Statistics for the Relative Detectability of Chemicals in Weak Gaseous Plumes in LWIR Hyperspectral Imagery

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

Metoyer, Candace N.; Walsh, Stephen J.; Tardiff, Mark F.

2008-10-30

The detection and identification of weak gaseous plumes using thermal imaging data is complicated by many factors. These include variability due to atmosphere, ground and plume temperature, and background clutter. This paper presents an analysis of one formulation of the physics-based model that describes the at-sensor observed radiance. The motivating question for the analyses performed in this paper is as follows. Given a set of backgrounds, is there a way to predict the background over which the probability of detecting a given chemical will be the highest? Two statistics were developed to address this question. These statistics incorporate data frommore » the long-wave infrared band to predict the background over which chemical detectability will be the highest. These statistics can be computed prior to data collection. As a preliminary exploration into the predictive ability of these statistics, analyses were performed on synthetic hyperspectral images. Each image contained one chemical (either carbon tetrachloride or ammonia) spread across six distinct background types. The statistics were used to generate predictions for the background ranks. Then, the predicted ranks were compared to the empirical ranks obtained from the analyses of the synthetic images. For the simplified images under consideration, the predicted and empirical ranks showed a promising amount of agreement. One statistic accurately predicted the best and worst background for detection in all of the images. Future work may include explorations of more complicated plume ingredients, background types, and noise structures.« less

Grey-Markov prediction model based on background value optimization and central-point triangular whitenization weight function

NASA Astrophysics Data System (ADS)

Ye, Jing; Dang, Yaoguo; Li, Bingjun

2018-01-01

Grey-Markov forecasting model is a combination of grey prediction model and Markov chain which show obvious optimization effects for data sequences with characteristics of non-stationary and volatility. However, the state division process in traditional Grey-Markov forecasting model is mostly based on subjective real numbers that immediately affects the accuracy of forecasting values. To seek the solution, this paper introduces the central-point triangular whitenization weight function in state division to calculate possibilities of research values in each state which reflect preference degrees in different states in an objective way. On the other hand, background value optimization is applied in the traditional grey model to generate better fitting data. By this means, the improved Grey-Markov forecasting model is built. Finally, taking the grain production in Henan Province as an example, it verifies this model's validity by comparing with GM(1,1) based on background value optimization and the traditional Grey-Markov forecasting model.

Quasar X-Ray Spectra At z=1.5

NASA Technical Reports Server (NTRS)

Siemiginowska, Aneta

2001-01-01

The predicted counts for ASCA observation was much higher than actually observed counts in the quasar. However, there are three weak hard x-ray sources in the GIS field. We are adding them to the source counts in modeling of hard x-ray background. The work is in progress. We have published a paper in Ap.J. on the luminosity function and the quasar evolution. Based on the theory described in this paper we are predicting a number of sources and their contribution to the x-ray background at different redshifts. These model predictions will be compared to the observed data in the final paper.

The background in the experiment Gerda

NASA Astrophysics Data System (ADS)

Agostini, M.; Allardt, M.; Andreotti, E.; Bakalyarov, A. M.; Balata, M.; Barabanov, I.; Barnabé Heider, M.; Barros, N.; Baudis, L.; Bauer, C.; Becerici-Schmidt, N.; Bellotti, E.; Belogurov, S.; Belyaev, S. T.; Benato, G.; Bettini, A.; Bezrukov, L.; Bode, T.; Brudanin, V.; Brugnera, R.; Budjáš, D.; Caldwell, A.; Cattadori, C.; Chernogorov, A.; Cossavella, F.; Demidova, E. V.; Domula, A.; Egorov, V.; Falkenstein, R.; Ferella, A.; Freund, K.; Frodyma, N.; Gangapshev, A.; Garfagnini, A.; Gotti, C.; Grabmayr, P.; Gurentsov, V.; Gusev, K.; Guthikonda, K. K.; Hampel, W.; Hegai, A.; Heisel, M.; Hemmer, S.; Heusser, G.; Hofmann, W.; Hult, M.; Inzhechik, L. V.; Ioannucci, L.; Csáthy, J. Janicskó; Jochum, J.; Junker, M.; Kihm, T.; Kirpichnikov, I. V.; Kirsch, A.; Klimenko, A.; Knöpfle, K. T.; Kochetov, O.; Kornoukhov, V. N.; Kuzminov, V. V.; Laubenstein, M.; Lazzaro, A.; Lebedev, V. I.; Lehnert, B.; Liao, H. Y.; Lindner, M.; Lippi, I.; Liu, X.; Lubashevskiy, A.; Lubsandorzhiev, B.; Lutter, G.; Macolino, C.; Machado, A. A.; Majorovits, B.; Maneschg, W.; Nemchenok, I.; Nisi, S.; O'Shaughnessy, C.; Palioselitis, D.; Pandola, L.; Pelczar, K.; Pessina, G.; Pullia, A.; Riboldi, S.; Sada, C.; Salathe, M.; Schmitt, C.; Schreiner, J.; Schulz, O.; Schwingenheuer, B.; Schönert, S.; Shevchik, E.; Shirchenko, M.; Simgen, H.; Smolnikov, A.; Stanco, L.; Strecker, H.; Tarka, M.; Ur, C. A.; Vasenko, A. A.; Volynets, O.; von Sturm, K.; Wagner, V.; Walter, M.; Wegmann, A.; Wester, T.; Wojcik, M.; Yanovich, E.; Zavarise, P.; Zhitnikov, I.; Zhukov, S. V.; Zinatulina, D.; Zuber, K.; Zuzel, G.

2014-04-01

The GERmanium Detector Array ( Gerda) experiment at the Gran Sasso underground laboratory (LNGS) of INFN is searching for neutrinoless double beta () decay of Ge. The signature of the signal is a monoenergetic peak at 2039 keV, the value of the decay. To avoid bias in the signal search, the present analysis does not consider all those events, that fall in a 40 keV wide region centered around . The main parameters needed for the analysis are described. A background model was developed to describe the observed energy spectrum. The model contains several contributions, that are expected on the basis of material screening or that are established by the observation of characteristic structures in the energy spectrum. The model predicts a flat energy spectrum for the blinding window around with a background index ranging from 17.6 to 23.8 cts/(keV kg yr). A part of the data not considered before has been used to test if the predictions of the background model are consistent. The observed number of events in this energy region is consistent with the background model. The background at is dominated by close sources, mainly due to K, Bi, Th, Co and emitting isotopes from the Ra decay chain. The individual fractions depend on the assumed locations of the contaminants. It is shown, that after removal of the known peaks, the energy spectrum can be fitted in an energy range of 200 keV around with a constant background. This gives a background index consistent with the full model and uncertainties of the same size.

Modeling background radiation using geochemical data: A case study in and around Cameron, Arizona.

PubMed

Marsac, Kara E; Burnley, Pamela C; Adcock, Christopher T; Haber, Daniel A; Malchow, Russell L; Hausrath, Elisabeth M

2016-12-01

This study compares high resolution forward models of natural gamma-ray background with that measured by high resolution aerial gamma-ray surveys. The ability to predict variations in natural background radiation levels should prove useful for those engaged in measuring anthropogenic contributions to background radiation for the purpose of emergency response and homeland security operations. The forward models are based on geologic maps and remote sensing multi-spectral imagery combined with two different sources of data: 1) bedrock geochemical data (uranium, potassium and thorium concentrations) collected from national databases, the scientific literature and private companies, and 2) the low spatial resolution NURE (National Uranium Resource Evaluation) aerial gamma-ray survey. The study area near Cameron, Arizona, is located in an arid region with minimal vegetation and, due to the presence of abandoned uranium mines, was the subject of a previous high resolution gamma-ray survey. We found that, in general, geologic map units form a good basis for predicting the geographic distribution of the gamma-ray background. Predictions of background gamma-radiation levels based on bedrock geochemical analyses were not as successful as those based on the NURE aerial survey data sorted by geologic unit. The less successful result of the bedrock geochemical model is most likely due to a number of factors including the need to take into account the evolution of soil geochemistry during chemical weathering and the influence of aeolian addition. Refinements to the forward models were made using ASTER visualizations to create subunits of similar exposure rate within the Chinle Formation, which contains multiple lithologies and by grouping alluvial units by drainage basin rather than age. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

Modeling background radiation using geochemical data: A case study in and around Cameron, Arizona

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

Marsac, Kara E.; Burnley, Pamela C.; Adcock, Christopher T.

Here, this study compares high-resolution forward models of natural gamma-ray background with that measured by high resolution aerial gamma-ray surveys. The ability to predict variations in natural background radiation levels should prove useful for those engaged in measuring anthropogenic contributions to background radiation for the purpose of emergency response and homeland security operations. The forward models are based on geologic maps and remote sensing multi-spectral imagery combined with two different sources of data: 1) bedrock geochemical data (uranium, potassium and thorium concentrations) collected from national databases, the scientific literature and private companies, and 2) the low spatial resolution NURE (Nationalmore » Uranium Resource Evaluation) aerial gamma-ray survey. The study area near Cameron, Arizona, is located in an arid region with minimal vegetation and, due to the presence of abandoned uranium mines, was the subject of a previous high resolution gamma-ray survey. We found that, in general, geologic map units form a good basis for predicting the geographic distribution of the gamma-ray background. Predictions of background gamma-radiation levels based on bedrock geochemical analyses were not as successful as those based on the NURE aerial survey data sorted by geologic unit. The less successful result of the bedrock geochemical model is most likely due to a number of factors including the need to take into account the evolution of soil geochemistry during chemical weathering and the influence of aeolian addition. Refinements to the forward models were made using ASTER visualizations to create subunits of similar exposure rate within the Chinle Formation, which contains multiple lithologies and by grouping alluvial units by drainage basin rather than age.« less

Modeling background radiation using geochemical data: A case study in and around Cameron, Arizona

DOE PAGES

Marsac, Kara E.; Burnley, Pamela C.; Adcock, Christopher T.; ...

2016-09-16

Here, this study compares high-resolution forward models of natural gamma-ray background with that measured by high resolution aerial gamma-ray surveys. The ability to predict variations in natural background radiation levels should prove useful for those engaged in measuring anthropogenic contributions to background radiation for the purpose of emergency response and homeland security operations. The forward models are based on geologic maps and remote sensing multi-spectral imagery combined with two different sources of data: 1) bedrock geochemical data (uranium, potassium and thorium concentrations) collected from national databases, the scientific literature and private companies, and 2) the low spatial resolution NURE (Nationalmore » Uranium Resource Evaluation) aerial gamma-ray survey. The study area near Cameron, Arizona, is located in an arid region with minimal vegetation and, due to the presence of abandoned uranium mines, was the subject of a previous high resolution gamma-ray survey. We found that, in general, geologic map units form a good basis for predicting the geographic distribution of the gamma-ray background. Predictions of background gamma-radiation levels based on bedrock geochemical analyses were not as successful as those based on the NURE aerial survey data sorted by geologic unit. The less successful result of the bedrock geochemical model is most likely due to a number of factors including the need to take into account the evolution of soil geochemistry during chemical weathering and the influence of aeolian addition. Refinements to the forward models were made using ASTER visualizations to create subunits of similar exposure rate within the Chinle Formation, which contains multiple lithologies and by grouping alluvial units by drainage basin rather than age.« less

Cosmic Microwave Background Timeline

Science.gov Websites

about 2.3 K 1948: George Gamow, Ralph Alpher, and Robert Herman predict that a Big Bang universe perfect blackbody spectrum and thereby strongly supporting the hot big bang model, the thermal history of anisotropy in the cosmic microwave background, this strongly supports the big bang model with gravitational

Modeled summer background concentration nutrients and suspended sediment in the mid-continent (USA) great rivers

EPA Science Inventory

We used regression models to predict background concentration of four water quality indictors: total nitrogen (N), total phosphorus (P), chloride, and total suspended solids (TSS), in the mid-continent (USA) great rivers, the Upper Mississippi, the Lower Missouri, and the Ohio. F...

Starobinsky-like inflation, supercosmology and neutrino masses in no-scale flipped SU(5)

NASA Astrophysics Data System (ADS)

Ellis, John; Garcia, Marcos A. G.; Nagata, Natsumi; Nanopoulos, Dimitri V.; Olive, Keith A.

2017-07-01

We embed a flipped SU(5) × U(1) GUT model in a no-scale supergravity framework, and discuss its predictions for cosmic microwave background observables, which are similar to those of the Starobinsky model of inflation. Measurements of the tilt in the spectrum of scalar perturbations in the cosmic microwave background, ns, constrain significantly the model parameters. We also discuss the model's predictions for neutrino masses, and pay particular attention to the behaviours of scalar fields during and after inflation, reheating and the GUT phase transition. We argue in favor of strong reheating in order to avoid excessive entropy production which could dilute the generated baryon asymmetry.

The background in the $$0\

DOE PAGES

Agostini, M.; Allardt, M.; Andreotti, E.; ...

2014-04-04

The GERmanium Detector Array (Gerda) experiment at the Gran Sasso underground laboratory (LNGS) of INFN is searching for neutrinoless double beta (0νββ) decay of 76 Ge. The signature of the signal is a monoenergetic peak at 2039 keV, the Q ββ value of the decay. To avoid bias in the signal search, the present analysis does not consider all those events, that fall in a 40 keV wide region centered around Q ββ. The main parameters needed for the 0νββ analysis are described. A background model was developed to describe the observed energy spectrum. The model contains severalmore » contributions, that are expected on the basis of material screening or that are established by the observation of characteristic structures in the energy spectrum. The model predicts a flat energy spectrum for the blinding window around Qββ with a background index ranging from 17.6 to 23.8 × 10 -3 cts/(keV kg yr). A part of the data not considered before has been used to test if the predictions of the background model are consistent. The observed number of events in this energy region is consistent with the background model. The background at Q ββ is dominated by close sources, mainly due to 42 K, 214 Bi, 228 60 Co and α emitting isotopes from the 226 Ra decay chain. The individual fractions depend on the assumed locations of the contaminants. It is shown, that after removal of the known γ peaks, the energy spectrum can be fitted in an energy range of 200 keV around Q ββ with a constant background. This gives a background index consistent with the full model and uncertainties of the same size.« less

Can personality traits and intelligence compensate for background disadvantage? Predicting status attainment in adulthood.

PubMed

Damian, Rodica Ioana; Su, Rong; Shanahan, Michael; Trautwein, Ulrich; Roberts, Brent W

2015-09-01

This study investigated the interplay of family background and individual differences, such as personality traits and intelligence (measured in a large U.S. representative sample of high school students; N = 81,000) in predicting educational attainment, annual income, and occupational prestige 11 years later. Specifically, we tested whether individual differences followed 1 of 3 patterns in relation to parental socioeconomic status (SES) when predicting attained status: (a) the independent effects hypothesis (i.e., individual differences predict attainments independent of parental SES level), (b) the resource substitution hypothesis (i.e., individual differences are stronger predictors of attainments at lower levels of parental SES), and (c) the Matthew effect hypothesis (i.e., "the rich get richer"; individual differences are stronger predictors of attainments at higher levels of parental SES). We found that personality traits and intelligence in adolescence predicted later attained status above and beyond parental SES. A standard deviation increase in individual differences translated to up to 8 additional months of education, $4,233 annually, and more prestigious occupations. Furthermore, although we did find some evidence for both the resource substitution and the Matthew effect hypotheses, the most robust pattern across all models supported the independent effects hypothesis. Intelligence was the exception, the interaction models being more robust. Finally, we found that although personality traits may help compensate for background disadvantage to a small extent, they do not usually lead to a "full catch-up" effect, unlike intelligence. This was the first longitudinal study of status attainment to test interactive models of individual differences and background factors. (c) 2015 APA, all rights reserved).

Can Personality Traits and Intelligence Compensate for Background Disadvantage? Predicting Status Attainment in Adulthood

PubMed Central

Damian, Rodica Ioana; Su, Rong; Shanahan, Michael; Trautwein, Ulrich; Roberts, Brent W.

2014-01-01

This paper investigates the interplay of family background and individual differences, such as personality traits and intelligence (measured in a large US representative sample of high school students; N = 81,000) in predicting educational attainment, annual income, and occupational prestige eleven years later. Specifically, we tested whether individual differences followed one of three patterns in relation to parental SES when predicting attained status: (a) the independent effects hypothesis (i.e., individual differences predict attainments independent of parental SES level), (b) the resource substitution hypothesis (i.e., individual differences are stronger predictors of attainments at lower levels of parental SES), and (c) the Matthew effect hypothesis (i.e., “the rich get richer,” individual differences are stronger predictors of attainments at higher levels of parental SES). We found that personality traits and intelligence in adolescence predicted later attained status above and beyond parental SES. A standard deviation increase in individual differences translated to up to 8 additional months of education, $4,233 annually, and more prestigious occupations. Furthermore, although we did find some evidence for both the resource substitution and the Matthew effect hypotheses, the most robust pattern across all models supported the independent effects hypothesis. Intelligence was the exception, where interaction models were more robust. Finally, we found that although personality traits may help compensate for background disadvantage to a small extent, they do not usually lead to a “full catch up” effect, unlike intelligence. This was the first longitudinal study of status attainment to test interactive models of individual differences and background factors. PMID:25402679

Use of sediment-trace element geochemical models for the identification of local fluvial baseline concentrations

USGS Publications Warehouse

Horowitz, A.J.; Elrick, K.A.; Demas, C.R.; Demcheck, D.K.

1991-01-01

Studies have demonstrated the utility of fluvial bed sediment chemical data in assesing local water-quality conditions. However, establishing local background trace element levels can be difficult. Reference to published average concentrations or the use of dated cores are often of little use in small areas of diverse local petrology, geology, land use, or hydrology. An alternative approach entails the construction of a series of sediment-trace element predictive models based on data from environmentally diverse but unaffected areas. Predicted values could provide a measure of local background concentrations and comparison with actual measured concentrations could identify elevated trace elements and affected sites. Such a model set was developed from surface bed sediments collected nationwide in the United States. Tests of the models in a small Louisiana basin indicated that they could be used to establish local trace element background levels, but required recalibration to account for local geochemical conditions outside the range of samples used to generate the nationwide models.

Prediction of Maternal Cytomegalovirus Serostatus in Early Pregnancy: A Retrospective Analysis in Western Europe

PubMed Central

Kuessel, Lorenz; Husslein, Heinrich; Marschalek, Julian; Brunner, Julia; Ristl, Robin; Popow-Kraupp, Theresia; Kiss, Herbert

2015-01-01

Background Cytomegalovirus (CMV) is the most prevalent congenital viral infection and thus places an enormous disease burden on newborn infants. Seroprevalence of maternal antibodies to CMV due to CMV exposure prior to pregnancy is currently the most important protective factor against congenital CMV disease. The aim of this study was to identify potential predictors, and to develop and evaluate a risk-predicting model for the maternal CMV serostatus in early pregnancy. Methods Maternal and paternal background information, as well as maternal CMV serostatus in early pregnancy from 882 pregnant women were analyzed. Women were divided into two groups based on their CMV serostatus, and were compared using univariate analysis. To predict serostatus based on epidemiological baseline characteristics, a multiple logistic regression model was calculated using stepwise model selection. Sensitivity and specificity were analyzed using ROC curves. A nomogram based on the model was developed. Results 646 women were CMV seropositive (73.2%), and 236 were seronegative (26.8%). The groups differed significantly with respect to maternal age (p = 0.006), gravidity (p<0.001), parity (p<0.001), use of assisted reproduction techniques (p = 0.018), maternal and paternal migration background (p<0.001), and maternal and paternal education level (p<0.001). ROC evaluation of the selected prediction model revealed an area under the curve of 0.83 (95%CI: 0.8–0.86), yielding sensitivity and specificity values of 0.69 and 0.86, respectively. Conclusion We identified predictors of maternal CMV serostatus in early pregnancy and developed a risk-predicting model based on baseline epidemiological characteristics. Our findings provide easy accessible information that can influence the counseling of pregnant woman in terms of their CMV-associated risk. PMID:26693714

Starobinsky-like inflation, supercosmology and neutrino masses in no-scale flipped SU(5)

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

Ellis, John; Garcia, Marcos A.G.; Nagata, Natsumi

2017-07-01

We embed a flipped SU(5) × U(1) GUT model in a no-scale supergravity framework, and discuss its predictions for cosmic microwave background observables, which are similar to those of the Starobinsky model of inflation. Measurements of the tilt in the spectrum of scalar perturbations in the cosmic microwave background, n {sub s} , constrain significantly the model parameters. We also discuss the model's predictions for neutrino masses, and pay particular attention to the behaviours of scalar fields during and after inflation, reheating and the GUT phase transition. We argue in favor of strong reheating in order to avoid excessive entropymore » production which could dilute the generated baryon asymmetry.« less

Improving Marital Prediction: A Model and a Pilot Study.

ERIC Educational Resources Information Center

Dean, Dwight G.; Lucas, Wayne L.

A model for the prediction of marital adjustment is proposed which presents selected social background factors (e.g., education) and interactive factors (e.g., Bienvenu's Communication scale, Hurvitz' Role Inventory, Dean's Emotional Maturity and Commitment scales, Rosenberg's Self-Esteem scale) in order to account for as much of the variance in…

Implementation and Research on the Operational Use of the Mesoscale Prediction Model COAMPS in Poland

DTIC Science & Technology

2007-09-30

COAMPS model. Bogumil Jakubiak, University of Warsaw – participated in EGU General Assembly , Vienna Austria 15-20 April 2007 giving one oral and two...conditional forecast (background) error probability density function using an ensemble of the model forecast to generate background error statistics...COAMPS system on ICM machines at Warsaw University for the purpose of providing operational support to the general public using the ICM meteorological

Construction of a model for predicting creatinine clearance in Japanese patients treated with Cisplatin therapy.

PubMed

Yajima, Airi; Uesawa, Yoshihiro; Ogawa, Chiaki; Yatabe, Megumi; Kondo, Naoki; Saito, Shinichiro; Suzuki, Yoshihiko; Atsuda, Kouichiro; Kagaya, Hajime

2015-05-01

There exist various useful predictive models, such as the Cockcroft-Gault model, for estimating creatinine clearance (CLcr). However, the prediction of renal function is difficult in patients with cancer treated with cisplatin. Therefore, we attempted to construct a new model for predicting CLcr in such patients. Japanese patients with head and neck cancer who had received cisplatin-based chemotherapy were used as subjects. A multiple regression equation was constructed as a model for predicting CLcr values based on background and laboratory data. A model for predicting CLcr, which included body surface area, serum creatinine and albumin, was constructed. The model exhibited good performance prior to cisplatin therapy. In addition, it performed better than previously reported models after cisplatin therapy. The predictive model constructed in the present study displayed excellent potential and was useful for estimating the renal function of patients treated with cisplatin therapy. Copyright© 2015 International Institute of Anticancer Research (Dr. John G. Delinassios), All rights reserved.

Cosmic microwave background probes models of inflation

NASA Technical Reports Server (NTRS)

Davis, Richard L.; Hodges, Hardy M.; Smoot, George F.; Steinhardt, Paul J.; Turner, Michael S.

1992-01-01

Inflation creates both scalar (density) and tensor (gravity wave) metric perturbations. We find that the tensor-mode contribution to the cosmic microwave background anisotropy on large-angular scales can only exceed that of the scalar mode in models where the spectrum of perturbations deviates significantly from scale invariance. If the tensor mode dominates at large-angular scales, then the value of DeltaT/T predicted on 1 deg is less than if the scalar mode dominates, and, for cold-dark-matter models, bias factors greater than 1 can be made consistent with Cosmic Background Explorer (COBE) DMR results.

Science with the space-based interferometer eLISA. II: gravitational waves from cosmological phase transitions

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

Caprini, Chiara, E-mail: chiara.caprini@cea.fr; Hindmarsh, Mark; Huber, Stephan

We investigate the potential for the eLISA space-based interferometer to detect the stochastic gravitational wave background produced by strong first-order cosmological phase transitions. We discuss the resulting contributions from bubble collisions, magnetohydrodynamic turbulence, and sound waves to the stochastic background, and estimate the total corresponding signal predicted in gravitational waves. The projected sensitivity of eLISA to cosmological phase transitions is computed in a model-independent way for various detector designs and configurations. By applying these results to several specific models, we demonstrate that eLISA is able to probe many well-motivated scenarios beyond the Standard Model of particle physics predicting strong first-ordermore » cosmological phase transitions in the early Universe.« less

Predicting performance: relative importance of students' background and past performance.

PubMed

Stegers-Jager, Karen M; Themmen, Axel P N; Cohen-Schotanus, Janke; Steyerberg, Ewout W

2015-09-01

Despite evidence for the predictive value of both pre-admission characteristics and past performance at medical school, their relative contribution to predicting medical school performance has not been thoroughly investigated. This study was designed to determine the relative importance of pre-admission characteristics and past performance in medical school in predicting student performance in pre-clinical and clinical training. This longitudinal prospective study followed six cohorts of students admitted to a Dutch, 6-year, undergraduate medical course during 2002-2007 (n = 2357). Four prediction models were developed using multivariate logistic regression analysis. Main outcome measures were 'Year 1 course completion within 1 year' (models 1a, 1b), 'Pre-clinical course completion within 4 years' (model 2) and 'Achievement of at least three of five clerkship grades of ≥ 8.0' (model 3). Pre-admission characteristics (models 1a, 1b, 2, 3) and past performance at medical school (models 1b, 2, 3) were included as predictor variables. In model 1a - including pre-admission characteristics only - the strongest predictor for Year 1 course completion was pre-university grade point average (GPA). Success factors were 'selected by admission testing' and 'age > 21 years'; risk factors were 'Surinamese/Antillean background', 'foreign pre-university degree', 'doctor parent' and male gender. In model 1b, number of attempts and GPA at 4 months were the strongest predictors for Year 1 course completion, and male gender remained a risk factor. Year 1 GPA was the strongest predictor for pre-clinical course completion, whereas being male or aged 19-21 years were risk factors. Pre-clinical course GPA positively predicted clinical performance, whereas being non-Dutch or a first-generation university student were important risk factors for lower clinical grades. Nagelkerke's R(2) ranged from 0.16 to 0.62. This study not only confirms the importance of past performance as a predictor of future performance in pre-clinical training, but also reveals the importance of a student's background as a predictor in clinical training. These findings have important practical implications for selection and support during medical school. © 2015 John Wiley & Sons Ltd.

A refined methodology for modeling volume quantification performance in CT

NASA Astrophysics Data System (ADS)

Chen, Baiyu; Wilson, Joshua; Samei, Ehsan

2014-03-01

The utility of CT lung nodule volume quantification technique depends on the precision of the quantification. To enable the evaluation of quantification precision, we previously developed a mathematical model that related precision to image resolution and noise properties in uniform backgrounds in terms of an estimability index (e'). The e' was shown to predict empirical precision across 54 imaging and reconstruction protocols, but with different correlation qualities for FBP and iterative reconstruction (IR) due to the non-linearity of IR impacted by anatomical structure. To better account for the non-linearity of IR, this study aimed to refine the noise characterization of the model in the presence of textured backgrounds. Repeated scans of an anthropomorphic lung phantom were acquired. Subtracted images were used to measure the image quantum noise, which was then used to adjust the noise component of the e' calculation measured from a uniform region. In addition to the model refinement, the validation of the model was further extended to 2 nodule sizes (5 and 10 mm) and 2 segmentation algorithms. Results showed that the magnitude of IR's quantum noise was significantly higher in structured backgrounds than in uniform backgrounds (ASiR, 30-50%; MBIR, 100-200%). With the refined model, the correlation between e' values and empirical precision no longer depended on reconstruction algorithm. In conclusion, the model with refined noise characterization relfected the nonlinearity of iterative reconstruction in structured background, and further showed successful prediction of quantification precision across a variety of nodule sizes, dose levels, slice thickness, reconstruction algorithms, and segmentation software.

Mean and turbulent flow downstream of a low-intensity fire: influence of canopy and background atmospheric conditions

Treesearch

Michael T. Kiefer; Warren E. Heilman; Shiyuan Zhong; Joseph J. Charney; Xindi Bian

2015-01-01

This study examines the sensitivity of mean and turbulent flow in the planetary boundary layer and roughness sublayer to a low-intensity fire and evaluates whether the sensitivity is dependent on canopy and background atmospheric properties. The ARPS-CANOPY model, a modified version of the Advanced Regional Prediction System (ARPS) model with a canopy parameterization...

Revealing infinite derivative gravity's true potential: The weak-field limit around de Sitter backgrounds

NASA Astrophysics Data System (ADS)

Edholm, James

2018-03-01

General Relativity is known to produce singularities in the potential generated by a point source. Our universe can be modeled as a de Sitter (dS) metric and we show that ghost-free infinite derivative gravity (IDG) produces a nonsingular potential around a dS background, while returning to the GR prediction at large distances. We also show that although there are an apparently infinite number of coefficients in the theory, only a finite number actually affect the predictions. By writing the linearized equations of motion in a simplified form, we find that at distances below the Hubble length scale, the difference between the IDG potential around a flat background and around a de Sitter background is negligible.

The treatment of uncertainties in reactive pollution dispersion models at urban scales.

PubMed

Tomlin, A S; Ziehn, T; Goodman, P; Tate, J E; Dixon, N S

2016-07-18

The ability to predict NO2 concentrations ([NO2]) within urban street networks is important for the evaluation of strategies to reduce exposure to NO2. However, models aiming to make such predictions involve the coupling of several complex processes: traffic emissions under different levels of congestion; dispersion via turbulent mixing; chemical processes of relevance at the street-scale. Parameterisations of these processes are challenging to quantify with precision. Predictions are therefore subject to uncertainties which should be taken into account when using models within decision making. This paper presents an analysis of mean [NO2] predictions from such a complex modelling system applied to a street canyon within the city of York, UK including the treatment of model uncertainties and their causes. The model system consists of a micro-scale traffic simulation and emissions model, and a Reynolds averaged turbulent flow model coupled to a reactive Lagrangian particle dispersion model. The analysis focuses on the sensitivity of predicted in-street increments of [NO2] at different locations in the street to uncertainties in the model inputs. These include physical characteristics such as background wind direction, temperature and background ozone concentrations; traffic parameters such as overall demand and primary NO2 fraction; as well as model parameterisations such as roughness lengths, turbulent time- and length-scales and chemical reaction rate coefficients. Predicted [NO2] is shown to be relatively robust with respect to model parameterisations, although there are significant sensitivities to the activation energy for the reaction NO + O3 as well as the canyon wall roughness length. Under off-peak traffic conditions, demand is the key traffic parameter. Under peak conditions where the network saturates, road-side [NO2] is relatively insensitive to changes in demand and more sensitive to the primary NO2 fraction. The most important physical parameter was found to be the background wind direction. The study highlights the key parameters required for reliable [NO2] estimations suggesting that accurate reference measurements for wind direction should be a critical part of air quality assessments for in-street locations. It also highlights the importance of street scale chemical processes in forming road-side [NO2], particularly for regions of high NOx emissions such as close to traffic queues.

Prediction of sub-surface 37Ar concentrations at locations in the Northwestern United States.

PubMed

Fritz, Bradley G; Aalseth, Craig E; Back, Henning O; Hayes, James C; Humble, Paul H; Ivanusa, Pavlo; Mace, Emily K

2018-01-01

The Comprehensive Nuclear-Test-Ban Treaty, which is intended to prevent nuclear weapon test explosions and any other nuclear explosions, includes a verification regime, which provides monitoring to identify potential nuclear explosions. The presence of elevated 37 Ar is one way to identify subsurface nuclear explosive testing. However, the naturally occurring formation of 37 Ar in the subsurface adds a complicating factor. Prediction of the naturally occurring concentration of 37 Ar can help to determine if a measured 37 Ar concentration is elevated relative to background. The naturally occurring 37 Ar background concentration has been shown to vary between less than 1 mBq/m 3 to greater than 100 mBq/m 3 (Riedmann and Purtschert, 2011). The purpose of this work was to enhance the understanding of the naturally occurring background concentrations of 37 Ar, allowing for better interpretation of results. To that end, we present and evaluate a computationally efficient model for predicting the average concentration of 37 Ar at any depth under transient barometric pressures. Further, measurements of 37 Ar concentrations in samples collected at multiple locations are provided as validation of the concentration prediction model. The model is shown to compare favorably with concentrations of 37 Ar measured at multiple locations in the Northwestern United States. Copyright © 2017 Elsevier Ltd. All rights reserved.

Implementation of a GPS-RO data processing system for the KIAPS-LETKF data assimilation system

NASA Astrophysics Data System (ADS)

Kwon, H.; Kang, J.-S.; Jo, Y.; Kang, J. H.

2014-11-01

The Korea Institute of Atmospheric Prediction Systems (KIAPS) has been developing a new global numerical weather prediction model and an advanced data assimilation system. As part of the KIAPS Package for Observation Processing (KPOP) system for data assimilation, preprocessing and quality control modules for bending angle measurements of global positioning system radio occultation (GPS-RO) data have been implemented and examined. GPS-RO data processing system is composed of several steps for checking observation locations, missing values, physical values for Earth radius of curvature, and geoid undulation. An observation-minus-background check is implemented by use of a one-dimensional observational bending angle operator and tangent point drift is also considered in the quality control process. We have tested GPS-RO observations utilized by the Korean Meteorological Administration (KMA) within KPOP, based on both the KMA global model and the National Center for Atmospheric Research (NCAR) Community Atmosphere Model-Spectral Element (CAM-SE) as a model background. Background fields from the CAM-SE model are incorporated for the preparation of assimilation experiments with the KIAPS-LETKF data assimilation system, which has been successfully implemented to a cubed-sphere model with fully unstructured quadrilateral meshes. As a result of data processing, the bending angle departure statistics between observation and background shows significant improvement. Also, the first experiment in assimilating GPS-RO bending angle resulting from KPOP within KIAPS-LETKF shows encouraging results.

Regional and global modeling estimates of policy relevant background ozone over the United States

NASA Astrophysics Data System (ADS)

Emery, Christopher; Jung, Jaegun; Downey, Nicole; Johnson, Jeremiah; Jimenez, Michele; Yarwood, Greg; Morris, Ralph

2012-02-01

Policy Relevant Background (PRB) ozone, as defined by the US Environmental Protection Agency (EPA), refers to ozone concentrations that would occur in the absence of all North American anthropogenic emissions. PRB enters into the calculation of health risk benefits, and as the US ozone standard approaches background levels, PRB is increasingly important in determining the feasibility and cost of compliance. As PRB is a hypothetical construct, modeling is a necessary tool. Since 2006 EPA has relied on global modeling to establish PRB for their regulatory analyses. Recent assessments with higher resolution global models exhibit improved agreement with remote observations and modest upward shifts in PRB estimates. This paper shifts the paradigm to a regional model (CAMx) run at 12 km resolution, for which North American boundary conditions were provided by a low-resolution version of the GEOS-Chem global model. We conducted a comprehensive model inter-comparison, from which we elucidate differences in predictive performance against ozone observations and differences in temporal and spatial background variability over the US. In general, CAMx performed better in replicating observations at remote monitoring sites, and performance remained better at higher concentrations. While spring and summer mean PRB predicted by GEOS-Chem ranged 20-45 ppb, CAMx predicted PRB ranged 25-50 ppb and reached well over 60 ppb in the west due to event-oriented phenomena such as stratospheric intrusion and wildfires. CAMx showed a higher correlation between modeled PRB and total observed ozone, which is significant for health risk assessments. A case study during April 2006 suggests that stratospheric exchange of ozone is underestimated in both models on an event basis. We conclude that wildfires, lightning NO x and stratospheric intrusions contribute a significant level of uncertainty in estimating PRB, and that PRB will require careful consideration in the ozone standard setting process.

Heterogeneity effects in visual search predicted from the group scanning model.

PubMed

Macquistan, A D

1994-12-01

The group scanning model of feature integration theory (Treisman & Gormican, 1988) suggests that subjects search visual displays serially by groups, but process items within each group in parallel. The size of these groups is determined by the discriminability of the targets in the background of distractors. When the target is poorly discriminable, the size of the scanned group will be small, and search will be slow. The model predicts that group size will be smallest when targets of an intermediate value on a perceptual dimension are presented in a heterogeneous background of distractors that have higher and lower values on the same dimension. Experiment 1 demonstrates this effect. Experiment 2 controls for a possible confound of decision complexity in Experiment 1. For simple feature targets, the group scanning model provides a good account of the visual search process.

Artificial neural network models for prediction of cardiovascular autonomic dysfunction in general Chinese population

PubMed Central

2013-01-01

Background The present study aimed to develop an artificial neural network (ANN) based prediction model for cardiovascular autonomic (CA) dysfunction in the general population. Methods We analyzed a previous dataset based on a population sample consisted of 2,092 individuals aged 30–80 years. The prediction models were derived from an exploratory set using ANN analysis. Performances of these prediction models were evaluated in the validation set. Results Univariate analysis indicated that 14 risk factors showed statistically significant association with CA dysfunction (P < 0.05). The mean area under the receiver-operating curve was 0.762 (95% CI 0.732–0.793) for prediction model developed using ANN analysis. The mean sensitivity, specificity, positive and negative predictive values were similar in the prediction models was 0.751, 0.665, 0.330 and 0.924, respectively. All HL statistics were less than 15.0. Conclusion ANN is an effective tool for developing prediction models with high value for predicting CA dysfunction among the general population. PMID:23902963

A New Determination of the Extragalactic Diffuse X-Ray Background from EGRET Data

NASA Technical Reports Server (NTRS)

Strong, Andrew W.; Moskalenko, Igor V.; Reimer, Olaf

2004-01-01

We use the GALPROP model for cosmic-ray propagation to obtain a new estimate of the Galactic component of gamma rays, and show that away from the Galactic plane it gives an accurate prediction of the observed EGRET intensities in the energy range 30 MeV - 50 GeV. On this basis we re-evaluate the extragalactic gamma-ray background. We find that for some energies previous work underestimated the Galactic contribution at high latitudes and hence overestimated the background. Our new background spectrum shows a positive curvature similar to that expected for models of the extragalactic emission based on the blazar population.

Air pollution dispersion models for human exposure predictions in London.

PubMed

Beevers, Sean D; Kitwiroon, Nutthida; Williams, Martin L; Kelly, Frank J; Ross Anderson, H; Carslaw, David C

2013-01-01

The London household survey has shown that people travel and are exposed to air pollutants differently. This argues for human exposure to be based upon space-time-activity data and spatio-temporal air quality predictions. For the latter, we have demonstrated the role that dispersion models can play by using two complimentary models, KCLurban, which gives source apportionment information, and Community Multi-scale Air Quality Model (CMAQ)-urban, which predicts hourly air quality. The KCLurban model is in close agreement with observations of NO(X), NO(2) and particulate matter (PM)(10/2.5), having a small normalised mean bias (-6% to 4%) and a large Index of Agreement (0.71-0.88). The temporal trends of NO(X) from the CMAQ-urban model are also in reasonable agreement with observations. Spatially, NO(2) predictions show that within 10's of metres of major roads, concentrations can range from approximately 10-20 p.p.b. up to 70 p.p.b. and that for PM(10/2.5) central London roadside concentrations are approximately double the suburban background concentrations. Exposure to different PM sources is important and we predict that brake wear-related PM(10) concentrations are approximately eight times greater near major roads than at suburban background locations. Temporally, we have shown that average NO(X) concentrations close to roads can range by a factor of approximately six between the early morning minimum and morning rush hour maximum periods. These results present strong arguments for the hybrid exposure model under development at King's and, in future, for in-building models and a model for the London Underground.

Evaluation of a Revised Interplanetary Shock Prediction Model: 1D CESE-HD-2 Solar-Wind Model

NASA Astrophysics Data System (ADS)

Zhang, Y.; Du, A. M.; Du, D.; Sun, W.

2014-08-01

We modified the one-dimensional conservation element and solution element (CESE) hydrodynamic (HD) model into a new version [ 1D CESE-HD-2], by considering the direction of the shock propagation. The real-time performance of the 1D CESE-HD-2 model during Solar Cycle 23 (February 1997 - December 2006) is investigated and compared with those of the Shock Time of Arrival Model ( STOA), the Interplanetary-Shock-Propagation Model ( ISPM), and the Hakamada-Akasofu-Fry version 2 ( HAFv.2). Of the total of 584 flare events, 173 occurred during the rising phase, 166 events during the maximum phase, and 245 events during the declining phase. The statistical results show that the success rates of the predictions by the 1D CESE-HD-2 model for the rising, maximum, declining, and composite periods are 64 %, 62 %, 57 %, and 61 %, respectively, with a hit window of ± 24 hours. The results demonstrate that the 1D CESE-HD-2 model shows the highest success rates when the background solar-wind speed is relatively fast. Thus, when the background solar-wind speed at the time of shock initiation is enhanced, the forecasts will provide potential values to the customers. A high value (27.08) of χ 2 and low p-value (< 0.0001) for the 1D CESE-HD-2 model give considerable confidence for real-time forecasts by using this new model. Furthermore, the effects of various shock characteristics (initial speed, shock duration, background solar wind, longitude, etc.) and background solar wind on the forecast are also investigated statistically.

The gravitational wave contribution to cosmic microwave background anisotropies and the amplitude of mass fluctuations from COBE results

NASA Technical Reports Server (NTRS)

Lucchin, Francesco; Matarrese, Sabino; Mollerach, Silvia

1992-01-01

A stochastic background of primordial gravitational waves may substantially contribute, via the Sachs-Wolfe effect, to the large-scale cosmic microwave background (CMB) anisotropies recently detected by COBE. This implies a bias in any resulting determination of the primordial amplitude of density fluctuations. We consider the constraints imposed on n is less than 1 ('tilted') power-law fluctuation spectra, taking into account the contribution from both scalar and tensor waves, as predicted by power-law inflation. The gravitational wave contribution to CMB anisotropies generally reduces the required rms level of mass fluctuation, thereby increasing the linear bias parameter, even in models where the spectral index is close to the Harrison-Zel'dovich value n = 1. This 'gravitational wave bias' helps to reconcile the predictions of CDM models with observations on pairwise galaxy velocity dispersion on small scales.

Neural network model for the prediction of PM10 daily concentrations in two sites in the Western Mediterranean.

PubMed

de Gennaro, Gianluigi; Trizio, Livia; Di Gilio, Alessia; Pey, Jorge; Pérez, Noemi; Cusack, Michael; Alastuey, Andrés; Querol, Xavier

2013-10-01

An artificial neural network (ANN) was developed and tested to forecast PM10 daily concentration in two contrasted environments in NE Spain, a regional background site (Montseny), and an urban background site (Barcelona-CSIC), which was highly influenced by vehicular emissions. In order to predict 24-h average PM10 concentrations, the artificial neural network previously developed by Caselli et al. (2009) was improved by using hourly PM concentrations and deterministic factors such as a Saharan dust alert. In particular, the model input data for prediction were the hourly PM10 concentrations 1-day in advance, local meteorological data and information about air masses origin. The forecasted performance indexes for both sites were calculated and they showed better results for the regional background site in Montseny (R(2)=0.86, SI=0.75) than for urban site in Barcelona (R(2)=0.73, SI=0.58), influenced by local and sometimes unexpected sources. Moreover, a sensitivity analysis conducted to understand the importance of the different variables included among the input data, showed that local meteorology and air masses origin are key factors in the model forecasts. This result explains the reason for the improvement of ANN's forecasting performance at the Montseny site with respect to the Barcelona site. Moreover, the artificial neural network developed in this work could prove useful to predict PM10 concentrations, especially, at regional background sites such as those on the Mediterranean Basin which are primarily affected by long-range transports. Hence, the artificial neural network presented here could be a powerful tool for obtaining real time information on air quality status and could aid stakeholders in their development of cost-effective control strategies. © 2013 Elsevier B.V. All rights reserved.

A neural model of border-ownership from kinetic occlusion.

PubMed

Layton, Oliver W; Yazdanbakhsh, Arash

2015-01-01

Camouflaged animals that have very similar textures to their surroundings are difficult to detect when stationary. However, when an animal moves, humans readily see a figure at a different depth than the background. How do humans perceive a figure breaking camouflage, even though the texture of the figure and its background may be statistically identical in luminance? We present a model that demonstrates how the primate visual system performs figure-ground segregation in extreme cases of breaking camouflage based on motion alone. Border-ownership signals develop as an emergent property in model V2 units whose receptive fields are nearby kinetically defined borders that separate the figure and background. Model simulations support border-ownership as a general mechanism by which the visual system performs figure-ground segregation, despite whether figure-ground boundaries are defined by luminance or motion contrast. The gradient of motion- and luminance-related border-ownership signals explains the perceived depth ordering of the foreground and background surfaces. Our model predicts that V2 neurons, which are sensitive to kinetic edges, are selective to border-ownership (magnocellular B cells). A distinct population of model V2 neurons is selective to border-ownership in figures defined by luminance contrast (parvocellular B cells). B cells in model V2 receive feedback from neurons in V4 and MT with larger receptive fields to bias border-ownership signals toward the figure. We predict that neurons in V4 and MT sensitive to kinetically defined figures play a crucial role in determining whether the foreground surface accretes, deletes, or produces a shearing motion with respect to the background. Copyright © 2014 Elsevier Ltd. All rights reserved.

Predicting hepatotoxicity using ToxCast in vitro bioactivity and chemical structure

EPA Science Inventory

Background: The U.S. EPA ToxCastTM program is screening thousands of environmental chemicals for bioactivity using hundreds of high-throughput in vitro assays to build predictive models of toxicity. We represented chemicals based on bioactivity and chemical structure descriptors ...

Investigation of Oil Fluorescence as a Technique for the Remote Sensing of Oil Spills

DOT National Transportation Integrated Search

1971-06-01

The flexibility of remote sensing of oil spills by laser-excited oil fluorescence is investigated. The required parameters are fed into a physical model to predict signal and background levels; and the predictions are verified by field experiments. A...

Quantitative Structure--Activity Relationship Modeling of Rat Acute Toxicity by Oral Exposure

EPA Science Inventory

Background: Few Quantitative Structure-Activity Relationship (QSAR) studies have successfully modeled large, diverse rodent toxicity endpoints. Objective: In this study, a combinatorial QSAR approach has been employed for the creation of robust and predictive models of acute toxi...

Can We Predict CME Deflections Based on Solar Magnetic Field Configuration Alone?

NASA Astrophysics Data System (ADS)

Kay, C.; Opher, M.; Evans, R. M.

2013-12-01

Accurate space weather forecasting requires knowledge of the trajectory of coronal mass ejections (CMEs), including predicting CME deflections close to the Sun and through interplanetary space. Deflections of CMEs occur due to variations in the background magnetic field or solar wind speed, magnetic reconnection, and interactions with other CMEs. Using our newly developed model of CME deflections due to gradients in the background solar magnetic field, ForeCAT (Kay et al. 2013), we explore the questions: (a) do all simulated CMEs ultimately deflect to the minimum in the background solar magnetic field? (b) does the majority of the deflection occur in the lower corona below 4 Rs? ForeCAT does not include temporal variations in the magnetic field of active regions (ARs), spatial variations in the background solar wind speed, magnetic reconnection, or interactions with other CMEs. Therefore we focus on the effects of the steady state solar magnetic field. We explore two different Carrington Rotations (CRs): CR 2029 (April-May 2005) and CR 2077 (November-December 2008). Little is known about how the density and magnetic field fall with distance in the lower corona. We consider four density models derived from observations (Chen 1996, Mann et al. 2003, Guhathakurta et al. 2006, Leblanc et al. 1996) and two magnetic field models (PFSS and a scaled model). ForeCAT includes drag resulting from both CME propagation and deflection through the background solar wind. We vary the drag coefficient to explore the effect of drag on the deflection at 1 AU.

Testing model for prediction system of 1-AU arrival times of CME-associated interplanetary shocks

NASA Astrophysics Data System (ADS)

Ogawa, Tomoya; den, Mitsue; Tanaka, Takashi; Sugihara, Kohta; Takei, Toshifumi; Amo, Hiroyoshi; Watari, Shinichi

We test a model to predict arrival times of interplanetary shock waves associated with coronal mass ejections (CMEs) using a three-dimensional adaptive mesh refinement (AMR) code. The model is used for the prediction system we develop, which has a Web-based user interface and aims at people who is not familiar with operation of computers and numerical simulations or is not researcher. We apply the model to interplanetary CME events. We first choose coronal parameters so that property of background solar wind observed by ACE space craft is reproduced. Then we input CME parameters observed by SOHO/LASCO. Finally we compare the predicted arrival times with observed ones. We describe results of the test and discuss tendency of the model.

Use of model calibration to achieve high accuracy in analysis of computer networks

DOEpatents

Frogner, Bjorn; Guarro, Sergio; Scharf, Guy

2004-05-11

A system and method are provided for creating a network performance prediction model, and calibrating the prediction model, through application of network load statistical analyses. The method includes characterizing the measured load on the network, which may include background load data obtained over time, and may further include directed load data representative of a transaction-level event. Probabilistic representations of load data are derived to characterize the statistical persistence of the network performance variability and to determine delays throughout the network. The probabilistic representations are applied to the network performance prediction model to adapt the model for accurate prediction of network performance. Certain embodiments of the method and system may be used for analysis of the performance of a distributed application characterized as data packet streams.

Implementation of a GPS-RO data processing system for the KIAPS-LETKF data assimilation system

NASA Astrophysics Data System (ADS)

Kwon, H.; Kang, J.-S.; Jo, Y.; Kang, J. H.

2015-03-01

The Korea Institute of Atmospheric Prediction Systems (KIAPS) has been developing a new global numerical weather prediction model and an advanced data assimilation system. As part of the KIAPS package for observation processing (KPOP) system for data assimilation, preprocessing, and quality control modules for bending-angle measurements of global positioning system radio occultation (GPS-RO) data have been implemented and examined. The GPS-RO data processing system is composed of several steps for checking observation locations, missing values, physical values for Earth radius of curvature, and geoid undulation. An observation-minus-background check is implemented by use of a one-dimensional observational bending-angle operator, and tangent point drift is also considered in the quality control process. We have tested GPS-RO observations utilized by the Korean Meteorological Administration (KMA) within KPOP, based on both the KMA global model and the National Center for Atmospheric Research Community Atmosphere Model with Spectral Element dynamical core (CAM-SE) as a model background. Background fields from the CAM-SE model are incorporated for the preparation of assimilation experiments with the KIAPS local ensemble transform Kalman filter (LETKF) data assimilation system, which has been successfully implemented to a cubed-sphere model with unstructured quadrilateral meshes. As a result of data processing, the bending-angle departure statistics between observation and background show significant improvement. Also, the first experiment in assimilating GPS-RO bending angle from KPOP within KIAPS-LETKF shows encouraging results.

Standard Clock in primordial density perturbations and cosmic microwave background

NASA Astrophysics Data System (ADS)

Chen, Xingang; Namjoo, Mohammad Hossein

2014-12-01

Standard Clocks in the primordial epoch leave a special type of features in the primordial perturbations, which can be used to directly measure the scale factor of the primordial universe as a function of time a (t), thus discriminating between inflation and alternatives. We have started to search for such signals in the Planck 2013 data using the key predictions of the Standard Clock. In this Letter, we summarize the key predictions of the Standard Clock and present an interesting candidate example in Planck 2013 data. Motivated by this candidate, we construct and compute full Standard Clock models and use the more complete prediction to make more extensive comparison with data. Although this candidate is not yet statistically significant, we use it to illustrate how Standard Clocks appear in Cosmic Microwave Background (CMB) and how they can be further tested by future data. We also use it to motivate more detailed theoretical model building.

Fine Particulate Matter and Cardiovascular Disease: Comparison of Assessment Methods for Long-term Exposure

EPA Science Inventory

Background Adverse cardiovascular events have been linked with PM2.5 exposure obtained primarily from air quality monitors, which rarely co-locate with participant residences. Modeled PM2.5 predictions at finer resolution may more accurately predict residential exposure; however...

[Evaluation of eco-environmental quality based on artificial neural network and remote sensing techniques].

PubMed

Li, Hongyi; Shi, Zhou; Sha, Jinming; Cheng, Jieliang

2006-08-01

In the present study, vegetation, soil brightness, and moisture indices were extracted from Landsat ETM remote sensing image, heat indices were extracted from MODIS land surface temperature product, and climate index and other auxiliary geographical information were selected as the input of neural network. The remote sensing eco-environmental background value of standard interest region evaluated in situ was selected as the output of neural network, and the back propagation (BP) neural network prediction model containing three layers was designed. The network was trained, and the remote sensing eco-environmental background value of Fuzhou in China was predicted by using software MATLAB. The class mapping of remote sensing eco-environmental background values based on evaluation standard showed that the total classification accuracy was 87. 8%. The method with a scheme of prediction first and classification then could provide acceptable results in accord with the regional eco-environment types.

JUNO Photovoltaic Power at Jupiter

NASA Technical Reports Server (NTRS)

Dawson, Stephen F.; Stella, Paul; McAlpine, William; Smith, Brian

2012-01-01

This paper summarizes the Juno modeling team work on predicting the Juno solar array performance at critical mission points including Juno Orbit Insertion (JOI) and End of Mission (EOM). This report consists of background on Juno solar array design, a summary of power estimates, an explanation of the modeling approach used by Aerospace, a detailed discussion of loss factors and performance predictions, a thermal analysis, and a review of risks to solar array performance

Research on Fault Rate Prediction Method of T/R Component

NASA Astrophysics Data System (ADS)

Hou, Xiaodong; Yang, Jiangping; Bi, Zengjun; Zhang, Yu

2017-07-01

T/R component is an important part of the large phased array radar antenna array, because of its large numbers, high fault rate, it has important significance for fault prediction. Aiming at the problems of traditional grey model GM(1,1) in practical operation, the discrete grey model is established based on the original model in this paper, and the optimization factor is introduced to optimize the background value, and the linear form of the prediction model is added, the improved discrete grey model of linear regression is proposed, finally, an example is simulated and compared with other models. The results show that the method proposed in this paper has higher accuracy and the solution is simple and the application scope is more extensive.

Factors Influencing Physical Activity among Postpartum Iranian Women

ERIC Educational Resources Information Center

Roozbahani, Nasrin; Ghofranipour, Fazlollah; Eftekhar Ardabili, Hassan; Hajizadeh, Ebrahim

2014-01-01

Background: Postpartum women are a population at risk for sedentary living. Physical activity (PA) prior to pregnancy may be effective in predicting similar behaviour in the postpartum period. Objective: To test a composite version of the extended transtheoretical model (TTM) by adding "past behaviour" in order to predict PA behaviour…

Learning Management System with Prediction Model and Course-Content Recommendation Module

ERIC Educational Resources Information Center

Evale, Digna S.

2017-01-01

Aim/Purpose: This study is an attempt to enhance the existing learning management systems today through the integration of technology, particularly with educational data mining and recommendation systems. Background: It utilized five-year historical data to find patterns for predicting student performance in Java Programming to generate…

Coulomb thermal properties and stability of the Io plasma torus

NASA Technical Reports Server (NTRS)

Barbosa, D. D.; Coroniti, F. V.; Eviatar, A.

1983-01-01

Coulomb collisional energy exchange rates are computed for a model of the Io plasma torus consisting of newly created pickup ions, a background of thermally degraded intermediary ions, and a population of cooler electrons. The electrons are collisionally heated by both the pickup ions and background ions and are cooled by electron impact excitation of plasma ions which radiate in the EUV. It is found that a relative concentration of S III pickup ions forbidden S III/electrons = 0.1 with a temperature of 340 eV can deliver energy to the electrons at a rate of 3 x 10 to the -13th erg/cu cm per sec, sufficient to power the EUV emissions in the Io torus. The model predicts a background ion temperature Ti of about 53 eV and an electron temperature Te of about 5.5 eV on the basis of steady-state energy balance relations at Coulomb rates. The model also predicts electron temperature fluctuations at the 30 percent level on a time scale of less than 11 hours, consistent with recent observations of this phenomenon.

Late time neutrino masses, the LSND experiment, and the cosmic microwave background.

PubMed

Chacko, Z; Hall, Lawrence J; Oliver, Steven J; Perelstein, Maxim

2005-03-25

Models with low-scale breaking of global symmetries in the neutrino sector provide an alternative to the seesaw mechanism for understanding why neutrinos are light. Such models can easily incorporate light sterile neutrinos required by the Liquid Scintillator Neutrino Detector experiment. Furthermore, the constraints on the sterile neutrino properties from nucleosynthesis and large-scale structure can be removed due to the nonconventional cosmological evolution of neutrino masses and densities. We present explicit, fully realistic supersymmetric models, and discuss the characteristic signatures predicted in the angular distributions of the cosmic microwave background.

Retina-V1 model of detectability across the visual field

PubMed Central

Bradley, Chris; Abrams, Jared; Geisler, Wilson S.

2014-01-01

A practical model is proposed for predicting the detectability of targets at arbitrary locations in the visual field, in arbitrary gray scale backgrounds, and under photopic viewing conditions. The major factors incorporated into the model include (a) the optical point spread function of the eye, (b) local luminance gain control (Weber's law), (c) the sampling array of retinal ganglion cells, (d) orientation and spatial frequency–dependent contrast masking, (e) broadband contrast masking, and (f) efficient response pooling. The model is tested against previously reported threshold measurements on uniform backgrounds (the ModelFest data set and data from Foley, Varadharajan, Koh, & Farias, 2007) and against new measurements reported here for several ModelFest targets presented on uniform, 1/f noise, and natural backgrounds at retinal eccentricities ranging from 0° to 10°. Although the model has few free parameters, it is able to account quite well for all the threshold measurements. PMID:25336179

Global, spatial, and temporal sensitivity analysis for a complex pesticide fate and transport model.

EPA Science Inventory

Background/Questions/Methods As one ofthe most heavily used exposure models by U.S. EPA, Pesticide Root Zone Model (PRZM) is a one-dimensional, dynamic, compartment model that predicts the fate and transport of a pesticide in the unsaturated soil system around a plant's root zo...

Role of Family Background, Student Behaviors, and School-Related Beliefs in Predicting High School Dropout

ERIC Educational Resources Information Center

Parr, Alyssa K.; Bonitz, Verena S.

2015-01-01

The authors' purpose was to test a parsimonious model derived from social cognitive career theory (R. W. Lent, S. D. Brown, & G. Hackett, 1994) and expectancy value theory (J. S. Eccles & A. Wigfield, 2002) that integrates groups of variables (demographic background, student behaviors, and school-related beliefs) with the goal of…

Comparison of Prediction Model for Cardiovascular Autonomic Dysfunction Using Artificial Neural Network and Logistic Regression Analysis

PubMed Central

Zeng, Fangfang; Li, Zhongtao; Yu, Xiaoling; Zhou, Linuo

2013-01-01

Background This study aimed to develop the artificial neural network (ANN) and multivariable logistic regression (LR) analyses for prediction modeling of cardiovascular autonomic (CA) dysfunction in the general population, and compare the prediction models using the two approaches. Methods and Materials We analyzed a previous dataset based on a Chinese population sample consisting of 2,092 individuals aged 30–80 years. The prediction models were derived from an exploratory set using ANN and LR analysis, and were tested in the validation set. Performances of these prediction models were then compared. Results Univariate analysis indicated that 14 risk factors showed statistically significant association with the prevalence of CA dysfunction (P<0.05). The mean area under the receiver-operating curve was 0.758 (95% CI 0.724–0.793) for LR and 0.762 (95% CI 0.732–0.793) for ANN analysis, but noninferiority result was found (P<0.001). The similar results were found in comparisons of sensitivity, specificity, and predictive values in the prediction models between the LR and ANN analyses. Conclusion The prediction models for CA dysfunction were developed using ANN and LR. ANN and LR are two effective tools for developing prediction models based on our dataset. PMID:23940593

Long-term prediction of fish growth under varying ambient temperature using a multiscale dynamic model

PubMed Central

2009-01-01

Background Feed composition has a large impact on the growth of animals, particularly marine fish. We have developed a quantitative dynamic model that can predict the growth and body composition of marine fish for a given feed composition over a timespan of several months. The model takes into consideration the effects of environmental factors, particularly temperature, on growth, and it incorporates detailed kinetics describing the main metabolic processes (protein, lipid, and central metabolism) known to play major roles in growth and body composition. Results For validation, we compared our model's predictions with the results of several experimental studies. We showed that the model gives reliable predictions of growth, nutrient utilization (including amino acid retention), and body composition over a timespan of several months, longer than most of the previously developed predictive models. Conclusion We demonstrate that, despite the difficulties involved, multiscale models in biology can yield reasonable and useful results. The model predictions are reliable over several timescales and in the presence of strong temperature fluctuations, which are crucial factors for modeling marine organism growth. The model provides important improvements over existing models. PMID:19903354

Comparison of Measured Galactic Background Radiation at L-Band with Model

NASA Technical Reports Server (NTRS)

LeVine, David M.; Abraham, Saji; Kerr, Yann H.; Wilson, William J.; Skou, Niels; Sobjaerg, Sten

2004-01-01

Radiation from the celestial sky in the spectral window at 1.413 GHz is strong and an accurate accounting of this background radiation is needed for calibration and retrieval algorithms. Modern radio astronomy measurements in this window have been converted into a brightness temperature map of the celestial sky at L-band suitable for such applications. This paper presents a comparison of the background predicted by this map with the measurements of several modern L-band remote sensing radiometer Keywords-Galactic background, microwave radiometry; remote sensing;

The faint galaxy contribution to the diffuse extragalactic background light

NASA Technical Reports Server (NTRS)

Cole, Shaun; Treyer, Marie-Agnes; Silk, Joseph

1992-01-01

Models of the faint galaxy contribution to the diffuse extragalactic background light are presented, which are consistent with current data on faint galaxy number counts and redshifts. The autocorrelation function of surface brightness fluctuations in the extragalactic diffuse light is predicted, and the way in which these predictions depend on the cosmological model and assumptions of biasing is determined. It is confirmed that the recent deep infrared number counts are most compatible with a high density universe (Omega-0 is approximately equal to 1) and that the steep blue counts then require an extra population of rapidly evolving blue galaxies. The faintest presently detectable galaxies produce an interesting contribution to the extragalactic diffuse light, and still fainter galaxies may also produce a significant contribution. These faint galaxies still only produce a small fraction of the total optical diffuse background light, but on scales of a few arcminutes to a few degrees, they produce a substantial fraction of the fluctuations in the diffuse light.

How Reliable Is the Prediction of Solar Wind Background?

NASA Astrophysics Data System (ADS)

Jian, Lan K.; MacNeice, Peter; Taktakishvili, Aleksandre; Odstrcil, Dusan; Jackson, Bernard; Yu, Hsiu-Shan; Riley, Pete; Sokolov, Igor

2015-04-01

The prediction of solar wind background is a necessary part of space weather forecasting. Multiple coronal and heliospheric models have been installed at the Community Coordinated Modeling Center (CCMC) to produce the solar wind, including the Wang-Sheely-Arge (WSA)-Enlil model, MHD-Around-a-Sphere (MAS)-Enlil model, Space Weather Modeling Framework (SWMF), and heliospheric tomography using interplanetary scintillation (IPS) data. By comparing the modeling results with the OMNI data over 7 Carrington rotations in 2007, we have conducted a third-party validation of these models for the near-Earth solar wind. This work will help the models get ready for the transition from research to operation. Besides visual comparison, we have quantitatively assessed the models’ capabilities in reproducing the time series and statistics of solar wind parameters. Using improved algorithms, we have identified magnetic field sector boundaries (SBs) and slow-to-fast stream interaction regions (SIRs) as focused structures. The success rate of capturing them and the time offset vary largely with models. For this period, the 2014 version of MAS-Enlil model works best for SBs, and the heliospheric tomography works best for SIRs. General strengths and weaknesses for each model are identified to provide an unbiased reference to model developers and users.

Sensitivity analysis for simulating pesticide impacts on honey bee colonies

EPA Science Inventory

Background/Question/Methods Regulatory agencies assess risks to honey bees from pesticides through a tiered process that includes predictive modeling with empirical toxicity and chemical data of pesticides as a line of evidence. We evaluate the Varroapop colony model, proposed by...

A Novel Two-Step Hierarchial Quantitative Structure-Activity Relationship Modeling Workflow for Predicting Acute Toxicity of Chemicals in Rodents

EPA Science Inventory

Background: Accurate prediction of in vivo toxicity from in vitro testing is a challenging problem. Large public–private consortia have been formed with the goal of improving chemical safety assessment by the means of high-throughput screening. Methods and results: A database co...

Monitoring and Depth of Strategy Use in Computer-Based Learning Environments for Science and History

ERIC Educational Resources Information Center

Deekens, Victor M.; Greene, Jeffrey A.; Lobczowski, Nikki G.

2018-01-01

Background: Self-regulated learning (SRL) models position metacognitive monitoring as central to SRL processing and predictive of student learning outcomes (Winne & Hadwin, 2008; Zimmerman, 2000). A body of research evidence also indicates that depth of strategy use, ranging from surface to deep processing, is predictive of learning…

High-energy gamma-ray and neutrino production in star-forming galaxies across cosmic time: Difficulties in explaining the IceCube data

NASA Astrophysics Data System (ADS)

Sudoh, Takahiro; Totani, Tomonori; Kawanaka, Norita

2018-06-01

We present new theoretical modeling to predict the luminosity and spectrum of gamma-ray and neutrino emission of a star-forming galaxy, from the star formation rate (ψ), gas mass (Mgas), stellar mass, and disk size, taking into account production, propagation, and interactions of cosmic rays. The model reproduces the observed gamma-ray luminosities of nearby galaxies detected by Fermi better than the simple power-law models as a function of ψ or ψMgas. This model is then used to predict the cosmic background flux of gamma-rays and neutrinos from star-forming galaxies, by using a semi-analytical model of cosmological galaxy formation that reproduces many observed quantities of local and high-redshift galaxies. Calibration of the model using gamma-ray luminosities of nearby galaxies allows us to make a more reliable prediction than previous studies. In our baseline model, star-forming galaxies produce about 20% of the isotropic gamma-ray background unresolved by Fermi, and only 0.5% of IceCube neutrinos. Even with an extreme model assuming a hard injection cosmic-ray spectral index of 2.0 for all galaxies, at most 22% of IceCube neutrinos can be accounted for. These results indicate that it is difficult to explain most of the IceCube neutrinos by star-forming galaxies, without violating the gamma-ray constraints from nearby galaxies.

High-energy gamma-ray and neutrino production in star-forming galaxies across cosmic time: Difficulties in explaining the IceCube data

NASA Astrophysics Data System (ADS)

Sudoh, Takahiro; Totani, Tomonori; Kawanaka, Norita

2018-04-01

We present new theoretical modeling to predict the luminosity and spectrum of gamma-ray and neutrino emission of a star-forming galaxy, from the star formation rate (ψ), gas mass (Mgas), stellar mass, and disk size, taking into account production, propagation, and interactions of cosmic rays. The model reproduces the observed gamma-ray luminosities of nearby galaxies detected by Fermi better than the simple power-law models as a function of ψ or ψMgas. This model is then used to predict the cosmic background flux of gamma-rays and neutrinos from star-forming galaxies, by using a semi-analytical model of cosmological galaxy formation that reproduces many observed quantities of local and high-redshift galaxies. Calibration of the model using gamma-ray luminosities of nearby galaxies allows us to make a more reliable prediction than previous studies. In our baseline model, star-forming galaxies produce about 20% of the isotropic gamma-ray background unresolved by Fermi, and only 0.5% of IceCube neutrinos. Even with an extreme model assuming a hard injection cosmic-ray spectral index of 2.0 for all galaxies, at most 22% of IceCube neutrinos can be accounted for. These results indicate that it is difficult to explain most of the IceCube neutrinos by star-forming galaxies, without violating the gamma-ray constraints from nearby galaxies.

Role of highway traffic on spatial and temporal distributions of air pollutants in a Swiss Alpine valley.

PubMed

Ducret-Stich, Regina E; Tsai, Ming-Yi; Ragettli, Martina S; Ineichen, Alex; Kuenzli, Nino; Phuleria, Harish C

2013-07-01

Traffic-related air pollutants show high spatial variability near roads, posing a challenge to adequately assess exposures. Recent modeling approaches (e.g. dispersion models, land-use regression (LUR) models) have addressed this but mostly in urban areas where traffic is abundant. In contrast, our study area was located in a rural Swiss Alpine valley crossed by the main North-south transit highway of Switzerland. We conducted an extensive measurement campaign collecting continuous nitrogen dioxide (NO₂), particulate number concentrations (PN), daily respirable particulate matter (PM10), elemental carbon (EC) and organic carbon (OC) at one background, one highway and seven mobile stations from November 2007 to June 2009. Using these measurements, we built a hybrid model to predict daily outdoor NO₂ concentrations at residences of children participating in an asthma panel study. With the exception of OC, daily variations of the pollutants followed the temporal trends of heavy-duty traffic counts on the highway. In contrast, variations of weekly/seasonal means were strongly determined by meteorological conditions, e.g., winter inversion episodes. For pollutants related to primary exhaust emissions (i.e. NO₂, EC and PN) local spatial variation strongly depended on proximity to the highway. Pollutant concentrations decayed to background levels within 150 to 200 m from the highway. Two separate daily NO₂ prediction models were built using LUR approaches with (a) short-term traffic and weather data (model 1) and (b) subsequent addition of daily background NO₂ to previous model (model 2). Models 1 and 2 explained 70% and 91% of the variability in outdoor NO₂ concentrations, respectively. The biweekly averaged predictions from the final model 2 agreed very well with the independent biweekly integrated passive measurements taken at thirteen homes and nine community sites (validation R(2)=0.74). The excellent spatio-temporal performance of our model provides a very promising basis for the health effect assessment of the panel study. Copyright © 2013 Elsevier B.V. All rights reserved.

The Anisotropy of the Microwave Background to l = 3500: Deep Field Observations with the Cosmic Background Imager

NASA Technical Reports Server (NTRS)

Mason, B. S.; Pearson, T. J.; Readhead, A. C. S.; Shepherd, M. C.; Sievers, J.; Udomprasert, P. S.; Cartwright, J. K.; Farmer, A. J.; Padin, S.; Myers, S. T.;

Background Noise Analysis in a Few-Photon-Level Qubit Memory

NASA Astrophysics Data System (ADS)

Mittiga, Thomas; Kupchak, Connor; Jordaan, Bertus; Namazi, Mehdi; Nolleke, Christian; Figeroa, Eden

2014-05-01

We have developed an Electromagnetically Induced Transparency based polarization qubit memory. The device is composed of a dual-rail probe field polarization setup colinear with an intense control field to store and retrieve any arbitrary polarization state by addressing a Λ-type energy level scheme in a 87Rb vapor cell. To achieve a signal-to-background ratio at the few photon level sufficient for polarization tomography of the retrieved state, the intense control field is filtered out through an etalon filtrating system. We have developed an analytical model predicting the influence of the signal-to-background ratio on the fidelities and compared it to experimental data. Experimentally measured global fidelities have been found to follow closely the theoretical prediction as signal-to-background decreases. These results suggest the plausibility of employing room temperature memories to store photonic qubits at the single photon level and for future applications in long distance quantum communication schemes.

A consensus least squares support vector regression (LS-SVR) for analysis of near-infrared spectra of plant samples.

PubMed

Li, Yankun; Shao, Xueguang; Cai, Wensheng

2007-04-15

Consensus modeling of combining the results of multiple independent models to produce a single prediction avoids the instability of single model. Based on the principle of consensus modeling, a consensus least squares support vector regression (LS-SVR) method for calibrating the near-infrared (NIR) spectra was proposed. In the proposed approach, NIR spectra of plant samples were firstly preprocessed using discrete wavelet transform (DWT) for filtering the spectral background and noise, then, consensus LS-SVR technique was used for building the calibration model. With an optimization of the parameters involved in the modeling, a satisfied model was achieved for predicting the content of reducing sugar in plant samples. The predicted results show that consensus LS-SVR model is more robust and reliable than the conventional partial least squares (PLS) and LS-SVR methods.

Understanding how roadside concentrations of NOx are influenced by the background levels, traffic density, and meteorological conditions using Boosted Regression Trees

NASA Astrophysics Data System (ADS)

Sayegh, Arwa; Tate, James E.; Ropkins, Karl

2016-02-01

Oxides of Nitrogen (NOx) is a major component of photochemical smog and its constituents are considered principal traffic-related pollutants affecting human health. This study investigates the influence of background concentrations of NOx, traffic density, and prevailing meteorological conditions on roadside concentrations of NOx at UK urban, open motorway, and motorway tunnel sites using the statistical approach Boosted Regression Trees (BRT). BRT models have been fitted using hourly concentration, traffic, and meteorological data for each site. The models predict, rank, and visualise the relationship between model variables and roadside NOx concentrations. A strong relationship between roadside NOx and monitored local background concentrations is demonstrated. Relationships between roadside NOx and other model variables have been shown to be strongly influenced by the quality and resolution of background concentrations of NOx, i.e. if it were based on monitored data or modelled prediction. The paper proposes a direct method of using site-specific fundamental diagrams for splitting traffic data into four traffic states: free-flow, busy-flow, congested, and severely congested. Using BRT models, the density of traffic (vehicles per kilometre) was observed to have a proportional influence on the concentrations of roadside NOx, with different fitted regression line slopes for the different traffic states. When other influences are conditioned out, the relationship between roadside concentrations and ambient air temperature suggests NOx concentrations reach a minimum at around 22 °C with high concentrations at low ambient air temperatures which could be associated to restricted atmospheric dispersion and/or to changes in road traffic exhaust emission characteristics at low ambient air temperatures. This paper uses BRT models to study how different critical factors, and their relative importance, influence the variation of roadside NOx concentrations. The paper highlights the importance of either setting up local background continuous monitors or improving the quality and resolution of modelled UK background maps and the need to further investigate the influence of ambient air temperature on NOx emissions and roadside NOx concentrations.

Spatio-temporal variation of urban ultrafine particle number concentrations

NASA Astrophysics Data System (ADS)

Ragettli, Martina S.; Ducret-Stich, Regina E.; Foraster, Maria; Morelli, Xavier; Aguilera, Inmaculada; Basagaña, Xavier; Corradi, Elisabetta; Ineichen, Alex; Tsai, Ming-Yi; Probst-Hensch, Nicole; Rivera, Marcela; Slama, Rémy; Künzli, Nino; Phuleria, Harish C.

2014-10-01

Methods are needed to characterize short-term exposure to ultrafine particle number concentrations (UFP) for epidemiological studies on the health effects of traffic-related UFP. Our aims were to assess season-specific spatial variation of short-term (20-min) UFP within the city of Basel, Switzerland, and to develop hybrid models for predicting short-term median and mean UFP levels on sidewalks. We collected measurements of UFP for periods of 20 min (MiniDiSC particle counter) and determined traffic volume along sidewalks at 60 locations across the city, during non-rush hours in three seasons. For each monitoring location, detailed spatial characteristics were locally recorded and potential predictor variables were derived from geographic information systems (GIS). We built multivariate regression models to predict local UFP, using concurrent UFP levels measured at a suburban background station, and combinations of meteorological, temporal, GIS and observed site characteristic variables. For a subset of sites, we assessed the relationship between UFP measured on the sidewalk and at the nearby residence (i.e., home outdoor exposure on e.g. balconies). The average median 20-min UFP levels at street and urban background sites were 14,700 ± 9100 particles cm-3 and 9900 ± 8600 particles cm-3, respectively, with the highest levels occurring in winter and the lowest in summer. The most important predictor for all models was the suburban background UFP concentration, explaining 50% and 38% of the variability of the median and mean, respectively. While the models with GIS-derived variables (R2 = 0.61) or observed site characteristics (R2 = 0.63) predicted median UFP levels equally well, mean UFP predictions using only site characteristic variables (R2 = 0.62) showed a better fit than models using only GIS variables (R2 = 0.55). The best model performance was obtained by using a combination of GIS-derived variables and locally observed site characteristics (median: R2 = 0.66; mean: R2 = 0.65). The 20-min UFP concentrations measured at the sidewalk were strongly related (R2 = 0.8) to the concurrent 20-min residential UFP levels nearby. Our results indicate that median UFP can be moderately predicted by means of a suburban background site and GIS-derived traffic and land use variables. In areas and regions where large-scale GIS data are not available, the spatial distribution of traffic-related UFP may be assessed reasonably well by collecting on-site short-term traffic and land-use data.

Constraints on the power spectrum of the primordial density field from large-scale data - Microwave background and predictions of inflation

NASA Technical Reports Server (NTRS)

Kashlinsky, A.

1992-01-01

It is shown here that, by using galaxy catalog correlation data as input, measurements of microwave background radiation (MBR) anisotropies should soon be able to test two of the inflationary scenario's most basic predictions: (1) that the primordial density fluctuations produced were scale-invariant and (2) that the universe is flat. They should also be able to detect anisotropies of large-scale structure formed by gravitational evolution of density fluctuations present at the last scattering epoch. Computations of MBR anisotropies corresponding to the minimum of the large-scale variance of the MBR anisotropy are presented which favor an open universe with P(k) significantly different from the Harrison-Zeldovich spectrum predicted by most inflationary models.

Predicted distribution of visible and near-infrared radiant flux above and below a transmittant leaf

NASA Technical Reports Server (NTRS)

Roberts, Dar A.; Adams, John B.; Smith, Milton O.

1990-01-01

The effects are studied analytically of leaf size, leaf height, and background reflectance on the upward and downward radiant flux (RF) of a leaf. The leaf is horizontal and isotropically scattering in the computer model which examines the light environment in three regions about the leaf. The spectral properties of the leaf are based on measurements of the big-leaf maple, and the model is interpreted in terms of relative RF which is defined as a percentage of the total light in the model. The results demonstrate the dependence of upward relative RF on the light's wavelength and background reflectance with large variations in the NIR. Brightness varies directly with distance from background with maximum brightness achieved at lower heights for smaller leaves. These and other results suggest that NIR canopy reflectance due to leaves is highly dependent on the background reflectance.

PREDICTION METRICS FOR CHEMICAL DETECTION IN LONG-WAVE INFRARED HYPERSPECTRAL IMAGERY

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

Chilton, M.; Walsh, S.J.; Daly, D.S.

2009-01-01

Natural and man-made chemical processes generate gaseous plumes that may be detected by hyperspectral imaging, which produces a matrix of spectra affected by the chemical constituents of the plume, the atmosphere, the bounding background surface and instrument noise. A physics-based model of observed radiance shows that high chemical absorbance and low background emissivity result in a larger chemical signature. Using simulated hyperspectral imagery, this study investigated two metrics which exploited this relationship. The objective was to explore how well the chosen metrics predicted when a chemical would be more easily detected when comparing one background type to another. The twomore » predictor metrics correctly rank ordered the backgrounds for about 94% of the chemicals tested as compared to the background rank orders from Whitened Matched Filtering (a detection algorithm) of the simulated spectra. These results suggest that the metrics provide a reasonable summary of how the background emissivity and chemical absorbance interact to produce the at-sensor chemical signal. This study suggests that similarly effective predictors that account for more general physical conditions may be derived.« less

The Reciprocal Internal/External Frame of Reference Model Using Grades and Test Scores

ERIC Educational Resources Information Center

Möller, Jens; Zimmermann, Friederike; Köller, Olaf

2014-01-01

Background: The reciprocal I/E model (RI/EM) combines the internal/external frame of reference model (I/EM) with the reciprocal effects model (REM). The RI/EM extends the I/EM longitudinally and the REM across domains. The model predicts that, within domains, mathematics and verbal achievement (VACH) and academic self-concept have positive effects…

Data-driven modeling of background and mine-related acidity and metals in river basins

USGS Publications Warehouse

Friedel, Michael J

2013-01-01

A novel application of self-organizing map (SOM) and multivariate statistical techniques is used to model the nonlinear interaction among basin mineral-resources, mining activity, and surface-water quality. First, the SOM is trained using sparse measurements from 228 sample sites in the Animas River Basin, Colorado. The model performance is validated by comparing stochastic predictions of basin-alteration assemblages and mining activity at 104 independent sites. The SOM correctly predicts (>98%) the predominant type of basin hydrothermal alteration and presence (or absence) of mining activity. Second, application of the Davies–Bouldin criteria to k-means clustering of SOM neurons identified ten unique environmental groups. Median statistics of these groups define a nonlinear water-quality response along the spatiotemporal hydrothermal alteration-mining gradient. These results reveal that it is possible to differentiate among the continuum between inputs of background and mine-related acidity and metals, and it provides a basis for future research and empirical model development.

Signal detection in power-law noise: effect of spectrum exponents.

PubMed

Burgess, Arthur E; Judy, Philip F

2007-12-01

Many natural backgrounds have approximately isotropic power spectra of the power-law form, P(f)=K/f(beta), where f is radial frequency. For natural scenes and mammograms, the values of the exponent, beta, range from 1.5 to 3.5. The ideal observer model predicts that for signals with certain properties and backgrounds that can be treated as random noise, a plot of log (contrast threshold) versus log (signal size) will be linear with slope, m, given by: m=(beta-2)/2. This plot is referred to as a contrast-detail (CD) diagram. It is interesting that this predicts a detection threshold that is independent of signal size for beta equal to 2. We present two-alternative forced-choice (2AFC) detection results for human and channelized model observers of a simple signal in filtered noise with exponents from 1.5 to 3.5. The CD diagram results are in good agreement with the prediction of this equation.

Monte Carlo Simulations of Background Spectra in Integral Imager Detectors

NASA Technical Reports Server (NTRS)

Armstrong, T. W.; Colborn, B. L.; Dietz, K. L.; Ramsey, B. D.; Weisskopf, M. C.

1998-01-01

Predictions of the expected gamma-ray backgrounds in the ISGRI (CdTe) and PiCsIT (Csl) detectors on INTEGRAL due to cosmic-ray interactions and the diffuse gamma-ray background have been made using a coupled set of Monte Carlo radiation transport codes (HETC, FLUKA, EGS4, and MORSE) and a detailed, 3-D mass model of the spacecraft and detector assemblies. The simulations include both the prompt background component from induced hadronic and electromagnetic cascades and the delayed component due to emissions from induced radioactivity. Background spectra have been obtained with and without the use of active (BGO) shielding and charged particle rejection to evaluate the effectiveness of anticoincidence counting on background rejection.

Projected electric power demands for the Potomac Electric Power Company

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

Wilson, J.W.

1975-07-01

Included are chapters on the background of the Potomac Electric Power Company, forecasting future power demand, demand modeling, accuracy of market predictions, and total power system requirements. (DG)

Culture and Social Relationship as Factors of Affecting Communicative Non-verbal Behaviors

NASA Astrophysics Data System (ADS)

Akhter Lipi, Afia; Nakano, Yukiko; Rehm, Mathias

The goal of this paper is to link a bridge between social relationship and cultural variation to predict conversants' non-verbal behaviors. This idea serves as a basis of establishing a parameter based socio-cultural model, which determines non-verbal expressive parameters that specify the shapes of agent's nonverbal behaviors in HAI. As the first step, a comparative corpus analysis is done for two cultures in two specific social relationships. Next, by integrating the cultural and social parameters factors with the empirical data from corpus analysis, we establish a model that predicts posture. The predictions from our model successfully demonstrate that both cultural background and social relationship moderate communicative non-verbal behaviors.

The Interplay between Expressed Parental Anxiety and Infant Behavioural Inhibition Predicts Infant Avoidance in a Social Referencing Paradigm

ERIC Educational Resources Information Center

Aktar, Evin; Majdandzic, Mirjana; de Vente, Wieke; Bogels, Susan M.

2013-01-01

Background: Anxiety aggregates in families. Environmental factors, such as modelling of anxious behaviours, are assumed to play a causal role in the development of child anxiety. We investigated the predictive value of paternal and maternal anxiety (lifetime anxiety disorders and expressed parental anxiety) on infants' fear and avoidance during…

Using the Integrative Model of Behavioral Prediction to Predict Vegetable Subgroup Consumption among College Students

ERIC Educational Resources Information Center

Senkowski, Valerie; Branscum, Paul; Maness, Sarah; Larson, Daniel

2017-01-01

Background: The United States Department of Agriculture (USDA) currently recommends that young adults consume 2.5-3 cups of vegetables daily, while also providing weekly recommendations for 5 vegetable subgroups: dark green, red and orange, beans and peas, starchy, and other. Purpose: The purpose of this study was to explore theory-based…

Integrated species distribution models: combining presence-background data and site-occupancy data with imperfect detection

USGS Publications Warehouse

Koshkina, Vira; Wang, Yang; Gordon, Ascelin; Dorazio, Robert; White, Matthew; Stone, Lewi

2017-01-01

Two main sources of data for species distribution models (SDMs) are site-occupancy (SO) data from planned surveys, and presence-background (PB) data from opportunistic surveys and other sources. SO surveys give high quality data about presences and absences of the species in a particular area. However, due to their high cost, they often cover a smaller area relative to PB data, and are usually not representative of the geographic range of a species. In contrast, PB data is plentiful, covers a larger area, but is less reliable due to the lack of information on species absences, and is usually characterised by biased sampling. Here we present a new approach for species distribution modelling that integrates these two data types.We have used an inhomogeneous Poisson point process as the basis for constructing an integrated SDM that fits both PB and SO data simultaneously. It is the first implementation of an Integrated SO–PB Model which uses repeated survey occupancy data and also incorporates detection probability.The Integrated Model's performance was evaluated, using simulated data and compared to approaches using PB or SO data alone. It was found to be superior, improving the predictions of species spatial distributions, even when SO data is sparse and collected in a limited area. The Integrated Model was also found effective when environmental covariates were significantly correlated. Our method was demonstrated with real SO and PB data for the Yellow-bellied glider (Petaurus australis) in south-eastern Australia, with the predictive performance of the Integrated Model again found to be superior.PB models are known to produce biased estimates of species occupancy or abundance. The small sample size of SO datasets often results in poor out-of-sample predictions. Integrated models combine data from these two sources, providing superior predictions of species abundance compared to using either data source alone. Unlike conventional SDMs which have restrictive scale-dependence in their predictions, our Integrated Model is based on a point process model and has no such scale-dependency. It may be used for predictions of abundance at any spatial-scale while still maintaining the underlying relationship between abundance and area.

Prediction of physical workload in reduced gravity environments

NASA Technical Reports Server (NTRS)

Goldberg, Joseph H.

1987-01-01

The background, development, and application of a methodology to predict human energy expenditure and physical workload in low gravity environments, such as a Lunar or Martian base, is described. Based on a validated model to predict energy expenditures in Earth-based industrial jobs, the model relies on an elemental analysis of the proposed job. Because the job itself need not physically exist, many alternative job designs may be compared in their physical workload. The feasibility of using the model for prediction of low gravity work was evaluated by lowering body and load weights, while maintaining basal energy expenditure. Comparison of model results was made both with simulated low gravity energy expenditure studies and with reported Apollo 14 Lunar EVA expenditure. Prediction accuracy was very good for walking and for cart pulling on slopes less than 15 deg, but the model underpredicted the most difficult work conditions. This model was applied to example core sampling and facility construction jobs, as presently conceptualized for a Lunar or Martian base. Resultant energy expenditures and suggested work-rest cycles were well within the range of moderate work difficulty. Future model development requirements were also discussed.

Mechanistic Enzyme Models: Pyridoxal and Metal Ions.

ERIC Educational Resources Information Center

Hamilton, S. E.; And Others

1984-01-01

Background information, procedures, and results are presented for experiments on the pyridoxal/metal ion model system. These experiments illustrate catalysis through Schiff's base formation between aldehydes/ketones and primary amines, catalysis by metal ions, and the predictable manner in which metal ions inhibit or catalyze reactions. (JN)

Modelling vehicle colour and pattern for multiple deployment environments

NASA Astrophysics Data System (ADS)

Liggins, Eric; Moorhead, Ian R.; Pearce, Daniel A.; Baker, Christopher J.; Serle, William P.

2016-10-01

Military land platforms are often deployed around the world in very different climate zones. Procuring vehicles in a large range of camouflage patterns and colour schemes is expensive and may limit the environments in which they can be effectively used. As such this paper reports a modelling approach for use in the optimisation and selection of a colour palette, to support operations in diverse environments and terrains. Three different techniques were considered based upon the differences between vehicle and background in L*a*b* colour space, to predict the optimum (initially single) colour to reduce the vehicle signature in the visible band. Calibrated digital imagery was used as backgrounds and a number of scenes were sampled. The three approaches used, and reported here are a) background averaging behind the vehicle b) background averaging in the area surrounding the vehicle and c) use of the spatial extension to CIE L*a*b*; S-CIELAB (Zhang and Wandell, Society for Information Display Symposium Technical Digest, vol. 27, pp. 731-734, 1996). Results are compared with natural scene colour statistics. The models used showed good agreement in the colour predictions for individual and multiple terrains or climate zones. A further development of the technique examines the effect of different patterns and colour combinations on the S-CIELAB spatial colour difference metric, when scaled for appropriate viewing ranges.

Modeling the effect of succimer (DMSA; dimercaptosuccinic acid) chelation therapy in patients poisoned by lead.

PubMed

van Eijkeren, Jan C H; Olie, J Daniël N; Bradberry, Sally M; Vale, J Allister; de Vries, Irma; Clewell, Harvey J; Meulenbelt, Jan; Hunault, Claudine C

2017-02-01

Kinetic models could assist clinicians potentially in managing cases of lead poisoning. Several models exist that can simulate lead kinetics but none of them can predict the effect of chelation in lead poisoning. Our aim was to devise a model to predict the effect of succimer (dimercaptosuccinic acid; DMSA) chelation therapy on blood lead concentrations. We integrated a two-compartment kinetic succimer model into an existing PBPK lead model and produced a Chelation Lead Therapy (CLT) model. The accuracy of the model's predictions was assessed by simulating clinical observations in patients poisoned by lead and treated with succimer. The CLT model calculates blood lead concentrations as the sum of the background exposure and the acute or chronic lead poisoning. The latter was due either to ingestion of traditional remedies or occupational exposure to lead-polluted ambient air. The exposure duration was known. The blood lead concentrations predicted by the CLT model were compared to the measured blood lead concentrations. Pre-chelation blood lead concentrations ranged between 99 and 150 μg/dL. The model was able to simulate accurately the blood lead concentrations during and after succimer treatment. The pattern of urine lead excretion was successfully predicted in some patients, while poorly predicted in others. Our model is able to predict blood lead concentrations after succimer therapy, at least, in situations where the duration of lead exposure is known.

The Joint Effects of Background Selection and Genetic Recombination on Local Gene Genealogies

PubMed Central

Zeng, Kai; Charlesworth, Brian

2011-01-01

Background selection, the effects of the continual removal of deleterious mutations by natural selection on variability at linked sites, is potentially a major determinant of DNA sequence variability. However, the joint effects of background selection and genetic recombination on the shape of the neutral gene genealogy have proved hard to study analytically. The only existing formula concerns the mean coalescent time for a pair of alleles, making it difficult to assess the importance of background selection from genome-wide data on sequence polymorphism. Here we develop a structured coalescent model of background selection with recombination and implement it in a computer program that efficiently generates neutral gene genealogies for an arbitrary sample size. We check the validity of the structured coalescent model against forward-in-time simulations and show that it accurately captures the effects of background selection. The model produces more accurate predictions of the mean coalescent time than the existing formula and supports the conclusion that the effect of background selection is greater in the interior of a deleterious region than at its boundaries. The level of linkage disequilibrium between sites is elevated by background selection, to an extent that is well summarized by a change in effective population size. The structured coalescent model is readily extendable to more realistic situations and should prove useful for analyzing genome-wide polymorphism data. PMID:21705759

The joint effects of background selection and genetic recombination on local gene genealogies.

PubMed

Zeng, Kai; Charlesworth, Brian

2011-09-01

Background selection, the effects of the continual removal of deleterious mutations by natural selection on variability at linked sites, is potentially a major determinant of DNA sequence variability. However, the joint effects of background selection and genetic recombination on the shape of the neutral gene genealogy have proved hard to study analytically. The only existing formula concerns the mean coalescent time for a pair of alleles, making it difficult to assess the importance of background selection from genome-wide data on sequence polymorphism. Here we develop a structured coalescent model of background selection with recombination and implement it in a computer program that efficiently generates neutral gene genealogies for an arbitrary sample size. We check the validity of the structured coalescent model against forward-in-time simulations and show that it accurately captures the effects of background selection. The model produces more accurate predictions of the mean coalescent time than the existing formula and supports the conclusion that the effect of background selection is greater in the interior of a deleterious region than at its boundaries. The level of linkage disequilibrium between sites is elevated by background selection, to an extent that is well summarized by a change in effective population size. The structured coalescent model is readily extendable to more realistic situations and should prove useful for analyzing genome-wide polymorphism data.

Predictive modeling of terrestrial radiation exposure from geologic materials

NASA Astrophysics Data System (ADS)

Haber, Daniel A.

Aerial gamma ray surveys are an important tool for national security, scientific, and industrial interests in determining locations of both anthropogenic and natural sources of radioactivity. There is a relationship between radioactivity and geology and in the past this relationship has been used to predict geology from an aerial survey. The purpose of this project is to develop a method to predict the radiologic exposure rate of the geologic materials in an area by creating a model using geologic data, images from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), geochemical data, and pre-existing low spatial resolution aerial surveys from the National Uranium Resource Evaluation (NURE) Survey. Using these data, geospatial areas, referred to as background radiation units, homogenous in terms of K, U, and Th are defined and the gamma ray exposure rate is predicted. The prediction is compared to data collected via detailed aerial survey by our partner National Security Technologies, LLC (NSTec), allowing for the refinement of the technique. High resolution radiation exposure rate models have been developed for two study areas in Southern Nevada that include the alluvium on the western shore of Lake Mohave, and Government Wash north of Lake Mead; both of these areas are arid with little soil moisture and vegetation. We determined that by using geologic units to define radiation background units of exposed bedrock and ASTER visualizations to subdivide radiation background units of alluvium, regions of homogeneous geochemistry can be defined allowing for the exposure rate to be predicted. Soil and rock samples have been collected at Government Wash and Lake Mohave as well as a third site near Cameron, Arizona. K, U, and Th concentrations of these samples have been determined using inductively coupled mass spectrometry (ICP-MS) and laboratory counting using radiation detection equipment. In addition, many sample locations also have concentrations determined via in situ radiation measurements with high purity germanium detectors (HPGe) and aerial survey measurements. These various measurement techniques have been compared and found to produce consistent results. Finally, modeling using Monte Carlo N-Particle Transport Code (MCNP), a particle physics modeling code, has allowed us to derive concentration to exposure rate coefficients. These simulations also have shown that differences in major element chemistry have little impact on the gamma ray emissions of geologic materials.

Planetary X ray experiment: Supporting research for outer planets mission: Experiment definition phase

NASA Technical Reports Server (NTRS)

Hurley, K.; Anderson, K. A.

1972-01-01

Models of Jupiter's magnetosphere were examined to predict the X-ray flux that would be emitted in auroral or radiation zone processes. Various types of X-ray detection were investigated for energy resolution, efficiency, reliability, and background. From the model fluxes it was determined under what models Jovian X-rays could be detected.

Man-Machine Communication in Remote Manipulation: Task-Oriented Supervisory Command Language (TOSC).

DTIC Science & Technology

1980-03-01

ORIENTED SUPERVISORY CONTROL SYSTEM METHODOLOGY 3-1 3.1 Overview 3-1 3.2 Background 3-3 3.2.1 General 3-3 3.2.2 Preliminary Principles of Command Language...Design 3-4 3.2.3 Preliminary Principles of Feedback Display Design 3-9 3.3 Man-Machine Communication Models 3-12 3.3.1 Background 3-12 3.3.2 Adapted...and feedback mode. The work ends with the presentation of a performance prediction model and a set of principles and guidelines, applicable to the

An evaluation of 1D loss model collections for the off-design performance prediction of automotive turbocharger compressors

NASA Astrophysics Data System (ADS)

Harley, P.; Spence, S.; Early, J.; Filsinger, D.; Dietrich, M.

2013-12-01

Single-zone modelling is used to assess different collections of impeller 1D loss models. Three collections of loss models have been identified in literature, and the background to each of these collections is discussed. Each collection is evaluated using three modern automotive turbocharger style centrifugal compressors; comparisons of performance for each of the collections are made. An empirical data set taken from standard hot gas stand tests for each turbocharger is used as a baseline for comparison. Compressor range is predicted in this study; impeller diffusion ratio is shown to be a useful method of predicting compressor surge in 1D, and choke is predicted using basic compressible flow theory. The compressor designer can use this as a guide to identify the most compatible collection of losses for turbocharger compressor design applications. The analysis indicates the most appropriate collection for the design of automotive turbocharger centrifugal compressors.

Attractiveness Compensates for Low Status Background in the Prediction of Educational Attainment

PubMed Central

Bauldry, Shawn; Shanahan, Michael J.; Russo, Rosemary; Roberts, Brent W.; Damian, Rodica

2016-01-01

Background People who are perceived as good looking or as having a pleasant personality enjoy many advantages, including higher educational attainment. This study examines (1) whether associations between physical/personality attractiveness and educational attainment vary by parental socioeconomic resources and (2) whether parental socioeconomic resources predict these forms of attractiveness. Based on the theory of resource substitution with structural amplification, we hypothesized that both types of attractiveness would have a stronger association with educational attainment for people from disadvantaged backgrounds (resource substitution), but also that people from disadvantaged backgrounds would be less likely to be perceived as attractive (amplification). Methods This study draws on data from the National Longitudinal Study of Adolescent to Adult Health—including repeated interviewer ratings of respondents’ attractiveness—and trait-state structural equation models to examine the moderation (substitution) and mediation (amplification) of physical and personality attractiveness in the link between parental socioeconomic resources and educational attainment. Results Both perceived personality and physical attractiveness have stronger associations with educational attainment for people from families with lower levels of parental education (substitution). Further, parental education and income are associated with both dimensions of perceived attractiveness, and personality attractiveness is positively associated with educational attainment (amplification). Results do not differ by sex and race/ethnicity. Further, associations between perceived attractiveness and educational attainment remain after accounting for unmeasured family-level confounders using a sibling fixed-effects model. Conclusions Perceived attractiveness, particularly personality attractiveness, is a more important psychosocial resource for educational attainment for people from disadvantaged backgrounds than for people from advantaged backgrounds. People from disadvantaged backgrounds, however, are less likely to be perceived as attractive than people from advantaged backgrounds. PMID:27249216

Use of the Transtheoretical Model to Predict Stages of Smoking Cessation in Korean Adolescents

ERIC Educational Resources Information Center

Ham, Ok Kyung; Lee, Young Ja

2007-01-01

Background: Smoking is popular among Korean male high school adolescents, with the prevalence of 20.7% differing markedly with the type of school, being 16.3% and 27.6% in academic and vocational technical high schools, respectively. The purpose of this study was to identify significant variables that predict stages of smoking cessation among…

Comparison of Model Prediction with Measurements of Galactic Background Noise at L-Band

NASA Technical Reports Server (NTRS)

LeVine, David M.; Abraham, Saji; Kerr, Yann H.; Wilson, Willam J.; Skou, Niels; Sobjaerg, S.

2004-01-01

The spectral window at L-band (1.413 GHz) is important for passive remote sensing of surface parameters such as soil moisture and sea surface salinity that are needed to understand the hydrological cycle and ocean circulation. Radiation from celestial (mostly galactic) sources is strong in this window and an accurate accounting for this background radiation is often needed for calibration. Modem radio astronomy measurements in this spectral window have been converted into a brightness temperature map of the celestial sky at L-band suitable for use in correcting passive measurements. This paper presents a comparison of the background radiation predicted by this map with measurements made with several modem L-band remote sensing radiometers. The agreement validates the map and the procedure for locating the source of down-welling radiation.

GW150914: Implications for the Stochastic Gravitational-Wave Background from Binary Black Holes

NASA Astrophysics Data System (ADS)

Abbott, B. P.; Abbott, R.; Abbott, T. D.; Abernathy, M. R.; Acernese, F.; Ackley, K.; Adams, C.; Adams, T.; Addesso, P.; Adhikari, R. X.; Adya, V. B.; Affeldt, C.; Agathos, M.; Agatsuma, K.; Aggarwal, N.; Aguiar, O. D.; Aiello, L.; Ain, A.; Ajith, P.; Allen, B.; Allocca, A.; Altin, P. A.; Anderson, S. B.; Anderson, W. G.; Arai, K.; Araya, M. C.; Arceneaux, C. C.; Areeda, J. S.; Arnaud, N.; Arun, K. G.; Ascenzi, S.; Ashton, G.; Ast, M.; Aston, S. M.; Astone, P.; Aufmuth, P.; Aulbert, C.; Babak, S.; Bacon, P.; Bader, M. K. M.; Baker, P. T.; Baldaccini, F.; Ballardin, G.; Ballmer, S. W.; Barayoga, J. C.; Barclay, S. E.; Barish, B. C.; Barker, D.; Barone, F.; Barr, B.; Barsotti, L.; Barsuglia, M.; Barta, D.; Bartlett, J.; Bartos, I.; Bassiri, R.; Basti, A.; Batch, J. C.; Baune, C.; Bavigadda, V.; Bazzan, M.; Behnke, B.; Bejger, M.; Bell, A. S.; Bell, C. J.; Berger, B. K.; Bergman, J.; Bergmann, G.; Berry, C. P. L.; Bersanetti, D.; Bertolini, A.; Betzwieser, J.; Bhagwat, S.; Bhandare, R.; Bilenko, I. A.; Billingsley, G.; Birch, J.; Birney, R.; Biscans, S.; Bisht, A.; Bitossi, M.; Biwer, C.; Bizouard, M. A.; Blackburn, J. K.; Blair, C. D.; Blair, D. G.; Blair, R. M.; Bloemen, S.; Bock, O.; Bodiya, T. P.; Boer, M.; Bogaert, G.; Bogan, C.; Bohe, A.; Bojtos, P.; Bond, C.; Bondu, F.; Bonnand, R.; Boom, B. A.; Bork, R.; Boschi, V.; Bose, S.; Bouffanais, Y.; Bozzi, A.; Bradaschia, C.; Brady, P. R.; Braginsky, V. B.; Branchesi, M.; Brau, J. E.; Briant, T.; Brillet, A.; Brinkmann, M.; Brisson, V.; Brockill, P.; Brooks, A. F.; Brown, D. D.; Brown, N. M.; Buchanan, C. C.; Buikema, A.; Bulik, T.; Bulten, H. J.; Buonanno, A.; Buskulic, D.; Buy, C.; Byer, R. L.; Cadonati, L.; Cagnoli, G.; Cahillane, C.; Bustillo, J. Calderón; Callister, T.; Calloni, E.; Camp, J. B.; Cannon, K. C.; Cao, J.; Capano, C. D.; Capocasa, E.; Carbognani, F.; Caride, S.; Diaz, J. Casanueva; Casentini, C.; Caudill, S.; Cavaglià, M.; Cavalier, F.; Cavalieri, R.; Cella, G.; Cepeda, C. B.; Baiardi, L. Cerboni; Cerretani, G.; Cesarini, E.; Chakraborty, R.; Chalermsongsak, T.; Chamberlin, S. J.; Chan, M.; Chao, S.; Charlton, P.; Chassande-Mottin, E.; Chen, H. Y.; Chen, Y.; Cheng, C.; Chincarini, A.; Chiummo, A.; Cho, H. S.; Cho, M.; Chow, J. H.; Christensen, N.; Chu, Q.; Chua, S.; Chung, S.; Ciani, G.; Clara, F.; Clark, J. A.; Cleva, F.; Coccia, E.; Cohadon, P.-F.; Colla, A.; Collette, C. G.; Cominsky, L.; Constancio, M.; Conte, A.; Conti, L.; Cook, D.; Corbitt, T. R.; Cornish, N.; Corsi, A.; Cortese, S.; Costa, C. A.; Coughlin, M. W.; Coughlin, S. B.; Coulon, J.-P.; Countryman, S. T.; Couvares, P.; Cowan, E. E.; Coward, D. M.; Cowart, M. J.; Coyne, D. C.; Coyne, R.; Craig, K.; Creighton, J. D. E.; Cripe, J.; Crowder, S. G.; Cumming, A.; Cunningham, L.; Cuoco, E.; Canton, T. Dal; Danilishin, S. L.; D'Antonio, S.; Danzmann, K.; Darman, N. S.; Dattilo, V.; Dave, I.; Daveloza, H. P.; Davier, M.; Davies, G. S.; Daw, E. J.; Day, R.; DeBra, D.; Debreczeni, G.; Degallaix, J.; De Laurentis, M.; Deléglise, S.; Del Pozzo, W.; Denker, T.; Dent, T.; Dereli, H.; Dergachev, V.; DeRosa, R. T.; De Rosa, R.; DeSalvo, R.; Dhurandhar, S.; Díaz, M. C.; Di Fiore, L.; Di Giovanni, M.; Di Lieto, A.; Di Pace, S.; Di Palma, I.; Di Virgilio, A.; Dojcinoski, G.; Dolique, V.; Donovan, F.; Dooley, K. L.; Doravari, S.; Douglas, R.; Downes, T. P.; Drago, M.; Drever, R. W. P.; Driggers, J. C.; Du, Z.; Ducrot, M.; Dwyer, S. E.; Edo, T. B.; Edwards, M. C.; Effler, A.; Eggenstein, H.-B.; Ehrens, P.; Eichholz, J.; Eikenberry, S. S.; Engels, W.; Essick, R. C.; Etzel, T.; Evans, M.; Evans, T. M.; Everett, R.; Factourovich, M.; Fafone, V.; Fair, H.; Fairhurst, S.; Fan, X.; Fang, Q.; Farinon, S.; Farr, B.; Farr, W. M.; Favata, M.; Fays, M.; Fehrmann, H.; Fejer, M. M.; Ferrante, I.; Ferreira, E. C.; Ferrini, F.; Fidecaro, F.; Fiori, I.; Fiorucci, D.; Fisher, R. P.; Flaminio, R.; Fletcher, M.; Fournier, J.-D.; Franco, S.; Frasca, S.; Frasconi, F.; Frei, Z.; Freise, A.; Frey, R.; Frey, V.; Fricke, T. T.; Fritschel, P.; Frolov, V. V.; Fulda, P.; Fyffe, M.; Gabbard, H. A. G.; Gair, J. R.; Gammaitoni, L.; Gaonkar, S. G.; Garufi, F.; Gatto, A.; Gaur, G.; Gehrels, N.; Gemme, G.; Gendre, B.; Genin, E.; Gennai, A.; George, J.; Gergely, L.; Germain, V.; Ghosh, Archisman; Ghosh, S.; Giaime, J. A.; Giardina, K. D.; Giazotto, A.; Gill, K.; Glaefke, A.; Goetz, E.; Goetz, R.; Gondan, L.; González, G.; Castro, J. M. Gonzalez; Gopakumar, A.; Gordon, N. A.; Gorodetsky, M. L.; Gossan, S. E.; Gosselin, M.; Gouaty, R.; Graef, C.; Graff, P. B.; Granata, M.; Grant, A.; Gras, S.; Gray, C.; Greco, G.; Green, A. C.; Groot, P.; Grote, H.; Grunewald, S.; Guidi, G. M.; Guo, X.; Gupta, A.; Gupta, M. K.; Gushwa, K. E.; Gustafson, E. K.; Gustafson, R.; Hacker, J. J.; Hall, B. R.; Hall, E. D.; Hammond, G.; Haney, M.; Hanke, M. M.; Hanks, J.; Hanna, C.; Hannam, M. D.; Hanson, J.; Hardwick, T.; Haris, K.; Harms, J.; Harry, G. M.; Harry, I. W.; Hart, M. J.; Hartman, M. T.; Haster, C.-J.; Haughian, K.; Heidmann, A.; Heintze, M. C.; Heitmann, H.; Hello, P.; Hemming, G.; Hendry, M.; Heng, I. S.; Hennig, J.; Heptonstall, A. W.; Heurs, M.; Hild, S.; Hoak, D.; Hodge, K. A.; Hofman, D.; Hollitt, S. E.; Holt, K.; Holz, D. E.; Hopkins, P.; Hosken, D. J.; Hough, J.; Houston, E. A.; Howell, E. J.; Hu, Y. M.; Huang, S.; Huerta, E. A.; Huet, D.; Hughey, B.; Husa, S.; Huttner, S. H.; Huynh-Dinh, T.; Idrisy, A.; Indik, N.; Ingram, D. R.; Inta, R.; Isa, H. N.; Isac, J.-M.; Isi, M.; Islas, G.; Isogai, T.; Iyer, B. R.; Izumi, K.; Jacqmin, T.; Jang, H.; Jani, K.; Jaranowski, P.; Jawahar, S.; Jiménez-Forteza, F.; Johnson, W. W.; Jones, D. I.; Jones, R.; Jonker, R. J. G.; Ju, L.; Kalaghatgi, C. V.; Kalogera, V.; Kandhasamy, S.; Kang, G.; Kanner, J. B.; Karki, S.; Kasprzack, M.; Katsavounidis, E.; Katzman, W.; Kaufer, S.; Kaur, T.; Kawabe, K.; Kawazoe, F.; Kéfélian, F.; Kehl, M. S.; Keitel, D.; Kelley, D. B.; Kells, W.; Kennedy, R.; Key, J. S.; Khalaidovski, A.; Khalili, F. Y.; Khan, I.; Khan, S.; Khan, Z.; Khazanov, E. A.; Kijbunchoo, N.; Kim, C.; Kim, J.; Kim, K.; Kim, Nam-Gyu; Kim, Namjun; Kim, Y.-M.; King, E. J.; King, P. J.; Kinzel, D. L.; Kissel, J. S.; Kleybolte, L.; Klimenko, S.; Koehlenbeck, S. M.; Kokeyama, K.; Koley, S.; Kondrashov, V.; Kontos, A.; Korobko, M.; Korth, W. Z.; Kowalska, I.; Kozak, D. B.; Kringel, V.; Królak, A.; Krueger, C.; Kuehn, G.; Kumar, P.; Kuo, L.; Kutynia, A.; Lackey, B. D.; Landry, M.; Lange, J.; Lantz, B.; Lasky, P. D.; Lazzarini, A.; Lazzaro, C.; Leaci, P.; Leavey, S.; Lebigot, E. O.; Lee, C. H.; Lee, H. K.; Lee, H. M.; Lee, K.; Lenon, A.; Leonardi, M.; Leong, J. R.; Leroy, N.; Letendre, N.; Levin, Y.; Levine, B. M.; Li, T. G. F.; Libson, A.; Littenberg, T. B.; Lockerbie, N. A.; Logue, J.; Lombardi, A. L.; Lord, J. E.; Lorenzini, M.; Loriette, V.; Lormand, M.; Losurdo, G.; Lough, J. D.; Lück, H.; Lundgren, A. P.; Luo, J.; Lynch, R.; Ma, Y.; MacDonald, T.; Machenschalk, B.; MacInnis, M.; Macleod, D. M.; Magaña-Sandoval, F.; Magee, R. M.; Mageswaran, M.; Majorana, E.; Maksimovic, I.; Malvezzi, V.; Man, N.; Mandel, I.; Mandic, V.; Mangano, V.; Mansell, G. L.; Manske, M.; Mantovani, M.; Marchesoni, F.; Marion, F.; Márka, S.; Márka, Z.; Markosyan, A. S.; Maros, E.; Martelli, F.; Martellini, L.; Martin, I. W.; Martin, R. M.; Martynov, D. V.; Marx, J. N.; Mason, K.; Masserot, A.; Massinger, T. J.; Masso-Reid, M.; Matichard, F.; Matone, L.; Mavalvala, N.; Mazumder, N.; Mazzolo, G.; McCarthy, R.; McClelland, D. E.; McCormick, S.; McGuire, S. C.; McIntyre, G.; McIver, J.; McManus, D. J.; McWilliams, S. T.; Meacher, D.; Meadors, G. D.; Meidam, J.; Melatos, A.; Mendell, G.; Mendoza-Gandara, D.; Mercer, R. A.; Merilh, E.; Merzougui, M.; Meshkov, S.; Messenger, C.; Messick, C.; Meyers, P. M.; Mezzani, F.; Miao, H.; Michel, C.; Middleton, H.; Mikhailov, E. E.; Milano, L.; Miller, J.; Millhouse, M.; Minenkov, Y.; Ming, J.; Mirshekari, S.; Mishra, C.; Mitra, S.; Mitrofanov, V. P.; Mitselmakher, G.; Mittleman, R.; Moggi, A.; Mohan, M.; Mohapatra, S. R. P.; Montani, M.; Moore, B. C.; Moore, C. J.; Moraru, D.; Moreno, G.; Morriss, S. R.; Mossavi, K.; Mours, B.; Mow-Lowry, C. M.; Mueller, C. L.; Mueller, G.; Muir, A. W.; Mukherjee, Arunava; Mukherjee, D.; Mukherjee, S.; Mukund, N.; Mullavey, A.; Munch, J.; Murphy, D. J.; Murray, P. G.; Mytidis, A.; Nardecchia, I.; Naticchioni, L.; Nayak, R. K.; Necula, V.; Nedkova, K.; Nelemans, G.; Neri, M.; Neunzert, A.; Newton, G.; Nguyen, T. T.; Nielsen, A. B.; Nissanke, S.; Nitz, A.; Nocera, F.; Nolting, D.; Normandin, M. E. N.; Nuttall, L. K.; Oberling, J.; Ochsner, E.; O'Dell, J.; Oelker, E.; Ogin, G. H.; Oh, J. J.; Oh, S. H.; Ohme, F.; Oliver, M.; Oppermann, P.; Oram, Richard J.; O'Reilly, B.; O'Shaughnessy, R.; Ottaway, D. J.; Ottens, R. S.; Overmier, H.; Owen, B. J.; Pai, A.; Pai, S. A.; Palamos, J. R.; Palashov, O.; Palomba, C.; Pal-Singh, A.; Pan, H.; Pankow, C.; Pannarale, F.; Pant, B. C.; Paoletti, F.; Paoli, A.; Papa, M. A.; Paris, H. R.; Parker, W.; Pascucci, D.; Pasqualetti, A.; Passaquieti, R.; Passuello, D.; Patricelli, B.; Patrick, Z.; Pearlstone, B. L.; Pedraza, M.; Pedurand, R.; Pekowsky, L.; Pele, A.; Penn, S.; Perreca, A.; Phelps, M.; Piccinni, O.; Pichot, M.; Piergiovanni, F.; Pierro, V.; Pillant, G.; Pinard, L.; Pinto, I. M.; Pitkin, M.; Poggiani, R.; Popolizio, P.; Post, A.; Powell, J.; Prasad, J.; Predoi, V.; Premachandra, S. S.; Prestegard, T.; Price, L. R.; Prijatelj, M.; Principe, M.; Privitera, S.; Prodi, G. A.; Prokhorov, L.; Puncken, O.; Punturo, M.; Puppo, P.; Pürrer, M.; Qi, H.; Qin, J.; Quetschke, V.; Quintero, E. A.; Quitzow-James, R.; Raab, F. J.; Rabeling, D. S.; Radkins, H.; Raffai, P.; Raja, S.; Rakhmanov, M.; Rapagnani, P.; Raymond, V.; Razzano, M.; Re, V.; Read, J.; Reed, C. M.; Regimbau, T.; Rei, L.; Reid, S.; Reitze, D. H.; Rew, H.; Reyes, S. D.; Ricci, F.; Riles, K.; Robertson, N. A.; Robie, R.; Robinet, F.; Rocchi, A.; Rolland, L.; Rollins, J. G.; Roma, V. J.; Romano, J. D.; Romano, R.; Romanov, G.; Romie, J. H.; Rosińska, D.; Rowan, S.; Rüdiger, A.; Ruggi, P.; Ryan, K.; Sachdev, S.; Sadecki, T.; Sadeghian, L.; Salconi, L.; Saleem, M.; Salemi, F.; Samajdar, A.; Sammut, L.; Sanchez, E. J.; Sandberg, V.; Sandeen, B.; Sanders, J. R.; Sassolas, B.; Sathyaprakash, B. S.; Saulson, P. R.; Sauter, O.; Savage, R. L.; Sawadsky, A.; Schale, P.; Schilling, R.; Schmidt, J.; Schmidt, P.; Schnabel, R.; Schofield, R. M. S.; Schönbeck, A.; Schreiber, E.; Schuette, D.; Schutz, B. F.; Scott, J.; Scott, S. M.; Sellers, D.; Sentenac, D.; Sequino, V.; Sergeev, A.; Serna, G.; Setyawati, Y.; Sevigny, A.; Shaddock, D. A.; Shah, S.; Shahriar, M. S.; Shaltev, M.; Shao, Z.; Shapiro, B.; Shawhan, P.; Sheperd, A.; Shoemaker, D. H.; Shoemaker, D. M.; Siellez, K.; Siemens, X.; Sigg, D.; Silva, A. D.; Simakov, D.; Singer, A.; Singer, L. P.; Singh, A.; Singh, R.; Singhal, A.; Sintes, A. M.; Slagmolen, B. J. J.; Smith, J. R.; Smith, N. D.; Smith, R. J. E.; Son, E. J.; Sorazu, B.; Sorrentino, F.; Souradeep, T.; Srivastava, A. K.; Staley, A.; Steinke, M.; Steinlechner, J.; Steinlechner, S.; Steinmeyer, D.; Stephens, B. C.; Stone, R.; Strain, K. A.; Straniero, N.; Stratta, G.; Strauss, N. A.; Strigin, S.; Sturani, R.; Stuver, A. L.; Summerscales, T. Z.; Sun, L.; Sutton, P. J.; Swinkels, B. L.; Szczepańczyk, M. J.; Tacca, M.; Talukder, D.; Tanner, D. B.; Tápai, M.; Tarabrin, S. P.; Taracchini, A.; Taylor, R.; Theeg, T.; Thirugnanasambandam, M. P.; Thomas, E. G.; Thomas, M.; Thomas, P.; Thorne, K. A.; Thorne, K. S.; Thrane, E.; Tiwari, S.; Tiwari, V.; Tokmakov, K. V.; Tomlinson, C.; Tonelli, M.; Torres, C. V.; Torrie, C. I.; Töyrä, D.; Travasso, F.; Traylor, G.; Trifirò, D.; Tringali, M. C.; Trozzo, L.; Tse, M.; Turconi, M.; Tuyenbayev, D.; Ugolini, D.; Unnikrishnan, C. S.; Urban, A. L.; Usman, S. A.; Vahlbruch, H.; Vajente, G.; Valdes, G.; van Bakel, N.; van Beuzekom, M.; van den Brand, J. F. J.; Van Den Broeck, C.; Vander-Hyde, D. C.; van der Schaaf, L.; van Heijningen, J. V.; van Veggel, A. A.; Vardaro, M.; Vass, S.; Vasúth, M.; Vaulin, R.; Vecchio, A.; Vedovato, G.; Veitch, J.; Veitch, P. J.; Venkateswara, K.; Verkindt, D.; Vetrano, F.; Viceré, A.; Vinciguerra, S.; Vine, D. J.; Vinet, J.-Y.; Vitale, S.; Vo, T.; Vocca, H.; Vorvick, C.; Voss, D.; Vousden, W. D.; Vyatchanin, S. P.; Wade, A. R.; Wade, L. E.; Wade, M.; Walker, M.; Wallace, L.; Walsh, S.; Wang, G.; Wang, H.; Wang, M.; Wang, X.; Wang, Y.; Ward, R. L.; Warner, J.; Was, M.; Weaver, B.; Wei, L.-W.; Weinert, M.; Weinstein, A. J.; Weiss, R.; Welborn, T.; Wen, L.; Weßels, P.; Westphal, T.; Wette, K.; Whelan, J. T.; White, D. J.; Whiting, B. F.; Williams, R. D.; Williamson, A. R.; Willis, J. L.; Willke, B.; Wimmer, M. H.; Winkler, W.; Wipf, C. C.; Wittel, H.; Woan, G.; Worden, J.; Wright, J. L.; Wu, G.; Yablon, J.; Yam, W.; Yamamoto, H.; Yancey, C. C.; Yap, M. J.; Yu, H.; Yvert, M.; ZadroŻny, A.; Zangrando, L.; Zanolin, M.; Zendri, J.-P.; Zevin, M.; Zhang, F.; Zhang, L.; Zhang, M.; Zhang, Y.; Zhao, C.; Zhou, M.; Zhou, Z.; Zhu, X. J.; Zucker, M. E.; Zuraw, S. E.; Zweizig, J.; LIGO Scientific Collaboration; Virgo Collaboration

2016-04-01

The LIGO detection of the gravitational wave transient GW150914, from the inspiral and merger of two black holes with masses ≳30 M⊙, suggests a population of binary black holes with relatively high mass. This observation implies that the stochastic gravitational-wave background from binary black holes, created from the incoherent superposition of all the merging binaries in the Universe, could be higher than previously expected. Using the properties of GW150914, we estimate the energy density of such a background from binary black holes. In the most sensitive part of the Advanced LIGO and Advanced Virgo band for stochastic backgrounds (near 25 Hz), we predict ΩGW(f =25 Hz )=1. 1-0.9+2.7×10-9 with 90% confidence. This prediction is robustly demonstrated for a variety of formation scenarios with different parameters. The differences between models are small compared to the statistical uncertainty arising from the currently poorly constrained local coalescence rate. We conclude that this background is potentially measurable by the Advanced LIGO and Advanced Virgo detectors operating at their projected final sensitivity.

GW150914: Implications for the Stochastic Gravitational-Wave Background from Binary Black Holes.

PubMed

Abbott, B P; Abbott, R; Abbott, T D; Abernathy, M R; Acernese, F; Ackley, K; Adams, C; Adams, T; Addesso, P; Adhikari, R X; Adya, V B; Affeldt, C; Agathos, M; Agatsuma, K; Aggarwal, N; Aguiar, O D; Aiello, L; Ain, A; Ajith, P; Allen, B; Allocca, A; Altin, P A; Anderson, S B; Anderson, W G; Arai, K; Araya, M C; Arceneaux, C C; Areeda, J S; Arnaud, N; Arun, K G; Ascenzi, S; Ashton, G; Ast, M; Aston, S M; Astone, P; Aufmuth, P; Aulbert, C; Babak, S; Bacon, P; Bader, M K M; Baker, P T; Baldaccini, F; Ballardin, G; Ballmer, S W; Barayoga, J C; Barclay, S E; Barish, B C; Barker, D; Barone, F; Barr, B; Barsotti, L; Barsuglia, M; Barta, D; Bartlett, J; Bartos, I; Bassiri, R; Basti, A; Batch, J C; Baune, C; Bavigadda, V; Bazzan, M; Behnke, B; Bejger, M; Bell, A S; Bell, C J; Berger, B K; Bergman, J; Bergmann, G; Berry, C P L; Bersanetti, D; Bertolini, A; Betzwieser, J; Bhagwat, S; Bhandare, R; Bilenko, I A; Billingsley, G; Birch, J; Birney, R; Biscans, S; Bisht, A; Bitossi, M; Biwer, C; Bizouard, M A; Blackburn, J K; Blair, C D; Blair, D G; Blair, R M; Bloemen, S; Bock, O; Bodiya, T P; Boer, M; Bogaert, G; Bogan, C; Bohe, A; Bojtos, P; Bond, C; Bondu, F; Bonnand, R; Boom, B A; Bork, R; Boschi, V; Bose, S; Bouffanais, Y; Bozzi, A; Bradaschia, C; Brady, P R; Braginsky, V B; Branchesi, M; Brau, J E; Briant, T; Brillet, A; Brinkmann, M; Brisson, V; Brockill, P; Brooks, A F; Brown, D D; Brown, N M; Buchanan, C C; Buikema, A; Bulik, T; Bulten, H J; Buonanno, A; Buskulic, D; Buy, C; Byer, R L; Cadonati, L; Cagnoli, G; Cahillane, C; Bustillo, J Calderón; Callister, T; Calloni, E; Camp, J B; Cannon, K C; Cao, J; Capano, C D; Capocasa, E; Carbognani, F; Caride, S; Diaz, J Casanueva; Casentini, C; Caudill, S; Cavaglià, M; Cavalier, F; Cavalieri, R; Cella, G; Cepeda, C B; Baiardi, L Cerboni; Cerretani, G; Cesarini, E; Chakraborty, R; Chalermsongsak, T; Chamberlin, S J; Chan, M; Chao, S; Charlton, P; Chassande-Mottin, E; Chen, H Y; Chen, Y; Cheng, C; Chincarini, A; Chiummo, A; Cho, H S; Cho, M; Chow, J H; Christensen, N; Chu, Q; Chua, S; Chung, S; Ciani, G; Clara, F; Clark, J A; Cleva, F; Coccia, E; Cohadon, P-F; Colla, A; Collette, C G; Cominsky, L; Constancio, M; Conte, A; Conti, L; Cook, D; Corbitt, T R; Cornish, N; Corsi, A; Cortese, S; Costa, C A; Coughlin, M W; Coughlin, S B; Coulon, J-P; Countryman, S T; Couvares, P; Cowan, E E; Coward, D M; Cowart, M J; Coyne, D C; Coyne, R; Craig, K; Creighton, J D E; Cripe, J; Crowder, S G; Cumming, A; Cunningham, L; Cuoco, E; Canton, T Dal; Danilishin, S L; D'Antonio, S; Danzmann, K; Darman, N S; Dattilo, V; Dave, I; Daveloza, H P; Davier, M; Davies, G S; Daw, E J; Day, R; DeBra, D; Debreczeni, G; Degallaix, J; De Laurentis, M; Deléglise, S; Del Pozzo, W; Denker, T; Dent, T; Dereli, H; Dergachev, V; DeRosa, R T; De Rosa, R; DeSalvo, R; Dhurandhar, S; Díaz, M C; Di Fiore, L; Di Giovanni, M; Di Lieto, A; Di Pace, S; Di Palma, I; Di Virgilio, A; Dojcinoski, G; Dolique, V; Donovan, F; Dooley, K L; Doravari, S; Douglas, R; Downes, T P; Drago, M; Drever, R W P; Driggers, J C; Du, Z; Ducrot, M; Dwyer, S E; Edo, T B; Edwards, M C; Effler, A; Eggenstein, H-B; Ehrens, P; Eichholz, J; Eikenberry, S S; Engels, W; Essick, R C; Etzel, T; Evans, M; Evans, T M; Everett, R; Factourovich, M; Fafone, V; Fair, H; Fairhurst, S; Fan, X; Fang, Q; Farinon, S; Farr, B; Farr, W M; Favata, M; Fays, M; Fehrmann, H; Fejer, M M; Ferrante, I; Ferreira, E C; Ferrini, F; Fidecaro, F; Fiori, I; Fiorucci, D; Fisher, R P; Flaminio, R; Fletcher, M; Fournier, J-D; Franco, S; Frasca, S; Frasconi, F; Frei, Z; Freise, A; Frey, R; Frey, V; Fricke, T T; Fritschel, P; Frolov, V V; Fulda, P; Fyffe, M; Gabbard, H A G; Gair, J R; Gammaitoni, L; Gaonkar, S G; Garufi, F; Gatto, A; Gaur, G; Gehrels, N; Gemme, G; Gendre, B; Genin, E; Gennai, A; George, J; Gergely, L; Germain, V; Ghosh, Archisman; Ghosh, S; Giaime, J A; Giardina, K D; Giazotto, A; Gill, K; Glaefke, A; Goetz, E; Goetz, R; Gondan, L; González, G; Castro, J M Gonzalez; Gopakumar, A; Gordon, N A; Gorodetsky, M L; Gossan, S E; Gosselin, M; Gouaty, R; Graef, C; Graff, P B; Granata, M; Grant, A; Gras, S; Gray, C; Greco, G; Green, A C; Groot, P; Grote, H; Grunewald, S; Guidi, G M; Guo, X; Gupta, A; Gupta, M K; Gushwa, K E; Gustafson, E K; Gustafson, R; Hacker, J J; Hall, B R; Hall, E D; Hammond, G; Haney, M; Hanke, M M; Hanks, J; Hanna, C; Hannam, M D; Hanson, J; Hardwick, T; Haris, K; Harms, J; Harry, G M; Harry, I W; Hart, M J; Hartman, M T; Haster, C-J; Haughian, K; Heidmann, A; Heintze, M C; Heitmann, H; Hello, P; Hemming, G; Hendry, M; Heng, I S; Hennig, J; Heptonstall, A W; Heurs, M; Hild, S; Hoak, D; Hodge, K A; Hofman, D; Hollitt, S E; Holt, K; Holz, D E; Hopkins, P; Hosken, D J; Hough, J; Houston, E A; Howell, E J; Hu, Y M; Huang, S; Huerta, E A; Huet, D; Hughey, B; Husa, S; Huttner, S H; Huynh-Dinh, T; Idrisy, A; Indik, N; Ingram, D R; Inta, R; Isa, H N; Isac, J-M; Isi, M; Islas, G; Isogai, T; Iyer, B R; Izumi, K; Jacqmin, T; Jang, H; Jani, K; Jaranowski, P; Jawahar, S; Jiménez-Forteza, F; Johnson, W W; Jones, D I; Jones, R; Jonker, R J G; Ju, L; Kalaghatgi, C V; Kalogera, V; Kandhasamy, S; Kang, G; Kanner, J B; Karki, S; Kasprzack, M; Katsavounidis, E; Katzman, W; Kaufer, S; Kaur, T; Kawabe, K; Kawazoe, F; Kéfélian, F; Kehl, M S; Keitel, D; Kelley, D B; Kells, W; Kennedy, R; Key, J S; Khalaidovski, A; Khalili, F Y; Khan, I; Khan, S; Khan, Z; Khazanov, E A; Kijbunchoo, N; Kim, C; Kim, J; Kim, K; Kim, Nam-Gyu; Kim, Namjun; Kim, Y-M; King, E J; King, P J; Kinzel, D L; Kissel, J S; Kleybolte, L; Klimenko, S; Koehlenbeck, S M; Kokeyama, K; Koley, S; Kondrashov, V; Kontos, A; Korobko, M; Korth, W Z; Kowalska, I; Kozak, D B; Kringel, V; Królak, A; Krueger, C; Kuehn, G; Kumar, P; Kuo, L; Kutynia, A; Lackey, B D; Landry, M; Lange, J; Lantz, B; Lasky, P D; Lazzarini, A; Lazzaro, C; Leaci, P; Leavey, S; Lebigot, E O; Lee, C H; Lee, H K; Lee, H M; Lee, K; Lenon, A; Leonardi, M; Leong, J R; Leroy, N; Letendre, N; Levin, Y; Levine, B M; Li, T G F; Libson, A; Littenberg, T B; Lockerbie, N A; Logue, J; Lombardi, A L; Lord, J E; Lorenzini, M; Loriette, V; Lormand, M; Losurdo, G; Lough, J D; Lück, H; Lundgren, A P; Luo, J; Lynch, R; Ma, Y; MacDonald, T; Machenschalk, B; MacInnis, M; Macleod, D M; Magaña-Sandoval, F; Magee, R M; Mageswaran, M; Majorana, E; Maksimovic, I; Malvezzi, V; Man, N; Mandel, I; Mandic, V; Mangano, V; Mansell, G L; Manske, M; Mantovani, M; Marchesoni, F; Marion, F; Márka, S; Márka, Z; Markosyan, A S; Maros, E; Martelli, F; Martellini, L; Martin, I W; Martin, R M; Martynov, D V; Marx, J N; Mason, K; Masserot, A; Massinger, T J; Masso-Reid, M; Matichard, F; Matone, L; Mavalvala, N; Mazumder, N; Mazzolo, G; McCarthy, R; McClelland, D E; McCormick, S; McGuire, S C; McIntyre, G; McIver, J; McManus, D J; McWilliams, S T; Meacher, D; Meadors, G D; Meidam, J; Melatos, A; Mendell, G; Mendoza-Gandara, D; Mercer, R A; Merilh, E; Merzougui, M; Meshkov, S; Messenger, C; Messick, C; Meyers, P M; Mezzani, F; Miao, H; Michel, C; Middleton, H; Mikhailov, E E; Milano, L; Miller, J; Millhouse, M; Minenkov, Y; Ming, J; Mirshekari, S; Mishra, C; Mitra, S; Mitrofanov, V P; Mitselmakher, G; Mittleman, R; Moggi, A; Mohan, M; Mohapatra, S R P; Montani, M; Moore, B C; Moore, C J; Moraru, D; Moreno, G; Morriss, S R; Mossavi, K; Mours, B; Mow-Lowry, C M; Mueller, C L; Mueller, G; Muir, A W; Mukherjee, Arunava; Mukherjee, D; Mukherjee, S; Mukund, N; Mullavey, A; Munch, J; Murphy, D J; Murray, P G; Mytidis, A; Nardecchia, I; Naticchioni, L; Nayak, R K; Necula, V; Nedkova, K; Nelemans, G; Neri, M; Neunzert, A; Newton, G; Nguyen, T T; Nielsen, A B; Nissanke, S; Nitz, A; Nocera, F; Nolting, D; Normandin, M E N; Nuttall, L K; Oberling, J; Ochsner, E; O'Dell, J; Oelker, E; Ogin, G H; Oh, J J; Oh, S H; Ohme, F; Oliver, M; Oppermann, P; Oram, Richard J; O'Reilly, B; O'Shaughnessy, R; Ottaway, D J; Ottens, R S; Overmier, H; Owen, B J; Pai, A; Pai, S A; Palamos, J R; Palashov, O; Palomba, C; Pal-Singh, A; Pan, H; Pankow, C; Pannarale, F; Pant, B C; Paoletti, F; Paoli, A; Papa, M A; Paris, H R; Parker, W; Pascucci, D; Pasqualetti, A; Passaquieti, R; Passuello, D; Patricelli, B; Patrick, Z; Pearlstone, B L; Pedraza, M; Pedurand, R; Pekowsky, L; Pele, A; Penn, S; Perreca, A; Phelps, M; Piccinni, O; Pichot, M; Piergiovanni, F; Pierro, V; Pillant, G; Pinard, L; Pinto, I M; Pitkin, M; Poggiani, R; Popolizio, P; Post, A; Powell, J; Prasad, J; Predoi, V; Premachandra, S S; Prestegard, T; Price, L R; Prijatelj, M; Principe, M; Privitera, S; Prodi, G A; Prokhorov, L; Puncken, O; Punturo, M; Puppo, P; Pürrer, M; Qi, H; Qin, J; Quetschke, V; Quintero, E A; Quitzow-James, R; Raab, F J; Rabeling, D S; Radkins, H; Raffai, P; Raja, S; Rakhmanov, M; Rapagnani, P; Raymond, V; Razzano, M; Re, V; Read, J; Reed, C M; Regimbau, T; Rei, L; Reid, S; Reitze, D H; Rew, H; Reyes, S D; Ricci, F; Riles, K; Robertson, N A; Robie, R; Robinet, F; Rocchi, A; Rolland, L; Rollins, J G; Roma, V J; Romano, J D; Romano, R; Romanov, G; Romie, J H; Rosińska, D; Rowan, S; Rüdiger, A; Ruggi, P; Ryan, K; Sachdev, S; Sadecki, T; Sadeghian, L; Salconi, L; Saleem, M; Salemi, F; Samajdar, A; Sammut, L; Sanchez, E J; Sandberg, V; Sandeen, B; Sanders, J R; Sassolas, B; Sathyaprakash, B S; Saulson, P R; Sauter, O; Savage, R L; Sawadsky, A; Schale, P; Schilling, R; Schmidt, J; Schmidt, P; Schnabel, R; Schofield, R M S; Schönbeck, A; Schreiber, E; Schuette, D; Schutz, B F; Scott, J; Scott, S M; Sellers, D; Sentenac, D; Sequino, V; Sergeev, A; Serna, G; Setyawati, Y; Sevigny, A; Shaddock, D A; Shah, S; Shahriar, M S; Shaltev, M; Shao, Z; Shapiro, B; Shawhan, P; Sheperd, A; Shoemaker, D H; Shoemaker, D M; Siellez, K; Siemens, X; Sigg, D; Silva, A D; Simakov, D; Singer, A; Singer, L P; Singh, A; Singh, R; Singhal, A; Sintes, A M; Slagmolen, B J J; Smith, J R; Smith, N D; Smith, R J E; Son, E J; Sorazu, B; Sorrentino, F; Souradeep, T; Srivastava, A K; Staley, A; Steinke, M; Steinlechner, J; Steinlechner, S; Steinmeyer, D; Stephens, B C; Stone, R; Strain, K A; Straniero, N; Stratta, G; Strauss, N A; Strigin, S; Sturani, R; Stuver, A L; Summerscales, T Z; Sun, L; Sutton, P J; Swinkels, B L; Szczepańczyk, M J; Tacca, M; Talukder, D; Tanner, D B; Tápai, M; Tarabrin, S P; Taracchini, A; Taylor, R; Theeg, T; Thirugnanasambandam, M P; Thomas, E G; Thomas, M; Thomas, P; Thorne, K A; Thorne, K S; Thrane, E; Tiwari, S; Tiwari, V; Tokmakov, K V; Tomlinson, C; Tonelli, M; Torres, C V; Torrie, C I; Töyrä, D; Travasso, F; Traylor, G; Trifirò, D; Tringali, M C; Trozzo, L; Tse, M; Turconi, M; Tuyenbayev, D; Ugolini, D; Unnikrishnan, C S; Urban, A L; Usman, S A; Vahlbruch, H; Vajente, G; Valdes, G; van Bakel, N; van Beuzekom, M; van den Brand, J F J; Van Den Broeck, C; Vander-Hyde, D C; van der Schaaf, L; van Heijningen, J V; van Veggel, A A; Vardaro, M; Vass, S; Vasúth, M; Vaulin, R; Vecchio, A; Vedovato, G; Veitch, J; Veitch, P J; Venkateswara, K; Verkindt, D; Vetrano, F; Viceré, A; Vinciguerra, S; Vine, D J; Vinet, J-Y; Vitale, S; Vo, T; Vocca, H; Vorvick, C; Voss, D; Vousden, W D; Vyatchanin, S P; Wade, A R; Wade, L E; Wade, M; Walker, M; Wallace, L; Walsh, S; Wang, G; Wang, H; Wang, M; Wang, X; Wang, Y; Ward, R L; Warner, J; Was, M; Weaver, B; Wei, L-W; Weinert, M; Weinstein, A J; Weiss, R; Welborn, T; Wen, L; Weßels, P; Westphal, T; Wette, K; Whelan, J T; White, D J; Whiting, B F; Williams, R D; Williamson, A R; Willis, J L; Willke, B; Wimmer, M H; Winkler, W; Wipf, C C; Wittel, H; Woan, G; Worden, J; Wright, J L; Wu, G; Yablon, J; Yam, W; Yamamoto, H; Yancey, C C; Yap, M J; Yu, H; Yvert, M; Zadrożny, A; Zangrando, L; Zanolin, M; Zendri, J-P; Zevin, M; Zhang, F; Zhang, L; Zhang, M; Zhang, Y; Zhao, C; Zhou, M; Zhou, Z; Zhu, X J; Zucker, M E; Zuraw, S E; Zweizig, J

2016-04-01

The LIGO detection of the gravitational wave transient GW150914, from the inspiral and merger of two black holes with masses ≳30M_{⊙}, suggests a population of binary black holes with relatively high mass. This observation implies that the stochastic gravitational-wave background from binary black holes, created from the incoherent superposition of all the merging binaries in the Universe, could be higher than previously expected. Using the properties of GW150914, we estimate the energy density of such a background from binary black holes. In the most sensitive part of the Advanced LIGO and Advanced Virgo band for stochastic backgrounds (near 25 Hz), we predict Ω_{GW}(f=25  Hz)=1.1_{-0.9}^{+2.7}×10^{-9} with 90% confidence. This prediction is robustly demonstrated for a variety of formation scenarios with different parameters. The differences between models are small compared to the statistical uncertainty arising from the currently poorly constrained local coalescence rate. We conclude that this background is potentially measurable by the Advanced LIGO and Advanced Virgo detectors operating at their projected final sensitivity.

Evolution of cosmic string networks

NASA Technical Reports Server (NTRS)

Albrecht, Andreas; Turok, Neil

1989-01-01

A discussion of the evolution and observable consequences of a network of cosmic strings is given. A simple model for the evolution of the string network is presented, and related to the statistical mechanics of string networks. The model predicts the long string density throughout the history of the universe from a single parameter, which researchers calculate in radiation era simulations. The statistical mechanics arguments indicate a particular thermal form for the spectrum of loops chopped off the network. Detailed numerical simulations of string networks in expanding backgrounds are performed to test the model. Consequences for large scale structure, the microwave and gravity wave backgrounds, nucleosynthesis and gravitational lensing are calculated.

Biological indicators for monitoring water quality of MTF canals system

NASA Technical Reports Server (NTRS)

Sethi, S. L.

1975-01-01

Biological models, diversity indexes, were developed to predict environmental effects of NASA's Mississippi test facility (MTF) chemical operations on canal systems in the area. To predict the effects on local streams, a physical model of unpolluted streams was established. The model is fed by artesian well water free of background levels of pollutants. The species diversity and biota composition of unpolluted MTF stream was determined; resulting information will be used to form baseline data for future comparisons. Biological modeling was accomplished by adding controlled quantities or kinds of chemical pollutants and evaluating the effects of these chemicals on the biological life of the stream.

Vesicular stomatitis forecasting based on Google Trends

PubMed Central

Lu, Yi; Zhou, GuangYa; Chen, Qin

2018-01-01

Background Vesicular stomatitis (VS) is an important viral disease of livestock. The main feature of VS is irregular blisters that occur on the lips, tongue, oral mucosa, hoof crown and nipple. Humans can also be infected with vesicular stomatitis and develop meningitis. This study analyses 2014 American VS outbreaks in order to accurately predict vesicular stomatitis outbreak trends. Methods American VS outbreaks data were collected from OIE. The data for VS keywords were obtained by inputting 24 disease-related keywords into Google Trends. After calculating the Pearson and Spearman correlation coefficients, it was found that there was a relationship between outbreaks and keywords derived from Google Trends. Finally, the predicted model was constructed based on qualitative classification and quantitative regression. Results For the regression model, the Pearson correlation coefficients between the predicted outbreaks and actual outbreaks are 0.953 and 0.948, respectively. For the qualitative classification model, we constructed five classification predictive models and chose the best classification predictive model as the result. The results showed, SN (sensitivity), SP (specificity) and ACC (prediction accuracy) values of the best classification predictive model are 78.52%,72.5% and 77.14%, respectively. Conclusion This study applied Google search data to construct a qualitative classification model and a quantitative regression model. The results show that the method is effective and that these two models obtain more accurate forecast. PMID:29385198

Which egg features predict egg rejection responses in American robins? Replicating Rothstein's (1982) study.

PubMed

Luro, Alec B; Igic, Branislav; Croston, Rebecca; López, Analía V; Shawkey, Matthew D; Hauber, Mark E

2018-02-01

Rothstein (Behavioral Ecology and Sociobiology, 11, 1982, 229) was one of the first comprehensive studies to examine how different egg features influence egg rejection behaviors of avian brood parasite-hosts. The methods and conclusions of Rothstein (1982) laid the foundation for subsequent experimental brood parasitism studies over the past thirty years, but its results have never been evaluated with replication. Here, we partially replicated Rothstein's (1982) experiments using parallel artificial model egg treatments to simulate cowbird ( Molothrus ater ) parasitism in American robin ( Turdus migratorius ) nests. We compared our data with those of Rothstein (1982) and confirmed most of its original findings: (1) robins reject model eggs that differ from the appearance of a natural robin egg toward that of a natural cowbird egg in background color, size, and maculation; (2) rejection responses were best predicted by model egg background color; and (3) model eggs differing by two or more features from natural robin eggs were more likely to be rejected than model eggs differing by one feature alone. In contrast with Rothstein's (1982) conclusion that American robin egg recognition is not specifically tuned toward rejection of brown-headed cowbird eggs, we argue that our results and those of other recent studies of robin egg rejection suggest a discrimination bias toward rejection of cowbird eggs. Future work on egg recognition will benefit from utilizing a range of model eggs varying continuously in background color, maculation patterning, and size in combination with avian visual modeling, rather than using model eggs which vary only discretely.

LAI inversion from optical reflectance using a neural network trained with a multiple scattering model

NASA Technical Reports Server (NTRS)

Smith, James A.

1992-01-01

The inversion of the leaf area index (LAI) canopy parameter from optical spectral reflectance measurements is obtained using a backpropagation artificial neural network trained using input-output pairs generated by a multiple scattering reflectance model. The problem of LAI estimation over sparse canopies (LAI < 1.0) with varying soil reflectance backgrounds is particularly difficult. Standard multiple regression methods applied to canopies within a single homogeneous soil type yield good results but perform unacceptably when applied across soil boundaries, resulting in absolute percentage errors of >1000 percent for low LAI. Minimization methods applied to merit functions constructed from differences between measured reflectances and predicted reflectances using multiple-scattering models are unacceptably sensitive to a good initial guess for the desired parameter. In contrast, the neural network reported generally yields absolute percentage errors of <30 percent when weighting coefficients trained on one soil type were applied to predicted canopy reflectance at a different soil background.

Adsorption Equilibrium and Kinetics at Goethite-Water and Related Interfaces

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

Katz, Lynn Ellen

This research study is an important component of a broader comprehensive project, “Geochemistry of Interfaces: From Surfaces to Interlayers to Clusters,” which sought to identify and evaluate the critical molecular phenomena at metal-oxide interfaces that control many geochemical and environmental processes. The primary goal of this research study was to better understand and predict adsorption of metal ions at mineral/water surfaces. Macroscopic data in traditional batch experiments was used to develop predictive models that characterize sorption in complex systems containing a wide range of background solution compositions. Our studies focused on systems involving alkaline earth metal (Mg 2+, Ca 2+,more » Sr 2+, Ba 2+) and heavy metal (Hg 2+, Co 2+, Cd 2+, Cu 2+, Zn 2+, Pb 2+) cations. The anions we selected for study included Cl -, NO 3 -, ClO 4 -, SO 4 2-, CO 3 2- and SeO 3 2- and the background electrolyte cations we examined included (Na +, K +, Rb + and Cs +) because these represent a range of ion sizes and have varying potentials for forming ion-pairs or ternary complexes with the metal ions studied. The research led to the development of a modified titration congruency approach for estimating site densities for mineral oxides such as goethite. The CD-MUSIC version of the surface complexation modeling approach was applied to potentiometric titration data and macroscopic adsorption data for single-solute heavy metals, oxyanions, alkaline earth metals and background electrolytes over a range of pH and ionic strength. The model was capable of predicting sorption in bi-solute systems containing multiple cations, cations and oxyanions, and transition metal cations and alkaline earth metal ions. Incorporation of ternary complexes was required for modeling Pb(II)-Se(IV) and Cd(II)-Se(IV) systems. -Both crystal face contributions and capacitance values were shown to be sensitive to varying specific surface area but were successfully accounted for in the modeling strategy. The insights gained from the macroscopic, spectroscopic and CD-MUSIC modeling developed in this study can be used to guide the implementation of less complex models which may be more applicable to field conditions. The findings of this research suggest that surface complexation models can be used as a predictive tool for fate and transport modeling of metal ions and oxyanions in fresh and saline systems typical of energy production waters and wastewaters.« less

Improved partition equilibrium model for predicting analyte response in electrospray ionization mass spectrometry.

PubMed

Du, Lihong; White, Robert L

2009-02-01

A previously proposed partition equilibrium model for quantitative prediction of analyte response in electrospray ionization mass spectrometry is modified to yield an improved linear relationship. Analyte mass spectrometer response is modeled by a competition mechanism between analyte and background electrolytes that is based on partition equilibrium considerations. The correlation between analyte response and solution composition is described by the linear model over a wide concentration range and the improved model is shown to be valid for a wide range of experimental conditions. The behavior of an analyte in a salt solution, which could not be explained by the original model, is correctly predicted. The ion suppression effects of 16:0 lysophosphatidylcholine (LPC) on analyte signals are attributed to a combination of competition for excess charge and reduction of total charge due to surface tension effects. In contrast to the complicated mathematical forms that comprise the original model, the simplified model described here can more easily be employed to predict analyte mass spectrometer responses for solutions containing multiple components. Copyright (c) 2008 John Wiley & Sons, Ltd.

Incorporating geographical factors with artificial neural networks to predict reference values of erythrocyte sedimentation rate

PubMed Central

2013-01-01

Background The measurement of the Erythrocyte Sedimentation Rate (ESR) value is a standard procedure performed during a typical blood test. In order to formulate a unified standard of establishing reference ESR values, this paper presents a novel prediction model in which local normal ESR values and corresponding geographical factors are used to predict reference ESR values using multi-layer feed-forward artificial neural networks (ANN). Methods and findings Local normal ESR values were obtained from hospital data, while geographical factors that include altitude, sunshine hours, relative humidity, temperature and precipitation were obtained from the National Geographical Data Information Centre in China. The results show that predicted values are statistically in agreement with measured values. Model results exhibit significant agreement between training data and test data. Consequently, the model is used to predict the unseen local reference ESR values. Conclusions Reference ESR values can be established with geographical factors by using artificial intelligence techniques. ANN is an effective method for simulating and predicting reference ESR values because of its ability to model nonlinear and complex relationships. PMID:23497145

A measurement by BOOMERANG of multiple peaks in the angular power spectrum of the cosmic microwave background

NASA Technical Reports Server (NTRS)

Netterfield, C. B.; Ade, P. A. R.; Bock, J. J.; Bond, J. R.; Borrill, J.; Boscaleri, A.; Coble, K.; Contaldi, C. R.; Crill, B. P.; Bernardis, P. de;

Mathematical modeling and validation of growth of Salmonella Enteritidis and background microorganisms in potato salad – one-step kinetic analysis and model development

USDA-ARS?s Scientific Manuscript database

This study was conducted to examine the growth of Salmonella Enteritidis (SE) in potato salad caused by cross-contamination and temperature abuse, and develop mathematical models to predict its growth. The growth of SE was investigated under constant temperature conditions (8, 10, 15, 20, 25, 30, a...

Modeling the Effects of Meteorological Conditions on the Neutron Flux

DTIC Science & Technology

2017-05-22

a statistical model that predicts environmental neutron background as a function of five meteorological variables: inverse barometric pressure...variable of the model was inverse barometric pressure with a contribution an order of magnitude larger than any other variable’s contribution. The...is based on the sensitivity of each sensor. . . . . . . . . . . . . . . . . . . . . . . . . . 25 3.2 Neutron counts from the LNS and inverse pressure

Prediction Metrics for Chemical Detection in Long-Wave Infrared Hyperspectral Imagery

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

Chilton, Marie C.; Walsh, Stephen J.; Daly, Don S.

2009-01-29

A natural or anthropogenic process often generates a signature gas plume whose chemical constituents may be identified using hyperspectral imagery. A hyperspectral image is a pixel-indexed set of spectra where each spectrum reflects the chemical constituents of the plume, the atmosphere, the bounding background surface, and instrument noise. This study explored the relationship between gas absorbance and background emissivity across the long-wave infrared (LWIR) spectrum and how they affect relative gas detection sensitivity. The physics-based model for the observed radiance shows that high gas absorbance coupled with low background emissivity at a single wavenumber results in a stronger recorded radiance.more » Two sensitivity measures were developed to predict relative probability of detection using chemical absorbance and background emissivity: one focused on a single wavenumber while another accounted for the entire spectrum. The predictive abilities of these measures were compared to synthetic image analysis. This study simulated images with 499 distinct gases at each of 6 concentrations over 6 different background surfaces with the atmosphere and level of instrument noise held constant. The Whitened Matched Filter was used to define gas detection from an image spectrum. The estimate of a chemical’s probability of detection at a given concentration over a specific background was the proportion of detections in 500 trials. Of the 499 chemicals used in the images, 276 had estimated probabilities of detection below 0.2 across all backgrounds and concentrations; these chemicals were removed from the study. For 92.8 percent of the remaining chemicals, the single channel measure correctly predicted the background over which the chemical had the largest relative probability of detection. Further, the measure which accounted for information across all wavenumbers predicted the background over which the chemical had the largest relative probability of detection for 93.3 percent of the chemicals. These results suggest that the wavenumber with largest gas absorbance has the most influence over gas detection for this data. By furthering the in-silico experimentation with higher concentrations of gases not detectable in this experiment or by standardizing the gas absorbance spectra to unit vectors, these conclusions may be confirmed and generalized to more gases. This will help simplify image acquisition planning and the identification of unknowns in field collected images.« less

Modeling fate and transport of "Contaminants of Emerging Concern" (CECs): is the Soil Water Assessment Tool (SWAT) the appropriate model?

USDA-ARS?s Scientific Manuscript database

Background/Question/Methods As the scientific and regulatory communities realize the significant environmental impacts and ubiquity of “contaminants of emerging concern” (CECs), it is increasingly imperative to develop quantitative assessment tools to evaluate and predict the fate and transport of...

Using the Information-Motivation Behavioral Model to Predict Sexual Behavior among Underserved Minority Youth

ERIC Educational Resources Information Center

Bazargan, Mohsen; Stein, Judith A.; Bazargan-Hejazi, Shahrzad; Hindman, David W.

2010-01-01

Background: Testing, refining, and tailoring theoretical approaches that are hypothesized to reduce sexual risk behaviors among adolescent subpopulations is an important task. Relatively little is known about the relationship between components of the information-motivation-behavior (IMB) model and sexual behaviors among underage minority youth.…

Computational Model of Steroidogenesis in Human H295R Cells to Predict Biochemical Response to Endocrine Active Chemicals: Model Development for Metyrapone

EPA Science Inventory

BACKGROUND: An in vitro steroidogenesis assay using the human adrenocortical carcinoma cells H295R is being evaluated as a possible toxicity screening approach to detect and assess the impact of endocrine active chemicals (EAC) capable of altering steroid biosynthesis. Interpreta...

Recent development of risk-prediction models for incident hypertension: An updated systematic review

PubMed Central

Xiao, Lei; Liu, Ya; Wang, Zuoguang; Li, Chuang; Jin, Yongxin; Zhao, Qiong

2017-01-01

Background Hypertension is a leading global health threat and a major cardiovascular disease. Since clinical interventions are effective in delaying the disease progression from prehypertension to hypertension, diagnostic prediction models to identify patient populations at high risk for hypertension are imperative. Methods Both PubMed and Embase databases were searched for eligible reports of either prediction models or risk scores of hypertension. The study data were collected, including risk factors, statistic methods, characteristics of study design and participants, performance measurement, etc. Results From the searched literature, 26 studies reporting 48 prediction models were selected. Among them, 20 reports studied the established models using traditional risk factors, such as body mass index (BMI), age, smoking, blood pressure (BP) level, parental history of hypertension, and biochemical factors, whereas 6 reports used genetic risk score (GRS) as the prediction factor. AUC ranged from 0.64 to 0.97, and C-statistic ranged from 60% to 90%. Conclusions The traditional models are still the predominant risk prediction models for hypertension, but recently, more models have begun to incorporate genetic factors as part of their model predictors. However, these genetic predictors need to be well selected. The current reported models have acceptable to good discrimination and calibration ability, but whether the models can be applied in clinical practice still needs more validation and adjustment. PMID:29084293

Simulation and theory of spontaneous TAE frequency sweeping

NASA Astrophysics Data System (ADS)

Wang, Ge; Berk, H. L.

2012-09-01

A simulation model, based on the linear tip model of Rosenbluth, Berk and Van Dam (RBV), is developed to study frequency sweeping of toroidal Alfvén eigenmodes (TAEs). The time response of the background wave in the RBV model is given by a Volterra integral equation. This model captures the properties of TAE waves both in the gap and in the continuum. The simulation shows that phase space structures form spontaneously at frequencies close to the linearly predicted frequency, due to resonant particle-wave interactions and background dissipation. The frequency sweeping signals are found to chirp towards the upper and lower continua. However, the chirping signals penetrate only the lower continuum, whereupon the frequency chirps and mode amplitude increases in synchronism to produce an explosive solution. An adiabatic theory describing the evolution of a chirping signal is developed which replicates the chirping dynamics of the simulation in the lower continuum. This theory predicts that a decaying chirping signal will terminate at the upper continuum though in the numerical simulation the hole disintegrates before the upper continuum is reached.

The mathematical limits of genetic prediction for complex chronic disease.

PubMed

Keyes, Katherine M; Smith, George Davey; Koenen, Karestan C; Galea, Sandro

2015-06-01

Attempts at predicting individual risk of disease based on common germline genetic variation have largely been disappointing. The present paper formalises why genetic prediction at the individual level is and will continue to have limited utility given the aetiological architecture of most common complex diseases. Data were simulated on one million populations with 10 000 individuals in each populations with varying prevalences of a genetic risk factor, an interacting environmental factor and the background rate of disease. The determinant risk ratio and risk difference magnitude for the association between a gene variant and disease is a function of the prevalence of the interacting factors that activate the gene, and the background rate of disease. The risk ratio and total excess cases due to the genetic factor increase as the prevalence of interacting factors increase, and decrease as the background rate of disease increases. Germline genetic variations have high predictive capacity for individual disease only under conditions of high heritability of particular genetic sequences, plausible only under rare variant hypotheses. Under a model of common germline genetic variants that interact with other genes and/or environmental factors in order to cause disease, the predictive capacity of common genetic variants is determined by the prevalence of the factors that interact with the variant and the background rate. A focus on estimating genetic associations for the purpose of prediction without explicitly grounding such work in an understanding of modifiable (including environmentally influenced) factors will be limited in its ability to yield important insights about the risk of disease. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

Prediction models for intracranial hemorrhage or major bleeding in patients on antiplatelet therapy: a systematic review and external validation study.

PubMed

Hilkens, N A; Algra, A; Greving, J P

2016-01-01

ESSENTIALS: Prediction models may help to identify patients at high risk of bleeding on antiplatelet therapy. We identified existing prediction models for bleeding and validated them in patients with cerebral ischemia. Five prediction models were identified, all of which had some methodological shortcomings. Performance in patients with cerebral ischemia was poor. Background Antiplatelet therapy is widely used in secondary prevention after a transient ischemic attack (TIA) or ischemic stroke. Bleeding is the main adverse effect of antiplatelet therapy and is potentially life threatening. Identification of patients at increased risk of bleeding may help target antiplatelet therapy. This study sought to identify existing prediction models for intracranial hemorrhage or major bleeding in patients on antiplatelet therapy and evaluate their performance in patients with cerebral ischemia. We systematically searched PubMed and Embase for existing prediction models up to December 2014. The methodological quality of the included studies was assessed with the CHARMS checklist. Prediction models were externally validated in the European Stroke Prevention Study 2, comprising 6602 patients with a TIA or ischemic stroke. We assessed discrimination and calibration of included prediction models. Five prediction models were identified, of which two were developed in patients with previous cerebral ischemia. Three studies assessed major bleeding, one studied intracerebral hemorrhage and one gastrointestinal bleeding. None of the studies met all criteria of good quality. External validation showed poor discriminative performance, with c-statistics ranging from 0.53 to 0.64 and poor calibration. A limited number of prediction models is available that predict intracranial hemorrhage or major bleeding in patients on antiplatelet therapy. The methodological quality of the models varied, but was generally low. Predictive performance in patients with cerebral ischemia was poor. In order to reliably predict the risk of bleeding in patients with cerebral ischemia, development of a prediction model according to current methodological standards is needed. © 2015 International Society on Thrombosis and Haemostasis.

Predicting Non Return to Work after Orthopaedic Trauma: The Wallis Occupational Rehabilitation RisK (WORRK) Model

PubMed Central

Luthi, François; Deriaz, Olivier; Vuistiner, Philippe; Burrus, Cyrille; Hilfiker, Roger

2014-01-01

Background Workers with persistent disabilities after orthopaedic trauma may need occupational rehabilitation. Despite various risk profiles for non-return-to-work (non-RTW), there is no available predictive model. Moreover, injured workers may have various origins (immigrant workers), which may either affect their return to work or their eligibility for research purposes. The aim of this study was to develop and validate a predictive model that estimates the likelihood of non-RTW after occupational rehabilitation using predictors which do not rely on the worker’s background. Methods Prospective cohort study (3177 participants, native (51%) and immigrant workers (49%)) with two samples: a) Development sample with patients from 2004 to 2007 with Full and Reduced Models, b) External validation of the Reduced Model with patients from 2008 to March 2010. We collected patients’ data and biopsychosocial complexity with an observer rated interview (INTERMED). Non-RTW was assessed two years after discharge from the rehabilitation. Discrimination was assessed by the area under the receiver operating curve (AUC) and calibration was evaluated with a calibration plot. The model was reduced with random forests. Results At 2 years, the non-RTW status was known for 2462 patients (77.5% of the total sample). The prevalence of non-RTW was 50%. The full model (36 items) and the reduced model (19 items) had acceptable discrimination performance (AUC 0.75, 95% CI 0.72 to 0.78 and 0.74, 95% CI 0.71 to 0.76, respectively) and good calibration. For the validation model, the discrimination performance was acceptable (AUC 0.73; 95% CI 0.70 to 0.77) and calibration was also adequate. Conclusions Non-RTW may be predicted with a simple model constructed with variables independent of the patient’s education and language fluency. This model is useful for all kinds of trauma in order to adjust for case mix and it is applicable to vulnerable populations like immigrant workers. PMID:24718689

Validation of TxDOT flexible pavement skid prediction model : workshop : student guide.

DOT National Transportation Integrated Search

2017-05-01

Course Materials: : Background summary of Research Project 0-5627. : Short presentation of research tasks and findings from Research Project 0-6746. : Aggregate characterization with Aggregate Imaging Measurement System (AIMS) and Micro-D...

Radiative transfer in shrub savanna sites in Niger: Preliminary results from HAPEX-Sahel. Part 1: Modelling surface reflectance using a geometric-optical approach

NASA Technical Reports Server (NTRS)

Franklin, Janet; Duncan, Jeff; Huete, Alfredo R.; vanLeeuwen, W. J. D.; Li, Xiaowen; Begue, Agnes

1994-01-01

To use optical remote sensing to monitor land surface-climate interactions over large areas, algorithms must be developed to relate multispectral measurements to key variables controlling the exchange of matter (water, carbon dioxide) and energy between the land surface and the atmosphere. The proportion of the ground covered by vegetation and the interception of photosynthetically active radiation (PAR) by vegetation are examples of two variables related to evapotranspiration and primary production, respectively. An areal-proportion model of the multispectral reflectance of shrub savanna, composed of scattered shrubs with a grass, forb or soil understory, predicted the reflectance of two 0.5 km(exp 2) sites as the area-weighted average of the shrub and understory or 'background' reflectances. Although the shaded crown and shaded background have darker reflectances, ignoring them in the area-weighted model is not serious when shrub cover is low and solar zenith angle is small. A submodel predicted the reflectance of the shrub crown as a function of the foliage reflectance and amount of plant material within the crown, and the background reflectance scattered or transmitted through canopy gaps (referred to as a soil-plant 'spectral interaction' term). One may be able to combine these two models to estimate both the fraction of vegetation cover and interception of PAR by green vegetation in a shrub savanna.

Search for production of an $$\\Upsilon$$(1S) meson in association with a W or Z boson using the full 1.96 TeV $$p\\bar{p}$$ collision data set at CDF

DOE PAGES

Aaltonen, T.

2015-03-17

Production of the Υ(1S) meson in association with a vector boson is a rare process in the standard model with a cross section predicted to be below the sensitivity of the Tevatron. Observation of this process could signify contributions not described by the standard model or reveal limitations with the current nonrelativistic quantum-chromodynamic models used to calculate the cross section. We perform a search for this process using the full Run II data set collected by the CDF II detector corresponding to an integrated luminosity of 9.4 fb -1. Our search considers the Υ→μμ decay and the decay of themore » W and Z bosons into muons and electrons. Furthermore, in these purely leptonic decay channels, we observe one ΥW candidate with an expected background of 1.2±0.5 events, and one ΥZcandidate with an expected background of 0.1±0.1 events. Both observations are consistent with the predicted background contributions. The resulting upper limits on the cross section for Υ+W/Zproduction are the most sensitive reported from a single experiment and place restrictions on potential contributions from non-standard-model physics.« less

Fingerprint Liveness Detection in the Presence of Capable Intruders.

PubMed

Sequeira, Ana F; Cardoso, Jaime S

2015-06-19

Fingerprint liveness detection methods have been developed as an attempt to overcome the vulnerability of fingerprint biometric systems to spoofing attacks. Traditional approaches have been quite optimistic about the behavior of the intruder assuming the use of a previously known material. This assumption has led to the use of supervised techniques to estimate the performance of the methods, using both live and spoof samples to train the predictive models and evaluate each type of fake samples individually. Additionally, the background was often included in the sample representation, completely distorting the decision process. Therefore, we propose that an automatic segmentation step should be performed to isolate the fingerprint from the background and truly decide on the liveness of the fingerprint and not on the characteristics of the background. Also, we argue that one cannot aim to model the fake samples completely since the material used by the intruder is unknown beforehand. We approach the design by modeling the distribution of the live samples and predicting as fake the samples very unlikely according to that model. Our experiments compare the performance of the supervised approaches with the semi-supervised ones that rely solely on the live samples. The results obtained differ from the ones obtained by the more standard approaches which reinforces our conviction that the results in the literature are misleadingly estimating the true vulnerability of the biometric system.

Fingerprint Liveness Detection in the Presence of Capable Intruders

PubMed Central

Sequeira, Ana F.; Cardoso, Jaime S.

2015-01-01

Fingerprint liveness detection methods have been developed as an attempt to overcome the vulnerability of fingerprint biometric systems to spoofing attacks. Traditional approaches have been quite optimistic about the behavior of the intruder assuming the use of a previously known material. This assumption has led to the use of supervised techniques to estimate the performance of the methods, using both live and spoof samples to train the predictive models and evaluate each type of fake samples individually. Additionally, the background was often included in the sample representation, completely distorting the decision process. Therefore, we propose that an automatic segmentation step should be performed to isolate the fingerprint from the background and truly decide on the liveness of the fingerprint and not on the characteristics of the background. Also, we argue that one cannot aim to model the fake samples completely since the material used by the intruder is unknown beforehand. We approach the design by modeling the distribution of the live samples and predicting as fake the samples very unlikely according to that model. Our experiments compare the performance of the supervised approaches with the semi-supervised ones that rely solely on the live samples. The results obtained differ from the ones obtained by the more standard approaches which reinforces our conviction that the results in the literature are misleadingly estimating the true vulnerability of the biometric system. PMID:26102491

Dependence of the Radiation Pressure on the Background Refractive Index

NASA Astrophysics Data System (ADS)

Webb, Kevin J.

2013-07-01

The 1978 experiments by Jones and Leslie showing that the radiation pressure on a mirror depends on the background medium refractive index have yet to be adequately explained using a force model and have provided a leading challenge to the Abraham form of the electromagnetic momentum. Those experimental results are predicted for the first time using a force representation that incorporates the Abraham momentum by utilizing the power calibration method employed in the Jones and Leslie experiments. With an extension of the same procedure, the polarization and angle independence of the experimental data are also explained by this model. Prospects are good for this general form of the electromagnetic force density to be effective in predicting other experiments with macroscopic materials. Furthermore, the rigorous representation of material dispersion makes the representation important for metamaterials that operate in the vicinity of homogenized material resonances.

Cosmic background radiation anisotropies in universes dominated by nonbaryonic dark matter

NASA Technical Reports Server (NTRS)

Bond, J. R.; Efstathiou, G.

1984-01-01

Detailed calculations of the temperature fluctuations in the cosmic background radiation for universes dominated by massive collisionless relics of the big bang are presented. An initially adiabatic constant curvature perturbation spectrum is assumed. In models with cold dark matter, the simplest hypothesis - that galaxies follow the mass distribution leads to small-scale anisotropies which exceed current observational limits if omega is less than 0.2 h to the -4/3. Since low values of omega are indicated by dynamical studies of galaxy clustering, cold particle models in which light traces mass are probably incorrect. Reheating of the pregalactic medium is unlikely to modify this conclusion. In cold particle or neutrino-dominated universes with omega = 1, presented predictions for small-scale and quadrupole anisotropies are below current limits. In all cases, the small-scale fluctuations are predicted to be about 10 percent linearly polarized.

The search for the pair production of second-generation scalar leptoquarks and measurements of the differential cross sections of the W boson produced in association with jets with the CMS detector at the LHC

NASA Astrophysics Data System (ADS)

Baumgartel, Darin C.

Since the formulation of the Standard Model of particle physics, numerous experiments have sought to observe the signatures of the subatomic particles by examining the outcomes of charged particle collisions. Over time, advances in detector technology and scientific computing have allowed for unprecedented precision measurements of Standard Model phenomena and particle properties. Although the Standard Model has displayed remarkable predictive power, extensions to the Standard Model have been formulated to account for unexplained phenomena, and these extensions often infer the existence of additional subatomic particles. Consequently, experiments at particle colliders often endeavor to search for signatures of physics beyond the Standard Model. These searches and measurements are often complementary pursuits, as searches are often limited by the precision of estimations of the Standard Model backgrounds. At the forefront of present-day collider experiments is the Large Hadron Collider at CERN, which delivers proton-proton collisions with unprecedented energy and luminosity. Collisions are recorded with detectors located at interaction points along the ring of the Large Hadron Collider. The CMS detector is one of two general-purpose detectors at the Large Hadron Collider, and the high-precision detection of particles from collision events in the CMS detector make the CMS detector a powerful tool for both Standard-Model measurements and searches for new physics. The Standard Model is characterized by three generation of quarks and leptons. This correspondence between the generations of quarks and leptons is necessary to allow for the renormalizability of the Standard Model, but it is not an inherent property of the Standard Model. Motivated by this compelling symmetry, many theories and models propose the existence of leptoquark bosons which mediate transitions between quarks and leptons. Experimental constraints indicate that leptoquarks would couple to a single generation, and this thesis describes searches for leptoquarks produced in pairs and decaying to final states containing either two muons and two jets, or one muon, one muon-neutrino, and two jets. Searches are conducted with collision data at center-of-mass energies of both 7 TeV and 8 TeV. No compelling evidence for the existence of leptoquarks is found, and upper limits on the leptoquark mass and cross section are placed at the 95% confidence level. These limits are the most stringent to date, and are several times larger than limits placed previously at hadron collider experiments. While the pair production of massive leptoquark bosons yields final states which have strong kinematic differences from the Standard Model processes, the ability to exploit these differences is limited by the ability to accurately model the backgrounds. The most notable of these backgrounds is the production of a W boson in association with one or more jets. Since the W+jets process has a very large cross section and a final state containing missing energy, its contribution to the total Standard Model background is both nominally large and more difficult to discriminate against than backgrounds with only visible final state objects. Furthermore, estimates of this background are not easily improved by comparisons with data in control regions, and simulations of the background are often limited to leading-order predictions. To improve the understanding and modeling of this background for future endeavors, this thesis also presents measurements of the W+jets process differentially as a function of several variables, including the jet multiplicity, the individual jet transverse momenta and pseudorapidities, the angular separation between the jets and the muon, and the scalar sum of the transverse momenta of all jets. The agreement of these measurements with respect to predictions from event leading-order generators and next-to-leading-order calculations is assessed.

Moon meteoritic seismic hum: Steady state prediction

USGS Publications Warehouse

Lognonne, P.; Feuvre, M.L.; Johnson, C.L.; Weber, R.C.

2009-01-01

We use three different statistical models describing the frequency of meteoroid impacts on Earth to estimate the seismic background noise due to impacts on the lunar surface. Because of diffraction, seismic events on the Moon are typically characterized by long codas, lasting 1 h or more. We find that the small but frequent impacts generate seismic signals whose codas overlap in time, resulting in a permanent seismic noise that we term the "lunar hum" by analogy with the Earth's continuous seismic background seismic hum. We find that the Apollo era impact detection rates and amplitudes are well explained by a model that parameterizes (1) the net seismic impulse due to the impactor and resulting ejecta and (2) the effects of diffraction and attenuation. The formulation permits the calculation of a composite waveform at any point on the Moon due to simulated impacts at any epicentral distance. The root-mean-square amplitude of this waveform yields a background noise level that is about 100 times lower than the resolution of the Apollo long-period seismometers. At 2 s periods, this noise level is more than 1000 times lower than the low noise model prediction for Earth's microseismic noise. Sufficiently sensitive seismometers will allow the future detection of several impacts per day at body wave frequencies. Copyright 2009 by the American Geophysical Union.

Predicting Student Success on the Texas Chemistry STAAR Test: A Logistic Regression Analysis

ERIC Educational Resources Information Center

Johnson, William L.; Johnson, Annabel M.; Johnson, Jared

2012-01-01

Background: The context is the new Texas STAAR end-of-course testing program. Purpose: The authors developed a logistic regression model to predict who would pass-or-fail the new Texas chemistry STAAR end-of-course exam. Setting: Robert E. Lee High School (5A) with an enrollment of 2700 students, Tyler, Texas. Date of the study was the 2011-2012…

Classroom Dimensions Predict Early Peer Interaction when Children Are Diverse in Ethnicity, Race, and Home Language

ERIC Educational Resources Information Center

Howes, Carollee; Guerra, Alison Wishard; Fuligni, Allison; Zucker, Eleanor; Lee, Linda; Obregon, Nora B.; Spivak, Asha

2011-01-01

The purpose of this study was to test a model for predicting preschool-age children's behaviors with peers from dimensions of the classroom and teacher-child relationship quality when the children were from diverse race, ethnic, and home language backgrounds. Eight hundred children, (M=age 63 months, SD=8.1 months), part of the National Evaluation…

Estimating community health needs against a Triple Aim background: What can we learn from current predictive risk models?

PubMed

Elissen, Arianne M J; Struijs, Jeroen N; Baan, Caroline A; Ruwaard, Dirk

2015-05-01

To support providers and commissioners in accurately assessing their local populations' health needs, this study produces an overview of Dutch predictive risk models for health care, focusing specifically on the type, combination and relevance of included determinants for achieving the Triple Aim (improved health, better care experience, and lower costs). We conducted a mixed-methods study combining document analyses, interviews and a Delphi study. Predictive risk models were identified based on a web search and expert input. Participating in the study were Dutch experts in predictive risk modelling (interviews; n=11) and experts in healthcare delivery, insurance and/or funding methodology (Delphi panel; n=15). Ten predictive risk models were analysed, comprising 17 unique determinants. Twelve were considered relevant by experts for estimating community health needs. Although some compositional similarities were identified between models, the combination and operationalisation of determinants varied considerably. Existing predictive risk models provide a good starting point, but optimally balancing resources and targeting interventions on the community level will likely require a more holistic approach to health needs assessment. Development of additional determinants, such as measures of people's lifestyle and social network, may require policies pushing the integration of routine data from different (healthcare) sources. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Disease-Free Survival after Hepatic Resection in Hepatocellular Carcinoma Patients: A Prediction Approach Using Artificial Neural Network

PubMed Central

Ho, Wen-Hsien; Lee, King-Teh; Chen, Hong-Yaw; Ho, Te-Wei; Chiu, Herng-Chia

2012-01-01

Background A database for hepatocellular carcinoma (HCC) patients who had received hepatic resection was used to develop prediction models for 1-, 3- and 5-year disease-free survival based on a set of clinical parameters for this patient group. Methods The three prediction models included an artificial neural network (ANN) model, a logistic regression (LR) model, and a decision tree (DT) model. Data for 427, 354 and 297 HCC patients with histories of 1-, 3- and 5-year disease-free survival after hepatic resection, respectively, were extracted from the HCC patient database. From each of the three groups, 80% of the cases (342, 283 and 238 cases of 1-, 3- and 5-year disease-free survival, respectively) were selected to provide training data for the prediction models. The remaining 20% of cases in each group (85, 71 and 59 cases in the three respective groups) were assigned to validation groups for performance comparisons of the three models. Area under receiver operating characteristics curve (AUROC) was used as the performance index for evaluating the three models. Conclusions The ANN model outperformed the LR and DT models in terms of prediction accuracy. This study demonstrated the feasibility of using ANNs in medical decision support systems for predicting disease-free survival based on clinical databases in HCC patients who have received hepatic resection. PMID:22235270

Attractiveness Compensates for Low Status Background in the Prediction of Educational Attainment.

PubMed

Bauldry, Shawn; Shanahan, Michael J; Russo, Rosemary; Roberts, Brent W; Damian, Rodica

2016-01-01

People who are perceived as good looking or as having a pleasant personality enjoy many advantages, including higher educational attainment. This study examines (1) whether associations between physical/personality attractiveness and educational attainment vary by parental socioeconomic resources and (2) whether parental socioeconomic resources predict these forms of attractiveness. Based on the theory of resource substitution with structural amplification, we hypothesized that both types of attractiveness would have a stronger association with educational attainment for people from disadvantaged backgrounds (resource substitution), but also that people from disadvantaged backgrounds would be less likely to be perceived as attractive (amplification). This study draws on data from the National Longitudinal Study of Adolescent to Adult Health-including repeated interviewer ratings of respondents' attractiveness-and trait-state structural equation models to examine the moderation (substitution) and mediation (amplification) of physical and personality attractiveness in the link between parental socioeconomic resources and educational attainment. Both perceived personality and physical attractiveness have stronger associations with educational attainment for people from families with lower levels of parental education (substitution). Further, parental education and income are associated with both dimensions of perceived attractiveness, and personality attractiveness is positively associated with educational attainment (amplification). Results do not differ by sex and race/ethnicity. Further, associations between perceived attractiveness and educational attainment remain after accounting for unmeasured family-level confounders using a sibling fixed-effects model. Perceived attractiveness, particularly personality attractiveness, is a more important psychosocial resource for educational attainment for people from disadvantaged backgrounds than for people from advantaged backgrounds. People from disadvantaged backgrounds, however, are less likely to be perceived as attractive than people from advantaged backgrounds.

Stress induction in the bacteria Shewanella oneidensis and Deinococcus radiodurans in response to below-background ionizing radiation.

PubMed

Castillo, Hugo; Schoderbek, Donald; Dulal, Santosh; Escobar, Gabriela; Wood, Jeffrey; Nelson, Roger; Smith, Geoffrey

2015-01-01

The 'Linear no-threshold' (LNT) model predicts that any amount of radiation increases the risk of organisms to accumulate negative effects. Several studies at below background radiation levels (4.5-11.4 nGy h(-1)) show decreased growth rates and an increased susceptibility to oxidative stress. The purpose of our study is to obtain molecular evidence of a stress response in Shewanella oneidensis and Deinococcus radiodurans grown at a gamma dose rate of 0.16 nGy h(-1), about 400 times less than normal background radiation. Bacteria cultures were grown at a dose rate of 0.16 or 71.3 nGy h(-1) gamma irradiation. Total RNA was extracted from samples at early-exponential and stationary phases for the rt-PCR relative quantification (radiation-deprived treatment/background radiation control) of the stress-related genes katB (catalase), recA (recombinase), oxyR (oxidative stress transcriptional regulator), lexA (SOS regulon transcriptional repressor), dnaK (heat shock protein 70) and SOA0154 (putative heavy metal efflux pump). Deprivation of normal levels of radiation caused a reduction in growth of both bacterial species, accompanied by the upregulation of katB, recA, SOA0154 genes in S. oneidensis and the upregulation of dnaK in D. radiodurans. When cells were returned to background radiation levels, growth rates recovered and the stress response dissipated. Our results indicate that below-background levels of radiation inhibited growth and elicited a stress response in two species of bacteria, contrary to the LNT model prediction.

Biological Networks for Predicting Chemical Hepatocarcinogenicity Using Gene Expression Data from Treated Mice and Relevance across Human and Rat Species

PubMed Central

Thomas, Reuben; Thomas, Russell S.; Auerbach, Scott S.; Portier, Christopher J.

2013-01-01

Background Several groups have employed genomic data from subchronic chemical toxicity studies in rodents (90 days) to derive gene-centric predictors of chronic toxicity and carcinogenicity. Genes are annotated to belong to biological processes or molecular pathways that are mechanistically well understood and are described in public databases. Objectives To develop a molecular pathway-based prediction model of long term hepatocarcinogenicity using 90-day gene expression data and to evaluate the performance of this model with respect to both intra-species, dose-dependent and cross-species predictions. Methods Genome-wide hepatic mRNA expression was retrospectively measured in B6C3F1 mice following subchronic exposure to twenty-six (26) chemicals (10 were positive, 2 equivocal and 14 negative for liver tumors) previously studied by the US National Toxicology Program. Using these data, a pathway-based predictor model for long-term liver cancer risk was derived using random forests. The prediction model was independently validated on test sets associated with liver cancer risk obtained from mice, rats and humans. Results Using 5-fold cross validation, the developed prediction model had reasonable predictive performance with the area under receiver-operator curve (AUC) equal to 0.66. The developed prediction model was then used to extrapolate the results to data associated with rat and human liver cancer. The extrapolated model worked well for both extrapolated species (AUC value of 0.74 for rats and 0.91 for humans). The prediction models implied a balanced interplay between all pathway responses leading to carcinogenicity predictions. Conclusions Pathway-based prediction models estimated from sub-chronic data hold promise for predicting long-term carcinogenicity and also for its ability to extrapolate results across multiple species. PMID:23737943

Starobinsky-like inflation and neutrino masses in a no-scale SO(10) model

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

Ellis, John; Theoretical Physics Department, CERN,CH-1211 Geneva 23; Garcia, Marcos A.G.

2016-11-08

Using a no-scale supergravity framework, we construct an SO(10) model that makes predictions for cosmic microwave background observables similar to those of the Starobinsky model of inflation, and incorporates a double-seesaw model for neutrino masses consistent with oscillation experiments and late-time cosmology. We pay particular attention to the behaviour of the scalar fields during inflation and the subsequent reheating.

Wavelet Analysis Used for Spectral Background Removal in the Determination of Glucose from Near-Infrared Single-Beam Spectra

PubMed Central

Wan, Boyong; Small, Gary W.

2010-01-01

Wavelet analysis is developed as a preprocessing tool for use in removing background information from near-infrared (near-IR) single-beam spectra before the construction of multivariate calibration models. Three data sets collected with three different near-IR spectrometers are investigated that involve the determination of physiological levels of glucose (1-30 mM) in a simulated biological matrix containing alanine, ascorbate, lactate, triacetin, and urea in phosphate buffer. A factorial design is employed to optimize the specific wavelet function used and the level of decomposition applied, in addition to the spectral range and number of latent variables associated with a partial least-squares calibration model. The prediction performance of the computed models is studied with separate data acquired after the collection of the calibration spectra. This evaluation includes one data set collected over a period of more than six months. Preprocessing with wavelet analysis is also compared to the calculation of second-derivative spectra. Over the three data sets evaluated, wavelet analysis is observed to produce better-performing calibration models, with improvements in concentration predictions on the order of 30% being realized relative to models based on either second-derivative spectra or spectra preprocessed with simple additive and multiplicative scaling correction. This methodology allows the construction of stable calibrations directly with single-beam spectra, thereby eliminating the need for the collection of a separate background or reference spectrum. PMID:21035604

Wavelet analysis used for spectral background removal in the determination of glucose from near-infrared single-beam spectra.

PubMed

Wan, Boyong; Small, Gary W

2010-11-29

Wavelet analysis is developed as a preprocessing tool for use in removing background information from near-infrared (near-IR) single-beam spectra before the construction of multivariate calibration models. Three data sets collected with three different near-IR spectrometers are investigated that involve the determination of physiological levels of glucose (1-30 mM) in a simulated biological matrix containing alanine, ascorbate, lactate, triacetin, and urea in phosphate buffer. A factorial design is employed to optimize the specific wavelet function used and the level of decomposition applied, in addition to the spectral range and number of latent variables associated with a partial least-squares calibration model. The prediction performance of the computed models is studied with separate data acquired after the collection of the calibration spectra. This evaluation includes one data set collected over a period of more than 6 months. Preprocessing with wavelet analysis is also compared to the calculation of second-derivative spectra. Over the three data sets evaluated, wavelet analysis is observed to produce better-performing calibration models, with improvements in concentration predictions on the order of 30% being realized relative to models based on either second-derivative spectra or spectra preprocessed with simple additive and multiplicative scaling correction. This methodology allows the construction of stable calibrations directly with single-beam spectra, thereby eliminating the need for the collection of a separate background or reference spectrum. Copyright © 2010 Elsevier B.V. All rights reserved.

Models for short term malaria prediction in Sri Lanka

PubMed Central

Briët, Olivier JT; Vounatsou, Penelope; Gunawardena, Dissanayake M; Galappaththy, Gawrie NL; Amerasinghe, Priyanie H

2008-01-01

Background Malaria in Sri Lanka is unstable and fluctuates in intensity both spatially and temporally. Although the case counts are dwindling at present, given the past history of resurgence of outbreaks despite effective control measures, the control programmes have to stay prepared. The availability of long time series of monitored/diagnosed malaria cases allows for the study of forecasting models, with an aim to developing a forecasting system which could assist in the efficient allocation of resources for malaria control. Methods Exponentially weighted moving average models, autoregressive integrated moving average (ARIMA) models with seasonal components, and seasonal multiplicative autoregressive integrated moving average (SARIMA) models were compared on monthly time series of district malaria cases for their ability to predict the number of malaria cases one to four months ahead. The addition of covariates such as the number of malaria cases in neighbouring districts or rainfall were assessed for their ability to improve prediction of selected (seasonal) ARIMA models. Results The best model for forecasting and the forecasting error varied strongly among the districts. The addition of rainfall as a covariate improved prediction of selected (seasonal) ARIMA models modestly in some districts but worsened prediction in other districts. Improvement by adding rainfall was more frequent at larger forecasting horizons. Conclusion Heterogeneity of patterns of malaria in Sri Lanka requires regionally specific prediction models. Prediction error was large at a minimum of 22% (for one of the districts) for one month ahead predictions. The modest improvement made in short term prediction by adding rainfall as a covariate to these prediction models may not be sufficient to merit investing in a forecasting system for which rainfall data are routinely processed. PMID:18460204

Predictive Monitoring for Improved Management of Glucose Levels

PubMed Central

Reifman, Jaques; Rajaraman, Srinivasan; Gribok, Andrei; Ward, W. Kenneth

2007-01-01

Background Recent developments and expected near-future improvements in continuous glucose monitoring (CGM) devices provide opportunities to couple them with mathematical forecasting models to produce predictive monitoring systems for early, proactive glycemia management of diabetes mellitus patients before glucose levels drift to undesirable levels. This article assesses the feasibility of data-driven models to serve as the forecasting engine of predictive monitoring systems. Methods We investigated the capabilities of data-driven autoregressive (AR) models to (1) capture the correlations in glucose time-series data, (2) make accurate predictions as a function of prediction horizon, and (3) be made portable from individual to individual without any need for model tuning. The investigation is performed by employing CGM data from nine type 1 diabetic subjects collected over a continuous 5-day period. Results With CGM data serving as the gold standard, AR model-based predictions of glucose levels assessed over nine subjects with Clarke error grid analysis indicated that, for a 30-minute prediction horizon, individually tuned models yield 97.6 to 100.0% of data in the clinically acceptable zones A and B, whereas cross-subject, portable models yield 95.8 to 99.7% of data in zones A and B. Conclusions This study shows that, for a 30-minute prediction horizon, data-driven AR models provide sufficiently-accurate and clinically-acceptable estimates of glucose levels for timely, proactive therapy and should be considered as the modeling engine for predictive monitoring of patients with type 1 diabetes mellitus. It also suggests that AR models can be made portable from individual to individual with minor performance penalties, while greatly reducing the burden associated with model tuning and data collection for model development. PMID:19885110

Slowing of Femtosecond Laser-Generated Nanoparticles in a Background Gas

DOE PAGES

Rouleau, Christopher M.; Puretzky, Alexander A.; Geohegan, David B.

2014-11-25

The slowing of Pt nanoparticles in argon background gas was characterized by Rayleigh scattering imaging using a plume of nanoparticles generated by femtosecond laser through thin film ablation (fs-TTFA) of 20 nanometers-thick Pt films. The ablation was performed at threshold laser energy fluences for complete film removal to provide a well-defined plume consisting almost entirely of nanoparticles traveling with a narrow velocity distribution, providing a unique system to unambiguously characterize the slowing of nanoparticles during interaction with background gases. Nanoparticles of ~200 nm diameter were found to decelerate in background Ar gas with pressures less than 50 Torr in goodmore » agreement with a linear drag model in the Epstein regime. Based on this model, the stopping distance of small nanoparticles in the plume was predicted and tested by particle collection in an off-axis geometry, and size distribution analysis by transmission electron microscopy. These results permit a basis to interpret nanoparticle propagation through background gases in laser ablation plumes that contain mixed components.« less

MetaDP: a comprehensive web server for disease prediction of 16S rRNA metagenomic datasets.

PubMed

Xu, Xilin; Wu, Aiping; Zhang, Xinlei; Su, Mingming; Jiang, Taijiao; Yuan, Zhe-Ming

2016-01-01

High-throughput sequencing-based metagenomics has garnered considerable interest in recent years. Numerous methods and tools have been developed for the analysis of metagenomic data. However, it is still a daunting task to install a large number of tools and complete a complicated analysis, especially for researchers with minimal bioinformatics backgrounds. To address this problem, we constructed an automated software named MetaDP for 16S rRNA sequencing data analysis, including data quality control, operational taxonomic unit clustering, diversity analysis, and disease risk prediction modeling. Furthermore, a support vector machine-based prediction model for intestinal bowel syndrome (IBS) was built by applying MetaDP to microbial 16S sequencing data from 108 children. The success of the IBS prediction model suggests that the platform may also be applied to other diseases related to gut microbes, such as obesity, metabolic syndrome, or intestinal cancer, among others (http://metadp.cn:7001/).

Status of the Simbol-X Background Simulation Activities

NASA Astrophysics Data System (ADS)

Tenzer, C.; Briel, U.; Bulgarelli, A.; Chipaux, R.; Claret, A.; Cusumano, G.; Dell'Orto, E.; Fioretti, V.; Foschini, L.; Hauf, S.; Kendziorra, E.; Kuster, M.; Laurent, P.; Tiengo, A.

2009-05-01

The Simbol-X background simulation group is working towards a simulation based background and mass model which can be used before and during the mission. Using the Geant4 toolkit, a Monte-Carlo code to simulate the detector background of the Simbol-X focal plane instrument has been developed with the aim to optimize the design of the instrument. Achieving an overall low instrument background has direct impact on the sensitivity of Simbol-X and thus will be crucial for the success of the mission. We present results of recent simulation studies concerning the shielding of the detectors with respect to the diffuse cosmic hard X-ray background and to the cosmic-ray proton induced background. Besides estimates of the level and spectral shape of the remaining background expected in the low and high energy detector, also anti-coincidence rates and resulting detector dead time predictions are discussed.

Accurate prediction of personalized olfactory perception from large-scale chemoinformatic features.

PubMed

Li, Hongyang; Panwar, Bharat; Omenn, Gilbert S; Guan, Yuanfang

2018-02-01

The olfactory stimulus-percept problem has been studied for more than a century, yet it is still hard to precisely predict the odor given the large-scale chemoinformatic features of an odorant molecule. A major challenge is that the perceived qualities vary greatly among individuals due to different genetic and cultural backgrounds. Moreover, the combinatorial interactions between multiple odorant receptors and diverse molecules significantly complicate the olfaction prediction. Many attempts have been made to establish structure-odor relationships for intensity and pleasantness, but no models are available to predict the personalized multi-odor attributes of molecules. In this study, we describe our winning algorithm for predicting individual and population perceptual responses to various odorants in the DREAM Olfaction Prediction Challenge. We find that random forest model consisting of multiple decision trees is well suited to this prediction problem, given the large feature spaces and high variability of perceptual ratings among individuals. Integrating both population and individual perceptions into our model effectively reduces the influence of noise and outliers. By analyzing the importance of each chemical feature, we find that a small set of low- and nondegenerative features is sufficient for accurate prediction. Our random forest model successfully predicts personalized odor attributes of structurally diverse molecules. This model together with the top discriminative features has the potential to extend our understanding of olfactory perception mechanisms and provide an alternative for rational odorant design.

A Synthesis Of Cosmic X-ray And Infrared Background

NASA Astrophysics Data System (ADS)

Shi, Yong; Helou, G.; Armus, L.; Stierwalt, S.

2012-01-01

We present a synthesis model of cosmic IR and X-ray background, with the goal to derive a complete census of cosmic evolution of star formation (SF) and black-hole (BH) growth by complementing advantages of X-ray and IR surveys to each other. By assuming that individual galaxies are experiencing both SF and BH accretion, our model decomposes the total IR LF into SF and BH components while taking into account the luminosity-dependent SED and its dispersion of the SF component, and the extinction-dependent SED of the BH component. The best-fit parameters are derived by fitting to the number counts and redshift distributions at X-ray including both hard and soft bands, and mid-IR to submm bands including IRAS, Spitzer, Herschel, SCUBA, Aztec and MAMBO. Based on the fit result, our models provide a series of predictions on galaxy evolution and black-hole growth. For evolution of infrared galaxies, the model predicts that the total infrared luminosity function is best described through evolution in both luminosity and density. For evolution of AGN populations, the model predicts that the evolution of X-ray LF also shows luminosity and density dependent, that the type-1/type-2 AGN fraction is a function of both luminosity and redshift, and that the Compton-thick AGN number density evolves strongly with redshift, contributing about 20% to the total cosmic BH growth. For BH growth in IR galaxies, the model predicts that the majority of BH growth at z>1 occurs in infrared luminous galaxies and the AGN fraction as a function of IR survey is a strong function of the survey depth, ranging from >50% at bright end to below 10% at faint end. We also evaluates various AGN selection techniques at X-ray and IR wavelengths and offer predictions for future missions at X-ray and IR.

Lipoprotein metabolism indicators improve cardiovascular risk prediction

USDA-ARS?s Scientific Manuscript database

Background: Cardiovascular disease risk increases when lipoprotein metabolism is dysfunctional. We have developed a computational model able to derive indicators of lipoprotein production, lipolysis, and uptake processes from a single lipoprotein profile measurement. This is the first study to inves...

Exploring Gender Differences in Predicting Physical Activity among Elementary Aged Children: An Application of the Integrated Behavioral Model

ERIC Educational Resources Information Center

Branscum, Paul; Bhochhibhoya, Amir

2016-01-01

Background: The integrated behavioral model (IBM) is a new and emerging theory in the field of health promotion and health education, and more applications are needed to test the usefulness of the model for research and practice. Purpose: The purpose of this study was to operationalize the IBM as it relates to physical activity (PA) among children…

Statistical Signal Models and Algorithms for Image Analysis

DTIC Science & Technology

1984-10-25

In this report, two-dimensional stochastic linear models are used in developing algorithms for image analysis such as classification, segmentation, and object detection in images characterized by textured backgrounds. These models generate two-dimensional random processes as outputs to which statistical inference procedures can naturally be applied. A common thread throughout our algorithms is the interpretation of the inference procedures in terms of linear prediction

How much crosstalk can be allowed in a stereoscopic system at various grey levels?

NASA Astrophysics Data System (ADS)

Shestak, Sergey; Kim, Daesik; Kim, Yongie

2012-03-01

We have calculated a perceptual threshold of stereoscopic crosstalk on the basis of mathematical model of human vision sensitivity. Instead of linear model of just noticeable difference (JND) known as Weber's law we applied nonlinear Barten's model. The predicted crosstalk threshold varies with the background luminance. The calculated values of threshold are in a reasonable agreement with known experimental data. We calculated perceptual threshold of crosstalk for various combinations of the applied grey level. This result can be applied for the assessment of grey-to-grey crosstalk compensation. Further computational analysis of the applied model predicts the increase of the displayable image contrast with reduction of the maximum displayable luminance.

Characterizing and Modeling the Noise and Complex Impedance of Feedhorn-Coupled TES Polarimeters

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

Appel, J. W.; Beall, J. A.; Essinger-Hileman, T.

2009-12-16

We present results from modeling the electrothermal performance of feedhorn-coupled transition edge sensor (TES) polarimeters under development for use in cosmic microwave background (CMB) polarization experiments. Each polarimeter couples radiation from a corrugated feedhorn through a planar orthomode transducer, which transmits power from orthogonal polarization modes to two TES bolometers. We model our TES with two- and three-block thermal architectures. We fit the complex impedance data at multiple points in the TES transition. From the fits, we predict the noise spectra. We present comparisons of these predictions to the data for two TESes on a prototype polarimeter.

Levels of naturally occurring gamma radiation measured in British homes and their prediction in particular residences.

PubMed

Kendall, G M; Wakeford, R; Athanson, M; Vincent, T J; Carter, E J; McColl, N P; Little, M P

2016-03-01

Gamma radiation from natural sources (including directly ionising cosmic rays) is an important component of background radiation. In the present paper, indoor measurements of naturally occurring gamma rays that were undertaken as part of the UK Childhood Cancer Study are summarised, and it is shown that these are broadly compatible with an earlier UK National Survey. The distribution of indoor gamma-ray dose rates in Great Britain is approximately normal with mean 96 nGy/h and standard deviation 23 nGy/h. Directly ionising cosmic rays contribute about one-third of the total. The expanded dataset allows a more detailed description than previously of indoor gamma-ray exposures and in particular their geographical variation. Various strategies for predicting indoor natural background gamma-ray dose rates were explored. In the first of these, a geostatistical model was fitted, which assumes an underlying geologically determined spatial variation, superimposed on which is a Gaussian stochastic process with Matérn correlation structure that models the observed tendency of dose rates in neighbouring houses to correlate. In the second approach, a number of dose-rate interpolation measures were first derived, based on averages over geologically or administratively defined areas or using distance-weighted averages of measurements at nearest-neighbour points. Linear regression was then used to derive an optimal linear combination of these interpolation measures. The predictive performances of the two models were compared via cross-validation, using a randomly selected 70 % of the data to fit the models and the remaining 30 % to test them. The mean square error (MSE) of the linear-regression model was lower than that of the Gaussian-Matérn model (MSE 378 and 411, respectively). The predictive performance of the two candidate models was also evaluated via simulation; the OLS model performs significantly better than the Gaussian-Matérn model.

An objective function exploiting suboptimal solutions in metabolic networks

PubMed Central

2013-01-01

Background Flux Balance Analysis is a theoretically elegant, computationally efficient, genome-scale approach to predicting biochemical reaction fluxes. Yet FBA models exhibit persistent mathematical degeneracy that generally limits their predictive power. Results We propose a novel objective function for cellular metabolism that accounts for and exploits degeneracy in the metabolic network to improve flux predictions. In our model, regulation drives metabolism toward a region of flux space that allows nearly optimal growth. Metabolic mutants deviate minimally from this region, a function represented mathematically as a convex cone. Near-optimal flux configurations within this region are considered equally plausible and not subject to further optimizing regulation. Consistent with relaxed regulation near optimality, we find that the size of the near-optimal region predicts flux variability under experimental perturbation. Conclusion Accounting for suboptimal solutions can improve the predictive power of metabolic FBA models. Because fluctuations of enzyme and metabolite levels are inevitable, tolerance for suboptimality may support a functionally robust metabolic network. PMID:24088221

Assessment of Prevalence of Persons with Down Syndrome: A Theory-Based Demographic Model

ERIC Educational Resources Information Center

de Graaf, Gert; Vis, Jeroen C.; Haveman, Meindert; van Hove, Geert; de Graaf, Erik A. B.; Tijssen, Jan G. P.; Mulder, Barbara J. M.

2011-01-01

Background: The Netherlands are lacking reliable empirical data in relation to the development of birth and population prevalence of Down syndrome. For the UK and Ireland there are more historical empirical data available. A theory-based model is developed for predicting Down syndrome prevalence in the Netherlands from the 1950s onwards. It is…

78 FR 54835 - Air Quality Implementation Plan; Alabama; Attainment Plan for the Troy Area 2008 Lead...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-09-06

... input options commensurate with the Regulatory Modeling Guidance. Perform current and post control.... Table 2--Post-Control Modeling Results \\4\\ Sanders lead facility Max 3-mth Background Total Year maximum...\\ (Facility MET data). The post-control analysis resulted in a predicted impact of 0.15 [mu]g/m\\3\\ (NWS MET...

Simulations of eddy kinetic energy transport in barotropic turbulence

NASA Astrophysics Data System (ADS)

Grooms, Ian

2017-11-01

Eddy energy transport in rotating two-dimensional turbulence is investigated using numerical simulation. Stochastic forcing is used to generate an inhomogeneous field of turbulence and the time-mean energy profile is diagnosed. An advective-diffusive model for the transport is fit to the simulation data by requiring the model to accurately predict the observed time-mean energy distribution. Isotropic harmonic diffusion of energy is found to be an accurate model in the case of uniform, solid-body background rotation (the f plane), with a diffusivity that scales reasonably well with a mixing-length law κ ∝V ℓ , where V and ℓ are characteristic eddy velocity and length scales. Passive tracer dynamics are added and it is found that the energy diffusivity is 75 % of the tracer diffusivity. The addition of a differential background rotation with constant vorticity gradient β leads to significant changes to the energy transport. The eddies generate and interact with a mean flow that advects the eddy energy. Mean advection plus anisotropic diffusion (with reduced diffusivity in the direction of the background vorticity gradient) is moderately accurate for flows with scale separation between the eddies and mean flow, but anisotropic diffusion becomes a much less accurate model of the transport when scale separation breaks down. Finally, it is observed that the time-mean eddy energy does not look like the actual eddy energy distribution at any instant of time. In the future, stochastic models of the eddy energy transport may prove more useful than models of the mean transport for predicting realistic eddy energy distributions.

Can we predict 4-year graduation in podiatric medical school using admission data?

PubMed

Sesodia, Sanjay; Molnar, David; Shaw, Graham P

2012-01-01

This study examined the predictive ability of educational background and demographic variables, available at the admission stage, to identify applicants who will graduate in 4 years from podiatric medical school. A logistic regression model was used to identify two predictors of 4-year graduation: age at matriculation and total Medical College Admission Test score. The model was cross-validated using a second independent sample from the same population. Cross-validation gives greater confidence that the results could be more generally applied. Total Medical College Admission Test score was the strongest predictor of 4-year graduation, with age at matriculation being a statistically significant but weaker predictor. Despite the model's capacity to predict 4-year graduation better than random assignment, a sufficient amount of error in prediction remained, suggesting that important predictors are missing from the model. Furthermore, the high rate of false-positives makes it inappropriate to use age and Medical College Admission Test score as admission screens in an attempt to eliminate attrition by not accepting at-risk students.

Extension and refinement of the predictive value of different classes of markers in ADNI: Four year follow-up data

PubMed Central

Gomar, Jesus J; Conejero-Goldberg, Concepcion; Davies, Peter; Goldberg, Terry E

2014-01-01

Background This study examined the predictive value of different classes of markers in the progression from Mild Cognitive Impairment (MCI) to Alzheimer’s disease (AD) over an extended 4 year follow-up in ADNI. Methods MCI patients assessed on clinical, cognitive, MRI, PET-FDG, and CSF markers at baseline, and followed on a yearly basis for four years to ascertain progression to AD. Logistic regression models were fitted in clusters including demographics, APOE genotype, cognitive markers, and biomarkers (morphometric, PET-FDG, CSF Abeta and tau). Results The predictive model at four years revealed that two cognitive measures, an episodic memory measure and a clock drawing screening test, were the best predictors of conversion (AUC= 0.78). Conclusions This model of prediction is consistent to the previous model at two years, thus highlighting the importance of cognitive measures in progression from MCI to AD. Cognitive markers were more robust predictors than biomarkers. PMID:24613706

Report to the National Park Service for Permit LAKE-2014-SCI-002

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

Burnley, Pamela C.

The overall purpose of the study is to determine how to use existing geologic data to predict gamma-ray background levels as measured during aerial radiological surveys. Aerial radiological surveys have typically been for resource exploration purposes but are now also used for homeland security purposes and nuclear disaster assessment as well as determining the depth of snowpack. Foreknowledge of the background measured during aerial radiological survey will be valuable for all the above applications. The gamma-ray background comes from the rocks and soil within the first 30 cm of the earth’s surface in the area where the survey is beingmore » made. The background should therefore be predictable based on an understanding of the distribution and geochemistry of the rocks on the surface. We are using a combination of geologic maps, remote sensing imagery and geochemical data from existing databases and the scientific literature to develop a method for predicting gamma-ray backgrounds. As part of this project we have an opportunity to ground truth our technique along a survey calibration line near Lake Mojave that is used by the Remote Sensing Lab (RSL) of National Security Technologies, LLC (NSTec). RSL makes aerial measurements along this line on a regular basis, so the aerial background in the area is well known. By making ground-based measurements of the gamma-ray background and detailed observations of the geology of the ground surface as well as local topography we will have the data we need to make corrections to the models we build based on the remote sensing and geologic data. Our project involves collaborators from the Airborne Geophysics Section of the Geological Survey of Canada as well as from NSTec’s RSL. Findings« less

A computer program for predicting nonlinear uniaxial material responses using viscoplastic models

NASA Technical Reports Server (NTRS)

Chang, T. Y.; Thompson, R. L.

1984-01-01

A computer program was developed for predicting nonlinear uniaxial material responses using viscoplastic constitutive models. Four specific models, i.e., those due to Miller, Walker, Krieg-Swearengen-Rhode, and Robinson, are included. Any other unified model is easily implemented into the program in the form of subroutines. Analysis features include stress-strain cycling, creep response, stress relaxation, thermomechanical fatigue loop, or any combination of these responses. An outline is given on the theoretical background of uniaxial constitutive models, analysis procedure, and numerical integration methods for solving the nonlinear constitutive equations. In addition, a discussion on the computer program implementation is also given. Finally, seven numerical examples are included to demonstrate the versatility of the computer program developed.

An infrared sky model based on the IRAS point source data

NASA Technical Reports Server (NTRS)

Cohen, Martin; Walker, Russell; Wainscoat, Richard; Volk, Kevin; Walker, Helen; Schwartz, Deborah

1990-01-01

A detailed model for the infrared point source sky is presented that comprises geometrically and physically realistic representations of the galactic disk, bulge, spheroid, spiral arms, molecular ring, and absolute magnitudes. The model was guided by a parallel Monte Carlo simulation of the Galaxy. The content of the galactic source table constitutes an excellent match to the 12 micrometer luminosity function in the simulation, as well as the luminosity functions at V and K. Models are given for predicting the density of asteroids to be observed, and the diffuse background radiance of the Zodiacal cloud. The model can be used to predict the character of the point source sky expected for observations from future infrared space experiments.

Mathematical modelling of growth of Listeria  monocytogenes in raw chilled pork.

PubMed

Ye, K; Wang, K; Liu, M; Liu, J; Zhu, L; Zhou, G

2017-04-01

The aim of this study was to analyse the growth kinetics of Listeria monocytogenes in naturally contaminated chilled pork. A cocktail of 26 meat-borne L. monocytogenes was inoculated to raw or sterile chilled pork to observe its growth at 4, 10, 16, 22 and 28°C respectively. The growth data were fitted by the Baranyi model and Ratkowsky square-root model. Results showed that the Baranyi model and Ratkowsky square-root model could describe the growth characteristics of L. monocytogenes at different temperatures reasonably well in raw chilled pork (1·0 ≤ Bf ≤ Af ≤ 1·1). Compared with the growth of L. monocytogenes in sterile chilled pork, the background microflora had no impact on the growth parameters of L. monocytogenes, except for the lag phase at low temperature storage. The microbial predictive models developed in this study can be used to predict the growth of L. monocytogenes during natural spoilage, and construct quantitative risk assessments in chilled pork. This study simulated the actual growth of Listeria monocytogenes in chilled pork to the maximum extent, and described its growth characteristics of L. monocytogenes during natural spoilage. This study showed that the background microflora had no impact on the growth parameters of L. monocytogenes, except for the lag phase at low temperature storage. The models developed in this study can be used to predict the growth of L. monocytogenes during refrigerated storage. © 2017 The Society for Applied Microbiology.

Foveated model observers to predict human performance in 3D images

NASA Astrophysics Data System (ADS)

Lago, Miguel A.; Abbey, Craig K.; Eckstein, Miguel P.

2017-03-01

We evaluate 3D search requires model observers that take into account the peripheral human visual processing (foveated models) to predict human observer performance. We show that two different 3D tasks, free search and location-known detection, influence the relative human visual detectability of two signals of different sizes in synthetic backgrounds mimicking the noise found in 3D digital breast tomosynthesis. One of the signals resembled a microcalcification (a small and bright sphere), while the other one was designed to look like a mass (a larger Gaussian blob). We evaluated current standard models observers (Hotelling; Channelized Hotelling; non-prewhitening matched filter with eye filter, NPWE; and non-prewhitening matched filter model, NPW) and showed that they incorrectly predict the relative detectability of the two signals in 3D search. We propose a new model observer (3D Foveated Channelized Hotelling Observer) that incorporates the properties of the visual system over a large visual field (fovea and periphery). We show that the foveated model observer can accurately predict the rank order of detectability of the signals in 3D images for each task. Together, these results motivate the use of a new generation of foveated model observers for predicting image quality for search tasks in 3D imaging modalities such as digital breast tomosynthesis or computed tomography.

Stata Modules for Calculating Novel Predictive Performance Indices for Logistic Models

PubMed Central

Barkhordari, Mahnaz; Padyab, Mojgan; Hadaegh, Farzad; Azizi, Fereidoun; Bozorgmanesh, Mohammadreza

2016-01-01

Background Prediction is a fundamental part of prevention of cardiovascular diseases (CVD). The development of prediction algorithms based on the multivariate regression models loomed several decades ago. Parallel with predictive models development, biomarker researches emerged in an impressively great scale. The key question is how best to assess and quantify the improvement in risk prediction offered by new biomarkers or more basically how to assess the performance of a risk prediction model. Discrimination, calibration, and added predictive value have been recently suggested to be used while comparing the predictive performances of the predictive models’ with and without novel biomarkers. Objectives Lack of user-friendly statistical software has restricted implementation of novel model assessment methods while examining novel biomarkers. We intended, thus, to develop a user-friendly software that could be used by researchers with few programming skills. Materials and Methods We have written a Stata command that is intended to help researchers obtain cut point-free and cut point-based net reclassification improvement index and (NRI) and relative and absolute Integrated discriminatory improvement index (IDI) for logistic-based regression analyses.We applied the commands to a real data on women participating the Tehran lipid and glucose study (TLGS) to examine if information of a family history of premature CVD, waist circumference, and fasting plasma glucose can improve predictive performance of the Framingham’s “general CVD risk” algorithm. Results The command is addpred for logistic regression models. Conclusions The Stata package provided herein can encourage the use of novel methods in examining predictive capacity of ever-emerging plethora of novel biomarkers. PMID:27279830

Testing the Li-Strahler four-component canopy reflectance model in the HAPEX-Sahel shrub savanna sites using ground reflectance data

NASA Technical Reports Server (NTRS)

Franklin, J.; Duncan, J.

1992-01-01

The rate at which a light field decays in water is characterized by the diffuse attenuation coefficient k. The Li-Strahler discrete-object canopy reflectance model was tested in two sites, a shrub grass savanna and a degraded shrub savanna on bare soil, in the proposed HAPEX (Hydrologic Atmospheric Pilot Experiment) II/Sahel study area in Niger, West Africa. Average site reflectance was predicted for each site from the reflectances and cover proportions of four components: shrub canopy, background (soil or grass and soil), shaded canopy, and shaded background. Component reflectances were sampled in the SPOT wavebands using a hand-held radiometer. Predicted reflectance was compared to average site reflectance measured using the same radiometer mounted on a backpack with measurements recorded every 5 m along two 1-km transects, also in the SPOT (Systeme Probatoire d'Observation de la Terre) bands. Measurements and predictions were made for each of the three days during the summer growing season, approximately two weeks apart. Red, near infrared reflectance, and the NDVI (normalized difference vegetation index) were all predicted with a high degree of accuracy for the shrub/grass site and with reasonable accuracy for the degraded shrub site.

Towards a Rigorous Assessment of Systems Biology Models: The DREAM3 Challenges

PubMed Central

Prill, Robert J.; Marbach, Daniel; Saez-Rodriguez, Julio; Sorger, Peter K.; Alexopoulos, Leonidas G.; Xue, Xiaowei; Clarke, Neil D.; Altan-Bonnet, Gregoire; Stolovitzky, Gustavo

2010-01-01

Background Systems biology has embraced computational modeling in response to the quantitative nature and increasing scale of contemporary data sets. The onslaught of data is accelerating as molecular profiling technology evolves. The Dialogue for Reverse Engineering Assessments and Methods (DREAM) is a community effort to catalyze discussion about the design, application, and assessment of systems biology models through annual reverse-engineering challenges. Methodology and Principal Findings We describe our assessments of the four challenges associated with the third DREAM conference which came to be known as the DREAM3 challenges: signaling cascade identification, signaling response prediction, gene expression prediction, and the DREAM3 in silico network challenge. The challenges, based on anonymized data sets, tested participants in network inference and prediction of measurements. Forty teams submitted 413 predicted networks and measurement test sets. Overall, a handful of best-performer teams were identified, while a majority of teams made predictions that were equivalent to random. Counterintuitively, combining the predictions of multiple teams (including the weaker teams) can in some cases improve predictive power beyond that of any single method. Conclusions DREAM provides valuable feedback to practitioners of systems biology modeling. Lessons learned from the predictions of the community provide much-needed context for interpreting claims of efficacy of algorithms described in the scientific literature. PMID:20186320

Hyperspectral target detection analysis of a cluttered scene from a virtual airborne sensor platform using MuSES

NASA Astrophysics Data System (ADS)

Packard, Corey D.; Viola, Timothy S.; Klein, Mark D.

2017-10-01

The ability to predict spectral electro-optical (EO) signatures for various targets against realistic, cluttered backgrounds is paramount for rigorous signature evaluation. Knowledge of background and target signatures, including plumes, is essential for a variety of scientific and defense-related applications including contrast analysis, camouflage development, automatic target recognition (ATR) algorithm development and scene material classification. The capability to simulate any desired mission scenario with forecast or historical weather is a tremendous asset for defense agencies, serving as a complement to (or substitute for) target and background signature measurement campaigns. In this paper, a systematic process for the physical temperature and visible-through-infrared radiance prediction of several diverse targets in a cluttered natural environment scene is presented. The ability of a virtual airborne sensor platform to detect and differentiate targets from a cluttered background, from a variety of sensor perspectives and across numerous wavelengths in differing atmospheric conditions, is considered. The process described utilizes the thermal and radiance simulation software MuSES and provides a repeatable, accurate approach for analyzing wavelength-dependent background and target (including plume) signatures in multiple band-integrated wavebands (multispectral) or hyperspectrally. The engineering workflow required to combine 3D geometric descriptions, thermal material properties, natural weather boundary conditions, all modes of heat transfer and spectral surface properties is summarized. This procedure includes geometric scene creation, material and optical property attribution, and transient physical temperature prediction. Radiance renderings, based on ray-tracing and the Sandford-Robertson BRDF model, are coupled with MODTRAN for the inclusion of atmospheric effects. This virtual hyperspectral/multispectral radiance prediction methodology has been extensively validated and provides a flexible process for signature evaluation and algorithm development.

Expanding models of lake trophic state to predict cyanobacteria in lakes

EPA Science Inventory

Background/Question/Methods: Cyanobacteria are a primary taxonomic group associated with harmful algal blooms in lakes. Understanding the drivers of cyanobacteria presence has important implications for lake management and for the protection of human and ecosystem health. Chlor...

Chandra ACIS-I particle background: an analytical model

NASA Astrophysics Data System (ADS)

Bartalucci, I.; Mazzotta, P.; Bourdin, H.; Vikhlinin, A.

2014-06-01

Aims: Imaging and spectroscopy of X-ray extended sources require a proper characterisation of a spatially unresolved background signal. This background includes sky and instrumental components, each of which are characterised by its proper spatial and spectral behaviour. While the X-ray sky background has been extensively studied in previous work, here we analyse and model the instrumental background of the ACIS-I detector on board the Chandra X-ray observatory in very faint mode. Methods: Caused by interaction of highly energetic particles with the detector, the ACIS-I instrumental background is spectrally characterised by the superimposition of several fluorescence emission lines onto a continuum. To isolate its flux from any sky component, we fitted an analytical model of the continuum to observations performed in very faint mode with the detector in the stowed position shielded from the sky, and gathered over the eight-year period starting in 2001. The remaining emission lines were fitted to blank-sky observations of the same period. We found 11 emission lines. Analysing the spatial variation of the amplitude, energy and width of these lines has further allowed us to infer that three lines of these are presumably due to an energy correction artefact produced in the frame store. Results: We provide an analytical model that predicts the instrumental background with a precision of 2% in the continuum and 5% in the lines. We use this model to measure the flux of the unresolved cosmic X-ray background in the Chandra deep field south. We obtain a flux of 10.2+0.5-0.4 × 10-13 erg cm-2 deg-2 s-1 for the [1-2] keV band and (3.8 ± 0.2) × 10-12 erg cm-2 deg-2 s-1 for the [2-8] keV band.

Effects of pedagogical ideology on the perceived loudness and noise levels in preschools.

PubMed

Jonsdottir, Valdis; Rantala, Leena M; Oskarsson, Gudmundur Kr; Sala, Eeva

2015-01-01

High activity noise levels that result in detrimental effects on speech communication have been measured in preschools. To find out if different pedagogical ideologies affect the perceived loudness and levels of noise, a questionnaire study inquiring about the experience of loudness and voice symptoms was carried out in Iceland in eight private preschools, called "Hjalli model", and in six public preschools. Noise levels were also measured in the preschools. Background variables (stress level, age, length of working career, education, smoking, and number of children per teacher) were also analyzed in order to determine how much they contributed toward voice symptoms and the experience of noisiness. Results indicate that pedagogical ideology is a significant factor for predicting noise and its consequences. Teachers in the preschool with tighter pedagogical control of discipline (the "Hjalli model") experienced lower activity noise loudness than teachers in the preschool with a more relaxed control of behavior (public preschool). Lower noise levels were also measured in the "Hjalli model" preschool and fewer "Hjalli model" teachers reported voice symptoms. Public preschool teachers experienced more stress than "Hjalli model" teachers and the stress level was, indeed, the background variable that best explained the voice symptoms and the teacher's perception of a noisy environment. Discipline, structure, and organization in the type of activity predicted the activity noise level better than the number of children in the group. Results indicate that pedagogical ideology is a significant factor for predicting self-reported noise and its consequences.

Extragalactic gamma-ray background from AGN winds and star-forming galaxies in cosmological galaxy-formation models

NASA Astrophysics Data System (ADS)

Lamastra, A.; Menci, N.; Fiore, F.; Antonelli, L. A.; Colafrancesco, S.; Guetta, D.; Stamerra, A.

2017-10-01

We derive the contribution to the extragalactic gamma-ray background (EGB) from active galactic nuclei (AGN) winds and star-forming galaxies by including a physical model for the γ-ray emission produced by relativistic protons accelerated by AGN-driven and supernova-driven shocks into a state-of-the-art semi-analytic model of galaxy formation. This is based on galaxy interactions as triggers of AGN accretion and starburst activity and on expanding blast waves as the mechanism to communicate outwards the energy injected into the interstellar medium by the active nucleus. We compare the model predictions with the latest measurement of the EGB spectrum performed by the Large Area Telescope (LAT) on board the Fermi Gamma-ray Space Telescope (Fermi) in the range between 100 MeV and 820 GeV. We find that AGN winds can provide 35 ± 15% of the observed EGB in the energy interval Eγ = 0.1-1 GeV, for 73 ± 15% at Eγ = 1-10 GeV, and for 60 ± 20% at Eγ ≳10 GeV. The AGN wind contribution to the EGB is predicted to be larger by a factor of 3-5 than that provided by star-forming galaxies (quiescent plus starburst) in the hierarchical clustering scenario. The cumulative γ-ray emission from AGN winds and blazars can account for the amplitude and spectral shape of the EGB, assuming the standard acceleration theory, and AGN wind parameters that agree with observations. We also compare the model prediction for the cumulative neutrino background from AGN winds with the most recent IceCube data. We find that for AGN winds with accelerated proton spectral index p = 2.2-2.3, and taking into account internal absorption of γ-rays, the Fermi-LAT and IceCube data could be reproduced simultaneously.

Use of population exposure frequency distributions to simulate effects of policy interventions on NO2 exposure

NASA Astrophysics Data System (ADS)

Dimitroulopoulou, C.; Ashmore, M. R.; Terry, A. C.

2017-02-01

Health effects of air pollution on individuals depend on their personal exposure, but few modelling tools are available which can predict how the distribution of personal exposures within a city will change in response to policies to reduce emissions both indoors and outdoors. We describe a new probabilistic modelling framework (INDAIR-2/EXPAIR), which provides predictions of the personal exposure frequency distribution (PEFD) across a city to assess the effects of both reduced emissions from home sources and reduced roadside concentrations on population exposure. The model uses a national time activity database, which gives the percentage of each population group in different residential and non-residential micro-environments, and links this, for the home, to predictions of concentrations from a three-compartment model, and for non-residential microenvironments to empirical indoor/outdoor ratios. This paper presents modelled PEFDs for NO2 in the city of Leicester, for children, the elderly, and office workers, comparing results in different seasons and on different days of the week. While the mean NO2 population exposure was close to, or below the urban background concentration, the 95%ile of the PEFD was well above the urban background concentration. The relationship between both mean and 95%ile PEFD and urban background concentrations was strongly influenced by air exchange rate. The 24 h mean PEFD showed relative small differences between the population groups, with both removal of home sources and reductions of roadside concentrations on roads with a high traffic density having similar effects in reducing mean exposure. In contrast, the 1 h maximum of the PEFD was significantly higher for children and the elderly than for office workers, and showed a much greater response to reduced home emissions in these groups. The results demonstrate the importance of understanding the dynamics of NO2 exposure at a population level within different groups, if the benefits of policy interventions are to be accurately assessed.

A Bridge from Optical to Infrared Galaxies: Explaining Local Properties, Predicting Galaxy Counts and the Cosmic Background Radiation

NASA Astrophysics Data System (ADS)

Totani, T.; Takeuchi, T. T.

2001-12-01

A new model of infrared galaxy counts and the cosmic background radiation (CBR) is developed by extending a model for optical/near-infrared galaxies. Important new characteristics of this model are that mass scale dependence of dust extinction is introduced based on the size-luminosity relation of optical galaxies, and that the big grain dust temperature T dust is calculated based on a physical consideration for energy balance, rather than using the empirical relation between T dust and total infrared luminosity L IR found in local galaxies, which has been employed in most of previous works. Consequently, the local properties of infrared galaxies, i.e., optical/infrared luminosity ratios, L IR-T dust correlation, and infrared luminosity function are outputs predicted by the model, while these have been inputs in a number of previous models. Our model indeed reproduces these local properties reasonably well. Then we make predictions for faint infrared counts (in 15, 60, 90, 170, 450, and 850 μ m) and CBR by this model. We found considerably different results from most of previous works based on the empirical L IR-T dust relation; especially, it is shown that the dust temperature of starbursting primordial elliptical galaxies is expected to be very high (40--80K). This indicates that intense starbursts of forming elliptical galaxies should have occurred at z ~ 2--3, in contrast to the previous results that significant starbursts beyond z ~ 1 tend to overproduce the far-infrared (FIR) CBR detected by COBE/FIRAS. On the other hand, our model predicts that the mid-infrared (MIR) flux from warm/nonequilibrium dust is relatively weak in such galaxies making FIR CBR, and this effect reconciles the prima facie conflict between the upper limit on MIR CBR from TeV gamma-ray observations and the COBE\\ detections of FIR CBR. The authors thank the financial support by the Japan Society for Promotion of Science.

Assessing Strategies Against Gambiense Sleeping Sickness Through Mathematical Modeling

PubMed Central

Rock, Kat S; Ndeffo-Mbah, Martial L; Castaño, Soledad; Palmer, Cody; Pandey, Abhishek; Atkins, Katherine E; Ndung’u, Joseph M; Hollingsworth, T Déirdre; Galvani, Alison; Bever, Caitlin; Chitnis, Nakul; Keeling, Matt J

2018-01-01

Abstract Background Control of gambiense sleeping sickness relies predominantly on passive and active screening of people, followed by treatment. Methods Mathematical modeling explores the potential of 3 complementary interventions in high- and low-transmission settings. Results Intervention strategies that included vector control are predicted to halt transmission most quickly. Targeted active screening, with better and more focused coverage, and enhanced passive surveillance, with improved access to diagnosis and treatment, are both estimated to avert many new infections but, when used alone, are unlikely to halt transmission before 2030 in high-risk settings. Conclusions There was general model consensus in the ranking of the 3 complementary interventions studied, although with discrepancies between the quantitative predictions due to differing epidemiological assumptions within the models. While these predictions provide generic insights into improving control, the most effective strategy in any situation depends on the specific epidemiology in the region and the associated costs. PMID:29860287

Predicting K0Λ photoproduction observables by using the multipole approach

NASA Astrophysics Data System (ADS)

Mart, T.; Rusli, A.

2017-12-01

We present an isobar model for kaon photoproduction on the proton γ p\\to K^+Λ that can nicely reproduce the available experimental data from threshold up to W=2.0 GeV. The background amplitude of the model is constructed from a covariant Feynman diagrammatic method, whereas the resonance one is formulated by using the multipole approach. All unknown parameters in both background and resonance amplitudes are extracted by adjusting the calculated observables to experimental data. With the help of SU(3) isospin symmetry and some information obtained from the Particle Data Group we estimate the cross section and polarization observables for the neutral kaon photoproduction on the neutron γ n\\to K^0Λ. The result indicates no sharp peak in the K^0Λ total cross section. The predicted differential cross section exhibits resonance structures only at cosθ=-1. To obtain sizable observables the present work recommends measurement of the K^0Λ cross section with W≳ 1.70 GeV, whereas for the recoiled Λ polarization measurement with W≈ 1.65-1.90 GeV would be advised, since the predictions of existing models show a large variance at this kinematics. The predicted electric and magnetic multipoles are found to be mostly different from those obtained in previous works. For W=1.75 and 1.95 GeV it is found that most of the single and double polarization observables demonstrate large asymmetries.

Extracting harmonic signal from a chaotic background with local linear model

NASA Astrophysics Data System (ADS)

Li, Chenlong; Su, Liyun

2017-02-01

In this paper, the problems of blind detection and estimation of harmonic signal in strong chaotic background are analyzed, and new methods by using local linear (LL) model are put forward. The LL model has been exhaustively researched and successfully applied for fitting and forecasting chaotic signal in many chaotic fields. We enlarge the modeling capacity substantially. Firstly, we can predict the short-term chaotic signal and obtain the fitting error based on the LL model. Then we detect the frequencies from the fitting error by periodogram, a property on the fitting error is proposed which has not been addressed before, and this property ensures that the detected frequencies are similar to that of harmonic signal. Secondly, we establish a two-layer LL model to estimate the determinate harmonic signal in strong chaotic background. To estimate this simply and effectively, we develop an efficient backfitting algorithm to select and optimize the parameters that are hard to be exhaustively searched for. In the method, based on sensitivity to initial value of chaos motion, the minimum fitting error criterion is used as the objective function to get the estimation of the parameters of the two-layer LL model. Simulation shows that the two-layer LL model and its estimation technique have appreciable flexibility to model the determinate harmonic signal in different chaotic backgrounds (Lorenz, Henon and Mackey-Glass (M-G) equations). Specifically, the harmonic signal can be extracted well with low SNR and the developed background algorithm satisfies the condition of convergence in repeated 3-5 times.

Long‐Term Post‐CABG Survival: Performance of Clinical Risk Models Versus Actuarial Predictions

PubMed Central

Carr, Brendan M.; Romeiser, Jamie; Ruan, Joyce; Gupta, Sandeep; Seifert, Frank C.; Zhu, Wei

2015-01-01

Abstract Background/aim Clinical risk models are commonly used to predict short‐term coronary artery bypass grafting (CABG) mortality but are less commonly used to predict long‐term mortality. The added value of long‐term mortality clinical risk models over traditional actuarial models has not been evaluated. To address this, the predictive performance of a long‐term clinical risk model was compared with that of an actuarial model to identify the clinical variable(s) most responsible for any differences observed. Methods Long‐term mortality for 1028 CABG patients was estimated using the Hannan New York State clinical risk model and an actuarial model (based on age, gender, and race/ethnicity). Vital status was assessed using the Social Security Death Index. Observed/expected (O/E) ratios were calculated, and the models' predictive performances were compared using a nested c‐index approach. Linear regression analyses identified the subgroup of risk factors driving the differences observed. Results Mortality rates were 3%, 9%, and 17% at one‐, three‐, and five years, respectively (median follow‐up: five years). The clinical risk model provided more accurate predictions. Greater divergence between model estimates occurred with increasing long‐term mortality risk, with baseline renal dysfunction identified as a particularly important driver of these differences. Conclusions Long‐term mortality clinical risk models provide enhanced predictive power compared to actuarial models. Using the Hannan risk model, a patient's long‐term mortality risk can be accurately assessed and subgroups of higher‐risk patients can be identified for enhanced follow‐up care. More research appears warranted to refine long‐term CABG clinical risk models. doi: 10.1111/jocs.12665 (J Card Surg 2016;31:23–30) PMID:26543019

Prediction of Return-to-original-work after an Industrial Accident Using Machine Learning and Comparison of Techniques

PubMed Central

2018-01-01

Background Many studies have tried to develop predictors for return-to-work (RTW). However, since complex factors have been demonstrated to predict RTW, it is difficult to use them practically. This study investigated whether factors used in previous studies could predict whether an individual had returned to his/her original work by four years after termination of the worker's recovery period. Methods An initial logistic regression analysis of 1,567 participants of the fourth Panel Study of Worker's Compensation Insurance yielded odds ratios. The participants were divided into two subsets, a training dataset and a test dataset. Using the training dataset, logistic regression, decision tree, random forest, and support vector machine models were established, and important variables of each model were identified. The predictive abilities of the different models were compared. Results The analysis showed that only earned income and company-related factors significantly affected return-to-original-work (RTOW). The random forest model showed the best accuracy among the tested machine learning models; however, the difference was not prominent. Conclusion It is possible to predict a worker's probability of RTOW using machine learning techniques with moderate accuracy. PMID:29736160

Prediction of High Incidence of Dengue in the Philippines

PubMed Central

Buczak, Anna L.; Baugher, Benjamin; Babin, Steven M.; Ramac-Thomas, Liane C.; Guven, Erhan; Elbert, Yevgeniy; Koshute, Phillip T.; Velasco, John Mark S.; Roque, Vito G.; Tayag, Enrique A.; Yoon, In-Kyu; Lewis, Sheri H.

2014-01-01

Background Accurate prediction of dengue incidence levels weeks in advance of an outbreak may reduce the morbidity and mortality associated with this neglected disease. Therefore, models were developed to predict high and low dengue incidence in order to provide timely forewarnings in the Philippines. Methods Model inputs were chosen based on studies indicating variables that may impact dengue incidence. The method first uses Fuzzy Association Rule Mining techniques to extract association rules from these historical epidemiological, environmental, and socio-economic data, as well as climate data indicating future weather patterns. Selection criteria were used to choose a subset of these rules for a classifier, thereby generating a Prediction Model. The models predicted high or low incidence of dengue in a Philippines province four weeks in advance. The threshold between high and low was determined relative to historical incidence data. Principal Findings Model accuracy is described by Positive Predictive Value (PPV), Negative Predictive Value (NPV), Sensitivity, and Specificity computed on test data not previously used to develop the model. Selecting a model using the F0.5 measure, which gives PPV more importance than Sensitivity, gave these results: PPV = 0.780, NPV = 0.938, Sensitivity = 0.547, Specificity = 0.978. Using the F3 measure, which gives Sensitivity more importance than PPV, the selected model had PPV = 0.778, NPV = 0.948, Sensitivity = 0.627, Specificity = 0.974. The decision as to which model has greater utility depends on how the predictions will be used in a particular situation. Conclusions This method builds prediction models for future dengue incidence in the Philippines and is capable of being modified for use in different situations; for diseases other than dengue; and for regions beyond the Philippines. The Philippines dengue prediction models predicted high or low incidence of dengue four weeks in advance of an outbreak with high accuracy, as measured by PPV, NPV, Sensitivity, and Specificity. PMID:24722434

SMOQ: a tool for predicting the absolute residue-specific quality of a single protein model with support vector machines

PubMed Central

2014-01-01

Background It is important to predict the quality of a protein structural model before its native structure is known. The method that can predict the absolute local quality of individual residues in a single protein model is rare, yet particularly needed for using, ranking and refining protein models. Results We developed a machine learning tool (SMOQ) that can predict the distance deviation of each residue in a single protein model. SMOQ uses support vector machines (SVM) with protein sequence and structural features (i.e. basic feature set), including amino acid sequence, secondary structures, solvent accessibilities, and residue-residue contacts to make predictions. We also trained a SVM model with two new additional features (profiles and SOV scores) on 20 CASP8 targets and found that including them can only improve the performance when real deviations between native and model are higher than 5Å. The SMOQ tool finally released uses the basic feature set trained on 85 CASP8 targets. Moreover, SMOQ implemented a way to convert predicted local quality scores into a global quality score. SMOQ was tested on the 84 CASP9 single-domain targets. The average difference between the residue-specific distance deviation predicted by our method and the actual distance deviation on the test data is 2.637Å. The global quality prediction accuracy of the tool is comparable to other good tools on the same benchmark. Conclusion SMOQ is a useful tool for protein single model quality assessment. Its source code and executable are available at: http://sysbio.rnet.missouri.edu/multicom_toolbox/. PMID:24776231

Model robustness as a confirmatory virtue: The case of climate science.

PubMed

Lloyd, Elisabeth A

2015-02-01

I propose a distinct type of robustness, which I suggest can support a confirmatory role in scientific reasoning, contrary to the usual philosophical claims. In model robustness, repeated production of the empirically successful model prediction or retrodiction against a background of independently-supported and varying model constructions, within a group of models containing a shared causal factor, may suggest how confident we can be in the causal factor and predictions/retrodictions, especially once supported by a variety of evidence framework. I present climate models of greenhouse gas global warming of the 20th Century as an example, and emphasize climate scientists' discussions of robust models and causal aspects. The account is intended as applicable to a broad array of sciences that use complex modeling techniques. Copyright © 2014 Elsevier Ltd. All rights reserved.

Spectral analysis-based risk score enables early prediction of mortality and cerebral performance in patients undergoing therapeutic hypothermia for ventricular fibrillation and comatose status

PubMed Central

Filgueiras-Rama, David; Calvo, Conrado J.; Salvador-Montañés, Óscar; Cádenas, Rosalía; Ruiz-Cantador, Jose; Armada, Eduardo; Rey, Juan Ramón; Merino, J.L.; Peinado, Rafael; Pérez-Castellano, Nicasio; Pérez-Villacastín, Julián; Quintanilla, Jorge G.; Jiménez, Santiago; Castells, Francisco; Chorro, Francisco J.; López-Sendón, J.L.; Berenfeld, Omer; Jalife, José; López de Sá, Esteban; Millet, José

2017-01-01

Background Early prognosis in comatose survivors after cardiac arrest due to ventricular fibrillation (VF) is unreliable, especially in patients undergoing mild hypothermia. We aimed at developing a reliable risk-score to enable early prediction of cerebral performance and survival. Methods Sixty-one out of 239 consecutive patients undergoing mild hypothermia after cardiac arrest, with eventual return of spontaneous circulation (ROSC), and comatose status on admission fulfilled the inclusion criteria. Background clinical variables, VF time and frequency domain fundamental variables were considered. The primary and secondary outcomes were a favorable neurological performance (FNP) during hospitalization and survival to hospital discharge, respectively. The predictive model was developed in a retrospective cohort (n=32; September 2006–September 2011, 48.5 ± 10.5 months of follow-up) and further validated in a prospective cohort (n = 29; October 2011–July 2013, 5 ± 1.8 months of follow-up). Results FNP was present in 16 (50.0%) and 21 patients (72.4%) in the retrospective and prospective cohorts, respectively. Seventeen (53.1%) and 21 patients (72.4%), respectively, survived to hospital discharge. Both outcomes were significantly associated (p < 0.001). Retrospective multivariate analysis provided a prediction model (sensitivity= 0.94, specificity = 1) that included spectral dominant frequency, derived power density and peak ratios between high and low frequency bands, and the number of shocks delivered before ROSC. Validation on the prospective cohort showed sensitivity = 0.88 and specificity = 0.91. A model-derived risk-score properly predicted 93% of FNP. Testing the model on follow-up showed a c-statistic ≥ 0.89. Conclusions A spectral analysis-based model reliably correlates time-dependent VF spectral changes with acute cerebral injury in comatose survivors undergoing mild hypothermia after cardiac arrest. PMID:25828128

Robust constraint on cosmic textures from the cosmic microwave background.

PubMed

Feeney, Stephen M; Johnson, Matthew C; Mortlock, Daniel J; Peiris, Hiranya V

2012-06-15

Fluctuations in the cosmic microwave background (CMB) contain information which has been pivotal in establishing the current cosmological model. These data can also be used to test well-motivated additions to this model, such as cosmic textures. Textures are a type of topological defect that can be produced during a cosmological phase transition in the early Universe, and which leave characteristic hot and cold spots in the CMB. We apply bayesian methods to carry out a rigorous test of the texture hypothesis, using full-sky data from the Wilkinson Microwave Anisotropy Probe. We conclude that current data do not warrant augmenting the standard cosmological model with textures. We rule out at 95% confidence models that predict more than 6 detectable cosmic textures on the full sky.

Ground cloud effluent measurements during the May 30, 1974, Titan 3 launch at the Air Force eastern test range

NASA Technical Reports Server (NTRS)

Bendura, R. J.; Crumbly, K. H.

1977-01-01

Surface-level exhaust effluent measurements of HCl, CO, and particulates, ground-cloud behavior, and some comparisons with model predictions for the launch of a Titan 3 rocket are presented along with a limited amount of airborne sampling measurements of other cloud species (O3, NO, NOX). Values above background levels for these effluents were obtained at 20 of the 30 instrument sites; these values were lower than model predictions and did not exceed public health standards. Cloud rise rate, stabilization altitude, and volume are compared with results from previous launches.

Development of a computer model to predict platform station keeping requirements in the Gulf of Mexico using remote sensing data

NASA Technical Reports Server (NTRS)

Barber, Bryan; Kahn, Laura; Wong, David

1990-01-01

Offshore operations such as oil drilling and radar monitoring require semisubmersible platforms to remain stationary at specific locations in the Gulf of Mexico. Ocean currents, wind, and waves in the Gulf of Mexico tend to move platforms away from their desired locations. A computer model was created to predict the station keeping requirements of a platform. The computer simulation uses remote sensing data from satellites and buoys as input. A background of the project, alternate approaches to the project, and the details of the simulation are presented.

Experiment-specific cosmic microwave background calculations made easier - Approximation formula for smoothed delta T/T windows

NASA Technical Reports Server (NTRS)

Gorski, Krzysztof M.

1993-01-01

Simple and easy to implement elementary function approximations are introduced to the spectral window functions needed in calculations of model predictions of the cosmic microwave backgrond (CMB) anisotropy. These approximations allow the investigator to obtain model delta T/T predictions in terms of single integrals over the power spectrum of cosmological perturbations and to avoid the necessity of performing the additional integrations. The high accuracy of these approximations is demonstrated here for the CDM theory-based calculations of the expected delta T/T signal in several experiments searching for the CMB anisotropy.

On the Limitations of Variational Bias Correction

NASA Technical Reports Server (NTRS)

Moradi, Isaac; Mccarty, Will; Gelaro, Ronald

2018-01-01

Satellite radiances are the largest dataset assimilated into Numerical Weather Prediction (NWP) models, however the data are subject to errors and uncertainties that need to be accounted for before assimilating into the NWP models. Variational bias correction uses the time series of observation minus background to estimate the observations bias. This technique does not distinguish between the background error, forward operator error, and observations error so that all these errors are summed up together and counted as observation error. We identify some sources of observations errors (e.g., antenna emissivity, non-linearity in the calibration, and antenna pattern) and show the limitations of variational bias corrections on estimating these errors.

Cosmic microwave background radiation anisotropies in brane worlds.

PubMed

Koyama, Kazuya

2003-11-28

We propose a new formulation to calculate the cosmic microwave background (CMB) spectrum in the Randall-Sundrum two-brane model based on recent progress in solving the bulk geometry using a low energy approximation. The evolution of the anisotropic stress imprinted on the brane by the 5D Weyl tensor is calculated. An impact of the dark radiation perturbation on the CMB spectrum is investigated in a simple model assuming an initially scale-invariant adiabatic perturbation. The dark radiation perturbation induces isocurvature perturbations, but the resultant spectrum can be quite different from the prediction of simple mixtures of adiabatic and isocurvature perturbations due to Weyl anisotropic stress.

Insights into the physico-chemical evolution of pyrogenic organic carbon emissions from biomass burning using coupled Lagrangian-Eulerian simulations

NASA Astrophysics Data System (ADS)

Suciu, L. G.; Griffin, R. J.; Masiello, C. A.

2017-12-01

Wildfires and prescribed burning are important sources of particulate and gaseous pyrogenic organic carbon (PyOC) emissions to the atmosphere. These emissions impact atmospheric chemistry, air quality and climate, but the spatial and temporal variabilities of these impacts are poorly understood, primarily because small and fresh fire plumes are not well predicted by three-dimensional Eulerian chemical transport models due to their coarser grid size. Generally, this results in underestimation of downwind deposition of PyOC, hydroxyl radical reactivity, secondary organic aerosol formation and ozone (O3) production. However, such models are very good for simulation of multiple atmospheric processes that could affect the lifetimes of PyOC emissions over large spatiotemporal scales. Finer resolution models, such as Lagrangian reactive plumes models (or plume-in-grid), could be used to trace fresh emissions at the sub-grid level of the Eulerian model. Moreover, Lagrangian plume models need background chemistry predicted by the Eulerian models to accurately simulate the interactions of the plume material with the background air during plume aging. Therefore, by coupling the two models, the physico-chemical evolution of the biomass burning plumes can be tracked from local to regional scales. In this study, we focus on the physico-chemical changes of PyOC emissions from sub-grid to grid levels using an existing chemical mechanism. We hypothesize that finer scale Lagrangian-Eulerian simulations of several prescribed burns in the U.S. will allow more accurate downwind predictions (validated by airborne observations from smoke plumes) of PyOC emissions (i.e., submicron particulate matter, organic aerosols, refractory black carbon) as well as O3 and other trace gases. Simulation results could be used to optimize the implementation of additional PyOC speciation in the existing chemical mechanism.

A Modified Obesity Proneness Model Predicts Adolescent Weight Concerns and Inability to Self-Regulate Eating

ERIC Educational Resources Information Center

Nickelson, Jen; Bryant, Carol A.; McDermott, Robert J.; Buhi, Eric R.; DeBate, Rita D.

2012-01-01

Background: The prevalence of obesity among high school students has risen in recent decades. Many high school students report trying to lose weight and some engage in disordered eating to do so. The obesity proneness model suggests that parents may influence their offspring's development of disordered eating. This study examined the viability of…

The Search for New Resonant Phenomena using Dijet Events at the ATLAS Detector

NASA Astrophysics Data System (ADS)

Frate, Meghan

A search for new physics resonances in the dijet invariant mass spectrum is presented here. Dijet events are collected at center of mass energy of 13 TeV with the ATLAS detector at the Large Hadron Collider in 2015 and 2016, equating to a total integrated luminosity of 37 fb-1. This data is compared to background predictions, and no significant deviations from the expected is seen. Therefore, the dataset is used to set improved upper limits on the mass of four benchmark signal models and one generic model at 95% CL. These limits exclude excited quarks with masses below 6.0 TeV, quantum black holes below 8.9 TeV, heavy W' boson masses below 3.6 TeV, and W* bosons masses below 3.4 TeV and between 3.77-3.85 TeV; as well as limits on a range of masses and couplings in a Z' dark matter mediator model. Model-independent limits are also set on signals with a Gaussian shape at various mass resolutions. Finally, a proof of concept study is done on a new method to predict dijet backgrounds, which may be implemented in future analyses.

Predicting the performance of linear optical detectors in free space laser communication links

NASA Astrophysics Data System (ADS)

Farrell, Thomas C.

2018-05-01

While the fundamental performance limit for optical communications is set by the quantum nature of light, in practical systems background light, dark current, and thermal noise of the electronics also degrade performance. In this paper, we derive a set of equations predicting the performance of PIN diodes and linear mode avalanche photo diodes (APDs) in the presence of such noise sources. Electrons generated by signal, background, and dark current shot noise are well modeled in PIN diodes as Poissonian statistical processes. In APDs, on the other hand, the amplifying effects of the device result in statistics that are distinctly non-Poissonian. Thermal noise is well modeled as Gaussian. In this paper, we appeal to the central limit theorem and treat both the variability of the signal and the sum of noise sources as Gaussian. Comparison against Monte-Carlo simulation of PIN diode performance (where we do model shot noise with draws from a Poissonian distribution) validates the legitimacy of this approximation. On-off keying, M-ary pulse position, and binary differential phase shift keying modulation are modeled. We conclude with examples showing how the equations may be used in a link budget to estimate the performance of optical links using linear receivers.

Modeling Neutral Densities Downstream of a Gridded Ion Thruster

NASA Technical Reports Server (NTRS)

Soulas, George C.

2010-01-01

The details of a model for determining the neutral density downstream of a gridded ion thruster are presented. An investigation of the possible sources of neutrals emanating from and surrounding a NEXT ion thruster determined that the most significant contributors to the downstream neutral density include discharge chamber neutrals escaping through the perforated grids, neutrals escaping from the neutralizer, and vacuum facility background neutrals. For the neutral flux through the grids, near- and far-field equations are presented for rigorously determining the neutral density downstream of a cylindrical aperture. These equations are integrated into a spherically-domed convex grid geometry with a hexagonal array of apertures for determining neutral densities downstream of the ion thruster grids. The neutrals escaping from an off-center neutralizer are also modeled assuming diffuse neutral emission from the neutralizer keeper orifice. Finally, the effect of the surrounding vacuum facility neutrals is included and assumed to be constant. The model is used to predict the neutral density downstream of a NEXT ion thruster with and without neutralizer flow and a vacuum facility background pressure. The impacts of past simplifying assumptions for predicting downstream neutral densities are also examined for a NEXT ion thruster.

Hard X-ray emission from accretion shocks around galaxy clusters

NASA Astrophysics Data System (ADS)

Kushnir, Doron; Waxman, Eli

2010-02-01

We show that the hard X-ray (HXR) emission observed from several galaxy clusters is consistent with a simple model, in which the nonthermal emission is produced by inverse Compton scattering of cosmic microwave background photons by electrons accelerated in cluster accretion shocks: The dependence of HXR surface brightness on cluster temperature is consistent with that predicted by the model, and the observed HXR luminosity is consistent with the fraction of shock thermal energy deposited in relativistic electrons being lesssim0.1. Alternative models, where the HXR emission is predicted to be correlated with the cluster thermal emission, are disfavored by the data. The implications of our predictions to future HXR observations (e.g. by NuStar, Simbol-X) and to (space/ground based) γ-ray observations (e.g. by Fermi, HESS, MAGIC, VERITAS) are discussed.

Hyperbolastic growth models: theory and application

PubMed Central

Tabatabai, Mohammad; Williams, David Keith; Bursac, Zoran

2005-01-01

Background Mathematical models describing growth kinetics are very important for predicting many biological phenomena such as tumor volume, speed of disease progression, and determination of an optimal radiation and/or chemotherapy schedule. Growth models such as logistic, Gompertz, Richards, and Weibull have been extensively studied and applied to a wide range of medical and biological studies. We introduce a class of three and four parameter models called "hyperbolastic models" for accurately predicting and analyzing self-limited growth behavior that occurs e.g. in tumors. To illustrate the application and utility of these models and to gain a more complete understanding of them, we apply them to two sets of data considered in previously published literature. Results The results indicate that volumetric tumor growth follows the principle of hyperbolastic growth model type III, and in both applications at least one of the newly proposed models provides a better fit to the data than the classical models used for comparison. Conclusion We have developed a new family of growth models that predict the volumetric growth behavior of multicellular tumor spheroids with a high degree of accuracy. We strongly believe that the family of hyperbolastic models can be a valuable predictive tool in many areas of biomedical and epidemiological research such as cancer or stem cell growth and infectious disease outbreaks. PMID:15799781

Mortality Probability Model III and Simplified Acute Physiology Score II

PubMed Central

Vasilevskis, Eduard E.; Kuzniewicz, Michael W.; Cason, Brian A.; Lane, Rondall K.; Dean, Mitzi L.; Clay, Ted; Rennie, Deborah J.; Vittinghoff, Eric; Dudley, R. Adams

2009-01-01

Background: To develop and compare ICU length-of-stay (LOS) risk-adjustment models using three commonly used mortality or LOS prediction models. Methods: Between 2001 and 2004, we performed a retrospective, observational study of 11,295 ICU patients from 35 hospitals in the California Intensive Care Outcomes Project. We compared the accuracy of the following three LOS models: a recalibrated acute physiology and chronic health evaluation (APACHE) IV-LOS model; and models developed using risk factors in the mortality probability model III at zero hours (MPM0) and the simplified acute physiology score (SAPS) II mortality prediction model. We evaluated models by calculating the following: (1) grouped coefficients of determination; (2) differences between observed and predicted LOS across subgroups; and (3) intraclass correlations of observed/expected LOS ratios between models. Results: The grouped coefficients of determination were APACHE IV with coefficients recalibrated to the LOS values of the study cohort (APACHE IVrecal) [R2 = 0.422], mortality probability model III at zero hours (MPM0 III) [R2 = 0.279], and simplified acute physiology score (SAPS II) [R2 = 0.008]. For each decile of predicted ICU LOS, the mean predicted LOS vs the observed LOS was significantly different (p ≤ 0.05) for three, two, and six deciles using APACHE IVrecal, MPM0 III, and SAPS II, respectively. Plots of the predicted vs the observed LOS ratios of the hospitals revealed a threefold variation in LOS among hospitals with high model correlations. Conclusions: APACHE IV and MPM0 III were more accurate than SAPS II for the prediction of ICU LOS. APACHE IV is the most accurate and best calibrated model. Although it is less accurate, MPM0 III may be a reasonable option if the data collection burden or the treatment effect bias is a consideration. PMID:19363210

Oxidative DNA damage background estimated by a system model of base excision repair

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

Sokhansanj, B A; Wilson, III, D M

Human DNA can be damaged by natural metabolism through free radical production. It has been suggested that the equilibrium between innate damage and cellular DNA repair results in an oxidative DNA damage background that potentially contributes to disease and aging. Efforts to quantitatively characterize the human oxidative DNA damage background level based on measuring 8-oxoguanine lesions as a biomarker have led to estimates varying over 3-4 orders of magnitude, depending on the method of measurement. We applied a previously developed and validated quantitative pathway model of human DNA base excision repair, integrating experimentally determined endogenous damage rates and model parametersmore » from multiple sources. Our estimates of at most 100 8-oxoguanine lesions per cell are consistent with the low end of data from biochemical and cell biology experiments, a result robust to model limitations and parameter variation. Our results show the power of quantitative system modeling to interpret composite experimental data and make biologically and physiologically relevant predictions for complex human DNA repair pathway mechanisms and capacity.« less

Development and Validation of a Risk Model for Prediction of Hazardous Alcohol Consumption in General Practice Attendees: The PredictAL Study

PubMed Central

King, Michael; Marston, Louise; Švab, Igor; Maaroos, Heidi-Ingrid; Geerlings, Mirjam I.; Xavier, Miguel; Benjamin, Vicente; Torres-Gonzalez, Francisco; Bellon-Saameno, Juan Angel; Rotar, Danica; Aluoja, Anu; Saldivia, Sandra; Correa, Bernardo; Nazareth, Irwin

2011-01-01

Background Little is known about the risk of progression to hazardous alcohol use in people currently drinking at safe limits. We aimed to develop a prediction model (predictAL) for the development of hazardous drinking in safe drinkers. Methods A prospective cohort study of adult general practice attendees in six European countries and Chile followed up over 6 months. We recruited 10,045 attendees between April 2003 to February 2005. 6193 European and 2462 Chilean attendees recorded AUDIT scores below 8 in men and 5 in women at recruitment and were used in modelling risk. 38 risk factors were measured to construct a risk model for the development of hazardous drinking using stepwise logistic regression. The model was corrected for over fitting and tested in an external population. The main outcome was hazardous drinking defined by an AUDIT score ≥8 in men and ≥5 in women. Results 69.0% of attendees were recruited, of whom 89.5% participated again after six months. The risk factors in the final predictAL model were sex, age, country, baseline AUDIT score, panic syndrome and lifetime alcohol problem. The predictAL model's average c-index across all six European countries was 0.839 (95% CI 0.805, 0.873). The Hedge's g effect size for the difference in log odds of predicted probability between safe drinkers in Europe who subsequently developed hazardous alcohol use and those who did not was 1.38 (95% CI 1.25, 1.51). External validation of the algorithm in Chilean safe drinkers resulted in a c-index of 0.781 (95% CI 0.717, 0.846) and Hedge's g of 0.68 (95% CI 0.57, 0.78). Conclusions The predictAL risk model for development of hazardous consumption in safe drinkers compares favourably with risk algorithms for disorders in other medical settings and can be a useful first step in prevention of alcohol misuse. PMID:21853028

Designing and benchmarking the MULTICOM protein structure prediction system

PubMed Central

2013-01-01

Background Predicting protein structure from sequence is one of the most significant and challenging problems in bioinformatics. Numerous bioinformatics techniques and tools have been developed to tackle almost every aspect of protein structure prediction ranging from structural feature prediction, template identification and query-template alignment to structure sampling, model quality assessment, and model refinement. How to synergistically select, integrate and improve the strengths of the complementary techniques at each prediction stage and build a high-performance system is becoming a critical issue for constructing a successful, competitive protein structure predictor. Results Over the past several years, we have constructed a standalone protein structure prediction system MULTICOM that combines multiple sources of information and complementary methods at all five stages of the protein structure prediction process including template identification, template combination, model generation, model assessment, and model refinement. The system was blindly tested during the ninth Critical Assessment of Techniques for Protein Structure Prediction (CASP9) in 2010 and yielded very good performance. In addition to studying the overall performance on the CASP9 benchmark, we thoroughly investigated the performance and contributions of each component at each stage of prediction. Conclusions Our comprehensive and comparative study not only provides useful and practical insights about how to select, improve, and integrate complementary methods to build a cutting-edge protein structure prediction system but also identifies a few new sources of information that may help improve the design of a protein structure prediction system. Several components used in the MULTICOM system are available at: http://sysbio.rnet.missouri.edu/multicom_toolbox/. PMID:23442819

Extended charge banking model of dual path shocks for implantable cardioverter defibrillators

PubMed Central

Dosdall, Derek J; Sweeney, James D

2008-01-01

Background Single path defibrillation shock methods have been improved through the use of the Charge Banking Model of defibrillation, which predicts the response of the heart to shocks as a simple resistor-capacitor (RC) circuit. While dual path defibrillation configurations have significantly reduced defibrillation thresholds, improvements to dual path defibrillation techniques have been limited to experimental observations without a practical model to aid in improving dual path defibrillation techniques. Methods The Charge Banking Model has been extended into a new Extended Charge Banking Model of defibrillation that represents small sections of the heart as separate RC circuits, uses a weighting factor based on published defibrillation shock field gradient measures, and implements a critical mass criteria to predict the relative efficacy of single and dual path defibrillation shocks. Results The new model reproduced the results from several published experimental protocols that demonstrated the relative efficacy of dual path defibrillation shocks. The model predicts that time between phases or pulses of dual path defibrillation shock configurations should be minimized to maximize shock efficacy. Discussion Through this approach the Extended Charge Banking Model predictions may be used to improve dual path and multi-pulse defibrillation techniques, which have been shown experimentally to lower defibrillation thresholds substantially. The new model may be a useful tool to help in further improving dual path and multiple pulse defibrillation techniques by predicting optimal pulse durations and shock timing parameters. PMID:18673561

Predicting Survival of De Novo Metastatic Breast Cancer in Asian Women: Systematic Review and Validation Study

PubMed Central

Miao, Hui; Hartman, Mikael; Bhoo-Pathy, Nirmala; Lee, Soo-Chin; Taib, Nur Aishah; Tan, Ern-Yu; Chan, Patrick; Moons, Karel G. M.; Wong, Hoong-Seam; Goh, Jeremy; Rahim, Siti Mastura; Yip, Cheng-Har; Verkooijen, Helena M.

2014-01-01

Background In Asia, up to 25% of breast cancer patients present with distant metastases at diagnosis. Given the heterogeneous survival probabilities of de novo metastatic breast cancer, individual outcome prediction is challenging. The aim of the study is to identify existing prognostic models for patients with de novo metastatic breast cancer and validate them in Asia. Materials and Methods We performed a systematic review to identify prediction models for metastatic breast cancer. Models were validated in 642 women with de novo metastatic breast cancer registered between 2000 and 2010 in the Singapore Malaysia Hospital Based Breast Cancer Registry. Survival curves for low, intermediate and high-risk groups according to each prognostic score were compared by log-rank test and discrimination of the models was assessed by concordance statistic (C-statistic). Results We identified 16 prediction models, seven of which were for patients with brain metastases only. Performance status, estrogen receptor status, metastatic site(s) and disease-free interval were the most common predictors. We were able to validate nine prediction models. The capacity of the models to discriminate between poor and good survivors varied from poor to fair with C-statistics ranging from 0.50 (95% CI, 0.48–0.53) to 0.63 (95% CI, 0.60–0.66). Conclusion The discriminatory performance of existing prediction models for de novo metastatic breast cancer in Asia is modest. Development of an Asian-specific prediction model is needed to improve prognostication and guide decision making. PMID:24695692

ENSO Bred Vectors in Coupled Ocean-Atmosphere General Circulation Models

NASA Technical Reports Server (NTRS)

Yang, S. C.; Cai, Ming; Kalnay, E.; Rienecker, M.; Yuan, G.; Toth, ZA.

2004-01-01

The breeding method has been implemented in the NASA Seasonal-to-Interannual Prediction Project (NSIPP) Coupled General Circulation Model (CGCM) with the goal of improving operational seasonal to interannual climate predictions through ensemble forecasting and data assimilation. The coupled instability as cap'tured by the breeding method is the first attempt to isolate the evolving ENSO instability and its corresponding global atmospheric response in a fully coupled ocean-atmosphere GCM. Our results show that the growth rate of the coupled bred vectors (BV) peaks at about 3 months before a background ENSO event. The dominant growing BV modes are reminiscent of the background ENSO anomalies and show a strong tropical response with wind/SST/thermocline interrelated in a manner similar to the background ENSO mode. They exhibit larger amplitudes in the eastern tropical Pacific, reflecting the natural dynamical sensitivity associated with the presence of the shallow thermocline. Moreover, the extratropical perturbations associated with these coupled BV modes reveal the variations related to the atmospheric teleconnection patterns associated with background ENSO variability, e.g. over the North Pacific and North America. A similar experiment was carried out with the NCEP/CFS03 CGCM. Comparisons between bred vectors from the NSIPP CGCM and NCEP/CFS03 CGCM demonstrate the robustness of the results. Our results strongly suggest that the breeding method can serve as a natural filter to identify the slowly varying, coupled instabilities in a coupled GCM, which can be used to construct ensemble perturbations for ensemble forecasts and to estimate the coupled background error covariance for coupled data assimilation.

Discriminability measures for predicting readability of text on textured backgrounds

NASA Technical Reports Server (NTRS)

Scharff, L. F.; Hill, A. L.; Ahumada, A. J. Jr; Watson, A. B. (Principal Investigator)

2000-01-01

Several discriminability measures were examined for their ability to predict reading search times for three levels of text contrast and a range of backgrounds (plain, a periodic texture, and four spatial-frequency-filtered textures created from the periodic texture). Search times indicate that these background variations only affect readability when the text contrast is low, and that spatial frequency content of the background affects readability. These results were not well predicted by the single variables of text contrast (Spearman rank correlation = -0.64) and background RMS contrast (0.08), but a global masking index and a spatial-frequency-selective masking index led to better predictions (-0.84 and -0.81, respectively). c2000 Optical Society of America.

Projections of Future Summertime Ozone over the U.S.

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

Pfister, G. G.; Walters, Stacy; Lamarque, J. F.

This study uses a regional fully coupled chemistry-transport model to assess changes in surface ozone over the summertime U.S. between present and a 2050 future time period at high spatial resolution (12 km grid spacing) under the SRES A2 climate and RCP8.5 anthropogenic pre-cursor emission scenario. The impact of predicted changes in climate and global background ozone is estimated to increase surface ozone over most of the U.S; the 5th - 95th percentile range for daily 8-hour maximum surface ozone increases from 31-79 ppbV to 30-87 ppbV between the present and future time periods. The analysis of a set ofmore » meteorological drivers suggests that these mostly will add to increasing ozone, but the set of simulations conducted does not allow to separate this effect from that through enhanced global background ozone. Statistically the most robust positive feedbacks are through increased temperature, biogenic emissions and solar radiation. Stringent emission controls can counteract these feedbacks and if considered, we estimate large reductions in surface ozone with the 5th-95th percentile reduced to 27-55 ppbV. A comparison of the high-resolution projections to global model projections shows that even though the global model is biased high in surface ozone compared to the regional model and compared to observations, both the global and the regional model predict similar changes in ozone between the present and future time periods. However, on smaller spatial scales, the regional predictions show more pronounced changes between urban and rural regimes that cannot be resolved at the coarse resolution of global model. In addition, the sign of the changes in overall ozone mixing ratios can be different between the global and the regional predictions in certain regions, such as the Western U.S. This study confirms the key role of emission control strategies in future air quality predictions and demonstrates the need for considering degradation of air quality with future climate change in emission policy making. It also illustrates the need for high resolution modeling when the objective is to address regional and local air quality or establish links to human health and society.« less

A Bridge from Optical to Infrared Galaxies: Explaining Local Properties and Predicting Galaxy Counts and the Cosmic Background Radiation

NASA Astrophysics Data System (ADS)

Totani, Tomonori; Takeuchi, Tsutomu T.

2002-05-01

We give an explanation for the origin of various properties observed in local infrared galaxies and make predictions for galaxy counts and cosmic background radiation (CBR) using a new model extended from that for optical/near-infrared galaxies. Important new characteristics of this study are that (1) mass scale dependence of dust extinction is introduced based on the size-luminosity relation of optical galaxies and that (2) the large-grain dust temperature Tdust is calculated based on a physical consideration for energy balance rather than by using the empirical relation between Tdust and total infrared luminosity LIR found in local galaxies, which has been employed in most previous works. Consequently, the local properties of infrared galaxies, i.e., optical/infrared luminosity ratios, LIR-Tdust correlation, and infrared luminosity function are outputs predicted by the model, while these have been inputs in a number of previous models. Our model indeed reproduces these local properties reasonably well. Then we make predictions for faint infrared counts (in 15, 60, 90, 170, 450, and 850 μm) and CBR using this model. We found results considerably different from those of most previous works based on the empirical LIR-Tdust relation; especially, it is shown that the dust temperature of starbursting primordial elliptical galaxies is expected to be very high (40-80 K), as often seen in starburst galaxies or ultraluminous infrared galaxies in the local and high-z universe. This indicates that intense starbursts of forming elliptical galaxies should have occurred at z~2-3, in contrast to the previous results that significant starbursts beyond z~1 tend to overproduce the far-infrared (FIR) CBR detected by COBE/FIRAS. On the other hand, our model predicts that the mid-infrared (MIR) flux from warm/nonequilibrium dust is relatively weak in such galaxies making FIR CBR, and this effect reconciles the prima facie conflict between the upper limit on MIR CBR from TeV gamma-ray observations and the COBE detections of FIR CBR. The intergalactic optical depth of TeV gamma rays based on our model is also presented.

The background puzzle: how identical mutations in the same gene lead to different disease symptoms.

PubMed

Kammenga, Jan E

2017-10-01

Identical disease-causing mutations can lead to different symptoms in different people. The reason for this has been a puzzling problem for geneticists. Differential penetrance and expressivity of mutations has been observed within individuals with different and similar genetic backgrounds. Attempts have been made to uncover the underlying mechanisms that determine differential phenotypic effects of identical mutations through studies of model organisms. From these studies evidence is accumulating that to understand disease mechanism or predict disease prevalence, an understanding of the influence of genetic background is as important as the putative disease-causing mutations of relatively large effect. This review highlights current insights into phenotypic variation due to gene interactions, epigenetics and stochasticity in model organisms, and discusses their importance for understanding the mutational effect on disease symptoms. © 2017 Federation of European Biochemical Societies.

Expanding Models of Lake Trophic State to Predict Cyanobacteria in Lakes: A Data Mining Approach

EPA Science Inventory

Background/Question/Methods: Cyanobacteria are a primary taxonomic group associated with harmful algal blooms in lakes. Understanding the drivers of cyanobacteria presence has important implications for lake management and for the protection of human and ecosystem health. Chloro...

Perplexities and Provocations of Eating Disorders

ERIC Educational Resources Information Center

Halmi, Katherine A.

2009-01-01

Background: Etiological hypotheses of eating disorders, anorexia nervosa and bulimia nervosa have not produced informative research for predictably effective treatment. Methods: The rationale for applying a model of allostasis, a dysregulation of reward circuits with activation of brain and hormonal stress responses to maintain apparent stability,…

Search for Ultra-High Energy Photons with the Pierre Auger Observatory

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

Homola, Piotr

One of key scientific objectives of the Pierre Auger Observatory is the search for ultra-high energy photons. Such photons could originate either in the interactions of energetic cosmic-ray nuclei with the cosmic microwave background (so-called cosmogenic photons) or in the exotic scenarios, e.g. those assuming a production and decay of some hypothetical super-massive particles. The latter category of models would imply relatively large fluxes of photons with ultra-high energies at Earth, while the former, involving interactions of cosmic-ray nuclei with the microwave background - just the contrary: very small fractions. The investigations on the data collected so far in themore » Pierre Auger Observatory led to placing very stringent limits to ultra-high energy photon fluxes: below the predictions of the most of the exotic models and nearing the predicted fluxes of the cosmogenic photons. In this paper the status of these investigations and perspectives for further studies are summarized.« less

Field determination and QSPR prediction of equilibrium-status soil/vegetation partition coefficient of PCDD/Fs.

PubMed

Li, Li; Wang, Qiang; Qiu, Xinghua; Dong, Yian; Jia, Shenglan; Hu, Jianxin

2014-07-15

Characterizing pseudo equilibrium-status soil/vegetation partition coefficient KSV, the quotient of respective concentrations in soil and vegetation of a certain substance at remote background areas, is essential in ecological risk assessment, however few previous attempts have been made for field determination and developing validated and reproducible structure-based estimates. In this study, KSV was calculated based on measurements of seventeen 2,3,7,8-substituted PCDD/F congeners in soil and moss (Dicranum angustum), and rouzi grass (Thylacospermum caespitosum) of two background sites, Ny-Ålesund of the Arctic and Zhangmu-Nyalam region of the Tibet Plateau, respectively. By both fugacity modeling and stepwise regression of field data, the air-water partition coefficient (KAW) and aqueous solubility (SW) were identified as the influential physicochemical properties. Furthermore, validated quantitative structure-property relationship (QSPR) model was developed to extrapolate the KSV prediction to all 210 PCDD/F congeners. Molecular polarizability, molecular size and molecular energy demonstrated leading effects on KSV. Copyright © 2014 Elsevier B.V. All rights reserved.

Detection of polarization in the cosmic microwave background using DASI. Degree Angular Scale Interferometer.

PubMed

Kovac, J M; Leitch, E M; Pryke, C; Carlstrom, J E; Halverson, N W; Holzapfel, W L

The past several years have seen the emergence of a standard cosmological model, in which small temperature differences in the cosmic microwave background (CMB) radiation on angular scales of the order of a degree are understood to arise from acoustic oscillations in the hot plasma of the early Universe, arising from primordial density fluctuations. Within the context of this model, recent measurements of the temperature fluctuations have led to profound conclusions about the origin, evolution and composition of the Universe. Using the measured temperature fluctuations, the theoretical framework predicts the level of polarization of the CMB with essentially no free parameters. Therefore, a measurement of the polarization is a critical test of the theory and thus of the validity of the cosmological parameters derived from the CMB measurements. Here we report the detection of polarization of the CMB with the Degree Angular Scale Interferometer (DASI). The polarization is deteced with high confidence, and its level and spatial distribution are in excellent agreement with the predictions of the standard theory.

Computational Assessment of Naturally Occurring Neutron and Photon Background Radiation Produced by Extraterrestrial Sources

DOE PAGES

Miller, Thomas Martin; de Wet, Wouter C.; Patton, Bruce W.

2015-10-28

In this study, a computational assessment of the variation in terrestrial neutron and photon background from extraterrestrial sources is presented. The motivation of this assessment is to evaluate the practicality of developing a tool or database to estimate background in real time (or near–real time) during an experimental measurement or to even predict the background for future measurements. The extraterrestrial source focused on during this assessment is naturally occurring galactic cosmic rays (GCRs). The MCNP6 transport code was used to perform the computational assessment. However, the GCR source available in MCNP6 was not used. Rather, models developed and maintained bymore » NASA were used to generate the GCR sources. The largest variation in both neutron and photon background spectra was found to be caused by changes in elevation on Earth's surface, which can be as large as an order of magnitude. All other perturbations produced background variations on the order of a factor of 3 or less. The most interesting finding was that ~80% and 50% of terrestrial background neutrons and photons, respectively, are generated by interactions in Earth's surface and other naturally occurring and man-made objects near a detector of particles from extraterrestrial sources and their progeny created in Earth's atmosphere. In conclusion, this assessment shows that it will be difficult to estimate the terrestrial background from extraterrestrial sources without a good understanding of a detector's surroundings. Therefore, estimating or predicting background during a measurement environment like a mobile random search will be difficult.« less

The Threshold Bias Model: A Mathematical Model for the Nomothetic Approach of Suicide

PubMed Central

Folly, Walter Sydney Dutra

2011-01-01

Background Comparative and predictive analyses of suicide data from different countries are difficult to perform due to varying approaches and the lack of comparative parameters. Methodology/Principal Findings A simple model (the Threshold Bias Model) was tested for comparative and predictive analyses of suicide rates by age. The model comprises of a six parameter distribution that was applied to the USA suicide rates by age for the years 2001 and 2002. Posteriorly, linear extrapolations are performed of the parameter values previously obtained for these years in order to estimate the values corresponding to the year 2003. The calculated distributions agreed reasonably well with the aggregate data. The model was also used to determine the age above which suicide rates become statistically observable in USA, Brazil and Sri Lanka. Conclusions/Significance The Threshold Bias Model has considerable potential applications in demographic studies of suicide. Moreover, since the model can be used to predict the evolution of suicide rates based on information extracted from past data, it will be of great interest to suicidologists and other researchers in the field of mental health. PMID:21909431

CXBN: a blueprint for an improved measurement of the cosmological x-ray background

NASA Astrophysics Data System (ADS)

Simms, Lance M.; Jernigan, J. G.; Malphrus, Benjamin K.; McNeil, Roger; Brown, Kevin Z.; Rose, Tyler G.; Lim, Hyoung S.; Anderson, Steven; Kruth, Jeffrey A.; Doty, John P.; Wampler-Doty, Matthew; Cominsky, Lynn R.; Prasad, Kamal S.; Thomas, Eric T.; Combs, Michael S.; Kroll, Robert T.; Cahall, Benjamin J.; Turba, Tyler T.; Molton, Brandon L.; Powell, Margaret M.; Fitzpatrick, Jonathan F.; Graves, Daniel C.; Gaalema, Stephen D.; Sun, Shunming

2012-10-01

A precise measurement of the Cosmic X-ray Background (CXB) is crucial for constraining models of the evolution and composition of the universe. While several large, expensive satellites have measured the CXB as a secondary mission, there is still disagreement about normalization of its spectrum. The Cosmic X-ray Background NanoSat (CXBN) is a small, low-cost satellite whose primary goal is to measure the CXB over its two-year lifetime. Benefiting from a low instrument-induced background due to its small mass and size, CXBN will use a novel, pixelated Cadmium Zinc Telluride (CZT) detector with energy resolution < 1 keV over the range 1-60 keV to measure the CXBN with unprecedented accuracy. This paper describes CXBN and its science payload, including the GEANT4 model that has been used to predict overall performance and the backgrounds from secondary particles in Low Earth Orbit. It also addresses the strategy for scanning the sky and calibrating the data, and presents the expected results over the two-year mission lifetime.

Search for the Chiral Magnetic Effect in Heavy-Ion Collisions and Quantification of the Background with the AMPT Model

NASA Astrophysics Data System (ADS)

Bryon, Jacob

2017-09-01

The chiral magnetic effect (CME) arises from the chirality imbalance of quarks and its interaction to the strong magnetic field generated in non-central heavy-ion collisions. Possible formation of domains of quarks with chirality imbalances is an intrinsic property of the Quantum ChromoDynamics (QCD), which describes the fundamental strong interactions among quarks and gluons. Azimuthal-angle correlations have been used to measure the magnitude of charge- separation across the reaction plane, which was predicted to arise from the CME. However, backgrounds from collective motion (flow) of the collision system can also contribute to the correlation observable. In this poster, we investigate the magnitude of the background utilizing the AMPT model, which contains no CME signals. We demonstrate, for Au +Au collisions at 200 and 39 GeV, a scheme to remove the flow background via the event-shape engineering with the vanishing magnitude of the flow vector. We also calculate the ensemble average of the charge-separation observable, and provide a background baseline for the experimental data.

Delusions and prediction error: clarifying the roles of behavioural and brain responses

PubMed Central

Corlett, Philip Robert; Fletcher, Paul Charles

2015-01-01

Griffiths and colleagues provided a clear and thoughtful review of the prediction error model of delusion formation [Cognitive Neuropsychiatry, 2014 April 4 (Epub ahead of print)]. As well as reviewing the central ideas and concluding that the existing evidence base is broadly supportive of the model, they provide a detailed critique of some of the experiments that we have performed to study it. Though they conclude that the shortcomings that they identify in these experiments do not fundamentally challenge the prediction error model, we nevertheless respond to these criticisms. We begin by providing a more detailed outline of the model itself as there are certain important aspects of it that were not covered in their review. We then respond to their specific criticisms of the empirical evidence. We defend the neuroimaging contrasts that we used to explore this model of psychosis arguing that, while any single contrast entails some ambiguity, our assumptions have been justified by our extensive background work before and since. PMID:25559871

Predictive Validation of an Influenza Spread Model

PubMed Central

Hyder, Ayaz; Buckeridge, David L.; Leung, Brian

2013-01-01

Background Modeling plays a critical role in mitigating impacts of seasonal influenza epidemics. Complex simulation models are currently at the forefront of evaluating optimal mitigation strategies at multiple scales and levels of organization. Given their evaluative role, these models remain limited in their ability to predict and forecast future epidemics leading some researchers and public-health practitioners to question their usefulness. The objective of this study is to evaluate the predictive ability of an existing complex simulation model of influenza spread. Methods and Findings We used extensive data on past epidemics to demonstrate the process of predictive validation. This involved generalizing an individual-based model for influenza spread and fitting it to laboratory-confirmed influenza infection data from a single observed epidemic (1998–1999). Next, we used the fitted model and modified two of its parameters based on data on real-world perturbations (vaccination coverage by age group and strain type). Simulating epidemics under these changes allowed us to estimate the deviation/error between the expected epidemic curve under perturbation and observed epidemics taking place from 1999 to 2006. Our model was able to forecast absolute intensity and epidemic peak week several weeks earlier with reasonable reliability and depended on the method of forecasting-static or dynamic. Conclusions Good predictive ability of influenza epidemics is critical for implementing mitigation strategies in an effective and timely manner. Through the process of predictive validation applied to a current complex simulation model of influenza spread, we provided users of the model (e.g. public-health officials and policy-makers) with quantitative metrics and practical recommendations on mitigating impacts of seasonal influenza epidemics. This methodology may be applied to other models of communicable infectious diseases to test and potentially improve their predictive ability. PMID:23755236

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

An enhanced MMW and SMMW/THz imaging system performance prediction and analysis tool for concealed weapon detection and pilotage obstacle avoidance

NASA Astrophysics Data System (ADS)

Murrill, Steven R.; Jacobs, Eddie L.; Franck, Charmaine C.; Petkie, Douglas T.; De Lucia, Frank C.

2015-10-01

The U.S. Army Research Laboratory (ARL) has continued to develop and enhance a millimeter-wave (MMW) and submillimeter- wave (SMMW)/terahertz (THz)-band imaging system performance prediction and analysis tool for both the detection and identification of concealed weaponry, and for pilotage obstacle avoidance. The details of the MATLAB-based model which accounts for the effects of all critical sensor and display components, for the effects of atmospheric attenuation, concealment material attenuation, and active illumination, were reported on at the 2005 SPIE Europe Security and Defence Symposium (Brugge). An advanced version of the base model that accounts for both the dramatic impact that target and background orientation can have on target observability as related to specular and Lambertian reflections captured by an active-illumination-based imaging system, and for the impact of target and background thermal emission, was reported on at the 2007 SPIE Defense and Security Symposium (Orlando). Further development of this tool that includes a MODTRAN-based atmospheric attenuation calculator and advanced system architecture configuration inputs that allow for straightforward performance analysis of active or passive systems based on scanning (single- or line-array detector element(s)) or staring (focal-plane-array detector elements) imaging architectures was reported on at the 2011 SPIE Europe Security and Defence Symposium (Prague). This paper provides a comprehensive review of a newly enhanced MMW and SMMW/THz imaging system analysis and design tool that now includes an improved noise sub-model for more accurate and reliable performance predictions, the capability to account for postcapture image contrast enhancement, and the capability to account for concealment material backscatter with active-illumination- based systems. Present plans for additional expansion of the model's predictive capabilities are also outlined.

Modelling seagrass growth and development to evaluate transplanting strategies for restoration

PubMed Central

Renton, Michael; Airey, Michael; Cambridge, Marion L.; Kendrick, Gary A.

2011-01-01

Background and Aims Seagrasses are important marine plants that are under threat globally. Restoration by transplanting vegetative fragments or seedlings into areas where seagrasses have been lost is possible, but long-term trial data are limited. The goal of this study is to use available short-term data to predict long-term outcomes of transplanting seagrass. Methods A functional–structural plant model of seagrass growth that integrates data collected from short-term trials and experiments is presented. The model was parameterized for the species Posidonia australis, a limited validation of the model against independent data and a sensitivity analysis were conducted and the model was used to conduct a preliminary evaluation of different transplanting strategies. Key Results The limited validation was successful, and reasonable long-term outcomes could be predicted, based only on short-term data. Conclusions This approach for modelling seagrass growth and development enables long-term predictions of the outcomes to be made from different strategies for transplanting seagrass, even when empirical long-term data are difficult or impossible to collect. More validation is required to improve confidence in the model's predictions, and inclusion of more mechanism will extend the model's usefulness. Marine restoration represents a novel application of functional–structural plant modelling. PMID:21821624

Development of background-free tame fluorescent probes for intracellular live cell imaging

PubMed Central

Alamudi, Samira Husen; Satapathy, Rudrakanta; Kim, Jihyo; Su, Dongdong; Ren, Haiyan; Das, Rajkumar; Hu, Lingna; Alvarado-Martínez, Enrique; Lee, Jung Yeol; Hoppmann, Christian; Peña-Cabrera, Eduardo; Ha, Hyung-Ho; Park, Hee-Sung; Wang, Lei; Chang, Young-Tae

2016-01-01

Fluorescence labelling of an intracellular biomolecule in native living cells is a powerful strategy to achieve in-depth understanding of the biomolecule's roles and functions. Besides being nontoxic and specific, desirable labelling probes should be highly cell permeable without nonspecific interactions with other cellular components to warrant high signal-to-noise ratio. While it is critical, rational design for such probes is tricky. Here we report the first predictive model for cell permeable background-free probe development through optimized lipophilicity, water solubility and charged van der Waals surface area. The model was developed by utilizing high-throughput screening in combination with cheminformatics. We demonstrate its reliability by developing CO-1 and AzG-1, a cyclooctyne- and azide-containing BODIPY probe, respectively, which specifically label intracellular target organelles and engineered proteins with minimum background. The results provide an efficient strategy for development of background-free probes, referred to as ‘tame' probes, and novel tools for live cell intracellular imaging. PMID:27321135

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

Rouleau, Christopher M.; Puretzky, Alexander A.; Geohegan, David B.

The slowing of Pt nanoparticles in argon background gas was characterized by Rayleigh scattering imaging using a plume of nanoparticles generated by femtosecond laser through thin film ablation (fs-TTFA) of 20 nanometers-thick Pt films. The ablation was performed at threshold laser energy fluences for complete film removal to provide a well-defined plume consisting almost entirely of nanoparticles traveling with a narrow velocity distribution, providing a unique system to unambiguously characterize the slowing of nanoparticles during interaction with background gases. Nanoparticles of ~200 nm diameter were found to decelerate in background Ar gas with pressures less than 50 Torr in goodmore » agreement with a linear drag model in the Epstein regime. Based on this model, the stopping distance of small nanoparticles in the plume was predicted and tested by particle collection in an off-axis geometry, and size distribution analysis by transmission electron microscopy. These results permit a basis to interpret nanoparticle propagation through background gases in laser ablation plumes that contain mixed components.« less

Measurement of Neutrino-Induced Coherent Pion Production and the Diffractive Background in MINERvA

NASA Astrophysics Data System (ADS)

Gomez, Alicia; Minerva Collaboration

2015-04-01

Neutrino-induced coherent charged pion production is a unique neutrino-nucleus scattering process in which a muon and pion are produced while the nucleus is left in its ground state. The MINERvA experiment has made a model-independent differential cross section measurement of this process on carbon by selecting events with a muon and a pion, no evidence of nuclear break-up, and small momentum transfer to the nucleus | t | . A similar process which is a background to the measurement on carbon is diffractive pion production off the free protons in MINERvA's scintillator. This process is not modeled in the neutrino event generator GENIE. At low | t | these events have a similar final state to the aforementioned process. A study to quantify this diffractive event contribution to the background is done by emulating these diffractive events by reweighting all other GENIE-generated background events to the predicted | t | distribution of diffractive events, and then scaling to the diffractive cross section.

Influences of acute alcohol consumption, sexual precedence, and relationship motivation on women’s relationship and sex appraisals and unprotected sex intentions

PubMed Central

Jacques-Tiura, Angela J.; Norris, Jeanette; Kiekel, Preston A.; Davis, Kelly Cue; Zawacki, Tina; Morrison, Diane M.; George, William H.; Abdallah, Devon Alisa

2014-01-01

Guided by the cognitive mediation model of sexual decision making (Norris, Masters, & Zawacki, 2004. Cognitive mediation of women’s sexual decision making: The influence of alcohol, contextual factors, and background variables. Annual Review of Sex Research, 15, 258–296), we examined female social drinkers’ (N = 162) in-the-moment risky sexual decision making by testing how individual differences (relationship motivation) and situational factors (alcohol consumption and sexual precedence conditions) influenced cognitive appraisals and sexual outcomes in a hypothetical sexual scenario. In a path model, acute intoxication, sexual precedence, and relationship motivation interactively predicted primary relationship appraisals and independently predicted primary sex appraisals. Primary appraisals predicted secondary appraisals related to relationship and unprotected sex, which predicted unprotected sex intentions. Sexual precedence directly increased unprotected sex intentions. Findings support the cognitive mediation model and suggest that sexual risk reduction interventions should address alcohol, relationship, sexual, and cognitive factors. PMID:25755302

A Systematic Investigation of Computation Models for Predicting Adverse Drug Reactions (ADRs)

PubMed Central

Kuang, Qifan; Wang, MinQi; Li, Rong; Dong, YongCheng; Li, Yizhou; Li, Menglong

2014-01-01

Background Early and accurate identification of adverse drug reactions (ADRs) is critically important for drug development and clinical safety. Computer-aided prediction of ADRs has attracted increasing attention in recent years, and many computational models have been proposed. However, because of the lack of systematic analysis and comparison of the different computational models, there remain limitations in designing more effective algorithms and selecting more useful features. There is therefore an urgent need to review and analyze previous computation models to obtain general conclusions that can provide useful guidance to construct more effective computational models to predict ADRs. Principal Findings In the current study, the main work is to compare and analyze the performance of existing computational methods to predict ADRs, by implementing and evaluating additional algorithms that have been earlier used for predicting drug targets. Our results indicated that topological and intrinsic features were complementary to an extent and the Jaccard coefficient had an important and general effect on the prediction of drug-ADR associations. By comparing the structure of each algorithm, final formulas of these algorithms were all converted to linear model in form, based on this finding we propose a new algorithm called the general weighted profile method and it yielded the best overall performance among the algorithms investigated in this paper. Conclusion Several meaningful conclusions and useful findings regarding the prediction of ADRs are provided for selecting optimal features and algorithms. PMID:25180585

The State of the Art of Neutrino Cross Section Measurements

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

Harris, Deborah A.

2015-06-08

The study of neutrino interactions has recently experienced a renaissance, motivated by the fact that neutrino oscillation experiments depend critically on an accurate models of neutrino interactions. These models have to predict not only the signal and background populations that oscillation experiments see at near and far detectors, but they must also predict how the neutrino's energy which enters a nucleus gets transferred to energies of the particles that leave the nucleus after the neutrino interacts. Over the past year there have been a number of new results on many different neutrino (and antineutrino) interaction channels using several different targetmore » nuclei. These results are often not in agreement with predictions extraolated from charged lepton scattering measurements, or even from predictions anchored to neutrino measurements on deuterium. These new measurements are starting to give the community the handles needed to improve the theoretical description of neutrino interactions, which ultimately pave the way for precision oscillation measurements. This report briefly summarizes recent results and points out where those results differ from the predictions based on current models.« less

Leuconostoc Mesenteroides Growth in Food Products: Prediction and Sensitivity Analysis by Adaptive-Network-Based Fuzzy Inference Systems

PubMed Central

Wang, Hue-Yu; Wen, Ching-Feng; Chiu, Yu-Hsien; Lee, I-Nong; Kao, Hao-Yun; Lee, I-Chen; Ho, Wen-Hsien

2013-01-01

Background An adaptive-network-based fuzzy inference system (ANFIS) was compared with an artificial neural network (ANN) in terms of accuracy in predicting the combined effects of temperature (10.5 to 24.5°C), pH level (5.5 to 7.5), sodium chloride level (0.25% to 6.25%) and sodium nitrite level (0 to 200 ppm) on the growth rate of Leuconostoc mesenteroides under aerobic and anaerobic conditions. Methods The ANFIS and ANN models were compared in terms of six statistical indices calculated by comparing their prediction results with actual data: mean absolute percentage error (MAPE), root mean square error (RMSE), standard error of prediction percentage (SEP), bias factor (Bf), accuracy factor (Af), and absolute fraction of variance (R 2). Graphical plots were also used for model comparison. Conclusions The learning-based systems obtained encouraging prediction results. Sensitivity analyses of the four environmental factors showed that temperature and, to a lesser extent, NaCl had the most influence on accuracy in predicting the growth rate of Leuconostoc mesenteroides under aerobic and anaerobic conditions. The observed effectiveness of ANFIS for modeling microbial kinetic parameters confirms its potential use as a supplemental tool in predictive mycology. Comparisons between growth rates predicted by ANFIS and actual experimental data also confirmed the high accuracy of the Gaussian membership function in ANFIS. Comparisons of the six statistical indices under both aerobic and anaerobic conditions also showed that the ANFIS model was better than all ANN models in predicting the four kinetic parameters. Therefore, the ANFIS model is a valuable tool for quickly predicting the growth rate of Leuconostoc mesenteroides under aerobic and anaerobic conditions. PMID:23705023

Variations on Bayesian Prediction and Inference

DTIC Science & Technology

2016-05-09

inference 2.2.1 Background There are a number of statistical inference problems that are not generally formulated via a full probability model...problem of inference about an unknown parameter, the Bayesian approach requires a full probability 1. REPORT DATE (DD-MM-YYYY) 4. TITLE AND...the problem of inference about an unknown parameter, the Bayesian approach requires a full probability model/likelihood which can be an obstacle

Granular activated carbon adsorption of MIB in the presence of dissolved organic matter.

PubMed

Summers, R Scott; Kim, Soo Myung; Shimabuku, Kyle; Chae, Seon-Ha; Corwin, Christopher J

2013-06-15

Based on the results of over twenty laboratory granular activated carbon (GAC) column runs, models were developed and utilized for the prediction of 2-methylisoborneol (MIB) breakthrough behavior at parts per trillion levels and verified with pilot-scale data. The influent MIB concentration was found not to impact the concentration normalized breakthrough. Increasing influent background dissolved organic matter (DOM) concentration was found to systematically decrease the GAC adsorption capacity for MIB. A series of empirical models were developed that related the throughput in bed volumes for a range of MIB breakthrough targets to the influent DOM concentration. The proportional diffusivity (PD) designed rapid small-scale column test (RSSCT) could be directly used to scale-up MIB breakthrough performance below 15% breakthrough. The empirical model to predict the throughput to 50% breakthrough based on the influent DOM concentration served as input to the pore diffusion model (PDM) and well-predicted the MIB breakthrough performance below a 50% breakthrough. The PDM predictions of throughput to 10% breakthrough well simulated the PD-RSSCT and pilot-scale 10% MIB breakthrough. Copyright © 2013 Elsevier Ltd. All rights reserved.

Concentration of folate in colorectal tissue biopsies predicts prevalence of adenomatous polyps

USDA-ARS?s Scientific Manuscript database

Background and aims: Folate has been implicated as a potential aetiological factor for colorectal cancer. Previous research has not adequately exploited concentrations of folate in normal colonic mucosal biopsies to examine the issue. Methods: Logistic regression models were used to estimate ORs ...

Background radiation in inelastic X-ray scattering and X-ray emission spectroscopy. A study for Johann-type spectrometers

NASA Astrophysics Data System (ADS)

Paredes Mellone, O. A.; Bianco, L. M.; Ceppi, S. A.; Goncalves Honnicke, M.; Stutz, G. E.

2018-06-01

A study of the background radiation in inelastic X-ray scattering (IXS) and X-ray emission spectroscopy (XES) based on an analytical model is presented. The calculation model considers spurious radiation originated from elastic and inelastic scattering processes along the beam paths of a Johann-type spectrometer. The dependence of the background radiation intensity on the medium of the beam paths (air and helium), analysed energy and radius of the Rowland circle was studied. The present study shows that both for IXS and XES experiments the background radiation is dominated by spurious radiation owing to scattering processes along the sample-analyser beam path. For IXS experiments the spectral distribution of the main component of the background radiation shows a weak linear dependence on the energy for the most cases. In the case of XES, a strong non-linear behaviour of the background radiation intensity was predicted for energy analysis very close to the backdiffraction condition, with a rapid increase in intensity as the analyser Bragg angle approaches π / 2. The contribution of the analyser-detector beam path is significantly weaker and resembles the spectral distribution of the measured spectra. Present results show that for usual experimental conditions no appreciable structures are introduced by the background radiation into the measured spectra, both in IXS and XES experiments. The usefulness of properly calculating the background profile is demonstrated in a background subtraction procedure for a real experimental situation. The calculation model was able to simulate with high accuracy the energy dependence of the background radiation intensity measured in a particular XES experiment with air beam paths.

Predicting non-isometric fatigue induced by electrical stimulation pulse trains as a function of pulse duration

PubMed Central

2013-01-01

Background Our previous model of the non-isometric muscle fatigue that occurs during repetitive functional electrical stimulation included models of force, motion, and fatigue and accounted for applied load but not stimulation pulse duration. Our objectives were to: 1) further develop, 2) validate, and 3) present outcome measures for a non-isometric fatigue model that can predict the effect of a range of pulse durations on muscle fatigue. Methods A computer-controlled stimulator sent electrical pulses to electrodes on the thighs of 25 able-bodied human subjects. Isometric and non-isometric non-fatiguing and fatiguing knee torques and/or angles were measured. Pulse duration (170–600 μs) was the independent variable. Measurements were divided into parameter identification and model validation subsets. Results The fatigue model was simplified by removing two of three non-isometric parameters. The third remained a function of other model parameters. Between 66% and 77% of the variability in the angle measurements was explained by the new model. Conclusion Muscle fatigue in response to different stimulation pulse durations can be predicted during non-isometric repetitive contractions. PMID:23374142

Cosmic microwave background anisotropies in cold dark matter models with cosmological constant: The intermediate versus large angular scales

NASA Technical Reports Server (NTRS)

Stompor, Radoslaw; Gorski, Krzysztof M.

1994-01-01

We obtain predictions for cosmic microwave background anisotropies at angular scales near 1 deg in the context of cold dark matter models with a nonzero cosmological constant, normalized to the Cosmic Background Explorer (COBE) Differential Microwave Radiometer (DMR) detection. The results are compared to those computed in the matter-dominated models. We show that the coherence length of the Cosmic Microwave Background (CMB) anisotropy is almost insensitive to cosmological parameters, and the rms amplitude of the anisotropy increases moderately with decreasing total matter density, while being most sensitive to the baryon abundance. We apply these results in the statistical analysis of the published data from the UCSB South Pole (SP) experiment (Gaier et al. 1992; Schuster et al. 1993). We reject most of the Cold Dark Matter (CDM)-Lambda models at the 95% confidence level when both SP scans are simulated together (although the combined data set renders less stringent limits than the Gaier et al. data alone). However, the Schuster et al. data considered alone as well as the results of some other recent experiments (MAX, MSAM, Saskatoon), suggest that typical temperature fluctuations on degree scales may be larger than is indicated by the Gaier et al. scan. If so, CDM-Lambda models may indeed provide, from a point of view of CMB anisotropies, an acceptable alternative to flat CDM models.

The novel application of artificial neural network on bioelectrical impedance analysis to assess the body composition in elderly

PubMed Central

2013-01-01

Background This study aims to improve accuracy of Bioelectrical Impedance Analysis (BIA) prediction equations for estimating fat free mass (FFM) of the elderly by using non-linear Back Propagation Artificial Neural Network (BP-ANN) model and to compare the predictive accuracy with the linear regression model by using energy dual X-ray absorptiometry (DXA) as reference method. Methods A total of 88 Taiwanese elderly adults were recruited in this study as subjects. Linear regression equations and BP-ANN prediction equation were developed using impedances and other anthropometrics for predicting the reference FFM measured by DXA (FFMDXA) in 36 male and 26 female Taiwanese elderly adults. The FFM estimated by BIA prediction equations using traditional linear regression model (FFMLR) and BP-ANN model (FFMANN) were compared to the FFMDXA. The measuring results of an additional 26 elderly adults were used to validate than accuracy of the predictive models. Results The results showed the significant predictors were impedance, gender, age, height and weight in developed FFMLR linear model (LR) for predicting FFM (coefficient of determination, r2 = 0.940; standard error of estimate (SEE) = 2.729 kg; root mean square error (RMSE) = 2.571kg, P < 0.001). The above predictors were set as the variables of the input layer by using five neurons in the BP-ANN model (r2 = 0.987 with a SD = 1.192 kg and relatively lower RMSE = 1.183 kg), which had greater (improved) accuracy for estimating FFM when compared with linear model. The results showed a better agreement existed between FFMANN and FFMDXA than that between FFMLR and FFMDXA. Conclusion When compared the performance of developed prediction equations for estimating reference FFMDXA, the linear model has lower r2 with a larger SD in predictive results than that of BP-ANN model, which indicated ANN model is more suitable for estimating FFM. PMID:23388042

Analysis on spectra of hydroacoustic field in sonar cavity of the sandwich elastic wall structure

NASA Astrophysics Data System (ADS)

Xuetao, W.; Rui, H.; Weike, W.

2017-09-01

In this paper, the characteristics of the mechanical self - noise in sonar array cavity are studied by using the elastic flatbed - filled rectangular cavity parameterization model. Firstly, the analytic derivation of the vibration differential equation of the single layer, sandwich elastic wall plate structure and internal fluid coupling is carried out, and the modal method is used to solve it. Finally, the spectral characteristics of the acoustic field of rectangular cavity of different elastic wallboard materials are simulated and analyzed, which provides a theoretical reference for the prediction and control of sonar mechanical self-noise. In this paper, the sandwich board as control inside the dome background noise of a potential means were discussed, the dome background noise of qualitative prediction analysis and control has important theoretical significance.

Structural models used in real-time biosurveillance outbreak detection and outbreak curve isolation from noisy background morbidity levels

PubMed Central

Cheng, Karen Elizabeth; Crary, David J; Ray, Jaideep; Safta, Cosmin

2013-01-01

Objective We discuss the use of structural models for the analysis of biosurveillance related data. Methods and results Using a combination of real and simulated data, we have constructed a data set that represents a plausible time series resulting from surveillance of a large scale bioterrorist anthrax attack in Miami. We discuss the performance of anomaly detection with structural models for these data using receiver operating characteristic (ROC) and activity monitoring operating characteristic (AMOC) analysis. In addition, we show that these techniques provide a method for predicting the level of the outbreak valid for approximately 2 weeks, post-alarm. Conclusions Structural models provide an effective tool for the analysis of biosurveillance data, in particular for time series with noisy, non-stationary background and missing data. PMID:23037798

Interpretation of the cosmic microwave background radiation anisotropy detected by the COBE Differential Microwave Radiometer

NASA Technical Reports Server (NTRS)

Wright, E. L.; Meyer, S. S.; Bennett, C. L.; Boggess, N. W.; Cheng, E. S.; Hauser, M. G.; Kogut, A.; Lineweaver, C.; Mather, J. C.; Smoot, G. F.

1992-01-01

The large-scale cosmic background anisotropy detected by the COBE Differential Microwave Radiometer (DMR) instrument is compared to the sensitive previous measurements on various angular scales, and to the predictions of a wide variety of models of structure formation driven by gravitational instability. The observed anisotropy is consistent with all previously measured upper limits and with a number of dynamical models of structure formation. For example, the data agree with an unbiased cold dark matter (CDM) model with H0 = 50 km/s Mpc and Delta-M/M = 1 in a 16 Mpc radius sphere. Other models, such as CDM plus massive neutrinos (hot dark matter (HDM)), or CDM with a nonzero cosmological constant are also consistent with the COBE detection and can provide the extra power seen on 5-10,000 km/s scales.

Prediction of pulmonary hypertension in idiopathic pulmonary fibrosis☆

PubMed Central

Zisman, David A.; Ross, David J.; Belperio, John A.; Saggar, Rajan; Lynch, Joseph P.; Ardehali, Abbas; Karlamangla, Arun S.

2007-01-01

Summary Background Reliable, noninvasive approaches to the diagnosis of pulmonary hypertension in idiopathic pulmonary fibrosis are needed. We tested the hypothesis that the forced vital capacity to diffusing capacity ratio and room air resting pulse oximetry may be combined to predict mean pulmonary artery pressure (MPAP) in idiopathic pulmonary fibrosis. Methods Sixty-one idiopathic pulmonary fibrosis patients with available right-heart catheterization were studied. We regressed measured MPAP as a continuous variable on pulse oximetry (SpO2) and percent predicted forced vital capacity (FVC) to percent-predicted diffusing capacity ratio (% FVC/% DLco) in a multivariable linear regression model. Results Linear regression generated the following equation: MPAP = −11.9+0.272 × SpO2+0.0659 × (100−SpO2)2+3.06 × (% FVC/% DLco); adjusted R2 = 0.55, p<0.0001. The sensitivity, specificity, positive predictive and negative predictive value of model-predicted pulmonary hypertension were 71% (95% confidence interval (CI): 50–89%), 81% (95% CI: 68–92%), 71% (95% CI: 51–87%) and 81% (95% CI: 68–94%). Conclusions A pulmonary hypertension predictor based on room air resting pulse oximetry and FVC to diffusing capacity ratio has a relatively high negative predictive value. However, this model will require external validation before it can be used in clinical practice. PMID:17604151

Outbreaks of Tularemia in a Boreal Forest Region Depends on Mosquito Prevalence

PubMed Central

Rydén, Patrik; Björk, Rafael; Schäfer, Martina L.; Lundström, Jan O.; Petersén, Bodil; Lindblom, Anders; Forsman, Mats; Sjöstedt, Anders

2012-01-01

Background. We aimed to evaluate the potential association of mosquito prevalence in a boreal forest area with transmission of the bacterial disease tularemia to humans, and model the annual variation of disease using local weather data. Methods. A prediction model for mosquito abundance was built using weather and mosquito catch data. Then a negative binomial regression model based on the predicted mosquito abundance and local weather data was built to predict annual numbers of humans contracting tularemia in Dalarna County, Sweden. Results. Three hundred seventy humans were diagnosed with tularemia between 1981 and 2007, 94% of them during 7 summer outbreaks. Disease transmission was concentrated along rivers in the area. The predicted mosquito abundance was correlated (0.41, P < .05) with the annual number of human cases. The predicted mosquito peaks consistently preceded the median onset time of human tularemia (temporal correlation, 0.76; P < .05). Our final predictive model included 5 environmental variables and identified 6 of the 7 outbreaks. Conclusions. This work suggests that a high prevalence of mosquitoes in late summer is a prerequisite for outbreaks of tularemia in a tularemia-endemic boreal forest area of Sweden and that environmental variables can be used as risk indicators. PMID:22124130

Preface: Current perspectives in modelling, monitoring, and predicting geophysical fluid dynamics

NASA Astrophysics Data System (ADS)

Mancho, Ana M.; Hernández-García, Emilio; López, Cristóbal; Turiel, Antonio; Wiggins, Stephen; Pérez-Muñuzuri, Vicente

2018-02-01

The third edition of the international workshop Nonlinear Processes in Oceanic and Atmospheric Flows was held at the Institute of Mathematical Sciences (ICMAT) in Madrid from 6 to 8 July 2016. The event gathered oceanographers, atmospheric scientists, physicists, and applied mathematicians sharing a common interest in the nonlinear dynamics of geophysical fluid flows. The philosophy of this meeting was to bring together researchers from a variety of backgrounds into an environment that favoured a vigorous discussion of concepts across different disciplines. The present Special Issue on Current perspectives in modelling, monitoring, and predicting geophysical fluid dynamics contains selected contributions, mainly from attendants of the workshop, providing an updated perspective on modelling aspects of geophysical flows as well as issues on prediction and assimilation of observational data and novel tools for describing transport and mixing processes in these contexts. More details on these aspects are discussed in this preface.

A Model for Oil-Gas Pipelines Cost Prediction Based on a Data Mining Process

NASA Astrophysics Data System (ADS)

Batzias, Fragiskos A.; Spanidis, Phillip-Mark P.

2009-08-01

This paper addresses the problems associated with the cost estimation of oil/gas pipelines during the elaboration of feasibility assessments. Techno-economic parameters, i.e., cost, length and diameter, are critical for such studies at the preliminary design stage. A methodology for the development of a cost prediction model based on Data Mining (DM) process is proposed. The design and implementation of a Knowledge Base (KB), maintaining data collected from various disciplines of the pipeline industry, are presented. The formulation of a cost prediction equation is demonstrated by applying multiple regression analysis using data sets extracted from the KB. Following the methodology proposed, a learning context is inductively developed as background pipeline data are acquired, grouped and stored in the KB, and through a linear regression model provide statistically substantial results, useful for project managers or decision makers.

Background Signal as an in Situ Predictor of Dopamine Oxidation Potential: Improving Interpretation of Fast-Scan Cyclic Voltammetry Data.

PubMed

Meunier, Carl J; Roberts, James G; McCarty, Gregory S; Sombers, Leslie A

2017-02-15

Background-subtracted fast-scan cyclic voltammetry (FSCV) has emerged as a powerful analytical technique for monitoring subsecond molecular fluctuations in live brain tissue. Despite increasing utilization of FSCV, efforts to improve the accuracy of quantification have been limited due to the complexity of the technique and the dynamic recording environment. It is clear that variable electrode performance renders calibration necessary for accurate quantification; however, the nature of in vivo measurements can make conventional postcalibration difficult, or even impossible. Analyte-specific voltammograms and scaling factors that are critical for quantification can shift or fluctuate in vivo. This is largely due to impedance changes, and the effects of impedance on these measurements have not been characterized. We have previously reported that the background current can be used to predict electrode-specific scaling factors in situ. In this work, we employ model circuits to investigate the impact of impedance on FSCV measurements. Additionally, we take another step toward in situ electrode calibration by using the oxidation potential of quinones on the electrode surface to accurately predict the oxidation potential for dopamine at any point in an electrochemical experiment, as both are dependent on impedance. The model, validated both in adrenal slice and live brain tissue, enables information encoded in the shape of the background voltammogram to determine electrochemical parameters that are critical for accurate quantification. This improves data interpretation and provides a significant next step toward more automated methods for in vivo data analysis.

Epithelial–mesenchymal transition biomarkers and support vector machine guided model in preoperatively predicting regional lymph node metastasis for rectal cancer

PubMed Central

Fan, X-J; Wan, X-B; Huang, Y; Cai, H-M; Fu, X-H; Yang, Z-L; Chen, D-K; Song, S-X; Wu, P-H; Liu, Q; Wang, L; Wang, J-P

2012-01-01

Background: Current imaging modalities are inadequate in preoperatively predicting regional lymph node metastasis (RLNM) status in rectal cancer (RC). Here, we designed support vector machine (SVM) model to address this issue by integrating epithelial–mesenchymal-transition (EMT)-related biomarkers along with clinicopathological variables. Methods: Using tissue microarrays and immunohistochemistry, the EMT-related biomarkers expression was measured in 193 RC patients. Of which, 74 patients were assigned to the training set to select the robust variables for designing SVM model. The SVM model predictive value was validated in the testing set (119 patients). Results: In training set, eight variables, including six EMT-related biomarkers and two clinicopathological variables, were selected to devise SVM model. In testing set, we identified 63 patients with high risk to RLNM and 56 patients with low risk. The sensitivity, specificity and overall accuracy of SVM in predicting RLNM were 68.3%, 81.1% and 72.3%, respectively. Importantly, multivariate logistic regression analysis showed that SVM model was indeed an independent predictor of RLNM status (odds ratio, 11.536; 95% confidence interval, 4.113–32.361; P<0.0001). Conclusion: Our SVM-based model displayed moderately strong predictive power in defining the RLNM status in RC patients, providing an important approach to select RLNM high-risk subgroup for neoadjuvant chemoradiotherapy. PMID:22538975

Neural Network Prediction of ICU Length of Stay Following Cardiac Surgery Based on Pre-Incision Variables

PubMed Central

Pothula, Venu M.; Yuan, Stanley C.; Maerz, David A.; Montes, Lucresia; Oleszkiewicz, Stephen M.; Yusupov, Albert; Perline, Richard

2015-01-01

Background Advanced predictive analytical techniques are being increasingly applied to clinical risk assessment. This study compared a neural network model to several other models in predicting the length of stay (LOS) in the cardiac surgical intensive care unit (ICU) based on pre-incision patient characteristics. Methods Thirty six variables collected from 185 cardiac surgical patients were analyzed for contribution to ICU LOS. The Automatic Linear Modeling (ALM) module of IBM-SPSS software identified 8 factors with statistically significant associations with ICU LOS; these factors were also analyzed with the Artificial Neural Network (ANN) module of the same software. The weighted contributions of each factor (“trained” data) were then applied to data for a “new” patient to predict ICU LOS for that individual. Results Factors identified in the ALM model were: use of an intra-aortic balloon pump; O2 delivery index; age; use of positive cardiac inotropic agents; hematocrit; serum creatinine ≥ 1.3 mg/deciliter; gender; arterial pCO2. The r2 value for ALM prediction of ICU LOS in the initial (training) model was 0.356, p <0.0001. Cross validation in prediction of a “new” patient yielded r2 = 0.200, p <0.0001. The same 8 factors analyzed with ANN yielded a training prediction r2 of 0.535 (p <0.0001) and a cross validation prediction r2 of 0.410, p <0.0001. Two additional predictive algorithms were studied, but they had lower prediction accuracies. Our validated neural network model identified the upper quartile of ICU LOS with an odds ratio of 9.8(p <0.0001). Conclusions ANN demonstrated a 2-fold greater accuracy than ALM in prediction of observed ICU LOS. This greater accuracy would be presumed to result from the capacity of ANN to capture nonlinear effects and higher order interactions. Predictive modeling may be of value in early anticipation of risks of post-operative morbidity and utilization of ICU facilities. PMID:26710254

Evaluation in Appalachian pasture systems of the 1996 (update 2000) National Research Council model for weaning cattle.

PubMed

Whetsell, M S; Rayburn, E B; Osborne, P I

2006-05-01

This study was conducted to evaluate the accuracy of the National Research Council's (2000) Nutrient Requirements of Beef Cattle computer model when used to predict calf performance during on-farm pasture or dry-lot weaning and backgrounding. Calf performance was measured on 22 farms in 2002 and 8 farms in 2003 that participated in West Virginia Beef Quality Assurance Sale marketing pools. Calves were weaned on pasture (25 farms) or dry-lot (5 farms) and fed supplemental hay, haylage, ground shell corn, soybean hulls, or a commercial concentrate. Concentrates were fed at a rate of 0.0 to 1.5% of BW. The National Research Council (2000) model was used to predict ADG of each group of calves observed on each farm. The model error was measured by calculating residuals (the difference between predicted ADG minus observed ADG). Predicted animal performance was determined using level 1 of the model. Results show that, when using normal on-farm pasture sampling and forage analysis methods, the model error for ADG is high and did not accurately predict the performance of steers or heifers fed high-forage pasture-based diets; the predicted ADG was lower (P < 0.05) than the observed ADG. The estimated intake of low-producing animals was similar to the expected DMI, but for the greater-producing animals it was not. The NRC (2000) beef model may more accurately predict on-farm animal performance in pastured situations if feed analysis values reflect the energy value of the feed, account for selective grazing, and relate empty BW and shrunk BW to NDF.

Variational dynamic background model for keyword spotting in handwritten documents

NASA Astrophysics Data System (ADS)

Kumar, Gaurav; Wshah, Safwan; Govindaraju, Venu

2013-12-01

We propose a bayesian framework for keyword spotting in handwritten documents. This work is an extension to our previous work where we proposed dynamic background model, DBM for keyword spotting that takes into account the local character level scores and global word level scores to learn a logistic regression classifier to separate keywords from non-keywords. In this work, we add a bayesian layer on top of the DBM called the variational dynamic background model, VDBM. The logistic regression classifier uses the sigmoid function to separate keywords from non-keywords. The sigmoid function being neither convex nor concave, exact inference of VDBM becomes intractable. An expectation maximization step is proposed to do approximate inference. The advantage of VDBM over the DBM is multi-fold. Firstly, being bayesian, it prevents over-fitting of data. Secondly, it provides better modeling of data and an improved prediction of unseen data. VDBM is evaluated on the IAM dataset and the results prove that it outperforms our prior work and other state of the art line based word spotting system.

Unifying Type-II Strings by Exceptional Groups

NASA Astrophysics Data System (ADS)

Arvanitakis, Alex S.; Blair, Chris D. A.

2018-05-01

We construct the exceptional sigma model: a two-dimensional sigma model coupled to a supergravity background in a manifestly (formally) ED (D )-covariant manner. This formulation of the background is provided by exceptional field theory (EFT), which unites the metric and form fields of supergravity in ED (D ) multiplets before compactification. The realization of the symmetries of EFT on the world sheet uniquely fixes the Weyl-invariant Lagrangian and allows us to relate our action to the usual type-IIA fundamental string action and a form of the type-IIB (m , n ) action. This uniqueness "predicts" the correct form of the couplings to gauge fields in both Neveu-Schwarz and Ramond sectors, without invoking supersymmetry.

Predicting plant biomass accumulation from image-derived parameters

PubMed Central

Chen, Dijun; Shi, Rongli; Pape, Jean-Michel; Neumann, Kerstin; Graner, Andreas; Chen, Ming; Klukas, Christian

2018-01-01

Abstract Background Image-based high-throughput phenotyping technologies have been rapidly developed in plant science recently, and they provide a great potential to gain more valuable information than traditionally destructive methods. Predicting plant biomass is regarded as a key purpose for plant breeders and ecologists. However, it is a great challenge to find a predictive biomass model across experiments. Results In the present study, we constructed 4 predictive models to examine the quantitative relationship between image-based features and plant biomass accumulation. Our methodology has been applied to 3 consecutive barley (Hordeum vulgare) experiments with control and stress treatments. The results proved that plant biomass can be accurately predicted from image-based parameters using a random forest model. The high prediction accuracy based on this model will contribute to relieving the phenotyping bottleneck in biomass measurement in breeding applications. The prediction performance is still relatively high across experiments under similar conditions. The relative contribution of individual features for predicting biomass was further quantified, revealing new insights into the phenotypic determinants of the plant biomass outcome. Furthermore, methods could also be used to determine the most important image-based features related to plant biomass accumulation, which would be promising for subsequent genetic mapping to uncover the genetic basis of biomass. Conclusions We have developed quantitative models to accurately predict plant biomass accumulation from image data. We anticipate that the analysis results will be useful to advance our views of the phenotypic determinants of plant biomass outcome, and the statistical methods can be broadly used for other plant species. PMID:29346559

Threshold Values for Identification of Contamination Predicted by Reduced-Order Models

DOE PAGES

Last, George V.; Murray, Christopher J.; Bott, Yi-Ju; ...

2014-12-31

The U.S. Department of Energy’s (DOE’s) National Risk Assessment Partnership (NRAP) Project is developing reduced-order models to evaluate potential impacts on underground sources of drinking water (USDWs) if CO2 or brine leaks from deep CO2 storage reservoirs. Threshold values, below which there would be no predicted impacts, were determined for portions of two aquifer systems. These threshold values were calculated using an interwell approach for determining background groundwater concentrations that is an adaptation of methods described in the U.S. Environmental Protection Agency’s Unified Guidance for Statistical Analysis of Groundwater Monitoring Data at RCRA Facilities.

Voxel inversion of airborne electromagnetic data for improved groundwater model construction and prediction accuracy

NASA Astrophysics Data System (ADS)

Kruse Christensen, Nikolaj; Ferre, Ty Paul A.; Fiandaca, Gianluca; Christensen, Steen

2017-03-01

We present a workflow for efficient construction and calibration of large-scale groundwater models that includes the integration of airborne electromagnetic (AEM) data and hydrological data. In the first step, the AEM data are inverted to form a 3-D geophysical model. In the second step, the 3-D geophysical model is translated, using a spatially dependent petrophysical relationship, to form a 3-D hydraulic conductivity distribution. The geophysical models and the hydrological data are used to estimate spatially distributed petrophysical shape factors. The shape factors primarily work as translators between resistivity and hydraulic conductivity, but they can also compensate for structural defects in the geophysical model. The method is demonstrated for a synthetic case study with sharp transitions among various types of deposits. Besides demonstrating the methodology, we demonstrate the importance of using geophysical regularization constraints that conform well to the depositional environment. This is done by inverting the AEM data using either smoothness (smooth) constraints or minimum gradient support (sharp) constraints, where the use of sharp constraints conforms best to the environment. The dependency on AEM data quality is also tested by inverting the geophysical model using data corrupted with four different levels of background noise. Subsequently, the geophysical models are used to construct competing groundwater models for which the shape factors are calibrated. The performance of each groundwater model is tested with respect to four types of prediction that are beyond the calibration base: a pumping well's recharge area and groundwater age, respectively, are predicted by applying the same stress as for the hydrologic model calibration; and head and stream discharge are predicted for a different stress situation. As expected, in this case the predictive capability of a groundwater model is better when it is based on a sharp geophysical model instead of a smoothness constraint. This is true for predictions of recharge area, head change, and stream discharge, while we find no improvement for prediction of groundwater age. Furthermore, we show that the model prediction accuracy improves with AEM data quality for predictions of recharge area, head change, and stream discharge, while there appears to be no accuracy improvement for the prediction of groundwater age.

A Field Synopsis of Sex in Clinical Prediction Models for Cardiovascular Disease

PubMed Central

Paulus, Jessica K.; Wessler, Benjamin S.; Lundquist, Christine; Lai, Lana L.Y.; Raman, Gowri; Lutz, Jennifer S.; Kent, David M.

2017-01-01

Background Several widely-used risk scores for cardiovascular disease (CVD) incorporate sex effects, yet there has been no systematic summary of the role of sex in clinical prediction models (CPMs). To better understand the potential of these models to support sex-specific care, we conducted a field synopsis of sex effects in CPMs for CVD. Methods and Results We identified CPMs in the Tufts Predictive Analytics and Comparative Effectiveness (PACE) CPM Registry, a comprehensive database of CVD CPMs published from 1/1990–5/2012. We report the proportion of models including sex effects on CVD incidence or prognosis, summarize the directionality of the predictive effects of sex, and explore factors influencing the inclusion of sex. Of 592 CVD-related CPMs, 193 (33%) included sex as a predictor or presented sex-stratified models. Sex effects were included in 78% (53/68) of models predicting incidence of CVD in a general population, versus only 35% (59/171), 21% (12/58) and 17% (12/72) of models predicting outcomes in patients with coronary artery disease (CAD), stroke, and heart failure, respectively. Among sex-including CPMs, women with heart failure were at lower mortality risk in 8/8 models; women undergoing revascularization for CAD were at higher mortality risk in 10/12 models. Factors associated with the inclusion of sex effects included the number of outcome events and using cohorts at-risk for CVD (rather than with established CVD). Conclusions While CPMs hold promise for supporting sex-specific decision making in CVD clinical care, sex effects are included in only one third of published CPMs. PMID:26908865

A Comparison of a Machine Learning Model with EuroSCORE II in Predicting Mortality after Elective Cardiac Surgery: A Decision Curve Analysis

PubMed Central

Allyn, Jérôme; Allou, Nicolas; Augustin, Pascal; Philip, Ivan; Martinet, Olivier; Belghiti, Myriem; Provenchere, Sophie; Montravers, Philippe; Ferdynus, Cyril

2017-01-01

Background The benefits of cardiac surgery are sometimes difficult to predict and the decision to operate on a given individual is complex. Machine Learning and Decision Curve Analysis (DCA) are recent methods developed to create and evaluate prediction models. Methods and finding We conducted a retrospective cohort study using a prospective collected database from December 2005 to December 2012, from a cardiac surgical center at University Hospital. The different models of prediction of mortality in-hospital after elective cardiac surgery, including EuroSCORE II, a logistic regression model and a machine learning model, were compared by ROC and DCA. Of the 6,520 patients having elective cardiac surgery with cardiopulmonary bypass, 6.3% died. Mean age was 63.4 years old (standard deviation 14.4), and mean EuroSCORE II was 3.7 (4.8) %. The area under ROC curve (IC95%) for the machine learning model (0.795 (0.755–0.834)) was significantly higher than EuroSCORE II or the logistic regression model (respectively, 0.737 (0.691–0.783) and 0.742 (0.698–0.785), p < 0.0001). Decision Curve Analysis showed that the machine learning model, in this monocentric study, has a greater benefit whatever the probability threshold. Conclusions According to ROC and DCA, machine learning model is more accurate in predicting mortality after elective cardiac surgery than EuroSCORE II. These results confirm the use of machine learning methods in the field of medical prediction. PMID:28060903

Detection of enhancement in number densities of background galaxies due to magnification by massive galaxy clusters

DOE PAGES

Chiu, I.; Dietrich, J. P.; Mohr, J.; ...

2016-02-18

We present a detection of the enhancement in the number densities of background galaxies induced from lensing magnification and use it to test the Sunyaev-Zel'dovich effect (SZE) inferred masses in a sample of 19 galaxy clusters with median redshift z≃0.42 selected from the South Pole Telescope SPT-SZ survey. Two background galaxy populations are selected for this study through their photometric colours; they have median redshifts z median≃0.9 (low-z background) and z median≃1.8 (high-z background). Stacking these populations, we detect the magnification bias effect at 3.3σ and 1.3σ for the low- and high-z backgrounds, respectively. We fit NFW models simultaneously tomore » all observed magnification bias profiles to estimate the multiplicative factor η that describes the ratio of the weak lensing mass to the mass inferred from the SZE observable-mass relation. We further quantify systematic uncertainties in η resulting from the photometric noise and bias, the cluster galaxy contamination and the estimations of the background properties. The resulting η for the combined background populations with 1σ uncertainties is 0.83 ± 0.24(stat) ± 0.074(sys), indicating good consistency between the lensing and the SZE-inferred masses. We also use our best-fit η to predict the weak lensing shear profiles and compare these predictions with observations, showing agreement between the magnification and shear mass constraints. Our work demonstrates the promise of using the magnification as a complementary method to estimate cluster masses in large surveys.« less

Fatigue crack growth model RANDOM2 user manual. Appendix 1: Development of advanced methodologies for probabilistic constitutive relationships of material strength models

NASA Technical Reports Server (NTRS)

Boyce, Lola; Lovelace, Thomas B.

1989-01-01

FORTRAN program RANDOM2 is presented in the form of a user's manual. RANDOM2 is based on fracture mechanics using a probabilistic fatigue crack growth model. It predicts the random lifetime of an engine component to reach a given crack size. Details of the theoretical background, input data instructions, and a sample problem illustrating the use of the program are included.

Fatigue strength reduction model: RANDOM3 and RANDOM4 user manual. Appendix 2: Development of advanced methodologies for probabilistic constitutive relationships of material strength models

NASA Technical Reports Server (NTRS)

Boyce, Lola; Lovelace, Thomas B.

1989-01-01

FORTRAN programs RANDOM3 and RANDOM4 are documented in the form of a user's manual. Both programs are based on fatigue strength reduction, using a probabilistic constitutive model. The programs predict the random lifetime of an engine component to reach a given fatigue strength. The theoretical backgrounds, input data instructions, and sample problems illustrating the use of the programs are included.

MJO prediction using the sub-seasonal to seasonal forecast model of Beijing Climate Center

NASA Astrophysics Data System (ADS)

Liu, Xiangwen; Wu, Tongwen; Yang, Song; Li, Tim; Jie, Weihua; Zhang, Li; Wang, Zaizhi; Liang, Xiaoyun; Li, Qiaoping; Cheng, Yanjie; Ren, Hongli; Fang, Yongjie; Nie, Suping

2017-05-01

By conducting several sets of hindcast experiments using the Beijing Climate Center Climate System Model, which participates in the Sub-seasonal to Seasonal (S2S) Prediction Project, we systematically evaluate the model's capability in forecasting MJO and its main deficiencies. In the original S2S hindcast set, MJO forecast skill is about 16 days. Such a skill shows significant seasonal-to-interannual variations. It is found that the model-dependent MJO forecast skill is more correlated with the Indian Ocean Dipole (IOD) than with the El Niño-Southern Oscillation. The highest skill is achieved in autumn when the IOD attains its maturity. Extended skill is found when the IOD is in its positive phase. MJO forecast skill's close association with the IOD is partially due to the quickly strengthening relationship between MJO amplitude and IOD intensity as lead time increases to about 15 days, beyond which a rapid weakening of the relationship is shown. This relationship transition may cause the forecast skill to decrease quickly with lead time, and is related to the unrealistic amplitude and phase evolutions of predicted MJO over or near the equatorial Indian Ocean during anomalous IOD phases, suggesting a possible influence of exaggerated IOD variability in the model. The results imply that the upper limit of intraseasonal predictability is modulated by large-scale external forcing background state in the tropical Indian Ocean. Two additional sets of hindcast experiments with improved atmosphere and ocean initial conditions (referred to as S2S_IEXP1 and S2S_IEXP2, respectively) are carried out, and the results show that the overall MJO forecast skill is increased to 21-22 days. It is found that the optimization of initial sea surface temperature condition largely accounts for the increase of the overall MJO forecast skill, even though the improved initial atmosphere conditions also play a role. For the DYNAMO/CINDY field campaign period, the forecast skill increases to 27 days in S2S_IEXP2. Nevertheless, even with improved initialization, it is still difficult for the model to predict MJO propagation across the western hemisphere-western Indian Ocean area and across the eastern Indian Ocean-Maritime Continent area. Especially, MJO prediction is apparently limited by various interrelated deficiencies (e.g., overestimated IOD, shorter-than-observed MJO life cycle, Maritime Continent prediction barrier), due possibly to the model bias in the background moisture field over the eastern Indian Ocean and Maritime Continent. Thus, more efforts are needed to correct the deficiency in model physics in this region, in order to overcome the well-known Maritime Continent predictability barrier.

Genotype and plant trait effects on soil CO2 efflux responses to altered precipitation in switchgrass

USDA-ARS?s Scientific Manuscript database

Background/Question/Methods Global climate change models predict increasing drought during the growing season, which will alter many ecosystem processes including soil CO2 efflux (JCO2), with potential consequences for carbon retention in soils. Soil moisture, soil temperature and plant traits such...

An Experimental Test of Parenting Practices as a Mediator of Early Childhood Physical Aggression

ERIC Educational Resources Information Center

Brotman, Laurie Miller; O'Neal, Colleen R.; Huang, Keng-Yen; Gouley, Kathleen Kiely; Rosenfelt, Amanda; Shrout, Patrick E.

2009-01-01

Background: Parenting practices predict early childhood physical aggression. Preventive interventions that alter parenting practices and aggression during early childhood provide the opportunity to test causal models of early childhood psychopathology. Although there have been several informative preventive intervention studies that test mediation…

Cognitive Processes in University Learning: A Developmental Framework Using Structural Equation Modelling

ERIC Educational Resources Information Center

Phan, Huy P.

2011-01-01

Background: Both achievement goals and study processing strategies theories have been shown to contribute to the prediction of students' academic performance. Existing research studies (Fenollar, Roman, & Cuestas, 2007; Liem, Lau, & Nie, 2008; Simons, Dewitte, & Lens, 2004) amalgamating these two theoretical orientations in different causal models…

Undergraduate Nurse Variables that Predict Academic Achievement and Clinical Competence in Nursing

ERIC Educational Resources Information Center

Blackman, Ian; Hall, Margaret; Darmawan, I Gusti Ngurah.

2007-01-01

A hypothetical model was formulated to explore factors that influenced academic and clinical achievement for undergraduate nursing students. Sixteen latent variables were considered including the students' background, gender, type of first language, age, their previous successes with their undergraduate nursing studies and status given for…

Importance of positive feedbacks and overconfidence in a self-fulfilling Ising model of financial markets

NASA Astrophysics Data System (ADS)

Sornette, Didier; Zhou, Wei-Xing

2006-10-01

Following a long tradition of physicists who have noticed that the Ising model provides a general background to build realistic models of social interactions, we study a model of financial price dynamics resulting from the collective aggregate decisions of agents. This model incorporates imitation, the impact of external news and private information. It has the structure of a dynamical Ising model in which agents have two opinions (buy or sell) with coupling coefficients, which evolve in time with a memory of how past news have explained realized market returns. We study two versions of the model, which differ on how the agents interpret the predictive power of news. We show that the stylized facts of financial markets are reproduced only when agents are overconfident and mis-attribute the success of news to predict return to herding effects, thereby providing positive feedbacks leading to the model functioning close to the critical point. Our model exhibits a rich multifractal structure characterized by a continuous spectrum of exponents of the power law relaxation of endogenous bursts of volatility, in good agreement with previous analytical predictions obtained with the multifractal random walk model and with empirical facts.

Ocean Predictability and Uncertainty Forecasts Using Local Ensemble Transfer Kalman Filter (LETKF)

NASA Astrophysics Data System (ADS)

Wei, M.; Hogan, P. J.; Rowley, C. D.; Smedstad, O. M.; Wallcraft, A. J.; Penny, S. G.

2017-12-01

Ocean predictability and uncertainty are studied with an ensemble system that has been developed based on the US Navy's operational HYCOM using the Local Ensemble Transfer Kalman Filter (LETKF) technology. One of the advantages of this method is that the best possible initial analysis states for the HYCOM forecasts are provided by the LETKF which assimilates operational observations using ensemble method. The background covariance during this assimilation process is implicitly supplied with the ensemble avoiding the difficult task of developing tangent linear and adjoint models out of HYCOM with the complicated hybrid isopycnal vertical coordinate for 4D-VAR. The flow-dependent background covariance from the ensemble will be an indispensable part in the next generation hybrid 4D-Var/ensemble data assimilation system. The predictability and uncertainty for the ocean forecasts are studied initially for the Gulf of Mexico. The results are compared with another ensemble system using Ensemble Transfer (ET) method which has been used in the Navy's operational center. The advantages and disadvantages are discussed.

Development and validation of a set of six adaptable prognosis prediction (SAP) models based on time-series real-world big data analysis for patients with cancer receiving chemotherapy: A multicenter case crossover study

PubMed Central

Kanai, Masashi; Okamoto, Kazuya; Yamamoto, Yosuke; Yoshioka, Akira; Hiramoto, Shuji; Nozaki, Akira; Nishikawa, Yoshitaka; Yamaguchi, Daisuke; Tomono, Teruko; Nakatsui, Masahiko; Baba, Mika; Morita, Tatsuya; Matsumoto, Shigemi; Kuroda, Tomohiro; Okuno, Yasushi; Muto, Manabu

2017-01-01

Background We aimed to develop an adaptable prognosis prediction model that could be applied at any time point during the treatment course for patients with cancer receiving chemotherapy, by applying time-series real-world big data. Methods Between April 2004 and September 2014, 4,997 patients with cancer who had received systemic chemotherapy were registered in a prospective cohort database at the Kyoto University Hospital. Of these, 2,693 patients with a death record were eligible for inclusion and divided into training (n = 1,341) and test (n = 1,352) cohorts. In total, 3,471,521 laboratory data at 115,738 time points, representing 40 laboratory items [e.g., white blood cell counts and albumin (Alb) levels] that were monitored for 1 year before the death event were applied for constructing prognosis prediction models. All possible prediction models comprising three different items from 40 laboratory items (40C3 = 9,880) were generated in the training cohort, and the model selection was performed in the test cohort. The fitness of the selected models was externally validated in the validation cohort from three independent settings. Results A prognosis prediction model utilizing Alb, lactate dehydrogenase, and neutrophils was selected based on a strong ability to predict death events within 1–6 months and a set of six prediction models corresponding to 1,2, 3, 4, 5, and 6 months was developed. The area under the curve (AUC) ranged from 0.852 for the 1 month model to 0.713 for the 6 month model. External validation supported the performance of these models. Conclusion By applying time-series real-world big data, we successfully developed a set of six adaptable prognosis prediction models for patients with cancer receiving chemotherapy. PMID:28837592

Designing and evaluating the MULTICOM protein local and global model quality prediction methods in the CASP10 experiment

PubMed Central

2014-01-01

Background Protein model quality assessment is an essential component of generating and using protein structural models. During the Tenth Critical Assessment of Techniques for Protein Structure Prediction (CASP10), we developed and tested four automated methods (MULTICOM-REFINE, MULTICOM-CLUSTER, MULTICOM-NOVEL, and MULTICOM-CONSTRUCT) that predicted both local and global quality of protein structural models. Results MULTICOM-REFINE was a clustering approach that used the average pairwise structural similarity between models to measure the global quality and the average Euclidean distance between a model and several top ranked models to measure the local quality. MULTICOM-CLUSTER and MULTICOM-NOVEL were two new support vector machine-based methods of predicting both the local and global quality of a single protein model. MULTICOM-CONSTRUCT was a new weighted pairwise model comparison (clustering) method that used the weighted average similarity between models in a pool to measure the global model quality. Our experiments showed that the pairwise model assessment methods worked better when a large portion of models in the pool were of good quality, whereas single-model quality assessment methods performed better on some hard targets when only a small portion of models in the pool were of reasonable quality. Conclusions Since digging out a few good models from a large pool of low-quality models is a major challenge in protein structure prediction, single model quality assessment methods appear to be poised to make important contributions to protein structure modeling. The other interesting finding was that single-model quality assessment scores could be used to weight the models by the consensus pairwise model comparison method to improve its accuracy. PMID:24731387

Expanding a dynamic flux balance model of yeast fermentation to genome-scale

PubMed Central

2011-01-01

Background Yeast is considered to be a workhorse of the biotechnology industry for the production of many value-added chemicals, alcoholic beverages and biofuels. Optimization of the fermentation is a challenging task that greatly benefits from dynamic models able to accurately describe and predict the fermentation profile and resulting products under different genetic and environmental conditions. In this article, we developed and validated a genome-scale dynamic flux balance model, using experimentally determined kinetic constraints. Results Appropriate equations for maintenance, biomass composition, anaerobic metabolism and nutrient uptake are key to improve model performance, especially for predicting glycerol and ethanol synthesis. Prediction profiles of synthesis and consumption of the main metabolites involved in alcoholic fermentation closely agreed with experimental data obtained from numerous lab and industrial fermentations under different environmental conditions. Finally, fermentation simulations of genetically engineered yeasts closely reproduced previously reported experimental results regarding final concentrations of the main fermentation products such as ethanol and glycerol. Conclusion A useful tool to describe, understand and predict metabolite production in batch yeast cultures was developed. The resulting model, if used wisely, could help to search for new metabolic engineering strategies to manage ethanol content in batch fermentations. PMID:21595919

Advanced Online Survival Analysis Tool for Predictive Modelling in Clinical Data Science.

PubMed

Montes-Torres, Julio; Subirats, José Luis; Ribelles, Nuria; Urda, Daniel; Franco, Leonardo; Alba, Emilio; Jerez, José Manuel

2016-01-01

One of the prevailing applications of machine learning is the use of predictive modelling in clinical survival analysis. In this work, we present our view of the current situation of computer tools for survival analysis, stressing the need of transferring the latest results in the field of machine learning to biomedical researchers. We propose a web based software for survival analysis called OSA (Online Survival Analysis), which has been developed as an open access and user friendly option to obtain discrete time, predictive survival models at individual level using machine learning techniques, and to perform standard survival analysis. OSA employs an Artificial Neural Network (ANN) based method to produce the predictive survival models. Additionally, the software can easily generate survival and hazard curves with multiple options to personalise the plots, obtain contingency tables from the uploaded data to perform different tests, and fit a Cox regression model from a number of predictor variables. In the Materials and Methods section, we depict the general architecture of the application and introduce the mathematical background of each of the implemented methods. The study concludes with examples of use showing the results obtained with public datasets.

Advanced Online Survival Analysis Tool for Predictive Modelling in Clinical Data Science

PubMed Central

Montes-Torres, Julio; Subirats, José Luis; Ribelles, Nuria; Urda, Daniel; Franco, Leonardo; Alba, Emilio; Jerez, José Manuel

2016-01-01

One of the prevailing applications of machine learning is the use of predictive modelling in clinical survival analysis. In this work, we present our view of the current situation of computer tools for survival analysis, stressing the need of transferring the latest results in the field of machine learning to biomedical researchers. We propose a web based software for survival analysis called OSA (Online Survival Analysis), which has been developed as an open access and user friendly option to obtain discrete time, predictive survival models at individual level using machine learning techniques, and to perform standard survival analysis. OSA employs an Artificial Neural Network (ANN) based method to produce the predictive survival models. Additionally, the software can easily generate survival and hazard curves with multiple options to personalise the plots, obtain contingency tables from the uploaded data to perform different tests, and fit a Cox regression model from a number of predictor variables. In the Materials and Methods section, we depict the general architecture of the application and introduce the mathematical background of each of the implemented methods. The study concludes with examples of use showing the results obtained with public datasets. PMID:27532883

Evaluation of 3D-Jury on CASP7 models

PubMed Central

Kaján, László; Rychlewski, Leszek

2007-01-01

Background 3D-Jury, the structure prediction consensus method publicly available in the Meta Server , was evaluated using models gathered in the 7th round of the Critical Assessment of Techniques for Protein Structure Prediction (CASP7). 3D-Jury is an automated expert process that generates protein structure meta-predictions from sets of models obtained from partner servers. Results The performance of 3D-Jury was analysed for three aspects. First, we examined the correlation between the 3D-Jury score and a model quality measure: the number of correctly predicted residues. The 3D-Jury score was shown to correlate significantly with the number of correctly predicted residues, the correlation is good enough to be used for prediction. 3D-Jury was also found to improve upon the competing servers' choice of the best structure model in most cases. The value of the 3D-Jury score as a generic reliability measure was also examined. We found that the 3D-Jury score separates bad models from good models better than the reliability score of the original server in 27 cases and falls short of it in only 5 cases out of a total of 38. We report the release of a new Meta Server feature: instant 3D-Jury scoring of uploaded user models. Conclusion The 3D-Jury score continues to be a good indicator of structural model quality. It also provides a generic reliability score, especially important for models that were not assigned such by the original server. Individual structure modellers can also benefit from the 3D-Jury scoring system by testing their models in the new instant scoring feature available in the Meta Server. PMID:17711571

Multispectral infrared target detection: phenomenology and modeling

NASA Astrophysics Data System (ADS)

Cederquist, Jack N.; Rogne, Timothy J.; Schwartz, Craig R.

1993-10-01

Many targets of interest provide only very small signature differences from the clutter background. The ability to detect these small difference targets should be improved by using data which is diverse in space, time, wavelength or some other observable. Target materials often differ from background materials in the variation of their reflectance or emittance with wavelength. A multispectral sensor is therefore considered as a means to improve detection of small signal targets. If this sensor operates in the thermal infrared, it will not need solar illumination and will be useful at night as well as during the day. An understanding of the phenomenology of the spectral properties of materials and an ability to model and simulate target and clutter signatures is needed to understand potential target detection performance from multispectral infrared sensor data. Spectral variations in material emittance are due to vibrational energy transitions in molecular bonds. The spectral emittances of many materials of interest have been measured. Examples are vegetation, soil, construction and road materials, and paints. A multispectral infrared signature model has been developed which includes target and background temperature and emissivity, sky, sun, cloud and background irradiance, multiple reflection effects, path radiance, and atmospheric attenuation. This model can be used to predict multispectral infrared signatures for small signal targets.

Cardiovascular disease risk scores in the current practice: which to use in rheumatoid arthritis?

PubMed

Purcarea, A; Sovaila, S; Gheorghe, A; Udrea, G; Stoica, V

2014-01-01

Cardiovascular disease (CVD) is the highest prevalence disease in the general population (GP) and it accounts for 20 million deaths worldwide each year. Its prevalence is even higher in rheumatoid arthritis. Early detection of subclinical disease is critical and the use of cardiovascular risk prediction models and calculators is widely spread. The impact of such techniques in the GP was previously studied. Despite their common background and similarities, some disagreement exists between most scores and their importance in special high-risk populations like rheumatoid arthritis (RA), having a low level of evidence. The current article aims to single out those predictive models (models) that could be most useful in the care of rheumatoid arthritis patients.

Cardiovascular disease risk scores in the current practice: which to use in rheumatoid arthritis?

PubMed Central

Purcarea, A; Sovaila, S; Gheorghe, A; Udrea, G; Stoica, V

2014-01-01

Cardiovascular disease (CVD) is the highest prevalence disease in the general population (GP) and it accounts for 20 million deaths worldwide each year. Its prevalence is even higher in rheumatoid arthritis. Early detection of subclinical disease is critical and the use of cardiovascular risk prediction models and calculators is widely spread. The impact of such techniques in the GP was previously studied. Despite their common background and similarities, some disagreement exists between most scores and their importance in special high-risk populations like rheumatoid arthritis (RA), having a low level of evidence. The current article aims to single out those predictive models (models) that could be most useful in the care of rheumatoid arthritis patients. PMID:25713603

Proposal for a New Predictive Model of Short-Term Mortality After Living Donor Liver Transplantation due to Acute Liver Failure.

PubMed

Chung, Hyun Sik; Lee, Yu Jung; Jo, Yun Sung

2017-02-21

BACKGROUND Acute liver failure (ALF) is known to be a rapidly progressive and fatal disease. Various models which could help to estimate the post-transplant outcome for ALF have been developed; however, none of them have been proved to be the definitive predictive model of accuracy. We suggest a new predictive model, and investigated which model has the highest predictive accuracy for the short-term outcome in patients who underwent living donor liver transplantation (LDLT) due to ALF. MATERIAL AND METHODS Data from a total 88 patients were collected retrospectively. King's College Hospital criteria (KCH), Child-Turcotte-Pugh (CTP) classification, and model for end-stage liver disease (MELD) score were calculated. Univariate analysis was performed, and then multivariate statistical adjustment for preoperative variables of ALF prognosis was performed. A new predictive model was developed, called the MELD conjugated serum phosphorus model (MELD-p). The individual diagnostic accuracy and cut-off value of models in predicting 3-month post-transplant mortality were evaluated using the area under the receiver operating characteristic curve (AUC). The difference in AUC between MELD-p and the other models was analyzed. The diagnostic improvement in MELD-p was assessed using the net reclassification improvement (NRI) and integrated discrimination improvement (IDI). RESULTS The MELD-p and MELD scores had high predictive accuracy (AUC >0.9). KCH and serum phosphorus had an acceptable predictive ability (AUC >0.7). The CTP classification failed to show discriminative accuracy in predicting 3-month post-transplant mortality. The difference in AUC between MELD-p and the other models had statistically significant associations with CTP and KCH. The cut-off value of MELD-p was 3.98 for predicting 3-month post-transplant mortality. The NRI was 9.9% and the IDI was 2.9%. CONCLUSIONS MELD-p score can predict 3-month post-transplant mortality better than other scoring systems after LDLT due to ALF. The recommended cut-off value of MELD-p is 3.98.

Enhanced backgrounds in scene rendering with GTSIMS

NASA Astrophysics Data System (ADS)

Prussing, Keith F.; Pierson, Oliver; Cordell, Chris; Stewart, John; Nielson, Kevin

2018-05-01

A core component to modeling visible and infrared sensor responses is the ability to faithfully recreate background noise and clutter in a synthetic image. Most tracking and detection algorithms use a combination of signal to noise or clutter to noise ratios to determine if a signature is of interest. A primary source of clutter is the background that defines the environment in which a target is placed. Over the past few years, the Electro-Optical Systems Laboratory (EOSL) at the Georgia Tech Research Institute has made significant improvements to its in house simulation framework GTSIMS. First, we have expanded our terrain models to include the effects of terrain orientation on emission and reflection. Second, we have included the ability to model dynamic reflections with full BRDF support. Third, we have added the ability to render physically accurate cirrus clouds. And finally, we have updated the overall rendering procedure to reduce the time necessary to generate a single frame by taking advantage of hardware acceleration. Here, we present the updates to GTSIMS to better predict clutter and noise doe to non-uniform backgrounds. Specifically, we show how the addition of clouds, terrain, and improved non-uniform sky rendering improve our ability to represent clutter during scene generation.

Achieving Superior Tropical Cyclone Intensity Forecasts by Improving the Assimilation of High-Resolution Satellite Data into Mesoscale Prediction Models

DTIC Science & Technology

2013-09-30

using polar orbit microwave and infrared sounder measurements from the Global Telecommunication System (GTS). The SDAT system was developed as a...WRF/GSI initial conditions and WRF boundary conditions. • WRF system to do short-range forecasts (6 hours) to provide the background fields for GSI...UCAR is related to a NASA GNSS proposal: “Improving Tropical Prediction and Analysis using COSMIC Radio Occultation Observations and an Ensemble Data

Prediction models for clustered data: comparison of a random intercept and standard regression model

PubMed Central

2013-01-01

Background When study data are clustered, standard regression analysis is considered inappropriate and analytical techniques for clustered data need to be used. For prediction research in which the interest of predictor effects is on the patient level, random effect regression models are probably preferred over standard regression analysis. It is well known that the random effect parameter estimates and the standard logistic regression parameter estimates are different. Here, we compared random effect and standard logistic regression models for their ability to provide accurate predictions. Methods Using an empirical study on 1642 surgical patients at risk of postoperative nausea and vomiting, who were treated by one of 19 anesthesiologists (clusters), we developed prognostic models either with standard or random intercept logistic regression. External validity of these models was assessed in new patients from other anesthesiologists. We supported our results with simulation studies using intra-class correlation coefficients (ICC) of 5%, 15%, or 30%. Standard performance measures and measures adapted for the clustered data structure were estimated. Results The model developed with random effect analysis showed better discrimination than the standard approach, if the cluster effects were used for risk prediction (standard c-index of 0.69 versus 0.66). In the external validation set, both models showed similar discrimination (standard c-index 0.68 versus 0.67). The simulation study confirmed these results. For datasets with a high ICC (≥15%), model calibration was only adequate in external subjects, if the used performance measure assumed the same data structure as the model development method: standard calibration measures showed good calibration for the standard developed model, calibration measures adapting the clustered data structure showed good calibration for the prediction model with random intercept. Conclusion The models with random intercept discriminate better than the standard model only if the cluster effect is used for predictions. The prediction model with random intercept had good calibration within clusters. PMID:23414436

The derivation and validation of a simple model for predicting in-hospital mortality of acutely admitted patients to internal medicine wards.

PubMed

Sakhnini, Ali; Saliba, Walid; Schwartz, Naama; Bisharat, Naiel

2017-06-01

Limited information is available about clinical predictors of in-hospital mortality in acute unselected medical admissions. Such information could assist medical decision-making.To develop a clinical model for predicting in-hospital mortality in unselected acute medical admissions and to test the impact of secondary conditions on hospital mortality.This is an analysis of the medical records of patients admitted to internal medicine wards at one university-affiliated hospital. Data obtained from the years 2013 to 2014 were used as a derivation dataset for creating a prediction model, while data from 2015 was used as a validation dataset to test the performance of the model. For each admission, a set of clinical and epidemiological variables was obtained. The main diagnosis at hospitalization was recorded, and all additional or secondary conditions that coexisted at hospital admission or that developed during hospital stay were considered secondary conditions.The derivation and validation datasets included 7268 and 7843 patients, respectively. The in-hospital mortality rate averaged 7.2%. The following variables entered the final model; age, body mass index, mean arterial pressure on admission, prior admission within 3 months, background morbidity of heart failure and active malignancy, and chronic use of statins and antiplatelet agents. The c-statistic (ROC-AUC) of the prediction model was 80.5% without adjustment for main or secondary conditions, 84.5%, with adjustment for the main diagnosis, and 89.5% with adjustment for the main diagnosis and secondary conditions. The accuracy of the predictive model reached 81% on the validation dataset.A prediction model based on clinical data with adjustment for secondary conditions exhibited a high degree of prediction accuracy. We provide a proof of concept that there is an added value for incorporating secondary conditions while predicting probabilities of in-hospital mortality. Further improvement of the model performance and validation in other cohorts are needed to aid hospitalists in predicting health outcomes.

Implementation and Research on the Operational Use of the Mesoscale Prediction Model COAMPS in Poland

DTIC Science & Technology

2008-09-30

participated in EGU General Assembly , Vienna Austria 13-18 April 2008, giving a poster presentation. Bogumil Jakubiak, University of Warsaw...participated in EGU General Assembly , Vienna Austria 13-18 April 2008, giving two posters presentation. Mikolaj Sierzega, University of Warwick – participated...model forecast to generate background error statistics. This helps us to identify and understand the uncertainties in high-resolution NWP forecasts

Systematics errors in strong lens modeling

NASA Astrophysics Data System (ADS)

Johnson, Traci L.; Sharon, Keren; Bayliss, Matthew B.

We investigate how varying the number of multiple image constraints and the available redshift information can influence the systematic errors of strong lens models, specifically, the image predictability, mass distribution, and magnifications of background sources. This work will not only inform upon Frontier Field science, but also for work on the growing collection of strong lensing galaxy clusters, most of which are less massive and are capable of lensing a handful of galaxies.

Comparison of the predictions of two road dust emission models with the measurements of a mobile van

NASA Astrophysics Data System (ADS)

Kauhaniemi, M.; Stojiljkovic, A.; Pirjola, L.; Karppinen, A.; Härkönen, J.; Kupiainen, K.; Kangas, L.; Aarnio, M. A.; Omstedt, G.; Denby, B. R.; Kukkonen, J.

2014-02-01

The predictions of two road dust suspension emission models were compared with the on-site mobile measurements of suspension emission factors. Such a quantitative comparison has not previously been reported in the reviewed literature. The models used were the Nordic collaboration model NORTRIP (NOn-exhaust Road TRaffic Induced Particle emissions) and the Swedish-Finnish FORE model (Forecasting Of Road dust Emissions). These models describe particulate matter generated by the wear of road surface due to traction control methods and processes that control the suspension of road dust particles into the air. An experimental measurement campaign was conducted using a mobile laboratory called SNIFFER, along two selected road segments in central Helsinki in 2007 and 2008. The suspended PM10 concentration was measured behind the left rear tyre and the street background PM10 concentration in front of the van. Both models reproduced the measured seasonal variation of suspension emission factors fairly well during both years at both measurement sites. However, both models substantially under-predicted the measured emission values. The results indicate that road dust emission models can be directly compared with mobile measurements; however, more extensive and versatile measurement campaigns will be needed in the future.

Modelling pollination services across agricultural landscapes

PubMed Central

Lonsdorf, Eric; Kremen, Claire; Ricketts, Taylor; Winfree, Rachael; Williams, Neal; Greenleaf, Sarah

2009-01-01

Background and Aims Crop pollination by bees and other animals is an essential ecosystem service. Ensuring the maintenance of the service requires a full understanding of the contributions of landscape elements to pollinator populations and crop pollination. Here, the first quantitative model that predicts pollinator abundance on a landscape is described and tested. Methods Using information on pollinator nesting resources, floral resources and foraging distances, the model predicts the relative abundance of pollinators within nesting habitats. From these nesting areas, it then predicts relative abundances of pollinators on the farms requiring pollination services. Model outputs are compared with data from coffee in Costa Rica, watermelon and sunflower in California and watermelon in New Jersey–Pennsylvania (NJPA). Key Results Results from Costa Rica and California, comparing field estimates of pollinator abundance, richness or services with model estimates, are encouraging, explaining up to 80 % of variance among farms. However, the model did not predict observed pollinator abundances on NJPA, so continued model improvement and testing are necessary. The inability of the model to predict pollinator abundances in the NJPA landscape may be due to not accounting for fine-scale floral and nesting resources within the landscapes surrounding farms, rather than the logic of our model. Conclusions The importance of fine-scale resources for pollinator service delivery was supported by sensitivity analyses indicating that the model's predictions depend largely on estimates of nesting and floral resources within crops. Despite the need for more research at the finer-scale, the approach fills an important gap by providing quantitative and mechanistic model from which to evaluate policy decisions and develop land-use plans that promote pollination conservation and service delivery. PMID:19324897

Developing and validating risk prediction models in an individual participant data meta-analysis

PubMed Central

2014-01-01

Background Risk prediction models estimate the risk of developing future outcomes for individuals based on one or more underlying characteristics (predictors). We review how researchers develop and validate risk prediction models within an individual participant data (IPD) meta-analysis, in order to assess the feasibility and conduct of the approach. Methods A qualitative review of the aims, methodology, and reporting in 15 articles that developed a risk prediction model using IPD from multiple studies. Results The IPD approach offers many opportunities but methodological challenges exist, including: unavailability of requested IPD, missing patient data and predictors, and between-study heterogeneity in methods of measurement, outcome definitions and predictor effects. Most articles develop their model using IPD from all available studies and perform only an internal validation (on the same set of data). Ten of the 15 articles did not allow for any study differences in baseline risk (intercepts), potentially limiting their model’s applicability and performance in some populations. Only two articles used external validation (on different data), including a novel method which develops the model on all but one of the IPD studies, tests performance in the excluded study, and repeats by rotating the omitted study. Conclusions An IPD meta-analysis offers unique opportunities for risk prediction research. Researchers can make more of this by allowing separate model intercept terms for each study (population) to improve generalisability, and by using ‘internal-external cross-validation’ to simultaneously develop and validate their model. Methodological challenges can be reduced by prospectively planned collaborations that share IPD for risk prediction. PMID:24397587

Hypoglycemia Early Alarm Systems Based on Recursive Autoregressive Partial Least Squares Models

PubMed Central

Bayrak, Elif Seyma; Turksoy, Kamuran; Cinar, Ali; Quinn, Lauretta; Littlejohn, Elizabeth; Rollins, Derrick

2013-01-01

Background Hypoglycemia caused by intensive insulin therapy is a major challenge for artificial pancreas systems. Early detection and prevention of potential hypoglycemia are essential for the acceptance of fully automated artificial pancreas systems. Many of the proposed alarm systems are based on interpretation of recent values or trends in glucose values. In the present study, subject-specific linear models are introduced to capture glucose variations and predict future blood glucose concentrations. These models can be used in early alarm systems of potential hypoglycemia. Methods A recursive autoregressive partial least squares (RARPLS) algorithm is used to model the continuous glucose monitoring sensor data and predict future glucose concentrations for use in hypoglycemia alarm systems. The partial least squares models constructed are updated recursively at each sampling step with a moving window. An early hypoglycemia alarm algorithm using these models is proposed and evaluated. Results Glucose prediction models based on real-time filtered data has a root mean squared error of 7.79 and a sum of squares of glucose prediction error of 7.35% for six-step-ahead (30 min) glucose predictions. The early alarm systems based on RARPLS shows good performance. A sensitivity of 86% and a false alarm rate of 0.42 false positive/day are obtained for the early alarm system based on six-step-ahead predicted glucose values with an average early detection time of 25.25 min. Conclusions The RARPLS models developed provide satisfactory glucose prediction with relatively smaller error than other proposed algorithms and are good candidates to forecast and warn about potential hypoglycemia unless preventive action is taken far in advance. PMID:23439179

Association of Coronary Artery Calcification with Estimated Coronary Heart Disease Risk from Prediction Models in a Community-Based Sample of Japanese Men: The Shiga Epidemiological Study of Subclinical Atherosclerosis (SESSA).

PubMed

Fujiyoshi, Akira; Arima, Hisatomi; Tanaka-Mizuno, Sachiko; Hisamatsu, Takahashi; Kadowaki, Sayaka; Kadota, Aya; Zaid, Maryam; Sekikawa, Akira; Yamamoto, Takashi; Horie, Minoru; Miura, Katsuyuki; Ueshima, Hirotsugu

2017-12-05

The clinical significance of coronary artery calcification (CAC) is not fully determined in general East Asian populations where background coronary heart disease (CHD) is less common than in USA/Western countries. We cross-sectionally assessed the association between CAC and estimated CHD risk as well as each major risk factor in general Japanese men. Participants were 996 randomly selected Japanese men aged 40-79 y, free of stroke, myocardial infarction, or revascularization. We examined an independent relationship between each risk factor used in prediction models and CAC score ≥100 by logistic regression. We then divided the participants into quintiles of estimated CHD risk per prediction model to calculate odds ratio of having CAC score ≥100. Receiver operating characteristic curve and c-index were used to examine discriminative ability of prevalent CAC for each prediction model. Age, smoking status, and systolic blood pressure were significantly associated with CAC score ≥100 in the multivariable analysis. The odds of having CAC score ≥100 were higher for those in higher quintiles in all prediction models (p-values for trend across quintiles ＜0.0001 for all models). All prediction models showed fair and similar discriminative abilities to detect CAC score ≥100, with similar c-statistics (around 0.70). In a community-based sample of Japanese men free of CHD and stroke, CAC score ≥100 was significantly associated with higher estimated CHD risk by prediction models. This finding supports the potential utility of CAC as a biomarker for CHD in a general Japanese male population.

On rate-state and Coulomb failure models

USGS Publications Warehouse

Gomberg, J.; Beeler, N.; Blanpied, M.

2000-01-01

We examine the predictions of Coulomb failure stress and rate-state frictional models. We study the change in failure time (clock advance) Δt due to stress step perturbations (i.e., coseismic static stress increases) added to "background" stressing at a constant rate (i.e., tectonic loading) at time t0. The predictability of Δt implies a predictable change in seismicity rate r(t)/r0, testable using earthquake catalogs, where r0 is the constant rate resulting from tectonic stressing. Models of r(t)/r0, consistent with general properties of aftershock sequences, must predict an Omori law seismicity decay rate, a sequence duration that is less than a few percent of the mainshock cycle time and a return directly to the background rate. A Coulomb model requires that a fault remains locked during loading, that failure occur instantaneously, and that Δt is independent of t0. These characteristics imply an instantaneous infinite seismicity rate increase of zero duration. Numerical calculations of r(t)/r0 for different state evolution laws show that aftershocks occur on faults extremely close to failure at the mainshock origin time, that these faults must be "Coulomb-like," and that the slip evolution law can be precluded. Real aftershock population characteristics also may constrain rate-state constitutive parameters; a may be lower than laboratory values, the stiffness may be high, and/or normal stress may be lower than lithostatic. We also compare Coulomb and rate-state models theoretically. Rate-state model fault behavior becomes more Coulomb-like as constitutive parameter a decreases relative to parameter b. This is because the slip initially decelerates, representing an initial healing of fault contacts. The deceleration is more pronounced for smaller a, more closely simulating a locked fault. Even when the rate-state Δt has Coulomb characteristics, its magnitude may differ by some constant dependent on b. In this case, a rate-state model behaves like a modified Coulomb failure model in which the failure stress threshold is lowered due to weakening, increasing the clock advance. The deviation from a non-Coulomb response also depends on the loading rate, elastic stiffness, initial conditions, and assumptions about how state evolves.

Biokinetic food chain modeling of waterborne selenium pulses into aquatic food chains: Implications for water quality criteria.

PubMed

DeForest, David K; Pargee, Suzanne; Claytor, Carrie; Canton, Steven P; Brix, Kevin V

2016-04-01

We evaluated the use of biokinetic models to predict selenium (Se) bioaccumulation into model food chains after short-term pulses of selenate or selenite into water. Both periphyton- and phytoplankton-based food chains were modeled, with Se trophically transferred to invertebrates and then to fish. Whole-body fish Se concentrations were predicted based on 1) the background waterborne Se concentration, 2) the magnitude of the Se pulse, and 3) the duration of the Se pulse. The models were used to evaluate whether the US Environmental Protection Agency's (USEPA's) existing acute Se criteria and their recently proposed intermittent Se criteria would be protective of a whole-body fish Se tissue-based criterion of 8.1 μg g(-1) dry wt. Based on a background waterborne Se concentration of 1 μg L(-1) and pulse durations of 1 d and 4 d, the Se pulse concentrations predicted to result in a whole-body fish Se concentration of 8.1 μg g(-1) dry wt in the most conservative model food chains were 144 and 35 μg L(-1), respectively, for selenate and 57 and 16 μg L(-1), respectively, for selenite. These concentrations fall within the range of various acute Se criteria recommended by the USEPA based on direct waterborne toxicity, suggesting that these criteria may not always be protective against bioaccumulation-based toxicity that could occur after short-term pulses. Regarding the USEPA's draft intermittent Se criteria, the biokinetic modeling indicates that they may be overly protective for selenate pulses but potentially underprotective for selenite pulses. Predictions of whole-body fish Se concentrations were highly dependent on whether the food chain was periphyton- or phytoplankton-based, because the latter had much greater Se uptake rate constants. Overall, biokinetic modeling provides an approach for developing acute Se criteria that are protective against bioaccumulation-based toxicity after trophic transfer, and it is also a useful tool for evaluating averaging periods for chronic Se criteria. © 2015 SETAC.

A search for the higgs boson and a search for dark-matter particle with jets and missing transverse energy at collider detector at Fermilab

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

Liu, Qiuguang

Finding the standard model Higgs boson and discovering beyond-standard model physics phenomena have been the most important goals for the high-energy physics in the last decades. In this thesis, we present two such searches. First is the search for the low mass standard model Higgs boson produced in association with a vector boson; second is the rst search for a dark-matter candidate (D) produced in association with a top quark (t) in particle colliders. We search in events with energetic jets and large missing transverse energy { a signature characterized by complicated backgrounds { in data collected by the CDFmore » detector with proton-antiproton collisions at p s = 1:96 TeV. We discuss the techniques that have been developed for background modeling, for discriminating signal from background, and for reducing background resulting from detector e ects. In the Higgs search, we report the 95% con dence level upper limits on the pro- duction cross section across masses of 90 to 150 GeV/c2. The expected limits are improved by an average of 14% relative to the previous analysis. The Large Hadron Collider experiments reported a Higgs-like particle with mass of 125 GeV/c2 by study- ing the data collected in year 2011/12. At a Higgs boson mass of 125 GeV/c2, our observed (expected) limit is 3.06 (3.33) times the standard model prediction, corre- sponding to one of the most sensitive searches to date in this nal state. In the dark matter search, we nd the data are consistent with the standard model prediction, thus set 95% con dence level upper limits on the cross section of the process p p ! t + D as a function of the mass of the dark-matter candidate. The xviii upper limits are approximately 0.5 pb for a dark-matter particle with masses in the range of 0 150 GeV/c2.« less

Development of a Melanoma Risk Prediction Model Incorporating MC1R Genotype and Indoor Tanning Exposure: Impact of Mole Phenotype on Model Performance

PubMed Central

Penn, Lauren A.; Qian, Meng; Zhang, Enhan; Ng, Elise; Shao, Yongzhao; Berwick, Marianne; Lazovich, DeAnn; Polsky, David

2014-01-01

Background Identifying individuals at increased risk for melanoma could potentially improve public health through targeted surveillance and early detection. Studies have separately demonstrated significant associations between melanoma risk, melanocortin receptor (MC1R) polymorphisms, and indoor ultraviolet light (UV) exposure. Existing melanoma risk prediction models do not include these factors; therefore, we investigated their potential to improve the performance of a risk model. Methods Using 875 melanoma cases and 765 controls from the population-based Minnesota Skin Health Study we compared the predictive ability of a clinical melanoma risk model (Model A) to an enhanced model (Model F) using receiver operating characteristic (ROC) curves. Model A used self-reported conventional risk factors including mole phenotype categorized as “none”, “few”, “some” or “many” moles. Model F added MC1R genotype and measures of indoor and outdoor UV exposure to Model A. We also assessed the predictive ability of these models in subgroups stratified by mole phenotype (e.g. nevus-resistant (“none” and “few” moles) and nevus-prone (“some” and “many” moles)). Results Model A (the reference model) yielded an area under the ROC curve (AUC) of 0.72 (95% CI = 0.69, 0.74). Model F was improved with an AUC = 0.74 (95% CI = 0.71–0.76, p<0.01). We also observed substantial variations in the AUCs of Models A & F when examined in the nevus-prone and nevus-resistant subgroups. Conclusions These results demonstrate that adding genotypic information and environmental exposure data can increase the predictive ability of a clinical melanoma risk model, especially among nevus-prone individuals. PMID:25003831

Predicting medical students' intentions to take up rural practice after graduation.

PubMed

Jones, Michael; Humphreys, John; Prideaux, David

2009-10-01

Using a novel longitudinal tracking project, this study develops and evaluates the performance of a predictive model and index of rural medical practice intention based on the characteristics of incoming medical students. Medical school entry survey data were obtained from the Medical Schools Outcome Database (MSOD) project implemented in all Australian and New Zealand medical schools and coordinated through Medical Deans Australia and New Zealand, the representative body for the Deans of 18 Australian and two New Zealand medical schools and faculties. The medical school commencement survey collects data on students' education and family background, including rural upbringing, personal circumstances and scholarships, and on their practice intentions in terms of location and specialty. The MSOD will also allow tracking of medical graduates after graduation. Logistic regression modelling was used to develop a predictive model of rural practice intention. Split-sample validation was used to gain some insight into the stability of performance of the model. Response rates to the MSOD survey exceeded 90% on average. The model findings confirm and extend previous research examining the association of medical student characteristics with intention to take up rural medical practice. The statistically significant independent factors in the model included students' rural backgrounds, financial arrangements and intentions regarding specialist versus generalist practice upon graduation. Model performance was good, with an area under the receiver-operator characteristics curve of 0.86, and reproducible, with an area in a validation sample of 0.83. The model and related index provide important insights into individual factors associated with rural practice intention among students commencing medical studies. The model can also provide a means for optimising the use of scarce medical programme resources, thereby helping to improve the supply of rural medical practitioners. This study illustrates the power and potential of a robust, consistent, systematic longitudinal tracking project.

Issues related to aircraft take-off plumes in a mesoscale photochemical model.

PubMed

Bossioli, Elissavet; Tombrou, Maria; Helmis, Costas; Kurtenbach, Ralf; Wiesen, Peter; Schäfer, Klaus; Dandou, Aggeliki; Varotsos, Kostas V

2013-07-01

The physical and chemical characteristics of aircraft plumes at the take-off phase are simulated with the mesoscale CAMx model using the individual plume segment approach, in a highly resolved domain, covering the Athens International Airport. Emission indices during take-off measured at the Athens International Airport are incorporated. Model predictions are compared with in situ point and path-averaged observations (NO, NO₂) downwind of the runway at the ground. The influence of modeling process, dispersion properties and background air composition on the chemical evolution of the aircraft plumes is examined. It is proven that the mixing properties mainly determine the plume dispersion. The initial plume properties become significant for the selection of the appropriate vertical resolution. Besides these factors, the background NOx and O₃ concentration levels control NOx distribution and their conversion to nitrogen reservoir species. Copyright © 2013 Elsevier B.V. All rights reserved.

Fitting cosmic microwave background data with cosmic strings and inflation.

PubMed

Bevis, Neil; Hindmarsh, Mark; Kunz, Martin; Urrestilla, Jon

2008-01-18

We perform a multiparameter likelihood analysis to compare measurements of the cosmic microwave background (CMB) power spectra with predictions from models involving cosmic strings. Adding strings to the standard case of a primordial spectrum with power-law tilt ns, we find a 2sigma detection of strings: f10=0.11+/-0.05, where f10 is the fractional contribution made by strings in the temperature power spectrum (at l=10). CMB data give moderate preference to the model ns=1 with cosmic strings over the standard zero-strings model with variable tilt. When additional non-CMB data are incorporated, the two models become on a par. With variable ns and these extra data, we find that f10<0.11, which corresponds to Gmicro<0.7x10(-6) (where micro is the string tension and G is the gravitational constant).

Optical modeling of stratopheric aerosols - Present status

NASA Technical Reports Server (NTRS)

Rosen, J. M.; Hofmann, D. J.

1986-01-01

A stratospheric aerosol optical model is developed which is based on a size distribution conforming to direct measurements. Additional constraints are consistent with large data sets of independently measured macroscopic aerosol properties such as mass and backscatter. The period under study covers background as well as highly disturbed volcanic conditions and an altitude interval ranging from the tropopause to about 30 km. The predictions of the model are used to form a basis for interpreting and intercomparing several diverse types of stratospheric aerosol measurement.

Observation of coherent elastic neutrino-nucleus scattering

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

Akimov, D.; Albert, J. B.; An, P.

The coherent elastic scattering of neutrinos off nuclei has eluded detection for four decades, even though its predicted cross section is by far the largest of all low-energy neutrino couplings. This mode of interaction offers new opportunities to study neutrino properties and leads to a miniaturization of detector size, with potential technological applications. In this paper, we observed this process at a 6.7σ confidence level, using a low-background, 14.6-kilogram CsI[Na] scintillator exposed to the neutrino emissions from the Spallation Neutron Source at Oak Ridge National Laboratory. Characteristic signatures in energy and time, predicted by the standard model for this process,more » were observed in high signal-to-background conditions. Finally, improved constraints on nonstandard neutrino interactions with quarks are derived from this initial data set.« less

Observation of coherent elastic neutrino-nucleus scattering

DOE PAGES

Akimov, D.; Albert, J. B.; An, P.; ...

2017-08-03

The coherent elastic scattering of neutrinos off nuclei has eluded detection for four decades, even though its predicted cross section is by far the largest of all low-energy neutrino couplings. This mode of interaction offers new opportunities to study neutrino properties and leads to a miniaturization of detector size, with potential technological applications. In this paper, we observed this process at a 6.7σ confidence level, using a low-background, 14.6-kilogram CsI[Na] scintillator exposed to the neutrino emissions from the Spallation Neutron Source at Oak Ridge National Laboratory. Characteristic signatures in energy and time, predicted by the standard model for this process,more » were observed in high signal-to-background conditions. Finally, improved constraints on nonstandard neutrino interactions with quarks are derived from this initial data set.« less

Predicting protein concentrations with ELISA microarray assays, monotonic splines and Monte Carlo simulation

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

Daly, Don S.; Anderson, Kevin K.; White, Amanda M.

Background: A microarray of enzyme-linked immunosorbent assays, or ELISA microarray, predicts simultaneously the concentrations of numerous proteins in a small sample. These predictions, however, are uncertain due to processing error and biological variability. Making sound biological inferences as well as improving the ELISA microarray process require require both concentration predictions and creditable estimates of their errors. Methods: We present a statistical method based on monotonic spline statistical models, penalized constrained least squares fitting (PCLS) and Monte Carlo simulation (MC) to predict concentrations and estimate prediction errors in ELISA microarray. PCLS restrains the flexible spline to a fit of assay intensitymore » that is a monotone function of protein concentration. With MC, both modeling and measurement errors are combined to estimate prediction error. The spline/PCLS/MC method is compared to a common method using simulated and real ELISA microarray data sets. Results: In contrast to the rigid logistic model, the flexible spline model gave credible fits in almost all test cases including troublesome cases with left and/or right censoring, or other asymmetries. For the real data sets, 61% of the spline predictions were more accurate than their comparable logistic predictions; especially the spline predictions at the extremes of the prediction curve. The relative errors of 50% of comparable spline and logistic predictions differed by less than 20%. Monte Carlo simulation rendered acceptable asymmetric prediction intervals for both spline and logistic models while propagation of error produced symmetric intervals that diverged unrealistically as the standard curves approached horizontal asymptotes. Conclusions: The spline/PCLS/MC method is a flexible, robust alternative to a logistic/NLS/propagation-of-error method to reliably predict protein concentrations and estimate their errors. The spline method simplifies model selection and fitting, and reliably estimates believable prediction errors. For the 50% of the real data sets fit well by both methods, spline and logistic predictions are practically indistinguishable, varying in accuracy by less than 15%. The spline method may be useful when automated prediction across simultaneous assays of numerous proteins must be applied routinely with minimal user intervention.« less

Calibration of Discrete Random Walk (DRW) Model via G.I Taylor's Dispersion Theory

NASA Astrophysics Data System (ADS)

Javaherchi, Teymour; Aliseda, Alberto

2012-11-01

Prediction of particle dispersion in turbulent flows is still an important challenge with many applications to environmental, as well as industrial, fluid mechanics. Several models of dispersion have been developed to predict particle trajectories and their relative velocities, in combination with a RANS-based simulation of the background flow. The interaction of the particles with the velocity fluctuations at different turbulent scales represents a significant difficulty in generalizing the models to the wide range of flows where they are used. We focus our attention on the Discrete Random Walk (DRW) model applied to flow in a channel, particularly to the selection of eddies lifetimes as realizations of a Poisson distribution with a mean value proportional to κ / ɛ . We present a general method to determine the constant of this proportionality by matching the DRW model dispersion predictions for fluid element and particle dispersion to G.I Taylor's classical dispersion theory. This model parameter is critical to the magnitude of predicted dispersion. A case study of its influence on sedimentation of suspended particles in a tidal channel with an array of Marine Hydrokinetic (MHK) turbines highlights the dependency of results on this time scale parameter. Support from US DOE through the Northwest National Marine Renewable Energy Center, a UW-OSU partnership.

Comparison of different risk stratification systems in predicting short-term serious outcome of syncope patients

PubMed Central

Safari, Saeed; Baratloo, Alireza; Hashemi, Behrooz; Rahmati, Farhad; Forouzanfar, Mohammad Mehdi; Motamedi, Maryam; Mirmohseni, Ladan

2016-01-01

Background: Determining etiologic causes and prognosis can significantly improve management of syncope patients. The present study aimed to compare the values of San Francisco, Osservatorio Epidemiologico sulla Sincope nel Lazio (OESIL), Boston, and Risk Stratification of Syncope in the Emergency Department (ROSE) score clinical decision rules in predicting the short-term serious outcome of syncope patients. Materials and Methods: The present diagnostic accuracy study with 1-week follow-up was designed to evaluate the predictive values of the four mentioned clinical decision rules. Screening performance characteristics of each model in predicting mortality, myocardial infarction (MI), and cerebrovascular accidents (CVAs) were calculated and compared. To evaluate the value of each aforementioned model in predicting the outcome, sensitivity, specificity, positive likelihood ratio, and negative likelihood ratio were calculated and receiver-operating curve (ROC) curve analysis was done. Results: A total of 187 patients (mean age: 64.2 ± 17.2 years) were enrolled in the study. Mortality, MI, and CVA were seen in 19 (10.2%), 12 (6.4%), and 36 (19.2%) patients, respectively. Area under the ROC curve for OESIL, San Francisco, Boston, and ROSE models in prediction the risk of 1-week mortality, MI, and CVA was in the 30–70% range, with no significant difference among models (P > 0.05). The pooled model did not show higher accuracy in prediction of mortality, MI, and CVA compared to others (P > 0.05). Conclusion: This study revealed the weakness of all four evaluated models in predicting short-term serious outcome of syncope patients referred to the emergency department without any significant advantage for one among others. PMID:27904602

SnowyOwl: accurate prediction of fungal genes by using RNA-Seq and homology information to select among ab initio models

PubMed Central

2014-01-01

Background Locating the protein-coding genes in novel genomes is essential to understanding and exploiting the genomic information but it is still difficult to accurately predict all the genes. The recent availability of detailed information about transcript structure from high-throughput sequencing of messenger RNA (RNA-Seq) delineates many expressed genes and promises increased accuracy in gene prediction. Computational gene predictors have been intensively developed for and tested in well-studied animal genomes. Hundreds of fungal genomes are now or will soon be sequenced. The differences of fungal genomes from animal genomes and the phylogenetic sparsity of well-studied fungi call for gene-prediction tools tailored to them. Results SnowyOwl is a new gene prediction pipeline that uses RNA-Seq data to train and provide hints for the generation of Hidden Markov Model (HMM)-based gene predictions and to evaluate the resulting models. The pipeline has been developed and streamlined by comparing its predictions to manually curated gene models in three fungal genomes and validated against the high-quality gene annotation of Neurospora crassa; SnowyOwl predicted N. crassa genes with 83% sensitivity and 65% specificity. SnowyOwl gains sensitivity by repeatedly running the HMM gene predictor Augustus with varied input parameters and selectivity by choosing the models with best homology to known proteins and best agreement with the RNA-Seq data. Conclusions SnowyOwl efficiently uses RNA-Seq data to produce accurate gene models in both well-studied and novel fungal genomes. The source code for the SnowyOwl pipeline (in Python) and a web interface (in PHP) is freely available from http://sourceforge.net/projects/snowyowl/. PMID:24980894

Dispositional and Situational Avoidance and Approach as Predictors of Physical Symptom Bother Following Breast Cancer Diagnosis

PubMed Central

Bauer, Margaret R.; Harris, Lauren N.; Wiley, Joshua F.; Crespi, Catherine M.; Krull, Jennifer L.; Weihs, Karen L.; Stanton, Annette L.

2016-01-01

Background Few studies examine whether dispositional approach and avoidance coping and stressor-specific coping strategies differentially predict physical adjustment to cancer-related stress. Purpose This study examines dispositional and situational avoidance and approach coping as unique predictors of the bother women experience from physical symptoms after breast cancer treatment, as well as whether situational coping mediates the prediction of bother from physical symptoms by dispositional coping. Method Breast cancer patients (N=460) diagnosed within the past 3 months completed self-report measures of dispositional coping at study entry and of situational coping and bother from physical symptoms every 6 weeks through 6 months. Results In multilevel structural equation modeling analyses, both dispositional and situational avoidance predict greater symptom bother. Dispositional, but not situational, approach predicts less symptom bother. Supporting mediation models, dispositional avoidance predicts more symptom bother indirectly through greater situational avoidance. Dispositional approach predicts less symptom bother through less situational avoidance. Conclusion Psychosocial interventions to reduce cancer-related avoidance coping are warranted for cancer survivors who are high in dispositional avoidance and/or low in dispositional approach. PMID:26769023

The mediating roles of stress and maladaptive behaviors on self-harm and suicide attempts among runaway and homeless youth.

PubMed

Moskowitz, Amanda; Stein, Judith A; Lightfoot, Marguerita

2013-07-01

Runaway and homeless youth often have a constellation of background behavioral, emotional, and familial problems that contribute to stress and maladaptive behaviors, which, in turn, can lead to self-harming and suicidal behaviors. The current study examined the roles of stress and maladaptive behaviors as mediators between demographic and psychosocial background characteristics and self-injurious outcomes through the lens of the stress process paradigm. The model was tested in a sample of runaway and homeless youth from Los Angeles County (N = 474, age 12-24, 41 % female, 17 % White, 32.5 % African American, 21.5 % Hispanic/Latino). Background variables (gender, age, sexual minority status, parental drug use history, and emotional distress) predicted hypothesized mediators of maladaptive behaviors and recent stress. In turn, it was hypothesized that the mediators would predict self-harming behaviors and suicide attempts in the last 3 months. Females and LGBT (lesbian, gay, bisexual, transgender) youth were more likely to have self-harmed and attempted suicide; younger participants reported more self-harming. The mediating constructs were associated more highly with self-harming than suicide attempts bivariately, although differences were modest. Maladaptive behaviors and recent stress were significant predictors of self-harm, whereas only recent stress was a significant predictor of suicide attempts. All background factors were significant predictors of recent stress. Older age, a history of parental drug use, and greater emotional distress predicted problem drug use. Males, younger participants, and participants with emotional distress reported more delinquent behaviors. Significant indirect effects on self-harming behaviors were mediated through stress and maladaptive behaviors. The hypothesized paradigm was useful in explaining the associations among background factors and self-injurious outcomes and the influence of mediating factors on these associations.

The role of motion streaks in the perception of the kinetic Zollner illusion.

PubMed

Khuu, Sieu K

2012-06-12

In classic geometric illusions such as the Zollner illusion, vertical lines superimposed on oriented background lines appear tilted in the direction opposite to the background. In kinetic forms of this illusion, an object moving over oriented background lines appears to follow a titled path, again in the direction opposite to the background. Existing literature does not proffer a complete explanation of the effect. Here, it is suggested that motion streaks underpin the illusion; that the effect is a consequence of interactions between detectors tuned to the orientation of background lines and those sensing the motion streaks that arise from fast object motion. This account was examined in the present study by measuring motion-tilt induction under different conditions in which the strength or salience of motion streaks was attenuated: by varying object speed (Experiment 1), contrast (Experiment 2), and trajectory/length by changing the element life-time within the stimulus (Experiment 3). It was predicted that, as motion streaks become less available, background lines would less affect the perceived direction of motion. Consistent with this prediction, the results indicated that, with a reduction in object speed below that required to generate motion streaks (< 1.12°/s), Weber contrast (< 0.125) and motion streak length (two frames) reduced or extinguished the motion-tilt-induction effect. The findings of the present study are consistent with previous reports and computational models that directly combine form and motion information to provide an effective determinant of motion direction.

Model-driven analysis of experimentally determined growth phenotypes for 465 yeast gene deletion mutants under 16 different conditions

PubMed Central

Snitkin, Evan S; Dudley, Aimée M; Janse, Daniel M; Wong, Kaisheen; Church, George M; Segrè, Daniel

2008-01-01

Background Understanding the response of complex biochemical networks to genetic perturbations and environmental variability is a fundamental challenge in biology. Integration of high-throughput experimental assays and genome-scale computational methods is likely to produce insight otherwise unreachable, but specific examples of such integration have only begun to be explored. Results In this study, we measured growth phenotypes of 465 Saccharomyces cerevisiae gene deletion mutants under 16 metabolically relevant conditions and integrated them with the corresponding flux balance model predictions. We first used discordance between experimental results and model predictions to guide a stage of experimental refinement, which resulted in a significant improvement in the quality of the experimental data. Next, we used discordance still present in the refined experimental data to assess the reliability of yeast metabolism models under different conditions. In addition to estimating predictive capacity based on growth phenotypes, we sought to explain these discordances by examining predicted flux distributions visualized through a new, freely available platform. This analysis led to insight into the glycerol utilization pathway and the potential effects of metabolic shortcuts on model results. Finally, we used model predictions and experimental data to discriminate between alternative raffinose catabolism routes. Conclusions Our study demonstrates how a new level of integration between high throughput measurements and flux balance model predictions can improve understanding of both experimental and computational results. The added value of a joint analysis is a more reliable platform for specific testing of biological hypotheses, such as the catabolic routes of different carbon sources. PMID:18808699

Local gravity and large-scale structure

NASA Technical Reports Server (NTRS)

Juszkiewicz, Roman; Vittorio, Nicola; Wyse, Rosemary F. G.

1990-01-01

The magnitude and direction of the observed dipole anisotropy of the galaxy distribution can in principle constrain the amount of large-scale power present in the spectrum of primordial density fluctuations. This paper confronts the data, provided by a recent redshift survey of galaxies detected by the IRAS satellite, with the predictions of two cosmological models with very different levels of large-scale power: the biased Cold Dark Matter dominated model (CDM) and a baryon-dominated model (BDM) with isocurvature initial conditions. Model predictions are investigated for the Local Group peculiar velocity, v(R), induced by mass inhomogeneities distributed out to a given radius, R, for R less than about 10,000 km/s. Several convergence measures for v(R) are developed, which can become powerful cosmological tests when deep enough samples become available. For the present data sets, the CDM and BDM predictions are indistinguishable at the 2 sigma level and both are consistent with observations. A promising discriminant between cosmological models is the misalignment angle between v(R) and the apex of the dipole anisotropy of the microwave background.

Space Environment Effects: Low-Altitude Trapped Radiation Model

NASA Technical Reports Server (NTRS)

Huston, S. L.; Pfitzer, K. A.

1998-01-01

Accurate models of the Earth's trapped energetic proton environment are required for both piloted and robotic space missions. For piloted missions, the concern is mainly total dose to the astronauts, particularly in long-duration missions and during extravehicular activity (EVA). As astronomical and remote-sensing detectors become more sensitive, the proton flux can induce unwanted backgrounds in these instruments. Due to this unwanted background, the following description details the development of a new model for the low-trapped proton environment. The model is based on nearly 20 years of data from the TIRO/NOAA weather satellites. The model, which has been designated NOAAPRO (for NOAA protons), predicts the integral omnidirectional proton flux in three energy ranges: >16, >36, and >80 MeV. It contains a true solar cycle variation and accounts for the secular variation in the Earth's magnetic field. It also extends to lower values of the magnetic L parameter than does AP8. Thus, the model addresses the major shortcomings of AP8.

Cosmic superstrings: Observable remnants of brane inflation

NASA Astrophysics Data System (ADS)

Wyman, Mark Charles

Brane inflation provides a natural dynamical model for the physics which underlie the inflationary paradigm. Besides their inflationary predictions, brane models imply another observable consequence: cosmic strings. In this dissertation I outline the background of how cosmic strings arise in brane inflationary models and how the properties of the strings and the models are mutually tied (Chapter 2). I then use cosmological observations to put limits on the properties of any actually-existing cosmic string network (Chapter 3). Next, I study the question of how cosmic superstrings, as the cosmic strings arising from string theory are known, could be distinct from classical gauge- theory cosmic strings. In particular, I propose an analytical model for the cosmological evolution of a network of binding cosmic strings (Chapter 4); I also describe the distinctive gravitational lensing phenomena that are caused by binding strings (Chapter 5). Finally, I lay out the background for the numerical study of a gauge theory model for the dynamics of cosmic superstring binding (Chapter 6).

Evaluation of the DAVROS (Development And Validation of Risk-adjusted Outcomes for Systems of emergency care) risk-adjustment model as a quality indicator for healthcare

PubMed Central

Wilson, Richard; Goodacre, Steve W; Klingbajl, Marcin; Kelly, Anne-Maree; Rainer, Tim; Coats, Tim; Holloway, Vikki; Townend, Will; Crane, Steve

2014-01-01

Background and objective Risk-adjusted mortality rates can be used as a quality indicator if it is assumed that the discrepancy between predicted and actual mortality can be attributed to the quality of healthcare (ie, the model has attributional validity). The Development And Validation of Risk-adjusted Outcomes for Systems of emergency care (DAVROS) model predicts 7-day mortality in emergency medical admissions. We aimed to test this assumption by evaluating the attributional validity of the DAVROS risk-adjustment model. Methods We selected cases that had the greatest discrepancy between observed mortality and predicted probability of mortality from seven hospitals involved in validation of the DAVROS risk-adjustment model. Reviewers at each hospital assessed hospital records to determine whether the discrepancy between predicted and actual mortality could be explained by the healthcare provided. Results We received 232/280 (83%) completed review forms relating to 179 unexpected deaths and 53 unexpected survivors. The healthcare system was judged to have potentially contributed to 10/179 (8%) of the unexpected deaths and 26/53 (49%) of the unexpected survivors. Failure of the model to appropriately predict risk was judged to be responsible for 135/179 (75%) of the unexpected deaths and 2/53 (4%) of the unexpected survivors. Some 10/53 (19%) of the unexpected survivors died within a few months of the 7-day period of model prediction. Conclusions We found little evidence that deaths occurring in patients with a low predicted mortality from risk-adjustment could be attributed to the quality of healthcare provided. PMID:23605036

Astrophysical tests for radiative decay of neutrinos and fundamental physics implications

NASA Technical Reports Server (NTRS)

Stecker, F. W.; Brown, R. W.

1981-01-01

The radiative lifetime tau for the decay of massious neutrinos was calculated using various physical models for neutrino decay. The results were then related to the astrophysical problem of the detectability of the decay photons from cosmic neutrinos. Conversely, the astrophysical data were used to place lower limits on tau. These limits are all well below predicted values. However, an observed feature at approximately 1700 A in the ultraviolet background radiation at high galactic latitudes may be from the decay of neutrinos with mass approximately 14 eV. This would require a decay rate much larger than the predictions of standard models but could be indicative of a decay rate possible in composite models or other new physics. Thus an important test for substructure in leptons and quarks or other physics beyond the standard electroweak model may have been found.

Family history and obesity in youth, their effect on acylcarnitine/aminoacids metabolomics and non-alcoholic fatty liver disease (NAFLD). Structural equation modeling approach

PubMed Central

Vadillo-Ortega, Felipe; Caballero, Augusto Enrique; Ibarra-González, Isabel; Herrera-Rosas, Arturo; Serratos-Canales, María Fabiola; León-Hernández, Mireya; González-Chávez, Antonio; Mummidi, Srinivas; Duggirala, Ravindranath

2018-01-01

Background Structural equation modeling (SEM) can help understanding complex functional relationships among obesity, non-alcoholic fatty liver disease (NAFLD), family history of obesity, targeted metabolomics and pro-inflammatory markers. We tested two hypotheses: 1) If obesity precedes an excess of free fatty acids that increase oxidative stress and mitochondrial dysfunction, there would be an increase of serum acylcarnitines, amino acids and cytokines in obese subjects. Acylcarnitines would be related to non-alcoholic fatty disease that will induce insulin resistance. 2) If a positive family history of obesity and type 2 diabetes are the major determinants of the metabolomic profile, there would be higher concentration of amino acids and acylcarnitines in patients with this background that will induce obesity and NAFLD which in turn will induce insulin resistance. Methods/Results 137 normoglycemic subjects, mean age (SD) of 30.61 (8.6) years divided in three groups: BMI<25 with absence of NAFLD (G1), n = 82; BMI>30 with absence of NAFLD (G2), n = 24; and BMI>30 with NAFLD (G3), n = 31. Family history of obesity (any) was present in 53%. Both models were adjusted in SEM. Family history of obesity predicted obesity but could not predict acylcarnitines and amino acid concentrations (effect size <0.2), but did predict obesity phenotype. Conclusion Family history of obesity is the major predictor of obesity, and the metabolic abnormalities on amino acids, acylcarnitines, inflammation, insulin resistance, and NAFLD. PMID:29466466

Factors Predicting Science Achievement of Immigrant and Non-Immigrant Students: A Multilevel Analysis

ERIC Educational Resources Information Center

Areepattamannil, Shaljan; Kaur, Berinderjeet

2013-01-01

This study, employing hierarchical linear modeling (HLM), sought to investigate the student-level and school-level factors associated with the science achievement of immigrant and non-immigrant students among a national sample of 22,646 students from 896 schools in Canada. While student background characteristics such as home language, family…

The Use of Longitudinal Analysis to Identify More and Less Effective Schools.

ERIC Educational Resources Information Center

Goldberg, Zandra S.; Spartz, James L.

Three longitudinal studies of student achievement test data were conducted to examine the extent to which the variation in student achievement can be explained by differences in student background (including socioeconomic and prior achievement data) and school resources. This model assumes that achievement test performance may be predicted by…

Utilizing Chinese Admission Records for MACE Prediction of Acute Coronary Syndrome

PubMed Central

Hu, Danqing; Huang, Zhengxing; Chan, Tak-Ming; Dong, Wei; Lu, Xudong; Duan, Huilong

2016-01-01

Background: Clinical major adverse cardiovascular event (MACE) prediction of acute coronary syndrome (ACS) is important for a number of applications including physician decision support, quality of care assessment, and efficient healthcare service delivery on ACS patients. Admission records, as typical media to contain clinical information of patients at the early stage of their hospitalizations, provide significant potential to be explored for MACE prediction in a proactive manner. Methods: We propose a hybrid approach for MACE prediction by utilizing a large volume of admission records. Firstly, both a rule-based medical language processing method and a machine learning method (i.e., Conditional Random Fields (CRFs)) are developed to extract essential patient features from unstructured admission records. After that, state-of-the-art supervised machine learning algorithms are applied to construct MACE prediction models from data. Results: We comparatively evaluate the performance of the proposed approach on a real clinical dataset consisting of 2930 ACS patient samples collected from a Chinese hospital. Our best model achieved 72% AUC in MACE prediction. In comparison of the performance between our models and two well-known ACS risk score tools, i.e., GRACE and TIMI, our learned models obtain better performances with a significant margin. Conclusions: Experimental results reveal that our approach can obtain competitive performance in MACE prediction. The comparison of classifiers indicates the proposed approach has a competitive generality with datasets extracted by different feature extraction methods. Furthermore, our MACE prediction model obtained a significant improvement by comparison with both GRACE and TIMI. It indicates that using admission records can effectively provide MACE prediction service for ACS patients at the early stage of their hospitalizations. PMID:27649220

Predicting Droplet Formation on Centrifugal Microfluidic Platforms

NASA Astrophysics Data System (ADS)

Moebius, Jacob Alfred

Centrifugal microfluidics is a widely known research tool for biological sample and water quality analysis. Currently, the standard equipment used for such diagnostic applications include slow, bulky machines controlled by multiple operators. These machines can be condensed into a smaller, faster benchtop sample-to-answer system. Sample processing is an important step taken to extract, isolate, and convert biological factors, such as nucleic acids or proteins, from a raw sample to an analyzable solution. Volume definition is one such step. The focus of this thesis is the development of a model predicting monodispersed droplet formation and the application of droplets as a technique for volume definition. First, a background of droplet microfluidic platforms is presented, along with current biological analysis technologies and the advantages of integrating such technologies onto microfluidic platforms. Second, background and theories of centrifugal microfluidics is given, followed by theories relevant to droplet emulsions. Third, fabrication techniques for centrifugal microfluidic designs are discussed. Finally, the development of a model for predicting droplet formation on the centrifugal microfluidic platform are presented for the rest of the thesis. Predicting droplet formation analytically based on the volumetric flow rates of the continuous and dispersed phases, the ratios of these two flow rates, and the interfacial tension between the continuous and dispersed phases presented many challenges, which will be discussed in this work. Experimental validation was completed using continuous phase solutions of different interfacial tensions. To conclude, prospective applications are discussed with expected challenges.

Search for new resonances decaying via WZ to leptons in proton–proton collisions at $$\\sqrt s =$$ 8 TeV

DOE PAGES

Khachatryan, V.

2014-11-18

A search is performed in proton–proton collisions at √s=8 TeV for exotic particles decaying via WZ to fully leptonic final states with electrons, muons, and neutrinos. The data set corresponds to an integrated luminosity of 19.5 fb -1. No significant excess is observed above the expected standard model background. Upper bounds at 95% confidence level are set on the production cross section of a W' boson as predicted by an extended gauge model, and on the W'WZ coupling. The expected and observed mass limits for a W' boson, as predicted by this model, are 1.55 and 1.47 TeV, respectively. Stringentmore » limits are also set in the context of low-scale technicolor models under a range of assumptions for the model parameters.« less

Optimizing Chemotherapy Dose and Schedule by Norton-Simon Mathematical Modeling

PubMed Central

Traina, Tiffany A.; Dugan, Ute; Higgins, Brian; Kolinsky, Kenneth; Theodoulou, Maria; Hudis, Clifford A.; Norton, Larry

2011-01-01

Background To hasten and improve anticancer drug development, we created a novel approach to generating and analyzing preclinical dose-scheduling data so as to optimize benefit-to-toxicity ratios. Methods We applied mathematical methods based upon Norton-Simon growth kinetic modeling to tumor-volume data from breast cancer xenografts treated with capecitabine (Xeloda®, Roche) at the conventional schedule of 14 days of treatment followed by a 7-day rest (14 - 7). Results The model predicted that 7 days of treatment followed by a 7-day rest (7 - 7) would be superior. Subsequent preclinical studies demonstrated that this biweekly capecitabine schedule allowed for safe delivery of higher daily doses, improved tumor response, and prolonged animal survival. Conclusions We demonstrated that the application of Norton-Simon modeling to the design and analysis of preclinical data predicts an improved capecitabine dosing schedule in xenograft models. This method warrants further investigation and application in clinical drug development. PMID:20519801

Predictive modeling of nanomaterial exposure effects in biological systems

PubMed Central

Liu, Xiong; Tang, Kaizhi; Harper, Stacey; Harper, Bryan; Steevens, Jeffery A; Xu, Roger

2013-01-01

Background Predictive modeling of the biological effects of nanomaterials is critical for industry and policymakers to assess the potential hazards resulting from the application of engineered nanomaterials. Methods We generated an experimental dataset on the toxic effects experienced by embryonic zebrafish due to exposure to nanomaterials. Several nanomaterials were studied, such as metal nanoparticles, dendrimer, metal oxide, and polymeric materials. The embryonic zebrafish metric (EZ Metric) was used as a screening-level measurement representative of adverse effects. Using the dataset, we developed a data mining approach to model the toxic endpoints and the overall biological impact of nanomaterials. Data mining techniques, such as numerical prediction, can assist analysts in developing risk assessment models for nanomaterials. Results We found several important attributes that contribute to the 24 hours post-fertilization (hpf) mortality, such as dosage concentration, shell composition, and surface charge. These findings concur with previous studies on nanomaterial toxicity using embryonic zebrafish. We conducted case studies on modeling the overall effect/impact of nanomaterials and the specific toxic endpoints such as mortality, delayed development, and morphological malformations. The results show that we can achieve high prediction accuracy for certain biological effects, such as 24 hpf mortality, 120 hpf mortality, and 120 hpf heart malformation. The results also show that the weighting scheme for individual biological effects has a significant influence on modeling the overall impact of nanomaterials. Sample prediction models can be found at http://neiminer.i-a-i.com/nei_models. Conclusion The EZ Metric-based data mining approach has been shown to have predictive power. The results provide valuable insights into the modeling and understanding of nanomaterial exposure effects. PMID:24098077

Cosmological backreaction within the Szekeres model and emergence of spatial curvature

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

Bolejko, Krzysztof, E-mail: krzysztof.bolejko@sydney.edu.au

This paper discusses the phenomenon of backreaction within the Szekeres model. Cosmological backreaction describes how the mean global evolution of the Universe deviates from the Friedmannian evolution. The analysis is based on models of a single cosmological environment and the global ensemble of the Szekeres models (of the Swiss-Cheese-type and Styrofoam-type). The obtained results show that non-linear growth of cosmic structures is associated with the growth of the spatial curvature Ω{sub R} (in the FLRW limit Ω{sub R} → Ω {sub k} ). If averaged over global scales the result depends on the assumed global model of the Universe. Withinmore » the Swiss-Cheese model, which does have a fixed background, the volume average follows the evolution of the background, and the global spatial curvature averages out to zero (the background model is the ΛCDM model, which is spatially flat). In the Styrofoam-type model, which does not have a fixed background, the mean evolution deviates from the spatially flat ΛCDM model, and the mean spatial curvature evolves from Ω{sub R} =0 at the CMB to Ω{sub R} ∼ 0.1 at 0 z =. If the Styrofoam-type model correctly captures evolutionary features of the real Universe then one should expect that in our Universe, the spatial curvature should build up (local growth of cosmic structures) and its mean global average should deviate from zero (backreaction). As a result, this paper predicts that the low-redshift Universe should not be spatially flat (i.e. Ω {sub k} ≠ 0, even if in the early Universe Ω {sub k} = 0) and therefore when analysing low- z cosmological data one should keep Ω {sub k} as a free parameter and independent from the CMB constraints.« less

Cosmological backreaction within the Szekeres model and emergence of spatial curvature

NASA Astrophysics Data System (ADS)

Bolejko, Krzysztof

2017-06-01

This paper discusses the phenomenon of backreaction within the Szekeres model. Cosmological backreaction describes how the mean global evolution of the Universe deviates from the Friedmannian evolution. The analysis is based on models of a single cosmological environment and the global ensemble of the Szekeres models (of the Swiss-Cheese-type and Styrofoam-type). The obtained results show that non-linear growth of cosmic structures is associated with the growth of the spatial curvature ΩScript R (in the FLRW limit ΩScript R → Ωk). If averaged over global scales the result depends on the assumed global model of the Universe. Within the Swiss-Cheese model, which does have a fixed background, the volume average follows the evolution of the background, and the global spatial curvature averages out to zero (the background model is the ΛCDM model, which is spatially flat). In the Styrofoam-type model, which does not have a fixed background, the mean evolution deviates from the spatially flat ΛCDM model, and the mean spatial curvature evolves from ΩScript R =0 at the CMB to ΩScript R ~ 0.1 at 0z =. If the Styrofoam-type model correctly captures evolutionary features of the real Universe then one should expect that in our Universe, the spatial curvature should build up (local growth of cosmic structures) and its mean global average should deviate from zero (backreaction). As a result, this paper predicts that the low-redshift Universe should not be spatially flat (i.e. Ωk ≠ 0, even if in the early Universe Ωk = 0) and therefore when analysing low-z cosmological data one should keep Ωk as a free parameter and independent from the CMB constraints.

Development and Current Status of the “Cambridge” Loudness Models

PubMed Central

2014-01-01

This article reviews the evolution of a series of models of loudness developed in Cambridge, UK. The first model, applicable to stationary sounds, was based on modifications of the model developed by Zwicker, including the introduction of a filter to allow for the effects of transfer of sound through the outer and middle ear prior to the calculation of an excitation pattern, and changes in the way that the excitation pattern was calculated. Later, modifications were introduced to the assumed middle-ear transfer function and to the way that specific loudness was calculated from excitation level. These modifications led to a finite calculated loudness at absolute threshold, which made it possible to predict accurately the absolute thresholds of broadband and narrowband sounds, based on the assumption that the absolute threshold corresponds to a fixed small loudness. The model was also modified to give predictions of partial loudness—the loudness of one sound in the presence of another. This allowed predictions of masked thresholds based on the assumption that the masked threshold corresponds to a fixed small partial loudness. Versions of the model for time-varying sounds were developed, which allowed prediction of the masked threshold of any sound in a background of any other sound. More recent extensions incorporate binaural processing to account for the summation of loudness across ears. In parallel, versions of the model for predicting loudness for hearing-impaired ears have been developed and have been applied to the development of methods for fitting multichannel compression hearing aids. PMID:25315375

Validity of Models for Predicting BRCA1 and BRCA2 Mutations

PubMed Central

Parmigiani, Giovanni; Chen, Sining; Iversen, Edwin S.; Friebel, Tara M.; Finkelstein, Dianne M.; Anton-Culver, Hoda; Ziogas, Argyrios; Weber, Barbara L.; Eisen, Andrea; Malone, Kathleen E.; Daling, Janet R.; Hsu, Li; Ostrander, Elaine A.; Peterson, Leif E.; Schildkraut, Joellen M.; Isaacs, Claudine; Corio, Camille; Leondaridis, Leoni; Tomlinson, Gail; Amos, Christopher I.; Strong, Louise C.; Berry, Donald A.; Weitzel, Jeffrey N.; Sand, Sharon; Dutson, Debra; Kerber, Rich; Peshkin, Beth N.; Euhus, David M.

2008-01-01

Background Deleterious mutations of the BRCA1 and BRCA2 genes confer susceptibility to breast and ovarian cancer. At least 7 models for estimating the probabilities of having a mutation are used widely in clinical and scientific activities; however, the merits and limitations of these models are not fully understood. Objective To systematically quantify the accuracy of the following publicly available models to predict mutation carrier status: BRCAPRO, family history assessment tool, Finnish, Myriad, National Cancer Institute, University of Pennsylvania, and Yale University. Design Cross-sectional validation study, using model predictions and BRCA1 or BRCA2 mutation status of patients different from those used to develop the models. Setting Multicenter study across Cancer Genetics Network participating centers. Patients 3 population-based samples of participants in research studies and 8 samples from genetic counseling clinics. Measurements Discrimination between individuals testing positive for a mutation in BRCA1 or BRCA2 from those testing negative, as measured by the c-statistic, and sensitivity and specificity of model predictions. Results The 7 models differ in their predictions. The better-performing models have a c-statistic around 80%. BRCAPRO has the largest c-statistic overall and in all but 2 patient subgroups, although the margin over other models is narrow in many strata. Outside of high-risk populations, all models have high false-negative and false-positive rates across a range of probability thresholds used to refer for mutation testing. Limitation Three recently published models were not included. Conclusions All models identify women who probably carry a deleterious mutation of BRCA1 or BRCA2 with adequate discrimination to support individualized genetic counseling, although discrimination varies across models and populations. PMID:17909205

Insights from the Source Physics Experiments on P/S Amplitude Ratio Methods of Identifying Explosions in a Background of Earthquakes

NASA Astrophysics Data System (ADS)

Walter, W. R.; Ford, S. R.; Xu, H.; Pasyanos, M. E.; Pyle, M. L.; Matzel, E.; Mellors, R. J.; Hauk, T. F.

2012-12-01

It is well established empirically that regional distance (200-1600 km) amplitude ratios of seismic P-to-S waves at sufficiently high frequencies (~>2 Hz) can identify explosions among a background of natural earthquakes. However the physical basis for the generation of explosion S-waves, and therefore the predictability of this P/S technique as a function of event properties such as size, depth, geology and path, remains incompletely understood. A goal of the Source Physics Experiments (SPE) at the Nevada National Security Site (NNSS, formerly the Nevada Test Site (NTS)) is to improve our physical understanding of the mechanisms of explosion S-wave generation and advance our ability to numerically model and predict them. Current models of explosion P/S values suggest they are frequency dependent with poor performance below the source corner frequencies and good performance above. This leads to expectations that small magnitude explosions might require much higher frequencies (>10 Hz) to identify them. Interestingly the 1-ton chemical source physics explosions SPE2 and SPE3 appear to discriminate well from background earthquakes in the frequency band 6-8 Hz, where P and S signals are visible at the NVAR array located near Mina, NV about 200 km away. NVAR is a primary seismic station in the International Monitoring System (IMS), part of the Comprehensive nuclear-Test-Ban Treaty (CTBT). The NVAR broadband element NV31 is co-located with the LLNL station MNV that recorded many NTS nuclear tests, allowing the comparison. We find the small SPE explosions in granite have similar Pn/Lg values at 6-8 Hz as the past nuclear tests mainly in softer rocks. We are currently examining a number of other stations in addition to NVAR, including the dedicated SPE stations that recorded the SPE explosions at much closer distances with very high sample rates, in order to better understand the observed frequency dependence as compared with the model predictions. We plan to use these observations to improve our explosion models and our ability to understand and predict where P/S methods of identifying explosions work and any circumstances where they may not. This work performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.

Local reionization histories with a merger tree of the HII regions

NASA Astrophysics Data System (ADS)

Chardin, Jonathan; Aubert, Dominique; Ocvirk, Pierre

2014-08-01

Aims: We investigate simple properties of the initial stage of the reionization process around progenitors of galaxies, such as the extent of the initial HII region before its fusion with the UV background, and the duration of its propagation. Methods: We used a set of four reionization simulations with different resolutions and ionizing source prescriptions. By using a merger tree of the HII regions we compiled a catalog of the HII region properties. When the ionized regions undergo a major-merger event, we considered that they belong to the global UV background. From the lifetime of the region and from their volume until this moment we drew typical local reionization histories as a function of time and investigated the relation between these histories and the halo mass progenitors of the regions. We then used an average mass accretion history model (AMAH) to extrapolate the halo mass inside the region from high z to z = 0 to predict the past reionization histories of galaxies we see today. Results: We found that the later an HII region appears during the reionization period, the shorter their related lifetime is and the smaller their volume before they merge with the global UV background. Quantitatively, the duration and extent of the initial growth of an HII region is strongly dependent on the mass of the inner halo and can be as long as ~50% of the reionization epoch. We found that the more massive a halo is today, the earlier it appears and the larger is the extension and the longer the propagation duration of its HII region. Quantitative predictions differ depending on the box size or the source model: small simulated volumes are affected by proximity effects between HII regions, and halo-based source models predict smaller regions and slower I-front expansion than models that use star particles as ionizing sources. Applying this extrapolation to Milky Way-type halos leads to a maximal extent of 1.1 Mpc/h for the initial HII region that established itself in ~150-200 ± 20 Myr. This is consistent with the prediction made using constrained Local Group simulations. For halos with masses similar to those of the Local Group (MW + M31), our result suggests that statistically it has not been influenced by an external front coming from a Virgo-like cluster.

Improved Upper Limits on the Stochastic Gravitational-Wave Background from 2009-2010 LIGO and Virgo Data

NASA Astrophysics Data System (ADS)

Aasi, J.; Abbott, B. P.; Abbott, R.; Abbott, T.; Abernathy, M. R.; Accadia, T.; Acernese, F.; Ackley, K.; Adams, C.; Adams, T.; Addesso, P.; Adhikari, R. X.; Affeldt, C.; Agathos, M.; Aggarwal, N.; Aguiar, O. D.; Ain, A.; Ajith, P.; Alemic, A.; Allen, B.; Allocca, A.; Amariutei, D.; Andersen, M.; Anderson, R.; Anderson, S. B.; Anderson, W. G.; Arai, K.; Araya, M. C.; Arceneaux, C.; Areeda, J.; Aston, S. M.; Astone, P.; Aufmuth, P.; Aulbert, C.; Austin, L.; Aylott, B. E.; Babak, S.; Baker, P. T.; Ballardin, G.; Ballmer, S. W.; Barayoga, J. C.; Barbet, M.; Barish, B. C.; Barker, D.; Barone, F.; Barr, B.; Barsotti, L.; Barsuglia, M.; Barton, M. A.; Bartos, I.; Bassiri, R.; Basti, A.; Batch, J. C.; Bauchrowitz, J.; Bauer, Th. S.; Behnke, B.; Bejger, M.; Beker, M. G.; Belczynski, C.; Bell, A. S.; Bell, C.; Bergmann, G.; Bersanetti, D.; Bertolini, A.; Betzwieser, J.; Beyersdorf, P. T.; Bilenko, I. A.; Billingsley, G.; Birch, J.; Biscans, S.; Bitossi, M.; Bizouard, M. A.; Black, E.; Blackburn, J. K.; Blackburn, L.; Blair, D.; Bloemen, S.; Blom, M.; Bock, O.; Bodiya, T. P.; Boer, M.; Bogaert, G.; Bogan, C.; Bond, C.; Bondu, F.; Bonelli, L.; Bonnand, R.; Bork, R.; Born, M.; Boschi, V.; Bose, Sukanta; Bosi, L.; Bradaschia, C.; Brady, P. R.; Braginsky, V. B.; Branchesi, M.; Brau, J. E.; Briant, T.; Bridges, D. O.; Brillet, A.; Brinkmann, M.; Brisson, V.; Brooks, A. F.; Brown, D. A.; Brown, D. D.; Brückner, F.; Buchman, S.; Bulik, T.; Bulten, H. J.; Buonanno, A.; Burman, R.; Buskulic, D.; Buy, C.; Cadonati, L.; Cagnoli, G.; Bustillo, J. Calderón; Calloni, E.; Camp, J. B.; Campsie, P.; Cannon, K. C.; Canuel, B.; Cao, J.; Capano, C. D.; Carbognani, F.; Carbone, L.; Caride, S.; Castiglia, A.; Caudill, S.; Cavaglià, M.; Cavalier, F.; Cavalieri, R.; Celerier, C.; Cella, G.; Cepeda, C.; Cesarini, E.; Chakraborty, R.; Chalermsongsak, T.; Chamberlin, S. J.; Chao, S.; Charlton, P.; Chassande-Mottin, E.; Chen, X.; Chen, Y.; Chincarini, A.; Chiummo, A.; Cho, H. S.; Chow, J.; Christensen, N.; Chu, Q.; Chua, S. S. Y.; Chung, S.; Ciani, G.; Clara, F.; Clark, J. A.; Cleva, F.; Coccia, E.; Cohadon, P.-F.; Colla, A.; Collette, C.; Colombini, M.; Cominsky, L.; Constancio, M.; Conte, A.; Cook, D.; Corbitt, T. R.; Cordier, M.; Cornish, N.; Corpuz, A.; Corsi, A.; Costa, C. A.; Coughlin, M. W.; Coughlin, S.; Coulon, J.-P.; Countryman, S.; Couvares, P.; Coward, D. M.; Cowart, M.; Coyne, D. C.; Coyne, R.; Craig, K.; Creighton, J. D. E.; Crowder, S. G.; Cumming, A.; Cunningham, L.; Cuoco, E.; Dahl, K.; Canton, T. Dal; Damjanic, M.; Danilishin, S. L.; D'Antonio, S.; Danzmann, K.; Dattilo, V.; Daveloza, H.; Davier, M.; Davies, G. S.; Daw, E. J.; Day, R.; Dayanga, T.; Debreczeni, G.; Degallaix, J.; Deléglise, S.; Del Pozzo, W.; Denker, T.; Dent, T.; Dereli, H.; Dergachev, V.; De Rosa, R.; DeRosa, R. T.; DeSalvo, R.; Dhurandhar, S.; Díaz, M.; Di Fiore, L.; Di Lieto, A.; Di Palma, I.; Di Virgilio, A.; Donath, A.; Donovan, F.; Dooley, K. L.; Doravari, S.; Dossa, S.; Douglas, R.; Downes, T. P.; Drago, M.; Drever, R. W. P.; Driggers, J. C.; Du, Z.; Dwyer, S.; Eberle, T.; Edo, T.; Edwards, M.; Effler, A.; Eggenstein, H.; Ehrens, P.; Eichholz, J.; Eikenberry, S. S.; Endrőczi, G.; Essick, R.; Etzel, T.; Evans, M.; Evans, T.; Factourovich, M.; Fafone, V.; Fairhurst, S.; Fang, Q.; Farinon, S.; Farr, B.; Farr, W. M.; Favata, M.; Fehrmann, H.; Fejer, M. M.; Feldbaum, D.; Feroz, F.; Ferrante, I.; Ferrini, F.; Fidecaro, F.; Finn, L. S.; Fiori, I.; Fisher, R. P.; Flaminio, R.; Fournier, J.-D.; Franco, S.; Frasca, S.; Frasconi, F.; Frede, M.; Frei, Z.; Freise, A.; Frey, R.; Fricke, T. T.; Fritschel, P.; Frolov, V. V.; Fulda, P.; Fyffe, M.; Gair, J.; Gammaitoni, L.; Gaonkar, S.; Garufi, F.; Gehrels, N.; Gemme, G.; Genin, E.; Gennai, A.; Ghosh, S.; Giaime, J. A.; Giardina, K. D.; Giazotto, A.; Gill, C.; Gleason, J.; Goetz, E.; Goetz, R.; Gondan, L.; González, G.; Gordon, N.; Gorodetsky, M. L.; Gossan, S.; Goßler, S.; Gouaty, R.; Gräf, C.; Graff, P. B.; Granata, M.; Grant, A.; Gras, S.; Gray, C.; Greenhalgh, R. J. S.; Gretarsson, A. M.; Groot, P.; Grote, H.; Grover, K.; Grunewald, S.; Guidi, G. M.; Guido, C.; Gushwa, K.; Gustafson, E. K.; Gustafson, R.; Hammer, D.; Hammond, G.; Hanke, M.; Hanks, J.; Hanna, C.; Hanson, J.; Harms, J.; Harry, G. M.; Harry, I. W.; Harstad, E. D.; Hart, M.; Hartman, M. T.; Haster, C.-J.; Haughian, K.; Heidmann, A.; Heintze, M.; Heitmann, H.; Hello, P.; Hemming, G.; Hendry, M.; Heng, I. S.; Heptonstall, A. W.; Heurs, M.; Hewitson, M.; Hild, S.; Hoak, D.; Hodge, K. A.; Holt, K.; Hooper, S.; Hopkins, P.; Hosken, D. J.; Hough, J.; Howell, E. J.; Hu, Y.; Huerta, E.; Hughey, B.; Husa, S.; Huttner, S. H.; Huynh, M.; Huynh-Dinh, T.; Ingram, D. R.; Inta, R.; Isogai, T.; Ivanov, A.; Iyer, B. R.; Izumi, K.; Jacobson, M.; James, E.; Jang, H.; Jaranowski, P.; Ji, Y.; Jiménez-Forteza, F.; Johnson, W. W.; Jones, D. I.; Jones, R.; Jonker, R. J. G.; Ju, L.; Haris, K.; Kalmus, P.; Kalogera, V.; Kandhasamy, S.; Kang, G.; Kanner, J. B.; Karlen, J.; Kasprzack, M.; Katsavounidis, E.; Katzman, W.; Kaufer, H.; Kawabe, K.; Kawazoe, F.; Kéfélian, F.; Keiser, G. M.; Keitel, D.; Kelley, D. B.; Kells, W.; Khalaidovski, A.; Khalili, F. Y.; Khazanov, E. A.; Kim, C.; Kim, K.; Kim, N.; Kim, N. G.; Kim, Y.-M.; King, E. J.; King, P. J.; Kinzel, D. L.; Kissel, J. S.; Klimenko, S.; Kline, J.; Koehlenbeck, S.; Kokeyama, K.; Kondrashov, V.; Koranda, S.; Korth, W. Z.; Kowalska, I.; Kozak, D. B.; Kremin, A.; Kringel, V.; Królak, A.; Kuehn, G.; Kumar, A.; Kumar, P.; Kumar, R.; Kuo, L.; Kutynia, A.; Kwee, P.; Landry, M.; Lantz, B.; Larson, S.; Lasky, P. D.; Lawrie, C.; Lazzarini, A.; Lazzaro, C.; Leaci, P.; Leavey, S.; Lebigot, E. O.; Lee, C.-H.; Lee, H. K.; Lee, H. M.; Lee, J.; Leonardi, M.; Leong, J. R.; Le Roux, A.; Leroy, N.; Letendre, N.; Levin, Y.; Levine, B.; Lewis, J.; Li, T. G. F.; Libbrecht, K.; Libson, A.; Lin, A. C.; Littenberg, T. B.; Litvine, V.; Lockerbie, N. A.; Lockett, V.; Lodhia, D.; Loew, K.; Logue, J.; Lombardi, A. L.; Lorenzini, M.; Loriette, V.; Lormand, M.; Losurdo, G.; Lough, J.; Lubinski, M. J.; Lück, H.; Luijten, E.; Lundgren, A. P.; Lynch, R.; Ma, Y.; Macarthur, J.; Macdonald, E. P.; MacDonald, T.; Machenschalk, B.; MacInnis, M.; Macleod, D. M.; Magana-Sandoval, F.; Mageswaran, M.; Maglione, C.; Mailand, K.; Majorana, E.; Maksimovic, I.; Malvezzi, V.; Man, N.; Manca, G. M.; Mandel, I.; Mandic, V.; Mangano, V.; Mangini, N.; Mantovani, M.; Marchesoni, F.; Marion, F.; Márka, S.; Márka, Z.; Markosyan, A.; Maros, E.; Marque, J.; Martelli, F.; Martin, I. W.; Martin, R. M.; Martinelli, L.; Martynov, D.; Marx, J. N.; Mason, K.; Masserot, A.; Massinger, T. J.; Matichard, F.; Matone, L.; Matzner, R. A.; Mavalvala, N.; Mazumder, N.; Mazzolo, G.; McCarthy, R.; McClelland, D. E.; McGuire, S. C.; McIntyre, G.; McIver, J.; McLin, K.; Meacher, D.; Meadors, G. D.; Mehmet, M.; Meidam, J.; Meinders, M.; Melatos, A.; Mendell, G.; Mercer, R. A.; Meshkov, S.; Messenger, C.; Meyers, P.; Miao, H.; Michel, C.; Mikhailov, E. E.; Milano, L.; Milde, S.; Miller, J.; Minenkov, Y.; Mingarelli, C. M. F.; Mishra, C.; Mitra, S.; Mitrofanov, V. P.; Mitselmakher, G.; Mittleman, R.; Moe, B.; Moesta, P.; Mohan, M.; Mohapatra, S. R. P.; Moraru, D.; Moreno, G.; Morgado, N.; Morriss, S. R.; Mossavi, K.; Mours, B.; Mow-Lowry, C. M.; Mueller, C. L.; Mueller, G.; Mukherjee, S.; Mullavey, A.; Munch, J.; Murphy, D.; Murray, P. G.; Mytidis, A.; Nagy, M. F.; Kumar, D. Nanda; Nardecchia, I.; Naticchioni, L.; Nayak, R. K.; Necula, V.; Nelemans, G.; Neri, I.; Neri, M.; Newton, G.; Nguyen, T.; Nitz, A.; Nocera, F.; Nolting, D.; Normandin, M. E. N.; Nuttall, L. K.; Ochsner, E.; O'Dell, J.; Oelker, E.; Oh, J. J.; Oh, S. H.; Ohme, F.; Oppermann, P.; O'Reilly, B.; O'Shaughnessy, R.; Osthelder, C.; Ottaway, D. J.; Ottens, R. S.; Overmier, H.; Owen, B. J.; Padilla, C.; Pai, A.; Palashov, O.; Palomba, C.; Pan, H.; Pan, Y.; Pankow, C.; Paoletti, F.; Paoletti, R.; Paris, H.; Pasqualetti, A.; Passaquieti, R.; Passuello, D.; Pedraza, M.; Penn, S.; Perreca, A.; Phelps, M.; Pichot, M.; Pickenpack, M.; Piergiovanni, F.; Pierro, V.; Pinard, L.; Pinto, I. M.; Pitkin, M.; Poeld, J.; Poggiani, R.; Poteomkin, A.; Powell, J.; Prasad, J.; Premachandra, S.; Prestegard, T.; Price, L. R.; Prijatelj, M.; Privitera, S.; Prodi, G. A.; Prokhorov, L.; Puncken, O.; Punturo, M.; Puppo, P.; Qin, J.; Quetschke, V.; Quintero, E.; Quiroga, G.; Quitzow-James, R.; Raab, F. J.; Rabeling, D. S.; Rácz, I.; Radkins, H.; Raffai, P.; Raja, S.; Rajalakshmi, G.; Rakhmanov, M.; Ramet, C.; Ramirez, K.; Rapagnani, P.; Raymond, V.; Re, V.; Read, J.; Reed, C. M.; Regimbau, T.; Reid, S.; Reitze, D. H.; Rhoades, E.; Ricci, F.; Riles, K.; Robertson, N. A.; Robinet, F.; Rocchi, A.; Rodruck, M.; Rolland, L.; Rollins, J. G.; Romano, J. D.; Romano, R.; Romanov, G.; Romie, J. H.; Rosińska, D.; Rowan, S.; Rüdiger, A.; Ruggi, P.; Ryan, K.; Salemi, F.; Sammut, L.; Sandberg, V.; Sanders, J. R.; Sannibale, V.; Santiago-Prieto, I.; Saracco, E.; Sassolas, B.; Sathyaprakash, B. S.; Saulson, P. R.; Savage, R.; Scheuer, J.; Schilling, R.; Schnabel, R.; Schofield, R. M. S.; Schreiber, E.; Schuette, D.; Schutz, B. F.; Scott, J.; Scott, S. M.; Sellers, D.; Sengupta, A. S.; Sentenac, D.; Sequino, V.; Sergeev, A.; Shaddock, D.; Shah, S.; Shahriar, M. S.; Shaltev, M.; Shapiro, B.; Shawhan, P.; Shoemaker, D. H.; Sidery, T. L.; Siellez, K.; Siemens, X.; Sigg, D.; Simakov, D.; Singer, A.; Singer, L.; Singh, R.; Sintes, A. M.; Slagmolen, B. J. J.; Slutsky, J.; Smith, J. R.; Smith, M.; Smith, R. J. E.; Smith-Lefebvre, N. D.; Son, E. J.; Sorazu, B.; Souradeep, T.; Sperandio, L.; Staley, A.; Stebbins, J.; Steinlechner, J.; Steinlechner, S.; Stephens, B. C.; Steplewski, S.; Stevenson, S.; Stone, R.; Stops, D.; Strain, K. A.; Straniero, N.; Strigin, S.; Sturani, R.; Stuver, A. L.; Summerscales, T. Z.; Susmithan, S.; Sutton, P. J.; Swinkels, B.; Tacca, M.; Talukder, D.; Tanner, D. B.; Tarabrin, S. P.; Taylor, R.; ter Braack, A. P. M.; Thirugnanasambandam, M. P.; Thomas, M.; Thomas, P.; Thorne, K. A.; Thorne, K. S.; Thrane, E.; Tiwari, V.; Tokmakov, K. V.; Tomlinson, C.; Toncelli, A.; Tonelli, M.; Torre, O.; Torres, C. V.; Torrie, C. I.; Travasso, F.; Traylor, G.; Tse, M.; Ugolini, D.; Unnikrishnan, C. S.; Urban, A. L.; Urbanek, K.; Vahlbruch, H.; Vajente, G.; Valdes, G.; Vallisneri, M.; van den Brand, J. F. J.; Van Den Broeck, C.; van der Putten, S.; van der Sluys, M. V.; van Heijningen, J.; van Veggel, A. A.; Vass, S.; Vasúth, M.; Vaulin, R.; Vecchio, A.; Vedovato, G.; Veitch, J.; Veitch, P. J.; Venkateswara, K.; Verkindt, D.; Verma, S. S.; Vetrano, F.; Viceré, A.; Vincent-Finley, R.; Vinet, J.-Y.; Vitale, S.; Vo, T.; Vocca, H.; Vorvick, C.; Vousden, W. D.; Vyachanin, S. P.; Wade, A.; Wade, L.; Wade, M.; Walker, M.; Wallace, L.; Wang, M.; Wang, X.; Ward, R. L.; Was, M.; Weaver, B.; Wei, L.-W.; Weinert, M.; Weinstein, A. J.; Weiss, R.; Welborn, T.; Wen, L.; Wessels, P.; West, M.; Westphal, T.; Wette, K.; Whelan, J. T.; White, D. J.; Whiting, B. F.; Wiesner, K.; Wilkinson, C.; Williams, K.; Williams, L.; Williams, R.; Williams, T.; Williamson, A. R.; Willis, J. L.; Willke, B.; Wimmer, M.; Winkler, W.; Wipf, C. C.; Wiseman, A. G.; Wittel, H.; Woan, G.; Worden, J.; Yablon, J.; Yakushin, I.; Yamamoto, H.; Yancey, C. C.; Yang, H.; Yang, Z.; Yoshida, S.; Yvert, M.; ZadroŻny, A.; Zanolin, M.; Zendri, J.-P.; Zhang, Fan; Zhang, L.; Zhao, C.; Zhu, X. J.; Zucker, M. E.; Zuraw, S.; Zweizig, J.; LIGO; Virgo Collaboration

2014-12-01

Gravitational waves from a variety of sources are predicted to superpose to create a stochastic background. This background is expected to contain unique information from throughout the history of the Universe that is unavailable through standard electromagnetic observations, making its study of fundamental importance to understanding the evolution of the Universe. We carry out a search for the stochastic background with the latest data from the LIGO and Virgo detectors. Consistent with predictions from most stochastic gravitational-wave background models, the data display no evidence of a stochastic gravitational-wave signal. Assuming a gravitational-wave spectrum of ΩGW(f )=Ωα(f/fref ) α , we place 95% confidence level upper limits on the energy density of the background in each of four frequency bands spanning 41.5-1726 Hz. In the frequency band of 41.5-169.25 Hz for a spectral index of α =0 , we constrain the energy density of the stochastic background to be ΩGW(f )<5.6 ×1 0-6 . For the 600-1000 Hz band, ΩGW(f )<0.14 (f /900 Hz )3 , a factor of 2.5 lower than the best previously reported upper limits. We find ΩGW(f )<1.8 ×1 0-4 using a spectral index of zero for 170-600 Hz and ΩGW(f )<1.0 (f /1300 Hz )3 for 1000-1726 Hz, bands in which no previous direct limits have been placed. The limits in these four bands are the lowest direct measurements to date on the stochastic background. We discuss the implications of these results in light of the recent claim by the BICEP2 experiment of the possible evidence for inflationary gravitational waves.

Detection of enhancement in number densities of background galaxies due to magnification by massive galaxy clusters

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

Chiu, I.; Dietrich, J. P.; Mohr, J.

2016-02-18

We present a detection of the enhancement in the number densities of background galaxies induced from lensing magnification and use it to test the Sunyaev-Zel'dovich effect (SZE-) inferred masses in a sample of 19 galaxy clusters with median redshift z similar or equal to 0.42 selected from the South Pole Telescope SPT-SZ survey. These clusters are observed by the Megacam on the Magellan Clay Telescope though gri filters. Two background galaxy populations are selected for this study through their photometric colours; they have median redshifts zmedian similar or equal to 0.9 (low-z background) and z(median) similar or equal to 1.8more » (high-z background). Stacking these populations, we detect the magnification bias effect at 3.3 sigma and 1.3 sigma for the low-and high-z backgrounds, respectively. We fit Navarro, Frenk and White models simultaneously to all observed magnification bias profiles to estimate the multiplicative factor. that describes the ratio of the weak lensing mass to the mass inferred from the SZE observable-mass relation. We further quantify systematic uncertainties in. resulting from the photometric noise and bias, the cluster galaxy contamination and the estimations of the background properties. The resulting. for the combined background populations with 1 sigma uncertainties is 0.83 +/- 0.24(stat) +/- 0.074(sys), indicating good consistency between the lensing and the SZE-inferred masses. We use our best-fitting eta to predict the weak lensing shear profiles and compare these predictions with observations, showing agreement between the magnification and shear mass constraints. This work demonstrates the promise of using the magnification as a complementary method to estimate cluster masses in large surveys.« less

Improved upper limits on the stochastic gravitational-wave background from 2009-2010 LIGO and Virgo data.

PubMed

Aasi, J; Abbott, B P; Abbott, R; Abbott, T; Abernathy, M R; Accadia, T; Acernese, F; Ackley, K; Adams, C; Adams, T; Addesso, P; Adhikari, R X; Affeldt, C; Agathos, M; Aggarwal, N; Aguiar, O D; Ain, A; Ajith, P; Alemic, A; Allen, B; Allocca, A; Amariutei, D; Andersen, M; Anderson, R; Anderson, S B; Anderson, W G; Arai, K; Araya, M C; Arceneaux, C; Areeda, J; Aston, S M; Astone, P; Aufmuth, P; Aulbert, C; Austin, L; Aylott, B E; Babak, S; Baker, P T; Ballardin, G; Ballmer, S W; Barayoga, J C; Barbet, M; Barish, B C; Barker, D; Barone, F; Barr, B; Barsotti, L; Barsuglia, M; Barton, M A; Bartos, I; Bassiri, R; Basti, A; Batch, J C; Bauchrowitz, J; Bauer, Th S; Behnke, B; Bejger, M; Beker, M G; Belczynski, C; Bell, A S; Bell, C; Bergmann, G; Bersanetti, D; Bertolini, A; Betzwieser, J; Beyersdorf, P T; Bilenko, I A; Billingsley, G; Birch, J; Biscans, S; Bitossi, M; Bizouard, M A; Black, E; Blackburn, J K; Blackburn, L; Blair, D; Bloemen, S; Blom, M; Bock, O; Bodiya, T P; Boer, M; Bogaert, G; 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Corbitt, T R; Cordier, M; Cornish, N; Corpuz, A; Corsi, A; Costa, C A; Coughlin, M W; Coughlin, S; Coulon, J-P; Countryman, S; Couvares, P; Coward, D M; Cowart, M; Coyne, D C; Coyne, R; Craig, K; Creighton, J D E; Crowder, S G; Cumming, A; Cunningham, L; Cuoco, E; Dahl, K; Canton, T Dal; Damjanic, M; Danilishin, S L; D'Antonio, S; Danzmann, K; Dattilo, V; Daveloza, H; Davier, M; Davies, G S; Daw, E J; Day, R; Dayanga, T; Debreczeni, G; Degallaix, J; Deléglise, S; Del Pozzo, W; Denker, T; Dent, T; Dereli, H; Dergachev, V; De Rosa, R; DeRosa, R T; DeSalvo, R; Dhurandhar, S; Díaz, M; Di Fiore, L; Di Lieto, A; Di Palma, I; Di Virgilio, A; Donath, A; Donovan, F; Dooley, K L; Doravari, S; Dossa, S; Douglas, R; Downes, T P; Drago, M; Drever, R W P; Driggers, J C; Du, Z; Dwyer, S; Eberle, T; Edo, T; Edwards, M; Effler, A; Eggenstein, H; Ehrens, P; Eichholz, J; Eikenberry, S S; Endrőczi, G; Essick, R; Etzel, T; Evans, M; Evans, T; Factourovich, M; Fafone, V; Fairhurst, S; Fang, Q; Farinon, S; Farr, B; Farr, W M; Favata, M; Fehrmann, H; Fejer, M M; Feldbaum, D; Feroz, F; Ferrante, I; Ferrini, F; Fidecaro, F; Finn, L S; Fiori, I; Fisher, R P; Flaminio, R; Fournier, J-D; Franco, S; Frasca, S; Frasconi, F; Frede, M; Frei, Z; Freise, A; Frey, R; Fricke, T T; Fritschel, P; Frolov, V V; Fulda, P; Fyffe, M; Gair, J; Gammaitoni, L; Gaonkar, S; Garufi, F; Gehrels, N; Gemme, G; Genin, E; Gennai, A; Ghosh, S; Giaime, J A; Giardina, K D; Giazotto, A; Gill, C; Gleason, J; Goetz, E; Goetz, R; Gondan, L; González, G; Gordon, N; Gorodetsky, M L; Gossan, S; Gossler, S; Gouaty, R; Gräf, C; Graff, P B; Granata, M; Grant, A; Gras, S; Gray, C; Greenhalgh, R J S; Gretarsson, A M; Groot, P; Grote, H; Grover, K; Grunewald, S; Guidi, G M; Guido, C; Gushwa, K; Gustafson, E K; Gustafson, R; Hammer, D; Hammond, G; Hanke, M; Hanks, J; Hanna, C; Hanson, J; Harms, J; Harry, G M; Harry, I W; Harstad, E D; Hart, M; Hartman, M T; Haster, C-J; Haughian, K; Heidmann, A; Heintze, M; Heitmann, H; Hello, P; Hemming, G; Hendry, M; Heng, I S; Heptonstall, A W; Heurs, M; Hewitson, M; Hild, S; Hoak, D; Hodge, K A; Holt, K; Hooper, S; Hopkins, P; Hosken, D J; Hough, J; Howell, E J; Hu, Y; Huerta, E; Hughey, B; Husa, S; Huttner, S H; Huynh, M; Huynh-Dinh, T; Ingram, D R; Inta, R; Isogai, T; Ivanov, A; Iyer, B R; Izumi, K; Jacobson, M; James, E; Jang, H; Jaranowski, P; Ji, Y; Jiménez-Forteza, F; Johnson, W W; Jones, D I; Jones, R; Jonker, R J G; Ju, L; K, Haris; Kalmus, P; Kalogera, V; Kandhasamy, S; Kang, G; Kanner, J B; Karlen, J; Kasprzack, M; Katsavounidis, E; Katzman, W; Kaufer, H; Kawabe, K; Kawazoe, F; Kéfélian, F; Keiser, G M; Keitel, D; Kelley, D B; Kells, W; Khalaidovski, A; Khalili, F Y; Khazanov, E A; Kim, C; Kim, K; Kim, N; Kim, N G; Kim, Y-M; King, E J; King, P J; Kinzel, D L; Kissel, J S; Klimenko, S; Kline, J; Koehlenbeck, S; Kokeyama, K; Kondrashov, V; Koranda, S; Korth, W Z; Kowalska, I; Kozak, D B; Kremin, A; Kringel, V; Królak, A; Kuehn, G; Kumar, A; Kumar, P; Kumar, R; Kuo, L; Kutynia, A; Kwee, P; Landry, M; Lantz, B; Larson, S; Lasky, P D; Lawrie, C; Lazzarini, A; Lazzaro, C; Leaci, P; Leavey, S; Lebigot, E O; Lee, C-H; Lee, H K; Lee, H M; Lee, J; Leonardi, M; Leong, J R; Le Roux, A; Leroy, N; Letendre, N; Levin, Y; Levine, B; Lewis, J; Li, T G F; Libbrecht, K; Libson, A; Lin, A C; Littenberg, T B; Litvine, V; Lockerbie, N A; Lockett, V; Lodhia, D; Loew, K; Logue, J; Lombardi, A L; Lorenzini, M; Loriette, V; Lormand, M; Losurdo, G; Lough, J; Lubinski, M J; Lück, H; Luijten, E; Lundgren, A P; Lynch, R; Ma, Y; Macarthur, J; Macdonald, E P; MacDonald, T; Machenschalk, B; MacInnis, M; Macleod, D M; Magana-Sandoval, F; Mageswaran, M; Maglione, C; Mailand, K; Majorana, E; Maksimovic, I; Malvezzi, V; Man, N; Manca, G M; Mandel, I; Mandic, V; Mangano, V; Mangini, N; Mantovani, M; Marchesoni, F; Marion, F; Márka, S; Márka, Z; Markosyan, A; Maros, E; Marque, J; Martelli, F; Martin, I W; Martin, R M; Martinelli, L; Martynov, D; Marx, J N; Mason, K; Masserot, A; Massinger, T J; Matichard, F; Matone, L; Matzner, R A; Mavalvala, N; Mazumder, N; Mazzolo, G; McCarthy, R; McClelland, D E; McGuire, S C; McIntyre, G; McIver, J; McLin, K; Meacher, D; Meadors, G D; Mehmet, M; Meidam, J; Meinders, M; Melatos, A; Mendell, G; Mercer, R A; Meshkov, S; Messenger, C; Meyers, P; Miao, H; Michel, C; Mikhailov, E E; Milano, L; Milde, S; Miller, J; Minenkov, Y; Mingarelli, C M F; Mishra, C; Mitra, S; Mitrofanov, V P; Mitselmakher, G; Mittleman, R; Moe, B; Moesta, P; Mohan, M; Mohapatra, S R P; Moraru, D; Moreno, G; Morgado, N; Morriss, S R; Mossavi, K; Mours, B; Mow-Lowry, C M; Mueller, C L; Mueller, G; Mukherjee, S; Mullavey, A; Munch, J; Murphy, D; Murray, P G; Mytidis, A; Nagy, M F; Kumar, D Nanda; Nardecchia, I; Naticchioni, L; Nayak, R K; Necula, V; Nelemans, G; Neri, I; Neri, M; Newton, G; Nguyen, T; Nitz, A; Nocera, F; Nolting, D; Normandin, M E N; Nuttall, L K; Ochsner, E; O'Dell, J; Oelker, E; Oh, J J; Oh, S H; Ohme, F; Oppermann, P; O'Reilly, B; O'Shaughnessy, R; Osthelder, C; Ottaway, D J; Ottens, R S; Overmier, H; Owen, B J; Padilla, C; Pai, A; Palashov, O; Palomba, C; Pan, H; Pan, Y; Pankow, C; Paoletti, F; Paoletti, R; Paris, H; Pasqualetti, A; Passaquieti, R; Passuello, D; Pedraza, M; Penn, S; Perreca, A; Phelps, M; Pichot, M; Pickenpack, M; Piergiovanni, F; Pierro, V; Pinard, L; Pinto, I M; Pitkin, M; Poeld, J; Poggiani, R; Poteomkin, A; Powell, J; Prasad, J; Premachandra, S; Prestegard, T; Price, L R; Prijatelj, M; Privitera, S; Prodi, G A; Prokhorov, L; Puncken, O; Punturo, M; Puppo, P; Qin, J; Quetschke, V; Quintero, E; Quiroga, G; Quitzow-James, R; Raab, F J; Rabeling, D S; Rácz, I; Radkins, H; Raffai, P; Raja, S; Rajalakshmi, G; Rakhmanov, M; Ramet, C; Ramirez, K; Rapagnani, P; Raymond, V; Re, V; Read, J; Reed, C M; Regimbau, T; Reid, S; Reitze, D H; Rhoades, E; Ricci, F; Riles, K; Robertson, N A; Robinet, F; Rocchi, A; Rodruck, M; Rolland, L; Rollins, J G; Romano, J D; Romano, R; Romanov, G; Romie, J H; Rosińska, D; Rowan, S; Rüdiger, A; Ruggi, P; Ryan, K; Salemi, F; Sammut, L; Sandberg, V; Sanders, J R; Sannibale, V; Santiago-Prieto, I; Saracco, E; Sassolas, B; Sathyaprakash, B S; Saulson, P R; Savage, R; Scheuer, J; Schilling, R; Schnabel, R; Schofield, R M S; Schreiber, E; Schuette, D; Schutz, B F; Scott, J; Scott, S M; Sellers, D; Sengupta, A S; Sentenac, D; Sequino, V; Sergeev, A; Shaddock, D; Shah, S; Shahriar, M S; Shaltev, M; Shapiro, B; Shawhan, P; Shoemaker, D H; Sidery, T L; Siellez, K; Siemens, X; Sigg, D; Simakov, D; Singer, A; Singer, L; Singh, R; Sintes, A M; Slagmolen, B J J; Slutsky, J; Smith, J R; Smith, M; Smith, R J E; Smith-Lefebvre, N D; Son, E J; Sorazu, B; Souradeep, T; Sperandio, L; Staley, A; Stebbins, J; Steinlechner, J; Steinlechner, S; Stephens, B C; Steplewski, S; Stevenson, S; Stone, R; Stops, D; Strain, K A; Straniero, N; Strigin, S; Sturani, R; Stuver, A L; Summerscales, T Z; Susmithan, S; Sutton, P J; Swinkels, B; Tacca, M; Talukder, D; Tanner, D B; Tarabrin, S P; Taylor, R; Ter Braack, A P M; Thirugnanasambandam, M P; Thomas, M; Thomas, P; Thorne, K A; Thorne, K S; Thrane, E; Tiwari, V; Tokmakov, K V; Tomlinson, C; Toncelli, A; Tonelli, M; Torre, O; Torres, C V; Torrie, C I; Travasso, F; Traylor, G; Tse, M; Ugolini, D; Unnikrishnan, C S; Urban, A L; Urbanek, K; Vahlbruch, H; Vajente, G; Valdes, G; Vallisneri, M; van den Brand, J F J; Van Den Broeck, C; van der Putten, S; van der Sluys, M V; van Heijningen, J; van Veggel, A A; Vass, S; Vasúth, M; Vaulin, R; Vecchio, A; Vedovato, G; Veitch, J; Veitch, P J; Venkateswara, K; Verkindt, D; Verma, S S; Vetrano, F; Viceré, A; Vincent-Finley, R; Vinet, J-Y; Vitale, S; Vo, T; Vocca, H; Vorvick, C; Vousden, W D; Vyachanin, S P; Wade, A; Wade, L; Wade, M; Walker, M; Wallace, L; Wang, M; Wang, X; Ward, R L; Was, M; Weaver, B; Wei, L-W; Weinert, M; Weinstein, A J; Weiss, R; Welborn, T; Wen, L; Wessels, P; West, M; Westphal, T; Wette, K; Whelan, J T; White, D J; Whiting, B F; Wiesner, K; Wilkinson, C; Williams, K; Williams, L; Williams, R; Williams, T; Williamson, A R; Willis, J L; Willke, B; Wimmer, M; Winkler, W; Wipf, C C; Wiseman, A G; Wittel, H; Woan, G; Worden, J; Yablon, J; Yakushin, I; Yamamoto, H; Yancey, C C; Yang, H; Yang, Z; Yoshida, S; Yvert, M; Zadrożny, A; Zanolin, M; Zendri, J-P; Zhang, Fan; Zhang, L; Zhao, C; Zhu, X J; Zucker, M E; Zuraw, S; Zweizig, J

2014-12-05

Gravitational waves from a variety of sources are predicted to superpose to create a stochastic background. This background is expected to contain unique information from throughout the history of the Universe that is unavailable through standard electromagnetic observations, making its study of fundamental importance to understanding the evolution of the Universe. We carry out a search for the stochastic background with the latest data from the LIGO and Virgo detectors. Consistent with predictions from most stochastic gravitational-wave background models, the data display no evidence of a stochastic gravitational-wave signal. Assuming a gravitational-wave spectrum of Ω_{GW}(f)=Ω_{α}(f/f_{ref})^{α}, we place 95% confidence level upper limits on the energy density of the background in each of four frequency bands spanning 41.5-1726 Hz. In the frequency band of 41.5-169.25 Hz for a spectral index of α=0, we constrain the energy density of the stochastic background to be Ω_{GW}(f)<5.6×10^{-6}. For the 600-1000 Hz band, Ω_{GW}(f)<0.14(f/900  Hz)^{3}, a factor of 2.5 lower than the best previously reported upper limits. We find Ω_{GW}(f)<1.8×10^{-4} using a spectral index of zero for 170-600 Hz and Ω_{GW}(f)<1.0(f/1300  Hz)^{3} for 1000-1726 Hz, bands in which no previous direct limits have been placed. The limits in these four bands are the lowest direct measurements to date on the stochastic background. We discuss the implications of these results in light of the recent claim by the BICEP2 experiment of the possible evidence for inflationary gravitational waves.

Integration of Irma tactical scene generator into directed-energy weapon system simulation

NASA Astrophysics Data System (ADS)

Owens, Monte A.; Cole, Madison B., III; Laine, Mark R.

2003-08-01

Integrated high-fidelity physics-based simulations that include engagement models, image generation, electro-optical hardware models and control system algorithms have previously been developed by Boeing-SVS for various tracking and pointing systems. These simulations, however, had always used images with featureless or random backgrounds and simple target geometries. With the requirement to engage tactical ground targets in the presence of cluttered backgrounds, a new type of scene generation tool was required to fully evaluate system performance in this challenging environment. To answer this need, Irma was integrated into the existing suite of Boeing-SVS simulation tools, allowing scene generation capabilities with unprecedented realism. Irma is a US Air Force research tool used for high-resolution rendering and prediction of target and background signatures. The MATLAB/Simulink-based simulation achieves closed-loop tracking by running track algorithms on the Irma-generated images, processing the track errors through optical control algorithms, and moving simulated electro-optical elements. The geometry of these elements determines the sensor orientation with respect to the Irma database containing the three-dimensional background and target models. This orientation is dynamically passed to Irma through a Simulink S-function to generate the next image. This integrated simulation provides a test-bed for development and evaluation of tracking and control algorithms against representative images including complex background environments and realistic targets calibrated using field measurements.

Model-based video segmentation for vision-augmented interactive games

NASA Astrophysics Data System (ADS)

Liu, Lurng-Kuo

2000-04-01

This paper presents an architecture and algorithms for model based video object segmentation and its applications to vision augmented interactive game. We are especially interested in real time low cost vision based applications that can be implemented in software in a PC. We use different models for background and a player object. The object segmentation algorithm is performed in two different levels: pixel level and object level. At pixel level, the segmentation algorithm is formulated as a maximizing a posteriori probability (MAP) problem. The statistical likelihood of each pixel is calculated and used in the MAP problem. Object level segmentation is used to improve segmentation quality by utilizing the information about the spatial and temporal extent of the object. The concept of an active region, which is defined based on motion histogram and trajectory prediction, is introduced to indicate the possibility of a video object region for both background and foreground modeling. It also reduces the overall computation complexity. In contrast with other applications, the proposed video object segmentation system is able to create background and foreground models on the fly even without introductory background frames. Furthermore, we apply different rate of self-tuning on the scene model so that the system can adapt to the environment when there is a scene change. We applied the proposed video object segmentation algorithms to several prototype virtual interactive games. In our prototype vision augmented interactive games, a player can immerse himself/herself inside a game and can virtually interact with other animated characters in a real time manner without being constrained by helmets, gloves, special sensing devices, or background environment. The potential applications of the proposed algorithms including human computer gesture interface and object based video coding such as MPEG-4 video coding.

Binary recursive partitioning: background, methods, and application to psychology.

PubMed

Merkle, Edgar C; Shaffer, Victoria A

2011-02-01

Binary recursive partitioning (BRP) is a computationally intensive statistical method that can be used in situations where linear models are often used. Instead of imposing many assumptions to arrive at a tractable statistical model, BRP simply seeks to accurately predict a response variable based on values of predictor variables. The method outputs a decision tree depicting the predictor variables that were related to the response variable, along with the nature of the variables' relationships. No significance tests are involved, and the tree's 'goodness' is judged based on its predictive accuracy. In this paper, we describe BRP methods in a detailed manner and illustrate their use in psychological research. We also provide R code for carrying out the methods.

Small traveling clusters in attractive and repulsive Hamiltonian mean-field models.

PubMed

Barré, Julien; Yamaguchi, Yoshiyuki Y

2009-03-01

Long-lasting small traveling clusters are studied in the Hamiltonian mean-field model by comparing between attractive and repulsive interactions. Nonlinear Landau damping theory predicts that a Gaussian momentum distribution on a spatially homogeneous background permits the existence of traveling clusters in the repulsive case, as in plasma systems, but not in the attractive case. Nevertheless, extending the analysis to a two-parameter family of momentum distributions of Fermi-Dirac type, we theoretically predict the existence of traveling clusters in the attractive case; these findings are confirmed by direct N -body numerical simulations. The parameter region with the traveling clusters is much reduced in the attractive case with respect to the repulsive case.

Study of moving object detecting and tracking algorithm for video surveillance system

NASA Astrophysics Data System (ADS)

Wang, Tao; Zhang, Rongfu

2010-10-01

This paper describes a specific process of moving target detecting and tracking in the video surveillance.Obtain high-quality background is the key to achieving differential target detecting in the video surveillance.The paper is based on a block segmentation method to build clear background,and using the method of background difference to detecing moving target,after a series of treatment we can be extracted the more comprehensive object from original image,then using the smallest bounding rectangle to locate the object.In the video surveillance system, the delay of camera and other reasons lead to tracking lag,the model of Kalman filter based on template matching was proposed,using deduced and estimated capacity of Kalman,the center of smallest bounding rectangle for predictive value,predicted the position in the next moment may appare,followed by template matching in the region as the center of this position,by calculate the cross-correlation similarity of current image and reference image,can determine the best matching center.As narrowed the scope of searching,thereby reduced the searching time,so there be achieve fast-tracking.

A Predictive Model for Medical Events Based on Contextual Embedding of Temporal Sequences

PubMed Central

Wang, Zhimu; Huang, Yingxiang; Wang, Shuang; Wang, Fei; Jiang, Xiaoqian

2016-01-01

Background Medical concepts are inherently ambiguous and error-prone due to human fallibility, which makes it hard for them to be fully used by classical machine learning methods (eg, for tasks like early stage disease prediction). Objective Our work was to create a new machine-friendly representation that resembles the semantics of medical concepts. We then developed a sequential predictive model for medical events based on this new representation. Methods We developed novel contextual embedding techniques to combine different medical events (eg, diagnoses, prescriptions, and labs tests). Each medical event is converted into a numerical vector that resembles its “semantics,” via which the similarity between medical events can be easily measured. We developed simple and effective predictive models based on these vectors to predict novel diagnoses. Results We evaluated our sequential prediction model (and standard learning methods) in estimating the risk of potential diseases based on our contextual embedding representation. Our model achieved an area under the receiver operating characteristic (ROC) curve (AUC) of 0.79 on chronic systolic heart failure and an average AUC of 0.67 (over the 80 most common diagnoses) using the Medical Information Mart for Intensive Care III (MIMIC-III) dataset. Conclusions We propose a general early prognosis predictor for 80 different diagnoses. Our method computes numeric representation for each medical event to uncover the potential meaning of those events. Our results demonstrate the efficiency of the proposed method, which will benefit patients and physicians by offering more accurate diagnosis. PMID:27888170

Search for Anomalous Production of Photon + Jets + Missing Transverse Energy in $$p\\bar{p}$$ Collisions at $$\\sqrt{s}=1.96$$~TeV Using the CDF II Detector

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

Hewamanage, Samantha Kaushalya

2011-01-01

A model-independent signature-based search for physics beyond the Standard Model is performed in the photon + jets + missing transverse energy channel in \\ppbar collisions at a center of mass energy of 1.96 TeV using the CDF II detector. Events with a photon + jets are predicted by the Standard Model and also by many theoretical models beyond the Standard Model. In the Standard Model, the main mechanisms for photon + jets production include quark-antiquark annihilation and quark-gluon scattering. No intrinsic missing transverse energy is present in any of these Standard Model processes. In this search, photon +more » $$\\geq$$1 jet and photon + $$\\geq$$2 jet events are analyzed with and without a minimum requirement on the missing transverse energy. Numerous mass distributions and kinematic distributions are studied and no significant excess over the background prediction is found. All results indicate good agreement with expectations of the Standard Model.« less

Validating neural-network refinements of nuclear mass models

NASA Astrophysics Data System (ADS)

Utama, R.; Piekarewicz, J.

2018-01-01

Background: Nuclear astrophysics centers on the role of nuclear physics in the cosmos. In particular, nuclear masses at the limits of stability are critical in the development of stellar structure and the origin of the elements. Purpose: We aim to test and validate the predictions of recently refined nuclear mass models against the newly published AME2016 compilation. Methods: The basic paradigm underlining the recently refined nuclear mass models is based on existing state-of-the-art models that are subsequently refined through the training of an artificial neural network. Bayesian inference is used to determine the parameters of the neural network so that statistical uncertainties are provided for all model predictions. Results: We observe a significant improvement in the Bayesian neural network (BNN) predictions relative to the corresponding "bare" models when compared to the nearly 50 new masses reported in the AME2016 compilation. Further, AME2016 estimates for the handful of impactful isotopes in the determination of r -process abundances are found to be in fairly good agreement with our theoretical predictions. Indeed, the BNN-improved Duflo-Zuker model predicts a root-mean-square deviation relative to experiment of σrms≃400 keV. Conclusions: Given the excellent performance of the BNN refinement in confronting the recently published AME2016 compilation, we are confident of its critical role in our quest for mass models of the highest quality. Moreover, as uncertainty quantification is at the core of the BNN approach, the improved mass models are in a unique position to identify those nuclei that will have the strongest impact in resolving some of the outstanding questions in nuclear astrophysics.

Ecology and geography of avian influenza (HPAI H5N1) transmission in the Middle East and northeastern Africa

PubMed Central

Williams, Richard AJ; Peterson, A Townsend

2009-01-01

Background The emerging highly pathogenic avian influenza strain H5N1 ("HPAI-H5N1") has spread broadly in the past decade, and is now the focus of considerable concern. We tested the hypothesis that spatial distributions of HPAI-H5N1 cases are related consistently and predictably to coarse-scale environmental features in the Middle East and northeastern Africa. We used ecological niche models to relate virus occurrences to 8 km resolution digital data layers summarizing parameters of monthly surface reflectance and landform. Predictive challenges included a variety of spatial stratification schemes in which models were challenged to predict case distributions in broadly unsampled areas. Results In almost all tests, HPAI-H5N1 cases were indeed occurring under predictable sets of environmental conditions, generally predicted absent from areas with low NDVI values and minimal seasonal variation, and present in areas with a broad range of and appreciable seasonal variation in NDVI values. Although we documented significant predictive ability of our models, even between our study region and West Africa, case occurrences in the Arabian Peninsula appear to follow a distinct environmental regime. Conclusion Overall, we documented a variable environmental "fingerprint" for areas suitable for HPAI-H5N1 transmission. PMID:19619336

Search for supersymmetry in pp collisions at $$\\sqrt{s} =7$$ TeV in events with a single lepton, jets, and missing transverse momentum

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

Chatrchyan, S.; Khachatryan, V.; Sirunyan, A. M.

Results are reported from a search for new physics processes in events containing a single isolated high-transverse-momentum lepton (electron or muon), energetic jets, and large missing transverse momentum. The analysis is based on a 4.98 fb -1 sample of proton–proton collisions at a center-of-mass energy of 7 TeV, obtained with the CMS detector at the LHC. Three separate background estimation methods, each relying primarily on control samples in the data, are applied to a range of signal regions, providing complementary approaches for estimating the background yields. The observed yields are consistent with the predicted standard model backgrounds. The results are interpreted inmore » terms of limits on the parameter space for the constrained minimal supersymmetric extension of the standard model, as well as on cross sections for simplified models, which provide a generic description of the production and decay of new particles in specific, topology based final states.« less

Amplitude of primeval fluctuations from cosmological mass density reconstructions

NASA Technical Reports Server (NTRS)

Seljak, Uros; Bertschinger, Edmund

1994-01-01

We use the POTENT reconstruction of the mass density field in the nearby universe to estimate the amplitude of the density fluctuation power spectrum for various cosmological models. We find that sigma(sub 8) Omega(sub m sup 0.6) = 1.3(sub -0.3 sup +0.4), almost independently of the power spectrum. This value agrees well with the Cosmic Background Explorer (COBE) normalization for the standard cold dark matter model, while alternative models predict an excessive amplitude compared with COBE. Flat, low Omega(sub m) models and tilted models with spectral index n less than 0.8 are particularly discordant.

Three-dimensional (3D) structure prediction of the American and African oil-palms β-ketoacyl-[ACP] synthase-II protein by comparative modelling

PubMed Central

Wang, Edina; Chinni, Suresh; Bhore, Subhash Janardhan

2014-01-01

Background: The fatty-acid profile of the vegetable oils determines its properties and nutritional value. Palm-oil obtained from the African oil-palm [Elaeis guineensis Jacq. (Tenera)] contains 44% palmitic acid (C16:0), but, palm-oil obtained from the American oilpalm [Elaeis oleifera] contains only 25% C16:0. In part, the b-ketoacyl-[ACP] synthase II (KASII) [EC: 2.3.1.179] protein is responsible for the high level of C16:0 in palm-oil derived from the African oil-palm. To understand more about E. guineensis KASII (EgKASII) and E. oleifera KASII (EoKASII) proteins, it is essential to know its structures. Hence, this study was undertaken. Objective: The objective of this study was to predict three-dimensional (3D) structure of EgKASII and EoKASII proteins using molecular modelling tools. Materials and Methods: The amino-acid sequences for KASII proteins were retrieved from the protein database of National Center for Biotechnology Information (NCBI), USA. The 3D structures were predicted for both proteins using homology modelling and ab-initio technique approach of protein structure prediction. The molecular dynamics (MD) simulation was performed to refine the predicted structures. The predicted structure models were evaluated and root mean square deviation (RMSD) and root mean square fluctuation (RMSF) values were calculated. Results: The homology modelling showed that EgKASII and EoKASII proteins are 78% and 74% similar with Streptococcus pneumonia KASII and Brucella melitensis KASII, respectively. The EgKASII and EoKASII structures predicted by using ab-initio technique approach shows 6% and 9% deviation to its structures predicted by homology modelling, respectively. The structure refinement and validation confirmed that the predicted structures are accurate. Conclusion: The 3D structures for EgKASII and EoKASII proteins were predicted. However, further research is essential to understand the interaction of EgKASII and EoKASII proteins with its substrates. PMID:24748752

Predicting Progression from Mild Cognitive Impairment to Alzheimer's Dementia Using Clinical, MRI, and Plasma Biomarkers via Probabilistic Pattern Classification

PubMed Central

Korolev, Igor O.; Symonds, Laura L.; Bozoki, Andrea C.

2016-01-01

Background Individuals with mild cognitive impairment (MCI) have a substantially increased risk of developing dementia due to Alzheimer's disease (AD). In this study, we developed a multivariate prognostic model for predicting MCI-to-dementia progression at the individual patient level. Methods Using baseline data from 259 MCI patients and a probabilistic, kernel-based pattern classification approach, we trained a classifier to distinguish between patients who progressed to AD-type dementia (n = 139) and those who did not (n = 120) during a three-year follow-up period. More than 750 variables across four data sources were considered as potential predictors of progression. These data sources included risk factors, cognitive and functional assessments, structural magnetic resonance imaging (MRI) data, and plasma proteomic data. Predictive utility was assessed using a rigorous cross-validation framework. Results Cognitive and functional markers were most predictive of progression, while plasma proteomic markers had limited predictive utility. The best performing model incorporated a combination of cognitive/functional markers and morphometric MRI measures and predicted progression with 80% accuracy (83% sensitivity, 76% specificity, AUC = 0.87). Predictors of progression included scores on the Alzheimer's Disease Assessment Scale, Rey Auditory Verbal Learning Test, and Functional Activities Questionnaire, as well as volume/cortical thickness of three brain regions (left hippocampus, middle temporal gyrus, and inferior parietal cortex). Calibration analysis revealed that the model is capable of generating probabilistic predictions that reliably reflect the actual risk of progression. Finally, we found that the predictive accuracy of the model varied with patient demographic, genetic, and clinical characteristics and could be further improved by taking into account the confidence of the predictions. Conclusions We developed an accurate prognostic model for predicting MCI-to-dementia progression over a three-year period. The model utilizes widely available, cost-effective, non-invasive markers and can be used to improve patient selection in clinical trials and identify high-risk MCI patients for early treatment. PMID:26901338

A Data Assimilation System For Operational Weather Forecast In Galicia Region (nw Spain)

NASA Astrophysics Data System (ADS)

Balseiro, C. F.; Souto, M. J.; Pérez-Muñuzuri, V.; Brewster, K.; Xue, M.

Regional weather forecast models, such as the Advanced Regional Prediction System (ARPS), over complex environments with varying local influences require an accurate meteorological analysis that should include all local meteorological measurements available. In this work, the ARPS Data Analysis System (ADAS) (Xue et al. 2001) is applied as a three-dimensional weather analysis tool to include surface station and rawinsonde data with the NCEP AVN forecasts as the analysis background. Currently in ADAS, a set of five meteorological variables are considered during the analysis: horizontal grid-relative wind components, pressure, potential temperature and spe- cific humidity. The analysis is used for high resolution numerical weather prediction for the Galicia region. The analysis method used in ADAS is based on the successive corrective scheme of Bratseth (1986), which asymptotically approaches the result of a statistical (optimal) interpolation, but at lower computational cost. As in the optimal interpolation scheme, the Bratseth interpolation method can take into account the rel- ative error between background and observational data, therefore they are relatively insensitive to large variations in data density and can integrate data of mixed accuracy. This method can be applied economically in an operational setting, providing signifi- cant improvement over the background model forecast as well as any analysis without high-resolution local observations. A one-way nesting is applied for weather forecast in Galicia region, and the use of this assimilation system in both domains shows better results not only in initial conditions but also in all forecast periods. Bratseth, A.M. (1986): "Statistical interpolation by means of successive corrections." Tellus, 38A, 439-447. Souto, M. J., Balseiro, C. F., Pérez-Muñuzuri, V., Xue, M. Brewster, K., (2001): "Im- pact of cloud analysis on numerical weather prediction in the galician region of Spain". Submitted to Journal of Applied Meteorology. Xue, M., Wang. D., Gao, J., Brewster, K, Droegemeier, K. K., (2001): "The Advanced Regional Prediction System (ARPS), storm-scale numerical weather prediction and data assimilation". Meteor. Atmos Physics. Accepted

Prediction of unwanted pregnancies using logistic regression, probit regression and discriminant analysis

PubMed Central

Ebrahimzadeh, Farzad; Hajizadeh, Ebrahim; Vahabi, Nasim; Almasian, Mohammad; Bakhteyar, Katayoon

2015-01-01

Background: Unwanted pregnancy not intended by at least one of the parents has undesirable consequences for the family and the society. In the present study, three classification models were used and compared to predict unwanted pregnancies in an urban population. Methods: In this cross-sectional study, 887 pregnant mothers referring to health centers in Khorramabad, Iran, in 2012 were selected by the stratified and cluster sampling; relevant variables were measured and for prediction of unwanted pregnancy, logistic regression, discriminant analysis, and probit regression models and SPSS software version 21 were used. To compare these models, indicators such as sensitivity, specificity, the area under the ROC curve, and the percentage of correct predictions were used. Results: The prevalence of unwanted pregnancies was 25.3%. The logistic and probit regression models indicated that parity and pregnancy spacing, contraceptive methods, household income and number of living male children were related to unwanted pregnancy. The performance of the models based on the area under the ROC curve was 0.735, 0.733, and 0.680 for logistic regression, probit regression, and linear discriminant analysis, respectively. Conclusion: Given the relatively high prevalence of unwanted pregnancies in Khorramabad, it seems necessary to revise family planning programs. Despite the similar accuracy of the models, if the researcher is interested in the interpretability of the results, the use of the logistic regression model is recommended. PMID:26793655

Visual detection following retinal damage: predictions of an inhomogeneous retino-cortical model

NASA Astrophysics Data System (ADS)

Arnow, Thomas L.; Geisler, Wilson S.

1996-04-01

A model of human visual detection performance has been developed, based on available anatomical and physiological data for the primate visual system. The inhomogeneous retino- cortical (IRC) model computes detection thresholds by comparing simulated neural responses to target patterns with responses to a uniform background of the same luminance. The model incorporates human ganglion cell sampling distributions; macaque monkey ganglion cell receptive field properties; macaque cortical cell contrast nonlinearities; and a optical decision rule based on ideal observer theory. Spatial receptive field properties of cortical neurons were not included. Two parameters were allowed to vary while minimizing the squared error between predicted and observed thresholds. One parameter was decision efficiency, the other was the relative strength of the ganglion-cell center and surround. The latter was only allowed to vary within a small range consistent with known physiology. Contrast sensitivity was measured for sinewave gratings as a function of spatial frequency, target size and eccentricity. Contrast sensitivity was also measured for an airplane target as a function of target size, with and without artificial scotomas. The results of these experiments, as well as contrast sensitivity data from the literature were compared to predictions of the IRC model. Predictions were reasonably good for grating and airplane targets.

An integrated physiology model to study regional lung damage effects and the physiologic response

PubMed Central

2014-01-01

Background This work expands upon a previously developed exercise dynamic physiology model (DPM) with the addition of an anatomic pulmonary system in order to quantify the impact of lung damage on oxygen transport and physical performance decrement. Methods A pulmonary model is derived with an anatomic structure based on morphometric measurements, accounting for heterogeneous ventilation and perfusion observed experimentally. The model is incorporated into an existing exercise physiology model; the combined system is validated using human exercise data. Pulmonary damage from blast, blunt trauma, and chemical injury is quantified in the model based on lung fluid infiltration (edema) which reduces oxygen delivery to the blood. The pulmonary damage component is derived and calibrated based on published animal experiments; scaling laws are used to predict the human response to lung injury in terms of physical performance decrement. Results The augmented dynamic physiology model (DPM) accurately predicted the human response to hypoxia, altitude, and exercise observed experimentally. The pulmonary damage parameters (shunt and diffusing capacity reduction) were fit to experimental animal data obtained in blast, blunt trauma, and chemical damage studies which link lung damage to lung weight change; the model is able to predict the reduced oxygen delivery in damage conditions. The model accurately estimates physical performance reduction with pulmonary damage. Conclusions We have developed a physiologically-based mathematical model to predict performance decrement endpoints in the presence of thoracic damage; simulations can be extended to estimate human performance and escape in extreme situations. PMID:25044032

A genome-scale metabolic flux model of Escherichia coli K–12 derived from the EcoCyc database

PubMed Central

2014-01-01

Background Constraint-based models of Escherichia coli metabolic flux have played a key role in computational studies of cellular metabolism at the genome scale. We sought to develop a next-generation constraint-based E. coli model that achieved improved phenotypic prediction accuracy while being frequently updated and easy to use. We also sought to compare model predictions with experimental data to highlight open questions in E. coli biology. Results We present EcoCyc–18.0–GEM, a genome-scale model of the E. coli K–12 MG1655 metabolic network. The model is automatically generated from the current state of EcoCyc using the MetaFlux software, enabling the release of multiple model updates per year. EcoCyc–18.0–GEM encompasses 1445 genes, 2286 unique metabolic reactions, and 1453 unique metabolites. We demonstrate a three-part validation of the model that breaks new ground in breadth and accuracy: (i) Comparison of simulated growth in aerobic and anaerobic glucose culture with experimental results from chemostat culture and simulation results from the E. coli modeling literature. (ii) Essentiality prediction for the 1445 genes represented in the model, in which EcoCyc–18.0–GEM achieves an improved accuracy of 95.2% in predicting the growth phenotype of experimental gene knockouts. (iii) Nutrient utilization predictions under 431 different media conditions, for which the model achieves an overall accuracy of 80.7%. The model’s derivation from EcoCyc enables query and visualization via the EcoCyc website, facilitating model reuse and validation by inspection. We present an extensive investigation of disagreements between EcoCyc–18.0–GEM predictions and experimental data to highlight areas of interest to E. coli modelers and experimentalists, including 70 incorrect predictions of gene essentiality on glucose, 80 incorrect predictions of gene essentiality on glycerol, and 83 incorrect predictions of nutrient utilization. Conclusion Significant advantages can be derived from the combination of model organism databases and flux balance modeling represented by MetaFlux. Interpretation of the EcoCyc database as a flux balance model results in a highly accurate metabolic model and provides a rigorous consistency check for information stored in the database. PMID:24974895

Predictability of a Coupled Model of ENSO Using Singular Vector Analysis: Optimal Growth and Forecast Skill.

NASA Astrophysics Data System (ADS)

Xue, Yan

The optimal growth and its relationship with the forecast skill of the Zebiak and Cane model are studied using a simple statistical model best fit to the original nonlinear model and local linear tangent models about idealized climatic states (the mean background and ENSO cycles in a long model run), and the actual forecast states, including two sets of runs using two different initialization procedures. The seasonally varying Markov model best fit to a suite of 3-year forecasts in a reduced EOF space (18 EOFs) fits the original nonlinear model reasonably well and has comparable or better forecast skill. The initial error growth in a linear evolution operator A is governed by the eigenvalues of A^{T}A, and the square roots of eigenvalues and eigenvectors of A^{T}A are named singular values and singular vectors. One dominant growing singular vector is found, and the optimal 6 month growth rate is largest for a (boreal) spring start and smallest for a fall start. Most of the variation in the optimal growth rate of the two forecasts is seasonal, attributable to the seasonal variations in the mean background, except that in the cold events it is substantially suppressed. It is found that the mean background (zero anomaly) is the most unstable state, and the "forecast IC states" are more unstable than the "coupled model states". One dominant growing singular vector is found, characterized by north-south and east -west dipoles, convergent winds on the equator in the eastern Pacific and a deepened thermocline in the whole equatorial belt. This singular vector is insensitive to initial time and optimization time, but its final pattern is a strong function of initial states. The ENSO system is inherently unpredictable for the dominant singular vector can amplify 5-fold to 24-fold in 6 months and evolve into the large scales characteristic of ENSO. However, the inherent ENSO predictability is only a secondary factor, while the mismatches between the model and data is a primary factor controlling the current forecast skill.

Simple inflationary models in Gauss-Bonnet brane-world cosmology

NASA Astrophysics Data System (ADS)

Okada, Nobuchika; Okada, Satomi

2016-06-01

In light of the recent Planck 2015 results for the measurement of the cosmic microwave background (CMB) anisotropy, we study simple inflationary models in the context of the Gauss-Bonnet (GB) brane-world cosmology. The brane-world cosmological effect modifies the power spectra of scalar and tensor perturbations generated by inflation and causes a dramatic change for the inflationary predictions of the spectral index (n s) and the tensor-to-scalar ratio (r) from those obtained in the standard cosmology. In particular, the predicted r values in the inflationary models favored by the Planck 2015 results are suppressed due to the GB brane-world cosmological effect, which is in sharp contrast with inflationary scenario in the Randall-Sundrum brane-world cosmology, where the r values are enhanced. Hence, these two brane-world cosmological scenarios are distinguishable. With the dramatic change of the inflationary predictions, the inflationary scenario in the GB brane-world cosmology can be tested by more precise measurements of n s and future observations of the CMB B-mode polarization.

Langmuir wave turbulence transition in a model of stimulated Raman scatter

NASA Astrophysics Data System (ADS)

Rose, Harvey A.

2000-06-01

In a one-dimensional stationary slab model, it is found that once the stimulated Raman scatter (SRS) homogeneous growth rate, γ0, exceeds a threshold value, γT, there exists a local, finite amplitude instability, which leads to Langmuir wave turbulence (LWT). Given energetic enough initial conditions, this allows forward SRS, a linearly convective instability, to be nonlinearly self-sustaining for γ0>γT. Levels of forward scatter, much larger than predicted by the linear amplification of thermal fluctuations, are then accessible. The Stochastic quasilinear Markovian (SQM) model of SRS interacting with LWT predicts a jump in the value of <ɛ>, the mean energy injection rate from the laser to the plasma, across this threshold, while one-dimensional plasma slab simulations reveal large fluctuations in ɛ, and a smooth variation of <ɛ> with γ0. Away from γT, <ɛ> is well predicted by the SQM. If a background density ramp is imposed, LWT may lead to loss of SRS gradient stabilization for γ0≪γT.

The microwave background anisotropies: Observations

PubMed Central

Wilkinson, David

1998-01-01

Most cosmologists now believe that we live in an evolving universe that has been expanding and cooling since its origin about 15 billion years ago. Strong evidence for this standard cosmological model comes from studies of the cosmic microwave background radiation (CMBR), the remnant heat from the initial fireball. The CMBR spectrum is blackbody, as predicted from the hot Big Bang model before the discovery of the remnant radiation in 1964. In 1992 the cosmic background explorer (COBE) satellite finally detected the anisotropy of the radiation—fingerprints left by tiny temperature fluctuations in the initial bang. Careful design of the COBE satellite, and a bit of luck, allowed the 30 μK fluctuations in the CMBR temperature (2.73 K) to be pulled out of instrument noise and spurious foreground emissions. Further advances in detector technology and experiment design are allowing current CMBR experiments to search for predicted features in the anisotropy power spectrum at angular scales of 1° and smaller. If they exist, these features were formed at an important epoch in the evolution of the universe—the decoupling of matter and radiation at a temperature of about 4,000 K and a time about 300,000 years after the bang. CMBR anisotropy measurements probe directly some detailed physics of the early universe. Also, parameters of the cosmological model can be measured because the anisotropy power spectrum depends on constituent densities and the horizon scale at a known cosmological epoch. As sophisticated experiments on the ground and on balloons pursue these measurements, two CMBR anisotropy satellite missions are being prepared for launch early in the next century. PMID:9419320

Validation of prediction models: examining temporal and geographic stability of baseline risk and estimated covariate effects

PubMed Central

Austin, Peter C.; van Klaveren, David; Vergouwe, Yvonne; Nieboer, Daan; Lee, Douglas S.; Steyerberg, Ewout W.

2018-01-01

Background Stability in baseline risk and estimated predictor effects both geographically and temporally is a desirable property of clinical prediction models. However, this issue has received little attention in the methodological literature. Our objective was to examine methods for assessing temporal and geographic heterogeneity in baseline risk and predictor effects in prediction models. Methods We studied 14,857 patients hospitalized with heart failure at 90 hospitals in Ontario, Canada, in two time periods. We focussed on geographic and temporal variation in baseline risk (intercept) and predictor effects (regression coefficients) of the EFFECT-HF mortality model for predicting 1-year mortality in patients hospitalized for heart failure. We used random effects logistic regression models for the 14,857 patients. Results The baseline risk of mortality displayed moderate geographic variation, with the hospital-specific probability of 1-year mortality for a reference patient lying between 0.168 and 0.290 for 95% of hospitals. Furthermore, the odds of death were 11% lower in the second period than in the first period. However, we found minimal geographic or temporal variation in predictor effects. Among 11 tests of differences in time for predictor variables, only one had a modestly significant P value (0.03). Conclusions This study illustrates how temporal and geographic heterogeneity of prediction models can be assessed in settings with a large sample of patients from a large number of centers at different time periods. PMID:29350215

Exploiting proteomic data for genome annotation and gene model validation in Aspergillus niger

PubMed Central

Wright, James C; Sugden, Deana; Francis-McIntyre, Sue; Riba-Garcia, Isabel; Gaskell, Simon J; Grigoriev, Igor V; Baker, Scott E; Beynon, Robert J; Hubbard, Simon J

2009-01-01

Background Proteomic data is a potentially rich, but arguably unexploited, data source for genome annotation. Peptide identifications from tandem mass spectrometry provide prima facie evidence for gene predictions and can discriminate over a set of candidate gene models. Here we apply this to the recently sequenced Aspergillus niger fungal genome from the Joint Genome Institutes (JGI) and another predicted protein set from another A.niger sequence. Tandem mass spectra (MS/MS) were acquired from 1d gel electrophoresis bands and searched against all available gene models using Average Peptide Scoring (APS) and reverse database searching to produce confident identifications at an acceptable false discovery rate (FDR). Results 405 identified peptide sequences were mapped to 214 different A.niger genomic loci to which 4093 predicted gene models clustered, 2872 of which contained the mapped peptides. Interestingly, 13 (6%) of these loci either had no preferred predicted gene model or the genome annotators' chosen "best" model for that genomic locus was not found to be the most parsimonious match to the identified peptides. The peptides identified also boosted confidence in predicted gene structures spanning 54 introns from different gene models. Conclusion This work highlights the potential of integrating experimental proteomics data into genomic annotation pipelines much as expressed sequence tag (EST) data has been. A comparison of the published genome from another strain of A.niger sequenced by DSM showed that a number of the gene models or proteins with proteomics evidence did not occur in both genomes, further highlighting the utility of the method. PMID:19193216

Time series analysis as input for clinical predictive modeling: Modeling cardiac arrest in a pediatric ICU

PubMed Central

2011-01-01

Background Thousands of children experience cardiac arrest events every year in pediatric intensive care units. Most of these children die. Cardiac arrest prediction tools are used as part of medical emergency team evaluations to identify patients in standard hospital beds that are at high risk for cardiac arrest. There are no models to predict cardiac arrest in pediatric intensive care units though, where the risk of an arrest is 10 times higher than for standard hospital beds. Current tools are based on a multivariable approach that does not characterize deterioration, which often precedes cardiac arrests. Characterizing deterioration requires a time series approach. The purpose of this study is to propose a method that will allow for time series data to be used in clinical prediction models. Successful implementation of these methods has the potential to bring arrest prediction to the pediatric intensive care environment, possibly allowing for interventions that can save lives and prevent disabilities. Methods We reviewed prediction models from nonclinical domains that employ time series data, and identified the steps that are necessary for building predictive models using time series clinical data. We illustrate the method by applying it to the specific case of building a predictive model for cardiac arrest in a pediatric intensive care unit. Results Time course analysis studies from genomic analysis provided a modeling template that was compatible with the steps required to develop a model from clinical time series data. The steps include: 1) selecting candidate variables; 2) specifying measurement parameters; 3) defining data format; 4) defining time window duration and resolution; 5) calculating latent variables for candidate variables not directly measured; 6) calculating time series features as latent variables; 7) creating data subsets to measure model performance effects attributable to various classes of candidate variables; 8) reducing the number of candidate features; 9) training models for various data subsets; and 10) measuring model performance characteristics in unseen data to estimate their external validity. Conclusions We have proposed a ten step process that results in data sets that contain time series features and are suitable for predictive modeling by a number of methods. We illustrated the process through an example of cardiac arrest prediction in a pediatric intensive care setting. PMID:22023778

Hybrid multiscale modeling and prediction of cancer cell behavior

PubMed Central

Habibi, Jafar

2017-01-01

Background Understanding cancer development crossing several spatial-temporal scales is of great practical significance to better understand and treat cancers. It is difficult to tackle this challenge with pure biological means. Moreover, hybrid modeling techniques have been proposed that combine the advantages of the continuum and the discrete methods to model multiscale problems. Methods In light of these problems, we have proposed a new hybrid vascular model to facilitate the multiscale modeling and simulation of cancer development with respect to the agent-based, cellular automata and machine learning methods. The purpose of this simulation is to create a dataset that can be used for prediction of cell phenotypes. By using a proposed Q-learning based on SVR-NSGA-II method, the cells have the capability to predict their phenotypes autonomously that is, to act on its own without external direction in response to situations it encounters. Results Computational simulations of the model were performed in order to analyze its performance. The most striking feature of our results is that each cell can select its phenotype at each time step according to its condition. We provide evidence that the prediction of cell phenotypes is reliable. Conclusion Our proposed model, which we term a hybrid multiscale modeling of cancer cell behavior, has the potential to combine the best features of both continuum and discrete models. The in silico results indicate that the 3D model can represent key features of cancer growth, angiogenesis, and its related micro-environment and show that the findings are in good agreement with biological tumor behavior. To the best of our knowledge, this paper is the first hybrid vascular multiscale modeling of cancer cell behavior that has the capability to predict cell phenotypes individually by a self-generated dataset. PMID:28846712

An efficient approach for site-specific scenery prediction in surveillance imaging near Earth's surface

NASA Astrophysics Data System (ADS)

Jylhä, Juha; Marjanen, Kalle; Rantala, Mikko; Metsäpuro, Petri; Visa, Ari

2006-09-01

Surveillance camera automation and camera network development are growing areas of interest. This paper proposes a competent approach to enhance the camera surveillance with Geographic Information Systems (GIS) when the camera is located at the height of 10-1000 m. A digital elevation model (DEM), a terrain class model, and a flight obstacle register comprise exploited auxiliary information. The approach takes into account spherical shape of the Earth and realistic terrain slopes. Accordingly, considering also forests, it determines visible and shadow regions. The efficiency arises out of reduced dimensionality in the visibility computation. Image processing is aided by predicting certain advance features of visible terrain. The features include distance from the camera and the terrain or object class such as coniferous forest, field, urban site, lake, or mast. The performance of the approach is studied by comparing a photograph of Finnish forested landscape with the prediction. The predicted background is well-fitting, and potential knowledge-aid for various purposes becomes apparent.

Deep Residual Network Predicts Cortical Representation and Organization of Visual Features for Rapid Categorization.

PubMed

Wen, Haiguang; Shi, Junxing; Chen, Wei; Liu, Zhongming

2018-02-28

The brain represents visual objects with topographic cortical patterns. To address how distributed visual representations enable object categorization, we established predictive encoding models based on a deep residual network, and trained them to predict cortical responses to natural movies. Using this predictive model, we mapped human cortical representations to 64,000 visual objects from 80 categories with high throughput and accuracy. Such representations covered both the ventral and dorsal pathways, reflected multiple levels of object features, and preserved semantic relationships between categories. In the entire visual cortex, object representations were organized into three clusters of categories: biological objects, non-biological objects, and background scenes. In a finer scale specific to each cluster, object representations revealed sub-clusters for further categorization. Such hierarchical clustering of category representations was mostly contributed by cortical representations of object features from middle to high levels. In summary, this study demonstrates a useful computational strategy to characterize the cortical organization and representations of visual features for rapid categorization.

Parametric Decay Instability and Dissipation of Low-frequency Alfvén Waves in Low-beta Turbulent Plasmas

NASA Astrophysics Data System (ADS)

Fu, Xiangrong; Li, Hui; Guo, Fan; Li, Xiaocan; Roytershteyn, Vadim

2018-03-01

Evolution of the parametric decay instability (PDI) of a circularly polarized Alfvén wave in a turbulent low-beta plasma background is investigated using 3D hybrid simulations. It is shown that the turbulence reduces the growth rate of PDI as compared to the linear theory predictions, but PDI can still exist. Interestingly, the damping rate of the ion acoustic mode (as the product of PDI) is also reduced as compared to the linear Vlasov predictions. Nonetheless, significant heating of ions in the direction parallel to the background magnetic field is observed due to resonant Landau damping of the ion acoustic waves. In low-beta turbulent plasmas, PDI can provide an important channel for energy dissipation of low-frequency Alfvén waves at a scale much larger than the ion kinetic scales, different from the traditional turbulence dissipation models.

Large-scale structure in mimetic Horndeski gravity

NASA Astrophysics Data System (ADS)

Arroja, Frederico; Okumura, Teppei; Bartolo, Nicola; Karmakar, Purnendu; Matarrese, Sabino

2018-05-01

In this paper, we propose to use the mimetic Horndeski model as a model for the dark universe. Both cold dark matter (CDM) and dark energy (DE) phenomena are described by a single component, the mimetic field. In linear theory, we show that this component effectively behaves like a perfect fluid with zero sound speed and clusters on all scales. For the simpler mimetic cubic Horndeski model, if the background expansion history is chosen to be identical to a perfect fluid DE (PFDE) then the mimetic model predicts the same power spectrum of the Newtonian potential as the PFDE model with zero sound speed. In particular, if the background is chosen to be the same as that of LCDM, then also in this case the power spectrum of the Newtonian potential in the mimetic model becomes indistinguishable from the power spectrum in LCDM on linear scales. A different conclusion may be found in the case of non-adiabatic perturbations. We also discuss the distinguishability, using power spectrum measurements from LCDM N-body simulations as a proxy for future observations, between these mimetic models and other popular models of DE. For instance, we find that if the background has an equation of state equal to ‑0.95 then we will be able to distinguish the mimetic model from the PFDE model with unity sound speed. On the other hand, it will be hard to do this distinction with respect to the LCDM model.

Comparison of the predictions of two road dust emission models with the measurements of a mobile van

NASA Astrophysics Data System (ADS)

Kauhaniemi, M.; Stojiljkovic, A.; Pirjola, L.; Karppinen, A.; Härkönen, J.; Kupiainen, K.; Kangas, L.; Aarnio, M. A.; Omstedt, G.; Denby, B. R.; Kukkonen, J.

2014-09-01

The predictions of two road dust suspension emission models were compared with the on-site mobile measurements of suspension emission factors. Such a quantitative comparison has not previously been reported in the reviewed literature. The models used were the Nordic collaboration model NORTRIP (NOn-exhaust Road TRaffic Induced Particle emissions) and the Swedish-Finnish FORE model (Forecasting Of Road dust Emissions). These models describe particulate matter generated by the wear of road surface due to traction control methods and processes that control the suspension of road dust particles into the air. An experimental measurement campaign was conducted using a mobile laboratory called SNIFFER, along two selected road segments in central Helsinki in 2007 and 2008. The suspended PM10 concentration was measured behind the left rear tyre and the street background PM10 concentration in front of the van. Both models reproduced the measured seasonal variation of suspension emission factors fairly well during both years at both measurement sites. However, both models substantially under-predicted the measured emission values. The article illustrates the challenges in conducting road suspension measurements in densely trafficked urban conditions, and the numerous requirements for input data that are needed for accurately applying road suspension emission models.

Neutron radiative capture cross section of Cu,6563 between 0.4 and 7.5 MeV

NASA Astrophysics Data System (ADS)

Newsome, I.; Bhike, M.; Krishichayan, Tornow, W.

2018-04-01

Natural copper is commonly used as cooling and shielding medium in detector arrangements designed to search for neutrinoless double-β decay. Neutron-induced background reactions on copper could potentially produce signals that are indistinguishable from the signals of interest. The present work focuses on radiative neutron capture experiments on Cu,6563 in the 0.4 to 7.5 MeV neutron energy range. The new data provide evaluations and model calculations with benchmark data needed to extend their applicability in predicting background rates in neutrinoless double-β decay experiments.

Natural Covariant Planck Scale Cutoffs and the Cosmic Microwave Background Spectrum.

PubMed

Chatwin-Davies, Aidan; Kempf, Achim; Martin, Robert T W

2017-07-21

We calculate the impact of quantum gravity-motivated ultraviolet cutoffs on inflationary predictions for the cosmic microwave background spectrum. We model the ultraviolet cutoffs fully covariantly to avoid possible artifacts of covariance breaking. Imposing these covariant cutoffs results in the production of small, characteristically k-dependent oscillations in the spectrum. The size of the effect scales linearly with the ratio of the Planck to Hubble lengths during inflation. Consequently, the relative size of the effect could be as large as one part in 10^{5}; i.e., eventual observability may not be ruled out.

Tropical climate and vegetation changes during Heinrich Event 1: comparing climate model output to pollen-based vegetation reconstructions with emphasis on the region around the tropical Atlantic Ocean

NASA Astrophysics Data System (ADS)

Handiani, D.; Paul, A.; Dupont, L.

2011-06-01

Abrupt climate changes associated with Heinrich Event 1 (HE1) about 18 to 15 thousand years before present (ka BP) strongly affected climate and vegetation patterns not only in the Northern Hemisphere, but also in tropical regions in the South Atlantic Ocean. We used the University of Victoria (UVic) Earth System-Climate Model (ESCM) with dynamical vegetation and land surface components to simulate four scenarios of climate-vegetation interaction: the pre-industrial era (PI), the Last Glacial Maximum (LGM), and a Heinrich-like event with two different climate backgrounds (interglacial and glacial). The HE1-like simulation with a glacial climate background produced sea surface temperature patterns and enhanced interhemispheric thermal gradients in accordance with the "bipolar seesaw" hypothesis. It allowed us to investigate the vegetation changes that result from a transition to a drier climate as predicted for northern tropical Africa due to a southward shift of the Intertropical Convergence Zone (ITCZ). We found that a cooling of the Northern Hemisphere caused a southward shift of those plant-functional types (PFTs) in Northern Tropical Africa that are indicative of an increased desertification, and a retreat of broadleaf forests in Western Africa and Northern South America. We used the PFTs generated by the model to calculate mega-biomes to allow for a direct comparison between paleodata and palynological vegetation reconstructions. Our calculated mega-biomes for the pre-industrial period and the LGM corresponded well to the modern and LGM sites of the BIOME6000 (v.4.2) reconstruction, except that our present-day simulation predicted the dominance of grassland in Southern Europe and our LGM simulation simulated more forest cover in tropical and sub-tropical South America. The mega-biomes from the HE1 simulation with glacial background climate were in agreement with paleovegetation data from land and ocean proxies in West, Central, and Northern Tropical Africa as well as Northeast South America. However, our model did not agree well with predicted biome distributions in Eastern South America.

A Search for Kilonovae in the Dark Energy Survey

DOE PAGES

Doctor, Z.; Kessler, R.; Chen, H. Y.; ...

2017-03-01

The coalescence of a binary neutron star pair is expected to produce gravitational waves (GW) and electromagnetic radiation, both of which may be detectable with currently available instruments. In this paper, we describe a search for a predicted r-process optical transient from these mergers, dubbed the “kilonova” (KN), using griz broadband data from the Dark Energy Survey Supernova Program (DES-SN). Some models predict KNe to be redder, shorter-lived, and dimmer than supernovae (SNe), but the event rate of KNe is poorly constrained. We simulate KN and SN light curves with the Monte-Carlo simulation code SNANA to optimize selection requirements, determine search efficiency, and predict SN backgrounds. Our analysis of the first two seasons of DES-SN data results in 0 events, and is consistent with our prediction of 1.1 ± 0.2 background events based on simulations of SNe. From our prediction, there is a 33% chance of finding 0 events in the data. Assuming no underlying galaxy flux, our search sets 90% upper limits on the KN volumetric rate of 1.0 x 10more » $$^{7}$$ Gpc $-$3 yr $-$1 for the dimmest KN model we consider (peak i-band absolute magnitude $${M}_{i}=-11.4$$ mag) and 2.4x 10$$^{4}$$ Gpc $-$3 yr $-$1 for the brightest ($${M}_{i}=-16.2$$ mag). Accounting for anomalous subtraction artifacts on bright galaxies, these limits are ~3 times higher. This analysis is the first untriggered optical KN search and informs selection requirements and strategies for future KN searches. Finally, our upper limits on the KN rate are consistent with those measured by GW and gamma-ray burst searches.« less

A Search for Kilonovae in the Dark Energy Survey

NASA Astrophysics Data System (ADS)

Doctor, Z.; Kessler, R.; Chen, H. Y.; Farr, B.; Finley, D. A.; Foley, R. J.; Goldstein, D. A.; Holz, D. E.; Kim, A. G.; Morganson, E.; Sako, M.; Scolnic, D.; Smith, M.; Soares-Santos, M.; Spinka, H.; Abbott, T. M. C.; Abdalla, F. B.; Allam, S.; Annis, J.; Bechtol, K.; Benoit-Lévy, A.; Bertin, E.; Brooks, D.; Buckley-Geer, E.; Burke, D. L.; Carnero Rosell, A.; Carrasco Kind, M.; Carretero, J.; Cunha, C. E.; D'Andrea, C. B.; da Costa, L. N.; DePoy, D. L.; Desai, S.; Diehl, H. T.; Drlica-Wagner, A.; Eifler, T. F.; Frieman, J.; García-Bellido, J.; Gaztanaga, E.; Gerdes, D. W.; Gruendl, R. A.; Gschwend, J.; Gutierrez, G.; James, D. J.; Krause, E.; Kuehn, K.; Kuropatkin, N.; Lahav, O.; Li, T. S.; Lima, M.; Maia, M. A. G.; March, M.; Marshall, J. L.; Menanteau, F.; Miquel, R.; Neilsen, E.; Nichol, R. C.; Nord, B.; Plazas, A. A.; Romer, A. K.; Sanchez, E.; Scarpine, V.; Schubnell, M.; Sevilla-Noarbe, I.; Smith, R. C.; Sobreira, F.; Suchyta, E.; Swanson, M. E. C.; Tarle, G.; Walker, A. R.; Wester, W.; DES Collaboration

2017-03-01

The coalescence of a binary neutron star pair is expected to produce gravitational waves (GW) and electromagnetic radiation, both of which may be detectable with currently available instruments. We describe a search for a predicted r-process optical transient from these mergers, dubbed the “kilonova” (KN), using griz broadband data from the Dark Energy Survey Supernova Program (DES-SN). Some models predict KNe to be redder, shorter-lived, and dimmer than supernovae (SNe), but the event rate of KNe is poorly constrained. We simulate KN and SN light curves with the Monte-Carlo simulation code SNANA to optimize selection requirements, determine search efficiency, and predict SN backgrounds. Our analysis of the first two seasons of DES-SN data results in 0 events, and is consistent with our prediction of 1.1 ± 0.2 background events based on simulations of SNe. From our prediction, there is a 33% chance of finding 0 events in the data. Assuming no underlying galaxy flux, our search sets 90% upper limits on the KN volumetric rate of 1.0 × {10}7 Gpc-3 yr-1 for the dimmest KN model we consider (peak I-band absolute magnitude {M}I=-11.4 mag) and 2.4 × {10}4 Gpc-3 yr-1 for the brightest ({M}I=-16.2 mag). Accounting for anomalous subtraction artifacts on bright galaxies, these limits are ˜3 times higher. This analysis is the first untriggered optical KN search and informs selection requirements and strategies for future KN searches. Our upper limits on the KN rate are consistent with those measured by GW and gamma-ray burst searches.

A model to predict when a cholera outbreak might hit the Congo

NASA Astrophysics Data System (ADS)

Schultz, Colin

2014-09-01

In 2011, as many as 600,000 people in 58 countries contracted cholera, with thousands succumbing to the disease. In most countries, cholera is rare. In others, like the Democratic Republic of the Congo, cholera is an endemic threat, always lurking in the background waiting for the right set of conditions to spark an outbreak.

"Your Model Is Predictive-- but Is It Useful?" Theoretical and Empirical Considerations of a New Paradigm for Adaptive Tutoring Evaluation

ERIC Educational Resources Information Center

González-Brenes, José P.; Huang, Yun

2015-01-01

Classification evaluation metrics are often used to evaluate adaptive tutoring systems-- programs that teach and adapt to humans. Unfortunately, it is not clear how intuitive these metrics are for practitioners with little machine learning background. Moreover, our experiments suggest that existing convention for evaluating tutoring systems may…

High Achievers from Low Socioeconomic Backgrounds: The Critical Role of Disciplinary Climate and Grit

ERIC Educational Resources Information Center

Huang, Haigen; Zhu, Hao

2017-01-01

The purpose of this study was to examine whether school disciplinary climate and grit predicted low socioeconomic status (SES) students being high achievers in mathematics and science with a representative sample of 15-year-old students in the United States. Our analysis, using a two-level logistic hierarchical linear model (HLM), indicated both…

Cognitive Impairment among the Aging Population in a Community in Southwest Nigeria

ERIC Educational Resources Information Center

Adebiyi, Akindele O.; Ogunniyi, Adesola; Adediran, Babatunde A.; Olakehinde, Olaide O.; Siwoku, Akeem A.

2016-01-01

Background: Vascular risk models can be quite informative in assisting the clinician to make a prediction of an individual's risk of cognitive impairment. Thus, a simple marker is a priority for low-capacity settings. This study examines the association of selected simple to deploy vascular markers with cognitive impairment in an elderly…

Analysis of Intelligence and Academic Scores as a Predictor of Promotion Rate for U.S. Army Noncommissioned Officers.

DTIC Science & Technology

1987-07-06

levels of intellegence tests and academic background as values to predict promotion. The model, however, demonstrated only limited utility as a preditive...attributes in the form of promotion points or a minimum threshold scale would be one approach. Unfortunately, this may artificially force NCO’s of less

The Effect of Exposure to an Agressive Cartoon on Children's Play.

ERIC Educational Resources Information Center

Cameron, Samuel M.; And Others

The authors discuss their replications of 2 prominent studies in the area of modeling aggressive behavior; those of Lovaas and Bandura. In the first, they predicted that, given the same socio-economic background, there would be no differences between black and white children in the amount of aggressive play subsequent to viewing an aggressive…

Social Support and Socioeconomic Status Predict Secondary Students' Grades and Educational Plans Indifferently across Immigrant Group and Gender

ERIC Educational Resources Information Center

Ulriksen, Robin; Sagatun, Åse; Zachrisson, Henrik Daae; Waaktaar, Trine; Lervåg, Arne Ola

2015-01-01

Social support and socioeconomic status (SES) have received considerable attention in explaining academic achievement and the achievement gap between students with ethic majority and immigrant background, and between boys and girls. Using a Structural Equation Modeling approach we examine (1) if there exist a gap in school achievements between…

Evaluation of Alternative Life Assessment Approaches Using P-3 SLAP Test Results

DTIC Science & Technology

2010-06-01

modelling of fatigue crack growth, infrared NDT technologies and fibre optic corrosion detection devices. He joined DSTO in 2007 in the Air Vehicles...10 3.4 Spectra Properties ...the previously conducted (truly ‘blind’) predictions for RAAF usage . DSTO-TR-2418 4 2. Background to DSTO P-3 SLAP Test Interpretation The P

Improving Emergency Department Triage Classification with Computerized Clinical Decision Support at a Pediatric Hospital

ERIC Educational Resources Information Center

Kunisch, Joseph Martin

2012-01-01

Background: The Emergency Severity Index (ESI) is an emergency department (ED) triage classification system based on estimated patient-specific resource utilization. Rules for a computerized clinical decision support (CDS) system based on a patient's chief complaint were developed and tested using a stochastic model for predicting ESI scores.…

Dodecahedral space topology as an explanation for weak wide-angle temperature correlations in the cosmic microwave background.

PubMed

Luminet, Jean-Pierre; Weeks, Jeffrey R; Riazuelo, Alain; Lehoucq, Roland; Uzan, Jean-Philippe

2003-10-09

The current 'standard model' of cosmology posits an infinite flat universe forever expanding under the pressure of dark energy. First-year data from the Wilkinson Microwave Anisotropy Probe (WMAP) confirm this model to spectacular precision on all but the largest scales. Temperature correlations across the microwave sky match expectations on angular scales narrower than 60 degrees but, contrary to predictions, vanish on scales wider than 60 degrees. Several explanations have been proposed. One natural approach questions the underlying geometry of space--namely, its curvature and topology. In an infinite flat space, waves from the Big Bang would fill the universe on all length scales. The observed lack of temperature correlations on scales beyond 60 degrees means that the broadest waves are missing, perhaps because space itself is not big enough to support them. Here we present a simple geometrical model of a finite space--the Poincaré dodecahedral space--which accounts for WMAP's observations with no fine-tuning required. The predicted density is Omega(0) approximately 1.013 > 1, and the model also predicts temperature correlations in matching circles on the sky.

Axion gauge field inflation and gravitational leptogenesis: A lower bound on B modes from the matter-antimatter asymmetry of the Universe

NASA Astrophysics Data System (ADS)

Caldwell, R. R.; Devulder, C.

2018-01-01

We present a toy model of an axion gauge field inflation scenario that yields viable density and gravitational wave spectra. The scenario consists of an axionic inflaton in a steep potential that is effectively flattened by a coupling to a collection of non-Abelian gauge fields. The model predicts a blue-tilted gravitational wave spectrum that is dominated by one circular polarization, resulting in unique observational targets for cosmic microwave background and gravitational wave experiments. The handedness of the gravitational wave spectrum is incorporated in a model of leptogenesis through the axial-gravitational anomaly; assuming electroweak sphaeleron processes convert the lepton asymmetry into baryons, we predict an approximate lower bound on the tensor-to-scalar ratio r ˜3 - 4 ×10-2 for models that also explain the matter-antimatter asymmetry of the Universe.

Physiologically-Based Pharmacokinetic Modelling to Inform Development of Intramuscular Long Acting Nanoformulations for HIV

PubMed Central

Rajoli, Rajith KR; Back, David J; Rannard, Steve; Meyers, Caren Freel; Flexner, Charles; Owen, Andrew; Siccardi, Marco

2014-01-01

Background and Objectives Antiretrovirals (ARVs) are currently used for the treatment and prevention of HIV infection. Poor adherence and low tolerability of some existing oral formulations can hinder their efficacy. Long-acting (LA) injectable nanoformulations could help address these complications by simplifying ARV administration. The aim of this study is to inform the optimisation of intramuscular LA formulations for eight ARVs through physiologically-based pharmacokinetic (PBPK) modelling. Methods A whole-body PBPK model was constructed using mathematical descriptions of molecular, physiological and anatomical processes defining pharmacokinetics. These models were validated against available clinical data and subsequently used to predict the pharmacokinetics of injectable LA formulations Results The predictions suggest that monthly intramuscular injections are possible for dolutegravir, efavirenz, emtricitabine, raltegravir, rilpivirine and tenofovir provided that technological challenges to control release rate can be addressed. Conclusions These data may help inform the target product profiles for LA ARV reformulation strategies. PMID:25523214

Combined search for the standard model Higgs boson decaying to bb using the D0 run II data set.

PubMed

Abazov, V M; Abbott, B; Acharya, B S; Adams, M; Adams, T; Alexeev, G D; Alkhazov, G; Alton, A; Alverson, G; Askew, A; Atkins, S; Augsten, K; Avila, C; Badaud, F; Bagby, L; Baldin, B; Bandurin, D V; Banerjee, S; Barberis, E; Baringer, P; Bartlett, J F; Bassler, U; Bazterra, V; Bean, A; Begalli, M; Bellantoni, L; Beri, S B; Bernardi, G; Bernhard, R; Bertram, I; Besançon, M; Beuselinck, R; Bhat, P C; Bhatia, S; Bhatnagar, V; Blazey, G; Blessing, S; Bloom, K; Boehnlein, A; Boline, D; Boos, E E; Borissov, G; Bose, T; Brandt, A; Brandt, O; Brock, R; Bross, A; Brown, D; Brown, J; Bu, X B; Buehler, M; Buescher, V; Bunichev, V; Burdin, S; Buszello, C P; Camacho-Pérez, E; Casey, B C K; Castilla-Valdez, H; Caughron, S; Chakrabarti, S; Chakraborty, D; Chan, K M; Chandra, A; Chapon, E; Chen, G; Chevalier-Théry, S; Cho, D K; Cho, S W; Choi, S; Choudhary, B; Cihangir, S; Claes, D; Clutter, J; Cooke, M; Cooper, W E; Corcoran, M; Couderc, F; Cousinou, M-C; Croc, A; Cutts, D; Das, A; Davies, G; de Jong, S J; De La Cruz-Burelo, E; Déliot, F; Demina, R; Denisov, D; Denisov, S P; Desai, S; Deterre, C; Devaughan, K; Diehl, H T; Diesburg, M; Ding, P F; Dominguez, A; Dubey, A; Dudko, L V; Duggan, D; Duperrin, A; Dutt, S; Dyshkant, A; Eads, M; Edmunds, D; Ellison, J; Elvira, V D; Enari, Y; Evans, H; Evdokimov, A; Evdokimov, V N; Facini, G; Feng, L; Ferbel, T; Fiedler, F; Filthaut, F; Fisher, W; Fisk, H E; Fortner, M; Fox, H; Fuess, S; Garcia-Bellido, A; García-González, J A; García-Guerra, G A; Gavrilov, V; Gay, P; Geng, W; Gerbaudo, D; Gerber, C E; Gershtein, Y; Ginther, G; Golovanov, G; Goussiou, A; Grannis, P D; Greder, S; Greenlee, H; Grenier, G; Gris, Ph; Grivaz, J-F; Grohsjean, A; Grünendahl, S; Grünewald, M W; Guillemin, T; Gutierrez, G; Gutierrez, P; Hagopian, S; Haley, J; Han, L; Harder, K; Harel, A; Hauptman, J M; Hays, J; Head, T; Hebbeker, T; Hedin, D; Hegab, H; Heinson, A P; Heintz, U; Hensel, C; Heredia De La Cruz, I; Herner, K; Hesketh, G; Hildreth, M D; Hirosky, R; Hoang, T; Hobbs, J D; Hoeneisen, B; Hogan, J; Hohlfeld, M; Howley, I; Hubacek, Z; Hynek, V; Iashvili, I; Ilchenko, Y; Illingworth, R; Ito, A S; Jabeen, S; Jaffré, M; Jayasinghe, A; Jeong, M S; Jesik, R; Jiang, P; Johns, K; Johnson, E; Johnson, M; Jonckheere, A; Jonsson, P; Joshi, J; Jung, A W; Juste, A; Kaadze, K; Kajfasz, E; Karmanov, D; Kasper, P A; Katsanos, I; Kehoe, R; Kermiche, S; Khalatyan, N; Khanov, A; Kharchilava, A; Kharzheev, Y N; Kiselevich, I; Kohli, J M; Kozelov, A V; Kraus, J; Kulikov, S; Kumar, A; Kupco, A; Kurča, T; Kuzmin, V A; Lammers, S; Landsberg, G; Lebrun, P; Lee, H S; Lee, S W; Lee, W M; Lei, X; Lellouch, J; Li, D; Li, H; Li, L; Li, Q Z; Lim, J K; Lincoln, D; Linnemann, J; Lipaev, V V; Lipton, R; Liu, H; Liu, Y; Lobodenko, A; Lokajicek, M; Lopes de Sa, R; Lubatti, H J; Luna-Garcia, R; Lyon, A L; Maciel, A K A; Madar, R; Magaña-Villalba, R; Malik, S; Malyshev, V L; Maravin, Y; Martínez-Ortega, J; McCarthy, R; McGivern, C L; Meijer, M M; Melnitchouk, A; Menezes, D; Mercadante, P G; Merkin, M; Meyer, A; Meyer, J; Miconi, F; Mondal, N K; Mulhearn, M; Nagy, E; Naimuddin, M; Narain, M; Nayyar, R; Neal, H A; Negret, J P; Neustroev, P; Nguyen, H T; Nunnemann, T; Orduna, J; Osman, N; Osta, J; Padilla, M; Pal, A; Parashar, N; Parihar, V; Park, S K; Partridge, R; Parua, N; Patwa, A; Penning, B; Perfilov, M; Peters, Y; Petridis, K; Petrillo, G; Pétroff, P; Pleier, M-A; Podesta-Lerma, P L M; Podstavkov, V M; Popov, A V; Prewitt, M; Price, D; Prokopenko, N; Qian, J; Quadt, A; Quinn, B; Rangel, M S; Ranjan, K; Ratoff, P N; Razumov, I; Renkel, P; Ripp-Baudot, I; Rizatdinova, F; Rominsky, M; Ross, A; Royon, C; Rubinov, P; Ruchti, R; Sajot, G; Salcido, P; Sánchez-Hernández, A; Sanders, M P; Santos, A S; Savage, G; Sawyer, L; Scanlon, T; Schamberger, R D; Scheglov, Y; Schellman, H; Schlobohm, S; Schwanenberger, C; Schwienhorst, R; Sekaric, J; Severini, H; Shabalina, E; Shary, V; Shaw, S; Shchukin, A A; Shivpuri, R K; Simak, V; Skubic, P; Slattery, P; Smirnov, D; Smith, K J; Snow, G R; Snow, J; Snyder, S; Söldner-Rembold, S; Sonnenschein, L; Soustruznik, K; Stark, J; Stoyanova, D A; Strauss, M; Suter, L; Svoisky, P; Takahashi, M; Titov, M; Tokmenin, V V; Tsai, Y-T; Tschann-Grimm, K; Tsybychev, D; Tuchming, B; Tully, C; Uvarov, L; Uvarov, S; Uzunyan, S; Van Kooten, R; van Leeuwen, W M; Varelas, N; Varnes, E W; Vasilyev, I A; Verdier, P; Verkheev, A Y; Vertogradov, L S; Verzocchi, M; Vesterinen, M; Vilanova, D; Vokac, P; Wahl, H D; Wang, M H L S; Wang, R-J; Warchol, J; Watts, G; Wayne, M; Weichert, J; Welty-Rieger, L; White, A; Wicke, D; Williams, M R J; Wilson, G W; Wobisch, M; Wood, D R; Wyatt, T R; Xie, Y; Yamada, R; Yang, S; Yang, W-C; Yasuda, T; Yatsunenko, Y A; Ye, W; Ye, Z; Yin, H; Yip, K; Youn, S W; Yu, J M; Zennamo, J; Zhao, T; Zhao, T G; Zhou, B; Zhu, J; Zielinski, M; Zieminska, D; Zivkovic, L

2012-09-21

We present the results of the combination of searches for the standard model Higgs boson produced in association with a W or Z boson and decaying into bb using the data sample collected with the D0 detector in pp collisions at √s = 1.96 TeV at the Fermilab Tevatron Collider. We derive 95% C.L. upper limits on the Higgs boson cross section relative to the standard model prediction in the mass range 100 GeV ≤ M(H) ≤ 150 GeV, and we exclude Higgs bosons with masses smaller than 102 GeV at the 95% C.L. In the mass range 120 GeV ≤ M(H) ≤145 GeV, the data exhibit an excess above the background prediction with a global significance of 1.5 standard deviations, consistent with the expectation in the presence of a standard model Higgs boson.

Measurement of Coherent π+ Production in Low Energy Neutrino-Carbon Scattering

NASA Astrophysics Data System (ADS)

Abe, K.; Andreopoulos, C.; Antonova, M.; Aoki, S.; Ariga, A.; Assylbekov, S.; Autiero, D.; Ban, S.; Barbi, M.; Barker, G. J.; Barr, G.; Bartet-Friburg, P.; Batkiewicz, M.; Bay, F.; Berardi, V.; Berkman, S.; Bhadra, S.; Blondel, A.; Bolognesi, S.; Bordoni, S.; Boyd, S. B.; Brailsford, D.; Bravar, A.; Bronner, C.; Buizza Avanzini, M.; Calland, R. G.; Campbell, T.; Cao, S.; Caravaca Rodríguez, J.; Cartwright, S. L.; Castillo, R.; Catanesi, M. G.; Cervera, A.; Cherdack, D.; Chikuma, N.; Christodoulou, G.; Clifton, A.; Coleman, J.; Collazuol, G.; Coplowe, D.; Cremonesi, L.; Dabrowska, A.; De Rosa, G.; Dealtry, T.; Denner, P. F.; Dennis, S. R.; Densham, C.; Dewhurst, D.; Di Lodovico, F.; Di Luise, S.; Dolan, S.; Drapier, O.; Duffy, K. E.; Dumarchez, J.; Dytman, S.; Dziewiecki, M.; Emery-Schrenk, S.; Ereditato, A.; Feusels, T.; Finch, A. J.; Fiorentini, G. A.; Friend, M.; Fujii, Y.; Fukuda, D.; Fukuda, Y.; Furmanski, A. P.; Galymov, V.; Garcia, A.; Giffin, S. G.; Giganti, C.; Gizzarelli, F.; Gonin, M.; Grant, N.; Hadley, D. R.; Haegel, L.; Haigh, M. D.; Hamilton, P.; Hansen, D.; Harada, J.; Hara, T.; Hartz, M.; Hasegawa, T.; Hastings, N. C.; Hayashino, T.; Hayato, Y.; Helmer, R. L.; Hierholzer, M.; Hillairet, A.; Himmel, A.; Hiraki, T.; Hirota, S.; Hogan, M.; Holeczek, J.; Horikawa, S.; Hosomi, F.; Huang, K.; Ichikawa, A. K.; Ieki, K.; Ikeda, M.; Imber, J.; Insler, J.; Intonti, R. A.; Irvine, T. J.; Ishida, T.; Ishii, T.; Iwai, E.; Iwamoto, K.; Izmaylov, A.; Jacob, A.; Jamieson, B.; Jiang, M.; Johnson, S.; Jo, J. H.; Jonsson, P.; Jung, C. K.; Kabirnezhad, M.; Kaboth, A. C.; Kajita, T.; Kakuno, H.; Kameda, J.; Karlen, D.; Karpikov, I.; Katori, T.; Kearns, E.; Khabibullin, M.; Khotjantsev, A.; Kielczewska, D.; Kikawa, T.; Kim, H.; Kim, J.; King, S.; Kisiel, J.; Knight, A.; Knox, A.; Kobayashi, T.; Koch, L.; Koga, T.; Konaka, A.; Kondo, K.; Kopylov, A.; Kormos, L. L.; Korzenev, A.; Koshio, Y.; Kropp, W.; Kudenko, Y.; Kurjata, R.; Kutter, T.; Lagoda, J.; Lamont, I.; Larkin, E.; Lasorak, P.; Laveder, M.; Lawe, M.; Lazos, M.; Lindner, T.; Liptak, Z. J.; Litchfield, R. P.; Li, X.; Longhin, A.; Lopez, J. P.; Ludovici, L.; Lu, X.; Magaletti, L.; Mahn, K.; Malek, M.; Manly, S.; Marino, A. D.; Marteau, J.; Martin, J. F.; Martins, P.; Martynenko, S.; Maruyama, T.; Matveev, V.; Mavrokoridis, K.; Ma, W. Y.; Mazzucato, E.; McCarthy, M.; McCauley, N.; McFarland, K. S.; McGrew, C.; Mefodiev, A.; Metelko, C.; Mezzetto, M.; Mijakowski, P.; Minamino, A.; Mineev, O.; Mine, S.; Missert, A.; Miura, M.; Moriyama, S.; Mueller, Th. A.; Murphy, S.; Myslik, J.; Nakadaira, T.; Nakahata, M.; Nakamura, K. G.; Nakamura, K.; Nakamura, K. D.; Nakayama, S.; Nakaya, T.; Nakayoshi, K.; Nantais, C.; Nielsen, C.; Nirkko, M.; Nishikawa, K.; Nishimura, Y.; Novella, P.; Nowak, J.; O'Keeffe, H. M.; Ohta, R.; Okumura, K.; Okusawa, T.; Oryszczak, W.; Oser, S. M.; Ovsyannikova, T.; Owen, R. A.; Oyama, Y.; Palladino, V.; Palomino, J. L.; Paolone, V.; Patel, N. D.; Pavin, M.; Payne, D.; Perkin, J. D.; Petrov, Y.; Pickard, L.; Pickering, L.; Pinzon Guerra, E. S.; Pistillo, C.; Popov, B.; Posiadala-Zezula, M.; Poutissou, J.-M.; Poutissou, R.; Przewlocki, P.; Quilain, B.; Radermacher, T.; Radicioni, E.; Ratoff, P. N.; Ravonel, M.; Rayner, M. A. M.; Redij, A.; Reinherz-Aronis, E.; Riccio, C.; Rojas, P.; Rondio, E.; Roth, S.; Rubbia, A.; Rychter, A.; Sacco, R.; Sakashita, K.; Sánchez, F.; Sato, F.; Scantamburlo, E.; Scholberg, K.; Schoppmann, S.; Schwehr, J.; Scott, M.; Seiya, Y.; Sekiguchi, T.; Sekiya, H.; Sgalaberna, D.; Shah, R.; Shaikhiev, A.; Shaker, F.; Shaw, D.; Shiozawa, M.; Shirahige, T.; Short, S.; Smy, M.; Sobczyk, J. T.; Sobel, H.; Sorel, M.; Southwell, L.; Stamoulis, P.; Steinmann, J.; Stewart, T.; Stowell, P.; Suda, Y.; Suvorov, S.; Suzuki, A.; Suzuki, K.; Suzuki, S. Y.; Suzuki, Y.; Tacik, R.; Tada, M.; Takahashi, S.; Takeda, A.; Takeuchi, Y.; Tanaka, H. K.; Tanaka, H. A.; Terhorst, D.; Terri, R.; Thakore, T.; Thompson, L. F.; Tobayama, S.; Toki, W.; Tomura, T.; Touramanis, C.; Tsukamoto, T.; Tzanov, M.; Uchida, Y.; Vacheret, A.; Vagins, M.; Vallari, Z.; Vasseur, G.; Wachala, T.; Wakamatsu, K.; Walter, C. W.; Wark, D.; Warzycha, W.; Wascko, M. O.; Weber, A.; Wendell, R.; Wilkes, R. J.; Wilking, M. J.; Wilkinson, C.; Wilson, J. R.; Wilson, R. J.; Yamada, Y.; Yamamoto, K.; Yamamoto, M.; Yanagisawa, C.; Yano, T.; Yen, S.; Yershov, N.; Yokoyama, M.; Yoo, J.; Yoshida, K.; Yuan, T.; Yu, M.; Zalewska, A.; Zalipska, J.; Zambelli, L.; Zaremba, K.; Ziembicki, M.; Zimmerman, E. D.; Zito, M.; Żmuda, J.; T2K Collaboration

2016-11-01

We report the first measurement of the flux-averaged cross section for charged current coherent π+ production on carbon for neutrino energies less than 1.5 GeV, and with a restriction on the final state phase space volume in the T2K near detector, ND280. Comparisons are made with predictions from the Rein-Sehgal coherent production model and the model by Alvarez-Ruso et al., the latter representing the first implementation of an instance of the new class of microscopic coherent models in a neutrino interaction Monte Carlo event generator. We observe a clear event excess above background, disagreeing with the null results reported by K2K and SciBooNE in a similar neutrino energy region. The measured flux-averaged cross sections are below those predicted by both the Rein-Sehgal and Alvarez-Ruso et al. models.

Real­-Time Ensemble Forecasting of Coronal Mass Ejections Using the Wsa-Enlil+Cone Model

NASA Astrophysics Data System (ADS)

Mays, M. L.; Taktakishvili, A.; Pulkkinen, A. A.; Odstrcil, D.; MacNeice, P. J.; Rastaetter, L.; LaSota, J. A.

2014-12-01

Ensemble forecasting of coronal mass ejections (CMEs) provides significant information in that it provides an estimation of the spread or uncertainty in CME arrival time predictions. Real-time ensemble modeling of CME propagation is performed by forecasters at the Space Weather Research Center (SWRC) using the WSA-ENLIL+cone model available at the Community Coordinated Modeling Center (CCMC). To estimate the effect of uncertainties in determining CME input parameters on arrival time predictions, a distribution of n (routinely n=48) CME input parameter sets are generated using the CCMC Stereo CME Analysis Tool (StereoCAT) which employs geometrical triangulation techniques. These input parameters are used to perform n different simulations yielding an ensemble of solar wind parameters at various locations of interest, including a probability distribution of CME arrival times (for hits), and geomagnetic storm strength (for Earth-directed hits). We present the results of ensemble simulations for a total of 38 CME events in 2013-2014. For 28 of the ensemble runs containing hits, the observed CME arrival was within the range of ensemble arrival time predictions for 14 runs (half). The average arrival time prediction was computed for each of the 28 ensembles predicting hits and using the actual arrival time, an average absolute error of 10.0 hours (RMSE=11.4 hours) was found for all 28 ensembles, which is comparable to current forecasting errors. Some considerations for the accuracy of ensemble CME arrival time predictions include the importance of the initial distribution of CME input parameters, particularly the mean and spread. When the observed arrivals are not within the predicted range, this still allows the ruling out of prediction errors caused by tested CME input parameters. Prediction errors can also arise from ambient model parameters such as the accuracy of the solar wind background, and other limitations. Additionally the ensemble modeling sysem was used to complete a parametric event case study of the sensitivity of the CME arrival time prediction to free parameters for ambient solar wind model and CME. The parameter sensitivity study suggests future directions for the system, such as running ensembles using various magnetogram inputs to the WSA model.

Data-driven model-independent searches for long-lived particles at the LHC

NASA Astrophysics Data System (ADS)

Coccaro, Andrea; Curtin, David; Lubatti, H. J.; Russell, Heather; Shelton, Jessie

2016-12-01

Neutral long-lived particles (LLPs) are highly motivated by many beyond the Standard Model scenarios, such as theories of supersymmetry, baryogenesis, and neutral naturalness, and present both tremendous discovery opportunities and experimental challenges for the LHC. A major bottleneck for current LLP searches is the prediction of Standard Model backgrounds, which are often impossible to simulate accurately. In this paper, we propose a general strategy for obtaining differential, data-driven background estimates in LLP searches, thereby notably extending the range of LLP masses and lifetimes that can be discovered at the LHC. We focus on LLPs decaying in the ATLAS muon system, where triggers providing both signal and control samples are available at LHC run 2. While many existing searches require two displaced decays, a detailed knowledge of backgrounds will allow for very inclusive searches that require just one detected LLP decay. As we demonstrate for the h →X X signal model of LLP pair production in exotic Higgs decays, this results in dramatic sensitivity improvements for proper lifetimes ≳10 m . In theories of neutral naturalness, this extends reach to glueball masses far below the b ¯b threshold. Our strategy readily generalizes to other signal models and other detector subsystems. This framework therefore lends itself to the development of a systematic, model-independent LLP search program, in analogy to the highly successful simplified-model framework of prompt searches.

Interpretation of the COBE FIRAS CMBR spectrum

NASA Technical Reports Server (NTRS)

Wright, E. L.; Mather, J. C.; Fixsen, D. J.; Kogut, A.; Shafer, R. A.; Bennett, C. L.; Boggess, N. W.; Cheng, E. S.; Silverberg, R. F.; Smoot, G. F.

1994-01-01

The cosmic microwave background radiation (CMBR) spectrum measured by the Far-Infrared Absolute Spectrophotometer (FIRAS) instrument on NASA's Cosmic Background Explorer (COBE) is indistinguishable from a blackbody, implying stringent limits on energy release in the early universe later than the time t = 1 yr after the big bang. We compare the FIRAS data to previous precise measurements of the cosmic microwave background spectrum and find a reasonable agreement. We discuss the implications of the absolute value of y is less than 2.5 x 10(exp -5) and the absolute value of mu is less than 3.3 x 10(exp -4) 95% confidence limits found by Mather et al. (1994) on many processes occurring after t = 1 yr, such as explosive structure formation, reionization, and dissipation of small-scale density perturbations. We place limits on models with dust plus Population III stars, or evolving populations of IR galaxies, by directly comparing the Mather et al. spectrum to the model predictions.

Cross-comparison of spacecraft-environment interaction model predictions applied to Solar Probe Plus near perihelion

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

Marchand, R.; Miyake, Y.; Usui, H.

2014-06-15

Five spacecraft-plasma models are used to simulate the interaction of a simplified geometry Solar Probe Plus (SPP) satellite with the space environment under representative solar wind conditions near perihelion. By considering similarities and differences between results obtained with different numerical approaches under well defined conditions, the consistency and validity of our models can be assessed. The impact on model predictions of physical effects of importance in the SPP mission is also considered by comparing results obtained with and without these effects. Simulation results are presented and compared with increasing levels of complexity in the physics of interaction between solar environmentmore » and the SPP spacecraft. The comparisons focus particularly on spacecraft floating potentials, contributions to the currents collected and emitted by the spacecraft, and on the potential and density spatial profiles near the satellite. The physical effects considered include spacecraft charging, photoelectron and secondary electron emission, and the presence of a background magnetic field. Model predictions obtained with our different computational approaches are found to be in agreement within 2% when the same physical processes are taken into account and treated similarly. The comparisons thus indicate that, with the correct description of important physical effects, our simulation models should have the required skill to predict details of satellite-plasma interaction physics under relevant conditions, with a good level of confidence. Our models concur in predicting a negative floating potential V{sub fl}∼−10V for SPP at perihelion. They also predict a “saturated emission regime” whereby most emitted photo- and secondary electron will be reflected by a potential barrier near the surface, back to the spacecraft where they will be recollected.« less

Prediction of clinical response based on pharmacokinetic/pharmacodynamic models of 5-hydroxytryptamine reuptake inhibitors in mice

PubMed Central

Kreilgaard, M; Smith, D G; Brennum, L T; Sánchez, C

2008-01-01

Background and purpose: Bridging the gap between preclinical research and clinical trials is vital for drug development. Predicting clinically relevant steady-state drug concentrations (Css) in serum from preclinical animal models may facilitate this transition. Here we used a pharmacokinetic/pharmacodynamic (PK/PD) modelling approach to evaluate the predictive validity of 5-hydroxytryptamine (5-HT; serotonin) transporter (SERT) occupancy and 5-hydroxytryptophan (5-HTP)-potentiated behavioral syndrome induced by 5-HT reuptake inhibitor (SRI) antidepressants in mice. Experimental approach: Serum and whole brain drug concentrations, cortical SERT occupancy and 5-HTP-potentiated behavioral syndrome were measured over 6 h after a single subcutaneous injection of escitalopram, paroxetine or sertraline. [3H]2-(2-dimethylaminomethylphenylsulphanyl)-5-methyl-phenylamine ([3H]MADAM) was used to assess SERT occupancy. For PK/PD modelling, an effect-compartment model was applied to collapse the hysteresis and predict the steady-state relationship between drug exposure and PD response. Key results: The predicted Css for escitalopram, paroxetine and sertraline at 80% SERT occupancy in mice are 18 ng mL−1, 18 ng mL−1 and 24 ng mL−1, respectively, with corresponding responses in the 5-HTP behavioral model being between 20–40% of the maximum. Conclusions and implications: Therapeutically effective SERT occupancy for SRIs in depressed patients is approximately 80%, and the corresponding plasma Css are 6–21 ng mL−1, 21-95 ng mL−1 and 20–48 ng mL−1 for escitalopram, paroxetine and sertraline, respectively. Thus, PK/PD modelling using SERT occupancy and 5-HTP-potentiated behavioral syndrome as response markers in mice may be a useful tool to predict clinically relevant plasma Css values. PMID:18552871

Modeling Particle Exposure in US Trucking Terminals

PubMed Central

Davis, ME; Smith, TJ; Laden, F; Hart, JE; Ryan, LM; Garshick, E

2007-01-01

Multi-tiered sampling approaches are common in environmental and occupational exposure assessment, where exposures for a given individual are often modeled based on simultaneous measurements taken at multiple indoor and outdoor sites. The monitoring data from such studies is hierarchical by design, imposing a complex covariance structure that must be accounted for in order to obtain unbiased estimates of exposure. Statistical methods such as structural equation modeling (SEM) represent a useful alternative to simple linear regression in these cases, providing simultaneous and unbiased predictions of each level of exposure based on a set of covariates specific to the exposure setting. We test the SEM approach using data from a large exposure assessment of diesel and combustion particles in the US trucking industry. The exposure assessment includes data from 36 different trucking terminals across the United States sampled between 2001 and 2005, measuring PM2.5 and its elemental carbon (EC), organic carbon (OC) components, by personal monitoring, and sampling at two indoor work locations and an outdoor “background” location. Using the SEM method, we predict: 1) personal exposures as a function of work related exposure and smoking status; 2) work related exposure as a function of terminal characteristics, indoor ventilation, job location, and background exposure conditions; and 3) background exposure conditions as a function of weather, nearby source pollution, and other regional differences across terminal sites. The primary advantage of SEMs in this setting is the ability to simultaneously predict exposures at each of the sampling locations, while accounting for the complex covariance structure among the measurements and descriptive variables. The statistically significant results and high R2 values observed from the trucking industry application supports the broader use of this approach in exposure assessment modeling. PMID:16856739

A systematic review of models to predict recruitment to multicentre clinical trials

PubMed Central

2010-01-01

Background Less than one third of publicly funded trials managed to recruit according to their original plan often resulting in request for additional funding and/or time extensions. The aim was to identify models which might be useful to a major public funder of randomised controlled trials when estimating likely time requirements for recruiting trial participants. The requirements of a useful model were identified as usability, based on experience, able to reflect time trends, accounting for centre recruitment and contribution to a commissioning decision. Methods A systematic review of English language articles using MEDLINE and EMBASE. Search terms included: randomised controlled trial, patient, accrual, predict, enrol, models, statistical; Bayes Theorem; Decision Theory; Monte Carlo Method and Poisson. Only studies discussing prediction of recruitment to trials using a modelling approach were included. Information was extracted from articles by one author, and checked by a second, using a pre-defined form. Results Out of 326 identified abstracts, only 8 met all the inclusion criteria. Of these 8 studies examined, there are five major classes of model discussed: the unconditional model, the conditional model, the Poisson model, Bayesian models and Monte Carlo simulation of Markov models. None of these meet all the pre-identified needs of the funder. Conclusions To meet the needs of a number of research programmes, a new model is required as a matter of importance. Any model chosen should be validated against both retrospective and prospective data, to ensure the predictions it gives are superior to those currently used. PMID:20604946

Patient Similarity in Prediction Models Based on Health Data: A Scoping Review

PubMed Central

Sharafoddini, Anis; Dubin, Joel A

2017-01-01

Background Physicians and health policy makers are required to make predictions during their decision making in various medical problems. Many advances have been made in predictive modeling toward outcome prediction, but these innovations target an average patient and are insufficiently adjustable for individual patients. One developing idea in this field is individualized predictive analytics based on patient similarity. The goal of this approach is to identify patients who are similar to an index patient and derive insights from the records of similar patients to provide personalized predictions.. Objective The aim is to summarize and review published studies describing computer-based approaches for predicting patients’ future health status based on health data and patient similarity, identify gaps, and provide a starting point for related future research. Methods The method involved (1) conducting the review by performing automated searches in Scopus, PubMed, and ISI Web of Science, selecting relevant studies by first screening titles and abstracts then analyzing full-texts, and (2) documenting by extracting publication details and information on context, predictors, missing data, modeling algorithm, outcome, and evaluation methods into a matrix table, synthesizing data, and reporting results. Results After duplicate removal, 1339 articles were screened in abstracts and titles and 67 were selected for full-text review. In total, 22 articles met the inclusion criteria. Within included articles, hospitals were the main source of data (n=10). Cardiovascular disease (n=7) and diabetes (n=4) were the dominant patient diseases. Most studies (n=18) used neighborhood-based approaches in devising prediction models. Two studies showed that patient similarity-based modeling outperformed population-based predictive methods. Conclusions Interest in patient similarity-based predictive modeling for diagnosis and prognosis has been growing. In addition to raw/coded health data, wavelet transform and term frequency-inverse document frequency methods were employed to extract predictors. Selecting predictors with potential to highlight special cases and defining new patient similarity metrics were among the gaps identified in the existing literature that provide starting points for future work. Patient status prediction models based on patient similarity and health data offer exciting potential for personalizing and ultimately improving health care, leading to better patient outcomes. PMID:28258046

A simple semi-empirical technique for apportioning the impact of roadways on air quality in an urban neighbourhood

NASA Astrophysics Data System (ADS)

Elangasinghe, M. A.; Dirks, K. N.; Singhal, N.; Costello, S. B.; Longley, I.; Salmond, J. A.

2014-02-01

Air pollution from the transport sector has a marked effect on human health, so isolating the pollutant contribution from a roadway is important in understanding its impact on the local neighbourhood. This paper proposes a novel technique based on a semi-empirical air pollution model to quantify the impact from a roadway on the air quality of a local neighbourhood using ambient records of a single air pollution monitor. We demonstrate the proposed technique using a case study, in which we quantify the contribution from a major highway with respect to the local background concentration in Auckland, New Zealand. Comparing the diurnal variation of the model-separated background contribution with real measurements from a site upwind of the highway shows that the model estimates are reliable. Amongst all of the pollutants considered, the best estimations of the background were achieved for nitrogen oxides. Although the multi-pronged approach worked well for predominantly vehicle-related pollutants, it could not be used effectively to isolate emissions of PM10 due to the complex and less predictable influence of natural sources (such as marine aerosols). The proposed approach is useful in situations where ambient records from an upwind background station are not available (as required by other techniques) and is potentially transferable to situations such as intersections and arterial roads. Applying this technique to longer time series could help to understand the changes in pollutant concentrations from the road and background sources for different emission scenarios, for different years or seasons. Modelling results also show the potential of such a hybrid semi-empirical models to contribute to our understanding of the physical parameters determining air quality and to validate emissions inventory data.

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

Khachatryan, Vardan

A dedicated search is presented for new phenomena in inclusive 8- and 10-jet final states with low missing transverse momentum, with and without identification of jets originating from b quarks. The analysis is based on data from proton–proton collisions corresponding to an integrated luminosity of 19.7fb –1 collected with the CMS detector at the LHC at √s = 8TeV. The dominant multijet background expectations are obtained from low jet multiplicity control samples. Data agree well with the standard model background predictions, and limits are set in several benchmark models. Colorons (axigluons) with masses between 0.6 and 0.75 (up to 1.15)more » TeV are excluded at 95% confidence level. Similar exclusion limits for gluinos in R-parity violating supersymmetric scenarios are from 0.6 up to 1.1 TeV. Finally, these results comprise the first experimental probe of the coloron and axigluon models in multijet final states.« less

New efficient optimizing techniques for Kalman filters and numerical weather prediction models

NASA Astrophysics Data System (ADS)

Famelis, Ioannis; Galanis, George; Liakatas, Aristotelis

2016-06-01

The need for accurate local environmental predictions and simulations beyond the classical meteorological forecasts are increasing the last years due to the great number of applications that are directly or not affected: renewable energy resource assessment, natural hazards early warning systems, global warming and questions on the climate change can be listed among them. Within this framework the utilization of numerical weather and wave prediction systems in conjunction with advanced statistical techniques that support the elimination of the model bias and the reduction of the error variability may successfully address the above issues. In the present work, new optimization methods are studied and tested in selected areas of Greece where the use of renewable energy sources is of critical. The added value of the proposed work is due to the solid mathematical background adopted making use of Information Geometry and Statistical techniques, new versions of Kalman filters and state of the art numerical analysis tools.

Linear and non-linear perturbations in dark energy models

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

Escamilla-Rivera, Celia; Casarini, Luciano; Fabris, Júlio C.

2016-11-01

In this work we discuss observational aspects of three time-dependent parameterisations of the dark energy equation of state w ( z ). In order to determine the dynamics associated with these models, we calculate their background evolution and perturbations in a scalar field representation. After performing a complete treatment of linear perturbations, we also show that the non-linear contribution of the selected w ( z ) parameterisations to the matter power spectra is almost the same for all scales, with no significant difference from the predictions of the standard ΛCDM model.

Copula based prediction models: an application to an aortic regurgitation study

PubMed Central

Kumar, Pranesh; Shoukri, Mohamed M

2007-01-01

Background: An important issue in prediction modeling of multivariate data is the measure of dependence structure. The use of Pearson's correlation as a dependence measure has several pitfalls and hence application of regression prediction models based on this correlation may not be an appropriate methodology. As an alternative, a copula based methodology for prediction modeling and an algorithm to simulate data are proposed. Methods: The method consists of introducing copulas as an alternative to the correlation coefficient commonly used as a measure of dependence. An algorithm based on the marginal distributions of random variables is applied to construct the Archimedean copulas. Monte Carlo simulations are carried out to replicate datasets, estimate prediction model parameters and validate them using Lin's concordance measure. Results: We have carried out a correlation-based regression analysis on data from 20 patients aged 17–82 years on pre-operative and post-operative ejection fractions after surgery and estimated the prediction model: Post-operative ejection fraction = - 0.0658 + 0.8403 (Pre-operative ejection fraction); p = 0.0008; 95% confidence interval of the slope coefficient (0.3998, 1.2808). From the exploratory data analysis, it is noted that both the pre-operative and post-operative ejection fractions measurements have slight departures from symmetry and are skewed to the left. It is also noted that the measurements tend to be widely spread and have shorter tails compared to normal distribution. Therefore predictions made from the correlation-based model corresponding to the pre-operative ejection fraction measurements in the lower range may not be accurate. Further it is found that the best approximated marginal distributions of pre-operative and post-operative ejection fractions (using q-q plots) are gamma distributions. The copula based prediction model is estimated as: Post -operative ejection fraction = - 0.0933 + 0.8907 × (Pre-operative ejection fraction); p = 0.00008 ; 95% confidence interval for slope coefficient (0.4810, 1.3003). For both models differences in the predicted post-operative ejection fractions in the lower range of pre-operative ejection measurements are considerably different and prediction errors due to copula model are smaller. To validate the copula methodology we have re-sampled with replacement fifty independent bootstrap samples and have estimated concordance statistics 0.7722 (p = 0.0224) for the copula model and 0.7237 (p = 0.0604) for the correlation model. The predicted and observed measurements are concordant for both models. The estimates of accuracy components are 0.9233 and 0.8654 for copula and correlation models respectively. Conclusion: Copula-based prediction modeling is demonstrated to be an appropriate alternative to the conventional correlation-based prediction modeling since the correlation-based prediction models are not appropriate to model the dependence in populations with asymmetrical tails. Proposed copula-based prediction model has been validated using the independent bootstrap samples. PMID:17573974

An inventory of the Aspergillus niger secretome by combining in silico predictions with shotgun proteomics data

PubMed Central

2010-01-01

Background The ecological niche occupied by a fungal species, its pathogenicity and its usefulness as a microbial cell factory to a large degree depends on its secretome. Protein secretion usually requires the presence of a N-terminal signal peptide (SP) and by scanning for this feature using available highly accurate SP-prediction tools, the fraction of potentially secreted proteins can be directly predicted. However, prediction of a SP does not guarantee that the protein is actually secreted and current in silico prediction methods suffer from gene-model errors introduced during genome annotation. Results A majority rule based classifier that also evaluates signal peptide predictions from the best homologs of three neighbouring Aspergillus species was developed to create an improved list of potential signal peptide containing proteins encoded by the Aspergillus niger genome. As a complement to these in silico predictions, the secretome associated with growth and upon carbon source depletion was determined using a shotgun proteomics approach. Overall, some 200 proteins with a predicted signal peptide were identified to be secreted proteins. Concordant changes in the secretome state were observed as a response to changes in growth/culture conditions. Additionally, two proteins secreted via a non-classical route operating in A. niger were identified. Conclusions We were able to improve the in silico inventory of A. niger secretory proteins by combining different gene-model predictions from neighbouring Aspergilli and thereby avoiding prediction conflicts associated with inaccurate gene-models. The expected accuracy of signal peptide prediction for proteins that lack homologous sequences in the proteomes of related species is 85%. An experimental validation of the predicted proteome confirmed in silico predictions. PMID:20959013

Interrelationship of Cytokines, Hypothalamic-Pituitary-Adrenal Axis Hormones, and Psychosocial Variables in the Prediction of Preterm Birth

PubMed Central

Pearce, B.D.; Grove, J.; Bonney, E.A.; Bliwise, N.; Dudley, D.J.; Schendel, D.E.; Thorsen, P.

2010-01-01

Background/Aims To examine the relationship of biological mediators (cytokines, stress hormones), psychosocial, obstetric history, and demographic factors in the early prediction of preterm birth (PTB) using a comprehensive logistic regression model incorporating diverse risk factors. Methods In this prospective case-control study, maternal serum biomarkers were quantified at 9–23 weeks’ gestation in 60 women delivering at <37 weeks compared to 123 women delivering at term. Biomarker data were combined with maternal sociodemographic factors and stress data into regression models encompassing 22 preterm risk factors and 1st-order interactions. Results Among individual biomarkers, we found that macrophage migration inhibitory factor (MIF), interleukin-10, C-reactive protein (CRP), and tumor necrosis factor-α were statistically significant predictors of PTB at all cutoff levels tested (75th, 85th, and 90th percentiles). We fit multifactor models for PTB prediction at each biomarker cutoff. Our best models revealed that MIF, CRP, risk-taking behavior, and low educational attainment were consistent predictors of PTB at all biomarker cutoffs. The 75th percentile cutoff yielded the best predicting model with an area under the ROC curve of 0.808 (95% CI 0.743–0.874). Conclusion Our comprehensive models highlight the prominence of behavioral risk factors for PTB and point to MIF as a possible psychobiological mediator. PMID:20160447

Simulated effect of pneumococcal vaccination in the Netherlands on existing rules constructed in a non-vaccinated cohort predicting sequelae after bacterial meningitis

PubMed Central

2010-01-01

Background Previously two prediction rules identifying children at risk of hearing loss and academic or behavioral limitations after bacterial meningitis were developed. Streptococcus pneumoniae as causative pathogen was an important risk factor in both. Since 2006 Dutch children receive seven-valent conjugate vaccination against S. pneumoniae. The presumed effect of vaccination was simulated by excluding all children infected by S. pneumoniae with the serotypes included in the vaccine, from both previous collected cohorts (between 1990-1995). Methods Children infected by one of the vaccine serotypes were excluded from both original cohorts (hearing loss: 70 of 628 children; academic or behavioral limitations: 26 of 182 children). All identified risk factors were included in multivariate logistic regression models. The discriminative ability of both new models was calculated. Results The same risk factors as in the original models were significant. The discriminative ability of the original hearing loss model was 0.84 and of the new model 0.87. In the academic or behavioral limitations model it was 0.83 and 0.84 respectively. Conclusion It can be assumed that the prediction rules will also be applicable on a vaccinated population. However, vaccination does not provide 100% coverage and evidence is available that serotype replacement will occur. The impact of vaccination on serotype replacement needs to be investigated, and the prediction rules must be validated externally. PMID:20815866

BOADICEA breast cancer risk prediction model: updates to cancer incidences, tumour pathology and web interface

PubMed Central

Lee, A J; Cunningham, A P; Kuchenbaecker, K B; Mavaddat, N; Easton, D F; Antoniou, A C

2014-01-01

Background: The Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) is a risk prediction model that is used to compute probabilities of carrying mutations in the high-risk breast and ovarian cancer susceptibility genes BRCA1 and BRCA2, and to estimate the future risks of developing breast or ovarian cancer. In this paper, we describe updates to the BOADICEA model that extend its capabilities, make it easier to use in a clinical setting and yield more accurate predictions. Methods: We describe: (1) updates to the statistical model to include cancer incidences from multiple populations; (2) updates to the distributions of tumour pathology characteristics using new data on BRCA1 and BRCA2 mutation carriers and women with breast cancer from the general population; (3) improvements to the computational efficiency of the algorithm so that risk calculations now run substantially faster; and (4) updates to the model's web interface to accommodate these new features and to make it easier to use in a clinical setting. Results: We present results derived using the updated model, and demonstrate that the changes have a significant impact on risk predictions. Conclusion: All updates have been implemented in a new version of the BOADICEA web interface that is now available for general use: http://ccge.medschl.cam.ac.uk/boadicea/. PMID:24346285

Dengue Baidu Search Index data can improve the prediction of local dengue epidemic: A case study in Guangzhou, China

PubMed Central

Liu, Tao; Zhu, Guanghu; Lin, Hualiang; Zhang, Yonghui; He, Jianfeng; Deng, Aiping; Peng, Zhiqiang; Xiao, Jianpeng; Rutherford, Shannon; Xie, Runsheng; Zeng, Weilin; Li, Xing; Ma, Wenjun

2017-01-01

Background Dengue fever (DF) in Guangzhou, Guangdong province in China is an important public health issue. The problem was highlighted in 2014 by a large, unprecedented outbreak. In order to respond in a more timely manner and hence better control such potential outbreaks in the future, this study develops an early warning model that integrates internet-based query data into traditional surveillance data. Methodology and principal findings A Dengue Baidu Search Index (DBSI) was collected from the Baidu website for developing a predictive model of dengue fever in combination with meteorological and demographic factors. Generalized additive models (GAM) with or without DBSI were established. The generalized cross validation (GCV) score and deviance explained indexes, intraclass correlation coefficient (ICC) and root mean squared error (RMSE), were respectively applied to measure the fitness and the prediction capability of the models. Our results show that the DBSI with one-week lag has a positive linear relationship with the local DF occurrence, and the model with DBSI (ICC:0.94 and RMSE:59.86) has a better prediction capability than the model without DBSI (ICC:0.72 and RMSE:203.29). Conclusions Our study suggests that a DSBI combined with traditional disease surveillance and meteorological data can improve the dengue early warning system in Guangzhou. PMID:28263988

Predictive genetic testing for the identification of high-risk groups: a simulation study on the impact of predictive ability

PubMed Central

2011-01-01

Background Genetic risk models could potentially be useful in identifying high-risk groups for the prevention of complex diseases. We investigated the performance of this risk stratification strategy by examining epidemiological parameters that impact the predictive ability of risk models. Methods We assessed sensitivity, specificity, and positive and negative predictive value for all possible risk thresholds that can define high-risk groups and investigated how these measures depend on the frequency of disease in the population, the frequency of the high-risk group, and the discriminative accuracy of the risk model, as assessed by the area under the receiver-operating characteristic curve (AUC). In a simulation study, we modeled genetic risk scores of 50 genes with equal odds ratios and genotype frequencies, and varied the odds ratios and the disease frequency across scenarios. We also performed a simulation of age-related macular degeneration risk prediction based on published odds ratios and frequencies for six genetic risk variants. Results We show that when the frequency of the high-risk group was lower than the disease frequency, positive predictive value increased with the AUC but sensitivity remained low. When the frequency of the high-risk group was higher than the disease frequency, sensitivity was high but positive predictive value remained low. When both frequencies were equal, both positive predictive value and sensitivity increased with increasing AUC, but higher AUC was needed to maximize both measures. Conclusions The performance of risk stratification is strongly determined by the frequency of the high-risk group relative to the frequency of disease in the population. The identification of high-risk groups with appreciable combinations of sensitivity and positive predictive value requires higher AUC. PMID:21797996

Evidence for the associated production of a W boson and a top quark at ATLAS

NASA Astrophysics Data System (ADS)

Koll, James

This thesis discusses a search for the Standard Model single top Wt-channel process. An analysis has been performed searching for the Wt-channel process using 4.7 fb-1 of integrated luminosity collected with the ATLAS detector at the Large Hadron Collider. A boosted decision tree is trained using machine learning techniques to increase the separation between signal and background. A profile likelihood fit is used to measure the cross-section of the Wt-channel process at sigma(pp → Wt + X) = 16.8 +/-2.9 (stat) +/- 4.9(syst) pb, consistent with the Standard Model prediction. This fit is also used to generate pseudoexperiments to calculate the significance, finding an observed (expected) 3.3 sigma (3.4 sigma) excess over background.

The Radio Background below 100 MHz

NASA Astrophysics Data System (ADS)

Dowell, Jayce; Taylor, Greg B.

2018-05-01

The recent detection of the “cosmic dawn” redshifted 21 cm signal at 78 MHz by the Experiment to Detect the Global EoR Signatures (EDGES) differs significantly from theoretical predictions. In particular, the absorption trough is roughly a factor of two stronger than the most optimistic theoretical models. The early interpretations of the origin of this discrepancy fall into two categories. The first is that there is increased cooling of the gas due to interactions with dark matter, while the second is that the background radiation field includes a contribution from a component in addition to the cosmic microwave background (CMB). In this Letter we examine the feasibility of the second idea using new data from the first station of the Long Wavelength Array. The data span 40–80 MHz and provide important constraints on the present-day background in a frequency range where there are few surveys with absolute temperature calibration suitable for measuring the strength of the radio monopole. We find support for a strong, diffuse radio background that was suggested by the ARCARDE 2 results in the 3–10 GHz range. We find that this background is well modeled by a power law with a spectral index of ‑2.58 ± 0.05 and a temperature at the rest frame 21 cm frequency of {603}-92+102 mK.

The burden of typhoid fever in low- and middle-income countries: A meta-regression approach

PubMed Central

Warren, Joshua L.; Crawford, Forrest W.; Weinberger, Daniel M.; Kürüm, Esra; Pak, Gi Deok; Marks, Florian; Pitzer, Virginia E.

2017-01-01

Background Upcoming vaccination efforts against typhoid fever require an assessment of the baseline burden of disease in countries at risk. There are no typhoid incidence data from most low- and middle-income countries (LMICs), so model-based estimates offer insights for decision-makers in the absence of readily available data. Methods We developed a mixed-effects model fit to data from 32 population-based studies of typhoid incidence in 22 locations in 14 countries. We tested the contribution of economic and environmental indices for predicting typhoid incidence using a stochastic search variable selection algorithm. We performed out-of-sample validation to assess the predictive performance of the model. Results We estimated that 17.8 million cases of typhoid fever occur each year in LMICs (95% credible interval: 6.9–48.4 million). Central Africa was predicted to experience the highest incidence of typhoid, followed by select countries in Central, South, and Southeast Asia. Incidence typically peaked in the 2–4 year old age group. Models incorporating widely available economic and environmental indicators were found to describe incidence better than null models. Conclusions Recent estimates of typhoid burden may under-estimate the number of cases and magnitude of uncertainty in typhoid incidence. Our analysis permits prediction of overall as well as age-specific incidence of typhoid fever in LMICs, and incorporates uncertainty around the model structure and estimates of the predictors. Future studies are needed to further validate and refine model predictions and better understand year-to-year variation in cases. PMID:28241011

Artificial neural network modeling using clinical and knowledge independent variables predicts salt intake reduction behavior

PubMed Central

Isma’eel, Hussain A.; Sakr, George E.; Almedawar, Mohamad M.; Fathallah, Jihan; Garabedian, Torkom; Eddine, Savo Bou Zein

2015-01-01

Background High dietary salt intake is directly linked to hypertension and cardiovascular diseases (CVDs). Predicting behaviors regarding salt intake habits is vital to guide interventions and increase their effectiveness. We aim to compare the accuracy of an artificial neural network (ANN) based tool that predicts behavior from key knowledge questions along with clinical data in a high cardiovascular risk cohort relative to the least square models (LSM) method. Methods We collected knowledge, attitude and behavior data on 115 patients. A behavior score was calculated to classify patients’ behavior towards reducing salt intake. Accuracy comparison between ANN and regression analysis was calculated using the bootstrap technique with 200 iterations. Results Starting from a 69-item questionnaire, a reduced model was developed and included eight knowledge items found to result in the highest accuracy of 62% CI (58-67%). The best prediction accuracy in the full and reduced models was attained by ANN at 66% and 62%, respectively, compared to full and reduced LSM at 40% and 34%, respectively. The average relative increase in accuracy over all in the full and reduced models is 82% and 102%, respectively. Conclusions Using ANN modeling, we can predict salt reduction behaviors with 66% accuracy. The statistical model has been implemented in an online calculator and can be used in clinics to estimate the patient’s behavior. This will help implementation in future research to further prove clinical utility of this tool to guide therapeutic salt reduction interventions in high cardiovascular risk individuals. PMID:26090333

Earthquake triggering by transient and static deformations

USGS Publications Warehouse

Gomberg, J.; Beeler, N.M.; Blanpied, M.L.; Bodin, P.

1998-01-01

Observational evidence for both static and transient near-field and far-field triggered seismicity are explained in terms of a frictional instability model, based on a single degree of freedom spring-slider system and rate- and state-dependent frictional constitutive equations. In this study a triggered earthquake is one whose failure time has been advanced by ??t (clock advance) due to a stress perturbation. Triggering stress perturbations considered include square-wave transients and step functions, analogous to seismic waves and coseismic static stress changes, respectively. Perturbations are superimposed on a constant background stressing rate which represents the tectonic stressing rate. The normal stress is assumed to be constant. Approximate, closed-form solutions of the rate-and-state equations are derived for these triggering and background loads, building on the work of Dieterich [1992, 1994]. These solutions can be used to simulate the effects of static and transient stresses as a function of amplitude, onset time t0, and in the case of square waves, duration. The accuracies of the approximate closed-form solutions are also evaluated with respect to the full numerical solution and t0. The approximate solutions underpredict the full solutions, although the difference decreases as t0, approaches the end of the earthquake cycle. The relationship between ??t and t0 differs for transient and static loads: a static stress step imposed late in the cycle causes less clock advance than an equal step imposed earlier, whereas a later applied transient causes greater clock advance than an equal one imposed earlier. For equal ??t, transient amplitudes must be greater than static loads by factors of several tens to hundreds depending on t0. We show that the rate-and-state model requires that the total slip at failure is a constant, regardless of the loading history. Thus a static load applied early in the cycle, or a transient applied at any time, reduces the stress at the initiation of failure, whereas static loads that are applied sufficiently late raise it. Rate-and-state friction predictions differ markedly from those based on Coulomb failure stress changes (??CFS) in which ??t equals the amplitude of the static stress change divided by the background stressing rate. The ??CFS model assumes a stress failure threshold, while the rate-and-state equations require a slip failure threshold. The complete rale-and-state equations predict larger ??t than the ??CFS model does for static stress steps at small t0, and smaller ??t than the ??CFS model for stress steps at large t0. The ??CFS model predicts nonzero ??t only for transient loads that raise the stress to failure stress levels during the transient. In contrast, the rate-and-state model predicts nonzero ??t for smaller loads, and triggered failure may occur well after the transient is finished. We consider heuristically the effects of triggering on a population of faults, as these effects might be evident in seismicity data. Triggering is manifest as an initial increase in seismicity rate that may be followed by a quiescence or by a return to the background rate. Available seismicity data are insufficient to discriminate whether triggered earthquakes are "new" or clock advanced. However, if triggering indeed results from advancing the failure time of inevitable earthquakes, then our modeling suggests that a quiescence always follows transient triggering and that the duration of increased seismicity also cannot exceed the duration of a triggering transient load. Quiescence follows static triggering only if the population of available faults is finite.

Predicting Curriculum and Test Performance at Age 7 Years from Pupil Background, Baseline Skills and Phonological Awareness at Age 5

ERIC Educational Resources Information Center

Savage, R.; Carless, S.

2004-01-01

Background: Phonological awareness tests are known to be amongst the best predictors of literacy; however their predictive validity alongside current school screening practice (baseline assessment, pupil background data) and to National Curricular outcome measures is unknown. Aim: We explored the validity of phonological awareness and orthographic…

Signature-based search for delayed photons in exclusive photon plus missing transverse energy events from pp¯ collisions with s=1.96TeV

NASA Astrophysics Data System (ADS)

Aaltonen, T.; Amerio, S.; Amidei, D.; Anastassov, A.; Annovi, A.; Antos, J.; Apollinari, G.; Appel, J. A.; Arisawa, T.; Artikov, A.; Asaadi, J.; Ashmanskas, W.; Auerbach, B.; Aurisano, A.; Azfar, F.; Badgett, W.; Bae, T.; Barbaro-Galtieri, A.; Barnes, V. E.; Barnett, B. A.; Barria, P.; Bartos, P.; Bauce, M.; Bedeschi, F.; Behari, S.; Bellettini, G.; Bellinger, J.; Benjamin, D.; Beretvas, A.; Bhatti, A.; Bland, K. R.; Blumenfeld, B.; Bocci, A.; Bodek, A.; Bortoletto, D.; Boudreau, J.; Boveia, A.; Brigliadori, L.; Bromberg, C.; Brucken, E.; Budagov, J.; Budd, H. S.; Burkett, K.; Busetto, G.; Bussey, P.; Butti, P.; Buzatu, A.; Calamba, A.; Camarda, S.; Campanelli, M.; Canelli, F.; Carls, B.; Carlsmith, D.; Carosi, R.; Carrillo, S.; Casal, B.; Casarsa, M.; Castro, A.; Catastini, P.; Cauz, D.; Cavaliere, V.; Cavalli-Sforza, M.; Cerri, A.; Cerrito, L.; Chen, Y. C.; Chertok, M.; Chiarelli, G.; Chlachidze, G.; Cho, K.; Chokheli, D.; Ciocci, M. A.; Clark, A.; Clarke, C.; Convery, M. E.; Conway, J.; Corbo, M.; Cordelli, M.; Cox, C. A.; Cox, D. J.; Cremonesi, M.; Cruz, D.; Cuevas, J.; Culbertson, R.; d'Ascenzo, N.; Datta, M.; De Barbaro, P.; Demortier, L.; Deninno, M.; d'Errico, M.; Devoto, F.; Di Canto, A.; Di Ruzza, B.; Dittmann, J. R.; D'Onofrio, M.; Donati, S.; Dorigo, M.; Driutti, A.; Ebina, K.; Edgar, R.; Elagin, A.; Erbacher, R.; Errede, S.; Esham, B.; Eusebi, R.; Farrington, S.; Fernández Ramos, J. P.; Field, R.; Flanagan, G.; Forrest, R.; Franklin, M.; Freeman, J. C.; Frisch, H.; Funakoshi, Y.; Garfinkel, A. F.; Garosi, P.; Gerberich, H.; Gerchtein, E.; Giagu, S.; Giakoumopoulou, V.; Gibson, K.; Ginsburg, C. M.; Giokaris, N.; Giromini, P.; Giurgiu, G.; Glagolev, V.; Glenzinski, D.; Gold, M.; Goldin, D.; Golossanov, A.; Gomez, G.; Gomez-Ceballos, G.; Goncharov, M.; González López, O.; Gorelov, I.; Goshaw, A. T.; Goulianos, K.; Gramellini, E.; Grinstein, S.; Grosso-Pilcher, C.; Group, R. C.; Guimaraes da Costa, J.; Hahn, S. R.; Han, J. Y.; Happacher, F.; Hara, K.; Hare, M.; Harr, R. F.; Harrington-Taber, T.; Hatakeyama, K.; Hays, C.; Heinrich, J.; Herndon, M.; Hocker, A.; Hong, Z.; Hopkins, W.; Hou, S.; Hughes, R. E.; Husemann, U.; Hussein, M.; Huston, J.; Introzzi, G.; Iori, M.; Ivanov, A.; James, E.; Jang, D.; Jayatilaka, B.; Jeon, E. J.; Jindariani, S.; Jones, M.; Joo, K. K.; Jun, S. Y.; Junk, T. R.; Kambeitz, M.; Kamon, T.; Karchin, P. E.; Kasmi, A.; Kato, Y.; Ketchum, W.; Keung, J.; Kilminster, B.; Kim, D. H.; Kim, H. S.; Kim, J. E.; Kim, M. J.; Kim, S. B.; Kim, S. H.; Kim, Y. J.; Kim, Y. K.; Kimura, N.; Kirby, M.; Knoepfel, K.; Kondo, K.; Kong, D. J.; Konigsberg, J.; Kotwal, A. V.; Kreps, M.; Kroll, J.; Kruse, M.; Kuhr, T.; Kurata, M.; Laasanen, A. T.; Lammel, S.; Lancaster, M.; Lannon, K.; Latino, G.; Lee, H. S.; Lee, J. S.; Leo, S.; Leone, S.; Lewis, J. D.; Limosani, A.; Lipeles, E.; Lister, A.; Liu, H.; Liu, Q.; Liu, T.; Lockwitz, S.; Loginov, A.; Lucà, A.; Lucchesi, D.; Lueck, J.; Lujan, P.; Lukens, P.; Lungu, G.; Lys, J.; Lysak, R.; Madrak, R.; Maestro, P.; Malik, S.; Manca, G.; Manousakis-Katsikakis, A.; Margaroli, F.; Marino, P.; Martínez, M.; Matera, K.; Mattson, M. E.; Mazzacane, A.; Mazzanti, P.; McNulty, R.; Mehta, A.; Mehtala, P.; Mesropian, C.; Miao, T.; Mietlicki, D.; Mitra, A.; Miyake, H.; Moed, S.; Moggi, N.; Moon, C. S.; Moore, R.; Morello, M. J.; Mukherjee, A.; Muller, Th.; Murat, P.; Mussini, M.; Nachtman, J.; Nagai, Y.; Naganoma, J.; Nakano, I.; Napier, A.; Nett, J.; Neu, C.; Nigmanov, T.; Nodulman, L.; Noh, S. Y.; Norniella, O.; Oakes, L.; Oh, S. H.; Oh, Y. D.; Oksuzian, I.; Okusawa, T.; Orava, R.; Ortolan, L.; Pagliarone, C.; Palencia, E.; Palni, P.; Papadimitriou, V.; Parker, W.; Pauletta, G.; Paulini, M.; Paus, C.; Phillips, T. J.; Piacentino, G.; Pianori, E.; Pilot, J.; Pitts, K.; Plager, C.; Pondrom, L.; Poprocki, S.; Potamianos, K.; Pranko, A.; Prokoshin, F.; Ptohos, F.; Punzi, G.; Ranjan, N.; Redondo Fernández, I.; Renton, P.; Rescigno, M.; Rimondi, F.; Ristori, L.; Robson, A.; Rodriguez, T.; Rolli, S.; Ronzani, M.; Roser, R.; Rosner, J. L.; Ruffini, F.; Ruiz, A.; Russ, J.; Rusu, V.; Sakumoto, W. K.; Sakurai, Y.; Santi, L.; Sato, K.; Saveliev, V.; Savoy-Navarro, A.; Schlabach, P.; Schmidt, E. E.; Schwarz, T.; Scodellaro, L.; Scuri, F.; Seidel, S.; Seiya, Y.; Semenov, A.; Sforza, F.; Shalhout, S. Z.; Shears, T.; Shepard, P. F.; Shimojima, M.; Shochet, M.; Shreyber-Tecker, I.; Simonenko, A.; Sinervo, P.; Sliwa, K.; Smith, J. R.; Snider, F. D.; Song, H.; Sorin, V.; Stancari, M.; Denis, R. St.; Stelzer, B.; Stelzer-Chilton, O.; Stentz, D.; Strologas, J.; Sudo, Y.; Sukhanov, A.; Suslov, I.; Takemasa, K.; Takeuchi, Y.; Tang, J.; Tecchio, M.; Teng, P. K.; Thom, J.; Thomson, E.; Thukral, V.; Toback, D.; Tokar, S.; Tollefson, K.; Tomura, T.; Tonelli, D.; Torre, S.; Torretta, D.; Totaro, P.; Trovato, M.; Ukegawa, F.; Uozumi, S.; Vázquez, F.; Velev, G.; Vellidis, C.; Vernieri, C.; Vidal, M.; Vilar, R.; Vizán, J.; Vogel, M.; Volpi, G.; Wagner, P.; Wallny, R.; Wang, S. M.; Warburton, A.; Waters, D.; Wester, W. C., III; Whiteson, D.; Wicklund, A. B.; Wilbur, S.; Williams, H. H.; Wilson, J. S.; Wilson, P.; Winer, B. L.; Wittich, P.; Wolbers, S.; Wolfe, H.; Wright, T.; Wu, X.; Wu, Z.; Yamamoto, K.; Yamato, D.; Yang, T.; Yang, U. K.; Yang, Y. C.; Yao, W.-M.; Yeh, G. P.; Yi, K.; Yoh, J.; Yorita, K.; Yoshida, T.; Yu, G. B.; Yu, I.; Zanetti, A. M.; Zeng, Y.; Zhou, C.; Zucchelli, S.

2013-08-01

We present the first signature-based search for delayed photons using an exclusive photon plus missing transverse energy final state. Events are reconstructed in a data sample from the CDF II detector corresponding to 6.3fb-1 of integrated luminosity from s=1.96TeV proton-antiproton collisions. Candidate events are selected if they contain a photon with an arrival time in the detector larger than expected from a promptly produced photon. The mean number of events from standard model sources predicted by the data-driven background model based on the photon timing distribution is 286±24. A total of 322 events are observed. A p value of 12% is obtained, showing consistency of the data with standard model predictions.

Estimates of ground level TSP, SO sub 2 and HCI for a municipal waste incinerator to be located at Tynes Bay - Bermuda

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

Kent Simmons, J.A.; Knap, A.H.

1991-04-01

The computer model Industrial Source Complex Short Term (ISCST) was used to study the stack emissions from a refuse incinerator proposed for the inland of Bermuda. The model predicts that the highest ground level pollutant concentrations will occur near Prospect, 800 m to 1,000 m due south of the stack. The authors installed a portable laboratory and instruments at Prospect to begin making air quality baseline measurements. By comparing the model's estimates of the incinerator contribution to the background levels measured at the site they predicted that stack emissions would not cause an increase in TSP or SO{sub 2}. Themore » incinerator will be a significant source of HCI to Bermuda air with ambient levels approaching air quality guidelines.« less

Predicting the synaptic information efficacy in cortical layer 5 pyramidal neurons using a minimal integrate-and-fire model.

PubMed

London, Michael; Larkum, Matthew E; Häusser, Michael

2008-11-01

Synaptic information efficacy (SIE) is a statistical measure to quantify the efficacy of a synapse. It measures how much information is gained, on the average, about the output spike train of a postsynaptic neuron if the input spike train is known. It is a particularly appropriate measure for assessing the input-output relationship of neurons receiving dynamic stimuli. Here, we compare the SIE of simulated synaptic inputs measured experimentally in layer 5 cortical pyramidal neurons in vitro with the SIE computed from a minimal model constructed to fit the recorded data. We show that even with a simple model that is far from perfect in predicting the precise timing of the output spikes of the real neuron, the SIE can still be accurately predicted. This arises from the ability of the model to predict output spikes influenced by the input more accurately than those driven by the background current. This indicates that in this context, some spikes may be more important than others. Lastly we demonstrate another aspect where using mutual information could be beneficial in evaluating the quality of a model, by measuring the mutual information between the model's output and the neuron's output. The SIE, thus, could be a useful tool for assessing the quality of models of single neurons in preserving input-output relationship, a property that becomes crucial when we start connecting these reduced models to construct complex realistic neuronal networks.

Predicting the Impact of Vaccination on the Transmission Dynamics of Typhoid in South Asia: A Mathematical Modeling Study

PubMed Central

Pitzer, Virginia E.; Bowles, Cayley C.; Baker, Stephen; Kang, Gagandeep; Balaji, Veeraraghavan; Farrar, Jeremy J.; Grenfell, Bryan T.

2014-01-01

Background Modeling of the transmission dynamics of typhoid allows for an evaluation of the potential direct and indirect effects of vaccination; however, relevant typhoid models rooted in data have rarely been deployed. Methodology/Principal Findings We developed a parsimonious age-structured model describing the natural history and immunity to typhoid infection. The model was fit to data on culture-confirmed cases of typhoid fever presenting to Christian Medical College hospital in Vellore, India from 2000–2012. The model was then used to evaluate the potential impact of school-based vaccination strategies using live oral, Vi-polysaccharide, and Vi-conjugate vaccines. The model was able to reproduce the incidence and age distribution of typhoid cases in Vellore. The basic reproductive number (R 0) of typhoid was estimated to be 2.8 in this setting. Vaccination was predicted to confer substantial indirect protection leading to a decrease in the incidence of typhoid in the short term, but (intuitively) typhoid incidence was predicted to rebound 5–15 years following a one-time campaign. Conclusions/Significance We found that model predictions for the overall and indirect effects of vaccination depend strongly on the role of chronic carriers in transmission. Carrier transmissibility was tentatively estimated to be low, consistent with recent studies, but was identified as a pivotal area for future research. It is unlikely that typhoid can be eliminated from endemic settings through vaccination alone. PMID:24416466

Search for Higgs boson production in dilepton and missing energy final states with 5.4 fb(-1) of pp collisions at square root(s) = 1.96 TeV.

PubMed

Abazov, V M; Abbott, B; Abolins, M; Acharya, B S; Adams, M; Adams, T; Aguilo, E; Alexeev, G D; Alkhazov, G; Alton, A; Alverson, G; Alves, G A; Ancu, L S; Aoki, M; Arnoud, Y; Arov, M; Askew, A; Asman, B; Atramentov, O; Avila, C; BackusMayes, J; Badaud, F; Bagby, L; Baldin, B; Bandurin, D V; Banerjee, S; Barberis, E; Barfuss, A-F; Baringer, P; Barreto, J; Bartlett, J F; Bassler, U; Bauer, D; Beale, S; Bean, A; Begalli, M; Begel, M; Belanger-Champagne, C; Bellantoni, L; Benitez, J A; Beri, S B; Bernardi, G; Bernhard, R; Bertram, I; Besançon, M; Beuselinck, R; Bezzubov, V A; Bhat, P C; Bhatnagar, V; Blazey, G; Blessing, S; Bloom, K; Boehnlein, A; Boline, D; Bolton, T A; Boos, E E; Borissov, G; Bose, T; Brandt, A; Brock, R; Brooijmans, G; Bross, A; Brown, D; Bu, X B; Buchholz, D; Buehler, M; Buescher, V; Bunichev, V; Burdin, S; Burnett, T H; Buszello, C P; Calfayan, P; Calpas, B; Calvet, S; Camacho-Pérez, E; Cammin, J; Carrasco-Lizarraga, M A; Carrera, E; Casey, B C K; Castilla-Valdez, H; Chakrabarti, S; Chakraborty, D; Chan, K M; Chandra, A; Cheu, E; Chevalier-Théry, S; Cho, D K; Cho, S W; Choi, S; Choudhary, B; Christoudias, T; Cihangir, S; Claes, D; Clutter, J; Cooke, M; Cooper, W E; Corcoran, M; Couderc, F; Cousinou, M-C; Cutts, D; Cwiok, M; Das, A; Davies, G; De, K; de Jong, S J; De la Cruz-Burelo, E; DeVaughan, K; Déliot, F; Demarteau, M; Demina, R; Denisov, D; Denisov, S P; Desai, S; Diehl, H T; Diesburg, M; Dominguez, A; Dorland, T; Dubey, A; Dudko, L V; Duflot, L; Duggan, D; Duperrin, A; Dutt, S; Dyshkant, A; Eads, M; Edmunds, D; Ellison, J; Elvira, V D; Enari, Y; Eno, S; Evans, H; Evdokimov, A; Evdokimov, V N; Facini, G; Ferapontov, A V; Ferbel, T; Fiedler, F; Filthaut, F; Fisher, W; Fisk, H E; Fortner, M; Fox, H; Fuess, S; Gadfort, T; Galea, C F; Garcia-Bellido, A; Gavrilov, V; Gay, P; Geist, W; Geng, W; Gerbaudo, D; Gerber, C E; Gershtein, Y; Gillberg, D; Ginther, G; Golovanov, G; Gómez, B; Goussiou, A; Grannis, P D; Greder, S; Greenlee, H; Greenwood, Z D; Gregores, E M; Grenier, G; Gris, Ph; Grivaz, J-F; Grohsjean, A; Grünendahl, S; Grünewald, M W; Guo, F; Guo, J; Gutierrez, G; Gutierrez, P; Haas, A; Haefner, P; Hagopian, S; Haley, J; Hall, I; Han, L; Harder, K; Harel, A; Hauptman, J M; Hays, J; Hebbeker, T; Hedin, D; Hegeman, J G; Heinson, A P; Heintz, U; Hensel, C; Heredia-De la Cruz, I; Herner, K; Hesketh, G; Hildreth, M D; Hirosky, R; Hoang, T; Hobbs, J D; Hoeneisen, B; Hohlfeld, M; Hossain, S; Houben, P; Hu, Y; Hubacek, Z; Huske, N; Hynek, V; Iashvili, I; Illingworth, R; Ito, A S; Jabeen, S; Jaffré, M; Jain, S; Jamin, D; Jesik, R; Johns, K; Johnson, C; Johnson, M; Johnston, D; Jonckheere, A; Jonsson, P; Juste, A; Kajfasz, E; Karmanov, D; Kasper, P A; Katsanos, I; Kaushik, V; Kehoe, R; Kermiche, S; Khalatyan, N; Khanov, A; Kharchilava, A; Kharzheev, Y N; Khatidze, D; Kirby, M H; Kirsch, M; Kohli, J M; Kozelov, A V; Kraus, J; Kumar, A; Kupco, A; Kurca, T; Kuzmin, V A; Kvita, J; Lam, D; Lammers, S; Landsberg, G; Lebrun, P; Lee, H S; Lee, W M; Leflat, A; Lellouch, J; Li, L; Li, Q Z; Lietti, S M; Lim, J K; Lincoln, D; Linnemann, J; Lipaev, V V; Lipton, R; Liu, Y; Liu, Z; Lobodenko, A; Lokajicek, M; Love, P; Lubatti, H J; Luna-Garcia, R; Lyon, A L; Maciel, A K A; Mackin, D; Mättig, P; Magaña-Villalba, R; Mal, P K; Malik, S; Malyshev, V L; Maravin, Y; Martínez-Ortega, J; McCarthy, R; McGivern, C L; Meijer, M M; Melnitchouk, A; Mendoza, L; Menezes, D; Mercadante, P G; Merkin, M; Meyer, A; Meyer, J; Mondal, N K; Moulik, T; Muanza, G S; Mulhearn, M; Mundal, O; Mundim, L; Nagy, E; Naimuddin, M; Narain, M; Nayyar, R; Neal, H A; Negret, J P; Neustroev, P; Nilsen, H; Nogima, H; Novaes, S F; Nunnemann, T; Obrant, G; Onoprienko, D; Orduna, J; Osman, N; Osta, J; Otec, R; Otero y Garzón, G J; Owen, M; Padilla, M; Padley, P; Pangilinan, M; Parashar, N; Parihar, V; Park, S-J; Park, S K; Parsons, J; Partridge, R; Parua, N; Patwa, A; Penning, B; Perfilov, M; Peters, K; Peters, Y; Pétroff, P; Piegaia, R; Piper, J; Pleier, M-A; Podesta-Lerma, P L M; Podstavkov, V M; Pol, M-E; Polozov, P; Popov, A V; Prewitt, M; Price, D; Protopopescu, S; Qian, J; Quadt, A; Quinn, B; Rangel, M S; Ranjan, K; Ratoff, P N; Razumov, I; Renkel, P; Rich, P; Rijssenbeek, M; Ripp-Baudot, I; Rizatdinova, F; Robinson, S; Rominsky, M; Royon, C; Rubinov, P; Ruchti, R; Safronov, G; Sajot, G; Sánchez-Hernández, A; Sanders, M P; Sanghi, B; Savage, G; Sawyer, L; Scanlon, T; Schaile, D; Schamberger, R D; Scheglov, Y; Schellman, H; Schliephake, T; Schlobohm, S; Schwanenberger, C; Schwienhorst, R; Sekaric, J; Severini, H; Shabalina, E; Shary, V; Shchukin, A A; Shivpuri, R K; Simak, V; Sirotenko, V; Skubic, P; Slattery, P; Smirnov, D; Snow, G R; Snow, J; Snyder, S; Söldner-Rembold, S; Sonnenschein, L; Sopczak, A; Sosebee, M; Soustruznik, K; Spurlock, B; Stark, J; Stolin, V; Stoyanova, D A; Strandberg, J; Strang, M A; Strauss, E; Strauss, M; Ströhmer, R; Strom, D; Stutte, L; Svoisky, P; Takahashi, M; Tanasijczuk, A; Taylor, W; Tiller, B; Titov, M; Tokmenin, V V; Tsybychev, D; Tuchming, B; Tully, C; Tuts, P M; Unalan, R; Uvarov, L; Uvarov, S; Uzunyan, S; van den Berg, P J; Van Kooten, R; van Leeuwen, W M; Varelas, N; Varnes, E W; Vasilyev, I A; Verdier, P; Vertogradov, L S; Verzocchi, M; Vesterinen, M; Vilanova, D; Vint, P; Vokac, P; Wahl, H D; Wang, M H L S; Warchol, J; Watts, G; Wayne, M; Weber, G; Weber, M; Wetstein, M; White, A; Wicke, D; Williams, M R J; Wilson, G W; Wimpenny, S J; Wobisch, M; Wood, D R; Wyatt, T R; Xie, Y; Xu, C; Yacoob, S; Yamada, R; Yang, W-C; Yasuda, T; Yatsunenko, Y A; Ye, Z; Yin, H; Yip, K; Yoo, H D; Youn, S W; Yu, J; Zeitnitz, C; Zelitch, S; Zhao, T; Zhou, B; Zhu, J; Zielinski, M; Zieminska, D; Zivkovic, L; Zutshi, V; Zverev, E G

2010-02-12

A search for the standard model Higgs boson is presented using events with two charged leptons and large missing transverse energy selected from 5.4 fb(-1) of integrated luminosity in pp collisions at square root(s) = 1.96 TeV collected with the D0 detector at the Fermilab Tevatron collider. No significant excess of events above background predictions is found, and observed (expected) upper limits at 95% confidence level on the rate of Higgs boson production are derived that are a factor of 1.55 (1.36) above the predicted standard model cross section at m(H) = 165 GeV.

Modeling the plant uptake of organic chemicals, including the soil-air-plant pathway.

PubMed

Collins, Chris D; Finnegan, Eilis

2010-02-01

The soil-air-plant pathway is potentially important in the vegetative accumulation of organic pollutants from contaminated soils. While a number of qualitative frameworks exist for the prediction of plant accumulation of organic chemicals by this pathway, there are few quantitative models that incorporate this pathway. The aim of the present study was to produce a model that included this pathway and could quantify its contribution to the total plant contamination for a range of organic pollutants. A new model was developed from three submodels for the processes controlling plant contamination via this pathway: aerial deposition, soil volatilization, and systemic translocation. Using the combined model, the soil-air-plant pathway was predicted to account for a significant proportion of the total shoot contamination for those compounds with log K(OA) > 9 and log K(AW) < -3. For those pollutants with log K(OA) < 9 and log K(AW) > -3 there was a higher deposition of pollutant via the soil-air-plant pathway than for those chemicals with log K(OA) > 9 and log K(AW) < -3, but this was an insignificant proportion of the total shoot contamination because of the higher mobility of these compounds via the soil-root-shoot pathway. The incorporation of the soil-air-plant pathway into the plant uptake model did not significantly improve the prediction of the contamination of vegetation from polluted soils when compared across a range of studies. This was a result of the high variability between the experimental studies where the bioconcentration factors varied by 2 orders of magnitude at an equivalent log K(OA). One potential reason for this is the background air concentration of the pollutants under study. It was found background air concentrations would dominate those from soil volatilization in many situations unless there was a soil hot spot of contamination, i.e., >100 mg kg(-1).

Background selection as null hypothesis in population genomics: insights and challenges from Drosophila studies

PubMed Central

2017-01-01

The consequences of selection at linked sites are multiple and widespread across the genomes of most species. Here, I first review the main concepts behind models of selection and linkage in recombining genomes, present the difficulty in parametrizing these models simply as a reduction in effective population size (Ne) and discuss the predicted impact of recombination rates on levels of diversity across genomes. Arguments are then put forward in favour of using a model of selection and linkage with neutral and deleterious mutations (i.e. the background selection model, BGS) as a sensible null hypothesis for investigating the presence of other forms of selection, such as balancing or positive. I also describe and compare two studies that have generated high-resolution landscapes of the predicted consequences of selection at linked sites in Drosophila melanogaster. Both studies show that BGS can explain a very large fraction of the observed variation in diversity across the whole genome, thus supporting its use as null model. Finally, I identify and discuss a number of caveats and challenges in studies of genetic hitchhiking that have been often overlooked, with several of them sharing a potential bias towards overestimating the evidence supporting recent selective sweeps to the detriment of a BGS explanation. One potential source of bias is the analysis of non-equilibrium populations: it is precisely because models of selection and linkage predict variation in Ne across chromosomes that demographic dynamics are not expected to be equivalent chromosome- or genome-wide. Other challenges include the use of incomplete genome annotations, the assumption of temporally stable recombination landscapes, the presence of genes under balancing selection and the consequences of ignoring non-crossover (gene conversion) recombination events. This article is part of the themed issue ‘Evolutionary causes and consequences of recombination rate variation in sexual organisms’. PMID:29109230

Improvement of Advanced Storm-scale Analysis and Prediction System (ASAPS) on Seoul Metropolitan Area, Korea

NASA Astrophysics Data System (ADS)

Park, Jeong-Gyun; Jee, Joon-Bum

2017-04-01

Dangerous weather such as severe rain, heavy snow, drought and heat wave caused by climate change make more damage in the urban area that dense populated and industry areas. Urban areas, unlike the rural area, have big population and transportation, dense the buildings and fuel consumption. Anthropogenic factors such as road energy balance, the flow of air in the urban is unique meteorological phenomena. However several researches are in process about prediction of urban meteorology. ASAPS (Advanced Storm-scale Analysis and Prediction System) predicts a severe weather with very short range (prediction with 6 hour) and high resolution (every hour with time and 1 km with space) on Seoul metropolitan area based on KLAPS (Korea Local Analysis and Prediction System) from KMA (Korea Meteorological Administration). This system configured three parts that make a background field (SUF5), analysis field (SU01) with observation and forecast field with high resolution (SUF1). In this study, we improve a high-resolution ASAPS model and perform a sensitivity test for the rainfall case. The improvement of ASAPS include model domain configuration, high resolution topographic data and data assimilation with WISE observation data.

Moral Attitudes Predict Cheating and Gamesmanship Behaviors Among Competitive Tennis Players

PubMed Central

Lucidi, Fabio; Zelli, Arnaldo; Mallia, Luca; Nicolais, Giampaolo; Lazuras, Lambros; Hagger, Martin S.

2017-01-01

Background: The present study tested Lee et al.’s (2008) model of moral attitudes and cheating behavior in sports in an Italian sample of young tennis players and extended it to predict behavior in actual match play. In the first phase of the study we proposed that moral, competence and status values would predict prosocial and antisocial moral attitudes directly, and indirectly through athletes’ goal orientations. In the second phase, we hypothesized that moral attitudes would directly predict actual cheating behavior observed during match play. Method: Adolescent competitive tennis players (N = 314, 76.75% males, M age = 14.36 years, SD = 1.50) completed measures of values, goal orientations, and moral attitudes. A sub-sample (n = 90) was observed in 45 competitive tennis matches by trained observers who recorded their cheating and gamesmanship behaviors on a validated checklist. Results: Consistent with hypotheses, athletes’ values predicted their moral attitudes through the effects of goal orientations. Anti-social attitudes directly predicted cheating behavior in actual match play providing support for a direct link between moral attitude and actual behavior. Conclusion: The present study findings support key propositions of Lee and colleagues’ model, and extended its application to competitive athletes in actual match play. PMID:28446891

Multivariate predictors of music perception and appraisal by adult cochlear implant users.

PubMed

Gfeller, Kate; Oleson, Jacob; Knutson, John F; Breheny, Patrick; Driscoll, Virginia; Olszewski, Carol

2008-02-01

The research examined whether performance by adult cochlear implant recipients on a variety of recognition and appraisal tests derived from real-world music could be predicted from technological, demographic, and life experience variables, as well as speech recognition scores. A representative sample of 209 adults implanted between 1985 and 2006 participated. Using multiple linear regression models and generalized linear mixed models, sets of optimal predictor variables were selected that effectively predicted performance on a test battery that assessed different aspects of music listening. These analyses established the importance of distinguishing between the accuracy of music perception and the appraisal of musical stimuli when using music listening as an index of implant success. Importantly, neither device type nor processing strategy predicted music perception or music appraisal. Speech recognition performance was not a strong predictor of music perception, and primarily predicted music perception when the test stimuli included lyrics. Additionally, limitations in the utility of speech perception in predicting musical perception and appraisal underscore the utility of music perception as an alternative outcome measure for evaluating implant outcomes. Music listening background, residual hearing (i.e., hearing aid use), cognitive factors, and some demographic factors predicted several indices of perceptual accuracy or appraisal of music.

A Comparison between Multiple Regression Models and CUN-BAE Equation to Predict Body Fat in Adults

PubMed Central

Fuster-Parra, Pilar; Bennasar-Veny, Miquel; Tauler, Pedro; Yañez, Aina; López-González, Angel A.; Aguiló, Antoni

2015-01-01

Background Because the accurate measure of body fat (BF) is difficult, several prediction equations have been proposed. The aim of this study was to compare different multiple regression models to predict BF, including the recently reported CUN-BAE equation. Methods Multi regression models using body mass index (BMI) and body adiposity index (BAI) as predictors of BF will be compared. These models will be also compared with the CUN-BAE equation. For all the analysis a sample including all the participants and another one including only the overweight and obese subjects will be considered. The BF reference measure was made using Bioelectrical Impedance Analysis. Results The simplest models including only BMI or BAI as independent variables showed that BAI is a better predictor of BF. However, adding the variable sex to both models made BMI a better predictor than the BAI. For both the whole group of participants and the group of overweight and obese participants, using simple models (BMI, age and sex as variables) allowed obtaining similar correlations with BF as when the more complex CUN-BAE was used (ρ = 0:87 vs. ρ = 0:86 for the whole sample and ρ = 0:88 vs. ρ = 0:89 for overweight and obese subjects, being the second value the one for CUN-BAE). Conclusions There are simpler models than CUN-BAE equation that fits BF as well as CUN-BAE does. Therefore, it could be considered that CUN-BAE overfits. Using a simple linear regression model, the BAI, as the only variable, predicts BF better than BMI. However, when the sex variable is introduced, BMI becomes the indicator of choice to predict BF. PMID:25821960

Fractional differential equations based modeling of microbial survival and growth curves: model development and experimental validation.

PubMed

Kaur, A; Takhar, P S; Smith, D M; Mann, J E; Brashears, M M

2008-10-01

A fractional differential equations (FDEs)-based theory involving 1- and 2-term equations was developed to predict the nonlinear survival and growth curves of foodborne pathogens. It is interesting to note that the solution of 1-term FDE leads to the Weibull model. Nonlinear regression (Gauss-Newton method) was performed to calculate the parameters of the 1-term and 2-term FDEs. The experimental inactivation data of Salmonella cocktail in ground turkey breast, ground turkey thigh, and pork shoulder; and cocktail of Salmonella, E. coli, and Listeria monocytogenes in ground beef exposed at isothermal cooking conditions of 50 to 66 degrees C were used for validation. To evaluate the performance of 2-term FDE in predicting the growth curves-growth of Salmonella typhimurium, Salmonella Enteritidis, and background flora in ground pork and boneless pork chops; and E. coli O157:H7 in ground beef in the temperature range of 22.2 to 4.4 degrees C were chosen. A program was written in Matlab to predict the model parameters and survival and growth curves. Two-term FDE was more successful in describing the complex shapes of microbial survival and growth curves as compared to the linear and Weibull models. Predicted curves of 2-term FDE had higher magnitudes of R(2) (0.89 to 0.99) and lower magnitudes of root mean square error (0.0182 to 0.5461) for all experimental cases in comparison to the linear and Weibull models. This model was capable of predicting the tails in survival curves, which was not possible using Weibull and linear models. The developed model can be used for other foodborne pathogens in a variety of food products to study the destruction and growth behavior.

One vs. Two Breast Density Measures to Predict 5- and 10- Year Breast Cancer Risk

PubMed Central

Kerlikowske, Karla; Gard, Charlotte C.; Sprague, Brian L.; Tice, Jeffrey A.; Miglioretti, Diana L.

2015-01-01

Background One measure of Breast Imaging Reporting and Data System (BI-RADS) breast density improves 5-year breast cancer risk prediction, but the value of sequential measures is unknown. We determined if two BI-RADS density measures improves the predictive accuracy of the Breast Cancer Surveillance Consortium 5-year risk model compared to one measure. Methods We included 722,654 women aged 35–74 years with two mammograms with BI-RADS density measures on average 1.8 years apart; 13,715 developed invasive breast cancer. We used Cox regression to estimate the relative hazards of breast cancer for age, race/ethnicity, family history of breast cancer, history of breast biopsy, and one or two density measures. We developed a risk prediction model by combining these estimates with 2000–2010 Surveillance, Epidemiology, and End Results incidence and 2010 vital statistics for competing risk of death. Results The two-measure density model had marginally greater discriminatory accuracy than the one-measure model (AUC=0.640 vs. 0.635). Of 18.6% of women (134,404/722,654) who decreased density categories, 15.4% (20,741/134,404) of women whose density decreased from heterogeneously or extremely dense to a lower density category with one other risk factor had a clinically meaningful increase in 5-year risk from <1.67% with the one-density model to ≥1.67% with the two-density model. Conclusion The two-density model has similar overall discrimination to the one-density model for predicting 5-year breast cancer risk and improves risk classification for women with risk factors and a decrease in density. Impact A two-density model should be considered for women whose density decreases when calculating breast cancer risk. PMID:25824444

Zn2+ and Sr2+ Adsorption at the TiO2 (110)-Electrolyte Interface: Influence of Ionic Strength, Coverage, and Anions

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

Zhang,Z.; Fenter, P.; Cheng, L.

2006-01-01

The X-ray standing wave technique was used to probe the sensitivity of Zn{sup 2+} and Sr{sup 2+} ion adsorption to changes in both the adsorbed ion coverage and the background electrolyte species and concentrations at the rutile ({alpha}-TiO{sub 2}) (110)-aqueous interface. Measurements were made with various background electrolytes (NaCl, NaTr, RbCl, NaBr) at concentrations as high as 1 m. The results demonstrate that Zn{sub 2+} and Sr{sub 2+} reside primarily in the condensed layer and that the ion heights above the Ti-O surface plane are insensitive to ionic strength and the choice of background electrolyte (with <0.1 Angstroms changes overmore » the full compositional range). The lack of any specific anion coadsorption upon probing with Br{sup -}, coupled with the insensitivity of Zn{sup 2+} and Sr{sup 2+} cation heights to changes in the background electrolyte, implies that anions do not play a significant role in the adsorption of these divalent metal ions to the rutile (110) surface. Absolute ion coverage measurements for Zn{sup 2+} and Sr{sup 2+} show a maximum Stern-layer coverage of {approx}0.5 monolayer, with no significant variation in height as a function of Stern-layer coverage. These observations are discussed in the context of Gouy-Chapman-Stern models of the electrical double layer developed from macroscopic sorption and pH-titration studies of rutile powder suspensions. Direct comparison between these experimental observations and the MUltiSIte Complexation (MUSIC) model predictions of cation surface coverage as a function of ionic strength revealed good agreement between measured and predicted surface coverages with no adjustable parameters.« less

Calibration of a Spatial-Temporal Discrimination Model from Forward, Simultaneous, and Backward Masking

NASA Technical Reports Server (NTRS)

Ahumada, Albert J.; Beard, B. L.; Stone, Leland (Technical Monitor)

1997-01-01

We have been developing a simplified spatial-temporal discrimination model similar to our simplified spatial model in that masking is assumed to be a function of the local visible contrast energy. The overall spatial-temporal sensitivity of the model is calibrated to predict the detectability of targets on a uniform background. To calibrate the spatial-temporal integration functions that define local visible contrast energy, spatial-temporal masking data are required. Observer thresholds were measured (2IFC) for the detection of a 12 msec target stimulus in the presence of a 700 msec mask. Targets were 1, 3 or 9 c/deg sine wave gratings. Masks were either one of these gratings or two of them combined. The target was presented in 17 temporal positions with respect to the mask, including positions before, during and after the mask. Peak masking was found near mask onset and offset for 1 and 3 c/deg targets, while masking effects were more nearly uniform during the mask for the 9 c/deg target. As in the purely spatial case, the simplified model can not predict all the details of masking as a function of masking component spatial frequencies, but overall the prediction errors are small.

Individualized prediction of lung-function decline in chronic obstructive pulmonary disease

PubMed Central

Zafari, Zafar; Sin, Don D.; Postma, Dirkje S.; Löfdahl, Claes-Göran; Vonk, Judith; Bryan, Stirling; Lam, Stephen; Tammemagi, C. Martin; Khakban, Rahman; Man, S.F. Paul; Tashkin, Donald; Wise, Robert A.; Connett, John E.; McManus, Bruce; Ng, Raymond; Hollander, Zsuszanna; Sadatsafavi, Mohsen

2016-01-01

Background: The rate of lung-function decline in chronic obstructive pulmonary disease (COPD) varies substantially among individuals. We sought to develop and validate an individualized prediction model for forced expiratory volume at 1 second (FEV1) in current smokers with mild-to-moderate COPD. Methods: Using data from a large long-term clinical trial (the Lung Health Study), we derived mixed-effects regression models to predict future FEV1 values over 11 years according to clinical traits. We modelled heterogeneity by allowing regression coefficients to vary across individuals. Two independent cohorts with COPD were used for validating the equations. Results: We used data from 5594 patients (mean age 48.4 yr, 63% men, mean baseline FEV1 2.75 L) to create the individualized prediction equations. There was significant between-individual variability in the rate of FEV1 decline, with the interval for the annual rate of decline that contained 95% of individuals being −124 to −15 mL/yr for smokers and −83 to 15 mL/yr for sustained quitters. Clinical variables in the final model explained 88% of variation around follow-up FEV1. The C statistic for predicting severity grades was 0.90. Prediction equations performed robustly in the 2 external data sets. Interpretation: A substantial part of individual variation in FEV1 decline can be explained by easily measured clinical variables. The model developed in this work can be used for prediction of future lung health in patients with mild-to-moderate COPD. Trial registration: Lung Health Study — ClinicalTrials.gov, no. NCT00000568; Pan-Canadian Early Detection of Lung Cancer Study — ClinicalTrials.gov, no. NCT00751660 PMID:27486205

Predicting complex traits using a diffusion kernel on genetic markers with an application to dairy cattle and wheat data

PubMed Central

2013-01-01

Background Arguably, genotypes and phenotypes may be linked in functional forms that are not well addressed by the linear additive models that are standard in quantitative genetics. Therefore, developing statistical learning models for predicting phenotypic values from all available molecular information that are capable of capturing complex genetic network architectures is of great importance. Bayesian kernel ridge regression is a non-parametric prediction model proposed for this purpose. Its essence is to create a spatial distance-based relationship matrix called a kernel. Although the set of all single nucleotide polymorphism genotype configurations on which a model is built is finite, past research has mainly used a Gaussian kernel. Results We sought to investigate the performance of a diffusion kernel, which was specifically developed to model discrete marker inputs, using Holstein cattle and wheat data. This kernel can be viewed as a discretization of the Gaussian kernel. The predictive ability of the diffusion kernel was similar to that of non-spatial distance-based additive genomic relationship kernels in the Holstein data, but outperformed the latter in the wheat data. However, the difference in performance between the diffusion and Gaussian kernels was negligible. Conclusions It is concluded that the ability of a diffusion kernel to capture the total genetic variance is not better than that of a Gaussian kernel, at least for these data. Although the diffusion kernel as a choice of basis function may have potential for use in whole-genome prediction, our results imply that embedding genetic markers into a non-Euclidean metric space has very small impact on prediction. Our results suggest that use of the black box Gaussian kernel is justified, given its connection to the diffusion kernel and its similar predictive performance. PMID:23763755

High beta effects and nonlinear evolution of the TAE instability

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

Spong, D.A.

1992-12-31

The toroidal Alfven eigenmode has recently been observed experimentally on DIII-D and TFTR when neutral beams are injected near the Alfven velocity. This instability is also of concern for future high {beta} D-T devices where fusion by-product alpha populations will generally be super-Alfvenic. We have developed a gyrofluid model (with Landau closure) of the TAE mode which can include most of the relevant damping mechanisms (continuum damping, ion and electron damping, ion FLR and collisional trapped electron damping) as well as reproducing analytically predicted undamped growth rates relatively accurately. An important consideration in predicting future unstable TAE regimes is themore » effect of finite beta in the background plasma. Due to the Shafranov shift and distortion of the flux surfaces, the location of the stable TAE root and the continuum will shift with increasing {beta}. The net effect of this is to generally enhance continuum damping and stabilize the TAF instability. Also, as the pressure gradient drive from the background becomes increasingly important, coupling between TAE and background driven modes can alter the TAE mode. A further application of our gyrofluid model which will be discussed is the nonlinear evolution of the TAE instability. Gyrofluid models offer a convenient reduced description which is more amenable to computational nonlinear modeling than full kinetic particle models. Our results demonstrate the rise and crash phases of TAE activity similar to experimental observations. The saturation is caused by generation of m=0 n=0 components through nonlinear beatings of the n > 1 modes; these cause modifications to the original equilibrium profiles in such a direction as to decrease the instability drive. This is the gyrofluid analog of direct particle losses. The peak magnetic fluctuation level increases with increasing energetic species beta, resulting in non-resonant stochastization of magnetic field lines.« less

High beta effects and nonlinear evolution of the TAE instability

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

Spong, D.A.

1992-01-01

The toroidal Alfven eigenmode has recently been observed experimentally on DIII-D and TFTR when neutral beams are injected near the Alfven velocity. This instability is also of concern for future high [beta] D-T devices where fusion by-product alpha populations will generally be super-Alfvenic. We have developed a gyrofluid model (with Landau closure) of the TAE mode which can include most of the relevant damping mechanisms (continuum damping, ion and electron damping, ion FLR and collisional trapped electron damping) as well as reproducing analytically predicted undamped growth rates relatively accurately. An important consideration in predicting future unstable TAE regimes is themore » effect of finite beta in the background plasma. Due to the Shafranov shift and distortion of the flux surfaces, the location of the stable TAE root and the continuum will shift with increasing [beta]. The net effect of this is to generally enhance continuum damping and stabilize the TAF instability. Also, as the pressure gradient drive from the background becomes increasingly important, coupling between TAE and background driven modes can alter the TAE mode. A further application of our gyrofluid model which will be discussed is the nonlinear evolution of the TAE instability. Gyrofluid models offer a convenient reduced description which is more amenable to computational nonlinear modeling than full kinetic particle models. Our results demonstrate the rise and crash phases of TAE activity similar to experimental observations. The saturation is caused by generation of m=0 n=0 components through nonlinear beatings of the n > 1 modes; these cause modifications to the original equilibrium profiles in such a direction as to decrease the instability drive. This is the gyrofluid analog of direct particle losses. The peak magnetic fluctuation level increases with increasing energetic species beta, resulting in non-resonant stochastization of magnetic field lines.« less

Impact from Magnitude-Rupture Length Uncertainty on Seismic Hazard and Risk

NASA Astrophysics Data System (ADS)

Apel, E. V.; Nyst, M.; Kane, D. L.

2015-12-01

In probabilistic seismic hazard and risk assessments seismic sources are typically divided into two groups: fault sources (to model known faults) and background sources (to model unknown faults). In areas like the Central and Eastern United States and Hawaii the hazard and risk is driven primarily by background sources. Background sources can be modeled as areas, points or pseudo-faults. When background sources are modeled as pseudo-faults, magnitude-length or magnitude-area scaling relationships are required to construct these pseudo-faults. However the uncertainty associated with these relationships is often ignored or discarded in hazard and risk models, particularly when faults sources are the dominant contributor. Conversely, in areas modeled only with background sources these uncertainties are much more significant. In this study we test the impact of using various relationships and the resulting epistemic uncertainties on the seismic hazard and risk in the Central and Eastern United States and Hawaii. It is common to use only one magnitude length relationship when calculating hazard. However, Stirling et al. (2013) showed that for a given suite of magnitude-rupture length relationships the variability can be quite large. The 2014 US National Seismic Hazard Maps (Petersen et al., 2014) used one magnitude-rupture length relationship (Somerville, et al., 2001) in the Central and Eastern United States, and did not consider variability in the seismogenic rupture plane width. Here we use a suite of metrics to compare the USGS approach with these variable uncertainty models to assess 1) the impact on hazard and risk and 2) the epistemic uncertainty associated with choice of relationship. In areas where the seismic hazard is dominated by larger crustal faults (e.g. New Madrid) the choice of magnitude-rupture length relationship has little impact on the hazard or risk. However away from these regions, the choice of relationship is more significant and may approach the size of the uncertainty associated with the ground motion prediction equation suite.

A cosmic microwave background feature consistent with a cosmic texture.

PubMed

Cruz, M; Turok, N; Vielva, P; Martínez-González, E; Hobson, M

2007-12-07

The Cosmic Microwave Background provides our most ancient image of the universe and our best tool for studying its early evolution. Theories of high-energy physics predict the formation of various types of topological defects in the very early universe, including cosmic texture, which would generate hot and cold spots in the Cosmic Microwave Background. We show through a Bayesian statistical analysis that the most prominent 5 degrees -radius cold spot observed in all-sky images, which is otherwise hard to explain, is compatible with having being caused by a texture. From this model, we constrain the fundamental symmetry-breaking energy scale to be (0) approximately 8.7 x 10(15) gigaelectron volts. If confirmed, this detection of a cosmic defect will probe physics at energies exceeding any conceivable terrestrial experiment.

Dynamical prediction of flu seasonality driven by ambient temperature: influenza vs. common cold

NASA Astrophysics Data System (ADS)

Postnikov, Eugene B.

2016-01-01

This work presents a comparative analysis of Influenzanet data for influenza itself and common cold in the Netherlands during the last 5 years, from the point of view of modelling by linearised SIRS equations parametrically driven by the ambient temperature. It is argued that this approach allows for the forecast of common cold, but not of influenza in a strict sense. The difference in their kinetic models is discussed with reference to the clinical background.

Predicting Health Care Utilization in Marginalized Populations: Black, Female, Street-based Sex Workers

PubMed Central

Varga, Leah M.; Surratt, Hilary L.

2014-01-01

Background Patterns of social and structural factors experienced by vulnerable populations may negatively affect willingness and ability to seek out health care services, and ultimately, their health. Methods The outcome variable was utilization of health care services in the previous 12 months. Using Andersen’s Behavioral Model for Vulnerable Populations, we examined self-reported data on utilization of health care services among a sample of 546 Black, street-based female sex workers in Miami, Florida. To evaluate the impact of each domain of the model on predicting health care utilization, domains were included in the logistic regression analysis by blocks using the traditional variables first and then adding the vulnerable domain variables. Findings The most consistent variables predicting health care utilization were having a regular source of care and self-rated health. The model that included only enabling variables was the most efficient model in predicting health care utilization. Conclusions Any type of resource, link, or connection to or with an institution, or any consistent point of care contributes significantly to health care utilization behaviors. A consistent and reliable source for health care may increase health care utilization and subsequently decrease health disparities among vulnerable and marginalized populations, as well as contribute to public health efforts that encourage preventive health. PMID:24657047

People counting in classroom based on video surveillance

NASA Astrophysics Data System (ADS)

Zhang, Quanbin; Huang, Xiang; Su, Juan

2014-11-01

Currently, the switches of the lights and other electronic devices in the classroom are mainly relied on manual control, as a result, many lights are on while no one or only few people in the classroom. It is important to change the current situation and control the electronic devices intelligently according to the number and the distribution of the students in the classroom, so as to reduce the considerable waste of electronic resources. This paper studies the problem of people counting in classroom based on video surveillance. As the camera in the classroom can not get the full shape contour information of bodies and the clear features information of faces, most of the classical algorithms such as the pedestrian detection method based on HOG (histograms of oriented gradient) feature and the face detection method based on machine learning are unable to obtain a satisfied result. A new kind of dual background updating model based on sparse and low-rank matrix decomposition is proposed in this paper, according to the fact that most of the students in the classroom are almost in stationary state and there are body movement occasionally. Firstly, combining the frame difference with the sparse and low-rank matrix decomposition to predict the moving areas, and updating the background model with different parameters according to the positional relationship between the pixels of current video frame and the predicted motion regions. Secondly, the regions of moving objects are determined based on the updated background using the background subtraction method. Finally, some operations including binarization, median filtering and morphology processing, connected component detection, etc. are performed on the regions acquired by the background subtraction, in order to induce the effects of the noise and obtain the number of people in the classroom. The experiment results show the validity of the algorithm of people counting.

Middle School-Aged Child Enjoyment of Food Tastings Predicted Interest in Nutrition Education on Osteoporosis Prevention

ERIC Educational Resources Information Center

Cheng, Feon W.; Monnat, Shannon M.; Lohse, Barbara

2015-01-01

Background: "NEEDs for Bones" (NFB), based on the Health Belief Model, is a 4-lesson osteoporosis-prevention curriculum for 11- to 14-year-olds. This study examined the relationship between enjoyment of food tastings and interest in NFB. Methods:NFB was administered by teachers as part of standard practice and evaluated after the fourth…

The Application of the Theory of Planned Behaviour to Diet in Carers of People with an Intellectual Disability

ERIC Educational Resources Information Center

Jenkins, Catherine M.; McKenzie, Karen

2011-01-01

Background: The utility of the theory of planned behaviour (TPB) in predicting the intentions of care staff to encourage healthy eating behaviour in those they supported was examined. Method: A quantitative, within-participant, questionnaire based design was used with 112 carers to assess the performance of two TPB models. The first contained the…

Polybrominated Diphenyl Ethers in Human Milk and Serum from the U.S. EPA MAMA Study: Modeled Predictions of Infant Exposure and Considerations for Risk Assessment

EPA Science Inventory

Background: Serum concentrations of polybrominated diphenyl ethers (PBDEs) in U.S. women are believed to be among the world’s highest; however, little information exists on the partitioning of PBDEs between serum and breast milk and how this may affect infant exposure. Obj...

Polybrominated Diphenyl Ethers in Human Milk and Serum from the U.S. EPA MAMA Study: Modeled Predictions of Infant Exposure and Considerations for Risk Assessment

EPA Science Inventory

Background: Serum concentrations of polybrominated diphenyl ethers (PBDEs) in U.S. women are believed to be among the world’s highest; however, little information exists on the partitioning of PBDEs between serum and breast milk and how this may affect infant exposure. Objecti...

How much detail is needed in modeling a transcranial magnetic stimulation figure-8 coil: Measurements and brain simulations

PubMed Central

Mandija, Stefano; Sommer, Iris E. C.; van den Berg, Cornelis A. T.; Neggers, Sebastiaan F. W.

2017-01-01

Background Despite TMS wide adoption, its spatial and temporal patterns of neuronal effects are not well understood. Although progress has been made in predicting induced currents in the brain using realistic finite element models (FEM), there is little consensus on how a magnetic field of a typical TMS coil should be modeled. Empirical validation of such models is limited and subject to several limitations. Methods We evaluate and empirically validate models of a figure-of-eight TMS coil that are commonly used in published modeling studies, of increasing complexity: simple circular coil model; coil with in-plane spiral winding turns; and finally one with stacked spiral winding turns. We will assess the electric fields induced by all 3 coil models in the motor cortex using a computer FEM model. Biot-Savart models of discretized wires were used to approximate the 3 coil models of increasing complexity. We use a tailored MR based phase mapping technique to get a full 3D validation of the incident magnetic field induced in a cylindrical phantom by our TMS coil. FEM based simulations on a meshed 3D brain model consisting of five tissues types were performed, using two orthogonal coil orientations. Results Substantial differences in the induced currents are observed, both theoretically and empirically, between highly idealized coils and coils with correctly modeled spiral winding turns. Thickness of the coil winding turns affect minimally the induced electric field, and it does not influence the predicted activation. Conclusion TMS coil models used in FEM simulations should include in-plane coil geometry in order to make reliable predictions of the incident field. Modeling the in-plane coil geometry is important to correctly simulate the induced electric field and to correctly make reliable predictions of neuronal activation PMID:28640923

Optimizing countershading camouflage.

PubMed

Cuthill, Innes C; Sanghera, N Simon; Penacchio, Olivier; Lovell, Paul George; Ruxton, Graeme D; Harris, Julie M

2016-11-15

Countershading, the widespread tendency of animals to be darker on the side that receives strongest illumination, has classically been explained as an adaptation for camouflage: obliterating cues to 3D shape and enhancing background matching. However, there have only been two quantitative tests of whether the patterns observed in different species match the optimal shading to obliterate 3D cues, and no tests of whether optimal countershading actually improves concealment or survival. We use a mathematical model of the light field to predict the optimal countershading for concealment that is specific to the light environment and then test this prediction with correspondingly patterned model "caterpillars" exposed to avian predation in the field. We show that the optimal countershading is strongly illumination-dependent. A relatively sharp transition in surface patterning from dark to light is only optimal under direct solar illumination; if there is diffuse illumination from cloudy skies or shade, the pattern provides no advantage over homogeneous background-matching coloration. Conversely, a smoother gradation between dark and light is optimal under cloudy skies or shade. The demonstration of these illumination-dependent effects of different countershading patterns on predation risk strongly supports the comparative evidence showing that the type of countershading varies with light environment.

Search for direct pair production of supersymmetric top quarks decaying to all-hadronic final states in pp collisions at √{s} = 8 {TeV}

NASA Astrophysics Data System (ADS)

Khachatryan, V.; Sirunyan, A. M.; Tumasyan, A.; Adam, W.; Asilar, E.; Bergauer, T.; Brandstetter, J.; Brondolin, E.; Dragicevic, M.; Erö, J.; Flechl, M.; Friedl, M.; Frühwirth, R.; Ghete, V. M.; Hartl, C.; Hörmann, N.; Hrubec, J.; Jeitler, M.; Knünz, V.; König, A.; Krammer, M.; Krätschmer, I.; Liko, D.; Matsushita, T.; Mikulec, I.; Rabady, D.; Rahbaran, B.; Rohringer, H.; Schieck, J.; Schöfbeck, R.; Strauss, J.; Treberer-Treberspurg, W.; Waltenberger, W.; Wulz, C.-E.; Mossolov, V.; Shumeiko, N.; Suarez Gonzalez, J.; Alderweireldt, S.; Cornelis, T.; De Wolf, E. A.; Janssen, X.; Knutsson, A.; Lauwers, J.; Luyckx, S.; Van De Klundert, M.; Van Haevermaet, H.; Van Mechelen, P.; Van Remortel, N.; Van Spilbeeck, A.; Abu Zeid, S.; Blekman, F.; D'Hondt, J.; Daci, N.; De Bruyn, I.; Deroover, K.; Heracleous, N.; Keaveney, J.; Lowette, S.; Moreels, L.; Olbrechts, A.; Python, Q.; Strom, D.; Tavernier, S.; Van Doninck, W.; Van Mulders, P.; Van Onsem, G. P.; Van Parijs, I.; Barria, P.; Brun, H.; Caillol, C.; Clerbaux, B.; De Lentdecker, G.; Fasanella, G.; Favart, L.; Grebenyuk, A.; Karapostoli, G.; Lenzi, T.; Léonard, A.; Maerschalk, T.; Marinov, A.; Perniè, L.; Randle-conde, A.; Seva, T.; Vander Velde, C.; Vanlaer, P.; Yonamine, R.; Zenoni, F.; Zhang, F.; Beernaert, K.; Benucci, L.; Cimmino, A.; Crucy, S.; Dobur, D.; Fagot, A.; Garcia, G.; Gul, M.; Mccartin, J.; Ocampo Rios, A. A.; Poyraz, D.; Ryckbosch, D.; Salva, S.; Sigamani, M.; Tytgat, M.; Van Driessche, W.; Yazgan, E.; Zaganidis, N.; Basegmez, S.; Beluffi, C.; Bondu, O.; Brochet, S.; Bruno, G.; Caudron, A.; Ceard, L.; Da Silveira, G. G.; Delaere, C.; Favart, D.; Forthomme, L.; Giammanco, A.; Hollar, J.; Jafari, A.; Jez, P.; Komm, M.; Lemaitre, V.; Mertens, A.; Musich, M.; Nuttens, C.; Perrini, L.; Pin, A.; Piotrzkowski, K.; Popov, A.; Quertenmont, L.; Selvaggi, M.; Vidal Marono, M.; Beliy, N.; Hammad, G. H.; Aldá Júnior, W. L.; Alves, F. L.; Alves, G. A.; Brito, L.; Correa Martins Junior, M.; Hamer, M.; Hensel, C.; Mora Herrera, C.; Moraes, A.; Pol, M. E.; Rebello Teles, P.; Belchior Batista Das Chagas, E.; Carvalho, W.; Chinellato, J.; Custódio, A.; Da Costa, E. M.; De Jesus Damiao, D.; De Oliveira Martins, C.; Fonseca De Souza, S.; Huertas Guativa, L. M.; Malbouisson, H.; Matos Figueiredo, D.; Mundim, L.; Nogima, H.; Prado Da Silva, W. L.; Santoro, A.; Sznajder, A.; Tonelli Manganote, E. J.; Vilela Pereira, A.; Ahuja, S.; Bernardes, C. A.; De Souza Santos, A.; Dogra, S.; Fernandez Perez Tomei, T. R.; Gregores, E. M.; Mercadante, P. G.; Moon, C. S.; Novaes, S. F.; Padula, Sandra S.; Romero Abad, D.; Ruiz Vargas, J. C.; Aleksandrov, A.; Hadjiiska, R.; Iaydjiev, P.; Rodozov, M.; Stoykova, S.; Sultanov, G.; Vutova, M.; Dimitrov, A.; Glushkov, I.; Litov, L.; Pavlov, B.; Petkov, P.; Ahmad, M.; Bian, J. G.; Chen, G. M.; Chen, H. S.; Chen, M.; Cheng, T.; Du, R.; Jiang, C. H.; Plestina, R.; Romeo, F.; Shaheen, S. M.; Spiezia, A.; Tao, J.; Wang, C.; Wang, Z.; Zhang, H.; Asawatangtrakuldee, C.; Ban, Y.; Li, Q.; Liu, S.; Mao, Y.; Qian, S. J.; Wang, D.; Xu, Z.; Avila, C.; Cabrera, A.; Chaparro Sierra, L. F.; Florez, C.; Gomez, J. P.; Gomez Moreno, B.; Sanabria, J. C.; Godinovic, N.; Lelas, D.; Puljak, I.; Ribeiro Cipriano, P. M.; Antunovic, Z.; Kovac, M.; Brigljevic, V.; Kadija, K.; Luetic, J.; Micanovic, S.; Sudic, L.; Attikis, A.; Mavromanolakis, G.; Mousa, J.; Nicolaou, C.; Ptochos, F.; Razis, P. A.; Rykaczewski, H.; Bodlak, M.; Finger, M.; Finger, M.; Assran, Y.; El Sawy, M.; Elgammal, S.; Ellithi Kamel, A.; Mahmoud, M. A.; Mahrous, A.; Radi, A.; Calpas, B.; Kadastik, M.; Murumaa, M.; Raidal, M.; Tiko, A.; Veelken, C.; Eerola, P.; Pekkanen, J.; Voutilainen, M.; Härkönen, J.; Karimäki, V.; Kinnunen, R.; Lampén, T.; Lassila-Perini, K.; Lehti, S.; Lindén, T.; Luukka, P.; Mäenpää, T.; Peltola, T.; Tuominen, E.; Tuominiemi, J.; Tuovinen, E.; Wendland, L.; Talvitie, J.; Tuuva, T.; Besancon, M.; Couderc, F.; Dejardin, M.; Denegri, D.; Fabbro, B.; Faure, J. L.; Favaro, C.; Ferri, F.; Ganjour, S.; Givernaud, A.; Gras, P.; Hamel de Monchenault, G.; Jarry, P.; Locci, E.; Machet, M.; Malcles, J.; Rander, J.; Rosowsky, A.; Titov, M.; Zghiche, A.; Antropov, I.; Baffioni, S.; Beaudette, F.; Busson, P.; Cadamuro, L.; Chapon, E.; Charlot, C.; Dahms, T.; Davignon, O.; Filipovic, N.; Granier de Cassagnac, R.; Jo, M.; Lisniak, S.; Mastrolorenzo, L.; Miné, P.; Naranjo, I. N.; Nguyen, M.; Ochando, C.; Ortona, G.; Paganini, P.; Pigard, P.; Regnard, S.; Salerno, R.; Sauvan, J. B.; Sirois, Y.; Strebler, T.; Yilmaz, Y.; Zabi, A.; Agram, J.-L.; Andrea, J.; Aubin, A.; Bloch, D.; Brom, J.-M.; Buttignol, M.; Chabert, E. C.; Chanon, N.; Collard, C.; Conte, E.; Coubez, X.; Fontaine, J.-C.; Gelé, D.; Goerlach, U.; Goetzmann, C.; Le Bihan, A.-C.; Merlin, J. A.; Skovpen, K.; Van Hove, P.; Gadrat, S.; Beauceron, S.; Bernet, C.; Boudoul, G.; Bouvier, E.; Carrillo Montoya, C. A.; Chierici, R.; Contardo, D.; Courbon, B.; Depasse, P.; El Mamouni, H.; Fan, J.; Fay, J.; Gascon, S.; Gouzevitch, M.; Ille, B.; Lagarde, F.; Laktineh, I. B.; Lethuillier, M.; Mirabito, L.; Pequegnot, A. L.; Perries, S.; Ruiz Alvarez, J. D.; Sabes, D.; Sgandurra, L.; Sordini, V.; Vander Donckt, M.; Verdier, P.; Viret, S.; Toriashvili, T.; Tsamalaidze, Z.; Autermann, C.; Beranek, S.; Edelhoff, M.; Feld, L.; Heister, A.; Kiesel, M. K.; Klein, K.; Lipinski, M.; Ostapchuk, A.; Preuten, M.; Raupach, F.; Schael, S.; Schulte, J. 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D.; Symonds, P.; Teodorescu, L.; Turner, M.; Borzou, A.; Call, K.; Dittmann, J.; Hatakeyama, K.; Liu, H.; Pastika, N.; Charaf, O.; Cooper, S. I.; Henderson, C.; Rumerio, P.; Arcaro, D.; Avetisyan, A.; Bose, T.; Fantasia, C.; Gastler, D.; Lawson, P.; Rankin, D.; Richardson, C.; Rohlf, J.; St. John, J.; Sulak, L.; Zou, D.; Alimena, J.; Berry, E.; Bhattacharya, S.; Cutts, D.; Dhingra, N.; Ferapontov, A.; Garabedian, A.; Hakala, J.; Heintz, U.; Laird, E.; Landsberg, G.; Mao, Z.; Narain, M.; Piperov, S.; Sagir, S.; Syarif, R.; Breedon, R.; Breto, G.; De La Barca Sanchez, M. Calderon; Chauhan, S.; Chertok, M.; Conway, J.; Conway, R.; Cox, P. T.; Erbacher, R.; Funk, G.; Gardner, M.; Ko, W.; Lander, R.; Mulhearn, M.; Pellett, D.; Pilot, J.; Ricci-Tam, F.; Shalhout, S.; Smith, J.; Squires, M.; Stolp, D.; Tripathi, M.; Wilbur, S.; Yohay, R.; Bravo, C.; Cousins, R.; Everaerts, P.; Farrell, C.; Florent, A.; Hauser, J.; Ignatenko, M.; Saltzberg, D.; Schnaible, C.; Takasugi, E.; Valuev, V.; Weber, M.; Burt, K.; Clare, R.; Ellison, J.; Gary, J. W.; Hanson, G.; Heilman, J.; Ivova Paneva, M.; Jandir, P.; Kennedy, E.; Lacroix, F.; Long, O. R.; Luthra, A.; Malberti, M.; Negrete, M. Olmedo; Shrinivas, A.; Wei, H.; Wimpenny, S.; Yates, B. R.; Branson, J. G.; Cerati, G. B.; Cittolin, S.; D'Agnolo, R. T.; Derdzinski, M.; Holzner, A.; Kelley, R.; Klein, D.; Letts, J.; Macneill, I.; Olivito, D.; Padhi, S.; Pieri, M.; Sani, M.; Sharma, V.; Simon, S.; Tadel, M.; Vartak, A.; Wasserbaech, S.; Welke, C.; Würthwein, F.; Yagil, A.; Zevi Della Porta, G.; Bradmiller-Feld, J.; Campagnari, C.; Dishaw, A.; Dutta, V.; Flowers, K.; Franco Sevilla, M.; Geffert, P.; George, C.; Golf, F.; Gouskos, L.; Gran, J.; Incandela, J.; Mccoll, N.; Mullin, S. D.; Richman, J.; Stuart, D.; Suarez, I.; West, C.; Yoo, J.; Anderson, D.; Apresyan, A.; Bornheim, A.; Bunn, J.; Chen, Y.; Duarte, J.; Mott, A.; Newman, H. B.; Pena, C.; Pierini, M.; Spiropulu, M.; Vlimant, J. R.; Xie, S.; Zhu, R. Y.; Andrews, M. B.; Azzolini, V.; Calamba, A.; Carlson, B.; Ferguson, T.; Paulini, M.; Russ, J.; Sun, M.; Vogel, H.; Vorobiev, I.; Cumalat, J. P.; Ford, W. T.; Gaz, A.; Jensen, F.; Johnson, A.; Krohn, M.; Mulholland, T.; Nauenberg, U.; Stenson, K.; Wagner, S. R.; Alexander, J.; Chatterjee, A.; Chaves, J.; Chu, J.; Dittmer, S.; Eggert, N.; Mirman, N.; Nicolas Kaufman, G.; Patterson, J. R.; Rinkevicius, A.; Ryd, A.; Skinnari, L.; Soffi, L.; Sun, W.; Tan, S. M.; Teo, W. D.; Thom, J.; Thompson, J.; Tucker, J.; Weng, Y.; Wittich, P.; Abdullin, S.; Albrow, M.; Apollinari, G.; Banerjee, S.; Bauerdick, L. A. T.; Beretvas, A.; Berryhill, J.; Bhat, P. C.; Bolla, G.; Burkett, K.; Butler, J. N.; Cheung, H. W. K.; Chlebana, F.; Cihangir, S.; Elvira, V. D.; Fisk, I.; Freeman, J.; Gottschalk, E.; Gray, L.; Green, D.; Grünendahl, S.; Gutsche, O.; Hanlon, J.; Hare, D.; Harris, R. M.; Hasegawa, S.; Hirschauer, J.; Hu, Z.; Jayatilaka, B.; Jindariani, S.; Johnson, M.; Joshi, U.; Jung, A. W.; Klima, B.; Kreis, B.; Lammel, S.; Linacre, J.; Lincoln, D.; Lipton, R.; Liu, T.; Lopes De Sá, R.; Lykken, J.; Maeshima, K.; Marraffino, J. M.; Martinez Outschoorn, V. I.; Maruyama, S.; Mason, D.; McBride, P.; Merkel, P.; Mishra, K.; Mrenna, S.; Nahn, S.; Newman-Holmes, C.; O'Dell, V.; Pedro, K.; Prokofyev, O.; Rakness, G.; Sexton-Kennedy, E.; Soha, A.; Spalding, W. J.; Spiegel, L.; Strobbe, N.; Taylor, L.; Tkaczyk, S.; Tran, N. V.; Uplegger, L.; Vaandering, E. W.; Vernieri, C.; Verzocchi, M.; Vidal, R.; Weber, H. A.; Whitbeck, A.; Acosta, D.; Avery, P.; Bortignon, P.; Bourilkov, D.; Carnes, A.; Carver, M.; Curry, D.; Das, S.; Field, R. D.; Furic, I. K.; Gleyzer, S. V.; Hugon, J.; Konigsberg, J.; Korytov, A.; Low, J. F.; Ma, P.; Matchev, K.; Mei, H.; Milenovic, P.; Mitselmakher, G.; Rank, D.; Rossin, R.; Shchutska, L.; Snowball, M.; Sperka, D.; Terentyev, N.; Thomas, L.; Wang, J.; Wang, S.; Yelton, J.; Hewamanage, S.; Linn, S.; Markowitz, P.; Martinez, G.; Rodriguez, J. L.; Ackert, A.; Adams, J. R.; Adams, T.; Askew, A.; Bein, S.; Bochenek, J.; Diamond, B.; Haas, J.; Hagopian, S.; Hagopian, V.; Johnson, K. F.; Khatiwada, A.; Prosper, H.; Weinberg, M.; Baarmand, M. M.; Bhopatkar, V.; Colafranceschi, S.; Hohlmann, M.; Kalakhety, H.; Noonan, D.; Roy, T.; Yumiceva, F.; Adams, M. R.; Apanasevich, L.; Berry, D.; Betts, R. R.; Bucinskaite, I.; Cavanaugh, R.; Evdokimov, O.; Gauthier, L.; Gerber, C. E.; Hofman, D. J.; Kurt, P.; O'Brien, C.; Sandoval Gonzalez, l. D.; Silkworth, C.; Turner, P.; Varelas, N.; Wu, Z.; Zakaria, M.; Bilki, B.; Clarida, W.; Dilsiz, K.; Durgut, S.; Gandrajula, R. P.; Haytmyradov, M.; Khristenko, V.; Merlo, J.-P.; Mermerkaya, H.; Mestvirishvili, A.; Moeller, A.; Nachtman, J.; Ogul, H.; Onel, Y.; Ozok, F.; Penzo, A.; Snyder, C.; Tiras, E.; Wetzel, J.; Yi, K.; Anderson, I.; Barnett, B. A.; Blumenfeld, B.; Eminizer, N.; Fehling, D.; Feng, L.; Gritsan, A. V.; Maksimovic, P.; Martin, C.; Osherson, M.; Roskes, J.; Sady, A.; Sarica, U.; Swartz, M.; Xiao, M.; Xin, Y.; You, C.; Baringer, P.; Bean, A.; Benelli, G.; Bruner, C.; Kenny, R. P.; Majumder, D.; Malek, M.; Murray, M.; Sanders, S.; Stringer, R.; Wang, Q.; Ivanov, A.; Kaadze, K.; Khalil, S.; Makouski, M.; Maravin, Y.; Mohammadi, A.; Saini, L. K.; Skhirtladze, N.; Toda, S.; Lange, D.; Rebassoo, F.; Wright, D.; Anelli, C.; Baden, A.; Baron, O.; Belloni, A.; Calvert, B.; Eno, S. C.; Ferraioli, C.; Gomez, J. A.; Hadley, N. J.; Jabeen, S.; Kellogg, R. G.; Kolberg, T.; Kunkle, J.; Lu, Y.; Mignerey, A. C.; Shin, Y. H.; Skuja, A.; Tonjes, M. B.; Tonwar, S. C.; Apyan, A.; Barbieri, R.; Baty, A.; Bierwagen, K.; Brandt, S.; Busza, W.; Cali, I. A.; Demiragli, Z.; Di Matteo, L.; Gomez Ceballos, G.; Goncharov, M.; Gulhan, D.; Iiyama, Y.; Innocenti, G. M.; Klute, M.; Kovalskyi, D.; Lai, Y. S.; Lee, Y.-J.; Levin, A.; Luckey, P. D.; Marini, A. C.; Mcginn, C.; Mironov, C.; Narayanan, S.; Niu, X.; Paus, C.; Ralph, D.; Roland, C.; Roland, G.; Salfeld-Nebgen, J.; Stephans, G. S. F.; Sumorok, K.; Varma, M.; Velicanu, D.; Veverka, J.; Wang, J.; Wang, T. W.; Wyslouch, B.; Yang, M.; Zhukova, V.; Dahmes, B.; Evans, A.; Finkel, A.; Gude, A.; Hansen, P.; Kalafut, S.; Kao, S. C.; Klapoetke, K.; Kubota, Y.; Lesko, Z.; Mans, J.; Nourbakhsh, S.; Ruckstuhl, N.; Rusack, R.; Tambe, N.; Turkewitz, J.; Acosta, J. G.; Oliveros, S.; Avdeeva, E.; Bloom, K.; Bose, S.; Claes, D. R.; Dominguez, A.; Fangmeier, C.; Gonzalez Suarez, R.; Kamalieddin, R.; Keller, J.; Knowlton, D.; Kravchenko, I.; Meier, F.; Monroy, J.; Ratnikov, F.; Siado, J. E.; Snow, G. R.; Alyari, M.; Dolen, J.; George, J.; Godshalk, A.; Harrington, C.; Iashvili, I.; Kaisen, J.; Kharchilava, A.; Kumar, A.; Rappoccio, S.; Roozbahani, B.; Alverson, G.; Barberis, E.; Baumgartel, D.; Chasco, M.; Hortiangtham, A.; Massironi, A.; Morse, D. M.; Nash, D.; Orimoto, T.; Teixeira De Lima, R.; Trocino, D.; Wang, R.-J.; Wood, D.; Zhang, J.; Hahn, K. A.; Kubik, A.; Mucia, N.; Odell, N.; Pollack, B.; Pozdnyakov, A.; Schmitt, M.; Stoynev, S.; Sung, K.; Trovato, M.; Velasco, M.; Brinkerhoff, A.; Dev, N.; Hildreth, M.; Jessop, C.; Karmgard, D. J.; Kellams, N.; Lannon, K.; Marinelli, N.; Meng, F.; Mueller, C.; Musienko, Y.; Planer, M.; Reinsvold, A.; Ruchti, R.; Smith, G.; Taroni, S.; Valls, N.; Wayne, M.; Wolf, M.; Woodard, A.; Antonelli, L.; Brinson, J.; Bylsma, B.; Durkin, L. S.; Flowers, S.; Hart, A.; Hill, C.; Hughes, R.; Ji, W.; Kotov, K.; Ling, T. Y.; Liu, B.; Luo, W.; Puigh, D.; Rodenburg, M.; Winer, B. L.; Wulsin, H. W.; Driga, O.; Elmer, P.; Hardenbrook, J.; Hebda, P.; Koay, S. A.; Lujan, P.; Marlow, D.; Medvedeva, T.; Mooney, M.; Olsen, J.; Palmer, C.; Piroué, P.; Saka, H.; Stickland, D.; Tully, C.; Zuranski, A.; Malik, S.; Barnes, V. E.; Benedetti, D.; Bortoletto, D.; Gutay, L.; Jha, M. K.; Jones, M.; Jung, K.; Miller, D. H.; Neumeister, N.; Radburn-Smith, B. C.; Shi, X.; Shipsey, I.; Silvers, D.; Sun, J.; Svyatkovskiy, A.; Wang, F.; Xie, W.; Xu, L.; Parashar, N.; Stupak, J.; Adair, A.; Akgun, B.; Chen, Z.; Ecklund, K. M.; Geurts, F. J. M.; Guilbaud, M.; Li, W.; Michlin, B.; Northup, M.; Padley, B. P.; Redjimi, R.; Roberts, J.; Rorie, J.; Tu, Z.; Zabel, J.; Betchart, B.; Bodek, A.; de Barbaro, P.; Demina, R.; Eshaq, Y.; Ferbel, T.; Galanti, M.; Garcia-Bellido, A.; Han, J.; Harel, A.; Hindrichs, O.; Khukhunaishvili, A.; Petrillo, G.; Tan, P.; Verzetti, M.; Arora, S.; Barker, A.; Chou, J. P.; Contreras-Campana, C.; Contreras-Campana, E.; Ferencek, D.; Gershtein, Y.; Gray, R.; Halkiadakis, E.; Hidas, D.; Hughes, E.; Kaplan, S.; Kunnawalkam Elayavalli, R.; Lath, A.; Nash, K.; Panwalkar, S.; Park, M.; Salur, S.; Schnetzer, S.; Sheffield, D.; Somalwar, S.; Stone, R.; Thomas, S.; Thomassen, P.; Walker, M.; Foerster, M.; Riley, G.; Rose, K.; Spanier, S.; York, A.; Bouhali, O.; Castaneda Hernandez, A.; Celik, A.; Dalchenko, M.; De Mattia, M.; Delgado, A.; Dildick, S.; Eusebi, R.; Gilmore, J.; Huang, T.; Kamon, T.; Krutelyov, V.; Mueller, R.; Osipenkov, I.; Pakhotin, Y.; Patel, R.; Perloff, A.; Rose, A.; Safonov, A.; Tatarinov, A.; Ulmer, K. A.; Akchurin, N.; Cowden, C.; Damgov, J.; Dragoiu, C.; Dudero, P. R.; Faulkner, J.; Kunori, S.; Lamichhane, K.; Lee, S. W.; Libeiro, T.; Undleeb, S.; Volobouev, I.; Appelt, E.; Delannoy, A. G.; Greene, S.; Gurrola, A.; Janjam, R.; Johns, W.; Maguire, C.; Mao, Y.; Melo, A.; Ni, H.; Sheldon, P.; Snook, B.; Tuo, S.; Velkovska, J.; Xu, Q.; Arenton, M. W.; Cox, B.; Francis, B.; Goodell, J.; Hirosky, R.; Ledovskoy, A.; Li, H.; Lin, C.; Neu, C.; Sinthuprasith, T.; Sun, X.; Wang, Y.; Wolfe, E.; Wood, J.; Xia, F.; Clarke, C.; Harr, R.; Karchin, P. E.; Kottachchi Kankanamge Don, C.; Lamichhane, P.; Sturdy, J.; Belknap, D. A.; Carlsmith, D.; Cepeda, M.; Dasu, S.; Dodd, L.; Duric, S.; Gomber, B.; Grothe, M.; Hall-Wilton, R.; Herndon, M.; Hervé, A.; Klabbers, P.; Lanaro, A.; Levine, A.; Long, K.; Loveless, R.; Mohapatra, A.; Ojalvo, I.; Perry, T.; Pierro, G. A.; Polese, G.; Ruggles, T.; Sarangi, T.; Savin, A.; Sharma, A.; Smith, N.; Smith, W. H.; Taylor, D.; Woods, N.; CMS Collaboration

2016-08-01

Results are reported from a search for the pair production of top squarks, the supersymmetric partners of top quarks, in final states with jets and missing transverse momentum. The data sample used in this search was collected by the CMS detector and corresponds to an integrated luminosity of 18.9 {fb}^ {-1} of proton-proton collisions at a centre-of-mass energy of 8 {TeV} produced by the LHC. The search features novel background suppression and prediction methods, including a dedicated top quark pair reconstruction algorithm. The data are found to be in agreement with the predicted backgrounds. Exclusion limits are set in simplified supersymmetry models with the top squark decaying to jets and an undetected neutralino, either through a top quark or through a bottom quark and chargino. Models with the top squark decaying via a top quark are excluded for top squark masses up to 755 {GeV} in the case of neutralino masses below 200 {GeV}. For decays via a chargino, top squark masses up to 620 {GeV} are excluded, depending on the masses of the chargino and neutralino.

NIR small arms muzzle flash

NASA Astrophysics Data System (ADS)

Montoya, Joseph; Kennerly, Stephen; Rede, Edward

2010-04-01

Utilization of Near-Infrared (NIR) spectral features in a muzzle flash will allow for small arms detection using low cost silicon (Si)-based imagers. Detection of a small arms muzzle flash in a particular wavelength region is dependent on the intensity of that emission, the efficiency of source emission transmission through the atmosphere, and the relative intensity of the background scene. The NIR muzzle flash signature exists in the relatively large Si spectral response wavelength region of 300 nm-1100 nm, which allows for use of commercial-off-the-shelf (COTS) Si-based detectors. The alkali metal origin of the NIR spectral features in the 7.62 × 39-mm round muzzle flash is discussed, and the basis for the spectral bandwidth is examined, using a calculated Voigt profile. This report will introduce a model of the 7.62 × 39-mm NIR muzzle flash signature based on predicted source characteristics. Atmospheric limitations based on NIR spectral regions are investigated in relation to the NIR muzzle flash signature. A simple signal-to-clutter ratio (SCR) metric is used to predict sensor performance based on a model of radiance for the source and solar background and pixel registered image subtraction.

Search for direct pair production of supersymmetric top quarks decaying to all-hadronic final states in pp collisions at [Formula: see text].

PubMed

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De La Barca Sanchez, M Calderon; Chauhan, S; Chertok, M; Conway, J; Conway, R; Cox, P T; Erbacher, R; Funk, G; Gardner, M; Ko, W; Lander, R; Mulhearn, M; Pellett, D; Pilot, J; Ricci-Tam, F; Shalhout, S; Smith, J; Squires, M; Stolp, D; Tripathi, M; Wilbur, S; Yohay, R; Bravo, C; Cousins, R; Everaerts, P; Farrell, C; Florent, A; Hauser, J; Ignatenko, M; Saltzberg, D; Schnaible, C; Takasugi, E; Valuev, V; Weber, M; Burt, K; Clare, R; Ellison, J; Gary, J W; Hanson, G; Heilman, J; Ivova Paneva, M; Jandir, P; Kennedy, E; Lacroix, F; Long, O R; Luthra, A; Malberti, M; Negrete, M Olmedo; Shrinivas, A; Wei, H; Wimpenny, S; Yates, B R; Branson, J G; Cerati, G B; Cittolin, S; D'Agnolo, R T; Derdzinski, M; Holzner, A; Kelley, R; Klein, D; Letts, J; Macneill, I; Olivito, D; Padhi, S; Pieri, M; Sani, M; Sharma, V; Simon, S; Tadel, M; Vartak, A; Wasserbaech, S; Welke, C; Würthwein, F; Yagil, A; Zevi Della Porta, G; Bradmiller-Feld, J; Campagnari, C; Dishaw, A; Dutta, V; Flowers, K; Franco Sevilla, M; Geffert, P; George, C; Golf, F; Gouskos, L; Gran, J; Incandela, J; Mccoll, N; Mullin, S D; Richman, J; Stuart, D; Suarez, I; West, C; Yoo, J; Anderson, D; Apresyan, A; Bornheim, A; Bunn, J; Chen, Y; Duarte, J; Mott, A; Newman, H B; Pena, C; Pierini, M; Spiropulu, M; Vlimant, J R; Xie, S; Zhu, R Y; Andrews, M B; Azzolini, V; Calamba, A; Carlson, B; Ferguson, T; Paulini, M; Russ, J; Sun, M; Vogel, H; Vorobiev, I; Cumalat, J P; Ford, W T; Gaz, A; Jensen, F; Johnson, A; Krohn, M; Mulholland, T; Nauenberg, U; Stenson, K; Wagner, S R; Alexander, J; Chatterjee, A; Chaves, J; Chu, J; Dittmer, S; Eggert, N; Mirman, N; Nicolas Kaufman, G; Patterson, J R; Rinkevicius, A; Ryd, A; Skinnari, L; Soffi, L; Sun, W; Tan, S M; Teo, W D; Thom, J; Thompson, J; Tucker, J; Weng, Y; Wittich, P; Abdullin, S; Albrow, M; Apollinari, G; Banerjee, S; Bauerdick, L A T; Beretvas, A; Berryhill, J; Bhat, P C; Bolla, G; Burkett, K; Butler, J N; Cheung, H W K; Chlebana, F; Cihangir, S; Elvira, V D; Fisk, I; Freeman, J; Gottschalk, E; Gray, L; Green, D; Grünendahl, S; Gutsche, O; Hanlon, J; Hare, D; Harris, R M; Hasegawa, S; Hirschauer, J; Hu, Z; Jayatilaka, B; Jindariani, S; Johnson, M; Joshi, U; Jung, A W; Klima, B; Kreis, B; Lammel, S; Linacre, J; Lincoln, D; Lipton, R; Liu, T; Lopes De Sá, R; Lykken, J; Maeshima, K; Marraffino, J M; Martinez Outschoorn, V I; Maruyama, S; Mason, D; McBride, P; Merkel, P; Mishra, K; Mrenna, S; Nahn, S; Newman-Holmes, C; O'Dell, V; Pedro, K; Prokofyev, O; Rakness, G; Sexton-Kennedy, E; Soha, A; Spalding, W J; Spiegel, L; Strobbe, N; Taylor, L; Tkaczyk, S; Tran, N V; Uplegger, L; Vaandering, E W; Vernieri, C; Verzocchi, M; Vidal, R; Weber, H A; Whitbeck, A; Acosta, D; Avery, P; Bortignon, P; Bourilkov, D; Carnes, A; Carver, M; Curry, D; Das, S; Field, R D; Furic, I K; Gleyzer, S V; Hugon, J; Konigsberg, J; Korytov, A; Low, J F; Ma, P; Matchev, K; Mei, H; Milenovic, P; Mitselmakher, G; Rank, D; Rossin, R; Shchutska, L; Snowball, M; Sperka, D; Terentyev, N; Thomas, L; Wang, J; Wang, S; Yelton, J; Hewamanage, S; Linn, S; Markowitz, P; Martinez, G; Rodriguez, J L; Ackert, A; Adams, J R; Adams, T; Askew, A; Bein, S; Bochenek, J; Diamond, B; Haas, J; Hagopian, S; Hagopian, V; Johnson, K F; Khatiwada, A; Prosper, H; Weinberg, M; Baarmand, M M; Bhopatkar, V; Colafranceschi, S; Hohlmann, M; Kalakhety, H; Noonan, D; Roy, T; Yumiceva, F; Adams, M R; Apanasevich, L; Berry, D; Betts, R R; Bucinskaite, I; Cavanaugh, R; Evdokimov, O; Gauthier, L; Gerber, C E; Hofman, D J; Kurt, P; O'Brien, C; Sandoval Gonzalez, L D; Silkworth, C; Turner, P; Varelas, N; Wu, Z; Zakaria, M; Bilki, B; Clarida, W; Dilsiz, K; Durgut, S; Gandrajula, R P; Haytmyradov, M; Khristenko, V; Merlo, J-P; Mermerkaya, H; Mestvirishvili, A; Moeller, A; Nachtman, J; Ogul, H; Onel, Y; Ozok, F; Penzo, A; Snyder, C; Tiras, E; Wetzel, J; Yi, K; Anderson, I; Barnett, B A; Blumenfeld, B; Eminizer, N; Fehling, D; Feng, L; Gritsan, A V; Maksimovic, P; Martin, C; Osherson, M; Roskes, J; Sady, A; Sarica, U; Swartz, M; Xiao, M; Xin, Y; You, C; Baringer, P; Bean, A; Benelli, G; Bruner, C; Kenny, R P; Majumder, D; Malek, M; Murray, M; Sanders, S; Stringer, R; Wang, Q; Ivanov, A; Kaadze, K; Khalil, S; Makouski, M; Maravin, Y; Mohammadi, A; Saini, L K; Skhirtladze, N; Toda, S; Lange, D; Rebassoo, F; Wright, D; Anelli, C; Baden, A; Baron, O; Belloni, A; Calvert, B; Eno, S C; Ferraioli, C; Gomez, J A; Hadley, N J; Jabeen, S; Kellogg, R G; Kolberg, T; Kunkle, J; Lu, Y; Mignerey, A C; Shin, Y H; Skuja, A; Tonjes, M B; Tonwar, S C; Apyan, A; Barbieri, R; Baty, A; Bierwagen, K; Brandt, S; Busza, W; Cali, I A; Demiragli, Z; Di Matteo, L; Gomez Ceballos, G; Goncharov, M; Gulhan, D; Iiyama, Y; Innocenti, G M; Klute, M; Kovalskyi, D; Lai, Y S; Lee, Y-J; Levin, A; Luckey, P D; Marini, A C; Mcginn, C; Mironov, C; Narayanan, S; Niu, X; Paus, C; Ralph, D; Roland, C; Roland, G; Salfeld-Nebgen, J; Stephans, G S F; Sumorok, K; Varma, M; Velicanu, D; Veverka, J; Wang, J; Wang, T W; Wyslouch, B; Yang, M; Zhukova, V; Dahmes, B; Evans, A; Finkel, A; Gude, A; Hansen, P; Kalafut, S; Kao, S C; Klapoetke, K; Kubota, Y; Lesko, Z; Mans, J; Nourbakhsh, S; Ruckstuhl, N; Rusack, R; Tambe, N; Turkewitz, J; Acosta, J G; Oliveros, S; Avdeeva, E; Bloom, K; Bose, S; Claes, D R; Dominguez, A; Fangmeier, C; Gonzalez Suarez, R; Kamalieddin, R; Keller, J; Knowlton, D; Kravchenko, I; Meier, F; Monroy, J; Ratnikov, F; Siado, J E; Snow, G R; Alyari, M; Dolen, J; George, J; Godshalk, A; Harrington, C; Iashvili, I; Kaisen, J; Kharchilava, A; Kumar, A; Rappoccio, S; Roozbahani, B; Alverson, G; Barberis, E; Baumgartel, D; Chasco, M; Hortiangtham, A; Massironi, A; Morse, D M; Nash, D; Orimoto, T; Teixeira De Lima, R; Trocino, D; Wang, R-J; Wood, D; Zhang, J; Hahn, K A; Kubik, A; Mucia, N; Odell, N; Pollack, B; Pozdnyakov, A; Schmitt, M; Stoynev, S; Sung, K; Trovato, M; Velasco, M; Brinkerhoff, A; Dev, N; Hildreth, M; Jessop, C; Karmgard, D J; Kellams, N; Lannon, K; Marinelli, N; Meng, F; Mueller, C; Musienko, Y; Planer, M; Reinsvold, A; Ruchti, R; Smith, G; Taroni, S; Valls, N; Wayne, M; Wolf, M; Woodard, A; Antonelli, L; Brinson, J; Bylsma, B; Durkin, L S; Flowers, S; Hart, A; Hill, C; Hughes, R; Ji, W; Kotov, K; Ling, T Y; Liu, B; Luo, W; Puigh, D; Rodenburg, M; Winer, B L; Wulsin, H W; Driga, O; Elmer, P; Hardenbrook, J; Hebda, P; Koay, S A; Lujan, P; Marlow, D; Medvedeva, T; Mooney, M; Olsen, J; Palmer, C; Piroué, P; Saka, H; Stickland, D; Tully, C; Zuranski, A; Malik, S; Barnes, V E; Benedetti, D; Bortoletto, D; Gutay, L; Jha, M K; Jones, M; Jung, K; Miller, D H; Neumeister, N; Radburn-Smith, B C; Shi, X; Shipsey, I; Silvers, D; Sun, J; Svyatkovskiy, A; Wang, F; Xie, W; Xu, L; Parashar, N; Stupak, J; Adair, A; Akgun, B; Chen, Z; Ecklund, K M; Geurts, F J M; Guilbaud, M; Li, W; Michlin, B; Northup, M; Padley, B P; Redjimi, R; Roberts, J; Rorie, J; Tu, Z; Zabel, J; Betchart, B; Bodek, A; de Barbaro, P; Demina, R; Eshaq, Y; Ferbel, T; Galanti, M; Garcia-Bellido, A; Han, J; Harel, A; Hindrichs, O; Khukhunaishvili, A; Petrillo, G; Tan, P; Verzetti, M; Arora, S; Barker, A; Chou, J P; Contreras-Campana, C; Contreras-Campana, E; Ferencek, D; Gershtein, Y; Gray, R; Halkiadakis, E; Hidas, D; Hughes, E; Kaplan, S; Kunnawalkam Elayavalli, R; Lath, A; Nash, K; Panwalkar, S; Park, M; Salur, S; Schnetzer, S; Sheffield, D; Somalwar, S; Stone, R; Thomas, S; Thomassen, P; Walker, M; Foerster, M; Riley, G; Rose, K; Spanier, S; York, A; Bouhali, O; Castaneda Hernandez, A; Celik, A; Dalchenko, M; De Mattia, M; Delgado, A; Dildick, S; Eusebi, R; Gilmore, J; Huang, T; Kamon, T; Krutelyov, V; Mueller, R; Osipenkov, I; Pakhotin, Y; Patel, R; Perloff, A; Rose, A; Safonov, A; Tatarinov, A; Ulmer, K A; Akchurin, N; Cowden, C; Damgov, J; Dragoiu, C; Dudero, P R; Faulkner, J; Kunori, S; Lamichhane, K; Lee, S W; Libeiro, T; Undleeb, S; Volobouev, I; Appelt, E; Delannoy, A G; Greene, S; Gurrola, A; Janjam, R; Johns, W; Maguire, C; Mao, Y; Melo, A; Ni, H; Sheldon, P; Snook, B; Tuo, S; Velkovska, J; Xu, Q; Arenton, M W; Cox, B; Francis, B; Goodell, J; Hirosky, R; Ledovskoy, A; Li, H; Lin, C; Neu, C; Sinthuprasith, T; Sun, X; Wang, Y; Wolfe, E; Wood, J; Xia, F; Clarke, C; Harr, R; Karchin, P E; Kottachchi Kankanamge Don, C; Lamichhane, P; Sturdy, J; Belknap, D A; Carlsmith, D; Cepeda, M; Dasu, S; Dodd, L; Duric, S; Gomber, B; Grothe, M; Hall-Wilton, R; Herndon, M; Hervé, A; Klabbers, P; Lanaro, A; Levine, A; Long, K; Loveless, R; Mohapatra, A; Ojalvo, I; Perry, T; Pierro, G A; Polese, G; Ruggles, T; Sarangi, T; Savin, A; Sharma, A; Smith, N; Smith, W H; Taylor, D; Woods, N; Collaboration, Authorinst The Cms

2016-01-01

Results are reported from a search for the pair production of top squarks, the supersymmetric partners of top quarks, in final states with jets and missing transverse momentum. The data sample used in this search was collected by the CMS detector and corresponds to an integrated luminosity of 18.9[Formula: see text] of proton-proton collisions at a centre-of-mass energy of 8[Formula: see text] produced by the LHC. The search features novel background suppression and prediction methods, including a dedicated top quark pair reconstruction algorithm. The data are found to be in agreement with the predicted backgrounds. Exclusion limits are set in simplified supersymmetry models with the top squark decaying to jets and an undetected neutralino, either through a top quark or through a bottom quark and chargino. Models with the top squark decaying via a top quark are excluded for top squark masses up to 755[Formula: see text] in the case of neutralino masses below 200[Formula: see text]. For decays via a chargino, top squark masses up to 620[Formula: see text] are excluded, depending on the masses of the chargino and neutralino.

Dispersion of a Passive Scalar Fluctuating Plume in a Turbulent Boundary Layer. Part III: Stochastic Modelling

NASA Astrophysics Data System (ADS)

Marro, Massimo; Salizzoni, Pietro; Soulhac, Lionel; Cassiani, Massimo

2018-06-01

We analyze the reliability of the Lagrangian stochastic micromixing method in predicting higher-order statistics of the passive scalar concentration induced by an elevated source (of varying diameter) placed in a turbulent boundary layer. To that purpose we analyze two different modelling approaches by testing their results against the wind-tunnel measurements discussed in Part I (Nironi et al., Boundary-Layer Meteorology, 2015, Vol. 156, 415-446). The first is a probability density function (PDF) micromixing model that simulates the effects of the molecular diffusivity on the concentration fluctuations by taking into account the background particles. The second is a new model, named VPΓ, conceived in order to minimize the computational costs. This is based on the volumetric particle approach providing estimates of the first two concentration moments with no need for the simulation of the background particles. In this second approach, higher-order moments are computed based on the estimates of these two moments and under the assumption that the concentration PDF is a Gamma distribution. The comparisons concern the spatial distribution of the first four moments of the concentration and the evolution of the PDF along the plume centreline. The novelty of this work is twofold: (i) we perform a systematic comparison of the results of micro-mixing Lagrangian models against experiments providing profiles of the first four moments of the concentration within an inhomogeneous and anisotropic turbulent flow, and (ii) we show the reliability of the VPΓ model as an operational tool for the prediction of the PDF of the concentration.

Dispersion of a Passive Scalar Fluctuating Plume in a Turbulent Boundary Layer. Part III: Stochastic Modelling

NASA Astrophysics Data System (ADS)

Marro, Massimo; Salizzoni, Pietro; Soulhac, Lionel; Cassiani, Massimo

2018-01-01

We analyze the reliability of the Lagrangian stochastic micromixing method in predicting higher-order statistics of the passive scalar concentration induced by an elevated source (of varying diameter) placed in a turbulent boundary layer. To that purpose we analyze two different modelling approaches by testing their results against the wind-tunnel measurements discussed in Part I (Nironi et al., Boundary-Layer Meteorology, 2015, Vol. 156, 415-446). The first is a probability density function (PDF) micromixing model that simulates the effects of the molecular diffusivity on the concentration fluctuations by taking into account the background particles. The second is a new model, named VPΓ, conceived in order to minimize the computational costs. This is based on the volumetric particle approach providing estimates of the first two concentration moments with no need for the simulation of the background particles. In this second approach, higher-order moments are computed based on the estimates of these two moments and under the assumption that the concentration PDF is a Gamma distribution. The comparisons concern the spatial distribution of the first four moments of the concentration and the evolution of the PDF along the plume centreline. The novelty of this work is twofold: (i) we perform a systematic comparison of the results of micro-mixing Lagrangian models against experiments providing profiles of the first four moments of the concentration within an inhomogeneous and anisotropic turbulent flow, and (ii) we show the reliability of the VPΓ model as an operational tool for the prediction of the PDF of the concentration.

Prediction of human observer performance in a 2-alternative forced choice low-contrast detection task using channelized Hotelling observer: Impact of radiation dose and reconstruction algorithms

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

Yu Lifeng; Leng Shuai; Chen Lingyun

2013-04-15

Purpose: Efficient optimization of CT protocols demands a quantitative approach to predicting human observer performance on specific tasks at various scan and reconstruction settings. The goal of this work was to investigate how well a channelized Hotelling observer (CHO) can predict human observer performance on 2-alternative forced choice (2AFC) lesion-detection tasks at various dose levels and two different reconstruction algorithms: a filtered-backprojection (FBP) and an iterative reconstruction (IR) method. Methods: A 35 Multiplication-Sign 26 cm{sup 2} torso-shaped phantom filled with water was used to simulate an average-sized patient. Three rods with different diameters (small: 3 mm; medium: 5 mm; large:more » 9 mm) were placed in the center region of the phantom to simulate small, medium, and large lesions. The contrast relative to background was -15 HU at 120 kV. The phantom was scanned 100 times using automatic exposure control each at 60, 120, 240, 360, and 480 quality reference mAs on a 128-slice scanner. After removing the three rods, the water phantom was again scanned 100 times to provide signal-absent background images at the exact same locations. By extracting regions of interest around the three rods and on the signal-absent images, the authors generated 21 2AFC studies. Each 2AFC study had 100 trials, with each trial consisting of a signal-present image and a signal-absent image side-by-side in randomized order. In total, 2100 trials were presented to both the model and human observers. Four medical physicists acted as human observers. For the model observer, the authors used a CHO with Gabor channels, which involves six channel passbands, five orientations, and two phases, leading to a total of 60 channels. The performance predicted by the CHO was compared with that obtained by four medical physicists at each 2AFC study. Results: The human and model observers were highly correlated at each dose level for each lesion size for both FBP and IR. The Pearson's product-moment correlation coefficients were 0.986 [95% confidence interval (CI): 0.958-0.996] for FBP and 0.985 (95% CI: 0.863-0.998) for IR. Bland-Altman plots showed excellent agreement for all dose levels and lesions sizes with a mean absolute difference of 1.0%{+-} 1.1% for FBP and 2.1%{+-} 3.3% for IR. Conclusions: Human observer performance on a 2AFC lesion detection task in CT with a uniform background can be accurately predicted by a CHO model observer at different radiation dose levels and for both FBP and IR methods.« less

A mesoscale hybrid data assimilation system based on the JMA nonhydrostatic model

NASA Astrophysics Data System (ADS)

Ito, K.; Kunii, M.; Kawabata, T. T.; Saito, K. K.; Duc, L. L.

2015-12-01

This work evaluates the potential of a hybrid ensemble Kalman filter and four-dimensional variational (4D-Var) data assimilation system for predicting severe weather events from a deterministic point of view. This hybrid system is an adjoint-based 4D-Var system using a background error covariance matrix constructed from the mixture of a so-called NMC method and perturbations in a local ensemble transform Kalman filter data assimilation system, both of which are based on the Japan Meteorological Agency nonhydrostatic model. To construct the background error covariance matrix, we investigated two types of schemes. One is a spatial localization scheme and the other is neighboring ensemble approach, which regards the result at a horizontally spatially shifted point in each ensemble member as that obtained from a different realization of ensemble simulation. An assimilation of a pseudo single-observation located to the north of a tropical cyclone (TC) yielded an analysis increment of wind and temperature physically consistent with what is expected for a mature TC in both hybrid systems, whereas an analysis increment in a 4D-Var system using a static background error covariance distorted a structure of the mature TC. Real data assimilation experiments applied to 4 TCs and 3 local heavy rainfall events showed that hybrid systems and EnKF provided better initial conditions than the NMC-based 4D-Var, both for predicting the intensity and track forecast of TCs and for the location and amount of local heavy rainfall events.

Guidelines for Developing and Reporting Machine Learning Predictive Models in Biomedical Research: A Multidisciplinary View

PubMed Central

2016-01-01

Background As more and more researchers are turning to big data for new opportunities of biomedical discoveries, machine learning models, as the backbone of big data analysis, are mentioned more often in biomedical journals. However, owing to the inherent complexity of machine learning methods, they are prone to misuse. Because of the flexibility in specifying machine learning models, the results are often insufficiently reported in research articles, hindering reliable assessment of model validity and consistent interpretation of model outputs. Objective To attain a set of guidelines on the use of machine learning predictive models within clinical settings to make sure the models are correctly applied and sufficiently reported so that true discoveries can be distinguished from random coincidence. Methods A multidisciplinary panel of machine learning experts, clinicians, and traditional statisticians were interviewed, using an iterative process in accordance with the Delphi method. Results The process produced a set of guidelines that consists of (1) a list of reporting items to be included in a research article and (2) a set of practical sequential steps for developing predictive models. Conclusions A set of guidelines was generated to enable correct application of machine learning models and consistent reporting of model specifications and results in biomedical research. We believe that such guidelines will accelerate the adoption of big data analysis, particularly with machine learning methods, in the biomedical research community. PMID:27986644

Modelling of lindane transport in groundwater of metropolitan city Vadodara, Gujarat, India.

PubMed

Sharma, M K; Jain, C K; Rao, G Tamma; Rao, V V S Gurunadha

2015-05-01

Migration pattern of organochloro pesticide lindane has been studied in groundwater of metropolitan city Vadodara. Groundwater flow was simulated using the groundwater flow model constructed up to a depth of 60 m considering a three-layer structure with grid size of 40 × 40 × 40 m(3). The general groundwater flow direction is from northeast to south and southwest. The river Vishwamitri and river Jambua form natural hydrologic boundary. The constant head in the north and south end of the study area is taken as another boundary condition in the model. The hydraulic head distribution in the multilayer aquifer has been computed from the visual MODFLOW groundwater flow model. TDS has been computed though MT3D mass transport model starting with a background concentration of 500 mg/l and using a porosity value of 0.3. Simulated TDS values from the model matches well with the observed data. Model MT3D was run for lindane pesticide with a background concentration of 0.5 μg/l. The predictions of the mass transport model for next 50 years indicate that advancement of containment of plume size in the aquifer system both spatially and depth wise as a result of increasing level of pesticide in river Vishwamitri. The restoration of the aquifer system may take a very long time as seen from slow improvement in the groundwater quality from the predicted scenarios, thereby, indicating alarming situation of groundwater quality deterioration in different layers. It is recommended that all the industries operating in the region should install efficient effluent treatment plants to abate the pollution problem.

Evolution of non-interacting entropic dark energy and its phantom nature

NASA Astrophysics Data System (ADS)

Mathew, Titus K.; Murali, Chinthak; Shejeelammal, J.

2016-04-01

Assuming the form of the entropic dark energy (EDE) as it arises from the surface term in the Einstein-Hilbert’s action, its evolution was analyzed in an expanding flat universe. The model parameters were evaluated by constraining the model using the Union data on Type Ia supernovae. We found that in the non-interacting case, the model predicts an early decelerated phase and a later accelerated phase at the background level. The evolutions of the Hubble parameter, dark energy (DE) density, equation of state parameter and deceleration parameter were obtained. The model hardly seems to be supporting the linear perturbation growth for the structure formation. We also found that the EDE shows phantom nature for redshifts z < 0.257. During the phantom epoch, the model predicts big rip effect at which both the scale factor of expansion and the DE density become infinitely large and the big rip time is found to be around 36 Giga years from now.

Prediction of TF target sites based on atomistic models of protein-DNA complexes

PubMed Central

Angarica, Vladimir Espinosa; Pérez, Abel González; Vasconcelos, Ana T; Collado-Vides, Julio; Contreras-Moreira, Bruno

2008-01-01

Background The specific recognition of genomic cis-regulatory elements by transcription factors (TFs) plays an essential role in the regulation of coordinated gene expression. Studying the mechanisms determining binding specificity in protein-DNA interactions is thus an important goal. Most current approaches for modeling TF specific recognition rely on the knowledge of large sets of cognate target sites and consider only the information contained in their primary sequence. Results Here we describe a structure-based methodology for predicting sequence motifs starting from the coordinates of a TF-DNA complex. Our algorithm combines information regarding the direct and indirect readout of DNA into an atomistic statistical model, which is used to estimate the interaction potential. We first measure the ability of our method to correctly estimate the binding specificities of eight prokaryotic and eukaryotic TFs that belong to different structural superfamilies. Secondly, the method is applied to two homology models, finding that sampling of interface side-chain rotamers remarkably improves the results. Thirdly, the algorithm is compared with a reference structural method based on contact counts, obtaining comparable predictions for the experimental complexes and more accurate sequence motifs for the homology models. Conclusion Our results demonstrate that atomic-detail structural information can be feasibly used to predict TF binding sites. The computational method presented here is universal and might be applied to other systems involving protein-DNA recognition. PMID:18922190

Morbidity Rate Prediction of Dengue Hemorrhagic Fever (DHF) Using the Support Vector Machine and the Aedes aegypti Infection Rate in Similar Climates and Geographical Areas

PubMed Central

Kesorn, Kraisak; Ongruk, Phatsavee; Chompoosri, Jakkrawarn; Phumee, Atchara; Thavara, Usavadee; Tawatsin, Apiwat; Siriyasatien, Padet

2015-01-01

Background In the past few decades, several researchers have proposed highly accurate prediction models that have typically relied on climate parameters. However, climate factors can be unreliable and can lower the effectiveness of prediction when they are applied in locations where climate factors do not differ significantly. The purpose of this study was to improve a dengue surveillance system in areas with similar climate by exploiting the infection rate in the Aedes aegypti mosquito and using the support vector machine (SVM) technique for forecasting the dengue morbidity rate. Methods and Findings Areas with high incidence of dengue outbreaks in central Thailand were studied. The proposed framework consisted of the following three major parts: 1) data integration, 2) model construction, and 3) model evaluation. We discovered that the Ae. aegypti female and larvae mosquito infection rates were significantly positively associated with the morbidity rate. Thus, the increasing infection rate of female mosquitoes and larvae led to a higher number of dengue cases, and the prediction performance increased when those predictors were integrated into a predictive model. In this research, we applied the SVM with the radial basis function (RBF) kernel to forecast the high morbidity rate and take precautions to prevent the development of pervasive dengue epidemics. The experimental results showed that the introduced parameters significantly increased the prediction accuracy to 88.37% when used on the test set data, and these parameters led to the highest performance compared to state-of-the-art forecasting models. Conclusions The infection rates of the Ae. aegypti female mosquitoes and larvae improved the morbidity rate forecasting efficiency better than the climate parameters used in classical frameworks. We demonstrated that the SVM-R-based model has high generalization performance and obtained the highest prediction performance compared to classical models as measured by the accuracy, sensitivity, specificity, and mean absolute error (MAE). PMID:25961289

Modelling Predictors of Molecular Response to Frontline Imatinib for Patients with Chronic Myeloid Leukaemia

PubMed Central

Brown, Fred; Adelson, David; White, Deborah; Hughes, Timothy; Chaudhri, Naeem

2017-01-01

Background Treatment of patients with chronic myeloid leukaemia (CML) has become increasingly difficult in recent years due to the variety of treatment options available and challenge deciding on the most appropriate treatment strategy for an individual patient. To facilitate the treatment strategy decision, disease assessment should involve molecular response to initial treatment for an individual patient. Patients predicted not to achieve major molecular response (MMR) at 24 months to frontline imatinib may be better treated with alternative frontline therapies, such as nilotinib or dasatinib. The aims of this study were to i) understand the clinical prediction ‘rules’ for predicting MMR at 24 months for CML patients treated with imatinib using clinical, molecular, and cell count observations (predictive factors collected at diagnosis and categorised based on available knowledge) and ii) develop a predictive model for CML treatment management. This predictive model was developed, based on CML patients undergoing imatinib therapy enrolled in the TIDEL II clinical trial with an experimentally identified achieving MMR group and non-achieving MMR group, by addressing the challenge as a machine learning problem. The recommended model was validated externally using an independent data set from King Faisal Specialist Hospital and Research Centre, Saudi Arabia. Principle Findings The common prognostic scores yielded similar sensitivity performance in testing and validation datasets and are therefore good predictors of the positive group. The G-mean and F-score values in our models outperformed the common prognostic scores in testing and validation datasets and are therefore good predictors for both the positive and negative groups. Furthermore, a high PPV above 65% indicated that our models are appropriate for making decisions at diagnosis and pre-therapy. Study limitations include that prior knowledge may change based on varying expert opinions; hence, representing the category boundaries of each predictive factor could dramatically change performance of the models. PMID:28045960

Adaptive Modeling of the International Space Station Electrical Power System

NASA Technical Reports Server (NTRS)

Thomas, Justin Ray

2007-01-01

Software simulations provide NASA engineers the ability to experiment with spacecraft systems in a computer-imitated environment. Engineers currently develop software models that encapsulate spacecraft system behavior. These models can be inaccurate due to invalid assumptions, erroneous operation, or system evolution. Increasing accuracy requires manual calibration and domain-specific knowledge. This thesis presents a method for automatically learning system models without any assumptions regarding system behavior. Data stream mining techniques are applied to learn models for critical portions of the International Space Station (ISS) Electrical Power System (EPS). We also explore a knowledge fusion approach that uses traditional engineered EPS models to supplement the learned models. We observed that these engineered EPS models provide useful background knowledge to reduce predictive error spikes when confronted with making predictions in situations that are quite different from the training scenarios used when learning the model. Evaluations using ISS sensor data and existing EPS models demonstrate the success of the adaptive approach. Our experimental results show that adaptive modeling provides reductions in model error anywhere from 80% to 96% over these existing models. Final discussions include impending use of adaptive modeling technology for ISS mission operations and the need for adaptive modeling in future NASA lunar and Martian exploration.

Approaches to highly parameterized inversion: A guide to using PEST for model-parameter and predictive-uncertainty analysis

USGS Publications Warehouse

Doherty, John E.; Hunt, Randall J.; Tonkin, Matthew J.

2010-01-01

Analysis of the uncertainty associated with parameters used by a numerical model, and with predictions that depend on those parameters, is fundamental to the use of modeling in support of decisionmaking. Unfortunately, predictive uncertainty analysis with regard to models can be very computationally demanding, due in part to complex constraints on parameters that arise from expert knowledge of system properties on the one hand (knowledge constraints) and from the necessity for the model parameters to assume values that allow the model to reproduce historical system behavior on the other hand (calibration constraints). Enforcement of knowledge and calibration constraints on parameters used by a model does not eliminate the uncertainty in those parameters. In fact, in many cases, enforcement of calibration constraints simply reduces the uncertainties associated with a number of broad-scale combinations of model parameters that collectively describe spatially averaged system properties. The uncertainties associated with other combinations of parameters, especially those that pertain to small-scale parameter heterogeneity, may not be reduced through the calibration process. To the extent that a prediction depends on system-property detail, its postcalibration variability may be reduced very little, if at all, by applying calibration constraints; knowledge constraints remain the only limits on the variability of predictions that depend on such detail. Regrettably, in many common modeling applications, these constraints are weak. Though the PEST software suite was initially developed as a tool for model calibration, recent developments have focused on the evaluation of model-parameter and predictive uncertainty. As a complement to functionality that it provides for highly parameterized inversion (calibration) by means of formal mathematical regularization techniques, the PEST suite provides utilities for linear and nonlinear error-variance and uncertainty analysis in these highly parameterized modeling contexts. Availability of these utilities is particularly important because, in many cases, a significant proportion of the uncertainty associated with model parameters-and the predictions that depend on them-arises from differences between the complex properties of the real world and the simplified representation of those properties that is expressed by the calibrated model. This report is intended to guide intermediate to advanced modelers in the use of capabilities available with the PEST suite of programs for evaluating model predictive error and uncertainty. A brief theoretical background is presented on sources of parameter and predictive uncertainty and on the means for evaluating this uncertainty. Applications of PEST tools are then discussed for overdetermined and underdetermined problems, both linear and nonlinear. PEST tools for calculating contributions to model predictive uncertainty, as well as optimization of data acquisition for reducing parameter and predictive uncertainty, are presented. The appendixes list the relevant PEST variables, files, and utilities required for the analyses described in the document.

Search for supersymmetry in pp collisions at [Formula: see text] TeV in events with a single lepton, jets, and missing transverse momentum.

PubMed

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Hollingsworth, M; Spanier, S; Yang, Z C; York, A; Eusebi, R; Flanagan, W; Gilmore, J; Kamon, T; Khotilovich, V; Montalvo, R; Osipenkov, I; Pakhotin, Y; Perloff, A; Roe, J; Safonov, A; Sakuma, T; Sengupta, S; Suarez, I; Tatarinov, A; Toback, D; Akchurin, N; Damgov, J; Dragoiu, C; Dudero, P R; Jeong, C; Kovitanggoon, K; Lee, S W; Libeiro, T; Roh, Y; Volobouev, I; Appelt, E; Delannoy, A G; Florez, C; Greene, S; Gurrola, A; Johns, W; Kurt, P; Maguire, C; Melo, A; Sharma, M; Sheldon, P; Snook, B; Tuo, S; Velkovska, J; Arenton, M W; Balazs, M; Boutle, S; Cox, B; Francis, B; Goodell, J; Hirosky, R; Ledovskoy, A; Lin, C; Neu, C; Wood, J; Gollapinni, S; Harr, R; Karchin, P E; Kottachchi Kankanamge Don, C; Lamichhane, P; Sakharov, A; Anderson, M; Belknap, D A; Borrello, L; Carlsmith, D; Cepeda, M; Dasu, S; Friis, E; Gray, L; Grogg, K S; Grothe, M; Hall-Wilton, R; Herndon, M; Hervé, A; Klabbers, P; Klukas, J; Lanaro, A; Lazaridis, C; Leonard, J; Loveless, R; Mohapatra, A; Ojalvo, I; Palmonari, F; Pierro, G A; Ross, I; Savin, A; Smith, W H; Swanson, J

Results are reported from a search for new physics processes in events containing a single isolated high-transverse-momentum lepton (electron or muon), energetic jets, and large missing transverse momentum. The analysis is based on a 4.98 fb -1 sample of proton-proton collisions at a center-of-mass energy of 7 TeV, obtained with the CMS detector at the LHC. Three separate background estimation methods, each relying primarily on control samples in the data, are applied to a range of signal regions, providing complementary approaches for estimating the background yields. The observed yields are consistent with the predicted standard model backgrounds. The results are interpreted in terms of limits on the parameter space for the constrained minimal supersymmetric extension of the standard model, as well as on cross sections for simplified models, which provide a generic description of the production and decay of new particles in specific, topology based final states. The online version of this article (doi:10.1140/epjc/s10052-013-2404-z) contains supplementary material, which is available to authorized users.

Investigation of micromixing by acoustically oscillated sharp-edges

PubMed Central

Nama, Nitesh; Huang, Po-Hsun; Huang, Tony Jun; Costanzo, Francesco

2016-01-01

Recently, acoustically oscillated sharp-edges have been utilized to achieve rapid and homogeneous mixing in microchannels. Here, we present a numerical model to investigate acoustic mixing inside a sharp-edge-based micromixer in the presence of a background flow. We extend our previously reported numerical model to include the mixing phenomena by using perturbation analysis and the Generalized Lagrangian Mean (GLM) theory in conjunction with the convection-diffusion equation. We divide the flow variables into zeroth-order, first-order, and second-order variables. This results in three sets of equations representing the background flow, acoustic response, and the time-averaged streaming flow, respectively. These equations are then solved successively to obtain the mean Lagrangian velocity which is combined with the convection-diffusion equation to predict the concentration profile. We validate our numerical model via a comparison of the numerical results with the experimentally obtained values of the mixing index for different flow rates. Further, we employ our model to study the effect of the applied input power and the background flow on the mixing performance of the sharp-edge-based micromixer. We also suggest potential design changes to the previously reported sharp-edge-based micromixer to improve its performance. Finally, we investigate the generation of a tunable concentration gradient by a linear arrangement of the sharp-edge structures inside the microchannel. PMID:27158292

Investigation of micromixing by acoustically oscillated sharp-edges.

PubMed

Nama, Nitesh; Huang, Po-Hsun; Huang, Tony Jun; Costanzo, Francesco

2016-03-01

Recently, acoustically oscillated sharp-edges have been utilized to achieve rapid and homogeneous mixing in microchannels. Here, we present a numerical model to investigate acoustic mixing inside a sharp-edge-based micromixer in the presence of a background flow. We extend our previously reported numerical model to include the mixing phenomena by using perturbation analysis and the Generalized Lagrangian Mean (GLM) theory in conjunction with the convection-diffusion equation. We divide the flow variables into zeroth-order, first-order, and second-order variables. This results in three sets of equations representing the background flow, acoustic response, and the time-averaged streaming flow, respectively. These equations are then solved successively to obtain the mean Lagrangian velocity which is combined with the convection-diffusion equation to predict the concentration profile. We validate our numerical model via a comparison of the numerical results with the experimentally obtained values of the mixing index for different flow rates. Further, we employ our model to study the effect of the applied input power and the background flow on the mixing performance of the sharp-edge-based micromixer. We also suggest potential design changes to the previously reported sharp-edge-based micromixer to improve its performance. Finally, we investigate the generation of a tunable concentration gradient by a linear arrangement of the sharp-edge structures inside the microchannel.

Projecting future expansion of invasive species: comparing and improving methodologies for species distribution modeling.

PubMed

Mainali, Kumar P; Warren, Dan L; Dhileepan, Kunjithapatham; McConnachie, Andrew; Strathie, Lorraine; Hassan, Gul; Karki, Debendra; Shrestha, Bharat B; Parmesan, Camille

2015-12-01

Modeling the distributions of species, especially of invasive species in non-native ranges, involves multiple challenges. Here, we developed some novel approaches to species distribution modeling aimed at reducing the influences of such challenges and improving the realism of projections. We estimated species-environment relationships for Parthenium hysterophorus L. (Asteraceae) with four modeling methods run with multiple scenarios of (i) sources of occurrences and geographically isolated background ranges for absences, (ii) approaches to drawing background (absence) points, and (iii) alternate sets of predictor variables. We further tested various quantitative metrics of model evaluation against biological insight. Model projections were very sensitive to the choice of training dataset. Model accuracy was much improved using a global dataset for model training, rather than restricting data input to the species' native range. AUC score was a poor metric for model evaluation and, if used alone, was not a useful criterion for assessing model performance. Projections away from the sampled space (i.e., into areas of potential future invasion) were very different depending on the modeling methods used, raising questions about the reliability of ensemble projections. Generalized linear models gave very unrealistic projections far away from the training region. Models that efficiently fit the dominant pattern, but exclude highly local patterns in the dataset and capture interactions as they appear in data (e.g., boosted regression trees), improved generalization of the models. Biological knowledge of the species and its distribution was important in refining choices about the best set of projections. A post hoc test conducted on a new Parthenium dataset from Nepal validated excellent predictive performance of our 'best' model. We showed that vast stretches of currently uninvaded geographic areas on multiple continents harbor highly suitable habitats for parthenium. However, discrepancies between model predictions and parthenium invasion in Australia indicate successful management for this globally significant weed. © 2015 John Wiley & Sons Ltd.

Forecasting influenza in Hong Kong with Google search queries and statistical model fusion

PubMed Central

Ramirez Ramirez, L. Leticia; Nezafati, Kusha; Zhang, Qingpeng; Tsui, Kwok-Leung

2017-01-01

Background The objective of this study is to investigate predictive utility of online social media and web search queries, particularly, Google search data, to forecast new cases of influenza-like-illness (ILI) in general outpatient clinics (GOPC) in Hong Kong. To mitigate the impact of sensitivity to self-excitement (i.e., fickle media interest) and other artifacts of online social media data, in our approach we fuse multiple offline and online data sources. Methods Four individual models: generalized linear model (GLM), least absolute shrinkage and selection operator (LASSO), autoregressive integrated moving average (ARIMA), and deep learning (DL) with Feedforward Neural Networks (FNN) are employed to forecast ILI-GOPC both one week and two weeks in advance. The covariates include Google search queries, meteorological data, and previously recorded offline ILI. To our knowledge, this is the first study that introduces deep learning methodology into surveillance of infectious diseases and investigates its predictive utility. Furthermore, to exploit the strength from each individual forecasting models, we use statistical model fusion, using Bayesian model averaging (BMA), which allows a systematic integration of multiple forecast scenarios. For each model, an adaptive approach is used to capture the recent relationship between ILI and covariates. Results DL with FNN appears to deliver the most competitive predictive performance among the four considered individual models. Combing all four models in a comprehensive BMA framework allows to further improve such predictive evaluation metrics as root mean squared error (RMSE) and mean absolute predictive error (MAPE). Nevertheless, DL with FNN remains the preferred method for predicting locations of influenza peaks. Conclusions The proposed approach can be viewed a feasible alternative to forecast ILI in Hong Kong or other countries where ILI has no constant seasonal trend and influenza data resources are limited. The proposed methodology is easily tractable and computationally efficient. PMID:28464015

Clinical Predictive Models for Chemotherapy-Induced Febrile Neutropenia in Breast Cancer Patients: A Validation Study

PubMed Central

Zhu, Liling; Su, Fengxi; Jia, Weijuan; Deng, Xiaogeng

2014-01-01

Background Predictive models for febrile neutropenia (FN) would be informative for physicians in clinical decision making. This study aims to validate a predictive model (Jenkin’s model) that comprises pretreatment hematological parameters in early-stage breast cancer patients. Patients and Methods A total of 428 breast cancer patients who received neoadjuvant/adjuvant chemotherapy without any prophylactic use of colony-stimulating factor were included. Pretreatment absolute neutrophil counts (ANC) and absolute lymphocyte counts (ALC) were used by the Jenkin’s model to assess the risk of FN. In addition, we modified the threshold of Jenkin’s model and generated Model-A and B. We also developed Model-C by incorporating the absolute monocyte count (AMC) as a predictor into Model-A. The rates of FN in the 1st chemotherapy cycle were calculated. A valid model should be able to significantly identify high-risk subgroup of patients with FN rate >20%. Results Jenkin’s model (Predicted as high-risk when ANC≦3.1*10∧9/L;ALC≦1.5*10∧9/L) did not identify any subgroups with significantly high risk (>20%) of FN in our population, even if we used different thresholds in Model-A(ANC≦4.4*10∧9/L;ALC≦2.1*10∧9/L) or B(ANC≦3.8*10∧9/L;ALC≦1.8*10∧9/L). However, with AMC added as an additional predictor, Model-C(ANC≦4.4*10∧9/L;ALC≦2.1*10∧9/L; AMC≦0.28*10∧9/L) identified a subgroup of patients with a significantly high risk of FN (23.1%). Conclusions In our population, Jenkin’s model, cannot accurately identify patients with a significant risk of FN. The threshold should be changed and the AMC should be incorporated as a predictor, to have excellent predictive ability. PMID:24945817

Modeling causes of death: an integrated approach using CODEm

PubMed Central

2012-01-01

Background Data on causes of death by age and sex are a critical input into health decision-making. Priority setting in public health should be informed not only by the current magnitude of health problems but by trends in them. However, cause of death data are often not available or are subject to substantial problems of comparability. We propose five general principles for cause of death model development, validation, and reporting. Methods We detail a specific implementation of these principles that is embodied in an analytical tool - the Cause of Death Ensemble model (CODEm) - which explores a large variety of possible models to estimate trends in causes of death. Possible models are identified using a covariate selection algorithm that yields many plausible combinations of covariates, which are then run through four model classes. The model classes include mixed effects linear models and spatial-temporal Gaussian Process Regression models for cause fractions and death rates. All models for each cause of death are then assessed using out-of-sample predictive validity and combined into an ensemble with optimal out-of-sample predictive performance. Results Ensemble models for cause of death estimation outperform any single component model in tests of root mean square error, frequency of predicting correct temporal trends, and achieving 95% coverage of the prediction interval. We present detailed results for CODEm applied to maternal mortality and summary results for several other causes of death, including cardiovascular disease and several cancers. Conclusions CODEm produces better estimates of cause of death trends than previous methods and is less susceptible to bias in model specification. We demonstrate the utility of CODEm for the estimation of several major causes of death. PMID:22226226

Vehicle license plate recognition in dense fog based on improved atmospheric scattering model

NASA Astrophysics Data System (ADS)

Tang, Chunming; Lin, Jun; Chen, Chunkai; Dong, Yancheng

2018-04-01

An effective method based on improved atmospheric scattering model is proposed in this paper to handle the problem of the vehicle license plate location and recognition in dense fog. Dense fog detection is performed firstly by the top-hat transformation and the vertical edge detection, and the moving vehicle image is separated from the traffic video image. After the vehicle image is decomposed into two layers: structure and texture layers, the glow layer is separated from the structure layer to get the background layer. Followed by performing the mean-pooling and the bicubic interpolation algorithm, the atmospheric light map of the background layer can be predicted, meanwhile the transmission of the background layer is estimated through the grayed glow layer, whose gray value is altered by linear mapping. Then, according to the improved atmospheric scattering model, the final restored image can be obtained by fusing the restored background layer and the optimized texture layer. License plate location is performed secondly by a series of morphological operations, connected domain analysis and various validations. Characters extraction is achieved according to the projection. Finally, an offline trained pattern classifier of hybrid discriminative restricted boltzmann machines (HDRBM) is applied to recognize the characters. Experimental results on thorough data sets are reported to demonstrate that the proposed method can achieve high recognition accuracy and works robustly in the dense fog traffic environment during 24h or one day.

Using built environment characteristics to predict walking for exercise

PubMed Central

Lovasi, Gina S; Moudon, Anne V; Pearson, Amber L; Hurvitz, Philip M; Larson, Eric B; Siscovick, David S; Berke, Ethan M; Lumley, Thomas; Psaty, Bruce M

2008-01-01

Background Environments conducive to walking may help people avoid sedentary lifestyles and associated diseases. Recent studies developed walkability models combining several built environment characteristics to optimally predict walking. Developing and testing such models with the same data could lead to overestimating one's ability to predict walking in an independent sample of the population. More accurate estimates of model fit can be obtained by splitting a single study population into training and validation sets (holdout approach) or through developing and evaluating models in different populations. We used these two approaches to test whether built environment characteristics near the home predict walking for exercise. Study participants lived in western Washington State and were adult members of a health maintenance organization. The physical activity data used in this study were collected by telephone interview and were selected for their relevance to cardiovascular disease. In order to limit confounding by prior health conditions, the sample was restricted to participants in good self-reported health and without a documented history of cardiovascular disease. Results For 1,608 participants meeting the inclusion criteria, the mean age was 64 years, 90 percent were white, 37 percent had a college degree, and 62 percent of participants reported that they walked for exercise. Single built environment characteristics, such as residential density or connectivity, did not significantly predict walking for exercise. Regression models using multiple built environment characteristics to predict walking were not successful at predicting walking for exercise in an independent population sample. In the validation set, none of the logistic models had a C-statistic confidence interval excluding the null value of 0.5, and none of the linear models explained more than one percent of the variance in time spent walking for exercise. We did not detect significant differences in walking for exercise among census areas or postal codes, which were used as proxies for neighborhoods. Conclusion None of the built environment characteristics significantly predicted walking for exercise, nor did combinations of these characteristics predict walking for exercise when tested using a holdout approach. These results reflect a lack of neighborhood-level variation in walking for exercise for the population studied. PMID:18312660

A New Hybrid Spatio-Temporal Model For Estimating Daily Multi-Year PM2.5 Concentrations Across Northeastern USA Using High Resolution Aerosol Optical Depth Data

PubMed Central

Kloog, Itai; Chudnovsky, Alexandra A.; Just, Allan C.; Nordio, Francesco; Koutrakis, Petros; Coull, Brent A.; Lyapustin, Alexei; Wang, Yujie; Schwartz, Joel

2017-01-01

Background The use of satellite-based aerosol optical depth (AOD) to estimate fine particulate matter (PM2.5) for epidemiology studies has increased substantially over the past few years. These recent studies often report moderate predictive power, which can generate downward bias in effect estimates. In addition, AOD measurements have only moderate spatial resolution, and have substantial missing data. Methods We make use of recent advances in MODIS satellite data processing algorithms (Multi-Angle Implementation of Atmospheric Correction (MAIAC), which allow us to use 1 km (versus currently available 10 km) resolution AOD data. We developed and cross validated models to predict daily PM2.5 at a 1×1km resolution across the northeastern USA (New England, New York and New Jersey) for the years 2003–2011, allowing us to better differentiate daily and long term exposure between urban, suburban, and rural areas. Additionally, we developed an approach that allows us to generate daily high-resolution 200 m localized predictions representing deviations from the area 1×1 km grid predictions. We used mixed models regressing PM2.5 measurements against day-specific random intercepts, and fixed and random AOD and temperature slopes. We then use generalized additive mixed models with spatial smoothing to generate grid cell predictions when AOD was missing. Finally, to get 200 m localized predictions, we regressed the residuals from the final model for each monitor against the local spatial and temporal variables at each monitoring site. Results Our model performance was excellent (mean out-of-sample R2=0.88). The spatial and temporal components of the out-of-sample results also presented very good fits to the withheld data (R2=0.87, R2=0.87). In addition, our results revealed very little bias in the predicted concentrations (Slope of predictions versus withheld observations = 0.99). Conclusion Our daily model results show high predictive accuracy at high spatial resolutions and will be useful in reconstructing exposure histories for epidemiological studies across this region. PMID:28966552

Plant microRNA-Target Interaction Identification Model Based on the Integration of Prediction Tools and Support Vector Machine

PubMed Central

Meng, Jun; Shi, Lin; Luan, Yushi

2014-01-01

Background Confident identification of microRNA-target interactions is significant for studying the function of microRNA (miRNA). Although some computational miRNA target prediction methods have been proposed for plants, results of various methods tend to be inconsistent and usually lead to more false positive. To address these issues, we developed an integrated model for identifying plant miRNA–target interactions. Results Three online miRNA target prediction toolkits and machine learning algorithms were integrated to identify and analyze Arabidopsis thaliana miRNA-target interactions. Principle component analysis (PCA) feature extraction and self-training technology were introduced to improve the performance. Results showed that the proposed model outperformed the previously existing methods. The results were validated by using degradome sequencing supported Arabidopsis thaliana miRNA-target interactions. The proposed model constructed on Arabidopsis thaliana was run over Oryza sativa and Vitis vinifera to demonstrate that our model is effective for other plant species. Conclusions The integrated model of online predictors and local PCA-SVM classifier gained credible and high quality miRNA-target interactions. The supervised learning algorithm of PCA-SVM classifier was employed in plant miRNA target identification for the first time. Its performance can be substantially improved if more experimentally proved training samples are provided. PMID:25051153

Current and future background ozone simulations for Mexico using a multi-scale regional climate modeling system

NASA Astrophysics Data System (ADS)

Lamb, B. K.; Gonzalez Abraham, R.; Avise, J. C.; Chung, S. H.; Salathe, E. P.; Zhang, Y.; Guenther, A. B.; Wiedinmyer, C.; Duhl, T.; Streets, D. G.

2013-05-01

Global change will clearly have a significant impact on the environment. Among the concerns for future air quality in North America, intercontinental transport of pollution has become increasingly important. In this study, we examined the effect of projected changes in Asian emissions and emissions from lightning and wildfires to produce ozone background concentrations within Mexico and the continental US. This provides a basis for developing an understanding of North American background levels and how they may change in the future. Meteorological fields were downscaled from the results of the ECHAM5 global climate model using the Weather Research Forecast (WRF) model. Two nested domains were employed, one covering most of the Northern Hemisphere from eastern Asia to North America using 220 km grid cells (semi-hemispheric domain) and one covering the continental US and northern Mexico using 36 km grid cells. Meteorological results from WRF were used to drive the MEGAN biogenic emissions model, the SMOKE emissions processing tool, and the CMAQ chemical transport model to predict ozone concentrations for current (1995-2004) and future (2045-2054) summertime conditions. The MEGAN model was used to calculate biogenic emissions for all simulations. For the semi-hemispheric domain, year 2000 global emissions of gases (ozone precursors) from anthropogenic (outside of North America), natural, and biomass burning sources from the POET and EDGAR emission inventories were used. The global tabulation for black and organic carbon (BC and OC respectively) was obtained from Bond et al. (2004) For the future decade, the current emissions were projected to the year 2050 following the Intergovernmental Panel for Climate Change (IPCC) A1B emission scenario. Anthropogenic emissions from the US, Canada, and Mexico were omitted so that only global background concentrations, and local biogenic, wildfire, and lightning emissions were treated. In this paper, we focus on background ozone levels in Mexico due to changes in future climate, local biogenic emissions and global emissions.

Search for Standard Model Production of Four Top Quarks at CMS in the Dilepton Channel at a Center of Mass Energy of 13 TeV

NASA Astrophysics Data System (ADS)

Heilman, Jesse Alan

The search for the production of four top quarks decaying in the dileptonic channel in proton-proton collisions at the LHC is presented. The analysis utilises the data recorded by the CMS experiment at sqrt{s} = 13 TeV in 2015, which corresponds to an integrated luminosity of 2.6 inverse femtobarns. A boosted decision tree algorithm is used to select signal and suppress background events. Upper limits on dileptonic four top quark production of 14.9 times the predicted standard model cross section observed and 22.3 +16.2-8.4 times the predicted standard model cross section expected are calculated at the 95% confidence level. A combination is then performed with a parallel analysis of the single lepton channel to extend the reach of the search.

Measurement of Coherent π^{+} Production in Low Energy Neutrino-Carbon Scattering.

PubMed

Abe, K; Andreopoulos, C; Antonova, M; Aoki, S; Ariga, A; Assylbekov, S; Autiero, D; Ban, S; Barbi, M; Barker, G J; Barr, G; Bartet-Friburg, P; Batkiewicz, M; Bay, F; Berardi, V; Berkman, S; Bhadra, S; Blondel, A; Bolognesi, S; Bordoni, S; Boyd, S B; Brailsford, D; Bravar, A; Bronner, C; Buizza Avanzini, M; Calland, R G; Campbell, T; Cao, S; Caravaca Rodríguez, J; Cartwright, S L; Castillo, R; Catanesi, M G; Cervera, A; Cherdack, D; Chikuma, N; Christodoulou, G; Clifton, A; Coleman, J; Collazuol, G; Coplowe, D; Cremonesi, L; Dabrowska, A; De Rosa, G; Dealtry, T; Denner, P F; Dennis, S R; Densham, C; Dewhurst, D; Di Lodovico, F; Di Luise, S; Dolan, S; Drapier, O; Duffy, K E; Dumarchez, J; Dytman, S; Dziewiecki, M; Emery-Schrenk, S; Ereditato, A; Feusels, T; Finch, A J; Fiorentini, G A; Friend, M; Fujii, Y; Fukuda, D; Fukuda, Y; Furmanski, A P; Galymov, V; Garcia, A; Giffin, S G; Giganti, C; Gizzarelli, F; Gonin, M; Grant, N; Hadley, D R; Haegel, L; Haigh, M D; Hamilton, P; Hansen, D; Harada, J; Hara, T; Hartz, M; Hasegawa, T; Hastings, N C; Hayashino, T; Hayato, Y; Helmer, R L; Hierholzer, M; Hillairet, A; Himmel, A; Hiraki, T; Hirota, S; Hogan, M; Holeczek, J; Horikawa, S; Hosomi, F; Huang, K; Ichikawa, A K; Ieki, K; Ikeda, M; Imber, J; Insler, J; Intonti, R A; Irvine, T J; Ishida, T; Ishii, T; Iwai, E; Iwamoto, K; Izmaylov, A; Jacob, A; Jamieson, B; Jiang, M; Johnson, S; Jo, J H; Jonsson, P; Jung, C K; Kabirnezhad, M; Kaboth, A C; Kajita, T; Kakuno, H; Kameda, J; Karlen, D; Karpikov, I; Katori, T; Kearns, E; Khabibullin, M; Khotjantsev, A; Kielczewska, D; Kikawa, T; Kim, H; Kim, J; King, S; Kisiel, J; Knight, A; Knox, A; Kobayashi, T; Koch, L; Koga, T; Konaka, A; Kondo, K; Kopylov, A; Kormos, L L; Korzenev, A; Koshio, Y; Kropp, W; Kudenko, Y; Kurjata, R; Kutter, T; Lagoda, J; Lamont, I; Larkin, E; Lasorak, P; Laveder, M; Lawe, M; Lazos, M; Lindner, T; Liptak, Z J; Litchfield, R P; Li, X; Longhin, A; Lopez, J P; Ludovici, L; Lu, X; Magaletti, L; Mahn, K; Malek, M; Manly, S; Marino, A D; Marteau, J; Martin, J F; Martins, P; Martynenko, S; Maruyama, T; Matveev, V; Mavrokoridis, K; Ma, W Y; Mazzucato, E; McCarthy, M; McCauley, N; McFarland, K S; McGrew, C; Mefodiev, A; Metelko, C; Mezzetto, M; Mijakowski, P; Minamino, A; Mineev, O; Mine, S; Missert, A; Miura, M; Moriyama, S; Mueller, Th A; Murphy, S; Myslik, J; Nakadaira, T; Nakahata, M; Nakamura, K G; Nakamura, K; Nakamura, K D; Nakayama, S; Nakaya, T; Nakayoshi, K; Nantais, C; Nielsen, C; Nirkko, M; Nishikawa, K; Nishimura, Y; Novella, P; Nowak, J; O'Keeffe, H M; Ohta, R; Okumura, K; Okusawa, T; Oryszczak, W; Oser, S M; Ovsyannikova, T; Owen, R A; Oyama, Y; Palladino, V; Palomino, J L; Paolone, V; Patel, N D; Pavin, M; Payne, D; Perkin, J D; Petrov, Y; Pickard, L; Pickering, L; Pinzon Guerra, E S; Pistillo, C; Popov, B; Posiadala-Zezula, M; Poutissou, J-M; Poutissou, R; Przewlocki, P; Quilain, B; Radermacher, T; Radicioni, E; Ratoff, P N; Ravonel, M; Rayner, M A M; Redij, A; Reinherz-Aronis, E; Riccio, C; Rojas, P; Rondio, E; Roth, S; Rubbia, A; Rychter, A; Sacco, R; Sakashita, K; Sánchez, F; Sato, F; Scantamburlo, E; Scholberg, K; Schoppmann, S; Schwehr, J; Scott, M; Seiya, Y; Sekiguchi, T; Sekiya, H; Sgalaberna, D; Shah, R; Shaikhiev, A; Shaker, F; Shaw, D; Shiozawa, M; Shirahige, T; Short, S; Smy, M; Sobczyk, J T; Sobel, H; Sorel, M; Southwell, L; Stamoulis, P; Steinmann, J; Stewart, T; Stowell, P; Suda, Y; Suvorov, S; Suzuki, A; Suzuki, K; Suzuki, S Y; Suzuki, Y; Tacik, R; Tada, M; Takahashi, S; Takeda, A; Takeuchi, Y; Tanaka, H K; Tanaka, H A; Terhorst, D; Terri, R; Thakore, T; Thompson, L F; Tobayama, S; Toki, W; Tomura, T; Touramanis, C; Tsukamoto, T; Tzanov, M; Uchida, Y; Vacheret, A; Vagins, M; Vallari, Z; Vasseur, G; Wachala, T; Wakamatsu, K; Walter, C W; Wark, D; Warzycha, W; Wascko, M O; Weber, A; Wendell, R; Wilkes, R J; Wilking, M J; Wilkinson, C; Wilson, J R; Wilson, R J; Yamada, Y; Yamamoto, K; Yamamoto, M; Yanagisawa, C; Yano, T; Yen, S; Yershov, N; Yokoyama, M; Yoo, J; Yoshida, K; Yuan, T; Yu, M; Zalewska, A; Zalipska, J; Zambelli, L; Zaremba, K; Ziembicki, M; Zimmerman, E D; Zito, M; Żmuda, J

2016-11-04

We report the first measurement of the flux-averaged cross section for charged current coherent π^{+} production on carbon for neutrino energies less than 1.5 GeV, and with a restriction on the final state phase space volume in the T2K near detector, ND280. Comparisons are made with predictions from the Rein-Sehgal coherent production model and the model by Alvarez-Ruso et al., the latter representing the first implementation of an instance of the new class of microscopic coherent models in a neutrino interaction Monte Carlo event generator. We observe a clear event excess above background, disagreeing with the null results reported by K2K and SciBooNE in a similar neutrino energy region. The measured flux-averaged cross sections are below those predicted by both the Rein-Sehgal and Alvarez-Ruso et al.

A proof for loop-law constraints in stoichiometric metabolic networks

PubMed Central

2012-01-01

Background Constraint-based modeling is increasingly employed for metabolic network analysis. Its underlying assumption is that natural metabolic phenotypes can be predicted by adding physicochemical constraints to remove unrealistic metabolic flux solutions. The loopless-COBRA approach provides an additional constraint that eliminates thermodynamically infeasible internal cycles (or loops) from the space of solutions. This allows the prediction of flux solutions that are more consistent with experimental data. However, it is not clear if this approach over-constrains the models by removing non-loop solutions as well. Results Here we apply Gordan’s theorem from linear algebra to prove for the first time that the constraints added in loopless-COBRA do not over-constrain the problem beyond the elimination of the loops themselves. Conclusions The loopless-COBRA constraints can be reliably applied. Furthermore, this proof may be adapted to evaluate the theoretical soundness for other methods in constraint-based modeling. PMID:23146116

Modeling the NF-κB mediated inflammatory response predicts cytokine waves in tissue

PubMed Central

2011-01-01

Background Waves propagating in "excitable media" is a reliable way to transmit signals in space. A fascinating example where living cells comprise such a medium is Dictyostelium D. which propagates waves of chemoattractant to attract distant cells. While neutrophils chemotax in a similar fashion as Dictyostelium D., it is unclear if chemoattractant waves exist in mammalian tissues and what mechanisms could propagate them. Results We propose that chemoattractant cytokine waves may naturally develop as a result of NF-κB response. Using a heuristic mathematical model of NF-κB-like circuits coupled in space we show that the known characteristics of NF-κB response favor cytokine waves. Conclusions While the propagating wave of cytokines is generally beneficial for inflammation resolution, our model predicts that there exist special conditions that can cause chronic inflammation and re-occurrence of acute inflammatory response. PMID:21771307

Search for gluinos and scalar quarks in pp collisions at square root[s] = 1.8 TeV using the missing energy plus multijets signature.

PubMed

Affolder, T; Akimoto, H; Akopian, A; Albrow, M G; Amaral, P; Amidei, D; Anikeev, K; Antos, J; Apollinari, G; Arisawa, T; Artikov, A; Asakawa, T; Ashmanskas, W; Azfar, F; Azzi-Bacchetta, P; Bacchetta, N; Bachacou, H; Bailey, S; de Barbaro, P; Barbaro-Galtieri, A; Barnes, V E; Barnett, B A; Baroiant, S; Barone, M; Bauer, G; Bedeschi, F; Belforte, S; Bell, W H; Bellettini, G; Bellinger, J; Benjamin, D; Bensinger, J; Beretvas, A; Berge, J P; Berryhill, J; Bhatti, A; Binkley, M; Bisello, D; Bishai, M; Blair, R E; Blocker, C; Bloom, K; Blumenfeld, B; Blusk, S R; Bocci, A; Bodek, A; Bokhari, W; Bolla, G; Bonushkin, Y; Bortoletto, D; Boudreau, J; Brandl, A; van den Brink, S; Bromberg, C; Brozovic, M; Brubaker, E; Bruner, N; Buckley-Geer, E; Budagov, J; Budd, H S; Burkett, K; Busetto, G; Byon-Wagner, A; Byrum, K L; Cabrera, S; Calafiura, P; Campbell, M; Carithers, W; Carlson, J; Carlsmith, D; Caskey, W; Castro, A; Cauz, D; Cerri, A; Chan, A W; Chang, P S; Chang, P T; Chapman, J; Chen, C; Chen, Y C; Cheng, M-T; Chertok, M; Chiarelli, G; Chirikov-Zorin, I; Chlachidze, G; Chlebana, F; Christofek, L; Chu, M L; Chung, Y S; Ciobanu, C I; Clark, A G; Connolly, A; Conway, J; Cordelli, M; Cranshaw, J; Cropp, R; Culbertson, R; Dagenhart, D; D'Auria, S; DeJongh, F; Dell'Agnello, S; Dell'Orso, M; Demortier, L; Deninno, M; Derwent, P F; Devlin, T; Dittmann, J R; Dominguez, A; Donati, S; Done, J; D'Onofrio, M; Dorigo, T; Eddy, N; Einsweiler, K; Elias, J E; Engels, E; Erbacher, R; Errede, D; Errede, S; Fan, Q; Feild, R G; Fernandez, J P; Ferretti, C; Field, R D; Fiori, I; Flaugher, B; Foster, G W; Franklin, M; Freeman, J; Friedman, J; Frisch, H J; Fukui, Y; Furic, I; Galeotti, S; Gallas, A; Gallinaro, M; Gao, T; Garcia-Sciveres, M; Garfinkel, A F; Gatti, P; Gay, C; Gerdes, D W; Giannetti, P; Giromini, P; Glagolev, V; Glenzinski, D; Gold, M; Goldstein, J; Gorelov, I; Goshaw, A T; Gotra, Y; Goulianos, K; Green, C; Grim, G; Gris, P; Groer, L; Grosso-Pilcher, C; Guenther, M; Guillian, G; Guimaraes da Costa, J; Haas, R M; Haber, C; Hahn, S R; Hall, C; Handa, T; Handler, R; Hao, W; Happacher, F; Hara, K; Hardman, A D; Harris, R M; Hartmann, F; Hatakeyama, K; Hauser, J; Heinrich, J; Heiss, A; Herndon, M; Hill, C; Hoffman, K D; Holck, C; Hollebeek, R; Holloway, L; Hughes, R; Huston, J; Huth, J; Ikeda, H; Incandela, J; Introzzi, G; Iwai, J; Iwata, Y; James, E; Jones, M; Joshi, U; Kambara, H; Kamon, T; Kaneko, T; Karr, K; Kasha, H; Kato, Y; Keaffaber, T A; Kelley, K; Kelly, M; Kennedy, R D; Kephart, R; Khazins, D; Kikuchi, T; Kilminster, B; Kim, B J; Kim, D H; Kim, H S; Kim, M J; Kim, S B; Kim, S H; Kim, Y K; Kirby, M; Kirk, M; Kirsch, L; Klimenko, S; Koehn, P; Kondo, K; Konigsberg, J; Korn, A; Korytov, A; Kovacs, E; Kroll, J; Kruse, M; Kuhlmann, S E; Kurino, K; Kuwabara, T; Laasanen, A T; Lai, N; Lami, S; Lammel, S; Lancaster, J; Lancaster, M; Lander, R; Lath, A; Latino, G; LeCompte, T; Lee, A M; Lee, K; Leone, S; Lewis, J D; Lindgren, M; Liss, T M; Liu, J B; Liu, Y C; Litvintsev, D O; Lobban, O; Lockyer, N; Loken, J; Loreti, M; Lucchesi, D; Lukens, P; Lusin, S; Lyons, L; Lys, J; Madrak, R; Maeshima, K; Maksimovic, P; Malferrari, L; Mangano, M; Mariotti, M; Martignon, G; Martin, A; Matthews, J A J; Mayer, J; Mazzanti, P; McFarland, K S; McIntyre, P; McKigney, E; Menguzzato, M; Menzione, A; Mesropian, C; Meyer, A; Miao, T; Miller, R; Miller, J S; Minato, H; Miscetti, S; Mishina, M; Mitselmakher, G; Moggi, N; Moore, E; Moore, R; Morita, Y; Moulik, T; Mulhearn, M; Mukherjee, A; Muller, T; Munar, A; Murat, P; Murgia, S; Nachtman, J; Nagaslaev, V; Nahn, S; Nakada, H; Nakano, I; Nelson, C; Nelson, T; Neu, C; Neuberger, D; Newman-Holmes, C; Ngan, C-Y P; Niu, H; Nodulman, L; Nomerotski, A; Oh, S H; Oh, Y D; Ohmoto, T; Ohsugi, T; Oishi, R; Okusawa, T; Olsen, J; Orejudos, W; Pagliarone, C; Palmonari, F; Paoletti, R; Papadimitriou, V; Partos, D; Patrick, J; Pauletta, G; Paulini, M; Paus, C; Pescara, L; Phillips, T J; Piacentino, G; Pitts, K T; Pompos, A; Pondrom, L; Pope, G; Popovic, M; Prokoshin, F; Proudfoot, J; Ptohos, F; Pukhov, O; Punzi, G; Rakitine, A; Ratnikov, F; Reher, D; Reichold, A; Ribon, A; Riegler, W; Rimondi, F; Ristori, L; Riveline, M; Robertson, W J; Robinson, A; Rodrigo, T; Rolli, S; Rosenson, L; Roser, R; Rossin, R; Roy, A; Ruiz, A; Safonov, A; St Denis, R; Sakumoto, W K; Saltzberg, D; Sanchez, C; Sansoni, A; Santi, L; Sato, H; Savard, P; Schlabach, P; Schmidt, E E; Schmidt, M P; Schmitt, M; Scodellaro, L; Scott, A; Scribano, A; Segler, S; Seidel, S; Seiya, Y; Semenov, A; Semeria, F; Shah, T; Shapiro, M D; Shepard, P F; Shibayama, T; Shimojima, M; Shochet, M; Sidoti, A; Siegrist, J; Sill, A; Sinervo, P; Singh, P; Slaughter, A J; Sliwa, K; Smith, C; Snider, F D; Solodsky, A; Spalding, J; Speer, T; Sphicas, P; Spinella, F; Spiropulu, M; Spiegel, L; Steele, J; Stefanini, A; Strologas, J; Strumia, F; Stuart, D; Sumorok, K; Suzuki, T; Takano, T; Takashima, R; Takikawa, K; Tamburello, P; Tanaka, M; Tannenbaum, B; Tecchio, M; Tesarek, R; Teng, P K; Terashi, K; Tether, S; Thompson, A S; Thurman-Keup, R; Tipton, P; Tkaczyk, S; Toback, D; Tollefson, K; Tollestrup, A; Tonelli, D; Toyoda, H; Trischuk, W; de Troconiz, J F; Tseng, J; Turini, N; Ukegawa, F; Vaiciulis, T; Valls, J; Vejcik, S; Velev, G; Veramendi, G; Vidal, R; Vila, I; Vilar, R; Volobouev, I; von der Mey, M; Vucinic, D; Wagner, R G; Wagner, R L; Wallace, N B; Wan, Z; Wang, C; Wang, M J; Ward, B; Waschke, S; Watanabe, T; Waters, D; Watts, T; Webb, R; Wenzel, H; Wester, W C; Wicklund, A B; Wicklund, E; Wilkes, T; Williams, H H; Wilson, P; Winer, B L; Winn, D; Wolbers, S; Wolinski, D; Wolinski, J; Wolinski, S; Worm, S; Wu, X; Wyss, J; Yao, W; Yagil, A; Yeh, G P; Yoh, J; Yosef, C; Yoshida, T; Yu, I; Yu, S; Yu, Z; Zanetti, A; Zetti, F; Zucchelli, S

2002-01-28

We have performed a search for gluinos (g) and scalar quarks (q) in a data sample of 84 pb(-1) of pp collisions at square root[s] = 1.8 TeV, recorded by the Collider Detector at Fermilab. We investigate the final state of large missing transverse energy and three or more jets, a characteristic signature in R-parity-conserving supersymmetric models. The analysis has been performed "blind," in that the inspection of the signal region is made only after the predictions from standard model backgrounds have been calculated. Comparing the data with predictions of constrained supersymmetric models, we exclude gluino masses below 195 GeV/c2 (95% C.L.), independent of the squark mass. For the case m(q) approximately m(g), gluino masses below 300 GeV/c2 are excluded.

Prediction of Multiple Infections After Severe Burn Trauma: a Prospective Cohort Study

PubMed Central

Yan, Shuangchun; Tsurumi, Amy; Que, Yok-Ai; Ryan, Colleen M.; Bandyopadhaya, Arunava; Morgan, Alexander A.; Flaherty, Patrick J.; Tompkins, Ronald G.; Rahme, Laurence G.

2014-01-01

Objective To develop predictive models for early triage of burn patients based on hyper-susceptibility to repeated infections. Background Infection remains a major cause of mortality and morbidity after severe trauma, demanding new strategies to combat infections. Models for infection prediction are lacking. Methods Secondary analysis of 459 burn patients (≥16 years old) with ≥20% total body surface area burns recruited from six US burn centers. We compared blood transcriptomes with a 180-h cut-off on the injury-to-transcriptome interval of 47 patients (≤1 infection episode) to those of 66 hyper-susceptible patients (multiple [≥2] infection episodes [MIE]). We used LASSO regression to select biomarkers and multivariate logistic regression to built models, accuracy of which were assessed by area under receiver operating characteristic curve (AUROC) and cross-validation. Results Three predictive models were developed covariates of: (1) clinical characteristics; (2) expression profiles of 14 genomic probes; (3) combining (1) and (2). The genomic and clinical models were highly predictive of MIE status (AUROCGenomic = 0.946 [95% CI, 0.906–0.986]); AUROCClinical = 0.864 [CI, 0.794–0.933]; AUROCGenomic/AUROCClinical P = 0.044). Combined model has an increased AUROCCombined of 0.967 (CI, 0.940–0.993) compared to the individual models (AUROCCombined/AUROCClinical P = 0.0069). Hyper-susceptible patients show early alterations in immune-related signaling pathways, epigenetic modulation and chromatin remodeling. Conclusions Early triage of burn patients more susceptible to infections can be made using clinical characteristics and/or genomic signatures. Genomic signature suggests new insights into the pathophysiology of hyper-susceptibility to infection may lead to novel potential therapeutic or prophylactic targets. PMID:24950278

A biological network-based regularized artificial neural network model for robust phenotype prediction from gene expression data.

PubMed

Kang, Tianyu; Ding, Wei; Zhang, Luoyan; Ziemek, Daniel; Zarringhalam, Kourosh

2017-12-19

Stratification of patient subpopulations that respond favorably to treatment or experience and adverse reaction is an essential step toward development of new personalized therapies and diagnostics. It is currently feasible to generate omic-scale biological measurements for all patients in a study, providing an opportunity for machine learning models to identify molecular markers for disease diagnosis and progression. However, the high variability of genetic background in human populations hampers the reproducibility of omic-scale markers. In this paper, we develop a biological network-based regularized artificial neural network model for prediction of phenotype from transcriptomic measurements in clinical trials. To improve model sparsity and the overall reproducibility of the model, we incorporate regularization for simultaneous shrinkage of gene sets based on active upstream regulatory mechanisms into the model. We benchmark our method against various regression, support vector machines and artificial neural network models and demonstrate the ability of our method in predicting the clinical outcomes using clinical trial data on acute rejection in kidney transplantation and response to Infliximab in ulcerative colitis. We show that integration of prior biological knowledge into the classification as developed in this paper, significantly improves the robustness and generalizability of predictions to independent datasets. We provide a Java code of our algorithm along with a parsed version of the STRING DB database. In summary, we present a method for prediction of clinical phenotypes using baseline genome-wide expression data that makes use of prior biological knowledge on gene-regulatory interactions in order to increase robustness and reproducibility of omic-scale markers. The integrated group-wise regularization methods increases the interpretability of biological signatures and gives stable performance estimates across independent test sets.

Integration of Gravitational Torques in Cerebellar Pathways Allows for the Dynamic Inverse Computation of Vertical Pointing Movements of a Robot Arm

PubMed Central

Gentili, Rodolphe J.; Papaxanthis, Charalambos; Ebadzadeh, Mehdi; Eskiizmirliler, Selim; Ouanezar, Sofiane; Darlot, Christian

2009-01-01

Background Several authors suggested that gravitational forces are centrally represented in the brain for planning, control and sensorimotor predictions of movements. Furthermore, some studies proposed that the cerebellum computes the inverse dynamics (internal inverse model) whereas others suggested that it computes sensorimotor predictions (internal forward model). Methodology/Principal Findings This study proposes a model of cerebellar pathways deduced from both biological and physical constraints. The model learns the dynamic inverse computation of the effect of gravitational torques from its sensorimotor predictions without calculating an explicit inverse computation. By using supervised learning, this model learns to control an anthropomorphic robot arm actuated by two antagonists McKibben artificial muscles. This was achieved by using internal parallel feedback loops containing neural networks which anticipate the sensorimotor consequences of the neural commands. The artificial neural networks architecture was similar to the large-scale connectivity of the cerebellar cortex. Movements in the sagittal plane were performed during three sessions combining different initial positions, amplitudes and directions of movements to vary the effects of the gravitational torques applied to the robotic arm. The results show that this model acquired an internal representation of the gravitational effects during vertical arm pointing movements. Conclusions/Significance This is consistent with the proposal that the cerebellar cortex contains an internal representation of gravitational torques which is encoded through a learning process. Furthermore, this model suggests that the cerebellum performs the inverse dynamics computation based on sensorimotor predictions. This highlights the importance of sensorimotor predictions of gravitational torques acting on upper limb movements performed in the gravitational field. PMID:19384420

External Validation of the Garvan Nomograms for Predicting Absolute Fracture Risk: The Tromsø Study

PubMed Central

Ahmed, Luai A.; Nguyen, Nguyen D.; Bjørnerem, Åshild; Joakimsen, Ragnar M.; Jørgensen, Lone; Størmer, Jan; Bliuc, Dana; Center, Jacqueline R.; Eisman, John A.; Nguyen, Tuan V.; Emaus, Nina

2014-01-01

Background Absolute risk estimation is a preferred approach for assessing fracture risk and treatment decision making. This study aimed to evaluate and validate the predictive performance of the Garvan Fracture Risk Calculator in a Norwegian cohort. Methods The analysis included 1637 women and 1355 aged 60+ years from the Tromsø study. All incident fragility fractures between 2001 and 2009 were registered. The predicted probabilities of non-vertebral osteoporotic and hip fractures were determined using models with and without BMD. The discrimination and calibration of the models were assessed. Reclassification analysis was used to compare the models performance. Results The incidence of osteoporotic and hip fracture was 31.5 and 8.6 per 1000 population in women, respectively; in men the corresponding incidence was 12.2 and 5.1. The predicted 5-year and 10-year probability of fractures was consistently higher in the fracture group than the non-fracture group for all models. The 10-year predicted probabilities of hip fracture in those with fracture was 2.8 (women) to 3.1 times (men) higher than those without fracture. There was a close agreement between predicted and observed risk in both sexes and up to the fifth quintile. Among those in the highest quintile of risk, the models over-estimated the risk of fracture. Models with BMD performed better than models with body weight in correct classification of risk in individuals with and without fracture. The overall net decrease in reclassification of the model with weight compared to the model with BMD was 10.6% (p = 0.008) in women and 17.2% (p = 0.001) in men for osteoporotic fractures, and 13.3% (p = 0.07) in women and 17.5% (p = 0.09) in men for hip fracture. Conclusions The Garvan Fracture Risk Calculator is valid and clinically useful in identifying individuals at high risk of fracture. The models with BMD performed better than those with body weight in fracture risk prediction. PMID:25255221

[Predictors of remission from major depressive disorder in secondary care].

PubMed

Salvo, Lilian; Saldivia, Sandra; Parra, Carlos; Cifuentes, Manuel; Bustos, Claudio; Acevedo, Paola; Díaz, Marcela; Ormazabal, Mitza; Guerra, Ivonne; Navarrete, Nicol; Bravo, Verónica; Castro, Andrea

2017-12-01

Background The knowledge of predictive factors in depression should help to deal with the disease. Aim To assess potential predictors of remission of major depressive disorders (MDD) in secondary care and to propose a predictive model. Material and Methods A 12 month follow-up study was conducted in a sample of 112 outpatients at three psychiatric care centers of Chile, with baseline and quarterly assessments. Demographic, psychosocial, clinical and treatment factors as potential predictors, were assessed. A clinical interview with the checklist of DSM-IV diagnostic criteria, the Hamilton Depression Scale and the List of Threatening Experiences and Multidimensional Scale of Perceived Social Support were applied. Results The number of stressful events, perceived social support, baseline depression scores, melancholic features, time prior to beginning treatment at the secondary level and psychotherapeutic sessions were included in the model as predictors of remission. Sex, age, number of previous depressive episodes, psychiatric comorbidity and medical comorbidity were not significantly related with remission. Conclusions This model allows to predict depression score at six months with 70% of accuracy and the score at 12 months with 72% of accuracy.

Spatial predictive mapping using artificial neural networks

NASA Astrophysics Data System (ADS)

Noack, S.; Knobloch, A.; Etzold, S. H.; Barth, A.; Kallmeier, E.

2014-11-01

The modelling or prediction of complex geospatial phenomena (like formation of geo-hazards) is one of the most important tasks for geoscientists. But in practice it faces various difficulties, caused mainly by the complexity of relationships between the phenomena itself and the controlling parameters, as well by limitations of our knowledge about the nature of physical/ mathematical relationships and by restrictions regarding accuracy and availability of data. In this situation methods of artificial intelligence, like artificial neural networks (ANN) offer a meaningful alternative modelling approach compared to the exact mathematical modelling. In the past, the application of ANN technologies in geosciences was primarily limited due to difficulties to integrate it into geo-data processing algorithms. In consideration of this background, the software advangeo® was developed to provide a normal GIS user with a powerful tool to use ANNs for prediction mapping and data preparation within his standard ESRI ArcGIS environment. In many case studies, such as land use planning, geo-hazards analysis and prevention, mineral potential mapping, agriculture & forestry advangeo® has shown its capabilities and strengths. The approach is able to add considerable value to existing data.

Visual fatigue modeling for stereoscopic video shot based on camera motion

NASA Astrophysics Data System (ADS)

Shi, Guozhong; Sang, Xinzhu; Yu, Xunbo; Liu, Yangdong; Liu, Jing

2014-11-01

As three-dimensional television (3-DTV) and 3-D movie become popular, the discomfort of visual feeling limits further applications of 3D display technology. The cause of visual discomfort from stereoscopic video conflicts between accommodation and convergence, excessive binocular parallax, fast motion of objects and so on. Here, a novel method for evaluating visual fatigue is demonstrated. Influence factors including spatial structure, motion scale and comfortable zone are analyzed. According to the human visual system (HVS), people only need to converge their eyes to the specific objects for static cameras and background. Relative motion should be considered for different camera conditions determining different factor coefficients and weights. Compared with the traditional visual fatigue prediction model, a novel visual fatigue predicting model is presented. Visual fatigue degree is predicted using multiple linear regression method combining with the subjective evaluation. Consequently, each factor can reflect the characteristics of the scene, and the total visual fatigue score can be indicated according to the proposed algorithm. Compared with conventional algorithms which ignored the status of the camera, our approach exhibits reliable performance in terms of correlation with subjective test results.

A model of chromosome aberration induction: applications to space research.

PubMed

Ballarini, Francesca; Ottolenghi, Andrea

2005-10-01

A mechanistic model and Monte Carlo code simulating chromosome aberration induction in human lymphocytes is presented. The model is based on the assumption that aberrations arise from clustered DNA lesions and that only the free ends of clustered lesions created in neighboring chromosome territories or in the same territory can join and produce exchanges. The lesions are distributed in the cell nucleus according to the radiation track structure. Interphase chromosome territories are modeled as compact intranuclear regions with volumes proportional to the chromosome DNA contents. Both Giemsa staining and FISH painting can be simulated, and background aberrations can be taken into account. The good agreement with in vitro data provides validation of the model in terms of both the assumptions adopted and the simulation techniques. As an application in the field of space research, the model predictions were compared with aberration yields measured among crew members of long-term missions on board Mir and ISS, assuming an average radiation quality factor of 2.4. The agreement obtained also validated the model for in vivo exposure scenarios and suggested possible applications to the prediction of other relevant aberrations, typically translocations.

Thermal modeling of lesion growth with radiofrequency ablation devices

PubMed Central

Chang, Isaac A; Nguyen, Uyen D

2004-01-01

Background Temperature is a frequently used parameter to describe the predicted size of lesions computed by computational models. In many cases, however, temperature correlates poorly with lesion size. Although many studies have been conducted to characterize the relationship between time-temperature exposure of tissue heating to cell damage, to date these relationships have not been employed in a finite element model. Methods We present an axisymmetric two-dimensional finite element model that calculates cell damage in tissues and compare lesion sizes using common tissue damage and iso-temperature contour definitions. The model accounts for both temperature-dependent changes in the electrical conductivity of tissue as well as tissue damage-dependent changes in local tissue perfusion. The data is validated using excised porcine liver tissues. Results The data demonstrate the size of thermal lesions is grossly overestimated when calculated using traditional temperature isocontours of 42°C and 47°C. The computational model results predicted lesion dimensions that were within 5% of the experimental measurements. Conclusion When modeling radiofrequency ablation problems, temperature isotherms may not be representative of actual tissue damage patterns. PMID:15298708

Reranking candidate gene models with cross-species comparison for improved gene prediction

PubMed Central

Liu, Qian; Crammer, Koby; Pereira, Fernando CN; Roos, David S

2008-01-01

Background Most gene finders score candidate gene models with state-based methods, typically HMMs, by combining local properties (coding potential, splice donor and acceptor patterns, etc). Competing models with similar state-based scores may be distinguishable with additional information. In particular, functional and comparative genomics datasets may help to select among competing models of comparable probability by exploiting features likely to be associated with the correct gene models, such as conserved exon/intron structure or protein sequence features. Results We have investigated the utility of a simple post-processing step for selecting among a set of alternative gene models, using global scoring rules to rerank competing models for more accurate prediction. For each gene locus, we first generate the K best candidate gene models using the gene finder Evigan, and then rerank these models using comparisons with putative orthologous genes from closely-related species. Candidate gene models with lower scores in the original gene finder may be selected if they exhibit strong similarity to probable orthologs in coding sequence, splice site location, or signal peptide occurrence. Experiments on Drosophila melanogaster demonstrate that reranking based on cross-species comparison outperforms the best gene models identified by Evigan alone, and also outperforms the comparative gene finders GeneWise and Augustus+. Conclusion Reranking gene models with cross-species comparison improves gene prediction accuracy. This straightforward method can be readily adapted to incorporate additional lines of evidence, as it requires only a ranked source of candidate gene models. PMID:18854050

Modeling the effects of exercise during 100% oxygen prebreathe on the risk of hypobaric decompression sickness

NASA Technical Reports Server (NTRS)

Loftin, K. C.; Conkin, J.; Powell, M. R.

1997-01-01

BACKGROUND: Several previous studies indicated that exercise during prebreathe with 100% O2 decreased the incidence of hypobaric decompression sickness (DCS). We report a meta-analysis of these investigations combined with a new study in our laboratory to develop a statistical model as a predictive tool for DCS. HYPOTHESIS: Exercise during prebreathe increases N2 elimination in a theoretical 360-min half-time compartment decreasing the incidence of DCS. METHODS: A dose-response probability tissue ratio (TR) model with 95% confidence limits was created for two groups, prebreathe with exercise (n = 113) and resting prebreathe (n = 113), using nonlinear regression analysis with maximum likelihood optimization. RESULTS: The model predicted that prebreathe exercise would reduce the residual N2 in a 360-min half-time compartment to a level analogous to that in a 180-min compartment. This finding supported the hypothesis. The incidence of DCS for the exercise prebreathe group was significantly decreased (Chi-Square = 17.1, p < 0.0001) from the resting prebreathe group. CONCLUSIONS: The results suggested that exercise during prebreathe increases tissue perfusion and N2 elimination approximately 2-fold and markedly lowers the risk of DCS. Based on the model, the prebreathe duration may be reduced from 240 min to a predicted 91 min for the protocol in our study, but this remains to be verified. The model provides a useful planning tool to develop and test appropriate prebreathe exercise protocols and to predict DCS risks for astronauts.

Non-stationary background intensity and Caribbean seismic events

NASA Astrophysics Data System (ADS)

Valmy, Larissa; Vaillant, Jean

2014-05-01

We consider seismic risk calculation based on models with non-stationary background intensity. The aim is to improve predictive strategies in the framework of seismic risk assessment from models describing at best the seismic activity in the Caribbean arc. Appropriate statistical methods are required for analyzing the volumes of data collected. The focus is on calculating earthquakes occurrences probability and analyzing spatiotemporal evolution of these probabilities. The main modeling tool is the point process theory in order to take into account past history prior to a given date. Thus, the seismic event conditional intensity is expressed by means of the background intensity and the self exciting component. This intensity can be interpreted as the expected event rate per time and / or surface unit. The most popular intensity model in seismology is the ETAS (Epidemic Type Aftershock Sequence) model introduced and then generalized by Ogata [2, 3]. We extended this model and performed a comparison of different probability density functions for the triggered event times [4]. We illustrate our model by considering the CDSA (Centre de Données Sismiques des Antilles) catalog [1] which contains more than 7000 seismic events occurred in the Lesser Antilles arc. Statistical tools for testing the background intensity stationarity and for dynamical segmentation are presented. [1] Bengoubou-Valérius M., Bazin S., Bertil D., Beauducel F. and Bosson A. (2008). CDSA: a new seismological data center for the French Lesser Antilles, Seismol. Res. Lett., 79 (1), 90-102. [2] Ogata Y. (1998). Space-time point-process models for earthquake occurrences, Annals of the Institute of Statistical Mathematics, 50 (2), 379-402. [3] Ogata, Y. (2011). Significant improvements of the space-time ETAS model for forecasting of accurate baseline seismicity, Earth, Planets and Space, 63 (3), 217-229. [4] Valmy L. and Vaillant J. (2013). Statistical models in seismology: Lesser Antilles arc case, Bull. Soc. géol. France, 2013, 184 (1), 61-67.

Visual attention in egocentric field-of-view using RGB-D data

NASA Astrophysics Data System (ADS)

Olesova, Veronika; Benesova, Wanda; Polatsek, Patrik

2017-03-01

Most of the existing solutions predicting visual attention focus solely on referenced 2D images and disregard any depth information. This aspect has always represented a weak point since the depth is an inseparable part of the biological vision. This paper presents a novel method of saliency map generation based on results of our experiments with egocentric visual attention and investigation of its correlation with perceived depth. We propose a model to predict the attention using superpixel representation with an assumption that contrast objects are usually salient and have a sparser spatial distribution of superpixels than their background. To incorporate depth information into this model, we propose three different depth techniques. The evaluation is done on our new RGB-D dataset created by SMI eye-tracker glasses and KinectV2 device.

Differentiating epileptic from non-epileptic high frequency intracerebral EEG signals with measures of wavelet entropy.

PubMed

Mooij, Anne H; Frauscher, Birgit; Amiri, Mina; Otte, Willem M; Gotman, Jean

2016-12-01

To assess whether there is a difference in the background activity in the ripple band (80-200Hz) between epileptic and non-epileptic channels, and to assess whether this difference is sufficient for their reliable separation. We calculated mean and standard deviation of wavelet entropy in 303 non-epileptic and 334 epileptic channels from 50 patients with intracerebral depth electrodes and used these measures as predictors in a multivariable logistic regression model. We assessed sensitivity, positive predictive value (PPV) and negative predictive value (NPV) based on a probability threshold corresponding to 90% specificity. The probability of a channel being epileptic increased with higher mean (p=0.004) and particularly with higher standard deviation (p<0.0001). The performance of the model was however not sufficient for fully classifying the channels. With a threshold corresponding to 90% specificity, sensitivity was 37%, PPV was 80%, and NPV was 56%. A channel with a high standard deviation of entropy is likely to be epileptic; with a threshold corresponding to 90% specificity our model can reliably select a subset of epileptic channels. Most studies have concentrated on brief ripple events. We showed that background activity in the ripple band also has some ability to discriminate epileptic channels. Copyright © 2016 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

A hybrid modeling with data assimilation to evaluate human exposure level

NASA Astrophysics Data System (ADS)

Koo, Y. S.; Cheong, H. K.; Choi, D.; Kim, A. L.; Yun, H. Y.

2015-12-01

Exposure models are designed to better represent human contact with PM (Particulate Matter) and other air pollutants such as CO, SO2, O3, and NO2. The exposure concentrations of the air pollutants to human are determined by global and regional long range transport of global and regional scales from Europe and China as well as local emissions from urban and road vehicle sources. To assess the exposure level in detail, the multiple scale influence from background to local sources should be considered. A hybrid air quality modeling methodology combing a grid-based chemical transport model with a local plume dispersion model was used to provide spatially and temporally resolved air quality concentration for human exposure levels in Korea. In the hybrid modeling approach, concentrations from a grid-based chemical transport model and a local plume dispersion model are added to provide contributions from photochemical interactions, long-range (regional) transport and local-scale dispersion. The CAMx (Comprehensive Air quality Model with Extensions was used for the background concentrations from anthropogenic and natural emissions in East Asia including Korea while the road dispersion by vehicle emission was calculated by CALPUFF model. The total exposure level of the pollutants was finally assessed by summing the background and road contributions. In the hybrid modeling, the data assimilation method based on the optimal interpolation was applied to overcome the discrepancies between the model predicted concentrations and observations. The air quality data from the air quality monitoring stations in Korea. The spatial resolution of the hybrid model was 50m for the Seoul Metropolitan Ares. This example clearly demonstrates that the exposure level could be estimated to the fine scale for the exposure assessment by using the hybrid modeling approach with data assimilation.

Predicting Degree of Benefit From Adjuvant Trastuzumab in NSABP Trial B-31

PubMed Central

Pogue-Geile, Katherine L.; Kim, Chungyeul; Jeong, Jong-Hyeon; Tanaka, Noriko; Bandos, Hanna; Gavin, Patrick G.; Fumagalli, Debora; Goldstein, Lynn C.; Sneige, Nour; Burandt, Eike; Taniyama, Yusuke; Bohn, Olga L.; Lee, Ahwon; Kim, Seung-Il; Reilly, Megan L.; Remillard, Matthew Y.; Blackmon, Nicole L.; Kim, Seong-Rim; Horne, Zachary D.; Rastogi, Priya; Fehrenbacher, Louis; Romond, Edward H.; Swain, Sandra M.; Mamounas, Eleftherios P.; Wickerham, D. Lawrence; Geyer, Charles E.; Costantino, Joseph P.; Wolmark, Norman

2013-01-01

Background National Surgical Adjuvant Breast and Bowel Project (NSABP) trial B-31 suggested the efficacy of adjuvant trastuzumab, even in HER2-negative breast cancer. This finding prompted us to develop a predictive model for degree of benefit from trastuzumab using archived tumor blocks from B-31. Methods Case subjects with tumor blocks were randomly divided into discovery (n = 588) and confirmation cohorts (n = 991). A predictive model was built from the discovery cohort through gene expression profiling of 462 genes with nCounter assay. A predefined cut point for the predictive model was tested in the confirmation cohort. Gene-by-treatment interaction was tested with Cox models, and correlations between variables were assessed with Spearman correlation. Principal component analysis was performed on the final set of selected genes. All statistical tests were two-sided. Results Eight predictive genes associated with HER2 (ERBB2, c17orf37, GRB7) or ER (ESR1, NAT1, GATA3, CA12, IGF1R) were selected for model building. Three-dimensional subset treatment effect pattern plot using two principal components of these genes was used to identify a subset with no benefit from trastuzumab, characterized by intermediate-level ERBB2 and high-level ESR1 mRNA expression. In the confirmation set, the predefined cut points for this model classified patients into three subsets with differential benefit from trastuzumab with hazard ratios of 1.58 (95% confidence interval [CI] = 0.67 to 3.69; P = .29; n = 100), 0.60 (95% CI = 0.41 to 0.89; P = .01; n = 449), and 0.28 (95% CI = 0.20 to 0.41; P < .001; n = 442; P interaction between the model and trastuzumab < .001). Conclusions We developed a gene expression–based predictive model for degree of benefit from trastuzumab and demonstrated that HER2-negative tumors belong to the moderate benefit group, thus providing justification for testing trastuzumab in HER2-negative patients (NSABP B-47). PMID:24262440

Evaluation of internal noise methods for Hotelling observers

NASA Astrophysics Data System (ADS)

Zhang, Yani; Pham, Binh T.; Eckstein, Miguel P.

2005-04-01

Including internal noise in computer model observers to degrade model observer performance to human levels is a common method to allow for quantitatively comparisons of human and model performance. In this paper, we studied two different types of methods for injecting internal noise to Hotelling model observers. The first method adds internal noise to the output of the individual channels: a) Independent non-uniform channel noise, b) Independent uniform channel noise. The second method adds internal noise to the decision variable arising from the combination of channel responses: a) internal noise standard deviation proportional to decision variable's standard deviation due to the external noise, b) internal noise standard deviation proportional to decision variable's variance caused by the external noise. We tested the square window Hotelling observer (HO), channelized Hotelling observer (CHO), and Laguerre-Gauss Hotelling observer (LGHO). The studied task was detection of a filling defect of varying size/shape in one of four simulated arterial segment locations with real x-ray angiography backgrounds. Results show that the internal noise method that leads to the best prediction of human performance differs across the studied models observers. The CHO model best predicts human observer performance with the channel internal noise. The HO and LGHO best predict human observer performance with the decision variable internal noise. These results might help explain why previous studies have found different results on the ability of each Hotelling model to predict human performance. Finally, the present results might guide researchers with the choice of method to include internal noise into their Hotelling models.

Prediction of two month modified Rankin Scale with an ordinal prediction model in patients with aneurysmal subarachnoid haemorrhage

PubMed Central

2010-01-01

Background Aneurysmal subarachnoid haemorrhage (aSAH) is a devastating event with a frequently disabling outcome. Our aim was to develop a prognostic model to predict an ordinal clinical outcome at two months in patients with aSAH. Methods We studied patients enrolled in the International Subarachnoid Aneurysm Trial (ISAT), a randomized multicentre trial to compare coiling and clipping in aSAH patients. Several models were explored to estimate a patient's outcome according to the modified Rankin Scale (mRS) at two months after aSAH. Our final model was validated internally with bootstrapping techniques. Results The study population comprised of 2,128 patients of whom 159 patients died within 2 months (8%). Multivariable proportional odds analysis identified World Federation of Neurosurgical Societies (WFNS) grade as the most important predictor, followed by age, sex, lumen size of the aneurysm, Fisher grade, vasospasm on angiography, and treatment modality. The model discriminated moderately between those with poor and good mRS scores (c statistic = 0.65), with minor optimism according to bootstrap re-sampling (optimism corrected c statistic = 0.64). Conclusion We presented a calibrated and internally validated ordinal prognostic model to predict two month mRS in aSAH patients who survived the early stage up till a treatment decision. Although generalizability of the model is limited due to the selected population in which it was developed, this model could eventually be used to support clinical decision making after external validation. Trial Registration International Standard Randomised Controlled Trial, Number ISRCTN49866681 PMID:20920243

Physiologically Based Pharmacokinetic (PBPK) Modeling of ...

EPA Pesticide Factsheets

Background: Quantitative estimation of toxicokinetic variability in the human population is a persistent challenge in risk assessment of environmental chemicals. Traditionally, inter-individual differences in the population are accounted for by default assumptions or, in rare cases, are based on human toxicokinetic data.Objectives: To evaluate the utility of genetically diverse mouse strains for estimating toxicokinetic population variability for risk assessment, using trichloroethylene (TCE) metabolism as a case study. Methods: We used data on oxidative and glutathione conjugation metabolism of TCE in 16 inbred and one hybrid mouse strains to calibrate and extend existing physiologically-based pharmacokinetic (PBPK) models. We added one-compartment models for glutathione metabolites and a two-compartment model for dichloroacetic acid (DCA). A Bayesian population analysis of inter-strain variability was used to quantify variability in TCE metabolism. Results: Concentration-time profiles for TCE metabolism to oxidative and glutathione conjugation metabolites varied across strains. Median predictions for the metabolic flux through oxidation was less variable (5-fold range) than that through glutathione conjugation (10-fold range). For oxidative metabolites, median predictions of trichloroacetic acid production was less variable (2-fold range) than DCA production (5-fold range), although uncertainty bounds for DCA exceeded the predicted variability. Conclusions:

Open-source Software for Demand Forecasting of Clinical Laboratory Test Volumes Using Time-series Analysis

PubMed Central

Mohammed, Emad A.; Naugler, Christopher

2017-01-01

Background: Demand forecasting is the area of predictive analytics devoted to predicting future volumes of services or consumables. Fair understanding and estimation of how demand will vary facilitates the optimal utilization of resources. In a medical laboratory, accurate forecasting of future demand, that is, test volumes, can increase efficiency and facilitate long-term laboratory planning. Importantly, in an era of utilization management initiatives, accurately predicted volumes compared to the realized test volumes can form a precise way to evaluate utilization management initiatives. Laboratory test volumes are often highly amenable to forecasting by time-series models; however, the statistical software needed to do this is generally either expensive or highly technical. Method: In this paper, we describe an open-source web-based software tool for time-series forecasting and explain how to use it as a demand forecasting tool in clinical laboratories to estimate test volumes. Results: This tool has three different models, that is, Holt-Winters multiplicative, Holt-Winters additive, and simple linear regression. Moreover, these models are ranked and the best one is highlighted. Conclusion: This tool will allow anyone with historic test volume data to model future demand. PMID:28400996

MGGPOD: a Monte Carlo Suite for Modeling Instrumental Line and Continuum Backgrounds in Gamma-Ray Astronomy

NASA Technical Reports Server (NTRS)

Weidenspointner, G.; Harris, M. J.; Sturner, S.; Teegarden, B. J.; Ferguson, C.

2004-01-01

Intense and complex instrumental backgrounds, against which the much smaller signals from celestial sources have to be discerned, are a notorious problem for low and intermediate energy gamma-ray astronomy (approximately 50 keV - 10 MeV). Therefore a detailed qualitative and quantitative understanding of instrumental line and continuum backgrounds is crucial for most stages of gamma-ray astronomy missions, ranging from the design and development of new instrumentation through performance prediction to data reduction. We have developed MGGPOD, a user-friendly suite of Monte Carlo codes built around the widely used GEANT (Version 3.21) package, to simulate ab initio the physical processes relevant for the production of instrumental backgrounds. These include the build-up and delayed decay of radioactive isotopes as well as the prompt de-excitation of excited nuclei, both of which give rise to a plethora of instrumental gamma-ray background lines in addition t o continuum backgrounds. The MGGPOD package and documentation are publicly available for download. We demonstrate the capabilities of the MGGPOD suite by modeling high resolution gamma-ray spectra recorded by the Transient Gamma-Ray Spectrometer (TGRS) on board Wind during 1995. The TGRS is a Ge spectrometer operating in the 40 keV to 8 MeV range. Due to its fine energy resolution, these spectra reveal the complex instrumental background in formidable detail, particularly the many prompt and delayed gamma-ray lines. We evaluate the successes and failures of the MGGPOD package in reproducing TGRS data, and provide identifications for the numerous instrumental lines.