Sample records for atlas level1 endcap

  1. The Common Cryogenic Test Facility for the ATLAS Barrel and End-Cap Toroid Magnets

    NASA Astrophysics Data System (ADS)

    Delruelle, N.; Haug, F.; Junker, S.; Passardi, G.; Pengo, R.; Pirotte, O.

    2004-06-01

    The large ATLAS toroidal superconducting magnet made of the Barrel and two End-Caps needs extensive testing at the surface of the individual components prior to their final assembly into the underground cavern of LHC. A cryogenic test facility specifically designed for cooling sequentially the eight coils making the Barrel Toroid (BT) has been fully commissioned and is now ready for final acceptance of these magnets. This facility, originally designed for testing individually the 46 tons BT coils, will be upgraded to allow the acceptance tests of the two End-Caps, each of them having a 160 tons cold mass. The integrated system mainly comprises a 1.2 kW@4.5 K refrigerator, a 10 kW liquid-nitrogen precooler, two cryostats housing liquid helium centrifugal pumps of respectively 80 g/s and 600 g/s nominal flow and specific instrumentation to measure the thermal performances of the magnets. This paper describes the overall facility with particular emphasis to the cryogenic features adopted to match the specific requirements of the magnets in the various operating scenarios.

  2. Sensors for the End-cap prototype of the Inner Tracker in the ATLAS Detector Upgrade

    NASA Astrophysics Data System (ADS)

    Benítez, V.; Ullán, M.; Quirion, D.; Pellegrini, G.; Fleta, C.; Lozano, M.; Sperlich, D.; Hauser, M.; Wonsak, S.; Parzefall, U.; Mahboubi, K.; Kuehn, S.; Mori, R.; Jakobs, K.; Bernabeu, J.; García, C.; Lacasta, C.; Marco, R.; Rodriguez, D.; Santoyo, D.; Solaz, C.; Soldevila, U.; Ariza, D.; Bloch, I.; Diez, S.; Gregor, I. M.; Keller, J.; Lohwasser, K.; Peschke, R.; Poley, L.; Brenner, R.; Affolder, A.

    2016-10-01

    The new silicon microstrip sensors of the End-cap part of the HL-LHC ATLAS Inner Tracker (ITk) present a number of challenges due to their complex design features such as the multiple different sensor shapes, the varying strip pitch, or the built-in stereo angle. In order to investigate these specific problems, the "petalet" prototype was defined as a small End-cap prototype. The sensors for the petalet prototype include several new layout and technological solutions to investigate the issues, they have been tested in detail by the collaboration. The sensor description and detailed test results are presented in this paper. New software tools have been developed for the automatic layout generation of the complex designs. The sensors have been fabricated, characterized and delivered to the institutes in the collaboration for their assembly on petalet prototypes. This paper describes the lessons learnt from the design and tests of the new solutions implemented on these sensors, which are being used for the full petal sensor development. This has resulted in the ITk strip community acquiring the necessary expertise to develop the full End-cap structure, the petal.

  3. ATLAS event display: Virtual Point-1 visualization software

    NASA Astrophysics Data System (ADS)

    Seeley, Kaelyn; Dimond, David; Bianchi, R. M.; Boudreau, Joseph; Hong, Tae Min; Atlas Collaboration

    2017-01-01

    Virtual Point-1 (VP1) is an event display visualization software for the ATLAS Experiment. VP1 is a software framework that makes use of ATHENA, the ATLAS software infrastructure, to access the complete detector geometry. This information is used to draw graphics representing the components of the detector at any scale. Two new features are added to VP1. The first is a traditional ``lego'' plot, displaying the calorimeter energy deposits in eta-phi space. The second is another lego plot focusing on the forward endcap region, displaying the energy deposits in r-phi space. Currently, these new additions display the energy deposits based on the granularity of the middle layer of the liquid-Argon electromagnetic calorimeter. Since VP1 accesses the complete detector geometry and all experimental data, future developments are outlined for a more detailed display involving multiple layers of the calorimeter along with their distinct granularities.

  4. CMS endcap RPC performance analysis

    NASA Astrophysics Data System (ADS)

    Teng, H.; CMS Collaboration

    2014-08-01

    The Resistive Plate Chamber (RPC) detector system in LHC-CMS experiment is designed for the trigger purpose. The endcap RPC system has been successfully operated since the commissioning period (2008) to the end of RUN1 (2013). We have developed an analysis tool for endcap RPC performance and validated the efficiency calculation algorithm, focusing on the first endcap station which was assembled and tested by the Peking University group. We cross checked the results obtained with those extracted with alternative methods and we found good agreement in terms of performance parameters [1]. The results showed that the CMS-RPC endcap system fulfilled the performance expected in the Technical Design Report [2].

  5. Electronics for CMS Endcap Muon Level-1 Trigger System Phase-1 and HL LHC upgrades

    NASA Astrophysics Data System (ADS)

    Madorsky, A.

    2017-07-01

    To accommodate high-luminosity LHC operation at a 13 TeV collision energy, the CMS Endcap Muon Level-1 Trigger system had to be significantly modified. To provide robust track reconstruction, the trigger system must now import all available trigger primitives generated by the Cathode Strip Chambers and by certain other subsystems, such as Resistive Plate Chambers (RPC). In addition to massive input bandwidth, this also required significant increase in logic and memory resources. To satisfy these requirements, a new Sector Processor unit has been designed. It consists of three modules. The Core Logic module houses the large FPGA that contains the track-finding logic and multi-gigabit serial links for data exchange. The Optical module contains optical receivers and transmitters; it communicates with the Core Logic module via a custom backplane section. The Pt Lookup table (PTLUT) module contains 1 GB of low-latency memory that is used to assign the final Pt to reconstructed muon tracks. The μ TCA architecture (adopted by CMS) was used for this design. The talk presents the details of the hardware and firmware design of the production system based on Xilinx Virtex-7 FPGA family. The next round of LHC and CMS upgrades starts in 2019, followed by a major High-Luminosity (HL) LHC upgrade starting in 2024. In the course of these upgrades, new Gas Electron Multiplier (GEM) detectors and more RPC chambers will be added to the Endcap Muon system. In order to keep up with all these changes, a new Advanced Processor unit is being designed. This device will be based on Xilinx UltraScale+ FPGAs. It will be able to accommodate up to 100 serial links with bit rates of up to 25 Gb/s, and provide up to 2.5 times more logic resources than the device used currently. The amount of PTLUT memory will be significantly increased to provide more flexibility for the Pt assignment algorithm. The talk presents preliminary details of the hardware design program.

  6. Resource atlases for multi-atlas brain segmentations with multiple ontology levels based on T1-weighted MRI.

    PubMed

    Wu, Dan; Ma, Ting; Ceritoglu, Can; Li, Yue; Chotiyanonta, Jill; Hou, Zhipeng; Hsu, John; Xu, Xin; Brown, Timothy; Miller, Michael I; Mori, Susumu

    2016-01-15

    Technologies for multi-atlas brain segmentation of T1-weighted MRI images have rapidly progressed in recent years, with highly promising results. This approach, however, relies on a large number of atlases with accurate and consistent structural identifications. Here, we introduce our atlas inventories (n=90), which cover ages 4-82years with unique hierarchical structural definitions (286 structures at the finest level). This multi-atlas library resource provides the flexibility to choose appropriate atlases for various studies with different age ranges and structure-definition criteria. In this paper, we describe the details of the atlas resources and demonstrate the improved accuracy achievable with a dynamic age-matching approach, in which atlases that most closely match the subject's age are dynamically selected. The advanced atlas creation strategy, together with atlas pre-selection principles, is expected to support the further development of multi-atlas image segmentation. Copyright © 2015 Elsevier Inc. All rights reserved.

  7. ATLAS level-1 calorimeter trigger: Run-2 performance and Phase-1 upgrades

    NASA Astrophysics Data System (ADS)

    Carlson, Ben; Hong, Tae Min; Atlas Collaboration

    2017-01-01

    The Run-2 performance and Phase-1 upgrade are presented for the hardware-based level-1 calorimeter trigger (L1Calo) for the ATLAS Experiment. This trigger has a latency of about 2.2 microseconds to make a decision to help ATLAS select about 100 kHz of the most interesting collisions from the nominal LHC rate of 40 MHz. We summarize the upgrade after Run-1 (2009-2012) and discuss its performance in Run-2 (2015-current). We also outline the on-going Phase-1 upgrade for the next run (2021-2024) and its expected performance.

  8. Cooled turbine vane with endcaps

    DOEpatents

    Cunha, Frank J.; Schiavo, Jr., Anthony L.; Nordlund, Raymond Scott; Malow, Thomas; McKinley, Barry L.

    2002-01-01

    A turbine vane assembly which includes an outer endcap having a plurality of generally straight passages and passage segments therethrough, an inner endcap having a plurality of passages and passage segments therethrough, and a vane assembly having an outer shroud, an airfoil body, and an inner shroud. The outer shroud, airfoil body and inner shroud each have a plurality of generally straight passages and passage segments therethrough as well. The outer endcap is coupled to the outer shroud so that outer endcap passages and said outer shroud passages form a fluid circuit. The inner endcap is coupled to the inner shroud so that the inner end cap passages and the inner shroud passages from a fluid circuit. Passages in the vane casting are in fluid communication with both the outer shroud passages and the inner shroud passages. Passages in the outer endcap may be coupled to a cooling system that supplies a coolant and takes away the heated exhaust.

  9. Simulation of the High Performance Time to Digital Converter for the ATLAS Muon Spectrometer trigger upgrade

    NASA Astrophysics Data System (ADS)

    Meng, X. T.; Levin, D. S.; Chapman, J. W.; Zhou, B.

    2016-09-01

    The ATLAS Muon Spectrometer endcap thin-Resistive Plate Chamber trigger project compliments the New Small Wheel endcap Phase-1 upgrade for higher luminosity LHC operation. These new trigger chambers, located in a high rate region of ATLAS, will improve overall trigger acceptance and reduce the fake muon trigger incidence. These chambers must generate a low level muon trigger to be delivered to a remote high level processor within a stringent latency requirement of 43 bunch crossings (1075 ns). To help meet this requirement the High Performance Time to Digital Converter (HPTDC), a multi-channel ASIC designed by CERN Microelectronics group, has been proposed for the digitization of the fast front end detector signals. This paper investigates the HPTDC performance in the context of the overall muon trigger latency, employing detailed behavioral Verilog simulations in which the latency in triggerless mode is measured for a range of configurations and under realistic hit rate conditions. The simulation results show that various HPTDC operational configurations, including leading edge and pair measurement modes can provide high efficiency (>98%) to capture and digitize hits within a time interval satisfying the Phase-1 latency tolerance.

  10. The PANDA Endcap Disc DIRC

    NASA Astrophysics Data System (ADS)

    Föhl, K.; Ali, A.; Belias, A.; Dzhygadlo, R.; Gerhardt, A.; Götzen, K.; Kalicy, G.; Krebs, M.; Lehmann, D.; Nerling, F.; Patsyuk, M.; Peters, K.; Schepers, G.; Schmitt, L.; Schwarz, C.; Schwiening, J.; Traxler, M.; Böhm, M.; Eyrich, W.; Lehmann, A.; Pfaffinger, M.; Uhlig, F.; Düren, M.; Etzelmüller, E.; Hayrapetyan, A.; Kreutzfeld, K.; Merle, O.; Rieke, J.; Schmidt, M.; Wasem, T.; Achenbach, P.; Cardinali, M.; Hoek, M.; Lauth, W.; Schlimme, S.; Sfienti, C.; Thiel, M.

    2018-02-01

    Positively identifying charged kaons in the PANDA forward endcap solid angle range can be achieved with the Endcap Disc DIRC, allowing kaon-pion separation from 1 up to 4 GeV/c with a separation power of at least 3 standard deviations. Design, performance, and components of this DIRC are given, including the recently introduced TOFPET-ASIC based read-out. Results of a prototype operated in a test beam at DESY in 2016 are shown.

  11. The ATLAS Level-1 Calorimeter Trigger: PreProcessor implementation and performance

    NASA Astrophysics Data System (ADS)

    Åsman, B.; Achenbach, R.; Allbrooke, B. M. M.; Anders, G.; Andrei, V.; Büscher, V.; Bansil, H. S.; Barnett, B. M.; Bauss, B.; Bendtz, K.; Bohm, C.; Bracinik, J.; Brawn, I. P.; Brock, R.; Buttinger, W.; Caputo, R.; Caughron, S.; Cerrito, L.; Charlton, D. G.; Childers, J. T.; Curtis, C. J.; Daniells, A. C.; Davis, A. O.; Davygora, Y.; Dorn, M.; Eckweiler, S.; Edmunds, D.; Edwards, J. P.; Eisenhandler, E.; Ellis, K.; Ermoline, Y.; Föhlisch, F.; Faulkner, P. J. W.; Fedorko, W.; Fleckner, J.; French, S. T.; Gee, C. N. P.; Gillman, A. R.; Goeringer, C.; Hülsing, T.; Hadley, D. R.; Hanke, P.; Hauser, R.; Heim, S.; Hellman, S.; Hickling, R. S.; Hidvégi, A.; Hillier, S. J.; Hofmann, J. I.; Hristova, I.; Ji, W.; Johansen, M.; Keller, M.; Khomich, A.; Kluge, E.-E.; Koll, J.; Laier, H.; Landon, M. P. J.; Lang, V. S.; Laurens, P.; Lepold, F.; Lilley, J. N.; Linnemann, J. T.; Müller, F.; Müller, T.; Mahboubi, K.; Martin, T. A.; Mass, A.; Meier, K.; Meyer, C.; Middleton, R. P.; Moa, T.; Moritz, S.; Morris, J. D.; Mudd, R. D.; Narayan, R.; zur Nedden, M.; Neusiedl, A.; Newman, P. R.; Nikiforov, A.; Ohm, C. C.; Perera, V. J. O.; Pfeiffer, U.; Plucinski, P.; Poddar, S.; Prieur, D. P. F.; Qian, W.; Rieck, P.; Rizvi, E.; Sankey, D. P. C.; Schäfer, U.; Scharf, V.; Schmitt, K.; Schröder, C.; Schultz-Coulon, H.-C.; Schumacher, C.; Schwienhorst, R.; Silverstein, S. B.; Simioni, E.; Snidero, G.; Staley, R. J.; Stamen, R.; Stock, P.; Stockton, M. C.; Tan, C. L. A.; Tapprogge, S.; Thomas, J. P.; Thompson, P. D.; Thomson, M.; True, P.; Watkins, P. M.; Watson, A. T.; Watson, M. F.; Weber, P.; Wessels, M.; Wiglesworth, C.; Williams, S. L.

    2012-12-01

    The PreProcessor system of the ATLAS Level-1 Calorimeter Trigger (L1Calo) receives about 7200 analogue signals from the electromagnetic and hadronic components of the calorimetric detector system. Lateral division results in cells which are pre-summed to so-called Trigger Towers of size 0.1 × 0.1 along azimuth (phi) and pseudorapidity (η). The received calorimeter signals represent deposits of transverse energy. The system consists of 124 individual PreProcessor modules that digitise the input signals for each LHC collision, and provide energy and timing information to the digital processors of the L1Calo system, which identify physics objects forming much of the basis for the full ATLAS first level trigger decision. This paper describes the architecture of the PreProcessor, its hardware realisation, functionality, and performance.

  12. Drift Time Measurement in the ATLAS Liquid Argon Electromagnetic Calorimeter using Cosmic Muons

    DOE PAGES

    Aad, G.; Abbott, B.; Abdallah, J.; ...

    2010-10-23

    The ionization signals in the liquid argon of the ATLAS electromagnetic calorimeter are studied in detail using cosmic muons. In particular, the drift time of the ionization electrons is measured and used to assess the intrinsic uniformity of the calorimeter gaps and estimate its impact on the constant term of the energy resolution. The drift times of electrons in the cells of the second layer of the calorimeter are uniform at the level of 1.3% in the barrel and 2.8% in the endcaps. This leads to an estimated contribution to the constant term of (0.29more » $$+0.05\\atop{-0.04}$$) % in the barrel and (0.54$$+0.06\\atop{-0.04}$$)% in the endcaps. Lastly, the same data are used to measure the drift velocity of ionization electrons in liquid argon, which is found to be 4.61 ± 0.07 mm/μs at 88.5 K and 1 kV/mm.« less

  13. Drift Time Measurement in the ATLAS Liquid Argon Electromagnetic Calorimeter using Cosmic Muons

    NASA Astrophysics Data System (ADS)

    Aad, G.; Abbott, B.; Abdallah, J.; Abdelalim, A. A.; Abdesselam, A.; Abdinov, O.; Abi, B.; Abolins, M.; Abramowicz, H.; Abreu, H.; Acharya, B. S.; Adams, D. L.; Addy, T. N.; Adelman, J.; Adorisio, C.; Adragna, P.; Adye, T.; Aefsky, S.; Aguilar-Saavedra, J. A.; Aharrouche, M.; Ahlen, S. P.; Ahles, F.; Ahmad, A.; Ahmed, H.; Ahsan, M.; Aielli, G.; Akdogan, T.; Åkesson, T. P. A.; Akimoto, G.; Akimov, A. V.; Aktas, A.; Alam, M. S.; Alam, M. A.; Albert, J.; Albrand, S.; Aleksa, M.; Aleksandrov, I. N.; Alessandria, F.; Alexa, C.; Alexander, G.; Alexandre, G.; Alexopoulos, T.; Alhroob, M.; Aliev, M.; Alimonti, G.; Alison, J.; Aliyev, M.; Allport, P. P.; Allwood-Spiers, S. E.; Almond, J.; Aloisio, A.; Alon, R.; Alonso, A.; Alviggi, M. G.; Amako, K.; Amelung, C.; Ammosov, V. V.; Amorim, A.; Amorós, G.; Amram, N.; Anastopoulos, C.; Andeen, T.; Anders, C. F.; Anderson, K. J.; Andreazza, A.; Andrei, V.; Anduaga, X. S.; Angerami, A.; Anghinolfi, F.; Anjos, N.; Antonaki, A.; Antonelli, M.; Antonelli, S.; Antos, J.; Antunovic, B.; Anulli, F.; Aoun, S.; Arabidze, G.; Aracena, I.; Arai, Y.; Arce, A. T. H.; Archambault, J. P.; Arfaoui, S.; Arguin, J.-F.; Argyropoulos, T.; Arik, E.; Arik, M.; Armbruster, A. J.; Arnaez, O.; Arnault, C.; Artamonov, A.; Arutinov, D.; Asai, M.; Asai, S.; Asfandiyarov, R.; Ask, S.; Åsman, B.; Asner, D.; Asquith, L.; Assamagan, K.; Astbury, A.; Astvatsatourov, A.; Atoian, G.; Auerbach, B.; Auge, E.; Augsten, K.; Aurousseau, M.; Austin, N.; Avolio, G.; Avramidou, R.; Axen, D.; Ay, C.; Azuelos, G.; Azuma, Y.; Baak, M. A.; Bacci, C.; Bach, A.; Bachacou, H.; Bachas, K.; Backes, M.; Badescu, E.; Bagnaia, P.; Bai, Y.; Bailey, D. C.; Bain, T.; Baines, J. T.; Baker, O. K.; Baker, M. D.; Baker, S.; Baltasar Dos Santos Pedrosa, F.; Banas, E.; Banerjee, P.; Banerjee, S.; Banfi, D.; Bangert, A.; Bansal, V.; Baranov, S. P.; Baranov, S.; Barashkou, A.; Barber, T.; Barberio, E. L.; Barberis, D.; Barbero, M.; Bardin, D. Y.; Barillari, T.; Barisonzi, M.; Barklow, T.; Barlow, N.; Barnett, B. M.; Barnett, R. M.; Baron, S.; Baroncelli, A.; Barr, A. J.; Barreiro, F.; Barreiro Guimarães da Costa, J.; Barrillon, P.; Barros, N.; Bartoldus, R.; Bartsch, D.; Bastos, J.; Bates, R. L.; Batkova, L.; Batley, J. R.; Battaglia, A.; Battistin, M.; Bauer, F.; Bawa, H. S.; Bazalova, M.; Beare, B.; Beau, T.; Beauchemin, P. H.; Beccherle, R.; Becerici, N.; Bechtle, P.; Beck, G. A.; Beck, H. P.; Beckingham, M.; Becks, K. H.; Bedajanek, I.; Beddall, A. J.; Beddall, A.; Bednár, P.; Bednyakov, V. A.; Bee, C.; Begel, M.; Behar Harpaz, S.; Behera, P. K.; Beimforde, M.; Belanger-Champagne, C.; Bell, P. J.; Bell, W. H.; Bella, G.; Bellagamba, L.; Bellina, F.; Bellomo, M.; Belloni, A.; Belotskiy, K.; Beltramello, O.; Ami, S. Ben; Benary, O.; Benchekroun, D.; Bendel, M.; Benedict, B. H.; Benekos, N.; Benhammou, Y.; Benincasa, G. P.; Benjamin, D. P.; Benoit, M.; Bensinger, J. R.; Benslama, K.; Bentvelsen, S.; Beretta, M.; Berge, D.; Bergeaas Kuutmann, E.; Berger, N.; Berghaus, F.; Berglund, E.; Beringer, J.; Bernardet, K.; Bernat, P.; Bernhard, R.; Bernius, C.; Berry, T.; Bertin, A.; Besana, M. I.; Besson, N.; Bethke, S.; Bianchi, R. M.; Bianco, M.; Biebel, O.; Biesiada, J.; Biglietti, M.; Bilokon, H.; Bindi, M.; Binet, S.; Bingul, A.; Bini, C.; Biscarat, C.; Bitenc, U.; Black, K. M.; Blair, R. E.; Blanchard, J.-B.; Blanchot, G.; Blocker, C.; Blocki, J.; Blondel, A.; Blum, W.; Blumenschein, U.; Bobbink, G. J.; Bocci, A.; Boehler, M.; Boek, J.; Boelaert, N.; Böser, S.; Bogaerts, J. A.; Bogouch, A.; Bohm, C.; Bohm, J.; Boisvert, V.; Bold, T.; Boldea, V.; Boldyrev, A.; Bondarenko, V. G.; Bondioli, M.; Boonekamp, M.; Bordoni, S.; Borer, C.; Borisov, A.; Borissov, G.; Borjanovic, I.; Borroni, S.; Bos, K.; Boscherini, D.; Bosman, M.; Bosteels, M.; Boterenbrood, H.; Bouchami, J.; Boudreau, J.; Bouhova-Thacker, E. V.; Boulahouache, C.; Bourdarios, C.; Boyd, J.; Boyko, I. R.; Bozovic-Jelisavcic, I.; Bracinik, J.; Braem, A.; Branchini, P.; Brandenburg, G. W.; Brandt, A.; Brandt, G.; Brandt, O.; Bratzler, U.; Brau, B.; Brau, J. E.; Braun, H. M.; Brelier, B.; Bremer, J.; Brenner, R.; Bressler, S.; Breton, D.; Britton, D.; Brochu, F. M.; Brock, I.; Brock, R.; Brodbeck, T. J.; Brodet, E.; Broggi, F.; Bromberg, C.; Brooijmans, G.; Brooks, W. K.; Brown, G.; Brubaker, E.; Bruckman de Renstrom, P. A.; Bruncko, D.; Bruneliere, R.; Brunet, S.; Bruni, A.; Bruni, G.; Bruschi, M.; Buanes, T.; Bucci, F.; Buchanan, J.; Buchholz, P.; Buckley, A. G.; Budagov, I. A.; Budick, B.; Büscher, V.; Bugge, L.; Bulekov, O.; Bunse, M.; Buran, T.; Burckhart, H.; Burdin, S.; Burgess, T.; Burke, S.; Busato, E.; Bussey, P.; Buszello, C. P.; Butin, F.; Butler, B.; Butler, J. M.; Buttar, C. M.; Butterworth, J. M.; Byatt, T.; Caballero, J.; Cabrera Urbán, S.; Caforio, D.; Cakir, O.; Calafiura, P.; Calderini, G.; Calfayan, P.; Calkins, R.; Caloba, L. P.; Caloi, R.; Calvet, D.; Camarri, P.; Cambiaghi, M.; Cameron, D.; Campabadal Segura, F.; Campana, S.; Campanelli, M.; Canale, V.; Canelli, F.; Canepa, A.; Cantero, J.; Capasso, L.; Capeans Garrido, M. D. M.; Caprini, I.; Caprini, M.; Capua, M.; Caputo, R.; Caracinha, D.; Caramarcu, C.; Cardarelli, R.; Carli, T.; Carlino, G.; Carminati, L.; Caron, B.; Caron, S.; Carrillo Montoya, G. D.; Carron Montero, S.; Carter, A. A.; Carter, J. R.; Carvalho, J.; Casadei, D.; Casado, M. P.; Cascella, M.; Caso, C.; Castaneda Hernadez, A. M.; Castaneda-Miranda, E.; Castillo Gimenez, V.; Castro, N.; Cataldi, G.; Catinaccio, A.; Catmore, J. R.; Cattai, A.; Cattani, G.; Caughron, S.; Cauz, D.; Cavalleri, P.; Cavalli, D.; Cavalli-Sforza, M.; Cavasinni, V.; Ceradini, F.; Cerqueira, A. S.; Cerri, A.; Cerrito, L.; Cerutti, F.; Cetin, S. A.; Cevenini, F.; Chafaq, A.; Chakraborty, D.; Chan, K.; Chapman, J. D.; Chapman, J. W.; Chareyre, E.; Charlton, D. G.; Chavda, V.; Cheatham, S.; Chekanov, S.; Chekulaev, S. V.; Chelkov, G. A.; Chen, H.; Chen, S.; Chen, T.; Chen, X.; Cheng, S.; Cheplakov, A.; Chepurnov, V. F.; Cherkaoui El Moursli, R.; Tcherniatine, V.; Chesneanu, D.; Cheu, E.; Cheung, S. L.; Chevalier, L.; Chevallier, F.; Chiarella, V.; Chiefari, G.; Chikovani, L.; Childers, J. T.; Chilingarov, A.; Chiodini, G.; Chizhov, M.; Choudalakis, G.; Chouridou, S.; Christidi, I. A.; Christov, A.; Chromek-Burckhart, D.; Chu, M. L.; Chudoba, J.; Ciapetti, G.; Ciftci, A. K.; Ciftci, R.; Cinca, D.; Cindro, V.; Ciobotaru, M. D.; Ciocca, C.; Ciocio, A.; Cirilli, M.; Citterio, M.; Clark, A.; Cleland, W.; Clemens, J. C.; Clement, B.; Clement, C.; Coadou, Y.; Cobal, M.; Coccaro, A.; Cochran, J.; Coelli, S.; Coggeshall, J.; Cogneras, E.; Cojocaru, C. D.; Colas, J.; Cole, B.; Colijn, A. P.; Collard, C.; Collins, N. J.; Collins-Tooth, C.; Collot, J.; Colon, G.; Conde Muiño, P.; Coniavitis, E.; Consonni, M.; Constantinescu, S.; Conta, C.; Conventi, F.; Cook, J.; Cooke, M.; Cooper, B. D.; Cooper-Sarkar, A. M.; Cooper-Smith, N. J.; Copic, K.; Cornelissen, T.; Corradi, M.; Corriveau, F.; Corso-Radu, A.; Cortes-Gonzalez, A.; Cortiana, G.; Costa, G.; Costa, M. J.; Costanzo, D.; Costin, T.; Côté, D.; Coura Torres, R.; Courneyea, L.; Cowan, G.; Cowden, C.; Cox, B. E.; Cranmer, K.; Cranshaw, J.; Cristinziani, M.; Crosetti, G.; Crupi, R.; Crépé-Renaudin, S.; Cuenca Almenar, C.; Cuhadar Donszelmann, T.; Curatolo, M.; Curtis, C. J.; Cwetanski, P.; Czyczula, Z.; D'Auria, S.; D'Onofrio, M.; D'Orazio, A.; da Silva, P. V. M.; da Via, C.; Dabrowski, W.; Dai, T.; Dallapiccola, C.; Dallison, S. J.; Daly, C. H.; Dam, M.; Danielsson, H. O.; Dannheim, D.; Dao, V.; Darbo, G.; Darlea, G. L.; Davey, W.; Davidek, T.; Davidson, N.; Davidson, R.; Davies, M.; Davison, A. R.; Dawson, I.; Dawson, J. W.; Daya, R. K.; de, K.; de Asmundis, R.; de Castro, S.; de Castro Faria Salgado, P. E.; de Cecco, S.; de Graat, J.; de Groot, N.; de Jong, P.; de La Cruz-Burelo, E.; de La Taille, C.; de Mora, L.; de Oliveira Branco, M.; de Pedis, D.; de Salvo, A.; de Sanctis, U.; de Santo, A.; de Vivie de Regie, J. B.; de Zorzi, G.; Dean, S.; Deberg, H.; Dedes, G.; Dedovich, D. V.; Defay, P. O.; Degenhardt, J.; Dehchar, M.; Del Papa, C.; Del Peso, J.; Del Prete, T.; Dell'Acqua, A.; Dell'Asta, L.; Della Pietra, M.; Della Volpe, D.; Delmastro, M.; Delruelle, N.; Delsart, P. A.; Deluca, C.; Demers, S.; Demichev, M.; Demirkoz, B.; Deng, J.; Deng, W.; Denisov, S. P.; Dennis, C.; Derkaoui, J. E.; Derue, F.; Dervan, P.; Desch, K.; Deviveiros, P. O.; Dewhurst, A.; Dewilde, B.; Dhaliwal, S.; Dhullipudi, R.; di Ciaccio, A.; di Ciaccio, L.; di Domenico, A.; di Girolamo, A.; di Girolamo, B.; di Luise, S.; di Mattia, A.; di Nardo, R.; di Simone, A.; di Sipio, R.; Diaz, M. A.; Diblen, F.; Diehl, E. B.; Dietrich, J.; Dietzsch, T. A.; Diglio, S.; Dindar Yagci, K.; Dingfelder, D. J.; Dionisi, C.; Dita, P.; Dita, S.; Dittus, F.; Djama, F.; Djilkibaev, R.; Djobava, T.; Do Vale, M. A. B.; Do Valle Wemans, A.; Doan, T. K. O.; Dobbs, M.; Dobos, D.; Dobson, E.; Dobson, M.; Dodd, J.; Doherty, T.; Doi, Y.; Dolejsi, J.; Dolenc, I.; Dolezal, Z.; Dolgoshein, B. A.; Dohmae, T.; Donega, M.; Donini, J.; Dopke, J.; Doria, A.; Dos Anjos, A.; Dotti, A.; Dova, M. T.; Doxiadis, A.; Doyle, A. T.; Drasal, Z.; Driouichi, C.; Dris, M.; Dubbert, J.; Duchovni, E.; Duckeck, G.; Dudarev, A.; Dudziak, F.; Dührssen, M.; Duflot, L.; Dufour, M.-A.; Dunford, M.; Duperrin, A.; Yildiz, H. Duran; Dushkin, A.; Duxfield, R.; Dwuznik, M.; Düren, M.; Ebenstein, W. L.; Ebke, J.; Eckert, S.; Eckweiler, S.; Edmonds, K.; Edwards, C. A.; Eerola, P.; Egorov, K.; Ehrenfeld, W.; Ehrich, T.; Eifert, T.; Eigen, G.; Einsweiler, K.; Eisenhandler, E.; Ekelof, T.; El Kacimi, M.; Ellert, M.; Elles, S.; Ellinghaus, F.; Ellis, K.; Ellis, N.; Elmsheuser, J.; Elsing, M.; Ely, R.; Emeliyanov, D.; Engelmann, R.; Engl, A.; Epp, B.; Eppig, A.; Epshteyn, V. S.; Ereditato, A.; Eriksson, D.; Ermoline, I.; Ernst, J.; Ernst, M.; Ernwein, J.; Errede, D.; Errede, S.; Ertel, E.; Escalier, M.; Escobar, C.; Espinal Curull, X.; Esposito, B.; Etienne, F.; Etienvre, A. I.; Etzion, E.; Evans, H.; Fabbri, L.; Fabre, C.; Facius, K.; Fakhrutdinov, R. M.; Falciano, S.; Falou, A. C.; Fang, Y.; Fanti, M.; Farbin, A.; Farilla, A.; Farley, J.; Farooque, T.; Farrington, S. M.; Farthouat, P.; Fassi, F.; Fassnacht, P.; Fassouliotis, D.; Fatholahzadeh, B.; Fayard, L.; Fayette, F.; Febbraro, R.; Federic, P.; Fedin, O. L.; Fedorko, I.; Fedorko, W.; Feligioni, L.; Felzmann, C. U.; Feng, C.; Feng, E. J.; Fenyuk, A. B.; Ferencei, J.; Ferland, J.; Fernandes, B.; Fernando, W.; Ferrag, S.; Ferrando, J.; Ferrari, A.; Ferrari, P.; Ferrari, R.; Ferrer, A.; Ferrer, M. L.; Ferrere, D.; Ferretti, C.; Fiascaris, M.; Fiedler, F.; Filipčič, A.; Filippas, A.; Filthaut, F.; Fincke-Keeler, M.; Fiolhais, M. C. N.; Fiorini, L.; Firan, A.; Fischer, G.; Fisher, M. J.; Flechl, M.; Fleck, I.; Fleckner, J.; Fleischmann, P.; Fleischmann, S.; Flick, T.; Flores Castillo, L. R.; Flowerdew, M. J.; Föhlisch, F.; Fokitis, M.; Fonseca Martin, T.; Forbush, D. A.; Formica, A.; Forti, A.; Fortin, D.; Foster, J. M.; Fournier, D.; Foussat, A.; Fowler, A. J.; Fowler, K.; Fox, H.; Francavilla, P.; Franchino, S.; Francis, D.; Franklin, M.; Franz, S.; Fraternali, M.; Fratina, S.; Freestone, J.; French, S. T.; Froeschl, R.; Froidevaux, D.; Frost, J. A.; Fukunaga, C.; Fullana Torregrosa, E.; Fuster, J.; Gabaldon, C.; Gabizon, O.; Gadfort, T.; Gadomski, S.; Gagliardi, G.; Gagnon, P.; Galea, C.; Gallas, E. J.; Gallas, M. V.; Gallo, V.; Gallop, B. J.; Gallus, P.; Galyaev, E.; Gan, K. K.; Gao, Y. S.; Gaponenko, A.; Garcia-Sciveres, M.; García, C.; García Navarro, J. E.; Gardner, R. W.; Garelli, N.; Garitaonandia, H.; Garonne, V.; Gatti, C.; Gaudio, G.; Gaumer, O.; Gauzzi, P.; Gavrilenko, I. L.; Gay, C.; Gaycken, G.; Gayde, J.-C.; Gazis, E. N.; Ge, P.; Gee, C. N. P.; Geich-Gimbel, Ch.; Gellerstedt, K.; Gemme, C.; Genest, M. H.; Gentile, S.; Georgatos, F.; George, S.; Gerlach, P.; Gershon, A.; Geweniger, C.; Ghazlane, H.; Ghez, P.; Ghodbane, N.; Giacobbe, B.; Giagu, S.; Giakoumopoulou, V.; Giangiobbe, V.; Gianotti, F.; Gibbard, B.; Gibson, A.; Gibson, S. M.; Gilbert, L. M.; Gilchriese, M.; Gilewsky, V.; Gillman, A. R.; Gingrich, D. M.; Ginzburg, J.; Giokaris, N.; Giordani, M. P.; Giordano, R.; Giovannini, P.; Giraud, P. F.; Girtler, P.; Giugni, D.; Giusti, P.; Gjelsten, B. K.; Gladilin, L. K.; Glasman, C.; Glazov, A.; Glitza, K. W.; Glonti, G. L.; Godfrey, J.; Godlewski, J.; Goebel, M.; Göpfert, T.; Goeringer, C.; Gössling, C.; Göttfert, T.; Goggi, V.; Goldfarb, S.; Goldin, D.; Golling, T.; Gollub, N. P.; Gomes, A.; Gomez Fajardo, L. S.; Gonçalo, R.; Gonella, L.; Gong, C.; González de La Hoz, S.; Gonzalez Silva, M. L.; Gonzalez-Sevilla, S.; Goodson, J. J.; Goossens, L.; Gorbounov, P. A.; Gordon, H. A.; Gorelov, I.; Gorfine, G.; Gorini, B.; Gorini, E.; Gorišek, A.; Gornicki, E.; Goryachev, V. N.; Gosdzik, B.; Gosselink, M.; Gostkin, M. I.; Gough Eschrich, I.; Gouighri, M.; Goujdami, D.; Goulette, M. P.; Goussiou, A. G.; Goy, C.; Grabowska-Bold, I.; Grafström, P.; Grahn, K.-J.; Granado Cardoso, L.; Grancagnolo, F.; Grancagnolo, S.; Grassi, V.; Gratchev, V.; Grau, N.; Gray, H. M.; Gray, J. A.; Graziani, E.; Green, B.; Greenshaw, T.; Greenwood, Z. D.; Gregor, I. M.; Grenier, P.; Griesmayer, E.; Griffiths, J.; Grigalashvili, N.; Grillo, A. A.; Grimm, K.; Grinstein, S.; Grishkevich, Y. V.; Groer, L. S.; Grognuz, J.; Groh, M.; Groll, M.; Gross, E.; Grosse-Knetter, J.; Groth-Jensen, J.; Grybel, K.; Guarino, V. J.; Guicheney, C.; Guida, A.; Guillemin, T.; Guler, H.; Gunther, J.; Guo, B.; Gupta, A.; Gusakov, Y.; Gutierrez, A.; Gutierrez, P.; Guttman, N.; Gutzwiller, O.; Guyot, C.; Gwenlan, C.; Gwilliam, C. B.; Haas, A.; Haas, S.; Haber, C.; Hackenburg, R.; Hadavand, H. K.; Hadley, D. R.; Haefner, P.; Härtel, R.; Hajduk, Z.; Hakobyan, H.; Haller, J.; Hamacher, K.; Hamilton, A.; Hamilton, S.; Han, H.; Han, L.; Hanagaki, K.; Hance, M.; Handel, C.; Hanke, P.; Hansen, J. R.; Hansen, J. B.; Hansen, J. D.; Hansen, P. H.; Hansl-Kozanecka, T.; Hansson, P.; Hara, K.; Hare, G. A.; Harenberg, T.; Harrington, R. D.; Harris, O. M.; Harrison, K.; Hartert, J.; Hartjes, F.; Haruyama, T.; Harvey, A.; Hasegawa, S.; Hasegawa, Y.; Hashemi, K.; Hassani, S.; Hatch, M.; Haug, F.; Haug, S.; Hauschild, M.; Hauser, R.; Havranek, M.; Hawkes, C. M.; Hawkings, R. J.; Hawkins, D.; Hayakawa, T.; Hayward, H. S.; Haywood, S. J.; He, M.; Head, S. J.; Hedberg, V.; Heelan, L.; Heim, S.; Heinemann, B.; Heisterkamp, S.; Helary, L.; Heller, M.; Hellman, S.; Helsens, C.; Hemperek, T.; Henderson, R. C. W.; Henke, M.; Henrichs, A.; Henriques Correia, A. M.; Henrot-Versille, S.; Hensel, C.; Henß, T.; Hernández Jiménez, Y.; Hershenhorn, A. D.; Herten, G.; Hertenberger, R.; Hervas, L.; Hessey, N. P.; Hidvegi, A.; Higón-Rodriguez, E.; Hill, D.; Hill, J. C.; Hiller, K. H.; Hillert, S.; Hillier, S. J.; Hinchliffe, I.; Hines, E.; Hirose, M.; Hirsch, F.; Hirschbuehl, D.; Hobbs, J.; Hod, N.; Hodgkinson, M. C.; Hodgson, P.; Hoecker, A.; Hoeferkamp, M. R.; Hoffman, J.; Hoffmann, D.; Hohlfeld, M.; Holmgren, S. O.; Holy, T.; Holzbauer, J. L.; Homma, Y.; Homola, P.; Horazdovsky, T.; Hori, T.; Horn, C.; Horner, S.; Horvat, S.; Hostachy, J.-Y.; Hou, S.; Houlden, M. A.; Hoummada, A.; Howe, T.; Hrivnac, J.; Hryn'ova, T.; Hsu, P. J.; Hsu, S.-C.; Huang, G. S.; Hubacek, Z.; Hubaut, F.; Huegging, F.; Hughes, E. W.; Hughes, G.; Hughes-Jones, R. E.; Hurst, P.; Hurwitz, M.; Husemann, U.; Huseynov, N.; Huston, J.; Huth, J.; Iacobucci, G.; Iakovidis, G.; Ibragimov, I.; Iconomidou-Fayard, L.; Idarraga, J.; Iengo, P.; Igonkina, O.; Ikegami, Y.; Ikeno, M.; Ilchenko, Y.; Iliadis, D.; Ilyushenka, Y.; Imori, M.; Ince, T.; Ioannou, P.; Iodice, M.; Irles Quiles, A.; Ishikawa, A.; Ishino, M.; Ishmukhametov, R.; Isobe, T.; Issakov, V.; Issever, C.; Istin, S.; Itoh, Y.; Ivashin, A. V.; Iwasaki, H.; Izen, J. M.; Izzo, V.; Jackson, B.; Jackson, J. N.; Jackson, P.; Jaekel, M.; Jahoda, M.; Jain, V.; Jakobs, K.; Jakobsen, S.; Jakubek, J.; Jana, D.; Jansen, E.; Jantsch, A.; Janus, M.; Jared, R. C.; Jarlskog, G.; Jarron, P.; Jeanty, L.; Jen-La Plante, I.; Jenni, P.; Jez, P.; Jézéquel, S.; Ji, W.; Jia, J.; Jiang, Y.; Jimenez Belenguer, M.; Jin, G.; Jin, S.; Jinnouchi, O.; Joffe, D.; Johansen, M.; Johansson, K. E.; Johansson, P.; Johnert, S.; Johns, K. A.; Jon-And, K.; Jones, G.; Jones, R. W. L.; Jones, T. W.; Jones, T. J.; Jonsson, O.; Joos, D.; Joram, C.; Jorge, P. M.; Juranek, V.; Jussel, P.; Kabachenko, V. V.; Kabana, S.; Kaci, M.; Kaczmarska, A.; Kado, M.; Kagan, H.; Kagan, M.; Kaiser, S.; Kajomovitz, E.; Kalinin, S.; Kalinovskaya, L. V.; Kalinowski, A.; Kama, S.; Kanaya, N.; Kaneda, M.; Kantserov, V. A.; Kanzaki, J.; Kaplan, B.; Kapliy, A.; Kaplon, J.; Karagounis, M.; Karagoz Unel, M.; Kartvelishvili, V.; Karyukhin, A. N.; Kashif, L.; Kasmi, A.; Kass, R. D.; Kastanas, A.; Kastoryano, M.; Kataoka, M.; Kataoka, Y.; Katsoufis, E.; Katzy, J.; Kaushik, V.; Kawagoe, K.; Kawamoto, T.; Kawamura, G.; Kayl, M. S.; Kayumov, F.; Kazanin, V. A.; Kazarinov, M. Y.; Kazi, S. I.; Keates, J. R.; Keeler, R.; Keener, P. T.; Kehoe, R.; Keil, M.; Kekelidze, G. D.; Kelly, M.; Kennedy, J.; Kenyon, M.; Kepka, O.; Kerschen, N.; Kerševan, B. P.; Kersten, S.; Kessoku, K.; Khakzad, M.; Khalil-Zada, F.; Khandanyan, H.; Khanov, A.; Kharchenko, D.; Khodinov, A.; Kholodenko, A. G.; Khomich, A.; Khoriauli, G.; Khovanskiy, N.; Khovanskiy, V.; Khramov, E.; Khubua, J.; Kilvington, G.; Kim, H.; Kim, M. S.; Kim, P. C.; Kim, S. H.; Kind, O.; Kind, P.; King, B. T.; Kirk, J.; Kirsch, G. P.; Kirsch, L. E.; Kiryunin, A. E.; Kisielewska, D.; Kittelmann, T.; Kiyamura, H.; Kladiva, E.; Klein, M.; Klein, U.; Kleinknecht, K.; Klemetti, M.; Klier, A.; Klimentov, A.; Klingenberg, R.; Klinkby, E. B.; Klioutchnikova, T.; Klok, P. F.; Klous, S.; Kluge, E.-E.; Kluge, T.; Kluit, P.; Klute, M.; Kluth, S.; Knecht, N. S.; Kneringer, E.; Ko, B. R.; Kobayashi, T.; Kobel, M.; Koblitz, B.; Kocian, M.; Kocnar, A.; Kodys, P.; Köneke, K.; König, A. C.; Köpke, L.; Koetsveld, F.; Koevesarki, P.; Koffas, T.; Koffeman, E.; Kohn, F.; Kohout, Z.; Kohriki, T.; Kokott, T.; Kolanoski, H.; Kolesnikov, V.; Koletsou, I.; Koll, J.; Kollar, D.; Kolos, S.; Kolya, S. D.; Komar, A. A.; Komaragiri, J. R.; Kondo, T.; Kono, T.; Kononov, A. I.; Konoplich, R.; Konovalov, S. P.; Konstantinidis, N.; Koperny, S.; Korcyl, K.; Kordas, K.; Koreshev, V.; Korn, A.; Korolkov, I.; Korolkova, E. V.; Korotkov, V. A.; Kortner, O.; Kostka, P.; Kostyukhin, V. V.; Kotamäki, M. J.; Kotov, S.; Kotov, V. M.; Kotov, K. Y.; Koupilova, Z.; Kourkoumelis, C.; Koutsman, A.; Kowalewski, R.; Kowalski, H.; Kowalski, T. Z.; Kozanecki, W.; Kozhin, A. S.; Kral, V.; Kramarenko, V. A.; Kramberger, G.; Krasny, M. W.; Krasznahorkay, A.; Kreisel, A.; Krejci, F.; Krepouri, A.; Kretzschmar, J.; Krieger, P.; Krobath, G.; Kroeninger, K.; Kroha, H.; Kroll, J.; Kroseberg, J.; Krstic, J.; Kruchonak, U.; Krüger, H.; Krumshteyn, Z. V.; Kubota, T.; Kuehn, S.; Kugel, A.; Kuhl, T.; Kuhn, D.; Kukhtin, V.; Kulchitsky, Y.; Kuleshov, S.; Kummer, C.; Kuna, M.; Kunkle, J.; Kupco, A.; Kurashige, H.; Kurata, M.; Kurchaninov, L. L.; Kurochkin, Y. A.; Kus, V.; Kuznetsova, E.; Kvasnicka, O.; Kwee, R.; La Rotonda, L.; Labarga, L.; Labbe, J.; Lacasta, C.; Lacava, F.; Lacker, H.; Lacour, D.; Lacuesta, V. R.; Ladygin, E.; Lafaye, R.; Laforge, B.; Lagouri, T.; Lai, S.; Lamanna, M.; Lampen, C. L.; Lampl, W.; Lancon, E.; Landgraf, U.; Landon, M. P. J.; Lane, J. L.; Lankford, A. J.; Lanni, F.; Lantzsch, K.; Lanza, A.; Laplace, S.; Lapoire, C.; Laporte, J. F.; Lari, T.; Larionov, A. V.; Larner, A.; Lasseur, C.; Lassnig, M.; Laurelli, P.; Lavrijsen, W.; Laycock, P.; Lazarev, A. B.; Lazzaro, A.; Le Dortz, O.; Le Guirriec, E.; Le Maner, C.; Le Menedeu, E.; Le Vine, M.; Leahu, M.; Lebedev, A.; Lebel, C.; Lecompte, T.; Ledroit-Guillon, F.; Lee, H.; Lee, J. S. H.; Lee, S. C.; Lefebvre, M.; Legendre, M.; Legeyt, B. C.; Legger, F.; Leggett, C.; Lehmacher, M.; Lehmann Miotto, G.; Lei, X.; Leitner, R.; Lelas, D.; Lellouch, D.; Lellouch, J.; Leltchouk, M.; Lendermann, V.; Leney, K. J. C.; Lenz, T.; Lenzen, G.; Lenzi, B.; Leonhardt, K.; Leroy, C.; Lessard, J.-R.; Lester, C. G.; Leung Fook Cheong, A.; Levêque, J.; Levin, D.; Levinson, L. J.; Levitski, M. S.; Levonian, S.; Lewandowska, M.; Leyton, M.; Li, H.; Li, J.; Li, S.; Li, X.; Liang, Z.; Liang, Z.; Liberti, B.; Lichard, P.; Lichtnecker, M.; Lie, K.; Liebig, W.; Liko, D.; Lilley, J. N.; Lim, H.; Limosani, A.; Limper, M.; Lin, S. C.; Lindsay, S. W.; Linhart, V.; Linnemann, J. T.; Liolios, A.; Lipeles, E.; Lipinsky, L.; Lipniacka, A.; Liss, T. M.; Lissauer, D.; Lister, A.; Litke, A. M.; Liu, C.; Liu, D.; Liu, H.; Liu, J. B.; Liu, M.; Liu, S.; Liu, T.; Liu, Y.; Livan, M.; Lleres, A.; Lloyd, S. L.; Lobodzinska, E.; Loch, P.; Lockman, W. S.; Lockwitz, S.; Loddenkoetter, T.; Loebinger, F. K.; Loginov, A.; Loh, C. W.; Lohse, T.; Lohwasser, K.; Lokajicek, M.; Loken, J.; Lopes, L.; Lopez Mateos, D.; Losada, M.; Loscutoff, P.; Losty, M. J.; Lou, X.; Lounis, A.; Loureiro, K. F.; Lovas, L.; Love, J.; Love, P.; Lowe, A. J.; Lu, F.; Lu, J.; Lubatti, H. J.; Luci, C.; Lucotte, A.; Ludwig, A.; Ludwig, D.; Ludwig, I.; Ludwig, J.; Luehring, F.; Luisa, L.; Lumb, D.; Luminari, L.; Lund, E.; Lund-Jensen, B.; Lundberg, B.; Lundberg, J.; Lundquist, J.; Lutz, G.; Lynn, D.; Lys, J.; Lytken, E.; Ma, H.; Ma, L. L.; Macana Goia, J. A.; Maccarrone, G.; Macchiolo, A.; Maček, B.; Machado Miguens, J.; Mackeprang, R.; Madaras, R. J.; Mader, W. F.; Maenner, R.; Maeno, T.; Mättig, P.; Mättig, S.; Magalhaes Martins, P. J.; Magradze, E.; Magrath, C. A.; Mahalalel, Y.; Mahboubi, K.; Mahmood, A.; Mahout, G.; Maiani, C.; Maidantchik, C.; Maio, A.; Majewski, S.; Makida, Y.; Makouski, M.; Makovec, N.; Malecki, Pa.; Malecki, P.; Maleev, V. P.; Malek, F.; Mallik, U.; Malon, D.; Maltezos, S.; Malyshev, V.; Malyukov, S.; Mambelli, M.; Mameghani, R.; Mamuzic, J.; Manabe, A.; Mandelli, L.; Mandić, I.; Mandrysch, R.; Maneira, J.; Mangeard, P. S.; Manjavidze, I. D.; Manning, P. M.; Manousakis-Katsikakis, A.; Mansoulie, B.; Mapelli, A.; Mapelli, L.; March, L.; Marchand, J. F.; Marchese, F.; Marchiori, G.; Marcisovsky, M.; Marino, C. P.; Marques, C. N.; Marroquim, F.; Marshall, R.; Marshall, Z.; Martens, F. K.; Marti I Garcia, S.; Martin, A. J.; Martin, A. J.; Martin, B.; Martin, B.; Martin, F. F.; Martin, J. P.; Martin, T. A.; Martin Dit Latour, B.; Martinez, M.; Martinez Outschoorn, V.; Martini, A.; Martyniuk, A. C.; Maruyama, T.; Marzano, F.; Marzin, A.; Masetti, L.; Mashimo, T.; Mashinistov, R.; Masik, J.; Maslennikov, A. L.; Massaro, G.; Massol, N.; Mastroberardino, A.; Masubuchi, T.; Mathes, M.; Matricon, P.; Matsunaga, H.; Matsushita, T.; Mattravers, C.; Maxfield, S. J.; May, E. N.; Mayne, A.; Mazini, R.; Mazur, M.; Mazzanti, M.; Mazzanti, P.; Mc Donald, J.; Mc Kee, S. P.; McCarn, A.; McCarthy, R. L.; McCubbin, N. A.; McFarlane, K. W.; McGlone, H.; McHedlidze, G.; McLaren, R. A.; McMahon, S. J.; McMahon, T. R.; McPherson, R. A.; Meade, A.; Mechnich, J.; Mechtel, M.; Medinnis, M.; Meera-Lebbai, R.; Meguro, T. M.; Mehdiyev, R.; Mehlhase, S.; Mehta, A.; Meier, K.; Meirose, B.; Melachrinos, C.; Melamed-Katz, A.; Mellado Garcia, B. R.; Meng, Z.; Menke, S.; Meoni, E.; Merkl, D.; Mermod, P.; Merola, L.; Meroni, C.; Merritt, F. S.; Messina, A. M.; Messmer, I.; Metcalfe, J.; Mete, A. S.; Meyer, J.-P.; Meyer, J.; Meyer, J.; Meyer, T. C.; Meyer, W. T.; Miao, J.; Michal, S.; Micu, L.; Middleton, R. P.; Migas, S.; Mijović, L.; Mikenberg, G.; Mikuž, M.; Miller, D. W.; Mills, W. J.; Mills, C. M.; Milov, A.; Milstead, D. A.; Minaenko, A. A.; Miñano, M.; Minashvili, I. A.; Mincer, A. I.; Mindur, B.; Mineev, M.; Ming, Y.; Mir, L. M.; Mirabelli, G.; Misawa, S.; Miscetti, S.; Misiejuk, A.; Mitrevski, J.; Mitsou, V. A.; Miyagawa, P. S.; Mjörnmark, J. U.; Mladenov, D.; Moa, T.; Moed, S.; Moeller, V.; Mönig, K.; Möser, N.; Mohn, B.; Mohr, W.; Mohrdieck-Möck, S.; Moles-Valls, R.; Molina-Perez, J.; Moloney, G.; Monk, J.; Monnier, E.; Montesano, S.; Monticelli, F.; Moore, R. W.; Mora Herrera, C.; Moraes, A.; Morais, A.; Morel, J.; Morello, G.; Moreno, D.; Llácer, M. Moreno; Morettini, P.; Morii, M.; Morley, A. K.; Mornacchi, G.; Morozov, S. V.; Morris, J. D.; Moser, H. G.; Mosidze, M.; Moss, J.; Mount, R.; Mountricha, E.; Mouraviev, S. V.; Moyse, E. J. W.; Mudrinic, M.; Mueller, F.; Mueller, J.; Mueller, K.; Müller, T. A.; Muenstermann, D.; Muir, A.; Munwes, Y.; Murillo Garcia, R.; Murray, W. J.; Mussche, I.; Musto, E.; Myagkov, A. G.; Myska, M.; Nadal, J.; Nagai, K.; Nagano, K.; Nagasaka, Y.; Nairz, A. M.; Nakamura, K.; Nakano, I.; Nakatsuka, H.; Nanava, G.; Napier, A.; Nash, M.; Nation, N. R.; Nattermann, T.; Naumann, T.; Navarro, G.; Nderitu, S. K.; Neal, H. A.; Nebot, E.; Nechaeva, P.; Negri, A.; Negri, G.; Nelson, A.; Nelson, T. K.; Nemecek, S.; Nemethy, P.; Nepomuceno, A. A.; Nessi, M.; Neubauer, M. S.; Neusiedl, A.; Neves, R. N.; Nevski, P.; Newcomer, F. M.; Nickerson, R. B.; Nicolaidou, R.; Nicolas, L.; Nicoletti, G.; Niedercorn, F.; Nielsen, J.; Nikiforov, A.; Nikolaev, K.; Nikolic-Audit, I.; Nikolopoulos, K.; Nilsen, H.; Nilsson, P.; Nisati, A.; Nishiyama, T.; Nisius, R.; Nodulman, L.; Nomachi, M.; Nomidis, I.; Nordberg, M.; Nordkvist, B.; Notz, D.; Novakova, J.; Nozaki, M.; Nožička, M.; Nugent, I. M.; Nuncio-Quiroz, A.-E.; Nunes Hanninger, G.; Nunnemann, T.; Nurse, E.; O'Neil, D. C.; O'Shea, V.; Oakham, F. G.; Oberlack, H.; Ochi, A.; Oda, S.; Odaka, S.; Odier, J.; Odino, G. A.; Ogren, H.; Oh, A.; Oh, S. H.; Ohm, C. C.; Ohshima, T.; Ohshita, H.; Ohsugi, T.; Okada, S.; Okawa, H.; Okumura, Y.; Olcese, M.; Olchevski, A. G.; Oliveira, M.; Oliveira Damazio, D.; Oliver, J.; Oliver Garcia, E.; Olivito, D.; Olszewski, A.; Olszowska, J.; Omachi, C.; Onofre, A.; Onyisi, P. U. E.; Oram, C. J.; Ordonez, G.; Oreglia, M. J.; Oren, Y.; Orestano, D.; Orlov, I.; Oropeza Barrera, C.; Orr, R. S.; Ortega, E. O.; Osculati, B.; Ospanov, R.; Osuna, C.; Otec, R.; P Ottersbach, J.; Ould-Saada, F.; Ouraou, A.; Ouyang, Q.; Owen, M.; Owen, S.; Oyarzun, A.; Ozcan, V. E.; Ozone, K.; Ozturk, N.; Pacheco Pages, A.; Padhi, S.; Padilla Aranda, C.; Paganis, E.; Pahl, C.; Paige, F.; Pajchel, K.; Palestini, S.; Pallin, D.; Palma, A.; Palmer, J. D.; Pan, Y. B.; Panagiotopoulou, E.; Panes, B.; Panikashvili, N.; Panitkin, S.; Pantea, D.; Panuskova, M.; Paolone, V.; Papadopoulou, Th. D.; Park, S. J.; Park, W.; Parker, M. A.; Parker, S. I.; Parodi, F.; Parsons, J. A.; Parzefall, U.; Pasqualucci, E.; Passardi, G.; Passeri, A.; Pastore, F.; Pastore, Fr.; Pásztor, G.; Pataraia, S.; Pater, J. R.; Patricelli, S.; Patwa, A.; Pauly, T.; Peak, L. S.; Pecsy, M.; Pedraza Morales, M. I.; Peleganchuk, S. V.; Peng, H.; Penson, A.; Penwell, J.; Perantoni, M.; Perez, K.; Perez Codina, E.; Pérez García-Estañ, M. T.; Perez Reale, V.; Perini, L.; Pernegger, H.; Perrino, R.; Perrodo, P.; Persembe, S.; Perus, P.; Peshekhonov, V. D.; Petersen, B. A.; Petersen, J.; Petersen, T. C.; Petit, E.; Petridou, C.; Petrolo, E.; Petrucci, F.; Petschull, D.; Petteni, M.; Pezoa, R.; Pfeifer, B.; Phan, A.; Phillips, A. W.; Piacquadio, G.; Piccinini, M.; Piegaia, R.; Pilcher, J. E.; Pilkington, A. D.; Pina, J.; Pinamonti, M.; Pinfold, J. L.; Ping, J.; Pinto, B.; Pizio, C.; Placakyte, R.; Plamondon, M.; Plano, W. G.; Pleier, M.-A.; Poblaguev, A.; Poddar, S.; Podlyski, F.; Poffenberger, P.; Poggioli, L.; Pohl, M.; Polci, F.; Polesello, G.; Policicchio, A.; Polini, A.; Poll, J.; Polychronakos, V.; Pomarede, D. M.; Pomeroy, D.; Pommès, K.; Pontecorvo, L.; Pope, B. G.; Popovic, D. S.; Poppleton, A.; Popule, J.; Portell Bueso, X.; Porter, R.; Pospelov, G. E.; Pospichal, P.; Pospisil, S.; Potekhin, M.; Potrap, I. N.; Potter, C. J.; Potter, C. T.; Potter, K. P.; Poulard, G.; Poveda, J.; Prabhu, R.; Pralavorio, P.; Prasad, S.; Pravahan, R.; Preda, T.; Pretzl, K.; Pribyl, L.; Price, D.; Price, L. E.; Prichard, P. M.; Prieur, D.; Primavera, M.; Prokofiev, K.; Prokoshin, F.; Protopopescu, S.; Proudfoot, J.; Prudent, X.; Przysiezniak, H.; Psoroulas, S.; Ptacek, E.; Puigdengoles, C.; Purdham, J.; Purohit, M.; Puzo, P.; Pylypchenko, Y.; Qi, M.; Qian, J.; Qian, W.; Qian, Z.; Qin, Z.; Qing, D.; Quadt, A.; Quarrie, D. R.; Quayle, W. B.; Quinonez, F.; Raas, M.; Radeka, V.; Radescu, V.; Radics, B.; Rador, T.; Ragusa, F.; Rahal, G.; Rahimi, A. M.; Rahm, D.; Rajagopalan, S.; Rammes, M.; Ratoff, P. N.; Rauscher, F.; Rauter, E.; Raymond, M.; Read, A. L.; Rebuzzi, D. M.; Redelbach, A.; Redlinger, G.; Reece, R.; Reeves, K.; Reinherz-Aronis, E.; Reinsch, A.; Reisinger, I.; Reljic, D.; Rembser, C.; Ren, Z. L.; Renkel, P.; Rescia, S.; Rescigno, M.; Resconi, S.; Resende, B.; Reznicek, P.; Rezvani, R.; Richards, A.; Richards, R. A.; Richter, R.; Richter-Was, E.; Ridel, M.; Rieke, S.; Rijpstra, M.; Rijssenbeek, M.; Rimoldi, A.; Rinaldi, L.; Rios, R. R.; Riu, I.; Rivoltella, G.; Rizatdinova, F.; Rizvi, E. R.; Roa Romero, D. A.; Robertson, S. H.; Robichaud-Veronneau, A.; Robinson, D.; Robinson, J.; Robinson, M.; Robson, A.; Rocha de Lima, J. G.; Roda, C.; Roda Dos Santos, D.; Rodriguez, D.; Rodriguez Garcia, Y.; Roe, S.; Røhne, O.; Rojo, V.; Rolli, S.; Romaniouk, A.; Romanov, V. M.; Romeo, G.; Romero Maltrana, D.; Roos, L.; Ros, E.; Rosati, S.; Rosenbaum, G. A.; Rosenberg, E. I.; Rosselet, L.; Rossetti, V.; Rossi, L. P.; Rotaru, M.; Rothberg, J.; Rottländer, I.; Rousseau, D.; Royon, C. R.; Rozanov, A.; Rozen, Y.; Ruan, X.; Ruckert, B.; Ruckstuhl, N.; Rud, V. I.; Rudolph, G.; Rühr, F.; Ruggieri, F.; Ruiz-Martinez, A.; Rumyantsev, L.; Rusakovich, N. A.; Rutherfoord, J. P.; Ruwiedel, C.; Ruzicka, P.; Ryabov, Y. F.; Ryadovikov, V.; Ryan, P.; Rybkin, G.; Rzaeva, S.; Saavedra, A. F.; Sadrozinski, H. F.-W.; Sadykov, R.; Sakamoto, H.; Salamanna, G.; Salamon, A.; Saleem, M.; Salihagic, D.; Salnikov, A.; Salt, J.; Salvachua Ferrando, B. M.; Salvatore, D.; Salvatore, F.; Salvucci, A.; Salzburger, A.; Sampsonidis, D.; Samset, B. H.; Sanchis Lozano, M. A.; Sandaker, H.; Sander, H. G.; Sanders, M. P.; Sandhoff, M.; Sandstroem, R.; Sandvoss, S.; Sankey, D. P. C.; Sanny, B.; Sansoni, A.; Santamarina Rios, C.; Santi, L.; Santoni, C.; Santonico, R.; Santos, J.; Saraiva, J. G.; Sarangi, T.; Sarkisyan-Grinbaum, E.; Sarri, F.; Sasaki, O.; Sasaki, T.; Sasao, N.; Satsounkevitch, I.; Sauvage, G.; Savard, P.; Savine, A. Y.; Savinov, V.; Sawyer, L.; Saxon, D. H.; Says, L. P.; Sbarra, C.; Sbrizzi, A.; Scannicchio, D. A.; Schaarschmidt, J.; Schacht, P.; Schäfer, U.; Schaetzel, S.; Schaffer, A. C.; Schaile, D.; Schamberger, R. D.; Schamov, A. G.; Schegelsky, V. A.; Scheirich, D.; Schernau, M.; Scherzer, M. I.; Schiavi, C.; Schieck, J.; Schioppa, M.; Schlenker, S.; Schlereth, J. L.; Schmid, P.; Schmieden, K.; Schmitt, C.; Schmitz, M.; Schott, M.; Schouten, D.; Schovancova, J.; Schram, M.; Schreiner, A.; Schroeder, C.; Schroer, N.; Schroers, M.; Schuler, G.; Schultes, J.; Schultz-Coulon, H.-C.; Schumacher, J. W.; Schumacher, M.; Schumm, B. A.; Schune, Ph.; Schwanenberger, C.; Schwartzman, A.; Schwemling, Ph.; Schwienhorst, R.; Schwierz, R.; Schwindling, J.; Scott, W. G.; Searcy, J.; Sedykh, E.; Segura, E.; Seidel, S. C.; Seiden, A.; Seifert, F.; Seixas, J. M.; Sekhniaidze, G.; Seliverstov, D. M.; Sellden, B.; Seman, M.; Semprini-Cesari, N.; Serfon, C.; Serin, L.; Seuster, R.; Severini, H.; Sevior, M. E.; Sfyrla, A.; Shabalina, E.; Shamim, M.; Shan, L. Y.; Shank, J. T.; Shao, Q. T.; Shapiro, M.; Shatalov, P. B.; Shaver, L.; Shaw, K.; Sherman, D.; Sherwood, P.; Shibata, A.; Shimojima, M.; Shin, T.; Shmeleva, A.; Shochet, M. J.; Shupe, M. A.; Sicho, P.; Sidoti, A.; Siebel, A.; Siegert, F.; Siegrist, J.; Sijacki, Dj.; Silbert, O.; Silva, J.; Silver, Y.; Silverstein, D.; Silverstein, S. B.; Simak, V.; Simic, Lj.; Simion, S.; Simmons, B.; Simonyan, M.; Sinervo, P.; Sinev, N. B.; Sipica, V.; Siragusa, G.; Sisakyan, A. N.; Sivoklokov, S. Yu.; Sjoelin, J.; Sjursen, T. B.; Skubic, P.; Skvorodnev, N.; Slater, M.; Slavicek, T.; Sliwa, K.; Sloper, J.; Sluka, T.; Smakhtin, V.; Smirnov, S. Yu.; Smirnov, Y.; Smirnova, L. N.; Smirnova, O.; Smith, B. C.; Smith, D.; Smith, K. M.; Smizanska, M.; Smolek, K.; Snesarev, A. A.; Snow, S. W.; Snow, J.; Snuverink, J.; Snyder, S.; Soares, M.; Sobie, R.; Sodomka, J.; Soffer, A.; Solans, C. A.; Solar, M.; Solc, J.; Solfaroli Camillocci, E.; Solodkov, A. A.; Solovyanov, O. V.; Soluk, R.; Sondericker, J.; Sopko, V.; Sopko, B.; Sosebee, M.; Sosnovtsev, V. V.; Sospedra Suay, L.; Soukharev, A.; Spagnolo, S.; Spanò, F.; Speckmayer, P.; Spencer, E.; Spighi, R.; Spigo, G.; Spila, F.; Spiwoks, R.; Spousta, M.; Spreitzer, T.; Spurlock, B.; Denis, R. D. St.; Stahl, T.; Stahlman, J.; Stamen, R.; Stancu, S. N.; Stanecka, E.; Stanek, R. W.; Stanescu, C.; Stapnes, S.; Starchenko, E. A.; Stark, J.; Staroba, P.; Starovoitov, P.; Stastny, J.; Staude, A.; Stavina, P.; Stavropoulos, G.; Steele, G.; Steinbach, P.; Steinberg, P.; Stekl, I.; Stelzer, B.; Stelzer, H. J.; Stelzer-Chilton, O.; Stenzel, H.; Stevenson, K.; Stewart, G.; Stockton, M. C.; Stoerig, K.; Stoicea, G.; Stonjek, S.; Strachota, P.; Stradling, A.; Straessner, A.; Strandberg, J.; Strandberg, S.; Strandlie, A.; Strauss, M.; Strizenec, P.; Ströhmer, R.; Strom, D. M.; Strong, J. A.; Stroynowski, R.; Strube, J.; Stugu, B.; Stumer, I.; Soh, D. A.; Su, D.; Suchkov, S. I.; Sugaya, Y.; Sugimoto, T.; Suhr, C.; Suk, M.; Sulin, V. V.; Sultansoy, S.; Sumida, T.; Sun, X.; Sundermann, J. E.; Suruliz, K.; Sushkov, S.; Susinno, G.; Sutton, M. R.; Suzuki, T.; Suzuki, Y.; Sviridov, Yu. M.; Sykora, I.; Sykora, T.; Szymocha, T.; Sánchez, J.; Ta, D.; Tackmann, K.; Taffard, A.; Tafirout, R.; Taga, A.; Takahashi, Y.; Takai, H.; Takashima, R.; Takeda, H.; Takeshita, T.; Talby, M.; Talyshev, A.; Tamsett, M. C.; Tanaka, J.; Tanaka, R.; Tanaka, S.; Tanaka, S.; Tappern, G. P.; Tapprogge, S.; Tardif, D.; Tarem, S.; Tarrade, F.; Tartarelli, G. F.; Tas, P.; Tasevsky, M.; Tassi, E.; Tatarkhanov, M.; Taylor, C.; Taylor, F. E.; Taylor, G. N.; Taylor, R. P.; Taylor, W.; Teixeira-Dias, P.; Ten Kate, H.; Teng, P. K.; Tennenbaum-Katan, Y. D.; Terada, S.; Terashi, K.; Terron, J.; Terwort, M.; Testa, M.; Teuscher, R. J.; Tevlin, C. M.; Thadome, J.; Thananuwong, R.; Thioye, M.; Thoma, S.; Thomas, J. P.; Thomas, T. L.; Thompson, E. N.; Thompson, P. D.; Thompson, P. D.; Thompson, R. J.; Thompson, A. S.; Thomson, E.; Thun, R. P.; Tic, T.; Tikhomirov, V. O.; Tikhonov, Y. A.; Timmermans, C. J. W. P.; Tipton, P.; Tique Aires Viegas, F. J.; Tisserant, S.; Tobias, J.; Toczek, B.; Todorov, T.; Todorova-Nova, S.; Toggerson, B.; Tojo, J.; Tokár, S.; Tokushuku, K.; Tollefson, K.; Tomasek, L.; Tomasek, M.; Tomasz, F.; Tomoto, M.; Tompkins, D.; Tompkins, L.; Toms, K.; Tong, G.; Tonoyan, A.; Topfel, C.; Topilin, N. D.; Torrence, E.; Torró Pastor, E.; Toth, J.; Touchard, F.; Tovey, D. R.; Tovey, S. N.; Trefzger, T.; Tremblet, L.; Tricoli, A.; Trigger, I. M.; Trincaz-Duvoid, S.; Trinh, T. N.; Tripiana, M. F.; Triplett, N.; Trischuk, W.; Trivedi, A.; Trocmé, B.; Troncon, C.; Trzupek, A.; Tsarouchas, C.; Tseng, J. C.-L.; Tsiafis, I.; Tsiakiris, M.; Tsiareshka, P. V.; Tsionou, D.; Tsipolitis, G.; Tsiskaridze, V.; Tskhadadze, E. G.; Tsukerman, I. I.; Tsulaia, V.; Tsung, J.-W.; Tsuno, S.; Tsybychev, D.; Turala, M.; Turecek, D.; Turk Cakir, I.; Turlay, E.; Tuts, P. M.; Twomey, M. S.; Tylmad, M.; Tyndel, M.; Tzanakos, G.; Uchida, K.; Ueda, I.; Ugland, M.; Uhlenbrock, M.; Uhrmacher, M.; Ukegawa, F.; Unal, G.; Underwood, D. G.; Undrus, A.; Unel, G.; Unno, Y.; Urbaniec, D.; Urkovsky, E.; Urquijo, P.; Urrejola, P.; Usai, G.; Uslenghi, M.; Vacavant, L.; Vacek, V.; Vachon, B.; Vahsen, S.; Valenta, J.; Valente, P.; Valentinetti, S.; Valkar, S.; Valladolid Gallego, E.; Vallecorsa, S.; Valls Ferrer, J. A.; van Berg, R.; van der Graaf, H.; van der Kraaij, E.; van der Poel, E.; van der Ster, D.; van Eldik, N.; van Gemmeren, P.; van Kesteren, Z.; van Vulpen, I.; Vandelli, W.; Vandoni, G.; Vaniachine, A.; Vankov, P.; Vannucci, F.; Varela Rodriguez, F.; Vari, R.; Varnes, E. W.; Varouchas, D.; Vartapetian, A.; Varvell, K. E.; Vasilyeva, L.; Vassilakopoulos, V. I.; Vazeille, F.; Vegni, G.; Veillet, J. J.; Vellidis, C.; Veloso, F.; Veness, R.; Veneziano, S.; Ventura, A.; Ventura, D.; Venturi, M.; Venturi, N.; Vercesi, V.; Verducci, M.; Verkerke, W.; Vermeulen, J. C.; Vetterli, M. C.; Vichou, I.; Vickey, T.; Viehhauser, G. H. A.; Villa, M.; Villani, E. G.; Villaplana Perez, M.; Villate, J.; Vilucchi, E.; Vincter, M. G.; Vinek, E.; Vinogradov, V. B.; Viret, S.; Virzi, J.; Vitale, A.; Vitells, O. V.; Vivarelli, I.; Vives Vaques, F.; Vlachos, S.; Vlasak, M.; Vlasov, N.; Vogel, A.; Vokac, P.; Volpi, M.; Volpini, G.; von der Schmitt, H.; von Loeben, J.; von Radziewski, H.; von Toerne, E.; Vorobel, V.; Vorobiev, A. P.; Vorwerk, V.; Vos, M.; Voss, R.; Voss, T. T.; Vossebeld, J. H.; Vranjes, N.; Vranjes Milosavljevic, M.; Vrba, V.; Vreeswijk, M.; Vu Anh, T.; Vudragovic, D.; Vuillermet, R.; Vukotic, I.; Wagner, P.; Wahlen, H.; Walbersloh, J.; Walder, J.; Walker, R.; Walkowiak, W.; Wall, R.; Wang, C.; Wang, H.; Wang, J.; Wang, J. C.; Wang, S. M.; Ward, C. P.; Warsinsky, M.; Wastie, R.; Watkins, P. M.; Watson, A. T.; Watson, M. F.; Watts, G.; Watts, S.; Waugh, A. T.; Waugh, B. M.; Webel, M.; Weber, J.; Weber, M. D.; Weber, M.; Weber, M. S.; Weber, P.; Weidberg, A. R.; Weingarten, J.; Weiser, C.; Wellenstein, H.; Wells, P. S.; Wen, M.; Wenaus, T.; Wendler, S.; Wengler, T.; Wenig, S.; Wermes, N.; Werner, M.; Werner, P.; Werth, M.; Werthenbach, U.; Wessels, M.; Whalen, K.; Wheeler-Ellis, S. J.; Whitaker, S. P.; White, A.; White, M. J.; White, S.; Whiteson, D.; Whittington, D.; Wicek, F.; Wicke, D.; Wickens, F. J.; Wiedenmann, W.; Wielers, M.; Wienemann, P.; Wiglesworth, C.; Wiik, L. A. M.; Wildauer, A.; Wildt, M. A.; Wilhelm, I.; Wilkens, H. G.; Williams, E.; Williams, H. H.; Willis, W.; Willocq, S.; Wilson, J. A.; Wilson, M. G.; Wilson, A.; Wingerter-Seez, I.; Winklmeier, F.; Wittgen, M.; Wolter, M. W.; Wolters, H.; Wosiek, B. K.; Wotschack, J.; Woudstra, M. J.; Wraight, K.; Wright, C.; Wright, D.; Wrona, B.; Wu, S. L.; Wu, X.; Wulf, E.; Xella, S.; Xie, S.; Xie, Y.; Xu, D.; Xu, N.; Yamada, M.; Yamamoto, A.; Yamamoto, S.; Yamamura, T.; Yamanaka, K.; Yamaoka, J.; Yamazaki, T.; Yamazaki, Y.; Yan, Z.; Yang, H.; Yang, U. K.; Yang, Y.; Yang, Z.; Yao, W.-M.; Yao, Y.; Yasu, Y.; Ye, J.; Ye, S.; Yilmaz, M.; Yoosoofmiya, R.; Yorita, K.; Yoshida, R.; Young, C.; Youssef, S. P.; Yu, D.; Yu, J.; Yu, M.; Yu, X.; Yuan, J.; Yuan, L.; Yurkewicz, A.; Zaidan, R.; Zaitsev, A. M.; Zajacova, Z.; Zambrano, V.; Zanello, L.; Zarzhitsky, P.; Zaytsev, A.; Zeitnitz, C.; Zeller, M.; Zema, P. F.; Zemla, A.; Zendler, C.; Zenin, O.; Zenis, T.; Zenonos, Z.; Zenz, S.; Zerwas, D.; Zevi Della Porta, G.; Zhan, Z.; Zhang, H.; Zhang, J.; Zhang, Q.; Zhang, X.; Zhao, L.; Zhao, T.; Zhao, Z.; Zhemchugov, A.; Zheng, S.; Zhong, J.; Zhou, B.; Zhou, N.; Zhou, Y.; Zhu, C. G.; Zhu, H.; Zhu, Y.; Zhuang, X.; Zhuravlov, V.; Zimmermann, R.; Zimmermann, S.; Zimmermann, S.; Ziolkowski, M.; Zitoun, R.; Živković, L.; Zmouchko, V. V.; Zobernig, G.; Zoccoli, A.; Zur Nedden, M.; Zutshi, V.

    2010-12-01

    The ionization signals in the liquid argon of the ATLAS electromagnetic calorimeter are studied in detail using cosmic muons. In particular, the drift time of the ionization electrons is measured and used to assess the intrinsic uniformity of the calorimeter gaps and estimate its impact on the constant term of the energy resolution. The drift times of electrons in the cells of the second layer of the calorimeter are uniform at the level of 1.3% in the barrel and 2.8% in the endcaps. This leads to an estimated contribution to the constant term of (0.29^{+0.05}_{-0.04})% in the barrel and (0.54^{+0.06}_{-0.04})% in the endcaps. The same data are used to measure the drift velocity of ionization electrons in liquid argon, which is found to be 4.61±0.07 mm/μs at 88.5 K and 1 kV/mm.

  14. Lower temperature curing thermoset polyimides utilizing a substituted norbornene endcap

    NASA Technical Reports Server (NTRS)

    Waters, John F.; Sukenik, Chaim N.; Kennedy, Vance O.; Livneh, Mordechai; Youngs, Wiley J.; Sutter, James K.; Meador, Mary A. B.; Burke, Luke A.; Ahn, Myong K.

    1992-01-01

    Methoxycarbonyl bridgehead substituted nadic diacid monomethyl ester, when used as an endcapping monomer, lowered the cure temperature of thermoset PMR polyimides without seriously affecting other desirable properties, such as glass transition temperature and thermal oxidative stability. The C-13 CP/MAS NMR of model compounds was used to follow the cure of resin systems using both the unmodified nadic endcap and the methoxycarbonyl-substituted endcap. Rheological analysis and differential scanning calorimetry DSC also provided evidence for the lower curing nature of the substituted endcap. Two regioisomers of the bridgehead-substituted endcap were isolated, and their chemical structures were elucidated by X-ray crystallography. The model compound and molecular modeling studies conducted ruled out the possibility of regioisomeric imide formation in the substituted endcaps.

  15. The Laser calibration of the ATLAS Tile Calorimeter during the LHC run 1

    DOE PAGES

    Abdallah, J.; Alexa, C.; Coutinho, Y. Amaral; ...

    2016-10-12

    This article describes the Laser calibration system of the ATLAS hadronic Tile Calorimeter that has been used during the run 1 of the LHC . First, the stability of the system associated readout electronics is studied. It is found to be stable with variations smaller than 0.6 %. Then, the method developed to compute the calibration constants, to correct for the variations of the gain of the calorimeter photomultipliers, is described. These constants were determined with a statistical uncertainty of 0.3 % and a systematic uncertainty of 0.2 % for the central part of the calorimeter and 0.5 % formore » the end-caps. Lastly, the detection and correction of timing mis-configuration of the Tile Calorimeter using the Laser system are also presented.« less

  16. The Laser calibration of the ATLAS Tile Calorimeter during the LHC run 1

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Abdallah, J.; Alexa, C.; Coutinho, Y. Amaral

    This article describes the Laser calibration system of the ATLAS hadronic Tile Calorimeter that has been used during the run 1 of the LHC . First, the stability of the system associated readout electronics is studied. It is found to be stable with variations smaller than 0.6 %. Then, the method developed to compute the calibration constants, to correct for the variations of the gain of the calorimeter photomultipliers, is described. These constants were determined with a statistical uncertainty of 0.3 % and a systematic uncertainty of 0.2 % for the central part of the calorimeter and 0.5 % formore » the end-caps. Lastly, the detection and correction of timing mis-configuration of the Tile Calorimeter using the Laser system are also presented.« less

  17. Ceramic pressure housing with metal endcaps

    DOEpatents

    Downing, Jr., John P.; DeRoos, Bradley G.; Hackman, Donald J.

    1995-01-01

    A housing for the containment of instrumentation in a high pressure fluid environment that consists of a metallic endcap and ceramic cylinder bonded together. The improvement comprises a structure which results in the improved sealing of said housing as the fluid pressure increases. The cylindrical ceramic tube and endcap are dimensioned such that mechanical failure does not occur when exposed to the desired external operating pressures which includes up to 36,000 feet of water. The housing is designed to withstand the external operating pressures without being subject to mechanical failure or excessive deformation which results in the loss of pressure housing integrity via cracking or deformation of the ceramic tube, deformation of the endcap, or from failure of the bonding agent.

  18. Ceramic pressure housing with metal endcaps

    DOEpatents

    Downing, J.P. Jr.; DeRoos, B.G.; Hackman, D.J.

    1995-06-27

    A housing is disclosed for the containment of instrumentation in a high pressure fluid environment that consists of a metallic endcap and ceramic cylinder bonded together. The improvement comprises a structure which results in the improved sealing of said housing as the fluid pressure increases. The cylindrical ceramic tube and endcap are dimensioned such that mechanical failure does not occur when exposed to the desired external operating pressures which includes up to 36,000 feet of water. The housing is designed to withstand the external operating pressures without being subject to mechanical failure or excessive deformation which results in the loss of pressure housing integrity via cracking or deformation of the ceramic tube, deformation of the endcap, or from failure of the bonding agent. 9 figs.

  19. The ATLAS Level-1 Topological Trigger performance in Run 2

    NASA Astrophysics Data System (ADS)

    Riu, Imma; ATLAS Collaboration

    2017-10-01

    The Level-1 trigger is the first event rate reducing step in the ATLAS detector trigger system, with an output rate of up to 100 kHz and decision latency smaller than 2.5 μs. During the LHC shutdown after Run 1, the Level-1 trigger system was upgraded at hardware, firmware and software levels. In particular, a new electronics sub-system was introduced in the real-time data processing path: the Level-1 Topological trigger system. It consists of a single electronics shelf equipped with two Level-1 Topological processor blades. They receive real-time information from the Level-1 calorimeter and muon triggers, which is processed to measure angles between trigger objects, invariant masses or other kinematic variables. Complementary to other requirements, these measurements are taken into account in the final Level-1 trigger decision. The system was installed and commissioning started in 2015 and continued during 2016. As part of the commissioning, the decisions from individual algorithms were simulated and compared with the hardware response. An overview of the Level-1 Topological trigger system design, commissioning process and impact on several event selections are illustrated.

  20. Study of surface properties of ATLAS12 strip sensors and their radiation resistance

    NASA Astrophysics Data System (ADS)

    Mikestikova, M.; Allport, P. P.; Baca, M.; Broughton, J.; Chisholm, A.; Nikolopoulos, K.; Pyatt, S.; Thomas, J. P.; Wilson, J. A.; Kierstead, J.; Kuczewski, P.; Lynn, D.; Hommels, L. B. A.; Ullan, M.; Bloch, I.; Gregor, I. M.; Tackmann, K.; Hauser, M.; Jakobs, K.; Kuehn, S.; Mahboubi, K.; Mori, R.; Parzefall, U.; Clark, A.; Ferrere, D.; Sevilla, S. Gonzalez; Ashby, J.; Blue, A.; Bates, R.; Buttar, C.; Doherty, F.; McMullen, T.; McEwan, F.; O'Shea, V.; Kamada, S.; Yamamura, K.; Ikegami, Y.; Nakamura, K.; Takubo, Y.; Unno, Y.; Takashima, R.; Chilingarov, A.; Fox, H.; Affolder, A. A.; Casse, G.; Dervan, P.; Forshaw, D.; Greenall, A.; Wonsak, S.; Wormald, M.; Cindro, V.; Kramberger, G.; Mandić, I.; Mikuž, M.; Gorelov, I.; Hoeferkamp, M.; Palni, P.; Seidel, S.; Taylor, A.; Toms, K.; Wang, R.; Hessey, N. P.; Valencic, N.; Hanagaki, K.; Dolezal, Z.; Kodys, P.; Bohm, J.; Stastny, J.; Bevan, A.; Beck, G.; Milke, C.; Domingo, M.; Fadeyev, V.; Galloway, Z.; Hibbard-Lubow, D.; Liang, Z.; Sadrozinski, H. F.-W.; Seiden, A.; To, K.; French, R.; Hodgson, P.; Marin-Reyes, H.; Parker, K.; Jinnouchi, O.; Hara, K.; Sato, K.; Hagihara, M.; Iwabuchi, S.; Bernabeu, J.; Civera, J. V.; Garcia, C.; Lacasta, C.; Marti i Garcia, S.; Rodriguez, D.; Santoyo, D.; Solaz, C.; Soldevila, U.

    2016-09-01

    A radiation hard n+-in-p micro-strip sensor for the use in the Upgrade of the strip tracker of the ATLAS experiment at the High Luminosity Large Hadron Collider (HL-LHC) has been developed by the "ATLAS ITk Strip Sensor collaboration" and produced by Hamamatsu Photonics. Surface properties of different types of end-cap and barrel miniature sensors of the latest sensor design ATLAS12 have been studied before and after irradiation. The tested barrel sensors vary in "punch-through protection" (PTP) structure, and the end-cap sensors, whose stereo-strips differ in fan geometry, in strip pitch and in edge strip ganging options. Sensors have been irradiated with proton fluences of up to 1×1016 neq/cm2, by reactor neutron fluence of 1×1015 neq/cm2 and by gamma rays from 60Co up to dose of 1 MGy. The main goal of the present study is to characterize the leakage current for micro-discharge breakdown voltage estimation, the inter-strip resistance and capacitance, the bias resistance and the effectiveness of PTP structures as a function of bias voltage and fluence. It has been verified that the ATLAS12 sensors have high breakdown voltage well above the operational voltage which implies that different geometries of sensors do not influence their stability. The inter-strip isolation is a strong function of irradiation fluence, however the sensor performance is acceptable in the expected range for HL-LHC. New gated PTP structure exhibits low PTP onset voltage and sharp cut-off of effective resistance even at the highest tested radiation fluence. The inter-strip capacitance complies with the technical specification required before irradiation and no radiation-induced degradation was observed. A summary of ATLAS12 sensors tests is presented including a comparison of results from different irradiation sites. The measured characteristics are compared with the previous prototype of the sensor design, ATLAS07.

  1. Better End-Cap Processing for Oxidation-Resistant Polyimides

    NASA Technical Reports Server (NTRS)

    Meador, Mary Ann B.; Frimer, Aryeh A.

    2004-01-01

    A class of end-cap compounds that increase the thermo-oxidative stab ility of polyimides of the polymerization of monomeric reactants (PM R) type has been extended. In addition, an improved processing proto col for this class of end-cap compounds has been invented.

  2. (3-aminopropyl)-4-methylpiperazine end-capped poly(1,4-butanediol diacrylate-co-4-amino-1-butanol)-based multilayer films for gene delivery.

    PubMed

    Li, Cuicui; Tzeng, Stephany Y; Tellier, Liane E; Green, Jordan J

    2013-07-10

    Biodegradable polyelectrolyte surfaces for gene delivery were created through electrospinning of biodegradable polycations combined with iterative solution-based multilayer coating. Poly(β-amino ester) (PBAE) poly(1,4-butanediol diacrylate-co-4-amino-1-butanol) end-capped with 1-(3-aminopropyl)-4-methylpiperazine was utilized because of its ability to electrostatically interact with anionic molecules like DNA, its biodegradability, and its low cytotoxicity. A new DNA release system was developed for sustained release of DNA over 24 h, accompanied by high exogenous gene expression in primary human glioblastoma (GB) cells. Electrospinning a different PBAE, poly(1,4-butanediol diacrylate-co-4,4'-trimethylenedipiperidine), and its combination with polyelectrolyte 1-(3-aminopropyl)-4-methylpiperazine end-capped poly(1,4-butanediol diacrylate-co-4-amino-1-butanol)-based multilayers are promising for DNA release and intracellular delivery from a surface.

  3. (3-Aminopropyl)-4-methylpiperazine End-capped Poly(1,4-butanediol diacrylate-co-4-amino-1-butanol)-based Multilayer Films for Gene Delivery

    PubMed Central

    Li, Cuicui; Tzeng, Stephany Y; Tellier, Liane E.; Green, Jordan J

    2013-01-01

    Biodegradable polyelectrolyte surfaces for gene delivery were created through electrospinning of biodegradable polycations combined with iterative solution-based multilayer coating. Poly(β-amino ester) (PBAE) poly(1,4-butanediol diacrylate-co-4-amino-1-butanol) end-capped with 1-(3-aminopropyl)-4-methylpiperazine was utilized due to its ability to electrostatically interact with anionic molecules like DNA, its biodegradability, and its low cytotoxicity. A new DNA release system was developed for sustained release of DNA over 24 hours, accompanied by high exogenous gene expression in primary human glioblastoma (GB) cells. Electrospinning a different PBAE, poly(1,4-butanediol diacrylate-co-4,4′-trimethylenedipiperidine), and its combination with polyelectrolyte 1-(3-aminopropyl)-4-methylpiperazine end-capped poly(1,4-butanediol diacrylate-co-4-amino-1-butanol)-based multilayers are promising for DNA release and intracellular delivery from a surface. PMID:23755861

  4. Laser rods with undoped, flanged end-caps for end-pumped laser applications

    DOEpatents

    Meissner, Helmuth E.; Beach, Raymond J.; Bibeau, Camille; Sutton, Steven B.; Mitchell, Scott; Bass, Isaac; Honea, Eric

    1999-01-01

    A method and apparatus for achieving improved performance in a solid state laser is provided. A flanged, at least partially undoped end-cap is attached to at least one end of a laserable medium. Preferably flanged, undoped end-caps are attached to both ends of the laserable medium. Due to the low scatter requirements for the interface between the end-caps and the laser rod, a non-adhesive method of bonding is utilized such as optical contacting combined with a subsequent heat treatment of the optically contacted composite. The non-bonded end surfaces of the flanged end-caps are coated with laser cavity coatings appropriate for the lasing wavelength of the laser rod. A cooling jacket, sealably coupled to the flanged end-caps, surrounds the entire length of the laserable medium. Radiation from a pump source is focussed by a lens duct and passed through at least one flanged end-cap into the laser rod.

  5. Laser rods with undoped, flanged end-caps for end-pumped laser applications

    DOEpatents

    Meissner, H.E.; Beach, R.J.; Bibeau, C.; Sutton, S.B.; Mitchell, S.; Bass, I.; Honea, E.

    1999-08-10

    A method and apparatus for achieving improved performance in a solid state laser is provided. A flanged, at least partially undoped end-cap is attached to at least one end of a laserable medium. Preferably flanged, undoped end-caps are attached to both ends of the laserable medium. Due to the low scatter requirements for the interface between the end-caps and the laser rod, a non-adhesive method of bonding is utilized such as optical contacting combined with a subsequent heat treatment of the optically contacted composite. The non-bonded end surfaces of the flanged end-caps are coated with laser cavity coatings appropriate for the lasing wavelength of the laser rod. A cooling jacket, sealably coupled to the flanged end-caps, surrounds the entire length of the laserable medium. Radiation from a pump source is focused by a lens duct and passed through at least one flanged end-cap into the laser rod. 14 figs.

  6. Site-specific multipoint fluorescence measurement system with end-capped optical fibers.

    PubMed

    Song, Woosub; Moon, Sucbei; Lee, Byoung-Cheol; Park, Chul-Seung; Kim, Dug Young; Kwon, Hyuk Sang

    2011-07-10

    We present the development and implementation of a spatially and spectrally resolved multipoint fluorescence correlation spectroscopy (FCS) system utilizing multiple end-capped optical fibers and an inexpensive laser source. Specially prepared end-capped optical fibers placed in an image plane were used to both collect fluorescence signals from the sample and to deliver signals to the detectors. The placement of independently selected optical fibers on the image plane was done by monitoring the end-capped fiber tips at the focus using a CCD, and fluorescence from specific positions of a sample were collected by an end-capped fiber, which could accurately represent light intensities or spectral data without incurring any disturbance. A fast multipoint spectroscopy system with a time resolution of ∼1.5 ms was then implemented using a prism and an electron multiplying charge coupled device with a pixel binning for the region of interest. The accuracy of our proposed system was subsequently confirmed by experimental results, based on an FCS analysis of microspheres in distilled water. We expect that the proposed multipoint site-specific fluorescence measurement system can be used as an inexpensive fluorescence measurement tool to study many intracellular and molecular dynamics in cell biology. © 2011 Optical Society of America

  7. The upgrade of the ATLAS first-level calorimeter trigger

    NASA Astrophysics Data System (ADS)

    Yamamoto, Shimpei; Atlas Collaboration

    2016-07-01

    The first-level calorimeter trigger (L1Calo) had operated successfully through the first data taking phase of the ATLAS experiment at the CERN Large Hadron Collider. Towards forthcoming LHC runs, a series of upgrades is planned for L1Calo to face new challenges posed by the upcoming increases of the beam energy and the luminosity. This paper reviews the ATLAS L1Calo trigger upgrade project that introduces new architectures for the liquid-argon calorimeter trigger readout and the L1Calo trigger processing system.

  8. The Phase-II ATLAS ITk pixel upgrade

    NASA Astrophysics Data System (ADS)

    Terzo, S.

    2017-07-01

    The entire tracking system of the ATLAS experiment will be replaced during the LHC Phase-II shutdown (foreseen to take place around 2025) by an all-silicon detector called the ``ITk'' (Inner Tracker). The innermost portion of ITk will consist of a pixel detector with five layers in the barrel region and ring-shaped supports in the end-cap regions. It will be instrumented with new sensor and readout electronics technologies to improve the tracking performance and cope with the HL-LHC environment, which will be severe in terms of occupancy and radiation levels. The new pixel system could include up to 14 m2 of silicon, depending on the final layout, which is expected to be decided in 2017. Several layout options are being investigated at the moment, including some with novel inclined support structures in the barrel end-cap overlap region and others with very long innermost barrel layers. Forward coverage could be as high as |eta| <4. Supporting structures will be based on low mass, highly stable and highly thermally conductive carbon-based materials cooled by evaporative carbon dioxide circulated in thin-walled titanium pipes embedded in the structures. Planar, 3D, and CMOS sensors are being investigated to identify the optimal technology, which may be different for the various layers. The RD53 Collaboration is developing the new readout chip. The pixel off-detector readout electronics will be implemented in the framework of the general ATLAS trigger and DAQ system. A readout speed of up to 5 Gb/s per data link will be needed in the innermost layers going down to 640 Mb/s for the outermost. Because of the very high radiation level inside the detector, the first part of the transmission has to be implemented electrically, with signals converted for optical transmission at larger radii. Extensive tests are being carried out to prove the feasibility of implementing serial powering, which has been chosen as the baseline for the ITk pixel system due to the reduced

  9. Multi-Threaded Algorithms for GPGPU in the ATLAS High Level Trigger

    NASA Astrophysics Data System (ADS)

    Conde Muíño, P.; ATLAS Collaboration

    2017-10-01

    General purpose Graphics Processor Units (GPGPU) are being evaluated for possible future inclusion in an upgraded ATLAS High Level Trigger farm. We have developed a demonstrator including GPGPU implementations of Inner Detector and Muon tracking and Calorimeter clustering within the ATLAS software framework. ATLAS is a general purpose particle physics experiment located on the LHC collider at CERN. The ATLAS Trigger system consists of two levels, with Level-1 implemented in hardware and the High Level Trigger implemented in software running on a farm of commodity CPU. The High Level Trigger reduces the trigger rate from the 100 kHz Level-1 acceptance rate to 1.5 kHz for recording, requiring an average per-event processing time of ∼ 250 ms for this task. The selection in the high level trigger is based on reconstructing tracks in the Inner Detector and Muon Spectrometer and clusters of energy deposited in the Calorimeter. Performing this reconstruction within the available farm resources presents a significant challenge that will increase significantly with future LHC upgrades. During the LHC data taking period starting in 2021, luminosity will reach up to three times the original design value. Luminosity will increase further to 7.5 times the design value in 2026 following LHC and ATLAS upgrades. Corresponding improvements in the speed of the reconstruction code will be needed to provide the required trigger selection power within affordable computing resources. Key factors determining the potential benefit of including GPGPU as part of the HLT processor farm are: the relative speed of the CPU and GPGPU algorithm implementations; the relative execution times of the GPGPU algorithms and serial code remaining on the CPU; the number of GPGPU required, and the relative financial cost of the selected GPGPU. We give a brief overview of the algorithms implemented and present new measurements that compare the performance of various configurations exploiting GPGPU cards.

  10. All-aromatic biphenylene end-capped polyquinoline and polyimide matrix resins

    NASA Technical Reports Server (NTRS)

    Droske, J. P.; Stille, J. K.; Alston, W. B.

    1985-01-01

    Biphenylene end-capped polyquinoline and polyimide resins afford low void content graphite-reinforced composites with good initial properties. However, with both resins, rapid degradation occurs during oxidative isothermal aging at elevated temperatures. The degradation is not observed during isothermal aging under a nitrogen atmosphere which suggests that the biphenylene end-cap (or the resulting crosslink/chain extension structures) is not particularly thermooxidatively stable. The nature of the thermooxidative instability is currently under investigation.

  11. NMR Guided Design of Endcaps With Improved Oxidation Resistance

    NASA Technical Reports Server (NTRS)

    Meador, Mary Ann B.; Frimer, Aryeh A.

    2002-01-01

    A polyimide is a polymer composed of alternating units of diamine and dianhydride, linked to each other via an imide bond. PMR polyimides, commonly used in the aerospace industry, are generally capped at each end by a norbornene endcap which serves a double function: (1) It limits the number of repeating units and, hence, the average molecular weight of the various polymer chains (oligomers), thereby improving processibility; (2) Upon further treatment (curing), the endcap crosslinks the various oligomer strands into a tough heat-resistant piece. Norbornenyl-end capped PMR polyimide resins' are widely used as polymer matrix composite materials for aircraft engine applications,2 since they combine ease of processing with good oxidative stability up to 300 C. PMR resins are prepared by a twestep approach involving the initial formation of oligomeric pre-polymers capped at both ends by a latent reactive end cap. The end cap undergoes cross-linking during higher temperature processing, producing the desired low density, high specific strength materials, as shown for PMR-15.

  12. Spectroscopic and chromatographic characterisation of a pentafluorophenylpropyl silica phase end-capped in supercritical carbon dioxide as a reaction solvent.

    PubMed

    Ashu-Arrah, Benjamin A; Glennon, Jeremy D; Albert, Klaus

    2013-07-12

    This research uses solid-state nuclear magnetic resonance (NMR) spectroscopy to characterise the nature and amount of different surface species, and chromatography to evaluate phase properties of a pentafluorophenylpropyl (PFPP) bonded silica phase prepared and end-capped using supercritical carbon dioxide (sc-CO2) as a reaction solvent. Under sc-CO2 reaction conditions (at temperature of 100 °C and pressure of 414 bar), a PFPP silica phase was prepared using 3-[(pentafluorophenyl)propyldimethylchlorosilane] within 1h. The bonded PFPP phase was subsequently end-capped with bis-N,O-trimethylsilylacetamide (BSA), hexamethyldisilazane (HMDS) and trimethylchlorosilane (TMCS) within 1h under the same sc-CO2 reaction conditions (100 °C/4141 bar). Elemental microanalysis, thermogravimetric analysis (TGA), and scanning electron microscopy (SEM) were used to provide support data to solid-state NMR and chromatographic evaluation. Results revealed a surface coverage of 2.2 μmol/m(2) for the non-end-capped PFPP silica phase while the PFPP phase end-capped with BSA gave a higher surface coverage (3.9 μmol/m(2)) compared to HMDS (2.9 μmol/m(2)) and TMCS (2.8 μmol/m(2)). (29)Si CP/MAS NMR analysis of the PFPP end-capped with BSA shows a significant decrease in the amount of Q(3) (free silanols) and Q(4) (siloxane groups) species, coupled with the absence of the most reactive Q(2) (geminal silanols) in addition to increased amount of a single resonance peak centred at +13 ppm (MH) corresponding to -Si-O-*Si-CH3 bond. (13)C CP/MAS NMR shows the resonance corresponding to the propyl linkage (CH3CH2CH2-) and methyl groups (Si(CH3)n) confirming successful silanisation and endcapping reactions in sc-CO2. Chromatographic evaluation of the BSA end-capped PFPP phase with Neue text mixture revealed improved chromatographic separation as evidenced in the enhanced retention of hydrophobic markers and decreased retention for basic solutes. Moreover, chromatography revealed a change in

  13. Effect of the endcapping of reversed-phase high-performance liquid chromatography adsorbents on the adsorption isotherm

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Gritti, Fabrice; Guiochon, Georges A

    2005-09-01

    The retention mechanisms of n-propylbenzoate, 4-t ert-butylphenol, and caffeine on the endcapped Symmetry-C{sub 18} and the non-endcapped Resolve-C{sub 18} are compared. The adsorption isotherms were measured by frontal analysis (FA), using as the mobile phase mixtures of methanol or acetonitrile and water of various compositions. The isotherm data were modeled and the adsorption energy distributions calculated. The surface heterogeneity increases faster with decreasing methanol concentration on the non-endcapped than on the endcapped adsorbent. For instance, for methanol concentrations exceeding 30% (v/v), the adsorption of caffeine is accounted for by assuming three and two different types of adsorption sites on Resolve-C{submore » 18} and Symmetry-C{sub 18}, respectively. This is explained by the effect of the mobile phase composition on the structure of the C{sub 18}-bonded layer. The bare surface of bonded silica appears more accessible to solute molecules at high water contents in the mobile phase. On the other hand, replacing methanol by a stronger organic modifier like acetonitrile dampens the differences between non-endcapped and endcapped stationary phase and decreases the degree of surface heterogeneity of the adsorbent. For instance, at acetonitrile concentrations exceeding 20%, the surface appears nearly homogeneous for the adsorption of caffeine.« less

  14. ATLAS, an integrated structural analysis and design system. Volume 1: ATLAS user's guide

    NASA Technical Reports Server (NTRS)

    Dreisbach, R. L. (Editor)

    1979-01-01

    Some of the many analytical capabilities provided by the ATLAS Version 4.0 System in the logical sequence are described in which model-definition data are prepared and the subsequent computer job is executed. The example data presented and the fundamental technical considerations that are highlighted can be used as guides during the problem solving process. This guide does not describe the details of the ATLAS capabilities, but provides an introduction to the new user of ATLAS to the level at which the complete array of capabilities described in the ATLAS User's Manual can be exploited fully.

  15. Fabrication of versatile cladding light strippers and fiber end-caps with CO2 laser radiation

    NASA Astrophysics Data System (ADS)

    Steinke, M.; Theeg, T.; Wysmolek, M.; Ottenhues, C.; Pulzer, T.; Neumann, J.; Kracht, D.

    2018-02-01

    We report on novel fabrication schemes of versatile cladding light strippers and end-caps via CO2 laser radiation. We integrated cladding light strippers in SMA-like connectors for reliable and stable fiber-coupling of high-power laser diodes. Moreover, the application of cladding light strippers in typical fiber geometries for high-power fiber lasers was evaluated. In addition, we also developed processes to fuse end-caps to fiber end faces via CO2 laser radiation and inscribe the fibers with cladding light strippers near the end-cap. Corresponding results indicate the great potential of such devices as a monolithic and low-cost alternative to SMA connectors.

  16. The ATLAS-1 Shuttle mission

    NASA Technical Reports Server (NTRS)

    Torr, Marsha R.; Sullivan, Kathryn D.

    1992-01-01

    The Atmospheric Laboratory for Applications and Science (ATLAS-1) encompasses instruments which will be useful in determining long-term solar variability as well as in forging links to the measurements obtained by other spacecraft for the perturbed middle and upper atmosphere. The simultaneous measurements that will be conducted by ATLAS-1 of stratospheric concentrations of ozone, chlorine monoxide and water vapor, at relatively high latitudes during the northern spring, will be especially timely.

  17. Scaling up ATLAS Event Service to production levels on opportunistic computing platforms

    NASA Astrophysics Data System (ADS)

    Benjamin, D.; Caballero, J.; Ernst, M.; Guan, W.; Hover, J.; Lesny, D.; Maeno, T.; Nilsson, P.; Tsulaia, V.; van Gemmeren, P.; Vaniachine, A.; Wang, F.; Wenaus, T.; ATLAS Collaboration

    2016-10-01

    Continued growth in public cloud and HPC resources is on track to exceed the dedicated resources available for ATLAS on the WLCG. Examples of such platforms are Amazon AWS EC2 Spot Instances, Edison Cray XC30 supercomputer, backfill at Tier 2 and Tier 3 sites, opportunistic resources at the Open Science Grid (OSG), and ATLAS High Level Trigger farm between the data taking periods. Because of specific aspects of opportunistic resources such as preemptive job scheduling and data I/O, their efficient usage requires workflow innovations provided by the ATLAS Event Service. Thanks to the finer granularity of the Event Service data processing workflow, the opportunistic resources are used more efficiently. We report on our progress in scaling opportunistic resource usage to double-digit levels in ATLAS production.

  18. Latency study of the High Performance Time to Digital Converter for the ATLAS Muon Spectrometer trigger upgrade

    NASA Astrophysics Data System (ADS)

    Meng, X. T.; Levin, D. S.; Chapman, J. W.; Li, D. C.; Yao, Z. E.; Zhou, B.

    2017-02-01

    The High Performance Time to Digital Converter (HPTDC), a multi-channel ASIC designed by the CERN Microelectronics group, has been proposed for the digitization of the thin-Resistive Plate Chambers (tRPC) in the ATLAS Muon Spectrometer Phase-1 upgrade project. These chambers, to be staged for higher luminosity LHC operation, will increase trigger acceptance and reduce or eliminate the fake muon trigger rates in the barrel-endcap transition region, corresponding to pseudo-rapidity range 1<|η|<1.3. Low level trigger candidates must be flagged within a maximum latency of 1075 ns, thus imposing stringent signal processing time performance requirements on the readout system in general, and on the digitization electronics in particular. This paper investigates the HPTDC signal latency performance based on a specially designed evaluation board coupled with an external FPGA evaluation board, when operated in triggerless mode, and under hit rate conditions expected in Phase-I. This hardware based study confirms previous simulations and demonstrates that the HPTDC in triggerless operation satisfies the digitization timing requirements in both leading edge and pair modes.

  19. Cementitious building material incorporating end-capped polyethylene glycol as a phase change material

    DOEpatents

    Salyer, Ival O.; Griffen, Charles W.

    1986-01-01

    A cementitious composition comprising a cementitious material and polyethylene glycol or end-capped polyethylene glycol as a phase change material, said polyethylene glycol and said end-capped polyethylene glycol having a molecular weight greater than about 400 and a heat of fusion greater than about 30 cal/g; the compositions are useful in making pre-formed building materials such as concrete blocks, brick, dry wall and the like or in making poured structures such as walls or floor pads; the glycols can be encapsulated to reduce their tendency to retard set.

  20. Synthesis and Properties of Benzil End-Capped Acetylene Terminated Phenylquinoxalines (BATQs)

    DTIC Science & Technology

    1978-12-01

    ratio of reactants. The ortho diamine end-capped quinoxaline oligomers were then reacted with excess I in m- cresol . All the oligomers prepared were...displacement of the nitro group on 4-nitrobenzil provided a 47% overall yield of I. The material exhibits excellent shelf-life in contrast to the...benzene 1,4-Bis(4-benziloxy)benzene was prepared by the nitro -displacement procedure of Relles et al (Reference 6). 3. Other Bis-benzils Other bis

  1. Correlative measurement opportunities between ATLAS-1 and UARS experiments

    NASA Technical Reports Server (NTRS)

    Harrison, Edwin F.; Denn, Fred M.; Gibson, Gary G.

    1992-01-01

    The first ATmospheric Laboratory for Applications and Science (ATLAS-1) mission was flown aboard the Space Shuttle from March 24 to April 2, 1992. The ATLAS-1 instruments provided a large number of measurements which were coincident with observations from experiments on the Upper Atmosphere Research Satellite (UARS). During the ATLAS-1 mission, simulations were performed to predict when and where coincident measurements between ATLAS and UARS instruments would occur. These predictions were used to develop instrument operation schedules to maximize the correlative opportunities between the two satellites. Results of the simulations provide valuable information for the ATLAS and UARS scientists to compare measurements between various instruments on the two satellites.

  2. A sandwiched piezoelectric transducer with flex end-caps for energy harvesting in large force environments

    NASA Astrophysics Data System (ADS)

    Kuang, Yang; Daniels, Alice; Zhu, Meiling

    2017-08-01

    This paper presents a sandwiched piezoelectric transducer (SPT) for energy harvesting in large force environments with increased load capacity and electric power output. The SPT uses (1) flex end-caps to amplify the applied load force so as to increase its power output and (2) a sandwiched piezoelectric-substrate structure to reduce the stress concentration in the piezoelectric material so as to increase the load capacity. A coupled piezoelectric-circuit finite element model (CPC-FEM) was developed, which is able to directly predict the electric power output of the SPT connected to a load resistor. The CPC-FEM was used to study the effects of various parameters of the SPT on the performance to obtain an optimal design. These parameters included the substrate thickness, the end-cap material and thickness, the electrode length, the joint length, the end-cap internal angle and the PZT thickness. A prototype with optimised parameters was tested on a loading machine, and the experimental results were compared with simulation. A good agreement was observed between simulation and experiment. When subjected to a 1 kN 2 Hz sinusoidal force applied by the loading machine, the SPT produced an average power of 4.68 mW. The application of the SPT as a footwear energy harvester was demonstrated by fitting the SPT into a boot and performing the tests on a treadmill, and the SPT generated an average power of 2.5 mW at a walking speed of 4.8 km h-1.

  3. (Photo)physical Properties of New Molecular Glasses End-Capped with Thiophene Rings Composed of Diimide and Imine Units

    PubMed Central

    2014-01-01

    New symmetrical arylene bisimide derivatives formed by using electron-donating–electron-accepting systems were synthesized. They consist of a phthalic diimide or naphthalenediimide core and imine linkages and are end-capped with thiophene, bithiophene, and (ethylenedioxy)thiophene units. Moreover, polymers were obtained from a new diamine, N,N′-bis(5-aminonaphthalenyl)naphthalene-1,4,5,8-dicarboximide and 2,5-thiophenedicarboxaldehyde or 2,2′-bithiophene-5,5′-dicarboxaldehyde. The prepared azomethine diimides exhibited glass-forming properties. The obtained compounds emitted blue light with the emission maximum at 470 nm. The value of the absorption coefficient was determined as a function of the photon energy using spectroscopic ellipsometry. All compounds are electrochemically active and undergo reversible electrochemical reduction and irreversible oxidation processes as was found in cyclic voltammetry and differential pulse voltammetry (DPV) studies. They exhibited a low electrochemically (DPV) calculated energy band gap (Eg) from 1.14 to 1.70 eV. The highest occupied molecular orbital and lowest unoccupied molecular orbital levels and Eg were additionally calculated theoretically by density functional theory at the B3LYP/6-31G(d,p) level. The photovoltaic properties of two model compounds as the active layer in organic solar cells in the configuration indium tin oxide/poly(3,4-(ethylenedioxy)thiophene):poly(styrenesulfonate)/active layer/Al under an illumination of 1.3 mW/cm2 were studied. The device comprising poly(3-hexylthiophene) with the compound end-capped with bithiophene rings showed the highest value of Voc (above 1 V). The conversion efficiency of the fabricated solar cell was in the range of 0.69–0.90%. PMID:24966893

  4. Imide Oligomers Endcapped with Phenylethynl Phthalic Anhydrides and Polymers Therefrom

    NASA Technical Reports Server (NTRS)

    Hergenrother, Paul M. (Inventor); Smith, Joseph G., Jr. (Inventor)

    1998-01-01

    Controlled molecular weight phenylethynyl terminated imide oligomers (PETIs) have been prepared by the cyclodehydration of precursor phenylethynyl terminated amic acid oligomers. Amino terminated amic acid oligomers are prepared from the reaction of dianhydride(s) with an excess of diamine(s) and subsequently endcapped with phenylethynyl phthalic anhydride(s) (PEPA). The polymerizations are carried out in polar aprotic solvents such as N-methyl-2-pyrrolidinone or N.N-dimethylacetamide under nitrogen at room temperature. The amic acid oligomers are subsequently cyclodehydrated either thermally or cheznicauy to the corresponding imide oligomers. Direct preparation of PETIs from the reaction of dianhydxide(s) with an excess of diamine(s) and endcapped with phenylethynyl phthalic anhydride(s) has been performed in m-cresol. Phenylethynyl phthalic anhydrides are synthesized by the palladium catalyzed reaction of phenylacetylene with bromo substituted phthalic anhydrides in triethylamine. These new materials exhibit excellent properties and are potentially useful as adhesives, coatings, films, moldings and composite matrices.

  5. Imide oligomers endcapped with phenylethynyl phthalic anhydrides and polymers therefrom

    NASA Technical Reports Server (NTRS)

    Hergenrother, Paul M. (Inventor); Smith, Jr., Joseph G. (Inventor)

    1996-01-01

    Controlled molecular weight phenylethynyl terminated imide oligomers (PETIs) have been prepared by the cyclodehydration of precursor phenylethynyl terminated amic acid oligomers. Amino terminated amic acid oligomers are prepared from the reaction of dianhydride(s) with an excess of diamine(s) and subsequently endcapped with phenylethynyl phthalic anhydride(s) (PEPA). The polymerizations are carried out in polar aprotic solvents such as N-methyl-2-pyrrolidinone or N,N-dimethylacetamide under nitrogen at room temperature. The amic acid oligomers are subsequently cyclodehydrated either thermally or chemically to the corresponding imide oligomers. Direct preparation of PETIs from the reaction of dianhydride(s) with an excess of diamine(s) and endcapped with phenylethynyl phthalic anhydride(s) has been performed in m-cresol. Phenylethynyl phthalic anhydrides are synthesized by the palladium catalyzed reaction of phenylacetylene with bromo substituted phthalic anhydrides in triethylamine. These new materials exhibit excellent properties and are potentially useful as adhesives, coatings, films, moldings and composite matrices.

  6. L1 track triggers for ATLAS in the HL-LHC

    DOE PAGES

    Lipeles, E.

    2012-01-01

    The HL-LHC, the planned high luminosity upgrade for the LHC, will increase the collision rate in the ATLAS detector approximately a factor of 5 beyond the luminosity for which the detectors were designed, while also increasing the number of pile-up collisions in each event by a similar factor. This means that the level-1 trigger must achieve a higher rejection factor in a more difficult environment. This presentation discusses the challenges that arise in this environment and strategies being considered by ATLAS to include information from the tracking systems in the level-1 decision. The main challenges involve reducing the data volumemore » exported from the tracking system for which two options are under consideration: a region of interest based system and an intelligent sensor method which filters on hits likely to come from higher transverse momentum tracks.« less

  7. Atlas Fractures and Atlas Osteosynthesis: A Comprehensive Narrative Review.

    PubMed

    Kandziora, Frank; Chapman, Jens R; Vaccaro, Alexander R; Schroeder, Gregory D; Scholz, Matti

    2017-09-01

    Most atlas fractures are the result of compression forces. They are often combined with fractures of the axis and especially with the odontoid process. Multiple classification systems for atlas fractures have been described. For an adequate diagnosis, a computed tomography is mandatory. To distinguish between stable and unstable atlas injury, it is necessary to evaluate the integrity of the transverse atlantal ligament (TAL) by magnetic resonance imaging and to classify the TAL lesion. Studies comparing conservative and operative management of unstable atlas fractures are unfortunately not available in the literature; neither are studies comparing different operative treatment strategies. Hence all treatment recommendations are based on low level evidence. Most of atlas fractures are stable and will be successfully managed by immobilization in a soft/hard collar. Unstable atlas fractures may be treated conservatively by halo-fixation, but nowadays more and more surgeons prefer surgery because of the potential discomfort and complications of halo-traction. Atlas fractures with a midsubstance ligamentous disruption of TAL or severe bony ligamentous avulsion can be treated by a C1/2 fusion. Unstable atlas fractures with moderate bony ligamentous avulsion may be treated by atlas osteosynthesis. Although the evidence for the different treatment strategies of atlas fractures is low, atlas osteosynthesis has the potential to change treatment philosophies. The reasons for this are described in this review.

  8. A High-Granularity Timing Detector for the Phase-II upgrade of the ATLAS calorimeter system: detector concept description and first beam test results

    NASA Astrophysics Data System (ADS)

    Lacour, D.

    2018-02-01

    The expected increase of the particle flux at the high luminosity phase of the LHC (HL-LHC) with instantaneous luminosities up to 7.5ṡ1034 cm-2s-1 will have a severe impact on the ATLAS detector performance. The pile-up is expected to increase on average to 200 interactions per bunch crossing. The reconstruction performance for electrons, photons as well as jets and transverse missing energy will be severely degraded in the end-cap and forward region. A High Granularity Timing Detector (HGTD) is proposed in front of the liquid Argon end-cap and forward calorimeters for pile-up mitigation. This device should cover the pseudo-rapidity range of 2.4 to about 4.0. Low Gain Avalanche Detectors (LGAD) technology has been chosen as it provides an internal gain good enough to reach large signal over noise ratio needed for excellent time resolution. The requirements and overall specifications of the High Granular Timing Detector at the HL-LHC will be presented as well as the conceptual design of its mechanics and electronics. Beam test results and measurements of irradiated LGAD silicon sensors, such as gain and timing resolution, will be shown.

  9. Algorithm and implementation of muon trigger and data transmission system for barrel-endcap overlap region of the CMS detector

    NASA Astrophysics Data System (ADS)

    Zabolotny, W. M.; Byszuk, A.

    2016-03-01

    The CMS experiment Level-1 trigger system is undergoing an upgrade. In the barrel-endcap transition region, it is necessary to merge data from 3 types of muon detectors—RPC, DT and CSC. The Overlap Muon Track Finder (OMTF) uses the novel approach to concentrate and process those data in a uniform manner to identify muons and their transversal momentum. The paper presents the algorithm and FPGA firmware implementation of the OMTF and its data transmission system in CMS. It is foreseen that the OMTF will be subject to significant changes resulting from optimization which will be done with the aid of physics simulations. Therefore, a special, high-level, parameterized HDL implementation is necessary.

  10. Radioactive source calibration test of the CMS Hadron Endcap Calorimeter test wedge with Phase I upgrade electronics

    NASA Astrophysics Data System (ADS)

    Chatrchyan, S.; Sirunyan, A. M.; Tumasyan, A.; Litomin, A.; Mossolov, V.; Shumeiko, N.; Van De Klundert, M.; Van Haevermaet, H.; Van Mechelen, P.; Van Spilbeeck, A.; Alves, G. A.; Aldá Júnior, W. L.; Hensel, C.; Carvalho, W.; Chinellato, J.; De Oliveira Martins, C.; Matos Figueiredo, D.; Mora Herrera, C.; Nogima, H.; Prado Da Silva, W. L.; Tonelli Manganote, E. J.; Vilela Pereira, A.; Finger, M.; Finger, M., Jr.; Kveton, A.; Tomsa, J.; Adamov, G.; Tsamalaidze, Z.; Behrens, U.; Borras, K.; Campbell, A.; Costanza, F.; Gunnellini, P.; Lobanov, A.; Melzer-Pellmann, I.-A.; Muhl, C.; Roland, B.; Sahin, M.; Saxena, P.; Hegde, V.; Kothekar, K.; Pandey, S.; Sharma, S.; Beri, S. B.; Bhawandeep, B.; Chawla, R.; Kalsi, A.; Kaur, A.; Kaur, M.; Walia, G.; Bhattacharya, S.; Ghosh, S.; Nandan, S.; Purohit, A.; Sharan, M.; Banerjee, S.; Bhattacharya, S.; Chatterjee, S.; Das, P.; Guchait, M.; Jain, S.; Kumar, S.; Maity, M.; Majumder, G.; Mazumdar, K.; Patil, M.; Sarkar, T.; Juodagalvis, A.; Afanasiev, S.; Bunin, P.; Ershov, Y.; Golutvin, I.; Malakhov, A.; Moisenz, P.; Smirnov, V.; Zarubin, A.; Chadeeva, M.; Chistov, R.; Danilov, M.; Popova, E.; Rusinov, V.; Andreev, Yu.; Dermenev, A.; Karneyeu, A.; Krasnikov, N.; Tlisov, D.; Toropin, A.; Epshteyn, V.; Gavrilov, V.; Lychkovskaya, N.; Popov, V.; Pozdnyakov, I.; Safronov, G.; Toms, M.; Zhokin, A.; Baskakov, A.; Belyaev, A.; Boos, E.; Dubinin, M.; Dudko, L.; Ershov, A.; Gribushin, A.; Kaminskiy, A.; Klyukhin, V.; Kodolova, O.; Lokhtin, I.; Miagkov, I.; Obraztsov, S.; Petrushanko, S.; Savrin, V.; Snigirev, A.; Andreev, V.; Azarkin, M.; Dremin, I.; Kirakosyan, M.; Leonidov, A.; Terkulov, A.; Bitioukov, S.; Elumakhov, D.; Kalinin, A.; Krychkine, V.; Mandrik, P.; Petrov, V.; Ryutin, R.; Sobol, A.; Troshin, S.; Volkov, A.; Sekmen, S.; Rumerio, P.; Adiguzel, A.; Bakirci, N.; Cerci, S.; Damarseckin, S.; Demiroglu, Z. S.; Dölek, F.; Dozen, C.; Dumanoglu, I.; Eskut, E.; Girgis, S.; Gokbulut, G.; Guler, Y.; Hos, I.; Kangal, E. E.; Kara, O.; Kayis Topaksu, A.; Işik, C.; Kiminsu, U.; Oglakci, M.; Onengut, G.; Ozdemir, K.; Ozturk, S.; Polatoz, A.; Sunar Cerci, D.; Tali, B.; Topakli, H.; Turkcapar, S.; Zorbakir, I. S.; Zorbilmez, C.; Bilin, B.; Isildak, B.; Karapinar, G.; Murat Guler, A.; Ocalan, K.; Yalvac, M.; Zeyrek, M.; Atakisi, I. O.; Gülmez, E.; Kaya, M.; Kaya, O.; Koseyan, O. K.; Ozcelik, O.; Ozkorucuklu, S.; Tekten, S.; Yetkin, E. A.; Yetkin, T.; Cankocak, K.; Sen, S.; Boyarintsev, A.; Grynyov, B.; Levchuk, L.; Popov, V.; Sorokin, P.; Flacher, H.; Borzou, A.; Call, K.; Dittmann, J.; Hatakeyama, K.; Liu, H.; Pastika, N.; Buccilli, A.; Cooper, S. I.; Henderson, C.; West, C.; Arcaro, D.; Gastler, D.; Hazen, E.; Rohlf, J.; Sulak, L.; Wu, S.; Zou, D.; Hakala, J.; Heintz, U.; Kwok, K. H. M.; Laird, E.; Landsberg, G.; Mao, Z.; Yu, D. R.; Gary, J. W.; Ghiasi Shirazi, S. M.; Lacroix, F.; Long, O. R.; Wei, H.; Bhandari, R.; Heller, R.; Stuart, D.; Yoo, J. H.; Chen, Y.; Duarte, J.; Lawhorn, J. M.; Nguyen, T.; Spiropulu, M.; Winn, D.; Abdullin, S.; Apresyan, A.; Apyan, A.; Banerjee, S.; Chlebana, F.; Freeman, J.; Green, D.; Hare, D.; Hirschauer, J.; Joshi, U.; Lincoln, D.; Los, S.; Pedro, K.; Spalding, W. J.; Strobbe, N.; Tkaczyk, S.; Whitbeck, A.; Linn, S.; Markowitz, P.; Martinez, G.; Bertoldi, M.; Hagopian, S.; Hagopian, V.; Kolberg, T.; Baarmand, M. M.; Noonan, D.; Roy, T.; Yumiceva, F.; Bilki, B.; Clarida, W.; Debbins, P.; Dilsiz, K.; Durgut, S.; Gandrajula, R. P.; Haytmyradov, M.; Khristenko, V.; Merlo, J.-P.; Mermerkaya, H.; Mestvirishvili, A.; Miller, M.; Moeller, A.; Nachtman, J.; Ogul, H.; Onel, Y.; Ozok, F.; Penzo, A.; Schmidt, I.; Snyder, C.; Southwick, D.; Tiras, E.; Yi, K.; Al-bataineh, A.; Bowen, J.; Castle, J.; McBrayer, W.; Murray, M.; Wang, Q.; Kaadze, K.; Maravin, Y.; Mohammadi, A.; Saini, L. K.; Baden, A.; Belloni, A.; Calderon, J. D.; Eno, S. C.; Feng, Y. B.; Ferraioli, C.; Grassi, T.; Hadley, N. J.; Jeng, G.-Y.; Kellogg, R. G.; Kunkle, J.; Mignerey, A.; Ricci-Tam, F.; Shin, Y. H.; Skuja, A.; Yang, Z. S.; Yao, Y.; Brandt, S.; D'Alfonso, M.; Hu, M.; Klute, M.; Niu, X.; Chatterjee, R. M.; Evans, A.; Frahm, E.; Kubota, Y.; Lesko, Z.; Mans, J.; Ruckstuhl, N.; Heering, A.; Karmgard, D. J.; Musienko, Y.; Ruchti, R.; Wayne, M.; Benaglia, A. D.; Medvedeva, T.; Mei, K.; Tully, C.; Bodek, A.; de Barbaro, P.; Galanti, M.; Garcia-Bellido, A.; Khukhunaishvili, A.; Lo, K. H.; Vishnevskiy, D.; Zielinski, M.; Agapitos, A.; Amouzegar, M.; Chou, J. P.; Hughes, E.; Saka, H.; Sheffield, D.; Akchurin, N.; Damgov, J.; De Guio, F.; Dudero, P. R.; Faulkner, J.; Gurpinar, E.; Kunori, S.; Lamichhane, K.; Lee, S. W.; Libeiro, T.; Mengke, T.; Muthumuni, S.; Undleeb, S.; Volobouev, I.; Wang, Z.; Goadhouse, S.; Hirosky, R.; Wang, Y.

    2017-12-01

    The Phase I upgrade of the CMS Hadron Endcap Calorimeters consists of new photodetectors (Silicon Photomultipliers in place of Hybrid Photo-Diodes) and front-end electronics. The upgrade will eliminate the noise and the calibration drift of the Hybrid Photo-Diodes and enable the mitigation of the radiation damage of the scintillators and the wavelength shifting fibers with a larger spectral acceptance of the Silicon Photomultipliers. The upgrade also includes increased longitudinal segmentation of the calorimeter readout, which allows pile-up mitigation and recalibration due to depth-dependent radiation damage. As a realistic operational test, the responses of the Hadron Endcap Calorimeter wedges were calibrated with a 60Co radioactive source with upgrade electronics. The test successfully established the procedure for future source calibrations of the Hadron Endcap Calorimeters. Here we describe the instrumentation details and the operational experiences related to the sourcing test.

  11. Atmospheric Laboratory for Applications and Science (ATLAS), mission 1: Introduction

    NASA Technical Reports Server (NTRS)

    1988-01-01

    The first Atmospheric Laboratory for Applications and Science (ATLAS 1) is a NASA mission with an international payload, with the European Space Agency providing operational support for the European investigations. The ATLAS 1 represents the first of a series of shuttle-borne payloads which are intended to study the composition of the middle atmosphere and its possible variations due to solar changes over the course of an 11-year solar cycle. One of the ATLAS missions will coincide with NASA's Upper Atmospheric Research Satellite (UARS) mission and will provide crucial parameters not measured by the instrument complement on the satellite. A first in this evolutionary program, the ATLAS 1 will carry a payload of instruments originally flown on the Spacelab 1 and Spacelab 3 missions. The ATLAS mission therefore exploits the shuttle capability to return sophisticated instruments to the ground for refurbishment and updating, and the multi-mission reflight of the instruments at intervals required by the scientific goals. In addition to the investigations specific to the ATLAS objectives, the first mission payload includes others that are intended to study or use the near earth environment.

  12. Design, Results, Evolution and Status of the ATLAS Simulation at Point1 Project

    NASA Astrophysics Data System (ADS)

    Ballestrero, S.; Batraneanu, S. M.; Brasolin, F.; Contescu, C.; Fazio, D.; Di Girolamo, A.; Lee, C. J.; Pozo Astigarraga, M. E.; Scannicchio, D. A.; Sedov, A.; Twomey, M. S.; Wang, F.; Zaytsev, A.

    2015-12-01

    During the LHC Long Shutdown 1 (LSI) period, that started in 2013, the Simulation at Point1 (Sim@P1) project takes advantage, in an opportunistic way, of the TDAQ (Trigger and Data Acquisition) HLT (High-Level Trigger) farm of the ATLAS experiment. This farm provides more than 1300 compute nodes, which are particularly suited for running event generation and Monte Carlo production jobs that are mostly CPU and not I/O bound. It is capable of running up to 2700 Virtual Machines (VMs) each with 8 CPU cores, for a total of up to 22000 parallel jobs. This contribution gives a review of the design, the results, and the evolution of the Sim@P1 project, operating a large scale OpenStack based virtualized platform deployed on top of the ATLAS TDAQ HLT farm computing resources. During LS1, Sim@P1 was one of the most productive ATLAS sites: it delivered more than 33 million CPU-hours and it generated more than 1.1 billion Monte Carlo events. The design aspects are presented: the virtualization platform exploited by Sim@P1 avoids interferences with TDAQ operations and it guarantees the security and the usability of the ATLAS private network. The cloud mechanism allows the separation of the needed support on both infrastructural (hardware, virtualization layer) and logical (Grid site support) levels. This paper focuses on the operational aspects of such a large system during the upcoming LHC Run 2 period: simple, reliable, and efficient tools are needed to quickly switch from Sim@P1 to TDAQ mode and back, to exploit the resources when they are not used for the data acquisition, even for short periods. The evolution of the central OpenStack infrastructure is described, as it was upgraded from Folsom to the Icehouse release, including the scalability issues addressed.

  13. DC Potentials Applied to an End-cap Electrode of a 3-D Ion Trap for Enhanced MSn Functionality

    PubMed Central

    Prentice, Boone M.; Xu, Wei; Ouyang, Zheng; McLuckey, Scott A.

    2010-01-01

    The effects of the application of various DC magnitudes and polarities to an end-cap of a 3-D quadrupole ion trap throughout a mass spectrometry experiment were investigated. Application of a monopolar DC field was achieved by applying a DC potential to the exit end-cap electrode, while maintaining the entrance end-cap electrode at ground potential. Control over the monopolar DC magnitude and polarity during time periods associated with ion accumulation, mass analysis, ion isolation, ion/ion reaction, and ion activation can have various desirable effects. Included amongst these are increased ion capture efficiency, increased ion ejection efficiency during mass analysis, effective isolation of ions using lower AC resonance ejection amplitudes, improved temporal control of the overlap of oppositely charged ion populations, and the performance of “broad-band” collision induced dissociation (CID). These results suggest general means to improve the performance of the 3-D ion trap in a variety of mass spectrometry and tandem mass spectrometry experiments. PMID:21927573

  14. ATLAS 1: Encountering Planet Earth

    NASA Technical Reports Server (NTRS)

    Shea, Charlotte; Mcmahan, Tracy; Accardi, Denise; Tygielski, Michele; Mikatarian, Jeff; Wiginton, Margaret (Editor)

    1984-01-01

    Several NASA science programs examine the dynamic balance of sunlight, atmosphere, water, land, and life that governs Earth's environment. Among these is a series of Space Shuttle-Spacelab missions, named the Atmospheric Laboratory for Applications and Science (ATLAS). During the ATLAS missions, international teams of scientists representing many disciplines combine their expertise to seek answers to complex questions about the atmospheric and solar conditions that sustain life on Earth. The ATLAS program specifically investigates how Earth's middle atmosphere and upper atmospheres and climate are affected by both the Sun and by products of industrial and agricultural activities on Earth.

  15. Upgrade plans for the ATLAS Forward Calorimeter at the HL-LHC

    NASA Astrophysics Data System (ADS)

    Rutherfoord, John; ATLAS Liquid Argon Calorimeter Group

    2012-12-01

    Although data-taking at CERN's Large Hadron Collider (LHC) is expected to continue for a number of years, plans are already being developed for operation of the LHC and associated detectors at an increased instantaneous luminosity about 5 times the original design value of 1034 cm-2 s-1. The increased particle flux at this high luminosity (HL) will have an impact on many sub-systems of the ATLAS detector. In particular, in the liquid argon forward calorimeter (FCal), which was designed for operation at LHC luminosities, the associated increase in the ionization load at HL-LHC luminosities creates a number of problems which can degrade its performance. These include space-charge effects in the liquid argon gaps, excessive drop in potential across the gaps due to large HV supply currents through the protection resistors, and an increase in temperature which may cause the liquid argon to boil. One solution, which would require opening both End-Cap cryostats, is the construction and installation of new FCals with narrower liquid argon gaps, lowering the values of the protection resistors, and the addition of cooling loops. A second proposed solution, which does not require opening the cryostat cold volume, is the addition of a small, warm calorimeter in front of each existing FCal, resulting in a reduction of the particle flux to levels at which the existing FCal can operate normally.

  16. Characterization of non-endcapped polymeric ODS column for the separation of triacylglycerol positional isomers.

    PubMed

    Gotoh, Naohiro; Matsumoto, Yumiko; Yuji, Hiromi; Nagai, Toshiharu; Mizobe, Hoyo; Ichioka, Kenji; Kuroda, Ikuma; Noguchi, Noriko; Wada, Shun

    2010-01-01

    The characteristics of a non-endcapped polymeric ODS column for the resolution of triacylglycerol positional isomers (TAG-PI) were examined using a recycle HPLC-atmospheric pressure chemical ionization/mass spectrometry system. A pair of TAG-PI containing saturated fatty acids at least 12 carbons was separated. Except for TAG-PI containing elaidic acid, pairs of TAG-PI containing three unsaturated fatty acids were not separated, even by recycle runs. These results indicate that the resolution of TAG-PI on a non-endcapped polymeric ODS stationary phase is realized by the recognition of the linear structure of the fatty acid and the binding position of the saturated fatty acid in TAG-PI. Chain length was also an important factor for resolution. This method may be a useful and simple for measuring the abundance ratio of TAG-PI containing saturated fatty acids in natural oils.

  17. The design of a fast Level 1 Track trigger for the ATLAS High Luminosity Upgrade

    NASA Astrophysics Data System (ADS)

    Miller Allbrooke, Benedict Marc; ATLAS Collaboration

    2017-10-01

    The ATLAS experiment at the high-luminosity LHC will face a five-fold increase in the number of interactions per collision relative to the ongoing Run 2. This will require a proportional improvement in rejection power at the earliest levels of the detector trigger system, while preserving good signal efficiency, due to the increase in the likelihood of individual trigger thresholds being passed as a result of pile-up related activity. One critical aspect of this improvement will be the implementation of precise track reconstruction, through which sharper turn-on curves, b-tagging and tau-tagging techniques can in principle be implemented. The challenge of such a project comes in the development of a fast, precise custom electronic device integrated in the hardware-based first trigger level of the experiment, with repercussions propagating as far as the detector read-out philosophy.

  18. Substituted Cyclohexene Endcaps for Polymers with Thermal-Oxidative Stability

    NASA Technical Reports Server (NTRS)

    2005-01-01

    This invention relates to polyimides having improved thermal-oxidative stability, to the process of preparing said polyimides, and the use of polyimide prepolymers in the preparation of prepregs and composites. The polyimides are particularly usefull in the preparation of fiber-reinforced, high-temperature composites for use in various engine parts including inlets, fan ducts, exit flaps and other parts of high speed aircraft. The polyimides are derived from the polymerization of effective amounts of at least one tetracarboxylic dianhydride, at least one polyamine and a novel dicarboxylic endcap having the formula presented.

  19. Foale holds the top endcap for the TVIS Gyroscope in SM during Expedition 8

    NASA Image and Video Library

    2003-12-09

    ISS008-E-07384 (9 Dec. 2003) --- Astronaut C. Michael Foale, Expedition 8 commander and NASA ISS science officer, holds the top end-cap for the Treadmill Vibration Isolation System (TVIS) gyroscope in the Zvezda Service Module on the International Space Station (ISS).

  20. LiverAtlas: a unique integrated knowledge database for systems-level research of liver and hepatic disease.

    PubMed

    Zhang, Yanqiong; Yang, Chunyuan; Wang, Shaochuang; Chen, Tao; Li, Mansheng; Wang, Xue; Li, Dongsheng; Wang, Kang; Ma, Jie; Wu, Songfeng; Zhang, Xueli; Zhu, Yunping; Wu, Jinsheng; He, Fuchu

    2013-09-01

    A large amount of liver-related physiological and pathological data exist in publicly available biological and bibliographic databases, which are usually far from comprehensive or integrated. Data collection, integration and mining processes pose a great challenge to scientific researchers and clinicians interested in the liver. To address these problems, we constructed LiverAtlas (http://liveratlas.hupo.org.cn), a comprehensive resource of biomedical knowledge related to the liver and various hepatic diseases by incorporating 53 databases. In the present version, LiverAtlas covers data on liver-related genomics, transcriptomics, proteomics, metabolomics and hepatic diseases. Additionally, LiverAtlas provides a wealth of manually curated information, relevant literature citations and cross-references to other databases. Importantly, an expert-confirmed Human Liver Disease Ontology, including relevant information for 227 types of hepatic disease, has been constructed and is used to annotate LiverAtlas data. Furthermore, we have demonstrated two examples of applying LiverAtlas data to identify candidate markers for hepatocellular carcinoma (HCC) at the systems level and to develop a systems biology-based classifier by combining the differential gene expression with topological features of human protein interaction networks to enhance the ability of HCC differential diagnosis. LiverAtlas is the most comprehensive liver and hepatic disease resource, which helps biologists and clinicians to analyse their data at the systems level and will contribute much to the biomarker discovery and diagnostic performance enhancement for liver diseases. © 2013 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

  1. Phospholipid End-Capped Acid-Degradable Polyurethane Micelles for Intracellular Delivery of Cancer Therapeutics.

    PubMed

    John, Johnson V; Thomas, Reju George; Lee, Hye Ri; Chen, Hongyu; Jeong, Yong Yeon; Kim, Il

    2016-08-01

    Nanoscale drug carriers fabricated by phospholipid end-capped polyurethane bearing acetal backbones that degrade in acidic conditions are fabricated. These micelles effectively allow drugs to enter the blood circulation, and then disintegrate in acidic endosomes and lysosomes for intelligent delivery of payloads. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  2. The Endcap Disc DIRC of PANDA

    NASA Astrophysics Data System (ADS)

    Düren, M.; Etzelmüller, E.; Föhl, K.; Hayrapetyan, A.; Kröck, B.; Merle, O.; Rieke, J.; Schmidt, M.; Wasem, T.; Britting, A.; Eyrich, W.; Lehmann, A.; Pfaffinger, M.; Uhlig, F.; Belias, A.; Dzhygadlo, R.; Gerhardt, A.; Götzen, K.; Kalicy, G.; Krebs, M.; Lehmann, D.; Nerling, F.; Patsyuk, M.; Peters, K.; Schepers, G.; Schmitt, L.; Schwarz, C.; Schwiening, J.; Traxler, M.; Zühlsdorf, M.; Cowie, E.; Keri, T.; Achenbach, P.; Cardinali, M.; Hoek, M.; Lauth, W.; Schlimme, S.; Sfienti, C.; Thiel, M.

    2017-12-01

    The Endcap Disc DIRC (EDD) for PANDA has been designed to identify traversing pions, kaons and protons in the future PANDA experiment. Its central part is a 2 cm thick fused silica plate. Focussing optics are attached to the outer rim of the plate, outside of the acceptance of the experiment. Fast, high-resolution MCP-PMTs, designed to register single Cherenkov photons, have been tested in magnetic field. Filters limit the spectral acceptance of the sensors to reduce dispersion effects and to extend their lifetime. A compact and fast readout is realized with ASICs. Analytical reconstruction algorithms allow for fast particle identification. The angular resolution of a DIRC prototype has been simulated in Monte Carlo and confirmed in a test beam. The final detector will be able to provide a 4 σπ / K separation up to a momentum of 4 GeV / c .

  3. Measurement of Magnetic Field Uniformity For a Neutron Electric Dipole Moment Detector with New Lead Endcaps

    NASA Astrophysics Data System (ADS)

    Kulkarni, Anita; Filippone, Bradley; Slutsky, Simon; Swank, Christopher; Carr, Robert; Osthelder, Charles; Biswas, Aritra; Molina, Daniel

    2016-09-01

    Over the last several decades, physicists have been measuring the neutron electric dipole moment (nEDM) with greater and greater sensitivity. The latest experiment we are developing will have 100 times more sensitivity than the previous leading experiment. A nonzero nEDM could, among other consequences, explain the presence of more matter than antimatter in the universe. To measure the nEDM with high accuracy, it is necessary to have a very uniform magnetic field inside the detector since non-uniformities can create false signals via the geometric phase effect. One way to improve field uniformity is to add superconducting lead endcaps to the detector, which constrain the fields at their surfaces to be parallel to them. Here, we test how the endcaps improve field uniformity by measuring the magnetic field at various points in a 1/3-scale experimental volume, inferring what the field must be at all other points, and calculating gradients in the field. This knowledge could help guide further steps needed to improve field uniformity and characterize limitations to the sensitivity of nEDM measurements for the full-scale experiment. Rose Hills Foundation, National Science Foundation Grant 1506459, and Department of Energy.

  4. [Spinal stenosis at the level of atlas in a boy with Down syndrome. A case report and literature review].

    PubMed

    Pascual-Gallego, María; Budke, Marcelo; Villarejo, Francisco

    2014-01-01

    The appearance of congenital anomalies at the level of atlas is frequent in patients with neural alterations, as well as in the Down syndrome. The presence of clinical stenosis for alteration in the posterior arch of C1 without a previous atlantoaxial subluxation hasn't been described in the literature thus far. We report an exceptional case of myelopathy due to compression at the level of the atlas in a 5-year-old boy with Down syndrome provoked by a developmental anomaly of the posterior arch of C1. A posterior laminectomy was achieved at that level with improvement of the previous symptoms. We have to pay special attention in children with syndromes associated with chondrogenesis alterations, as in the case of those with Down syndrome, to benefit from early treatment, since in most of the time they are diagnosed when symptoms are very severe. Copyright © 2012 Sociedad Española de Neurocirugía. Published by Elsevier España. All rights reserved.

  5. A chromatographic estimate of the degree of surface heterogeneity of reversed-phase liquid chromatography packing materials II-Endcapped monomeric C18-bonded stationary phase

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Gritti, Fabrice; Guiochon, Georges A

    2006-01-01

    In a previous report, the heterogeneity of a non-endcapped C{sub 30}-bonded stationary phase was investigated, based on the results of the measurements of the adsorption isotherms of two neutral compounds (phenol and caffeine) and two ionizable compounds (sodium naphthalene sulfonate and propranololium chloride) by frontal analysis (FA). The same method is applied here for the characterization of the surface heterogeneity of two new brands of endcapped C{sub 18}-bonded stationary phases (Gemini and Sunfire). The adsorption isotherms of the same four chemicals were measured by FA and the results confirmed by the independent calculation of the adsorption energy distribution (AED), usingmore » the expectation-maximization (EM) method. The effect of the length of the bonded alkyl chain was investigated. Shorter alkyl-bonded-chains (C{sub 18} versus C{sub 30}) and the end-capping of the silica surface contribute to decrease the surface heterogeneity under the same experimental conditions (30% methanol, 25 mM NaCl). The AEDs of phenol and caffeine are bimodal with the C{sub 18}-bonded columns while they are trimodal and quadrimodal, respectively, with a non-endcapped C{sub 30}-bonded column. The 'supersites' (adsorption energy >20 kJ/mol) found on the C{sub 30}-Prontosil column and attributed to a cation exchange mechanism completely disappear on the C{sub 18}-Gemini and C{sub 18}-Sunfire, probably because the end-capping of the silica surface eliminates most if not all the ionic interactions.« less

  6. What Is the Most Representative Parameter for Describing the Size of the Atlas? CT Morphometric Analysis of the Atlas with Special Reference to Atlas Hypoplasia.

    PubMed

    Yamahata, Hitoshi; Hirano, Hirofumi; Yamaguchi, Satoshi; Mori, Masanao; Niiro, Tadaaki; Tokimura, Hiroshi; Arita, Kazunori

    2017-09-15

    The spinal canal diameter (SCD) is one of the most studied factors for the assessment of cervical spinal canal stenosis. The inner anteroposterior diameter (IAP), the SCD, and the cross-sectional area (CSA) of the atlas have been used for the evaluation of the size of the atlas in patients with atlas hypoplasia, a rare form of developmental spinal canal stenosis, however, there is little information on their relationship. The aim of this study was to identify the most useful parameter for depicting the size of the atlas. The CSA, the IAP, and the SCD were measured on computed tomography (CT) images at the C1 level of 213 patients and compared in this retrospective study. These three parameters increased with increasing patient height and weight. There was a strong correlation between IAP and SCD (r = 0.853) or CSA (r = 0.822), while correlation between SCD and CSA (r = 0.695) was weaker than between IAP and CSA. Partial correlation analysis showed that IAP was positively correlated with SCD (r = 0.687) and CSA (r = 0.612) when CSA or SCD were controlled. SCD was negatively correlated with CSA when IAP was controlled (r = -0.21). The IAP can serve as the CSA for the evaluation of the size of the atlas ring, while the SCD does not correlate with the CSA. As the patient height and weight affect the size of the atlas, analysis of the spinal canal at the C1 level should take into account physiologic patient data.

  7. A pediatric brain structure atlas from T1-weighted MR images

    NASA Astrophysics Data System (ADS)

    Shan, Zuyao Y.; Parra, Carlos; Ji, Qing; Ogg, Robert J.; Zhang, Yong; Laningham, Fred H.; Reddick, Wilburn E.

    2006-03-01

    In this paper, we have developed a digital atlas of the pediatric human brain. Human brain atlases, used to visualize spatially complex structures of the brain, are indispensable tools in model-based segmentation and quantitative analysis of brain structures. However, adult brain atlases do not adequately represent the normal maturational patterns of the pediatric brain, and the use of an adult model in pediatric studies may introduce substantial bias. Therefore, we proposed to develop a digital atlas of the pediatric human brain in this study. The atlas was constructed from T1 weighted MR data set of a 9 year old, right-handed girl. Furthermore, we extracted and simplified boundary surfaces of 25 manually defined brain structures (cortical and subcortical) based on surface curvature. Higher curvature surfaces were simplified with more reference points; lower curvature surfaces, with fewer. We constructed a 3D triangular mesh model for each structure by triangulation of the structure's reference points. Kappa statistics (cortical, 0.97; subcortical, 0.91) indicated substantial similarities between the mesh-defined and the original volumes. Our brain atlas and structural mesh models (www.stjude.org/BrainAtlas) can be used to plan treatment, to conduct knowledge and modeldriven segmentation, and to analyze the shapes of brain structures in pediatric patients.

  8. Self-designed posterior atlas polyaxial lateral mass screw-plate fixation for unstable atlas fracture.

    PubMed

    He, Baorong; Yan, Liang; Zhao, Qinpeng; Chang, Zhen; Hao, Dingjun

    2014-12-01

    Most atlas fractures can be effectively treated nonoperatively with external immobilization unless there is an injury to the transverse atlantal ligament. Surgical stabilization is most commonly achieved using a posterior approach with fixation of C1-C2 or C0-C2, but these treatments usually result in loss of the normal motion of the C1-C2 and C0-C1 joints. To clinically validate feasibility, safety, and value of open reduction and fixation using an atlas polyaxial lateral mass screw-plate construct in unstable atlas fractures. Retrospective review of patients who sustained unstable atlas fractures treated with polyaxial lateral mass screw-plate construct. Twenty-two patients with unstable atlas fractures who underwent posterior atlas polyaxial lateral mass screw-plate fixation were analyzed. Visual analog scale, neurologic status, and radiographs for fusion. From January 2011 to September 2012, 22 patients with unstable atlas fractures were treated with this technique. Patients' charts and radiographs were reviewed. Bone fusion, internal fixation placement, and integrity of spinal cord and vertebral arteries were assessed via intraoperative and follow-up imaging. Neurologic function, range of motion, and pain levels were assessed clinically on follow-up. All patients were followed up from 12 to 32 months, with an average of 22.5±18.0 months. A total of 22 plates were placed, and all 44 screws were inserted into the atlas lateral masses. The mean duration of the procedure was 86 minutes, and the average estimated blood loss was 120 mL. Computed tomography scans 9 months after surgery confirmed that fusion was achieved in all cases. There was no screw or plate loosening or breakage in any patient. All patients had well-preserved range of motion. No vascular or neurologic complication was noted, and all patients had a good clinical outcome. An open reduction and posterior internal fixation with atlas polyaxial lateral mass screw-plate is a safe and effective

  9. The ATLAS high level trigger steering

    NASA Astrophysics Data System (ADS)

    Berger, N.; Bold, T.; Eifert, T.; Fischer, G.; George, S.; Haller, J.; Hoecker, A.; Masik, J.; Nedden, M. Z.; Reale, V. P.; Risler, C.; Schiavi, C.; Stelzer, J.; Wu, X.

    2008-07-01

    The High Level Trigger (HLT) of the ATLAS experiment at the Large Hadron Collider receives events which pass the LVL1 trigger at ~75 kHz and has to reduce the rate to ~200 Hz while retaining the most interesting physics. It is a software trigger and performs the reduction in two stages: the LVL2 trigger and the Event Filter (EF). At the heart of the HLT is the Steering software. To minimise processing time and data transfers it implements the novel event selection strategies of seeded, step-wise reconstruction and early rejection. The HLT is seeded by regions of interest identified at LVL1. These and the static configuration determine which algorithms are run to reconstruct event data and test the validity of trigger signatures. The decision to reject the event or continue is based on the valid signatures, taking into account pre-scale and pass-through. After the EF, event classification tags are assigned for streaming purposes. Several new features for commissioning and operation have been added: comprehensive monitoring is now built in to the framework; for validation and debugging, reconstructed data can be written out; the steering is integrated with the new configuration (presented separately), and topological and global triggers have been added. This paper will present details of the final design and its implementation, the principles behind it, and the requirements and constraints it is subject to. The experience gained from technical runs with realistic trigger menus will be described.

  10. Enzymatic Synthesis of Amino Acids Endcapped Polycaprolactone: A Green Route Towards Functional Polyesters.

    PubMed

    Duchiron, Stéphane W; Pollet, Eric; Givry, Sébastien; Avérous, Luc

    2018-01-30

    ε-caprolactone (CL) has been enzymatically polymerized using α-amino acids based on sulfur (methionine and cysteine) as (co-)initiators and immobilized lipase B of Candida antarctica (CALB) as biocatalyst. In-depth characterizations allowed determining the corresponding involved mechanisms and the polymers thermal properties. Two synthetic strategies were tested, a first one with direct polymerization of CL with the native amino acids and a second one involving the use of an amino acid with protected functional groups. The first route showed that mainly polycaprolactone (PCL) homopolymer could be obtained and highlighted the lack of reactivity of the unmodified amino acids due to poor solubility and affinity with the lipase active site. The second strategy based on protected cysteine showed higher monomer conversion, with the amino acids acting as (co-)initiators, but their insertion along the PCL chains remained limited to chain endcapping. These results thus showed the possibility to synthesize enzymatically polycaprolactone-based chains bearing amino acids units. Such cysteine endcapped PCL materials could then find application in the biomedical field. Indeed, subsequent functionalization of these polyesters with drugs or bioactive molecules can be obtained, by derivatization of the amino acids, after removal of the protecting group.

  11. Atlas 1.1: An Update to the Theory of Effective Systems Engineers

    DTIC Science & Technology

    2018-01-16

    Proficiency Model ........................................................................................................... 21 5.1.1 Area 1: Math ... Math /Science/General Engineering: Foundational concepts from mathematics, physical sciences, and general engineering; 2. System’s Domain...Table 5. Atlas Proficiency Areas, Categories, and Topics Area Category Topic 1. Math / Science / General Engineering 1.1. Natural Science

  12. Virtual planets atlas 1.0 freeware

    NASA Astrophysics Data System (ADS)

    Legrand, C.; Chevalley, P.

    2015-10-01

    Since 2002, we develop the "Virtual Moon Atlas -http://www.ap-i.net/avl/en/start" a freeware to help Moon observing and to improve interest for Moon in general public. VMA freeware has been downloaded near 900000 times all over the world and is or has been used by several professional organizations such as Kitt Peak Observatory, National Japan Observatory, Birkbeck College / University College London (K. Joy), BBC Sky at night, several French astronomy magazines and astronomy writers (P. Harrington, S. French...) . Recommended by ESA, registered as educational software by French ministry for education, it has also yet been presented at 2006 & 2007 LPSC and PCC2 in 2011 We have declined this freeware in a new tool with the same goals, but for the telluric planets and satellites, the "Virtual Planets Atlas (VPA / http://www.ap-i.net/avp/en/start") now in version 1.0.

  13. Thermal and Alignment Analysis of the Instrument-Level ATLAS Thermal Vacuum Test

    NASA Technical Reports Server (NTRS)

    Bradshaw, Heather

    2012-01-01

    This paper describes the thermal analysis and test design performed in preparation for the ATLAS thermal vacuum test. NASA's Advanced Topographic Laser Altimeter System (ATLAS) will be flown as the sole instrument aboard the Ice, Cloud, and land Elevation Satellite-2 (ICESat-2). It will be used to take measurements of topography and ice thickness for Arctic and Antarctic regions, providing crucial data used to predict future changes in worldwide sea levels. Due to the precise measurements ATLAS is taking, the laser altimeter has very tight pointing requirements. Therefore, the instrument is very sensitive to temperature-induced thermal distortions. For this reason, it is necessary to perform a Structural, Thermal, Optical Performance (STOP) analysis not only for flight, but also to ensure performance requirements can be operationally met during instrument-level thermal vacuum testing. This paper describes the thermal model created for the chamber setup, which was used to generate inputs for the environmental STOP analysis. This paper also presents the results of the STOP analysis, which indicate that the test predictions adequately replicate the thermal distortions predicted for flight. This is a new application of an existing process, as STOP analyses are generally performed to predict flight behavior only. Another novel aspect of this test is that it presents the opportunity to verify pointing results of a STOP model, which is not generally done. It is possible in this case, however, because the actual pointing will be measured using flight hardware during thermal vacuum testing and can be compared to STOP predictions.

  14. Particle identification algorithms for the PANDA Endcap Disc DIRC

    NASA Astrophysics Data System (ADS)

    Schmidt, M.; Ali, A.; Belias, A.; Dzhygadlo, R.; Gerhardt, A.; Götzen, K.; Kalicy, G.; Krebs, M.; Lehmann, D.; Nerling, F.; Patsyuk, M.; Peters, K.; Schepers, G.; Schmitt, L.; Schwarz, C.; Schwiening, J.; Traxler, M.; Böhm, M.; Eyrich, W.; Lehmann, A.; Pfaffinger, M.; Uhlig, F.; Düren, M.; Etzelmüller, E.; Föhl, K.; Hayrapetyan, A.; Kreutzfeld, K.; Merle, O.; Rieke, J.; Wasem, T.; Achenbach, P.; Cardinali, M.; Hoek, M.; Lauth, W.; Schlimme, S.; Sfienti, C.; Thiel, M.

    2017-12-01

    The Endcap Disc DIRC has been developed to provide an excellent particle identification for the future PANDA experiment by separating pions and kaons up to a momentum of 4 GeV/c with a separation power of 3 standard deviations in the polar angle region from 5o to 22o. This goal will be achieved using dedicated particle identification algorithms based on likelihood methods and will be applied in an offline analysis and online event filtering. This paper evaluates the resulting PID performance using Monte-Carlo simulations to study basic single track PID as well as the analysis of complex physics channels. The online reconstruction algorithm has been tested with a Virtex4 FGPA card and optimized regarding the resulting constraints.

  15. High-resolution in vivo Wistar rodent brain atlas based on T1 weighted image

    NASA Astrophysics Data System (ADS)

    Huang, Su; Lu, Zhongkang; Huang, Weimin; Seramani, Sankar; Ramasamy, Boominathan; Sekar, Sakthivel; Guan, Cuntai; Bhakoo, Kishore

    2016-03-01

    Image based atlases for rats brain have a significant impact on pre-clinical research. In this project we acquired T1-weighted images from Wistar rodent brains with fine 59μm isotropical resolution for generation of the atlas template image. By applying post-process procedures using a semi-automatic brain extraction method, we delineated the brain tissues from source data. Furthermore, we applied a symmetric group-wise normalization method to generate an optimized template of T1 image of rodent brain, then aligned our template to the Waxholm Space. In addition, we defined several simple and explicit landmarks to corresponding our template with the well known Paxinos stereotaxic reference system. Anchoring at the origin of the Waxholm Space, we applied piece-wise linear transformation method to map the voxels of the template into the coordinates system in Paxinos' stereotoxic coordinates to facilitate the labelling task. We also cross-referenced our data with both published rodent brain atlas and image atlases available online, methodologically labelling the template to produce a Wistar brain atlas identifying more than 130 structures. Particular attention was paid to the cortex and cerebellum, as these areas encompass the most researched aspects of brain functions. Moreover, we adopted the structure hierarchy and naming nomenclature common to various atlases, so that the names and hierarchy structure presented in the atlas are readily recognised for easy use. It is believed the atlas will present a useful tool in rodent brain functional and pharmaceutical studies.

  16. Multiple brain atlas database and atlas-based neuroimaging system.

    PubMed

    Nowinski, W L; Fang, A; Nguyen, B T; Raphel, J K; Jagannathan, L; Raghavan, R; Bryan, R N; Miller, G A

    1997-01-01

    For the purpose of developing multiple, complementary, fully labeled electronic brain atlases and an atlas-based neuroimaging system for analysis, quantification, and real-time manipulation of cerebral structures in two and three dimensions, we have digitized, enhanced, segmented, and labeled the following print brain atlases: Co-Planar Stereotaxic Atlas of the Human Brain by Talairach and Tournoux, Atlas for Stereotaxy of the Human Brain by Schaltenbrand and Wahren, Referentially Oriented Cerebral MRI Anatomy by Talairach and Tournoux, and Atlas of the Cerebral Sulci by Ono, Kubik, and Abernathey. Three-dimensional extensions of these atlases have been developed as well. All two- and three-dimensional atlases are mutually preregistered and may be interactively registered with an actual patient's data. An atlas-based neuroimaging system has been developed that provides support for reformatting, registration, visualization, navigation, image processing, and quantification of clinical data. The anatomical index contains about 1,000 structures and over 400 sulcal patterns. Several new applications of the brain atlas database also have been developed, supported by various technologies such as virtual reality, the Internet, and electronic publishing. Fusion of information from multiple atlases assists the user in comprehensively understanding brain structures and identifying and quantifying anatomical regions in clinical data. The multiple brain atlas database and atlas-based neuroimaging system have substantial potential impact in stereotactic neurosurgery and radiotherapy by assisting in visualization and real-time manipulation in three dimensions of anatomical structures, in quantitative neuroradiology by allowing interactive analysis of clinical data, in three-dimensional neuroeducation, and in brain function studies.

  17. Dual stimuli-responsive nano-vehicles for controlled drug delivery: mesoporous silica nanoparticles end-capped with natural chitosan.

    PubMed

    Hakeem, Abdul; Duan, Ruixue; Zahid, Fouzia; Dong, Chao; Wang, Boya; Hong, Fan; Ou, Xiaowen; Jia, Yongmei; Lou, Xiaoding; Xia, Fan

    2014-11-11

    Herein, we report natural chitosan end-capped MCM-41 type MSNPs as novel, dual stimuli, responsive nano-vehicles for controlled anticancer drug delivery. The chitosan nanovalves tightly close the pores of the MSNPs to control premature cargo release under physiological conditions but respond to lysozyme and acidic media to release the trapped cargo.

  18. The Herschel-ATLAS Data Release 1 - II. Multi-wavelength counterparts to submillimetre sources

    NASA Astrophysics Data System (ADS)

    Bourne, N.; Dunne, L.; Maddox, S. J.; Dye, S.; Furlanetto, C.; Hoyos, C.; Smith, D. J. B.; Eales, S.; Smith, M. W. L.; Valiante, E.; Alpaslan, M.; Andrae, E.; Baldry, I. K.; Cluver, M. E.; Cooray, A.; Driver, S. P.; Dunlop, J. S.; Grootes, M. W.; Ivison, R. J.; Jarrett, T. H.; Liske, J.; Madore, B. F.; Popescu, C. C.; Robotham, A. G.; Rowlands, K.; Seibert, M.; Thompson, M. A.; Tuffs, R. J.; Viaene, S.; Wright, A. H.

    2016-10-01

    This paper is the second in a pair of papers presenting data release 1 (DR1) of the Herschel Astrophysical Terahertz Large Area Survey (H-ATLAS), the largest single open-time key project carried out with the Herschel Space Observatory. The H-ATLAS is a wide-area imaging survey carried out in five photometric bands at 100, 160, 250, 350 and 500 μm covering a total area of 600 deg2. In this paper, we describe the identification of optical counterparts to submillimetre sources in DR1, comprising an area of 161 deg2 over three equatorial fields of roughly 12 × 4.5 deg centred at 9h, 12h and 14{^h.}5, respectively. Of all the H-ATLAS fields, the equatorial regions benefit from the greatest overlap with current multi-wavelength surveys spanning ultraviolet (UV) to mid-infrared regimes, as well as extensive spectroscopic coverage. We use a likelihood ratio technique to identify Sloan Digital Sky Survey counterparts at r < 22.4 for 250-μm-selected sources detected at ≥4σ (≈28 mJy). We find `reliable' counterparts (reliability R ≥ 0.8) for 44 835 sources (39 per cent), with an estimated completeness of 73.0 per cent and contamination rate of 4.7 per cent. Using redshifts and multi-wavelength photometry from GAMA and other public catalogues, we show that H-ATLAS-selected galaxies at z < 0.5 span a wide range of optical colours, total infrared (IR) luminosities and IR/UV ratios, with no strong disposition towards mid-IR-classified active galactic nuclei in comparison with optical selection. The data described herein, together with all maps and catalogues described in the companion paper, are available from the H-ATLAS website at www.h-atlas.org.

  19. Hints from Run 1 and prospects from Run 2 at ATLAS

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Bernius, Catrin, E-mail: Catrin.Bernius@cern.ch

    2016-06-21

    The Large Hadron Collider at CERN has allowed the ATLAS experiment to collect a large amount of proton-proton collision data at 7 TeV and 8 TeV centre-of-mass energies throughout Run 1. This dataset was used to discover a Higgs boson with Standard Model-like properties at a mass of about 125 GeV. Furthermore, an impressive number of searches for deviations from the Standard Model expectations have been carried out. To date, no evidence for new physics beyond the SM has been found. However, a few hints in form of 2-3 σ deviations have been observed. After an 18-month shutdown, in whichmore » the ATLAS detector has undergone various upgrades, the LHC has again started to deliver collision data at an increased centre-of-mass energy of 13 TeV, providing a much improved sensitivity for various searches, in particular for high mass particles. Some representative hints at the LHC Run 1 are presented, a brief overview of ATLAS upgrades and prospects for SUSY searches with early Run 2 data are given.« less

  20. Large Area Coverage of a TPC Endcap with GridPix Detectors

    NASA Astrophysics Data System (ADS)

    Kaminski, Jochen

    2018-02-01

    The Large Prototype TPC at DESY, Hamburg, was built by the LCTPC collaboration as a testbed for new readout technologies of Time Projection Chambers. Up to seven modules of about 400 cm2 each can be placed in the endcap. Three of these modules were equipped with a total of 160 GridPix detectors. This is a combination of a highly pixelated readout ASIC and a Micromegas built on top. GridPix detectors have a very high efficiency of detecting primary electrons, which leads to excellent spatial and energy resolutions. For the first time a large number of GridPix detectors has been operated and long segments of tracks have been recorded with excellent precision.

  1. Wave Energy Prize - 1/50th Testing - Atlas Ocean Systems

    DOE Data Explorer

    Wesley Scharmen

    2015-12-04

    This submission of data includes all the 1/50th scale testing data completed on the Wave Energy Prize for the Atlas Ocean Systems team, and includes: 1/50th test data (raw & processed) 1/50th test data video and pictures 1/50th Test plans and testing documents SSTF_Submission (summarized results)

  2. EnviroAtlas - Austin, TX - Atlas Area Boundary

    EPA Pesticide Factsheets

    This EnviroAtlas dataset shows the boundary of the Austin, TX Atlas Area. It represents the outside edge of all the block groups included in the EnviroAtlas Area.This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

  3. Event selection services in ATLAS

    NASA Astrophysics Data System (ADS)

    Cranshaw, J.; Cuhadar-Donszelmann, T.; Gallas, E.; Hrivnac, J.; Kenyon, M.; McGlone, H.; Malon, D.; Mambelli, M.; Nowak, M.; Viegas, F.; Vinek, E.; Zhang, Q.

    2010-04-01

    ATLAS has developed and deployed event-level selection services based upon event metadata records ("TAGS") and supporting file and database technology. These services allow physicists to extract events that satisfy their selection predicates from any stage of data processing and use them as input to later analyses. One component of these services is a web-based Event-Level Selection Service Interface (ELSSI). ELSSI supports event selection by integrating run-level metadata, luminosity-block-level metadata (e.g., detector status and quality information), and event-by-event information (e.g., triggers passed and physics content). The list of events that survive after some selection criterion is returned in a form that can be used directly as input to local or distributed analysis; indeed, it is possible to submit a skimming job directly from the ELSSI interface using grid proxy credential delegation. ELSSI allows physicists to explore ATLAS event metadata as a means to understand, qualitatively and quantitatively, the distributional characteristics of ATLAS data. In fact, the ELSSI service provides an easy interface to see the highest missing ET events or the events with the most leptons, to count how many events passed a given set of triggers, or to find events that failed a given trigger but nonetheless look relevant to an analysis based upon the results of offline reconstruction, and more. This work provides an overview of ATLAS event-level selection services, with an emphasis upon the interactive Event-Level Selection Service Interface.

  4. Learning to rank atlases for multiple-atlas segmentation.

    PubMed

    Sanroma, Gerard; Wu, Guorong; Gao, Yaozong; Shen, Dinggang

    2014-10-01

    Recently, multiple-atlas segmentation (MAS) has achieved a great success in the medical imaging area. The key assumption is that multiple atlases have greater chances of correctly labeling a target image than a single atlas. However, the problem of atlas selection still remains unexplored. Traditionally, image similarity is used to select a set of atlases. Unfortunately, this heuristic criterion is not necessarily related to the final segmentation performance. To solve this seemingly simple but critical problem, we propose a learning-based atlas selection method to pick up the best atlases that would lead to a more accurate segmentation. Our main idea is to learn the relationship between the pairwise appearance of observed instances (i.e., a pair of atlas and target images) and their final labeling performance (e.g., using the Dice ratio). In this way, we select the best atlases based on their expected labeling accuracy. Our atlas selection method is general enough to be integrated with any existing MAS method. We show the advantages of our atlas selection method in an extensive experimental evaluation in the ADNI, SATA, IXI, and LONI LPBA40 datasets. As shown in the experiments, our method can boost the performance of three widely used MAS methods, outperforming other learning-based and image-similarity-based atlas selection methods.

  5. Bis(carbazol-9-ylphenyl)aniline end-capped oligoarylenes as solution-processed nondoped emitters for full-emission color tuning organic light-emitting diodes.

    PubMed

    Khanasa, Tanika; Prachumrak, Narid; Rattanawan, Rattanawaree; Jungsuttiwong, Siriporn; Keawin, Tinnagon; Sudyoadsuk, Taweesak; Tuntulani, Thawatchai; Promarak, Vinich

    2013-07-05

    A series of bis(3,6-di-tert-butylcarbazol-9-ylphenyl)aniline end-capped oligoarylenes, BCPA-Ars, are synthesized by double palladium-catalyzed cross-coupling reactions. By using this bis(carbazol-9-yl)triphenylamine moiety as an end-cap, we are able to reduce the crystallization and retain the high-emission ability of these planar fluorescent oligoarylene cores in the solid state, as well as improve the amorphous stability and solubility of the materials. The results of optical and electrochemical studies show that their HOMOs, LUMOs, and energy gaps can be easily modified or fine-tuned by either varying the degree of π-conjugation or using electron affinities of the aryl cores which include fluorene, oligothiophenes, 2,1,3-benzothiadiazole, 4,7-diphenyl-4-yl-2,1,3-benzothiadiazole, and 4,7-dithien-2-yl-2,1,3-benzothiadiazole. As a result, their emission spectra measured in solution and thin films can cover the full UV-vis spectrum (426-644 nm). Remarkably, solution-processed nondoped BCPA-Ars-based OLEDs could show moderate to excellent device performance with emission colors spanning the whole visible spectrum (deep blue to red). Particularly, the RGB (red, green, blue) OLEDs exhibit good color purity close to the pure RGB colors. This report offers a practical approach for both decorating the highly efficient but planar fluorophores and tuning their emission colors to be suitable for applications in nondoped and solution-processable full-color emission OLEDs.

  6. Epidemiology of atlas fractures--a national registry-based cohort study of 1,537 cases.

    PubMed

    Matthiessen, Christian; Robinson, Yohan

    2015-11-01

    The epidemiology of fractures of the first cervical vertebra-the atlas-has not been well documented. Previous studies concerning atlas fractures focus on treatment and form a weak platform for epidemiologic study. This study aims to provide reliable epidemiologic data on atlas fractures. This was a national registry-based cohort study. A total of 1,537 cases of atlas fractures between 1997 and 2011 from the Swedish National Patient Registry (NPR). The outcome measures were annual incidence and mortality. Data from the NPR and the Swedish Cause of Death Registry were extracted, including age, gender, diagnosis, comorbidity, treatment codes, and date of death. The Charlson Comorbidity Index was calculated and a survival analysis performed. A total of 869 (56.5%) cases were men, and 668 (43.5%) were women. The mean age of the entire population was 64 years. The proportion of atlas fractures of all registered cervical fractures was 10.6%. In 19% of all cases, there was an additional fracture of the axis, and 7% of all cases had additional subaxial cervical fractures. Patients with fractures of the axis were older than patients with isolated atlas fractures. The annual incidence almost doubled during the study period, and in 2011, it was 17 per million inhabitants. The greatest increase in incidence occurred in the elderly population. Atlas fractures occurred predominantly in the elderly population. Further study is needed to determine the cause of the increasing incidence. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

  7. Glance Information System for ATLAS Management

    NASA Astrophysics Data System (ADS)

    Grael, F. F.; Maidantchik, C.; Évora, L. H. R. A.; Karam, K.; Moraes, L. O. F.; Cirilli, M.; Nessi, M.; Pommès, K.; ATLAS Collaboration

    2011-12-01

    ATLAS Experiment is an international collaboration where more than 37 countries, 172 institutes and laboratories, 2900 physicists, engineers, and computer scientists plus 700 students participate. The management of this teamwork involves several aspects such as institute contribution, employment records, members' appointment, authors' list, preparation and publication of papers and speakers nomination. Previously, most of the information was accessible by a limited group and developers had to face problems such as different terminology, diverse data modeling, heterogeneous databases and unlike users needs. Moreover, the systems were not designed to handle new requirements. The maintenance has to be an easy task due to the long lifetime experiment and professionals turnover. The Glance system, a generic mechanism for accessing any database, acts as an intermediate layer isolating the user from the particularities of each database. It retrieves, inserts and updates the database independently of its technology and modeling. Relying on Glance, a group of systems were built to support the ATLAS management and operation aspects: ATLAS Membership, ATLAS Appointments, ATLAS Speakers, ATLAS Analysis Follow-Up, ATLAS Conference Notes, ATLAS Thesis, ATLAS Traceability and DSS Alarms Viewer. This paper presents the overview of the Glance information framework and describes the privilege mechanism developed to grant different level of access for each member and system.

  8. ATLAS Cloud R&D

    NASA Astrophysics Data System (ADS)

    Panitkin, Sergey; Barreiro Megino, Fernando; Caballero Bejar, Jose; Benjamin, Doug; Di Girolamo, Alessandro; Gable, Ian; Hendrix, Val; Hover, John; Kucharczyk, Katarzyna; Medrano Llamas, Ramon; Love, Peter; Ohman, Henrik; Paterson, Michael; Sobie, Randall; Taylor, Ryan; Walker, Rodney; Zaytsev, Alexander; Atlas Collaboration

    2014-06-01

    The computing model of the ATLAS experiment was designed around the concept of grid computing and, since the start of data taking, this model has proven very successful. However, new cloud computing technologies bring attractive features to improve the operations and elasticity of scientific distributed computing. ATLAS sees grid and cloud computing as complementary technologies that will coexist at different levels of resource abstraction, and two years ago created an R&D working group to investigate the different integration scenarios. The ATLAS Cloud Computing R&D has been able to demonstrate the feasibility of offloading work from grid to cloud sites and, as of today, is able to integrate transparently various cloud resources into the PanDA workload management system. The ATLAS Cloud Computing R&D is operating various PanDA queues on private and public resources and has provided several hundred thousand CPU days to the experiment. As a result, the ATLAS Cloud Computing R&D group has gained a significant insight into the cloud computing landscape and has identified points that still need to be addressed in order to fully utilize this technology. This contribution will explain the cloud integration models that are being evaluated and will discuss ATLAS' learning during the collaboration with leading commercial and academic cloud providers.

  9. Radiation Hard Active Media R&D for CMS Hadron Endcap Calorimetry

    NASA Astrophysics Data System (ADS)

    Tiras, Emrah; CMS-HCAL Collaboration

    2015-04-01

    The High Luminosity LHC era imposes unprecedented radiation conditions on the CMS detectors targeting a factor of 5-10 higher than the LHC design luminosity. The CMS detectors will need to be upgraded in order to withstand these conditions yet maintain/improve the physics measurement capabilities. One of the upgrade options is reconstructing the CMS Endcap Calorimeters with a shashlik design electromagnetic section and replacing active media of the hadronic section with radiation-hard scintillation materials. In this context, we have studied various radiation-hard materials such as Polyethylene Naphthalate (PEN), Polyethylene Terephthalate (PET), HEM and quartz plates coated with various organic materials such as p-Terphenyl (pTp), Gallium doped Zinc Oxide (ZnO:Ga) and Anthracene. Here we discuss the related test beam activities, laboratory measurements and recent developments.

  10. EnviroAtlas - NHDPlus V2 WBD Snapshot, EnviroAtlas version - Conterminous United States

    EPA Pesticide Factsheets

    This EnviroAtlas dataset is a digital hydrologic unit boundary layer to the Subwatershed (12-digit) 6th level for the conterminous United States, based on the January 6, 2015 NHDPlus V2 WBD (Watershed Boundary Dataset) Snapshot (NHDPlusV21_NationalData_WBDSnapshot_FileGDB_05). The feature class has been edited for use in for EPA ORD's EnviroAtlas. Features in Canada and Mexico have been removed, the boundaries of three 12-digit HUCs have been edited to eliminate gaps and overlaps, the dataset has been dissolved on HUC_12 to create multipart polygons, and information on the percent land area has been added. Hawaii, Puerto Rico, and the U.S. Virgin Islands have been removed, and can be downloaded separately. Other than these modifications, the dataset is the same as the WBD Snapshot included in NHDPlus V2.This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

  11. AICHA: An atlas of intrinsic connectivity of homotopic areas.

    PubMed

    Joliot, Marc; Jobard, Gaël; Naveau, Mikaël; Delcroix, Nicolas; Petit, Laurent; Zago, Laure; Crivello, Fabrice; Mellet, Emmanuel; Mazoyer, Bernard; Tzourio-Mazoyer, Nathalie

    2015-10-30

    Atlases of brain anatomical ROIs are widely used for functional MRI data analysis. Recently, it was proposed that an atlas of ROIs derived from a functional brain parcellation could be advantageous, in particular for understanding how different regions share information. However, functional atlases so far proposed do not account for a crucial aspect of cerebral organization, namely homotopy, i.e. that each region in one hemisphere has a homologue in the other hemisphere. We present AICHA (for Atlas of Intrinsic Connectivity of Homotopic Areas), a functional brain ROIs atlas based on resting-state fMRI data acquired in 281 individuals. AICHA ROIs cover the whole cerebrum, each having 1-homogeneity of its constituting voxels intrinsic activity, and 2-a unique homotopic contralateral counterpart with which it has maximal intrinsic connectivity. AICHA was built in 4 steps: (1) estimation of resting-state networks (RSNs) using individual resting-state fMRI independent components, (2) k-means clustering of voxel-wise group level profiles of connectivity, (3) homotopic regional grouping based on maximal inter-hemispheric functional correlation, and (4) ROI labeling. AICHA includes 192 homotopic region pairs (122 gyral, 50 sulcal, and 20 gray nuclei). As an application, we report inter-hemispheric (homotopic and heterotopic) and intra-hemispheric connectivity patterns at different sparsities. ROI functional homogeneity was higher for AICHA than for anatomical ROI atlases, but slightly lower than for another functional ROI atlas not accounting for homotopy. AICHA is ideally suited for intrinsic/effective connectivity analyses, as well as for investigating brain hemispheric specialization. Copyright © 2015 Elsevier B.V. All rights reserved.

  12. Gateways to the FANTOM5 promoter level mammalian expression atlas

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Lizio, Marina; Harshbarger, Jayson; Shimoji, Hisashi

    The FANTOM5 project investigates transcription initiation activities in more than 1,000 human and mouse primary cells, cell lines and tissues using CAGE. Based on manual curation of sample information and development of an ontology for sample classification, we assemble the resulting data into a centralized data resource (http://fantom.gsc.riken.jp/5/). In conclusion, this resource contains web-based tools and data-access points for the research community to search and extract data related to samples, genes, promoter activities, transcription factors and enhancers across the FANTOM5 atlas.

  13. Gateways to the FANTOM5 promoter level mammalian expression atlas

    DOE PAGES

    Lizio, Marina; Harshbarger, Jayson; Shimoji, Hisashi; ...

    2015-01-05

    The FANTOM5 project investigates transcription initiation activities in more than 1,000 human and mouse primary cells, cell lines and tissues using CAGE. Based on manual curation of sample information and development of an ontology for sample classification, we assemble the resulting data into a centralized data resource (http://fantom.gsc.riken.jp/5/). In conclusion, this resource contains web-based tools and data-access points for the research community to search and extract data related to samples, genes, promoter activities, transcription factors and enhancers across the FANTOM5 atlas.

  14. Improving vertebra segmentation through joint vertebra-rib atlases

    NASA Astrophysics Data System (ADS)

    Wang, Yinong; Yao, Jianhua; Roth, Holger R.; Burns, Joseph E.; Summers, Ronald M.

    2016-03-01

    Accurate spine segmentation allows for improved identification and quantitative characterization of abnormalities of the vertebra, such as vertebral fractures. However, in existing automated vertebra segmentation methods on computed tomography (CT) images, leakage into nearby bones such as ribs occurs due to the close proximity of these visibly intense structures in a 3D CT volume. To reduce this error, we propose the use of joint vertebra-rib atlases to improve the segmentation of vertebrae via multi-atlas joint label fusion. Segmentation was performed and evaluated on CTs containing 106 thoracic and lumbar vertebrae from 10 pathological and traumatic spine patients on an individual vertebra level basis. Vertebra atlases produced errors where the segmentation leaked into the ribs. The use of joint vertebra-rib atlases produced a statistically significant increase in the Dice coefficient from 92.5 +/- 3.1% to 93.8 +/- 2.1% for the left and right transverse processes and a decrease in the mean and max surface distance from 0.75 +/- 0.60mm and 8.63 +/- 4.44mm to 0.30 +/- 0.27mm and 3.65 +/- 2.87mm, respectively.

  15. NCHHSTP Atlas

    MedlinePlus

    ... disease and a combined set for all diseases. Social Determinants of Health Slide Sets County-level data on poverty, income, ... Hepatitis Slideset HIV Slideset STD Slideset Tuberculosis Slideset Social Determinants of Health Slideset Buttons AtlasPlus Application File Formats Help: How ...

  16. The ATLAS Data Acquisition System: from Run 1 to Run 2

    NASA Astrophysics Data System (ADS)

    Panduro Vazquez, William; ATLAS Collaboration

    2016-04-01

    The experience gained during the first period of very successful data taking of the ATLAS experiment (Run 1) has inspired a number of ideas for improvement of the Data Acquisition (DAQ) system that are being put in place during the so-called Long Shutdown 1 of the Large Hadron Collider (LHC), in 2013/14. We have updated the data-flow architecture, rewritten an important fraction of the software and replaced hardware, profiting from state of the art technologies. This paper summarizes the main changes that have been applied to the ATLAS DAQ system and highlights the expected performance and functional improvements that will be available for the LHC Run 2. Particular emphasis will be put on explaining the reasons for our architectural and technical choices, as well as on the simulation and testing approach used to validate this system.

  17. EnviroAtlas - Austin, TX - Proximity to Parks

    EPA Pesticide Factsheets

    This EnviroAtlas dataset shows the approximate walking distance from a park entrance at any given location within the EnviroAtlas community boundary. The zones are estimated in 1/4 km intervals up to 1km then in 1km intervals up to 5km. Park entrances were included in this analysis if they were within 5km of the community boundary. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

  18. A Glimpse of Atlas

    NASA Technical Reports Server (NTRS)

    2005-01-01

    Saturn's little moon Atlas orbits Saturn between the outer edge of the A ring and the fascinating, twisted F ring. This image just barely resolves the disk of Atlas, and also shows some of the knotted structure for which the F ring is known. Atlas is 32 kilometers (20 miles) across.

    The bright outer edge of the A ring is overexposed here, but farther down the image several bright ring features can be seen.

    The image was taken in visible light with the Cassini spacecraft narrow-angle camera on April 25, 2005, at a distance of approximately 2.4 million kilometers (1.5 million miles) from Atlas and at a Sun-Atlas-spacecraft, or phase, angle of 60 degrees. Resolution in the original image was 14 kilometers (9 miles) per pixel.

  19. Multi atlas based segmentation: Should we prefer the best atlas group over the group of best atlases?

    PubMed

    Zaffino, Paolo; Ciardo, Delia; Raudaschl, Patrik; Fritscher, Karl; Ricotti, Rosalinda; Alterio, Daniela; Marvaso, Giulia; Fodor, Cristiana; Baroni, Guido; Amato, Francesco; Orecchia, Roberto; Jereczek-Fossa, Barbara Alicja; Sharp, Gregory C; Spadea, Maria Francesca

    2018-05-22

    Multi Atlas Based Segmentation (MABS) uses a database of atlas images, and an atlas selection process is used to choose an atlas subset for registration and voting. In the current state of the art, atlases are chosen according to a similarity criterion between the target subject and each atlas in the database. In this paper, we propose a new concept for atlas selection that relies on selecting the best performing group of atlases rather than the group of highest scoring individual atlases. Experiments were performed using CT images of 50 patients, with contours of brainstem and parotid glands. The dataset was randomly split in 2 groups: 20 volumes were used as an atlas database and 30 served as target subjects for testing. Classic oracle group selection, where atlases are chosen by the highest Dice Similarity Coefficient (DSC) with the target, was performed. This was compared to oracle Group selection, where all the combinations of atlas subgroups were considered and scored by computing DSC with the target subject. Subsequently, Convolutional Neural Networks (CNNs) were designed to predict the best group of atlases. The results were compared also with the selection strategy based on Normalized Mutual Information (NMI). Oracle group was proved to be significantly better that classic oracle selection (p<10-5). Atlas group selection led to a median±interquartile DSC of 0.740±0.084, 0.718±0.086 and 0.670±0.097 for brainstem and left/right parotid glands respectively, outperforming NMI selection 0.676±0.113, 0.632±0.104 and 0.606±0.118 (p<0.001) as well as classic oracle selection. The implemented methodology is a proof of principle that selecting the atlases by considering the performance of the entire group of atlases instead of each single atlas leads to higher segmentation accuracy, being even better then current oracle strategy. This finding opens a new discussion about the most appropriate atlas selection criterion for MABS. © 2018

  20. Atlas of Mars: the 1:5,000,000 map series

    USGS Publications Warehouse

    Batson, R.M.; Bridges, P.M.; Inge, J.L.

    1979-01-01

    This atlas comprises small-scale maps and photomosaics covering the entire surface of the planet Mars. The cartographic contents are reduced-scale versions of the 1:5,000,000 topographic series of 30 quadrangles compiled by the U.S. Geological Survey in cooperation with the National Aeronautics and Space Administration (NASA).

  1. ICESat-2 / ATLAS Flight Science Receiver Algorithms

    NASA Astrophysics Data System (ADS)

    Mcgarry, J.; Carabajal, C. C.; Degnan, J. J.; Mallama, A.; Palm, S. P.; Ricklefs, R.; Saba, J. L.

    2013-12-01

    NASA's Advanced Topographic Laser Altimeter System (ATLAS) will be the single instrument on the ICESat-2 spacecraft which is expected to launch in 2016 with a 3 year mission lifetime. The ICESat-2 orbital altitude will be 500 km with a 92 degree inclination and 91-day repeat tracks. ATLAS is a single photon detection system transmitting at 532nm with a laser repetition rate of 10 kHz and a 6 spot pattern on the Earth's surface. Without some method of eliminating solar background noise in near real-time, the volume of ATLAS telemetry would far exceed the normal X-band downlink capability. To reduce the data volume to an acceptable level a set of onboard Receiver Algorithms has been developed. These Algorithms limit the daily data volume by distinguishing surface echoes from the background noise and allow the instrument to telemeter only a small vertical region about the signal. This is accomplished through the use of an onboard Digital Elevation Model (DEM), signal processing techniques, and an onboard relief map. Similar to what was flown on the ATLAS predecessor GLAS (Geoscience Laser Altimeter System) the DEM provides minimum and maximum heights for each 1 degree x 1 degree tile on the Earth. This information allows the onboard algorithm to limit its signal search to the region between minimum and maximum heights (plus some margin for errors). The understanding that the surface echoes will tend to clump while noise will be randomly distributed led us to histogram the received event times. The selection of the signal locations is based on those histogram bins with statistically significant counts. Once the signal location has been established the onboard Digital Relief Map (DRM) is used to determine the vertical width of the telemetry band about the signal. The ATLAS Receiver Algorithms are nearing completion of the development phase and are currently being tested using a Monte Carlo Software Simulator that models the instrument, the orbit and the environment

  2. Anatomical Variant of Atlas : Arcuate Foramen, Occpitalization of Atlas, and Defect of Posterior Arch of Atlas.

    PubMed

    Kim, Myoung Soo

    2015-12-01

    We sought to examine anatomic variations of the atlas and the clinical significance of these variations. We retrospectively reviewed 1029 cervical 3-dimensional (3D) CT images. Cervical 3D CT was performed between November 2011 and August 2014. Arcuate foramina were classified as partial or complete and left and/or right. Occipitalization of the atlas was classified in accordance with criteria specified by Mudaliar et al. Posterior arch defects of the atlas were classified in accordance with criteria specified by Currarino et al. One hundred and eight vertebrae (108/1029, 10.5%) showed an arcuate foramen. Bilateral arcuate foramina were present in 41 of these vertebrae and the remaining 67 arcuate foramina were unilateral (right 31, left 36). Right-side arcuate foramina were partial on 18 sides and complete on 54 sides. Left-side arcuate foramina were partial on 24 sides and complete on 53 sides. One case of atlas assimilation was found. Twelve patients (12/1029, 1.17%) had a defect of the atlantal posterior arch. Nine of these patients (9/1029, 0.87%) had a type A posterior arch defect. We also identified one type B, one type D, and one type E defect. Preoperative diagnosis of occipitalization of the atlas and arcuate foramina using 3D CT is of paramount importance in avoiding neurovascular injury during surgery. It is important to be aware of posterior arch defects of the atlas because they may be misdiagnosed as a fracture.

  3. Development, deployment and operations of ATLAS databases

    NASA Astrophysics Data System (ADS)

    Vaniachine, A. V.; Schmitt, J. G. v. d.

    2008-07-01

    In preparation for ATLAS data taking, a coordinated shift from development towards operations has occurred in ATLAS database activities. In addition to development and commissioning activities in databases, ATLAS is active in the development and deployment (in collaboration with the WLCG 3D project) of the tools that allow the worldwide distribution and installation of databases and related datasets, as well as the actual operation of this system on ATLAS multi-grid infrastructure. We describe development and commissioning of major ATLAS database applications for online and offline. We present the first scalability test results and ramp-up schedule over the initial LHC years of operations towards the nominal year of ATLAS running, when the database storage volumes are expected to reach 6.1 TB for the Tag DB and 1.0 TB for the Conditions DB. ATLAS database applications require robust operational infrastructure for data replication between online and offline at Tier-0, and for the distribution of the offline data to Tier-1 and Tier-2 computing centers. We describe ATLAS experience with Oracle Streams and other technologies for coordinated replication of databases in the framework of the WLCG 3D services.

  4. Status of HVCMOS developments for ATLAS

    NASA Astrophysics Data System (ADS)

    Perić, I.; Blanco, R.; Casanova Mohr, R.; Ehrler, F.; Guezzi Messaoud, F.; Krämer, C.; Leys, R.; Prathapan, M.; Schimassek, R.; Schöning, A.; Vilella Figueras, E.; Weber, A.; Zhang, H.

    2017-02-01

    This paper describes the status of the developments made by ATLAS HVCMOS and HVMAPS collaborations. We have proposed two HVCMOS sensor concepts for ATLAS pixels—the capacitive coupled pixel detector (CCPD) and the monolithic detector. The sensors have been implemented in three semiconductor processes AMS H18, AMS H35 and LFoundry LFA15. Efficiency of 99.7% after neutron irradiation to 1015 neq/cm2W has been measured with the small area CCPD prototype in AMS H18 technology. About 84% of the particles are detected with a time resolution better than 25 ns. The sensor was implemented on a low resistivity substrate. The large area demonstrator sensor in AMS H35 process has been designed, produced and successfully tested. The sensor has been produced on different high resistivity substrates ranging from 80 Ωcm to more than 1 kΩ. Monolithic- and hybrid readout are both possible. In August 2016, six different monolithic pixel matrices for ATLAS with a total area of 1 cm2 have been submitted in LFoundry LFA15 process. The matrices implement column drain and triggered readout as well as waveform sampling capability on pixel level. Design details will be presented.

  5. 25. "TEST STAND 1A UTILIZED TO TEST THE ATLAS ICBM", ...

    Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

    25. "TEST STAND 1-A UTILIZED TO TEST THE ATLAS ICBM", CROPPED OUT: "DIRECTORATE OF MISSILE CAPTIVE TEST, EDWARDS AFB." Photo no. 11,371 57; G-AFFTC 15 OCT 57. Looking southwest from below the stand. - Edwards Air Force Base, Air Force Rocket Propulsion Laboratory, Test Stand 1-A, Test Area 1-120, north end of Jupiter Boulevard, Boron, Kern County, CA

  6. BNL ATLAS Grid Computing

    ScienceCinema

    Michael Ernst

    2017-12-09

    As the sole Tier-1 computing facility for ATLAS in the United States and the largest ATLAS computing center worldwide Brookhaven provides a large portion of the overall computing resources for U.S. collaborators and serves as the central hub for storing,

  7. Improving atlas methodology

    USGS Publications Warehouse

    Robbins, C.S.; Dowell, B.A.; O'Brien, J.

    1987-01-01

    We are studying a sample of Maryland (2 %) and New Hampshire (4 %) Atlas blocks and a small sample in Maine. These three States used different sampling methods and block sizes. We compare sampling techniques, roadside with off-road coverage, our coverage with that of the volunteers, and different methods of quantifying Atlas results. The 7 1/2' (12-km) blocks used in the Maine Atlas are satisfactory for coarse mapping, but are too large to enable changes to be detected in the future. Most states are subdividing the standard 7 1/2' maps into six 5-km blocks. The random 1/6 sample of 5-km blocks used in New Hampshire, Vermont (published 1985), and many other states has the advantage of permitting detection of some changes in the future, but the disadvantage of leaving important habitats unsampled. The Maryland system of atlasing all 1,200 5-km blocks and covering one out of each six by quarterblocks (2 1/2-km) is far superior if enough observers can be found. A good compromise, not yet attempted, would be to Atlas a 1/6 random sample of 5-km blocks and also one other carefully selected (non-random) block on the same 7 1/2' map--the block that would include the best sample of habitats or elevations not in the random block. In our sample the second block raised the percentage of birds found from 86% of the birds recorded in the 7 1/2' quadrangle to 93%. It was helpful to list the expected species in each block and to revise this list annually. We estimate that 90-100 species could be found with intensive effort in most Maryland blocks; perhaps 95-105 in New Hampshire. It was also helpful to know which species were under-sampled so we could make a special effort to search for these. A total of 75 species per block (or 75% of the expected species in blocks with very restricted habitat diversity) is considered a practical and adequate goal in these States. When fewer than 60 species are found per block, a high proportion of the rarer species are missed, as well as some of

  8. ATLAS FTK a - very complex - custom super computer

    NASA Astrophysics Data System (ADS)

    Kimura, N.; ATLAS Collaboration

    2016-10-01

    In the LHC environment for high interaction pile-up, advanced techniques of analysing the data in real time are required in order to maximize the rate of physics processes of interest with respect to background processes. The Fast TracKer (FTK) is a track finding implementation at the hardware level that is designed to deliver full-scan tracks with pT above 1 GeV to the ATLAS trigger system for events passing the Level-1 accept (at a maximum rate of 100 kHz). In order to achieve this performance, a highly parallel system was designed and currently it is being commissioned within in ATLAS. Starting in 2016 it will provide tracks for the trigger system in a region covering the central part of the ATLAS detector, and will be extended to the full detector coverage. The system relies on matching hits coming from the silicon tracking detectors against one billion patterns stored in custom ASIC chips (Associative memory chip - AM06). In a first stage, coarse resolution hits are matched against the patterns and the accepted hits undergo track fitting implemented in FPGAs. Tracks with pT > 1GeV are delivered to the High Level Trigger within about 100 ps. Resolution of the tracks coming from FTK is close to the offline tracking and it will allow for reliable detection of primary and secondary vertexes at trigger level and improved trigger performance for b-jets and tau leptons. This contribution will give an overview of the FTK system and present the status of commissioning of the system. Additionally, the expected FTK performance will be briefly described.

  9. EnviroAtlas - NHDPlus V2 Hydrologic Unit Boundaries Web Service - Conterminous United States

    EPA Pesticide Factsheets

    This EnviroAtlas web service contains layers depicting hydrologic unit boundary layers and labels for the Subregion level (4-digit HUCs), Subbasin level (8-digit HUCs), and Subwatershed level (12-digit HUCs) for the conterminous United States. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

  10. The National Atlas of the United States now on the Web and in print

    USGS Publications Warehouse

    Hutchinson, John A.

    2004-01-01

    The National Atlas of the United States of America® was published in 1970 as a book, with more than 400 pages and 765 maps. Since then, many people have called for a new edition, and many maps have been published as single sheets using the classic National Atlas 1:7,500,000-scale format. Work began in 1997 on a new, web-based edition of the National Atlas of the United States®. Accessible at http://nationalatlas.gov, the new atlas features an interactive mapmaker with more than 1,000 data layers. Developed as a coordinated package of dynamic webbased map products and services, and printed and printable maps for selected themes, the National Atlas of the United States of America® has grown beyond a book. Yet, the cartographer’s fundamental job remains the same as it was in 1970—to translate national-level geographic data into an understandable view of the nation.

  11. 27 CFR 9.140 - Atlas Peak.

    Code of Federal Regulations, 2010 CFR

    2010-04-01

    ... 27 Alcohol, Tobacco Products and Firearms 1 2010-04-01 2010-04-01 false Atlas Peak. 9.140 Section... THE TREASURY LIQUORS AMERICAN VITICULTURAL AREAS Approved American Viticultural Areas § 9.140 Atlas Peak. (a) Name. The name of the viticultural area described in this section is “Atlas Peak.” (b...

  12. SpS1-The Spitzer atlas of stellar spectra

    NASA Astrophysics Data System (ADS)

    Ardila, David R.; Makowiecki, W.; van Dyk, S.; Song, I.; Stauffer, J.; Rho, J.; Fajardo-Acosta, S.; Hoard, D. W.; Wachter, S.

    2010-11-01

    We present Spitzer Space Telescope spectra of 147 stars (R~64 - 128, λλ = 5 - 35 μm, S/N~100) covering most spectral and luminosity classes within the HR diagram. The spectra are available from the NASA/IPAC Infrared Science Archive (IRSA) and from the first author's webpage (http://web.ipac.caltech.edu/staff/ardila/Atlas/). The Atlas contains spectra of ‘typical’ stars, which may serve to refine galactic synthesis models, study stellar atmospheres, and establish a legacy for future IR missions, such as JWST.

  13. Synthesis and surface properties of polyurethane end-capped with hybrid hydrocarbon/fluorocarbon double-chain phospholipid.

    PubMed

    Li, Jiehua; Zhang, Yi; Yang, Jian; Tan, Hong; Li, Jianshu; Fu, Qiang

    2013-05-01

    To improve hemocompatibility of biomedical polyurethanes (PUs), a series of new fluorinated phospholipid end-capped polyurethanes (FPCPUs) as blending PU additives were designed and synthesized using diphenyl methane diisocyanate and 1,4-butanediol as hard segment, poly(tetramethylene glycol), polypropylene glycol, polycarbonate diols, and polyethylene glycol as soft segments, respectively, aminofunctionalized hybrid hydrocarbon/fluorocarbon double-chain phospholipid as end-capper. The bulk structures and surface properties of the obtained FPCPUs were fully characterized by (1)H NMR, Fourier transform infrared, gel permeation chromatography, X-ray photoelectron spectroscopy, differential scanning calorimetry, atomic force microscopy, and water contact angle measurement. It was found that the phosphatidylcholine groups could enrich on the surfaces and subsurfaces with the help of the fluorocarbon chains and self-assemble into mimic biomembrane on these polymer surfaces. These surfaces could effectively suppress fibrinogen adsorption, as evaluated by enzyme-linked immunosorbent assay method. Our work indicates that the FPCPUs should be one of the most potential modified additives for enhancing hemocompatibility of traditional medical PUs. Copyright © 2012 Wiley Periodicals, Inc.

  14. Abasy Atlas: a comprehensive inventory of systems, global network properties and systems-level elements across bacteria.

    PubMed

    Ibarra-Arellano, Miguel A; Campos-González, Adrián I; Treviño-Quintanilla, Luis G; Tauch, Andreas; Freyre-González, Julio A

    2016-01-01

    The availability of databases electronically encoding curated regulatory networks and of high-throughput technologies and methods to discover regulatory interactions provides an invaluable source of data to understand the principles underpinning the organization and evolution of these networks responsible for cellular regulation. Nevertheless, data on these sources never goes beyond the regulon level despite the fact that regulatory networks are complex hierarchical-modular structures still challenging our understanding. This brings the necessity for an inventory of systems across a large range of organisms, a key step to rendering feasible comparative systems biology approaches. In this work, we take the first step towards a global understanding of the regulatory networks organization by making a cartography of the functional architectures of diverse bacteria. Abasy ( A: cross- BA: cteria SY: stems) Atlas provides a comprehensive inventory of annotated functional systems, global network properties and systems-level elements (global regulators, modular genes shaping functional systems, basal machinery genes and intermodular genes) predicted by the natural decomposition approach for reconstructed and meta-curated regulatory networks across a large range of bacteria, including pathogenically and biotechnologically relevant organisms. The meta-curation of regulatory datasets provides the most complete and reliable set of regulatory interactions currently available, which can even be projected into subsets by considering the force or weight of evidence supporting them or the systems that they belong to. Besides, Abasy Atlas provides data enabling large-scale comparative systems biology studies aimed at understanding the common principles and particular lifestyle adaptions of systems across bacteria. Abasy Atlas contains systems and system-level elements for 50 regulatory networks comprising 78 649 regulatory interactions covering 42 bacteria in nine taxa, containing

  15. Abasy Atlas: a comprehensive inventory of systems, global network properties and systems-level elements across bacteria

    PubMed Central

    Ibarra-Arellano, Miguel A.; Campos-González, Adrián I.; Treviño-Quintanilla, Luis G.; Tauch, Andreas; Freyre-González, Julio A.

    2016-01-01

    The availability of databases electronically encoding curated regulatory networks and of high-throughput technologies and methods to discover regulatory interactions provides an invaluable source of data to understand the principles underpinning the organization and evolution of these networks responsible for cellular regulation. Nevertheless, data on these sources never goes beyond the regulon level despite the fact that regulatory networks are complex hierarchical-modular structures still challenging our understanding. This brings the necessity for an inventory of systems across a large range of organisms, a key step to rendering feasible comparative systems biology approaches. In this work, we take the first step towards a global understanding of the regulatory networks organization by making a cartography of the functional architectures of diverse bacteria. Abasy (Across-bacteria systems) Atlas provides a comprehensive inventory of annotated functional systems, global network properties and systems-level elements (global regulators, modular genes shaping functional systems, basal machinery genes and intermodular genes) predicted by the natural decomposition approach for reconstructed and meta-curated regulatory networks across a large range of bacteria, including pathogenically and biotechnologically relevant organisms. The meta-curation of regulatory datasets provides the most complete and reliable set of regulatory interactions currently available, which can even be projected into subsets by considering the force or weight of evidence supporting them or the systems that they belong to. Besides, Abasy Atlas provides data enabling large-scale comparative systems biology studies aimed at understanding the common principles and particular lifestyle adaptions of systems across bacteria. Abasy Atlas contains systems and system-level elements for 50 regulatory networks comprising 78 649 regulatory interactions covering 42 bacteria in nine taxa, containing 3708

  16. Development and Implementation of a Corriedale Ovine Brain Atlas for Use in Atlas-Based Segmentation.

    PubMed

    Liyanage, Kishan Andre; Steward, Christopher; Moffat, Bradford Armstrong; Opie, Nicholas Lachlan; Rind, Gil Simon; John, Sam Emmanuel; Ronayne, Stephen; May, Clive Newton; O'Brien, Terence John; Milne, Marjorie Eileen; Oxley, Thomas James

    2016-01-01

    Segmentation is the process of partitioning an image into subdivisions and can be applied to medical images to isolate anatomical or pathological areas for further analysis. This process can be done manually or automated by the use of image processing computer packages. Atlas-based segmentation automates this process by the use of a pre-labelled template and a registration algorithm. We developed an ovine brain atlas that can be used as a model for neurological conditions such as Parkinson's disease and focal epilepsy. 17 female Corriedale ovine brains were imaged in-vivo in a 1.5T (low-resolution) MRI scanner. 13 of the low-resolution images were combined using a template construction algorithm to form a low-resolution template. The template was labelled to form an atlas and tested by comparing manual with atlas-based segmentations against the remaining four low-resolution images. The comparisons were in the form of similarity metrics used in previous segmentation research. Dice Similarity Coefficients were utilised to determine the degree of overlap between eight independent, manual and atlas-based segmentations, with values ranging from 0 (no overlap) to 1 (complete overlap). For 7 of these 8 segmented areas, we achieved a Dice Similarity Coefficient of 0.5-0.8. The amygdala was difficult to segment due to its variable location and similar intensity to surrounding tissues resulting in Dice Coefficients of 0.0-0.2. We developed a low resolution ovine brain atlas with eight clinically relevant areas labelled. This brain atlas performed comparably to prior human atlases described in the literature and to intra-observer error providing an atlas that can be used to guide further research using ovine brains as a model and is hosted online for public access.

  17. Global GIS database; digital atlas of Africa

    USGS Publications Warehouse

    Hearn, P.P.; Hare, T.M.; Schruben, P.; Sherrill, D.; LaMar, C.; Tsushima, P.

    2001-01-01

    This CD-ROM contains a digital atlas of the countries of Africa. This atlas is part of a global database compiled from USGS and other data sources at a nominal scale of 1:1 million and is intended to be used as a regional-scale reference and analytical tool by government officials, researchers, the private sector, and the general public. The atlas includes free GIS software or may be used with ESRI's ArcView software. Customized ArcView tools, specifically designed to make this atlas easier to use, are also included.

  18. Multi-atlas segmentation enables robust multi-contrast MRI spleen segmentation for splenomegaly

    NASA Astrophysics Data System (ADS)

    Huo, Yuankai; Liu, Jiaqi; Xu, Zhoubing; Harrigan, Robert L.; Assad, Albert; Abramson, Richard G.; Landman, Bennett A.

    2017-02-01

    Non-invasive spleen volume estimation is essential in detecting splenomegaly. Magnetic resonance imaging (MRI) has been used to facilitate splenomegaly diagnosis in vivo. However, achieving accurate spleen volume estimation from MR images is challenging given the great inter-subject variance of human abdomens and wide variety of clinical images/modalities. Multi-atlas segmentation has been shown to be a promising approach to handle heterogeneous data and difficult anatomical scenarios. In this paper, we propose to use multi-atlas segmentation frameworks for MRI spleen segmentation for splenomegaly. To the best of our knowledge, this is the first work that integrates multi-atlas segmentation for splenomegaly as seen on MRI. To address the particular concerns of spleen MRI, automated and novel semi-automated atlas selection approaches are introduced. The automated approach interactively selects a subset of atlases using selective and iterative method for performance level estimation (SIMPLE) approach. To further control the outliers, semi-automated craniocaudal length based SIMPLE atlas selection (L-SIMPLE) is proposed to introduce a spatial prior in a fashion to guide the iterative atlas selection. A dataset from a clinical trial containing 55 MRI volumes (28 T1 weighted and 27 T2 weighted) was used to evaluate different methods. Both automated and semi-automated methods achieved median DSC > 0.9. The outliers were alleviated by the L-SIMPLE (≍1 min manual efforts per scan), which achieved 0.9713 Pearson correlation compared with the manual segmentation. The results demonstrated that the multi-atlas segmentation is able to achieve accurate spleen segmentation from the multi-contrast splenomegaly MRI scans.

  19. Multi-atlas Segmentation Enables Robust Multi-contrast MRI Spleen Segmentation for Splenomegaly.

    PubMed

    Huo, Yuankai; Liu, Jiaqi; Xu, Zhoubing; Harrigan, Robert L; Assad, Albert; Abramson, Richard G; Landman, Bennett A

    2017-02-11

    Non-invasive spleen volume estimation is essential in detecting splenomegaly. Magnetic resonance imaging (MRI) has been used to facilitate splenomegaly diagnosis in vivo. However, achieving accurate spleen volume estimation from MR images is challenging given the great inter-subject variance of human abdomens and wide variety of clinical images/modalities. Multi-atlas segmentation has been shown to be a promising approach to handle heterogeneous data and difficult anatomical scenarios. In this paper, we propose to use multi-atlas segmentation frameworks for MRI spleen segmentation for splenomegaly. To the best of our knowledge, this is the first work that integrates multi-atlas segmentation for splenomegaly as seen on MRI. To address the particular concerns of spleen MRI, automated and novel semi-automated atlas selection approaches are introduced. The automated approach interactively selects a subset of atlases using selective and iterative method for performance level estimation (SIMPLE) approach. To further control the outliers, semi-automated craniocaudal length based SIMPLE atlas selection (L-SIMPLE) is proposed to introduce a spatial prior in a fashion to guide the iterative atlas selection. A dataset from a clinical trial containing 55 MRI volumes (28 T1 weighted and 27 T2 weighted) was used to evaluate different methods. Both automated and semi-automated methods achieved median DSC > 0.9. The outliers were alleviated by the L-SIMPLE (≈1 min manual efforts per scan), which achieved 0.9713 Pearson correlation compared with the manual segmentation. The results demonstrated that the multi-atlas segmentation is able to achieve accurate spleen segmentation from the multi-contrast splenomegaly MRI scans.

  20. Evolution of the ATLAS distributed computing system during the LHC long shutdown

    NASA Astrophysics Data System (ADS)

    Campana, S.; Atlas Collaboration

    2014-06-01

    The ATLAS Distributed Computing project (ADC) was established in 2007 to develop and operate a framework, following the ATLAS computing model, to enable data storage, processing and bookkeeping on top of the Worldwide LHC Computing Grid (WLCG) distributed infrastructure. ADC development has always been driven by operations and this contributed to its success. The system has fulfilled the demanding requirements of ATLAS, daily consolidating worldwide up to 1 PB of data and running more than 1.5 million payloads distributed globally, supporting almost one thousand concurrent distributed analysis users. Comprehensive automation and monitoring minimized the operational manpower required. The flexibility of the system to adjust to operational needs has been important to the success of the ATLAS physics program. The LHC shutdown in 2013-2015 affords an opportunity to improve the system in light of operational experience and scale it to cope with the demanding requirements of 2015 and beyond, most notably a much higher trigger rate and event pileup. We will describe the evolution of the ADC software foreseen during this period. This includes consolidating the existing Production and Distributed Analysis framework (PanDA) and ATLAS Grid Information System (AGIS), together with the development and commissioning of next generation systems for distributed data management (DDM/Rucio) and production (Prodsys-2). We will explain how new technologies such as Cloud Computing and NoSQL databases, which ATLAS investigated as R&D projects in past years, will be integrated in production. Finally, we will describe more fundamental developments such as breaking job-to-data locality by exploiting storage federations and caches, and event level (rather than file or dataset level) workload engines.

  1. EnviroAtlas - Austin, TX - Impervious Proximity Gradient

    EPA Pesticide Factsheets

    In any given 1-square meter point in this EnviroAtlas dataset, the value shown gives the percentage of impervious surface within 1 square kilometer centered over the given point. Water is shown as '-99999' in this dataset to distinguish it from land areas with low impervious. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

  2. Task Management in the New ATLAS Production System

    NASA Astrophysics Data System (ADS)

    De, K.; Golubkov, D.; Klimentov, A.; Potekhin, M.; Vaniachine, A.; Atlas Collaboration

    2014-06-01

    This document describes the design of the new Production System of the ATLAS experiment at the LHC [1]. The Production System is the top level workflow manager which translates physicists' needs for production level processing and analysis into actual workflows executed across over a hundred Grid sites used globally by ATLAS. As the production workload increased in volume and complexity in recent years (the ATLAS production tasks count is above one million, with each task containing hundreds or thousands of jobs) there is a need to upgrade the Production System to meet the challenging requirements of the next LHC run while minimizing the operating costs. In the new design, the main subsystems are the Database Engine for Tasks (DEFT) and the Job Execution and Definition Interface (JEDI). Based on users' requests, DEFT manages inter-dependent groups of tasks (Meta-Tasks) and generates corresponding data processing workflows. The JEDI component then dynamically translates the task definitions from DEFT into actual workload jobs executed in the PanDA Workload Management System [2]. We present the requirements, design parameters, basics of the object model and concrete solutions utilized in building the new Production System and its components.

  3. Upgrading the ATLAS Tile Calorimeter Electronics

    NASA Astrophysics Data System (ADS)

    Carrió, Fernando

    2013-11-01

    This work summarizes the status of the on-detector and off-detector electronics developments for the Phase 2 Upgrade of the ATLAS Tile Calorimeter at the LHC scheduled around 2022. A demonstrator prototype for a slice of the calorimeter including most of the new electronics is planned to be installed in ATLAS in the middle of 2014 during the first Long Shutdown. For the on-detector readout, three different front-end boards (FEB) alternatives are being studied: a new version of the 3-in-1 card, the QIE chip and a dedicated ASIC called FATALIC. The Main Board will provide communication and control to the FEBs and the Daughter Board will transmit the digitized data to the off-detector electronics in the counting room, where the super Read-Out Driver (sROD) will perform processing tasks on them and will be the interface to the trigger levels 0, 1 and 2.

  4. Thermospheric nitric oxide from the ATLAS 1 and Spacelab 1 missions

    NASA Technical Reports Server (NTRS)

    Torr, Marsha R.; Torr, D. G.; Chang, T.; Richards, P.; Swift, W.; Li, N.

    1995-01-01

    Spectral and spatial images obtained with the Imaging Spectrometric Observatory on the ATLAS 1 and Spacelab 1 missions are used to study the ultraviolet emissions of nitric oxide in the thermosphere. By synthetically fitting the measured NO gamma bands, intensities are derived as a function of altitude and latitude. We find that the NO concentrations inferred from the ATLAS 1 measurements are higher than predicted by our thermospheric airglow model and tend to lie to the high side of a number of earlier measurements. By comparison with synthetic spectral fits, the shape of the NO gamma bands is used to derive temperature as a function of altitude. Using the simultaneous spectral and spatial imaging capability of the instrument, we present the first simultaneously acquired altitude images of NO gamma band temperature and intensity in the thermosphere. The lower thermospheric temperature images show structure as a function of altitude. The spatial imaging technique appears to be a viable means of obtaining temperatures in the middle and lower thermosphere, provided that good information is also obtained at the higher altitudes, as the contribution of the overlying, hotter NO is nonnegligible. By fitting both self-absorbed and nonabsorbed bands of the NO gamma system, we show that the self absorption effects are observable up to 200 km, although small above 150 km. The spectral resolution of the instrument (1.6 A) allows separation of the N(+)(S-5) doublet, and we show the contribution of this feature to the combination of the NO gamma (1, 0) band and the N(+)(S-5) doublet as a function of altitude (less than 10% below 200 km). Spectral images including the NO delta bands support previous findings that the fluorescence efficiency is much higher than that determined from laboratory measurements. The Spacelab 1 data indicate the presence of a significant population of hot NO in the vehicle environment of that early shuttle mission.

  5. An MRI Von Economo - Koskinas atlas.

    PubMed

    Scholtens, Lianne H; de Reus, Marcel A; de Lange, Siemon C; Schmidt, Ruben; van den Heuvel, Martijn P

    2018-04-15

    The cerebral cortex displays substantial variation in cellular architecture, a regional patterning that has been of great interest to anatomists for centuries. In 1925, Constantin von Economo and George Koskinas published a detailed atlas of the human cerebral cortex, describing a cytoarchitectonic division of the cortical mantle into over 40 distinct areas. Von Economo and Koskinas accompanied their seminal work with large photomicrographic plates of their histological slides, together with tables containing for each described region detailed morphological layer-specific information on neuronal count, neuron size and thickness of the cortical mantle. Here, we aimed to make this legacy data accessible and relatable to in vivo neuroimaging data by constructing a digital Von Economo - Koskinas atlas compatible with the widely used FreeSurfer software suite. In this technical note we describe the procedures used for manual segmentation of the Von Economo - Koskinas atlas onto individual T1 scans and the subsequent construction of the digital atlas. We provide the files needed to run the atlas on new FreeSurfer data, together with some simple code of how to apply the atlas to T1 scans within the FreeSurfer software suite. The digital Von Economo - Koskinas atlas is easily applicable to modern day anatomical MRI data and is made publicly available online. Copyright © 2017 Elsevier Inc. All rights reserved.

  6. 'Whose atlas I use, his song I sing?' - The impact of anatomical atlases on fiber tract contributions to cognitive deficits after stroke.

    PubMed

    de Haan, Bianca; Karnath, Hans-Otto

    2017-12-01

    Nowadays, different anatomical atlases exist for the anatomical interpretation of the results from neuroimaging and lesion analysis studies that investigate the contribution of white matter fiber tract integrity to cognitive (dys)function. A major problem with the use of different atlases in different studies, however, is that the anatomical interpretation of neuroimaging and lesion analysis results might vary as a function of the atlas used. This issue might be particularly prominent in studies that investigate the contribution of white matter fiber tract integrity to cognitive (dys)function. We used a single large-sample dataset of right brain damaged stroke patients with and without cognitive deficit (here: spatial neglect) to systematically compare the influence of three different, widely-used white matter fiber tract atlases (1 histology-based atlas and 2 DTI tractography-based atlases) on conclusions concerning the involvement of white matter fiber tracts in the pathogenesis of cognitive dysfunction. We both calculated the overlap between the statistical lesion analysis results and each long association fiber tract (topological analyses) and performed logistic regressions on the extent of fiber tract damage in each individual for each long association white matter fiber tract (hodological analyses). For the topological analyses, our results suggest that studies that use tractography-based atlases are more likely to conclude that white matter integrity is critical for a cognitive (dys)function than studies that use a histology-based atlas. The DTI tractography-based atlases classified approximately 10 times as many voxels of the statistical map as being located in a long association white matter fiber tract than the histology-based atlas. For hodological analyses on the other hand, we observed that the conclusions concerning the overall importance of long association fiber tract integrity to cognitive function do not necessarily depend on the white matter

  7. Performance of the Prototype Readout System for the CMS Endcap Hadron Calorimeter Upgrade

    NASA Astrophysics Data System (ADS)

    Chaverin, Nate; Dittmann, Jay; Hatakeyama, Kenichi; Pastika, Nathaniel; CMS Collaboration

    2016-03-01

    The Compact Muon Solenoid (CMS) experiment at the CERN Large Hadron Collider (LHC) will upgrade the photodetectors and readout systems of the endcap hadron calorimeter during the technical stop scheduled for late 2016 and early 2017. A major milestone for this project was a highly successful testbeam run at CERN in August 2015. The testbeam run served as a full integration test of the electronics, allowing a study of the response of the preproduction electronics to the true detector light profile, as well as a test of the light yield of various new plastic scintillator materials. We present implications for the performance of the hadron calorimeter front-end electronics based on testbeam data, and we report on the production status of various components of the system in preparation for the upgrade.

  8. Multi-Atlas-Based Attenuation Correction for Brain 18F-FDG PET Imaging Using a Time-of-Flight PET/MR Scanner: Comparison with Clinical Single-Atlas- and CT-Based Attenuation Correction.

    PubMed

    Sekine, Tetsuro; Burgos, Ninon; Warnock, Geoffrey; Huellner, Martin; Buck, Alfred; Ter Voert, Edwin E G W; Cardoso, M Jorge; Hutton, Brian F; Ourselin, Sebastien; Veit-Haibach, Patrick; Delso, Gaspar

    2016-08-01

    In this work, we assessed the feasibility of attenuation correction (AC) based on a multi-atlas-based method (m-Atlas) by comparing it with a clinical AC method (single-atlas-based method [s-Atlas]), on a time-of-flight (TOF) PET/MRI scanner. We enrolled 15 patients. The median patient age was 59 y (age range, 31-80). All patients underwent clinically indicated whole-body (18)F-FDG PET/CT for staging, restaging, or follow-up of malignant disease. All patients volunteered for an additional PET/MRI scan of the head (no additional tracer being injected). For each patient, 3 AC maps were generated. Both s-Atlas and m-Atlas AC maps were generated from the same patient-specific LAVA-Flex T1-weighted images being acquired by default on the PET/MRI scanner during the first 18 s of the PET scan. An s-Atlas AC map was extracted by the PET/MRI scanner, and an m-Atlas AC map was created using a Web service tool that automatically generates m-Atlas pseudo-CT images. For comparison, the AC map generated by PET/CT was registered and used as a gold standard. PET images were reconstructed from raw data on the TOF PET/MRI scanner using each AC map. All PET images were normalized to the SPM5 PET template, and (18)F-FDG accumulation was quantified in 67 volumes of interest (VOIs; automated anatomic labeling atlas). Relative (%diff) and absolute differences (|%diff|) between images based on each atlas AC and CT-AC were calculated. (18)F-FDG uptake in all VOIs and generalized merged VOIs were compared using the paired t test and Bland-Altman test. The range of error on m-Atlas in all 1,005 VOIs was -4.99% to 4.09%. The |%diff| on the m-Atlas was improved by about 20% compared with s-Atlas (s-Atlas vs. m-Atlas: 1.49% ± 1.06% vs. 1.21% ± 0.89%, P < 0.01). In generalized VOIs, %diff on m-Atlas in the temporal lobe and cerebellum was significantly smaller (s-Atlas vs. m-Atlas: temporal lobe, 1.49% ± 1.37% vs. -0.37% ± 1.41%, P < 0.01; cerebellum, 1.55% ± 1.97% vs. -1.15% ± 1.72%, P

  9. Mesospheric nightglow spectral survey taken by the ISO spectral spatial imager on ATLAS 1

    NASA Technical Reports Server (NTRS)

    Owens, J. K.; Torr, D. G.; Torr, M. R.; Chang, T.; Fennelly, J. A.; Richards, P. G.; Morgan, M. F.; Baldridge, T. W.; Fellows, C. W.; Dougani, H.

    1993-01-01

    This paper reports the first comprehensive spectral survey of the mesospheric airglow between 260 and 832 nm taken by the Imaging Spectrometric Observatory on the ATLAS 1 mission. We select data taken in the spectral window between 275 and 300 nm to determine the variation with altitude of the Herzberg I bands originating from the vibrational levels v-prime = 3 to 8. These data provide the first spatially resolved spectral measurements of the system. The data are used to demonstrate that to within an uncertainty of +/- 10 percent, the vibrational distribution remains invariant with altitude. The deficit reported previously for the v-prime = 5 level is not observed although there is a suggestion of depletion in v-prime = 6. The data could be used to place tight constraints on the vibrational dependence of quenching rate coefficients, and on the abundance of atomic oxygen.

  10. The effect of morphometric atlas selection on multi-atlas-based automatic brachial plexus segmentation.

    PubMed

    Van de Velde, Joris; Wouters, Johan; Vercauteren, Tom; De Gersem, Werner; Achten, Eric; De Neve, Wilfried; Van Hoof, Tom

    2015-12-23

    The present study aimed to measure the effect of a morphometric atlas selection strategy on the accuracy of multi-atlas-based BP autosegmentation using the commercially available software package ADMIRE® and to determine the optimal number of selected atlases to use. Autosegmentation accuracy was measured by comparing all generated automatic BP segmentations with anatomically validated gold standard segmentations that were developed using cadavers. Twelve cadaver computed tomography (CT) atlases were included in the study. One atlas was selected as a patient in ADMIRE®, and multi-atlas-based BP autosegmentation was first performed with a group of morphometrically preselected atlases. In this group, the atlases were selected on the basis of similarity in the shoulder protraction position with the patient. The number of selected atlases used started at two and increased up to eight. Subsequently, a group of randomly chosen, non-selected atlases were taken. In this second group, every possible combination of 2 to 8 random atlases was used for multi-atlas-based BP autosegmentation. For both groups, the average Dice similarity coefficient (DSC), Jaccard index (JI) and Inclusion index (INI) were calculated, measuring the similarity of the generated automatic BP segmentations and the gold standard segmentation. Similarity indices of both groups were compared using an independent sample t-test, and the optimal number of selected atlases was investigated using an equivalence trial. For each number of atlases, average similarity indices of the morphometrically selected atlas group were significantly higher than the random group (p < 0,05). In this study, the highest similarity indices were achieved using multi-atlas autosegmentation with 6 selected atlases (average DSC = 0,598; average JI = 0,434; average INI = 0,733). Morphometric atlas selection on the basis of the protraction position of the patient significantly improves multi-atlas-based BP

  11. Global GIS database; digital atlas of South Pacific

    USGS Publications Warehouse

    Hearn, P.P.; Hare, T.M.; Schruben, P.; Sherrill, D.; LaMar, C.; Tsushima, P.

    2001-01-01

    This CD-ROM contains a digital atlas of the countries of the South Pacific. This atlas is part of a global database compiled from USGS and other data sources at a nominal scale of 1:1 million and is intended to be used as a regional-scale reference and analytical tool by government officials, researchers, the private sector, and the general public. The atlas includes free GIS software or may be used with ESRI's ArcView software. Customized ArcView tools, specifically designed to make the atlas easier to use, are also included.

  12. Global GIS database; digital atlas of South Asia

    USGS Publications Warehouse

    Hearn, P.P.; Hare, T.M.; Schruben, P.; Sherrill, D.; LaMar, C.; Tsushima, P.

    2001-01-01

    This CD-ROM contains a digital atlas of the countries of South Asia. This atlas is part of a global database compiled from USGS and other data sources at a nominal scale 1:1 million and is intended to be used as a regional-scale reference and analytical tool by government officials, researchers, the private sector, and the general public. The atlas includes free GIS software or may be used with ESRI's ArcView software. Customized ArcView tools, specifically designed to make the atlas easier to use, are also included.

  13. White matter atlas of the human spinal cord with estimation of partial volume effect.

    PubMed

    Lévy, S; Benhamou, M; Naaman, C; Rainville, P; Callot, V; Cohen-Adad, J

    2015-10-01

    Template-based analysis has proven to be an efficient, objective and reproducible way of extracting relevant information from multi-parametric MRI data. Using common atlases, it is possible to quantify MRI metrics within specific regions without the need for manual segmentation. This method is therefore free from user-bias and amenable to group studies. While template-based analysis is common procedure for the brain, there is currently no atlas of the white matter (WM) spinal pathways. The goals of this study were: (i) to create an atlas of the white matter tracts compatible with the MNI-Poly-AMU template and (ii) to propose methods to quantify metrics within the atlas that account for partial volume effect. The WM atlas was generated by: (i) digitalizing an existing WM atlas from a well-known source (Gray's Anatomy), (ii) registering this atlas to the MNI-Poly-AMU template at the corresponding slice (C4 vertebral level), (iii) propagating the atlas throughout all slices of the template (C1 to T6) using regularized diffeomorphic transformations and (iv) computing partial volume values for each voxel and each tract. Several approaches were implemented and validated to quantify metrics within the atlas, including weighted-average and Gaussian mixture models. Proof-of-concept application was done in five subjects for quantifying magnetization transfer ratio (MTR) in each tract of the atlas. The resulting WM atlas showed consistent topological organization and smooth transitions along the rostro-caudal axis. The median MTR across tracts was 26.2. Significant differences were detected across tracts, vertebral levels and subjects, but not across laterality (right-left). Among the different tested approaches to extract metrics, the maximum a posteriori showed highest performance with respect to noise, inter-tract variability, tract size and partial volume effect. This new WM atlas of the human spinal cord overcomes the biases associated with manual delineation and partial

  14. A High-Resolution In Vivo Atlas of the Human Brain's Serotonin System.

    PubMed

    Beliveau, Vincent; Ganz, Melanie; Feng, Ling; Ozenne, Brice; Højgaard, Liselotte; Fisher, Patrick M; Svarer, Claus; Greve, Douglas N; Knudsen, Gitte M

    2017-01-04

    The serotonin (5-hydroxytryptamine, 5-HT) system modulates many important brain functions and is critically involved in many neuropsychiatric disorders. Here, we present a high-resolution, multidimensional, in vivo atlas of four of the human brain's 5-HT receptors (5-HT 1A , 5-HT 1B , 5-HT 2A , and 5-HT 4 ) and the 5-HT transporter (5-HTT). The atlas is created from molecular and structural high-resolution neuroimaging data consisting of positron emission tomography (PET) and magnetic resonance imaging (MRI) scans acquired in a total of 210 healthy individuals. Comparison of the regional PET binding measures with postmortem human brain autoradiography outcomes showed a high correlation for the five 5-HT targets and this enabled us to transform the atlas to represent protein densities (in picomoles per milliliter). We also assessed the regional association between protein concentration and mRNA expression in the human brain by comparing the 5-HT density across the atlas with data from the Allen Human Brain atlas and identified receptor- and transporter-specific associations that show the regional relation between the two measures. Together, these data provide unparalleled insight into the serotonin system of the human brain. We present a high-resolution positron emission tomography (PET)- and magnetic resonance imaging-based human brain atlas of important serotonin receptors and the transporter. The regional PET-derived binding measures correlate strongly with the corresponding autoradiography protein levels. The strong correlation enables the transformation of the PET-derived human brain atlas into a protein density map of the serotonin (5-hydroxytryptamine, 5-HT) system. Next, we compared the regional receptor/transporter protein densities with mRNA levels and uncovered unique associations between protein expression and density at high detail. This new in vivo neuroimaging atlas of the 5-HT system not only provides insight in the human brain's regional protein

  15. Construction and Design of a full size sTGC prototype for the ATLAS New Small Wheel upgrade

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    NONE

    For the forthcoming Phase-I upgrade to the LHC (2018/19), the first station of the ATLAS muon end-cap system, Small Wheel, will need to be replaced. The New Small Wheel (NSW) will have to operate in a high background radiation region while reconstructing muon tracks with high precision as well as furnishing information for the Level-1 trigger. In particular, the precision reconstruction of tracks requires a spatial resolution of about 100 μm, and the Level-1 trigger track segments have to be reconstructed with an angular resolution of approximately 1 mrad. The NSW will have two chamber technologies, one primarily devoted tomore » the Level-1 trigger function the small-strip Thin Gap Chambers (sTGC) and one dedicated to precision tracking, Micromegas detectors, (MM). The single sTGC planes of a quadruplet consists of an anode layer of 50 μm gold plated tungsten wire sandwiched between two resistive cathode layers. Behind one of the resistive cathode layers, a PCB with precise machined strips (thus the name sTGC's) spaced every 3.2 mm allows to achieve the position resolution that ranges from 70 to 150 μm, depending on the incident particle angle. Behind the second cathode, a PCB that contains an arrangement of pads, allows for a fast coincidence between successive sTGC layers to tag the passage of a track and reads only the corresponding strips for triggering. To be able to profit from the high accuracy of each of the sTGC planes for trigger purposes, their relative geometrical position between planes has to be controlled to within a precision of about 40 μm in their parallelism, as well (due to the various incident angles), to within a precision of 80 μm in the relative distance between the planes to achieve the overall angular resolution of 1 mrad. The needed accuracy in the position and parallelism of the strips is achieved by machining brass inserts together when machining the strip patterns into the cathode boards in a single step. The inserts can then

  16. Probabilistic atlas-based segmentation of combined T1-weighted and DUTE MRI for calculation of head attenuation maps in integrated PET/MRI scanners.

    PubMed

    Poynton, Clare B; Chen, Kevin T; Chonde, Daniel B; Izquierdo-Garcia, David; Gollub, Randy L; Gerstner, Elizabeth R; Batchelor, Tracy T; Catana, Ciprian

    2014-01-01

    We present a new MRI-based attenuation correction (AC) approach for integrated PET/MRI systems that combines both segmentation- and atlas-based methods by incorporating dual-echo ultra-short echo-time (DUTE) and T1-weighted (T1w) MRI data and a probabilistic atlas. Segmented atlases were constructed from CT training data using a leave-one-out framework and combined with T1w, DUTE, and CT data to train a classifier that computes the probability of air/soft tissue/bone at each voxel. This classifier was applied to segment the MRI of the subject of interest and attenuation maps (μ-maps) were generated by assigning specific linear attenuation coefficients (LACs) to each tissue class. The μ-maps generated with this "Atlas-T1w-DUTE" approach were compared to those obtained from DUTE data using a previously proposed method. For validation of the segmentation results, segmented CT μ-maps were considered to the "silver standard"; the segmentation accuracy was assessed qualitatively and quantitatively through calculation of the Dice similarity coefficient (DSC). Relative change (RC) maps between the CT and MRI-based attenuation corrected PET volumes were also calculated for a global voxel-wise assessment of the reconstruction results. The μ-maps obtained using the Atlas-T1w-DUTE classifier agreed well with those derived from CT; the mean DSCs for the Atlas-T1w-DUTE-based μ-maps across all subjects were higher than those for DUTE-based μ-maps; the atlas-based μ-maps also showed a lower percentage of misclassified voxels across all subjects. RC maps from the atlas-based technique also demonstrated improvement in the PET data compared to the DUTE method, both globally as well as regionally.

  17. Probabilistic atlas-based segmentation of combined T1-weighted and DUTE MRI for calculation of head attenuation maps in integrated PET/MRI scanners

    PubMed Central

    Poynton, Clare B; Chen, Kevin T; Chonde, Daniel B; Izquierdo-Garcia, David; Gollub, Randy L; Gerstner, Elizabeth R; Batchelor, Tracy T; Catana, Ciprian

    2014-01-01

    We present a new MRI-based attenuation correction (AC) approach for integrated PET/MRI systems that combines both segmentation- and atlas-based methods by incorporating dual-echo ultra-short echo-time (DUTE) and T1-weighted (T1w) MRI data and a probabilistic atlas. Segmented atlases were constructed from CT training data using a leave-one-out framework and combined with T1w, DUTE, and CT data to train a classifier that computes the probability of air/soft tissue/bone at each voxel. This classifier was applied to segment the MRI of the subject of interest and attenuation maps (μ-maps) were generated by assigning specific linear attenuation coefficients (LACs) to each tissue class. The μ-maps generated with this “Atlas-T1w-DUTE” approach were compared to those obtained from DUTE data using a previously proposed method. For validation of the segmentation results, segmented CT μ-maps were considered to the “silver standard”; the segmentation accuracy was assessed qualitatively and quantitatively through calculation of the Dice similarity coefficient (DSC). Relative change (RC) maps between the CT and MRI-based attenuation corrected PET volumes were also calculated for a global voxel-wise assessment of the reconstruction results. The μ-maps obtained using the Atlas-T1w-DUTE classifier agreed well with those derived from CT; the mean DSCs for the Atlas-T1w-DUTE-based μ-maps across all subjects were higher than those for DUTE-based μ-maps; the atlas-based μ-maps also showed a lower percentage of misclassified voxels across all subjects. RC maps from the atlas-based technique also demonstrated improvement in the PET data compared to the DUTE method, both globally as well as regionally. PMID:24753982

  18. SU-E-J-128: Two-Stage Atlas Selection in Multi-Atlas-Based Image Segmentation

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Zhao, T; Ruan, D

    2015-06-15

    Purpose: In the new era of big data, multi-atlas-based image segmentation is challenged by heterogeneous atlas quality and high computation burden from extensive atlas collection, demanding efficient identification of the most relevant atlases. This study aims to develop a two-stage atlas selection scheme to achieve computational economy with performance guarantee. Methods: We develop a low-cost fusion set selection scheme by introducing a preliminary selection to trim full atlas collection into an augmented subset, alleviating the need for extensive full-fledged registrations. More specifically, fusion set selection is performed in two successive steps: preliminary selection and refinement. An augmented subset is firstmore » roughly selected from the whole atlas collection with a simple registration scheme and the corresponding preliminary relevance metric; the augmented subset is further refined into the desired fusion set size, using full-fledged registration and the associated relevance metric. The main novelty of this work is the introduction of an inference model to relate the preliminary and refined relevance metrics, based on which the augmented subset size is rigorously derived to ensure the desired atlases survive the preliminary selection with high probability. Results: The performance and complexity of the proposed two-stage atlas selection method were assessed using a collection of 30 prostate MR images. It achieved comparable segmentation accuracy as the conventional one-stage method with full-fledged registration, but significantly reduced computation time to 1/3 (from 30.82 to 11.04 min per segmentation). Compared with alternative one-stage cost-saving approach, the proposed scheme yielded superior performance with mean and medium DSC of (0.83, 0.85) compared to (0.74, 0.78). Conclusion: This work has developed a model-guided two-stage atlas selection scheme to achieve significant cost reduction while guaranteeing high segmentation accuracy. The

  19. The ATLAS Tier-3 in Geneva and the Trigger Development Facility

    NASA Astrophysics Data System (ADS)

    Gadomski, S.; Meunier, Y.; Pasche, P.; Baud, J.-P.; ATLAS Collaboration

    2011-12-01

    The ATLAS Tier-3 farm at the University of Geneva provides storage and processing power for analysis of ATLAS data. In addition the facility is used for development, validation and commissioning of the High Level Trigger of ATLAS [1]. The latter purpose leads to additional requirements on the availability of latest software and data, which will be presented. The farm is also a part of the WLCG [2], and is available to all members of the ATLAS Virtual Organization. The farm currently provides 268 CPU cores and 177 TB of storage space. A grid Storage Element, implemented with the Disk Pool Manager software [3], is available and integrated with the ATLAS Distributed Data Management system [4]. The batch system can be used directly by local users, or with a grid interface provided by NorduGrid ARC middleware [5]. In this article we will present the use cases that we support, as well as the experience with the software and the hardware we are using. Results of I/O benchmarking tests, which were done for our DPM Storage Element and for the NFS servers we are using, will also be presented.

  20. A hardware fast tracker for the ATLAS trigger

    NASA Astrophysics Data System (ADS)

    Asbah, Nedaa

    2016-09-01

    The trigger system of the ATLAS experiment is designed to reduce the event rate from the LHC nominal bunch crossing at 40 MHz to about 1 kHz, at the design luminosity of 1034 cm-2 s-1. After a successful period of data taking from 2010 to early 2013, the LHC already started with much higher instantaneous luminosity. This will increase the load on High Level Trigger system, the second stage of the selection based on software algorithms. More sophisticated algorithms will be needed to achieve higher background rejection while maintaining good efficiency for interesting physics signals. The Fast TracKer (FTK) is part of the ATLAS trigger upgrade project. It is a hardware processor that will provide, at every Level-1 accepted event (100 kHz) and within 100 microseconds, full tracking information for tracks with momentum as low as 1 GeV. Providing fast, extensive access to tracking information, with resolution comparable to the offline reconstruction, FTK will help in precise detection of the primary and secondary vertices to ensure robust selections and improve the trigger performance. FTK exploits hardware technologies with massive parallelism, combining Associative Memory ASICs, FPGAs and high-speed communication links.

  1. ATLAS F MISSILE FIELDS IN THE UNITED STATES, ATLAS F ...

    Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

    ATLAS F MISSILE FIELDS IN THE UNITED STATES, ATLAS F- TEXAS RING OF TWELVE - Dyess Air Force Base, Atlas F Missle Site S-8, Approximately 3 miles east of Winters, 500 feet southwest of Highway 177, Winters, Runnels County, TX

  2. Atlas of Vega: 3850-6860 Å

    NASA Astrophysics Data System (ADS)

    Kim, Hyun-Sook; Han, Inwoo; Valyavin, G.; Lee, Byeong-Cheol; Shimansky, V.; Galazutdinov, G. A.

    2009-10-01

    We present a high resolving power (λ/Δλ = 90,000) and high signal-to-noise ratio (˜700) spectral atlas of Vega covering the 3850-6860 Å wavelength range. The atlas is a result of averaging of spectra recorded with the aid of the echelle spectrograph BOES fed by the 1.8 m telescope at Bohyunsan Observatory (Korea). The atlas is provided only in machine-readable form (electronic data file) and will be available in the SIMBAD database upon publication. Based on data collected with the 1.8 m telescope operated at BOAO Observatory, Korea.

  3. Brief retrospection on Hungarian school atlases

    NASA Astrophysics Data System (ADS)

    Klinghammer, István; Jesús Reyes Nuñez, José

    2018-05-01

    The first part of this article is dedicated to the history of Hungarian school atlases to the end of the 1st World War. Although the first maps included in a Hungarian textbook were probably made in 1751, the publication of atlases for schools is dated almost 50 years later, when professor Ézsáiás Budai created his "New School Atlas for elementary pupils" in 1800. This was followed by a long period of 90 years, when the school atlases were mostly translations and adaptations of foreign atlases, the majority of which were made in German-speaking countries. In those years, a school atlas made by a Hungarian astronomer, Antal Vállas, should be highlighted as a prominent independent piece of work. In 1890, a talented cartographer, Manó Kogutowicz founded the Hungarian Geographical Institute, which was the institution responsible for producing school atlases for the different types of schools in Hungary. The professional quality of the school atlases published by his institute was also recognized beyond the Hungarian borders by prizes won in international exhibitions. Kogutowicz laid the foundations of the current Hungarian school cartography: this statement is confirmed in the second part of this article, when three of his school atlases are presented in more detail to give examples of how the pupils were introduced to the basic cartographic and astronomic concepts as well as how different innovative solutions were used on the maps.

  4. EnviroAtlas - Austin, TX - Green Space Proximity Gradient

    EPA Pesticide Factsheets

    In any given 1-square meter point in this EnviroAtlas dataset, the value shown gives the percentage of square meters of greenspace within 1/4 square kilometer centered over the given point. Green space is defined as Trees & Forest, Grass & Herbaceous, and Agriculture. Water is shown as -99999 in this dataset to distinguish it from land areas with very low green space. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

  5. Profiles of Stratospheric Chlorine Nitrate from ATMOS/ATLAS 1 Infrared Solar Occultation Spectra

    NASA Technical Reports Server (NTRS)

    Rinsland, C. P.; Gunson, M. R.; Abrams, M. C.; Zander, R.; Mahieu, E.; Goldman, A.; Ko, M. K. W.; Rodriguez, J. M.; Sze, N. D.

    1994-01-01

    Stratospheric volume mixing ration profiles of chlorine nitrate have been retrieved from 0.01-cm(sub -1) resolution infrared solar occutation spectra recorded at latitudes between 14 degrees N and 54 degrees S by the Atmospheric Trace Molecule Spectroscopy (ATMOS) Fourier transform spectrometer during the ATLAS 1 shuttle mission (March 24 to April 2, 1992).

  6. EnviroAtlas - Des Moines, IA - Green Space Proximity Gradient

    EPA Pesticide Factsheets

    In any given 1-square meter point in this EnviroAtlas dataset, the value shown gives the percentage of square meters of greenspace within 1/4 square kilometer centered over the given point. Green space is defined as Trees & Forest, Grass & Herbaceous, and Agriculture. Water is shown as -99999 in this dataset to distinguish it from land areas with very low green space. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://enviroatlas.epa.gov/EnviroAtlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

  7. Mesure des champs de radiation dans le detecteur ATLAS et sa caverne avec les detecteurs au silicium a pixels ATLAS-MPX

    NASA Astrophysics Data System (ADS)

    Bouchami, Jihene

    The LHC proton-proton collisions create a hard radiation environment in the ATLAS detector. In order to quantify the effects of this environment on the detector performance and human safety, several Monte Carlo simulations have been performed. However, direct measurement is indispensable to monitor radiation levels in ATLAS and also to verify the simulation predictions. For this purpose, sixteen ATLAS-MPX devices have been installed at various positions in the ATLAS experimental and technical areas. They are composed of a pixelated silicon detector called MPX whose active surface is partially covered with converter layers for the detection of thermal, slow and fast neutrons. The ATLAS-MPX devices perform real-time measurement of radiation fields by recording the detected particle tracks as raster images. The analysis of the acquired images allows the identification of the detected particle types by the shapes of their tracks. For this aim, a pattern recognition software called MAFalda has been conceived. Since the tracks of strongly ionizing particles are influenced by charge sharing between adjacent pixels, a semi-empirical model describing this effect has been developed. Using this model, the energy of strongly ionizing particles can be estimated from the size of their tracks. The converter layers covering each ATLAS-MPX device form six different regions. The efficiency of each region to detect thermal, slow and fast neutrons has been determined by calibration measurements with known sources. The study of the ATLAS-MPX devices response to the radiation produced by proton-proton collisions at a center of mass energy of 7 TeV has demonstrated that the number of recorded tracks is proportional to the LHC luminosity. This result allows the ATLAS-MPX devices to be employed as luminosity monitors. To perform an absolute luminosity measurement and calibration with these devices, the van der Meer method based on the LHC beam parameters has been proposed. Since the ATLAS

  8. Development and test of the DAQ system for a Micromegas prototype to be installed in the ATLAS experiment

    NASA Astrophysics Data System (ADS)

    Bianco, M.; Martoiu, S.; Sidiropoulou, O.; Zibell, A.

    2015-12-01

    A Micromegas (MM) quadruplet prototype with an active area of 0.5 m2 that adopts the general design foreseen for the upgrade of the innermost forward muon tracking systems (Small Wheels) of the ATLAS detector in 2018-2019, has been built at CERN and is going to be tested in the ATLAS cavern environment during the LHC RUN-II period 2015-2017. The integration of this prototype detector into the ATLAS data acquisition system using custom ATCA equipment is presented. An ATLAS compatible Read Out Driver (ROD) based on the Scalable Readout System (SRS), the Scalable Readout Unit (SRU), will be used in order to transmit the data after generating valid event fragments to the high-level Read Out System (ROS). The SRU will be synchronized with the LHC bunch crossing clock (40.08 MHz) and will receive the Level-1 trigger signals from the Central Trigger Processor (CTP) through the TTCrx receiver ASIC. The configuration of the system will be driven directly from the ATLAS Run Control System. By using the ATLAS TDAQ Software, a dedicated Micromegas segment has been implemented, in order to include the detector inside the main ATLAS DAQ partition. A full set of tests, on the hardware and software aspects, is presented.

  9. Search for a heavy neutral particle decaying into an electron and a muon using 1 fb -1 of ATLAS data

    DOE PAGES

    Aad, G.; Abbott, B.; Abdallah, J.; ...

    2011-12-07

    A search is presented for a high mass neutral particle that decays directly to the e < sup > ± < /sup > μ < sup > ∓ < /sup > final state. The data sample was recorded by the ATLAS detector in √ s = 7 TeV pp collisions at the LHC from March to June 2011 and corresponds to an integrated luminosity of 1.07 fb < sup > -1 < /sup > . The data are found to be consistent with the Standard Model background. The high e < sup > ± < /sup > μ < supmore » > ∓ < /sup > mass region is used to set 95% confidence level upper limits on the production of two possible new physics processes: tau sneutrinos in an R-parity violating supersymmetric model and Z'-like vector bosons in a lepton flavor violating model.« less

  10. Welcome - TampaBay.WaterAtlas.org

    Science.gov Websites

    An edition of: WaterAtlas.orgPresented By: USF Water Institute Choose a Water Atlas Charlotte Harbor NEP Water Atlas Hillsborough County Water Atlas Lake County Water Atlas Manatee County Water Atlas Orange County Water Atlas Pinellas County Water Atlas Polk County Water Atlas Sarasota County Water Atlas

  11. Construction and performance of the sTGC and MicroMegas chambers for ATLAS NSW upgrade

    NASA Astrophysics Data System (ADS)

    Sekhniaidze, G.

    2017-03-01

    The innermost stations of the current ATLAS muon end-cap system, the Small Wheels, must be upgraded in 2019 to retain their good precision tracking and trigger capabilities in the high background environment expected with the upcoming luminosity increase of the LHC. The New Small Wheels (NSW) will employ two chamber technologies: eight layers of MicroMegas (MM) arranged in two quadruplets, sandwiched between two quadruplets of small-strip Thin Gap Chambers (sTGC) for a total of about 2400 m2 of detection planes. All quadruplets have trapezoidal shapes with surface areas between 1 and 3 m2. Both MM and sTGC systems will independently provide trigger and tracking capabilities. The readout boards are industrially produced for both technologies and an accurate quality control is needed. In order to achieve a 15% transverse momentum resolution for 1 TeV muons, in addition to an excellent intrinsic resolution (010 μm), the mechanical precision of each plane of the assembled modules must be as good as 30 μm along the precision coordinate and 80 μm perpendicular to the chamber. In 2016 the milestone to build the first module-0 prototypes for both technologies has been reached. The construction procedure of the module-0 detectors will be reviewed, along with the results of the quality control checks performed during construction. The module-0 have been measured and subjected to a thorough validation. Results obtained with high-energy particle beams, with cosmic rays and with X-rays will be presented.

  12. Atlas - a data warehouse for integrative bioinformatics.

    PubMed

    Shah, Sohrab P; Huang, Yong; Xu, Tao; Yuen, Macaire M S; Ling, John; Ouellette, B F Francis

    2005-02-21

    We present a biological data warehouse called Atlas that locally stores and integrates biological sequences, molecular interactions, homology information, functional annotations of genes, and biological ontologies. The goal of the system is to provide data, as well as a software infrastructure for bioinformatics research and development. The Atlas system is based on relational data models that we developed for each of the source data types. Data stored within these relational models are managed through Structured Query Language (SQL) calls that are implemented in a set of Application Programming Interfaces (APIs). The APIs include three languages: C++, Java, and Perl. The methods in these API libraries are used to construct a set of loader applications, which parse and load the source datasets into the Atlas database, and a set of toolbox applications which facilitate data retrieval. Atlas stores and integrates local instances of GenBank, RefSeq, UniProt, Human Protein Reference Database (HPRD), Biomolecular Interaction Network Database (BIND), Database of Interacting Proteins (DIP), Molecular Interactions Database (MINT), IntAct, NCBI Taxonomy, Gene Ontology (GO), Online Mendelian Inheritance in Man (OMIM), LocusLink, Entrez Gene and HomoloGene. The retrieval APIs and toolbox applications are critical components that offer end-users flexible, easy, integrated access to this data. We present use cases that use Atlas to integrate these sources for genome annotation, inference of molecular interactions across species, and gene-disease associations. The Atlas biological data warehouse serves as data infrastructure for bioinformatics research and development. It forms the backbone of the research activities in our laboratory and facilitates the integration of disparate, heterogeneous biological sources of data enabling new scientific inferences. Atlas achieves integration of diverse data sets at two levels. First, Atlas stores data of similar types using common data

  13. "ATLAS" Advanced Technology Life-cycle Analysis System

    NASA Technical Reports Server (NTRS)

    Lollar, Louis F.; Mankins, John C.; ONeil, Daniel A.

    2004-01-01

    Making good decisions concerning research and development portfolios-and concerning the best systems concepts to pursue - as early as possible in the life cycle of advanced technologies is a key goal of R&D management This goal depends upon the effective integration of information from a wide variety of sources as well as focused, high-level analyses intended to inform such decisions Life-cycle Analysis System (ATLAS) methodology and tool kit. ATLAS encompasses a wide range of methods and tools. A key foundation for ATLAS is the NASA-created Technology Readiness. The toolkit is largely spreadsheet based (as of August 2003). This product is being funded by the Human and Robotics The presentation provides a summary of the Advanced Technology Level (TRL) systems Technology Program Office, Office of Exploration Systems, NASA Headquarters, Washington D.C. and is being integrated by Dan O Neil of the Advanced Projects Office, NASA/MSFC, Huntsville, AL

  14. Atlas of NATO.

    ERIC Educational Resources Information Center

    Young, Harry F.

    This atlas provides basic information about the North Atlantic Treaty Organization (NATO). Formed in response to growing concern for the security of Western Europe after World War II, NATO is a vehicle for Western efforts to reduce East-West tensions and the level of armaments. NATO promotes political and economic collaboration as well as military…

  15. EnviroAtlas - Cleveland, OH - Green Space Proximity Gradient

    EPA Pesticide Factsheets

    In any given 1-square meter point in this EnviroAtlas dataset, the value shown gives the percentage of square meters of greenspace within 1/4 square kilometer centered over the given point. In this community, green space is defined as Trees & Forest, Grass & Herbaceous, Woody Wetlands, and Emergent Wetlands. Water is shown as -99999 in this dataset to distinguish it from land areas with very low green space. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas ) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

  16. EnviroAtlas - Memphis, TN - Green Space Proximity Gradient

    EPA Pesticide Factsheets

    In any given 1-square meter point in this EnviroAtlas dataset, the value shown gives the percentage of square meters of greenspace within 1/4 square kilometer centered over the given point. Green space is defined as Trees & Forest, Grass & Herbaceous, Agriculture, Woody Wetlands, and Emergent Wetlands. Water is shown as -99999 in this dataset to distinguish it from land areas with very low green space. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

  17. Enhancing atlas based segmentation with multiclass linear classifiers

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Sdika, Michaël, E-mail: michael.sdika@creatis.insa-lyon.fr

    Purpose: To present a method to enrich atlases for atlas based segmentation. Such enriched atlases can then be used as a single atlas or within a multiatlas framework. Methods: In this paper, machine learning techniques have been used to enhance the atlas based segmentation approach. The enhanced atlas defined in this work is a pair composed of a gray level image alongside an image of multiclass classifiers with one classifier per voxel. Each classifier embeds local information from the whole training dataset that allows for the correction of some systematic errors in the segmentation and accounts for the possible localmore » registration errors. The authors also propose to use these images of classifiers within a multiatlas framework: results produced by a set of such local classifier atlases can be combined using a label fusion method. Results: Experiments have been made on the in vivo images of the IBSR dataset and a comparison has been made with several state-of-the-art methods such as FreeSurfer and the multiatlas nonlocal patch based method of Coupé or Rousseau. These experiments show that their method is competitive with state-of-the-art methods while having a low computational cost. Further enhancement has also been obtained with a multiatlas version of their method. It is also shown that, in this case, nonlocal fusion is unnecessary. The multiatlas fusion can therefore be done efficiently. Conclusions: The single atlas version has similar quality as state-of-the-arts multiatlas methods but with the computational cost of a naive single atlas segmentation. The multiatlas version offers a improvement in quality and can be done efficiently without a nonlocal strategy.« less

  18. Depolymerization of Trityl End-Capped Poly(Ethyl Glyoxylate): Potential Applications in Smart Packaging.

    PubMed

    Fan, Bo; Salazar, Rómulo; Gillies, Elizabeth R

    2018-06-01

    The temperature-dependent depolymerization of self-immolative poly(ethyl glyoxylate) (PEtG) capped with triphenylmethyl (trityl) groups is studied and its potential application for smart packaging is explored. PEtGs with four different trityl end-caps are prepared and found to undergo depolymerization to volatile products from the solid state at different rates depending on temperature and the electron-donating substituents on the trityl aromatic rings. Through the incorporation of hydrophobic dyes including Nile red and IR-780, the depolymerization is visualized as a color change of the dye as it changes from a dispersed to aggregated state. The ability of this platform to provide information on thermal history through an easily readable signal makes it promising in smart packaging applications for sensitive products such a food and other cargo that is susceptible to degradation. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  19. Probabilistic liver atlas construction.

    PubMed

    Dura, Esther; Domingo, Juan; Ayala, Guillermo; Marti-Bonmati, Luis; Goceri, E

    2017-01-13

    Anatomical atlases are 3D volumes or shapes representing an organ or structure of the human body. They contain either the prototypical shape of the object of interest together with other shapes representing its statistical variations (statistical atlas) or a probability map of belonging to the object (probabilistic atlas). Probabilistic atlases are mostly built with simple estimations only involving the data at each spatial location. A new method for probabilistic atlas construction that uses a generalized linear model is proposed. This method aims to improve the estimation of the probability to be covered by the liver. Furthermore, all methods to build an atlas involve previous coregistration of the sample of shapes available. The influence of the geometrical transformation adopted for registration in the quality of the final atlas has not been sufficiently investigated. The ability of an atlas to adapt to a new case is one of the most important quality criteria that should be taken into account. The presented experiments show that some methods for atlas construction are severely affected by the previous coregistration step. We show the good performance of the new approach. Furthermore, results suggest that extremely flexible registration methods are not always beneficial, since they can reduce the variability of the atlas and hence its ability to give sensible values of probability when used as an aid in segmentation of new cases.

  20. Repeatability of Brain Volume Measurements Made with the Atlas-based Method from T1-weighted Images Acquired Using a 0.4 Tesla Low Field MR Scanner.

    PubMed

    Goto, Masami; Suzuki, Makoto; Mizukami, Shinya; Abe, Osamu; Aoki, Shigeki; Miyati, Tosiaki; Fukuda, Michinari; Gomi, Tsutomu; Takeda, Tohoru

    2016-10-11

    An understanding of the repeatability of measured results is important for both the atlas-based and voxel-based morphometry (VBM) methods of magnetic resonance (MR) brain volumetry. However, many recent studies that have investigated the repeatability of brain volume measurements have been performed using static magnetic fields of 1-4 tesla, and no study has used a low-strength static magnetic field. The aim of this study was to investigate the repeatability of measured volumes using the atlas-based method and a low-strength static magnetic field (0.4 tesla). Ten healthy volunteers participated in this study. Using a 0.4 tesla magnetic resonance imaging (MRI) scanner and a quadrature head coil, three-dimensional T 1 -weighted images (3D-T 1 WIs) were obtained from each subject, twice on the same day. VBM8 software was used to construct segmented normalized images [gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) images]. The regions-of-interest (ROIs) of GM, WM, CSF, hippocampus (HC), orbital gyrus (OG), and cerebellum posterior lobe (CPL) were generated using WFU PickAtlas. The percentage change was defined as[100 × (measured volume with first segmented image - mean volume in each subject)/(mean volume in each subject)]The average percentage change was calculated as the percentage change in the 6 ROIs of the 10 subjects. The mean of the average percentage changes for each ROI was as follows: GM, 0.556%; WM, 0.324%; CSF, 0.573%; HC, 0.645%; OG, 1.74%; and CPL, 0.471%. The average percentage change was higher for the orbital gyrus than for the other ROIs. We consider that repeatability of the atlas-based method is similar between 0.4 and 1.5 tesla MR scanners. To our knowledge, this is the first report to show that the level of repeatability with a 0.4 tesla MR scanner is adequate for the estimation of brain volume change by the atlas-based method.

  1. EnviroAtlas - New York, NY - Green Space Proximity Gradient

    EPA Pesticide Factsheets

    In any given 1-square meter point in this EnviroAtlas dataset, the value shown gives the percentage of square meters of greenspace within 1/4 square kilometer centered over the given point. In this community, green space is defined as Trees & Forest and Grass & Herbaceous. Water is shown as -99999 in this dataset to distinguish it from land areas with very low green space. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

  2. ATLAS@AWS

    NASA Astrophysics Data System (ADS)

    Gehrcke, Jan-Philip; Kluth, Stefan; Stonjek, Stefan

    2010-04-01

    We show how the ATLAS offline software is ported on the Amazon Elastic Compute Cloud (EC2). We prepare an Amazon Machine Image (AMI) on the basis of the standard ATLAS platform Scientific Linux 4 (SL4). Then an instance of the SLC4 AMI is started on EC2 and we install and validate a recent release of the ATLAS offline software distribution kit. The installed software is archived as an image on the Amazon Simple Storage Service (S3) and can be quickly retrieved and connected to new SL4 AMI instances using the Amazon Elastic Block Store (EBS). ATLAS jobs can then configure against the release kit using the ATLAS configuration management tool (cmt) in the standard way. The output of jobs is exported to S3 before the SL4 AMI is terminated. Job status information is transferred to the Amazon SimpleDB service. The whole process of launching instances of our AMI, starting, monitoring and stopping jobs and retrieving job output from S3 is controlled from a client machine using python scripts implementing the Amazon EC2/S3 API via the boto library working together with small scripts embedded in the SL4 AMI. We report our experience with setting up and operating the system using standard ATLAS job transforms.

  3. Argonne Physics Division - ATLAS

    Science.gov Websites

    Strategic Plan (2014) ATLAS Gus Savard Guy Savard, Director of ATLAS Welcome to ATLAS, the Argonne Tandem users. ATLAS mission statement and strategic plan guide the operation of the facility. The strategic plan defines the facilities main goals and is aligned with the US Nuclear Physics long-range plan

  4. A digital 3D atlas of the marmoset brain based on multi-modal MRI.

    PubMed

    Liu, Cirong; Ye, Frank Q; Yen, Cecil Chern-Chyi; Newman, John D; Glen, Daniel; Leopold, David A; Silva, Afonso C

    2018-04-01

    The common marmoset (Callithrix jacchus) is a New-World monkey of growing interest in neuroscience. Magnetic resonance imaging (MRI) is an essential tool to unveil the anatomical and functional organization of the marmoset brain. To facilitate identification of regions of interest, it is desirable to register MR images to an atlas of the brain. However, currently available atlases of the marmoset brain are mainly based on 2D histological data, which are difficult to apply to 3D imaging techniques. Here, we constructed a 3D digital atlas based on high-resolution ex-vivo MRI images, including magnetization transfer ratio (a T1-like contrast), T2w images, and multi-shell diffusion MRI. Based on the multi-modal MRI images, we manually delineated 54 cortical areas and 16 subcortical regions on one hemisphere of the brain (the core version). The 54 cortical areas were merged into 13 larger cortical regions according to their locations to yield a coarse version of the atlas, and also parcellated into 106 sub-regions using a connectivity-based parcellation method to produce a refined atlas. Finally, we compared the new atlas set with existing histology atlases and demonstrated its applications in connectome studies, and in resting state and stimulus-based fMRI. The atlas set has been integrated into the widely-distributed neuroimaging data analysis software AFNI and SUMA, providing a readily usable multi-modal template space with multi-level anatomical labels (including labels from the Paxinos atlas) that can facilitate various neuroimaging studies of marmosets. Published by Elsevier Inc.

  5. Evaluation of Atlas-Based White Matter Segmentation with Eve.

    PubMed

    Plassard, Andrew J; Hinton, Kendra E; Venkatraman, Vijay; Gonzalez, Christopher; Resnick, Susan M; Landman, Bennett A

    2015-03-20

    Multi-atlas labeling has come in wide spread use for whole brain labeling on magnetic resonance imaging. Recent challenges have shown that leading techniques are near (or at) human expert reproducibility for cortical gray matter labels. However, these approaches tend to treat white matter as essentially homogeneous (as white matter exhibits isointense signal on structural MRI). The state-of-the-art for white matter atlas is the single-subject Johns Hopkins Eve atlas. Numerous approaches have attempted to use tractography and/or orientation information to identify homologous white matter structures across subjects. Despite success with large tracts, these approaches have been plagued by difficulties in with subtle differences in course, low signal to noise, and complex structural relationships for smaller tracts. Here, we investigate use of atlas-based labeling to propagate the Eve atlas to unlabeled datasets. We evaluate single atlas labeling and multi-atlas labeling using synthetic atlases derived from the single manually labeled atlas. On 5 representative tracts for 10 subjects, we demonstrate that (1) single atlas labeling generally provides segmentations within 2mm mean surface distance, (2) morphologically constraining DTI labels within structural MRI white matter reduces variability, and (3) multi-atlas labeling did not improve accuracy. These efforts present a preliminary indication that single atlas labels with correction is reasonable, but caution should be applied. To purse multi-atlas labeling and more fully characterize overall performance, more labeled datasets would be necessary.

  6. Atlas-based segmentation of 3D cerebral structures with competitive level sets and fuzzy control.

    PubMed

    Ciofolo, Cybèle; Barillot, Christian

    2009-06-01

    We propose a novel approach for the simultaneous segmentation of multiple structures with competitive level sets driven by fuzzy control. To this end, several contours evolve simultaneously toward previously defined anatomical targets. A fuzzy decision system combines the a priori knowledge provided by an anatomical atlas with the intensity distribution of the image and the relative position of the contours. This combination automatically determines the directional term of the evolution equation of each level set. This leads to a local expansion or contraction of the contours, in order to match the boundaries of their respective targets. Two applications are presented: the segmentation of the brain hemispheres and the cerebellum, and the segmentation of deep internal structures. Experimental results on real magnetic resonance (MR) images are presented, quantitatively assessed and discussed.

  7. Search for WW and WZ production in lepton, neutrino plus jets final states at CDF Run II and Silicon module production and detector control system for the ATLAS SemiConductor Tracker

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Sfyrla, Anna

    2008-03-10

    In the first part of this work, we present a search for WW and WZ production in charged lepton, neutrino plus jets final states produced in pmore » $$\\bar{p}$$ collisions with √s = 1.96 TeV at the Fermilab Tevatron, using 1.2 fb -1 of data accumulated with the CDF II detector. This channel is yet to be observed in hadron colliders due to the large singleWplus jets background. However, this decay mode has a much larger branching fraction than the cleaner fully leptonic mode making it more sensitive to anomalous triple gauge couplings that manifest themselves at higher transverse W momentum. Because the final state is topologically similar to associated production of a Higgs boson with a W, the techniques developed in this analysis are also applicable in that search. An Artificial Neural Network has been used for the event selection optimization. The theoretical prediction for the cross section is σ WW/WZ theory x Br(W → ℓv; W/Z → jj) = 2.09 ± 0.14 pb. They measured N Signal = 410 ± 212(stat) ± 102(sys) signal events that correspond to a cross section σ WW/WZ x Br(W → ℓv; W/Z → jj) = 1.47 ± 0.77(stat) ± 0.38(sys) pb. The 95% CL upper limit to the cross section is estimated to be σ x Br(W → ℓv; W/Z → jj) < 2.88 pb. The second part of the present work is technical and concerns the ATLAS SemiConductor Tracker (SCT) assembly phase. Although technical, the work in the SCT assembly phase is of prime importance for the good performance of the detector during data taking. The production at the University of Geneva of approximately one third of the silicon microstrip end-cap modules is presented. This collaborative effort of the university of Geneva group that lasted two years, resulted in 655 produced modules, 97% of which were good modules, constructed within the mechanical and electrical specifications and delivered in the SCT collaboration for assembly on the end-cap disks. The SCT end-caps and barrels consist of 4088 silicon modules, with a

  8. Multi-atlas pancreas segmentation: Atlas selection based on vessel structure.

    PubMed

    Karasawa, Ken'ichi; Oda, Masahiro; Kitasaka, Takayuki; Misawa, Kazunari; Fujiwara, Michitaka; Chu, Chengwen; Zheng, Guoyan; Rueckert, Daniel; Mori, Kensaku

    2017-07-01

    Automated organ segmentation from medical images is an indispensable component for clinical applications such as computer-aided diagnosis (CAD) and computer-assisted surgery (CAS). We utilize a multi-atlas segmentation scheme, which has recently been used in different approaches in the literature to achieve more accurate and robust segmentation of anatomical structures in computed tomography (CT) volume data. Among abdominal organs, the pancreas has large inter-patient variability in its position, size and shape. Moreover, the CT intensity of the pancreas closely resembles adjacent tissues, rendering its segmentation a challenging task. Due to this, conventional intensity-based atlas selection for pancreas segmentation often fails to select atlases that are similar in pancreas position and shape to those of the unlabeled target volume. In this paper, we propose a new atlas selection strategy based on vessel structure around the pancreatic tissue and demonstrate its application to a multi-atlas pancreas segmentation. Our method utilizes vessel structure around the pancreas to select atlases with high pancreatic resemblance to the unlabeled volume. Also, we investigate two types of applications of the vessel structure information to the atlas selection. Our segmentations were evaluated on 150 abdominal contrast-enhanced CT volumes. The experimental results showed that our approach can segment the pancreas with an average Jaccard index of 66.3% and an average Dice overlap coefficient of 78.5%. Copyright © 2017 Elsevier B.V. All rights reserved.

  9. Herschel-ATLAS: Dusty early-type galaxies

    NASA Astrophysics Data System (ADS)

    Rowlands, K.; Dunne, L.; Maddox, S.

    2015-03-01

    Early-type galaxies (ETGs) are thought to be devoid of dust and star-formation, having formed most of their stars at early epochs. We present the detection of the dustiest ETGs in a large-area blind submillimetre survey with Herschel (H-ATLAS, Eales et al. 2010), where the lack of pre-selection in other bands makes it the first unbiased survey for cold dust in ETGs. The parent sample of 1087 H-ATLAS galaxies in this study have a >= 5σ detection at 250μm, a reliable optical counterpart to the submillimetre source (Smith et al. 2011) and a spectroscopic redshift from the GAMA survey (Driver et al. 2011). Additionally, we construct a control sample of 1052 optically selected galaxies undetected at 250μm and matched in stellar mass to the H-ATLAS parent sample to eliminate selection effects. ETGs were selected from both samples via visual classifications using SDSS images. Further details can be found in Rowlands et al. (2012). Physical parameters are derived for each galaxy using the multiwavelength spectral energy distribution (SED) fitting code of da Cunha, Charlot and Elbaz (2008), Smith et al. 2012, using an energy balance argument. We investigate the differences between the dusty ETGs and the general ETG population, and find that the H-ATLAS ETGs are more than an order of magnitude dustier than the control ETGs. The mean dust mass of the 42 H-ATLAS ETGs is 5.5 × 107M⊙ (comparable to the dust mass of spirals in our sample), whereas the dust mass of the 233 control ETGs inferred from stacking at optical positions on the 250μm map is (0.8 - 4.0) × 106M⊙ for 25-15 K dust. The average star-formation rate of the H-ATLAS ETGs is 1.0 dex higher than that of control ETGs, and the mean r-band light-weighted age of the H-ATLAS ETGs is 1.8 Gyr younger than the control ETGs. The rest-frame NUV - r colours of the H-ATLAS ETGs are 1.0 magnitudes bluer than the control ETGs, and some ETGs may be transitioning from the blue cloud to the red sequence. Some H-ATLAS ETGs

  10. Anti-Atlas Mountains, Morocco

    NASA Technical Reports Server (NTRS)

    2002-01-01

    The Anti-Atlas Mountains of northern Africa and the nearby Atlas mountains were created by the prolonged collision of the African and Eurasian tectonic plates, beginning about 80 million years ago. Massive sandstone and limestone layers have been crumpled and uplifted more than 4,000 meters in the High Atlas and to lower elevations in the Anti-Atlas. Between more continuous major fold structures, such as the Jbel Ouarkziz in the southwestern Anti-Atlas, tighter secondary folds (arrow) have developed. Earlier, the supercontinent of Pangea rifted apart to form precursors to the Mediterranean and the Atlantic Ocean (Beauchamp and others, 1996). In those seas sands, clays, limey sediments, and evaporite layers (gypsum, rock salt) were deposited. Later, during the mountain-building plate collision, the gypsum layers flowed under the pressure and provided a slippery surface on which overlying rigid rocks could glide (Burkhard, 2001). The broad, open style of folds seen in this view is common where evaporites are involved in the deformation. Other examples can be found in the Southern Zagros of Iran and the Sierra Madre Oriental of Mexico. Information Sources: Beauchamp, W., Barazangi, M., Demnati, A., and El Alji, M., 1996, Intracontinental rifting and inversion: Missour Basin and Atlas Mountains, Morocco: Tulsa, American Association of Petroleum Geologists Bulletin, v. 80, No. 9, p. 1459-1482. Burkhard, Martin, 2001, Tectonics of the Anti-Atlas of Morocco -- Thin-skin/thick-skin relationships in an atypical foreland fold belt. University of Neuchatel, Switzerland: http://www-geol.unine.ch/Structural/Antiatlas.html (accessed 1/29/02). STS108-711-25 was taken in December, 2001 by the crew of Space Shuttle mission 108 using a Hasselblad camera with 250-mm lens. The image is provided by the Earth Sciences and Image Analysis Laboratory at Johnson Space Center. Additional images taken by astronauts and cosmonauts can be viewed at the NASA-JSC Gateway to Astronaut Photography

  11. Deploying the ATLAS Metadata Interface (AMI) on the cloud with Jenkins

    NASA Astrophysics Data System (ADS)

    Lambert, F.; Odier, J.; Fulachier, J.; ATLAS Collaboration

    2017-10-01

    The ATLAS Metadata Interface (AMI) is a mature application of more than 15 years of existence. Mainly used by the ATLAS experiment at CERN, it consists of a very generic tool ecosystem for metadata aggregation and cataloguing. AMI is used by the ATLAS production system, therefore the service must guarantee a high level of availability. We describe our monitoring and administration systems, and the Jenkins-based strategy used to dynamically test and deploy cloud OpenStack nodes on demand.

  12. ATLAS offline software performance monitoring and optimization

    NASA Astrophysics Data System (ADS)

    Chauhan, N.; Kabra, G.; Kittelmann, T.; Langenberg, R.; Mandrysch, R.; Salzburger, A.; Seuster, R.; Ritsch, E.; Stewart, G.; van Eldik, N.; Vitillo, R.; Atlas Collaboration

    2014-06-01

    In a complex multi-developer, multi-package software environment, such as the ATLAS offline framework Athena, tracking the performance of the code can be a non-trivial task in itself. In this paper we describe improvements in the instrumentation of ATLAS offline software that have given considerable insight into the performance of the code and helped to guide the optimization work. The first tool we used to instrument the code is PAPI, which is a programing interface for accessing hardware performance counters. PAPI events can count floating point operations, cycles, instructions and cache accesses. Triggering PAPI to start/stop counting for each algorithm and processed event results in a good understanding of the algorithm level performance of ATLAS code. Further data can be obtained using Pin, a dynamic binary instrumentation tool. Pin tools can be used to obtain similar statistics as PAPI, but advantageously without requiring recompilation of the code. Fine grained routine and instruction level instrumentation is also possible. Pin tools can additionally interrogate the arguments to functions, like those in linear algebra libraries, so that a detailed usage profile can be obtained. These tools have characterized the extensive use of vector and matrix operations in ATLAS tracking. Currently, CLHEP is used here, which is not an optimal choice. To help evaluate replacement libraries a testbed has been setup allowing comparison of the performance of different linear algebra libraries (including CLHEP, Eigen and SMatrix/SVector). Results are then presented via the ATLAS Performance Management Board framework, which runs daily with the current development branch of the code and monitors reconstruction and Monte-Carlo jobs. This framework analyses the CPU and memory performance of algorithms and an overview of results are presented on a web page. These tools have provided the insight necessary to plan and implement performance enhancements in ATLAS code by identifying

  13. Atlas Escaping

    NASA Image and Video Library

    2015-12-08

    NASA Cassini spacecraft captured this view of Saturn moon Atlas 30 kilometers, or 19 miles across, with its smooth equatorial ridge, during a moderately close flyby on Dec. 6, 2015. The view offers one of Cassini best glimpses of Atlas.

  14. High-Performance Scalable Information Service for the ATLAS Experiment

    NASA Astrophysics Data System (ADS)

    Kolos, S.; Boutsioukis, G.; Hauser, R.

    2012-12-01

    The ATLAS[1] experiment is operated by a highly distributed computing system which is constantly producing a lot of status information which is used to monitor the experiment operational conditions as well as to assess the quality of the physics data being taken. For example the ATLAS High Level Trigger(HLT) algorithms are executed on the online computing farm consisting from about 1500 nodes. Each HLT algorithm is producing few thousands histograms, which have to be integrated over the whole farm and carefully analyzed in order to properly tune the event rejection. In order to handle such non-physics data the Information Service (IS) facility has been developed in the scope of the ATLAS Trigger and Data Acquisition (TDAQ)[2] project. The IS provides a high-performance scalable solution for information exchange in distributed environment. In the course of an ATLAS data taking session the IS handles about a hundred gigabytes of information which is being constantly updated with the update interval varying from a second to a few tens of seconds. IS provides access to any information item on request as well as distributing notification to all the information subscribers. In the latter case IS subscribers receive information within a few milliseconds after it was updated. IS can handle arbitrary types of information, including histograms produced by the HLT applications, and provides C++, Java and Python API. The Information Service is a unique source of information for the majority of the online monitoring analysis and GUI applications used to control and monitor the ATLAS experiment. Information Service provides streaming functionality allowing efficient replication of all or part of the managed information. This functionality is used to duplicate the subset of the ATLAS monitoring data to the CERN public network with a latency of a few milliseconds, allowing efficient real-time monitoring of the data taking from outside the protected ATLAS network. Each information

  15. Bone age assessment in Hispanic children: digital hand atlas compared with the Greulich and Pyle (G&P) atlas

    NASA Astrophysics Data System (ADS)

    Fernandez, James Reza; Zhang, Aifeng; Vachon, Linda; Tsao, Sinchai

    2008-03-01

    Bone age assessment is most commonly performed with the use of the Greulich and Pyle (G&P) book atlas, which was developed in the 1950s. The population of theUnited States is not as homogenous as the Caucasian population in the Greulich and Pyle in the 1950s, especially in the Los Angeles, California area. A digital hand atlas (DHA) based on 1,390 hand images of children of different racial backgrounds (Caucasian, African American, Hispanic, and Asian) aged 0-18 years was collected from Children's Hospital Los Angeles. Statistical analysis discovered significant discrepancies exist between Hispanic and the G&P atlas standard. To validate the usage of DHA as a clinical standard, diagnostic radiologists performed reads on Hispanic pediatric hand and wrist computed radiography images using either the G&P pediatric radiographic atlas or the Children's Hospital Los Angeles Digital Hand Atlas (DHA) as reference. The order in which the atlas is used (G&P followed by DHA or vice versa) for each image was prepared before actual reading begins. Statistical analysis of the results was then performed to determine if a discrepancy exists between the two readings.

  16. The Copernicus ultraviolet spectral atlas of Sirius

    NASA Technical Reports Server (NTRS)

    Rogerson, John B., Jr.

    1987-01-01

    A near-ultraviolet spectral atlas for the A1 V star Alpha CMa (Sirius) has been prepared from data taken by the Princeton spectrometer aboard the Copernicus satellite. The spectral region from 1649 to 3170 A has been scanned with a resolution of 0.1 A. The atlas is presented in graphs, and line identifications for the absorption features have been tabulated.

  17. ATLAS Distributed Computing Experience and Performance During the LHC Run-2

    NASA Astrophysics Data System (ADS)

    Filipčič, A.; ATLAS Collaboration

    2017-10-01

    ATLAS Distributed Computing during LHC Run-1 was challenged by steadily increasing computing, storage and network requirements. In addition, the complexity of processing task workflows and their associated data management requirements led to a new paradigm in the ATLAS computing model for Run-2, accompanied by extensive evolution and redesign of the workflow and data management systems. The new systems were put into production at the end of 2014, and gained robustness and maturity during 2015 data taking. ProdSys2, the new request and task interface; JEDI, the dynamic job execution engine developed as an extension to PanDA; and Rucio, the new data management system, form the core of Run-2 ATLAS distributed computing engine. One of the big changes for Run-2 was the adoption of the Derivation Framework, which moves the chaotic CPU and data intensive part of the user analysis into the centrally organized train production, delivering derived AOD datasets to user groups for final analysis. The effectiveness of the new model was demonstrated through the delivery of analysis datasets to users just one week after data taking, by completing the calibration loop, Tier-0 processing and train production steps promptly. The great flexibility of the new system also makes it possible to execute part of the Tier-0 processing on the grid when Tier-0 resources experience a backlog during high data-taking periods. The introduction of the data lifetime model, where each dataset is assigned a finite lifetime (with extensions possible for frequently accessed data), was made possible by Rucio. Thanks to this the storage crises experienced in Run-1 have not reappeared during Run-2. In addition, the distinction between Tier-1 and Tier-2 disk storage, now largely artificial given the quality of Tier-2 resources and their networking, has been removed through the introduction of dynamic ATLAS clouds that group the storage endpoint nucleus and its close-by execution satellite sites. All stable

  18. The Forward Endcap of the Electromagnetic Calorimeter for the PANDA Detector at FAIR

    NASA Astrophysics Data System (ADS)

    Albrecht, Malte; PANDA Collaboration

    2015-02-01

    The versatile 4π-detector PANDA will be built at the Facility for Antiproton and Ion Research (FAIR), an accelerator complex, currently under construction near Darmstadt, Germany. A cooled antiproton beam in a momentum range of 1.5 - 15GeV/c will be provided by the High Energy Storage Ring (HESR). All measurements at PANDA rely on an excellent performance of the detector with respect to tracking, particle identification and energy measurement. The electromagnetic calorimeter (EMC) of the PANDA detector will be equipped with 15744 PbWO4 crystals (PWO-II), which will be operated at a temperature of - 25° C in order to increase the light output. The design of the forward endcap of the EMC has been finalized. The crystals will be read out with Large Area Avalanche Photo Diodes (LAAPDs) in the outer regions and with Vacuum Photo Tetrodes (VPTTs) in the innermost part. Production of photosensor units utilizing charge integrating preamplifiers has begun. A prototype comprised of 216 PbWO4 crystals has been built and tested at various accelerators (CERN SPS, ELSA/Bonn, MAMI/Mainz), where the crystals have been exposed to electron and photon beams of 25MeV up to 15GeV. The results of these test measurements regarding the energy and position resolution are presented.

  19. Novel in-situ nanocomposites of phthalonitrile end-capped polyarylene ether nitrile/copper phthalocyanine

    NASA Astrophysics Data System (ADS)

    Tong, Lifen; Wei, Renbo; Liu, Xiaobo

    2017-01-01

    A novel phthalonitrile end-capped polyarylene ether nitrile (PEN-Ph)/copper phthalocyanine (CuPc) nanocomposites which possesses crosslinking reaction combined with crystallization behaviour were prepared successfully through in-situ reaction and hot-compression. In the presence of copper ion, CuPc were formed through crosslinking reaction among the phthalonitrile at the end of the PEN-Ph main chain and 1, 3, 5-Tri-(3, 4-dicyanophenoxy) benzene (TPh). Besides, the formed CuPc can play the role of nucleating agent to improve the crystallinity of the polymers. The influence of the crosslinking reaction and crystallization behaviour were investigated. The results show that the crystallization and crosslinking coexist in the system at the same time. Scanning electron microscope (SEM) images show that the crystals of the PEN-Ph grow after the hot-compressing procedure. Moreover, the glass transition temperature (Tg) increases while the crystallinity declines slightly with the low amount of copper ions. The increase of Tg is mainly caused by the crosslinking reaction, indicating that the copper can be used as a crosslinking agent in this system. Due to formation of the CuPc and the crystallization behaviour, the dielectric constant increased as expected from 3.2 to 4.9 while the dielectric loss decreased. Therefore, the PEN-Ph/CuPc in-situ nanocomposites will have a good prospect for application in electronic field.

  20. The Human Brainnetome Atlas: A New Brain Atlas Based on Connectional Architecture.

    PubMed

    Fan, Lingzhong; Li, Hai; Zhuo, Junjie; Zhang, Yu; Wang, Jiaojian; Chen, Liangfu; Yang, Zhengyi; Chu, Congying; Xie, Sangma; Laird, Angela R; Fox, Peter T; Eickhoff, Simon B; Yu, Chunshui; Jiang, Tianzi

    2016-08-01

    The human brain atlases that allow correlating brain anatomy with psychological and cognitive functions are in transition from ex vivo histology-based printed atlases to digital brain maps providing multimodal in vivo information. Many current human brain atlases cover only specific structures, lack fine-grained parcellations, and fail to provide functionally important connectivity information. Using noninvasive multimodal neuroimaging techniques, we designed a connectivity-based parcellation framework that identifies the subdivisions of the entire human brain, revealing the in vivo connectivity architecture. The resulting human Brainnetome Atlas, with 210 cortical and 36 subcortical subregions, provides a fine-grained, cross-validated atlas and contains information on both anatomical and functional connections. Additionally, we further mapped the delineated structures to mental processes by reference to the BrainMap database. It thus provides an objective and stable starting point from which to explore the complex relationships between structure, connectivity, and function, and eventually improves understanding of how the human brain works. The human Brainnetome Atlas will be made freely available for download at http://atlas.brainnetome.org, so that whole brain parcellations, connections, and functional data will be readily available for researchers to use in their investigations into healthy and pathological states. © The Author 2016. Published by Oxford University Press.

  1. Two-stage atlas subset selection in multi-atlas based image segmentation

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Zhao, Tingting, E-mail: tingtingzhao@mednet.ucla.edu; Ruan, Dan, E-mail: druan@mednet.ucla.edu

    2015-06-15

    Purpose: Fast growing access to large databases and cloud stored data presents a unique opportunity for multi-atlas based image segmentation and also presents challenges in heterogeneous atlas quality and computation burden. This work aims to develop a novel two-stage method tailored to the special needs in the face of large atlas collection with varied quality, so that high-accuracy segmentation can be achieved with low computational cost. Methods: An atlas subset selection scheme is proposed to substitute a significant portion of the computationally expensive full-fledged registration in the conventional scheme with a low-cost alternative. More specifically, the authors introduce a two-stagemore » atlas subset selection method. In the first stage, an augmented subset is obtained based on a low-cost registration configuration and a preliminary relevance metric; in the second stage, the subset is further narrowed down to a fusion set of desired size, based on full-fledged registration and a refined relevance metric. An inference model is developed to characterize the relationship between the preliminary and refined relevance metrics, and a proper augmented subset size is derived to ensure that the desired atlases survive the preliminary selection with high probability. Results: The performance of the proposed scheme has been assessed with cross validation based on two clinical datasets consisting of manually segmented prostate and brain magnetic resonance images, respectively. The proposed scheme demonstrates comparable end-to-end segmentation performance as the conventional single-stage selection method, but with significant computation reduction. Compared with the alternative computation reduction method, their scheme improves the mean and medium Dice similarity coefficient value from (0.74, 0.78) to (0.83, 0.85) and from (0.82, 0.84) to (0.95, 0.95) for prostate and corpus callosum segmentation, respectively, with statistical significance. Conclusions: The

  2. Two-stage atlas subset selection in multi-atlas based image segmentation.

    PubMed

    Zhao, Tingting; Ruan, Dan

    2015-06-01

    Fast growing access to large databases and cloud stored data presents a unique opportunity for multi-atlas based image segmentation and also presents challenges in heterogeneous atlas quality and computation burden. This work aims to develop a novel two-stage method tailored to the special needs in the face of large atlas collection with varied quality, so that high-accuracy segmentation can be achieved with low computational cost. An atlas subset selection scheme is proposed to substitute a significant portion of the computationally expensive full-fledged registration in the conventional scheme with a low-cost alternative. More specifically, the authors introduce a two-stage atlas subset selection method. In the first stage, an augmented subset is obtained based on a low-cost registration configuration and a preliminary relevance metric; in the second stage, the subset is further narrowed down to a fusion set of desired size, based on full-fledged registration and a refined relevance metric. An inference model is developed to characterize the relationship between the preliminary and refined relevance metrics, and a proper augmented subset size is derived to ensure that the desired atlases survive the preliminary selection with high probability. The performance of the proposed scheme has been assessed with cross validation based on two clinical datasets consisting of manually segmented prostate and brain magnetic resonance images, respectively. The proposed scheme demonstrates comparable end-to-end segmentation performance as the conventional single-stage selection method, but with significant computation reduction. Compared with the alternative computation reduction method, their scheme improves the mean and medium Dice similarity coefficient value from (0.74, 0.78) to (0.83, 0.85) and from (0.82, 0.84) to (0.95, 0.95) for prostate and corpus callosum segmentation, respectively, with statistical significance. The authors have developed a novel two-stage atlas

  3. Diabetes Interactive Atlas

    PubMed Central

    Burrows, Nilka R.; Geiss, Linda S.

    2014-01-01

    The Diabetes Interactive Atlas is a recently released Web-based collection of maps that allows users to view geographic patterns and examine trends in diabetes and its risk factors over time across the United States and within states. The atlas provides maps, tables, graphs, and motion charts that depict national, state, and county data. Large amounts of data can be viewed in various ways simultaneously. In this article, we describe the design and technical issues for developing the atlas and provide an overview of the atlas’ maps and graphs. The Diabetes Interactive Atlas improves visualization of geographic patterns, highlights observation of trends, and demonstrates the concomitant geographic and temporal growth of diabetes and obesity. PMID:24503340

  4. EnviroAtlas: Two Use Cases in the EnviroAtlas

    EPA Science Inventory

    EnviroAtlas is an online spatial decision support tool for viewing and analyzing the supply, demand, and drivers of change related to natural and built infrastructure at multiple scales for the nation. To maximize usefulness to a broad range of users, EnviroAtlas contains trainin...

  5. Atlas – a data warehouse for integrative bioinformatics

    PubMed Central

    Shah, Sohrab P; Huang, Yong; Xu, Tao; Yuen, Macaire MS; Ling, John; Ouellette, BF Francis

    2005-01-01

    Background We present a biological data warehouse called Atlas that locally stores and integrates biological sequences, molecular interactions, homology information, functional annotations of genes, and biological ontologies. The goal of the system is to provide data, as well as a software infrastructure for bioinformatics research and development. Description The Atlas system is based on relational data models that we developed for each of the source data types. Data stored within these relational models are managed through Structured Query Language (SQL) calls that are implemented in a set of Application Programming Interfaces (APIs). The APIs include three languages: C++, Java, and Perl. The methods in these API libraries are used to construct a set of loader applications, which parse and load the source datasets into the Atlas database, and a set of toolbox applications which facilitate data retrieval. Atlas stores and integrates local instances of GenBank, RefSeq, UniProt, Human Protein Reference Database (HPRD), Biomolecular Interaction Network Database (BIND), Database of Interacting Proteins (DIP), Molecular Interactions Database (MINT), IntAct, NCBI Taxonomy, Gene Ontology (GO), Online Mendelian Inheritance in Man (OMIM), LocusLink, Entrez Gene and HomoloGene. The retrieval APIs and toolbox applications are critical components that offer end-users flexible, easy, integrated access to this data. We present use cases that use Atlas to integrate these sources for genome annotation, inference of molecular interactions across species, and gene-disease associations. Conclusion The Atlas biological data warehouse serves as data infrastructure for bioinformatics research and development. It forms the backbone of the research activities in our laboratory and facilitates the integration of disparate, heterogeneous biological sources of data enabling new scientific inferences. Atlas achieves integration of diverse data sets at two levels. First, Atlas stores data of

  6. Synthesis of a basket-shaped C56H38 hydrocarbon as a precursor toward an end-cap template for carbon [6,6]nanotubes.

    PubMed

    Cui, Hu; Akhmedov, Novruz G; Petersen, Jeffrey L; Wang, Kung K

    2010-03-19

    A basket-shaped C(56)H(38) hydrocarbon (3) possessing a 30-carbon difluorenonaphthacenyl core that can be mapped onto the surface of C(78) was synthesized from 4-bromo-1-indanone. The first stage of the synthesis involved the preparation of tetraketone 10 as a key intermediate. The use of cascade cyclization reactions of benzannulated enyne-allenes as key features in the next stage of the synthetic sequence provides an efficient route to 3 from 4-bromo-1-indanone in 12 steps. The all-cis relationship among the methyl groups and the methine hydrogens causes the two benzofluorenyl units in 3 to be in an essentially perpendicular orientation to each other. Hydrocarbon 3 and its derivatives could serve as attractive precursors leading to a geodesic C(68)H(26) end-cap template for carbon [6,6]nanotubes.

  7. Automatic Structural Parcellation of Mouse Brain MRI Using Multi-Atlas Label Fusion

    PubMed Central

    Ma, Da; Cardoso, Manuel J.; Modat, Marc; Powell, Nick; Wells, Jack; Holmes, Holly; Wiseman, Frances; Tybulewicz, Victor; Fisher, Elizabeth; Lythgoe, Mark F.; Ourselin, Sébastien

    2014-01-01

    Multi-atlas segmentation propagation has evolved quickly in recent years, becoming a state-of-the-art methodology for automatic parcellation of structural images. However, few studies have applied these methods to preclinical research. In this study, we present a fully automatic framework for mouse brain MRI structural parcellation using multi-atlas segmentation propagation. The framework adopts the similarity and truth estimation for propagated segmentations (STEPS) algorithm, which utilises a locally normalised cross correlation similarity metric for atlas selection and an extended simultaneous truth and performance level estimation (STAPLE) framework for multi-label fusion. The segmentation accuracy of the multi-atlas framework was evaluated using publicly available mouse brain atlas databases with pre-segmented manually labelled anatomical structures as the gold standard, and optimised parameters were obtained for the STEPS algorithm in the label fusion to achieve the best segmentation accuracy. We showed that our multi-atlas framework resulted in significantly higher segmentation accuracy compared to single-atlas based segmentation, as well as to the original STAPLE framework. PMID:24475148

  8. Atlas of Seasonal Means Simulated by the NSIPP 1 Atmospheric GCM. Volume 17

    NASA Technical Reports Server (NTRS)

    Suarez, Max J. (Editor); Bacmeister, Julio; Pegion, Philip J.; Schubert, Siegfried D.; Busalacchi, Antonio J. (Technical Monitor)

    2000-01-01

    This atlas documents the climate characteristics of version 1 of the NASA Seasonal-to-Interannual Prediction Project (NSIPP) Atmospheric General Circulation Model (AGCM). The AGCM includes an interactive land model (the Mosaic scheme), and is part of the NSIPP coupled atmosphere-land-ocean model. The results presented here are based on a 20-year (December 1979-November 1999) "ANIIP-style" integration of the AGCM in which the monthly-mean sea-surface temperature and sea ice are specified from observations. The climate characteristics of the AGCM are compared with the National Centers for Environmental Prediction (NCEP) and the European Center for Medium-Range Weather Forecasting (ECMWF) reanalyses. Other verification data include Special Sensor Microwave/Imager (SSNM) total precipitable water, the Xie-Arkin estimates of precipitation, and Earth Radiation Budget Experiment (ERBE) measurements of short and long wave radiation. The atlas is organized by season. The basic quantities include seasonal mean global maps and zonal and vertical averages of circulation, variance/covariance statistics, and selected physics quantities.

  9. Neonatal Atlas Construction Using Sparse Representation

    PubMed Central

    Shi, Feng; Wang, Li; Wu, Guorong; Li, Gang; Gilmore, John H.; Lin, Weili; Shen, Dinggang

    2014-01-01

    Atlas construction generally includes first an image registration step to normalize all images into a common space and then an atlas building step to fuse the information from all the aligned images. Although numerous atlas construction studies have been performed to improve the accuracy of the image registration step, unweighted or simply weighted average is often used in the atlas building step. In this article, we propose a novel patch-based sparse representation method for atlas construction after all images have been registered into the common space. By taking advantage of local sparse representation, more anatomical details can be recovered in the built atlas. To make the anatomical structures spatially smooth in the atlas, the anatomical feature constraints on group structure of representations and also the overlapping of neighboring patches are imposed to ensure the anatomical consistency between neighboring patches. The proposed method has been applied to 73 neonatal MR images with poor spatial resolution and low tissue contrast, for constructing a neonatal brain atlas with sharp anatomical details. Experimental results demonstrate that the proposed method can significantly enhance the quality of the constructed atlas by discovering more anatomical details especially in the highly convoluted cortical regions. The resulting atlas demonstrates superior performance of our atlas when applied to spatially normalizing three different neonatal datasets, compared with other start-of-the-art neonatal brain atlases. PMID:24638883

  10. Scoring haemophilic arthropathy on X-rays: improving inter- and intra-observer reliability and agreement using a consensus atlas.

    PubMed

    Foppen, Wouter; van der Schaaf, Irene C; Beek, Frederik J A; Verkooijen, Helena M; Fischer, Kathelijn

    2016-06-01

    The radiological Pettersson score (PS) is widely applied for classification of arthropathy to evaluate costly haemophilia treatment. This study aims to assess and improve inter- and intra-observer reliability and agreement of the PS. Two series of X-rays (bilateral elbows, knees, and ankles) of 10 haemophilia patients (120 joints) with haemophilic arthropathy were scored by three observers according to the PS (maximum score 13/joint). Subsequently, (dis-)agreement in scoring was discussed until consensus. Example images were collected in an atlas. Thereafter, second series of 120 joints were scored using the atlas. One observer rescored the second series after three months. Reliability was assessed by intraclass correlation coefficients (ICC), agreement by limits of agreement (LoA). Median Pettersson score at joint level (PSjoint) of affected joints was 6 (interquartile range 3-9). Using the consensus atlas, inter-observer reliability of the PSjoint improved significantly from 0.94 (95 % confidence interval (CI) 0.91-0.96) to 0.97 (CI 0.96-0.98). LoA improved from ±1.7 to ±1.1 for the PSjoint. Therefore, true differences in arthropathy were differences in the PSjoint of >2 points. Intra-observer reliability of the PSjoint was 0.98 (CI 0.97-0.98), intra-observer LoA were ±0.9 points. Reliability and agreement of the PS improved by using a consensus atlas. • Reliability of the Pettersson score significantly improved using the consensus atlas. • The presented consensus atlas improved the agreement among observers. • The consensus atlas could be recommended to obtain a reproducible Pettersson score.

  11. Event visualization in ATLAS

    NASA Astrophysics Data System (ADS)

    Bianchi, R. M.; Boudreau, J.; Konstantinidis, N.; Martyniuk, A. C.; Moyse, E.; Thomas, J.; Waugh, B. M.; Yallup, D. P.; ATLAS Collaboration

    2017-10-01

    At the beginning, HEP experiments made use of photographical images both to record and store experimental data and to illustrate their findings. Then the experiments evolved and needed to find ways to visualize their data. With the availability of computer graphics, software packages to display event data and the detector geometry started to be developed. Here, an overview of the usage of event display tools in HEP is presented. Then the case of the ATLAS experiment is considered in more detail and two widely used event display packages are presented, Atlantis and VP1, focusing on the software technologies they employ, as well as their strengths, differences and their usage in the experiment: from physics analysis to detector development, and from online monitoring to outreach and communication. Towards the end, the other ATLAS visualization tools will be briefly presented as well. Future development plans and improvements in the ATLAS event display packages will also be discussed.

  12. Evaluation of Atlas-Based Attenuation Correction for Integrated PET/MR in Human Brain: Application of a Head Atlas and Comparison to True CT-Based Attenuation Correction.

    PubMed

    Sekine, Tetsuro; Buck, Alfred; Delso, Gaspar; Ter Voert, Edwin E G W; Huellner, Martin; Veit-Haibach, Patrick; Warnock, Geoffrey

    2016-02-01

    Attenuation correction (AC) for integrated PET/MR imaging in the human brain is still an open problem. In this study, we evaluated a simplified atlas-based AC (Atlas-AC) by comparing (18)F-FDG PET data corrected using either Atlas-AC or true CT data (CT-AC). We enrolled 8 patients (median age, 63 y). All patients underwent clinically indicated whole-body (18)F-FDG PET/CT for staging, restaging, or follow-up of malignant disease. All patients volunteered for an additional PET/MR of the head (additional tracer was not injected). For each patient, 2 AC maps were generated: an Atlas-AC map registered to a patient-specific liver accelerated volume acquisition-Flex MR sequence and using a vendor-provided head atlas generated from multiple CT head images and a CT-based AC map. For comparative AC, the CT-AC map generated from PET/CT was superimposed on the Atlas-AC map. PET images were reconstructed from the list-mode raw data from the PET/MR imaging scanner using each AC map. All PET images were normalized to the SPM5 PET template, and (18)F-FDG accumulation was quantified in 67 volumes of interest (VOIs; automated anatomic labeling atlas). Relative difference (%diff) between images based on Atlas-AC and CT-AC was calculated, and averaged difference images were generated. (18)F-FDG uptake in all VOIs was compared using Bland-Altman analysis. The range of error in all 536 VOIs was -3.0%-7.3%. Whole-brain (18)F-FDG uptake based on Atlas-AC was slightly underestimated (%diff = 2.19% ± 1.40%). The underestimation was most pronounced in the regions below the anterior/posterior commissure line, such as the cerebellum, temporal lobe, and central structures (%diff = 3.69% ± 1.43%, 3.25% ± 1.42%, and 3.05% ± 1.18%), suggesting that Atlas-AC tends to underestimate the attenuation values of the skull base bone. When compared with the gold-standard CT-AC, errors introduced using Atlas-AC did not exceed 8% in any brain region investigated. Underestimation of (18)F-FDG uptake was

  13. Influence of signal intensity non-uniformity on brain volumetry using an atlas-based method.

    PubMed

    Goto, Masami; Abe, Osamu; Miyati, Tosiaki; Kabasawa, Hiroyuki; Takao, Hidemasa; Hayashi, Naoto; Kurosu, Tomomi; Iwatsubo, Takeshi; Yamashita, Fumio; Matsuda, Hiroshi; Mori, Harushi; Kunimatsu, Akira; Aoki, Shigeki; Ino, Kenji; Yano, Keiichi; Ohtomo, Kuni

    2012-01-01

    Many studies have reported pre-processing effects for brain volumetry; however, no study has investigated whether non-parametric non-uniform intensity normalization (N3) correction processing results in reduced system dependency when using an atlas-based method. To address this shortcoming, the present study assessed whether N3 correction processing provides reduced system dependency in atlas-based volumetry. Contiguous sagittal T1-weighted images of the brain were obtained from 21 healthy participants, by using five magnetic resonance protocols. After image preprocessing using the Statistical Parametric Mapping 5 software, we measured the structural volume of the segmented images with the WFU-PickAtlas software. We applied six different bias-correction levels (Regularization 10, Regularization 0.0001, Regularization 0, Regularization 10 with N3, Regularization 0.0001 with N3, and Regularization 0 with N3) to each set of images. The structural volume change ratio (%) was defined as the change ratio (%) = (100 × [measured volume - mean volume of five magnetic resonance protocols] / mean volume of five magnetic resonance protocols) for each bias-correction level. A low change ratio was synonymous with lower system dependency. The results showed that the images with the N3 correction had a lower change ratio compared with those without the N3 correction. The present study is the first atlas-based volumetry study to show that the precision of atlas-based volumetry improves when using N3-corrected images. Therefore, correction for signal intensity non-uniformity is strongly advised for multi-scanner or multi-site imaging trials.

  14. Influence of Signal Intensity Non-Uniformity on Brain Volumetry Using an Atlas-Based Method

    PubMed Central

    Abe, Osamu; Miyati, Tosiaki; Kabasawa, Hiroyuki; Takao, Hidemasa; Hayashi, Naoto; Kurosu, Tomomi; Iwatsubo, Takeshi; Yamashita, Fumio; Matsuda, Hiroshi; Mori, Harushi; Kunimatsu, Akira; Aoki, Shigeki; Ino, Kenji; Yano, Keiichi; Ohtomo, Kuni

    2012-01-01

    Objective Many studies have reported pre-processing effects for brain volumetry; however, no study has investigated whether non-parametric non-uniform intensity normalization (N3) correction processing results in reduced system dependency when using an atlas-based method. To address this shortcoming, the present study assessed whether N3 correction processing provides reduced system dependency in atlas-based volumetry. Materials and Methods Contiguous sagittal T1-weighted images of the brain were obtained from 21 healthy participants, by using five magnetic resonance protocols. After image preprocessing using the Statistical Parametric Mapping 5 software, we measured the structural volume of the segmented images with the WFU-PickAtlas software. We applied six different bias-correction levels (Regularization 10, Regularization 0.0001, Regularization 0, Regularization 10 with N3, Regularization 0.0001 with N3, and Regularization 0 with N3) to each set of images. The structural volume change ratio (%) was defined as the change ratio (%) = (100 × [measured volume - mean volume of five magnetic resonance protocols] / mean volume of five magnetic resonance protocols) for each bias-correction level. Results A low change ratio was synonymous with lower system dependency. The results showed that the images with the N3 correction had a lower change ratio compared with those without the N3 correction. Conclusion The present study is the first atlas-based volumetry study to show that the precision of atlas-based volumetry improves when using N3-corrected images. Therefore, correction for signal intensity non-uniformity is strongly advised for multi-scanner or multi-site imaging trials. PMID:22778560

  15. EnviroAtlas - Austin, TX - Block Groups

    EPA Pesticide Factsheets

    This EnviroAtlas dataset is the base layer for the Austin, TX EnviroAtlas area. The block groups are from the US Census Bureau and are included/excluded based on EnviroAtlas criteria described in the procedure log. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

  16. An Open-Source Label Atlas Correction Tool and Preliminary Results on Huntingtons Disease Whole-Brain MRI Atlases

    PubMed Central

    Forbes, Jessica L.; Kim, Regina E. Y.; Paulsen, Jane S.; Johnson, Hans J.

    2016-01-01

    The creation of high-quality medical imaging reference atlas datasets with consistent dense anatomical region labels is a challenging task. Reference atlases have many uses in medical image applications and are essential components of atlas-based segmentation tools commonly used for producing personalized anatomical measurements for individual subjects. The process of manual identification of anatomical regions by experts is regarded as a so-called gold standard; however, it is usually impractical because of the labor-intensive costs. Further, as the number of regions of interest increases, these manually created atlases often contain many small inconsistently labeled or disconnected regions that need to be identified and corrected. This project proposes an efficient process to drastically reduce the time necessary for manual revision in order to improve atlas label quality. We introduce the LabelAtlasEditor tool, a SimpleITK-based open-source label atlas correction tool distributed within the image visualization software 3D Slicer. LabelAtlasEditor incorporates several 3D Slicer widgets into one consistent interface and provides label-specific correction tools, allowing for rapid identification, navigation, and modification of the small, disconnected erroneous labels within an atlas. The technical details for the implementation and performance of LabelAtlasEditor are demonstrated using an application of improving a set of 20 Huntingtons Disease-specific multi-modal brain atlases. Additionally, we present the advantages and limitations of automatic atlas correction. After the correction of atlas inconsistencies and small, disconnected regions, the number of unidentified voxels for each dataset was reduced on average by 68.48%. PMID:27536233

  17. An Open-Source Label Atlas Correction Tool and Preliminary Results on Huntingtons Disease Whole-Brain MRI Atlases.

    PubMed

    Forbes, Jessica L; Kim, Regina E Y; Paulsen, Jane S; Johnson, Hans J

    2016-01-01

    The creation of high-quality medical imaging reference atlas datasets with consistent dense anatomical region labels is a challenging task. Reference atlases have many uses in medical image applications and are essential components of atlas-based segmentation tools commonly used for producing personalized anatomical measurements for individual subjects. The process of manual identification of anatomical regions by experts is regarded as a so-called gold standard; however, it is usually impractical because of the labor-intensive costs. Further, as the number of regions of interest increases, these manually created atlases often contain many small inconsistently labeled or disconnected regions that need to be identified and corrected. This project proposes an efficient process to drastically reduce the time necessary for manual revision in order to improve atlas label quality. We introduce the LabelAtlasEditor tool, a SimpleITK-based open-source label atlas correction tool distributed within the image visualization software 3D Slicer. LabelAtlasEditor incorporates several 3D Slicer widgets into one consistent interface and provides label-specific correction tools, allowing for rapid identification, navigation, and modification of the small, disconnected erroneous labels within an atlas. The technical details for the implementation and performance of LabelAtlasEditor are demonstrated using an application of improving a set of 20 Huntingtons Disease-specific multi-modal brain atlases. Additionally, we present the advantages and limitations of automatic atlas correction. After the correction of atlas inconsistencies and small, disconnected regions, the number of unidentified voxels for each dataset was reduced on average by 68.48%.

  18. A chromatographic estimate of the degree of surface heterogeneity of RPLC packing materials. III. Endcapped amido-embedded reversed phase

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Gritti, Fabrice; Guiochon, Georges A

    2006-01-01

    The difference in adsorption behavior between a conventional monomeric endcapped C{sub 18} stationary phase (3.43 {micro}mol/m{sup 2}) and an endcapped polymeric RP-Amide phase (3.31 {micro}mol/m{sup 2}) was investigated. The adsorption isotherms of four compounds (phenol, caffeine, sodium 2-naphthalene sulfonate, and propranololium chloride) were measured by frontal analysis (FA) and the degree of heterogeneity of each phase for each solute was characterized by their adsorption energy distributions (AED), derived using the Expectation-Maximization method. The results show that only certain analytes (phenol and 2-naphthalene sulfonate) are sensitive to the presence of the polar embedded amide groups within the RP phase. Their bindingmore » constants on the amide-bonded phase are significantly higher than on conventional RPLC phases. Furthermore, an additional type of adsorption sites was observed for these two compounds. However, these sites having a low density, their presence does not affect much the retention factors of the two analytes. On the other hand, the adsorption behavior of the other two analytes (caffeine and propranololium chloride) is almost unaffected by the presence of the amide group in the bonded layer. Strong selective interactions may explain these observations. For example, hydrogen-bond interactions between an analyte (e.g., phenol or naphthalene sulfonate) and the carbonyl group (acceptor) or the nitrogen (donor) of the amido-embedded group may take place. No such interactions may take place with either caffeine or the cation propranololium chloride. This study confirms the hypothesis that analytes have ready access to locations deep inside the bonded layer, where the amide groups are present.« less

  19. AGIS: The ATLAS Grid Information System

    NASA Astrophysics Data System (ADS)

    Anisenkov, Alexey; Belov, Sergey; Di Girolamo, Alessandro; Gayazov, Stavro; Klimentov, Alexei; Oleynik, Danila; Senchenko, Alexander

    2012-12-01

    ATLAS is a particle physics experiment at the Large Hadron Collider at CERN. The experiment produces petabytes of data annually through simulation production and tens petabytes of data per year from the detector itself. The ATLAS Computing model embraces the Grid paradigm and a high degree of decentralization and computing resources able to meet ATLAS requirements of petabytes scale data operations. In this paper we present ATLAS Grid Information System (AGIS) designed to integrate configuration and status information about resources, services and topology of whole ATLAS Grid needed by ATLAS Distributed Computing applications and services.

  20. Generation of an Atlas of the Proximal Femur and Its Application to Trabecular Bone Analysis

    PubMed Central

    Carballido-Gamio, Julio; Folkesson, Jenny; Karampinos, Dimitrios C.; Baum, Thomas; Link, Thomas M.; Majumdar, Sharmila; Krug, Roland

    2013-01-01

    Automatic placement of anatomically corresponding volumes of interest and comparison of parameters against a standard of reference are essential components in studies of trabecular bone. Only recently, in vivo MR images of the proximal femur, an important fracture site, could be acquired with high-spatial resolution. The purpose of this MRI trabecular bone study was two-fold: (1) to generate an atlas of the proximal femur to automatically place anatomically corresponding volumes of interest in a population study and (2) to demonstrate how mean models of geodesic topological analysis parameters can be generated to be used as potential standard of reference. Ten females were used to generate the atlas and geodesic topological analysis models, and 10 females were used to demonstrate the atlas-based trabecular bone analysis. All alignments were based on three-dimensional (3D) multiresolution affine transformations followed by 3D multiresolution free-form deformations. Mean distances less than 1 mm between aligned femora, and sharp edges in the atlas and in fused gray-level images of registered femora indicated that the anatomical variability was well accommodated and explained by the free-form deformations. PMID:21432904

  1. Computational and mathematical methods in brain atlasing.

    PubMed

    Nowinski, Wieslaw L

    2017-12-01

    Brain atlases have a wide range of use from education to research to clinical applications. Mathematical methods as well as computational methods and tools play a major role in the process of brain atlas building and developing atlas-based applications. Computational methods and tools cover three areas: dedicated editors for brain model creation, brain navigators supporting multiple platforms, and atlas-assisted specific applications. Mathematical methods in atlas building and developing atlas-aided applications deal with problems in image segmentation, geometric body modelling, physical modelling, atlas-to-scan registration, visualisation, interaction and virtual reality. Here I overview computational and mathematical methods in atlas building and developing atlas-assisted applications, and share my contribution to and experience in this field.

  2. Global GIS database; digital atlas of Central and South America

    USGS Publications Warehouse

    Hearn,, Paul P.; Hare, T.; Schruben, P.; Sherrill, D.; LaMar, C.; Tsushima, P.

    2000-01-01

    This CD-ROM contains a digital atlas of the countries of Central and South America. This atlas is part of a global database compiled from USGS and other data sources at the nominal scale of 1:1 million and is intended to be used as a regional-scale reference and analytical tool by government officials, researchers, the private sector, and the general public. The atlas includes free GIS software or may also be used with ESRI's ArcView software. Customized ArcView tools, specifically designed to make the atlas easier to use, are also included. The atlas contains the following datasets: country political boundaries, digital shaded relief map, elevation, slope, hydrology, locations of cities and towns, airfields, roads, railroads, utility lines, population density, geology, ecological regions, historical seismicity, volcanoes, ore deposits, oil and gas fields, climate data, landcover, vegetation index, and lights at night.

  3. EnviroAtlas - Woodbine, IA - Ecosystem Services by Block Group

    EPA Pesticide Factsheets

    This EnviroAtlas dataset presents environmental benefits of the urban forest in 1 block group in Woodbine, Iowa. Carbon attributes, temperature reduction, pollution removal and value, and runoff effects are calculated for each block group using i-Tree models (www.itreetools.org), local weather data, pollution data, EPA provided city boundary and land cover data, and U.S. Census derived block group boundary data. This dataset was produced by the US Forest Service to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

  4. ATLAS Probe: Exploring Frontiers in Galaxy Evolution, Cosmology, and Milky Way Science

    NASA Astrophysics Data System (ADS)

    Wang, Yun; Robberto, Massimo; Dickinson, Mark; Ferguson, Henry C.; Hillenbrand, Lynne; Hirata, Christopher M.; Cimatti, Andrea; Bartlett, James; Barkhouser, Robert; Benjamin, Robert A.; Brinchmann, Jarle; Chary, Ranga-Ram; Conroy, Charlie; Daddi, Emanuele; Donahue, Megan; Dore, Olivier; Eisenhardt, Peter; Fraser, Wesley C.; Helou, George; Kirkpatrick, J. Davy; Malhotra, Sangeeta; Moscardini, Lauro; Ninkov, Zoran; Ressler, Michael; Rhoads, James; Rhodes, Jason; Shapley, Alice; Smee, Stephen; ATLAS Probe Team

    2018-01-01

    ATLAS (Astrophysics Telescope for Large Area Spectroscopy) Probe is a concept for a NASA probe-class space mission that leverages WFIRST imaging for targeted spectroscopy. ATLAS Probe will obtain spectra of 90% of all galaxies imaged by the WFIRST High Latitude Survey at z > 0.5, with slit spectra of 300 million galaxies to z = 7. ATLAS Probe and WFIRST together will produce a 3D map of the Universe with Mpc resolution over 2200 sq deg, the definitive data sets for studying galaxy evolution, probing dark matter, dark energy and modification of general relativity, and quantifying the 3D structure and stellar content of the Milky Way.ATLAS Probe science spans four broad categories: (1) Revolutionize galaxy evolution studies by tracing the relation between galaxies and dark matter from the local group to cosmic voids and filaments, from the epoch of reionization through the peak era of galaxy assembly. (2) Open a new window into the Universe by mapping the dark matter filaments using 3D weak lensing with spectroscopic redshifts to unveil the nature of the dark Universe, and obtaining definitive measurements of dark energy and possible modification of general relativity using cosmic large-scale structure. (3) Probe the Milky Way's dust-shrouded regions, reaching the far side of our Galaxy. (4) Characterize asteroids and comets in the outer Solar System.ATLAS Probe is a 1.5m telescope with a field of view (FoV) of 0.4 sq deg, and uses Digital Micromirror Devices (DMDs) as slit selectors. It has a spectroscopic resolution of R = 600, and a wavelength range of 1-4μm. The lack of slit spectroscopy from space over a wide FoV is the obvious gap in current and planned future space missions; ATLAS fills this big gap with an unprecedented spectroscopic capability (with an estimated spectroscopic multiplex factor of 5000-10000). It has an estimated cost under $1B, with a single instrument, a telescope aperture that allows for a lighter launch vehicle, and mature technology

  5. Using EnviroAtlas Data to Identify Cost-Effective Locations for ...

    EPA Pesticide Factsheets

    Manure Management Use Case A use case walks through an example application of how EnviroAtlas data, in conjunction with other available data or resources, may be used to address real-world questions. Use cases may be hypothetical or based on actual cases where EnviroAtlas has been used in decision making at the local, regional or national scale. The use case is a deliverable for the EnviroAtlas project under the SHC 1.62 and will be incorporated on the EnviroAtlas website.

  6. MERCURY-ATLAS (MA)-2 - LIFTOFF - CAPE

    NASA Image and Video Library

    1961-02-21

    S61-01226 (21 Feb. 1961) --- Launch of the unmanned Mercury-Atlas 2 (MA-2) vehicle for a suborbital test flight of the Mercury capsule. The upper part of Atlas is stengthened by an eight-inch wide stainless steel band. The capsule was recovered less than one hour after launch. The altitude was 108 miles. Speed was 13,000 mph. Recovered 1,425 miles downrange. Photo credit: NASA

  7. Bird atlasing in the United States

    USGS Publications Warehouse

    Robbins, C.S.

    1977-01-01

    Since the Breeding Bird Survey provides an annual quantitative sample of about 75% of the 1? blocks of latitude and longitude in every state except Alaska and Hawaii, and 47% of the 1/2? blocks (equivalent on the average to a 48 km square), no national Atlas based on merely presence or absence has been contemplated. Conventional atlases are in progress in the states of Maryland (2.5 km), Massaohusetts (5 km) and Vermont (5 km) and in parts of 3 other states. Quantitative studies including mapping have been published for North Dakota (10 km) and are in progress in Wyoming (1?). A Montana project (1?) is continuing.

  8. Thermal Design, Tvac Testing, and Lessons Learned for Critical GSE of ATLAS and the ICESat-2 Mission

    NASA Technical Reports Server (NTRS)

    Bradshaw, Heather

    2016-01-01

    This presentation describes the thermal design of the three main of optical components which comprise the Bench Checkout Equipment (BCE) for the Advanced Topographic Laser Altimeter System (ATLAS) instrument, which is flying on the ICESat-2 mission. Thermal vacuum testing of these components is also described in this presentation, as well as a few lessons learned. These BCE components serve as critical GSE for the mission; their purpose is to verify ATLAS is performing well. It has been said that, in one light, the BCE is the most important part of ATLAS, since, without it, ATLAS cannot be aligned properly or its performance verified before flight. Therefore, careful attention was paid to the BCEs thermal design, development, and component-level Tvac testing prior to its use in instrument-level and spacecraft-level Tvac tests with ATLAS. This presentation describes that thermal design, development, and testing, as well as a few lessons learned.

  9. AGIS: Evolution of Distributed Computing information system for ATLAS

    NASA Astrophysics Data System (ADS)

    Anisenkov, A.; Di Girolamo, A.; Alandes, M.; Karavakis, E.

    2015-12-01

    ATLAS, a particle physics experiment at the Large Hadron Collider at CERN, produces petabytes of data annually through simulation production and tens of petabytes of data per year from the detector itself. The ATLAS computing model embraces the Grid paradigm and a high degree of decentralization of computing resources in order to meet the ATLAS requirements of petabytes scale data operations. It has been evolved after the first period of LHC data taking (Run-1) in order to cope with new challenges of the upcoming Run- 2. In this paper we describe the evolution and recent developments of the ATLAS Grid Information System (AGIS), developed in order to integrate configuration and status information about resources, services and topology of the computing infrastructure used by the ATLAS Distributed Computing applications and services.

  10. Performance of the ATLAS Transition Radiation Tracker in Run 1 of the LHC: tracker properties

    DOE PAGES

    Aaboud, M.; Aad, G.; Abbott, B.; ...

    2017-05-03

    The tracking performance parameters of the ATLAS Transition Radiation Tracker (TRT) as part of the ATLAS inner detector are described in this paper for different data-taking conditions in proton-proton, proton-lead and lead-lead collisions at the Large Hadron Collider (LHC). The performance is studied using data collected during the first period of LHC operation (Run 1) and is compared with Monte Carlo simulations. The performance of the TRT, operating with two different gas mixtures (xenon-based and argon-based) and its dependence on the TRT occupancy is presented. Furthermore, these studies show that the tracking performance of the TRT is similar for themore » two gas mixtures and that a significant contribution to the particle momentum resolution is made by the TRT up to high particle densities.« less

  11. Multi-Atlas Segmentation using Partially Annotated Data: Methods and Annotation Strategies.

    PubMed

    Koch, Lisa M; Rajchl, Martin; Bai, Wenjia; Baumgartner, Christian F; Tong, Tong; Passerat-Palmbach, Jonathan; Aljabar, Paul; Rueckert, Daniel

    2017-08-22

    Multi-atlas segmentation is a widely used tool in medical image analysis, providing robust and accurate results by learning from annotated atlas datasets. However, the availability of fully annotated atlas images for training is limited due to the time required for the labelling task. Segmentation methods requiring only a proportion of each atlas image to be labelled could therefore reduce the workload on expert raters tasked with annotating atlas images. To address this issue, we first re-examine the labelling problem common in many existing approaches and formulate its solution in terms of a Markov Random Field energy minimisation problem on a graph connecting atlases and the target image. This provides a unifying framework for multi-atlas segmentation. We then show how modifications in the graph configuration of the proposed framework enable the use of partially annotated atlas images and investigate different partial annotation strategies. The proposed method was evaluated on two Magnetic Resonance Imaging (MRI) datasets for hippocampal and cardiac segmentation. Experiments were performed aimed at (1) recreating existing segmentation techniques with the proposed framework and (2) demonstrating the potential of employing sparsely annotated atlas data for multi-atlas segmentation.

  12. An integrated expression atlas of miRNAs and their promoters in human and mouse

    PubMed Central

    de Rie, Derek; Abugessaisa, Imad; Alam, Tanvir; Arner, Erik; Arner, Peter; Ashoor, Haitham; Åström, Gaby; Babina, Magda; Bertin, Nicolas; Burroughs, A. Maxwell; Carlisle, Ailsa J.; Daub, Carsten O.; Detmar, Michael; Deviatiiarov, Ruslan; Fort, Alexandre; Gebhard, Claudia; Goldowitz, Daniel; Guhl, Sven; Ha, Thomas J.; Harshbarger, Jayson; Hasegawa, Akira; Hashimoto, Kosuke; Herlyn, Meenhard; Heutink, Peter; Hitchens, Kelly J.; Hon, Chung Chau; Huang, Edward; Ishizu, Yuri; Kai, Chieko; Kasukawa, Takeya; Klinken, Peter; Lassmann, Timo; Lecellier, Charles-Henri; Lee, Weonju; Lizio, Marina; Makeev, Vsevolod; Mathelier, Anthony; Medvedeva, Yulia A.; Mejhert, Niklas; Mungall, Christopher J.; Noma, Shohei; Ohshima, Mitsuhiro; Okada-Hatakeyama, Mariko; Persson, Helena; Rizzu, Patrizia; Roudnicky, Filip; Sætrom, Pål; Sato, Hiroki; Severin, Jessica; Shin, Jay W.; Swoboda, Rolf K.; Tarui, Hiroshi; Toyoda, Hiroo; Vitting-Seerup, Kristoffer; Winteringham, Louise; Yamaguchi, Yoko; Yasuzawa, Kayoko; Yoneda, Misako; Yumoto, Noriko; Zabierowski, Susan; Zhang, Peter G.; Wells, Christine A.; Summers, Kim M.; Kawaji, Hideya; Sandelin, Albin; Rehli, Michael; Hayashizaki, Yoshihide; Carninci, Piero; Forrest, Alistair R. R.; de Hoon, Michiel J. L.

    2018-01-01

    MicroRNAs (miRNAs) are short non-coding RNAs with key roles in cellular regulation. As part of the fifth edition of the Functional Annotation of Mammalian Genome (FANTOM5) project, we created an integrated expression atlas of miRNAs and their promoters by deep-sequencing 492 short RNA (sRNA) libraries, with matching Cap Analysis Gene Expression (CAGE) data, from 396 human and 47 mouse RNA samples. Promoters were identified for 1,357 human and 804 mouse miRNAs and showed strong sequence conservation between species. We also found that primary and mature miRNA expression levels were correlated, allowing us to use the primary miRNA measurements as a proxy for mature miRNA levels in a total of 1,829 human and 1,029 mouse CAGE libraries. We thus provide a broad atlas of miRNA expression and promoters in primary mammalian cells, establishing a foundation for detailed analysis of miRNA expression patterns and transcriptional control regions. PMID:28829439

  13. EnviroAtlas - Metrics for Austin, TX

    EPA Pesticide Factsheets

    This EnviroAtlas web service supports research and online mapping activities related to EnviroAtlas (https://enviroatlas.epa.gov/EnviroAtlas). The layers in this web service depict ecosystem services at the census block group level for the community of Austin, Texas. These layers illustrate the ecosystems and natural resources that are associated with clean air (https://enviroatlas.epa.gov/arcgis/rest/services/Communities/ESC_ATX_CleanAir/MapServer); clean and plentiful water (https://enviroatlas.epa.gov/arcgis/rest/services/Communities/ESC_ATX_CleanPlentifulWater/MapServer); natural hazard mitigation (https://enviroatlas.epa.gov/arcgis/rest/services/Communities/ESC_ATX_NaturalHazardMitigation/MapServer); climate stabilization (https://enviroatlas.epa.gov/arcgis/rest/services/Communities/ESC_ATX_ClimateStabilization/MapServer); food, fuel, and materials (https://enviroatlas.epa.gov/arcgis/rest/services/Communities/ESC_ATX_FoodFuelMaterials/MapServer); recreation, culture, and aesthetics (https://enviroatlas.epa.gov/arcgis/rest/services/Communities/ESC_ATX_RecreationCultureAesthetics/MapServer); and biodiversity conservation (https://enviroatlas.epa.gov/arcgis/rest/services/Communities/ESC_ATX_BiodiversityConservation/MapServer), and factors that place stress on those resources. EnviroAtlas allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the conterminous United States as well as de

  14. Launch mission summary: INTELSAT 5(F1) ATLAS/CENTAUR-56

    NASA Technical Reports Server (NTRS)

    1981-01-01

    The technology and capability of the INTELSAT 5 series satellites and the Atlas-Centaur launch vehicle are described. Data relative to launch windows, flight plans, radar, and telemetry are included along with selected trajectory information and a sequence of flight events.

  15. Subcortical structure segmentation using probabilistic atlas priors

    NASA Astrophysics Data System (ADS)

    Gouttard, Sylvain; Styner, Martin; Joshi, Sarang; Smith, Rachel G.; Cody Hazlett, Heather; Gerig, Guido

    2007-03-01

    The segmentation of the subcortical structures of the brain is required for many forms of quantitative neuroanatomic analysis. The volumetric and shape parameters of structures such as lateral ventricles, putamen, caudate, hippocampus, pallidus and amygdala are employed to characterize a disease or its evolution. This paper presents a fully automatic segmentation of these structures via a non-rigid registration of a probabilistic atlas prior and alongside a comprehensive validation. Our approach is based on an unbiased diffeomorphic atlas with probabilistic spatial priors built from a training set of MR images with corresponding manual segmentations. The atlas building computes an average image along with transformation fields mapping each training case to the average image. These transformation fields are applied to the manually segmented structures of each case in order to obtain a probabilistic map on the atlas. When applying the atlas for automatic structural segmentation, an MR image is first intensity inhomogeneity corrected, skull stripped and intensity calibrated to the atlas. Then the atlas image is registered to the image using an affine followed by a deformable registration matching the gray level intensity. Finally, the registration transformation is applied to the probabilistic maps of each structures, which are then thresholded at 0.5 probability. Using manual segmentations for comparison, measures of volumetric differences show high correlation with our results. Furthermore, the dice coefficient, which quantifies the volumetric overlap, is higher than 62% for all structures and is close to 80% for basal ganglia. The intraclass correlation coefficient computed on these same datasets shows a good inter-method correlation of the volumetric measurements. Using a dataset of a single patient scanned 10 times on 5 different scanners, reliability is shown with a coefficient of variance of less than 2 percents over the whole dataset. Overall, these validation

  16. ATLAS I/O performance optimization in as-deployed environments

    NASA Astrophysics Data System (ADS)

    Maier, T.; Benjamin, D.; Bhimji, W.; Elmsheuser, J.; van Gemmeren, P.; Malon, D.; Krumnack, N.

    2015-12-01

    This paper provides an overview of an integrated program of work underway within the ATLAS experiment to optimise I/O performance for large-scale physics data analysis in a range of deployment environments. It proceeds to examine in greater detail one component of that work, the tuning of job-level I/O parameters in response to changes to the ATLAS event data model, and considers the implications of such tuning for a number of measures of I/O performance.

  17. Toward the holistic, reference, and extendable atlas of the human brain, head, and neck.

    PubMed

    Nowinski, Wieslaw L

    2015-06-01

    Despite numerous efforts, a fairly complete (holistic) anatomical model of the whole, normal, adult human brain, which is required as the reference in brain studies and clinical applications, has not yet been constructed. Our ultimate objective is to build this kind of atlas from advanced in vivo imaging. This work presents the taxonomy of our currently developed brain atlases and addresses the design, content, functionality, and current results in the holistic atlas development as well as atlas usefulness and future directions. We have developed to date 35 commercial brain atlases (along with numerous research prototypes), licensed to 63 companies and institutions, and made available to medical societies, organizations, medical schools, and individuals. These atlases have been applied in education, research, and clinical applications. Hundreds of thousands of patients have been treated by using our atlases. Based on this experience, the first version of the holistic and reference atlas of the brain, head, and neck has been developed and made available. The atlas has been created from multispectral 3 and 7 Tesla and high-resolution CT in vivo scans. It is fully 3D, scalable, interactive, and highly detailed with about 3,000 labeled components. This atlas forms a foundation for the development of a multi-level molecular, cellular, anatomical, physiological, and behavioral brain atlas platform.

  18. Atlas Basemaps in Web 2.0 Epoch

    NASA Astrophysics Data System (ADS)

    Chabaniuk, V.; Dyshlyk, O.

    2016-06-01

    The authors have analyzed their experience of the production of various Electronic Atlases (EA) and Atlas Information Systems (AtIS) of so-called "classical type". These EA/AtIS have been implemented in the past decade in the Web 1.0 architecture (e.g., National Atlas of Ukraine, Atlas of radioactive contamination of Ukraine, and others). One of the main distinguishing features of these atlases was their static nature - the end user could not change the content of EA/AtIS. Base maps are very important element of any EA/AtIS. In classical type EA/AtIS they were static datasets, which consisted of two parts: the topographic data of a fixed scale and data of the administrative-territorial division of Ukraine. It is important to note that the technique of topographic data production was based on the use of direct channels of topographic entity observation (such as aerial photography) for the selected scale. Changes in the information technology of the past half-decade are characterized by the advent of the "Web 2.0 epoch". Due to this, in cartography appeared such phenomena as, for example, "neo-cartography" and various mapping platforms like OpenStreetMap. These changes have forced developers of EA/AtIS to use new atlas basemaps. Our approach is described in the article. The phenomenon of neo-cartography and/or Web 2.0 cartography are analysed by authors using previously developed Conceptual framework of EA/AtIS. This framework logically explains the cartographic phenomena relations of three formations: Web 1.0, Web 1.0x1.0 and Web 2.0. Atlas basemaps of the Web 2.0 epoch are integrated information systems. We use several ways to integrate separate atlas basemaps into the information system - by building: weak integrated information system, structured system and meta-system. This integrated information system consists of several basemaps and falls under the definition of "big data". In real projects it is already used the basemaps of three strata: Conceptual

  19. SU-E-J-129: Atlas Development for Cardiac Automatic Contouring Using Multi-Atlas Segmentation

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Zhou, R; Yang, J; Pan, T

    Purpose: To develop a set of atlases for automatic contouring of cardiac structures to determine heart radiation dose and the associated toxicity. Methods: Six thoracic cancer patients with both contrast and non-contrast CT images were acquired for this study. Eight radiation oncologists manually and independently delineated cardiac contours on the non-contrast CT by referring to the fused contrast CT and following the RTOG 1106 atlas contouring guideline. Fifteen regions of interest (ROIs) were delineated, including heart, four chambers, four coronary arteries, pulmonary artery and vein, inferior and superior vena cava, and ascending and descending aorta. Individual expert contours were fusedmore » using the simultaneous truth and performance level estimation (STAPLE) algorithm for each ROI and each patient. The fused contours became atlases for an in-house multi-atlas segmentation. Using leave-one-out test, we generated auto-segmented contours for each ROI and each patient. The auto-segmented contours were compared with the fused contours using the Dice similarity coefficient (DSC) and the mean surface distance (MSD). Results: Inter-observer variability was not obvious for heart, chambers, and aorta but was large for other structures that were not clearly distinguishable on CT image. The average DSC between individual expert contours and the fused contours were less than 50% for coronary arteries and pulmonary vein, and the average MSD were greater than 4.0 mm. The largest MSD of expert contours deviating from the fused contours was 2.5 cm. The mean DSC and MSD of auto-segmented contours were within one standard deviation of expert contouring variability except the right coronary artery. The coronary arteries, vena cava, and pulmonary vein had DSC<70% and MSD>3.0 mm. Conclusion: A set of cardiac atlases was created for cardiac automatic contouring, the accuracy of which was comparable to the variability in expert contouring. However, substantial modification

  20. Estimate of the neutron fields in ATLAS based on ATLAS-MPX detectors data

    NASA Astrophysics Data System (ADS)

    Bouchami, J.; Dallaire, F.; Gutiérrez, A.; Idarraga, J.; Král, V.; Leroy, C.; Picard, S.; Pospíšil, S.; Scallon, O.; Solc, J.; Suk, M.; Turecek, D.; Vykydal, Z.; Žemlièka, J.

    2011-01-01

    The ATLAS-MPX detectors are based on Medipix2 silicon devices designed by CERN for the detection of different types of radiation. These detectors are covered with converting layers of 6LiF and polyethylene (PE) to increase their sensitivity to thermal and fast neutrons, respectively. These devices allow the measurement of the composition and spectroscopic characteristics of the radiation field in ATLAS, particularly of neutrons. These detectors can operate in low or high preset energy threshold mode. The signature of particles interacting in a ATLAS-MPX detector at low threshold are clusters of adjacent pixels with different size and form depending on their type, energy and incidence angle. The classification of particles into different categories can be done using the geometrical parameters of these clusters. The Medipix analysis framework (MAFalda) — based on the ROOT application — allows the recognition of particle tracks left in ATLAS-MPX devices located at various positions in the ATLAS detector and cavern. The pattern recognition obtained from the application of MAFalda was configured to distinguish the response of neutrons from other radiation. The neutron response at low threshold is characterized by clusters of adjoining pixels (heavy tracks and heavy blobs) left by protons and heavy ions resulting from neutron interactions in the converting layers of the ATLAS-MPX devices. The neutron detection efficiency of ATLAS-MPX devices has been determined by the exposure of two detectors of reference to radionuclide sources of neutrons (252Cf and 241AmBe). With these results, an estimate of the neutrons fields produced at the devices locations during ATLAS operation was done.

  1. The performance of the jet trigger for the ATLAS detector during 2011 data taking

    NASA Astrophysics Data System (ADS)

    Aad, G.; Abbott, B.; Abdallah, J.; Abdinov, O.; Abeloos, B.; Aben, R.; Abolins, M.; AbouZeid, O. S.; Abraham, N. L.; Abramowicz, H.; Abreu, H.; Abreu, R.; Abulaiti, Y.; Acharya, B. S.; Adamczyk, L.; Adams, D. L.; Adelman, J.; Adomeit, S.; Adye, T.; Affolder, A. A.; Agatonovic-Jovin, T.; Agricola, J.; Aguilar-Saavedra, J. A.; Ahlen, S. P.; Ahmadov, F.; Aielli, G.; Akerstedt, H.; Åkesson, T. P. A.; Akimov, A. V.; Alberghi, G. L.; Albert, J.; Albrand, S.; Verzini, M. J. Alconada; Aleksa, M.; Aleksandrov, I. N.; Alexa, C.; Alexander, G.; Alexopoulos, T.; Alhroob, M.; Aliev, M.; Alimonti, G.; Alison, J.; Alkire, S. P.; Allbrooke, B. M. M.; Allen, B. W.; Allport, P. P.; Aloisio, A.; Alonso, A.; Alonso, F.; Alpigiani, C.; Gonzalez, B. Alvarez; Piqueras, D. Álvarez; Alviggi, M. G.; Amadio, B. T.; Amako, K.; Coutinho, Y. Amaral; Amelung, C.; Amidei, D.; Santos, S. P. Amor Dos; Amorim, A.; Amoroso, S.; Amram, N.; Amundsen, G.; Anastopoulos, C.; Ancu, L. S.; Andari, N.; Andeen, T.; Anders, C. F.; Anders, G.; Anders, J. K.; Anderson, K. J.; Andreazza, A.; Andrei, V.; Angelidakis, S.; Angelozzi, I.; Anger, P.; Angerami, A.; Anghinolfi, F.; Anisenkov, A. V.; Anjos, N.; Annovi, A.; Antonelli, M.; Antonov, A.; Antos, J.; Anulli, F.; Aoki, M.; Bella, L. Aperio; Arabidze, G.; Arai, Y.; Araque, J. P.; Arce, A. T. H.; Arduh, F. A.; Arguin, J.-F.; Argyropoulos, S.; Arik, M.; Armbruster, A. J.; Armitage, L. J.; Arnaez, O.; Arnold, H.; Arratia, M.; Arslan, O.; Artamonov, A.; Artoni, G.; Artz, S.; Asai, S.; Asbah, N.; Ashkenazi, A.; Åsman, B.; Asquith, L.; Assamagan, K.; Astalos, R.; Atkinson, M.; Atlay, N. B.; Augsten, K.; Avolio, G.; Axen, B.; Ayoub, M. K.; Azuelos, G.; Baak, M. A.; Baas, A. E.; Baca, M. J.; Bachacou, H.; Bachas, K.; Backes, M.; Backhaus, M.; Bagiacchi, P.; Bagnaia, P.; Bai, Y.; Baines, J. T.; Baker, O. K.; Baldin, E. M.; Balek, P.; Balestri, T.; Balli, F.; Balunas, W. K.; Banas, E.; Banerjee, Sw.; Bannoura, A. A. E.; Barak, L.; Barberio, E. L.; Barberis, D.; Barbero, M.; Barillari, T.; Barklow, T.; Barlow, N.; Barnes, S. L.; Barnett, B. M.; Barnett, R. M.; Barnovska, Z.; Baroncelli, A.; Barone, G.; Barr, A. J.; Navarro, L. Barranco; Barreiro, F.; da Costa, J. Barreiro Guimarães; Bartoldus, R.; Barton, A. E.; Bartos, P.; Basalaev, A.; Bassalat, A.; Basye, A.; Bates, R. L.; Batista, S. J.; Batley, J. R.; Battaglia, M.; Bauce, M.; Bauer, F.; Bawa, H. S.; Beacham, J. B.; Beattie, M. D.; Beau, T.; Beauchemin, P. H.; Bechtle, P.; Beck, H. P.; Becker, K.; Becker, M.; Beckingham, M.; Becot, C.; Beddall, A. J.; Beddall, A.; Bednyakov, V. A.; Bedognetti, M.; Bee, C. P.; Beemster, L. J.; Beermann, T. A.; Begel, M.; Behr, J. K.; Belanger-Champagne, C.; Bell, A. S.; Bella, G.; Bellagamba, L.; Bellerive, A.; Bellomo, M.; Belotskiy, K.; Beltramello, O.; Belyaev, N. L.; Benary, O.; Benchekroun, D.; Bender, M.; Bendtz, K.; Benekos, N.; Benhammou, Y.; Noccioli, E. Benhar; Benitez, J.; Garcia, J. A. Benitez; Benjamin, D. P.; Bensinger, J. R.; Bentvelsen, S.; Beresford, L.; Beretta, M.; Berge, D.; Kuutmann, E. Bergeaas; Berger, N.; Berghaus, F.; Beringer, J.; Berlendis, S.; Bernard, N. R.; Bernius, C.; Bernlochner, F. U.; Berry, T.; Berta, P.; Bertella, C.; Bertoli, G.; Bertolucci, F.; Bertram, I. A.; Bertsche, C.; Bertsche, D.; Besjes, G. J.; Bylund, O. Bessidskaia; Bessner, M.; Besson, N.; Betancourt, C.; Bethke, S.; Bevan, A. J.; Bhimji, W.; Bianchi, R. M.; Bianchini, L.; Bianco, M.; Biebel, O.; Biedermann, D.; Bielski, R.; Biesuz, N. V.; Biglietti, M.; De Mendizabal, J. Bilbao; Bilokon, H.; Bindi, M.; Binet, S.; Bingul, A.; Bini, C.; Biondi, S.; Bjergaard, D. M.; Black, C. W.; Black, J. E.; Black, K. M.; Blackburn, D.; Blair, R. E.; Blanchard, J.-B.; Blanco, J. E.; Blazek, T.; Bloch, I.; Blocker, C.; Blum, W.; Blumenschein, U.; Blunier, S.; Bobbink, G. J.; Bobrovnikov, V. S.; Bocchetta, S. S.; Bocci, A.; Bock, C.; Boehler, M.; Boerner, D.; Bogaerts, J. A.; Bogavac, D.; Bogdanchikov, A. G.; Bohm, C.; Boisvert, V.; Bold, T.; Boldea, V.; Boldyrev, A. S.; Bomben, M.; Bona, M.; Boonekamp, M.; Borisov, A.; Borissov, G.; Bortfeldt, J.; Bortoletto, D.; Bortolotto, V.; Bos, K.; Boscherini, D.; Bosman, M.; Sola, J. D. Bossio; Boudreau, J.; Bouffard, J.; Bouhova-Thacker, E. V.; Boumediene, D.; Bourdarios, C.; Boutle, S. K.; Boveia, A.; Boyd, J.; Boyko, I. R.; Bracinik, J.; Brandt, A.; Brandt, G.; Brandt, O.; Bratzler, U.; Brau, B.; Brau, J. E.; Braun, H. M.; Madden, W. D. Breaden; Brendlinger, K.; Brennan, A. J.; Brenner, L.; Brenner, R.; Bressler, S.; Bristow, T. M.; Britton, D.; Britzger, D.; Brochu, F. M.; Brock, I.; Brock, R.; Brooijmans, G.; Brooks, T.; Brooks, W. K.; Brosamer, J.; Brost, E.; Broughton, J. H.; de Renstrom, P. A. Bruckman; Bruncko, D.; Bruneliere, R.; Bruni, A.; Bruni, G.; Brunt, BH; Bruschi, M.; Bruscino, N.; Bryant, P.; Bryngemark, L.; Buanes, T.; Buat, Q.; Buchholz, P.; Buckley, A. G.; Budagov, I. A.; Buehrer, F.; Bugge, M. K.; Bulekov, O.; Bullock, D.; Burckhart, H.; Burdin, S.; Burgard, C. D.; Burghgrave, B.; Burka, K.; Burke, S.; Burmeister, I.; Busato, E.; Büscher, D.; Büscher, V.; Bussey, P.; Butler, J. M.; Butt, A. I.; Buttar, C. M.; Butterworth, J. M.; Butti, P.; Buttinger, W.; Buzatu, A.; Buzykaev, A. R.; Urbán, S. Cabrera; Caforio, D.; Cairo, V. M.; Cakir, O.; Calace, N.; Calafiura, P.; Calandri, A.; Calderini, G.; Calfayan, P.; Caloba, L. P.; Calvet, D.; Calvet, S.; Calvet, T. P.; Toro, R. Camacho; Camarda, S.; Camarri, P.; Cameron, D.; Armadans, R. Caminal; Camincher, C.; Campana, S.; Campanelli, M.; Campoverde, A.; Canale, V.; Canepa, A.; Bret, M. Cano; Cantero, J.; Cantrill, R.; Cao, T.; Garrido, M. D. M. Capeans; Caprini, I.; Caprini, M.; Capua, M.; Caputo, R.; Carbone, R. M.; Cardarelli, R.; Cardillo, F.; Carli, I.; Carli, T.; Carlino, G.; Carminati, L.; Caron, S.; Carquin, E.; Carrillo-Montoya, G. D.; Carter, J. R.; Carvalho, J.; Casadei, D.; Casado, M. P.; Casolino, M.; Casper, D. W.; Castaneda-Miranda, E.; Castelli, A.; Gimenez, V. Castillo; Castro, N. F.; Catinaccio, A.; Catmore, J. R.; Cattai, A.; Caudron, J.; Cavaliere, V.; Cavallaro, E.; Cavalli, D.; Cavalli-Sforza, M.; Cavasinni, V.; Ceradini, F.; Alberich, L. Cerda; Cerio, B. C.; Cerqueira, A. S.; Cerri, A.; Cerrito, L.; Cerutti, F.; Cerv, M.; Cervelli, A.; Cetin, S. A.; Chafaq, A.; Chakraborty, D.; Chan, S. K.; Chan, Y. L.; Chang, P.; Chapman, J. D.; Charlton, D. G.; Chatterjee, A.; Chau, C. C.; Barajas, C. A. Chavez; Che, S.; Cheatham, S.; Chegwidden, A.; Chekanov, S.; Chekulaev, S. V.; Chelkov, G. A.; Chelstowska, M. A.; Chen, C.; Chen, H.; Chen, K.; Chen, S.; Chen, S.; Chen, X.; Chen, Y.; Cheng, H. C.; Cheng, H. J.; Cheng, Y.; Cheplakov, A.; Cheremushkina, E.; Moursli, R. Cherkaoui El; Chernyatin, V.; Cheu, E.; Chevalier, L.; Chiarella, V.; Chiarelli, G.; Chiodini, G.; Chisholm, A. S.; Chitan, A.; Chizhov, M. V.; Choi, K.; Chomont, A. R.; Chouridou, S.; Chow, B. K. B.; Christodoulou, V.; Chromek-Burckhart, D.; Chudoba, J.; Chuinard, A. J.; Chwastowski, J. J.; Chytka, L.; Ciapetti, G.; Ciftci, A. K.; Cinca, D.; Cindro, V.; Cioara, I. A.; Ciocio, A.; Cirotto, F.; Citron, Z. H.; Ciubancan, M.; Clark, A.; Clark, B. L.; Clark, M. R.; Clark, P. J.; Clarke, R. N.; Clement, C.; Coadou, Y.; Cobal, M.; Coccaro, A.; Cochran, J.; Coffey, L.; Colasurdo, L.; Cole, B.; Cole, S.; Colijn, A. P.; Collot, J.; Colombo, T.; Compostella, G.; Muiño, P. Conde; Coniavitis, E.; Connell, S. H.; Connelly, I. A.; Consorti, V.; Constantinescu, S.; Conta, C.; Conti, G.; Conventi, F.; Cooke, M.; Cooper, B. D.; Cooper-Sarkar, A. M.; Cornelissen, T.; Corradi, M.; Corriveau, F.; Corso-Radu, A.; Cortes-Gonzalez, A.; Cortiana, G.; Costa, G.; Costa, M. J.; Costanzo, D.; Cottin, G.; Cowan, G.; Cox, B. E.; Cranmer, K.; Crawley, S. J.; Cree, G.; Crépé-Renaudin, S.; Crescioli, F.; Cribbs, W. A.; Ortuzar, M. Crispin; Cristinziani, M.; Croft, V.; Crosetti, G.; Donszelmann, T. Cuhadar; Cummings, J.; Curatolo, M.; Cúth, J.; Cuthbert, C.; Czirr, H.; Czodrowski, P.; D'Auria, S.; D'Onofrio, M.; De Sousa, M. J. Da Cunha Sargedas; Via, C. Da; Dabrowski, W.; Dai, T.; Dale, O.; Dallaire, F.; Dallapiccola, C.; Dam, M.; Dandoy, J. R.; Dang, N. P.; Daniells, A. C.; Dann, N. S.; Danninger, M.; Hoffmann, M. Dano; Dao, V.; Darbo, G.; Darmora, S.; Dassoulas, J.; Dattagupta, A.; Davey, W.; David, C.; Davidek, T.; Davies, M.; Davison, P.; Davygora, Y.; Dawe, E.; Dawson, I.; Daya-Ishmukhametova, R. K.; De, K.; de Asmundis, R.; De Benedetti, A.; De Castro, S.; De Cecco, S.; De Groot, N.; de Jong, P.; De la Torre, H.; De Lorenzi, F.; De Pedis, D.; De Salvo, A.; De Sanctis, U.; De Santo, A.; De Regie, J. B. De Vivie; Dearnaley, W. J.; Debbe, R.; Debenedetti, C.; Dedovich, D. V.; Deigaard, I.; Del Peso, J.; Del Prete, T.; Delgove, D.; Deliot, F.; Delitzsch, C. M.; Deliyergiyev, M.; Dell'Acqua, A.; Dell'Asta, L.; Dell'Orso, M.; Della Pietra, M.; della Volpe, D.; Delmastro, M.; Delsart, P. A.; Deluca, C.; DeMarco, D. A.; Demers, S.; Demichev, M.; Demilly, A.; Denisov, S. P.; Denysiuk, D.; Derendarz, D.; Derkaoui, J. E.; Derue, F.; Dervan, P.; Desch, K.; Deterre, C.; Dette, K.; Deviveiros, P. O.; Dewhurst, A.; Dhaliwal, S.; Di Ciaccio, A.; Di Ciaccio, L.; Di Clemente, W. K.; Di Donato, C.; Di Girolamo, A.; Di Girolamo, B.; Di Micco, B.; Di Nardo, R.; Di Simone, A.; Di Sipio, R.; Di Valentino, D.; Diaconu, C.; Diamond, M.; Dias, F. A.; Diaz, M. A.; Diehl, E. B.; Dietrich, J.; Diglio, S.; Dimitrievska, A.; Dingfelder, J.; Dita, P.; Dita, S.; Dittus, F.; Djama, F.; Djobava, T.; Djuvsland, J. I.; do Vale, M. A. B.; Dobos, D.; Dobre, M.; Doglioni, C.; Dohmae, T.; Dolejsi, J.; Dolezal, Z.; Dolgoshein, B. A.; Donadelli, M.; Donati, S.; Dondero, P.; Donini, J.; Dopke, J.; Doria, A.; Dova, M. T.; Doyle, A. T.; Drechsler, E.; Dris, M.; Du, Y.; Duarte-Campderros, J.; Duchovni, E.; Duckeck, G.; Ducu, O. A.; Duda, D.; Dudarev, A.; Duflot, L.; Duguid, L.; Dührssen, M.; Dunford, M.; Yildiz, H. Duran; Düren, M.; Durglishvili, A.; Duschinger, D.; Dutta, B.; Dyndal, M.; Eckardt, C.; Ecker, K. M.; Edgar, R. C.; Edson, W.; Edwards, N. C.; Eifert, T.; Eigen, G.; Einsweiler, K.; Ekelof, T.; Kacimi, M. El; Ellajosyula, V.; Ellert, M.; Elles, S.; Ellinghaus, F.; Elliot, A. A.; Ellis, N.; Elmsheuser, J.; Elsing, M.; Emeliyanov, D.; Enari, Y.; Endner, O. C.; Endo, M.; Ennis, J. S.; Erdmann, J.; Ereditato, A.; Ernis, G.; Ernst, J.; Ernst, M.; Errede, S.; Ertel, E.; Escalier, M.; Esch, H.; Escobar, C.; Esposito, B.; Etienvre, A. I.; Etzion, E.; Evans, H.; Ezhilov, A.; Fabbri, F.; Fabbri, L.; Facini, G.; Fakhrutdinov, R. M.; Falciano, S.; Falla, R. J.; Faltova, J.; Fang, Y.; Fanti, M.; Farbin, A.; Farilla, A.; Farina, C.; Farooque, T.; Farrell, S.; Farrington, S. M.; Farthouat, P.; Fassi, F.; Fassnacht, P.; Fassouliotis, D.; Giannelli, M. Faucci; Favareto, A.; Fawcett, W. J.; Fayard, L.; Fedin, O. L.; Fedorko, W.; Feigl, S.; Feligioni, L.; Feng, C.; Feng, E. J.; Feng, H.; Fenyuk, A. B.; Feremenga, L.; Martinez, P. Fernandez; Perez, S. Fernandez; Ferrando, J.; Ferrari, A.; Ferrari, P.; Ferrari, R.; de Lima, D. E. Ferreira; Ferrer, A.; Ferrere, D.; Ferretti, C.; Parodi, A. Ferretto; Fiedler, F.; Filipčič, A.; Filipuzzi, M.; Filthaut, F.; Fincke-Keeler, M.; Finelli, K. D.; Fiolhais, M. C. N.; Fiorini, L.; Firan, A.; Fischer, A.; Fischer, C.; Fischer, J.; Fisher, W. C.; Flaschel, N.; Fleck, I.; Fleischmann, P.; Fletcher, G. T.; Fletcher, G.; Fletcher, R. R. M.; Flick, T.; Floderus, A.; Castillo, L. R. Flores; Flowerdew, M. J.; Forcolin, G. T.; Formica, A.; Forti, A.; Foster, A. G.; Fournier, D.; Fox, H.; Fracchia, S.; Francavilla, P.; Franchini, M.; Francis, D.; Franconi, L.; Franklin, M.; Frate, M.; Fraternali, M.; Freeborn, D.; Fressard-Batraneanu, S. M.; Friedrich, F.; Froidevaux, D.; Frost, J. A.; Fukunaga, C.; Torregrosa, E. Fullana; Fusayasu, T.; Fuster, J.; Gabaldon, C.; Gabizon, O.; Gabrielli, A.; Gabrielli, A.; Gach, G. P.; Gadatsch, S.; Gadomski, S.; Gagliardi, G.; Gagnon, L. G.; Gagnon, P.; Galea, C.; Galhardo, B.; Gallas, E. J.; Gallop, B. J.; Gallus, P.; Galster, G.; Gan, K. K.; Gao, J.; Gao, Y.; Gao, Y. S.; Walls, F. M. Garay; García, C.; Navarro, J. E. García; Garcia-Sciveres, M.; Gardner, R. W.; Garelli, N.; Garonne, V.; Bravo, A. Gascon; Gatti, C.; Gaudiello, A.; Gaudio, G.; Gaur, B.; Gauthier, L.; Gavrilenko, I. L.; Gay, C.; Gaycken, G.; Gazis, E. N.; Gecse, Z.; Gee, C. N. P.; Geich-Gimbel, Ch.; Geisler, M. P.; Gemme, C.; Genest, M. H.; Geng, C.; Gentile, S.; George, S.; Gerbaudo, D.; Gershon, A.; Ghasemi, S.; Ghazlane, H.; Ghneimat, M.; Giacobbe, B.; Giagu, S.; Giannetti, P.; Gibbard, B.; Gibson, S. M.; Gignac, M.; Gilchriese, M.; Gillam, T. P. S.; Gillberg, D.; Gilles, G.; Gingrich, D. M.; Giokaris, N.; Giordani, M. P.; Giorgi, F. M.; Giorgi, F. M.; Giraud, P. F.; Giromini, P.; Giugni, D.; Giuli, F.; Giuliani, C.; Giulini, M.; Gjelsten, B. K.; Gkaitatzis, S.; Gkialas, I.; Gkougkousis, E. L.; Gladilin, L. K.; Glasman, C.; Glatzer, J.; Glaysher, P. C. F.; Glazov, A.; Goblirsch-Kolb, M.; Godlewski, J.; Goldfarb, S.; Golling, T.; Golubkov, D.; Gomes, A.; Gonçalo, R.; Costa, J. Goncalves Pinto Firmino Da; Gonella, L.; Gongadze, A.; de la Hoz, S. González; Parra, G. Gonzalez; Gonzalez-Sevilla, S.; Goossens, L.; Gorbounov, P. A.; Gordon, H. A.; Gorelov, I.; Gorini, B.; Gorini, E.; Gorišek, A.; Gornicki, E.; Goshaw, A. T.; Gössling, C.; Gostkin, M. I.; Goudet, C. R.; Goujdami, D.; Goussiou, A. G.; Govender, N.; Gozani, E.; Graber, L.; Grabowska-Bold, I.; Gradin, P. O. J.; Grafström, P.; Gramling, J.; Gramstad, E.; Grancagnolo, S.; Gratchev, V.; Gray, H. M.; Graziani, E.; Greenwood, Z. D.; Grefe, C.; Gregersen, K.; Gregor, I. M.; Grenier, P.; Grevtsov, K.; Griffiths, J.; Grillo, A. A.; Grimm, K.; Grinstein, S.; Gris, Ph.; Grivaz, J.-F.; Groh, S.; Grohs, J. P.; Gross, E.; Grosse-Knetter, J.; Grossi, G. C.; Grout, Z. J.; Guan, L.; Guan, W.; Guenther, J.; Guescini, F.; Guest, D.; Gueta, O.; Guido, E.; Guillemin, T.; Guindon, S.; Gul, U.; Gumpert, C.; Guo, J.; Guo, Y.; Gupta, S.; Gustavino, G.; Gutierrez, P.; Ortiz, N. G. Gutierrez; Gutschow, C.; Guyot, C.; Gwenlan, C.; Gwilliam, C. B.; Haas, A.; Haber, C.; Hadavand, H. K.; Haddad, N.; Hadef, A.; Haefner, P.; Hageböck, S.; Hajduk, Z.; Hakobyan, H.; Haleem, M.; Haley, J.; Hall, D.; Halladjian, G.; Hallewell, G. D.; Hamacher, K.; Hamal, P.; Hamano, K.; Hamilton, A.; Hamity, G. N.; Hamnett, P. G.; Han, L.; Hanagaki, K.; Hanawa, K.; Hance, M.; Haney, B.; Hanke, P.; Hanna, R.; Hansen, J. B.; Hansen, J. D.; Hansen, M. C.; Hansen, P. H.; Hara, K.; Hard, A. S.; Harenberg, T.; Hariri, F.; Harkusha, S.; Harrington, R. D.; Harrison, P. F.; Hartjes, F.; Hasegawa, M.; Hasegawa, Y.; Hasib, A.; Hassani, S.; Haug, S.; Hauser, R.; Hauswald, L.; Havranek, M.; Hawkes, C. M.; Hawkings, R. J.; Hawkins, A. D.; Hayden, D.; Hays, C. P.; Hays, J. M.; Hayward, H. S.; Haywood, S. J.; Head, S. J.; Heck, T.; Hedberg, V.; Heelan, L.; Heim, S.; Heim, T.; Heinemann, B.; Heinrich, J. J.; Heinrich, L.; Heinz, C.; Hejbal, J.; Helary, L.; Hellman, S.; Helsens, C.; Henderson, J.; Henderson, R. C. W.; Heng, Y.; Henkelmann, S.; Correia, A. M. Henriques; Henrot-Versille, S.; Herbert, G. H.; Jiménez, Y. Hernández; Herten, G.; Hertenberger, R.; Hervas, L.; Hesketh, G. G.; Hessey, N. P.; Hetherly, J. W.; Hickling, R.; Higón-Rodriguez, E.; Hill, E.; Hill, J. C.; Hiller, K. H.; Hillier, S. J.; Hinchliffe, I.; Hines, E.; Hinman, R. R.; Hirose, M.; Hirschbuehl, D.; Hobbs, J.; Hod, N.; Hodgkinson, M. C.; Hodgson, P.; Hoecker, A.; Hoeferkamp, M. R.; Hoenig, F.; Hohlfeld, M.; Hohn, D.; Holmes, T. R.; Homann, M.; Hong, T. M.; Hooberman, B. H.; Hopkins, W. H.; Horii, Y.; Horton, A. J.; Hostachy, J.-Y.; Hou, S.; Hoummada, A.; Howard, J.; Howarth, J.; Hrabovsky, M.; Hristova, I.; Hrivnac, J.; Hryn'ova, T.; Hrynevich, A.; Hsu, C.; Hsu, P. J.; Hsu, S.-C.; Hu, D.; Hu, Q.; Huang, Y.; Hubacek, Z.; Hubaut, F.; Huegging, F.; Huffman, T. B.; Hughes, E. W.; Hughes, G.; Huhtinen, M.; Hülsing, T. A.; Huseynov, N.; Huston, J.; Huth, J.; Iacobucci, G.; Iakovidis, G.; Ibragimov, I.; Iconomidou-Fayard, L.; Ideal, E.; Idrissi, Z.; Iengo, P.; Igonkina, O.; Iizawa, T.; Ikegami, Y.; Ikeno, M.; Ilchenko, Y.; Iliadis, D.; Ilic, N.; Ince, T.; Introzzi, G.; Ioannou, P.; Iodice, M.; Iordanidou, K.; Ippolito, V.; Quiles, A. Irles; Isaksson, C.; Ishino, M.; Ishitsuka, M.; Ishmukhametov, R.; Issever, C.; Istin, S.; Ito, F.; Ponce, J. M. Iturbe; Iuppa, R.; Ivarsson, J.; Iwanski, W.; Iwasaki, H.; Izen, J. M.; Izzo, V.; Jabbar, S.; Jackson, B.; Jackson, M.; Jackson, P.; Jain, V.; Jakobi, K. B.; Jakobs, K.; Jakobsen, S.; Jakoubek, T.; Jamin, D. O.; Jana, D. K.; Jansen, E.; Jansky, R.; Janssen, J.; Janus, M.; Jarlskog, G.; Javadov, N.; Javůrek, T.; Jeanneau, F.; Jeanty, L.; Jejelava, J.; Jeng, G.-Y.; Jennens, D.; Jenni, P.; Jentzsch, J.; Jeske, C.; Jézéquel, S.; Ji, H.; Jia, J.; Jiang, H.; Jiang, Y.; Jiggins, S.; Pena, J. Jimenez; Jin, S.; Jinaru, A.; Jinnouchi, O.; Johansson, P.; Johns, K. A.; Johnson, W. J.; Jon-And, K.; Jones, G.; Jones, R. W. L.; Jones, S.; Jones, T. J.; Jongmanns, J.; Jorge, P. M.; Jovicevic, J.; Ju, X.; Rozas, A. Juste; Köhler, M. K.; Kaczmarska, A.; Kado, M.; Kagan, H.; Kagan, M.; Kahn, S. J.; Kajomovitz, E.; Kalderon, C. W.; Kaluza, A.; Kama, S.; Kamenshchikov, A.; Kanaya, N.; Kaneti, S.; Kantserov, V. A.; Kanzaki, J.; Kaplan, B.; Kaplan, L. S.; Kapliy, A.; Kar, D.; Karakostas, K.; Karamaoun, A.; Karastathis, N.; Kareem, M. J.; Karentzos, E.; Karnevskiy, M.; Karpov, S. N.; Karpova, Z. M.; Karthik, K.; Kartvelishvili, V.; Karyukhin, A. N.; Kasahara, K.; Kashif, L.; Kass, R. D.; Kastanas, A.; Kataoka, Y.; Kato, C.; Katre, A.; Katzy, J.; Kawagoe, K.; Kawamoto, T.; Kawamura, G.; Kazama, S.; Kazanin, V. F.; Keeler, R.; Kehoe, R.; Keller, J. S.; Kempster, J. J.; Kentaro, K.; Keoshkerian, H.; Kepka, O.; Kerševan, B. P.; Kersten, S.; Keyes, R. A.; Khalil-zada, F.; Khandanyan, H.; Khanov, A.; Kharlamov, A. G.; Khoo, T. J.; Khovanskiy, V.; Khramov, E.; Khubua, J.; Kido, S.; Kim, H. Y.; Kim, S. H.; Kim, Y. K.; Kimura, N.; Kind, O. M.; King, B. T.; King, M.; King, S. B.; Kirk, J.; Kiryunin, A. E.; Kishimoto, T.; Kisielewska, D.; Kiss, F.; Kiuchi, K.; Kivernyk, O.; Kladiva, E.; Klein, M. H.; Klein, M.; Klein, U.; Kleinknecht, K.; Klimek, P.; Klimentov, A.; Klingenberg, R.; Klinger, J. A.; Klioutchnikova, T.; Kluge, E.-E.; Kluit, P.; Kluth, S.; Knapik, J.; Kneringer, E.; Knoops, E. B. F. G.; Knue, A.; Kobayashi, A.; Kobayashi, D.; Kobayashi, T.; Kobel, M.; Kocian, M.; Kodys, P.; Koffas, T.; Koffeman, E.; Kogan, L. A.; Koi, T.; Kolanoski, H.; Kolb, M.; Koletsou, I.; Komar, A. A.; Komori, Y.; Kondo, T.; Kondrashova, N.; Köneke, K.; König, A. C.; Kono, T.; Konoplich, R.; Konstantinidis, N.; Kopeliansky, R.; Koperny, S.; Köpke, L.; Kopp, A. K.; Korcyl, K.; Kordas, K.; Korn, A.; Korol, A. A.; Korolkov, I.; Korolkova, E. V.; Kortner, O.; Kortner, S.; Kosek, T.; Kostyukhin, V. V.; Kotwal, A.; Kourkoumeli-Charalampidi, A.; Kourkoumelis, C.; Kouskoura, V.; Koutsman, A.; Kowalewska, A. B.; Kowalewski, R.; Kowalski, T. Z.; Kozanecki, W.; Kozhin, A. S.; Kramarenko, V. A.; Kramberger, G.; Krasnopevtsev, D.; Krasny, M. W.; Krasznahorkay, A.; Kraus, J. K.; Kravchenko, A.; Kretz, M.; Kretzschmar, J.; Kreutzfeldt, K.; Krieger, P.; Krizka, K.; Kroeninger, K.; Kroha, H.; Kroll, J.; Kroseberg, J.; Krstic, J.; Kruchonak, U.; Krüger, H.; Krumnack, N.; Kruse, A.; Kruse, M. C.; Kruskal, M.; Kubota, T.; Kucuk, H.; Kuday, S.; Kuechler, J. T.; Kuehn, S.; Kugel, A.; Kuger, F.; Kuhl, A.; Kuhl, T.; Kukhtin, V.; Kukla, R.; Kulchitsky, Y.; Kuleshov, S.; Kuna, M.; Kunigo, T.; Kupco, A.; Kurashige, H.; Kurochkin, Y. A.; Kus, V.; Kuwertz, E. S.; Kuze, M.; Kvita, J.; Kwan, T.; Kyriazopoulos, D.; Rosa, A. La; Navarro, J. L. La Rosa; Rotonda, L. La; Lacasta, C.; Lacava, F.; Lacey, J.; Lacker, H.; Lacour, D.; Lacuesta, V. R.; Ladygin, E.; Lafaye, R.; Laforge, B.; Lagouri, T.; Lai, S.; Lammers, S.; Lampl, W.; Lançon, E.; Landgraf, U.; Landon, M. P. J.; Lang, V. S.; Lange, J. C.; Lankford, A. J.; Lanni, F.; Lantzsch, K.; Lanza, A.; Laplace, S.; Lapoire, C.; Laporte, J. F.; Lari, T.; Manghi, F. Lasagni; Lassnig, M.; Laurelli, P.; Lavrijsen, W.; Law, A. T.; Laycock, P.; Lazovich, T.; Lazzaroni, M.; Dortz, O. Le; Guirriec, E. Le; Menedeu, E. Le; Quilleuc, E. P. Le; LeBlanc, M.; LeCompte, T.; Ledroit-Guillon, F.; Lee, C. A.; Lee, S. C.; Lee, L.; Lefebvre, G.; Lefebvre, M.; Legger, F.; Leggett, C.; Lehan, A.; Miotto, G. Lehmann; Lei, X.; Leight, W. A.; Leisos, A.; Leister, A. G.; Leite, M. A. L.; Leitner, R.; Lellouch, D.; Lemmer, B.; Leney, K. J. C.; Lenz, T.; Lenzi, B.; Leone, R.; Leone, S.; Leonidopoulos, C.; Leontsinis, S.; Lerner, G.; Leroy, C.; Lesage, A. A. J.; Lester, C. G.; Levchenko, M.; Levêque, J.; Levin, D.; Levinson, L. J.; Levy, M.; Leyko, A. M.; Leyton, M.; Li, B.; Li, H.; Li, H. L.; Li, L.; Li, L.; Li, Q.; Li, S.; Li, X.; Li, Y.; Liang, Z.; Liao, H.; Liberti, B.; Liblong, A.; Lichard, P.; Lie, K.; Liebal, J.; Liebig, W.; Limbach, C.; Limosani, A.; Lin, S. C.; Lin, T. H.; Lindquist, B. E.; Lipeles, E.; Lipniacka, A.; Lisovyi, M.; Liss, T. M.; Lissauer, D.; Lister, A.; Litke, A. M.; Liu, B.; Liu, D.; Liu, H.; Liu, H.; Liu, J.; Liu, J. B.; Liu, K.; Liu, L.; Liu, M.; Liu, M.; Liu, Y. L.; Liu, Y.; Livan, M.; Lleres, A.; Merino, J. Llorente; Lloyd, S. L.; Sterzo, F. Lo; Lobodzinska, E.; Loch, P.; Lockman, W. S.; Loebinger, F. K.; Loevschall-Jensen, A. E.; Loew, K. M.; Loginov, A.; Lohse, T.; Lohwasser, K.; Lokajicek, M.; Long, B. A.; Long, J. D.; Long, R. E.; Longo, L.; Looper, K. A.; Lopes, L.; Mateos, D. Lopez; Paredes, B. Lopez; Paz, I. Lopez; Solis, A. Lopez; Lorenz, J.; Martinez, N. Lorenzo; Losada, M.; Lösel, P. J.; Lou, X.; Lounis, A.; Love, J.; Love, P. A.; Lu, H.; Lu, N.; Lubatti, H. J.; Luci, C.; Lucotte, A.; Luedtke, C.; Luehring, F.; Lukas, W.; Luminari, L.; Lundberg, O.; Lund-Jensen, B.; Lynn, D.; Lysak, R.; Lytken, E.; Lyubushkin, V.; Ma, H.; Ma, L. L.; Ma, Y.; Maccarrone, G.; Macchiolo, A.; Macdonald, C. M.; Maček, B.; Miguens, J. Machado; Madaffari, D.; Madar, R.; Maddocks, H. J.; Mader, W. F.; Madsen, A.; Maeda, J.; Maeland, S.; Maeno, T.; Maevskiy, A.; Magradze, E.; Mahlstedt, J.; Maiani, C.; Maidantchik, C.; Maier, A. A.; Maier, T.; Maio, A.; Majewski, S.; Makida, Y.; Makovec, N.; Malaescu, B.; Malecki, Pa.; Maleev, V. P.; Malek, F.; Mallik, U.; Malon, D.; Malone, C.; Maltezos, S.; Malyukov, S.; Mamuzic, J.; Mancini, G.; Mandelli, B.; Mandelli, L.; Mandić, I.; Maneira, J.; Filho, L. Manhaes de Andrade; Ramos, J. Manjarres; Mann, A.; Mansoulie, B.; Mantifel, R.; Mantoani, M.; Manzoni, S.; Mapelli, L.; Marceca, G.; March, L.; Marchiori, G.; Marcisovsky, M.; Marjanovic, M.; Marley, D. E.; Marroquim, F.; Marsden, S. P.; Marshall, Z.; Marti, L. F.; Marti-Garcia, S.; Martin, B.; Martin, T. A.; Martin, V. J.; Latour, B. Martin dit; Martinez, M.; Martin-Haugh, S.; Martoiu, V. S.; Martyniuk, A. C.; Marx, M.; Marzano, F.; Marzin, A.; Masetti, L.; Mashimo, T.; Mashinistov, R.; Masik, J.; Maslennikov, A. L.; Massa, I.; Massa, L.; Mastrandrea, P.; Mastroberardino, A.; Masubuchi, T.; Mättig, P.; Mattmann, J.; Maurer, J.; Maxfield, S. J.; Maximov, D. A.; Mazini, R.; Mazza, S. M.; Fadden, N. C. Mc; Goldrick, G. Mc; Kee, S. P. Mc; McCarn, A.; McCarthy, R. L.; McCarthy, T. G.; McClymont, L. I.; McFarlane, K. W.; Mcfayden, J. A.; Mchedlidze, G.; McMahon, S. J.; McPherson, R. A.; Medinnis, M.; Meehan, S.; Mehlhase, S.; Mehta, A.; Meier, K.; Meineck, C.; Meirose, B.; Garcia, B. R. Mellado; Meloni, F.; Mengarelli, A.; Menke, S.; Meoni, E.; Mercurio, K. M.; Mergelmeyer, S.; Mermod, P.; Merola, L.; Meroni, C.; Merritt, F. S.; Messina, A.; Metcalfe, J.; Mete, A. S.; Meyer, C.; Meyer, C.; Meyer, J.-P.; Meyer, J.; Theenhausen, H. Meyer Zu; Middleton, R. P.; Miglioranzi, S.; Mijović, L.; Mikenberg, G.; Mikestikova, M.; Mikuž, M.; Milesi, M.; Milic, A.; Miller, D. W.; Mills, C.; Milov, A.; Milstead, D. A.; Minaenko, A. A.; Minami, Y.; Minashvili, I. A.; Mincer, A. I.; Mindur, B.; Mineev, M.; Ming, Y.; Mir, L. M.; Mistry, K. P.; Mitani, T.; Mitrevski, J.; Mitsou, V. A.; Miucci, A.; Miyagawa, P. S.; Mjörnmark, J. U.; Moa, T.; Mochizuki, K.; Mohapatra, S.; Mohr, W.; Molander, S.; Moles-Valls, R.; Monden, R.; Mondragon, M. C.; Mönig, K.; Monk, J.; Monnier, E.; Montalbano, A.; Berlingen, J. Montejo; Monticelli, F.; Monzani, S.; Moore, R. W.; Morange, N.; Moreno, D.; Llácer, M. Moreno; Morettini, P.; Mori, D.; Mori, T.; Morii, M.; Morinaga, M.; Morisbak, V.; Moritz, S.; Morley, A. K.; Mornacchi, G.; Morris, J. D.; Mortensen, S. S.; Morvaj, L.; Mosidze, M.; Moss, J.; Motohashi, K.; Mount, R.; Mountricha, E.; Mouraviev, S. V.; Moyse, E. J. W.; Muanza, S.; Mudd, R. D.; Mueller, F.; Mueller, J.; Mueller, R. S. P.; Mueller, T.; Muenstermann, D.; Mullen, P.; Mullier, G. A.; Sanchez, F. J. Munoz; Quijada, J. A. Murillo; Murray, W. J.; Musheghyan, H.; Muškinja, M.; Myagkov, A. G.; Myska, M.; Nachman, B. P.; Nackenhorst, O.; Nadal, J.; Nagai, K.; Nagai, R.; Nagano, K.; Nagasaka, Y.; Nagata, K.; Nagel, M.; Nagy, E.; Nairz, A. M.; Nakahama, Y.; Nakamura, K.; Nakamura, T.; Nakano, I.; Namasivayam, H.; Garcia, R. F. Naranjo; Narayan, R.; Villar, D. I. Narrias; Naryshkin, I.; Naumann, T.; Navarro, G.; Nayyar, R.; Neal, H. A.; Nechaeva, P. Yu.; Neep, T. J.; Nef, P. D.; Negri, A.; Negrini, M.; Nektarijevic, S.; Nellist, C.; Nelson, A.; Nemecek, S.; Nemethy, P.; Nepomuceno, A. A.; Nessi, M.; Neubauer, M. S.; Neumann, M.; Neves, R. M.; Nevski, P.; Newman, P. R.; Nguyen, D. H.; Nickerson, R. B.; Nicolaidou, R.; Nicquevert, B.; Nielsen, J.; Nikiforov, A.; Nikolaenko, V.; Nikolic-Audit, I.; Nikolopoulos, K.; Nilsen, J. K.; Nilsson, P.; Ninomiya, Y.; Nisati, A.; Nisius, R.; Nobe, T.; Nodulman, L.; Nomachi, M.; Nomidis, I.; Nooney, T.; Norberg, S.; Nordberg, M.; Norjoharuddeen, N.; Novgorodova, O.; Nowak, S.; Nozaki, M.; Nozka, L.; Ntekas, K.; Nurse, E.; Nuti, F.; O'grady, F.; O'Neil, D. C.; O'Rourke, A. A.; O'Shea, V.; Oakham, F. G.; Oberlack, H.; Obermann, T.; Ocariz, J.; Ochi, A.; Ochoa, I.; Ochoa-Ricoux, J. P.; Oda, S.; Odaka, S.; Ogren, H.; Oh, A.; Oh, S. H.; Ohm, C. C.; Ohman, H.; Oide, H.; Okawa, H.; Okumura, Y.; Okuyama, T.; Olariu, A.; Seabra, L. F. Oleiro; Pino, S. A. Olivares; Damazio, D. Oliveira; Olszewski, A.; Olszowska, J.; Onofre, A.; Onogi, K.; Onyisi, P. U. E.; Oram, C. J.; Oreglia, M. J.; Oren, Y.; Orestano, D.; Orlando, N.; Orr, R. S.; Osculati, B.; Ospanov, R.; Garzon, G. Otero y.; Otono, H.; Ouchrif, M.; Ould-Saada, F.; Ouraou, A.; Oussoren, K. P.; Ouyang, Q.; Ovcharova, A.; Owen, M.; Owen, R. E.; Ozcan, V. E.; Ozturk, N.; Pachal, K.; Pages, A. Pacheco; Aranda, C. Padilla; Pagáčová, M.; Griso, S. Pagan; Paige, F.; Pais, P.; Pajchel, K.; Palacino, G.; Palestini, S.; Palka, M.; Pallin, D.; Palma, A.; Panagiotopoulou, E. St.; Pandini, C. E.; Vazquez, J. G. Panduro; Pani, P.; Panitkin, S.; Pantea, D.; Paolozzi, L.; Papadopoulou, Th. D.; Papageorgiou, K.; Paramonov, A.; Hernandez, D. Paredes; Parker, A. J.; Parker, M. A.; Parker, K. A.; Parodi, F.; Parsons, J. A.; Parzefall, U.; Pascuzzi, V. R.; Pasqualucci, E.; Passaggio, S.; Pastore, F.; Pastore, Fr.; Pásztor, G.; Pataraia, S.; Patel, N. D.; Pater, J. R.; Pauly, T.; Pearce, J.; Pearson, B.; Pedersen, L. E.; Pedersen, M.; Lopez, S. Pedraza; Pedro, R.; Peleganchuk, S. V.; Pelikan, D.; Penc, O.; Peng, C.; Peng, H.; Penwell, J.; Peralva, B. S.; Perego, M. M.; Perepelitsa, D. V.; Codina, E. Perez; Perini, L.; Pernegger, H.; Perrella, S.; Peschke, R.; Peshekhonov, V. D.; Peters, K.; Peters, R. F. Y.; Petersen, B. A.; Petersen, T. C.; Petit, E.; Petridis, A.; Petridou, C.; Petroff, P.; Petrolo, E.; Petrov, M.; Petrucci, F.; Pettersson, N. E.; Peyaud, A.; Pezoa, R.; Phillips, P. W.; Piacquadio, G.; Pianori, E.; Picazio, A.; Piccaro, E.; Piccinini, M.; Pickering, M. A.; Piegaia, R.; Pilcher, J. E.; Pilkington, A. D.; Pin, A. W. J.; Pina, J.; Pinamonti, M.; Pinfold, J. L.; Pingel, A.; Pires, S.; Pirumov, H.; Pitt, M.; Plazak, L.; Pleier, M.-A.; Pleskot, V.; Plotnikova, E.; Plucinski, P.; Pluth, D.; Poettgen, R.; Poggioli, L.; Pohl, D.; Polesello, G.; Poley, A.; Policicchio, A.; Polifka, R.; Polini, A.; Pollard, C. S.; Polychronakos, V.; Pommès, K.; Pontecorvo, L.; Pope, B. G.; Popeneciu, G. A.; Popovic, D. S.; Poppleton, A.; Pospisil, S.; Potamianos, K.; Potrap, I. N.; Potter, C. J.; Potter, C. T.; Poulard, G.; Poveda, J.; Pozdnyakov, V.; Astigarraga, M. E. Pozo; Pralavorio, P.; Pranko, A.; Prell, S.; Price, D.; Price, L. E.; Primavera, M.; Prince, S.; Proissl, M.; Prokofiev, K.; Prokoshin, F.; Protopopescu, S.; Proudfoot, J.; Przybycien, M.; Puddu, D.; Puldon, D.; Purohit, M.; Puzo, P.; Qian, J.; Qin, G.; Qin, Y.; Quadt, A.; Quayle, W. B.; Queitsch-Maitland, M.; Quilty, D.; Raddum, S.; Radeka, V.; Radescu, V.; Radhakrishnan, S. K.; Radloff, P.; Rados, P.; Ragusa, F.; Rahal, G.; Raine, J. A.; Rajagopalan, S.; Rammensee, M.; Rangel-Smith, C.; Ratti, M. G.; Rauscher, F.; Rave, S.; Ravenscroft, T.; Raymond, M.; Read, A. L.; Readioff, N. P.; Rebuzzi, D. M.; Redelbach, A.; Redlinger, G.; Reece, R.; Reeves, K.; Rehnisch, L.; Reichert, J.; Reisin, H.; Rembser, C.; Ren, H.; Rescigno, M.; Resconi, S.; Rezanova, O. L.; Reznicek, P.; Rezvani, R.; Richter, R.; Richter, S.; Richter-Was, E.; Ricken, O.; Ridel, M.; Rieck, P.; Riegel, C. J.; Rieger, J.; Rifki, O.; Rijssenbeek, M.; Rimoldi, A.; Rinaldi, L.; Ristić, B.; Ritsch, E.; Riu, I.; Rizatdinova, F.; Rizvi, E.; Rizzi, C.; Robertson, S. H.; Robichaud-Veronneau, A.; Robinson, D.; Robinson, J. E. M.; Robson, A.; Roda, C.; Rodina, Y.; Perez, A. Rodriguez; Rodriguez, D. Rodriguez; Roe, S.; Rogan, C. S.; Røhne, O.; Romaniouk, A.; Romano, M.; Saez, S. M. Romano; Adam, E. Romero; Rompotis, N.; Ronzani, M.; Roos, L.; Ros, E.; Rosati, S.; Rosbach, K.; Rose, P.; Rosenthal, O.; Rossetti, V.; Rossi, E.; Rossi, L. P.; Rosten, J. H. N.; Rosten, R.; Rotaru, M.; Roth, I.; Rothberg, J.; Rousseau, D.; Royon, C. R.; Rozanov, A.; Rozen, Y.; Ruan, X.; Rubbo, F.; Rubinskiy, I.; Rud, V. I.; Rudolph, M. S.; Rühr, F.; Ruiz-Martinez, A.; Rurikova, Z.; Rusakovich, N. A.; Ruschke, A.; Russell, H. L.; Rutherfoord, J. P.; Ruthmann, N.; Ryabov, Y. F.; Rybar, M.; Rybkin, G.; Ryu, S.; Ryzhov, A.; Saavedra, A. F.; Sabato, G.; Sacerdoti, S.; Sadrozinski, H. F.-W.; Sadykov, R.; Tehrani, F. Safai; Saha, P.; Sahinsoy, M.; Saimpert, M.; Saito, T.; Sakamoto, H.; Sakurai, Y.; Salamanna, G.; Salamon, A.; Loyola, J. E. Salazar; Salek, D.; De Bruin, P. H. Sales; Salihagic, D.; Salnikov, A.; Salt, J.; Salvatore, D.; Salvatore, F.; Salvucci, A.; Salzburger, A.; Sammel, D.; Sampsonidis, D.; Sanchez, A.; Sánchez, J.; Martinez, V. Sanchez; Sandaker, H.; Sandbach, R. L.; Sander, H. G.; Sanders, M. P.; Sandhoff, M.; Sandoval, C.; Sandstroem, R.; Sankey, D. P. C.; Sannino, M.; Sansoni, A.; Santoni, C.; Santonico, R.; Santos, H.; Castillo, I. Santoyo; Sapp, K.; Sapronov, A.; Saraiva, J. G.; Sarrazin, B.; Sasaki, O.; Sasaki, Y.; Sato, K.; Sauvage, G.; Sauvan, E.; Savage, G.; Savard, P.; Sawyer, C.; Sawyer, L.; Saxon, J.; Sbarra, C.; Sbrizzi, A.; Scanlon, T.; Scannicchio, D. A.; Scarcella, M.; Scarfone, V.; Schaarschmidt, J.; Schacht, P.; Schaefer, D.; Schaefer, R.; Schaeffer, J.; Schaepe, S.; Schaetzel, S.; Schäfer, U.; Schaffer, A. C.; Schaile, D.; Schamberger, R. D.; Scharf, V.; Schegelsky, V. A.; Scheirich, D.; Schernau, M.; Schiavi, C.; Schillo, C.; Schioppa, M.; Schlenker, S.; Schmieden, K.; Schmitt, C.; Schmitt, S.; Schmitz, S.; Schneider, B.; Schnellbach, Y. J.; Schnoor, U.; Schoeffel, L.; Schoening, A.; Schoenrock, B. D.; Schopf, E.; Schorlemmer, A. L. S.; Schott, M.; Schovancova, J.; Schramm, S.; Schreyer, M.; Schuh, N.; Schultens, M. J.; Schultz-Coulon, H.-C.; Schulz, H.; Schumacher, M.; Schumm, B. A.; Schune, Ph.; Schwanenberger, C.; Schwartzman, A.; Schwarz, T. A.; Schwegler, Ph.; Schweiger, H.; Schwemling, Ph.; Schwienhorst, R.; Schwindling, J.; Schwindt, T.; Sciolla, G.; Scuri, F.; Scutti, F.; Searcy, J.; Seema, P.; Seidel, S. C.; Seiden, A.; Seifert, F.; Seixas, J. M.; Sekhniaidze, G.; Sekhon, K.; Sekula, S. J.; Seliverstov, D. M.; Semprini-Cesari, N.; Serfon, C.; Serin, L.; Serkin, L.; Sessa, M.; Seuster, R.; Severini, H.; Sfiligoj, T.; Sforza, F.; Sfyrla, A.; Shabalina, E.; Shaikh, N. W.; Shan, L. Y.; Shang, R.; Shank, J. T.; Shapiro, M.; Shatalov, P. B.; Shaw, K.; Shaw, S. M.; Shcherbakova, A.; Shehu, C. Y.; Sherwood, P.; Shi, L.; Shimizu, S.; Shimmin, C. O.; Shimojima, M.; Shiyakova, M.; Shmeleva, A.; Saadi, D. Shoaleh; Shochet, M. J.; Shojaii, S.; Shrestha, S.; Shulga, E.; Shupe, M. A.; Sicho, P.; Sidebo, P. E.; Sidiropoulou, O.; Sidorov, D.; Sidoti, A.; Siegert, F.; Sijacki, Dj.; Silva, J.; Silverstein, S. B.; Simak, V.; Simard, O.; Simic, Lj.; Simion, S.; Simioni, E.; Simmons, B.; Simon, D.; Simon, M.; Sinervo, P.; Sinev, N. B.; Sioli, M.; Siragusa, G.; Sivoklokov, S. Yu.; Sjölin, J.; Sjursen, T. B.; Skinner, M. B.; Skottowe, H. P.; Skubic, P.; Slater, M.; Slavicek, T.; Slawinska, M.; Sliwa, K.; Slovak, R.; Smakhtin, V.; Smart, B. H.; Smestad, L.; Smirnov, S. Yu.; Smirnov, Y.; Smirnova, L. N.; Smirnova, O.; Smith, M. N. K.; Smith, R. W.; Smizanska, M.; Smolek, K.; Snesarev, A. A.; Snidero, G.; Snyder, S.; Sobie, R.; Socher, F.; Soffer, A.; Soh, D. A.; Sokhrannyi, G.; Sanchez, C. A. Solans; Solar, M.; Soldatov, E. Yu.; Soldevila, U.; Solodkov, A. A.; Soloshenko, A.; Solovyanov, O. V.; Solovyev, V.; Sommer, P.; Son, H.; Song, H. Y.; Sood, A.; Sopczak, A.; Sopko, V.; Sorin, V.; Sosa, D.; Sotiropoulou, C. L.; Soualah, R.; Soukharev, A. M.; South, D.; Sowden, B. C.; Spagnolo, S.; Spalla, M.; Spangenberg, M.; Spanò, F.; Sperlich, D.; Spettel, F.; Spighi, R.; Spigo, G.; Spiller, L. A.; Spousta, M.; Denis, R. D. St.; Stabile, A.; Stahlman, J.; Stamen, R.; Stamm, S.; Stanecka, E.; Stanek, R. W.; Stanescu, C.; Stanescu-Bellu, M.; Stanitzki, M. M.; Stapnes, S.; Starchenko, E. A.; Stark, G. H.; Stark, J.; Staroba, P.; Starovoitov, P.; Stärz, S.; Staszewski, R.; Steinberg, P.; Stelzer, B.; Stelzer, H. J.; Stelzer-Chilton, O.; Stenzel, H.; Stewart, G. A.; Stillings, J. A.; Stockton, M. C.; Stoebe, M.; Stoicea, G.; Stolte, P.; Stonjek, S.; Stradling, A. R.; Straessner, A.; Stramaglia, M. E.; Strandberg, J.; Strandberg, S.; Strandlie, A.; Strauss, M.; Strizenec, P.; Ströhmer, R.; Strom, D. M.; Stroynowski, R.; Strubig, A.; Stucci, S. A.; Stugu, B.; Styles, N. A.; Su, D.; Su, J.; Subramaniam, R.; Suchek, S.; Sugaya, Y.; Suk, M.; Sulin, V. V.; Sultansoy, S.; Sumida, T.; Sun, S.; Sun, X.; Sundermann, J. E.; Suruliz, K.; Susinno, G.; Sutton, M. R.; Suzuki, S.; Svatos, M.; Swiatlowski, M.; Sykora, I.; Sykora, T.; Ta, D.; Taccini, C.; Tackmann, K.; Taenzer, J.; Taffard, A.; Tafirout, R.; Taiblum, N.; Takai, H.; Takashima, R.; Takeda, H.; Takeshita, T.; Takubo, Y.; Talby, M.; Talyshev, A. A.; Tam, J. Y. C.; Tan, K. G.; Tanaka, J.; Tanaka, R.; Tanaka, S.; Tannenwald, B. B.; Araya, S. Tapia; Tapprogge, S.; Tarem, S.; Tartarelli, G. F.; Tas, P.; Tasevsky, M.; Tashiro, T.; Tassi, E.; Delgado, A. Tavares; Tayalati, Y.; Taylor, A. C.; Taylor, G. N.; Taylor, P. T. E.; Taylor, W.; Teischinger, F. A.; Teixeira-Dias, P.; Temming, K. K.; Temple, D.; Kate, H. Ten; Teng, P. K.; Teoh, J. J.; Tepel, F.; Terada, S.; Terashi, K.; Terron, J.; Terzo, S.; Testa, M.; Teuscher, R. J.; Theveneaux-Pelzer, T.; Thomas, J. P.; Thomas-Wilsker, J.; Thompson, E. N.; Thompson, P. D.; Thompson, R. J.; Thompson, A. S.; Thomsen, L. A.; Thomson, E.; Thomson, M.; Tibbetts, M. J.; Torres, R. E. Ticse; Tikhomirov, V. O.; Tikhonov, Yu. A.; Timoshenko, S.; Tipton, P.; Tisserant, S.; Todome, K.; Todorov, T.; Todorova-Nova, S.; Tojo, J.; Tokár, S.; Tokushuku, K.; Tolley, E.; Tomlinson, L.; Tomoto, M.; Tompkins, L.; Toms, K.; Tong, B.; Torrence, E.; Torres, H.; Pastor, E. Torró; Toth, J.; Touchard, F.; Tovey, D. R.; Trefzger, T.; Tricoli, A.; Trigger, I. M.; Trincaz-Duvoid, S.; Tripiana, M. F.; Trischuk, W.; Trocmé, B.; Trofymov, A.; Troncon, C.; Trottier-McDonald, M.; Trovatelli, M.; Truong, L.; Trzebinski, M.; Trzupek, A.; Tseng, J. C.-L.; Tsiareshka, P. V.; Tsipolitis, G.; Tsirintanis, N.; Tsiskaridze, S.; Tsiskaridze, V.; Tskhadadze, E. G.; Tsui, K. M.; Tsukerman, I. I.; Tsulaia, V.; Tsuno, S.; Tsybychev, D.; Tudorache, A.; Tudorache, V.; Tuna, A. N.; Tupputi, S. A.; Turchikhin, S.; Turecek, D.; Turgeman, D.; Turra, R.; Turvey, A. J.; Tuts, P. M.; Tyndel, M.; Ucchielli, G.; Ueda, I.; Ueno, R.; Ughetto, M.; Ukegawa, F.; Unal, G.; Undrus, A.; Unel, G.; Ungaro, F. C.; Unno, Y.; Unverdorben, C.; Urban, J.; Urquijo, P.; Urrejola, P.; Usai, G.; Usanova, A.; Vacavant, L.; Vacek, V.; Vachon, B.; Valderanis, C.; Santurio, E. Valdes; Valencic, N.; Valentinetti, S.; Valero, A.; Valery, L.; Valkar, S.; Vallecorsa, S.; Ferrer, J. A. Valls; Van Den Wollenberg, W.; Van Der Deijl, P. C.; van der Geer, R.; van der Graaf, H.; van Eldik, N.; van Gemmeren, P.; Van Nieuwkoop, J.; van Vulpen, I.; van Woerden, M. C.; Vanadia, M.; Vandelli, W.; Vanguri, R.; Vaniachine, A.; Vankov, P.; Vardanyan, G.; Vari, R.; Varnes, E. W.; Varol, T.; Varouchas, D.; Vartapetian, A.; Varvell, K. E.; Vasquez, J. G.; Vazeille, F.; Schroeder, T. Vazquez; Veatch, J.; Veloce, L. M.; Veloso, F.; Veneziano, S.; Ventura, A.; Venturi, M.; Venturi, N.; Venturini, A.; Vercesi, V.; Verducci, M.; Verkerke, W.; Vermeulen, J. C.; Vest, A.; Vetterli, M. C.; Viazlo, O.; Vichou, I.; Vickey, T.; Boeriu, O. E. Vickey; Viehhauser, G. H. A.; Viel, S.; Vigani, L.; Vigne, R.; Villa, M.; Perez, M. Villaplana; Vilucchi, E.; Vincter, M. G.; Vinogradov, V. B.; Vittori, C.; Vivarelli, I.; Vlachos, S.; Vlasak, M.; Vogel, M.; Vokac, P.; Volpi, G.; Volpi, M.; von der Schmitt, H.; von Toerne, E.; Vorobel, V.; Vorobev, K.; Vos, M.; Voss, R.; Vossebeld, J. H.; Vranjes, N.; Milosavljevic, M. Vranjes; Vrba, V.; Vreeswijk, M.; Vuillermet, R.; Vukotic, I.; Vykydal, Z.; Wagner, P.; Wagner, W.; Wahlberg, H.; Wahrmund, S.; Wakabayashi, J.; Walder, J.; Walker, R.; Walkowiak, W.; Wallangen, V.; Wang, C.; Wang, C.; Wang, F.; Wang, H.; Wang, H.; Wang, J.; Wang, J.; Wang, K.; Wang, R.; Wang, S. M.; Wang, T.; Wang, T.; Wang, X.; Wanotayaroj, C.; Warburton, A.; Ward, C. P.; Wardrope, D. R.; Washbrook, A.; Watkins, P. M.; Watson, A. T.; Watson, I. J.; Watson, M. F.; Watts, G.; Watts, S.; Waugh, B. M.; Webb, S.; Weber, M. S.; Weber, S. W.; Webster, J. S.; Weidberg, A. R.; Weinert, B.; Weingarten, J.; Weiser, C.; Weits, H.; Wells, P. S.; Wenaus, T.; Wengler, T.; Wenig, S.; Wermes, N.; Werner, M.; Werner, P.; Wessels, M.; Wetter, J.; Whalen, K.; Whallon, N. L.; Wharton, A. M.; White, A.; White, M. J.; White, R.; White, S.; Whiteson, D.; Wickens, F. J.; Wiedenmann, W.; Wielers, M.; Wienemann, P.; Wiglesworth, C.; Wiik-Fuchs, L. A. M.; Wildauer, A.; Wilk, F.; Wilkens, H. G.; Williams, H. H.; Williams, S.; Willis, C.; Willocq, S.; Wilson, J. A.; Wingerter-Seez, I.; Winklmeier, F.; Winston, O. J.; Winter, B. T.; Wittgen, M.; Wittkowski, J.; Wollstadt, S. J.; Wolter, M. W.; Wolters, H.; Wosiek, B. K.; Wotschack, J.; Woudstra, M. J.; Wozniak, K. W.; Wu, M.; Wu, M.; Wu, S. L.; Wu, X.; Wu, Y.; Wyatt, T. R.; Wynne, B. M.; Xella, S.; Xu, D.; Xu, L.; Yabsley, B.; Yacoob, S.; Yakabe, R.; Yamaguchi, D.; Yamaguchi, Y.; Yamamoto, A.; Yamamoto, S.; Yamanaka, T.; Yamauchi, K.; Yamazaki, Y.; Yan, Z.; Yang, H.; Yang, H.; Yang, Y.; Yang, Z.; Yao, W.-M.; Yap, Y. C.; Yasu, Y.; Yatsenko, E.; Wong, K. H. Yau; Ye, J.; Ye, S.; Yeletskikh, I.; Yen, A. L.; Yildirim, E.; Yorita, K.; Yoshida, R.; Yoshihara, K.; Young, C.; Young, C. J. S.; Youssef, S.; Yu, D. R.; Yu, J.; Yu, J. M.; Yu, J.; Yuan, L.; Yuen, S. P. Y.; Yusuff, I.; Zabinski, B.; Zaidan, R.; Zaitsev, A. M.; Zakharchuk, N.; Zalieckas, J.; Zaman, A.; Zambito, S.; Zanello, L.; Zanzi, D.; Zeitnitz, C.; Zeman, M.; Zemla, A.; Zeng, J. C.; Zeng, Q.; Zengel, K.; Zenin, O.; Ženiš, T.; Zerwas, D.; Zhang, D.; Zhang, F.; Zhang, G.; Zhang, H.; Zhang, J.; Zhang, L.; Zhang, R.; Zhang, R.; Zhang, X.; Zhang, Z.; Zhao, X.; Zhao, Y.; Zhao, Z.; Zhemchugov, A.; Zhong, J.; Zhou, B.; Zhou, C.; Zhou, L.; Zhou, L.; Zhou, M.; Zhou, N.; Zhu, C. G.; Zhu, H.; Zhu, J.; Zhu, Y.; Zhuang, X.; Zhukov, K.; Zibell, A.; Zieminska, D.; Zimine, N. I.; Zimmermann, C.; Zimmermann, S.; Zinonos, Z.; Zinser, M.; Ziolkowski, M.; Živković, L.; Zobernig, G.; Zoccoli, A.; Nedden, M. zur; Zurzolo, G.; Zwalinski, L.

    2016-10-01

    The performance of the jet trigger for the ATLAS detector at the LHC during the 2011 data taking period is described. During 2011 the LHC provided proton-proton collisions with a centre-of-mass energy of 7 TeV and heavy ion collisions with a 2.76 TeV per nucleon-nucleon collision energy. The ATLAS trigger is a three level system designed to reduce the rate of events from the 40 MHz nominal maximum bunch crossing rate to the approximate 400 Hz which can be written to offline storage. The ATLAS jet trigger is the primary means for the online selection of events containing jets. Events are accepted by the trigger if they contain one or more jets above some transverse energy threshold. During 2011 data taking the jet trigger was fully efficient for jets with transverse energy above 25 GeV for triggers seeded randomly at Level 1. For triggers which require a jet to be identified at each of the three trigger levels, full efficiency is reached for offline jets with transverse energy above 60 GeV. Jets reconstructed in the final trigger level and corresponding to offline jets with transverse energy greater than 60 GeV, are reconstructed with a resolution in transverse energy with respect to offline jets, of better than 4 % in the central region and better than 2.5 % in the forward direction.

  2. Challenges of a Modern Atlas of the Ageing Society

    NASA Astrophysics Data System (ADS)

    Bleisch, S.; Hil, D.; Korkut, S.; Meyer, P.

    2016-06-01

    Atlases are collections of illustrated data, often maps, which give an overview - as well as some details - of one or several topic areas. We noted that this description serves well especially for traditional paper and digital atlases. However, in our today's world of entertainment it might give a somewhat dated impression. For the topic area 'Ageing Society' we aim to visualise age related data in an interactive digital way that supports not only the content but also engages the users, offers opportunities for different stakeholders and levels of interest, and is able to accommodate a range of data as well as future updates. A set of guiding principles for the development process addresses these challenges. First implementations show that following the principles is feasible but expensive in terms of time and attention to detail needed. For each selected topic, a story guides the users through the data and highlights interesting aspects. The user can interrupt the story at any time and explore the data further through interacting with the detailed data representations, and switch back to the story when needed. This allows different levels of access which in combination with the specifically designed navigation concept as well as through the adherence to user aware design principles are very promising for the future developments of the Atlas of the Ageing Society and potentially other atlas products.

  3. Atlases in the Collection of Moellering Memorial Library, Valparaiso University. A Selected and Annotated Bibliography.

    ERIC Educational Resources Information Center

    Hess, Elmer B., Comp.

    Following a brief discussion of the evolution of the atlas and its importance as a library reference tool, an annotated description is provided of each atlas found in this university library collection. Items in the bibliography are arranged in the following categories: (1) world atlases; (2) regional atlases; (3) national atlases; (4) state…

  4. Atlas ranking and selection for automatic segmentation of the esophagus from CT scans

    NASA Astrophysics Data System (ADS)

    Yang, Jinzhong; Haas, Benjamin; Fang, Raymond; Beadle, Beth M.; Garden, Adam S.; Liao, Zhongxing; Zhang, Lifei; Balter, Peter; Court, Laurence

    2017-12-01

    In radiation treatment planning, the esophagus is an important organ-at-risk that should be spared in patients with head and neck cancer or thoracic cancer who undergo intensity-modulated radiation therapy. However, automatic segmentation of the esophagus from CT scans is extremely challenging because of the structure’s inconsistent intensity, low contrast against the surrounding tissues, complex and variable shape and location, and random air bubbles. The goal of this study is to develop an online atlas selection approach to choose a subset of optimal atlases for multi-atlas segmentation to the delineate esophagus automatically. We performed atlas selection in two phases. In the first phase, we used the correlation coefficient of the image content in a cubic region between each atlas and the new image to evaluate their similarity and to rank the atlases in an atlas pool. A subset of atlases based on this ranking was selected, and deformable image registration was performed to generate deformed contours and deformed images in the new image space. In the second phase of atlas selection, we used Kullback-Leibler divergence to measure the similarity of local-intensity histograms between the new image and each of the deformed images, and the measurements were used to rank the previously selected atlases. Deformed contours were overlapped sequentially, from the most to the least similar, and the overlap ratio was examined. We further identified a subset of optimal atlases by analyzing the variation of the overlap ratio versus the number of atlases. The deformed contours from these optimal atlases were fused together using a modified simultaneous truth and performance level estimation algorithm to produce the final segmentation. The approach was validated with promising results using both internal data sets (21 head and neck cancer patients and 15 thoracic cancer patients) and external data sets (30 thoracic patients).

  5. EnviroAtlas - Pittsburgh, PA - Ecosystem Services by Block Group

    EPA Pesticide Factsheets

    This EnviroAtlas dataset presents environmental benefits of the urban forest in 1,089 block groups in Pittsburgh, Pennsylvania. Carbon attributes, temperature reduction, pollution removal and value, and runoff effects are calculated for each block group using i-Tree models (www.itreetools.org), local weather data, pollution data, EPA provided city boundary and land cover data, and U.S. Census derived block group boundary data. This dataset was produced by the US Forest Service to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

  6. EnviroAtlas - Tampa, FL - Ecosystem Services by Block Group

    EPA Pesticide Factsheets

    This EnviroAtlas dataset presents environmental benefits of the urban forest in 1,833 block groups in Tampa Bay, Florida. Carbon attributes, temperature reduction, pollution removal and value, and runoff effects are calculated for each block group using i-Tree models (www.itreetools.org), local weather data, pollution data, EPA provided city boundary and land cover data, and U.S. Census derived block group boundary data. This dataset was produced by the US Forest Service to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

  7. EnviroAtlas - Cleveland, OH - Ecosystem Services by Block Group

    EPA Pesticide Factsheets

    This EnviroAtlas dataset presents environmental benefits of the urban forest in 1,442 block groups in Cleveland, Ohio. Carbon attributes, temperature reduction, pollution removal and value, and runoff effects are calculated for each block group using i-Tree models (www.itreetools.org), local weather data, pollution data, EPA provided city boundary and land cover data, and U.S. Census derived block group boundary data. This dataset was produced by the US Forest Service to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas ) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

  8. EnviroAtlas - Milwaukee, WI - Ecosystem Services by Block Group

    EPA Pesticide Factsheets

    This EnviroAtlas dataset presents environmental benefits of the urban forest in 1,175 block groups in Milwaukee, Wisconsin. Carbon attributes, temperature reduction, pollution removal and value, and runoff effects are calculated for each block group using i-Tree models (www.itreetools.org), local weather data, pollution data, EPA provided city boundary and land cover data, and U.S. Census derived block group boundary data. This dataset was produced by the US Forest Service to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

  9. Shape-intensity prior level set combining probabilistic atlas and probability map constrains for automatic liver segmentation from abdominal CT images.

    PubMed

    Wang, Jinke; Cheng, Yuanzhi; Guo, Changyong; Wang, Yadong; Tamura, Shinichi

    2016-05-01

    Propose a fully automatic 3D segmentation framework to segment liver on challenging cases that contain the low contrast of adjacent organs and the presence of pathologies from abdominal CT images. First, all of the atlases are weighted in the selected training datasets by calculating the similarities between the atlases and the test image to dynamically generate a subject-specific probabilistic atlas for the test image. The most likely liver region of the test image is further determined based on the generated atlas. A rough segmentation is obtained by a maximum a posteriori classification of probability map, and the final liver segmentation is produced by a shape-intensity prior level set in the most likely liver region. Our method is evaluated and demonstrated on 25 test CT datasets from our partner site, and its results are compared with two state-of-the-art liver segmentation methods. Moreover, our performance results on 10 MICCAI test datasets are submitted to the organizers for comparison with the other automatic algorithms. Using the 25 test CT datasets, average symmetric surface distance is [Formula: see text] mm (range 0.62-2.12 mm), root mean square symmetric surface distance error is [Formula: see text] mm (range 0.97-3.01 mm), and maximum symmetric surface distance error is [Formula: see text] mm (range 12.73-26.67 mm) by our method. Our method on 10 MICCAI test data sets ranks 10th in all the 47 automatic algorithms on the site as of July 2015. Quantitative results, as well as qualitative comparisons of segmentations, indicate that our method is a promising tool to improve the efficiency of both techniques. The applicability of the proposed method to some challenging clinical problems and the segmentation of the liver are demonstrated with good results on both quantitative and qualitative experimentations. This study suggests that the proposed framework can be good enough to replace the time-consuming and tedious slice-by-slice manual

  10. Energy Frontier Research With ATLAS: Final Report

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Butler, John; Black, Kevin; Ahlen, Steve

    2016-06-14

    The Boston University (BU) group is playing key roles across the ATLAS experiment: in detector operations, the online trigger, the upgrade, computing, and physics analysis. Our team has been critical to the maintenance and operations of the muon system since its installation. During Run 1 we led the muon trigger group and that responsibility continues into Run 2. BU maintains and operates the ATLAS Northeast Tier 2 computing center. We are actively engaged in the analysis of ATLAS data from Run 1 and Run 2. Physics analyses we have contributed to include Standard Model measurements (W and Z cross sections,more » t\\bar{t} differential cross sections, WWW^* production), evidence for the Higgs decaying to \\tau^+\\tau^-, and searches for new phenomena (technicolor, Z' and W', vector-like quarks, dark matter).« less

  11. Associations among sella turcica bridging, atlas arcuate foramen (ponticulus posticus) development, atlas posterior arch deficiency, and the occurrence of palatally displaced canine impaction.

    PubMed

    Haji Ghadimi, Mona; Amini, Fariborz; Hamedi, Shayesteh; Rakhshan, Vahid

    2017-03-01

    Head and neck skeletal anomalies or normal variants might predict the occurrence of palatally displaced impacted maxillary canines. Despite their clinical importance, studies in this regard are rare, especially when it comes to vertebral anomalies. This case-control study was performed on cephalographs of 35 orthodontic patients (11 male, 24 female) with palatally displaced canines (PDC) and 75 patients without them (29 male, 46 female). PDC were diagnosed on panoramic and lateral cephalographs and from clinical reports. The occurrence and severity of sella turcica bridge and the atlas ponticulus posticus, and deficiency of the posterior atlas arch were evaluated twice on lateral cephalographs. The associations between the occurrence and level of these skeletal anomalies and variations of PDC occurrence as well as additional correlations were assessed using multivariable and bivariate statistics (α = 0.05; β ≤0.2). The patients' mean age was 18.4 ± 1.9 years. In the control and patient groups, 23 (30.7%) and 21 subjects (60%) had sella turcica bridging, respectively (chi-square, P = 0.003). Ponticulus posticus was observed in 14 (18.7%) controls and 15 (42.9%) patients (chi-square, P = 0.007). Posterior atlas arch deficiency was observed in 4 (5.3%) controls and 5 (14.3%) patients (chi-square, P = 0.111). The presence of ponticulus posticus and sella turcica bridging might be associated with increased odds of PDC occurrence for about odds ratios of 3.1 and 3.5 times, respectively (binary logistic regression). PDC is positively associated with the occurrence and severity of sella turcica bridging and ponticulus posticus. The association between PDC and posterior atlas arch deficiency was inconclusive. Copyright © 2016 American Association of Orthodontists. Published by Elsevier Inc. All rights reserved.

  12. Medical Students' Assessment of Eduard Pernkopf's Atlas: Topographical Anatomy of Man.

    PubMed

    Coombs, Demetrius M; Peitzman, Steven J

    2017-07-01

    To date, there has been no study examining the perceptions of first-year medical students regarding Eduard Pernkopf's atlas, particularly during their study of gross anatomy and prior to coursework in medical ethics. We present a discussion of Pernkopf's Atlas: Topographical Anatomy of Man from the perspective of U.S. medical students, and sought to determine whether medical students view Pernkopf's Topographical Anatomy of Man as a resource of greater accuracy, detail, and potential educational utility as compared to Netter's Atlas of Human Anatomy. The entire first-year class at Drexel University College of Medicine (265 students) was surveyed at approximately the midpoint of their gross anatomy course and 192 responses were collected (72% response rate). Of these, 176 (95%) were unaware of the existence of Pernkopf's atlas. Another 71% of students found the Pernkopf atlas more likely complete and accurate, whereas 76% thought the Netter atlas more useful for learning (p<.001). When presented with a hypothetical scenario in which the subjects used in creating Pernkopf's atlas were donated, or unclaimed, but with knowledge that Pernkopf was an active member of the Nazi party, 133 students (72%) retained their original position (p=.001). About 94% desired discussion of Pernkopf within a medical school bioethics course. The relationship between level of self-reported knowledge and whether or not students would advocate removal of the atlas was statistically significant (p=.013). Discussing ethical violations in medical history, especially the Pernkopf atlas, must attain a secure place in medical school curricula, and more specifically, within a bioethics course. Copyright © 2017 Elsevier GmbH. All rights reserved.

  13. Crustal Scale Magnetotelluric Imaging of the Central Atlas in Moocco

    NASA Astrophysics Data System (ADS)

    Ledo, J.; Jones, A. G.; Sinischalchi, A.; Rouais, M.; Campanyà, J.; Kiyan, D.; Moretti, P.; Piña, P.; Hogg, C.; Romano, G.; Picasso Team

    2010-12-01

    The Central Atlas of Morocco is an intracontinental fold-thrust belt with an ENE-WSW main strike that extends about 2000 km and 100 km wide, located in the foreland of the Mediterranean Alpine belt. The structure of the Atlas resulted from the tectonic inversion of a Mesozoic extensional basin, due to compression related to convergence between Africa and Europe occurred from cenozoic to present times. Previous MT data models based on stitched 1D inversion or using only the phases and the induction vector data following and trial and error approach (Schwarz et al., 1992), therefore the overall geoelectrical structure is partly unresolved. In this paper we will expose and discuss the results of new magnetotelluric data acquired along a profile crossing the Atlas that allows imaging its electrical crustal structure.In the lower crust two conductive units appear. One below the Moulouya plains that coincides with a minimum of the Bouguer anomaly, less earthquakes than the adjacent Middle and High Atlas and a low velocity anomaly at lower crustal levels. Moreover, the Moulouya plain and the Middle Atlas to the north are host of the largest Neogene-Quaternary intraplate alkaline volcanic field in Morocco. This feature has been associated either to a Canary mantle plume flow beneath Africa or to the interplay between reactivation of inherited geological structures and the thermal erosion of the metasomatized lithosphere. In any case, all the authors agree that are originated by low degree partial melting of sublithospheric mantle sources. Another low resistivity anomaly appears at lower crustal depths below the Anti-Atlas, that could be either a remnant of tectonic processes in the pre-mesozoic or a more recent overprint of the lower crust due to mantle processes. Two main events during the Pan African orogeny may be the cause of this anomaly, a relic of a subduction process or a deep mineralization associated to magmatism. The Anti-Atlas consists of of a Precambrian

  14. Image database for digital hand atlas

    NASA Astrophysics Data System (ADS)

    Cao, Fei; Huang, H. K.; Pietka, Ewa; Gilsanz, Vicente; Dey, Partha S.; Gertych, Arkadiusz; Pospiech-Kurkowska, Sywia

    2003-05-01

    Bone age assessment is a procedure frequently performed in pediatric patients to evaluate their growth disorder. A commonly used method is atlas matching by a visual comparison of a hand radiograph with a small reference set of old Greulich-Pyle atlas. We have developed a new digital hand atlas with a large set of clinically normal hand images of diverse ethnic groups. In this paper, we will present our system design and implementation of the digital atlas database to support the computer-aided atlas matching for bone age assessment. The system consists of a hand atlas image database, a computer-aided diagnostic (CAD) software module for image processing and atlas matching, and a Web user interface. Users can use a Web browser to push DICOM images, directly or indirectly from PACS, to the CAD server for a bone age assessment. Quantitative features on the examined image, which reflect the skeletal maturity, are then extracted and compared with patterns from the atlas image database to assess the bone age. The digital atlas method built on a large image database and current Internet technology provides an alternative to supplement or replace the traditional one for a quantitative, accurate and cost-effective assessment of bone age.

  15. TDRS-M Atlas V 1st Stage Erection Launch Vehicle on Stand

    NASA Image and Video Library

    2017-07-12

    A United Launch Alliance Atlas V first stage is lifted at the Vertical Integration Facility at Space Launch Complex 41 at Cape Canaveral Air Force Station in Florida. The rocket is scheduled to launch the Tracking and Data Relay Satellite, TDRS-M. It will be the latest spacecraft destined for the agency's constellation of communications satellites that allows nearly continuous contact with orbiting spacecraft ranging from the International Space Station and Hubble Space Telescope to the array of scientific observatories. Liftoff atop the ULA Atlas V rocket is scheduled to take place from Cape Canaveral's Space Launch Complex 41 on Aug. 3, 2017 at 9:02 a.m. EDT.

  16. The Copernicus ultraviolet spectral atlas of Vega

    NASA Technical Reports Server (NTRS)

    Rogerson, John B., Jr.

    1989-01-01

    A near-ultraviolet spectral atlas for the A0 V star Alpha Lyr (Vega) has been prepared from data taken by the Princeton spectrometer aboard the Copernicus satellite. The spectral region from 2000 to 3187 A has been scanned with a resolution of 0.1 A. The atlas is presented in graphs with a normalized continuum, and an identification table for the absorption features has been prepared.

  17. The Copernicus ultraviolet spectral atlas Tau Scorpii

    NASA Technical Reports Server (NTRS)

    Rogerson, J. B., Jr.; Upson, W. L., II

    1977-01-01

    An ultraviolet spectral atlas was presented for the B0 V star, Tau Scorpii. It was scanned from 949 to 1560 A by the Princeton spectrometer aboard the Copernicus satellite. From 949 to 1420 A the observations have a nominal resolution of 0.05 A. At the longer wavelengths, the resolution was 0.1 A. The atlas was presented in both tables and graphs.

  18. Spinal pedicle screw planning using deformable atlas registration.

    PubMed

    Goerres, J; Uneri, A; De Silva, T; Ketcha, M; Reaungamornrat, S; Jacobson, M; Vogt, S; Kleinszig, G; Osgood, G; Wolinsky, J-P; Siewerdsen, J H

    2017-04-07

    Spinal screw placement is a challenging task due to small bone corridors and high risk of neurological or vascular complications, benefiting from precision guidance/navigation and quality assurance (QA). Implicit to both guidance and QA is the definition of a surgical plan-i.e. the desired trajectories and device selection for target vertebrae-conventionally requiring time-consuming manual annotations by a skilled surgeon. We propose automation of such planning by deriving the pedicle trajectory and device selection from a patient's preoperative CT or MRI. An atlas of vertebrae surfaces was created to provide the underlying basis for automatic planning-in this work, comprising 40 exemplary vertebrae at three levels of the spine (T7, T8, and L3). The atlas was enriched with ideal trajectory annotations for 60 pedicles in total. To define trajectories for a given patient, sparse deformation fields from the atlas surfaces to the input (CT or MR image) are applied on the annotated trajectories. Mean value coordinates are used to interpolate dense deformation fields. The pose of a straight trajectory is optimized by image-based registration to an accumulated volume of the deformed annotations. For evaluation, input deformation fields were created using coherent point drift (CPD) to perform a leave-one-out analysis over the atlas surfaces. CPD registration demonstrated surface error of 0.89  ±  0.10 mm (median  ±  interquartile range) for T7/T8 and 1.29  ±  0.15 mm for L3. At the pedicle center, registered trajectories deviated from the expert reference by 0.56  ±  0.63 mm (T7/T8) and 1.12  ±  0.67 mm (L3). The predicted maximum screw diameter differed by 0.45  ±  0.62 mm (T7/T8), and 1.26  ±  1.19 mm (L3). The automated planning method avoided screw collisions in all cases and demonstrated close agreement overall with expert reference plans, offering a potentially valuable tool in support of

  19. Spinal pedicle screw planning using deformable atlas registration

    NASA Astrophysics Data System (ADS)

    Goerres, J.; Uneri, A.; De Silva, T.; Ketcha, M.; Reaungamornrat, S.; Jacobson, M.; Vogt, S.; Kleinszig, G.; Osgood, G.; Wolinsky, J.-P.; Siewerdsen, J. H.

    2017-04-01

    Spinal screw placement is a challenging task due to small bone corridors and high risk of neurological or vascular complications, benefiting from precision guidance/navigation and quality assurance (QA). Implicit to both guidance and QA is the definition of a surgical plan—i.e. the desired trajectories and device selection for target vertebrae—conventionally requiring time-consuming manual annotations by a skilled surgeon. We propose automation of such planning by deriving the pedicle trajectory and device selection from a patient’s preoperative CT or MRI. An atlas of vertebrae surfaces was created to provide the underlying basis for automatic planning—in this work, comprising 40 exemplary vertebrae at three levels of the spine (T7, T8, and L3). The atlas was enriched with ideal trajectory annotations for 60 pedicles in total. To define trajectories for a given patient, sparse deformation fields from the atlas surfaces to the input (CT or MR image) are applied on the annotated trajectories. Mean value coordinates are used to interpolate dense deformation fields. The pose of a straight trajectory is optimized by image-based registration to an accumulated volume of the deformed annotations. For evaluation, input deformation fields were created using coherent point drift (CPD) to perform a leave-one-out analysis over the atlas surfaces. CPD registration demonstrated surface error of 0.89  ±  0.10 mm (median  ±  interquartile range) for T7/T8 and 1.29  ±  0.15 mm for L3. At the pedicle center, registered trajectories deviated from the expert reference by 0.56  ±  0.63 mm (T7/T8) and 1.12  ±  0.67 mm (L3). The predicted maximum screw diameter differed by 0.45  ±  0.62 mm (T7/T8), and 1.26  ±  1.19 mm (L3). The automated planning method avoided screw collisions in all cases and demonstrated close agreement overall with expert reference plans, offering a potentially valuable tool in support

  20. Multi-atlas learner fusion: An efficient segmentation approach for large-scale data.

    PubMed

    Asman, Andrew J; Huo, Yuankai; Plassard, Andrew J; Landman, Bennett A

    2015-12-01

    We propose multi-atlas learner fusion (MLF), a framework for rapidly and accurately replicating the highly accurate, yet computationally expensive, multi-atlas segmentation framework based on fusing local learners. In the largest whole-brain multi-atlas study yet reported, multi-atlas segmentations are estimated for a training set of 3464 MR brain images. Using these multi-atlas estimates we (1) estimate a low-dimensional representation for selecting locally appropriate example images, and (2) build AdaBoost learners that map a weak initial segmentation to the multi-atlas segmentation result. Thus, to segment a new target image we project the image into the low-dimensional space, construct a weak initial segmentation, and fuse the trained, locally selected, learners. The MLF framework cuts the runtime on a modern computer from 36 h down to 3-8 min - a 270× speedup - by completely bypassing the need for deformable atlas-target registrations. Additionally, we (1) describe a technique for optimizing the weak initial segmentation and the AdaBoost learning parameters, (2) quantify the ability to replicate the multi-atlas result with mean accuracies approaching the multi-atlas intra-subject reproducibility on a testing set of 380 images, (3) demonstrate significant increases in the reproducibility of intra-subject segmentations when compared to a state-of-the-art multi-atlas framework on a separate reproducibility dataset, (4) show that under the MLF framework the large-scale data model significantly improve the segmentation over the small-scale model under the MLF framework, and (5) indicate that the MLF framework has comparable performance as state-of-the-art multi-atlas segmentation algorithms without using non-local information. Copyright © 2015 Elsevier B.V. All rights reserved.

  1. ATLAS. Association of Teachers of Latin American Studies. [Newsletter] Volume 3, Number 1. October, 1973.

    ERIC Educational Resources Information Center

    Association of Teachers of Latin American Studies, Brooklyn, NY.

    The October 1973 issue of ATLAS, a newsletter for the Association of Teachers of Latin American Studies, is entered into the ERIC system on a one time basis to acquaint teachers with this resource. This issue reports summer activities and reviews new materials in this subject area. The events of the 1973 summer ATLAS-Fulbright Seminar to Mexico…

  2. ATLAS TDAQ System Administration: evolution and re-design

    NASA Astrophysics Data System (ADS)

    Ballestrero, S.; Bogdanchikov, A.; Brasolin, F.; Contescu, C.; Dubrov, S.; Fazio, D.; Korol, A.; Lee, C. J.; Scannicchio, D. A.; Twomey, M. S.

    2015-12-01

    The ATLAS Trigger and Data Acquisition system is responsible for the online processing of live data, streaming from the ATLAS experiment at the Large Hadron Collider at CERN. The online farm is composed of ∼3000 servers, processing the data read out from ∼100 million detector channels through multiple trigger levels. During the two years of the first Long Shutdown there has been a tremendous amount of work done by the ATLAS Trigger and Data Acquisition System Administrators, implementing numerous new software applications, upgrading the OS and the hardware, changing some design philosophies and exploiting the High- Level Trigger farm with different purposes. The OS version has been upgraded to SLC6; for the largest part of the farm, which is composed of net booted nodes, this required a completely new design of the net booting system. In parallel, the migration to Puppet of the Configuration Management systems has been completed for both net booted and local booted hosts; the Post-Boot Scripts system and Quattor have been consequently dismissed. Virtual Machine usage has been investigated and tested and many of the core servers are now running on Virtual Machines. Virtualisation has also been used to adapt the High-Level Trigger farm as a batch system, which has been used for running Monte Carlo production jobs that are mostly CPU and not I/O bound. Finally, monitoring the health and the status of ∼3000 machines in the experimental area is obviously of the utmost importance, so the obsolete Nagios v2 has been replaced with Icinga, complemented by Ganglia as a performance data provider. This paper serves for reporting of the actions taken by the Systems Administrators in order to improve and produce a system capable of performing for the next three years of ATLAS data taking.

  3. Taus at ATLAS

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Demers, Sarah M.

    2017-12-06

    The grant "Taus at ATLAS" supported the group of Sarah Demers at Yale University over a period of 8.5 months, bridging the time between her Early Career Award and her inclusion on Yale's grant cycle within the Department of Energy's Office of Science. The work supported the functioning of the ATLAS Experiment at CERN's Large Hadron Collider and the analysis of ATLAS data. The work included searching for the Higgs Boson in a particular mode of its production (with a W or Z boson) and decay (to a pair of tau leptons.) This was part of a broad program ofmore » characterizing the Higgs boson as we try to understand this recently discovered particle, and whether or not it matches our expectations within the current standard model of particle physics. In addition, group members worked with simulation to understand the physics reach of planned upgrades to the ATLAS experiment. Supported group members include postdoctoral researcher Lotte Thomsen and graduate student Mariel Pettee.« less

  4. AGIS: The ATLAS Grid Information System

    NASA Astrophysics Data System (ADS)

    Anisenkov, A.; Di Girolamo, A.; Klimentov, A.; Oleynik, D.; Petrosyan, A.; Atlas Collaboration

    2014-06-01

    ATLAS, a particle physics experiment at the Large Hadron Collider at CERN, produced petabytes of data annually through simulation production and tens of petabytes of data per year from the detector itself. The ATLAS computing model embraces the Grid paradigm and a high degree of decentralization and computing resources able to meet ATLAS requirements of petabytes scale data operations. In this paper we describe the ATLAS Grid Information System (AGIS), designed to integrate configuration and status information about resources, services and topology of the computing infrastructure used by the ATLAS Distributed Computing applications and services.

  5. Schaltenbrand-Wahren-Talairach-Tournoux brain atlas registration

    NASA Astrophysics Data System (ADS)

    Nowinski, Wieslaw L.; Fang, Anthony; Nguyen, Bonnie T.

    1995-04-01

    The CIeMed electronic brain atlas system contains electronic versions of multiple paper brain atlases with 3D extensions; some other 3D brain atlases are under development. Its primary goal is to provide automatic labeling and quantification of brains. The atlas data are digitized, enhanced, color coded, labeled, and organized into volumes. The atlas system provides several tools for registration, 3D display and real-time manipulation, object extraction/editing, quantification, image processing and analysis, reformatting, anatomical index operations, and file handling. The two main stereotactic atlases provided by the system are electronic and enhanced versions of Atlas of Stereotaxy of the Human Brain by Schaltenbrand and Wahren and Co-Planar Stereotactic Atlas of the Human Brain by Talairach and Tournoux. Each of these atlases has its own strengths and their combination has several advantages. First, a complementary information is merged and provided to the user. Second, the user can register data with a single atlas only, as the Schaltenbrand-Wahren-Talairach-Tournoux registration is data-independent. And last but not least, a direct registration of the Schaltenbrand-Wahren microseries with MRI data may not be feasible, since cerebral deep structures are usually not clearly discernible on MRI images. This paper addresses registration of the Schaltenbrand- Wahren and Talairach-Tournoux brain atlases. A modified proportional grid system transformation is introduced and suitable sets of landmarks identifiable in both atlases are defined. The accuracy of registration is discussed. A continuous navigation in the multi- atlas/patient data space is presented.

  6. The ATLAS multi-user upgrade and potential applications

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Mustapha, B.; Nolen, J. A.; Savard, G.

    With the recent integration of the CARIBU-EBIS charge breeder into the ATLAS accelerator system to provide for more pure and efficient charge breeding of radioactive beams, a multi-user upgrade of the ATLAS facility is being proposed to serve multiple users simultaneously. ATLAS was the first superconducting ion linac in the world and is the US DOE low-energy Nuclear Physics National User Facility. The proposed upgrade will take advantage of the continuous-wave nature of ATLAS and the pulsed nature of the EBIS charge breeder in order to simultaneously accelerate two beams with very close mass-to-charge ratios; one stable from the existingmore » ECR ion source and one radioactive from the newly commissioned EBIS charge breeder. In addition to enhancing the nuclear physics program, beam extraction at different points along the linac will open up the opportunity for other potential applications; for instance, material irradiation studies at ~ 1 MeV/u and isotope production at ~ 6 MeV/u or at the full ATLAS energy of ~ 15 MeV/u. The concept and proposed implementation of the ATLAS multi-user upgrade will be presented. Future plans to enhance the flexibility of this upgrade will also be presented.« less

  7. The ATLAS multi-user upgrade and potential applications

    NASA Astrophysics Data System (ADS)

    Mustapha, B.; Nolen, J. A.; Savard, G.; Ostroumov, P. N.

    2017-12-01

    With the recent integration of the CARIBU-EBIS charge breeder into the ATLAS accelerator system to provide for more pure and efficient charge breeding of radioactive beams, a multi-user upgrade of the ATLAS facility is being proposed to serve multiple users simultaneously. ATLAS was the first superconducting ion linac in the world and is the US DOE low-energy Nuclear Physics National User Facility. The proposed upgrade will take advantage of the continuous-wave nature of ATLAS and the pulsed nature of the EBIS charge breeder in order to simultaneously accelerate two beams with very close mass-to-charge ratios; one stable from the existing ECR ion source and one radioactive from the newly commissioned EBIS charge breeder. In addition to enhancing the nuclear physics program, beam extraction at different points along the linac will open up the opportunity for other potential applications; for instance, material irradiation studies at ~1 MeV/u, isotope production and radiobiological studies at ~6 MeV/u and at the full ATLAS energy of ~15 MeV/u. The concept and proposed implementation of the ATLAS multi-user upgrade will be discussed. Future plans to enhance the flexibility of this upgrade will be presented.

  8. EnviroAtlas - Reptile Biodiversity Ecosystem Services Metrics by 12-digit HUC for the Conterminous United States

    EPA Pesticide Factsheets

    This EnviroAtlas dataset contains biodiversity metrics reflecting ecosystem services or other aspects of biodiversity for reptile species, based on the number of reptile species as measured by predicted habitat present within a pixel. These metrics were created from grouping national level single species habitat models created by the USGS Gap Analysis Program into smaller ecologically based, phylogeny based, or stakeholder suggested composites. The dataset includes reptile species richness metrics for all reptile species, lizards, snakes, turtles, poisonous reptiles, Natureserve-listed G1,G2, and G3 reptile species, and reptile species listed by IUCN (International Union for Conservation of Nature), PARC (Partners in Amphibian and Reptile Conservation) and SWPARC (Southwest Partners in Amphibian and Reptile Conservation). This dataset was produced by a joint effort of New Mexico State University, US EPA, and USGS to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa

  9. The Cerefy Neuroradiology Atlas: a Talairach-Tournoux atlas-based tool for analysis of neuroimages available over the internet.

    PubMed

    Nowinski, Wieslaw L; Belov, Dmitry

    2003-09-01

    The article introduces an atlas-assisted method and a tool called the Cerefy Neuroradiology Atlas (CNA), available over the Internet for neuroradiology and human brain mapping. The CNA contains an enhanced, extended, and fully segmented and labeled electronic version of the Talairach-Tournoux brain atlas, including parcelated gyri and Brodmann's areas. To our best knowledge, this is the first online, publicly available application with the Talairach-Tournoux atlas. The process of atlas-assisted neuroimage analysis is done in five steps: image data loading, Talairach landmark setting, atlas normalization, image data exploration and analysis, and result saving. Neuroimage analysis is supported by a near-real-time, atlas-to-data warping based on the Talairach transformation. The CNA runs on multiple platforms; is able to process simultaneously multiple anatomical and functional data sets; and provides functions for a rapid atlas-to-data registration, interactive structure labeling and annotating, and mensuration. It is also empowered with several unique features, including interactive atlas warping facilitating fine tuning of atlas-to-data fit, navigation on the triplanar formed by the image data and the atlas, multiple-images-in-one display with interactive atlas-anatomy-function blending, multiple label display, and saving of labeled and annotated image data. The CNA is useful for fast atlas-assisted analysis of neuroimage data sets. It increases accuracy and reduces time in localization analysis of activation regions; facilitates to communicate the information on the interpreted scans from the neuroradiologist to other clinicians and medical students; increases the neuroradiologist's confidence in terms of anatomy and spatial relationships; and serves as a user-friendly, public domain tool for neuroeducation. At present, more than 700 users from five continents have subscribed to the CNA.

  10. ATLAS (Automatic Tool for Local Assembly Structures) - A Comprehensive Infrastructure for Assembly, Annotation, and Genomic Binning of Metagenomic and Metaranscripomic Data

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    White, Richard A.; Brown, Joseph M.; Colby, Sean M.

    ATLAS (Automatic Tool for Local Assembly Structures) is a comprehensive multiomics data analysis pipeline that is massively parallel and scalable. ATLAS contains a modular analysis pipeline for assembly, annotation, quantification and genome binning of metagenomics and metatranscriptomics data and a framework for reference metaproteomic database construction. ATLAS transforms raw sequence data into functional and taxonomic data at the microbial population level and provides genome-centric resolution through genome binning. ATLAS provides robust taxonomy based on majority voting of protein coding open reading frames rolled-up at the contig level using modified lowest common ancestor (LCA) analysis. ATLAS provides robust taxonomy based onmore » majority voting of protein coding open reading frames rolled-up at the contig level using modified lowest common ancestor (LCA) analysis. ATLAS is user-friendly, easy install through bioconda maintained as open-source on GitHub, and is implemented in Snakemake for modular customizable workflows.« less

  11. Atlas-guided prostate intensity modulated radiation therapy (IMRT) planning.

    PubMed

    Sheng, Yang; Li, Taoran; Zhang, You; Lee, W Robert; Yin, Fang-Fang; Ge, Yaorong; Wu, Q Jackie

    2015-09-21

    An atlas-based IMRT planning technique for prostate cancer was developed and evaluated. A multi-dose atlas was built based on the anatomy patterns of the patients, more specifically, the percent distance to the prostate and the concaveness angle formed by the seminal vesicles relative to the anterior-posterior axis. A 70-case dataset was classified using a k-medoids clustering analysis to recognize anatomy pattern variations in the dataset. The best classification, defined by the number of classes or medoids, was determined by the largest value of the average silhouette width. Reference plans from each class formed a multi-dose atlas. The atlas-guided planning (AGP) technique started with matching the new case anatomy pattern to one of the reference cases in the atlas; then a deformable registration between the atlas and new case anatomies transferred the dose from the atlas to the new case to guide inverse planning with full automation. 20 additional clinical cases were re-planned to evaluate the AGP technique. Dosimetric properties between AGP and clinical plans were evaluated. The classification analysis determined that the 5-case atlas would best represent anatomy patterns for the patient cohort. AGP took approximately 1 min on average (corresponding to 70 iterations of optimization) for all cases. When dosimetric parameters were compared, the differences between AGP and clinical plans were less than 3.5%, albeit some statistical significances observed: homogeneity index (p  >  0.05), conformity index (p  <  0.01), bladder gEUD (p  <  0.01), and rectum gEUD (p  =  0.02). Atlas-guided treatment planning is feasible and efficient. Atlas predicted dose can effectively guide the optimizer to achieve plan quality comparable to that of clinical plans.

  12. Search for lepton-flavour-violating decays of the Higgs and Z bosons with the ATLAS detector

    NASA Astrophysics Data System (ADS)

    Aad, G.; Abbott, B.; Abdallah, J.; Abdinov, O.; Abeloos, B.; Aben, R.; Abolins, M.; AbouZeid, O. S.; Abraham, N. L.; Abramowicz, H.; Abreu, H.; Abreu, R.; Abulaiti, Y.; Acharya, B. S.; Adamczyk, L.; Adams, D. L.; Adelman, J.; Adomeit, S.; Adye, T.; Affolder, A. A.; Agatonovic-Jovin, T.; Agricola, J.; Aguilar-Saavedra, J. A.; Ahlen, S. P.; Ahmadov, F.; Aielli, G.; Akerstedt, H.; Åkesson, T. P. A.; Akimov, A. V.; Alberghi, G. L.; Albert, J.; Albrand, S.; Verzini, M. J. Alconada; Aleksa, M.; Aleksandrov, I. N.; Alexa, C.; Alexander, G.; Alexopoulos, T.; Alhroob, M.; Aliev, M.; Alimonti, G.; Alison, J.; Alkire, S. P.; Allbrooke, B. M. M.; Allen, B. W.; Allport, P. P.; Aloisio, A.; Alonso, A.; Alonso, F.; Alpigiani, C.; Alstaty, M.; Gonzalez, B. Alvarez; Piqueras, D. Álvarez; Alviggi, M. G.; Amadio, B. T.; Amako, K.; Coutinho, Y. Amaral; Amelung, C.; Amidei, D.; Santos, S. P. Amor Dos; Amorim, A.; Amoroso, S.; Amundsen, G.; Anastopoulos, C.; Ancu, L. S.; Andari, N.; Andeen, T.; Anders, C. F.; Anders, G.; Anders, J. K.; Anderson, K. J.; Andreazza, A.; Andrei, V.; Angelidakis, S.; Angelozzi, I.; Anger, P.; Angerami, A.; Anghinolfi, F.; Anisenkov, A. V.; Anjos, N.; Annovi, A.; Antonelli, M.; Antonov, A.; Antos, J.; Anulli, F.; Aoki, M.; Bella, L. Aperio; Arabidze, G.; Arai, Y.; Araque, J. P.; Arce, A. T. H.; Arduh, F. A.; Arguin, J.-F.; Argyropoulos, S.; Arik, M.; Armbruster, A. J.; Armitage, L. J.; Arnaez, O.; Arnold, H.; Arratia, M.; Arslan, O.; Artamonov, A.; Artoni, G.; Artz, S.; Asai, S.; Asbah, N.; Ashkenazi, A.; Åsman, B.; Asquith, L.; Assamagan, K.; Astalos, R.; Atkinson, M.; Atlay, N. B.; Augsten, K.; Avolio, G.; Axen, B.; Ayoub, M. K.; Azuelos, G.; Baak, M. A.; Baas, A. E.; Baca, M. J.; Bachacou, H.; Bachas, K.; Backes, M.; Backhaus, M.; Bagiacchi, P.; Bagnaia, P.; Bai, Y.; Baines, J. T.; Baker, O. K.; Baldin, E. M.; Balek, P.; Balestri, T.; Balli, F.; Balunas, W. K.; Banas, E.; Banerjee, Sw.; Bannoura, A. A. E.; Barak, L.; Barberio, E. L.; Barberis, D.; Barbero, M.; Barillari, T.; Barklow, T.; Barlow, N.; Barnes, S. L.; Barnett, B. M.; Barnett, R. M.; Barnovska, Z.; Baroncelli, A.; Barone, G.; Barr, A. J.; Navarro, L. Barranco; Barreiro, F.; da Costa, J. Barreiro Guimarães; Bartoldus, R.; Barton, A. E.; Bartos, P.; Basalaev, A.; Bassalat, A.; Bates, R. L.; Batista, S. J.; Batley, J. R.; Battaglia, M.; Bauce, M.; Bauer, F.; Bawa, H. S.; Beacham, J. B.; Beattie, M. D.; Beau, T.; Beauchemin, P. H.; Bechtle, P.; Beck, H. P.; Becker, K.; Becker, M.; Beckingham, M.; Becot, C.; Beddall, A. J.; Beddall, A.; Bednyakov, V. A.; Bedognetti, M.; Bee, C. P.; Beemster, L. J.; Beermann, T. A.; Begel, M.; Behr, J. K.; Belanger-Champagne, C.; Bell, A. S.; Bella, G.; Bellagamba, L.; Bellerive, A.; Bellomo, M.; Belotskiy, K.; Beltramello, O.; Belyaev, N. L.; Benary, O.; Benchekroun, D.; Bender, M.; Bendtz, K.; Benekos, N.; Benhammou, Y.; Noccioli, E. Benhar; Benitez, J.; Garcia, J. A. Benitez; Benjamin, D. P.; Bensinger, J. R.; Bentvelsen, S.; Beresford, L.; Beretta, M.; Berge, D.; Kuutmann, E. Bergeaas; Berger, N.; Beringer, J.; Berlendis, S.; Bernard, N. R.; Bernius, C.; Bernlochner, F. U.; Berry, T.; Berta, P.; Bertella, C.; Bertoli, G.; Bertolucci, F.; Bertram, I. A.; Bertsche, C.; Bertsche, D.; Besjes, G. J.; Bylund, O. Bessidskaia; Bessner, M.; Besson, N.; Betancourt, C.; Bethke, S.; Bevan, A. J.; Bhimji, W.; Bianchi, R. M.; Bianchini, L.; Bianco, M.; Biebel, O.; Biedermann, D.; Bielski, R.; Biesuz, N. V.; Biglietti, M.; De Mendizabal, J. Bilbao; Bilokon, H.; Bindi, M.; Binet, S.; Bingul, A.; Bini, C.; Biondi, S.; Bjergaard, D. M.; Black, C. W.; Black, J. E.; Black, K. M.; Blackburn, D.; Blair, R. E.; Blanchard, J.-B.; Blanco, J. E.; Blazek, T.; Bloch, I.; Blocker, C.; Blum, W.; Blumenschein, U.; Blunier, S.; Bobbink, G. J.; Bobrovnikov, V. S.; Bocchetta, S. S.; Bocci, A.; Bock, C.; Boehler, M.; Boerner, D.; Bogaerts, J. A.; Bogavac, D.; Bogdanchikov, A. G.; Bohm, C.; Boisvert, V.; Bokan, P.; Bold, T.; Boldyrev, A. S.; Bomben, M.; Bona, M.; Boonekamp, M.; Borisov, A.; Borissov, G.; Bortfeldt, J.; Bortoletto, D.; Bortolotto, V.; Bos, K.; Boscherini, D.; Bosman, M.; Sola, J. D. Bossio; Boudreau, J.; Bouffard, J.; Bouhova-Thacker, E. V.; Boumediene, D.; Bourdarios, C.; Boutle, S. K.; Boveia, A.; Boyd, J.; Boyko, I. R.; Bracinik, J.; Brandt, A.; Brandt, G.; Brandt, O.; Bratzler, U.; Brau, B.; Brau, J. E.; Braun, H. M.; Madden, W. D. Breaden; Brendlinger, K.; Brennan, A. J.; Brenner, L.; Brenner, R.; Bressler, S.; Bristow, T. M.; Britton, D.; Britzger, D.; Brochu, F. M.; Brock, I.; Brock, R.; Brooijmans, G.; Brooks, T.; Brooks, W. K.; Brosamer, J.; Brost, E.; Broughton, J. H.; de Renstrom, P. A. Bruckman; Bruncko, D.; Bruneliere, R.; Bruni, A.; Bruni, G.; Brunt, BH; Bruschi, M.; Bruscino, N.; Bryant, P.; Bryngemark, L.; Buanes, T.; Buat, Q.; Buchholz, P.; Buckley, A. G.; Budagov, I. A.; Buehrer, F.; Bugge, M. K.; Bulekov, O.; Bullock, D.; Burckhart, H.; Burdin, S.; Burgard, C. D.; Burghgrave, B.; Burka, K.; Burke, S.; Burmeister, I.; Busato, E.; Büscher, D.; Büscher, V.; Bussey, P.; Butler, J. M.; Buttar, C. M.; Butterworth, J. M.; Butti, P.; Buttinger, W.; Buzatu, A.; Buzykaev, A. R.; Urbán, S. Cabrera; Caforio, D.; Cairo, V. M.; Cakir, O.; Calace, N.; Calafiura, P.; Calandri, A.; Calderini, G.; Calfayan, P.; Caloba, L. P.; Calvet, D.; Calvet, S.; Calvet, T. P.; Toro, R. Camacho; Camarda, S.; Camarri, P.; Cameron, D.; Armadans, R. Caminal; Camincher, C.; Campana, S.; Campanelli, M.; Camplani, A.; Campoverde, A.; Canale, V.; Canepa, A.; Bret, M. Cano; Cantero, J.; Cantrill, R.; Cao, T.; Garrido, M. D. M. Capeans; Caprini, I.; Caprini, M.; Capua, M.; Caputo, R.; Carbone, R. M.; Cardarelli, R.; Cardillo, F.; Carli, I.; Carli, T.; Carlino, G.; Carminati, L.; Caron, S.; Carquin, E.; Carrillo-Montoya, G. D.; Carter, J. R.; Carvalho, J.; Casadei, D.; Casado, M. P.; Casolino, M.; Casper, D. W.; Castaneda-Miranda, E.; Castelli, A.; Gimenez, V. Castillo; Castro, N. F.; Catinaccio, A.; Catmore, J. R.; Cattai, A.; Caudron, J.; Cavaliere, V.; Cavallaro, E.; Cavalli, D.; Cavalli-Sforza, M.; Cavasinni, V.; Ceradini, F.; Alberich, L. Cerda; Cerio, B. C.; Cerqueira, A. S.; Cerri, A.; Cerrito, L.; Cerutti, F.; Cerv, M.; Cervelli, A.; Cetin, S. A.; Chafaq, A.; Chakraborty, D.; Chan, S. K.; Chan, Y. L.; Chang, P.; Chapman, J. D.; Charlton, D. G.; Chatterjee, A.; Chau, C. C.; Barajas, C. A. Chavez; Che, S.; Cheatham, S.; Chegwidden, A.; Chekanov, S.; Chekulaev, S. V.; Chelkov, G. A.; Chelstowska, M. A.; Chen, C.; Chen, H.; Chen, K.; Chen, S.; Chen, S.; Chen, X.; Chen, Y.; Cheng, H. C.; Cheng, H. J.; Cheng, Y.; Cheplakov, A.; Cheremushkina, E.; Moursli, R. Cherkaoui El; Chernyatin, V.; Cheu, E.; Chevalier, L.; Chiarella, V.; Chiarelli, G.; Chiodini, G.; Chisholm, A. S.; Chitan, A.; Chizhov, M. V.; Choi, K.; Chomont, A. R.; Chouridou, S.; Chow, B. K. B.; Christodoulou, V.; Chromek-Burckhart, D.; Chudoba, J.; Chuinard, A. J.; Chwastowski, J. J.; Chytka, L.; Ciapetti, G.; Ciftci, A. K.; Cinca, D.; Cindro, V.; Cioara, I. A.; Ciocio, A.; Cirotto, F.; Citron, Z. H.; Citterio, M.; Ciubancan, M.; Clark, A.; Clark, B. L.; Clark, M. R.; Clark, P. J.; Clarke, R. N.; Clement, C.; Coadou, Y.; Cobal, M.; Coccaro, A.; Cochran, J.; Coffey, L.; Colasurdo, L.; Cole, B.; Cole, S.; Colijn, A. P.; Collot, J.; Colombo, T.; Compostella, G.; Muiño, P. Conde; Coniavitis, E.; Connell, S. H.; Connelly, I. A.; Consorti, V.; Constantinescu, S.; Conta, C.; Conti, G.; Conventi, F.; Cooke, M.; Cooper, B. D.; Cooper-Sarkar, A. M.; Cormier, K. J. R.; Cornelissen, T.; Corradi, M.; Corriveau, F.; Corso-Radu, A.; Cortes-Gonzalez, A.; Cortiana, G.; Costa, G.; Costa, M. J.; Costanzo, D.; Cottin, G.; Cowan, G.; Cox, B. E.; Cranmer, K.; Crawley, S. J.; Cree, G.; Crépé-Renaudin, S.; Crescioli, F.; Cribbs, W. A.; Ortuzar, M. Crispin; Cristinziani, M.; Croft, V.; Crosetti, G.; Donszelmann, T. Cuhadar; Cummings, J.; Curatolo, M.; Cúth, J.; Cuthbert, C.; Czirr, H.; Czodrowski, P.; D'Auria, S.; D'Onofrio, M.; De Sousa, M. J. Da Cunha Sargedas; Via, C. Da; Dabrowski, W.; Dado, T.; Dai, T.; Dale, O.; Dallaire, F.; Dallapiccola, C.; Dam, M.; Dandoy, J. R.; Dang, N. P.; Daniells, A. C.; Dann, N. S.; Danninger, M.; Hoffmann, M. Dano; Dao, V.; Darbo, G.; Darmora, S.; Dassoulas, J.; Dattagupta, A.; Davey, W.; David, C.; Davidek, T.; Davies, M.; Davison, P.; Dawe, E.; Dawson, I.; Daya-Ishmukhametova, R. K.; De, K.; de Asmundis, R.; De Benedetti, A.; De Castro, S.; De Cecco, S.; De Groot, N.; de Jong, P.; De la Torre, H.; De Lorenzi, F.; De Pedis, D.; De Salvo, A.; De Sanctis, U.; De Santo, A.; De Regie, J. B. De Vivie; Dearnaley, W. J.; Debbe, R.; Debenedetti, C.; Dedovich, D. V.; Deigaard, I.; Del Gaudio, M.; Del Peso, J.; Del Prete, T.; Delgove, D.; Deliot, F.; Delitzsch, C. M.; Deliyergiyev, M.; Dell'Acqua, A.; Dell'Asta, L.; Dell'Orso, M.; Della Pietra, M.; della Volpe, D.; Delmastro, M.; Delsart, P. A.; Deluca, C.; DeMarco, D. A.; Demers, S.; Demichev, M.; Demilly, A.; Denisov, S. P.; Denysiuk, D.; Derendarz, D.; Derkaoui, J. E.; Derue, F.; Dervan, P.; Desch, K.; Deterre, C.; Dette, K.; Deviveiros, P. O.; Dewhurst, A.; Dhaliwal, S.; Di Ciaccio, A.; Di Ciaccio, L.; Di Clemente, W. K.; Di Donato, C.; Di Girolamo, A.; Di Girolamo, B.; Di Micco, B.; Di Nardo, R.; Di Simone, A.; Di Sipio, R.; Di Valentino, D.; Diaconu, C.; Diamond, M.; Dias, F. A.; Diaz, M. A.; Diehl, E. B.; Dietrich, J.; Diglio, S.; Dimitrievska, A.; Dingfelder, J.; Dita, P.; Dita, S.; Dittus, F.; Djama, F.; Djobava, T.; Djuvsland, J. I.; do Vale, M. A. B.; Dobos, D.; Dobre, M.; Doglioni, C.; Dohmae, T.; Dolejsi, J.; Dolezal, Z.; Dolgoshein, B. A.; Donadelli, M.; Donati, S.; Dondero, P.; Donini, J.; Dopke, J.; Doria, A.; Dova, M. T.; Doyle, A. T.; Drechsler, E.; Dris, M.; Du, Y.; Duarte-Campderros, J.; Duchovni, E.; Duckeck, G.; Ducu, O. A.; Duda, D.; Dudarev, A.; Duflot, L.; Duguid, L.; Dührssen, M.; Dumancic, M.; Dunford, M.; Yildiz, H. Duran; Düren, M.; Durglishvili, A.; Duschinger, D.; Dutta, B.; Dyndal, M.; Eckardt, C.; Ecker, K. M.; Edgar, R. C.; Edwards, N. C.; Eifert, T.; Eigen, G.; Einsweiler, K.; Ekelof, T.; Kacimi, M. El; Ellajosyula, V.; Ellert, M.; Elles, S.; Ellinghaus, F.; Elliot, A. A.; Ellis, N.; Elmsheuser, J.; Elsing, M.; Emeliyanov, D.; Enari, Y.; Endner, O. C.; Endo, M.; Ennis, J. S.; Erdmann, J.; Ereditato, A.; Ernis, G.; Ernst, J.; Ernst, M.; Errede, S.; Ertel, E.; Escalier, M.; Esch, H.; Escobar, C.; Esposito, B.; Etienvre, A. I.; Etzion, E.; Evans, H.; Ezhilov, A.; Fabbri, F.; Fabbri, L.; Facini, G.; Fakhrutdinov, R. M.; Falciano, S.; Falla, R. J.; Faltova, J.; Fang, Y.; Fanti, M.; Farbin, A.; Farilla, A.; Farina, C.; Farooque, T.; Farrell, S.; Farrington, S. M.; Farthouat, P.; Fassi, F.; Fassnacht, P.; Fassouliotis, D.; Giannelli, M. Faucci; Favareto, A.; Fawcett, W. J.; Fayard, L.; Fedin, O. L.; Fedorko, W.; Feigl, S.; Feligioni, L.; Feng, C.; Feng, E. J.; Feng, H.; Fenyuk, A. B.; Feremenga, L.; Martinez, P. Fernandez; Perez, S. Fernandez; Ferrando, J.; Ferrari, A.; Ferrari, P.; Ferrari, R.; de Lima, D. E. Ferreira; Ferrer, A.; Ferrere, D.; Ferretti, C.; Parodi, A. Ferretto; Fiedler, F.; Filipčič, A.; Filipuzzi, M.; Filthaut, F.; Fincke-Keeler, M.; Finelli, K. D.; Fiolhais, M. C. N.; Fiorini, L.; Firan, A.; Fischer, A.; Fischer, C.; Fischer, J.; Fisher, W. C.; Flaschel, N.; Fleck, I.; Fleischmann, P.; Fletcher, G. T.; Fletcher, R. R. M.; Flick, T.; Floderus, A.; Castillo, L. R. Flores; Flowerdew, M. J.; Forcolin, G. T.; Formica, A.; Forti, A.; Foster, A. G.; Fournier, D.; Fox, H.; Fracchia, S.; Francavilla, P.; Franchini, M.; Francis, D.; Franconi, L.; Franklin, M.; Frate, M.; Fraternali, M.; Freeborn, D.; Fressard-Batraneanu, S. M.; Friedrich, F.; Froidevaux, D.; Frost, J. A.; Fukunaga, C.; Torregrosa, E. Fullana; Fusayasu, T.; Fuster, J.; Gabaldon, C.; Gabizon, O.; Gabrielli, A.; Gabrielli, A.; Gach, G. P.; Gadatsch, S.; Gadomski, S.; Gagliardi, G.; Gagnon, L. G.; Gagnon, P.; Galea, C.; Galhardo, B.; Gallas, E. J.; Gallop, B. J.; Gallus, P.; Galster, G.; Gan, K. K.; Gao, J.; Gao, Y.; Gao, Y. S.; Walls, F. M. Garay; García, C.; Navarro, J. E. García; Garcia-Sciveres, M.; Gardner, R. W.; Garelli, N.; Garonne, V.; Bravo, A. Gascon; Gatti, C.; Gaudiello, A.; Gaudio, G.; Gaur, B.; Gauthier, L.; Gavrilenko, I. L.; Gay, C.; Gaycken, G.; Gazis, E. N.; Gecse, Z.; Gee, C. N. P.; Geich-Gimbel, Ch.; Geisler, M. P.; Gemme, C.; Genest, M. H.; Geng, C.; Gentile, S.; George, S.; Gerbaudo, D.; Gershon, A.; Ghasemi, S.; Ghazlane, H.; Ghneimat, M.; Giacobbe, B.; Giagu, S.; Giannetti, P.; Gibbard, B.; Gibson, S. M.; Gignac, M.; Gilchriese, M.; Gillam, T. P. S.; Gillberg, D.; Gilles, G.; Gingrich, D. M.; Giokaris, N.; Giordani, M. P.; Giorgi, F. M.; Giorgi, F. M.; Giraud, P. F.; Giromini, P.; Giugni, D.; Giuli, F.; Giuliani, C.; Giulini, M.; Gjelsten, B. K.; Gkaitatzis, S.; Gkialas, I.; Gkougkousis, E. L.; Gladilin, L. K.; Glasman, C.; Glatzer, J.; Glaysher, P. C. F.; Glazov, A.; Goblirsch-Kolb, M.; Godlewski, J.; Goldfarb, S.; Golling, T.; Golubkov, D.; Gomes, A.; Gonçalo, R.; Costa, J. Goncalves Pinto Firmino Da; Gonella, L.; Gongadze, A.; de la Hoz, S. González; Parra, G. Gonzalez; Gonzalez-Sevilla, S.; Goossens, L.; Gorbounov, P. A.; Gordon, H. A.; Gorelov, I.; Gorini, B.; Gorini, E.; Gorišek, A.; Gornicki, E.; Goshaw, A. T.; Gössling, C.; Gostkin, M. I.; Goudet, C. R.; Goujdami, D.; Goussiou, A. G.; Govender, N.; Gozani, E.; Graber, L.; Grabowska-Bold, I.; Gradin, P. O. J.; Grafström, P.; Gramling, J.; Gramstad, E.; Grancagnolo, S.; Gratchev, V.; Gray, H. M.; Graziani, E.; Greenwood, Z. D.; Grefe, C.; Gregersen, K.; Gregor, I. M.; Grenier, P.; Grevtsov, K.; Griffiths, J.; Grillo, A. A.; Grimm, K.; Grinstein, S.; Gris, Ph.; Grivaz, J.-F.; Groh, S.; Grohs, J. P.; Gross, E.; Grosse-Knetter, J.; Grossi, G. C.; Grout, Z. J.; Guan, L.; Guan, W.; Guenther, J.; Guescini, F.; Guest, D.; Gueta, O.; Guido, E.; Guillemin, T.; Guindon, S.; Gul, U.; Gumpert, C.; Guo, J.; Guo, Y.; Gupta, S.; Gustavino, G.; Gutierrez, P.; Ortiz, N. G. Gutierrez; Gutschow, C.; Guyot, C.; Gwenlan, C.; Gwilliam, C. B.; Haas, A.; Haber, C.; Hadavand, H. K.; Haddad, N.; Hadef, A.; Haefner, P.; Hageböck, S.; Hajduk, Z.; Hakobyan, H.; Haleem, M.; Haley, J.; Halladjian, G.; Hallewell, G. D.; Hamacher, K.; Hamal, P.; Hamano, K.; Hamilton, A.; Hamity, G. N.; Hamnett, P. G.; Han, L.; Hanagaki, K.; Hanawa, K.; Hance, M.; Haney, B.; Hanke, P.; Hanna, R.; Hansen, J. B.; Hansen, J. D.; Hansen, M. C.; Hansen, P. H.; Hara, K.; Hard, A. S.; Harenberg, T.; Hariri, F.; Harkusha, S.; Harrington, R. D.; Harrison, P. F.; Hartjes, F.; Hasegawa, M.; Hasegawa, Y.; Hasib, A.; Hassani, S.; Haug, S.; Hauser, R.; Hauswald, L.; Havranek, M.; Hawkes, C. M.; Hawkings, R. J.; Hawkins, A. D.; Hayden, D.; Hays, C. P.; Hays, J. M.; Hayward, H. S.; Haywood, S. J.; Head, S. J.; Heck, T.; Hedberg, V.; Heelan, L.; Heim, S.; Heim, T.; Heinemann, B.; Heinrich, J. J.; Heinrich, L.; Heinz, C.; Hejbal, J.; Helary, L.; Hellman, S.; Helsens, C.; Henderson, J.; Henderson, R. C. W.; Heng, Y.; Henkelmann, S.; Correia, A. M. Henriques; Henrot-Versille, S.; Herbert, G. H.; Jiménez, Y. Hernández; Herten, G.; Hertenberger, R.; Hervas, L.; Hesketh, G. G.; Hessey, N. P.; Hetherly, J. W.; Hickling, R.; Higón-Rodriguez, E.; Hill, E.; Hill, J. C.; Hiller, K. H.; Hillier, S. J.; Hinchliffe, I.; Hines, E.; Hinman, R. R.; Hirose, M.; Hirschbuehl, D.; Hobbs, J.; Hod, N.; Hodgkinson, M. C.; Hodgson, P.; Hoecker, A.; Hoeferkamp, M. R.; Hoenig, F.; Hohlfeld, M.; Hohn, D.; Holmes, T. R.; Homann, M.; Hong, T. M.; Hooberman, B. H.; Hopkins, W. H.; Horii, Y.; Horton, A. J.; Hostachy, J.-Y.; Hou, S.; Hoummada, A.; Howarth, J.; Hrabovsky, M.; Hristova, I.; Hrivnac, J.; Hryn'ova, T.; Hrynevich, A.; Hsu, C.; Hsu, P. J.; Hsu, S.-C.; Hu, D.; Hu, Q.; Huang, Y.; Hubacek, Z.; Hubaut, F.; Huegging, F.; Huffman, T. B.; Hughes, E. W.; Hughes, G.; Huhtinen, M.; Hülsing, T. A.; Huo, P.; Huseynov, N.; Huston, J.; Huth, J.; Iacobucci, G.; Iakovidis, G.; Ibragimov, I.; Iconomidou-Fayard, L.; Ideal, E.; Idrissi, Z.; Iengo, P.; Igonkina, O.; Iizawa, T.; Ikegami, Y.; Ikeno, M.; Ilchenko, Y.; Iliadis, D.; Ilic, N.; Ince, T.; Introzzi, G.; Ioannou, P.; Iodice, M.; Iordanidou, K.; Ippolito, V.; Ishino, M.; Ishitsuka, M.; Ishmukhametov, R.; Issever, C.; Istin, S.; Ito, F.; Ponce, J. M. Iturbe; Iuppa, R.; Iwanski, W.; Iwasaki, H.; Izen, J. M.; Izzo, V.; Jabbar, S.; Jackson, B.; Jackson, M.; Jackson, P.; Jain, V.; Jakobi, K. B.; Jakobs, K.; Jakobsen, S.; Jakoubek, T.; Jamin, D. O.; Jana, D. K.; Jansen, E.; Jansky, R.; Janssen, J.; Janus, M.; Jarlskog, G.; Javadov, N.; Javůrek, T.; Jeanneau, F.; Jeanty, L.; Jejelava, J.; Jeng, G.-Y.; Jennens, D.; Jenni, P.; Jentzsch, J.; Jeske, C.; Jézéquel, S.; Ji, H.; Jia, J.; Jiang, H.; Jiang, Y.; Jiggins, S.; Pena, J. Jimenez; Jin, S.; Jinaru, A.; Jinnouchi, O.; Johansson, P.; Johns, K. A.; Johnson, W. J.; Jon-And, K.; Jones, G.; Jones, R. W. L.; Jones, S.; Jones, T. J.; Jongmanns, J.; Jorge, P. M.; Jovicevic, J.; Ju, X.; Rozas, A. Juste; Köhler, M. K.; Kaczmarska, A.; Kado, M.; Kagan, H.; Kagan, M.; Kahn, S. J.; Kajomovitz, E.; Kalderon, C. W.; Kaluza, A.; Kama, S.; Kamenshchikov, A.; Kanaya, N.; Kaneti, S.; Kanjir, L.; Kantserov, V. A.; Kanzaki, J.; Kaplan, B.; Kaplan, L. S.; Kapliy, A.; Kar, D.; Karakostas, K.; Karamaoun, A.; Karastathis, N.; Kareem, M. J.; Karentzos, E.; Karnevskiy, M.; Karpov, S. N.; Karpova, Z. M.; Karthik, K.; Kartvelishvili, V.; Karyukhin, A. N.; Kasahara, K.; Kashif, L.; Kass, R. D.; Kastanas, A.; Kataoka, Y.; Kato, C.; Katre, A.; Katzy, J.; Kawagoe, K.; Kawamoto, T.; Kawamura, G.; Kazama, S.; Kazanin, V. F.; Keeler, R.; Kehoe, R.; Keller, J. S.; Kempster, J. J.; Kentaro, K.; Keoshkerian, H.; Kepka, O.; Kerševan, B. P.; Kersten, S.; Keyes, R. A.; Khalil-zada, F.; Khanov, A.; Kharlamov, A. G.; Khoo, T. J.; Khovanskiy, V.; Khramov, E.; Khubua, J.; Kido, S.; Kim, H. Y.; Kim, S. H.; Kim, Y. K.; Kimura, N.; Kind, O. M.; King, B. T.; King, M.; King, S. B.; Kirk, J.; Kiryunin, A. E.; Kishimoto, T.; Kisielewska, D.; Kiss, F.; Kiuchi, K.; Kivernyk, O.; Kladiva, E.; Klein, M. H.; Klein, M.; Klein, U.; Kleinknecht, K.; Klimek, P.; Klimentov, A.; Klingenberg, R.; Klinger, J. A.; Klioutchnikova, T.; Kluge, E.-E.; Kluit, P.; Kluth, S.; Knapik, J.; Kneringer, E.; Knoops, E. B. F. G.; Knue, A.; Kobayashi, A.; Kobayashi, D.; Kobayashi, T.; Kobel, M.; Kocian, M.; Kodys, P.; Koffas, T.; Koffeman, E.; Koi, T.; Kolanoski, H.; Kolb, M.; Koletsou, I.; Komar, A. A.; Komori, Y.; Kondo, T.; Kondrashova, N.; Köneke, K.; König, A. C.; Kono, T.; Konoplich, R.; Konstantinidis, N.; Kopeliansky, R.; Koperny, S.; Köpke, L.; Kopp, A. K.; Korcyl, K.; Kordas, K.; Korn, A.; Korol, A. A.; Korolkov, I.; Korolkova, E. V.; Kortner, O.; Kortner, S.; Kosek, T.; Kostyukhin, V. V.; Kotwal, A.; Kourkoumeli-Charalampidi, A.; Kourkoumelis, C.; Kouskoura, V.; Kowalewska, A. B.; Kowalewski, R.; Kowalski, T. Z.; Kozakai, C.; Kozanecki, W.; Kozhin, A. S.; Kramarenko, V. A.; Kramberger, G.; Krasnopevtsev, D.; Krasny, M. W.; Krasznahorkay, A.; Kraus, J. K.; Kravchenko, A.; Kretz, M.; Kretzschmar, J.; Kreutzfeldt, K.; Krieger, P.; Krizka, K.; Kroeninger, K.; Kroha, H.; Kroll, J.; Kroseberg, J.; Krstic, J.; Kruchonak, U.; Krüger, H.; Krumnack, N.; Kruse, A.; Kruse, M. C.; Kruskal, M.; Kubota, T.; Kucuk, H.; Kuday, S.; Kuechler, J. T.; Kuehn, S.; Kugel, A.; Kuger, F.; Kuhl, A.; Kuhl, T.; Kukhtin, V.; Kukla, R.; Kulchitsky, Y.; Kuleshov, S.; Kuna, M.; Kunigo, T.; Kupco, A.; Kurashige, H.; Kurochkin, Y. A.; Kus, V.; Kuwertz, E. S.; Kuze, M.; Kvita, J.; Kwan, T.; Kyriazopoulos, D.; Rosa, A. La; Navarro, J. L. La Rosa; Rotonda, L. La; Lacasta, C.; Lacava, F.; Lacey, J.; Lacker, H.; Lacour, D.; Lacuesta, V. R.; Ladygin, E.; Lafaye, R.; Laforge, B.; Lagouri, T.; Lai, S.; Lammers, S.; Lampl, W.; Lançon, E.; Landgraf, U.; Landon, M. P. J.; Lang, V. S.; Lange, J. C.; Lankford, A. J.; Lanni, F.; Lantzsch, K.; Lanza, A.; Laplace, S.; Lapoire, C.; Laporte, J. F.; Lari, T.; Manghi, F. Lasagni; Lassnig, M.; Laurelli, P.; Lavrijsen, W.; Law, A. T.; Laycock, P.; Lazovich, T.; Lazzaroni, M.; Le, B.; Dortz, O. Le; Guirriec, E. Le; Quilleuc, E. P. Le; LeBlanc, M.; LeCompte, T.; Ledroit-Guillon, F.; Lee, C. A.; Lee, S. C.; Lee, L.; Lefebvre, G.; Lefebvre, M.; Legger, F.; Leggett, C.; Lehan, A.; Miotto, G. Lehmann; Lei, X.; Leight, W. A.; Leisos, A.; Leister, A. G.; Leite, M. A. L.; Leitner, R.; Lellouch, D.; Lemmer, B.; Leney, K. J. C.; Lenz, T.; Lenzi, B.; Leone, R.; Leone, S.; Leonidopoulos, C.; Leontsinis, S.; Lerner, G.; Leroy, C.; Lesage, A. A. J.; Lester, C. G.; Levchenko, M.; Levêque, J.; Levin, D.; Levinson, L. J.; Levy, M.; Leyko, A. M.; Leyton, M.; Li, B.; Li, H.; Li, H. L.; Li, L.; Li, L.; Li, Q.; Li, S.; Li, X.; Li, Y.; Liang, Z.; Liberti, B.; Liblong, A.; Lichard, P.; Lie, K.; Liebal, J.; Liebig, W.; Limosani, A.; Lin, S. C.; Lin, T. H.; Lindquist, B. E.; Lipeles, E.; Lipniacka, A.; Lisovyi, M.; Liss, T. M.; Lissauer, D.; Lister, A.; Litke, A. M.; Liu, B.; Liu, D.; Liu, H.; Liu, H.; Liu, J.; Liu, J. B.; Liu, K.; Liu, L.; Liu, M.; Liu, M.; Liu, Y. L.; Liu, Y.; Livan, M.; Lleres, A.; Merino, J. Llorente; Lloyd, S. L.; Sterzo, F. Lo; Lobodzinska, E.; Loch, P.; Lockman, W. S.; Loebinger, F. K.; Loevschall-Jensen, A. E.; Loew, K. M.; Loginov, A.; Lohse, T.; Lohwasser, K.; Lokajicek, M.; Long, B. A.; Long, J. D.; Long, R. E.; Longo, L.; Looper, K. A.; Lopes, L.; Mateos, D. Lopez; Paredes, B. Lopez; Paz, I. Lopez; Solis, A. Lopez; Lorenz, J.; Martinez, N. Lorenzo; Losada, M.; Lösel, P. J.; Lou, X.; Lounis, A.; Love, J.; Love, P. A.; Lu, H.; Lu, N.; Lubatti, H. J.; Luci, C.; Lucotte, A.; Luedtke, C.; Luehring, F.; Lukas, W.; Luminari, L.; Lundberg, O.; Lund-Jensen, B.; Lynn, D.; Lysak, R.; Lytken, E.; Lyubushkin, V.; Ma, H.; Ma, L. L.; Ma, Y.; Maccarrone, G.; Macchiolo, A.; Macdonald, C. M.; Maček, B.; Miguens, J. Machado; Madaffari, D.; Madar, R.; Maddocks, H. J.; Mader, W. F.; Madsen, A.; Maeda, J.; Maeland, S.; Maeno, T.; Maevskiy, A.; Magradze, E.; Mahlstedt, J.; Maiani, C.; Maidantchik, C.; Maier, A. A.; Maier, T.; Maio, A.; Majewski, S.; Makida, Y.; Makovec, N.; Malaescu, B.; Malecki, Pa.; Maleev, V. P.; Malek, F.; Mallik, U.; Malon, D.; Malone, C.; Maltezos, S.; Malyukov, S.; Mamuzic, J.; Mancini, G.; Mandelli, B.; Mandelli, L.; Mandić, I.; Maneira, J.; Filho, L. Manhaes de Andrade; Ramos, J. Manjarres; Mann, A.; Mansoulie, B.; Mansour, J. D.; Mantifel, R.; Mantoani, M.; Manzoni, S.; Mapelli, L.; Marceca, G.; March, L.; Marchiori, G.; Marcisovsky, M.; Marjanovic, M.; Marley, D. E.; Marroquim, F.; Marsden, S. P.; Marshall, Z.; Marti-Garcia, S.; Martin, B.; Martin, T. A.; Martin, V. J.; Latour, B. Martin dit; Martinez, M.; Martin-Haugh, S.; Martoiu, V. S.; Martyniuk, A. C.; Marx, M.; Marzin, A.; Masetti, L.; Mashimo, T.; Mashinistov, R.; Masik, J.; Maslennikov, A. L.; Massa, I.; Massa, L.; Mastrandrea, P.; Mastroberardino, A.; Masubuchi, T.; Mättig, P.; Mattmann, J.; Maurer, J.; Maxfield, S. J.; Maximov, D. A.; Mazini, R.; Mazza, S. M.; Fadden, N. C. Mc; Goldrick, G. Mc; Kee, S. P. Mc; McCarn, A.; McCarthy, R. L.; McCarthy, T. G.; McClymont, L. I.; McDonald, E. F.; McFarlane, K. W.; Mcfayden, J. A.; Mchedlidze, G.; McMahon, S. J.; McPherson, R. A.; Medinnis, M.; Meehan, S.; Mehlhase, S.; Mehta, A.; Meier, K.; Meineck, C.; Meirose, B.; Melini, D.; Garcia, B. R. Mellado; Melo, M.; Meloni, F.; Mengarelli, A.; Menke, S.; Meoni, E.; Mergelmeyer, S.; Mermod, P.; Merola, L.; Meroni, C.; Merritt, F. S.; Messina, A.; Metcalfe, J.; Mete, A. S.; Meyer, C.; Meyer, C.; Meyer, J.-P.; Meyer, J.; Theenhausen, H. Meyer Zu; Miano, F.; Middleton, R. P.; Miglioranzi, S.; Mijović, L.; Mikenberg, G.; Mikestikova, M.; Mikuž, M.; Milesi, M.; Milic, A.; Miller, D. W.; Mills, C.; Milov, A.; Milstead, D. A.; Minaenko, A. A.; Minami, Y.; Minashvili, I. A.; Mincer, A. I.; Mindur, B.; Mineev, M.; Ming, Y.; Mir, L. M.; Mistry, K. P.; Mitani, T.; Mitrevski, J.; Mitsou, V. A.; Miucci, A.; Miyagawa, P. S.; Mjörnmark, J. U.; Moa, T.; Mochizuki, K.; Mohapatra, S.; Mohr, W.; Molander, S.; Moles-Valls, R.; Monden, R.; Mondragon, M. C.; Mönig, K.; Monk, J.; Monnier, E.; Montalbano, A.; Berlingen, J. Montejo; Monticelli, F.; Monzani, S.; Moore, R. W.; Morange, N.; Moreno, D.; Llácer, M. Moreno; Morettini, P.; Mori, D.; Mori, T.; Morii, M.; Morinaga, M.; Morisbak, V.; Moritz, S.; Morley, A. K.; Mornacchi, G.; Morris, J. D.; Mortensen, S. S.; Morvaj, L.; Mosidze, M.; Moss, J.; Motohashi, K.; Mount, R.; Mountricha, E.; Mouraviev, S. V.; Moyse, E. J. W.; Muanza, S.; Mudd, R. D.; Mueller, F.; Mueller, J.; Mueller, R. S. P.; Mueller, T.; Muenstermann, D.; Mullen, P.; Mullier, G. A.; Sanchez, F. J. Munoz; Quijada, J. A. Murillo; Murray, W. J.; Musheghyan, H.; Muškinja, M.; Myagkov, A. G.; Myska, M.; Nachman, B. P.; Nackenhorst, O.; Nadal, J.; Nagai, K.; Nagai, R.; Nagano, K.; Nagasaka, Y.; Nagata, K.; Nagel, M.; Nagy, E.; Nairz, A. M.; Nakahama, Y.; Nakamura, K.; Nakamura, T.; Nakano, I.; Namasivayam, H.; Garcia, R. F. Naranjo; Narayan, R.; Villar, D. I. Narrias; Naryshkin, I.; Naumann, T.; Navarro, G.; Nayyar, R.; Neal, H. A.; Nechaeva, P. Yu.; Neep, T. J.; Nef, P. D.; Negri, A.; Negrini, M.; Nektarijevic, S.; Nellist, C.; Nelson, A.; Nemecek, S.; Nemethy, P.; Nepomuceno, A. A.; Nessi, M.; Neubauer, M. S.; Neumann, M.; Neves, R. M.; Nevski, P.; Newman, P. R.; Nguyen, D. H.; Manh, T. Nguyen; Nickerson, R. B.; Nicolaidou, R.; Nielsen, J.; Nikiforov, A.; Nikolaenko, V.; Nikolic-Audit, I.; Nikolopoulos, K.; Nilsen, J. K.; Nilsson, P.; Ninomiya, Y.; Nisati, A.; Nisius, R.; Nobe, T.; Nodulman, L.; Nomachi, M.; Nomidis, I.; Nooney, T.; Norberg, S.; Nordberg, M.; Norjoharuddeen, N.; Novgorodova, O.; Nowak, S.; Nozaki, M.; Nozka, L.; Ntekas, K.; Nurse, E.; Nuti, F.; O'grady, F.; O'Neil, D. C.; O'Rourke, A. A.; O'Shea, V.; Oakham, F. G.; Oberlack, H.; Obermann, T.; Ocariz, J.; Ochi, A.; Ochoa, I.; Ochoa-Ricoux, J. P.; Oda, S.; Odaka, S.; Ogren, H.; Oh, A.; Oh, S. H.; Ohm, C. C.; Ohman, H.; Oide, H.; Okawa, H.; Okumura, Y.; Okuyama, T.; Olariu, A.; Seabra, L. F. Oleiro; Pino, S. A. Olivares; Damazio, D. Oliveira; Olszewski, A.; Olszowska, J.; Onofre, A.; Onogi, K.; Onyisi, P. U. E.; Oreglia, M. J.; Oren, Y.; Orestano, D.; Orlando, N.; Orr, R. S.; Osculati, B.; Ospanov, R.; Garzon, G. Otero y.; Otono, H.; Ouchrif, M.; Ould-Saada, F.; Ouraou, A.; Oussoren, K. P.; Ouyang, Q.; Owen, M.; Owen, R. E.; Ozcan, V. E.; Ozturk, N.; Pachal, K.; Pages, A. Pacheco; Aranda, C. Padilla; Pagáčová, M.; Griso, S. Pagan; Paige, F.; Pais, P.; Pajchel, K.; Palacino, G.; Palestini, S.; Palka, M.; Pallin, D.; Palma, A.; Panagiotopoulou, E. St.; Pandini, C. E.; Vazquez, J. G. Panduro; Pani, P.; Panitkin, S.; Pantea, D.; Paolozzi, L.; Papadopoulou, Th. D.; Papageorgiou, K.; Paramonov, A.; Hernandez, D. Paredes; Parker, A. J.; Parker, M. A.; Parker, K. A.; Parodi, F.; Parsons, J. A.; Parzefall, U.; Pascuzzi, V. R.; Pasqualucci, E.; Passaggio, S.; Pastore, F.; Pastore, Fr.; Pásztor, G.; Pataraia, S.; Pater, J. R.; Pauly, T.; Pearce, J.; Pearson, B.; Pedersen, L. E.; Pedersen, M.; Lopez, S. Pedraza; Pedro, R.; Peleganchuk, S. V.; Pelikan, D.; Penc, O.; Peng, C.; Peng, H.; Penwell, J.; Peralva, B. S.; Perego, M. M.; Perepelitsa, D. V.; Codina, E. Perez; Perini, L.; Pernegger, H.; Perrella, S.; Peschke, R.; Peshekhonov, V. D.; Peters, K.; Peters, R. F. Y.; Petersen, B. A.; Petersen, T. C.; Petit, E.; Petridis, A.; Petridou, C.; Petroff, P.; Petrolo, E.; Petrov, M.; Petrucci, F.; Pettersson, N. E.; Peyaud, A.; Pezoa, R.; Phillips, P. W.; Piacquadio, G.; Pianori, E.; Picazio, A.; Piccaro, E.; Piccinini, M.; Pickering, M. A.; Piegaia, R.; Pilcher, J. E.; Pilkington, A. D.; Pin, A. W. J.; Pinamonti, M.; Pinfold, J. L.; Pingel, A.; Pires, S.; Pirumov, H.; Pitt, M.; Plazak, L.; Pleier, M.-A.; Pleskot, V.; Plotnikova, E.; Plucinski, P.; Pluth, D.; Poettgen, R.; Poggioli, L.; Pohl, D.; Polesello, G.; Poley, A.; Policicchio, A.; Polifka, R.; Polini, A.; Pollard, C. S.; Polychronakos, V.; Pommès, K.; Pontecorvo, L.; Pope, B. G.; Popeneciu, G. A.; Popovic, D. S.; Poppleton, A.; Pospisil, S.; Potamianos, K.; Potrap, I. N.; Potter, C. J.; Potter, C. T.; Poulard, G.; Poveda, J.; Pozdnyakov, V.; Astigarraga, M. E. Pozo; Pralavorio, P.; Pranko, A.; Prell, S.; Price, D.; Price, L. E.; Primavera, M.; Prince, S.; Proissl, M.; Prokofiev, K.; Prokoshin, F.; Protopopescu, S.; Proudfoot, J.; Przybycien, M.; Puddu, D.; Puldon, D.; Purohit, M.; Puzo, P.; Qian, J.; Qin, G.; Qin, Y.; Quadt, A.; Quayle, W. B.; Queitsch-Maitland, M.; Quilty, D.; Raddum, S.; Radeka, V.; Radescu, V.; Radhakrishnan, S. K.; Radloff, P.; Rados, P.; Ragusa, F.; Rahal, G.; Raine, J. A.; Rajagopalan, S.; Rammensee, M.; Rangel-Smith, C.; Ratti, M. G.; Rauscher, F.; Rave, S.; Ravenscroft, T.; Raymond, M.; Read, A. L.; Readioff, N. P.; Rebuzzi, D. M.; Redelbach, A.; Redlinger, G.; Reece, R.; Reeves, K.; Rehnisch, L.; Reichert, J.; Reisin, H.; Rembser, C.; Ren, H.; Rescigno, M.; Resconi, S.; Rezanova, O. L.; Reznicek, P.; Rezvani, R.; Richter, R.; Richter, S.; Richter-Was, E.; Ricken, O.; Ridel, M.; Rieck, P.; Riegel, C. J.; Rieger, J.; Rifki, O.; Rijssenbeek, M.; Rimoldi, A.; Rinaldi, L.; Ristić, B.; Ritsch, E.; Riu, I.; Rizatdinova, F.; Rizvi, E.; Rizzi, C.; Robertson, S. H.; Robichaud-Veronneau, A.; Robinson, D.; Robinson, J. E. M.; Robson, A.; Roda, C.; Rodina, Y.; Perez, A. Rodriguez; Rodriguez, D. Rodriguez; Roe, S.; Rogan, C. S.; Røhne, O.; Romaniouk, A.; Romano, M.; Saez, S. M. Romano; Adam, E. Romero; Rompotis, N.; Ronzani, M.; Roos, L.; Ros, E.; Rosati, S.; Rosbach, K.; Rose, P.; Rosenthal, O.; Rosien, N.-A.; Rossetti, V.; Rossi, E.; Rossi, L. P.; Rosten, J. H. N.; Rosten, R.; Rotaru, M.; Roth, I.; Rothberg, J.; Rousseau, D.; Royon, C. R.; Rozanov, A.; Rozen, Y.; Ruan, X.; Rubbo, F.; Rud, V. I.; Rudolph, M. S.; Rühr, F.; Ruiz-Martinez, A.; Rurikova, Z.; Rusakovich, N. A.; Ruschke, A.; Russell, H. L.; Rutherfoord, J. P.; Ruthmann, N.; Ryabov, Y. F.; Rybar, M.; Rybkin, G.; Ryu, S.; Ryzhov, A.; Rzehorz, G. F.; Saavedra, A. F.; Sabato, G.; Sacerdoti, S.; Sadrozinski, H. F.-W.; Sadykov, R.; Tehrani, F. Safai; Saha, P.; Sahinsoy, M.; Saimpert, M.; Saito, T.; Sakamoto, H.; Sakurai, Y.; Salamanna, G.; Salamon, A.; Loyola, J. E. Salazar; Salek, D.; De Bruin, P. H. Sales; Salihagic, D.; Salnikov, A.; Salt, J.; Salvatore, D.; Salvatore, F.; Salvucci, A.; Salzburger, A.; Sammel, D.; Sampsonidis, D.; Sanchez, A.; Sánchez, J.; Martinez, V. Sanchez; Sandaker, H.; Sandbach, R. L.; Sander, H. G.; Sandhoff, M.; Sandoval, C.; Sandstroem, R.; Sankey, D. P. C.; Sannino, M.; Sansoni, A.; Santoni, C.; Santonico, R.; Santos, H.; Castillo, I. Santoyo; Sapp, K.; Sapronov, A.; Saraiva, J. G.; Sarrazin, B.; Sasaki, O.; Sasaki, Y.; Sato, K.; Sauvage, G.; Sauvan, E.; Savage, G.; Savard, P.; Sawyer, C.; Sawyer, L.; Saxon, J.; Sbarra, C.; Sbrizzi, A.; Scanlon, T.; Scannicchio, D. A.; Scarcella, M.; Scarfone, V.; Schaarschmidt, J.; Schacht, P.; Schaefer, D.; Schaefer, R.; Schaeffer, J.; Schaepe, S.; Schaetzel, S.; Schäfer, U.; Schaffer, A. C.; Schaile, D.; Schamberger, R. D.; Scharf, V.; Schegelsky, V. A.; Scheirich, D.; Schernau, M.; Schiavi, C.; Schillo, C.; Schioppa, M.; Schlenker, S.; Schmieden, K.; Schmitt, C.; Schmitt, S.; Schmitz, S.; Schneider, B.; Schnoor, U.; Schoeffel, L.; Schoening, A.; Schoenrock, B. D.; Schopf, E.; Schorlemmer, A. L. S.; Schott, M.; Schovancova, J.; Schramm, S.; Schreyer, M.; Schuh, N.; Schultens, M. J.; Schultz-Coulon, H.-C.; Schulz, H.; Schumacher, M.; Schumm, B. A.; Schune, Ph.; Schwanenberger, C.; Schwartzman, A.; Schwarz, T. A.; Schwegler, Ph.; Schweiger, H.; Schwemling, Ph.; Schwienhorst, R.; Schwindling, J.; Schwindt, T.; Sciolla, G.; Scuri, F.; Scutti, F.; Searcy, J.; Seema, P.; Seidel, S. C.; Seiden, A.; Seifert, F.; Seixas, J. M.; Sekhniaidze, G.; Sekhon, K.; Sekula, S. J.; Seliverstov, D. M.; Semprini-Cesari, N.; Serfon, C.; Serin, L.; Serkin, L.; Sessa, M.; Seuster, R.; Severini, H.; Sfiligoj, T.; Sforza, F.; Sfyrla, A.; Shabalina, E.; Shaikh, N. W.; Shan, L. Y.; Shang, R.; Shank, J. T.; Shapiro, M.; Shatalov, P. B.; Shaw, K.; Shaw, S. M.; Shcherbakova, A.; Shehu, C. Y.; Sherwood, P.; Shi, L.; Shimizu, S.; Shimmin, C. O.; Shimojima, M.; Shiyakova, M.; Shmeleva, A.; Saadi, D. Shoaleh; Shochet, M. J.; Shojaii, S.; Shrestha, S.; Shulga, E.; Shupe, M. A.; Sicho, P.; Sidebo, P. E.; Sidiropoulou, O.; Sidorov, D.; Sidoti, A.; Siegert, F.; Sijacki, Dj.; Silva, J.; Silverstein, S. B.; Simak, V.; Simard, O.; Simic, Lj.; Simion, S.; Simioni, E.; Simmons, B.; Simon, D.; Simon, M.; Sinervo, P.; Sinev, N. B.; Sioli, M.; Siragusa, G.; Sivoklokov, S. Yu.; Sjölin, J.; Sjursen, T. B.; Skinner, M. B.; Skottowe, H. P.; Skubic, P.; Slater, M.; Slavicek, T.; Slawinska, M.; Sliwa, K.; Slovak, R.; Smakhtin, V.; Smart, B. H.; Smestad, L.; Smirnov, S. Yu.; Smirnov, Y.; Smirnova, L. N.; Smirnova, O.; Smith, M. N. K.; Smith, R. W.; Smizanska, M.; Smolek, K.; Snesarev, A. A.; Snyder, S.; Sobie, R.; Socher, F.; Soffer, A.; Soh, D. A.; Sokhrannyi, G.; Sanchez, C. A. Solans; Solar, M.; Soldatov, E. Yu.; Soldevila, U.; Solodkov, A. A.; Soloshenko, A.; Solovyanov, O. V.; Solovyev, V.; Sommer, P.; Son, H.; Song, H. Y.; Sood, A.; Sopczak, A.; Sopko, V.; Sorin, V.; Sosa, D.; Sotiropoulou, C. L.; Soualah, R.; Soukharev, A. M.; South, D.; Sowden, B. C.; Spagnolo, S.; Spalla, M.; Spangenberg, M.; Spanò, F.; Sperlich, D.; Spettel, F.; Spighi, R.; Spigo, G.; Spiller, L. A.; Spousta, M.; Denis, R. D. St.; Stabile, A.; Stamen, R.; Stamm, S.; Stanecka, E.; Stanek, R. W.; Stanescu, C.; Stanescu-Bellu, M.; Stanitzki, M. M.; Stapnes, S.; Starchenko, E. A.; Stark, G. H.; Stark, J.; Staroba, P.; Starovoitov, P.; Stärz, S.; Staszewski, R.; Steinberg, P.; Stelzer, B.; Stelzer, H. J.; Stelzer-Chilton, O.; Stenzel, H.; Stewart, G. A.; Stillings, J. A.; Stockton, M. C.; Stoebe, M.; Stoicea, G.; Stolte, P.; Stonjek, S.; Stradling, A. R.; Straessner, A.; Stramaglia, M. E.; Strandberg, J.; Strandberg, S.; Strandlie, A.; Strauss, M.; Strizenec, P.; Ströhmer, R.; Strom, D. M.; Stroynowski, R.; Strubig, A.; Stucci, S. A.; Stugu, B.; Styles, N. A.; Su, D.; Su, J.; Subramaniam, R.; Suchek, S.; Sugaya, Y.; Suk, M.; Sulin, V. V.; Sultansoy, S.; Sumida, T.; Sun, S.; Sun, X.; Sundermann, J. E.; Suruliz, K.; Susinno, G.; Sutton, M. R.; Suzuki, S.; Svatos, M.; Swiatlowski, M.; Sykora, I.; Sykora, T.; Ta, D.; Taccini, C.; Tackmann, K.; Taenzer, J.; Taffard, A.; Tafirout, R.; Taiblum, N.; Takai, H.; Takashima, R.; Takeshita, T.; Takubo, Y.; Talby, M.; Talyshev, A. A.; Tam, J. Y. C.; Tan, K. G.; Tanaka, J.; Tanaka, R.; Tanaka, S.; Tannenwald, B. B.; Araya, S. Tapia; Tapprogge, S.; Tarem, S.; Tartarelli, G. F.; Tas, P.; Tasevsky, M.; Tashiro, T.; Tassi, E.; Delgado, A. Tavares; Tayalati, Y.; Taylor, A. C.; Taylor, G. N.; Taylor, P. T. E.; Taylor, W.; Teischinger, F. A.; Teixeira-Dias, P.; Temming, K. K.; Temple, D.; Kate, H. Ten; Teng, P. K.; Teoh, J. J.; Tepel, F.; Terada, S.; Terashi, K.; Terron, J.; Terzo, S.; Testa, M.; Teuscher, R. J.; Theveneaux-Pelzer, T.; Thomas, J. P.; Thomas-Wilsker, J.; Thompson, E. N.; Thompson, P. D.; Thompson, A. S.; Thomsen, L. A.; Thomson, E.; Thomson, M.; Tibbetts, M. J.; Torres, R. E. Ticse; Tikhomirov, V. O.; Tikhonov, Yu. A.; Timoshenko, S.; Tipton, P.; Tisserant, S.; Todome, K.; Todorov, T.; Todorova-Nova, S.; Tojo, J.; Tokár, S.; Tokushuku, K.; Tolley, E.; Tomlinson, L.; Tomoto, M.; Tompkins, L.; Toms, K.; Tong, B.; Torrence, E.; Torres, H.; Pastor, E. Torró; Toth, J.; Touchard, F.; Tovey, D. R.; Trefzger, T.; Tricoli, A.; Trigger, I. M.; Trincaz-Duvoid, S.; Tripiana, M. F.; Trischuk, W.; Trocmé, B.; Trofymov, A.; Troncon, C.; Trottier-McDonald, M.; Trovatelli, M.; Truong, L.; Trzebinski, M.; Trzupek, A.; Tseng, J. C.-L.; Tsiareshka, P. V.; Tsipolitis, G.; Tsirintanis, N.; Tsiskaridze, S.; Tsiskaridze, V.; Tskhadadze, E. G.; Tsui, K. M.; Tsukerman, I. I.; Tsulaia, V.; Tsuno, S.; Tsybychev, D.; Tudorache, A.; Tudorache, V.; Tuna, A. N.; Tupputi, S. A.; Turchikhin, S.; Turecek, D.; Turgeman, D.; Turra, R.; Turvey, A. J.; Tuts, P. M.; Tyndel, M.; Ucchielli, G.; Ueda, I.; Ueno, R.; Ughetto, M.; Ukegawa, F.; Unal, G.; Undrus, A.; Unel, G.; Ungaro, F. C.; Unno, Y.; Unverdorben, C.; Urban, J.; Urquijo, P.; Urrejola, P.; Usai, G.; Usanova, A.; Vacavant, L.; Vacek, V.; Vachon, B.; Valderanis, C.; Santurio, E. Valdes; Valencic, N.; Valentinetti, S.; Valero, A.; Valery, L.; Valkar, S.; Vallecorsa, S.; Ferrer, J. A. Valls; Van Den Wollenberg, W.; Van Der Deijl, P. C.; van der Geer, R.; van der Graaf, H.; van Eldik, N.; van Gemmeren, P.; Van Nieuwkoop, J.; van Vulpen, I.; van Woerden, M. C.; Vanadia, M.; Vandelli, W.; Vanguri, R.; Vaniachine, A.; Vankov, P.; Vardanyan, G.; Vari, R.; Varnes, E. W.; Varol, T.; Varouchas, D.; Vartapetian, A.; Varvell, K. E.; Vasquez, J. G.; Vazeille, F.; Schroeder, T. Vazquez; Veatch, J.; Veloce, L. M.; Veloso, F.; Veneziano, S.; Ventura, A.; Venturi, M.; Venturi, N.; Venturini, A.; Vercesi, V.; Verducci, M.; Verkerke, W.; Vermeulen, J. C.; Vest, A.; Vetterli, M. C.; Viazlo, O.; Vichou, I.; Vickey, T.; Boeriu, O. E. Vickey; Viehhauser, G. H. A.; Viel, S.; Vigani, L.; Vigne, R.; Villa, M.; Perez, M. Villaplana; Vilucchi, E.; Vincter, M. G.; Vinogradov, V. B.; Vittori, C.; Vivarelli, I.; Vlachos, S.; Vlasak, M.; Vogel, M.; Vokac, P.; Volpi, G.; Volpi, M.; von der Schmitt, H.; von Toerne, E.; Vorobel, V.; Vorobev, K.; Vos, M.; Voss, R.; Vossebeld, J. H.; Vranjes, N.; Milosavljevic, M. Vranjes; Vrba, V.; Vreeswijk, M.; Vuillermet, R.; Vukotic, I.; Vykydal, Z.; Wagner, P.; Wagner, W.; Wahlberg, H.; Wahrmund, S.; Wakabayashi, J.; Walder, J.; Walker, R.; Walkowiak, W.; Wallangen, V.; Wang, C.; Wang, C.; Wang, F.; Wang, H.; Wang, H.; Wang, J.; Wang, J.; Wang, K.; Wang, R.; Wang, S. M.; Wang, T.; Wang, T.; Wang, X.; Wanotayaroj, C.; Warburton, A.; Ward, C. P.; Wardrope, D. R.; Washbrook, A.; Watkins, P. M.; Watson, A. T.; Watson, M. F.; Watts, G.; Watts, S.; Waugh, B. M.; Webb, S.; Weber, M. S.; Weber, S. W.; Webster, J. S.; Weidberg, A. R.; Weinert, B.; Weingarten, J.; Weiser, C.; Weits, H.; Wells, P. S.; Wenaus, T.; Wengler, T.; Wenig, S.; Wermes, N.; Werner, M.; Werner, P.; Wessels, M.; Wetter, J.; Whalen, K.; Whallon, N. L.; Wharton, A. M.; White, A.; White, M. J.; White, R.; White, S.; Whiteson, D.; Wickens, F. J.; Wiedenmann, W.; Wielers, M.; Wienemann, P.; Wiglesworth, C.; Wiik-Fuchs, L. A. M.; Wildauer, A.; Wilk, F.; Wilkens, H. G.; Williams, H. H.; Williams, S.; Willis, C.; Willocq, S.; Wilson, J. A.; Wingerter-Seez, I.; Winklmeier, F.; Winston, O. J.; Winter, B. T.; Wittgen, M.; Wittkowski, J.; Wollstadt, S. J.; Wolter, M. W.; Wolters, H.; Wosiek, B. K.; Wotschack, J.; Woudstra, M. J.; Wozniak, K. W.; Wu, M.; Wu, M.; Wu, S. L.; Wu, X.; Wu, Y.; Wyatt, T. R.; Wynne, B. M.; Xella, S.; Xu, D.; Xu, L.; Yabsley, B.; Yacoob, S.; Yakabe, R.; Yamaguchi, D.; Yamaguchi, Y.; Yamamoto, A.; Yamamoto, S.; Yamanaka, T.; Yamauchi, K.; Yamazaki, Y.; Yan, Z.; Yang, H.; Yang, H.; Yang, Y.; Yang, Z.; Yao, W.-M.; Yap, Y. C.; Yasu, Y.; Yatsenko, E.; Wong, K. H. Yau; Ye, J.; Ye, S.; Yeletskikh, I.; Yen, A. L.; Yildirim, E.; Yorita, K.; Yoshida, R.; Yoshihara, K.; Young, C.; Young, C. J. S.; Youssef, S.; Yu, D. R.; Yu, J.; Yu, J. M.; Yu, J.; Yuan, L.; Yuen, S. P. Y.; Yusuff, I.; Zabinski, B.; Zaidan, R.; Zaitsev, A. M.; Zakharchuk, N.; Zalieckas, J.; Zaman, A.; Zambito, S.; Zanello, L.; Zanzi, D.; Zeitnitz, C.; Zeman, M.; Zemla, A.; Zeng, J. C.; Zeng, Q.; Zengel, K.; Zenin, O.; Ženiš, T.; Zerwas, D.; Zhang, D.; Zhang, F.; Zhang, G.; Zhang, H.; Zhang, J.; Zhang, L.; Zhang, R.; Zhang, R.; Zhang, X.; Zhang, Z.; Zhao, X.; Zhao, Y.; Zhao, Z.; Zhemchugov, A.; Zhong, J.; Zhou, B.; Zhou, C.; Zhou, L.; Zhou, L.; Zhou, M.; Zhou, N.; Zhu, C. G.; Zhu, H.; Zhu, J.; Zhu, Y.; Zhuang, X.; Zhukov, K.; Zibell, A.; Zieminska, D.; Zimine, N. I.; Zimmermann, C.; Zimmermann, S.; Zinonos, Z.; Zinser, M.; Ziolkowski, M.; Živković, L.; Zobernig, G.; Zoccoli, A.; Nedden, M. zur; Zurzolo, G.; Zwalinski, L.

    2017-02-01

    Direct searches for lepton flavour violation in decays of the Higgs and Z bosons with the ATLAS detector at the LHC are presented. The following three decays are considered: H→ eτ , H→ μ τ , and Z→ μ τ . The searches are based on the data sample of proton-proton collisions collected by the ATLAS detector corresponding to an integrated luminosity of 20.3 fb^{-1} at a centre-of-mass energy of √{s}=8 TeV. No significant excess is observed, and upper limits on the lepton-flavour-violating branching ratios are set at the 95% confidence level: Br(H→ eτ )<1.04%, Br(H→ μ τ )<1.43%, and Br(Z→ μ τ )<1.69× 10^{-5}.

  13. Upgrade project and plans for the ATLAS detector and trigger

    NASA Astrophysics Data System (ADS)

    Pastore, Francesca; Atlas Collaboration

    2013-08-01

    The LHC is expected to under go upgrades over the coming years in order to extend its scientific potential. Through two different phases (namely Phase-I and Phase-II), the average luminosity will be increased by a factor 5-10 above the design luminosity, 1034 cm-2 s-1. Consequently, the LHC experiments will need upgraded detectors and new infrastructure of the trigger and DAQ systems, to take into account the increase of radiation level and of particle rates foreseen at such high luminosity. In this paper we describe the planned changes and the investigations for the ATLAS experiment, focusing on the requirements for the trigger system to handle the increase rate of collisions per beam crossing, while maintaining widely inclusive selections. In different steps, the trigger detectors will improve their selectivity by benefiting from increased granularity. To improve the flexibility of the system, the use of the tracking information in the lower levels of the trigger selection is also discussed. Lastly different scenarios are compared, based on the expected physics potential of ATLAS in this high luminosity regime.

  14. EnviroAtlas National Layers Master Web Service

    EPA Pesticide Factsheets

    This EnviroAtlas web service supports research and online mapping activities related to EnviroAtlas (https://www.epa.gov/enviroatlas). This web service includes layers depicting EnviroAtlas national metrics mapped at the 12-digit HUC within the conterminous United States. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

  15. Three-dimensional Talairach-Tournoux brain atlas

    NASA Astrophysics Data System (ADS)

    Fang, Anthony; Nowinski, Wieslaw L.; Nguyen, Bonnie T.; Bryan, R. Nick

    1995-04-01

    The Talairach-Tournoux Stereotaxic Atlas of the human brain is a frequently consulted resource in stereotaxic neurosurgery and computer-based neuroradiology. Its primary application lies in the 2-D analysis and interpretation of neurological images. However, for the purpose of the analysis and visualization of shapes and forms, accurate mensuration of volumes, or 3-D models matching, a 3-D representation of the atlas is essential. This paper proposes and describes, along with its difficulties, a 3-D geometric extension of the atlas. We introduce a `zero-potential' surface smoothing technique, along with a space-dependent convolution kernel and space-dependent normalization. The mesh-based atlas structures are hierarchically organized, and anatomically conform to the original atlas. Structures and their constituents can be independently selected and manipulated in real-time within an integrated system. The extended atlas may be navigated by itself, or interactively registered with patient data with the proportional grid system (piecewise linear) transformation. Visualization of the geometric atlas along with patient data gives a remarkable visual `feel' of the biological structures, not usually perceivable to the untrained eyes in conventional 2-D atlas to image analysis.

  16. Search for a heavy gauge boson decaying to a charged lepton and a neutrino in 1 fb -1 of pp collisions at √s = 7 TeV using the ATLAS detector

    DOE PAGES

    Aad, G.

    2011-11-01

    The ATLAS detector at the LHC is used to search for heavy charged gauge bosons (W'), decaying to a charged lepton (electron or muon) and a neutrino. Results are presented based on the analysis of pp collisions at a center-of-mass energy of 7 TeV corresponding to an integrated luminosity of 1.04 fb⁻¹. No excess beyond Standard Model expectations is observed. A W' with Sequential Standard Model couplings is excluded at the 95% confidence level for masses up to 2.15 TeV.

  17. Large scale digital atlases in neuroscience

    NASA Astrophysics Data System (ADS)

    Hawrylycz, M.; Feng, D.; Lau, C.; Kuan, C.; Miller, J.; Dang, C.; Ng, L.

    2014-03-01

    Imaging in neuroscience has revolutionized our current understanding of brain structure, architecture and increasingly its function. Many characteristics of morphology, cell type, and neuronal circuitry have been elucidated through methods of neuroimaging. Combining this data in a meaningful, standardized, and accessible manner is the scope and goal of the digital brain atlas. Digital brain atlases are used today in neuroscience to characterize the spatial organization of neuronal structures, for planning and guidance during neurosurgery, and as a reference for interpreting other data modalities such as gene expression and connectivity data. The field of digital atlases is extensive and in addition to atlases of the human includes high quality brain atlases of the mouse, rat, rhesus macaque, and other model organisms. Using techniques based on histology, structural and functional magnetic resonance imaging as well as gene expression data, modern digital atlases use probabilistic and multimodal techniques, as well as sophisticated visualization software to form an integrated product. Toward this goal, brain atlases form a common coordinate framework for summarizing, accessing, and organizing this knowledge and will undoubtedly remain a key technology in neuroscience in the future. Since the development of its flagship project of a genome wide image-based atlas of the mouse brain, the Allen Institute for Brain Science has used imaging as a primary data modality for many of its large scale atlas projects. We present an overview of Allen Institute digital atlases in neuroscience, with a focus on the challenges and opportunities for image processing and computation.

  18. The Atlases of Vesta derived from Dawn Framing Camera images

    NASA Astrophysics Data System (ADS)

    Roatsch, T.; Kersten, E.; Matz, K.; Preusker, F.; Scholten, F.; Jaumann, R.; Raymond, C. A.; Russell, C. T.

    2013-12-01

    The Dawn Framing Camera acquired during its two HAMO (High Altitude Mapping Orbit) phases in 2011 and 2012 about 6,000 clear filter images with a resolution of about 60 m/pixel. We combined these images in a global ortho-rectified mosaic of Vesta (60 m/pixel resolution). Only very small areas near the northern pole were still in darkness and are missing in the mosaic. The Dawn Framing Camera also acquired about 10,000 high-resolution clear filter images (about 20 m/pixel) of Vesta during its Low Altitude Mapping Orbit (LAMO). Unfortunately, the northern part of Vesta was still in darkness during this phase, good illumination (incidence angle < 70°) was only available for 66.8 % of the surface [1]. We used the LAMO images to calculate another global mosaic of Vesta, this time with 20 m/pixel resolution. Both global mosaics were used to produce atlases of Vesta: a HAMO atlas with 15 tiles at a scale of 1:500,000 and a LAMO atlas with 30 tiles at a scale between 1:200,000 and 1:225,180. The nomenclature used in these atlases is based on names and places historically associated with the Roman goddess Vesta, and is compliant with the rules of the IAU. 65 names for geological features were already approved by the IAU, 39 additional names are currently under review. Selected examples of both atlases will be shown in this presentation. Reference: [1]Roatsch, Th., etal., High-resolution Vesta Low Altitude Mapping Orbit Atlas derived from Dawn Framing Camera images. Planetary and Space Science (2013), http://dx.doi.org/10.1016/j.pss.2013.06.024i

  19. A three-dimensional histological atlas of the human basal ganglia. II. Atlas deformation strategy and evaluation in deep brain stimulation for Parkinson disease.

    PubMed

    Bardinet, Eric; Bhattacharjee, Manik; Dormont, Didier; Pidoux, Bernard; Malandain, Grégoire; Schüpbach, Michael; Ayache, Nicholas; Cornu, Philippe; Agid, Yves; Yelnik, Jérôme

    2009-02-01

    The localization of any given target in the brain has become a challenging issue because of the increased use of deep brain stimulation to treat Parkinson disease, dystonia, and nonmotor diseases (for example, Tourette syndrome, obsessive compulsive disorders, and depression). The aim of this study was to develop an automated method of adapting an atlas of the human basal ganglia to the brains of individual patients. Magnetic resonance images of the brain specimen were obtained before extraction from the skull and histological processing. Adaptation of the atlas to individual patient anatomy was performed by reshaping the atlas MR images to the images obtained in the individual patient using a hierarchical registration applied to a region of interest centered on the basal ganglia, and then applying the reshaping matrix to the atlas surfaces. Results were evaluated by direct visual inspection of the structures visible on MR images and atlas anatomy, by comparison with electrophysiological intraoperative data, and with previous atlas studies in patients with Parkinson disease. The method was both robust and accurate, never failing to provide an anatomically reliable atlas to patient registration. The registration obtained did not exceed a 1-mm mismatch with the electrophysiological signatures in the region of the subthalamic nucleus. This registration method applied to the basal ganglia atlas forms a powerful and reliable method for determining deep brain stimulation targets within the basal ganglia of individual patients.

  20. Sonar atlas of caverns comprising the U.S. Strategic Petroleum Reserve. Volume 1, Bayou Choctaw site, Louisiana.

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Rautman, Christopher Arthur; Lord, Anna Snider

    2007-10-01

    Downhole sonar surveys from the four active U.S. Strategic Petroleum Reserve sites have been modeled and used to generate a four-volume sonar atlas, showing the three-dimensional geometry of each cavern. This volume 1 focuses on the Bayou Choctaw SPR site, located in southern Louisiana. Volumes 2, 3, and 4, respectively, present images for the Big Hill SPR site, Texas, the Bryan Mound SPR site, Texas, and the West Hackberry SPR site, Louisiana. The atlas uses a consistent presentation format throughout. The basic geometric measurements provided by the down-cavern surveys have also been used to generate a number of geometric attributes,more » the values of which have been mapped onto the geometric form of each cavern using a color-shading scheme. The intent of the various geometrical attributes is to highlight deviations of the cavern shape from the idealized cylindrical form of a carefully leached underground storage cavern in salt. The atlas format does not allow interpretation of such geometric deviations and anomalies. However, significant geometric anomalies, not directly related to the leaching history of the cavern, may provide insight into the internal structure of the relevant salt dome.« less

  1. Probabilistic atlas and geometric variability estimation to drive tissue segmentation.

    PubMed

    Xu, Hao; Thirion, Bertrand; Allassonnière, Stéphanie

    2014-09-10

    Computerized anatomical atlases play an important role in medical image analysis. While an atlas usually refers to a standard or mean image also called template, which presumably represents well a given population, it is not enough to characterize the observed population in detail. A template image should be learned jointly with the geometric variability of the shapes represented in the observations. These two quantities will in the sequel form the atlas of the corresponding population. The geometric variability is modeled as deformations of the template image so that it fits the observations. In this paper, we provide a detailed analysis of a new generative statistical model based on dense deformable templates that represents several tissue types observed in medical images. Our atlas contains both an estimation of probability maps of each tissue (called class) and the deformation metric. We use a stochastic algorithm for the estimation of the probabilistic atlas given a dataset. This atlas is then used for atlas-based segmentation method to segment the new images. Experiments are shown on brain T1 MRI datasets. Copyright © 2014 John Wiley & Sons, Ltd.

  2. Summary of the ATLAS experiment’s sensitivity to supersymmetry after LHC Run 1 -- interpreted in the phenomenological MSSM

    DOE PAGES

    Aad, G.; Abbott, B.; Abdallah, J.; ...

    2015-10-21

    A summary of the constraints from the ATLAS experiment on R -parity-conserving supersymmetry is presented. Results from 22 separate ATLAS searches are considered, each based on analysis of up to 20.3 fb –1 of proton-proton collision data at centre-of-mass energies of √s =7 and 8 TeV at the Large Hadron Collider. The results are interpreted in the context of the 19-parameter phenomenological minimal supersymmetric standard model, in which the lightest supersymmetric particle is a neutralino, taking into account constraints from previous precision electroweak and flavour measurements as well as from dark matter related measurements. The results are presented in termsmore » of constraints on supersymmetric particle masses and are compared to limits from simplified models. The impact of ATLAS searches on parameters such as the dark matter relic density, the couplings of the observed Higgs boson, and the degree of electroweak fine-tuning is also shown. As a result, spectra for surviving supersymmetry model points with low fine-tunings are presented.« less

  3. Preliminary measurements of mesospheric OH X 2Pi by ISO on Atlas 1

    NASA Technical Reports Server (NTRS)

    Morgan, M. F.; Torr, D. G.; Torr, M. R.

    1993-01-01

    The Imaging Spectrometric Observatory (ISO) carried by the Atlas 1 mission conducted observations of the resonance fluorescence of the OH radical, on whose bases a density profile has been determined at 70-80 km altitude. OH observations were conducted during most of the dayside phases during the mission, covering much of the Northern Hemisphere to 57 deg N latitude. Attention is given to results from an observation at 39 deg N, where OH densities rapidly decreased above 80 km.

  4. Multi-atlas attenuation correction supports full quantification of static and dynamic brain PET data in PET-MR

    NASA Astrophysics Data System (ADS)

    Mérida, Inés; Reilhac, Anthonin; Redouté, Jérôme; Heckemann, Rolf A.; Costes, Nicolas; Hammers, Alexander

    2017-04-01

    In simultaneous PET-MR, attenuation maps are not directly available. Essential for absolute radioactivity quantification, they need to be derived from MR or PET data to correct for gamma photon attenuation by the imaged object. We evaluate a multi-atlas attenuation correction method for brain imaging (MaxProb) on static [18F]FDG PET and, for the first time, on dynamic PET, using the serotoninergic tracer [18F]MPPF. A database of 40 MR/CT image pairs (atlases) was used. The MaxProb method synthesises subject-specific pseudo-CTs by registering each atlas to the target subject space. Atlas CT intensities are then fused via label propagation and majority voting. Here, we compared these pseudo-CTs with the real CTs in a leave-one-out design, contrasting the MaxProb approach with a simplified single-atlas method (SingleAtlas). We evaluated the impact of pseudo-CT accuracy on reconstructed PET images, compared to PET data reconstructed with real CT, at the regional and voxel levels for the following: radioactivity images; time-activity curves; and kinetic parameters (non-displaceable binding potential, BPND). On static [18F]FDG, the mean bias for MaxProb ranged between 0 and 1% for 73 out of 84 regions assessed, and exceptionally peaked at 2.5% for only one region. Statistical parametric map analysis of MaxProb-corrected PET data showed significant differences in less than 0.02% of the brain volume, whereas SingleAtlas-corrected data showed significant differences in 20% of the brain volume. On dynamic [18F]MPPF, most regional errors on BPND ranged from -1 to  +3% (maximum bias 5%) for the MaxProb method. With SingleAtlas, errors were larger and had higher variability in most regions. PET quantification bias increased over the duration of the dynamic scan for SingleAtlas, but not for MaxProb. We show that this effect is due to the interaction of the spatial tracer-distribution heterogeneity variation over time with the degree of accuracy of the attenuation maps. This

  5. Multi-atlas attenuation correction supports full quantification of static and dynamic brain PET data in PET-MR.

    PubMed

    Mérida, Inés; Reilhac, Anthonin; Redouté, Jérôme; Heckemann, Rolf A; Costes, Nicolas; Hammers, Alexander

    2017-04-07

    In simultaneous PET-MR, attenuation maps are not directly available. Essential for absolute radioactivity quantification, they need to be derived from MR or PET data to correct for gamma photon attenuation by the imaged object. We evaluate a multi-atlas attenuation correction method for brain imaging (MaxProb) on static [ 18 F]FDG PET and, for the first time, on dynamic PET, using the serotoninergic tracer [ 18 F]MPPF. A database of 40 MR/CT image pairs (atlases) was used. The MaxProb method synthesises subject-specific pseudo-CTs by registering each atlas to the target subject space. Atlas CT intensities are then fused via label propagation and majority voting. Here, we compared these pseudo-CTs with the real CTs in a leave-one-out design, contrasting the MaxProb approach with a simplified single-atlas method (SingleAtlas). We evaluated the impact of pseudo-CT accuracy on reconstructed PET images, compared to PET data reconstructed with real CT, at the regional and voxel levels for the following: radioactivity images; time-activity curves; and kinetic parameters (non-displaceable binding potential, BP ND ). On static [ 18 F]FDG, the mean bias for MaxProb ranged between 0 and 1% for 73 out of 84 regions assessed, and exceptionally peaked at 2.5% for only one region. Statistical parametric map analysis of MaxProb-corrected PET data showed significant differences in less than 0.02% of the brain volume, whereas SingleAtlas-corrected data showed significant differences in 20% of the brain volume. On dynamic [ 18 F]MPPF, most regional errors on BP ND ranged from -1 to  +3% (maximum bias 5%) for the MaxProb method. With SingleAtlas, errors were larger and had higher variability in most regions. PET quantification bias increased over the duration of the dynamic scan for SingleAtlas, but not for MaxProb. We show that this effect is due to the interaction of the spatial tracer-distribution heterogeneity variation over time with the degree of accuracy of the attenuation

  6. ATLAS DataFlow Infrastructure: Recent results from ATLAS cosmic and first-beam data-taking

    NASA Astrophysics Data System (ADS)

    Vandelli, Wainer; ATLAS TDAQ Collaboration

    2010-04-01

    The ATLAS DataFlow infrastructure is responsible for the collection and conveyance of event data from the detector front-end electronics to the mass storage. Several optimized and multi-threaded applications fulfill this purpose operating over a multi-stage Gigabit Ethernet network which is the backbone of the ATLAS Trigger and Data Acquisition System. The system must be able to efficiently transport event-data with high reliability, while providing aggregated bandwidths larger than 5 GByte/s and coping with many thousands network connections. Nevertheless, routing and streaming capabilities and monitoring and data accounting functionalities are also fundamental requirements. During 2008, a few months of ATLAS cosmic data-taking and the first experience with the LHC beams provided an unprecedented test-bed for the evaluation of the performance of the ATLAS DataFlow, in terms of functionality, robustness and stability. Besides, operating the system far from its design specifications helped in exercising its flexibility and contributed in understanding its limitations. Moreover, the integration with the detector and the interfacing with the off-line data processing and management have been able to take advantage of this extended data taking-period as well. In this paper we report on the usage of the DataFlow infrastructure during the ATLAS data-taking. These results, backed-up by complementary performance tests, validate the architecture of the ATLAS DataFlow and prove that the system is robust, flexible and scalable enough to cope with the final requirements of the ATLAS experiment.

  7. A cross-validated cytoarchitectonic atlas of the human ventral visual stream.

    PubMed

    Rosenke, Mona; Weiner, Kevin S; Barnett, Michael A; Zilles, Karl; Amunts, Katrin; Goebel, Rainer; Grill-Spector, Kalanit

    2018-04-15

    The human ventral visual stream consists of several areas that are considered processing stages essential for perception and recognition. A fundamental microanatomical feature differentiating areas is cytoarchitecture, which refers to the distribution, size, and density of cells across cortical layers. Because cytoarchitectonic structure is measured in 20-micron-thick histological slices of postmortem tissue, it is difficult to assess (a) how anatomically consistent these areas are across brains and (b) how they relate to brain parcellations obtained with prevalent neuroimaging methods, acquired at the millimeter and centimeter scale. Therefore, the goal of this study was to (a) generate a cross-validated cytoarchitectonic atlas of the human ventral visual stream on a whole brain template that is commonly used in neuroimaging studies and (b) to compare this atlas to a recently published retinotopic parcellation of visual cortex (Wang et al., 2014). To achieve this goal, we generated an atlas of eight cytoarchitectonic areas: four areas in the occipital lobe (hOc1-hOc4v) and four in the fusiform gyrus (FG1-FG4), then we tested how the different alignment techniques affect the accuracy of the resulting atlas. Results show that both cortex-based alignment (CBA) and nonlinear volumetric alignment (NVA) generate an atlas with better cross-validation performance than affine volumetric alignment (AVA). Additionally, CBA outperformed NVA in 6/8 of the cytoarchitectonic areas. Finally, the comparison of the cytoarchitectonic atlas to a retinotopic atlas shows a clear correspondence between cytoarchitectonic and retinotopic areas in the ventral visual stream. The successful performance of CBA suggests a coupling between cytoarchitectonic areas and macroanatomical landmarks in the human ventral visual stream, and furthermore, that this coupling can be utilized for generating an accurate group atlas. In addition, the coupling between cytoarchitecture and retinotopy highlights

  8. The Dutch National Atlas of Public Health.

    PubMed

    Zwakhals, S L N; Giesbers, H; Mac Gillavry, E; van Boven, P F; van der Veen, A A

    2004-09-01

    The Dutch National Atlas of Public Health (http://www.zorgatlas.nl) maps the regional distribution of demand and usage of health care, public health status and influencing factors. The Atlas provides answers to locational questions, e. g. 'Where are the highest mortality rates?', 'Where are the longest waiting lists?' and 'Where are hospitals located?' Maps play a pivotal role in the Atlas. Texts, graphics and diagrams support the interpretation of the maps. The information in the Atlas specifically targets policy makers at the Ministry of Health, Welfare and Sport. For them, the Atlas is a tool for problem detection, policy making and policy evaluation. The Atlas is also aimed at all professionals in health care. In practice, also the general public appears to access and use the Atlas. The Atlas is part of the Dutch Public Health Status and Forecasts (PHSF). The PHSF is made by the National Institute of Public Health and the Environment mandated by the Ministry of Health, Welfare and Sport.

  9. The Imaging Spectrometric Observatory for the ATLAS 1 mission

    NASA Technical Reports Server (NTRS)

    Torr, Douglas G.

    1995-01-01

    The Imaging Spectrometric Observatory (ISO) was flown on the ATLAS 1 mission and was enormously successful, providing a baseline database on the coupled stratospheric, mesospheric, thermospheric, and ionospheric regions. Specific ISO accomplishments include measurements of the hydroxyl radical, studies of the global ionosphere, retrieval of the concentrations of neutral species from the ISO data, studies of mesospheric oxygen emissions, retrieval of mesospheric O from oxygen emissions, studies of the OH Meinel bands and the search for the Herzberg III bands, search for metallic species, studies of thermospheric nitric oxide, auroral study of molecular nitrogen emissions, and studies of thermospheric species. Apart from participation in the data analysis, the primary post-flight responsibility of Marshall Space Flight Center was the delivery of the final post mission dataset. Support provided by the University of Alabama in Huntsville is described.

  10. The performance of the jet trigger for the ATLAS detector during 2011 data taking

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Aad, G.; Abbott, B.; Abdallah, J.

    The performance of the jet trigger for the ATLAS detector at the LHC during the 2011 data taking period is described. During 2011 the LHC provided proton–proton collisions with a centre-of-mass energy of 7 TeV and heavy ion collisions with a 2.76 TeV per nucleon–nucleon collision energy. The ATLAS trigger is a three level system designed to reduce the rate of events from the 40 MHz nominal maximum bunch crossing rate to the approximate 400 Hz which can be written to offline storage. The ATLAS jet trigger is the primary means for the online selection of events containing jets. Events are accepted by themore » trigger if they contain one or more jets above some transverse energy threshold. During 2011 data taking the jet trigger was fully efficient for jets with transverse energy above 25 GeV for triggers seeded randomly at Level 1. For triggers which require a jet to be identified at each of the three trigger levels, full efficiency is reached for offline jets with transverse energy above 60 GeV. Jets reconstructed in the final trigger level and corresponding to offline jets with transverse energy greater than 60 GeV, are reconstructed with a resolution in transverse energy with respect to offline jets, of better than 4 % in the central region and better than 2.5 % in the forward direction.« less

  11. The performance of the jet trigger for the ATLAS detector during 2011 data taking

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Aad, G.; Abbott, B.; Abdallah, J.

    The performance of the jet trigger for the ATLAS detector at the LHC during the 2011 data taking period is described. During 2011 the LHC provided proton–proton collisions with a centre-of-mass energy of 7 TeV and heavy ion collisions with a 2.76 TeV per nucleon–nucleon collision energy. The ATLAS trigger is a three level system designed to reduce the rate of events from the 40 MHz nominal maximum bunch crossing rate to the approximate 400 Hz which can be written to offline storage. The ATLAS jet trigger is the primary means for the online selection of events containing jets. Eventsmore » are accepted by the trigger if they contain one or more jets above some transverse energy threshold. During 2011 data taking the jet trigger was fully efficient for jets with transverse energy above 25 GeV for triggers seeded randomly at Level 1. For triggers which require a jet to be identified at each of the three trigger levels, full efficiency is reached for offline jets with transverse energy above 60 GeV. Jets reconstructed in the final trigger level and corresponding to offline jets with transverse energy greater than 60 GeV, are reconstructed with a resolution in transverse energy with respect to offline jets, of better than 4 % in the central region and better than 2.5 % in the forward direction.« less

  12. The performance of the jet trigger for the ATLAS detector during 2011 data taking

    DOE PAGES

    Aad, G.; Abbott, B.; Abdallah, J.; ...

    2016-09-27

    The performance of the jet trigger for the ATLAS detector at the LHC during the 2011 data taking period is described. During 2011 the LHC provided proton–proton collisions with a centre-of-mass energy of 7 TeV and heavy ion collisions with a 2.76 TeV per nucleon–nucleon collision energy. The ATLAS trigger is a three level system designed to reduce the rate of events from the 40 MHz nominal maximum bunch crossing rate to the approximate 400 Hz which can be written to offline storage. The ATLAS jet trigger is the primary means for the online selection of events containing jets. Events are accepted by themore » trigger if they contain one or more jets above some transverse energy threshold. During 2011 data taking the jet trigger was fully efficient for jets with transverse energy above 25 GeV for triggers seeded randomly at Level 1. For triggers which require a jet to be identified at each of the three trigger levels, full efficiency is reached for offline jets with transverse energy above 60 GeV. Jets reconstructed in the final trigger level and corresponding to offline jets with transverse energy greater than 60 GeV, are reconstructed with a resolution in transverse energy with respect to offline jets, of better than 4 % in the central region and better than 2.5 % in the forward direction.« less

  13. The performance of the jet trigger for the ATLAS detector during 2011 data taking.

    PubMed

    Aad, G; Abbott, B; Abdallah, J; Abdinov, O; Abeloos, B; Aben, R; Abolins, M; AbouZeid, O S; Abraham, N L; Abramowicz, H; Abreu, H; Abreu, R; Abulaiti, Y; Acharya, B S; Adamczyk, L; Adams, D L; Adelman, J; Adomeit, S; Adye, T; Affolder, A A; Agatonovic-Jovin, T; Agricola, J; Aguilar-Saavedra, J A; Ahlen, S P; Ahmadov, F; Aielli, G; Akerstedt, H; Åkesson, T P A; Akimov, A V; Alberghi, G L; Albert, J; Albrand, S; Verzini, M J Alconada; Aleksa, M; Aleksandrov, I N; Alexa, C; Alexander, G; Alexopoulos, T; Alhroob, M; Aliev, M; Alimonti, G; Alison, J; Alkire, S P; Allbrooke, B M M; Allen, B W; Allport, P P; Aloisio, A; Alonso, A; Alonso, F; Alpigiani, C; Gonzalez, B Alvarez; Piqueras, D Álvarez; Alviggi, M G; Amadio, B T; Amako, K; Coutinho, Y Amaral; Amelung, C; Amidei, D; Santos, S P Amor Dos; Amorim, A; Amoroso, S; Amram, N; Amundsen, G; Anastopoulos, C; Ancu, L S; Andari, N; Andeen, T; Anders, C F; Anders, G; Anders, J K; Anderson, K J; Andreazza, A; Andrei, V; Angelidakis, S; Angelozzi, I; Anger, P; Angerami, A; Anghinolfi, F; Anisenkov, A V; Anjos, N; Annovi, A; Antonelli, M; Antonov, A; Antos, J; Anulli, F; Aoki, M; Bella, L Aperio; Arabidze, G; Arai, Y; Araque, J P; Arce, A T H; Arduh, F A; Arguin, J-F; Argyropoulos, S; Arik, M; Armbruster, A J; Armitage, L J; Arnaez, O; Arnold, H; Arratia, M; Arslan, O; Artamonov, A; Artoni, G; Artz, S; Asai, S; Asbah, N; Ashkenazi, A; Åsman, B; Asquith, L; Assamagan, K; Astalos, R; Atkinson, M; Atlay, N B; Augsten, K; Avolio, G; Axen, B; Ayoub, M K; Azuelos, G; Baak, M A; Baas, A E; Baca, M J; Bachacou, H; Bachas, K; Backes, M; Backhaus, M; Bagiacchi, P; Bagnaia, P; Bai, Y; Baines, J T; Baker, O K; Baldin, E M; Balek, P; Balestri, T; Balli, F; Balunas, W K; Banas, E; Banerjee, Sw; Bannoura, A A E; Barak, L; Barberio, E L; Barberis, D; Barbero, M; Barillari, T; Barklow, T; Barlow, N; Barnes, S L; Barnett, B M; Barnett, R M; Barnovska, Z; Baroncelli, A; Barone, G; Barr, A J; Navarro, L Barranco; Barreiro, F; da Costa, J Barreiro Guimarães; Bartoldus, R; Barton, A E; Bartos, P; Basalaev, A; Bassalat, A; Basye, A; Bates, R L; Batista, S J; Batley, J R; Battaglia, M; Bauce, M; Bauer, F; Bawa, H S; Beacham, J B; Beattie, M D; Beau, T; Beauchemin, P H; Bechtle, P; Beck, H P; Becker, K; Becker, M; Beckingham, M; Becot, C; Beddall, A J; Beddall, A; Bednyakov, V A; Bedognetti, M; Bee, C P; Beemster, L J; Beermann, T A; Begel, M; Behr, J K; Belanger-Champagne, C; Bell, A S; Bella, G; Bellagamba, L; Bellerive, A; Bellomo, M; Belotskiy, K; Beltramello, O; Belyaev, N L; Benary, O; Benchekroun, D; Bender, M; Bendtz, K; Benekos, N; Benhammou, Y; Noccioli, E Benhar; Benitez, J; Garcia, J A Benitez; Benjamin, D P; Bensinger, J R; Bentvelsen, S; Beresford, L; Beretta, M; Berge, D; Kuutmann, E Bergeaas; Berger, N; Berghaus, F; Beringer, J; Berlendis, S; Bernard, N R; Bernius, C; Bernlochner, F U; Berry, T; Berta, P; Bertella, C; Bertoli, G; Bertolucci, F; Bertram, I A; Bertsche, C; Bertsche, D; Besjes, G J; Bylund, O Bessidskaia; Bessner, M; Besson, N; Betancourt, C; Bethke, S; Bevan, A J; Bhimji, W; Bianchi, R M; Bianchini, L; Bianco, M; Biebel, O; Biedermann, D; Bielski, R; Biesuz, N V; Biglietti, M; De Mendizabal, J Bilbao; Bilokon, H; Bindi, M; Binet, S; Bingul, A; Bini, C; Biondi, S; Bjergaard, D M; Black, C W; Black, J E; Black, K M; Blackburn, D; Blair, R E; Blanchard, J-B; Blanco, J E; Blazek, T; Bloch, I; Blocker, C; Blum, W; Blumenschein, U; Blunier, S; Bobbink, G J; Bobrovnikov, V S; Bocchetta, S S; Bocci, A; Bock, C; Boehler, M; Boerner, D; Bogaerts, J A; Bogavac, D; Bogdanchikov, A G; Bohm, C; Boisvert, V; Bold, T; Boldea, V; Boldyrev, A S; Bomben, M; Bona, M; Boonekamp, M; Borisov, A; Borissov, G; Bortfeldt, J; Bortoletto, D; Bortolotto, V; Bos, K; Boscherini, D; Bosman, M; Sola, J D Bossio; Boudreau, J; Bouffard, J; Bouhova-Thacker, E V; Boumediene, D; Bourdarios, C; Boutle, S K; Boveia, A; Boyd, J; Boyko, I R; Bracinik, J; Brandt, A; Brandt, G; Brandt, O; Bratzler, U; Brau, B; Brau, J E; Braun, H M; Madden, W D Breaden; Brendlinger, K; Brennan, A J; Brenner, L; Brenner, R; Bressler, S; Bristow, T M; Britton, D; Britzger, D; Brochu, F M; Brock, I; Brock, R; Brooijmans, G; Brooks, T; Brooks, W K; Brosamer, J; Brost, E; Broughton, J H; de Renstrom, P A Bruckman; Bruncko, D; Bruneliere, R; Bruni, A; Bruni, G; Brunt, B H; Bruschi, M; Bruscino, N; Bryant, P; Bryngemark, L; Buanes, T; Buat, Q; Buchholz, P; Buckley, A G; Budagov, I A; Buehrer, F; Bugge, M K; Bulekov, O; Bullock, D; Burckhart, H; Burdin, S; Burgard, C D; Burghgrave, B; Burka, K; Burke, S; Burmeister, I; Busato, E; Büscher, D; Büscher, V; Bussey, P; Butler, J M; Butt, A I; Buttar, C M; Butterworth, J M; Butti, P; Buttinger, W; Buzatu, A; Buzykaev, A R; Urbán, S Cabrera; Caforio, D; Cairo, V M; Cakir, O; Calace, N; Calafiura, P; Calandri, A; Calderini, G; Calfayan, P; Caloba, L P; Calvet, D; Calvet, S; Calvet, T P; Toro, R Camacho; Camarda, S; Camarri, P; Cameron, D; Armadans, R Caminal; Camincher, C; Campana, S; Campanelli, M; Campoverde, A; Canale, V; Canepa, A; Bret, M Cano; Cantero, J; Cantrill, R; Cao, T; Garrido, M D M Capeans; Caprini, I; Caprini, M; Capua, M; Caputo, R; Carbone, R M; Cardarelli, R; Cardillo, F; Carli, I; Carli, T; Carlino, G; Carminati, L; Caron, S; Carquin, E; Carrillo-Montoya, G D; Carter, J R; Carvalho, J; Casadei, D; Casado, M P; Casolino, M; Casper, D W; Castaneda-Miranda, E; Castelli, A; Gimenez, V Castillo; Castro, N F; Catinaccio, A; Catmore, J R; Cattai, A; Caudron, J; Cavaliere, V; Cavallaro, E; Cavalli, D; Cavalli-Sforza, M; Cavasinni, V; Ceradini, F; Alberich, L Cerda; Cerio, B C; Cerqueira, A S; Cerri, A; Cerrito, L; Cerutti, F; Cerv, M; Cervelli, A; Cetin, S A; Chafaq, A; Chakraborty, D; Chan, S K; Chan, Y L; Chang, P; Chapman, J D; Charlton, D G; Chatterjee, A; Chau, C C; Barajas, C A Chavez; Che, S; Cheatham, S; Chegwidden, A; Chekanov, S; Chekulaev, S V; Chelkov, G A; Chelstowska, M A; Chen, C; Chen, H; Chen, K; Chen, S; Chen, S; Chen, X; Chen, Y; Cheng, H C; Cheng, H J; Cheng, Y; Cheplakov, A; Cheremushkina, E; Moursli, R Cherkaoui El; Chernyatin, V; Cheu, E; Chevalier, L; Chiarella, V; Chiarelli, G; Chiodini, G; Chisholm, A S; Chitan, A; Chizhov, M V; Choi, K; Chomont, A R; Chouridou, S; Chow, B K B; Christodoulou, V; Chromek-Burckhart, D; Chudoba, J; Chuinard, A J; Chwastowski, J J; Chytka, L; Ciapetti, G; Ciftci, A K; Cinca, D; Cindro, V; Cioara, I A; Ciocio, A; Cirotto, F; Citron, Z H; Ciubancan, M; Clark, A; Clark, B L; Clark, M R; Clark, P J; Clarke, R N; Clement, C; Coadou, Y; Cobal, M; Coccaro, A; Cochran, J; Coffey, L; Colasurdo, L; Cole, B; Cole, S; Colijn, A P; Collot, J; Colombo, T; Compostella, G; Muiño, P Conde; Coniavitis, E; Connell, S H; Connelly, I A; Consorti, V; Constantinescu, S; Conta, C; Conti, G; Conventi, F; Cooke, M; Cooper, B D; Cooper-Sarkar, A M; Cornelissen, T; Corradi, M; Corriveau, F; Corso-Radu, A; Cortes-Gonzalez, A; Cortiana, G; Costa, G; Costa, M J; Costanzo, D; Cottin, G; Cowan, G; Cox, B E; Cranmer, K; Crawley, S J; Cree, G; Crépé-Renaudin, S; Crescioli, F; Cribbs, W A; Ortuzar, M Crispin; Cristinziani, M; Croft, V; Crosetti, G; Donszelmann, T Cuhadar; Cummings, J; Curatolo, M; Cúth, J; Cuthbert, C; Czirr, H; Czodrowski, P; D'Auria, S; D'Onofrio, M; De Sousa, M J Da Cunha Sargedas; Via, C Da; Dabrowski, W; Dai, T; Dale, O; Dallaire, F; Dallapiccola, C; Dam, M; Dandoy, J R; Dang, N P; Daniells, A C; Dann, N S; Danninger, M; Hoffmann, M Dano; Dao, V; Darbo, G; Darmora, S; Dassoulas, J; Dattagupta, A; Davey, W; David, C; Davidek, T; Davies, M; Davison, P; Davygora, Y; Dawe, E; Dawson, I; Daya-Ishmukhametova, R K; De, K; de Asmundis, R; De Benedetti, A; De Castro, S; De Cecco, S; De Groot, N; de Jong, P; De la Torre, H; De Lorenzi, F; De Pedis, D; De Salvo, A; De Sanctis, U; De Santo, A; De Regie, J B De Vivie; Dearnaley, W J; Debbe, R; Debenedetti, C; Dedovich, D V; Deigaard, I; Del Peso, J; Del Prete, T; Delgove, D; Deliot, F; Delitzsch, C M; Deliyergiyev, M; Dell'Acqua, A; Dell'Asta, L; Dell'Orso, M; Della Pietra, M; Della Volpe, D; Delmastro, M; Delsart, P A; Deluca, C; DeMarco, D A; Demers, S; Demichev, M; Demilly, A; Denisov, S P; Denysiuk, D; Derendarz, D; Derkaoui, J E; Derue, F; Dervan, P; Desch, K; Deterre, C; Dette, K; Deviveiros, P O; Dewhurst, A; Dhaliwal, S; Di Ciaccio, A; Di Ciaccio, L; Di Clemente, W K; Di Donato, C; Di Girolamo, A; Di Girolamo, B; Di Micco, B; Di Nardo, R; Di Simone, A; Di Sipio, R; Di Valentino, D; Diaconu, C; Diamond, M; Dias, F A; Diaz, M A; Diehl, E B; Dietrich, J; Diglio, S; Dimitrievska, A; Dingfelder, J; Dita, P; Dita, S; Dittus, F; Djama, F; Djobava, T; Djuvsland, J I; do Vale, M A B; Dobos, D; Dobre, M; Doglioni, C; Dohmae, T; Dolejsi, J; Dolezal, Z; Dolgoshein, B A; Donadelli, M; Donati, S; Dondero, P; Donini, J; Dopke, J; Doria, A; Dova, M T; Doyle, A T; Drechsler, E; Dris, M; Du, Y; Duarte-Campderros, J; Duchovni, E; Duckeck, G; Ducu, O A; Duda, D; Dudarev, A; Duflot, L; Duguid, L; Dührssen, M; Dunford, M; Yildiz, H Duran; Düren, M; Durglishvili, A; Duschinger, D; Dutta, B; Dyndal, M; Eckardt, C; Ecker, K M; Edgar, R C; Edson, W; Edwards, N C; Eifert, T; Eigen, G; Einsweiler, K; Ekelof, T; Kacimi, M El; Ellajosyula, V; Ellert, M; Elles, S; Ellinghaus, F; Elliot, A A; Ellis, N; Elmsheuser, J; Elsing, M; Emeliyanov, D; Enari, Y; Endner, O C; Endo, M; Ennis, J S; Erdmann, J; Ereditato, A; Ernis, G; Ernst, J; Ernst, M; Errede, S; Ertel, E; Escalier, M; Esch, H; Escobar, C; Esposito, B; Etienvre, A I; Etzion, E; Evans, H; Ezhilov, A; Fabbri, F; Fabbri, L; Facini, G; Fakhrutdinov, R M; Falciano, S; Falla, R J; Faltova, J; Fang, Y; Fanti, M; Farbin, A; Farilla, A; Farina, C; Farooque, T; Farrell, S; Farrington, S M; Farthouat, P; Fassi, F; Fassnacht, P; Fassouliotis, D; Giannelli, M Faucci; Favareto, A; Fawcett, W J; Fayard, L; Fedin, O L; Fedorko, W; Feigl, S; Feligioni, L; Feng, C; Feng, E J; Feng, H; Fenyuk, A B; Feremenga, L; Martinez, P Fernandez; Perez, S Fernandez; Ferrando, J; Ferrari, A; Ferrari, P; Ferrari, R; de Lima, D E Ferreira; Ferrer, A; Ferrere, D; Ferretti, C; Parodi, A Ferretto; Fiedler, F; Filipčič, A; Filipuzzi, M; Filthaut, F; Fincke-Keeler, M; Finelli, K D; Fiolhais, M C N; Fiorini, L; Firan, A; Fischer, A; Fischer, C; Fischer, J; Fisher, W C; Flaschel, N; Fleck, I; Fleischmann, P; Fletcher, G T; Fletcher, G; Fletcher, R R M; Flick, T; Floderus, A; Castillo, L R Flores; Flowerdew, M J; Forcolin, G T; Formica, A; Forti, A; Foster, A G; Fournier, D; Fox, H; Fracchia, S; Francavilla, P; Franchini, M; Francis, D; Franconi, L; Franklin, M; Frate, M; Fraternali, M; Freeborn, D; Fressard-Batraneanu, S M; Friedrich, F; Froidevaux, D; Frost, J A; Fukunaga, C; Torregrosa, E Fullana; Fusayasu, T; Fuster, J; Gabaldon, C; Gabizon, O; Gabrielli, A; Gabrielli, A; Gach, G P; Gadatsch, S; Gadomski, S; Gagliardi, G; Gagnon, L G; Gagnon, P; Galea, C; Galhardo, B; Gallas, E J; Gallop, B J; Gallus, P; Galster, G; Gan, K K; Gao, J; Gao, Y; Gao, Y S; Walls, F M Garay; García, C; Navarro, J E García; Garcia-Sciveres, M; Gardner, R W; Garelli, N; Garonne, V; Bravo, A Gascon; Gatti, C; Gaudiello, A; Gaudio, G; Gaur, B; Gauthier, L; Gavrilenko, I L; Gay, C; Gaycken, G; Gazis, E N; Gecse, Z; Gee, C N P; Geich-Gimbel, Ch; Geisler, M P; Gemme, C; Genest, M H; Geng, C; Gentile, S; George, S; Gerbaudo, D; Gershon, A; Ghasemi, S; Ghazlane, H; Ghneimat, M; Giacobbe, B; Giagu, S; Giannetti, P; Gibbard, B; Gibson, S M; Gignac, M; Gilchriese, M; Gillam, T P S; Gillberg, D; Gilles, G; Gingrich, D M; Giokaris, N; Giordani, M P; Giorgi, F M; Giorgi, F M; Giraud, P F; Giromini, P; Giugni, D; Giuli, F; Giuliani, C; Giulini, M; Gjelsten, B K; Gkaitatzis, S; Gkialas, I; Gkougkousis, E L; Gladilin, L K; Glasman, C; Glatzer, J; Glaysher, P C F; Glazov, A; Goblirsch-Kolb, M; Godlewski, J; Goldfarb, S; Golling, T; Golubkov, D; Gomes, A; Gonçalo, R; Costa, J Goncalves Pinto Firmino Da; Gonella, L; Gongadze, A; de la Hoz, S González; Parra, G Gonzalez; Gonzalez-Sevilla, S; Goossens, L; Gorbounov, P A; Gordon, H A; Gorelov, I; Gorini, B; Gorini, E; Gorišek, A; Gornicki, E; Goshaw, A T; Gössling, C; Gostkin, M I; Goudet, C R; Goujdami, D; Goussiou, A G; Govender, N; Gozani, E; Graber, L; Grabowska-Bold, I; Gradin, P O J; Grafström, P; Gramling, J; Gramstad, E; Grancagnolo, S; Gratchev, V; Gray, H M; Graziani, E; Greenwood, Z D; Grefe, C; Gregersen, K; Gregor, I M; Grenier, P; Grevtsov, K; Griffiths, J; Grillo, A A; Grimm, K; Grinstein, S; Gris, Ph; Grivaz, J-F; Groh, S; Grohs, J P; Gross, E; Grosse-Knetter, J; Grossi, G C; Grout, Z J; Guan, L; Guan, W; Guenther, J; Guescini, F; Guest, D; Gueta, O; Guido, E; Guillemin, T; Guindon, S; Gul, U; Gumpert, C; Guo, J; Guo, Y; Gupta, S; Gustavino, G; Gutierrez, P; Ortiz, N G Gutierrez; Gutschow, C; Guyot, C; Gwenlan, C; Gwilliam, C B; Haas, A; Haber, C; Hadavand, H K; Haddad, N; Hadef, A; Haefner, P; Hageböck, S; Hajduk, Z; Hakobyan, H; Haleem, M; Haley, J; Hall, D; Halladjian, G; Hallewell, G D; Hamacher, K; Hamal, P; Hamano, K; Hamilton, A; Hamity, G N; Hamnett, P G; Han, L; Hanagaki, K; Hanawa, K; Hance, M; Haney, B; Hanke, P; Hanna, R; Hansen, J B; Hansen, J D; Hansen, M C; Hansen, P H; Hara, K; Hard, A S; Harenberg, T; Hariri, F; Harkusha, S; Harrington, R D; Harrison, P F; Hartjes, F; Hasegawa, M; Hasegawa, Y; Hasib, A; Hassani, S; Haug, S; Hauser, R; Hauswald, L; Havranek, M; Hawkes, C M; Hawkings, R J; Hawkins, A D; Hayden, D; Hays, C P; Hays, J M; Hayward, H S; Haywood, S J; Head, S J; Heck, T; Hedberg, V; Heelan, L; Heim, S; Heim, T; Heinemann, B; Heinrich, J J; Heinrich, L; Heinz, C; Hejbal, J; Helary, L; Hellman, S; Helsens, C; Henderson, J; Henderson, R C W; Heng, Y; Henkelmann, S; Correia, A M Henriques; Henrot-Versille, S; Herbert, G H; Jiménez, Y Hernández; Herten, G; Hertenberger, R; Hervas, L; Hesketh, G G; Hessey, N P; Hetherly, J W; Hickling, R; Higón-Rodriguez, E; Hill, E; Hill, J C; Hiller, K H; Hillier, S J; Hinchliffe, I; Hines, E; Hinman, R R; Hirose, M; Hirschbuehl, D; Hobbs, J; Hod, N; Hodgkinson, M C; Hodgson, P; Hoecker, A; Hoeferkamp, M R; Hoenig, F; Hohlfeld, M; Hohn, D; Holmes, T R; Homann, M; Hong, T M; Hooberman, B H; Hopkins, W H; Horii, Y; Horton, A J; Hostachy, J-Y; Hou, S; Hoummada, A; Howard, J; Howarth, J; Hrabovsky, M; Hristova, I; Hrivnac, J; Hryn'ova, T; Hrynevich, A; Hsu, C; Hsu, P J; Hsu, S-C; Hu, D; Hu, Q; Huang, Y; Hubacek, Z; Hubaut, F; Huegging, F; Huffman, T B; Hughes, E W; Hughes, G; Huhtinen, M; Hülsing, T A; Huseynov, N; Huston, J; Huth, J; Iacobucci, G; Iakovidis, G; Ibragimov, I; Iconomidou-Fayard, L; Ideal, E; Idrissi, Z; Iengo, P; Igonkina, O; Iizawa, T; Ikegami, Y; Ikeno, M; Ilchenko, Y; Iliadis, D; Ilic, N; Ince, T; Introzzi, G; Ioannou, P; Iodice, M; Iordanidou, K; Ippolito, V; Quiles, A Irles; Isaksson, C; Ishino, M; Ishitsuka, M; Ishmukhametov, R; Issever, C; Istin, S; Ito, F; Ponce, J M Iturbe; Iuppa, R; Ivarsson, J; Iwanski, W; Iwasaki, H; Izen, J M; Izzo, V; Jabbar, S; Jackson, B; Jackson, M; Jackson, P; Jain, V; Jakobi, K B; Jakobs, K; Jakobsen, S; Jakoubek, T; Jamin, D O; Jana, D K; Jansen, E; Jansky, R; Janssen, J; Janus, M; Jarlskog, G; Javadov, N; Javůrek, T; Jeanneau, F; Jeanty, L; Jejelava, J; Jeng, G-Y; Jennens, D; Jenni, P; Jentzsch, J; Jeske, C; Jézéquel, S; Ji, H; Jia, J; Jiang, H; Jiang, Y; Jiggins, S; Pena, J Jimenez; Jin, S; Jinaru, A; Jinnouchi, O; Johansson, P; Johns, K A; Johnson, W J; Jon-And, K; Jones, G; Jones, R W L; Jones, S; Jones, T J; Jongmanns, J; Jorge, P M; Jovicevic, J; Ju, X; Rozas, A Juste; Köhler, M K; Kaczmarska, A; Kado, M; Kagan, H; Kagan, M; Kahn, S J; Kajomovitz, E; Kalderon, C W; Kaluza, A; Kama, S; Kamenshchikov, A; Kanaya, N; Kaneti, S; Kantserov, V A; Kanzaki, J; Kaplan, B; Kaplan, L S; Kapliy, A; Kar, D; Karakostas, K; Karamaoun, A; Karastathis, N; Kareem, M J; Karentzos, E; Karnevskiy, M; Karpov, S N; Karpova, Z M; Karthik, K; Kartvelishvili, V; Karyukhin, A N; Kasahara, K; Kashif, L; Kass, R D; Kastanas, A; Kataoka, Y; Kato, C; Katre, A; Katzy, J; Kawagoe, K; Kawamoto, T; Kawamura, G; Kazama, S; Kazanin, V F; Keeler, R; Kehoe, R; Keller, J S; Kempster, J J; Kentaro, K; Keoshkerian, H; Kepka, O; Kerševan, B P; Kersten, S; Keyes, R A; Khalil-Zada, F; Khandanyan, H; Khanov, A; Kharlamov, A G; Khoo, T J; Khovanskiy, V; Khramov, E; Khubua, J; Kido, S; Kim, H Y; Kim, S H; Kim, Y K; Kimura, N; Kind, O M; King, B T; King, M; King, S B; Kirk, J; Kiryunin, A E; Kishimoto, T; Kisielewska, D; Kiss, F; Kiuchi, K; Kivernyk, O; Kladiva, E; Klein, M H; Klein, M; Klein, U; Kleinknecht, K; Klimek, P; Klimentov, A; Klingenberg, R; Klinger, J A; Klioutchnikova, T; Kluge, E-E; Kluit, P; Kluth, S; Knapik, J; Kneringer, E; Knoops, E B F G; Knue, A; Kobayashi, A; Kobayashi, D; Kobayashi, T; Kobel, M; Kocian, M; Kodys, P; Koffas, T; Koffeman, E; Kogan, L A; Koi, T; Kolanoski, H; Kolb, M; Koletsou, I; Komar, A A; Komori, Y; Kondo, T; Kondrashova, N; Köneke, K; König, A C; Kono, T; Konoplich, R; Konstantinidis, N; Kopeliansky, R; Koperny, S; Köpke, L; Kopp, A K; Korcyl, K; Kordas, K; Korn, A; Korol, A A; Korolkov, I; Korolkova, E V; Kortner, O; Kortner, S; Kosek, T; Kostyukhin, V V; Kotwal, A; Kourkoumeli-Charalampidi, A; Kourkoumelis, C; Kouskoura, V; Koutsman, A; Kowalewska, A B; Kowalewski, R; Kowalski, T Z; Kozanecki, W; Kozhin, A S; Kramarenko, V A; Kramberger, G; Krasnopevtsev, D; Krasny, M W; Krasznahorkay, A; Kraus, J K; Kravchenko, A; Kretz, M; Kretzschmar, J; Kreutzfeldt, K; Krieger, P; Krizka, K; Kroeninger, K; Kroha, H; Kroll, J; Kroseberg, J; Krstic, J; Kruchonak, U; Krüger, H; Krumnack, N; Kruse, A; Kruse, M C; Kruskal, M; Kubota, T; Kucuk, H; Kuday, S; Kuechler, J T; Kuehn, S; Kugel, A; Kuger, F; Kuhl, A; Kuhl, T; Kukhtin, V; Kukla, R; Kulchitsky, Y; Kuleshov, S; Kuna, M; Kunigo, T; Kupco, A; Kurashige, H; Kurochkin, Y A; Kus, V; Kuwertz, E S; Kuze, M; Kvita, J; Kwan, T; Kyriazopoulos, D; Rosa, A La; Navarro, J L La Rosa; Rotonda, L La; Lacasta, C; Lacava, F; Lacey, J; Lacker, H; Lacour, D; Lacuesta, V R; Ladygin, E; Lafaye, R; Laforge, B; Lagouri, T; Lai, S; Lammers, S; Lampl, W; Lançon, E; Landgraf, U; Landon, M P J; Lang, V S; Lange, J C; Lankford, A J; Lanni, F; Lantzsch, K; Lanza, A; Laplace, S; Lapoire, C; Laporte, J F; Lari, T; Manghi, F Lasagni; Lassnig, M; Laurelli, P; Lavrijsen, W; Law, A T; Laycock, P; Lazovich, T; Lazzaroni, M; Dortz, O Le; Guirriec, E Le; Menedeu, E Le; Quilleuc, E P Le; LeBlanc, M; LeCompte, T; Ledroit-Guillon, F; Lee, C A; Lee, S C; Lee, L; Lefebvre, G; Lefebvre, M; Legger, F; Leggett, C; Lehan, A; Miotto, G Lehmann; Lei, X; Leight, W A; Leisos, A; Leister, A G; Leite, M A L; Leitner, R; Lellouch, D; Lemmer, B; Leney, K J C; Lenz, T; Lenzi, B; Leone, R; Leone, S; Leonidopoulos, C; Leontsinis, S; Lerner, G; Leroy, C; Lesage, A A J; Lester, C G; Levchenko, M; Levêque, J; Levin, D; Levinson, L J; Levy, M; Leyko, A M; Leyton, M; Li, B; Li, H; Li, H L; Li, L; Li, L; Li, Q; Li, S; Li, X; Li, Y; Liang, Z; Liao, H; Liberti, B; Liblong, A; Lichard, P; Lie, K; Liebal, J; Liebig, W; Limbach, C; Limosani, A; Lin, S C; Lin, T H; Lindquist, B E; Lipeles, E; Lipniacka, A; Lisovyi, M; Liss, T M; Lissauer, D; Lister, A; Litke, A M; Liu, B; Liu, D; Liu, H; Liu, H; Liu, J; Liu, J B; Liu, K; Liu, L; Liu, M; Liu, M; Liu, Y L; Liu, Y; Livan, M; Lleres, A; Merino, J Llorente; Lloyd, S L; Sterzo, F Lo; Lobodzinska, E; Loch, P; Lockman, W S; Loebinger, F K; Loevschall-Jensen, A E; Loew, K M; Loginov, A; Lohse, T; Lohwasser, K; Lokajicek, M; Long, B A; Long, J D; Long, R E; Longo, L; Looper, K A; Lopes, L; Mateos, D Lopez; Paredes, B Lopez; Paz, I Lopez; Solis, A Lopez; Lorenz, J; Martinez, N Lorenzo; Losada, M; Lösel, P J; Lou, X; Lounis, A; Love, J; Love, P A; Lu, H; Lu, N; Lubatti, H J; Luci, C; Lucotte, A; Luedtke, C; Luehring, F; Lukas, W; Luminari, L; Lundberg, O; Lund-Jensen, B; Lynn, D; Lysak, R; Lytken, E; Lyubushkin, V; Ma, H; Ma, L L; Ma, Y; Maccarrone, G; Macchiolo, A; Macdonald, C M; Maček, B; Miguens, J Machado; Madaffari, D; Madar, R; Maddocks, H J; Mader, W F; Madsen, A; Maeda, J; Maeland, S; Maeno, T; Maevskiy, A; Magradze, E; Mahlstedt, J; Maiani, C; Maidantchik, C; Maier, A A; Maier, T; Maio, A; Majewski, S; Makida, Y; Makovec, N; Malaescu, B; Malecki, Pa; Maleev, V P; Malek, F; Mallik, U; Malon, D; Malone, C; Maltezos, S; Malyukov, S; Mamuzic, J; Mancini, G; Mandelli, B; Mandelli, L; Mandić, I; Maneira, J; Filho, L Manhaes de Andrade; Ramos, J Manjarres; Mann, A; Mansoulie, B; Mantifel, R; Mantoani, M; Manzoni, S; Mapelli, L; Marceca, G; March, L; Marchiori, G; Marcisovsky, M; Marjanovic, M; Marley, D E; Marroquim, F; Marsden, S P; Marshall, Z; Marti, L F; Marti-Garcia, S; Martin, B; Martin, T A; Martin, V J; Latour, B Martin Dit; Martinez, M; Martin-Haugh, S; Martoiu, V S; Martyniuk, A C; Marx, M; Marzano, F; Marzin, A; Masetti, L; Mashimo, T; Mashinistov, R; Masik, J; Maslennikov, A L; Massa, I; Massa, L; Mastrandrea, P; Mastroberardino, A; Masubuchi, T; Mättig, P; Mattmann, J; Maurer, J; Maxfield, S J; Maximov, D A; Mazini, R; Mazza, S M; Fadden, N C Mc; Goldrick, G Mc; Kee, S P Mc; McCarn, A; McCarthy, R L; McCarthy, T G; McClymont, L I; McFarlane, K W; Mcfayden, J A; Mchedlidze, G; McMahon, S J; McPherson, R A; Medinnis, M; Meehan, S; Mehlhase, S; Mehta, A; Meier, K; Meineck, C; Meirose, B; Garcia, B R Mellado; Meloni, F; Mengarelli, A; Menke, S; Meoni, E; Mercurio, K M; Mergelmeyer, S; Mermod, P; Merola, L; Meroni, C; Merritt, F S; Messina, A; Metcalfe, J; Mete, A S; Meyer, C; Meyer, C; Meyer, J-P; Meyer, J; Theenhausen, H Meyer Zu; Middleton, R P; Miglioranzi, S; Mijović, L; Mikenberg, G; Mikestikova, M; Mikuž, M; Milesi, M; Milic, A; Miller, D W; Mills, C; Milov, A; Milstead, D A; Minaenko, A A; Minami, Y; Minashvili, I A; Mincer, A I; Mindur, B; Mineev, M; Ming, Y; Mir, L M; Mistry, K P; Mitani, T; Mitrevski, J; Mitsou, V A; Miucci, A; Miyagawa, P S; Mjörnmark, J U; Moa, T; Mochizuki, K; Mohapatra, S; Mohr, W; Molander, S; Moles-Valls, R; Monden, R; Mondragon, M C; Mönig, K; Monk, J; Monnier, E; Montalbano, A; Berlingen, J Montejo; Monticelli, F; Monzani, S; Moore, R W; Morange, N; Moreno, D; Llácer, M Moreno; Morettini, P; Mori, D; Mori, T; Morii, M; Morinaga, M; Morisbak, V; Moritz, S; Morley, A K; Mornacchi, G; Morris, J D; Mortensen, S S; Morvaj, L; Mosidze, M; Moss, J; Motohashi, K; Mount, R; Mountricha, E; Mouraviev, S V; Moyse, E J W; Muanza, S; Mudd, R D; Mueller, F; Mueller, J; Mueller, R S P; Mueller, T; Muenstermann, D; Mullen, P; Mullier, G A; Sanchez, F J Munoz; Quijada, J A Murillo; Murray, W J; Musheghyan, H; Muškinja, M; Myagkov, A G; Myska, M; Nachman, B P; Nackenhorst, O; Nadal, J; Nagai, K; Nagai, R; Nagano, K; Nagasaka, Y; Nagata, K; Nagel, M; Nagy, E; Nairz, A M; Nakahama, Y; Nakamura, K; Nakamura, T; Nakano, I; Namasivayam, H; Garcia, R F Naranjo; Narayan, R; Villar, D I Narrias; Naryshkin, I; Naumann, T; Navarro, G; Nayyar, R; Neal, H A; Nechaeva, P Yu; Neep, T J; Nef, P D; Negri, A; Negrini, M; Nektarijevic, S; Nellist, C; Nelson, A; Nemecek, S; Nemethy, P; Nepomuceno, A A; Nessi, M; Neubauer, M S; Neumann, M; Neves, R M; Nevski, P; Newman, P R; Nguyen, D H; Nickerson, R B; Nicolaidou, R; Nicquevert, B; Nielsen, J; Nikiforov, A; Nikolaenko, V; Nikolic-Audit, I; Nikolopoulos, K; Nilsen, J K; Nilsson, P; Ninomiya, Y; Nisati, A; Nisius, R; Nobe, T; Nodulman, L; Nomachi, M; Nomidis, I; Nooney, T; Norberg, S; Nordberg, M; Norjoharuddeen, N; Novgorodova, O; Nowak, S; Nozaki, M; Nozka, L; Ntekas, K; Nurse, E; Nuti, F; O'grady, F; O'Neil, D C; O'Rourke, A A; O'Shea, V; Oakham, F G; Oberlack, H; Obermann, T; Ocariz, J; Ochi, A; Ochoa, I; Ochoa-Ricoux, J P; Oda, S; Odaka, S; Ogren, H; Oh, A; Oh, S H; Ohm, C C; Ohman, H; Oide, H; Okawa, H; Okumura, Y; Okuyama, T; Olariu, A; Seabra, L F Oleiro; Pino, S A Olivares; Damazio, D Oliveira; Olszewski, A; Olszowska, J; Onofre, A; Onogi, K; Onyisi, P U E; Oram, C J; Oreglia, M J; Oren, Y; Orestano, D; Orlando, N; Orr, R S; Osculati, B; Ospanov, R; Garzon, G Otero Y; Otono, H; Ouchrif, M; Ould-Saada, F; Ouraou, A; Oussoren, K P; Ouyang, Q; Ovcharova, A; Owen, M; Owen, R E; Ozcan, V E; Ozturk, N; Pachal, K; Pages, A Pacheco; Aranda, C Padilla; Pagáčová, M; Griso, S Pagan; Paige, F; Pais, P; Pajchel, K; Palacino, G; Palestini, S; Palka, M; Pallin, D; Palma, A; Panagiotopoulou, E St; Pandini, C E; Vazquez, J G Panduro; Pani, P; Panitkin, S; Pantea, D; Paolozzi, L; Papadopoulou, Th D; Papageorgiou, K; Paramonov, A; Hernandez, D Paredes; Parker, A J; Parker, M A; Parker, K A; Parodi, F; Parsons, J A; Parzefall, U; Pascuzzi, V R; Pasqualucci, E; Passaggio, S; Pastore, F; Pastore, Fr; Pásztor, G; Pataraia, S; Patel, N D; Pater, J R; Pauly, T; Pearce, J; Pearson, B; Pedersen, L E; Pedersen, M; Lopez, S Pedraza; Pedro, R; Peleganchuk, S V; Pelikan, D; Penc, O; Peng, C; Peng, H; Penwell, J; Peralva, B S; Perego, M M; Perepelitsa, D V; Codina, E Perez; Perini, L; Pernegger, H; Perrella, S; Peschke, R; Peshekhonov, V D; Peters, K; Peters, R F Y; Petersen, B A; Petersen, T C; Petit, E; Petridis, A; Petridou, C; Petroff, P; Petrolo, E; Petrov, M; Petrucci, F; Pettersson, N E; Peyaud, A; Pezoa, R; Phillips, P W; Piacquadio, G; Pianori, E; Picazio, A; Piccaro, E; Piccinini, M; Pickering, M A; Piegaia, R; Pilcher, J E; Pilkington, A D; Pin, A W J; Pina, J; Pinamonti, M; Pinfold, J L; Pingel, A; Pires, S; Pirumov, H; Pitt, M; Plazak, L; Pleier, M-A; Pleskot, V; Plotnikova, E; Plucinski, P; Pluth, D; Poettgen, R; Poggioli, L; Pohl, D; Polesello, G; Poley, A; Policicchio, A; Polifka, R; Polini, A; Pollard, C S; Polychronakos, V; Pommès, K; Pontecorvo, L; Pope, B G; Popeneciu, G A; Popovic, D S; Poppleton, A; Pospisil, S; Potamianos, K; Potrap, I N; Potter, C J; Potter, C T; Poulard, G; Poveda, J; Pozdnyakov, V; Astigarraga, M E Pozo; Pralavorio, P; Pranko, A; Prell, S; Price, D; Price, L E; Primavera, M; Prince, S; Proissl, M; Prokofiev, K; Prokoshin, F; Protopopescu, S; Proudfoot, J; Przybycien, M; Puddu, D; Puldon, D; Purohit, M; Puzo, P; Qian, J; Qin, G; Qin, Y; Quadt, A; Quayle, W B; Queitsch-Maitland, M; Quilty, D; Raddum, S; Radeka, V; Radescu, V; Radhakrishnan, S K; Radloff, P; Rados, P; Ragusa, F; Rahal, G; Raine, J A; Rajagopalan, S; Rammensee, M; Rangel-Smith, C; Ratti, M G; Rauscher, F; Rave, S; Ravenscroft, T; Raymond, M; Read, A L; Readioff, N P; Rebuzzi, D M; Redelbach, A; Redlinger, G; Reece, R; Reeves, K; Rehnisch, L; Reichert, J; Reisin, H; Rembser, C; Ren, H; Rescigno, M; Resconi, S; Rezanova, O L; Reznicek, P; Rezvani, R; Richter, R; Richter, S; Richter-Was, E; Ricken, O; Ridel, M; Rieck, P; Riegel, C J; Rieger, J; Rifki, O; Rijssenbeek, M; Rimoldi, A; Rinaldi, L; Ristić, B; Ritsch, E; Riu, I; Rizatdinova, F; Rizvi, E; Rizzi, C; Robertson, S H; Robichaud-Veronneau, A; Robinson, D; Robinson, J E M; Robson, A; Roda, C; Rodina, Y; Perez, A Rodriguez; Rodriguez, D Rodriguez; Roe, S; Rogan, C S; Røhne, O; Romaniouk, A; Romano, M; Saez, S M Romano; Adam, E Romero; Rompotis, N; Ronzani, M; Roos, L; Ros, E; Rosati, S; Rosbach, K; Rose, P; Rosenthal, O; Rossetti, V; Rossi, E; Rossi, L P; Rosten, J H N; Rosten, R; Rotaru, M; Roth, I; Rothberg, J; Rousseau, D; Royon, C R; Rozanov, A; Rozen, Y; Ruan, X; Rubbo, F; Rubinskiy, I; Rud, V I; Rudolph, M S; Rühr, F; Ruiz-Martinez, A; Rurikova, Z; Rusakovich, N A; Ruschke, A; Russell, H L; Rutherfoord, J P; Ruthmann, N; Ryabov, Y F; Rybar, M; Rybkin, G; Ryu, S; Ryzhov, A; Saavedra, A F; Sabato, G; Sacerdoti, S; Sadrozinski, H F-W; Sadykov, R; Tehrani, F Safai; Saha, P; Sahinsoy, M; Saimpert, M; Saito, T; Sakamoto, H; Sakurai, Y; Salamanna, G; Salamon, A; Loyola, J E Salazar; Salek, D; De Bruin, P H Sales; Salihagic, D; Salnikov, A; Salt, J; Salvatore, D; Salvatore, F; Salvucci, A; Salzburger, A; Sammel, D; Sampsonidis, D; Sanchez, A; Sánchez, J; Martinez, V Sanchez; Sandaker, H; Sandbach, R L; Sander, H G; Sanders, M P; Sandhoff, M; Sandoval, C; Sandstroem, R; Sankey, D P C; Sannino, M; Sansoni, A; Santoni, C; Santonico, R; Santos, H; Castillo, I Santoyo; Sapp, K; Sapronov, A; Saraiva, J G; Sarrazin, B; Sasaki, O; Sasaki, Y; Sato, K; Sauvage, G; Sauvan, E; Savage, G; Savard, P; Sawyer, C; Sawyer, L; Saxon, J; Sbarra, C; Sbrizzi, A; Scanlon, T; Scannicchio, D A; Scarcella, M; Scarfone, V; Schaarschmidt, J; Schacht, P; Schaefer, D; Schaefer, R; Schaeffer, J; Schaepe, S; Schaetzel, S; Schäfer, U; Schaffer, A C; Schaile, D; Schamberger, R D; Scharf, V; Schegelsky, V A; Scheirich, D; Schernau, M; Schiavi, C; Schillo, C; Schioppa, M; Schlenker, S; Schmieden, K; Schmitt, C; Schmitt, S; Schmitz, S; Schneider, B; Schnellbach, Y J; Schnoor, U; Schoeffel, L; Schoening, A; Schoenrock, B D; Schopf, E; Schorlemmer, A L S; Schott, M; Schovancova, J; Schramm, S; Schreyer, M; Schuh, N; Schultens, M J; Schultz-Coulon, H-C; Schulz, H; Schumacher, M; Schumm, B A; Schune, Ph; Schwanenberger, C; Schwartzman, A; Schwarz, T A; Schwegler, Ph; Schweiger, H; Schwemling, Ph; Schwienhorst, R; Schwindling, J; Schwindt, T; Sciolla, G; Scuri, F; Scutti, F; Searcy, J; Seema, P; Seidel, S C; Seiden, A; Seifert, F; Seixas, J M; Sekhniaidze, G; Sekhon, K; Sekula, S J; Seliverstov, D M; Semprini-Cesari, N; Serfon, C; Serin, L; Serkin, L; Sessa, M; Seuster, R; Severini, H; Sfiligoj, T; Sforza, F; Sfyrla, A; Shabalina, E; Shaikh, N W; Shan, L Y; Shang, R; Shank, J T; Shapiro, M; Shatalov, P B; Shaw, K; Shaw, S M; Shcherbakova, A; Shehu, C Y; Sherwood, P; Shi, L; Shimizu, S; Shimmin, C O; Shimojima, M; Shiyakova, M; Shmeleva, A; Saadi, D Shoaleh; Shochet, M J; Shojaii, S; Shrestha, S; Shulga, E; Shupe, M A; Sicho, P; Sidebo, P E; Sidiropoulou, O; Sidorov, D; Sidoti, A; Siegert, F; Sijacki, Dj; Silva, J; Silverstein, S B; Simak, V; Simard, O; Simic, Lj; Simion, S; Simioni, E; Simmons, B; Simon, D; Simon, M; Sinervo, P; Sinev, N B; Sioli, M; Siragusa, G; Sivoklokov, S Yu; Sjölin, J; Sjursen, T B; Skinner, M B; Skottowe, H P; Skubic, P; Slater, M; Slavicek, T; Slawinska, M; Sliwa, K; Slovak, R; Smakhtin, V; Smart, B H; Smestad, L; Smirnov, S Yu; Smirnov, Y; Smirnova, L N; Smirnova, O; Smith, M N K; Smith, R W; Smizanska, M; Smolek, K; Snesarev, A A; Snidero, G; Snyder, S; Sobie, R; Socher, F; Soffer, A; Soh, D A; Sokhrannyi, G; Sanchez, C A Solans; Solar, M; Soldatov, E Yu; Soldevila, U; Solodkov, A A; Soloshenko, A; Solovyanov, O V; Solovyev, V; Sommer, P; Son, H; Song, H Y; Sood, A; Sopczak, A; Sopko, V; Sorin, V; Sosa, D; Sotiropoulou, C L; Soualah, R; Soukharev, A M; South, D; Sowden, B C; Spagnolo, S; Spalla, M; Spangenberg, M; Spanò, F; Sperlich, D; Spettel, F; Spighi, R; Spigo, G; Spiller, L A; Spousta, M; Denis, R D St; Stabile, A; Stahlman, J; Stamen, R; Stamm, S; Stanecka, E; Stanek, R W; Stanescu, C; Stanescu-Bellu, M; Stanitzki, M M; Stapnes, S; Starchenko, E A; Stark, G H; Stark, J; Staroba, P; Starovoitov, P; Stärz, S; Staszewski, R; Steinberg, P; Stelzer, B; Stelzer, H J; Stelzer-Chilton, O; Stenzel, H; Stewart, G A; Stillings, J A; Stockton, M C; Stoebe, M; Stoicea, G; Stolte, P; Stonjek, S; Stradling, A R; Straessner, A; Stramaglia, M E; Strandberg, J; Strandberg, S; Strandlie, A; Strauss, M; Strizenec, P; Ströhmer, R; Strom, D M; Stroynowski, R; Strubig, A; Stucci, S A; Stugu, B; Styles, N A; Su, D; Su, J; Subramaniam, R; Suchek, S; Sugaya, Y; Suk, M; Sulin, V V; Sultansoy, S; Sumida, T; Sun, S; Sun, X; Sundermann, J E; Suruliz, K; Susinno, G; Sutton, M R; Suzuki, S; Svatos, M; Swiatlowski, M; Sykora, I; Sykora, T; Ta, D; Taccini, C; Tackmann, K; Taenzer, J; Taffard, A; Tafirout, R; Taiblum, N; Takai, H; Takashima, R; Takeda, H; Takeshita, T; Takubo, Y; Talby, M; Talyshev, A A; Tam, J Y C; Tan, K G; Tanaka, J; Tanaka, R; Tanaka, S; Tannenwald, B B; Araya, S Tapia; Tapprogge, S; Tarem, S; Tartarelli, G F; Tas, P; Tasevsky, M; Tashiro, T; Tassi, E; Delgado, A Tavares; Tayalati, Y; Taylor, A C; Taylor, G N; Taylor, P T E; Taylor, W; Teischinger, F A; Teixeira-Dias, P; Temming, K K; Temple, D; Kate, H Ten; Teng, P K; Teoh, J J; Tepel, F; Terada, S; Terashi, K; Terron, J; Terzo, S; Testa, M; Teuscher, R J; Theveneaux-Pelzer, T; Thomas, J P; Thomas-Wilsker, J; Thompson, E N; Thompson, P D; Thompson, R J; Thompson, A S; Thomsen, L A; Thomson, E; Thomson, M; Tibbetts, M J; Torres, R E Ticse; Tikhomirov, V O; Tikhonov, Yu A; Timoshenko, S; Tipton, P; Tisserant, S; Todome, K; Todorov, T; Todorova-Nova, S; Tojo, J; Tokár, S; Tokushuku, K; Tolley, E; Tomlinson, L; Tomoto, M; Tompkins, L; Toms, K; Tong, B; Torrence, E; Torres, H; Pastor, E Torró; Toth, J; Touchard, F; Tovey, D R; Trefzger, T; Tricoli, A; Trigger, I M; Trincaz-Duvoid, S; Tripiana, M F; Trischuk, W; Trocmé, B; Trofymov, A; Troncon, C; Trottier-McDonald, M; Trovatelli, M; Truong, L; Trzebinski, M; Trzupek, A; Tseng, J C-L; Tsiareshka, P V; Tsipolitis, G; Tsirintanis, N; Tsiskaridze, S; Tsiskaridze, V; Tskhadadze, E G; Tsui, K M; Tsukerman, I I; Tsulaia, V; Tsuno, S; Tsybychev, D; Tudorache, A; Tudorache, V; Tuna, A N; Tupputi, S A; Turchikhin, S; Turecek, D; Turgeman, D; Turra, R; Turvey, A J; Tuts, P M; Tyndel, M; Ucchielli, G; Ueda, I; Ueno, R; Ughetto, M; Ukegawa, F; Unal, G; Undrus, A; Unel, G; Ungaro, F C; Unno, Y; Unverdorben, C; Urban, J; Urquijo, P; Urrejola, P; Usai, G; Usanova, A; Vacavant, L; Vacek, V; Vachon, B; Valderanis, C; Santurio, E Valdes; Valencic, N; Valentinetti, S; Valero, A; Valery, L; Valkar, S; Vallecorsa, S; Ferrer, J A Valls; Van Den Wollenberg, W; Van Der Deijl, P C; van der Geer, R; van der Graaf, H; van Eldik, N; van Gemmeren, P; Van Nieuwkoop, J; van Vulpen, I; van Woerden, M C; Vanadia, M; Vandelli, W; Vanguri, R; Vaniachine, A; Vankov, P; Vardanyan, G; Vari, R; Varnes, E W; Varol, T; Varouchas, D; Vartapetian, A; Varvell, K E; Vasquez, J G; Vazeille, F; Schroeder, T Vazquez; Veatch, J; Veloce, L M; Veloso, F; Veneziano, S; Ventura, A; Venturi, M; Venturi, N; Venturini, A; Vercesi, V; Verducci, M; Verkerke, W; Vermeulen, J C; Vest, A; Vetterli, M C; Viazlo, O; Vichou, I; Vickey, T; Boeriu, O E Vickey; Viehhauser, G H A; Viel, S; Vigani, L; Vigne, R; Villa, M; Perez, M Villaplana; Vilucchi, E; Vincter, M G; Vinogradov, V B; Vittori, C; Vivarelli, I; Vlachos, S; Vlasak, M; Vogel, M; Vokac, P; Volpi, G; Volpi, M; von der Schmitt, H; von Toerne, E; Vorobel, V; Vorobev, K; Vos, M; Voss, R; Vossebeld, J H; Vranjes, N; Milosavljevic, M Vranjes; Vrba, V; Vreeswijk, M; Vuillermet, R; Vukotic, I; Vykydal, Z; Wagner, P; Wagner, W; Wahlberg, H; Wahrmund, S; Wakabayashi, J; Walder, J; Walker, R; Walkowiak, W; Wallangen, V; Wang, C; Wang, C; Wang, F; Wang, H; Wang, H; Wang, J; Wang, J; Wang, K; Wang, R; Wang, S M; Wang, T; Wang, T; Wang, X; Wanotayaroj, C; Warburton, A; Ward, C P; Wardrope, D R; Washbrook, A; Watkins, P M; Watson, A T; Watson, I J; Watson, M F; Watts, G; Watts, S; Waugh, B M; Webb, S; Weber, M S; Weber, S W; Webster, J S; Weidberg, A R; Weinert, B; Weingarten, J; Weiser, C; Weits, H; Wells, P S; Wenaus, T; Wengler, T; Wenig, S; Wermes, N; Werner, M; Werner, P; Wessels, M; Wetter, J; Whalen, K; Whallon, N L; Wharton, A M; White, A; White, M J; White, R; White, S; Whiteson, D; Wickens, F J; Wiedenmann, W; Wielers, M; Wienemann, P; Wiglesworth, C; Wiik-Fuchs, L A M; Wildauer, A; Wilk, F; Wilkens, H G; Williams, H H; Williams, S; Willis, C; Willocq, S; Wilson, J A; Wingerter-Seez, I; Winklmeier, F; Winston, O J; Winter, B T; Wittgen, M; Wittkowski, J; Wollstadt, S J; Wolter, M W; Wolters, H; Wosiek, B K; Wotschack, J; Woudstra, M J; Wozniak, K W; Wu, M; Wu, M; Wu, S L; Wu, X; Wu, Y; Wyatt, T R; Wynne, B M; Xella, S; Xu, D; Xu, L; Yabsley, B; Yacoob, S; Yakabe, R; Yamaguchi, D; Yamaguchi, Y; Yamamoto, A; Yamamoto, S; Yamanaka, T; Yamauchi, K; Yamazaki, Y; Yan, Z; Yang, H; Yang, H; Yang, Y; Yang, Z; Yao, W-M; Yap, Y C; Yasu, Y; Yatsenko, E; Wong, K H Yau; Ye, J; Ye, S; Yeletskikh, I; Yen, A L; Yildirim, E; Yorita, K; Yoshida, R; Yoshihara, K; Young, C; Young, C J S; Youssef, S; Yu, D R; Yu, J; Yu, J M; Yu, J; Yuan, L; Yuen, S P Y; Yusuff, I; Zabinski, B; Zaidan, R; Zaitsev, A M; Zakharchuk, N; Zalieckas, J; Zaman, A; Zambito, S; Zanello, L; Zanzi, D; Zeitnitz, C; Zeman, M; Zemla, A; Zeng, J C; Zeng, Q; Zengel, K; Zenin, O; Ženiš, T; Zerwas, D; Zhang, D; Zhang, F; Zhang, G; Zhang, H; Zhang, J; Zhang, L; Zhang, R; Zhang, R; Zhang, X; Zhang, Z; Zhao, X; Zhao, Y; Zhao, Z; Zhemchugov, A; Zhong, J; Zhou, B; Zhou, C; Zhou, L; Zhou, L; Zhou, M; Zhou, N; Zhu, C G; Zhu, H; Zhu, J; Zhu, Y; Zhuang, X; Zhukov, K; Zibell, A; Zieminska, D; Zimine, N I; Zimmermann, C; Zimmermann, S; Zinonos, Z; Zinser, M; Ziolkowski, M; Živković, L; Zobernig, G; Zoccoli, A; Nedden, M Zur; Zurzolo, G; Zwalinski, L

    2016-01-01

    The performance of the jet trigger for the ATLAS detector at the LHC during the 2011 data taking period is described. During 2011 the LHC provided proton-proton collisions with a centre-of-mass energy of 7 TeV and heavy ion collisions with a 2.76 TeV per nucleon-nucleon collision energy. The ATLAS trigger is a three level system designed to reduce the rate of events from the 40 MHz nominal maximum bunch crossing rate to the approximate 400 Hz which can be written to offline storage. The ATLAS jet trigger is the primary means for the online selection of events containing jets. Events are accepted by the trigger if they contain one or more jets above some transverse energy threshold. During 2011 data taking the jet trigger was fully efficient for jets with transverse energy above 25 GeV for triggers seeded randomly at Level 1. For triggers which require a jet to be identified at each of the three trigger levels, full efficiency is reached for offline jets with transverse energy above 60 GeV. Jets reconstructed in the final trigger level and corresponding to offline jets with transverse energy greater than 60 GeV, are reconstructed with a resolution in transverse energy with respect to offline jets, of better than 4 % in the central region and better than 2.5 % in the forward direction.

  14. Comprehensive cellular‐resolution atlas of the adult human brain

    PubMed Central

    Royall, Joshua J.; Sunkin, Susan M.; Ng, Lydia; Facer, Benjamin A.C.; Lesnar, Phil; Guillozet‐Bongaarts, Angie; McMurray, Bergen; Szafer, Aaron; Dolbeare, Tim A.; Stevens, Allison; Tirrell, Lee; Benner, Thomas; Caldejon, Shiella; Dalley, Rachel A.; Dee, Nick; Lau, Christopher; Nyhus, Julie; Reding, Melissa; Riley, Zackery L.; Sandman, David; Shen, Elaine; van der Kouwe, Andre; Varjabedian, Ani; Write, Michelle; Zollei, Lilla; Dang, Chinh; Knowles, James A.; Koch, Christof; Phillips, John W.; Sestan, Nenad; Wohnoutka, Paul; Zielke, H. Ronald; Hohmann, John G.; Jones, Allan R.; Bernard, Amy; Hawrylycz, Michael J.; Hof, Patrick R.; Fischl, Bruce

    2016-01-01

    ABSTRACT Detailed anatomical understanding of the human brain is essential for unraveling its functional architecture, yet current reference atlases have major limitations such as lack of whole‐brain coverage, relatively low image resolution, and sparse structural annotation. We present the first digital human brain atlas to incorporate neuroimaging, high‐resolution histology, and chemoarchitecture across a complete adult female brain, consisting of magnetic resonance imaging (MRI), diffusion‐weighted imaging (DWI), and 1,356 large‐format cellular resolution (1 µm/pixel) Nissl and immunohistochemistry anatomical plates. The atlas is comprehensively annotated for 862 structures, including 117 white matter tracts and several novel cyto‐ and chemoarchitecturally defined structures, and these annotations were transferred onto the matching MRI dataset. Neocortical delineations were done for sulci, gyri, and modified Brodmann areas to link macroscopic anatomical and microscopic cytoarchitectural parcellations. Correlated neuroimaging and histological structural delineation allowed fine feature identification in MRI data and subsequent structural identification in MRI data from other brains. This interactive online digital atlas is integrated with existing Allen Institute for Brain Science gene expression atlases and is publicly accessible as a resource for the neuroscience community. J. Comp. Neurol. 524:3127–3481, 2016. © 2016 The Authors The Journal of Comparative Neurology Published by Wiley Periodicals, Inc. PMID:27418273

  15. National Atlas of the United States Maps

    USGS Publications Warehouse

    ,

    2001-01-01

    The "National Atlas of the United States of America®", published by the U.S. Geological Survey (USGS) in 1970, is out of print, but many of its maps can be purchased separately. Maps that span facing pages in the atlas are printed on one sheet. Maps dated after 1970 and before 1997 are either revisions of original atlas maps or new maps published in the original atlas format. The USGS and its partners in government and industry began work on a new "National Atlas" in 1997. Though most new atlas products are designed for the World Wide Web, we are continuing our tradition of printing high-quality maps of America. In 1998, the first completely redesigned maps of the "National Atlas of the United States®" were published.

  16. About the group - ATLAS group

    Science.gov Websites

    ATLAS group Studies of particle collisions at highest energy frontiers Home • About the group About the group Welcom to the home page of the ATLAS group of High-Energy Physics division of the Argonne National Laboratory ATLAS is one of the two general purpose detectors for the Large Hadron

  17. Integration of the Eventlndex with other ATLAS systems

    NASA Astrophysics Data System (ADS)

    Barberis, D.; Cárdenas Zárate, S. E.; Gallas, E. J.; Prokoshin, F.

    2015-12-01

    The ATLAS EventIndex System, developed for use in LHC Run 2, is designed to index every processed event in ATLAS, replacing the TAG System used in Run 1. Its storage infrastructure, based on Hadoop open-source software framework, necessitates revamping how information in this system relates to other ATLAS systems. It will store more indexes since the fundamental mechanisms for retrieving these indexes will be better integrated into all stages of data processing, allowing more events from later stages of processing to be indexed than was possible with the previous system. Connections with other systems (conditions database, monitoring) are fundamentally critical to assess dataset completeness, identify data duplication, and check data integrity, and also enhance access to information in EventIndex by user and system interfaces. This paper gives an overview of the ATLAS systems involved, the relevant metadata, and describe the technologies we are deploying to complete these connections.

  18. A Deformable Atlas of the Laboratory Mouse

    PubMed Central

    Wang, Hongkai; Stout, David B.; Chatziioannou, Arion F.

    2015-01-01

    Purpose This paper presents a deformable mouse atlas of the laboratory mouse anatomy. This atlas is fully articulated and can be positioned into arbitrary body poses. The atlas can also adapt body weight by changing body length and fat amount. Procedures A training set of 103 micro-CT images was used to construct the atlas. A cage-based deformation method was applied to realize the articulated pose change. The weight-related body deformation was learned from the training set using a linear regression method. A conditional Gaussian model and thin-plate spline mapping were used to deform the internal organs following the changes of pose and weight. Results The atlas was deformed into different body poses and weights, and the deformation results were more realistic compared to the results achieved with other mouse atlases. The organ weights of this atlas matched well with the measurements of real mouse organ weights. This atlas can also be converted into voxelized images with labeled organs, pseudo CT images and tetrahedral mesh for phantom studies. Conclusions With the unique ability of articulated pose and weight changes, the deformable laboratory mouse atlas can become a valuable tool for preclinical image analysis. PMID:25049072

  19. Evolution of the ATLAS Nightly Build System

    NASA Astrophysics Data System (ADS)

    Undrus, A.

    2012-12-01

    The ATLAS Nightly Build System is a major component in the ATLAS collaborative software organization, validation, and code approval scheme. For over 10 years of development it has evolved into a factory for automatic release production and grid distribution. The 50 multi-platform branches of ATLAS releases provide vast opportunities for testing new packages, verification of patches to existing software, and migration to new platforms and compilers for ATLAS code that currently contains 2200 packages with 4 million C++ and 1.4 million python scripting lines written by about 1000 developers. Recent development was focused on the integration of ATLAS Nightly Build and Installation systems. The nightly releases are distributed and validated and some are transformed into stable releases used for data processing worldwide. The ATLAS Nightly System is managed by the NICOS control tool on a computing farm with 50 powerful multiprocessor nodes. NICOS provides the fully automated framework for the release builds, testing, and creation of distribution kits. The ATN testing framework of the Nightly System runs unit and integration tests in parallel suites, fully utilizing the resources of multi-core machines, and provides the first results even before compilations complete. The NICOS error detection system is based on several techniques and classifies the compilation and test errors according to their severity. It is periodically tuned to place greater emphasis on certain software defects by highlighting the problems on NICOS web pages and sending automatic e-mail notifications to responsible developers. These and other recent developments will be presented and future plans will be described.

  20. Difficulties in distinguishing between an atlas fracture and a congenital posterior atlas arch defect in postmortem analysis.

    PubMed

    Sanchis-Gimeno, Juan A; Blanco-Perez, Esther; Aparicio, Luis; Martinez-Soriano, Francisco; Martinez-Sanjuan, Vicente

    2014-09-01

    We found one atlas from a sample of 148 skeletons (0.67%) that presented different anatomical variations which made it difficult to determine whether the vertebra had an atlas fracture, an unusual Type B posterior atlas arch defect, or a combination of both. We carried out a stereomicroscopy, radiographic, and computerized tomography scan study that revealed that the dry atlas we found presented a very uncommon congenital Type B posterior atlas arch defect, simulating a fracture. In short, the present paper has revealed that differentiating Type B posterior atlas arch defects from fractures in post-mortem dry vertebrae is more difficult than expected. Thus we believe that it can be easier than expected to mistake Type B posterior arch defects for fractures and vice versa in postmortem studies. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

  1. Big Data Tools as Applied to ATLAS Event Data

    NASA Astrophysics Data System (ADS)

    Vukotic, I.; Gardner, R. W.; Bryant, L. A.

    2017-10-01

    Big Data technologies have proven to be very useful for storage, processing and visualization of derived metrics associated with ATLAS distributed computing (ADC) services. Logfiles, database records, and metadata from a diversity of systems have been aggregated and indexed to create an analytics platform for ATLAS ADC operations analysis. Dashboards, wide area data access cost metrics, user analysis patterns, and resource utilization efficiency charts are produced flexibly through queries against a powerful analytics cluster. Here we explore whether these techniques and associated analytics ecosystem can be applied to add new modes of open, quick, and pervasive access to ATLAS event data. Such modes would simplify access and broaden the reach of ATLAS public data to new communities of users. An ability to efficiently store, filter, search and deliver ATLAS data at the event and/or sub-event level in a widely supported format would enable or significantly simplify usage of machine learning environments and tools like Spark, Jupyter, R, SciPy, Caffe, TensorFlow, etc. Machine learning challenges such as the Higgs Boson Machine Learning Challenge, the Tracking challenge, Event viewers (VP1, ATLANTIS, ATLASrift), and still to be developed educational and outreach tools would be able to access the data through a simple REST API. In this preliminary investigation we focus on derived xAOD data sets. These are much smaller than the primary xAODs having containers, variables, and events of interest to a particular analysis. Being encouraged with the performance of Elasticsearch for the ADC analytics platform, we developed an algorithm for indexing derived xAOD event data. We have made an appropriate document mapping and have imported a full set of standard model W/Z datasets. We compare the disk space efficiency of this approach to that of standard ROOT files, the performance in simple cut flow type of data analysis, and will present preliminary results on its scaling

  2. Distributed analysis in ATLAS

    NASA Astrophysics Data System (ADS)

    Dewhurst, A.; Legger, F.

    2015-12-01

    The ATLAS experiment accumulated more than 140 PB of data during the first run of the Large Hadron Collider (LHC) at CERN. The analysis of such an amount of data is a challenging task for the distributed physics community. The Distributed Analysis (DA) system of the ATLAS experiment is an established and stable component of the ATLAS distributed computing operations. About half a million user jobs are running daily on DA resources, submitted by more than 1500 ATLAS physicists. The reliability of the DA system during the first run of the LHC and the following shutdown period has been high thanks to the continuous automatic validation of the distributed analysis sites and the user support provided by a dedicated team of expert shifters. During the LHC shutdown, the ATLAS computing model has undergone several changes to improve the analysis workflows, including the re-design of the production system, a new analysis data format and event model, and the development of common reduction and analysis frameworks. We report on the impact such changes have on the DA infrastructure, describe the new DA components, and include recent performance measurements.

  3. Some non-atlas work at ESO Sky Atlas Laboratory.

    NASA Astrophysics Data System (ADS)

    Madsen, C.

    The ESO Sky Atlas Laboratory (SAL) was set up in 1972 with the aim of producing the ESO Quick Blue Survey and later the joint ESO/SERC Survey of the Southern Sky. With the establishment of a Scientific Group, it became apparent that ESO had additional photographic needs, the fullfilment of which was also entrusted to SAL. Thus, in the course of the years, the "Photographic Section" evolved as a subdivision of the Sky Atlas Laboratory.

  4. Atlas International de la vitalite Linguistique, Volume 1. Les langues constitutionnelles de l'Indie = International Atlas of Language Vitality. Volume 1: Constitutional Languages of India.

    ERIC Educational Resources Information Center

    McConnell, Grant D., Ed.; Gendron, Jean-Denis, Ed.

    This International atlas of language vitality offers a cartography of language functions, based on a quantitative measure of the vitality of each language. It covers the constitutional languages of India, and offers maps, graphs, and data tables illustrating the vitality ratings of 14 languages by state, domain of usage (religion, schools, mass…

  5. The PeptideAtlas Project.

    PubMed

    Deutsch, Eric W

    2010-01-01

    PeptideAtlas is a multi-species compendium of peptides observed with tandem mass spectrometry methods. Raw mass spectrometer output files are collected from the community and reprocessed through a uniform analysis and validation pipeline that continues to advance. The results are loaded into a database and the information derived from the raw data is returned to the community via several web-based data exploration tools. The PeptideAtlas resource is useful for experiment planning, improving genome annotation, and other data mining projects. PeptideAtlas has become especially useful for planning targeted proteomics experiments.

  6. Atlas of Variations in Medical Practice in Spain: the Spanish National Health Service under scrutiny.

    PubMed

    Bernal-Delgado, Enrique; García-Armesto, Sandra; Peiró, Salvador

    2014-01-01

    Early in the 2000s, a countrywide health services research initiative was launched under the acronym of Atlas VPM: Atlas of Variations in Medical Practice in the Spanish National Health System. This initiative aimed at describing systematic and unwarranted variations in medical practice at geographic level-building upon the seminal experience of the Dartmouth Atlas of Health Care. The paper aims at explaining the Spanish Atlas experience, built upon the pioneer Dartmouth inspiration. A few selected examples will be used along the following sections to illustrate the outlined conceptual framework, the different factors that may affect variation, and some methodological challenges. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

  7. Design and performance of the virtualization platform for offline computing on the ATLAS TDAQ Farm

    NASA Astrophysics Data System (ADS)

    Ballestrero, S.; Batraneanu, S. M.; Brasolin, F.; Contescu, C.; Di Girolamo, A.; Lee, C. J.; Pozo Astigarraga, M. E.; Scannicchio, D. A.; Twomey, M. S.; Zaytsev, A.

    2014-06-01

    With the LHC collider at CERN currently going through the period of Long Shutdown 1 there is an opportunity to use the computing resources of the experiments' large trigger farms for other data processing activities. In the case of the ATLAS experiment, the TDAQ farm, consisting of more than 1500 compute nodes, is suitable for running Monte Carlo (MC) production jobs that are mostly CPU and not I/O bound. This contribution gives a thorough review of the design and deployment of a virtualized platform running on this computing resource and of its use to run large groups of CernVM based virtual machines operating as a single CERN-P1 WLCG site. This platform has been designed to guarantee the security and the usability of the ATLAS private network, and to minimize interference with TDAQ's usage of the farm. Openstack has been chosen to provide a cloud management layer. The experience gained in the last 3.5 months shows that the use of the TDAQ farm for the MC simulation contributes to the ATLAS data processing at the level of a large Tier-1 WLCG site, despite the opportunistic nature of the underlying computing resources being used.

  8. Radiation Tolerant Electronics and Digital Processing for the Phase-I Trigger Readout Upgrade of the ATLAS Liquid Argon Calorimeters

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Milic, A.

    The high luminosities of L > 10{sup 34} cm{sup -2}s{sup -1} at the Large Hadron Collider (LHC) at CERN produce an intense radiation environment that the detectors and their electronics must withstand. The ATLAS detector is a multi-purpose apparatus constructed to explore the new particle physics regime opened by the LHC. Of the many decay particles observed by the ATLAS detector, the energy of the created electrons and photons is measured by a sampling calorimeter technique that uses Liquid Argon (LAr) as its active medium. The front end (FE) electronic readout of the ATLAS LAr calorimeter located on the detectormore » itself consists of a combined analog and digital processing system. In order to exploit the higher luminosity while keeping the same trigger bandwidth of 100 kHz, higher transverse granularity, higher resolution and longitudinal shower shape information will be provided from the LAr calorimeter to the Level-l trigger processors. New trigger readout electronics have been designed for this purpose, which will withstand the radiation dose levels expected for an integrated luminosity of 3000 fb{sup -1} during the high luminosity LHC (HL-LHC), which is well above the original LHC design qualifications. (authors)« less

  9. A chromatographic estimate of the degree of heterogeneity of RPLC packing materials. 1. Non-endcapped polymeric C30-bonded stationary phase

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Gritti, Fabrice; Guiochon, Georges A

    2006-01-01

    A new chromatographic method estimating the degree of heterogeneity of RPLC packing materials is based on the results of systematic measurements of the adsorption data in a wide concentration range for selected probe compounds. These data are acquired by frontal analysis (FA), modeled, and used for the calculation of the adsorption energy distribution (AED). Four compounds were used, two neutral compounds of different molecular sizes (caffeine and phenol) and two ionizable compounds of opposite charges, 2-naphthalene sulfonate, an anion, and propranololium, a cation. This work was done on a C{sub 30}-bonded silica stationary phase (Prontosil-C{sub 30}), using the same aqueousmore » mobile phase (30% methanol, v/v) for all compounds, except that sodium chloride (25 mM) was added to elute the ionizable compounds. All four adsorption isotherms have Langmuirian behavior. The AEDs are tri-modal for phenol, quadri-modal for caffeine. The total saturation capacity of the stationary phase is four-fold lower for caffeine than for phenol, due in part to its larger molecular size. The equilibrium constants on the low-energy sites of types 1 and 2 are eight-fold larger. These two types of sites characterize the heterogeneity of the bonded layer itself. The density of the high-energy sites of types 3 and 4 is higher for caffeine, suggesting that caffeine molecules can be accommodated in some hydrophobic cages into which smaller molecules like phenol cannot. These high-energy types of sites characterize the heterogeneity of the whole stationary phase (silica support included). The ionizable compounds have larger molecules than the neutral ones and, accordingly, a lower relative density of sites of type 2 to sites of type 1. A tri-modal and a quadri-modal energy distributions were observed for the 2-naphthalene sulfonate anion and the propranololium cation, respectively. The fourth types of sites measured and its unusually high equilibrium constant are most probably due to ion

  10. A computational atlas of the hippocampal formation using ex vivo, ultra-high resolution MRI: Application to adaptive segmentation of in vivo MRI.

    PubMed

    Iglesias, Juan Eugenio; Augustinack, Jean C; Nguyen, Khoa; Player, Christopher M; Player, Allison; Wright, Michelle; Roy, Nicole; Frosch, Matthew P; McKee, Ann C; Wald, Lawrence L; Fischl, Bruce; Van Leemput, Koen

    2015-07-15

    Automated analysis of MRI data of the subregions of the hippocampus requires computational atlases built at a higher resolution than those that are typically used in current neuroimaging studies. Here we describe the construction of a statistical atlas of the hippocampal formation at the subregion level using ultra-high resolution, ex vivo MRI. Fifteen autopsy samples were scanned at 0.13 mm isotropic resolution (on average) using customized hardware. The images were manually segmented into 13 different hippocampal substructures using a protocol specifically designed for this study; precise delineations were made possible by the extraordinary resolution of the scans. In addition to the subregions, manual annotations for neighboring structures (e.g., amygdala, cortex) were obtained from a separate dataset of in vivo, T1-weighted MRI scans of the whole brain (1mm resolution). The manual labels from the in vivo and ex vivo data were combined into a single computational atlas of the hippocampal formation with a novel atlas building algorithm based on Bayesian inference. The resulting atlas can be used to automatically segment the hippocampal subregions in structural MRI images, using an algorithm that can analyze multimodal data and adapt to variations in MRI contrast due to differences in acquisition hardware or pulse sequences. The applicability of the atlas, which we are releasing as part of FreeSurfer (version 6.0), is demonstrated with experiments on three different publicly available datasets with different types of MRI contrast. The results show that the atlas and companion segmentation method: 1) can segment T1 and T2 images, as well as their combination, 2) replicate findings on mild cognitive impairment based on high-resolution T2 data, and 3) can discriminate between Alzheimer's disease subjects and elderly controls with 88% accuracy in standard resolution (1mm) T1 data, significantly outperforming the atlas in FreeSurfer version 5.3 (86% accuracy) and

  11. EnviroAtlas - Austin, TX - Tree Cover Configuration and Connectivity, Water Background

    EPA Pesticide Factsheets

    This EnviroAtlas dataset categorizes forest land cover into structural elements (e.g. core, edge, connector, etc.). In this community, Forest is defined as Trees & Forest (Trees & Forest - 40 = 1; All Else = 0). Water was considered background (value 129) during the analysis to create this dataset, however it has been converted into value 10 to distinguish it from land area background. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

  12. 23. "A CAPTIVE ATLAS MISSILE EXPLODED DURING THE TEST ON ...

    Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

    23. "A CAPTIVE ATLAS MISSILE EXPLODED DURING THE TEST ON TEST STAND 1-A, 27 MARCH 1959, PUTTING THAT TEST STAND OUT-OF-COMMISSION. STAND WAS NOT REPAIRED FOR THE ATLAS PROGRAM BUT TRANSFERRED TO ROCKETDYNE AND MODIFIED FOR THE F-l ENGINE PROGRAM." - Edwards Air Force Base, Air Force Rocket Propulsion Laboratory, Test Stand 1-A, Test Area 1-120, north end of Jupiter Boulevard, Boron, Kern County, CA

  13. The Copernicus ultraviolet spectral atlas of Gamma Pegasi

    NASA Technical Reports Server (NTRS)

    Rogerson, J. B., Jr.

    1985-01-01

    An ultraviolet spectral atlas is presented for the B2 IV star Gamma Pegasi, which has been scanned from 970 to 1501 A by the Princeton spectrometer aboard the Copernicus satellite. From 970 to 1430 A the observations have a nominal resolution of 0.05 A. At the longer wavelengths the resolution is 0.1 A. The atlas is presented in graphs. Line identifications are also listed.

  14. The Copernicus ultraviolet spectral atlas of Tau Scorpii

    NASA Technical Reports Server (NTRS)

    Rogerson, J. B., Jr.; Upson, W. L., II

    1977-01-01

    An ultraviolet spectral atlas is presented for the B0 V star, Tau Scorpii. It has been scanned from 949 to 1560 A by the Princeton spectrometer aboard the Copernicus satellite. From 949 to 1420 A the observations have a nominal resolution of 0.05 A. At the longer wavelengths, the resolution is 0.1 A. The atlas is presented in both tables and graphs.

  15. Digital version of the European Atlas of natural radiation.

    PubMed

    Cinelli, Giorgia; Tollefsen, Tore; Bossew, Peter; Gruber, Valeria; Bogucarskis, Konstantins; De Felice, Luca; De Cort, Marc

    2018-02-26

    The European Atlas of Natural Radiation is a collection of maps displaying the levels of natural radioactivity caused by different sources. It has been developed and is being maintained by the Joint Research Centre (JRC) of the European Commission, in line with its mission, based on the Euratom Treaty: to collect, validate and report information on radioactivity levels in the environment of the EU Member States. This work describes the first version of the European Atlas of Natural Radiation, available in digital format through a web portal, as well as the methodology and results for the maps already developed. So far the digital Atlas contains: an annual cosmic-ray dose map; a map of indoor radon concentration; maps of uranium, thorium and potassium concentration in soil and in bedrock; a terrestrial gamma dose rate map; and a map of soil permeability. Through these maps, the public will be able to: familiarize itself with natural environmental radioactivity; be informed about the levels of natural radioactivity caused by different sources; have a more balanced view of the annual dose received by the European population, to which natural radioactivity is the largest contributor; and make direct comparisons between doses from natural sources of ionizing radiation and those from man-made (artificial) ones, hence, to better assess the latter. Work will continue on the European Geogenic Radon Map and on estimating the annual dose that the public may receive from natural radioactivity, by combining all the information from the different maps. More maps could be added to the Atlas, such us radon in outdoor air and in water and concentration of radionuclides in water, even if these sources usually contribute less to the total exposure. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

  16. Atlas-based head modeling and spatial normalization for high-density diffuse optical tomography: in vivo validation against fMRI.

    PubMed

    Ferradal, Silvina L; Eggebrecht, Adam T; Hassanpour, Mahlega; Snyder, Abraham Z; Culver, Joseph P

    2014-01-15

    Diffuse optical imaging (DOI) is increasingly becoming a valuable neuroimaging tool when fMRI is precluded. Recent developments in high-density diffuse optical tomography (HD-DOT) overcome previous limitations of sparse DOI systems, providing improved image quality and brain specificity. These improvements in instrumentation prompt the need for advancements in both i) realistic forward light modeling for accurate HD-DOT image reconstruction, and ii) spatial normalization for voxel-wise comparisons across subjects. Individualized forward light models derived from subject-specific anatomical images provide the optimal inverse solutions, but such modeling may not be feasible in all situations. In the absence of subject-specific anatomical images, atlas-based head models registered to the subject's head using cranial fiducials provide an alternative solution. In addition, a standard atlas is attractive because it defines a common coordinate space in which to compare results across subjects. The question therefore arises as to whether atlas-based forward light modeling ensures adequate HD-DOT image quality at the individual and group level. Herein, we demonstrate the feasibility of using atlas-based forward light modeling and spatial normalization methods. Both techniques are validated using subject-matched HD-DOT and fMRI data sets for visual evoked responses measured in five healthy adult subjects. HD-DOT reconstructions obtained with the registered atlas anatomy (i.e. atlas DOT) had an average localization error of 2.7mm relative to reconstructions obtained with the subject-specific anatomical images (i.e. subject-MRI DOT), and 6.6mm relative to fMRI data. At the group level, the localization error of atlas DOT reconstruction was 4.2mm relative to subject-MRI DOT reconstruction, and 6.1mm relative to fMRI. These results show that atlas-based image reconstruction provides a viable approach to individual head modeling for HD-DOT when anatomical imaging is not available

  17. Applying the algorithm "assessing quality using image registration circuits" (AQUIRC) to multi-atlas segmentation

    NASA Astrophysics Data System (ADS)

    Datteri, Ryan; Asman, Andrew J.; Landman, Bennett A.; Dawant, Benoit M.

    2014-03-01

    Multi-atlas registration-based segmentation is a popular technique in the medical imaging community, used to transform anatomical and functional information from a set of atlases onto a new patient that lacks this information. The accuracy of the projected information on the target image is dependent on the quality of the registrations between the atlas images and the target image. Recently, we have developed a technique called AQUIRC that aims at estimating the error of a non-rigid registration at the local level and was shown to correlate to error in a simulated case. Herein, we extend upon this work by applying AQUIRC to atlas selection at the local level across multiple structures in cases in which non-rigid registration is difficult. AQUIRC is applied to 6 structures, the brainstem, optic chiasm, left and right optic nerves, and the left and right eyes. We compare the results of AQUIRC to that of popular techniques, including Majority Vote, STAPLE, Non-Local STAPLE, and Locally-Weighted Vote. We show that AQUIRC can be used as a method to combine multiple segmentations and increase the accuracy of the projected information on a target image, and is comparable to cutting edge methods in the multi-atlas segmentation field.

  18. Atlas-based system for functional neurosurgery

    NASA Astrophysics Data System (ADS)

    Nowinski, Wieslaw L.; Yeo, Tseng T.; Yang, Guo L.; Dow, Douglas E.

    1997-05-01

    This paper addresses the development of an atlas-based system for preoperative functional neurosurgery planning and training, intraoperative support and postoperative analysis. The system is based on Atlas of Stereotaxy of the Human Brain by Schaltenbrand and Wahren used for interactive segmentation and labeling of clinical data in 2D/3D, and for assisting stereotactic targeting. The atlas microseries are digitized, enhanced, segmented, labeled, aligned and organized into mutually preregistered atlas volumes 3D models of the structures are also constructed. The atlas may be interactively registered with the actual patient's data. Several other features are also provided including data reformatting, visualization, navigation, mensuration, and stereotactic path display and editing in 2D/3D. The system increases the accuracy of target definition, reduces the time of planning and time of the procedure itself. It also constitutes a research platform for the construction of more advanced neurosurgery supporting tools and brain atlases.

  19. Mesospheric nightglow spectral survey taken by the ISO spectral spatial imager on Atlas 1

    NASA Technical Reports Server (NTRS)

    Owens, J. K.; Torr, D. G.; Torr, M. R.; Chang, T.; Fennelly, J. A.; Richards, P. G.; Morgan, M. F.; Baldridge, T. W.; Dougani, H.; Swift, W.

    1993-01-01

    This paper reports the first comprehensive spectral survey of the mesospheric airglow between 260 and 832 nm taken by the Imaging Spectrometric Observatory (ISO) on the ATLAS I mission. We select data taken in the spectral window between 275 and 300 nm to determine the variation with altitude of the Herzberg I bands originating from the vibrational levels v' = 3 to 8. These data provide the first spatially resolved spectral measurements of the system. The data are used to demonstrate that to within an uncertainty of + 10%, the vibrational distribution remains invariant with altitude. The deficit reported previously for the v' = 5 level is not observed although there is a suggestion of depletion in v' = 6. The data could be used to place tight constraints on the vibrational dependence of quenching rate coefficients, and on the abundance of atomic oxygen.

  20. Combining multi-atlas segmentation with brain surface estimation

    NASA Astrophysics Data System (ADS)

    Huo, Yuankai; Carass, Aaron; Resnick, Susan M.; Pham, Dzung L.; Prince, Jerry L.; Landman, Bennett A.

    2016-03-01

    Whole brain segmentation (with comprehensive cortical and subcortical labels) and cortical surface reconstruction are two essential techniques for investigating the human brain. The two tasks are typically conducted independently, however, which leads to spatial inconsistencies and hinders further integrated cortical analyses. To obtain self-consistent whole brain segmentations and surfaces, FreeSurfer segregates the subcortical and cortical segmentations before and after the cortical surface reconstruction. However, this "segmentation to surface to parcellation" strategy has shown limitation in various situations. In this work, we propose a novel "multi-atlas segmentation to surface" method called Multi-atlas CRUISE (MaCRUISE), which achieves self-consistent whole brain segmentations and cortical surfaces by combining multi-atlas segmentation with the cortical reconstruction method CRUISE. To our knowledge, this is the first work that achieves the reliability of state-of-the-art multi-atlas segmentation and labeling methods together with accurate and consistent cortical surface reconstruction. Compared with previous methods, MaCRUISE has three features: (1) MaCRUISE obtains 132 cortical/subcortical labels simultaneously from a single multi-atlas segmentation before reconstructing volume consistent surfaces; (2) Fuzzy tissue memberships are combined with multi-atlas segmentations to address partial volume effects; (3) MaCRUISE reconstructs topologically consistent cortical surfaces by using the sulci locations from multi-atlas segmentation. Two data sets, one consisting of five subjects with expertly traced landmarks and the other consisting of 100 volumes from elderly subjects are used for validation. Compared with CRUISE, MaCRUISE achieves self-consistent whole brain segmentation and cortical reconstruction without compromising on surface accuracy. MaCRUISE is comparably accurate to FreeSurfer while achieving greater robustness across an elderly population.

  1. Combining Multi-atlas Segmentation with Brain Surface Estimation.

    PubMed

    Huo, Yuankai; Carass, Aaron; Resnick, Susan M; Pham, Dzung L; Prince, Jerry L; Landman, Bennett A

    2016-02-27

    Whole brain segmentation (with comprehensive cortical and subcortical labels) and cortical surface reconstruction are two essential techniques for investigating the human brain. The two tasks are typically conducted independently, however, which leads to spatial inconsistencies and hinders further integrated cortical analyses. To obtain self-consistent whole brain segmentations and surfaces, FreeSurfer segregates the subcortical and cortical segmentations before and after the cortical surface reconstruction. However, this "segmentation to surface to parcellation" strategy has shown limitations in various situations. In this work, we propose a novel "multi-atlas segmentation to surface" method called Multi-atlas CRUISE (MaCRUISE), which achieves self-consistent whole brain segmentations and cortical surfaces by combining multi-atlas segmentation with the cortical reconstruction method CRUISE. To our knowledge, this is the first work that achieves the reliability of state-of-the-art multi-atlas segmentation and labeling methods together with accurate and consistent cortical surface reconstruction. Compared with previous methods, MaCRUISE has three features: (1) MaCRUISE obtains 132 cortical/subcortical labels simultaneously from a single multi-atlas segmentation before reconstructing volume consistent surfaces; (2) Fuzzy tissue memberships are combined with multi-atlas segmentations to address partial volume effects; (3) MaCRUISE reconstructs topologically consistent cortical surfaces by using the sulci locations from multi-atlas segmentation. Two data sets, one consisting of five subjects with expertly traced landmarks and the other consisting of 100 volumes from elderly subjects are used for validation. Compared with CRUISE, MaCRUISE achieves self-consistent whole brain segmentation and cortical reconstruction without compromising on surface accuracy. MaCRUISE is comparably accurate to FreeSurfer while achieving greater robustness across an elderly population.

  2. The ADAM project: a generic web interface for retrieval and display of ATLAS TDAQ information

    NASA Astrophysics Data System (ADS)

    Harwood, A.; Lehmann Miotto, G.; Magnoni, L.; Vandelli, W.; Savu, D.

    2012-06-01

    This paper describes a new approach to the visualization of information about the operation of the ATLAS Trigger and Data Acquisition system. ATLAS is one of the two general purpose detectors positioned along the Large Hadron Collider at CERN. Its data acquisition system consists of several thousand computers interconnected via multiple gigabit Ethernet networks, that are constantly monitored via different tools. Operational parameters ranging from the temperature of the computers to the network utilization are stored in several databases for later analysis. Although the ability to view these data-sets individually is already in place, currently there is no way to view this data together, in a uniform format, from one location. The ADAM project has been launched in order to overcome this limitation. It defines a uniform web interface to collect data from multiple providers that have different structures. It is capable of aggregating and correlating the data according to user defined criteria. Finally, it visualizes the collected data using a flexible and interactive front-end web system. Structurally, the project comprises of 3 main levels of the data collection cycle: The Level 0 represents the information sources within ATLAS. These providers do not store information in a uniform fashion. The first step of the project was to define a common interface with which to expose stored data. The interface designed for the project originates from the Google Data Protocol API. The idea is to allow read-only access to data providers, through HTTP requests similar in format to the SQL query structure. This provides a standardized way to access this different information sources within ATLAS. The Level 1 can be considered the engine of the system. The primary task of the Level 1 is to gather data from multiple data sources via the common interface, to correlate this data together, or over a defined time series, and expose the combined data as a whole to the Level 2 web

  3. Commissioning of the first chambers of the CMS GE1/1 muon station

    NASA Astrophysics Data System (ADS)

    Ressegotti, Martina; CMS Muon Group

    2017-12-01

    The upgrades of the LHC planned in the next years will increase the instantaneous luminosity up to 5 × 1034 cm -2 s -1 after Long Shutdown 3, a value about five times higher than the nominal one for which the CMS experiment was designed. The resulting larger rate of interactions will produce a higher pileup environment that will challenge the trigger system of the CMS experiment in its original configuration, in particular in the endcap region. As part of the upgrade program of the CMS muon endcaps, additional muon detectors based on Gas Electron Multiplier (GEM) technology will be installed, in order to be able to sustain a physics program during high-luminosity operation without performance losses. The installation of the GE1/1 station is scheduled for Long Shutdown 2 in 2019-2020 already a demonstrator composed of five superchambers has been installed during the Extended Year-End Technical Stop at the beginning of 2017. Its goal is to test the system’s operational conditions and also to demonstrate the integration of the GE1/1 chambers into the CMS online system. The status of the installation and commissioning of the GE1/1 demonstrator is presented.

  4. EnviroAtlas - Number of Water Markets per HUC8 Watershed, U.S., 2015, Forest Trends' Ecosystem Marketplace

    EPA Pesticide Factsheets

    This EnviroAtlas dataset contains polygons depicting the number of watershed-level market-based programs, referred to herein as markets, in operation per 8-digit HUC watershed throughout the United States. The data were collected via surveys and desk research conducted by Forest Trends' Ecosystem Marketplace during 2014 regarding markets operating to protect watershed ecosystem services. Utilizing these data, the number of water market coverage areas overlaying each HUC8 watershed were calculated to produce this dataset. Only water markets identified as operating at the watershed level (i.e., single or multiple watersheds define the market boundaries) were included in the count of water markets per HUC8 watershed. Excluded were water markets operating at the national, state, county, or federal lands level and all water projects. Attribute data include the watershed's 8-digit hydrologic unit code and name, in addition to the watershed-level water market count associated with the watershed. This dataset was produced by Forest Trends' Ecosystem Marketplace to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Addi

  5. Localized-atlas-based segmentation of breast MRI in a decision-making framework.

    PubMed

    Fooladivanda, Aida; Shokouhi, Shahriar B; Ahmadinejad, Nasrin

    2017-03-01

    Breast-region segmentation is an important step for density estimation and Computer-Aided Diagnosis (CAD) systems in Magnetic Resonance Imaging (MRI). Detection of breast-chest wall boundary is often a difficult task due to similarity between gray-level values of fibroglandular tissue and pectoral muscle. This paper proposes a robust breast-region segmentation method which is applicable for both complex cases with fibroglandular tissue connected to the pectoral muscle, and simple cases with high contrast boundaries. We present a decision-making framework based on geometric features and support vector machine (SVM) to classify breasts in two main groups, complex and simple. For complex cases, breast segmentation is done using a combination of intensity-based and atlas-based techniques; however, only intensity-based operation is employed for simple cases. A novel atlas-based method, that is called localized-atlas, accomplishes the processes of atlas construction and registration based on the region of interest (ROI). Atlas-based segmentation is performed by relying on the chest wall template. Our approach is validated using a dataset of 210 cases. Based on similarity between automatic and manual segmentation results, the proposed method achieves Dice similarity coefficient, Jaccard coefficient, total overlap, false negative, and false positive values of 96.3, 92.9, 97.4, 2.61 and 4.77%, respectively. The localization error of the breast-chest wall boundary is 1.97 mm, in terms of averaged deviation distance. The achieved results prove that the suggested framework performs the breast segmentation with negligible errors and efficient computational time for different breasts from the viewpoints of size, shape, and density pattern.

  6. EnviroAtlas - Austin, TX - Demographics by Block Group

    EPA Pesticide Factsheets

    This EnviroAtlas dataset is a summary of key demographic groups for the EnviroAtlas community. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

  7. ATLAS/AGENA - ON PAD - CAPE

    NASA Image and Video Library

    1965-10-25

    S65-57967 (25 Oct. 1965) --- View at Pad 14 during prelaunch operations for the Atlas/Agena. The Agena is mounted atop its Atlas launch vehicle. The Atlas/Agena liftoff was at 10 a.m. (EST) on Oct. 25, 1965. Intended as a rendezvous target vehicle in the Gemini-6 mission, the Agena failed to achieve orbit, and the Oct. 25 Gemini-6 launch was scrubbed. Photo credit: NASA or National Aeronautics and Space Administration

  8. The Copernicus ultraviolet spectral atlas of Beta Orionis

    NASA Technical Reports Server (NTRS)

    Rogerson, J. B., Jr.; Upson, W. L., II

    1982-01-01

    An ultraviolet spectral atlas is presented for the B8 Ia star Beta Orionis, which has been scanned from 999 to 1561 A by the Princeton spectrometer aboard the Copernicus satellite. From 999 to 1420 A the observations have a nominal resolution of 0.05 A. At the longer wavelengths the resolution is 0.1 A. The atlas is presented in graphs. Lines identified in the spectrum are also listed.

  9. The Copernicus ultraviolet spectral atlas of Iota Herculis

    NASA Technical Reports Server (NTRS)

    Upson, W. L., II; Rogerson, J. B., Jr.

    1980-01-01

    An ultraviolet spectral atlas is presented for the B3 IV star Iota Herculis, which has been scanned from 999 to 1467 A by the Princeton spectrometer aboard the Copernicus satellite. From 999 to 1422 A the observations have a nominal resolution of 0.05 A. At the longer wavelengths the resolution is 0.1 A. The atlas is presented in graphs. Lines identified in the spectrum are also listed.

  10. EnviroAtlas - Austin, TX - Tree Cover Configuration and Connectivity, Water Background Web Service

    EPA Pesticide Factsheets

    This EnviroAtlas web service supports research and online mapping activities related to EnviroAtlas (https://enviroatlas.epa.gov/EnviroAtlas). The EnviroAtlas Austin, TX tree cover configuration and connectivity map categorizes forest land cover into structural elements (e.g. core, edge, connector, etc.). In this community, Forest is defined as Trees & Forest (Trees & Forest - 40 = 1; All Else = 0). Water was considered background (value 129) during the analysis to create this dataset, however it has been converted into value 10 to distinguish it from land area background. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

  11. GOES-S Atlas V Booster and Centaur Stages Arrival, Offload, and

    NASA Image and Video Library

    2018-01-22

    After being offloaded from the Mariner transport ship at the Army Wharf at Cape Canaveral Air Force Station in Florida, the United Launch Alliance Atlas V booster for NOAA's Geostationary Operational Environmental Satellite-S (GOES-S) is being transported to the Atlas Spaceflight Operations Center near Space Launch Complex 41 at CCAFS. GOES-S is the second in a series of four advanced geostationary weather satellites. The satellite is slated to launch aboard the Atlas V rocket March 1.

  12. Augmenting atlas-based liver segmentation for radiotherapy treatment planning by incorporating image features proximal to the atlas contours

    NASA Astrophysics Data System (ADS)

    Li, Dengwang; Liu, Li; Chen, Jinhu; Li, Hongsheng; Yin, Yong; Ibragimov, Bulat; Xing, Lei

    2017-01-01

    Atlas-based segmentation utilizes a library of previously delineated contours of similar cases to facilitate automatic segmentation. The problem, however, remains challenging because of limited information carried by the contours in the library. In this studying, we developed a narrow-shell strategy to enhance the information of each contour in the library and to improve the accuracy of the exiting atlas-based approach. This study presented a new concept of atlas based segmentation method. Instead of using the complete volume of the target organs, only information along the organ contours from the atlas images was used for guiding segmentation of the new image. In setting up an atlas-based library, we included not only the coordinates of contour points, but also the image features adjacent to the contour. In this work, 139 CT images with normal appearing livers collected for radiotherapy treatment planning were used to construct the library. The CT images within the library were first registered to each other using affine registration. The nonlinear narrow shell was generated alongside the object contours of registered images. Matching voxels were selected inside common narrow shell image features of a library case and a new case using a speed-up robust features (SURF) strategy. A deformable registration was then performed using a thin plate splines (TPS) technique. The contour associated with the library case was propagated automatically onto the new image by exploiting the deformation field vectors. The liver contour was finally obtained by employing level set based energy optimization within the narrow shell. The performance of the proposed method was evaluated by comparing quantitatively the auto-segmentation results with that delineated by physicians. A novel atlas-based segmentation technique with inclusion of neighborhood image features through the introduction of a narrow-shell surrounding the target objects was established. Application of the technique to

  13. Augmenting atlas-based liver segmentation for radiotherapy treatment planning by incorporating image features proximal to the atlas contours.

    PubMed

    Li, Dengwang; Liu, Li; Chen, Jinhu; Li, Hongsheng; Yin, Yong; Ibragimov, Bulat; Xing, Lei

    2017-01-07

    Atlas-based segmentation utilizes a library of previously delineated contours of similar cases to facilitate automatic segmentation. The problem, however, remains challenging because of limited information carried by the contours in the library. In this studying, we developed a narrow-shell strategy to enhance the information of each contour in the library and to improve the accuracy of the exiting atlas-based approach. This study presented a new concept of atlas based segmentation method. Instead of using the complete volume of the target organs, only information along the organ contours from the atlas images was used for guiding segmentation of the new image. In setting up an atlas-based library, we included not only the coordinates of contour points, but also the image features adjacent to the contour. In this work, 139 CT images with normal appearing livers collected for radiotherapy treatment planning were used to construct the library. The CT images within the library were first registered to each other using affine registration. The nonlinear narrow shell was generated alongside the object contours of registered images. Matching voxels were selected inside common narrow shell image features of a library case and a new case using a speed-up robust features (SURF) strategy. A deformable registration was then performed using a thin plate splines (TPS) technique. The contour associated with the library case was propagated automatically onto the new image by exploiting the deformation field vectors. The liver contour was finally obtained by employing level set based energy optimization within the narrow shell. The performance of the proposed method was evaluated by comparing quantitatively the auto-segmentation results with that delineated by physicians. A novel atlas-based segmentation technique with inclusion of neighborhood image features through the introduction of a narrow-shell surrounding the target objects was established. Application of the technique to

  14. EnviroAtlas: Incorporation of EnviroAtlas as a Major Component of EcoInforma

    EPA Science Inventory

    EnviroAtlas is a collection of interactive tools and resources that help inform decision-making and allow users to explore the many benefits people receive from nature, often referred to as ecosystem services. EnviroAtlas was publicly released in May 2014. Ecoinformatics-based O...

  15. ATLAS Live: Collaborative Information Streams

    NASA Astrophysics Data System (ADS)

    Goldfarb, Steven; ATLAS Collaboration

    2011-12-01

    I report on a pilot project launched in 2010 focusing on facilitating communication and information exchange within the ATLAS Collaboration, through the combination of digital signage software and webcasting. The project, called ATLAS Live, implements video streams of information, ranging from detailed detector and data status to educational and outreach material. The content, including text, images, video and audio, is collected, visualised and scheduled using digital signage software. The system is robust and flexible, utilizing scripts to input data from remote sources, such as the CERN Document Server, Indico, or any available URL, and to integrate these sources into professional-quality streams, including text scrolling, transition effects, inter and intra-screen divisibility. Information is published via the encoding and webcasting of standard video streams, viewable on all common platforms, using a web browser or other common video tool. Authorisation is enforced at the level of the streaming and at the web portals, using the CERN SSO system.

  16. TU-AB-BRA-02: An Efficient Atlas-Based Synthetic CT Generation Method

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Han, X

    2016-06-15

    Purpose: A major obstacle for MR-only radiotherapy is the need to generate an accurate synthetic CT (sCT) from MR image(s) of a patient for the purposes of dose calculation and DRR generation. We propose here an accurate and efficient atlas-based sCT generation method, which has a computation speed largely independent of the number of atlases used. Methods: Atlas-based sCT generation requires a set of atlases with co-registered CT and MR images. Unlike existing methods that align each atlas to the new patient independently, we first create an average atlas and pre-align every atlas to the average atlas space. When amore » new patient arrives, we compute only one deformable image registration to align the patient MR image to the average atlas, which indirectly aligns the patient to all pre-aligned atlases. A patch-based non-local weighted fusion is performed in the average atlas space to generate the sCT for the patient, which is then warped back to the original patient space. We further adapt a PatchMatch algorithm that can quickly find top matches between patches of the patient image and all atlas images, which makes the patch fusion step also independent of the number of atlases used. Results: Nineteen brain tumour patients with both CT and T1-weighted MR images are used as testing data and a leave-one-out validation is performed. Each sCT generated is compared against the original CT image of the same patient on a voxel-by-voxel basis. The proposed method produces a mean absolute error (MAE) of 98.6±26.9 HU overall. The accuracy is comparable with a conventional implementation scheme, but the computation time is reduced from over an hour to four minutes. Conclusion: An average atlas space patch fusion approach can produce highly accurate sCT estimations very efficiently. Further validation on dose computation accuracy and using a larger patient cohort is warranted. The author is a full time employee of Elekta, Inc.« less

  17. Search for lepton-flavour-violating decays of the Higgs and Z bosons with the ATLAS detector

    DOE PAGES

    Aad, G.; Abbott, B.; Abdallah, J.; ...

    2017-02-04

    Direct searches for lepton flavour violation in decays of the Higgs and Z bosons with the ATLAS detector at the LHC are presented. The following three decays are considered: H → eτ, H → μτ, and Z → μτ. The searches are based on the data sample of proton–proton collisions collected by the ATLAS detector corresponding to an integrated luminosity of 20.3 fb –1 at a centre-of-mass energy of √s = 8 TeV. No significant excess is observed, and upper limits on the lepton-flavour-violating branching ratios are set at the 95% confidence level: Br(H → eτ) < 1.04%, Br(H →more » μτ) < 1.43%, and Br(Z → μτ) < 1.69 × 10 –5.« less

  18. EnviroAtlas - Minneapolis/St. Paul, MN - Ecosystem Services by Block Group

    EPA Pesticide Factsheets

    This EnviroAtlas dataset presents environmental benefits of the urban forest in 1,772 block groups in Minneapolis/St. Paul, Minnesota. Carbon attributes, temperature reduction, pollution removal and value, and runoff effects are calculated for each block group using i-Tree models (www.itreetools.org), local weather data, pollution data, EPA provided city boundary and land cover data, and U.S. Census derived block group boundary data. This dataset was produced by the US Forest Service to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

  19. Robust multi-atlas label propagation by deep sparse representation

    PubMed Central

    Zu, Chen; Wang, Zhengxia; Zhang, Daoqiang; Liang, Peipeng; Shi, Yonghong; Shen, Dinggang; Wu, Guorong

    2016-01-01

    Recently, multi-atlas patch-based label fusion has achieved many successes in medical imaging area. The basic assumption in the current state-of-the-art approaches is that the image patch at the target image point can be represented by a patch dictionary consisting of atlas patches from registered atlas images. Therefore, the label at the target image point can be determined by fusing labels of atlas image patches with similar anatomical structures. However, such assumption on image patch representation does not always hold in label fusion since (1) the image content within the patch may be corrupted due to noise and artifact; and (2) the distribution of morphometric patterns among atlas patches might be unbalanced such that the majority patterns can dominate label fusion result over other minority patterns. The violation of the above basic assumptions could significantly undermine the label fusion accuracy. To overcome these issues, we first consider forming label-specific group for the atlas patches with the same label. Then, we alter the conventional flat and shallow dictionary to a deep multi-layer structure, where the top layer (label-specific dictionaries) consists of groups of representative atlas patches and the subsequent layers (residual dictionaries) hierarchically encode the patchwise residual information in different scales. Thus, the label fusion follows the representation consensus across representative dictionaries. However, the representation of target patch in each group is iteratively optimized by using the representative atlas patches in each label-specific dictionary exclusively to match the principal patterns and also using all residual patterns across groups collaboratively to overcome the issue that some groups might be absent of certain variation patterns presented in the target image patch. Promising segmentation results have been achieved in labeling hippocampus on ADNI dataset, as well as basal ganglia and brainstem structures, compared

  20. Robust multi-atlas label propagation by deep sparse representation.

    PubMed

    Zu, Chen; Wang, Zhengxia; Zhang, Daoqiang; Liang, Peipeng; Shi, Yonghong; Shen, Dinggang; Wu, Guorong

    2017-03-01

    Recently, multi-atlas patch-based label fusion has achieved many successes in medical imaging area. The basic assumption in the current state-of-the-art approaches is that the image patch at the target image point can be represented by a patch dictionary consisting of atlas patches from registered atlas images. Therefore, the label at the target image point can be determined by fusing labels of atlas image patches with similar anatomical structures. However, such assumption on image patch representation does not always hold in label fusion since (1) the image content within the patch may be corrupted due to noise and artifact; and (2) the distribution of morphometric patterns among atlas patches might be unbalanced such that the majority patterns can dominate label fusion result over other minority patterns. The violation of the above basic assumptions could significantly undermine the label fusion accuracy. To overcome these issues, we first consider forming label-specific group for the atlas patches with the same label. Then, we alter the conventional flat and shallow dictionary to a deep multi-layer structure, where the top layer ( label-specific dictionaries ) consists of groups of representative atlas patches and the subsequent layers ( residual dictionaries ) hierarchically encode the patchwise residual information in different scales. Thus, the label fusion follows the representation consensus across representative dictionaries. However, the representation of target patch in each group is iteratively optimized by using the representative atlas patches in each label-specific dictionary exclusively to match the principal patterns and also using all residual patterns across groups collaboratively to overcome the issue that some groups might be absent of certain variation patterns presented in the target image patch. Promising segmentation results have been achieved in labeling hippocampus on ADNI dataset, as well as basal ganglia and brainstem structures

  1. Fine grained event processing on HPCs with the ATLAS Yoda system

    NASA Astrophysics Data System (ADS)

    Calafiura, Paolo; De, Kaushik; Guan, Wen; Maeno, Tadashi; Nilsson, Paul; Oleynik, Danila; Panitkin, Sergey; Tsulaia, Vakhtang; Van Gemmeren, Peter; Wenaus, Torre

    2015-12-01

    High performance computing facilities present unique challenges and opportunities for HEP event processing. The massive scale of many HPC systems means that fractionally small utilization can yield large returns in processing throughput. Parallel applications which can dynamically and efficiently fill any scheduling opportunities the resource presents benefit both the facility (maximal utilization) and the (compute-limited) science. The ATLAS Yoda system provides this capability to HEP-like event processing applications by implementing event-level processing in an MPI-based master-client model that integrates seamlessly with the more broadly scoped ATLAS Event Service. Fine grained, event level work assignments are intelligently dispatched to parallel workers to sustain full utilization on all cores, with outputs streamed off to destination object stores in near real time with similarly fine granularity, such that processing can proceed until termination with full utilization. The system offers the efficiency and scheduling flexibility of preemption without requiring the application actually support or employ check-pointing. We will present the new Yoda system, its motivations, architecture, implementation, and applications in ATLAS data processing at several US HPC centers.

  2. MRI-based treatment planning with pseudo CT generated through atlas registration.

    PubMed

    Uh, Jinsoo; Merchant, Thomas E; Li, Yimei; Li, Xingyu; Hua, Chiaho

    2014-05-01

    least 95% of the prescription dose in the planning target volume differed from the original values by less than 2% of the prescription dose (root-mean-square, RMS < 1%). The PRGP scheme did not perform better than the arithmetic mean process with the same number of atlases. Increasing the number of atlases from 6 to 12 often resulted in improvements, but statistical significance was not always found. MRI-based treatment planning with pseudo CTs generated through atlas registration is feasible for pediatric brain tumor patients. The doses calculated from pseudo CTs agreed well with those from real CTs, showing dosimetric accuracy within 2% for the PTV when multiple atlases were used. The arithmetic mean process may be a reasonable choice over PRGP for the synthesis scheme considering performance and computational costs.

  3. MRI-based treatment planning with pseudo CT generated through atlas registration

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Uh, Jinsoo, E-mail: jinsoo.uh@stjude.org; Merchant, Thomas E.; Hua, Chiaho

    2014-05-15

    the percentage of volume receiving at least 95% of the prescription dose in the planning target volume differed from the original values by less than 2% of the prescription dose (root-mean-square, RMS < 1%). The PRGP scheme did not perform better than the arithmetic mean process with the same number of atlases. Increasing the number of atlases from 6 to 12 often resulted in improvements, but statistical significance was not always found. Conclusions: MRI-based treatment planning with pseudo CTs generated through atlas registration is feasible for pediatric brain tumor patients. The doses calculated from pseudo CTs agreed well with those from real CTs, showing dosimetric accuracy within 2% for the PTV when multiple atlases were used. The arithmetic mean process may be a reasonable choice over PRGP for the synthesis scheme considering performance and computational costs.« less

  4. MRI-based treatment planning with pseudo CT generated through atlas registration

    PubMed Central

    Uh, Jinsoo; Merchant, Thomas E.; Li, Yimei; Li, Xingyu; Hua, Chiaho

    2014-01-01

    percentage of volume receiving at least 95% of the prescription dose in the planning target volume differed from the original values by less than 2% of the prescription dose (root-mean-square, RMS < 1%). The PRGP scheme did not perform better than the arithmetic mean process with the same number of atlases. Increasing the number of atlases from 6 to 12 often resulted in improvements, but statistical significance was not always found. Conclusions: MRI-based treatment planning with pseudo CTs generated through atlas registration is feasible for pediatric brain tumor patients. The doses calculated from pseudo CTs agreed well with those from real CTs, showing dosimetric accuracy within 2% for the PTV when multiple atlases were used. The arithmetic mean process may be a reasonable choice over PRGP for the synthesis scheme considering performance and computational costs. PMID:24784377

  5. A digital rat atlas of sectional anatomy

    NASA Astrophysics Data System (ADS)

    Yu, Li; Liu, Qian; Bai, Xueling; Liao, Yinping; Luo, Qingming; Gong, Hui

    2006-09-01

    This paper describes a digital rat alias of sectional anatomy made by milling. Two healthy Sprague-Dawley (SD) rat weighing 160-180 g were used for the generation of this atlas. The rats were depilated completely, then euthanized by Co II. One was via vascular perfusion, the other was directly frozen at -85 °C over 24 hour. After that, the frozen specimens were transferred into iron molds for embedding. A 3% gelatin solution colored blue was used to fill the molds and then frozen at -85 °C for one or two days. The frozen specimen-blocks were subsequently sectioned on the cryosection-milling machine in a plane oriented approximately transverse to the long axis of the body. The surface of specimen-blocks were imaged by a scanner and digitalized into 4,600 x2,580 x 24 bit array through a computer. Finally 9,475 sectional images (arterial vessel were not perfused) and 1,646 sectional images (arterial vessel were perfused) were captured, which made the volume of the digital atlas up to 369.35 Gbyte. This digital rat atlas is aimed at the whole rat and the rat arterial vessels are also presented. We have reconstructed this atlas. The information from the two-dimensional (2-D) images of serial sections and three-dimensional (3-D) surface model all shows that the digital rat atlas we constructed is high quality. This work lays the foundation for a deeper study of digital rat.

  6. Book review: Oklahoma Breeding Bird Atlas

    USGS Publications Warehouse

    Peterjohn, Bruce G.

    2004-01-01

    The first North American breeding bird atlases were initiated during the 1970s. With atlases completed or ongoing in more than 40 U.S. states and most Canadian provinces, these projects are now familiar to professional ornithologists and amateur birders. This book provides the results of the Oklahoma Breeding Bird Atlas, the data for which were collected during 1997–2001. Its appearance less than 3 years after completing fieldwork is remarkable and everyone associated with its timely publication should be congratulated for their efforts.Review info: Oklahoma Breeding Bird Atlas. By Dan L. Reinking, 2004. ISBN: 0806136146, 528 pp.

  7. Ground Water Atlas of the United States: Introduction and national summary

    USGS Publications Warehouse

    Miller, James A.

    1999-01-01

    The Ground Water Atlas of the United States provides a summary of the most important information available for each principal aquifer, or rock unit that will yield usable quantities of water to wells, throughout the 50 States, Puerto Rico, and the U.S. Virgin Islands. The Atlas is an outgrowth of the Regional Aquifer-System Analysis (RASA) program of the U.S. Geological Survey (USGS), a program that investigated 24 of the most important aquifers and aquifer systems of the Nation and one in the Caribbean Islands (fig. 1). The objectives of the RASA program were to define the geologic and hydrologic frameworks of each aquifer system, to assess the geochemistry of the water in the system, to characterize the ground-water flow system, and to describe the effects of development on the flow system. Although the RASA studies did not cover the entire Nation, they compiled much of the data needed to make the National assessments of ground-water resources presented in the Ground Water Atlas of the United States. The Atlas, however, describes the location, extent, and geologic and hydrologic characteristics of all the important aquifers in the United States, including those not studied by the RASA program. The Atlas is written so that it can be understood by readers who are not hydrologists. Simple language is used to explain technical terms. The principles that control the presence, movement, and chemical quality of ground water in different climatic, topographic, and geologic settings are clearly illustrated. The Atlas is, therefore, useful as a teaching tool for introductory courses in hydrology or hydrogeology at the college level and as an overview of ground-water conditions for consultants who need information about an individual aquifer. It also serves as an introduction to regional and National ground-water resources for lawmakers, personnel of local, State, or Federal agencies, or anyone who needs to understand ground-water occurrence, movement, and quality. The

  8. GOES-S Atlas V Booster and Centaur Stages Arrival, Offload, and

    NASA Image and Video Library

    2018-01-22

    The United Launch Alliance Atlas V booster for NOAA's Geostationary Operational Environmental Satellite-S (GOES-S) was offloaded from the Mariner transport ship at the Army Wharf at Cape Canaveral Air Force Station in Florida. The booster will be transported to the Atlas Spaceflight Operations Center near Space Launch Complex 41 at CCAFS. GOES-S is the second in a series of four advanced geostationary weather satellites. The satellite is slated to launch aboard the Atlas V rocket March 1.

  9. GOES-S Atlas V Booster and Centaur Stages Arrival, Offload, and

    NASA Image and Video Library

    2018-01-22

    The United Launch Alliance Atlas V booster for NOAA's Geostationary Operational Environmental Satellite-S (GOES-S) is offloaded from the Mariner transport ship at the Army Wharf at Cape Canaveral Air Force Station in Florida. The booster will be transported to the Atlas Spaceflight Operations Center near Space Launch Complex 41 at CCAFS. GOES-S is the second in a series of four advanced geostationary weather satellites. The satellite is slated to launch aboard the Atlas V rocket March 1.

  10. GOES-S Atlas V Booster and Centaur Stages Arrival, Offload, and

    NASA Image and Video Library

    2018-01-22

    The United Launch Alliance Atlas V booster and Centaur stage for NOAA's Geostationary Operational Environmental Satellite-S (GOES-S) are offloaded from the Mariner transport ship at the Army Wharf at Cape Canaveral Air Force Station in Florida. They will be transported to the Atlas Spaceflight Operations Center near Space Launch Complex 41 at CCAFS. GOES-S is the second in a series of four advanced geostationary weather satellites. The satellite is slated to launch aboard the Atlas V rocket March 1.

  11. Advanced technologies for scalable ATLAS conditions database access on the grid

    NASA Astrophysics Data System (ADS)

    Basset, R.; Canali, L.; Dimitrov, G.; Girone, M.; Hawkings, R.; Nevski, P.; Valassi, A.; Vaniachine, A.; Viegas, F.; Walker, R.; Wong, A.

    2010-04-01

    During massive data reprocessing operations an ATLAS Conditions Database application must support concurrent access from numerous ATLAS data processing jobs running on the Grid. By simulating realistic work-flow, ATLAS database scalability tests provided feedback for Conditions Db software optimization and allowed precise determination of required distributed database resources. In distributed data processing one must take into account the chaotic nature of Grid computing characterized by peak loads, which can be much higher than average access rates. To validate database performance at peak loads, we tested database scalability at very high concurrent jobs rates. This has been achieved through coordinated database stress tests performed in series of ATLAS reprocessing exercises at the Tier-1 sites. The goal of database stress tests is to detect scalability limits of the hardware deployed at the Tier-1 sites, so that the server overload conditions can be safely avoided in a production environment. Our analysis of server performance under stress tests indicates that Conditions Db data access is limited by the disk I/O throughput. An unacceptable side-effect of the disk I/O saturation is a degradation of the WLCG 3D Services that update Conditions Db data at all ten ATLAS Tier-1 sites using the technology of Oracle Streams. To avoid such bottlenecks we prototyped and tested a novel approach for database peak load avoidance in Grid computing. Our approach is based upon the proven idea of pilot job submission on the Grid: instead of the actual query, an ATLAS utility library sends to the database server a pilot query first.

  12. DISTRIBUTED CONTROL AND DA FOR ATLAS

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    D. SCUDDER; ET AL

    1999-05-01

    The control system for the Atlas pulsed power generator being built at Los Alamos National Laboratory will utilize a significant level of distributed control. Other principal design characteristics include noise immunity, modularity and use of commercial products wherever possible. The data acquisition system is tightly coordinated with the control system. Both share a common database server and a fiber-optic ethernet communications backbone.

  13. A three-dimensional single-cell-resolution whole-brain atlas using CUBIC-X expansion microscopy and tissue clearing.

    PubMed

    Murakami, Tatsuya C; Mano, Tomoyuki; Saikawa, Shu; Horiguchi, Shuhei A; Shigeta, Daichi; Baba, Kousuke; Sekiya, Hiroshi; Shimizu, Yoshihiro; Tanaka, Kenji F; Kiyonari, Hiroshi; Iino, Masamitsu; Mochizuki, Hideki; Tainaka, Kazuki; Ueda, Hiroki R

    2018-04-01

    A three-dimensional single-cell-resolution mammalian brain atlas will accelerate systems-level identification and analysis of cellular circuits underlying various brain functions. However, its construction requires efficient subcellular-resolution imaging throughout the entire brain. To address this challenge, we developed a fluorescent-protein-compatible, whole-organ clearing and homogeneous expansion protocol based on an aqueous chemical solution (CUBIC-X). The expanded, well-cleared brain enabled us to construct a point-based mouse brain atlas with single-cell annotation (CUBIC-Atlas). CUBIC-Atlas reflects inhomogeneous whole-brain development, revealing a significant decrease in the cerebral visual and somatosensory cortical areas during postnatal development. Probabilistic activity mapping of pharmacologically stimulated Arc-dVenus reporter mouse brains onto CUBIC-Atlas revealed the existence of distinct functional structures in the hippocampal dentate gyrus. CUBIC-Atlas is shareable by an open-source web-based viewer, providing a new platform for whole-brain cell profiling.

  14. Probing lepton flavour violation via neutrinoless τ→3μ decays with the ATLAS detector

    DOE PAGES

    Aad, G.; Abbott, B.; Abdallah, J.; ...

    2016-04-26

    This article presents the sensitivity of the ATLAS experiment to the lepton-flavour-violating decays of τ→3μ. A method utilising the production of τ leptons via W→τν decays is used. This method is applied to the sample of 20.3 fb -1 of pp collision data at a centre-of-mass energy of 8 TeV collected by the ATLAS experiment at the LHC in 2012. Lastly, no event is observed passing the selection criteria, and the observed (expected) upper limit on the τ lepton branching fraction into three muons, Br(τ→3μ), is 3.76×10 -7 (3.94×10 -7 ) at 90 % confidence level.

  15. EnviroAtlas -- Austin, TX -- One Meter Resolution Urban Land Cover Data (2010)

    EPA Pesticide Factsheets

    The Austin, TX EnviroAtlas One Meter-scale Urban Land Cover (MULC) Data were generated from United States Department of Agriculture (USDA) National Agricultural Imagery Program (NAIP) four band (red, green, blue, and near infrared) aerial photography at 1 m spatial resolution from multiple dates in May, 2010. Six land cover classes were mapped: water, impervious surfaces, soil and barren land, trees, grass-herbaceous non-woody vegetation, and agriculture. An accuracy assessment of 600 completely random and 55 stratified random photo interpreted reference points yielded an overall User's fuzzy accuracy of 87 percent. The area mapped is the US Census Bureau's 2010 Urban Statistical Area for Austin, TX plus a 1 km buffer. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

  16. Assessment Atlas, 1982-83.

    ERIC Educational Resources Information Center

    Yosemite Community Coll. District, Modesto, CA.

    Designed to provide information of value in establishing a base for decisionmaking in the Yosemite Community College District (YCCD), this assessment atlas graphically presents statistical data on the District as a whole, its two campuses, and YCCD Central Services for 1982-83. After an introduction to the use of the assessment atlas and…

  17. Use of statecharts in the modelling of dynamic behaviour in the ATLAS DAQ prototype-1

    NASA Astrophysics Data System (ADS)

    Croll, P.; Duval, P.-Y.; Jones, R.; Kolos, S.; Sari, R. F.; Wheeler, S.

    1998-08-01

    Many applications within the ATLAS DAQ prototype-1 system have complicated dynamic behaviour which can be successfully modelled in terms of states and transitions between states. Previously, state diagrams implemented as finite-state machines have been used. Although effective, they become ungainly as system size increases. Harel statecharts address this problem by implementing additional features such as hierarchy and concurrency. The CHSM object-oriented language system is freeware which implements Harel statecharts as concurrent, hierarchical, finite-state machines (CHSMs). An evaluation of this language system by the ATLAS DAQ group has shown it to be suitable for describing the dynamic behaviour of typical DAQ applications. The language is currently being used to model the dynamic behaviour of the prototype-1 run-control system. The design is specified by means of a CHSM description file, and C++ code is obtained by running the CHSM compiler on the file. In parallel with the modelling work, a code generator has been developed which translates statecharts, drawn using the StP CASE tool, into the CHSM language. C++ code, describing the dynamic behaviour of the run-control system, has been successfully generated directly from StP statecharts using the CHSM generator and compiler. The validity of the design was tested using the simulation features of the Statemate CASE tool.

  18. System Architecture Modeling for Technology Portfolio Management using ATLAS

    NASA Technical Reports Server (NTRS)

    Thompson, Robert W.; O'Neil, Daniel A.

    2006-01-01

    Strategic planners and technology portfolio managers have traditionally relied on consensus-based tools, such as Analytical Hierarchy Process (AHP) and Quality Function Deployment (QFD) in planning the funding of technology development. While useful to a certain extent, these tools are limited in the ability to fully quantify the impact of a technology choice on system mass, system reliability, project schedule, and lifecycle cost. The Advanced Technology Lifecycle Analysis System (ATLAS) aims to provide strategic planners a decision support tool for analyzing technology selections within a Space Exploration Architecture (SEA). Using ATLAS, strategic planners can select physics-based system models from a library, configure the systems with technologies and performance parameters, and plan the deployment of a SEA. Key parameters for current and future technologies have been collected from subject-matter experts and other documented sources in the Technology Tool Box (TTB). ATLAS can be used to compare the technical feasibility and economic viability of a set of technology choices for one SEA, and compare it against another set of technology choices or another SEA. System architecture modeling in ATLAS is a multi-step process. First, the modeler defines the system level requirements. Second, the modeler identifies technologies of interest whose impact on an SEA. Third, the system modeling team creates models of architecture elements (e.g. launch vehicles, in-space transfer vehicles, crew vehicles) if they are not already in the model library. Finally, the architecture modeler develops a script for the ATLAS tool to run, and the results for comparison are generated.

  19. Introduction to the Canadian Cardiovascular Outcomes Research Team's (CCORT) Canadian Cardiovascular Atlas project.

    PubMed

    Tu, Jack V; Brien, Susan E; Kennedy, Courtney C; Pilote, Louise; Ghali, William A

    2003-03-15

    The Canadian Cardiovascular Outcomes Research Team's (CCORT) Canadian Cardiovascular Atlas project was developed to provide Canadians with a national report on the state of cardiovascular health and health services in Canada. Written by a group of Canada's leading experts in cardiovascular outcomes research, the CCORT cardiac Atlas will cover a wide variety of topics ranging from cardiac risk factors and cardiac mortality rates to the treatment of patients with acute myocardial infarction and congestive heart failure and the outcomes of invasive cardiac procedures across Canada. Data in the Atlas will be presented at a national, provincial and health region level. The Atlas will be published as a series of 20 articles and chapters in future issues of The Canadian Journal of Cardiology and on CCORT's web site (www.ccort.ca). The journal version of the Atlas chapters will be written for a clinical audience and will include editorials written by invited experts, whereas the web-based version of each chapter will be written for a more general audience and will include additional supplemental information (for example, interactive colour maps and tables) that cannot be included in the journal version. Material from the Journal and the web will eventually be compiled into a book that will be distributed across Canada. This article serves as an introduction to the Atlas project and describes the rationale for and objectives of the CCORT national cardiac Atlas project.

  20. Digital hand atlas for web-based bone age assessment: system design and implementation

    NASA Astrophysics Data System (ADS)

    Cao, Fei; Huang, H. K.; Pietka, Ewa; Gilsanz, Vicente

    2000-04-01

    A frequently used assessment method of skeletal age is atlas matching by a radiological examination of a hand image against a small set of Greulich-Pyle patterns of normal standards. The method however can lead to significant deviation in age assessment, due to a variety of observers with different levels of training. The Greulich-Pyle atlas based on middle upper class white populations in the 1950s, is also not fully applicable for children of today, especially regarding the standard development in other racial groups. In this paper, we present our system design and initial implementation of a digital hand atlas and computer-aided diagnostic (CAD) system for Web-based bone age assessment. The digital atlas will remove the disadvantages of the currently out-of-date one and allow the bone age assessment to be computerized and done conveniently via Web. The system consists of a hand atlas database, a CAD module and a Java-based Web user interface. The atlas database is based on a large set of clinically normal hand images of diverse ethnic groups. The Java-based Web user interface allows users to interact with the hand image database form browsers. Users can use a Web browser to push a clinical hand image to the CAD server for a bone age assessment. Quantitative features on the examined image, which reflect the skeletal maturity, is then extracted and compared with patterns from the atlas database to assess the bone age.

  1. Automating usability of ATLAS Distributed Computing resources

    NASA Astrophysics Data System (ADS)

    Tupputi, S. A.; Di Girolamo, A.; Kouba, T.; Schovancová, J.; Atlas Collaboration

    2014-06-01

    The automation of ATLAS Distributed Computing (ADC) operations is essential to reduce manpower costs and allow performance-enhancing actions, which improve the reliability of the system. In this perspective a crucial case is the automatic handling of outages of ATLAS computing sites storage resources, which are continuously exploited at the edge of their capabilities. It is challenging to adopt unambiguous decision criteria for storage resources of non-homogeneous types, sizes and roles. The recently developed Storage Area Automatic Blacklisting (SAAB) tool has provided a suitable solution, by employing an inference algorithm which processes history of storage monitoring tests outcome. SAAB accomplishes both the tasks of providing global monitoring as well as automatic operations on single sites. The implementation of the SAAB tool has been the first step in a comprehensive review of the storage areas monitoring and central management at all levels. Such review has involved the reordering and optimization of SAM tests deployment and the inclusion of SAAB results in the ATLAS Site Status Board with both dedicated metrics and views. The resulting structure allows monitoring the storage resources status with fine time-granularity and automatic actions to be taken in foreseen cases, like automatic outage handling and notifications to sites. Hence, the human actions are restricted to reporting and following up problems, where and when needed. In this work we show SAAB working principles and features. We present also the decrease of human interactions achieved within the ATLAS Computing Operation team. The automation results in a prompt reaction to failures, which leads to the optimization of resource exploitation.

  2. Structural styles of the western onshore and offshore termination of the High Atlas, Morocco

    NASA Astrophysics Data System (ADS)

    Hafid, Mohamad; Zizi, Mahmoud; Bally, Albert W.; Ait Salem, Abdellah

    2006-01-01

    The present work aims (1) at documenting, by regional seismic transects, how the structural style varies in the western High Atlas system and its prolongation under the present-day Atlantic margin, (2) at understanding how this variation is related to the local geological framework, especially the presence of salt within the sedimentary cover, and (3) at discussing the exact geographic location of the northern front of the western High Atlas and how it links with the most western Atlas front in the offshore Cap Tafelney High Atlas. Previous work showed that the structural style of the Atlas belt changes eastward from a dominantly thick-skinned one in central and eastern High Atlas and Middle Atlas of Morocco to a dominantly thin-skinned one in Algeria and Tunisia. We propose here to show that a similar structural style change can be observed in the other direction of the Atlas Belt within its western termination, where the western High Atlas intersects at right angle the Atlantic passive margin and develops into a distinct segment, namely the High Atlas of Cap Tafelney, where salt/evaporite-based décollement tectonics prevail. To cite this article: M. Hafid et al., C. R. Geoscience 338 (2006).

  3. GOES-S Atlas V Booster and Centaur Stages Arrival, Offload, and

    NASA Image and Video Library

    2018-01-22

    Preparations are underway to offload the United Launch Alliance Atlas V booster and Centaur stage for NOAA's Geostationary Operational Environmental Satellite-S (GOES-S) from the Mariner transport ship at the Army Wharf at Cape Canaveral Air Force Station in Florida. They will be transported to the Atlas Spaceflight Operations Center near Space Launch Complex 41 at CCAFS. GOES-S is the second in a series of four advanced geostationary weather satellites. The satellite is slated to launch aboard the Atlas V rocket March 1.

  4. Improved Neuroimaging Atlas of the Dentate Nucleus.

    PubMed

    He, Naying; Langley, Jason; Huddleston, Daniel E; Ling, Huawei; Xu, Hongmin; Liu, Chunlei; Yan, Fuhua; Hu, Xiaoping P

    2017-12-01

    The dentate nucleus (DN) of the cerebellum is the major output nucleus of the cerebellum and is rich in iron. Quantitative susceptibility mapping (QSM) provides better iron-sensitive MRI contrast to delineate the boundary of the DN than either T 2 -weighted images or susceptibility-weighted images. Prior DN atlases used T 2 -weighted or susceptibility-weighted images to create DN atlases. Here, we employ QSM images to develop an improved dentate nucleus atlas for use in imaging studies. The DN was segmented in QSM images from 38 healthy volunteers. The resulting DN masks were transformed to a common space and averaged to generate the DN atlas. The center of mass of the left and right sides of the QSM-based DN atlas in the Montreal Neurological Institute space was -13.8, -55.8, and -36.4 mm, and 13.8, -55.7, and -36.4 mm, respectively. The maximal probability and mean probability of the DN atlas with the individually segmented DNs in this cohort were 100 and 39.3%, respectively, in contrast to the maximum probability of approximately 75% and the mean probability of 23.4 to 33.7% with earlier DN atlases. Using QSM, which provides superior iron-sensitive MRI contrast for delineating iron-rich structures, an improved atlas for the dentate nucleus has been generated. The atlas can be applied to investigate the role of the DN in both normal cortico-cerebellar physiology and the variety of disease states in which it is implicated.

  5. Education modules using EnviroAtlas

    EPA Science Inventory

    Proposal #1: Exploration and Discovery through Maps: Teaching Science with Technology (Elementary)Online maps have the power to bring students closer to their local natural environments. EnviroAtlas is an interactive, web-based tool that was designed by the EPA and its partners ...

  6. Assessment Atlas, 1983-84.

    ERIC Educational Resources Information Center

    Yosemite Community Coll. District, Modesto, CA.

    Designed to provide information of value in establishing a base for decision making in the Yosemite Community College District (YCCD), this assessment atlas graphically presents statistical data for the District as a whole, its two campuses, and YCCD Central Services for 1983-84. After an introduction to the use of the assessment atlas and…

  7. Brain transcriptome atlases: a computational perspective.

    PubMed

    Mahfouz, Ahmed; Huisman, Sjoerd M H; Lelieveldt, Boudewijn P F; Reinders, Marcel J T

    2017-05-01

    The immense complexity of the mammalian brain is largely reflected in the underlying molecular signatures of its billions of cells. Brain transcriptome atlases provide valuable insights into gene expression patterns across different brain areas throughout the course of development. Such atlases allow researchers to probe the molecular mechanisms which define neuronal identities, neuroanatomy, and patterns of connectivity. Despite the immense effort put into generating such atlases, to answer fundamental questions in neuroscience, an even greater effort is needed to develop methods to probe the resulting high-dimensional multivariate data. We provide a comprehensive overview of the various computational methods used to analyze brain transcriptome atlases.

  8. The VALiDATe29 MRI Based Multi-Channel Atlas of the Squirrel Monkey Brain.

    PubMed

    Schilling, Kurt G; Gao, Yurui; Stepniewska, Iwona; Wu, Tung-Lin; Wang, Feng; Landman, Bennett A; Gore, John C; Chen, Li Min; Anderson, Adam W

    2017-10-01

    We describe the development of the first digital atlas of the normal squirrel monkey brain and present the resulting product, VALiDATe29. The VALiDATe29 atlas is based on multiple types of magnetic resonance imaging (MRI) contrast acquired on 29 squirrel monkeys, and is created using unbiased, nonlinear registration techniques, resulting in a population-averaged stereotaxic coordinate system. The atlas consists of multiple anatomical templates (proton density, T1, and T2* weighted), diffusion MRI templates (fractional anisotropy and mean diffusivity), and ex vivo templates (fractional anisotropy and a structural MRI). In addition, the templates are combined with histologically defined cortical labels, and diffusion tractography defined white matter labels. The combination of intensity templates and image segmentations make this atlas suitable for the fundamental atlas applications of spatial normalization and label propagation. Together, this atlas facilitates 3D anatomical localization and region of interest delineation, and enables comparisons of experimental data across different subjects or across different experimental conditions. This article describes the atlas creation and its contents, and demonstrates the use of the VALiDATe29 atlas in typical applications. The atlas is freely available to the scientific community.

  9. Performance of the ATLAS trigger system in 2015

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Aaboud, M.; Aad, G.; Abbott, B.

    During 2015 the ATLAS experiment recorded 3.8fb –1 of proton–proton collision data at a centre-of-mass energy of 13TeV. The ATLAS trigger system is a crucial component of the experiment, responsible for selecting events of interest at a recording rate of approximately 1 kHz from up to 40 MHz of collisions. This paper presents a short overview of the changes to the trigger and data acquisition systems during the first long shutdown of the LHC and shows the performance of the trigger system and its components based on the 2015 proton–proton collision data.

  10. Performance of the ATLAS trigger system in 2015

    DOE PAGES

    Aaboud, M.; Aad, G.; Abbott, B.; ...

    2017-05-18

    During 2015 the ATLAS experiment recorded 3.8fb –1 of proton–proton collision data at a centre-of-mass energy of 13TeV. The ATLAS trigger system is a crucial component of the experiment, responsible for selecting events of interest at a recording rate of approximately 1 kHz from up to 40 MHz of collisions. This paper presents a short overview of the changes to the trigger and data acquisition systems during the first long shutdown of the LHC and shows the performance of the trigger system and its components based on the 2015 proton–proton collision data.

  11. Evaluation and optimization of the parameters used in multiple-atlas-based segmentation of prostate cancers in radiation therapy.

    PubMed

    Wong, Wicger K H; Leung, Lucullus H T; Kwong, Dora L W

    2016-01-01

    To evaluate and optimize the parameters used in multiple-atlas-based segmentation of prostate cancers in radiation therapy. A retrospective study was conducted, and the accuracy of the multiple-atlas-based segmentation was tested on 30 patients. The effect of library size (LS), number of atlases used for contour averaging and the contour averaging strategy were also studied. The autogenerated contours were compared with the manually drawn contours. Dice similarity coefficient (DSC) and Hausdorff distance were used to evaluate the segmentation agreement. Mixed results were found between simultaneous truth and performance level estimation (STAPLE) and majority vote (MV) strategies. Multiple-atlas approaches were relatively insensitive to LS. A LS of ten was adequate, and further increase in the LS only showed insignificant gain. Multiple atlas performed better than single atlas for most of the time. Using more atlases did not guarantee better performance, with five atlases performing better than ten atlases. With our recommended setting, the median DSC for the bladder, rectum, prostate, seminal vesicle and femurs was 0.90, 0.77, 0.84, 0.56 and 0.95, respectively. Our study shows that multiple-atlas-based strategies have better accuracy than single-atlas approach. STAPLE is preferred, and a LS of ten is adequate for prostate cases. Using five atlases for contour averaging is recommended. The contouring accuracy of seminal vesicle still needs improvement, and manual editing is still required for the other structures. This article provides a better understanding of the influence of the parameters used in multiple-atlas-based segmentation of prostate cancers.

  12. Atlas V OA-7 LVOS Atlas Booster on Stand

    NASA Image and Video Library

    2017-02-22

    The first stage of the United Launch Alliance (ULA) Atlas V rocket is lifted by crane to vertical as it is moved into the Vertical Integration Facility at Space Launch Complex 41 at Cape Canaveral Air Force Station in Florida. The rocket is being prepared for Orbital ATK's seventh commercial resupply mission, CRS-7, to the International Space Station. Orbital ATK's CYGNUS pressurized cargo module is scheduled to launch atop ULA's Atlas V rocket from Pad 41 on March 19, 2017. CYGNUS will deliver thousands of pounds of supplies, equipment and scientific research materials to the space station

  13. EnviroAtlas - Austin, TX - Demographics by Block Group Web Service

    EPA Pesticide Factsheets

    This EnviroAtlas web service supports research and online mapping activities related to EnviroAtlas (https://enviroatlas.epa.gov/EnviroAtlas). This EnviroAtlas dataset is a summary of key demographic groups for the EnviroAtlas community. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

  14. Atlas/Centaur Pioneer G operations summary

    NASA Technical Reports Server (NTRS)

    1973-01-01

    Specifications of the Pioneer G and Atlas/Centaur 30 Launch Vehicle are provided, along with information concerning mission objectives. The Atlas/Centaur engine group will generate a 431,383 lb. thrust for an injection velocity of approximately 32,400 miles per hour using liquid oxygen and RP-1 propellants. In addition to detailed diagrams of equipment aboard the Pioneer G, an account is given of intended investigations of the interplanetary medium beyond the orbit of Mars, the nature of the asteroid belt, and the environmental and atmospheric characteristics of Jupiter. Pertinent data regarding the option of a Saturn-oriented trajectory are also reviewed and evaluated.

  15. National Transportation Atlas Databases : 1995

    DOT National Transportation Integrated Search

    1995-01-01

    BTS has compiled the initial version of a geographic atlas : database to support research, analysis, and decision making : across all modes of transportation. The atlas databases are : designed primarily to meet the needs of DOT at the national : lev...

  16. Networks in ATLAS

    NASA Astrophysics Data System (ADS)

    McKee, Shawn; ATLAS Collaboration

    2017-10-01

    Networks have played a critical role in high-energy physics (HEP), enabling us to access and effectively utilize globally distributed resources to meet the needs of our physicists. Because of their importance in enabling our grid computing infrastructure many physicists have taken leading roles in research and education (R&E) networking, participating in, and even convening, network related meetings and research programs with the broader networking community worldwide. This has led to HEP benefiting from excellent global networking capabilities for little to no direct cost. However, as other science domains ramp-up their need for similar networking it becomes less clear that this situation will continue unchanged. What this means for ATLAS in particular needs to be understood. ATLAS has evolved its computing model since the LHC started based upon its experience with using globally distributed resources. The most significant theme of those changes has been increased reliance upon, and use of, its networks. We will report on a number of networking initiatives in ATLAS including participation in the global perfSONAR network monitoring and measuring efforts of WLCG and OSG, the collaboration with the LHCOPN/LHCONE effort, the integration of network awareness into PanDA, the use of the evolving ATLAS analytics framework to better understand our networks and the changes in our DDM system to allow remote access to data. We will also discuss new efforts underway that are exploring the inclusion and use of software defined networks (SDN) and how ATLAS might benefit from: • Orchestration and optimization of distributed data access and data movement. • Better control of workflows, end to end. • Enabling prioritization of time-critical vs normal tasks • Improvements in the efficiency of resource usage

  17. A Roadmap to Continuous Integration for ATLAS Software Development

    NASA Astrophysics Data System (ADS)

    Elmsheuser, J.; Krasznahorkay, A.; Obreshkov, E.; Undrus, A.; ATLAS Collaboration

    2017-10-01

    The ATLAS software infrastructure facilitates efforts of more than 1000 developers working on the code base of 2200 packages with 4 million lines of C++ and 1.4 million lines of python code. The ATLAS offline code management system is the powerful, flexible framework for processing new package versions requests, probing code changes in the Nightly Build System, migration to new platforms and compilers, deployment of production releases for worldwide access and supporting physicists with tools and interfaces for efficient software use. It maintains multi-stream, parallel development environment with about 70 multi-platform branches of nightly releases and provides vast opportunities for testing new packages, for verifying patches to existing software and for migrating to new platforms and compilers. The system evolution is currently aimed on the adoption of modern continuous integration (CI) practices focused on building nightly releases early and often, with rigorous unit and integration testing. This paper describes the CI incorporation program for the ATLAS software infrastructure. It brings modern open source tools such as Jenkins and GitLab into the ATLAS Nightly System, rationalizes hardware resource allocation and administrative operations, provides improved feedback and means to fix broken builds promptly for developers. Once adopted, ATLAS CI practices will improve and accelerate innovation cycles and result in increased confidence in new software deployments. The paper reports the status of Jenkins integration with the ATLAS Nightly System as well as short and long term plans for the incorporation of CI practices.

  18. Tampa Bay environmental atlas

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Kunneke, J.T.; Palik, T.F.

    1984-12-01

    Biological and water resource data for Tampa Bay were compiled and mapped at a scale of 1:24,000. This atlas consists of (1) composited information overlain on 18 biological and 20 water resource base maps and (2) an accompanying map narrative. Subjects mapped on the water resource maps are contours of the mean middepth specific conductivity which can be converted to salinity; bathymetry, sediments, tidal currents, the freshwater/saltwater interface, dredge spoil disposal sites; locations of industrial and municipal point source discharges, tide stations, and water quality sampling stations. The point source discharge locations show permitted capacity and the water quality samplingmore » stations show 5-year averages for chlorophyll, conductivity, turbidity, temperature, and total nitrogen. The subjects shown on the biological resource maps are clam and oyster beds, shellfish harvest areas, colonial bird nesting sites, manatee habitat, seagrass beds and artificial reefs. Spawning seasons, nursery habitats, and adult habitats are identified for major fish species. The atlas will provide useful information for coastal planning and management in Tampa Bay.« less

  19. Commissioning of the ATLAS pixel detector

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    ATLAS Collaboration; Golling, Tobias

    2008-09-01

    The ATLAS pixel detector is a high precision silicon tracking device located closest to the LHC interaction point. It belongs to the first generation of its kind in a hadron collider experiment. It will provide crucial pattern recognition information and will largely determine the ability of ATLAS to precisely track particle trajectories and find secondary vertices. It was the last detector to be installed in ATLAS in June 2007, has been fully connected and tested in-situ during spring and summer 2008, and is ready for the imminent LHC turn-on. The highlights of the past and future commissioning activities of themore » ATLAS pixel system are presented.« less

  20. Encoding probabilistic brain atlases using Bayesian inference.

    PubMed

    Van Leemput, Koen

    2009-06-01

    This paper addresses the problem of creating probabilistic brain atlases from manually labeled training data. Probabilistic atlases are typically constructed by counting the relative frequency of occurrence of labels in corresponding locations across the training images. However, such an "averaging" approach generalizes poorly to unseen cases when the number of training images is limited, and provides no principled way of aligning the training datasets using deformable registration. In this paper, we generalize the generative image model implicitly underlying standard "average" atlases, using mesh-based representations endowed with an explicit deformation model. Bayesian inference is used to infer the optimal model parameters from the training data, leading to a simultaneous group-wise registration and atlas estimation scheme that encompasses standard averaging as a special case. We also use Bayesian inference to compare alternative atlas models in light of the training data, and show how this leads to a data compression problem that is intuitive to interpret and computationally feasible. Using this technique, we automatically determine the optimal amount of spatial blurring, the best deformation field flexibility, and the most compact mesh representation. We demonstrate, using 2-D training datasets, that the resulting models are better at capturing the structure in the training data than conventional probabilistic atlases. We also present experiments of the proposed atlas construction technique in 3-D, and show the resulting atlases' potential in fully-automated, pulse sequence-adaptive segmentation of 36 neuroanatomical structures in brain MRI scans.

  1. EnviroAtlas -- Austin, TX -- One Meter Resolution Urban Land Cover Data (2010) Web Service

    EPA Pesticide Factsheets

    This EnviroAtlas web service supports research and online mapping activities related to EnviroAtlas (https://www.epa.gov/enviroatlas ). The Austin, TX EnviroAtlas One Meter-scale Urban Land Cover (MULC) Data were generated from United States Department of Agriculture (USDA) National Agricultural Imagery Program (NAIP) four band (red, green, blue, and near infrared) aerial photography at 1 m spatial resolution from multiple dates in May, 2010. Six land cover classes were mapped: water, impervious surfaces, soil and barren land, trees, grass-herbaceous non-woody vegetation, and agriculture. An accuracy assessment of 600 completely random and 55 stratified random photo interpreted reference points yielded an overall User's fuzzy accuracy of 87 percent. The area mapped is the US Census Bureau's 2010 Urban Statistical Area for Austin, TX plus a 1 km buffer. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas

  2. A prostate MRI atlas of biochemical failures following cancer treatment

    NASA Astrophysics Data System (ADS)

    Rusu, Mirabela; Kurhanewicz, John; Tewari, Ashutosh; Madabhushi, Anant

    2014-03-01

    Radical prostatectomy (RP) and radiation therapy (RT) are the most common treatment options for prostate cancer (PCa). Despite advancements in radiation delivery and surgical procedures, RP and RT can result in failure rates as high as 40% and >25%, respectively. Treatment failure is characterized by biochemical recurrence (BcR), which is defined in terms of prostate specific antigen (PSA) concentrations and its variation following treatment. PSA is expected to decrease following treatment, thereby its presence in even small concentrations (e.g 0.2 ng/ml for surgery or 2 ng/ml over the nadir PSA for radiation therapy) is indicative of treatment failure. Early identification of treatment failure may enable the use of more aggressive or neo-adjuvant therapies. Moreover, predicting failure prior to treatment may spare the patient from a procedure that is unlikely to be successful. Our goal is to identify differences on pre-treatment MRI between patients who have BcR and those who remain disease-free at 5 years post-treatment. Specifically, we focus on (1) identifying statistically significant differences in MRI intensities, (2) establishing morphological differences in prostatic anatomic structures, and (3) comparing these differences with the natural variability of prostatic structures. In order to attain these objectives, we use an anatomically constrained registration framework to construct BcR and non-BcR statistical atlases based on the pre-treatment magnetic resonance images (MRI) of the prostate. The patients included in the atlas either underwent RP or RT and were followed up for at least 5 years. The BcR atlas was constructed from a combined population of 12 pre-RT 1.5 Tesla (T) MRI and 33 pre-RP 3T MRI from patients with BcR within 5 years of treatment. Similarly, the non-BcR atlas was built based on a combined cohort of 20 pre-RT 1.5T MRI and 41 pre-RP 3T MRI from patients who remain disease-free 5 years post treatment. We chose the atlas framework as it

  3. Atlas of Yellowstone

    USGS Publications Warehouse

    Pierce, Kenneth L.; Marcus, A. W.; Meachan, J. E.; Rodman, A. W.; Steingisser, A. Y.; Allan, Stuart; West, Ross

    2012-01-01

    Established in 1872, Yellowstone National Park was the world’s first national park. In a fitting tribute to this diverse and beautiful region, the Atlas of Yellowstone is a compelling visual guide to this unique national park and its surrounding area. Ranging from art to wolves, from American Indians to the Yellowstone Volcano, and from geysers to population, each page explains something new about the dynamic forces shaping Yellowstone. Equal parts reference and travel guide, the Atlas of Yellowstone is an unsurpassed resource.

  4. Production experience with the ATLAS Event Service

    NASA Astrophysics Data System (ADS)

    Benjamin, D.; Calafiura, P.; Childers, T.; De, K.; Guan, W.; Maeno, T.; Nilsson, P.; Tsulaia, V.; Van Gemmeren, P.; Wenaus, T.; ATLAS Collaboration

    2017-10-01

    The ATLAS Event Service (AES) has been designed and implemented for efficient running of ATLAS production workflows on a variety of computing platforms, ranging from conventional Grid sites to opportunistic, often short-lived resources, such as spot market commercial clouds, supercomputers and volunteer computing. The Event Service architecture allows real time delivery of fine grained workloads to running payload applications which process dispatched events or event ranges and immediately stream the outputs to highly scalable Object Stores. Thanks to its agile and flexible architecture the AES is currently being used by grid sites for assigning low priority workloads to otherwise idle computing resources; similarly harvesting HPC resources in an efficient back-fill mode; and massively scaling out to the 50-100k concurrent core level on the Amazon spot market to efficiently utilize those transient resources for peak production needs. Platform ports in development include ATLAS@Home (BOINC) and the Google Compute Engine, and a growing number of HPC platforms. After briefly reviewing the concept and the architecture of the Event Service, we will report the status and experience gained in AES commissioning and production operations on supercomputers, and our plans for extending ES application beyond Geant4 simulation to other workflows, such as reconstruction and data analysis.

  5. The ATLAS PanDA Pilot in Operation

    NASA Astrophysics Data System (ADS)

    Nilsson, P.; Caballero, J.; De, K.; Maeno, T.; Stradling, A.; Wenaus, T.; ATLAS Collaboration

    2011-12-01

    The Production and Distributed Analysis system (PanDA) [1-2] was designed to meet ATLAS [3] requirements for a data-driven workload management system capable of operating at LHC data processing scale. Submitted jobs are executed on worker nodes by pilot jobs sent to the grid sites by pilot factories. This paper provides an overview of the PanDA pilot [4] system and presents major features added in light of recent operational experience, including multi-job processing, advanced job recovery for jobs with output storage failures, gLExec [5-6] based identity switching from the generic pilot to the actual user, and other security measures. The PanDA system serves all ATLAS distributed processing and is the primary system for distributed analysis; it is currently used at over 100 sites worldwide. We analyze the performance of the pilot system in processing real LHC data on the OSG [7], EGI [8] and Nordugrid [9-10] infrastructures used by ATLAS, and describe plans for its evolution.

  6. Baily's Beads Atlas in 2005 - 2008 Eclipses

    NASA Astrophysics Data System (ADS)

    Sigismondi, C.; Dunham, D. W.; Guhl, K.; Andersson, S.; Bode, H.; Canales, O.; Colona, P.; Farago, O.; Fernández-Ocaña, M.; Gabel, A.; Haupt, M.; Herold, C.; Nugent, R.; Oliva, P.; Patel, M.; Perello, C.; Rothe, W.; Rovira, J.; Schaefer, T.; Schnabel, C.; Schwartz, D.; Selva, A.; Strickling, W.; Tegtmeier, A.; Tegtmeier, C.; Thome, B.; Warren, W. H.

    2009-09-01

    In the annular or total eclipses of 3 October 2005, 29 March 2006, 22 September 2006, and 1 August 2008, observational campaigns were organized to record the phenomenon of Baily’s beads. These campaigns were internationally coordinated through the International Occultation Timing Association (IOTA) at both its American and European sections. From the stations in the northern and southern zones of grazing eclipse, the eclipses have been recorded on video. Afterward, as many beads as possible have been identified by analyzing the video data of each observing station. The atlas presented in this paper includes 598 data points, obtained by 23 observers operating at 28 different observing stations. The atlas lists the geographic positions of the observing stations and the observed time instants of disappearance or reappearance of beads, identified by an angle measured relative to the Moon’s axis of rotation. The atlas will serve as a basis for determining the solar diameter.

  7. Teaching science with technology: Using EPA's EnviroAtlas in ...

    EPA Pesticide Factsheets

    Background/Question/Methods U.S. EPA’s EnviroAtlas provides a collection of web-based, interactive tools and resources for exploring ecosystem goods and services. EnviroAtlas contains two primary tools: An Interactive Map, which provides access to 300+ maps at multiple extents for the U.S., and an Eco-Health Relationship Browser, which displays evidence from hundreds of scientific publications on the linkages between ecosystems, the services they provide, and human health. EnviroAtlas is readily available, only requires an internet browser to use, and can be used by anyone with some introduction, which this session will provide. This session introduces an educational curriculum that has been designed for use with the tools in EnviroAtlas. The curriculum contains three lesson plan packages for varying grade levels: Exploring Your Watershed for 4th and 5th grades, Making Connections Between Ecosystems and Human Health for 7th-12th grades, and a lesson that encourages students to be collaborative decision-makers in a role-playing exercise that integrates ecology, public health, and city-planning in Building a Greenway Case Study for high school and undergraduate classes. All lesson plans are free and available for download. Results/Conclusions These educational activities encourage critical thinking and engage students and community users in a variety of ways, including physical engagement and technological exploration of their local environment and communities.

  8. Search for lepton-flavour-violating H → μτ decays of the Higgs boson with the ATLAS detector

    DOE PAGES

    Aad, G.; Abbott, B.; Abdallah, J.; ...

    2015-11-30

    A direct search for lepton-flavour-violating H → μτ decays of the recently discovered Higgs boson with the ATLAS detector at the LHC is presented. The analysis is performed in the H → μτ had channel, where τ had is a hadronically decaying τ -lepton. The search is based on the data sample of proton-proton collisions collected by the ATLAS experiment corresponding to an integrated luminosity of 20.3 fb –1 at a centre-of-mass energy of √s = 8 TeV. No statistically significant excess of data over the predicted background is observed. As a result, the observed (expected) 95% confidence-level upper limitmore » on the branching fraction, Br( H → μτ ), is 1.85% (1.24%).« less

  9. Trigger readout electronics upgrade for the ATLAS Liquid Argon Calorimeters

    NASA Astrophysics Data System (ADS)

    Dinkespiler, B.

    2017-09-01

    The upgrade of the Large Hadron Collider (LHC) scheduled for the 2019-2020 shut-down period, referred to as Phase-I upgrade, will increase the instantaneous luminosity to about three times the design value. Since the current ATLAS trigger system does not allow sufficient increase of the trigger rate, an improvement of the trigger system is required. The Liquid Argon (LAr) Calorimeter read-out will therefore be modified to deliver digital trigger signals with a higher spatial granularity in order to improve the identification efficiencies of electrons, photons, tau, jets and missing energy, at high background rejection rates at the Level-1 trigger. The new trigger signals will be arranged in 34000 so-called Super Cells which achieves 5-10 times better granularity than the trigger towers currently used and allows an improved background rejection. The readout of the trigger signals will process the signal of the Super Cells at every LHC bunch-crossing at 12-bit precision and a frequency of 40 MHz. The data will be transmitted to the Back End using a custom serializer and optical converter and 5.12 Gb/s optical links. In order to verify the full functionality of the future Liquid Argon trigger system, a demonstrator set-up has been installed on the ATLAS detector and is operated in parallel to the regular ATLAS data taking during the LHC Run-2 in 2015 and 2016. Noise level and linearity on the energy measurement have been verified to be within our requirements. In addition, we have collected data from 13 TeV proton collisions during the LHC 2015 and 2016 runs, and have observed real pulses from the detector through the demonstrator system. The talk will give an overview of the Phase-I Upgrade of the ATLAS Liquid Argon Calorimeter readout and present the custom developed hardware including their role in real-time data processing and fast data transfer. This contribution will also report on the performance of the newly developed ASICs including their radiation tolerance

  10. Automated Loads Analysis System (ATLAS)

    NASA Technical Reports Server (NTRS)

    Gardner, Stephen; Frere, Scot; O’Reilly, Patrick

    2013-01-01

    ATLAS is a generalized solution that can be used for launch vehicles. ATLAS is used to produce modal transient analysis and quasi-static analysis results (i.e., accelerations, displacements, and forces) for the payload math models on a specific Shuttle Transport System (STS) flight using the shuttle math model and associated forcing functions. This innovation solves the problem of coupling of payload math models into a shuttle math model. It performs a transient loads analysis simulating liftoff, landing, and all flight events between liftoff and landing. ATLAS utilizes efficient and numerically stable algorithms available in MSC/NASTRAN.

  11. Cassini Tour Atlas Automated Generation

    NASA Technical Reports Server (NTRS)

    Grazier, Kevin R.; Roumeliotis, Chris; Lange, Robert D.

    2011-01-01

    During the Cassini spacecraft s cruise phase and nominal mission, the Cassini Science Planning Team developed and maintained an online database of geometric and timing information called the Cassini Tour Atlas. The Tour Atlas consisted of several hundreds of megabytes of EVENTS mission planning software outputs, tables, plots, and images used by mission scientists for observation planning. Each time the nominal mission trajectory was altered or tweaked, a new Tour Atlas had to be regenerated manually. In the early phases of Cassini s Equinox Mission planning, an a priori estimate suggested that mission tour designers would develop approximately 30 candidate tours within a short period of time. So that Cassini scientists could properly analyze the science opportunities in each candidate tour quickly and thoroughly so that the optimal series of orbits for science return could be selected, a separate Tour Atlas was required for each trajectory. The task of manually generating the number of trajectory analyses in the allotted time would have been impossible, so the entire task was automated using code written in five different programming languages. This software automates the generation of the Cassini Tour Atlas database. It performs with one UNIX command what previously took a day or two of human labor.

  12. Voxel-based dose prediction with multi-patient atlas selection for automated radiotherapy treatment planning

    NASA Astrophysics Data System (ADS)

    McIntosh, Chris; Purdie, Thomas G.

    2017-01-01

    Automating the radiotherapy treatment planning process is a technically challenging problem. The majority of automated approaches have focused on customizing and inferring dose volume objectives to be used in plan optimization. In this work we outline a multi-patient atlas-based dose prediction approach that learns to predict the dose-per-voxel for a novel patient directly from the computed tomography planning scan without the requirement of specifying any objectives. Our method learns to automatically select the most effective atlases for a novel patient, and then map the dose from those atlases onto the novel patient. We extend our previous work to include a conditional random field for the optimization of a joint distribution prior that matches the complementary goals of an accurately spatially distributed dose distribution while still adhering to the desired dose volume histograms. The resulting distribution can then be used for inverse-planning with a new spatial dose objective, or to create typical dose volume objectives for the canonical optimization pipeline. We investigated six treatment sites (633 patients for training and 113 patients for testing) and evaluated the mean absolute difference in all DVHs for the clinical and predicted dose distribution. The results on average are favorable in comparison to our previous approach (1.91 versus 2.57). Comparing our method with and without atlas-selection further validates that atlas-selection improved dose prediction on average in whole breast (0.64 versus 1.59), prostate (2.13 versus 4.07), and rectum (1.46 versus 3.29) while it is less important in breast cavity (0.79 versus 0.92) and lung (1.33 versus 1.27) for which there is high conformity and minimal dose shaping. In CNS brain, atlas-selection has the potential to be impactful (3.65 versus 5.09), but selecting the ideal atlas is the most challenging.

  13. A new strips tracker for the upgraded ATLAS ITk detector

    NASA Astrophysics Data System (ADS)

    David, C.

    2018-01-01

    The ATLAS detector has been designed and developed to function in the environment of the present Large Hadron Collider (LHC). At the next-generation tracking detector proposed for the High Luminosity LHC (HL-LHC), the so-called ATLAS Phase-II Upgrade, the fluences and radiation levels will be higher by as much as a factor of ten. The new sub-detectors must thus be faster, of larger area, more segmented and more radiation hard while the amount of inactive material should be minimized and the power supply to the front-end systems should be increased. For those reasons, the current inner tracker of the ATLAS detector will be fully replaced by an all-silicon tracking system that consists of a pixel detector at small radius close to the beam line and a large area strip tracker surrounding it. This document gives an overview of the design of the strip inner tracker (Strip ITk) and summarises the intensive R&D activities performed over the last years by the numerous institutes within the Strips ITk collaboration. These studies are accompanied with a strong prototyping effort to contribute to the optimisation of the Strip ITk's structure and components. This effort culminated recently in the release of the ATLAS Strips ITk Technical Design Report (TDR).

  14. Segmentation of Image Ensembles via Latent Atlases

    PubMed Central

    Van Leemput, Koen; Menze, Bjoern H.; Wells, William M.; Golland, Polina

    2010-01-01

    Spatial priors, such as probabilistic atlases, play an important role in MRI segmentation. However, the availability of comprehensive, reliable and suitable manual segmentations for atlas construction is limited. We therefore propose a method for joint segmentation of corresponding regions of interest in a collection of aligned images that does not require labeled training data. Instead, a latent atlas, initialized by at most a single manual segmentation, is inferred from the evolving segmentations of the ensemble. The algorithm is based on probabilistic principles but is solved using partial differential equations (PDEs) and energy minimization criteria. We evaluate the method on two datasets, segmenting subcortical and cortical structures in a multi-subject study and extracting brain tumors in a single-subject multi-modal longitudinal experiment. We compare the segmentation results to manual segmentations, when those exist, and to the results of a state-of-the-art atlas-based segmentation method. The quality of the results supports the latent atlas as a promising alternative when existing atlases are not compatible with the images to be segmented. PMID:20580305

  15. Automated tissue classification of pediatric brains from magnetic resonance images using age-specific atlases

    NASA Astrophysics Data System (ADS)

    Metzger, Andrew; Benavides, Amanda; Nopoulos, Peg; Magnotta, Vincent

    2016-03-01

    The goal of this project was to develop two age appropriate atlases (neonatal and one year old) that account for the rapid growth and maturational changes that occur during early development. Tissue maps from this age group were initially created by manually correcting the resulting tissue maps after applying an expectation maximization (EM) algorithm and an adult atlas to pediatric subjects. The EM algorithm classified each voxel into one of ten possible tissue types including several subcortical structures. This was followed by a novel level set segmentation designed to improve differentiation between distal cortical gray matter and white matter. To minimize the req uired manual corrections, the adult atlas was registered to the pediatric scans using high -dimensional, symmetric image normalization (SyN) registration. The subject images were then mapped to an age specific atlas space, again using SyN registration, and the resulting transformation applied to the manually corrected tissue maps. The individual maps were averaged in the age specific atlas space and blurred to generate the age appropriate anatomical priors. The resulting anatomical priors were then used by the EM algorithm to re-segment the initial training set as well as an independent testing set. The results from the adult and age-specific anatomical priors were compared to the manually corrected results. The age appropriate atlas provided superior results as compared to the adult atlas. The image analysis pipeline used in this work was built using the open source software package BRAINSTools.

  16. An Atlas of O-C Diagrams of Eclipsing Binary Stars

    NASA Astrophysics Data System (ADS)

    Kreiner, Jerzy M.; Kim, Chun-Hwey; Nha, Il-Seong

    The Atlas contains data for 1,138 eclipsing binaries represented by 91,798 minima timings, collected from the usual international and local journals, observatory publications and unpublished minima. Among this source material there is a considerable representation of amateur astronomers. Some timings were found in the card-index catalogue of the Astronomical Observatory of the Jagiellonian University, Cracow. Stars were included in the Atlas provided that they satisfied 3 criteria: (1) at least 20 minima had been times; (2) these minima spanned at least 2,500 cycles; and (3) the 2,500 cycles represented no fewer than 40 years. Some additional stars not strictly satisfying these criteria were also included if useful information was available. For each star, the Atlas contains the (O-C) diagram calculated by the authors and a table of general information containing: binary characteristics; assorted catalogue numbers; the statistics of the collected minima timings; the light elements (light ephemeris); comments and literature references. All of the data and diagrams in the Atlas are also available in electronic form on the Internet at http://www.as.ap.krakow.pl/o- c".

  17. An atlas of the prenatal mouse brain: gestational day 14.

    PubMed

    Schambra, U B; Silver, J; Lauder, J M

    1991-11-01

    A prenatal atlas of the mouse brain is presently unavailable and is needed for studies of normal and abnormal development, using techniques including immunocytochemistry and in situ hybridization. This atlas will be especially useful for researchers studying transgenic and mutant mice. This collection of photomicrographs and corresponding drawings of Gestational Day (GD) 14 mouse brain sections is an excerpt from a larger atlas encompassing GD 12-18. In composing this atlas, available published studies on the developing rodent brain were consulted to aid in the detailed labeling of embryonic brain structures. C57Bl/6J mice were mated for 1 h, and the presence of a copulation plug was designated as GD 0. GD 14 embryos were perfused transcardially with 4% paraformaldehyde in 0.1 M phosphate buffer and embedded in paraffin. Serial sections (10 microns thickness) were cut through whole heads in sagittal and horizontal planes. They were stained with hematoxylin and eosin and photographed. Magnifications were 43X and 31X for the horizontal and sagittal sections, respectively. Photographs were traced and line drawings prepared using an Adobe Illustrator on a Macintosh computer.

  18. Three-dimensional atlas of iron, copper, and zinc in the mouse cerebrum and brainstem.

    PubMed

    Hare, Dominic J; Lee, Jason K; Beavis, Alison D; van Gramberg, Amanda; George, Jessica; Adlard, Paul A; Finkelstein, David I; Doble, Philip A

    2012-05-01

    Atlases depicting molecular and functional features of the brain are becoming an integral part of modern neuroscience. In this study we used laser ablation-inductively coupled plasma-mass spectrometry (LA-ICPMS) to quantitatively measure iron (Fe), copper (Cu), and zinc (Zn) levels in a serially sectioned C57BL/6 mouse brain (cerebrum and brainstem). Forty-six sections were analyzed in a single experiment of approximately 158 h in duration. We constructed a 46-plate reference atlas by aligning quantified images of metal distribution with corresponding coronal sections from the Allen Mouse Brain Reference Atlas. The 46 plates were also used to construct three-dimensional models of Fe, Cu, and Zn distribution. This atlas represents the first reconstruction of quantitative trace metal distribution through the brain by LA-ICPMS and will facilitate the study of trace metals in the brain and help to elucidate their role in neurobiology.

  19. EnviroAtlas - Phoenix, AZ - Domestic Water Demand per Day by U.S. Census Block Group

    EPA Pesticide Factsheets

    As included in this EnviroAtlas dataset, community level domestic water demand is calculated using locally available water use data per capita in gallons of water per day (GPD), distributed dasymetrically, and summarized by census block group. Domestic water use, as defined in this case, is intended to represent residential indoor and outdoor water use (e.g., cooking hygiene, landscaping, pools, etc.) for primary residences (i.e., excluding second homes and tourism rentals). For the purposes of this metric, these publicly-supplied estimates are also applied and considered representative of local self-supplied water use. Within the EnviroAtlas Phoenix boundary, there are 53 service providers with 2000-2009 water use estimates ranging from 108 to 366 GPD.This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

  20. EnviroAtlas - Fresno, CA - Near Road Tree Buffer

    EPA Pesticide Factsheets

    This EnviroAtlas dataset addresses the tree buffer along heavily traveled roads. The roads are interstates, arterials, and collectors within the EnviroAtlas community boundary. Forest is defined as Trees & Forest and Orchards. Sufficient tree bufferage is defined as 25% coverage within the circular moving window with a radius of 14.5m at any given point along the roadway. There are potential negative health effects for those living in a location without a sufficient tree buffer. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

  1. EnviroAtlas - Pittsburgh, PA - Near Road Tree Buffer

    EPA Pesticide Factsheets

    This EnviroAtlas dataset addresses the tree buffer along heavily traveled roads. The roads are interstates, arterials, and collectors within the EnviroAtlas community boundary. Forest is defined as Trees & Forest. Sufficient tree bufferage is defined as 25% coverage within the circular moving window with a radius of 14.5m at any given point along the roadway. There are potential negative health effects for those living in a location without a sufficient tree buffer. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

  2. EnviroAtlas - Milwaukee, WI - Near Road Tree Buffer

    EPA Pesticide Factsheets

    This EnviroAtlas dataset addresses the tree buffer along heavily traveled roads. The roads are interstates, arterials, and collectors within the EnviroAtlas community boundary. Forest is defined as Trees & Forest and Woody Wetlands. Sufficient tree bufferage is defined as 25% coverage within the circular moving window with a radius of 14.5m at any given point along the roadway. There are potential negative health effects for those living in a location without a sufficient tree buffer. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

  3. EnviroAtlas - Cleveland, OH - Near Road Tree Buffer

    EPA Pesticide Factsheets

    This EnviroAtlas dataset addresses the tree buffer along heavily traveled roads. The roads are interstates, arterials, and collectors within the EnviroAtlas community boundary. In this community, forest is defined as Trees & Forest and Woody Wetlands. Sufficient tree bufferage is defined as 25% coverage within the circular moving window with a radius of 14.5m at any given point along the roadway. There are potential negative health effects for those living in a location without a sufficient tree buffer. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas ) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

  4. EnviroAtlas - Portland, OR - Near Road Tree Buffer

    EPA Pesticide Factsheets

    This EnviroAtlas dataset addresses the tree buffer along heavily traveled roads. The roads are interstates, arterials, and collectors within the EnviroAtlas community boundary. Forest is defined as Trees & Forest and Woody Wetlands. Sufficient tree bufferage is defined as 25% coverage within the circular moving window with a radius of 14.5m at any given point along the roadway. There are potential negative health effects for those living in a location without a sufficient tree buffer. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (http:/www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

  5. EnviroAtlas - Tampa, FL - Near Road Tree Buffer

    EPA Pesticide Factsheets

    This EnviroAtlas dataset addresses the tree buffer along heavily traveled roads. The roads are interstates, arterials, and collectors within the EnviroAtlas community boundary. Forest is defined as Trees & Forest and Woody Wetlands. Sufficient tree bufferage is defined as 25% coverage within the circular moving window with a radius of 14.5m at any given point along the roadway. There are potential negative health effects for those living in a location without a sufficient tree buffer. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

  6. EnviroAtlas - Memphis, TN - Near Road Tree Buffer

    EPA Pesticide Factsheets

    This EnviroAtlas dataset addresses the tree buffer along heavily traveled roads. The roads are interstates, arterials, and collectors within the EnviroAtlas community boundary. Forest is defined as Trees & Forest and Woody Wetlands. Sufficient tree bufferage is defined as 25% coverage within the circular moving window with a radius of 14.5m at any given point along the roadway. There are potential negative health effects for those living in a location without a sufficient tree buffer. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

  7. EnviroAtlas - Paterson, NJ - Near Road Tree Buffer

    EPA Pesticide Factsheets

    This EnviroAtlas dataset addresses the tree buffer along heavily traveled roads. The roads are interstates, arterials, and collectors within the EnviroAtlas community boundary. Forest is defined as Trees & Forest. Sufficient tree bufferage is defined as 25% coverage within the circular moving window with a radius of 14.5m at any given point along the roadway. There are potential negative health effects for those living in a location without a sufficient tree buffer. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

  8. EnviroAtlas - Portland, ME - Near Road Tree Buffer

    EPA Pesticide Factsheets

    This EnviroAtlas dataset addresses the tree buffer along heavily traveled roads. The roads are interstates, arterials, and collectors within the EnviroAtlas community boundary. Forest is defined as Trees & Forest and Woody Wetlands. Sufficient tree bufferage is defined as 25% coverage within the circular moving window with a radius of 14.5m at any given point along the roadway. There are potential negative health effects for those living in a location without a sufficient tree buffer. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

  9. EnviroAtlas - Durham, NC - Near Road Tree Buffer

    EPA Pesticide Factsheets

    This EnviroAtlas dataset addresses the tree buffer along heavily traveled roads. The roads are interstates, arterials, and collectors within the EnviroAtlas community boundary. Forest is defined as Trees & Forest. Sufficient tree bufferage is defined as 25% coverage within the circular moving window with a radius of 14.5m at any given point along the roadway. There are potential negative health effects for those living in a location without a sufficient tree buffer. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas ) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets ).

  10. EnviroAtlas - Woodbine, IA - Near Road Tree Buffer

    EPA Pesticide Factsheets

    This EnviroAtlas dataset addresses the tree buffer along heavily traveled roads. The roads are interstates, arterials, and collectors within the EnviroAtlas community boundary. Forest is defined as Trees & Forest. Sufficient tree bufferage is defined as 25% coverage within the circular moving window with a radius of 14.5m at any given point along the roadway. There are potential negative health effects for those living in a location without a sufficient tree buffer. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

  11. EnviroAtlas - Phoenix, AZ - Near Road Tree Buffer

    EPA Pesticide Factsheets

    This EnviroAtlas dataset addresses the tree buffer along heavily traveled roads. The roads are interstates, arterials, and collectors within the EnviroAtlas community boundary. Forest is defined as Trees & Forest. Sufficient tree bufferage is defined as 25% coverage within the circular moving window with a radius of 14.5m at any given point along the roadway. There are potential negative health effects for those living in a location without a sufficient tree buffer. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

  12. EnviroAtlas - Austin, TX - Near Road Tree Buffer

    EPA Pesticide Factsheets

    This EnviroAtlas dataset addresses the tree buffer along heavily traveled roads. The roads are interstates, arterials, and collectors within the EnviroAtlas community boundary. Forest is defined as Trees & Forest. Sufficient tree bufferage is defined as 25% coverage within the circular moving window with a radius of 14.5m at any given point along the roadway. There are potential negative health effects for those living in a location without a sufficient tree buffer. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

  13. ATLAS Series of Shuttle Missions. Volume 23

    NASA Technical Reports Server (NTRS)

    1996-01-01

    This technical paper contains selected papers from Geophysical Research Letters (Volume 23, Number 17) on ATLAS series of shuttle missions. The ATLAS space shuttle missions were conducted in March 1992, April 1993, and November 1994. This paper discusses solar irradiance, middle atmospheric temperatures, and trace gas concentrations measurements made by the ATLAS payload and companion instruments.

  14. Optimal atlas construction through hierarchical image registration

    NASA Astrophysics Data System (ADS)

    Grevera, George J.; Udupa, Jayaram K.; Odhner, Dewey; Torigian, Drew A.

    2016-03-01

    Atlases (digital or otherwise) are common in medicine. However, there is no standard framework for creating them from medical images. One traditional approach is to pick a representative subject and then proceed to label structures/regions of interest in this image. Another is to create a "mean" or average subject. Atlases may also contain more than a single representative (e.g., the Visible Human contains both a male and a female data set). Other criteria besides gender may be used as well, and the atlas may contain many examples for a given criterion. In this work, we propose that atlases be created in an optimal manner using a well-established graph theoretic approach using a min spanning tree (or more generally, a collection of them). The resulting atlases may contain many examples for a given criterion. In fact, our framework allows for the addition of new subjects to the atlas to allow it to evolve over time. Furthermore, one can apply segmentation methods to the graph (e.g., graph-cut, fuzzy connectedness, or cluster analysis) which allow it to be separated into "sub-atlases" as it evolves. We demonstrate our method by applying it to 50 3D CT data sets of the chest region, and by comparing it to a number of traditional methods using measures such as Mean Squared Difference, Mattes Mutual Information, and Correlation, and for rigid registration. Our results demonstrate that optimal atlases can be constructed in this manner and outperform other methods of construction using freely available software.

  15. Radiation Tolerant Electronics and Digital Processing for the Phase-1 Read-out Upgrade of the ATLAS Liquid Argon Calorimeters

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Milic, A.

    The ATLAS Liquid Argon calorimeters are designed and built to study proton-proton collisions produced at the LHC at centre-of-mass energies up to 14 TeV. Liquid argon (LAr) sampling calorimeters are employed for all electromagnetic calorimetry in the pseudorapidity region |η|<3.2, and for hadronic calorimetry in the region from |η|=1.5 to |η|=4.9. Although the nominal LHC experimental programme is still in progress, an upgrade of the read-out electronics is being launched to cope with luminosities of up to 3x10{sup 34} cm{sup -2}s{sup -1}, which are beyond the original design by a factor of 3. An improved spatial granularity of the triggermore » primitives is therefore proposed in order to improve the identification performance for trigger signatures, like electrons, photons, tau leptons, jets, total and missing energy, at high background rejection rates. For the upgrade Phase-1 in 2018, new LAr Trigger Digitizer Boards (LTDB) are being designed to receive higher granularity signals, digitize them on detector and send them via fast optical links to a new LAr digital processing system (LDPS). The LDPS applies a digital filtering and identifies significant energy depositions in each trigger channel. The refined trigger primitives are then transmitted to the first level trigger system to extract improved trigger signatures. The read-out of the trigger signals will process 34000 so-called Super Cells at every LHC bunch-crossing at a frequency of 40 MHz. The new LTDB on-detector electronics is designed to be radiation tolerant in order to be operated for the remaining live-time of the ATLAS detector up to a total luminosity of 3000 fb{sup -1}. For the analog-to-digital conversion (12-bit ADC at 40 MSPS), the data serialization and the fast optical link (5.44 Gb/s) custom components have been developed. They have been qualified for the expected radiation environment of a total ionization dose of 1.3 kGy and a hadron fluence of 6 x 10{sup 13} h/cm{sup 2} with

  16. Atlas of the Soviet Union.

    ERIC Educational Resources Information Center

    Young, Harry F.

    This atlas consists of 20 maps, tables, charts, and graphs with complementary text illustrating Soviet government machinery, trade and political relations, and military stance. Some topics depicted by charts and graphs include: (1) Soviet foreign affairs machinery; (2) Soviet intelligence and security services; (4) Soviet position in the United…

  17. Generation and evaluation of an ultra-high-field atlas with applications in DBS planning

    NASA Astrophysics Data System (ADS)

    Wang, Brian T.; Poirier, Stefan; Guo, Ting; Parrent, Andrew G.; Peters, Terry M.; Khan, Ali R.

    2016-03-01

    Purpose Deep brain stimulation (DBS) is a common treatment for Parkinson's disease (PD) and involves the use of brain atlases or intrinsic landmarks to estimate the location of target deep brain structures, such as the subthalamic nucleus (STN) and the globus pallidus pars interna (GPi). However, these structures can be difficult to localize with conventional clinical magnetic resonance imaging (MRI), and thus targeting can be prone to error. Ultra-high-field imaging at 7T has the ability to clearly resolve these structures and thus atlases built with these data have the potential to improve targeting accuracy. Methods T1 and T2-weighted images of 12 healthy control subjects were acquired using a 7T MR scanner. These images were then used with groupwise registration to generate an unbiased average template with T1w and T2w contrast. Deep brain structures were manually labelled in each subject by two raters and rater reliability was assessed. We compared the use of this unbiased atlas with two other methods of atlas-based segmentation (single-template and multi-template) for subthalamic nucleus (STN) segmentation on 7T MRI data. We also applied this atlas to clinical DBS data acquired at 1.5T to evaluate its efficacy for DBS target localization as compared to using a standard atlas. Results The unbiased templates provide superb detail of subcortical structures. Through one-way ANOVA tests, the unbiased template is significantly (p <0.05) more accurate than a single-template in atlas-based segmentation and DBS target localization tasks. Conclusion The generated unbiased averaged templates provide better visualization of deep brain nuclei and an increase in accuracy over single-template and lower field strength atlases.

  18. EnviroAtlas -Portland, ME- One Meter Resolution Urban Land Cover (2010)

    EPA Pesticide Factsheets

    The EnviroAtlas Portland, ME land cover map was generated from USDA NAIP (National Agricultural Imagery Program) four band (red, green, blue and near infrared) aerial photography from Late Summer 2010 at 1 m spatial resolution. Eight land cover classes were mapped: water, impervious surfaces, soil and barren land, trees and forest, grass and herbaceous non-woody vegetation, agriculture, and wetlands (woody and emergent). An accuracy assessment using a stratified random sampling of 600 samples yielded an overall accuracy of 87.5 percent using a minimum mapping unit of 9 pixels (3x3 pixel window). The area mapped is defined by the US Census Bureau's 2010 Urban Statistical Area for Portland. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

  19. A practical workflow for making anatomical atlases for biological research.

    PubMed

    Wan, Yong; Lewis, A Kelsey; Colasanto, Mary; van Langeveld, Mark; Kardon, Gabrielle; Hansen, Charles

    2012-01-01

    The anatomical atlas has been at the intersection of science and art for centuries. These atlases are essential to biological research, but high-quality atlases are often scarce. Recent advances in imaging technology have made high-quality 3D atlases possible. However, until now there has been a lack of practical workflows using standard tools to generate atlases from images of biological samples. With certain adaptations, CG artists' workflow and tools, traditionally used in the film industry, are practical for building high-quality biological atlases. Researchers have developed a workflow for generating a 3D anatomical atlas using accessible artists' tools. They used this workflow to build a mouse limb atlas for studying the musculoskeletal system's development. This research aims to raise the awareness of using artists' tools in scientific research and promote interdisciplinary collaborations between artists and scientists. This video (http://youtu.be/g61C-nia9ms) demonstrates a workflow for creating an anatomical atlas.

  20. EnviroAtlas - Des Moines, IA - Near Road Tree Buffer

    EPA Pesticide Factsheets

    This EnviroAtlas dataset addresses the tree buffer along heavily traveled roads. The roads are interstates, arterials, and collectors within the EnviroAtlas community boundary. Forest is defined as Trees & Forest. Sufficient tree bufferage is defined as 25% coverage within the circular moving window with a radius of 14.5m at any given point along the roadway. There are potential negative health effects for those living in a location without a sufficient tree buffer. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://enviroatlas.epa.gov/EnviroAtlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

  1. Self-correcting multi-atlas segmentation

    NASA Astrophysics Data System (ADS)

    Gao, Yi; Wilford, Andrew; Guo, Liang

    2016-03-01

    In multi-atlas segmentation, one typically registers several atlases to the new image, and their respective segmented label images are transformed and fused to form the final segmentation. After each registration, the quality of the registration is reflected by the single global value: the final registration cost. Ideally, if the quality of the registration can be evaluated at each point, independent of the registration process, which also provides a direction in which the deformation can further be improved, the overall segmentation performance can be improved. We propose such a self-correcting multi-atlas segmentation method. The method is applied on hippocampus segmentation from brain images and statistically significantly improvement is observed.

  2. Pre-Launch Performance Testing of the ICESat-2/ATLAS Flight Science Receiver Algorithms

    NASA Astrophysics Data System (ADS)

    Mcgarry, J.; Carabajal, C. C.; Saba, J. L.; Rackley, A.; Holland, S.

    2016-12-01

    NASA's Advanced Topographic Laser Altimeter System (ATLAS) will be the single instrument on the ICESat-2 spacecraft which is expected to launch in late 2017 with a 3 year mission lifetime. The ICESat-2 planned orbital altitude is 500 km with a 92 degree inclination and 91-day repeat tracks. ATLAS is a single-photon detection system transmitting at 532nm with a laser repetition rate of 10 kHz and a 6 spot pattern on the Earth's surface. Without some method of reducing the received data, the volume of ATLAS telemetry would far exceed the normal X-band downlink capability. To reduce the data volume to an acceptable level a set of onboard Receiver Algorithms has been developed. These Algorithms limit the daily data volume by distinguishing surface echoes from the background noise and allowing the instrument to telemeter data from only a small vertical region about the signal. This is accomplished through the use of an onboard Digital Elevation Model (DEM), signal processing techniques, and onboard relief and surface reference maps. The ATLAS Receiver Algorithms have been completed and have been verified during Instrument testing in the spacecraft assembly area at the Goddard Space Flight Center in late 2015 and early 2016. Testing has been performed at ambient temperature with a pressure of one atmosphere as well as at the expected hot and cold temperatures in a vacuum. Results from testing to date show the Receiver Algorithms have the ability to handle a wide range of signal and noise levels with a very good sensitivity at relatively low signal to noise ratios. Testing with the ATLAS instrument and flight software shows very good agreement with previous Simulator testing and all of the requirements for ATLAS Receiver Algorithms were successfully verified during Run for the Record Testing in December 2015. This poster will describe the performance of the ATLAS Flight Science Receiver Algorithms during the Run for Record and Comprehensive Performance Testing performed

  3. Volunteer Computing Experience with ATLAS@Home

    NASA Astrophysics Data System (ADS)

    Adam-Bourdarios, C.; Bianchi, R.; Cameron, D.; Filipčič, A.; Isacchini, G.; Lançon, E.; Wu, W.; ATLAS Collaboration

    2017-10-01

    ATLAS@Home is a volunteer computing project which allows the public to contribute to computing for the ATLAS experiment through their home or office computers. The project has grown continuously since its creation in mid-2014 and now counts almost 100,000 volunteers. The combined volunteers’ resources make up a sizeable fraction of overall resources for ATLAS simulation. This paper takes stock of the experience gained so far and describes the next steps in the evolution of the project. These improvements include running natively on Linux to ease the deployment on for example university clusters, using multiple cores inside one task to reduce the memory requirements and running different types of workload such as event generation. In addition to technical details the success of ATLAS@Home as an outreach tool is evaluated.

  4. The NeuARt II system: a viewing tool for neuroanatomical data based on published neuroanatomical atlases

    PubMed Central

    Burns, Gully APC; Cheng, Wei-Cheng; Thompson, Richard H; Swanson, Larry W

    2006-01-01

    Background Anatomical studies of neural circuitry describing the basic wiring diagram of the brain produce intrinsically spatial, highly complex data of great value to the neuroscience community. Published neuroanatomical atlases provide a spatial framework for these studies. We have built an informatics framework based on these atlases for the representation of neuroanatomical knowledge. This framework not only captures current methods of anatomical data acquisition and analysis, it allows these studies to be collated, compared and synthesized within a single system. Results We have developed an atlas-viewing application ('NeuARt II') in the Java language with unique functional properties. These include the ability to use copyrighted atlases as templates within which users may view, save and retrieve data-maps and annotate them with volumetric delineations. NeuARt II also permits users to view multiple levels on multiple atlases at once. Each data-map in this system is simply a stack of vector images with one image per atlas level, so any set of accurate drawings made onto a supported atlas (in vector graphics format) could be uploaded into NeuARt II. Presently the database is populated with a corpus of high-quality neuroanatomical data from the laboratory of Dr Larry Swanson (consisting 64 highly-detailed maps of PHAL tract-tracing experiments, made up of 1039 separate drawings that were published in 27 primary research publications over 17 years). Herein we take selective examples from these data to demonstrate the features of NeuArt II. Our informatics tool permits users to browse, query and compare these maps. The NeuARt II tool operates within a bioinformatics knowledge management platform (called 'NeuroScholar') either as a standalone or a plug-in application. Conclusion Anatomical localization is fundamental to neuroscientific work and atlases provide an easily-understood framework that is widely used by neuroanatomists and non-neuroanatomists alike. Neu

  5. Search for squarks and gluinos in final states with jets and missing transverse momentum using 36 fb-1 of √{s }=13 TeV p p collision data with the ATLAS detector

    NASA Astrophysics Data System (ADS)

    Aaboud, M.; Aad, G.; Abbott, B.; Abdinov, O.; Abeloos, B.; Abidi, S. H.; Abouzeid, O. S.; Abraham, N. L.; Abramowicz, H.; Abreu, H.; Abreu, R.; Abulaiti, Y.; Acharya, B. S.; Adachi, S.; Adamczyk, L.; Adelman, J.; Adersberger, M.; Adye, T.; Affolder, A. A.; Agatonovic-Jovin, T.; Agheorghiesei, C.; Aguilar-Saavedra, J. A.; Ahlen, S. P.; Ahmadov, F.; Aielli, G.; Akatsuka, S.; Akerstedt, H.; Åkesson, T. P. A.; Akilli, E.; Akimov, A. V.; Alberghi, G. L.; Albert, J.; Albicocco, P.; Alconada Verzini, M. J.; Alderweireldt, S. C.; Aleksa, M.; Aleksandrov, I. N.; Alexa, C.; Alexander, G.; Alexopoulos, T.; Alhroob, M.; Ali, B.; Aliev, M.; Alimonti, G.; Alison, J.; Alkire, S. P.; Allbrooke, B. M. M.; Allen, B. W.; Allport, P. P.; Aloisio, A.; Alonso, A.; Alonso, F.; Alpigiani, C.; Alshehri, A. A.; Alstaty, M. I.; Alvarez Gonzalez, B.; Álvarez Piqueras, D.; Alviggi, M. G.; Amadio, B. T.; Amaral Coutinho, Y.; Amelung, C.; Amidei, D.; Amor Dos Santos, S. P.; Amorim, A.; Amoroso, S.; Amundsen, G.; Anastopoulos, C.; Ancu, L. S.; Andari, N.; Andeen, T.; Anders, C. F.; Anders, J. K.; Anderson, K. J.; Andreazza, A.; Andrei, V.; Angelidakis, S.; Angelozzi, I.; Angerami, A.; Anisenkov, A. V.; Anjos, N.; Annovi, A.; Antel, C.; Antonelli, M.; Antonov, A.; Antrim, D. J.; Anulli, F.; Aoki, M.; Aperio Bella, L.; Arabidze, G.; Arai, Y.; Araque, J. P.; Araujo Ferraz, V.; Arce, A. T. H.; Ardell, R. E.; Arduh, F. A.; Arguin, J.-F.; Argyropoulos, S.; Arik, M.; Armbruster, A. J.; Armitage, L. J.; Arnaez, O.; Arnold, H.; Arratia, M.; Arslan, O.; Artamonov, A.; Artoni, G.; Artz, S.; Asai, S.; Asbah, N.; Ashkenazi, A.; Asquith, L.; Assamagan, K.; Astalos, R.; Atkinson, M.; Atlay, N. B.; Augsten, K.; Avolio, G.; Axen, B.; Ayoub, M. K.; Azuelos, G.; Baas, A. E.; Baca, M. J.; Bachacou, H.; Bachas, K.; Backes, M.; Backhaus, M.; Bagnaia, P.; Bahmani, M.; Bahrasemani, H.; Baines, J. T.; Bajic, M.; Baker, O. K.; Baldin, E. M.; Balek, P.; Balli, F.; Balunas, W. K.; Banas, E.; Bandyopadhyay, A.; Banerjee, Sw.; Bannoura, A. A. E.; Barak, L.; Barberio, E. L.; Barberis, D.; Barbero, M.; Barillari, T.; Barisits, M.-S.; Barkeloo, J. T.; Barklow, T.; Barlow, N.; Barnes, S. L.; Barnett, B. M.; Barnett, R. M.; Barnovska-Blenessy, Z.; Baroncelli, A.; Barone, G.; Barr, A. J.; Barranco Navarro, L.; Barreiro, F.; Barreiro Guimarães da Costa, J.; Bartoldus, R.; Barton, A. E.; Bartos, P.; Basalaev, A.; Bassalat, A.; Bates, R. L.; Batista, S. J.; Batley, J. R.; Battaglia, M.; Bauce, M.; Bauer, F.; Bawa, H. S.; Beacham, J. B.; Beattie, M. D.; Beau, T.; Beauchemin, P. H.; Bechtle, P.; Beck, H. P.; Beck, H. C.; Becker, K.; Becker, M.; Beckingham, M.; Becot, C.; Beddall, A. J.; Beddall, A.; Bednyakov, V. A.; Bedognetti, M.; Bee, C. P.; Beermann, T. A.; Begalli, M.; Begel, M.; Behr, J. K.; Bell, A. S.; Bella, G.; Bellagamba, L.; Bellerive, A.; Bellomo, M.; Belotskiy, K.; Beltramello, O.; Belyaev, N. L.; Benary, O.; Benchekroun, D.; Bender, M.; Bendtz, K.; Benekos, N.; Benhammou, Y.; Benhar Noccioli, E.; Benitez, J.; Benjamin, D. P.; Benoit, M.; Bensinger, J. R.; Bentvelsen, S.; Beresford, L.; Beretta, M.; Berge, D.; Bergeaas Kuutmann, E.; Berger, N.; Beringer, J.; Berlendis, S.; Bernard, N. R.; Bernardi, G.; Bernius, C.; Bernlochner, F. U.; Berry, T.; Berta, P.; Bertella, C.; Bertoli, G.; Bertolucci, F.; Bertram, I. A.; Bertsche, C.; Bertsche, D.; Besjes, G. J.; Bessidskaia Bylund, O.; Bessner, M.; Besson, N.; Betancourt, C.; Bethani, A.; Bethke, S.; Bevan, A. J.; Beyer, J.; Bianchi, R. M.; Biebel, O.; Biedermann, D.; Bielski, R.; Bierwagen, K.; Biesuz, N. V.; Biglietti, M.; Billoud, T. R. V.; Bilokon, H.; Bindi, M.; Bingul, A.; Bini, C.; Biondi, S.; Bisanz, T.; Bittrich, C.; Bjergaard, D. M.; Black, C. W.; Black, J. E.; Black, K. M.; Blair, R. E.; Blazek, T.; Bloch, I.; Blocker, C.; Blue, A.; Blum, W.; Blumenschein, U.; Blunier, S.; Bobbink, G. J.; Bobrovnikov, V. S.; Bocchetta, S. S.; Bocci, A.; Bock, C.; Boehler, M.; Boerner, D.; Bogavac, D.; Bogdanchikov, A. G.; Bohm, C.; Boisvert, V.; Bokan, P.; Bold, T.; Boldyrev, A. S.; Bolz, A. E.; Bomben, M.; Bona, M.; Boonekamp, M.; Borisov, A.; Borissov, G.; Bortfeldt, J.; Bortoletto, D.; Bortolotto, V.; Boscherini, D.; Bosman, M.; Bossio Sola, J. D.; Boudreau, J.; Bouffard, J.; Bouhova-Thacker, E. V.; Boumediene, D.; Bourdarios, C.; Boutle, S. K.; Boveia, A.; Boyd, J.; Boyko, I. R.; Bracinik, J.; Brandt, A.; Brandt, G.; Brandt, O.; Bratzler, U.; Brau, B.; Brau, J. E.; Breaden Madden, W. D.; Brendlinger, K.; Brennan, A. J.; Brenner, L.; Brenner, R.; Bressler, S.; Briglin, D. L.; Bristow, T. M.; Britton, D.; Britzger, D.; Brochu, F. M.; Brock, I.; Brock, R.; Brooijmans, G.; Brooks, T.; Brooks, W. K.; Brosamer, J.; Brost, E.; Broughton, J. H.; Bruckman de Renstrom, P. A.; Bruncko, D.; Bruni, A.; Bruni, G.; Bruni, L. S.; Brunt, Bh; Bruschi, M.; Bruscino, N.; Bryant, P.; Bryngemark, L.; Buanes, T.; Buat, Q.; Buchholz, P.; Buckley, A. G.; Budagov, I. A.; Buehrer, F.; Bugge, M. K.; Bulekov, O.; Bullock, D.; Burch, T. J.; Burdin, S.; Burgard, C. D.; Burger, A. M.; Burghgrave, B.; Burka, K.; Burke, S.; Burmeister, I.; Burr, J. T. P.; Busato, E.; Büscher, D.; Büscher, V.; Bussey, P.; Butler, J. M.; Buttar, C. M.; Butterworth, J. M.; Butti, P.; Buttinger, W.; Buzatu, A.; Buzykaev, A. R.; Cabrera Urbán, S.; Caforio, D.; Cairo, V. M.; Cakir, O.; Calace, N.; Calafiura, P.; Calandri, A.; Calderini, G.; Calfayan, P.; Callea, G.; Caloba, L. P.; Calvente Lopez, S.; Calvet, D.; Calvet, S.; Calvet, T. P.; Camacho Toro, R.; Camarda, S.; Camarri, P.; Cameron, D.; Caminal Armadans, R.; Camincher, C.; Campana, S.; Campanelli, M.; Camplani, A.; Campoverde, A.; Canale, V.; Cano Bret, M.; Cantero, J.; Cao, T.; Capeans Garrido, M. D. M.; Caprini, I.; Caprini, M.; Capua, M.; Carbone, R. M.; Cardarelli, R.; Cardillo, F.; Carli, I.; Carli, T.; Carlino, G.; Carlson, B. T.; Carminati, L.; Carney, R. M. D.; Caron, S.; Carquin, E.; Carrá, S.; Carrillo-Montoya, G. D.; Carvalho, J.; Casadei, D.; Casado, M. P.; Casolino, M.; Casper, D. W.; Castelijn, R.; Castillo Gimenez, V.; Castro, N. F.; Catinaccio, A.; Catmore, J. R.; Cattai, A.; Caudron, J.; Cavaliere, V.; Cavallaro, E.; Cavalli, D.; Cavalli-Sforza, M.; Cavasinni, V.; Celebi, E.; Ceradini, F.; Cerda Alberich, L.; Cerqueira, A. S.; Cerri, A.; Cerrito, L.; Cerutti, F.; Cervelli, A.; Cetin, S. A.; Chafaq, A.; Chakraborty, D.; Chan, S. K.; Chan, W. S.; Chan, Y. L.; Chang, P.; Chapman, J. D.; Charlton, D. G.; Chau, C. C.; Chavez Barajas, C. A.; Che, S.; Cheatham, S.; Chegwidden, A.; Chekanov, S.; Chekulaev, S. V.; Chelkov, G. A.; Chelstowska, M. A.; Chen, C.; Chen, H.; Chen, J.; Chen, S.; Chen, S.; Chen, X.; Chen, Y.; Cheng, H. C.; Cheng, H. J.; Cheplakov, A.; Cheremushkina, E.; Cherkaoui El Moursli, R.; Cheu, E.; Cheung, K.; Chevalier, L.; Chiarella, V.; Chiarelli, G.; Chiodini, G.; Chisholm, A. S.; Chitan, A.; Chiu, Y. H.; Chizhov, M. V.; Choi, K.; Chomont, A. R.; Chouridou, S.; Christodoulou, V.; Chromek-Burckhart, D.; Chu, M. C.; Chudoba, J.; Chuinard, A. J.; Chwastowski, J. J.; Chytka, L.; Ciftci, A. K.; Cinca, D.; Cindro, V.; Cioara, I. A.; Ciocca, C.; Ciocio, A.; Cirotto, F.; Citron, Z. H.; Citterio, M.; Ciubancan, M.; Clark, A.; Clark, B. L.; Clark, M. R.; Clark, P. J.; Clarke, R. N.; Clement, C.; Coadou, Y.; Cobal, M.; Coccaro, A.; Cochran, J.; Colasurdo, L.; Cole, B.; Colijn, A. P.; Collot, J.; Colombo, T.; Conde Muiño, P.; Coniavitis, E.; Connell, S. H.; Connelly, I. A.; Constantinescu, S.; Conti, G.; Conventi, F.; Cooke, M.; Cooper-Sarkar, A. M.; Cormier, F.; Cormier, K. J. R.; Corradi, M.; Corriveau, F.; Cortes-Gonzalez, A.; Cortiana, G.; Costa, G.; Costa, M. J.; Costanzo, D.; Cottin, G.; Cowan, G.; Cox, B. E.; Cranmer, K.; Crawley, S. J.; Creager, R. A.; Cree, G.; Crépé-Renaudin, S.; Crescioli, F.; Cribbs, W. A.; Cristinziani, M.; Croft, V.; Crosetti, G.; Cueto, A.; Cuhadar Donszelmann, T.; Cukierman, A. R.; Cummings, J.; Curatolo, M.; Cúth, J.; Czodrowski, P.; D'Amen, G.; D'Auria, S.; D'Eramo, L.; D'Onofrio, M.; da Cunha Sargedas de Sousa, M. J.; da Via, C.; Dabrowski, W.; Dado, T.; Dai, T.; Dale, O.; Dallaire, F.; Dallapiccola, C.; Dam, M.; Dandoy, J. R.; Daneri, M. F.; Dang, N. P.; Daniells, A. C.; Dann, N. S.; Danninger, M.; Dano Hoffmann, M.; Dao, V.; Darbo, G.; Darmora, S.; Dassoulas, J.; Dattagupta, A.; Daubney, T.; Davey, W.; David, C.; Davidek, T.; Davis, D. R.; Davison, P.; Dawe, E.; Dawson, I.; de, K.; de Asmundis, R.; de Benedetti, A.; de Castro, S.; de Cecco, S.; de Groot, N.; de Jong, P.; de la Torre, H.; de Lorenzi, F.; de Maria, A.; de Pedis, D.; de Salvo, A.; de Sanctis, U.; de Santo, A.; de Vasconcelos Corga, K.; de Vivie de Regie, J. B.; Dearnaley, W. J.; Debbe, R.; Debenedetti, C.; Dedovich, D. V.; Dehghanian, N.; Deigaard, I.; Del Gaudio, M.; Del Peso, J.; Delgove, D.; Deliot, F.; Delitzsch, C. M.; Dell'Acqua, A.; Dell'Asta, L.; Dell'Orso, M.; Della Pietra, M.; Della Volpe, D.; Delmastro, M.; Delporte, C.; Delsart, P. A.; Demarco, D. A.; Demers, S.; Demichev, M.; Demilly, A.; Denisov, S. P.; Denysiuk, D.; Derendarz, D.; Derkaoui, J. E.; Derue, F.; Dervan, P.; Desch, K.; Deterre, C.; Dette, K.; Devesa, M. R.; Deviveiros, P. O.; Dewhurst, A.; Dhaliwal, S.; di Bello, F. A.; di Ciaccio, A.; di Ciaccio, L.; di Clemente, W. K.; di Donato, C.; di Girolamo, A.; di Girolamo, B.; di Micco, B.; di Nardo, R.; di Petrillo, K. F.; di Simone, A.; di Sipio, R.; di Valentino, D.; Diaconu, C.; Diamond, M.; Dias, F. A.; Diaz, M. A.; Diehl, E. B.; Dietrich, J.; Díez Cornell, S.; Dimitrievska, A.; Dingfelder, J.; Dita, P.; Dita, S.; Dittus, F.; Djama, F.; Djobava, T.; Djuvsland, J. I.; Do Vale, M. A. B.; Dobos, D.; Dobre, M.; Doglioni, C.; Dolejsi, J.; Dolezal, Z.; Donadelli, M.; Donati, S.; Dondero, P.; Donini, J.; Dopke, J.; Doria, A.; Dova, M. T.; Doyle, A. T.; Drechsler, E.; Dris, M.; Du, Y.; Duarte-Campderros, J.; Dubreuil, A.; Duchovni, E.; Duckeck, G.; Ducourthial, A.; Ducu, O. A.; Duda, D.; Dudarev, A.; Dudder, A. Chr.; Duffield, E. M.; Duflot, L.; Dührssen, M.; Dumancic, M.; Dumitriu, A. E.; Duncan, A. K.; Dunford, M.; Duran Yildiz, H.; Düren, M.; Durglishvili, A.; Duschinger, D.; Dutta, B.; Duvnjak, D.; Dyndal, M.; Dziedzic, B. S.; Eckardt, C.; Ecker, K. M.; Edgar, R. C.; Eifert, T.; Eigen, G.; Einsweiler, K.; Ekelof, T.; El Kacimi, M.; El Kosseifi, R.; Ellajosyula, V.; Ellert, M.; Elles, S.; Ellinghaus, F.; Elliot, A. A.; Ellis, N.; Elmsheuser, J.; Elsing, M.; Emeliyanov, D.; Enari, Y.; Endner, O. C.; Ennis, J. S.; Erdmann, J.; Ereditato, A.; Ernst, M.; Errede, S.; Escalier, M.; Escobar, C.; Esposito, B.; Estrada Pastor, O.; Etienvre, A. I.; Etzion, E.; Evans, H.; Ezhilov, A.; Ezzi, M.; Fabbri, F.; Fabbri, L.; Fabiani, V.; Facini, G.; Fakhrutdinov, R. M.; Falciano, S.; Falla, R. J.; Faltova, J.; Fang, Y.; Fanti, M.; Farbin, A.; Farilla, A.; Farina, C.; Farina, E. M.; Farooque, T.; Farrell, S.; Farrington, S. M.; Farthouat, P.; Fassi, F.; Fassnacht, P.; Fassouliotis, D.; Faucci Giannelli, M.; Favareto, A.; Fawcett, W. J.; Fayard, L.; Fedin, O. L.; Fedorko, W.; Feigl, S.; Feligioni, L.; Feng, C.; Feng, E. J.; Feng, H.; Fenton, M. J.; Fenyuk, A. B.; Feremenga, L.; Fernandez Martinez, P.; Fernandez Perez, S.; Ferrando, J.; Ferrari, A.; Ferrari, P.; Ferrari, R.; Ferreira de Lima, D. E.; Ferrer, A.; Ferrere, D.; Ferretti, C.; Fiedler, F.; Filipčič, A.; Filipuzzi, M.; Filthaut, F.; Fincke-Keeler, M.; Finelli, K. D.; Fiolhais, M. C. N.; Fiorini, L.; Fischer, A.; Fischer, C.; Fischer, J.; Fisher, W. C.; Flaschel, N.; Fleck, I.; Fleischmann, P.; Fletcher, R. R. M.; Flick, T.; Flierl, B. M.; Flores Castillo, L. R.; Flowerdew, M. J.; Forcolin, G. T.; Formica, A.; Förster, F. A.; Forti, A.; Foster, A. G.; Fournier, D.; Fox, H.; Fracchia, S.; Francavilla, P.; Franchini, M.; Franchino, S.; Francis, D.; Franconi, L.; Franklin, M.; Frate, M.; Fraternali, M.; Freeborn, D.; Fressard-Batraneanu, S. M.; Freund, B.; Froidevaux, D.; Frost, J. A.; Fukunaga, C.; Fusayasu, T.; Fuster, J.; Gabaldon, C.; Gabizon, O.; Gabrielli, A.; Gabrielli, A.; Gach, G. P.; Gadatsch, S.; Gadomski, S.; Gagliardi, G.; Gagnon, L. G.; Galea, C.; Galhardo, B.; Gallas, E. J.; Gallop, B. J.; Gallus, P.; Galster, G.; Gan, K. K.; Ganguly, S.; Gao, Y.; Gao, Y. S.; Garay Walls, F. M.; García, C.; García Navarro, J. E.; García Pascual, J. A.; Garcia-Sciveres, M.; Gardner, R. W.; Garelli, N.; Garonne, V.; Gascon Bravo, A.; Gasnikova, K.; Gatti, C.; Gaudiello, A.; Gaudio, G.; Gavrilenko, I. L.; Gay, C.; Gaycken, G.; Gazis, E. N.; Gee, C. N. P.; Geisen, J.; Geisen, M.; Geisler, M. P.; Gellerstedt, K.; Gemme, C.; Genest, M. H.; Geng, C.; Gentile, S.; Gentsos, C.; George, S.; Gerbaudo, D.; Gershon, A.; Geßner, G.; Ghasemi, S.; Ghneimat, M.; Giacobbe, B.; Giagu, S.; Giangiacomi, N.; Giannetti, P.; Gibson, S. M.; Gignac, M.; Gilchriese, M.; Gillberg, D.; Gilles, G.; Gingrich, D. M.; Giokaris, N.; Giordani, M. P.; Giorgi, F. M.; Giraud, P. F.; Giromini, P.; Giugliarelli, G.; Giugni, D.; Giuli, F.; Giuliani, C.; Giulini, M.; Gjelsten, B. K.; Gkaitatzis, S.; Gkialas, I.; Gkougkousis, E. L.; Gkountoumis, P.; Gladilin, L. K.; Glasman, C.; Glatzer, J.; Glaysher, P. C. F.; Glazov, A.; Goblirsch-Kolb, M.; Godlewski, J.; Goldfarb, S.; Golling, T.; Golubkov, D.; Gomes, A.; Gonçalo, R.; Goncalves Gama, R.; Goncalves Pinto Firmino da Costa, J.; Gonella, G.; Gonella, L.; Gongadze, A.; González de La Hoz, S.; Gonzalez-Sevilla, S.; Goossens, L.; Gorbounov, P. A.; Gordon, H. A.; Gorelov, I.; Gorini, B.; Gorini, E.; Gorišek, A.; Goshaw, A. T.; Gössling, C.; Gostkin, M. I.; Gottardo, C. A.; Goudet, C. R.; Goujdami, D.; Goussiou, A. G.; Govender, N.; Gozani, E.; Graber, L.; Grabowska-Bold, I.; Gradin, P. O. J.; Gramling, J.; Gramstad, E.; Grancagnolo, S.; Gratchev, V.; Gravila, P. M.; Gray, C.; Gray, H. M.; Greenwood, Z. D.; Grefe, C.; Gregersen, K.; Gregor, I. M.; Grenier, P.; Grevtsov, K.; Griffiths, J.; Grillo, A. A.; Grimm, K.; Grinstein, S.; Gris, Ph.; Grivaz, J.-F.; Groh, S.; Gross, E.; Grosse-Knetter, J.; Grossi, G. C.; Grout, Z. J.; Grummer, A.; Guan, L.; Guan, W.; Guenther, J.; Guescini, F.; Guest, D.; Gueta, O.; Gui, B.; Guido, E.; Guillemin, T.; Guindon, S.; Gul, U.; Gumpert, C.; Guo, J.; Guo, W.; Guo, Y.; Gupta, R.; Gupta, S.; Gustavino, G.; Gutierrez, P.; Gutierrez Ortiz, N. G.; Gutschow, C.; Guyot, C.; Guzik, M. P.; Gwenlan, C.; Gwilliam, C. B.; Haas, A.; Haber, C.; Hadavand, H. K.; Haddad, N.; Hadef, A.; Hageböck, S.; Hagihara, M.; Hakobyan, H.; Haleem, M.; Haley, J.; Halladjian, G.; Hallewell, G. D.; Hamacher, K.; Hamal, P.; Hamano, K.; Hamilton, A.; Hamity, G. N.; Hamnett, P. G.; Han, L.; Han, S.; Hanagaki, K.; Hanawa, K.; Hance, M.; Haney, B.; Hanke, P.; Hansen, J. B.; Hansen, J. D.; Hansen, M. C.; Hansen, P. H.; Hara, K.; Hard, A. S.; Harenberg, T.; Hariri, F.; Harkusha, S.; Harrington, R. D.; Harrison, P. F.; Hartmann, N. M.; Hasegawa, M.; Hasegawa, Y.; Hasib, A.; Hassani, S.; Haug, S.; Hauser, R.; Hauswald, L.; Havener, L. B.; Havranek, M.; Hawkes, C. M.; Hawkings, R. J.; Hayakawa, D.; Hayden, D.; Hays, C. P.; Hays, J. M.; Hayward, H. S.; Haywood, S. J.; Head, S. J.; Heck, T.; Hedberg, V.; Heelan, L.; Heer, S.; Heidegger, K. K.; Heim, S.; Heim, T.; Heinemann, B.; Heinrich, J. J.; Heinrich, L.; Heinz, C.; Hejbal, J.; Helary, L.; Held, A.; Hellman, S.; Helsens, C.; Henderson, R. C. W.; Heng, Y.; Henkelmann, S.; Henriques Correia, A. M.; Henrot-Versille, S.; Herbert, G. H.; Herde, H.; Herget, V.; Hernández Jiménez, Y.; Herr, H.; Herten, G.; Hertenberger, R.; Hervas, L.; Herwig, T. C.; Hesketh, G. G.; Hessey, N. P.; Hetherly, J. W.; Higashino, S.; Higón-Rodriguez, E.; Hildebrand, K.; Hill, E.; Hill, J. C.; Hiller, K. H.; Hillier, S. J.; Hils, M.; Hinchliffe, I.; Hirose, M.; Hirschbuehl, D.; Hiti, B.; Hladik, O.; Hoad, X.; Hobbs, J.; Hod, N.; Hodgkinson, M. C.; Hodgson, P.; Hoecker, A.; Hoeferkamp, M. R.; Hoenig, F.; Hohn, D.; Holmes, T. R.; Homann, M.; Honda, S.; Honda, T.; Hong, T. M.; Hooberman, B. H.; Hopkins, W. H.; Horii, Y.; Horton, A. J.; Hostachy, J.-Y.; Hou, S.; Hoummada, A.; Howarth, J.; Hoya, J.; Hrabovsky, M.; Hrdinka, J.; Hristova, I.; Hrivnac, J.; Hryn'ova, T.; Hrynevich, A.; Hsu, P. J.; Hsu, S.-C.; Hu, Q.; Hu, S.; Huang, Y.; Hubacek, Z.; Hubaut, F.; Huegging, F.; Huffman, T. B.; Hughes, E. W.; Hughes, G.; Huhtinen, M.; Huo, P.; Huseynov, N.; Huston, J.; Huth, J.; Iacobucci, G.; Iakovidis, G.; Ibragimov, I.; Iconomidou-Fayard, L.; Idrissi, Z.; Iengo, P.; Igonkina, O.; Iizawa, T.; Ikegami, Y.; Ikeno, M.; Ilchenko, Y.; Iliadis, D.; Ilic, N.; Introzzi, G.; Ioannou, P.; Iodice, M.; Iordanidou, K.; Ippolito, V.; Isacson, M. F.; Ishijima, N.; Ishino, M.; Ishitsuka, M.; Issever, C.; Istin, S.; Ito, F.; Iturbe Ponce, J. M.; Iuppa, R.; Iwasaki, H.; Izen, J. M.; Izzo, V.; Jabbar, S.; Jackson, P.; Jacobs, R. M.; Jain, V.; Jakobi, K. B.; Jakobs, K.; Jakobsen, S.; Jakoubek, T.; Jamin, D. O.; Jana, D. K.; Jansky, R.; Janssen, J.; Janus, M.; Janus, P. A.; Jarlskog, G.; Javadov, N.; Javå¯Rek, T.; Javurkova, M.; Jeanneau, F.; Jeanty, L.; Jejelava, J.; Jelinskas, A.; Jenni, P.; Jeske, C.; Jézéquel, S.; Ji, H.; Jia, J.; Jiang, H.; Jiang, Y.; Jiang, Z.; Jiggins, S.; Jimenez Pena, J.; Jin, S.; Jinaru, A.; Jinnouchi, O.; Jivan, H.; Johansson, P.; Johns, K. A.; Johnson, C. A.; Johnson, W. J.; Jon-And, K.; Jones, R. W. L.; Jones, S. D.; Jones, S.; Jones, T. J.; Jongmanns, J.; Jorge, P. M.; Jovicevic, J.; Ju, X.; Juste Rozas, A.; Köhler, M. K.; Kaczmarska, A.; Kado, M.; Kagan, H.; Kagan, M.; Kahn, S. J.; Kaji, T.; Kajomovitz, E.; Kalderon, C. W.; Kaluza, A.; Kama, S.; Kamenshchikov, A.; Kanaya, N.; Kanjir, L.; Kantserov, V. A.; Kanzaki, J.; Kaplan, B.; Kaplan, L. S.; Kar, D.; Karakostas, K.; Karastathis, N.; Kareem, M. J.; Karentzos, E.; Karpov, S. N.; Karpova, Z. M.; Karthik, K.; Kartvelishvili, V.; Karyukhin, A. N.; Kasahara, K.; Kashif, L.; Kass, R. D.; Kastanas, A.; Kataoka, Y.; Kato, C.; Katre, A.; Katzy, J.; Kawade, K.; Kawagoe, K.; Kawamoto, T.; Kawamura, G.; Kay, E. F.; Kazanin, V. F.; Keeler, R.; Kehoe, R.; Keller, J. S.; Kempster, J. J.; Kendrick, J.; Keoshkerian, H.; Kepka, O.; Kerševan, B. P.; Kersten, S.; Keyes, R. A.; Khader, M.; Khalil-Zada, F.; Khanov, A.; Kharlamov, A. G.; Kharlamova, T.; Khodinov, A.; Khoo, T. J.; Khovanskiy, V.; Khramov, E.; Khubua, J.; Kido, S.; Kilby, C. R.; Kim, H. Y.; Kim, S. H.; Kim, Y. K.; Kimura, N.; Kind, O. M.; King, B. T.; Kirchmeier, D.; Kirk, J.; Kiryunin, A. E.; Kishimoto, T.; Kisielewska, D.; Kitali, V.; Kiuchi, K.; Kivernyk, O.; Kladiva, E.; Klapdor-Kleingrothaus, T.; Klein, M. H.; Klein, M.; Klein, U.; Kleinknecht, K.; Klimek, P.; Klimentov, A.; Klingenberg, R.; Klingl, T.; Klioutchnikova, T.; Kluge, E.-E.; Kluit, P.; Kluth, S.; Kneringer, E.; Knoops, E. B. F. G.; Knue, A.; Kobayashi, A.; Kobayashi, D.; Kobayashi, T.; Kobel, M.; Kocian, M.; Kodys, P.; Koffas, T.; Koffeman, E.; Köhler, N. M.; Koi, T.; Kolb, M.; Koletsou, I.; Komar, A. A.; Komori, Y.; Kondo, T.; Kondrashova, N.; Köneke, K.; König, A. C.; Kono, T.; Konoplich, R.; Konstantinidis, N.; Kopeliansky, R.; Koperny, S.; Kopp, A. K.; Korcyl, K.; Kordas, K.; Korn, A.; Korol, A. A.; Korolkov, I.; Korolkova, E. V.; Kortner, O.; Kortner, S.; Kosek, T.; Kostyukhin, V. V.; Kotwal, A.; Koulouris, A.; Kourkoumeli-Charalampidi, A.; Kourkoumelis, C.; Kourlitis, E.; Kouskoura, V.; Kowalewska, A. B.; Kowalewski, R.; Kowalski, T. Z.; Kozakai, C.; Kozanecki, W.; Kozhin, A. S.; Kramarenko, V. A.; Kramberger, G.; Krasnopevtsev, D.; Krasny, M. W.; Krasznahorkay, A.; Krauss, D.; Kremer, J. A.; Kretzschmar, J.; Kreutzfeldt, K.; Krieger, P.; Krizka, K.; Kroeninger, K.; Kroha, H.; Kroll, J.; Kroll, J.; Kroseberg, J.; Krstic, J.; Kruchonak, U.; Krüger, H.; Krumnack, N.; Kruse, M. C.; Kubota, T.; Kucuk, H.; Kuday, S.; Kuechler, J. T.; Kuehn, S.; Kugel, A.; Kuger, F.; Kuhl, T.; Kukhtin, V.; Kukla, R.; Kulchitsky, Y.; Kuleshov, S.; Kulinich, Y. P.; Kuna, M.; Kunigo, T.; Kupco, A.; Kupfer, T.; Kuprash, O.; Kurashige, H.; Kurchaninov, L. L.; Kurochkin, Y. A.; Kurth, M. G.; Kus, V.; Kuwertz, E. S.; Kuze, M.; Kvita, J.; Kwan, T.; Kyriazopoulos, D.; La Rosa, A.; La Rosa Navarro, J. L.; La Rotonda, L.; La Ruffa, F.; Lacasta, C.; Lacava, F.; Lacey, J.; Lacker, H.; Lacour, D.; Ladygin, E.; Lafaye, R.; Laforge, B.; Lagouri, T.; Lai, S.; Lammers, S.; Lampl, W.; Lançon, E.; Landgraf, U.; Landon, M. P. J.; Lanfermann, M. C.; Lang, V. S.; Lange, J. C.; Langenberg, R. J.; Lankford, A. J.; Lanni, F.; Lantzsch, K.; Lanza, A.; Lapertosa, A.; Laplace, S.; Laporte, J. F.; Lari, T.; Lasagni Manghi, F.; Lassnig, M.; Laurelli, P.; Lavrijsen, W.; Law, A. T.; Laycock, P.; Lazovich, T.; Lazzaroni, M.; Le, B.; Le Dortz, O.; Le Guirriec, E.; Le Quilleuc, E. P.; Leblanc, M.; Lecompte, T.; Ledroit-Guillon, F.; Lee, C. A.; Lee, G. R.; Lee, S. C.; Lee, L.; Lefebvre, B.; Lefebvre, G.; Lefebvre, M.; Legger, F.; Leggett, C.; Lehmann Miotto, G.; Lei, X.; Leight, W. A.; Leite, M. A. L.; Leitner, R.; Lellouch, D.; Lemmer, B.; Leney, K. J. C.; Lenz, T.; Lenzi, B.; Leone, R.; Leone, S.; Leonidopoulos, C.; Lerner, G.; Leroy, C.; Lesage, A. A. J.; Lester, C. G.; Levchenko, M.; Levêque, J.; Levin, D.; Levinson, L. J.; Levy, M.; Lewis, D.; Li, B.; Li, C.-Q.; Li, H.; Li, L.; Li, Q.; Li, S.; Li, X.; Li, Y.; Liang, Z.; Liberti, B.; Liblong, A.; Lie, K.; Liebal, J.; Liebig, W.; Limosani, A.; Lin, S. C.; Lin, T. H.; Linck, R. A.; Lindquist, B. E.; Lionti, A. E.; Lipeles, E.; Lipniacka, A.; Lisovyi, M.; Liss, T. M.; Lister, A.; Litke, A. M.; Liu, B.; Liu, H.; Liu, H.; Liu, J. K. K.; Liu, J.; Liu, J. B.; Liu, K.; Liu, L.; Liu, M.; Liu, Y. L.; Liu, Y.; Livan, M.; Lleres, A.; Llorente Merino, J.; Lloyd, S. L.; Lo, C. Y.; Lo Sterzo, F.; Lobodzinska, E. M.; Loch, P.; Loebinger, F. K.; Loesle, A.; Loew, K. M.; Loginov, A.; Lohse, T.; Lohwasser, K.; Lokajicek, M.; Long, B. A.; Long, J. D.; Long, R. E.; Longo, L.; Looper, K. A.; Lopez, J. A.; Lopez Mateos, D.; Lopez Paz, I.; Lopez Solis, A.; Lorenz, J.; Lorenzo Martinez, N.; Losada, M.; Lösel, P. J.; Lou, X.; Lounis, A.; Love, J.; Love, P. A.; Lu, H.; Lu, N.; Lu, Y. J.; Lubatti, H. J.; Luci, C.; Lucotte, A.; Luedtke, C.; Luehring, F.; Lukas, W.; Luminari, L.; Lundberg, O.; Lund-Jensen, B.; Lutz, M. S.; Luzi, P. M.; Lynn, D.; Lysak, R.; Lytken, E.; Lyu, F.; Lyubushkin, V.; Ma, H.; Ma, L. L.; Ma, Y.; Maccarrone, G.; Macchiolo, A.; MacDonald, C. M.; Maček, B.; Machado Miguens, J.; Madaffari, D.; Madar, R.; Mader, W. F.; Madsen, A.; Maeda, J.; Maeland, S.; Maeno, T.; Maevskiy, A. S.; Magerl, V.; Mahlstedt, J.; Maiani, C.; Maidantchik, C.; Maier, A. A.; Maier, T.; Maio, A.; Majersky, O.; Majewski, S.; Makida, Y.; Makovec, N.; Malaescu, B.; Malecki, Pa.; Maleev, V. P.; Malek, F.; Mallik, U.; Malon, D.; Malone, C.; Maltezos, S.; Malyukov, S.; Mamuzic, J.; Mancini, G.; Mandić, I.; Maneira, J.; Manhaes de Andrade Filho, L.; Manjarres Ramos, J.; Mankinen, K. H.; Mann, A.; Manousos, A.; Mansoulie, B.; Mansour, J. D.; Mantifel, R.; Mantoani, M.; Manzoni, S.; Mapelli, L.; Marceca, G.; March, L.; Marchese, L.; Marchiori, G.; Marcisovsky, M.; Marjanovic, M.; Marley, D. E.; Marroquim, F.; Marsden, S. P.; Marshall, Z.; Martensson, M. U. F.; Marti-Garcia, S.; Martin, C. B.; Martin, T. A.; Martin, V. J.; Martin Dit Latour, B.; Martinez, M.; Martinez Outschoorn, V. I.; Martin-Haugh, S.; Martoiu, V. S.; Martyniuk, A. C.; Marzin, A.; Masetti, L.; Mashimo, T.; Mashinistov, R.; Masik, J.; Maslennikov, A. L.; Massa, L.; Mastrandrea, P.; Mastroberardino, A.; Masubuchi, T.; Mättig, P.; Maurer, J.; Maxfield, S. J.; Maximov, D. A.; Mazini, R.; Maznas, I.; Mazza, S. M.; Mc Fadden, N. C.; Mc Goldrick, G.; Mc Kee, S. P.; McCarn, A.; McCarthy, R. L.; McCarthy, T. G.; McClymont, L. I.; McDonald, E. F.; McFayden, J. A.; McHedlidze, G.; McMahon, S. J.; McNamara, P. C.; McPherson, R. A.; Meehan, S.; Megy, T. J.; Mehlhase, S.; Mehta, A.; Meideck, T.; Meier, K.; Meirose, B.; Melini, D.; Mellado Garcia, B. R.; Mellenthin, J. D.; Melo, M.; Meloni, F.; Melzer, A.; Menary, S. B.; Meng, L.; Meng, X. T.; Mengarelli, A.; Menke, S.; Meoni, E.; Mergelmeyer, S.; Mermod, P.; Merola, L.; Meroni, C.; Merritt, F. S.; Messina, A.; Metcalfe, J.; Mete, A. S.; Meyer, C.; Meyer, J.-P.; Meyer, J.; Meyer Zu Theenhausen, H.; Miano, F.; Middleton, R. P.; Miglioranzi, S.; Mijović, L.; Mikenberg, G.; Mikestikova, M.; Mikuž, M.; Milesi, M.; Milic, A.; Miller, D. W.; Mills, C.; Milov, A.; Milstead, D. A.; Minaenko, A. A.; Minami, Y.; Minashvili, I. A.; Mincer, A. I.; Mindur, B.; Mineev, M.; Minegishi, Y.; Ming, Y.; Mir, L. M.; Mistry, K. P.; Mitani, T.; Mitrevski, J.; Mitsou, V. A.; Miucci, A.; Miyagawa, P. S.; Mizukami, A.; Mjörnmark, J. U.; Mkrtchyan, T.; Mlynarikova, M.; Moa, T.; Mochizuki, K.; Mogg, P.; Mohapatra, S.; Molander, S.; Moles-Valls, R.; Monden, R.; Mondragon, M. C.; Mönig, K.; Monk, J.; Monnier, E.; Montalbano, A.; Montejo Berlingen, J.; Monticelli, F.; Monzani, S.; Moore, R. W.; Morange, N.; Moreno, D.; Moreno Llácer, M.; Morettini, P.; Morgenstern, S.; Mori, D.; Mori, T.; Morii, M.; Morinaga, M.; Morisbak, V.; Morley, A. K.; Mornacchi, G.; Morris, J. D.; Morvaj, L.; Moschovakos, P.; Mosidze, M.; Moss, H. J.; Moss, J.; Motohashi, K.; Mount, R.; Mountricha, E.; Moyse, E. J. W.; Muanza, S.; Mueller, F.; Mueller, J.; Mueller, R. S. P.; Muenstermann, D.; Mullen, P.; Mullier, G. A.; Munoz Sanchez, F. J.; Murray, W. J.; Musheghyan, H.; Muškinja, M.; Myagkov, A. G.; Myska, M.; Nachman, B. P.; Nackenhorst, O.; Nagai, K.; Nagai, R.; Nagano, K.; Nagasaka, Y.; Nagata, K.; Nagel, M.; Nagy, E.; Nairz, A. M.; Nakahama, Y.; Nakamura, K.; Nakamura, T.; Nakano, I.; Naranjo Garcia, R. F.; Narayan, R.; Narrias Villar, D. I.; Naryshkin, I.; Naumann, T.; Navarro, G.; Nayyar, R.; Neal, H. A.; Nechaeva, P. Yu.; Neep, T. J.; Negri, A.; Negrini, M.; Nektarijevic, S.; Nellist, C.; Nelson, A.; Nelson, M. E.; Nemecek, S.; Nemethy, P.; Nessi, M.; Neubauer, M. S.; Neumann, M.; Newman, P. R.; Ng, T. Y.; Nguyen Manh, T.; Nickerson, R. B.; Nicolaidou, R.; Nielsen, J.; Nikolaenko, V.; Nikolic-Audit, I.; Nikolopoulos, K.; Nilsen, J. K.; Nilsson, P.; Ninomiya, Y.; Nisati, A.; Nishu, N.; Nisius, R.; Nitsche, I.; Nitta, T.; Nobe, T.; Noguchi, Y.; Nomachi, M.; Nomidis, I.; Nomura, M. A.; Nooney, T.; Nordberg, M.; Norjoharuddeen, N.; Novgorodova, O.; Nozaki, M.; Nozka, L.; Ntekas, K.; Nurse, E.; Nuti, F.; O'Connor, K.; O'Neil, D. C.; O'Rourke, A. A.; O'Shea, V.; Oakham, F. G.; Oberlack, H.; Obermann, T.; Ocariz, J.; Ochi, A.; Ochoa, I.; Ochoa-Ricoux, J. P.; Oda, S.; Odaka, S.; Oh, A.; Oh, S. H.; Ohm, C. C.; Ohman, H.; Oide, H.; Okawa, H.; Okumura, Y.; Okuyama, T.; Olariu, A.; Oleiro Seabra, L. F.; Olivares Pino, S. A.; Oliveira Damazio, D.; Olszewski, A.; Olszowska, J.; Onofre, A.; Onogi, K.; Onyisi, P. U. E.; Oppen, H.; Oreglia, M. J.; Oren, Y.; Orestano, D.; Orlando, N.; Orr, R. S.; Osculati, B.; Ospanov, R.; Otero Y Garzon, G.; Otono, H.; Ouchrif, M.; Ould-Saada, F.; Ouraou, A.; Oussoren, K. P.; Ouyang, Q.; Owen, M.; Owen, R. E.; Ozcan, V. E.; Ozturk, N.; Pachal, K.; Pacheco Pages, A.; Pacheco Rodriguez, L.; Padilla Aranda, C.; Pagan Griso, S.; Paganini, M.; Paige, F.; Palacino, G.; Palazzo, S.; Palestini, S.; Palka, M.; Pallin, D.; Panagiotopoulou, E. St.; Panagoulias, I.; Pandini, C. E.; Panduro Vazquez, J. G.; Pani, P.; Panitkin, S.; Pantea, D.; Paolozzi, L.; Papadopoulou, Th. D.; Papageorgiou, K.; Paramonov, A.; Paredes Hernandez, D.; Parker, A. J.; Parker, M. A.; Parker, K. A.; Parodi, F.; Parsons, J. A.; Parzefall, U.; Pascuzzi, V. R.; Pasner, J. M.; Pasqualucci, E.; Passaggio, S.; Pastore, Fr.; Pataraia, S.; Pater, J. R.; Pauly, T.; Pearson, B.; Pedraza Lopez, S.; Pedro, R.; Peleganchuk, S. V.; Penc, O.; Peng, C.; Peng, H.; Penwell, J.; Peralva, B. S.; Perego, M. M.; Perepelitsa, D. V.; Peri, F.; Perini, L.; Pernegger, H.; Perrella, S.; Peschke, R.; Peshekhonov, V. D.; Peters, K.; Peters, R. F. Y.; Petersen, B. A.; Petersen, T. C.; Petit, E.; Petridis, A.; Petridou, C.; Petroff, P.; Petrolo, E.; Petrov, M.; Petrucci, F.; Pettersson, N. E.; Peyaud, A.; Pezoa, R.; Phillips, F. H.; Phillips, P. W.; Piacquadio, G.; Pianori, E.; Picazio, A.; Piccaro, E.; Pickering, M. A.; Piegaia, R.; Pilcher, J. E.; Pilkington, A. D.; Pin, A. W. J.; Pinamonti, M.; Pinfold, J. L.; Pirumov, H.; Pitt, M.; Plazak, L.; Pleier, M.-A.; Pleskot, V.; Plotnikova, E.; Pluth, D.; Podberezko, P.; Poettgen, R.; Poggi, R.; Poggioli, L.; Pohl, D.; Polesello, G.; Poley, A.; Policicchio, A.; Polifka, R.; Polini, A.; Pollard, C. S.; Polychronakos, V.; Pommès, K.; Ponomarenko, D.; Pontecorvo, L.; Popeneciu, G. A.; Poppleton, A.; Pospisil, S.; Potamianos, K.; Potrap, I. N.; Potter, C. J.; Poulard, G.; Poulsen, T.; Poveda, J.; Pozo Astigarraga, M. E.; Pralavorio, P.; Pranko, A.; Prell, S.; Price, D.; Primavera, M.; Prince, S.; Proklova, N.; Prokofiev, K.; Prokoshin, F.; Protopopescu, S.; Proudfoot, J.; Przybycien, M.; Puri, A.; Puzo, P.; Qian, J.; Qin, G.; Qin, Y.; Quadt, A.; Queitsch-Maitland, M.; Quilty, D.; Raddum, S.; Radeka, V.; Radescu, V.; Radhakrishnan, S. K.; Radloff, P.; Rados, P.; Ragusa, F.; Rahal, G.; Raine, J. A.; Rajagopalan, S.; Rangel-Smith, C.; Rashid, T.; Raspopov, S.; Ratti, M. G.; Rauch, D. M.; Rauscher, F.; Rave, S.; Ravinovich, I.; Rawling, J. H.; Raymond, M.; Read, A. L.; Readioff, N. P.; Reale, M.; Rebuzzi, D. M.; Redelbach, A.; Redlinger, G.; Reece, R.; Reed, R. G.; Reeves, K.; Rehnisch, L.; Reichert, J.; Reiss, A.; Rembser, C.; Ren, H.; Rescigno, M.; Resconi, S.; Resseguie, E. D.; Rettie, S.; Reynolds, E.; Rezanova, O. L.; Reznicek, P.; Rezvani, R.; Richter, R.; Richter, S.; Richter-Was, E.; Ricken, O.; Ridel, M.; Rieck, P.; Riegel, C. J.; Rieger, J.; Rifki, O.; Rijssenbeek, M.; Rimoldi, A.; Rimoldi, M.; Rinaldi, L.; Ripellino, G.; Ristić, B.; Ritsch, E.; Riu, I.; Rizatdinova, F.; Rizvi, E.; Rizzi, C.; Roberts, R. T.; Robertson, S. H.; Robichaud-Veronneau, A.; Robinson, D.; Robinson, J. E. M.; Robson, A.; Rocco, E.; Roda, C.; Rodina, Y.; Rodriguez Bosca, S.; Rodriguez Perez, A.; Rodriguez Rodriguez, D.; Roe, S.; Rogan, C. S.; Røhne, O.; Roloff, J.; Romaniouk, A.; Romano, M.; Romano Saez, S. M.; Romero Adam, E.; Rompotis, N.; Ronzani, M.; Roos, L.; Rosati, S.; Rosbach, K.; Rose, P.; Rosien, N.-A.; Rossi, E.; Rossi, L. P.; Rosten, J. H. N.; Rosten, R.; Rotaru, M.; Rothberg, J.; Rousseau, D.; Rozanov, A.; Rozen, Y.; Ruan, X.; Rubbo, F.; Rühr, F.; Ruiz-Martinez, A.; Rurikova, Z.; Rusakovich, N. A.; Russell, H. L.; Rutherfoord, J. P.; Ruthmann, N.; Ryabov, Y. F.; Rybar, M.; Rybkin, G.; Ryu, S.; Ryzhov, A.; Rzehorz, G. F.; Saavedra, A. F.; Sabato, G.; Sacerdoti, S.; Sadrozinski, H. F.-W.; Sadykov, R.; Safai Tehrani, F.; Saha, P.; Sahinsoy, M.; Saimpert, M.; Saito, M.; Saito, T.; Sakamoto, H.; Sakurai, Y.; Salamanna, G.; Salazar Loyola, J. E.; Salek, D.; Sales de Bruin, P. H.; Salihagic, D.; Salnikov, A.; Salt, J.; Salvatore, D.; Salvatore, F.; Salvucci, A.; Salzburger, A.; Sammel, D.; Sampsonidis, D.; Sampsonidou, D.; Sánchez, J.; Sanchez Martinez, V.; Sanchez Pineda, A.; Sandaker, H.; Sandbach, R. L.; Sander, C. O.; Sandhoff, M.; Sandoval, C.; Sankey, D. P. C.; Sannino, M.; Sano, Y.; Sansoni, A.; Santoni, C.; Santos, H.; Santoyo Castillo, I.; Sapronov, A.; Saraiva, J. G.; Sarrazin, B.; Sasaki, O.; Sato, K.; Sauvan, E.; Savage, G.; Savard, P.; Savic, N.; Sawyer, C.; Sawyer, L.; Saxon, J.; Sbarra, C.; Sbrizzi, A.; Scanlon, T.; Scannicchio, D. A.; Scarcella, M.; Schaarschmidt, J.; Schacht, P.; Schachtner, B. M.; Schaefer, D.; Schaefer, L.; Schaefer, R.; Schaeffer, J.; Schaepe, S.; Schaetzel, S.; Schäfer, U.; Schaffer, A. C.; Schaile, D.; Schamberger, R. D.; Schegelsky, V. A.; Scheirich, D.; Schernau, M.; Schiavi, C.; Schier, S.; Schildgen, L. K.; Schillo, C.; Schioppa, M.; Schlenker, S.; Schmidt-Sommerfeld, K. R.; Schmieden, K.; Schmitt, C.; Schmitt, S.; Schmitz, S.; Schnoor, U.; Schoeffel, L.; Schoening, A.; Schoenrock, B. D.; Schopf, E.; Schott, M.; Schouwenberg, J. F. P.; Schovancova, J.; Schramm, S.; Schuh, N.; Schulte, A.; Schultens, M. J.; Schultz-Coulon, H.-C.; Schulz, H.; Schumacher, M.; Schumm, B. A.; Schune, Ph.; Schwartzman, A.; Schwarz, T. A.; Schweiger, H.; Schwemling, Ph.; Schwienhorst, R.; Schwindling, J.; Sciandra, A.; Sciolla, G.; Scornajenghi, M.; Scuri, F.; Scutti, F.; Searcy, J.; Seema, P.; Seidel, S. C.; Seiden, A.; Seixas, J. M.; Sekhniaidze, G.; Sekhon, K.; Sekula, S. J.; Semprini-Cesari, N.; Senkin, S.; Serfon, C.; Serin, L.; Serkin, L.; Sessa, M.; Seuster, R.; Severini, H.; Sfiligoj, T.; Sforza, F.; Sfyrla, A.; Shabalina, E.; Shaikh, N. W.; Shan, L. Y.; Shang, R.; Shank, J. T.; Shapiro, M.; Shatalov, P. B.; Shaw, K.; Shaw, S. M.; Shcherbakova, A.; Shehu, C. Y.; Shen, Y.; Sherafati, N.; Sherwood, P.; Shi, L.; Shimizu, S.; Shimmin, C. O.; Shimojima, M.; Shipsey, I. P. J.; Shirabe, S.; Shiyakova, M.; Shlomi, J.; Shmeleva, A.; Shoaleh Saadi, D.; Shochet, M. J.; Shojaii, S.; Shope, D. R.; Shrestha, S.; Shulga, E.; Shupe, M. A.; Sicho, P.; Sickles, A. M.; Sidebo, P. E.; Sideras Haddad, E.; Sidiropoulou, O.; Sidoti, A.; Siegert, F.; Sijacki, Dj.; Silva, J.; Silverstein, S. B.; Simak, V.; Simic, Lj.; Simion, S.; Simioni, E.; Simmons, B.; Simon, M.; Sinervo, P.; Sinev, N. B.; Sioli, M.; Siragusa, G.; Siral, I.; Sivoklokov, S. Yu.; Sjölin, J.; Skinner, M. B.; Skubic, P.; Slater, M.; Slavicek, T.; Slawinska, M.; Sliwa, K.; Slovak, R.; Smakhtin, V.; Smart, B. H.; Smiesko, J.; Smirnov, N.; Smirnov, S. Yu.; Smirnov, Y.; Smirnova, L. N.; Smirnova, O.; Smith, J. W.; Smith, M. N. K.; Smith, R. W.; Smizanska, M.; Smolek, K.; Snesarev, A. A.; Snyder, I. M.; Snyder, S.; Sobie, R.; Socher, F.; Soffer, A.; Søgaard, A.; Soh, D. A.; Sokhrannyi, G.; Solans Sanchez, C. A.; Solar, M.; Soldatov, E. Yu.; Soldevila, U.; Solodkov, A. A.; Soloshenko, A.; Solovyanov, O. V.; Solovyev, V.; Sommer, P.; Son, H.; Sopczak, A.; Sosa, D.; Sotiropoulou, C. L.; Soualah, R.; Soukharev, A. M.; South, D.; Sowden, B. C.; Spagnolo, S.; Spalla, M.; Spangenberg, M.; Spanò, F.; Sperlich, D.; Spettel, F.; Spieker, T. M.; Spighi, R.; Spigo, G.; Spiller, L. A.; Spousta, M.; St. Denis, R. D.; Stabile, A.; Stamen, R.; Stamm, S.; Stanecka, E.; Stanek, R. W.; Stanescu, C.; Stanitzki, M. M.; Stapf, B. S.; Stapnes, S.; Starchenko, E. A.; Stark, G. H.; Stark, J.; Stark, S. H.; Staroba, P.; Starovoitov, P.; Stärz, S.; Staszewski, R.; Steinberg, P.; Stelzer, B.; Stelzer, H. J.; Stelzer-Chilton, O.; Stenzel, H.; Stewart, G. A.; Stockton, M. C.; Stoebe, M.; Stoicea, G.; Stolte, P.; Stonjek, S.; Stradling, A. R.; Straessner, A.; Stramaglia, M. E.; Strandberg, J.; Strandberg, S.; Strauss, M.; Strizenec, P.; Ströhmer, R.; Strom, D. M.; Stroynowski, R.; Strubig, A.; Stucci, S. A.; Stugu, B.; Styles, N. A.; Su, D.; Su, J.; Suchek, S.; Sugaya, Y.; Suk, M.; Sulin, V. V.; Sultan, Dms; Sultansoy, S.; Sumida, T.; Sun, S.; Sun, X.; Suruliz, K.; Suster, C. J. E.; Sutton, M. R.; Suzuki, S.; Svatos, M.; Swiatlowski, M.; Swift, S. P.; Sykora, I.; Sykora, T.; Ta, D.; Tackmann, K.; Taenzer, J.; Taffard, A.; Tafirout, R.; Taiblum, N.; Takai, H.; Takashima, R.; Takasugi, E. H.; Takeshita, T.; Takubo, Y.; Talby, M.; Talyshev, A. A.; Tanaka, J.; Tanaka, M.; Tanaka, R.; Tanaka, S.; Tanioka, R.; Tannenwald, B. B.; Tapia Araya, S.; Tapprogge, S.; Tarem, S.; Tartarelli, G. F.; Tas, P.; Tasevsky, M.; Tashiro, T.; Tassi, E.; Tavares Delgado, A.; Tayalati, Y.; Taylor, A. C.; Taylor, G. N.; Taylor, P. T. E.; Taylor, W.; Teixeira-Dias, P.; Temple, D.; Ten Kate, H.; Teng, P. K.; Teoh, J. J.; Tepel, F.; Terada, S.; Terashi, K.; Terron, J.; Terzo, S.; Testa, M.; Teuscher, R. J.; Theveneaux-Pelzer, T.; Thiele, F.; Thomas, J. P.; Thomas-Wilsker, J.; Thompson, P. D.; Thompson, A. S.; Thomsen, L. A.; Thomson, E.; Tibbetts, M. J.; Ticse Torres, R. E.; Tikhomirov, V. O.; Tikhonov, Yu. A.; Timoshenko, S.; Tipton, P.; Tisserant, S.; Todome, K.; Todorova-Nova, S.; Todt, S.; Tojo, J.; Tokár, S.; Tokushuku, K.; Tolley, E.; Tomlinson, L.; Tomoto, M.; Tompkins, L.; Toms, K.; Tong, B.; Tornambe, P.; Torrence, E.; Torres, H.; Torró Pastor, E.; Toth, J.; Touchard, F.; Tovey, D. R.; Treado, C. J.; Trefzger, T.; Tresoldi, F.; Tricoli, A.; Trigger, I. M.; Trincaz-Duvoid, S.; Tripiana, M. F.; Trischuk, W.; Trocmé, B.; Trofymov, A.; Troncon, C.; Trottier-McDonald, M.; Trovatelli, M.; Truong, L.; Trzebinski, M.; Trzupek, A.; Tsang, K. W.; Tseng, J. C.-L.; Tsiareshka, P. V.; Tsipolitis, G.; Tsirintanis, N.; Tsiskaridze, S.; Tsiskaridze, V.; Tskhadadze, E. G.; Tsui, K. M.; Tsukerman, I. I.; Tsulaia, V.; Tsuno, S.; Tsybychev, D.; Tu, Y.; Tudorache, A.; Tudorache, V.; Tulbure, T. T.; Tuna, A. N.; Tupputi, S. A.; Turchikhin, S.; Turgeman, D.; Turk Cakir, I.; Turra, R.; Tuts, P. M.; Ucchielli, G.; Ueda, I.; Ughetto, M.; Ukegawa, F.; Unal, G.; Undrus, A.; Unel, G.; Ungaro, F. C.; Unno, Y.; Unverdorben, C.; Urban, J.; Urquijo, P.; Urrejola, P.; Usai, G.; Usui, J.; Vacavant, L.; Vacek, V.; Vachon, B.; Vadla, K. O. H.; Vaidya, A.; Valderanis, C.; Valdes Santurio, E.; Valentinetti, S.; Valero, A.; Valéry, L.; Valkar, S.; Vallier, A.; Valls Ferrer, J. A.; van den Wollenberg, W.; van der Graaf, H.; van Gemmeren, P.; van Nieuwkoop, J.; van Vulpen, I.; van Woerden, M. C.; Vanadia, M.; Vandelli, W.; Vaniachine, A.; Vankov, P.; Vardanyan, G.; Vari, R.; Varnes, E. W.; Varni, C.; Varol, T.; Varouchas, D.; Vartapetian, A.; Varvell, K. E.; Vasquez, J. G.; Vasquez, G. A.; Vazeille, F.; Vazquez Schroeder, T.; Veatch, J.; Veeraraghavan, V.; Veloce, L. M.; Veloso, F.; Veneziano, S.; Ventura, A.; Venturi, M.; Venturi, N.; Venturini, A.; Vercesi, V.; Verducci, M.; Verkerke, W.; Vermeulen, A. T.; Vermeulen, J. C.; Vetterli, M. C.; Viaux Maira, N.; Viazlo, O.; Vichou, I.; Vickey, T.; Vickey Boeriu, O. E.; Viehhauser, G. H. A.; Viel, S.; Vigani, L.; Villa, M.; Villaplana Perez, M.; Vilucchi, E.; Vincter, M. G.; Vinogradov, V. B.; Vishwakarma, A.; Vittori, C.; Vivarelli, I.; Vlachos, S.; Vogel, M.; Vokac, P.; Volpi, G.; von der Schmitt, H.; von Toerne, E.; Vorobel, V.; Vorobev, K.; Vos, M.; Voss, R.; Vossebeld, J. H.; Vranjes, N.; Vranjes Milosavljevic, M.; Vrba, V.; Vreeswijk, M.; Vuillermet, R.; Vukotic, I.; Wagner, P.; Wagner, W.; Wagner-Kuhr, J.; Wahlberg, H.; Wahrmund, S.; Wakabayashi, J.; Walder, J.; Walker, R.; Walkowiak, W.; Wallangen, V.; Wang, A. M.; Wang, C.; Wang, C.; Wang, F.; Wang, H.; Wang, H.; Wang, J.; Wang, J.; Wang, Q.; Wang, R.; Wang, S. M.; Wang, T.; Wang, W.; Wang, W.; Wang, Z.; Wanotayaroj, C.; Warburton, A.; Ward, C. P.; Wardrope, D. R.; Washbrook, A.; Watkins, P. M.; Watson, A. T.; Watson, M. F.; Watts, G.; Watts, S.; Waugh, B. M.; Webb, A. F.; Webb, S.; Weber, M. S.; Weber, S. W.; Weber, S. A.; Webster, J. S.; Weidberg, A. R.; Weinert, B.; Weingarten, J.; Weirich, M.; Weiser, C.; Weits, H.; Wells, P. S.; Wenaus, T.; Wengler, T.; Wenig, S.; Wermes, N.; Werner, M. D.; Werner, P.; Wessels, M.; Whalen, K.; Whallon, N. L.; Wharton, A. M.; White, A. S.; White, A.; White, M. J.; White, R.; Whiteson, D.; Whitmore, B. W.; Wickens, F. J.; Wiedenmann, W.; Wielers, M.; Wiglesworth, C.; Wiik-Fuchs, L. A. M.; Wildauer, A.; Wilk, F.; Wilkens, H. G.; Williams, H. H.; Williams, S.; Willis, C.; Willocq, S.; Wilson, J. A.; Wingerter-Seez, I.; Winkels, E.; Winklmeier, F.; Winston, O. J.; Winter, B. T.; Wittgen, M.; Wobisch, M.; Wolf, T. M. H.; Wolff, R.; Wolter, M. W.; Wolters, H.; Wong, V. W. S.; Worm, S. D.; Wosiek, B. K.; Wotschack, J.; Wozniak, K. W.; Wu, M.; Wu, S. L.; Wu, X.; Wu, Y.; Wyatt, T. R.; Wynne, B. M.; Xella, S.; Xi, Z.; Xia, L.; Xu, D.; Xu, L.; Xu, T.; Yabsley, B.; Yacoob, S.; Yamaguchi, D.; Yamaguchi, Y.; Yamamoto, A.; Yamamoto, S.; Yamanaka, T.; Yamatani, M.; Yamauchi, K.; Yamazaki, Y.; Yan, Z.; Yang, H.; Yang, H.; Yang, Y.; Yang, Z.; Yao, W.-M.; Yap, Y. C.; Yasu, Y.; Yatsenko, E.; Yau Wong, K. H.; Ye, J.; Ye, S.; Yeletskikh, I.; Yigitbasi, E.; Yildirim, E.; Yorita, K.; Yoshihara, K.; Young, C.; Young, C. J. S.; Yu, J.; Yu, J.; Yuen, S. P. Y.; Yusuff, I.; Zabinski, B.; Zacharis, G.; Zaidan, R.; Zaitsev, A. M.; Zakharchuk, N.; Zalieckas, J.; Zaman, A.; Zambito, S.; Zanzi, D.; Zeitnitz, C.; Zemaityte, G.; Zemla, A.; Zeng, J. C.; Zeng, Q.; Zenin, O.; Ženiš, T.; Zerwas, D.; Zhang, D.; Zhang, F.; Zhang, G.; Zhang, H.; Zhang, J.; Zhang, L.; Zhang, L.; Zhang, M.; Zhang, P.; Zhang, R.; Zhang, R.; Zhang, X.; Zhang, Y.; Zhang, Z.; Zhao, X.; Zhao, Y.; Zhao, Z.; Zhemchugov, A.; Zhou, B.; Zhou, C.; Zhou, L.; Zhou, M.; Zhou, M.; Zhou, N.; Zhu, C. G.; Zhu, H.; Zhu, J.; Zhu, Y.; Zhuang, X.; Zhukov, K.; Zibell, A.; Zieminska, D.; Zimine, N. I.; Zimmermann, C.; Zimmermann, S.; Zinonos, Z.; Zinser, M.; Ziolkowski, M.; Živković, L.; Zobernig, G.; Zoccoli, A.; Zou, R.; Zur Nedden, M.; Zwalinski, L.; Atlas Collaboration

    2018-06-01

    A search for the supersymmetric partners of quarks and gluons (squarks and gluinos) in final states containing hadronic jets and missing transverse momentum, but no electrons or muons, is presented. The data used in this search were recorded in 2015 and 2016 by the ATLAS experiment in √{s }=13 TeV proton-proton collisions at the Large Hadron Collider, corresponding to an integrated luminosity of 36.1 fb-1 . The results are interpreted in the context of various models where squarks and gluinos are pair produced and the neutralino is the lightest supersymmetric particle. An exclusion limit at the 95% confidence level on the mass of the gluino is set at 2.03 TeV for a simplified model incorporating only a gluino and the lightest neutralino, assuming the lightest neutralino is massless. For a simplified model involving the strong production of mass-degenerate first- and second-generation squarks, squark masses below 1.55 TeV are excluded if the lightest neutralino is massless. These limits substantially extend the region of supersymmetric parameter space previously excluded by searches with the ATLAS detector.

  6. The SRI24 multichannel brain atlas: construction and applications

    NASA Astrophysics Data System (ADS)

    Rohlfing, Torsten; Zahr, Natalie M.; Sullivan, Edith V.; Pfefferbaum, Adolf

    2008-03-01

    We present a new standard atlas of the human brain based on magnetic resonance images. The atlas was generated using unbiased population registration from high-resolution images obtained by multichannel-coil acquisition at 3T in a group of 24 normal subjects. The final atlas comprises three anatomical channels (T I-weighted, early and late spin echo), three diffusion-related channels (fractional anisotropy, mean diffusivity, diffusion-weighted image), and three tissue probability maps (CSF, gray matter, white matter). The atlas is dynamic in that it is implicitly represented by nonrigid transformations between the 24 subject images, as well as distortion-correction alignments between the image channels in each subject. The atlas can, therefore, be generated at essentially arbitrary image resolutions and orientations (e.g., AC/PC aligned), without compounding interpolation artifacts. We demonstrate in this paper two different applications of the atlas: (a) region definition by label propagation in a fiber tracking study is enabled by the increased sharpness of our atlas compared with other available atlases, and (b) spatial normalization is enabled by its average shape property. In summary, our atlas has unique features and will be made available to the scientific community as a resource and reference system for future imaging-based studies of the human brain.

  7. ATLAS Metadata Infrastructure Evolution for Run 2 and Beyond

    NASA Astrophysics Data System (ADS)

    van Gemmeren, P.; Cranshaw, J.; Malon, D.; Vaniachine, A.

    2015-12-01

    ATLAS developed and employed for Run 1 of the Large Hadron Collider a sophisticated infrastructure for metadata handling in event processing jobs. This infrastructure profits from a rich feature set provided by the ATLAS execution control framework, including standardized interfaces and invocation mechanisms for tools and services, segregation of transient data stores with concomitant object lifetime management, and mechanisms for handling occurrences asynchronous to the control framework's state machine transitions. This metadata infrastructure is evolving and being extended for Run 2 to allow its use and reuse in downstream physics analyses, analyses that may or may not utilize the ATLAS control framework. At the same time, multiprocessing versions of the control framework and the requirements of future multithreaded frameworks are leading to redesign of components that use an incident-handling approach to asynchrony. The increased use of scatter-gather architectures, both local and distributed, requires further enhancement of metadata infrastructure in order to ensure semantic coherence and robust bookkeeping. This paper describes the evolution of ATLAS metadata infrastructure for Run 2 and beyond, including the transition to dual-use tools—tools that can operate inside or outside the ATLAS control framework—and the implications thereof. It further examines how the design of this infrastructure is changing to accommodate the requirements of future frameworks and emerging event processing architectures.

  8. EnviroAtlas - Austin, TX - Park Access by Block Group

    EPA Pesticide Factsheets

    This EnviroAtlas dataset shows the block group population that is within and beyond an easy walking distance (500m) of a park entrance. Park entrances were included in this analysis if they were within 5km of the EnviroAtlas community boundary. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

  9. Atmospheric Detectives. Atlas 2 Teacher's Guide with Activities.

    ERIC Educational Resources Information Center

    National Aeronautics and Space Administration, Washington, DC. Educational Affairs Div.

    As part of the National Aeronautics and Space Administration Mission to Planet Earth, ATLAS 2 will help develop a thorough picture of the Sun's output, its interaction with the atmosphere, and the well-being of Earth's middle atmosphere. This middle school level guide probes the connection between the activities of scientists and the observable…

  10. 7T MRI subthalamic nucleus atlas for use with 3T MRI.

    PubMed

    Milchenko, Mikhail; Norris, Scott A; Poston, Kathleen; Campbell, Meghan C; Ushe, Mwiza; Perlmutter, Joel S; Snyder, Abraham Z

    2018-01-01

    Deep brain stimulation (DBS) of the subthalamic nucleus (STN) reduces motor symptoms in most patients with Parkinson disease (PD), yet may produce untoward effects. Investigation of DBS effects requires accurate localization of the STN, which can be difficult to identify on magnetic resonance images collected with clinically available 3T scanners. The goal of this study is to develop a high-quality STN atlas that can be applied to standard 3T images. We created a high-definition STN atlas derived from seven older participants imaged at 7T. This atlas was nonlinearly registered to a standard template representing 56 patients with PD imaged at 3T. This process required development of methodology for nonlinear multimodal image registration. We demonstrate mm-scale STN localization accuracy by comparison of our 3T atlas with a publicly available 7T atlas. We also demonstrate less agreement with an earlier histological atlas. STN localization error in the 56 patients imaged at 3T was less than 1 mm on average. Our methodology enables accurate STN localization in individuals imaged at 3T. The STN atlas and underlying 3T average template in MNI space are freely available to the research community. The image registration methodology developed in the course of this work may be generally applicable to other datasets.

  11. Readout Electronics for the ATLAS LAr Calorimeter at HL-LHC

    NASA Astrophysics Data System (ADS)

    Chen, Hucheng; ATLAS Liquid Argon Calorimeter Group

    The ATLAS Liquid Argon (LAr) calorimeters are high precision, high sensitivity and high granularity detectors designed to provide precision measurements of electrons, photons, jets and missing transverse energy. ATLAS and its LAr calorimeters have been operating and collecting proton-proton collisions at LHC since 2009. The current front-end electronics of the LAr calorimeters need to be upgraded to sustain the higher radiation levels and data rates expected at the upgraded high luminosity LHC machine (HL-LHC), which will have 5 times more luminosity than the LHC in its ultimate configuration. The complexity of the present electronics and the obsolescence of some of components of which it is made, will not allow a partial replacement of the system. A completely new readout architecture scheme is under study and many components are being developed in various R&D programs of the LAr Calorimeter Group.The new front-end readout electronics will send data continuously at each bunch crossing through high speed radiation resistant optical links. The data will be processed real-time with the possibility of implementing trigger algorithms for clusters and electron/photon identification at a higher granularity than that which is currently implemented. The new architecture will eliminate the intrinsic limitation presently existing on Level-1 trigger acceptance. This article is an overview of the R&D activities which covers architectural design aspects of the new electronics as well as some detailed progress on the development of several ASICs needed, and preliminary studies with FPGAs to cover the backend functions including part of the Level-1 trigger requirements. A recently proposed staged upgrade with hybrid Tower Builder Board (TBB) is also described.

  12. Mercury-Atlas 9 spacecraft Faith 7 is shown during mating of spacecraft to Atlas booster

    NASA Technical Reports Server (NTRS)

    1963-01-01

    The Mercury-Atlas-9 spacecraft, #20, Faith 7, is shown during mating of spacecraft to the Atlas booster at Pad 14, Cape Canaveral, Fla. Faith 7 named by Astronaut L. Gordon Cooper is programmed for a 22-orbit mission, lasting 30 hours and 20 minutes, with impact near Midway Island.

  13. Education modules using EnviroAtlas (#2)

    EPA Science Inventory

    Session Title #1: Exploration and Discovery through Maps: Teaching Science with Technology. Online maps have the power to spark student interest and bring students closer to their local natural environments. EnviroAtlas is an interactive, web-based tool that was designed by the...

  14. Topographical atlas sheets

    USGS Publications Warehouse

    Wheeler, George Montague

    1877-01-01

    The following topographical atlas maps, published during the year, accompany the copies of Appendix N.N. of the Annual Report of the Chief of Engineers for 1877, beinig Annual Report of Lieut. Geo. M. Wheeler, Corps of Engineers, in charge of U. S. Geographical Surveys, are in continuation of the series ninety-five in number, on a scale of 1 inch to 8 miles, embracing the territory of the United States lying west of the 100th meridian.

  15. The Offshore New European Wind Atlas

    NASA Astrophysics Data System (ADS)

    Karagali, I.; Hahmann, A. N.; Badger, M.; Hasager, C.; Mann, J.

    2017-12-01

    The New European Wind Atlas (NEWA) is a joint effort of research agencies from eight European countries, co-funded under the ERANET Plus Program. The project is structured around two areas of work: development of dynamical downscaling methodologies and measurement campaigns to validate these methodologies, leading to the creation and publication of a European wind atlas in electronic form. This atlas will contain an offshore component extending 100 km from the European coasts. To achieve this, mesoscale models along with various observational datasets are utilised. Scanning lidars located at the coastline were used to compare the coastal wind gradient reproduced by the meso-scale model. Currently, an experimental campaign is occurring in the Baltic Sea, with a lidar located in a commercial ship sailing from Germany to Lithuania, thus covering the entire span of the south Baltic basin. In addition, satellite wind retrievals from scatterometers and Synthetic Aperture Radar (SAR) instruments were used to generate mean wind field maps and validate offshore modelled wind fields and identify the optimal model set-up parameters.The aim of this study is to compare the initial outputs from the offshore wind atlas produced by the Weather & Research Forecasting (WRF) model, still in pre-operational phase, and the METOP-A/B Advanced Scatterometer (ASCAT) wind fields, reprocessed to stress equivalent winds at 10m. Different experiments were set-up to evaluate the model sensitivity for the various domains covered by the NEWA offshore atlas. ASCAT winds were utilised to assess the performance of the WRF offshore atlases. In addition, ASCAT winds were used to create an offshore atlas covering the years 2007 to 2016, capturing the signature of various spatial wind features, such as channelling and lee effects from complex coastal topographical elements.

  16. ATLAS: Big Data in a Small Package

    NASA Astrophysics Data System (ADS)

    Denneau, Larry; Tonry, John

    2015-08-01

    For even small telescope projects, the petabyte scale is now upon us. The Asteroid Terrestrial-impact Last Alert System (ATLAS; Tonry 2011) will robotically survey the entire visible sky from Hawaii multiple times per night to search for near-Earth asteroids (NEAs) on impact trajectories. While the ATLAS optical system is modest by modern astronomical standards -- two 0.5 m F/2.0 telescopes -- each year the ATLAS system will obtain ~103 measurements of 109 astronomical sources to a photometric accuracy of <5%. This ever-growing dataset must be searched in real-time for moving objects then archived for further analysis, and alerts for newly discovered near-Earth NEAs disseminated within tens of minutes from detection. ATLAS's all-sky coverage ensures it will discover many ``rifle shot'' near-misses moving rapidly on the sky as they shoot past the Earth, so the system will need software to automatically detect highly-trailed sources and discriminate them from the thousands of satellites and pieces of space junk that ATLAS will see each night. Additional interrogation will identify interesting phenomena from beyond the solar system occurring over millions of transient sources per night. The data processing and storage requirements for ATLAS demand a ``big data'' approach typical of commercial Internet enterprises. We describe our approach to deploying a nimble, scalable and reliable data processing infrastructure, and promote ATLAS as steppingstone to eventual processing scales in the era of LSST.

  17. Digital atlas of fetal brain MRI.

    PubMed

    Chapman, Teresa; Matesan, Manuela; Weinberger, Ed; Bulas, Dorothy I

    2010-02-01

    Fetal MRI can be performed in the second and third trimesters. During this time, the fetal brain undergoes profound structural changes. Interpretation of appropriate development might require comparison with normal age-based models. Consultation of a hard-copy atlas is limited by the inability to compare multiple ages simultaneously. To provide images of normal fetal brains from weeks 18 through 37 in a digital format that can be reviewed interactively. This will facilitate recognition of abnormal brain development. T2-W images for the atlas were obtained from fetal MR studies of normal brains scanned for other indications from 2005 to 2007. Images were oriented in standard axial, coronal and sagittal projections, with laterality established by situs. Gestational age was determined by last menstrual period, earliest US measurements and sonogram performed on the same day as the MR. The software program used for viewing the atlas, written in C#, permits linked scrolling and resizing the images. Simultaneous comparison of varying gestational ages is permissible. Fetal brain images across gestational ages 18 to 37 weeks are provided as an interactive digital atlas and are available for free download from http://radiology.seattlechildrens.org/teaching/fetal_brain . Improved interpretation of fetal brain abnormalities can be facilitated by the use of digital atlas cataloging of the normal changes throughout fetal development. Here we provide a description of the atlas and a discussion of normal fetal brain development.

  18. 4D Infant Cortical Surface Atlas Construction using Spherical Patch-based Sparse Representation.

    PubMed

    Wu, Zhengwang; Li, Gang; Meng, Yu; Wang, Li; Lin, Weili; Shen, Dinggang

    2017-09-01

    The 4D infant cortical surface atlas with densely sampled time points is highly needed for neuroimaging analysis of early brain development. In this paper, we build the 4D infant cortical surface atlas firstly covering 6 postnatal years with 11 time points (i.e., 1, 3, 6, 9, 12, 18, 24, 36, 48, 60, and 72 months), based on 339 longitudinal MRI scans from 50 healthy infants. To build the 4D cortical surface atlas, first , we adopt a two-stage groupwise surface registration strategy to ensure both longitudinal consistency and unbiasedness. Second , instead of simply averaging over the co-registered surfaces, a spherical patch-based sparse representation is developed to overcome possible surface registration errors across different subjects. The central idea is that, for each local spherical patch in the atlas space, we build a dictionary, which includes the samples of current local patches and their spatially-neighboring patches of all co-registered surfaces, and then the current local patch in the atlas is sparsely represented using the built dictionary. Compared to the atlas built with the conventional methods, the 4D infant cortical surface atlas constructed by our method preserves more details of cortical folding patterns, thus leading to boosted accuracy in registration of new infant cortical surfaces.

  19. New Release of the High-Resolution Mimas Atlas derived from Cassini-ISS Images

    NASA Astrophysics Data System (ADS)

    Roatsch, T.; Kersten, E.; Matz, K.-D.; Porco, C. C.

    2017-09-01

    The Cassini Imaging Science Subsystem (ISS) acquired 128 high-resolution images (< 1 km/pixel) of Mimas during its tour through the Saturnian system since 2004. We combined new images from orbit 249 (Nov. 2016) and orbit 259 (Jan. 2017) with the high-resolution global semi-controlled mosaic of Mimas from 2012. This global mosaic is the baseline for the new high-resolution Mimas atlas that still consists of three tiles mapped at a scale of 1:1,000,000 [1]. The nomenclature used in this atlas was proposed by the Cassini imaging team and was approved by the International Astronomical Union (IAU). The entire atlas will become available to the public through the Imaging Team's website [http://ciclops.org/maps] and the Planetary Data System (PDS) [https://pds- imaging.jpl.nasa.gov/volumes/carto.html].

  20. EnviroAtlas - Austin, TX - Domestic Water Use per Day by U.S. Census Block Group

    EPA Pesticide Factsheets

    As included in this EnviroAtlas dataset, the community level domestic water use is calculated using locally available water use data per capita in gallons of water per day (GPD), distributed dasymetrically, and summarized by census block group. Domestic water use, as defined in this case, is intended to represent residential indoor and outdoor water use (e.g., cooking, hygiene, landscaping, pools, etc.) for primary residences (i.e., excluding second homes and tourism rentals). For the purposes of this metric, these publicly-supplied estimates are also applied and considered representative of local self-supplied water use. Residential water use reporting in the EnviroAtlas-defined study area is available through the Texas Water Development Board. Within the Austin study area, there are thirteen community estimates from 2012 ranging from 65 to 303 GPD. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-f

  1. HYPATIA--An Online Tool for ATLAS Event Visualization

    ERIC Educational Resources Information Center

    Kourkoumelis, C.; Vourakis, S.

    2014-01-01

    This paper describes an interactive tool for analysis of data from the ATLAS experiment taking place at the world's highest energy particle collider at CERN. The tool, called HYPATIA/applet, enables students of various levels to become acquainted with particle physics and look for discoveries in a similar way to that of real research.

  2. Morphometric Atlas Selection for Automatic Brachial Plexus Segmentation

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Van de Velde, Joris, E-mail: joris.vandevelde@ugent.be; Department of Radiotherapy, Ghent University, Ghent; Wouters, Johan

    Purpose: The purpose of this study was to determine the effects of atlas selection based on different morphometric parameters, on the accuracy of automatic brachial plexus (BP) segmentation for radiation therapy planning. The segmentation accuracy was measured by comparing all of the generated automatic segmentations with anatomically validated gold standard atlases developed using cadavers. Methods and Materials: Twelve cadaver computed tomography (CT) atlases (3 males, 9 females; mean age: 73 years) were included in the study. One atlas was selected to serve as a patient, and the other 11 atlases were registered separately onto this “patient” using deformable image registration. Thismore » procedure was repeated for every atlas as a patient. Next, the Dice and Jaccard similarity indices and inclusion index were calculated for every registered BP with the original gold standard BP. In parallel, differences in several morphometric parameters that may influence the BP segmentation accuracy were measured for the different atlases. Specific brachial plexus-related CT-visible bony points were used to define the morphometric parameters. Subsequently, correlations between the similarity indices and morphometric parameters were calculated. Results: A clear negative correlation between difference in protraction-retraction distance and the similarity indices was observed (mean Pearson correlation coefficient = −0.546). All of the other investigated Pearson correlation coefficients were weak. Conclusions: Differences in the shoulder protraction-retraction position between the atlas and the patient during planning CT influence the BP autosegmentation accuracy. A greater difference in the protraction-retraction distance between the atlas and the patient reduces the accuracy of the BP automatic segmentation result.« less

  3. Overview of ATLAS PanDA Workload Management

    NASA Astrophysics Data System (ADS)

    Maeno, T.; De, K.; Wenaus, T.; Nilsson, P.; Stewart, G. A.; Walker, R.; Stradling, A.; Caballero, J.; Potekhin, M.; Smith, D.; ATLAS Collaboration

    2011-12-01

    The Production and Distributed Analysis System (PanDA) plays a key role in the ATLAS distributed computing infrastructure. All ATLAS Monte-Carlo simulation and data reprocessing jobs pass through the PanDA system. We will describe how PanDA manages job execution on the grid using dynamic resource estimation and data replication together with intelligent brokerage in order to meet the scaling and automation requirements of ATLAS distributed computing. PanDA is also the primary ATLAS system for processing user and group analysis jobs, bringing further requirements for quick, flexible adaptation to the rapidly evolving analysis use cases of the early datataking phase, in addition to the high reliability, robustness and usability needed to provide efficient and transparent utilization of the grid for analysis users. We will describe how PanDA meets ATLAS requirements, the evolution of the system in light of operational experience, how the system has performed during the first LHC data-taking phase and plans for the future.

  4. Overview of ATLAS PanDA Workload Management

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Maeno T.; De K.; Wenaus T.

    2011-01-01

    The Production and Distributed Analysis System (PanDA) plays a key role in the ATLAS distributed computing infrastructure. All ATLAS Monte-Carlo simulation and data reprocessing jobs pass through the PanDA system. We will describe how PanDA manages job execution on the grid using dynamic resource estimation and data replication together with intelligent brokerage in order to meet the scaling and automation requirements of ATLAS distributed computing. PanDA is also the primary ATLAS system for processing user and group analysis jobs, bringing further requirements for quick, flexible adaptation to the rapidly evolving analysis use cases of the early datataking phase, in additionmore » to the high reliability, robustness and usability needed to provide efficient and transparent utilization of the grid for analysis users. We will describe how PanDA meets ATLAS requirements, the evolution of the system in light of operational experience, how the system has performed during the first LHC data-taking phase and plans for the future.« less

  5. Digital atlas of the upper Washita River basin, southwestern Oklahoma

    USGS Publications Warehouse

    Becker, Carol J.; Masoner, Jason R.; Scott, Jonathon C.

    2008-01-01

    Numerous types of environmental data have been collected in the upper Washita River basin in southwestern Oklahoma. However, to date these data have not been compiled into a format that can be comprehensively queried for the purpose of evaluating the effects of various conservation practices implemented to reduce agricultural runoff and erosion in parts of the upper Washita River basin. This U.S. Geological Survey publication, 'Digital atlas of the upper Washita River basin, southwestern Oklahoma' was created to assist with environmental analysis. This atlas contains 30 spatial data sets that can be used in environmental assessment and decision making for the upper Washita River basin. This digital atlas includes U.S. Geological Survey sampling sites and associated water-quality, biological, water-level, and streamflow data collected from 1903 to 2005. The data were retrieved from the U.S. Geological Survey National Water Information System database on September 29, 2005. Data sets are from the Geology, Geography, and Water disciplines of the U.S. Geological Survey and cover parts of Beckham, Caddo, Canadian, Comanche, Custer, Dewey, Grady, Kiowa, and Washita Counties in southwestern Oklahoma. A bibliography of past reports from the U.S. Geological Survey and other State and Federal agencies from 1949 to 2004 is included in the atlas. Additionally, reports by Becker (2001), Martin (2002), Fairchild and others (2004), and Miller and Stanley (2005) are provided in electronic format.

  6. MUSE: MUlti-atlas region Segmentation utilizing Ensembles of registration algorithms and parameters, and locally optimal atlas selection

    PubMed Central

    Ou, Yangming; Resnick, Susan M.; Gur, Ruben C.; Gur, Raquel E.; Satterthwaite, Theodore D.; Furth, Susan; Davatzikos, Christos

    2016-01-01

    Atlas-based automated anatomical labeling is a fundamental tool in medical image segmentation, as it defines regions of interest for subsequent analysis of structural and functional image data. The extensive investigation of multi-atlas warping and fusion techniques over the past 5 or more years has clearly demonstrated the advantages of consensus-based segmentation. However, the common approach is to use multiple atlases with a single registration method and parameter set, which is not necessarily optimal for every individual scan, anatomical region, and problem/data-type. Different registration criteria and parameter sets yield different solutions, each providing complementary information. Herein, we present a consensus labeling framework that generates a broad ensemble of labeled atlases in target image space via the use of several warping algorithms, regularization parameters, and atlases. The label fusion integrates two complementary sources of information: a local similarity ranking to select locally optimal atlases and a boundary modulation term to refine the segmentation consistently with the target image's intensity profile. The ensemble approach consistently outperforms segmentations using individual warping methods alone, achieving high accuracy on several benchmark datasets. The MUSE methodology has been used for processing thousands of scans from various datasets, producing robust and consistent results. MUSE is publicly available both as a downloadable software package, and as an application that can be run on the CBICA Image Processing Portal (https://ipp.cbica.upenn.edu), a web based platform for remote processing of medical images. PMID:26679328

  7. Mapping Dialectal Variation Using the Algonquian Linguistic Atlas

    ERIC Educational Resources Information Center

    Cenerini, Chantale; Junker, Marie-Odile; Rosen, Nicole

    2017-01-01

    The Algonquian Linguistic Atlas (www.atlas-ling.ca) is an online multimedia linguistic atlas of Algonquian languages in Canada, built based on a template of conversational topics. It includes Algonquian languages primarily from the CreeInnu-Naskapi continuum, but also from Blackfoot, Mi'kmaw, and Ojibwe (including Algonquin), with other languages…

  8. A Molecular atlas of Xenopus respiratory system development.

    PubMed

    Rankin, Scott A; Thi Tran, Hong; Wlizla, Marcin; Mancini, Pamela; Shifley, Emily T; Bloor, Sean D; Han, Lu; Vleminckx, Kris; Wert, Susan E; Zorn, Aaron M

    2015-01-01

    Respiratory system development is regulated by a complex series of endoderm-mesoderm interactions that are not fully understood. Recently Xenopus has emerged as an alternative model to investigate early respiratory system development, but the extent to which the morphogenesis and molecular pathways involved are conserved between Xenopus and mammals has not been systematically documented. In this study, we provide a histological and molecular atlas of Xenopus respiratory system development, focusing on Nkx2.1+ respiratory cell fate specification in the developing foregut. We document the expression patterns of Wnt/β-catenin, fibroblast growth factor (FGF), and bone morphogenetic protein (BMP) signaling components in the foregut and show that the molecular mechanisms of respiratory lineage induction are remarkably conserved between Xenopus and mice. Finally, using several functional experiments we refine the epistatic relationships among FGF, Wnt, and BMP signaling in early Xenopus respiratory system development. We demonstrate that Xenopus trachea and lung development, before metamorphosis, is comparable at the cellular and molecular levels to embryonic stages of mouse respiratory system development between embryonic days 8.5 and 10.5. This molecular atlas provides a fundamental starting point for further studies using Xenopus as a model to define the conserved genetic programs controlling early respiratory system development. © 2014 Wiley Periodicals, Inc.

  9. The Herschel ATLAS

    NASA Technical Reports Server (NTRS)

    Eales, S.; Dunne, L.; Clements, D.; Cooray, A.; De Zotti, G.; Dye, S.; Ivison, R.; Jarvis, M.; Lagache, G.; Maddox, S.; hide

    2010-01-01

    The Herschel ATLAS is the largest open-time key project that will be carried out on the Herschel Space Observatory. It will survey 570 sq deg of the extragalactic sky, 4 times larger than all the other Herschel extragalactic surveys combined, in five far-infrared and submillimeter bands. We describe the survey, the complementary multiwavelength data sets that will be combined with the Herschel data, and the six major science programs we are undertaking. Using new models based on a previous submillimeter survey of galaxies, we present predictions of the properties of the ATLAS sources in other wave bands.

  10. A Three-Dimensional Atlas of the Honeybee Neck

    PubMed Central

    Berry, Richard P.; Ibbotson, Michael R.

    2010-01-01

    Three-dimensional digital atlases are rapidly becoming indispensible in modern biology. We used serial sectioning combined with manual registration and segmentation of images to develop a comprehensive and detailed three-dimensional atlas of the honeybee head-neck system. This interactive atlas includes skeletal structures of the head and prothorax, the neck musculature, and the nervous system. The scope and resolution of the model exceeds atlases previously developed on similar sized animals, and the interactive nature of the model provides a far more accessible means of interpreting and comprehending insect anatomy and neuroanatomy. PMID:20520729

  11. Complete occipitalization of the atlas with bilateral external auditory canal atresia.

    PubMed

    Dolenšek, Janez; Cvetko, Erika; Snoj, Žiga; Meznaric, Marija

    2017-09-01

    Fusion of the atlas with the occipital bone is a rare congenital dysplasia known as occipitalization of the atlas, occipitocervical synostosis, assimilation of the atlas, or atlanto-occipital fusion. It is a component of the paraxial mesodermal maldevelopment and commonly associated with other dysplasias of the craniovertebral junction. External auditory canal atresia or external aural atresia is a rare congenital absence of the external auditory canal. It occurs as the consequence of the maldevelopment of the first pharyngeal cleft due to defects of cranial neural crest cells migration and/or differentiation. It is commonly associated with the dysplasias of the structures derived from the first and second pharyngeal arches including microtia. We present the coexistence of the occipitalization of the atlas and congenital aural atresia, an uncommon combination of the paraxial mesodermal maldevelopment, and defects of cranial neural crest cells. The association is most probably syndromic as minimal diagnostic criteria for the oculoariculovertebral spectrum are fulfilled. From the clinical point of view, it is important to be aware that patients with microtia must obtain also appropriate diagnostic imaging studies of the craniovetebral junction due to eventual concomitant occipitalization of the atlas and frequently associated C1-C2 instability.

  12. An atlas of high-resolution IRAS maps on nearby galaxies

    NASA Technical Reports Server (NTRS)

    Rice, Walter

    1993-01-01

    An atlas of far-infrared IRAS maps with near 1 arcmin angular resolution of 30 optically large galaxies is presented. The high-resolution IRAS maps were produced with the Maximum Correlation Method (MCM) image construction and enhancement technique developed at IPAC. The MCM technique, which recovers the spatial information contained in the overlapping detector data samples of the IRAS all-sky survey scans, is outlined and tests to verify the structural reliability and photometric integrity of the high-resolution maps are presented. The infrared structure revealed in individual galaxies is discussed. The atlas complements the IRAS Nearby Galaxy High-Resolution Image Atlas, the high-resolution galaxy images encoded in FITS format, which is provided to the astronomical community as an IPAC product.

  13. Search for the X b and other hidden-beauty states in the π +π -Υ(1S) channel at ATLAS

    DOE PAGES

    Aad, G.; Abbott, B.; Abdallah, J.; ...

    2014-12-02

    This Letter presents a search for a hidden-beauty counterpart of the X(3872) in the mass ranges of 10.05–10.31 GeV and 10.40–11.00 GeV, in the channel X b → π +π -Υ (1S)(→ μ +μ -), using 16.2 fb -1 of √s = 8 TeV pp collision data collected by the ATLAS detector at the LHC. No evidence for new narrow states is found, and upper limits are set on the product of the X b cross section and branching fraction, relative to those of the Υ(2S), at the 95% confidence level using the CL S approach. These limits range frommore » 0.8% to 4.0%, depending on mass. For masses above 10.1 GeV, the expected upper limits from this analysis are the most restrictive to date. As a result, searches for production of the Υ (1 3D J), Υ(10860), and Υ(11020) states also reveal no significant signals« less

  14. Search for the X b and other hidden-beauty states in the π +π -Υ(1S) channel at ATLAS

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Aad, G.; Abbott, B.; Abdallah, J.

    This Letter presents a search for a hidden-beauty counterpart of the X(3872) in the mass ranges of 10.05–10.31 GeV and 10.40–11.00 GeV, in the channel X b → π +π -Υ (1S)(→ μ +μ -), using 16.2 fb -1 of √s = 8 TeV pp collision data collected by the ATLAS detector at the LHC. No evidence for new narrow states is found, and upper limits are set on the product of the X b cross section and branching fraction, relative to those of the Υ(2S), at the 95% confidence level using the CL S approach. These limits range frommore » 0.8% to 4.0%, depending on mass. For masses above 10.1 GeV, the expected upper limits from this analysis are the most restrictive to date. As a result, searches for production of the Υ (1 3D J), Υ(10860), and Υ(11020) states also reveal no significant signals« less

  15. ATCA follow-up of blazar candidates in the H-ATLAS fields

    NASA Astrophysics Data System (ADS)

    Massardi, Marcella; Ricci, Roberto; de Zotti, Gianfranco; White, Glenn; Michalowski, Michal; Ivison, Rob; Baes, Maarten; Lapi, Andrea; Temi, Pasquale; Lopez-Caniego, Marcos; Herranz, Diego; Seymour, Nick; Gonzalez-Nuevo, Joaquin; Bonavera, Laura; Negrello, Mattia

    2012-04-01

    The Herschel-ATLAS (H-ATLAS) survey that is covering 550 sq.deg. in 5 bands from 100 to 500 micron, allows for the first time a flux limited selection of blazars at sub-mm wavelengths. This wavelength range is particularly interesting because it is where the most luminous blazars are expected to show the synchrotron peak. The peak frequency and luminosity carry key information on the blazar physics. However, blazars constitute a tiny fraction of H-ATLAS sources and therefore picking them up isn't easy. A criterion to efficiently select candidate blazars exploiting the roughly flat blazar continuum spectrum from radio to sub-mm wavelengths has been devised by Lopez-Caniego et al. (in prep.). Multifrequency radio follow-up is however a necessary step to assess the nature of candidates. We propose to complete the validation of candidates in the H-ATLAS equatorial fields (partly done during few hours of ATCA DDT allocated time and with Medicina radiotelescope observations) and to extend the investigation to the Southern (SGP) fields reconstructing the blazars SED between 1.1 and 40 GHz. This will provide the first statistically significant blazar sample selected at sub-mm wavelengths.

  16. Readiness of the ATLAS liquid argon calorimeter for LHC collisions

    NASA Astrophysics Data System (ADS)

    Aad, G.; Abbott, B.; Abdallah, J.; Abdelalim, A. A.; Abdesselam, A.; Abdinov, O.; Abi, B.; Abolins, M.; Abramowicz, H.; Abreu, H.; Acharya, B. S.; Adams, D. L.; Addy, T. N.; Adelman, J.; Adorisio, C.; Adragna, P.; Adye, T.; Aefsky, S.; Aguilar-Saavedra, J. A.; Aharrouche, M.; Ahlen, S. P.; Ahles, F.; Ahmad, A.; Ahmed, H.; Ahsan, M.; Aielli, G.; Akdogan, T.; Åkesson, T. P. A.; Akimoto, G.; Akimov, A. V.; Aktas, A.; Alam, M. S.; Alam, M. A.; Albert, J.; Albrand, S.; Aleksa, M.; Aleksandrov, I. N.; Alessandria, F.; Alexa, C.; Alexander, G.; Alexandre, G.; Alexopoulos, T.; Alhroob, M.; Aliev, M.; Alimonti, G.; Alison, J.; Aliyev, M.; Allport, P. P.; Allwood-Spiers, S. E.; Almond, J.; Aloisio, A.; Alon, R.; Alonso, A.; Alviggi, M. G.; Amako, K.; Amelung, C.; Ammosov, V. V.; Amorim, A.; Amorós, G.; Amram, N.; Anastopoulos, C.; Andeen, T.; Anders, C. F.; Anderson, K. J.; Andreazza, A.; Andrei, V.; Anduaga, X. S.; Angerami, A.; Anghinolfi, F.; Anjos, N.; Antonaki, A.; Antonelli, M.; Antonelli, S.; Antunovic, B.; Anulli, F.; Aoun, S.; Arabidze, G.; Aracena, I.; Arai, Y.; Arce, A. T. H.; Archambault, J. P.; Arfaoui, S.; Arguin, J.-F.; Argyropoulos, T.; Arik, E.; Arik, M.; Armbruster, A. J.; Arnaez, O.; Arnault, C.; Artamonov, A.; Arutinov, D.; Asai, M.; Asai, S.; Asfandiyarov, R.; Ask, S.; Åsman, B.; Asner, D.; Asquith, L.; Assamagan, K.; Astbury, A.; Astvatsatourov, A.; Atoian, G.; Auerbach, B.; Auge, E.; Augsten, K.; Aurousseau, M.; Austin, N.; Avolio, G.; Avramidou, R.; Axen, D.; Ay, C.; Azuelos, G.; Azuma, Y.; Baak, M. A.; Baccaglioni, G.; Bacci, C.; Bach, A.; Bachacou, H.; Bachas, K.; Backes, M.; Badescu, E.; Bagnaia, P.; Bai, Y.; Bailey, D. C.; Bain, T.; Baines, J. T.; Baker, O. K.; Baker, M. D.; Dos Santos Pedrosa, F. Baltasar; Banas, E.; Banerjee, P.; Banerjee, S.; Banfi, D.; Bangert, A.; Bansal, V.; Baranov, S. P.; Baranov, S.; Barashkou, A.; Barber, T.; Barberio, E. L.; Barberis, D.; Barbero, M.; Bardin, D. Y.; Barillari, T.; Barisonzi, M.; Barklow, T.; Barlow, N.; Barnett, B. M.; Barnett, R. M.; Baron, S.; Baroncelli, A.; Barr, A. J.; Barreiro, F.; Barreiro Guimarães da Costa, J.; Barrillon, P.; Barros, N.; Bartoldus, R.; Bartsch, D.; Bastos, J.; Bates, R. L.; Bathe, S.; Batkova, L.; Batley, J. R.; Battaglia, A.; Battistin, M.; Bauer, F.; Bawa, H. S.; Bazalova, M.; Beare, B.; Beau, T.; Beauchemin, P. H.; Beccherle, R.; Becerici, N.; Bechtle, P.; Beck, G. A.; Beck, H. P.; Beckingham, M.; Becks, K. H.; Bedajanek, I.; Beddall, A. J.; Beddall, A.; Bednár, P.; Bednyakov, V. A.; Bee, C.; Begel, M.; Behar Harpaz, S.; Behera, P. K.; Beimforde, M.; Belanger-Champagne, C.; Bell, P. J.; Bell, W. H.; Bella, G.; Bellagamba, L.; Bellina, F.; Bellomo, M.; Belloni, A.; Belotskiy, K.; Beltramello, O.; Ben Ami, S.; Benary, O.; Benchekroun, D.; Bendel, M.; Benedict, B. H.; Benekos, N.; Benhammou, Y.; Benincasa, G. P.; Benjamin, D. P.; Benoit, M.; Bensinger, J. R.; Benslama, K.; Bentvelsen, S.; Beretta, M.; Berge, D.; Bergeaas Kuutmann, E.; Berger, N.; Berghaus, F.; Berglund, E.; Beringer, J.; Bernardet, K.; Bernat, P.; Bernhard, R.; Bernius, C.; Berry, T.; Bertin, A.; Besson, N.; Bethke, S.; Bianchi, R. M.; Bianco, M.; Biebel, O.; Biesiada, J.; Biglietti, M.; Bilokon, H.; Bindi, M.; Binet, S.; Bingul, A.; Bini, C.; Biscarat, C.; Bitenc, U.; Black, K. M.; Blair, R. E.; Blanchard, J.-B.; Blanchot, G.; Blocker, C.; Blocki, J.; Blondel, A.; Blum, W.; Blumenschein, U.; Bobbink, G. J.; Bocci, A.; Boehler, M.; Boek, J.; Boelaert, N.; Böser, S.; Bogaerts, J. A.; Bogouch, A.; Bohm, C.; Bohm, J.; Boisvert, V.; Bold, T.; Boldea, V.; Boldyrev, A.; Bondarenko, V. G.; Bondioli, M.; Boonekamp, M.; Booth, J. R. A.; Bordoni, S.; Borer, C.; Borisov, A.; Borissov, G.; Borjanovic, I.; Borroni, S.; Bos, K.; Boscherini, D.; Bosman, M.; Bosteels, M.; Boterenbrood, H.; Bouchami, J.; Boudreau, J.; Bouhova-Thacker, E. V.; Boulahouache, C.; Bourdarios, C.; Boyd, J.; Boyko, I. R.; Bozovic-Jelisavcic, I.; Bracinik, J.; Braem, A.; Branchini, P.; Brandenburg, G. W.; Brandt, A.; Brandt, G.; Brandt, O.; Bratzler, U.; Brau, B.; Brau, J. E.; Braun, H. M.; Brelier, B.; Bremer, J.; Brenner, R.; Bressler, S.; Breton, D.; Brett, N. D.; Britton, D.; Brochu, F. M.; Brock, I.; Brock, R.; Brodbeck, T. J.; Brodet, E.; Broggi, F.; Bromberg, C.; Brooijmans, G.; Brooks, W. K.; Brown, G.; Brubaker, E.; Bruckman de Renstrom, P. A.; Bruncko, D.; Bruneliere, R.; Brunet, S.; Bruni, A.; Bruni, G.; Bruschi, M.; Buanes, T.; Bucci, F.; Buchanan, J.; Buchholz, P.; Buckley, A. G.; Budagov, I. A.; Budick, B.; Büscher, V.; Bugge, L.; Bulekov, O.; Bunse, M.; Buran, T.; Burckhart, H.; Burdin, S.; Burgess, T.; Burke, S.; Busato, E.; Bussey, P.; Buszello, C. P.; Butin, F.; Butler, B.; Butler, J. M.; Buttar, C. M.; Butterworth, J. M.; Byatt, T.; Caballero, J.; Cabrera Urbán, S.; Caforio, D.; Cakir, O.; Calafiura, P.; Calderini, G.; Calfayan, P.; Calkins, R.; Caloba, L. P.; Caloi, R.; Calvet, D.; Camarri, P.; Cambiaghi, M.; Cameron, D.; Campabadal Segura, F.; Campana, S.; Campanelli, M.; Canale, V.; Canelli, F.; Canepa, A.; Cantero, J.; Capasso, L.; Capeans Garrido, M. D. M.; Caprini, I.; Caprini, M.; Capua, M.; Caputo, R.; Caracinha, D.; Caramarcu, C.; Cardarelli, R.; Carli, T.; Carlino, G.; Carminati, L.; Caron, B.; Caron, S.; Carrillo Montoya, G. D.; Carron Montero, S.; Carter, A. A.; Carter, J. R.; Carvalho, J.; Casadei, D.; Casado, M. P.; Cascella, M.; Caso, C.; Castaneda Hernadez, A. M.; Castaneda-Miranda, E.; Castillo Gimenez, V.; Castro, N.; Cataldi, G.; Catinaccio, A.; Catmore, J. R.; Cattai, A.; Cattani, G.; Caughron, S.; Cauz, D.; Cavalleri, P.; Cavalli, D.; Cavalli-Sforza, M.; Cavasinni, V.; Ceradini, F.; Cerqueira, A. S.; Cerri, A.; Cerrito, L.; Cerutti, F.; Cetin, S. A.; Cevenini, F.; Chafaq, A.; Chakraborty, D.; Chan, K.; Chapman, J. D.; Chapman, J. W.; Chareyre, E.; Charlton, D. G.; Chavda, V.; Cheatham, S.; Chekanov, S.; Chekulaev, S. V.; Chelkov, G. A.; Chen, H.; Chen, S.; Chen, T.; Chen, X.; Cheng, S.; Cheplakov, A.; Chepurnov, V. F.; Cherkaoui El Moursli, R.; Tcherniatine, V.; Chesneanu, D.; Cheu, E.; Cheung, S. L.; Chevalier, L.; Chevallier, F.; Chiarella, V.; Chiefari, G.; Chikovani, L.; Childers, J. T.; Chilingarov, A.; Chiodini, G.; Chizhov, M.; Choudalakis, G.; Chouridou, S.; Chren, D.; Christidi, I. A.; Christov, A.; Chromek-Burckhart, D.; Chu, M. L.; Chudoba, J.; Ciapetti, G.; Ciftci, A. K.; Ciftci, R.; Cinca, D.; Cindro, V.; Ciobotaru, M. D.; Ciocca, C.; Ciocio, A.; Cirilli, M.; Citterio, M.; Clark, A.; Cleland, W.; Clemens, J. C.; Clement, B.; Clement, C.; Clements, D.; Coadou, Y.; Cobal, M.; Coccaro, A.; Cochran, J.; Coelli, S.; Coggeshall, J.; Cogneras, E.; Cojocaru, C. D.; Colas, J.; Cole, B.; Colijn, A. P.; Collard, C.; Collins, N. J.; Collins-Tooth, C.; Collot, J.; Colon, G.; Coluccia, R.; Conde Muiño, P.; Coniavitis, E.; Consonni, M.; Constantinescu, S.; Conta, C.; Conventi, F.; Cook, J.; Cooke, M.; Cooper, B. D.; Cooper-Sarkar, A. M.; Cooper-Smith, N. J.; Copic, K.; Cornelissen, T.; Corradi, M.; Corriveau, F.; Corso-Radu, A.; Cortes-Gonzalez, A.; Cortiana, G.; Costa, G.; Costa, M. J.; Costanzo, D.; Costin, T.; Côté, D.; Coura Torres, R.; Courneyea, L.; Cowan, G.; Cowden, C.; Cox, B. E.; Cranmer, K.; Cranshaw, J.; Cristinziani, M.; Crosetti, G.; Crupi, R.; Crépé-Renaudin, S.; Cuenca Almenar, C.; Cuhadar Donszelmann, T.; Curatolo, M.; Curtis, C. J.; Cwetanski, P.; Czyczula, Z.; D'Auria, S.; D'Onofrio, M.; D'Orazio, A.; da Silva, P. V. M.; da Via, C.; Dabrowski, W.; Dai, T.; Dallapiccola, C.; Dallison, S. J.; Daly, C. H.; Dam, M.; Danielsson, H. O.; Dannheim, D.; Dao, V.; Darbo, G.; Darlea, G. L.; Davey, W.; Davidek, T.; Davidson, N.; Davidson, R.; Davison, A. R.; Dawson, I.; Dawson, J. W.; Daya, R. K.; de, K.; de Asmundis, R.; de Castro, S.; de Castro Faria Salgado, P. E.; de Cecco, S.; de Graat, J.; de Groot, N.; de Jong, P.; de La Cruz-Burelo, E.; de La Taille, C.; de Mora, L.; de Oliveira Branco, M.; de Pedis, D.; de Salvo, A.; de Sanctis, U.; de Santo, A.; de Vivie de Regie, J. B.; de Zorzi, G.; Dean, S.; Deberg, H.; Dedes, G.; Dedovich, D. V.; Defay, P. O.; Degenhardt, J.; Dehchar, M.; Del Papa, C.; Del Peso, J.; Del Prete, T.; Dell'Acqua, A.; Dell'Asta, L.; Della Pietra, M.; Della Volpe, D.; Delmastro, M.; Delruelle, N.; Delsart, P. A.; Deluca, C.; Demers, S.; Demichev, M.; Demirkoz, B.; Deng, J.; Deng, W.; Denisov, S. P.; Dennis, C.; Derkaoui, J. E.; Derue, F.; Dervan, P.; Desch, K.; Deviveiros, P. O.; Dewhurst, A.; Dewilde, B.; Dhaliwal, S.; Dhullipudi, R.; di Ciaccio, A.; di Ciaccio, L.; di Domenico, A.; di Girolamo, A.; di Girolamo, B.; di Luise, S.; di Mattia, A.; di Nardo, R.; di Simone, A.; di Sipio, R.; Diaz, M. A.; Diblen, F.; Diehl, E. B.; Dietrich, J.; Diglio, S.; Dindar Yagci, K.; Dingfelder, D. J.; Dionisi, C.; Dita, P.; Dita, S.; Dittus, F.; Djama, F.; Djilkibaev, R.; Djobava, T.; Do Vale, M. A. B.; Do Valle Wemans, A.; Dobbs, M.; Dobos, D.; Dobson, E.; Dobson, M.; Dodd, J.; Dogan, O. B.; Doherty, T.; Doi, Y.; Dolejsi, J.; Dolenc, I.; Dolezal, Z.; Dolgoshein, B. A.; Dohmae, T.; Donega, M.; Donini, J.; Dopke, J.; Doria, A.; Dos Anjos, A.; Dotti, A.; Dova, M. T.; Doxiadis, A.; Doyle, A. T.; Drasal, Z.; Driouichi, C.; Dris, M.; Dubbert, J.; Duchovni, E.; Duckeck, G.; Dudarev, A.; Dudziak, F.; Dührssen, M.; Duflot, L.; Dufour, M.-A.; Dunford, M.; Duperrin, A.; Duran Yildiz, H.; Dushkin, A.; Duxfield, R.; Dwuznik, M.; Düren, M.; Ebenstein, W. L.; Ebke, J.; Eckert, S.; Eckweiler, S.; Edmonds, K.; Edwards, C. A.; Eerola, P.; Egorov, K.; Ehrenfeld, W.; Ehrich, T.; Eifert, T.; Eigen, G.; Einsweiler, K.; Eisenhandler, E.; Ekelof, T.; El Kacimi, M.; Ellert, M.; Elles, S.; Ellinghaus, F.; Ellis, K.; Ellis, N.; Elmsheuser, J.; Elsing, M.; Ely, R.; Emeliyanov, D.; Engelmann, R.; Engl, A.; Epp, B.; Eppig, A.; Epshteyn, V. S.; Ereditato, A.; Eriksson, D.; Ermoline, I.; Ernst, J.; Ernst, M.; Ernwein, J.; Errede, D.; Errede, S.; Ertel, E.; Escalier, M.; Escobar, C.; Espinal Curull, X.; Esposito, B.; Etienne, F.; Etienvre, A. I.; Etzion, E.; Evans, H.; Fabbri, L.; Fabre, C.; Faccioli, P.; Facius, K.; Fakhrutdinov, R. M.; Falciano, S.; Falou, A. C.; Fang, Y.; Fanti, M.; Farbin, A.; Farilla, A.; Farley, J.; Farooque, T.; Farrington, S. M.; Farthouat, P.; Fassi, F.; Fassnacht, P.; Fassouliotis, D.; Fatholahzadeh, B.; Fayard, L.; Fayette, F.; Febbraro, R.; Federic, P.; Fedin, O. L.; Fedorko, I.; Fedorko, W.; Feligioni, L.; Felzmann, C. U.; Feng, C.; Feng, E. J.; Fenyuk, A. B.; Ferencei, J.; Ferland, J.; Fernandes, B.; Fernando, W.; Ferrag, S.; Ferrando, J.; Ferrari, A.; Ferrari, P.; Ferrari, R.; Ferrer, A.; Ferrer, M. L.; Ferrere, D.; Ferretti, C.; Fiascaris, M.; Fiedler, F.; Filipčič, A.; Filippas, A.; Filthaut, F.; Fincke-Keeler, M.; Fiolhais, M. C. N.; Fiorini, L.; Firan, A.; Fischer, G.; Fisher, M. J.; Flechl, M.; Fleck, I.; Fleckner, J.; Fleischmann, P.; Fleischmann, S.; Flick, T.; Flores Castillo, L. R.; Flowerdew, M. J.; Föhlisch, F.; Fokitis, M.; Fonseca Martin, T.; Forbush, D. A.; Formica, A.; Forti, A.; Fortin, D.; Foster, J. M.; Fournier, D.; Foussat, A.; Fowler, A. J.; Fowler, K.; Fox, H.; Francavilla, P.; Franchino, S.; Francis, D.; Franklin, M.; Franz, S.; Fraternali, M.; Fratina, S.; Freestone, J.; French, S. T.; Froeschl, R.; Froidevaux, D.; Frost, J. A.; Fukunaga, C.; Fullana Torregrosa, E.; Fuster, J.; Gabaldon, C.; Gabizon, O.; Gadfort, T.; Gadomski, S.; Gagliardi, G.; Gagnon, P.; Galea, C.; Gallas, E. J.; Gallas, M. V.; Gallop, B. J.; Gallus, P.; Galyaev, E.; Gan, K. K.; Gao, Y. S.; Gaponenko, A.; Garcia-Sciveres, M.; García, C.; García Navarro, J. E.; Gardner, R. W.; Garelli, N.; Garitaonandia, H.; Garonne, V.; Gatti, C.; Gaudio, G.; Gaumer, O.; Gauzzi, P.; Gavrilenko, I. L.; Gay, C.; Gaycken, G.; Gayde, J.-C.; Gazis, E. N.; Ge, P.; Gee, C. N. P.; Geich-Gimbel, Ch.; Gellerstedt, K.; Gemme, C.; Genest, M. H.; Gentile, S.; Georgatos, F.; George, S.; Gerlach, P.; Gershon, A.; Geweniger, C.; Ghazlane, H.; Ghez, P.; Ghodbane, N.; Giacobbe, B.; Giagu, S.; Giakoumopoulou, V.; Giangiobbe, V.; Gianotti, F.; Gibbard, B.; Gibson, A.; Gibson, S. M.; Gilbert, L. M.; Gilchriese, M.; Gilewsky, V.; Gillberg, D.; Gillman, A. R.; Gingrich, D. M.; Ginzburg, J.; Giokaris, N.; Giordani, M. P.; Giordano, R.; Giovannini, P.; Giraud, P. F.; Girtler, P.; Giugni, D.; Giusti, P.; Gjelsten, B. K.; Gladilin, L. K.; Glasman, C.; Glazov, A.; Glitza, K. W.; Glonti, G. L.; Godfrey, J.; Godlewski, J.; Goebel, M.; Göpfert, T.; Goeringer, C.; Gössling, C.; Göttfert, T.; Goggi, V.; Goldfarb, S.; Goldin, D.; Golling, T.; Gollub, N. P.; Gomes, A.; Gomez Fajardo, L. S.; Gonçalo, R.; Gonella, L.; Gong, C.; González de La Hoz, S.; Gonzalez Silva, M. L.; Gonzalez-Sevilla, S.; Goodson, J. J.; Goossens, L.; Gorbounov, P. A.; Gordon, H. A.; Gorelov, I.; Gorfine, G.; Gorini, B.; Gorini, E.; Gorišek, A.; Gornicki, E.; Goryachev, S. V.; Goryachev, V. N.; Gosdzik, B.; Gosselink, M.; Gostkin, M. I.; Gough Eschrich, I.; Gouighri, M.; Goujdami, D.; Goulette, M. P.; Goussiou, A. G.; Goy, C.; Grabowska-Bold, I.; Grafström, P.; Grahn, K.-J.; Granado Cardoso, L.; Grancagnolo, F.; Grancagnolo, S.; Grassi, V.; Gratchev, V.; Grau, N.; Gray, H. M.; Gray, J. A.; Graziani, E.; Green, B.; Greenshaw, T.; Greenwood, Z. D.; Gregor, I. M.; Grenier, P.; Griesmayer, E.; Griffiths, J.; Grigalashvili, N.; Grillo, A. A.; Grimm, K.; Grinstein, S.; Grishkevich, Y. V.; Groer, L. S.; Grognuz, J.; Groh, M.; Groll, M.; Gross, E.; Grosse-Knetter, J.; Groth-Jensen, J.; Grybel, K.; Guarino, V. J.; Guicheney, C.; Guida, A.; Guillemin, T.; Guler, H.; Gunther, J.; Guo, B.; Gupta, A.; Gusakov, Y.; Gutierrez, A.; Gutierrez, P.; Guttman, N.; Gutzwiller, O.; Guyot, C.; Gwenlan, C.; Gwilliam, C. B.; Haas, A.; Haas, S.; Haber, C.; Hackenburg, R.; Hadavand, H. K.; Hadley, D. R.; Haefner, P.; Härtel, R.; Hajduk, Z.; Hakobyan, H.; Haller, J.; Hamacher, K.; Hamilton, A.; Hamilton, S.; Han, H.; Han, L.; Hanagaki, K.; Hance, M.; Handel, C.; Hanke, P.; Hansen, J. R.; Hansen, J. B.; Hansen, J. D.; Hansen, P. H.; Hansl-Kozanecka, T.; Hansson, P.; Hara, K.; Hare, G. A.; Harenberg, T.; Harrington, R. D.; Harris, O. B.; Harris, O. M.; Harrison, K.; Hartert, J.; Hartjes, F.; Haruyama, T.; Harvey, A.; Hasegawa, S.; Hasegawa, Y.; Hashemi, K.; Hassani, S.; Hatch, M.; Haug, F.; Haug, S.; Hauschild, M.; Hauser, R.; Havranek, M.; Hawkes, C. M.; Hawkings, R. J.; Hawkins, D.; Hayakawa, T.; Hayward, H. S.; Haywood, S. J.; He, M.; Head, S. J.; Hedberg, V.; Heelan, L.; Heim, S.; Heinemann, B.; Heisterkamp, S.; Helary, L.; Heller, M.; Hellman, S.; Helsens, C.; Hemperek, T.; Henderson, R. C. W.; Henke, M.; Henrichs, A.; Correia, A. M. Henriques; Henrot-Versille, S.; Hensel, C.; Henß, T.; Hershenhorn, A. D.; Herten, G.; Hertenberger, R.; Hervas, L.; Hessey, N. P.; Hidvegi, A.; Higón-Rodriguez, E.; Hill, D.; Hill, J. C.; Hiller, K. H.; Hillier, S. J.; Hinchliffe, I.; Hirose, M.; Hirsch, F.; Hobbs, J.; Hod, N.; Hodgkinson, M. C.; Hodgson, P.; Hoecker, A.; Hoeferkamp, M. R.; Hoffman, J.; Hoffmann, D.; Hohlfeld, M.; Holmgren, S. O.; Holy, T.; Holzbauer, J. L.; Homma, Y.; Homola, P.; Horazdovsky, T.; Hori, T.; Horn, C.; Horner, S.; Horvat, S.; Hostachy, J.-Y.; Hou, S.; Houlden, M. A.; Hoummada, A.; Howe, T.; Hrivnac, J.; Hryn'ova, T.; Hsu, P. J.; Hsu, S.-C.; Huang, G. S.; Hubacek, Z.; Hubaut, F.; Huegging, F.; Hughes, E. W.; Hughes, G.; Hughes-Jones, R. E.; Hurst, P.; Hurwitz, M.; Husemann, U.; Huseynov, N.; Huston, J.; Huth, J.; Iacobucci, G.; Iakovidis, G.; Ibragimov, I.; Iconomidou-Fayard, L.; Idarraga, J.; Iengo, P.; Igonkina, O.; Ikegami, Y.; Ikeno, M.; Ilchenko, Y.; Iliadis, D.; Ilyushenka, Y.; Imori, M.; Ince, T.; Ioannou, P.; Iodice, M.; Irles Quiles, A.; Ishikawa, A.; Ishino, M.; Ishmukhametov, R.; Isobe, T.; Issakov, V.; Issever, C.; Istin, S.; Itoh, Y.; Ivashin, A. V.; Iwanski, W.; Iwasaki, H.; Izen, J. M.; Izzo, V.; Jackson, J. N.; Jackson, P.; Jaekel, M.; Jahoda, M.; Jain, V.; Jakobs, K.; Jakobsen, S.; Jakubek, J.; Jana, D.; Jansen, E.; Jantsch, A.; Janus, M.; Jared, R. C.; Jarlskog, G.; Jarron, P.; Jeanty, L.; Jelen, K.; Jen-La Plante, I.; Jenni, P.; Jez, P.; Jézéquel, S.; Ji, W.; Jia, J.; Jiang, Y.; Jimenez Belenguer, M.; Jin, G.; Jin, S.; Jinnouchi, O.; Joffe, D.; Johansen, M.; Johansson, K. E.; Johansson, P.; Johnert, S.; Johns, K. A.; Jon-And, K.; Jones, G.; Jones, R. W. L.; Jones, T. W.; Jones, T. J.; Jonsson, O.; Joos, D.; Joram, C.; Jorge, P. M.; Juranek, V.; Jussel, P.; Kabachenko, V. V.; Kabana, S.; Kaci, M.; Kaczmarska, A.; Kado, M.; Kagan, H.; Kagan, M.; Kaiser, S.; Kajomovitz, E.; Kalinovskaya, L. V.; Kalinowski, A.; Kama, S.; Kanaya, N.; Kaneda, M.; Kantserov, V. A.; Kanzaki, J.; Kaplan, B.; Kapliy, A.; Kaplon, J.; Karagounis, M.; Karagoz Unel, M.; Kartvelishvili, V.; Karyukhin, A. N.; Kashif, L.; Kasmi, A.; Kass, R. D.; Kastanas, A.; Kastoryano, M.; Kataoka, M.; Kataoka, Y.; Katsoufis, E.; Katzy, J.; Kaushik, V.; Kawagoe, K.; Kawamoto, T.; Kawamura, G.; Kayl, M. S.; Kayumov, F.; Kazanin, V. A.; Kazarinov, M. Y.; Kazi, S. I.; Keates, J. R.; Keeler, R.; Keener, P. T.; Kehoe, R.; Keil, M.; Kekelidze, G. D.; Kelly, M.; Kennedy, J.; Kenyon, M.; Kepka, O.; Kerschen, N.; Kerševan, B. P.; Kersten, S.; Kessoku, K.; Khakzad, M.; Khalil-Zada, F.; Khandanyan, H.; Khanov, A.; Kharchenko, D.; Khodinov, A.; Kholodenko, A. G.; Khomich, A.; Khoriauli, G.; Khovanskiy, N.; Khovanskiy, V.; Khramov, E.; Khubua, J.; Kilvington, G.; Kim, H.; Kim, M. S.; Kim, P. C.; Kim, S. H.; Kind, O.; Kind, P.; King, B. T.; Kirk, J.; Kirsch, G. P.; Kirsch, L. E.; Kiryunin, A. E.; Kisielewska, D.; Kittelmann, T.; Kiyamura, H.; Kladiva, E.; Klein, M.; Klein, U.; Kleinknecht, K.; Klemetti, M.; Klier, A.; Klimentov, A.; Klingenberg, R.; Klinkby, E. B.; Klioutchnikova, T.; Klok, P. F.; Klous, S.; Kluge, E.-E.; Kluge, T.; Kluit, P.; Klute, M.; Kluth, S.; Knecht, N. S.; Kneringer, E.; Ko, B. R.; Kobayashi, T.; Kobel, M.; Koblitz, B.; Kocian, M.; Kocnar, A.; Kodys, P.; Köneke, K.; König, A. C.; Köpke, L.; Koetsveld, F.; Koevesarki, P.; Koffas, T.; Koffeman, E.; Kohn, F.; Kohout, Z.; Kohriki, T.; Kokott, T.; Kolanoski, H.; Kolesnikov, V.; Koletsou, I.; Koll, J.; Kollar, D.; Kolos, S.; Kolya, S. D.; Komar, A. A.; Komaragiri, J. R.; Kondo, T.; Kono, T.; Kononov, A. I.; Konoplich, R.; Konovalov, S. P.; Konstantinidis, N.; Koperny, S.; Korcyl, K.; Kordas, K.; Koreshev, V.; Korn, A.; Korolkov, I.; Korolkova, E. V.; Korotkov, V. A.; Kortner, O.; Kostka, P.; Kostyukhin, V. V.; Kotamäki, M. J.; Kotov, S.; Kotov, V. M.; Kotov, K. Y.; Koupilova, Z.; Kourkoumelis, C.; Koutsman, A.; Kowalewski, R.; Kowalski, H.; Kowalski, T. Z.; Kozanecki, W.; Kozhin, A. S.; Kral, V.; Kramarenko, V. A.; Kramberger, G.; Krasny, M. W.; Krasznahorkay, A.; Kreisel, A.; Krejci, F.; Krepouri, A.; Kretzschmar, J.; Krieger, P.; Krobath, G.; Kroeninger, K.; Kroha, H.; Kroll, J.; Kroseberg, J.; Krstic, J.; Kruchonak, U.; Krüger, H.; Krumshteyn, Z. V.; Kubota, T.; Kuehn, S.; Kugel, A.; Kuhl, T.; Kuhn, D.; Kukhtin, V.; Kulchitsky, Y.; Kuleshov, S.; Kummer, C.; Kuna, M.; Kupco, A.; Kurashige, H.; Kurata, M.; Kurchaninov, L. L.; Kurochkin, Y. A.; Kus, V.; Kuykendall, W.; Kuznetsova, E.; Kvasnicka, O.; Kwee, R.; La Rosa, M.; La Rotonda, L.; Labarga, L.; Labbe, J.; Lacasta, C.; Lacava, F.; Lacker, H.; Lacour, D.; Lacuesta, V. R.; Ladygin, E.; Lafaye, R.; Laforge, B.; Lagouri, T.; Lai, S.; Lamanna, M.; Lampen, C. L.; Lampl, W.; Lancon, E.; Landgraf, U.; Landon, M. P. J.; Lane, J. L.; Lankford, A. J.; Lanni, F.; Lantzsch, K.; Lanza, A.; Laplace, S.; Lapoire, C.; Laporte, J. F.; Lari, T.; Larionov, A. V.; Larner, A.; Lasseur, C.; Lassnig, M.; Laurelli, P.; Lavrijsen, W.; Laycock, P.; Lazarev, A. B.; Lazzaro, A.; Le Dortz, O.; Le Guirriec, E.; Le Maner, C.; Le Menedeu, E.; Le Vine, M.; Leahu, M.; Lebedev, A.; Lebel, C.; Lecompte, T.; Ledroit-Guillon, F.; Lee, H.; Lee, J. S. H.; Lee, S. C.; Lefebvre, M.; Legendre, M.; Legeyt, B. C.; Legger, F.; Leggett, C.; Lehmacher, M.; Lehmann Miotto, G.; Lei, X.; Leitner, R.; Lelas, D.; Lellouch, D.; Lellouch, J.; Leltchouk, M.; Lendermann, V.; Leney, K. J. C.; Lenz, T.; Lenzen, G.; Lenzi, B.; Leonhardt, K.; Leroy, C.; Lessard, J.-R.; Lester, C. G.; Leung Fook Cheong, A.; Levêque, J.; Levin, D.; Levinson, L. J.; Levitski, M. S.; Levonian, S.; Lewandowska, M.; Leyton, M.; Li, H.; Li, J.; Li, S.; Li, X.; Liang, Z.; Liang, Z.; Liberti, B.; Lichard, P.; Lichtnecker, M.; Lie, K.; Liebig, W.; Liko, D.; Lilley, J. N.; Lim, H.; Limosani, A.; Limper, M.; Lin, S. C.; Lindsay, S. W.; Linhart, V.; Linnemann, J. T.; Liolios, A.; Lipeles, E.; Lipinsky, L.; Lipniacka, A.; Liss, T. M.; Lissauer, D.; Litke, A. M.; Liu, C.; Liu, D.; Liu, H.; Liu, J. B.; Liu, M.; Liu, S.; Liu, T.; Liu, Y.; Livan, M.; Lleres, A.; Lloyd, S. L.; Lobodzinska, E.; Loch, P.; Lockman, W. S.; Lockwitz, S.; Loddenkoetter, T.; Loebinger, F. K.; Loginov, A.; Loh, C. W.; Lohse, T.; Lohwasser, K.; Lokajicek, M.; Loken, J.; Lopes, L.; Lopez Mateos, D.; Losada, M.; Loscutoff, P.; Losty, M. J.; Lou, X.; Lounis, A.; Loureiro, K. F.; Lovas, L.; Love, J.; Love, P.; Lowe, A. J.; Lu, F.; Lu, J.; Lubatti, H. J.; Luci, C.; Lucotte, A.; Ludwig, A.; Ludwig, D.; Ludwig, I.; Ludwig, J.; Luehring, F.; Luisa, L.; Lumb, D.; Luminari, L.; Lund, E.; Lund-Jensen, B.; Lundberg, B.; Lundberg, J.; Lundquist, J.; Lutz, G.; Lynn, D.; Lys, J.; Lytken, E.; Ma, H.; Ma, L. L.; Maccarrone, G.; Macchiolo, A.; Maček, B.; Miguens, J. Machado; Mackeprang, R.; Madaras, R. J.; Mader, W. F.; Maenner, R.; Maeno, T.; Mättig, P.; Mättig, S.; Magalhaes Martins, P. J.; Magradze, E.; Magrath, C. A.; Mahalalel, Y.; Mahboubi, K.; Mahmood, A.; Mahout, G.; Maiani, C.; Maidantchik, C.; Maio, A.; Majewski, S.; Makida, Y.; Makouski, M.; Makovec, N.; Malecki, Pa.; Malecki, P.; Maleev, V. P.; Malek, F.; Mallik, U.; Malon, D.; Maltezos, S.; Malyshev, V.; Malyukov, S.; Mambelli, M.; Mameghani, R.; Mamuzic, J.; Manabe, A.; Mandelli, L.; Mandić, I.; Mandrysch, R.; Maneira, J.; Mangeard, P. S.; Manjavidze, I. D.; Manousakis-Katsikakis, A.; Mansoulie, B.; Mapelli, A.; Mapelli, L.; March, L.; Marchand, J. F.; Marchese, F.; Marcisovsky, M.; Marino, C. P.; Marques, C. N.; Marroquim, F.; Marshall, R.; Marshall, Z.; Martens, F. K.; Marti I Garcia, S.; Martin, A. J.; Martin, A. J.; Martin, B.; Martin, B.; Martin, F. F.; Martin, J. P.; Martin, T. A.; Martin Dit Latour, B.; Martinez, M.; Martinez Outschoorn, V.; Martini, A.; Martynenko, V.; Martyniuk, A. C.; Maruyama, T.; Marzano, F.; Marzin, A.; Masetti, L.; Mashimo, T.; Mashinistov, R.; Masik, J.; Maslennikov, A. L.; Massaro, G.; Massol, N.; Mastroberardino, A.; Masubuchi, T.; Mathes, M.; Matricon, P.; Matsumoto, H.; Matsunaga, H.; Matsushita, T.; Mattravers, C.; Maxfield, S. J.; May, E. N.; Mayne, A.; Mazini, R.; Mazur, M.; Mazzanti, M.; Mazzanti, P.; Mc Donald, J.; Mc Kee, S. P.; McCarn, A.; McCarthy, R. L.; McCubbin, N. A.; McFarlane, K. W.; McGlone, H.; McHedlidze, G.; McLaren, R. A.; McMahon, S. J.; McMahon, T. R.; McPherson, R. A.; Meade, A.; Mechnich, J.; Mechtel, M.; Medinnis, M.; Meera-Lebbai, R.; Meguro, T. M.; Mehdiyev, R.; Mehlhase, S.; Mehta, A.; Meier, K.; Meirose, B.; Melamed-Katz, A.; Mellado Garcia, B. R.; Meng, Z.; Menke, S.; Meoni, E.; Merkl, D.; Mermod, P.; Merola, L.; Meroni, C.; Merritt, F. S.; Messina, A. M.; Messmer, I.; Metcalfe, J.; Mete, A. S.; Meyer, J.-P.; Meyer, J.; Meyer, T. C.; Meyer, W. T.; Miao, J.; Micu, L.; Middleton, R. P.; Migas, S.; Mijović, L.; Mikenberg, G.; Mikuž, M.; Miller, D. W.; Mills, W. J.; Mills, C. M.; Milov, A.; Milstead, D. A.; Minaenko, A. A.; Miñano, M.; Minashvili, I. A.; Mincer, A. I.; Mindur, B.; Mineev, M.; Mir, L. M.; Mirabelli, G.; Misawa, S.; Miscetti, S.; Misiejuk, A.; Mitrevski, J.; Mitsou, V. A.; Miyagawa, P. S.; Mjörnmark, J. U.; Mladenov, D.; Moa, T.; Mockett, P.; Moed, S.; Moeller, V.; Mönig, K.; Möser, N.; Mohn, B.; Mohr, W.; Mohrdieck-Möck, S.; Moles-Valls, R.; Molina-Perez, J.; Moloney, G.; Monk, J.; Monnier, E.; Montesano, S.; Monticelli, F.; Moore, R. W.; Herrera, C. Mora; Moraes, A.; Morais, A.; Morel, J.; Morello, G.; Moreno, D.; Moreno Llácer, M.; Morettini, P.; Morii, M.; Morley, A. K.; Mornacchi, G.; Morozov, S. V.; Morris, J. D.; Moser, H. G.; Mosidze, M.; Moss, J.; Mount, R.; Mountricha, E.; Mouraviev, S. V.; Moyse, E. J. W.; Mudrinic, M.; Mueller, F.; Mueller, J.; Mueller, K.; Müller, T. A.; Muenstermann, D.; Muir, A.; Murillo Garcia, R.; Murray, W. J.; Mussche, I.; Musto, E.; Myagkov, A. G.; Myska, M.; Nadal, J.; Nagai, K.; Nagano, K.; Nagasaka, Y.; Nairz, A. M.; Nakamura, K.; Nakano, I.; Nakatsuka, H.; Nanava, G.; Napier, A.; Nash, M.; Nation, N. R.; Nattermann, T.; Naumann, T.; Navarro, G.; Nderitu, S. K.; Neal, H. A.; Nebot, E.; Nechaeva, P.; Negri, A.; Negri, G.; Nelson, A.; Nelson, T. K.; Nemecek, S.; Nemethy, P.; Nepomuceno, A. A.; Nessi, M.; Neubauer, M. S.; Neusiedl, A.; Neves, R. N.; Nevski, P.; Newcomer, F. M.; Nicholson, C.; Nickerson, R. B.; Nicolaidou, R.; Nicolas, L.; Nicoletti, G.; Niedercorn, F.; Nielsen, J.; Nikiforov, A.; Nikolaev, K.; Nikolic-Audit, I.; Nikolopoulos, K.; Nilsen, H.; Nilsson, P.; Nisati, A.; Nishiyama, T.; Nisius, R.; Nodulman, L.; Nomachi, M.; Nomidis, I.; Nomoto, H.; Nordberg, M.; Nordkvist, B.; Notz, D.; Novakova, J.; Nozaki, M.; Nožička, M.; Nugent, I. M.; Nuncio-Quiroz, A.-E.; Nunes Hanninger, G.; Nunnemann, T.; Nurse, E.; O'Neil, D. C.; O'Shea, V.; Oakham, F. G.; Oberlack, H.; Ochi, A.; Oda, S.; Odaka, S.; Odier, J.; Odino, G. A.; Ogren, H.; Oh, S. H.; Ohm, C. C.; Ohshima, T.; Ohshita, H.; Ohsugi, T.; Okada, S.; Okawa, H.; Okumura, Y.; Olcese, M.; Olchevski, A. G.; Oliveira, M.; Oliveira Damazio, D.; Oliver, J.; Oliver Garcia, E.; Olivito, D.; Olszewski, A.; Olszowska, J.; Omachi, C.; Onofre, A.; Onyisi, P. U. E.; Oram, C. J.; Ordonez, G.; Oreglia, M. J.; Oren, Y.; Orestano, D.; Orlov, I.; Oropeza Barrera, C.; Orr, R. S.; Ortega, E. O.; Osculati, B.; Osuna, C.; Otec, R.; P Ottersbach, J.; Ould-Saada, F.; Ouraou, A.; Ouyang, Q.; Owen, M.; Owen, S.; Ozcan, V. E.; Ozone, K.; Ozturk, N.; Pacheco Pages, A.; Padhi, S.; Padilla Aranda, C.; Paganis, E.; Pahl, C.; Paige, F.; Pajchel, K.; Pal, A.; Palestini, S.; Pallin, D.; Palma, A.; Palmer, J. D.; Pan, Y. B.; Panagiotopoulou, E.; Panes, B.; Panikashvili, N.; Panitkin, S.; Pantea, D.; Panuskova, M.; Paolone, V.; Papadopoulou, Th. D.; Park, S. J.; Park, W.; Parker, M. A.; Parker, S. I.; Parodi, F.; Parsons, J. A.; Parzefall, U.; Pasqualucci, E.; Passardi, G.; Passeri, A.; Pastore, F.; Pastore, Fr.; Pásztor, G.; Pataraia, S.; Pater, J. R.; Patricelli, S.; Patwa, A.; Pauly, T.; Peak, L. S.; Pecsy, M.; Pedraza Morales, M. I.; Peleganchuk, S. V.; Peng, H.; Penson, A.; Penwell, J.; Perantoni, M.; Perez, K.; Perez Codina, E.; Pérez García-Estañ, M. T.; Perez Reale, V.; Perini, L.; Pernegger, H.; Perrino, R.; Perrodo, P.; Persembe, S.; Perus, P.; Peshekhonov, V. D.; Petersen, B. A.; Petersen, J.; Petersen, T. C.; Petit, E.; Petridou, C.; Petrolo, E.; Petrucci, F.; Petschull, D.; Petteni, M.; Pezoa, R.; Pfeifer, B.; Phan, A.; Phillips, A. W.; Piacquadio, G.; Piccinini, M.; Piegaia, R.; Pilcher, J. E.; Pilkington, A. D.; Pina, J.; Pinamonti, M.; Pinfold, J. L.; Ping, J.; Pinto, B.; Pirotte, O.; Pizio, C.; Placakyte, R.; Plamondon, M.; Plano, W. G.; Pleier, M.-A.; Poblaguev, A.; Poddar, S.; Podlyski, F.; Poffenberger, P.; Poggioli, L.; Pohl, M.; Polci, F.; Polesello, G.; Policicchio, A.; Polini, A.; Poll, J.; Polychronakos, V.; Pomarede, D. M.; Pomeroy, D.; Pommès, K.; Pontecorvo, L.; Pope, B. G.; Popovic, D. S.; Poppleton, A.; Popule, J.; Portell Bueso, X.; Porter, R.; Pospelov, G. E.; Pospichal, P.; Pospisil, S.; Potekhin, M.; Potrap, I. N.; Potter, C. J.; Potter, C. T.; Potter, K. P.; Poulard, G.; Poveda, J.; Prabhu, R.; Pralavorio, P.; Prasad, S.; Pravahan, R.; Preda, T.; Pretzl, K.; Pribyl, L.; Price, D.; Price, L. E.; Prichard, P. M.; Prieur, D.; Primavera, M.; Prokofiev, K.; Prokoshin, F.; Protopopescu, S.; Proudfoot, J.; Prudent, X.; Przysiezniak, H.; Psoroulas, S.; Ptacek, E.; Puigdengoles, C.; Purdham, J.; Purohit, M.; Puzo, P.; Pylypchenko, Y.; Qi, M.; Qian, J.; Qian, W.; Qian, Z.; Qin, Z.; Qing, D.; Quadt, A.; Quarrie, D. R.; Quayle, W. B.; Quinonez, F.; Raas, M.; Radeka, V.; Radescu, V.; Radics, B.; Rador, T.; Ragusa, F.; Rahal, G.; Rahimi, A. M.; Rahm, D.; Rajagopalan, S.; Rammes, M.; Ratoff, P. N.; Rauscher, F.; Rauter, E.; Raymond, M.; Read, A. L.; Rebuzzi, D. M.; Redelbach, A.; Redlinger, G.; Reece, R.; Reeves, K.; Reinherz-Aronis, E.; Reinsch, A.; Reisinger, I.; Reljic, D.; Rembser, C.; Ren, Z. L.; Renkel, P.; Rescia, S.; Rescigno, M.; Resconi, S.; Resende, B.; Reznicek, P.; Rezvani, R.; Richards, A.; Richards, R. A.; Richter, D.; Richter, R.; Richter-Was, E.; Ridel, M.; Rieke, S.; Rijpstra, M.; Rijssenbeek, M.; Rimoldi, A.; Rinaldi, L.; Rios, R. R.; Riu, I.; Rivoltella, G.; Rizatdinova, F.; Rizvi, E. R.; Roa Romero, D. A.; Robertson, S. H.; Robichaud-Veronneau, A.; Robinson, D.; Robinson, M.; Robson, A.; Rocha de Lima, J. G.; Roda, C.; Rodriguez, D.; Rodriguez Garcia, Y.; Roe, S.; Røhne, O.; Rojo, V.; Rolli, S.; Romaniouk, A.; Romanov, V. M.; Romeo, G.; Romero Maltrana, D.; Roos, L.; Ros, E.; Rosati, S.; Rosenbaum, G. A.; Rosenberg, E. I.; Rosselet, L.; Rossi, L. P.; Rotaru, M.; Rothberg, J.; Rottländer, I.; Rousseau, D.; Royon, C. R.; Rozanov, A.; Rozen, Y.; Ruan, X.; Ruckert, B.; Ruckstuhl, N.; Rud, V. I.; Rudolph, G.; Rühr, F.; Ruggieri, F.; Ruiz-Martinez, A.; Rumyantsev, L.; Rusakovich, N. A.; Rutherfoord, J. P.; Ruwiedel, C.; Ruzicka, P.; Ryabov, Y. F.; Ryadovikov, V.; Ryan, P.; Rybkin, G.; Rzaeva, S.; Saavedra, A. F.; Sadrozinski, H. F.-W.; Sadykov, R.; Sakamoto, H.; Salamanna, G.; Salamon, A.; Saleem, M.; Salihagic, D.; Salnikov, A.; Salt, J.; Salvachua Ferrando, B. M.; Salvatore, D.; Salvatore, F.; Salvucci, A.; Salzburger, A.; Sampsonidis, D.; Samset, B. H.; Sanchis Lozano, M. A.; Sandaker, H.; Sander, H. G.; Sanders, M. P.; Sandhoff, M.; Sandstroem, R.; Sandvoss, S.; Sankey, D. P. C.; Sanny, B.; Sansoni, A.; Santamarina Rios, C.; Santi, L.; Santoni, C.; Santonico, R.; Santos, D.; Santos, J.; Saraiva, J. G.; Sarangi, T.; Sarkisyan-Grinbaum, E.; Sarri, F.; Sasaki, O.; Sasaki, T.; Sasao, N.; Satsounkevitch, I.; Sauvage, G.; Savard, P.; Savine, A. Y.; Savinov, V.; Sawyer, L.; Saxon, D. H.; Says, L. P.; Sbarra, C.; Sbrizzi, A.; Scannicchio, D. A.; Schaarschmidt, J.; Schacht, P.; Schäfer, U.; Schaetzel, S.; Schaffer, A. C.; Schaile, D.; Schamberger, R. D.; Schamov, A. G.; Schegelsky, V. A.; Scheirich, D.; Schernau, M.; Scherzer, M. I.; Schiavi, C.; Schieck, J.; Schioppa, M.; Schlenker, S.; Schlereth, J. L.; Schmid, P.; Schmidt, M. P.; Schmieden, K.; Schmitt, C.; Schmitz, M.; Schott, M.; Schouten, D.; Schovancova, J.; Schram, M.; Schreiner, A.; Schroeder, C.; Schroer, N.; Schroers, M.; Schuler, G.; Schultes, J.; Schultz-Coulon, H.-C.; Schumacher, J.; Schumacher, M.; Schumm, B. A.; Schune, Ph.; Schwanenberger, C.; Schwartzman, A.; Schwemling, Ph.; Schwienhorst, R.; Schwierz, R.; Schwindling, J.; Scott, W. G.; Searcy, J.; Sedykh, E.; Segura, E.; Seidel, S. C.; Seiden, A.; Seifert, F.; Seixas, J. M.; Sekhniaidze, G.; Seliverstov, D. M.; Sellden, B.; Seman, M.; Semprini-Cesari, N.; Serfon, C.; Serin, L.; Seuster, R.; Severini, H.; Sevior, M. E.; Sfyrla, A.; Shamim, M.; Shan, L. Y.; Shank, J. T.; Shao, Q. T.; Shapiro, M.; Shatalov, P. B.; Shaver, L.; Shaw, C.; Shaw, K.; Sherman, D.; Sherwood, P.; Shibata, A.; Shimojima, M.; Shin, T.; Shmeleva, A.; Shochet, M. J.; Shupe, M. A.; Sicho, P.; Sidoti, A.; Siebel, A.; Siegert, F.; Siegrist, J.; Sijacki, Dj.; Silbert, O.; Silva, J.; Silver, Y.; Silverstein, D.; Silverstein, S. B.; Simak, V.; Simic, Lj.; Simion, S.; Simmons, B.; Simonyan, M.; Sinervo, P.; Sinev, N. B.; Sipica, V.; Siragusa, G.; Sisakyan, A. N.; Sivoklokov, S. Yu.; Sjoelin, J.; Sjursen, T. B.; Skubic, P.; Skvorodnev, N.; Slater, M.; Slavicek, T.; Sliwa, K.; Sloper, J.; Sluka, T.; Smakhtin, V.; Smirnov, S. Yu.; Smirnov, Y.; Smirnova, L. N.; Smirnova, O.; Smith, B. C.; Smith, D.; Smith, K. M.; Smizanska, M.; Smolek, K.; Snesarev, A. A.; Snow, S. W.; Snow, J.; Snuverink, J.; Snyder, S.; Soares, M.; Sobie, R.; Sodomka, J.; Soffer, A.; Solans, C. A.; Solar, M.; Solfaroli Camillocci, E.; Solodkov, A. A.; Solovyanov, O. V.; Soluk, R.; Sondericker, J.; Sopko, V.; Sopko, B.; Sosebee, M.; Sosnovtsev, V. V.; Sospedra Suay, L.; Soukharev, A.; Spagnolo, S.; Spanò, F.; Speckmayer, P.; Spencer, E.; Spighi, R.; Spigo, G.; Spila, F.; Spiwoks, R.; Spousta, M.; Spreitzer, T.; Spurlock, B.; Denis, R. D. St.; Stahl, T.; Stamen, R.; Stancu, S. N.; Stanecka, E.; Stanek, R. W.; Stanescu, C.; Stapnes, S.; Starchenko, E. A.; Stark, J.; Staroba, P.; Starovoitov, P.; Stastny, J.; Staude, A.; Stavina, P.; Stavropoulos, G.; Steinbach, P.; Steinberg, P.; Stekl, I.; Stelzer, B.; Stelzer, H. J.; Stelzer-Chilton, O.; Stenzel, H.; Stevenson, K.; Stewart, G.; Stockton, M. C.; Stoerig, K.; Stoicea, G.; Stonjek, S.; Strachota, P.; Stradling, A.; Straessner, A.; Strandberg, J.; Strandberg, S.; Strandlie, A.; Strauss, M.; Strizenec, P.; Ströhmer, R.; Strom, D. M.; Strong, J. A.; Stroynowski, R.; Strube, J.; Stugu, B.; Stumer, I.; Soh, D. A.; Su, D.; Suchkov, S. I.; Sugaya, Y.; Sugimoto, T.; Suhr, C.; Suk, M.; Sulin, V. V.; Sultansoy, S.; Sumida, T.; Sun, X.; Sundermann, J. E.; Suruliz, K.; Sushkov, S.; Susinno, G.; Sutton, M. R.; Suzuki, T.; Suzuki, Y.; Sviridov, Yu. M.; Sykora, I.; Sykora, T.; Szymocha, T.; Sánchez, J.; Ta, D.; Tackmann, K.; Taffard, A.; Tafirout, R.; Taga, A.; Takahashi, Y.; Takai, H.; Takashima, R.; Takeda, H.; Takeshita, T.; Talby, M.; Talyshev, A.; Tamsett, M. C.; Tanaka, J.; Tanaka, R.; Tanaka, S.; Tanaka, S.; Tappern, G. P.; Tapprogge, S.; Tardif, D.; Tarem, S.; Tarrade, F.; Tartarelli, G. F.; Tas, P.; Tasevsky, M.; Tassi, E.; Taylor, C.; Taylor, F. E.; Taylor, G. N.; Taylor, R. P.; Taylor, W.; Teixeira-Dias, P.; Ten Kate, H.; Teng, P. K.; Terada, S.; Terashi, K.; Terron, J.; Terwort, M.; Testa, M.; Teuscher, R. J.; Tevlin, C. M.; Thadome, J.; Thananuwong, R.; Thioye, M.; Thoma, S.; Thomas, J. P.; Thomas, T. L.; Thompson, E. N.; Thompson, P. D.; Thompson, P. D.; Thompson, R. J.; Thompson, A. S.; Thomson, E.; Thun, R. P.; Tic, T.; Tikhomirov, V. O.; Tikhonov, Y. A.; Timmermans, C. J. W. P.; Tipton, P.; Tique Aires Viegas, F. J.; Tisserant, S.; Tobias, J.; Toczek, B.; Todorov, T.; Todorova-Nova, S.; Toggerson, B.; Tojo, J.; Tokár, S.; Tokushuku, K.; Tollefson, K.; Tomasek, L.; Tomasek, M.; Tomasz, F.; Tomoto, M.; Tompkins, D.; Tompkins, L.; Toms, K.; Tong, G.; Tonoyan, A.; Topfel, C.; Topilin, N. D.; Torrence, E.; Torró Pastor, E.; Toth, J.; Touchard, F.; Tovey, D. R.; Tovey, S. N.; Trefzger, T.; Tremblet, L.; Tricoli, A.; Trigger, I. M.; Trincaz-Duvoid, S.; Trinh, T. N.; Tripiana, M. F.; Triplett, N.; Trivedi, A.; Trocmé, B.; Troncon, C.; Trzupek, A.; Tsarouchas, C.; Tseng, J. C.-L.; Tsiafis, I.; Tsiakiris, M.; Tsiareshka, P. V.; Tsionou, D.; Tsipolitis, G.; Tsiskaridze, V.; Tskhadadze, E. G.; Tsukerman, I. I.; Tsulaia, V.; Tsung, J.-W.; Tsuno, S.; Tsybychev, D.; Turala, M.; Turecek, D.; Turk Cakir, I.; Turlay, E.; Tuts, P. M.; Twomey, M. S.; Tylmad, M.; Tyndel, M.; Tzanakos, G.; Uchida, K.; Ueda, I.; Uhlenbrock, M.; Uhrmacher, M.; Ukegawa, F.; Unal, G.; Underwood, D. G.; Undrus, A.; Unel, G.; Unno, Y.; Urbaniec, D.; Urkovsky, E.; Urquijo, P.; Urrejola, P.; Usai, G.; Uslenghi, M.; Vacavant, L.; Vacek, V.; Vachon, B.; Vahsen, S.; Valenta, J.; Valente, P.; Valentinetti, S.; Valkar, S.; Valladolid Gallego, E.; Vallecorsa, S.; Valls Ferrer, J. A.; van Berg, R.; van der Graaf, H.; van der Kraaij, E.; van der Poel, E.; van der Ster, D.; van Eldik, N.; van Gemmeren, P.; van Kesteren, Z.; van Vulpen, I.; Vandelli, W.; Vandoni, G.; Vaniachine, A.; Vankov, P.; Vannucci, F.; Varela Rodriguez, F.; Vari, R.; Varnes, E. W.; Varouchas, D.; Vartapetian, A.; Varvell, K. E.; Vasilyeva, L.; Vassilakopoulos, V. I.; Vazeille, F.; Vegni, G.; Veillet, J. J.; Vellidis, C.; Veloso, F.; Veness, R.; Veneziano, S.; Ventura, A.; Ventura, D.; Venturi, M.; Venturi, N.; Vercesi, V.; Verducci, M.; Verkerke, W.; Vermeulen, J. C.; Vetterli, M. C.; Vichou, I.; Vickey, T.; Viehhauser, G. H. A.; Villa, M.; Villani, E. G.; Villaplana Perez, M.; Villate, J.; Vilucchi, E.; Vincter, M. G.; Vinek, E.; Vinogradov, V. B.; Viret, S.; Virzi, J.; Vitale, A.; Vitells, O. V.; Vivarelli, I.; Vives Vaques, F.; Vlachos, S.; Vlasak, M.; Vlasov, N.; Vogt, H.; Vokac, P.; Volpi, M.; Volpini, G.; von der Schmitt, H.; von Loeben, J.; von Radziewski, H.; von Toerne, E.; Vorobel, V.; Vorobiev, A. P.; Vorwerk, V.; Vos, M.; Voss, R.; Voss, T. T.; Vossebeld, J. H.; Vranjes, N.; Vranjes Milosavljevic, M.; Vrba, V.; Vreeswijk, M.; Vu Anh, T.; Vudragovic, D.; Vuillermet, R.; Vukotic, I.; Wagner, P.; Wahlen, H.; Walbersloh, J.; Walder, J.; Walker, R.; Walkowiak, W.; Wall, R.; Wang, C.; Wang, H.; Wang, J.; Wang, J. C.; Wang, S. M.; Ward, C. P.; Warsinsky, M.; Wastie, R.; Watkins, P. M.; Watson, A. T.; Watson, M. F.; Watts, G.; Watts, S.; Waugh, A. T.; Waugh, B. M.; Webel, M.; Weber, J.; Weber, M. D.; Weber, M.; Weber, M. S.; Weber, P.; Weidberg, A. R.; Weingarten, J.; Weiser, C.; Wellenstein, H.; Wells, P. S.; Wen, M.; Wenaus, T.; Wendler, S.; Wengler, T.; Wenig, S.; Wermes, N.; Werner, M.; Werner, P.; Werth, M.; Werthenbach, U.; Wessels, M.; Whalen, K.; Wheeler-Ellis, S. J.; Whitaker, S. P.; White, A.; White, M. J.; White, S.; Whiteson, D.; Whittington, D.; Wicek, F.; Wicke, D.; Wickens, F. J.; Wiedenmann, W.; Wielers, M.; Wienemann, P.; Wiglesworth, C.; Wiik, L. A. M.; Wildauer, A.; Wildt, M. A.; Wilhelm, I.; Wilkens, H. G.; Williams, E.; Williams, H. H.; Willis, W.; Willocq, S.; Wilson, J. A.; Wilson, M. G.; Wilson, A.; Wingerter-Seez, I.; Winklmeier, F.; Wittgen, M.; Wolter, M. W.; Wolters, H.; Wosiek, B. K.; Wotschack, J.; Woudstra, M. J.; Wraight, K.; Wright, C.; Wright, D.; Wrona, B.; Wu, S. L.; Wu, X.; Wulf, E.; Xella, S.; Xie, S.; Xie, Y.; Xu, D.; Xu, N.; Yamada, M.; Yamamoto, A.; Yamamoto, S.; Yamamura, T.; Yamanaka, K.; Yamaoka, J.; Yamazaki, T.; Yamazaki, Y.; Yan, Z.; Yang, H.; Yang, U. K.; Yang, Y.; Yang, Z.; Yao, W.-M.; Yao, Y.; Yasu, Y.; Ye, J.; Ye, S.; Yilmaz, M.; Yoosoofmiya, R.; Yorita, K.; Yoshida, R.; Young, C.; Youssef, S. P.; Yu, D.; Yu, J.; Yu, M.; Yu, X.; Yuan, J.; Yuan, L.; Yurkewicz, A.; Zaidan, R.; Zaitsev, A. M.; Zajacova, Z.; Zambrano, V.; Zanello, L.; Zarzhitsky, P.; Zaytsev, A.; Zeitnitz, C.; Zeller, M.; Zema, P. F.; Zemla, A.; Zendler, C.; Zenin, O.; Zenis, T.; Zenonos, Z.; Zenz, S.; Zerwas, D.; Zevi Della Porta, G.; Zhan, Z.; Zhang, H.; Zhang, J.; Zhang, Q.; Zhang, X.; Zhao, L.; Zhao, T.; Zhao, Z.; Zhemchugov, A.; Zheng, S.; Zhong, J.; Zhou, B.; Zhou, N.; Zhou, Y.; Zhu, C. G.; Zhu, H.; Zhu, Y.; Zhuang, X.; Zhuravlov, V.; Zilka, B.; Zimmermann, R.; Zimmermann, S.; Zimmermann, S.; Ziolkowski, M.; Zitoun, R.; Živković, L.; Zmouchko, V. V.; Zobernig, G.; Zoccoli, A.; Zur Nedden, M.; Zutshi, V.

    2010-12-01

    The ATLAS liquid argon calorimeter has been operating continuously since August 2006. At this time, only part of the calorimeter was readout, but since the beginning of 2008, all calorimeter cells have been connected to the ATLAS readout system in preparation for LHC collisions. This paper gives an overview of the liquid argon calorimeter performance measured in situ with random triggers, calibration data, cosmic muons, and LHC beam splash events. Results on the detector operation, timing performance, electronics noise, and gain stability are presented. High energy deposits from radiative cosmic muons and beam splash events allow to check the intrinsic constant term of the energy resolution. The uniformity of the electromagnetic barrel calorimeter response along η (averaged over φ) is measured at the percent level using minimum ionizing cosmic muons. Finally, studies of electromagnetic showers from radiative muons have been used to cross-check the Monte Carlo simulation. The performance results obtained using the ATLAS readout, data acquisition, and reconstruction software indicate that the liquid argon calorimeter is well-prepared for collisions at the dawn of the LHC era.

  17. Topological cell clustering in the ATLAS calorimeters and its performance in LHC Run 1

    NASA Astrophysics Data System (ADS)

    Aad, G.; Abbott, B.; Abdallah, J.; Abdinov, O.; Aben, R.; Abolins, M.; AbouZeid, O. S.; Abramowicz, H.; Abreu, H.; Abreu, R.; Abulaiti, Y.; Acharya, B. S.; Adamczyk, L.; Adams, D. L.; Adelman, J.; Adomeit, S.; Adye, T.; Affolder, A. A.; Agatonovic-Jovin, T.; Agricola, J.; Aguilar-Saavedra, J. A.; Ahlen, S. P.; Ahmadov, F.; Aielli, G.; Akerstedt, H.; Åkesson, T. P. A.; Akimov, A. V.; Alberghi, G. L.; Albert, J.; Albrand, S.; Verzini, M. J. Alconada; Aleksa, M.; Aleksandrov, I. N.; Alexa, C.; Alexander, G.; Alexopoulos, T.; Alhroob, M.; Alimonti, G.; Alio, L.; Alison, J.; Alkire, S. P.; Allbrooke, B. M. M.; Allport, P. P.; Aloisio, A.; Alonso, A.; Alonso, F.; Alpigiani, C.; Altheimer, A.; Gonzalez, B. Alvarez; Piqueras, D. Álvarez; Alviggi, M. G.; Amadio, B. T.; Amako, K.; Coutinho, Y. Amaral; Amelung, C.; Amidei, D.; Santos, S. P. Amor Dos; Amorim, A.; Amoroso, S.; Amram, N.; Amundsen, G.; Anastopoulos, C.; Ancu, L. S.; Andari, N.; Andeen, T.; Anders, C. F.; Anders, G.; Anders, J. K.; Anderson, K. J.; Andreazza, A.; Andrei, V.; Angelidakis, S.; Angelozzi, I.; Anger, P.; Angerami, A.; Anghinolfi, F.; Anisenkov, A. V.; Anjos, N.; Annovi, A.; Antonelli, M.; Antonov, A.; Antos, J.; Anulli, F.; Aoki, M.; Bella, L. Aperio; Arabidze, G.; Arai, Y.; Araque, J. P.; Arce, A. T. H.; Arduh, F. A.; Arguin, J.-F.; Argyropoulos, S.; Arik, M.; Armbruster, A. J.; Arnaez, O.; Arnold, H.; Arratia, M.; Arslan, O.; Artamonov, A.; Artoni, G.; Artz, S.; Asai, S.; Asbah, N.; Ashkenazi, A.; Åsman, B.; Asquith, L.; Assamagan, K.; Astalos, R.; Atkinson, M.; Atlay, N. B.; Augsten, K.; Aurousseau, M.; Avolio, G.; Axen, B.; Ayoub, M. K.; Azuelos, G.; Baak, M. A.; Baas, A. E.; Baca, M. J.; Bacci, C.; Bachacou, H.; Bachas, K.; Backes, M.; Backhaus, M.; Bagiacchi, P.; Bagnaia, P.; Bai, Y.; Bain, T.; Baines, J. T.; Baker, O. K.; Baldin, E. M.; Balek, P.; Balestri, T.; Balli, F.; Balunas, W. K.; Banas, E.; Banerjee, Sw.; Bannoura, A. A. E.; Barak, L.; Barberio, E. L.; Barberis, D.; Barbero, M.; Barillari, T.; Barisonzi, M.; Barklow, T.; Barlow, N.; Barnes, S. L.; Barnett, B. M.; Barnett, R. M.; Barnovska, Z.; Baroncelli, A.; Barone, G.; Barr, A. J.; Barreiro, F.; da Costa, J. Barreiro Guimarães; Bartoldus, R.; Barton, A. E.; Bartos, P.; Basalaev, A.; Bassalat, A.; Basye, A.; Bates, R. L.; Batista, S. J.; Batley, J. R.; Battaglia, M.; Bauce, M.; Bauer, F.; Bawa, H. S.; Beacham, J. B.; Beattie, M. D.; Beau, T.; Beauchemin, P. H.; Beccherle, R.; Bechtle, P.; Beck, H. P.; Becker, K.; Becker, M.; Beckingham, M.; Becot, C.; Beddall, A. J.; Beddall, A.; Bednyakov, V. A.; Bee, C. P.; Beemster, L. J.; Beermann, T. A.; Begel, M.; Behr, J. K.; Belanger-Champagne, C.; Bell, W. H.; Bella, G.; Bellagamba, L.; Bellerive, A.; Bellomo, M.; Belotskiy, K.; Beltramello, O.; Benary, O.; Benchekroun, D.; Bender, M.; Bendtz, K.; Benekos, N.; Benhammou, Y.; Noccioli, E. Benhar; Garcia, J. A. Benitez; Benjamin, D. P.; Bensinger, J. R.; Bentvelsen, S.; Beresford, L.; Beretta, M.; Berge, D.; Kuutmann, E. Bergeaas; Berger, N.; Berghaus, F.; Beringer, J.; Bernard, C.; Bernard, N. R.; Bernius, C.; Bernlochner, F. U.; Berry, T.; Berta, P.; Bertella, C.; Bertoli, G.; Bertolucci, F.; Bertsche, C.; Bertsche, D.; Besana, M. I.; Besjes, G. J.; Bylund, O. Bessidskaia; Bessner, M.; Besson, N.; Betancourt, C.; Bethke, S.; Bevan, A. J.; Bhimji, W.; Bianchi, R. M.; Bianchini, L.; Bianco, M.; Biebel, O.; Biedermann, D.; Biesuz, N. V.; Biglietti, M.; De Mendizabal, J. Bilbao; Bilokon, H.; Bindi, M.; Binet, S.; Bingul, A.; Bini, C.; Biondi, S.; Bjergaard, D. M.; Black, C. W.; Black, J. E.; Black, K. M.; Blackburn, D.; Blair, R. E.; Blanchard, J.-B.; Blanco, J. E.; Blazek, T.; Bloch, I.; Blocker, C.; Blum, W.; Blumenschein, U.; Blunier, S.; Bobbink, G. J.; Bobrovnikov, V. S.; Bocchetta, S. S.; Bocci, A.; Bock, C.; Boehler, M.; Bogaerts, J. A.; Bogavac, D.; Bogdanchikov, A. G.; Bohm, C.; Boisvert, V.; Bold, T.; Boldea, V.; Boldyrev, A. S.; Bomben, M.; Bona, M.; Boonekamp, M.; Borisov, A.; Borissov, G.; Borroni, S.; Bortfeldt, J.; Bortolotto, V.; Bos, K.; Boscherini, D.; Bosman, M.; Boudreau, J.; Bouffard, J.; Bouhova-Thacker, E. V.; Boumediene, D.; Bourdarios, C.; Bousson, N.; Boutle, S. K.; Boveia, A.; Boyd, J.; Boyko, I. R.; Bozic, I.; Bracinik, J.; Brandt, A.; Brandt, G.; Brandt, O.; Bratzler, U.; Brau, B.; Brau, J. E.; Braun, H. M.; Madden, W. D. Breaden; Brendlinger, K.; Brennan, A. J.; Brenner, L.; Brenner, R.; Bressler, S.; Bristow, T. M.; Britton, D.; Britzger, D.; Brochu, F. M.; Brock, I.; Brock, R.; Bronner, J.; Brooijmans, G.; Brooks, T.; Brooks, W. K.; Brosamer, J.; Brost, E.; de Renstrom, P. A. Bruckman; Bruncko, D.; Bruneliere, R.; Bruni, A.; Bruni, G.; Bruschi, M.; Bruscino, N.; Bryngemark, L.; Buanes, T.; Buat, Q.; Buchholz, P.; Buckley, A. G.; Budagov, I. A.; Buehrer, F.; Bugge, L.; Bugge, M. K.; Bulekov, O.; Bullock, D.; Burckhart, H.; Burdin, S.; Burgard, C. D.; Burghgrave, B.; Burke, S.; Burmeister, I.; Busato, E.; Büscher, D.; Büscher, V.; Bussey, P.; Butler, J. M.; Butt, A. I.; Buttar, C. M.; Butterworth, J. M.; Butti, P.; Buttinger, W.; Buzatu, A.; Buzykaev, A. R.; Urbán, S. Cabrera; Caforio, D.; Cairo, V. M.; Cakir, O.; Calace, N.; Calafiura, P.; Calandri, A.; Calderini, G.; Calfayan, P.; Caloba, L. P.; Calvet, D.; Calvet, S.; Toro, R. Camacho; Camarda, S.; Camarri, P.; Cameron, D.; Armadans, R. Caminal; Campana, S.; Campanelli, M.; Campoverde, A.; Canale, V.; Canepa, A.; Bret, M. Cano; Cantero, J.; Cantrill, R.; Cao, T.; Garrido, M. D. M. Capeans; Caprini, I.; Caprini, M.; Capua, M.; Caputo, R.; Carbone, R. M.; Cardarelli, R.; Cardillo, F.; Carli, T.; Carlino, G.; Carminati, L.; Caron, S.; Carquin, E.; Carrillo-Montoya, G. D.; Carter, J. R.; Carvalho, J.; Casadei, D.; Casado, M. P.; Casolino, M.; Casper, D. W.; Castaneda-Miranda, E.; Castelli, A.; Gimenez, V. Castillo; Castro, N. F.; Catastini, P.; Catinaccio, A.; Catmore, J. R.; Cattai, A.; Caudron, J.; Cavaliere, V.; Cavalli, D.; Cavalli-Sforza, M.; Cavasinni, V.; Ceradini, F.; Alberich, L. Cerda; Cerio, B. C.; Cerny, K.; Cerqueira, A. S.; Cerri, A.; Cerrito, L.; Cerutti, F.; Cerv, M.; Cervelli, A.; Cetin, S. A.; Chafaq, A.; Chakraborty, D.; Chalupkova, I.; Chan, Y. L.; Chang, P.; Chapman, J. D.; Charlton, D. G.; Chau, C. C.; Barajas, C. A. Chavez; Che, S.; Cheatham, S.; Chegwidden, A.; Chekanov, S.; Chekulaev, S. V.; Chelkov, G. A.; Chelstowska, M. A.; Chen, C.; Chen, H.; Chen, K.; Chen, L.; Chen, S.; Chen, S.; Chen, X.; Chen, Y.; Cheng, H. C.; Cheng, Y.; Cheplakov, A.; Cheremushkina, E.; Moursli, R. Cherkaoui El; Chernyatin, V.; Cheu, E.; Chevalier, L.; Chiarella, V.; Chiarelli, G.; Chiodini, G.; Chisholm, A. S.; Chislett, R. T.; Chitan, A.; Chizhov, M. V.; Choi, K.; Chouridou, S.; Chow, B. K. B.; Christodoulou, V.; Chromek-Burckhart, D.; Chudoba, J.; Chuinard, A. J.; Chwastowski, J. J.; Chytka, L.; Ciapetti, G.; Ciftci, A. K.; Cinca, D.; Cindro, V.; Cioara, I. A.; Ciocio, A.; Cirotto, F.; Citron, Z. H.; Ciubancan, M.; Clark, A.; Clark, B. L.; Clark, P. J.; Clarke, R. N.; Clement, C.; Coadou, Y.; Cobal, M.; Coccaro, A.; Cochran, J.; Coffey, L.; Cogan, J. G.; Colasurdo, L.; Cole, B.; Cole, S.; Colijn, A. P.; Collot, J.; Colombo, T.; Compostella, G.; Muiño, P. Conde; Coniavitis, E.; Connell, S. H.; Connelly, I. A.; Consorti, V.; Constantinescu, S.; Conta, C.; Conti, G.; Conventi, F.; Cooke, M.; Cooper, B. D.; Cooper-Sarkar, A. M.; Cornelissen, T.; Corradi, M.; Corriveau, F.; Corso-Radu, A.; Cortes-Gonzalez, A.; Cortiana, G.; Costa, G.; Costa, M. J.; Costanzo, D.; Côté, D.; Cottin, G.; Cowan, G.; Cox, B. E.; Cranmer, K.; Cree, G.; Crépé-Renaudin, S.; Crescioli, F.; Cribbs, W. A.; Ortuzar, M. Crispin; Cristinziani, M.; Croft, V.; Crosetti, G.; Donszelmann, T. Cuhadar; Cummings, J.; Curatolo, M.; Cúth, J.; Cuthbert, C.; Czirr, H.; Czodrowski, P.; D'Auria, S.; D'Onofrio, M.; De Sousa, M. J. Da Cunha Sargedas; Via, C. Da; Dabrowski, W.; Dafinca, A.; Dai, T.; Dale, O.; Dallaire, F.; Dallapiccola, C.; Dam, M.; Dandoy, J. R.; Dang, N. P.; Daniells, A. C.; Danninger, M.; Hoffmann, M. Dano; Dao, V.; Darbo, G.; Darmora, S.; Dassoulas, J.; Dattagupta, A.; Davey, W.; David, C.; Davidek, T.; Davies, E.; Davies, M.; Davison, P.; Davygora, Y.; Dawe, E.; Dawson, I.; Daya-Ishmukhametova, R. K.; De, K.; de Asmundis, R.; De Benedetti, A.; De Castro, S.; De Cecco, S.; De Groot, N.; de Jong, P.; De la Torre, H.; De Lorenzi, F.; De Pedis, D.; De Salvo, A.; De Sanctis, U.; De Santo, A.; De Regie, J. B. De Vivie; Dearnaley, W. J.; Debbe, R.; Debenedetti, C.; Dedovich, D. V.; Deigaard, I.; Del Peso, J.; Del Prete, T.; Delgove, D.; Deliot, F.; Delitzsch, C. M.; Deliyergiyev, M.; Dell'Acqua, A.; Dell'Asta, L.; Dell'Orso, M.; Della Pietra, M.; della Volpe, D.; Delmastro, M.; Delsart, P. A.; Deluca, C.; DeMarco, D. A.; Demers, S.; Demichev, M.; Demilly, A.; Denisov, S. P.; Derendarz, D.; Derkaoui, J. E.; Derue, F.; Dervan, P.; Desch, K.; Deterre, C.; Dette, K.; Deviveiros, P. O.; Dewhurst, A.; Dhaliwal, S.; Di Ciaccio, A.; Di Ciaccio, L.; Di Domenico, A.; Di Donato, C.; Di Girolamo, A.; Di Girolamo, B.; Di Mattia, A.; Di Micco, B.; Di Nardo, R.; Di Simone, A.; Di Sipio, R.; Di Valentino, D.; Diaconu, C.; Diamond, M.; Dias, F. A.; Diaz, M. A.; Diehl, E. B.; Dietrich, J.; Diglio, S.; Dimitrievska, A.; Dingfelder, J.; Dita, P.; Dita, S.; Dittus, F.; Djama, F.; Djobava, T.; Djuvsland, J. I.; do Vale, M. A. B.; Dobos, D.; Dobre, M.; Doglioni, C.; Dohmae, T.; Dolejsi, J.; Dolezal, Z.; Dolgoshein, B. A.; Donadelli, M.; Donati, S.; Dondero, P.; Donini, J.; Dopke, J.; Doria, A.; Dova, M. T.; Doyle, A. T.; Drechsler, E.; Dris, M.; Du, Y.; Dubreuil, E.; Duchovni, E.; Duckeck, G.; Ducu, O. A.; Duda, D.; Dudarev, A.; Duflot, L.; Duguid, L.; Dührssen, M.; Dunford, M.; Yildiz, H. Duran; Düren, M.; Durglishvili, A.; Duschinger, D.; Dutta, B.; Dyndal, M.; Eckardt, C.; Ecker, K. M.; Edgar, R. C.; Edson, W.; Edwards, N. C.; Ehrenfeld, W.; Eifert, T.; Eigen, G.; Einsweiler, K.; Ekelof, T.; Kacimi, M. El; Ellert, M.; Elles, S.; Ellinghaus, F.; Elliot, A. A.; Ellis, N.; Elmsheuser, J.; Elsing, M.; Emeliyanov, D.; Enari, Y.; Endner, O. C.; Endo, M.; Erdmann, J.; Ereditato, A.; Ernis, G.; Ernst, J.; Ernst, M.; Errede, S.; Ertel, E.; Escalier, M.; Esch, H.; Escobar, C.; Esposito, B.; Etienvre, A. I.; Etzion, E.; Evans, H.; Ezhilov, A.; Fabbri, L.; Facini, G.; Fakhrutdinov, R. M.; Falciano, S.; Falla, R. J.; Faltova, J.; Fang, Y.; Fanti, M.; Farbin, A.; Farilla, A.; Farooque, T.; Farrell, S.; Farrington, S. M.; Farthouat, P.; Fassi, F.; Fassnacht, P.; Fassouliotis, D.; Giannelli, M. Faucci; Favareto, A.; Fayard, L.; Fedin, O. L.; Fedorko, W.; Feigl, S.; Feligioni, L.; Feng, C.; Feng, E. J.; Feng, H.; Fenyuk, A. B.; Feremenga, L.; Martinez, P. Fernandez; Perez, S. Fernandez; Ferrando, J.; Ferrari, A.; Ferrari, P.; Ferrari, R.; de Lima, D. E. Ferreira; Ferrer, A.; Ferrere, D.; Ferretti, C.; Parodi, A. Ferretto; Fiascaris, M.; Fiedler, F.; Filipčič, A.; Filipuzzi, M.; Filthaut, F.; Fincke-Keeler, M.; Finelli, K. D.; Fiolhais, M. C. N.; Fiorini, L.; Firan, A.; Fischer, A.; Fischer, C.; Fischer, J.; Fisher, W. C.; Flaschel, N.; Fleck, I.; Fleischmann, P.; Fletcher, G. T.; Fletcher, G.; Fletcher, R. R. M.; Flick, T.; Floderus, A.; Castillo, L. R. Flores; Flowerdew, M. J.; Formica, A.; Forti, A.; Fournier, D.; Fox, H.; Fracchia, S.; Francavilla, P.; Franchini, M.; Francis, D.; Franconi, L.; Franklin, M.; Frate, M.; Fraternali, M.; Freeborn, D.; French, S. T.; Fressard-Batraneanu, S. M.; Friedrich, F.; Froidevaux, D.; Frost, J. A.; Fukunaga, C.; Torregrosa, E. Fullana; Fulsom, B. G.; Fusayasu, T.; Fuster, J.; Gabaldon, C.; Gabizon, O.; Gabrielli, A.; Gabrielli, A.; Gach, G. P.; Gadatsch, S.; Gadomski, S.; Gagliardi, G.; Gagnon, P.; Galea, C.; Galhardo, B.; Gallas, E. J.; Gallop, B. J.; Gallus, P.; Galster, G.; Gan, K. K.; Gao, J.; Gao, Y.; Gao, Y. S.; Walls, F. M. Garay; Garberson, F.; García, C.; Navarro, J. E. García; Garcia-Sciveres, M.; Gardner, R. W.; Garelli, N.; Garonne, V.; Gatti, C.; Gaudiello, A.; Gaudio, G.; Gaur, B.; Gauthier, L.; Gauzzi, P.; Gavrilenko, I. L.; Gay, C.; Gaycken, G.; Gazis, E. N.; Ge, P.; Gecse, Z.; Gee, C. N. P.; Geich-Gimbel, Ch.; Geisler, M. P.; Gemme, C.; Genest, M. H.; Geng, C.; Gentile, S.; George, M.; George, S.; Gerbaudo, D.; Gershon, A.; Ghasemi, S.; Ghazlane, H.; Giacobbe, B.; Giagu, S.; Giangiobbe, V.; Giannetti, P.; Gibbard, B.; Gibson, S. M.; Gignac, M.; Gilchriese, M.; Gillam, T. P. S.; Gillberg, D.; Gilles, G.; Gingrich, D. M.; Giokaris, N.; Giordani, M. P.; Giorgi, F. M.; Giorgi, F. M.; Giraud, P. F.; Giromini, P.; Giugni, D.; Giuliani, C.; Giulini, M.; Gjelsten, B. K.; Gkaitatzis, S.; Gkialas, I.; Gkougkousis, E. L.; Gladilin, L. K.; Glasman, C.; Glatzer, J.; Glaysher, P. C. F.; Glazov, A.; Goblirsch-Kolb, M.; Goddard, J. R.; Godlewski, J.; Goldfarb, S.; Golling, T.; Golubkov, D.; Gomes, A.; Gonçalo, R.; Costa, J. Goncalves Pinto Firmino Da; Gonella, L.; de la Hoz, S. González; Parra, G. Gonzalez; Gonzalez-Sevilla, S.; Goossens, L.; Gorbounov, P. A.; Gordon, H. A.; Gorelov, I.; Gorini, B.; Gorini, E.; Gorišek, A.; Gornicki, E.; Goshaw, A. T.; Gössling, C.; Gostkin, M. I.; Goujdami, D.; Goussiou, A. G.; Govender, N.; Gozani, E.; Grabas, H. M. X.; Graber, L.; Grabowska-Bold, I.; Gradin, P. O. J.; Grafström, P.; Gramling, J.; Gramstad, E.; Grancagnolo, S.; Gratchev, V.; Gray, H. M.; Graziani, E.; Greenwood, Z. D.; Grefe, C.; Gregersen, K.; Gregor, I. M.; Grenier, P.; Griffiths, J.; Grillo, A. A.; Grimm, K.; Grinstein, S.; Gris, Ph.; Grivaz, J.-F.; Groh, S.; Grohs, J. P.; Grohsjean, A.; Gross, E.; Grosse-Knetter, J.; Grossi, G. C.; Grout, Z. J.; Guan, L.; Guenther, J.; Guescini, F.; Guest, D.; Gueta, O.; Guido, E.; Guillemin, T.; Guindon, S.; Gul, U.; Gumpert, C.; Guo, J.; Guo, Y.; Gupta, S.; Gustavino, G.; Gutierrez, P.; Ortiz, N. G. Gutierrez; Gutschow, C.; Guyot, C.; Gwenlan, C.; Gwilliam, C. B.; Haas, A.; Haber, C.; Hadavand, H. K.; Haddad, N.; Haefner, P.; Hageböck, S.; Hajduk, Z.; Hakobyan, H.; Haleem, M.; Haley, J.; Hall, D.; Halladjian, G.; Hallewell, G. D.; Hamacher, K.; Hamal, P.; Hamano, K.; Hamilton, A.; Hamity, G. N.; Hamnett, P. G.; Han, L.; Hanagaki, K.; Hanawa, K.; Hance, M.; Haney, B.; Hanke, P.; Hanna, R.; Hansen, J. B.; Hansen, J. D.; Hansen, M. C.; Hansen, P. H.; Hara, K.; Hard, A. S.; Harenberg, T.; Hariri, F.; Harkusha, S.; Harrington, R. D.; Harrison, P. F.; Hartjes, F.; Hasegawa, M.; Hasegawa, Y.; Hasib, A.; Hassani, S.; Haug, S.; Hauser, R.; Hauswald, L.; Havranek, M.; Hawkes, C. M.; Hawkings, R. J.; Hawkins, A. D.; Hayashi, T.; Hayden, D.; Hays, C. P.; Hays, J. M.; Hayward, H. S.; Haywood, S. J.; Head, S. J.; Heck, T.; Hedberg, V.; Heelan, L.; Heim, S.; Heim, T.; Heinemann, B.; Heinrich, L.; Hejbal, J.; Helary, L.; Hellman, S.; Helsens, C.; Henderson, J.; Henderson, R. C. W.; Heng, Y.; Hengler, C.; Henkelmann, S.; Henrichs, A.; Correia, A. M. Henriques; Henrot-Versille, S.; Herbert, G. H.; Jiménez, Y. Hernández; Herten, G.; Hertenberger, R.; Hervas, L.; Hesketh, G. G.; Hessey, N. P.; Hetherly, J. W.; Hickling, R.; Higón-Rodriguez, E.; Hill, E.; Hill, J. C.; Hiller, K. H.; Hillier, S. J.; Hinchliffe, I.; Hines, E.; Hinman, R. R.; Hirose, M.; Hirschbuehl, D.; Hobbs, J.; Hod, N.; Hodgkinson, M. C.; Hodgson, P.; Hoecker, A.; Hoeferkamp, M. R.; Hoenig, F.; Hohlfeld, M.; Hohn, D.; Holmes, T. R.; Homann, M.; Hong, T. M.; Hopkins, W. H.; Horii, Y.; Horton, A. J.; Hostachy, J.-Y.; Hou, S.; Hoummada, A.; Howard, J.; Howarth, J.; Hrabovsky, M.; Hristova, I.; Hrivnac, J.; Hryn'ova, T.; Hrynevich, A.; Hsu, C.; Hsu, P. J.; Hsu, S.-C.; Hu, D.; Hu, Q.; Hu, X.; Huang, Y.; Hubacek, Z.; Hubaut, F.; Huegging, F.; Huffman, T. B.; Hughes, E. W.; Hughes, G.; Huhtinen, M.; Hülsing, T. A.; Huseynov, N.; Huston, J.; Huth, J.; Iacobucci, G.; Iakovidis, G.; Ibragimov, I.; Iconomidou-Fayard, L.; Ideal, E.; Idrissi, Z.; Iengo, P.; Igonkina, O.; Iizawa, T.; Ikegami, Y.; Ikeno, M.; Ilchenko, Y.; Iliadis, D.; Ilic, N.; Ince, T.; Introzzi, G.; Ioannou, P.; Iodice, M.; Iordanidou, K.; Ippolito, V.; Quiles, A. Irles; Isaksson, C.; Ishino, M.; Ishitsuka, M.; Ishmukhametov, R.; Issever, C.; Istin, S.; Ponce, J. M. Iturbe; Iuppa, R.; Ivarsson, J.; Iwanski, W.; Iwasaki, H.; Izen, J. M.; Izzo, V.; Jabbar, S.; Jackson, B.; Jackson, M.; Jackson, P.; Jaekel, M. R.; Jain, V.; Jakobi, K. B.; Jakobs, K.; Jakobsen, S.; Jakoubek, T.; Jakubek, J.; Jamin, D. O.; Jana, D. K.; Jansen, E.; Jansky, R.; Janssen, J.; Janus, M.; Jarlskog, G.; Javadov, N.; Javůrek, T.; Jeanty, L.; Jejelava, J.; Jeng, G.-Y.; Jennens, D.; Jenni, P.; Jentzsch, J.; Jeske, C.; Jézéquel, S.; Ji, H.; Jia, J.; Jiang, H.; Jiang, Y.; Jiggins, S.; Pena, J. Jimenez; Jin, S.; Jinaru, A.; Jinnouchi, O.; Joergensen, M. D.; Johansson, P.; Johns, K. A.; Johnson, W. J.; Jon-And, K.; Jones, G.; Jones, R. W. L.; Jones, T. J.; Jongmanns, J.; Jorge, P. M.; Joshi, K. D.; Jovicevic, J.; Ju, X.; Rozas, A. Juste; Kaci, M.; Kaczmarska, A.; Kado, M.; Kagan, H.; Kagan, M.; Kahn, S. J.; Kajomovitz, E.; Kalderon, C. W.; Kaluza, A.; Kama, S.; Kamenshchikov, A.; Kanaya, N.; Kaneti, S.; Kantserov, V. A.; Kanzaki, J.; Kaplan, B.; Kaplan, L. S.; Kapliy, A.; Kar, D.; Karakostas, K.; Karamaoun, A.; Karastathis, N.; Kareem, M. J.; Karentzos, E.; Karnevskiy, M.; Karpov, S. N.; Karpova, Z. M.; Karthik, K.; Kartvelishvili, V.; Karyukhin, A. N.; Kasahara, K.; Kashif, L.; Kass, R. D.; Kastanas, A.; Kataoka, Y.; Kato, C.; Katre, A.; Katzy, J.; Kawade, K.; Kawagoe, K.; Kawamoto, T.; Kawamura, G.; Kazama, S.; Kazanin, V. F.; Keeler, R.; Kehoe, R.; Keller, J. S.; Kempster, J. J.; Keoshkerian, H.; Kepka, O.; Kerševan, B. P.; Kersten, S.; Keyes, R. A.; Khalil-zada, F.; Khandanyan, H.; Khanov, A.; Kharlamov, A. G.; Khoo, T. J.; Khovanskiy, V.; Khramov, E.; Khubua, J.; Kido, S.; Kim, H. Y.; Kim, S. H.; Kim, Y. K.; Kimura, N.; Kind, O. M.; King, B. T.; King, M.; King, S. B.; Kirk, J.; Kiryunin, A. E.; Kishimoto, T.; Kisielewska, D.; Kiss, F.; Kiuchi, K.; Kivernyk, O.; Kladiva, E.; Klein, M. H.; Klein, M.; Klein, U.; Kleinknecht, K.; Klimek, P.; Klimentov, A.; Klingenberg, R.; Klinger, J. A.; Klioutchnikova, T.; Kluge, E.-E.; Kluit, P.; Kluth, S.; Knapik, J.; Kneringer, E.; Knoops, E. B. F. G.; Knue, A.; Kobayashi, A.; Kobayashi, D.; Kobayashi, T.; Kobel, M.; Kocian, M.; Kodys, P.; Koffas, T.; Koffeman, E.; Kogan, L. A.; Kohlmann, S.; Kohout, Z.; Kohriki, T.; Koi, T.; Kolanoski, H.; Kolb, M.; Koletsou, I.; Komar, A. A.; Komori, Y.; Kondo, T.; Kondrashova, N.; Köneke, K.; König, A. C.; Kono, T.; Konoplich, R.; Konstantinidis, N.; Kopeliansky, R.; Koperny, S.; Köpke, L.; Kopp, A. K.; Korcyl, K.; Kordas, K.; Korn, A.; Korol, A. A.; Korolkov, I.; Korolkova, E. V.; Kortner, O.; Kortner, S.; Kosek, T.; Kostyukhin, V. V.; Kotov, V. M.; Kotwal, A.; Kourkoumeli-Charalampidi, A.; Kourkoumelis, C.; Kouskoura, V.; Koutsman, A.; Kowalewski, R.; Kowalski, T. Z.; Kozanecki, W.; Kozhin, A. S.; Kramarenko, V. A.; Kramberger, G.; Krasnopevtsev, D.; Krasny, M. W.; Krasznahorkay, A.; Kraus, J. K.; Kravchenko, A.; Kreiss, S.; Kretz, M.; Kretzschmar, J.; Kreutzfeldt, K.; Krieger, P.; Krizka, K.; Kroeninger, K.; Kroha, H.; Kroll, J.; Kroseberg, J.; Krstic, J.; Kruchonak, U.; Krüger, H.; Krumnack, N.; Kruse, A.; Kruse, M. C.; Kruskal, M.; Kubota, T.; Kucuk, H.; Kuday, S.; Kuehn, S.; Kugel, A.; Kuger, F.; Kuhl, A.; Kuhl, T.; Kukhtin, V.; Kukla, R.; Kulchitsky, Y.; Kuleshov, S.; Kuna, M.; Kunigo, T.; Kupco, A.; Kurashige, H.; Kurochkin, Y. A.; Kus, V.; Kuwertz, E. S.; Kuze, M.; Kvita, J.; Kwan, T.; Kyriazopoulos, D.; Rosa, A. La; Navarro, J. L. La Rosa; Rotonda, L. La; Lacasta, C.; Lacava, F.; Lacey, J.; Lacker, H.; Lacour, D.; Lacuesta, V. R.; Ladygin, E.; Lafaye, R.; Laforge, B.; Lagouri, T.; Lai, S.; Lambourne, L.; Lammers, S.; Lampen, C. L.; Lampl, W.; Lançon, E.; Landgraf, U.; Landon, M. P. J.; Lang, V. S.; Lange, J. C.; Lankford, A. J.; Lanni, F.; Lantzsch, K.; Lanza, A.; Laplace, S.; Lapoire, C.; Laporte, J. F.; Lari, T.; Manghi, F. Lasagni; Lassnig, M.; Laurelli, P.; Lavrijsen, W.; Law, A. T.; Laycock, P.; Lazovich, T.; Dortz, O. Le; Guirriec, E. Le; Menedeu, E. Le; LeBlanc, M.; LeCompte, T.; Ledroit-Guillon, F.; Lee, C. A.; Lee, S. C.; Lee, L.; Lefebvre, G.; Lefebvre, M.; Legger, F.; Leggett, C.; Lehan, A.; Miotto, G. Lehmann; Lei, X.; Leight, W. A.; Leisos, A.; Leister, A. G.; Leite, M. A. L.; Leitner, R.; Lellouch, D.; Lemmer, B.; Leney, K. J. C.; Lenz, T.; Lenzi, B.; Leone, R.; Leone, S.; Leonidopoulos, C.; Leontsinis, S.; Leroy, C.; Lester, C. G.; Levchenko, M.; Levêque, J.; Levin, D.; Levinson, L. J.; Levy, M.; Lewis, A.; Leyko, A. M.; Leyton, M.; Li, B.; Li, H.; Li, H. L.; Li, L.; Li, L.; Li, S.; Li, X.; Li, Y.; Liang, Z.; Liao, H.; Liberti, B.; Liblong, A.; Lichard, P.; Lie, K.; Liebal, J.; Liebig, W.; Limbach, C.; Limosani, A.; Lin, S. C.; Lin, T. H.; Linde, F.; Lindquist, B. E.; Linnemann, J. T.; Lipeles, E.; Lipniacka, A.; Lisovyi, M.; Liss, T. M.; Lissauer, D.; Lister, A.; Litke, A. M.; Liu, B.; Liu, D.; Liu, H.; Liu, J.; Liu, J. B.; Liu, K.; Liu, L.; Liu, M.; Liu, M.; Liu, Y.; Livan, M.; Lleres, A.; Merino, J. Llorente; Lloyd, S. L.; Sterzo, F. Lo; Lobodzinska, E.; Loch, P.; Lockman, W. S.; Loebinger, F. K.; Loevschall-Jensen, A. E.; Loew, K. M.; Loginov, A.; Lohse, T.; Lohwasser, K.; Lokajicek, M.; Long, B. A.; Long, J. D.; Long, R. E.; Looper, K. A.; Lopes, L.; Mateos, D. Lopez; Paredes, B. Lopez; Paz, I. Lopez; Lorenz, J.; Martinez, N. Lorenzo; Losada, M.; Lösel, P. J.; Lou, X.; Lounis, A.; Love, J.; Love, P. A.; Lu, H.; Lu, N.; Lubatti, H. J.; Luci, C.; Lucotte, A.; Luedtke, C.; Luehring, F.; Lukas, W.; Luminari, L.; Lundberg, O.; Lund-Jensen, B.; Lynn, D.; Lysak, R.; Lytken, E.; Ma, H.; Ma, L. L.; Maccarrone, G.; Macchiolo, A.; Macdonald, C. M.; Maček, B.; Miguens, J. Machado; Macina, D.; Madaffari, D.; Madar, R.; Maddocks, H. J.; Mader, W. F.; Madsen, A.; Maeda, J.; Maeland, S.; Maeno, T.; Maevskiy, A.; Magradze, E.; Mahboubi, K.; Mahlstedt, J.; Maiani, C.; Maidantchik, C.; Maier, A. A.; Maier, T.; Maio, A.; Majewski, S.; Makida, Y.; Makovec, N.; Malaescu, B.; Malecki, Pa.; Maleev, V. P.; Malek, F.; Mallik, U.; Malon, D.; Malone, C.; Maltezos, S.; Malyshev, V. M.; Malyukov, S.; Mamuzic, J.; Mancini, G.; Mandelli, B.; Mandelli, L.; Mandić, I.; Mandrysch, R.; Maneira, J.; Filho, L. Manhaes de Andrade; Ramos, J. Manjarres; Mann, A.; Manousakis-Katsikakis, A.; Mansoulie, B.; Mantifel, R.; Mantoani, M.; Mapelli, L.; March, L.; Marchiori, G.; Marcisovsky, M.; Marino, C. P.; Marjanovic, M.; Marley, D. E.; Marroquim, F.; Marsden, S. P.; Marshall, Z.; Marti, L. F.; Marti-Garcia, S.; Martin, B.; Martin, T. A.; Martin, V. J.; Latour, B. Martin dit; Martinez, M.; Martin-Haugh, S.; Martoiu, V. S.; Martyniuk, A. C.; Marx, M.; Marzano, F.; Marzin, A.; Masetti, L.; Mashimo, T.; Mashinistov, R.; Masik, J.; Maslennikov, A. L.; Massa, I.; Massa, L.; Mastrandrea, P.; Mastroberardino, A.; Masubuchi, T.; Mättig, P.; Mattmann, J.; Maurer, J.; Maxfield, S. J.; Maximov, D. A.; Mazini, R.; Mazza, S. M.; Goldrick, G. Mc; Kee, S. P. Mc; McCarn, A.; McCarthy, R. L.; McCarthy, T. G.; McCubbin, N. A.; McFarlane, K. W.; Mcfayden, J. A.; Mchedlidze, G.; McMahon, S. J.; McPherson, R. A.; Medinnis, M.; Meehan, S.; Mehlhase, S.; Mehta, A.; Meier, K.; Meineck, C.; Meirose, B.; Garcia, B. R. Mellado; Meloni, F.; Mengarelli, A.; Menke, S.; Meoni, E.; Mercurio, K. M.; Mergelmeyer, S.; Mermod, P.; Merola, L.; Meroni, C.; Merritt, F. S.; Messina, A.; Metcalfe, J.; Mete, A. S.; Meyer, C.; Meyer, C.; Meyer, J.-P.; Meyer, J.; Theenhausen, H. Meyer Zu; Middleton, R. P.; Miglioranzi, S.; Mijović, L.; Mikenberg, G.; Mikestikova, M.; Mikuž, M.; Milesi, M.; Milic, A.; Miller, D. W.; Mills, C.; Milov, A.; Milstead, D. A.; Minaenko, A. A.; Minami, Y.; Minashvili, I. A.; Mincer, A. I.; Mindur, B.; Mineev, M.; Ming, Y.; Mir, L. M.; Mistry, K. P.; Mitani, T.; Mitrevski, J.; Mitsou, V. A.; Miucci, A.; Miyagawa, P. S.; Mjörnmark, J. U.; Moa, T.; Mochizuki, K.; Mohapatra, S.; Mohr, W.; Molander, S.; Moles-Valls, R.; Monden, R.; Mondragon, M. C.; Mönig, K.; Monini, C.; Monk, J.; Monnier, E.; Montalbano, A.; Berlingen, J. Montejo; Monticelli, F.; Monzani, S.; Moore, R. W.; Morange, N.; Moreno, D.; Llácer, M. Moreno; Morettini, P.; Mori, D.; Mori, T.; Morii, M.; Morinaga, M.; Morisbak, V.; Moritz, S.; Morley, A. K.; Mornacchi, G.; Morris, J. D.; Mortensen, S. S.; Morton, A.; Morvaj, L.; Mosidze, M.; Moss, J.; Motohashi, K.; Mount, R.; Mountricha, E.; Mouraviev, S. V.; Moyse, E. J. W.; Muanza, S.; Mudd, R. D.; Mueller, F.; Mueller, J.; Mueller, R. S. P.; Mueller, T.; Muenstermann, D.; Mullen, P.; Mullier, G. A.; Sanchez, F. J. Munoz; Quijada, J. A. Murillo; Murray, W. J.; Musheghyan, H.; Musto, E.; Myagkov, A. G.; Myska, M.; Nachman, B. P.; Nackenhorst, O.; Nadal, J.; Nagai, K.; Nagai, R.; Nagai, Y.; Nagano, K.; Nagarkar, A.; Nagasaka, Y.; Nagata, K.; Nagel, M.; Nagy, E.; Nairz, A. M.; Nakahama, Y.; Nakamura, K.; Nakamura, T.; Nakano, I.; Namasivayam, H.; Garcia, R. F. Naranjo; Narayan, R.; Villar, D. I. Narrias; Naumann, T.; Navarro, G.; Nayyar, R.; Neal, H. A.; Nechaeva, P. Yu.; Neep, T. J.; Nef, P. D.; Negri, A.; Negrini, M.; Nektarijevic, S.; Nellist, C.; Nelson, A.; Nemecek, S.; Nemethy, P.; Nepomuceno, A. A.; Nessi, M.; Neubauer, M. S.; Neumann, M.; Neves, R. M.; Nevski, P.; Newman, P. R.; Nguyen, D. H.; Nickerson, R. B.; Nicolaidou, R.; Nicquevert, B.; Nielsen, J.; Nikiforou, N.; Nikiforov, A.; Nikolaenko, V.; Nikolic-Audit, I.; Nikolopoulos, K.; Nilsen, J. K.; Nilsson, P.; Ninomiya, Y.; Nisati, A.; Nisius, R.; Nobe, T.; Nodulman, L.; Nomachi, M.; Nomidis, I.; Nooney, T.; Norberg, S.; Nordberg, M.; Novgorodova, O.; Nowak, S.; Nozaki, M.; Nozka, L.; Ntekas, K.; Hanninger, G. Nunes; Nunnemann, T.; Nurse, E.; Nuti, F.; O'grady, F.; O'Neil, D. C.; O'Shea, V.; Oakham, F. G.; Oberlack, H.; Obermann, T.; Ocariz, J.; Ochi, A.; Ochoa, I.; Ochoa-Ricoux, J. P.; Oda, S.; Odaka, S.; Ogren, H.; Oh, A.; Oh, S. H.; Ohm, C. C.; Ohman, H.; Oide, H.; Okamura, W.; Okawa, H.; Okumura, Y.; Okuyama, T.; Olariu, A.; Pino, S. A. Olivares; Damazio, D. Oliveira; Olszewski, A.; Olszowska, J.; Onofre, A.; Onogi, K.; Onyisi, P. U. E.; Oram, C. J.; Oreglia, M. J.; Oren, Y.; Orestano, D.; Orlando, N.; Barrera, C. Oropeza; Orr, R. S.; Osculati, B.; Ospanov, R.; Garzon, G. Otero y.; Otono, H.; Ouchrif, M.; Ould-Saada, F.; Ouraou, A.; Oussoren, K. P.; Ouyang, Q.; Ovcharova, A.; Owen, M.; Owen, R. E.; Ozcan, V. E.; Ozturk, N.; Pachal, K.; Pages, A. Pacheco; Aranda, C. Padilla; Pagáčová, M.; Griso, S. Pagan; Paganis, E.; Paige, F.; Pais, P.; Pajchel, K.; Palacino, G.; Palestini, S.; Palka, M.; Pallin, D.; Palma, A.; Pan, Y. B.; Panagiotopoulou, E. St.; Pandini, C. E.; Vazquez, J. G. Panduro; Pani, P.; Panitkin, S.; Pantea, D.; Paolozzi, L.; Papadopoulou, Th. D.; Papageorgiou, K.; Paramonov, A.; Hernandez, D. Paredes; Parker, M. A.; Parker, K. A.; Parodi, F.; Parsons, J. A.; Parzefall, U.; Pasqualucci, E.; Passaggio, S.; Pastore, F.; Pastore, Fr.; Pásztor, G.; Pataraia, S.; Patel, N. D.; Pater, J. R.; Pauly, T.; Pearce, J.; Pearson, B.; Pedersen, L. E.; Pedersen, M.; Lopez, S. Pedraza; Pedro, R.; Peleganchuk, S. V.; Pelikan, D.; Penc, O.; Peng, C.; Peng, H.; Penning, B.; Penwell, J.; Perepelitsa, D. V.; Codina, E. Perez; García-Estañ, M. T. Pérez; Perini, L.; Pernegger, H.; Perrella, S.; Peschke, R.; Peshekhonov, V. D.; Peters, K.; Peters, R. F. Y.; Petersen, B. A.; Petersen, T. C.; Petit, E.; Petridis, A.; Petridou, C.; Petroff, P.; Petrolo, E.; Petrucci, F.; Pettersson, N. E.; Pezoa, R.; Phillips, P. W.; Piacquadio, G.; Pianori, E.; Picazio, A.; Piccaro, E.; Piccinini, M.; Pickering, M. A.; Piegaia, R.; Pignotti, D. T.; Pilcher, J. E.; Pilkington, A. D.; Pin, A. W. J.; Pina, J.; Pinamonti, M.; Pinfold, J. L.; Pingel, A.; Pires, S.; Pirumov, H.; Pitt, M.; Pizio, C.; Plazak, L.; Pleier, M.-A.; Pleskot, V.; Plotnikova, E.; Plucinski, P.; Pluth, D.; Poettgen, R.; Poggioli, L.; Pohl, D.; Polesello, G.; Poley, A.; Policicchio, A.; Polifka, R.; Polini, A.; Pollard, C. S.; Polychronakos, V.; Pommès, K.; Pontecorvo, L.; Pope, B. G.; Popeneciu, G. A.; Popovic, D. S.; Poppleton, A.; Pospisil, S.; Potamianos, K.; Potrap, I. N.; Potter, C. J.; Potter, C. T.; Poulard, G.; Poveda, J.; Pozdnyakov, V.; Astigarraga, M. E. Pozo; Pralavorio, P.; Pranko, A.; Prasad, S.; Prell, S.; Price, D.; Price, L. E.; Primavera, M.; Prince, S.; Proissl, M.; Prokofiev, K.; Prokoshin, F.; Protopapadaki, E.; Protopopescu, S.; Proudfoot, J.; Przybycien, M.; Ptacek, E.; Puddu, D.; Pueschel, E.; Puldon, D.; Purohit, M.; Puzo, P.; Qian, J.; Qin, G.; Qin, Y.; Quadt, A.; Quarrie, D. R.; Quayle, W. B.; Queitsch-Maitland, M.; Quilty, D.; Raddum, S.; Radeka, V.; Radescu, V.; Radhakrishnan, S. K.; Radloff, P.; Rados, P.; Ragusa, F.; Rahal, G.; Rajagopalan, S.; Rammensee, M.; Rangel-Smith, C.; Rauscher, F.; Rave, S.; Ravenscroft, T.; Raymond, M.; Read, A. L.; Readioff, N. P.; Rebuzzi, D. M.; Redelbach, A.; Redlinger, G.; Reece, R.; Reeves, K.; Rehnisch, L.; Reichert, J.; Reisin, H.; Rembser, C.; Ren, H.; Renaud, A.; Rescigno, M.; Resconi, S.; Rezanova, O. L.; Reznicek, P.; Rezvani, R.; Richter, R.; Richter, S.; Richter-Was, E.; Ricken, O.; Ridel, M.; Rieck, P.; Riegel, C. J.; Rieger, J.; Rifki, O.; Rijssenbeek, M.; Rimoldi, A.; Rinaldi, L.; Ristić, B.; Ritsch, E.; Riu, I.; Rizatdinova, F.; Rizvi, E.; Robertson, S. H.; Robichaud-Veronneau, A.; Robinson, D.; Robinson, J. E. M.; Robson, A.; Roda, C.; Roe, S.; Røhne, O.; Romaniouk, A.; Romano, M.; Saez, S. M. Romano; Adam, E. Romero; Rompotis, N.; Ronzani, M.; Roos, L.; Ros, E.; Rosati, S.; Rosbach, K.; Rose, P.; Rosenthal, O.; Rossetti, V.; Rossi, E.; Rossi, L. P.; Rosten, J. H. N.; Rosten, R.; Rotaru, M.; Roth, I.; Rothberg, J.; Rousseau, D.; Royon, C. R.; Rozanov, A.; Rozen, Y.; Ruan, X.; Rubbo, F.; Rubinskiy, I.; Rud, V. I.; Rudolph, C.; Rudolph, M. S.; Rühr, F.; Ruiz-Martinez, A.; Rurikova, Z.; Rusakovich, N. A.; Ruschke, A.; Russell, H. L.; Rutherfoord, J. P.; Ruthmann, N.; Ryabov, Y. F.; Rybar, M.; Rybkin, G.; Ryder, N. C.; Ryzhov, A.; Saavedra, A. F.; Sabato, G.; Sacerdoti, S.; Saddique, A.; Sadrozinski, H. F.-W.; Sadykov, R.; Tehrani, F. Safai; Saha, P.; Sahinsoy, M.; Saimpert, M.; Saito, T.; Sakamoto, H.; Sakurai, Y.; Salamanna, G.; Salamon, A.; Loyola, J. E. Salazar; Saleem, M.; Salek, D.; De Bruin, P. H. Sales; Salihagic, D.; Salnikov, A.; Salt, J.; Salvatore, D.; Salvatore, F.; Salvucci, A.; Salzburger, A.; Sammel, D.; Sampsonidis, D.; Sanchez, A.; Sánchez, J.; Martinez, V. Sanchez; Sandaker, H.; Sandbach, R. L.; Sander, H. G.; Sanders, M. P.; Sandhoff, M.; Sandoval, C.; Sandstroem, R.; Sankey, D. P. C.; Sannino, M.; Sansoni, A.; Santoni, C.; Santonico, R.; Santos, H.; Castillo, I. Santoyo; Sapp, K.; Sapronov, A.; Saraiva, J. G.; Sarrazin, B.; Sasaki, O.; Sasaki, Y.; Sato, K.; Sauvage, G.; Sauvan, E.; Savage, G.; Savard, P.; Sawyer, C.; Sawyer, L.; Saxon, J.; Sbarra, C.; Sbrizzi, A.; Scanlon, T.; Scannicchio, D. A.; Scarcella, M.; Scarfone, V.; Schaarschmidt, J.; Schacht, P.; Schaefer, D.; Schaefer, R.; Schaeffer, J.; Schaepe, S.; Schaetzel, S.; Schäfer, U.; Schaffer, A. C.; Schaile, D.; Schamberger, R. D.; Scharf, V.; Schegelsky, V. A.; Scheirich, D.; Schernau, M.; Schiavi, C.; Schillo, C.; Schioppa, M.; Schlenker, S.; Schmieden, K.; Schmitt, C.; Schmitt, S.; Schmitt, S.; Schmitz, S.; Schneider, B.; Schnellbach, Y. J.; Schnoor, U.; Schoeffel, L.; Schoening, A.; Schoenrock, B. D.; Schopf, E.; Schorlemmer, A. L. S.; Schott, M.; Schouten, D.; Schovancova, J.; Schramm, S.; Schreyer, M.; Schuh, N.; Schultens, M. J.; Schultz-Coulon, H.-C.; Schulz, H.; Schumacher, M.; Schumm, B. A.; Schune, Ph.; Schwanenberger, C.; Schwartzman, A.; Schwarz, T. A.; Schwegler, Ph.; Schweiger, H.; Schwemling, Ph.; Schwienhorst, R.; Schwindling, J.; Schwindt, T.; Scifo, E.; Sciolla, G.; Scuri, F.; Scutti, F.; Searcy, J.; Sedov, G.; Sedykh, E.; Seema, P.; Seidel, S. C.; Seiden, A.; Seifert, F.; Seixas, J. M.; Sekhniaidze, G.; Sekhon, K.; Sekula, S. J.; Seliverstov, D. M.; Semprini-Cesari, N.; Serfon, C.; Serin, L.; Serkin, L.; Serre, T.; Sessa, M.; Seuster, R.; Severini, H.; Sfiligoj, T.; Sforza, F.; Sfyrla, A.; Shabalina, E.; Shamim, M.; Shan, L. Y.; Shang, R.; Shank, J. T.; Shapiro, M.; Shatalov, P. B.; Shaw, K.; Shaw, S. M.; Shcherbakova, A.; Shehu, C. Y.; Sherwood, P.; Shi, L.; Shimizu, S.; Shimmin, C. O.; Shimojima, M.; Shiyakova, M.; Shmeleva, A.; Saadi, D. Shoaleh; Shochet, M. J.; Shojaii, S.; Shrestha, S.; Shulga, E.; Shupe, M. A.; Sicho, P.; Sidebo, P. E.; Sidiropoulou, O.; Sidorov, D.; Sidoti, A.; Siegert, F.; Sijacki, Dj.; Silva, J.; Silver, Y.; Silverstein, S. B.; Simak, V.; Simard, O.; Simic, Lj.; Simion, S.; Simioni, E.; Simmons, B.; Simon, D.; Simon, M.; Sinervo, P.; Sinev, N. B.; Sioli, M.; Siragusa, G.; Sisakyan, A. N.; Sivoklokov, S. Yu.; Sjölin, J.; Sjursen, T. B.; Skinner, M. B.; Skottowe, H. P.; Skubic, P.; Slater, M.; Slavicek, T.; Slawinska, M.; Sliwa, K.; Smakhtin, V.; Smart, B. H.; Smestad, L.; Smirnov, S. Yu.; Smirnov, Y.; Smirnova, L. N.; Smirnova, O.; Smith, M. N. K.; Smith, R. W.; Smizanska, M.; Smolek, K.; Snesarev, A. A.; Snidero, G.; Snyder, S.; Sobie, R.; Socher, F.; Soffer, A.; Soh, D. A.; Sokhrannyi, G.; Sanchez, C. A. Solans; Solar, M.; Solc, J.; Soldatov, E. Yu.; Soldevila, U.; Solodkov, A. A.; Soloshenko, A.; Solovyanov, O. V.; Solovyev, V.; Sommer, P.; Song, H. Y.; Soni, N.; Sood, A.; Sopczak, A.; Sopko, B.; Sopko, V.; Sorin, V.; Sosa, D.; Sosebee, M.; Sotiropoulou, C. L.; Soualah, R.; Soukharev, A. M.; South, D.; Sowden, B. C.; Spagnolo, S.; Spalla, M.; Spangenberg, M.; Spanò, F.; Spearman, W. R.; Sperlich, D.; Spettel, F.; Spighi, R.; Spigo, G.; Spiller, L. A.; Spousta, M.; Denis, R. D. St.; Stabile, A.; Staerz, S.; Stahlman, J.; Stamen, R.; Stamm, S.; Stanecka, E.; Stanek, R. W.; Stanescu, C.; Stanescu-Bellu, M.; Stanitzki, M. M.; Stapnes, S.; Starchenko, E. A.; Stark, J.; Staroba, P.; Starovoitov, P.; Staszewski, R.; Steinberg, P.; Stelzer, B.; Stelzer, H. J.; Stelzer-Chilton, O.; Stenzel, H.; Stewart, G. A.; Stillings, J. A.; Stockton, M. C.; Stoebe, M.; Stoicea, G.; Stolte, P.; Stonjek, S.; Stradling, A. R.; Straessner, A.; Stramaglia, M. E.; Strandberg, J.; Strandberg, S.; Strandlie, A.; Strauss, E.; Strauss, M.; Strizenec, P.; Ströhmer, R.; Strom, D. M.; Stroynowski, R.; Strubig, A.; Stucci, S. A.; Stugu, B.; Styles, N. A.; Su, D.; Su, J.; Subramaniam, R.; Succurro, A.; Suchek, S.; Sugaya, Y.; Suk, M.; Sulin, V. V.; Sultansoy, S.; Sumida, T.; Sun, S.; Sun, X.; Sundermann, J. E.; Suruliz, K.; Susinno, G.; Sutton, M. R.; Suzuki, S.; Svatos, M.; Swiatlowski, M.; Sykora, I.; Sykora, T.; Ta, D.; Taccini, C.; Tackmann, K.; Taenzer, J.; Taffard, A.; Tafirout, R.; Taiblum, N.; Takai, H.; Takashima, R.; Takeda, H.; Takeshita, T.; Takubo, Y.; Talby, M.; Talyshev, A. A.; Tam, J. Y. C.; Tan, K. G.; Tanaka, J.; Tanaka, R.; Tanaka, S.; Tannenwald, B. B.; Araya, S. Tapia; Tapprogge, S.; Tarem, S.; Tarrade, F.; Tartarelli, G. F.; Tas, P.; Tasevsky, M.; Tashiro, T.; Tassi, E.; Delgado, A. Tavares; Tayalati, Y.; Taylor, A. C.; Taylor, F. E.; Taylor, G. N.; Taylor, P. T. E.; Taylor, W.; Teischinger, F. A.; Teixeira-Dias, P.; Temming, K. K.; Temple, D.; Kate, H. Ten; Teng, P. K.; Teoh, J. J.; Tepel, F.; Terada, S.; Terashi, K.; Terron, J.; Terzo, S.; Testa, M.; Teuscher, R. J.; Theveneaux-Pelzer, T.; Thomas, J. P.; Thomas-Wilsker, J.; Thompson, E. N.; Thompson, P. D.; Thompson, R. J.; Thompson, A. S.; Thomsen, L. A.; Thomson, E.; Thomson, M.; Thun, R. P.; Tibbetts, M. J.; Torres, R. E. Ticse; Tikhomirov, V. O.; Tikhonov, Yu. A.; Timoshenko, S.; Tiouchichine, E.; Tipton, P.; Tisserant, S.; Todome, K.; Todorov, T.; Todorova-Nova, S.; Tojo, J.; Tokár, S.; Tokushuku, K.; Tollefson, K.; Tolley, E.; Tomlinson, L.; Tomoto, M.; Tompkins, L.; Toms, K.; Torrence, E.; Torres, H.; Pastor, E. Torró; Toth, J.; Touchard, F.; Tovey, D. R.; Trefzger, T.; Tremblet, L.; Tricoli, A.; Trigger, I. M.; Trincaz-Duvoid, S.; Tripiana, M. F.; Trischuk, W.; Trocmé, B.; Troncon, C.; Trottier-McDonald, M.; Trovatelli, M.; Truong, L.; Trzebinski, M.; Trzupek, A.; Tsarouchas, C.; Tseng, J. C.-L.; Tsiareshka, P. V.; Tsionou, D.; Tsipolitis, G.; Tsirintanis, N.; Tsiskaridze, S.; Tsiskaridze, V.; Tskhadadze, E. G.; Tsui, K. M.; Tsukerman, I. I.; Tsulaia, V.; Tsuno, S.; Tsybychev, D.; Tudorache, A.; Tudorache, V.; Tuna, A. N.; Tupputi, S. A.; Turchikhin, S.; Turecek, D.; Turra, R.; Turvey, A. J.; Tuts, P. M.; Tykhonov, A.; Tylmad, M.; Tyndel, M.; Ueda, I.; Ueno, R.; Ughetto, M.; Ukegawa, F.; Unal, G.; Undrus, A.; Unel, G.; Ungaro, F. C.; Unno, Y.; Unverdorben, C.; Urban, J.; Urquijo, P.; Urrejola, P.; Usai, G.; Usanova, A.; Vacavant, L.; Vacek, V.; Vachon, B.; Valderanis, C.; Valencic, N.; Valentinetti, S.; Valero, A.; Valery, L.; Valkar, S.; Vallecorsa, S.; Ferrer, J. A. Valls; Van Den Wollenberg, W.; Van Der Deijl, P. C.; van der Geer, R.; van der Graaf, H.; van Eldik, N.; van Gemmeren, P.; Van Nieuwkoop, J.; van Vulpen, I.; van Woerden, M. C.; Vanadia, M.; Vandelli, W.; Vanguri, R.; Vaniachine, A.; Vannucci, F.; Vardanyan, G.; Vari, R.; Varnes, E. W.; Varol, T.; Varouchas, D.; Vartapetian, A.; Varvell, K. E.; Vazeille, F.; Schroeder, T. Vazquez; Veatch, J.; Veloce, L. M.; Veloso, F.; Velz, T.; Veneziano, S.; Ventura, A.; Ventura, D.; Venturi, M.; Venturi, N.; Venturini, A.; Vercesi, V.; Verducci, M.; Verkerke, W.; Vermeulen, J. C.; Vest, A.; Vetterli, M. C.; Viazlo, O.; Vichou, I.; Vickey, T.; Boeriu, O. E. Vickey; Viehhauser, G. H. A.; Viel, S.; Vigne, R.; Villa, M.; Perez, M. Villaplana; Vilucchi, E.; Vincter, M. G.; Vinogradov, V. B.; Vivarelli, I.; Vlachos, S.; Vladoiu, D.; Vlasak, M.; Vogel, M.; Vokac, P.; Volpi, G.; Volpi, M.; von der Schmitt, H.; von Radziewski, H.; von Toerne, E.; Vorobel, V.; Vorobev, K.; Vos, M.; Voss, R.; Vossebeld, J. H.; Vranjes, N.; Milosavljevic, M. Vranjes; Vrba, V.; Vreeswijk, M.; Vuillermet, R.; Vukotic, I.; Vykydal, Z.; Wagner, P.; Wagner, W.; Wahlberg, H.; Wahrmund, S.; Wakabayashi, J.; Walder, J.; Walker, R.; Walkowiak, W.; Wang, C.; Wang, F.; Wang, H.; Wang, H.; Wang, J.; Wang, J.; Wang, K.; Wang, R.; Wang, S. M.; Wang, T.; Wang, T.; Wang, X.; Wanotayaroj, C.; Warburton, A.; Ward, C. P.; Wardrope, D. R.; Washbrook, A.; Wasicki, C.; Watkins, P. M.; Watson, A. T.; Watson, I. J.; Watson, M. F.; Watts, G.; Watts, S.; Waugh, B. M.; Webb, S.; Weber, M. S.; Weber, S. W.; Webster, J. S.; Weidberg, A. R.; Weinert, B.; Weingarten, J.; Weiser, C.; Weits, H.; Wells, P. S.; Wenaus, T.; Wengler, T.; Wenig, S.; Wermes, N.; Werner, M.; Werner, P.; Wessels, M.; Wetter, J.; Whalen, K.; Wharton, A. M.; White, A.; White, M. J.; White, R.; White, S.; Whiteson, D.; Wickens, F. J.; Wiedenmann, W.; Wielers, M.; Wienemann, P.; Wiglesworth, C.; Wiik-Fuchs, L. A. M.; Wildauer, A.; Wilkens, H. G.; Williams, H. H.; Williams, S.; Willis, C.; Willocq, S.; Wilson, A.; Wilson, J. A.; Wingerter-Seez, I.; Winklmeier, F.; Winter, B. T.; Wittgen, M.; Wittkowski, J.; Wollstadt, S. J.; Wolter, M. W.; Wolters, H.; Wosiek, B. K.; Wotschack, J.; Woudstra, M. J.; Wozniak, K. W.; Wu, M.; Wu, M.; Wu, S. L.; Wu, X.; Wu, Y.; Wyatt, T. R.; Wynne, B. M.; Xella, S.; Xu, D.; Xu, L.; Yabsley, B.; Yacoob, S.; Yakabe, R.; Yamada, M.; Yamaguchi, D.; Yamaguchi, Y.; Yamamoto, A.; Yamamoto, S.; Yamanaka, T.; Yamauchi, K.; Yamazaki, Y.; Yan, Z.; Yang, H.; Yang, H.; Yang, Y.; Yao, W.-M.; Yap, Y. C.; Yasu, Y.; Yatsenko, E.; Wong, K. H. Yau; Ye, J.; Ye, S.; Yeletskikh, I.; Yen, A. L.; Yildirim, E.; Yorita, K.; Yoshida, R.; Yoshihara, K.; Young, C.; Young, C. J. S.; Youssef, S.; Yu, D. R.; Yu, J.; Yu, J. M.; Yu, J.; Yuan, L.; Yuen, S. P. Y.; Yurkewicz, A.; Yusuff, I.; Zabinski, B.; Zaidan, R.; Zaitsev, A. M.; Zalieckas, J.; Zaman, A.; Zambito, S.; Zanello, L.; Zanzi, D.; Zeitnitz, C.; Zeman, M.; Zemla, A.; Zeng, J. C.; Zeng, Q.; Zengel, K.; Zenin, O.; Ženiš, T.; Zerwas, D.; Zhang, D.; Zhang, F.; Zhang, G.; Zhang, H.; Zhang, J.; Zhang, L.; Zhang, R.; Zhang, X.; Zhang, Z.; Zhao, X.; Zhao, Y.; Zhao, Z.; Zhemchugov, A.; Zhong, J.; Zhou, B.; Zhou, C.; Zhou, L.; Zhou, L.; Zhou, M.; Zhou, N.; Zhu, C. G.; Zhu, H.; Zhu, J.; Zhu, Y.; Zhuang, X.; Zhukov, K.; Zibell, A.; Zieminska, D.; Zimine, N. I.; Zimmermann, C.; Zimmermann, S.; Zinonos, Z.; Zinser, M.; Ziolkowski, M.; Živković, L.; Zobernig, G.; Zoccoli, A.; Nedden, M. zur; Zurzolo, G.; Zwalinski, L.

    2017-07-01

    The reconstruction of the signal from hadrons and jets emerging from the proton-proton collisions at the Large Hadron Collider (LHC) and entering the ATLAS calorimeters is based on a three-dimensional topological clustering of individual calorimeter cell signals. The cluster formation follows cell signal-significance patterns generated by electromagnetic and hadronic showers. In this, the clustering algorithm implicitly performs a topological noise suppression by removing cells with insignificant signals which are not in close proximity to cells with significant signals. The resulting topological cell clusters have shape and location information, which is exploited to apply a local energy calibration and corrections depending on the nature of the cluster. Topological cell clustering is established as a well-performing calorimeter signal definition for jet and missing transverse momentum reconstruction in ATLAS.

  18. Topological cell clustering in the ATLAS calorimeters and its performance in LHC Run 1.

    PubMed

    Aad, G; Abbott, B; Abdallah, J; Abdinov, O; Aben, R; Abolins, M; AbouZeid, O S; Abramowicz, H; Abreu, H; Abreu, R; Abulaiti, Y; Acharya, B S; Adamczyk, L; Adams, D L; Adelman, J; Adomeit, S; Adye, T; Affolder, A A; Agatonovic-Jovin, T; Agricola, J; Aguilar-Saavedra, J A; Ahlen, S P; Ahmadov, F; Aielli, G; Akerstedt, H; Åkesson, T P A; Akimov, A V; Alberghi, G L; Albert, J; Albrand, S; Verzini, M J Alconada; Aleksa, M; Aleksandrov, I N; Alexa, C; Alexander, G; Alexopoulos, T; Alhroob, M; Alimonti, G; Alio, L; Alison, J; Alkire, S P; Allbrooke, B M M; Allport, P P; Aloisio, A; Alonso, A; Alonso, F; Alpigiani, C; Altheimer, A; Gonzalez, B Alvarez; Piqueras, D Álvarez; Alviggi, M G; Amadio, B T; Amako, K; Coutinho, Y Amaral; Amelung, C; Amidei, D; Santos, S P Amor Dos; Amorim, A; Amoroso, S; Amram, N; Amundsen, G; Anastopoulos, C; Ancu, L S; Andari, N; Andeen, T; Anders, C F; Anders, G; Anders, J K; Anderson, K J; Andreazza, A; Andrei, V; Angelidakis, S; Angelozzi, I; Anger, P; Angerami, A; Anghinolfi, F; Anisenkov, A V; Anjos, N; Annovi, A; Antonelli, M; Antonov, A; Antos, J; Anulli, F; Aoki, M; Bella, L Aperio; Arabidze, G; Arai, Y; Araque, J P; Arce, A T H; Arduh, F A; Arguin, J-F; Argyropoulos, S; Arik, M; Armbruster, A J; Arnaez, O; Arnold, H; Arratia, M; Arslan, O; Artamonov, A; Artoni, G; Artz, S; Asai, S; Asbah, N; Ashkenazi, A; Åsman, B; Asquith, L; Assamagan, K; Astalos, R; Atkinson, M; Atlay, N B; Augsten, K; Aurousseau, M; Avolio, G; Axen, B; Ayoub, M K; Azuelos, G; Baak, M A; Baas, A E; Baca, M J; Bacci, C; Bachacou, H; Bachas, K; Backes, M; Backhaus, M; Bagiacchi, P; Bagnaia, P; Bai, Y; Bain, T; Baines, J T; Baker, O K; Baldin, E M; Balek, P; Balestri, T; Balli, F; Balunas, W K; Banas, E; Banerjee, Sw; Bannoura, A A E; Barak, L; Barberio, E L; Barberis, D; Barbero, M; Barillari, T; Barisonzi, M; Barklow, T; Barlow, N; Barnes, S L; Barnett, B M; Barnett, R M; Barnovska, Z; Baroncelli, A; Barone, G; Barr, A J; Barreiro, F; da Costa, J Barreiro Guimarães; Bartoldus, R; Barton, A E; Bartos, P; Basalaev, A; Bassalat, A; Basye, A; Bates, R L; Batista, S J; Batley, J R; Battaglia, M; Bauce, M; Bauer, F; Bawa, H S; Beacham, J B; Beattie, M D; Beau, T; Beauchemin, P H; Beccherle, R; Bechtle, P; Beck, H P; Becker, K; Becker, M; Beckingham, M; Becot, C; Beddall, A J; Beddall, A; Bednyakov, V A; Bee, C P; Beemster, L J; Beermann, T A; Begel, M; Behr, J K; Belanger-Champagne, C; Bell, W H; Bella, G; Bellagamba, L; Bellerive, A; Bellomo, M; Belotskiy, K; Beltramello, O; Benary, O; Benchekroun, D; Bender, M; Bendtz, K; Benekos, N; Benhammou, Y; Noccioli, E Benhar; Garcia, J A Benitez; Benjamin, D P; Bensinger, J R; Bentvelsen, S; Beresford, L; Beretta, M; Berge, D; Kuutmann, E Bergeaas; Berger, N; Berghaus, F; Beringer, J; Bernard, C; Bernard, N R; Bernius, C; Bernlochner, F U; Berry, T; Berta, P; Bertella, C; Bertoli, G; Bertolucci, F; Bertsche, C; Bertsche, D; Besana, M I; Besjes, G J; Bylund, O Bessidskaia; Bessner, M; Besson, N; Betancourt, C; Bethke, S; Bevan, A J; Bhimji, W; Bianchi, R M; Bianchini, L; Bianco, M; Biebel, O; Biedermann, D; Biesuz, N V; Biglietti, M; De Mendizabal, J Bilbao; Bilokon, H; Bindi, M; Binet, S; Bingul, A; Bini, C; Biondi, S; Bjergaard, D M; Black, C W; Black, J E; Black, K M; Blackburn, D; Blair, R E; Blanchard, J-B; Blanco, J E; Blazek, T; Bloch, I; Blocker, C; Blum, W; Blumenschein, U; Blunier, S; Bobbink, G J; Bobrovnikov, V S; Bocchetta, S S; Bocci, A; Bock, C; Boehler, M; Bogaerts, J A; Bogavac, D; Bogdanchikov, A G; Bohm, C; Boisvert, V; Bold, T; Boldea, V; Boldyrev, A S; Bomben, M; Bona, M; Boonekamp, M; Borisov, A; Borissov, G; Borroni, S; Bortfeldt, J; Bortolotto, V; Bos, K; Boscherini, D; Bosman, M; Boudreau, J; Bouffard, J; Bouhova-Thacker, E V; Boumediene, D; Bourdarios, C; Bousson, N; Boutle, S K; Boveia, A; Boyd, J; Boyko, I R; Bozic, I; Bracinik, J; Brandt, A; Brandt, G; Brandt, O; Bratzler, U; Brau, B; Brau, J E; Braun, H M; Madden, W D Breaden; Brendlinger, K; Brennan, A J; Brenner, L; Brenner, R; Bressler, S; Bristow, T M; Britton, D; Britzger, D; Brochu, F M; Brock, I; Brock, R; Bronner, J; Brooijmans, G; Brooks, T; Brooks, W K; Brosamer, J; Brost, E; de Renstrom, P A Bruckman; Bruncko, D; Bruneliere, R; Bruni, A; Bruni, G; Bruschi, M; Bruscino, N; Bryngemark, L; Buanes, T; Buat, Q; Buchholz, P; Buckley, A G; Budagov, I A; Buehrer, F; Bugge, L; Bugge, M K; Bulekov, O; Bullock, D; Burckhart, H; Burdin, S; Burgard, C D; Burghgrave, B; Burke, S; Burmeister, I; Busato, E; Büscher, D; Büscher, V; Bussey, P; Butler, J M; Butt, A I; Buttar, C M; Butterworth, J M; Butti, P; Buttinger, W; Buzatu, A; Buzykaev, A R; Urbán, S Cabrera; Caforio, D; Cairo, V M; Cakir, O; Calace, N; Calafiura, P; Calandri, A; Calderini, G; Calfayan, P; Caloba, L P; Calvet, D; Calvet, S; Toro, R Camacho; Camarda, S; Camarri, P; Cameron, D; Armadans, R Caminal; Campana, S; Campanelli, M; Campoverde, A; Canale, V; Canepa, A; Bret, M Cano; Cantero, J; Cantrill, R; Cao, T; Garrido, M D M Capeans; Caprini, I; Caprini, M; Capua, M; Caputo, R; Carbone, R M; Cardarelli, R; Cardillo, F; Carli, T; Carlino, G; Carminati, L; Caron, S; Carquin, E; Carrillo-Montoya, G D; Carter, J R; Carvalho, J; Casadei, D; Casado, M P; Casolino, M; Casper, D W; Castaneda-Miranda, E; Castelli, A; Gimenez, V Castillo; Castro, N F; Catastini, P; Catinaccio, A; Catmore, J R; Cattai, A; Caudron, J; Cavaliere, V; Cavalli, D; Cavalli-Sforza, M; Cavasinni, V; Ceradini, F; Alberich, L Cerda; Cerio, B C; Cerny, K; Cerqueira, A S; Cerri, A; Cerrito, L; Cerutti, F; Cerv, M; Cervelli, A; Cetin, S A; Chafaq, A; Chakraborty, D; Chalupkova, I; Chan, Y L; Chang, P; Chapman, J D; Charlton, D G; Chau, C C; Barajas, C A Chavez; Che, S; Cheatham, S; Chegwidden, A; Chekanov, S; Chekulaev, S V; Chelkov, G A; Chelstowska, M A; Chen, C; Chen, H; Chen, K; Chen, L; Chen, S; Chen, S; Chen, X; Chen, Y; Cheng, H C; Cheng, Y; Cheplakov, A; Cheremushkina, E; Moursli, R Cherkaoui El; Chernyatin, V; Cheu, E; Chevalier, L; Chiarella, V; Chiarelli, G; Chiodini, G; Chisholm, A S; Chislett, R T; Chitan, A; Chizhov, M V; Choi, K; Chouridou, S; Chow, B K B; Christodoulou, V; Chromek-Burckhart, D; Chudoba, J; Chuinard, A J; Chwastowski, J J; Chytka, L; Ciapetti, G; Ciftci, A K; Cinca, D; Cindro, V; Cioara, I A; Ciocio, A; Cirotto, F; Citron, Z H; Ciubancan, M; Clark, A; Clark, B L; Clark, P J; Clarke, R N; Clement, C; Coadou, Y; Cobal, M; Coccaro, A; Cochran, J; Coffey, L; Cogan, J G; Colasurdo, L; Cole, B; Cole, S; Colijn, A P; Collot, J; Colombo, T; Compostella, G; Muiño, P Conde; Coniavitis, E; Connell, S H; Connelly, I A; Consorti, V; Constantinescu, S; Conta, C; Conti, G; Conventi, F; Cooke, M; Cooper, B D; Cooper-Sarkar, A M; Cornelissen, T; Corradi, M; Corriveau, F; Corso-Radu, A; Cortes-Gonzalez, A; Cortiana, G; Costa, G; Costa, M J; Costanzo, D; Côté, D; Cottin, G; Cowan, G; Cox, B E; Cranmer, K; Cree, G; Crépé-Renaudin, S; Crescioli, F; Cribbs, W A; Ortuzar, M Crispin; Cristinziani, M; Croft, V; Crosetti, G; Donszelmann, T Cuhadar; Cummings, J; Curatolo, M; Cúth, J; Cuthbert, C; Czirr, H; Czodrowski, P; D'Auria, S; D'Onofrio, M; De Sousa, M J Da Cunha Sargedas; Via, C Da; Dabrowski, W; Dafinca, A; Dai, T; Dale, O; Dallaire, F; Dallapiccola, C; Dam, M; Dandoy, J R; Dang, N P; Daniells, A C; Danninger, M; Hoffmann, M Dano; Dao, V; Darbo, G; Darmora, S; Dassoulas, J; Dattagupta, A; Davey, W; David, C; Davidek, T; Davies, E; Davies, M; Davison, P; Davygora, Y; Dawe, E; Dawson, I; Daya-Ishmukhametova, R K; De, K; de Asmundis, R; De Benedetti, A; De Castro, S; De Cecco, S; De Groot, N; de Jong, P; De la Torre, H; De Lorenzi, F; De Pedis, D; De Salvo, A; De Sanctis, U; De Santo, A; De Regie, J B De Vivie; Dearnaley, W J; Debbe, R; Debenedetti, C; Dedovich, D V; Deigaard, I; Del Peso, J; Del Prete, T; Delgove, D; Deliot, F; Delitzsch, C M; Deliyergiyev, M; Dell'Acqua, A; Dell'Asta, L; Dell'Orso, M; Della Pietra, M; Della Volpe, D; Delmastro, M; Delsart, P A; Deluca, C; DeMarco, D A; Demers, S; Demichev, M; Demilly, A; Denisov, S P; Derendarz, D; Derkaoui, J E; Derue, F; Dervan, P; Desch, K; Deterre, C; Dette, K; Deviveiros, P O; Dewhurst, A; Dhaliwal, S; Di Ciaccio, A; Di Ciaccio, L; Di Domenico, A; Di Donato, C; Di Girolamo, A; Di Girolamo, B; Di Mattia, A; Di Micco, B; Di Nardo, R; Di Simone, A; Di Sipio, R; Di Valentino, D; Diaconu, C; Diamond, M; Dias, F A; Diaz, M A; Diehl, E B; Dietrich, J; Diglio, S; Dimitrievska, A; Dingfelder, J; Dita, P; Dita, S; Dittus, F; Djama, F; Djobava, T; Djuvsland, J I; do Vale, M A B; Dobos, D; Dobre, M; Doglioni, C; Dohmae, T; Dolejsi, J; Dolezal, Z; Dolgoshein, B A; Donadelli, M; Donati, S; Dondero, P; Donini, J; Dopke, J; Doria, A; Dova, M T; Doyle, A T; Drechsler, E; Dris, M; Du, Y; Dubreuil, E; Duchovni, E; Duckeck, G; Ducu, O A; Duda, D; Dudarev, A; Duflot, L; Duguid, L; Dührssen, M; Dunford, M; Yildiz, H Duran; Düren, M; Durglishvili, A; Duschinger, D; Dutta, B; Dyndal, M; Eckardt, C; Ecker, K M; Edgar, R C; Edson, W; Edwards, N C; Ehrenfeld, W; Eifert, T; Eigen, G; Einsweiler, K; Ekelof, T; Kacimi, M El; Ellert, M; Elles, S; Ellinghaus, F; Elliot, A A; Ellis, N; Elmsheuser, J; Elsing, M; Emeliyanov, D; Enari, Y; Endner, O C; Endo, M; Erdmann, J; Ereditato, A; Ernis, G; Ernst, J; Ernst, M; Errede, S; Ertel, E; Escalier, M; Esch, H; Escobar, C; Esposito, B; Etienvre, A I; Etzion, E; Evans, H; Ezhilov, A; Fabbri, L; Facini, G; Fakhrutdinov, R M; Falciano, S; Falla, R J; Faltova, J; Fang, Y; Fanti, M; Farbin, A; Farilla, A; Farooque, T; Farrell, S; Farrington, S M; Farthouat, P; Fassi, F; Fassnacht, P; Fassouliotis, D; Giannelli, M Faucci; Favareto, A; Fayard, L; Fedin, O L; Fedorko, W; Feigl, S; Feligioni, L; Feng, C; Feng, E J; Feng, H; Fenyuk, A B; Feremenga, L; Martinez, P Fernandez; Perez, S Fernandez; Ferrando, J; Ferrari, A; Ferrari, P; Ferrari, R; de Lima, D E Ferreira; Ferrer, A; Ferrere, D; Ferretti, C; Parodi, A Ferretto; Fiascaris, M; Fiedler, F; Filipčič, A; Filipuzzi, M; Filthaut, F; Fincke-Keeler, M; Finelli, K D; Fiolhais, M C N; Fiorini, L; Firan, A; Fischer, A; Fischer, C; Fischer, J; Fisher, W C; Flaschel, N; Fleck, I; Fleischmann, P; Fletcher, G T; Fletcher, G; Fletcher, R R M; Flick, T; Floderus, A; Castillo, L R Flores; Flowerdew, M J; Formica, A; Forti, A; Fournier, D; Fox, H; Fracchia, S; Francavilla, P; Franchini, M; Francis, D; Franconi, L; Franklin, M; Frate, M; Fraternali, M; Freeborn, D; French, S T; Fressard-Batraneanu, S M; Friedrich, F; Froidevaux, D; Frost, J A; Fukunaga, C; Torregrosa, E Fullana; Fulsom, B G; Fusayasu, T; Fuster, J; Gabaldon, C; Gabizon, O; Gabrielli, A; Gabrielli, A; Gach, G P; Gadatsch, S; Gadomski, S; Gagliardi, G; Gagnon, P; Galea, C; Galhardo, B; Gallas, E J; Gallop, B J; Gallus, P; Galster, G; Gan, K K; Gao, J; Gao, Y; Gao, Y S; Walls, F M Garay; Garberson, F; García, C; Navarro, J E García; Garcia-Sciveres, M; Gardner, R W; Garelli, N; Garonne, V; Gatti, C; Gaudiello, A; Gaudio, G; Gaur, B; Gauthier, L; Gauzzi, P; Gavrilenko, I L; Gay, C; Gaycken, G; Gazis, E N; Ge, P; Gecse, Z; Gee, C N P; Geich-Gimbel, Ch; Geisler, M P; Gemme, C; Genest, M H; Geng, C; Gentile, S; George, M; George, S; Gerbaudo, D; Gershon, A; Ghasemi, S; Ghazlane, H; Giacobbe, B; Giagu, S; Giangiobbe, V; Giannetti, P; Gibbard, B; Gibson, S M; Gignac, M; Gilchriese, M; Gillam, T P S; Gillberg, D; Gilles, G; Gingrich, D M; Giokaris, N; Giordani, M P; Giorgi, F M; Giorgi, F M; Giraud, P F; Giromini, P; Giugni, D; Giuliani, C; Giulini, M; Gjelsten, B K; Gkaitatzis, S; Gkialas, I; Gkougkousis, E L; Gladilin, L K; Glasman, C; Glatzer, J; Glaysher, P C F; Glazov, A; Goblirsch-Kolb, M; Goddard, J R; Godlewski, J; Goldfarb, S; Golling, T; Golubkov, D; Gomes, A; Gonçalo, R; Costa, J Goncalves Pinto Firmino Da; Gonella, L; de la Hoz, S González; Parra, G Gonzalez; Gonzalez-Sevilla, S; Goossens, L; Gorbounov, P A; Gordon, H A; Gorelov, I; Gorini, B; Gorini, E; Gorišek, A; Gornicki, E; Goshaw, A T; Gössling, C; Gostkin, M I; Goujdami, D; Goussiou, A G; Govender, N; Gozani, E; Grabas, H M X; Graber, L; Grabowska-Bold, I; Gradin, P O J; Grafström, P; Gramling, J; Gramstad, E; Grancagnolo, S; Gratchev, V; Gray, H M; Graziani, E; Greenwood, Z D; Grefe, C; Gregersen, K; Gregor, I M; Grenier, P; Griffiths, J; Grillo, A A; Grimm, K; Grinstein, S; Gris, Ph; Grivaz, J-F; Groh, S; Grohs, J P; Grohsjean, A; Gross, E; Grosse-Knetter, J; Grossi, G C; Grout, Z J; Guan, L; Guenther, J; Guescini, F; Guest, D; Gueta, O; Guido, E; Guillemin, T; Guindon, S; Gul, U; Gumpert, C; Guo, J; Guo, Y; Gupta, S; Gustavino, G; Gutierrez, P; Ortiz, N G Gutierrez; Gutschow, C; Guyot, C; Gwenlan, C; Gwilliam, C B; Haas, A; Haber, C; Hadavand, H K; Haddad, N; Haefner, P; Hageböck, S; Hajduk, Z; Hakobyan, H; Haleem, M; Haley, J; Hall, D; Halladjian, G; Hallewell, G D; Hamacher, K; Hamal, P; Hamano, K; Hamilton, A; Hamity, G N; Hamnett, P G; Han, L; Hanagaki, K; Hanawa, K; Hance, M; Haney, B; Hanke, P; Hanna, R; Hansen, J B; Hansen, J D; Hansen, M C; Hansen, P H; Hara, K; Hard, A S; Harenberg, T; Hariri, F; Harkusha, S; Harrington, R D; Harrison, P F; Hartjes, F; Hasegawa, M; Hasegawa, Y; Hasib, A; Hassani, S; Haug, S; Hauser, R; Hauswald, L; Havranek, M; Hawkes, C M; Hawkings, R J; Hawkins, A D; Hayashi, T; Hayden, D; Hays, C P; Hays, J M; Hayward, H S; Haywood, S J; Head, S J; Heck, T; Hedberg, V; Heelan, L; Heim, S; Heim, T; Heinemann, B; Heinrich, L; Hejbal, J; Helary, L; Hellman, S; Helsens, C; Henderson, J; Henderson, R C W; Heng, Y; Hengler, C; Henkelmann, S; Henrichs, A; Correia, A M Henriques; Henrot-Versille, S; Herbert, G H; Jiménez, Y Hernández; Herten, G; Hertenberger, R; Hervas, L; Hesketh, G G; Hessey, N P; Hetherly, J W; Hickling, R; Higón-Rodriguez, E; Hill, E; Hill, J C; Hiller, K H; Hillier, S J; Hinchliffe, I; Hines, E; Hinman, R R; Hirose, M; Hirschbuehl, D; Hobbs, J; Hod, N; Hodgkinson, M C; Hodgson, P; Hoecker, A; Hoeferkamp, M R; Hoenig, F; Hohlfeld, M; Hohn, D; Holmes, T R; Homann, M; Hong, T M; Hopkins, W H; Horii, Y; Horton, A J; Hostachy, J-Y; Hou, S; Hoummada, A; Howard, J; Howarth, J; Hrabovsky, M; Hristova, I; Hrivnac, J; Hryn'ova, T; Hrynevich, A; Hsu, C; Hsu, P J; Hsu, S-C; Hu, D; Hu, Q; Hu, X; Huang, Y; Hubacek, Z; Hubaut, F; Huegging, F; Huffman, T B; Hughes, E W; Hughes, G; Huhtinen, M; Hülsing, T A; Huseynov, N; Huston, J; Huth, J; Iacobucci, G; Iakovidis, G; Ibragimov, I; Iconomidou-Fayard, L; Ideal, E; Idrissi, Z; Iengo, P; Igonkina, O; Iizawa, T; Ikegami, Y; Ikeno, M; Ilchenko, Y; Iliadis, D; Ilic, N; Ince, T; Introzzi, G; Ioannou, P; Iodice, M; Iordanidou, K; Ippolito, V; Quiles, A Irles; Isaksson, C; Ishino, M; Ishitsuka, M; Ishmukhametov, R; Issever, C; Istin, S; Ponce, J M Iturbe; Iuppa, R; Ivarsson, J; Iwanski, W; Iwasaki, H; Izen, J M; Izzo, V; Jabbar, S; Jackson, B; Jackson, M; Jackson, P; Jaekel, M R; Jain, V; Jakobi, K B; Jakobs, K; Jakobsen, S; Jakoubek, T; Jakubek, J; Jamin, D O; Jana, D K; Jansen, E; Jansky, R; Janssen, J; Janus, M; Jarlskog, G; Javadov, N; Javůrek, T; Jeanty, L; Jejelava, J; Jeng, G-Y; Jennens, D; Jenni, P; Jentzsch, J; Jeske, C; Jézéquel, S; Ji, H; Jia, J; Jiang, H; Jiang, Y; Jiggins, S; Pena, J Jimenez; Jin, S; Jinaru, A; Jinnouchi, O; Joergensen, M D; Johansson, P; Johns, K A; Johnson, W J; Jon-And, K; Jones, G; Jones, R W L; Jones, T J; Jongmanns, J; Jorge, P M; Joshi, K D; Jovicevic, J; Ju, X; Rozas, A Juste; Kaci, M; Kaczmarska, A; Kado, M; Kagan, H; Kagan, M; Kahn, S J; Kajomovitz, E; Kalderon, C W; Kaluza, A; Kama, S; Kamenshchikov, A; Kanaya, N; Kaneti, S; Kantserov, V A; Kanzaki, J; Kaplan, B; Kaplan, L S; Kapliy, A; Kar, D; Karakostas, K; Karamaoun, A; Karastathis, N; Kareem, M J; Karentzos, E; Karnevskiy, M; Karpov, S N; Karpova, Z M; Karthik, K; Kartvelishvili, V; Karyukhin, A N; Kasahara, K; Kashif, L; Kass, R D; Kastanas, A; Kataoka, Y; Kato, C; Katre, A; Katzy, J; Kawade, K; Kawagoe, K; Kawamoto, T; Kawamura, G; Kazama, S; Kazanin, V F; Keeler, R; Kehoe, R; Keller, J S; Kempster, J J; Keoshkerian, H; Kepka, O; Kerševan, B P; Kersten, S; Keyes, R A; Khalil-Zada, F; Khandanyan, H; Khanov, A; Kharlamov, A G; Khoo, T J; Khovanskiy, V; Khramov, E; Khubua, J; Kido, S; Kim, H Y; Kim, S H; Kim, Y K; Kimura, N; Kind, O M; King, B T; King, M; King, S B; Kirk, J; Kiryunin, A E; Kishimoto, T; Kisielewska, D; Kiss, F; Kiuchi, K; Kivernyk, O; Kladiva, E; Klein, M H; Klein, M; Klein, U; Kleinknecht, K; Klimek, P; Klimentov, A; Klingenberg, R; Klinger, J A; Klioutchnikova, T; Kluge, E-E; Kluit, P; Kluth, S; Knapik, J; Kneringer, E; Knoops, E B F G; Knue, A; Kobayashi, A; Kobayashi, D; Kobayashi, T; Kobel, M; Kocian, M; Kodys, P; Koffas, T; Koffeman, E; Kogan, L A; Kohlmann, S; Kohout, Z; Kohriki, T; Koi, T; Kolanoski, H; Kolb, M; Koletsou, I; Komar, A A; Komori, Y; Kondo, T; Kondrashova, N; Köneke, K; König, A C; Kono, T; Konoplich, R; Konstantinidis, N; Kopeliansky, R; Koperny, S; Köpke, L; Kopp, A K; Korcyl, K; Kordas, K; Korn, A; Korol, A A; Korolkov, I; Korolkova, E V; Kortner, O; Kortner, S; Kosek, T; Kostyukhin, V V; Kotov, V M; Kotwal, A; Kourkoumeli-Charalampidi, A; Kourkoumelis, C; Kouskoura, V; Koutsman, A; Kowalewski, R; Kowalski, T Z; Kozanecki, W; Kozhin, A S; Kramarenko, V A; Kramberger, G; Krasnopevtsev, D; Krasny, M W; Krasznahorkay, A; Kraus, J K; Kravchenko, A; Kreiss, S; Kretz, M; Kretzschmar, J; Kreutzfeldt, K; Krieger, P; Krizka, K; Kroeninger, K; Kroha, H; Kroll, J; Kroseberg, J; Krstic, J; Kruchonak, U; Krüger, H; Krumnack, N; Kruse, A; Kruse, M C; Kruskal, M; Kubota, T; Kucuk, H; Kuday, S; Kuehn, S; Kugel, A; Kuger, F; Kuhl, A; Kuhl, T; Kukhtin, V; Kukla, R; Kulchitsky, Y; Kuleshov, S; Kuna, M; Kunigo, T; Kupco, A; Kurashige, H; Kurochkin, Y A; Kus, V; Kuwertz, E S; Kuze, M; Kvita, J; Kwan, T; Kyriazopoulos, D; Rosa, A La; Navarro, J L La Rosa; Rotonda, L La; Lacasta, C; Lacava, F; Lacey, J; Lacker, H; Lacour, D; Lacuesta, V R; Ladygin, E; Lafaye, R; Laforge, B; Lagouri, T; Lai, S; Lambourne, L; Lammers, S; Lampen, C L; Lampl, W; Lançon, E; Landgraf, U; Landon, M P J; Lang, V S; Lange, J C; Lankford, A J; Lanni, F; Lantzsch, K; Lanza, A; Laplace, S; Lapoire, C; Laporte, J F; Lari, T; Manghi, F Lasagni; Lassnig, M; Laurelli, P; Lavrijsen, W; Law, A T; Laycock, P; Lazovich, T; Dortz, O Le; Guirriec, E Le; Menedeu, E Le; LeBlanc, M; LeCompte, T; Ledroit-Guillon, F; Lee, C A; Lee, S C; Lee, L; Lefebvre, G; Lefebvre, M; Legger, F; Leggett, C; Lehan, A; Miotto, G Lehmann; Lei, X; Leight, W A; Leisos, A; Leister, A G; Leite, M A L; Leitner, R; Lellouch, D; Lemmer, B; Leney, K J C; Lenz, T; Lenzi, B; Leone, R; Leone, S; Leonidopoulos, C; Leontsinis, S; Leroy, C; Lester, C G; Levchenko, M; Levêque, J; Levin, D; Levinson, L J; Levy, M; Lewis, A; Leyko, A M; Leyton, M; Li, B; Li, H; Li, H L; Li, L; Li, L; Li, S; Li, X; Li, Y; Liang, Z; Liao, H; Liberti, B; Liblong, A; Lichard, P; Lie, K; Liebal, J; Liebig, W; Limbach, C; Limosani, A; Lin, S C; Lin, T H; Linde, F; Lindquist, B E; Linnemann, J T; Lipeles, E; Lipniacka, A; Lisovyi, M; Liss, T M; Lissauer, D; Lister, A; Litke, A M; Liu, B; Liu, D; Liu, H; Liu, J; Liu, J B; Liu, K; Liu, L; Liu, M; Liu, M; Liu, Y; Livan, M; Lleres, A; Merino, J Llorente; Lloyd, S L; Sterzo, F Lo; Lobodzinska, E; Loch, P; Lockman, W S; Loebinger, F K; Loevschall-Jensen, A E; Loew, K M; Loginov, A; Lohse, T; Lohwasser, K; Lokajicek, M; Long, B A; Long, J D; Long, R E; Looper, K A; Lopes, L; Mateos, D Lopez; Paredes, B Lopez; Paz, I Lopez; Lorenz, J; Martinez, N Lorenzo; Losada, M; Lösel, P J; Lou, X; Lounis, A; Love, J; Love, P A; Lu, H; Lu, N; Lubatti, H J; Luci, C; Lucotte, A; Luedtke, C; Luehring, F; Lukas, W; Luminari, L; Lundberg, O; Lund-Jensen, B; Lynn, D; Lysak, R; Lytken, E; Ma, H; Ma, L L; Maccarrone, G; Macchiolo, A; Macdonald, C M; Maček, B; Miguens, J Machado; Macina, D; Madaffari, D; Madar, R; Maddocks, H J; Mader, W F; Madsen, A; Maeda, J; Maeland, S; Maeno, T; Maevskiy, A; Magradze, E; Mahboubi, K; Mahlstedt, J; Maiani, C; Maidantchik, C; Maier, A A; Maier, T; Maio, A; Majewski, S; Makida, Y; Makovec, N; Malaescu, B; Malecki, Pa; Maleev, V P; Malek, F; Mallik, U; Malon, D; Malone, C; Maltezos, S; Malyshev, V M; Malyukov, S; Mamuzic, J; Mancini, G; Mandelli, B; Mandelli, L; Mandić, I; Mandrysch, R; Maneira, J; Filho, L Manhaes de Andrade; Ramos, J Manjarres; Mann, A; Manousakis-Katsikakis, A; Mansoulie, B; Mantifel, R; Mantoani, M; Mapelli, L; March, L; Marchiori, G; Marcisovsky, M; Marino, C P; Marjanovic, M; Marley, D E; Marroquim, F; Marsden, S P; Marshall, Z; Marti, L F; Marti-Garcia, S; Martin, B; Martin, T A; Martin, V J; Latour, B Martin Dit; Martinez, M; Martin-Haugh, S; Martoiu, V S; Martyniuk, A C; Marx, M; Marzano, F; Marzin, A; Masetti, L; Mashimo, T; Mashinistov, R; Masik, J; Maslennikov, A L; Massa, I; Massa, L; Mastrandrea, P; Mastroberardino, A; Masubuchi, T; Mättig, P; Mattmann, J; Maurer, J; Maxfield, S J; Maximov, D A; Mazini, R; Mazza, S M; Goldrick, G Mc; Kee, S P Mc; McCarn, A; McCarthy, R L; McCarthy, T G; McCubbin, N A; McFarlane, K W; Mcfayden, J A; Mchedlidze, G; McMahon, S J; McPherson, R A; Medinnis, M; Meehan, S; Mehlhase, S; Mehta, A; Meier, K; Meineck, C; Meirose, B; Garcia, B R Mellado; Meloni, F; Mengarelli, A; Menke, S; Meoni, E; Mercurio, K M; Mergelmeyer, S; Mermod, P; Merola, L; Meroni, C; Merritt, F S; Messina, A; Metcalfe, J; Mete, A S; Meyer, C; Meyer, C; Meyer, J-P; Meyer, J; Theenhausen, H Meyer Zu; Middleton, R P; Miglioranzi, S; Mijović, L; Mikenberg, G; Mikestikova, M; Mikuž, M; Milesi, M; Milic, A; Miller, D W; Mills, C; Milov, A; Milstead, D A; Minaenko, A A; Minami, Y; Minashvili, I A; Mincer, A I; Mindur, B; Mineev, M; Ming, Y; Mir, L M; Mistry, K P; Mitani, T; Mitrevski, J; Mitsou, V A; Miucci, A; Miyagawa, P S; Mjörnmark, J U; Moa, T; Mochizuki, K; Mohapatra, S; Mohr, W; Molander, S; Moles-Valls, R; Monden, R; Mondragon, M C; Mönig, K; Monini, C; Monk, J; Monnier, E; Montalbano, A; Berlingen, J Montejo; Monticelli, F; Monzani, S; Moore, R W; Morange, N; Moreno, D; Llácer, M Moreno; Morettini, P; Mori, D; Mori, T; Morii, M; Morinaga, M; Morisbak, V; Moritz, S; Morley, A K; Mornacchi, G; Morris, J D; Mortensen, S S; Morton, A; Morvaj, L; Mosidze, M; Moss, J; Motohashi, K; Mount, R; Mountricha, E; Mouraviev, S V; Moyse, E J W; Muanza, S; Mudd, R D; Mueller, F; Mueller, J; Mueller, R S P; Mueller, T; Muenstermann, D; Mullen, P; Mullier, G A; Sanchez, F J Munoz; Quijada, J A Murillo; Murray, W J; Musheghyan, H; Musto, E; Myagkov, A G; Myska, M; Nachman, B P; Nackenhorst, O; Nadal, J; Nagai, K; Nagai, R; Nagai, Y; Nagano, K; Nagarkar, A; Nagasaka, Y; Nagata, K; Nagel, M; Nagy, E; Nairz, A M; Nakahama, Y; Nakamura, K; Nakamura, T; Nakano, I; Namasivayam, H; Garcia, R F Naranjo; Narayan, R; Villar, D I Narrias; Naumann, T; Navarro, G; Nayyar, R; Neal, H A; Nechaeva, P Yu; Neep, T J; Nef, P D; Negri, A; Negrini, M; Nektarijevic, S; Nellist, C; Nelson, A; Nemecek, S; Nemethy, P; Nepomuceno, A A; Nessi, M; Neubauer, M S; Neumann, M; Neves, R M; Nevski, P; Newman, P R; Nguyen, D H; Nickerson, R B; Nicolaidou, R; Nicquevert, B; Nielsen, J; Nikiforou, N; Nikiforov, A; Nikolaenko, V; Nikolic-Audit, I; Nikolopoulos, K; Nilsen, J K; Nilsson, P; Ninomiya, Y; Nisati, A; Nisius, R; Nobe, T; Nodulman, L; Nomachi, M; Nomidis, I; Nooney, T; Norberg, S; Nordberg, M; Novgorodova, O; Nowak, S; Nozaki, M; Nozka, L; Ntekas, K; Hanninger, G Nunes; Nunnemann, T; Nurse, E; Nuti, F; O'grady, F; O'Neil, D C; O'Shea, V; Oakham, F G; Oberlack, H; Obermann, T; Ocariz, J; Ochi, A; Ochoa, I; Ochoa-Ricoux, J P; Oda, S; Odaka, S; Ogren, H; Oh, A; Oh, S H; Ohm, C C; Ohman, H; Oide, H; Okamura, W; Okawa, H; Okumura, Y; Okuyama, T; Olariu, A; Pino, S A Olivares; Damazio, D Oliveira; Olszewski, A; Olszowska, J; Onofre, A; Onogi, K; Onyisi, P U E; Oram, C J; Oreglia, M J; Oren, Y; Orestano, D; Orlando, N; Barrera, C Oropeza; Orr, R S; Osculati, B; Ospanov, R; Garzon, G Otero Y; Otono, H; Ouchrif, M; Ould-Saada, F; Ouraou, A; Oussoren, K P; Ouyang, Q; Ovcharova, A; Owen, M; Owen, R E; Ozcan, V E; Ozturk, N; Pachal, K; Pages, A Pacheco; Aranda, C Padilla; Pagáčová, M; Griso, S Pagan; Paganis, E; Paige, F; Pais, P; Pajchel, K; Palacino, G; Palestini, S; Palka, M; Pallin, D; Palma, A; Pan, Y B; Panagiotopoulou, E St; Pandini, C E; Vazquez, J G Panduro; Pani, P; Panitkin, S; Pantea, D; Paolozzi, L; Papadopoulou, Th D; Papageorgiou, K; Paramonov, A; Hernandez, D Paredes; Parker, M A; Parker, K A; Parodi, F; Parsons, J A; Parzefall, U; Pasqualucci, E; Passaggio, S; Pastore, F; Pastore, Fr; Pásztor, G; Pataraia, S; Patel, N D; Pater, J R; Pauly, T; Pearce, J; Pearson, B; Pedersen, L E; Pedersen, M; Lopez, S Pedraza; Pedro, R; Peleganchuk, S V; Pelikan, D; Penc, O; Peng, C; Peng, H; Penning, B; Penwell, J; Perepelitsa, D V; Codina, E Perez; García-Estañ, M T Pérez; Perini, L; Pernegger, H; Perrella, S; Peschke, R; Peshekhonov, V D; Peters, K; Peters, R F Y; Petersen, B A; Petersen, T C; Petit, E; Petridis, A; Petridou, C; Petroff, P; Petrolo, E; Petrucci, F; Pettersson, N E; Pezoa, R; Phillips, P W; Piacquadio, G; Pianori, E; Picazio, A; Piccaro, E; Piccinini, M; Pickering, M A; Piegaia, R; Pignotti, D T; Pilcher, J E; Pilkington, A D; Pin, A W J; Pina, J; Pinamonti, M; Pinfold, J L; Pingel, A; Pires, S; Pirumov, H; Pitt, M; Pizio, C; Plazak, L; Pleier, M-A; Pleskot, V; Plotnikova, E; Plucinski, P; Pluth, D; Poettgen, R; Poggioli, L; Pohl, D; Polesello, G; Poley, A; Policicchio, A; Polifka, R; Polini, A; Pollard, C S; Polychronakos, V; Pommès, K; Pontecorvo, L; Pope, B G; Popeneciu, G A; Popovic, D S; Poppleton, A; Pospisil, S; Potamianos, K; Potrap, I N; Potter, C J; Potter, C T; Poulard, G; Poveda, J; Pozdnyakov, V; Astigarraga, M E Pozo; Pralavorio, P; Pranko, A; Prasad, S; Prell, S; Price, D; Price, L E; Primavera, M; Prince, S; Proissl, M; Prokofiev, K; Prokoshin, F; Protopapadaki, E; Protopopescu, S; Proudfoot, J; Przybycien, M; Ptacek, E; Puddu, D; Pueschel, E; Puldon, D; Purohit, M; Puzo, P; Qian, J; Qin, G; Qin, Y; Quadt, A; Quarrie, D R; Quayle, W B; Queitsch-Maitland, M; Quilty, D; Raddum, S; Radeka, V; Radescu, V; Radhakrishnan, S K; Radloff, P; Rados, P; Ragusa, F; Rahal, G; Rajagopalan, S; Rammensee, M; Rangel-Smith, C; Rauscher, F; Rave, S; Ravenscroft, T; Raymond, M; Read, A L; Readioff, N P; Rebuzzi, D M; Redelbach, A; Redlinger, G; Reece, R; Reeves, K; Rehnisch, L; Reichert, J; Reisin, H; Rembser, C; Ren, H; Renaud, A; Rescigno, M; Resconi, S; Rezanova, O L; Reznicek, P; Rezvani, R; Richter, R; Richter, S; Richter-Was, E; Ricken, O; Ridel, M; Rieck, P; Riegel, C J; Rieger, J; Rifki, O; Rijssenbeek, M; Rimoldi, A; Rinaldi, L; Ristić, B; Ritsch, E; Riu, I; Rizatdinova, F; Rizvi, E; Robertson, S H; Robichaud-Veronneau, A; Robinson, D; Robinson, J E M; Robson, A; Roda, C; Roe, S; Røhne, O; Romaniouk, A; Romano, M; Saez, S M Romano; Adam, E Romero; Rompotis, N; Ronzani, M; Roos, L; Ros, E; Rosati, S; Rosbach, K; Rose, P; Rosenthal, O; Rossetti, V; Rossi, E; Rossi, L P; Rosten, J H N; Rosten, R; Rotaru, M; Roth, I; Rothberg, J; Rousseau, D; Royon, C R; Rozanov, A; Rozen, Y; Ruan, X; Rubbo, F; Rubinskiy, I; Rud, V I; Rudolph, C; Rudolph, M S; Rühr, F; Ruiz-Martinez, A; Rurikova, Z; Rusakovich, N A; Ruschke, A; Russell, H L; Rutherfoord, J P; Ruthmann, N; Ryabov, Y F; Rybar, M; Rybkin, G; Ryder, N C; Ryzhov, A; Saavedra, A F; Sabato, G; Sacerdoti, S; Saddique, A; Sadrozinski, H F-W; Sadykov, R; Tehrani, F Safai; Saha, P; Sahinsoy, M; Saimpert, M; Saito, T; Sakamoto, H; Sakurai, Y; Salamanna, G; Salamon, A; Loyola, J E Salazar; Saleem, M; Salek, D; De Bruin, P H Sales; Salihagic, D; Salnikov, A; Salt, J; Salvatore, D; Salvatore, F; Salvucci, A; Salzburger, A; Sammel, D; Sampsonidis, D; Sanchez, A; Sánchez, J; Martinez, V Sanchez; Sandaker, H; Sandbach, R L; Sander, H G; Sanders, M P; Sandhoff, M; Sandoval, C; Sandstroem, R; Sankey, D P C; Sannino, M; Sansoni, A; Santoni, C; Santonico, R; Santos, H; Castillo, I Santoyo; Sapp, K; Sapronov, A; Saraiva, J G; Sarrazin, B; Sasaki, O; Sasaki, Y; Sato, K; Sauvage, G; Sauvan, E; Savage, G; Savard, P; Sawyer, C; Sawyer, L; Saxon, J; Sbarra, C; Sbrizzi, A; Scanlon, T; Scannicchio, D A; Scarcella, M; Scarfone, V; Schaarschmidt, J; Schacht, P; Schaefer, D; Schaefer, R; Schaeffer, J; Schaepe, S; Schaetzel, S; Schäfer, U; Schaffer, A C; Schaile, D; Schamberger, R D; Scharf, V; Schegelsky, V A; Scheirich, D; Schernau, M; Schiavi, C; Schillo, C; Schioppa, M; Schlenker, S; Schmieden, K; Schmitt, C; Schmitt, S; Schmitt, S; Schmitz, S; Schneider, B; Schnellbach, Y J; Schnoor, U; Schoeffel, L; Schoening, A; Schoenrock, B D; Schopf, E; Schorlemmer, A L S; Schott, M; Schouten, D; Schovancova, J; Schramm, S; Schreyer, M; Schuh, N; Schultens, M J; Schultz-Coulon, H-C; Schulz, H; Schumacher, M; Schumm, B A; Schune, Ph; Schwanenberger, C; Schwartzman, A; Schwarz, T A; Schwegler, Ph; Schweiger, H; Schwemling, Ph; Schwienhorst, R; Schwindling, J; Schwindt, T; Scifo, E; Sciolla, G; Scuri, F; Scutti, F; Searcy, J; Sedov, G; Sedykh, E; Seema, P; Seidel, S C; Seiden, A; Seifert, F; Seixas, J M; Sekhniaidze, G; Sekhon, K; Sekula, S J; Seliverstov, D M; Semprini-Cesari, N; Serfon, C; Serin, L; Serkin, L; Serre, T; Sessa, M; Seuster, R; Severini, H; Sfiligoj, T; Sforza, F; Sfyrla, A; Shabalina, E; Shamim, M; Shan, L Y; Shang, R; Shank, J T; Shapiro, M; Shatalov, P B; Shaw, K; Shaw, S M; Shcherbakova, A; Shehu, C Y; Sherwood, P; Shi, L; Shimizu, S; Shimmin, C O; Shimojima, M; Shiyakova, M; Shmeleva, A; Saadi, D Shoaleh; Shochet, M J; Shojaii, S; Shrestha, S; Shulga, E; Shupe, M A; Sicho, P; Sidebo, P E; Sidiropoulou, O; Sidorov, D; Sidoti, A; Siegert, F; Sijacki, Dj; Silva, J; Silver, Y; Silverstein, S B; Simak, V; Simard, O; Simic, Lj; Simion, S; Simioni, E; Simmons, B; Simon, D; Simon, M; Sinervo, P; Sinev, N B; Sioli, M; Siragusa, G; Sisakyan, A N; Sivoklokov, S Yu; Sjölin, J; Sjursen, T B; Skinner, M B; Skottowe, H P; Skubic, P; Slater, M; Slavicek, T; Slawinska, M; Sliwa, K; Smakhtin, V; Smart, B H; Smestad, L; Smirnov, S Yu; Smirnov, Y; Smirnova, L N; Smirnova, O; Smith, M N K; Smith, R W; Smizanska, M; Smolek, K; Snesarev, A A; Snidero, G; Snyder, S; Sobie, R; Socher, F; Soffer, A; Soh, D A; Sokhrannyi, G; Sanchez, C A Solans; Solar, M; Solc, J; Soldatov, E Yu; Soldevila, U; Solodkov, A A; Soloshenko, A; Solovyanov, O V; Solovyev, V; Sommer, P; Song, H Y; Soni, N; Sood, A; Sopczak, A; Sopko, B; Sopko, V; Sorin, V; Sosa, D; Sosebee, M; Sotiropoulou, C L; Soualah, R; Soukharev, A M; South, D; Sowden, B C; Spagnolo, S; Spalla, M; Spangenberg, M; Spanò, F; Spearman, W R; Sperlich, D; Spettel, F; Spighi, R; Spigo, G; Spiller, L A; Spousta, M; Denis, R D St; Stabile, A; Staerz, S; Stahlman, J; Stamen, R; Stamm, S; Stanecka, E; Stanek, R W; Stanescu, C; Stanescu-Bellu, M; Stanitzki, M M; Stapnes, S; Starchenko, E A; Stark, J; Staroba, P; Starovoitov, P; Staszewski, R; Steinberg, P; Stelzer, B; Stelzer, H J; Stelzer-Chilton, O; Stenzel, H; Stewart, G A; Stillings, J A; Stockton, M C; Stoebe, M; Stoicea, G; Stolte, P; Stonjek, S; Stradling, A R; Straessner, A; Stramaglia, M E; Strandberg, J; Strandberg, S; Strandlie, A; Strauss, E; Strauss, M; Strizenec, P; Ströhmer, R; Strom, D M; Stroynowski, R; Strubig, A; Stucci, S A; Stugu, B; Styles, N A; Su, D; Su, J; Subramaniam, R; Succurro, A; Suchek, S; Sugaya, Y; Suk, M; Sulin, V V; Sultansoy, S; Sumida, T; Sun, S; Sun, X; Sundermann, J E; Suruliz, K; Susinno, G; Sutton, M R; Suzuki, S; Svatos, M; Swiatlowski, M; Sykora, I; Sykora, T; Ta, D; Taccini, C; Tackmann, K; Taenzer, J; Taffard, A; Tafirout, R; Taiblum, N; Takai, H; Takashima, R; Takeda, H; Takeshita, T; Takubo, Y; Talby, M; Talyshev, A A; Tam, J Y C; Tan, K G; Tanaka, J; Tanaka, R; Tanaka, S; Tannenwald, B B; Araya, S Tapia; Tapprogge, S; Tarem, S; Tarrade, F; Tartarelli, G F; Tas, P; Tasevsky, M; Tashiro, T; Tassi, E; Delgado, A Tavares; Tayalati, Y; Taylor, A C; Taylor, F E; Taylor, G N; Taylor, P T E; Taylor, W; Teischinger, F A; Teixeira-Dias, P; Temming, K K; Temple, D; Kate, H Ten; Teng, P K; Teoh, J J; Tepel, F; Terada, S; Terashi, K; Terron, J; Terzo, S; Testa, M; Teuscher, R J; Theveneaux-Pelzer, T; Thomas, J P; Thomas-Wilsker, J; Thompson, E N; Thompson, P D; Thompson, R J; Thompson, A S; Thomsen, L A; Thomson, E; Thomson, M; Thun, R P; Tibbetts, M J; Torres, R E Ticse; Tikhomirov, V O; Tikhonov, Yu A; Timoshenko, S; Tiouchichine, E; Tipton, P; Tisserant, S; Todome, K; Todorov, T; Todorova-Nova, S; Tojo, J; Tokár, S; Tokushuku, K; Tollefson, K; Tolley, E; Tomlinson, L; Tomoto, M; Tompkins, L; Toms, K; Torrence, E; Torres, H; Pastor, E Torró; Toth, J; Touchard, F; Tovey, D R; Trefzger, T; Tremblet, L; Tricoli, A; Trigger, I M; Trincaz-Duvoid, S; Tripiana, M F; Trischuk, W; Trocmé, B; Troncon, C; Trottier-McDonald, M; Trovatelli, M; Truong, L; Trzebinski, M; Trzupek, A; Tsarouchas, C; Tseng, J C-L; Tsiareshka, P V; Tsionou, D; Tsipolitis, G; Tsirintanis, N; Tsiskaridze, S; Tsiskaridze, V; Tskhadadze, E G; Tsui, K M; Tsukerman, I I; Tsulaia, V; Tsuno, S; Tsybychev, D; Tudorache, A; Tudorache, V; Tuna, A N; Tupputi, S A; Turchikhin, S; Turecek, D; Turra, R; Turvey, A J; Tuts, P M; Tykhonov, A; Tylmad, M; Tyndel, M; Ueda, I; Ueno, R; Ughetto, M; Ukegawa, F; Unal, G; Undrus, A; Unel, G; Ungaro, F C; Unno, Y; Unverdorben, C; Urban, J; Urquijo, P; Urrejola, P; Usai, G; Usanova, A; Vacavant, L; Vacek, V; Vachon, B; Valderanis, C; Valencic, N; Valentinetti, S; Valero, A; Valery, L; Valkar, S; Vallecorsa, S; Ferrer, J A Valls; Van Den Wollenberg, W; Van Der Deijl, P C; van der Geer, R; van der Graaf, H; van Eldik, N; van Gemmeren, P; Van Nieuwkoop, J; van Vulpen, I; van Woerden, M C; Vanadia, M; Vandelli, W; Vanguri, R; Vaniachine, A; Vannucci, F; Vardanyan, G; Vari, R; Varnes, E W; Varol, T; Varouchas, D; Vartapetian, A; Varvell, K E; Vazeille, F; Schroeder, T Vazquez; Veatch, J; Veloce, L M; Veloso, F; Velz, T; Veneziano, S; Ventura, A; Ventura, D; Venturi, M; Venturi, N; Venturini, A; Vercesi, V; Verducci, M; Verkerke, W; Vermeulen, J C; Vest, A; Vetterli, M C; Viazlo, O; Vichou, I; Vickey, T; Boeriu, O E Vickey; Viehhauser, G H A; Viel, S; Vigne, R; Villa, M; Perez, M Villaplana; Vilucchi, E; Vincter, M G; Vinogradov, V B; Vivarelli, I; Vlachos, S; Vladoiu, D; Vlasak, M; Vogel, M; Vokac, P; Volpi, G; Volpi, M; von der Schmitt, H; von Radziewski, H; von Toerne, E; Vorobel, V; Vorobev, K; Vos, M; Voss, R; Vossebeld, J H; Vranjes, N; Milosavljevic, M Vranjes; Vrba, V; Vreeswijk, M; Vuillermet, R; Vukotic, I; Vykydal, Z; Wagner, P; Wagner, W; Wahlberg, H; Wahrmund, S; Wakabayashi, J; Walder, J; Walker, R; Walkowiak, W; Wang, C; Wang, F; Wang, H; Wang, H; Wang, J; Wang, J; Wang, K; Wang, R; Wang, S M; Wang, T; Wang, T; Wang, X; Wanotayaroj, C; Warburton, A; Ward, C P; Wardrope, D R; Washbrook, A; Wasicki, C; Watkins, P M; Watson, A T; Watson, I J; Watson, M F; Watts, G; Watts, S; Waugh, B M; Webb, S; Weber, M S; Weber, S W; Webster, J S; Weidberg, A R; Weinert, B; Weingarten, J; Weiser, C; Weits, H; Wells, P S; Wenaus, T; Wengler, T; Wenig, S; Wermes, N; Werner, M; Werner, P; Wessels, M; Wetter, J; Whalen, K; Wharton, A M; White, A; White, M J; White, R; White, S; Whiteson, D; Wickens, F J; Wiedenmann, W; Wielers, M; Wienemann, P; Wiglesworth, C; Wiik-Fuchs, L A M; Wildauer, A; Wilkens, H G; Williams, H H; Williams, S; Willis, C; Willocq, S; Wilson, A; Wilson, J A; Wingerter-Seez, I; Winklmeier, F; Winter, B T; Wittgen, M; Wittkowski, J; Wollstadt, S J; Wolter, M W; Wolters, H; Wosiek, B K; Wotschack, J; Woudstra, M J; Wozniak, K W; Wu, M; Wu, M; Wu, S L; Wu, X; Wu, Y; Wyatt, T R; Wynne, B M; Xella, S; Xu, D; Xu, L; Yabsley, B; Yacoob, S; Yakabe, R; Yamada, M; Yamaguchi, D; Yamaguchi, Y; Yamamoto, A; Yamamoto, S; Yamanaka, T; Yamauchi, K; Yamazaki, Y; Yan, Z; Yang, H; Yang, H; Yang, Y; Yao, W-M; Yap, Y C; Yasu, Y; Yatsenko, E; Wong, K H Yau; Ye, J; Ye, S; Yeletskikh, I; Yen, A L; Yildirim, E; Yorita, K; Yoshida, R; Yoshihara, K; Young, C; Young, C J S; Youssef, S; Yu, D R; Yu, J; Yu, J M; Yu, J; Yuan, L; Yuen, S P Y; Yurkewicz, A; Yusuff, I; Zabinski, B; Zaidan, R; Zaitsev, A M; Zalieckas, J; Zaman, A; Zambito, S; Zanello, L; Zanzi, D; Zeitnitz, C; Zeman, M; Zemla, A; Zeng, J C; Zeng, Q; Zengel, K; Zenin, O; Ženiš, T; Zerwas, D; Zhang, D; Zhang, F; Zhang, G; Zhang, H; Zhang, J; Zhang, L; Zhang, R; Zhang, X; Zhang, Z; Zhao, X; Zhao, Y; Zhao, Z; Zhemchugov, A; Zhong, J; Zhou, B; Zhou, C; Zhou, L; Zhou, L; Zhou, M; Zhou, N; Zhu, C G; Zhu, H; Zhu, J; Zhu, Y; Zhuang, X; Zhukov, K; Zibell, A; Zieminska, D; Zimine, N I; Zimmermann, C; Zimmermann, S; Zinonos, Z; Zinser, M; Ziolkowski, M; Živković, L; Zobernig, G; Zoccoli, A; Nedden, M Zur; Zurzolo, G; Zwalinski, L

    2017-01-01

    The reconstruction of the signal from hadrons and jets emerging from the proton-proton collisions at the Large Hadron Collider (LHC) and entering the ATLAS calorimeters is based on a three-dimensional topological clustering of individual calorimeter cell signals. The cluster formation follows cell signal-significance patterns generated by electromagnetic and hadronic showers. In this, the clustering algorithm implicitly performs a topological noise suppression by removing cells with insignificant signals which are not in close proximity to cells with significant signals. The resulting topological cell clusters have shape and location information, which is exploited to apply a local energy calibration and corrections depending on the nature of the cluster. Topological cell clustering is established as a well-performing calorimeter signal definition for jet and missing transverse momentum reconstruction in ATLAS.

  19. EnviroAtlas -Pittsburgh, PA- One Meter Resolution Urban Land Cover Data (2010)

    EPA Pesticide Factsheets

    The EnviroAtlas Pittsburgh, PA land cover map was generated from United States Department of Agriculture (USDA) National Agricultural Imagery Program (NAIP) four band (red, green, blue, and near infrared) aerial photography at 1 m spatial resolution. Imagery was collected on multiple dates in June 2010. Five land cover classes were mapped: water, impervious surfaces, soil and barren land, trees and forest, and grass and herbaceous non-woody vegetation. An accuracy assessment of 500 completely random and 81 stratified random points yielded an overall accuracy of 86.57 percent. The area mapped is defined by the US Census Bureau's 2010 Urban Statistical Area for Pittsburgh, PA. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

  20. Warping an atlas derived from serial histology to 5 high-resolution MRIs.

    PubMed

    Tullo, Stephanie; Devenyi, Gabriel A; Patel, Raihaan; Park, Min Tae M; Collins, D Louis; Chakravarty, M Mallar

    2018-06-19

    Previous work from our group demonstrated the use of multiple input atlases to a modified multi-atlas framework (MAGeT-Brain) to improve subject-based segmentation accuracy. Currently, segmentation of the striatum, globus pallidus and thalamus are generated from a single high-resolution and -contrast MRI atlas derived from annotated serial histological sections. Here, we warp this atlas to five high-resolution MRI templates to create five de novo atlases. The overall goal of this work is to use these newly warped atlases as input to MAGeT-Brain in an effort to consolidate and improve the workflow presented in previous manuscripts from our group, allowing for simultaneous multi-structure segmentation. The work presented details the methodology used for the creation of the atlases using a technique previously proposed, where atlas labels are modified to mimic the intensity and contrast profile of MRI to facilitate atlas-to-template nonlinear transformation estimation. Dice's Kappa metric was used to demonstrate high quality registration and segmentation accuracy of the atlases. The final atlases are available at https://github.com/CobraLab/atlases/tree/master/5-atlas-subcortical.

  1. Study of performance of the ATLAS transition radiation tracker in run 1 of the LHC: Tracking characteristics

    NASA Astrophysics Data System (ADS)

    Belyaev, N.; Krasnopevtsev, D.; Smirnov, N.

    2018-01-01

    The ATLAS Transition Radiation Tracker (TRT) contains more than 350000 large straw tubes and it is the outermost of the three subsystems of the ATLAS Inner Detector (ID). The TRT contributes substantially to the ATLAS ID resolution for the tracks of high-energy particles, providing excellent particle identification capabilities and electron-pion separation. Basic performance parameters of the TRT related to its tracking function are described in this paper. The data used in this study were collected during the first period of the Large Hadron Collider (LHC) operation in 2012 with a proton collision energy of 8 TeV. The tracking performance of the TRT has been studied in the case of operating with a Xe-based gas mixture and as a function of the straw occupancy. Special attention was paid to investigation of tracking parameters inside hadronic jets. The experimental data and simulation are in reasonable agreement, even within the dense cores of the most energetic jets.

  2. A multi-atlas based method for automated anatomical Macaca fascicularis brain MRI segmentation and PET kinetic extraction.

    PubMed

    Ballanger, Bénédicte; Tremblay, Léon; Sgambato-Faure, Véronique; Beaudoin-Gobert, Maude; Lavenne, Franck; Le Bars, Didier; Costes, Nicolas

    2013-08-15

    MRI templates and digital atlases are needed for automated and reproducible quantitative analysis of non-human primate PET studies. Segmenting brain images via multiple atlases outperforms single-atlas labelling in humans. We present a set of atlases manually delineated on brain MRI scans of the monkey Macaca fascicularis. We use this multi-atlas dataset to evaluate two automated methods in terms of accuracy, robustness and reliability in segmenting brain structures on MRI and extracting regional PET measures. Twelve individual Macaca fascicularis high-resolution 3DT1 MR images were acquired. Four individual atlases were created by manually drawing 42 anatomical structures, including cortical and sub-cortical structures, white matter regions, and ventricles. To create the MRI template, we first chose one MRI to define a reference space, and then performed a two-step iterative procedure: affine registration of individual MRIs to the reference MRI, followed by averaging of the twelve resampled MRIs. Automated segmentation in native space was obtained in two ways: 1) Maximum probability atlases were created by decision fusion of two to four individual atlases in the reference space, and transformation back into the individual native space (MAXPROB)(.) 2) One to four individual atlases were registered directly to the individual native space, and combined by decision fusion (PROPAG). Accuracy was evaluated by computing the Dice similarity index and the volume difference. The robustness and reproducibility of PET regional measurements obtained via automated segmentation was evaluated on four co-registered MRI/PET datasets, which included test-retest data. Dice indices were always over 0.7 and reached maximal values of 0.9 for PROPAG with all four individual atlases. There was no significant mean volume bias. The standard deviation of the bias decreased significantly when increasing the number of individual atlases. MAXPROB performed better when increasing the number of

  3. Two more, bright, z > 6 quasars from VST ATLAS and WISE

    NASA Astrophysics Data System (ADS)

    Chehade, B.; Carnall, A. C.; Shanks, T.; Diener, C.; Fumagalli, M.; Findlay, J. R.; Metcalfe, N.; Hennawi, J.; Leibler, C.; Murphy, D. N. A.; Prochaska, J. X.; Irwin, M. J.; Gonzalez-Solares, E.

    2018-03-01

    Recently, Carnall et al. discovered two bright high redshift quasars using the combination of the VST ATLAS and WISE surveys. The technique involved using the 3-D colour plane i - z: z - W1: W1 - W2 with the WISE W1(3.4 micron) and W2 (4.5 micron) bands taking the place of the usual NIR J band to help decrease stellar dwarf contamination. Here we report on our continued search for 5.7 < z < 6.4 quasars over an ≈2 × larger area of ≈3577 deg2 of the Southern Hemisphere. We have found two further z > 6 quasars, VST-ATLAS J158.6938-14.4211 at z = 6.07 and J332.8017-32.1036 at z = 6.32 with magnitudes of zAB = 19.4 and 19.7 mag respectively. J158.6938-14.4211 was confirmed by Keck LRIS observations and J332.8017-32.1036 was confirmed by ESO NTT EFOSC-2 observations. Here we present VLT X-shooter Visible and NIR spectra for the four ATLAS quasars. We have further independently rediscovered two z > 5.7 quasars previously found by the VIKING/KiDS and PanSTARRS surveys. This means that in ATLAS we have now discovered a total of six quasars in our target 5.7 < z < 6.4 redshift range. Making approximate corrections for incompleteness, we find that our quasar space density agrees with the SDSS results of Jiang et al. at M1450Å ≈ -27. Preliminary virial mass estimates based on the CIV and MgII emission lines give black hole masses in the range MBH ≈ 1 - 6 × 109M⊙ for the four ATLAS quasars.

  4. Performance of the ATLAS trigger system in 2015.

    PubMed

    Aaboud, M; Aad, G; Abbott, B; Abdallah, J; Abdinov, O; Abeloos, B; Aben, R; AbouZeid, O S; Abraham, N L; Abramowicz, H; Abreu, H; Abreu, R; Abulaiti, Y; Acharya, B S; Adachi, S; Adamczyk, L; Adams, D L; Adelman, J; Adomeit, S; Adye, T; Affolder, A A; Agatonovic-Jovin, T; Aguilar-Saavedra, J A; Ahlen, S P; Ahmadov, F; Aielli, G; Akerstedt, H; Åkesson, T P A; Akimov, A V; Alberghi, G L; Albert, J; Albrand, S; Alconada Verzini, M J; Aleksa, M; Aleksandrov, I N; Alexa, C; Alexander, G; Alexopoulos, T; Alhroob, M; Ali, B; Aliev, M; Alimonti, G; Alison, J; Alkire, S P; Allbrooke, B M M; Allen, B W; Allport, P P; Aloisio, A; Alonso, A; Alonso, F; Alpigiani, C; Alshehri, A A; Alstaty, M; Alvarez Gonzalez, B; Álvarez Piqueras, D; Alviggi, M G; Amadio, B T; Amaral Coutinho, Y; Amelung, C; Amidei, D; Amor Dos Santos, S P; Amorim, A; Amoroso, S; Amundsen, G; Anastopoulos, C; Ancu, L S; Andari, N; Andeen, T; Anders, C F; Anders, G; Anders, J K; Anderson, K J; Andreazza, A; Andrei, V; Angelidakis, S; Angelozzi, I; Angerami, A; Anghinolfi, F; Anisenkov, A V; Anjos, N; Annovi, A; Antel, C; Antonelli, M; Antonov, A; Antrim, D J; Anulli, F; Aoki, M; Aperio Bella, L; Arabidze, G; Arai, Y; Araque, J P; Arce, A T H; Arduh, F A; Arguin, J-F; Argyropoulos, S; Arik, M; Armbruster, A J; Armitage, L J; Arnaez, O; Arnold, H; Arratia, M; Arslan, O; Artamonov, A; Artoni, G; Artz, S; Asai, S; Asbah, N; Ashkenazi, A; Åsman, B; Asquith, L; Assamagan, K; Astalos, R; Atkinson, M; Atlay, N B; Augsten, K; Avolio, G; Axen, B; Ayoub, M K; Azuelos, G; Baak, M A; Baas, A E; Baca, M J; Bachacou, H; Bachas, K; Backes, M; Backhaus, M; Bagiacchi, P; Bagnaia, P; Bai, Y; Baines, J T; Bajic, M; Baker, O K; Baldin, E M; Balek, P; Balestri, T; Balli, F; Balunas, W K; Banas, E; Banerjee, Sw; Bannoura, A A E; Barak, L; Barberio, E L; Barberis, D; Barbero, M; Barillari, T; Barisits, M-S; Barklow, T; Barlow, N; Barnes, S L; Barnett, B M; Barnett, R M; Barnovska-Blenessy, Z; Baroncelli, A; Barone, G; Barr, A J; Barranco Navarro, L; Barreiro, F; Barreiro Guimarães da Costa, J; Bartoldus, R; Barton, A E; Bartos, P; Basalaev, A; Bassalat, A; Bates, R L; Batista, S J; Batley, J R; Battaglia, M; Bauce, M; Bauer, F; Bawa, H S; Beacham, J B; Beattie, M D; Beau, T; Beauchemin, P H; Bechtle, P; Beck, H P; Becker, K; Becker, M; Beckingham, M; Becot, C; Beddall, A J; Beddall, A; Bednyakov, V A; Bedognetti, M; Bee, C P; Beemster, L J; Beermann, T A; Begel, M; Behr, J K; Bell, A S; Bella, G; Bellagamba, L; Bellerive, A; Bellomo, M; Belotskiy, K; Beltramello, O; Belyaev, N L; Benary, O; Benchekroun, D; Bender, M; Bendtz, K; Benekos, N; Benhammou, Y; Benhar Noccioli, E; Benitez, J; Benjamin, D P; Bensinger, J R; Bentvelsen, S; Beresford, L; Beretta, M; Berge, D; Bergeaas Kuutmann, E; Berger, N; Beringer, J; Berlendis, S; Bernard, N R; Bernius, C; Bernlochner, F U; Berry, T; Berta, P; Bertella, C; Bertoli, G; Bertolucci, F; Bertram, I A; Bertsche, C; Bertsche, D; Besjes, G J; Bessidskaia Bylund, O; Bessner, M; Besson, N; Betancourt, C; Bethani, A; Bethke, S; Bevan, A J; Bianchi, R M; Bianco, M; Biebel, O; Biedermann, D; Bielski, R; Biesuz, N V; Biglietti, M; Bilbao De Mendizabal, J; Billoud, T R V; Bilokon, H; Bindi, M; Bingul, A; Bini, C; Biondi, S; Bisanz, T; Bjergaard, D M; Black, C W; Black, J E; Black, K M; Blackburn, D; Blair, R E; Blazek, T; Bloch, I; Blocker, C; Blue, A; Blum, W; Blumenschein, U; Blunier, S; Bobbink, G J; Bobrovnikov, V S; Bocchetta, S S; Bocci, A; Bock, C; Boehler, M; Boerner, D; Bogaerts, J A; Bogavac, D; Bogdanchikov, A G; Bohm, C; Boisvert, V; Bokan, P; Bold, T; Boldyrev, A S; Bomben, M; Bona, M; Boonekamp, M; Borisov, A; Borissov, G; Bortfeldt, J; Bortoletto, D; Bortolotto, V; Bos, K; Boscherini, D; Bosman, M; Bossio Sola, J D; Boudreau, J; Bouffard, J; Bouhova-Thacker, E V; Boumediene, D; Bourdarios, C; Boutle, S K; Boveia, A; Boyd, J; Boyko, I R; Bracinik, J; Brandt, A; Brandt, G; Brandt, O; Bratzler, U; Brau, B; Brau, J E; Breaden Madden, W D; Brendlinger, K; Brennan, A J; Brenner, L; Brenner, R; Bressler, S; Bristow, T M; Britton, D; Britzger, D; Brochu, F M; Brock, I; Brock, R; Brooijmans, G; Brooks, T; Brooks, W K; Brosamer, J; Brost, E; Broughton, J H; Bruckman de Renstrom, P A; Bruncko, D; Bruneliere, R; Bruni, A; Bruni, G; Bruni, L S; Brunt, B H; Bruschi, M; Bruscino, N; Bryant, P; Bryngemark, L; Buanes, T; Buat, Q; Buchholz, P; Buckley, A G; Budagov, I A; Buehrer, F; Bugge, M K; Bulekov, O; Bullock, D; Burckhart, H; Burdin, S; Burgard, C D; Burger, A M; Burghgrave, B; Burka, K; Burke, S; Burmeister, I; Burr, J T P; Busato, E; Büscher, D; Büscher, V; Bussey, P; Butler, J M; Buttar, C M; Butterworth, J M; Butti, P; Buttinger, W; Buzatu, A; Buzykaev, A R; Cabrera Urbán, S; Caforio, D; Cairo, V M; Cakir, O; Calace, N; Calafiura, P; Calandri, A; Calderini, G; Calfayan, P; Callea, G; Caloba, L P; Calvente Lopez, S; Calvet, D; Calvet, S; Calvet, T P; Camacho Toro, R; Camarda, S; Camarri, P; Cameron, D; Caminal Armadans, R; Camincher, C; Campana, S; Campanelli, M; Camplani, A; Campoverde, A; Canale, V; Canepa, A; Cano Bret, M; Cantero, J; Cao, T; Capeans Garrido, M D M; Caprini, I; Caprini, M; Capua, M; Carbone, R M; Cardarelli, R; Cardillo, F; Carli, I; Carli, T; Carlino, G; Carlson, B T; Carminati, L; Carney, R M D; Caron, S; Carquin, E; Carrillo-Montoya, G D; Carter, J R; Carvalho, J; Casadei, D; Casado, M P; Casolino, M; Casper, D W; Castaneda-Miranda, E; Castelijn, R; Castelli, A; Castillo Gimenez, V; Castro, N F; Catinaccio, A; Catmore, J R; Cattai, A; Caudron, J; Cavaliere, V; Cavallaro, E; Cavalli, D; Cavalli-Sforza, M; Cavasinni, V; Ceradini, F; Cerda Alberich, L; Cerqueira, A S; Cerri, A; Cerrito, L; Cerutti, F; Cervelli, A; Cetin, S A; Chafaq, A; Chakraborty, D; Chan, S K; Chan, Y L; Chang, P; Chapman, J D; Charlton, D G; Chatterjee, A; Chau, C C; Chavez Barajas, C A; Che, S; Cheatham, S; Chegwidden, A; Chekanov, S; Chekulaev, S V; Chelkov, G A; Chelstowska, M A; Chen, C; Chen, H; Chen, K; Chen, S; Chen, S; Chen, X; Chen, Y; Cheng, H C; Cheng, H J; Cheng, Y; Cheplakov, A; Cheremushkina, E; Cherkaoui El Moursli, R; Chernyatin, V; Cheu, E; Chevalier, L; Chiarella, V; Chiarelli, G; Chiodini, G; Chisholm, A S; Chitan, A; Chizhov, M V; Choi, K; Chomont, A R; Chouridou, S; Chow, B K B; Christodoulou, V; Chromek-Burckhart, D; Chudoba, J; Chuinard, A J; Chwastowski, J J; Chytka, L; Ciapetti, G; Ciftci, A K; Cinca, D; Cindro, V; Cioara, I A; Ciocca, C; Ciocio, A; Cirotto, F; Citron, Z H; Citterio, M; Ciubancan, M; Clark, A; Clark, B L; Clark, M R; Clark, P J; Clarke, R N; Clement, C; Coadou, Y; Cobal, M; Coccaro, A; Cochran, J; Colasurdo, L; Cole, B; Colijn, A P; Collot, J; Colombo, T; Compostella, G; Conde Muiño, P; Coniavitis, E; Connell, S H; Connelly, I A; Consorti, V; Constantinescu, S; Conti, G; Conventi, F; Cooke, M; Cooper, B D; Cooper-Sarkar, A M; Cormier, F; Cormier, K J R; Cornelissen, T; Corradi, M; Corriveau, F; Cortes-Gonzalez, A; Cortiana, G; Costa, G; Costa, M J; Costanzo, D; Cottin, G; Cowan, G; Cox, B E; Cranmer, K; Crawley, S J; Cree, G; Crépé-Renaudin, S; Crescioli, F; Cribbs, W A; Crispin Ortuzar, M; Cristinziani, M; Croft, V; Crosetti, G; Cueto, A; Cuhadar Donszelmann, T; Cummings, J; Curatolo, M; Cúth, J; Czirr, H; Czodrowski, P; D'amen, G; D'Auria, S; D'Onofrio, M; Da Cunha Sargedas De Sousa, M J; Da Via, C; Dabrowski, W; Dado, T; Dai, T; Dale, O; Dallaire, F; Dallapiccola, C; Dam, M; Dandoy, J R; Dang, N P; Daniells, A C; Dann, N S; Danninger, M; Dano Hoffmann, M; Dao, V; Darbo, G; Darmora, S; Dassoulas, J; Dattagupta, A; Davey, W; David, C; Davidek, T; Davies, M; Davison, P; Dawe, E; Dawson, I; De, K; de Asmundis, R; De Benedetti, A; De Castro, S; De Cecco, S; De Groot, N; de Jong, P; De la Torre, H; De Lorenzi, F; De Maria, A; De Pedis, D; De Salvo, A; De Sanctis, U; De Santo, A; De Vivie De Regie, J B; Dearnaley, W J; Debbe, R; Debenedetti, C; Dedovich, D V; Dehghanian, N; Deigaard, I; Del Gaudio, M; Del Peso, J; Del Prete, T; Delgove, D; Deliot, F; Delitzsch, C M; Dell'Acqua, A; Dell'Asta, L; Dell'Orso, M; Della Pietra, M; Della Volpe, D; Delmastro, M; Delsart, P A; DeMarco, D A; Demers, S; Demichev, M; Demilly, A; Denisov, S P; Denysiuk, D; Derendarz, D; Derkaoui, J E; Derue, F; Dervan, P; Desch, K; Deterre, C; Dette, K; Deviveiros, P O; Dewhurst, A; Dhaliwal, S; Di Ciaccio, A; Di Ciaccio, L; Di Clemente, W K; Di Donato, C; Di Girolamo, A; Di Girolamo, B; Di Micco, B; Di Nardo, R; Di Simone, A; Di Sipio, R; Di Valentino, D; Diaconu, C; Diamond, M; Dias, F A; Diaz, M A; Diehl, E B; Dietrich, J; Díez Cornell, S; Dimitrievska, A; Dingfelder, J; Dita, P; Dita, S; Dittus, F; Djama, F; Djobava, T; Djuvsland, J I; do Vale, M A B; Dobos, D; Dobre, M; Doglioni, C; Dolejsi, J; Dolezal, Z; Donadelli, M; Donati, S; Dondero, P; Donini, J; Dopke, J; Doria, A; Dova, M T; Doyle, A T; Drechsler, E; Dris, M; Du, Y; Duarte-Campderros, J; Duchovni, E; Duckeck, G; Ducu, O A; Duda, D; Dudarev, A; Dudder, A Chr; Duffield, E M; Duflot, L; Dührssen, M; Dumancic, M; Duncan, A K; Dunford, M; Duran Yildiz, H; Düren, M; Durglishvili, A; Duschinger, D; Dutta, B; Dyndal, M; Eckardt, C; Ecker, K M; Edgar, R C; Edwards, N C; Eifert, T; Eigen, G; Einsweiler, K; Ekelof, T; El Kacimi, M; Ellajosyula, V; Ellert, M; Elles, S; Ellinghaus, F; Elliot, A A; Ellis, N; Elmsheuser, J; Elsing, M; Emeliyanov, D; Enari, Y; Endner, O C; Ennis, J S; Erdmann, J; Ereditato, A; Ernis, G; Ernst, J; Ernst, M; Errede, S; Ertel, E; Escalier, M; Esch, H; Escobar, C; Esposito, B; Etienvre, A I; Etzion, E; Evans, H; Ezhilov, A; Ezzi, M; Fabbri, F; Fabbri, L; Facini, G; Fakhrutdinov, R M; Falciano, S; Falla, R J; Faltova, J; Fang, Y; Fanti, M; Farbin, A; Farilla, A; Farina, C; Farina, E M; Farooque, T; Farrell, S; Farrington, S M; Farthouat, P; Fassi, F; Fassnacht, P; Fassouliotis, D; Faucci Giannelli, M; Favareto, A; Fawcett, W J; Fayard, L; Fedin, O L; Fedorko, W; Feigl, S; Feligioni, L; Feng, C; Feng, E J; Feng, H; Fenyuk, A B; Feremenga, L; Fernandez Martinez, P; Fernandez Perez, S; Ferrando, J; Ferrari, A; Ferrari, P; Ferrari, R; Ferreira de Lima, D E; Ferrer, A; Ferrere, D; Ferretti, C; Fiedler, F; Filipčič, A; Filipuzzi, M; Filthaut, F; Fincke-Keeler, M; Finelli, K D; Fiolhais, M C N; Fiorini, L; Fischer, A; Fischer, C; Fischer, J; Fisher, W C; Flaschel, N; Fleck, I; Fleischmann, P; Fletcher, G T; Fletcher, R R M; Flick, T; Flierl, B M; Flores Castillo, L R; Flowerdew, M J; Forcolin, G T; Formica, A; Forti, A; Foster, A G; Fournier, D; Fox, H; Fracchia, S; Francavilla, P; Franchini, M; Francis, D; Franconi, L; Franklin, M; Frate, M; Fraternali, M; Freeborn, D; Fressard-Batraneanu, S M; Friedrich, F; Froidevaux, D; Frost, J A; Fukunaga, C; Fullana Torregrosa, E; Fusayasu, T; Fuster, J; Gabaldon, C; Gabizon, O; Gabrielli, A; Gabrielli, A; Gach, G P; Gadatsch, S; Gagliardi, G; Gagnon, L G; Gagnon, P; Galea, C; Galhardo, B; Gallas, E J; Gallop, B J; Gallus, P; Galster, G; Gan, K K; Ganguly, S; Gao, J; Gao, Y; Gao, Y S; Garay Walls, F M; García, C; García Navarro, J E; Garcia-Sciveres, M; Gardner, R W; Garelli, N; Garonne, V; Gascon Bravo, A; Gasnikova, K; Gatti, C; Gaudiello, A; Gaudio, G; Gauthier, L; Gavrilenko, I L; Gay, C; Gaycken, G; Gazis, E N; Gecse, Z; Gee, C N P; Geich-Gimbel, Ch; Geisen, M; Geisler, M P; Gellerstedt, K; Gemme, C; Genest, M H; Geng, C; Gentile, S; Gentsos, C; George, S; Gerbaudo, D; Gershon, A; Ghasemi, S; Ghneimat, M; Giacobbe, B; Giagu, S; Giannetti, P; Gibson, S M; Gignac, M; Gilchriese, M; Gillam, T P S; Gillberg, D; Gilles, G; Gingrich, D M; Giokaris, N; Giordani, M P; Giorgi, F M; Giraud, P F; Giromini, P; Giugni, D; Giuli, F; Giuliani, C; Giulini, M; Gjelsten, B K; Gkaitatzis, S; Gkialas, I; Gkougkousis, E L; Gladilin, L K; Glasman, C; Glatzer, J; Glaysher, P C F; Glazov, A; Goblirsch-Kolb, M; Godlewski, J; Goldfarb, S; Golling, T; Golubkov, D; Gomes, A; Gonçalo, R; Goncalves Pinto Firmino Da Costa, J; Gonella, G; Gonella, L; Gongadze, A; González de la Hoz, S; Gonzalez-Sevilla, S; Goossens, L; Gorbounov, P A; Gordon, H A; Gorelov, I; Gorini, B; Gorini, E; Gorišek, A; Gornicki, E; Goshaw, A T; Gössling, C; Gostkin, M I; Goudet, C R; Goujdami, D; Goussiou, A G; Govender, N; Gozani, E; Graber, L; Grabowska-Bold, I; Gradin, P O J; Grafström, P; Gramling, J; Gramstad, E; Grancagnolo, S; Gratchev, V; Gravila, P M; Gray, H M; Graziani, E; Greenwood, Z D; Grefe, C; Gregersen, K; Gregor, I M; Grenier, P; Grevtsov, K; Griffiths, J; Grillo, A A; Grimm, K; Grinstein, S; Gris, Ph; Grivaz, J-F; Groh, S; Gross, E; Grosse-Knetter, J; Grossi, G C; Grout, Z J; Guan, L; Guan, W; Guenther, J; Guescini, F; Guest, D; Gueta, O; Gui, B; Guido, E; Guillemin, T; Guindon, S; Gul, U; Gumpert, C; Guo, J; Guo, Y; Gupta, R; Gupta, S; Gustavino, G; Gutierrez, P; Gutierrez Ortiz, N G; Gutschow, C; Guyot, C; Gwenlan, C; Gwilliam, C B; Haas, A; Haber, C; Hadavand, H K; Haddad, N; Hadef, A; Hageböck, S; Hagihara, M; Hajduk, Z; Hakobyan, H; Haleem, M; Haley, J; Halladjian, G; Hallewell, G D; Hamacher, K; Hamal, P; Hamano, K; Hamilton, A; Hamity, G N; Hamnett, P G; Han, L; Hanagaki, K; Hanawa, K; Hance, M; Haney, B; Hanke, P; Hanna, R; Hansen, J B; Hansen, J D; Hansen, M C; Hansen, P H; Hara, K; Hard, A S; Harenberg, T; Hariri, F; Harkusha, S; Harrington, R D; Harrison, P F; Hartjes, F; Hartmann, N M; Hasegawa, M; Hasegawa, Y; Hasib, A; Hassani, S; Haug, S; Hauser, R; Hauswald, L; Havranek, M; Hawkes, C M; Hawkings, R J; Hayakawa, D; Hayden, D; Hays, C P; Hays, J M; Hayward, H S; Haywood, S J; Head, S J; Heck, T; Hedberg, V; Heelan, L; Heim, S; Heim, T; Heinemann, B; Heinrich, J J; Heinrich, L; Heinz, C; Hejbal, J; Helary, L; Hellman, S; Helsens, C; Henderson, J; Henderson, R C W; Heng, Y; Henkelmann, S; Henriques Correia, A M; Henrot-Versille, S; Herbert, G H; Herde, H; Herget, V; Hernández Jiménez, Y; Herten, G; Hertenberger, R; Hervas, L; Hesketh, G G; Hessey, N P; Hetherly, J W; Higón-Rodriguez, E; Hill, E; Hill, J C; Hiller, K H; Hillier, S J; Hinchliffe, I; Hines, E; Hirose, M; Hirschbuehl, D; Hoad, X; Hobbs, J; Hod, N; Hodgkinson, M C; Hodgson, P; Hoecker, A; Hoeferkamp, M R; Hoenig, F; Hohn, D; Holmes, T R; Homann, M; Honda, T; Hong, T M; Hooberman, B H; Hopkins, W H; Horii, Y; Horton, A J; Hostachy, J-Y; Hou, S; Hoummada, A; Howarth, J; Hoya, J; Hrabovsky, M; Hristova, I; Hrivnac, J; Hryn'ova, T; Hrynevich, A; Hsu, P J; Hsu, S-C; Hu, Q; Hu, S; Huang, Y; Hubacek, Z; Hubaut, F; Huegging, F; Huffman, T B; Hughes, E W; Hughes, G; Huhtinen, M; Huo, P; Huseynov, N; Huston, J; Huth, J; Iacobucci, G; Iakovidis, G; Ibragimov, I; Iconomidou-Fayard, L; Ideal, E; Idrissi, Z; Iengo, P; Igonkina, O; Iizawa, T; Ikai, T; Ikegami, Y; Ikeno, M; Ilchenko, Y; Iliadis, D; Ilic, N; Introzzi, G; Ioannou, P; Iodice, M; Iordanidou, K; Ippolito, V; Ishijima, N; Ishino, M; Ishitsuka, M; Ishmukhametov, R; Issever, C; Istin, S; Ito, F; Iturbe Ponce, J M; Iuppa, R; Iwanski, W; Iwasaki, H; Izen, J M; Izzo, V; Jabbar, S; Jackson, B; Jackson, P; Jain, V; Jakobi, K B; Jakobs, K; Jakobsen, S; Jakoubek, T; Jamin, D O; Jana, D K; Jansky, R; Janssen, J; Janus, M; Janus, P A; Jarlskog, G; Javadov, N; Javůrek, T; Jeanneau, F; Jeanty, L; Jejelava, J; Jeng, G-Y; Jennens, D; Jenni, P; Jeske, C; Jézéquel, S; Ji, H; Jia, J; Jiang, H; Jiang, Y; Jiang, Z; Jiggins, S; Jimenez Pena, J; Jin, S; Jinaru, A; Jinnouchi, O; Jivan, H; Johansson, P; Johns, K A; Johnson, W J; Jon-And, K; Jones, G; Jones, R W L; Jones, S; Jones, T J; Jongmanns, J; Jorge, P M; Jovicevic, J; Ju, X; Juste Rozas, A; Köhler, M K; Kaczmarska, A; Kado, M; Kagan, H; Kagan, M; Kahn, S J; Kaji, T; Kajomovitz, E; Kalderon, C W; Kaluza, A; Kama, S; Kamenshchikov, A; Kanaya, N; Kaneti, S; Kanjir, L; Kantserov, V A; Kanzaki, J; Kaplan, B; Kaplan, L S; Kapliy, A; Kar, D; Karakostas, K; Karamaoun, A; Karastathis, N; Kareem, M J; Karentzos, E; Karnevskiy, M; Karpov, S N; Karpova, Z M; Karthik, K; Kartvelishvili, V; Karyukhin, A N; Kasahara, K; Kashif, L; Kass, R D; Kastanas, A; Kataoka, Y; Kato, C; Katre, A; Katzy, J; Kawade, K; Kawagoe, K; Kawamoto, T; Kawamura, G; Kazanin, V F; Keeler, R; Kehoe, R; Keller, J S; Kempster, J J; Keoshkerian, H; Kepka, O; Kerševan, B P; Kersten, S; Keyes, R A; Khader, M; Khalil-Zada, F; Khanov, A; Kharlamov, A G; Kharlamova, T; Khoo, T J; Khovanskiy, V; Khramov, E; Khubua, J; Kido, S; Kilby, C R; Kim, H Y; Kim, S H; Kim, Y K; Kimura, N; Kind, O M; King, B T; King, M; Kirk, J; Kiryunin, A E; Kishimoto, T; Kisielewska, D; Kiss, F; Kiuchi, K; Kivernyk, O; Kladiva, E; Klein, M H; Klein, M; Klein, U; Kleinknecht, K; Klimek, P; Klimentov, A; Klingenberg, R; Klioutchnikova, T; Kluge, E-E; Kluit, P; Kluth, S; Knapik, J; Kneringer, E; Knoops, E B F G; Knue, A; Kobayashi, A; Kobayashi, D; Kobayashi, T; Kobel, M; Kocian, M; Kodys, P; Koffas, T; Koffeman, E; Köhler, N M; Koi, T; Kolanoski, H; Kolb, M; Koletsou, I; Komar, A A; Komori, Y; Kondo, T; Kondrashova, N; Köneke, K; König, A C; Kono, T; Konoplich, R; Konstantinidis, N; Kopeliansky, R; Koperny, S; Köpke, L; Kopp, A K; Korcyl, K; Kordas, K; Korn, A; Korol, A A; Korolkov, I; Korolkova, E V; Kortner, O; Kortner, S; Kosek, T; Kostyukhin, V V; Kotwal, A; Koulouris, A; Kourkoumeli-Charalampidi, A; Kourkoumelis, C; Kouskoura, V; Kowalewska, A B; Kowalewski, R; Kowalski, T Z; Kozakai, C; Kozanecki, W; Kozhin, A S; Kramarenko, V A; Kramberger, G; Krasnopevtsev, D; Krasny, M W; Krasznahorkay, A; Kravchenko, A; Kretz, M; Kretzschmar, J; Kreutzfeldt, K; Krieger, P; Krizka, K; Kroeninger, K; Kroha, H; Kroll, J; Kroseberg, J; Krstic, J; Kruchonak, U; Krüger, H; Krumnack, N; Kruse, M C; Kruskal, M; Kubota, T; Kucuk, H; Kuday, S; Kuechler, J T; Kuehn, S; Kugel, A; Kuger, F; Kuhl, T; Kukhtin, V; Kukla, R; Kulchitsky, Y; Kuleshov, S; Kuna, M; Kunigo, T; Kupco, A; Kurashige, H; Kurchaninov, L L; Kurochkin, Y A; Kurth, M G; Kus, V; Kuwertz, E S; Kuze, M; Kvita, J; Kwan, T; Kyriazopoulos, D; La Rosa, A; La Rosa Navarro, J L; Rotonda, L La; Lacasta, C; Lacava, F; Lacey, J; Lacker, H; Lacour, D; Lacuesta, V R; Ladygin, E; Lafaye, R; Laforge, B; Lagouri, T; Lai, S; Lammers, S; Lampl, W; Lançon, E; Landgraf, U; Landon, M P J; Lanfermann, M C; Lang, V S; Lange, J C; Lankford, A J; Lanni, F; Lantzsch, K; Lanza, A; Laplace, S; Lapoire, C; Laporte, J F; Lari, T; Lasagni Manghi, F; Lassnig, M; Laurelli, P; Lavrijsen, W; Law, A T; Laycock, P; Lazovich, T; Lazzaroni, M; Le, B; Le Dortz, O; Le Guirriec, E; Le Quilleuc, E P; LeBlanc, M; LeCompte, T; Ledroit-Guillon, F; Lee, C A; Lee, S C; Lee, L; Lefebvre, B; Lefebvre, G; Lefebvre, M; Legger, F; Leggett, C; Lehan, A; Lehmann Miotto, G; Lei, X; Leight, W A; Leister, A G; Leite, M A L; Leitner, R; Lellouch, D; Lemmer, B; Leney, K J C; Lenz, T; Lenzi, B; Leone, R; Leone, S; Leonidopoulos, C; Leontsinis, S; Lerner, G; Leroy, C; Lesage, A A J; Lester, C G; Levchenko, M; Levêque, J; Levin, D; Levinson, L J; Levy, M; Lewis, D; Leyton, M; Li, B; Li, C; Li, H; Li, L; Li, L; Li, Q; Li, S; Li, X; Li, Y; Liang, Z; Liberti, B; Liblong, A; Lichard, P; Lie, K; Liebal, J; Liebig, W; Limosani, A; Lin, S C; Lin, T H; Lindquist, B E; Lionti, A E; Lipeles, E; Lipniacka, A; Lisovyi, M; Liss, T M; Lister, A; Litke, A M; Liu, B; Liu, D; Liu, H; Liu, H; Liu, J; Liu, J B; Liu, K; Liu, L; Liu, M; Liu, Y L; Liu, Y; Livan, M; Lleres, A; Llorente Merino, J; Lloyd, S L; Lo Sterzo, F; Lobodzinska, E M; Loch, P; Loebinger, F K; Loew, K M; Loginov, A; Lohse, T; Lohwasser, K; Lokajicek, M; Long, B A; Long, J D; Long, R E; Longo, L; Looper, K A; Lopez Lopez, J A; Lopez Mateos, D; Lopez Paredes, B; Lopez Paz, I; Lopez Solis, A; Lorenz, J; Lorenzo Martinez, N; Losada, M; Lösel, P J; Lou, X; Lounis, A; Love, J; Love, P A; Lu, H; Lu, N; Lubatti, H J; Luci, C; Lucotte, A; Luedtke, C; Luehring, F; Lukas, W; Luminari, L; Lundberg, O; Lund-Jensen, B; Luzi, P M; Lynn, D; Lysak, R; Lytken, E; Lyubushkin, V; Ma, H; Ma, L L; Ma, Y; Maccarrone, G; Macchiolo, A; Macdonald, C M; Maček, B; Machado Miguens, J; Madaffari, D; Madar, R; Maddocks, H J; Mader, W F; Madsen, A; Maeda, J; Maeland, S; Maeno, T; Maevskiy, A; Magradze, E; Mahlstedt, J; Maiani, C; Maidantchik, C; Maier, A A; Maier, T; Maio, A; Majewski, S; Makida, Y; Makovec, N; Malaescu, B; Malecki, Pa; Maleev, V P; Malek, F; Mallik, U; Malon, D; Malone, C; Malone, C; Maltezos, S; Malyukov, S; Mamuzic, J; Mancini, G; Mandelli, L; Mandić, I; Maneira, J; Manhaes de Andrade Filho, L; Manjarres Ramos, J; Mann, A; Manousos, A; Mansoulie, B; Mansour, J D; Mantifel, R; Mantoani, M; Manzoni, S; Mapelli, L; Marceca, G; March, L; Marchiori, G; Marcisovsky, M; Marjanovic, M; Marley, D E; Marroquim, F; Marsden, S P; Marshall, Z; Marti-Garcia, S; Martin, B; Martin, T A; Martin, V J; Martin Dit Latour, B; Martinez, M; Martinez Outschoorn, V I; Martin-Haugh, S; Martoiu, V S; Martyniuk, A C; Marzin, A; Masetti, L; Mashimo, T; Mashinistov, R; Masik, J; Maslennikov, A L; Massa, I; Massa, L; Mastrandrea, P; Mastroberardino, A; Masubuchi, T; Mättig, P; Mattmann, J; Maurer, J; Maxfield, S J; Maximov, D A; Mazini, R; Maznas, I; Mazza, S M; Mc Fadden, N C; Mc Goldrick, G; Mc Kee, S P; McCarn, A; McCarthy, R L; McCarthy, T G; McClymont, L I; McDonald, E F; Mcfayden, J A; Mchedlidze, G; McMahon, S J; McNamara, P C; McPherson, R A; Medinnis, M; Meehan, S; Mehlhase, S; Mehta, A; Meier, K; Meineck, C; Meirose, B; Melini, D; Mellado Garcia, B R; Melo, M; Meloni, F; Menary, S B; Meng, L; Meng, X T; Mengarelli, A; Menke, S; Meoni, E; Mergelmeyer, S; Mermod, P; Merola, L; Meroni, C; Merritt, F S; Messina, A; Metcalfe, J; Mete, A S; Meyer, C; Meyer, C; Meyer, J-P; Meyer, J; Meyer Zu Theenhausen, H; Miano, F; Middleton, R P; Miglioranzi, S; Mijović, L; Mikenberg, G; Mikestikova, M; Mikuž, M; Milesi, M; Milic, A; Miller, D W; Mills, C; Milov, A; Milstead, D A; Minaenko, A A; Minami, Y; Minashvili, I A; Mincer, A I; Mindur, B; Mineev, M; Minegishi, Y; Ming, Y; Mir, L M; Mistry, K P; Mitani, T; Mitrevski, J; Mitsou, V A; Miucci, A; Miyagawa, P S; Mizukami, A; Mjörnmark, J U; Mlynarikova, M; Moa, T; Mochizuki, K; Mogg, P; Mohapatra, S; Molander, S; Moles-Valls, R; Monden, R; Mondragon, M C; Mönig, K; Monk, J; Monnier, E; Montalbano, A; Montejo Berlingen, J; Monticelli, F; Monzani, S; Moore, R W; Morange, N; Moreno, D; Moreno Llácer, M; Morettini, P; Morgenstern, S; Mori, D; Mori, T; Morii, M; Morinaga, M; Morisbak, V; Moritz, S; Morley, A K; Mornacchi, G; Morris, J D; Mortensen, S S; Morvaj, L; Moschovakos, P; Mosidze, M; Moss, H J; Moss, J; Motohashi, K; Mount, R; Mountricha, E; Moyse, E J W; Muanza, S; Mudd, R D; Mueller, F; Mueller, J; Mueller, R S P; Mueller, T; Muenstermann, D; Mullen, P; Mullier, G A; Munoz Sanchez, F J; Murillo Quijada, J A; Murray, W J; Musheghyan, H; Muškinja, M; Myagkov, A G; Myska, M; Nachman, B P; Nackenhorst, O; Nagai, K; Nagai, R; Nagano, K; Nagasaka, Y; Nagata, K; Nagel, M; Nagy, E; Nairz, A M; Nakahama, Y; Nakamura, K; Nakamura, T; Nakano, I; Naranjo Garcia, R F; Narayan, R; Narrias Villar, D I; Naryshkin, I; Naumann, T; Navarro, G; Nayyar, R; Neal, H A; Nechaeva, P Yu; Neep, T J; Negri, A; Negrini, M; Nektarijevic, S; Nellist, C; Nelson, A; Nemecek, S; Nemethy, P; Nepomuceno, A A; Nessi, M; Neubauer, M S; Neumann, M; Neves, R M; Nevski, P; Newman, P R; Nguyen, D H; Nguyen Manh, T; Nickerson, R B; Nicolaidou, R; Nielsen, J; Nikiforov, A; Nikolaenko, V; Nikolic-Audit, I; Nikolopoulos, K; Nilsen, J K; Nilsson, P; Ninomiya, Y; Nisati, A; Nisius, R; Nobe, T; Nomachi, M; Nomidis, I; Nooney, T; Norberg, S; Nordberg, M; Norjoharuddeen, N; Novgorodova, O; Nowak, S; Nozaki, M; Nozka, L; Ntekas, K; Nurse, E; Nuti, F; O'grady, F; O'Neil, D C; O'Rourke, A A; O'Shea, V; Oakham, F G; Oberlack, H; Obermann, T; Ocariz, J; Ochi, A; Ochoa, I; Ochoa-Ricoux, J P; Oda, S; Odaka, S; Ogren, H; Oh, A; Oh, S H; Ohm, C C; Ohman, H; Oide, H; Okawa, H; Okumura, Y; Okuyama, T; Olariu, A; Oleiro Seabra, L F; Olivares Pino, S A; Damazio, D Oliveira; Olszewski, A; Olszowska, J; Onofre, A; Onogi, K; Onyisi, P U E; Oreglia, M J; Oren, Y; Orestano, D; Orlando, N; Orr, R S; Osculati, B; Ospanov, R; Otero Y Garzon, G; Otono, H; Ouchrif, M; Ould-Saada, F; Ouraou, A; Oussoren, K P; Ouyang, Q; Owen, M; Owen, R E; Ozcan, V E; Ozturk, N; Pachal, K; Pacheco Pages, A; Pacheco Rodriguez, L; Padilla Aranda, C; Pagáčová, M; Pagan Griso, S; Paganini, M; Paige, F; Pais, P; Pajchel, K; Palacino, G; Palazzo, S; Palestini, S; Palka, M; Pallin, D; St Panagiotopoulou, E; Panagoulias, I; Pandini, C E; Panduro Vazquez, J G; Pani, P; Panitkin, S; Pantea, D; Paolozzi, L; Papadopoulou, Th D; Papageorgiou, K; Paramonov, A; Paredes Hernandez, D; Parker, A J; Parker, M A; Parker, K A; Parodi, F; Parsons, J A; Parzefall, U; Pascuzzi, V R; Pasqualucci, E; Passaggio, S; Pastore, Fr; Pásztor, G; Pataraia, S; Pater, J R; Pauly, T; Pearce, J; Pearson, B; Pedersen, L E; Pedersen, M; Pedraza Lopez, S; Pedro, R; Peleganchuk, S V; Penc, O; Peng, C; Peng, H; Penwell, J; Peralva, B S; Perego, M M; Perepelitsa, D V; Perez Codina, E; Perini, L; Pernegger, H; Perrella, S; Peschke, R; Peshekhonov, V D; Peters, K; Peters, R F Y; Petersen, B A; Petersen, T C; Petit, E; Petridis, A; Petridou, C; Petroff, P; Petrolo, E; Petrov, M; Petrucci, F; Pettersson, N E; Peyaud, A; Pezoa, R; Phillips, P W; Piacquadio, G; Pianori, E; Picazio, A; Piccaro, E; Piccinini, M; Pickering, M A; Piegaia, R; Pilcher, J E; Pilkington, A D; Pin, A W J; Pinamonti, M; Pinfold, J L; Pingel, A; Pires, S; Pirumov, H; Pitt, M; Plazak, L; Pleier, M-A; Pleskot, V; Plotnikova, E; Pluth, D; Poettgen, R; Poggioli, L; Pohl, D; Polesello, G; Poley, A; Policicchio, A; Polifka, R; Polini, A; Pollard, C S; Polychronakos, V; Pommès, K; Pontecorvo, L; Pope, B G; Popeneciu, G A; Poppleton, A; Pospisil, S; Potamianos, K; Potrap, I N; Potter, C J; Potter, C T; Poulard, G; Poveda, J; Pozdnyakov, V; Pozo Astigarraga, M E; Pralavorio, P; Pranko, A; Prell, S; Price, D; Price, L E; Primavera, M; Prince, S; Prokofiev, K; Prokoshin, F; Protopopescu, S; Proudfoot, J; Przybycien, M; Puddu, D; Purohit, M; Puzo, P; Qian, J; Qin, G; Qin, Y; Quadt, A; Quayle, W B; Queitsch-Maitland, M; Quilty, D; Raddum, S; Radeka, V; Radescu, V; Radhakrishnan, S K; Radloff, P; Rados, P; Ragusa, F; Rahal, G; Raine, J A; Rajagopalan, S; Rammensee, M; Rangel-Smith, C; Ratti, M G; Rauch, D M; Rauscher, F; Rave, S; Ravenscroft, T; Ravinovich, I; Raymond, M; Read, A L; Readioff, N P; Reale, M; Rebuzzi, D M; Redelbach, A; Redlinger, G; Reece, R; Reed, R G; Reeves, K; Rehnisch, L; Reichert, J; Reiss, A; Rembser, C; Ren, H; Rescigno, M; Resconi, S; Rezanova, O L; Reznicek, P; Rezvani, R; Richter, R; Richter, S; Richter-Was, E; Ricken, O; Ridel, M; Rieck, P; Riegel, C J; Rieger, J; Rifki, O; Rijssenbeek, M; Rimoldi, A; Rimoldi, M; Rinaldi, L; Ristić, B; Ritsch, E; Riu, I; Rizatdinova, F; Rizvi, E; Rizzi, C; Robertson, S H; Robichaud-Veronneau, A; Robinson, D; Robinson, J E M; Robson, A; Roda, C; Rodina, Y; Rodriguez Perez, A; Rodriguez Rodriguez, D; Roe, S; Rogan, C S; Røhne, O; Roloff, J; Romaniouk, A; Romano, M; Romano Saez, S M; Romero Adam, E; Rompotis, N; Ronzani, M; Roos, L; Ros, E; Rosati, S; Rosbach, K; Rose, P; Rosien, N-A; Rossetti, V; Rossi, E; Rossi, L P; Rosten, J H N; Rosten, R; Rotaru, M; Roth, I; Rothberg, J; Rousseau, D; Rozanov, A; Rozen, Y; Ruan, X; Rubbo, F; Rudolph, M S; Rühr, F; Ruiz-Martinez, A; Rurikova, Z; Rusakovich, N A; Ruschke, A; Russell, H L; Rutherfoord, J P; Ruthmann, N; Ryabov, Y F; Rybar, M; Rybkin, G; Ryu, S; Ryzhov, A; Rzehorz, G F; Saavedra, A F; Sabato, G; Sacerdoti, S; Sadrozinski, H F-W; Sadykov, R; Safai Tehrani, F; Saha, P; Sahinsoy, M; Saimpert, M; Saito, T; Sakamoto, H; Sakurai, Y; Salamanna, G; Salamon, A; Salazar Loyola, J E; Salek, D; Sales De Bruin, P H; Salihagic, D; Salnikov, A; Salt, J; Salvatore, D; Salvatore, F; Salvucci, A; Salzburger, A; Sammel, D; Sampsonidis, D; Sánchez, J; Sanchez Martinez, V; Sanchez Pineda, A; Sandaker, H; Sandbach, R L; Sandhoff, M; Sandoval, C; Sankey, D P C; Sannino, M; Sansoni, A; Santoni, C; Santonico, R; Santos, H; Santoyo Castillo, I; Sapp, K; Sapronov, A; Saraiva, J G; Sarrazin, B; Sasaki, O; Sato, K; Sauvan, E; Savage, G; Savard, P; Savic, N; Sawyer, C; Sawyer, L; Saxon, J; Sbarra, C; Sbrizzi, A; Scanlon, T; Scannicchio, D A; Scarcella, M; Scarfone, V; Schaarschmidt, J; Schacht, P; Schachtner, B M; Schaefer, D; Schaefer, L; Schaefer, R; Schaeffer, J; Schaepe, S; Schaetzel, S; Schäfer, U; Schaffer, A C; Schaile, D; Schamberger, R D; Scharf, V; Schegelsky, V A; Scheirich, D; Schernau, M; Schiavi, C; Schier, S; Schillo, C; Schioppa, M; Schlenker, S; Schmidt-Sommerfeld, K R; Schmieden, K; Schmitt, C; Schmitt, S; Schmitz, S; Schneider, B; Schnoor, U; Schoeffel, L; Schoening, A; Schoenrock, B D; Schopf, E; Schott, M; Schouwenberg, J F P; Schovancova, J; Schramm, S; Schreyer, M; Schuh, N; Schulte, A; Schultens, M J; Schultz-Coulon, H-C; Schulz, H; Schumacher, M; Schumm, B A; Schune, Ph; Schwartzman, A; Schwarz, T A; Schweiger, H; Schwemling, Ph; Schwienhorst, R; Schwindling, J; Schwindt, T; Sciolla, G; Scuri, F; Scutti, F; Searcy, J; Seema, P; Seidel, S C; Seiden, A; Seifert, F; Seixas, J M; Sekhniaidze, G; Sekhon, K; Sekula, S J; Seliverstov, D M; Semprini-Cesari, N; Serfon, C; Serin, L; Serkin, L; Sessa, M; Seuster, R; Severini, H; Sfiligoj, T; Sforza, F; Sfyrla, A; Shabalina, E; Shaikh, N W; Shan, L Y; Shang, R; Shank, J T; Shapiro, M; Shatalov, P B; Shaw, K; Shaw, S M; Shcherbakova, A; Shehu, C Y; Sherwood, P; Shi, L; Shimizu, S; Shimmin, C O; Shimojima, M; Shirabe, S; Shiyakova, M; Shmeleva, A; Shoaleh Saadi, D; Shochet, M J; Shojaii, S; Shope, D R; Shrestha, S; Shulga, E; Shupe, M A; Sicho, P; Sickles, A M; Sidebo, P E; Sideras Haddad, E; Sidiropoulou, O; Sidorov, D; Sidoti, A; Siegert, F; Sijacki, Dj; Silva, J; Silverstein, S B; Simak, V; Simic, Lj; Simion, S; Simioni, E; Simmons, B; Simon, D; Simon, M; Sinervo, P; Sinev, N B; Sioli, M; Siragusa, G; Sivoklokov, S Yu; Sjölin, J; Skinner, M B; Skottowe, H P; Skubic, P; Slater, M; Slavicek, T; Slawinska, M; Sliwa, K; Slovak, R; Smakhtin, V; Smart, B H; Smestad, L; Smiesko, J; Smirnov, S Yu; Smirnov, Y; Smirnova, L N; Smirnova, O; Smith, J W; Smith, M N K; Smith, R W; Smizanska, M; Smolek, K; Snesarev, A A; Snyder, I M; Snyder, S; Sobie, R; Socher, F; Soffer, A; Soh, D A; Sokhrannyi, G; Solans Sanchez, C A; Solar, M; Soldatov, E Yu; Soldevila, U; Solodkov, A A; Soloshenko, A; Solovyanov, O V; Solovyev, V; Sommer, P; Son, H; Song, H Y; Sood, A; Sopczak, A; Sopko, V; Sorin, V; Sosa, D; Sotiropoulou, C L; Soualah, R; Soukharev, A M; South, D; Sowden, B C; Spagnolo, S; Spalla, M; Spangenberg, M; Spanò, F; Sperlich, D; Spettel, F; Spieker, T M; Spighi, R; Spigo, G; Spiller, L A; Spousta, M; St Denis, R D; Stabile, A; Stamen, R; Stamm, S; Stanecka, E; Stanek, R W; Stanescu, C; Stanescu-Bellu, M; Stanitzki, M M; Stapnes, S; Starchenko, E A; Stark, G H; Stark, J; Staroba, P; Starovoitov, P; Stärz, S; Staszewski, R; Steinberg, P; Stelzer, B; Stelzer, H J; Stelzer-Chilton, O; Stenzel, H; Stewart, G A; Stillings, J A; Stockton, M C; Stoebe, M; Stoicea, G; Stolte, P; Stonjek, S; Stradling, A R; Straessner, A; Stramaglia, M E; Strandberg, J; Strandberg, S; Strandlie, A; Strauss, M; Strizenec, P; Ströhmer, R; Strom, D M; Stroynowski, R; Strubig, A; Stucci, S A; Stugu, B; Styles, N A; Su, D; Su, J; Suchek, S; Sugaya, Y; Suk, M; Sulin, V V; Sultansoy, S; Sumida, T; Sun, S; Sun, X; Sundermann, J E; Suruliz, K; Suster, C J E; Sutton, M R; Suzuki, S; Svatos, M; Swiatlowski, M; Swift, S P; Sykora, I; Sykora, T; Ta, D; Taccini, C; Tackmann, K; Taenzer, J; Taffard, A; Tafirout, R; Taiblum, N; Takai, H; Takashima, R; Takeshita, T; Takubo, Y; Talby, M; Talyshev, A A; Tan, K G; Tanaka, J; Tanaka, M; Tanaka, R; Tanaka, S; Tanioka, R; Tannenwald, B B; Tapia Araya, S; Tapprogge, S; Tarem, S; Tartarelli, G F; Tas, P; Tasevsky, M; Tashiro, T; Tassi, E; Tavares Delgado, A; Tayalati, Y; Taylor, A C; Taylor, G N; Taylor, P T E; Taylor, W; Teischinger, F A; Teixeira-Dias, P; Temming, K K; Temple, D; Ten Kate, H; Teng, P K; Teoh, J J; Tepel, F; Terada, S; Terashi, K; Terron, J; Terzo, S; Testa, M; Teuscher, R J; Theveneaux-Pelzer, T; Thomas, J P; Thomas-Wilsker, J; Thompson, P D; Thompson, A S; Thomsen, L A; Thomson, E; Tibbetts, M J; Ticse Torres, R E; Tikhomirov, V O; Tikhonov, Yu A; Timoshenko, S; Tipton, P; Tisserant, S; Todome, K; Todorov, T; Todorova-Nova, S; Tojo, J; Tokár, S; Tokushuku, K; Tolley, E; Tomlinson, L; Tomoto, M; Tompkins, L; Toms, K; Tong, B; Tornambe, P; Torrence, E; Torres, H; Torró Pastor, E; Toth, J; Touchard, F; Tovey, D R; Trefzger, T; Tricoli, A; Trigger, I M; Trincaz-Duvoid, S; Tripiana, M F; Trischuk, W; Trocmé, B; Trofymov, A; Troncon, C; Trottier-McDonald, M; Trovatelli, M; Truong, L; Trzebinski, M; Trzupek, A; Tseng, J C-L; Tsiareshka, P V; Tsipolitis, G; Tsirintanis, N; Tsiskaridze, S; Tsiskaridze, V; Tskhadadze, E G; Tsui, K M; Tsukerman, I I; Tsulaia, V; Tsuno, S; Tsybychev, D; Tu, Y; Tudorache, A; Tudorache, V; Tulbure, T T; Tuna, A N; Tupputi, S A; Turchikhin, S; Turgeman, D; Turk Cakir, I; Turra, R; Tuts, P M; Ucchielli, G; Ueda, I; Ughetto, M; Ukegawa, F; Unal, G; Undrus, A; Unel, G; Ungaro, F C; Unno, Y; Unverdorben, C; Urban, J; Urquijo, P; Urrejola, P; Usai, G; Usui, J; Vacavant, L; Vacek, V; Vachon, B; Valderanis, C; Valdes Santurio, E; Valencic, N; Valentinetti, S; Valero, A; Valery, L; Valkar, S; Valls Ferrer, J A; Van Den Wollenberg, W; Van Der Deijl, P C; van der Graaf, H; van Eldik, N; van Gemmeren, P; Van Nieuwkoop, J; van Vulpen, I; van Woerden, M C; Vanadia, M; Vandelli, W; Vanguri, R; Vaniachine, A; Vankov, P; Vardanyan, G; Vari, R; Varnes, E W; Varol, T; Varouchas, D; Vartapetian, A; Varvell, K E; Vasquez, J G; Vasquez, G A; Vazeille, F; Vazquez Schroeder, T; Veatch, J; Veeraraghavan, V; Veloce, L M; Veloso, F; Veneziano, S; Ventura, A; Venturi, M; Venturi, N; Venturini, A; Vercesi, V; Verducci, M; Verkerke, W; Vermeulen, J C; Vest, A; Vetterli, M C; Viazlo, O; Vichou, I; Vickey, T; Vickey Boeriu, O E; Viehhauser, G H A; Viel, S; Vigani, L; Villa, M; Villaplana Perez, M; Vilucchi, E; Vincter, M G; Vinogradov, V B; Vittori, C; Vivarelli, I; Vlachos, S; Vlasak, M; Vogel, M; Vokac, P; Volpi, G; Volpi, M; von der Schmitt, H; von Toerne, E; Vorobel, V; Vorobev, K; Vos, M; Voss, R; Vossebeld, J H; Vranjes, N; Vranjes Milosavljevic, M; Vrba, V; Vreeswijk, M; Vuillermet, R; Vukotic, I; Wagner, P; Wagner, W; Wahlberg, H; Wahrmund, S; Wakabayashi, J; Walder, J; Walker, R; Walkowiak, W; Wallangen, V; Wang, C; Wang, C; Wang, F; Wang, H; Wang, H; Wang, J; Wang, J; Wang, K; Wang, R; Wang, S M; Wang, T; Wang, W; Wanotayaroj, C; Warburton, A; Ward, C P; Wardrope, D R; Washbrook, A; Watkins, P M; Watson, A T; Watson, M F; Watts, G; Watts, S; Waugh, B M; Webb, S; Weber, M S; Weber, S W; Weber, S A; Webster, J S; Weidberg, A R; Weinert, B; Weingarten, J; Weiser, C; Weits, H; Wells, P S; Wenaus, T; Wengler, T; Wenig, S; Wermes, N; Werner, M D; Werner, P; Wessels, M; Wetter, J; Whalen, K; Whallon, N L; Wharton, A M; White, A; White, M J; White, R; Whiteson, D; Wickens, F J; Wiedenmann, W; Wielers, M; Wiglesworth, C; Wiik-Fuchs, L A M; Wildauer, A; Wilk, F; Wilkens, H G; Williams, H H; Williams, S; Willis, C; Willocq, S; Wilson, J A; Wingerter-Seez, I; Winklmeier, F; Winston, O J; Winter, B T; Wittgen, M; Wolf, T M H; Wolff, R; Wolter, M W; Wolters, H; Worm, S D; Wosiek, B K; Wotschack, J; Woudstra, M J; Wozniak, K W; Wu, M; Wu, M; Wu, S L; Wu, X; Wu, Y; Wyatt, T R; Wynne, B M; Xella, S; Xi, Z; Xu, D; Xu, L; Yabsley, B; Yacoob, S; Yamaguchi, D; Yamaguchi, Y; Yamamoto, A; Yamamoto, S; Yamanaka, T; Yamauchi, K; Yamazaki, Y; Yan, Z; Yang, H; Yang, H; Yang, Y; Yang, Z; Yao, W-M; Yap, Y C; Yasu, Y; Yatsenko, E; Yau Wong, K H; Ye, J; Ye, S; Yeletskikh, I; Yildirim, E; Yorita, K; Yoshida, R; Yoshihara, K; Young, C; Young, C J S; Youssef, S; Yu, D R; Yu, J; Yu, J M; Yu, J; Yuan, L; Yuen, S P Y; Yusuff, I; Zabinski, B; Zacharis, G; Zaidan, R; Zaitsev, A M; Zakharchuk, N; Zalieckas, J; Zaman, A; Zambito, S; Zanello, L; Zanzi, D; Zeitnitz, C; Zeman, M; Zemla, A; Zeng, J C; Zeng, Q; Zenin, O; Ženiš, T; Zerwas, D; Zhang, D; Zhang, F; Zhang, G; Zhang, H; Zhang, J; Zhang, L; Zhang, L; Zhang, M; Zhang, R; Zhang, R; Zhang, X; Zhang, Z; Zhao, X; Zhao, Y; Zhao, Z; Zhemchugov, A; Zhong, J; Zhou, B; Zhou, C; Zhou, L; Zhou, L; Zhou, M; Zhou, N; Zhu, C G; Zhu, H; Zhu, J; Zhu, Y; Zhuang, X; Zhukov, K; Zibell, A; Zieminska, D; Zimine, N I; Zimmermann, C; Zimmermann, S; Zinonos, Z; Zinser, M; Ziolkowski, M; Živković, L; Zobernig, G; Zoccoli, A; Zur Nedden, M; Zwalinski, L

    2017-01-01

    During 2015 the ATLAS experiment recorded [Formula: see text] of proton-proton collision data at a centre-of-mass energy of [Formula: see text]. The ATLAS trigger system is a crucial component of the experiment, responsible for selecting events of interest at a recording rate of approximately 1 kHz from up to 40 MHz of collisions. This paper presents a short overview of the changes to the trigger and data acquisition systems during the first long shutdown of the LHC and shows the performance of the trigger system and its components based on the 2015 proton-proton collision data.

  5. Wind Energy Resource Atlas of the Dominican Republic

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Elliott, D.; Schwartz, M.; George, R.

    2001-10-01

    The Wind Energy Resource Atlas of the Dominican Republic identifies the wind characteristics and the distribution of the wind resource in this country. This major project is the first of its kind undertaken for the Dominican Republic. The information contained in the atlas is necessary to facilitate the use of wind energy technologies, both for utility-scale power generation and off-grid wind energy applications. A computerized wind mapping system developed by NREL generated detailed wind resource maps for the entire country. This technique uses Geographic Information Systems (GIS) to produce high-resolution (1-square kilometer) annual average wind resource maps.

  6. EnviroAtlas - Phoenix, AZ - Near Road Block Group Summary

    EPA Pesticide Factsheets

    This EnviroAtlas dataset addresses the tree buffer along heavily traveled roads. The roads are interstates, arterials, and collectors within the EnviroAtlas community boundary. Forest is defined as Trees & Forest. Sufficient tree bufferage is defined as 25% coverage within the circular moving window with a radius of 14.5m at any given point along the roadway. There are potential negative health effects for those living in a location without a sufficient tree buffer. Those populations are estimated here using dasymetric data calculated for the EnviroAtlas. There are potential negative health effects for those living in a location without a sufficient tree buffer. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

  7. EnviroAtlas - Pittsburgh, PA - Near Road Block Group Summary

    EPA Pesticide Factsheets

    This EnviroAtlas dataset addresses the tree buffer along heavily traveled roads. The roads are interstates, arterials, and collectors within the EnviroAtlas community boundary. Forest is defined as Trees & Forest. Sufficient tree bufferage is defined as 25% coverage within the circular moving window with a radius of 14.5m at any given point along the roadway. There are potential negative health effects for those living in a location without a sufficient tree buffer. Those populations are estimated here using dasymetric data calculated for the EnviroAtlas. There are potential negative health effects for those living in a location without a sufficient tree buffer. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

  8. EnviroAtlas - Austin, TX - Near Road Block Group Summary

    EPA Pesticide Factsheets

    This EnviroAtlas dataset addresses the tree buffer along heavily traveled roads. The roads are interstates, arterials, and collectors within the EnviroAtlas community boundary. Forest is defined as Trees & Forest. Sufficient tree bufferage is defined as 25% coverage within the circular moving window with a radius of 14.5m at any given point along the roadway. There are potential negative health affects for those living in a location without a sufficient tree buffer. Those populations are estimated here using dasymetric data calculated for the EnviroAtlas. There are potential negative health effects for those living in a location without a sufficient tree buffer. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

  9. EnviroAtlas - Woodbine, IA - Near Road Block Group Summary

    EPA Pesticide Factsheets

    This EnviroAtlas dataset addresses the tree buffer along heavily traveled roads. The roads are interstates, arterials, and collectors within the EnviroAtlas community boundary. Forest is defined as Trees & Forest. Sufficient tree bufferage is defined as 25% coverage within the circular moving window with a radius of 14.5m at any given point along the roadway. There are potential negative health effects for those living in a location without a sufficient tree buffer. Those populations are estimated here using dasymetric data calculated for the EnviroAtlas. There are potential negative health effects for those living in a location without a sufficient tree buffer. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

  10. EnviroAtlas - Paterson, NJ - Near Road Block Group Summary

    EPA Pesticide Factsheets

    This EnviroAtlas dataset addresses the tree buffer along heavily traveled roads. The roads are interstates, arterials, and collectors within the EnviroAtlas community boundary. Forest is defined as Trees & Forest. Sufficient tree bufferage is defined as 25% coverage within the circular moving window with a radius of 14.5m at any given point along the roadway. There are potential negative health effects for those living in a location without a sufficient tree buffer. Those populations are estimated here using dasymetric data calculated for the EnviroAtlas. There are potential negative health effects for those living in a location without a sufficient tree buffer. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

  11. EnviroAtlas - Milwaukee, WI - Near Road Block Group Summary

    EPA Pesticide Factsheets

    This EnviroAtlas dataset addresses the tree buffer along heavily traveled roads. The roads are interstates, arterials, and collectors within the EnviroAtlas community boundary. Forest is defined as Trees & Forest and Woody Wetlands. Sufficient tree bufferage is defined as 25% coverage within the circular moving window with a radius of 14.5m at any given point along the roadway. There are potential negative health effects for those living in a location without a sufficient tree buffer. Those populations are estimated here using dasymetric data calculated for the EnviroAtlas. There are potential negative health effects for those living in a location without a sufficient tree buffer. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

  12. EnviroAtlas - Portland, OR - Near Road Block Group Summary

    EPA Pesticide Factsheets

    This EnviroAtlas dataset addresses the tree buffer along heavily traveled roads. The roads are interstates, arterials, and collectors within the EnviroAtlas community boundary. Forest is defined as Trees & Forest and Woody Wetlands. Sufficient tree bufferage is defined as 25% coverage within the circular moving window with a radius of 14.5m at any given point along the roadway. There are potential negative health effects for those living in a location without a sufficient tree buffer. Those populations are estimated here using dasymetric data calculated for the EnviroAtlas. There are potential negative health effects for those living in a location without a sufficient tree buffer. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (http:/www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

  13. EnviroAtlas - Memphis, TN - Near Road Block Group Summary

    EPA Pesticide Factsheets

    This EnviroAtlas dataset addresses the tree buffer along heavily traveled roads. The roads are interstates, arterials, and collectors within the EnviroAtlas community boundary. Forest is defined as Trees & Forest and Woody Wetlands. Sufficient tree bufferage is defined as 25% coverage within the circular moving window with a radius of 14.5m at any given point along the roadway. There are potential negative health effects for those living in a location without a sufficient tree buffer. Those populations are estimated here using dasymetric data calculated for the EnviroAtlas. There are potential negative health effects for those living in a location without a sufficient tree buffer. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

  14. EnviroAtlas - Fresno, CA - Near Road Block Group Summary

    EPA Pesticide Factsheets

    This EnviroAtlas dataset addresses the tree buffer along heavily traveled roads. The roads are interstates, arterials, and collectors within the EnviroAtlas community boundary. Forest is defined as Trees & Forest and Orchards. Sufficient tree bufferage is defined as 25% coverage within the circular moving window with a radius of 14.5m at any given point along the roadway. There are potential negative health effects for those living in a location without a sufficient tree buffer. Those populations are estimated here using dasymetric data calculated for the EnviroAtlas. There are potential negative health effects for those living in a location without a sufficient tree buffer. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

  15. EnviroAtlas - Tampa, FL - Near Road Block Group Summary

    EPA Pesticide Factsheets

    This EnviroAtlas dataset addresses the tree buffer along heavily traveled roads. The roads are interstates, arterials, and collectors within the EnviroAtlas community boundary. Forest is defined as Trees & Forest and Woody Wetlands. Sufficient tree bufferage is defined as 25% coverage within the circular moving window with a radius of 14.5m at any given point along the roadway. There are potential negative health effects for those living in a location without a sufficient tree buffer. Those populations are estimated here using dasymetric data calculated for the EnviroAtlas. There are potential negative health effects for those living in a location without a sufficient tree buffer. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

  16. EnviroAtlas - Cleveland, OH - Near Road Block Group Summary

    EPA Pesticide Factsheets

    This EnviroAtlas dataset addresses the tree buffer along heavily traveled roads. The roads are interstates, arterials, and collectors within the EnviroAtlas community boundary. In this community, forest is defined as Trees & Forest and Woody Wetlands. Sufficient tree bufferage is defined as 25% coverage within the circular moving window with a radius of 14.5m at any given point along the roadway. There are potential negative health affects for those living in a location without a sufficient tree buffer. Those populations are estimated here using dasymetric data calculated for the EnviroAtlas. There are potential negative health effects for those living in a location without a sufficient tree buffer. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas ) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

  17. EnviroAtlas - Durham, NC - Near Road Block Group Summary

    EPA Pesticide Factsheets

    This EnviroAtlas dataset addresses the tree buffer along heavily traveled roads. The roads are interstates, arterials, and collectors within the EnviroAtlas community boundary. Forest is defined as Trees & Forest. Sufficient tree bufferage is defined as 25% coverage within the circular moving window with a radius of 14.5m at any given point along the roadway. There are potential negative health effects for those living in a location without a sufficient tree buffer. Those populations are estimated here using dasymetric data calculated for the EnviroAtlas. There are potential negative health effects for those living in a location without a sufficient tree buffer. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas ) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets ).

  18. EnviroAtlas - Portland, ME - Near Road Block Group Summary

    EPA Pesticide Factsheets

    This EnviroAtlas dataset addresses the tree buffer along heavily traveled roads. The roads are interstates, arterials, and collectors within the EnviroAtlas community boundary. Forest is defined as Trees & Forest and Woody Wetlands. Sufficient tree bufferage is defined as 25% coverage within the circular moving window with a radius of 14.5m at any given point along the roadway. There are potential negative health effects for those living in a location without a sufficient tree buffer. Those populations are estimated here using dasymetric data calculated for the EnviroAtlas. There are potential negative health effects for those living in a location without a sufficient tree buffer. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

  19. EnviroAtlas -Pittsburgh, PA- One Meter Resolution Urban Land Cover Data (2010) Web Service

    EPA Pesticide Factsheets

    This EnviroAtlas web service supports research and online mapping activities related to EnviroAtlas (https://www.epa.gov/enviroatlas).The EnviroAtlas Pittsburgh, PA land cover map was generated from United States Department of Agriculture (USDA) National Agricultural Imagery Program (NAIP) four band (red, green, blue, and near infrared) aerial photography at 1 m spatial resolution. Imagery was collected on multiple dates in June 2010. Five land cover classes were mapped: water, impervious surfaces, soil and barren land, trees and forest, and grass and herbaceous non-woody vegetation. An accuracy assessment of 500 completely random and 81 stratified random points yielded an overall accuracy of 86.57 percent. The area mapped is defined by the US Census Bureau's 2010 Urban Statistical Area for Pittsburgh, PA. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

  20. EnviroAtlas - Green Bay, WI - Near Road Tree Buffer

    EPA Pesticide Factsheets

    This EnviroAtlas dataset addresses the tree buffer along heavily traveled roads. The roads are interstates, arterials, and collectors within the EnviroAtlas community boundary. Forest is defined as Trees & Forest and Woody Wetlands. Sufficient tree bufferage is defined as 25% coverage within the circular moving window with a radius of 14.5m at any given point along the roadway. There are potential negative health effects for those living in a location without a sufficient tree buffer. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets ).

  1. EnviroAtlas - New Bedford, MA - Near Road Tree Buffer

    EPA Pesticide Factsheets

    This EnviroAtlas dataset addresses the tree buffer along heavily traveled roads. The roads are interstates, arterials, and collectors within the EnviroAtlas community boundary. Forest is defined as Trees & Forest and Woody Wetlands. Sufficient tree bufferage is defined as 25% coverage within the circular moving window with a radius of 14.5m at any given point along the roadway. There are potential negative health effects for those living in a location without a sufficient tree buffer. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

  2. EnviroAtlas - New York, NY - Near Road Tree Buffer

    EPA Pesticide Factsheets

    This EnviroAtlas dataset addresses the tree buffer along heavily traveled roads. The roads are interstates, arterials, and collectors within the EnviroAtlas community boundary. In this community, forest is defined as Trees & Forest. Sufficient tree bufferage is defined as 25% coverage within the circular moving window with a radius of 14.5m at any given point along the roadway. There are potential negative health effects for those living in a location without a sufficient tree buffer. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

  3. The WITS Atlas: A Black Southern African dental atlas for permanent tooth formation and emergence.

    PubMed

    Esan, Temitope A; Schepartz, Lynne A

    2018-05-01

    Current dental maturity charts, such as the widely applied London atlas, do not take into consideration advanced tooth emergence and formation patterns observed in children of African ancestry. The result is inaccurate age estimation in Southern Africa, a region where there is great forensic and anthropological need for reliable age estimation. To develop a population-specific atlas of permanent tooth emergence and formation for age estimation of Black Southern Africans. Using data from a cross-sectional study of 642 school children aged 5-20 years, panoramic radiographs taken during routine dental examination in a mobile treatment van were analyzed using the Demirjian method of eight (A-H) tooth formation stages. Tables of the stages of tooth development for each tooth, including the third molars, were generated separately for age cohorts and by sex. The most frequently occurring (modal) stage of tooth formation was considered the signature developmental stage for the age. The relationship of the third molar occlusal surfaces with occlusal tables on the radiographs were checked and compared with the findings recorded during intra oral examination. Comparison with the London atlas shows that at age 9.5 years, the canine and premolar emergence are at least one year ahead and the third molar formation completes four years earlier in the WITS Atlas. Similarities in advancement in tooth formation and emergence across sub-Saharan Africa suggest that the WITS Atlas can be used for those populations as well. © 2018 Wiley Periodicals, Inc.

  4. Efficient multi-atlas abdominal segmentation on clinically acquired CT with SIMPLE context learning.

    PubMed

    Xu, Zhoubing; Burke, Ryan P; Lee, Christopher P; Baucom, Rebeccah B; Poulose, Benjamin K; Abramson, Richard G; Landman, Bennett A

    2015-08-01

    Abdominal segmentation on clinically acquired computed tomography (CT) has been a challenging problem given the inter-subject variance of human abdomens and complex 3-D relationships among organs. Multi-atlas segmentation (MAS) provides a potentially robust solution by leveraging label atlases via image registration and statistical fusion. We posit that the efficiency of atlas selection requires further exploration in the context of substantial registration errors. The selective and iterative method for performance level estimation (SIMPLE) method is a MAS technique integrating atlas selection and label fusion that has proven effective for prostate radiotherapy planning. Herein, we revisit atlas selection and fusion techniques for segmenting 12 abdominal structures using clinically acquired CT. Using a re-derived SIMPLE algorithm, we show that performance on multi-organ classification can be improved by accounting for exogenous information through Bayesian priors (so called context learning). These innovations are integrated with the joint label fusion (JLF) approach to reduce the impact of correlated errors among selected atlases for each organ, and a graph cut technique is used to regularize the combined segmentation. In a study of 100 subjects, the proposed method outperformed other comparable MAS approaches, including majority vote, SIMPLE, JLF, and the Wolz locally weighted vote technique. The proposed technique provides consistent improvement over state-of-the-art approaches (median improvement of 7.0% and 16.2% in DSC over JLF and Wolz, respectively) and moves toward efficient segmentation of large-scale clinically acquired CT data for biomarker screening, surgical navigation, and data mining. Copyright © 2015 Elsevier B.V. All rights reserved.

  5. El Yunque National Forest Atlas

    Treesearch

    Maya Quiñones; Isabel K. Parés-Ramos; William A. Gould; Grizelle Gonzalez; Kathleen McGinley; Pedro Ríos

    2018-01-01

    El Yunque National Forest Atlas is a collaborative effort by the International Institute of Tropical Forestry and El Yunque National Forest to provide upto-date maps and analyses of spatial information of an important natural reserve in Puerto Rico and the only tropical forest in the National Forest System of the United States. El Yunque National Forest Atlas serves as...

  6. ATLAS@Home: Harnessing Volunteer Computing for HEP

    NASA Astrophysics Data System (ADS)

    Adam-Bourdarios, C.; Cameron, D.; Filipčič, A.; Lancon, E.; Wu, W.; ATLAS Collaboration

    2015-12-01

    A recent common theme among HEP computing is exploitation of opportunistic resources in order to provide the maximum statistics possible for Monte Carlo simulation. Volunteer computing has been used over the last few years in many other scientific fields and by CERN itself to run simulations of the LHC beams. The ATLAS@Home project was started to allow volunteers to run simulations of collisions in the ATLAS detector. So far many thousands of members of the public have signed up to contribute their spare CPU cycles for ATLAS, and there is potential for volunteer computing to provide a significant fraction of ATLAS computing resources. Here we describe the design of the project, the lessons learned so far and the future plans.

  7. EnviroAtlas - Pittsburgh, PA - Domestic Water Use per Day by U.S. Census Block Group

    EPA Pesticide Factsheets

    As included in this EnviroAtlas dataset, the community level domestic water use was calculated using locally available water use data per capita in gallons of water per day (GPD), distributed dasymetrically, and summarized by census block group. Domestic water use, as defined in this case, is intended to represent residential indoor and outdoor water use (e.g., cooking hygiene, landscaping, pools, etc.) for primary residences (i.e., excluding second homes and tourism rentals). For the purposes of this metric, these publicly-supplied estimates are also applied and considered representative of local self-supplied water use. Domestic water demand was calculated and applied using the Pennsylvania Department of Environmental Protection (PADEP) PWS Service Areas layer, population served per provider, and average water use per provider. Within the EnviroAtlas study area, there are 43 service providers with 2010-2013 estimates ranging from 34 to 102 GPD.This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can

  8. EnviroAtlas -- Fresno, California -- One Meter Resolution Urban Land Cover Data (2010) Web Service

    EPA Pesticide Factsheets

    This EnviroAtlas web service supports research and online mapping activities related to EnviroAtlas (https://www.epa.gov/enviroatlas). The Fresno, CA EnviroAtlas One-Meter-scale Urban Land Cover Data were generated via supervised classification of combined aerial photography and LiDAR data. The air photos were United States Department of Agriculture (USDA) National Agricultural Imagery Program (NAIP) four band (red, green, blue, and near infrared) aerial photography at 1-m spatial resolution. Aerial photography ('imagery') was collected on multiple dates in summer 2010. Seven land cover classes were mapped: Water, impervious surfaces (Impervious), soil and barren (Soil), trees and forest (Tree), and grass and herbaceous non-woody vegetation (Grass), agriculture (Ag), and Orchards. An accuracy assessment of 500 completely random and 103 stratified random points yielded an overall User's fuzzy accuracy of 81.1 percent (see below). The area mapped is defined by the US Census Bureau's 2010 Urban Statistical Area for Fresno, CA plus a 1-km buffer. Where imagery was available, additional areas outside the 1-km boundary were also mapped but not included in the accuracy assessment. We expect the accuracy of the areas outside of the 1-km boundary to be consistent with those within. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with

  9. Role of the ATLAS Grid Information System (AGIS) in Distributed Data Analysis and Simulation

    NASA Astrophysics Data System (ADS)

    Anisenkov, A. V.

    2018-03-01

    In modern high-energy physics experiments, particular attention is paid to the global integration of information and computing resources into a unified system for efficient storage and processing of experimental data. Annually, the ATLAS experiment performed at the Large Hadron Collider at the European Organization for Nuclear Research (CERN) produces tens of petabytes raw data from the recording electronics and several petabytes of data from the simulation system. For processing and storage of such super-large volumes of data, the computing model of the ATLAS experiment is based on heterogeneous geographically distributed computing environment, which includes the worldwide LHC computing grid (WLCG) infrastructure and is able to meet the requirements of the experiment for processing huge data sets and provide a high degree of their accessibility (hundreds of petabytes). The paper considers the ATLAS grid information system (AGIS) used by the ATLAS collaboration to describe the topology and resources of the computing infrastructure, to configure and connect the high-level software systems of computer centers, to describe and store all possible parameters, control, configuration, and other auxiliary information required for the effective operation of the ATLAS distributed computing applications and services. The role of the AGIS system in the development of a unified description of the computing resources provided by grid sites, supercomputer centers, and cloud computing into a consistent information model for the ATLAS experiment is outlined. This approach has allowed the collaboration to extend the computing capabilities of the WLCG project and integrate the supercomputers and cloud computing platforms into the software components of the production and distributed analysis workload management system (PanDA, ATLAS).

  10. EnviroAtlas - Paterson, NJ - 51m Riparian Buffer Forest Cover

    EPA Pesticide Factsheets

    This EnviroAtlas dataset describes the percentage of a 51-m riparian buffer that is forested. There is a potential for decreased water quality in areas where the riparian buffer is less forested. The displayed line represents the center of the analyzed riparian buffer. The water bodies analyzed include hydrologically connected streams, rivers, connectors, reservoirs, lakes/ponds, ice masses, washes, locks, and rapids within the Atlas Area. EnviroAtlas defines tree buffer for this community as only trees and forest. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

  11. Concepts and Plans towards fast large scale Monte Carlo production for the ATLAS Experiment

    NASA Astrophysics Data System (ADS)

    Ritsch, E.; Atlas Collaboration

    2014-06-01

    The huge success of the physics program of the ATLAS experiment at the Large Hadron Collider (LHC) during Run 1 relies upon a great number of simulated Monte Carlo events. This Monte Carlo production takes the biggest part of the computing resources being in use by ATLAS as of now. In this document we describe the plans to overcome the computing resource limitations for large scale Monte Carlo production in the ATLAS Experiment for Run 2, and beyond. A number of fast detector simulation, digitization and reconstruction techniques are being discussed, based upon a new flexible detector simulation framework. To optimally benefit from these developments, a redesigned ATLAS MC production chain is presented at the end of this document.

  12. Vector and Scalar Bosons at DØ and ATLAS

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Lammers, Sabine Sabine

    2014-09-26

    Vector Boson Fusion (VBF) has never been measured in hadron collisions, but it is one of the most sensitive modes for low mass Standard Model Higgs production at ATLAS. The objective of this proposal is to measure VBF production of W and Z bosons at the DØ Experiment taking place at the Tevatron Collider near Chicago, Illinois, and at the ATLAS Experiment, running at the Large Hadron Collider in Geneva, Switzerland. The framework developed in these measurements will be used to discover and study the Higgs Boson produced through the same mechanism (VBF) at ATLAS. The 10 f b-1 datasetmore » recently collected by the DØ experiment provides a unique opportunity to observe evidence of VBF production of W Bosons, which will provide the required theoretical knowledge - VBF cross sections - and experimental knowledge - tuning of measurement techniques - on which to base the VBF measurements at the LHC. At the time of this writing, the ATLAS experiment has recorded 5 fb-1 of data at √s = 7 TeV, and expects to collect at least another 5 in 2012. Assuming Standard Model cross sections, this dataset will allow for the observation of VBF production of W, Z and Higgs bosons. The major challenges for the first observation of VBF interactions are: developing highly optimized forward jet identification algorithms, and accurately modeling both rates and kinematics of background processes. With the research program outlined in this grant proposal, I plan to address each of these areas, paving the way for VBF observation. The concentration on VBF production for the duration of this grant will be at ATLAS where the anticipated high pileup rates necessitates a cleaner signal. My past experience with forward jet identification at the ZEUS experiment, and with W+(n)Jets measurements at DØ , puts me in a unique position to lead this effort. The proposed program will have a dual focus: on DØ where the VBF analysis effort is mature and efforts of a postdoc will be required to bring

  13. The use of atlas registration and graph cuts for prostate segmentation in magnetic resonance images

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Korsager, Anne Sofie, E-mail: asko@hst.aau.dk; Østergaard, Lasse Riis; Fortunati, Valerio

    2015-04-15

    Purpose: An automatic method for 3D prostate segmentation in magnetic resonance (MR) images is presented for planning image-guided radiotherapy treatment of prostate cancer. Methods: A spatial prior based on intersubject atlas registration is combined with organ-specific intensity information in a graph cut segmentation framework. The segmentation is tested on 67 axial T{sub 2}-weighted MR images in a leave-one-out cross validation experiment and compared with both manual reference segmentations and with multiatlas-based segmentations using majority voting atlas fusion. The impact of atlas selection is investigated in both the traditional atlas-based segmentation and the new graph cut method that combines atlas andmore » intensity information in order to improve the segmentation accuracy. Best results were achieved using the method that combines intensity information, shape information, and atlas selection in the graph cut framework. Results: A mean Dice similarity coefficient (DSC) of 0.88 and a mean surface distance (MSD) of 1.45 mm with respect to the manual delineation were achieved. Conclusions: This approaches the interobserver DSC of 0.90 and interobserver MSD 0f 1.15 mm and is comparable to other studies performing prostate segmentation in MR.« less

  14. EnviroAtlas - Des Moines, IA - Near Road Block Group Summary

    EPA Pesticide Factsheets

    This EnviroAtlas dataset addresses the tree buffer along heavily traveled roads. The roads are interstates, arterials, and collectors within the EnviroAtlas community boundary. Forest is defined as Trees & Forest. Sufficient tree bufferage is defined as 25% coverage within the circular moving window with a radius of 14.5m at any given point along the roadway. There are potential negative health effects for those living in a location without a sufficient tree buffer. Those populations are estimated here using dasymetric data calculated for the EnviroAtlas. There are potential negative health effects for those living in a location without a sufficient tree buffer. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://enviroatlas.epa.gov/EnviroAtlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

  15. EnviroAtlas -Tampa, FL- One Meter Resolution Urban Land Cover (2010)

    EPA Pesticide Factsheets

    The EnviroAtlas Tampa, FL land cover map was generated from USDA NAIP (National Agricultural Imagery Program) four band (red, green, blue and near infrared) aerial photography from April-May 2010 at 1 m spatial resolution. Eight land cover classes were mapped: impervious surface, soil and barren, grass and herbaceous, trees and forest, water, agriculture, woody wetland, and emergent wetland. The area mapped is defined by the US Census Bureau's 2010 Urban Statistical Area for Tampa, and includes the cities of Clearwater and St. Petersburg, as well as additional out-lying areas. An accuracy assessment using a stratified random sampling of 600 samples (100 per class) yielded an overall accuracy of 70.67 percent and an area weighted accuracy of 81.87 percent using a minimum mapping unit of 9 pixels (3x3 pixel window). This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

  16. Computation of an MRI brain atlas from a population of Parkinson’s disease patients

    NASA Astrophysics Data System (ADS)

    Angelidakis, L.; Papageorgiou, I. E.; Damianou, C.; Psychogios, M. N.; Lingor, P.; von Eckardstein, K.; Hadjidemetriou, S.

    2017-11-01

    Parkinson’s Disease (PD) is a degenerative disorder of the brain. This study presents an MRI-based brain atlas of PD to characterize associated alterations for diagnostic and interventional purposes. The atlas standardizes primarily the implicated subcortical regions such as the globus pallidus (GP), substantia nigra (SN), subthalamic nucleus (STN), caudate nucleus (CN), thalamus (TH), putamen (PUT), and red nucleus (RN). The data were 3.0 T MRI brain images from 16 PD patients and 10 matched controls. The images used were T1-weighted (T 1 w), T2-weighted (T 2 w) images, and Susceptibility Weighted Images (SWI). The T1w images were the reference for the inter-subject non-rigid registration available from 3DSlicer. Anatomic labeling was achieved with BrainSuite and regions were refined with the level sets segmentation of ITK-Snap. The subcortical centers were analyzed for their volume and signal intensity. Comparison with an age-matched control group unravels a significant PD-related T1w signal loss in the striatum (CN and PUT) centers, but approximately a constant volume. The results in this study improve MRI based PD localization and can lead to the development of novel biomarkers.

  17. Topological cell clustering in the ATLAS calorimeters and its performance in LHC Run 1

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Aad, G.; Abbott, B.; Abdallah, J.

    The reconstruction of the signal from hadrons and jets emerging from the proton–proton collisions at the Large Hadron Collider (LHC) and entering the ATLAS calorimeters is based on a three-dimensional topological clustering of individual calorimeter cell signals. The cluster formation follows cell signal-significance patterns generated by electromagnetic and hadronic showers. In this, the clustering algorithm implicitly performs a topological noise suppression by removing cells with insignificant signals which are not in close proximity to cells with significant signals. The resulting topological cell clusters have shape and location information, which is exploited to apply a local energy calibration and corrections dependingmore » on the nature of the cluster. Lastly, topological cell clustering is established as a well-performing calorimeter signal definition for jet and missing transverse momentum reconstruction in ATLAS.« less

  18. Topological cell clustering in the ATLAS calorimeters and its performance in LHC Run 1

    DOE PAGES

    Aad, G.; Abbott, B.; Abdallah, J.; ...

    2017-07-24

    The reconstruction of the signal from hadrons and jets emerging from the proton–proton collisions at the Large Hadron Collider (LHC) and entering the ATLAS calorimeters is based on a three-dimensional topological clustering of individual calorimeter cell signals. The cluster formation follows cell signal-significance patterns generated by electromagnetic and hadronic showers. In this, the clustering algorithm implicitly performs a topological noise suppression by removing cells with insignificant signals which are not in close proximity to cells with significant signals. The resulting topological cell clusters have shape and location information, which is exploited to apply a local energy calibration and corrections dependingmore » on the nature of the cluster. Lastly, topological cell clustering is established as a well-performing calorimeter signal definition for jet and missing transverse momentum reconstruction in ATLAS.« less

  19. Dynamic updating atlas for heart segmentation with a nonlinear field-based model.

    PubMed

    Cai, Ken; Yang, Rongqian; Yue, Hongwei; Li, Lihua; Ou, Shanxing; Liu, Feng

    2017-09-01

    Segmentation of cardiac computed tomography (CT) images is an effective method for assessing the dynamic function of the heart and lungs. In the atlas-based heart segmentation approach, the quality of segmentation usually relies upon atlas images, and the selection of those reference images is a key step. The optimal goal in this selection process is to have the reference images as close to the target image as possible. This study proposes an atlas dynamic update algorithm using a scheme of nonlinear deformation field. The proposed method is based on the features among double-source CT (DSCT) slices. The extraction of these features will form a base to construct an average model and the created reference atlas image is updated during the registration process. A nonlinear field-based model was used to effectively implement a 4D cardiac segmentation. The proposed segmentation framework was validated with 14 4D cardiac CT sequences. The algorithm achieved an acceptable accuracy (1.0-2.8 mm). Our proposed method that combines a nonlinear field-based model and dynamic updating atlas strategies can provide an effective and accurate way for whole heart segmentation. The success of the proposed method largely relies on the effective use of the prior knowledge of the atlas and the similarity explored among the to-be-segmented DSCT sequences. Copyright © 2016 John Wiley & Sons, Ltd.

  20. EnviroAtlas -Milwaukee, WI- One Meter Resolution Urban Land Cover Data (2010)

    EPA Pesticide Factsheets

    The EnviroAtlas Milwaukee, WI land cover data and map were generated from USDA NAIP (National Agricultural Imagery Program) four band (red, green, blue and near infrared) aerial photography from Late Summer 2010 at 1 m spatial resolution. Nine land cover classes were mapped: water, impervious surfaces (dark and light), soil and barren land, trees and forest, grass and herbaceous non-woody vegetation, agriculture, and wetlands (woody and emergent). An accuracy assessment using a completely random sampling of 600 samples yielded an overall accuracy of 85.39% percent using a minimum mapping unit of 9 pixels (3x3 pixel window). The area mapped is defined by the US Census Bureau's 2010 Urban Statistical Area for Milwaukee. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

  1. EnviroAtlas -- Woodbine, IA -- One Meter Resolution Urban Land Cover Data (2011)

    EPA Pesticide Factsheets

    The EnviroAtlas Woodbine, IA land cover (LC) data and map were generated from USDA NAIP (National Agricultural Imagery Program) four band (red, green, blue and near infrared) aerial photography from Late Summer 2011 at 1 m spatial resolution. Six land cover classes were mapped: water, impervious surfaces (dark and light), soil and barren land, trees and forest, grass and herbaceous non-woody vegetation, and agriculture. An accuracy assessment using a completely random sampling of 600 samples yielded an overall accuracy of 87.03% percent using a minimum mapping unit of 9 pixels (3x3 pixel window). The area mapped is defined by the US Census Bureau's 2010 Urban Statistical Area for Woodbine. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

  2. A human brain atlas derived via n-cut parcellation of resting-state and task-based fMRI data

    PubMed Central

    James, G. Andrew; Hazaroglu, Onder; Bush, Keith A.

    2015-01-01

    The growth of functional MRI has led to development of human brain atlases derived by parcellating resting-state connectivity patterns into functionally independent regions of interest (ROIs). All functional atlases to date have been derived from resting-state fMRI data. But given that functional connectivity between regions varies with task, we hypothesized that an atlas incorporating both resting-state and task-based fMRI data would produce an atlas with finer characterization of task-relevant regions than an atlas derived from resting-state alone. To test this hypothesis, we derived parcellation atlases from twenty-nine healthy adult participants enrolled in the Cognitive Connectome project, an initiative to improve functional MRI’s translation into clinical decision-making by mapping normative variance in brain-behavior relationships. Participants underwent resting-state and task-based fMRI spanning nine cognitive domains: motor, visuospatial, attention, language, memory, affective processing, decision-making, working memory, and executive function. Spatially constrained n-cut parcellation derived brain atlases using (1) all participants’ functional data (Task) or (2) a single resting-state scan (Rest). An atlas was also derived from random parcellation for comparison purposes (Random). Two methods were compared: (1) a parcellation applied to the group’s mean edge weights (mean), and (2) a two-stage approach with parcellation of individual edge weights followed by parcellation of mean binarized edges (two-stage). The resulting Task and Rest atlases had significantly greater similarity with each other (mean Jaccard indices JI= 0.72–0.85) than with the Random atlases (JI=0.59–0.63; all p<0.001 after Bonferroni correction). Task and Rest atlas similarity was greatest for the two-stage method (JI=0.85), which has been shown as more robust than the mean method; these atlases also better reproduced voxelwise seed maps of the left dorsolateral prefrontal

  3. A human brain atlas derived via n-cut parcellation of resting-state and task-based fMRI data.

    PubMed

    James, George Andrew; Hazaroglu, Onder; Bush, Keith A

    2016-02-01

    The growth of functional MRI has led to development of human brain atlases derived by parcellating resting-state connectivity patterns into functionally independent regions of interest (ROIs). All functional atlases to date have been derived from resting-state fMRI data. But given that functional connectivity between regions varies with task, we hypothesized that an atlas incorporating both resting-state and task-based fMRI data would produce an atlas with finer characterization of task-relevant regions than an atlas derived from resting-state alone. To test this hypothesis, we derived parcellation atlases from twenty-nine healthy adult participants enrolled in the Cognitive Connectome project, an initiative to improve functional MRI's translation into clinical decision-making by mapping normative variance in brain-behavior relationships. Participants underwent resting-state and task-based fMRI spanning nine cognitive domains: motor, visuospatial, attention, language, memory, affective processing, decision-making, working memory, and executive function. Spatially constrained n-cut parcellation derived brain atlases using (1) all participants' functional data (Task) or (2) a single resting-state scan (Rest). An atlas was also derived from random parcellation for comparison purposes (Random). Two methods were compared: (1) a parcellation applied to the group's mean edge weights (mean), and (2) a two-stage approach with parcellation of individual edge weights followed by parcellation of mean binarized edges (two-stage). The resulting Task and Rest atlases had significantly greater similarity with each other (mean Jaccard indices JI=0.72-0.85) than with the Random atlases (JI=0.59-0.63; all p<0.001 after Bonferroni correction). Task and Rest atlas similarity was greatest for the two-stage method (JI=0.85), which has been shown as more robust than the mean method; these atlases also better reproduced voxelwise seed maps of the left dorsolateral prefrontal cortex during

  4. The new ATLAS Fast Calorimeter Simulation

    NASA Astrophysics Data System (ADS)

    Schaarschmidt, J.; ATLAS Collaboration

    2017-10-01

    Current and future need for large scale simulated samples motivate the development of reliable fast simulation techniques. The new Fast Calorimeter Simulation is an improved parameterized response of single particles in the ATLAS calorimeter that aims to accurately emulate the key features of the detailed calorimeter response as simulated with Geant4, yet approximately ten times faster. Principal component analysis and machine learning techniques are used to improve the performance and decrease the memory need compared to the current version of the ATLAS Fast Calorimeter Simulation. A prototype of this new Fast Calorimeter Simulation is in development and its integration into the ATLAS simulation infrastructure is ongoing.

  5. EnviroAtlas -Milwaukee, WI- One Meter Resolution Urban Land Cover Data (2010) Web Service

    EPA Pesticide Factsheets

    This EnviroAtlas web service supports research and online mapping activities related to EnviroAtlas (https://www.epa.gov/enviroatlas). The EnviroAtlas Milwaukee, WI land cover data and map were generated from USDA NAIP (National Agricultural Imagery Program) four band (red, green, blue and near infrared) aerial photography from Late Summer 2010 at 1 m spatial resolution. Nine land cover classes were mapped: water, impervious surfaces (dark and light), soil and barren land, trees and forest, grass and herbaceous non-woody vegetation, agriculture, and wetlands (woody and emergent). An accuracy assessment using a completely random sampling of 600 samples yielded an overall accuracy of 85.39% percent using a minimum mapping unit of 9 pixels (3x3 pixel window). The area mapped is defined by the US Census Bureau's 2010 Urban Statistical Area for Milwaukee. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-

  6. EnviroAtlas -- Woodbine, IA -- One Meter Resolution Urban Land Cover Data (2011) Web Service

    EPA Pesticide Factsheets

    This EnviroAtlas web service supports research and online mapping activities related to EnviroAtlas (https://www.epa.gov/enviroatlas). The EnviroAtlas Woodbine, IA land cover (LC) data and map were generated from USDA NAIP (National Agricultural Imagery Program) four band (red, green, blue and near infrared) aerial photography from Late Summer 2011 at 1 m spatial resolution. Six land cover classes were mapped: water, impervious surfaces (dark and light), soil and barren land, trees and forest, grass and herbaceous non-woody vegetation, and agriculture. An accuracy assessment using a completely random sampling of 600 samples yielded an overall accuracy of 87.03% percent using a minimum mapping unit of 9 pixels (3x3 pixel window). The area mapped is defined by the US Census Bureau's 2010 Urban Statistical Area for Woodbine. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

  7. EnviroAtlas - Portland, OR - Ecosystem Services by Block Group

    EPA Pesticide Factsheets

    This EnviroAtlas dataset presents environmental benefits of the urban forest in 1176 block groups in Portland, Oregon. Carbon attributes, temperature reduction, pollution removal and value, and runoff effects are calculated for each block group using i-Tree models (www.itreetools.org), local weather data, pollution data, EPA provided city boundary and land cover data, and U.S. Census derived block group boundary data. This dataset was produced by the US Forest Service to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (http:/www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

  8. EnviroAtlas - Fresno, CA - Ecosystem Services by Block Group

    EPA Pesticide Factsheets

    This EnviroAtlas dataset presents environmental benefits of the urban forest in 405 block groups in Fresno, California. Carbon attributes, temperature reduction, pollution removal and value, and runoff effects are calculated for each block group using i-Tree models (www.itreetools.org), local weather data, pollution data, EPA provided city boundary and land cover data, and U.S. Census derived block group boundary data. This dataset was produced by the US Forest Service to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

  9. EnviroAtlas - Portland, ME - Ecosystem Services by Block Group

    EPA Pesticide Factsheets

    This EnviroAtlas dataset presents environmental benefits of the urban forest in 146 block groups in Portland, Maine. Carbon attributes, temperature reduction, pollution removal and value, and runoff effects are calculated for each block group using i-Tree models (www.itreetools.org), local weather data, pollution data, EPA provided city boundary and land cover data, and U.S. Census derived block group boundary data. This dataset was produced by the US Forest Service to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

  10. EnviroAtlas - Memphis, TN - Ecosystem Services by Block Group

    EPA Pesticide Factsheets

    This EnviroAtlas dataset presents environmental benefits of the urban forest in 703 block groups in Memphis, Tennessee. Carbon attributes, temperature reduction, pollution removal and value, and runoff effects are calculated for each block group using i-Tree models (www.itreetools.org), local weather data, pollution data, EPA provided city boundary and land cover data, and U.S. Census derived block group boundary data. This dataset was produced by the US Forest Service to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

  11. EnviroAtlas - Austin, TX - Land Cover by Block Group

    EPA Pesticide Factsheets

    This EnviroAtlas dataset describes the percentage of each block group that is classified as impervious, forest, green space, and agriculture. Forest is defined as Trees & Forest. Green space is defined as Trees & Forest, Grass & Herbaceous, and Agriculture. This dataset also includes the area per capita for each block group for some land cover types. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

  12. EnviroAtlas - Austin, TX - Ecosystem Services by Block Group

    EPA Pesticide Factsheets

    This EnviroAtlas dataset presents environmental benefits of the urban forest in 750 block groups in Austin, Texas. Carbon attributes, temperature reduction, pollution removal and value, and runoff effects are calculated for each block group using i-Tree models (www.itreetools.org), local weather data, pollution data, EPA provided city boundary and land cover data, and U.S. Census derived block group boundary data. This dataset was produced by the US Forest Service to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

  13. EnviroAtlas - Austin, TX - Greenspace Around Schools by Block Group

    EPA Pesticide Factsheets

    This EnviroAtlas data set shows the number of schools in each block group in the EnviroAtlas community boundary as well as the number of schools where less than 25% of the area within 100 meters of the school is classified as greenspace. Green space is defined as Trees & Forest, Grass & Herbaceous, and Agriculture. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

  14. From the European indoor radon map towards an atlas of natural radiation.

    PubMed

    Tollefsen, T; Cinelli, G; Bossew, P; Gruber, V; De Cort, M

    2014-11-01

    In 2006, the Joint Research Centre of the European Commission launched a project to map radon at the European level, as part of a planned European Atlas of Natural Radiation. It started with a map of indoor radon concentrations. As of May 2014, this map includes data from 24 countries, covering a fair part of Europe. Next, a European map of geogenic radon, intended to show 'what earth delivers' in terms of radon potential (RP), was started in 2008. A first trial map has been created, and a database was established to collect all available data relevant to the RP. The Atlas should eventually display the geographical distribution of physical quantities related to natural radiation. In addition to radon, it will comprise maps of quantities such as cosmic rays and terrestrial gamma radiation. In this paper, the authors present the current state of the radon maps and the Atlas. © The Author 2014. Published by Oxford University Press.

  15. Brain templates and atlases.

    PubMed

    Evans, Alan C; Janke, Andrew L; Collins, D Louis; Baillet, Sylvain

    2012-08-15

    The core concept within the field of brain mapping is the use of a standardized, or "stereotaxic", 3D coordinate frame for data analysis and reporting of findings from neuroimaging experiments. This simple construct allows brain researchers to combine data from many subjects such that group-averaged signals, be they structural or functional, can be detected above the background noise that would swamp subtle signals from any single subject. Where the signal is robust enough to be detected in individuals, it allows for the exploration of inter-individual variance in the location of that signal. From a larger perspective, it provides a powerful medium for comparison and/or combination of brain mapping findings from different imaging modalities and laboratories around the world. Finally, it provides a framework for the creation of large-scale neuroimaging databases or "atlases" that capture the population mean and variance in anatomical or physiological metrics as a function of age or disease. However, while the above benefits are not in question at first order, there are a number of conceptual and practical challenges that introduce second-order incompatibilities among experimental data. Stereotaxic mapping requires two basic components: (i) the specification of the 3D stereotaxic coordinate space, and (ii) a mapping function that transforms a 3D brain image from "native" space, i.e. the coordinate frame of the scanner at data acquisition, to that stereotaxic space. The first component is usually expressed by the choice of a representative 3D MR image that serves as target "template" or atlas. The native image is re-sampled from native to stereotaxic space under the mapping function that may have few or many degrees of freedom, depending upon the experimental design. The optimal choice of atlas template and mapping function depend upon considerations of age, gender, hemispheric asymmetry, anatomical correspondence, spatial normalization methodology and disease

  16. Interoperability Between Coastal Web Atlases Using Semantic Mediation: A Case Study of the International Coastal Atlas Network (ICAN)

    NASA Astrophysics Data System (ADS)

    Wright, D. J.; Lassoued, Y.; Dwyer, N.; Haddad, T.; Bermudez, L. E.; Dunne, D.

    2009-12-01

    Coastal mapping plays an important role in informing marine spatial planning, resource management, maritime safety, hazard assessment and even national sovereignty. As such, there is now a plethora of data/metadata catalogs, pre-made maps, tabular and text information on resource availability and exploitation, and decision-making tools. A recent trend has been to encapsulate these in a special class of web-enabled geographic information systems called a coastal web atlas (CWA). While multiple benefits are derived from tailor-made atlases, there is great value added from the integration of disparate CWAs. CWAs linked to one another can query more successfully to optimize planning and decision-making. If a dataset is missing in one atlas, it may be immediately located in another. Similar datasets in two atlases may be combined to enhance study in either region. *But how best to achieve semantic interoperability to mitigate vague data queries, concepts or natural language semantics when retrieving and integrating data and information?* We report on the development of a new prototype seeking to interoperate between two initial CWAs: the Marine Irish Digital Atlas (MIDA) and the Oregon Coastal Atlas (OCA). These two mature atlases are used as a testbed for more regional connections, with the intent for the OCA to use lessons learned to develop a regional network of CWAs along the west coast, and for MIDA to do the same in building and strengthening atlas networks with the UK, Belgium, and other parts of Europe. Our prototype uses semantic interoperability via services harmonization and ontology mediation, allowing local atlases to use their own data structures, and vocabularies (ontologies). We use standard technologies such as OGC Web Map Services (WMS) for delivering maps, and OGC Catalogue Service for the Web (CSW) for delivering and querying ISO-19139 metadata. The metadata records of a given CWA use a given ontology of terms called local ontology. Human or machine

  17. Search for Higgs and Z Boson Decays to ϕ γ with the ATLAS Detector

    NASA Astrophysics Data System (ADS)

    Aaboud, M.; Aad, G.; Abbott, B.; Abdallah, J.; Abdinov, O.; Abeloos, B.; Aben, R.; Abouzeid, O. S.; Abraham, N. L.; Abramowicz, H.; Abreu, H.; Abreu, R.; Abulaiti, Y.; Acharya, B. S.; Adamczyk, L.; Adams, D. L.; Adelman, J.; Adomeit, S.; Adye, T.; Affolder, A. A.; Agatonovic-Jovin, T.; Agricola, J.; Aguilar-Saavedra, J. A.; Ahlen, S. P.; Ahmadov, F.; Aielli, G.; Akerstedt, H.; Åkesson, T. P. A.; Akimov, A. V.; Alberghi, G. L.; Albert, J.; Albrand, S.; Alconada Verzini, M. J.; Aleksa, M.; Aleksandrov, I. N.; Alexa, C.; Alexander, G.; Alexopoulos, T.; Alhroob, M.; Ali, B.; Aliev, M.; Alimonti, G.; Alison, J.; Alkire, S. P.; Allbrooke, B. M. M.; Allen, B. W.; Allport, P. P.; Aloisio, A.; Alonso, A.; Alonso, F.; Alpigiani, C.; Alstaty, M.; Alvarez Gonzalez, B.; Álvarez Piqueras, D.; Alviggi, M. G.; Amadio, B. T.; Amako, K.; Amaral Coutinho, Y.; Amelung, C.; Amidei, D.; Amor Dos Santos, S. P.; Amorim, A.; Amoroso, S.; Amundsen, G.; Anastopoulos, C.; Ancu, L. S.; Andari, N.; Andeen, T.; Anders, C. F.; Anders, G.; Anders, J. K.; Anderson, K. J.; Andreazza, A.; Andrei, V.; Angelidakis, S.; Angelozzi, I.; Anger, P.; Angerami, A.; Anghinolfi, F.; Anisenkov, A. V.; Anjos, N.; Annovi, A.; Antel, C.; Antonelli, M.; Antonov, A.; Anulli, F.; Aoki, M.; Aperio Bella, L.; Arabidze, G.; Arai, Y.; Araque, J. P.; Arce, A. T. H.; Arduh, F. A.; Arguin, J.-F.; Argyropoulos, S.; Arik, M.; Armbruster, A. J.; Armitage, L. J.; Arnaez, O.; Arnold, H.; Arratia, M.; Arslan, O.; Artamonov, A.; Artoni, G.; Artz, S.; Asai, S.; Asbah, N.; Ashkenazi, A.; Åsman, B.; Asquith, L.; Assamagan, K.; Astalos, R.; Atkinson, M.; Atlay, N. B.; Augsten, K.; Avolio, G.; Axen, B.; Ayoub, M. K.; Azuelos, G.; Baak, M. A.; Baas, A. E.; Baca, M. J.; Bachacou, H.; Bachas, K.; Backes, M.; Backhaus, M.; Bagiacchi, P.; Bagnaia, P.; Bai, Y.; Baines, J. T.; Baker, O. K.; Baldin, E. M.; Balek, P.; Balestri, T.; Balli, F.; Balunas, W. K.; Banas, E.; Banerjee, Sw.; Bannoura, A. A. E.; Barak, L.; Barberio, E. L.; Barberis, D.; Barbero, M.; Barillari, T.; Barisits, M.-S.; Barklow, T.; Barlow, N.; Barnes, S. L.; Barnett, B. M.; Barnett, R. M.; Barnovska, Z.; Baroncelli, A.; Barone, G.; Barr, A. J.; Barranco Navarro, L.; Barreiro, F.; Barreiro Guimarães da Costa, J.; Bartoldus, R.; Barton, A. E.; Bartos, P.; Basalaev, A.; Bassalat, A.; Bates, R. L.; Batista, S. J.; Batley, J. R.; Battaglia, M.; Bauce, M.; Bauer, F.; Bawa, H. S.; Beacham, J. B.; Beattie, M. D.; Beau, T.; Beauchemin, P. H.; Bechtle, P.; Beck, H. P.; Becker, K.; Becker, M.; Beckingham, M.; Becot, C.; Beddall, A. J.; Beddall, A.; Bednyakov, V. A.; Bedognetti, M.; Bee, C. P.; Beemster, L. J.; Beermann, T. A.; Begel, M.; Behr, J. K.; Belanger-Champagne, C.; Bell, A. S.; Bella, G.; Bellagamba, L.; Bellerive, A.; Bellomo, M.; Belotskiy, K.; Beltramello, O.; Belyaev, N. L.; Benary, O.; Benchekroun, D.; Bender, M.; Bendtz, K.; Benekos, N.; Benhammou, Y.; Benhar Noccioli, E.; Benitez, J.; Benjamin, D. P.; Bensinger, J. R.; Bentvelsen, S.; Beresford, L.; Beretta, M.; Berge, D.; Bergeaas Kuutmann, E.; Berger, N.; Beringer, J.; Berlendis, S.; Bernard, N. R.; Bernius, C.; Bernlochner, F. U.; Berry, T.; Berta, P.; Bertella, C.; Bertoli, G.; Bertolucci, F.; Bertram, I. A.; Bertsche, C.; Bertsche, D.; Besjes, G. J.; Bessidskaia Bylund, O.; Bessner, M.; Besson, N.; Betancourt, C.; Bethani, A.; Bethke, S.; Bevan, A. J.; Bianchi, R. M.; Bianchini, L.; Bianco, M.; Biebel, O.; Biedermann, D.; Bielski, R.; Biesuz, N. V.; Biglietti, M.; Bilbao de Mendizabal, J.; Billoud, T. R. V.; Bilokon, H.; Bindi, M.; Binet, S.; Bingul, A.; Bini, C.; Biondi, S.; Bisanz, T.; Bjergaard, D. M.; Black, C. W.; Black, J. E.; Black, K. M.; Blackburn, D.; Blair, R. E.; Blanchard, J.-B.; Blazek, T.; Bloch, I.; Blocker, C.; Blum, W.; Blumenschein, U.; Blunier, S.; Bobbink, G. J.; Bobrovnikov, V. S.; Bocchetta, S. S.; Bocci, A.; Bock, C.; Boehler, M.; Boerner, D.; Bogaerts, J. A.; Bogavac, D.; Bogdanchikov, A. G.; Bohm, C.; Boisvert, V.; Bokan, P.; Bold, T.; Boldyrev, A. S.; Bomben, M.; Bona, M.; Boonekamp, M.; Borisov, A.; Borissov, G.; Bortfeldt, J.; Bortoletto, D.; Bortolotto, V.; Bos, K.; Boscherini, D.; Bosman, M.; Bossio Sola, J. D.; Boudreau, J.; Bouffard, J.; Bouhova-Thacker, E. V.; Boumediene, D.; Bourdarios, C.; Boutle, S. K.; Boveia, A.; Boyd, J.; Boyko, I. R.; Bracinik, J.; Brandt, A.; Brandt, G.; Brandt, O.; Bratzler, U.; Brau, B.; Brau, J. E.; Braun, H. M.; Breaden Madden, W. D.; Brendlinger, K.; Brennan, A. J.; Brenner, L.; Brenner, R.; Bressler, S.; Bristow, T. M.; Britton, D.; Britzger, D.; Brochu, F. M.; Brock, I.; Brock, R.; Brooijmans, G.; Brooks, T.; Brooks, W. K.; Brosamer, J.; Brost, E.; Broughton, J. H.; Bruckman de Renstrom, P. A.; Bruncko, D.; Bruneliere, R.; Bruni, A.; Bruni, G.; Bruni, L. S.; Brunt, Bh; Bruschi, M.; Bruscino, N.; Bryant, P.; Bryngemark, L.; Buanes, T.; Buat, Q.; Buchholz, P.; Buckley, A. G.; Budagov, I. A.; Buehrer, F.; Bugge, M. K.; Bulekov, O.; Bullock, D.; Burckhart, H.; Burdin, S.; Burgard, C. D.; Burghgrave, B.; Burka, K.; Burke, S.; Burmeister, I.; Burr, J. T. P.; Busato, E.; Büscher, D.; Büscher, V.; Bussey, P.; Butler, J. M.; Buttar, C. M.; Butterworth, J. M.; Butti, P.; Buttinger, W.; Buzatu, A.; Buzykaev, A. R.; Cabrera Urbán, S.; Caforio, D.; Cairo, V. M.; Cakir, O.; Calace, N.; Calafiura, P.; Calandri, A.; Calderini, G.; Calfayan, P.; Callea, G.; Caloba, L. P.; Calvente Lopez, S.; Calvet, D.; Calvet, S.; Calvet, T. P.; Camacho Toro, R.; Camarda, S.; Camarri, P.; Cameron, D.; Caminal Armadans, R.; Camincher, C.; Campana, S.; Campanelli, M.; Camplani, A.; Campoverde, A.; Canale, V.; Canepa, A.; Cano Bret, M.; Cantero, J.; Cantrill, R.; Cao, T.; Capeans Garrido, M. D. M.; Caprini, I.; Caprini, M.; Capua, M.; Caputo, R.; Carbone, R. M.; Cardarelli, R.; Cardillo, F.; Carli, I.; Carli, T.; Carlino, G.; Carminati, L.; Caron, S.; Carquin, E.; Carrillo-Montoya, G. D.; Carter, J. R.; Carvalho, J.; Casadei, D.; Casado, M. P.; Casolino, M.; Casper, D. W.; Castaneda-Miranda, E.; Castelijn, R.; Castelli, A.; Castillo Gimenez, V.; Castro, N. F.; Catinaccio, A.; Catmore, J. R.; Cattai, A.; Caudron, J.; Cavaliere, V.; Cavallaro, E.; Cavalli, D.; Cavalli-Sforza, M.; Cavasinni, V.; Ceradini, F.; Cerda Alberich, L.; Cerio, B. C.; Cerqueira, A. S.; Cerri, A.; Cerrito, L.; Cerutti, F.; Cerv, M.; Cervelli, A.; Cetin, S. A.; Chafaq, A.; Chakraborty, D.; Chan, S. K.; Chan, Y. L.; Chang, P.; Chapman, J. D.; Charlton, D. G.; Chatterjee, A.; Chau, C. C.; Chavez Barajas, C. A.; Che, S.; Cheatham, S.; Chegwidden, A.; Chekanov, S.; Chekulaev, S. V.; Chelkov, G. A.; Chelstowska, M. A.; Chen, C.; Chen, H.; Chen, K.; Chen, S.; Chen, S.; Chen, X.; Chen, Y.; Cheng, H. C.; Cheng, H. J.; Cheng, Y.; Cheplakov, A.; Cheremushkina, E.; Cherkaoui El Moursli, R.; Chernyatin, V.; Cheu, E.; Chevalier, L.; Chiarella, V.; Chiarelli, G.; Chiodini, G.; Chisholm, A. S.; Chitan, A.; Chizhov, M. V.; Choi, K.; Chomont, A. R.; Chouridou, S.; Chow, B. K. B.; Christodoulou, V.; Chromek-Burckhart, D.; Chudoba, J.; Chuinard, A. J.; Chwastowski, J. J.; Chytka, L.; Ciapetti, G.; Ciftci, A. K.; Cinca, D.; Cindro, V.; Cioara, I. A.; Ciocca, C.; Ciocio, A.; Cirotto, F.; Citron, Z. H.; Citterio, M.; Ciubancan, M.; Clark, A.; Clark, B. L.; Clark, M. R.; Clark, P. J.; Clarke, R. N.; Clement, C.; Coadou, Y.; Cobal, M.; Coccaro, A.; Cochran, J.; Colasurdo, L.; Cole, B.; Colijn, A. P.; Collot, J.; Colombo, T.; Compostella, G.; Conde Muiño, P.; Coniavitis, E.; Connell, S. H.; Connelly, I. A.; Consorti, V.; Constantinescu, S.; Conti, G.; Conventi, F.; Cooke, M.; Cooper, B. D.; Cooper-Sarkar, A. M.; Cormier, K. J. R.; Cornelissen, T.; Corradi, M.; Corriveau, F.; Corso-Radu, A.; Cortes-Gonzalez, A.; Cortiana, G.; Costa, G.; Costa, M. J.; Costanzo, D.; Cottin, G.; Cowan, G.; Cox, B. E.; Cranmer, K.; Crawley, S. J.; Cree, G.; Crépé-Renaudin, S.; Crescioli, F.; Cribbs, W. A.; Crispin Ortuzar, M.; Cristinziani, M.; Croft, V.; Crosetti, G.; Cueto, A.; Cuhadar Donszelmann, T.; Cummings, J.; Curatolo, M.; Cúth, J.; Czirr, H.; Czodrowski, P.; D'Amen, G.; D'Auria, S.; D'Onofrio, M.; da Cunha Sargedas de Sousa, M. J.; da Via, C.; Dabrowski, W.; Dado, T.; Dai, T.; Dale, O.; Dallaire, F.; Dallapiccola, C.; Dam, M.; Dandoy, J. R.; Dang, N. P.; Daniells, A. C.; Dann, N. S.; Danninger, M.; Dano Hoffmann, M.; Dao, V.; Darbo, G.; Darmora, S.; Dassoulas, J.; Dattagupta, A.; Davey, W.; David, C.; Davidek, T.; Davies, M.; Davison, P.; Dawe, E.; Dawson, I.; Daya-Ishmukhametova, R. K.; de, K.; de Asmundis, R.; de Benedetti, A.; de Castro, S.; de Cecco, S.; de Groot, N.; de Jong, P.; de la Torre, H.; de Lorenzi, F.; de Maria, A.; de Pedis, D.; de Salvo, A.; de Sanctis, U.; de Santo, A.; de Vivie de Regie, J. B.; Dearnaley, W. J.; Debbe, R.; Debenedetti, C.; Dedovich, D. V.; Dehghanian, N.; Deigaard, I.; Del Gaudio, M.; Del Peso, J.; Del Prete, T.; Delgove, D.; Deliot, F.; Delitzsch, C. M.; Dell'Acqua, A.; Dell'Asta, L.; Dell'Orso, M.; Della Pietra, M.; Della Volpe, D.; Delmastro, M.; Delsart, P. A.; Demarco, D. A.; Demers, S.; Demichev, M.; Demilly, A.; Denisov, S. P.; Denysiuk, D.; Derendarz, D.; Derkaoui, J. E.; Derue, F.; Dervan, P.; Desch, K.; Deterre, C.; Dette, K.; Deviveiros, P. O.; Dewhurst, A.; Dhaliwal, S.; di Ciaccio, A.; di Ciaccio, L.; di Clemente, W. K.; di Donato, C.; di Girolamo, A.; di Girolamo, B.; di Micco, B.; di Nardo, R.; di Simone, A.; di Sipio, R.; di Valentino, D.; Diaconu, C.; Diamond, M.; Dias, F. A.; Diaz, M. A.; Diehl, E. B.; Dietrich, J.; Diglio, S.; Dimitrievska, A.; Dingfelder, J.; Dita, P.; Dita, S.; Dittus, F.; Djama, F.; Djobava, T.; Djuvsland, J. I.; Do Vale, M. A. B.; Dobos, D.; Dobre, M.; Doglioni, C.; Dolejsi, J.; Dolezal, Z.; Donadelli, M.; Donati, S.; Dondero, P.; Donini, J.; Dopke, J.; Doria, A.; Dova, M. T.; Doyle, A. T.; Drechsler, E.; Dris, M.; Du, Y.; Duarte-Campderros, J.; Duchovni, E.; Duckeck, G.; Ducu, O. A.; Duda, D.; Dudarev, A.; Dudder, A. Chr.; Duffield, E. M.; Duflot, L.; Dührssen, M.; Dumancic, M.; Dunford, M.; Duran Yildiz, H.; Düren, M.; Durglishvili, A.; Duschinger, D.; Dutta, B.; Dyndal, M.; Eckardt, C.; Ecker, K. M.; Edgar, R. C.; Edwards, N. C.; Eifert, T.; Eigen, G.; Einsweiler, K.; Ekelof, T.; El Kacimi, M.; Ellajosyula, V.; Ellert, M.; Elles, S.; Ellinghaus, F.; Elliot, A. A.; Ellis, N.; Elmsheuser, J.; Elsing, M.; Emeliyanov, D.; Enari, Y.; Endner, O. C.; Ennis, J. S.; Erdmann, J.; Ereditato, A.; Ernis, G.; Ernst, J.; Ernst, M.; Errede, S.; Ertel, E.; Escalier, M.; Esch, H.; Escobar, C.; Esposito, B.; Etienvre, A. I.; Etzion, E.; Evans, H.; Ezhilov, A.; Fabbri, F.; Fabbri, L.; Facini, G.; Fakhrutdinov, R. M.; Falciano, S.; Falla, R. J.; Faltova, J.; Fang, Y.; Fanti, M.; Farbin, A.; Farilla, A.; Farina, C.; Farina, E. M.; Farooque, T.; Farrell, S.; Farrington, S. M.; Farthouat, P.; Fassi, F.; Fassnacht, P.; Fassouliotis, D.; Faucci Giannelli, M.; Favareto, A.; Fawcett, W. J.; Fayard, L.; Fedin, O. L.; Fedorko, W.; Feigl, S.; Feligioni, L.; Feng, C.; Feng, E. J.; Feng, H.; Fenyuk, A. B.; Feremenga, L.; Fernandez Martinez, P.; Fernandez Perez, S.; Ferrando, J.; Ferrari, A.; Ferrari, P.; Ferrari, R.; Ferreira de Lima, D. E.; Ferrer, A.; Ferrere, D.; Ferretti, C.; Ferretto Parodi, A.; Fiedler, F.; Filipčič, A.; Filipuzzi, M.; Filthaut, F.; Fincke-Keeler, M.; Finelli, K. D.; Fiolhais, M. C. N.; Fiorini, L.; Firan, A.; Fischer, A.; Fischer, C.; Fischer, J.; Fisher, W. C.; Flaschel, N.; Fleck, I.; Fleischmann, P.; Fletcher, G. T.; Fletcher, R. R. M.; Flick, T.; Floderus, A.; Flores Castillo, L. R.; Flowerdew, M. J.; Forcolin, G. T.; Formica, A.; Forti, A.; Foster, A. G.; Fournier, D.; Fox, H.; Fracchia, S.; Francavilla, P.; Franchini, M.; Francis, D.; Franconi, L.; Franklin, M.; Frate, M.; Fraternali, M.; Freeborn, D.; Fressard-Batraneanu, S. M.; Friedrich, F.; Froidevaux, D.; Frost, J. A.; Fukunaga, C.; Fullana Torregrosa, E.; Fusayasu, T.; Fuster, J.; Gabaldon, C.; Gabizon, O.; Gabrielli, A.; Gabrielli, A.; Gach, G. P.; Gadatsch, S.; Gadomski, S.; Gagliardi, G.; Gagnon, L. G.; Gagnon, P.; Galea, C.; Galhardo, B.; Gallas, E. J.; Gallop, B. J.; Gallus, P.; Galster, G.; Gan, K. K.; Gao, J.; Gao, Y.; Gao, Y. S.; Garay Walls, F. M.; García, C.; García Navarro, J. E.; Garcia-Sciveres, M.; Gardner, R. W.; Garelli, N.; Garonne, V.; Gascon Bravo, A.; Gasnikova, K.; Gatti, C.; Gaudiello, A.; Gaudio, G.; Gauthier, L.; Gavrilenko, I. L.; Gay, C.; Gaycken, G.; Gazis, E. N.; Gecse, Z.; Gee, C. N. P.; Geich-Gimbel, Ch.; Geisen, M.; Geisler, M. P.; Gemme, C.; Genest, M. H.; Geng, C.; Gentile, S.; Gentsos, C.; George, S.; Gerbaudo, D.; Gershon, A.; Ghasemi, S.; Ghazlane, H.; Ghneimat, M.; Giacobbe, B.; Giagu, S.; Giannetti, P.; Gibbard, B.; Gibson, S. M.; Gignac, M.; Gilchriese, M.; Gillam, T. P. S.; Gillberg, D.; Gilles, G.; Gingrich, D. M.; Giokaris, N.; Giordani, M. P.; Giorgi, F. M.; Giorgi, F. M.; Giraud, P. F.; Giromini, P.; Giugni, D.; Giuli, F.; Giuliani, C.; Giulini, M.; Gjelsten, B. K.; Gkaitatzis, S.; Gkialas, I.; Gkougkousis, E. L.; Gladilin, L. K.; Glasman, C.; Glatzer, J.; Glaysher, P. C. F.; Glazov, A.; Goblirsch-Kolb, M.; Godlewski, J.; Goldfarb, S.; Golling, T.; Golubkov, D.; Gomes, A.; Gonçalo, R.; Goncalves Pinto Firmino da Costa, J.; Gonella, G.; Gonella, L.; Gongadze, A.; González de La Hoz, S.; Gonzalez Parra, G.; Gonzalez-Sevilla, S.; Goossens, L.; Gorbounov, P. A.; Gordon, H. A.; Gorelov, I.; Gorini, B.; Gorini, E.; Gorišek, A.; Gornicki, E.; Goshaw, A. T.; Gössling, C.; Gostkin, M. I.; Goudet, C. R.; Goujdami, D.; Goussiou, A. G.; Govender, N.; Gozani, E.; Graber, L.; Grabowska-Bold, I.; Gradin, P. O. J.; Grafström, P.; Gramling, J.; Gramstad, E.; Grancagnolo, S.; Gratchev, V.; Gravila, P. M.; Gray, H. M.; Graziani, E.; Greenwood, Z. D.; Grefe, C.; Gregersen, K.; Gregor, I. M.; Grenier, P.; Grevtsov, K.; Griffiths, J.; Grillo, A. A.; Grimm, K.; Grinstein, S.; Gris, Ph.; Grivaz, J.-F.; Groh, S.; Grohs, J. P.; Gross, E.; Grosse-Knetter, J.; Grossi, G. C.; Grout, Z. J.; Guan, L.; Guan, W.; Guenther, J.; Guescini, F.; Guest, D.; Gueta, O.; Guido, E.; Guillemin, T.; Guindon, S.; Gul, U.; Gumpert, C.; Guo, J.; Guo, Y.; Gupta, R.; Gupta, S.; Gustavino, G.; Gutierrez, P.; Gutierrez Ortiz, N. G.; Gutschow, C.; Guyot, C.; Gwenlan, C.; Gwilliam, C. B.; Haas, A.; Haber, C.; Hadavand, H. K.; Haddad, N.; Hadef, A.; Hageböck, S.; Hajduk, Z.; Hakobyan, H.; Haleem, M.; Haley, J.; Halladjian, G.; Hallewell, G. D.; Hamacher, K.; Hamal, P.; Hamano, K.; Hamilton, A.; Hamity, G. N.; Hamnett, P. G.; Han, L.; Hanagaki, K.; Hanawa, K.; Hance, M.; Haney, B.; Hanisch, S.; Hanke, P.; Hanna, R.; Hansen, J. B.; Hansen, J. D.; Hansen, M. C.; Hansen, P. H.; Hara, K.; Hard, A. S.; Harenberg, T.; Hariri, F.; Harkusha, S.; Harrington, R. D.; Harrison, P. F.; Hartjes, F.; Hartmann, N. M.; Hasegawa, M.; Hasegawa, Y.; Hasib, A.; Hassani, S.; Haug, S.; Hauser, R.; Hauswald, L.; Havranek, M.; Hawkes, C. M.; Hawkings, R. J.; Hayakawa, D.; Hayden, D.; Hays, C. P.; Hays, J. M.; Hayward, H. S.; Haywood, S. J.; Head, S. J.; Heck, T.; Hedberg, V.; Heelan, L.; Heim, S.; Heim, T.; Heinemann, B.; Heinrich, J. J.; Heinrich, L.; Heinz, C.; Hejbal, J.; Helary, L.; Hellman, S.; Helsens, C.; Henderson, J.; Henderson, R. C. W.; Heng, Y.; Henkelmann, S.; Henriques Correia, A. M.; Henrot-Versille, S.; Herbert, G. H.; Herget, V.; Hernández Jiménez, Y.; Herten, G.; Hertenberger, R.; Hervas, L.; Hesketh, G. G.; Hessey, N. P.; Hetherly, J. W.; Hickling, R.; Higón-Rodriguez, E.; Hill, E.; Hill, J. C.; Hiller, K. H.; Hillier, S. J.; Hinchliffe, I.; Hines, E.; Hinman, R. R.; Hirose, M.; Hirschbuehl, D.; Hobbs, J.; Hod, N.; Hodgkinson, M. C.; Hodgson, P.; Hoecker, A.; Hoeferkamp, M. R.; Hoenig, F.; Hohn, D.; Holmes, T. R.; Homann, M.; Hong, T. M.; Hooberman, B. H.; Hopkins, W. H.; Horii, Y.; Horton, A. J.; Hostachy, J.-Y.; Hou, S.; Hoummada, A.; Howarth, J.; Hrabovsky, M.; Hristova, I.; Hrivnac, J.; Hryn'ova, T.; Hrynevich, A.; Hsu, C.; Hsu, P. J.; Hsu, S.-C.; Hu, D.; Hu, Q.; Hu, S.; Huang, Y.; Hubacek, Z.; Hubaut, F.; Huegging, F.; Huffman, T. B.; Hughes, E. W.; Hughes, G.; Huhtinen, M.; Huo, P.; Huseynov, N.; Huston, J.; Huth, J.; Iacobucci, G.; Iakovidis, G.; Ibragimov, I.; Iconomidou-Fayard, L.; Ideal, E.; Idrissi, Z.; Iengo, P.; Igonkina, O.; Iizawa, T.; Ikegami, Y.; Ikeno, M.; Ilchenko, Y.; Iliadis, D.; Ilic, N.; Ince, T.; Introzzi, G.; Ioannou, P.; Iodice, M.; Iordanidou, K.; Ippolito, V.; Ishijima, N.; Ishino, M.; Ishitsuka, M.; Ishmukhametov, R.; Issever, C.; Istin, S.; Ito, F.; Iturbe Ponce, J. M.; Iuppa, R.; Iwanski, W.; Iwasaki, H.; Izen, J. M.; Izzo, V.; Jabbar, S.; Jackson, B.; Jackson, P.; Jain, V.; Jakobi, K. B.; Jakobs, K.; Jakobsen, S.; Jakoubek, T.; Jamin, D. O.; Jana, D. K.; Jansen, E.; Jansky, R.; Janssen, J.; Janus, M.; Jarlskog, G.; Javadov, N.; Javå¯Rek, T.; Jeanneau, F.; Jeanty, L.; Jejelava, J.; Jeng, G.-Y.; Jennens, D.; Jenni, P.; Jeske, C.; Jézéquel, S.; Ji, H.; Jia, J.; Jiang, H.; Jiang, Y.; Jiggins, S.; Jimenez Pena, J.; Jin, S.; Jinaru, A.; Jinnouchi, O.; Jivan, H.; Johansson, P.; Johns, K. A.; Johnson, W. J.; Jon-And, K.; Jones, G.; Jones, R. W. L.; Jones, S.; Jones, T. J.; Jongmanns, J.; Jorge, P. M.; Jovicevic, J.; Ju, X.; Juste Rozas, A.; Köhler, M. K.; Kaczmarska, A.; Kado, M.; Kagan, H.; Kagan, M.; Kahn, S. J.; Kaji, T.; Kajomovitz, E.; Kalderon, C. W.; Kaluza, A.; Kama, S.; Kamenshchikov, A.; Kanaya, N.; Kaneti, S.; Kanjir, L.; Kantserov, V. A.; Kanzaki, J.; Kaplan, B.; Kaplan, L. S.; Kapliy, A.; Kar, D.; Karakostas, K.; Karamaoun, A.; Karastathis, N.; Kareem, M. J.; Karentzos, E.; Karnevskiy, M.; Karpov, S. N.; Karpova, Z. M.; Karthik, K.; Kartvelishvili, V.; Karyukhin, A. N.; Kasahara, K.; Kashif, L.; Kass, R. D.; Kastanas, A.; Kataoka, Y.; Kato, C.; Katre, A.; Katzy, J.; Kawagoe, K.; Kawamoto, T.; Kawamura, G.; Kazanin, V. F.; Keeler, R.; Kehoe, R.; Keller, J. S.; Kempster, J. J.; Kentaro, K.; Keoshkerian, H.; Kepka, O.; Kerševan, B. P.; Kersten, S.; Keyes, R. A.; Khader, M.; Khalil-Zada, F.; Khanov, A.; Kharlamov, A. G.; Khoo, T. J.; Khovanskiy, V.; Khramov, E.; Khubua, J.; Kido, S.; Kilby, C. R.; Kim, H. Y.; Kim, S. H.; Kim, Y. K.; Kimura, N.; Kind, O. M.; King, B. T.; King, M.; King, S. B.; Kirk, J.; Kiryunin, A. E.; Kishimoto, T.; Kisielewska, D.; Kiss, F.; Kiuchi, K.; Kivernyk, O.; Kladiva, E.; Klein, M. H.; Klein, M.; Klein, U.; Kleinknecht, K.; Klimek, P.; Klimentov, A.; Klingenberg, R.; Klinger, J. A.; Klioutchnikova, T.; Kluge, E.-E.; Kluit, P.; Kluth, S.; Knapik, J.; Kneringer, E.; Knoops, E. B. F. G.; Knue, A.; Kobayashi, A.; Kobayashi, D.; Kobayashi, T.; Kobel, M.; Kocian, M.; Kodys, P.; Koehler, N. M.; Koffas, T.; Koffeman, E.; Koi, T.; Kolanoski, H.; Kolb, M.; Koletsou, I.; Komar, A. A.; Komori, Y.; Kondo, T.; Kondrashova, N.; Köneke, K.; König, A. C.; Kono, T.; Konoplich, R.; Konstantinidis, N.; Kopeliansky, R.; Koperny, S.; Köpke, L.; Kopp, A. K.; Korcyl, K.; Kordas, K.; Korn, A.; Korol, A. A.; Korolkov, I.; Korolkova, E. V.; Kortner, O.; Kortner, S.; Kosek, T.; Kostyukhin, V. V.; Kotwal, A.; Kourkoumeli-Charalampidi, A.; Kourkoumelis, C.; Kouskoura, V.; Kowalewska, A. B.; Kowalewski, R.; Kowalski, T. Z.; Kozakai, C.; Kozanecki, W.; Kozhin, A. S.; Kramarenko, V. A.; Kramberger, G.; Krasnopevtsev, D.; Krasny, M. W.; Krasznahorkay, A.; Kravchenko, A.; Kretz, M.; Kretzschmar, J.; Kreutzfeldt, K.; Krieger, P.; Krizka, K.; Kroeninger, K.; Kroha, H.; Kroll, J.; Kroseberg, J.; Krstic, J.; Kruchonak, U.; Krüger, H.; Krumnack, N.; Kruse, A.; Kruse, M. C.; Kruskal, M.; Kubota, T.; Kucuk, H.; Kuday, S.; Kuechler, J. T.; Kuehn, S.; Kugel, A.; Kuger, F.; Kuhl, A.; Kuhl, T.; Kukhtin, V.; Kukla, R.; Kulchitsky, Y.; Kuleshov, S.; Kuna, M.; Kunigo, T.; Kupco, A.; Kurashige, H.; Kurochkin, Y. A.; Kus, V.; Kuwertz, E. S.; Kuze, M.; Kvita, J.; Kwan, T.; Kyriazopoulos, D.; La Rosa, A.; La Rosa Navarro, J. L.; La Rotonda, L.; Lacasta, C.; Lacava, F.; Lacey, J.; Lacker, H.; Lacour, D.; Lacuesta, V. R.; Ladygin, E.; Lafaye, R.; Laforge, B.; Lagouri, T.; Lai, S.; Lammers, S.; Lampl, W.; Lançon, E.; Landgraf, U.; Landon, M. P. J.; Lanfermann, M. C.; Lang, V. S.; Lange, J. C.; Lankford, A. J.; Lanni, F.; Lantzsch, K.; Lanza, A.; Laplace, S.; Lapoire, C.; Laporte, J. F.; Lari, T.; Lasagni Manghi, F.; Lassnig, M.; Laurelli, P.; Lavrijsen, W.; Law, A. T.; Laycock, P.; Lazovich, T.; Lazzaroni, M.; Le, B.; Le Dortz, O.; Le Guirriec, E.; Le Quilleuc, E. P.; Leblanc, M.; Lecompte, T.; Ledroit-Guillon, F.; Lee, C. A.; Lee, S. C.; Lee, L.; Lefebvre, B.; Lefebvre, G.; Lefebvre, M.; Legger, F.; Leggett, C.; Lehan, A.; Lehmann Miotto, G.; Lei, X.; Leight, W. A.; Leisos, A.; Leister, A. G.; Leite, M. A. L.; Leitner, R.; Lellouch, D.; Lemmer, B.; Leney, K. J. C.; Lenz, T.; Lenzi, B.; Leone, R.; Leone, S.; Leonidopoulos, C.; Leontsinis, S.; Lerner, G.; Leroy, C.; Lesage, A. A. J.; Lester, C. G.; Levchenko, M.; Levêque, J.; Levin, D.; Levinson, L. J.; Levy, M.; Lewis, D.; Leyko, A. M.; Leyton, M.; Li, B.; Li, C.; Li, H.; Li, H. L.; Li, L.; Li, L.; Li, Q.; Li, S.; Li, X.; Li, Y.; Liang, Z.; Liberti, B.; Liblong, A.; Lichard, P.; Lie, K.; Liebal, J.; Liebig, W.; Limosani, A.; Lin, S. C.; Lin, T. H.; Lindquist, B. E.; Lionti, A. E.; Lipeles, E.; Lipniacka, A.; Lisovyi, M.; Liss, T. M.; Lister, A.; Litke, A. M.; Liu, B.; Liu, D.; Liu, H.; Liu, H.; Liu, J.; Liu, J. B.; Liu, K.; Liu, L.; Liu, M.; Liu, M.; Liu, Y. L.; Liu, Y.; Livan, M.; Lleres, A.; Llorente Merino, J.; Lloyd, S. L.; Lo Sterzo, F.; Lobodzinska, E.; Loch, P.; Lockman, W. S.; Loebinger, F. K.; Loevschall-Jensen, A. E.; Loew, K. M.; Loginov, A.; Lohse, T.; Lohwasser, K.; Lokajicek, M.; Long, B. A.; Long, J. D.; Long, R. E.; Longo, L.; Looper, K. A.; Lopes, L.; Lopez Mateos, D.; Lopez Paredes, B.; Lopez Paz, I.; Lopez Solis, A.; Lorenz, J.; Lorenzo Martinez, N.; Losada, M.; Lösel, P. J.; Lou, X.; Lounis, A.; Love, J.; Love, P. A.; Lu, H.; Lu, N.; Lubatti, H. J.; Luci, C.; Lucotte, A.; Luedtke, C.; Luehring, F.; Lukas, W.; Luminari, L.; Lundberg, O.; Lund-Jensen, B.; Luzi, P. M.; Lynn, D.; Lysak, R.; Lytken, E.; Lyubushkin, V.; Ma, H.; Ma, L. L.; Ma, Y.; Maccarrone, G.; Macchiolo, A.; MacDonald, C. M.; Maček, B.; Machado Miguens, J.; Madaffari, D.; Madar, R.; Maddocks, H. J.; Mader, W. F.; Madsen, A.; Maeda, J.; Maeland, S.; Maeno, T.; Maevskiy, A.; Magradze, E.; Mahlstedt, J.; Maiani, C.; Maidantchik, C.; Maier, A. A.; Maier, T.; Maio, A.; Majewski, S.; Makida, Y.; Makovec, N.; Malaescu, B.; Malecki, Pa.; Maleev, V. P.; Malek, F.; Mallik, U.; Malon, D.; Malone, C.; Maltezos, S.; Malyukov, S.; Mamuzic, J.; Mancini, G.; Mandelli, B.; Mandelli, L.; Mandić, I.; Maneira, J.; Manhaes de Andrade Filho, L.; Manjarres Ramos, J.; Mann, A.; Manousos, A.; Mansoulie, B.; Mansour, J. D.; Mantifel, R.; Mantoani, M.; Manzoni, S.; Mapelli, L.; Marceca, G.; March, L.; Marchiori, G.; Marcisovsky, M.; Marjanovic, M.; Marley, D. E.; Marroquim, F.; Marsden, S. P.; Marshall, Z.; Marti-Garcia, S.; Martin, B.; Martin, T. A.; Martin, V. J.; Martin Dit Latour, B.; Martinez, M.; Martinez Outschoorn, V. I.; Martin-Haugh, S.; Martoiu, V. S.; Martyniuk, A. C.; Marx, M.; Marzin, A.; Masetti, L.; Mashimo, T.; Mashinistov, R.; Masik, J.; Maslennikov, A. L.; Massa, I.; Massa, L.; Mastrandrea, P.; Mastroberardino, A.; Masubuchi, T.; Mättig, P.; Mattmann, J.; Maurer, J.; Maxfield, S. J.; Maximov, D. A.; Mazini, R.; Mazza, S. M.; Mc Fadden, N. C.; Mc Goldrick, G.; Mc Kee, S. P.; McCarn, A.; McCarthy, R. L.; McCarthy, T. G.; McClymont, L. I.; McDonald, E. F.; McFayden, J. A.; McHedlidze, G.; McMahon, S. J.; McPherson, R. A.; Medinnis, M.; Meehan, S.; Mehlhase, S.; Mehta, A.; Meier, K.; Meineck, C.; Meirose, B.; Melini, D.; Mellado Garcia, B. R.; Melo, M.; Meloni, F.; Mengarelli, A.; Menke, S.; Meoni, E.; Mergelmeyer, S.; Mermod, P.; Merola, L.; Meroni, C.; Merritt, F. S.; Messina, A.; Metcalfe, J.; Mete, A. S.; Meyer, C.; Meyer, C.; Meyer, J.-P.; Meyer, J.; Meyer Zu Theenhausen, H.; Miano, F.; Middleton, R. P.; Miglioranzi, S.; Mijović, L.; Mikenberg, G.; Mikestikova, M.; Mikuž, M.; Milesi, M.; Milic, A.; Miller, D. W.; Mills, C.; Milov, A.; Milstead, D. A.; Minaenko, A. A.; Minami, Y.; Minashvili, I. A.; Mincer, A. I.; Mindur, B.; Mineev, M.; Ming, Y.; Mir, L. M.; Mistry, K. P.; Mitani, T.; Mitrevski, J.; Mitsou, V. A.; Miucci, A.; Miyagawa, P. S.; Mjörnmark, J. U.; Moa, T.; Mochizuki, K.; Mohapatra, S.; Molander, S.; Moles-Valls, R.; Monden, R.; Mondragon, M. C.; Mönig, K.; Monk, J.; Monnier, E.; Montalbano, A.; Montejo Berlingen, J.; Monticelli, F.; Monzani, S.; Moore, R. W.; Morange, N.; Moreno, D.; Moreno Llácer, M.; Morettini, P.; Mori, D.; Mori, T.; Morii, M.; Morinaga, M.; Morisbak, V.; Moritz, S.; Morley, A. K.; Mornacchi, G.; Morris, J. D.; Mortensen, S. S.; Morvaj, L.; Mosidze, M.; Moss, J.; Motohashi, K.; Mount, R.; Mountricha, E.; Mouraviev, S. V.; Moyse, E. J. W.; Muanza, S.; Mudd, R. D.; Mueller, F.; Mueller, J.; Mueller, R. S. P.; Mueller, T.; Muenstermann, D.; Mullen, P.; Mullier, G. A.; Munoz Sanchez, F. J.; Murillo Quijada, J. A.; Murray, W. J.; Musheghyan, H.; Muškinja, M.; Myagkov, A. G.; Myska, M.; Nachman, B. P.; Nackenhorst, O.; Nagai, K.; Nagai, R.; Nagano, K.; Nagasaka, Y.; Nagata, K.; Nagel, M.; Nagy, E.; Nairz, A. M.; Nakahama, Y.; Nakamura, K.; Nakamura, T.; Nakano, I.; Namasivayam, H.; Naranjo Garcia, R. F.; Narayan, R.; Narrias Villar, D. I.; Naryshkin, I.; Naumann, T.; Navarro, G.; Nayyar, R.; Neal, H. A.; Nechaeva, P. Yu.; Neep, T. J.; Negri, A.; Negrini, M.; Nektarijevic, S.; Nellist, C.; Nelson, A.; Nemecek, S.; Nemethy, P.; Nepomuceno, A. A.; Nessi, M.; Neubauer, M. S.; Neumann, M.; Neves, R. M.; Nevski, P.; Newman, P. R.; Nguyen, D. H.; Nguyen Manh, T.; Nickerson, R. B.; Nicolaidou, R.; Nielsen, J.; Nikiforov, A.; Nikolaenko, V.; Nikolic-Audit, I.; Nikolopoulos, K.; Nilsen, J. K.; Nilsson, P.; Ninomiya, Y.; Nisati, A.; Nisius, R.; Nobe, T.; Nomachi, M.; Nomidis, I.; Nooney, T.; Norberg, S.; Nordberg, M.; Norjoharuddeen, N.; Novgorodova, O.; Nowak, S.; Nozaki, M.; Nozka, L.; Ntekas, K.; Nurse, E.; Nuti, F.; O'Grady, F.; O'Neil, D. C.; O'Rourke, A. A.; O'Shea, V.; Oakham, F. G.; Oberlack, H.; Obermann, T.; Ocariz, J.; Ochi, A.; Ochoa, I.; Ochoa-Ricoux, J. P.; Oda, S.; Odaka, S.; Ogren, H.; Oh, A.; Oh, S. H.; Ohm, C. C.; Ohman, H.; Oide, H.; Okawa, H.; Okumura, Y.; Okuyama, T.; Olariu, A.; Oleiro Seabra, L. F.; Olivares Pino, S. A.; Oliveira Damazio, D.; Olszewski, A.; Olszowska, J.; Onofre, A.; Onogi, K.; Onyisi, P. U. E.; Oreglia, M. J.; Oren, Y.; Orestano, D.; Orlando, N.; Orr, R. S.; Osculati, B.; Ospanov, R.; Otero Y Garzon, G.; Otono, H.; Ouchrif, M.; Ould-Saada, F.; Ouraou, A.; Oussoren, K. P.; Ouyang, Q.; Owen, M.; Owen, R. E.; Ozcan, V. E.; Ozturk, N.; Pachal, K.; Pacheco Pages, A.; Pacheco Rodriguez, L.; Padilla Aranda, C.; Pagáčová, M.; Pagan Griso, S.; Paige, F.; Pais, P.; Pajchel, K.; Palacino, G.; Palazzo, S.; Palestini, S.; Palka, M.; Pallin, D.; Panagiotopoulou, E. St.; Pandini, C. E.; Panduro Vazquez, J. G.; Pani, P.; Panitkin, S.; Pantea, D.; Paolozzi, L.; Papadopoulou, Th. D.; Papageorgiou, K.; Paramonov, A.; Paredes Hernandez, D.; Parker, A. J.; Parker, M. A.; Parker, K. A.; Parodi, F.; Parsons, J. A.; Parzefall, U.; Pascuzzi, V. R.; Pasqualucci, E.; Passaggio, S.; Pastore, Fr.; Pásztor, G.; Pataraia, S.; Pater, J. R.; Pauly, T.; Pearce, J.; Pearson, B.; Pedersen, L. E.; Pedersen, M.; Pedraza Lopez, S.; Pedro, R.; Peleganchuk, S. V.; Penc, O.; Peng, C.; Peng, H.; Penwell, J.; Peralva, B. S.; Perego, M. M.; Perepelitsa, D. V.; Perez Codina, E.; Perini, L.; Pernegger, H.; Perrella, S.; Peschke, R.; Peshekhonov, V. D.; Peters, K.; Peters, R. F. Y.; Petersen, B. A.; Petersen, T. C.; Petit, E.; Petridis, A.; Petridou, C.; Petroff, P.; Petrolo, E.; Petrov, M.; Petrucci, F.; Pettersson, N. E.; Peyaud, A.; Pezoa, R.; Phillips, P. W.; Piacquadio, G.; Pianori, E.; Picazio, A.; Piccaro, E.; Piccinini, M.; Pickering, M. A.; Piegaia, R.; Pilcher, J. E.; Pilkington, A. D.; Pin, A. W. J.; Pinamonti, M.; Pinfold, J. L.; Pingel, A.; Pires, S.; Pirumov, H.; Pitt, M.; Plazak, L.; Pleier, M.-A.; Pleskot, V.; Plotnikova, E.; Plucinski, P.; Pluth, D.; Poettgen, R.; Poggioli, L.; Pohl, D.; Polesello, G.; Poley, A.; Policicchio, A.; Polifka, R.; Polini, A.; Pollard, C. S.; Polychronakos, V.; Pommès, K.; Pontecorvo, L.; Pope, B. G.; Popeneciu, G. A.; Poppleton, A.; Pospisil, S.; Potamianos, K.; Potrap, I. N.; Potter, C. J.; Potter, C. T.; Poulard, G.; Poveda, J.; Pozdnyakov, V.; Pozo Astigarraga, M. E.; Pralavorio, P.; Pranko, A.; Prell, S.; Price, D.; Price, L. E.; Primavera, M.; Prince, S.; Prokofiev, K.; Prokoshin, F.; Protopopescu, S.; Proudfoot, J.; Przybycien, M.; Puddu, D.; Purohit, M.; Puzo, P.; Qian, J.; Qin, G.; Qin, Y.; Quadt, A.; Quayle, W. B.; Queitsch-Maitland, M.; Quilty, D.; Raddum, S.; Radeka, V.; Radescu, V.; Radhakrishnan, S. K.; Radloff, P.; Rados, P.; Ragusa, F.; Rahal, G.; Raine, J. A.; Rajagopalan, S.; Rammensee, M.; Rangel-Smith, C.; Ratti, M. G.; Rauscher, F.; Rave, S.; Ravenscroft, T.; Ravinovich, I.; Raymond, M.; Read, A. L.; Readioff, N. P.; Reale, M.; Rebuzzi, D. M.; Redelbach, A.; Redlinger, G.; Reece, R.; Reeves, K.; Rehnisch, L.; Reichert, J.; Reisin, H.; Rembser, C.; Ren, H.; Rescigno, M.; Resconi, S.; Rezanova, O. L.; Reznicek, P.; Rezvani, R.; Richter, R.; Richter, S.; Richter-Was, E.; Ricken, O.; Ridel, M.; Rieck, P.; Riegel, C. J.; Rieger, J.; Rifki, O.; Rijssenbeek, M.; Rimoldi, A.; Rimoldi, M.; Rinaldi, L.; Ristić, B.; Ritsch, E.; Riu, I.; Rizatdinova, F.; Rizvi, E.; Rizzi, C.; Robertson, S. H.; Robichaud-Veronneau, A.; Robinson, D.; Robinson, J. E. M.; Robson, A.; Roda, C.; Rodina, Y.; Rodriguez Perez, A.; Rodriguez Rodriguez, D.; Roe, S.; Rogan, C. S.; Røhne, O.; Romaniouk, A.; Romano, M.; Romano Saez, S. M.; Romero Adam, E.; Rompotis, N.; Ronzani, M.; Roos, L.; Ros, E.; Rosati, S.; Rosbach, K.; Rose, P.; Rosenthal, O.; Rosien, N.-A.; Rossetti, V.; Rossi, E.; Rossi, L. P.; Rosten, J. H. N.; Rosten, R.; Rotaru, M.; Roth, I.; Rothberg, J.; Rousseau, D.; Royon, C. R.; Rozanov, A.; Rozen, Y.; Ruan, X.; Rubbo, F.; Rudolph, M. S.; Rühr, F.; Ruiz-Martinez, A.; Rurikova, Z.; Rusakovich, N. A.; Ruschke, A.; Russell, H. L.; Rutherfoord, J. P.; Ruthmann, N.; Ryabov, Y. F.; Rybar, M.; Rybkin, G.; Ryu, S.; Ryzhov, A.; Rzehorz, G. F.; Saavedra, A. F.; Sabato, G.; Sacerdoti, S.; Sadrozinski, H. F.-W.; Sadykov, R.; Safai Tehrani, F.; Saha, P.; Sahinsoy, M.; Saimpert, M.; Saito, T.; Sakamoto, H.; Sakurai, Y.; Salamanna, G.; Salamon, A.; Salazar Loyola, J. E.; Salek, D.; Sales de Bruin, P. H.; Salihagic, D.; Salnikov, A.; Salt, J.; Salvatore, D.; Salvatore, F.; Salvucci, A.; Salzburger, A.; Sammel, D.; Sampsonidis, D.; Sanchez, A.; Sánchez, J.; Sanchez Martinez, V.; Sandaker, H.; Sandbach, R. L.; Sander, H. G.; Sandhoff, M.; Sandoval, C.; Sandstroem, R.; Sankey, D. P. C.; Sannino, M.; Sansoni, A.; Santoni, C.; Santonico, R.; Santos, H.; Santoyo Castillo, I.; Sapp, K.; Sapronov, A.; Saraiva, J. G.; Sarrazin, B.; Sasaki, O.; Sasaki, Y.; Sato, K.; Sauvage, G.; Sauvan, E.; Savage, G.; Savard, P.; Savic, N.; Sawyer, C.; Sawyer, L.; Saxon, J.; Sbarra, C.; Sbrizzi, A.; Scanlon, T.; Scannicchio, D. A.; Scarcella, M.; Scarfone, V.; Schaarschmidt, J.; Schacht, P.; Schachtner, B. M.; Schaefer, D.; Schaefer, L.; Schaefer, R.; Schaeffer, J.; Schaepe, S.; Schaetzel, S.; Schäfer, U.; Schaffer, A. C.; Schaile, D.; Schamberger, R. D.; Scharf, V.; Schegelsky, V. A.; Scheirich, D.; Schernau, M.; Schiavi, C.; Schier, S.; Schillo, C.; Schioppa, M.; Schlenker, S.; Schmidt-Sommerfeld, K. R.; Schmieden, K.; Schmitt, C.; Schmitt, S.; Schmitz, S.; Schneider, B.; Schnoor, U.; Schoeffel, L.; Schoening, A.; Schoenrock, B. D.; Schopf, E.; Schott, M.; Schovancova, J.; Schramm, S.; Schreyer, M.; Schuh, N.; Schulte, A.; Schultens, M. J.; Schultz-Coulon, H.-C.; Schulz, H.; Schumacher, M.; Schumm, B. A.; Schune, Ph.; Schwartzman, A.; Schwarz, T. A.; Schweiger, H.; Schwemling, Ph.; Schwienhorst, R.; Schwindling, J.; Schwindt, T.; Sciolla, G.; Scuri, F.; Scutti, F.; Searcy, J.; Seema, P.; Seidel, S. C.; Seiden, A.; Seifert, F.; Seixas, J. M.; Sekhniaidze, G.; Sekhon, K.; Sekula, S. J.; Seliverstov, D. M.; Semprini-Cesari, N.; Serfon, C.; Serin, L.; Serkin, L.; Sessa, M.; Seuster, R.; Severini, H.; Sfiligoj, T.; Sforza, F.; Sfyrla, A.; Shabalina, E.; Shaikh, N. W.; Shan, L. Y.; Shang, R.; Shank, J. T.; Shapiro, M.; Shatalov, P. B.; Shaw, K.; Shaw, S. M.; Shcherbakova, A.; Shehu, C. Y.; Sherwood, P.; Shi, L.; Shimizu, S.; Shimmin, C. O.; Shimojima, M.; Shiyakova, M.; Shmeleva, A.; Shoaleh Saadi, D.; Shochet, M. J.; Shojaii, S.; Shrestha, S.; Shulga, E.; Shupe, M. A.; Sicho, P.; Sickles, A. M.; Sidebo, P. E.; Sidiropoulou, O.; Sidorov, D.; Sidoti, A.; Siegert, F.; Sijacki, Dj.; Silva, J.; Silverstein, S. B.; Simak, V.; Simic, Lj.; Simion, S.; Simioni, E.; Simmons, B.; Simon, D.; Simon, M.; Sinervo, P.; Sinev, N. B.; Sioli, M.; Siragusa, G.; Sivoklokov, S. Yu.; Sjölin, J.; Skinner, M. B.; Skottowe, H. P.; Skubic, P.; Slater, M.; Slavicek, T.; Slawinska, M.; Sliwa, K.; Slovak, R.; Smakhtin, V.; Smart, B. H.; Smestad, L.; Smiesko, J.; Smirnov, S. Yu.; Smirnov, Y.; Smirnova, L. N.; Smirnova, O.; Smith, M. N. K.; Smith, R. W.; Smizanska, M.; Smolek, K.; Snesarev, A. A.; Snyder, S.; Sobie, R.; Socher, F.; Soffer, A.; Soh, D. A.; Sokhrannyi, G.; Solans Sanchez, C. A.; Solar, M.; Soldatov, E. Yu.; Soldevila, U.; Solodkov, A. A.; Soloshenko, A.; Solovyanov, O. V.; Solovyev, V.; Sommer, P.; Son, H.; Song, H. Y.; Sood, A.; Sopczak, A.; Sopko, V.; Sorin, V.; Sosa, D.; Sotiropoulou, C. L.; Soualah, R.; Soukharev, A. M.; South, D.; Sowden, B. C.; Spagnolo, S.; Spalla, M.; Spangenberg, M.; Spanò, F.; Sperlich, D.; Spettel, F.; Spighi, R.; Spigo, G.; Spiller, L. A.; Spousta, M.; St. Denis, R. D.; Stabile, A.; Stamen, R.; Stamm, S.; Stanecka, E.; Stanek, R. W.; Stanescu, C.; Stanescu-Bellu, M.; Stanitzki, M. M.; Stapnes, S.; Starchenko, E. A.; Stark, G. H.; Stark, J.; Staroba, P.; Starovoitov, P.; Stärz, S.; Staszewski, R.; Steinberg, P.; Stelzer, B.; Stelzer, H. J.; Stelzer-Chilton, O.; Stenzel, H.; Stewart, G. A.; Stillings, J. A.; Stockton, M. C.; Stoebe, M.; Stoicea, G.; Stolte, P.; Stonjek, S.; Stradling, A. R.; Straessner, A.; Stramaglia, M. E.; Strandberg, J.; Strandberg, S.; Strandlie, A.; Strauss, M.; Strizenec, P.; Ströhmer, R.; Strom, D. M.; Stroynowski, R.; Strubig, A.; Stucci, S. A.; Stugu, B.; Styles, N. A.; Su, D.; Su, J.; Suchek, S.; Sugaya, Y.; Suk, M.; Sulin, V. V.; Sultansoy, S.; Sumida, T.; Sun, S.; Sun, X.; Sundermann, J. E.; Suruliz, K.; Susinno, G.; Sutton, M. R.; Suzuki, S.; Svatos, M.; Swiatlowski, M.; Sykora, I.; Sykora, T.; Ta, D.; Taccini, C.; Tackmann, K.; Taenzer, J.; Taffard, A.; Tafirout, R.; Taiblum, N.; Takai, H.; Takashima, R.; Takeshita, T.; Takubo, Y.; Talby, M.; Talyshev, A. A.; Tan, K. G.; Tanaka, J.; Tanaka, M.; Tanaka, R.; Tanaka, S.; Tannenwald, B. B.; Tapia Araya, S.; Tapprogge, S.; Tarem, S.; Tartarelli, G. F.; Tas, P.; Tasevsky, M.; Tashiro, T.; Tassi, E.; Tavares Delgado, A.; Tayalati, Y.; Taylor, A. C.; Taylor, G. N.; Taylor, P. T. E.; Taylor, W.; Teischinger, F. A.; Teixeira-Dias, P.; Temming, K. K.; Temple, D.; Ten Kate, H.; Teng, P. K.; Teoh, J. J.; Tepel, F.; Terada, S.; Terashi, K.; Terron, J.; Terzo, S.; Testa, M.; Teuscher, R. J.; Theveneaux-Pelzer, T.; Thomas, J. P.; Thomas-Wilsker, J.; Thompson, E. N.; Thompson, P. D.; Thompson, A. S.; Thomsen, L. A.; Thomson, E.; Thomson, M.; Tibbetts, M. J.; Ticse Torres, R. E.; Tikhomirov, V. O.; Tikhonov, Yu. A.; Timoshenko, S.; Tipton, P.; Tisserant, S.; Todome, K.; Todorov, T.; Todorova-Nova, S.; Tojo, J.; Tokár, S.; Tokushuku, K.; Tolley, E.; Tomlinson, L.; Tomoto, M.; Tompkins, L.; Toms, K.; Tong, B.; Torrence, E.; Torres, H.; Torró Pastor, E.; Toth, J.; Touchard, F.; Tovey, D. R.; Trefzger, T.; Tricoli, A.; Trigger, I. M.; Trincaz-Duvoid, S.; Tripiana, M. F.; Trischuk, W.; Trocmé, B.; Trofymov, A.; Troncon, C.; Trottier-McDonald, M.; Trovatelli, M.; Truong, L.; Trzebinski, M.; Trzupek, A.; Tseng, J. C.-L.; Tsiareshka, P. V.; Tsipolitis, G.; Tsirintanis, N.; Tsiskaridze, S.; Tsiskaridze, V.; Tskhadadze, E. G.; Tsui, K. M.; Tsukerman, I. I.; Tsulaia, V.; Tsuno, S.; Tsybychev, D.; Tu, Y.; Tudorache, A.; Tudorache, V.; Tuna, A. N.; Tupputi, S. A.; Turchikhin, S.; Turecek, D.; Turgeman, D.; Turra, R.; Turvey, A. J.; Tuts, P. M.; Tyndel, M.; Ucchielli, G.; Ueda, I.; Ughetto, M.; Ukegawa, F.; Unal, G.; Undrus, A.; Unel, G.; Ungaro, F. C.; Unno, Y.; Unverdorben, C.; Urban, J.; Urquijo, P.; Urrejola, P.; Usai, G.; Usanova, A.; Vacavant, L.; Vacek, V.; Vachon, B.; Valderanis, C.; Valdes Santurio, E.; Valencic, N.; Valentinetti, S.; Valero, A.; Valery, L.; Valkar, S.; Valls Ferrer, J. A.; van den Wollenberg, W.; van der Deijl, P. C.; van der Graaf, H.; van Eldik, N.; van Gemmeren, P.; van Nieuwkoop, J.; van Vulpen, I.; van Woerden, M. C.; Vanadia, M.; Vandelli, W.; Vanguri, R.; Vaniachine, A.; Vankov, P.; Vardanyan, G.; Vari, R.; Varnes, E. W.; Varol, T.; Varouchas, D.; Vartapetian, A.; Varvell, K. E.; Vasquez, J. G.; Vazeille, F.; Vazquez Schroeder, T.; Veatch, J.; Veeraraghavan, V.; Veloce, L. M.; Veloso, F.; Veneziano, S.; Ventura, A.; Venturi, M.; Venturi, N.; Venturini, A.; Vercesi, V.; Verducci, M.; Verkerke, W.; Vermeulen, J. C.; Vest, A.; Vetterli, M. C.; Viazlo, O.; Vichou, I.; Vickey, T.; Vickey Boeriu, O. E.; Viehhauser, G. H. A.; Viel, S.; Vigani, L.; Villa, M.; Villaplana Perez, M.; Vilucchi, E.; Vincter, M. G.; Vinogradov, V. B.; Vittori, C.; Vivarelli, I.; Vlachos, S.; Vlasak, M.; Vogel, M.; Vokac, P.; Volpi, G.; Volpi, M.; von der Schmitt, H.; von Toerne, E.; Vorobel, V.; Vorobev, K.; Vos, M.; Voss, R.; Vossebeld, J. H.; Vranjes, N.; Vranjes Milosavljevic, M.; Vrba, V.; Vreeswijk, M.; Vuillermet, R.; Vukotic, I.; Vykydal, Z.; Wagner, P.; Wagner, W.; Wahlberg, H.; Wahrmund, S.; Wakabayashi, J.; Walder, J.; Walker, R.; Walkowiak, W.; Wallangen, V.; Wang, C.; Wang, C.; Wang, F.; Wang, H.; Wang, H.; Wang, J.; Wang, J.; Wang, K.; Wang, R.; Wang, S. M.; Wang, T.; Wang, T.; Wang, W.; Wang, X.; Wanotayaroj, C.; Warburton, A.; Ward, C. P.; Wardrope, D. R.; Washbrook, A.; Watkins, P. M.; Watson, A. T.; Watson, M. F.; Watts, G.; Watts, S.; Waugh, B. M.; Webb, S.; Weber, M. S.; Weber, S. W.; Webster, J. S.; Weidberg, A. R.; Weinert, B.; Weingarten, J.; Weiser, C.; Weits, H.; Wells, P. S.; Wenaus, T.; Wengler, T.; Wenig, S.; Wermes, N.; Werner, M.; Werner, M. D.; Werner, P.; Wessels, M.; Wetter, J.; Whalen, K.; Whallon, N. L.; Wharton, A. M.; White, A.; White, M. J.; White, R.; Whiteson, D.; Wickens, F. J.; Wiedenmann, W.; Wielers, M.; Wienemann, P.; Wiglesworth, C.; Wiik-Fuchs, L. A. M.; Wildauer, A.; Wilk, F.; Wilkens, H. G.; Williams, H. H.; Williams, S.; Willis, C.; Willocq, S.; Wilson, J. A.; Wingerter-Seez, I.; Winklmeier, F.; Winston, O. J.; Winter, B. T.; Wittgen, M.; Wittkowski, J.; Wolf, T. M. H.; Wolter, M. W.; Wolters, H.; Worm, S. D.; Wosiek, B. K.; Wotschack, J.; Woudstra, M. J.; Wozniak, K. W.; Wu, M.; Wu, M.; Wu, S. L.; Wu, X.; Wu, Y.; Wyatt, T. R.; Wynne, B. M.; Xella, S.; Xu, D.; Xu, L.; Yabsley, B.; Yacoob, S.; Yamaguchi, D.; Yamaguchi, Y.; Yamamoto, A.; Yamamoto, S.; Yamanaka, T.; Yamauchi, K.; Yamazaki, Y.; Yan, Z.; Yang, H.; Yang, H.; Yang, Y.; Yang, Z.; Yao, W.-M.; Yap, Y. C.; Yasu, Y.; Yatsenko, E.; Yau Wong, K. H.; Ye, J.; Ye, S.; Yeletskikh, I.; Yen, A. L.; Yildirim, E.; Yorita, K.; Yoshida, R.; Yoshihara, K.; Young, C.; Young, C. J. S.; Youssef, S.; Yu, D. R.; Yu, J.; Yu, J. M.; Yu, J.; Yuan, L.; Yuen, S. P. Y.; Yusuff, I.; Zabinski, B.; Zaidan, R.; Zaitsev, A. M.; Zakharchuk, N.; Zalieckas, J.; Zaman, A.; Zambito, S.; Zanello, L.; Zanzi, D.; Zeitnitz, C.; Zeman, M.; Zemla, A.; Zeng, J. C.; Zeng, Q.; Zengel, K.; Zenin, O.; Ženiš, T.; Zerwas, D.; Zhang, D.; Zhang, F.; Zhang, G.; Zhang, H.; Zhang, J.; Zhang, L.; Zhang, R.; Zhang, R.; Zhang, X.; Zhang, Z.; Zhao, X.; Zhao, Y.; Zhao, Z.; Zhemchugov, A.; Zhong, J.; Zhou, B.; Zhou, C.; Zhou, L.; Zhou, L.; Zhou, M.; Zhou, N.; Zhu, C. G.; Zhu, H.; Zhu, J.; Zhu, Y.; Zhuang, X.; Zhukov, K.; Zibell, A.; Zieminska, D.; Zimine, N. I.; Zimmermann, C.; Zimmermann, S.; Zinonos, Z.; Zinser, M.; Ziolkowski, M.; Živković, L.; Zobernig, G.; Zoccoli, A.; Zur Nedden, M.; Zwalinski, L.; Atlas Collaboration

    2016-09-01

    A search for the decays of the Higgs and Z bosons to a ϕ meson and a photon is performed with a p p collision data sample corresponding to an integrated luminosity of 2.7 fb-1 collected at √{s }=13 TeV with the ATLAS detector at the LHC. No significant excess of events is observed above the background, and 95% confidence level upper limits on the branching fractions of the Higgs and Z boson decays to ϕ γ of 1.4 ×1 0-3 and 8.3 ×1 0-6 , respectively, are obtained.

  18. EnviroAtlas - Paterson, NJ - 51m Riparian Buffer Vegetated Cover

    EPA Pesticide Factsheets

    This EnviroAtlas dataset describes the percentage of a 51-m riparian buffer that is vegetated. There is a potential for decreased water quality in areas where the riparian buffer is less vegetated. The displayed line represents the center of the analyzed riparian buffer. The water bodies analyzed include hydrologically connected streams, rivers, connectors, reservoirs, lakes/ponds, ice masses, washes, locks, and rapids within the Atlas Area. EnviroAtlas defines vegetated buffer for this community as trees and forest and grass and herbaceous. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

  19. Multi-scale hippocampal parcellation improves atlas-based segmentation accuracy

    NASA Astrophysics Data System (ADS)

    Plassard, Andrew J.; McHugo, Maureen; Heckers, Stephan; Landman, Bennett A.

    2017-02-01

    Known for its distinct role in memory, the hippocampus is one of the most studied regions of the brain. Recent advances in magnetic resonance imaging have allowed for high-contrast, reproducible imaging of the hippocampus. Typically, a trained rater takes 45 minutes to manually trace the hippocampus and delineate the anterior from the posterior segment at millimeter resolution. As a result, there has been a significant desire for automated and robust segmentation of the hippocampus. In this work we use a population of 195 atlases based on T1-weighted MR images with the left and right hippocampus delineated into the head and body. We initialize the multi-atlas segmentation to a region directly around each lateralized hippocampus to both speed up and improve the accuracy of registration. This initialization allows for incorporation of nearly 200 atlases, an accomplishment which would typically involve hundreds of hours of computation per target image. The proposed segmentation results in a Dice similiarity coefficient over 0.9 for the full hippocampus. This result outperforms a multi-atlas segmentation using the BrainCOLOR atlases (Dice 0.85) and FreeSurfer (Dice 0.75). Furthermore, the head and body delineation resulted in a Dice coefficient over 0.87 for both structures. The head and body volume measurements also show high reproducibility on the Kirby 21 reproducibility population (R2 greater than 0.95, p < 0.05 for all structures). This work signifies the first result in an ongoing work to develop a robust tool for measurement of the hippocampus and other temporal lobe structures.

  20. The Herschel-ATLAS data release 1 - I. Maps, catalogues and number counts

    NASA Astrophysics Data System (ADS)

    Valiante, E.; Smith, M. W. L.; Eales, S.; Maddox, S. J.; Ibar, E.; Hopwood, R.; Dunne, L.; Cigan, P. J.; Dye, S.; Pascale, E.; Rigby, E. E.; Bourne, N.; Furlanetto, C.; Ivison, R. J.

    2016-11-01

    We present the first major data release of the largest single key-project in area carried out in open time with the Herschel Space Observatory. The Herschel Astrophysical Terahertz Large Area Survey (H-ATLAS) is a survey of 600 deg2 in five photometric bands - 100, 160, 250, 350 and 500 μm - with the Photoconductor Array Camera and Spectrometer and Spectral and Photometric Imaging Receiver (SPIRE) cameras. In this paper and the companion Paper II, we present the survey of three fields on the celestial equator, covering a total area of 161.6 deg2 and previously observed in the Galaxy and Mass Assembly (GAMA) spectroscopic survey. This paper describes the Herschel images and catalogues of the sources detected on the SPIRE 250 μm images. The 1σ noise for source detection, including both confusion and instrumental noise, is 7.4, 9.4 and 10.2 mJy at 250, 350 and 500 μm. Our catalogue includes 120 230 sources in total, with 113 995, 46 209 and 11 011 sources detected at >4σ at 250, 350 and 500 μm. The catalogue contains detections at >3σ at 100 and 160 μm for 4650 and 5685 sources, and the typical noise at these wavelengths is 44 and 49 mJy. We include estimates of the completeness of the survey and of the effects of flux bias and also describe a novel method for determining the true source counts. The H-ATLAS source counts are very similar to the source counts from the deeper HerMES survey at 250 and 350 μm, with a small difference at 500 μm. Appendix A provides a quick start in using the released data sets, including instructions and cautions on how to use them.