Carbon Nanofiber Nanoelectrodes for Neural Stimulation and Chemical Detection: The Era of "Smart" Deep Brain Stimulation

NASA Technical Reports Server (NTRS)

Koehne, Jessica E.

2016-01-01

A sensor platform based on vertically aligned carbon nanofibers (CNFs) has been developed. Their inherent nanometer scale, high conductivity, wide potential window, good biocompatibility and well-defined surface chemistry make them ideal candidates as biosensor electrodes. Here, we report two studies using vertically aligned CNF nanoelectrodes for biomedical applications. CNF arrays are investigated as neural stimulation and neurotransmitter recording electrodes for application in deep brain stimulation (DBS). Polypyrrole coated CNF nanoelectrodes have shown great promise as stimulating electrodes due to their large surface area, low impedance, biocompatibility and capacity for highly localized stimulation. CNFs embedded in SiO2 have been used as sensing electrodes for neurotransmitter detection. Our approach combines a multiplexed CNF electrode chip, developed at NASA Ames Research Center, with the Wireless Instantaneous Neurotransmitter Concentration Sensor (WINCS) system, developed at the Mayo Clinic. Preliminary results indicate that the CNF nanoelectrode arrays are easily integrated with WINCS for neurotransmitter detection in a multiplexed array format. In the future, combining CNF based stimulating and recording electrodes with WINCS may lay the foundation for an implantable smart therapeutic system that utilizes neurochemical feedback control while likely resulting in increased DBS application in various neuropsychiatric disorders. In total, our goal is to take advantage of the nanostructure of CNF arrays for biosensing studies requiring ultrahigh sensitivity, high-degree of miniaturization, and selective biofunctionalization.

Carbon Nanofiber Nanoelectrodes for Neural Stimulation and Chemical Detection: The Era of Smart Deep Brain Stimulation

NASA Technical Reports Server (NTRS)

Koehne, Jessica E.

2016-01-01

A sensor platform based on vertically aligned carbon nanofibers (CNFs) has been developed. Their inherent nanometer scale, high conductivity, wide potential window, good biocompatibility and well-defined surface chemistry make them ideal candidates as biosensor electrodes. Here, we report two studies using vertically aligned CNF nanoelectrodes for biomedical applications. CNF arrays are investigated as neural stimulation and neurotransmitter recording electrodes for application in deep brain stimulation (DBS). Polypyrrole coated CNF nanoelectrodes have shown great promise as stimulating electrodes due to their large surface area, low impedance, biocompatibility and capacity for highly localized stimulation. CNFs embedded in SiO2 have been used as sensing electrodes for neurotransmitter detection. Our approach combines a multiplexed CNF electrode chip, developed at NASA Ames Research Center, with the Wireless Instantaneous Neurotransmitter Concentration Sensor (WINCS) system, developed at the Mayo Clinic. Preliminary results indicate that the CNF nanoelectrode arrays are easily integrated with WINCS for neurotransmitter detection in a multiplexed array format. In the future, combining CNF based stimulating and recording electrodes with WINCS may lay the foundation for an implantable "smart" therapeutic system that utilizes neurochemical feedback control while likely resulting in increased DBS application in various neuropsychiatric disorders. In total, our goal is to take advantage of the nanostructure of CNF arrays for biosensing studies requiring ultrahigh sensitivity, high-degree of miniaturization, and selective biofunctionalization.

Processing arctic eddy-flux data using a simple carbon-exchange model embedded in the ensemble Kalman filter.

PubMed

Rastetter, Edward B; Williams, Mathew; Griffin, Kevin L; Kwiatkowski, Bonnie L; Tomasky, Gabrielle; Potosnak, Mark J; Stoy, Paul C; Shaver, Gaius R; Stieglitz, Marc; Hobbie, John E; Kling, George W

2010-07-01

Continuous time-series estimates of net ecosystem carbon exchange (NEE) are routinely made using eddy covariance techniques. Identifying and compensating for errors in the NEE time series can be automated using a signal processing filter like the ensemble Kalman filter (EnKF). The EnKF compares each measurement in the time series to a model prediction and updates the NEE estimate by weighting the measurement and model prediction relative to a specified measurement error estimate and an estimate of the model-prediction error that is continuously updated based on model predictions of earlier measurements in the time series. Because of the covariance among model variables, the EnKF can also update estimates of variables for which there is no direct measurement. The resulting estimates evolve through time, enabling the EnKF to be used to estimate dynamic variables like changes in leaf phenology. The evolving estimates can also serve as a means to test the embedded model and reconcile persistent deviations between observations and model predictions. We embedded a simple arctic NEE model into the EnKF and filtered data from an eddy covariance tower located in tussock tundra on the northern foothills of the Brooks Range in northern Alaska, USA. The model predicts NEE based only on leaf area, irradiance, and temperature and has been well corroborated for all the major vegetation types in the Low Arctic using chamber-based data. This is the first application of the model to eddy covariance data. We modified the EnKF by adding an adaptive noise estimator that provides a feedback between persistent model data deviations and the noise added to the ensemble of Monte Carlo simulations in the EnKF. We also ran the EnKF with both a specified leaf-area trajectory and with the EnKF sequentially recalibrating leaf-area estimates to compensate for persistent model-data deviations. When used together, adaptive noise estimation and sequential recalibration substantially improved filter

Variation of NEE and its affecting factors in a vineyard of arid region of northwest China

NASA Astrophysics Data System (ADS)

Guo, W. H.; Kang, S. Z.; Li, F. S.; Li, S. E.

2014-02-01

To understand the variation of net ecosystem CO2 exchange (NEE) in orchard ecosystem and it's affecting factors, carbon flux was measured using eddy covariance system in a wine vineyard in arid northwest China during 2008-2010. Results show that vineyard NEE was positive value at the early growth stage, higher negative value at the mid-growth stage, and lower negative value at the later growth stage. Diurnal variation of NEE was "W" shaped curve in sunny day, but "U" shaped curve in cloudy day. Irrigation and pruning did not affect diurnal variation shape of NEE, however, irrigation reduced the difference between maximal and minimal value of NEE and pruning reduced the carbon sink capacity. The main factors affecting hourly NEE were canopy conductance (gc) and net radiation (Rn). The hourly NEE increased with the increase of gc or Rn when gc was less than 0.02 m·s-1 or Rn was between 0 and 200 W·m-2. The main factors affecting both daily and seasonal NEE were gc, air temperature (Ta), atmospheric CO2 density, vapour pressure deficit (VPD) and soil moisture content.

A decade of continuous NEE measurements at a Scottish peatland

NASA Astrophysics Data System (ADS)

Helfter, Carole; Campbell, Claire; Coyle, Mhairi; Anderson, Margaret; Drewer, Julia; Levy, Peter; Famulari, Daniela; Twigg, Marsailidh; Skiba, Ute; Billett, Michael; Dinsmore, Kerry; Nemitz, Eiko; Sutton, Mark

2013-04-01

Eddy-covariance measurements of carbon dioxide (CO2) fluxes have been running continuously at the Auchencorth Moss peatland site in Scotland (55o47'32N, 3o14'35W, 267 m a.s.l.) since the spring of 2002 which makes this study one of the longest ones to date on a peatland system. Auchencorth Moss is a low-lying, ombrotrophic peatland situated ca. 20 km south-west of Edinburgh. Peat depth ranges from <0.5 m to >0.5 m and the site has a mean annual precipitation of 1155 mm. The open moorland site has an extensive uniform fetch of blanket bog to the south, west and north. The vegetation present within the flux measurement footprint comprises mixed grass species, heather and substantial areas of moss species (Sphagnum spp. and Polytrichum spp.). The eddy-covariance system consists of a Licor 7000 closed-path infrared gas analyser operating at 10 Hz for the simultaneous measurement of carbon dioxide and water vapour and of a Gill Windmaster Pro ultrasonic anemometer, operating at 20 Hz, and mounted atop a 3 m mast. The effective measurement height is 3.5 m with a vertical separation of 20 cm between the anemometer and the inlet of the sampling line. Air is sampled at 20 litres per minute through a 40 m long Dekabon line (internal diameter 4 mm). In addition to eddy-covariance measurements, the site is equipped with a weather station, soil temperature measurements, total solar radiation and photosynthetically active radiation (PAR) sensors, a tipping bucket for rainfall and, since April 2007, water table depth has been recorded at half-hourly interval. On an annual basis, the peatland at Auchencorth Moss has consistently been a net sink of CO2 in the study period 2002-2012 with a mean net ecosystem exchange (NEE) of - 69.1 ± 33.6 g C-CO2 m-2 yr-1. This value is at the high end of other recent studies as is the inter-annual range of NEE (-31.4 to -135.9 g C-CO2 m-2 yr-1). Inter-annual variations in NEE are significant and strongly correlated to the length of the growing

Fabrication of silicon-based template-assisted nanoelectrode arrays and ohmic contact properties investigation.

PubMed

Bai, Anqi; Cheng, Buwen; Wang, Xiaofeng; Xue, Chunlai; Zuo, Yuhua; Wang, Qiming

2010-11-01

A convenient fabrication technology for large-area, highly-ordered nanoelectrode arrays on silicon substrate has been described here, using porous anodic alumina (PAA) as a template. The ultrathin PAA membranes were anodic oxidized utilizing a two-step anodization method, from Al film evaporated on substrate. The purposes for the use of two-step anodization were, first, improving the regularity of the porous structures, and second reducing the thickness of the membranes to 100-200 nm we desired. Then the nanoelectrode arrays were obtained by electroless depositing Ni-W alloy into the through pores of PAA membranes, making the alloy isolated by the insulating pore walls and contacting with the silicon substrates at the bottoms of pores. The Ni-W alloy was also electroless deposited at the back surface of silicon to form back electrode. Then ohmic contact properties between silicon and Ni-W alloy were investigated after rapid thermal annealing. Scanning electron microscopy (SEM) observations showed the structure characteristics, and the influence factors of fabrication effect were discussed. The current-voltage (I-V) curves revealed the contact properties. After annealing in N2 at 700 degrees C, good linear property was shown with contact resistance of 33 omega, which confirmed ohmic contacts between silicon and electrodes. These results presented significant application potential of this technology in nanosize current-injection devices in optoelectronics, microelectronics and bio-medical fields.

[Simulating of carbon fluxes in bamboo forest ecosystem using BEPS model based on the LAI assimilated with Dual Ensemble Kalman Filter].

PubMed

Li, Xue Jian; Mao, Fang Jie; Du, Hua Qiang; Zhou, Guo Mo; Xu, Xiao Jun; Li, Ping Heng; Liu, Yu Li; Cui, Lu

2016-12-01

LAI is one of the most important observation data in the research of carbon cycle of forest ecosystem, and it is also an important parameter to drive process-based ecosystem model. The Moso bamboo forest (MBF) and Lei bamboo forest (LBF) were selected as the study targets. Firstly, the MODIS LAI time series data during 2014-2015 was assimilated with Dual Ensemble Kalman Filter method. Secondly, the high quality assimilated MBF LAI and LBF LAI were used as input dataset to drive BEPS model for simulating the gross primary productivity (GPP), net ecosystem exchange (NEE) and total ecosystem respiration (TER) of the two types of bamboo forest ecosystem, respectively. The modeled carbon fluxes were evaluated by the observed carbon fluxes data, and the effects of different quality LAI inputs on carbon cycle simulation were also studied. The LAI assimilated using Dual Ensemble Kalman Filter of MBF and LBF were significantly correlated with the observed LAI, with high R 2 of 0.81 and 0.91 respectively, and lower RMSE and absolute bias, which represented the great improvement of the accuracy of MODIS LAI products. With the driving of assimilated LAI, the modeled GPP, NEE, and TER were also highly correlated with the flux observation data, with the R 2 of 0.66, 0.47, and 0.64 for MBF, respectively, and 0.66, 0.45, and 0.73 for LBF, respectively. The accuracy of carbon fluxes modeled with assimilated LAI was higher than that acquired by the locally adjusted cubic-spline capping method, in which, the accuracy of mo-deled NEE for MBF and LBF increased by 11.2% and 11.8% at the most degrees, respectively.

Effects of convergent diffusion and charge transfer kinetics on the diffusion layer thickness of spherical micro- and nanoelectrodes.

PubMed

Molina, A; Laborda, E; González, J; Compton, R G

2013-05-21

Nuances of the linear diffusion layer approximation are examined for slow charge transfer reactions at (hemi)spherical micro- and nanoelectrodes. This approximation is widely employed in Electrochemistry to evaluate the extent of electrolyte solution perturbed by the electrode process, which is essential to the understanding of the effects arising from thin-layer diffusion, convergent diffusion, convection, coupled chemical reactions and the double layer. The concept was well established for fast charge transfer processes at macroelectrodes, but remains unclear under other conditions such that a thorough assessment of its meaning was necessary. In a previous publication [A. Molina, J. González, E. Laborda and R. G. Compton, Phys. Chem. Chem. Phys., 2013, 15, 2381-2388] we shed some light on the influence of the reversibility degree. In the present work, the meaning of the diffusion layer thickness is investigated when very small electrodes are employed and so the contribution of convergent diffusion to the mass transport is very important. An analytical expression is given to calculate the linear diffusion layer thickness at (hemi)spherical electrodes and its behaviour is studied for a wide range of conditions of reversibility (from reversible to fully-irreversible processes) and electrode size (from macro- to nano-electrodes). Rigorous analytical solutions are deduced for true concentration profiles, surface concentrations, linear diffusion layer thickness and current densities when a potential pulse is applied at (hemi)spherical electrodes. The expressions for the magnitudes mentioned above are valid for electrodes of any size (including (hemi)spherical nanoelectrodes) and for any degree of reversibility, provided that mass transport occurs exclusively via diffusion. The variation of the above with the electrode size, applied potential and charge transfer kinetics is studied.

A comparative study of the plasmon effect in nanoelectrode THz emitters: Pulse vs. continuous-wave radiation

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

Moon, Kiwon; Lee, Eui Su; Lee, Il-Min

Plasmonic field enhancement in terahertz (THz) generation is one of the recently arisen techniques in the THz field that has attracted considerable interest. However, the reported levels of enhancement of THz output power in the literature are significantly different from each other, from less than two times to about two orders of magnitude of enhancement in power, which implies the existence of other major limiting factors yet to be revealed. In this work, the contribution of the plasmonic effect to the power enhancement of THz emitters is revisited. We show that the carrier collection efficiency in a THz emitter withmore » plasmonic nanostructures is more critical to the device performance than the plasmonic field enhancement itself. The strong reverse fields induced by the highly localized plasmonic carriers in the vicinity of the nanoelectrodes screen the carrier collections and seriously limit the power enhancement. This is supported by our experimental observations of the significantly enhanced power in a plasmonic nanoelectrode THz emitter in continuous-wave radiation mode, while the same device has limited enhancement with pulsed radiation. We hope that our study may provide an intuitive but practical guideline in adopting plasmonic nanostructures with an aim of enhancing the efficiency of optoelectronic devices.« less

Numerical Simulation of the Diffusion Processes in Nanoelectrode Arrays Using an Axial Neighbor Symmetry Approximation.

PubMed

Peinetti, Ana Sol; Gilardoni, Rodrigo S; Mizrahi, Martín; Requejo, Felix G; González, Graciela A; Battaglini, Fernando

2016-06-07

Nanoelectrode arrays have introduced a complete new battery of devices with fascinating electrocatalytic, sensitivity, and selectivity properties. To understand and predict the electrochemical response of these arrays, a theoretical framework is needed. Cyclic voltammetry is a well-fitted experimental technique to understand the undergoing diffusion and kinetics processes. Previous works describing microelectrode arrays have exploited the interelectrode distance to simulate its behavior as the summation of individual electrodes. This approach becomes limited when the size of the electrodes decreases to the nanometer scale due to their strong radial effect with the consequent overlapping of the diffusional fields. In this work, we present a computational model able to simulate the electrochemical behavior of arrays working either as the summation of individual electrodes or being affected by the overlapping of the diffusional fields without previous considerations. Our computational model relays in dividing a regular electrode array in cells. In each of them, there is a central electrode surrounded by neighbor electrodes; these neighbor electrodes are transformed in a ring maintaining the same active electrode area than the summation of the closest neighbor electrodes. Using this axial neighbor symmetry approximation, the problem acquires a cylindrical symmetry, being applicable to any diffusion pattern. The model is validated against micro- and nanoelectrode arrays showing its ability to predict their behavior and therefore to be used as a designing tool.

Scaling for Robust Empirical Modeling and Predictions of Net Ecosystem Exchange (NEE) from Diverse Wetland Ecosystems

NASA Astrophysics Data System (ADS)

Ishtiaq, K. S.; Abdul-Aziz, O. I.

2014-12-01

We developed a scaling-based, simple empirical model for spatio-temporally robust prediction of the diurnal cycles of wetland net ecosystem exchange (NEE) by using an extended stochastic harmonic algorithm (ESHA). A reference-time observation from each diurnal cycle was utilized as the scaling parameter to normalize and collapse hourly observed NEE of different days into a single, dimensionless diurnal curve. The modeling concept was tested by parameterizing the unique diurnal curve and predicting hourly NEE of May to October (summer growing and fall seasons) between 2002-12 for diverse wetland ecosystems, as available in the U.S. AmeriFLUX network. As an example, the Taylor Slough short hydroperiod marsh site in the Florida Everglades had data for four consecutive growing seasons from 2009-12; results showed impressive modeling efficiency (coefficient of determination, R2 = 0.66) and accuracy (ratio of root-mean-square-error to the standard deviation of observations, RSR = 0.58). Model validation was performed with an independent year of NEE data, indicating equally impressive performance (R2 = 0.68, RSR = 0.57). The model included a parsimonious set of estimated parameters, which exhibited spatio-temporal robustness by collapsing onto narrow ranges. Model robustness was further investigated by analytically deriving and quantifying parameter sensitivity coefficients and a first-order uncertainty measure. The relatively robust, empirical NEE model can be applied for simulating continuous (e.g., hourly) NEE time-series from a single reference observation (or a set of limited observations) at different wetland sites of comparable hydro-climatology, biogeochemistry, and ecology. The method can also be used for a robust gap-filling of missing data in observed time-series of periodic ecohydrological variables for wetland or other ecosystems.

Small-volume multiparametric electrochemical detection at low cost polymeric devices featuring nanoelectrodes

NASA Astrophysics Data System (ADS)

Kitsara, Maria; Cirera, Josep Maria; Aller-Pellitero, Miguel; Sabaté, Neus; Punter, Jaume; Colomer-Farrarons, Jordi; Miribel-Català, Pere; del Campo, F. Javier

2015-06-01

The development of a low-cost multiparametric platform for enzymatic electrochemical biosensing that can be integrated in a disposable, energy autonomous analytical device is the target of the current work. We propose a technology to fabricate nano-electrodes and ultimately biosensors on flexible polymeric-based substrates (cyclo olefin polymer, and polyimide) using standard microfabrication (step and repeat lithography and lift-off) and rapid prototyping techniques (blade cutting). Our target is towards the fabrication of a miniaturized prototype that can work with small sample volumes in the range of 5-10μL without the need for external pumps for sample loading and handling. This device can be used for the simultaneous detection of metabolites such as glucose, cholesterol and triglycerides for the early diagnosis of diabetes.

Bias field tailored plasmonic nano-electrode for high-power terahertz photonic devices.

PubMed

Moon, Kiwon; Lee, Il-Min; Shin, Jun-Hwan; Lee, Eui Su; Kim, Namje; Lee, Won-Hui; Ko, Hyunsung; Han, Sang-Pil; Park, Kyung Hyun

2015-09-08

Photoconductive antennas with nano-structured electrodes and which show significantly improved performances have been proposed to satisfy the demand for compact and efficient terahertz (THz) sources. Plasmonic field enhancement was previously considered the dominant mechanism accounting for the improvements in the underlying physics. However, we discovered that the role of plasmonic field enhancement is limited and near-field distribution of bias field should be considered as well. In this paper, we clearly show that the locally enhanced bias field due to the size effect is much more important than the plasmonic enhanced absorption in the nano-structured electrodes for the THz emitters. Consequently, an improved nano-electrode design is presented by tailoring bias field distribution and plasmonic enhancement. Our findings will pave the way for new perspectives in the design and analysis of plasmonic nano-structures for more efficient THz photonic devices.

Erythropoietic activity of Asteracantha longifolia (Nees.) in rats.

PubMed

Pawar, Rajesh Singh; Jain, Alok Pal; Lodhi, Santram; Singhai, Abhay K

2010-05-27

Asteracantha longifolia Nees. (Family-Acanthaceae) is a wild herb commonly used in traditional ayurvedic medicine as Kokilaaksha and the Unani drug as Talimakhana in India and Srilanka for various medicinal uses as aphrodisiac, tonic, sedative and blood diseases etc. The aim of the current study was to validate and explore the folk use of Asteracantha longifolia Nees. (AL) (Leaf part) on pharmacological grounds using haloperidol induced iron deficiency anemia for the assessment of erythropoietic activity. Determination of iron in plant extracts was carried out using spectrophotometric method. Plant extract was obtained from crude drugs using extraction with ethanol. In vivo study, haloperidol induced iron deficiency anemia model was used in experimental studies. An administration of ethanolic extract of AL at the doses of 100 mg/kg and 200 mg/kg body weight, i.p., demonstrated a significant (P<0.05) increase in erythrocyte count, haemoglobin count, serum iron and serum protein etc. This effect may be due to the presence of iron (622 microg/50 mg) in extract estimated by spectrophotometric method. An ethanolic extract of AL effectively restored the hematological parameters, serum iron and serum protein and normalized the microcytic (smaller in size), anisocytosis (disturbed shape) and hypochromic RBCs. These observations could justify the inclusion of this plant in the management of iron deficiency anemia due the presence of iron and other constituents as flavonoids, terpenoids, steroids, lupeol and betulin. Copyright 2010 Elsevier Ireland Ltd. All rights reserved.

Bias field tailored plasmonic nano-electrode for high-power terahertz photonic devices

PubMed Central

Moon, Kiwon; Lee, Il-Min; Shin, Jun-Hwan; Lee, Eui Su; Kim, Namje; Lee, Won-Hui; Ko, Hyunsung; Han, Sang-Pil; Park, Kyung Hyun

2015-01-01

Photoconductive antennas with nano-structured electrodes and which show significantly improved performances have been proposed to satisfy the demand for compact and efficient terahertz (THz) sources. Plasmonic field enhancement was previously considered the dominant mechanism accounting for the improvements in the underlying physics. However, we discovered that the role of plasmonic field enhancement is limited and near-field distribution of bias field should be considered as well. In this paper, we clearly show that the locally enhanced bias field due to the size effect is much more important than the plasmonic enhanced absorption in the nano-structured electrodes for the THz emitters. Consequently, an improved nano-electrode design is presented by tailoring bias field distribution and plasmonic enhancement. Our findings will pave the way for new perspectives in the design and analysis of plasmonic nano-structures for more efficient THz photonic devices. PMID:26347288

Simulating the impacts of land use in northwest Europe on Net Ecosystem Exchange (NEE): the role of arable ecosystems, grasslands and forest plantations in climate change mitigation.

PubMed

Abdalla, Mohamed; Saunders, Matthew; Hastings, Astley; Williams, Mike; Smith, Pete; Osborne, Bruce; Lanigan, Gary; Jones, Mike B

2013-11-01

In this study, we compared measured and simulated Net Ecosystem Exchange (NEE) values from three wide spread ecosystems in the southeast of Ireland (forest, arable and grassland), and investigated the suitability of the DNDC (the DeNitrification-DeComposition) model to estimate present and future NEE. Although, the field-DNDC version overestimated NEE at temperatures >5 °C, forest-DNDC under-estimated NEE at temperatures >5 °C. The results suggest that the field/forest DNDC models can successfully estimate changes in seasonal and annual NEE from these ecosystems. Differences in NEE were found to be primarily land cover specific. The annual NEE was similar for the grassland and arable sites, but due to the contribution of exported carbon, the soil carbon increased at the grassland site and decreased at the arable site. The NEE of the forest site was an order of magnitude larger than that of the grassland or arable ecosystems, with large amounts of carbon stored in woody biomass and the soil. The average annual NEE, GPP and Reco values over the measurement period were -904, 2379 and 1475 g C m(-2) (forest plantations), -189, 906 and 715 g C m(-2) (arable systems) and -212, 1653 and 1444 g C m(-2) (grasslands), respectively. The average RMSE values were 3.8 g C m(-2) (forest plantations), 0.12 g C m(-2) (arable systems) and 0.21 g C m(-2) (grasslands). When these models were run with climate change scenarios to 2060, predictions show that all three ecosystems will continue to operate as carbon sinks. Further, climate change may decrease the carbon sink strength in the forest plantations by up to 50%. This study supports the use of the DNDC model as a valid tool to predict the consequences of climate change on NEE from different ecosystems. Copyright © 2012 Elsevier B.V. All rights reserved.

MEAs and 3D nanoelectrodes: electrodeposition as tool for a precisely controlled nanofabrication.

PubMed

Weidlich, Sabrina; Krause, Kay J; Schnitker, Jan; Wolfrum, Bernhard; Offenhäusser, Andreas

2017-01-31

Microelectrode arrays (MEAs) are gaining increasing importance for the investigation of signaling processes between electrogenic cells. However, efficient cell-chip coupling for robust and long-term electrophysiological recording and stimulation still remains a challenge. A possible approach for the improvement of the cell-electrode contact is the utilization of three-dimensional structures. In recent years, various 3D electrode geometries have been developed, but we are still lacking a fabrication approach that enables the formation of different 3D structures on a single chip in a controlled manner. This, however, is needed to enable a direct and reliable comparison of the recording capabilities of the different structures. Here, we present a method for a precisely controlled deposition of nanoelectrodes, enabling the fabrication of multiple, well-defined types of structures on our 64 electrode MEAs towards a rapid-prototyping approach to 3D electrodes.

Generalized canonical ensembles and ensemble equivalence

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

Costeniuc, M.; Ellis, R.S.; Turkington, B.

2006-02-15

This paper is a companion piece to our previous work [J. Stat. Phys. 119, 1283 (2005)], which introduced a generalized canonical ensemble obtained by multiplying the usual Boltzmann weight factor e{sup -{beta}}{sup H} of the canonical ensemble with an exponential factor involving a continuous function g of the Hamiltonian H. We provide here a simplified introduction to our previous work, focusing now on a number of physical rather than mathematical aspects of the generalized canonical ensemble. The main result discussed is that, for suitable choices of g, the generalized canonical ensemble reproduces, in the thermodynamic limit, all the microcanonical equilibriummore » properties of the many-body system represented by H even if this system has a nonconcave microcanonical entropy function. This is something that in general the standard (g=0) canonical ensemble cannot achieve. Thus a virtue of the generalized canonical ensemble is that it can often be made equivalent to the microcanonical ensemble in cases in which the canonical ensemble cannot. The case of quadratic g functions is discussed in detail; it leads to the so-called Gaussian ensemble.« less

Multi-temporal Linkages of Net Ecosystem Exchanges (NEE) with the Climatic and Ecohydrologic Drivers in a Florida Everglades Short-hydroperiod Freshwater Marsh

NASA Astrophysics Data System (ADS)

Zaki, M. T.; Abdul-Aziz, O. I.; Ishtiaq, K. S.

2017-12-01

Wetlands are considered one of the most productive and ecologically valuable ecosystems on earth. We investigated the multi-temporal linkages of net ecosystem exchange (NEE) with the relevant climatic and ecohydrological drivers for a Florida Everglades short-hydroperiod freshwater wetland. Hourly NEE observations and the associated driving variables during 2008-12 were collected from the AmeriFlux and EDEN databases, and then averaged for the four temporal scales (1-day, 8-day, 15-day, and 30-day). Pearson correlation and factor analysis were employed to identify the interrelations and grouping patterns among the participatory variables for each time scale. The climatic and ecohydrological linkages of NEE were then reliably estimated using bootstrapped (1000 iterations) partial least squares regressions by resolving multicollinearity. The analytics identified four bio-physical components exhibiting relatively robust interrelations and grouping patterns with NEE across the temporal scales. In general, NEE was most strongly linked with the `radiation-energy (RE)' component, while having a moderate linkage with the `temperature-hydrology (TH)' and `aerodynamic (AD)' components. However, the `ambient atmospheric CO2 (AC)' component was very weakly linked to NEE. Further, RE and TH had a decreasing trend with the increasing time scales (1-30 days). In contrast, the linkages of AD and AC components increased from 1-day to 8-day scales, and then remained relatively invariable at the longer scales of aggregation. The estimated linkages provide insights into the dominant biophysical process components and drivers of ecosystem carbon in the Everglades. The invariant linking pattern and linkages would help to develop low-dimensional models to reliably predict CO2 fluxes from the tidal freshwater wetlands.

In vivo test of bitter (andrographis paniculata nees.) extract to ejaculated sperm quality

NASA Astrophysics Data System (ADS)

Sumarmin, R.; Huda, NK; Yuniarti, E.; Violita

2018-03-01

Sambiloto or Bitter (Andrographis paniculata Nees.), are often used to treat various diseases, such as influenza, cancer, anti-inflammation, anti-HIV, anti-mitotic and anti-fertility. This study aimed to determine the effects of the bitter (Andrographis paniculata Nees.) extract to ejaculated sperm mice quality (Mus musculus L. Swiss Webster). This research was conducted using Completely Randomized Design with 4 treatments, which are 0.0 g/b.w., (P0), 0.2 g/b.w., (P1), 0,4 g/b.w., (P3), or 0.6 g/b.w., (P4) bitter extract orally for 36 days. After treatment, the mice decapitated, dissected and collected the sperm from vas deferens. Then, the number of sperm counted by used the improved Neubauer and then stained by Eosin to count the abnormal sperm. Data analyzed by ANOVA (Analysis of Variance) then DNMRT. The results showed that the average numbers of sperm are 28.80 x 105 (P0), 19.50 x 105 (P1), 12.50 x105 (P2) and 9.50 x 105 (P3). The average abnormal sperm numbers are 18.33 x 105 (P0), 22.50 x 105 (P1), 31.50 x105 (P2) and 39.33 x 105 (P3). It showed that the effective treatment to decrease sperm number was 0.2 g/b.w., of bitter extract. It can conclude that the bitter (Andrographis paniculata Nees.) extract decreases the quality of the ejaculated sperm of mice (Mus musculus L.)

Chip-Based Sensors for Disease Diagnosis

NASA Astrophysics Data System (ADS)

Fang, Zhichao

Nucleic acid analysis is one of the most important disease diagnostic approaches in medical practice, and has been commonly used in cancer biomarker detection, bacterial speciation and many other fields in laboratory. Currently, the application of powerful research methods for genetic analysis, including the polymerase chain reaction (PCR), DNA sequencing, and gene expression profiling using fluorescence microarrays, are not widely used in hospitals and extended-care units due to high-cost, long detection times, and extensive sample preparation. Bioassays, especially chip-based electrochemical sensors, may be suitable for the next generation of rapid, sensitive, and multiplexed detection tools. Herein, we report three different microelectrode platforms with capabilities enabled by nano- and microtechnology: nanoelectrode ensembles (NEEs), nanostructured microelectrodes (NMEs), and hierarchical nanostructured microelectrodes (HNMEs), all of which are able to directly detect unpurified RNA in clinical samples without enzymatic amplification. Biomarkers that are cancer and infectious disease relevant to clinical medicine were chosen to be the targets. Markers were successfully detected with clinically-relevant sensitivity. Using peptide nucleic acids (PNAs) as probes and an electrocatalytic reporter system, NEEs were able to detect prostate cancer-related gene fusions in tumor tissue samples with 100 ng of RNA. The development of NMEs improved the sensitivity of the assay further to 10 aM of DNA target, and multiplexed detection of RNA sequences of different prostate cancer-related gene fusion types was achieved on the chip-based NMEs platform. An HNMEs chip integrated with a bacterial lysis device was able to detect as few as 25 cfu bacteria in 30 minutes and monitor the detection in real time. Bacterial detection could also be performed in neat urine samples. The development of these versatile clinical diagnostic tools could be extended to the detection of various

Separation and counting of single molecules through nanofluidics, programmable electrophoresis, and nanoelectrode-gated tunneling and dielectric detection

DOEpatents

Lee, James W.; Thundat, Thomas G.

2006-04-25

An apparatus for carrying out the separation, detection, and/or counting of single molecules at nanometer scale. Molecular separation is achieved by driving single molecules through a microfluidic or nanofluidic medium using programmable and coordinated electric fields. In various embodiments, the fluidic medium is a strip of hydrophilic material on nonconductive hydrophobic surface, a trough produced by parallel strips of hydrophobic nonconductive material on a hydrophilic base, or a covered passageway produced by parallel strips of hydrophobic nonconductive material on a hydrophilic base together with a nonconductive cover on the parallel strips of hydrophobic nonconductive material. The molecules are detected and counted using nanoelectrode-gated electron tunneling methods, dielectric monitoring, and other methods.

The Ensembl REST API: Ensembl Data for Any Language.

PubMed

Yates, Andrew; Beal, Kathryn; Keenan, Stephen; McLaren, William; Pignatelli, Miguel; Ritchie, Graham R S; Ruffier, Magali; Taylor, Kieron; Vullo, Alessandro; Flicek, Paul

2015-01-01

We present a Web service to access Ensembl data using Representational State Transfer (REST). The Ensembl REST server enables the easy retrieval of a wide range of Ensembl data by most programming languages, using standard formats such as JSON and FASTA while minimizing client work. We also introduce bindings to the popular Ensembl Variant Effect Predictor tool permitting large-scale programmatic variant analysis independent of any specific programming language. The Ensembl REST API can be accessed at http://rest.ensembl.org and source code is freely available under an Apache 2.0 license from http://github.com/Ensembl/ensembl-rest. © The Author 2014. Published by Oxford University Press.

The Ensembl REST API: Ensembl Data for Any Language

PubMed Central

Yates, Andrew; Beal, Kathryn; Keenan, Stephen; McLaren, William; Pignatelli, Miguel; Ritchie, Graham R. S.; Ruffier, Magali; Taylor, Kieron; Vullo, Alessandro; Flicek, Paul

2015-01-01

Motivation: We present a Web service to access Ensembl data using Representational State Transfer (REST). The Ensembl REST server enables the easy retrieval of a wide range of Ensembl data by most programming languages, using standard formats such as JSON and FASTA while minimizing client work. We also introduce bindings to the popular Ensembl Variant Effect Predictor tool permitting large-scale programmatic variant analysis independent of any specific programming language. Availability and implementation: The Ensembl REST API can be accessed at http://rest.ensembl.org and source code is freely available under an Apache 2.0 license from http://github.com/Ensembl/ensembl-rest. Contact: ayates@ebi.ac.uk or flicek@ebi.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online. PMID:25236461

Electrochemical enzymatic biosensors using carbon nanofiber nanoelectrode arrays

NASA Astrophysics Data System (ADS)

Li, Jun; Li, Yi-fen; Swisher, Luxi Z.; Syed, Lateef U.; Prior, Allan M.; Nguyen, Thu A.; Hua, Duy H.

2012-10-01

The reduction of electrode size down to nanometers could dramatically enhance detection sensitivity and temporal resolution. Nanoelectrode arrays (NEAs) are of particular interest for ultrasensitive biosensors. Here we report the study of two types of biosensors for measuring enzyme activities using NEAs fabricated with vertically aligned carbon nanofibers (VACNFs). VACNFs of ~100 nm in average diameter and 3-5 μm in length were grown on conductive substrates as uniform vertical arrays which were then encapsulated in SiO2 matrix leaving only the tips exposed. We demonstrate that such VACNF NEAs can be used in profiling enzyme activities through monitoring the change in electrochemical signals induced by enzymatic reactions to the peptides attached to the VACNF tip. The cleavage of the tetrapeptide with a ferrocene tag by a cancerrelated protease (legumain) was monitored with AC voltammetry. Real-time electrochemical impedance spectroscopy (REIS) was used for fast label-free detection of two reversible processes, i.e. phosphorylation by c-Src tyrosine kinase and dephosphorylation by protein tyrosine phosphatase 1B (PTP1B). The REIS data of phosphorylation were slow and unreliable, but those of dephosphorylation showed large and fast exponential decay due to much higher activity of phosphatase PTP1B. The kinetic data were analyzed with a heterogeneous Michaelis-Menten model to derive the "specificity constant" kcat/Km, which is 8.2x103 M-1s-1 for legumain and (2.1 ± 0.1) x 107 M-1s-1 for phosphatase (PTP1B), well consistent with literature. It is promising to develop VACNF NEA based electrochemical enzymatic biosensors as portable multiplex electronic techniques for rapid cancer diagnosis and treatment monitoring.

Antidiabetic and antihiperlipidemic effect of Andrographis paniculata (Burm. f.) Nees and andrographolide in high-fructose-fat-fed rats

PubMed Central

Nugroho, Agung Endro; Andrie, Mohamad; Warditiani, Ni Kadek; Siswanto, Eka; Pramono, Suwidjiyo; Lukitaningsih, Endang

2012-01-01

Objectives: Andrographis paniculata (Burm. f.) Nees originates from India and grows widely in many areas in Southeast Asian countries. Andrographis paniculata (Burm. f.) Nees has shown an antidiabetic effect in type 1 DM rats. The present study investigates the purified extract of the plant and its active compound andrographolide for antidiabetic and antihyperlipidemic effects in high-fructose-fat-fed rats, a model of type 2 DM rats. Materials and Methods: Hyperglycemia in rats was induced by high-fructose-fat diet containing 36% fructose, 15% lard, and 5% egg yolks in 0.36 g/200 gb.wt. 55 days. The rats were treated with the extract or test compound on the 50th day. Antidiabetic activity was measured by estimating mainly the pre– and postprandial blood glucose levels and other parameters such as cholesterol, LDL, triglyceride, and body weight. Results: The purified extract and andrographolide significantly (P<0.05) decreased the levels of blood glucose, triglyceride, and LDL compared to controls. However, no changes were observed in serum cholesterol and rat body weight. Metformin also showed similar effects on these parameters. Conclusions: Andrographis paniculata (Burm. f.) Nees or its active compound andrographolide showed hypoglycemic and hypolipidemic effects in high-fat-fructose-fed rat. PMID:22701250

Simulation's Ensemble is Better Than Ensemble Simulation

NASA Astrophysics Data System (ADS)

Yan, X.

2017-12-01

Simulation's ensemble is better than ensemble simulation Yan Xiaodong State Key Laboratory of Earth Surface Processes and Resource Ecology (ESPRE) Beijing Normal University,19 Xinjiekouwai Street, Haidian District, Beijing 100875, China Email: yxd@bnu.edu.cnDynamical system is simulated from initial state. However initial state data is of great uncertainty, which leads to uncertainty of simulation. Therefore, multiple possible initial states based simulation has been used widely in atmospheric science, which has indeed been proved to be able to lower the uncertainty, that was named simulation's ensemble because multiple simulation results would be fused . In ecological field, individual based model simulation (forest gap models for example) can be regarded as simulation's ensemble compared with community based simulation (most ecosystem models). In this talk, we will address the advantage of individual based simulation and even their ensembles.

BOREAS TGB-1/TGB-3 NEE Data over the NSA Fen

NASA Technical Reports Server (NTRS)

Bellisario, Lianne; Hall, Forrest G. (Editor); Conrad, Sara K. (Editor); Moore, Tim R.

2000-01-01

The BOReal Ecosystem-Atmosphere Study Trace Gas Biogeochemistry (BOREAS TGB-1) and TGB-3 teams collected several data sets that contributed to understanding the measured trace gas fluxes over sites in the Northern Study Area (NSA). This data set contains Net Ecosystem Exchange of CO2 (NEE) measurements collected with chambers at the NSA fen in 1994 and 1996. Gas samples were extracted approximately every 7 days from chambers and analyzed at the NSA lab facility. The data are provided in tabular ASCII files.

Ensemble Methods

NASA Astrophysics Data System (ADS)

Re, Matteo; Valentini, Giorgio

2012-03-01

Ensemble methods are statistical and computational learning procedures reminiscent of the human social learning behavior of seeking several opinions before making any crucial decision. The idea of combining the opinions of different "experts" to obtain an overall “ensemble” decision is rooted in our culture at least from the classical age of ancient Greece, and it has been formalized during the Enlightenment with the Condorcet Jury Theorem[45]), which proved that the judgment of a committee is superior to those of individuals, provided the individuals have reasonable competence. Ensembles are sets of learning machines that combine in some way their decisions, or their learning algorithms, or different views of data, or other specific characteristics to obtain more reliable and more accurate predictions in supervised and unsupervised learning problems [48,116]. A simple example is represented by the majority vote ensemble, by which the decisions of different learning machines are combined, and the class that receives the majority of “votes” (i.e., the class predicted by the majority of the learning machines) is the class predicted by the overall ensemble [158]. In the literature, a plethora of terms other than ensembles has been used, such as fusion, combination, aggregation, and committee, to indicate sets of learning machines that work together to solve a machine learning problem [19,40,56,66,99,108,123], but in this chapter we maintain the term ensemble in its widest meaning, in order to include the whole range of combination methods. Nowadays, ensemble methods represent one of the main current research lines in machine learning [48,116], and the interest of the research community on ensemble methods is witnessed by conferences and workshops specifically devoted to ensembles, first of all the multiple classifier systems (MCS) conference organized by Roli, Kittler, Windeatt, and other researchers of this area [14,62,85,149,173]. Several theories have been

Residue-level global and local ensemble-ensemble comparisons of protein domains.

PubMed

Clark, Sarah A; Tronrud, Dale E; Karplus, P Andrew

2015-09-01

Many methods of protein structure generation such as NMR-based solution structure determination and template-based modeling do not produce a single model, but an ensemble of models consistent with the available information. Current strategies for comparing ensembles lose information because they use only a single representative structure. Here, we describe the ENSEMBLATOR and its novel strategy to directly compare two ensembles containing the same atoms to identify significant global and local backbone differences between them on per-atom and per-residue levels, respectively. The ENSEMBLATOR has four components: eePREP (ee for ensemble-ensemble), which selects atoms common to all models; eeCORE, which identifies atoms belonging to a cutoff-distance dependent common core; eeGLOBAL, which globally superimposes all models using the defined core atoms and calculates for each atom the two intraensemble variations, the interensemble variation, and the closest approach of members of the two ensembles; and eeLOCAL, which performs a local overlay of each dipeptide and, using a novel measure of local backbone similarity, reports the same four variations as eeGLOBAL. The combination of eeGLOBAL and eeLOCAL analyses identifies the most significant differences between ensembles. We illustrate the ENSEMBLATOR's capabilities by showing how using it to analyze NMR ensembles and to compare NMR ensembles with crystal structures provides novel insights compared to published studies. One of these studies leads us to suggest that a "consistency check" of NMR-derived ensembles may be a useful analysis step for NMR-based structure determinations in general. The ENSEMBLATOR 1.0 is available as a first generation tool to carry out ensemble-ensemble comparisons. © 2015 The Protein Society.

Residue-level global and local ensemble-ensemble comparisons of protein domains

PubMed Central

Clark, Sarah A; Tronrud, Dale E; Andrew Karplus, P

2015-01-01

Many methods of protein structure generation such as NMR-based solution structure determination and template-based modeling do not produce a single model, but an ensemble of models consistent with the available information. Current strategies for comparing ensembles lose information because they use only a single representative structure. Here, we describe the ENSEMBLATOR and its novel strategy to directly compare two ensembles containing the same atoms to identify significant global and local backbone differences between them on per-atom and per-residue levels, respectively. The ENSEMBLATOR has four components: eePREP (ee for ensemble-ensemble), which selects atoms common to all models; eeCORE, which identifies atoms belonging to a cutoff-distance dependent common core; eeGLOBAL, which globally superimposes all models using the defined core atoms and calculates for each atom the two intraensemble variations, the interensemble variation, and the closest approach of members of the two ensembles; and eeLOCAL, which performs a local overlay of each dipeptide and, using a novel measure of local backbone similarity, reports the same four variations as eeGLOBAL. The combination of eeGLOBAL and eeLOCAL analyses identifies the most significant differences between ensembles. We illustrate the ENSEMBLATOR's capabilities by showing how using it to analyze NMR ensembles and to compare NMR ensembles with crystal structures provides novel insights compared to published studies. One of these studies leads us to suggest that a “consistency check” of NMR-derived ensembles may be a useful analysis step for NMR-based structure determinations in general. The ENSEMBLATOR 1.0 is available as a first generation tool to carry out ensemble-ensemble comparisons. PMID:26032515

Using model-data fusion to analyze the interannual variability of NEE of an alpine grassland

NASA Astrophysics Data System (ADS)

Scholz, Katharina; Hammerle, Albin; Hiltbrunner, Erika; Wohlfahrt, Georg

2017-04-01

To understand the processes and magnitude of carbon dynamics of the biosphere, modeling approaches are an important tool to analyze carbon budgets from regional to global scale. Here, a simple process-based ecosystem carbon model was used to investigate differences in CO2 fluxes of a high mountain grassland near Furka Pass in the Swiss central Alps at an elevation of about 2400 m a.s.l. during two growing seasons differing in snow melt date. Data on net ecosystem CO2 exchange (NEE) as well as meteorological conditions was available from 20.06.2013 - 08.10.2014 covering two snow free periods. The NEE data indicates that the carbon uptake during the growing season in 2013 was considerably lower than in 2014. To investigate whether the lower carbon uptake in 2013 was mainly due to the short growing season, an effect of biotic response to spring environmental conditions, or the direct effect of the weather conditions during the growing season, a modeling approach was applied. For this purpose, an ecosystem mass balance C model with 13 unknown parameters was constructed based on the DALEC model to represent the major C fluxes among six carbon pools (foliage, roots, necromass, litter, soil organic carbon and a labile pool to support leaf onset in spring) of the grassland ecosystem. Daily gross primary production was estimated by use of a sun/shade big-leaf model of canopy photosynthesis. By calibrating the model with NEE data from individual years, two sets of parameters were retrieved which were then used to run the model under environmental conditions of the same as well as the other year. The parameter estimation was done using DREAM, an algorithm for statistical inference of parameters using Bayesian statistics. In order to account for non-normality, heteroscedasticity and correlation of model residuals, a common problem in ecological modeling, a generalized likelihood function was applied. The results indicate that the late growing season start in 2013 led to a

A comparison of breeding and ensemble transform vectors for global ensemble generation

NASA Astrophysics Data System (ADS)

Deng, Guo; Tian, Hua; Li, Xiaoli; Chen, Jing; Gong, Jiandong; Jiao, Meiyan

2012-02-01

To compare the initial perturbation techniques using breeding vectors and ensemble transform vectors, three ensemble prediction systems using both initial perturbation methods but with different ensemble member sizes based on the spectral model T213/L31 are constructed at the National Meteorological Center, China Meteorological Administration (NMC/CMA). A series of ensemble verification scores such as forecast skill of the ensemble mean, ensemble resolution, and ensemble reliability are introduced to identify the most important attributes of ensemble forecast systems. The results indicate that the ensemble transform technique is superior to the breeding vector method in light of the evaluation of anomaly correlation coefficient (ACC), which is a deterministic character of the ensemble mean, the root-mean-square error (RMSE) and spread, which are of probabilistic attributes, and the continuous ranked probability score (CRPS) and its decomposition. The advantage of the ensemble transform approach is attributed to its orthogonality among ensemble perturbations as well as its consistence with the data assimilation system. Therefore, this study may serve as a reference for configuration of the best ensemble prediction system to be used in operation.

The Nucleation Rate of Single O2 Nanobubbles at Pt Nanoelectrodes.

PubMed

Soto, Álvaro Moreno; German, Sean R; Ren, Hang; van der Meer, Devaraj; Lohse, Detlef; Edwards, Martin A; White, Henry S

2018-06-13

Nanobubble nucleation is a problem that affects efficiency in electrocatalytic reactions since those bubbles can block the surface of the catalytic sites. In this article, we focus on the nucleation rate of O 2 nanobubbles resulting from the electrooxidation of H 2 O 2 at Pt disk nanoelectrodes. Bubbles form almost instantaneously when a critical peak current, i nb p , is applied, but for lower currents, bubble nucleation is a stochastic process in which the nucleation (induction) time, t ind , dramatically decreases as the applied current approaches i nb p , a consequence of the local supersaturation level, ζ, increasing at high currents. Here, by applying different currents below i nb p , nanobubbles take some time to nucleate and block the surface of the Pt electrode at which the reaction occurs, providing a means to measure the stochastic t ind . We study in detail the different conditions in which nanobubbles appear, concluding that the electrode surface needs to be preconditioned to achieve reproducible results. We also measure the activation energy for bubble nucleation, E a , which varies in the range from (6 to 30) kT, and assuming a spherically cap-shaped nanobubble nucleus, we determine the footprint diameter L = 8-15 nm, the contact angle to the electrode surface θ = 135-155°, and the number of O 2 molecules contained in the nucleus (50 to 900 molecules).

Inter-annual variability of year-round NEE over 18 years, and environmental controls on seasonal patterns at an arctic wet sedge tundra, CMDL Barrow, Alaska

NASA Astrophysics Data System (ADS)

Kalhori, A. A. M.; Oechel, W. C.; Goodrich, J. P.; Gioli, B.; Burba, G. G.; Shen, S. S. P.; Murphy, P.; Zona, D.

2016-12-01

To refine understanding of the total annual carbon balance in the Arctic, it is critical to extend long-term site-level CO2 flux data collection. These data are critical to addressing the environmental controls and processes responsible for temporal variability and seasonal patterns of net ecosystem exchange of CO2 (NEE). This dataset represents the longest running eddy covariance tower in the Arctic, which is located in an Alaskan wet sedge tundra ecosystem and is adjacent to the NOAA Climate Monitoring & Diagnostic Laboratory (CMDL). In addition to analyzing the year-to-year controls on NEE and its long-term trends, this work will complement a parallel study of the 40 year record of CO2 concentration measurements from the NOAA Barrow synoptic sampling station. For long-term, retrospective measurements, missing values are unavoidable, resulting from system failure, sensors icing-up during winter, losing network connections due to the harsh conditions, necessary instrument repairs, etc. Therefore, to analyze the annual sums, diurnal patterns, and seasonal vs. annual fluxes, the choice of gap-filling approach is critical and can dominate the magnitude of uncertainties, especially for periods with long gaps (> 1 month). We have applied different gap-filling methods such as artificial neural networks (ANN), and multiple linear regression (MLR) driven by micrometeorological parameters in an effort to minimize the associated uncertainties. Following gap-filling, a stepwise multiple regression against meteorological drivers including average summer PAR, average air and soil temperature, growing season length, duration of the zero curtain, growing degree days (GDD), date of snow melt, date of freeze up, and length of the summer was applied to determine the parameters that best explain the magnitude and sign of NEE in different seasons. These statistical analyses show that growing degree days were strongly correlated with summer NEE, which increased with higher GDD

DNA and RNA sequencing by nanoscale reading through programmable electrophoresis and nanoelectrode-gated tunneling and dielectric detection

DOEpatents

Lee, James W.; Thundat, Thomas G.

2005-06-14

An apparatus and method for performing nucleic acid (DNA and/or RNA) sequencing on a single molecule. The genetic sequence information is obtained by probing through a DNA or RNA molecule base by base at nanometer scale as though looking through a strip of movie film. This DNA sequencing nanotechnology has the theoretical capability of performing DNA sequencing at a maximal rate of about 1,000,000 bases per second. This enhanced performance is made possible by a series of innovations including: novel applications of a fine-tuned nanometer gap for passage of a single DNA or RNA molecule; thin layer microfluidics for sample loading and delivery; and programmable electric fields for precise control of DNA or RNA movement. Detection methods include nanoelectrode-gated tunneling current measurements, dielectric molecular characterization, and atomic force microscopy/electrostatic force microscopy (AFM/EFM) probing for nanoscale reading of the nucleic acid sequences.

Overview of the NEES-Soft Experimental Program for Seismic Risk Reduction of Soft-Story Woodframe Buildings

Treesearch

John W. van de Lindt; Pouria Bahmani; Steven E. Pryor; Gary Mochizuki; Mikhail Gershfeld; Weichiang Pang; Ershad Ziaei; Elaina N. Jennings; Michael D. Symans; Xiaoyun Shao; Jingjing Tian; Doug Rammer

2014-01-01

The existence of thousands of soft-story woodframe buildings in California has been recognized as a disaster preparedness problem resulting in mitigation efforts throughout the state. The considerable presence of these large multi-family buildings in San Francisco prompted the city to mandate their retrofitting over the next seven years. The NEES-Soft project, whose...

HLPI-Ensemble: Prediction of human lncRNA-protein interactions based on ensemble strategy.

PubMed

Hu, Huan; Zhang, Li; Ai, Haixin; Zhang, Hui; Fan, Yetian; Zhao, Qi; Liu, Hongsheng

2018-03-27

LncRNA plays an important role in many biological and disease progression by binding to related proteins. However, the experimental methods for studying lncRNA-protein interactions are time-consuming and expensive. Although there are a few models designed to predict the interactions of ncRNA-protein, they all have some common drawbacks that limit their predictive performance. In this study, we present a model called HLPI-Ensemble designed specifically for human lncRNA-protein interactions. HLPI-Ensemble adopts the ensemble strategy based on three mainstream machine learning algorithms of Support Vector Machines (SVM), Random Forests (RF) and Extreme Gradient Boosting (XGB) to generate HLPI-SVM Ensemble, HLPI-RF Ensemble and HLPI-XGB Ensemble, respectively. The results of 10-fold cross-validation show that HLPI-SVM Ensemble, HLPI-RF Ensemble and HLPI-XGB Ensemble achieved AUCs of 0.95, 0.96 and 0.96, respectively, in the test dataset. Furthermore, we compared the performance of the HLPI-Ensemble models with the previous models through external validation dataset. The results show that the false positives (FPs) of HLPI-Ensemble models are much lower than that of the previous models, and other evaluation indicators of HLPI-Ensemble models are also higher than those of the previous models. It is further showed that HLPI-Ensemble models are superior in predicting human lncRNA-protein interaction compared with previous models. The HLPI-Ensemble is publicly available at: http://ccsipb.lnu.edu.cn/hlpiensemble/ .

Individual differences in ensemble perception reveal multiple, independent levels of ensemble representation.

PubMed

Haberman, Jason; Brady, Timothy F; Alvarez, George A

2015-04-01

Ensemble perception, including the ability to "see the average" from a group of items, operates in numerous feature domains (size, orientation, speed, facial expression, etc.). Although the ubiquity of ensemble representations is well established, the large-scale cognitive architecture of this process remains poorly defined. We address this using an individual differences approach. In a series of experiments, observers saw groups of objects and reported either a single item from the group or the average of the entire group. High-level ensemble representations (e.g., average facial expression) showed complete independence from low-level ensemble representations (e.g., average orientation). In contrast, low-level ensemble representations (e.g., orientation and color) were correlated with each other, but not with high-level ensemble representations (e.g., facial expression and person identity). These results suggest that there is not a single domain-general ensemble mechanism, and that the relationship among various ensemble representations depends on how proximal they are in representational space. (c) 2015 APA, all rights reserved).

Ensembl regulation resources

PubMed Central

Zerbino, Daniel R.; Johnson, Nathan; Juetteman, Thomas; Sheppard, Dan; Wilder, Steven P.; Lavidas, Ilias; Nuhn, Michael; Perry, Emily; Raffaillac-Desfosses, Quentin; Sobral, Daniel; Keefe, Damian; Gräf, Stefan; Ahmed, Ikhlak; Kinsella, Rhoda; Pritchard, Bethan; Brent, Simon; Amode, Ridwan; Parker, Anne; Trevanion, Steven; Birney, Ewan; Dunham, Ian; Flicek, Paul

2016-01-01

New experimental techniques in epigenomics allow researchers to assay a diversity of highly dynamic features such as histone marks, DNA modifications or chromatin structure. The study of their fluctuations should provide insights into gene expression regulation, cell differentiation and disease. The Ensembl project collects and maintains the Ensembl regulation data resources on epigenetic marks, transcription factor binding and DNA methylation for human and mouse, as well as microarray probe mappings and annotations for a variety of chordate genomes. From this data, we produce a functional annotation of the regulatory elements along the human and mouse genomes with plans to expand to other species as data becomes available. Starting from well-studied cell lines, we will progressively expand our library of measurements to a greater variety of samples. Ensembl’s regulation resources provide a central and easy-to-query repository for reference epigenomes. As with all Ensembl data, it is freely available at http://www.ensembl.org, from the Perl and REST APIs and from the public Ensembl MySQL database server at ensembldb.ensembl.org. Database URL: http://www.ensembl.org PMID:26888907

MSEBAG: a dynamic classifier ensemble generation based on `minimum-sufficient ensemble' and bagging

NASA Astrophysics Data System (ADS)

Chen, Lei; Kamel, Mohamed S.

2016-01-01

In this paper, we propose a dynamic classifier system, MSEBAG, which is characterised by searching for the 'minimum-sufficient ensemble' and bagging at the ensemble level. It adopts an 'over-generation and selection' strategy and aims to achieve a good bias-variance trade-off. In the training phase, MSEBAG first searches for the 'minimum-sufficient ensemble', which maximises the in-sample fitness with the minimal number of base classifiers. Then, starting from the 'minimum-sufficient ensemble', a backward stepwise algorithm is employed to generate a collection of ensembles. The objective is to create a collection of ensembles with a descending fitness on the data, as well as a descending complexity in the structure. MSEBAG dynamically selects the ensembles from the collection for the decision aggregation. The extended adaptive aggregation (EAA) approach, a bagging-style algorithm performed at the ensemble level, is employed for this task. EAA searches for the competent ensembles using a score function, which takes into consideration both the in-sample fitness and the confidence of the statistical inference, and averages the decisions of the selected ensembles to label the test pattern. The experimental results show that the proposed MSEBAG outperforms the benchmarks on average.

Ensembl 2004.

PubMed

Birney, E; Andrews, D; Bevan, P; Caccamo, M; Cameron, G; Chen, Y; Clarke, L; Coates, G; Cox, T; Cuff, J; Curwen, V; Cutts, T; Down, T; Durbin, R; Eyras, E; Fernandez-Suarez, X M; Gane, P; Gibbins, B; Gilbert, J; Hammond, M; Hotz, H; Iyer, V; Kahari, A; Jekosch, K; Kasprzyk, A; Keefe, D; Keenan, S; Lehvaslaiho, H; McVicker, G; Melsopp, C; Meidl, P; Mongin, E; Pettett, R; Potter, S; Proctor, G; Rae, M; Searle, S; Slater, G; Smedley, D; Smith, J; Spooner, W; Stabenau, A; Stalker, J; Storey, R; Ureta-Vidal, A; Woodwark, C; Clamp, M; Hubbard, T

2004-01-01

The Ensembl (http://www.ensembl.org/) database project provides a bioinformatics framework to organize biology around the sequences of large genomes. It is a comprehensive and integrated source of annotation of large genome sequences, available via interactive website, web services or flat files. As well as being one of the leading sources of genome annotation, Ensembl is an open source software engineering project to develop a portable system able to handle very large genomes and associated requirements. The facilities of the system range from sequence analysis to data storage and visualization and installations exist around the world both in companies and at academic sites. With a total of nine genome sequences available from Ensembl and more genomes to follow, recent developments have focused mainly on closer integration between genomes and external data.

Exploring the Electrical Conductivity of Cytochrome P450 by Nano-Electrode and Conductive Atomic Force Microscopy

NASA Astrophysics Data System (ADS)

Li, Debin; Gu, Jianhua; Chye, Yewhee; Lederman, David; Kabulski, Jarod; Gannett, Peter; Tracy, Timothy

2006-03-01

There is a growing interest in measuring the conductivity of electron-transfer proteins. The cytochrome P450 (CP450) enzymes represent an important class of heme-containing enzymes. Immobilizing CP450 enzymes on a surface can be used for studying a single enzyme with respect to electron transfer. The spin state of the heme iron can change upon binding of a substrate. In our experiment, CP450 (diameter ˜ 5 nm) has been bonded to a metal surface. Nano-electrodes (gap < 10 nm) were fabricated by defining a bridge via e-beam lithography and then breaking the junction by electromigration at low temperatures. We have examined the electronic properties of CP450 by itself and after binding CP450 with flurbiprofen. The room temperature I-V conductivity is reminiscent to cyclic voltammetry measurements, indicating the presence of strong ionic transfer. At lower temperatures (100 K) the I-V characteristics indicate electronic transport dominated by tunneling processes. The conductive AFM is an additional method used to examine the enzyme's electronic properties. The results from two methods will be discussed..

The Ensemble Canon

NASA Technical Reports Server (NTRS)

MIittman, David S

2011-01-01

Ensemble is an open architecture for the development, integration, and deployment of mission operations software. Fundamentally, it is an adaptation of the Eclipse Rich Client Platform (RCP), a widespread, stable, and supported framework for component-based application development. By capitalizing on the maturity and availability of the Eclipse RCP, Ensemble offers a low-risk, politically neutral path towards a tighter integration of operations tools. The Ensemble project is a highly successful, ongoing collaboration among NASA Centers. Since 2004, the Ensemble project has supported the development of mission operations software for NASA's Exploration Systems, Science, and Space Operations Directorates.

Soft electroporation for delivering molecules into tightly adherent mammalian cells through 3D hollow nanoelectrodes.

PubMed

Caprettini, Valeria; Cerea, Andrea; Melle, Giovanni; Lovato, Laura; Capozza, Rosario; Huang, Jian-An; Tantussi, Francesco; Dipalo, Michele; De Angelis, Francesco

2017-08-17

Electroporation of in-vitro cultured cells is widely used in biological and medical areas to deliver molecules of interest inside cells. Since very high electric fields are required to electroporate the plasma membrane, depending on the geometry of the electrodes the required voltages can be very high and often critical to cell viability. Furthermore, in traditional electroporation configuration based on planar electrodes there is no a priori certain feedback about which cell has been targeted and delivered and the addition of fluorophores may be needed to gain this information. In this study we present a nanofabricated platform able to perform intracellular delivery of membrane-impermeable molecules by opening transient nanopores into the lipid membrane of adherent cells with high spatial precision and with the application of low voltages (1.5-2 V). This result is obtained by exploiting the tight seal that the cells present with 3D fluidic hollow gold-coated nanostructures that act as nanochannels and nanoelectrodes at the same time. The final soft-electroporation platform provides an accessible approach for controlled and selective drug delivery on ordered arrangements of cells.

Ensemble Models

EPA Science Inventory

Ensemble forecasting has been used for operational numerical weather prediction in the United States and Europe since the early 1990s. An ensemble of weather or climate forecasts is used to characterize the two main sources of uncertainty in computer models of physical systems: ...

Ensemble Eclipse: A Process for Prefab Development Environment for the Ensemble Project

NASA Technical Reports Server (NTRS)

Wallick, Michael N.; Mittman, David S.; Shams, Khawaja, S.; Bachmann, Andrew G.; Ludowise, Melissa

2013-01-01

This software simplifies the process of having to set up an Eclipse IDE programming environment for the members of the cross-NASA center project, Ensemble. It achieves this by assembling all the necessary add-ons and custom tools/preferences. This software is unique in that it allows developers in the Ensemble Project (approximately 20 to 40 at any time) across multiple NASA centers to set up a development environment almost instantly and work on Ensemble software. The software automatically has the source code repositories and other vital information and settings included. The Eclipse IDE is an open-source development framework. The NASA (Ensemble-specific) version of the software includes Ensemble-specific plug-ins as well as settings for the Ensemble project. This software saves developers the time and hassle of setting up a programming environment, making sure that everything is set up in the correct manner for Ensemble development. Existing software (i.e., standard Eclipse) requires an intensive setup process that is both time-consuming and error prone. This software is built once by a single user and tested, allowing other developers to simply download and use the software

The role of ensemble post-processing for modeling the ensemble tail

NASA Astrophysics Data System (ADS)

Van De Vyver, Hans; Van Schaeybroeck, Bert; Vannitsem, Stéphane

2016-04-01

The past decades the numerical weather prediction community has witnessed a paradigm shift from deterministic to probabilistic forecast and state estimation (Buizza and Leutbecher, 2015; Buizza et al., 2008), in an attempt to quantify the uncertainties associated with initial-condition and model errors. An important benefit of a probabilistic framework is the improved prediction of extreme events. However, one may ask to what extent such model estimates contain information on the occurrence probability of extreme events and how this information can be optimally extracted. Different approaches have been proposed and applied on real-world systems which, based on extreme value theory, allow the estimation of extreme-event probabilities conditional on forecasts and state estimates (Ferro, 2007; Friederichs, 2010). Using ensemble predictions generated with a model of low dimensionality, a thorough investigation is presented quantifying the change of predictability of extreme events associated with ensemble post-processing and other influencing factors including the finite ensemble size, lead time and model assumption and the use of different covariates (ensemble mean, maximum, spread...) for modeling the tail distribution. Tail modeling is performed by deriving extreme-quantile estimates using peak-over-threshold representation (generalized Pareto distribution) or quantile regression. Common ensemble post-processing methods aim to improve mostly the ensemble mean and spread of a raw forecast (Van Schaeybroeck and Vannitsem, 2015). Conditional tail modeling, on the other hand, is a post-processing in itself, focusing on the tails only. Therefore, it is unclear how applying ensemble post-processing prior to conditional tail modeling impacts the skill of extreme-event predictions. This work is investigating this question in details. Buizza, Leutbecher, and Isaksen, 2008: Potential use of an ensemble of analyses in the ECMWF Ensemble Prediction System, Q. J. R. Meteorol

Entropy of network ensembles

NASA Astrophysics Data System (ADS)

Bianconi, Ginestra

2009-03-01

In this paper we generalize the concept of random networks to describe network ensembles with nontrivial features by a statistical mechanics approach. This framework is able to describe undirected and directed network ensembles as well as weighted network ensembles. These networks might have nontrivial community structure or, in the case of networks embedded in a given space, they might have a link probability with a nontrivial dependence on the distance between the nodes. These ensembles are characterized by their entropy, which evaluates the cardinality of networks in the ensemble. In particular, in this paper we define and evaluate the structural entropy, i.e., the entropy of the ensembles of undirected uncorrelated simple networks with given degree sequence. We stress the apparent paradox that scale-free degree distributions are characterized by having small structural entropy while they are so widely encountered in natural, social, and technological complex systems. We propose a solution to the paradox by proving that scale-free degree distributions are the most likely degree distribution with the corresponding value of the structural entropy. Finally, the general framework we present in this paper is able to describe microcanonical ensembles of networks as well as canonical or hidden-variable network ensembles with significant implications for the formulation of network-constructing algorithms.

Ensembl variation resources

PubMed Central

2010-01-01

Background The maturing field of genomics is rapidly increasing the number of sequenced genomes and producing more information from those previously sequenced. Much of this additional information is variation data derived from sampling multiple individuals of a given species with the goal of discovering new variants and characterising the population frequencies of the variants that are already known. These data have immense value for many studies, including those designed to understand evolution and connect genotype to phenotype. Maximising the utility of the data requires that it be stored in an accessible manner that facilitates the integration of variation data with other genome resources such as gene annotation and comparative genomics. Description The Ensembl project provides comprehensive and integrated variation resources for a wide variety of chordate genomes. This paper provides a detailed description of the sources of data and the methods for creating the Ensembl variation databases. It also explores the utility of the information by explaining the range of query options available, from using interactive web displays, to online data mining tools and connecting directly to the data servers programmatically. It gives a good overview of the variation resources and future plans for expanding the variation data within Ensembl. Conclusions Variation data is an important key to understanding the functional and phenotypic differences between individuals. The development of new sequencing and genotyping technologies is greatly increasing the amount of variation data known for almost all genomes. The Ensembl variation resources are integrated into the Ensembl genome browser and provide a comprehensive way to access this data in the context of a widely used genome bioinformatics system. All Ensembl data is freely available at http://www.ensembl.org and from the public MySQL database server at ensembldb.ensembl.org. PMID:20459805

NEE and GPP dynamic evolution at two biomes in the upper Spanish plateau

NASA Astrophysics Data System (ADS)

Sánchez, María Luisa; Pardo, Nuria; Pérez, Isidro Alberto; García, Maria de los Angeles

2014-05-01

In order to assess the ability of dominant biomes to act as a CO2 sink, two eddy correlation stations close to each other in central Spain have been concurrently operational since March 2008 until the present. The land use of the first station, AC, is a rapeseed rotating crop consisting of annual rotation of non-irrigated rapeseed, barley, peas, rye, and sunflower, respectively. The land use of the second, CIBA, is a mixture of open shrubs/crops, with open shrubs being markedly dominant. The period of measurements covered variable general meteorological conditions. 2009 and 2012 were dominated by drought, whereas 2010 was the rainiest year. Annual rainfall during 2008 and 2009 was close to the historical averaged annual means. This paper presents the dynamic evolution of NEE-8d and GPP-8d observed at the AC station over five years and compares the results with those concurrently observed at the CIBA station. GGP 8-d estimates at both stations were determined using a Light Use Efficiency Model, LUE. Input data for the LUE model were the FPAR 8-d products supplied by MODIS, PAR in situ measurements, and a scalar f, varying between 0 and 1, to take account of the reduction in maximum PAR conversion efficiency, ɛ0, under limiting environmental conditions. f values were assumed to be dependent on air temperature and evaporative fraction, EF, which was considered a proxy of soil moisture. ɛ0, a key parameter, which depends on land use types, was derived through the results of a linear regression fit between the GPP 8-d eddy covariance composites observed and the LUE concurrent 8-d model estimates. Over the five-year study period, both biomes behaved as CO2 sinks. However, the ratio of the NEE-8d total accumulated at AC and CIBA, respectively, was close to a factor two, revealing the effectiveness of the studied crops as CO2 sinks. On an annual basis, accumulated NEE-8d exhibited major variability in both biomes. At CIBA, the results were largely dominated by the

Historical Mortality of Valley Oak (Quercus lobata, Nee) in the Santa Ynez Valley, Santa Barbara County, 1938-1989

Treesearch

Rodney W. Brown; Frank W. Davis

1991-01-01

The range and abundance of valley oak (Quercus lobata, Nee) have steadily decreased in the last 100 years due to low rates of regeneration during this period. Documented low rates of sapling recruitment must be compared to adult mortality rates in order to evaluate the severity of this decline. The purpose of this research is to measure and analyze...

Locally Weighted Ensemble Clustering.

PubMed

Huang, Dong; Wang, Chang-Dong; Lai, Jian-Huang

2018-05-01

Due to its ability to combine multiple base clusterings into a probably better and more robust clustering, the ensemble clustering technique has been attracting increasing attention in recent years. Despite the significant success, one limitation to most of the existing ensemble clustering methods is that they generally treat all base clusterings equally regardless of their reliability, which makes them vulnerable to low-quality base clusterings. Although some efforts have been made to (globally) evaluate and weight the base clusterings, yet these methods tend to view each base clustering as an individual and neglect the local diversity of clusters inside the same base clustering. It remains an open problem how to evaluate the reliability of clusters and exploit the local diversity in the ensemble to enhance the consensus performance, especially, in the case when there is no access to data features or specific assumptions on data distribution. To address this, in this paper, we propose a novel ensemble clustering approach based on ensemble-driven cluster uncertainty estimation and local weighting strategy. In particular, the uncertainty of each cluster is estimated by considering the cluster labels in the entire ensemble via an entropic criterion. A novel ensemble-driven cluster validity measure is introduced, and a locally weighted co-association matrix is presented to serve as a summary for the ensemble of diverse clusters. With the local diversity in ensembles exploited, two novel consensus functions are further proposed. Extensive experiments on a variety of real-world datasets demonstrate the superiority of the proposed approach over the state-of-the-art.

CLINICAL STUDIES ON KALMEGH (ANDROGRAPHIS PANICULATA NEES) IN INFECTIVE HEPATITIS

PubMed Central

Chturvedi, G. N.; Tomar, G. S.; Tiwari, S. K.; Singh, K. P.

1983-01-01

Infective hepatitis ia an acute inflamatory condition of liver. It is usually manifested in the form of Jaundice. In this clinical study Kalmegh(Andrographis paniculata Nees) was given in the decoction form to the patients of infective hepitis. The results were assessed on the basis of clinical and biochemical parameters. A marked symptomatic improvement in majority of the cases was observed. A statistically highly significant decrease was noted in various liver function tests viz., serum bilirubin, thymol turbidity, alkaline phosphatase, S.G.O.T.; S.G.P.T. and serum globulin fraction of protein. Moreover it increased significantly total serum globulin fraction of protien. Moreover it increased significantly total serum protein level along with albumin fraction. On the total assessment 80% cases of this series were cured and 20% patients were relieved. Therefore, Kalmegh appears to be a useful remedy for the treatment of infective hepatitis. PMID:22556984

Clinical studies on kalmegh (andrographis paniculata nees) in infective hepatitis.

PubMed

Chturvedi, G N; Tomar, G S; Tiwari, S K; Singh, K P

1983-04-01

Infective hepatitis ia an acute inflamatory condition of liver. It is usually manifested in the form of Jaundice. In this clinical study Kalmegh(Andrographis paniculata Nees) was given in the decoction form to the patients of infective hepitis. The results were assessed on the basis of clinical and biochemical parameters. A marked symptomatic improvement in majority of the cases was observed. A statistically highly significant decrease was noted in various liver function tests viz., serum bilirubin, thymol turbidity, alkaline phosphatase, S.G.O.T.; S.G.P.T. and serum globulin fraction of protein. Moreover it increased significantly total serum globulin fraction of protien. Moreover it increased significantly total serum protein level along with albumin fraction. On the total assessment 80% cases of this series were cured and 20% patients were relieved. Therefore, Kalmegh appears to be a useful remedy for the treatment of infective hepatitis.

Intracellular and Extracellular Recording of Spontaneous Action Potentials in Mammalian Neurons and Cardiac Cells with 3D Plasmonic Nanoelectrodes.

PubMed

Dipalo, Michele; Amin, Hayder; Lovato, Laura; Moia, Fabio; Caprettini, Valeria; Messina, Gabriele C; Tantussi, Francesco; Berdondini, Luca; De Angelis, Francesco

2017-06-14

Three-dimensional vertical micro- and nanostructures can enhance the signal quality of multielectrode arrays and promise to become the prime methodology for the investigation of large networks of electrogenic cells. So far, access to the intracellular environment has been obtained via spontaneous poration, electroporation, or by surface functionalization of the micro/nanostructures; however, these methods still suffer from some limitations due to their intrinsic characteristics that limit their widespread use. Here, we demonstrate the ability to continuously record both extracellular and intracellular-like action potentials at each electrode site in spontaneously active mammalian neurons and HL-1 cardiac-derived cells via the combination of vertical nanoelectrodes with plasmonic optoporation. We demonstrate long-term and stable recordings with a very good signal-to-noise ratio. Additionally, plasmonic optoporation does not perturb the spontaneous electrical activity; it permits continuous recording even during the poration process and can regulate extracellular and intracellular contributions by means of partial cellular poration.

Imprinting and recalling cortical ensembles.

PubMed

Carrillo-Reid, Luis; Yang, Weijian; Bando, Yuki; Peterka, Darcy S; Yuste, Rafael

2016-08-12

Neuronal ensembles are coactive groups of neurons that may represent building blocks of cortical circuits. These ensembles could be formed by Hebbian plasticity, whereby synapses between coactive neurons are strengthened. Here we report that repetitive activation with two-photon optogenetics of neuronal populations from ensembles in the visual cortex of awake mice builds neuronal ensembles that recur spontaneously after being imprinted and do not disrupt preexisting ones. Moreover, imprinted ensembles can be recalled by single- cell stimulation and remain coactive on consecutive days. Our results demonstrate the persistent reconfiguration of cortical circuits by two-photon optogenetics into neuronal ensembles that can perform pattern completion. Copyright © 2016, American Association for the Advancement of Science.

World Music Ensemble: Kulintang

ERIC Educational Resources Information Center

Beegle, Amy C.

2012-01-01

As instrumental world music ensembles such as steel pan, mariachi, gamelan and West African drums are becoming more the norm than the exception in North American school music programs, there are other world music ensembles just starting to gain popularity in particular parts of the United States. The kulintang ensemble, a drum and gong ensemble…

Evaluation and uncertainty analysis of regional-scale CLM4.5 net carbon flux estimates

NASA Astrophysics Data System (ADS)

Post, Hanna; Hendricks Franssen, Harrie-Jan; Han, Xujun; Baatz, Roland; Montzka, Carsten; Schmidt, Marius; Vereecken, Harry

2018-01-01

Modeling net ecosystem exchange (NEE) at the regional scale with land surface models (LSMs) is relevant for the estimation of regional carbon balances, but studies on it are very limited. Furthermore, it is essential to better understand and quantify the uncertainty of LSMs in order to improve them. An important key variable in this respect is the prognostic leaf area index (LAI), which is very sensitive to forcing data and strongly affects the modeled NEE. We applied the Community Land Model (CLM4.5-BGC) to the Rur catchment in western Germany and compared estimated and default ecological key parameters for modeling carbon fluxes and LAI. The parameter estimates were previously estimated with the Markov chain Monte Carlo (MCMC) approach DREAM(zs) for four of the most widespread plant functional types in the catchment. It was found that the catchment-scale annual NEE was strongly positive with default parameter values but negative (and closer to observations) with the estimated values. Thus, the estimation of CLM parameters with local NEE observations can be highly relevant when determining regional carbon balances. To obtain a more comprehensive picture of model uncertainty, CLM ensembles were set up with perturbed meteorological input and uncertain initial states in addition to uncertain parameters. C3 grass and C3 crops were particularly sensitive to the perturbed meteorological input, which resulted in a strong increase in the standard deviation of the annual NEE sum (σ ∑ NEE) for the different ensemble members from ˜ 2 to 3 g C m-2 yr-1 (with uncertain parameters) to ˜ 45 g C m-2 yr-1 (C3 grass) and ˜ 75 g C m-2 yr-1 (C3 crops) with perturbed forcings. This increase in uncertainty is related to the impact of the meteorological forcings on leaf onset and senescence, and enhanced/reduced drought stress related to perturbation of precipitation. The NEE uncertainty for the forest plant functional type (PFT) was considerably lower (�

Ensemble data assimilation in the Red Sea: sensitivity to ensemble selection and atmospheric forcing

NASA Astrophysics Data System (ADS)

Toye, Habib; Zhan, Peng; Gopalakrishnan, Ganesh; Kartadikaria, Aditya R.; Huang, Huang; Knio, Omar; Hoteit, Ibrahim

2017-07-01

We present our efforts to build an ensemble data assimilation and forecasting system for the Red Sea. The system consists of the high-resolution Massachusetts Institute of Technology general circulation model (MITgcm) to simulate ocean circulation and of the Data Research Testbed (DART) for ensemble data assimilation. DART has been configured to integrate all members of an ensemble adjustment Kalman filter (EAKF) in parallel, based on which we adapted the ensemble operations in DART to use an invariant ensemble, i.e., an ensemble Optimal Interpolation (EnOI) algorithm. This approach requires only single forward model integration in the forecast step and therefore saves substantial computational cost. To deal with the strong seasonal variability of the Red Sea, the EnOI ensemble is then seasonally selected from a climatology of long-term model outputs. Observations of remote sensing sea surface height (SSH) and sea surface temperature (SST) are assimilated every 3 days. Real-time atmospheric fields from the National Center for Environmental Prediction (NCEP) and the European Center for Medium-Range Weather Forecasts (ECMWF) are used as forcing in different assimilation experiments. We investigate the behaviors of the EAKF and (seasonal-) EnOI and compare their performances for assimilating and forecasting the circulation of the Red Sea. We further assess the sensitivity of the assimilation system to various filtering parameters (ensemble size, inflation) and atmospheric forcing.

Enhanced synthesis of andrographolide by Aspergillus niger and Penicillium expansum elicitors in cell suspension culture of Andrographis paniculata (Burm. f.) Nees.

PubMed

Vakil, Moinuddin M A; Mendhulkar, Vijay D

2013-12-01

Andrographis paniculata (Burm. f.) Nees is an important medicinal plant which has enormous applications in pharmaceutical industries. Cell suspension culture of Andrographis paniculata (Burm. f.) Nees. was treated with Aspergillus niger and Penicillium expansum elicitors to enhance the synthesis of andrographolide, the bioactive constituent of A. paniculata. The elicitation treatment with fungal elicitors (A. niger and P. expansum) was observed to be most suitable for eliciting andrographolide production in the culture. The quantification of andrographolide was done using High Performance Liquid Chromatography (HPLC) technique. A. niger extract (1.5 ml with10 days treatment duration) revealed 6.94 fold increase in andrographolide content (132 μg) which was higher than the control (19 μg). P. expansum elicitor (0.6% with 8 days treatment duration) could reveal 6.23 fold enhancement in andrographolide content (81.0 μg) over control (13 μg). The results obtained reveal that the longer treatment duration is most favorable for the elicitation of andrographolide using both the fungal elicitors.

Herb-drug interaction of Andrographis paniculata (Nees) extract and andrographolide on pharmacokinetic and pharmacodynamic of naproxen in rats.

PubMed

Balap, Aishwarya; Lohidasan, Sathiyanarayanan; Sinnathambi, Arulmozhi; Mahadik, Kakasaheb

2017-01-04

Andrographis paniculata Nees (Acanthacae) have broad range of pharmacological effects such as hepatoprotective, antifertility, antimalarial, antidiabetic, suppression of various cancer cells and anti-inflammatory properties and is widely used medicinal plant in the traditional Unani and Ayurvedic medicinal systems. Andrographolide (AN) is one of the active constituent of the A. paniculata Nees extract (APE). They have been found in many traditional herbal formulations in India and proven to be effective as anti-inflammatory drug. To evaluate the pharmacokinetic and pharmacodynamic (anti arthritic) herb-drug interactions of A. paniculata Nees extract (APE) and pure andrographolide (AN) with naproxen (NP) after oral co-administration in wistar rats. After oral co-administration of APE (200mg/Kg) and AN (60mg/kg) with NP (7.5mg/kg) in rats, drug concentrations in plasma were determined using HPLC method. The main pharmacokinetic parameters of C max , t max , t 1/2 , MRT, Vd, CL, and AUC were calculated by non-compartment model. Change in paw volume, mechanical nociceptive threshold, mechanical hyperalgesia, histopathology and hematological parameters were evaluated to study antiarthritic activity. Co-administration of NP with APE and pure AN decreased systemic exposure level of NP in vivo. The C max , t max, AUC 0-t of NP was decreased. In pharmacodynamic study, NP (10mg/kg) alone and NP+AN (10+60mg/kg) groups exhibited significant synergistic anti-arthritic activity as compared to groups NP+APE, APE and AN alone. The results obtained from this study suggested that NP, APE and pure AN existed pharmacokinetic herb-drug interactions in rat which is correlated with anti-arthritic study. The knowledge regarding possible herb-drug interaction of NP might be helpful for physicians as well as patients using AP. So further studies should be done to understand the effect of other herbal ingredients of APE on NP as well as to predict the herb-drug interaction in humans

The genus Spathius Nees (Hymenoptera, Braconidae, Doryctinae) in Mexico: occurrence of a highly diverse Old World taxon in the Neotropics.

PubMed

Belokobylskij, Sergey A; Zaldívar-Riverón, Alejandro

2014-01-01

Two new species of the parasitoid wasp genus Spathius Nees (Braconidae: Doryctinae) from Mexico, S. mexicanus sp. n. and S. chamelae sp. n., are described and illustrated. These represent the second and third described species of this highly diverse Old World genus in the Neotropics, and the first described species recorded for the Mexican territory.

Ensembl comparative genomics resources

PubMed Central

Muffato, Matthieu; Beal, Kathryn; Fitzgerald, Stephen; Gordon, Leo; Pignatelli, Miguel; Vilella, Albert J.; Searle, Stephen M. J.; Amode, Ridwan; Brent, Simon; Spooner, William; Kulesha, Eugene; Yates, Andrew; Flicek, Paul

2016-01-01

Evolution provides the unifying framework with which to understand biology. The coherent investigation of genic and genomic data often requires comparative genomics analyses based on whole-genome alignments, sets of homologous genes and other relevant datasets in order to evaluate and answer evolutionary-related questions. However, the complexity and computational requirements of producing such data are substantial: this has led to only a small number of reference resources that are used for most comparative analyses. The Ensembl comparative genomics resources are one such reference set that facilitates comprehensive and reproducible analysis of chordate genome data. Ensembl computes pairwise and multiple whole-genome alignments from which large-scale synteny, per-base conservation scores and constrained elements are obtained. Gene alignments are used to define Ensembl Protein Families, GeneTrees and homologies for both protein-coding and non-coding RNA genes. These resources are updated frequently and have a consistent informatics infrastructure and data presentation across all supported species. Specialized web-based visualizations are also available including synteny displays, collapsible gene tree plots, a gene family locator and different alignment views. The Ensembl comparative genomics infrastructure is extensively reused for the analysis of non-vertebrate species by other projects including Ensembl Genomes and Gramene and much of the information here is relevant to these projects. The consistency of the annotation across species and the focus on vertebrates makes Ensembl an ideal system to perform and support vertebrate comparative genomic analyses. We use robust software and pipelines to produce reference comparative data and make it freely available. Database URL: http://www.ensembl.org. PMID:26896847

The genus Spathius Nees (Hymenoptera, Braconidae, Doryctinae) in Mexico: occurrence of a highly diverse Old World taxon in the Neotropics

PubMed Central

Belokobylskij, Sergey A.; Zaldívar-Riverón, Alejandro

2014-01-01

Abstract Two new species of the parasitoid wasp genus Spathius Nees (Braconidae: Doryctinae) from Mexico, S. mexicanus sp. n. and S. chamelae sp. n., are described and illustrated. These represent the second and third described species of this highly diverse Old World genus in the Neotropics, and the first described species recorded for the Mexican territory. PMID:25147464

Impacts of calibration strategies and ensemble methods on ensemble flood forecasting over Lanjiang basin, Southeast China

NASA Astrophysics Data System (ADS)

Liu, Li; Xu, Yue-Ping

2017-04-01

Ensemble flood forecasting driven by numerical weather prediction products is becoming more commonly used in operational flood forecasting applications.In this study, a hydrological ensemble flood forecasting system based on Variable Infiltration Capacity (VIC) model and quantitative precipitation forecasts from TIGGE dataset is constructed for Lanjiang Basin, Southeast China. The impacts of calibration strategies and ensemble methods on the performance of the system are then evaluated.The hydrological model is optimized by parallel programmed ɛ-NSGAII multi-objective algorithm and two respectively parameterized models are determined to simulate daily flows and peak flows coupled with a modular approach.The results indicatethat the ɛ-NSGAII algorithm permits more efficient optimization and rational determination on parameter setting.It is demonstrated that the multimodel ensemble streamflow mean have better skills than the best singlemodel ensemble mean (ECMWF) and the multimodel ensembles weighted on members and skill scores outperform other multimodel ensembles. For typical flood event, it is proved that the flood can be predicted 3-4 days in advance, but the flows in rising limb can be captured with only 1-2 days ahead due to the flash feature. With respect to peak flows selected by Peaks Over Threshold approach, the ensemble means from either singlemodel or multimodels are generally underestimated as the extreme values are smoothed out by ensemble process.

Ensembl comparative genomics resources.

PubMed

Herrero, Javier; Muffato, Matthieu; Beal, Kathryn; Fitzgerald, Stephen; Gordon, Leo; Pignatelli, Miguel; Vilella, Albert J; Searle, Stephen M J; Amode, Ridwan; Brent, Simon; Spooner, William; Kulesha, Eugene; Yates, Andrew; Flicek, Paul

2016-01-01

Evolution provides the unifying framework with which to understand biology. The coherent investigation of genic and genomic data often requires comparative genomics analyses based on whole-genome alignments, sets of homologous genes and other relevant datasets in order to evaluate and answer evolutionary-related questions. However, the complexity and computational requirements of producing such data are substantial: this has led to only a small number of reference resources that are used for most comparative analyses. The Ensembl comparative genomics resources are one such reference set that facilitates comprehensive and reproducible analysis of chordate genome data. Ensembl computes pairwise and multiple whole-genome alignments from which large-scale synteny, per-base conservation scores and constrained elements are obtained. Gene alignments are used to define Ensembl Protein Families, GeneTrees and homologies for both protein-coding and non-coding RNA genes. These resources are updated frequently and have a consistent informatics infrastructure and data presentation across all supported species. Specialized web-based visualizations are also available including synteny displays, collapsible gene tree plots, a gene family locator and different alignment views. The Ensembl comparative genomics infrastructure is extensively reused for the analysis of non-vertebrate species by other projects including Ensembl Genomes and Gramene and much of the information here is relevant to these projects. The consistency of the annotation across species and the focus on vertebrates makes Ensembl an ideal system to perform and support vertebrate comparative genomic analyses. We use robust software and pipelines to produce reference comparative data and make it freely available. Database URL: http://www.ensembl.org. © The Author(s) 2016. Published by Oxford University Press.

EnsembleGraph: Interactive Visual Analysis of Spatial-Temporal Behavior for Ensemble Simulation Data

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

Shu, Qingya; Guo, Hanqi; Che, Limei

We present a novel visualization framework—EnsembleGraph— for analyzing ensemble simulation data, in order to help scientists understand behavior similarities between ensemble members over space and time. A graph-based representation is used to visualize individual spatiotemporal regions with similar behaviors, which are extracted by hierarchical clustering algorithms. A user interface with multiple-linked views is provided, which enables users to explore, locate, and compare regions that have similar behaviors between and then users can investigate and analyze the selected regions in detail. The driving application of this paper is the studies on regional emission influences over tropospheric ozone, which is based onmore » ensemble simulations conducted with different anthropogenic emission absences using the MOZART-4 (model of ozone and related tracers, version 4) model. We demonstrate the effectiveness of our method by visualizing the MOZART-4 ensemble simulation data and evaluating the relative regional emission influences on tropospheric ozone concentrations. Positive feedbacks from domain experts and two case studies prove efficiency of our method.« less

Measuring social interaction in music ensembles

PubMed Central

D'Ausilio, Alessandro; Badino, Leonardo; Camurri, Antonio; Fadiga, Luciano

2016-01-01

Music ensembles are an ideal test-bed for quantitative analysis of social interaction. Music is an inherently social activity, and music ensembles offer a broad variety of scenarios which are particularly suitable for investigation. Small ensembles, such as string quartets, are deemed a significant example of self-managed teams, where all musicians contribute equally to a task. In bigger ensembles, such as orchestras, the relationship between a leader (the conductor) and a group of followers (the musicians) clearly emerges. This paper presents an overview of recent research on social interaction in music ensembles with a particular focus on (i) studies from cognitive neuroscience; and (ii) studies adopting a computational approach for carrying out automatic quantitative analysis of ensemble music performances. PMID:27069054

Measuring social interaction in music ensembles.

PubMed

Volpe, Gualtiero; D'Ausilio, Alessandro; Badino, Leonardo; Camurri, Antonio; Fadiga, Luciano

2016-05-05

Music ensembles are an ideal test-bed for quantitative analysis of social interaction. Music is an inherently social activity, and music ensembles offer a broad variety of scenarios which are particularly suitable for investigation. Small ensembles, such as string quartets, are deemed a significant example of self-managed teams, where all musicians contribute equally to a task. In bigger ensembles, such as orchestras, the relationship between a leader (the conductor) and a group of followers (the musicians) clearly emerges. This paper presents an overview of recent research on social interaction in music ensembles with a particular focus on (i) studies from cognitive neuroscience; and (ii) studies adopting a computational approach for carrying out automatic quantitative analysis of ensemble music performances. © 2016 The Author(s).

Synthesis and characterization of lithium ion nanobatteries and lithium battery nanoelectrode arrays

NASA Astrophysics Data System (ADS)

Vullum, Fride

2005-07-01

Arrays of individual nanobatteries were constructed by confining V 2O5 ambigel and a PEO wax electrolyte containing lithium triflate in the porous structure of an alumina membrane. The pores had an average diameter of 200 nm. Cyclic voltammetry data indicated that this configuration could be described by a nanoelectrode array model. A.C. impedance data of the macro cell coupled with a lithium anode showed that there was little or no unstable passivation behavior of the lithium anode in contact with the PEO wax electrolyte. This was attributed to a self-assembled hydrocarbon layer that formed at the surface of the wax preventing the lithium metal from chemically reacting with oxygen atoms in the PEO backbone. Individual nanobatteries were characterized by charge/discharge analysis. Electrical contact with individual nanocathodes was achieved using the cantilever tip of an atomic force microscope. Of the three different anode materials that were investigated SnO2 seemed to perform better than either graphite or lithium metal. This was attributed to SnO2 being able to accept more lithium ions into its structure than graphite. The favorable capacity values compared to the lithium anode batteries were attributed to better contact between the electrolyte and the anode. Average volumetric capacities for the SnO2 system were found to be around 45 muAh/cm2mum, which compare favorably to similar systems reported in literature. These nanobatteries also exhibited capacitor-like behavior, having capacitances around 300-400 F/g, which is in the range of what is expected for a supercapacitor. An electrochemical cell combining battery-like and capacitor-like behavior is a very promising power supply for applications such as electric vehicle propulsion systems.

Statistical Ensemble of Large Eddy Simulations

NASA Technical Reports Server (NTRS)

Carati, Daniele; Rogers, Michael M.; Wray, Alan A.; Mansour, Nagi N. (Technical Monitor)

2001-01-01

A statistical ensemble of large eddy simulations (LES) is run simultaneously for the same flow. The information provided by the different large scale velocity fields is used to propose an ensemble averaged version of the dynamic model. This produces local model parameters that only depend on the statistical properties of the flow. An important property of the ensemble averaged dynamic procedure is that it does not require any spatial averaging and can thus be used in fully inhomogeneous flows. Also, the ensemble of LES's provides statistics of the large scale velocity that can be used for building new models for the subgrid-scale stress tensor. The ensemble averaged dynamic procedure has been implemented with various models for three flows: decaying isotropic turbulence, forced isotropic turbulence, and the time developing plane wake. It is found that the results are almost independent of the number of LES's in the statistical ensemble provided that the ensemble contains at least 16 realizations.

Clara Haber, nee Immerwahr (1870-1915): Life, Work and Legacy.

PubMed

Friedrich, Bretislav; Hoffmann, Dieter

2016-03-01

We examine the life, work, and legacy of Clara Haber, nee Immerwahr, who became the first woman to earn a doctorate from the University of Breslau, in 1900. In 1901 she married the chemist Fritz Haber. With no employment available for female scientists, Clara freelanced as an instructor in the continued education of women, mainly housewives, while struggling not to become a housewife herself. Her duties as a designated head of a posh household hardly brought fulfillment to her life. The outbreak of WWI further exacerbated the situation, as Fritz Haber applied himself in extraordinary ways to aid the German war effort. The night that he celebrated the "success" of the first chlorine cloud attack, Clara committed suicide. We found little evidence to support claims that Clara was an outspoken pacifist who took her life because of her disapproval of Fritz Haber's involvement in chemical warfare. We conclude by examining "the myth of Clara Immerwahr" that took root in the 1990s from the perspective offered by the available scholarly sources, including some untapped ones.

Joys of Community Ensemble Playing: The Case of the Happy Roll Elastic Ensemble in Taiwan

ERIC Educational Resources Information Center

Hsieh, Yuan-Mei; Kao, Kai-Chi

2012-01-01

The Happy Roll Elastic Ensemble (HREE) is a community music ensemble supported by Tainan Culture Centre in Taiwan. With enjoyment and friendship as its primary goals, it aims to facilitate the joys of ensemble playing and the spirit of social networking. This article highlights the key aspects of HREE's development in its first two years…

Ensemble Data Mining Methods

NASA Technical Reports Server (NTRS)

Oza, Nikunj C.

2004-01-01

Ensemble Data Mining Methods, also known as Committee Methods or Model Combiners, are machine learning methods that leverage the power of multiple models to achieve better prediction accuracy than any of the individual models could on their own. The basic goal when designing an ensemble is the same as when establishing a committee of people: each member of the committee should be as competent as possible, but the members should be complementary to one another. If the members are not complementary, Le., if they always agree, then the committee is unnecessary---any one member is sufficient. If the members are complementary, then when one or a few members make an error, the probability is high that the remaining members can correct this error. Research in ensemble methods has largely revolved around designing ensembles consisting of competent yet complementary models.

Assessing the impact of land use change on hydrology by ensemble modelling (LUCHEM) II: Ensemble combinations and predictions

USGS Publications Warehouse

Viney, N.R.; Bormann, H.; Breuer, L.; Bronstert, A.; Croke, B.F.W.; Frede, H.; Graff, T.; Hubrechts, L.; Huisman, J.A.; Jakeman, A.J.; Kite, G.W.; Lanini, J.; Leavesley, G.; Lettenmaier, D.P.; Lindstrom, G.; Seibert, J.; Sivapalan, M.; Willems, P.

2009-01-01

This paper reports on a project to compare predictions from a range of catchment models applied to a mesoscale river basin in central Germany and to assess various ensemble predictions of catchment streamflow. The models encompass a large range in inherent complexity and input requirements. In approximate order of decreasing complexity, they are DHSVM, MIKE-SHE, TOPLATS, WASIM-ETH, SWAT, PRMS, SLURP, HBV, LASCAM and IHACRES. The models are calibrated twice using different sets of input data. The two predictions from each model are then combined by simple averaging to produce a single-model ensemble. The 10 resulting single-model ensembles are combined in various ways to produce multi-model ensemble predictions. Both the single-model ensembles and the multi-model ensembles are shown to give predictions that are generally superior to those of their respective constituent models, both during a 7-year calibration period and a 9-year validation period. This occurs despite a considerable disparity in performance of the individual models. Even the weakest of models is shown to contribute useful information to the ensembles they are part of. The best model combination methods are a trimmed mean (constructed using the central four or six predictions each day) and a weighted mean ensemble (with weights calculated from calibration performance) that places relatively large weights on the better performing models. Conditional ensembles, in which separate model weights are used in different system states (e.g. summer and winter, high and low flows) generally yield little improvement over the weighted mean ensemble. However a conditional ensemble that discriminates between rising and receding flows shows moderate improvement. An analysis of ensemble predictions shows that the best ensembles are not necessarily those containing the best individual models. Conversely, it appears that some models that predict well individually do not necessarily combine well with other models in

The Weighted-Average Lagged Ensemble.

PubMed

DelSole, T; Trenary, L; Tippett, M K

2017-11-01

A lagged ensemble is an ensemble of forecasts from the same model initialized at different times but verifying at the same time. The skill of a lagged ensemble mean can be improved by assigning weights to different forecasts in such a way as to maximize skill. If the forecasts are bias corrected, then an unbiased weighted lagged ensemble requires the weights to sum to one. Such a scheme is called a weighted-average lagged ensemble. In the limit of uncorrelated errors, the optimal weights are positive and decay monotonically with lead time, so that the least skillful forecasts have the least weight. In more realistic applications, the optimal weights do not always behave this way. This paper presents a series of analytic examples designed to illuminate conditions under which the weights of an optimal weighted-average lagged ensemble become negative or depend nonmonotonically on lead time. It is shown that negative weights are most likely to occur when the errors grow rapidly and are highly correlated across lead time. The weights are most likely to behave nonmonotonically when the mean square error is approximately constant over the range forecasts included in the lagged ensemble. An extreme example of the latter behavior is presented in which the optimal weights vanish everywhere except at the shortest and longest lead times.

Gridded Calibration of Ensemble Wind Vector Forecasts Using Ensemble Model Output Statistics

NASA Astrophysics Data System (ADS)

Lazarus, S. M.; Holman, B. P.; Splitt, M. E.

2017-12-01

A computationally efficient method is developed that performs gridded post processing of ensemble wind vector forecasts. An expansive set of idealized WRF model simulations are generated to provide physically consistent high resolution winds over a coastal domain characterized by an intricate land / water mask. Ensemble model output statistics (EMOS) is used to calibrate the ensemble wind vector forecasts at observation locations. The local EMOS predictive parameters (mean and variance) are then spread throughout the grid utilizing flow-dependent statistical relationships extracted from the downscaled WRF winds. Using data withdrawal and 28 east central Florida stations, the method is applied to one year of 24 h wind forecasts from the Global Ensemble Forecast System (GEFS). Compared to the raw GEFS, the approach improves both the deterministic and probabilistic forecast skill. Analysis of multivariate rank histograms indicate the post processed forecasts are calibrated. Two downscaling case studies are presented, a quiescent easterly flow event and a frontal passage. Strengths and weaknesses of the approach are presented and discussed.

Adaptive correction of ensemble forecasts

NASA Astrophysics Data System (ADS)

Pelosi, Anna; Battista Chirico, Giovanni; Van den Bergh, Joris; Vannitsem, Stephane

2017-04-01

Forecasts from numerical weather prediction (NWP) models often suffer from both systematic and non-systematic errors. These are present in both deterministic and ensemble forecasts, and originate from various sources such as model error and subgrid variability. Statistical post-processing techniques can partly remove such errors, which is particularly important when NWP outputs concerning surface weather variables are employed for site specific applications. Many different post-processing techniques have been developed. For deterministic forecasts, adaptive methods such as the Kalman filter are often used, which sequentially post-process the forecasts by continuously updating the correction parameters as new ground observations become available. These methods are especially valuable when long training data sets do not exist. For ensemble forecasts, well-known techniques are ensemble model output statistics (EMOS), and so-called "member-by-member" approaches (MBM). Here, we introduce a new adaptive post-processing technique for ensemble predictions. The proposed method is a sequential Kalman filtering technique that fully exploits the information content of the ensemble. One correction equation is retrieved and applied to all members, however the parameters of the regression equations are retrieved by exploiting the second order statistics of the forecast ensemble. We compare our new method with two other techniques: a simple method that makes use of a running bias correction of the ensemble mean, and an MBM post-processing approach that rescales the ensemble mean and spread, based on minimization of the Continuous Ranked Probability Score (CRPS). We perform a verification study for the region of Campania in southern Italy. We use two years (2014-2015) of daily meteorological observations of 2-meter temperature and 10-meter wind speed from 18 ground-based automatic weather stations distributed across the region, comparing them with the corresponding COSMO

On the generation of climate model ensembles

NASA Astrophysics Data System (ADS)

Haughton, Ned; Abramowitz, Gab; Pitman, Andy; Phipps, Steven J.

2014-10-01

Climate model ensembles are used to estimate uncertainty in future projections, typically by interpreting the ensemble distribution for a particular variable probabilistically. There are, however, different ways to produce climate model ensembles that yield different results, and therefore different probabilities for a future change in a variable. Perhaps equally importantly, there are different approaches to interpreting the ensemble distribution that lead to different conclusions. Here we use a reduced-resolution climate system model to compare three common ways to generate ensembles: initial conditions perturbation, physical parameter perturbation, and structural changes. Despite these three approaches conceptually representing very different categories of uncertainty within a modelling system, when comparing simulations to observations of surface air temperature they can be very difficult to separate. Using the twentieth century CMIP5 ensemble for comparison, we show that initial conditions ensembles, in theory representing internal variability, significantly underestimate observed variance. Structural ensembles, perhaps less surprisingly, exhibit over-dispersion in simulated variance. We argue that future climate model ensembles may need to include parameter or structural perturbation members in addition to perturbed initial conditions members to ensure that they sample uncertainty due to internal variability more completely. We note that where ensembles are over- or under-dispersive, such as for the CMIP5 ensemble, estimates of uncertainty need to be treated with care.

Ensembl 2002: accommodating comparative genomics.

PubMed

Clamp, M; Andrews, D; Barker, D; Bevan, P; Cameron, G; Chen, Y; Clark, L; Cox, T; Cuff, J; Curwen, V; Down, T; Durbin, R; Eyras, E; Gilbert, J; Hammond, M; Hubbard, T; Kasprzyk, A; Keefe, D; Lehvaslaiho, H; Iyer, V; Melsopp, C; Mongin, E; Pettett, R; Potter, S; Rust, A; Schmidt, E; Searle, S; Slater, G; Smith, J; Spooner, W; Stabenau, A; Stalker, J; Stupka, E; Ureta-Vidal, A; Vastrik, I; Birney, E

2003-01-01

The Ensembl (http://www.ensembl.org/) database project provides a bioinformatics framework to organise biology around the sequences of large genomes. It is a comprehensive source of stable automatic annotation of human, mouse and other genome sequences, available as either an interactive web site or as flat files. Ensembl also integrates manually annotated gene structures from external sources where available. As well as being one of the leading sources of genome annotation, Ensembl is an open source software engineering project to develop a portable system able to handle very large genomes and associated requirements. These range from sequence analysis to data storage and visualisation and installations exist around the world in both companies and at academic sites. With both human and mouse genome sequences available and more vertebrate sequences to follow, many of the recent developments in Ensembl have focusing on developing automatic comparative genome analysis and visualisation.

Imprinting and Recalling Cortical Ensembles

PubMed Central

Carrillo-Reid, Luis; Yang, Weijian; Bando, Yuki; Peterka, Darcy S.; Yuste, Rafael

2017-01-01

Neuronal ensembles are coactive groups of neurons that may represent emergent building blocks of neural circuits. They could be formed by Hebbian plasticity, whereby synapses between coactive neurons are strengthened. Here we report that repetitive activation with two-photon optogenetics of neuronal populations in visual cortex of awake mice generates artificially induced ensembles which recur spontaneously after being imprinted and do not disrupt preexistent ones. Moreover, imprinted ensembles can be recalled by single cell stimulation and remain coactive on consecutive days. Our results demonstrate the persistent reconfiguration of cortical circuits by two-photon optogenetics into neuronal ensembles that can perform pattern completion. PMID:27516599

Ensemble Pulsar Time Scale

NASA Astrophysics Data System (ADS)

Yin, Dong-shan; Gao, Yu-ping; Zhao, Shu-hong

2017-07-01

Millisecond pulsars can generate another type of time scale that is totally independent of the atomic time scale, because the physical mechanisms of the pulsar time scale and the atomic time scale are quite different from each other. Usually the pulsar timing observations are not evenly sampled, and the internals between two data points range from several hours to more than half a month. Further more, these data sets are sparse. All this makes it difficult to generate an ensemble pulsar time scale. Hence, a new algorithm to calculate the ensemble pulsar time scale is proposed. Firstly, a cubic spline interpolation is used to densify the data set, and make the intervals between data points uniform. Then, the Vondrak filter is employed to smooth the data set, and get rid of the high-frequency noises, and finally the weighted average method is adopted to generate the ensemble pulsar time scale. The newly released NANOGRAV (North American Nanohertz Observatory for Gravitational Waves) 9-year data set is used to generate the ensemble pulsar time scale. This data set includes the 9-year observational data of 37 millisecond pulsars observed by the 100-meter Green Bank telescope and the 305-meter Arecibo telescope. It is found that the algorithm used in this paper can reduce effectively the influence caused by the noises in pulsar timing residuals, and improve the long-term stability of the ensemble pulsar time scale. Results indicate that the long-term (> 1 yr) stability of the ensemble pulsar time scale is better than 3.4 × 10-15.

Pharmacokinetic and pharmacodynamic herb-drug interaction of Andrographis paniculata (Nees) extract and andrographolide with etoricoxib after oral administration in rats.

PubMed

Balap, Aishwarya; Atre, Bhagyashri; Lohidasan, Sathiyanarayanan; Sinnathambi, Arulmozhi; Mahadik, Kakasaheb

2016-05-13

Andrographis paniculata Nees (Acanthacae) is commonly used medicinal plant in the traditional. Unani and Ayurvedic medicinal systems. It has broad range of pharmacological effects such as hepatoprotective, antioxidant, antivenom, antifertility, inhibition of replication of the HIV virus, antimalarial, antifungal, antibacterial, antidiabetic, suppression of various cancer cells and anti-inflammatory properties. Andrographolide (AN) is one of the active constituent of the A. paniculata Nees extract (APE). They have been found in many traditional herbal formulations in India and proven to be effective as anti-inflammatory drug To evaluate the pharmacokinetic and pharmacodynamic (anti-arthritic) herb-drug interactions of A. paniculata Nees extract (APE) and pure andrographolide (AN) with etoricoxib (ETO) after oral co-administration in wistar rats. After oral co-administration of APE (200mg/Kg) and AN (60mg/kg) with ETO (10mg/kg) in rats, drug concentrations in plasma were determined using HPLC method. The main pharmacokinetic parameters of Cmax, tmax, t1/2, MRT, Vd, CL, and AUC were calculated by non-compartment model. Change in paw volume, mechanical nociceptive threshold, mechanical hyperalgesia, histopathology and hematological parameters were evaluated to study antiarthritic activity. Co-administration of ETO with APE and pure AN decreased systemic exposure level of each compound in vivo. The Cmax, AUC, t1/2 of ETO was decreased whereas Vd and CL of ETO was increased significantly after co-administration of ETO with pure AN and APE. In pharmacodynamic study, ETO alone and ETO+APE (10+200mg/kg) groups exhibited significant synergistic anti-arthritic activity as compared to groups ETO+AN, APE and AN alone. The results obtained from this study suggested that ETO, APE and pure AN existed pharmacokinetic herb-drug interactions in rat which is correlated with anti-arthritic study. Physicians and patients using A. paniculata should have the knowledge about its possible

Quantum ensembles of quantum classifiers.

PubMed

Schuld, Maria; Petruccione, Francesco

2018-02-09

Quantum machine learning witnesses an increasing amount of quantum algorithms for data-driven decision making, a problem with potential applications ranging from automated image recognition to medical diagnosis. Many of those algorithms are implementations of quantum classifiers, or models for the classification of data inputs with a quantum computer. Following the success of collective decision making with ensembles in classical machine learning, this paper introduces the concept of quantum ensembles of quantum classifiers. Creating the ensemble corresponds to a state preparation routine, after which the quantum classifiers are evaluated in parallel and their combined decision is accessed by a single-qubit measurement. This framework naturally allows for exponentially large ensembles in which - similar to Bayesian learning - the individual classifiers do not have to be trained. As an example, we analyse an exponentially large quantum ensemble in which each classifier is weighed according to its performance in classifying the training data, leading to new results for quantum as well as classical machine learning.

Multi-Model Ensemble Wake Vortex Prediction

NASA Technical Reports Server (NTRS)

Koerner, Stephan; Holzaepfel, Frank; Ahmad, Nash'at N.

2015-01-01

Several multi-model ensemble methods are investigated for predicting wake vortex transport and decay. This study is a joint effort between National Aeronautics and Space Administration and Deutsches Zentrum fuer Luft- und Raumfahrt to develop a multi-model ensemble capability using their wake models. An overview of different multi-model ensemble methods and their feasibility for wake applications is presented. The methods include Reliability Ensemble Averaging, Bayesian Model Averaging, and Monte Carlo Simulations. The methodologies are evaluated using data from wake vortex field experiments.

A Statistical Description of Neural Ensemble Dynamics

PubMed Central

Long, John D.; Carmena, Jose M.

2011-01-01

The growing use of multi-channel neural recording techniques in behaving animals has produced rich datasets that hold immense potential for advancing our understanding of how the brain mediates behavior. One limitation of these techniques is they do not provide important information about the underlying anatomical connections among the recorded neurons within an ensemble. Inferring these connections is often intractable because the set of possible interactions grows exponentially with ensemble size. This is a fundamental challenge one confronts when interpreting these data. Unfortunately, the combination of expert knowledge and ensemble data is often insufficient for selecting a unique model of these interactions. Our approach shifts away from modeling the network diagram of the ensemble toward analyzing changes in the dynamics of the ensemble as they relate to behavior. Our contribution consists of adapting techniques from signal processing and Bayesian statistics to track the dynamics of ensemble data on time-scales comparable with behavior. We employ a Bayesian estimator to weigh prior information against the available ensemble data, and use an adaptive quantization technique to aggregate poorly estimated regions of the ensemble data space. Importantly, our method is capable of detecting changes in both the magnitude and structure of correlations among neurons missed by firing rate metrics. We show that this method is scalable across a wide range of time-scales and ensemble sizes. Lastly, the performance of this method on both simulated and real ensemble data is used to demonstrate its utility. PMID:22319486

Monthly ENSO Forecast Skill and Lagged Ensemble Size

PubMed Central

DelSole, T.; Tippett, M.K.; Pegion, K.

2018-01-01

Abstract The mean square error (MSE) of a lagged ensemble of monthly forecasts of the Niño 3.4 index from the Climate Forecast System (CFSv2) is examined with respect to ensemble size and configuration. Although the real‐time forecast is initialized 4 times per day, it is possible to infer the MSE for arbitrary initialization frequency and for burst ensembles by fitting error covariances to a parametric model and then extrapolating to arbitrary ensemble size and initialization frequency. Applying this method to real‐time forecasts, we find that the MSE consistently reaches a minimum for a lagged ensemble size between one and eight days, when four initializations per day are included. This ensemble size is consistent with the 8–10 day lagged ensemble configuration used operationally. Interestingly, the skill of both ensemble configurations is close to the estimated skill of the infinite ensemble. The skill of the weighted, lagged, and burst ensembles are found to be comparable. Certain unphysical features of the estimated error growth were tracked down to problems with the climatology and data discontinuities. PMID:29937973

Monthly ENSO Forecast Skill and Lagged Ensemble Size

NASA Astrophysics Data System (ADS)

Trenary, L.; DelSole, T.; Tippett, M. K.; Pegion, K.

2018-04-01

The mean square error (MSE) of a lagged ensemble of monthly forecasts of the Niño 3.4 index from the Climate Forecast System (CFSv2) is examined with respect to ensemble size and configuration. Although the real-time forecast is initialized 4 times per day, it is possible to infer the MSE for arbitrary initialization frequency and for burst ensembles by fitting error covariances to a parametric model and then extrapolating to arbitrary ensemble size and initialization frequency. Applying this method to real-time forecasts, we find that the MSE consistently reaches a minimum for a lagged ensemble size between one and eight days, when four initializations per day are included. This ensemble size is consistent with the 8-10 day lagged ensemble configuration used operationally. Interestingly, the skill of both ensemble configurations is close to the estimated skill of the infinite ensemble. The skill of the weighted, lagged, and burst ensembles are found to be comparable. Certain unphysical features of the estimated error growth were tracked down to problems with the climatology and data discontinuities.

Similarity Measures for Protein Ensembles

PubMed Central

Lindorff-Larsen, Kresten; Ferkinghoff-Borg, Jesper

2009-01-01

Analyses of similarities and changes in protein conformation can provide important information regarding protein function and evolution. Many scores, including the commonly used root mean square deviation, have therefore been developed to quantify the similarities of different protein conformations. However, instead of examining individual conformations it is in many cases more relevant to analyse ensembles of conformations that have been obtained either through experiments or from methods such as molecular dynamics simulations. We here present three approaches that can be used to compare conformational ensembles in the same way as the root mean square deviation is used to compare individual pairs of structures. The methods are based on the estimation of the probability distributions underlying the ensembles and subsequent comparison of these distributions. We first validate the methods using a synthetic example from molecular dynamics simulations. We then apply the algorithms to revisit the problem of ensemble averaging during structure determination of proteins, and find that an ensemble refinement method is able to recover the correct distribution of conformations better than standard single-molecule refinement. PMID:19145244

Improving database enrichment through ensemble docking

NASA Astrophysics Data System (ADS)

Rao, Shashidhar; Sanschagrin, Paul C.; Greenwood, Jeremy R.; Repasky, Matthew P.; Sherman, Woody; Farid, Ramy

2008-09-01

While it may seem intuitive that using an ensemble of multiple conformations of a receptor in structure-based virtual screening experiments would necessarily yield improved enrichment of actives relative to using just a single receptor, it turns out that at least in the p38 MAP kinase model system studied here, a very large majority of all possible ensembles do not yield improved enrichment of actives. However, there are combinations of receptor structures that do lead to improved enrichment results. We present here a method to select the ensembles that produce the best enrichments that does not rely on knowledge of active compounds or sophisticated analyses of the 3D receptor structures. In the system studied here, the small fraction of ensembles of up to 3 receptors that do yield good enrichments of actives were identified by selecting ensembles that have the best mean GlideScore for the top 1% of the docked ligands in a database screen of actives and drug-like "decoy" ligands. Ensembles of two receptors identified using this mean GlideScore metric generally outperform single receptors, while ensembles of three receptors identified using this metric consistently give optimal enrichment factors in which, for example, 40% of the known actives outrank all the other ligands in the database.

New technique for ensemble dressing combining Multimodel SuperEnsemble and precipitation PDF

NASA Astrophysics Data System (ADS)

Cane, D.; Milelli, M.

2009-09-01

The Multimodel SuperEnsemble technique (Krishnamurti et al., Science 285, 1548-1550, 1999) is a postprocessing method for the estimation of weather forecast parameters reducing direct model output errors. It differs from other ensemble analysis techniques by the use of an adequate weighting of the input forecast models to obtain a combined estimation of meteorological parameters. Weights are calculated by least-square minimization of the difference between the model and the observed field during a so-called training period. Although it can be applied successfully on the continuous parameters like temperature, humidity, wind speed and mean sea level pressure (Cane and Milelli, Meteorologische Zeitschrift, 15, 2, 2006), the Multimodel SuperEnsemble gives good results also when applied on the precipitation, a parameter quite difficult to handle with standard post-processing methods. Here we present our methodology for the Multimodel precipitation forecasts applied on a wide spectrum of results over Piemonte very dense non-GTS weather station network. We will focus particularly on an accurate statistical method for bias correction and on the ensemble dressing in agreement with the observed precipitation forecast-conditioned PDF. Acknowledgement: this work is supported by the Italian Civil Defence Department.

Verifying and Postprocesing the Ensemble Spread-Error Relationship

NASA Astrophysics Data System (ADS)

Hopson, Tom; Knievel, Jason; Liu, Yubao; Roux, Gregory; Wu, Wanli

2013-04-01

With the increased utilization of ensemble forecasts in weather and hydrologic applications, there is a need to verify their benefit over less expensive deterministic forecasts. One such potential benefit of ensemble systems is their capacity to forecast their own forecast error through the ensemble spread-error relationship. The paper begins by revisiting the limitations of the Pearson correlation alone in assessing this relationship. Next, we introduce two new metrics to consider in assessing the utility an ensemble's varying dispersion. We argue there are two aspects of an ensemble's dispersion that should be assessed. First, and perhaps more fundamentally: is there enough variability in the ensembles dispersion to justify the maintenance of an expensive ensemble prediction system (EPS), irrespective of whether the EPS is well-calibrated or not? To diagnose this, the factor that controls the theoretical upper limit of the spread-error correlation can be useful. Secondly, does the variable dispersion of an ensemble relate to variable expectation of forecast error? Representing the spread-error correlation in relation to its theoretical limit can provide a simple diagnostic of this attribute. A context for these concepts is provided by assessing two operational ensembles: 30-member Western US temperature forecasts for the U.S. Army Test and Evaluation Command and 51-member Brahmaputra River flow forecasts of the Climate Forecast and Applications Project for Bangladesh. Both of these systems utilize a postprocessing technique based on quantile regression (QR) under a step-wise forward selection framework leading to ensemble forecasts with both good reliability and sharpness. In addition, the methodology utilizes the ensemble's ability to self-diagnose forecast instability to produce calibrated forecasts with informative skill-spread relationships. We will describe both ensemble systems briefly, review the steps used to calibrate the ensemble forecast, and present

Mixture models for protein structure ensembles.

PubMed

Hirsch, Michael; Habeck, Michael

2008-10-01

Protein structure ensembles provide important insight into the dynamics and function of a protein and contain information that is not captured with a single static structure. However, it is not clear a priori to what extent the variability within an ensemble is caused by internal structural changes. Additional variability results from overall translations and rotations of the molecule. And most experimental data do not provide information to relate the structures to a common reference frame. To report meaningful values of intrinsic dynamics, structural precision, conformational entropy, etc., it is therefore important to disentangle local from global conformational heterogeneity. We consider the task of disentangling local from global heterogeneity as an inference problem. We use probabilistic methods to infer from the protein ensemble missing information on reference frames and stable conformational sub-states. To this end, we model a protein ensemble as a mixture of Gaussian probability distributions of either entire conformations or structural segments. We learn these models from a protein ensemble using the expectation-maximization algorithm. Our first model can be used to find multiple conformers in a structure ensemble. The second model partitions the protein chain into locally stable structural segments or core elements and less structured regions typically found in loops. Both models are simple to implement and contain only a single free parameter: the number of conformers or structural segments. Our models can be used to analyse experimental ensembles, molecular dynamics trajectories and conformational change in proteins. The Python source code for protein ensemble analysis is available from the authors upon request.

Image Change Detection via Ensemble Learning

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

Martin, Benjamin W; Vatsavai, Raju

2013-01-01

The concept of geographic change detection is relevant in many areas. Changes in geography can reveal much information about a particular location. For example, analysis of changes in geography can identify regions of population growth, change in land use, and potential environmental disturbance. A common way to perform change detection is to use a simple method such as differencing to detect regions of change. Though these techniques are simple, often the application of these techniques is very limited. Recently, use of machine learning methods such as neural networks for change detection has been explored with great success. In this work,more » we explore the use of ensemble learning methodologies for detecting changes in bitemporal synthetic aperture radar (SAR) images. Ensemble learning uses a collection of weak machine learning classifiers to create a stronger classifier which has higher accuracy than the individual classifiers in the ensemble. The strength of the ensemble lies in the fact that the individual classifiers in the ensemble create a mixture of experts in which the final classification made by the ensemble classifier is calculated from the outputs of the individual classifiers. Our methodology leverages this aspect of ensemble learning by training collections of weak decision tree based classifiers to identify regions of change in SAR images collected of a region in the Staten Island, New York area during Hurricane Sandy. Preliminary studies show that the ensemble method has approximately 11.5% higher change detection accuracy than an individual classifier.« less

A Theoretical Analysis of Why Hybrid Ensembles Work.

PubMed

Hsu, Kuo-Wei

2017-01-01

Inspired by the group decision making process, ensembles or combinations of classifiers have been found favorable in a wide variety of application domains. Some researchers propose to use the mixture of two different types of classification algorithms to create a hybrid ensemble. Why does such an ensemble work? The question remains. Following the concept of diversity, which is one of the fundamental elements of the success of ensembles, we conduct a theoretical analysis of why hybrid ensembles work, connecting using different algorithms to accuracy gain. We also conduct experiments on classification performance of hybrid ensembles of classifiers created by decision tree and naïve Bayes classification algorithms, each of which is a top data mining algorithm and often used to create non-hybrid ensembles. Therefore, through this paper, we provide a complement to the theoretical foundation of creating and using hybrid ensembles.

Clara Haber, nee Immerwahr (1870–1915): Life, Work and Legacy

PubMed Central

2016-01-01

Abstract We examine the life, work, and legacy of Clara Haber, nee Immerwahr, who became the first woman to earn a doctorate from the University of Breslau, in 1900. In 1901 she married the chemist Fritz Haber. With no employment available for female scientists, Clara freelanced as an instructor in the continued education of women, mainly housewives, while struggling not to become a housewife herself. Her duties as a designated head of a posh household hardly brought fulfillment to her life. The outbreak of WWI further exacerbated the situation, as Fritz Haber applied himself in extraordinary ways to aid the German war effort. The night that he celebrated the “success” of the first chlorine cloud attack, Clara committed suicide. We found little evidence to support claims that Clara was an outspoken pacifist who took her life because of her disapproval of Fritz Haber's involvement in chemical warfare. We conclude by examining “the myth of Clara Immerwahr” that took root in the 1990s from the perspective offered by the available scholarly sources, including some untapped ones. PMID:27099403

Anti-inflammatory, analgesic and antipyretic effects of Lepidagathis anobrya Nees (Acanthaceae).

PubMed

Richard, Sawadogo Wamtinga; Marius, Lompo; Noya, Somé; Innocent Pierre, Guissou; Germaine, Nacoulma-Ouedraogo Odile

2011-01-01

This study investigated the general acute, anti-inflammatory, analgesic and antipyretic effects of methanol extract of Lepidagathis anobrya Nees (Acanthaceae). Carrageenan-induced rat paw edema and croton oil-induced ear edema in rats were used for the evaluation of general acute anti-inflammatory effects. Acetic acid-induced writhing response and yeast-induced hyperpyrexia in mice were used to evaluate the analgesic and antipyretic activities respectively. The extract at doses of 10, 25, 50 and 100 mgkg(-1) for carrageenan test and doses of 0.5 mg/ear for croton oil test induced a significant reduction (p < 0.001) of paw and ear edemas in rats. In the analgesic and antipyretic tests, the extract has shown a significant inhibition of writhes and hyperpyrexia with all the doses used when compared to the untreated control group. These results clearly show the anti-inflammatory, analgesic and antipyretic effects of the methanol extract of Lepidagathis anobrya and give the scientific basis for its traditional use. Further studies are needed to clarify the mechanism of action and the components responsible for these pharmacological effects.

Direct Analysis in Real Time by Mass Spectrometric Technique for Determining the Variation in Metabolite Profiles of Cinnamomum tamala Nees and Eberm Genotypes

PubMed Central

Singh, Vineeta; Gupta, Atul Kumar; Singh, S. P.; Kumar, Anil

2012-01-01

Cinnamomum tamala Nees & Eberm. is an important traditional medicinal plant, mentioned in various ancient literatures such as Ayurveda. Several of its medicinal properties have recently been proved. To characterize diversity in terms of metabolite profiles of Cinnamomum tamala Nees and Eberm genotypes, a newly emerging mass spectral ionization technique direct time in real time (DART) is very helpful. The DART ion source has been used to analyze an extremely wide range of phytochemicals present in leaves of Cinnamomum tamala. Ten genotypes were assessed for the presence of different phytochemicals. Phytochemical analysis showed the presence of mainly terpenes and phenols. These constituents vary in the different genotypes of Cinnamomum tamala. Principal component analysis has also been employed to analyze the DART data of these Cinnamomum genotypes. The result shows that the genotype of Cinnamomum tamala could be differentiated using DART MS data. The active components present in Cinnamomum tamala may be contributing significantly to high amount of antioxidant property of leaves and, in turn, conditional effects for diabetic patients. PMID:22701361

Input Decimated Ensembles

NASA Technical Reports Server (NTRS)

Tumer, Kagan; Oza, Nikunj C.; Clancy, Daniel (Technical Monitor)

2001-01-01

Using an ensemble of classifiers instead of a single classifier has been shown to improve generalization performance in many pattern recognition problems. However, the extent of such improvement depends greatly on the amount of correlation among the errors of the base classifiers. Therefore, reducing those correlations while keeping the classifiers' performance levels high is an important area of research. In this article, we explore input decimation (ID), a method which selects feature subsets for their ability to discriminate among the classes and uses them to decouple the base classifiers. We provide a summary of the theoretical benefits of correlation reduction, along with results of our method on two underwater sonar data sets, three benchmarks from the Probenl/UCI repositories, and two synthetic data sets. The results indicate that input decimated ensembles (IDEs) outperform ensembles whose base classifiers use all the input features; randomly selected subsets of features; and features created using principal components analysis, on a wide range of domains.

A Theoretical Analysis of Why Hybrid Ensembles Work

PubMed Central

2017-01-01

Inspired by the group decision making process, ensembles or combinations of classifiers have been found favorable in a wide variety of application domains. Some researchers propose to use the mixture of two different types of classification algorithms to create a hybrid ensemble. Why does such an ensemble work? The question remains. Following the concept of diversity, which is one of the fundamental elements of the success of ensembles, we conduct a theoretical analysis of why hybrid ensembles work, connecting using different algorithms to accuracy gain. We also conduct experiments on classification performance of hybrid ensembles of classifiers created by decision tree and naïve Bayes classification algorithms, each of which is a top data mining algorithm and often used to create non-hybrid ensembles. Therefore, through this paper, we provide a complement to the theoretical foundation of creating and using hybrid ensembles. PMID:28255296

Effect of land model ensemble versus coupled model ensemble on the simulation of precipitation climatology and variability

NASA Astrophysics Data System (ADS)

Wei, Jiangfeng; Dirmeyer, Paul A.; Yang, Zong-Liang; Chen, Haishan

2017-10-01

Through a series of model simulations with an atmospheric general circulation model coupled to three different land surface models, this study investigates the impacts of land model ensembles and coupled model ensemble on precipitation simulation. It is found that coupling an ensemble of land models to an atmospheric model has a very minor impact on the improvement of precipitation climatology and variability, but a simple ensemble average of the precipitation from three individually coupled land-atmosphere models produces better results, especially for precipitation variability. The generally weak impact of land processes on precipitation should be the main reason that the land model ensembles do not improve precipitation simulation. However, if there are big biases in the land surface model or land surface data set, correcting them could improve the simulated climate, especially for well-constrained regional climate simulations.

Evaluation of an Ensemble Dispersion Calculation.

NASA Astrophysics Data System (ADS)

Draxler, Roland R.

2003-02-01

A Lagrangian transport and dispersion model was modified to generate multiple simulations from a single meteorological dataset. Each member of the simulation was computed by assuming a ±1-gridpoint shift in the horizontal direction and a ±250-m shift in the vertical direction of the particle position, with respect to the meteorological data. The configuration resulted in 27 ensemble members. Each member was assumed to have an equal probability. The model was tested by creating an ensemble of daily average air concentrations for 3 months at 75 measurement locations over the eastern half of the United States during the Across North America Tracer Experiment (ANATEX). Two generic graphical displays were developed to summarize the ensemble prediction and the resulting concentration probabilities for a specific event: a probability-exceed plot and a concentration-probability plot. Although a cumulative distribution of the ensemble probabilities compared favorably with the measurement data, the resulting distribution was not uniform. This result was attributed to release height sensitivity. The trajectory ensemble approach accounts for about 41%-47% of the variance in the measurement data. This residual uncertainty is caused by other model and data errors that are not included in the ensemble design.

On the predictability of outliers in ensemble forecasts

NASA Astrophysics Data System (ADS)

Siegert, S.; Bröcker, J.; Kantz, H.

2012-03-01

In numerical weather prediction, ensembles are used to retrieve probabilistic forecasts of future weather conditions. We consider events where the verification is smaller than the smallest, or larger than the largest ensemble member of a scalar ensemble forecast. These events are called outliers. In a statistically consistent K-member ensemble, outliers should occur with a base rate of 2/(K+1). In operational ensembles this base rate tends to be higher. We study the predictability of outlier events in terms of the Brier Skill Score and find that forecast probabilities can be calculated which are more skillful than the unconditional base rate. This is shown analytically for statistically consistent ensembles. Using logistic regression, forecast probabilities for outlier events in an operational ensemble are calculated. These probabilities exhibit positive skill which is quantitatively similar to the analytical results. Possible causes of these results as well as their consequences for ensemble interpretation are discussed.

Rethinking the Default Construction of Multimodel Climate Ensembles

DOE PAGES

Rauser, Florian; Gleckler, Peter; Marotzke, Jochem

2015-07-21

Here, we discuss the current code of practice in the climate sciences to routinely create climate model ensembles as ensembles of opportunity from the newest phase of the Coupled Model Intercomparison Project (CMIP). We give a two-step argument to rethink this process. First, the differences between generations of ensembles corresponding to different CMIP phases in key climate quantities are not large enough to warrant an automatic separation into generational ensembles for CMIP3 and CMIP5. Second, we suggest that climate model ensembles cannot continue to be mere ensembles of opportunity but should always be based on a transparent scientific decision process.more » If ensembles can be constrained by observation, then they should be constructed as target ensembles that are specifically tailored to a physical question. If model ensembles cannot be constrained by observation, then they should be constructed as cross-generational ensembles, including all available model data to enhance structural model diversity and to better sample the underlying uncertainties. To facilitate this, CMIP should guide the necessarily ongoing process of updating experimental protocols for the evaluation and documentation of coupled models. Finally, with an emphasis on easy access to model data and facilitating the filtering of climate model data across all CMIP generations and experiments, our community could return to the underlying idea of using model data ensembles to improve uncertainty quantification, evaluation, and cross-institutional exchange.« less

Ensemble Data Assimilation Without Ensembles: Methodology and Application to Ocean Data Assimilation

NASA Technical Reports Server (NTRS)

Keppenne, Christian L.; Rienecker, Michele M.; Kovach, Robin M.; Vernieres, Guillaume

2013-01-01

Two methods to estimate background error covariances for data assimilation are introduced. While both share properties with the ensemble Kalman filter (EnKF), they differ from it in that they do not require the integration of multiple model trajectories. Instead, all the necessary covariance information is obtained from a single model integration. The first method is referred-to as SAFE (Space Adaptive Forecast error Estimation) because it estimates error covariances from the spatial distribution of model variables within a single state vector. It can thus be thought of as sampling an ensemble in space. The second method, named FAST (Flow Adaptive error Statistics from a Time series), constructs an ensemble sampled from a moving window along a model trajectory. The underlying assumption in these methods is that forecast errors in data assimilation are primarily phase errors in space and/or time.

Ensembl genomes 2016: more genomes, more complexity

USDA-ARS?s Scientific Manuscript database

Ensembl Genomes (http://www.ensemblgenomes.org) is an integrating resource for genome-scale data from non-vertebrate species, complementing the resources for vertebrate genomics developed in the context of the Ensembl project (http://www.ensembl.org). Together, the two resources provide a consistent...

Hybrid Data Assimilation without Ensemble Filtering

NASA Technical Reports Server (NTRS)

Todling, Ricardo; Akkraoui, Amal El

2014-01-01

The Global Modeling and Assimilation Office is preparing to upgrade its three-dimensional variational system to a hybrid approach in which the ensemble is generated using a square-root ensemble Kalman filter (EnKF) and the variational problem is solved using the Grid-point Statistical Interpolation system. As in most EnKF applications, we found it necessary to employ a combination of multiplicative and additive inflations, to compensate for sampling and modeling errors, respectively and, to maintain the small-member ensemble solution close to the variational solution; we also found it necessary to re-center the members of the ensemble about the variational analysis. During tuning of the filter we have found re-centering and additive inflation to play a considerably larger role than expected, particularly in a dual-resolution context when the variational analysis is ran at larger resolution than the ensemble. This led us to consider a hybrid strategy in which the members of the ensemble are generated by simply converting the variational analysis to the resolution of the ensemble and applying additive inflation, thus bypassing the EnKF. Comparisons of this, so-called, filter-free hybrid procedure with an EnKF-based hybrid procedure and a control non-hybrid, traditional, scheme show both hybrid strategies to provide equally significant improvement over the control; more interestingly, the filter-free procedure was found to give qualitatively similar results to the EnKF-based procedure.

Motor-motor interactions in ensembles of muscle myosin: using theory to connect single molecule to ensemble measurements

NASA Astrophysics Data System (ADS)

Walcott, Sam

2013-03-01

Interactions between the proteins actin and myosin drive muscle contraction. Properties of a single myosin interacting with an actin filament are largely known, but a trillion myosins work together in muscle. We are interested in how single-molecule properties relate to ensemble function. Myosin's reaction rates depend on force, so ensemble models keep track of both molecular state and force on each molecule. These models make subtle predictions, e.g. that myosin, when part of an ensemble, moves actin faster than when isolated. This acceleration arises because forces between molecules speed reaction kinetics. Experiments support this prediction and allow parameter estimates. A model based on this analysis describes experiments from single molecule to ensemble. In vivo, actin is regulated by proteins that, when present, cause the binding of one myosin to speed the binding of its neighbors; binding becomes cooperative. Although such interactions preclude the mean field approximation, a set of linear ODEs describes these ensembles under simplified experimental conditions. In these experiments cooperativity is strong, with the binding of one molecule affecting ten neighbors on either side. We progress toward a description of myosin ensembles under physiological conditions.

The Ensembl genome database project.

PubMed

Hubbard, T; Barker, D; Birney, E; Cameron, G; Chen, Y; Clark, L; Cox, T; Cuff, J; Curwen, V; Down, T; Durbin, R; Eyras, E; Gilbert, J; Hammond, M; Huminiecki, L; Kasprzyk, A; Lehvaslaiho, H; Lijnzaad, P; Melsopp, C; Mongin, E; Pettett, R; Pocock, M; Potter, S; Rust, A; Schmidt, E; Searle, S; Slater, G; Smith, J; Spooner, W; Stabenau, A; Stalker, J; Stupka, E; Ureta-Vidal, A; Vastrik, I; Clamp, M

2002-01-01

The Ensembl (http://www.ensembl.org/) database project provides a bioinformatics framework to organise biology around the sequences of large genomes. It is a comprehensive source of stable automatic annotation of the human genome sequence, with confirmed gene predictions that have been integrated with external data sources, and is available as either an interactive web site or as flat files. It is also an open source software engineering project to develop a portable system able to handle very large genomes and associated requirements from sequence analysis to data storage and visualisation. The Ensembl site is one of the leading sources of human genome sequence annotation and provided much of the analysis for publication by the international human genome project of the draft genome. The Ensembl system is being installed around the world in both companies and academic sites on machines ranging from supercomputers to laptops.

Decadal climate prediction in the large ensemble limit

NASA Astrophysics Data System (ADS)

Yeager, S. G.; Rosenbloom, N. A.; Strand, G.; Lindsay, K. T.; Danabasoglu, G.; Karspeck, A. R.; Bates, S. C.; Meehl, G. A.

2017-12-01

In order to quantify the benefits of initialization for climate prediction on decadal timescales, two parallel sets of historical simulations are required: one "initialized" ensemble that incorporates observations of past climate states and one "uninitialized" ensemble whose internal climate variations evolve freely and without synchronicity. In the large ensemble limit, ensemble averaging isolates potentially predictable forced and internal variance components in the "initialized" set, but only the forced variance remains after averaging the "uninitialized" set. The ensemble size needed to achieve this variance decomposition, and to robustly distinguish initialized from uninitialized decadal predictions, remains poorly constrained. We examine a large ensemble (LE) of initialized decadal prediction (DP) experiments carried out using the Community Earth System Model (CESM). This 40-member CESM-DP-LE set of experiments represents the "initialized" complement to the CESM large ensemble of 20th century runs (CESM-LE) documented in Kay et al. (2015). Both simulation sets share the same model configuration, historical radiative forcings, and large ensemble sizes. The twin experiments afford an unprecedented opportunity to explore the sensitivity of DP skill assessment, and in particular the skill enhancement associated with initialization, to ensemble size. This talk will highlight the benefits of a large ensemble size for initialized predictions of seasonal climate over land in the Atlantic sector as well as predictions of shifts in the likelihood of climate extremes that have large societal impact.

Layered Ensemble Architecture for Time Series Forecasting.

PubMed

Rahman, Md Mustafizur; Islam, Md Monirul; Murase, Kazuyuki; Yao, Xin

2016-01-01

Time series forecasting (TSF) has been widely used in many application areas such as science, engineering, and finance. The phenomena generating time series are usually unknown and information available for forecasting is only limited to the past values of the series. It is, therefore, necessary to use an appropriate number of past values, termed lag, for forecasting. This paper proposes a layered ensemble architecture (LEA) for TSF problems. Our LEA consists of two layers, each of which uses an ensemble of multilayer perceptron (MLP) networks. While the first ensemble layer tries to find an appropriate lag, the second ensemble layer employs the obtained lag for forecasting. Unlike most previous work on TSF, the proposed architecture considers both accuracy and diversity of the individual networks in constructing an ensemble. LEA trains different networks in the ensemble by using different training sets with an aim of maintaining diversity among the networks. However, it uses the appropriate lag and combines the best trained networks to construct the ensemble. This indicates LEAs emphasis on accuracy of the networks. The proposed architecture has been tested extensively on time series data of neural network (NN)3 and NN5 competitions. It has also been tested on several standard benchmark time series data. In terms of forecasting accuracy, our experimental results have revealed clearly that LEA is better than other ensemble and nonensemble methods.

Perception of ensemble statistics requires attention.

PubMed

Jackson-Nielsen, Molly; Cohen, Michael A; Pitts, Michael A

2017-02-01

To overcome inherent limitations in perceptual bandwidth, many aspects of the visual world are represented as summary statistics (e.g., average size, orientation, or density of objects). Here, we investigated the relationship between summary (ensemble) statistics and visual attention. Recently, it was claimed that one ensemble statistic in particular, color diversity, can be perceived without focal attention. However, a broader debate exists over the attentional requirements of conscious perception, and it is possible that some form of attention is necessary for ensemble perception. To test this idea, we employed a modified inattentional blindness paradigm and found that multiple types of summary statistics (color and size) often go unnoticed without attention. In addition, we found attentional costs in dual-task situations, further implicating a role for attention in statistical perception. Overall, we conclude that while visual ensembles may be processed efficiently, some amount of attention is necessary for conscious perception of ensemble statistics. Copyright © 2016 Elsevier Inc. All rights reserved.

AUC-Maximizing Ensembles through Metalearning.

PubMed

LeDell, Erin; van der Laan, Mark J; Petersen, Maya

2016-05-01

Area Under the ROC Curve (AUC) is often used to measure the performance of an estimator in binary classification problems. An AUC-maximizing classifier can have significant advantages in cases where ranking correctness is valued or if the outcome is rare. In a Super Learner ensemble, maximization of the AUC can be achieved by the use of an AUC-maximining metalearning algorithm. We discuss an implementation of an AUC-maximization technique that is formulated as a nonlinear optimization problem. We also evaluate the effectiveness of a large number of different nonlinear optimization algorithms to maximize the cross-validated AUC of the ensemble fit. The results provide evidence that AUC-maximizing metalearners can, and often do, out-perform non-AUC-maximizing metalearning methods, with respect to ensemble AUC. The results also demonstrate that as the level of imbalance in the training data increases, the Super Learner ensemble outperforms the top base algorithm by a larger degree.

AUC-Maximizing Ensembles through Metalearning

PubMed Central

LeDell, Erin; van der Laan, Mark J.; Peterson, Maya

2016-01-01

Area Under the ROC Curve (AUC) is often used to measure the performance of an estimator in binary classification problems. An AUC-maximizing classifier can have significant advantages in cases where ranking correctness is valued or if the outcome is rare. In a Super Learner ensemble, maximization of the AUC can be achieved by the use of an AUC-maximining metalearning algorithm. We discuss an implementation of an AUC-maximization technique that is formulated as a nonlinear optimization problem. We also evaluate the effectiveness of a large number of different nonlinear optimization algorithms to maximize the cross-validated AUC of the ensemble fit. The results provide evidence that AUC-maximizing metalearners can, and often do, out-perform non-AUC-maximizing metalearning methods, with respect to ensemble AUC. The results also demonstrate that as the level of imbalance in the training data increases, the Super Learner ensemble outperforms the top base algorithm by a larger degree. PMID:27227721

Advanced Atmospheric Ensemble Modeling Techniques

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

Buckley, R.; Chiswell, S.; Kurzeja, R.

Ensemble modeling (EM), the creation of multiple atmospheric simulations for a given time period, has become an essential tool for characterizing uncertainties in model predictions. We explore two novel ensemble modeling techniques: (1) perturbation of model parameters (Adaptive Programming, AP), and (2) data assimilation (Ensemble Kalman Filter, EnKF). The current research is an extension to work from last year and examines transport on a small spatial scale (<100 km) in complex terrain, for more rigorous testing of the ensemble technique. Two different release cases were studied, a coastal release (SF6) and an inland release (Freon) which consisted of two releasemore » times. Observations of tracer concentration and meteorology are used to judge the ensemble results. In addition, adaptive grid techniques have been developed to reduce required computing resources for transport calculations. Using a 20- member ensemble, the standard approach generated downwind transport that was quantitatively good for both releases; however, the EnKF method produced additional improvement for the coastal release where the spatial and temporal differences due to interior valley heating lead to the inland movement of the plume. The AP technique showed improvements for both release cases, with more improvement shown in the inland release. This research demonstrated that transport accuracy can be improved when models are adapted to a particular location/time or when important local data is assimilated into the simulation and enhances SRNL’s capability in atmospheric transport modeling in support of its current customer base and local site missions, as well as our ability to attract new customers within the intelligence community.« less

Improvement in the yield and quality of kalmegh (Andrographis paniculata Nees) under the sustainable production system.

PubMed

Verma, Rajesh Kumar; Verma, Sanjeet K; Pankaj, Umesh; Gupta, Anand K; Khan, Khushboo; Shankar, Karuna

2015-02-01

Andrographis paniculata Nees is an annual erect herb with wide medicinal and pharmacological applications due to the presence of andrographolide and other active chemical constituents. The large-scale cultivation of the kalmegh is not in practice. The aim of this study was to establish sustainable production systems of A. paniculata cv CIM-Megha with the application of different bioinoculants and chemical fertilisers. A. paniculata herb and andrographolide yield in the dried leaves was found to be highest (218% and 61.3%, respectively) in treatment T3 (NPK+Bacillus sp.) compared with T1 (control). The soil organic carbon, soil microbial respiration, soil enzymes activity and available nutrients improved significantly with combined application of bioinoculants and chemical fertilisers.

Bayesian ensemble refinement by replica simulations and reweighting.

PubMed

Hummer, Gerhard; Köfinger, Jürgen

2015-12-28

We describe different Bayesian ensemble refinement methods, examine their interrelation, and discuss their practical application. With ensemble refinement, the properties of dynamic and partially disordered (bio)molecular structures can be characterized by integrating a wide range of experimental data, including measurements of ensemble-averaged observables. We start from a Bayesian formulation in which the posterior is a functional that ranks different configuration space distributions. By maximizing this posterior, we derive an optimal Bayesian ensemble distribution. For discrete configurations, this optimal distribution is identical to that obtained by the maximum entropy "ensemble refinement of SAXS" (EROS) formulation. Bayesian replica ensemble refinement enhances the sampling of relevant configurations by imposing restraints on averages of observables in coupled replica molecular dynamics simulations. We show that the strength of the restraints should scale linearly with the number of replicas to ensure convergence to the optimal Bayesian result in the limit of infinitely many replicas. In the "Bayesian inference of ensembles" method, we combine the replica and EROS approaches to accelerate the convergence. An adaptive algorithm can be used to sample directly from the optimal ensemble, without replicas. We discuss the incorporation of single-molecule measurements and dynamic observables such as relaxation parameters. The theoretical analysis of different Bayesian ensemble refinement approaches provides a basis for practical applications and a starting point for further investigations.

Bayesian ensemble refinement by replica simulations and reweighting

NASA Astrophysics Data System (ADS)

Hummer, Gerhard; Köfinger, Jürgen

2015-12-01

We describe different Bayesian ensemble refinement methods, examine their interrelation, and discuss their practical application. With ensemble refinement, the properties of dynamic and partially disordered (bio)molecular structures can be characterized by integrating a wide range of experimental data, including measurements of ensemble-averaged observables. We start from a Bayesian formulation in which the posterior is a functional that ranks different configuration space distributions. By maximizing this posterior, we derive an optimal Bayesian ensemble distribution. For discrete configurations, this optimal distribution is identical to that obtained by the maximum entropy "ensemble refinement of SAXS" (EROS) formulation. Bayesian replica ensemble refinement enhances the sampling of relevant configurations by imposing restraints on averages of observables in coupled replica molecular dynamics simulations. We show that the strength of the restraints should scale linearly with the number of replicas to ensure convergence to the optimal Bayesian result in the limit of infinitely many replicas. In the "Bayesian inference of ensembles" method, we combine the replica and EROS approaches to accelerate the convergence. An adaptive algorithm can be used to sample directly from the optimal ensemble, without replicas. We discuss the incorporation of single-molecule measurements and dynamic observables such as relaxation parameters. The theoretical analysis of different Bayesian ensemble refinement approaches provides a basis for practical applications and a starting point for further investigations.

Ensemble perception of color in autistic adults.

PubMed

Maule, John; Stanworth, Kirstie; Pellicano, Elizabeth; Franklin, Anna

2017-05-01

Dominant accounts of visual processing in autism posit that autistic individuals have an enhanced access to details of scenes [e.g., weak central coherence] which is reflected in a general bias toward local processing. Furthermore, the attenuated priors account of autism predicts that the updating and use of summary representations is reduced in autism. Ensemble perception describes the extraction of global summary statistics of a visual feature from a heterogeneous set (e.g., of faces, sizes, colors), often in the absence of local item representation. The present study investigated ensemble perception in autistic adults using a rapidly presented (500 msec) ensemble of four, eight, or sixteen elements representing four different colors. We predicted that autistic individuals would be less accurate when averaging the ensembles, but more accurate in recognizing individual ensemble colors. The results were consistent with the predictions. Averaging was impaired in autism, but only when ensembles contained four elements. Ensembles of eight or sixteen elements were averaged equally accurately across groups. The autistic group also showed a corresponding advantage in rejecting colors that were not originally seen in the ensemble. The results demonstrate the local processing bias in autism, but also suggest that the global perceptual averaging mechanism may be compromised under some conditions. The theoretical implications of the findings and future avenues for research on summary statistics in autism are discussed. Autism Res 2017, 10: 839-851. © 2016 International Society for Autism Research, Wiley Periodicals, Inc. © 2016 International Society for Autism Research, Wiley Periodicals, Inc.

Ensemble perception of color in autistic adults

PubMed Central

Stanworth, Kirstie; Pellicano, Elizabeth; Franklin, Anna

2016-01-01

Dominant accounts of visual processing in autism posit that autistic individuals have an enhanced access to details of scenes [e.g., weak central coherence] which is reflected in a general bias toward local processing. Furthermore, the attenuated priors account of autism predicts that the updating and use of summary representations is reduced in autism. Ensemble perception describes the extraction of global summary statistics of a visual feature from a heterogeneous set (e.g., of faces, sizes, colors), often in the absence of local item representation. The present study investigated ensemble perception in autistic adults using a rapidly presented (500 msec) ensemble of four, eight, or sixteen elements representing four different colors. We predicted that autistic individuals would be less accurate when averaging the ensembles, but more accurate in recognizing individual ensemble colors. The results were consistent with the predictions. Averaging was impaired in autism, but only when ensembles contained four elements. Ensembles of eight or sixteen elements were averaged equally accurately across groups. The autistic group also showed a corresponding advantage in rejecting colors that were not originally seen in the ensemble. The results demonstrate the local processing bias in autism, but also suggest that the global perceptual averaging mechanism may be compromised under some conditions. The theoretical implications of the findings and future avenues for research on summary statistics in autism are discussed. Autism Res 2017, 10: 839–851. © 2016 The Authors Autism Research published by Wiley Periodicals, Inc. on behalf of International Society for Autism Research PMID:27874263

Evaluation of medium-range ensemble flood forecasting based on calibration strategies and ensemble methods in Lanjiang Basin, Southeast China

NASA Astrophysics Data System (ADS)

Liu, Li; Gao, Chao; Xuan, Weidong; Xu, Yue-Ping

2017-11-01

Ensemble flood forecasts by hydrological models using numerical weather prediction products as forcing data are becoming more commonly used in operational flood forecasting applications. In this study, a hydrological ensemble flood forecasting system comprised of an automatically calibrated Variable Infiltration Capacity model and quantitative precipitation forecasts from TIGGE dataset is constructed for Lanjiang Basin, Southeast China. The impacts of calibration strategies and ensemble methods on the performance of the system are then evaluated. The hydrological model is optimized by the parallel programmed ε-NSGA II multi-objective algorithm. According to the solutions by ε-NSGA II, two differently parameterized models are determined to simulate daily flows and peak flows at each of the three hydrological stations. Then a simple yet effective modular approach is proposed to combine these daily and peak flows at the same station into one composite series. Five ensemble methods and various evaluation metrics are adopted. The results show that ε-NSGA II can provide an objective determination on parameter estimation, and the parallel program permits a more efficient simulation. It is also demonstrated that the forecasts from ECMWF have more favorable skill scores than other Ensemble Prediction Systems. The multimodel ensembles have advantages over all the single model ensembles and the multimodel methods weighted on members and skill scores outperform other methods. Furthermore, the overall performance at three stations can be satisfactory up to ten days, however the hydrological errors can degrade the skill score by approximately 2 days, and the influence persists until a lead time of 10 days with a weakening trend. With respect to peak flows selected by the Peaks Over Threshold approach, the ensemble means from single models or multimodels are generally underestimated, indicating that the ensemble mean can bring overall improvement in forecasting of flows. For

Ensemble training to improve recognition using 2D ear

NASA Astrophysics Data System (ADS)

Middendorff, Christopher; Bowyer, Kevin W.

2009-05-01

The ear has gained popularity as a biometric feature due to the robustness of the shape over time and across emotional expression. Popular methods of ear biometrics analyze the ear as a whole, leaving these methods vulnerable to error due to occlusion. Many researchers explore ear recognition using an ensemble, but none present a method for designing the individual parts that comprise the ensemble. In this work, we introduce a method of modifying the ensemble shapes to improve performance. We determine how different properties of an ensemble training system can affect overall performance. We show that ensembles built from small parts will outperform ensembles built with larger parts, and that incorporating a large number of parts improves the performance of the ensemble.

Ensemble method for dengue prediction

PubMed Central

Baugher, Benjamin; Moniz, Linda J.; Bagley, Thomas; Babin, Steven M.; Guven, Erhan

2018-01-01

Background In the 2015 NOAA Dengue Challenge, participants made three dengue target predictions for two locations (Iquitos, Peru, and San Juan, Puerto Rico) during four dengue seasons: 1) peak height (i.e., maximum weekly number of cases during a transmission season; 2) peak week (i.e., week in which the maximum weekly number of cases occurred); and 3) total number of cases reported during a transmission season. A dengue transmission season is the 12-month period commencing with the location-specific, historical week with the lowest number of cases. At the beginning of the Dengue Challenge, participants were provided with the same input data for developing the models, with the prediction testing data provided at a later date. Methods Our approach used ensemble models created by combining three disparate types of component models: 1) two-dimensional Method of Analogues models incorporating both dengue and climate data; 2) additive seasonal Holt-Winters models with and without wavelet smoothing; and 3) simple historical models. Of the individual component models created, those with the best performance on the prior four years of data were incorporated into the ensemble models. There were separate ensembles for predicting each of the three targets at each of the two locations. Principal findings Our ensemble models scored higher for peak height and total dengue case counts reported in a transmission season for Iquitos than all other models submitted to the Dengue Challenge. However, the ensemble models did not do nearly as well when predicting the peak week. Conclusions The Dengue Challenge organizers scored the dengue predictions of the Challenge participant groups. Our ensemble approach was the best in predicting the total number of dengue cases reported for transmission season and peak height for Iquitos, Peru. PMID:29298320

Ensemble method for dengue prediction.

PubMed

Buczak, Anna L; Baugher, Benjamin; Moniz, Linda J; Bagley, Thomas; Babin, Steven M; Guven, Erhan

2018-01-01

In the 2015 NOAA Dengue Challenge, participants made three dengue target predictions for two locations (Iquitos, Peru, and San Juan, Puerto Rico) during four dengue seasons: 1) peak height (i.e., maximum weekly number of cases during a transmission season; 2) peak week (i.e., week in which the maximum weekly number of cases occurred); and 3) total number of cases reported during a transmission season. A dengue transmission season is the 12-month period commencing with the location-specific, historical week with the lowest number of cases. At the beginning of the Dengue Challenge, participants were provided with the same input data for developing the models, with the prediction testing data provided at a later date. Our approach used ensemble models created by combining three disparate types of component models: 1) two-dimensional Method of Analogues models incorporating both dengue and climate data; 2) additive seasonal Holt-Winters models with and without wavelet smoothing; and 3) simple historical models. Of the individual component models created, those with the best performance on the prior four years of data were incorporated into the ensemble models. There were separate ensembles for predicting each of the three targets at each of the two locations. Our ensemble models scored higher for peak height and total dengue case counts reported in a transmission season for Iquitos than all other models submitted to the Dengue Challenge. However, the ensemble models did not do nearly as well when predicting the peak week. The Dengue Challenge organizers scored the dengue predictions of the Challenge participant groups. Our ensemble approach was the best in predicting the total number of dengue cases reported for transmission season and peak height for Iquitos, Peru.

An information-theoretical perspective on weighted ensemble forecasts

NASA Astrophysics Data System (ADS)

Weijs, Steven V.; van de Giesen, Nick

2013-08-01

This paper presents an information-theoretical method for weighting ensemble forecasts with new information. Weighted ensemble forecasts can be used to adjust the distribution that an existing ensemble of time series represents, without modifying the values in the ensemble itself. The weighting can, for example, add new seasonal forecast information in an existing ensemble of historically measured time series that represents climatic uncertainty. A recent article in this journal compared several methods to determine the weights for the ensemble members and introduced the pdf-ratio method. In this article, a new method, the minimum relative entropy update (MRE-update), is presented. Based on the principle of minimum discrimination information, an extension of the principle of maximum entropy (POME), the method ensures that no more information is added to the ensemble than is present in the forecast. This is achieved by minimizing relative entropy, with the forecast information imposed as constraints. From this same perspective, an information-theoretical view on the various weighting methods is presented. The MRE-update is compared with the existing methods and the parallels with the pdf-ratio method are analysed. The paper provides a new, information-theoretical justification for one version of the pdf-ratio method that turns out to be equivalent to the MRE-update. All other methods result in sets of ensemble weights that, seen from the information-theoretical perspective, add either too little or too much (i.e. fictitious) information to the ensemble.

The effects of Andrographis paniculata (Burm.f.) Nees extract and diterpenoids on the CYP450 isoforms' activities, a review of possible herb-drug interaction risks.

PubMed

Tan, Mei Lan; Lim, Lin Ee

2015-01-01

Andrographis paniculata (Burm.f.) Nees is a popular medicinal plant and its components are used in various traditional product preparations. However, its herb-drug interactions risks remain unclear. This review specifically discusses the various published studies carried out to evaluate the effects of Andrographis paniculata (Burm.f.) Nees plant extracts and diterpenoids on the CYP450 metabolic enzyme and if the plant components pose a possible herb-drug interaction risk. Unfortunately, the current data are insufficient to indicate if the extracts or diterpenoids can be labeled as in vitro CYP1A2, CYP2C9 or CYP3A4 inhibitors. A complete CYP inhibition assay utilizing human liver microsomes and the derivation of relevant parameters to predict herb-drug interaction risks may be necessary for these isoforms. However, based on the current studies, none of the extracts and diterpenoids exhibited CYP450 induction activity in human hepatocytes or human-derived cell lines. It is crucial that a well-defined experimental design is needed to make a meaningful herb-drug interaction prediction.

Contact planarization of ensemble nanowires

NASA Astrophysics Data System (ADS)

Chia, A. C. E.; LaPierre, R. R.

2011-06-01

The viability of four organic polymers (S1808, SC200, SU8 and Cyclotene) as filling materials to achieve planarization of ensemble nanowire arrays is reported. Analysis of the porosity, surface roughness and thermal stability of each filling material was performed. Sonication was used as an effective method to remove the tops of the nanowires (NWs) to achieve complete planarization. Ensemble nanowire devices were fully fabricated and I-V measurements confirmed that Cyclotene effectively planarizes the NWs while still serving the role as an insulating layer between the top and bottom contacts. These processes and analysis can be easily implemented into future characterization and fabrication of ensemble NWs for optoelectronic device applications.

Contact planarization of ensemble nanowires.

PubMed

Chia, A C E; LaPierre, R R

2011-06-17

The viability of four organic polymers (S1808, SC200, SU8 and Cyclotene) as filling materials to achieve planarization of ensemble nanowire arrays is reported. Analysis of the porosity, surface roughness and thermal stability of each filling material was performed. Sonication was used as an effective method to remove the tops of the nanowires (NWs) to achieve complete planarization. Ensemble nanowire devices were fully fabricated and I-V measurements confirmed that Cyclotene effectively planarizes the NWs while still serving the role as an insulating layer between the top and bottom contacts. These processes and analysis can be easily implemented into future characterization and fabrication of ensemble NWs for optoelectronic device applications.

Challenges in Visual Analysis of Ensembles

DOE PAGES

Crossno, Patricia

2018-04-12

Modeling physical phenomena through computational simulation increasingly relies on generating a collection of related runs, known as an ensemble. In this paper, we explore the challenges we face in developing analysis and visualization systems for large and complex ensemble data sets, which we seek to understand without having to view the results of every simulation run. Implementing approaches and ideas developed in response to this goal, we demonstrate the analysis of a 15K run material fracturing study using Slycat, our ensemble analysis system.

Challenges in Visual Analysis of Ensembles

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

Crossno, Patricia

Modeling physical phenomena through computational simulation increasingly relies on generating a collection of related runs, known as an ensemble. In this paper, we explore the challenges we face in developing analysis and visualization systems for large and complex ensemble data sets, which we seek to understand without having to view the results of every simulation run. Implementing approaches and ideas developed in response to this goal, we demonstrate the analysis of a 15K run material fracturing study using Slycat, our ensemble analysis system.

Random matrix ensembles for many-body quantum systems

NASA Astrophysics Data System (ADS)

Vyas, Manan; Seligman, Thomas H.

2018-04-01

Classical random matrix ensembles were originally introduced in physics to approximate quantum many-particle nuclear interactions. However, there exists a plethora of quantum systems whose dynamics is explained in terms of few-particle (predom-inantly two-particle) interactions. The random matrix models incorporating the few-particle nature of interactions are known as embedded random matrix ensembles. In the present paper, we provide a brief overview of these two ensembles and illustrate how the embedded ensembles can be successfully used to study decoherence of a qubit interacting with an environment, both for fermionic and bosonic embedded ensembles. Numerical calculations show the dependence of decoherence on the nature of the environment.

Reproducing multi-model ensemble average with Ensemble-averaged Reconstructed Forcings (ERF) in regional climate modeling

NASA Astrophysics Data System (ADS)

Erfanian, A.; Fomenko, L.; Wang, G.

2016-12-01

Multi-model ensemble (MME) average is considered the most reliable for simulating both present-day and future climates. It has been a primary reference for making conclusions in major coordinated studies i.e. IPCC Assessment Reports and CORDEX. The biases of individual models cancel out each other in MME average, enabling the ensemble mean to outperform individual members in simulating the mean climate. This enhancement however comes with tremendous computational cost, which is especially inhibiting for regional climate modeling as model uncertainties can originate from both RCMs and the driving GCMs. Here we propose the Ensemble-based Reconstructed Forcings (ERF) approach to regional climate modeling that achieves a similar level of bias reduction at a fraction of cost compared with the conventional MME approach. The new method constructs a single set of initial and boundary conditions (IBCs) by averaging the IBCs of multiple GCMs, and drives the RCM with this ensemble average of IBCs to conduct a single run. Using a regional climate model (RegCM4.3.4-CLM4.5), we tested the method over West Africa for multiple combination of (up to six) GCMs. Our results indicate that the performance of the ERF method is comparable to that of the MME average in simulating the mean climate. The bias reduction seen in ERF simulations is achieved by using more realistic IBCs in solving the system of equations underlying the RCM physics and dynamics. This endows the new method with a theoretical advantage in addition to reducing computational cost. The ERF output is an unaltered solution of the RCM as opposed to a climate state that might not be physically plausible due to the averaging of multiple solutions with the conventional MME approach. The ERF approach should be considered for use in major international efforts such as CORDEX. Key words: Multi-model ensemble, ensemble analysis, ERF, regional climate modeling

Minimalist ensemble algorithms for genome-wide protein localization prediction.

PubMed

Lin, Jhih-Rong; Mondal, Ananda Mohan; Liu, Rong; Hu, Jianjun

2012-07-03

Computational prediction of protein subcellular localization can greatly help to elucidate its functions. Despite the existence of dozens of protein localization prediction algorithms, the prediction accuracy and coverage are still low. Several ensemble algorithms have been proposed to improve the prediction performance, which usually include as many as 10 or more individual localization algorithms. However, their performance is still limited by the running complexity and redundancy among individual prediction algorithms. This paper proposed a novel method for rational design of minimalist ensemble algorithms for practical genome-wide protein subcellular localization prediction. The algorithm is based on combining a feature selection based filter and a logistic regression classifier. Using a novel concept of contribution scores, we analyzed issues of algorithm redundancy, consensus mistakes, and algorithm complementarity in designing ensemble algorithms. We applied the proposed minimalist logistic regression (LR) ensemble algorithm to two genome-wide datasets of Yeast and Human and compared its performance with current ensemble algorithms. Experimental results showed that the minimalist ensemble algorithm can achieve high prediction accuracy with only 1/3 to 1/2 of individual predictors of current ensemble algorithms, which greatly reduces computational complexity and running time. It was found that the high performance ensemble algorithms are usually composed of the predictors that together cover most of available features. Compared to the best individual predictor, our ensemble algorithm improved the prediction accuracy from AUC score of 0.558 to 0.707 for the Yeast dataset and from 0.628 to 0.646 for the Human dataset. Compared with popular weighted voting based ensemble algorithms, our classifier-based ensemble algorithms achieved much better performance without suffering from inclusion of too many individual predictors. We proposed a method for rational design

Minimalist ensemble algorithms for genome-wide protein localization prediction

PubMed Central

2012-01-01

Background Computational prediction of protein subcellular localization can greatly help to elucidate its functions. Despite the existence of dozens of protein localization prediction algorithms, the prediction accuracy and coverage are still low. Several ensemble algorithms have been proposed to improve the prediction performance, which usually include as many as 10 or more individual localization algorithms. However, their performance is still limited by the running complexity and redundancy among individual prediction algorithms. Results This paper proposed a novel method for rational design of minimalist ensemble algorithms for practical genome-wide protein subcellular localization prediction. The algorithm is based on combining a feature selection based filter and a logistic regression classifier. Using a novel concept of contribution scores, we analyzed issues of algorithm redundancy, consensus mistakes, and algorithm complementarity in designing ensemble algorithms. We applied the proposed minimalist logistic regression (LR) ensemble algorithm to two genome-wide datasets of Yeast and Human and compared its performance with current ensemble algorithms. Experimental results showed that the minimalist ensemble algorithm can achieve high prediction accuracy with only 1/3 to 1/2 of individual predictors of current ensemble algorithms, which greatly reduces computational complexity and running time. It was found that the high performance ensemble algorithms are usually composed of the predictors that together cover most of available features. Compared to the best individual predictor, our ensemble algorithm improved the prediction accuracy from AUC score of 0.558 to 0.707 for the Yeast dataset and from 0.628 to 0.646 for the Human dataset. Compared with popular weighted voting based ensemble algorithms, our classifier-based ensemble algorithms achieved much better performance without suffering from inclusion of too many individual predictors. Conclusions We

Total probabilities of ensemble runoff forecasts

NASA Astrophysics Data System (ADS)

Olav Skøien, Jon; Bogner, Konrad; Salamon, Peter; Smith, Paul; Pappenberger, Florian

2017-04-01

Ensemble forecasting has a long history from meteorological modelling, as an indication of the uncertainty of the forecasts. However, it is necessary to calibrate and post-process the ensembles as the they often exhibit both bias and dispersion errors. Two of the most common methods for this are Bayesian Model Averaging (Raftery et al., 2005) and Ensemble Model Output Statistics (EMOS) (Gneiting et al., 2005). There are also methods for regionalizing these methods (Berrocal et al., 2007) and for incorporating the correlation between lead times (Hemri et al., 2013). Engeland and Steinsland Engeland and Steinsland (2014) developed a framework which can estimate post-processing parameters varying in space and time, while giving a spatially and temporally consistent output. However, their method is computationally complex for our larger number of stations, which makes it unsuitable for our purpose. Our post-processing method of the ensembles is developed in the framework of the European Flood Awareness System (EFAS - http://www.efas.eu), where we are making forecasts for whole Europe, and based on observations from around 700 catchments. As the target is flood forecasting, we are also more interested in improving the forecast skill for high-flows rather than in a good prediction of the entire flow regime. EFAS uses a combination of ensemble forecasts and deterministic forecasts from different meteorological forecasters to force a distributed hydrologic model and to compute runoff ensembles for each river pixel within the model domain. Instead of showing the mean and the variability of each forecast ensemble individually, we will now post-process all model outputs to estimate the total probability, the post-processed mean and uncertainty of all ensembles. The post-processing parameters are first calibrated for each calibration location, but we are adding a spatial penalty in the calibration process to force a spatial correlation of the parameters. The penalty takes

Modality-Driven Classification and Visualization of Ensemble Variance

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

Bensema, Kevin; Gosink, Luke; Obermaier, Harald

Advances in computational power now enable domain scientists to address conceptual and parametric uncertainty by running simulations multiple times in order to sufficiently sample the uncertain input space. While this approach helps address conceptual and parametric uncertainties, the ensemble datasets produced by this technique present a special challenge to visualization researchers as the ensemble dataset records a distribution of possible values for each location in the domain. Contemporary visualization approaches that rely solely on summary statistics (e.g., mean and variance) cannot convey the detailed information encoded in ensemble distributions that are paramount to ensemble analysis; summary statistics provide no informationmore » about modality classification and modality persistence. To address this problem, we propose a novel technique that classifies high-variance locations based on the modality of the distribution of ensemble predictions. Additionally, we develop a set of confidence metrics to inform the end-user of the quality of fit between the distribution at a given location and its assigned class. We apply a similar method to time-varying ensembles to illustrate the relationship between peak variance and bimodal or multimodal behavior. These classification schemes enable a deeper understanding of the behavior of the ensemble members by distinguishing between distributions that can be described by a single tendency and distributions which reflect divergent trends in the ensemble.« less

Renoprotective effects of Andrographis paniculata (Burm. f.) Nees in rats

PubMed Central

Singh, Pratibha; Srivastava, Man Mohan

2009-01-01

Background Renal failure is an increasingly common condition with limited treatment options that is causing a major financial and emotional burden on the community. Andrographis paniculata is the plant used in Ayurveda for several remedies. Scientific evidence suggests its versatile biological functions that support its traditional use in the Orient. The plant is claimed to possess immunological, antibacterial, anti-inflammatory, antithrombotic, and hepatoprotective properties. But, to date, there is no study demonstrating the protective effect of A. paniculata on gentamicin-induced renal failure. The present study aims to highlight the first ever reported, antirenal failure activity of A. paniculata. Methods Male Wistar albino rats were divided into three groups: normal control, gentamicin control, and aqueous extract of A. paniculata (200 mg/kg, per oral (p.o.))-treated. The nephrotoxic model was induced by gentamicin (80 mg/kg, intraperitoeal (i.p.)). Blood samples were examined for serum creatinine, serum urea, and blood urea nitrogen after the 10 days of treatment. Results A gentamicin-induced nephrotoxic animal model was successfully prepared. Aqueous extract of A. paniculata attenuated the gentamicin-induced increase in serum creatinine, serum urea, and blood urea nitrogen levels by 176.92%, 106.27%, and 202.90%, respectively. Conclusion The present study reports that the aqueous extract (whole plant) of A. paniculata (Burm. f.) Nees exhibits a significant renoprotective effect in gentamicin-induced nephrotoxicity in male Wistar albino rats. PMID:19736602

Simulation studies of the fidelity of biomolecular structure ensemble recreation

NASA Astrophysics Data System (ADS)

Lätzer, Joachim; Eastwood, Michael P.; Wolynes, Peter G.

2006-12-01

We examine the ability of Bayesian methods to recreate structural ensembles for partially folded molecules from averaged data. Specifically we test the ability of various algorithms to recreate different transition state ensembles for folding proteins using a multiple replica simulation algorithm using input from "gold standard" reference ensembles that were first generated with a Gō-like Hamiltonian having nonpairwise additive terms. A set of low resolution data, which function as the "experimental" ϕ values, were first constructed from this reference ensemble. The resulting ϕ values were then treated as one would treat laboratory experimental data and were used as input in the replica reconstruction algorithm. The resulting ensembles of structures obtained by the replica algorithm were compared to the gold standard reference ensemble, from which those "data" were, in fact, obtained. It is found that for a unimodal transition state ensemble with a low barrier, the multiple replica algorithm does recreate the reference ensemble fairly successfully when no experimental error is assumed. The Kolmogorov-Smirnov test as well as principal component analysis show that the overlap of the recovered and reference ensembles is significantly enhanced when multiple replicas are used. Reduction of the multiple replica ensembles by clustering successfully yields subensembles with close similarity to the reference ensembles. On the other hand, for a high barrier transition state with two distinct transition state ensembles, the single replica algorithm only samples a few structures of one of the reference ensemble basins. This is due to the fact that the ϕ values are intrinsically ensemble averaged quantities. The replica algorithm with multiple copies does sample both reference ensemble basins. In contrast to the single replica case, the multiple replicas are constrained to reproduce the average ϕ values, but allow fluctuations in ϕ for each individual copy. These

Evaluating Alignment of Shapes by Ensemble Visualization

PubMed Central

Raj, Mukund; Mirzargar, Mahsa; Preston, J. Samuel; Kirby, Robert M.; Whitaker, Ross T.

2016-01-01

The visualization of variability in surfaces embedded in 3D, which is a type of ensemble uncertainty visualization, provides a means of understanding the underlying distribution of a collection or ensemble of surfaces. Although ensemble visualization for isosurfaces has been described in the literature, we conduct an expert-based evaluation of various ensemble visualization techniques in a particular medical imaging application: the construction of atlases or templates from a population of images. In this work, we extend contour boxplot to 3D, allowing us to evaluate it against an enumeration-style visualization of the ensemble members and other conventional visualizations used by atlas builders, namely examining the atlas image and the corresponding images/data provided as part of the construction process. We present feedback from domain experts on the efficacy of contour boxplot compared to other modalities when used as part of the atlas construction and analysis stages of their work. PMID:26186768

Bioactive focus in conformational ensembles: a pluralistic approach

NASA Astrophysics Data System (ADS)

Habgood, Matthew

2017-12-01

Computational generation of conformational ensembles is key to contemporary drug design. Selecting the members of the ensemble that will approximate the conformation most likely to bind to a desired target (the bioactive conformation) is difficult, given that the potential energy usually used to generate and rank the ensemble is a notoriously poor discriminator between bioactive and non-bioactive conformations. In this study an approach to generating a focused ensemble is proposed in which each conformation is assigned multiple rankings based not just on potential energy but also on solvation energy, hydrophobic or hydrophilic interaction energy, radius of gyration, and on a statistical potential derived from Cambridge Structural Database data. The best ranked structures derived from each system are then assembled into a new ensemble that is shown to be better focused on bioactive conformations. This pluralistic approach is tested on ensembles generated by the Molecular Operating Environment's Low Mode Molecular Dynamics module, and by the Cambridge Crystallographic Data Centre's conformation generator software.

Entropy of spatial network ensembles

NASA Astrophysics Data System (ADS)

Coon, Justin P.; Dettmann, Carl P.; Georgiou, Orestis

2018-04-01

We analyze complexity in spatial network ensembles through the lens of graph entropy. Mathematically, we model a spatial network as a soft random geometric graph, i.e., a graph with two sources of randomness, namely nodes located randomly in space and links formed independently between pairs of nodes with probability given by a specified function (the "pair connection function") of their mutual distance. We consider the general case where randomness arises in node positions as well as pairwise connections (i.e., for a given pair distance, the corresponding edge state is a random variable). Classical random geometric graph and exponential graph models can be recovered in certain limits. We derive a simple bound for the entropy of a spatial network ensemble and calculate the conditional entropy of an ensemble given the node location distribution for hard and soft (probabilistic) pair connection functions. Under this formalism, we derive the connection function that yields maximum entropy under general constraints. Finally, we apply our analytical framework to study two practical examples: ad hoc wireless networks and the US flight network. Through the study of these examples, we illustrate that both exhibit properties that are indicative of nearly maximally entropic ensembles.

Ensemble Weight Enumerators for Protograph LDPC Codes

NASA Technical Reports Server (NTRS)

Divsalar, Dariush

2006-01-01

Recently LDPC codes with projected graph, or protograph structures have been proposed. In this paper, finite length ensemble weight enumerators for LDPC codes with protograph structures are obtained. Asymptotic results are derived as the block size goes to infinity. In particular we are interested in obtaining ensemble average weight enumerators for protograph LDPC codes which have minimum distance that grows linearly with block size. As with irregular ensembles, linear minimum distance property is sensitive to the proportion of degree-2 variable nodes. In this paper the derived results on ensemble weight enumerators show that linear minimum distance condition on degree distribution of unstructured irregular LDPC codes is a sufficient but not a necessary condition for protograph LDPC codes.

Total probabilities of ensemble runoff forecasts

NASA Astrophysics Data System (ADS)

Olav Skøien, Jon; Bogner, Konrad; Salamon, Peter; Smith, Paul; Pappenberger, Florian

2016-04-01

Ensemble forecasting has for a long time been used as a method in meteorological modelling to indicate the uncertainty of the forecasts. However, as the ensembles often exhibit both bias and dispersion errors, it is necessary to calibrate and post-process them. Two of the most common methods for this are Bayesian Model Averaging (Raftery et al., 2005) and Ensemble Model Output Statistics (EMOS) (Gneiting et al., 2005). There are also methods for regionalizing these methods (Berrocal et al., 2007) and for incorporating the correlation between lead times (Hemri et al., 2013). Engeland and Steinsland Engeland and Steinsland (2014) developed a framework which can estimate post-processing parameters which are different in space and time, but still can give a spatially and temporally consistent output. However, their method is computationally complex for our larger number of stations, and cannot directly be regionalized in the way we would like, so we suggest a different path below. The target of our work is to create a mean forecast with uncertainty bounds for a large number of locations in the framework of the European Flood Awareness System (EFAS - http://www.efas.eu) We are therefore more interested in improving the forecast skill for high-flows rather than the forecast skill of lower runoff levels. EFAS uses a combination of ensemble forecasts and deterministic forecasts from different forecasters to force a distributed hydrologic model and to compute runoff ensembles for each river pixel within the model domain. Instead of showing the mean and the variability of each forecast ensemble individually, we will now post-process all model outputs to find a total probability, the post-processed mean and uncertainty of all ensembles. The post-processing parameters are first calibrated for each calibration location, but assuring that they have some spatial correlation, by adding a spatial penalty in the calibration process. This can in some cases have a slight negative

Ensembl BioMarts: a hub for data retrieval across taxonomic space.

PubMed

Kinsella, Rhoda J; Kähäri, Andreas; Haider, Syed; Zamora, Jorge; Proctor, Glenn; Spudich, Giulietta; Almeida-King, Jeff; Staines, Daniel; Derwent, Paul; Kerhornou, Arnaud; Kersey, Paul; Flicek, Paul

2011-01-01

For a number of years the BioMart data warehousing system has proven to be a valuable resource for scientists seeking a fast and versatile means of accessing the growing volume of genomic data provided by the Ensembl project. The launch of the Ensembl Genomes project in 2009 complemented the Ensembl project by utilizing the same visualization, interactive and programming tools to provide users with a means for accessing genome data from a further five domains: protists, bacteria, metazoa, plants and fungi. The Ensembl and Ensembl Genomes BioMarts provide a point of access to the high-quality gene annotation, variation data, functional and regulatory annotation and evolutionary relationships from genomes spanning the taxonomic space. This article aims to give a comprehensive overview of the Ensembl and Ensembl Genomes BioMarts as well as some useful examples and a description of current data content and future objectives. Database URLs: http://www.ensembl.org/biomart/martview/; http://metazoa.ensembl.org/biomart/martview/; http://plants.ensembl.org/biomart/martview/; http://protists.ensembl.org/biomart/martview/; http://fungi.ensembl.org/biomart/martview/; http://bacteria.ensembl.org/biomart/martview/.

Application of an Ensemble Smoother to Precipitation Assimilation

NASA Technical Reports Server (NTRS)

Zhang, Sara; Zupanski, Dusanka; Hou, Arthur; Zupanski, Milija

2008-01-01

Assimilation of precipitation in a global modeling system poses a special challenge in that the observation operators for precipitation processes are highly nonlinear. In the variational approach, substantial development work and model simplifications are required to include precipitation-related physical processes in the tangent linear model and its adjoint. An ensemble based data assimilation algorithm "Maximum Likelihood Ensemble Smoother (MLES)" has been developed to explore the ensemble representation of the precipitation observation operator with nonlinear convection and large-scale moist physics. An ensemble assimilation system based on the NASA GEOS-5 GCM has been constructed to assimilate satellite precipitation data within the MLES framework. The configuration of the smoother takes the time dimension into account for the relationship between state variables and observable rainfall. The full nonlinear forward model ensembles are used to represent components involving the observation operator and its transpose. Several assimilation experiments using satellite precipitation observations have been carried out to investigate the effectiveness of the ensemble representation of the nonlinear observation operator and the data impact of assimilating rain retrievals from the TMI and SSM/I sensors. Preliminary results show that this ensemble assimilation approach is capable of extracting information from nonlinear observations to improve the analysis and forecast if ensemble size is adequate, and a suitable localization scheme is applied. In addition to a dynamically consistent precipitation analysis, the assimilation system produces a statistical estimate of the analysis uncertainty.

Improving land resource evaluation using fuzzy neural network ensembles

USGS Publications Warehouse

Xue, Yue-Ju; HU, Y.-M.; Liu, S.-G.; YANG, J.-F.; CHEN, Q.-C.; BAO, S.-T.

2007-01-01

Land evaluation factors often contain continuous-, discrete- and nominal-valued attributes. In traditional land evaluation, these different attributes are usually graded into categorical indexes by land resource experts, and the evaluation results rely heavily on experts' experiences. In order to overcome the shortcoming, we presented a fuzzy neural network ensemble method that did not require grading the evaluation factors into categorical indexes and could evaluate land resources by using the three kinds of attribute values directly. A fuzzy back propagation neural network (BPNN), a fuzzy radial basis function neural network (RBFNN), a fuzzy BPNN ensemble, and a fuzzy RBFNN ensemble were used to evaluate the land resources in Guangdong Province. The evaluation results by using the fuzzy BPNN ensemble and the fuzzy RBFNN ensemble were much better than those by using the single fuzzy BPNN and the single fuzzy RBFNN, and the error rate of the single fuzzy RBFNN or fuzzy RBFNN ensemble was lower than that of the single fuzzy BPNN or fuzzy BPNN ensemble, respectively. By using the fuzzy neural network ensembles, the validity of land resource evaluation was improved and reliance on land evaluators' experiences was considerably reduced. ?? 2007 Soil Science Society of China.

Sequential ensemble-based optimal design for parameter estimation: SEQUENTIAL ENSEMBLE-BASED OPTIMAL DESIGN

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

Man, Jun; Zhang, Jiangjiang; Li, Weixuan

2016-10-01

The ensemble Kalman filter (EnKF) has been widely used in parameter estimation for hydrological models. The focus of most previous studies was to develop more efficient analysis (estimation) algorithms. On the other hand, it is intuitively understandable that a well-designed sampling (data-collection) strategy should provide more informative measurements and subsequently improve the parameter estimation. In this work, a Sequential Ensemble-based Optimal Design (SEOD) method, coupled with EnKF, information theory and sequential optimal design, is proposed to improve the performance of parameter estimation. Based on the first-order and second-order statistics, different information metrics including the Shannon entropy difference (SD), degrees ofmore » freedom for signal (DFS) and relative entropy (RE) are used to design the optimal sampling strategy, respectively. The effectiveness of the proposed method is illustrated by synthetic one-dimensional and two-dimensional unsaturated flow case studies. It is shown that the designed sampling strategies can provide more accurate parameter estimation and state prediction compared with conventional sampling strategies. Optimal sampling designs based on various information metrics perform similarly in our cases. The effect of ensemble size on the optimal design is also investigated. Overall, larger ensemble size improves the parameter estimation and convergence of optimal sampling strategy. Although the proposed method is applied to unsaturated flow problems in this study, it can be equally applied in any other hydrological problems.« less

Pancreatic effect of andrographolide isolated from Andrographis paniculata (Burm. f.) Nees.

PubMed

Nugroho, Agung Endro; Rais, Ichwan Ridwan; Setiawan, Iwan; Pratiwi, Pramita Yuli; Hadibarata, Tony; Tegar, Maulana; Pramono, Suwidjiyo

2014-01-01

Andrographis paniculata (Burm. f.) Nees is a plant that originates from India and grows widely to Southeast which used for several purposes mainly as treatment of diabetes mellitus so the aim of this study was evaluate andrographolide for its pancreatic effect in neonatal streptozotocin (STZ)-induced diabetic rats, a model of type 2 diabetic rats. Diabetic condition was induced with an intraperitoneal injection of 90 mg kg(-1) streptozotocin in two-day-old rats. After three months, the neonatal STZ-induced diabetic rats were treated with andrographolide or andrographolide-enriched extract of A. paniculata (AEEAP) for 8 consecutive days. Pancreatic effect was evaluated by estimating mainly the preprandial and postprandial blood glucose levels and other parameters such as morphology of pancreatic islet, beta cells density and morphology and immunohistochemically pancreatic insulin. Andrographolide significantly (p < 0.05) decreased the levels of blood glucose and improved diabetic rat islet and beta cells. However, AEEAP exhibited moderate hypoglycaemic effects on the blood glucose levels. Moderate changes in beta cells were observed after AEEAP treatment. They could restore decreasing of pancreatic insulin contents. Based on these results andrographolide and AEEAP exhibited pancreatic actions in neonatal STZ-induced diabetic rats. The activity of andrographolide was more effective than this of AEEAP.

Fine-Tuning Your Ensemble's Jazz Style.

ERIC Educational Resources Information Center

Garcia, Antonio J.

1991-01-01

Proposes instructional strategies for directors of jazz groups, including guidelines for developing of skills necessary for good performance. Includes effective methods for positive changes in ensemble style. Addresses jazz group problems such as beat, tempo, staying in tune, wind power, and solo/ensemble lines. Discusses percussionists, bassists,…

Thermodynamic-ensemble independence of solvation free energy.

PubMed

Chong, Song-Ho; Ham, Sihyun

2015-02-10

Solvation free energy is the fundamental thermodynamic quantity in solution chemistry. Recently, it has been suggested that the partial molar volume correction is necessary to convert the solvation free energy determined in different thermodynamic ensembles. Here, we demonstrate ensemble-independence of the solvation free energy on general thermodynamic grounds. Theoretical estimates of the solvation free energy based on the canonical or grand-canonical ensemble are pertinent to experiments carried out under constant pressure without any conversion.

Long-range interacting systems in the unconstrained ensemble.

PubMed

Latella, Ivan; Pérez-Madrid, Agustín; Campa, Alessandro; Casetti, Lapo; Ruffo, Stefano

2017-01-01

Completely open systems can exchange heat, work, and matter with the environment. While energy, volume, and number of particles fluctuate under completely open conditions, the equilibrium states of the system, if they exist, can be specified using the temperature, pressure, and chemical potential as control parameters. The unconstrained ensemble is the statistical ensemble describing completely open systems and the replica energy is the appropriate free energy for these control parameters from which the thermodynamics must be derived. It turns out that macroscopic systems with short-range interactions cannot attain equilibrium configurations in the unconstrained ensemble, since temperature, pressure, and chemical potential cannot be taken as a set of independent variables in this case. In contrast, we show that systems with long-range interactions can reach states of thermodynamic equilibrium in the unconstrained ensemble. To illustrate this fact, we consider a modification of the Thirring model and compare the unconstrained ensemble with the canonical and grand-canonical ones: The more the ensemble is constrained by fixing the volume or number of particles, the larger the space of parameters defining the equilibrium configurations.

Ensemble habitat mapping of invasive plant species

USGS Publications Warehouse

Stohlgren, T.J.; Ma, P.; Kumar, S.; Rocca, M.; Morisette, J.T.; Jarnevich, C.S.; Benson, N.

2010-01-01

Ensemble species distribution models combine the strengths of several species environmental matching models, while minimizing the weakness of any one model. Ensemble models may be particularly useful in risk analysis of recently arrived, harmful invasive species because species may not yet have spread to all suitable habitats, leaving species-environment relationships difficult to determine. We tested five individual models (logistic regression, boosted regression trees, random forest, multivariate adaptive regression splines (MARS), and maximum entropy model or Maxent) and ensemble modeling for selected nonnative plant species in Yellowstone and Grand Teton National Parks, Wyoming; Sequoia and Kings Canyon National Parks, California, and areas of interior Alaska. The models are based on field data provided by the park staffs, combined with topographic, climatic, and vegetation predictors derived from satellite data. For the four invasive plant species tested, ensemble models were the only models that ranked in the top three models for both field validation and test data. Ensemble models may be more robust than individual species-environment matching models for risk analysis. ?? 2010 Society for Risk Analysis.

Skill of Ensemble Seasonal Probability Forecasts

NASA Astrophysics Data System (ADS)

Smith, Leonard A.; Binter, Roman; Du, Hailiang; Niehoerster, Falk

2010-05-01

In operational forecasting, the computational complexity of large simulation models is, ideally, justified by enhanced performance over simpler models. We will consider probability forecasts and contrast the skill of ENSEMBLES-based seasonal probability forecasts of interest to the finance sector (specifically temperature forecasts for Nino 3.4 and the Atlantic Main Development Region (MDR)). The ENSEMBLES model simulations will be contrasted against forecasts from statistical models based on the observations (climatological distributions) and empirical dynamics based on the observations but conditioned on the current state (dynamical climatology). For some start dates, individual ENSEMBLES models yield significant skill even at a lead-time of 14 months. The nature of this skill is discussed, and chances of application are noted. Questions surrounding the interpretation of probability forecasts based on these multi-model ensemble simulations are then considered; the distributions considered are formed by kernel dressing the ensemble and blending with the climatology. The sources of apparent (RMS) skill in distributions based on multi-model simulations is discussed, and it is demonstrated that the inclusion of "zero-skill" models in the long range can improve Root-Mean-Square-Error scores, casting some doubt on the common justification for the claim that all models should be included in forming an operational probability forecast. It is argued that the rational response varies with lead time.

A multiphysical ensemble system of numerical snow modelling

NASA Astrophysics Data System (ADS)

Lafaysse, Matthieu; Cluzet, Bertrand; Dumont, Marie; Lejeune, Yves; Vionnet, Vincent; Morin, Samuel

2017-05-01

Physically based multilayer snowpack models suffer from various modelling errors. To represent these errors, we built the new multiphysical ensemble system ESCROC (Ensemble System Crocus) by implementing new representations of different physical processes in the deterministic coupled multilayer ground/snowpack model SURFEX/ISBA/Crocus. This ensemble was driven and evaluated at Col de Porte (1325 m a.s.l., French alps) over 18 years with a high-quality meteorological and snow data set. A total number of 7776 simulations were evaluated separately, accounting for the uncertainties of evaluation data. The ability of the ensemble to capture the uncertainty associated to modelling errors is assessed for snow depth, snow water equivalent, bulk density, albedo and surface temperature. Different sub-ensembles of the ESCROC system were studied with probabilistic tools to compare their performance. Results show that optimal members of the ESCROC system are able to explain more than half of the total simulation errors. Integrating members with biases exceeding the range corresponding to observational uncertainty is necessary to obtain an optimal dispersion, but this issue can also be a consequence of the fact that meteorological forcing uncertainties were not accounted for. The ESCROC system promises the integration of numerical snow-modelling errors in ensemble forecasting and ensemble assimilation systems in support of avalanche hazard forecasting and other snowpack-modelling applications.

Bidirectional Modulation of Intrinsic Excitability in Rat Prelimbic Cortex Neuronal Ensembles and Non-Ensembles after Operant Learning.

PubMed

Whitaker, Leslie R; Warren, Brandon L; Venniro, Marco; Harte, Tyler C; McPherson, Kylie B; Beidel, Jennifer; Bossert, Jennifer M; Shaham, Yavin; Bonci, Antonello; Hope, Bruce T

2017-09-06

Learned associations between environmental stimuli and rewards drive goal-directed learning and motivated behavior. These memories are thought to be encoded by alterations within specific patterns of sparsely distributed neurons called neuronal ensembles that are activated selectively by reward-predictive stimuli. Here, we use the Fos promoter to identify strongly activated neuronal ensembles in rat prelimbic cortex (PLC) and assess altered intrinsic excitability after 10 d of operant food self-administration training (1 h/d). First, we used the Daun02 inactivation procedure in male FosLacZ-transgenic rats to ablate selectively Fos-expressing PLC neurons that were active during operant food self-administration. Selective ablation of these neurons decreased food seeking. We then used male FosGFP-transgenic rats to assess selective alterations of intrinsic excitability in Fos-expressing neuronal ensembles (FosGFP + ) that were activated during food self-administration and compared these with alterations in less activated non-ensemble neurons (FosGFP - ). Using whole-cell recordings of layer V pyramidal neurons in an ex vivo brain slice preparation, we found that operant self-administration increased excitability of FosGFP + neurons and decreased excitability of FosGFP - neurons. Increased excitability of FosGFP + neurons was driven by increased steady-state input resistance. Decreased excitability of FosGFP - neurons was driven by increased contribution of small-conductance calcium-activated potassium (SK) channels. Injections of the specific SK channel antagonist apamin into PLC increased Fos expression but had no effect on food seeking. Overall, operant learning increased intrinsic excitability of PLC Fos-expressing neuronal ensembles that play a role in food seeking but decreased intrinsic excitability of Fos - non-ensembles. SIGNIFICANCE STATEMENT Prefrontal cortex activity plays a critical role in operant learning, but the underlying cellular mechanisms are

Exploring the calibration of a wind forecast ensemble for energy applications

NASA Astrophysics Data System (ADS)

Heppelmann, Tobias; Ben Bouallegue, Zied; Theis, Susanne

2015-04-01

In the German research project EWeLiNE, Deutscher Wetterdienst (DWD) and Fraunhofer Institute for Wind Energy and Energy System Technology (IWES) are collaborating with three German Transmission System Operators (TSO) in order to provide the TSOs with improved probabilistic power forecasts. Probabilistic power forecasts are derived from probabilistic weather forecasts, themselves derived from ensemble prediction systems (EPS). Since the considered raw ensemble wind forecasts suffer from underdispersiveness and bias, calibration methods are developed for the correction of the model bias and the ensemble spread bias. The overall aim is to improve the ensemble forecasts such that the uncertainty of the possible weather deployment is depicted by the ensemble spread from the first forecast hours. Additionally, the ensemble members after calibration should remain physically consistent scenarios. We focus on probabilistic hourly wind forecasts with horizon of 21 h delivered by the convection permitting high-resolution ensemble system COSMO-DE-EPS which has become operational in 2012 at DWD. The ensemble consists of 20 ensemble members driven by four different global models. The model area includes whole Germany and parts of Central Europe with a horizontal resolution of 2.8 km and a vertical resolution of 50 model levels. For verification we use wind mast measurements around 100 m height that corresponds to the hub height of wind energy plants that belong to wind farms within the model area. Calibration of the ensemble forecasts can be performed by different statistical methods applied to the raw ensemble output. Here, we explore local bivariate Ensemble Model Output Statistics at individual sites and quantile regression with different predictors. Applying different methods, we already show an improvement of ensemble wind forecasts from COSMO-DE-EPS for energy applications. In addition, an ensemble copula coupling approach transfers the time-dependencies of the raw

Nationwide validation of ensemble streamflow forecasts from the Hydrologic Ensemble Forecast Service (HEFS) of the U.S. National Weather Service

NASA Astrophysics Data System (ADS)

Lee, H. S.; Liu, Y.; Ward, J.; Brown, J.; Maestre, A.; Herr, H.; Fresch, M. A.; Wells, E.; Reed, S. M.; Jones, E.

2017-12-01

The National Weather Service's (NWS) Office of Water Prediction (OWP) recently launched a nationwide effort to verify streamflow forecasts from the Hydrologic Ensemble Forecast Service (HEFS) for a majority of forecast locations across the 13 River Forecast Centers (RFCs). Known as the HEFS Baseline Validation (BV), the project involves a joint effort between the OWP and the RFCs. It aims to provide a geographically consistent, statistically robust validation, and a benchmark to guide the operational implementation of the HEFS, inform practical applications, such as impact-based decision support services, and to provide an objective framework for evaluating strategic investments in the HEFS. For the BV, HEFS hindcasts are issued once per day on a 12Z cycle for the period of 1985-2015 with a forecast horizon of 30 days. For the first two weeks, the hindcasts are forced with precipitation and temperature ensemble forecasts from the Global Ensemble Forecast System of the National Centers for Environmental Prediction, and by resampled climatology for the remaining period. The HEFS-generated ensemble streamflow hindcasts are verified using the Ensemble Verification System. Skill is assessed relative to streamflow hindcasts generated from NWS' current operational system, namely climatology-based Ensemble Streamflow Prediction. In this presentation, we summarize the results and findings to date.

Argumentation Based Joint Learning: A Novel Ensemble Learning Approach

PubMed Central

Xu, Junyi; Yao, Li; Li, Le

2015-01-01

Recently, ensemble learning methods have been widely used to improve classification performance in machine learning. In this paper, we present a novel ensemble learning method: argumentation based multi-agent joint learning (AMAJL), which integrates ideas from multi-agent argumentation, ensemble learning, and association rule mining. In AMAJL, argumentation technology is introduced as an ensemble strategy to integrate multiple base classifiers and generate a high performance ensemble classifier. We design an argumentation framework named Arena as a communication platform for knowledge integration. Through argumentation based joint learning, high quality individual knowledge can be extracted, and thus a refined global knowledge base can be generated and used independently for classification. We perform numerous experiments on multiple public datasets using AMAJL and other benchmark methods. The results demonstrate that our method can effectively extract high quality knowledge for ensemble classifier and improve the performance of classification. PMID:25966359

Regionalization of post-processed ensemble runoff forecasts

NASA Astrophysics Data System (ADS)

Olav Skøien, Jon; Bogner, Konrad; Salamon, Peter; Smith, Paul; Pappenberger, Florian

2016-05-01

For many years, meteorological models have been run with perturbated initial conditions or parameters to produce ensemble forecasts that are used as a proxy of the uncertainty of the forecasts. However, the ensembles are usually both biased (the mean is systematically too high or too low, compared with the observed weather), and has dispersion errors (the ensemble variance indicates a too low or too high confidence in the forecast, compared with the observed weather). The ensembles are therefore commonly post-processed to correct for these shortcomings. Here we look at one of these techniques, referred to as Ensemble Model Output Statistics (EMOS) (Gneiting et al., 2005). Originally, the post-processing parameters were identified as a fixed set of parameters for a region. The application of our work is the European Flood Awareness System (http://www.efas.eu), where a distributed model is run with meteorological ensembles as input. We are therefore dealing with a considerably larger data set than previous analyses. We also want to regionalize the parameters themselves for other locations than the calibration gauges. The post-processing parameters are therefore estimated for each calibration station, but with a spatial penalty for deviations from neighbouring stations, depending on the expected semivariance between the calibration catchment and these stations. The estimated post-processed parameters can then be used for regionalization of the postprocessing parameters also for uncalibrated locations using top-kriging in the rtop-package (Skøien et al., 2006, 2014). We will show results from cross-validation of the methodology and although our interest is mainly in identifying exceedance probabilities for certain return levels, we will also show how the rtop package can be used for creating a set of post-processed ensembles through simulations.

Pauci ex tanto numero: reducing redundancy in multi-model ensembles

NASA Astrophysics Data System (ADS)

Solazzo, E.; Riccio, A.; Kioutsioukis, I.; Galmarini, S.

2013-02-01

We explicitly address the fundamental issue of member diversity in multi-model ensembles. To date no attempts in this direction are documented within the air quality (AQ) community, although the extensive use of ensembles in this field. Common biases and redundancy are the two issues directly deriving from lack of independence, undermining the significance of a multi-model ensemble, and are the subject of this study. Shared biases among models will determine a biased ensemble, making therefore essential the errors of the ensemble members to be independent so that bias can cancel out. Redundancy derives from having too large a portion of common variance among the members of the ensemble, producing overconfidence in the predictions and underestimation of the uncertainty. The two issues of common biases and redundancy are analysed in detail using the AQMEII ensemble of AQ model results for four air pollutants in two European regions. We show that models share large portions of bias and variance, extending well beyond those induced by common inputs. We make use of several techniques to further show that subsets of models can explain the same amount of variance as the full ensemble with the advantage of being poorly correlated. Selecting the members for generating skilful, non-redundant ensembles from such subsets proved, however, non-trivial. We propose and discuss various methods of member selection and rate the ensemble performance they produce. In most cases, the full ensemble is outscored by the reduced ones. We conclude that, although independence of outputs may not always guarantee enhancement of scores (but this depends upon the skill being investigated) we discourage selecting the members of the ensemble simply on the basis of scores, that is, independence and skills need to be considered disjointly.

EnsembleGASVR: a novel ensemble method for classifying missense single nucleotide polymorphisms.

PubMed

Rapakoulia, Trisevgeni; Theofilatos, Konstantinos; Kleftogiannis, Dimitrios; Likothanasis, Spiros; Tsakalidis, Athanasios; Mavroudi, Seferina

2014-08-15

Single nucleotide polymorphisms (SNPs) are considered the most frequently occurring DNA sequence variations. Several computational methods have been proposed for the classification of missense SNPs to neutral and disease associated. However, existing computational approaches fail to select relevant features by choosing them arbitrarily without sufficient documentation. Moreover, they are limited to the problem of missing values, imbalance between the learning datasets and most of them do not support their predictions with confidence scores. To overcome these limitations, a novel ensemble computational methodology is proposed. EnsembleGASVR facilitates a two-step algorithm, which in its first step applies a novel evolutionary embedded algorithm to locate close to optimal Support Vector Regression models. In its second step, these models are combined to extract a universal predictor, which is less prone to overfitting issues, systematizes the rebalancing of the learning sets and uses an internal approach for solving the missing values problem without loss of information. Confidence scores support all the predictions and the model becomes tunable by modifying the classification thresholds. An extensive study was performed for collecting the most relevant features for the problem of classifying SNPs, and a superset of 88 features was constructed. Experimental results show that the proposed framework outperforms well-known algorithms in terms of classification performance in the examined datasets. Finally, the proposed algorithmic framework was able to uncover the significant role of certain features such as the solvent accessibility feature, and the top-scored predictions were further validated by linking them with disease phenotypes. Datasets and codes are freely available on the Web at http://prlab.ceid.upatras.gr/EnsembleGASVR/dataset-codes.zip. All the required information about the article is available through http://prlab.ceid.upatras.gr/Ensemble

20060530 - Global Ensemble Upgrade - NWS ftp

Science.gov Websites

the NWS ftp server and to describe some changes to data locations described in the earlier message on .{YYYYMMDD}/ MAJOR PRODUCT CHANGES TO NCEP GLOBAL ENSEMBLE OUTPUT -- PLEASE READ CAREFULLY Starting with the 12 UTC cycle on 30 May 2006 NCEP Central Operations will implement changes to the global ensemble

Ensemble pharmacophore meets ensemble docking: a novel screening strategy for the identification of RIPK1 inhibitors

NASA Astrophysics Data System (ADS)

Fayaz, S. M.; Rajanikant, G. K.

2014-07-01

Programmed cell death has been a fascinating area of research since it throws new challenges and questions in spite of the tremendous ongoing research in this field. Recently, necroptosis, a programmed form of necrotic cell death, has been implicated in many diseases including neurological disorders. Receptor interacting serine/threonine protein kinase 1 (RIPK1) is an important regulatory protein involved in the necroptosis and inhibition of this protein is essential to stop necroptotic process and eventually cell death. Current structure-based virtual screening methods involve a wide range of strategies and recently, considering the multiple protein structures for pharmacophore extraction has been emphasized as a way to improve the outcome. However, using the pharmacophoric information completely during docking is very important. Further, in such methods, using the appropriate protein structures for docking is desirable. If not, potential compound hits, obtained through pharmacophore-based screening, may not have correct ranks and scores after docking. Therefore, a comprehensive integration of different ensemble methods is essential, which may provide better virtual screening results. In this study, dual ensemble screening, a novel computational strategy was used to identify diverse and potent inhibitors against RIPK1. All the pharmacophore features present in the binding site were captured using both the apo and holo protein structures and an ensemble pharmacophore was built by combining these features. This ensemble pharmacophore was employed in pharmacophore-based screening of ZINC database. The compound hits, thus obtained, were subjected to ensemble docking. The leads acquired through docking were further validated through feature evaluation and molecular dynamics simulation.

The Hydrologic Ensemble Prediction Experiment (HEPEX)

NASA Astrophysics Data System (ADS)

Wood, A. W.; Thielen, J.; Pappenberger, F.; Schaake, J. C.; Hartman, R. K.

2012-12-01

The Hydrologic Ensemble Prediction Experiment was established in March, 2004, at a workshop hosted by the European Center for Medium Range Weather Forecasting (ECMWF). With support from the US National Weather Service (NWS) and the European Commission (EC), the HEPEX goal was to bring the international hydrological and meteorological communities together to advance the understanding and adoption of hydrological ensemble forecasts for decision support in emergency management and water resources sectors. The strategy to meet this goal includes meetings that connect the user, forecast producer and research communities to exchange ideas, data and methods; the coordination of experiments to address specific challenges; and the formation of testbeds to facilitate shared experimentation. HEPEX has organized about a dozen international workshops, as well as sessions at scientific meetings (including AMS, AGU and EGU) and special issues of scientific journals where workshop results have been published. Today, the HEPEX mission is to demonstrate the added value of hydrological ensemble prediction systems (HEPS) for emergency management and water resources sectors to make decisions that have important consequences for economy, public health, safety, and the environment. HEPEX is now organised around six major themes that represent core elements of a hydrologic ensemble prediction enterprise: input and pre-processing, ensemble techniques, data assimilation, post-processing, verification, and communication and use in decision making. This poster presents an overview of recent and planned HEPEX activities, highlighting case studies that exemplify the focus and objectives of HEPEX.

Pauci ex tanto numero: reduce redundancy in multi-model ensembles

NASA Astrophysics Data System (ADS)

Solazzo, E.; Riccio, A.; Kioutsioukis, I.; Galmarini, S.

2013-08-01

We explicitly address the fundamental issue of member diversity in multi-model ensembles. To date, no attempts in this direction have been documented within the air quality (AQ) community despite the extensive use of ensembles in this field. Common biases and redundancy are the two issues directly deriving from lack of independence, undermining the significance of a multi-model ensemble, and are the subject of this study. Shared, dependant biases among models do not cancel out but will instead determine a biased ensemble. Redundancy derives from having too large a portion of common variance among the members of the ensemble, producing overconfidence in the predictions and underestimation of the uncertainty. The two issues of common biases and redundancy are analysed in detail using the AQMEII ensemble of AQ model results for four air pollutants in two European regions. We show that models share large portions of bias and variance, extending well beyond those induced by common inputs. We make use of several techniques to further show that subsets of models can explain the same amount of variance as the full ensemble with the advantage of being poorly correlated. Selecting the members for generating skilful, non-redundant ensembles from such subsets proved, however, non-trivial. We propose and discuss various methods of member selection and rate the ensemble performance they produce. In most cases, the full ensemble is outscored by the reduced ones. We conclude that, although independence of outputs may not always guarantee enhancement of scores (but this depends upon the skill being investigated), we discourage selecting the members of the ensemble simply on the basis of scores; that is, independence and skills need to be considered disjointly.

The Road to DLCZ Protocol in Rubidium Ensemble

NASA Astrophysics Data System (ADS)

Li, Chang; Pu, Yunfei; Jiang, Nan; Chang, Wei; Zhang, Sheng; CenterQuantum Information, InstituteInterdisciplinary Information Sciences, Tsinghua Univ Team

2017-04-01

Quantum communication is the powerful approach achieving a fully secure information transferal. The DLCZ protocol ensures that photon linearly decays with transferring distance increasing, which improves the success potential and shortens the time to build up an entangled channel. Apart from that, it provides an advanced idea that building up a quantum internet based on different nodes connected to different sites and themselves. In our laboratory, three sets of laser-cooled Rubidium 87 ensemble have been built. Two of them serve as the single photon emitter, which generate the entanglement between ensemble and photon. What's more, crossed AODs are equipped to multiplex and demultiplex optical circuit so that ensemble is divided into 2 hundred of 2D sub-memory cells. And the third ensemble is used as quantum telecommunication, which converts 780nm photon into telecom-wavelength one. And we have been building double-MOT system, which provides more atoms in ensemble and larger optical density.

Quantum Gibbs ensemble Monte Carlo

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

Fantoni, Riccardo, E-mail: rfantoni@ts.infn.it; Moroni, Saverio, E-mail: moroni@democritos.it

We present a path integral Monte Carlo method which is the full quantum analogue of the Gibbs ensemble Monte Carlo method of Panagiotopoulos to study the gas-liquid coexistence line of a classical fluid. Unlike previous extensions of Gibbs ensemble Monte Carlo to include quantum effects, our scheme is viable even for systems with strong quantum delocalization in the degenerate regime of temperature. This is demonstrated by an illustrative application to the gas-superfluid transition of {sup 4}He in two dimensions.

Decadal climate predictions improved by ocean ensemble dispersion filtering

NASA Astrophysics Data System (ADS)

Kadow, C.; Illing, S.; Kröner, I.; Ulbrich, U.; Cubasch, U.

2017-06-01

Decadal predictions by Earth system models aim to capture the state and phase of the climate several years in advance. Atmosphere-ocean interaction plays an important role for such climate forecasts. While short-term weather forecasts represent an initial value problem and long-term climate projections represent a boundary condition problem, the decadal climate prediction falls in-between these two time scales. In recent years, more precise initialization techniques of coupled Earth system models and increased ensemble sizes have improved decadal predictions. However, climate models in general start losing the initialized signal and its predictive skill from one forecast year to the next. Here we show that the climate prediction skill of an Earth system model can be improved by a shift of the ocean state toward the ensemble mean of its individual members at seasonal intervals. We found that this procedure, called ensemble dispersion filter, results in more accurate results than the standard decadal prediction. Global mean and regional temperature, precipitation, and winter cyclone predictions show an increased skill up to 5 years ahead. Furthermore, the novel technique outperforms predictions with larger ensembles and higher resolution. Our results demonstrate how decadal climate predictions benefit from ocean ensemble dispersion filtering toward the ensemble mean.