Sample records for accurately predict behavior

  1. Accurate Identification of Fear Facial Expressions Predicts Prosocial Behavior

    PubMed Central

    Marsh, Abigail A.; Kozak, Megan N.; Ambady, Nalini

    2009-01-01

    The fear facial expression is a distress cue that is associated with the provision of help and prosocial behavior. Prior psychiatric studies have found deficits in the recognition of this expression by individuals with antisocial tendencies. However, no prior study has shown accuracy for recognition of fear to predict actual prosocial or antisocial behavior in an experimental setting. In 3 studies, the authors tested the prediction that individuals who recognize fear more accurately will behave more prosocially. In Study 1, participants who identified fear more accurately also donated more money and time to a victim in a classic altruism paradigm. In Studies 2 and 3, participants’ ability to identify the fear expression predicted prosocial behavior in a novel task designed to control for confounding variables. In Study 3, accuracy for recognizing fear proved a better predictor of prosocial behavior than gender, mood, or scores on an empathy scale. PMID:17516803

  2. Accurate identification of fear facial expressions predicts prosocial behavior.

    PubMed

    Marsh, Abigail A; Kozak, Megan N; Ambady, Nalini

    2007-05-01

    The fear facial expression is a distress cue that is associated with the provision of help and prosocial behavior. Prior psychiatric studies have found deficits in the recognition of this expression by individuals with antisocial tendencies. However, no prior study has shown accuracy for recognition of fear to predict actual prosocial or antisocial behavior in an experimental setting. In 3 studies, the authors tested the prediction that individuals who recognize fear more accurately will behave more prosocially. In Study 1, participants who identified fear more accurately also donated more money and time to a victim in a classic altruism paradigm. In Studies 2 and 3, participants' ability to identify the fear expression predicted prosocial behavior in a novel task designed to control for confounding variables. In Study 3, accuracy for recognizing fear proved a better predictor of prosocial behavior than gender, mood, or scores on an empathy scale.

  3. Base Rates, Contingencies, and Prediction Behavior

    ERIC Educational Resources Information Center

    Kareev, Yaakov; Fiedler, Klaus; Avrahami, Judith

    2009-01-01

    A skew in the base rate of upcoming events can often provide a better cue for accurate predictions than a contingency between signals and events. The authors study prediction behavior and test people's sensitivity to both base rate and contingency; they also examine people's ability to compare the benefits of both for prediction. They formalize…

  4. An accurate model for predicting high frequency noise of nanoscale NMOS SOI transistors

    NASA Astrophysics Data System (ADS)

    Shen, Yanfei; Cui, Jie; Mohammadi, Saeed

    2017-05-01

    A nonlinear and scalable model suitable for predicting high frequency noise of N-type Metal Oxide Semiconductor (NMOS) transistors is presented. The model is developed for a commercial 45 nm CMOS SOI technology and its accuracy is validated through comparison with measured performance of a microwave low noise amplifier. The model employs the virtual source nonlinear core and adds parasitic elements to accurately simulate the RF behavior of multi-finger NMOS transistors up to 40 GHz. For the first time, the traditional long-channel thermal noise model is supplemented with an injection noise model to accurately represent the noise behavior of these short-channel transistors up to 26 GHz. The developed model is simple and easy to extract, yet very accurate.

  5. Mental models accurately predict emotion transitions.

    PubMed

    Thornton, Mark A; Tamir, Diana I

    2017-06-06

    Successful social interactions depend on people's ability to predict others' future actions and emotions. People possess many mechanisms for perceiving others' current emotional states, but how might they use this information to predict others' future states? We hypothesized that people might capitalize on an overlooked aspect of affective experience: current emotions predict future emotions. By attending to regularities in emotion transitions, perceivers might develop accurate mental models of others' emotional dynamics. People could then use these mental models of emotion transitions to predict others' future emotions from currently observable emotions. To test this hypothesis, studies 1-3 used data from three extant experience-sampling datasets to establish the actual rates of emotional transitions. We then collected three parallel datasets in which participants rated the transition likelihoods between the same set of emotions. Participants' ratings of emotion transitions predicted others' experienced transitional likelihoods with high accuracy. Study 4 demonstrated that four conceptual dimensions of mental state representation-valence, social impact, rationality, and human mind-inform participants' mental models. Study 5 used 2 million emotion reports on the Experience Project to replicate both of these findings: again people reported accurate models of emotion transitions, and these models were informed by the same four conceptual dimensions. Importantly, neither these conceptual dimensions nor holistic similarity could fully explain participants' accuracy, suggesting that their mental models contain accurate information about emotion dynamics above and beyond what might be predicted by static emotion knowledge alone.

  6. Mental models accurately predict emotion transitions

    PubMed Central

    Thornton, Mark A.; Tamir, Diana I.

    2017-01-01

    Successful social interactions depend on people’s ability to predict others’ future actions and emotions. People possess many mechanisms for perceiving others’ current emotional states, but how might they use this information to predict others’ future states? We hypothesized that people might capitalize on an overlooked aspect of affective experience: current emotions predict future emotions. By attending to regularities in emotion transitions, perceivers might develop accurate mental models of others’ emotional dynamics. People could then use these mental models of emotion transitions to predict others’ future emotions from currently observable emotions. To test this hypothesis, studies 1–3 used data from three extant experience-sampling datasets to establish the actual rates of emotional transitions. We then collected three parallel datasets in which participants rated the transition likelihoods between the same set of emotions. Participants’ ratings of emotion transitions predicted others’ experienced transitional likelihoods with high accuracy. Study 4 demonstrated that four conceptual dimensions of mental state representation—valence, social impact, rationality, and human mind—inform participants’ mental models. Study 5 used 2 million emotion reports on the Experience Project to replicate both of these findings: again people reported accurate models of emotion transitions, and these models were informed by the same four conceptual dimensions. Importantly, neither these conceptual dimensions nor holistic similarity could fully explain participants’ accuracy, suggesting that their mental models contain accurate information about emotion dynamics above and beyond what might be predicted by static emotion knowledge alone. PMID:28533373

  7. Measuring the value of accurate link prediction for network seeding.

    PubMed

    Wei, Yijin; Spencer, Gwen

    2017-01-01

    The influence-maximization literature seeks small sets of individuals whose structural placement in the social network can drive large cascades of behavior. Optimization efforts to find the best seed set often assume perfect knowledge of the network topology. Unfortunately, social network links are rarely known in an exact way. When do seeding strategies based on less-than-accurate link prediction provide valuable insight? We introduce optimized-against-a-sample ([Formula: see text]) performance to measure the value of optimizing seeding based on a noisy observation of a network. Our computational study investigates [Formula: see text] under several threshold-spread models in synthetic and real-world networks. Our focus is on measuring the value of imprecise link information. The level of investment in link prediction that is strategic appears to depend closely on spread model: in some parameter ranges investments in improving link prediction can pay substantial premiums in cascade size. For other ranges, such investments would be wasted. Several trends were remarkably consistent across topologies.

  8. Predicting wildfire behavior in black spruce forests in Alaska.

    Treesearch

    Rodney A. Norum

    1982-01-01

    The current fire behavior system, when properly adjusted, accurately predicts forward rate of spread and flame length of wildfires in black spruce (Picea mariana (Mill.) B.S.P.) forests in Alaska. After fire behavior was observed and quantified, adjustment factors were calculated and assigned to the selected fuel models to correct the outputs to...

  9. Predicting Violent Behavior: What Can Neuroscience Add?

    PubMed

    Poldrack, Russell A; Monahan, John; Imrey, Peter B; Reyna, Valerie; Raichle, Marcus E; Faigman, David; Buckholtz, Joshua W

    2018-02-01

    The ability to accurately predict violence and other forms of serious antisocial behavior would provide important societal benefits, and there is substantial enthusiasm for the potential predictive accuracy of neuroimaging techniques. Here, we review the current status of violence prediction using actuarial and clinical methods, and assess the current state of neuroprediction. We then outline several questions that need to be addressed by future studies of neuroprediction if neuroimaging and other neuroscientific markers are to be successfully translated into public policy. Copyright © 2017 Elsevier Ltd. All rights reserved.

  10. Behavior-Based Budget Management Using Predictive Analytics

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

    Troy Hiltbrand

    Historically, the mechanisms to perform forecasting have primarily used two common factors as a basis for future predictions: time and money. While time and money are very important aspects of determining future budgetary spend patterns, organizations represent a complex system of unique individuals with a myriad of associated behaviors and all of these behaviors have bearing on how budget is utilized. When looking to forecasted budgets, it becomes a guessing game about how budget managers will behave under a given set of conditions. This becomes relatively messy when human nature is introduced, as different managers will react very differently undermore » similar circumstances. While one manager becomes ultra conservative during periods of financial austerity, another might be un-phased and continue to spend as they have in the past. Both might revert into a state of budgetary protectionism masking what is truly happening at a budget holder level, in order to keep as much budget and influence as possible while at the same time sacrificing the greater good of the organization. To more accurately predict future outcomes, the models should consider both time and money and other behavioral patterns that have been observed across the organization. The field of predictive analytics is poised to provide the tools and methodologies needed for organizations to do just this: capture and leverage behaviors of the past to predict the future.« less

  11. High Order Schemes in Bats-R-US for Faster and More Accurate Predictions

    NASA Astrophysics Data System (ADS)

    Chen, Y.; Toth, G.; Gombosi, T. I.

    2014-12-01

    BATS-R-US is a widely used global magnetohydrodynamics model that originally employed second order accurate TVD schemes combined with block based Adaptive Mesh Refinement (AMR) to achieve high resolution in the regions of interest. In the last years we have implemented fifth order accurate finite difference schemes CWENO5 and MP5 for uniform Cartesian grids. Now the high order schemes have been extended to generalized coordinates, including spherical grids and also to the non-uniform AMR grids including dynamic regridding. We present numerical tests that verify the preservation of free-stream solution and high-order accuracy as well as robust oscillation-free behavior near discontinuities. We apply the new high order accurate schemes to both heliospheric and magnetospheric simulations and show that it is robust and can achieve the same accuracy as the second order scheme with much less computational resources. This is especially important for space weather prediction that requires faster than real time code execution.

  12. Biomarker Surrogates Do Not Accurately Predict Sputum Eosinophils and Neutrophils in Asthma

    PubMed Central

    Hastie, Annette T.; Moore, Wendy C.; Li, Huashi; Rector, Brian M.; Ortega, Victor E.; Pascual, Rodolfo M.; Peters, Stephen P.; Meyers, Deborah A.; Bleecker, Eugene R.

    2013-01-01

    Background Sputum eosinophils (Eos) are a strong predictor of airway inflammation, exacerbations, and aid asthma management, whereas sputum neutrophils (Neu) indicate a different severe asthma phenotype, potentially less responsive to TH2-targeted therapy. Variables such as blood Eos, total IgE, fractional exhaled nitric oxide (FeNO) or FEV1% predicted, may predict airway Eos, while age, FEV1%predicted, or blood Neu may predict sputum Neu. Availability and ease of measurement are useful characteristics, but accuracy in predicting airway Eos and Neu, individually or combined, is not established. Objectives To determine whether blood Eos, FeNO, and IgE accurately predict sputum eosinophils, and age, FEV1% predicted, and blood Neu accurately predict sputum neutrophils (Neu). Methods Subjects in the Wake Forest Severe Asthma Research Program (N=328) were characterized by blood and sputum cells, healthcare utilization, lung function, FeNO, and IgE. Multiple analytical techniques were utilized. Results Despite significant association with sputum Eos, blood Eos, FeNO and total IgE did not accurately predict sputum Eos, and combinations of these variables failed to improve prediction. Age, FEV1%predicted and blood Neu were similarly unsatisfactory for prediction of sputum Neu. Factor analysis and stepwise selection found FeNO, IgE and FEV1% predicted, but not blood Eos, correctly predicted 69% of sputum Eospredicted 64% of sputum Neupredict both sputum Eos and Neu accurately assigned only 41% of samples. Conclusion Despite statistically significant associations FeNO, IgE, blood Eos and Neu, FEV1%predicted, and age are poor surrogates, separately and combined, for accurately predicting sputum eosinophils and neutrophils. PMID:23706399

  13. Do fertility intentions predict subsequent behavior? Evidence from Peninsular Malaysia.

    PubMed

    Tan, P C; Tey, N P

    1994-01-01

    Data from the 1984 Malaysian Population and Family Survey were matched with birth registration records for 1985-87 to determine the accuracy of statements regarding desired family size that were reported in a household survey in predicting subsequent reproductive behavior. The findings of this study were that stated fertility intention provides fairly accurate forecasts of fertility behavior in the subsequent period. In other words, whether a woman has another child is predicted closely by whether she wanted an additional child. Informational, educational, and motivational activities of family planning programs would, therefore, have greater success in reducing family size if fertility intentions were taken into account.

  14. Efficient and accurate two-scale FE-FFT-based prediction of the effective material behavior of elasto-viscoplastic polycrystals

    NASA Astrophysics Data System (ADS)

    Kochmann, Julian; Wulfinghoff, Stephan; Ehle, Lisa; Mayer, Joachim; Svendsen, Bob; Reese, Stefanie

    2018-06-01

    Recently, two-scale FE-FFT-based methods (e.g., Spahn et al. in Comput Methods Appl Mech Eng 268:871-883, 2014; Kochmann et al. in Comput Methods Appl Mech Eng 305:89-110, 2016) have been proposed to predict the microscopic and overall mechanical behavior of heterogeneous materials. The purpose of this work is the extension to elasto-viscoplastic polycrystals, efficient and robust Fourier solvers and the prediction of micromechanical fields during macroscopic deformation processes. Assuming scale separation, the macroscopic problem is solved using the finite element method. The solution of the microscopic problem, which is embedded as a periodic unit cell (UC) in each macroscopic integration point, is found by employing fast Fourier transforms, fixed-point and Newton-Krylov methods. The overall material behavior is defined by the mean UC response. In order to ensure spatially converged micromechanical fields as well as feasible overall CPU times, an efficient but simple solution strategy for two-scale simulations is proposed. As an example, the constitutive behavior of 42CrMo4 steel is predicted during macroscopic three-point bending tests.

  15. Efficient and accurate two-scale FE-FFT-based prediction of the effective material behavior of elasto-viscoplastic polycrystals

    NASA Astrophysics Data System (ADS)

    Kochmann, Julian; Wulfinghoff, Stephan; Ehle, Lisa; Mayer, Joachim; Svendsen, Bob; Reese, Stefanie

    2017-09-01

    Recently, two-scale FE-FFT-based methods (e.g., Spahn et al. in Comput Methods Appl Mech Eng 268:871-883, 2014; Kochmann et al. in Comput Methods Appl Mech Eng 305:89-110, 2016) have been proposed to predict the microscopic and overall mechanical behavior of heterogeneous materials. The purpose of this work is the extension to elasto-viscoplastic polycrystals, efficient and robust Fourier solvers and the prediction of micromechanical fields during macroscopic deformation processes. Assuming scale separation, the macroscopic problem is solved using the finite element method. The solution of the microscopic problem, which is embedded as a periodic unit cell (UC) in each macroscopic integration point, is found by employing fast Fourier transforms, fixed-point and Newton-Krylov methods. The overall material behavior is defined by the mean UC response. In order to ensure spatially converged micromechanical fields as well as feasible overall CPU times, an efficient but simple solution strategy for two-scale simulations is proposed. As an example, the constitutive behavior of 42CrMo4 steel is predicted during macroscopic three-point bending tests.

  16. Accurate Binding Free Energy Predictions in Fragment Optimization.

    PubMed

    Steinbrecher, Thomas B; Dahlgren, Markus; Cappel, Daniel; Lin, Teng; Wang, Lingle; Krilov, Goran; Abel, Robert; Friesner, Richard; Sherman, Woody

    2015-11-23

    Predicting protein-ligand binding free energies is a central aim of computational structure-based drug design (SBDD)--improved accuracy in binding free energy predictions could significantly reduce costs and accelerate project timelines in lead discovery and optimization. The recent development and validation of advanced free energy calculation methods represents a major step toward this goal. Accurately predicting the relative binding free energy changes of modifications to ligands is especially valuable in the field of fragment-based drug design, since fragment screens tend to deliver initial hits of low binding affinity that require multiple rounds of synthesis to gain the requisite potency for a project. In this study, we show that a free energy perturbation protocol, FEP+, which was previously validated on drug-like lead compounds, is suitable for the calculation of relative binding strengths of fragment-sized compounds as well. We study several pharmaceutically relevant targets with a total of more than 90 fragments and find that the FEP+ methodology, which uses explicit solvent molecular dynamics and physics-based scoring with no parameters adjusted, can accurately predict relative fragment binding affinities. The calculations afford R(2)-values on average greater than 0.5 compared to experimental data and RMS errors of ca. 1.1 kcal/mol overall, demonstrating significant improvements over the docking and MM-GBSA methods tested in this work and indicating that FEP+ has the requisite predictive power to impact fragment-based affinity optimization projects.

  17. Predicting mixture phase equilibria and critical behavior using the SAFT-VRX approach.

    PubMed

    Sun, Lixin; Zhao, Honggang; Kiselev, Sergei B; McCabe, Clare

    2005-05-12

    The SAFT-VRX equation of state combines the SAFT-VR equation with a crossover function that smoothly transforms the classical equation into a nonanalytical form close to the critical point. By a combinination of the accuracy of the SAFT-VR approach away from the critical region with the asymptotic scaling behavior seen at the critical point of real fluids, the SAFT-VRX equation can accurately describe the global fluid phase diagram. In previous work, we demonstrated that the SAFT-VRX equation very accurately describes the pvT and phase behavior of both nonassociating and associating pure fluids, with a minimum of fitting to experimental data. Here, we present a generalized SAFT-VRX equation of state for binary mixtures that is found to accurately predict the vapor-liquid equilibrium and pvT behavior of the systems studied. In particular, we examine binary mixtures of n-alkanes and carbon dioxide + n-alkanes. The SAFT-VRX equation accurately describes not only the gas-liquid critical locus for these systems but also the vapor-liquid equilibrium phase diagrams and thermal properties in single-phase regions.

  18. Modeling and prediction of human word search behavior in interactive machine translation

    NASA Astrophysics Data System (ADS)

    Ji, Duo; Yu, Bai; Ma, Bin; Ye, Na

    2017-12-01

    As a kind of computer aided translation method, Interactive Machine Translation technology reduced manual translation repetitive and mechanical operation through a variety of methods, so as to get the translation efficiency, and played an important role in the practical application of the translation work. In this paper, we regarded the behavior of users' frequently searching for words in the translation process as the research object, and transformed the behavior to the translation selection problem under the current translation. The paper presented a prediction model, which is a comprehensive utilization of alignment model, translation model and language model of the searching words behavior. It achieved a highly accurate prediction of searching words behavior, and reduced the switching of mouse and keyboard operations in the users' translation process.

  19. Can phenological models predict tree phenology accurately under climate change conditions?

    NASA Astrophysics Data System (ADS)

    Chuine, Isabelle; Bonhomme, Marc; Legave, Jean Michel; García de Cortázar-Atauri, Inaki; Charrier, Guillaume; Lacointe, André; Améglio, Thierry

    2014-05-01

    The onset of the growing season of trees has been globally earlier by 2.3 days/decade during the last 50 years because of global warming and this trend is predicted to continue according to climate forecast. The effect of temperature on plant phenology is however not linear because temperature has a dual effect on bud development. On one hand, low temperatures are necessary to break bud dormancy, and on the other hand higher temperatures are necessary to promote bud cells growth afterwards. Increasing phenological changes in temperate woody species have strong impacts on forest trees distribution and productivity, as well as crops cultivation areas. Accurate predictions of trees phenology are therefore a prerequisite to understand and foresee the impacts of climate change on forests and agrosystems. Different process-based models have been developed in the last two decades to predict the date of budburst or flowering of woody species. They are two main families: (1) one-phase models which consider only the ecodormancy phase and make the assumption that endodormancy is always broken before adequate climatic conditions for cell growth occur; and (2) two-phase models which consider both the endodormancy and ecodormancy phases and predict a date of dormancy break which varies from year to year. So far, one-phase models have been able to predict accurately tree bud break and flowering under historical climate. However, because they do not consider what happens prior to ecodormancy, and especially the possible negative effect of winter temperature warming on dormancy break, it seems unlikely that they can provide accurate predictions in future climate conditions. It is indeed well known that a lack of low temperature results in abnormal pattern of bud break and development in temperate fruit trees. An accurate modelling of the dormancy break date has thus become a major issue in phenology modelling. Two-phases phenological models predict that global warming should delay

  20. Vision drives accurate approach behavior during prey capture in laboratory mice

    PubMed Central

    Hoy, Jennifer L.; Yavorska, Iryna; Wehr, Michael; Niell, Cristopher M.

    2016-01-01

    Summary The ability to genetically identify and manipulate neural circuits in the mouse is rapidly advancing our understanding of visual processing in the mammalian brain [1,2]. However, studies investigating the circuitry that underlies complex ethologically-relevant visual behaviors in the mouse have been primarily restricted to fear responses [3–5]. Here, we show that a laboratory strain of mouse (Mus musculus, C57BL/6J) robustly pursues, captures and consumes live insect prey, and that vision is necessary for mice to perform the accurate orienting and approach behaviors leading to capture. Specifically, we differentially perturbed visual or auditory input in mice and determined that visual input is required for accurate approach, allowing maintenance of bearing to within 11 degrees of the target on average during pursuit. While mice were able to capture prey without vision, the accuracy of their approaches and capture rate dramatically declined. To better explore the contribution of vision to this behavior, we developed a simple assay that isolated visual cues and simplified analysis of the visually guided approach. Together, our results demonstrate that laboratory mice are capable of exhibiting dynamic and accurate visually-guided approach behaviors, and provide a means to estimate the visual features that drive behavior within an ethological context. PMID:27773567

  1. ASTRAL, DRAGON and SEDAN scores predict stroke outcome more accurately than physicians.

    PubMed

    Ntaios, G; Gioulekas, F; Papavasileiou, V; Strbian, D; Michel, P

    2016-11-01

    ASTRAL, SEDAN and DRAGON scores are three well-validated scores for stroke outcome prediction. Whether these scores predict stroke outcome more accurately compared with physicians interested in stroke was investigated. Physicians interested in stroke were invited to an online anonymous survey to provide outcome estimates in randomly allocated structured scenarios of recent real-life stroke patients. Their estimates were compared to scores' predictions in the same scenarios. An estimate was considered accurate if it was within 95% confidence intervals of actual outcome. In all, 244 participants from 32 different countries responded assessing 720 real scenarios and 2636 outcomes. The majority of physicians' estimates were inaccurate (1422/2636, 53.9%). 400 (56.8%) of physicians' estimates about the percentage probability of 3-month modified Rankin score (mRS) > 2 were accurate compared with 609 (86.5%) of ASTRAL score estimates (P < 0.0001). 394 (61.2%) of physicians' estimates about the percentage probability of post-thrombolysis symptomatic intracranial haemorrhage were accurate compared with 583 (90.5%) of SEDAN score estimates (P < 0.0001). 160 (24.8%) of physicians' estimates about post-thrombolysis 3-month percentage probability of mRS 0-2 were accurate compared with 240 (37.3%) DRAGON score estimates (P < 0.0001). 260 (40.4%) of physicians' estimates about the percentage probability of post-thrombolysis mRS 5-6 were accurate compared with 518 (80.4%) DRAGON score estimates (P < 0.0001). ASTRAL, DRAGON and SEDAN scores predict outcome of acute ischaemic stroke patients with higher accuracy compared to physicians interested in stroke. © 2016 EAN.

  2. Predicting consumer behavior with Web search.

    PubMed

    Goel, Sharad; Hofman, Jake M; Lahaie, Sébastien; Pennock, David M; Watts, Duncan J

    2010-10-12

    Recent work has demonstrated that Web search volume can "predict the present," meaning that it can be used to accurately track outcomes such as unemployment levels, auto and home sales, and disease prevalence in near real time. Here we show that what consumers are searching for online can also predict their collective future behavior days or even weeks in advance. Specifically we use search query volume to forecast the opening weekend box-office revenue for feature films, first-month sales of video games, and the rank of songs on the Billboard Hot 100 chart, finding in all cases that search counts are highly predictive of future outcomes. We also find that search counts generally boost the performance of baseline models fit on other publicly available data, where the boost varies from modest to dramatic, depending on the application in question. Finally, we reexamine previous work on tracking flu trends and show that, perhaps surprisingly, the utility of search data relative to a simple autoregressive model is modest. We conclude that in the absence of other data sources, or where small improvements in predictive performance are material, search queries provide a useful guide to the near future.

  3. Prediction of shear critical behavior of high-strength reinforced concrete columns using finite element methods

    NASA Astrophysics Data System (ADS)

    Alrasyid, Harun; Safi, Fahrudin; Iranata, Data; Chen-Ou, Yu

    2017-11-01

    This research shows the prediction of shear behavior of High-Strength Reinforced Concrete Columns using Finite-Element Method. The experimental data of nine half scale high-strength reinforced concrete were selected. These columns using specified concrete compressive strength of 70 MPa, specified yield strength of longitudinal and transverse reinforcement of 685 and 785 MPa, respectively. The VecTor2 finite element software was used to simulate the shear critical behavior of these columns. The combination axial compression load and monotonic loading were applied at this prediction. It is demonstrated that VecTor2 finite element software provides accurate prediction of load-deflection up to peak at applied load, but provide similar behavior at post peak load. The shear strength prediction provide by VecTor 2 are slightly conservative compare to test result.

  4. Predicting Team Performance through Human Behavioral Sensing and Quantitative Workflow Instrumentation

    DTIC Science & Technology

    2016-07-27

    make risk-informed decisions during serious games . Statistical models of intra- game performance were developed to determine whether behaviors in...specific facets of the gameplay workflow were predictive of analytical performance and games outcomes. A study of over seventy instrumented teams revealed...more accurate game decisions. 2 Keywords: Humatics · Serious Games · Human-System Interaction · Instrumentation · Teamwork · Communication Analysis

  5. Predicting consumer behavior with Web search

    PubMed Central

    Goel, Sharad; Hofman, Jake M.; Lahaie, Sébastien; Pennock, David M.; Watts, Duncan J.

    2010-01-01

    Recent work has demonstrated that Web search volume can “predict the present,” meaning that it can be used to accurately track outcomes such as unemployment levels, auto and home sales, and disease prevalence in near real time. Here we show that what consumers are searching for online can also predict their collective future behavior days or even weeks in advance. Specifically we use search query volume to forecast the opening weekend box-office revenue for feature films, first-month sales of video games, and the rank of songs on the Billboard Hot 100 chart, finding in all cases that search counts are highly predictive of future outcomes. We also find that search counts generally boost the performance of baseline models fit on other publicly available data, where the boost varies from modest to dramatic, depending on the application in question. Finally, we reexamine previous work on tracking flu trends and show that, perhaps surprisingly, the utility of search data relative to a simple autoregressive model is modest. We conclude that in the absence of other data sources, or where small improvements in predictive performance are material, search queries provide a useful guide to the near future. PMID:20876140

  6. Multiphase, multicomponent phase behavior prediction

    NASA Astrophysics Data System (ADS)

    Dadmohammadi, Younas

    Accurate prediction of phase behavior of fluid mixtures in the chemical industry is essential for designing and operating a multitude of processes. Reliable generalized predictions of phase equilibrium properties, such as pressure, temperature, and phase compositions offer an attractive alternative to costly and time consuming experimental measurements. The main purpose of this work was to assess the efficacy of recently generalized activity coefficient models based on binary experimental data to (a) predict binary and ternary vapor-liquid equilibrium systems, and (b) characterize liquid-liquid equilibrium systems. These studies were completed using a diverse binary VLE database consisting of 916 binary and 86 ternary systems involving 140 compounds belonging to 31 chemical classes. Specifically the following tasks were undertaken: First, a comprehensive assessment of the two common approaches (gamma-phi (gamma-ϕ) and phi-phi (ϕ-ϕ)) used for determining the phase behavior of vapor-liquid equilibrium systems is presented. Both the representation and predictive capabilities of these two approaches were examined, as delineated form internal and external consistency tests of 916 binary systems. For the purpose, the universal quasi-chemical (UNIQUAC) model and the Peng-Robinson (PR) equation of state (EOS) were used in this assessment. Second, the efficacy of recently developed generalized UNIQUAC and the nonrandom two-liquid (NRTL) for predicting multicomponent VLE systems were investigated. Third, the abilities of recently modified NRTL model (mNRTL2 and mNRTL1) to characterize liquid-liquid equilibria (LLE) phase conditions and attributes, including phase stability, miscibility, and consolute point coordinates, were assessed. The results of this work indicate that the ϕ-ϕ approach represents the binary VLE systems considered within three times the error of the gamma-ϕ approach. A similar trend was observed for the for the generalized model predictions using

  7. Robust and accurate decoding of motoneuron behavior and prediction of the resulting force output.

    PubMed

    Thompson, Christopher K; Negro, Francesco; Johnson, Michael D; Holmes, Matthew R; McPherson, Laura Miller; Powers, Randall K; Farina, Dario; Heckman, Charles J

    2018-05-03

    The spinal alpha motoneuron is the only cell in the human CNS whose discharge can be routinely recorded in humans. We have reengineered motor unit collection and decomposition approaches, originally developed in humans, to measure the neural drive to muscle and estimate muscle force generation in the decerebrate cat model. Experimental, computational, and predictive approaches are used to demonstrate the validity of this approach across a wide range of modes to activate the motor pool. The utility of this approach is shown through the ability to track individual motor units across trials, allowing for better predictions of muscle force than the electromyography signal, and providing insights in to the stereotypical discharge characteristics in response to synaptic activation of the motor pool. This approach now allows for a direct link between the intracellular data of single motoneurons, the discharge properties of motoneuron populations, and muscle force generation in the same preparation. The discharge of a spinal alpha motoneuron and the resulting contraction of its muscle fibers represents the functional quantum of the motor system. Recent advances in the recording and decomposition of the electromyographic signal allows for the identification of several tens of concurrently active motor units. These detailed population data provide the potential to achieve deep insights into the synaptic organization of motor commands. Yet most of our understanding of the synaptic input to motoneurons is derived from intracellular recordings in animal preparations. Thus, it is necessary to extend the new electrode and decomposition methods to recording of motor unit populations in these same preparations. To achieve this goal, we use high-density electrode arrays and decomposition techniques, analogous to those developed for humans, to record and decompose the activity of tens of concurrently active motor units in a hindlimb muscle in the decerebrate cat. Our results showed

  8. Mothers' Predictions of Their Son's Executive Functioning Skills: Relations to Child Behavior Problems

    ERIC Educational Resources Information Center

    Johnston, Charlotte

    2011-01-01

    This study examined mothers' ability to accurately predict their sons' performance on executive functioning tasks in relation to the child's behavior problems. One-hundred thirteen mothers and their 4-7 year old sons participated. From behind a one-way mirror, mothers watched their sons perform tasks assessing inhibition and planning skills.…

  9. Accurate Prediction of Motor Failures by Application of Multi CBM Tools: A Case Study

    NASA Astrophysics Data System (ADS)

    Dutta, Rana; Singh, Veerendra Pratap; Dwivedi, Jai Prakash

    2018-02-01

    Motor failures are very difficult to predict accurately with a single condition-monitoring tool as both electrical and the mechanical systems are closely related. Electrical problem, like phase unbalance, stator winding insulation failures can, at times, lead to vibration problem and at the same time mechanical failures like bearing failure, leads to rotor eccentricity. In this case study of a 550 kW blower motor it has been shown that a rotor bar crack was detected by current signature analysis and vibration monitoring confirmed the same. In later months in a similar motor vibration monitoring predicted bearing failure and current signature analysis confirmed the same. In both the cases, after dismantling the motor, the predictions were found to be accurate. In this paper we will be discussing the accurate predictions of motor failures through use of multi condition monitoring tools with two case studies.

  10. Imagine All the People: How the Brain Creates and Uses Personality Models to Predict Behavior

    PubMed Central

    Hassabis, Demis; Spreng, R. Nathan; Rusu, Andrei A.; Robbins, Clifford A.; Mar, Raymond A.; Schacter, Daniel L.

    2014-01-01

    The behaviors of other people are often central to envisioning the future. The ability to accurately predict the thoughts and actions of others is essential for successful social interactions, with far-reaching consequences. Despite its importance, little is known about how the brain represents people in order to predict behavior. In this functional magnetic resonance imaging study, participants learned the unique personality of 4 protagonists and imagined how each would behave in different scenarios. The protagonists' personalities were composed of 2 traits: Agreeableness and Extraversion. Which protagonist was being imagined was accurately inferred based solely on activity patterns in the medial prefrontal cortex using multivariate pattern classification, providing novel evidence that brain activity can reveal whom someone is thinking about. Lateral temporal and posterior cingulate cortex discriminated between different degrees of agreeableness and extraversion, respectively. Functional connectivity analysis confirmed that regions associated with trait-processing and individual identities were functionally coupled. Activity during the imagination task, and revealed by functional connectivity, was consistent with the default network. Our results suggest that distinct regions code for personality traits, and that the brain combines these traits to represent individuals. The brain then uses this “personality model” to predict the behavior of others in novel situations. PMID:23463340

  11. Radiomics biomarkers for accurate tumor progression prediction of oropharyngeal cancer

    NASA Astrophysics Data System (ADS)

    Hadjiiski, Lubomir; Chan, Heang-Ping; Cha, Kenny H.; Srinivasan, Ashok; Wei, Jun; Zhou, Chuan; Prince, Mark; Papagerakis, Silvana

    2017-03-01

    Accurate tumor progression prediction for oropharyngeal cancers is crucial for identifying patients who would best be treated with optimized treatment and therefore minimize the risk of under- or over-treatment. An objective decision support system that can merge the available radiomics, histopathologic and molecular biomarkers in a predictive model based on statistical outcomes of previous cases and machine learning may assist clinicians in making more accurate assessment of oropharyngeal tumor progression. In this study, we evaluated the feasibility of developing individual and combined predictive models based on quantitative image analysis from radiomics, histopathology and molecular biomarkers for oropharyngeal tumor progression prediction. With IRB approval, 31, 84, and 127 patients with head and neck CT (CT-HN), tumor tissue microarrays (TMAs) and molecular biomarker expressions, respectively, were collected. For 8 of the patients all 3 types of biomarkers were available and they were sequestered in a test set. The CT-HN lesions were automatically segmented using our level sets based method. Morphological, texture and molecular based features were extracted from CT-HN and TMA images, and selected features were merged by a neural network. The classification accuracy was quantified using the area under the ROC curve (AUC). Test AUCs of 0.87, 0.74, and 0.71 were obtained with the individual predictive models based on radiomics, histopathologic, and molecular features, respectively. Combining the radiomics and molecular models increased the test AUC to 0.90. Combining all 3 models increased the test AUC further to 0.94. This preliminary study demonstrates that the individual domains of biomarkers are useful and the integrated multi-domain approach is most promising for tumor progression prediction.

  12. SCPRED: accurate prediction of protein structural class for sequences of twilight-zone similarity with predicting sequences.

    PubMed

    Kurgan, Lukasz; Cios, Krzysztof; Chen, Ke

    2008-05-01

    Protein structure prediction methods provide accurate results when a homologous protein is predicted, while poorer predictions are obtained in the absence of homologous templates. However, some protein chains that share twilight-zone pairwise identity can form similar folds and thus determining structural similarity without the sequence similarity would be desirable for the structure prediction. The folding type of a protein or its domain is defined as the structural class. Current structural class prediction methods that predict the four structural classes defined in SCOP provide up to 63% accuracy for the datasets in which sequence identity of any pair of sequences belongs to the twilight-zone. We propose SCPRED method that improves prediction accuracy for sequences that share twilight-zone pairwise similarity with sequences used for the prediction. SCPRED uses a support vector machine classifier that takes several custom-designed features as its input to predict the structural classes. Based on extensive design that considers over 2300 index-, composition- and physicochemical properties-based features along with features based on the predicted secondary structure and content, the classifier's input includes 8 features based on information extracted from the secondary structure predicted with PSI-PRED and one feature computed from the sequence. Tests performed with datasets of 1673 protein chains, in which any pair of sequences shares twilight-zone similarity, show that SCPRED obtains 80.3% accuracy when predicting the four SCOP-defined structural classes, which is superior when compared with over a dozen recent competing methods that are based on support vector machine, logistic regression, and ensemble of classifiers predictors. The SCPRED can accurately find similar structures for sequences that share low identity with sequence used for the prediction. The high predictive accuracy achieved by SCPRED is attributed to the design of the features, which are

  13. SCPRED: Accurate prediction of protein structural class for sequences of twilight-zone similarity with predicting sequences

    PubMed Central

    Kurgan, Lukasz; Cios, Krzysztof; Chen, Ke

    2008-01-01

    Background Protein structure prediction methods provide accurate results when a homologous protein is predicted, while poorer predictions are obtained in the absence of homologous templates. However, some protein chains that share twilight-zone pairwise identity can form similar folds and thus determining structural similarity without the sequence similarity would be desirable for the structure prediction. The folding type of a protein or its domain is defined as the structural class. Current structural class prediction methods that predict the four structural classes defined in SCOP provide up to 63% accuracy for the datasets in which sequence identity of any pair of sequences belongs to the twilight-zone. We propose SCPRED method that improves prediction accuracy for sequences that share twilight-zone pairwise similarity with sequences used for the prediction. Results SCPRED uses a support vector machine classifier that takes several custom-designed features as its input to predict the structural classes. Based on extensive design that considers over 2300 index-, composition- and physicochemical properties-based features along with features based on the predicted secondary structure and content, the classifier's input includes 8 features based on information extracted from the secondary structure predicted with PSI-PRED and one feature computed from the sequence. Tests performed with datasets of 1673 protein chains, in which any pair of sequences shares twilight-zone similarity, show that SCPRED obtains 80.3% accuracy when predicting the four SCOP-defined structural classes, which is superior when compared with over a dozen recent competing methods that are based on support vector machine, logistic regression, and ensemble of classifiers predictors. Conclusion The SCPRED can accurately find similar structures for sequences that share low identity with sequence used for the prediction. The high predictive accuracy achieved by SCPRED is attributed to the design of

  14. Prediction of Happy-Sad mood from daily behaviors and previous sleep history.

    PubMed

    Sano, Akane; Yu, Amy Z; McHill, Andrew W; Phillips, Andrew J K; Taylor, Sara; Jaques, Natasha; Klerman, Elizabeth B; Picard, Rosalind W

    2015-01-01

    We collected and analyzed subjective and objective data using surveys and wearable sensors worn day and night from 68 participants for ~30 days each, to address questions related to the relationships among sleep duration, sleep irregularity, self-reported Happy-Sad mood and other daily behavioral factors in college students. We analyzed this behavioral and physiological data to (i) identify factors that classified the participants into Happy-Sad mood using support vector machines (SVMs); and (ii) analyze how accurately sleep duration and sleep regularity for the past 1-5 days classified morning Happy-Sad mood. We found statistically significant associations amongst Sad mood and poor health-related factors. Behavioral factors including the frequency of negative social interactions, and negative emails, and total academic activity hours showed the best performance in separating the Happy-Sad mood groups. Sleep regularity and sleep duration predicted daily Happy-Sad mood with 65-80% accuracy. The number of nights giving the best prediction of Happy-Sad mood varied for different individuals.

  15. A Unified Framework for Activity Recognition-Based Behavior Analysis and Action Prediction in Smart Homes

    PubMed Central

    Fatima, Iram; Fahim, Muhammad; Lee, Young-Koo; Lee, Sungyoung

    2013-01-01

    In recent years, activity recognition in smart homes is an active research area due to its applicability in many applications, such as assistive living and healthcare. Besides activity recognition, the information collected from smart homes has great potential for other application domains like lifestyle analysis, security and surveillance, and interaction monitoring. Therefore, discovery of users common behaviors and prediction of future actions from past behaviors become an important step towards allowing an environment to provide personalized service. In this paper, we develop a unified framework for activity recognition-based behavior analysis and action prediction. For this purpose, first we propose kernel fusion method for accurate activity recognition and then identify the significant sequential behaviors of inhabitants from recognized activities of their daily routines. Moreover, behaviors patterns are further utilized to predict the future actions from past activities. To evaluate the proposed framework, we performed experiments on two real datasets. The results show a remarkable improvement of 13.82% in the accuracy on average of recognized activities along with the extraction of significant behavioral patterns and precise activity predictions with 6.76% increase in F-measure. All this collectively help in understanding the users” actions to gain knowledge about their habits and preferences. PMID:23435057

  16. PredictSNP: Robust and Accurate Consensus Classifier for Prediction of Disease-Related Mutations

    PubMed Central

    Bendl, Jaroslav; Stourac, Jan; Salanda, Ondrej; Pavelka, Antonin; Wieben, Eric D.; Zendulka, Jaroslav; Brezovsky, Jan; Damborsky, Jiri

    2014-01-01

    Single nucleotide variants represent a prevalent form of genetic variation. Mutations in the coding regions are frequently associated with the development of various genetic diseases. Computational tools for the prediction of the effects of mutations on protein function are very important for analysis of single nucleotide variants and their prioritization for experimental characterization. Many computational tools are already widely employed for this purpose. Unfortunately, their comparison and further improvement is hindered by large overlaps between the training datasets and benchmark datasets, which lead to biased and overly optimistic reported performances. In this study, we have constructed three independent datasets by removing all duplicities, inconsistencies and mutations previously used in the training of evaluated tools. The benchmark dataset containing over 43,000 mutations was employed for the unbiased evaluation of eight established prediction tools: MAPP, nsSNPAnalyzer, PANTHER, PhD-SNP, PolyPhen-1, PolyPhen-2, SIFT and SNAP. The six best performing tools were combined into a consensus classifier PredictSNP, resulting into significantly improved prediction performance, and at the same time returned results for all mutations, confirming that consensus prediction represents an accurate and robust alternative to the predictions delivered by individual tools. A user-friendly web interface enables easy access to all eight prediction tools, the consensus classifier PredictSNP and annotations from the Protein Mutant Database and the UniProt database. The web server and the datasets are freely available to the academic community at http://loschmidt.chemi.muni.cz/predictsnp. PMID:24453961

  17. Predictability of the future development of aggressive behavior of cranial dural arteriovenous fistulas based on decision tree analysis.

    PubMed

    Satomi, Junichiro; Ghaibeh, A Ammar; Moriguchi, Hiroki; Nagahiro, Shinji

    2015-07-01

    The severity of clinical signs and symptoms of cranial dural arteriovenous fistulas (DAVFs) are well correlated with their pattern of venous drainage. Although the presence of cortical venous drainage can be considered a potential predictor of aggressive DAVF behaviors, such as intracranial hemorrhage or progressive neurological deficits due to venous congestion, accurate statistical analyses are currently not available. Using a decision tree data mining method, the authors aimed at clarifying the predictability of the future development of aggressive behaviors of DAVF and at identifying the main causative factors. Of 266 DAVF patients, 89 were eligible for analysis. Under observational management, 51 patients presented with intracranial hemorrhage/infarction during the follow-up period. The authors created a decision tree able to assess the risk for the development of aggressive DAVF behavior. Evaluated by 10-fold cross-validation, the decision tree's accuracy, sensitivity, and specificity were 85.28%, 88.33%, and 80.83%, respectively. The tree shows that the main factor in symptomatic patients was the presence of cortical venous drainage. In its absence, the lesion location determined the risk of a DAVF developing aggressive behavior. Decision tree analysis accurately predicts the future development of aggressive DAVF behavior.

  18. A Weibull statistics-based lignocellulose saccharification model and a built-in parameter accurately predict lignocellulose hydrolysis performance.

    PubMed

    Wang, Mingyu; Han, Lijuan; Liu, Shasha; Zhao, Xuebing; Yang, Jinghua; Loh, Soh Kheang; Sun, Xiaomin; Zhang, Chenxi; Fang, Xu

    2015-09-01

    Renewable energy from lignocellulosic biomass has been deemed an alternative to depleting fossil fuels. In order to improve this technology, we aim to develop robust mathematical models for the enzymatic lignocellulose degradation process. By analyzing 96 groups of previously published and newly obtained lignocellulose saccharification results and fitting them to Weibull distribution, we discovered Weibull statistics can accurately predict lignocellulose saccharification data, regardless of the type of substrates, enzymes and saccharification conditions. A mathematical model for enzymatic lignocellulose degradation was subsequently constructed based on Weibull statistics. Further analysis of the mathematical structure of the model and experimental saccharification data showed the significance of the two parameters in this model. In particular, the λ value, defined the characteristic time, represents the overall performance of the saccharification system. This suggestion was further supported by statistical analysis of experimental saccharification data and analysis of the glucose production levels when λ and n values change. In conclusion, the constructed Weibull statistics-based model can accurately predict lignocellulose hydrolysis behavior and we can use the λ parameter to assess the overall performance of enzymatic lignocellulose degradation. Advantages and potential applications of the model and the λ value in saccharification performance assessment were discussed. Copyright © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  19. An instrument for rapid, accurate, determination of fuel moisture content

    Treesearch

    Stephen S. Sackett

    1980-01-01

    Moisture contents of dead and living fuels are key variables in fire behavior. Accurate, real-time fuel moisture data are required for prescribed burning and wildfire behavior predictions. The convection oven method has become the standard for direct fuel moisture content determination. Efforts to quantify fuel moisture through indirect methods have not been...

  20. Heart rate during basketball game play and volleyball drills accurately predicts oxygen uptake and energy expenditure.

    PubMed

    Scribbans, T D; Berg, K; Narazaki, K; Janssen, I; Gurd, B J

    2015-09-01

    There is currently little information regarding the ability of metabolic prediction equations to accurately predict oxygen uptake and exercise intensity from heart rate (HR) during intermittent sport. The purpose of the present study was to develop and, cross-validate equations appropriate for accurately predicting oxygen cost (VO2) and energy expenditure from HR during intermittent sport participation. Eleven healthy adult males (19.9±1.1yrs) were recruited to establish the relationship between %VO2peak and %HRmax during low-intensity steady state endurance (END), moderate-intensity interval (MOD) and high intensity-interval exercise (HI), as performed on a cycle ergometer. Three equations (END, MOD, and HI) for predicting %VO2peak based on %HRmax were developed. HR and VO2 were directly measured during basketball games (6 male, 20.8±1.0 yrs; 6 female, 20.0±1.3yrs) and volleyball drills (12 female; 20.8±1.0yrs). Comparisons were made between measured and predicted VO2 and energy expenditure using the 3 equations developed and 2 previously published equations. The END and MOD equations accurately predicted VO2 and energy expenditure, while the HI equation underestimated, and the previously published equations systematically overestimated VO2 and energy expenditure. Intermittent sport VO2 and energy expenditure can be accurately predicted from heart rate data using either the END (%VO2peak=%HRmax x 1.008-17.17) or MOD (%VO2peak=%HRmax x 1.2-32) equations. These 2 simple equations provide an accessible and cost-effective method for accurate estimation of exercise intensity and energy expenditure during intermittent sport.

  1. Predicting Persuasion-Induced Behavior Change from the Brain

    PubMed Central

    Falk, Emily B.; Berkman, Elliot T.; Mann, Traci; Harrison, Brittany; Lieberman, Matthew D.

    2011-01-01

    Although persuasive messages often alter people’s self-reported attitudes and intentions to perform behaviors, these self-reports do not necessarily predict behavior change. We demonstrate that neural responses to persuasive messages can predict variability in behavior change in the subsequent week. Specifically, an a priori region of interest (ROI) in medial prefrontal cortex (MPFC) was reliably associated with behavior change (r = 0.49, p < 0.05). Additionally, an iterative cross-validation approach using activity in this MPFC ROI predicted an average 23% of the variance in behavior change beyond the variance predicted by self-reported attitudes and intentions. Thus, neural signals can predict behavioral changes that are not predicted from self-reported attitudes and intentions alone. Additionally, this is the first functional magnetic resonance imaging study to demonstrate that a neural signal can predict complex real world behavior days in advance. PMID:20573889

  2. Accurate prediction of cation-π interaction energy using substituent effects.

    PubMed

    Sayyed, Fareed Bhasha; Suresh, Cherumuttathu H

    2012-06-14

    (M(+))' and ΔV(min). All the Φ-X···M(+) systems showed good agreement between the calculated and predicted E(M(+))() values, suggesting that the ΔV(min) approach to substituent effect is accurate and useful for predicting the interactive behavior of substituted π-systems with cations.

  3. Accurate prediction of protein–protein interactions from sequence alignments using a Bayesian method

    PubMed Central

    Burger, Lukas; van Nimwegen, Erik

    2008-01-01

    Accurate and large-scale prediction of protein–protein interactions directly from amino-acid sequences is one of the great challenges in computational biology. Here we present a new Bayesian network method that predicts interaction partners using only multiple alignments of amino-acid sequences of interacting protein domains, without tunable parameters, and without the need for any training examples. We first apply the method to bacterial two-component systems and comprehensively reconstruct two-component signaling networks across all sequenced bacteria. Comparisons of our predictions with known interactions show that our method infers interaction partners genome-wide with high accuracy. To demonstrate the general applicability of our method we show that it also accurately predicts interaction partners in a recent dataset of polyketide synthases. Analysis of the predicted genome-wide two-component signaling networks shows that cognates (interacting kinase/regulator pairs, which lie adjacent on the genome) and orphans (which lie isolated) form two relatively independent components of the signaling network in each genome. In addition, while most genes are predicted to have only a small number of interaction partners, we find that 10% of orphans form a separate class of ‘hub' nodes that distribute and integrate signals to and from up to tens of different interaction partners. PMID:18277381

  4. A Novel Method for Accurate Operon Predictions in All SequencedProkaryotes

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

    Price, Morgan N.; Huang, Katherine H.; Alm, Eric J.

    2004-12-01

    We combine comparative genomic measures and the distance separating adjacent genes to predict operons in 124 completely sequenced prokaryotic genomes. Our method automatically tailors itself to each genome using sequence information alone, and thus can be applied to any prokaryote. For Escherichia coli K12 and Bacillus subtilis, our method is 85 and 83% accurate, respectively, which is similar to the accuracy of methods that use the same features but are trained on experimentally characterized transcripts. In Halobacterium NRC-1 and in Helicobacterpylori, our method correctly infers that genes in operons are separated by shorter distances than they are in E.coli, andmore » its predictions using distance alone are more accurate than distance-only predictions trained on a database of E.coli transcripts. We use microarray data from sixphylogenetically diverse prokaryotes to show that combining intergenic distance with comparative genomic measures further improves accuracy and that our method is broadly effective. Finally, we survey operon structure across 124 genomes, and find several surprises: H.pylori has many operons, contrary to previous reports; Bacillus anthracis has an unusual number of pseudogenes within conserved operons; and Synechocystis PCC6803 has many operons even though it has unusually wide spacings between conserved adjacent genes.« less

  5. Accurate prediction of energy expenditure using a shoe-based activity monitor.

    PubMed

    Sazonova, Nadezhda; Browning, Raymond C; Sazonov, Edward

    2011-07-01

    The aim of this study was to develop and validate a method for predicting energy expenditure (EE) using a footwear-based system with integrated accelerometer and pressure sensors. We developed a footwear-based device with an embedded accelerometer and insole pressure sensors for the prediction of EE. The data from the device can be used to perform accurate recognition of major postures and activities and to estimate EE using the acceleration, pressure, and posture/activity classification information in a branched algorithm without the need for individual calibration. We measured EE via indirect calorimetry as 16 adults (body mass index=19-39 kg·m) performed various low- to moderate-intensity activities and compared measured versus predicted EE using several models based on the acceleration and pressure signals. Inclusion of pressure data resulted in better accuracy of EE prediction during static postures such as sitting and standing. The activity-based branched model that included predictors from accelerometer and pressure sensors (BACC-PS) achieved the lowest error (e.g., root mean squared error (RMSE)=0.69 METs) compared with the accelerometer-only-based branched model BACC (RMSE=0.77 METs) and nonbranched model (RMSE=0.94-0.99 METs). Comparison of EE prediction models using data from both legs versus models using data from a single leg indicates that only one shoe needs to be equipped with sensors. These results suggest that foot acceleration combined with insole pressure measurement, when used in an activity-specific branched model, can accurately estimate the EE associated with common daily postures and activities. The accuracy and unobtrusiveness of a footwear-based device may make it an effective physical activity monitoring tool.

  6. Predicting clinical image delivery time by monitoring PACS queue behavior.

    PubMed

    King, Nelson E; Documet, Jorge; Liu, Brent

    2006-01-01

    The expectation of rapid image retrieval from PACS users contributes to increased information technology (IT) infrastructure investments to increase performance as well as continuing demands upon PACS administrators to respond to "slow" system performance. The ability to provide predicted delivery times to a PACS user may curb user expectations for "fastest" response especially during peak hours. This, in turn, could result in a PACS infrastructure tailored to more realistic performance demands. A PACS with a stand-alone architecture under peak load typically holds study requests in a queue until the DICOM C-Move command can take place. We investigate the contents of a stand-alone architecture PACS RetrieveSend queue and identified parameters and behaviors that enable a more accurate prediction of delivery time. A prediction algorithm for studies delayed in a stand-alone PACS queue can be extendible to other potential bottlenecks such as long-term storage archives. Implications of a queue monitor in other PACS architectures are also discussed.

  7. Are EMS call volume predictions based on demand pattern analysis accurate?

    PubMed

    Brown, Lawrence H; Lerner, E Brooke; Larmon, Baxter; LeGassick, Todd; Taigman, Michael

    2007-01-01

    Most EMS systems determine the number of crews they will deploy in their communities and when those crews will be scheduled based on anticipated call volumes. Many systems use historical data to calculate their anticipated call volumes, a method of prediction known as demand pattern analysis. To evaluate the accuracy of call volume predictions calculated using demand pattern analysis. Seven EMS systems provided 73 consecutive weeks of hourly call volume data. The first 20 weeks of data were used to calculate three common demand pattern analysis constructs for call volume prediction: average peak demand (AP), smoothed average peak demand (SAP), and 90th percentile rank (90%R). The 21st week served as a buffer. Actual call volumes in the last 52 weeks were then compared to the predicted call volumes by using descriptive statistics. There were 61,152 hourly observations in the test period. All three constructs accurately predicted peaks and troughs in call volume but not exact call volume. Predictions were accurate (+/-1 call) 13% of the time using AP, 10% using SAP, and 19% using 90%R. Call volumes were overestimated 83% of the time using AP, 86% using SAP, and 74% using 90%R. When call volumes were overestimated, predictions exceeded actual call volume by a median (Interquartile range) of 4 (2-6) calls for AP, 4 (2-6) for SAP, and 3 (2-5) for 90%R. Call volumes were underestimated 4% of time using AP, 4% using SAP, and 7% using 90%R predictions. When call volumes were underestimated, call volumes exceeded predictions by a median (Interquartile range; maximum under estimation) of 1 (1-2; 18) call for AP, 1 (1-2; 18) for SAP, and 2 (1-3; 20) for 90%R. Results did not vary between systems. Generally, demand pattern analysis estimated or overestimated call volume, making it a reasonable predictor for ambulance staffing patterns. However, it did underestimate call volume between 4% and 7% of the time. Communities need to determine if these rates of over

  8. Predicting phase behavior of mixtures of reservoir fluids with carbon dioxide

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

    Grigg, R.B.; Lingane, P.J.

    1983-01-01

    The use of an equation of state to predict phase behavior during carbon dioxide flooding is well established. The characterization of the C/sub 7/ fraction and the selection of interaction parameters are the most important variables. Single-contact phase behavior is presented for mixtures of Ford Geraldine (Delaware), Maljamar (Grayburg), West Sussex (Shannon), and Reservoir D reservoir fluids, and of a synthetic oil with carbon dioxide. The phase behavior of these mixtures can be reproduced using 3 to 5 pseudo components and common interaction parameters. The critical properties of the pseudo components are calculated from detailed oil characterizations. Because the parametersmore » are not further adjusted, this approach reduces the empiricism in fitting phase data and may result in a more accurate representation of the system as the composition of the oil changes during the approach to miscibility. 21 references.« less

  9. Rapid and accurate prediction and scoring of water molecules in protein binding sites.

    PubMed

    Ross, Gregory A; Morris, Garrett M; Biggin, Philip C

    2012-01-01

    Water plays a critical role in ligand-protein interactions. However, it is still challenging to predict accurately not only where water molecules prefer to bind, but also which of those water molecules might be displaceable. The latter is often seen as a route to optimizing affinity of potential drug candidates. Using a protocol we call WaterDock, we show that the freely available AutoDock Vina tool can be used to predict accurately the binding sites of water molecules. WaterDock was validated using data from X-ray crystallography, neutron diffraction and molecular dynamics simulations and correctly predicted 97% of the water molecules in the test set. In addition, we combined data-mining, heuristic and machine learning techniques to develop probabilistic water molecule classifiers. When applied to WaterDock predictions in the Astex Diverse Set of protein ligand complexes, we could identify whether a water molecule was conserved or displaced to an accuracy of 75%. A second model predicted whether water molecules were displaced by polar groups or by non-polar groups to an accuracy of 80%. These results should prove useful for anyone wishing to undertake rational design of new compounds where the displacement of water molecules is being considered as a route to improved affinity.

  10. Bimanual coordination positively predicts episodic memory: A combined behavioral and MRI investigation.

    PubMed

    Lyle, Keith B; Dombroski, Brynn A; Faul, Leonard; Hopkins, Robin F; Naaz, Farah; Switala, Andrew E; Depue, Brendan E

    2017-11-01

    Some people remember events more completely and accurately than other people, but the origins of individual differences in episodic memory are poorly understood. One way to advance understanding is by identifying characteristics of individuals that reliably covary with memory performance. Recent research suggests motor behavior is related to memory performance, with individuals who consistently use a single preferred hand for unimanual actions performing worse than individuals who make greater use of both hands. This research has relied on self-reports of behavior. It is unknown whether objective measures of motor behavior also predict memory performance. Here, we tested the predictive power of bimanual coordination, an important form of manual dexterity. Bimanual coordination, as measured objectively on the Purdue Pegboard Test, was positively related to correct recall on the California Verbal Learning Test-II and negatively related to false recall. Furthermore, MRI data revealed that cortical surface area in right lateral prefrontal regions was positively related to correct recall. In one of these regions, cortical thickness was negatively related to bimanual coordination. These results suggest that individual differences in episodic memory may partially reflect morphological variation in right lateral prefrontal cortex and suggest a relationship between neural correlates of episodic memory and motor behavior. Copyright © 2017 Elsevier Inc. All rights reserved.

  11. Simple Learned Weighted Sums of Inferior Temporal Neuronal Firing Rates Accurately Predict Human Core Object Recognition Performance

    PubMed Central

    Hong, Ha; Solomon, Ethan A.; DiCarlo, James J.

    2015-01-01

    database of images for evaluating object recognition performance. We used multielectrode arrays to characterize hundreds of neurons in the visual ventral stream of nonhuman primates and measured the object recognition performance of >100 human observers. Remarkably, we found that simple learned weighted sums of firing rates of neurons in monkey inferior temporal (IT) cortex accurately predicted human performance. Although previous work led us to expect that IT would outperform V4, we were surprised by the quantitative precision with which simple IT-based linking hypotheses accounted for human behavior. PMID:26424887

  12. Simple Learned Weighted Sums of Inferior Temporal Neuronal Firing Rates Accurately Predict Human Core Object Recognition Performance.

    PubMed

    Majaj, Najib J; Hong, Ha; Solomon, Ethan A; DiCarlo, James J

    2015-09-30

    database of images for evaluating object recognition performance. We used multielectrode arrays to characterize hundreds of neurons in the visual ventral stream of nonhuman primates and measured the object recognition performance of >100 human observers. Remarkably, we found that simple learned weighted sums of firing rates of neurons in monkey inferior temporal (IT) cortex accurately predicted human performance. Although previous work led us to expect that IT would outperform V4, we were surprised by the quantitative precision with which simple IT-based linking hypotheses accounted for human behavior. Copyright © 2015 the authors 0270-6474/15/3513402-17$15.00/0.

  13. Does Early Childhood Callous-Unemotional Behavior Uniquely Predict Behavior Problems or Callous-Unemotional Behavior in Late Childhood?

    ERIC Educational Resources Information Center

    Waller, Rebecca; Dishion, Thomas J.; Shaw, Daniel S.; Gardner, Frances; Wilson, Melvin N.; Hyde, Luke W.

    2016-01-01

    Callous-unemotional (CU) behavior has been linked to behavior problems in children and adolescents. However, few studies have examined whether CU behavior in "early childhood" predicts behavior problems or CU behavior in "late childhood". This study examined whether indicators of CU behavior at ages 2-4 predicted aggression,…

  14. Multi-fidelity machine learning models for accurate bandgap predictions of solids

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

    Pilania, Ghanshyam; Gubernatis, James E.; Lookman, Turab

    Here, we present a multi-fidelity co-kriging statistical learning framework that combines variable-fidelity quantum mechanical calculations of bandgaps to generate a machine-learned model that enables low-cost accurate predictions of the bandgaps at the highest fidelity level. Additionally, the adopted Gaussian process regression formulation allows us to predict the underlying uncertainties as a measure of our confidence in the predictions. In using a set of 600 elpasolite compounds as an example dataset and using semi-local and hybrid exchange correlation functionals within density functional theory as two levels of fidelities, we demonstrate the excellent learning performance of the method against actual high fidelitymore » quantum mechanical calculations of the bandgaps. The presented statistical learning method is not restricted to bandgaps or electronic structure methods and extends the utility of high throughput property predictions in a significant way.« less

  15. Multi-fidelity machine learning models for accurate bandgap predictions of solids

    DOE PAGES

    Pilania, Ghanshyam; Gubernatis, James E.; Lookman, Turab

    2016-12-28

    Here, we present a multi-fidelity co-kriging statistical learning framework that combines variable-fidelity quantum mechanical calculations of bandgaps to generate a machine-learned model that enables low-cost accurate predictions of the bandgaps at the highest fidelity level. Additionally, the adopted Gaussian process regression formulation allows us to predict the underlying uncertainties as a measure of our confidence in the predictions. In using a set of 600 elpasolite compounds as an example dataset and using semi-local and hybrid exchange correlation functionals within density functional theory as two levels of fidelities, we demonstrate the excellent learning performance of the method against actual high fidelitymore » quantum mechanical calculations of the bandgaps. The presented statistical learning method is not restricted to bandgaps or electronic structure methods and extends the utility of high throughput property predictions in a significant way.« less

  16. Gambling and the Reasoned Action Model: Predicting Past Behavior, Intentions, and Future Behavior.

    PubMed

    Dahl, Ethan; Tagler, Michael J; Hohman, Zachary P

    2018-03-01

    Gambling is a serious concern for society because it is highly addictive and is associated with a myriad of negative outcomes. The current study applied the Reasoned Action Model (RAM) to understand and predict gambling intentions and behavior. Although prior studies have taken a reasoned action approach to understand gambling, no prior study has fully applied the RAM or used the RAM to predict future gambling. Across two studies the RAM was used to predict intentions to gamble, past gambling behavior, and future gambling behavior. In study 1 the model significantly predicted intentions and past behavior in both a college student and Amazon Mechanical Turk sample. In study 2 the model predicted future gambling behavior, measured 2 weeks after initial measurement of the RAM constructs. This study stands as the first to show the utility of the RAM in predicting future gambling behavior. Across both studies, attitudes and perceived normative pressure were the strongest predictors of intentions to gamble. These findings provide increased understanding of gambling and inform the development of gambling interventions based on the RAM.

  17. Predicting fire behavior in U.S. Mediterranean ecosystems

    Treesearch

    Frank A. Albini; Earl B. Anderson

    1982-01-01

    Quantification and methods of prediction of wildland fire behavior are discussed briefly and factors of particular relevance to the prediction of fire behavior in Mediterranean ecosystems are reviewed. A computer-based system which uses relevant fuel information and current weather data to predict fire behavior is in operation in southern California. Some of the...

  18. Utility of the theory of planned behavior to predict nursing staff blood pressure monitoring behaviours.

    PubMed

    Nelson, Joan M; Cook, Paul F; Ingram, Jennifer C

    2014-02-01

    To evaluate constructs from the theory of planned behavior (TPB, Ajzen 2002) - attitudes, sense of control, subjective norms and intentions - as predictors of accuracy in blood pressure monitoring. Despite numerous initiatives aimed at teaching blood pressure measurement techniques, many healthcare providers measure blood pressures incorrectly. Descriptive, cohort design. Medical assistants and licensed practical nurses were asked to complete a questionnaire on TPB variables. These nursing staff's patients had their blood pressures measured and completed a survey about techniques used to measure their blood pressure. We correlated nursing staff's responses on the TBP questionnaire with their intention to measure an accurate blood pressure and with the difference between their actual blood pressure measurement and a second measurement taken by a researcher immediately after the clinic visit. Patients' perceptions of MAs' and LPNs' blood pressure measurement techniques were examined descriptively. Perceived control and social norm predicted intention to measure an accurate blood pressure, with a negative relationship between knowledge and intention. Consistent with the TPB, intention was the only significant predictor of blood pressure measurement accuracy. Theory of planned behavior constructs predicted the healthcare providers' intention to measure blood pressure accurately and intention predicted the actual accuracy of systolic blood pressure measurement. However, participants' knowledge about blood pressure measurement had an unexpected negative relationship with their intentions. These findings have important implications for nursing education departments and organisations which traditionally invest significant time and effort in annual competency training focused on knowledge enhancement by staff. This study suggests that a better strategy might involve efforts to enhance providers' intention to change, particularly by changing social norms or increasing

  19. Accurate prediction of secondary metabolite gene clusters in filamentous fungi.

    PubMed

    Andersen, Mikael R; Nielsen, Jakob B; Klitgaard, Andreas; Petersen, Lene M; Zachariasen, Mia; Hansen, Tilde J; Blicher, Lene H; Gotfredsen, Charlotte H; Larsen, Thomas O; Nielsen, Kristian F; Mortensen, Uffe H

    2013-01-02

    Biosynthetic pathways of secondary metabolites from fungi are currently subject to an intense effort to elucidate the genetic basis for these compounds due to their large potential within pharmaceutics and synthetic biochemistry. The preferred method is methodical gene deletions to identify supporting enzymes for key synthases one cluster at a time. In this study, we design and apply a DNA expression array for Aspergillus nidulans in combination with legacy data to form a comprehensive gene expression compendium. We apply a guilt-by-association-based analysis to predict the extent of the biosynthetic clusters for the 58 synthases active in our set of experimental conditions. A comparison with legacy data shows the method to be accurate in 13 of 16 known clusters and nearly accurate for the remaining 3 clusters. Furthermore, we apply a data clustering approach, which identifies cross-chemistry between physically separate gene clusters (superclusters), and validate this both with legacy data and experimentally by prediction and verification of a supercluster consisting of the synthase AN1242 and the prenyltransferase AN11080, as well as identification of the product compound nidulanin A. We have used A. nidulans for our method development and validation due to the wealth of available biochemical data, but the method can be applied to any fungus with a sequenced and assembled genome, thus supporting further secondary metabolite pathway elucidation in the fungal kingdom.

  20. Simple prediction scores predict good and devastating outcomes after stroke more accurately than physicians.

    PubMed

    Reid, John Michael; Dai, Dingwei; Delmonte, Susanna; Counsell, Carl; Phillips, Stephen J; MacLeod, Mary Joan

    2017-05-01

    physicians are often asked to prognosticate soon after a patient presents with stroke. This study aimed to compare two outcome prediction scores (Five Simple Variables [FSV] score and the PLAN [Preadmission comorbidities, Level of consciousness, Age, and focal Neurologic deficit]) with informal prediction by physicians. demographic and clinical variables were prospectively collected from consecutive patients hospitalised with acute ischaemic or haemorrhagic stroke (2012-13). In-person or telephone follow-up at 6 months established vital and functional status (modified Rankin score [mRS]). Area under the receiver operating curves (AUC) was used to establish prediction score performance. five hundred and seventy-five patients were included; 46% female, median age 76 years, 88% ischaemic stroke. Six months after stroke, 47% of patients had a good outcome (alive and independent, mRS 0-2) and 26% a devastating outcome (dead or severely dependent, mRS 5-6). The FSV and PLAN scores were superior to physician prediction (AUCs of 0.823-0.863 versus 0.773-0.805, P < 0.0001) for good and devastating outcomes. The FSV score was superior to the PLAN score for predicting good outcomes and vice versa for devastating outcomes (P < 0.001). Outcome prediction was more accurate for those with later presentations (>24 hours from onset). the FSV and PLAN scores are validated in this population for outcome prediction after both ischaemic and haemorrhagic stroke. The FSV score is the least complex of all developed scores and can assist outcome prediction by physicians. © The Author 2016. Published by Oxford University Press on behalf of the British Geriatrics Society. All rights reserved. For permissions, please email: journals.permissions@oup.com

  1. Accurate Prediction of Contact Numbers for Multi-Spanning Helical Membrane Proteins

    PubMed Central

    Li, Bian; Mendenhall, Jeffrey; Nguyen, Elizabeth Dong; Weiner, Brian E.; Fischer, Axel W.; Meiler, Jens

    2017-01-01

    Prediction of the three-dimensional (3D) structures of proteins by computational methods is acknowledged as an unsolved problem. Accurate prediction of important structural characteristics such as contact number is expected to accelerate the otherwise slow progress being made in the prediction of 3D structure of proteins. Here, we present a dropout neural network-based method, TMH-Expo, for predicting the contact number of transmembrane helix (TMH) residues from sequence. Neuronal dropout is a strategy where certain neurons of the network are excluded from back-propagation to prevent co-adaptation of hidden-layer neurons. By using neuronal dropout, overfitting was significantly reduced and performance was noticeably improved. For multi-spanning helical membrane proteins, TMH-Expo achieved a remarkable Pearson correlation coefficient of 0.69 between predicted and experimental values and a mean absolute error of only 1.68. In addition, among those membrane protein–membrane protein interface residues, 76.8% were correctly predicted. Mapping of predicted contact numbers onto structures indicates that contact numbers predicted by TMH-Expo reflect the exposure patterns of TMHs and reveal membrane protein–membrane protein interfaces, reinforcing the potential of predicted contact numbers to be used as restraints for 3D structure prediction and protein–protein docking. TMH-Expo can be accessed via a Web server at www.meilerlab.org. PMID:26804342

  2. Basophile: Accurate Fragment Charge State Prediction Improves Peptide Identification Rates

    DOE PAGES

    Wang, Dong; Dasari, Surendra; Chambers, Matthew C.; ...

    2013-03-07

    In shotgun proteomics, database search algorithms rely on fragmentation models to predict fragment ions that should be observed for a given peptide sequence. The most widely used strategy (Naive model) is oversimplified, cleaving all peptide bonds with equal probability to produce fragments of all charges below that of the precursor ion. More accurate models, based on fragmentation simulation, are too computationally intensive for on-the-fly use in database search algorithms. We have created an ordinal-regression-based model called Basophile that takes fragment size and basic residue distribution into account when determining the charge retention during CID/higher-energy collision induced dissociation (HCD) of chargedmore » peptides. This model improves the accuracy of predictions by reducing the number of unnecessary fragments that are routinely predicted for highly-charged precursors. Basophile increased the identification rates by 26% (on average) over the Naive model, when analyzing triply-charged precursors from ion trap data. Basophile achieves simplicity and speed by solving the prediction problem with an ordinal regression equation, which can be incorporated into any database search software for shotgun proteomic identification.« less

  3. Does early childhood callous-unemotional behavior uniquely predict behavior problems or callous-unemotional behavior in late childhood?

    PubMed Central

    Waller, Rebecca; Dishion, Thomas J.; Shaw, Daniel S.; Gardner, Frances; Wilson, Melvin N.; Hyde, Luke W.

    2016-01-01

    Callous unemotional (CU) behavior has been linked to behavior problems in children and adolescents. However, few studies have examined whether CU behavior in early childhood predicts behavior problems or CU behavior in late childhood. This study examined whether indicators of CU behavior at ages 2–4 predicted aggression, rule-breaking, and CU behavior across informants at age 9.5. To test the unique predictive and convergent validity of CU behavior in early childhood, we accounted for stability in behavior problems and method effects to rule out the possibility that rater biases inflated the magnitude of any associations found. Cross-informant data were collected from a multi-ethnic, high-risk sample (N = 731; female = 49%) at ages 2–4 and again at age 9.5. From age 3, CU behavior uniquely predicted aggression and rule-breaking across informants. There were also unique associations between CU behavior assessed at ages 3 and 4 and CU behavior assessed at age 9.5. Findings demonstrate that early-childhood indicators of CU behavior account for unique variance in later childhood behavior problems and CU behavior, taking into account stability in behavior problems over time and method effects. Convergence with a traditional measure of CU behavior in late childhood provides support for the construct validity of a brief early childhood measure of CU behavior. PMID:27598253

  4. A study of hyperelastic models for predicting the mechanical behavior of extensor apparatus.

    PubMed

    Elyasi, Nahid; Taheri, Kimia Karimi; Narooei, Keivan; Taheri, Ali Karimi

    2017-06-01

    In this research, the nonlinear elastic behavior of human extensor apparatus was investigated. To this goal, firstly the best material parameters of hyperelastic strain energy density functions consisting of the Mooney-Rivlin, Ogden, invariants, and general exponential models were derived for the simple tension experimental data. Due to the significance of stress response in other deformation modes of nonlinear models, the calculated parameters were used to study the pure shear and balance biaxial tension behavior of the extensor apparatus. The results indicated that the Mooney-Rivlin model predicts an unstable behavior in the balance biaxial deformation of the extensor apparatus, while the Ogden order 1 represents a stable behavior, although the fitting of experimental data and theoretical model was not satisfactory. However, the Ogden order 6 model was unstable in the simple tension mode and the Ogden order 5 and general exponential models presented accurate and stable results. In order to reduce the material parameters, the invariants model with four material parameters was investigated and this model presented the minimum error and stable behavior in all deformation modes. The ABAQUS Explicit solver was coupled with the VUMAT subroutine code of the invariants model to simulate the mechanical behavior of the central and terminal slips of the extensor apparatus during the passive finger flexion, which is important in the prediction of boutonniere deformity and chronic mallet finger injuries, respectively. Also, to evaluate the adequacy of constitutive models in simulations, the results of the Ogden order 5 were presented. The difference between the predictions was attributed to the better fittings of the invariants model compared with the Ogden model.

  5. Perceived behavioral alcohol norms predict drinking for college students while studying abroad.

    PubMed

    Pedersen, Eric R; LaBrie, Joseph W; Hummer, Justin F

    2009-11-01

    College students who study abroad may represent a subgroup at risk for increased drinking while living in foreign countries. The present study explores this idea as well as the extent to which students' pre-abroad perceptions of study-abroad student drinking are related to actual drinking while abroad. Ninety-one students planning to study abroad completed an online survey of demographics, pre-abroad drinking behavior, perceptions of study-abroad student drinking behavior while abroad, and intentions to drink while abroad. Halfway into their study-abroad experience, participants completed a follow-up survey assessing drinking while abroad. Pre-abroad intentions of drinking and pre-abroad perceptions of study-abroad drinking were associated with actual drinking while abroad. However, perceptions predicted actual drinking while abroad over and above intended drinking. In addition, although participants overall did not significantly increase their drinking while studying abroad, participants with higher pre-abroad perceived norms significantly increased their own drinking behavior while abroad. As in other samples of college students, perceived norms appear to be an important correlate of study-abroad student drinking behavior. Findings suggest that perceptions of study-abroad student-specific drinking predicted not only actual drinking while abroad but also increases in drinking from pre-abroad levels. Findings provide preliminary support for the idea that presenting prospective study-abroad students with accurate norms of study-abroad student-drinking behavior may help prevent increased or heavy drinking during this period.

  6. Perceived Behavioral Alcohol Norms Predict Drinking for College Students While Studying Abroad*

    PubMed Central

    Pedersen, Eric R.; LaBrie, Joseph W.; Hummer, Justin F.

    2009-01-01

    Objective: College students who study abroad may represent a subgroup at risk for increased drinking while living in foreign countries. The present study explores this idea as well as the extent to which students' pre-abroad perceptions of study-abroad student drinking are related to actual drinking while abroad. Method: Ninety-one students planning to study abroad completed an online survey of demographics, pre-abroad drinking behavior, perceptions of study-abroad student drinking behavior while abroad, and intentions to drink while abroad. Halfway into their study-abroad experience, participants completed a follow-up survey assessing drinking while abroad. Results: Pre-abroad intentions of drinking and pre-abroad perceptions of study-abroad drinking were associated with actual drinking while abroad. However, perceptions predicted actual drinking while abroad over and above intended drinking. In addition, although participants overall did not significantly increase their drinking while studying abroad, participants with higher pre-abroad perceived norms significantly increased their own drinking behavior while abroad. Conclusions: As in other samples of college students, perceived norms appear to be an important correlate of study-abroad student drinking behavior. Findings suggest that perceptions of study-abroad student-specific drinking predicted not only actual drinking while abroad but also increases in drinking from pre-abroad levels. Findings provide preliminary support for the idea that presenting prospective study-abroad students with accurate norms of study-abroad student-drinking behavior may help prevent increased or heavy drinking during this period. PMID:19895769

  7. Modification of the Feline-Ality™ Assessment and the Ability to Predict Adopted Cats’ Behaviors in Their New Homes

    PubMed Central

    Weiss, Emily; Gramann, Shannon; Drain, Natasha; Dolan, Emily; Slater, Margaret

    2015-01-01

    Simple Summary While millions of cats enter animal shelters every year, only 11.5% of pet cats are obtained from a shelter in the United States. Previous research has indicated that unrealistic expectations set by adopters can increase the chances of an adopted cat returning to the shelter. The ASPCA®’s Meet Your Match® Feline-ality™ adoption program was designed to provide adopters with accurate information about an adult cat’s future behavior in the home. This research explored the ability of the modified Feline-ality™ assessment when done one day after the cat entered the shelter. Our modified version was predictive of feline behavior post adoption. Abstract It is estimated that 2.5 million cats enter animal shelters in the United States every year and as few as 20% leave the shelter alive. Of those adopted, the greatest risk to post-adoption human animal bond is unrealistic expectations set by the adopter. The ASPCA®’s Meet Your Match® Feline-ality™ adoption program was developed to provide adopters with an accurate assessment of an adult cat’s future behavior in the home. However, the original Feline-ality™ required a three-day hold time to collect cat behaviors on a data card, which was challenging for some shelters. This research involved creating a survey to determine in-home feline behavior post adoption and explored the predictive ability of the in-shelter assessment without the data card. Our results show that the original Feline-ality™ assessment and our modified version were predictive of feline behavior post adoption. Our modified version also decreased hold time for cats to one day. Shelters interested in increasing cat adoptions, decreasing length of stay and improving the adoption experience can now implement the modified version for future feline adoption success. PMID:26479138

  8. Fast and Accurate Prediction of Stratified Steel Temperature During Holding Period of Ladle

    NASA Astrophysics Data System (ADS)

    Deodhar, Anirudh; Singh, Umesh; Shukla, Rishabh; Gautham, B. P.; Singh, Amarendra K.

    2017-04-01

    Thermal stratification of liquid steel in a ladle during the holding period and the teeming operation has a direct bearing on the superheat available at the caster and hence on the caster set points such as casting speed and cooling rates. The changes in the caster set points are typically carried out based on temperature measurements at the end of tundish outlet. Thermal prediction models provide advance knowledge of the influence of process and design parameters on the steel temperature at various stages. Therefore, they can be used in making accurate decisions about the caster set points in real time. However, this requires both fast and accurate thermal prediction models. In this work, we develop a surrogate model for the prediction of thermal stratification using data extracted from a set of computational fluid dynamics (CFD) simulations, pre-determined using design of experiments technique. Regression method is used for training the predictor. The model predicts the stratified temperature profile instantaneously, for a given set of process parameters such as initial steel temperature, refractory heat content, slag thickness, and holding time. More than 96 pct of the predicted values are within an error range of ±5 K (±5 °C), when compared against corresponding CFD results. Considering its accuracy and computational efficiency, the model can be extended for thermal control of casting operations. This work also sets a benchmark for developing similar thermal models for downstream processes such as tundish and caster.

  9. Prediction of Happy-Sad Mood from Daily Behaviors and Previous Sleep History

    PubMed Central

    Sano, Akane; Yu, Amy; McHill, Andrew W.; Phillips, Andrew J. K.; Taylor, Sara; Jaques, Natasha; Klerman, Elizabeth B.; Picard, Rosalind W.

    2016-01-01

    We collected and analyzed subjective and objective data using surveys and wearable sensors worn day and night from 68 participants, for 30 days each, to address questions related to the relationships among sleep duration, sleep irregularity, self-reported Happy-Sad mood and other factors in college students. We analyzed daily and monthly behavior and physiology and identified factors that affect mood, including how accurately sleep duration and sleep regularity for the past 1-5 days classified the participants into high/low mood using support vector machines. We found statistically significant associations among sad mood and poor health-related factors. Behavioral factors such as the percentage of neutral social interactions and the total academic activity hours showed the best performance in separating the Happy-Sad mood groups. Sleep regularity was a more important discriminator of mood than sleep duration for most participants, although both variables predicted happy/sad mood with from 70-82% accuracy. The number of nights giving the best prediction of happy/sad mood varied for different groups of individuals. PMID:26737854

  10. Estimating the state of a geophysical system with sparse observations: time delay methods to achieve accurate initial states for prediction

    NASA Astrophysics Data System (ADS)

    An, Zhe; Rey, Daniel; Ye, Jingxin; Abarbanel, Henry D. I.

    2017-01-01

    The problem of forecasting the behavior of a complex dynamical system through analysis of observational time-series data becomes difficult when the system expresses chaotic behavior and the measurements are sparse, in both space and/or time. Despite the fact that this situation is quite typical across many fields, including numerical weather prediction, the issue of whether the available observations are "sufficient" for generating successful forecasts is still not well understood. An analysis by Whartenby et al. (2013) found that in the context of the nonlinear shallow water equations on a β plane, standard nudging techniques require observing approximately 70 % of the full set of state variables. Here we examine the same system using a method introduced by Rey et al. (2014a), which generalizes standard nudging methods to utilize time delayed measurements. We show that in certain circumstances, it provides a sizable reduction in the number of observations required to construct accurate estimates and high-quality predictions. In particular, we find that this estimate of 70 % can be reduced to about 33 % using time delays, and even further if Lagrangian drifter locations are also used as measurements.

  11. Competitive Abilities in Experimental Microcosms Are Accurately Predicted by a Demographic Index for R*

    PubMed Central

    Murrell, Ebony G.; Juliano, Steven A.

    2012-01-01

    Resource competition theory predicts that R*, the equilibrium resource amount yielding zero growth of a consumer population, should predict species' competitive abilities for that resource. This concept has been supported for unicellular organisms, but has not been well-tested for metazoans, probably due to the difficulty of raising experimental populations to equilibrium and measuring population growth rates for species with long or complex life cycles. We developed an index (Rindex) of R* based on demography of one insect cohort, growing from egg to adult in a non-equilibrium setting, and tested whether Rindex yielded accurate predictions of competitive abilities using mosquitoes as a model system. We estimated finite rate of increase (λ′) from demographic data for cohorts of three mosquito species raised with different detritus amounts, and estimated each species' Rindex using nonlinear regressions of λ′ vs. initial detritus amount. All three species' Rindex differed significantly, and accurately predicted competitive hierarchy of the species determined in simultaneous pairwise competition experiments. Our Rindex could provide estimates and rigorous statistical comparisons of competitive ability for organisms for which typical chemostat methods and equilibrium population conditions are impractical. PMID:22970128

  12. Searching for an Accurate Marker-Based Prediction of an Individual Quantitative Trait in Molecular Plant Breeding

    PubMed Central

    Fu, Yong-Bi; Yang, Mo-Hua; Zeng, Fangqin; Biligetu, Bill

    2017-01-01

    Molecular plant breeding with the aid of molecular markers has played an important role in modern plant breeding over the last two decades. Many marker-based predictions for quantitative traits have been made to enhance parental selection, but the trait prediction accuracy remains generally low, even with the aid of dense, genome-wide SNP markers. To search for more accurate trait-specific prediction with informative SNP markers, we conducted a literature review on the prediction issues in molecular plant breeding and on the applicability of an RNA-Seq technique for developing function-associated specific trait (FAST) SNP markers. To understand whether and how FAST SNP markers could enhance trait prediction, we also performed a theoretical reasoning on the effectiveness of these markers in a trait-specific prediction, and verified the reasoning through computer simulation. To the end, the search yielded an alternative to regular genomic selection with FAST SNP markers that could be explored to achieve more accurate trait-specific prediction. Continuous search for better alternatives is encouraged to enhance marker-based predictions for an individual quantitative trait in molecular plant breeding. PMID:28729875

  13. SIFTER search: a web server for accurate phylogeny-based protein function prediction

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

    Sahraeian, Sayed M.; Luo, Kevin R.; Brenner, Steven E.

    We are awash in proteins discovered through high-throughput sequencing projects. As only a minuscule fraction of these have been experimentally characterized, computational methods are widely used for automated annotation. Here, we introduce a user-friendly web interface for accurate protein function prediction using the SIFTER algorithm. SIFTER is a state-of-the-art sequence-based gene molecular function prediction algorithm that uses a statistical model of function evolution to incorporate annotations throughout the phylogenetic tree. Due to the resources needed by the SIFTER algorithm, running SIFTER locally is not trivial for most users, especially for large-scale problems. The SIFTER web server thus provides access tomore » precomputed predictions on 16 863 537 proteins from 232 403 species. Users can explore SIFTER predictions with queries for proteins, species, functions, and homologs of sequences not in the precomputed prediction set. Lastly, the SIFTER web server is accessible at http://sifter.berkeley.edu/ and the source code can be downloaded.« less

  14. SIFTER search: a web server for accurate phylogeny-based protein function prediction

    DOE PAGES

    Sahraeian, Sayed M.; Luo, Kevin R.; Brenner, Steven E.

    2015-05-15

    We are awash in proteins discovered through high-throughput sequencing projects. As only a minuscule fraction of these have been experimentally characterized, computational methods are widely used for automated annotation. Here, we introduce a user-friendly web interface for accurate protein function prediction using the SIFTER algorithm. SIFTER is a state-of-the-art sequence-based gene molecular function prediction algorithm that uses a statistical model of function evolution to incorporate annotations throughout the phylogenetic tree. Due to the resources needed by the SIFTER algorithm, running SIFTER locally is not trivial for most users, especially for large-scale problems. The SIFTER web server thus provides access tomore » precomputed predictions on 16 863 537 proteins from 232 403 species. Users can explore SIFTER predictions with queries for proteins, species, functions, and homologs of sequences not in the precomputed prediction set. Lastly, the SIFTER web server is accessible at http://sifter.berkeley.edu/ and the source code can be downloaded.« less

  15. XenoSite: accurately predicting CYP-mediated sites of metabolism with neural networks.

    PubMed

    Zaretzki, Jed; Matlock, Matthew; Swamidass, S Joshua

    2013-12-23

    Understanding how xenobiotic molecules are metabolized is important because it influences the safety, efficacy, and dose of medicines and how they can be modified to improve these properties. The cytochrome P450s (CYPs) are proteins responsible for metabolizing 90% of drugs on the market, and many computational methods can predict which atomic sites of a molecule--sites of metabolism (SOMs)--are modified during CYP-mediated metabolism. This study improves on prior methods of predicting CYP-mediated SOMs by using new descriptors and machine learning based on neural networks. The new method, XenoSite, is faster to train and more accurate by as much as 4% or 5% for some isozymes. Furthermore, some "incorrect" predictions made by XenoSite were subsequently validated as correct predictions by revaluation of the source literature. Moreover, XenoSite output is interpretable as a probability, which reflects both the confidence of the model that a particular atom is metabolized and the statistical likelihood that its prediction for that atom is correct.

  16. Understanding Eating Behaviors through Parental Communication and the Integrative Model of Behavioral Prediction.

    PubMed

    Scheinfeld, Emily; Shim, Minsun

    2017-05-01

    Emerging adulthood (EA) is an important yet overlooked period for developing long-term health behaviors. During these years, emerging adults adopt health behaviors that persist throughout life. This study applies the Integrative Model of Behavioral Prediction (IMBP) to examine the role of childhood parental communication in predicting engagement in healthful eating during EA. Participants included 239 college students, ages 18 to 25, from a large university in the southern United States. Participants were recruited and data collection occurred spring 2012. Participants responded to measures to assess perceived parental communication, eating behaviors, attitudes, subjective norms, and behavioral control over healthful eating. SEM and mediation analyses were used to address the hypotheses posited. Data demonstrated that perceived parent-child communication - specifically, its quality and target-specific content - significantly predicted emerging adults' eating behaviors, mediated through subjective norm and perceived behavioral control. This study sets the stage for further exploration and understanding of different ways parental communication influences emerging adults' healthy behavior enactment.

  17. 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.

  18. Individual laboratory-measured discount rates predict field behavior

    PubMed Central

    Chabris, Christopher F.; Laibson, David; Morris, Carrie L.; Schuldt, Jonathon P.; Taubinsky, Dmitry

    2009-01-01

    We estimate discount rates of 555 subjects using a laboratory task and find that these individual discount rates predict inter-individual variation in field behaviors (e.g., exercise, BMI, smoking). The correlation between the discount rate and each field behavior is small: none exceeds 0.28 and many are near 0. However, the discount rate has at least as much predictive power as any variable in our dataset (e.g., sex, age, education). The correlation between the discount rate and field behavior rises when field behaviors are aggregated: these correlations range from 0.09-0.38. We present a model that explains why specific intertemporal choice behaviors are only weakly correlated with discount rates, even though discount rates robustly predict aggregates of intertemporal decisions. PMID:19412359

  19. Individual laboratory-measured discount rates predict field behavior.

    PubMed

    Chabris, Christopher F; Laibson, David; Morris, Carrie L; Schuldt, Jonathon P; Taubinsky, Dmitry

    2008-12-01

    We estimate discount rates of 555 subjects using a laboratory task and find that these individual discount rates predict inter-individual variation in field behaviors (e.g., exercise, BMI, smoking). The correlation between the discount rate and each field behavior is small: none exceeds 0.28 and many are near 0. However, the discount rate has at least as much predictive power as any variable in our dataset (e.g., sex, age, education). The correlation between the discount rate and field behavior rises when field behaviors are aggregated: these correlations range from 0.09-0.38. We present a model that explains why specific intertemporal choice behaviors are only weakly correlated with discount rates, even though discount rates robustly predict aggregates of intertemporal decisions.

  20. A Critical Review for Developing Accurate and Dynamic Predictive Models Using Machine Learning Methods in Medicine and Health Care.

    PubMed

    Alanazi, Hamdan O; Abdullah, Abdul Hanan; Qureshi, Kashif Naseer

    2017-04-01

    Recently, Artificial Intelligence (AI) has been used widely in medicine and health care sector. In machine learning, the classification or prediction is a major field of AI. Today, the study of existing predictive models based on machine learning methods is extremely active. Doctors need accurate predictions for the outcomes of their patients' diseases. In addition, for accurate predictions, timing is another significant factor that influences treatment decisions. In this paper, existing predictive models in medicine and health care have critically reviewed. Furthermore, the most famous machine learning methods have explained, and the confusion between a statistical approach and machine learning has clarified. A review of related literature reveals that the predictions of existing predictive models differ even when the same dataset is used. Therefore, existing predictive models are essential, and current methods must be improved.

  1. Components of Young Children's Trait Understanding: Behavior-to-Trait Inferences and Trait-to-Behavior Predictions

    ERIC Educational Resources Information Center

    Liu, David; Gelman, Susan A.; Wellman, Henry M.

    2007-01-01

    Trait attribution is central to people's naive theories of people and their actions. Previous developmental research indicates that young children are poor at predicting behaviors from past trait-relevant behaviors. We propose that the cognitive process of behavior-to-behavior predictions consists of two component processes: (1) behavior-to-trait…

  2. Life prediction and constitutive behavior

    NASA Technical Reports Server (NTRS)

    Halford, G. R.

    1983-01-01

    One of the primary drivers that prompted the initiation of the hot section technology (HOST) program was the recognized need for improved cyclic durability of costly hot section components. All too frequently, fatigue in one form or another was directly responsible for the less than desired durability, and prospects for the future weren't going to improve unless a significant effort was mounted to increase our knowledge and understanding of the elements governing cyclic crack initiation and propagation lifetime. Certainly one of the important factors is the ability to perform accurate structural stress-strain analyses on a routine basis to determine the magnitudes of the localized stresses and strains since it is these localized conditions that govern the initiation and crack growth processes. Developing the ability to more accurately predict crack initiation lifetimes and cyclic crack growth rates for the complex loading conditions found in turbine engine hot sections is of course the ultimate goal of the life prediction research efforts. It has been found convenient to divide the research efforts into those dealing with nominally isotropic and anisotropic alloys; the latter for application to directionally solidified and single crystal turbine blades.

  3. Ensemble predictive model for more accurate soil organic carbon spectroscopic estimation

    NASA Astrophysics Data System (ADS)

    Vašát, Radim; Kodešová, Radka; Borůvka, Luboš

    2017-07-01

    A myriad of signal pre-processing strategies and multivariate calibration techniques has been explored in attempt to improve the spectroscopic prediction of soil organic carbon (SOC) over the last few decades. Therefore, to come up with a novel, more powerful, and accurate predictive approach to beat the rank becomes a challenging task. However, there may be a way, so that combine several individual predictions into a single final one (according to ensemble learning theory). As this approach performs best when combining in nature different predictive algorithms that are calibrated with structurally different predictor variables, we tested predictors of two different kinds: 1) reflectance values (or transforms) at each wavelength and 2) absorption feature parameters. Consequently we applied four different calibration techniques, two per each type of predictors: a) partial least squares regression and support vector machines for type 1, and b) multiple linear regression and random forest for type 2. The weights to be assigned to individual predictions within the ensemble model (constructed as a weighted average) were determined by an automated procedure that ensured the best solution among all possible was selected. The approach was tested at soil samples taken from surface horizon of four sites differing in the prevailing soil units. By employing the ensemble predictive model the prediction accuracy of SOC improved at all four sites. The coefficient of determination in cross-validation (R2cv) increased from 0.849, 0.611, 0.811 and 0.644 (the best individual predictions) to 0.864, 0.650, 0.824 and 0.698 for Site 1, 2, 3 and 4, respectively. Generally, the ensemble model affected the final prediction so that the maximal deviations of predicted vs. observed values of the individual predictions were reduced, and thus the correlation cloud became thinner as desired.

  4. Alcohol operant self-administration: Investigating how alcohol-seeking behaviors predict drinking in mice using two operant approaches

    PubMed Central

    Blegen, Mariah B.; Silva, Daniel da Silva E; Bock, Roland; Morisot, Nadege; Ron, Dorit; Alvarez, Veronica A.

    2018-01-01

    Alcohol operant self-administration paradigms are critical tools for studying the neural circuits implicated in both alcohol-seeking and consummatory behaviors and for understanding the neural basis underlying alcohol-use disorders. In this study, we investigate the predictive value of two operant models of oral alcohol self-administration in mice, one in which alcohol is delivered into a cup following nose-poke responses with no accurate measurement of consumed alcohol solution, and another paradigm that provides access to alcohol via a sipper tube following lever presses and where lick rate and consumed alcohol volume can be measured. The goal was to identify a paradigm where operant behaviors such as lever presses and nose pokes, as well as other tracked behavior such as licks and head entries, can be used to reliably predict blood alcohol concentration (BAC). All mice were first exposed to alcohol in the home cage using the “drinking in the dark” (DID) procedure for 3 weeks and then were trained in alcohol self-administration using either of the operant paradigms for several weeks. Even without sucrose fading or food pre-training, mice acquired alcohol self-administration with both paradigms. However, neither lever press nor nose-poke rates were good predictors of alcohol intake or BAC. Only the lick rate and consumed alcohol were consistently and significantly correlated with BAC. Using this paradigm that accurately measures alcohol intake, unsupervised cluster analysis revealed three groups of mice: high-drinking (43%), low-drinking (37%), and non-drinking mice (20%). High-drinking mice showed faster acquisition of operant responding and achieved higher BACs than low-drinking mice. Lick rate and volume consumed varied with the alcohol concentration made available only for high- and low-drinking mice, but not for non-drinking mice. In addition, high- and low-drinking mice showed similar patterns during extinction and significant cue-induced reinstatement

  5. Accurate Behavioral Simulator of All-Digital Time-Domain Smart Temperature Sensors by Using SIMULINK

    PubMed Central

    Chen, Chun-Chi; Chen, Chao-Lieh; Lin, You-Ting

    2016-01-01

    This study proposes a new behavioral simulator that uses SIMULINK for all-digital CMOS time-domain smart temperature sensors (TDSTSs) for performing rapid and accurate simulations. Inverter-based TDSTSs offer the benefits of low cost and simple structure for temperature-to-digital conversion and have been developed. Typically, electronic design automation tools, such as HSPICE, are used to simulate TDSTSs for performance evaluations. However, such tools require extremely long simulation time and complex procedures to analyze the results and generate figures. In this paper, we organize simple but accurate equations into a temperature-dependent model (TDM) by which the TDSTSs evaluate temperature behavior. Furthermore, temperature-sensing models of a single CMOS NOT gate were devised using HSPICE simulations. Using the TDM and these temperature-sensing models, a novel simulator in SIMULINK environment was developed to substantially accelerate the simulation and simplify the evaluation procedures. Experiments demonstrated that the simulation results of the proposed simulator have favorable agreement with those obtained from HSPICE simulations, showing that the proposed simulator functions successfully. This is the first behavioral simulator addressing the rapid simulation of TDSTSs. PMID:27509507

  6. Using connectome-based predictive modeling to predict individual behavior from brain connectivity

    PubMed Central

    Shen, Xilin; Finn, Emily S.; Scheinost, Dustin; Rosenberg, Monica D.; Chun, Marvin M.; Papademetris, Xenophon; Constable, R Todd

    2017-01-01

    Neuroimaging is a fast developing research area where anatomical and functional images of human brains are collected using techniques such as functional magnetic resonance imaging (fMRI), diffusion tensor imaging (DTI), and electroencephalography (EEG). Technical advances and large-scale datasets have allowed for the development of models capable of predicting individual differences in traits and behavior using brain connectivity measures derived from neuroimaging data. Here, we present connectome-based predictive modeling (CPM), a data-driven protocol for developing predictive models of brain-behavior relationships from connectivity data using cross-validation. This protocol includes the following steps: 1) feature selection, 2) feature summarization, 3) model building, and 4) assessment of prediction significance. We also include suggestions for visualizing the most predictive features (i.e., brain connections). The final result should be a generalizable model that takes brain connectivity data as input and generates predictions of behavioral measures in novel subjects, accounting for a significant amount of the variance in these measures. It has been demonstrated that the CPM protocol performs equivalently or better than most of the existing approaches in brain-behavior prediction. However, because CPM focuses on linear modeling and a purely data-driven driven approach, neuroscientists with limited or no experience in machine learning or optimization would find it easy to implement the protocols. Depending on the volume of data to be processed, the protocol can take 10–100 minutes for model building, 1–48 hours for permutation testing, and 10–20 minutes for visualization of results. PMID:28182017

  7. Towards First Principles-Based Prediction of Highly Accurate Electrochemical Pourbaix Diagrams

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

    Zeng, Zhenhua; Chan, Maria K. Y.; Zhao, Zhi-Jian

    2015-08-13

    Electrochemical potential/pH (Pourbaix) diagrams underpin many aqueous electrochemical processes and are central to the identification of stable phases of metals for processes ranging from electrocatalysis to corrosion. Even though standard DFT calculations are potentially powerful tools for the prediction of such diagrams, inherent errors in the description of transition metal (hydroxy)oxides, together with neglect of van der Waals interactions, have limited the reliability of such predictions for even the simplest pure metal bulk compounds, and corresponding predictions for more complex alloy or surface structures are even more challenging. In the present work, through synergistic use of a Hubbard U correction,more » a state-of-the-art dispersion correction, and a water-based bulk reference state for the calculations, these errors are systematically corrected. The approach describes the weak binding that occurs between hydroxyl-containing functional groups in certain compounds in Pourbaix diagrams, corrects for self-interaction errors in transition metal compounds, and reduces residual errors on oxygen atoms by preserving a consistent oxidation state between the reference state, water, and the relevant bulk phases. The strong performance is illustrated on a series of bulk transition metal (Mn, Fe, Co and Ni) hydroxides, oxyhydroxides, binary, and ternary oxides, where the corresponding thermodynamics of redox and (de)hydration are described with standard errors of 0.04 eV per (reaction) formula unit. The approach further preserves accurate descriptions of the overall thermodynamics of electrochemically-relevant bulk reactions, such as water formation, which is an essential condition for facilitating accurate analysis of reaction energies for electrochemical processes on surfaces. The overall generality and transferability of the scheme suggests that it may find useful application in the construction of a broad array of electrochemical phase diagrams, including

  8. Gender perspective on the factors predicting recycling behavior: Implications from the theory of planned behavior.

    PubMed

    Oztekin, Ceren; Teksöz, Gaye; Pamuk, Savas; Sahin, Elvan; Kilic, Dilek Sultan

    2017-04-01

    This study aimed to assess the role of some socio-psychological attributes in explaining recycling behavior of Turkish university community from a gender perspective within the context of the theory of planned behavior with an additional variable (past experience). The recycling behavior of whole sample, females and males, has been examined in 3 sessions -depending on the arguments that explain gendered pattern of private and public environmental behavior and sticking to the fact why females' stronger environmental values, beliefs, and attitudes do not translate consistently into greater engagement in public behavior. As a result of model runs, different variables shaping intention for behavior have been found, namely perceived behavior control for females and past behavior for males. Due to the low percent of the variance in explaining recycling behavior of females, they have been identified as the ones who do not carry out intentions (non-recyclers). Since intentions alone are capable of identifying recyclers accurately but not non-recyclers, there may be other factors to be considered to understand the reason for females not carrying out the intentions. The results of descriptive statistics supported the identification by attitudes toward recycling. Female attitudes were innate (recycling is good, necessary, useful and sensitive), whereas those of males were learnt (recycling is healthy, valuable and correct). Thus, it has been concluded that males' intention for recycling is shaped by their past behavior and the conclusion is supported by males having learnt attitude toward recycling whereas females' lack of intention for recycling is shaped by their perceived behavior control and is supported by their innate attitude for recycling. All in all, the results of the present study provide further support for the utility of the TPB as a model of behavioral prediction and concur with other studies examining the utility of the TPB in the context of recycling

  9. Highly accurate prediction of emotions surrounding the attacks of September 11, 2001 over 1-, 2-, and 7-year prediction intervals.

    PubMed

    Doré, Bruce P; Meksin, Robert; Mather, Mara; Hirst, William; Ochsner, Kevin N

    2016-06-01

    In the aftermath of a national tragedy, important decisions are predicated on judgments of the emotional significance of the tragedy in the present and future. Research in affective forecasting has largely focused on ways in which people fail to make accurate predictions about the nature and duration of feelings experienced in the aftermath of an event. Here we ask a related but understudied question: can people forecast how they will feel in the future about a tragic event that has already occurred? We found that people were strikingly accurate when predicting how they would feel about the September 11 attacks over 1-, 2-, and 7-year prediction intervals. Although people slightly under- or overestimated their future feelings at times, they nonetheless showed high accuracy in forecasting (a) the overall intensity of their future negative emotion, and (b) the relative degree of different types of negative emotion (i.e., sadness, fear, or anger). Using a path model, we found that the relationship between forecasted and actual future emotion was partially mediated by current emotion and remembered emotion. These results extend theories of affective forecasting by showing that emotional responses to an event of ongoing national significance can be predicted with high accuracy, and by identifying current and remembered feelings as independent sources of this accuracy. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

  10. Highly accurate prediction of emotions surrounding the attacks of September 11, 2001 over 1-, 2-, and 7-year prediction intervals

    PubMed Central

    Doré, B.P.; Meksin, R.; Mather, M.; Hirst, W.; Ochsner, K.N

    2016-01-01

    In the aftermath of a national tragedy, important decisions are predicated on judgments of the emotional significance of the tragedy in the present and future. Research in affective forecasting has largely focused on ways in which people fail to make accurate predictions about the nature and duration of feelings experienced in the aftermath of an event. Here we ask a related but understudied question: can people forecast how they will feel in the future about a tragic event that has already occurred? We found that people were strikingly accurate when predicting how they would feel about the September 11 attacks over 1-, 2-, and 7-year prediction intervals. Although people slightly under- or overestimated their future feelings at times, they nonetheless showed high accuracy in forecasting 1) the overall intensity of their future negative emotion, and 2) the relative degree of different types of negative emotion (i.e., sadness, fear, or anger). Using a path model, we found that the relationship between forecasted and actual future emotion was partially mediated by current emotion and remembered emotion. These results extend theories of affective forecasting by showing that emotional responses to an event of ongoing national significance can be predicted with high accuracy, and by identifying current and remembered feelings as independent sources of this accuracy. PMID:27100309

  11. An accurate behavioral model for single-photon avalanche diode statistical performance simulation

    NASA Astrophysics Data System (ADS)

    Xu, Yue; Zhao, Tingchen; Li, Ding

    2018-01-01

    An accurate behavioral model is presented to simulate important statistical performance of single-photon avalanche diodes (SPADs), such as dark count and after-pulsing noise. The derived simulation model takes into account all important generation mechanisms of the two kinds of noise. For the first time, thermal agitation, trap-assisted tunneling and band-to-band tunneling mechanisms are simultaneously incorporated in the simulation model to evaluate dark count behavior of SPADs fabricated in deep sub-micron CMOS technology. Meanwhile, a complete carrier trapping and de-trapping process is considered in afterpulsing model and a simple analytical expression is derived to estimate after-pulsing probability. In particular, the key model parameters of avalanche triggering probability and electric field dependence of excess bias voltage are extracted from Geiger-mode TCAD simulation and this behavioral simulation model doesn't include any empirical parameters. The developed SPAD model is implemented in Verilog-A behavioral hardware description language and successfully operated on commercial Cadence Spectre simulator, showing good universality and compatibility. The model simulation results are in a good accordance with the test data, validating high simulation accuracy.

  12. Do Implicit Attitudes Predict Actual Voting Behavior Particularly for Undecided Voters?

    PubMed Central

    Friese, Malte; Smith, Colin Tucker; Plischke, Thomas; Bluemke, Matthias; Nosek, Brian A.

    2012-01-01

    The prediction of voting behavior of undecided voters poses a challenge to psychologists and pollsters. Recently, researchers argued that implicit attitudes would predict voting behavior particularly for undecided voters whereas explicit attitudes would predict voting behavior particularly for decided voters. We tested this assumption in two studies in two countries with distinct political systems in the context of real political elections. Results revealed that (a) explicit attitudes predicted voting behavior better than implicit attitudes for both decided and undecided voters, and (b) implicit attitudes predicted voting behavior better for decided than undecided voters. We propose that greater elaboration of attitudes produces stronger convergence between implicit and explicit attitudes resulting in better predictive validity of both, and less incremental validity of implicit over explicit attitudes for the prediction of voting behavior. However, greater incremental predictive validity of implicit over explicit attitudes may be associated with less elaboration. PMID:22952898

  13. Estimating the state of a geophysical system with sparse observations: time delay methods to achieve accurate initial states for prediction

    DOE PAGES

    An, Zhe; Rey, Daniel; Ye, Jingxin; ...

    2017-01-16

    The problem of forecasting the behavior of a complex dynamical system through analysis of observational time-series data becomes difficult when the system expresses chaotic behavior and the measurements are sparse, in both space and/or time. Despite the fact that this situation is quite typical across many fields, including numerical weather prediction, the issue of whether the available observations are "sufficient" for generating successful forecasts is still not well understood. An analysis by Whartenby et al. (2013) found that in the context of the nonlinear shallow water equations on a β plane, standard nudging techniques require observing approximately 70 % of themore » full set of state variables. Here we examine the same system using a method introduced by Rey et al. (2014a), which generalizes standard nudging methods to utilize time delayed measurements. Here, we show that in certain circumstances, it provides a sizable reduction in the number of observations required to construct accurate estimates and high-quality predictions. In particular, we find that this estimate of 70 % can be reduced to about 33 % using time delays, and even further if Lagrangian drifter locations are also used as measurements.« less

  14. Estimating the state of a geophysical system with sparse observations: time delay methods to achieve accurate initial states for prediction

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

    An, Zhe; Rey, Daniel; Ye, Jingxin

    The problem of forecasting the behavior of a complex dynamical system through analysis of observational time-series data becomes difficult when the system expresses chaotic behavior and the measurements are sparse, in both space and/or time. Despite the fact that this situation is quite typical across many fields, including numerical weather prediction, the issue of whether the available observations are "sufficient" for generating successful forecasts is still not well understood. An analysis by Whartenby et al. (2013) found that in the context of the nonlinear shallow water equations on a β plane, standard nudging techniques require observing approximately 70 % of themore » full set of state variables. Here we examine the same system using a method introduced by Rey et al. (2014a), which generalizes standard nudging methods to utilize time delayed measurements. Here, we show that in certain circumstances, it provides a sizable reduction in the number of observations required to construct accurate estimates and high-quality predictions. In particular, we find that this estimate of 70 % can be reduced to about 33 % using time delays, and even further if Lagrangian drifter locations are also used as measurements.« less

  15. Predicting phase behavior of mixtures of reservoir fluids with carbon dioxide

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

    Grigg, R.B.; Lingane, P.J.

    1983-10-01

    The use of an equation of state to predict phase behavior during carbon dioxide flooding is well established. There is consensus that the characterization of the C fraction, the grouping of this fraction into ''pseudo components'', and the selection of interaction parameters are the most important variables. However, the literature is vague as to how to best select the pseudo components, especially when aiming for a few-component representation as for a field scale compositional simulation. Single-contact phase behavior is presented for mixtures of Ford Geraldine (Delaware), Maljamar (Grayburg), West Sussex (Shannon), and Reservoir D reservoir fluids, and of a syntheticmore » oil C/C/C, with carbon dioxide. One can reproduce the phase behavior of these mixtures using 3-5 pseudo components and common interaction parameters. The critical properties of the pseudo components are calculated from detailed oil characterizations. Because the parameters are not further adjusted, this approach reduces the empiricism in fitting phase data and may result in a more accurate representation of the system as the composition of the oil changes during the approach to miscibility.« less

  16. Accurate high-throughput structure mapping and prediction with transition metal ion FRET

    PubMed Central

    Yu, Xiaozhen; Wu, Xiongwu; Bermejo, Guillermo A.; Brooks, Bernard R.; Taraska, Justin W.

    2013-01-01

    Mapping the landscape of a protein’s conformational space is essential to understanding its functions and regulation. The limitations of many structural methods have made this process challenging for most proteins. Here, we report that transition metal ion FRET (tmFRET) can be used in a rapid, highly parallel screen, to determine distances from multiple locations within a protein at extremely low concentrations. The distances generated through this screen for the protein Maltose Binding Protein (MBP) match distances from the crystal structure to within a few angstroms. Furthermore, energy transfer accurately detects structural changes during ligand binding. Finally, fluorescence-derived distances can be used to guide molecular simulations to find low energy states. Our results open the door to rapid, accurate mapping and prediction of protein structures at low concentrations, in large complex systems, and in living cells. PMID:23273426

  17. Neural and hybrid modeling: an alternative route to efficiently predict the behavior of biotechnological processes aimed at biofuels obtainment.

    PubMed

    Curcio, Stefano; Saraceno, Alessandra; Calabrò, Vincenza; Iorio, Gabriele

    2014-01-01

    The present paper was aimed at showing that advanced modeling techniques, based either on artificial neural networks or on hybrid systems, might efficiently predict the behavior of two biotechnological processes designed for the obtainment of second-generation biofuels from waste biomasses. In particular, the enzymatic transesterification of waste-oil glycerides, the key step for the obtainment of biodiesel, and the anaerobic digestion of agroindustry wastes to produce biogas were modeled. It was proved that the proposed modeling approaches provided very accurate predictions of systems behavior. Both neural network and hybrid modeling definitely represented a valid alternative to traditional theoretical models, especially when comprehensive knowledge of the metabolic pathways, of the true kinetic mechanisms, and of the transport phenomena involved in biotechnological processes was difficult to be achieved.

  18. Anticipating Their Future: Adolescent Values for the Future Predict Adult Behaviors

    PubMed Central

    Finlay, Andrea; Wray-Lake, Laura; Warren, Michael; Maggs, Jennifer L.

    2014-01-01

    Adolescent future values – beliefs about what will matter to them in the future – may shape their adult behavior. Utilizing a national longitudinal British sample, this study examined whether adolescent future values in six domains (i.e., family responsibility, full-time job, personal responsibility, autonomy, civic responsibility, and hedonistic privilege) predicted adult social roles, civic behaviors, and alcohol use. Future values positively predicted behaviors within the same domain; fewer cross-domain associations were evident. Civic responsibility positively predicted adult civic behaviors, but negatively predicted having children. Hedonistic privilege positively predicted adult alcohol use and negatively predicted civic behaviors. Results suggest that attention should be paid to how adolescents are thinking about their futures due to the associated links with long-term social and health behaviors. PMID:26279595

  19. Private traits and attributes are predictable from digital records of human behavior.

    PubMed

    Kosinski, Michal; Stillwell, David; Graepel, Thore

    2013-04-09

    We show that easily accessible digital records of behavior, Facebook Likes, can be used to automatically and accurately predict a range of highly sensitive personal attributes including: sexual orientation, ethnicity, religious and political views, personality traits, intelligence, happiness, use of addictive substances, parental separation, age, and gender. The analysis presented is based on a dataset of over 58,000 volunteers who provided their Facebook Likes, detailed demographic profiles, and the results of several psychometric tests. The proposed model uses dimensionality reduction for preprocessing the Likes data, which are then entered into logistic/linear regression to predict individual psychodemographic profiles from Likes. The model correctly discriminates between homosexual and heterosexual men in 88% of cases, African Americans and Caucasian Americans in 95% of cases, and between Democrat and Republican in 85% of cases. For the personality trait "Openness," prediction accuracy is close to the test-retest accuracy of a standard personality test. We give examples of associations between attributes and Likes and discuss implications for online personalization and privacy.

  20. Private traits and attributes are predictable from digital records of human behavior

    PubMed Central

    Kosinski, Michal; Stillwell, David; Graepel, Thore

    2013-01-01

    We show that easily accessible digital records of behavior, Facebook Likes, can be used to automatically and accurately predict a range of highly sensitive personal attributes including: sexual orientation, ethnicity, religious and political views, personality traits, intelligence, happiness, use of addictive substances, parental separation, age, and gender. The analysis presented is based on a dataset of over 58,000 volunteers who provided their Facebook Likes, detailed demographic profiles, and the results of several psychometric tests. The proposed model uses dimensionality reduction for preprocessing the Likes data, which are then entered into logistic/linear regression to predict individual psychodemographic profiles from Likes. The model correctly discriminates between homosexual and heterosexual men in 88% of cases, African Americans and Caucasian Americans in 95% of cases, and between Democrat and Republican in 85% of cases. For the personality trait “Openness,” prediction accuracy is close to the test–retest accuracy of a standard personality test. We give examples of associations between attributes and Likes and discuss implications for online personalization and privacy. PMID:23479631

  1. Accurate approximation method for prediction of class I MHC affinities for peptides of length 8, 10 and 11 using prediction tools trained on 9mers.

    PubMed

    Lundegaard, Claus; Lund, Ole; Nielsen, Morten

    2008-06-01

    Several accurate prediction systems have been developed for prediction of class I major histocompatibility complex (MHC):peptide binding. Most of these are trained on binding affinity data of primarily 9mer peptides. Here, we show how prediction methods trained on 9mer data can be used for accurate binding affinity prediction of peptides of length 8, 10 and 11. The method gives the opportunity to predict peptides with a different length than nine for MHC alleles where no such peptides have been measured. As validation, the performance of this approach is compared to predictors trained on peptides of the peptide length in question. In this validation, the approximation method has an accuracy that is comparable to or better than methods trained on a peptide length identical to the predicted peptides. The algorithm has been implemented in the web-accessible servers NetMHC-3.0: http://www.cbs.dtu.dk/services/NetMHC-3.0, and NetMHCpan-1.1: http://www.cbs.dtu.dk/services/NetMHCpan-1.1

  2. Predicting school adjustment from multiple perspectives on parental behaviors.

    PubMed

    Ratelle, Catherine F; Duchesne, Stéphane; Guay, Frédéric

    2017-01-01

    Past research supported the importance of parental autonomy support, involvement, and structure for student outcomes. The goal of this study was to test the contribution of these behaviors from mothers and fathers in predicting adolescents' adjustment in school using a multi-informant approach. A sample of 522 adolescents (233 boys, 389 girls), their mothers (n = 535), and fathers (n = 296) participated in the study. Results revealed that parents' self-evaluations explained additional variance in children's school adjustment, over and beyond the contribution of children's evaluation of their parents. Maternal reports on their positive behaviors (autonomy support, involvement, and structure) predicted their child's academic and emotional adjustment while their reported control predicted lower levels of these. Fathers' self-reported positive behaviors predicted academic adjustment while their control predicted lower academic and personal-emotional adjustment. These findings support the importance of multiple assessments of parental behaviors for improving the prediction of adjustment in school. Copyright © 2016 The Foundation for Professionals in Services for Adolescents. Published by Elsevier Ltd. All rights reserved.

  3. Soil-pipe interaction modeling for pipe behavior prediction with super learning based methods

    NASA Astrophysics Data System (ADS)

    Shi, Fang; Peng, Xiang; Liu, Huan; Hu, Yafei; Liu, Zheng; Li, Eric

    2018-03-01

    Underground pipelines are subject to severe distress from the surrounding expansive soil. To investigate the structural response of water mains to varying soil movements, field data, including pipe wall strains in situ soil water content, soil pressure and temperature, was collected. The research on monitoring data analysis has been reported, but the relationship between soil properties and pipe deformation has not been well-interpreted. To characterize the relationship between soil property and pipe deformation, this paper presents a super learning based approach combining feature selection algorithms to predict the water mains structural behavior in different soil environments. Furthermore, automatic variable selection method, e.i. recursive feature elimination algorithm, were used to identify the critical predictors contributing to the pipe deformations. To investigate the adaptability of super learning to different predictive models, this research employed super learning based methods to three different datasets. The predictive performance was evaluated by R-squared, root-mean-square error and mean absolute error. Based on the prediction performance evaluation, the superiority of super learning was validated and demonstrated by predicting three types of pipe deformations accurately. In addition, a comprehensive understand of the water mains working environments becomes possible.

  4. Predicting sun protection behaviors using protection motivation variables.

    PubMed

    Ch'ng, Joanne W M; Glendon, A Ian

    2014-04-01

    Protection motivation theory components were used to predict sun protection behaviors (SPBs) using four outcome measures: typical reported behaviors, previous reported behaviors, current sunscreen use as determined by interview, and current observed behaviors (clothing worn) to control for common method bias. Sampled from two SE Queensland public beaches during summer, 199 participants aged 18-29 years completed a questionnaire measuring perceived severity, perceived vulnerability, response efficacy, response costs, and protection motivation (PM). Personal perceived risk (similar to threat appraisal) and response likelihood (similar to coping appraisal) were derived from their respective PM components. Protection motivation predicted all four SPB criterion variables. Personal perceived risk and response likelihood predicted protection motivation. Protection motivation completely mediated the effect of response likelihood on all four criterion variables. Alternative models are considered. Strengths and limitations of the study are outlined and suggestions made for future research.

  5. Can phenological models predict tree phenology accurately in the future? The unrevealed hurdle of endodormancy break.

    PubMed

    Chuine, Isabelle; Bonhomme, Marc; Legave, Jean-Michel; García de Cortázar-Atauri, Iñaki; Charrier, Guillaume; Lacointe, André; Améglio, Thierry

    2016-10-01

    The onset of the growing season of trees has been earlier by 2.3 days per decade during the last 40 years in temperate Europe because of global warming. The effect of temperature on plant phenology is, however, not linear because temperature has a dual effect on bud development. On one hand, low temperatures are necessary to break bud endodormancy, and, on the other hand, higher temperatures are necessary to promote bud cell growth afterward. Different process-based models have been developed in the last decades to predict the date of budbreak of woody species. They predict that global warming should delay or compromise endodormancy break at the species equatorward range limits leading to a delay or even impossibility to flower or set new leaves. These models are classically parameterized with flowering or budbreak dates only, with no information on the endodormancy break date because this information is very scarce. Here, we evaluated the efficiency of a set of phenological models to accurately predict the endodormancy break dates of three fruit trees. Our results show that models calibrated solely with budbreak dates usually do not accurately predict the endodormancy break date. Providing endodormancy break date for the model parameterization results in much more accurate prediction of this latter, with, however, a higher error than that on budbreak dates. Most importantly, we show that models not calibrated with endodormancy break dates can generate large discrepancies in forecasted budbreak dates when using climate scenarios as compared to models calibrated with endodormancy break dates. This discrepancy increases with mean annual temperature and is therefore the strongest after 2050 in the southernmost regions. Our results claim for the urgent need of massive measurements of endodormancy break dates in forest and fruit trees to yield more robust projections of phenological changes in a near future. © 2016 John Wiley & Sons Ltd.

  6. How accurate is our clinical prediction of "minimal prostate cancer"?

    PubMed

    Leibovici, Dan; Shikanov, Sergey; Gofrit, Ofer N; Zagaja, Gregory P; Shilo, Yaniv; Shalhav, Arieh L

    2013-07-01

    Recommendations for active surveillance versus immediate treatment for low risk prostate cancer are based on biopsy and clinical data, assuming that a low volume of well-differentiated carcinoma will be associated with a low progression risk. However, the accuracy of clinical prediction of minimal prostate cancer (MPC) is unclear. To define preoperative predictors for MPC in prostatectomy specimens and to examine the accuracy of such prediction. Data collected on 1526 consecutive radical prostatectomy patients operated in a single center between 2003 and 2008 included: age, body mass index, preoperative prostate-specific antigen level, biopsy Gleason score, clinical stage, percentage of positive biopsy cores, and maximal core length (MCL) involvement. MPC was defined as < 5% of prostate volume involvement with organ-confined Gleason score < or = 6. Univariate and multivariate logistic regression analyses were used to define independent predictors of minimal disease. Classification and Regression Tree (CART) analysis was used to define cutoff values for the predictors and measure the accuracy of prediction. MPC was found in 241 patients (15.8%). Clinical stage, biopsy Gleason's score, percent of positive biopsy cores, and maximal involved core length were associated with minimal disease (OR 0.42, 0.1, 0.92, and 0.9, respectively). Independent predictors of MPC included: biopsy Gleason score, percent of positive cores and MCL (OR 0.21, 095 and 0.95, respectively). CART showed that when the MCL exceeded 11.5%, the likelihood of MPC was 3.8%. Conversely, when applying the most favorable preoperative conditions (Gleason < or = 6, < 20% positive cores, MCL < or = 11.5%) the chance of minimal disease was 41%. Biopsy Gleason score, the percent of positive cores and MCL are independently associated with MPC. While preoperative prediction of significant prostate cancer was accurate, clinical prediction of MPC was incorrect 59% of the time. Caution is necessary when

  7. Extending Theory-Based Quantitative Predictions to New Health Behaviors.

    PubMed

    Brick, Leslie Ann D; Velicer, Wayne F; Redding, Colleen A; Rossi, Joseph S; Prochaska, James O

    2016-04-01

    Traditional null hypothesis significance testing suffers many limitations and is poorly adapted to theory testing. A proposed alternative approach, called Testing Theory-based Quantitative Predictions, uses effect size estimates and confidence intervals to directly test predictions based on theory. This paper replicates findings from previous smoking studies and extends the approach to diet and sun protection behaviors using baseline data from a Transtheoretical Model behavioral intervention (N = 5407). Effect size predictions were developed using two methods: (1) applying refined effect size estimates from previous smoking research or (2) using predictions developed by an expert panel. Thirteen of 15 predictions were confirmed for smoking. For diet, 7 of 14 predictions were confirmed using smoking predictions and 6 of 16 using expert panel predictions. For sun protection, 3 of 11 predictions were confirmed using smoking predictions and 5 of 19 using expert panel predictions. Expert panel predictions and smoking-based predictions poorly predicted effect sizes for diet and sun protection constructs. Future studies should aim to use previous empirical data to generate predictions whenever possible. The best results occur when there have been several iterations of predictions for a behavior, such as with smoking, demonstrating that expected values begin to converge on the population effect size. Overall, the study supports necessity in strengthening and revising theory with empirical data.

  8. Ongoing behavior predicts perceptual report of interval duration

    PubMed Central

    Gouvêa, Thiago S.; Monteiro, Tiago; Soares, Sofia; Atallah, Bassam V.; Paton, Joseph J.

    2014-01-01

    The ability to estimate the passage of time is essential for adaptive behavior in complex environments. Yet, it is not known how the brain encodes time over the durations necessary to explain animal behavior. Under temporally structured reinforcement schedules, animals tend to develop temporally structured behavior, and interval timing has been suggested to be accomplished by learning sequences of behavioral states. If this is true, trial to trial fluctuations in behavioral sequences should be predictive of fluctuations in time estimation. We trained rodents in an duration categorization task while continuously monitoring their behavior with a high speed camera. Animals developed highly reproducible behavioral sequences during the interval being timed. Moreover, those sequences were often predictive of perceptual report from early in the trial, providing support to the idea that animals may use learned behavioral patterns to estimate the duration of time intervals. To better resolve the issue, we propose that continuous and simultaneous behavioral and neural monitoring will enable identification of neural activity related to time perception that is not explained by ongoing behavior. PMID:24672473

  9. Predicting Sympathy and Prosocial Behavior from Young Children's Dispositional Sadness.

    PubMed

    Edwards, Alison; Eisenberg, Nancy; Spinrad, Tracy L; Reiser, Mark; Eggum-Wilkens, Natalie D; Liew, Jeffrey

    2015-02-01

    The purpose of this study was to examine whether dispositional sadness predicted children's prosocial behavior and if sympathy mediated this relation. Constructs were measured when children ( N = 256 at Time 1) were 18-, 30-, and 42-months old. Mothers and non-parental caregivers rated children's sadness; mothers, caregivers, and fathers rated children's prosocial behavior; sympathy (concern and hypothesis testing) and prosocial behavior (indirect and direct, as well as verbal at older ages) were assessed with a task in which the experimenter feigned injury. In a panel path analysis, 30-month dispositional sadness predicted marginally higher 42-month sympathy; in addition, 30-month sympathy predicted 42-month sadness. Moreover, when controlling for prior levels of prosocial behavior, 30-month sympathy significantly predicted reported and observed prosocial behavior at 42 months. Sympathy did not mediate the relation between sadness and prosocial behavior (either reported or observed).

  10. Machine learning predictions of molecular properties: Accurate many-body potentials and nonlocality in chemical space

    DOE PAGES

    Hansen, Katja; Biegler, Franziska; Ramakrishnan, Raghunathan; ...

    2015-06-04

    Simultaneously accurate and efficient prediction of molecular properties throughout chemical compound space is a critical ingredient toward rational compound design in chemical and pharmaceutical industries. Aiming toward this goal, we develop and apply a systematic hierarchy of efficient empirical methods to estimate atomization and total energies of molecules. These methods range from a simple sum over atoms, to addition of bond energies, to pairwise interatomic force fields, reaching to the more sophisticated machine learning approaches that are capable of describing collective interactions between many atoms or bonds. In the case of equilibrium molecular geometries, even simple pairwise force fields demonstratemore » prediction accuracy comparable to benchmark energies calculated using density functional theory with hybrid exchange-correlation functionals; however, accounting for the collective many-body interactions proves to be essential for approaching the “holy grail” of chemical accuracy of 1 kcal/mol for both equilibrium and out-of-equilibrium geometries. This remarkable accuracy is achieved by a vectorized representation of molecules (so-called Bag of Bonds model) that exhibits strong nonlocality in chemical space. The same representation allows us to predict accurate electronic properties of molecules, such as their polarizability and molecular frontier orbital energies.« less

  11. Machine Learning Predictions of Molecular Properties: Accurate Many-Body Potentials and Nonlocality in Chemical Space

    PubMed Central

    2015-01-01

    Simultaneously accurate and efficient prediction of molecular properties throughout chemical compound space is a critical ingredient toward rational compound design in chemical and pharmaceutical industries. Aiming toward this goal, we develop and apply a systematic hierarchy of efficient empirical methods to estimate atomization and total energies of molecules. These methods range from a simple sum over atoms, to addition of bond energies, to pairwise interatomic force fields, reaching to the more sophisticated machine learning approaches that are capable of describing collective interactions between many atoms or bonds. In the case of equilibrium molecular geometries, even simple pairwise force fields demonstrate prediction accuracy comparable to benchmark energies calculated using density functional theory with hybrid exchange-correlation functionals; however, accounting for the collective many-body interactions proves to be essential for approaching the “holy grail” of chemical accuracy of 1 kcal/mol for both equilibrium and out-of-equilibrium geometries. This remarkable accuracy is achieved by a vectorized representation of molecules (so-called Bag of Bonds model) that exhibits strong nonlocality in chemical space. In addition, the same representation allows us to predict accurate electronic properties of molecules, such as their polarizability and molecular frontier orbital energies. PMID:26113956

  12. Neural and Hybrid Modeling: An Alternative Route to Efficiently Predict the Behavior of Biotechnological Processes Aimed at Biofuels Obtainment

    PubMed Central

    Saraceno, Alessandra; Calabrò, Vincenza; Iorio, Gabriele

    2014-01-01

    The present paper was aimed at showing that advanced modeling techniques, based either on artificial neural networks or on hybrid systems, might efficiently predict the behavior of two biotechnological processes designed for the obtainment of second-generation biofuels from waste biomasses. In particular, the enzymatic transesterification of waste-oil glycerides, the key step for the obtainment of biodiesel, and the anaerobic digestion of agroindustry wastes to produce biogas were modeled. It was proved that the proposed modeling approaches provided very accurate predictions of systems behavior. Both neural network and hybrid modeling definitely represented a valid alternative to traditional theoretical models, especially when comprehensive knowledge of the metabolic pathways, of the true kinetic mechanisms, and of the transport phenomena involved in biotechnological processes was difficult to be achieved. PMID:24516363

  13. How accurate are resting energy expenditure prediction equations in obese trauma and burn patients?

    PubMed

    Stucky, Chee-Chee H; Moncure, Michael; Hise, Mary; Gossage, Clint M; Northrop, David

    2008-01-01

    While the prevalence of obesity continues to increase in our society, outdated resting energy expenditure (REE) prediction equations may overpredict energy requirements in obese patients. Accurate feeding is essential since overfeeding has been demonstrated to adversely affect outcomes. The first objective was to compare REE calculated by prediction equations to the measured REE in obese trauma and burn patients. Our hypothesis was that an equation using fat-free mass would give a more accurate prediction. The second objective was to consider the effect of a commonly used injury factor on the predicted REE. A retrospective chart review was performed on 28 patients. REE was measured using indirect calorimetry and compared with the Harris-Benedict and Cunningham equations, and an equation using type II diabetes as a factor. Statistical analyses used were paired t test, +/-95% confidence interval, and the Bland-Altman method. Measured average REE in trauma and burn patients was 21.37 +/- 5.26 and 21.81 +/- 3.35 kcal/kg/d, respectively. Harris-Benedict underpredicted REE in trauma and burn patients to the least extent, while the Cunningham equation underpredicted REE in both populations to the greatest extent. Using an injury factor of 1.2, Cunningham continued to underestimate REE in both populations, while the Harris-Benedict and Diabetic equations overpredicted REE in both populations. The measured average REE is significantly less than current guidelines. This finding suggests that a hypocaloric regimen is worth considering for ICU patients. Also, if an injury factor of 1.2 is incorporated in certain equations, patients may be given too many calories.

  14. ChIP-seq Accurately Predicts Tissue-Specific Activity of Enhancers

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

    Visel, Axel; Blow, Matthew J.; Li, Zirong

    2009-02-01

    A major yet unresolved quest in decoding the human genome is the identification of the regulatory sequences that control the spatial and temporal expression of genes. Distant-acting transcriptional enhancers are particularly challenging to uncover since they are scattered amongst the vast non-coding portion of the genome. Evolutionary sequence constraint can facilitate the discovery of enhancers, but fails to predict when and where they are active in vivo. Here, we performed chromatin immunoprecipitation with the enhancer-associated protein p300, followed by massively-parallel sequencing, to map several thousand in vivo binding sites of p300 in mouse embryonic forebrain, midbrain, and limb tissue. Wemore » tested 86 of these sequences in a transgenic mouse assay, which in nearly all cases revealed reproducible enhancer activity in those tissues predicted by p300 binding. Our results indicate that in vivo mapping of p300 binding is a highly accurate means for identifying enhancers and their associated activities and suggest that such datasets will be useful to study the role of tissue-specific enhancers in human biology and disease on a genome-wide scale.« less

  15. Predicting Active Users' Personality Based on Micro-Blogging Behaviors

    PubMed Central

    Hao, Bibo; Guan, Zengda; Zhu, Tingshao

    2014-01-01

    Because of its richness and availability, micro-blogging has become an ideal platform for conducting psychological research. In this paper, we proposed to predict active users' personality traits through micro-blogging behaviors. 547 Chinese active users of micro-blogging participated in this study. Their personality traits were measured by the Big Five Inventory, and digital records of micro-blogging behaviors were collected via web crawlers. After extracting 845 micro-blogging behavioral features, we first trained classification models utilizing Support Vector Machine (SVM), differentiating participants with high and low scores on each dimension of the Big Five Inventory. The classification accuracy ranged from 84% to 92%. We also built regression models utilizing PaceRegression methods, predicting participants' scores on each dimension of the Big Five Inventory. The Pearson correlation coefficients between predicted scores and actual scores ranged from 0.48 to 0.54. Results indicated that active users' personality traits could be predicted by micro-blogging behaviors. PMID:24465462

  16. Simple Mathematical Models Do Not Accurately Predict Early SIV Dynamics

    PubMed Central

    Noecker, Cecilia; Schaefer, Krista; Zaccheo, Kelly; Yang, Yiding; Day, Judy; Ganusov, Vitaly V.

    2015-01-01

    Upon infection of a new host, human immunodeficiency virus (HIV) replicates in the mucosal tissues and is generally undetectable in circulation for 1–2 weeks post-infection. Several interventions against HIV including vaccines and antiretroviral prophylaxis target virus replication at this earliest stage of infection. Mathematical models have been used to understand how HIV spreads from mucosal tissues systemically and what impact vaccination and/or antiretroviral prophylaxis has on viral eradication. Because predictions of such models have been rarely compared to experimental data, it remains unclear which processes included in these models are critical for predicting early HIV dynamics. Here we modified the “standard” mathematical model of HIV infection to include two populations of infected cells: cells that are actively producing the virus and cells that are transitioning into virus production mode. We evaluated the effects of several poorly known parameters on infection outcomes in this model and compared model predictions to experimental data on infection of non-human primates with variable doses of simian immunodifficiency virus (SIV). First, we found that the mode of virus production by infected cells (budding vs. bursting) has a minimal impact on the early virus dynamics for a wide range of model parameters, as long as the parameters are constrained to provide the observed rate of SIV load increase in the blood of infected animals. Interestingly and in contrast with previous results, we found that the bursting mode of virus production generally results in a higher probability of viral extinction than the budding mode of virus production. Second, this mathematical model was not able to accurately describe the change in experimentally determined probability of host infection with increasing viral doses. Third and finally, the model was also unable to accurately explain the decline in the time to virus detection with increasing viral dose. These results

  17. Improving medical decisions for incapacitated persons: does focusing on "accurate predictions" lead to an inaccurate picture?

    PubMed

    Kim, Scott Y H

    2014-04-01

    The Patient Preference Predictor (PPP) proposal places a high priority on the accuracy of predicting patients' preferences and finds the performance of surrogates inadequate. However, the quest to develop a highly accurate, individualized statistical model has significant obstacles. First, it will be impossible to validate the PPP beyond the limit imposed by 60%-80% reliability of people's preferences for future medical decisions--a figure no better than the known average accuracy of surrogates. Second, evidence supports the view that a sizable minority of persons may not even have preferences to predict. Third, many, perhaps most, people express their autonomy just as much by entrusting their loved ones to exercise their judgment than by desiring to specifically control future decisions. Surrogate decision making faces none of these issues and, in fact, it may be more efficient, accurate, and authoritative than is commonly assumed.

  18. Expectations are more predictive of behavior than behavioral intentions: evidence from two prospective studies.

    PubMed

    Armitage, Christopher J; Norman, Paul; Alganem, Soud; Conner, Mark

    2015-04-01

    Understanding the gap between people's behavioral intentions and their subsequent behavior is a key problem for behavioral scientists, but little attention has been paid to how behavioral intentions are operationalized. Test the distinction between asking people what they intend to do, as opposed to what they expect they will do. Two studies were conducted in the domains of alcohol consumption (N = 152) and weight loss (N = 141). Participants completed questionnaires assessing their behavioral intentions, expectations, and self-efficacy at baseline; alcohol consumption/weight were assessed at both baseline and follow-up. In study 1, expectations were more predictive of alcohol consumption than behavioral intentions, controlling for baseline alcohol consumption and self-efficacy. In study 2, changes in expectations were more predictive of weight loss than changes in behavioral intentions, controlling for baseline weight and self-efficacy. The findings support a potentially important distinction between behavioral intentions and expectations.

  19. Rapid and accurate prediction of degradant formation rates in pharmaceutical formulations using high-performance liquid chromatography-mass spectrometry.

    PubMed

    Darrington, Richard T; Jiao, Jim

    2004-04-01

    Rapid and accurate stability prediction is essential to pharmaceutical formulation development. Commonly used stability prediction methods include monitoring parent drug loss at intended storage conditions or initial rate determination of degradants under accelerated conditions. Monitoring parent drug loss at the intended storage condition does not provide a rapid and accurate stability assessment because often <0.5% drug loss is all that can be observed in a realistic time frame, while the accelerated initial rate method in conjunction with extrapolation of rate constants using the Arrhenius or Eyring equations often introduces large errors in shelf-life prediction. In this study, the shelf life prediction of a model pharmaceutical preparation utilizing sensitive high-performance liquid chromatography-mass spectrometry (LC/MS) to directly quantitate degradant formation rates at the intended storage condition is proposed. This method was compared to traditional shelf life prediction approaches in terms of time required to predict shelf life and associated error in shelf life estimation. Results demonstrated that the proposed LC/MS method using initial rates analysis provided significantly improved confidence intervals for the predicted shelf life and required less overall time and effort to obtain the stability estimation compared to the other methods evaluated. Copyright 2004 Wiley-Liss, Inc. and the American Pharmacists Association.

  20. How the brain predicts people's behavior in relation to rules and desires. Evidence of a medio-prefrontal dissociation.

    PubMed

    Corradi-Dell'Acqua, Corrado; Turri, Francesco; Kaufmann, Laurence; Clément, Fabrice; Schwartz, Sophie

    2015-09-01

    Forming and updating impressions about others is critical in everyday life and engages portions of the dorsomedial prefrontal cortex (dMPFC), the posterior cingulate cortex (PCC) and the amygdala. Some of these activations are attributed to "mentalizing" functions necessary to represent people's mental states, such as beliefs or desires. Evolutionary psychology and developmental studies, however, suggest that interpersonal inferences can also be obtained through the aid of deontic heuristics, which dictate what must (or must not) be done in given circumstances. We used fMRI and asked 18 participants to predict whether unknown characters would follow their desires or obey external rules. Participants had no means, at the beginning, to make accurate predictions, but slowly learned (throughout the experiment) each character's behavioral profile. We isolated brain regions whose activity changed during the experiment, as a neural signature of impression updating: whereas dMPFC was progressively more involved in predicting characters' behavior in relation to their desires, the medial orbitofrontal cortex and the amygdala were progressively more recruited in predicting rule-based behavior. Our data provide evidence of a neural dissociation between deontic inference and theory-of-mind (ToM), and support a differentiation of orbital and dorsal prefrontal cortex in terms of low- and high-level social cognition. Copyright © 2015 Elsevier Ltd. All rights reserved.

  1. Disorganized attachment and inhibitory capacity: predicting externalizing problem behaviors.

    PubMed

    Bohlin, Gunilla; Eninger, Lilianne; Brocki, Karin Cecilia; Thorell, Lisa B

    2012-04-01

    The aim of the present study was to investigate whether attachment insecurity, focusing on disorganized attachment, and the executive function (EF) component of inhibition, assessed at age 5, were longitudinally related to general externalizing problem behaviors as well as to specific symptoms of ADHD and Autism spectrum disorder (ASD), and callous-unemotional (CU) traits. General externalizing problem behaviors were also measured at age 5 to allow for a developmental analysis. Outcome variables were rated by parents and teachers. The sample consisted of 65 children with an oversampling of children with high levels of externalizing behaviors. Attachment was evaluated using a story stem attachment doll play procedure. Inhibition was measured using four different tasks. The results showed that both disorganized attachment and poor inhibition were longitudinally related to all outcome variables. Controlling for initial level of externalizing problem behavior, poor inhibition predicted ADHD symptoms and externalizing problem behaviors, independent of disorganized attachment, whereas for ASD symptoms no predictive relations remained. Disorganized attachment independently predicted CU traits.

  2. Using the Integrative Model of Behavioral Prediction to Understand College Students' STI Testing Beliefs, Intentions, and Behaviors.

    PubMed

    Wombacher, Kevin; Dai, Minhao; Matig, Jacob J; Harrington, Nancy Grant

    2018-03-22

    To identify salient behavioral determinants related to STI testing among college students by testing a model based on the integrative model of behavioral (IMBP) prediction. 265 undergraduate students from a large university in the Southeastern US. Formative and survey research to test an IMBP-based model that explores the relationships between determinants and STI testing intention and behavior. Results of path analyses supported a model in which attitudinal beliefs predicted intention and intention predicted behavior. Normative beliefs and behavioral control beliefs were not significant in the model; however, select individual normative and control beliefs were significantly correlated with intention and behavior. Attitudinal beliefs are the strongest predictor of STI testing intention and behavior. Future efforts to increase STI testing rates should identify and target salient attitudinal beliefs.

  3. Kinetic approach to degradation mechanisms in polymer solar cells and their accurate lifetime predictions

    NASA Astrophysics Data System (ADS)

    Arshad, Muhammad Azeem; Maaroufi, AbdelKrim

    2018-07-01

    A beginning has been made in the present study regarding the accurate lifetime predictions of polymer solar cells. Certain reservations about the conventionally employed temperature accelerated lifetime measurements test for its unworthiness of predicting reliable lifetimes of polymer solar cells are brought into light. Critical issues concerning the accelerated lifetime testing include, assuming reaction mechanism instead of determining it, and relying solely on the temperature acceleration of a single property of material. An advanced approach comprising a set of theoretical models to estimate the accurate lifetimes of polymer solar cells is therefore suggested in order to suitably alternate the accelerated lifetime testing. This approach takes into account systematic kinetic modeling of various possible polymer degradation mechanisms under natural weathering conditions. The proposed kinetic approach is substantiated by its applications on experimental aging data-sets of polymer solar materials/solar cells including, P3HT polymer film, bulk heterojunction (MDMO-PPV:PCBM) and dye-sensitized solar cells. Based on the suggested approach, an efficacious lifetime determination formula for polymer solar cells is derived and tested on dye-sensitized solar cells. Some important merits of the proposed method are also pointed out and its prospective applications are discussed.

  4. Predictability and hierarchy in Drosophila behavior.

    PubMed

    Berman, Gordon J; Bialek, William; Shaevitz, Joshua W

    2016-10-18

    Even the simplest of animals exhibit behavioral sequences with complex temporal dynamics. Prominent among the proposed organizing principles for these dynamics has been the idea of a hierarchy, wherein the movements an animal makes can be understood as a set of nested subclusters. Although this type of organization holds potential advantages in terms of motion control and neural circuitry, measurements demonstrating this for an animal's entire behavioral repertoire have been limited in scope and temporal complexity. Here, we use a recently developed unsupervised technique to discover and track the occurrence of all stereotyped behaviors performed by fruit flies moving in a shallow arena. Calculating the optimally predictive representation of the fly's future behaviors, we show that fly behavior exhibits multiple time scales and is organized into a hierarchical structure that is indicative of its underlying behavioral programs and its changing internal states.

  5. Modeling methodology for the accurate and prompt prediction of symptomatic events in chronic diseases.

    PubMed

    Pagán, Josué; Risco-Martín, José L; Moya, José M; Ayala, José L

    2016-08-01

    Prediction of symptomatic crises in chronic diseases allows to take decisions before the symptoms occur, such as the intake of drugs to avoid the symptoms or the activation of medical alarms. The prediction horizon is in this case an important parameter in order to fulfill the pharmacokinetics of medications, or the time response of medical services. This paper presents a study about the prediction limits of a chronic disease with symptomatic crises: the migraine. For that purpose, this work develops a methodology to build predictive migraine models and to improve these predictions beyond the limits of the initial models. The maximum prediction horizon is analyzed, and its dependency on the selected features is studied. A strategy for model selection is proposed to tackle the trade off between conservative but robust predictive models, with respect to less accurate predictions with higher horizons. The obtained results show a prediction horizon close to 40min, which is in the time range of the drug pharmacokinetics. Experiments have been performed in a realistic scenario where input data have been acquired in an ambulatory clinical study by the deployment of a non-intrusive Wireless Body Sensor Network. Our results provide an effective methodology for the selection of the future horizon in the development of prediction algorithms for diseases experiencing symptomatic crises. Copyright © 2016 Elsevier Inc. All rights reserved.

  6. Accurate prediction of interfacial residues in two-domain proteins using evolutionary information: implications for three-dimensional modeling.

    PubMed

    Bhaskara, Ramachandra M; Padhi, Amrita; Srinivasan, Narayanaswamy

    2014-07-01

    With the preponderance of multidomain proteins in eukaryotic genomes, it is essential to recognize the constituent domains and their functions. Often function involves communications across the domain interfaces, and the knowledge of the interacting sites is essential to our understanding of the structure-function relationship. Using evolutionary information extracted from homologous domains in at least two diverse domain architectures (single and multidomain), we predict the interface residues corresponding to domains from the two-domain proteins. We also use information from the three-dimensional structures of individual domains of two-domain proteins to train naïve Bayes classifier model to predict the interfacial residues. Our predictions are highly accurate (∼85%) and specific (∼95%) to the domain-domain interfaces. This method is specific to multidomain proteins which contain domains in at least more than one protein architectural context. Using predicted residues to constrain domain-domain interaction, rigid-body docking was able to provide us with accurate full-length protein structures with correct orientation of domains. We believe that these results can be of considerable interest toward rational protein and interaction design, apart from providing us with valuable information on the nature of interactions. © 2013 Wiley Periodicals, Inc.

  7. Using Multitheory Model of Health Behavior Change to Predict Adequate Sleep Behavior.

    PubMed

    Knowlden, Adam P; Sharma, Manoj; Nahar, Vinayak K

    The purpose of this article was to use the multitheory model of health behavior change in predicting adequate sleep behavior in college students. A valid and reliable survey was administered in a cross-sectional design (n = 151). For initiation of adequate sleep behavior, the construct of behavioral confidence (P < .001) was found to be significant and accounted for 24.4% of the variance. For sustenance of adequate sleep behavior, changes in social environment (P < .02), emotional transformation (P < .001), and practice for change (P < .001) were significant and accounted for 34.2% of the variance.

  8. The role of descriptive norm within the theory of planned behavior in predicting Korean Americans' exercise behavior.

    PubMed

    Lee, Hyo

    2011-08-01

    There are few studies investigating psychosocial mechanisms in Korean Americans' exercise behavior. The present study tested the usefulness of the theory of planned behavior in predicting Korean American's exercise behavior and whether the descriptive norm (i.e., perceptions of what others do) improved the predictive validity of the theory of planned behavior. Using a retrospective design and self-report measures, web-survey responses from 198 Korean-American adults were analyzed using hierarchical regression analyses. The theory of planned behavior constructs accounted for 31% of exercise behavior and 43% of exercise intention. Intention and perceived behavioral control were significant predictors of exercise behavior. Although the descriptive norm did not augment the theory of planned behavior, all original constructs--attitude, injunctive norm (a narrow definition of subjective norm), and perceived behavioral control--statistically significantly predicted leisure-time physical activity intention. Future studies should consider random sampling, prospective design, and objective measures of physical activity.

  9. Do dual-route models accurately predict reading and spelling performance in individuals with acquired alexia and agraphia?

    PubMed

    Rapcsak, Steven Z; Henry, Maya L; Teague, Sommer L; Carnahan, Susan D; Beeson, Pélagie M

    2007-06-18

    Coltheart and co-workers [Castles, A., Bates, T. C., & Coltheart, M. (2006). John Marshall and the developmental dyslexias. Aphasiology, 20, 871-892; Coltheart, M., Rastle, K., Perry, C., Langdon, R., & Ziegler, J. (2001). DRC: A dual route cascaded model of visual word recognition and reading aloud. Psychological Review, 108, 204-256] have demonstrated that an equation derived from dual-route theory accurately predicts reading performance in young normal readers and in children with reading impairment due to developmental dyslexia or stroke. In this paper, we present evidence that the dual-route equation and a related multiple regression model also accurately predict both reading and spelling performance in adult neurological patients with acquired alexia and agraphia. These findings provide empirical support for dual-route theories of written language processing.

  10. Predicting Sympathy and Prosocial Behavior from Young Children’s Dispositional Sadness

    PubMed Central

    Edwards, Alison; Eisenberg, Nancy; Spinrad, Tracy L.; Reiser, Mark; Eggum-Wilkens, Natalie D.; Liew, Jeffrey

    2014-01-01

    The purpose of this study was to examine whether dispositional sadness predicted children's prosocial behavior and if sympathy mediated this relation. Constructs were measured when children (N = 256 at Time 1) were 18-, 30-, and 42-months old. Mothers and non-parental caregivers rated children’s sadness; mothers, caregivers, and fathers rated children’s prosocial behavior; sympathy (concern and hypothesis testing) and prosocial behavior (indirect and direct, as well as verbal at older ages) were assessed with a task in which the experimenter feigned injury. In a panel path analysis, 30-month dispositional sadness predicted marginally higher 42-month sympathy; in addition, 30-month sympathy predicted 42-month sadness. Moreover, when controlling for prior levels of prosocial behavior, 30-month sympathy significantly predicted reported and observed prosocial behavior at 42 months. Sympathy did not mediate the relation between sadness and prosocial behavior (either reported or observed). PMID:25663753

  11. Intermolecular potentials and the accurate prediction of the thermodynamic properties of water

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

    Shvab, I.; Sadus, Richard J., E-mail: rsadus@swin.edu.au

    2013-11-21

    The ability of intermolecular potentials to correctly predict the thermodynamic properties of liquid water at a density of 0.998 g/cm{sup 3} for a wide range of temperatures (298–650 K) and pressures (0.1–700 MPa) is investigated. Molecular dynamics simulations are reported for the pressure, thermal pressure coefficient, thermal expansion coefficient, isothermal and adiabatic compressibilities, isobaric and isochoric heat capacities, and Joule-Thomson coefficient of liquid water using the non-polarizable SPC/E and TIP4P/2005 potentials. The results are compared with both experiment data and results obtained from the ab initio-based Matsuoka-Clementi-Yoshimine non-additive (MCYna) [J. Li, Z. Zhou, and R. J. Sadus, J. Chem. Phys.more » 127, 154509 (2007)] potential, which includes polarization contributions. The data clearly indicate that both the SPC/E and TIP4P/2005 potentials are only in qualitative agreement with experiment, whereas the polarizable MCYna potential predicts some properties within experimental uncertainty. This highlights the importance of polarizability for the accurate prediction of the thermodynamic properties of water, particularly at temperatures beyond 298 K.« less

  12. Artificial neural network prediction of aircraft aeroelastic behavior

    NASA Astrophysics Data System (ADS)

    Pesonen, Urpo Juhani

    An Artificial Neural Network that predicts aeroelastic behavior of aircraft is presented. The neural net was designed to predict the shape of a flexible wing in static flight conditions using results from a structural analysis and an aerodynamic analysis performed with traditional computational tools. To generate reliable training and testing data for the network, an aeroelastic analysis code using these tools as components was designed and validated. To demonstrate the advantages and reliability of Artificial Neural Networks, a network was also designed and trained to predict airfoil maximum lift at low Reynolds numbers where wind tunnel data was used for the training. Finally, a neural net was designed and trained to predict the static aeroelastic behavior of a wing without the need to iterate between the structural and aerodynamic solvers.

  13. ILT based defect simulation of inspection images accurately predicts mask defect printability on wafer

    NASA Astrophysics Data System (ADS)

    Deep, Prakash; Paninjath, Sankaranarayanan; Pereira, Mark; Buck, Peter

    2016-05-01

    At advanced technology nodes mask complexity has been increased because of large-scale use of resolution enhancement technologies (RET) which includes Optical Proximity Correction (OPC), Inverse Lithography Technology (ILT) and Source Mask Optimization (SMO). The number of defects detected during inspection of such mask increased drastically and differentiation of critical and non-critical defects are more challenging, complex and time consuming. Because of significant defectivity of EUVL masks and non-availability of actinic inspection, it is important and also challenging to predict the criticality of defects for printability on wafer. This is one of the significant barriers for the adoption of EUVL for semiconductor manufacturing. Techniques to decide criticality of defects from images captured using non actinic inspection images is desired till actinic inspection is not available. High resolution inspection of photomask images detects many defects which are used for process and mask qualification. Repairing all defects is not practical and probably not required, however it's imperative to know which defects are severe enough to impact wafer before repair. Additionally, wafer printability check is always desired after repairing a defect. AIMSTM review is the industry standard for this, however doing AIMSTM review for all defects is expensive and very time consuming. Fast, accurate and an economical mechanism is desired which can predict defect printability on wafer accurately and quickly from images captured using high resolution inspection machine. Predicting defect printability from such images is challenging due to the fact that the high resolution images do not correlate with actual mask contours. The challenge is increased due to use of different optical condition during inspection other than actual scanner condition, and defects found in such images do not have correlation with actual impact on wafer. Our automated defect simulation tool predicts

  14. Preschoolers Use Social Allegiances to Predict Behavior

    ERIC Educational Resources Information Center

    Chalik, Lisa; Rhodes, Marjorie

    2014-01-01

    Developing mechanisms for predicting human action is a critical task of early conceptual development. Three studies examined whether 4-year-old children (N = 149) use social allegiances to predict behavior, by testing whether they expect the experiences of social partners to influence individual action. After being exposed to a conflict between…

  15. Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model.

    PubMed

    Wang, Sheng; Sun, Siqi; Li, Zhen; Zhang, Renyu; Xu, Jinbo

    2017-01-01

    Protein contacts contain key information for the understanding of protein structure and function and thus, contact prediction from sequence is an important problem. Recently exciting progress has been made on this problem, but the predicted contacts for proteins without many sequence homologs is still of low quality and not very useful for de novo structure prediction. This paper presents a new deep learning method that predicts contacts by integrating both evolutionary coupling (EC) and sequence conservation information through an ultra-deep neural network formed by two deep residual neural networks. The first residual network conducts a series of 1-dimensional convolutional transformation of sequential features; the second residual network conducts a series of 2-dimensional convolutional transformation of pairwise information including output of the first residual network, EC information and pairwise potential. By using very deep residual networks, we can accurately model contact occurrence patterns and complex sequence-structure relationship and thus, obtain higher-quality contact prediction regardless of how many sequence homologs are available for proteins in question. Our method greatly outperforms existing methods and leads to much more accurate contact-assisted folding. Tested on 105 CASP11 targets, 76 past CAMEO hard targets, and 398 membrane proteins, the average top L long-range prediction accuracy obtained by our method, one representative EC method CCMpred and the CASP11 winner MetaPSICOV is 0.47, 0.21 and 0.30, respectively; the average top L/10 long-range accuracy of our method, CCMpred and MetaPSICOV is 0.77, 0.47 and 0.59, respectively. Ab initio folding using our predicted contacts as restraints but without any force fields can yield correct folds (i.e., TMscore>0.6) for 203 of the 579 test proteins, while that using MetaPSICOV- and CCMpred-predicted contacts can do so for only 79 and 62 of them, respectively. Our contact-assisted models also have

  16. Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model

    PubMed Central

    Li, Zhen; Zhang, Renyu

    2017-01-01

    Motivation Protein contacts contain key information for the understanding of protein structure and function and thus, contact prediction from sequence is an important problem. Recently exciting progress has been made on this problem, but the predicted contacts for proteins without many sequence homologs is still of low quality and not very useful for de novo structure prediction. Method This paper presents a new deep learning method that predicts contacts by integrating both evolutionary coupling (EC) and sequence conservation information through an ultra-deep neural network formed by two deep residual neural networks. The first residual network conducts a series of 1-dimensional convolutional transformation of sequential features; the second residual network conducts a series of 2-dimensional convolutional transformation of pairwise information including output of the first residual network, EC information and pairwise potential. By using very deep residual networks, we can accurately model contact occurrence patterns and complex sequence-structure relationship and thus, obtain higher-quality contact prediction regardless of how many sequence homologs are available for proteins in question. Results Our method greatly outperforms existing methods and leads to much more accurate contact-assisted folding. Tested on 105 CASP11 targets, 76 past CAMEO hard targets, and 398 membrane proteins, the average top L long-range prediction accuracy obtained by our method, one representative EC method CCMpred and the CASP11 winner MetaPSICOV is 0.47, 0.21 and 0.30, respectively; the average top L/10 long-range accuracy of our method, CCMpred and MetaPSICOV is 0.77, 0.47 and 0.59, respectively. Ab initio folding using our predicted contacts as restraints but without any force fields can yield correct folds (i.e., TMscore>0.6) for 203 of the 579 test proteins, while that using MetaPSICOV- and CCMpred-predicted contacts can do so for only 79 and 62 of them, respectively. Our contact

  17. Obtaining Accurate Probabilities Using Classifier Calibration

    ERIC Educational Resources Information Center

    Pakdaman Naeini, Mahdi

    2016-01-01

    Learning probabilistic classification and prediction models that generate accurate probabilities is essential in many prediction and decision-making tasks in machine learning and data mining. One way to achieve this goal is to post-process the output of classification models to obtain more accurate probabilities. These post-processing methods are…

  18. Corridor Use Predicted from Behaviors at Habitat Boundaries.

    PubMed

    Haddad, Nick M

    1999-02-01

    Through empirical studies and simulation, I demonstrate how simple behaviors can be used in lieu of detailed dispersal studies to predict the effects of corridors on interpatch movements. Movement paths of three butterfly species were measured in large (1.64 ha) experimental patches of open habitat, some of which were connected by corridors. Butterflies that "reflected" off boundaries between open patches and the surrounding forest also emigrated from patches through corridors at rates higher than expected from random movement. This was observed for two open-habitat species, Eurema nicippe and Phoebis sennae; however, edges and corridors had no effect on a habitat generalist, Papilio troilus. Behaviorally based simulation models, which departed from correlated random walks only at habitat boundaries, predicted that corridors increase interpatch movement rates of both open-habitat species. Models also predicted that corridors have proportionately greater effects as corridor width increases, that movement rates increase before leveling off as corridor width increases, and that corridor effects decrease as patch size increases. This study suggests that corridors direct movements of habitat-restricted species and that local behaviors may be used to predict the conservation potential of corridors in fragmented landscapes.

  19. Predicting Alumni/ae Gift Giving Behavior: A Structural Equation Model Approach.

    ERIC Educational Resources Information Center

    Mosser, John Wayne

    This dissertation focuses on predicting alumni gift giving behavior at a large public research university (University of Michigan). A conceptual model was developed for predicting alumni giving behavior in order to advance the theoretical understanding of how capacity to give, motivation to give, and their interaction effect gift giving behavior.…

  20. Limb-Enhancer Genie: An accessible resource of accurate enhancer predictions in the developing limb

    DOE PAGES

    Monti, Remo; Barozzi, Iros; Osterwalder, Marco; ...

    2017-08-21

    Epigenomic mapping of enhancer-associated chromatin modifications facilitates the genome-wide discovery of tissue-specific enhancers in vivo. However, reliance on single chromatin marks leads to high rates of false-positive predictions. More sophisticated, integrative methods have been described, but commonly suffer from limited accessibility to the resulting predictions and reduced biological interpretability. Here we present the Limb-Enhancer Genie (LEG), a collection of highly accurate, genome-wide predictions of enhancers in the developing limb, available through a user-friendly online interface. We predict limb enhancers using a combination of > 50 published limb-specific datasets and clusters of evolutionarily conserved transcription factor binding sites, taking advantage ofmore » the patterns observed at previously in vivo validated elements. By combining different statistical models, our approach outperforms current state-of-the-art methods and provides interpretable measures of feature importance. Our results indicate that including a previously unappreciated score that quantifies tissue-specific nuclease accessibility significantly improves prediction performance. We demonstrate the utility of our approach through in vivo validation of newly predicted elements. Moreover, we describe general features that can guide the type of datasets to include when predicting tissue-specific enhancers genome-wide, while providing an accessible resource to the general biological community and facilitating the functional interpretation of genetic studies of limb malformations.« less

  1. Predicting Pilot Behavior in Medium Scale Scenarios Using Game Theory and Reinforcement Learning

    NASA Technical Reports Server (NTRS)

    Yildiz, Yildiray; Agogino, Adrian; Brat, Guillaume

    2013-01-01

    Effective automation is critical in achieving the capacity and safety goals of the Next Generation Air Traffic System. Unfortunately creating integration and validation tools for such automation is difficult as the interactions between automation and their human counterparts is complex and unpredictable. This validation becomes even more difficult as we integrate wide-reaching technologies that affect the behavior of different decision makers in the system such as pilots, controllers and airlines. While overt short-term behavior changes can be explicitly modeled with traditional agent modeling systems, subtle behavior changes caused by the integration of new technologies may snowball into larger problems and be very hard to detect. To overcome these obstacles, we show how integration of new technologies can be validated by learning behavior models based on goals. In this framework, human participants are not modeled explicitly. Instead, their goals are modeled and through reinforcement learning their actions are predicted. The main advantage to this approach is that modeling is done within the context of the entire system allowing for accurate modeling of all participants as they interact as a whole. In addition such an approach allows for efficient trade studies and feasibility testing on a wide range of automation scenarios. The goal of this paper is to test that such an approach is feasible. To do this we implement this approach using a simple discrete-state learning system on a scenario where 50 aircraft need to self-navigate using Automatic Dependent Surveillance-Broadcast (ADS-B) information. In this scenario, we show how the approach can be used to predict the ability of pilots to adequately balance aircraft separation and fly efficient paths. We present results with several levels of complexity and airspace congestion.

  2. 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

  3. Combining Structural Modeling with Ensemble Machine Learning to Accurately Predict Protein Fold Stability and Binding Affinity Effects upon Mutation

    PubMed Central

    Garcia Lopez, Sebastian; Kim, Philip M.

    2014-01-01

    Advances in sequencing have led to a rapid accumulation of mutations, some of which are associated with diseases. However, to draw mechanistic conclusions, a biochemical understanding of these mutations is necessary. For coding mutations, accurate prediction of significant changes in either the stability of proteins or their affinity to their binding partners is required. Traditional methods have used semi-empirical force fields, while newer methods employ machine learning of sequence and structural features. Here, we show how combining both of these approaches leads to a marked boost in accuracy. We introduce ELASPIC, a novel ensemble machine learning approach that is able to predict stability effects upon mutation in both, domain cores and domain-domain interfaces. We combine semi-empirical energy terms, sequence conservation, and a wide variety of molecular details with a Stochastic Gradient Boosting of Decision Trees (SGB-DT) algorithm. The accuracy of our predictions surpasses existing methods by a considerable margin, achieving correlation coefficients of 0.77 for stability, and 0.75 for affinity predictions. Notably, we integrated homology modeling to enable proteome-wide prediction and show that accurate prediction on modeled structures is possible. Lastly, ELASPIC showed significant differences between various types of disease-associated mutations, as well as between disease and common neutral mutations. Unlike pure sequence-based prediction methods that try to predict phenotypic effects of mutations, our predictions unravel the molecular details governing the protein instability, and help us better understand the molecular causes of diseases. PMID:25243403

  4. Fixed recurrence and slip models better predict earthquake behavior than the time- and slip-predictable models 1: repeating earthquakes

    USGS Publications Warehouse

    Rubinstein, Justin L.; Ellsworth, William L.; Chen, Kate Huihsuan; Uchida, Naoki

    2012-01-01

    The behavior of individual events in repeating earthquake sequences in California, Taiwan and Japan is better predicted by a model with fixed inter-event time or fixed slip than it is by the time- and slip-predictable models for earthquake occurrence. Given that repeating earthquakes are highly regular in both inter-event time and seismic moment, the time- and slip-predictable models seem ideally suited to explain their behavior. Taken together with evidence from the companion manuscript that shows similar results for laboratory experiments we conclude that the short-term predictions of the time- and slip-predictable models should be rejected in favor of earthquake models that assume either fixed slip or fixed recurrence interval. This implies that the elastic rebound model underlying the time- and slip-predictable models offers no additional value in describing earthquake behavior in an event-to-event sense, but its value in a long-term sense cannot be determined. These models likely fail because they rely on assumptions that oversimplify the earthquake cycle. We note that the time and slip of these events is predicted quite well by fixed slip and fixed recurrence models, so in some sense they are time- and slip-predictable. While fixed recurrence and slip models better predict repeating earthquake behavior than the time- and slip-predictable models, we observe a correlation between slip and the preceding recurrence time for many repeating earthquake sequences in Parkfield, California. This correlation is not found in other regions, and the sequences with the correlative slip-predictable behavior are not distinguishable from nearby earthquake sequences that do not exhibit this behavior.

  5. Toward accurate prediction of pKa values for internal protein residues: the importance of conformational relaxation and desolvation energy.

    PubMed

    Wallace, Jason A; Wang, Yuhang; Shi, Chuanyin; Pastoor, Kevin J; Nguyen, Bao-Linh; Xia, Kai; Shen, Jana K

    2011-12-01

    Proton uptake or release controls many important biological processes, such as energy transduction, virus replication, and catalysis. Accurate pK(a) prediction informs about proton pathways, thereby revealing detailed acid-base mechanisms. Physics-based methods in the framework of molecular dynamics simulations not only offer pK(a) predictions but also inform about the physical origins of pK(a) shifts and provide details of ionization-induced conformational relaxation and large-scale transitions. One such method is the recently developed continuous constant pH molecular dynamics (CPHMD) method, which has been shown to be an accurate and robust pK(a) prediction tool for naturally occurring titratable residues. To further examine the accuracy and limitations of CPHMD, we blindly predicted the pK(a) values for 87 titratable residues introduced in various hydrophobic regions of staphylococcal nuclease and variants. The predictions gave a root-mean-square deviation of 1.69 pK units from experiment, and there were only two pK(a)'s with errors greater than 3.5 pK units. Analysis of the conformational fluctuation of titrating side-chains in the context of the errors of calculated pK(a) values indicate that explicit treatment of conformational flexibility and the associated dielectric relaxation gives CPHMD a distinct advantage. Analysis of the sources of errors suggests that more accurate pK(a) predictions can be obtained for the most deeply buried residues by improving the accuracy in calculating desolvation energies. Furthermore, it is found that the generalized Born implicit-solvent model underlying the current CPHMD implementation slightly distorts the local conformational environment such that the inclusion of an explicit-solvent representation may offer improvement of accuracy. Copyright © 2011 Wiley-Liss, Inc.

  6. Effectiveness of link prediction for face-to-face behavioral networks.

    PubMed

    Tsugawa, Sho; Ohsaki, Hiroyuki

    2013-01-01

    Research on link prediction for social networks has been actively pursued. In link prediction for a given social network obtained from time-windowed observation, new link formation in the network is predicted from the topology of the obtained network. In contrast, recent advances in sensing technology have made it possible to obtain face-to-face behavioral networks, which are social networks representing face-to-face interactions among people. However, the effectiveness of link prediction techniques for face-to-face behavioral networks has not yet been explored in depth. To clarify this point, here we investigate the accuracy of conventional link prediction techniques for networks obtained from the history of face-to-face interactions among participants at an academic conference. Our findings were (1) that conventional link prediction techniques predict new link formation with a precision of 0.30-0.45 and a recall of 0.10-0.20, (2) that prolonged observation of social networks often degrades the prediction accuracy, (3) that the proposed decaying weight method leads to higher prediction accuracy than can be achieved by observing all records of communication and simply using them unmodified, and (4) that the prediction accuracy for face-to-face behavioral networks is relatively high compared to that for non-social networks, but not as high as for other types of social networks.

  7. Effectiveness of Link Prediction for Face-to-Face Behavioral Networks

    PubMed Central

    Tsugawa, Sho; Ohsaki, Hiroyuki

    2013-01-01

    Research on link prediction for social networks has been actively pursued. In link prediction for a given social network obtained from time-windowed observation, new link formation in the network is predicted from the topology of the obtained network. In contrast, recent advances in sensing technology have made it possible to obtain face-to-face behavioral networks, which are social networks representing face-to-face interactions among people. However, the effectiveness of link prediction techniques for face-to-face behavioral networks has not yet been explored in depth. To clarify this point, here we investigate the accuracy of conventional link prediction techniques for networks obtained from the history of face-to-face interactions among participants at an academic conference. Our findings were (1) that conventional link prediction techniques predict new link formation with a precision of 0.30–0.45 and a recall of 0.10–0.20, (2) that prolonged observation of social networks often degrades the prediction accuracy, (3) that the proposed decaying weight method leads to higher prediction accuracy than can be achieved by observing all records of communication and simply using them unmodified, and (4) that the prediction accuracy for face-to-face behavioral networks is relatively high compared to that for non-social networks, but not as high as for other types of social networks. PMID:24339956

  8. How Accurately Can We Predict Eclipses for Algol? (Poster abstract)

    NASA Astrophysics Data System (ADS)

    Turner, D.

    2016-06-01

    (Abstract only) beta Persei, or Algol, is a very well known eclipsing binary system consisting of a late B-type dwarf that is regularly eclipsed by a GK subgiant every 2.867 days. Eclipses, which last about 8 hours, are regular enough that predictions for times of minima are published in various places, Sky & Telescope magazine and The Observer's Handbook, for example. But eclipse minimum lasts for less than a half hour, whereas subtle mistakes in the current ephemeris for the star can result in predictions that are off by a few hours or more. The Algol system is fairly complex, with the Algol A and Algol B eclipsing system also orbited by Algol C with an orbital period of nearly 2 years. Added to that are complex long-term O-C variations with a periodicity of almost two centuries that, although suggested by Hoffmeister to be spurious, fit the type of light travel time variations expected for a fourth star also belonging to the system. The AB sub-system also undergoes mass transfer events that add complexities to its O-C behavior. Is it actually possible to predict precise times of eclipse minima for Algol months in advance given such complications, or is it better to encourage ongoing observations of the star so that O-C variations can be tracked in real time?

  9. Accurate Descriptions of Hot Flow Behaviors Across β Transus of Ti-6Al-4V Alloy by Intelligence Algorithm GA-SVR

    NASA Astrophysics Data System (ADS)

    Wang, Li-yong; Li, Le; Zhang, Zhi-hua

    2016-09-01

    Hot compression tests of Ti-6Al-4V alloy in a wide temperature range of 1023-1323 K and strain rate range of 0.01-10 s-1 were conducted by a servo-hydraulic and computer-controlled Gleeble-3500 machine. In order to accurately and effectively characterize the highly nonlinear flow behaviors, support vector regression (SVR) which is a machine learning method was combined with genetic algorithm (GA) for characterizing the flow behaviors, namely, the GA-SVR. The prominent character of GA-SVR is that it with identical training parameters will keep training accuracy and prediction accuracy at a stable level in different attempts for a certain dataset. The learning abilities, generalization abilities, and modeling efficiencies of the mathematical regression model, ANN, and GA-SVR for Ti-6Al-4V alloy were detailedly compared. Comparison results show that the learning ability of the GA-SVR is stronger than the mathematical regression model. The generalization abilities and modeling efficiencies of these models were shown as follows in ascending order: the mathematical regression model < ANN < GA-SVR. The stress-strain data outside experimental conditions were predicted by the well-trained GA-SVR, which improved simulation accuracy of the load-stroke curve and can further improve the related research fields where stress-strain data play important roles, such as speculating work hardening and dynamic recovery, characterizing dynamic recrystallization evolution, and improving processing maps.

  10. Prognostic breast cancer signature identified from 3D culture model accurately predicts clinical outcome across independent datasets

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

    Martin, Katherine J.; Patrick, Denis R.; Bissell, Mina J.

    2008-10-20

    One of the major tenets in breast cancer research is that early detection is vital for patient survival by increasing treatment options. To that end, we have previously used a novel unsupervised approach to identify a set of genes whose expression predicts prognosis of breast cancer patients. The predictive genes were selected in a well-defined three dimensional (3D) cell culture model of non-malignant human mammary epithelial cell morphogenesis as down-regulated during breast epithelial cell acinar formation and cell cycle arrest. Here we examine the ability of this gene signature (3D-signature) to predict prognosis in three independent breast cancer microarray datasetsmore » having 295, 286, and 118 samples, respectively. Our results show that the 3D-signature accurately predicts prognosis in three unrelated patient datasets. At 10 years, the probability of positive outcome was 52, 51, and 47 percent in the group with a poor-prognosis signature and 91, 75, and 71 percent in the group with a good-prognosis signature for the three datasets, respectively (Kaplan-Meier survival analysis, p<0.05). Hazard ratios for poor outcome were 5.5 (95% CI 3.0 to 12.2, p<0.0001), 2.4 (95% CI 1.6 to 3.6, p<0.0001) and 1.9 (95% CI 1.1 to 3.2, p = 0.016) and remained significant for the two larger datasets when corrected for estrogen receptor (ER) status. Hence the 3D-signature accurately predicts breast cancer outcome in both ER-positive and ER-negative tumors, though individual genes differed in their prognostic ability in the two subtypes. Genes that were prognostic in ER+ patients are AURKA, CEP55, RRM2, EPHA2, FGFBP1, and VRK1, while genes prognostic in ER patients include ACTB, FOXM1 and SERPINE2 (Kaplan-Meier p<0.05). Multivariable Cox regression analysis in the largest dataset showed that the 3D-signature was a strong independent factor in predicting breast cancer outcome. The 3D-signature accurately predicts breast cancer outcome across multiple datasets and holds

  11. BEHAVE: fire behavior prediction and fuel modeling system-BURN Subsystem, part 1

    Treesearch

    Patricia L. Andrews

    1986-01-01

    Describes BURN Subsystem, Part 1, the operational fire behavior prediction subsystem of the BEHAVE fire behavior prediction and fuel modeling system. The manual covers operation of the computer program, assumptions of the mathematical models used in the calculations, and application of the predictions.

  12. Disruptions of network connectivity predict impairment in multiple behavioral domains after stroke

    PubMed Central

    Ramsey, Lenny E.; Metcalf, Nicholas V.; Chacko, Ravi V.; Weinberger, Kilian; Baldassarre, Antonello; Hacker, Carl D.; Shulman, Gordon L.; Corbetta, Maurizio

    2016-01-01

    Deficits following stroke are classically attributed to focal damage, but recent evidence suggests a key role of distributed brain network disruption. We measured resting functional connectivity (FC), lesion topography, and behavior in multiple domains (attention, visual memory, verbal memory, language, motor, and visual) in a cohort of 132 stroke patients, and used machine-learning models to predict neurological impairment in individual subjects. We found that visual memory and verbal memory were better predicted by FC, whereas visual and motor impairments were better predicted by lesion topography. Attention and language deficits were well predicted by both. Next, we identified a general pattern of physiological network dysfunction consisting of decrease of interhemispheric integration and intrahemispheric segregation, which strongly related to behavioral impairment in multiple domains. Network-specific patterns of dysfunction predicted specific behavioral deficits, and loss of interhemispheric communication across a set of regions was associated with impairment across multiple behavioral domains. These results link key organizational features of brain networks to brain–behavior relationships in stroke. PMID:27402738

  13. Fixed recurrence and slip models better predict earthquake behavior than the time- and slip-predictable models: 2. Laboratory earthquakes

    NASA Astrophysics Data System (ADS)

    Rubinstein, Justin L.; Ellsworth, William L.; Beeler, Nicholas M.; Kilgore, Brian D.; Lockner, David A.; Savage, Heather M.

    2012-02-01

    The behavior of individual stick-slip events observed in three different laboratory experimental configurations is better explained by a "memoryless" earthquake model with fixed inter-event time or fixed slip than it is by the time- and slip-predictable models for earthquake occurrence. We make similar findings in the companion manuscript for the behavior of natural repeating earthquakes. Taken together, these results allow us to conclude that the predictions of a characteristic earthquake model that assumes either fixed slip or fixed recurrence interval should be preferred to the predictions of the time- and slip-predictable models for all earthquakes. Given that the fixed slip and recurrence models are the preferred models for all of the experiments we examine, we infer that in an event-to-event sense the elastic rebound model underlying the time- and slip-predictable models does not explain earthquake behavior. This does not indicate that the elastic rebound model should be rejected in a long-term-sense, but it should be rejected for short-term predictions. The time- and slip-predictable models likely offer worse predictions of earthquake behavior because they rely on assumptions that are too simple to explain the behavior of earthquakes. Specifically, the time-predictable model assumes a constant failure threshold and the slip-predictable model assumes that there is a constant minimum stress. There is experimental and field evidence that these assumptions are not valid for all earthquakes.

  14. Predicting active school travel: the role of planned behavior and habit strength.

    PubMed

    Murtagh, Shemane; Rowe, David A; Elliott, Mark A; McMinn, David; Nelson, Norah M

    2012-05-30

    Despite strong support for predictive validity of the theory of planned behavior (TPB) substantial variance in both intention and behavior is unaccounted for by the model's predictors. The present study tested the extent to which habit strength augments the predictive validity of the TPB in relation to a currently under-researched behavior that has important health implications, namely children's active school travel. Participants (N = 126 children aged 8-9 years; 59 % males) were sampled from five elementary schools in the west of Scotland and completed questionnaire measures of all TPB constructs in relation to walking to school and both walking and car/bus use habit. Over the subsequent week, commuting steps on school journeys were measured objectively using an accelerometer. Hierarchical multiple regressions were used to test the predictive utility of the TPB and habit strength in relation to both intention and subsequent behavior. The TPB accounted for 41 % and 10 % of the variance in intention and objectively measured behavior, respectively. Together, walking habit and car/bus habit significantly increased the proportion of explained variance in both intention and behavior by 6 %. Perceived behavioral control and both walking and car/bus habit independently predicted intention. Intention and car/bus habit independently predicted behavior. The TPB significantly predicts children's active school travel. However, habit strength augments the predictive validity of the model. The results indicate that school travel is controlled by both intentional and habitual processes. In practice, interventions could usefully decrease the habitual use of motorized transport for travel to school and increase children's intention to walk (via increases in perceived behavioral control and walking habit, and decreases in car/bus habit). Further research is needed to identify effective strategies for changing these antecedents of children's active school travel.

  15. Predicting active school travel: The role of planned behavior and habit strength

    PubMed Central

    2012-01-01

    Background Despite strong support for predictive validity of the theory of planned behavior (TPB) substantial variance in both intention and behavior is unaccounted for by the model’s predictors. The present study tested the extent to which habit strength augments the predictive validity of the TPB in relation to a currently under-researched behavior that has important health implications, namely children’s active school travel. Method Participants (N = 126 children aged 8–9 years; 59 % males) were sampled from five elementary schools in the west of Scotland and completed questionnaire measures of all TPB constructs in relation to walking to school and both walking and car/bus use habit. Over the subsequent week, commuting steps on school journeys were measured objectively using an accelerometer. Hierarchical multiple regressions were used to test the predictive utility of the TPB and habit strength in relation to both intention and subsequent behavior. Results The TPB accounted for 41 % and 10 % of the variance in intention and objectively measured behavior, respectively. Together, walking habit and car/bus habit significantly increased the proportion of explained variance in both intention and behavior by 6 %. Perceived behavioral control and both walking and car/bus habit independently predicted intention. Intention and car/bus habit independently predicted behavior. Conclusions The TPB significantly predicts children’s active school travel. However, habit strength augments the predictive validity of the model. The results indicate that school travel is controlled by both intentional and habitual processes. In practice, interventions could usefully decrease the habitual use of motorized transport for travel to school and increase children’s intention to walk (via increases in perceived behavioral control and walking habit, and decreases in car/bus habit). Further research is needed to identify effective strategies for changing these

  16. MetaPSICOV: combining coevolution methods for accurate prediction of contacts and long range hydrogen bonding in proteins.

    PubMed

    Jones, David T; Singh, Tanya; Kosciolek, Tomasz; Tetchner, Stuart

    2015-04-01

    Recent developments of statistical techniques to infer direct evolutionary couplings between residue pairs have rendered covariation-based contact prediction a viable means for accurate 3D modelling of proteins, with no information other than the sequence required. To extend the usefulness of contact prediction, we have designed a new meta-predictor (MetaPSICOV) which combines three distinct approaches for inferring covariation signals from multiple sequence alignments, considers a broad range of other sequence-derived features and, uniquely, a range of metrics which describe both the local and global quality of the input multiple sequence alignment. Finally, we use a two-stage predictor, where the second stage filters the output of the first stage. This two-stage predictor is additionally evaluated on its ability to accurately predict the long range network of hydrogen bonds, including correctly assigning the donor and acceptor residues. Using the original PSICOV benchmark set of 150 protein families, MetaPSICOV achieves a mean precision of 0.54 for top-L predicted long range contacts-around 60% higher than PSICOV, and around 40% better than CCMpred. In de novo protein structure prediction using FRAGFOLD, MetaPSICOV is able to improve the TM-scores of models by a median of 0.05 compared with PSICOV. Lastly, for predicting long range hydrogen bonding, MetaPSICOV-HB achieves a precision of 0.69 for the top-L/10 hydrogen bonds compared with just 0.26 for the baseline MetaPSICOV. MetaPSICOV is available as a freely available web server at http://bioinf.cs.ucl.ac.uk/MetaPSICOV. Raw data (predicted contact lists and 3D models) and source code can be downloaded from http://bioinf.cs.ucl.ac.uk/downloads/MetaPSICOV. Supplementary data are available at Bioinformatics online. © The Author 2014. Published by Oxford University Press.

  17. Quasi-closed phase forward-backward linear prediction analysis of speech for accurate formant detection and estimation.

    PubMed

    Gowda, Dhananjaya; Airaksinen, Manu; Alku, Paavo

    2017-09-01

    Recently, a quasi-closed phase (QCP) analysis of speech signals for accurate glottal inverse filtering was proposed. However, the QCP analysis which belongs to the family of temporally weighted linear prediction (WLP) methods uses the conventional forward type of sample prediction. This may not be the best choice especially in computing WLP models with a hard-limiting weighting function. A sample selective minimization of the prediction error in WLP reduces the effective number of samples available within a given window frame. To counter this problem, a modified quasi-closed phase forward-backward (QCP-FB) analysis is proposed, wherein each sample is predicted based on its past as well as future samples thereby utilizing the available number of samples more effectively. Formant detection and estimation experiments on synthetic vowels generated using a physical modeling approach as well as natural speech utterances show that the proposed QCP-FB method yields statistically significant improvements over the conventional linear prediction and QCP methods.

  18. Comparison of Coarse-Grained Approaches in Predicting Polymer Nanocomposite Phase Behavior

    DOE PAGES

    Koski, Jason P.; Ferrier, Robert C.; Krook, Nadia M.; ...

    2017-11-02

    Because of the considerable parameter space, efficient theoretical and simulation methods are required to predict the morphology and guide experiments in polymer nanocomposites (PNCs). Unfortunately, theoretical and simulation methods are restricted in their ability to accurately map to experiments based on necessary approximations and numerical limitations. In this study, we provide direct comparisons of two recently developed coarse-grained approaches for modeling polymer nanocomposites (PNCs): polymer nanocomposite field theory (PNC-FT) and dynamic mean-field theory (DMFT). These methods are uniquely suited to efficiently capture mesoscale phase behavior of PNCs in comparison to other theoretical and simulation frameworks. We demonstrate the ability ofmore » both methods to capture macrophase separation and describe the thermodynamics of PNCs. We systematically test how the nanoparticle morphology in PNCs is affected by a uniform probability distribution of grafting sites, common in field-based methods, versus random discrete grafting sites on the nanoparticle surface. We also analyze the accuracy of the mean-field approximation in capturing the phase behavior of PNCs. Moreover, the DMFT method introduces the ability to describe nonequilibrium phase behavior while the PNC-FT method is strictly an equilibrium method. With the DMFT method we are able to show the evolution of nonequilibrium states toward their equilibrium state and a qualitative assessment of the dynamics in these systems. These simulations are compared to experiments consisting of polystyrene grafted gold nanorods in a poly(methyl methacrylate) matrix to ensure the model gives results that qualitatively agree with the experiments. This study reveals that nanoparticles in a relatively high matrix molecular weight are trapped in a nonequilibrium state and demonstrates the utility of the DMFT framework in capturing nonequilibrium phase behavior of PNCs. In conclusion, both the PNC-FT and DMFT

  19. Comparison of Coarse-Grained Approaches in Predicting Polymer Nanocomposite Phase Behavior

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

    Koski, Jason P.; Ferrier, Robert C.; Krook, Nadia M.

    Because of the considerable parameter space, efficient theoretical and simulation methods are required to predict the morphology and guide experiments in polymer nanocomposites (PNCs). Unfortunately, theoretical and simulation methods are restricted in their ability to accurately map to experiments based on necessary approximations and numerical limitations. In this study, we provide direct comparisons of two recently developed coarse-grained approaches for modeling polymer nanocomposites (PNCs): polymer nanocomposite field theory (PNC-FT) and dynamic mean-field theory (DMFT). These methods are uniquely suited to efficiently capture mesoscale phase behavior of PNCs in comparison to other theoretical and simulation frameworks. We demonstrate the ability ofmore » both methods to capture macrophase separation and describe the thermodynamics of PNCs. We systematically test how the nanoparticle morphology in PNCs is affected by a uniform probability distribution of grafting sites, common in field-based methods, versus random discrete grafting sites on the nanoparticle surface. We also analyze the accuracy of the mean-field approximation in capturing the phase behavior of PNCs. Moreover, the DMFT method introduces the ability to describe nonequilibrium phase behavior while the PNC-FT method is strictly an equilibrium method. With the DMFT method we are able to show the evolution of nonequilibrium states toward their equilibrium state and a qualitative assessment of the dynamics in these systems. These simulations are compared to experiments consisting of polystyrene grafted gold nanorods in a poly(methyl methacrylate) matrix to ensure the model gives results that qualitatively agree with the experiments. This study reveals that nanoparticles in a relatively high matrix molecular weight are trapped in a nonequilibrium state and demonstrates the utility of the DMFT framework in capturing nonequilibrium phase behavior of PNCs. In conclusion, both the PNC-FT and DMFT

  20. WegoLoc: accurate prediction of protein subcellular localization using weighted Gene Ontology terms.

    PubMed

    Chi, Sang-Mun; Nam, Dougu

    2012-04-01

    We present an accurate and fast web server, WegoLoc for predicting subcellular localization of proteins based on sequence similarity and weighted Gene Ontology (GO) information. A term weighting method in the text categorization process is applied to GO terms for a support vector machine classifier. As a result, WegoLoc surpasses the state-of-the-art methods for previously used test datasets. WegoLoc supports three eukaryotic kingdoms (animals, fungi and plants) and provides human-specific analysis, and covers several sets of cellular locations. In addition, WegoLoc provides (i) multiple possible localizations of input protein(s) as well as their corresponding probability scores, (ii) weights of GO terms representing the contribution of each GO term in the prediction, and (iii) a BLAST E-value for the best hit with GO terms. If the similarity score does not meet a given threshold, an amino acid composition-based prediction is applied as a backup method. WegoLoc and User's guide are freely available at the website http://www.btool.org/WegoLoc smchiks@ks.ac.kr; dougnam@unist.ac.kr Supplementary data is available at http://www.btool.org/WegoLoc.

  1. Sex-specific lean body mass predictive equations are accurate in the obese paediatric population

    PubMed Central

    Jackson, Lanier B.; Henshaw, Melissa H.; Carter, Janet; Chowdhury, Shahryar M.

    2015-01-01

    Background The clinical assessment of lean body mass (LBM) is challenging in obese children. A sex-specific predictive equation for LBM derived from anthropometric data was recently validated in children. Aim The purpose of this study was to independently validate these predictive equations in the obese paediatric population. Subjects and methods Obese subjects aged 4–21 were analysed retrospectively. Predicted LBM (LBMp) was calculated using equations previously developed in children. Measured LBM (LBMm) was derived from dual-energy x-ray absorptiometry. Agreement was expressed as [(LBMm-LBMp)/LBMm] with 95% limits of agreement. Results Of 310 enrolled patients, 195 (63%) were females. The mean age was 11.8 ± 3.4 years and mean BMI Z-score was 2.3 ± 0.4. The average difference between LBMm and LBMp was −0.6% (−17.0%, 15.8%). Pearson’s correlation revealed a strong linear relationship between LBMm and LBMp (r=0.97, p<0.01). Conclusion This study validates the use of these clinically-derived sex-specific LBM predictive equations in the obese paediatric population. Future studies should use these equations to improve the ability to accurately classify LBM in obese children. PMID:26287383

  2. Leveraging Call Center Logs for Customer Behavior Prediction

    NASA Astrophysics Data System (ADS)

    Parvathy, Anju G.; Vasudevan, Bintu G.; Kumar, Abhishek; Balakrishnan, Rajesh

    Most major businesses use business process outsourcing for performing a process or a part of a process including financial services like mortgage processing, loan origination, finance and accounting and transaction processing. Call centers are used for the purpose of receiving and transmitting a large volume of requests through outbound and inbound calls to customers on behalf of a business. In this paper we deal specifically with the call centers notes from banks. Banks as financial institutions provide loans to non-financial businesses and individuals. Their call centers act as the nuclei of their client service operations and log the transactions between the customer and the bank. This crucial conversation or information can be exploited for predicting a customer’s behavior which will in turn help these businesses to decide on the next action to be taken. Thus the banks save considerable time and effort in tracking delinquent customers to ensure minimum subsequent defaulters. Majority of the time the call center notes are very concise and brief and often the notes are misspelled and use many domain specific acronyms. In this paper we introduce a novel domain specific spelling correction algorithm which corrects the misspelled words in the call center logs to meaningful ones. We also discuss a procedure that builds the behavioral history sequences for the customers by categorizing the logs into one of the predefined behavioral states. We then describe a pattern based predictive algorithm that uses temporal behavioral patterns mined from these sequences to predict the customer’s next behavioral state.

  3. Using Formal Grammars to Predict I/O Behaviors in HPC: The Omnisc'IO Approach

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

    Dorier, Matthieu; Ibrahim, Shadi; Antoniu, Gabriel

    2016-08-01

    The increasing gap between the computation performance of post-petascale machines and the performance of their I/O subsystem has motivated many I/O optimizations including prefetching, caching, and scheduling. In order to further improve these techniques, modeling and predicting spatial and temporal I/O patterns of HPC applications as they run has become crucial. In this paper we present Omnisc'IO, an approach that builds a grammar-based model of the I/O behavior of HPC applications and uses it to predict when future I/O operations will occur, and where and how much data will be accessed. To infer grammars, Omnisc'IO is based on StarSequitur, amore » novel algorithm extending Nevill-Manning's Sequitur algorithm. Omnisc'IO is transparently integrated into the POSIX and MPI I/O stacks and does not require any modification in applications or higher-level I/O libraries. It works without any prior knowledge of the application and converges to accurate predictions of any N future I/O operations within a couple of iterations. Its implementation is efficient in both computation time and memory footprint.« less

  4. Adolescents' perception of peer groups: Psychological, behavioral, and relational determinants.

    PubMed

    Lee, Seungyoon; Foote, Jeremy; Wittrock, Zachary; Xu, Siyu; Niu, Li; French, Doran C

    2017-07-01

    Adolescents' social cognitive understanding of their social world is often inaccurate and biased. Focusing on peer groups, this study examines how adolescents' psychological, behavioral, and relational characteristics influence the extent to which they accurately identify their own and others' peer groups. Analyses were conducted with a sample of 1481 seventh- and tenth-grade Chinese students who are embedded with 346 peer groups. Overall, females and older students had more accurate perceptions. In addition, lower self-esteem, higher indegree centrality, and lower betweenness centrality in the friendship network predicted more accurate perception of one's own groups, whereas higher academic performance and lower betweenness centrality in the friendship network predicted more accurate perception of others' groups. Implications for understanding the connection between adolescents' psychological and behavioral traits, social relationships, and social cognition are discussed. Copyright © 2016 Elsevier Inc. All rights reserved.

  5. Accurate predictions of iron redox state in silicate glasses: A multivariate approach using X-ray absorption spectroscopy

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

    Dyar, M. Darby; McCanta, Molly; Breves, Elly

    2016-03-01

    Pre-edge features in the K absorption edge of X-ray absorption spectra are commonly used to predict Fe3+ valence state in silicate glasses. However, this study shows that using the entire spectral region from the pre-edge into the extended X-ray absorption fine-structure region provides more accurate results when combined with multivariate analysis techniques. The least absolute shrinkage and selection operator (lasso) regression technique yields %Fe3+ values that are accurate to ±3.6% absolute when the full spectral region is employed. This method can be used across a broad range of glass compositions, is easily automated, and is demonstrated to yield accurate resultsmore » from different synchrotrons. It will enable future studies involving X-ray mapping of redox gradients on standard thin sections at 1 × 1 μm pixel sizes.« less

  6. Accurate predictions of iron redox state in silicate glasses: A multivariate approach using X-ray absorption spectroscopy

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

    Dyar, M. Darby; McCanta, Molly; Breves, Elly

    2016-03-01

    Pre-edge features in the K absorption edge of X-ray absorption spectra are commonly used to predict Fe 3+ valence state in silicate glasses. However, this study shows that using the entire spectral region from the pre-edge into the extended X-ray absorption fine-structure region provides more accurate results when combined with multivariate analysis techniques. The least absolute shrinkage and selection operator (lasso) regression technique yields %Fe 3+ values that are accurate to ±3.6% absolute when the full spectral region is employed. This method can be used across a broad range of glass compositions, is easily automated, and is demonstrated to yieldmore » accurate results from different synchrotrons. It will enable future studies involving X-ray mapping of redox gradients on standard thin sections at 1 × 1 μm pixel sizes.« less

  7. Can We Predict Disruptive School Behavior?

    ERIC Educational Resources Information Center

    Eamon, Mary Keegan; Altshuler, Sandra J.

    2004-01-01

    This study examined whether child, parental, and socioenvironmental factors predict disruptive school behavior two years later. Data from a sample of 10-to 12-year-old youths, including 289 African American, 183 Hispanic/Latino, and 335 non-Hispanic white youths from the National Longitudinal Survey of Youth were analyzed. Findings indicate that…

  8. General predictive model of friction behavior regimes for metal contacts based on the formation stability and evolution of nanocrystalline surface films.

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

    Argibay, Nicolas; Cheng, Shengfeng; Sawyer, W. G.

    2015-09-01

    The prediction of macro-scale friction and wear behavior based on first principles and material properties has remained an elusive but highly desirable target for tribologists and material scientists alike. Stochastic processes (e.g. wear), statistically described parameters (e.g. surface topography) and their evolution tend to defeat attempts to establish practical general correlations between fundamental nanoscale processes and macro-scale behaviors. We present a model based on microstructural stability and evolution for the prediction of metal friction regimes, founded on recently established microstructural deformation mechanisms of nanocrystalline metals, that relies exclusively on material properties and contact stress models. We show through complementary experimentalmore » and simulation results that this model overcomes longstanding practical challenges and successfully makes accurate and consistent predictions of friction transitions for a wide range of contact conditions. This framework not only challenges the assumptions of conventional causal relationships between hardness and friction, and between friction and wear, but also suggests a pathway for the design of higher performance metal alloys.« less

  9. Computer-based personality judgments are more accurate than those made by humans.

    PubMed

    Youyou, Wu; Kosinski, Michal; Stillwell, David

    2015-01-27

    Judging others' personalities is an essential skill in successful social living, as personality is a key driver behind people's interactions, behaviors, and emotions. Although accurate personality judgments stem from social-cognitive skills, developments in machine learning show that computer models can also make valid judgments. This study compares the accuracy of human and computer-based personality judgments, using a sample of 86,220 volunteers who completed a 100-item personality questionnaire. We show that (i) computer predictions based on a generic digital footprint (Facebook Likes) are more accurate (r = 0.56) than those made by the participants' Facebook friends using a personality questionnaire (r = 0.49); (ii) computer models show higher interjudge agreement; and (iii) computer personality judgments have higher external validity when predicting life outcomes such as substance use, political attitudes, and physical health; for some outcomes, they even outperform the self-rated personality scores. Computers outpacing humans in personality judgment presents significant opportunities and challenges in the areas of psychological assessment, marketing, and privacy.

  10. Predicting personality traits related to consumer behavior using SNS analysis

    NASA Astrophysics Data System (ADS)

    Baik, Jongbum; Lee, Kangbok; Lee, Soowon; Kim, Yongbum; Choi, Jayoung

    2016-07-01

    Modeling a user profile is one of the important factors for devising a personalized recommendation. The traditional approach for modeling a user profile in computer science is to collect and generalize the user's buying behavior or preference history, generated from the user's interactions with recommender systems. According to consumer behavior research, however, internal factors such as personality traits influence a consumer's buying behavior. Existing studies have tried to adapt the Big 5 personality traits to personalized recommendations. However, although studies have shown that these traits can be useful to some extent for personalized recommendation, the causal relationship between the Big 5 personality traits and the buying behaviors of actual consumers has not been validated. In this paper, we propose a novel method for predicting the four personality traits-Extroversion, Public Self-consciousness, Desire for Uniqueness, and Self-esteem-that correlate with buying behaviors. The proposed method automatically constructs a user-personality-traits prediction model for each user by analyzing the user behavior on a social networking service. The experimental results from an analysis of the collected Facebook data show that the proposed method can predict user-personality traits with greater precision than methods that use the variables proposed in previous studies.

  11. Accurate indel prediction using paired-end short reads

    PubMed Central

    2013-01-01

    Background One of the major open challenges in next generation sequencing (NGS) is the accurate identification of structural variants such as insertions and deletions (indels). Current methods for indel calling assign scores to different types of evidence or counter-evidence for the presence of an indel, such as the number of split read alignments spanning the boundaries of a deletion candidate or reads that map within a putative deletion. Candidates with a score above a manually defined threshold are then predicted to be true indels. As a consequence, structural variants detected in this manner contain many false positives. Results Here, we present a machine learning based method which is able to discover and distinguish true from false indel candidates in order to reduce the false positive rate. Our method identifies indel candidates using a discriminative classifier based on features of split read alignment profiles and trained on true and false indel candidates that were validated by Sanger sequencing. We demonstrate the usefulness of our method with paired-end Illumina reads from 80 genomes of the first phase of the 1001 Genomes Project ( http://www.1001genomes.org) in Arabidopsis thaliana. Conclusion In this work we show that indel classification is a necessary step to reduce the number of false positive candidates. We demonstrate that missing classification may lead to spurious biological interpretations. The software is available at: http://agkb.is.tuebingen.mpg.de/Forschung/SV-M/. PMID:23442375

  12. Which behavioral, emotional and school problems in middle-childhood predict early sexual behavior?

    PubMed

    Parkes, Alison; Waylen, Andrea; Sayal, Kapil; Heron, Jon; Henderson, Marion; Wight, Daniel; Macleod, John

    2014-04-01

    Mental health and school adjustment problems are thought to distinguish early sexual behavior from normative timing (16-18 years), but little is known about how early sexual behavior originates from these problems in middle-childhood. Existing studies do not allow for co-occurring problems, differences in onset and persistence, and there is no information on middle-childhood school adjustment in relationship to early sexual activity. This study examined associations between several middle-childhood problems and early sexual behavior, using a subsample (N = 4,739, 53 % female, 98 % white, mean age 15 years 6 months) from a birth cohort study, the Avon Longitudinal Study of Parents and Children. Adolescents provided information at age 15 on early sexual behavior (oral sex and/or intercourse) and sexual risk-taking, and at age 13 on prior risk involvement (sexual behavior, antisocial behavior and substance use). Information on hyperactivity/inattention, conduct problems, depressive symptoms, peer relationship problems, school dislike and school performance was collected in middle-childhood at Time 1 (6-8 years) and Time 2 (10-11 years). In agreement with previous research, conduct problems predicted early sexual behavior, although this was found only for persistent early problems. In addition, Time 2 school dislike predicted early sexual behavior, while peer relationship problems were protective. Persistent early school dislike further characterized higher-risk groups (early sexual behavior preceded by age 13 risk, or accompanied by higher sexual risk-taking). The study establishes middle-childhood school dislike as a novel risk factor for early sexual behavior and higher-risk groups, and the importance of persistent conduct problems. Implications for the identification of children at risk and targeted intervention are discussed, as well as suggestions for further research.

  13. Accurate and robust genomic prediction of celiac disease using statistical learning.

    PubMed

    Abraham, Gad; Tye-Din, Jason A; Bhalala, Oneil G; Kowalczyk, Adam; Zobel, Justin; Inouye, Michael

    2014-02-01

    Practical application of genomic-based risk stratification to clinical diagnosis is appealing yet performance varies widely depending on the disease and genomic risk score (GRS) method. Celiac disease (CD), a common immune-mediated illness, is strongly genetically determined and requires specific HLA haplotypes. HLA testing can exclude diagnosis but has low specificity, providing little information suitable for clinical risk stratification. Using six European cohorts, we provide a proof-of-concept that statistical learning approaches which simultaneously model all SNPs can generate robust and highly accurate predictive models of CD based on genome-wide SNP profiles. The high predictive capacity replicated both in cross-validation within each cohort (AUC of 0.87-0.89) and in independent replication across cohorts (AUC of 0.86-0.9), despite differences in ethnicity. The models explained 30-35% of disease variance and up to ∼43% of heritability. The GRS's utility was assessed in different clinically relevant settings. Comparable to HLA typing, the GRS can be used to identify individuals without CD with ≥99.6% negative predictive value however, unlike HLA typing, fine-scale stratification of individuals into categories of higher-risk for CD can identify those that would benefit from more invasive and costly definitive testing. The GRS is flexible and its performance can be adapted to the clinical situation by adjusting the threshold cut-off. Despite explaining a minority of disease heritability, our findings indicate a genomic risk score provides clinically relevant information to improve upon current diagnostic pathways for CD and support further studies evaluating the clinical utility of this approach in CD and other complex diseases.

  14. Milieu matters: Evidence that ongoing lifestyle activities influence health behaviors

    PubMed Central

    Lowe, Rob; Norman, Paul

    2017-01-01

    Health behaviors occur within a milieu of lifestyle activities that could conflict with health actions. We examined whether cognitions about, and performance of, other lifestyle activities augment the prediction of health behaviors, and whether these lifestyle factors are especially influential among individuals with low health behavior engagement. Participants (N = 211) completed measures of past behavior and cognitions relating to five health behaviors (e.g., smoking, getting drunk) and 23 lifestyle activities (e.g., reading, socializing), as well as personality variables. All behaviors were measured again at two weeks. Data were analyzed using neural network and cluster analyses. The neural network accurately predicted health behaviors at follow-up (R2 = .71). As hypothesized, lifestyle cognitions and activities independently predicted health behaviors over and above behavior-specific cognitions and previous behavior. Additionally, lifestyle activities and poor self-regulatory capability were more influential among people exhibiting unhealthy behaviors. Considering ongoing lifestyle activities can enhance prediction and understanding of health behaviors and offer new targets for health behavior interventions. PMID:28662120

  15. Predicting fruit consumption: the role of habits, previous behavior and mediation effects

    PubMed Central

    2014-01-01

    Background This study assessed the role of habits and previous behavior in predicting fruit consumption as well as their additional predictive contribution besides socio-demographic and motivational factors. In the literature, habits are proposed as a stable construct that needs to be controlled for in longitudinal analyses that predict behavior. The aim of this study is to provide empirical evidence for the inclusion of either previous behavior or habits. Methods A random sample of 806 Dutch adults (>18 years) was invited by an online survey panel of a private research company to participate in an online study on fruit consumption. A longitudinal design (N = 574) was used with assessments at baseline and after one (T2) and two months (T3). Multivariate linear regression analysis was used to assess the differential value of habit and previous behavior in the prediction of fruit consumption. Results Eighty percent of habit strength could be explained by habit strength one month earlier, and 64% of fruit consumption could be explained by fruit consumption one month earlier. Regression analyses revealed that the model with motivational constructs explained 41% of the behavioral variance at T2 and 38% at T3. The addition of previous behavior and habit increased the explained variance up to 66% at T2 and to 59% at T3. Inclusion of these factors resulted in non-significant contributions of the motivational constructs. Furthermore, our findings showed that the effect of habit strength on future behavior was to a large extent mediated by previous behavior. Conclusions Both habit and previous behavior are important as predictors of future behavior, and as educational objectives for behavior change programs. Our results revealed less stability for the constructs over time than expected. Habit strength was to a large extent mediated by previous behavior and our results do not strongly suggest a need for the inclusion of both constructs. Future research needs to assess

  16. Sexing adult black-legged kittiwakes by DNA, behavior, and morphology

    USGS Publications Warehouse

    Jodice, P.G.R.; Lanctot, Richard B.; Gill, V.A.; Roby, D.D.; Hatch, Shyla A.

    2000-01-01

    We sexed adult Black-legged Kittiwakes (Rissa tridactyla) using DNA-based genetic techniques, behavior and morphology and compared results from these techniques. Genetic and morphology data were collected on 605 breeding kittiwakes and sex-specific behaviors were recorded for a sub-sample of 285 of these individuals. We compared sex classification based on both genetic and behavioral techniques for this sub-sample to assess the accuracy of the genetic technique. DNA-based techniques correctly sexed 97.2% and sex-specific behaviors, 96.5% of this sub-sample. We used the corrected genetic classifications from this sub-sample and the genetic classifications for the remaining birds, under the assumption they were correct, to develop predictive morphometric discriminant function models for all 605 birds. These models accurately predicted the sex of 73-96% of individuals examined, depending on the sample of birds used and the characters included. The most accurate single measurement for determining sex was length of head plus bill, which correctly classified 88% of individuals tested. When both members of a pair were measured, classification levels improved and approached the accuracy of both behavioral observations and genetic analyses. Morphometric techniques were only slightly less accurate than genetic techniques but were easier to implement in the field and less costly. Behavioral observations, while highly accurate, required that birds be easily observable during the breeding season and that birds be identifiable. As such, sex-specific behaviors may best be applied as a confirmation of sex for previously marked birds. All three techniques thus have the potential to be highly accurate, and the selection of one or more will depend on the circumstances of any particular field study.

  17. Predicting game-attending behavior in amateur athletes: the moderating role of intention stability.

    PubMed

    Lu, Wan Chen; Cheng, Chih-Fu; Chen, Lung Hung

    2013-10-01

    The theory of planned behavior is a well-established theory in predicting human behavior. However, there is evidence of an inconsistent relationship between intention and behavior. Therefore, the purpose of the current study is to further investigate the gap between intention and behavior. The study proposes intention stability as the moderator. Participants (N = 154, M age = 23 yr., SD = 6.7) were recruited from Internet volleyball forums and local volleyball courts in Taiwan. Multiple hierarchical regression was used to analyze the data. The results indicated that perceived behavioral control significantly predicted game-attending behavior through intention. However, attitude and subjective norms did not significantly predict behavioral intention. In addition, intention stability moderated the relationship between intention and behavior and indicated the relationship between intention and behavior was strong when intention stability was high. On the contrary, when intention stability was low, the relationship between intention and behavior was weak. Implications and applications are discussed.

  18. Neural response to pictorial health warning labels can predict smoking behavioral change

    PubMed Central

    Riddle, Philip J.; Newman-Norlund, Roger D.; Baer, Jessica; Thrasher, James F.

    2016-01-01

    In order to improve our understanding of how pictorial health warning labels (HWLs) influence smoking behavior, we examined whether brain activity helps to explain smoking behavior above and beyond self-reported effectiveness of HWLs. We measured the neural response in the ventromedial prefrontal cortex (vmPFC) and the amygdala while adult smokers viewed HWLs. Two weeks later, participants’ self-reported smoking behavior and biomarkers of smoking behavior were reassessed. We compared multiple models predicting change in self-reported smoking behavior (cigarettes per day [CPD]) and change in a biomarkers of smoke exposure (expired carbon monoxide [CO]). Brain activity in the vmPFC and amygdala not only predicted changes in CO, but also accounted for outcome variance above and beyond self-report data. Neural data were most useful in predicting behavioral change as quantified by the objective biomarker (CO). This pattern of activity was significantly modulated by individuals’ intention to quit. The finding that both cognitive (vmPFC) and affective (amygdala) brain areas contributed to these models supports the idea that smokers respond to HWLs in a cognitive-affective manner. Based on our findings, researchers may wish to consider using neural data from both cognitive and affective networks when attempting to predict behavioral change in certain populations (e.g. cigarette smokers). PMID:27405615

  19. Feedback about More Accurate versus Less Accurate Trials: Differential Effects on Self-Confidence and Activation

    ERIC Educational Resources Information Center

    Badami, Rokhsareh; VaezMousavi, Mohammad; Wulf, Gabriele; Namazizadeh, Mahdi

    2012-01-01

    One purpose of the present study was to examine whether self-confidence or anxiety would be differentially affected by feedback from more accurate rather than less accurate trials. The second purpose was to determine whether arousal variations (activation) would predict performance. On Day 1, participants performed a golf putting task under one of…

  20. Future missions studies: Combining Schatten's solar activity prediction model with a chaotic prediction model

    NASA Technical Reports Server (NTRS)

    Ashrafi, S.

    1991-01-01

    K. Schatten (1991) recently developed a method for combining his prediction model with our chaotic model. The philosophy behind this combined model and his method of combination is explained. Because the Schatten solar prediction model (KS) uses a dynamo to mimic solar dynamics, accurate prediction is limited to long-term solar behavior (10 to 20 years). The Chaotic prediction model (SA) uses the recently developed techniques of nonlinear dynamics to predict solar activity. It can be used to predict activity only up to the horizon. In theory, the chaotic prediction should be several orders of magnitude better than statistical predictions up to that horizon; beyond the horizon, chaotic predictions would theoretically be just as good as statistical predictions. Therefore, chaos theory puts a fundamental limit on predictability.

  1. An Interpretable Machine Learning Model for Accurate Prediction of Sepsis in the ICU.

    PubMed

    Nemati, Shamim; Holder, Andre; Razmi, Fereshteh; Stanley, Matthew D; Clifford, Gari D; Buchman, Timothy G

    2018-04-01

    Sepsis is among the leading causes of morbidity, mortality, and cost overruns in critically ill patients. Early intervention with antibiotics improves survival in septic patients. However, no clinically validated system exists for real-time prediction of sepsis onset. We aimed to develop and validate an Artificial Intelligence Sepsis Expert algorithm for early prediction of sepsis. Observational cohort study. Academic medical center from January 2013 to December 2015. Over 31,000 admissions to the ICUs at two Emory University hospitals (development cohort), in addition to over 52,000 ICU patients from the publicly available Medical Information Mart for Intensive Care-III ICU database (validation cohort). Patients who met the Third International Consensus Definitions for Sepsis (Sepsis-3) prior to or within 4 hours of their ICU admission were excluded, resulting in roughly 27,000 and 42,000 patients within our development and validation cohorts, respectively. None. High-resolution vital signs time series and electronic medical record data were extracted. A set of 65 features (variables) were calculated on hourly basis and passed to the Artificial Intelligence Sepsis Expert algorithm to predict onset of sepsis in the proceeding T hours (where T = 12, 8, 6, or 4). Artificial Intelligence Sepsis Expert was used to predict onset of sepsis in the proceeding T hours and to produce a list of the most significant contributing factors. For the 12-, 8-, 6-, and 4-hour ahead prediction of sepsis, Artificial Intelligence Sepsis Expert achieved area under the receiver operating characteristic in the range of 0.83-0.85. Performance of the Artificial Intelligence Sepsis Expert on the development and validation cohorts was indistinguishable. Using data available in the ICU in real-time, Artificial Intelligence Sepsis Expert can accurately predict the onset of sepsis in an ICU patient 4-12 hours prior to clinical recognition. A prospective study is necessary to determine the

  2. Development and Validation of a Multidisciplinary Tool for Accurate and Efficient Rotorcraft Noise Prediction (MUTE)

    NASA Technical Reports Server (NTRS)

    Liu, Yi; Anusonti-Inthra, Phuriwat; Diskin, Boris

    2011-01-01

    A physics-based, systematically coupled, multidisciplinary prediction tool (MUTE) for rotorcraft noise was developed and validated with a wide range of flight configurations and conditions. MUTE is an aggregation of multidisciplinary computational tools that accurately and efficiently model the physics of the source of rotorcraft noise, and predict the noise at far-field observer locations. It uses systematic coupling approaches among multiple disciplines including Computational Fluid Dynamics (CFD), Computational Structural Dynamics (CSD), and high fidelity acoustics. Within MUTE, advanced high-order CFD tools are used around the rotor blade to predict the transonic flow (shock wave) effects, which generate the high-speed impulsive noise. Predictions of the blade-vortex interaction noise in low speed flight are also improved by using the Particle Vortex Transport Method (PVTM), which preserves the wake flow details required for blade/wake and fuselage/wake interactions. The accuracy of the source noise prediction is further improved by utilizing a coupling approach between CFD and CSD, so that the effects of key structural dynamics, elastic blade deformations, and trim solutions are correctly represented in the analysis. The blade loading information and/or the flow field parameters around the rotor blade predicted by the CFD/CSD coupling approach are used to predict the acoustic signatures at far-field observer locations with a high-fidelity noise propagation code (WOPWOP3). The predicted results from the MUTE tool for rotor blade aerodynamic loading and far-field acoustic signatures are compared and validated with a variation of experimental data sets, such as UH60-A data, DNW test data and HART II test data.

  3. Affective decision-making predictive of Chinese adolescent drinking behaviors

    PubMed Central

    XIAO, LIN; BECHARA, ANTOINE; GRENARD, L. JERRY; STACY, W. ALAN; PALMER, PAULA; WEI, YONGLAN; JIA, YONG; FU, XIAOLU; JOHNSON, C. ANDERSON

    2013-01-01

    The goal of the current investigation was to address whether affective decision making would serve as a unique neuropsychological marker to predict drinking behaviors among adolescents. We conducted a longitudinal study of 181 Chinese adolescents in Chengdu city, China. In their 10th grade (ages 15–16), these adolescents were tested for their affective decision-making ability using the Iowa Gambling Task (IGT) and working memory capacity using the Self-Ordered Pointing Test. Self-report questionnaires were used to assess academic performance and drinking behaviors. At 1-year follow-up, questionnaires were completed to assess drinking behaviors, and the UPPS Impulsive Behavior Scale was used to examine four dimensions of impulsivity: urgency, lack of premeditation, lack of perseverance, and sensation seeking. Results indicated that those adolescents who progressed to binge drinking or exhibited consistent binge drinking not only performed poorly on the IGT but also scored significantly higher in urgency compared to those who never or occasionally drank. Moreover, better IGT scores predicted fewer drinking problems and fewer drinks 1 year later after controlling for demographic variables, the previous drinking behaviors, working memory, and impulsivity. These findings suggest that deficits in affective decision making may be important independent determinants of compulsive drinking and potentially addictive behavior in adolescents. PMID:19573273

  4. Language Ability Predicts the Development of Behavior Problems in Children

    PubMed Central

    Petersen, Isaac T.; Bates, John E.; D’Onofrio, Brian M.; Coyne, Claire A.; Lansford, Jennifer E.; Dodge, Kenneth A.; Pettit, Gregory S.; Van Hulle, Carol A.

    2013-01-01

    Prior studies have suggested, but not fully established, that language ability is important for regulating attention and behavior. Language ability may have implications for understanding attention-deficit hyperactivity disorder (ADHD) and conduct disorders, as well as subclinical problems. This article reports findings from two longitudinal studies to test (a) whether language ability has an independent effect on behavior problems, and (b) the direction of effect between language ability and behavior problems. In Study 1 (N = 585), language ability was measured annually from ages 7 to 13 years by language subtests of standardized academic achievement tests administered at the children’s schools. Inattentive-hyperactive (I-H) and externalizing (EXT) problems were reported annually by teachers and mothers. In Study 2 (N = 11,506), language ability (receptive vocabulary) and mother-rated I-H and EXT problems were measured biannually from ages 4 to 12 years. Analyses in both studies showed that language ability predicted within-individual variability in the development of I-H and EXT problems over and above the effects of sex, ethnicity, socioeconomic status (SES), and performance in other academic and intellectual domains (e.g., math, reading comprehension, reading recognition, and short-term memory [STM]). Even after controls for prior levels of behavior problems, language ability predicted later behavior problems more strongly than behavior problems predicted later language ability, suggesting that the direction of effect may be from language ability to behavior problems. The findings suggest that language ability may be a useful target for the prevention or even treatment of attention deficits and EXT problems in children. PMID:23713507

  5. Neural response to pictorial health warning labels can predict smoking behavioral change.

    PubMed

    Riddle, Philip J; Newman-Norlund, Roger D; Baer, Jessica; Thrasher, James F

    2016-11-01

    In order to improve our understanding of how pictorial health warning labels (HWLs) influence smoking behavior, we examined whether brain activity helps to explain smoking behavior above and beyond self-reported effectiveness of HWLs. We measured the neural response in the ventromedial prefrontal cortex (vmPFC) and the amygdala while adult smokers viewed HWLs. Two weeks later, participants' self-reported smoking behavior and biomarkers of smoking behavior were reassessed. We compared multiple models predicting change in self-reported smoking behavior (cigarettes per day [CPD]) and change in a biomarkers of smoke exposure (expired carbon monoxide [CO]). Brain activity in the vmPFC and amygdala not only predicted changes in CO, but also accounted for outcome variance above and beyond self-report data. Neural data were most useful in predicting behavioral change as quantified by the objective biomarker (CO). This pattern of activity was significantly modulated by individuals' intention to quit. The finding that both cognitive (vmPFC) and affective (amygdala) brain areas contributed to these models supports the idea that smokers respond to HWLs in a cognitive-affective manner. Based on our findings, researchers may wish to consider using neural data from both cognitive and affective networks when attempting to predict behavioral change in certain populations (e.g. cigarette smokers). © The Author (2016). Published by Oxford University Press.

  6. Combining transcription factor binding affinities with open-chromatin data for accurate gene expression prediction

    PubMed Central

    Schmidt, Florian; Gasparoni, Nina; Gasparoni, Gilles; Gianmoena, Kathrin; Cadenas, Cristina; Polansky, Julia K.; Ebert, Peter; Nordström, Karl; Barann, Matthias; Sinha, Anupam; Fröhler, Sebastian; Xiong, Jieyi; Dehghani Amirabad, Azim; Behjati Ardakani, Fatemeh; Hutter, Barbara; Zipprich, Gideon; Felder, Bärbel; Eils, Jürgen; Brors, Benedikt; Chen, Wei; Hengstler, Jan G.; Hamann, Alf; Lengauer, Thomas; Rosenstiel, Philip; Walter, Jörn; Schulz, Marcel H.

    2017-01-01

    The binding and contribution of transcription factors (TF) to cell specific gene expression is often deduced from open-chromatin measurements to avoid costly TF ChIP-seq assays. Thus, it is important to develop computational methods for accurate TF binding prediction in open-chromatin regions (OCRs). Here, we report a novel segmentation-based method, TEPIC, to predict TF binding by combining sets of OCRs with position weight matrices. TEPIC can be applied to various open-chromatin data, e.g. DNaseI-seq and NOMe-seq. Additionally, Histone-Marks (HMs) can be used to identify candidate TF binding sites. TEPIC computes TF affinities and uses open-chromatin/HM signal intensity as quantitative measures of TF binding strength. Using machine learning, we find low affinity binding sites to improve our ability to explain gene expression variability compared to the standard presence/absence classification of binding sites. Further, we show that both footprints and peaks capture essential TF binding events and lead to a good prediction performance. In our application, gene-based scores computed by TEPIC with one open-chromatin assay nearly reach the quality of several TF ChIP-seq data sets. Finally, these scores correctly predict known transcriptional regulators as illustrated by the application to novel DNaseI-seq and NOMe-seq data for primary human hepatocytes and CD4+ T-cells, respectively. PMID:27899623

  7. Accurate First-Principles Spectra Predictions for Planetological and Astrophysical Applications at Various T-Conditions

    NASA Astrophysics Data System (ADS)

    Rey, M.; Nikitin, A. V.; Tyuterev, V.

    2014-06-01

    Knowledge of near infrared intensities of rovibrational transitions of polyatomic molecules is essential for the modeling of various planetary atmospheres, brown dwarfs and for other astrophysical applications 1,2,3. For example, to analyze exoplanets, atmospheric models have been developed, thus making the need to provide accurate spectroscopic data. Consequently, the spectral characterization of such planetary objects relies on the necessity of having adequate and reliable molecular data in extreme conditions (temperature, optical path length, pressure). On the other hand, in the modeling of astrophysical opacities, millions of lines are generally involved and the line-by-line extraction is clearly not feasible in laboratory measurements. It is thus suggested that this large amount of data could be interpreted only by reliable theoretical predictions. There exists essentially two theoretical approaches for the computation and prediction of spectra. The first one is based on empirically-fitted effective spectroscopic models. Another way for computing energies, line positions and intensities is based on global variational calculations using ab initio surfaces. They do not yet reach the spectroscopic accuracy stricto sensu but implicitly account for all intramolecular interactions including resonance couplings in a wide spectral range. The final aim of this work is to provide reliable predictions which could be quantitatively accurate with respect to the precision of available observations and as complete as possible. All this thus requires extensive first-principles quantum mechanical calculations essentially based on three necessary ingredients which are (i) accurate intramolecular potential energy surface and dipole moment surface components well-defined in a large range of vibrational displacements and (ii) efficient computational methods combined with suitable choices of coordinates to account for molecular symmetry properties and to achieve a good numerical

  8. Accurate prediction of vaccine stability under real storage conditions and during temperature excursions.

    PubMed

    Clénet, Didier

    2018-04-01

    Due to their thermosensitivity, most vaccines must be kept refrigerated from production to use. To successfully carry out global immunization programs, ensuring the stability of vaccines is crucial. In this context, two important issues are critical, namely: (i) predicting vaccine stability and (ii) preventing product damage due to excessive temperature excursions outside of the recommended storage conditions (cold chain break). We applied a combination of advanced kinetics and statistical analyses on vaccine forced degradation data to accurately describe the loss of antigenicity for a multivalent freeze-dried inactivated virus vaccine containing three variants. The screening of large amounts of kinetic models combined with a statistical model selection approach resulted in the identification of two-step kinetic models. Predictions based on kinetic analysis and experimental stability data were in agreement, with approximately five percentage points difference from real values for long-term stability storage conditions, after excursions of temperature and during experimental shipments of freeze-dried products. Results showed that modeling a few months of forced degradation can be used to predict various time and temperature profiles endured by vaccines, i.e. long-term stability, short time excursions outside the labeled storage conditions or shipments at ambient temperature, with high accuracy. Pharmaceutical applications of the presented kinetics-based approach are discussed. Copyright © 2018 The Author. Published by Elsevier B.V. All rights reserved.

  9. Neuronal prediction of opponent's behavior during cooperative social interchange in primates.

    PubMed

    Haroush, Keren; Williams, Ziv M

    2015-03-12

    A cornerstone of successful social interchange is the ability to anticipate each other's intentions or actions. While generating these internal predictions is essential for constructive social behavior, their single neuronal basis and causal underpinnings are unknown. Here, we discover specific neurons in the primate dorsal anterior cingulate that selectively predict an opponent's yet unknown decision to invest in their common good or defect and distinct neurons that encode the monkey's own current decision based on prior outcomes. Mixed population predictions of the other was remarkably near optimal compared to behavioral decoders. Moreover, disrupting cingulate activity selectively biased mutually beneficial interactions between the monkeys but, surprisingly, had no influence on their decisions when no net-positive outcome was possible. These findings identify a group of other-predictive neurons in the primate anterior cingulate essential for enacting cooperative interactions and may pave a way toward the targeted treatment of social behavioral disorders. Copyright © 2015 Elsevier Inc. All rights reserved.

  10. The Utility of Thin Slice Ratings for Predicting Language Growth in Children with Autism Spectrum Disorder

    ERIC Educational Resources Information Center

    Walton, Katherine M.; Ingersoll, Brooke R.

    2016-01-01

    Literature on "Thin Slice" ratings indicates that a number of personality characteristics and behaviors can be accurately predicted by ratings of very short segments (<5?min) of behavior. This study examined the utility of Thin Slice ratings of young children with autism spectrum disorder for predicting developmental skills and…

  11. Accurate prediction of X-ray pulse properties from a free-electron laser using machine learning

    DOE PAGES

    Sanchez-Gonzalez, A.; Micaelli, P.; Olivier, C.; ...

    2017-06-05

    Free-electron lasers providing ultra-short high-brightness pulses of X-ray radiation have great potential for a wide impact on science, and are a critical element for unravelling the structural dynamics of matter. To fully harness this potential, we must accurately know the X-ray properties: intensity, spectrum and temporal profile. Owing to the inherent fluctuations in free-electron lasers, this mandates a full characterization of the properties for each and every pulse. While diagnostics of these properties exist, they are often invasive and many cannot operate at a high-repetition rate. Here, we present a technique for circumventing this limitation. Employing a machine learning strategy,more » we can accurately predict X-ray properties for every shot using only parameters that are easily recorded at high-repetition rate, by training a model on a small set of fully diagnosed pulses. Lastly, this opens the door to fully realizing the promise of next-generation high-repetition rate X-ray lasers.« less

  12. Accurate prediction of X-ray pulse properties from a free-electron laser using machine learning

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

    Sanchez-Gonzalez, A.; Micaelli, P.; Olivier, C.

    Free-electron lasers providing ultra-short high-brightness pulses of X-ray radiation have great potential for a wide impact on science, and are a critical element for unravelling the structural dynamics of matter. To fully harness this potential, we must accurately know the X-ray properties: intensity, spectrum and temporal profile. Owing to the inherent fluctuations in free-electron lasers, this mandates a full characterization of the properties for each and every pulse. While diagnostics of these properties exist, they are often invasive and many cannot operate at a high-repetition rate. Here, we present a technique for circumventing this limitation. Employing a machine learning strategy,more » we can accurately predict X-ray properties for every shot using only parameters that are easily recorded at high-repetition rate, by training a model on a small set of fully diagnosed pulses. Lastly, this opens the door to fully realizing the promise of next-generation high-repetition rate X-ray lasers.« less

  13. Do Motivational Interviewing Behaviors Predict Reductions in Partner Aggression for Men and Women?

    PubMed Central

    Woodin, Erica M.; Sotskova, Alina; O’Leary, K. Daniel

    2011-01-01

    Motivational interviewing is a directive, non-confrontational intervention to promote behavior change. The current study examined therapist behaviors during a successful brief motivational interviewing intervention for physically aggressive college dating couples (Woodin & O’Leary, 2010). Forty-five minute motivational interviews with each partner were videotaped and coded using the Motivational Interviewing Treatment Integrity scale (MITI; Moyers, Martin, Manuel, & Miller, 2003). Hierarchical modeling analyses demonstrated that therapist behaviors consistent with motivational interviewing competency predicted significantly greater reductions in physical aggression perpetration following the intervention. Specifically, greater reflection to question ratios by the therapists predicted reductions in aggression for both men and women, greater percentages of open versus closed questions predicted aggression reductions for women, and there was a trend for greater levels of global therapist empathy to predict aggression reductions for women. These findings provide evidence that motivational interviewing seems to have an effect on behavior change through therapist behaviors consistent with the theoretical underpinnings of motivational interviewing. PMID:22119133

  14. Accurate and dynamic predictive model for better prediction in medicine and healthcare.

    PubMed

    Alanazi, H O; Abdullah, A H; Qureshi, K N; Ismail, A S

    2018-05-01

    Information and communication technologies (ICTs) have changed the trend into new integrated operations and methods in all fields of life. The health sector has also adopted new technologies to improve the systems and provide better services to customers. Predictive models in health care are also influenced from new technologies to predict the different disease outcomes. However, still, existing predictive models have suffered from some limitations in terms of predictive outcomes performance. In order to improve predictive model performance, this paper proposed a predictive model by classifying the disease predictions into different categories. To achieve this model performance, this paper uses traumatic brain injury (TBI) datasets. TBI is one of the serious diseases worldwide and needs more attention due to its seriousness and serious impacts on human life. The proposed predictive model improves the predictive performance of TBI. The TBI data set is developed and approved by neurologists to set its features. The experiment results show that the proposed model has achieved significant results including accuracy, sensitivity, and specificity.

  15. Motivation is not enough: prediction of risk behavior following diagnosis of coronary heart disease from the theory of planned behavior.

    PubMed

    Johnston, Derek W; Johnston, Marie; Pollard, Beth; Kinmonth, Ann-Louise; Mant, David

    2004-09-01

    Perceived behavioral control (PBC) and intention, the proximal predictors from the theory of planned behavior (TPB), were used to predict cardiovascular risk behaviors in 597 patients 1 year after diagnosis with coronary heart disease. The outcome measures were self-report measures of exercise plus objective measures of fitness (distance walked in 6 min) and cotinine-confirmed smoking cessation. In multivariate analyses incorporating both PBC and intention, PBC predicted exercise, distance walked, and smoking cessation, but intention was not a reliable independent predictor of any health behavior measured. Thus, the effective theoretical component of the TPB was PBC. Similar predictions could derive from social-cognitive theory. In coronary patients, behavioral change needs to address issues of action implementation rather than motivational factors alone. ((c) 2004 APA, all rights reserved)

  16. Planning versus action: Different decision-making processes predict plans to change one's diet versus actual dietary behavior.

    PubMed

    Kiviniemi, Marc T; Brown-Kramer, Carolyn R

    2015-05-01

    Most health decision-making models posit that deciding to engage in a health behavior involves forming a behavioral intention which then leads to actual behavior. However, behavioral intentions and actual behavior may not be functionally equivalent. Two studies examined whether decision-making factors predicting dietary behaviors were the same as or distinct from those predicting intentions. Actual dietary behavior was proximally predicted by affective associations with the behavior. By contrast, behavioral intentions were predicted by cognitive beliefs about behaviors, with no contribution of affective associations. This dissociation has implications for understanding individual regulation of health behaviors and for behavior change interventions. © The Author(s) 2015.

  17. Accurate prediction of severe allergic reactions by a small set of environmental parameters (NDVI, temperature).

    PubMed

    Notas, George; Bariotakis, Michail; Kalogrias, Vaios; Andrianaki, Maria; Azariadis, Kalliopi; Kampouri, Errika; Theodoropoulou, Katerina; Lavrentaki, Katerina; Kastrinakis, Stelios; Kampa, Marilena; Agouridakis, Panagiotis; Pirintsos, Stergios; Castanas, Elias

    2015-01-01

    Severe allergic reactions of unknown etiology,necessitating a hospital visit, have an important impact in the life of affected individuals and impose a major economic burden to societies. The prediction of clinically severe allergic reactions would be of great importance, but current attempts have been limited by the lack of a well-founded applicable methodology and the wide spatiotemporal distribution of allergic reactions. The valid prediction of severe allergies (and especially those needing hospital treatment) in a region, could alert health authorities and implicated individuals to take appropriate preemptive measures. In the present report we have collecterd visits for serious allergic reactions of unknown etiology from two major hospitals in the island of Crete, for two distinct time periods (validation and test sets). We have used the Normalized Difference Vegetation Index (NDVI), a satellite-based, freely available measurement, which is an indicator of live green vegetation at a given geographic area, and a set of meteorological data to develop a model capable of describing and predicting severe allergic reaction frequency. Our analysis has retained NDVI and temperature as accurate identifiers and predictors of increased hospital severe allergic reactions visits. Our approach may contribute towards the development of satellite-based modules, for the prediction of severe allergic reactions in specific, well-defined geographical areas. It could also probably be used for the prediction of other environment related diseases and conditions.

  18. Accurate Prediction of Severe Allergic Reactions by a Small Set of Environmental Parameters (NDVI, Temperature)

    PubMed Central

    Andrianaki, Maria; Azariadis, Kalliopi; Kampouri, Errika; Theodoropoulou, Katerina; Lavrentaki, Katerina; Kastrinakis, Stelios; Kampa, Marilena; Agouridakis, Panagiotis; Pirintsos, Stergios; Castanas, Elias

    2015-01-01

    Severe allergic reactions of unknown etiology,necessitating a hospital visit, have an important impact in the life of affected individuals and impose a major economic burden to societies. The prediction of clinically severe allergic reactions would be of great importance, but current attempts have been limited by the lack of a well-founded applicable methodology and the wide spatiotemporal distribution of allergic reactions. The valid prediction of severe allergies (and especially those needing hospital treatment) in a region, could alert health authorities and implicated individuals to take appropriate preemptive measures. In the present report we have collecterd visits for serious allergic reactions of unknown etiology from two major hospitals in the island of Crete, for two distinct time periods (validation and test sets). We have used the Normalized Difference Vegetation Index (NDVI), a satellite-based, freely available measurement, which is an indicator of live green vegetation at a given geographic area, and a set of meteorological data to develop a model capable of describing and predicting severe allergic reaction frequency. Our analysis has retained NDVI and temperature as accurate identifiers and predictors of increased hospital severe allergic reactions visits. Our approach may contribute towards the development of satellite-based modules, for the prediction of severe allergic reactions in specific, well-defined geographical areas. It could also probably be used for the prediction of other environment related diseases and conditions. PMID:25794106

  19. A Machine Learned Classifier That Uses Gene Expression Data to Accurately Predict Estrogen Receptor Status

    PubMed Central

    Bastani, Meysam; Vos, Larissa; Asgarian, Nasimeh; Deschenes, Jean; Graham, Kathryn; Mackey, John; Greiner, Russell

    2013-01-01

    Background Selecting the appropriate treatment for breast cancer requires accurately determining the estrogen receptor (ER) status of the tumor. However, the standard for determining this status, immunohistochemical analysis of formalin-fixed paraffin embedded samples, suffers from numerous technical and reproducibility issues. Assessment of ER-status based on RNA expression can provide more objective, quantitative and reproducible test results. Methods To learn a parsimonious RNA-based classifier of hormone receptor status, we applied a machine learning tool to a training dataset of gene expression microarray data obtained from 176 frozen breast tumors, whose ER-status was determined by applying ASCO-CAP guidelines to standardized immunohistochemical testing of formalin fixed tumor. Results This produced a three-gene classifier that can predict the ER-status of a novel tumor, with a cross-validation accuracy of 93.17±2.44%. When applied to an independent validation set and to four other public databases, some on different platforms, this classifier obtained over 90% accuracy in each. In addition, we found that this prediction rule separated the patients' recurrence-free survival curves with a hazard ratio lower than the one based on the IHC analysis of ER-status. Conclusions Our efficient and parsimonious classifier lends itself to high throughput, highly accurate and low-cost RNA-based assessments of ER-status, suitable for routine high-throughput clinical use. This analytic method provides a proof-of-principle that may be applicable to developing effective RNA-based tests for other biomarkers and conditions. PMID:24312637

  20. Quokka: a comprehensive tool for rapid and accurate prediction of kinase family-specific phosphorylation sites in the human proteome.

    PubMed

    Li, Fuyi; Li, Chen; Marquez-Lago, Tatiana T; Leier, André; Akutsu, Tatsuya; Purcell, Anthony W; Smith, A Ian; Lithgow, Trevor; Daly, Roger J; Song, Jiangning; Chou, Kuo-Chen

    2018-06-27

    Kinase-regulated phosphorylation is a ubiquitous type of post-translational modification (PTM) in both eukaryotic and prokaryotic cells. Phosphorylation plays fundamental roles in many signalling pathways and biological processes, such as protein degradation and protein-protein interactions. Experimental studies have revealed that signalling defects caused by aberrant phosphorylation are highly associated with a variety of human diseases, especially cancers. In light of this, a number of computational methods aiming to accurately predict protein kinase family-specific or kinase-specific phosphorylation sites have been established, thereby facilitating phosphoproteomic data analysis. In this work, we present Quokka, a novel bioinformatics tool that allows users to rapidly and accurately identify human kinase family-regulated phosphorylation sites. Quokka was developed by using a variety of sequence scoring functions combined with an optimized logistic regression algorithm. We evaluated Quokka based on well-prepared up-to-date benchmark and independent test datasets, curated from the Phospho.ELM and UniProt databases, respectively. The independent test demonstrates that Quokka improves the prediction performance compared with state-of-the-art computational tools for phosphorylation prediction. In summary, our tool provides users with high-quality predicted human phosphorylation sites for hypothesis generation and biological validation. The Quokka webserver and datasets are freely available at http://quokka.erc.monash.edu/. Supplementary data are available at Bioinformatics online.

  1. Similarity of Cortical Activity Patterns Predicts generalization Behavior

    PubMed Central

    Engineer, Crystal T.; Perez, Claudia A.; Carraway, Ryan S.; Chang, Kevin Q.; Roland, Jarod L.; Sloan, Andrew M.; Kilgard, Michael P.

    2013-01-01

    Humans and animals readily generalize previously learned knowledge to new situations. Determining similarity is critical for assigning category membership to a novel stimulus. We tested the hypothesis that category membership is initially encoded by the similarity of the activity pattern evoked by a novel stimulus to the patterns from known categories. We provide behavioral and neurophysiological evidence that activity patterns in primary auditory cortex contain sufficient information to explain behavioral categorization of novel speech sounds by rats. Our results suggest that category membership might be encoded by the similarity of the activity pattern evoked by a novel speech sound to the patterns evoked by known sounds. Categorization based on featureless pattern matching may represent a general neural mechanism for ensuring accurate generalization across sensory and cognitive systems. PMID:24147140

  2. Computer-based personality judgments are more accurate than those made by humans

    PubMed Central

    Youyou, Wu; Kosinski, Michal; Stillwell, David

    2015-01-01

    Judging others’ personalities is an essential skill in successful social living, as personality is a key driver behind people’s interactions, behaviors, and emotions. Although accurate personality judgments stem from social-cognitive skills, developments in machine learning show that computer models can also make valid judgments. This study compares the accuracy of human and computer-based personality judgments, using a sample of 86,220 volunteers who completed a 100-item personality questionnaire. We show that (i) computer predictions based on a generic digital footprint (Facebook Likes) are more accurate (r = 0.56) than those made by the participants’ Facebook friends using a personality questionnaire (r = 0.49); (ii) computer models show higher interjudge agreement; and (iii) computer personality judgments have higher external validity when predicting life outcomes such as substance use, political attitudes, and physical health; for some outcomes, they even outperform the self-rated personality scores. Computers outpacing humans in personality judgment presents significant opportunities and challenges in the areas of psychological assessment, marketing, and privacy. PMID:25583507

  3. A Multiscale Red Blood Cell Model with Accurate Mechanics, Rheology, and Dynamics

    PubMed Central

    Fedosov, Dmitry A.; Caswell, Bruce; Karniadakis, George Em

    2010-01-01

    Abstract Red blood cells (RBCs) have highly deformable viscoelastic membranes exhibiting complex rheological response and rich hydrodynamic behavior governed by special elastic and bending properties and by the external/internal fluid and membrane viscosities. We present a multiscale RBC model that is able to predict RBC mechanics, rheology, and dynamics in agreement with experiments. Based on an analytic theory, the modeled membrane properties can be uniquely related to the experimentally established RBC macroscopic properties without any adjustment of parameters. The RBC linear and nonlinear elastic deformations match those obtained in optical-tweezers experiments. The rheological properties of the membrane are compared with those obtained in optical magnetic twisting cytometry, membrane thermal fluctuations, and creep followed by cell recovery. The dynamics of RBCs in shear and Poiseuille flows is tested against experiments and theoretical predictions, and the applicability of the latter is discussed. Our findings clearly indicate that a purely elastic model for the membrane cannot accurately represent the RBC's rheological properties and its dynamics, and therefore accurate modeling of a viscoelastic membrane is necessary. PMID:20483330

  4. In-Session Caregiver Behavior Predicts Symptom Change in Youth Receiving Trauma-Focused Cognitive Behavioral Therapy (TF-CBT)

    PubMed Central

    Yasinski, Carly; Hayes, Adele; Ready, C. Beth; Cummings, Jorden A.; Berman, Ilana S.; McCauley, Thomas; Webb, Charles; Deblinger, Esther

    2016-01-01

    Objective Involving caregivers in trauma-focused treatments for youth has been shown to result in better outcomes, but it is not clear which in-session caregiver behaviors enhance or inhibit this effect. The current study examined the associations between caregiver behaviors during Trauma-Focused Cognitive Behavioral Therapy (TF-CBT) and youth cognitive processes and symptoms. Method Participants were a racially diverse sample of Medicaid-eligible youth (ages 7–17) and their non-offending caregivers (N= 71 pairs) who received TF-CBT through an effectiveness study in a community setting. Caregiver and youth processes were coded from audio-recorded sessions, and outcomes were measured using the Child Behavior Checklist (CBCL) and UCLA PTSD Reaction Index for DSM-IV (UPID) at 3, 6, 9, and 12 months post-intake. Results Piecewise linear growth curve modeling revealed that during the trauma narrative phase of TF-CBT, caregivers’ cognitive-emotional processing of their own and their child's trauma-related reactions predicted decreases in youth internalizing and externalizing symptoms over treatment. Caregiver support predicted lower internalizing symptoms over follow-up. In contrast, caregiver avoidance and blame of the child predicted worsening of youth internalizing and externalizing symptoms over follow-up. Caregiver avoidance early in treatment also predicted worsening of externalizing symptoms over follow-up. During the narrative phase, caregiver blame and avoidance were correlated with more child overgeneralization of trauma beliefs, and blame was also associated with less child accommodation of balanced beliefs. Conclusions The association between in-session caregiver behaviors and youth symptomatology during and after TF-CBT highlights the importance of assessing and targeting these behaviors to improve clinical outcomes. PMID:27618641

  5. Can numerical simulations accurately predict hydrodynamic instabilities in liquid films?

    NASA Astrophysics Data System (ADS)

    Denner, Fabian; Charogiannis, Alexandros; Pradas, Marc; van Wachem, Berend G. M.; Markides, Christos N.; Kalliadasis, Serafim

    2014-11-01

    Understanding the dynamics of hydrodynamic instabilities in liquid film flows is an active field of research in fluid dynamics and non-linear science in general. Numerical simulations offer a powerful tool to study hydrodynamic instabilities in film flows and can provide deep insights into the underlying physical phenomena. However, the direct comparison of numerical results and experimental results is often hampered by several reasons. For instance, in numerical simulations the interface representation is problematic and the governing equations and boundary conditions may be oversimplified, whereas in experiments it is often difficult to extract accurate information on the fluid and its behavior, e.g. determine the fluid properties when the liquid contains particles for PIV measurements. In this contribution we present the latest results of our on-going, extensive study on hydrodynamic instabilities in liquid film flows, which includes direct numerical simulations, low-dimensional modelling as well as experiments. The major focus is on wave regimes, wave height and wave celerity as a function of Reynolds number and forcing frequency of a falling liquid film. Specific attention is paid to the differences in numerical and experimental results and the reasons for these differences. The authors are grateful to the EPSRC for their financial support (Grant EP/K008595/1).

  6. Maternal Characteristics Predicting Young Girls’ Disruptive Behavior

    PubMed Central

    van der Molen, Elsa; Hipwell, Alison E.; Vermeiren, Robert; Loeber, Rolf

    2011-01-01

    Little is known about the relative predictive utility of maternal characteristics and parenting skills on the development of girls’ disruptive behavior. The current study used five waves of parent and child-report data from the ongoing Pittsburgh Girls Study to examine these relationships in a sample of 1,942 girls from age 7 to 12 years. Multivariate Generalized Estimating Equation (GEE) analyses indicated that European American race, mother’s prenatal nicotine use, maternal depression, maternal conduct problems prior to age 15, and low maternal warmth explained unique variance. Maladaptive parenting partly mediated the effects of maternal depression and maternal conduct problems. Both current and early maternal risk factors have an impact on young girls’ disruptive behavior, providing support for the timing and focus of the prevention of girls’ disruptive behavior. PMID:21391016

  7. Maternal characteristics predicting young girls' disruptive behavior.

    PubMed

    van der Molen, Elsa; Hipwell, Alison E; Vermeiren, Robert; Loeber, Rolf

    2011-01-01

    Little is known about the relative predictive utility of maternal characteristics and parenting skills on the development of girls' disruptive behavior. The current study used five waves of parent- and child-report data from the ongoing Pittsburgh Girls Study to examine these relationships in a sample of 1,942 girls from age 7 to 12 years. Multivariate generalized estimating equation analyses indicated that European American race, mother's prenatal nicotine use, maternal depression, maternal conduct problems prior to age 15, and low maternal warmth explained unique variance. Maladaptive parenting partly mediated the effects of maternal depression and maternal conduct problems. Both current and early maternal risk factors have an impact on young girls' disruptive behavior, providing support for the timing and focus of the prevention of girls' disruptive behavior.

  8. Born Knowing: Tentacled Snakes Innately Predict Future Prey Behavior

    PubMed Central

    Catania, Kenneth C.

    2010-01-01

    Background Aquatic tentacled snakes (Erpeton tentaculatus) can take advantage of their prey's escape response by startling fish with their body before striking. The feint usually startles fish toward the snake's approaching jaws. But when fish are oriented at a right angle to the jaws, the C-start escape response translates fish parallel to the snake's head. To exploit this latter response, snakes must predict the future location of the fish. Adult snakes can make this prediction. Is it learned, or are tentacled snakes born able to predict future fish behavior? Methods and Findings Laboratory-born, naïve snakes were investigated as they struck at fish. Trials were recorded at 250 or 500 frames per second. To prevent learning, snakes were placed in a water container with a clear transparency sheet or glass bottom. The chamber was placed over a channel in a separate aquarium with fish below. Thus snakes could see and strike at fish, without contact. The snake's body feint elicited C-starts in the fish below the transparency sheet, allowing strike accuracy to be quantified in relationship to the C-starts. When fish were oriented at a right angle to the jaws, naïve snakes biased their strikes to the future location of the escaping fish's head, such that the snake's jaws and the fish's translating head usually converged. Several different types of predictive strikes were observed. Conclusions The results show that some predators have adapted their nervous systems to directly compensate for the future behavior of prey in a sensory realm that usually requires learning. Instead of behavior selected during their lifetime, newborn tentacled snakes exhibit behavior that has been selected on a different scale—over many generations. Counter adaptations in fish are not expected, as tentacled snakes are rare predators exploiting fish responses that are usually adaptive. PMID:20585384

  9. The Impact of Asking Intention or Self-Prediction Questions on Subsequent Behavior

    PubMed Central

    Wood, Chantelle; Conner, Mark; Miles, Eleanor; Sandberg, Tracy; Taylor, Natalie; Godin, Gaston; Sheeran, Paschal

    2015-01-01

    The current meta-analysis estimated the magnitude of the impact of asking intention and self-prediction questions on rates of subsequent behavior, and examined mediators and moderators of this question–behavior effect (QBE). Random-effects meta-analysis on 116 published tests of the effect indicated that intention/prediction questions have a small positive effect on behavior (d+ = 0.24). Little support was observed for attitude accessibility, cognitive dissonance, behavioral simulation, or processing fluency explanations of the QBE. Multivariate analyses indicated significant effects of social desirability of behavior/behavior domain (larger effects for more desirable and less risky behaviors), difficulty of behavior (larger effects for easy-to-perform behaviors), and sample type (larger effects among student samples). Although this review controls for co-occurrence of moderators in multivariate analyses, future primary research should systematically vary moderators in fully factorial designs. Further primary research is also needed to unravel the mechanisms underlying different variants of the QBE. PMID:26162771

  10. Combining transcription factor binding affinities with open-chromatin data for accurate gene expression prediction.

    PubMed

    Schmidt, Florian; Gasparoni, Nina; Gasparoni, Gilles; Gianmoena, Kathrin; Cadenas, Cristina; Polansky, Julia K; Ebert, Peter; Nordström, Karl; Barann, Matthias; Sinha, Anupam; Fröhler, Sebastian; Xiong, Jieyi; Dehghani Amirabad, Azim; Behjati Ardakani, Fatemeh; Hutter, Barbara; Zipprich, Gideon; Felder, Bärbel; Eils, Jürgen; Brors, Benedikt; Chen, Wei; Hengstler, Jan G; Hamann, Alf; Lengauer, Thomas; Rosenstiel, Philip; Walter, Jörn; Schulz, Marcel H

    2017-01-09

    The binding and contribution of transcription factors (TF) to cell specific gene expression is often deduced from open-chromatin measurements to avoid costly TF ChIP-seq assays. Thus, it is important to develop computational methods for accurate TF binding prediction in open-chromatin regions (OCRs). Here, we report a novel segmentation-based method, TEPIC, to predict TF binding by combining sets of OCRs with position weight matrices. TEPIC can be applied to various open-chromatin data, e.g. DNaseI-seq and NOMe-seq. Additionally, Histone-Marks (HMs) can be used to identify candidate TF binding sites. TEPIC computes TF affinities and uses open-chromatin/HM signal intensity as quantitative measures of TF binding strength. Using machine learning, we find low affinity binding sites to improve our ability to explain gene expression variability compared to the standard presence/absence classification of binding sites. Further, we show that both footprints and peaks capture essential TF binding events and lead to a good prediction performance. In our application, gene-based scores computed by TEPIC with one open-chromatin assay nearly reach the quality of several TF ChIP-seq data sets. Finally, these scores correctly predict known transcriptional regulators as illustrated by the application to novel DNaseI-seq and NOMe-seq data for primary human hepatocytes and CD4+ T-cells, respectively. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

  11. Employing Numerical Weather Models to Enhance Fire Weather and Fire Behavior Predictions

    Treesearch

    Joseph J. Charney; Lesley A. Fusina

    2006-01-01

    This paper presents an assessment of fire weather and fire behavior predictions produced by a numerical weather prediction model similar to those used by operational weather forecasters when preparing their forecasts. The PSU/NCAR MM5 model is used to simulate the weather conditions associated with three fire episodes in June 2005. Extreme fire behavior was reported...

  12. Predicting Networked Strategic Behavior via Machine Learning and Game Theory

    DTIC Science & Technology

    2015-01-13

    The funding for this project was used to develop basic models, methodology and algorithms for the application of machine learning and related tools to settings in which strategic behavior is central. Among the topics studied was the development of simple behavioral models explaining and predicting human subject behavior in networked strategic experiments from prior work. These included experiments in biased voting and networked trading, among others.

  13. Exchange-Hole Dipole Dispersion Model for Accurate Energy Ranking in Molecular Crystal Structure Prediction.

    PubMed

    Whittleton, Sarah R; Otero-de-la-Roza, A; Johnson, Erin R

    2017-02-14

    Accurate energy ranking is a key facet to the problem of first-principles crystal-structure prediction (CSP) of molecular crystals. This work presents a systematic assessment of B86bPBE-XDM, a semilocal density functional combined with the exchange-hole dipole moment (XDM) dispersion model, for energy ranking using 14 compounds from the first five CSP blind tests. Specifically, the set of crystals studied comprises 11 rigid, planar compounds and 3 co-crystals. The experimental structure was correctly identified as the lowest in lattice energy for 12 of the 14 total crystals. One of the exceptions is 4-hydroxythiophene-2-carbonitrile, for which the experimental structure was correctly identified once a quasi-harmonic estimate of the vibrational free-energy contribution was included, evidencing the occasional importance of thermal corrections for accurate energy ranking. The other exception is an organic salt, where charge-transfer error (also called delocalization error) is expected to cause the base density functional to be unreliable. Provided the choice of base density functional is appropriate and an estimate of temperature effects is used, XDM-corrected density-functional theory is highly reliable for the energetic ranking of competing crystal structures.

  14. Behavioral Profile Predicts Dominance Status in Mountain Chickadees.

    PubMed

    Fox, Rebecca A; Ladage, Lara D; Roth, Timothy C; Pravosudov, Vladimir V

    2009-06-01

    Individual variation in stable behavioral traits may explain variation in ecologically-relevant behaviors such as foraging, dispersal, anti-predator behavior, and dominance. We investigated behavioral variation in mountain chickadees (Poecile gambeli), a North American parid that lives in dominance-structured winter flocks, using two common measures of behavioral profile: exploration of a novel room and novel object exploration. We related those behavioral traits to dominance status in male chickadees following brief, pair-wise encounters. Low-exploring birds (birds that visited less than four locations in the novel room) were significantly more likely to become dominant in brief, pairwise encounters with high-exploring birds (i.e., birds that visited all perching locations within a novel room). On the other hand, there was no relationship between novel object exploration and dominance. Interestingly, novel room exploration was also not correlated with novel object exploration. These results suggest that behavioral profile may predict the social status of group-living individuals. Moreover, our results contradict the idea that novel object exploration and novel room exploration are always interchangeable measures of individuals' sensitivity to environmental novelty.

  15. Predicting adolescent's cyberbullying behavior: A longitudinal risk analysis.

    PubMed

    Barlett, Christopher P

    2015-06-01

    The current study used the risk factor approach to test the unique and combined influence of several possible risk factors for cyberbullying attitudes and behavior using a four-wave longitudinal design with an adolescent US sample. Participants (N = 96; average age = 15.50 years) completed measures of cyberbullying attitudes, perceptions of anonymity, cyberbullying behavior, and demographics four times throughout the academic school year. Several logistic regression equations were used to test the contribution of these possible risk factors. Results showed that (a) cyberbullying attitudes and previous cyberbullying behavior were important unique risk factors for later cyberbullying behavior, (b) anonymity and previous cyberbullying behavior were valid risk factors for later cyberbullying attitudes, and (c) the likelihood of engaging in later cyberbullying behavior increased with the addition of risk factors. Overall, results show the unique and combined influence of such risk factors for predicting later cyberbullying behavior. Results are discussed in terms of theory. Copyright © 2015 The Foundation for Professionals in Services for Adolescents. Published by Elsevier Ltd. All rights reserved.

  16. Using the Personality Assessment Inventory Antisocial and Borderline Features Scales to Predict Behavior Change.

    PubMed

    Penson, Brittany N; Ruchensky, Jared R; Morey, Leslie C; Edens, John F

    2016-11-01

    A substantial amount of research has examined the developmental trajectory of antisocial behavior and, in particular, the relationship between antisocial behavior and maladaptive personality traits. However, research typically has not controlled for previous behavior (e.g., past violence) when examining the utility of personality measures, such as self-report scales of antisocial and borderline traits, in predicting future behavior (e.g., subsequent violence). Examination of the potential interactive effects of measures of both antisocial and borderline traits also is relatively rare in longitudinal research predicting adverse outcomes. The current study utilizes a large sample of youthful offenders ( N = 1,354) from the Pathways to Desistance project to examine the separate effects of the Personality Assessment Inventory Antisocial Features (ANT) and Borderline Features (BOR) scales in predicting future offending behavior as well as trends in other negative outcomes (e.g., substance abuse, violence, employment difficulties) over a 1-year follow-up period. In addition, an ANT × BOR interaction term was created to explore the predictive effects of secondary psychopathy. ANT and BOR both explained unique variance in the prediction of various negative outcomes even after controlling for past indicators of those same behaviors during the preceding year.

  17. An Extrapolation of a Radical Equation More Accurately Predicts Shelf Life of Frozen Biological Matrices.

    PubMed

    De Vore, Karl W; Fatahi, Nadia M; Sass, John E

    2016-08-01

    Arrhenius modeling of analyte recovery at increased temperatures to predict long-term colder storage stability of biological raw materials, reagents, calibrators, and controls is standard practice in the diagnostics industry. Predicting subzero temperature stability using the same practice is frequently criticized but nevertheless heavily relied upon. We compared the ability to predict analyte recovery during frozen storage using 3 separate strategies: traditional accelerated studies with Arrhenius modeling, and extrapolation of recovery at 20% of shelf life using either ordinary least squares or a radical equation y = B1x(0.5) + B0. Computer simulations were performed to establish equivalence of statistical power to discern the expected changes during frozen storage or accelerated stress. This was followed by actual predictive and follow-up confirmatory testing of 12 chemistry and immunoassay analytes. Linear extrapolations tended to be the most conservative in the predicted percent recovery, reducing customer and patient risk. However, the majority of analytes followed a rate of change that slowed over time, which was fit best to a radical equation of the form y = B1x(0.5) + B0. Other evidence strongly suggested that the slowing of the rate was not due to higher-order kinetics, but to changes in the matrix during storage. Predicting shelf life of frozen products through extrapolation of early initial real-time storage analyte recovery should be considered the most accurate method. Although in this study the time required for a prediction was longer than a typical accelerated testing protocol, there are less potential sources of error, reduced costs, and a lower expenditure of resources. © 2016 American Association for Clinical Chemistry.

  18. Predicting Behaviors to Reduce Toxic Chemical Exposures among New and Expectant Mothers: The Role of Distal Variables within the Integrative Model of Behavioral Prediction

    ERIC Educational Resources Information Center

    Mello, Susan; Hovick, Shelly R.

    2016-01-01

    There is a growing body of evidence linking childhood exposure to environmental toxins and a range of adverse health outcomes, including preterm birth, cognitive deficits, and cancer. Little is known, however, about what drives mothers to engage in health behaviors to reduce such risks. Guided by the integrative model of behavioral prediction,…

  19. INFANT AVOIDANCE DURING A TACTILE TASK PREDICTS AUTISM SPECTRUM BEHAVIORS IN TODDLERHOOD.

    PubMed

    Mammen, Micah A; Moore, Ginger A; Scaramella, Laura V; Reiss, David; Ganiban, Jody M; Shaw, Daniel S; Leve, Leslie D; Neiderhiser, Jenae M

    2015-01-01

    The experience of touch is critical for early communication and social interaction; infants who show aversion to touch may be at risk for atypical development and behavior problems. The current study aimed to clarify predictive associations between infant responses to tactile stimuli and toddler autism spectrum, internalizing, and externalizing behaviors. This study measured 9-month-old infants' (N = 561; 58% male) avoidance and negative affect during a novel tactile task in which parents painted infants' hands and feet and pressed them to paper to make a picture. Parent reports on the Pervasive Developmental Problems (PDP), Internalizing, and Externalizing scales of the Child Behavior Checklist were used to measure toddler behaviors at 18 months. Infant observed avoidance and negative affect were significantly correlated; however, avoidance predicted subsequent PDP scores only, independent of negative affect, which did not predict any toddler behaviors. Findings suggest that incorporating measures of responses to touch in the study of early social interaction may provide an important and discriminating construct for identifying children at greater risk for social impairments related to autism spectrum behaviors. © 2015 Michigan Association for Infant Mental Health.

  20. Frontal Theta Links Prediction Errors to Behavioral Adaptation in Reinforcement Learning

    PubMed Central

    Cavanagh, James F.; Frank, Michael J.; Klein, Theresa J.; Allen, John J.B.

    2009-01-01

    Investigations into action monitoring have consistently detailed a fronto-central voltage deflection in the Event-Related Potential (ERP) following the presentation of negatively valenced feedback, sometimes termed the Feedback Related Negativity (FRN). The FRN has been proposed to reflect a neural response to prediction errors during reinforcement learning, yet the single trial relationship between neural activity and the quanta of expectation violation remains untested. Although ERP methods are not well suited to single trial analyses, the FRN has been associated with theta band oscillatory perturbations in the medial prefrontal cortex. Medio-frontal theta oscillations have been previously associated with expectation violation and behavioral adaptation and are well suited to single trial analysis. Here, we recorded EEG activity during a probabilistic reinforcement learning task and fit the performance data to an abstract computational model (Q-learning) for calculation of single-trial reward prediction errors. Single-trial theta oscillatory activities following feedback were investigated within the context of expectation (prediction error) and adaptation (subsequent reaction time change). Results indicate that interactive medial and lateral frontal theta activities reflect the degree of negative and positive reward prediction error in the service of behavioral adaptation. These different brain areas use prediction error calculations for different behavioral adaptations: with medial frontal theta reflecting the utilization of prediction errors for reaction time slowing (specifically following errors), but lateral frontal theta reflecting prediction errors leading to working memory-related reaction time speeding for the correct choice. PMID:19969093

  1. 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

  2. Control surface hinge moment prediction using computational fluid dynamics

    NASA Astrophysics Data System (ADS)

    Simpson, Christopher David

    The following research determines the feasibility of predicting control surface hinge moments using various computational methods. A detailed analysis is conducted using a 2D GA(W)-1 airfoil with a 20% plain flap. Simple hinge moment prediction methods are tested, including empirical Datcom relations and XFOIL. Steady-state and time-accurate turbulent, viscous, Navier-Stokes solutions are computed using Fun3D. Hinge moment coefficients are computed. Mesh construction techniques are discussed. An adjoint-based mesh adaptation case is also evaluated. An NACA 0012 45-degree swept horizontal stabilizer with a 25% elevator is also evaluated using Fun3D. Results are compared with experimental wind-tunnel data obtained from references. Finally, the costs of various solution methods are estimated. Results indicate that while a steady-state Navier-Stokes solution can accurately predict control surface hinge moments for small angles of attack and deflection angles, a time-accurate solution is necessary to accurately predict hinge moments in the presence of flow separation. The ability to capture the unsteady vortex shedding behavior present in moderate to large control surface deflections is found to be critical to hinge moment prediction accuracy. Adjoint-based mesh adaptation is shown to give hinge moment predictions similar to a globally-refined mesh for a steady-state 2D simulation.

  3. In-session caregiver behavior predicts symptom change in youth receiving trauma-focused cognitive behavioral therapy (TF-CBT).

    PubMed

    Yasinski, Carly; Hayes, Adele M; Ready, C Beth; Cummings, Jorden A; Berman, Ilana S; McCauley, Thomas; Webb, Charles; Deblinger, Esther

    2016-12-01

    Involving caregivers in trauma-focused treatments for youth has been shown to result in better outcomes, but it is not clear which in-session caregiver behaviors enhance or inhibit this effect. The current study examined the associations between caregiver behaviors during Trauma-Focused Cognitive Behavioral Therapy (TF-CBT) and youth cognitive processes and symptoms. Participants were a racially diverse sample of Medicaid-eligible youth (ages 7-17) and their nonoffending caregivers (N = 71 pairs) who received TF-CBT through an effectiveness study in a community setting. Caregiver and youth processes were coded from audio-recorded sessions, and outcomes were measured using the Child Behavior Checklist (CBCL) and UCLA PTSD Reaction Index for Diagnostic and Statistical Manual for Mental Disorders-Fourth Edition (DSM-IV; UPID) at 3, 6, 9, and 12 months postintake. Piecewise linear growth curve modeling revealed that during the trauma narrative phase of TF-CBT, caregivers' cognitive-emotional processing of their own and their child's trauma-related reactions predicted decreases in youth internalizing and externalizing symptoms over treatment. Caregiver support predicted lower internalizing symptoms over follow-up. In contrast, caregiver avoidance and blame of the child predicted worsening of youth internalizing and externalizing symptoms over follow-up. Caregiver avoidance early in treatment also predicted worsening of externalizing symptoms over follow-up. During the narrative phase, caregiver blame and avoidance were correlated with more child overgeneralization of trauma beliefs, and blame was also associated with less child accommodation of balanced beliefs. The association between in-session caregiver behaviors and youth symptomatology during and after TF-CBT highlights the importance of assessing and targeting these behaviors to improve clinical outcomes. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

  4. Maternal Characteristics Predicting Young Girls' Disruptive Behavior

    ERIC Educational Resources Information Center

    van der Molen, Elsa; Hipwell, Alison E.; Vermeiren, Robert; Loeber, Rolf

    2011-01-01

    Little is known about the relative predictive utility of maternal characteristics and parenting skills on the development of girls' disruptive behavior. The current study used five waves of parent- and child-report data from the ongoing Pittsburgh Girls Study to examine these relationships in a sample of 1,942 girls from age 7 to 12 years.…

  5. Oxytocin receptor gene variations predict neural and behavioral response to oxytocin in autism

    PubMed Central

    Watanabe, Takamitsu; Otowa, Takeshi; Abe, Osamu; Kuwabara, Hitoshi; Aoki, Yuta; Natsubori, Tatsunobu; Takao, Hidemasa; Kakiuchi, Chihiro; Kondo, Kenji; Ikeda, Masashi; Iwata, Nakao; Kasai, Kiyoto; Sasaki, Tsukasa

    2017-01-01

    Abstract Oxytocin appears beneficial for autism spectrum disorder (ASD), and more than 20 single-nucleotide polymorphisms (SNPs) in oxytocin receptor (OXTR) are relevant to ASD. However, neither biological functions of OXTR SNPs in ASD nor critical OXTR SNPs that determine oxytocin’s effects on ASD remains known. Here, using a machine-learning algorithm that was designed to evaluate collective effects of multiple SNPs and automatically identify most informative SNPs, we examined relationships between 27 representative OXTR SNPs and six types of behavioral/neural response to oxytocin in ASD individuals. The oxytocin effects were extracted from our previous placebo-controlled within-participant clinical trial administering single-dose intranasal oxytocin to 38 high-functioning adult Japanese ASD males. Consequently, we identified six different SNP sets that could accurately predict the six different oxytocin efficacies, and confirmed the robustness of these SNP selections against variations of the datasets and analysis parameters. Moreover, major alleles of several prominent OXTR SNPs—including rs53576 and rs2254298—were found to have dissociable effects on the oxytocin efficacies. These findings suggest biological functions of the OXTR SNP variants on autistic oxytocin responses, and implied that clinical oxytocin efficacy may be genetically predicted before its actual administration, which would contribute to establishment of future precision medicines for ASD. PMID:27798253

  6. Does the emergency surgery score accurately predict outcomes in emergent laparotomies?

    PubMed

    Peponis, Thomas; Bohnen, Jordan D; Sangji, Naveen F; Nandan, Anirudh R; Han, Kelsey; Lee, Jarone; Yeh, D Dante; de Moya, Marc A; Velmahos, George C; Chang, David C; Kaafarani, Haytham M A

    2017-08-01

    The emergency surgery score is a mortality-risk calculator for emergency general operation patients. We sought to examine whether the emergency surgery score predicts 30-day morbidity and mortality in a high-risk group of patients undergoing emergent laparotomy. Using the 2011-2012 American College of Surgeons National Surgical Quality Improvement Program database, we identified all patients who underwent emergent laparotomy using (1) the American College of Surgeons National Surgical Quality Improvement Program definition of "emergent," and (2) all Current Procedural Terminology codes denoting a laparotomy, excluding aortic aneurysm rupture. Multivariable logistic regression analyses were performed to measure the correlation (c-statistic) between the emergency surgery score and (1) 30-day mortality, and (2) 30-day morbidity after emergent laparotomy. As sensitivity analyses, the correlation between the emergency surgery score and 30-day mortality was also evaluated in prespecified subgroups based on Current Procedural Terminology codes. A total of 26,410 emergent laparotomy patients were included. Thirty-day mortality and morbidity were 10.2% and 43.8%, respectively. The emergency surgery score correlated well with mortality (c-statistic = 0.84); scores of 1, 11, and 22 correlated with mortalities of 0.4%, 39%, and 100%, respectively. Similarly, the emergency surgery score correlated well with morbidity (c-statistic = 0.74); scores of 0, 7, and 11 correlated with complication rates of 13%, 58%, and 79%, respectively. The morbidity rates plateaued for scores higher than 11. Sensitivity analyses demonstrated that the emergency surgery score effectively predicts mortality in patients undergoing emergent (1) splenic, (2) gastroduodenal, (3) intestinal, (4) hepatobiliary, or (5) incarcerated ventral hernia operation. The emergency surgery score accurately predicts outcomes in all types of emergent laparotomy patients and may prove valuable as a bedside decision

  7. Predicting Overt and Covert Antisocial Behaviors: Parents, Peers, and Homelessness

    ERIC Educational Resources Information Center

    Tompsett, Carolyn J.; Toro, Paul A.

    2010-01-01

    Parental deviance, parental monitoring, and deviant peers were examined as predictors of overt and covert antisocial behaviors. Homeless (N=231) and housed (N=143) adolescents were assessed in adolescence and again in early adulthood. Homelessness predicted both types of antisocial behaviors, and effects persisted in young adulthood. Parental…

  8. Brain-behavioral adaptability predicts response to cognitive behavioral therapy for emotional disorders: A person-centered event-related potential study.

    PubMed

    Stange, Jonathan P; MacNamara, Annmarie; Kennedy, Amy E; Hajcak, Greg; Phan, K Luan; Klumpp, Heide

    2017-06-23

    Single-trial-level analyses afford the ability to link neural indices of elaborative attention (such as the late positive potential [LPP], an event-related potential) with downstream markers of attentional processing (such as reaction time [RT]). This approach can provide useful information about individual differences in information processing, such as the ability to adapt behavior based on attentional demands ("brain-behavioral adaptability"). Anxiety and depression are associated with maladaptive information processing implicating aberrant cognition-emotion interactions, but whether brain-behavioral adaptability predicts response to psychotherapy is not known. We used a novel person-centered, trial-level analysis approach to link neural indices of stimulus processing to behavioral responses and to predict treatment outcome. Thirty-nine patients with anxiety and/or depression received 12 weeks of cognitive behavioral therapy (CBT). Prior to treatment, patients performed a speeded reaction-time task involving briefly-presented pairs of aversive and neutral pictures while electroencephalography was recorded. Multilevel modeling demonstrated that larger LPPs predicted slower responses on subsequent trials, suggesting that increased attention to the task-irrelevant nature of pictures interfered with reaction time on subsequent trials. Whereas using LPP and RT averages did not distinguish CBT responders from nonresponders, in trial-level analyses individuals who demonstrated greater ability to benefit behaviorally (i.e., faster RT) from smaller LPPs on the previous trial (greater brain-behavioral adaptability) were more likely to respond to treatment and showed greater improvements in depressive symptoms. These results highlight the utility of trial-level analyses to elucidate variability in within-subjects, brain-behavioral attentional coupling in the context of emotion processing, in predicting response to CBT for emotional disorders. Copyright © 2017 Elsevier Ltd

  9. Predicting Academics via Behavior within an Elementary Sample: An Evaluation of the Social, Academic, and Emotional Behavior Risk Screener (SAEBRS)

    ERIC Educational Resources Information Center

    Kilgus, Stephen P.; Bowman, Nicollette A.; Christ, Theodore J.; Taylor, Crystal N.

    2017-01-01

    This study examined the extent to which teacher ratings of student behavior via the "Social, Academic, and Emotional Behavior Risk Screener" (SAEBRS) predicted academic achievement in math and reading. A secondary purpose was to compare the predictive capacity of three SAEBRS subscales corresponding to social, academic, or emotional…

  10. Do behavioral scientists really understand HIV-related sexual risk behavior? A systematic review of longitudinal and experimental studies predicting sexual behavior.

    PubMed

    Huebner, David M; Perry, Nicholas S

    2015-10-01

    Behavioral interventions to reduce sexual risk behavior depend on strong health behavior theory. By identifying the psychosocial variables that lead causally to sexual risk, theories provide interventionists with a guide for how to change behavior. However, empirical research is critical to determining whether a particular theory adequately explains sexual risk behavior. A large body of cross-sectional evidence, which has been reviewed elsewhere, supports the notion that certain theory-based constructs (e.g., self-efficacy) are correlates of sexual behavior. However, given the limitations of inferring causality from correlational research, it is essential that we review the evidence from more methodologically rigorous studies (i.e., longitudinal and experimental designs). This systematic review identified 44 longitudinal studies in which investigators attempted to predict sexual risk from psychosocial variables over time. We also found 134 experimental studies (i.e., randomized controlled trials of HIV interventions), but of these only 9 (6.7 %) report the results of mediation analyses that might provide evidence for the validity of health behavior theories in predicting sexual behavior. Results show little convergent support across both types of studies for most traditional, theoretical predictors of sexual behavior. This suggests that the field must expand the body of empirical work that utilizes the most rigorous study designs to test our theoretical assumptions. The inconsistent results of existing research would indicate that current theoretical models of sexual risk behavior are inadequate, and may require expansion or adaptation.

  11. Forming Attitudes That Predict Future Behavior: A Meta-Analysis of the Attitude–Behavior Relation

    PubMed Central

    Glasman, Laura R.; Albarracín, Dolores

    2016-01-01

    A meta-analysis (k of conditions = 128; N = 4,598) examined the influence of factors present at the time an attitude is formed on the degree to which this attitude guides future behavior. The findings indicated that attitudes correlated with a future behavior more strongly when they were easy to recall (accessible) and stable over time. Because of increased accessibility, attitudes more strongly predicted future behavior when participants had direct experience with the attitude object and reported their attitudes frequently. Because of the resulting attitude stability, the attitude–behavior association was strongest when attitudes were confident, when participants formed their attitude on the basis of behavior-relevant information, and when they received or were induced to think about one- rather than two-sided information about the attitude object. PMID:16910754

  12. Fast and Accurate Circuit Design Automation through Hierarchical Model Switching.

    PubMed

    Huynh, Linh; Tagkopoulos, Ilias

    2015-08-21

    In computer-aided biological design, the trifecta of characterized part libraries, accurate models and optimal design parameters is crucial for producing reliable designs. As the number of parts and model complexity increase, however, it becomes exponentially more difficult for any optimization method to search the solution space, hence creating a trade-off that hampers efficient design. To address this issue, we present a hierarchical computer-aided design architecture that uses a two-step approach for biological design. First, a simple model of low computational complexity is used to predict circuit behavior and assess candidate circuit branches through branch-and-bound methods. Then, a complex, nonlinear circuit model is used for a fine-grained search of the reduced solution space, thus achieving more accurate results. Evaluation with a benchmark of 11 circuits and a library of 102 experimental designs with known characterization parameters demonstrates a speed-up of 3 orders of magnitude when compared to other design methods that provide optimality guarantees.

  13. Predicting Visual Distraction Using Driving Performance Data

    PubMed Central

    Kircher, Katja; Ahlstrom, Christer

    2010-01-01

    Behavioral variables are often used as performance indicators (PIs) of visual or internal distraction induced by secondary tasks. The objective of this study is to investigate whether visual distraction can be predicted by driving performance PIs in a naturalistic setting. Visual distraction is here defined by a gaze based real-time distraction detection algorithm called AttenD. Seven drivers used an instrumented vehicle for one month each in a small scale field operational test. For each of the visual distraction events detected by AttenD, seven PIs such as steering wheel reversal rate and throttle hold were calculated. Corresponding data were also calculated for time periods during which the drivers were classified as attentive. For each PI, means between distracted and attentive states were calculated using t-tests for different time-window sizes (2 – 40 s), and the window width with the smallest resulting p-value was selected as optimal. Based on the optimized PIs, logistic regression was used to predict whether the drivers were attentive or distracted. The logistic regression resulted in predictions which were 76 % correct (sensitivity = 77 % and specificity = 76 %). The conclusion is that there is a relationship between behavioral variables and visual distraction, but the relationship is not strong enough to accurately predict visual driver distraction. Instead, behavioral PIs are probably best suited as complementary to eye tracking based algorithms in order to make them more accurate and robust. PMID:21050615

  14. Artificial Neural Networks: A New Approach to Predicting Application Behavior.

    ERIC Educational Resources Information Center

    Gonzalez, Julie M. Byers; DesJardins, Stephen L.

    2002-01-01

    Applied the technique of artificial neural networks to predict which students were likely to apply to one research university. Compared the results to the traditional analysis tool, logistic regression modeling. Found that the addition of artificial intelligence models was a useful new tool for predicting student application behavior. (EV)

  15. Statistical Models for Predicting Threat Detection From Human Behavior

    PubMed Central

    Kelley, Timothy; Amon, Mary J.; Bertenthal, Bennett I.

    2018-01-01

    Users must regularly distinguish between secure and insecure cyber platforms in order to preserve their privacy and safety. Mouse tracking is an accessible, high-resolution measure that can be leveraged to understand the dynamics of perception, categorization, and decision-making in threat detection. Researchers have begun to utilize measures like mouse tracking in cyber security research, including in the study of risky online behavior. However, it remains an empirical question to what extent real-time information about user behavior is predictive of user outcomes and demonstrates added value compared to traditional self-report questionnaires. Participants navigated through six simulated websites, which resembled either secure “non-spoof” or insecure “spoof” versions of popular websites. Websites also varied in terms of authentication level (i.e., extended validation, standard validation, or partial encryption). Spoof websites had modified Uniform Resource Locator (URL) and authentication level. Participants chose to “login” to or “back” out of each website based on perceived website security. Mouse tracking information was recorded throughout the task, along with task performance. After completing the website identification task, participants completed a questionnaire assessing their security knowledge and degree of familiarity with the websites simulated during the experiment. Despite being primed to the possibility of website phishing attacks, participants generally showed a bias for logging in to websites versus backing out of potentially dangerous sites. Along these lines, participant ability to identify spoof websites was around the level of chance. Hierarchical Bayesian logistic models were used to compare the accuracy of two-factor (i.e., website security and encryption level), survey-based (i.e., security knowledge and website familiarity), and real-time measures (i.e., mouse tracking) in predicting risky online behavior during phishing

  16. Statistical Models for Predicting Threat Detection From Human Behavior.

    PubMed

    Kelley, Timothy; Amon, Mary J; Bertenthal, Bennett I

    2018-01-01

    Users must regularly distinguish between secure and insecure cyber platforms in order to preserve their privacy and safety. Mouse tracking is an accessible, high-resolution measure that can be leveraged to understand the dynamics of perception, categorization, and decision-making in threat detection. Researchers have begun to utilize measures like mouse tracking in cyber security research, including in the study of risky online behavior. However, it remains an empirical question to what extent real-time information about user behavior is predictive of user outcomes and demonstrates added value compared to traditional self-report questionnaires. Participants navigated through six simulated websites, which resembled either secure "non-spoof" or insecure "spoof" versions of popular websites. Websites also varied in terms of authentication level (i.e., extended validation, standard validation, or partial encryption). Spoof websites had modified Uniform Resource Locator (URL) and authentication level. Participants chose to "login" to or "back" out of each website based on perceived website security. Mouse tracking information was recorded throughout the task, along with task performance. After completing the website identification task, participants completed a questionnaire assessing their security knowledge and degree of familiarity with the websites simulated during the experiment. Despite being primed to the possibility of website phishing attacks, participants generally showed a bias for logging in to websites versus backing out of potentially dangerous sites. Along these lines, participant ability to identify spoof websites was around the level of chance. Hierarchical Bayesian logistic models were used to compare the accuracy of two-factor (i.e., website security and encryption level), survey-based (i.e., security knowledge and website familiarity), and real-time measures (i.e., mouse tracking) in predicting risky online behavior during phishing attacks

  17. NMRDSP: an accurate prediction of protein shape strings from NMR chemical shifts and sequence data.

    PubMed

    Mao, Wusong; Cong, Peisheng; Wang, Zhiheng; Lu, Longjian; Zhu, Zhongliang; Li, Tonghua

    2013-01-01

    Shape string is structural sequence and is an extremely important structure representation of protein backbone conformations. Nuclear magnetic resonance chemical shifts give a strong correlation with the local protein structure, and are exploited to predict protein structures in conjunction with computational approaches. Here we demonstrate a novel approach, NMRDSP, which can accurately predict the protein shape string based on nuclear magnetic resonance chemical shifts and structural profiles obtained from sequence data. The NMRDSP uses six chemical shifts (HA, H, N, CA, CB and C) and eight elements of structure profiles as features, a non-redundant set (1,003 entries) as the training set, and a conditional random field as a classification algorithm. For an independent testing set (203 entries), we achieved an accuracy of 75.8% for S8 (the eight states accuracy) and 87.8% for S3 (the three states accuracy). This is higher than only using chemical shifts or sequence data, and confirms that the chemical shift and the structure profile are significant features for shape string prediction and their combination prominently improves the accuracy of the predictor. We have constructed the NMRDSP web server and believe it could be employed to provide a solid platform to predict other protein structures and functions. The NMRDSP web server is freely available at http://cal.tongji.edu.cn/NMRDSP/index.jsp.

  18. Body odor quality predicts behavioral attractiveness in humans.

    PubMed

    Roberts, S Craig; Kralevich, Alexandra; Ferdenzi, Camille; Saxton, Tamsin K; Jones, Benedict C; DeBruine, Lisa M; Little, Anthony C; Havlicek, Jan

    2011-12-01

    Growing effort is being made to understand how different attractive physical traits co-vary within individuals, partly because this might indicate an underlying index of genetic quality. In humans, attention has focused on potential markers of quality such as facial attractiveness, axillary odor quality, the second-to-fourth digit (2D:4D) ratio and body mass index (BMI). Here we extend this approach to include visually-assessed kinesic cues (nonverbal behavior linked to movement) which are statistically independent of structural physical traits. The utility of such kinesic cues in mate assessment is controversial, particularly during everyday conversational contexts, as they could be unreliable and susceptible to deception. However, we show here that the attractiveness of nonverbal behavior, in 20 male participants, is predicted by perceived quality of their axillary body odor. This finding indicates covariation between two desirable traits in different sensory modalities. Depending on two different rating contexts (either a simple attractiveness rating or a rating for long-term partners by 10 female raters not using hormonal contraception), we also found significant relationships between perceived attractiveness of nonverbal behavior and BMI, and between axillary odor ratings and 2D:4D ratio. Axillary odor pleasantness was the single attribute that consistently predicted attractiveness of nonverbal behavior. Our results demonstrate that nonverbal kinesic cues could reliably reveal mate quality, at least in males, and could corroborate and contribute to mate assessment based on other physical traits.

  19. Free will beliefs predict attitudes toward unethical behavior and criminal punishment

    PubMed Central

    Martin, Nathan D.; Rigoni, Davide; Vohs, Kathleen D.

    2017-01-01

    Do free will beliefs influence moral judgments? Answers to this question from theoretical and empirical perspectives are controversial. This study attempted to replicate past research and offer theoretical insights by analyzing World Values Survey data from residents of 46 countries (n = 65,111 persons). Corroborating experimental findings, free will beliefs predicted intolerance of unethical behaviors and support for severe criminal punishment. Further, the link between free will beliefs and intolerance of unethical behavior was moderated by variations in countries’ institutional integrity, defined as the degree to which countries had accountable, corruption-free public sectors. Free will beliefs predicted intolerance of unethical behaviors for residents of countries with high and moderate institutional integrity, but this correlation was not seen for countries with low institutional integrity. Free will beliefs predicted support for criminal punishment regardless of countries’ institutional integrity. Results were robust across different operationalizations of institutional integrity and with or without statistical control variables. PMID:28652361

  20. Free will beliefs predict attitudes toward unethical behavior and criminal punishment.

    PubMed

    Martin, Nathan D; Rigoni, Davide; Vohs, Kathleen D

    2017-07-11

    Do free will beliefs influence moral judgments? Answers to this question from theoretical and empirical perspectives are controversial. This study attempted to replicate past research and offer theoretical insights by analyzing World Values Survey data from residents of 46 countries ( n = 65,111 persons). Corroborating experimental findings, free will beliefs predicted intolerance of unethical behaviors and support for severe criminal punishment. Further, the link between free will beliefs and intolerance of unethical behavior was moderated by variations in countries' institutional integrity, defined as the degree to which countries had accountable, corruption-free public sectors. Free will beliefs predicted intolerance of unethical behaviors for residents of countries with high and moderate institutional integrity, but this correlation was not seen for countries with low institutional integrity. Free will beliefs predicted support for criminal punishment regardless of countries' institutional integrity. Results were robust across different operationalizations of institutional integrity and with or without statistical control variables.

  1. Sleep spindles may predict response to cognitive-behavioral therapy for chronic insomnia.

    PubMed

    Dang-Vu, Thien Thanh; Hatch, Benjamin; Salimi, Ali; Mograss, Melodee; Boucetta, Soufiane; O'Byrne, Jordan; Brandewinder, Marie; Berthomier, Christian; Gouin, Jean-Philippe

    2017-11-01

    While cognitive-behavioral therapy for insomnia constitutes the first-line treatment for chronic insomnia, only few reports have investigated how sleep architecture relates to response to this treatment. In this pilot study, we aimed to determine whether pre-treatment sleep spindle density predicts treatment response to cognitive-behavioral therapy for insomnia. Twenty-four participants with chronic primary insomnia participated in a 6-week cognitive-behavioral therapy for insomnia performed in groups of 4-6 participants. Treatment response was assessed using the Pittsburgh Sleep Quality Index and the Insomnia Severity Index measured at pre- and post-treatment, and at 3- and 12-months' follow-up assessments. Secondary outcome measures were extracted from sleep diaries over 7 days and overnight polysomnography, obtained at pre- and post-treatment. Spindle density during stage N2-N3 sleep was extracted from polysomnography at pre-treatment. Hierarchical linear modeling analysis assessed whether sleep spindle density predicted response to cognitive-behavioral therapy. After adjusting for age, sex, and education level, lower spindle density at pre-treatment predicted poorer response over the 12-month follow-up, as reflected by a smaller reduction in Pittsburgh Sleep Quality Index over time. Reduced spindle density also predicted lower improvements in sleep diary sleep efficiency and wake after sleep onset immediately after treatment. There were no significant associations between spindle density and changes in the Insomnia Severity Index or polysomnography variables over time. These preliminary results suggest that inter-individual differences in sleep spindle density in insomnia may represent an endogenous biomarker predicting responsiveness to cognitive-behavioral therapy. Insomnia with altered spindle activity might constitute an insomnia subtype characterized by a neurophysiological vulnerability to sleep disruption associated with impaired responsiveness to

  2. Modeling behavioral thermoregulation in a climate change sentinel.

    PubMed

    Moyer-Horner, Lucas; Mathewson, Paul D; Jones, Gavin M; Kearney, Michael R; Porter, Warren P

    2015-12-01

    When possible, many species will shift in elevation or latitude in response to rising temperatures. However, before such shifts occur, individuals will first tolerate environmental change and then modify their behavior to maintain heat balance. Behavioral thermoregulation allows animals a range of climatic tolerances and makes predicting geographic responses under future warming scenarios challenging. Because behavioral modification may reduce an individual's fecundity by, for example, limiting foraging time and thus caloric intake, we must consider the range of behavioral options available for thermoregulation to accurately predict climate change impacts on individual species. To date, few studies have identified mechanistic links between an organism's daily activities and the need to thermoregulate. We used a biophysical model, Niche Mapper, to mechanistically model microclimate conditions and thermoregulatory behavior for a temperature-sensitive mammal, the American pika (Ochotona princeps). Niche Mapper accurately simulated microclimate conditions, as well as empirical metabolic chamber data for a range of fur properties, animal sizes, and environmental parameters. Niche Mapper predicted pikas would be behaviorally constrained because of the need to thermoregulate during the hottest times of the day. We also showed that pikas at low elevations could receive energetic benefits by being smaller in size and maintaining summer pelage during longer stretches of the active season under a future warming scenario. We observed pika behavior for 288 h in Glacier National Park, Montana, and thermally characterized their rocky, montane environment. We found that pikas were most active when temperatures were cooler, and at sites characterized by high elevations and north-facing slopes. Pikas became significantly less active across a suite of behaviors in the field when temperatures surpassed 20°C, which supported a metabolic threshold predicted by Niche Mapper. In general

  3. Fast and accurate predictions of covalent bonds in chemical space.

    PubMed

    Chang, K Y Samuel; Fias, Stijn; Ramakrishnan, Raghunathan; von Lilienfeld, O Anatole

    2016-05-07

    We assess the predictive accuracy of perturbation theory based estimates of changes in covalent bonding due to linear alchemical interpolations among molecules. We have investigated σ bonding to hydrogen, as well as σ and π bonding between main-group elements, occurring in small sets of iso-valence-electronic molecules with elements drawn from second to fourth rows in the p-block of the periodic table. Numerical evidence suggests that first order Taylor expansions of covalent bonding potentials can achieve high accuracy if (i) the alchemical interpolation is vertical (fixed geometry), (ii) it involves elements from the third and fourth rows of the periodic table, and (iii) an optimal reference geometry is used. This leads to near linear changes in the bonding potential, resulting in analytical predictions with chemical accuracy (∼1 kcal/mol). Second order estimates deteriorate the prediction. If initial and final molecules differ not only in composition but also in geometry, all estimates become substantially worse, with second order being slightly more accurate than first order. The independent particle approximation based second order perturbation theory performs poorly when compared to the coupled perturbed or finite difference approach. Taylor series expansions up to fourth order of the potential energy curve of highly symmetric systems indicate a finite radius of convergence, as illustrated for the alchemical stretching of H2 (+). Results are presented for (i) covalent bonds to hydrogen in 12 molecules with 8 valence electrons (CH4, NH3, H2O, HF, SiH4, PH3, H2S, HCl, GeH4, AsH3, H2Se, HBr); (ii) main-group single bonds in 9 molecules with 14 valence electrons (CH3F, CH3Cl, CH3Br, SiH3F, SiH3Cl, SiH3Br, GeH3F, GeH3Cl, GeH3Br); (iii) main-group double bonds in 9 molecules with 12 valence electrons (CH2O, CH2S, CH2Se, SiH2O, SiH2S, SiH2Se, GeH2O, GeH2S, GeH2Se); (iv) main-group triple bonds in 9 molecules with 10 valence electrons (HCN, HCP, HCAs, HSiN, HSi

  4. Does mesenteric venous imaging assessment accurately predict pathologic invasion in localized pancreatic ductal adenocarcinoma?

    PubMed

    Clanton, Jesse; Oh, Stephen; Kaplan, Stephen J; Johnson, Emily; Ross, Andrew; Kozarek, Richard; Alseidi, Adnan; Biehl, Thomas; Picozzi, Vincent J; Helton, William S; Coy, David; Dorer, Russell; Rocha, Flavio G

    2018-05-09

    Accurate prediction of mesenteric venous involvement in pancreatic ductal adenocarcinoma (PDAC) is necessary for adequate staging and treatment. A retrospective cohort study was conducted in PDAC patients at a single institution. All patients with resected PDAC and staging CT and EUS between 2003 and 2014 were included and sub-divided into "upfront resected" and "neoadjuvant chemotherapy (NAC)" groups. Independent imaging re-review was correlated to venous resection and venous invasion. Sensitivity, specificity, positive and negative predictive values were then calculated. A total of 109 patients underwent analysis, 60 received upfront resection, and 49 NAC. Venous resection (30%) and vein invasion (13%) was less common in patients resected upfront than those who received NAC (53% and 16%, respectively). Both CT and EUS had poor sensitivity (14-44%) but high specificity (75-95%) for detecting venous resection and vein invasion in patients resected upfront, whereas sensitivity was high (84-100%) and specificity was low (27-44%) after NAC. Preoperative CT and EUS in PDAC have similar efficacy but different predictive capacity in assessing mesenteric venous involvement depending on whether patients are resected upfront or received NAC. Both modalities appear to significantly overestimate true vascular involvement and should be interpreted in the appropriate clinical context. Copyright © 2018 International Hepato-Pancreato-Biliary Association Inc. Published by Elsevier Ltd. All rights reserved.

  5. Accurate load prediction by BEM with airfoil data from 3D RANS simulations

    NASA Astrophysics Data System (ADS)

    Schneider, Marc S.; Nitzsche, Jens; Hennings, Holger

    2016-09-01

    In this paper, two methods for the extraction of airfoil coefficients from 3D CFD simulations of a wind turbine rotor are investigated, and these coefficients are used to improve the load prediction of a BEM code. The coefficients are extracted from a number of steady RANS simulations, using either averaging of velocities in annular sections, or an inverse BEM approach for determination of the induction factors in the rotor plane. It is shown that these 3D rotor polars are able to capture the rotational augmentation at the inner part of the blade as well as the load reduction by 3D effects close to the blade tip. They are used as input to a simple BEM code and the results of this BEM with 3D rotor polars are compared to the predictions of BEM with 2D airfoil coefficients plus common empirical corrections for stall delay and tip loss. While BEM with 2D airfoil coefficients produces a very different radial distribution of loads than the RANS simulation, the BEM with 3D rotor polars manages to reproduce the loads from RANS very accurately for a variety of load cases, as long as the blade pitch angle is not too different from the cases from which the polars were extracted.

  6. Accurate secondary structure prediction and fold recognition for circular dichroism spectroscopy

    PubMed Central

    Micsonai, András; Wien, Frank; Kernya, Linda; Lee, Young-Ho; Goto, Yuji; Réfrégiers, Matthieu; Kardos, József

    2015-01-01

    Circular dichroism (CD) spectroscopy is a widely used technique for the study of protein structure. Numerous algorithms have been developed for the estimation of the secondary structure composition from the CD spectra. These methods often fail to provide acceptable results on α/β-mixed or β-structure–rich proteins. The problem arises from the spectral diversity of β-structures, which has hitherto been considered as an intrinsic limitation of the technique. The predictions are less reliable for proteins of unusual β-structures such as membrane proteins, protein aggregates, and amyloid fibrils. Here, we show that the parallel/antiparallel orientation and the twisting of the β-sheets account for the observed spectral diversity. We have developed a method called β-structure selection (BeStSel) for the secondary structure estimation that takes into account the twist of β-structures. This method can reliably distinguish parallel and antiparallel β-sheets and accurately estimates the secondary structure for a broad range of proteins. Moreover, the secondary structure components applied by the method are characteristic to the protein fold, and thus the fold can be predicted to the level of topology in the CATH classification from a single CD spectrum. By constructing a web server, we offer a general tool for a quick and reliable structure analysis using conventional CD or synchrotron radiation CD (SRCD) spectroscopy for the protein science research community. The method is especially useful when X-ray or NMR techniques fail. Using BeStSel on data collected by SRCD spectroscopy, we investigated the structure of amyloid fibrils of various disease-related proteins and peptides. PMID:26038575

  7. Early life stress, MAOA, and gene-environment interactions predict behavioral disinhibition in children.

    PubMed

    Enoch, M-A; Steer, C D; Newman, T K; Gibson, N; Goldman, D

    2010-02-01

    Several, but not all, studies have shown that the monoamine oxidase A functional promoter polymorphism (MAOA-LPR) interacts with childhood adversity to predict adolescent and adult antisocial behavior. However, it is not known whether MAOA-LPR interacts with early life (pre-birth-3 years) stressors to influence behavior in prepubertal children. The Avon Longitudinal Study of Parents and Children, UK, is a community-representative cohort study of children followed from pre-birth onwards. The impact of family adversity from pre-birth to age 3 years and stressful life events from 6 months to 7 years on behavioral disinhibition was determined in 7500 girls and boys. Behavioral disinhibition measures were: mother-reported hyperactivity and conduct disturbances (Strengths and Difficulties Questionnaire) at ages 4 and 7 years. In both sexes, exposure to family adversity and stressful life events in the first 3 years of life predicted behavioral disinhibition at age 4, persisting until age 7. In girls, MAOA-LPR interacted with stressful life events experienced from 6 months to 3.5 years to influence hyperactivity at ages 4 and 7. In boys, the interaction of MAOA-LPR with stressful life events between 1.5 and 2.5 years predicted hyperactivity at age 7 years. The low activity MAOA-LPR variant was associated with increased hyperactivity in girls and boys exposed to high stress. In contrast, there was no MAOA-LPR interaction with family adversity. In a general population sample of prepubertal children, exposure to common stressors from pre-birth to 3 years predicted behavioral disinhibition, and MAOA-LPR- stressful life event interactions specifically predicted hyperactivity.

  8. Accurate prediction of bacterial type IV secreted effectors using amino acid composition and PSSM profiles.

    PubMed

    Zou, Lingyun; Nan, Chonghan; Hu, Fuquan

    2013-12-15

    Various human pathogens secret effector proteins into hosts cells via the type IV secretion system (T4SS). These proteins play important roles in the interaction between bacteria and hosts. Computational methods for T4SS effector prediction have been developed for screening experimental targets in several isolated bacterial species; however, widely applicable prediction approaches are still unavailable In this work, four types of distinctive features, namely, amino acid composition, dipeptide composition, .position-specific scoring matrix composition and auto covariance transformation of position-specific scoring matrix, were calculated from primary sequences. A classifier, T4EffPred, was developed using the support vector machine with these features and their different combinations for effector prediction. Various theoretical tests were performed in a newly established dataset, and the results were measured with four indexes. We demonstrated that T4EffPred can discriminate IVA and IVB effectors in benchmark datasets with positive rates of 76.7% and 89.7%, respectively. The overall accuracy of 95.9% shows that the present method is accurate for distinguishing the T4SS effector in unidentified sequences. A classifier ensemble was designed to synthesize all single classifiers. Notable performance improvement was observed using this ensemble system in benchmark tests. To demonstrate the model's application, a genome-scale prediction of effectors was performed in Bartonella henselae, an important zoonotic pathogen. A number of putative candidates were distinguished. A web server implementing the prediction method and the source code are both available at http://bioinfo.tmmu.edu.cn/T4EffPred.

  9. Response of dorsomedial prefrontal cortex predicts altruistic behavior

    PubMed Central

    Waytz, Adam; Zaki, Jamil; Mitchell, Jason P.

    2012-01-01

    Human beings have an unusual proclivity for altruistic behavior, and recent commentators have suggested that these prosocial tendencies arise from our unique capacity to understand the minds of others (i.e., to mentalize). The current studies test this hypothesis by examining the relation between altruistic behavior and the reflexive engagement of a neural system reliably associated with mentalizing. Results indicated that activity in the dorsomedial prefrontal cortex (dorsal MPFC)—a region consistently involved in understanding others’ mental states—predicts both monetary donations to others and time spent helping others. These findings address long-standing questions about the proximate source of human altruism by suggesting that prosocial behavior results, in part, from our broader tendency for social-cognitive thought. PMID:22649243

  10. Towards accurate cosmological predictions for rapidly oscillating scalar fields as dark matter

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

    Ureña-López, L. Arturo; Gonzalez-Morales, Alma X., E-mail: lurena@ugto.mx, E-mail: alma.gonzalez@fisica.ugto.mx

    2016-07-01

    As we are entering the era of precision cosmology, it is necessary to count on accurate cosmological predictions from any proposed model of dark matter. In this paper we present a novel approach to the cosmological evolution of scalar fields that eases their analytic and numerical analysis at the background and at the linear order of perturbations. The new method makes use of appropriate angular variables that simplify the writing of the equations of motion, and which also show that the usual field variables play a secondary role in the cosmological dynamics. We apply the method to a scalar fieldmore » endowed with a quadratic potential and revisit its properties as dark matter. Some of the results known in the literature are recovered, and a better understanding of the physical properties of the model is provided. It is confirmed that there exists a Jeans wavenumber k {sub J} , directly related to the suppression of linear perturbations at wavenumbers k > k {sub J} , and which is verified to be k {sub J} = a √ mH . We also discuss some semi-analytical results that are well satisfied by the full numerical solutions obtained from an amended version of the CMB code CLASS. Finally we draw some of the implications that this new treatment of the equations of motion may have in the prediction of cosmological observables from scalar field dark matter models.« less

  11. A multiscale red blood cell model with accurate mechanics, rheology, and dynamics.

    PubMed

    Fedosov, Dmitry A; Caswell, Bruce; Karniadakis, George Em

    2010-05-19

    Red blood cells (RBCs) have highly deformable viscoelastic membranes exhibiting complex rheological response and rich hydrodynamic behavior governed by special elastic and bending properties and by the external/internal fluid and membrane viscosities. We present a multiscale RBC model that is able to predict RBC mechanics, rheology, and dynamics in agreement with experiments. Based on an analytic theory, the modeled membrane properties can be uniquely related to the experimentally established RBC macroscopic properties without any adjustment of parameters. The RBC linear and nonlinear elastic deformations match those obtained in optical-tweezers experiments. The rheological properties of the membrane are compared with those obtained in optical magnetic twisting cytometry, membrane thermal fluctuations, and creep followed by cell recovery. The dynamics of RBCs in shear and Poiseuille flows is tested against experiments and theoretical predictions, and the applicability of the latter is discussed. Our findings clearly indicate that a purely elastic model for the membrane cannot accurately represent the RBC's rheological properties and its dynamics, and therefore accurate modeling of a viscoelastic membrane is necessary. Copyright 2010 Biophysical Society. Published by Elsevier Inc. All rights reserved.

  12. Toward Objective Monitoring of Ingestive Behavior in Free-living Population

    PubMed Central

    Sazonov, Edward S.; Schuckers, Stephanie A.C.; Lopez-Meyer, Paulo; Makeyev, Oleksandr; Melanson, Edward L.; Neuman, Michael R.; Hill, James O.

    2010-01-01

    Understanding of eating behaviors associated with obesity requires objective and accurate monitoring of food intake patterns. Accurate methods are available for measuring total energy expenditure and its components in free-living populations, but methods for measuring food intake in free-living people are far less accurate and involve self-reporting or subjective monitoring. We suggest that chews and swallows can be used for objective monitoring of ingestive behavior. This hypothesis was verified in a human study involving 20 subjects. Chews and swallows were captured during periods of quiet resting, talking, and meals of varying size. The counts of chews and swallows along with other derived metrics were used to build prediction models for detection of food intake, differentiation between liquids and solids, and for estimation of the mass of ingested food. The proposed prediction models were able to detect periods of food intake with >95% accuracy and a fine time resolution of 30 s, differentiate solid foods from liquids with >91% accuracy, and predict mass of ingested food with >91% accuracy for solids and >83% accuracy for liquids. In earlier publications, we have shown that chews and swallows can be captured by noninvasive sensors that could be developed into a wearable device. Thus, the proposed methodology could lead to the development of an innovative new way of assessing human eating behavior in free-living conditions. PMID:19444225

  13. Toward objective monitoring of ingestive behavior in free-living population.

    PubMed

    Sazonov, Edward S; Schuckers, Stephanie A C; Lopez-Meyer, Paulo; Makeyev, Oleksandr; Melanson, Edward L; Neuman, Michael R; Hill, James O

    2009-10-01

    Understanding of eating behaviors associated with obesity requires objective and accurate monitoring of food intake patterns. Accurate methods are available for measuring total energy expenditure and its components in free-living populations, but methods for measuring food intake in free-living people are far less accurate and involve self-reporting or subjective monitoring. We suggest that chews and swallows can be used for objective monitoring of ingestive behavior. This hypothesis was verified in a human study involving 20 subjects. Chews and swallows were captured during periods of quiet resting, talking, and meals of varying size. The counts of chews and swallows along with other derived metrics were used to build prediction models for detection of food intake, differentiation between liquids and solids, and for estimation of the mass of ingested food. The proposed prediction models were able to detect periods of food intake with >95% accuracy and a fine time resolution of 30 s, differentiate solid foods from liquids with >91% accuracy, and predict mass of ingested food with >91% accuracy for solids and >83% accuracy for liquids. In earlier publications, we have shown that chews and swallows can be captured by noninvasive sensors that could be developed into a wearable device. Thus, the proposed methodology could lead to the development of an innovative new way of assessing human eating behavior in free-living conditions.

  14. Accurate prediction of cellular co-translational folding indicates proteins can switch from post- to co-translational folding

    PubMed Central

    Nissley, Daniel A.; Sharma, Ajeet K.; Ahmed, Nabeel; Friedrich, Ulrike A.; Kramer, Günter; Bukau, Bernd; O'Brien, Edward P.

    2016-01-01

    The rates at which domains fold and codons are translated are important factors in determining whether a nascent protein will co-translationally fold and function or misfold and malfunction. Here we develop a chemical kinetic model that calculates a protein domain's co-translational folding curve during synthesis using only the domain's bulk folding and unfolding rates and codon translation rates. We show that this model accurately predicts the course of co-translational folding measured in vivo for four different protein molecules. We then make predictions for a number of different proteins in yeast and find that synonymous codon substitutions, which change translation-elongation rates, can switch some protein domains from folding post-translationally to folding co-translationally—a result consistent with previous experimental studies. Our approach explains essential features of co-translational folding curves and predicts how varying the translation rate at different codon positions along a transcript's coding sequence affects this self-assembly process. PMID:26887592

  15. When Preferences Are in the Way: Children's Predictions of Goal-Directed Behaviors.

    PubMed

    Yang, Fan; Frye, Douglas

    2017-12-18

    Across three studies, we examined 4- to 7-year-olds' predictions of goal-directed behaviors when goals conflict with preferences. In Study 1, when presented with stories in which a character had to act against basic preferences to achieve an interpersonal goal (e.g., playing with a partner), 6- and 7-year-olds were more likely than 4- and 5-year-olds to predict the actor would act in accordance with the goal to play with the partner, instead of fulfilling the basic preference of playing a favored activity. Similar results were obtained in Study 2 with scenarios that each involved a single individual pursuing intrapersonal goals that conflicted with his or her basic preferences. In Study 3, younger children's predictions of goal-directed behaviors did not increase for novel goals and preferences, when the influences of their own preferences, future thinking, or a lack of impulse control were minimized. The results suggest that between ages 4 and 7, children increasingly integrate and give more weight to other sources of motivational information (e.g., goals) in addition to preferences when predicting people's behaviors. This increasing awareness may have implications for children's self-regulatory and goal pursuit behaviors. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

  16. Maternal Behavior Predicts Infant Neurophysiological and Behavioral Attention Processes in the First Year

    ERIC Educational Resources Information Center

    Swingler, Margaret M.; Perry, Nicole B.; Calkins, Susan D.; Bell, Martha Ann

    2017-01-01

    We apply a biopsychosocial conceptualization to attention development in the 1st year and examine the role of neurophysiological and social processes on the development of early attention processes. We tested whether maternal behavior measured during 2 mother-child interaction tasks when infants (N = 388) were 5 months predicted infant medial…

  17. Field procedures for verification and adjustment of fire behavior predictions

    Treesearch

    Richard C. Rothermel; George C. Rinehart

    1983-01-01

    The problem of verifying predictions of fire behavior, primarily rate of spread, is discussed in terms of the fire situation for which predictions are made, and the type of fire where data are to be collected. Procedures for collecting data and performing analysis are presented for both readily accessible fires where data should be complete, and for inaccessible fires...

  18. A gene expression biomarker accurately predicts estrogen ...

    EPA Pesticide Factsheets

    The EPA’s vision for the Endocrine Disruptor Screening Program (EDSP) in the 21st Century (EDSP21) includes utilization of high-throughput screening (HTS) assays coupled with computational modeling to prioritize chemicals with the goal of eventually replacing current Tier 1 screening tests. The ToxCast program currently includes 18 HTS in vitro assays that evaluate the ability of chemicals to modulate estrogen receptor α (ERα), an important endocrine target. We propose microarray-based gene expression profiling as a complementary approach to predict ERα modulation and have developed computational methods to identify ERα modulators in an existing database of whole-genome microarray data. The ERα biomarker consisted of 46 ERα-regulated genes with consistent expression patterns across 7 known ER agonists and 3 known ER antagonists. The biomarker was evaluated as a predictive tool using the fold-change rank-based Running Fisher algorithm by comparison to annotated gene expression data sets from experiments in MCF-7 cells. Using 141 comparisons from chemical- and hormone-treated cells, the biomarker gave a balanced accuracy for prediction of ERα activation or suppression of 94% or 93%, respectively. The biomarker was able to correctly classify 18 out of 21 (86%) OECD ER reference chemicals including “very weak” agonists and replicated predictions based on 18 in vitro ER-associated HTS assays. For 114 chemicals present in both the HTS data and the MCF-7 c

  19. Functional analysis screening for multiple topographies of problem behavior.

    PubMed

    Bell, Marlesha C; Fahmie, Tara A

    2018-04-23

    The current study evaluated a screening procedure for multiple topographies of problem behavior in the context of an ongoing functional analysis. Experimenters analyzed the function of a topography of primary concern while collecting data on topographies of secondary concern. We used visual analysis to predict the function of secondary topographies and a subsequent functional analysis to test those predictions. Results showed that a general function was accurately predicted for five of six (83%) secondary topographies. A specific function was predicted and supported for a subset of these topographies. The experimenters discuss the implication of these results for clinicians who have limited time for functional assessment. © 2018 Society for the Experimental Analysis of Behavior.

  20. Adaptive Encoding of Outcome Prediction by Prefrontal Cortex Ensembles Supports Behavioral Flexibility.

    PubMed

    Del Arco, Alberto; Park, Junchol; Wood, Jesse; Kim, Yunbok; Moghaddam, Bita

    2017-08-30

    The prefrontal cortex (PFC) is thought to play a critical role in behavioral flexibility by monitoring action-outcome contingencies. How PFC ensembles represent shifts in behavior in response to changes in these contingencies remains unclear. We recorded single-unit activity and local field potentials in the dorsomedial PFC (dmPFC) of male rats during a set-shifting task that required them to update their behavior, among competing options, in response to changes in action-outcome contingencies. As behavior was updated, a subset of PFC ensembles encoded the current trial outcome before the outcome was presented. This novel outcome-prediction encoding was absent in a control task, in which actions were rewarded pseudorandomly, indicating that PFC neurons are not merely providing an expectancy signal. In both control and set-shifting tasks, dmPFC neurons displayed postoutcome discrimination activity, indicating that these neurons also monitor whether a behavior is successful in generating rewards. Gamma-power oscillatory activity increased before the outcome in both tasks but did not differentiate between expected outcomes, suggesting that this measure is not related to set-shifting behavior but reflects expectation of an outcome after action execution. These results demonstrate that PFC neurons support flexible rule-based action selection by predicting outcomes that follow a particular action. SIGNIFICANCE STATEMENT Tracking action-outcome contingencies and modifying behavior when those contingencies change is critical to behavioral flexibility. We find that ensembles of dorsomedial prefrontal cortex neurons differentiate between expected outcomes when action-outcome contingencies change. This predictive mode of signaling may be used to promote a new response strategy at the service of behavioral flexibility. Copyright © 2017 the authors 0270-6474/17/378363-11$15.00/0.

  1. PredSTP: a highly accurate SVM based model to predict sequential cystine stabilized peptides.

    PubMed

    Islam, S M Ashiqul; Sajed, Tanvir; Kearney, Christopher Michel; Baker, Erich J

    2015-07-05

    Numerous organisms have evolved a wide range of toxic peptides for self-defense and predation. Their effective interstitial and macro-environmental use requires energetic and structural stability. One successful group of these peptides includes a tri-disulfide domain arrangement that offers toxicity and high stability. Sequential tri-disulfide connectivity variants create highly compact disulfide folds capable of withstanding a variety of environmental stresses. Their combination of toxicity and stability make these peptides remarkably valuable for their potential as bio-insecticides, antimicrobial peptides and peptide drug candidates. However, the wide sequence variation, sources and modalities of group members impose serious limitations on our ability to rapidly identify potential members. As a result, there is a need for automated high-throughput member classification approaches that leverage their demonstrated tertiary and functional homology. We developed an SVM-based model to predict sequential tri-disulfide peptide (STP) toxins from peptide sequences. One optimized model, called PredSTP, predicted STPs from training set with sensitivity, specificity, precision, accuracy and a Matthews correlation coefficient of 94.86%, 94.11%, 84.31%, 94.30% and 0.86, respectively, using 200 fold cross validation. The same model outperforms existing prediction approaches in three independent out of sample testsets derived from PDB. PredSTP can accurately identify a wide range of cystine stabilized peptide toxins directly from sequences in a species-agnostic fashion. The ability to rapidly filter sequences for potential bioactive peptides can greatly compress the time between peptide identification and testing structural and functional properties for possible antimicrobial and insecticidal candidates. A web interface is freely available to predict STP toxins from http://crick.ecs.baylor.edu/.

  2. Ambivalence About Interpersonal Problems and Traits Predicts Cross-Situational Variability of Social Behavior.

    PubMed

    Erickson, Thane M; Newman, Michelle G; Peterson, Jessica; Scarsella, Gina

    2015-08-01

    Multiple theoretical perspectives suggest that maladjusted personality is characterized by not only distress, but also opposing or "ambivalent" self-perceptions and behavioral lability across social interactions. However, the degree to which ambivalence about oneself predicts cross-situational variability in social behavior has not been examined empirically. Using the interpersonal circumplex (IPC) as a nomological framework, the present study investigated the extent to which endorsing opposing or "ambivalent" tendencies on IPC measures predicted variability in social behavior across a range of hypothetical interpersonal scenarios (Part 1; N = 288) and naturalistic social interactions (Part 2; N = 192). Ambivalent responding for interpersonal problems and traits was associated with measures of distress, maladaptive interpersonal tendencies, and greater variability of social behavior across both hypothetical and daily social interactions, though more consistently for interpersonal problems. More conservative tests suggested that ambivalence predicted some indexes of behavioral variability even when accounting for mean levels and squared means of social behaviors, vector length, gender, and depressive symptoms. Results suggest that processes theorized as typifying personality disorder may apply more broadly to personality maladjustment occurring outside of clinical samples. © 2014 Wiley Periodicals, Inc.

  3. Using implicit attitudes of exercise importance to predict explicit exercise dependence symptoms and exercise behaviors.

    PubMed

    Forrest, Lauren N; Smith, April R; Fussner, Lauren M; Dodd, Dorian R; Clerkin, Elise M

    2016-01-01

    "Fast" (i.e., implicit) processing is relatively automatic; "slow" (i.e., explicit) processing is relatively controlled and can override automatic processing. These different processing types often produce different responses that uniquely predict behaviors. In the present study, we tested if explicit, self-reported symptoms of exercise dependence and an implicit association of exercise as important predicted exercise behaviors and change in problematic exercise attitudes. We assessed implicit attitudes of exercise importance and self-reported symptoms of exercise dependence at Time 1. Participants reported daily exercise behaviors for approximately one month, and then completed a Time 2 assessment of self-reported exercise dependence symptoms. Undergraduate males and females (Time 1, N = 93; Time 2, N = 74) tracked daily exercise behaviors for one month and completed an Implicit Association Test assessing implicit exercise importance and subscales of the Exercise Dependence Questionnaire (EDQ) assessing exercise dependence symptoms. Implicit attitudes of exercise importance and Time 1 EDQ scores predicted Time 2 EDQ scores. Further, implicit exercise importance and Time 1 EDQ scores predicted daily exercise intensity while Time 1 EDQ scores predicted the amount of days exercised. Implicit and explicit processing appear to uniquely predict exercise behaviors and attitudes. Given that different implicit and explicit processes may drive certain exercise factors (e.g., intensity and frequency, respectively), these behaviors may contribute to different aspects of exercise dependence.

  4. Early Life Stress, MAOA, and Gene-Environment Interactions Predict Behavioral Disinhibition in Children

    PubMed Central

    Enoch, Mary-Anne; Steer, Colin D.; Newman, Timothy K.; Gibson, Nerea; Goldman, David

    2009-01-01

    Several, but not all, studies have shown that the monoamine oxidase A functional promoter polymorphism (MAOA-LPR) interacts with childhood adversity to predict adolescent and adult antisocial behavior. However, it is not known whether MAOA-LPR interacts with early life (pre-birth – 3 years) stressors to influence behavior in pre-pubertal children. The Avon Longitudinal Study of Parents and Children, U.K., is a community-representative cohort study of children followed from pre-birth onwards. The impact of family adversity from pre-birth to age 3 years and stressful life events from 6 months to 7 years on behavioral disinhibition was determined in 7500 girls and boys. Behavioral disinhibition measures were: mother-reported hyperactivity and conduct disturbances (Strengths and Difficulties Questionnaire) at ages 4 and 7 years. In both sexes, exposure to family adversity and stressful life events in the first three years of life predicted behavioral disinhibition at age 4, persisting until age 7. In girls, MAOA-LPR interacted with stressful life events experienced from 6 months to 3 ½ years to influence hyperactivity at ages 4 and 7. In boys, the interaction of MAOA-LPR with stressful life events between 1 ½ and 2 ½ years predicted hyperactivity at age 7 years. The low activity MAOA-LPR variant was associated with increased hyperactivity in girls and boys exposed to high stress. In contrast, there was no MAOA-LPR interaction with family adversity. In a general population sample of pre-pubertal children, exposure to common stressors from pre-birth to 3 years predicted behavioral disinhibition, and MAOA-LPR - stressful life event interactions specifically predicted hyperactivity. PMID:19804559

  5. Predicting Adolescents' Bullying Participation from Developmental Trajectories of Social Status and Behavior.

    PubMed

    Pouwels, J Loes; Salmivalli, Christina; Saarento, Silja; van den Berg, Yvonne H M; Lansu, Tessa A M; Cillessen, Antonius H N

    2017-03-28

    The aim of this study was to determine how trajectory clusters of social status (social preference and perceived popularity) and behavior (direct aggression and prosocial behavior) from age 9 to age 14 predicted adolescents' bullying participant roles at age 16 and 17 (n = 266). Clusters were identified with multivariate growth mixture modeling (GMM). The findings showed that participants' developmental trajectories of social status and social behavior across childhood and early adolescence predicted their bullying participant role involvement in adolescence. Practical implications and suggestions for further research are discussed. © 2017 The Authors. Child Development published by Wiley Periodicals, Inc. on behalf of Society for Research in Child Development.

  6. Attachment theory and theory of planned behavior: an integrative model predicting underage drinking.

    PubMed

    Lac, Andrew; Crano, William D; Berger, Dale E; Alvaro, Eusebio M

    2013-08-01

    Research indicates that peer and maternal bonds play important but sometimes contrasting roles in the outcomes of children. Less is known about attachment bonds to these 2 reference groups in young adults. Using a sample of 351 participants (18 to 20 years of age), the research integrated two theoretical traditions: attachment theory and theory of planned behavior (TPB). The predictive contribution of both theories was examined in the context of underage adult alcohol use. Using full structural equation modeling, results substantiated the hypotheses that secure peer attachment positively predicted norms and behavioral control toward alcohol, but secure maternal attachment inversely predicted attitudes and behavioral control toward alcohol. Alcohol attitudes, norms, and behavioral control each uniquely explained alcohol intentions, which anticipated an increase in alcohol behavior 1 month later. The hypothesized processes were statistically corroborated by tests of indirect and total effects. These findings support recommendations for programs designed to curtail risky levels of underage drinking using the tenets of attachment theory and TPB. (PsycINFO Database Record (c) 2013 APA, all rights reserved).

  7. Internet use and video gaming predict problem behavior in early adolescence.

    PubMed

    Holtz, Peter; Appel, Markus

    2011-02-01

    In early adolescence, the time spent using the Internet and video games is higher than in any other present-day age group. Due to age-inappropriate web and gaming content, the impact of new media use on teenagers is a matter of public and scientific concern. Based on current theories on inappropriate media use, a study was conducted that comprised 205 adolescents aged 10-14 years (Md = 13). Individuals were identified who showed clinically relevant problem behavior according to the problem scales of the Youth Self Report (YSR). Online gaming, communicational Internet use, and playing first-person shooters were predictive of externalizing behavior problems (aggression, delinquency). Playing online role-playing games was predictive of internalizing problem behavior (including withdrawal and anxiety). Parent-child communication about Internet activities was negatively related to problem behavior. Copyright © 2010 The Association for Professionals in Services for Adolescents. Published by Elsevier Ltd. All rights reserved.

  8. PSSP-RFE: accurate prediction of protein structural class by recursive feature extraction from PSI-BLAST profile, physical-chemical property and functional annotations.

    PubMed

    Li, Liqi; Cui, Xiang; Yu, Sanjiu; Zhang, Yuan; Luo, Zhong; Yang, Hua; Zhou, Yue; Zheng, Xiaoqi

    2014-01-01

    Protein structure prediction is critical to functional annotation of the massively accumulated biological sequences, which prompts an imperative need for the development of high-throughput technologies. As a first and key step in protein structure prediction, protein structural class prediction becomes an increasingly challenging task. Amongst most homological-based approaches, the accuracies of protein structural class prediction are sufficiently high for high similarity datasets, but still far from being satisfactory for low similarity datasets, i.e., below 40% in pairwise sequence similarity. Therefore, we present a novel method for accurate and reliable protein structural class prediction for both high and low similarity datasets. This method is based on Support Vector Machine (SVM) in conjunction with integrated features from position-specific score matrix (PSSM), PROFEAT and Gene Ontology (GO). A feature selection approach, SVM-RFE, is also used to rank the integrated feature vectors through recursively removing the feature with the lowest ranking score. The definitive top features selected by SVM-RFE are input into the SVM engines to predict the structural class of a query protein. To validate our method, jackknife tests were applied to seven widely used benchmark datasets, reaching overall accuracies between 84.61% and 99.79%, which are significantly higher than those achieved by state-of-the-art tools. These results suggest that our method could serve as an accurate and cost-effective alternative to existing methods in protein structural classification, especially for low similarity datasets.

  9. Observed fearlessness and positive parenting interact to predict childhood callous-unemotional behaviors among low-income boys

    PubMed Central

    Waller, Rebecca; Shaw, Daniel S.; Hyde, Luke W.

    2016-01-01

    Background Callous-unemotional behaviors identify children at risk for severe and chronic antisocial behavior. Research is needed to establish pathways from temperament and parenting factors that give rise to callous-unemotional behaviors, including interactions of positive versus harsh parenting with child fearlessness. Methods Multi-method data, including parent reports and observations of parent and child behavior, were drawn from a prospective, longitudinal sample of low-income boys (N=310) with assessments at 18, 24, and 42 months, and at ages 10–12 years old. Results Parent-reported callous-unemotional, oppositional, and attention-deficit factors were separable at 42 months. Callous-unemotional behaviors at 42 months predicted callous-unemotional behaviors at ages 10–12, accounting for earlier oppositional and attention-deficit behaviors and self-reported child delinquency at ages 10–12. Observations of fearlessness at 24 months predicted callous-unemotional behaviors at 42 months, but only when parents exhibited low observed levels of positive parenting. The interaction of fearlessness and low positive parenting indirectly predicted callous-unemotional behaviors at 10–12 via callous-unemotional behaviors at 42 months. Conclusions Early fearlessness interacts with low positive parenting to predict early callous-unemotional behaviors, with lasting effects of this person-by-context interaction on callous-unemotional behaviors into late-childhood. PMID:27917472

  10. Predicting intentions versus predicting behaviors: domestic violence prevention from a theory of reasoned action perspective.

    PubMed

    Nabi, Robin L; Southwell, Brian; Hornik, Robert

    2002-01-01

    A central assumption of many models of human behavior is that intention to perform a behavior is highly predictive of actual behavior. This article presents evidence that belies this notion. Based on a survey of 1,250 Philadelphia adults, a clear and consistent pattern emerged suggesting that beliefs related to domestic violence correlate with intentions to act with respect to domestic violence but rarely correlate with reported actions (e.g., talking to the abused woman). Numerous methodological and substantive explanations for this finding are offered with emphasis placed on the complexity of the context in which an action to prevent a domestic violence incident occurs. We conclude by arguing that despite the small, insignificant relationships between beliefs and behaviors found, worthwhile aggregate effects on behavior might still exist, thus reaffirming the role of communication campaign efforts.

  11. Can parenting practices predict externalizing behavior problems among children with hearing impairment?

    PubMed

    Pino, María J; Castillo, Rosa A; Raya, Antonio; Herruzo, Javier

    2017-11-09

    To identify possible differences in the level of externalizing behavior problems among children with and without hearing impairment and determine whether any relationship exists between this type of problem and parenting practices. The Behavior Assessment System for Children was used to evaluate externalizing variables in a sample of 118 boys and girls divided into two matched groups: 59 with hearing disorders and 59 normal-hearing controls. Significant between-group differences were found in hyperactivity, behavioral problems, and externalizing problems, but not in aggression. Significant differences were also found in various aspects of parenting styles. A model for predicting externalizing behavior problems was constructed, achieving a predicted explained variance of 50%. Significant differences do exist between adaptation levels in children with and without hearing impairment. Parenting style also plays an important role.

  12. A Time-Accurate Upwind Unstructured Finite Volume Method for Compressible Flow with Cure of Pathological Behaviors

    NASA Technical Reports Server (NTRS)

    Loh, Ching Y.; Jorgenson, Philip C. E.

    2007-01-01

    A time-accurate, upwind, finite volume method for computing compressible flows on unstructured grids is presented. The method is second order accurate in space and time and yields high resolution in the presence of discontinuities. For efficiency, the Roe approximate Riemann solver with an entropy correction is employed. In the basic Euler/Navier-Stokes scheme, many concepts of high order upwind schemes are adopted: the surface flux integrals are carefully treated, a Cauchy-Kowalewski time-stepping scheme is used in the time-marching stage, and a multidimensional limiter is applied in the reconstruction stage. However even with these up-to-date improvements, the basic upwind scheme is still plagued by the so-called "pathological behaviors," e.g., the carbuncle phenomenon, the expansion shock, etc. A solution to these limitations is presented which uses a very simple dissipation model while still preserving second order accuracy. This scheme is referred to as the enhanced time-accurate upwind (ETAU) scheme in this paper. The unstructured grid capability renders flexibility for use in complex geometry; and the present ETAU Euler/Navier-Stokes scheme is capable of handling a broad spectrum of flow regimes from high supersonic to subsonic at very low Mach number, appropriate for both CFD (computational fluid dynamics) and CAA (computational aeroacoustics). Numerous examples are included to demonstrate the robustness of the methods.

  13. 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

  14. Hybrid multiscale modeling and prediction of cancer cell behavior.

    PubMed

    Zangooei, Mohammad Hossein; Habibi, Jafar

    2017-01-01

    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. 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. 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. 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.

  15. Deformation behaviors of three-dimensional graphene honeycombs under out-of-plane compression: Atomistic simulations and predictive modeling

    NASA Astrophysics Data System (ADS)

    Meng, Fanchao; Chen, Cheng; Hu, Dianyin; Song, Jun

    2017-12-01

    Combining atomistic simulations and continuum modeling, a comprehensive study of the out-of-plane compressive deformation behaviors of equilateral three-dimensional (3D) graphene honeycombs was performed. It was demonstrated that under out-of-plane compression, the honeycomb exhibits two critical deformation events, i.e., elastic mechanical instability (including elastic buckling and structural transformation) and inelastic structural collapse. The above events were shown to be strongly dependent on the honeycomb cell size and affected by the local atomic bonding at the cell junction. By treating the 3D graphene honeycomb as a continuum cellular solid, and accounting for the structural heterogeneity and constraint at the junction, a set of analytical models were developed to accurately predict the threshold stresses corresponding to the onset of those deformation events. The present study elucidates key structure-property relationships of 3D graphene honeycombs under out-of-plane compression, and provides a comprehensive theoretical framework to predictively analyze their deformation responses, and more generally, offers critical new knowledge for the rational bottom-up design of 3D networks of two-dimensional nanomaterials.

  16. A randomized trial to identify accurate and cost-effective fidelity measurement methods for cognitive-behavioral therapy: project FACTS study protocol.

    PubMed

    Beidas, Rinad S; Maclean, Johanna Catherine; Fishman, Jessica; Dorsey, Shannon; Schoenwald, Sonja K; Mandell, David S; Shea, Judy A; McLeod, Bryce D; French, Michael T; Hogue, Aaron; Adams, Danielle R; Lieberman, Adina; Becker-Haimes, Emily M; Marcus, Steven C

    2016-09-15

    This randomized trial will compare three methods of assessing fidelity to cognitive-behavioral therapy (CBT) for youth to identify the most accurate and cost-effective method. The three methods include self-report (i.e., therapist completes a self-report measure on the CBT interventions used in session while circumventing some of the typical barriers to self-report), chart-stimulated recall (i.e., therapist reports on the CBT interventions used in session via an interview with a trained rater, and with the chart to assist him/her) and behavioral rehearsal (i.e., therapist demonstrates the CBT interventions used in session via a role-play with a trained rater). Direct observation will be used as the gold-standard comparison for each of the three methods. This trial will recruit 135 therapists in approximately 12 community agencies in the City of Philadelphia. Therapists will be randomized to one of the three conditions. Each therapist will provide data from three unique sessions, for a total of 405 sessions. All sessions will be audio-recorded and coded using the Therapy Process Observational Coding System for Child Psychotherapy-Revised Strategies scale. This will enable comparison of each measurement approach to direct observation of therapist session behavior to determine which most accurately assesses fidelity. Cost data associated with each method will be gathered. To gather stakeholder perspectives of each measurement method, we will use purposive sampling to recruit 12 therapists from each condition (total of 36 therapists) and 12 supervisors to participate in semi-structured qualitative interviews. Results will provide needed information on how to accurately and cost-effectively measure therapist fidelity to CBT for youth, as well as important information about stakeholder perspectives with regard to each measurement method. Findings will inform fidelity measurement practices in future implementation studies as well as in clinical practice. NCT02820623

  17. Teammate Prosocial and Antisocial Behaviors Predict Task Cohesion and Burnout: The Mediating Role of Affect.

    PubMed

    Al-Yaaribi, Ali; Kavussanu, Maria

    2017-06-01

    The manner in which teammates behave toward each other when playing sport could have important achievement-related consequences. However, this issue has received very little research attention. In this study, we investigated whether (a) prosocial and antisocial teammate behaviors predict task cohesion and burnout, and (b) positive and negative affect mediates these relationships. In total, 272 (M age  = 21.86, SD = 4.36) team-sport players completed a multisection questionnaire assessing the aforementioned variables. Structural equation modeling indicated that prosocial teammate behavior positively predicted task cohesion and negatively predicted burnout, and these relationships were mediated by positive affect. The reverse pattern of relationships was observed for antisocial teammate behavior which negatively predicted task cohesion and positively predicted burnout, and these relationships were mediated by negative affect. Our findings underscore the importance of promoting prosocial and reducing antisocial behaviors in sport and highlight the role of affect in explaining the identified relationships.

  18. Accurate multimodal probabilistic prediction of conversion to Alzheimer's disease in patients with mild cognitive impairment.

    PubMed

    Young, Jonathan; Modat, Marc; Cardoso, Manuel J; Mendelson, Alex; Cash, Dave; Ourselin, Sebastien

    2013-01-01

    Accurately identifying the patients that have mild cognitive impairment (MCI) who will go on to develop Alzheimer's disease (AD) will become essential as new treatments will require identification of AD patients at earlier stages in the disease process. Most previous work in this area has centred around the same automated techniques used to diagnose AD patients from healthy controls, by coupling high dimensional brain image data or other relevant biomarker data to modern machine learning techniques. Such studies can now distinguish between AD patients and controls as accurately as an experienced clinician. Models trained on patients with AD and control subjects can also distinguish between MCI patients that will convert to AD within a given timeframe (MCI-c) and those that remain stable (MCI-s), although differences between these groups are smaller and thus, the corresponding accuracy is lower. The most common type of classifier used in these studies is the support vector machine, which gives categorical class decisions. In this paper, we introduce Gaussian process (GP) classification to the problem. This fully Bayesian method produces naturally probabilistic predictions, which we show correlate well with the actual chances of converting to AD within 3 years in a population of 96 MCI-s and 47 MCI-c subjects. Furthermore, we show that GPs can integrate multimodal data (in this study volumetric MRI, FDG-PET, cerebrospinal fluid, and APOE genotype with the classification process through the use of a mixed kernel). The GP approach aids combination of different data sources by learning parameters automatically from training data via type-II maximum likelihood, which we compare to a more conventional method based on cross validation and an SVM classifier. When the resulting probabilities from the GP are dichotomised to produce a binary classification, the results for predicting MCI conversion based on the combination of all three types of data show a balanced accuracy

  19. Using Theory of Planned Behavior to Predict Healthy Eating among Danish Adolescents

    ERIC Educational Resources Information Center

    Gronhoj, Alice; Bech-Larsen, Tino; Chan, Kara; Tsang, Lennon

    2013-01-01

    Purpose: The purpose of the study was to apply the theory of planned behavior to predict Danish adolescents' behavioral intention for healthy eating. Design/methodology/approach: A cluster sample survey of 410 students aged 11 to 16 years studying in Grade 6 to Grade 10 was conducted in Denmark. Findings: Perceived behavioral control followed by…

  20. Using implicit attitudes of exercise importance to predict explicit exercise dependence symptoms and exercise behaviors

    PubMed Central

    Forrest, Lauren N.; Smith, April R.; Fussner, Lauren M.; Dodd, Dorian R.; Clerkin, Elise M.

    2015-01-01

    Objectives ”Fast” (i.e., implicit) processing is relatively automatic; “slow” (i.e., explicit) processing is relatively controlled and can override automatic processing. These different processing types often produce different responses that uniquely predict behaviors. In the present study, we tested if explicit, self-reported symptoms of exercise dependence and an implicit association of exercise as important predicted exercise behaviors and change in problematic exercise attitudes. Design We assessed implicit attitudes of exercise importance and self-reported symptoms of exercise dependence at Time 1. Participants reported daily exercise behaviors for approximately one month, and then completed a Time 2 assessment of self-reported exercise dependence symptoms. Method Undergraduate males and females (Time 1, N = 93; Time 2, N = 74) tracked daily exercise behaviors for one month and completed an Implicit Association Test assessing implicit exercise importance and subscales of the Exercise Dependence Questionnaire (EDQ) assessing exercise dependence symptoms. Results Implicit attitudes of exercise importance and Time 1 EDQ scores predicted Time 2 EDQ scores. Further, implicit exercise importance and Time 1 EDQ scores predicted daily exercise intensity while Time 1 EDQ scores predicted the amount of days exercised. Conclusion Implicit and explicit processing appear to uniquely predict exercise behaviors and attitudes. Given that different implicit and explicit processes may drive certain exercise factors (e.g., intensity and frequency, respectively), these behaviors may contribute to different aspects of exercise dependence. PMID:26195916

  1. Observed fearlessness and positive parenting interact to predict childhood callous-unemotional behaviors among low-income boys.

    PubMed

    Waller, Rebecca; Shaw, Daniel S; Hyde, Luke W

    2017-03-01

    Callous-unemotional behaviors identify children at risk for severe and chronic antisocial behavior. Research is needed to establish pathways from temperament and parenting factors that give rise to callous-unemotional behaviors, including interactions of positive versus harsh parenting with child fearlessness. Multimethod data, including parent reports and observations of parent and child behavior, were drawn from a prospective, longitudinal sample of low-income boys (N = 310) with assessments at 18, 24, and 42 months, and at ages 10-12 years old. Parent-reported callous-unemotional, oppositional, and attention-deficit factors were separable at 42 months. Callous-unemotional behaviors at 42 months predicted callous-unemotional behaviors at ages 10-12, accounting for earlier oppositional and attention-deficit behaviors and self-reported child delinquency at ages 10-12. Observations of fearlessness at 24 months predicted callous-unemotional behaviors at 42 months, but only when parents exhibited low observed levels of positive parenting. The interaction of fearlessness and low positive parenting indirectly predicted callous-unemotional behaviors at 10-12 via callous-unemotional behaviors at 42 months. Early fearlessness interacts with low positive parenting to predict early callous-unemotional behaviors, with lasting effects of this person-by-context interaction on callous-unemotional behaviors into late childhood. © 2016 Association for Child and Adolescent Mental Health.

  2. Observed Parenting Behavior with Teens: Measurement Invariance and Predictive Validity Across Race

    PubMed Central

    Skinner, Martie L.; MacKenzie, Elizabeth P.; Haggerty, Kevin P.; Hill, Karl G.; Roberson, Kendra C.

    2011-01-01

    Previous reports supporting measurement equality between European American and African American families have often focused on self-reported risk factors or observed parent behavior with young children. This study examines equality of measurement of observer ratings of parenting behavior with adolescents during structured tasks; mean levels of observed parenting; and predictive validity of teen self-reports of antisocial behaviors and beliefs using a sample of 163 African American and 168 European American families. Multiple-group confirmatory factor analyses supported measurement invariance across ethnic groups for 4 measures of observed parenting behavior: prosocial rewards, psychological costs, antisocial rewards, and problem solving. Some mean-level differences were found: African American parents exhibited lower levels of prosocial rewards, higher levels of psychological costs, and lower problem solving when compared to European Americans. No significant mean difference was found in rewards for antisocial behavior. Multigroup structural equation models suggested comparable relationships across race (predictive validity) between parenting constructs and youth antisocial constructs (i.e., drug initiation, positive drug attitudes, antisocial attitudes, problem behaviors) in all but one of the tested relationships. This study adds to existing evidence that family-based interventions targeting parenting behaviors can be generalized to African American families. PMID:21787057

  3. Interplay between Marital Attributions and Conflict Behavior in Predicting Depressive Symptoms

    PubMed Central

    Ellison, Jenna K.; Kouros, Chrystyna D.; Papp, Lauren M.; Cummings, E. Mark

    2015-01-01

    Marital attributions--i.e., causal inferences and explanations spouses make about their partners’ behavior--have been implicated as predictors of relationship functioning. Extending previous work, we examined marital attributions as a moderator of the link between marital conflict and depressive symptoms one year later. Participants were 284 couples who reported on marital attributions and depressive symptoms. Couples also engaged in a videotaped marital conflict interaction, which was later coded for specific conflict behaviors. The results showed that husbands’ and wives’ marital attributions about their partner moderated relations between marital conflict behavior and later depressive symptoms, controlling for global marital sentiments. For husbands, positive behavior and affect during marital conflict predicted a decrease in depressive symptoms, but only for husbands’ who made low levels of responsibility and causal attributions about their wives. Wives’ causal attributions about their partner also moderated relations between positive behavior and affect during marital conflict and husbands’ later depressive symptoms. Reflecting an unexpected finding, negative behavior and affect during marital conflict predicted increases in wives’ depressive symptoms, but only for wives who made low levels of responsibility attributions about their partner. The findings suggest that, for husbands, low levels of negative marital attributions for spouses may be protective, strengthening the positive effect of constructive conflict behaviors for their mental health, whereas for wives low levels of responsibility attributions about their spouse may be a risk factor, exacerbating the negative effect of negative marital conflict behaviors on their later depressive symptoms. PMID:26751758

  4. Risk prediction model: Statistical and artificial neural network approach

    NASA Astrophysics Data System (ADS)

    Paiman, Nuur Azreen; Hariri, Azian; Masood, Ibrahim

    2017-04-01

    Prediction models are increasingly gaining popularity and had been used in numerous areas of studies to complement and fulfilled clinical reasoning and decision making nowadays. The adoption of such models assist physician's decision making, individual's behavior, and consequently improve individual outcomes and the cost-effectiveness of care. The objective of this paper is to reviewed articles related to risk prediction model in order to understand the suitable approach, development and the validation process of risk prediction model. A qualitative review of the aims, methods and significant main outcomes of the nineteen published articles that developed risk prediction models from numerous fields were done. This paper also reviewed on how researchers develop and validate the risk prediction models based on statistical and artificial neural network approach. From the review done, some methodological recommendation in developing and validating the prediction model were highlighted. According to studies that had been done, artificial neural network approached in developing the prediction model were more accurate compared to statistical approach. However currently, only limited published literature discussed on which approach is more accurate for risk prediction model development.

  5. Spouses’ Attachment Pairings Predict Neuroendocrine, Behavioral, and Psychological Responses to Marital Conflict

    PubMed Central

    Beck, Lindsey A.; Pietromonaco, Paula R.; DeBuse, Casey J.; Powers, Sally I.; Sayer, Aline G.

    2014-01-01

    This research investigated how spouses’ attachment styles jointly contributed to their stress responses. Newlywed couples discussed relationship conflicts. Salivary cortisol indexed physiological stress; observer-rated behaviors indexed behavioral stress; self-reported distress indexed psychological stress. Multilevel modeling tested predictions that couples including one anxious and one avoidant partner or two anxious partners would show distinctive stress responses. As predicted, couples with anxious wives and avoidant husbands showed physiological reactivity in anticipation of conflict: Both spouses showed sharp increases in cortisol, followed by rapid declines. These couples also showed distinctive behaviors during conflict: Anxious wives had difficulty recognizing avoidant husbands’ distress, and avoidant husbands had difficulty approaching anxious wives for support. Contrary to predictions, couples including two anxious partners did not show distinctive stress responses. Findings suggest that the fit between partners’ attachment styles can improve understanding of relationships by specifying conditions under which partners’ attachment characteristics jointly influence individual and relationship outcomes. PMID:23773048

  6. Longitudinal Prediction of Disruptive Behavior Disorders in Adolescent Males from Multiple Risk Domains

    PubMed Central

    Trentacosta, Christopher J.; Hyde, Luke W.; Goodlett, Benjamin D.; Shaw, Daniel S.

    2012-01-01

    The disruptive behavior disorders are among the most prevalent youth psychiatric disorders, and they predict numerous problematic outcomes in adulthood. This study examined multiple domains of risk during early childhood and early adolescence as longitudinal predictors of disruptive behavior disorder diagnoses among adolescent males. Early adolescent risks in the domains of sociodemographic factors, the caregiving context, and youth attributes were examined as mediators of associations between early childhood risks and disruptive behavior disorder diagnoses. Participants were 309 males from a longitudinal study of low-income mothers and their sons. Caregiving and youth risk during early adolescence each predicted the likelihood of receiving a disruptive behavior disorder diagnosis. Furthermore, sociodemographic and caregiving risk during early childhood were indirectly associated with disruptive behavior disorder diagnoses via their association with early adolescent risk. The findings suggest that preventive interventions targeting risk across domains may reduce the prevalence of disruptive behavior disorders. PMID:23239427

  7. Maternal behavior predicts infant neurophysiological and behavioral attention processes in the first year.

    PubMed

    Swingler, Margaret M; Perry, Nicole B; Calkins, Susan D; Bell, Martha Ann

    2017-01-01

    We apply a biopsychosocial conceptualization to attention development in the 1st year and examine the role of neurophysiological and social processes on the development of early attention processes. We tested whether maternal behavior measured during 2 mother-child interaction tasks when infants (N = 388) were 5 months predicted infant medial frontal (F3/F4) EEG power and observed attention behavior during an attention task at 10 months. After controlling for infant attention behavior and EEG power in the same task measured at an earlier 5-month time point, results indicated a significant direct and positive association from 5-month maternal positive affect to infant attention behavior at 10 months. However, maternal positive affect was not related to medial frontal EEG power. In contrast, 5-month maternal intrusive behavior was associated with infants' task-related EEG power change at the left frontal location, F3, at 10 months of age. The test of indirect effects from 5-month maternal intrusiveness to 10-month infant attention behavior via infants' EEG power change at F3 was significant. These findings suggest that the development of neural networks serving attention processes may be 1 mechanism through which early maternal behavior is related to infant attention development in the 1st year and that intrusive maternal behavior may have a particularly disruptive effect on this process. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

  8. A behavioral economic measure of demand for alcohol predicts brief intervention outcomes.

    PubMed

    MacKillop, James; Murphy, James G

    2007-07-10

    Considerable basic and clinical research supports a behavioral economic conceptualization of alcohol and drug dependence. One behavioral economic approach to assess motivation for a drug is the use of demand curves, or quantitative representations of drug consumption and drug-reinforced responding across a range of prices. This study used a hypothetical alcohol purchase task to generate demand curves, and examined whether the resulting demand curve parameters predicted drinking outcomes following a brief intervention. Participants were 51 college student drinkers (67% female; 94% Caucasian; drinks/week: M=24.57, S.D.=8.77) who completed a brief alcohol intervention. Consistent with predictions, a number of demand curve indices significantly predicted post-intervention alcohol use and frequency of heavy drinking episodes, even after controlling for baseline drinking and other pertinent covariates. Most prominently, O(max) (i.e., maximum alcohol expenditure) and breakpoint (i.e., sensitivity of consumption to increasing price) predicted greater drinking at 6-month post-intervention follow-up. These results indicate that a behavioral economic measure of alcohol demand may have utility in characterizing the malleability of alcohol consumption. Moreover, these results support the utility of translating experimental assays of reinforcement into clinical research.

  9. A novel fibrosis index comprising a non-cholesterol sterol accurately predicts HCV-related liver cirrhosis.

    PubMed

    Ydreborg, Magdalena; Lisovskaja, Vera; Lagging, Martin; Brehm Christensen, Peer; Langeland, Nina; Buhl, Mads Rauning; Pedersen, Court; Mørch, Kristine; Wejstål, Rune; Norkrans, Gunnar; Lindh, Magnus; Färkkilä, Martti; Westin, Johan

    2014-01-01

    Diagnosis of liver cirrhosis is essential in the management of chronic hepatitis C virus (HCV) infection. Liver biopsy is invasive and thus entails a risk of complications as well as a potential risk of sampling error. Therefore, non-invasive diagnostic tools are preferential. The aim of the present study was to create a model for accurate prediction of liver cirrhosis based on patient characteristics and biomarkers of liver fibrosis, including a panel of non-cholesterol sterols reflecting cholesterol synthesis and absorption and secretion. We evaluated variables with potential predictive significance for liver fibrosis in 278 patients originally included in a multicenter phase III treatment trial for chronic HCV infection. A stepwise multivariate logistic model selection was performed with liver cirrhosis, defined as Ishak fibrosis stage 5-6, as the outcome variable. A new index, referred to as Nordic Liver Index (NoLI) in the paper, was based on the model: Log-odds (predicting cirrhosis) = -12.17+ (age × 0.11) + (BMI (kg/m(2)) × 0.23) + (D7-lathosterol (μg/100 mg cholesterol)×(-0.013)) + (Platelet count (x10(9)/L) × (-0.018)) + (Prothrombin-INR × 3.69). The area under the ROC curve (AUROC) for prediction of cirrhosis was 0.91 (95% CI 0.86-0.96). The index was validated in a separate cohort of 83 patients and the AUROC for this cohort was similar (0.90; 95% CI: 0.82-0.98). In conclusion, the new index may complement other methods in diagnosing cirrhosis in patients with chronic HCV infection.

  10. Towards more accurate vegetation mortality predictions

    DOE PAGES

    Sevanto, Sanna Annika; Xu, Chonggang

    2016-09-26

    Predicting the fate of vegetation under changing climate is one of the major challenges of the climate modeling community. Here, terrestrial vegetation dominates the carbon and water cycles over land areas, and dramatic changes in vegetation cover resulting from stressful environmental conditions such as drought feed directly back to local and regional climate, potentially leading to a vicious cycle where vegetation recovery after a disturbance is delayed or impossible.

  11. On their best behavior: how animal behavior can help determine the combined effects of species interactions and climate change.

    PubMed

    Harmon, Jason P; Barton, Brandon T

    2013-09-01

    The increasingly appreciated link between climate change and species interactions has the potential to help us understand and predict how organisms respond to a changing environment. As this connection grows, it becomes even more important to appreciate the mechanisms that create and control the combined effect of these factors. However, we believe one such important set of mechanisms comes from species' behavior and the subsequent trait-mediated interactions, as opposed to the more often studied density-mediated effects. Behavioral mechanisms are already well appreciated for mitigating the separate effects of the environment and species interactions. Thus, they could be at the forefront for understanding the combined effects. In this review, we (1) show some of the known behaviors that influence the individual and combined effects of climate change and species interactions; (2) conceptualize general ways behavior may mediate these combined effects; and (3) illustrate the potential importance of including behavior in our current tools for predicting climate change effects. In doing so, we hope to promote more research on behavior and other mechanistic factors that may increase our ability to accurately predict climate change effects. © 2013 New York Academy of Sciences.

  12. Uncertainty in Wildfire Behavior

    NASA Astrophysics Data System (ADS)

    Finney, M.; Cohen, J. D.

    2013-12-01

    The challenge of predicting or modeling fire behavior is well recognized by scientists and managers who attempt predictions of fire spread rate or growth. At the scale of the spreading fire, the uncertainty in winds, moisture, fuel structure, and fire location make accurate predictions difficult, and the non-linear response of fire spread to these conditions means that average behavior is poorly represented by average environmental parameters. Even more difficult are estimations of threshold behaviors (e.g. spread/no-spread, crown fire initiation, ember generation and spotting) because the fire responds as a step-function to small changes in one or more environmental variables, translating to dynamical feedbacks and unpredictability. Recent research shows that ignition of fuel particles, itself a threshold phenomenon, depends on flame contact which is absolutely not steady or uniform. Recent studies of flame structure in both spreading and stationary fires reveals that much of the non-steadiness of the flames as they contact fuel particles results from buoyant instabilities that produce quasi-periodic flame structures. With fuel particle ignition produced by time-varying heating and short-range flame contact, future improvements in fire behavior modeling will likely require statistical approaches to deal with the uncertainty at all scales, including the level of heat transfer, the fuel arrangement, and weather.

  13. The use of artificial neural networks to predict the muscle behavior

    NASA Astrophysics Data System (ADS)

    Kutilek, Patrik; Viteckova, Slavka; Svoboda, Zdenĕk; Smrcka, Pavel

    2013-09-01

    The aim of this article is to introduce methods of prediction of muscle behavior of the lower extremities based on artificial neural networks, which can be used for medical purposes. Our work focuses on predicting muscletendon forces and moments during human gait with the use of angle-time diagram. A group of healthy children and children with cerebral palsy were measured using a Vicon MoCap system. The kinematic data was recorded and the OpenSim software system was used to identify the joint angles, muscle-tendon forces and joint muscle moment, which are presented graphically with time diagrams. The musculus gastrocnemius medialis that is often studied in the context of cerebral palsy have been chosen to study the method of prediction. The diagrams of mean muscle-tendon force and mean moment are plotted and the data about the force-time and moment-time dependencies are used for training neural networks. The new way of prediction of muscle-tendon forces and moments based on neural networks was tested. Neural networks predicted the muscle forces and moments of healthy children and children with cerebral palsy. The designed method of prediction by neural networks could help to identify the difference between muscle behavior of healthy subjects and diseased subjects.

  14. INFLUENCE OF MATERIAL MODELS ON PREDICTING THE FIRE BEHAVIOR OF STEEL COLUMNS.

    PubMed

    Choe, Lisa; Zhang, Chao; Luecke, William E; Gross, John L; Varma, Amit H

    2017-01-01

    Finite-element (FE) analysis was used to compare the high-temperature responses of steel columns with two different stress-strain models: the Eurocode 3 model and the model proposed by National Institute of Standards and Technology (NIST). The comparisons were made in three different phases. The first phase compared the critical buckling temperatures predicted using forty seven column data from five different laboratories. The slenderness ratios varied from 34 to 137, and the applied axial load was 20-60 % of the room-temperature capacity. The results showed that the NIST model predicted the buckling temperature as or more accurately than the Eurocode 3 model for four of the five data sets. In the second phase, thirty unique FE models were developed to analyze the W8×35 and W14×53 column specimens with the slenderness ratio about 70. The column specimens were tested under steady-heating conditions with a target temperature in the range of 300-600 °C. The models were developed by combining the material model, temperature distributions in the specimens, and numerical scheme for non-linear analyses. Overall, the models with the NIST material properties and the measured temperature variations showed the results comparable to the test data. The deviations in the results from two different numerical approaches (modified Newton Raphson vs. arc-length) were negligible. The Eurocode 3 model made conservative predictions on the behavior of the column specimens since its retained elastic moduli are smaller than those of the NIST model at elevated temperatures. In the third phase, the column curves calibrated using the NIST model was compared with those prescribed in the ANSI/AISC-360 Appendix 4. The calibrated curve significantly deviated from the current design equation with increasing temperature, especially for the slenderness ratio from 50 to 100.

  15. Behavioral activation and inhibition system's role in predicting addictive behaviors of patients with bipolar disorder of Roozbeh Psychiatric Hospital

    PubMed Central

    Abbasi, Moslem; Sadeghi, Hasan; Pirani, Zabih; Vatandoust, Leyla

    2016-01-01

    Background: Nowadays, prevalence of addictive behaviors among bipolar patients is considered to be a serious health threat by the World Health Organization. The aim of this study is to investigate the role of behavioral activation and inhibition systems in predicting addictive behaviors of male patients with bipolar disorder at the Roozbeh Psychiatric Hospital. Materials and Methods: The research method used in this study is correlation. The study population consisted of 80 male patients with bipolar disorder referring to the psychiatrics clinics of Tehran city in 2014 who were referred to the Roozbeh Psychiatric Hospital. To collect data, the international and comprehensive inventory diagnostic interview, behavioral activation and inhibition systems scale, and addictive behaviors scale were used. Results: The results showed that there is a positive and significant relationship between behavioral activation systems and addictive behaviors (addictive eating, alcohol addiction, television addiction, cigarette addiction, mobile addiction, etc.). In addition, correlation between behavioral inhibition systems and addictive behaviors (addictive eating, alcohol addiction, TV addiction, cigarette addiction, mobile addiction) is significantly negative. Finally, regression analysis showed that behavioral activation and inhibition systems could significantly predict 47% of addictive behaviors in patients with bipolar disorder. Conclusions: It can be said that the patients with bipolar disorder use substance and addictive behaviors for enjoyment and as pleasure stimulants; they also use substances to suppress unpleasant stimulants and negative emotions. These results indicate that behavioral activation and inhibition systems have an important role in the incidence and exacerbation of addictive behaviors. Therefore, preventive interventions in this direction seem to be necessary. PMID:28194203

  16. A high order accurate finite element algorithm for high Reynolds number flow prediction

    NASA Technical Reports Server (NTRS)

    Baker, A. J.

    1978-01-01

    A Galerkin-weighted residuals formulation is employed to establish an implicit finite element solution algorithm for generally nonlinear initial-boundary value problems. Solution accuracy, and convergence rate with discretization refinement, are quantized in several error norms, by a systematic study of numerical solutions to several nonlinear parabolic and a hyperbolic partial differential equation characteristic of the equations governing fluid flows. Solutions are generated using selective linear, quadratic and cubic basis functions. Richardson extrapolation is employed to generate a higher-order accurate solution to facilitate isolation of truncation error in all norms. Extension of the mathematical theory underlying accuracy and convergence concepts for linear elliptic equations is predicted for equations characteristic of laminar and turbulent fluid flows at nonmodest Reynolds number. The nondiagonal initial-value matrix structure introduced by the finite element theory is determined intrinsic to improved solution accuracy and convergence. A factored Jacobian iteration algorithm is derived and evaluated to yield a consequential reduction in both computer storage and execution CPU requirements while retaining solution accuracy.

  17. Reliable and accurate point-based prediction of cumulative infiltration using soil readily available characteristics: A comparison between GMDH, ANN, and MLR

    NASA Astrophysics Data System (ADS)

    Rahmati, Mehdi

    2017-08-01

    Developing accurate and reliable pedo-transfer functions (PTFs) to predict soil non-readily available characteristics is one of the most concerned topic in soil science and selecting more appropriate predictors is a crucial factor in PTFs' development. Group method of data handling (GMDH), which finds an approximate relationship between a set of input and output variables, not only provide an explicit procedure to select the most essential PTF input variables, but also results in more accurate and reliable estimates than other mostly applied methodologies. Therefore, the current research was aimed to apply GMDH in comparison with multivariate linear regression (MLR) and artificial neural network (ANN) to develop several PTFs to predict soil cumulative infiltration point-basely at specific time intervals (0.5-45 min) using soil readily available characteristics (RACs). In this regard, soil infiltration curves as well as several soil RACs including soil primary particles (clay (CC), silt (Si), and sand (Sa)), saturated hydraulic conductivity (Ks), bulk (Db) and particle (Dp) densities, organic carbon (OC), wet-aggregate stability (WAS), electrical conductivity (EC), and soil antecedent (θi) and field saturated (θfs) water contents were measured at 134 different points in Lighvan watershed, northwest of Iran. Then, applying GMDH, MLR, and ANN methodologies, several PTFs have been developed to predict cumulative infiltrations using two sets of selected soil RACs including and excluding Ks. According to the test data, results showed that developed PTFs by GMDH and MLR procedures using all soil RACs including Ks resulted in more accurate (with E values of 0.673-0.963) and reliable (with CV values lower than 11 percent) predictions of cumulative infiltrations at different specific time steps. In contrast, ANN procedure had lower accuracy (with E values of 0.356-0.890) and reliability (with CV values up to 50 percent) compared to GMDH and MLR. The results also revealed

  18. Predicting Self-Management Behaviors in Familial Hypercholesterolemia Using an Integrated Theoretical Model: the Impact of Beliefs About Illnesses and Beliefs About Behaviors.

    PubMed

    Hagger, Martin S; Hardcastle, Sarah J; Hingley, Catherine; Strickland, Ella; Pang, Jing; Watts, Gerald F

    2016-06-01

    Patients with familial hypercholesterolemia (FH) are at markedly increased risk of coronary artery disease. Regular participation in three self-management behaviors, physical activity, healthy eating, and adherence to medication, can significantly reduce this risk in FH patients. We aimed to predict intentions to engage in these self-management behaviors in FH patients using a multi-theory, integrated model that makes the distinction between beliefs about illness and beliefs about self-management behaviors. Using a cross-sectional, correlational design, patients (N = 110) diagnosed with FH from a clinic in Perth, Western Australia, self-completed a questionnaire that measured constructs from three health behavior theories: the common sense model of illness representations (serious consequences, timeline, personal control, treatment control, illness coherence, emotional representations); theory of planned behavior (attitudes, subjective norms, perceived behavioral control); and social cognitive theory (self-efficacy). Structural equation models for each self-management behavior revealed consistent and statistically significant effects of attitudes on intentions across the three behaviors. Subjective norms predicted intentions for health eating only and self-efficacy predicted intentions for physical activity only. There were no effects for the perceived behavioral control and common sense model constructs in any model. Attitudes feature prominently in determining intentions to engage in self-management behaviors in FH patients. The prominence of these attitudinal beliefs about self-management behaviors, as opposed to illness beliefs, suggest that addressing these beliefs may be a priority in the management of FH.

  19. Perceived Physician-informed Weight Status Predicts Accurate Weight Self-Perception and Weight Self-Regulation in Low-income, African American Women.

    PubMed

    Harris, Charlie L; Strayhorn, Gregory; Moore, Sandra; Goldman, Brian; Martin, Michelle Y

    2016-01-01

    Obese African American women under-appraise their body mass index (BMI) classification and report fewer weight loss attempts than women who accurately appraise their weight status. This cross-sectional study examined whether physician-informed weight status could predict weight self-perception and weight self-regulation strategies in obese women. A convenience sample of 118 low-income women completed a survey assessing demographic characteristics, comorbidities, weight self-perception, and weight self-regulation strategies. BMI was calculated during nurse triage. Binary logistic regression models were performed to test hypotheses. The odds of obese accurate appraisers having been informed about their weight status were six times greater than those of under-appraisers. The odds of those using an "approach" self-regulation strategy having been physician-informed were four times greater compared with those using an "avoidance" strategy. Physicians are uniquely positioned to influence accurate weight self-perception and adaptive weight self-regulation strategies in underserved women, reducing their risk for obesity-related morbidity.

  20. Applying Social Psychological Models to Predicting HIV-Related Sexual Risk Behaviors Among African Americans

    PubMed Central

    Cochran, Susan D.; Mays, Vickie M.

    2011-01-01

    Existing models of attitude-behavior relationships, including the Health Belief Model, the Theory of Reasoned Action, and the Self-Efficacy Theory, are increasingly being used by psychologists to predict human immunodeficiency virus (HIV)-related risk behaviors. The authors briefly highlight some of the difficulties that might arise in applying these models to predicting the risk behaviors of African Americans. These social psychological models tend to emphasize the importance of individualistic, direct control of behavioral choices and deemphasize factors, such as racism and poverty, particularly relevant to that segment of the African American population most at risk for HIV infection. Applications of these models without taking into account the unique issues associated with behavioral choices within the African American community may fail to capture the relevant determinants of risk behaviors. PMID:23529205

  1. Predicting Study Abroad Intentions Based on the Theory of Planned Behavior

    ERIC Educational Resources Information Center

    Schnusenberg, Oliver; de Jong, Pieter; Goel, Lakshmi

    2012-01-01

    The emphasis on study abroad programs is growing in the academic context as U.S. based universities seek to incorporate a global perspective in education. Using a model that has underpinnings in the theory of planned behavior (TPB), we predict students' intention to participate in short-term study abroad program. We use TPB to identify behavioral,…

  2. A Theory of Planned Behavior Research Model for Predicting the Sleep Intentions and Behaviors of Undergraduate College Students

    ERIC Educational Resources Information Center

    Knowlden, Adam P.; Sharma, Manoj; Bernard, Amy L.

    2012-01-01

    The purpose of this study was to operationalize the constructs of the Theory of Planned Behavior (TPB) to predict the sleep intentions and behaviors of undergraduate college students attending a Midwestern University. Data collection spanned three phases. The first phase included a semi-structured qualitative interview (n = 11), readability by…

  3. Aggression, emotional self-regulation, attentional bias, and cognitive inhibition predict risky driving behavior.

    PubMed

    Sani, Susan Raouf Hadadi; Tabibi, Zahra; Fadardi, Javad Salehi; Stavrinos, Despina

    2017-12-01

    The present study explored whether aggression, emotional regulation, cognitive inhibition, and attentional bias towards emotional stimuli were related to risky driving behavior (driving errors, and driving violations). A total of 117 applicants for taxi driver positions (89% male, M age=36.59years, SD=9.39, age range 24-62years) participated in the study. Measures included the Ahwaz Aggression Inventory, the Difficulties in emotion regulation Questionnaire, the emotional Stroop task, the Go/No-go task, and the Driving Behavior Questionnaire. Correlation and regression analyses showed that aggression and emotional regulation predicted risky driving behavior. Difficulties in emotion regulation, the obstinacy and revengeful component of aggression, attentional bias toward emotional stimuli, and cognitive inhibition predicted driving errors. Aggression was the only significant predictive factor for driving violations. In conclusion, aggression and difficulties in regulating emotions may exacerbate risky driving behaviors. Deficits in cognitive inhibition and attentional bias toward negative emotional stimuli can increase driving errors. Predisposition to aggression has strong effect on making one vulnerable to violation of traffic rules and crashes. Copyright © 2017 Elsevier Ltd. All rights reserved.

  4. Interplay between marital attributions and conflict behavior in predicting depressive symptoms.

    PubMed

    Ellison, Jenna K; Kouros, Chrystyna D; Papp, Lauren M; Cummings, E Mark

    2016-03-01

    Marital attributions-that is, causal inferences and explanations spouses make about their partners' behavior-have been implicated as predictors of relationship functioning. Extending previous work, we examined marital attributions as a moderator of the link between marital conflict and depressive symptoms 1 year later. Participants were 284 couples who reported on marital attributions and depressive symptoms. Couples also engaged in a videotaped marital conflict interaction, which was later coded for specific conflict behaviors. The results showed that husbands' and wives' marital attributions about their partner moderated relations between marital conflict behavior and later depressive symptoms, controlling for global marital sentiments. For husbands, positive behavior and affect during marital conflict predicted a decrease in depressive symptoms, but only for husbands' who made low levels of responsibility and causal attributions about their wives. Wives' causal attributions about their partner also moderated relations between positive behavior and affect during marital conflict and husbands' later depressive symptoms. Reflecting an unexpected finding, negative behavior and affect during marital conflict predicted increases in wives' depressive symptoms, but only for wives who made low levels of responsibility attributions about their partner. The findings suggest that, for husbands, low levels of negative marital attributions for spouses may be protective, strengthening the positive effect of constructive conflict behaviors for their mental health, whereas for wives low levels of responsibility attributions about their spouse may be a risk factor, exacerbating the negative effect of negative marital conflict behaviors on their later depressive symptoms. (c) 2016 APA, all rights reserved).

  5. Predicted osteotomy planes are accurate when using patient-specific instrumentation for total knee arthroplasty in cadavers: a descriptive analysis.

    PubMed

    Kievit, A J; Dobbe, J G G; Streekstra, G J; Blankevoort, L; Schafroth, M U

    2018-06-01

    Malalignment of implants is a major source of failure during total knee arthroplasty. To achieve more accurate 3D planning and execution of the osteotomy cuts during surgery, the Signature (Biomet, Warsaw) patient-specific instrumentation (PSI) was used to produce pin guides for the positioning of the osteotomy blocks by means of computer-aided manufacture based on CT scan images. The research question of this study is: what is the transfer accuracy of osteotomy planes predicted by the Signature PSI system for preoperative 3D planning and intraoperative block-guided pin placement to perform total knee arthroplasty procedures? The transfer accuracy achieved by using the Signature PSI system was evaluated by comparing the osteotomy planes predicted preoperatively with the osteotomy planes seen intraoperatively in human cadaveric legs. Outcomes were measured in terms of translational and rotational errors (varus, valgus, flexion, extension and axial rotation) for both tibia and femur osteotomies. Average translational errors between the osteotomy planes predicted using the Signature system and the actual osteotomy planes achieved was 0.8 mm (± 0.5 mm) for the tibia and 0.7 mm (± 4.0 mm) for the femur. Average rotational errors in relation to predicted and achieved osteotomy planes were 0.1° (± 1.2°) of varus and 0.4° (± 1.7°) of anterior slope (extension) for the tibia, and 2.8° (± 2.0°) of varus and 0.9° (± 2.7°) of flexion and 1.4° (± 2.2°) of external rotation for the femur. The similarity between osteotomy planes predicted using the Signature system and osteotomy planes actually achieved was excellent for the tibia although some discrepancies were seen for the femur. The use of 3D system techniques in TKA surgery can provide accurate intraoperative guidance, especially for patients with deformed bone, tailored to individual patients and ensure better placement of the implant.

  6. Validation of behave fire behavior predictions in oak savannas

    USGS Publications Warehouse

    Grabner, Keith W.; Dwyer, John; Cutter, Bruce E.

    1997-01-01

    Prescribed fire is a valuable tool in the restoration and management of oak savannas. BEHAVE, a fire behavior prediction system developed by the United States Forest Service, can be a useful tool when managing oak savannas with prescribed fire. BEHAVE predictions of fire rate-of-spread and flame length were validated using four standardized fuel models: Fuel Model 1 (short grass), Fuel Model 2 (timber and grass), Fuel Model 3 (tall grass), and Fuel Model 9 (hardwood litter). Also, a customized oak savanna fuel model (COSFM) was created and validated. Results indicate that standardized fuel model 2 and the COSFM reliably estimate mean rate-of-spread (MROS). The COSFM did not appreciably reduce MROS variation when compared to fuel model 2. Fuel models 1, 3, and 9 did not reliably predict MROS. Neither the standardized fuel models nor the COSFM adequately predicted flame lengths. We concluded that standardized fuel model 2 should be used with BEHAVE when predicting fire rates-of-spread in established oak savannas.

  7. Infant Sleep Predicts Attention Regulation and Behavior Problems at 3-4 Years of Age.

    PubMed

    Sadeh, Avi; De Marcas, Gali; Guri, Yael; Berger, Andrea; Tikotzky, Liat; Bar-Haim, Yair

    2015-01-01

    This longitudinal study assessed the role of early sleep patterns in predicting attention regulation and behavior problems. Sleep of 43 infants was assessed using actigraphy at 12 months of age and then reassessed when the children were 3-4 years old. During this follow-up, their attention regulation and behavior problems were also assessed using a computerized test and parental reports. Lower quality of sleep in infancy significantly predicted compromised attention regulation and behavior problems. These findings underscore the need to identify and treat early sleep problems.

  8. Respiratory Sinus Arrhythmia Activity Predicts Internalizing and Externalizing Behaviors in Non-referred Boys

    PubMed Central

    Zhang, Wei; Fagan, Shawn E.; Gao, Yu

    2017-01-01

    Atypical respiratory sinus arrhythmia (RSA), a biomarker of emotion dysregulation, is associated with both externalizing and internalizing behaviors. In addition, social adversity and gender may moderate this association. In this study, we investigated if RSA (both resting RSA and RSA reactivity in an emotion regulation task) predicts externalizing and/or internalizing behaviors and the extent to which social adversity moderates this relationship. Two hundred and fifty-three children (at Time 1, mean age = 9.05, SD = 0.60, 48% boys) and their caregivers from the community participated in this study. Resting RSA and RSA reactivity were assessed, and caregivers reported children’s externalizing and internalizing behaviors at both Time 1 and Time 2 (1 year later). We found that lower resting RSA (but not RSA reactivity) at Time 1 was associated with increased externalizing and internalizing behaviors at Time 2 in boys, even after controlling for the effects of Time 1 behavioral problems and Time 2 age. Moreover, there was a significant interaction effect between Time 1 resting RSA and social adversity such that lower resting RSA predicted higher externalizing and internalizing behaviors in boys only under conditions of high social adversity. Follow-up analyses revealed that these predictive effects were stronger for externalizing behavior than for internalizing behavior. No significant effects were found for girls. Our findings provide further evidence that low resting RSA may be a transdiagnostic biomarker of emotion dysregulation and a predisposing risk factor for both types of behavior problems, in particular for boys who grow up in adverse environments. We conclude that biosocial interaction effects and gender differences should be considered when examining the etiological mechanisms of child psychopathology. PMID:28955262

  9. Prefrontal Brain Activity Predicts Temporally Extended Decision-Making Behavior

    ERIC Educational Resources Information Center

    Yarkoni, Tal; Braver, Todd S.; Gray, Jeremy R.; Green, Leonard

    2005-01-01

    Although functional neuroimaging studies of human decision-making processes are increasingly common, most of the research in this area has relied on passive tasks that generate little individual variability. Relatively little attention has been paid to the ability of brain activity to predict overt behavior. Using functional magnetic resonance…

  10. Predicting Sexual Risk Behaviors among Adolescent and Young Women Using a Prospective Diary Method

    PubMed Central

    Morrison-Beedy, Dianne; Carey, Michael P.; Feng, Changyong; Tu, Xin M.

    2008-01-01

    We describe the sexual risk behaviors, psychological distress, and substance use of 102 late adolescent girls and identify predictors of protected and unprotected vaginal sex. Participants completed questionnaires assessing hypothesized predictors and then daily behavioral diaries for 12 weeks. Protected intercourse was predicted by baseline sexual behavior, greater knowledge, positive condom attitudes, lower perceived condom-use difficulty, greater condom-use intentions, more drinking days, less binge drinking, less Ecstasy use, and lower psychological distress. Unprotected intercourse was predicted by baseline sexual behavior, binge drinking, Ecstasy and opiate use, fewer drinking days, and fewer daily drinks. These findings suggest that psychological distress, substance use, and sexual risk behavior are interconnected and should be considered collectively in interventions for adolescent females. PMID:18231976

  11. Brain indices of disagreement with one’s social values predict EU referendum voting behavior

    PubMed Central

    Sirota, Miroslav; Materassi, Maurizio; Zaninotto, Francesca; Terry, Philip

    2017-01-01

    Abstract Pre-electoral surveys typically attempt, and sometimes fail, to predict voting behavior on the basis of explicit measures of agreement or disagreement with a candidate or political position. Here, we assessed whether a specific brain signature of disagreement with one’s social values, the event-related potential component N400, could be predictive of voting behavior. We examined this possibility in the context of the EU referendum in the UK. In the 5 weeks preceding the referendum, we recorded the N400 while participants with different vote intentions expressed their agreement or disagreement with pro- and against-EU statements. We showed that the N400 responded to statements incongruent with one’s view regarding the EU. Crucially, this effect predicted actual voting behavior in decided as well as undecided voters. The N400 was a better predictor of voting choice than an explicit index of preference based on the behavioral responses. Our findings demonstrate that well-defined patterns of brain activity can forecast future voting behavior. PMID:28981799

  12. Changes in Pilot Behavior with Predictive System Status Information

    NASA Technical Reports Server (NTRS)

    Trujillo, Anna C.

    1998-01-01

    Research has shown a strong pilot preference for predictive information of aircraft system status in the flight deck. However, changes in pilot behavior associated with using this predictive information have not been ascertained. The study described here quantified these changes using three types of predictive information (none, whether a parameter was changing abnormally, and the time for a parameter to reach an alert range) and three initial time intervals until a parameter alert range was reached (ITIs) (1 minute, 5 minutes, and 15 minutes). With predictive information, subjects accomplished most of their tasks before an alert occurred. Subjects organized the time they did their tasks by locus-of-control with no predictive information and for the 1-minute ITI, and by aviatenavigate-communicate for the time for a parameter to reach an alert range and the 15-minute conditions. Overall, predictive information and the longer ITIs moved subjects to performing tasks before the alert actually occurred and had them more mission oriented as indicated by their tasks grouping of aviate-navigate-communicate.

  13. CodingQuarry: highly accurate hidden Markov model gene prediction in fungal genomes using RNA-seq transcripts.

    PubMed

    Testa, Alison C; Hane, James K; Ellwood, Simon R; Oliver, Richard P

    2015-03-11

    The impact of gene annotation quality on functional and comparative genomics makes gene prediction an important process, particularly in non-model species, including many fungi. Sets of homologous protein sequences are rarely complete with respect to the fungal species of interest and are often small or unreliable, especially when closely related species have not been sequenced or annotated in detail. In these cases, protein homology-based evidence fails to correctly annotate many genes, or significantly improve ab initio predictions. Generalised hidden Markov models (GHMM) have proven to be invaluable tools in gene annotation and, recently, RNA-seq has emerged as a cost-effective means to significantly improve the quality of automated gene annotation. As these methods do not require sets of homologous proteins, improving gene prediction from these resources is of benefit to fungal researchers. While many pipelines now incorporate RNA-seq data in training GHMMs, there has been relatively little investigation into additionally combining RNA-seq data at the point of prediction, and room for improvement in this area motivates this study. CodingQuarry is a highly accurate, self-training GHMM fungal gene predictor designed to work with assembled, aligned RNA-seq transcripts. RNA-seq data informs annotations both during gene-model training and in prediction. Our approach capitalises on the high quality of fungal transcript assemblies by incorporating predictions made directly from transcript sequences. Correct predictions are made despite transcript assembly problems, including those caused by overlap between the transcripts of adjacent gene loci. Stringent benchmarking against high-confidence annotation subsets showed CodingQuarry predicted 91.3% of Schizosaccharomyces pombe genes and 90.4% of Saccharomyces cerevisiae genes perfectly. These results are 4-5% better than those of AUGUSTUS, the next best performing RNA-seq driven gene predictor tested. Comparisons against

  14. Deformation, Failure, and Fatigue Life of SiC/Ti-15-3 Laminates Accurately Predicted by MAC/GMC

    NASA Technical Reports Server (NTRS)

    Bednarcyk, Brett A.; Arnold, Steven M.

    2002-01-01

    NASA Glenn Research Center's Micromechanics Analysis Code with Generalized Method of Cells (MAC/GMC) (ref.1) has been extended to enable fully coupled macro-micro deformation, failure, and fatigue life predictions for advanced metal matrix, ceramic matrix, and polymer matrix composites. Because of the multiaxial nature of the code's underlying micromechanics model, GMC--which allows the incorporation of complex local inelastic constitutive models--MAC/GMC finds its most important application in metal matrix composites, like the SiC/Ti-15-3 composite examined here. Furthermore, since GMC predicts the microscale fields within each constituent of the composite material, submodels for local effects such as fiber breakage, interfacial debonding, and matrix fatigue damage can and have been built into MAC/GMC. The present application of MAC/GMC highlights the combination of these features, which has enabled the accurate modeling of the deformation, failure, and life of titanium matrix composites.

  15. Personality patterns predict the risk of antisocial behavior in Spanish-speaking adolescents.

    PubMed

    Alcázar-Córcoles, Miguel A; Verdejo-García, Antonio; Bouso-Sáiz, José C; Revuelta-Menéndez, Javier; Ramírez-Lira, Ezequiel

    2017-05-01

    There is a renewed interest in incorporating personality variables in criminology theories in order to build models able to integrate personality variables and biological factors with psychosocial and sociocultural factors. The aim of this article is the assessment of personality dimensions that contribute to the prediction of antisocial behavior in adolescents. For this purpose, a sample of adolescents from El Salvador, Mexico, and Spain was obtained. The sample consisted of 1035 participants with a mean age of 16.2. There were 450 adolescents from a forensic population (those who committed a crime) and 585 adolescents from the normal population (no crime committed). All of participants answered personality tests about neuroticism, extraversion, psychoticism, sensation seeking, impulsivity, and violence risk. Principal component analysis of the data identified two independent factors: (i) the disinhibited behavior pattern (PDC), formed by the dimensions of neuroticism, psychoticism, impulsivity and risk of violence; and (ii) the extrovert behavior pattern (PEC), formed by the dimensions of sensation risk and extraversion. Both patterns significantly contributed to the prediction of adolescent antisocial behavior in a logistic regression model which properly classifies a global percentage of 81.9%, 86.8% for non-offense and 72.5% for offense behavior. The classification power of regression equations allows making very satisfactory predictions about adolescent offense commission. Educational level has been classified as a protective factor, while age and gender (male) have been classified as risk factors.

  16. Accurate prediction of cardiorespiratory fitness using cycle ergometry in minimally disabled persons with relapsing-remitting multiple sclerosis.

    PubMed

    Motl, Robert W; Fernhall, Bo

    2012-03-01

    To examine the accuracy of predicting peak oxygen consumption (VO(2peak)) primarily from peak work rate (WR(peak)) recorded during a maximal, incremental exercise test on a cycle ergometer among persons with relapsing-remitting multiple sclerosis (RRMS) who had minimal disability. Cross-sectional study. Clinical research laboratory. Women with RRMS (n=32) and sex-, age-, height-, and weight-matched healthy controls (n=16) completed an incremental exercise test on a cycle ergometer to volitional termination. Not applicable. Measured and predicted VO(2peak) and WR(peak). There were strong, statistically significant associations between measured and predicted VO(2peak) in the overall sample (R(2)=.89, standard error of the estimate=127.4 mL/min) and subsamples with (R(2)=.89, standard error of the estimate=131.3 mL/min) and without (R(2)=.85, standard error of the estimate=126.8 mL/min) multiple sclerosis (MS) based on the linear regression analyses. Based on the 95% confidence limits for worst-case errors, the equation predicted VO(2peak) within 10% of its true value in 95 of every 100 subjects with MS. Peak VO(2) can be accurately predicted in persons with RRMS who have minimal disability as it is in controls by using established equations and WR(peak) recorded from a maximal, incremental exercise test on a cycle ergometer. Copyright © 2012 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.

  17. Factors Influencing College Women's Contraceptive Behavior: An Application of the Integrative Model of Behavioral Prediction

    ERIC Educational Resources Information Center

    Sutton, Jazmyne A.; Walsh-Buhi, Eric R.

    2017-01-01

    Objective: This study investigated variables within the Integrative Model of Behavioral Prediction (IMBP) as well as differences across socioeconomic status (SES) levels within the context of inconsistent contraceptive use among college women. Participants: A nonprobability sample of 515 female college students completed an Internet-based survey…

  18. Point-Mass Aircraft Trajectory Prediction Using a Hierarchical, Highly-Adaptable Software Design

    NASA Technical Reports Server (NTRS)

    Karr, David A.; Vivona, Robert A.; Woods, Sharon E.; Wing, David J.

    2017-01-01

    A highly adaptable and extensible method for predicting four-dimensional trajectories of civil aircraft has been developed. This method, Behavior-Based Trajectory Prediction, is based on taxonomic concepts developed for the description and comparison of trajectory prediction software. A hierarchical approach to the "behavioral" layer of a point-mass model of aircraft flight, a clear separation between the "behavioral" and "mathematical" layers of the model, and an abstraction of the methods of integrating differential equations in the "mathematical" layer have been demonstrated to support aircraft models of different types (in particular, turbojet vs. turboprop aircraft) using performance models at different levels of detail and in different formats, and promise to be easily extensible to other aircraft types and sources of data. The resulting trajectories predict location, altitude, lateral and vertical speeds, and fuel consumption along the flight path of the subject aircraft accurately and quickly, accounting for local conditions of wind and outside air temperature. The Behavior-Based Trajectory Prediction concept was implemented in NASA's Traffic Aware Planner (TAP) flight-optimizing cockpit software application.

  19. Applying theory of planned behavior to predict exercise maintenance in sarcopenic elderly.

    PubMed

    Ahmad, Mohamad Hasnan; Shahar, Suzana; Teng, Nur Islami Mohd Fahmi; Manaf, Zahara Abdul; Sakian, Noor Ibrahim Mohd; Omar, Baharudin

    2014-01-01

    This study aimed to determine the factors associated with exercise behavior based on the theory of planned behavior (TPB) among the sarcopenic elderly people in Cheras, Kuala Lumpur. A total of 65 subjects with mean ages of 67.5±5.2 (men) and 66.1±5.1 (women) years participated in this study. Subjects were divided into two groups: 1) exercise group (n=34; 25 men, nine women); and 2) the control group (n=31; 22 men, nine women). Structural equation modeling, based on TPB components, was applied to determine specific factors that most contribute to and predict actual behavior toward exercise. Based on the TPB's model, attitude (β=0.60) and perceived behavioral control (β=0.24) were the major predictors of intention to exercise among men at the baseline. Among women, the subjective norm (β=0.82) was the major predictor of intention to perform the exercise at the baseline. After 12 weeks, attitude (men's, β=0.68; women's, β=0.24) and subjective norm (men's, β=0.12; women's, β=0.87) were the predictors of the intention to perform the exercise. "Feels healthier with exercise" was the specific factor to improve the intention to perform and to maintain exercise behavior in men (β=0.36) and women (β=0.49). "Not motivated to perform exercise" was the main barrier among men's intention to exercise. The intention to perform the exercise was able to predict actual behavior regarding exercise at the baseline and at 12 weeks of an intervention program. As a conclusion, TPB is a useful model to determine and to predict maintenance of exercise in the sarcopenic elderly.

  20. Ceramic Matrix Composites (CMC) Life Prediction Development - 2003

    NASA Technical Reports Server (NTRS)

    Levine, Stanley R.; Calomino, Anthony M.; Verrilli, Michael J.; Thomas, David J.; Halbig, Michael C.; Opila, Elizabeth J.; Ellis, John R.

    2003-01-01

    Accurate life prediction is critical to successful use of ceramic matrix composites (CMCs). The tools to accomplish this are immature and not oriented toward the behavior of carbon fiber reinforced silicon carbide (C/SiC), the primary system of interest for many reusable and single mission launch vehicle propulsion and airframe applications. This paper describes an approach and progress made to satisfy the need to develop an integrated life prediction system that addresses mechanical durability and environmental degradation of C/SiC.

  1. BEHAVE: fire behavior prediction and fuel modeling system--FUEL subsystem

    Treesearch

    Robert E. Burgan; Richard C. Rothermel

    1984-01-01

    This manual documents the fuel modeling procedures of BEHAVE--a state-of-the-art wildland fire behavior prediction system. Described are procedures for collecting fuel data, using the data with the program, and testing and adjusting the fuel model.

  2. Clusters of Behaviors and Beliefs Predicting Adolescent Depression: Implications for Prevention

    PubMed Central

    Paunesku, David; Ellis, Justin; Fogel, Joshua; Kuwabara, Sachiko A; Gollan, Jackie; Gladstone, Tracy; Reinecke, Mark; Van Voorhees, Benjamin W.

    2009-01-01

    OBJECTIVE Risk factors for various disorders are known to cluster. However, the factor structure for behaviors and beliefs predicting depressive disorder in adolescents is not known. Knowledge of this structure can facilitate prevention planning. METHODS We used the National Longitudinal Study of Adolescent Health (AddHealth) data set to conduct an exploratory factor analysis to identify clusters of behaviors/experiences predicting the onset of major depressive disorder (MDD) at 1-year follow-up (N=4,791). RESULTS Four factors were identified: family/interpersonal relations, self-emancipation, avoidant problem solving/low self-worth, and religious activity. Strong family/interpersonal relations were the most significantly protective against depression at one year follow-up. Avoidant problem solving/low self-worth was not predictive of MDD on its own, but significantly amplified the risks associated with delinquency. CONCLUSION Depression prevention interventions should consider giving family relationships a more central role in their efforts. Programs teaching problem solving skills may be most appropriate for reducing MDD risk in delinquent youth. PMID:20502621

  3. Predicting Intended and Self-perceived Sugar Restriction among Tanzanian Students using the Theory of Planned Behavior.

    PubMed

    Masalu, J R; Astrøm, A N

    2001-07-01

    This study examines the applicability and sufficiency of the Theory of Planned Behavior (TPB) in predicting intention and self-perceived behavior with respect to avoiding between-meal intake of sugared snacks and drinks. One thousand one hundred and twenty-three Tanzanian students (mean age 26.4 years) completed self-administered questionnaires designed to measure the components of the TPB during May-July, 1999. Self-perceived sugar consumption was obtained in a subsample of respondents (n = 228) four weeks later. The TPB provided a significant prediction of intention (R(2)= 0.44), with attitude (= 0.25), subjective norms (= 0.28) and perceived behavioral control (= 0.35) significant, and subsequent behavior (R(2) = 0.15, with intention (= 0.25) and perceived behavioral control (= 0.18) significant. Frequency of past behavior explained a significant, albeit small, amount of additional variance in intention (1 percent) and behavior (4 percent). The results indicate that the TPB is applicable to the prediction of food choice-related intention and behavior among young adult students living in a non-occidental setting.

  4. Do Skilled Elementary Teachers Hold Scientific Conceptions and Can They Accurately Predict the Type and Source of Students' Preconceptions of Electric Circuits?

    ERIC Educational Resources Information Center

    Lin, Jing-Wen

    2016-01-01

    Holding scientific conceptions and having the ability to accurately predict students' preconceptions are a prerequisite for science teachers to design appropriate constructivist-oriented learning experiences. This study explored the types and sources of students' preconceptions of electric circuits. First, 438 grade 3 (9 years old) students were…

  5. The use of messages in altering risky gambling behavior in experienced gamblers.

    PubMed

    Jardin, Bianca F; Wulfert, Edelgard

    2012-03-01

    The present study was an experimental analogue that examined the relationship between gambling-related irrational beliefs and risky gambling behavior. Eighty high-frequency gamblers were randomly assigned to four conditions and played a chance-based computer game in a laboratory setting. Depending on the condition, during the game a pop-up screen repeatedly displayed either accurate or inaccurate messages concerning the game, neutral messages, or no messages. Consistent with a cognitive-behavioral model of gambling, accurate messages that correctly described the random contingencies governing the game decreased risky gambling behavior. Contrary to predictions, inaccurate messages designed to mimic gamblers' irrational beliefs about their abilities to influence chance events did not lead to more risky gambling behavior than exposure to neutral or no messages. Participants in the latter three conditions did not differ significantly from one another and all showed riskier gambling behavior than participants in the accurate message condition. The results suggest that harm minimization strategies that help individuals maintain a rational perspective while gambling may protect them from unreasonable risk-taking. PsycINFO Database Record (c) 2012 APA, all rights reserved.

  6. Metamemory monitoring in mild cognitive impairment: Evidence of a less accurate episodic feeling-of-knowing.

    PubMed

    Perrotin, Audrey; Belleville, Sylvie; Isingrini, Michel

    2007-09-20

    This study aimed at exploring metamemory and specifically the accuracy of memory monitoring in mild cognitive impairment (MCI) using an episodic memory feeling-of-knowing (FOK) procedure. To this end, 20 people with MCI and 20 matched control participants were compared on the episodic FOK task. Results showed that the MCI group made less accurate FOK predictions than the control group by overestimating their memory performance on a recognition task. The MCI overestimation behavior was found to be critically related to the severity of their cognitive decline. In the light of recent neuroanatomical models showing the involvement of a temporal-frontal network underlying accurate FOK predictions, the role of memory and executive processes was evaluated. Thus, participants were also administered memory and executive neuropsychological tests. Correlation analysis revealed a between-group differential pattern indicating that FOK accuracy was primarily related to memory abilities in people with MCI, whereas it was specifically related to executive functioning in control participants. The lesser ability of people with MCI to assess their memory status accurately on an episodic FOK task is discussed in relation to both their subjective memory complaints and to their actual memory deficits which might be mediated by the brain vulnerability of their hippocampus and medial temporal system. It is suggested that their memory weakness may lead people with MCI to use other less reliable forms of memory monitoring.

  7. A Study of the Relationship of Parenting Styles, Child Temperament, and Operatory Behavior in Healthy Children.

    PubMed

    Tsoi, Amanda K; Wilson, Stephen; Thikkurissy, S

    2018-05-11

    The purpose of this study was to assess the relationship of the child's temperament, parenting styles, and parents' prediction of their child's behavior in the dental setting. Subjects were healthy children 4-12 years of age attending a dental clinic. A Parenting Styles and Dimensions Questionnaire (PSDQ) was given to parents to determine their parenting style. Parents completed the Emotionality, Activity, Sociability Temperament (EAS) survey to measure their child's temperament. Parents were asked to predict their child's behavior using the Frankl Scale. Data analysis included 113 parent/child dyads. Parents accurately predicted their child's behavior 58% of the time. Significant correlations were noted between parent's predictions of behavior and emotionality (r = -.497, p < .001), activity (r = -.217, p < .009), and shyness (r = -.282, p < .002) of EAS. Significant correlations were found between actual behavior and emotionality (r = -.586, p < .001), activity (r = -.196, p < .03), and shyness (r = -.281, p < .003). Parenting style scores did not correlate to predicted or actual behavior; however, categories of PSDQ were related to parental predictions of behavior. Relationships between temperament and parenting may aid in predicting children's behavior in the operatory.

  8. Applying a health action model to predict and improve healthy behaviors in coal miners.

    PubMed

    Vahedian-Shahroodi, Mohammad; Tehrani, Hadi; Mohammadi, Faeze; Gholian-Aval, Mahdi; Peyman, Nooshin

    2018-05-01

    One of the most important ways to prevent work-related diseases in occupations such as mining is to promote healthy behaviors among miners. This study aimed to predict and promote healthy behaviors among coal miners by using a health action model (HAM). The study was conducted on 200 coal miners in Iran in two steps. In the first step, a descriptive study was implemented to determine predictive constructs and effectiveness of HAM on behavioral intention. The second step involved a quasi-experimental study to determine the effect of an HAM-based education intervention. This intervention was implemented by the researcher and the head of the safety unit based on the predictive construct specified in the first step over 12 sessions of 60 min. The data was collected using an HAM questionnaire and a checklist of healthy behavior. The results of the first step of the study showed that attitude, belief, and normative constructs were meaningful predictors of behavioral intention. Also, the results of the second step revealed that the mean score of attitude and behavioral intention increased significantly after conducting the intervention in the experimental group, while the mean score of these constructs decreased significantly in the control group. The findings of this study showed that HAM-based educational intervention could improve the healthy behaviors of mine workers. Therefore, it is recommended to extend the application of this model to other working groups to improve healthy behaviors.

  9. Predicting Behavior from Normative Influences: What Insights Can the Fishbein Model Offer?

    ERIC Educational Resources Information Center

    Walster, Dian E.

    The Fishbein Model is an attitude behavior consistency model which is used in both laboratory and field settings for predicting and understanding attitudinal and normative influences on behavior. This paper examines controversy surrounding the Fishbein Model's normative component in the context of a study of library and information science (LIS)…

  10. Modeled changes of cerebellar activity in mutant mice are predictive of their learning impairments

    NASA Astrophysics Data System (ADS)

    Badura, Aleksandra; Clopath, Claudia; Schonewille, Martijn; de Zeeuw, Chris I.

    2016-11-01

    Translating neuronal activity to measurable behavioral changes has been a long-standing goal of systems neuroscience. Recently, we have developed a model of phase-reversal learning of the vestibulo-ocular reflex, a well-established, cerebellar-dependent task. The model, comprising both the cerebellar cortex and vestibular nuclei, reproduces behavioral data and accounts for the changes in neural activity during learning in wild type mice. Here, we used our model to predict Purkinje cell spiking as well as behavior before and after learning of five different lines of mutant mice with distinct cell-specific alterations of the cerebellar cortical circuitry. We tested these predictions by obtaining electrophysiological data depicting changes in neuronal spiking. We show that our data is largely consistent with the model predictions for simple spike modulation of Purkinje cells and concomitant behavioral learning in four of the mutants. In addition, our model accurately predicts a shift in simple spike activity in a mutant mouse with a brainstem specific mutation. This combination of electrophysiological and computational techniques opens a possibility of predicting behavioral impairments from neural activity.

  11. Modeled changes of cerebellar activity in mutant mice are predictive of their learning impairments

    PubMed Central

    Badura, Aleksandra; Clopath, Claudia; Schonewille, Martijn; De Zeeuw, Chris I.

    2016-01-01

    Translating neuronal activity to measurable behavioral changes has been a long-standing goal of systems neuroscience. Recently, we have developed a model of phase-reversal learning of the vestibulo-ocular reflex, a well-established, cerebellar-dependent task. The model, comprising both the cerebellar cortex and vestibular nuclei, reproduces behavioral data and accounts for the changes in neural activity during learning in wild type mice. Here, we used our model to predict Purkinje cell spiking as well as behavior before and after learning of five different lines of mutant mice with distinct cell-specific alterations of the cerebellar cortical circuitry. We tested these predictions by obtaining electrophysiological data depicting changes in neuronal spiking. We show that our data is largely consistent with the model predictions for simple spike modulation of Purkinje cells and concomitant behavioral learning in four of the mutants. In addition, our model accurately predicts a shift in simple spike activity in a mutant mouse with a brainstem specific mutation. This combination of electrophysiological and computational techniques opens a possibility of predicting behavioral impairments from neural activity. PMID:27805050

  12. Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties

    NASA Astrophysics Data System (ADS)

    Xie, Tian; Grossman, Jeffrey C.

    2018-04-01

    The use of machine learning methods for accelerating the design of crystalline materials usually requires manually constructed feature vectors or complex transformation of atom coordinates to input the crystal structure, which either constrains the model to certain crystal types or makes it difficult to provide chemical insights. Here, we develop a crystal graph convolutional neural networks framework to directly learn material properties from the connection of atoms in the crystal, providing a universal and interpretable representation of crystalline materials. Our method provides a highly accurate prediction of density functional theory calculated properties for eight different properties of crystals with various structure types and compositions after being trained with 1 04 data points. Further, our framework is interpretable because one can extract the contributions from local chemical environments to global properties. Using an example of perovskites, we show how this information can be utilized to discover empirical rules for materials design.

  13. Behavior-specific changes in transcriptional modules lead to distinct and predictable neurogenomic states

    PubMed Central

    Chandrasekaran, Sriram; Ament, Seth A.; Eddy, James A.; Rodriguez-Zas, Sandra L.; Schatz, Bruce R.; Price, Nathan D.; Robinson, Gene E.

    2011-01-01

    Using brain transcriptomic profiles from 853 individual honey bees exhibiting 48 distinct behavioral phenotypes in naturalistic contexts, we report that behavior-specific neurogenomic states can be inferred from the coordinated action of transcription factors (TFs) and their predicted target genes. Unsupervised hierarchical clustering of these transcriptomic profiles showed three clusters that correspond to three ecologically important behavioral categories: aggression, maturation, and foraging. To explore the genetic influences potentially regulating these behavior-specific neurogenomic states, we reconstructed a brain transcriptional regulatory network (TRN) model. This brain TRN quantitatively predicts with high accuracy gene expression changes of more than 2,000 genes involved in behavior, even for behavioral phenotypes on which it was not trained, suggesting that there is a core set of TFs that regulates behavior-specific gene expression in the bee brain, and other TFs more specific to particular categories. TFs playing key roles in the TRN include well-known regulators of neural and behavioral plasticity, e.g., Creb, as well as TFs better known in other biological contexts, e.g., NF-κB (immunity). Our results reveal three insights concerning the relationship between genes and behavior. First, distinct behaviors are subserved by distinct neurogenomic states in the brain. Second, the neurogenomic states underlying different behaviors rely upon both shared and distinct transcriptional modules. Third, despite the complexity of the brain, simple linear relationships between TFs and their putative target genes are a surprisingly prominent feature of the networks underlying behavior. PMID:21960440

  14. Dynamic Socialized Gaussian Process Models for Human Behavior Prediction in a Health Social Network

    PubMed Central

    Shen, Yelong; Phan, NhatHai; Xiao, Xiao; Jin, Ruoming; Sun, Junfeng; Piniewski, Brigitte; Kil, David; Dou, Dejing

    2016-01-01

    Modeling and predicting human behaviors, such as the level and intensity of physical activity, is a key to preventing the cascade of obesity and helping spread healthy behaviors in a social network. In our conference paper, we have developed a social influence model, named Socialized Gaussian Process (SGP), for socialized human behavior modeling. Instead of explicitly modeling social influence as individuals' behaviors influenced by their friends' previous behaviors, SGP models the dynamic social correlation as the result of social influence. The SGP model naturally incorporates personal behavior factor and social correlation factor (i.e., the homophily principle: Friends tend to perform similar behaviors) into a unified model. And it models the social influence factor (i.e., an individual's behavior can be affected by his/her friends) implicitly in dynamic social correlation schemes. The detailed experimental evaluation has shown the SGP model achieves better prediction accuracy compared with most of baseline methods. However, a Socialized Random Forest model may perform better at the beginning compared with the SGP model. One of the main reasons is the dynamic social correlation function is purely based on the users' sequential behaviors without considering other physical activity-related features. To address this issue, we further propose a novel “multi-feature SGP model” (mfSGP) which improves the SGP model by using multiple physical activity-related features in the dynamic social correlation learning. Extensive experimental results illustrate that the mfSGP model clearly outperforms all other models in terms of prediction accuracy and running time. PMID:27746515

  15. Are Nomothetic or Ideographic Approaches Superior in Predicting Daily Exercise Behaviors?

    PubMed

    Cheung, Ying Kuen; Hsueh, Pei-Yun Sabrina; Qian, Min; Yoon, Sunmoo; Meli, Laura; Diaz, Keith M; Schwartz, Joseph E; Kronish, Ian M; Davidson, Karina W

    2017-01-01

    The understanding of how stress influences health behavior can provide insights into developing healthy lifestyle interventions. This understanding is traditionally attained through observational studies that examine associations at a population level. This nomothetic approach, however, is fundamentally limited by the fact that the environment- person milieu that constitutes stress exposure and experience can vary substantially between individuals, and the modifiable elements of these exposures and experiences are individual-specific. With recent advances in smartphone and sensing technologies, it is now possible to conduct idiographic assessment in users' own environment, leveraging the full-range observations of actions and experiences that result in differential response to naturally occurring events. The aim of this paper is to explore the hypothesis that an ideographic N-of-1 model can better capture an individual's stress- behavior pathway (or the lack thereof) and provide useful person-specific predictors of exercise behavior. This paper used the data collected in an observational study in 79 participants who were followed for up to a 1-year period, wherein their physical activity was continuously and objectively monitored by actigraphy and their stress experience was recorded via ecological momentary assessment on a mobile app. In addition, our analyses considered exogenous and environmental variables retrieved from public archive such as day in a week, daylight time, temperature and precipitation. Leveraging the multiple data sources, we developed prediction algorithms for exercise behavior using random forest and classification tree techniques using a nomothetic approach and an N-of-1 approach. The two approaches were compared based on classification errors in predicting personalized exercise behavior. Eight factors were selected by random forest for the nomothetic decision model, which was used to predict whether a participant would exercise on a

  16. Study on elevated-temperature flow behavior of Ni-Cr-Mo-B ultra-heavy-plate steel via experiment and modelling

    NASA Astrophysics Data System (ADS)

    Gao, Zhi-yu; Kang, Yu; Li, Yan-shuai; Meng, Chao; Pan, Tao

    2018-04-01

    Elevated-temperature flow behavior of a novel Ni-Cr-Mo-B ultra-heavy-plate steel was investigated by conducting hot compressive deformation tests on a Gleeble-3800 thermo-mechanical simulator at a temperature range of 1123 K–1423 K with a strain rate range from 0.01 s‑1 to10 s‑1 and a height reduction of 70%. Based on the experimental results, classic strain-compensated Arrhenius-type, a new revised strain-compensated Arrhenius-type and classic modified Johnson-Cook constitutive models were developed for predicting the high-temperature deformation behavior of the steel. The predictability of these models were comparatively evaluated in terms of statistical parameters including correlation coefficient (R), average absolute relative error (AARE), average root mean square error (RMSE), normalized mean bias error (NMBE) and relative error. The statistical results indicate that the new revised strain-compensated Arrhenius-type model could give prediction of elevated-temperature flow stress for the steel accurately under the entire process conditions. However, the predicted values by the classic modified Johnson-Cook model could not agree well with the experimental values, and the classic strain-compensated Arrhenius-type model could track the deformation behavior more accurately compared with the modified Johnson-Cook model, but less accurately with the new revised strain-compensated Arrhenius-type model. In addition, reasons of differences in predictability of these models were discussed in detail.

  17. Accurate X-Ray Spectral Predictions: An Advanced Self-Consistent-Field Approach Inspired by Many-Body Perturbation Theory

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

    Liang, Yufeng; Vinson, John; Pemmaraju, Sri

    Constrained-occupancy delta-self-consistent-field (ΔSCF) methods and many-body perturbation theories (MBPT) are two strategies for obtaining electronic excitations from first principles. Using the two distinct approaches, we study the O 1s core excitations that have become increasingly important for characterizing transition-metal oxides and understanding strong electronic correlation. The ΔSCF approach, in its current single-particle form, systematically underestimates the pre-edge intensity for chosen oxides, despite its success in weakly correlated systems. By contrast, the Bethe-Salpeter equation within MBPT predicts much better line shapes. This motivates one to reexamine the many-electron dynamics of x-ray excitations. We find that the single-particle ΔSCF approach can bemore » rectified by explicitly calculating many-electron transition amplitudes, producing x-ray spectra in excellent agreement with experiments. This study paves the way to accurately predict x-ray near-edge spectral fingerprints for physics and materials science beyond the Bethe-Salpether equation.« less

  18. Accurate X-Ray Spectral Predictions: An Advanced Self-Consistent-Field Approach Inspired by Many-Body Perturbation Theory

    DOE PAGES

    Liang, Yufeng; Vinson, John; Pemmaraju, Sri; ...

    2017-03-03

    Constrained-occupancy delta-self-consistent-field (ΔSCF) methods and many-body perturbation theories (MBPT) are two strategies for obtaining electronic excitations from first principles. Using the two distinct approaches, we study the O 1s core excitations that have become increasingly important for characterizing transition-metal oxides and understanding strong electronic correlation. The ΔSCF approach, in its current single-particle form, systematically underestimates the pre-edge intensity for chosen oxides, despite its success in weakly correlated systems. By contrast, the Bethe-Salpeter equation within MBPT predicts much better line shapes. This motivates one to reexamine the many-electron dynamics of x-ray excitations. We find that the single-particle ΔSCF approach can bemore » rectified by explicitly calculating many-electron transition amplitudes, producing x-ray spectra in excellent agreement with experiments. This study paves the way to accurately predict x-ray near-edge spectral fingerprints for physics and materials science beyond the Bethe-Salpether equation.« less

  19. Accurate X-Ray Spectral Predictions: An Advanced Self-Consistent-Field Approach Inspired by Many-Body Perturbation Theory.

    PubMed

    Liang, Yufeng; Vinson, John; Pemmaraju, Sri; Drisdell, Walter S; Shirley, Eric L; Prendergast, David

    2017-03-03

    Constrained-occupancy delta-self-consistent-field (ΔSCF) methods and many-body perturbation theories (MBPT) are two strategies for obtaining electronic excitations from first principles. Using the two distinct approaches, we study the O 1s core excitations that have become increasingly important for characterizing transition-metal oxides and understanding strong electronic correlation. The ΔSCF approach, in its current single-particle form, systematically underestimates the pre-edge intensity for chosen oxides, despite its success in weakly correlated systems. By contrast, the Bethe-Salpeter equation within MBPT predicts much better line shapes. This motivates one to reexamine the many-electron dynamics of x-ray excitations. We find that the single-particle ΔSCF approach can be rectified by explicitly calculating many-electron transition amplitudes, producing x-ray spectra in excellent agreement with experiments. This study paves the way to accurately predict x-ray near-edge spectral fingerprints for physics and materials science beyond the Bethe-Salpether equation.

  20. Do guilt- and shame-proneness differentially predict prosocial, aggressive, and withdrawn behaviors during early adolescence?

    PubMed

    Roos, Sanna; Hodges, Ernest V E; Salmivalli, Christina

    2014-03-01

    In this short-term longitudinal study, we systematically examined the distinctiveness of guilt- and shame-proneness in early adolescents (N = 395, mean age = 11.8 years) in terms of differential relations with peer reported prosocial behavior, withdrawal, and aggression. Results from structural equation modeling indicated that guilt-proneness concurrently predicted more aggressive and less prosocial behavior as well as subsequent increases in prosocial behavior. Shame-proneness predicted subsequent decreases in prosocial behavior. Although girls reported a greater proneness to experience guilt and shame than boys, the associations between the two dispositional emotions and social behaviors were found to be similar across time and gender. PsycINFO Database Record (c) 2014 APA, all rights reserved.

  1. Discrete modes of social information processing predict individual behavior of fish in a group

    PubMed Central

    Harpaz, Roy; Tkačik, Gašper

    2017-01-01

    Individual computations and social interactions underlying collective behavior in groups of animals are of great ethological, behavioral, and theoretical interest. While complex individual behaviors have successfully been parsed into small dictionaries of stereotyped behavioral modes, studies of collective behavior largely ignored these findings; instead, their focus was on inferring single, mode-independent social interaction rules that reproduced macroscopic and often qualitative features of group behavior. Here, we bring these two approaches together to predict individual swimming patterns of adult zebrafish in a group. We show that fish alternate between an “active” mode, in which they are sensitive to the swimming patterns of conspecifics, and a “passive” mode, where they ignore them. Using a model that accounts for these two modes explicitly, we predict behaviors of individual fish with high accuracy, outperforming previous approaches that assumed a single continuous computation by individuals and simple metric or topological weighing of neighbors’ behavior. At the group level, switching between active and passive modes is uncorrelated among fish, but correlated directional swimming behavior still emerges. Our quantitative approach for studying complex, multimodal individual behavior jointly with emergent group behavior is readily extensible to additional behavioral modes and their neural correlates as well as to other species. PMID:28874581

  2. Predicting preschoolers' social-cognitive play behavior: attachment, peers, temperament, and physiological regulation.

    PubMed

    Porter, Christin L

    2009-04-01

    Research on children's social-cognitive play typologies (i.e., active and passive forms of solitary and social play) suggests links of early play behaviors and later social development and risk status. To date, few studies have examined simultaneously suspected links between children's social-cognitive play types and factors believed to shape these early social-play behaviors. This study examined a simultaneous model of individual (temperament, physiology) and relational variables (attachment, peer networks) believed to influence children's social-cognitive play types, including individual characteristics drawn from the Child Behavior Questionnaire which measures dimensions of shyness and impulsivity, a lab-based assessment of social withdrawal, and physiological markers linked to social regulation (cardiac vagal tone and vagal regulation). Children's attachment status to parents was gathered using Q-Sort methodology, and a measure of previous peer network size was obtained from parents' reports to examine potential links between relational history and social-cognitive play types. Predictive discriminant function analysis showed that children's (N = 54, age range 35 to 58 months) social-cognitive play was better predicted on the basis of multiple independent variables than individual, zero-order relations. When predicting children's social-cognitive play typologies, a multidimensional view which encompasses both individual characteristics and social-relational variables may best predict social -cognitive play types and help understanding of children's social trajectories.

  3. Early-onset Conduct Problems: Predictions from daring temperament and risk taking behavior.

    PubMed

    Bai, Sunhye; Lee, Steve S

    2017-12-01

    Given its considerable public health significance, identifying predictors of early expressions of conduct problems is a priority. We examined the predictive validity of daring, a key dimension of temperament, and the Balloon Analog Risk Task (BART), a laboratory-based measure of risk taking behavior, with respect to two-year change in parent, teacher-, and youth self-reported oppositional defiant disorder (ODD), conduct disorder (CD), and antisocial behavior. At baseline, 150 ethnically diverse 6- to 10-year old (M=7.8, SD=1.1; 69.3% male) youth with ( n =82) and without ( n =68) DSM-IV ADHD completed the BART whereas parents rated youth temperament (i.e., daring); parents and teachers also independently rated youth ODD and CD symptoms. Approximately 2 years later, multi-informant ratings of youth ODD, CD, and antisocial behavior were gathered from rating scales and interviews. Whereas risk taking on the BART was unrelated to conduct problems, individual differences in daring prospectively predicted multi-informant rated conduct problems, independent of baseline risk taking, conduct problems, and ADHD diagnostic status. Early differences in the propensity to show positive socio-emotional responses to risky or novel experiences uniquely predicted escalating conduct problems in childhood, even with control of other potent clinical correlates. We consider the role of temperament in the origins and development of significant conduct problems from childhood to adolescence, including possible explanatory mechanisms underlying these predictions.

  4. Ideal Teacher Behaviors: Student Motivation and Self-Efficacy Predict Preferences

    ERIC Educational Resources Information Center

    Komarraju, Meera

    2013-01-01

    Differences in students' academic self-efficacy and motivation were examined in predicting preferred teacher traits. Undergraduates (261) completed the Teaching Behavior Checklist, Academic Self-Concept scale, and Academic Motivation scale. Hierarchical regression analyses indicated that academic self-efficacy and extrinsic motivation explained…

  5. Computer program for predicting creep behavior of bodies of revolution

    NASA Technical Reports Server (NTRS)

    Adams, R.; Greenbaum, G.

    1971-01-01

    Computer program, CRAB, uses finite-element method to calculate creep behavior and predict steady-state stresses in an arbitrary body of revolution subjected to a time-dependent axisymmetric load. Creep strains follow a time hardening law and a Prandtl-Reuss stress-strain relationship.

  6. Accurate prediction of acute fish toxicity of fragrance chemicals with the RTgill-W1 cell assay.

    PubMed

    Natsch, Andreas; Laue, Heike; Haupt, Tina; von Niederhäusern, Valentin; Sanders, Gordon

    2018-03-01

    Testing for acute fish toxicity is an integral part of the environmental safety assessment of chemicals. A true replacement of primary fish tissue was recently proposed using cell viability in a fish gill cell line (RTgill-W1) as a means of predicting acute toxicity, showing good predictivity on 35 chemicals. To promote regulatory acceptance, the predictivity and applicability domain of novel tests need to be carefully evaluated on chemicals with existing high-quality in vivo data. We applied the RTgill-W1 cell assay to 38 fragrance chemicals with a wide range of both physicochemical properties and median lethal concentration (LC50) values and representing a diverse range of chemistries. A strong correlation (R 2  = 0.90-0.94) between the logarithmic in vivo LC50 values, based on fish mortality, and the logarithmic in vitro median effect concentration (EC50) values based on cell viability was observed. A leave-one-out analysis illustrates a median under-/overprediction from in vitro EC50 values to in vivo LC50 values by a factor of 1.5. This assay offers a simple, accurate, and reliable alternative to in vivo acute fish toxicity testing for chemicals, presumably acting mainly by a narcotic mode of action. Furthermore, the present study provides validation of the predictivity of the RTgill-W1 assay on a completely independent set of chemicals that had not been previously tested and indicates that fragrance chemicals are clearly within the applicability domain. Environ Toxicol Chem 2018;37:931-941. © 2017 SETAC. © 2017 SETAC.

  7. Machine-learning-based calving prediction from activity, lying, and ruminating behaviors in dairy cattle.

    PubMed

    Borchers, M R; Chang, Y M; Proudfoot, K L; Wadsworth, B A; Stone, A E; Bewley, J M

    2017-07-01

    The objective of this study was to use automated activity, lying, and rumination monitors to characterize prepartum behavior and predict calving in dairy cattle. Data were collected from 20 primiparous and 33 multiparous Holstein dairy cattle from September 2011 to May 2013 at the University of Kentucky Coldstream Dairy. The HR Tag (SCR Engineers Ltd., Netanya, Israel) automatically collected neck activity and rumination data in 2-h increments. The IceQube (IceRobotics Ltd., South Queensferry, United Kingdom) automatically collected number of steps, lying time, standing time, number of transitions from standing to lying (lying bouts), and total motion, summed in 15-min increments. IceQube data were summed in 2-h increments to match HR Tag data. All behavioral data were collected for 14 d before the predicted calving date. Retrospective data analysis was performed using mixed linear models to examine behavioral changes by day in the 14 d before calving. Bihourly behavioral differences from baseline values over the 14 d before calving were also evaluated using mixed linear models. Changes in daily rumination time, total motion, lying time, and lying bouts occurred in the 14 d before calving. In the bihourly analysis, extreme values for all behaviors occurred in the final 24 h, indicating that the monitored behaviors may be useful in calving prediction. To determine whether technologies were useful at predicting calving, random forest, linear discriminant analysis, and neural network machine-learning techniques were constructed and implemented using R version 3.1.0 (R Foundation for Statistical Computing, Vienna, Austria). These methods were used on variables from each technology and all combined variables from both technologies. A neural network analysis that combined variables from both technologies at the daily level yielded 100.0% sensitivity and 86.8% specificity. A neural network analysis that combined variables from both technologies in bihourly increments was

  8. Predicting violent behavior: The role of violence exposure and future educational aspirations during adolescence

    PubMed Central

    Stoddard, Sarah A.; Heinze, Justin E.; Choe, Daniel Ewon; Zimmerman, Marc A.

    2015-01-01

    Few researchers have explored future educational aspirations as a promotive factor against exposure to community violence in relation to adolescents’ violent behavior over time. The present study examined the direct and indirect effect of exposure to community violence prior to 9th grade on attitudes about violence and violent behavior in 12th grade, and violent behavior at age 22 via 9th grade future educational aspirations in a sample of urban African American youth (n = 681; 49% male). Multi-group SEM was used to test the moderating effect of gender. Exposure to violence was associated with lower future educational aspirations. For boys, attitudes about violence directly predicted violent behavior at age 22. For boys, future educational aspirations indirectly predicted less violent behavior at age 22. Implications of the findings and suggestions for future research are discussed. PMID:26282242

  9. Interactions between callous unemotional behaviors and executive function in early childhood predict later socioemotional functioning

    PubMed Central

    Waller, Rebecca; Hyde, Luke W.; Baskin-Sommers, Arielle; Olson, Sheryl L.

    2018-01-01

    Callous unemotional (CU) behaviors are linked to aggression, behavior problems, and difficulties in peer relationships in children and adolescents. However, few studies have examined whether early childhood CU behaviors predict aggression or peer-rejection during late-childhood or potential moderation of this relationship by executive function. The current study examined whether the interaction of CU behaviors and executive function in early childhood predicted different forms of aggression in late-childhood, including proactive, reactive, and relational aggression, as well as how much children were liked by their peers. Data from cross-informant reports and multiple observational tasks were collected from a high-risk sample (N=240; female=118) at ages 3 and 10 years old. Parent reports of CU behaviors at age 3 predicted teacher reports of reactive, proactive, and relational aggression, as well as lower peer-liking at age 10. Moderation analysis showed that specifically at high levels of CU behaviors and low levels of observed executive function, children were reported by teachers as showing greater reactive and proactive aggression, and were less-liked by peers. Findings demonstrate that early childhood CU behaviors and executive function have unique main and interactive effects on both later aggression and lower peer-liking even when taking into account stability in behavior problems over time. By elucidating how CU behaviors and deficits in executive function potentiate each other during early childhood, we can better characterize the emergence of severe and persistent behavior and interpersonal difficulties across development. PMID:27418255

  10. A Simplified Micromechanical Modeling Approach to Predict the Tensile Flow Curve Behavior of Dual-Phase Steels

    NASA Astrophysics Data System (ADS)

    Nanda, Tarun; Kumar, B. Ravi; Singh, Vishal

    2017-11-01

    Micromechanical modeling is used to predict material's tensile flow curve behavior based on microstructural characteristics. This research develops a simplified micromechanical modeling approach for predicting flow curve behavior of dual-phase steels. The existing literature reports on two broad approaches for determining tensile flow curve of these steels. The modeling approach developed in this work attempts to overcome specific limitations of the existing two approaches. This approach combines dislocation-based strain-hardening method with rule of mixtures. In the first step of modeling, `dislocation-based strain-hardening method' was employed to predict tensile behavior of individual phases of ferrite and martensite. In the second step, the individual flow curves were combined using `rule of mixtures,' to obtain the composite dual-phase flow behavior. To check accuracy of proposed model, four distinct dual-phase microstructures comprising of different ferrite grain size, martensite fraction, and carbon content in martensite were processed by annealing experiments. The true stress-strain curves for various microstructures were predicted with the newly developed micromechanical model. The results of micromechanical model matched closely with those of actual tensile tests. Thus, this micromechanical modeling approach can be used to predict and optimize the tensile flow behavior of dual-phase steels.

  11. Applying theory of planned behavior to predict exercise maintenance in sarcopenic elderly

    PubMed Central

    Ahmad, Mohamad Hasnan; Shahar, Suzana; Teng, Nur Islami Mohd Fahmi; Manaf, Zahara Abdul; Sakian, Noor Ibrahim Mohd; Omar, Baharudin

    2014-01-01

    This study aimed to determine the factors associated with exercise behavior based on the theory of planned behavior (TPB) among the sarcopenic elderly people in Cheras, Kuala Lumpur. A total of 65 subjects with mean ages of 67.5±5.2 (men) and 66.1±5.1 (women) years participated in this study. Subjects were divided into two groups: 1) exercise group (n=34; 25 men, nine women); and 2) the control group (n=31; 22 men, nine women). Structural equation modeling, based on TPB components, was applied to determine specific factors that most contribute to and predict actual behavior toward exercise. Based on the TPB’s model, attitude (β=0.60) and perceived behavioral control (β=0.24) were the major predictors of intention to exercise among men at the baseline. Among women, the subjective norm (β=0.82) was the major predictor of intention to perform the exercise at the baseline. After 12 weeks, attitude (men’s, β=0.68; women’s, β=0.24) and subjective norm (men’s, β=0.12; women’s, β=0.87) were the predictors of the intention to perform the exercise. “Feels healthier with exercise” was the specific factor to improve the intention to perform and to maintain exercise behavior in men (β=0.36) and women (β=0.49). “Not motivated to perform exercise” was the main barrier among men’s intention to exercise. The intention to perform the exercise was able to predict actual behavior regarding exercise at the baseline and at 12 weeks of an intervention program. As a conclusion, TPB is a useful model to determine and to predict maintenance of exercise in the sarcopenic elderly. PMID:25258524

  12. BEHAVE: fire behavior prediction and fuel modeling system - BURN subsystem, Part 2

    Treesearch

    Patricia L. Andrews; Carolyn H. Chase

    1989-01-01

    This is the third publication describing the BEHAVE system of computer programs for predicting behavior of wildland fires. This publication adds the following predictive capabilities: distance firebrands are lofted ahead of a wind-driven surface fire, probabilities of firebrands igniting spot fires, scorch height of trees, and percentage of tree mortality. The system...

  13. Physical activity behavior predicts endogenous pain modulation in older adults.

    PubMed

    Naugle, Kelly M; Ohlman, Thomas; Naugle, Keith E; Riley, Zachary A; Keith, NiCole R

    2017-03-01

    Older adults compared with younger adults are characterized by greater endogenous pain facilitation and a reduced capacity to endogenously inhibit pain, potentially placing them at a greater risk for chronic pain. Previous research suggests that higher levels of self-reported physical activity are associated with more effective pain inhibition and less pain facilitation on quantitative sensory tests in healthy adults. However, no studies have directly tested the relationship between physical activity behavior and pain modulatory function in older adults. This study examined whether objective measures of physical activity behavior cross-sectionally predicted pain inhibitory function on the conditioned pain modulation (CPM) test and pain facilitation on the temporal summation (TS) test in healthy older adults. Fifty-one older adults wore an accelerometer on the hip for 7 days and completed the CPM and TS tests. Measures of sedentary time, light physical activity (LPA), and moderate to vigorous physical activity (MVPA) were obtained from the accelerometer. Hierarchical linear regressions were conducted to determine the relationship of TS and CPM with levels of physical activity, while controlling for demographic, psychological, and test variables. The results indicated that sedentary time and LPA significantly predicted pain inhibitory function on the CPM test, with less sedentary time and greater LPA per day associated with greater pain inhibitory capacity. Additionally, MVPA predicted pain facilitation on the TS test, with greater MVPA associated with less TS of pain. These results suggest that different types of physical activity behavior may differentially impact pain inhibitory and facilitatory processes in older adults.

  14. Predicting early positive change in multisystemic therapy with youth exhibiting antisocial behaviors.

    PubMed

    Tiernan, Kristine; Foster, Sharon L; Cunningham, Phillippe B; Brennan, Patricia; Whitmore, Elizabeth

    2015-03-01

    This study examined individual and family characteristics that predicted early positive change in the context of Multisystemic Therapy (MST). Families (n = 185; 65% male; average youth age 15 years) receiving MST in community settings completed assessments at the outset of treatment and 6-12 weeks into treatment. Early positive changes in youth antisocial behavior were assessed using the caregiver report on the Child Behavior Checklist Externalizing Behaviors subscale and youth report on the Self-Report Delinquency Scale. Overall, families showed significant positive changes by 6-12 weeks into treatment; these early changes were maintained into midtreatment 6-12 weeks later. Families who exhibited clinically significant gains early in treatment were more likely to terminate treatment successfully compared with those who did not show these gains. Low youth internalizing behaviors and absence of youth drug use predicted early positive changes in MST. High levels of parental monitoring and low levels of affiliation with deviant peers (mechanisms known to be associated with MST success) were also associated with early positive change. (PsycINFO Database Record (c) 2015 APA, all rights reserved).

  15. Do infant behaviors following immunization predict attachment? An exploratory study.

    PubMed

    Horton, Rachel; Pillai Riddell, Rebecca; Moran, Greg; Lisi, Diana

    2016-01-01

    The relationship between infant behaviors during routine immunization, pre- and post-needle, and infant attachment was explored. A total of 130 parent-infant dyads were recruited from a larger longitudinal study and videotaped during routine immunization at 12 months and the Strange Situation Procedure (SSP) at 14 months. Six infant behaviors were coded for 1-minute pre-needle and 3-minutes post-needle. Attachment was operationalized according to the secure/avoidant/resistant/disorganized categories. As expected, none of the pre-needle behaviors predicted attachment. Proximity-seeking post-needle significantly discriminated attachment categorizations. Secure infants were more likely to seek proximity to caregivers post-needle in comparison with avoidant and disorganized infants. Proximity-seeking following immunization was positively correlated with proximity-seeking during the SSP and negatively correlated with avoidance and disorganization during the SSP. Infant proximity-seeking during immunization is associated with attachment security and parallels behaviors observed during the SSP. More research is needed to identify behavioral markers of disorganization.

  16. A cross-race effect in metamemory: Predictions of face recognition are more accurate for members of our own race

    PubMed Central

    Hourihan, Kathleen L.; Benjamin, Aaron S.; Liu, Xiping

    2012-01-01

    The Cross-Race Effect (CRE) in face recognition is the well-replicated finding that people are better at recognizing faces from their own race, relative to other races. The CRE reveals systematic limitations on eyewitness identification accuracy and suggests that some caution is warranted in evaluating cross-race identification. The CRE is a problem because jurors value eyewitness identification highly in verdict decisions. In the present paper, we explore how accurate people are in predicting their ability to recognize own-race and other-race faces. Caucasian and Asian participants viewed photographs of Caucasian and Asian faces, and made immediate judgments of learning during study. An old/new recognition test replicated the CRE: both groups displayed superior discriminability of own-race faces, relative to other-race faces. Importantly, relative metamnemonic accuracy was also greater for own-race faces, indicating that the accuracy of predictions about face recognition is influenced by race. This result indicates another source of concern when eliciting or evaluating eyewitness identification: people are less accurate in judging whether they will or will not recognize a face when that face is of a different race than they are. This new result suggests that a witness’s claim of being likely to recognize a suspect from a lineup should be interpreted with caution when the suspect is of a different race than the witness. PMID:23162788

  17. 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

  18. Accurate RNA 5-methylcytosine site prediction based on heuristic physical-chemical properties reduction and classifier ensemble.

    PubMed

    Zhang, Ming; Xu, Yan; Li, Lei; Liu, Zi; Yang, Xibei; Yu, Dong-Jun

    2018-06-01

    RNA 5-methylcytosine (m 5 C) is an important post-transcriptional modification that plays an indispensable role in biological processes. The accurate identification of m 5 C sites from primary RNA sequences is especially useful for deeply understanding the mechanisms and functions of m 5 C. Due to the difficulty and expensive costs of identifying m 5 C sites with wet-lab techniques, developing fast and accurate machine-learning-based prediction methods is urgently needed. In this study, we proposed a new m 5 C site predictor, called M5C-HPCR, by introducing a novel heuristic nucleotide physicochemical property reduction (HPCR) algorithm and classifier ensemble. HPCR extracts multiple reducts of physical-chemical properties for encoding discriminative features, while the classifier ensemble is applied to integrate multiple base predictors, each of which is trained based on a separate reduct of the physical-chemical properties obtained from HPCR. Rigorous jackknife tests on two benchmark datasets demonstrate that M5C-HPCR outperforms state-of-the-art m 5 C site predictors, with the highest values of MCC (0.859) and AUC (0.962). We also implemented the webserver of M5C-HPCR, which is freely available at http://cslab.just.edu.cn:8080/M5C-HPCR/. Copyright © 2018 Elsevier Inc. All rights reserved.

  19. Predicting risk behaviors: development and validation of a diagnostic scale.

    PubMed

    Witte, K; Cameron, K A; McKeon, J K; Berkowitz, J M

    1996-01-01

    The goal of this study was to develop and validate the Risk Behavior Diagnosis (RBD) Scale for use by health care providers and practitioners interested in promoting healthy behaviors. Theoretically guided by the Extended Parallel Process Model (EPPM; a fear appeal theory), the RBD scale was designed to work in conjunction with an easy-to-use formula to determine which types of health risk messages would be most appropriate for a given individual or audience. Because some health risk messages promote behavior change and others backfire, this type of scale offers guidance to practitioners on how to develop the best persuasive message possible to motivate healthy behaviors. The results of the study demonstrate the RBD scale to have a high degree of content, construct, and predictive validity. Specific examples and practical suggestions are offered to facilitate use of the scale for health practitioners.

  20. Impact of Predicting Health Care Utilization Via Web Search Behavior: A Data-Driven Analysis.

    PubMed

    Agarwal, Vibhu; Zhang, Liangliang; Zhu, Josh; Fang, Shiyuan; Cheng, Tim; Hong, Chloe; Shah, Nigam H

    2016-09-21

    By recent estimates, the steady rise in health care costs has deprived more than 45 million Americans of health care services and has encouraged health care providers to better understand the key drivers of health care utilization from a population health management perspective. Prior studies suggest the feasibility of mining population-level patterns of health care resource utilization from observational analysis of Internet search logs; however, the utility of the endeavor to the various stakeholders in a health ecosystem remains unclear. The aim was to carry out a closed-loop evaluation of the utility of health care use predictions using the conversion rates of advertisements that were displayed to the predicted future utilizers as a surrogate. The statistical models to predict the probability of user's future visit to a medical facility were built using effective predictors of health care resource utilization, extracted from a deidentified dataset of geotagged mobile Internet search logs representing searches made by users of the Baidu search engine between March 2015 and May 2015. We inferred presence within the geofence of a medical facility from location and duration information from users' search logs and putatively assigned medical facility visit labels to qualifying search logs. We constructed a matrix of general, semantic, and location-based features from search logs of users that had 42 or more search days preceding a medical facility visit as well as from search logs of users that had no medical visits and trained statistical learners for predicting future medical visits. We then carried out a closed-loop evaluation of the utility of health care use predictions using the show conversion rates of advertisements displayed to the predicted future utilizers. In the context of behaviorally targeted advertising, wherein health care providers are interested in minimizing their cost per conversion, the association between show conversion rate and predicted

  1. Predicting the onset of inappropriate compensatory behaviors in undergraduate college women.

    PubMed

    Jones, Michelle D; Crowther, Janis H

    2013-01-01

    The present study examined various factors from the interactive and sociocultural models of bulimia nervosa as predictors of the onset of compensatory behaviors. Participants (n=237) completed a series of questionnaires assessing dietary restraint, body dissatisfaction, negative affect, binge eating, perfectionism, and self-esteem at two time points one year apart. Women who did not engage in compensatory behaviors at time one but did engage in compensatory behaviors at time two (n=21) were compared to women who engaged in compensatory behaviors at both time points (n=28) and women who did not engage in compensatory behaviors at either time point (n=188). Body dissatisfaction and perfectionism at time one significantly predicted women who began using compensatory behaviors compared to women who did not engage in compensatory behaviors at either time point. Women who experienced an onset of compensatory behaviors also reported significantly greater body dissatisfaction at time 1 than women who engaged in compensatory behaviors for the duration of the study. Implications and limitations of these findings are discussed. Copyright © 2012 Elsevier Ltd. All rights reserved.

  2. Maternal feeding practices predict weight gain and obesogenic eating behaviors in young children: a prospective study

    PubMed Central

    2013-01-01

    Background Maternal feeding practices have been proposed to play an important role in early child weight gain and obesogenic eating behaviors. However, to date longitudinal investigations in young children exploring these relationships have been lacking. The aim of the present study was to explore prospective relationships between maternal feeding practices, child weight gain and obesogenic eating behaviors in 2-year-old children. The competing hypothesis that child eating behaviors predict changes in maternal feeding practices was also examined. Methods A sample of 323 mother (mean age = 35 years, ± 0.37) and child dyads (mean age = 2.03 years, ± 0.37 at recruitment) were participants. Mothers completed a questionnaire assessing parental feeding practices and child eating behaviors at baseline and again one year later. Child BMI (predominantly objectively measured) was obtained at both time points. Results Increases in child BMI z-scores over the follow-up period were predicted by maternal instrumental feeding practices. Furthermore, restriction, emotional feeding, encouragement to eat, weight-based restriction and fat restriction were associated prospectively with the development of obesogenic eating behaviors in children including emotional eating, tendency to overeat and food approach behaviors (such as enjoyment of food and good appetite). Maternal monitoring, however, predicted decreases in food approach eating behaviors. Partial support was also observed for child eating behaviors predicting maternal feeding practices. Conclusions Maternal feeding practices play an important role in the development of weight gain and obesogenic eating behaviors in young children and are potential targets for effective prevention interventions aiming to decrease child obesity. PMID:23414332

  3. Predictability of Interruptions During Medication Administration With Related Behavioral Management Strategies.

    PubMed

    Johnson, Maree; Weidemann, Gabrielle; Adams, Rebecca; Manias, Elizabeth; Levett-Jones, Tracy; Aguilar, Vicki; Everett, Bronwyn

    The aim of this qualitative study was to examine the nature of interruptions during medication administration. Focus groups were conducted with medical/surgical nurses (n = 15), critical care nurses (n = 13), and nurse managers/educators/specialists (n = 6). Most interruptions (78%) were predictable. Nurse-adopted strategies included blocking, engaging, mediating, multitasking, and preventing. Educational content was developed that relates behavioral strategies to respond to predictable and unpredictable interruptions.

  4. Visual prediction and perceptual expertise

    PubMed Central

    Cheung, Olivia S.; Bar, Moshe

    2012-01-01

    Making accurate predictions about what may happen in the environment requires analogies between perceptual input and associations in memory. These elements of predictions are based on cortical representations, but little is known about how these processes can be enhanced by experience and training. On the other hand, studies on perceptual expertise have revealed that the acquisition of expertise leads to strengthened associative processing among features or objects, suggesting that predictions and expertise may be tightly connected. Here we review the behavioral and neural findings regarding the mechanisms involving prediction and expert processing, and highlight important possible overlaps between them. Future investigation should examine the relations among perception, memory and prediction skills as a function of expertise. The knowledge gained by this line of research will have implications for visual cognition research, and will advance our understanding of how the human brain can improve its ability to predict by learning from experience. PMID:22123523

  5. Swimming behavior of zebrafish is accurately classified by direct modeling and behavioral space analysis

    NASA Astrophysics Data System (ADS)

    Feng, Ruopei; Chemla, Yann; Gruebele, Martin

    Larval zebrafish is a popular organism in the search for the correlation between locomotion behavior and neural pathways because of their highly stereotyped and temporally episodic swimming motion. This correlation is usually investigated using electrophysiological recordings of neural activities in partially immobilized fish. Seeking for a way to study animal behavior without constraints or intruding electrodes, which can in turn modify their behavior, our lab has introduced a parameter-free approach which allows automated classification of the locomotion behaviors of freely swimming fish. We looked into several types of swimming bouts including free swimming and two modes of escape responses and established a new classification of these behaviors. Combined with a neurokinematic model, our analysis showed the capability to probe intrinsic properties of the underlying neural pathways of freely swimming larval zebrafish by inspecting swimming movies only.

  6. “Feature Detection” vs. “Predictive Coding” Models of Plant Behavior

    PubMed Central

    Calvo, Paco; Baluška, František; Sims, Andrew

    2016-01-01

    In this article we consider the possibility that plants exhibit anticipatory behavior, a mark of intelligence. If plants are able to anticipate and respond accordingly to varying states of their surroundings, as opposed to merely responding online to environmental contingencies, then such capacity may be in principle testable, and subject to empirical scrutiny. Our main thesis is that adaptive behavior can only take place by way of a mechanism that predicts the environmental sources of sensory stimulation. We propose to test for anticipation in plants experimentally by contrasting two empirical hypotheses: “feature detection” and “predictive coding.” We spell out what these contrasting hypotheses consist of by way of illustration from the animal literature, and consider how to transfer the rationale involved to the plant literature. PMID:27757094

  7. The NAFLD Index: A Simple and Accurate Screening Tool for the Prediction of Non-Alcoholic Fatty Liver Disease.

    PubMed

    Ichino, Naohiro; Osakabe, Keisuke; Sugimoto, Keiko; Suzuki, Koji; Yamada, Hiroya; Takai, Hiroji; Sugiyama, Hiroko; Yukitake, Jun; Inoue, Takashi; Ohashi, Koji; Hata, Tadayoshi; Hamajima, Nobuyuki; Nishikawa, Toru; Hashimoto, Senju; Kawabe, Naoto; Yoshioka, Kentaro

    2015-01-01

    Non-alcoholic fatty liver disease (NAFLD) is a common debilitating condition in many industrialized countries that increases the risk of cardiovascular disease. The aim of this study was to derive a simple and accurate screening tool for the prediction of NAFLD in the Japanese population. A total of 945 participants, 279 men and 666 women living in Hokkaido, Japan, were enrolled among residents who attended a health check-up program from 2010 to 2014. Participants with an alcohol consumption > 20 g/day and/or a chronic liver disease, such as chronic hepatitis B, chronic hepatitis C or autoimmune hepatitis, were excluded from this study. Clinical and laboratory data were examined to identify predictive markers of NAFLD. A new predictive index for NAFLD, the NAFLD index, was constructed for men and for women. The NAFLD index for men = -15.5693+0.3264 [BMI] +0.0134 [triglycerides (mg/dl)], and for women = -31.4686+0.3683 [BMI] +2.5699 [albumin (g/dl)] +4.6740[ALT/AST] -0.0379 [HDL cholesterol (mg/dl)]. The AUROC of the NAFLD index for men and for women was 0.87(95% CI 0.88-1.60) and 0.90 (95% CI 0.66-1.02), respectively. The cut-off point of -5.28 for men predicted NAFLD with an accuracy of 82.8%. For women, the cut-off point of -7.65 predicted NAFLD with an accuracy of 87.7%. A new index for the non-invasive prediction of NAFLD, the NAFLD index, was constructed using available clinical and laboratory data. This index is a simple screening tool to predict the presence of NAFLD.

  8. Predictive Validity of a Student Self-Report Screener of Behavioral and Emotional Risk in an Urban High School

    ERIC Educational Resources Information Center

    Dowdy, Erin; Harrell-Williams, Leigh; Dever, Bridget V.; Furlong, Michael J.; Moore, Stephanie; Raines, Tara; Kamphaus, Randy W.

    2016-01-01

    Increasingly, schools are implementing school-based screening for risk of behavioral and emotional problems; hence, foundational evidence supporting the predictive validity of screening instruments is important to assess. This study examined the predictive validity of the Behavior Assessment System for Children-2 Behavioral and Emotional Screening…

  9. Predictive Validity of Delay Discounting Behavior in Adolescence: A Longitudinal Twin Study

    PubMed Central

    Isen, Joshua D.; Sparks, Jordan C.; Iacono, William G.

    2014-01-01

    A standard assumption in the delay discounting literature is that individuals who exhibit steeper discounting of hypothetical rewards also experience greater difficulty deferring gratification to real-world rewards. There is ample cross-sectional evidence that delay discounting paradigms reflect a variety of maladaptive psychosocial outcomes, including substance use pathology. We sought to determine whether a computerized assessment of hypothetical delay discounting (HDD) taps into behavioral impulsivity in a community sample of adolescent twins (N = 675). Using a longitudinal design, we hypothesized that greater HDD at age 14–15 predicts real-world impulsive choices and risk for substance use disorders in late adolescence. We also examined the genetic and environmental structure of HDD performance. Individual differences in HDD behavior showed moderate heritability, and were prospectively associated with real-world temporal discounting at age 17–18. Contrary to expectations, HDD was not consistently related to substance use or trait impulsivity. Although a significant association between HDD behavior and past substance use emerged in males, this effect was mediated by cognitive ability. In both sexes, HDD failed to predict a comprehensive index of substance use problems and behavioral disinhibition in late adolescence. In sum, we present some of the first evidence that HDD performance is heritable and predictive of real-world temporal discounting of rewards. Nevertheless, HDD might not serve as a valid marker of substance use disorder risk in younger adolescents, particularly females. PMID:24999868

  10. Modeling Stationary Lithium-Ion Batteries for Optimization and Predictive Control

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

    Baker, Kyri A; Shi, Ying; Christensen, Dane T

    Accurately modeling stationary battery storage behavior is crucial to understand and predict its limitations in demand-side management scenarios. In this paper, a lithium-ion battery model was derived to estimate lifetime and state-of-charge for building-integrated use cases. The proposed battery model aims to balance speed and accuracy when modeling battery behavior for real-time predictive control and optimization. In order to achieve these goals, a mixed modeling approach was taken, which incorporates regression fits to experimental data and an equivalent circuit to model battery behavior. A comparison of the proposed battery model output to actual data from the manufacturer validates the modelingmore » approach taken in the paper. Additionally, a dynamic test case demonstrates the effects of using regression models to represent internal resistance and capacity fading.« less

  11. A Deep Learning Framework for Robust and Accurate Prediction of ncRNA-Protein Interactions Using Evolutionary Information.

    PubMed

    Yi, Hai-Cheng; You, Zhu-Hong; Huang, De-Shuang; Li, Xiao; Jiang, Tong-Hai; Li, Li-Ping

    2018-06-01

    The interactions between non-coding RNAs (ncRNAs) and proteins play an important role in many biological processes, and their biological functions are primarily achieved by binding with a variety of proteins. High-throughput biological techniques are used to identify protein molecules bound with specific ncRNA, but they are usually expensive and time consuming. Deep learning provides a powerful solution to computationally predict RNA-protein interactions. In this work, we propose the RPI-SAN model by using the deep-learning stacked auto-encoder network to mine the hidden high-level features from RNA and protein sequences and feed them into a random forest (RF) model to predict ncRNA binding proteins. Stacked assembling is further used to improve the accuracy of the proposed method. Four benchmark datasets, including RPI2241, RPI488, RPI1807, and NPInter v2.0, were employed for the unbiased evaluation of five established prediction tools: RPI-Pred, IPMiner, RPISeq-RF, lncPro, and RPI-SAN. The experimental results show that our RPI-SAN model achieves much better performance than other methods, with accuracies of 90.77%, 89.7%, 96.1%, and 99.33%, respectively. It is anticipated that RPI-SAN can be used as an effective computational tool for future biomedical researches and can accurately predict the potential ncRNA-protein interacted pairs, which provides reliable guidance for biological research. Copyright © 2018 The Author(s). Published by Elsevier Inc. All rights reserved.

  12. Trait psychopathy, emotional intelligence, and criminal thinking: Predicting illegal behavior among college students.

    PubMed

    Fix, Rebecca L; Fix, Spencer T

    2015-01-01

    Research focusing on individuals high on trait psychopathy remains limited. Higher trait psychopathy is associated with lower levels of emotional intelligence and increased participation in illegal behavior. Additionally, research has confirmed significantly higher levels of criminal thinking and lower levels of empathy in the incarcerated psychopathic population. However, the relationships between trait psychopathy and criminal thinking have not been researched in the community or college population. To test for such differences, questionnaires containing relevant measures were administered to 111 college students. Results indicated that higher levels of trait psychopathy were significantly related to less caring for others, intrapersonal understanding, and general mood, and greater interpersonal functioning and stress management. Furthermore, trait psychopathy was a strong predictor of violent, property, drug, and status offenses. Power-oriented criminal thinking was also predictive of violent behaviors, and entitlement predicted property offending. Results suggest emotional intelligence is important for predicting psychopathy, and trait psychopathy is a strong predictor of all types of illegal behaviors among the non-incarcerated population. Published by Elsevier Ltd.

  13. Examining the Validity of Behavioral Self-Regulation Tools in Predicting Preschoolers' Academic Achievement

    ERIC Educational Resources Information Center

    Schmitt, Sara A.; Pratt, Megan E.; McClelland, Megan M.

    2014-01-01

    The current study investigated the predictive utility among teacher-rated, observed, and directly assessed behavioral self-regulation skills to academic achievement in preschoolers. Specifically, this study compared how a teacher report, the Child Behavior Rating Scale, an observer report, the Observed Child Engagement Scale, and a direct…

  14. Examining the Validity of Behavioral Self-Regulation Tools in Predicting Preschoolers' Academic Achievement

    ERIC Educational Resources Information Center

    Schmitt, Sara A.; Pratt, Megan E.; McClelland, Megan M.

    2014-01-01

    Research Findings: The current study investigated the predictive utility of teacher-rated, observed, and directly assessed behavioral self-regulation skills to academic achievement in preschoolers. Specifically, this study compared how a teacher report (the Child Behavior Rating Scale), an observer report (the Observed Child Engagement Scale), and…

  15. Observed parenting behaviors interact with a polymorphism of the brain-derived neurotrophic factor gene to predict the emergence of oppositional defiant and callous-unemotional behaviors at age 3 years.

    PubMed

    Willoughby, Michael T; Mills-Koonce, Roger; Propper, Cathi B; Waschbusch, Daniel A

    2013-11-01

    Using the Durham Child Health and Development Study, this study (N = 171) tested whether observed parenting behaviors in infancy (6 and 12 months) and toddlerhood/preschool (24 and 36 months) interacted with a child polymorphism of the brain-derived neurotrophic factor gene to predict oppositional defiant disorder (ODD) and callous-unemotional (CU) behaviors at age 3 years. Child genotype interacted with observed harsh and intrusive (but not sensitive) parenting to predict ODD and CU behaviors. Harsh-intrusive parenting was more strongly associated with ODD and CU for children with a methionine allele of the brain-derived neurotrophic factor gene. CU behaviors were uniquely predicted by harsh-intrusive parenting in infancy, whereas ODD behaviors were predicted by harsh-intrusive parenting in both infancy and toddlerhood/preschool. The results are discussed from the perspective of the contributions of caregiving behaviors as contributing to distinct aspects of early onset disruptive behavior.

  16. Prediction of hot deformation behavior of high phosphorus steel using artificial neural network

    NASA Astrophysics Data System (ADS)

    Singh, Kanchan; Rajput, S. K.; Soota, T.; Verma, Vijay; Singh, Dharmendra

    2018-03-01

    To predict the hot deformation behavior of high phosphorus steel, the hot compression experiments were performed with the help of thermo-mechanical simulator Gleeble® 3800 in the temperatures ranging from 750 °C to 1050 °C and strain rates of 0.001 s-1, 0.01 s-1, 0.1 s-1, 0.5 s-1, 1.0 s-1 and 10 s-1. The experimental stress-strain data are employed to develop artificial neural network (ANN) model and their predictability. Using different combination of temperature, strain and strain rate as a input parameter and obtained experimental stress as a target, a multi-layer ANN model based on feed-forward back-propagation algorithm is trained, to predict the flow stress for a given processing condition. The relative error between predicted and experimental stress are in the range of ±3.5%, whereas the correlation coefficient (R2) of training and testing data are 0.99986 and 0.99999 respectively. This shows that a well-trained ANN model has excellent capability to predict the hot deformation behavior of materials. Comparative study shows quite good agreement of predicted and experimental values.

  17. Accurate and Reliable Prediction of the Binding Affinities of Macrocycles to Their Protein Targets.

    PubMed

    Yu, Haoyu S; Deng, Yuqing; Wu, Yujie; Sindhikara, Dan; Rask, Amy R; Kimura, Takayuki; Abel, Robert; Wang, Lingle

    2017-12-12

    Macrocycles have been emerging as a very important drug class in the past few decades largely due to their expanded chemical diversity benefiting from advances in synthetic methods. Macrocyclization has been recognized as an effective way to restrict the conformational space of acyclic small molecule inhibitors with the hope of improving potency, selectivity, and metabolic stability. Because of their relatively larger size as compared to typical small molecule drugs and the complexity of the structures, efficient sampling of the accessible macrocycle conformational space and accurate prediction of their binding affinities to their target protein receptors poses a great challenge of central importance in computational macrocycle drug design. In this article, we present a novel method for relative binding free energy calculations between macrocycles with different ring sizes and between the macrocycles and their corresponding acyclic counterparts. We have applied the method to seven pharmaceutically interesting data sets taken from recent drug discovery projects including 33 macrocyclic ligands covering a diverse chemical space. The predicted binding free energies are in good agreement with experimental data with an overall root-mean-square error (RMSE) of 0.94 kcal/mol. This is to our knowledge the first time where the free energy of the macrocyclization of linear molecules has been directly calculated with rigorous physics-based free energy calculation methods, and we anticipate the outstanding accuracy demonstrated here across a broad range of target classes may have significant implications for macrocycle drug discovery.

  18. Global brain dynamics during social exclusion predict subsequent behavioral conformity

    PubMed Central

    Wasylyshyn, Nick; Hemenway Falk, Brett; Garcia, Javier O; Cascio, Christopher N; O’Donnell, Matthew Brook; Bingham, C Raymond; Simons-Morton, Bruce; Vettel, Jean M; Falk, Emily B

    2018-01-01

    Abstract Individuals react differently to social experiences; for example, people who are more sensitive to negative social experiences, such as being excluded, may be more likely to adapt their behavior to fit in with others. We examined whether functional brain connectivity during social exclusion in the fMRI scanner can be used to predict subsequent conformity to peer norms. Adolescent males (n = 57) completed a two-part study on teen driving risk: a social exclusion task (Cyberball) during an fMRI session and a subsequent driving simulator session in which they drove alone and in the presence of a peer who expressed risk-averse or risk-accepting driving norms. We computed the difference in functional connectivity between social exclusion and social inclusion from each node in the brain to nodes in two brain networks, one previously associated with mentalizing (medial prefrontal cortex, temporoparietal junction, precuneus, temporal poles) and another with social pain (dorsal anterior cingulate cortex, anterior insula). Using predictive modeling, this measure of global connectivity during exclusion predicted the extent of conformity to peer pressure during driving in the subsequent experimental session. These findings extend our understanding of how global neural dynamics guide social behavior, revealing functional network activity that captures individual differences. PMID:29529310

  19. Does life history predict risk-taking behavior of wintering dabbling ducks?

    USGS Publications Warehouse

    Ackerman, Joshua T.; Eadie, J.M.; Moore, T.G.

    2006-01-01

    Life-history theory predicts that longer-lived, less fecund species should take fewer risks when exposed to predation than shorter-lived, more fecund species. We tested this prediction for seven species of dabbling ducks (Anas) by measuring the approach behavior (behavior of ducks when approaching potential landing sites) of 1099 duck flocks during 37 hunting trials and 491 flocks during 13 trials conducted immediately after the 1999-2000 waterfowl hunting season in California, USA. We also experimentally manipulated the attractiveness of the study site by using two decoy treatments: (1) traditional, stationary decoys only, and (2) traditional decoys in conjunction with a mechanical spinning-wing decoy. Approach behavior of ducks was strongly correlated with their life history. Minimum approach distance was negatively correlated with reproductive output during each decoy treatment and trial type. Similarly, the proportion of flocks taking risk (approaching landing sites to within 45 m) was positively correlated with reproductive output. We found similar patterns of approach behavior in relation to other life-history parameters (i.e., adult female body mass and annual adult female survival rate). Thus, species characterized by a slower life-history strategy (e.g., Northern Pintail [A. acuta]) were more risk-averse than species with a faster life-history strategy (e.g., Cinnamon Teal [A. cyanoptera]). Furthermore, although we were able to reduce risk-averseness using the spinning-wing decoy, we were unable to override the influence of life history on risk-taking behavior. Alternative explanations did not account for the observed correlation between approach behavior and life-history parameters. These results suggest that life history influences the risk-taking behavior of dabbling ducks and provide an explanation for the differential vulnerability of waterfowl to harvest. ?? The Cooper Ornithological Society 2006.

  20. Methods for predicting properties and tailoring salt solutions for industrial processes

    NASA Technical Reports Server (NTRS)

    Ally, Moonis R.

    1993-01-01

    An algorithm developed at Oak Ridge National Laboratory accurately and quickly predicts thermodynamic properties of concentrated aqueous salt solutions. This algorithm is much simpler and much faster than other modeling schemes and is unique because it can predict solution behavior at very high concentrations and under varying conditions. Typical industrial applications of this algorithm would be in manufacture of inorganic chemicals by crystallization, thermal storage, refrigeration and cooling, extraction of metals, emissions controls, etc.

  1. Prediction of the Possibility a Right-Turn Driving Behavior at Intersection Leads to an Accident by Detecting Deviation of the Situation from Usual when the Behavior is Observed

    NASA Astrophysics Data System (ADS)

    Hayashi, Toshinori; Yamada, Keiichi

    Deviation of driving behavior from usual could be a sign of human error that increases the risk of traffic accidents. This paper proposes a novel method for predicting the possibility a driving behavior leads to an accident from the information on the driving behavior and the situation. In a previous work, a method of predicting the possibility by detecting the deviation of driving behavior from usual one in that situation has been proposed. In contrast, the method proposed in this paper predicts the possibility by detecting the deviation of the situation from usual one when the behavior is observed. An advantage of the proposed method is the number of the required models is independent of the variety of the situations. The method was applied to a problem of predicting accidents by right-turn driving behavior at an intersection, and the performance of the method was evaluated by experiments on a driving simulator.

  2. Predicting violent behavior: The role of violence exposure and future educational aspirations during adolescence.

    PubMed

    Stoddard, Sarah A; Heinze, Justin E; Choe, Daniel Ewon; Zimmerman, Marc A

    2015-10-01

    Few researchers have explored future educational aspirations as a promotive factor against exposure to community violence in relation to adolescents' violent behavior over time. The present study examined the direct and indirect effect of exposure to community violence prior to 9th grade on attitudes about violence and violent behavior in 12th grade, and violent behavior at age 22 via 9th grade future educational aspirations in a sample of urban African American youth (n = 681; 49% male). Multi-group SEM was used to test the moderating effect of gender. Exposure to violence was associated with lower future educational aspirations. For boys, attitudes about violence directly predicted violent behavior at age 22. For boys, future educational aspirations indirectly predicted less violent behavior at age 22. Implications of the findings and suggestions for future research are discussed. Copyright © 2015 The Foundation for Professionals in Services for Adolescents. Published by Elsevier Ltd. All rights reserved.

  3. Beating Heart Motion Accurate Prediction Method Based on Interactive Multiple Model: An Information Fusion Approach

    PubMed Central

    Xie, Weihong; Yu, Yang

    2017-01-01

    Robot-assisted motion compensated beating heart surgery has the advantage over the conventional Coronary Artery Bypass Graft (CABG) in terms of reduced trauma to the surrounding structures that leads to shortened recovery time. The severe nonlinear and diverse nature of irregular heart rhythm causes enormous difficulty for the robot to realize the clinic requirements, especially under arrhythmias. In this paper, we propose a fusion prediction framework based on Interactive Multiple Model (IMM) estimator, allowing each model to cover a distinguishing feature of the heart motion in underlying dynamics. We find that, at normal state, the nonlinearity of the heart motion with slow time-variant changing dominates the beating process. When an arrhythmia occurs, the irregularity mode, the fast uncertainties with random patterns become the leading factor of the heart motion. We deal with prediction problem in the case of arrhythmias by estimating the state with two behavior modes which can adaptively “switch” from one to the other. Also, we employed the signal quality index to adaptively determine the switch transition probability in the framework of IMM. We conduct comparative experiments to evaluate the proposed approach with four distinguished datasets. The test results indicate that the new proposed approach reduces prediction errors significantly. PMID:29124062

  4. Beating Heart Motion Accurate Prediction Method Based on Interactive Multiple Model: An Information Fusion Approach.

    PubMed

    Liang, Fan; Xie, Weihong; Yu, Yang

    2017-01-01

    Robot-assisted motion compensated beating heart surgery has the advantage over the conventional Coronary Artery Bypass Graft (CABG) in terms of reduced trauma to the surrounding structures that leads to shortened recovery time. The severe nonlinear and diverse nature of irregular heart rhythm causes enormous difficulty for the robot to realize the clinic requirements, especially under arrhythmias. In this paper, we propose a fusion prediction framework based on Interactive Multiple Model (IMM) estimator, allowing each model to cover a distinguishing feature of the heart motion in underlying dynamics. We find that, at normal state, the nonlinearity of the heart motion with slow time-variant changing dominates the beating process. When an arrhythmia occurs, the irregularity mode, the fast uncertainties with random patterns become the leading factor of the heart motion. We deal with prediction problem in the case of arrhythmias by estimating the state with two behavior modes which can adaptively "switch" from one to the other. Also, we employed the signal quality index to adaptively determine the switch transition probability in the framework of IMM. We conduct comparative experiments to evaluate the proposed approach with four distinguished datasets. The test results indicate that the new proposed approach reduces prediction errors significantly.

  5. Limited Sampling Strategy for Accurate Prediction of Pharmacokinetics of Saroglitazar: A 3-point Linear Regression Model Development and Successful Prediction of Human Exposure.

    PubMed

    Joshi, Shuchi N; Srinivas, Nuggehally R; Parmar, Deven V

    2018-03-01

    Our aim was to develop and validate the extrapolative performance of a regression model using a limited sampling strategy for accurate estimation of the area under the plasma concentration versus time curve for saroglitazar. Healthy subject pharmacokinetic data from a well-powered food-effect study (fasted vs fed treatments; n = 50) was used in this work. The first 25 subjects' serial plasma concentration data up to 72 hours and corresponding AUC 0-t (ie, 72 hours) from the fasting group comprised a training dataset to develop the limited sampling model. The internal datasets for prediction included the remaining 25 subjects from the fasting group and all 50 subjects from the fed condition of the same study. The external datasets included pharmacokinetic data for saroglitazar from previous single-dose clinical studies. Limited sampling models were composed of 1-, 2-, and 3-concentration-time points' correlation with AUC 0-t of saroglitazar. Only models with regression coefficients (R 2 ) >0.90 were screened for further evaluation. The best R 2 model was validated for its utility based on mean prediction error, mean absolute prediction error, and root mean square error. Both correlations between predicted and observed AUC 0-t of saroglitazar and verification of precision and bias using Bland-Altman plot were carried out. None of the evaluated 1- and 2-concentration-time points models achieved R 2 > 0.90. Among the various 3-concentration-time points models, only 4 equations passed the predefined criterion of R 2 > 0.90. Limited sampling models with time points 0.5, 2, and 8 hours (R 2 = 0.9323) and 0.75, 2, and 8 hours (R 2 = 0.9375) were validated. Mean prediction error, mean absolute prediction error, and root mean square error were <30% (predefined criterion) and correlation (r) was at least 0.7950 for the consolidated internal and external datasets of 102 healthy subjects for the AUC 0-t prediction of saroglitazar. The same models, when applied to the AUC 0-t

  6. Towards more accurate and reliable predictions for nuclear applications

    NASA Astrophysics Data System (ADS)

    Goriely, Stephane; Hilaire, Stephane; Dubray, Noel; Lemaître, Jean-François

    2017-09-01

    The need for nuclear data far from the valley of stability, for applications such as nuclear astrophysics or future nuclear facilities, challenges the robustness as well as the predictive power of present nuclear models. Most of the nuclear data evaluation and prediction are still performed on the basis of phenomenological nuclear models. For the last decades, important progress has been achieved in fundamental nuclear physics, making it now feasible to use more reliable, but also more complex microscopic or semi-microscopic models in the evaluation and prediction of nuclear data for practical applications. Nowadays mean-field models can be tuned at the same level of accuracy as the phenomenological models, renormalized on experimental data if needed, and therefore can replace the phenomenological inputs in the evaluation of nuclear data. The latest achievements to determine nuclear masses within the non-relativistic HFB approach, including the related uncertainties in the model predictions, are discussed. Similarly, recent efforts to determine fission observables within the mean-field approach are described and compared with more traditional existing models.

  7. Rational Emotive Behavior Therapy after Ellis: Predictions for the Future.

    ERIC Educational Resources Information Center

    Weinrach, Stephen G.; Ellis, Albert; DiGiuseppe, Raymond; Bernard, Michael E.; Dryden, Windy; Kassinove, Howard; Morris, G. Barry; Vernon, Ann; Wolfe, Janet

    1995-01-01

    Nine members of the institute for Rational-Emotive Therapy's (REBT) International Training Standards and Review Committee predicted the status of REBT 25 to 50 years after the death of Albert Ellis. Will REBT continue to exist in its own right or be incorporated into newer forms of cognitive behavior therapy? (EMK)

  8. Organic Foods: Do Eco-Friendly Attitudes Predict Eco-Friendly Behaviors?

    ERIC Educational Resources Information Center

    Dahm, Molly J.; Samonte, Aurelia V.; Shows, Amy R.

    2009-01-01

    Objective: The purpose of this study was to determine whether student awareness and attitudes about organic foods would predict their behaviors with regard to organic food consumption and other healthy lifestyle practices. A secondary purpose was to determine whether attitudes about similar eco-friendly practices would result in socially conscious…

  9. Predicting Risk-Taking Behavior from Prefrontal Resting-State Activity and Personality

    PubMed Central

    Studer, Bettina; Pedroni, Andreas; Rieskamp, Jörg

    2013-01-01

    Risk-taking is subject to considerable individual differences. In the current study, we tested whether resting-state activity in the prefrontal cortex and trait sensitivity to reward and punishment can help predict risk-taking behavior. Prefrontal activity at rest was assessed in seventy healthy volunteers using electroencephalography, and compared to their choice behavior on an economic risk-taking task. The Behavioral Inhibition System/Behavioral Activation System scale was used to measure participants’ trait sensitivity to reward and punishment. Our results confirmed both prefrontal resting-state activity and personality traits as sources of individual differences in risk-taking behavior. Right-left asymmetry in prefrontal activity and scores on the Behavioral Inhibition System scale, reflecting trait sensitivity to punishment, were correlated with the level of risk-taking on the task. We further discovered that scores on the Behavioral Inhibition System scale modulated the relationship between asymmetry in prefrontal resting-state activity and risk-taking. The results of this study demonstrate that heterogeneity in risk-taking behavior can be traced back to differences in the basic physiology of decision-makers’ brains, and suggest that baseline prefrontal activity and personality traits might interplay in guiding risk-taking behavior. PMID:24116176

  10. Accurate electrical prediction of memory array through SEM-based edge-contour extraction using SPICE simulation

    NASA Astrophysics Data System (ADS)

    Shauly, Eitan; Rotstein, Israel; Peltinov, Ram; Latinski, Sergei; Adan, Ofer; Levi, Shimon; Menadeva, Ovadya

    2009-03-01

    The continues transistors scaling efforts, for smaller devices, similar (or larger) drive current/um and faster devices, increase the challenge to predict and to control the transistor off-state current. Typically, electrical simulators like SPICE, are using the design intent (as-drawn GDS data). At more sophisticated cases, the simulators are fed with the pattern after lithography and etch process simulations. As the importance of electrical simulation accuracy is increasing and leakage is becoming more dominant, there is a need to feed these simulators, with more accurate information extracted from physical on-silicon transistors. Our methodology to predict changes in device performances due to systematic lithography and etch effects was used in this paper. In general, the methodology consists on using the OPCCmaxTM for systematic Edge-Contour-Extraction (ECE) from transistors, taking along the manufacturing and includes any image distortions like line-end shortening, corner rounding and line-edge roughness. These measurements are used for SPICE modeling. Possible application of this new metrology is to provide a-head of time, physical and electrical statistical data improving time to market. In this work, we applied our methodology to analyze a small and large array's of 2.14um2 6T-SRAM, manufactured using Tower Standard Logic for General Purposes Platform. 4 out of the 6 transistors used "U-Shape AA", known to have higher variability. The predicted electrical performances of the transistors drive current and leakage current, in terms of nominal values and variability are presented. We also used the methodology to analyze an entire SRAM Block array. Study of an isolation leakage and variability are presented.

  11. Comparing the predictive capacity of observed in-session resistance to self-reported motivation in cognitive behavioral therapy.

    PubMed

    Westra, Henny A

    2011-02-01

    Self-report measures of motivation for changing anxiety have been weakly and inconsistently related to outcome in cognitive behavioral therapy (CBT). While clients may not be able to accurately report their motivation, ambivalence about change may nonetheless be expressed in actual therapy sessions as opposition to the direction set by the therapist (i.e., resistance). In the context of CBT for generalized anxiety disorder, the present study compared the ability of observed in-session resistance in CBT session 1 and two self-report measures of motivation for changing anxiety (the Change Questionnaire & the Client Motivational for Therapy Scale) to (1) predict client and therapist rated homework compliance (2) predict post-CBT and one-year post-treatment worry reduction, and (3) differentiate those who received motivational interviewing prior to CBT from those who received no pre-treatment. Observed in-session resistance performed very well on each index, compared to the performance of self-reported motivation which was inconsistent and weaker relative to observed resistance. These findings strongly support both clinician sensitivity to moments of client resistance in actual therapy sessions as early as session 1, and the inclusion of observational process measures in CBT research. Crown Copyright © 2010. Published by Elsevier Ltd. All rights reserved.

  12. Predicting outcomes of group cognitive behavior therapy for patients with affective and neurotic disorders.

    PubMed

    Hooke, Geoffrey R; Page, Andrew C

    2002-10-01

    An attempt was made to predict outcomes following group Cognitive Behavior Therapy (CBT) for patients with affective and neurotic disorders. A group of 348 patients at a private psychiatric clinic, treated in a group CBT program, completed the Depression, Anxiety, and Stress Scale (DASS) before and after treatment. Prior to treatment, data from the Locus of Control of Behavior (LCB), a Global Assessment of Function (GAF), the Health of the Nation Outcome Scales (HoNOS), and the Rosenberg Self Esteem Scale (RSE) were also collected. Results indicated that posttreatment stress scores of all patients were predicted by pretreatment stress and self-esteem. Among patients with neurotic disorders, posttreatment anxiety was predicted by initial anxiety and self-esteem whereas among patients with affective disorders, posttreatment anxiety scores were predicted by initial anxiety and GAF. For patients with neurotic disorders, self-esteem did not predict variance in posttreatment depression in addition to that explained by pretreatment depression. In contrast, for patients with affective disorders, pretreatment depression and Locus of Control predicted posttreatment depression.

  13. Do self- or parent-reported dietary, physical activity, and sedentary behaviors predict worsening obesity in children?

    PubMed

    Dorsey, Karen B; Mauldon, Maria; Magraw, Ruth; Yu, Sunkyung; Krumholz, Harlan M

    2010-10-01

    To determine whether information gathered during routine healthcare visits regarding obesity related risk factors and risk behaviors predicts increases in BMI z-score over time among overweight and obese children. Medical records from 168 overweight and 441 obese patients seen for repeated visits between September 2003 and April 2006 were examined for reported dietary, physical activity, and sedentary behaviors, family history of obesity and diabetes mellitus, documented Acanthosis nigricans, and BMI values. Random-effects regression analysis was done to determine whether demographic, familial, or behavioral data predicted changes in BMI z-score over time. The presence of A nigricans and a family history of obesity were associated with an increase in BMI z-score (beta=0.56, SE=0.09, P<.001 and beta=0.31, SE=0.13, P=.021). These risk factors explained 8% and 7% of the variation in BMI z-score respectively. Self- or parent-reported dietary and physical activity behaviors did not predict change in BMI z-score. Our findings suggest that the risk factors and self- or parent-reported risk behaviors routinely assessed by pediatric clinicians have limited ability to predict future growth trends, demonstrating the difficulty in determining which patients have the greatest risk of progression of obesity. Copyright (c) 2010 Mosby, Inc. All rights reserved.

  14. Estimating energy expenditure in vascular surgery patients: Are predictive equations accurate enough?

    PubMed

    Suen, J; Thomas, J M; Delaney, C L; Spark, J I; Miller, M D

    2016-12-01

    Malnutrition is prevalent in vascular surgical patients who commonly seek tertiary care at advanced stages of disease. Adjunct nutrition support is therefore pertinent to optimise patient outcomes. To negate consequences related to excessive or suboptimal dietary energy intake, it is essential to accurately determine energy expenditure and subsequent requirements. This study aims to compare resting energy expenditure (REE) measured by indirect calorimetry, a commonly used comparator, to REE estimated by predictive equations (Schofield, Harris-Benedict equations and Miller equation) to determine the most suitable equation for vascular surgery patients. Data were collected from four studies that measured REE in 77 vascular surgery patients. Bland-Altman analyses were conducted to explore agreement. Presence of fixed or proportional bias was assessed by linear regression analyses. In comparison to measured REE, on average REE was overestimated when Schofield (+857 kJ/day), Harris-Benedict (+801 kJ/day) and Miller (+71 kJ/day) equations were used. Wide limits of agreement led to an over or underestimation from 1552 to 1755 kJ. Proportional bias was absent in Schofield (R 2  = 0.005, p = 0.54) and Harris-Benedict equations (R 2  = 0.045, p = 0.06) but was present in the Miller equation (R 2  = 0.210, p < 0.01) even after logarithmic transformation (R 2  = 0.213, p < 0.01). Whilst the Miller equation tended to overestimate resting energy expenditure and was affected by proportional bias, the limits of agreement and mean bias were smaller compared to Schofield and Harris-Benedict equations. This suggested that it is the preferred predictive equation for vascular surgery patients. Future research to refine the Miller equation to improve its overall accuracy will better inform the provision of nutritional support for vascular surgery patients and subsequently improve outcomes. Alternatively, an equation might be developed specifically for use with

  15. Characterization of normality of chaotic systems including prediction and detection of anomalies

    NASA Astrophysics Data System (ADS)

    Engler, Joseph John

    Accurate prediction and control pervades domains such as engineering, physics, chemistry, and biology. Often, it is discovered that the systems under consideration cannot be well represented by linear, periodic nor random data. It has been shown that these systems exhibit deterministic chaos behavior. Deterministic chaos describes systems which are governed by deterministic rules but whose data appear to be random or quasi-periodic distributions. Deterministically chaotic systems characteristically exhibit sensitive dependence upon initial conditions manifested through rapid divergence of states initially close to one another. Due to this characterization, it has been deemed impossible to accurately predict future states of these systems for longer time scales. Fortunately, the deterministic nature of these systems allows for accurate short term predictions, given the dynamics of the system are well understood. This fact has been exploited in the research community and has resulted in various algorithms for short term predictions. Detection of normality in deterministically chaotic systems is critical in understanding the system sufficiently to able to predict future states. Due to the sensitivity to initial conditions, the detection of normal operational states for a deterministically chaotic system can be challenging. The addition of small perturbations to the system, which may result in bifurcation of the normal states, further complicates the problem. The detection of anomalies and prediction of future states of the chaotic system allows for greater understanding of these systems. The goal of this research is to produce methodologies for determining states of normality for deterministically chaotic systems, detection of anomalous behavior, and the more accurate prediction of future states of the system. Additionally, the ability to detect subtle system state changes is discussed. The dissertation addresses these goals by proposing new representational

  16. Bidirectional-Compounding Effects of Rumination and Negative Emotion in Predicting Impulsive Behavior: Implications for Emotional Cascades.

    PubMed

    Selby, Edward A; Kranzler, Amy; Panza, Emily; Fehling, Kara B

    2016-04-01

    Influenced by chaos theory, the emotional cascade model proposes that rumination and negative emotion may promote each other in a self-amplifying cycle that increases over time. Accordingly, exponential-compounding effects may better describe the relationship between rumination and negative emotion when they occur in impulsive persons, and predict impulsive behavior. Forty-seven community and undergraduate participants who reported frequent engagement in impulsive behaviors monitored their ruminative thoughts and negative emotion multiple times daily for two weeks using digital recording devices. Hypotheses were tested using cross-lagged mixed model analyses. Findings indicated that rumination predicted subsequent elevations in rumination that lasted over extended periods of time. Rumination and negative emotion predicted increased levels of each other at subsequent assessments, and exponential functions for these associations were supported. Results also supported a synergistic effect between rumination and negative emotion, predicting larger elevations in subsequent rumination and negative emotion than when one variable alone was elevated. Finally, there were synergistic effects of rumination and negative emotion in predicting number of impulsive behaviors subsequently reported. These findings are consistent with the emotional cascade model in suggesting that momentary rumination and negative emotion progressively propagate and magnify each other over time in impulsive people, promoting impulsive behavior. © 2014 Wiley Periodicals, Inc.

  17. Modeling Stationary Lithium-Ion Batteries for Optimization and Predictive Control: Preprint

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

    Raszmann, Emma; Baker, Kyri; Shi, Ying

    Accurately modeling stationary battery storage behavior is crucial to understand and predict its limitations in demand-side management scenarios. In this paper, a lithium-ion battery model was derived to estimate lifetime and state-of-charge for building-integrated use cases. The proposed battery model aims to balance speed and accuracy when modeling battery behavior for real-time predictive control and optimization. In order to achieve these goals, a mixed modeling approach was taken, which incorporates regression fits to experimental data and an equivalent circuit to model battery behavior. A comparison of the proposed battery model output to actual data from the manufacturer validates the modelingmore » approach taken in the paper. Additionally, a dynamic test case demonstrates the effects of using regression models to represent internal resistance and capacity fading.« less

  18. A fast and accurate method to predict 2D and 3D aerodynamic boundary layer flows

    NASA Astrophysics Data System (ADS)

    Bijleveld, H. A.; Veldman, A. E. P.

    2014-12-01

    A quasi-simultaneous interaction method is applied to predict 2D and 3D aerodynamic flows. This method is suitable for offshore wind turbine design software as it is a very accurate and computationally reasonably cheap method. This study shows the results for a NACA 0012 airfoil. The two applied solvers converge to the experimental values when the grid is refined. We also show that in separation the eigenvalues remain positive thus avoiding the Goldstein singularity at separation. In 3D we show a flow over a dent in which separation occurs. A rotating flat plat is used to show the applicability of the method for rotating flows. The shown capabilities of the method indicate that the quasi-simultaneous interaction method is suitable for design methods for offshore wind turbine blades.

  19. Incorporating Communication into the Theory of Planned Behavior to Predict Condom Use Among African American Women

    PubMed Central

    Guan, Mengfei; Coles, Valerie B.; Samp, Jennifer A.; Sales, Jessica McDermott; DiClemente, Ralph J.; Monahan, Jennifer L.

    2016-01-01

    The present research extends the Theory of Planned Behavior (TPB) to investigate how communication-related variables influence condom use intention and behavior among African American women. According to the TPB, attitudes, subjective norms, and self-efficacy are associated with behavioral intent, which predicts behavior. For women, it was argued that condom negotiation self-efficacy was more important than condom use self-efficacy in predicting consistent condom use. Moreover, an important environmental factor that affects condom use for African American women is fear or worry when negotiating condom use because the sex partners might leave, threaten, or abuse them. Fears associated with negotiating condom use were predicted to be negatively associated with attitudes, subjective norms, and self-efficacy. African American women (N = 560; M age = 20.58) completed assessments of TPB variables at baseline and condom use three months later. Condom negotiation self-efficacy was a significant indicator of behavioral intent while condom use self-efficacy was not. Fear of condom negotiation was negatively associated with all TPB components, which was in turn significantly associated with behavioral intent and condom use. Implications for the TPB, safer sex literature, and STI prevention intervention design are discussed. PMID:27565192

  20. Modeling moisture content of fine dead wildland fuels: Input to the BEHAVE fire prediction system

    Treesearch

    Richard C. Rothermel; Ralph A. Wilson; Glen A. Morris; Stephen S. Sackett

    1986-01-01

    Describes a model for predicting moisture content of fine fuels for use with the BEHAVE fire behavior and fuel modeling system. The model is intended to meet the need for more accurate predictions of fine fuel moisture, particularly in northern conifer stands and on days following rain. The model is based on the Canadian Fine Fuel Moisture Code (FFMC), modified to...

  1. Decisions among the Undecided: Implicit Attitudes Predict Future Voting Behavior of Undecided Voters

    PubMed Central

    Lundberg, Kristjen B.; Payne, B. Keith

    2014-01-01

    Implicit attitudes have been suggested as a key to unlock the hidden preferences of undecided voters. Past research, however, offered mixed support for this hypothesis. The present research used a large nationally representative sample and a longitudinal design to examine the predictive utility of implicit and explicit attitude measures in the 2008 U.S. presidential election. In our analyses, explicit attitudes toward candidates predicted voting better for decided than undecided voters, but implicit candidate attitudes were predictive of voting for both decided and undecided voters. Extending our examination to implicit and explicit racial attitudes, we found the same pattern. Taken together, these results provide convergent evidence that implicit attitudes predict voting about as well for undecided as for decided voters. We also assessed a novel explanation for these effects by evaluating whether implicit attitudes may predict the choices of undecided voters, in part, because they are neglected when people introspect about their confidence. Consistent with this idea, we found that the extremity of explicit but not implicit attitudes was associated with greater confidence. These analyses shed new light on the utility of implicit measures in predicting future behavior among individuals who feel undecided. Considering the prior studies together with this new evidence, the data seem to be consistent that implicit attitudes may be successful in predicting the behavior of undecided voters. PMID:24489666

  2. Integrating metabolic performance, thermal tolerance, and plasticity enables for more accurate predictions on species vulnerability to acute and chronic effects of global warming.

    PubMed

    Magozzi, Sarah; Calosi, Piero

    2015-01-01

    Predicting species vulnerability to global warming requires a comprehensive, mechanistic understanding of sublethal and lethal thermal tolerances. To date, however, most studies investigating species physiological responses to increasing temperature have focused on the underlying physiological traits of either acute or chronic tolerance in isolation. Here we propose an integrative, synthetic approach including the investigation of multiple physiological traits (metabolic performance and thermal tolerance), and their plasticity, to provide more accurate and balanced predictions on species and assemblage vulnerability to both acute and chronic effects of global warming. We applied this approach to more accurately elucidate relative species vulnerability to warming within an assemblage of six caridean prawns occurring in the same geographic, hence macroclimatic, region, but living in different thermal habitats. Prawns were exposed to four incubation temperatures (10, 15, 20 and 25 °C) for 7 days, their metabolic rates and upper thermal limits were measured, and plasticity was calculated according to the concept of Reaction Norms, as well as Q10 for metabolism. Compared to species occupying narrower/more stable thermal niches, species inhabiting broader/more variable thermal environments (including the invasive Palaemon macrodactylus) are likely to be less vulnerable to extreme acute thermal events as a result of their higher upper thermal limits. Nevertheless, they may be at greater risk from chronic exposure to warming due to the greater metabolic costs they incur. Indeed, a trade-off between acute and chronic tolerance was apparent in the assemblage investigated. However, the invasive species P. macrodactylus represents an exception to this pattern, showing elevated thermal limits and plasticity of these limits, as well as a high metabolic control. In general, integrating multiple proxies for species physiological acute and chronic responses to increasing

  3. Observed Emotional and Behavioral Indicators of Motivation Predict School Readiness in Head Start Graduates

    PubMed Central

    Berhenke, Amanda; Miller, Alison L.; Brown, Eleanor; Seifer, Ronald; Dickstein, Susan

    2011-01-01

    Emotions and behaviors observed during challenging tasks are hypothesized to be valuable indicators of young children's motivation, the assessment of which may be particularly important for children at risk for school failure. The current study demonstrated reliability and concurrent validity of a new observational assessment of motivation in young children. Head Start graduates completed challenging puzzle and trivia tasks during their kindergarten year. Children's emotion expression and task engagement were assessed based on their observed facial and verbal expressions and behavioral cues. Hierarchical regression analyses revealed that observed persistence and shame predicted teacher ratings of children's academic achievement, whereas interest, anxiety, pride, shame, and persistence predicted children's social skills and learning-related behaviors. Children's emotional and behavioral responses to challenge thus appeared to be important indicators of school success. Observation of such responses may be a useful and valid alternative to self-report measures of motivation at this age. PMID:21949599

  4. Observed Emotional and Behavioral Indicators of Motivation Predict School Readiness in Head Start Graduates.

    PubMed

    Berhenke, Amanda; Miller, Alison L; Brown, Eleanor; Seifer, Ronald; Dickstein, Susan

    2011-01-01

    Emotions and behaviors observed during challenging tasks are hypothesized to be valuable indicators of young children's motivation, the assessment of which may be particularly important for children at risk for school failure. The current study demonstrated reliability and concurrent validity of a new observational assessment of motivation in young children. Head Start graduates completed challenging puzzle and trivia tasks during their kindergarten year. Children's emotion expression and task engagement were assessed based on their observed facial and verbal expressions and behavioral cues. Hierarchical regression analyses revealed that observed persistence and shame predicted teacher ratings of children's academic achievement, whereas interest, anxiety, pride, shame, and persistence predicted children's social skills and learning-related behaviors. Children's emotional and behavioral responses to challenge thus appeared to be important indicators of school success. Observation of such responses may be a useful and valid alternative to self-report measures of motivation at this age.

  5. Predicting the behavior of techno-social systems.

    PubMed

    Vespignani, Alessandro

    2009-07-24

    We live in an increasingly interconnected world of techno-social systems, in which infrastructures composed of different technological layers are interoperating within the social component that drives their use and development. Examples are provided by the Internet, the World Wide Web, WiFi communication technologies, and transportation and mobility infrastructures. The multiscale nature and complexity of these networks are crucial features in understanding and managing the networks. The accessibility of new data and the advances in the theory and modeling of complex networks are providing an integrated framework that brings us closer to achieving true predictive power of the behavior of techno-social systems.

  6. A sophisticated simulation for the fracture behavior of concrete material using XFEM

    NASA Astrophysics Data System (ADS)

    Zhai, Changhai; Wang, Xiaomin; Kong, Jingchang; Li, Shuang; Xie, Lili

    2017-10-01

    The development of a powerful numerical model to simulate the fracture behavior of concrete material has long been one of the dominant research areas in earthquake engineering. A reliable model should be able to adequately represent the discontinuous characteristics of cracks and simulate various failure behaviors under complicated loading conditions. In this paper, a numerical formulation, which incorporates a sophisticated rigid-plastic interface constitutive model coupling cohesion softening, contact, friction and shear dilatation into the XFEM, is proposed to describe various crack behaviors of concrete material. An effective numerical integration scheme for accurately assembling the contribution to the weak form on both sides of the discontinuity is introduced. The effectiveness of the proposed method has been assessed by simulating several well-known experimental tests. It is concluded that the numerical method can successfully capture the crack paths and accurately predict the fracture behavior of concrete structures. The influence of mode-II parameters on the mixed-mode fracture behavior is further investigated to better determine these parameters.

  7. Impact of Predicting Health Care Utilization Via Web Search Behavior: A Data-Driven Analysis

    PubMed Central

    Zhang, Liangliang; Zhu, Josh; Fang, Shiyuan; Cheng, Tim; Hong, Chloe; Shah, Nigam H

    2016-01-01

    Background By recent estimates, the steady rise in health care costs has deprived more than 45 million Americans of health care services and has encouraged health care providers to better understand the key drivers of health care utilization from a population health management perspective. Prior studies suggest the feasibility of mining population-level patterns of health care resource utilization from observational analysis of Internet search logs; however, the utility of the endeavor to the various stakeholders in a health ecosystem remains unclear. Objective The aim was to carry out a closed-loop evaluation of the utility of health care use predictions using the conversion rates of advertisements that were displayed to the predicted future utilizers as a surrogate. The statistical models to predict the probability of user’s future visit to a medical facility were built using effective predictors of health care resource utilization, extracted from a deidentified dataset of geotagged mobile Internet search logs representing searches made by users of the Baidu search engine between March 2015 and May 2015. Methods We inferred presence within the geofence of a medical facility from location and duration information from users’ search logs and putatively assigned medical facility visit labels to qualifying search logs. We constructed a matrix of general, semantic, and location-based features from search logs of users that had 42 or more search days preceding a medical facility visit as well as from search logs of users that had no medical visits and trained statistical learners for predicting future medical visits. We then carried out a closed-loop evaluation of the utility of health care use predictions using the show conversion rates of advertisements displayed to the predicted future utilizers. In the context of behaviorally targeted advertising, wherein health care providers are interested in minimizing their cost per conversion, the association between show

  8. A behavioral economic reward index predicts drinking resolutions: moderation revisited and compared with other outcomes.

    PubMed

    Tucker, Jalie A; Roth, David L; Vignolo, Mary J; Westfall, Andrew O

    2009-04-01

    Data were pooled from 3 studies of recently resolved community-dwelling problem drinkers to determine whether a behavioral economic index of the value of rewards available over different time horizons distinguished among moderation (n = 30), abstinent (n = 95), and unresolved (n = 77) outcomes. Moderation over 1- to 2-year prospective follow-up intervals was hypothesized to involve longer term behavior regulation processes than abstinence or relapse and to be predicted by more balanced preresolution monetary allocations between short-term and longer term objectives (i.e., drinking and saving for the future). Standardized odds ratios (ORs) based on changes in standard deviation units from a multinomial logistic regression indicated that increases on this "Alcohol-Savings Discretionary Expenditure" index predicted higher rates of abstinence (OR = 1.93, p = .004) and relapse (OR = 2.89, p < .0001) compared with moderation outcomes. The index had incremental utility in predicting moderation in complex models that included other established predictors. The study adds to evidence supporting a behavioral economic analysis of drinking resolutions and shows that a systematic analysis of preresolution spending patterns aids in predicting moderation.

  9. Ensemble MD simulations restrained via crystallographic data: Accurate structure leads to accurate dynamics

    PubMed Central

    Xue, Yi; Skrynnikov, Nikolai R

    2014-01-01

    Currently, the best existing molecular dynamics (MD) force fields cannot accurately reproduce the global free-energy minimum which realizes the experimental protein structure. As a result, long MD trajectories tend to drift away from the starting coordinates (e.g., crystallographic structures). To address this problem, we have devised a new simulation strategy aimed at protein crystals. An MD simulation of protein crystal is essentially an ensemble simulation involving multiple protein molecules in a crystal unit cell (or a block of unit cells). To ensure that average protein coordinates remain correct during the simulation, we introduced crystallography-based restraints into the MD protocol. Because these restraints are aimed at the ensemble-average structure, they have only minimal impact on conformational dynamics of the individual protein molecules. So long as the average structure remains reasonable, the proteins move in a native-like fashion as dictated by the original force field. To validate this approach, we have used the data from solid-state NMR spectroscopy, which is the orthogonal experimental technique uniquely sensitive to protein local dynamics. The new method has been tested on the well-established model protein, ubiquitin. The ensemble-restrained MD simulations produced lower crystallographic R factors than conventional simulations; they also led to more accurate predictions for crystallographic temperature factors, solid-state chemical shifts, and backbone order parameters. The predictions for 15N R1 relaxation rates are at least as accurate as those obtained from conventional simulations. Taken together, these results suggest that the presented trajectories may be among the most realistic protein MD simulations ever reported. In this context, the ensemble restraints based on high-resolution crystallographic data can be viewed as protein-specific empirical corrections to the standard force fields. PMID:24452989

  10. Momentary changes in craving predict smoking lapse behavior: a laboratory study.

    PubMed

    Motschman, Courtney A; Germeroth, Lisa J; Tiffany, Stephen T

    2018-04-27

    Current research on factors that predict smoking lapse behavior is limited in its ability to fully characterize the critical moments leading up to decisions to smoke. We used a validated and widely used experimental analogue for smoking lapse to assess how moment-to-moment dynamics of craving relate to decisions to smoke. Heavy smokers (N = 128, M age = 35.9) participated in a 50-min laboratory delay to smoking task on 2 consecutive days, earning money for each 5 min they remained abstinent or ending the task by choosing to smoke. Participants rated craving and negative affect levels immediately prior to each choice. Participants were randomized to smoking as usual (n = 50) or overnight abstinence (n = 50 successfully abstained, n = 22 failed abstaining) prior to session 2. Discrete-time hazard models were used to examine craving and negative affect as time-varying predictors of smoking. Higher craving levels prior to smoking opportunities predicted increased risk of smoking. When controlling for craving levels, incremental increases in craving predicted increased smoking risk. Increases in negative affect incrementally predicted increased smoking risk at session 2 only. Smokers who failed to abstain were at a higher risk of smoking than those who successfully abstained, whereas abstinent and non-abstinent smokers did not differ in smoking risk. Findings demonstrate an extension of the smoking lapse paradigm that can be utilized to capture momentary changes in craving that predict smoking behavior. Evaluations of nuanced craving experiences may inform clinical and pharmacological research on preventing smoking lapse and relapse.

  11. Highly Accurate Calculations of the Phase Diagram of Cold Lithium

    NASA Astrophysics Data System (ADS)

    Shulenburger, Luke; Baczewski, Andrew

    The phase diagram of lithium is particularly complicated, exhibiting many different solid phases under the modest application of pressure. Experimental efforts to identify these phases using diamond anvil cells have been complemented by ab initio theory, primarily using density functional theory (DFT). Due to the multiplicity of crystal structures whose enthalpy is nearly degenerate and the uncertainty introduced by density functional approximations, we apply the highly accurate many-body diffusion Monte Carlo (DMC) method to the study of the solid phases at low temperature. These calculations span many different phases, including several with low symmetry, demonstrating the viability of DMC as a method for calculating phase diagrams for complex solids. Our results can be used as a benchmark to test the accuracy of various density functionals. This can strengthen confidence in DFT based predictions of more complex phenomena such as the anomalous melting behavior predicted for lithium at high pressures. Sandia National Laboratories is a multi-program laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Corporation, for the U.S. DOE's National Nuclear Security Administration under contract DE-AC04-94AL85000.

  12. Predicting externalizing and internalizing behavior in kindergarten: examining the buffering role of early social support.

    PubMed

    Heberle, Amy E; Krill, Sarah C; Briggs-Gowan, Margaret J; Carter, Alice S

    2015-01-01

    This study tested an ecological model predicting children's behavior problems in kindergarten from risk and protective factors (parent psychological distress, parenting behavior, and social support) during early childhood. Study participants were 1,161 sociodemographically diverse mother-child pairs that participated in a longitudinal birth cohort study. The predictor variables were collected at two separate time points and based on parent reports; children were an average of 2 years old at Time 1 and 3 years old at Time 2. The outcome measures were collected when children reached kindergarten and were 6 years old on average. Our results show that early maternal psychological distress, mediated by suboptimal parenting behavior, predicts children's externalizing and internalizing behaviors in kindergarten. Moreover, early social support buffers the relations between psychological distress and later suboptimal parenting behavior and between suboptimal parenting behavior and later depressive/withdrawn behavior. Our findings have several implications for early intervention and prevention efforts. Of note, informal social support appears to play an important protective role in the development of externalizing and internalizing behavior problems, weakening the link between psychological distress and less optimal parenting behavior and between suboptimal parenting behavior and children's withdrawal/depression symptoms. Increasing social support may be a productive goal for family and community-level intervention.

  13. Encoded exposure to tobacco use in social media predicts subsequent smoking behavior.

    PubMed

    Depue, Jacob B; Southwell, Brian G; Betzner, Anne E; Walsh, Barbara M

    2015-01-01

    Assessing the potential link between smoking behavior and exposure to mass media depictions of smoking on social networking Web sites. A representative longitudinal panel of 200 young adults in Connecticut. Telephone surveys were conducted by using computer assisted telephone interviewing technology and electronic dialing for random digit dialing and listed samples. Connecticut residents aged 18 to 24 years. To measure encoded exposure, respondents were asked whether or not they had smoked a cigarette in the past 30 days and about how often they had seen tobacco use on television, in movies, and in social media content. Respondents were also asked about cigarette use in the past 30 days, and a series of additional questions that have been shown to be predictive of tobacco use. Logistic regression was used to test for our main prediction that reported exposure to social media tobacco depictions at time 1 would influence time 2 smoking behavior. Encoded exposure to social media tobacco depictions (B = .47, p < .05) was a significant predictor of time 2 smoking, even after controlling for all the aforementioned predictors. Our results suggest that social media depictions of tobacco use predict future smoking tendency, over and above the influence of TV and movie depictions of smoking. This is the first known study to specifically assess the role of social media in informing tobacco behavior.

  14. Identifying Future Drinkers: Behavioral Analysis of Monkeys Initiating Drinking to Intoxication is Predictive of Future Drinking Classification.

    PubMed

    Baker, Erich J; Walter, Nicole A R; Salo, Alex; Rivas Perea, Pablo; Moore, Sharon; Gonzales, Steven; Grant, Kathleen A

    2017-03-01

    The Monkey Alcohol Tissue Research Resource (MATRR) is a repository and analytics platform for detailed data derived from well-documented nonhuman primate (NHP) alcohol self-administration studies. This macaque model has demonstrated categorical drinking norms reflective of human drinking populations, resulting in consumption pattern classifications of very heavy drinking (VHD), heavy drinking (HD), binge drinking (BD), and low drinking (LD) individuals. Here, we expand on previous findings that suggest ethanol drinking patterns during initial drinking to intoxication can reliably predict future drinking category assignment. The classification strategy uses a machine-learning approach to examine an extensive set of daily drinking attributes during 90 sessions of induction across 7 cohorts of 5 to 8 monkeys for a total of 50 animals. A Random Forest classifier is employed to accurately predict categorical drinking after 12 months of self-administration. Predictive outcome accuracy is approximately 78% when classes are aggregated into 2 groups, "LD and BD" and "HD and VHD." A subsequent 2-step classification model distinguishes individual LD and BD categories with 90% accuracy and between HD and VHD categories with 95% accuracy. Average 4-category classification accuracy is 74%, and provides putative distinguishing behavioral characteristics between groupings. We demonstrate that data derived from the induction phase of this ethanol self-administration protocol have significant predictive power for future ethanol consumption patterns. Importantly, numerous predictive factors are longitudinal, measuring the change of drinking patterns through 3 stages of induction. Factors during induction that predict future heavy drinkers include being younger at the time of first intoxication and developing a shorter latency to first ethanol drink. Overall, this analysis identifies predictive characteristics in future very heavy drinkers that optimize intoxication, such as having

  15. Prediction of composites behavior undergoing an ATP process through data-mining

    NASA Astrophysics Data System (ADS)

    Martin, Clara Argerich; Collado, Angel Leon; Pinillo, Rubén Ibañez; Barasinski, Anaïs; Abisset-Chavanne, Emmanuelle; Chinesta, Francisco

    2018-05-01

    The need to characterize composite surfaces for distinct mechanical or physical processes leads to different manners of evaluate the state of the surface. During many manufacturing processes deformation occurs, thus hindering composite classification for fabrication processes. In this work we focus on the challenge of a priori identifying the surfaces' behavior in order to optimize manufacturing. We will propose and validate the curvature of the surface as a reliable parameter and we will develop a tool that allows the prediction of the surface behavior.

  16. Accurate disulfide-bonding network predictions improve ab initio structure prediction of cysteine-rich proteins

    PubMed Central

    Yang, Jing; He, Bao-Ji; Jang, Richard; Zhang, Yang; Shen, Hong-Bin

    2015-01-01

    Abstract Motivation: Cysteine-rich proteins cover many important families in nature but there are currently no methods specifically designed for modeling the structure of these proteins. The accuracy of disulfide connectivity pattern prediction, particularly for the proteins of higher-order connections, e.g. >3 bonds, is too low to effectively assist structure assembly simulations. Results: We propose a new hierarchical order reduction protocol called Cyscon for disulfide-bonding prediction. The most confident disulfide bonds are first identified and bonding prediction is then focused on the remaining cysteine residues based on SVR training. Compared with purely machine learning-based approaches, Cyscon improved the average accuracy of connectivity pattern prediction by 21.9%. For proteins with more than 5 disulfide bonds, Cyscon improved the accuracy by 585% on the benchmark set of PDBCYS. When applied to 158 non-redundant cysteine-rich proteins, Cyscon predictions helped increase (or decrease) the TM-score (or RMSD) of the ab initio QUARK modeling by 12.1% (or 14.4%). This result demonstrates a new avenue to improve the ab initio structure modeling for cysteine-rich proteins. Availability and implementation: http://www.csbio.sjtu.edu.cn/bioinf/Cyscon/ Contact: zhng@umich.edu or hbshen@sjtu.edu.cn Supplementary information: Supplementary data are available at Bioinformatics online. PMID:26254435

  17. Specific responsible environmental behavior among boaters on the Chesapeake Bay: a predictive model part II

    Treesearch

    Stuart P. Cottrell; Alan R. Graefe

    1995-01-01

    This paper examines predictors of boater behavior in a specific behavior situation, namely the percentage of raw sewage discharged from recreational vessels in a sanitation pumpout facility on the Chesapeake Bay. Results of a multiple regression analysis show knowledge predicts behavior in specific issue situations. In addition, the more specific the...

  18. Development of a New Model for Accurate Prediction of Cloud Water Deposition on Vegetation

    NASA Astrophysics Data System (ADS)

    Katata, G.; Nagai, H.; Wrzesinsky, T.; Klemm, O.; Eugster, W.; Burkard, R.

    2006-12-01

    Scarcity of water resources in arid and semi-arid areas is of great concern in the light of population growth and food shortages. Several experiments focusing on cloud (fog) water deposition on the land surface suggest that cloud water plays an important role in water resource in such regions. A one-dimensional vegetation model including the process of cloud water deposition on vegetation has been developed to better predict cloud water deposition on the vegetation. New schemes to calculate capture efficiency of leaf, cloud droplet size distribution, and gravitational flux of cloud water were incorporated in the model. Model calculations were compared with the data acquired at the Norway spruce forest at the Waldstein site, Germany. High performance of the model was confirmed by comparisons of calculated net radiation, sensible and latent heat, and cloud water fluxes over the forest with measurements. The present model provided a better prediction of measured turbulent and gravitational fluxes of cloud water over the canopy than the Lovett model, which is a commonly used cloud water deposition model. Detailed calculations of evapotranspiration and of turbulent exchange of heat and water vapor within the canopy and the modifications are necessary for accurate prediction of cloud water deposition. Numerical experiments to examine the dependence of cloud water deposition on the vegetation species (coniferous and broad-leaved trees, flat and cylindrical grasses) and structures (Leaf Area Index (LAI) and canopy height) are performed using the presented model. The results indicate that the differences of leaf shape and size have a large impact on cloud water deposition. Cloud water deposition also varies with the growth of vegetation and seasonal change of LAI. We found that the coniferous trees whose height and LAI are 24 m and 2.0 m2m-2, respectively, produce the largest amount of cloud water deposition in all combinations of vegetation species and structures in the

  19. Infant stranger fear trajectories predict anxious behaviors and diurnal cortisol rhythm during childhood.

    PubMed

    Van Hulle, Carol A; Moore, Mollie N; Lemery-Chalfant, Kathryn; Goldsmith, H Hill; Brooker, Rebecca J

    2017-08-01

    Although a robust literature has linked stable, high levels of fear across childhood to increased risk for anxiety problems, less is known about alternative pathways to anxiety. We tested two putatively normative developmental pathways of early fearfulness for their distinct associations with behavioral (anxiety-related behaviors and symptoms) and biological (diurnal cortisol) markers of anxiety risk in middle childhood in a community-based sample (n = 107). Steeper increases in fear from 6 to 36 months predicted more parent-reported anxiety symptoms at age 8 years. In addition, children who exhibited steep increases in fear during infancy were overrepresented among children with diagnoses of separation anxiety disorder at age 8 years. Finally, we showed that steeper increases in fearfulness in infancy predicted flatter slopes of diurnal cortisol at age 8 years for girls. Thus, differences in stranger fear across infancy may indicate varying degrees of risk for anxious behaviors in later childhood.

  20. Predicting substance use behavior among South African adolescents: The role of leisure experiences across time

    PubMed Central

    Sharp, Erin Hiley; Coffman, Donna L.; Caldwell, Linda L.; Smith, Edward A.; Wegner, Lisa; Vergnani, Tania; Mathews, Catherine

    2012-01-01

    Using seven waves of data, collected twice a year from the 8th through the 11th grades in a low-resource community in Cape Town, South Africa, we aimed to describe the developmental trends in three specific leisure experiences (leisure boredom, new leisure interests, and healthy leisure) and substance use (cigarettes, alcohol, and marijuana) behaviors and to investigate the ways in which changes in leisure experiences predict changes in substance use behaviors over time. Results indicated that adolescents’ substance use increased significantly across adolescence, but that leisure experiences remained fairly stable over time. We also found that adolescent leisure experiences predicted baseline substance use and that changes in leisure experiences predicted changes in substance use behaviors over time, with leisure boredom emerging as the most consistent and strongest predictor of alcohol, cigarette, and marijuana use. Implications for interventions that target time use and leisure experiences are discussed. PMID:22707811

  1. Accurate prediction of polarised high order electrostatic interactions for hydrogen bonded complexes using the machine learning method kriging.

    PubMed

    Hughes, Timothy J; Kandathil, Shaun M; Popelier, Paul L A

    2015-02-05

    As intermolecular interactions such as the hydrogen bond are electrostatic in origin, rigorous treatment of this term within force field methodologies should be mandatory. We present a method able of accurately reproducing such interactions for seven van der Waals complexes. It uses atomic multipole moments up to hexadecupole moment mapped to the positions of the nuclear coordinates by the machine learning method kriging. Models were built at three levels of theory: HF/6-31G(**), B3LYP/aug-cc-pVDZ and M06-2X/aug-cc-pVDZ. The quality of the kriging models was measured by their ability to predict the electrostatic interaction energy between atoms in external test examples for which the true energies are known. At all levels of theory, >90% of test cases for small van der Waals complexes were predicted within 1 kJ mol(-1), decreasing to 60-70% of test cases for larger base pair complexes. Models built on moments obtained at B3LYP and M06-2X level generally outperformed those at HF level. For all systems the individual interactions were predicted with a mean unsigned error of less than 1 kJ mol(-1). Copyright © 2013 Elsevier B.V. All rights reserved.

  2. Affective behavior and temperament predict the onset of smoking in adolescence.

    PubMed

    Cheetham, Ali; Allen, Nicholas B; Schwartz, Orli; Simmons, Julian G; Whittle, Sarah; Byrne, Michelle L; Sheeber, Lisa; Lubman, Dan I

    2015-06-01

    Earlier use of tobacco in adolescence is associated with numerous adverse outcomes later in life. Although a number of studies have linked individual differences in affective functioning to adolescent smoking, these have relied primarily on self-report measures, and the contribution of different dimensions of affect to the onset of tobacco use during this period remains unclear. The current study examined these issues in a sample of 180 adolescents recruited from an ongoing prospective, longitudinal study examining emotional development. At approximately age 12, participants completed a questionnaire measure of affective temperament and took part in a family interaction task that was coded observationally to provide measures of dysphoric, aggressive, and positive behaviors. At 2 subsequent assessments, which took place approximately 2.5 years and 4 years after the initial assessment, participants completed a questionnaire measure of substance use. In total, 70 participants initiated smoking between the ages of 12 and 17. An earlier onset of smoking was predicted by more aggressive and less positive observed behavior during the interaction task, as well as lower levels of self-reported temperamental Effortful Control. There were no associations between dysphoric behaviors, or temperamental measures of negative affectivity or surgency and the onset of smoking. The findings add to a small body of literature demonstrating that behavioral components of affect can prospectively predict substance use in adolescence and suggest that different dimensions of affect show unique relationships with early substance use. (PsycINFO Database Record (c) 2015 APA, all rights reserved).

  3. When do normative beliefs about aggression predict aggressive behavior? An application of I3 theory.

    PubMed

    Li, Jian-Bin; Nie, Yan-Gang; Boardley, Ian D; Dou, Kai; Situ, Qiao-Min

    2015-01-01

    I(3) theory assumes that aggressive behavior is dependent on three orthogonal processes (i.e., Instigator, Impellance, and Inhibition). Previous studies showed that Impellance (trait aggressiveness, retaliation tendencies) better predicted aggression when Instigator was strong and Inhibition was weak. In the current study, we predicted that another Impellance (i.e., normative beliefs about aggression) might predict aggression when Instigator was absent and Inhibition was high (i.e., the perfect calm proposition). In two experiments, participants first completed the normative beliefs about aggression questionnaire. Two weeks later, participants' self-control resources were manipulated either using the Stroop task (study 1, N = 148) or through an "e-crossing" task (study 2, N = 180). Afterwards, with or without being provoked, participants played a game with an ostensible partner where they had a chance to aggress against them. Study 1 found that normative beliefs about aggression negatively and significantly predicted aggressive behavior only when provocation was absent and self-control resources were not depleted. In Study 2, normative beliefs about aggression negatively predicted aggressive behavior at marginal significance level only in the "no-provocation and no-depletion" condition. In conclusion, the current study provides partial support for the perfect calm proposition and I(3) theory. © 2015 Wiley Periodicals, Inc.

  4. Validation of BEHAVE fire behavior predictions in oak savannas using five fuel models

    Treesearch

    Keith Grabner; John Dwyer; Bruce Cutter

    1997-01-01

    Prescribed fire is a valuable tool in the restoration and management of oak savannas. BEHAVE, a fire behavior prediction system developed by the United States Forest Service, can be a useful tool when managing oak savannas with prescribed fire. BEHAVE predictions of fire rate-of-spread and flame length were validated using four standardized fuel models: Fuel Model 1 (...

  5. Accurate Predictions of Mean Geomagnetic Dipole Excursion and Reversal Frequencies, Mean Paleomagnetic Field Intensity, and the Radius of Earth's Core Using McLeod's Rule

    NASA Technical Reports Server (NTRS)

    Voorhies, Coerte V.; Conrad, Joy

    1996-01-01

    The geomagnetic spatial power spectrum R(sub n)(r) is the mean square magnetic induction represented by degree n spherical harmonic coefficients of the internal scalar potential averaged over the geocentric sphere of radius r. McLeod's Rule for the magnetic field generated by Earth's core geodynamo says that the expected core surface power spectrum (R(sub nc)(c)) is inversely proportional to (2n + 1) for 1 less than n less than or equal to N(sub E). McLeod's Rule is verified by locating Earth's core with main field models of Magsat data; the estimated core radius of 3485 kn is close to the seismologic value for c of 3480 km. McLeod's Rule and similar forms are then calibrated with the model values of R(sub n) for 3 less than or = n less than or = 12. Extrapolation to the degree 1 dipole predicts the expectation value of Earth's dipole moment to be about 5.89 x 10(exp 22) Am(exp 2)rms (74.5% of the 1980 value) and the expected geomagnetic intensity to be about 35.6 (mu)T rms at Earth's surface. Archeo- and paleomagnetic field intensity data show these and related predictions to be reasonably accurate. The probability distribution chi(exp 2) with 2n+1 degrees of freedom is assigned to (2n + 1)R(sub nc)/(R(sub nc). Extending this to the dipole implies that an exceptionally weak absolute dipole moment (less than or = 20% of the 1980 value) will exist during 2.5% of geologic time. The mean duration for such major geomagnetic dipole power excursions, one quarter of which feature durable axial dipole reversal, is estimated from the modern dipole power time-scale and the statistical model of excursions. The resulting mean excursion duration of 2767 years forces us to predict an average of 9.04 excursions per million years, 2.26 axial dipole reversals per million years, and a mean reversal duration of 5533 years. Paleomagnetic data show these predictions to be quite accurate. McLeod's Rule led to accurate predictions of Earth's core radius, mean paleomagnetic field

  6. Using the Theory of Planned Behavior to predict HPV vaccination intentions of college men.

    PubMed

    Catalano, Hannah Priest; Knowlden, Adam P; Birch, David A; Leeper, James D; Paschal, Angelia M; Usdan, Stuart L

    2017-04-01

    The purpose of this study was to test Theory of Planned Behavior (TPB) constructs in predicting human papillomavirus (HPV) vaccination behavioral intentions of vaccine-eligible college men. Participants were unvaccinated college men aged 18-26 years attending a large public university in the southeastern United States during Spring 2015. A nonexperimental, cross-sectional study design was employed. Instrumentation comprised a qualitative elicitation study, expert panel review, pilot test, test-retest, and internal consistency, construct validity, and predictive validity assessments using data collected from an online self-report questionnaire. The sample consisted of 256 college men, and the final structural model exhibited acceptable fit of the data. Attitude toward the behavior (β = .169) and subjective norm (β = 0.667) were significant predictors of behavioral intention, accounting for 58% of its variance. Practitioners may utilize this instrument for the development and evaluation of TPB-based interventions to increase HPV vaccination intentions of undergraduate college men.

  7. Accurate prediction of pregnancy viability by means of a simple scoring system.

    PubMed

    Bottomley, Cecilia; Van Belle, Vanya; Kirk, Emma; Van Huffel, Sabine; Timmerman, Dirk; Bourne, Tom

    2013-01-01

    What is the performance of a simple scoring system to predict whether women will have an ongoing viable intrauterine pregnancy beyond the first trimester? A simple scoring system using demographic and initial ultrasound variables accurately predicts pregnancy viability beyond the first trimester with an area under the curve (AUC) in a receiver operating characteristic curve of 0.924 [95% confidence interval (CI) 0.900-0.947] on an independent test set. Individual demographic and ultrasound factors, such as maternal age, vaginal bleeding and gestational sac size, are strong predictors of miscarriage. Previous mathematical models have combined individual risk factors with reasonable performance. A simple scoring system derived from a mathematical model that can be easily implemented in clinical practice has not previously been described for the prediction of ongoing viability. This was a prospective observational study in a single early pregnancy assessment centre during a 9-month period. A cohort of 1881 consecutive women undergoing transvaginal ultrasound scan at a gestational age <84 days were included. Women were excluded if the first trimester outcome was not known. Demographic features, symptoms and ultrasound variables were tested for their influence on ongoing viability. Logistic regression was used to determine the influence on first trimester viability from demographics and symptoms alone, ultrasound findings alone and then from all the variables combined. Each model was developed on a training data set, and a simple scoring system was derived from this. This scoring system was tested on an independent test data set. The final outcome based on a total of 1435 participants was an ongoing viable pregnancy in 885 (61.7%) and early pregnancy loss in 550 (38.3%) women. The scoring system using significant demographic variables alone (maternal age and amount of bleeding) to predict ongoing viability gave an AUC of 0.724 (95% CI = 0.692-0.756) in the training set

  8. Accurate diode behavioral model with reverse recovery

    NASA Astrophysics Data System (ADS)

    Banáš, Stanislav; Divín, Jan; Dobeš, Josef; Paňko, Václav

    2018-01-01

    This paper deals with the comprehensive behavioral model of p-n junction diode containing reverse recovery effect, applicable to all standard SPICE simulators supporting Verilog-A language. The model has been successfully used in several production designs, which require its full complexity, robustness and set of tuning parameters comparable with standard compact SPICE diode model. The model is like standard compact model scalable with area and temperature and can be used as a stand-alone diode or as a part of more complex device macro-model, e.g. LDMOS, JFET, bipolar transistor. The paper briefly presents the state of the art followed by the chapter describing the model development and achieved solutions. During precise model verification some of them were found non-robust or poorly converging and replaced by more robust solutions, demonstrated in the paper. The measurement results of different technologies and different devices compared with a simulation using the new behavioral model are presented as the model validation. The comparison of model validation in time and frequency domains demonstrates that the implemented reverse recovery effect with correctly extracted parameters improves the model simulation results not only in switching from ON to OFF state, which is often published, but also its impedance/admittance frequency dependency in GHz range. Finally the model parameter extraction and the comparison with SPICE compact models containing reverse recovery effect is presented.

  9. A Comparative Study on Johnson Cook, Modified Zerilli-Armstrong and Arrhenius-Type Constitutive Models to Predict High-Temperature Flow Behavior of Ti-6Al-4V Alloy in α + β Phase

    NASA Astrophysics Data System (ADS)

    Cai, Jun; Wang, Kuaishe; Han, Yingying

    2016-03-01

    True stress and true strain values obtained from isothermal compression tests over a wide temperature range from 1,073 to 1,323 K and a strain rate range from 0.001 to 1 s-1 were employed to establish the constitutive equations based on Johnson Cook, modified Zerilli-Armstrong (ZA) and strain-compensated Arrhenius-type models, respectively, to predict the high-temperature flow behavior of Ti-6Al-4V alloy in α + β phase. Furthermore, a comparative study has been made on the capability of the three models to represent the elevated temperature flow behavior of Ti-6Al-4V alloy. Suitability of the three models was evaluated by comparing both the correlation coefficient R and the average absolute relative error (AARE). The results showed that the Johnson Cook model is inadequate to provide good description of flow behavior of Ti-6Al-4V alloy in α + β phase domain, while the predicted values of modified ZA model and the strain-compensated Arrhenius-type model could agree well with the experimental values except under some deformation conditions. Meanwhile, the modified ZA model could track the deformation behavior more accurately than other model throughout the entire temperature and strain rate range.

  10. Utility of the theory of reasoned action and theory of planned behavior for predicting Chinese adolescent smoking.

    PubMed

    Guo, Qian; Johnson, C Anderson; Unger, Jennifer B; Lee, Liming; Xie, Bin; Chou, Chih-Ping; Palmer, Paula H; Sun, Ping; Gallaher, Peggy; Pentz, MaryAnn

    2007-05-01

    One third of smokers worldwide live in China. Identifying predictors of smoking is important for prevention program development. This study explored whether the Theory of Reasoned Action (TRA) and Theory of Planned Behavior (TPB) predict adolescent smoking in China. Data were obtained from 14,434 middle and high school students (48.6% boys, 51.4% girls) in seven geographically varied cities in China. TRA and TPB were tested by multilevel mediation modeling, and compared by multilevel analyses and likelihood ratio tests. Perceived behavioral control was tested as a main effect in TPB and a moderation effect in TRA. The mediation effects of smoking intention were supported in both models (p<0.001). TPB accounted for significantly more variance than TRA (p<0.001). Perceived behavioral control significantly interacted with attitudes and social norms in TRA (p<0.001). Therefore, TRA and TPB are applicable to China to predict adolescent smoking. TPB is superior to TRA for the prediction and TRA can better predict smoking among students with lower than higher perceived behavioral control.

  11. Accurate perception of negative emotions predicts functional capacity in schizophrenia.

    PubMed

    Abram, Samantha V; Karpouzian, Tatiana M; Reilly, James L; Derntl, Birgit; Habel, Ute; Smith, Matthew J

    2014-04-30

    Several studies suggest facial affect perception (FAP) deficits in schizophrenia are linked to poorer social functioning. However, whether reduced functioning is associated with inaccurate perception of specific emotional valence or a global FAP impairment remains unclear. The present study examined whether impairment in the perception of specific emotional valences (positive, negative) and neutrality were uniquely associated with social functioning, using a multimodal social functioning battery. A sample of 59 individuals with schizophrenia and 41 controls completed a computerized FAP task, and measures of functional capacity, social competence, and social attainment. Participants also underwent neuropsychological testing and symptom assessment. Regression analyses revealed that only accurately perceiving negative emotions explained significant variance (7.9%) in functional capacity after accounting for neurocognitive function and symptoms. Partial correlations indicated that accurately perceiving anger, in particular, was positively correlated with functional capacity. FAP for positive, negative, or neutral emotions were not related to social competence or social attainment. Our findings were consistent with prior literature suggesting negative emotions are related to functional capacity in schizophrenia. Furthermore, the observed relationship between perceiving anger and performance of everyday living skills is novel and warrants further exploration. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

  12. Predicting change in early adolescent problem behavior in the middle school years: a mesosystemic perspective on parenting and peer experiences.

    PubMed

    Véronneau, Marie-Hélène; Dishion, Thomas J

    2010-11-01

    The transition into middle school may be a risky period in early adolescence. In particular, friendships, peer status, and parental monitoring during this developmental period can influence the development of problem behavior. This study examined interrelationships among peer and parenting factors that predict changes in problem behavior over the middle school years. A longitudinal sample (580 boys, 698 girls) was assessed in Grades 6 and 8. Peer acceptance, peer rejection, and their interaction predicted increases in problem behavior. Having high-achieving friends predicted less problem behavior. Parental monitoring predicted less problem behavior in general, but also acted as a buffer for students who were most vulnerable to developing problem behavior on the basis of being well liked by some peers, and also disliked by several others. These findings highlight the importance of studying the family-peer mesosystem when considering risk and resilience in early adolescence, and when considering implications for intervention.

  13. Using the Theory of Planned Behavior to Predict HPV Vaccination Intentions of College Men

    ERIC Educational Resources Information Center

    Catalano, Hannah Priest; Knowlden, Adam P.; Birch, David A.; Leeper, James D.; Paschal, Angelia M.; Usdan, Stuart L.

    2017-01-01

    Objective: The purpose of this study was to test Theory of Planned Behavior (TPB) constructs in predicting human papillomavirus (HPV) vaccination behavioral intentions of vaccine-eligible college men. Participants: Participants were unvaccinated college men aged 18-26 years attending a large public university in the southeastern United States…

  14. Parent cortisol and family relatedness predict anxious behavior in emerging adults.

    PubMed

    Johnson, Vanessa Kahen; Gans, Susan E

    2016-10-01

    Emerging-adult cortisol response during family interaction predicts change in emerging-adult anxious behavior during the transition to college (Gans & Johnson, in press). In the present study, we take an additional step toward integrating family systems research and physiology by including assessment of parent physiology. We collect salivary cortisol from parents and emerging adults during triadic family interaction. Emerging adults (N = 101) between the ages of 17 and 19 years were assessed at 3 time points across their first college year: the summer before college and the Fall and Spring semesters. Two parents accompanied the emerging-adult child to the summer assessment; all family members provided 4 saliva samples each at 20-min intervals. Later assessments of emerging adults included measures of internalizing behaviors. Parents' cortisol secretion patterns during family interaction predict their emerging-adult children's cortisol secretion pattern, parent perceptions of the family environment, and emerging-adult children's internalizing behavior during the college transition. Different patterns of results emerged for mothers' and fathers' cortisol response to family interaction and for families with sons or with daughters. The approach taken by this study provides a first step toward understanding how interrelationships among elements of physiology and family functioning contribute to adjustment during major life transitions. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

  15. Parent Cortisol and Family Relatedness Predict Anxious Behavior in Emerging Adults

    PubMed Central

    Johnson, Vanessa Kahen; Gans, Susan E.

    2016-01-01

    Emerging adult cortisol response during family interaction predicts change in EA anxious behavior during the transition to college (Gans & Johnson, in press). In the present study, we take an additional step toward integrating family systems research and physiology by including assessment of parent physiology. We collect salivary cortisol from parents and emerging-adults during triadic family interaction. Emerging adults (N = 101) between the ages of 17 and 19 were assessed at three time points across their first college year: the summer before college, fall and spring semesters. Two parents accompanied the emerging adult child to the summer assessment; all family members provided four saliva samples each at 20-minute intervals. Later assessments of emerging adults included measures of internalizing behaviors. Parents’ cortisol secretion patterns during family interaction predict their emerging adult child’s cortisol secretion pattern, parent perceptions of the family environment, and emerging adult children’s internalizing behavior during the college transition. Different patterns of results emerged for mothers’ and fathers’ cortisol response to family interaction, and for families with sons or with daughters. The approach taken by this study provides a first step toward understanding how interrelationships among elements of physiology and family functioning contribute to adjustment during major life transitions. PMID:27536860

  16. In-Kennel Behavior Predicts Length of Stay in Shelter Dogs

    PubMed Central

    Protopopova, Alexandra; Mehrkam, Lindsay Renee; Boggess, May Meredith; Wynne, Clive David Lawrence

    2014-01-01

    Previous empirical evaluations of training programs aimed at improving dog adoption rates assume that dogs exhibiting certain behaviors are more adoptable. However, no systematic data are available to indicate that the spontaneous behavior of shelter dogs has an effect on adopter preference. The aim of the present study was to determine whether any behaviors that dogs exhibit spontaneously in the presence of potential adopters were associated with the dogs' length of stay in the shelter. A sample of 289 dogs was videotaped for 1 min daily throughout their stay at a county shelter. To account for differences in adopter behavior, experimenters varied from solitary passive observers to pairs of interactive observers. Dogs behaved more attentively to active observers. To account for adopter preference for morphology, dogs were divided into “morphologically preferred” and “non-preferred” groups. Morphologically preferred dogs were small, long coated, ratters, herders, and lap dogs. No theoretically significant differences in behavior were observed between the two different dog morphologies. When accounting for morphological preference, three behaviors were found to have a significant effect on length of stay in all dogs: leaning or rubbing on the enclosure wall (increased median length of stay by 30 days), facing away from the front of the enclosure (increased by 15 days), and standing (increased by 7 days). When combinations of behaviors were assessed, back and forth motion was found to predict a longer stay (increased by 24 days). No consistent behavioral changes were observed due to time spent at the shelter. These findings will allow shelters to focus behavioral modification efforts only on behaviors likely to influence adopters' choices. PMID:25551460

  17. Birth timing and behavioral responsiveness predict individual differences in the mother-infant relationship and infant behavior during weaning and maternal breeding

    PubMed Central

    Vandeleest, Jessica J.; Capitanio, John P.

    2012-01-01

    There is a great deal of variability in mother-infant interactions and infant behavior across the first year of life in rhesus monkeys. The current paper has two specific aims: 1) to determine if birth timing predicts variability in the mother-infant relationship and infant behavior during weaning and maternal breeding, and 2) to identify predictors of infant behavior during a period of acute challenge, maternal breeding. Forty-one mother-infant pairs were observed during weaning when infants were 4.5 months old, and 33 were followed through maternal breeding. Subjective ratings of 16 adjectives reflecting qualities of maternal attitude, mother-infant interactions, and infant attitude were factor analyzed to construct factors relating to the mother-infant relationship (Relaxed and Aggressive), and infant behavior (Positive Engagement and Distress). During weaning, late born infants were more Positively Engaged than peak born infants (ANOVA, P < 0.05); however, birth timing did not affect the mother-infant relationship factors Relaxed and Aggressive or the infant attitude factor Distress. During maternal breeding early born infants had less Relaxed relationships with their mothers than peak or late born infants, higher Positive Engagement scores than peak or late born infants, and tended to have higher Distress scores than peak born infants (Repeated-measures ANOVA, P < 0.05). In addition, Distress scores were higher during maternal breeding than during the pre- and post-breeding phases. Finally, multiple regression (P < 0.05) indicated that while infant behavioral responsiveness predicted infant Positive Engagement during the acute challenge of maternal breeding, qualities of the mother-infant relationship predicted infant Distress. These data suggest that birth timing influences the patterns of mother-infant interactions during weaning and maternal breeding. Additionally, infant behavioral responsiveness and mother-infant relationship quality impact infant social

  18. Applicability of the Theory of Planned Behavior in Predicting Supportive Behaviors by Parents and Adult Siblings of Immediate Relatives with Intellectual Disability

    ERIC Educational Resources Information Center

    Rimmerman, Arie; Chen, Ariel

    2012-01-01

    This research examines the applicability of the Theory of Planned Behavior in predicting supportive behaviors by parents and adult siblings of immediate relatives with intellectual disability. Participants were 67 parents and 63 siblings whose immediate relatives with intellectual disability resided in two institutional care facilities. Three…

  19. Accurate prediction of collapse temperature using optical coherence tomography-based freeze-drying microscopy.

    PubMed

    Greco, Kristyn; Mujat, Mircea; Galbally-Kinney, Kristin L; Hammer, Daniel X; Ferguson, R Daniel; Iftimia, Nicusor; Mulhall, Phillip; Sharma, Puneet; Kessler, William J; Pikal, Michael J

    2013-06-01

    The objective of this study was to assess the feasibility of developing and applying a laboratory tool that can provide three-dimensional product structural information during freeze-drying and which can accurately characterize the collapse temperature (Tc ) of pharmaceutical formulations designed for freeze-drying. A single-vial freeze dryer coupled with optical coherence tomography freeze-drying microscopy (OCT-FDM) was developed to investigate the structure and Tc of formulations in pharmaceutically relevant products containers (i.e., freeze-drying in vials). OCT-FDM was used to measure the Tc and eutectic melt of three formulations in freeze-drying vials. The Tc as measured by OCT-FDM was found to be predictive of freeze-drying with a batch of vials in a conventional laboratory freeze dryer. The freeze-drying cycles developed using OCT-FDM data, as compared with traditional light transmission freeze-drying microscopy (LT-FDM), resulted in a significant reduction in primary drying time, which could result in a substantial reduction of manufacturing costs while maintaining product quality. OCT-FDM provides quantitative data to justify freeze-drying at temperatures higher than the Tc measured by LT-FDM and provides a reliable upper limit to setting a product temperature in primary drying. Copyright © 2013 Wiley Periodicals, Inc.

  20. Hindered rotor models with variable kinetic functions for accurate thermodynamic and kinetic predictions

    NASA Astrophysics Data System (ADS)

    Reinisch, Guillaume; Leyssale, Jean-Marc; Vignoles, Gérard L.

    2010-10-01

    We present an extension of some popular hindered rotor (HR) models, namely, the one-dimensional HR (1DHR) and the degenerated two-dimensional HR (d2DHR) models, allowing for a simple and accurate treatment of internal rotations. This extension, based on the use of a variable kinetic function in the Hamiltonian instead of a constant reduced moment of inertia, is extremely suitable in the case of rocking/wagging motions involved in dissociation or atom transfer reactions. The variable kinetic function is first introduced in the framework of a classical 1DHR model. Then, an effective temperature and potential dependent constant is proposed in the cases of quantum 1DHR and classical d2DHR models. These methods are finally applied to the atom transfer reaction SiCl3+BCl3→SiCl4+BCl2. We show, for this particular case, that a proper accounting of internal rotations greatly improves the accuracy of thermodynamic and kinetic predictions. Moreover, our results confirm (i) that using a suitably defined kinetic function appears to be very adapted to such problems; (ii) that the separability assumption of independent rotations seems justified; and (iii) that a quantum mechanical treatment is not a substantial improvement with respect to a classical one.

  1. Artificial intelligence exploration of unstable protocells leads to predictable properties and discovery of collective behavior.

    PubMed

    Points, Laurie J; Taylor, James Ward; Grizou, Jonathan; Donkers, Kevin; Cronin, Leroy

    2018-01-30

    Protocell models are used to investigate how cells might have first assembled on Earth. Some, like oil-in-water droplets, can be seemingly simple models, while able to exhibit complex and unpredictable behaviors. How such simple oil-in-water systems can come together to yield complex and life-like behaviors remains a key question. Herein, we illustrate how the combination of automated experimentation and image processing, physicochemical analysis, and machine learning allows significant advances to be made in understanding the driving forces behind oil-in-water droplet behaviors. Utilizing >7,000 experiments collected using an autonomous robotic platform, we illustrate how smart automation cannot only help with exploration, optimization, and discovery of new behaviors, but can also be core to developing fundamental understanding of such systems. Using this process, we were able to relate droplet formulation to behavior via predicted physical properties, and to identify and predict more occurrences of a rare collective droplet behavior, droplet swarming. Proton NMR spectroscopic and qualitative pH methods enabled us to better understand oil dissolution, chemical change, phase transitions, and droplet and aqueous phase flows, illustrating the utility of the combination of smart-automation and traditional analytical chemistry techniques. We further extended our study for the simultaneous exploration of both the oil and aqueous phases using a robotic platform. Overall, this work shows that the combination of chemistry, robotics, and artificial intelligence enables discovery, prediction, and mechanistic understanding in ways that no one approach could achieve alone.

  2. Ceramic Matrix Composites (CMC) Life Prediction Development

    NASA Technical Reports Server (NTRS)

    Levine, Stanley R.; Verrilli, Michael J.; Thomas, David J.; Halbig, Michael C.; Calomino, Anthony M.; Ellis, John R.; Opila, Elizabeth J.

    1990-01-01

    Advanced launch systems will very likely incorporate fiber reinforced ceramic matrix composites (CMC) in critical propulsion and airframe components. The use of CMC will save weight, increase operating margin, safety and performance, and improve reuse capability. For reusable and single mission use, accurate life prediction is critical to success. The tools to accomplish this are immature and not oriented toward the behavior of carbon fiber reinforced silicon carbide (C/SiC), the primary system of interest for many applications. This paper describes an approach and progress made to satisfy the need to develop an integrated life prediction system that addresses mechanical durability and environmental degradation.

  3. Predicting Externalizing and Internalizing Behavior in Kindergarten: Examining the Buffering Role of Early Social Support

    PubMed Central

    Heberle, Amy E.; Krill, Sarah C.; Briggs-Gowan, Margaret J.; Carter, Alice S.

    2014-01-01

    Objective This study tested an ecological model predicting children’s behavior problems in kindergarten from risk and protective factors (parent psychological distress, parenting behavior, and social support) during early childhood. Method Study participants were 1161 socio-demographically diverse mother-child pairs who participated in a longitudinal birth cohort study. The predictor variables were collected at two separate time points and based on parent reports; children were an average of two years old at Time 1 and three years old at Time 2. The outcome measures were collected when children reached Kindergarten and were six years old on average. Results Our results show that early maternal psychological distress, mediated by sub-optimal parenting behavior, predicts children’s externalizing and internalizing behaviors in kindergarten. Moreover, early social support buffers the relations between psychological distress and later sub-optimal parenting behaviors and between sub-optimal parenting behavior and later depressive/withdrawn behavior. Conclusions Our findings have several implications for early intervention and prevention efforts. Of note, informal social support appears to play an important protective role in the development of externalizing and internalizing behavior problems, weakening the link between psychological distress and less optimal parenting behavior and between sub-optimal parenting behavior and children’s withdrawal/depression symptoms. Increasing social support may be a productive goal for family and community-level intervention. PMID:24697587

  4. A Simple and Accurate Model to Predict Responses to Multi-electrode Stimulation in the Retina

    PubMed Central

    Maturana, Matias I.; Apollo, Nicholas V.; Hadjinicolaou, Alex E.; Garrett, David J.; Cloherty, Shaun L.; Kameneva, Tatiana; Grayden, David B.; Ibbotson, Michael R.; Meffin, Hamish

    2016-01-01

    Implantable electrode arrays are widely used in therapeutic stimulation of the nervous system (e.g. cochlear, retinal, and cortical implants). Currently, most neural prostheses use serial stimulation (i.e. one electrode at a time) despite this severely limiting the repertoire of stimuli that can be applied. Methods to reliably predict the outcome of multi-electrode stimulation have not been available. Here, we demonstrate that a linear-nonlinear model accurately predicts neural responses to arbitrary patterns of stimulation using in vitro recordings from single retinal ganglion cells (RGCs) stimulated with a subretinal multi-electrode array. In the model, the stimulus is projected onto a low-dimensional subspace and then undergoes a nonlinear transformation to produce an estimate of spiking probability. The low-dimensional subspace is estimated using principal components analysis, which gives the neuron’s electrical receptive field (ERF), i.e. the electrodes to which the neuron is most sensitive. Our model suggests that stimulation proportional to the ERF yields a higher efficacy given a fixed amount of power when compared to equal amplitude stimulation on up to three electrodes. We find that the model captures the responses of all the cells recorded in the study, suggesting that it will generalize to most cell types in the retina. The model is computationally efficient to evaluate and, therefore, appropriate for future real-time applications including stimulation strategies that make use of recorded neural activity to improve the stimulation strategy. PMID:27035143

  5. Neurophysiology of Reward-Guided Behavior: Correlates Related to Predictions, Value, Motivation, Errors, Attention, and Action.

    PubMed

    Bissonette, Gregory B; Roesch, Matthew R

    2016-01-01

    Many brain areas are activated by the possibility and receipt of reward. Are all of these brain areas reporting the same information about reward? Or are these signals related to other functions that accompany reward-guided learning and decision-making? Through carefully controlled behavioral studies, it has been shown that reward-related activity can represent reward expectations related to future outcomes, errors in those expectations, motivation, and signals related to goal- and habit-driven behaviors. These dissociations have been accomplished by manipulating the predictability of positively and negatively valued events. Here, we review single neuron recordings in behaving animals that have addressed this issue. We describe data showing that several brain areas, including orbitofrontal cortex, anterior cingulate, and basolateral amygdala signal reward prediction. In addition, anterior cingulate, basolateral amygdala, and dopamine neurons also signal errors in reward prediction, but in different ways. For these areas, we will describe how unexpected manipulations of positive and negative value can dissociate signed from unsigned reward prediction errors. All of these signals feed into striatum to modify signals that motivate behavior in ventral striatum and guide responding via associative encoding in dorsolateral striatum.

  6. Neurophysiology of Reward-Guided Behavior: Correlates Related to Predictions, Value, Motivation, Errors, Attention, and Action

    PubMed Central

    Roesch, Matthew R.

    2017-01-01

    Many brain areas are activated by the possibility and receipt of reward. Are all of these brain areas reporting the same information about reward? Or are these signals related to other functions that accompany reward-guided learning and decision-making? Through carefully controlled behavioral studies, it has been shown that reward-related activity can represent reward expectations related to future outcomes, errors in those expectations, motivation, and signals related to goal- and habit-driven behaviors. These dissociations have been accomplished by manipulating the predictability of positively and negatively valued events. Here, we review single neuron recordings in behaving animals that have addressed this issue. We describe data showing that several brain areas, including orbitofrontal cortex, anterior cingulate, and basolateral amygdala signal reward prediction. In addition, anterior cingulate, basolateral amygdala, and dopamine neurons also signal errors in reward prediction, but in different ways. For these areas, we will describe how unexpected manipulations of positive and negative value can dissociate signed from unsigned reward prediction errors. All of these signals feed into striatum to modify signals that motivate behavior in ventral striatum and guide responding via associative encoding in dorsolateral striatum. PMID:26276036

  7. Grid Quality and Resolution Issues from the Drag Prediction Workshop Series

    NASA Technical Reports Server (NTRS)

    Mavriplis, Dimitri J.; Vassberg, John C.; Tinoco, Edward N.; Mani, Mori; Brodersen, Olaf P.; Eisfeld, Bernhard; Wahls, Richard A.; Morrison, Joseph H.; Zickuhr, Tom; Levy, David; hide

    2008-01-01

    The drag prediction workshop series (DPW), held over the last six years, and sponsored by the AIAA Applied Aerodynamics Committee, has been extremely useful in providing an assessment of the state-of-the-art in computationally based aerodynamic drag prediction. An emerging consensus from the three workshop series has been the identification of spatial discretization errors as a dominant error source in absolute as well as incremental drag prediction. This paper provides an overview of the collective experience from the workshop series regarding the effect of grid-related issues on overall drag prediction accuracy. Examples based on workshop results are used to illustrate the effect of grid resolution and grid quality on drag prediction, and grid convergence behavior is examined in detail. For fully attached flows, various accurate and successful workshop results are demonstrated, while anomalous behavior is identified for a number of cases involving substantial regions of separated flow. Based on collective workshop experiences, recommendations for improvements in mesh generation technology which have the potential to impact the state-of-the-art of aerodynamic drag prediction are given.

  8. Non-Fourier based thermal-mechanical tissue damage prediction for thermal ablation.

    PubMed

    Li, Xin; Zhong, Yongmin; Smith, Julian; Gu, Chengfan

    2017-01-02

    Prediction of tissue damage under thermal loads plays important role for thermal ablation planning. A new methodology is presented in this paper by combing non-Fourier bio-heat transfer, constitutive elastic mechanics as well as non-rigid motion of dynamics to predict and analyze thermal distribution, thermal-induced mechanical deformation and thermal-mechanical damage of soft tissues under thermal loads. Simulations and comparison analysis demonstrate that the proposed methodology based on the non-Fourier bio-heat transfer can account for the thermal-induced mechanical behaviors of soft tissues and predict tissue thermal damage more accurately than classical Fourier bio-heat transfer based model.

  9. Observational Assessment of Preschool Disruptive Behavior, Part II: validity of the Disruptive Behavior Diagnostic Observation Schedule (DB-DOS).

    PubMed

    Wakschlag, Lauren S; Briggs-Gowan, Margaret J; Hill, Carri; Danis, Barbara; Leventhal, Bennett L; Keenan, Kate; Egger, Helen L; Cicchetti, Domenic; Burns, James; Carter, Alice S

    2008-06-01

    To examine the validity of the Disruptive Behavior Diagnostic Observation Schedule (DB-DOS), a new observational method for assessing preschool disruptive behavior. A total of 327 behaviorally heterogeneous preschoolers from low-income environments comprised the validation sample. Parent and teacher reports were used to identify children with clinically significant disruptive behavior. The DB-DOS assessed observed disruptive behavior in two domains, problems in Behavioral Regulation and Anger Modulation, across three interactional contexts: Examiner Engaged, Examiner Busy, and Parent. Convergent and divergent validity of the DB-DOS were tested in relation to parent and teacher reports and independently observed behavior. Clinical validity was tested in terms of criterion and incremental validity of the DB-DOS for discriminating disruptive behavior status and impairment, concurrently and longitudinally. DB-DOS scores were significantly associated with reported and independently observed behavior in a theoretically meaningful fashion. Scores from both DB-DOS domains and each of the three DB-DOS contexts contributed uniquely to discrimination of disruptive behavior status, concurrently and predictively. Observed behavior on the DB-DOS also contributed incrementally to prediction of impairment over time, beyond variance explained by meeting DSM-IV disruptive behavior disorder symptom criteria based on parent/teacher report. The multidomain, multicontext approach of the DB-DOS is a valid method for direct assessment of preschool disruptive behavior. This approach shows promise for enhancing accurate identification of clinically significant disruptive behavior in young children and for characterizing subtypes in a manner that can directly inform etiological and intervention research.

  10. Brain Activity in Self- and Value-Related Regions in Response to Online Antismoking Messages Predicts Behavior Change

    PubMed Central

    Cooper, Nicole; Tompson, Steve; O’Donnell, Matthew Brook; Falk, Emily B.

    2017-01-01

    In this study, we combined approaches from media psychology and neuroscience to ask whether brain activity in response to online antismoking messages can predict smoking behavior change. In particular, we examined activity in subregions of the medial prefrontal cortex linked to self- and value-related processing, to test whether these neurocognitive processes play a role in message-consistent behavior change. We observed significant relationships between activity in both brain regions of interest and behavior change (such that higher activity predicted a larger reduction in smoking). Furthermore, activity in these brain regions predicted variance independent of traditional, theory-driven self-report metrics such as intention, self-efficacy, and risk perceptions. We propose that valuation is an additional cognitive process that should be investigated further as we search for a mechanistic explanation of the relationship between brain activity and media effects relevant to health behavior change. PMID:29057013

  11. Residual Strength Prediction of Fuselage Structures with Multiple Site Damage

    NASA Technical Reports Server (NTRS)

    Chen, Chuin-Shan; Wawrzynek, Paul A.; Ingraffea, Anthony R.

    1999-01-01

    This paper summarizes recent results on simulating full-scale pressure tests of wide body, lap-jointed fuselage panels with multiple site damage (MSD). The crack tip opening angle (CTOA) fracture criterion and the FRANC3D/STAGS software program were used to analyze stable crack growth under conditions of general yielding. The link-up of multiple cracks and residual strength of damaged structures were predicted. Elastic-plastic finite element analysis based on the von Mises yield criterion and incremental flow theory with small strain assumption was used. A global-local modeling procedure was employed in the numerical analyses. Stress distributions from the numerical simulations are compared with strain gage measurements. Analysis results show that accurate representation of the load transfer through the rivets is crucial for the model to predict the stress distribution accurately. Predicted crack growth and residual strength are compared with test data. Observed and predicted results both indicate that the occurrence of small MSD cracks substantially reduces the residual strength. Modeling fatigue closure is essential to capture the fracture behavior during the early stable crack growth. Breakage of a tear strap can have a major influence on residual strength prediction.

  12. Empathy and nonattachment independently predict peer nominations of prosocial behavior of adolescents

    PubMed Central

    Sahdra, Baljinder K.; Ciarrochi, Joseph; Parker, Philip D.; Marshall, Sarah; Heaven, Patrick

    2015-01-01

    There is a plethora of research showing that empathy promotes prosocial behavior among young people. We examined a relatively new construct in the mindfulness literature, nonattachment, defined as a flexible way of relating to one's experiences without clinging to or suppressing them. We tested whether nonattachment could predict prosociality above and beyond empathy. Nonattachment implies high cognitive flexibility and sufficient mental resources to step out of excessive self-cherishing to be there for others in need. Multilevel Poisson models using a sample of 15-year olds (N = 1831) showed that empathy and nonattachment independently predicted prosocial behaviors of helpfulness and kindness, as judged by same-sex and opposite-sex peers, except for when boys nominated girls. The effects of nonattachment remained substantial in more conservative models including self-esteem and peer nominations of liking. PMID:25852590

  13. Oxytocin Receptor (OXTR) Single Nucleotide Polymorphisms Indirectly Predict Prosocial Behavior Through Perspective Taking and Empathic Concern.

    PubMed

    Christ, Christa C; Carlo, Gustavo; Stoltenberg, Scott F

    2016-04-01

    Engaging in prosocial behavior can provide positive outcomes for self and others. Prosocial tendencies contribute to the propensity to engage in prosocial behavior. The oxytocin receptor gene (OXTR) has also been associated with prosocial tendencies and behaviors. There has been little research, however, investigating whether the relationship between OXTR and prosocial behaviors is mediated by prosocial tendencies. This relationship may also vary among different types of prosocial behavior. The current study examines the relationship between OXTR, gender, prosocial tendencies, and both altruistic and public prosocial behavior endorsement. Students at a midwestern university (N = 398; 89.2% Caucasian; Mage  = 20.76; 26.6% male) provided self-report measures of prosocial tendencies and behaviors and buccal cells for genotyping OXTR polymorphisms. Results indicated that OXTR single nucleotide polymorphism (SNP) rs2268498 genotype significantly predicted empathic concern, whereas gender moderated the association between several other OXTR SNPs and prosocial tendencies. Increased prosocial tendencies predicted increased altruistic prosocial behavior endorsement and decreased public prosocial behavior endorsement. Our findings suggest an association between genetic variation in OXTR and endorsement of prosocial behavior indirectly through prosocial tendencies, and that the pathway is dependent on the type of prosocial behavior and gender. © 2014 Wiley Periodicals, Inc.

  14. Extending the Theory of Normative Social Behavior to Predict Hand-Washing among Koreans.

    PubMed

    Chung, Minwoong; Lapinski, Maria Knight

    2018-04-10

    The current study tests the predictions of the theory of normative social behavior (TNSB) in a hand-washing context in a Korean sample and extends the theory to examine the role of perceived publicness, a variable believed to activate face concerns, as a moderator of the norm-behavior relationship. The findings show substantial main effects for all of the study variables on behavior. In addition, the descriptive norm-behavior relationship is moderated by perceived publicness and outcome expectations, but the nature of the interactions is not consistent with that evidenced in previous literature on US samples. Implications for normative theory and communication campaigns are discussed.

  15. Combining Mean and Standard Deviation of Hounsfield Unit Measurements from Preoperative CT Allows More Accurate Prediction of Urinary Stone Composition Than Mean Hounsfield Units Alone.

    PubMed

    Tailly, Thomas; Larish, Yaniv; Nadeau, Brandon; Violette, Philippe; Glickman, Leonard; Olvera-Posada, Daniel; Alenezi, Husain; Amann, Justin; Denstedt, John; Razvi, Hassan

    2016-04-01

    The mineral composition of a urinary stone may influence its surgical and medical treatment. Previous attempts at identifying stone composition based on mean Hounsfield Units (HUm) have had varied success. We aimed to evaluate the additional use of standard deviation of HU (HUsd) to more accurately predict stone composition. We identified patients from two centers who had undergone urinary stone treatment between 2006 and 2013 and had mineral stone analysis and a computed tomography (CT) available. HUm and HUsd of the stones were compared with ANOVA. Receiver operative characteristic analysis with area under the curve (AUC), Youden index, and likelihood ratio calculations were performed. Data were available for 466 patients. The major components were calcium oxalate monohydrate (COM), uric acid, hydroxyapatite, struvite, brushite, cystine, and CO dihydrate (COD) in 41.4%, 19.3%, 12.4%, 7.5%, 5.8%, 5.4%, and 4.7% of patients, respectively. The HUm of UA and Br was significantly lower and higher than the HUm of any other stone type, respectively. HUm and HUsd were most accurate in predicting uric acid with an AUC of 0.969 and 0.851, respectively. The combined use of HUm and HUsd resulted in increased positive predictive value and higher likelihood ratios for identifying a stone's mineral composition for all stone types but COM. To the best of our knowledge, this is the first report of CT data aiding in the prediction of brushite stone composition. Both HUm and HUsd can help predict stone composition and their combined use results in higher likelihood ratios influencing probability.

  16. A computational model that predicts behavioral sensitivity to intracortical microstimulation.

    PubMed

    Kim, Sungshin; Callier, Thierri; Bensmaia, Sliman J

    2017-02-01

    Intracortical microstimulation (ICMS) is a powerful tool to investigate the neural mechanisms of perception and can be used to restore sensation for patients who have lost it. While sensitivity to ICMS has previously been characterized, no systematic framework has been developed to summarize the detectability of individual ICMS pulse trains or the discriminability of pairs of pulse trains. We develop a simple simulation that describes the responses of a population of neurons to a train of electrical pulses delivered through a microelectrode. We then perform an ideal observer analysis on the simulated population responses to predict the behavioral performance of non-human primates in ICMS detection and discrimination tasks. Our computational model can predict behavioral performance across a wide range of stimulation conditions with high accuracy (R 2  = 0.97) and generalizes to novel ICMS pulse trains that were not used to fit its parameters. Furthermore, the model provides a theoretical basis for the finding that amplitude discrimination based on ICMS violates Weber's law. The model can be used to characterize the sensitivity to ICMS across the range of perceptible and safe stimulation regimes. As such, it will be a useful tool for both neuroscience and neuroprosthetics.

  17. An Accurate GPS-IMU/DR Data Fusion Method for Driverless Car Based on a Set of Predictive Models and Grid Constraints

    PubMed Central

    Wang, Shiyao; Deng, Zhidong; Yin, Gang

    2016-01-01

    A high-performance differential global positioning system (GPS)  receiver with real time kinematics provides absolute localization for driverless cars. However, it is not only susceptible to multipath effect but also unable to effectively fulfill precise error correction in a wide range of driving areas. This paper proposes an accurate GPS–inertial measurement unit (IMU)/dead reckoning (DR) data fusion method based on a set of predictive models and occupancy grid constraints. First, we employ a set of autoregressive and moving average (ARMA) equations that have different structural parameters to build maximum likelihood models of raw navigation. Second, both grid constraints and spatial consensus checks on all predictive results and current measurements are required to have removal of outliers. Navigation data that satisfy stationary stochastic process are further fused to achieve accurate localization results. Third, the standard deviation of multimodal data fusion can be pre-specified by grid size. Finally, we perform a lot of field tests on a diversity of real urban scenarios. The experimental results demonstrate that the method can significantly smooth small jumps in bias and considerably reduce accumulated position errors due to DR. With low computational complexity, the position accuracy of our method surpasses existing state-of-the-arts on the same dataset and the new data fusion method is practically applied in our driverless car. PMID:26927108

  18. An Accurate GPS-IMU/DR Data Fusion Method for Driverless Car Based on a Set of Predictive Models and Grid Constraints.

    PubMed

    Wang, Shiyao; Deng, Zhidong; Yin, Gang

    2016-02-24

    A high-performance differential global positioning system (GPS)  receiver with real time kinematics provides absolute localization for driverless cars. However, it is not only susceptible to multipath effect but also unable to effectively fulfill precise error correction in a wide range of driving areas. This paper proposes an accurate GPS-inertial measurement unit (IMU)/dead reckoning (DR) data fusion method based on a set of predictive models and occupancy grid constraints. First, we employ a set of autoregressive and moving average (ARMA) equations that have different structural parameters to build maximum likelihood models of raw navigation. Second, both grid constraints and spatial consensus checks on all predictive results and current measurements are required to have removal of outliers. Navigation data that satisfy stationary stochastic process are further fused to achieve accurate localization results. Third, the standard deviation of multimodal data fusion can be pre-specified by grid size. Finally, we perform a lot of field tests on a diversity of real urban scenarios. The experimental results demonstrate that the method can significantly smooth small jumps in bias and considerably reduce accumulated position errors due to DR. With low computational complexity, the position accuracy of our method surpasses existing state-of-the-arts on the same dataset and the new data fusion method is practically applied in our driverless car.

  19. Transient Vibration Prediction for Rotors on Ball Bearings Using Load-dependent Non-linear Bearing Stiffness

    NASA Technical Reports Server (NTRS)

    Fleming, David P.; Poplawski, J. V.

    2002-01-01

    Rolling-element bearing forces vary nonlinearly with bearing deflection. Thus an accurate rotordynamic transient analysis requires bearing forces to be determined at each step of the transient solution. Analyses have been carried out to show the effect of accurate bearing transient forces (accounting for non-linear speed and load dependent bearing stiffness) as compared to conventional use of average rolling-element bearing stiffness. Bearing forces were calculated by COBRA-AHS (Computer Optimized Ball and Roller Bearing Analysis - Advanced High Speed) and supplied to the rotordynamics code ARDS (Analysis of Rotor Dynamic Systems) for accurate simulation of rotor transient behavior. COBRA-AHS is a fast-running 5 degree-of-freedom computer code able to calculate high speed rolling-element bearing load-displacement data for radial and angular contact ball bearings and also for cylindrical and tapered roller beatings. Results show that use of nonlinear bearing characteristics is essential for accurate prediction of rotordynamic behavior.

  20. Adolescents’ expectations for the future predict health behaviors in early adulthood

    PubMed Central

    McDade, Thomas W.; Chyu, Laura; Duncan, Greg J.; Hoyt, Lindsay T.; Doane, Leah D.; Adam, Emma K.

    2011-01-01

    Health-related behaviors in adolescence establish trajectories of risk for obesity and chronic degenerative diseases, and they represent an important pathway through which socio-economic environments shape patterns of morbidity and mortality. Most behaviors that promote health involve making choices that may not pay off until the future, but the factors that predict an individual's investment in future health are not known. In this paper we consider whether expectations for the future in two domains relevant to adolescents in the U.S.—perceived chances of living to middle age and perceived chances of attending college—are associated with an individual's engagement in behaviors that protect health in the long run. We focus on adolescence as an important life stage during which habits formed may shape trajectories of disease risk later in life. We use data from a large, nationally representative sample of American youth (the US National Longitudinal Study of Adolescent Health) to predict levels of physical activity, fast food consumption, and cigarette smoking in young adulthood in relation to perceived life chances in adolescence, controlling for baseline health behaviors and a wide range of potentially confounding factors. We found that adolescents who rated their chances of attending college more highly exercised more frequently and smoked fewer cigarettes in young adulthood. Adolescents with higher expectations of living to age 35 smoked fewer cigarettes as young adults. Parental education was a significant predictor of perceived life chances, as well as health behaviors, but for each outcome the effects of perceived life chances were independent of, and often stronger than, parental education. Perceived life chances in adolescence may therefore play an important role in establishing individual trajectories of health, and in contributing to social gradients in population health. PMID:21764487

  1. Adolescents' expectations for the future predict health behaviors in early adulthood.

    PubMed

    McDade, Thomas W; Chyu, Laura; Duncan, Greg J; Hoyt, Lindsay T; Doane, Leah D; Adam, Emma K

    2011-08-01

    Health-related behaviors in adolescence establish trajectories of risk for obesity and chronic degenerative diseases, and they represent an important pathway through which socio-economic environments shape patterns of morbidity and mortality. Most behaviors that promote health involve making choices that may not pay off until the future, but the factors that predict an individual's investment in future health are not known. In this paper we consider whether expectations for the future in two domains relevant to adolescents in the U.S.-perceived chances of living to middle age and perceived chances of attending college-are associated with an individual's engagement in behaviors that protect health in the long run. We focus on adolescence as an important life stage during which habits formed may shape trajectories of disease risk later in life. We use data from a large, nationally representative sample of American youth (the US National Longitudinal Study of Adolescent Health) to predict levels of physical activity, fast food consumption, and cigarette smoking in young adulthood in relation to perceived life chances in adolescence, controlling for baseline health behaviors and a wide range of potentially confounding factors. We found that adolescents who rated their chances of attending college more highly exercised more frequently and smoked fewer cigarettes in young adulthood. Adolescents with higher expectations of living to age 35 smoked fewer cigarettes as young adults. Parental education was a significant predictor of perceived life chances, as well as health behaviors, but for each outcome the effects of perceived life chances were independent of, and often stronger than, parental education. Perceived life chances in adolescence may therefore play an important role in establishing individual trajectories of health, and in contributing to social gradients in population health. Copyright © 2011 Elsevier Ltd. All rights reserved.

  2. Predicting Adaptive Behavior in the Environment from Central Nervous System Dynamics

    PubMed Central

    Proekt, Alex; Wong, Jane; Zhurov, Yuriy; Kozlova, Nataliya; Weiss, Klaudiusz R.; Brezina, Vladimir

    2008-01-01

    To generate adaptive behavior, the nervous system is coupled to the environment. The coupling constrains the dynamical properties that the nervous system and the environment must have relative to each other if adaptive behavior is to be produced. In previous computational studies, such constraints have been used to evolve controllers or artificial agents to perform a behavioral task in a given environment. Often, however, we already know the controller, the real nervous system, and its dynamics. Here we propose that the constraints can also be used to solve the inverse problem—to predict from the dynamics of the nervous system the environment to which they are adapted, and so reconstruct the production of the adaptive behavior by the entire coupled system. We illustrate how this can be done in the feeding system of the sea slug Aplysia. At the core of this system is a central pattern generator (CPG) that, with dynamics on both fast and slow time scales, integrates incoming sensory stimuli to produce ingestive and egestive motor programs. We run models embodying these CPG dynamics—in effect, autonomous Aplysia agents—in various feeding environments and analyze the performance of the entire system in a realistic feeding task. We find that the dynamics of the system are tuned for optimal performance in a narrow range of environments that correspond well to those that Aplysia encounter in the wild. In these environments, the slow CPG dynamics implement efficient ingestion of edible seaweed strips with minimal sensory information about them. The fast dynamics then implement a switch to a different behavioral mode in which the system ignores the sensory information completely and follows an internal “goal,” emergent from the dynamics, to egest again a strip that proves to be inedible. Key predictions of this reconstruction are confirmed in real feeding animals. PMID:18989362

  3. A proposed food breakdown classification system to predict food behavior during gastric digestion.

    PubMed

    Bornhorst, Gail M; Ferrua, Maria J; Singh, R Paul

    2015-05-01

    The pharmaceutical industry has implemented the Biopharmaceutics Classification System (BCS), which is used to classify drug products based on their solubility and intestinal permeability. The BCS can help predict drug behavior in vivo, the rate-limiting mechanism of absorption, and the likelihood of an in vitro-in vivo correlation. Based on this analysis, we have proposed a Food Breakdown Classification System (FBCS) framework that can be used to classify solid foods according to their initial hardness and their rate of softening during physiological gastric conditions. The proposed FBCS will allow for prediction of food behavior during gastric digestion. The applicability of the FBCS framework in differentiating between dissimilar solid foods was demonstrated using four example foods: raw carrot, boiled potato, white rice, and brown rice. The initial hardness and rate of softening parameter (softening half time) were determined for these foods as well as their hypothesized FBCS class. In addition, we have provided future suggestions as to the methodological and analytical challenges that need to be overcome prior to widespread use and adoption of this classification system. The FBCS gives a framework that may be used to classify food products based on their material properties and their behavior during in vitro gastric digestion, and may also be used to predict in vivo food behavior. As consumer demand increases for functional and "pharma" food products, the food industry will need widespread testing of food products for their structural and functional performance during digestion. © 2015 Institute of Food Technologists®

  4. Measured and predicted structural behavior of the HiMAT tailored composite wing

    NASA Technical Reports Server (NTRS)

    Nelson, Lawrence H.

    1987-01-01

    A series of load tests was conducted on the HiMAT tailored composite wing. Coupon tests were also run on a series of unbalanced laminates, including the ply configuration of the wing, the purpose of which was to compare the measured and predicted behavior of unbalanced laminates, including - in the case of the wing - a comparison between the behavior of the full scale structure and coupon tests. Both linear and nonlinear finite element (NASTRAN) analyses were carried out on the wing. Both linear and nonlinear point-stress analyses were performed on the coupons. All test articles were instrumented with strain gages, and wing deflections measured. The leading and trailing edges were found to have no effect on the response of the wing to applied loads. A decrease in the stiffness of the wing box was evident over the 27-test program. The measured load-strain behavior of the wing was found to be linear, in contrast to coupon tests of the same laminate, which were nonlinear. A linear NASTRAN analysis of the wing generally correlated more favorably with measurements than did a nonlinear analysis. An examination of the predicted deflections in the wing root region revealed an anomalous behavior of the structural model that cannot be explained. Both hysteresis and creep appear to be less significant in the wing tests than in the corresponding laminate coupon tests.

  5. Using the integrative model of behavioral prediction to identify promising message strategies to promote healthy sleep behavior among college students.

    PubMed

    Robbins, Rebecca; Niederdeppe, Jeff

    2015-01-01

    This research used the Integrative Model of Behavioral Prediction (IMBP) to examine cognitive predictors of intentions to engage in healthy sleep behavior among a population of college students. In doing so, we identify promising message strategies to increase healthy sleep behavior during college. In Phase 1, members of a small sample of undergraduates (n = 31) were asked to describe their beliefs about expected outcomes, norms, and perceived behavioral control associated with sleep on an open-ended questionnaire. We analyzed these qualitative responses to create a closed-ended survey about sleep-related attitudes, perceived norms, control beliefs, behavioral intentions, and behavior. In Phase 2, a larger sample of undergraduate students (n = 365) completed the survey. Attitudes and perceived behavioral control were the strongest predictors of both intentions to engage in sleep behavior and self-reported sleep behavior. Control beliefs associated with time management and stress also had substantial room to change, suggesting their potential as message strategies to better promote healthy sleep behavior in college. We conclude with a broader discussion of the study's implications for message design and intervention.

  6. Prediction of Flow Stress in Cadmium Using Constitutive Equation and Artificial Neural Network Approach

    NASA Astrophysics Data System (ADS)

    Sarkar, A.; Chakravartty, J. K.

    2013-10-01

    A model is developed to predict the constitutive flow behavior of cadmium during compression test using artificial neural network (ANN). The inputs of the neural network are strain, strain rate, and temperature, whereas flow stress is the output. Experimental data obtained from compression tests in the temperature range -30 to 70 °C, strain range 0.1 to 0.6, and strain rate range 10-3 to 1 s-1 are employed to develop the model. A three-layer feed-forward ANN is trained with Levenberg-Marquardt training algorithm. It has been shown that the developed ANN model can efficiently and accurately predict the deformation behavior of cadmium. This trained network could predict the flow stress better than a constitutive equation of the type.

  7. Behavioral oscillation in face priming: Prediction about face identity is updated at a theta-band rhythm.

    PubMed

    Wang, Yuanye; Luo, Huan

    2017-01-01

    In order to deal with external world efficiently, the brain constantly generates predictions about incoming sensory inputs, a process known as "predictive coding." Our recent studies, by employing visual priming paradigms in combination with a time-resolved behavioral measurement, reveal that perceptual predictions about simple features (e.g., left or right orientation) return to low sensory areas not continuously but recurrently in a theta-band (3-4Hz) rhythm. However, it remains unknown whether high-level object processing is also mediated by the oscillatory mechanism and if yes at which rhythm the mechanism works. In the present study, we employed a morph-face priming paradigm and the time-resolved behavioral measurements to examine the fine temporal dynamics of face identity priming performance. First, we reveal classical priming effects and a rhythmic trend within the prime-to-probe SOA of 600ms (Experiment 1). Next, we densely sampled the face priming behavioral performances within this SOA range (Experiment 2). Our results demonstrate a significant ~5Hz oscillatory component in the face priming behavioral performances, suggesting that a rhythmic process also coordinates the object-level prediction (i.e., face identity here). In comparison to our previous studies, the results suggest that the rhythm for the high-level object is faster than that for simple features. We propose that the seemingly distinctive priming rhythms might be attributable to that the object-level and simple feature-level predictions return to different stages along the visual pathway (e.g., FFA area for face priming and V1 area for simple feature priming). In summary, the findings support a general theta-band (3-6Hz) temporal organization mechanism in predictive coding, and that such wax-and-waning pattern in predictive coding may aid the brain to be more readily updated for new inputs. © 2017 Elsevier B.V. All rights reserved.

  8. Evidence for surprise minimization over value maximization in choice behavior

    PubMed Central

    Schwartenbeck, Philipp; FitzGerald, Thomas H. B.; Mathys, Christoph; Dolan, Ray; Kronbichler, Martin; Friston, Karl

    2015-01-01

    Classical economic models are predicated on the idea that the ultimate aim of choice is to maximize utility or reward. In contrast, an alternative perspective highlights the fact that adaptive behavior requires agents’ to model their environment and minimize surprise about the states they frequent. We propose that choice behavior can be more accurately accounted for by surprise minimization compared to reward or utility maximization alone. Minimizing surprise makes a prediction at variance with expected utility models; namely, that in addition to attaining valuable states, agents attempt to maximize the entropy over outcomes and thus ‘keep their options open’. We tested this prediction using a simple binary choice paradigm and show that human decision-making is better explained by surprise minimization compared to utility maximization. Furthermore, we replicated this entropy-seeking behavior in a control task with no explicit utilities. These findings highlight a limitation of purely economic motivations in explaining choice behavior and instead emphasize the importance of belief-based motivations. PMID:26564686

  9. Impulsive Approach Tendencies towards Physical Activity and Sedentary Behaviors, but Not Reflective Intentions, Prospectively Predict Non-Exercise Activity Thermogenesis

    PubMed Central

    Cheval, Boris; Sarrazin, Philippe; Pelletier, Luc

    2014-01-01

    Understanding the determinants of non-exercise activity thermogenesis (NEAT) is crucial, given its extensive health benefits. Some scholars have assumed that a proneness to react differently to environmental cues promoting sedentary versus active behaviors could be responsible for inter-individual differences in NEAT. In line with this reflection and grounded on the Reflective-Impulsive Model, we test the assumption that impulsive processes related to sedentary and physical activity behaviors can prospectively predict NEAT, operationalized as spontaneous effort exerted to maintain low intensity muscle contractions within the release phases of an intermittent maximal isometric contraction task. Participants (n = 91) completed a questionnaire assessing their intentions to adopt physical activity behaviors and a manikin task to assess impulsive approach tendencies towards physical activity behaviors (IAPA) and sedentary behaviors (IASB). Participants were then instructed to perform a maximal handgrip strength task and an intermittent maximal isometric contraction task. As hypothesized, multilevel regression analyses revealed that spontaneous effort was (a) positively predicted by IAPA, (b) negatively predicted by IASB, and (c) was not predicted by physical activity intentions, after controlling for some confounding variables such as age, sex, usual PA level and average force provided during the maximal-contraction phases of the task. These effects remained constant throughout all the phases of the task. This study demonstrated that impulsive processes may play a unique role in predicting spontaneous physical activity behaviors. Theoretically, this finding reinforces the utility of a motivational approach based on dual-process models to explain inter-individual differences in NEAT. Implications for health behavior theories and behavior change interventions are outlined. PMID:25526596

  10. Impulsive approach tendencies towards physical activity and sedentary behaviors, but not reflective intentions, prospectively predict non-exercise activity thermogenesis.

    PubMed

    Cheval, Boris; Sarrazin, Philippe; Pelletier, Luc

    2014-01-01

    Understanding the determinants of non-exercise activity thermogenesis (NEAT) is crucial, given its extensive health benefits. Some scholars have assumed that a proneness to react differently to environmental cues promoting sedentary versus active behaviors could be responsible for inter-individual differences in NEAT. In line with this reflection and grounded on the Reflective-Impulsive Model, we test the assumption that impulsive processes related to sedentary and physical activity behaviors can prospectively predict NEAT, operationalized as spontaneous effort exerted to maintain low intensity muscle contractions within the release phases of an intermittent maximal isometric contraction task. Participants (n = 91) completed a questionnaire assessing their intentions to adopt physical activity behaviors and a manikin task to assess impulsive approach tendencies towards physical activity behaviors (IAPA) and sedentary behaviors (IASB). Participants were then instructed to perform a maximal handgrip strength task and an intermittent maximal isometric contraction task. As hypothesized, multilevel regression analyses revealed that spontaneous effort was (a) positively predicted by IAPA, (b) negatively predicted by IASB, and (c) was not predicted by physical activity intentions, after controlling for some confounding variables such as age, sex, usual PA level and average force provided during the maximal-contraction phases of the task. These effects remained constant throughout all the phases of the task. This study demonstrated that impulsive processes may play a unique role in predicting spontaneous physical activity behaviors. Theoretically, this finding reinforces the utility of a motivational approach based on dual-process models to explain inter-individual differences in NEAT. Implications for health behavior theories and behavior change interventions are outlined.

  11. Callous-Unemotional Traits and Autonomic Functioning in Toddlerhood Interact to Predict Externalizing Behaviors in Preschool.

    PubMed

    Wagner, Nicholas J; Hastings, Paul D; Rubin, Kenneth H

    2017-11-29

    Callous-unemotional (CU) traits are one meaningful risk factor which helps to explain heterogeneity in the emergence of externalizing behavior problems. While the extant literature demonstrates links between early CU traits and later externalizing problems, there is a dearth of research examining for whom early CU traits confer risk. Data from a longitudinal study (n = 108) were used to examine the extent to which parasympathetic functioning moderated links between CU traits in toddlerhood (m age  = 24.99 months) and externalizing behavior problems at preschool-age (m age  = 51 months). Neither CU traits nor parasympathetic functioning at age 2 directly predicted later externalizing behaviors. However, results show that high levels of CU traits predict elevated externalizing behavior problems, but only for toddlers exhibiting either high baseline respiratory sinus arrhythmia (RSA) or little to no RSA suppression in response to a fear stimulus.

  12. Using self-reported and objective measures of self-control to predict exercise and academic behaviors among first-year university students.

    PubMed

    Stork, Matthew J; Graham, Jeffrey D; Bray, Steven R; Martin Ginis, Kathleen A

    2017-07-01

    Thirty students (mean age = 18 ± 0.5 years) completed self-report (Self-Control Scale) and objective (isometric handgrip squeeze performance) measures of self-control, provided their exercise and academic (study/schoolwork) plans for the next month, and then logged these behaviors over the subsequent 4-week period. Trait self-control predicted exercise and academic behavior. Handgrip squeeze performance predicted academic behavior and adherence to academic plans. Further, regression analysis revealed that trait self-control and handgrip performance explained significant variance in academic behavior. These findings provide a new understanding of how different self-control measures can be used to predict first-year students' participation in, and adherence to, exercise and academic behaviors concurrently.

  13. Profiles of observed infant anger predict preschool behavior problems: Moderation by life stress

    PubMed Central

    Brooker, Rebecca J.; Buss, Kristin A.; Lemery-Chalfant, Kathryn; Aksan, Nazan; Davidson, Richard J.; Goldsmith, H. Hill

    2014-01-01

    Using both traditional composites and novel profiles of anger, we examined associations between infant anger and preschool behavior problems in a large, longitudinal data set (N = 966). We also tested the role of life stress as a moderator of the link between early anger and the development of behavior problems. Although traditional measures of anger were largely unrelated to later behavior problems, profiles of anger that dissociated typical from atypical development predicted behavior problems during preschool. Moreover, the relation between infant anger profiles and preschool behavior problems was moderated such that, when early life stress was low, infants with atypical profiles of early anger showed more preschool behavior problems than did infants with normative anger profiles. However, when early life stress was high, infants with atypical and normative profiles of infant anger did not differ in preschool behavior problems. We conclude that a discrete emotions approach including latent profile analysis is useful for elucidating biological and environmental developmental pathways to early problem behaviors. PMID:25151247

  14. The effect of machine learning regression algorithms and sample size on individualized behavioral prediction with functional connectivity features.

    PubMed

    Cui, Zaixu; Gong, Gaolang

    2018-06-02

    Individualized behavioral/cognitive prediction using machine learning (ML) regression approaches is becoming increasingly applied. The specific ML regression algorithm and sample size are two key factors that non-trivially influence prediction accuracies. However, the effects of the ML regression algorithm and sample size on individualized behavioral/cognitive prediction performance have not been comprehensively assessed. To address this issue, the present study included six commonly used ML regression algorithms: ordinary least squares (OLS) regression, least absolute shrinkage and selection operator (LASSO) regression, ridge regression, elastic-net regression, linear support vector regression (LSVR), and relevance vector regression (RVR), to perform specific behavioral/cognitive predictions based on different sample sizes. Specifically, the publicly available resting-state functional MRI (rs-fMRI) dataset from the Human Connectome Project (HCP) was used, and whole-brain resting-state functional connectivity (rsFC) or rsFC strength (rsFCS) were extracted as prediction features. Twenty-five sample sizes (ranged from 20 to 700) were studied by sub-sampling from the entire HCP cohort. The analyses showed that rsFC-based LASSO regression performed remarkably worse than the other algorithms, and rsFCS-based OLS regression performed markedly worse than the other algorithms. Regardless of the algorithm and feature type, both the prediction accuracy and its stability exponentially increased with increasing sample size. The specific patterns of the observed algorithm and sample size effects were well replicated in the prediction using re-testing fMRI data, data processed by different imaging preprocessing schemes, and different behavioral/cognitive scores, thus indicating excellent robustness/generalization of the effects. The current findings provide critical insight into how the selected ML regression algorithm and sample size influence individualized predictions of

  15. Family Structure versus Family Relationships for Predicting to Substance Use/Abuse and Illegal Behavior.

    ERIC Educational Resources Information Center

    Friedman, Alfred S.; Terras, Arlene; Glassman, Kimberly

    2000-01-01

    Study looked at sample of African-American adolescent males to determine the degree to which family structure (e.g., single parent vs. two-parent families) vs. the nature of the family relationships predict sons' involvement in substance use/abuse and illegal behavior. Of 33 relationships measures analyzed, 3 predicted the degree of recent…

  16. Accurate pan-specific prediction of peptide-MHC class II binding affinity with improved binding core identification.

    PubMed

    Andreatta, Massimo; Karosiene, Edita; Rasmussen, Michael; Stryhn, Anette; Buus, Søren; Nielsen, Morten

    2015-11-01

    A key event in the generation of a cellular response against malicious organisms through the endocytic pathway is binding of peptidic antigens by major histocompatibility complex class II (MHC class II) molecules. The bound peptide is then presented on the cell surface where it can be recognized by T helper lymphocytes. NetMHCIIpan is a state-of-the-art method for the quantitative prediction of peptide binding to any human or mouse MHC class II molecule of known sequence. In this paper, we describe an updated version of the method with improved peptide binding register identification. Binding register prediction is concerned with determining the minimal core region of nine residues directly in contact with the MHC binding cleft, a crucial piece of information both for the identification and design of CD4(+) T cell antigens. When applied to a set of 51 crystal structures of peptide-MHC complexes with known binding registers, the new method NetMHCIIpan-3.1 significantly outperformed the earlier 3.0 version. We illustrate the impact of accurate binding core identification for the interpretation of T cell cross-reactivity using tetramer double staining with a CMV epitope and its variants mapped to the epitope binding core. NetMHCIIpan is publicly available at http://www.cbs.dtu.dk/services/NetMHCIIpan-3.1 .

  17. Moving Toward Integrating Gene Expression Profiling Into High-Throughput Testing: A Gene Expression Biomarker Accurately Predicts Estrogen Receptor α Modulation in a Microarray Compendium

    PubMed Central

    Ryan, Natalia; Chorley, Brian; Tice, Raymond R.; Judson, Richard; Corton, J. Christopher

    2016-01-01

    Microarray profiling of chemical-induced effects is being increasingly used in medium- and high-throughput formats. Computational methods are described here to identify molecular targets from whole-genome microarray data using as an example the estrogen receptor α (ERα), often modulated by potential endocrine disrupting chemicals. ERα biomarker genes were identified by their consistent expression after exposure to 7 structurally diverse ERα agonists and 3 ERα antagonists in ERα-positive MCF-7 cells. Most of the biomarker genes were shown to be directly regulated by ERα as determined by ESR1 gene knockdown using siRNA as well as through chromatin immunoprecipitation coupled with DNA sequencing analysis of ERα-DNA interactions. The biomarker was evaluated as a predictive tool using the fold-change rank-based Running Fisher algorithm by comparison to annotated gene expression datasets from experiments using MCF-7 cells, including those evaluating the transcriptional effects of hormones and chemicals. Using 141 comparisons from chemical- and hormone-treated cells, the biomarker gave a balanced accuracy for prediction of ERα activation or suppression of 94% and 93%, respectively. The biomarker was able to correctly classify 18 out of 21 (86%) ER reference chemicals including “very weak” agonists. Importantly, the biomarker predictions accurately replicated predictions based on 18 in vitro high-throughput screening assays that queried different steps in ERα signaling. For 114 chemicals, the balanced accuracies were 95% and 98% for activation or suppression, respectively. These results demonstrate that the ERα gene expression biomarker can accurately identify ERα modulators in large collections of microarray data derived from MCF-7 cells. PMID:26865669

  18. Prediction of dynamic behavior of mutant strains from limited wild-type data.

    PubMed

    Song, Hyun-Seob; Ramkrishna, Doraiswami

    2012-03-01

    Metabolic engineering is the field of introducing genetic changes in organisms so as to modify their function towards synthesizing new products of high impact to society. However, engineered cells frequently have impaired growth rates thus seriously limiting the rate at which such products are made. The problem is attributable to inadequate understanding of how a metabolic network functions in a dynamic sense. Predictions of mutant strain behavior in the past have been based on steady state theories such as flux balance analysis (FBA), minimization of metabolic adjustment (MOMA), and regulatory on/off minimization (ROOM). Such predictions are restricted to product yields and cannot address productivity, which is of focal interest to applications. We demonstrate that our framework ( [Song and Ramkrishna, 2010] and [Song and Ramkrishna, 2011]), based on a “cybernetic” view of metabolic systems, makes predictions of the dynamic behavior of mutant strains of Escherichia coli from a limited amount of data obtained from the wild-type. Dynamic frameworks must necessarily address the issue of metabolic regulation, which the cybernetic approach does by postulating that metabolism is an optimal dynamic response of the organism to the environment in driving reactions towards ensuring survival. The predictions made in this paper are without parallel in the literature and lay the foundation for rational metabolic engineering. Copyright © 2012 Elsevier Inc. All rights reserved.

  19. Analyzing Medical Image Search Behavior: Semantics and Prediction of Query Results.

    PubMed

    De-Arteaga, Maria; Eggel, Ivan; Kahn, Charles E; Müller, Henning

    2015-10-01

    Log files of information retrieval systems that record user behavior have been used to improve the outcomes of retrieval systems, understand user behavior, and predict events. In this article, a log file of the ARRS GoldMiner search engine containing 222,005 consecutive queries is analyzed. Time stamps are available for each query, as well as masked IP addresses, which enables to identify queries from the same person. This article describes the ways in which physicians (or Internet searchers interested in medical images) search and proposes potential improvements by suggesting query modifications. For example, many queries contain only few terms and therefore are not specific; others contain spelling mistakes or non-medical terms that likely lead to poor or empty results. One of the goals of this report is to predict the number of results a query will have since such a model allows search engines to automatically propose query modifications in order to avoid result lists that are empty or too large. This prediction is made based on characteristics of the query terms themselves. Prediction of empty results has an accuracy above 88%, and thus can be used to automatically modify the query to avoid empty result sets for a user. The semantic analysis and data of reformulations done by users in the past can aid the development of better search systems, particularly to improve results for novice users. Therefore, this paper gives important ideas to better understand how people search and how to use this knowledge to improve the performance of specialized medical search engines.

  20. Accurate Energy Consumption Modeling of IEEE 802.15.4e TSCH Using Dual-BandOpenMote Hardware.

    PubMed

    Daneels, Glenn; Municio, Esteban; Van de Velde, Bruno; Ergeerts, Glenn; Weyn, Maarten; Latré, Steven; Famaey, Jeroen

    2018-02-02

    The Time-Slotted Channel Hopping (TSCH) mode of the IEEE 802.15.4e amendment aims to improve reliability and energy efficiency in industrial and other challenging Internet-of-Things (IoT) environments. This paper presents an accurate and up-to-date energy consumption model for devices using this IEEE 802.15.4e TSCH mode. The model identifies all network-related CPU and radio state changes, thus providing a precise representation of the device behavior and an accurate prediction of its energy consumption. Moreover, energy measurements were performed with a dual-band OpenMote device, running the OpenWSN firmware. This allows the model to be used for devices using 2.4 GHz, as well as 868 MHz. Using these measurements, several network simulations were conducted to observe the TSCH energy consumption effects in end-to-end communication for both frequency bands. Experimental verification of the model shows that it accurately models the consumption for all possible packet sizes and that the calculated consumption on average differs less than 3% from the measured consumption. This deviation includes measurement inaccuracies and the variations of the guard time. As such, the proposed model is very suitable for accurate energy consumption modeling of TSCH networks.

  1. Accurate Energy Consumption Modeling of IEEE 802.15.4e TSCH Using Dual-BandOpenMote Hardware

    PubMed Central

    Municio, Esteban; Van de Velde, Bruno; Latré, Steven

    2018-01-01

    The Time-Slotted Channel Hopping (TSCH) mode of the IEEE 802.15.4e amendment aims to improve reliability and energy efficiency in industrial and other challenging Internet-of-Things (IoT) environments. This paper presents an accurate and up-to-date energy consumption model for devices using this IEEE 802.15.4e TSCH mode. The model identifies all network-related CPU and radio state changes, thus providing a precise representation of the device behavior and an accurate prediction of its energy consumption. Moreover, energy measurements were performed with a dual-band OpenMote device, running the OpenWSN firmware. This allows the model to be used for devices using 2.4 GHz, as well as 868 MHz. Using these measurements, several network simulations were conducted to observe the TSCH energy consumption effects in end-to-end communication for both frequency bands. Experimental verification of the model shows that it accurately models the consumption for all possible packet sizes and that the calculated consumption on average differs less than 3% from the measured consumption. This deviation includes measurement inaccuracies and the variations of the guard time. As such, the proposed model is very suitable for accurate energy consumption modeling of TSCH networks. PMID:29393900

  2. Positive Parenting Interacts with Child Temperament and Negative Parenting to Predict Children’s Socially Appropriate Behavior

    PubMed Central

    Danzig, Allison P.; Dyson, Margaret W.; Olino, Thomas M.; Laptook, Rebecca S.; Klein, Daniel N.

    2017-01-01

    This study examined the effects of parents’ positive and negative affect and behavior while interacting with their preschool child and the moderating role of child temperament in predicting children’s subsequent difficulty with socially appropriate behavior around school-entry. Independent observational measures were used to assess child temperament (dysphoria; exuberance) and parenting at age 3, and multi-informant reports of child socially appropriate behavior were collected at age 6 (N = 219). Hierarchical multiple regression analyses revealed that children’s temperamental dysphoria moderated the relationship between positive parenting and later socially appropriate behavior. High- and low-dysphoric children trended in opposite directions; highly dysphoric children experienced greater difficulty with socially appropriate behavior as levels of positive parenting increased, whereas low-dysphoric children experienced less difficulty with socially appropriate behavior with higher levels of positive parenting. There was also an interaction between positive and negative parenting, whereby the combination of elevated positive and negative parenting predicted children’s later difficulty with socially appropriate behavior. The findings suggest that positive parenting interacts with early child temperament and negative parenting to impact the development of children’s socially appropriate behavior. PMID:28824223

  3. Amygdala inputs to prefrontal cortex guide behavior amid conflicting cues of reward and punishment

    PubMed Central

    Burgos-Robles, Anthony; Kimchi, Eyal Y.; Izadmehr, Ehsan M.; Porzenheim, Mary Jane; Ramos-Guasp, William A.; Nieh, Edward H.; Felix-Ortiz, Ada C.; Namburi, Praneeth; Leppla, Christopher A.; Presbrey, Kara N.; Anandalingam, Kavitha K.; Pagan-Rivera, Pablo A.; Anahtar, Melodi; Beyeler, Anna; Tye, Kay M.

    2017-01-01

    Orchestrating appropriate behavioral responses in the face of competing signals that predict either rewards or threats in the environment is crucial for survival. The basolateral amygdala (BLA) and prelimbic (PL) medial prefrontal cortex (mPFC) have been implicated in reward-seeking and fear-related responses, but how information flows between these reciprocally-connected structures to coordinate behavior is unknown. We recorded neuronal activity from the BLA and PL while rats performed a task where in shock- and sucrose-predictive cues were simultaneously presented to induce competition. The correlated firing primarily displayed a BLA→PL directionality during the shock-associated cue. Furthermore, the majority of optogenetically-identified PL-projecting BLA neurons recorded encoded the shock-associated cue, and more accurately predicted behavioral responses during competition than unidentified BLA neurons. Finally, BLA→PL photostimulation increased freezing, whereas both chemogenetic and optogenetic inhibition reduced freezing. The BLA→PL circuit plays a critical role in governing the selection of behavioral responses in the face of competing signals. PMID:28436980

  4. An Anisotropic Hardening Model for Springback Prediction

    NASA Astrophysics Data System (ADS)

    Zeng, Danielle; Xia, Z. Cedric

    2005-08-01

    As more Advanced High-Strength Steels (AHSS) are heavily used for automotive body structures and closures panels, accurate springback prediction for these components becomes more challenging because of their rapid hardening characteristics and ability to sustain even higher stresses. In this paper, a modified Mroz hardening model is proposed to capture realistic Bauschinger effect at reverse loading, such as when material passes through die radii or drawbead during sheet metal forming process. This model accounts for material anisotropic yield surface and nonlinear isotropic/kinematic hardening behavior. Material tension/compression test data are used to accurately represent Bauschinger effect. The effectiveness of the model is demonstrated by comparison of numerical and experimental springback results for a DP600 straight U-channel test.

  5. Board-invited review: Using behavior to predict and identify ill health in animals.

    PubMed

    Weary, D M; Huzzey, J M; von Keyserlingk, M A G

    2009-02-01

    We review recent research in one of the oldest and most important applications of ethology: evaluating animal health. Traditionally, such evaluations have been based on subjective assessments of debilitative signs; animals are judged ill when they appear depressed or off feed. Such assessments are prone to error but can be dramatically improved with training using well-defined clinical criteria. The availability of new technology to automatically record behaviors allows for increased use of objective measures; automated measures of feeding behavior and intake are increasingly available in commercial agriculture, and recent work has shown these to be valuable indicators of illness. Research has also identified behaviors indicative of risk of disease or injury. For example, the time spent standing on wet, concrete surfaces can be used to predict susceptibility to hoof injuries in dairy cattle, and time spent nuzzling the udder of the sow can predict the risk of crushing in piglets. One conceptual advance has been to view decreased exploration, feeding, social, sexual, and other behaviors as a coordinated response that helps afflicted individuals recover from illness. We argue that the sickness behaviors most likely to decline are those that provide longer-term fitness benefits (such as play), as animals divert resources to those functions of critical short-term value such as maintaining body temperature. We urge future research assessing the strength of motivation to express sickness behaviors, allowing for quantitative estimates of how sick an animal feels. Finally, we call for new theoretical and empirical work on behaviors that may act to signal health status, including behaviors that have evolved as honest (i.e., reliable) signals of condition for offspring-parent, inter- and intra-sexual, and predator-prey communication.

  6. On accurate determination of contact angle

    NASA Technical Reports Server (NTRS)

    Concus, P.; Finn, R.

    1992-01-01

    Methods are proposed that exploit a microgravity environment to obtain highly accurate measurement of contact angle. These methods, which are based on our earlier mathematical results, do not require detailed measurement of a liquid free-surface, as they incorporate discontinuous or nearly-discontinuous behavior of the liquid bulk in certain container geometries. Physical testing is planned in the forthcoming IML-2 space flight and in related preparatory ground-based experiments.

  7. Wildland Fire Forecasting: Predicting Wildfire Behavior, Growth, and Feedbacks on Weather

    NASA Astrophysics Data System (ADS)

    Coen, J. L.

    2005-12-01

    Recent developments in wildland fire research models have represented more complex of fire behavior. The cost has been to increase the computational requirements. When operational constraints are included, such as the need to produce such forecasts faster than real time, the challenge becomes a balance of how much complexity (with corresponding gains in realism) and accuracy can be achieved in producing the quantities of interest while meeting the specified operational constraints. Current field tools are calculator or Palm-Pilot based algorithms such as BEHAVE and BEHAVE Plus that produce timely estimates of instantaneous fire spread rates, flame length, and fire intensity at a point using readily estimated inputs of fuel model, terrain slope, and atmospheric wind speed at a point. At the cost of requiring a PC and slower calculation, FARSITE represents two-dimensional fire spread and adds capabilities including a parameterized representation of crown fire ignition, This work describes how a coupled atmosphere-fire model previously used as a research tool has been adapted for production of real-time forecasts of fire growth and its interactions with weather over a domain focusing on Colorado during summer 2004. The coupled atmosphere-wildland fire-environment (CAWFE) model composed of a 3-dimensional atmospheric prediction model that has been two-way coupled with an empirical fire spread model. The models are connected in that atmospheric conditions (and fuel conditions influenced by the atmosphere) affect the rate and direction of fire propagation, which releases sensible and latent heat (i.e. thermal and water vapor fluxes) to the atmosphere that in turn alter the winds and atmospheric structure around the fire. Thus, it can represent time and spatially-varying weather and the fire feedbacks on the atmospheric which are at the heart of sudden changes in fire behavior and examples of extreme fire behavior such as blow ups, which are now not predictable with

  8. A comparison of naïve and sophisticated subject behavior with game theoretic predictions

    PubMed Central

    McCabe, Kevin A.; Smith, Vernon L.

    2000-01-01

    We use an extensive form two-person game as the basis for two experiments designed to compare the behavior of two groups of subjects with each other and with the subgame perfect theoretical prediction in an anonymous interaction protocol. The two subject groups are undergraduates and advanced graduate students, the latter having studied economics and game theory. There is no difference in their choice behavior, and both groups depart substantially from game theoretic predictions. We also compare a subsample of the same graduate students with a typical undergraduate sample in an asset trading environment in which inexperienced undergraduates invariably produce substantial departures from the rational expectations prediction. In this way, we examine how robust are the results across two distinct anonymous interactive environments. In the constant sum trading game, the graduate students closely track the predictions of rational theory. Our interpretation is that the graduate student subjects' departure from subgame perfection to achieve cooperative outcomes in the two-person bargaining game is a consequence of a deliberate strategy and is not the result of error or inadequate learning. PMID:10725349

  9. Selection of fire spread model for Russian fire behavior prediction system

    Treesearch

    Alexandra V. Volokitina; Kevin C. Ryan; Tatiana M. Sofronova; Mark A. Sofronov

    2010-01-01

    Mathematical modeling of fire behavior prediction is only possible if the models are supplied with an information database that provides spatially explicit input parameters for modeled area. Mathematical models can be of three kinds: 1) physical; 2) empirical; and 3) quasi-empirical (Sullivan, 2009). Physical models (Grishin, 1992) are of academic interest only because...

  10. Improved supervised classification of accelerometry data to distinguish behaviors of soaring birds.

    PubMed

    Sur, Maitreyi; Suffredini, Tony; Wessells, Stephen M; Bloom, Peter H; Lanzone, Michael; Blackshire, Sheldon; Sridhar, Srisarguru; Katzner, Todd

    2017-01-01

    Soaring birds can balance the energetic costs of movement by switching between flapping, soaring and gliding flight. Accelerometers can allow quantification of flight behavior and thus a context to interpret these energetic costs. However, models to interpret accelerometry data are still being developed, rarely trained with supervised datasets, and difficult to apply. We collected accelerometry data at 140Hz from a trained golden eagle (Aquila chrysaetos) whose flight we recorded with video that we used to characterize behavior. We applied two forms of supervised classifications, random forest (RF) models and K-nearest neighbor (KNN) models. The KNN model was substantially easier to implement than the RF approach but both were highly accurate in classifying basic behaviors such as flapping (85.5% and 83.6% accurate, respectively), soaring (92.8% and 87.6%) and sitting (84.1% and 88.9%) with overall accuracies of 86.6% and 92.3% respectively. More detailed classification schemes, with specific behaviors such as banking and straight flights were well classified only by the KNN model (91.24% accurate; RF = 61.64% accurate). The RF model maintained its accuracy of classifying basic behavior classification accuracy of basic behaviors at sampling frequencies as low as 10Hz, the KNN at sampling frequencies as low as 20Hz. Classification of accelerometer data collected from free ranging birds demonstrated a strong dependence of predicted behavior on the type of classification model used. Our analyses demonstrate the consequence of different approaches to classification of accelerometry data, the potential to optimize classification algorithms with validated flight behaviors to improve classification accuracy, ideal sampling frequencies for different classification algorithms, and a number of ways to improve commonly used analytical techniques and best practices for classification of accelerometry data.

  11. Improved supervised classification of accelerometry data to distinguish behaviors of soaring birds

    PubMed Central

    Suffredini, Tony; Wessells, Stephen M.; Bloom, Peter H.; Lanzone, Michael; Blackshire, Sheldon; Sridhar, Srisarguru; Katzner, Todd

    2017-01-01

    Soaring birds can balance the energetic costs of movement by switching between flapping, soaring and gliding flight. Accelerometers can allow quantification of flight behavior and thus a context to interpret these energetic costs. However, models to interpret accelerometry data are still being developed, rarely trained with supervised datasets, and difficult to apply. We collected accelerometry data at 140Hz from a trained golden eagle (Aquila chrysaetos) whose flight we recorded with video that we used to characterize behavior. We applied two forms of supervised classifications, random forest (RF) models and K-nearest neighbor (KNN) models. The KNN model was substantially easier to implement than the RF approach but both were highly accurate in classifying basic behaviors such as flapping (85.5% and 83.6% accurate, respectively), soaring (92.8% and 87.6%) and sitting (84.1% and 88.9%) with overall accuracies of 86.6% and 92.3% respectively. More detailed classification schemes, with specific behaviors such as banking and straight flights were well classified only by the KNN model (91.24% accurate; RF = 61.64% accurate). The RF model maintained its accuracy of classifying basic behavior classification accuracy of basic behaviors at sampling frequencies as low as 10Hz, the KNN at sampling frequencies as low as 20Hz. Classification of accelerometer data collected from free ranging birds demonstrated a strong dependence of predicted behavior on the type of classification model used. Our analyses demonstrate the consequence of different approaches to classification of accelerometry data, the potential to optimize classification algorithms with validated flight behaviors to improve classification accuracy, ideal sampling frequencies for different classification algorithms, and a number of ways to improve commonly used analytical techniques and best practices for classification of accelerometry data. PMID:28403159

  12. Improved supervised classification of accelerometry data to distinguish behaviors of soaring birds

    USGS Publications Warehouse

    Sur, Maitreyi; Suffredini, Tony; Wessells, Stephen M.; Bloom, Peter H.; Lanzone, Michael J.; Blackshire, Sheldon; Sridhar, Srisarguru; Katzner, Todd

    2017-01-01

    Soaring birds can balance the energetic costs of movement by switching between flapping, soaring and gliding flight. Accelerometers can allow quantification of flight behavior and thus a context to interpret these energetic costs. However, models to interpret accelerometry data are still being developed, rarely trained with supervised datasets, and difficult to apply. We collected accelerometry data at 140Hz from a trained golden eagle (Aquila chrysaetos) whose flight we recorded with video that we used to characterize behavior. We applied two forms of supervised classifications, random forest (RF) models and K-nearest neighbor (KNN) models. The KNN model was substantially easier to implement than the RF approach but both were highly accurate in classifying basic behaviors such as flapping (85.5% and 83.6% accurate, respectively), soaring (92.8% and 87.6%) and sitting (84.1% and 88.9%) with overall accuracies of 86.6% and 92.3% respectively. More detailed classification schemes, with specific behaviors such as banking and straight flights were well classified only by the KNN model (91.24% accurate; RF = 61.64% accurate). The RF model maintained its accuracy of classifying basic behavior classification accuracy of basic behaviors at sampling frequencies as low as 10Hz, the KNN at sampling frequencies as low as 20Hz. Classification of accelerometer data collected from free ranging birds demonstrated a strong dependence of predicted behavior on the type of classification model used. Our analyses demonstrate the consequence of different approaches to classification of accelerometry data, the potential to optimize classification algorithms with validated flight behaviors to improve classification accuracy, ideal sampling frequencies for different classification algorithms, and a number of ways to improve commonly used analytical techniques and best practices for classification of accelerometry data.

  13. Accurate thermoelastic tensor and acoustic velocities of NaCl

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

    Marcondes, Michel L., E-mail: michel@if.usp.br; Chemical Engineering and Material Science, University of Minnesota, Minneapolis, 55455; Shukla, Gaurav, E-mail: shukla@physics.umn.edu

    Despite the importance of thermoelastic properties of minerals in geology and geophysics, their measurement at high pressures and temperatures are still challenging. Thus, ab initio calculations are an essential tool for predicting these properties at extreme conditions. Owing to the approximate description of the exchange-correlation energy, approximations used in calculations of vibrational effects, and numerical/methodological approximations, these methods produce systematic deviations. Hybrid schemes combining experimental data and theoretical results have emerged as a way to reconcile available information and offer more reliable predictions at experimentally inaccessible thermodynamics conditions. Here we introduce a method to improve the calculated thermoelastic tensor bymore » using highly accurate thermal equation of state (EoS). The corrective scheme is general, applicable to crystalline solids with any symmetry, and can produce accurate results at conditions where experimental data may not exist. We apply it to rock-salt-type NaCl, a material whose structural properties have been challenging to describe accurately by standard ab initio methods and whose acoustic/seismic properties are important for the gas and oil industry.« less

  14. Learning a weighted sequence model of the nucleosome core and linker yields more accurate predictions in Saccharomyces cerevisiae and Homo sapiens.

    PubMed

    Reynolds, Sheila M; Bilmes, Jeff A; Noble, William Stafford

    2010-07-08

    DNA in eukaryotes is packaged into a chromatin complex, the most basic element of which is the nucleosome. The precise positioning of the nucleosome cores allows for selective access to the DNA, and the mechanisms that control this positioning are important pieces of the gene expression puzzle. We describe a large-scale nucleosome pattern that jointly characterizes the nucleosome core and the adjacent linkers and is predominantly characterized by long-range oscillations in the mono, di- and tri-nucleotide content of the DNA sequence, and we show that this pattern can be used to predict nucleosome positions in both Homo sapiens and Saccharomyces cerevisiae more accurately than previously published methods. Surprisingly, in both H. sapiens and S. cerevisiae, the most informative individual features are the mono-nucleotide patterns, although the inclusion of di- and tri-nucleotide features results in improved performance. Our approach combines a much longer pattern than has been previously used to predict nucleosome positioning from sequence-301 base pairs, centered at the position to be scored-with a novel discriminative classification approach that selectively weights the contributions from each of the input features. The resulting scores are relatively insensitive to local AT-content and can be used to accurately discriminate putative dyad positions from adjacent linker regions without requiring an additional dynamic programming step and without the attendant edge effects and assumptions about linker length modeling and overall nucleosome density. Our approach produces the best dyad-linker classification results published to date in H. sapiens, and outperforms two recently published models on a large set of S. cerevisiae nucleosome positions. Our results suggest that in both genomes, a comparable and relatively small fraction of nucleosomes are well-positioned and that these positions are predictable based on sequence alone. We believe that the bulk of the

  15. Learning a Weighted Sequence Model of the Nucleosome Core and Linker Yields More Accurate Predictions in Saccharomyces cerevisiae and Homo sapiens

    PubMed Central

    Reynolds, Sheila M.; Bilmes, Jeff A.; Noble, William Stafford

    2010-01-01

    DNA in eukaryotes is packaged into a chromatin complex, the most basic element of which is the nucleosome. The precise positioning of the nucleosome cores allows for selective access to the DNA, and the mechanisms that control this positioning are important pieces of the gene expression puzzle. We describe a large-scale nucleosome pattern that jointly characterizes the nucleosome core and the adjacent linkers and is predominantly characterized by long-range oscillations in the mono, di- and tri-nucleotide content of the DNA sequence, and we show that this pattern can be used to predict nucleosome positions in both Homo sapiens and Saccharomyces cerevisiae more accurately than previously published methods. Surprisingly, in both H. sapiens and S. cerevisiae, the most informative individual features are the mono-nucleotide patterns, although the inclusion of di- and tri-nucleotide features results in improved performance. Our approach combines a much longer pattern than has been previously used to predict nucleosome positioning from sequence—301 base pairs, centered at the position to be scored—with a novel discriminative classification approach that selectively weights the contributions from each of the input features. The resulting scores are relatively insensitive to local AT-content and can be used to accurately discriminate putative dyad positions from adjacent linker regions without requiring an additional dynamic programming step and without the attendant edge effects and assumptions about linker length modeling and overall nucleosome density. Our approach produces the best dyad-linker classification results published to date in H. sapiens, and outperforms two recently published models on a large set of S. cerevisiae nucleosome positions. Our results suggest that in both genomes, a comparable and relatively small fraction of nucleosomes are well-positioned and that these positions are predictable based on sequence alone. We believe that the bulk of the

  16. Accurate predictions of population-level changes in sequence and structural properties of HIV-1 Env using a volatility-controlled diffusion model

    PubMed Central

    DeLeon, Orlando; Hodis, Hagit; O’Malley, Yunxia; Johnson, Jacklyn; Salimi, Hamid; Zhai, Yinjie; Winter, Elizabeth; Remec, Claire; Eichelberger, Noah; Van Cleave, Brandon; Puliadi, Ramya; Harrington, Robert D.; Stapleton, Jack T.; Haim, Hillel

    2017-01-01

    The envelope glycoproteins (Envs) of HIV-1 continuously evolve in the host by random mutations and recombination events. The resulting diversity of Env variants circulating in the population and their continuing diversification process limit the efficacy of AIDS vaccines. We examined the historic changes in Env sequence and structural features (measured by integrity of epitopes on the Env trimer) in a geographically defined population in the United States. As expected, many Env features were relatively conserved during the 1980s. From this state, some features diversified whereas others remained conserved across the years. We sought to identify “clues” to predict the observed historic diversification patterns. Comparison of viruses that cocirculate in patients at any given time revealed that each feature of Env (sequence or structural) exists at a defined level of variance. The in-host variance of each feature is highly conserved among individuals but can vary between different HIV-1 clades. We designate this property “volatility” and apply it to model evolution of features as a linear diffusion process that progresses with increasing genetic distance. Volatilities of different features are highly correlated with their divergence in longitudinally monitored patients. Volatilities of features also correlate highly with their population-level diversification. Using volatility indices measured from a small number of patient samples, we accurately predict the population diversity that developed for each feature over the course of 30 years. Amino acid variants that evolved at key antigenic sites are also predicted well. Therefore, small “fluctuations” in feature values measured in isolated patient samples accurately describe their potential for population-level diversification. These tools will likely contribute to the design of population-targeted AIDS vaccines by effectively capturing the diversity of currently circulating strains and addressing properties

  17. A micromechanics-based strength prediction methodology for notched metal matrix composites

    NASA Technical Reports Server (NTRS)

    Bigelow, C. A.

    1992-01-01

    An analytical micromechanics based strength prediction methodology was developed to predict failure of notched metal matrix composites. The stress-strain behavior and notched strength of two metal matrix composites, boron/aluminum (B/Al) and silicon-carbide/titanium (SCS-6/Ti-15-3), were predicted. The prediction methodology combines analytical techniques ranging from a three dimensional finite element analysis of a notched specimen to a micromechanical model of a single fiber. In the B/Al laminates, a fiber failure criteria based on the axial and shear stress in the fiber accurately predicted laminate failure for a variety of layups and notch-length to specimen-width ratios with both circular holes and sharp notches when matrix plasticity was included in the analysis. For the SCS-6/Ti-15-3 laminates, a fiber failure based on the axial stress in the fiber correlated well with experimental results for static and post fatigue residual strengths when fiber matrix debonding and matrix cracking were included in the analysis. The micromechanics based strength prediction methodology offers a direct approach to strength prediction by modeling behavior and damage on a constituent level, thus, explicitly including matrix nonlinearity, fiber matrix debonding, and matrix cracking.

  18. A micromechanics-based strength prediction methodology for notched metal-matrix composites

    NASA Technical Reports Server (NTRS)

    Bigelow, C. A.

    1993-01-01

    An analytical micromechanics-based strength prediction methodology was developed to predict failure of notched metal matrix composites. The stress-strain behavior and notched strength of two metal matrix composites, boron/aluminum (B/Al) and silicon-carbide/titanium (SCS-6/Ti-15-3), were predicted. The prediction methodology combines analytical techniques ranging from a three-dimensional finite element analysis of a notched specimen to a micromechanical model of a single fiber. In the B/Al laminates, a fiber failure criteria based on the axial and shear stress in the fiber accurately predicted laminate failure for a variety of layups and notch-length to specimen-width ratios with both circular holes and sharp notches when matrix plasticity was included in the analysis. For the SCS-6/Ti-15-3 laminates, a fiber failure based on the axial stress in the fiber correlated well with experimental results for static and postfatigue residual strengths when fiber matrix debonding and matrix cracking were included in the analysis. The micromechanics-based strength prediction methodology offers a direct approach to strength prediction by modeling behavior and damage on a constituent level, thus, explicitly including matrix nonlinearity, fiber matrix debonding, and matrix cracking.

  19. Parenting and children's representations of family predict disruptive and callous-unemotional behaviors

    PubMed Central

    Wagner, Nicholas J.; Mills-Koonce, W. Roger; Willoughby, Michael T.; Zvara, Bharathi; Cox, Martha J.

    2015-01-01

    Data from a large prospective longitudinal study (n = 1,239) was used to investigate the association between observed sensitive parenting in early childhood and children's representations of family relationships as measured by the Family Drawing Paradigm (FDP) in first grade as well as the extent to which these representations partially mediate the influences of early caregiving experiences on later conduct problems and callous-unemotional behaviors. A structural equation modeling approach revealed that less sensitive parenting at 24, 36, and 58 months predicts higher levels of conduct problems (CP) and callous-unemotional (CU) behaviors in first grade controlling for earlier measures of CP and CU behaviors. Results also indicated that greater dysfunctional family representations, as assessed with the FDP, are significantly associated with higher CU behaviors in the first grade, but not CP. Finally, a test of the indirect pathway suggests that children's dysfunctional family representations may, in part, account for the association between sensitive parenting and CU behaviors. PMID:26010385

  20. How HEXACO Personality Traits Predict Different Selfie-Posting Behaviors among Adolescents and Young Adults

    PubMed Central

    Baiocco, Roberto; Chirumbolo, Antonio; Bianchi, Dora; Ioverno, Salvatore; Morelli, Mara; Nappa, Maria R.

    2017-01-01

    Selfies are self-portrait photos shared on Social Networks. Previous literature has investigated how personality traits, and specifically narcissism, are associated with selfie-posting behaviors. In this contribution we investigated how selfie-posting behaviors are predicted by the six HEXACO personality traits, controlling for age, gender and sexual orientation. The Kinsey scale, three questions about the frequency of own selfies, group selfies and selfies with partner, and 60-item HEXACO Personality Inventory-Revised were administered to 750 young people from 13 to 30 years. Females, adolescents and not-exclusively heterosexual people posted more own selfies, and adolescents posted also more group selfies and selfies with partner. Moreover, lower Honesty/Humility, lower Conscientiousness, higher Emotionality and higher Extraversion significantly predict own selfies and group selfies. Finally, only lower Honesty/Humility and higher Emotionality predict selfies with partner. Theoretical and practical implications are provided. PMID:28119662