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Sample records for accurately predict behavior

  1. A hierarchical approach to accurate predictions of macroscopic thermodynamic behavior from quantum mechanics and molecular simulations

    NASA Astrophysics Data System (ADS)

    Garrison, Stephen L.

    2005-07-01

    The combination of molecular simulations and potentials obtained from quantum chemistry is shown to be able to provide reasonably accurate thermodynamic property predictions. Gibbs ensemble Monte Carlo simulations are used to understand the effects of small perturbations to various regions of the model Lennard-Jones 12-6 potential. However, when the phase behavior and second virial coefficient are scaled by the critical properties calculated for each potential, the results obey a corresponding states relation suggesting a non-uniqueness problem for interaction potentials fit to experimental phase behavior. Several variations of a procedure collectively referred to as quantum mechanical Hybrid Methods for Interaction Energies (HM-IE) are developed and used to accurately estimate interaction energies from CCSD(T) calculations with a large basis set in a computationally efficient manner for the neon-neon, acetylene-acetylene, and nitrogen-benzene systems. Using these results and methods, an ab initio, pairwise-additive, site-site potential for acetylene is determined and then improved using results from molecular simulations using this initial potential. The initial simulation results also indicate that a limited range of energies important for accurate phase behavior predictions. Second virial coefficients calculated from the improved potential indicate that one set of experimental data in the literature is likely erroneous. This prescription is then applied to methanethiol. Difficulties in modeling the effects of the lone pair electrons suggest that charges on the lone pair sites negatively impact the ability of the intermolecular potential to describe certain orientations, but that the lone pair sites may be necessary to reasonably duplicate the interaction energies for several orientations. Two possible methods for incorporating the effects of three-body interactions into simulations within the pairwise-additivity formulation are also developed. A low density

  2. Predict amine solution properties accurately

    SciTech Connect

    Cheng, S.; Meisen, A.; Chakma, A.

    1996-02-01

    Improved process design begins with using accurate physical property data. Especially in the preliminary design stage, physical property data such as density viscosity, thermal conductivity and specific heat can affect the overall performance of absorbers, heat exchangers, reboilers and pump. These properties can also influence temperature profiles in heat transfer equipment and thus control or affect the rate of amine breakdown. Aqueous-amine solution physical property data are available in graphical form. However, it is not convenient to use with computer-based calculations. Developed equations allow improved correlations of derived physical property estimates with published data. Expressions are given which can be used to estimate physical properties of methyldiethanolamine (MDEA), monoethanolamine (MEA) and diglycolamine (DGA) solutions.

  3. Fishbein and Ajzen's Theory of Reasoned Action: Accurate Prediction of Behavioral Intentions for Enrolling in Distance Education Courses.

    ERIC Educational Resources Information Center

    Becker, Ellen A.; Gibson, Chere C.

    1998-01-01

    A survey of 365 respiratory care practitioners measured variables from the Theory of Reasoned Action (TRA): intention, attitude, social norm, behavioral and normative beliefs, personal norm, and perceived behavioral control. Attitude and subjective social norm were significant predictors of participation in continuing professional education. The…

  4. New model accurately predicts reformate composition

    SciTech Connect

    Ancheyta-Juarez, J.; Aguilar-Rodriguez, E. )

    1994-01-31

    Although naphtha reforming is a well-known process, the evolution of catalyst formulation, as well as new trends in gasoline specifications, have led to rapid evolution of the process, including: reactor design, regeneration mode, and operating conditions. Mathematical modeling of the reforming process is an increasingly important tool. It is fundamental to the proper design of new reactors and revamp of existing ones. Modeling can be used to optimize operating conditions, analyze the effects of process variables, and enhance unit performance. Instituto Mexicano del Petroleo has developed a model of the catalytic reforming process that accurately predicts reformate composition at the higher-severity conditions at which new reformers are being designed. The new AA model is more accurate than previous proposals because it takes into account the effects of temperature and pressure on the rate constants of each chemical reaction.

  5. Accurate Prediction of Docked Protein Structure Similarity.

    PubMed

    Akbal-Delibas, Bahar; Pomplun, Marc; Haspel, Nurit

    2015-09-01

    One of the major challenges for protein-protein docking methods is to accurately discriminate nativelike structures. The protein docking community agrees on the existence of a relationship between various favorable intermolecular interactions (e.g. Van der Waals, electrostatic, desolvation forces, etc.) and the similarity of a conformation to its native structure. Different docking algorithms often formulate this relationship as a weighted sum of selected terms and calibrate their weights against specific training data to evaluate and rank candidate structures. However, the exact form of this relationship is unknown and the accuracy of such methods is impaired by the pervasiveness of false positives. Unlike the conventional scoring functions, we propose a novel machine learning approach that not only ranks the candidate structures relative to each other but also indicates how similar each candidate is to the native conformation. We trained the AccuRMSD neural network with an extensive dataset using the back-propagation learning algorithm. Our method achieved predicting RMSDs of unbound docked complexes with 0.4Å error margin. PMID:26335807

  6. Quantitative Assessment of Protein Structural Models by Comparison of H/D Exchange MS Data with Exchange Behavior Accurately Predicted by DXCOREX

    NASA Astrophysics Data System (ADS)

    Liu, Tong; Pantazatos, Dennis; Li, Sheng; Hamuro, Yoshitomo; Hilser, Vincent J.; Woods, Virgil L.

    2012-01-01

    Peptide amide hydrogen/deuterium exchange mass spectrometry (DXMS) data are often used to qualitatively support models for protein structure. We have developed and validated a method (DXCOREX) by which exchange data can be used to quantitatively assess the accuracy of three-dimensional (3-D) models of protein structure. The method utilizes the COREX algorithm to predict a protein's amide hydrogen exchange rates by reference to a hypothesized structure, and these values are used to generate a virtual data set (deuteron incorporation per peptide) that can be quantitatively compared with the deuteration level of the peptide probes measured by hydrogen exchange experimentation. The accuracy of DXCOREX was established in studies performed with 13 proteins for which both high-resolution structures and experimental data were available. The DXCOREX-calculated and experimental data for each protein was highly correlated. We then employed correlation analysis of DXCOREX-calculated versus DXMS experimental data to assess the accuracy of a recently proposed structural model for the catalytic domain of a Ca2+-independent phospholipase A2. The model's calculated exchange behavior was highly correlated with the experimental exchange results available for the protein, supporting the accuracy of the proposed model. This method of analysis will substantially increase the precision with which experimental hydrogen exchange data can help decipher challenging questions regarding protein structure and dynamics.

  7. Predicting accurate probabilities with a ranking loss

    PubMed Central

    Menon, Aditya Krishna; Jiang, Xiaoqian J; Vembu, Shankar; Elkan, Charles; Ohno-Machado, Lucila

    2013-01-01

    In many real-world applications of machine learning classifiers, it is essential to predict the probability of an example belonging to a particular class. This paper proposes a simple technique for predicting probabilities based on optimizing a ranking loss, followed by isotonic regression. This semi-parametric technique offers both good ranking and regression performance, and models a richer set of probability distributions than statistical workhorses such as logistic regression. We provide experimental results that show the effectiveness of this technique on real-world applications of probability prediction. PMID:25285328

  8. You Can Accurately Predict Land Acquisition Costs.

    ERIC Educational Resources Information Center

    Garrigan, Richard

    1967-01-01

    Land acquisition costs were tested for predictability based upon the 1962 assessed valuations of privately held land acquired for campus expansion by the University of Wisconsin from 1963-1965. By correlating the land acquisition costs of 108 properties acquired during the 3 year period with--(1) the assessed value of the land, (2) the assessed…

  9. 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…

  10. Inverter Modeling For Accurate Energy Predictions Of Tracking HCPV Installations

    NASA Astrophysics Data System (ADS)

    Bowman, J.; Jensen, S.; McDonald, Mark

    2010-10-01

    High efficiency high concentration photovoltaic (HCPV) solar plants of megawatt scale are now operational, and opportunities for expanded adoption are plentiful. However, effective bidding for sites requires reliable prediction of energy production. HCPV module nameplate power is rated for specific test conditions; however, instantaneous HCPV power varies due to site specific irradiance and operating temperature, and is degraded by soiling, protective stowing, shading, and electrical connectivity. These factors interact with the selection of equipment typically supplied by third parties, e.g., wire gauge and inverters. We describe a time sequence model accurately accounting for these effects that predicts annual energy production, with specific reference to the impact of the inverter on energy output and interactions between system-level design decisions and the inverter. We will also show two examples, based on an actual field design, of inverter efficiency calculations and the interaction between string arrangements and inverter selection.

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

    DOE PAGESBeta

    Wang, Dong; Dasari, Surendra; Chambers, Matthew C.; Holman, Jerry D.; Chen, Kan; Liebler, Daniel; Orton, Daniel J.; Purvine, Samuel O.; Monroe, Matthew E.; Chung, Chang Y.; et al

    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

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

    SciTech Connect

    Wang, Dong; Dasari, Surendra; Chambers, Matthew C.; Holman, Jerry D.; Chen, Kan; Liebler, Daniel; Orton, Daniel J.; Purvine, Samuel O.; Monroe, Matthew E.; Chung, Chang Y.; Rose, Kristie L.; Tabb, David L.

    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 charged 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.

  13. Passive samplers accurately predict PAH levels in resident crayfish.

    PubMed

    Paulik, L Blair; Smith, Brian W; Bergmann, Alan J; Sower, Greg J; Forsberg, Norman D; Teeguarden, Justin G; Anderson, Kim A

    2016-02-15

    Contamination of resident aquatic organisms is a major concern for environmental risk assessors. However, collecting organisms to estimate risk is often prohibitively time and resource-intensive. Passive sampling accurately estimates resident organism contamination, and it saves time and resources. This study used low density polyethylene (LDPE) passive water samplers to predict polycyclic aromatic hydrocarbon (PAH) levels in signal crayfish, Pacifastacus leniusculus. Resident crayfish were collected at 5 sites within and outside of the Portland Harbor Superfund Megasite (PHSM) in the Willamette River in Portland, Oregon. LDPE deployment was spatially and temporally paired with crayfish collection. Crayfish visceral and tail tissue, as well as water-deployed LDPE, were extracted and analyzed for 62 PAHs using GC-MS/MS. Freely-dissolved concentrations (Cfree) of PAHs in water were calculated from concentrations in LDPE. Carcinogenic risks were estimated for all crayfish tissues, using benzo[a]pyrene equivalent concentrations (BaPeq). ∑PAH were 5-20 times higher in viscera than in tails, and ∑BaPeq were 6-70 times higher in viscera than in tails. Eating only tail tissue of crayfish would therefore significantly reduce carcinogenic risk compared to also eating viscera. Additionally, PAH levels in crayfish were compared to levels in crayfish collected 10 years earlier. PAH levels in crayfish were higher upriver of the PHSM and unchanged within the PHSM after the 10-year period. Finally, a linear regression model predicted levels of 34 PAHs in crayfish viscera with an associated R-squared value of 0.52 (and a correlation coefficient of 0.72), using only the Cfree PAHs in water. On average, the model predicted PAH concentrations in crayfish tissue within a factor of 2.4 ± 1.8 of measured concentrations. This affirms that passive water sampling accurately estimates PAH contamination in crayfish. Furthermore, the strong predictive ability of this simple model suggests

  14. A new generalized correlation for accurate vapor pressure prediction

    NASA Astrophysics Data System (ADS)

    An, Hui; Yang, Wenming

    2012-08-01

    An accurate knowledge of the vapor pressure of organic liquids is very important for the oil and gas processing operations. In combustion modeling, the accuracy of numerical predictions is also highly dependent on the fuel properties such as vapor pressure. In this Letter, a new generalized correlation is proposed based on the Lee-Kesler's method where a fuel dependent parameter 'A' is introduced. The proposed method only requires the input parameters of critical temperature, normal boiling temperature and the acentric factor of the fluid. With this method, vapor pressures have been calculated and compared with the data reported in data compilation for 42 organic liquids over 1366 data points, and the overall average absolute percentage deviation is only 1.95%.

  15. Mouse models of human AML accurately predict chemotherapy response

    PubMed Central

    Zuber, Johannes; Radtke, Ina; Pardee, Timothy S.; Zhao, Zhen; Rappaport, Amy R.; Luo, Weijun; McCurrach, Mila E.; Yang, Miao-Miao; Dolan, M. Eileen; Kogan, Scott C.; Downing, James R.; Lowe, Scott W.

    2009-01-01

    The genetic heterogeneity of cancer influences the trajectory of tumor progression and may underlie clinical variation in therapy response. To model such heterogeneity, we produced genetically and pathologically accurate mouse models of common forms of human acute myeloid leukemia (AML) and developed methods to mimic standard induction chemotherapy and efficiently monitor therapy response. We see that murine AMLs harboring two common human AML genotypes show remarkably diverse responses to conventional therapy that mirror clinical experience. Specifically, murine leukemias expressing the AML1/ETO fusion oncoprotein, associated with a favorable prognosis in patients, show a dramatic response to induction chemotherapy owing to robust activation of the p53 tumor suppressor network. Conversely, murine leukemias expressing MLL fusion proteins, associated with a dismal prognosis in patients, are drug-resistant due to an attenuated p53 response. Our studies highlight the importance of genetic information in guiding the treatment of human AML, functionally establish the p53 network as a central determinant of chemotherapy response in AML, and demonstrate that genetically engineered mouse models of human cancer can accurately predict therapy response in patients. PMID:19339691

  16. Mouse models of human AML accurately predict chemotherapy response.

    PubMed

    Zuber, Johannes; Radtke, Ina; Pardee, Timothy S; Zhao, Zhen; Rappaport, Amy R; Luo, Weijun; McCurrach, Mila E; Yang, Miao-Miao; Dolan, M Eileen; Kogan, Scott C; Downing, James R; Lowe, Scott W

    2009-04-01

    The genetic heterogeneity of cancer influences the trajectory of tumor progression and may underlie clinical variation in therapy response. To model such heterogeneity, we produced genetically and pathologically accurate mouse models of common forms of human acute myeloid leukemia (AML) and developed methods to mimic standard induction chemotherapy and efficiently monitor therapy response. We see that murine AMLs harboring two common human AML genotypes show remarkably diverse responses to conventional therapy that mirror clinical experience. Specifically, murine leukemias expressing the AML1/ETO fusion oncoprotein, associated with a favorable prognosis in patients, show a dramatic response to induction chemotherapy owing to robust activation of the p53 tumor suppressor network. Conversely, murine leukemias expressing MLL fusion proteins, associated with a dismal prognosis in patients, are drug-resistant due to an attenuated p53 response. Our studies highlight the importance of genetic information in guiding the treatment of human AML, functionally establish the p53 network as a central determinant of chemotherapy response in AML, and demonstrate that genetically engineered mouse models of human cancer can accurately predict therapy response in patients. PMID:19339691

  17. Turbulence Models for Accurate Aerothermal Prediction in Hypersonic Flows

    NASA Astrophysics Data System (ADS)

    Zhang, Xiang-Hong; Wu, Yi-Zao; Wang, Jiang-Feng

    Accurate description of the aerodynamic and aerothermal environment is crucial to the integrated design and optimization for high performance hypersonic vehicles. In the simulation of aerothermal environment, the effect of viscosity is crucial. The turbulence modeling remains a major source of uncertainty in the computational prediction of aerodynamic forces and heating. In this paper, three turbulent models were studied: the one-equation eddy viscosity transport model of Spalart-Allmaras, the Wilcox k-ω model and the Menter SST model. For the k-ω model and SST model, the compressibility correction, press dilatation and low Reynolds number correction were considered. The influence of these corrections for flow properties were discussed by comparing with the results without corrections. In this paper the emphasis is on the assessment and evaluation of the turbulence models in prediction of heat transfer as applied to a range of hypersonic flows with comparison to experimental data. This will enable establishing factor of safety for the design of thermal protection systems of hypersonic vehicle.

  18. 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?

  19. Accurate Prediction of Binding Thermodynamics for DNA on Surfaces

    PubMed Central

    Vainrub, Arnold; Pettitt, B. Montgomery

    2011-01-01

    For DNA mounted on surfaces for microarrays, microbeads and nanoparticles, the nature of the random attachment of oligonucleotide probes to an amorphous surface gives rise to a locally inhomogeneous probe density. These fluctuations of the probe surface density are inherent to all common surface or bead platforms, regardless if they exploit either an attachment of pre-synthesized probes or probes synthesized in situ on the surface. Here, we demonstrate for the first time the crucial role of the probe surface density fluctuations in performance of DNA arrays. We account for the density fluctuations with a disordered two-dimensional surface model and derive the corresponding array hybridization isotherm that includes a counter-ion screened electrostatic repulsion between the assayed DNA and probe array. The calculated melting curves are in excellent agreement with published experimental results for arrays with both pre-synthesized and in-situ synthesized oligonucleotide probes. The approach developed allows one to accurately predict the melting curves of DNA arrays using only the known sequence dependent hybridization enthalpy and entropy in solution and the experimental macroscopic surface density of probes. This opens the way to high precision theoretical design and optimization of probes and primers in widely used DNA array-based high-throughput technologies for gene expression, genotyping, next-generation sequencing, and surface polymerase extension. PMID:21972932

  20. 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

  1. CAPE: Automatically Predicting Changes in Group Behavior

    NASA Astrophysics Data System (ADS)

    Sliva, Amy; Subrahmanian, V. S.; Martinez, Vanina; Simari, Gerardo

    There is now intense interest in the problem of forecasting what a group will do in the future. Past work [1, 2, 3] has built complex models of a group’s behavior and used this to predict what the group might do in the future. However, almost all past work assumes that the group will not change its past behavior. Whether the group is a group of investors, or a political party, or a terror group, there is much interest in when and how the group will change its behavior. In this paper, we develop an architecture and algorithms called CAPE to forecast the conditions under which a group will change its behavior. We have tested CAPE on social science data about the behaviors of seven terrorist groups and show that CAPE is highly accurate in its predictions—at least in this limited setting.

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

    PubMed

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

    2016-05-01

    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

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

    NASA Astrophysics Data System (ADS)

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

    2016-05-01

    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 H 2+ . 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. IRIS: Towards an Accurate and Fast Stage Weight Prediction Method

    NASA Astrophysics Data System (ADS)

    Taponier, V.; Balu, A.

    2002-01-01

    The knowledge of the structural mass fraction (or the mass ratio) of a given stage, which affects the performance of a rocket, is essential for the analysis of new or upgraded launchers or stages, whose need is increased by the quick evolution of the space programs and by the necessity of their adaptation to the market needs. The availability of this highly scattered variable, ranging between 0.05 and 0.15, is of primary importance at the early steps of the preliminary design studies. At the start of the staging and performance studies, the lack of frozen weight data (to be obtained later on from propulsion, trajectory and sizing studies) leads to rely on rough estimates, generally derived from printed sources and adapted. When needed, a consolidation can be acquired trough a specific analysis activity involving several techniques and implying additional effort and time. The present empirical approach allows thus to get approximated values (i.e. not necessarily accurate or consistent), inducing some result inaccuracy as well as, consequently, difficulties of performance ranking for a multiple option analysis, and an increase of the processing duration. This forms a classical harsh fact of the preliminary design system studies, insufficiently discussed to date. It appears therefore highly desirable to have, for all the evaluation activities, a reliable, fast and easy-to-use weight or mass fraction prediction method. Additionally, the latter should allow for a pre selection of the alternative preliminary configurations, making possible a global system approach. For that purpose, an attempt at modeling has been undertaken, whose objective was the determination of a parametric formulation of the mass fraction, to be expressed from a limited number of parameters available at the early steps of the project. It is based on the innovative use of a statistical method applicable to a variable as a function of several independent parameters. A specific polynomial generator

  5. An Overview of Practical Applications of Protein Disorder Prediction and Drive for Faster, More Accurate Predictions.

    PubMed

    Deng, Xin; Gumm, Jordan; Karki, Suman; Eickholt, Jesse; Cheng, Jianlin

    2015-01-01

    Protein disordered regions are segments of a protein chain that do not adopt a stable structure. Thus far, a variety of protein disorder prediction methods have been developed and have been widely used, not only in traditional bioinformatics domains, including protein structure prediction, protein structure determination and function annotation, but also in many other biomedical fields. The relationship between intrinsically-disordered proteins and some human diseases has played a significant role in disorder prediction in disease identification and epidemiological investigations. Disordered proteins can also serve as potential targets for drug discovery with an emphasis on the disordered-to-ordered transition in the disordered binding regions, and this has led to substantial research in drug discovery or design based on protein disordered region prediction. Furthermore, protein disorder prediction has also been applied to healthcare by predicting the disease risk of mutations in patients and studying the mechanistic basis of diseases. As the applications of disorder prediction increase, so too does the need to make quick and accurate predictions. To fill this need, we also present a new approach to predict protein residue disorder using wide sequence windows that is applicable on the genomic scale. PMID:26198229

  6. An Overview of Practical Applications of Protein Disorder Prediction and Drive for Faster, More Accurate Predictions

    PubMed Central

    Deng, Xin; Gumm, Jordan; Karki, Suman; Eickholt, Jesse; Cheng, Jianlin

    2015-01-01

    Protein disordered regions are segments of a protein chain that do not adopt a stable structure. Thus far, a variety of protein disorder prediction methods have been developed and have been widely used, not only in traditional bioinformatics domains, including protein structure prediction, protein structure determination and function annotation, but also in many other biomedical fields. The relationship between intrinsically-disordered proteins and some human diseases has played a significant role in disorder prediction in disease identification and epidemiological investigations. Disordered proteins can also serve as potential targets for drug discovery with an emphasis on the disordered-to-ordered transition in the disordered binding regions, and this has led to substantial research in drug discovery or design based on protein disordered region prediction. Furthermore, protein disorder prediction has also been applied to healthcare by predicting the disease risk of mutations in patients and studying the mechanistic basis of diseases. As the applications of disorder prediction increase, so too does the need to make quick and accurate predictions. To fill this need, we also present a new approach to predict protein residue disorder using wide sequence windows that is applicable on the genomic scale. PMID:26198229

  7. Accurately Predicting Complex Reaction Kinetics from First Principles

    NASA Astrophysics Data System (ADS)

    Green, William

    Many important systems contain a multitude of reactive chemical species, some of which react on a timescale faster than collisional thermalization, i.e. they never achieve a Boltzmann energy distribution. Usually it is impossible to fully elucidate the processes by experiments alone. Here we report recent progress toward predicting the time-evolving composition of these systems a priori: how unexpected reactions can be discovered on the computer, how reaction rates are computed from first principles, and how the many individual reactions are efficiently combined into a predictive simulation for the whole system. Some experimental tests of the a priori predictions are also presented.

  8. Is Three-Dimensional Soft Tissue Prediction by Software Accurate?

    PubMed

    Nam, Ki-Uk; Hong, Jongrak

    2015-11-01

    The authors assessed whether virtual surgery, performed with a soft tissue prediction program, could correctly simulate the actual surgical outcome, focusing on soft tissue movement. Preoperative and postoperative computed tomography (CT) data for 29 patients, who had undergone orthognathic surgery, were obtained and analyzed using the Simplant Pro software. The program made a predicted soft tissue image (A) based on presurgical CT data. After the operation, we obtained actual postoperative CT data and an actual soft tissue image (B) was generated. Finally, the 2 images (A and B) were superimposed and analyzed differences between the A and B. Results were grouped in 2 classes: absolute values and vector values. In the absolute values, the left mouth corner was the most significant error point (2.36 mm). The right mouth corner (2.28 mm), labrale inferius (2.08 mm), and the pogonion (2.03 mm) also had significant errors. In vector values, prediction of the right-left side had a left-sided tendency, the superior-inferior had a superior tendency, and the anterior-posterior showed an anterior tendency. As a result, with this program, the position of points tended to be located more left, anterior, and superior than the "real" situation. There is a need to improve the prediction accuracy for soft tissue images. Such software is particularly valuable in predicting craniofacial soft tissues landmarks, such as the pronasale. With this software, landmark positions were most inaccurate in terms of anterior-posterior predictions. PMID:26594988

  9. 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. PMID:24524947

  10. Towards Accurate Ab Initio Predictions of the Spectrum of Methane

    NASA Technical Reports Server (NTRS)

    Schwenke, David W.; Kwak, Dochan (Technical Monitor)

    2001-01-01

    We have carried out extensive ab initio calculations of the electronic structure of methane, and these results are used to compute vibrational energy levels. We include basis set extrapolations, core-valence correlation, relativistic effects, and Born- Oppenheimer breakdown terms in our calculations. Our ab initio predictions of the lowest lying levels are superb.

  11. 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.

  12. Standardized EEG interpretation accurately predicts prognosis after cardiac arrest

    PubMed Central

    Rossetti, Andrea O.; van Rootselaar, Anne-Fleur; Wesenberg Kjaer, Troels; Horn, Janneke; Ullén, Susann; Friberg, Hans; Nielsen, Niklas; Rosén, Ingmar; Åneman, Anders; Erlinge, David; Gasche, Yvan; Hassager, Christian; Hovdenes, Jan; Kjaergaard, Jesper; Kuiper, Michael; Pellis, Tommaso; Stammet, Pascal; Wanscher, Michael; Wetterslev, Jørn; Wise, Matt P.; Cronberg, Tobias

    2016-01-01

    Objective: To identify reliable predictors of outcome in comatose patients after cardiac arrest using a single routine EEG and standardized interpretation according to the terminology proposed by the American Clinical Neurophysiology Society. Methods: In this cohort study, 4 EEG specialists, blinded to outcome, evaluated prospectively recorded EEGs in the Target Temperature Management trial (TTM trial) that randomized patients to 33°C vs 36°C. Routine EEG was performed in patients still comatose after rewarming. EEGs were classified into highly malignant (suppression, suppression with periodic discharges, burst-suppression), malignant (periodic or rhythmic patterns, pathological or nonreactive background), and benign EEG (absence of malignant features). Poor outcome was defined as best Cerebral Performance Category score 3–5 until 180 days. Results: Eight TTM sites randomized 202 patients. EEGs were recorded in 103 patients at a median 77 hours after cardiac arrest; 37% had a highly malignant EEG and all had a poor outcome (specificity 100%, sensitivity 50%). Any malignant EEG feature had a low specificity to predict poor prognosis (48%) but if 2 malignant EEG features were present specificity increased to 96% (p < 0.001). Specificity and sensitivity were not significantly affected by targeted temperature or sedation. A benign EEG was found in 1% of the patients with a poor outcome. Conclusions: Highly malignant EEG after rewarming reliably predicted poor outcome in half of patients without false predictions. An isolated finding of a single malignant feature did not predict poor outcome whereas a benign EEG was highly predictive of a good outcome. PMID:26865516

  13. 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

  14. Microstructure-Dependent Gas Adsorption: Accurate Predictions of Methane Uptake in Nanoporous Carbons

    SciTech Connect

    Ihm, Yungok; Cooper, Valentino R; Gallego, Nidia C; Contescu, Cristian I; Morris, James R

    2014-01-01

    We demonstrate a successful, efficient framework for predicting gas adsorption properties in real materials based on first-principles calculations, with a specific comparison of experiment and theory for methane adsorption in activated carbons. These carbon materials have different pore size distributions, leading to a variety of uptake characteristics. Utilizing these distributions, we accurately predict experimental uptakes and heats of adsorption without empirical potentials or lengthy simulations. We demonstrate that materials with smaller pores have higher heats of adsorption, leading to a higher gas density in these pores. This pore-size dependence must be accounted for, in order to predict and understand the adsorption behavior. The theoretical approach combines: (1) ab initio calculations with a van der Waals density functional to determine adsorbent-adsorbate interactions, and (2) a thermodynamic method that predicts equilibrium adsorption densities by directly incorporating the calculated potential energy surface in a slit pore model. The predicted uptake at P=20 bar and T=298 K is in excellent agreement for all five activated carbon materials used. This approach uses only the pore-size distribution as an input, with no fitting parameters or empirical adsorbent-adsorbate interactions, and thus can be easily applied to other adsorbent-adsorbate combinations.

  15. Accurate and predictive antibody repertoire profiling by molecular amplification fingerprinting

    PubMed Central

    Khan, Tarik A.; Friedensohn, Simon; de Vries, Arthur R. Gorter; Straszewski, Jakub; Ruscheweyh, Hans-Joachim; Reddy, Sai T.

    2016-01-01

    High-throughput antibody repertoire sequencing (Ig-seq) provides quantitative molecular information on humoral immunity. However, Ig-seq is compromised by biases and errors introduced during library preparation and sequencing. By using synthetic antibody spike-in genes, we determined that primer bias from multiplex polymerase chain reaction (PCR) library preparation resulted in antibody frequencies with only 42 to 62% accuracy. Additionally, Ig-seq errors resulted in antibody diversity measurements being overestimated by up to 5000-fold. To rectify this, we developed molecular amplification fingerprinting (MAF), which uses unique molecular identifier (UID) tagging before and during multiplex PCR amplification, which enabled tagging of transcripts while accounting for PCR efficiency. Combined with a bioinformatic pipeline, MAF bias correction led to measurements of antibody frequencies with up to 99% accuracy. We also used MAF to correct PCR and sequencing errors, resulting in enhanced accuracy of full-length antibody diversity measurements, achieving 98 to 100% error correction. Using murine MAF-corrected data, we established a quantitative metric of recent clonal expansion—the intraclonal diversity index—which measures the number of unique transcripts associated with an antibody clone. We used this intraclonal diversity index along with antibody frequencies and somatic hypermutation to build a logistic regression model for prediction of the immunological status of clones. The model was able to predict clonal status with high confidence but only when using MAF error and bias corrected Ig-seq data. Improved accuracy by MAF provides the potential to greatly advance Ig-seq and its utility in immunology and biotechnology. PMID:26998518

  16. Accurate and predictive antibody repertoire profiling by molecular amplification fingerprinting.

    PubMed

    Khan, Tarik A; Friedensohn, Simon; Gorter de Vries, Arthur R; Straszewski, Jakub; Ruscheweyh, Hans-Joachim; Reddy, Sai T

    2016-03-01

    High-throughput antibody repertoire sequencing (Ig-seq) provides quantitative molecular information on humoral immunity. However, Ig-seq is compromised by biases and errors introduced during library preparation and sequencing. By using synthetic antibody spike-in genes, we determined that primer bias from multiplex polymerase chain reaction (PCR) library preparation resulted in antibody frequencies with only 42 to 62% accuracy. Additionally, Ig-seq errors resulted in antibody diversity measurements being overestimated by up to 5000-fold. To rectify this, we developed molecular amplification fingerprinting (MAF), which uses unique molecular identifier (UID) tagging before and during multiplex PCR amplification, which enabled tagging of transcripts while accounting for PCR efficiency. Combined with a bioinformatic pipeline, MAF bias correction led to measurements of antibody frequencies with up to 99% accuracy. We also used MAF to correct PCR and sequencing errors, resulting in enhanced accuracy of full-length antibody diversity measurements, achieving 98 to 100% error correction. Using murine MAF-corrected data, we established a quantitative metric of recent clonal expansion-the intraclonal diversity index-which measures the number of unique transcripts associated with an antibody clone. We used this intraclonal diversity index along with antibody frequencies and somatic hypermutation to build a logistic regression model for prediction of the immunological status of clones. The model was able to predict clonal status with high confidence but only when using MAF error and bias corrected Ig-seq data. Improved accuracy by MAF provides the potential to greatly advance Ig-seq and its utility in immunology and biotechnology. PMID:26998518

  17. Accurate predictions for the production of vaporized water

    SciTech Connect

    Morin, E.; Montel, F.

    1995-12-31

    The production of water vaporized in the gas phase is controlled by the local conditions around the wellbore. The pressure gradient applied to the formation creates a sharp increase of the molar water content in the hydrocarbon phase approaching the well; this leads to a drop in the pore water saturation around the wellbore. The extent of the dehydrated zone which is formed is the key controlling the bottom-hole content of vaporized water. The maximum water content in the hydrocarbon phase at a given pressure, temperature and salinity is corrected by capillarity or adsorption phenomena depending on the actual water saturation. Describing the mass transfer of the water between the hydrocarbon phases and the aqueous phase into the tubing gives a clear idea of vaporization effects on the formation of scales. Field example are presented for gas fields with temperatures ranging between 140{degrees}C and 180{degrees}C, where water vaporization effects are significant. Conditions for salt plugging in the tubing are predicted.

  18. 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.

  19. Change in BMI Accurately Predicted by Social Exposure to Acquaintances

    PubMed Central

    Oloritun, Rahman O.; Ouarda, Taha B. M. J.; Moturu, Sai; Madan, Anmol; Pentland, Alex (Sandy); Khayal, Inas

    2013-01-01

    Research has mostly focused on obesity and not on processes of BMI change more generally, although these may be key factors that lead to obesity. Studies have suggested that obesity is affected by social ties. However these studies used survey based data collection techniques that may be biased toward select only close friends and relatives. In this study, mobile phone sensing techniques were used to routinely capture social interaction data in an undergraduate dorm. By automating the capture of social interaction data, the limitations of self-reported social exposure data are avoided. This study attempts to understand and develop a model that best describes the change in BMI using social interaction data. We evaluated a cohort of 42 college students in a co-located university dorm, automatically captured via mobile phones and survey based health-related information. We determined the most predictive variables for change in BMI using the least absolute shrinkage and selection operator (LASSO) method. The selected variables, with gender, healthy diet category, and ability to manage stress, were used to build multiple linear regression models that estimate the effect of exposure and individual factors on change in BMI. We identified the best model using Akaike Information Criterion (AIC) and R2. This study found a model that explains 68% (p<0.0001) of the variation in change in BMI. The model combined social interaction data, especially from acquaintances, and personal health-related information to explain change in BMI. This is the first study taking into account both interactions with different levels of social interaction and personal health-related information. Social interactions with acquaintances accounted for more than half the variation in change in BMI. This suggests the importance of not only individual health information but also the significance of social interactions with people we are exposed to, even people we may not consider as close friends. PMID

  20. Distance scaling method for accurate prediction of slowly varying magnetic fields in satellite missions

    NASA Astrophysics Data System (ADS)

    Zacharias, Panagiotis P.; Chatzineofytou, Elpida G.; Spantideas, Sotirios T.; Capsalis, Christos N.

    2016-07-01

    In the present work, the determination of the magnetic behavior of localized magnetic sources from near-field measurements is examined. The distance power law of the magnetic field fall-off is used in various cases to accurately predict the magnetic signature of an equipment under test (EUT) consisting of multiple alternating current (AC) magnetic sources. Therefore, parameters concerning the location of the observation points (magnetometers) are studied towards this scope. The results clearly show that these parameters are independent of the EUT's size and layout. Additionally, the techniques developed in the present study enable the placing of the magnetometers close to the EUT, thus achieving high signal-to-noise ratio (SNR). Finally, the proposed method is verified by real measurements, using a mobile phone as an EUT.

  1. 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

  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. PMID:20876140

  3. Hidden Behavior Prediction of Complex Systems Based on Hybrid Information.

    PubMed

    Zhou, Zhi-Jie; Hu, Chang-Hua; Zhang, Bang-Cheng; Xu, Dong-Ling; Chen, Yu-Wang

    2013-04-01

    It is important to predict both observable and hidden behaviors in complex engineering systems. However, compared with observable behavior, it is often difficult to establish a forecasting model for hidden behavior. The existing methods for predicting the hidden behavior cannot effectively and simultaneously use the hybrid information with uncertainties that include qualitative knowledge and quantitative data. Although belief rule base (BRB) has been employed to predict the observable behavior using the hybrid information with uncertainties, it is still not applicable to predict the hidden behavior directly. As such, in this paper, a new BRB-based model is proposed to predict the hidden behavior. In the proposed BRB-based model, the initial values of parameters are usually given by experts, thus some of them may not be accurate, which can lead to inaccurate prediction results. In order to solve the problem, a parameter estimation algorithm for training the parameters of the forecasting model is further proposed on the basis of maximum likelihood algorithm. Using the hybrid information with uncertainties, the proposed model can combine together with the parameter estimation algorithm and improve the forecasting precision in an integrated and effective manner. A case study is conducted to demonstrate the capability and potential applications of the proposed forecasting model with the parameter estimation algorithm. PMID:22907969

  4. 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. PMID:7985216

  5. PREDICTED AND OBSERVED BEHAVIOR OF PLATELET AEROSOLS

    EPA Science Inventory

    A theory describing the aerodynamic behavior of triaxial particles was recently presented. he formulation of particle motion was developed by regressing numerically predicted aerodynamic diameters on particle axial dimensions used in numerical simulations, and provided an excelle...

  6. Cas9-chromatin binding information enables more accurate CRISPR off-target prediction

    PubMed Central

    Singh, Ritambhara; Kuscu, Cem; Quinlan, Aaron; Qi, Yanjun; Adli, Mazhar

    2015-01-01

    The CRISPR system has become a powerful biological tool with a wide range of applications. However, improving targeting specificity and accurately predicting potential off-targets remains a significant goal. Here, we introduce a web-based CRISPR/Cas9 Off-target Prediction and Identification Tool (CROP-IT) that performs improved off-target binding and cleavage site predictions. Unlike existing prediction programs that solely use DNA sequence information; CROP-IT integrates whole genome level biological information from existing Cas9 binding and cleavage data sets. Utilizing whole-genome chromatin state information from 125 human cell types further enhances its computational prediction power. Comparative analyses on experimentally validated datasets show that CROP-IT outperforms existing computational algorithms in predicting both Cas9 binding as well as cleavage sites. With a user-friendly web-interface, CROP-IT outputs scored and ranked list of potential off-targets that enables improved guide RNA design and more accurate prediction of Cas9 binding or cleavage sites. PMID:26032770

  7. 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. PMID:27260782

  8. Accurate rotor loads prediction using the FLAP (Force and Loads Analysis Program) dynamics code

    SciTech Connect

    Wright, A.D.; Thresher, R.W.

    1987-10-01

    Accurately predicting wind turbine blade loads and response is very important in predicting the fatigue life of wind turbines. There is a clear need in the wind turbine community for validated and user-friendly structural dynamics codes for predicting blade loads and response. At the Solar Energy Research Institute (SERI), a Force and Loads Analysis Program (FLAP) has been refined and validated and is ready for general use. Currently, FLAP is operational on an IBM-PC compatible computer and can be used to analyze both rigid- and teetering-hub configurations. The results of this paper show that FLAP can be used to accurately predict the deterministic loads for rigid-hub rotors. This paper compares analytical predictions to field test measurements for a three-bladed, upwind turbine with a rigid-hub configuration. The deterministic loads predicted by FLAP are compared with 10-min azimuth averages of blade root flapwise bending moments for different wind speeds. 6 refs., 12 figs., 3 tabs.

  9. 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

  10. An accurate modeling, simulation, and analysis tool for predicting and estimating Raman LIDAR system performance

    NASA Astrophysics Data System (ADS)

    Grasso, Robert J.; Russo, Leonard P.; Barrett, John L.; Odhner, Jefferson E.; Egbert, Paul I.

    2007-09-01

    BAE Systems presents the results of a program to model the performance of Raman LIDAR systems for the remote detection of atmospheric gases, air polluting hydrocarbons, chemical and biological weapons, and other molecular species of interest. Our model, which integrates remote Raman spectroscopy, 2D and 3D LADAR, and USAF atmospheric propagation codes permits accurate determination of the performance of a Raman LIDAR system. The very high predictive performance accuracy of our model is due to the very accurate calculation of the differential scattering cross section for the specie of interest at user selected wavelengths. We show excellent correlation of our calculated cross section data, used in our model, with experimental data obtained from both laboratory measurements and the published literature. In addition, the use of standard USAF atmospheric models provides very accurate determination of the atmospheric extinction at both the excitation and Raman shifted wavelengths.

  11. Accurate Prediction of Ligand Affinities for a Proton-Dependent Oligopeptide Transporter.

    PubMed

    Samsudin, Firdaus; Parker, Joanne L; Sansom, Mark S P; Newstead, Simon; Fowler, Philip W

    2016-02-18

    Membrane transporters are critical modulators of drug pharmacokinetics, efficacy, and safety. One example is the proton-dependent oligopeptide transporter PepT1, also known as SLC15A1, which is responsible for the uptake of the ?-lactam antibiotics and various peptide-based prodrugs. In this study, we modeled the binding of various peptides to a bacterial homolog, PepTSt, and evaluated a range of computational methods for predicting the free energy of binding. Our results show that a hybrid approach (endpoint methods to classify peptides into good and poor binders and a theoretically exact method for refinement) is able to accurately predict affinities, which we validated using proteoliposome transport assays. Applying the method to a homology model of PepT1 suggests that the approach requires a high-quality structure to be accurate. Our study provides a blueprint for extending these computational methodologies to other pharmaceutically important transporter families. PMID:27028887

  12. Accurate Prediction of Ligand Affinities for a Proton-Dependent Oligopeptide Transporter

    PubMed Central

    Samsudin, Firdaus; Parker, Joanne L.; Sansom, Mark S.P.; Newstead, Simon; Fowler, Philip W.

    2016-01-01

    Summary Membrane transporters are critical modulators of drug pharmacokinetics, efficacy, and safety. One example is the proton-dependent oligopeptide transporter PepT1, also known as SLC15A1, which is responsible for the uptake of the β-lactam antibiotics and various peptide-based prodrugs. In this study, we modeled the binding of various peptides to a bacterial homolog, PepTSt, and evaluated a range of computational methods for predicting the free energy of binding. Our results show that a hybrid approach (endpoint methods to classify peptides into good and poor binders and a theoretically exact method for refinement) is able to accurately predict affinities, which we validated using proteoliposome transport assays. Applying the method to a homology model of PepT1 suggests that the approach requires a high-quality structure to be accurate. Our study provides a blueprint for extending these computational methodologies to other pharmaceutically important transporter families. PMID:27028887

  13. A Single Linear Prediction Filter that Accurately Predicts the AL Index

    NASA Astrophysics Data System (ADS)

    McPherron, R. L.; Chu, X.

    2015-12-01

    The AL index is a measure of the strength of the westward electrojet flowing along the auroral oval. It has two components: one from the global DP-2 current system and a second from the DP-1 current that is more localized near midnight. It is generally believed that the index a very poor measure of these currents because of its dependence on the distance of stations from the source of the two currents. In fact over season and solar cycle the coupling strength defined as the steady state ratio of the output AL to the input coupling function varies by a factor of four. There are four factors that lead to this variation. First is the equinoctial effect that modulates coupling strength with peaks (strongest coupling) at the equinoxes. Second is the saturation of the polar cap potential which decreases coupling strength as the strength of the driver increases. Since saturation occurs more frequently at solar maximum we obtain the result that maximum coupling strength occurs at equinox at solar minimum. A third factor is ionospheric conductivity with stronger coupling at summer solstice as compared to winter. The fourth factor is the definition of a solar wind coupling function appropriate to a given index. We have developed an optimum coupling function depending on solar wind speed, density, transverse magnetic field, and IMF clock angle which is better than previous functions. Using this we have determined the seasonal variation of coupling strength and developed an inverse function that modulates the optimum coupling function so that all seasonal variation is removed. In a similar manner we have determined the dependence of coupling strength on solar wind driver strength. The inverse of this function is used to scale a linear prediction filter thus eliminating the dependence on driver strength. Our result is a single linear filter that is adjusted in a nonlinear manner by driver strength and an optimum coupling function that is seasonal modulated. Together this

  14. A review of the kinetic detail required for accurate predictions of normal shock waves

    NASA Technical Reports Server (NTRS)

    Muntz, E. P.; Erwin, Daniel A.; Pham-Van-diep, Gerald C.

    1991-01-01

    Several aspects of the kinetic models used in the collision phase of Monte Carlo direct simulations have been studied. Accurate molecular velocity distribution function predictions require a significantly increased number of computational cells in one maximum slope shock thickness, compared to predictions of macroscopic properties. The shape of the highly repulsive portion of the interatomic potential for argon is not well modeled by conventional interatomic potentials; this portion of the potential controls high Mach number shock thickness predictions, indicating that the specification of the energetic repulsive portion of interatomic or intermolecular potentials must be chosen with care for correct modeling of nonequilibrium flows at high temperatures. It has been shown for inverse power potentials that the assumption of variable hard sphere scattering provides accurate predictions of the macroscopic properties in shock waves, by comparison with simulations in which differential scattering is employed in the collision phase. On the other hand, velocity distribution functions are not well predicted by the variable hard sphere scattering model for softer potentials at higher Mach numbers.

  15. 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

  16. 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. PMID:27272707

  17. Predicting fracture behavior of aluminum alloys

    SciTech Connect

    Chang, A.T.; Cordes, J.A.

    1997-12-31

    A computational method has been developed to predict the material fracture failure process in flawed or cracked specimens. This method does not require experimental material fracture data. Finite element technique is employed to model the physical shape of the specimen. Nonlinear spring elements are introduced to model the material damage behavior near a flaw or a crack tip. Crack initiation and crack propagation conditions are developed to predict the crack initiation load, the extent of material damage, and the crack growth behavior. The introduction of nonlinear spring elements and the development of crack initiation and crack growth conditions are unique features for fracture prediction with the development of this method. To prove the feasibility of the method, two types of specimen made by two aluminum alloys with similar material stress-strain data were studied. Fracture predictions by this method are comparable to experimental data.

  18. Development of modified cable models to simulate accurate neuronal active behaviors

    PubMed Central

    2014-01-01

    In large network and single three-dimensional (3-D) neuron simulations, high computing speed dictates using reduced cable models to simulate neuronal firing behaviors. However, these models are unwarranted under active conditions and lack accurate representation of dendritic active conductances that greatly shape neuronal firing. Here, realistic 3-D (R3D) models (which contain full anatomical details of dendrites) of spinal motoneurons were systematically compared with their reduced single unbranched cable (SUC, which reduces the dendrites to a single electrically equivalent cable) counterpart under passive and active conditions. The SUC models matched the R3D model's passive properties but failed to match key active properties, especially active behaviors originating from dendrites. For instance, persistent inward currents (PIC) hysteresis, frequency-current (FI) relationship secondary range slope, firing hysteresis, plateau potential partial deactivation, staircase currents, synaptic current transfer ratio, and regional FI relationships were not accurately reproduced by the SUC models. The dendritic morphology oversimplification and lack of dendritic active conductances spatial segregation in the SUC models caused significant underestimation of those behaviors. Next, SUC models were modified by adding key branching features in an attempt to restore their active behaviors. The addition of primary dendritic branching only partially restored some active behaviors, whereas the addition of secondary dendritic branching restored most behaviors. Importantly, the proposed modified models successfully replicated the active properties without sacrificing model simplicity, making them attractive candidates for running R3D single neuron and network simulations with accurate firing behaviors. The present results indicate that using reduced models to examine PIC behaviors in spinal motoneurons is unwarranted. PMID:25277743

  19. Bicluster Sampled Coherence Metric (BSCM) provides an accurate environmental context for phenotype predictions

    PubMed Central

    2015-01-01

    Background Biclustering is a popular method for identifying under which experimental conditions biological signatures are co-expressed. However, the general biclustering problem is NP-hard, offering room to focus algorithms on specific biological tasks. We hypothesize that conditional co-regulation of genes is a key factor in determining cell phenotype and that accurately segregating conditions in biclusters will improve such predictions. Thus, we developed a bicluster sampled coherence metric (BSCM) for determining which conditions and signals should be included in a bicluster. Results Our BSCM calculates condition and cluster size specific p-values, and we incorporated these into the popular integrated biclustering algorithm cMonkey. We demonstrate that incorporation of our new algorithm significantly improves bicluster co-regulation scores (p-value = 0.009) and GO annotation scores (p-value = 0.004). Additionally, we used a bicluster based signal to predict whether a given experimental condition will result in yeast peroxisome induction. Using the new algorithm, the classifier accuracy improves from 41.9% to 76.1% correct. Conclusions We demonstrate that the proposed BSCM helps determine which signals ought to be co-clustered, resulting in more accurately assigned bicluster membership. Furthermore, we show that BSCM can be extended to more accurately detect under which experimental conditions the genes are co-clustered. Features derived from this more accurate analysis of conditional regulation results in a dramatic improvement in the ability to predict a cellular phenotype in yeast. The latest cMonkey is available for download at https://github.com/baliga-lab/cmonkey2. The experimental data and source code featured in this paper is available http://AitchisonLab.com/BSCM. BSCM has been incorporated in the official cMonkey release. PMID:25881257

  20. Highly Accurate Structure-Based Prediction of HIV-1 Coreceptor Usage Suggests Intermolecular Interactions Driving Tropism

    PubMed Central

    Kieslich, Chris A.; Tamamis, Phanourios; Guzman, Yannis A.; Onel, Melis; Floudas, Christodoulos A.

    2016-01-01

    HIV-1 entry into host cells is mediated by interactions between the V3-loop of viral glycoprotein gp120 and chemokine receptor CCR5 or CXCR4, collectively known as HIV-1 coreceptors. Accurate genotypic prediction of coreceptor usage is of significant clinical interest and determination of the factors driving tropism has been the focus of extensive study. We have developed a method based on nonlinear support vector machines to elucidate the interacting residue pairs driving coreceptor usage and provide highly accurate coreceptor usage predictions. Our models utilize centroid-centroid interaction energies from computationally derived structures of the V3-loop:coreceptor complexes as primary features, while additional features based on established rules regarding V3-loop sequences are also investigated. We tested our method on 2455 V3-loop sequences of various lengths and subtypes, and produce a median area under the receiver operator curve of 0.977 based on 500 runs of 10-fold cross validation. Our study is the first to elucidate a small set of specific interacting residue pairs between the V3-loop and coreceptors capable of predicting coreceptor usage with high accuracy across major HIV-1 subtypes. The developed method has been implemented as a web tool named CRUSH, CoReceptor USage prediction for HIV-1, which is available at http://ares.tamu.edu/CRUSH/. PMID:26859389

  1. Accurate similarity index based on activity and connectivity of node for link prediction

    NASA Astrophysics Data System (ADS)

    Li, Longjie; Qian, Lvjian; Wang, Xiaoping; Luo, Shishun; Chen, Xiaoyun

    2015-05-01

    Recent years have witnessed the increasing of available network data; however, much of those data is incomplete. Link prediction, which can find the missing links of a network, plays an important role in the research and analysis of complex networks. Based on the assumption that two unconnected nodes which are highly similar are very likely to have an interaction, most of the existing algorithms solve the link prediction problem by computing nodes' similarities. The fundamental requirement of those algorithms is accurate and effective similarity indices. In this paper, we propose a new similarity index, namely similarity based on activity and connectivity (SAC), which performs link prediction more accurately. To compute the similarity between two nodes, this index employs the average activity of these two nodes in their common neighborhood and the connectivities between them and their common neighbors. The higher the average activity is and the stronger the connectivities are, the more similar the two nodes are. The proposed index not only commendably distinguishes the contributions of paths but also incorporates the influence of endpoints. Therefore, it can achieve a better predicting result. To verify the performance of SAC, we conduct experiments on 10 real-world networks. Experimental results demonstrate that SAC outperforms the compared baselines.

  2. 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 PMID:27100309

  3. Prediction of Accurate Thermochemistry of Medium and Large Sized Radicals Using Connectivity-Based Hierarchy (CBH).

    PubMed

    Sengupta, Arkajyoti; Raghavachari, Krishnan

    2014-10-14

    Accurate modeling of the chemical reactions in many diverse areas such as combustion, photochemistry, or atmospheric chemistry strongly depends on the availability of thermochemical information of the radicals involved. However, accurate thermochemical investigations of radical systems using state of the art composite methods have mostly been restricted to the study of hydrocarbon radicals of modest size. In an alternative approach, systematic error-canceling thermochemical hierarchy of reaction schemes can be applied to yield accurate results for such systems. In this work, we have extended our connectivity-based hierarchy (CBH) method to the investigation of radical systems. We have calibrated our method using a test set of 30 medium sized radicals to evaluate their heats of formation. The CBH-rad30 test set contains radicals containing diverse functional groups as well as cyclic systems. We demonstrate that the sophisticated error-canceling isoatomic scheme (CBH-2) with modest levels of theory is adequate to provide heats of formation accurate to ∼1.5 kcal/mol. Finally, we predict heats of formation of 19 other large and medium sized radicals for which the accuracy of available heats of formation are less well-known. PMID:26588131

  4. conSSert: Consensus SVM Model for Accurate Prediction of Ordered Secondary Structure.

    PubMed

    Kieslich, Chris A; Smadbeck, James; Khoury, George A; Floudas, Christodoulos A

    2016-03-28

    Accurate prediction of protein secondary structure remains a crucial step in most approaches to the protein-folding problem, yet the prediction of ordered secondary structure, specifically beta-strands, remains a challenge. We developed a consensus secondary structure prediction method, conSSert, which is based on support vector machines (SVM) and provides exceptional accuracy for the prediction of beta-strands with QE accuracy of over 0.82 and a Q2-EH of 0.86. conSSert uses as input probabilities for the three types of secondary structure (helix, strand, and coil) that are predicted by four top performing methods: PSSpred, PSIPRED, SPINE-X, and RAPTOR. conSSert was trained/tested using 4261 protein chains from PDBSelect25, and 8632 chains from PISCES. Further validation was performed using targets from CASP9, CASP10, and CASP11. Our data suggest that poor performance in strand prediction is likely a result of training bias and not solely due to the nonlocal nature of beta-sheet contacts. conSSert is freely available for noncommercial use as a webservice: http://ares.tamu.edu/conSSert/ . PMID:26928531

  5. Behavior-Based Budget Management Using Predictive Analytics

    SciTech Connect

    Troy Hiltbrand

    2013-03-01

    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 under 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.

  6. Predicting Death from Behavioral Test Performance

    ERIC Educational Resources Information Center

    Botwinick, Jack; And Others

    1978-01-01

    This study described several brief behavioral measures which, with further validation, could be useful in predicting the deaths of older adults within a five-year period following testing. Such tests can be used in routine biomedical examinations, alerting the physician to possible problems in the future. (Author)

  7. 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.…

  8. Planar Near-Field Phase Retrieval Using GPUs for Accurate THz Far-Field Prediction

    NASA Astrophysics Data System (ADS)

    Junkin, Gary

    2013-04-01

    With a view to using Phase Retrieval to accurately predict Terahertz antenna far-field from near-field intensity measurements, this paper reports on three fundamental advances that achieve very low algorithmic error penalties. The first is a new Gaussian beam analysis that provides accurate initial complex aperture estimates including defocus and astigmatic phase errors, based only on first and second moment calculations. The second is a powerful noise tolerant near-field Phase Retrieval algorithm that combines Anderson's Plane-to-Plane (PTP) with Fienup's Hybrid-Input-Output (HIO) and Successive Over-Relaxation (SOR) to achieve increased accuracy at reduced scan separations. The third advance employs teraflop Graphical Processing Units (GPUs) to achieve practically real time near-field phase retrieval and to obtain the optimum aperture constraint without any a priori information.

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

    SciTech Connect

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

    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, and 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.

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

    DOE PAGESBeta

    Hansen, Katja; Biegler, Franziska; Ramakrishnan, Raghunathan; Pronobis, Wiktor; von Lilienfeld, O. Anatole; Müller, Klaus -Robert; Tkatchenko, Alexandre

    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

    SciTech Connect

    Hansen, Katja; Biegler, Franziska; Ramakrishnan, Raghunathan; Pronobis, Wiktor; von Lilienfeld, O. Anatole; Müller, Klaus -Robert; Tkatchenko, Alexandre

    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 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. The same representation allows us to predict accurate electronic properties of molecules, such as their polarizability and molecular frontier orbital energies.

  12. 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.

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

    PubMed

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

    2015-07-01

    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 to 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. The SIFTER web server is accessible at http://sifter.berkeley.edu/ and the source code can be downloaded. PMID:25979264

  14. 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

  15. Accurate bearing remaining useful life prediction based on Weibull distribution and artificial neural network

    NASA Astrophysics Data System (ADS)

    Ben Ali, Jaouher; Chebel-Morello, Brigitte; Saidi, Lotfi; Malinowski, Simon; Fnaiech, Farhat

    2015-05-01

    Accurate remaining useful life (RUL) prediction of critical assets is an important challenge in condition based maintenance to improve reliability and decrease machine's breakdown and maintenance's cost. Bearing is one of the most important components in industries which need to be monitored and the user should predict its RUL. The challenge of this study is to propose an original feature able to evaluate the health state of bearings and to estimate their RUL by Prognostics and Health Management (PHM) techniques. In this paper, the proposed method is based on the data-driven prognostic approach. The combination of Simplified Fuzzy Adaptive Resonance Theory Map (SFAM) neural network and Weibull distribution (WD) is explored. WD is used just in the training phase to fit measurement and to avoid areas of fluctuation in the time domain. SFAM training process is based on fitted measurements at present and previous inspection time points as input. However, the SFAM testing process is based on real measurements at present and previous inspections. Thanks to the fuzzy learning process, SFAM has an important ability and a good performance to learn nonlinear time series. As output, seven classes are defined; healthy bearing and six states for bearing degradation. In order to find the optimal RUL prediction, a smoothing phase is proposed in this paper. Experimental results show that the proposed method can reliably predict the RUL of rolling element bearings (REBs) based on vibration signals. The proposed prediction approach can be applied to prognostic other various mechanical assets.

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

    SciTech Connect

    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 to 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.

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

    DOE PAGESBeta

    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

  18. Special purpose hybrid transfinite elements and unified computational methodology for accurately predicting thermoelastic stress waves

    NASA Technical Reports Server (NTRS)

    Tamma, Kumar K.; Railkar, Sudhir B.

    1988-01-01

    This paper represents an attempt to apply extensions of a hybrid transfinite element computational approach for accurately predicting thermoelastic stress waves. The applicability of the present formulations for capturing the thermal stress waves induced by boundary heating for the well known Danilovskaya problems is demonstrated. A unique feature of the proposed formulations for applicability to the Danilovskaya problem of thermal stress waves in elastic solids lies in the hybrid nature of the unified formulations and the development of special purpose transfinite elements in conjunction with the classical Galerkin techniques and transformation concepts. Numerical test cases validate the applicability and superior capability to capture the thermal stress waves induced due to boundary heating.

  19. Accurate verification of the conserved-vector-current and standard-model predictions

    SciTech Connect

    Sirlin, A.; Zucchini, R.

    1986-10-20

    An approximate analytic calculation of O(Z..cap alpha../sup 2/) corrections to Fermi decays is presented. When the analysis of Koslowsky et al. is modified to take into account the new results, it is found that each of the eight accurately studied scrFt values differs from the average by approx. <1sigma, thus significantly improving the comparison of experiments with conserved-vector-current predictions. The new scrFt values are lower than before, which also brings experiments into very good agreement with the three-generation standard model, at the level of its quantum corrections.

  20. 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. PMID:26121186

  1. Accurate Structure Prediction and Conformational Analysis of Cyclic Peptides with Residue-Specific Force Fields.

    PubMed

    Geng, Hao; Jiang, Fan; Wu, Yun-Dong

    2016-05-19

    Cyclic peptides (CPs) are promising candidates for drugs, chemical biology tools, and self-assembling nanomaterials. However, the development of reliable and accurate computational methods for their structure prediction has been challenging. Here, 20 all-trans CPs of 5-12 residues selected from Cambridge Structure Database have been simulated using replica-exchange molecular dynamics with four different force fields. Our recently developed residue-specific force fields RSFF1 and RSFF2 can correctly identify the crystal-like conformations of more than half CPs as the most populated conformation. The RSFF2 performs the best, which consistently predicts the crystal structures of 17 out of 20 CPs with rmsd < 1.1 Å. We also compared the backbone (ϕ, ψ) sampling of residues in CPs with those in short linear peptides and in globular proteins. In general, unlike linear peptides, CPs have local conformational free energies and entropies quite similar to globular proteins. PMID:27128113

  2. Accurate prediction of helix interactions and residue contacts in membrane proteins.

    PubMed

    Hönigschmid, Peter; Frishman, Dmitrij

    2016-04-01

    Accurate prediction of intra-molecular interactions from amino acid sequence is an important pre-requisite for obtaining high-quality protein models. Over the recent years, remarkable progress in this area has been achieved through the application of novel co-variation algorithms, which eliminate transitive evolutionary connections between residues. In this work we present a new contact prediction method for α-helical transmembrane proteins, MemConP, in which evolutionary couplings are combined with a machine learning approach. MemConP achieves a substantially improved accuracy (precision: 56.0%, recall: 17.5%, MCC: 0.288) compared to the use of either machine learning or co-evolution methods alone. The method also achieves 91.4% precision, 42.1% recall and a MCC of 0.490 in predicting helix-helix interactions based on predicted contacts. The approach was trained and rigorously benchmarked by cross-validation and independent testing on up-to-date non-redundant datasets of 90 and 30 experimental three dimensional structures, respectively. MemConP is a standalone tool that can be downloaded together with the associated training data from http://webclu.bio.wzw.tum.de/MemConP. PMID:26851352

  3. Base-resolution methylation patterns accurately predict transcription factor bindings in vivo

    PubMed Central

    Xu, Tianlei; Li, Ben; Zhao, Meng; Szulwach, Keith E.; Street, R. Craig; Lin, Li; Yao, Bing; Zhang, Feiran; Jin, Peng; Wu, Hao; Qin, Zhaohui S.

    2015-01-01

    Detecting in vivo transcription factor (TF) binding is important for understanding gene regulatory circuitries. ChIP-seq is a powerful technique to empirically define TF binding in vivo. However, the multitude of distinct TFs makes genome-wide profiling for them all labor-intensive and costly. Algorithms for in silico prediction of TF binding have been developed, based mostly on histone modification or DNase I hypersensitivity data in conjunction with DNA motif and other genomic features. However, technical limitations of these methods prevent them from being applied broadly, especially in clinical settings. We conducted a comprehensive survey involving multiple cell lines, TFs, and methylation types and found that there are intimate relationships between TF binding and methylation level changes around the binding sites. Exploiting the connection between DNA methylation and TF binding, we proposed a novel supervised learning approach to predict TF–DNA interaction using data from base-resolution whole-genome methylation sequencing experiments. We devised beta-binomial models to characterize methylation data around TF binding sites and the background. Along with other static genomic features, we adopted a random forest framework to predict TF–DNA interaction. After conducting comprehensive tests, we saw that the proposed method accurately predicts TF binding and performs favorably versus competing methods. PMID:25722376

  4. 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. PMID:24376713

  5. NMRDSP: An Accurate Prediction of Protein Shape Strings from NMR Chemical Shifts and Sequence Data

    PubMed Central

    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. PMID:24376713

  6. 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. PMID:26424887

  7. 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

  8. 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

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

    PubMed

    Shvab, I; Sadus, Richard J

    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(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. 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. PMID:24320337

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

    NASA Astrophysics Data System (ADS)

    Shvab, I.; Sadus, Richard J.

    2013-11-01

    The ability of intermolecular potentials to correctly predict the thermodynamic properties of liquid water at a density of 0.998 g/cm3 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. 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.

  11. Toward an Accurate Prediction of the Arrival Time of Geomagnetic-Effective Coronal Mass Ejections

    NASA Astrophysics Data System (ADS)

    Shi, T.; Wang, Y.; Wan, L.; Cheng, X.; Ding, M.; Zhang, J.

    2015-12-01

    Accurately predicting the arrival of coronal mass ejections (CMEs) to the Earth based on remote images is of critical significance for the study of space weather. Here we make a statistical study of 21 Earth-directed CMEs, specifically exploring the relationship between CME initial speeds and transit times. The initial speed of a CME is obtained by fitting the CME with the Graduated Cylindrical Shell model and is thus free of projection effects. We then use the drag force model to fit results of the transit time versus the initial speed. By adopting different drag regimes, i.e., the viscous, aerodynamics, and hybrid regimes, we get similar results, with a least mean estimation error of the hybrid model of 12.9 hr. CMEs with a propagation angle (the angle between the propagation direction and the Sun-Earth line) larger than their half-angular widths arrive at the Earth with an angular deviation caused by factors other than the radial solar wind drag. The drag force model cannot be reliably applied to such events. If we exclude these events in the sample, the prediction accuracy can be improved, i.e., the estimation error reduces to 6.8 hr. This work suggests that it is viable to predict the arrival time of CMEs to the Earth based on the initial parameters with fairly good accuracy. Thus, it provides a method of forecasting space weather 1-5 days following the occurrence of CMEs.

  12. Towards first-principles based prediction of highly accurate electrochemical Pourbiax diagrams

    NASA Astrophysics Data System (ADS)

    Zeng, Zhenhua; Chan, Maria; Greeley, Jeff

    2015-03-01

    Electrochemical Pourbaix diagrams lie at the heart of 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 Pourbaix diagrams, inherent errors in the description of strongly-correlated transition metal (hydr)oxides, together with neglect of weak van der Waals (vdW) interactions, has limited the reliability of the predictions for even the simplest bulk systems; corresponding predictions for more complex alloy or surface structures are even more challenging . Through introduction of a Hubbard U correction, employment of a state-of-the-art van der Waals functional, and use of pure water as a reference state for the calculations, these errors are systematically corrected. The strong performance is illustrated on a series of bulk transition metal (Mn, Fe, Co and Ni) hydroxide, oxyhydroxide, binary and ternary oxides where the corresponding thermodynamics of oxidation and reduction can be accurately described with standard errors of less than 0.04 eV in comparison with experiment.

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

    SciTech Connect

    Shvab, I.; Sadus, Richard J.

    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. 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.

  14. Using attribute behavior diversity to build accurate decision tree committees for microarray data.

    PubMed

    Han, Qian; Dong, Guozhu

    2012-08-01

    DNA microarrays (gene chips), frequently used in biological and medical studies, measure the expressions of thousands of genes per sample. Using microarray data to build accurate classifiers for diseases is an important task. This paper introduces an algorithm, called Committee of Decision Trees by Attribute Behavior Diversity (CABD), to build highly accurate ensembles of decision trees for such data. Since a committee's accuracy is greatly influenced by the diversity among its member classifiers, CABD uses two new ideas to "optimize" that diversity, namely (1) the concept of attribute behavior-based similarity between attributes, and (2) the concept of attribute usage diversity among trees. The ideas are effective for microarray data, since such data have many features and behavior similarity between genes can be high. Experiments on microarray data for six cancers show that CABD outperforms previous ensemble methods significantly and outperforms SVM, and show that the diversified features used by CABD's decision tree committee can be used to improve performance of other classifiers such as SVM. CABD has potential for other high-dimensional data, and its ideas may apply to ensembles of other classifier types. PMID:22809418

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

    PubMed

    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

  16. Direct Pressure Monitoring Accurately Predicts Pulmonary Vein Occlusion During Cryoballoon Ablation

    PubMed Central

    Kosmidou, Ioanna; Wooden, Shannnon; Jones, Brian; Deering, Thomas; Wickliffe, Andrew; Dan, Dan

    2013-01-01

    Cryoballoon ablation (CBA) is an established therapy for atrial fibrillation (AF). Pulmonary vein (PV) occlusion is essential for achieving antral contact and PV isolation and is typically assessed by contrast injection. We present a novel method of direct pressure monitoring for assessment of PV occlusion. Transcatheter pressure is monitored during balloon advancement to the PV antrum. Pressure is recorded via a single pressure transducer connected to the inner lumen of the cryoballoon. Pressure curve characteristics are used to assess occlusion in conjunction with fluoroscopic or intracardiac echocardiography (ICE) guidance. PV occlusion is confirmed when loss of typical left atrial (LA) pressure waveform is observed with recordings of PA pressure characteristics (no A wave and rapid V wave upstroke). Complete pulmonary vein occlusion as assessed with this technique has been confirmed with concurrent contrast utilization during the initial testing of the technique and has been shown to be highly accurate and readily reproducible. We evaluated the efficacy of this novel technique in 35 patients. A total of 128 veins were assessed for occlusion with the cryoballoon utilizing the pressure monitoring technique; occlusive pressure was demonstrated in 113 veins with resultant successful pulmonary vein isolation in 111 veins (98.2%). Occlusion was confirmed with subsequent contrast injection during the initial ten procedures, after which contrast utilization was rapidly reduced or eliminated given the highly accurate identification of occlusive pressure waveform with limited initial training. Verification of PV occlusive pressure during CBA is a novel approach to assessing effective PV occlusion and it accurately predicts electrical isolation. Utilization of this method results in significant decrease in fluoroscopy time and volume of contrast. PMID:23485956

  17. Direct pressure monitoring accurately predicts pulmonary vein occlusion during cryoballoon ablation.

    PubMed

    Kosmidou, Ioanna; Wooden, Shannnon; Jones, Brian; Deering, Thomas; Wickliffe, Andrew; Dan, Dan

    2013-01-01

    Cryoballoon ablation (CBA) is an established therapy for atrial fibrillation (AF). Pulmonary vein (PV) occlusion is essential for achieving antral contact and PV isolation and is typically assessed by contrast injection. We present a novel method of direct pressure monitoring for assessment of PV occlusion. Transcatheter pressure is monitored during balloon advancement to the PV antrum. Pressure is recorded via a single pressure transducer connected to the inner lumen of the cryoballoon. Pressure curve characteristics are used to assess occlusion in conjunction with fluoroscopic or intracardiac echocardiography (ICE) guidance. PV occlusion is confirmed when loss of typical left atrial (LA) pressure waveform is observed with recordings of PA pressure characteristics (no A wave and rapid V wave upstroke). Complete pulmonary vein occlusion as assessed with this technique has been confirmed with concurrent contrast utilization during the initial testing of the technique and has been shown to be highly accurate and readily reproducible. We evaluated the efficacy of this novel technique in 35 patients. A total of 128 veins were assessed for occlusion with the cryoballoon utilizing the pressure monitoring technique; occlusive pressure was demonstrated in 113 veins with resultant successful pulmonary vein isolation in 111 veins (98.2%). Occlusion was confirmed with subsequent contrast injection during the initial ten procedures, after which contrast utilization was rapidly reduced or eliminated given the highly accurate identification of occlusive pressure waveform with limited initial training. Verification of PV occlusive pressure during CBA is a novel approach to assessing effective PV occlusion and it accurately predicts electrical isolation. Utilization of this method results in significant decrease in fluoroscopy time and volume of contrast. PMID:23485956

  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. Fast and Accurate Prediction of Numerical Relativity Waveforms from Binary Black Hole Coalescences Using Surrogate Models

    NASA Astrophysics Data System (ADS)

    Blackman, Jonathan; Field, Scott E.; Galley, Chad R.; Szilágyi, Béla; Scheel, Mark A.; Tiglio, Manuel; Hemberger, Daniel A.

    2015-09-01

    Simulating a binary black hole coalescence by solving Einstein's equations is computationally expensive, requiring days to months of supercomputing time. Using reduced order modeling techniques, we construct an accurate surrogate model, which is evaluated in a millisecond to a second, for numerical relativity (NR) waveforms from nonspinning binary black hole coalescences with mass ratios in [1, 10] and durations corresponding to about 15 orbits before merger. We assess the model's uncertainty and show that our modeling strategy predicts NR waveforms not used for the surrogate's training with errors nearly as small as the numerical error of the NR code. Our model includes all spherical-harmonic -2Yℓm waveform modes resolved by the NR code up to ℓ=8 . We compare our surrogate model to effective one body waveforms from 50 M⊙ to 300 M⊙ for advanced LIGO detectors and find that the surrogate is always more faithful (by at least an order of magnitude in most cases).

  20. Measuring solar reflectance Part I: Defining a metric that accurately predicts solar heat gain

    SciTech Connect

    Levinson, Ronnen; Akbari, Hashem; Berdahl, Paul

    2010-05-14

    Solar reflectance can vary with the spectral and angular distributions of incident sunlight, which in turn depend on surface orientation, solar position and atmospheric conditions. A widely used solar reflectance metric based on the ASTM Standard E891 beam-normal solar spectral irradiance underestimates the solar heat gain of a spectrally selective 'cool colored' surface because this irradiance contains a greater fraction of near-infrared light than typically found in ordinary (unconcentrated) global sunlight. At mainland U.S. latitudes, this metric RE891BN can underestimate the annual peak solar heat gain of a typical roof or pavement (slope {le} 5:12 [23{sup o}]) by as much as 89 W m{sup -2}, and underestimate its peak surface temperature by up to 5 K. Using R{sub E891BN} to characterize roofs in a building energy simulation can exaggerate the economic value N of annual cool-roof net energy savings by as much as 23%. We define clear-sky air mass one global horizontal ('AM1GH') solar reflectance R{sub g,0}, a simple and easily measured property that more accurately predicts solar heat gain. R{sub g,0} predicts the annual peak solar heat gain of a roof or pavement to within 2 W m{sup -2}, and overestimates N by no more than 3%. R{sub g,0} is well suited to rating the solar reflectances of roofs, pavements and walls. We show in Part II that R{sub g,0} can be easily and accurately measured with a pyranometer, a solar spectrophotometer or version 6 of the Solar Spectrum Reflectometer.

  1. Measuring solar reflectance - Part I: Defining a metric that accurately predicts solar heat gain

    SciTech Connect

    Levinson, Ronnen; Akbari, Hashem; Berdahl, Paul

    2010-09-15

    Solar reflectance can vary with the spectral and angular distributions of incident sunlight, which in turn depend on surface orientation, solar position and atmospheric conditions. A widely used solar reflectance metric based on the ASTM Standard E891 beam-normal solar spectral irradiance underestimates the solar heat gain of a spectrally selective ''cool colored'' surface because this irradiance contains a greater fraction of near-infrared light than typically found in ordinary (unconcentrated) global sunlight. At mainland US latitudes, this metric R{sub E891BN} can underestimate the annual peak solar heat gain of a typical roof or pavement (slope {<=} 5:12 [23 ]) by as much as 89 W m{sup -2}, and underestimate its peak surface temperature by up to 5 K. Using R{sub E891BN} to characterize roofs in a building energy simulation can exaggerate the economic value N of annual cool roof net energy savings by as much as 23%. We define clear sky air mass one global horizontal (''AM1GH'') solar reflectance R{sub g,0}, a simple and easily measured property that more accurately predicts solar heat gain. R{sub g,0} predicts the annual peak solar heat gain of a roof or pavement to within 2 W m{sup -2}, and overestimates N by no more than 3%. R{sub g,0} is well suited to rating the solar reflectances of roofs, pavements and walls. We show in Part II that R{sub g,0} can be easily and accurately measured with a pyranometer, a solar spectrophotometer or version 6 of the Solar Spectrum Reflectometer. (author)

  2. Highly Accurate Prediction of Protein-Protein Interactions via Incorporating Evolutionary Information and Physicochemical Characteristics.

    PubMed

    Li, Zheng-Wei; You, Zhu-Hong; Chen, Xing; Gui, Jie; Nie, Ru

    2016-01-01

    Protein-protein interactions (PPIs) occur at almost all levels of cell functions and play crucial roles in various cellular processes. Thus, identification of PPIs is critical for deciphering the molecular mechanisms and further providing insight into biological processes. Although a variety of high-throughput experimental techniques have been developed to identify PPIs, existing PPI pairs by experimental approaches only cover a small fraction of the whole PPI networks, and further, those approaches hold inherent disadvantages, such as being time-consuming, expensive, and having high false positive rate. Therefore, it is urgent and imperative to develop automatic in silico approaches to predict PPIs efficiently and accurately. In this article, we propose a novel mixture of physicochemical and evolutionary-based feature extraction method for predicting PPIs using our newly developed discriminative vector machine (DVM) classifier. The improvements of the proposed method mainly consist in introducing an effective feature extraction method that can capture discriminative features from the evolutionary-based information and physicochemical characteristics, and then a powerful and robust DVM classifier is employed. To the best of our knowledge, it is the first time that DVM model is applied to the field of bioinformatics. When applying the proposed method to the Yeast and Helicobacter pylori (H. pylori) datasets, we obtain excellent prediction accuracies of 94.35% and 90.61%, respectively. The computational results indicate that our method is effective and robust for predicting PPIs, and can be taken as a useful supplementary tool to the traditional experimental methods for future proteomics research. PMID:27571061

  3. A novel approach for accurate prediction of spontaneous passage of ureteral stones: support vector machines.

    PubMed

    Dal Moro, F; Abate, A; Lanckriet, G R G; Arandjelovic, G; Gasparella, P; Bassi, P; Mancini, M; Pagano, F

    2006-01-01

    The objective of this study was to optimally predict the spontaneous passage of ureteral stones in patients with renal colic by applying for the first time support vector machines (SVM), an instance of kernel methods, for classification. After reviewing the results found in the literature, we compared the performances obtained with logistic regression (LR) and accurately trained artificial neural networks (ANN) to those obtained with SVM, that is, the standard SVM, and the linear programming SVM (LP-SVM); the latter techniques show an improved performance. Moreover, we rank the prediction factors according to their importance using Fisher scores and the LP-SVM feature weights. A data set of 1163 patients affected by renal colic has been analyzed and restricted to single out a statistically coherent subset of 402 patients. Nine clinical factors are used as inputs for the classification algorithms, to predict one binary output. The algorithms are cross-validated by training and testing on randomly selected train- and test-set partitions of the data and reporting the average performance on the test sets. The SVM-based approaches obtained a sensitivity of 84.5% and a specificity of 86.9%. The feature ranking based on LP-SVM gives the highest importance to stone size, stone position and symptom duration before check-up. We propose a statistically correct way of employing LR, ANN and SVM for the prediction of spontaneous passage of ureteral stones in patients with renal colic. SVM outperformed ANN, as well as LR. This study will soon be translated into a practical software toolbox for actual clinical usage. PMID:16374437

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

    SciTech Connect

    Visel, Axel; Blow, Matthew J.; Li, Zirong; Zhang, Tao; Akiyama, Jennifer A.; Holt, Amy; Plajzer-Frick, Ingrid; Shoukry, Malak; Wright, Crystal; Chen, Feng; Afzal, Veena; Ren, Bing; Rubin, Edward M.; Pennacchio, Len A.

    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. We 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.

  5. 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

  6. Can CO2 assimilation in maize leaves be predicted accurately from chlorophyll fluorescence analysis?

    PubMed

    Edwards, G E; Baker, N R

    1993-08-01

    Analysis is made of the energetics of CO2 fixation, the photochemical quantum requirement per CO2 fixed, and sinks for utilising reductive power in the C4 plant maize. CO2 assimilation is the primary sink for energy derived from photochemistry, whereas photorespiration and nitrogen assimilation are relatively small sinks, particularly in developed leaves. Measurement of O2 exchange by mass spectrometry and CO2 exchange by infrared gas analysis under varying levels of CO2 indicate that there is a very close relationship between the true rate of O2 evolution from PS II and the net rate of CO2 fixation. Consideration is given to measurements of the quantum yields of PS II (φ PS II) from fluorescence analysis and of CO2 assimilation ([Formula: see text]) in maize over a wide range of conditions. The[Formula: see text] ratio was found to remain reasonably constant (ca. 12) over a range of physiological conditions in developed leaves, with varying temperature, CO2 concentrations, light intensities (from 5% to 100% of full sunlight), and following photoinhibition under high light and low temperature. A simple model for predicting CO2 assimilation from fluorescence parameters is presented and evaluated. It is concluded that under a wide range of conditions fluorescence parameters can be used to predict accurately and rapidly CO2 assimilation rates in maize. PMID:24317706

  7. A Simple and Accurate Model to Predict Responses to Multi-electrode Stimulation in the Retina.

    PubMed

    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

    2016-04-01

    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

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

    PubMed Central

    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

  9. 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

  10. Accurate and Robust Genomic Prediction of Celiac Disease Using Statistical Learning

    PubMed Central

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

    2014-01-01

    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. PMID:24550740

  11. Energy expenditure during level human walking: seeking a simple and accurate predictive solution.

    PubMed

    Ludlow, Lindsay W; Weyand, Peter G

    2016-03-01

    Accurate prediction of the metabolic energy that walking requires can inform numerous health, bodily status, and fitness outcomes. We adopted a two-step approach to identifying a concise, generalized equation for predicting level human walking metabolism. Using literature-aggregated values we compared 1) the predictive accuracy of three literature equations: American College of Sports Medicine (ACSM), Pandolf et al., and Height-Weight-Speed (HWS); and 2) the goodness-of-fit possible from one- vs. two-component descriptions of walking metabolism. Literature metabolic rate values (n = 127; speed range = 0.4 to 1.9 m/s) were aggregated from 25 subject populations (n = 5-42) whose means spanned a 1.8-fold range of heights and a 4.2-fold range of weights. Population-specific resting metabolic rates (V̇o2 rest) were determined using standardized equations. Our first finding was that the ACSM and Pandolf et al. equations underpredicted nearly all 127 literature-aggregated values. Consequently, their standard errors of estimate (SEE) were nearly four times greater than those of the HWS equation (4.51 and 4.39 vs. 1.13 ml O2·kg(-1)·min(-1), respectively). For our second comparison, empirical best-fit relationships for walking metabolism were derived from the data set in one- and two-component forms for three V̇o2-speed model types: linear (∝V(1.0)), exponential (∝V(2.0)), and exponential/height (∝V(2.0)/Ht). We found that the proportion of variance (R(2)) accounted for, when averaged across the three model types, was substantially lower for one- vs. two-component versions (0.63 ± 0.1 vs. 0.90 ± 0.03) and the predictive errors were nearly twice as great (SEE = 2.22 vs. 1.21 ml O2·kg(-1)·min(-1)). Our final analysis identified the following concise, generalized equation for predicting level human walking metabolism: V̇o2 total = V̇o2 rest + 3.85 + 5.97·V(2)/Ht (where V is measured in m/s, Ht in meters, and V̇o2 in ml O2·kg(-1)·min(-1)). PMID:26679617

  12. Can radiation therapy treatment planning system accurately predict surface doses in postmastectomy radiation therapy patients?

    SciTech Connect

    Wong, Sharon; Back, Michael; Tan, Poh Wee; Lee, Khai Mun; Baggarley, Shaun; Lu, Jaide Jay

    2012-07-01

    Skin doses have been an important factor in the dose prescription for breast radiotherapy. Recent advances in radiotherapy treatment techniques, such as intensity-modulated radiation therapy (IMRT) and new treatment schemes such as hypofractionated breast therapy have made the precise determination of the surface dose necessary. Detailed information of the dose at various depths of the skin is also critical in designing new treatment strategies. The purpose of this work was to assess the accuracy of surface dose calculation by a clinically used treatment planning system and those measured by thermoluminescence dosimeters (TLDs) in a customized chest wall phantom. This study involved the construction of a chest wall phantom for skin dose assessment. Seven TLDs were distributed throughout each right chest wall phantom to give adequate representation of measured radiation doses. Point doses from the CMS Xio Registered-Sign treatment planning system (TPS) were calculated for each relevant TLD positions and results correlated. There were no significant difference between measured absorbed dose by TLD and calculated doses by the TPS (p > 0.05 (1-tailed). Dose accuracy of up to 2.21% was found. The deviations from the calculated absorbed doses were overall larger (3.4%) when wedges and bolus were used. 3D radiotherapy TPS is a useful and accurate tool to assess the accuracy of surface dose. Our studies have shown that radiation treatment accuracy expressed as a comparison between calculated doses (by TPS) and measured doses (by TLD dosimetry) can be accurately predicted for tangential treatment of the chest wall after mastectomy.

  13. How can single sensory neurons predict behavior?

    PubMed Central

    Pitkow, Xaq; Liu, Sheng; Angelaki, Dora E.; DeAngelis, Gregory C.; Pouget, Alex

    2015-01-01

    Summary Single sensory neurons can be surprisingly predictive of behavior in discrimination tasks. We propose this is possible because sensory information extracted from neural populations is severely restricted, either by near-optimal decoding of a population with information-limiting correlations or suboptimal decoding that is blind to correlations. These have different consequences for choice correlations, the correlations between neural responses and behavioral choices. In the vestibular and cerebellar nuclei and the dorsal medial superior temporal area, we found that choice correlations during heading discrimination are consistent with near-optimal decoding of neuronal responses corrupted by information-limiting correlations. In the ventral intraparietal area, the choice correlations are also consistent with the presence of information-limiting correlations, but this area does not appear to influence behavior although the choice correlations are particularly large. These findings demonstrate how choice correlations can be used to assess the efficiency of the downstream read-out and detect the presence of information-limiting correlations. PMID:26182422

  14. New consensus definition for acute kidney injury accurately predicts 30-day mortality in cirrhosis with infection

    PubMed Central

    Wong, Florence; O’Leary, Jacqueline G; Reddy, K Rajender; Patton, Heather; Kamath, Patrick S; Fallon, Michael B; Garcia-Tsao, Guadalupe; Subramanian, Ram M.; Malik, Raza; Maliakkal, Benedict; Thacker, Leroy R; Bajaj, Jasmohan S

    2015-01-01

    Background & Aims A consensus conference proposed that cirrhosis-associated acute kidney injury (AKI) be defined as an increase in serum creatinine by >50% from the stable baseline value in <6 months or by ≥0.3mg/dL in <48 hrs. We prospectively evaluated the ability of these criteria to predict mortality within 30 days among hospitalized patients with cirrhosis and infection. Methods 337 patients with cirrhosis admitted with or developed an infection in hospital (56% men; 56±10 y old; model for end-stage liver disease score, 20±8) were followed. We compared data on 30-day mortality, hospital length-of-stay, and organ failure between patients with and without AKI. Results 166 (49%) developed AKI during hospitalization, based on the consensus criteria. Patients who developed AKI had higher admission Child-Pugh (11.0±2.1 vs 9.6±2.1; P<.0001), and MELD scores (23±8 vs17±7; P<.0001), and lower mean arterial pressure (81±16mmHg vs 85±15mmHg; P<.01) than those who did not. Also higher amongst patients with AKI were mortality in ≤30 days (34% vs 7%), intensive care unit transfer (46% vs 20%), ventilation requirement (27% vs 6%), and shock (31% vs 8%); AKI patients also had longer hospital stays (17.8±19.8 days vs 13.3±31.8 days) (all P<.001). 56% of AKI episodes were transient, 28% persistent, and 16% resulted in dialysis. Mortality was 80% among those without renal recovery, higher compared to partial (40%) or complete recovery (15%), or AKI-free patients (7%; P<.0001). Conclusions 30-day mortality is 10-fold higher among infected hospitalized cirrhotic patients with irreversible AKI than those without AKI. The consensus definition of AKI accurately predicts 30-day mortality, length of hospital stay, and organ failure. PMID:23999172

  15. 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.

  16. Fast and Accurate Prediction of Numerical Relativity Waveforms from Binary Black Hole Coalescences Using Surrogate Models.

    PubMed

    Blackman, Jonathan; Field, Scott E; Galley, Chad R; Szilágyi, Béla; Scheel, Mark A; Tiglio, Manuel; Hemberger, Daniel A

    2015-09-18

    Simulating a binary black hole coalescence by solving Einstein's equations is computationally expensive, requiring days to months of supercomputing time. Using reduced order modeling techniques, we construct an accurate surrogate model, which is evaluated in a millisecond to a second, for numerical relativity (NR) waveforms from nonspinning binary black hole coalescences with mass ratios in [1, 10] and durations corresponding to about 15 orbits before merger. We assess the model's uncertainty and show that our modeling strategy predicts NR waveforms not used for the surrogate's training with errors nearly as small as the numerical error of the NR code. Our model includes all spherical-harmonic _{-2}Y_{ℓm} waveform modes resolved by the NR code up to ℓ=8. We compare our surrogate model to effective one body waveforms from 50M_{⊙} to 300M_{⊙} for advanced LIGO detectors and find that the surrogate is always more faithful (by at least an order of magnitude in most cases). PMID:26430979

  17. How accurately can we predict the melting points of drug-like compounds?

    PubMed

    Tetko, Igor V; Sushko, Yurii; Novotarskyi, Sergii; Patiny, Luc; Kondratov, Ivan; Petrenko, Alexander E; Charochkina, Larisa; Asiri, Abdullah M

    2014-12-22

    This article contributes a highly accurate model for predicting the melting points (MPs) of medicinal chemistry compounds. The model was developed using the largest published data set, comprising more than 47k compounds. The distributions of MPs in drug-like and drug lead sets showed that >90% of molecules melt within [50,250]°C. The final model calculated an RMSE of less than 33 °C for molecules from this temperature interval, which is the most important for medicinal chemistry users. This performance was achieved using a consensus model that performed calculations to a significantly higher accuracy than the individual models. We found that compounds with reactive and unstable groups were overrepresented among outlying compounds. These compounds could decompose during storage or measurement, thus introducing experimental errors. While filtering the data by removing outliers generally increased the accuracy of individual models, it did not significantly affect the results of the consensus models. Three analyzed distance to models did not allow us to flag molecules, which had MP values fell outside the applicability domain of the model. We believe that this negative result and the public availability of data from this article will encourage future studies to develop better approaches to define the applicability domain of models. The final model, MP data, and identified reactive groups are available online at http://ochem.eu/article/55638. PMID:25489863

  18. How Accurately Can We Predict the Melting Points of Drug-like Compounds?

    PubMed Central

    2014-01-01

    This article contributes a highly accurate model for predicting the melting points (MPs) of medicinal chemistry compounds. The model was developed using the largest published data set, comprising more than 47k compounds. The distributions of MPs in drug-like and drug lead sets showed that >90% of molecules melt within [50,250]°C. The final model calculated an RMSE of less than 33 °C for molecules from this temperature interval, which is the most important for medicinal chemistry users. This performance was achieved using a consensus model that performed calculations to a significantly higher accuracy than the individual models. We found that compounds with reactive and unstable groups were overrepresented among outlying compounds. These compounds could decompose during storage or measurement, thus introducing experimental errors. While filtering the data by removing outliers generally increased the accuracy of individual models, it did not significantly affect the results of the consensus models. Three analyzed distance to models did not allow us to flag molecules, which had MP values fell outside the applicability domain of the model. We believe that this negative result and the public availability of data from this article will encourage future studies to develop better approaches to define the applicability domain of models. The final model, MP data, and identified reactive groups are available online at http://ochem.eu/article/55638. PMID:25489863

  19. Accurate prediction of V1 location from cortical folds in a surface coordinate system

    PubMed Central

    Hinds, Oliver P.; Rajendran, Niranjini; Polimeni, Jonathan R.; Augustinack, Jean C.; Wiggins, Graham; Wald, Lawrence L.; Rosas, H. Diana; Potthast, Andreas; Schwartz, Eric L.; Fischl, Bruce

    2008-01-01

    Previous studies demonstrated substantial variability of the location of primary visual cortex (V1) in stereotaxic coordinates when linear volume-based registration is used to match volumetric image intensities (Amunts et al., 2000). However, other qualitative reports of V1 location (Smith, 1904; Stensaas et al., 1974; Rademacher et al., 1993) suggested a consistent relationship between V1 and the surrounding cortical folds. Here, the relationship between folds and the location of V1 is quantified using surface-based analysis to generate a probabilistic atlas of human V1. High-resolution (about 200 μm) magnetic resonance imaging (MRI) at 7 T of ex vivo human cerebral hemispheres allowed identification of the full area via the stria of Gennari: a myeloarchitectonic feature specific to V1. Separate, whole-brain scans were acquired using MRI at 1.5 T to allow segmentation and mesh reconstruction of the cortical gray matter. For each individual, V1 was manually identified in the high-resolution volume and projected onto the cortical surface. Surface-based intersubject registration (Fischl et al., 1999b) was performed to align the primary cortical folds of individual hemispheres to those of a reference template representing the average folding pattern. An atlas of V1 location was constructed by computing the probability of V1 inclusion for each cortical location in the template space. This probabilistic atlas of V1 exhibits low prediction error compared to previous V1 probabilistic atlases built in volumetric coordinates. The increased predictability observed under surface-based registration suggests that the location of V1 is more accurately predicted by the cortical folds than by the shape of the brain embedded in the volume of the skull. In addition, the high quality of this atlas provides direct evidence that surface-based intersubject registration methods are superior to volume-based methods at superimposing functional areas of cortex, and therefore are better

  20. 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

  1. Life prediction and constitutive behavior: Overview

    NASA Technical Reports Server (NTRS)

    Halford, G. R.

    1982-01-01

    The evolution of programs to investigate high temperature consititutive behavior and develop cyclic life prediction methods is reviewed. Contracts granted for developing and verifying workable engineering methods for the calculation, in advance of service, of the local stress-strain response at the critical life governing location in typical hot section components as well as the resultant cyclic crack initiation and crack growth lifetimes are listed. The Langley fatigue facility is being upgraded to include: (1) a servocontrolled testing machine for high temperature crack growth; (2) three servocontrolled tension/torsion machines for biaxial studies; (3) a HOST/satellite computer for data acquisition, processing, storage, and retrieval; and (4) HCV/LCF machines for cumulative damage studies.

  2. Unilateral Prostate Cancer Cannot be Accurately Predicted in Low-Risk Patients

    SciTech Connect

    Isbarn, Hendrik; Karakiewicz, Pierre I.; Vogel, Susanne

    2010-07-01

    Purpose: Hemiablative therapy (HAT) is increasing in popularity for treatment of patients with low-risk prostate cancer (PCa). The validity of this therapeutic modality, which exclusively treats PCa within a single prostate lobe, rests on accurate staging. We tested the accuracy of unilaterally unremarkable biopsy findings in cases of low-risk PCa patients who are potential candidates for HAT. Methods and Materials: The study population consisted of 243 men with clinical stage {<=}T2a, a prostate-specific antigen (PSA) concentration of <10 ng/ml, a biopsy-proven Gleason sum of {<=}6, and a maximum of 2 ipsilateral positive biopsy results out of 10 or more cores. All men underwent a radical prostatectomy, and pathology stage was used as the gold standard. Univariable and multivariable logistic regression models were tested for significant predictors of unilateral, organ-confined PCa. These predictors consisted of PSA, %fPSA (defined as the quotient of free [uncomplexed] PSA divided by the total PSA), clinical stage (T2a vs. T1c), gland volume, and number of positive biopsy cores (2 vs. 1). Results: Despite unilateral stage at biopsy, bilateral or even non-organ-confined PCa was reported in 64% of all patients. In multivariable analyses, no variable could clearly and independently predict the presence of unilateral PCa. This was reflected in an overall accuracy of 58% (95% confidence interval, 50.6-65.8%). Conclusions: Two-thirds of patients with unilateral low-risk PCa, confirmed by clinical stage and biopsy findings, have bilateral or non-organ-confined PCa at radical prostatectomy. This alarming finding questions the safety and validity of HAT.

  3. Turtle utricle dynamic behavior using a combined anatomically accurate model and experimentally measured hair bundle stiffness

    PubMed Central

    Davis, J.L.; Grant, J.W.

    2014-01-01

    Anatomically correct turtle utricle geometry was incorporated into two finite element models. The geometrically accurate model included appropriately shaped macular surface and otoconial layer, compact gel and column filament (or shear) layer thicknesses and thickness distributions. The first model included a shear layer where the effects of hair bundle stiffness was included as part of the shear layer modulus. This solid model’s undamped natural frequency was matched to an experimentally measured value. This frequency match established a realistic value of the effective shear layer Young’s modulus of 16 Pascals. We feel this is the most accurate prediction of this shear layer modulus and fits with other estimates (Kondrachuk, 2001b). The second model incorporated only beam elements in the shear layer to represent hair cell bundle stiffness. The beam element stiffness’s were further distributed to represent their location on the neuroepithelial surface. Experimentally measured striola hair cell bundles mean stiffness values were used in the striolar region and the mean extrastriola hair cell bundles stiffness values were used in this region. The results from this second model indicated that hair cell bundle stiffness contributes approximately 40% to the overall stiffness of the shear layer– hair cell bundle complex. This analysis shows that high mass saccules, in general, achieve high gain at the sacrifice of frequency bandwidth. We propose the mechanism by which this can be achieved is through increase the otoconial layer mass. The theoretical difference in gain (deflection per acceleration) is shown for saccules with large otoconial layer mass relative to saccules and utricles with small otoconial layer mass. Also discussed is the necessity of these high mass saccules to increase their overall system shear layer stiffness. Undamped natural frequencies and mode shapes for these sensors are shown. PMID:25445820

  4. Predicting the Problem Behavior in Adolescents

    ERIC Educational Resources Information Center

    Karaman, Neslihan G.

    2013-01-01

    Problem statement: Problem behavior theory describes both protective factors and risk factors to explain adolescent problem behaviors, such as delinquency, alcohol use, and reckless driving. The theory holds that problem behaviors involving risky behavior are used by adolescents as a means to gain peer acceptance and respect. Problem behaviors…

  5. 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.…

  6. Predicting phase behavior in multicomponent mixtures.

    PubMed

    Jacobs, William M; Frenkel, Daan

    2013-07-14

    Mixtures with a large number of components can undergo phase transitions of a hybrid character, with both condensation and demixing contributions. We describe a robust Monte Carlo simulation method for calculating phase coexistence in multicomponent mixtures. We use this approach to study the phase behavior of lattice models of multicomponent mixtures with strongly varying pair interactions. Such a system can be thought of as a simplified model of the cytosol, with both specific and nonspecific interactions. We show that mapping a multicomponent mixture onto an approximately equivalent one-component system yields both upper and lower bounds on the maximum solute volume fraction of a stable, homogeneous phase. By following the minimum excess-free-energy path from the dilute phase free-energy minimum, we predict the difference in composition between the condensed and dilute phases at the boundary of the homogeneous phase. We find that this "direction" of phase separation rarely aligns with the dominant direction of density fluctuations in the dilute phase. We also show that demixing transitions tend to lower the maximum solute volume fraction at which the homogeneous phase is stable. By considering statistical ensembles of mixtures with random interactions, we show that the demixing contribution to phase separation is self-averaging and dependent only on the mean and variance of the distribution of interactions. PMID:23862930

  7. Accurate prediction model of bead geometry in crimping butt of the laser brazing using generalized regression neural network

    NASA Astrophysics Data System (ADS)

    Rong, Y. M.; Chang, Y.; Huang, Y.; Zhang, G. J.; Shao, X. Y.

    2015-12-01

    There are few researches that concentrate on the prediction of the bead geometry for laser brazing with crimping butt. This paper addressed the accurate prediction of the bead profile by developing a generalized regression neural network (GRNN) algorithm. Firstly GRNN model was developed and trained to decrease the prediction error that may be influenced by the sample size. Then the prediction accuracy was demonstrated by comparing with other articles and back propagation artificial neural network (BPNN) algorithm. Eventually the reliability and stability of GRNN model were discussed from the points of average relative error (ARE), mean square error (MSE) and root mean square error (RMSE), while the maximum ARE and MSE were 6.94% and 0.0303 that were clearly less than those (14.28% and 0.0832) predicted by BPNN. Obviously, it was proved that the prediction accuracy was improved at least 2 times, and the stability was also increased much more.

  8. Industrial Compositional Streamline Simulation for Efficient and Accurate Prediction of Gas Injection and WAG Processes

    SciTech Connect

    Margot Gerritsen

    2008-10-31

    Gas-injection processes are widely and increasingly used for enhanced oil recovery (EOR). In the United States, for example, EOR production by gas injection accounts for approximately 45% of total EOR production and has tripled since 1986. The understanding of the multiphase, multicomponent flow taking place in any displacement process is essential for successful design of gas-injection projects. Due to complex reservoir geometry, reservoir fluid properties and phase behavior, the design of accurate and efficient numerical simulations for the multiphase, multicomponent flow governing these processes is nontrivial. In this work, we developed, implemented and tested a streamline based solver for gas injection processes that is computationally very attractive: as compared to traditional Eulerian solvers in use by industry it computes solutions with a computational speed orders of magnitude higher and a comparable accuracy provided that cross-flow effects do not dominate. We contributed to the development of compositional streamline solvers in three significant ways: improvement of the overall framework allowing improved streamline coverage and partial streamline tracing, amongst others; parallelization of the streamline code, which significantly improves wall clock time; and development of new compositional solvers that can be implemented along streamlines as well as in existing Eulerian codes used by industry. We designed several novel ideas in the streamline framework. First, we developed an adaptive streamline coverage algorithm. Adding streamlines locally can reduce computational costs by concentrating computational efforts where needed, and reduce mapping errors. Adapting streamline coverage effectively controls mass balance errors that mostly result from the mapping from streamlines to pressure grid. We also introduced the concept of partial streamlines: streamlines that do not necessarily start and/or end at wells. This allows more efficient coverage and avoids

  9. Towards more accurate wind and solar power prediction by improving NWP model physics

    NASA Astrophysics Data System (ADS)

    Steiner, Andrea; Köhler, Carmen; von Schumann, Jonas; Ritter, Bodo

    2014-05-01

    The growing importance and successive expansion of renewable energies raise new challenges for decision makers, economists, transmission system operators, scientists and many more. In this interdisciplinary field, the role of Numerical Weather Prediction (NWP) is to reduce the errors and provide an a priori estimate of remaining uncertainties associated with the large share of weather-dependent power sources. For this purpose it is essential to optimize NWP model forecasts with respect to those prognostic variables which are relevant for wind and solar power plants. An improved weather forecast serves as the basis for a sophisticated power forecasts. Consequently, a well-timed energy trading on the stock market, and electrical grid stability can be maintained. The German Weather Service (DWD) currently is involved with two projects concerning research in the field of renewable energy, namely ORKA*) and EWeLiNE**). Whereas the latter is in collaboration with the Fraunhofer Institute (IWES), the project ORKA is led by energy & meteo systems (emsys). Both cooperate with German transmission system operators. The goal of the projects is to improve wind and photovoltaic (PV) power forecasts by combining optimized NWP and enhanced power forecast models. In this context, the German Weather Service aims to improve its model system, including the ensemble forecasting system, by working on data assimilation, model physics and statistical post processing. This presentation is focused on the identification of critical weather situations and the associated errors in the German regional NWP model COSMO-DE. First steps leading to improved physical parameterization schemes within the NWP-model are presented. Wind mast measurements reaching up to 200 m height above ground are used for the estimation of the (NWP) wind forecast error at heights relevant for wind energy plants. One particular problem is the daily cycle in wind speed. The transition from stable stratification during

  10. Hierarchy and predictability in spontaneous behavior

    NASA Astrophysics Data System (ADS)

    Berman, Gordon; Bialek, William; Shaevitz, Joshua

    2015-03-01

    Animals perform a complex array of behaviors, from changes in body posture to vocalizations to other dynamic outputs. Far from being a disordered collection of actions, however, there is thought to be an intrinsic structure to the set of behaviors and their temporal organization. This structure has often been hypothesized to be hierarchical, with certain behaviors grouped together into modules that interact with other modules at time scales that are long with respect to that of an individual behavior. There have been few measurements, however, showing that a particular animal's behavioral repertoire is organized hierarchically. This has largely resulted from an inability to measure the entirety of an animal's behavioral repertoire or even to definite precisely what a ``behavior'' is. In this talk, I will apply our novel method for mapping the behavioral space of animals to videos of freely-behaving fruit flies (D. melanogaster), showing that the organisms' behavioral repertoire consists of a hierarchically-organized set of stereotyped behaviors. This hierarchical patterning results in the emergence of long time scales of memory in the system, providing insight into the mechanisms of behavioral control and patterning.

  11. Combining Evolutionary Information and an Iterative Sampling Strategy for Accurate Protein Structure Prediction

    PubMed Central

    Braun, Tatjana; Koehler Leman, Julia; Lange, Oliver F.

    2015-01-01

    Recent work has shown that the accuracy of ab initio structure prediction can be significantly improved by integrating evolutionary information in form of intra-protein residue-residue contacts. Following this seminal result, much effort is put into the improvement of contact predictions. However, there is also a substantial need to develop structure prediction protocols tailored to the type of restraints gained by contact predictions. Here, we present a structure prediction protocol that combines evolutionary information with the resolution-adapted structural recombination approach of Rosetta, called RASREC. Compared to the classic Rosetta ab initio protocol, RASREC achieves improved sampling, better convergence and higher robustness against incorrect distance restraints, making it the ideal sampling strategy for the stated problem. To demonstrate the accuracy of our protocol, we tested the approach on a diverse set of 28 globular proteins. Our method is able to converge for 26 out of the 28 targets and improves the average TM-score of the entire benchmark set from 0.55 to 0.72 when compared to the top ranked models obtained by the EVFold web server using identical contact predictions. Using a smaller benchmark, we furthermore show that the prediction accuracy of our method is only slightly reduced when the contact prediction accuracy is comparatively low. This observation is of special interest for protein sequences that only have a limited number of homologs. PMID:26713437

  12. Accurate microRNA target prediction correlates with protein repression levels

    PubMed Central

    Maragkakis, Manolis; Alexiou, Panagiotis; Papadopoulos, Giorgio L; Reczko, Martin; Dalamagas, Theodore; Giannopoulos, George; Goumas, George; Koukis, Evangelos; Kourtis, Kornilios; Simossis, Victor A; Sethupathy, Praveen; Vergoulis, Thanasis; Koziris, Nectarios; Sellis, Timos; Tsanakas, Panagiotis; Hatzigeorgiou, Artemis G

    2009-01-01

    Background MicroRNAs are small endogenously expressed non-coding RNA molecules that regulate target gene expression through translation repression or messenger RNA degradation. MicroRNA regulation is performed through pairing of the microRNA to sites in the messenger RNA of protein coding genes. Since experimental identification of miRNA target genes poses difficulties, computational microRNA target prediction is one of the key means in deciphering the role of microRNAs in development and disease. Results DIANA-microT 3.0 is an algorithm for microRNA target prediction which is based on several parameters calculated individually for each microRNA and combines conserved and non-conserved microRNA recognition elements into a final prediction score, which correlates with protein production fold change. Specifically, for each predicted interaction the program reports a signal to noise ratio and a precision score which can be used as an indication of the false positive rate of the prediction. Conclusion Recently, several computational target prediction programs were benchmarked based on a set of microRNA target genes identified by the pSILAC method. In this assessment DIANA-microT 3.0 was found to achieve the highest precision among the most widely used microRNA target prediction programs reaching approximately 66%. The DIANA-microT 3.0 prediction results are available online in a user friendly web server at PMID:19765283

  13. A machine learning approach to the accurate prediction of multi-leaf collimator positional errors

    NASA Astrophysics Data System (ADS)

    Carlson, Joel N. K.; Park, Jong Min; Park, So-Yeon; In Park, Jong; Choi, Yunseok; Ye, Sung-Joon

    2016-03-01

    Discrepancies between planned and delivered movements of multi-leaf collimators (MLCs) are an important source of errors in dose distributions during radiotherapy. In this work we used machine learning techniques to train models to predict these discrepancies, assessed the accuracy of the model predictions, and examined the impact these errors have on quality assurance (QA) procedures and dosimetry. Predictive leaf motion parameters for the models were calculated from the plan files, such as leaf position and velocity, whether the leaf was moving towards or away from the isocenter of the MLC, and many others. Differences in positions between synchronized DICOM-RT planning files and DynaLog files reported during QA delivery were used as a target response for training of the models. The final model is capable of predicting MLC positions during delivery to a high degree of accuracy. For moving MLC leaves, predicted positions were shown to be significantly closer to delivered positions than were planned positions. By incorporating predicted positions into dose calculations in the TPS, increases were shown in gamma passing rates against measured dose distributions recorded during QA delivery. For instance, head and neck plans with 1%/2 mm gamma criteria had an average increase in passing rate of 4.17% (SD  =  1.54%). This indicates that the inclusion of predictions during dose calculation leads to a more realistic representation of plan delivery. To assess impact on the patient, dose volumetric histograms (DVH) using delivered positions were calculated for comparison with planned and predicted DVHs. In all cases, predicted dose volumetric parameters were in closer agreement to the delivered parameters than were the planned parameters, particularly for organs at risk on the periphery of the treatment area. By incorporating the predicted positions into the TPS, the treatment planner is given a more realistic view of the dose distribution as it will truly be

  14. A machine learning approach to the accurate prediction of multi-leaf collimator positional errors.

    PubMed

    Carlson, Joel N K; Park, Jong Min; Park, So-Yeon; Park, Jong In; Choi, Yunseok; Ye, Sung-Joon

    2016-03-21

    Discrepancies between planned and delivered movements of multi-leaf collimators (MLCs) are an important source of errors in dose distributions during radiotherapy. In this work we used machine learning techniques to train models to predict these discrepancies, assessed the accuracy of the model predictions, and examined the impact these errors have on quality assurance (QA) procedures and dosimetry. Predictive leaf motion parameters for the models were calculated from the plan files, such as leaf position and velocity, whether the leaf was moving towards or away from the isocenter of the MLC, and many others. Differences in positions between synchronized DICOM-RT planning files and DynaLog files reported during QA delivery were used as a target response for training of the models. The final model is capable of predicting MLC positions during delivery to a high degree of accuracy. For moving MLC leaves, predicted positions were shown to be significantly closer to delivered positions than were planned positions. By incorporating predicted positions into dose calculations in the TPS, increases were shown in gamma passing rates against measured dose distributions recorded during QA delivery. For instance, head and neck plans with 1%/2 mm gamma criteria had an average increase in passing rate of 4.17% (SD  =  1.54%). This indicates that the inclusion of predictions during dose calculation leads to a more realistic representation of plan delivery. To assess impact on the patient, dose volumetric histograms (DVH) using delivered positions were calculated for comparison with planned and predicted DVHs. In all cases, predicted dose volumetric parameters were in closer agreement to the delivered parameters than were the planned parameters, particularly for organs at risk on the periphery of the treatment area. By incorporating the predicted positions into the TPS, the treatment planner is given a more realistic view of the dose distribution as it will truly be

  15. LOCUSTRA: accurate prediction of local protein structure using a two-layer support vector machine approach.

    PubMed

    Zimmermann, Olav; Hansmann, Ulrich H E

    2008-09-01

    Constraint generation for 3d structure prediction and structure-based database searches benefit from fine-grained prediction of local structure. In this work, we present LOCUSTRA, a novel scheme for the multiclass prediction of local structure that uses two layers of support vector machines (SVM). Using a 16-letter structural alphabet from de Brevern et al. (Proteins: Struct., Funct., Bioinf. 2000, 41, 271-287), we assess its prediction ability for an independent test set of 222 proteins and compare our method to three-class secondary structure prediction and direct prediction of dihedral angles. The prediction accuracy is Q16=61.0% for the 16 classes of the structural alphabet and Q3=79.2% for a simple mapping to the three secondary classes helix, sheet, and coil. We achieve a mean phi(psi) error of 24.74 degrees (38.35 degrees) and a median RMSDA (root-mean-square deviation of the (dihedral) angles) per protein chain of 52.1 degrees. These results compare favorably with related approaches. The LOCUSTRA web server is freely available to researchers at http://www.fz-juelich.de/nic/cbb/service/service.php. PMID:18763837

  16. Sensor Data Fusion for Accurate Cloud Presence Prediction Using Dempster-Shafer Evidence Theory

    PubMed Central

    Li, Jiaming; Luo, Suhuai; Jin, Jesse S.

    2010-01-01

    Sensor data fusion technology can be used to best extract useful information from multiple sensor observations. It has been widely applied in various applications such as target tracking, surveillance, robot navigation, signal and image processing. This paper introduces a novel data fusion approach in a multiple radiation sensor environment using Dempster-Shafer evidence theory. The methodology is used to predict cloud presence based on the inputs of radiation sensors. Different radiation data have been used for the cloud prediction. The potential application areas of the algorithm include renewable power for virtual power station where the prediction of cloud presence is the most challenging issue for its photovoltaic output. The algorithm is validated by comparing the predicted cloud presence with the corresponding sunshine occurrence data that were recorded as the benchmark. Our experiments have indicated that comparing to the approaches using individual sensors, the proposed data fusion approach can increase correct rate of cloud prediction by ten percent, and decrease unknown rate of cloud prediction by twenty three percent. PMID:22163414

  17. DISPLAR: an accurate method for predicting DNA-binding sites on protein surfaces

    PubMed Central

    Tjong, Harianto; Zhou, Huan-Xiang

    2007-01-01

    Structural and physical properties of DNA provide important constraints on the binding sites formed on surfaces of DNA-targeting proteins. Characteristics of such binding sites may form the basis for predicting DNA-binding sites from the structures of proteins alone. Such an approach has been successfully developed for predicting protein–protein interface. Here this approach is adapted for predicting DNA-binding sites. We used a representative set of 264 protein–DNA complexes from the Protein Data Bank to analyze characteristics and to train and test a neural network predictor of DNA-binding sites. The input to the predictor consisted of PSI-blast sequence profiles and solvent accessibilities of each surface residue and 14 of its closest neighboring residues. Predicted DNA-contacting residues cover 60% of actual DNA-contacting residues and have an accuracy of 76%. This method significantly outperforms previous attempts of DNA-binding site predictions. Its application to the prion protein yielded a DNA-binding site that is consistent with recent NMR chemical shift perturbation data, suggesting that it can complement experimental techniques in characterizing protein–DNA interfaces. PMID:17284455

  18. Using complete genome comparisons to identify sequences whose presence accurately predicts clinically important phenotypes.

    PubMed

    Hall, Barry G; Cardenas, Heliodoro; Barlow, Miriam

    2013-01-01

    In clinical settings it is often important to know not just the identity of a microorganism, but also the danger posed by that particular strain. For instance, Escherichia coli can range from being a harmless commensal to being a very dangerous enterohemorrhagic (EHEC) strain. Determining pathogenic phenotypes can be both time consuming and expensive. Here we propose a simple, rapid, and inexpensive method of predicting pathogenic phenotypes on the basis of the presence or absence of short homologous DNA segments in an isolate. Our method compares completely sequenced genomes without the necessity of genome alignments in order to identify the presence or absence of the segments to produce an automatic alignment of the binary string that describes each genome. Analysis of the segment alignment allows identification of those segments whose presence strongly predicts a phenotype. Clinical application of the method requires nothing more that PCR amplification of each of the set of predictive segments. Here we apply the method to identifying EHEC strains of E. coli and to distinguishing E. coli from Shigella. We show in silico that with as few as 8 predictive sequences, if even three of those predictive sequences are amplified the probability of being EHEC or Shigella is >0.99. The method is thus very robust to the occasional amplification failure for spurious reasons. Experimentally, we apply the method to screening a set of 98 isolates to distinguishing E. coli from Shigella, and EHEC from non-EHEC E. coli strains and show that all isolates are correctly identified. PMID:23935901

  19. Empirical approaches to more accurately predict benthic-pelagic coupling in biogeochemical ocean models

    NASA Astrophysics Data System (ADS)

    Dale, Andy; Stolpovsky, Konstantin; Wallmann, Klaus

    2016-04-01

    The recycling and burial of biogenic material in the sea floor plays a key role in the regulation of ocean chemistry. Proper consideration of these processes in ocean biogeochemical models is becoming increasingly recognized as an important step in model validation and prediction. However, the rate of organic matter remineralization in sediments and the benthic flux of redox-sensitive elements are difficult to predict a priori. In this communication, examples of empirical benthic flux models that can be coupled to earth system models to predict sediment-water exchange in the open ocean are presented. Large uncertainties hindering further progress in this field include knowledge of the reactivity of organic carbon reaching the sediment, the importance of episodic variability in bottom water chemistry and particle rain rates (for both the deep-sea and margins) and the role of benthic fauna. How do we meet the challenge?

  20. An endometrial gene expression signature accurately predicts recurrent implantation failure after IVF

    PubMed Central

    Koot, Yvonne E. M.; van Hooff, Sander R.; Boomsma, Carolien M.; van Leenen, Dik; Groot Koerkamp, Marian J. A.; Goddijn, Mariëtte; Eijkemans, Marinus J. C.; Fauser, Bart C. J. M.; Holstege, Frank C. P.; Macklon, Nick S.

    2016-01-01

    The primary limiting factor for effective IVF treatment is successful embryo implantation. Recurrent implantation failure (RIF) is a condition whereby couples fail to achieve pregnancy despite consecutive embryo transfers. Here we describe the collection of gene expression profiles from mid-luteal phase endometrial biopsies (n = 115) from women experiencing RIF and healthy controls. Using a signature discovery set (n = 81) we identify a signature containing 303 genes predictive of RIF. Independent validation in 34 samples shows that the gene signature predicts RIF with 100% positive predictive value (PPV). The strength of the RIF associated expression signature also stratifies RIF patients into distinct groups with different subsequent implantation success rates. Exploration of the expression changes suggests that RIF is primarily associated with reduced cellular proliferation. The gene signature will be of value in counselling and guiding further treatment of women who fail to conceive upon IVF and suggests new avenues for developing intervention. PMID:26797113

  1. Accurate and inexpensive prediction of the color optical properties of anthocyanins in solution.

    PubMed

    Ge, Xiaochuan; Timrov, Iurii; Binnie, Simon; Biancardi, Alessandro; Calzolari, Arrigo; Baroni, Stefano

    2015-04-23

    The simulation of the color optical properties of molecular dyes in liquid solution requires the calculation of time evolution of the solute absorption spectra fluctuating in the solvent at finite temperature. Time-averaged spectra can be directly evaluated by combining ab initio Car-Parrinello molecular dynamics and time-dependent density functional theory calculations. The inclusion of hybrid exchange-correlation functionals, necessary for the prediction of the correct transition frequencies, prevents one from using these techniques for the simulation of the optical properties of large realistic systems. Here we present an alternative approach for the prediction of the color of natural dyes in solution with a low computational cost. We applied this approach to representative anthocyanin dyes: the excellent agreement between the simulated and the experimental colors makes this method a straightforward and inexpensive tool for the high-throughput prediction of colors of molecules in liquid solvents. PMID:25830823

  2. Accurate ab initio prediction of NMR chemical shifts of nucleic acids and nucleic acids/protein complexes

    PubMed Central

    Victora, Andrea; Möller, Heiko M.; Exner, Thomas E.

    2014-01-01

    NMR chemical shift predictions based on empirical methods are nowadays indispensable tools during resonance assignment and 3D structure calculation of proteins. However, owing to the very limited statistical data basis, such methods are still in their infancy in the field of nucleic acids, especially when non-canonical structures and nucleic acid complexes are considered. Here, we present an ab initio approach for predicting proton chemical shifts of arbitrary nucleic acid structures based on state-of-the-art fragment-based quantum chemical calculations. We tested our prediction method on a diverse set of nucleic acid structures including double-stranded DNA, hairpins, DNA/protein complexes and chemically-modified DNA. Overall, our quantum chemical calculations yield highly/very accurate predictions with mean absolute deviations of 0.3–0.6 ppm and correlation coefficients (r2) usually above 0.9. This will allow for identifying misassignments and validating 3D structures. Furthermore, our calculations reveal that chemical shifts of protons involved in hydrogen bonding are predicted significantly less accurately. This is in part caused by insufficient inclusion of solvation effects. However, it also points toward shortcomings of current force fields used for structure determination of nucleic acids. Our quantum chemical calculations could therefore provide input for force field optimization. PMID:25404135

  3. Accurate ab initio prediction of NMR chemical shifts of nucleic acids and nucleic acids/protein complexes.

    PubMed

    Victora, Andrea; Möller, Heiko M; Exner, Thomas E

    2014-12-16

    NMR chemical shift predictions based on empirical methods are nowadays indispensable tools during resonance assignment and 3D structure calculation of proteins. However, owing to the very limited statistical data basis, such methods are still in their infancy in the field of nucleic acids, especially when non-canonical structures and nucleic acid complexes are considered. Here, we present an ab initio approach for predicting proton chemical shifts of arbitrary nucleic acid structures based on state-of-the-art fragment-based quantum chemical calculations. We tested our prediction method on a diverse set of nucleic acid structures including double-stranded DNA, hairpins, DNA/protein complexes and chemically-modified DNA. Overall, our quantum chemical calculations yield highly/very accurate predictions with mean absolute deviations of 0.3-0.6 ppm and correlation coefficients (r(2)) usually above 0.9. This will allow for identifying misassignments and validating 3D structures. Furthermore, our calculations reveal that chemical shifts of protons involved in hydrogen bonding are predicted significantly less accurately. This is in part caused by insufficient inclusion of solvation effects. However, it also points toward shortcomings of current force fields used for structure determination of nucleic acids. Our quantum chemical calculations could therefore provide input for force field optimization. PMID:25404135

  4. An Accurate, Clinically Feasible Multi-Gene Expression Assay for Predicting Metastasis in Uveal Melanoma

    PubMed Central

    Onken, Michael D.; Worley, Lori A.; Tuscan, Meghan D.; Harbour, J. William

    2010-01-01

    Uveal (ocular) melanoma is an aggressive cancer that often forms undetectable micrometastases before diagnosis of the primary tumor. These micrometastases later multiply to generate metastatic tumors that are resistant to therapy and are uniformly fatal. We have previously identified a gene expression profile derived from the primary tumor that is extremely accurate for identifying patients at high risk of metastatic disease. Development of a practical clinically feasible platform for analyzing this expression profile would benefit high-risk patients through intensified metastatic surveillance, earlier intervention for metastasis, and stratification for entry into clinical trials of adjuvant therapy. Here, we migrate the expression profile from a hybridization-based microarray platform to a robust, clinically practical, PCR-based 15-gene assay comprising 12 discriminating genes and three endogenous control genes. We analyze the technical performance of the assay in a prospective study of 609 tumor samples, including 421 samples sent from distant locations. We show that the assay can be performed accurately on fine needle aspirate biopsy samples, even when the quantity of RNA is below detectable limits. Preliminary outcome data from the prospective study affirm the prognostic accuracy of the assay. This prognostic assay provides an important addition to the armamentarium for managing patients with uveal melanoma, and it provides a proof of principle for the development of similar assays for other cancers. PMID:20413675

  5. Accurate Prediction of Transposon-Derived piRNAs by Integrating Various Sequential and Physicochemical Features

    PubMed Central

    Luo, Longqiang; Li, Dingfang; Zhang, Wen; Tu, Shikui; Zhu, Xiaopeng; Tian, Gang

    2016-01-01

    Background Piwi-interacting RNA (piRNA) is the largest class of small non-coding RNA molecules. The transposon-derived piRNA prediction can enrich the research contents of small ncRNAs as well as help to further understand generation mechanism of gamete. Methods In this paper, we attempt to differentiate transposon-derived piRNAs from non-piRNAs based on their sequential and physicochemical features by using machine learning methods. We explore six sequence-derived features, i.e. spectrum profile, mismatch profile, subsequence profile, position-specific scoring matrix, pseudo dinucleotide composition and local structure-sequence triplet elements, and systematically evaluate their performances for transposon-derived piRNA prediction. Finally, we consider two approaches: direct combination and ensemble learning to integrate useful features and achieve high-accuracy prediction models. Results We construct three datasets, covering three species: Human, Mouse and Drosophila, and evaluate the performances of prediction models by 10-fold cross validation. In the computational experiments, direct combination models achieve AUC of 0.917, 0.922 and 0.992 on Human, Mouse and Drosophila, respectively; ensemble learning models achieve AUC of 0.922, 0.926 and 0.994 on the three datasets. Conclusions Compared with other state-of-the-art methods, our methods can lead to better performances. In conclusion, the proposed methods are promising for the transposon-derived piRNA prediction. The source codes and datasets are available in S1 File. PMID:27074043

  6. Attentional bias toward safety predicts safety behaviors.

    PubMed

    Xu, Yaoshan; Li, Yongjuan; Wang, Guangxi; Yuan, Xiao; Ding, Weidong; Shen, Zhongxiang

    2014-10-01

    Safety studies have primarily focused on how explicit processes and measures affect safety behavior and subsequent accidents and injuries. Recently, safety researchers have paid greater attention to the role of implicit processes. Our research focuses on the role of attentional bias toward safety (ABS) in workplace safety. ABS is a basic, early-stage cognitive process involving the automatic and selective allocation of attentional resources toward safety cues, which reflect the implicit motivational state of employees regarding safety goal. In this study, we used two reaction time-based paradigms to measure the ABS of employees in three studies: two modified Stroop tasks (Studies 1 and 2) and a visual dot-probe task (Study 3). Results revealed that employees with better safety behavior showed significant ABS (Study 2), and greater ABS than employees with poorer safety behavior (Studies 1 and 2). Moreover, ABS was positively associated with the perceived safety climate and safety motivation of employees, both of which mediate the effect of ABS on safety behavior (Study 3). These results contributed to a deeper understanding of how early-stage automatic perceptual processing affects safety behavior. The practical implications of these results were also discussed. PMID:24922613

  7. Viewing men's faces does not lead to accurate predictions of trustworthiness

    PubMed Central

    Efferson, Charles; Vogt, Sonja

    2013-01-01

    The evolution of cooperation requires some mechanism that reduces the risk of exploitation for cooperative individuals. Recent studies have shown that men with wide faces are anti-social, and they are perceived that way by others. This suggests that people could use facial width to identify anti-social men and thus limit the risk of exploitation. To see if people can make accurate inferences like this, we conducted a two-part experiment. First, males played a sequential social dilemma, and we took photographs of their faces. Second, raters then viewed these photographs and guessed how second movers behaved. Raters achieved significant accuracy by guessing that second movers exhibited reciprocal behaviour. Raters were not able to use the photographs to further improve accuracy. Indeed, some raters used the photographs to their detriment; they could have potentially achieved greater accuracy and earned more money by ignoring the photographs and assuming all second movers reciprocate. PMID:23308340

  8. Accurate prediction of the ammonia probes of a variable proton-to-electron mass ratio

    NASA Astrophysics Data System (ADS)

    Owens, A.; Yurchenko, S. N.; Thiel, W.; Špirko, V.

    2015-07-01

    A comprehensive study of the mass sensitivity of the vibration-rotation-inversion transitions of 14NH3, 15NH3, 14ND3 and 15ND3 is carried out variationally using the TROVE approach. Variational calculations are robust and accurate, offering a new way to compute sensitivity coefficients. Particular attention is paid to the Δk = ±3 transitions between the accidentally coinciding rotation-inversion energy levels of the ν2 = 0+, 0-, 1+ and 1- states, and the inversion transitions in the ν4 = 1 state affected by the `giant' l-type doubling effect. These transitions exhibit highly anomalous sensitivities, thus appearing as promising probes of a possible cosmological variation of the proton-to-electron mass ratio μ. Moreover, a simultaneous comparison of the calculated sensitivities reveals a sizeable isotopic dependence which could aid an exclusive ammonia detection.

  9. Accurate, conformation-dependent predictions of solvent effects on protein ionization constants

    PubMed Central

    Barth, P.; Alber, T.; Harbury, P. B.

    2007-01-01

    Predicting how aqueous solvent modulates the conformational transitions and influences the pKa values that regulate the biological functions of biomolecules remains an unsolved challenge. To address this problem, we developed FDPB_MF, a rotamer repacking method that exhaustively samples side chain conformational space and rigorously calculates multibody protein–solvent interactions. FDPB_MF predicts the effects on pKa values of various solvent exposures, large ionic strength variations, strong energetic couplings, structural reorganizations and sequence mutations. The method achieves high accuracy, with root mean square deviations within 0.3 pH unit of the experimental values measured for turkey ovomucoid third domain, hen lysozyme, Bacillus circulans xylanase, and human and Escherichia coli thioredoxins. FDPB_MF provides a faithful, quantitative assessment of electrostatic interactions in biological macromolecules. PMID:17360348

  10. FastRNABindR: Fast and Accurate Prediction of Protein-RNA Interface Residues.

    PubMed

    El-Manzalawy, Yasser; Abbas, Mostafa; Malluhi, Qutaibah; Honavar, Vasant

    2016-01-01

    A wide range of biological processes, including regulation of gene expression, protein synthesis, and replication and assembly of many viruses are mediated by RNA-protein interactions. However, experimental determination of the structures of protein-RNA complexes is expensive and technically challenging. Hence, a number of computational tools have been developed for predicting protein-RNA interfaces. Some of the state-of-the-art protein-RNA interface predictors rely on position-specific scoring matrix (PSSM)-based encoding of the protein sequences. The computational efforts needed for generating PSSMs severely limits the practical utility of protein-RNA interface prediction servers. In this work, we experiment with two approaches, random sampling and sequence similarity reduction, for extracting a representative reference database of protein sequences from more than 50 million protein sequences in UniRef100. Our results suggest that random sampled databases produce better PSSM profiles (in terms of the number of hits used to generate the profile and the distance of the generated profile to the corresponding profile generated using the entire UniRef100 data as well as the accuracy of the machine learning classifier trained using these profiles). Based on our results, we developed FastRNABindR, an improved version of RNABindR for predicting protein-RNA interface residues using PSSM profiles generated using 1% of the UniRef100 sequences sampled uniformly at random. To the best of our knowledge, FastRNABindR is the only protein-RNA interface residue prediction online server that requires generation of PSSM profiles for query sequences and accepts hundreds of protein sequences per submission. Our approach for determining the optimal BLAST database for a protein-RNA interface residue classification task has the potential of substantially speeding up, and hence increasing the practical utility of, other amino acid sequence based predictors of protein-protein and protein

  11. FastRNABindR: Fast and Accurate Prediction of Protein-RNA Interface Residues

    PubMed Central

    EL-Manzalawy, Yasser; Abbas, Mostafa; Malluhi, Qutaibah; Honavar, Vasant

    2016-01-01

    A wide range of biological processes, including regulation of gene expression, protein synthesis, and replication and assembly of many viruses are mediated by RNA-protein interactions. However, experimental determination of the structures of protein-RNA complexes is expensive and technically challenging. Hence, a number of computational tools have been developed for predicting protein-RNA interfaces. Some of the state-of-the-art protein-RNA interface predictors rely on position-specific scoring matrix (PSSM)-based encoding of the protein sequences. The computational efforts needed for generating PSSMs severely limits the practical utility of protein-RNA interface prediction servers. In this work, we experiment with two approaches, random sampling and sequence similarity reduction, for extracting a representative reference database of protein sequences from more than 50 million protein sequences in UniRef100. Our results suggest that random sampled databases produce better PSSM profiles (in terms of the number of hits used to generate the profile and the distance of the generated profile to the corresponding profile generated using the entire UniRef100 data as well as the accuracy of the machine learning classifier trained using these profiles). Based on our results, we developed FastRNABindR, an improved version of RNABindR for predicting protein-RNA interface residues using PSSM profiles generated using 1% of the UniRef100 sequences sampled uniformly at random. To the best of our knowledge, FastRNABindR is the only protein-RNA interface residue prediction online server that requires generation of PSSM profiles for query sequences and accepts hundreds of protein sequences per submission. Our approach for determining the optimal BLAST database for a protein-RNA interface residue classification task has the potential of substantially speeding up, and hence increasing the practical utility of, other amino acid sequence based predictors of protein-protein and protein

  12. Accurate Fault Prediction of BlueGene/P RAS Logs Via Geometric Reduction

    SciTech Connect

    Jones, Terry R; Kirby, Michael; Ladd, Joshua S; Dreisigmeyer, David; Thompson, Joshua

    2010-01-01

    The authors are building two algorithms for fault prediction using raw system-log data. This work is preliminary, and has only been applied to a limited dataset, however the results seem promising. The conclusions are that: (1) obtaining useful data from RAS-logs is challenging; (2) extracting concentrated information improves efficiency and accuracy; and (3) function evaluation algorithms are fast and lend well to scaling.

  13. Accurate single-sequence prediction of solvent accessible surface area using local and global features

    PubMed Central

    Faraggi, Eshel; Zhou, Yaoqi; Kloczkowski, Andrzej

    2014-01-01

    We present a new approach for predicting the Accessible Surface Area (ASA) using a General Neural Network (GENN). The novelty of the new approach lies in not using residue mutation profiles generated by multiple sequence alignments as descriptive inputs. Instead we use solely sequential window information and global features such as single-residue and two-residue compositions of the chain. The resulting predictor is both highly more efficient than sequence alignment based predictors and of comparable accuracy to them. Introduction of the global inputs significantly helps achieve this comparable accuracy. The predictor, termed ASAquick, is tested on predicting the ASA of globular proteins and found to perform similarly well for so-called easy and hard cases indicating generalizability and possible usability for de-novo protein structure prediction. The source code and a Linux executables for GENN and ASAquick are available from Research and Information Systems at http://mamiris.com, from the SPARKS Lab at http://sparks-lab.org, and from the Battelle Center for Mathematical Medicine at http://mathmed.org. PMID:25204636

  14. Robust and Accurate Modeling Approaches for Migraine Per-Patient Prediction from Ambulatory Data.

    PubMed

    Pagán, Josué; De Orbe, M Irene; Gago, Ana; Sobrado, Mónica; Risco-Martín, José L; Mora, J Vivancos; Moya, José M; Ayala, José L

    2015-01-01

    Migraine is one of the most wide-spread neurological disorders, and its medical treatment represents a high percentage of the costs of health systems. In some patients, characteristic symptoms that precede the headache appear. However, they are nonspecific, and their prediction horizon is unknown and pretty variable; hence, these symptoms are almost useless for prediction, and they are not useful to advance the intake of drugs to be effective and neutralize the pain. To solve this problem, this paper sets up a realistic monitoring scenario where hemodynamic variables from real patients are monitored in ambulatory conditions with a wireless body sensor network (WBSN). The acquired data are used to evaluate the predictive capabilities and robustness against noise and failures in sensors of several modeling approaches. The obtained results encourage the development of per-patient models based on state-space models (N4SID) that are capable of providing average forecast windows of 47 min and a low rate of false positives. PMID:26134103

  15. Revisiting the blind tests in crystal structure prediction: accurate energy ranking of molecular crystals.

    PubMed

    Asmadi, Aldi; Neumann, Marcus A; Kendrick, John; Girard, Pascale; Perrin, Marc-Antoine; Leusen, Frank J J

    2009-12-24

    In the 2007 blind test of crystal structure prediction hosted by the Cambridge Crystallographic Data Centre (CCDC), a hybrid DFT/MM method correctly ranked each of the four experimental structures as having the lowest lattice energy of all the crystal structures predicted for each molecule. The work presented here further validates this hybrid method by optimizing the crystal structures (experimental and submitted) of the first three CCDC blind tests held in 1999, 2001, and 2004. Except for the crystal structures of compound IX, all structures were reminimized and ranked according to their lattice energies. The hybrid method computes the lattice energy of a crystal structure as the sum of the DFT total energy and a van der Waals (dispersion) energy correction. Considering all four blind tests, the crystal structure with the lowest lattice energy corresponds to the experimentally observed structure for 12 out of 14 molecules. Moreover, good geometrical agreement is observed between the structures determined by the hybrid method and those measured experimentally. In comparison with the correct submissions made by the blind test participants, all hybrid optimized crystal structures (apart from compound II) have the smallest calculated root mean squared deviations from the experimentally observed structures. It is predicted that a new polymorph of compound V exists under pressure. PMID:19950907

  16. Accurate single-sequence prediction of solvent accessible surface area using local and global features.

    PubMed

    Faraggi, Eshel; Zhou, Yaoqi; Kloczkowski, Andrzej

    2014-11-01

    We present a new approach for predicting the Accessible Surface Area (ASA) using a General Neural Network (GENN). The novelty of the new approach lies in not using residue mutation profiles generated by multiple sequence alignments as descriptive inputs. Instead we use solely sequential window information and global features such as single-residue and two-residue compositions of the chain. The resulting predictor is both highly more efficient than sequence alignment-based predictors and of comparable accuracy to them. Introduction of the global inputs significantly helps achieve this comparable accuracy. The predictor, termed ASAquick, is tested on predicting the ASA of globular proteins and found to perform similarly well for so-called easy and hard cases indicating generalizability and possible usability for de-novo protein structure prediction. The source code and a Linux executables for GENN and ASAquick are available from Research and Information Systems at http://mamiris.com, from the SPARKS Lab at http://sparks-lab.org, and from the Battelle Center for Mathematical Medicine at http://mathmed.org. PMID:25204636

  17. Accurate Prediction of Drug-Induced Liver Injury Using Stem Cell-Derived Populations

    PubMed Central

    Szkolnicka, Dagmara; Farnworth, Sarah L.; Lucendo-Villarin, Baltasar; Storck, Christopher; Zhou, Wenli; Iredale, John P.; Flint, Oliver

    2014-01-01

    Despite major progress in the knowledge and management of human liver injury, there are millions of people suffering from chronic liver disease. Currently, the only cure for end-stage liver disease is orthotopic liver transplantation; however, this approach is severely limited by organ donation. Alternative approaches to restoring liver function have therefore been pursued, including the use of somatic and stem cell populations. Although such approaches are essential in developing scalable treatments, there is also an imperative to develop predictive human systems that more effectively study and/or prevent the onset of liver disease and decompensated organ function. We used a renewable human stem cell resource, from defined genetic backgrounds, and drove them through developmental intermediates to yield highly active, drug-inducible, and predictive human hepatocyte populations. Most importantly, stem cell-derived hepatocytes displayed equivalence to primary adult hepatocytes, following incubation with known hepatotoxins. In summary, we have developed a serum-free, scalable, and shippable cell-based model that faithfully predicts the potential for human liver injury. Such a resource has direct application in human modeling and, in the future, could play an important role in developing renewable cell-based therapies. PMID:24375539

  18. Robust and Accurate Modeling Approaches for Migraine Per-Patient Prediction from Ambulatory Data

    PubMed Central

    Pagán, Josué; Irene De Orbe, M.; Gago, Ana; Sobrado, Mónica; Risco-Martín, José L.; Vivancos Mora, J.; Moya, José M.; Ayala, José L.

    2015-01-01

    Migraine is one of the most wide-spread neurological disorders, and its medical treatment represents a high percentage of the costs of health systems. In some patients, characteristic symptoms that precede the headache appear. However, they are nonspecific, and their prediction horizon is unknown and pretty variable; hence, these symptoms are almost useless for prediction, and they are not useful to advance the intake of drugs to be effective and neutralize the pain. To solve this problem, this paper sets up a realistic monitoring scenario where hemodynamic variables from real patients are monitored in ambulatory conditions with a wireless body sensor network (WBSN). The acquired data are used to evaluate the predictive capabilities and robustness against noise and failures in sensors of several modeling approaches. The obtained results encourage the development of per-patient models based on state-space models (N4SID) that are capable of providing average forecast windows of 47 min and a low rate of false positives. PMID:26134103

  19. Accurate structure prediction of peptide–MHC complexes for identifying highly immunogenic antigens

    SciTech Connect

    Park, Min-Sun; Park, Sung Yong; Miller, Keith R.; Collins, Edward J.; Lee, Ha Youn

    2013-11-01

    Designing an optimal HIV-1 vaccine faces the challenge of identifying antigens that induce a broad immune capacity. One factor to control the breadth of T cell responses is the surface morphology of a peptide–MHC complex. Here, we present an in silico protocol for predicting peptide–MHC structure. A robust signature of a conformational transition was identified during all-atom molecular dynamics, which results in a model with high accuracy. A large test set was used in constructing our protocol and we went another step further using a blind test with a wild-type peptide and two highly immunogenic mutants, which predicted substantial conformational changes in both mutants. The center residues at position five of the analogs were configured to be accessible to solvent, forming a prominent surface, while the residue of the wild-type peptide was to point laterally toward the side of the binding cleft. We then experimentally determined the structures of the blind test set, using high resolution of X-ray crystallography, which verified predicted conformational changes. Our observation strongly supports a positive association of the surface morphology of a peptide–MHC complex to its immunogenicity. Our study offers the prospect of enhancing immunogenicity of vaccines by identifying MHC binding immunogens.

  20. 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. PMID:24375512

  1. The impact of degree of hearing loss on auditory brainstem response predictions of behavioral thresholds

    PubMed Central

    McCreery, Ryan W.; Kaminski, Jan; Beauchaine, Kathryn; Lenzen, Natalie; Simms, Kendell; Gorga, Michael P.

    2014-01-01

    Objectives: Diagnosis of hearing loss and prescription of amplification for infants and young children require accurate estimates of ear- and frequency-specific behavioral thresholds based on auditory brainstem response measurements. Although the overall relationship between ABR and behavioral thresholds has been demonstrated, the agreement is imperfect, and the accuracy of predictions of behavioral threshold based on ABR may depend on degree of hearing loss. Behavioral thresholds are lower than ABR thresholds, at least in part due to differences in calibration interacting with the effects of temporal integration, which are manifest in behavioral measurements but not ABR measurements and depend on behavioral threshold. Listeners with sensory hearing loss exhibit reduced or absent temporal integration, which could impact the relationship between ABR and behavioral thresholds as degree of hearing loss increases. The current study evaluated the relationship between ABR and behavioral thresholds in infants and children over a range of hearing thresholds, and tested an approach for adjusting the correction factor based on degree of hearing loss as estimated by ABR measurements. Design: A retrospective review of clinical records was completed for 309 ears of 177 children with hearing thresholds ranging from normal to profound hearing loss and for whom both ABR and behavioral thresholds were available. Children were required to have the same middle-ear status at both evaluations. The relationship between ABR and behavioral thresholds was examined. Factors that potentially could affect the relationship between ABR and behavioral thresholds were analyzed, including degree of hearing loss observed on the ABR, behavioral test method (visual reinforcement, conditioned play or conventional audiometry), the length of time between ABR and behavioral assessments, and clinician-reported reliability of the behavioral assessment. Predictive accuracy of a correction factor based on

  2. Parental Behavior, TV Habits, IQ Predict Aggression.

    ERIC Educational Resources Information Center

    Greenberg, J.

    1983-01-01

    Highlights a longitudinal study on key factors in the metamorphosis of childhood aggression into adult crime in more than 400 males/females. Results (which began with study of 875 third graders in 1960) indicate that aggressive youngsters at age eight have much higher rates of criminal/violent behavior at age 30. (JN)

  3. Predicting community structure of ground-foraging ant assemblages with Markov models of behavioral dominance.

    PubMed

    Wittman, Sarah E; Gotelli, Nicholas J

    2011-05-01

    Although interference competition is a conspicuous component of many animal communities, it is still uncertain whether the competitive ability of a species determines its relative abundance and patterns of association with other species. We used replicated arena tests to quantify behavioral dominance of eight common species of co-occurring ground-foraging ants in the Siskiyou Mountains of southern Oregon. We found that behavior recorded in laboratory assays was an accurate representation of a colony's ability to monopolize resources in the field. We used interaction frequencies from the behavioral tests to estimate transition probabilities in a simple Markov chain model to predict patterns of relative abundance in a metacommunity that is dominated by behavioral interactions. We also tested whether behavioral interactions between each pair of species could be used to predict patterns of species co-occurrence. We found that the Markov model did not accurately predict patterns of observed relative abundance on either the local or the regional scale. However, we did detect a significant negative correlation at the local scale in which behaviorally dominant species occupied relatively few baits. Pairwise behavioral data also did not predict species co-occurrence in any site. Although interference competition is a conspicuous process in ant communities, our results suggest that it may not contribute much to patterns of relative abundance and species co-occurrence in the system studied here. However, the negative correlation between behavioral dominance and bait occupancy at the local scale suggests that competition-colonization trade-offs may be important in resource acquisition and persistence of behaviorally subordinate species. PMID:20978797

  4. More accurate predictions with transonic Navier-Stokes methods through improved turbulence modeling

    NASA Technical Reports Server (NTRS)

    Johnson, Dennis A.

    1989-01-01

    Significant improvements in predictive accuracies for off-design conditions are achievable through better turbulence modeling; and, without necessarily adding any significant complication to the numerics. One well established fact about turbulence is it is slow to respond to changes in the mean strain field. With the 'equilibrium' algebraic turbulence models no attempt is made to model this characteristic and as a consequence these turbulence models exaggerate the turbulent boundary layer's ability to produce turbulent Reynolds shear stresses in regions of adverse pressure gradient. As a consequence, too little momentum loss within the boundary layer is predicted in the region of the shock wave and along the aft part of the airfoil where the surface pressure undergoes further increases. Recently, a 'nonequilibrium' algebraic turbulence model was formulated which attempts to capture this important characteristic of turbulence. This 'nonequilibrium' algebraic model employs an ordinary differential equation to model the slow response of the turbulence to changes in local flow conditions. In its original form, there was some question as to whether this 'nonequilibrium' model performed as well as the 'equilibrium' models for weak interaction cases. However, this turbulence model has since been further improved wherein it now appears that this turbulence model performs at least as well as the 'equilibrium' models for weak interaction cases and for strong interaction cases represents a very significant improvement. The performance of this turbulence model relative to popular 'equilibrium' models is illustrated for three airfoil test cases of the 1987 AIAA Viscous Transonic Airfoil Workshop, Reno, Nevada. A form of this 'nonequilibrium' turbulence model is currently being applied to wing flows for which similar improvements in predictive accuracy are being realized.

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

    NASA Astrophysics Data System (ADS)

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

    2016-02-01

    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.

  6. Accurate prediction of lattice energies and structures of molecular crystals with molecular quantum chemistry methods.

    PubMed

    Fang, Tao; Li, Wei; Gu, Fangwei; Li, Shuhua

    2015-01-13

    We extend the generalized energy-based fragmentation (GEBF) approach to molecular crystals under periodic boundary conditions (PBC), and we demonstrate the performance of the method for a variety of molecular crystals. With this approach, the lattice energy of a molecular crystal can be obtained from the energies of a series of embedded subsystems, which can be computed with existing advanced molecular quantum chemistry methods. The use of the field compensation method allows the method to take long-range electrostatic interaction of the infinite crystal environment into account and make the method almost translationally invariant. The computational cost of the present method scales linearly with the number of molecules in the unit cell. Illustrative applications demonstrate that the PBC-GEBF method with explicitly correlated quantum chemistry methods is capable of providing accurate descriptions on the lattice energies and structures for various types of molecular crystals. In addition, this approach can be employed to quantify the contributions of various intermolecular interactions to the theoretical lattice energy. Such qualitative understanding is very useful for rational design of molecular crystals. PMID:26574207

  7. DisoMCS: Accurately Predicting Protein Intrinsically Disordered Regions Using a Multi-Class Conservative Score Approach

    PubMed Central

    Wang, Zhiheng; Yang, Qianqian; Li, Tonghua; Cong, Peisheng

    2015-01-01

    The precise prediction of protein intrinsically disordered regions, which play a crucial role in biological procedures, is a necessary prerequisite to further the understanding of the principles and mechanisms of protein function. Here, we propose a novel predictor, DisoMCS, which is a more accurate predictor of protein intrinsically disordered regions. The DisoMCS bases on an original multi-class conservative score (MCS) obtained by sequence-order/disorder alignment. Initially, near-disorder regions are defined on fragments located at both the terminus of an ordered region connecting a disordered region. Then the multi-class conservative score is generated by sequence alignment against a known structure database and represented as order, near-disorder and disorder conservative scores. The MCS of each amino acid has three elements: order, near-disorder and disorder profiles. Finally, the MCS is exploited as features to identify disordered regions in sequences. DisoMCS utilizes a non-redundant data set as the training set, MCS and predicted secondary structure as features, and a conditional random field as the classification algorithm. In predicted near-disorder regions a residue is determined as an order or a disorder according to the optimized decision threshold. DisoMCS was evaluated by cross-validation, large-scale prediction, independent tests and CASP (Critical Assessment of Techniques for Protein Structure Prediction) tests. All results confirmed that DisoMCS was very competitive in terms of accuracy of prediction when compared with well-established publicly available disordered region predictors. It also indicated our approach was more accurate when a query has higher homologous with the knowledge database. Availability The DisoMCS is available at http://cal.tongji.edu.cn/disorder/. PMID:26090958

  8. Size-extensivity-corrected multireference configuration interaction schemes to accurately predict bond dissociation energies of oxygenated hydrocarbons

    SciTech Connect

    Oyeyemi, Victor B.; Krisiloff, David B.; Keith, John A.; Libisch, Florian; Pavone, Michele; Carter, Emily A.

    2014-01-28

    Oxygenated hydrocarbons play important roles in combustion science as renewable fuels and additives, but many details about their combustion chemistry remain poorly understood. Although many methods exist for computing accurate electronic energies of molecules at equilibrium geometries, a consistent description of entire combustion reaction potential energy surfaces (PESs) requires multireference correlated wavefunction theories. Here we use bond dissociation energies (BDEs) as a foundational metric to benchmark methods based on multireference configuration interaction (MRCI) for several classes of oxygenated compounds (alcohols, aldehydes, carboxylic acids, and methyl esters). We compare results from multireference singles and doubles configuration interaction to those utilizing a posteriori and a priori size-extensivity corrections, benchmarked against experiment and coupled cluster theory. We demonstrate that size-extensivity corrections are necessary for chemically accurate BDE predictions even in relatively small molecules and furnish examples of unphysical BDE predictions resulting from using too-small orbital active spaces. We also outline the specific challenges in using MRCI methods for carbonyl-containing compounds. The resulting complete basis set extrapolated, size-extensivity-corrected MRCI scheme produces BDEs generally accurate to within 1 kcal/mol, laying the foundation for this scheme's use on larger molecules and for more complex regions of combustion PESs.

  9. Accurate predictions of dielectrophoretic force and torque on particles with strong mutual field, particle, and wall interactions

    NASA Astrophysics Data System (ADS)

    Liu, Qianlong; Reifsnider, Kenneth

    2012-11-01

    The basis of dielectrophoresis (DEP) is the prediction of the force and torque on particles. The classical approach to the prediction is based on the effective moment method, which, however, is an approximate approach, assumes infinitesimal particles. Therefore, it is well-known that for finite-sized particles, the DEP approximation is inaccurate as the mutual field, particle, wall interactions become strong, a situation presently attracting extensive research for practical significant applications. In the present talk, we provide accurate calculations of the force and torque on the particles from first principles, by directly resolving the local geometry and properties and accurately accounting for the mutual interactions for finite-sized particles with both dielectric polarization and conduction in a sinusoidally steady-state electric field. Since the approach has a significant advantage, compared to other numerical methods, to efficiently simulate many closely packed particles, it provides an important, unique, and accurate technique to investigate complex DEP phenomena, for example heterogeneous mixtures containing particle chains, nanoparticle assembly, biological cells, non-spherical effects, etc. This study was supported by the Department of Energy under funding for an EFRC (the HeteroFoaM Center), grant no. DE-SC0001061.

  10. Size-extensivity-corrected multireference configuration interaction schemes to accurately predict bond dissociation energies of oxygenated hydrocarbons.

    PubMed

    Oyeyemi, Victor B; Krisiloff, David B; Keith, John A; Libisch, Florian; Pavone, Michele; Carter, Emily A

    2014-01-28

    Oxygenated hydrocarbons play important roles in combustion science as renewable fuels and additives, but many details about their combustion chemistry remain poorly understood. Although many methods exist for computing accurate electronic energies of molecules at equilibrium geometries, a consistent description of entire combustion reaction potential energy surfaces (PESs) requires multireference correlated wavefunction theories. Here we use bond dissociation energies (BDEs) as a foundational metric to benchmark methods based on multireference configuration interaction (MRCI) for several classes of oxygenated compounds (alcohols, aldehydes, carboxylic acids, and methyl esters). We compare results from multireference singles and doubles configuration interaction to those utilizing a posteriori and a priori size-extensivity corrections, benchmarked against experiment and coupled cluster theory. We demonstrate that size-extensivity corrections are necessary for chemically accurate BDE predictions even in relatively small molecules and furnish examples of unphysical BDE predictions resulting from using too-small orbital active spaces. We also outline the specific challenges in using MRCI methods for carbonyl-containing compounds. The resulting complete basis set extrapolated, size-extensivity-corrected MRCI scheme produces BDEs generally accurate to within 1 kcal/mol, laying the foundation for this scheme's use on larger molecules and for more complex regions of combustion PESs. PMID:25669533

  11. Size-extensivity-corrected multireference configuration interaction schemes to accurately predict bond dissociation energies of oxygenated hydrocarbons

    NASA Astrophysics Data System (ADS)

    Oyeyemi, Victor B.; Krisiloff, David B.; Keith, John A.; Libisch, Florian; Pavone, Michele; Carter, Emily A.

    2014-01-01

    Oxygenated hydrocarbons play important roles in combustion science as renewable fuels and additives, but many details about their combustion chemistry remain poorly understood. Although many methods exist for computing accurate electronic energies of molecules at equilibrium geometries, a consistent description of entire combustion reaction potential energy surfaces (PESs) requires multireference correlated wavefunction theories. Here we use bond dissociation energies (BDEs) as a foundational metric to benchmark methods based on multireference configuration interaction (MRCI) for several classes of oxygenated compounds (alcohols, aldehydes, carboxylic acids, and methyl esters). We compare results from multireference singles and doubles configuration interaction to those utilizing a posteriori and a priori size-extensivity corrections, benchmarked against experiment and coupled cluster theory. We demonstrate that size-extensivity corrections are necessary for chemically accurate BDE predictions even in relatively small molecules and furnish examples of unphysical BDE predictions resulting from using too-small orbital active spaces. We also outline the specific challenges in using MRCI methods for carbonyl-containing compounds. The resulting complete basis set extrapolated, size-extensivity-corrected MRCI scheme produces BDEs generally accurate to within 1 kcal/mol, laying the foundation for this scheme's use on larger molecules and for more complex regions of combustion PESs.

  12. PSI: a comprehensive and integrative approach for accurate plant subcellular localization prediction.

    PubMed

    Liu, Lili; Zhang, Zijun; Mei, Qian; Chen, Ming

    2013-01-01

    Predicting the subcellular localization of proteins conquers the major drawbacks of high-throughput localization experiments that are costly and time-consuming. However, current subcellular localization predictors are limited in scope and accuracy. In particular, most predictors perform well on certain locations or with certain data sets while poorly on others. Here, we present PSI, a novel high accuracy web server for plant subcellular localization prediction. PSI derives the wisdom of multiple specialized predictors via a joint-approach of group decision making strategy and machine learning methods to give an integrated best result. The overall accuracy obtained (up to 93.4%) was higher than best individual (CELLO) by ~10.7%. The precision of each predicable subcellular location (more than 80%) far exceeds that of the individual predictors. It can also deal with multi-localization proteins. PSI is expected to be a powerful tool in protein location engineering as well as in plant sciences, while the strategy employed could be applied to other integrative problems. A user-friendly web server, PSI, has been developed for free access at http://bis.zju.edu.cn/psi/. PMID:24194827

  13. The Compensatory Reserve For Early and Accurate Prediction Of Hemodynamic Compromise: A Review of the Underlying Physiology.

    PubMed

    Convertino, Victor A; Wirt, Michael D; Glenn, John F; Lein, Brian C

    2016-06-01

    Shock is deadly and unpredictable if it is not recognized and treated in early stages of hemorrhage. Unfortunately, measurements of standard vital signs that are displayed on current medical monitors fail to provide accurate or early indicators of shock because of physiological mechanisms that effectively compensate for blood loss. As a result of new insights provided by the latest research on the physiology of shock using human experimental models of controlled hemorrhage, it is now recognized that measurement of the body's reserve to compensate for reduced circulating blood volume is the single most important indicator for early and accurate assessment of shock. We have called this function the "compensatory reserve," which can be accurately assessed by real-time measurements of changes in the features of the arterial waveform. In this paper, the physiology underlying the development and evaluation of a new noninvasive technology that allows for real-time measurement of the compensatory reserve will be reviewed, with its clinical implications for earlier and more accurate prediction of shock. PMID:26950588

  14. A novel method to predict visual field progression more accurately, using intraocular pressure measurements in glaucoma patients.

    PubMed

    2016-01-01

    Visual field (VF) data were retrospectively obtained from 491 eyes in 317 patients with open angle glaucoma who had undergone ten VF tests (Humphrey Field Analyzer, 24-2, SITA standard). First, mean of total deviation values (mTD) in the tenth VF was predicted using standard linear regression of the first five VFs (VF1-5) through to using all nine preceding VFs (VF1-9). Then an 'intraocular pressure (IOP)-integrated VF trend analysis' was carried out by simply using time multiplied by IOP as the independent term in the linear regression model. Prediction errors (absolute prediction error or root mean squared error: RMSE) for predicting mTD and also point wise TD values of the tenth VF were obtained from both approaches. The mTD absolute prediction errors associated with the IOP-integrated VF trend analysis were significantly smaller than those from the standard trend analysis when VF1-6 through to VF1-8 were used (p < 0.05). The point wise RMSEs from the IOP-integrated trend analysis were significantly smaller than those from the standard trend analysis when VF1-5 through to VF1-9 were used (p < 0.05). This was especially the case when IOP was measured more frequently. Thus a significantly more accurate prediction of VF progression is possible using a simple trend analysis that incorporates IOP measurements. PMID:27562553

  15. A novel method to predict visual field progression more accurately, using intraocular pressure measurements in glaucoma patients

    PubMed Central

    Asaoka, Ryo; Fujino, Yuri; Murata, Hiroshi; Miki, Atsuya; Tanito, Masaki; Mizoue, Shiro; Mori, Kazuhiko; Suzuki, Katsuyoshi; Yamashita, Takehiro; Kashiwagi, Kenji; Shoji, Nobuyuki

    2016-01-01

    Visual field (VF) data were retrospectively obtained from 491 eyes in 317 patients with open angle glaucoma who had undergone ten VF tests (Humphrey Field Analyzer, 24-2, SITA standard). First, mean of total deviation values (mTD) in the tenth VF was predicted using standard linear regression of the first five VFs (VF1-5) through to using all nine preceding VFs (VF1-9). Then an ‘intraocular pressure (IOP)-integrated VF trend analysis’ was carried out by simply using time multiplied by IOP as the independent term in the linear regression model. Prediction errors (absolute prediction error or root mean squared error: RMSE) for predicting mTD and also point wise TD values of the tenth VF were obtained from both approaches. The mTD absolute prediction errors associated with the IOP-integrated VF trend analysis were significantly smaller than those from the standard trend analysis when VF1-6 through to VF1-8 were used (p < 0.05). The point wise RMSEs from the IOP-integrated trend analysis were significantly smaller than those from the standard trend analysis when VF1-5 through to VF1-9 were used (p < 0.05). This was especially the case when IOP was measured more frequently. Thus a significantly more accurate prediction of VF progression is possible using a simple trend analysis that incorporates IOP measurements. PMID:27562553

  16. Combining multiple regression and principal component analysis for accurate predictions for column ozone in Peninsular Malaysia

    NASA Astrophysics Data System (ADS)

    Rajab, Jasim M.; MatJafri, M. Z.; Lim, H. S.

    2013-06-01

    This study encompasses columnar ozone modelling in the peninsular Malaysia. Data of eight atmospheric parameters [air surface temperature (AST), carbon monoxide (CO), methane (CH4), water vapour (H2Ovapour), skin surface temperature (SSKT), atmosphere temperature (AT), relative humidity (RH), and mean surface pressure (MSP)] data set, retrieved from NASA's Atmospheric Infrared Sounder (AIRS), for the entire period (2003-2008) was employed to develop models to predict the value of columnar ozone (O3) in study area. The combined method, which is based on using both multiple regressions combined with principal component analysis (PCA) modelling, was used to predict columnar ozone. This combined approach was utilized to improve the prediction accuracy of columnar ozone. Separate analysis was carried out for north east monsoon (NEM) and south west monsoon (SWM) seasons. The O3 was negatively correlated with CH4, H2Ovapour, RH, and MSP, whereas it was positively correlated with CO, AST, SSKT, and AT during both the NEM and SWM season periods. Multiple regression analysis was used to fit the columnar ozone data using the atmospheric parameter's variables as predictors. A variable selection method based on high loading of varimax rotated principal components was used to acquire subsets of the predictor variables to be comprised in the linear regression model of the atmospheric parameter's variables. It was found that the increase in columnar O3 value is associated with an increase in the values of AST, SSKT, AT, and CO and with a drop in the levels of CH4, H2Ovapour, RH, and MSP. The result of fitting the best models for the columnar O3 value using eight of the independent variables gave about the same values of the R (≈0.93) and R2 (≈0.86) for both the NEM and SWM seasons. The common variables that appeared in both regression equations were SSKT, CH4 and RH, and the principal precursor of the columnar O3 value in both the NEM and SWM seasons was SSKT.

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

    SciTech Connect

    Martin, Katherine J.; Patrick, Denis R.; Bissell, Mina J.; Fournier, Marcia V.

    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 datasets 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 prognostic

  18. A Foundation for the Accurate Prediction of the Soft Error Vulnerability of Scientific Applications

    SciTech Connect

    Bronevetsky, G; de Supinski, B; Schulz, M

    2009-02-13

    Understanding the soft error vulnerability of supercomputer applications is critical as these systems are using ever larger numbers of devices that have decreasing feature sizes and, thus, increasing frequency of soft errors. As many large scale parallel scientific applications use BLAS and LAPACK linear algebra routines, the soft error vulnerability of these methods constitutes a large fraction of the applications overall vulnerability. This paper analyzes the vulnerability of these routines to soft errors by characterizing how their outputs are affected by injected errors and by evaluating several techniques for predicting how errors propagate from the input to the output of each routine. The resulting error profiles can be used to understand the fault vulnerability of full applications that use these routines.

  19. Sequence features accurately predict genome-wide MeCP2 binding in vivo.

    PubMed

    Rube, H Tomas; Lee, Wooje; Hejna, Miroslav; Chen, Huaiyang; Yasui, Dag H; Hess, John F; LaSalle, Janine M; Song, Jun S; Gong, Qizhi

    2016-01-01

    Methyl-CpG binding protein 2 (MeCP2) is critical for proper brain development and expressed at near-histone levels in neurons, but the mechanism of its genomic localization remains poorly understood. Using high-resolution MeCP2-binding data, we show that DNA sequence features alone can predict binding with 88% accuracy. Integrating MeCP2 binding and DNA methylation in a probabilistic graphical model, we demonstrate that previously reported genome-wide association with methylation is in part due to MeCP2's affinity to GC-rich chromatin, a result replicated using published data. Furthermore, MeCP2 co-localizes with nucleosomes. Finally, MeCP2 binding downstream of promoters correlates with increased expression in Mecp2-deficient neurons. PMID:27008915

  20. nuMap: a web platform for accurate prediction of nucleosome positioning.

    PubMed

    Alharbi, Bader A; Alshammari, Thamir H; Felton, Nathan L; Zhurkin, Victor B; Cui, Feng

    2014-10-01

    Nucleosome positioning is critical for gene expression and of major biological interest. The high cost of experimentally mapping nucleosomal arrangement signifies the need for computational approaches to predict nucleosome positions at high resolution. Here, we present a web-based application to fulfill this need by implementing two models, YR and W/S schemes, for the translational and rotational positioning of nucleosomes, respectively. Our methods are based on sequence-dependent anisotropic bending that dictates how DNA is wrapped around a histone octamer. This application allows users to specify a number of options such as schemes and parameters for threading calculation and provides multiple layout formats. The nuMap is implemented in Java/Perl/MySQL and is freely available for public use at http://numap.rit.edu. The user manual, implementation notes, description of the methodology and examples are available at the site. PMID:25220945

  1. Sequence features accurately predict genome-wide MeCP2 binding in vivo

    PubMed Central

    Rube, H. Tomas; Lee, Wooje; Hejna, Miroslav; Chen, Huaiyang; Yasui, Dag H.; Hess, John F.; LaSalle, Janine M.; Song, Jun S.; Gong, Qizhi

    2016-01-01

    Methyl-CpG binding protein 2 (MeCP2) is critical for proper brain development and expressed at near-histone levels in neurons, but the mechanism of its genomic localization remains poorly understood. Using high-resolution MeCP2-binding data, we show that DNA sequence features alone can predict binding with 88% accuracy. Integrating MeCP2 binding and DNA methylation in a probabilistic graphical model, we demonstrate that previously reported genome-wide association with methylation is in part due to MeCP2's affinity to GC-rich chromatin, a result replicated using published data. Furthermore, MeCP2 co-localizes with nucleosomes. Finally, MeCP2 binding downstream of promoters correlates with increased expression in Mecp2-deficient neurons. PMID:27008915

  2. Simplified versus geometrically accurate models of forefoot anatomy to predict plantar pressures: A finite element study.

    PubMed

    Telfer, Scott; Erdemir, Ahmet; Woodburn, James; Cavanagh, Peter R

    2016-01-25

    Integration of patient-specific biomechanical measurements into the design of therapeutic footwear has been shown to improve clinical outcomes in patients with diabetic foot disease. The addition of numerical simulations intended to optimise intervention design may help to build on these advances, however at present the time and labour required to generate and run personalised models of foot anatomy restrict their routine clinical utility. In this study we developed second-generation personalised simple finite element (FE) models of the forefoot with varying geometric fidelities. Plantar pressure predictions from barefoot, shod, and shod with insole simulations using simplified models were compared to those obtained from CT-based FE models incorporating more detailed representations of bone and tissue geometry. A simplified model including representations of metatarsals based on simple geometric shapes, embedded within a contoured soft tissue block with outer geometry acquired from a 3D surface scan was found to provide pressure predictions closest to the more complex model, with mean differences of 13.3kPa (SD 13.4), 12.52kPa (SD 11.9) and 9.6kPa (SD 9.3) for barefoot, shod, and insole conditions respectively. The simplified model design could be produced in <1h compared to >3h in the case of the more detailed model, and solved on average 24% faster. FE models of the forefoot based on simplified geometric representations of the metatarsal bones and soft tissue surface geometry from 3D surface scans may potentially provide a simulation approach with improved clinical utility, however further validity testing around a range of therapeutic footwear types is required. PMID:26708965

  3. An accurate and efficient method for prediction of the long-term evolution of space debris in the geosynchronous region

    NASA Astrophysics Data System (ADS)

    McNamara, Roger P.; Eagle, C. D.

    1992-08-01

    Planetary Observer High Accuracy Orbit Prediction Program (POHOP), an existing numerical integrator, was modified with the solar and lunar formulae developed by T.C. Van Flandern and K.F. Pulkkinen to provide the accuracy required to evaluate long-term orbit characteristics of objects on the geosynchronous region. The orbit of a 1000 kg class spacecraft is numerically integrated over 50 years using both the original and the more accurate solar and lunar ephemerides methods. Results of this study demonstrate that, over the long term, for an object located in the geosynchronous region, the more accurate solar and lunar ephemerides effects on the objects's position are significantly different than using the current POHOP ephemeris.

  4. Prediction of composite hygral behavior made simple

    NASA Technical Reports Server (NTRS)

    Chamis, C. C.; Sinclair, J. H.

    1982-01-01

    A convenient procedure is described to determine the hygral behavior (moisture expansion coefficients and moisture stresses) of angleplied fiber composites using a pocket calculator. The procedure consists of equations and appropriate graphs for various (+ or - theta) ply combinations. These graphs present reduced stiffness and moisture expansion coefficients as functions of (+ or - theta) in order to simplify and expedite the use of the equations. The procedure is applicable to all types of balanced, symmetric fiber composites including interply and intraply hybrids. The versatility and generality of the procedure is illustrated using several step-by-step numerical examples.

  5. Prediction of composite thermal behavior made simple

    NASA Technical Reports Server (NTRS)

    Chamis, C. C.

    1981-01-01

    A convenient procedure is described to determine the thermal behavior (thermal expansion coefficients and thermal stresses) of angleplied fiber composites using a pocket calculator. The procedure consists of equations and appropriate graphs for various ( + or - theta) ply combinations. These graphs present reduced stiffness and thermal expansion coefficients as functions of (+ or - theta) in order to simplify and expedite the use of the equations. The procedure is applicable to all types of balanced, symmetric fiber composites including interply and intraply hybrids. The versatility and generality of the procedure is illustrated using several step-by-step numerical examples.

  6. Prediction of composite hygral behavior made simple

    NASA Technical Reports Server (NTRS)

    Chamis, C. C.; Sinclair, J. H.

    1982-01-01

    A convenient procedure is described to determine the hygral behavior (moisture expansion coefficients and moisture stresses) of angleplied fiber composites using a pocket calculator. The procedure consists of equations and appropriate graphs for various (+ or - theta) ply combinations. These graphs present reduced stiffness and moisture expansion coefficients as functions of (+ or - theta) in order to simplify and expedite the use of the equations. The procedure is applicable to all types of balanced, symmetric fiber composites including interply and intraply hybrids. The versatility and generality of the procedure is illustrated using several step-by-step numerical examples. Previously announced in STAR as N82-16181

  7. Single-subject prediction of response inhibition behavior by event-related potentials.

    PubMed

    Stock, Ann-Kathrin; Popescu, Florin; Neuhaus, Andres H; Beste, Christian

    2016-03-01

    Much research has been devoted to investigating response inhibition and the neuronal processes constituting this essential cognitive faculty. However, the nexus between cognitive subprocesses, behavior, and electrophysiological processes remains associative in nature. We therefore investigated whether neurophysiological correlates of inhibition subprocesses merely correlate with behavioral performance or actually provide information expedient to the prediction of behavior on a single-subject level. Tackling this question, we used different data-driven classification approaches in a sample of n = 262 healthy young subjects who completed a standard Go/Nogo task while an EEG was recorded. On the basis of median-split response inhibition performance, subjects were classified as "accurate/slow" and "less accurate/fast." Even though these behavioral group differences were associated with significant amplitude variations in classical electrophysiological correlates of response inhibition (i.e., N2 and P3), they were not predictive for group membership on a single-subject level. Instead, amplitude differences in the Go-P2 originating in the precuneus (BA7) were shown to predict group membership on a single-subject level with up to 64% accuracy. These findings strongly suggest that the behavioral outcome of response inhibition greatly depends on the amount of cognitive resources allocated to early stages of stimulus-response activation during responding. This suggests that research should focus more on early processing steps during responding when trying to understand the origin of interindividual differences in response inhibition processes. PMID:26683075

  8. 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…

  9. Integrative subcellular proteomic analysis allows accurate prediction of human disease-causing genes.

    PubMed

    Zhao, Li; Chen, Yiyun; Bajaj, Amol Onkar; Eblimit, Aiden; Xu, Mingchu; Soens, Zachry T; Wang, Feng; Ge, Zhongqi; Jung, Sung Yun; He, Feng; Li, Yumei; Wensel, Theodore G; Qin, Jun; Chen, Rui

    2016-05-01

    Proteomic profiling on subcellular fractions provides invaluable information regarding both protein abundance and subcellular localization. When integrated with other data sets, it can greatly enhance our ability to predict gene function genome-wide. In this study, we performed a comprehensive proteomic analysis on the light-sensing compartment of photoreceptors called the outer segment (OS). By comparing with the protein profile obtained from the retina tissue depleted of OS, an enrichment score for each protein is calculated to quantify protein subcellular localization, and 84% accuracy is achieved compared with experimental data. By integrating the protein OS enrichment score, the protein abundance, and the retina transcriptome, the probability of a gene playing an essential function in photoreceptor cells is derived with high specificity and sensitivity. As a result, a list of genes that will likely result in human retinal disease when mutated was identified and validated by previous literature and/or animal model studies. Therefore, this new methodology demonstrates the synergy of combining subcellular fractionation proteomics with other omics data sets and is generally applicable to other tissues and diseases. PMID:26912414

  10. Neural network approach to quantum-chemistry data: Accurate prediction of density functional theory energies

    NASA Astrophysics Data System (ADS)

    Balabin, Roman M.; Lomakina, Ekaterina I.

    2009-08-01

    Artificial neural network (ANN) approach has been applied to estimate the density functional theory (DFT) energy with large basis set using lower-level energy values and molecular descriptors. A total of 208 different molecules were used for the ANN training, cross validation, and testing by applying BLYP, B3LYP, and BMK density functionals. Hartree-Fock results were reported for comparison. Furthermore, constitutional molecular descriptor (CD) and quantum-chemical molecular descriptor (QD) were used for building the calibration model. The neural network structure optimization, leading to four to five hidden neurons, was also carried out. The usage of several low-level energy values was found to greatly reduce the prediction error. An expected error, mean absolute deviation, for ANN approximation to DFT energies was 0.6±0.2 kcal mol-1. In addition, the comparison of the different density functionals with the basis sets and the comparison of multiple linear regression results were also provided. The CDs were found to overcome limitation of the QD. Furthermore, the effective ANN model for DFT/6-311G(3df,3pd) and DFT/6-311G(2df,2pd) energy estimation was developed, and the benchmark results were provided.

  11. Towards Accurate Prediction of Turbulent, Three-Dimensional, Recirculating Flows with the NCC

    NASA Technical Reports Server (NTRS)

    Iannetti, A.; Tacina, R.; Jeng, S.-M.; Cai, J.

    2001-01-01

    The National Combustion Code (NCC) was used to calculate the steady state, nonreacting flow field of a prototype Lean Direct Injection (LDI) swirler. This configuration used nine groups of eight holes drilled at a thirty-five degree angle to induce swirl. These nine groups created swirl in the same direction, or a corotating pattern. The static pressure drop across the holes was fixed at approximately four percent. Computations were performed on one quarter of the geometry, because the geometry is considered rotationally periodic every ninety degrees. The final computational grid used was approximately 2.26 million tetrahedral cells, and a cubic nonlinear k - epsilon model was used to model turbulence. The NCC results were then compared to time averaged Laser Doppler Velocimetry (LDV) data. The LDV measurements were performed on the full geometry, but four ninths of the geometry was measured. One-, two-, and three-dimensional representations of both flow fields are presented. The NCC computations compare both qualitatively and quantitatively well to the LDV data, but differences exist downstream. The comparison is encouraging, and shows that NCC can be used for future injector design studies. To improve the flow prediction accuracy of turbulent, three-dimensional, recirculating flow fields with the NCC, recommendations are given.

  12. The human skin/chick chorioallantoic membrane model accurately predicts the potency of cosmetic allergens.

    PubMed

    Slodownik, Dan; Grinberg, Igor; Spira, Ram M; Skornik, Yehuda; Goldstein, Ronald S

    2009-04-01

    The current standard method for predicting contact allergenicity is the murine local lymph node assay (LLNA). Public objection to the use of animals in testing of cosmetics makes the development of a system that does not use sentient animals highly desirable. The chorioallantoic membrane (CAM) of the chick egg has been extensively used for the growth of normal and transformed mammalian tissues. The CAM is not innervated, and embryos are sacrificed before the development of pain perception. The aim of this study was to determine whether the sensitization phase of contact dermatitis to known cosmetic allergens can be quantified using CAM-engrafted human skin and how these results compare with published EC3 data obtained with the LLNA. We studied six common molecules used in allergen testing and quantified migration of epidermal Langerhans cells (LC) as a measure of their allergic potency. All agents with known allergic potential induced statistically significant migration of LC. The data obtained correlated well with published data for these allergens generated using the LLNA test. The human-skin CAM model therefore has great potential as an inexpensive, non-radioactive, in vivo alternative to the LLNA, which does not require the use of sentient animals. In addition, this system has the advantage of testing the allergic response of human, rather than animal skin. PMID:19054059

  13. Accurate prediction of the refractive index of polymers using first principles and data modeling

    NASA Astrophysics Data System (ADS)

    Afzal, Mohammad Atif Faiz; Cheng, Chong; Hachmann, Johannes

    Organic polymers with a high refractive index (RI) have recently attracted considerable interest due to their potential application in optical and optoelectronic devices. The ability to tailor the molecular structure of polymers is the key to increasing the accessible RI values. Our work concerns the creation of predictive in silico models for the optical properties of organic polymers, the screening of large-scale candidate libraries, and the mining of the resulting data to extract the underlying design principles that govern their performance. This work was set up to guide our experimentalist partners and allow them to target the most promising candidates. Our model is based on the Lorentz-Lorenz equation and thus includes the polarizability and number density values for each candidate. For the former, we performed a detailed benchmark study of different density functionals, basis sets, and the extrapolation scheme towards the polymer limit. For the number density we devised an exceedingly efficient machine learning approach to correlate the polymer structure and the packing fraction in the bulk material. We validated the proposed RI model against the experimentally known RI values of 112 polymers. We could show that the proposed combination of physical and data modeling is both successful and highly economical to characterize a wide range of organic polymers, which is a prerequisite for virtual high-throughput screening.

  14. Viscoelastic behavior and life-time predictions

    NASA Technical Reports Server (NTRS)

    Dillard, D. A.; Brinson, H. F.

    1985-01-01

    Fiber reinforced plastics were considered for many structural applications in automotive, aerospace and other industries. A major concern was and remains the failure modes associated with the polymer matrix which serves to bind the fibers together and transfer the load through connections, from fiber to fiber and ply to ply. An accelerated characterization procedure for prediction of delayed failures was developed. This method utilizes time-temperature-stress-moisture superposition principles in conjunction with laminated plate theory. Because failures are inherently nonlinear, the testing and analytic modeling for both moduli and strength is based upon nonlinear viscoelastic concepts.

  15. 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)

  16. School Dropout as Predicted by Peer Rejection and Antisocial Behavior.

    ERIC Educational Resources Information Center

    French, Doran C.; Conrad, Jody

    2001-01-01

    Assessed at eighth and tenth grades the prediction of school dropout from measures of antisocial behavior, social preference, and achievement. Found that social preference does not uniquely predict school dropout. The possibility exists, however, that youth who are both antisocial and rejected may be at heightened risk for school dropout.…

  17. The development and verification of a highly accurate collision prediction model for automated noncoplanar plan delivery

    SciTech Connect

    Yu, Victoria Y.; Tran, Angelia; Nguyen, Dan; Cao, Minsong; Ruan, Dan; Low, Daniel A.; Sheng, Ke

    2015-11-15

    attributed to phantom setup errors due to the slightly deformable and flexible phantom extremities. The estimated site-specific safety buffer distance with 0.001% probability of collision for (gantry-to-couch, gantry-to-phantom) was (1.23 cm, 3.35 cm), (1.01 cm, 3.99 cm), and (2.19 cm, 5.73 cm) for treatment to the head, lung, and prostate, respectively. Automated delivery to all three treatment sites was completed in 15 min and collision free using a digital Linac. Conclusions: An individualized collision prediction model for the purpose of noncoplanar beam delivery was developed and verified. With the model, the study has demonstrated the feasibility of predicting deliverable beams for an individual patient and then guiding fully automated noncoplanar treatment delivery. This work motivates development of clinical workflows and quality assurance procedures to allow more extensive use and automation of noncoplanar beam geometries.

  18. How Accurate Are the Anthropometry Equations in in Iranian Military Men in Predicting Body Composition?

    PubMed Central

    Shakibaee, Abolfazl; Faghihzadeh, Soghrat; Alishiri, Gholam Hossein; Ebrahimpour, Zeynab; Faradjzadeh, Shahram; Sobhani, Vahid; Asgari, Alireza

    2015-01-01

    Background: The body composition varies according to different life styles (i.e. intake calories and caloric expenditure). Therefore, it is wise to record military personnel’s body composition periodically and encourage those who abide to the regulations. Different methods have been introduced for body composition assessment: invasive and non-invasive. Amongst them, the Jackson and Pollock equation is most popular. Objectives: The recommended anthropometric prediction equations for assessing men’s body composition were compared with dual-energy X-ray absorptiometry (DEXA) gold standard to develop a modified equation to assess body composition and obesity quantitatively among Iranian military men. Patients and Methods: A total of 101 military men aged 23 - 52 years old with a mean age of 35.5 years were recruited and evaluated in the present study (average height, 173.9 cm and weight, 81.5 kg). The body-fat percentages of subjects were assessed both with anthropometric assessment and DEXA scan. The data obtained from these two methods were then compared using multiple regression analysis. Results: The mean and standard deviation of body fat percentage of the DEXA assessment was 21.2 ± 4.3 and body fat percentage obtained from three Jackson and Pollock 3-, 4- and 7-site equations were 21.1 ± 5.8, 22.2 ± 6.0 and 20.9 ± 5.7, respectively. There was a strong correlation between these three equations and DEXA (R² = 0.98). Conclusions: The mean percentage of body fat obtained from the three equations of Jackson and Pollock was very close to that of body fat obtained from DEXA; however, we suggest using a modified Jackson-Pollock 3-site equation for volunteer military men because the 3-site equation analysis method is simpler and faster than other methods. PMID:26715964

  19. Higher social class predicts increased unethical behavior

    PubMed Central

    Piff, Paul K.; Stancato, Daniel M.; Côté, Stéphane; Mendoza-Denton, Rodolfo; Keltner, Dacher

    2012-01-01

    Seven studies using experimental and naturalistic methods reveal that upper-class individuals behave more unethically than lower-class individuals. In studies 1 and 2, upper-class individuals were more likely to break the law while driving, relative to lower-class individuals. In follow-up laboratory studies, upper-class individuals were more likely to exhibit unethical decision-making tendencies (study 3), take valued goods from others (study 4), lie in a negotiation (study 5), cheat to increase their chances of winning a prize (study 6), and endorse unethical behavior at work (study 7) than were lower-class individuals. Mediator and moderator data demonstrated that upper-class individuals’ unethical tendencies are accounted for, in part, by their more favorable attitudes toward greed. PMID:22371585

  20. Winning a competition predicts dishonest behavior

    PubMed Central

    Schurr, Amos; Ritov, Ilana

    2016-01-01

    Winning a competition engenders subsequent unrelated unethical behavior. Five studies reveal that after a competition has taken place winners behave more dishonestly than competition losers. Studies 1 and 2 demonstrate that winning a competition increases the likelihood of winners to steal money from their counterparts in a subsequent unrelated task. Studies 3a and 3b demonstrate that the effect holds only when winning means performing better than others (i.e., determined in reference to others) but not when success is determined by chance or in reference to a personal goal. Finally, study 4 demonstrates that a possible mechanism underlying the effect is an enhanced sense of entitlement among competition winners. PMID:26831083

  1. Winning a competition predicts dishonest behavior.

    PubMed

    Schurr, Amos; Ritov, Ilana

    2016-02-16

    Winning a competition engenders subsequent unrelated unethical behavior. Five studies reveal that after a competition has taken place winners behave more dishonestly than competition losers. Studies 1 and 2 demonstrate that winning a competition increases the likelihood of winners to steal money from their counterparts in a subsequent unrelated task. Studies 3a and 3b demonstrate that the effect holds only when winning means performing better than others (i.e., determined in reference to others) but not when success is determined by chance or in reference to a personal goal. Finally, study 4 demonstrates that a possible mechanism underlying the effect is an enhanced sense of entitlement among competition winners. PMID:26831083

  2. Accurate prediction of interference minima in linear molecular harmonic spectra by a modified two-center model

    NASA Astrophysics Data System (ADS)

    Xin, Cui; Di-Yu, Zhang; Gao, Chen; Ji-Gen, Chen; Si-Liang, Zeng; Fu-Ming, Guo; Yu-Jun, Yang

    2016-03-01

    We demonstrate that the interference minima in the linear molecular harmonic spectra can be accurately predicted by a modified two-center model. Based on systematically investigating the interference minima in the linear molecular harmonic spectra by the strong-field approximation (SFA), it is found that the locations of the harmonic minima are related not only to the nuclear distance between the two main atoms contributing to the harmonic generation, but also to the symmetry of the molecular orbital. Therefore, we modify the initial phase difference between the double wave sources in the two-center model, and predict the harmonic minimum positions consistent with those simulated by SFA. Project supported by the National Basic Research Program of China (Grant No. 2013CB922200) and the National Natural Science Foundation of China (Grant Nos. 11274001, 11274141, 11304116, 11247024, and 11034003), and the Jilin Provincial Research Foundation for Basic Research, China (Grant Nos. 20130101012JC and 20140101168JC).

  3. 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.

  4. Evaluating Mesoscale Numerical Weather Predictions and Spatially Distributed Meteorologic Forcing Data for Developing Accurate SWE Forecasts over Large Mountain Basins

    NASA Astrophysics Data System (ADS)

    Hedrick, A. R.; Marks, D. G.; Winstral, A. H.; Marshall, H. P.

    2014-12-01

    The ability to forecast snow water equivalent, or SWE, in mountain catchments would benefit many different communities ranging from avalanche hazard mitigation to water resource management. Historical model runs of Isnobal, the physically based energy balance snow model, have been produced over the 2150 km2 Boise River Basin for water years 2012 - 2014 at 100-meter resolution. Spatially distributed forcing parameters such as precipitation, wind, and relative humidity are generated from automated weather stations located throughout the watershed, and are supplied to Isnobal at hourly timesteps. Similarly, the Weather Research & Forecasting (WRF) Model provides hourly predictions of the same forcing parameters from an atmospheric physics perspective. This work aims to quantitatively compare WRF model output to the spatial meteorologic fields developed to force Isnobal, with the hopes of eventually using WRF predictions to create accurate hourly forecasts of SWE over a large mountainous basin.

  5. A unified framework for activity recognition-based behavior analysis and action prediction in smart homes.

    PubMed

    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

  6. 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

  7. Testing the ability of children with attention deficit hyperactivity disorder to accurately report the effects of medication on their behavior.

    PubMed

    Ardoin, S P; Martens, B K

    2000-01-01

    Children with attention deficit hyperactivity disorder (ADHD) are often treated with central nervous system stimulants, making the evaluation of medication effects an important topic for applied behavior analysts. Because assessment protocols emphasize informant reports and direct observations of child behavior, little is known about the extent to which children themselves can accurately report medication effects. Double-blind placebo-controlled procedures were used to examine whether 6 children with ADHD could recognize the effects of their medication. The children were given math worksheets to complete for 15 min during each of 14 sessions while on medication and placebo. Children completed a self-evaluation form at the end of each session, and ratings were compared to observed behavior and academic performance. Results indicated that 3 children were able to accurately report their medication status at levels greater than chance, whereas the accuracy of reports by all children was related to dosage level, differences in behavior, and the presence of adverse effects. The implications of these results for placebo-controlled research, self-monitoring of dosage levels, and accuracy training are discussed. PMID:11214033

  8. 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. PMID:23292546

  9. Unified approach for predicting mechanical behaviors of textile composites

    SciTech Connect

    Hamada, H.; Fujita, A.; Maekawa, Z.; Yokoyama, A.

    1994-12-31

    The purpose of this study was to establish unified prediction method of mechanical properties and fracture behaviors in the composites reinforced with textile fabric preforms such as two and three-dimensional woven fabrics, braided fabrics and knitted fabrics. In this analysis model, factors deciding weaving structure such as fiber orientation state, crimp and continuity of fiber, transmission of force at cross part between fiber bundles and surface resin of the composite which affect on the mechanical properties and fracture behavior of the textile composites, were considered. The validity of this numerical analysis method was examined by comparing predicted results with experimental data. Consequently, it could be confirmed that this numerical analysis method was valid for predicting the mechanical properties and fracture behavior of the textile composites. In this analysis model, not only the mechanical properties but also local stress state and fracture behavior of the textile composites could be estimated.

  10. Accurate First-Principles Spectra Predictions for Ethylene and its Isotopologues from Full 12D AB Initio Surfaces

    NASA Astrophysics Data System (ADS)

    Delahaye, Thibault; Rey, Michael; Tyuterev, Vladimir; Nikitin, Andrei V.; Szalay, Peter

    2015-06-01

    Hydrocarbons such as ethylene (C_2H_4) and methane (CH_4) are of considerable interest for the modeling of planetary atmospheres and other astrophysical applications. Knowledge of rovibrational transitions of hydrocarbons is of primary importance in many fields but remains a formidable challenge for the theory and spectral analysis. Essentially two theoretical approaches for the computation and prediction of spectra exist. The first one is based on empirically-fitted effective spectroscopic models. Several databases aim at collecting the corresponding data but the information about C_2H_4 spectrum present in these databases remains limited, only some spectral ranges around 1000, 3000 and 6000 cm-1 being available. Another way for computing energies, line positions and intensities is based on global variational calculations using ab initio surfaces. Although they do not yet reach the spectroscopic accuracy, they could 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 two necessary ingredients: (i) accurate intramolecular potential energy surface and dipole moment surface components and (ii) efficient computational methods to achieve a good numerical convergence. We report predictions of vibrational and rovibrational energy levels of C_2H_4 using our new ground state potential energy surface obtained from extended ab initio calculations. Additionally we will introduce line positions and line intensities predictions based on a new dipole moment surface for ethylene. These results will be compared with previous works on ethylene and its isotopologues.

  11. Does a More Precise Chemical Description of Protein–Ligand Complexes Lead to More Accurate Prediction of Binding Affinity?

    PubMed Central

    2014-01-01

    Predicting the binding affinities of large sets of diverse molecules against a range of macromolecular targets is an extremely challenging task. The scoring functions that attempt such computational prediction are essential for exploiting and analyzing the outputs of docking, which is in turn an important tool in problems such as structure-based drug design. Classical scoring functions assume a predetermined theory-inspired functional form for the relationship between the variables that describe an experimentally determined or modeled structure of a protein–ligand complex and its binding affinity. The inherent problem of this approach is in the difficulty of explicitly modeling the various contributions of intermolecular interactions to binding affinity. New scoring functions based on machine-learning regression models, which are able to exploit effectively much larger amounts of experimental data and circumvent the need for a predetermined functional form, have already been shown to outperform a broad range of state-of-the-art scoring functions in a widely used benchmark. Here, we investigate the impact of the chemical description of the complex on the predictive power of the resulting scoring function using a systematic battery of numerical experiments. The latter resulted in the most accurate scoring function to date on the benchmark. Strikingly, we also found that a more precise chemical description of the protein–ligand complex does not generally lead to a more accurate prediction of binding affinity. We discuss four factors that may contribute to this result: modeling assumptions, codependence of representation and regression, data restricted to the bound state, and conformational heterogeneity in data. PMID:24528282

  12. 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

  13. 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. PMID:25828551

  14. Positive urgency predicts illegal drug use and risky sexual behavior.

    PubMed

    Zapolski, Tamika C B; Cyders, Melissa A; Smith, Gregory T

    2009-06-01

    There are several different personality traits that dispose individuals to engage in rash action. One such trait is positive urgency: the tendency to act rashly when experiencing extremely positive affect. This trait may be relevant for college student risky behavior, because it appears that a great deal of college student risky behavior is undertaken during periods of intensely positive mood states. To test this possibility, the authors conducted a longitudinal study designed to predict increases in risky sexual behavior and illegal drug use over the course of the first year of college (n=407). In a well-fitting structural model, positive urgency predicted increases in illegal drug use and risky sexual behavior, even after controlling for time 1 (T1) involvement in both risky behaviors, biological sex, and T1 scores on four other personality dispositions to rash action. The authors discuss the theoretical and practical implications of this finding. PMID:19586152

  15. A hyperspectral imaging system for an accurate prediction of the above-ground biomass of individual rice plants.

    PubMed

    Feng, Hui; Jiang, Ni; Huang, Chenglong; Fang, Wei; Yang, Wanneng; Chen, Guoxing; Xiong, Lizhong; Liu, Qian

    2013-09-01

    Biomass is an important component of the plant phenomics, and the existing methods for biomass estimation for individual plants are either destructive or lack accuracy. In this study, a hyperspectral imaging system was developed for the accurate prediction of the above-ground biomass of individual rice plants in the visible and near-infrared spectral region. First, the structure of the system and the influence of various parameters on the camera acquisition speed were established. Then the system was used to image 152 rice plants, which selected from the rice mini-core collection, in two stages, the tillering to elongation (T-E) stage and the booting to heading (B-H) stage. Several variables were extracted from the images. Following, linear stepwise regression analysis and 5-fold cross-validation were used to select effective variables for model construction and test the stability of the model, respectively. For the T-E stage, the R(2) value was 0.940 for the fresh weight (FW) and 0.935 for the dry weight (DW). For the B-H stage, the R(2) value was 0.891 for the FW and 0.783 for the DW. Moreover, estimations of the biomass using visible light images were also calculated. These comparisons showed that hyperspectral imaging performed better than the visible light imaging. Therefore, this study provides not only a stable hyperspectral imaging platform but also an accurate and nondestructive method for the prediction of biomass for individual rice plants. PMID:24089866

  16. Accurate prediction of unsteady and time-averaged pressure loads using a hybrid Reynolds-Averaged/large-eddy simulation technique

    NASA Astrophysics Data System (ADS)

    Bozinoski, Radoslav

    Significant research has been performed over the last several years on understanding the unsteady aerodynamics of various fluid flows. Much of this work has focused on quantifying the unsteady, three-dimensional flow field effects which have proven vital to the accurate prediction of many fluid and aerodynamic problems. Up until recently, engineers have predominantly relied on steady-state simulations to analyze the inherently three-dimensional ow structures that are prevalent in many of today's "real-world" problems. Increases in computational capacity and the development of efficient numerical methods can change this and allow for the solution of the unsteady Reynolds-Averaged Navier-Stokes (RANS) equations for practical three-dimensional aerodynamic applications. An integral part of this capability has been the performance and accuracy of the turbulence models coupled with advanced parallel computing techniques. This report begins with a brief literature survey of the role fully three-dimensional, unsteady, Navier-Stokes solvers have on the current state of numerical analysis. Next, the process of creating a baseline three-dimensional Multi-Block FLOw procedure called MBFLO3 is presented. Solutions for an inviscid circular arc bump, laminar at plate, laminar cylinder, and turbulent at plate are then presented. Results show good agreement with available experimental, numerical, and theoretical data. Scalability data for the parallel version of MBFLO3 is presented and shows efficiencies of 90% and higher for processes of no less than 100,000 computational grid points. Next, the description and implementation techniques used for several turbulence models are presented. Following the successful implementation of the URANS and DES procedures, the validation data for separated, non-reattaching flows over a NACA 0012 airfoil, wall-mounted hump, and a wing-body junction geometry are presented. Results for the NACA 0012 showed significant improvement in flow predictions

  17. 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

    2015-02-01

    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). PMID:24274986

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

    PubMed

    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

  19. 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

  20. Improving the Prediction of Suicidal Behavior in Youth

    PubMed Central

    Glenn, Catherine R.; Nock, Matthew K.

    2015-01-01

    Suicidal behaviors increase dramatically during adolescence. In order to effectively intervene and ultimately prevent suicide in youth, the field needs to be able to identify and predict which adolescents are at greatest suicide risk. However, present knowledge of risk factors for suicide and techniques for identifying at-risk youth are insufficient. The purpose of the current manuscript is to highlight some of the key, yet unanswered, questions about the prediction of suicidal behavior in youth, and to suggest the types of research advances needed to move the field forward. PMID:23850053

  1. A simple yet accurate correction for winner's curse can predict signals discovered in much larger genome scans

    PubMed Central

    Bigdeli, T. Bernard; Lee, Donghyung; Webb, Bradley Todd; Riley, Brien P.; Vladimirov, Vladimir I.; Fanous, Ayman H.; Kendler, Kenneth S.; Bacanu, Silviu-Alin

    2016-01-01

    Motivation: For genetic studies, statistically significant variants explain far less trait variance than ‘sub-threshold’ association signals. To dimension follow-up studies, researchers need to accurately estimate ‘true’ effect sizes at each SNP, e.g. the true mean of odds ratios (ORs)/regression coefficients (RRs) or Z-score noncentralities. Naïve estimates of effect sizes incur winner’s curse biases, which are reduced only by laborious winner’s curse adjustments (WCAs). Given that Z-scores estimates can be theoretically translated on other scales, we propose a simple method to compute WCA for Z-scores, i.e. their true means/noncentralities. Results:WCA of Z-scores shrinks these towards zero while, on P-value scale, multiple testing adjustment (MTA) shrinks P-values toward one, which corresponds to the zero Z-score value. Thus, WCA on Z-scores scale is a proxy for MTA on P-value scale. Therefore, to estimate Z-score noncentralities for all SNPs in genome scans, we propose FDR Inverse Quantile Transformation (FIQT). It (i) performs the simpler MTA of P-values using FDR and (ii) obtains noncentralities by back-transforming MTA P-values on Z-score scale. When compared to competitors, realistic simulations suggest that FIQT is more (i) accurate and (ii) computationally efficient by orders of magnitude. Practical application of FIQT to Psychiatric Genetic Consortium schizophrenia cohort predicts a non-trivial fraction of sub-threshold signals which become significant in much larger supersamples. Conclusions: FIQT is a simple, yet accurate, WCA method for Z-scores (and ORs/RRs, via simple transformations). Availability and Implementation: A 10 lines R function implementation is available at https://github.com/bacanusa/FIQT. Contact: sabacanu@vcu.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:27187203

  2. Small-scale field experiments accurately scale up to predict density dependence in reef fish populations at large scales

    PubMed Central

    Steele, Mark A.; Forrester, Graham E.

    2005-01-01

    Field experiments provide rigorous tests of ecological hypotheses but are usually limited to small spatial scales. It is thus unclear whether these findings extrapolate to larger scales relevant to conservation and management. We show that the results of experiments detecting density-dependent mortality of reef fish on small habitat patches scale up to have similar effects on much larger entire reefs that are the size of small marine reserves and approach the scale at which some reef fisheries operate. We suggest that accurate scaling is due to the type of species interaction causing local density dependence and the fact that localized events can be aggregated to describe larger-scale interactions with minimal distortion. Careful extrapolation from small-scale experiments identifying species interactions and their effects should improve our ability to predict the outcomes of alternative management strategies for coral reef fishes and their habitats. PMID:16150721

  3. Small-scale field experiments accurately scale up to predict density dependence in reef fish populations at large scales.

    PubMed

    Steele, Mark A; Forrester, Graham E

    2005-09-20

    Field experiments provide rigorous tests of ecological hypotheses but are usually limited to small spatial scales. It is thus unclear whether these findings extrapolate to larger scales relevant to conservation and management. We show that the results of experiments detecting density-dependent mortality of reef fish on small habitat patches scale up to have similar effects on much larger entire reefs that are the size of small marine reserves and approach the scale at which some reef fisheries operate. We suggest that accurate scaling is due to the type of species interaction causing local density dependence and the fact that localized events can be aggregated to describe larger-scale interactions with minimal distortion. Careful extrapolation from small-scale experiments identifying species interactions and their effects should improve our ability to predict the outcomes of alternative management strategies for coral reef fishes and their habitats. PMID:16150721

  4. Effects of the inlet conditions and blood models on accurate prediction of hemodynamics in the stented coronary arteries

    NASA Astrophysics Data System (ADS)

    Jiang, Yongfei; Zhang, Jun; Zhao, Wanhua

    2015-05-01

    Hemodynamics altered by stent implantation is well-known to be closely related to in-stent restenosis. Computational fluid dynamics (CFD) method has been used to investigate the hemodynamics in stented arteries in detail and help to analyze the performances of stents. In this study, blood models with Newtonian or non-Newtonian properties were numerically investigated for the hemodynamics at steady or pulsatile inlet conditions respectively employing CFD based on the finite volume method. The results showed that the blood model with non-Newtonian property decreased the area of low wall shear stress (WSS) compared with the blood model with Newtonian property and the magnitude of WSS varied with the magnitude and waveform of the inlet velocity. The study indicates that the inlet conditions and blood models are all important for accurately predicting the hemodynamics. This will be beneficial to estimate the performances of stents and also help clinicians to select the proper stents for the patients.

  5. The general AMBER force field (GAFF) can accurately predict thermodynamic and transport properties of many ionic liquids.

    PubMed

    Sprenger, K G; Jaeger, Vance W; Pfaendtner, Jim

    2015-05-01

    We have applied molecular dynamics to calculate thermodynamic and transport properties of a set of 19 room-temperature ionic liquids. Since accurately simulating the thermophysical properties of solvents strongly depends upon the force field of choice, we tested the accuracy of the general AMBER force field, without refinement, for the case of ionic liquids. Electrostatic point charges were developed using ab initio calculations and a charge scaling factor of 0.8 to more accurately predict dynamic properties. The density, heat capacity, molar enthalpy of vaporization, self-diffusivity, and shear viscosity of the ionic liquids were computed and compared to experimentally available data, and good agreement across a wide range of cation and anion types was observed. Results show that, for a wide range of ionic liquids, the general AMBER force field, with no tuning of parameters, can reproduce a variety of thermodynamic and transport properties with similar accuracy to that of other published, often IL-specific, force fields. PMID:25853313

  6. Behavior Laws And Their Influences On Numerical Prediction

    SciTech Connect

    Lemoine, Xavier

    2007-04-07

    Many studies show that the improvement of the forming numerical prediction for rolled sheets is done through laws of increasingly complex behavior, in particular by combination of the isotropic and kinematic hardening (mixed hardening) to take account of the Baushinger effect. This present work classifies the steel grades compared to the Baushinger effect. For some forming cases, it shows also the influence of a mixed hardening law on this numerical prediction, in term of deformation, thinning, residual stresses, and punch force..

  7. TIMP2•IGFBP7 biomarker panel accurately predicts acute kidney injury in high-risk surgical patients

    PubMed Central

    Gunnerson, Kyle J.; Shaw, Andrew D.; Chawla, Lakhmir S.; Bihorac, Azra; Al-Khafaji, Ali; Kashani, Kianoush; Lissauer, Matthew; Shi, Jing; Walker, Michael G.; Kellum, John A.

    2016-01-01

    BACKGROUND Acute kidney injury (AKI) is an important complication in surgical patients. Existing biomarkers and clinical prediction models underestimate the risk for developing AKI. We recently reported data from two trials of 728 and 408 critically ill adult patients in whom urinary TIMP2•IGFBP7 (NephroCheck, Astute Medical) was used to identify patients at risk of developing AKI. Here we report a preplanned analysis of surgical patients from both trials to assess whether urinary tissue inhibitor of metalloproteinase 2 (TIMP-2) and insulin-like growth factor–binding protein 7 (IGFBP7) accurately identify surgical patients at risk of developing AKI. STUDY DESIGN We enrolled adult surgical patients at risk for AKI who were admitted to one of 39 intensive care units across Europe and North America. The primary end point was moderate-severe AKI (equivalent to KDIGO [Kidney Disease Improving Global Outcomes] stages 2–3) within 12 hours of enrollment. Biomarker performance was assessed using the area under the receiver operating characteristic curve, integrated discrimination improvement, and category-free net reclassification improvement. RESULTS A total of 375 patients were included in the final analysis of whom 35 (9%) developed moderate-severe AKI within 12 hours. The area under the receiver operating characteristic curve for [TIMP-2]•[IGFBP7] alone was 0.84 (95% confidence interval, 0.76–0.90; p < 0.0001). Biomarker performance was robust in sensitivity analysis across predefined subgroups (urgency and type of surgery). CONCLUSION For postoperative surgical intensive care unit patients, a single urinary TIMP2•IGFBP7 test accurately identified patients at risk for developing AKI within the ensuing 12 hours and its inclusion in clinical risk prediction models significantly enhances their performance. LEVEL OF EVIDENCE Prognostic study, level I. PMID:26816218

  8. Anticipated emotions and personal experience for predicting behavioral intentions and behavioral expectations.

    PubMed

    Carrera, Pilar; Caballero, Amparo; Muñoz, Dolores; Oceja, Luis

    2011-11-01

    We tested how anticipated emotions interact with personal experience in risk behavior to improve predictions from TPB on behavioral intention (BI) and behavioral expectation (BE) for sex without condom (Study 1) and excessive drinking (Study 2). In the moderate-high experience group, anticipated emotional profiles (AEPs) improve TPB prediction from 28% to 45% in the case of BI and from 19% to 40% in that of BE in relation to sexual risk behavior (Study 1), and from 23% to 36% in the case of BI and from 17% to 31% in that of BE in relation to binge drinking (Study 2). However, in the low-experience group (Study 2) AEPs improve TPB predictions for BI (12% to 34%) but not for BE, showing that in less experienced people BI and BE are not equivalent: anticipated emotions have different relevance in their prediction. These results were replicated using a general negative anticipated emotion index (averaging emotional categories). PMID:22059300

  9. Mining Behavior Based Safety Data to Predict Safety Performance

    SciTech Connect

    Jeffrey C. Joe

    2010-06-01

    The Idaho National Laboratory (INL) operates a behavior based safety program called Safety Observations Achieve Results (SOAR). This peer-to-peer observation program encourages employees to perform in-field observations of each other's work practices and habits (i.e., behaviors). The underlying premise of conducting these observations is that more serious accidents are prevented from occurring because lower level “at risk” behaviors are identified and corrected before they can propagate into culturally accepted “unsafe” behaviors that result in injuries or fatalities. Although the approach increases employee involvement in safety, the premise of the program has not been subject to sufficient empirical evaluation. The INL now has a significant amount of SOAR data on these lower level “at risk” behaviors. This paper describes the use of data mining techniques to analyze these data to determine whether they can predict if and when a more serious accident will occur.

  10. Prenatal substance exposure: What predicts behavioral resilience by early adolescence?

    PubMed

    Liebschutz, Jane M; Crooks, Denise; Rose-Jacobs, Ruth; Cabral, Howard J; Heeren, Timothy C; Gerteis, Jessie; Appugliese, Danielle P; Heymann, Orlaith D; Lange, Allison V; Frank, Deborah A

    2015-06-01

    Understanding behavioral resilience among at-risk adolescents may guide public policy decisions and future programs. We examined factors predicting behavioral resilience following intrauterine substance exposure in a prospective longitudinal birth-cohort study of 136 early adolescents (ages 12.4-15.9 years) at risk for poor behavioral outcomes. We defined behavioral resilience as a composite measure of lack of early substance use initiation (before age 14), lack of risky sexual behavior, or lack of delinquency. Intrauterine substance exposures included in this analysis were cocaine, tobacco, alcohol, and marijuana. We recruited participants from Boston Medical Center as mother-infant dyads between 1990 and 1993. The majority of the sample was African American/Caribbean (88%) and 49% female. In bivariate analyses, none and lower intrauterine cocaine exposure level predicted resilience compared with higher cocaine exposure, but this effect was not found in an adjusted model. Instead, strict caregiver supervision (adjusted odds ratio [AOR] = 6.02, 95% confidence interval (CI) [1.90, 19.00], p = .002), lower violence exposure (AOR = 4.07, 95% CI [1.77, 9.38], p < .001), and absence of intrauterine tobacco exposure (AOR = 3.71, 95% CI [1.28, 10.74], p = .02) predicted behavioral resilience. In conclusion, caregiver supervision in early adolescence, lower violence exposure in childhood, and lack of intrauterine tobacco exposure predicted behavioral resilience among a cohort of early adolescents with significant social and environmental risk. Future interventions should work to enhance parental supervision as a way to mitigate the effects of adversity on high-risk groups of adolescents. (PsycINFO Database Record PMID:26076097

  11. Prenatal Substance Exposure: What Predicts Behavioral Resilience by Early Adolescence?

    PubMed Central

    Liebschutz, Jane; Crooks, Denise; Rose-Jacobs, Ruth; Cabral, Howard J; Heeren, Timothy C; Gerteis, Jessie; Appugliese, Danielle P.; Heymann, Orlaith D.; Lange, Allison V.; Frank, Deborah A.

    2015-01-01

    Understanding behavioral resilience among at-risk adolescents may guide public policy decisions and future programs. We examined factors predicting behavioral resilience following intrauterine substance exposure (IUSE) in a prospective longitudinal birth-cohort study of 136 early adolescents (age 12.4–15.9) at-risk for poor behavioral outcomes. We defined behavioral resilience as a composite measure of lack of early substance use initiation (before age 14), lack of risky sexual behavior, or lack of delinquency. IUSEs included in this analysis were cocaine (IUCE), tobacco (IUTE), alcohol (IUAE), and marijuana (IUME). We recruited participants from Boston Medical Center as mother-infant dyads between 1990 and 1993. The majority of the sample was African-American/Caribbean (88%) and 49% female. In bivariate analyses, none and lower IUCE level predicted resilience compared to higher IUCE, but this effect was not found in an adjusted model. Instead, strict caregiver supervision (adjusted odds ratio (AOR)=6.02, 95% confidence interval (CI)=1.90–19.00, p=0.002), lower violence exposure (AOR=4.07, 95% CI=1.77–9.38, p<0.001), and absence of intrauterine tobacco exposure (AOR=3.71, 95% CI= 1.28–10.74, p=0.02) predicted behavioral resilience. In conclusion, caregiver supervision in early adolescence, lower violence exposure in childhood, and lack of intrauterine tobacco exposure predict behavioral resilience among a cohort of early adolescents with significant social and environmental risk. Future interventions should work to enhance parental supervision as a way to mitigate the effects of adversity on high-risk groups of adolescents. PMID:26076097

  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. Noninvasive, accurate assessment of the behavior of representative populations of motor units in targeted reinnervated muscles.

    PubMed

    Farina, Dario; Rehbaum, Hubertus; Holobar, Aleš; Vujaklija, Ivan; Jiang, Ning; Hofer, Christian; Salminger, Stefan; van Vliet, Hans-Willem; Aszmann, Oskar C

    2014-07-01

    Targeted muscle reinnervation (TMR) redirects nerves that have lost their target, due to amputation, to remaining muscles in the region of the stump with the intent of establishing intuitive myosignals to control a complex prosthetic device. In order to directly recover the neural code underlying an attempted limb movement, in this paper, we present the decomposition of high-density surface electromyographic (EMG) signals detected from three TMR patients into the individual motor unit spike trains. The aim was to prove, for the first time, the feasibility of decoding the neural drive that would reach muscles of the missing limb in TMR patients, to show the accuracy of the decoding, and to demonstrate the representativeness of the pool of extracted motor units. Six to seven flexible EMG electrode grids of 64 electrodes each were mounted over the reinnervated muscles of each patient, resulting in up to 448 EMG signals. The subjects were asked to attempt elbow extension and flexion, hand open and close, wrist extension and flexion, wrist pronation and supination, of their missing limb. The EMG signals were decomposed using the Convolution Kernel Compensation technique and the decomposition accuracy was evaluated with a signal-based index of accuracy, called pulse-to-noise ratio (PNR). The results showed that the spike trains of 3 to 27 motor units could be identified for each task, with a sensitivity of the decomposition > 90%, as revealed by PNR. The motor unit discharge rates were within physiological values of normally innervated muscles. Moreover, the detected motor units showed a high degree of common drive so that the set of extracted units per task was representative of the behavior of the population of active units. The results open a path for a new generation of human-machine interfaces in which the control signals are extracted from noninvasive recordings and the obtained neural information is based directly on the spike trains of motor neurons. PMID

  14. Accurate prediction for atomic-level protein design and its application in diversifying the near-optimal sequence space.

    PubMed

    Fromer, Menachem; Yanover, Chen

    2009-05-15

    precisely. Examination of the predicted ensembles indicates that, for each structure, the amino acid identity at a majority of positions must be chosen extremely selectively so as to not incur significant energetic penalties. We investigate this high degree of similarity and demonstrate how more diverse near-optimal sequences can be predicted in order to systematically overcome this bottleneck for computational design. Finally, we exploit this in-depth analysis of a collection of the lowest energy sequences to suggest an explanation for previously observed experimental design results. The novel methodologies introduced here accurately portray the sequence space compatible with a protein structure and further supply a scheme to yield heterogeneous low-energy sequences, thus providing a powerful instrument for future work on protein design. PMID:19003998

  15. Predicting suitable optoelectronic properties of monoclinic VON semiconductor crystals for photovoltaics using accurate first-principles computations.

    PubMed

    Harb, Moussab

    2015-10-14

    Using accurate first-principles quantum calculations based on DFT (including the DFPT) with the range-separated hybrid HSE06 exchange-correlation functional, we can predict the essential fundamental properties (such as bandgap, optical absorption co-efficient, dielectric constant, charge carrier effective masses and exciton binding energy) of two stable monoclinic vanadium oxynitride (VON) semiconductor crystals for solar energy conversion applications. In addition to the predicted band gaps in the optimal range for making single-junction solar cells, both polymorphs exhibit a relatively high absorption efficiency in the visible range, high dielectric constant, high charge carrier mobility and much lower exciton binding energy than the thermal energy at room temperature. Moreover, their optical absorption, dielectric and exciton dissociation properties were found to be better than those obtained for semiconductors frequently utilized in photovoltaic devices such as Si, CdTe and GaAs. These novel results offer a great opportunity for this stoichiometric VON material to be properly synthesized and considered as a new good candidate for photovoltaic applications. PMID:26351755

  16. The Theory of Planned Behavior: Predicting Teachers' Intentions and Behavior during Fitness Testing

    ERIC Educational Resources Information Center

    Stanec, Amanda D. Stewart

    2009-01-01

    The twofold purpose of this study was to develop and validate an instrument that assessed teachers' intentions, attitudes, subjective norm, and perceived behavior control to administer fitness tests effectively, and to determine how well the instrument could predict teachers' intentions and actual behavior based on Ajzen's (1985, 1991) theory of…

  17. 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

  18. Predictions of Future Daily Behaviors: A Further Replication.

    ERIC Educational Resources Information Center

    Bull, C. Neil

    The present study is a continuation of a series of studies dealing with predictions of future daily behaviors. Because of the finite nature of time, people have to make choices among activities, bearing in mind the duration of such activities. The dimension of "discretion", which deals specifically with the duration of time a person spends on an…

  19. Behavioral Changes Predicting Temporal Changes in Perceived Popular Status

    ERIC Educational Resources Information Center

    Bowker, Julie C.; Rubin, Kenneth H.; Buskirk-Cohen, Allison; Rose-Krasnor, Linda; Booth-LaForce, Cathryn

    2010-01-01

    The primary objectives of this investigation were to determine the extent to which young adolescents are stable in high perceived popular status across the middle school transition and to examine whether changes in social behaviors predict the stability, gain, and loss of perceived popular status after the transition. The sample included 672 young…

  20. 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.

  1. 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)

  2. Predicting active users' personality based on micro-blogging behaviors.

    PubMed

    Li, Lin; Li, Ang; 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 839 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 [corrected]. 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

  3. An Integrated Calculation Method to Predict Arc Behavior

    NASA Astrophysics Data System (ADS)

    Li, Xingwen; Chen, Degui

    The precision of magnetic field calculation is crucial to predict the arc behavior using magnetohydrodynamic (MHD) model. A integrated calculation method is proposed to couple the calculation of magnetic field and fluid dynamics based on the commercial software ANSYS and FLUENT, which especially benefits to take into account the existence of the ferromagnetic parts. An example concerning air arc is presented using the method.

  4. 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. PMID:21947617

  5. 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.

  6. 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

    2016-07-01

    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.

  7. 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.

  8. 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

  9. Deficits in behavioral inhibition predict treatment engagement in prison inmates.

    PubMed

    Fishbein, Diana; Sheppard, Monica; Hyde, Christopher; Hubal, Robert; Newlin, David; Serin, Ralph; Chrousos, George; Alesci, Salvatore

    2009-10-01

    Many inmates do not respond favorably to standard treatments routinely offered in prison. Executive cognitive functioning and emotional regulation may play a key role in treatment responsivity. During intake into treatment, inmates (N = 224) were evaluated for executive functioning, emotional perception, stress reactivity (salivary cortisol), IQ, psychological and behavioral traits, prior drug use, child and family background, and criminal histories and institutional behavior. Outcome measures included program completion, treatment readiness, responsivity and gain, and the Novaco Reaction to Provocation Questionnaire. Relative deficits in behavioral inhibition significantly predicted treatment outcomes, more so than background, psychological, or behavioral variables, and other neurocognitive and emotional regulatory measures. Future replications of these results have potential to improve assessment and treatment of offenders who are otherwise intractable. PMID:19139980

  10. Prediction of Domain Behavior through Dynamic Well-Being Domain Model Analysis

    PubMed Central

    Bosems, Steven; van Sinderen, Marten

    2015-01-01

    As the concept of context-awareness is becoming more popular the demand for improved quality of context-aware systems increases too. Due to the inherent challenges posed by context-awareness, it is harder to predict what the behavior of the systems and their context will be once provided to the end-user than is the case for non-context-aware systems. A domain where such upfront knowledge is highly important is that of well-being. In this paper, we introduce a method to model the well-being domain and to predict the effects the system will have on its context when implemented. This analysis can be performed at design time. Using these predictions, the design can be fine-tuned to increase the chance that systems will have the desired effect. The method has been tested using three existing well-being applications. For these applications, domain models were created in the Dynamic Well-being Domain Model language. This language allows for causal reasoning over the application domain. The models created were used to perform the analysis and behavior prediction. The analysis results were compared to existing application end-user evaluation studies. Results showed that our analysis could accurately predict success and possible problems in the focus of the systems, although certain limitation regarding the predictions should be kept into consideration. PMID:26351660

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

    SciTech Connect

    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 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. 21 references.

  12. Mathematical models for accurate prediction of atmospheric visibility with particular reference to the seasonal and environmental patterns in Hong Kong.

    PubMed

    Mui, K W; Wong, L T; Chung, L Y

    2009-11-01

    Atmospheric visibility impairment has gained increasing concern as it is associated with the existence of a number of aerosols as well as common air pollutants and produces unfavorable conditions for observation, dispersion, and transportation. This study analyzed the atmospheric visibility data measured in urban and suburban Hong Kong (two selected stations) with respect to time-matched mass concentrations of common air pollutants including nitrogen dioxide (NO(2)), nitrogen monoxide (NO), respirable suspended particulates (PM(10)), sulfur dioxide (SO(2)), carbon monoxide (CO), and meteorological parameters including air temperature, relative humidity, and wind speed. No significant difference in atmospheric visibility was reported between the two measurement locations (p > or = 0.6, t test); and good atmospheric visibility was observed more frequently in summer and autumn than in winter and spring (p < 0.01, t test). It was also found that atmospheric visibility increased with temperature but decreased with the concentrations of SO(2), CO, PM(10), NO, and NO(2). The results showed that atmospheric visibility was season dependent and would have significant correlations with temperature, the mass concentrations of PM(10) and NO(2), and the air pollution index API (correlation coefficients mid R: R mid R: > or = 0.7, p < or = 0.0001, t test). Mathematical expressions catering to the seasonal variations of atmospheric visibility were thus proposed. By comparison, the proposed visibility prediction models were more accurate than some existing regional models. In addition to improving visibility prediction accuracy, this study would be useful for understanding the context of low atmospheric visibility, exploring possible remedial measures, and evaluating the impact of air pollution and atmospheric visibility impairment in this region. PMID:18951139

  13. A Support Vector Machine model for the prediction of proteotypic peptides for accurate mass and time proteomics

    SciTech Connect

    Webb-Robertson, Bobbie-Jo M.; Cannon, William R.; Oehmen, Christopher S.; Shah, Anuj R.; Gurumoorthi, Vidhya; Lipton, Mary S.; Waters, Katrina M.

    2008-07-01

    Motivation: The standard approach to identifying peptides based on accurate mass and elution time (AMT) compares these profiles obtained from a high resolution mass spectrometer to a database of peptides previously identified from tandem mass spectrometry (MS/MS) studies. It would be advantageous, with respect to both accuracy and cost, to only search for those peptides that are detectable by MS (proteotypic). Results: We present a Support Vector Machine (SVM) model that uses a simple descriptor space based on 35 properties of amino acid content, charge, hydrophilicity, and polarity for the quantitative prediction of proteotypic peptides. Using three independently derived AMT databases (Shewanella oneidensis, Salmonella typhimurium, Yersinia pestis) for training and validation within and across species, the SVM resulted in an average accuracy measure of ~0.8 with a standard deviation of less than 0.025. Furthermore, we demonstrate that these results are achievable with a small set of 12 variables and can achieve high proteome coverage. Availability: http://omics.pnl.gov/software/STEPP.php

  14. Applying Theory of Planned Behavior in Predicting of Patient Safety Behaviors of Nurses

    PubMed Central

    Javadi, Marzieh; Kadkhodaee, Maryam; Yaghoubi, Maryam; Maroufi, Maryam; Shams, Asadollah

    2013-01-01

    Background: Patient safety has become a major concern throughout the world. It is the absence of preventable harm to a patient during the process of health care, ensuring safer care is an enormous challenge, psychosocial variables influences behaviors of human. The theory of planned behavior (TPB) is a well-validated behavioral decision-making model that has been used to predict social and health behaviors. This study is aimed to investigate predictors of nurse’s patient safety intentions and behavior, using a TPB framework. Methods: Stratified sampling technique was used to choose 124 nurses who worked at the selected hospitals of Isfahan in 2011. Study tool was a questionnaire, designed by researchers team including 3 nurses a physician and a psychologist based on guideline of TPB model. Questionnaire Validity was confirmed by experts and its reliability was assessed by Cronbach’s alpha as 0.87. Binary logistic regression analysis was performed to evaluate how well each TPB variables predicted the variance in patient safety behavior. Analyzing was done by SPSS18. Results: Finding revealed that “normative beliefs” had the greatest influence on nurses intention to implement patient safety behaviors. Analyzing data by hospital types and workplace wards showed that both in public and private hospitals normative beliefs has affected safety behaviors of nurses more than other variables. Also in surgical wards, nurses behaviors have been affected by “control beliefs” and in medical wards by normative beliefs. Conclusion: Normative beliefs, and subjective norms were the most influential factor of safety behavior of nurses in this study. Considering the role of cultural context in these issues, it seemseducation of managers and top individuals about patient safety and its importance is a priority also control believes were another important predicting factor of behavior in surgical wards and intensive care units. Regarding the complexity of work in these

  15. High IFIT1 expression predicts improved clinical outcome, and IFIT1 along with MGMT more accurately predicts prognosis in newly diagnosed glioblastoma.

    PubMed

    Zhang, Jin-Feng; Chen, Yao; Lin, Guo-Shi; Zhang, Jian-Dong; Tang, Wen-Long; Huang, Jian-Huang; Chen, Jin-Shou; Wang, Xing-Fu; Lin, Zhi-Xiong

    2016-06-01

    Interferon-induced protein with tetratricopeptide repeat 1 (IFIT1) plays a key role in growth suppression and apoptosis promotion in cancer cells. Interferon was reported to induce the expression of IFIT1 and inhibit the expression of O-6-methylguanine-DNA methyltransferase (MGMT).This study aimed to investigate the expression of IFIT1, the correlation between IFIT1 and MGMT, and their impact on the clinical outcome in newly diagnosed glioblastoma. The expression of IFIT1 and MGMT and their correlation were investigated in the tumor tissues from 70 patients with newly diagnosed glioblastoma. The effects on progression-free survival and overall survival were evaluated. Of 70 cases, 57 (81.4%) tissue samples showed high expression of IFIT1 by immunostaining. The χ(2) test indicated that the expression of IFIT1 and MGMT was negatively correlated (r = -0.288, P = .016). Univariate and multivariate analyses confirmed high IFIT1 expression as a favorable prognostic indicator for progression-free survival (P = .005 and .017) and overall survival (P = .001 and .001), respectively. Patients with 2 favorable factors (high IFIT1 and low MGMT) had an improved prognosis as compared with others. The results demonstrated significantly increased expression of IFIT1 in newly diagnosed glioblastoma tissue. The negative correlation between IFIT1 and MGMT expression may be triggered by interferon. High IFIT1 can be a predictive biomarker of favorable clinical outcome, and IFIT1 along with MGMT more accurately predicts prognosis in newly diagnosed glioblastoma. PMID:26980050

  16. Attentional Bias toward Suicide-Related Stimuli Predicts Suicidal Behavior

    PubMed Central

    Cha, Christine B.; Najmi, Sadia; Park, Jennifer M.; Finn, Christine T.; Nock, Matthew K.

    2010-01-01

    A long-standing challenge for scientific and clinical work on suicidal behavior is that people often are motivated to deny or conceal suicidal thoughts. We proposed that people considering suicide would possess an objectively measurable attentional bias toward suicide-related stimuli, and that this bias would predict future suicidal behavior. Participants were 124 adults presenting to a psychiatric emergency department who were administered a modified emotional Stroop task and followed for six months. Suicide attempters showed an attentional bias toward suicide-related words relative to neutral words, and this bias was strongest among those who had made a more recent attempt. Importantly, this suicide-specific attentional bias predicted which people made a suicide attempt over the next six months, above and beyond other clinical predictors. Attentional bias toward more general negatively-valenced words did not predict any suicide-related outcomes, supporting the specificity of the observed effect. These results suggest that suicide-specific attentional bias can serve as a behavioral marker for suicidal risk, and ultimately improve scientific and clinical work on suicide-related outcomes. PMID:20677851

  17. Important behavioral traits for predicting guide dog qualification.

    PubMed

    Arata, Sayaka; Momozawa, Yukihide; Takeuchi, Yukari; Mori, Yuji

    2010-05-01

    Guide dogs for the blind help blind people physically and mentally in their daily lives. Their qualifications are based on health, working performance and temperament; approximately 70% of dogs that fail to qualify are disqualified for behavioral reasons. In order to achieve early prediction of qualification, it would be essential as the first step to identify important temperament traits for guide dogs. Therefore, we administered a questionnaire consisting of 22 temperament items to experienced trainers to assess candidate dogs at the Japan Guide Dog Association after three months of training, which was at least three months prior to the final success (qualified as a guide dog) or failure (disqualified for behavioral reasons) judgment. Factor analyses of question items stably extracted three factors with high internal consistency, Distraction, Sensitivity and Docility. When we compared factor points between successful dogs and failed dogs, the successful dogs showed significantly and consistently lower Distraction points and higher Docility points. Additionally, Distraction points could predict qualification with 80.6% accuracy and detect 28.2% of the failed dogs that had higher Distraction points than any of the successful dogs. Of the nine question items not included in the three factors, two items (;Aggression' and ;Animal interest') were consistently associated with qualification. These results suggest that Distraction is stably assessable and has the strongest impact on success or failure judgment; therefore, it will be the first target to establish a behavioral test that may lead to early prediction of guide dog qualification. PMID:20009419

  18. 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

  19. 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.

  20. A Systematic Review of Predictions of Survival in Palliative Care: How Accurate Are Clinicians and Who Are the Experts?

    PubMed Central

    Harris, Adam; Harries, Priscilla

    2016-01-01

    overall accuracy being reported. Data were extracted using a standardised tool, by one reviewer, which could have introduced bias. Devising search terms for prognostic studies is challenging. Every attempt was made to devise search terms that were sufficiently sensitive to detect all prognostic studies; however, it remains possible that some studies were not identified. Conclusion Studies of prognostic accuracy in palliative care are heterogeneous, but the evidence suggests that clinicians’ predictions are frequently inaccurate. No sub-group of clinicians was consistently shown to be more accurate than any other. Implications of Key Findings Further research is needed to understand how clinical predictions are formulated and how their accuracy can be improved. PMID:27560380

  1. Predictive models of procedural human supervisory control behavior

    NASA Astrophysics Data System (ADS)

    Boussemart, Yves

    Human supervisory control systems are characterized by the computer-mediated nature of the interactions between one or more operators and a given task. Nuclear power plants, air traffic management and unmanned vehicles operations are examples of such systems. In this context, the role of the operators is typically highly proceduralized due to the time and mission-critical nature of the tasks. Therefore, the ability to continuously monitor operator behavior so as to detect and predict anomalous situations is a critical safeguard for proper system operation. In particular, such models can help support the decision J]l8king process of a supervisor of a team of operators by providing alerts when likely anomalous behaviors are detected By exploiting the operator behavioral patterns which are typically reinforced through standard operating procedures, this thesis proposes a methodology that uses statistical learning techniques in order to detect and predict anomalous operator conditions. More specifically, the proposed methodology relies on hidden Markov models (HMMs) and hidden semi-Markov models (HSMMs) to generate predictive models of unmanned vehicle systems operators. Through the exploration of the resulting HMMs in two distinct single operator scenarios, the methodology presented in this thesis is validated and shown to provide models capable of reliably predicting operator behavior. In addition, the use of HSMMs on the same data scenarios provides the temporal component of the predictions missing from the HMMs. The final step of this work is to examine how the proposed methodology scales to more complex scenarios involving teams of operators. Adopting a holistic team modeling approach, both HMMs and HSMMs are learned based on two team-based data sets. The results show that the HSMMs can provide valuable timing information in the single operator case, whereas HMMs tend to be more robust to increased team complexity. In addition, this thesis discusses the

  2. Monoamine oxidase A gene (MAOA) predicts behavioral aggression following provocation

    PubMed Central

    McDermott, Rose; Tingley, Dustin; Cowden, Jonathan; Frazzetto, Giovanni; Johnson, Dominic D. P.

    2009-01-01

    Monoamine oxidase A gene (MAOA) has earned the nickname “warrior gene” because it has been linked to aggression in observational and survey-based studies. However, no controlled experimental studies have tested whether the warrior gene actually drives behavioral manifestations of these tendencies. We report an experiment, synthesizing work in psychology and behavioral economics, which demonstrates that aggression occurs with greater intensity and frequency as provocation is experimentally manipulated upwards, especially among low activity MAOA (MAOA-L) subjects. In this study, subjects paid to punish those they believed had taken money from them by administering varying amounts of unpleasantly hot (spicy) sauce to their opponent. There is some evidence of a main effect for genotype and some evidence for a gene by environment interaction, such that MAOA is less associated with the occurrence of aggression in a low provocation condition, but significantly predicts such behavior in a high provocation situation. This new evidence for genetic influences on aggression and punishment behavior complicates characterizations of humans as “altruistic” punishers and supports theories of cooperation that propose mixed strategies in the population. It also suggests important implications for the role of individual variance in genetic factors contributing to everyday behaviors and decisions. PMID:19168625

  3. Prediction of multiaxial mechanical behavior for conventional and highly crosslinked UHMWPE using a hybrid constitutive model.

    PubMed

    Bergström, J S; Rimnac, C M; Kurtz, S M

    2003-04-01

    The development of theoretical failure, fatigue, and wear models for ultra-high molecular weight polyethylene (UHMWPE) used in joint replacements has been hindered by the lack of a validated constitutive model that can accurately predict large deformation mechanical behavior under clinically relevant, multiaxial loading conditions. Recently, a new Hybrid constitutive model for unirradiated UHMWPE was developed Bergström et al., (Biomaterials 23 (2002) 2329) based on a physics-motivated framework which incorporates the governing micro-mechanisms of polymers into an effective and accurate continuum representation. The goal of the present study was to compare the predictive capability of the new Hybrid model with the J(2)-plasticity model for four conventional and highly crosslinked UHMWPE materials during multiaxial loading. After calibration under uniaxial loading, the predictive capabilities of the J(2)-plasticity and Hybrid model were tested by comparing the load-displacement curves from experimental multiaxial (small punch) tests with simulated load-displacement curves calculated using a finite element model of the experimental apparatus. The quality of the model predictions was quantified using the coefficient of determination (r(2)). The results of the study demonstrate that the Hybrid model outperforms the J(2)-plasticity model both for combined uniaxial tension and compression predictions and for simulating multiaxial large deformation mechanical behavior produced by the small punch test. The results further suggest that the parameters of the HM may be generalizable for a wide range of conventional, highly crosslinked, and thermally treated UHMWPE materials, based on the characterization of four material properties related to the elastic modulus, yield stress, rate of strain hardening, and locking stretch of the polymer chains. Most importantly, from a practical perspective, these four key material properties for the Hybrid constitutive model can be measured

  4. 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

  5. Behavioral Changes Predicting Temporal Changes in Perceived Popular Status

    PubMed Central

    Bowker, Julie C.; Rubin, Kenneth H.; Buskirk-Cohen, Alison; Rose-Krasnor, Linda; Booth-LaForce, Cathryn

    2009-01-01

    The primary objectives of this investigation were to determine the extent to which young adolescents are stable in high perceived popular status across the middle school transition and to examine whether changes in social behaviors predict the stability, gain, and loss of perceived popular status after the transition. The sample included 672 young adolescents (323 boys) who completed peer-nomination assessments of social behavior and perceived popularity at the end of elementary school (5th grade) and the beginning of middle school (6th grade). Findings indicated that 62 percent of perceived popular adolescents remained stable in their high popular status across the middle school transition. Multinomial logistic regression analyses revealed that a combination of aggression and arrogance/conceit was associated with stable and newly-gained perceived popular status after the middle school transition. Taken together, findings highlight the significance of contextual and temporal changes in adolescents’ perceived popular status. PMID:20209113

  6. 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. PMID:22049662

  7. Energy functions for knots: Beginning to predict physical behavior

    SciTech Connect

    Simon, J.

    1996-12-31

    Several definitions have been proposed for the {open_quotes}energy{close_quotes} of a knot. The intuitive goal is to define a number u(K) that somehow measures how {open_quotes}tangled{close_quotes} or {open_quotes}crumpled{close_quotes} a knot K is. Typically, one starts with the idea that a small piece of the knot somehow repels other pieces, and then adds up the contributions from all the pieces. From a purely mathematical standpoint, one may hope to define new knot-type invariants, e.g by considering the minimum of u(K) as K ranges over all the knots of a given knot-type. We also are motivated by the desire to understand and predict how knot-type affects the behavior of physically real knots, in particular DNA loops in gel electrophoresis or random knotting experiments. Despite the physical naivete of recently studied knot energies, there now is enough laboratory data on relative gel velocity, along with computer calculations of idealized knot energies, to justify the assertion that knot energies can predict relative knot behavior in physical systems. The relationships between random knot frequencies and either gel velocities or knot energies is less clear at this time. 50 refs., 8 figs., 2 tabs.

  8. A transport model for prediction of wildfire behavior

    SciTech Connect

    Linn, R.R.

    1997-07-01

    Wildfires are a threat to human life and property, yet they are an unavoidable part of nature. In the past people have tried to predict wildfire behavior through the use of point functional models but have been unsuccessful at adequately predicting the gross behavior of the broad spectrum of fires that occur in nature. The majority of previous models do not have self-determining propagation rates. The author uses a transport approach to represent this complicated problem and produce a model that utilizes a self-determining propagation rate. The transport approach allows one to represent a large number of environments including transition regions such as those with nonhomogeneous vegetation and terrain. Some of the most difficult features to treat are the imperfectly known boundary conditions and the fine scale structure that is unresolvable, such as the specific location of the fuel or the precise incoming winds. The author accounts for the microscopic details of a fire with macroscopic resolution by dividing quantities into mean and fluctuating parts similar to what is done in traditional turbulence modelling. The author develops a complicated model that includes the transport of multiple gas species, such as oxygen and volatile hydrocarbons, and tracks the depletion of various fuels and other stationary solids and liquids. From this model the author also forms a simplified local burning model with which he performs a number of simulations for the purpose of demonstrating the properties of a self-determining transport-based wildfire model.

  9. Corticosterone predicts foraging behavior and parental care in macaroni penguins.

    PubMed

    Crossin, Glenn T; Trathan, Phil N; Phillips, Richard A; Gorman, Kristen B; Dawson, Alistair; Sakamoto, Kentaro Q; Williams, Tony D

    2012-07-01

    Corticosterone has received considerable attention as the principal hormonal mediator of allostasis or physiological stress in wild animals. More recently, it has also been implicated in the regulation of parental care in breeding birds, particularly with respect to individual variation in foraging behavior and provisioning effort. There is also evidence that prolactin can work either inversely or additively with corticosterone to achieve this. Here we test the hypothesis that endogenous corticosterone plays a key physiological role in the control of foraging behavior and parental care, using a combination of exogenous corticosterone treatment, time-depth telemetry, and physiological sampling of female macaroni penguins (Eudyptes chrysolophus) during the brood-guard period of chick rearing, while simultaneously monitoring patterns of prolactin secretion. Plasma corticosterone levels were significantly higher in females given exogenous implants relative to those receiving sham implants. Increased corticosterone levels were associated with significantly higher levels of foraging and diving activity and greater mass gain in implanted females. Elevated plasma corticosterone was also associated with an apparent fitness benefit in the form of increased chick mass. Plasma prolactin levels did not correlate with corticosterone levels at any time, nor was prolactin correlated with any measure of foraging behavior or parental care. Our results provide support for the corticosterone-adaptation hypothesis, which predicts that higher corticosterone levels support increased foraging activity and parental effort. PMID:22673661

  10. 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. PMID:26737854

  11. Dimensionless Equation of State to Predict Microemulsion Phase Behavior.

    PubMed

    Ghosh, Soumyadeep; Johns, Russell T

    2016-09-01

    Prediction of microemulsion phase behavior for changing state variables is critical to formulation design of surfactant-oil-brine (SOB) systems. SOB systems find applications in various chemical and petroleum processes, including enhanced oil recovery. A dimensional equation-of-state (EoS) was recently presented by Ghosh and Johns1 that relied on estimation of the surfactant tail length and surface area. We give an algorithm for flash calculations for estimation of three-phase Winsor regions that is more robust, simpler, and noniterative by making the equations dimensionless so that estimates of tail length and surface area are no longer needed. We predict phase behavior as a function temperature, pressure, volume, salinity, oil type, oil-water ratio, and surfactant/alcohol concentration. The dimensionless EoS is based on coupling the HLD-NAC (Hydrophilic Lipophilic Difference-Net Average Curvature) equations with new relationships between optimum salinity and solubility. An updated HLD expression that includes pressure is also used to complete the state description. A significant advantage of the dimensionless form of the EoS over the dimensional version is that salinity scans are tuned based only on one parameter, the interfacial volume ratio. Further, stability conditions are developed in a simplified way to predict whether an overall compositions lies within the single, two-, or three-phase regions. Important new microemulsion relationships are also found, the most important of which is that optimum solubilization ratio is equal to the harmonic mean of the oil and water solubilization ratios in the type III region. Thus, only one experimental measurement is needed in the three-phase zone to estimate the optimum solubilization ratio, a result which can aid experimental design and improve estimates of optimum from noisy data. Predictions with changing state variables are illustrated by comparison to experimental data using standard diagrams including a new type

  12. Predicting subtle behavioral responses of invertebrates to soil contaminants

    SciTech Connect

    Donkin, S.G.

    1995-12-31

    At concentration levels well below those which cause death and injury to soil invertebrates, a toxic chemical plume may yet effectively damage a soil ecosystem by triggering avoidance behavior among sensitive invertebrates as they move along the concentration gradient. The result may be a soil ecosystem lacking the benefits of effective nutrient cycling and mineralization which a thriving invertebrate population provides. While determining actual detection limits of invertebrates for chemical gradients in soils is experimentally difficult, theoretical calculations have suggested that such limits may be extremely low, and hence many organisms may sense and avoid concentrations of chemicals far below levels commonly considered acceptable. The minimum gradient (G) that can be detected by a receptor depends on the receptor radius (R), the chemical concentration (C), the diffusion constant of the chemical (D), the velocity of the organism (v), and the time over which the receptor integrates the chemical signal (t). In addition, the characteristics of that gradient are determined by interactions between the chemical and the soil particles (sorption/desorption), and advection through the pore spaces. The example of lead (Pb), a neurotoxic metal with demonstrated behavioral effects on the free-living nematode Caenorhabditis elegans, is used to model a chemical migrating through a soil. Based on experimentally determined Pb concentrations which elicited avoidance behavior in nematodes, and sorption characteristics of defined Pb-soil systems, the minimum detectable gradient (G) produced by a solubilized Pb plume in several soils was modeled. The results predict maximum allowable Pb levels in a soil if a healthy invertebrate community is desired, and suggest areas for further research into the subtle behavioral effects of environmental toxicants ore sensitive invertebrates.

  13. Accurate prediction of protein secondary structure and solvent accessibility by consensus combiners of sequence and structure information

    PubMed Central

    Pollastri, Gianluca; Martin, Alberto JM; Mooney, Catherine; Vullo, Alessandro

    2007-01-01

    Background Structural properties of proteins such as secondary structure and solvent accessibility contribute to three-dimensional structure prediction, not only in the ab initio case but also when homology information to known structures is available. Structural properties are also routinely used in protein analysis even when homology is available, largely because homology modelling is lower throughput than, say, secondary structure prediction. Nonetheless, predictors of secondary structure and solvent accessibility are virtually always ab initio. Results Here we develop high-throughput machine learning systems for the prediction of protein secondary structure and solvent accessibility that exploit homology to proteins of known structure, where available, in the form of simple structural frequency profiles extracted from sets of PDB templates. We compare these systems to their state-of-the-art ab initio counterparts, and with a number of baselines in which secondary structures and solvent accessibilities are extracted directly from the templates. We show that structural information from templates greatly improves secondary structure and solvent accessibility prediction quality, and that, on average, the systems significantly enrich the information contained in the templates. For sequence similarity exceeding 30%, secondary structure prediction quality is approximately 90%, close to its theoretical maximum, and 2-class solvent accessibility roughly 85%. Gains are robust with respect to template selection noise, and significant for marginal sequence similarity and for short alignments, supporting the claim that these improved predictions may prove beneficial beyond the case in which clear homology is available. Conclusion The predictive system are publicly available at the address . PMID:17570843

  14. 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. PMID:24675610

  15. 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

  16. The eye in hand: predicting others' behavior by integrating multiple sources of information

    PubMed Central

    Pezzulo, Giovanni; Costantini, Marcello

    2015-01-01

    The ability to predict the outcome of other beings' actions confers significant adaptive advantages. Experiments have assessed that human action observation can use multiple information sources, but it is currently unknown how they are integrated and how conflicts between them are resolved. To address this issue, we designed an action observation paradigm requiring the integration of multiple, potentially conflicting sources of evidence about the action target: the actor's gaze direction, hand preshape, and arm trajectory, and their availability and relative uncertainty in time. In two experiments, we analyzed participants' action prediction ability by using eye tracking and behavioral measures. The results show that the information provided by the actor's gaze affected participants' explicit predictions. However, results also show that gaze information was disregarded as soon as information on the actor's hand preshape was available, and this latter information source had widespread effects on participants' prediction ability. Furthermore, as the action unfolded in time, participants relied increasingly more on the arm movement source, showing sensitivity to its increasing informativeness. Therefore, the results suggest that the brain forms a robust estimate of the actor's motor intention by integrating multiple sources of information. However, when informative motor cues such as a preshaped hand with a given grip are available and might help in selecting action targets, people tend to capitalize on such motor cues, thus turning out to be more accurate and fast in inferring the object to be manipulated by the other's hand. PMID:25568158

  17. The eye in hand: predicting others' behavior by integrating multiple sources of information.

    PubMed

    Ambrosini, Ettore; Pezzulo, Giovanni; Costantini, Marcello

    2015-04-01

    The ability to predict the outcome of other beings' actions confers significant adaptive advantages. Experiments have assessed that human action observation can use multiple information sources, but it is currently unknown how they are integrated and how conflicts between them are resolved. To address this issue, we designed an action observation paradigm requiring the integration of multiple, potentially conflicting sources of evidence about the action target: the actor's gaze direction, hand preshape, and arm trajectory, and their availability and relative uncertainty in time. In two experiments, we analyzed participants' action prediction ability by using eye tracking and behavioral measures. The results show that the information provided by the actor's gaze affected participants' explicit predictions. However, results also show that gaze information was disregarded as soon as information on the actor's hand preshape was available, and this latter information source had widespread effects on participants' prediction ability. Furthermore, as the action unfolded in time, participants relied increasingly more on the arm movement source, showing sensitivity to its increasing informativeness. Therefore, the results suggest that the brain forms a robust estimate of the actor's motor intention by integrating multiple sources of information. However, when informative motor cues such as a preshaped hand with a given grip are available and might help in selecting action targets, people tend to capitalize on such motor cues, thus turning out to be more accurate and fast in inferring the object to be manipulated by the other's hand. PMID:25568158

  18. The Use of Behavior Models for Predicting Complex Operations

    NASA Technical Reports Server (NTRS)

    Gore, Brian F.

    2010-01-01

    Modeling and simulation (M&S) plays an important role when complex human-system notions are being proposed, developed and tested within the system design process. National Aeronautics and Space Administration (NASA) as an agency uses many different types of M&S approaches for predicting human-system interactions, especially when it is early in the development phase of a conceptual design. NASA Ames Research Center possesses a number of M&S capabilities ranging from airflow, flight path models, aircraft models, scheduling models, human performance models (HPMs), and bioinformatics models among a host of other kinds of M&S capabilities that are used for predicting whether the proposed designs will benefit the specific mission criteria. The Man-Machine Integration Design and Analysis System (MIDAS) is a NASA ARC HPM software tool that integrates many models of human behavior with environment models, equipment models, and procedural / task models. The challenge to model comprehensibility is heightened as the number of models that are integrated and the requisite fidelity of the procedural sets are increased. Model transparency is needed for some of the more complex HPMs to maintain comprehensibility of the integrated model performance. This will be exemplified in a recent MIDAS v5 application model and plans for future model refinements will be presented.

  19. The Pupillary Orienting Response Predicts Adaptive Behavioral Adjustment after Errors

    PubMed Central

    Murphy, Peter R.; van Moort, Marianne L.; Nieuwenhuis, Sander

    2016-01-01

    Reaction time (RT) is commonly observed to slow down after an error. This post-error slowing (PES) has been thought to arise from the strategic adoption of a more cautious response mode following deployment of cognitive control. Recently, an alternative account has suggested that PES results from interference due to an error-evoked orienting response. We investigated whether error-related orienting may in fact be a pre-cursor to adaptive post-error behavioral adjustment when the orienting response resolves before subsequent trial onset. We measured pupil dilation, a prototypical measure of autonomic orienting, during performance of a choice RT task with long inter-stimulus intervals, and found that the trial-by-trial magnitude of the error-evoked pupil response positively predicted both PES magnitude and the likelihood that the following response would be correct. These combined findings suggest that the magnitude of the error-related orienting response predicts an adaptive change of response strategy following errors, and thereby promote a reconciliation of the orienting and adaptive control accounts of PES. PMID:27010472

  20. Adolescent Expectations of Early Death Predict Adult Risk Behaviors

    PubMed Central

    Nguyen, Quynh C.; Villaveces, Andres; Marshall, Stephen W.; Hussey, Jon M.; Halpern, Carolyn T.; Poole, Charles

    2012-01-01

    Only a handful of public health studies have investigated expectations of early death among adolescents. Associations have been found between these expectations and risk behaviors in adolescence. However, these beliefs may not only predict worse adolescent outcomes, but worse trajectories in health with ties to negative outcomes that endure into young adulthood. The objectives of this study were to investigate perceived chances of living to age 35 (Perceived Survival Expectations, PSE) as a predictor of suicidal ideation, suicide attempt and substance use in young adulthood. We examined the predictive capacity of PSE on future suicidal ideation/attempt after accounting for sociodemographics, depressive symptoms, and history of suicide among family and friends to more fully assess its unique contribution to suicide risk. We investigated the influence of PSE on legal and illegal substance use and varying levels of substance use. We utilized the National Longitudinal Study of Adolescent Health (Add Health) initiated in 1994–95 among 20,745 adolescents in grades 7–12 with follow-up interviews in 1996 (Wave II), 2001–02 (Wave III) and 2008 (Wave IV; ages 24–32). Compared to those who were almost certain of living to age 35, perceiving a 50–50 or less chance of living to age 35 at Waves I or III predicted suicide attempt and ideation as well as regular substance use (i.e., exceeding daily limits for moderate drinking; smoking ≥ a pack/day; and using illicit substances other than marijuana at least weekly) at Wave IV. Associations between PSE and detrimental adult outcomes were particularly strong for those reporting persistently low PSE at both Waves I and III. Low PSE at Wave I or Wave III was also related to a doubling and tripling, respectively, of death rates in young adulthood. Long-term and wide-ranging ties between PSE and detrimental outcomes suggest these expectations may contribute to identifying at-risk youth. PMID:22870260

  1. A mathematical recursive model for accurate description of the phase behavior in the near-critical region by Generalized van der Waals Equation

    NASA Astrophysics Data System (ADS)

    Kim, Jibeom; Jeon, Joonhyeon

    2015-01-01

    Recently, related studies on Equation Of State (EOS) have reported that generalized van der Waals (GvdW) shows poor representations in the near critical region for non-polar and non-sphere molecules. Hence, there are still remains a problem of GvdW parameters to minimize loss in describing saturated vapor densities and vice versa. This paper describes a recursive model GvdW (rGvdW) for an accurate representation of pure fluid materials in the near critical region. For the performance evaluation of rGvdW in the near critical region, other EOS models are also applied together with two pure molecule group: alkane and amine. The comparison results show rGvdW provides much more accurate and reliable predictions of pressure than the others. The calculating model of EOS through this approach gives an additional insight into the physical significance of accurate prediction of pressure in the nearcritical region.

  2. Conformations of 1,2-dimethoxypropane and 5-methoxy-1,3-dioxane: are ab initio quantum chemistry predictions accurate?

    NASA Astrophysics Data System (ADS)

    Smith, Grant D.; Jaffe, Richard L.; Yoon, Do. Y.

    1998-06-01

    High-level ab initio quantum chemistry calculations are shown to predict conformer populations of 1,2-dimethoxypropane and 5-methoxy-1,3-dioxane that are consistent with gas-phase NMR vicinal coupling constant measurements. The conformational energies of the cyclic ether 5-methoxy-1,3-dioxane are found to be consistent with those predicted by a rotational isomeric state (RIS) model based upon the acyclic analog 1,2-dimethoxypropane. The quantum chemistry and RIS calculations indicate the presence of strong attractive 1,5 C(H 3)⋯O electrostatic interactions in these molecules, similar to those found in 1,2-dimethoxyethane.

  3. 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.

  4. 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

  5. Survival outcomes scores (SOFT, BAR, and Pedi-SOFT) are accurate in predicting post-liver transplant survival in adolescents.

    PubMed

    Conjeevaram Selvakumar, Praveen Kumar; Maksimak, Brian; Hanouneh, Ibrahim; Youssef, Dalia H; Lopez, Rocio; Alkhouri, Naim

    2016-09-01

    SOFT and BAR scores utilize recipient, donor, and graft factors to predict the 3-month survival after LT in adults (≥18 years). Recently, Pedi-SOFT score was developed to predict 3-month survival after LT in young children (≤12 years). These scoring systems have not been studied in adolescent patients (13-17 years). We evaluated the accuracy of these scoring systems in predicting the 3-month post-LT survival in adolescents through a retrospective analysis of data from UNOS of patients aged 13-17 years who received LT between 03/01/2002 and 12/31/2012. Recipients of combined organ transplants, donation after cardiac death, or living donor graft were excluded. A total of 711 adolescent LT recipients were included with a mean age of 15.2±1.4 years. A total of 100 patients died post-LT including 33 within 3 months. SOFT, BAR, and Pedi-SOFT scores were all found to be good predictors of 3-month post-transplant survival outcome with areas under the ROC curve of 0.81, 0.80, and 0.81, respectively. All three scores provided good accuracy for predicting 3-month survival post-LT in adolescents and may help clinical decision making to optimize survival rate and organ utilization. PMID:27478012

  6. RepurposeVS: A Drug Repurposing-Focused Computational Method for Accurate Drug-Target Signature Predictions.

    PubMed

    Issa, Naiem T; Peters, Oakland J; Byers, Stephen W; Dakshanamurthy, Sivanesan

    2015-01-01

    We describe here RepurposeVS for the reliable prediction of drug-target signatures using X-ray protein crystal structures. RepurposeVS is a virtual screening method that incorporates docking, drug-centric and protein-centric 2D/3D fingerprints with a rigorous mathematical normalization procedure to account for the variability in units and provide high-resolution contextual information for drug-target binding. Validity was confirmed by the following: (1) providing the greatest enrichment of known drug binders for multiple protein targets in virtual screening experiments, (2) determining that similarly shaped protein target pockets are predicted to bind drugs of similar 3D shapes when RepurposeVS is applied to 2,335 human protein targets, and (3) determining true biological associations in vitro for mebendazole (MBZ) across many predicted kinase targets for potential cancer repurposing. Since RepurposeVS is a drug repurposing-focused method, benchmarking was conducted on a set of 3,671 FDA approved and experimental drugs rather than the Database of Useful Decoys (DUDE) so as to streamline downstream repurposing experiments. We further apply RepurposeVS to explore the overall potential drug repurposing space for currently approved drugs. RepurposeVS is not computationally intensive and increases performance accuracy, thus serving as an efficient and powerful in silico tool to predict drug-target associations in drug repurposing. PMID:26234515

  7. Is demography destiny? Application of machine learning techniques to accurately predict population health outcomes from a minimal demographic dataset.

    PubMed

    Luo, Wei; Nguyen, Thin; Nichols, Melanie; Tran, Truyen; Rana, Santu; Gupta, Sunil; Phung, Dinh; Venkatesh, Svetha; Allender, Steve

    2015-01-01

    For years, we have relied on population surveys to keep track of regional public health statistics, including the prevalence of non-communicable diseases. Because of the cost and limitations of such surveys, we often do not have the up-to-date data on health outcomes of a region. In this paper, we examined the feasibility of inferring regional health outcomes from socio-demographic data that are widely available and timely updated through national censuses and community surveys. Using data for 50 American states (excluding Washington DC) from 2007 to 2012, we constructed a machine-learning model to predict the prevalence of six non-communicable disease (NCD) outcomes (four NCDs and two major clinical risk factors), based on population socio-demographic characteristics from the American Community Survey. We found that regional prevalence estimates for non-communicable diseases can be reasonably predicted. The predictions were highly correlated with the observed data, in both the states included in the derivation model (median correlation 0.88) and those excluded from the development for use as a completely separated validation sample (median correlation 0.85), demonstrating that the model had sufficient external validity to make good predictions, based on demographics alone, for areas not included in the model development. This highlights both the utility of this sophisticated approach to model development, and the vital importance of simple socio-demographic characteristics as both indicators and determinants of chronic disease. PMID:25938675

  8. A Maximal Graded Exercise Test to Accurately Predict VO2max in 18-65-Year-Old Adults

    ERIC Educational Resources Information Center

    George, James D.; Bradshaw, Danielle I.; Hyde, Annette; Vehrs, Pat R.; Hager, Ronald L.; Yanowitz, Frank G.

    2007-01-01

    The purpose of this study was to develop an age-generalized regression model to predict maximal oxygen uptake (VO sub 2 max) based on a maximal treadmill graded exercise test (GXT; George, 1996). Participants (N = 100), ages 18-65 years, reached a maximal level of exertion (mean plus or minus standard deviation [SD]; maximal heart rate [HR sub…

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

    SciTech Connect

    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 synthetic 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.

  10. Prediction of thermal behavior and trajectory of stratospheric airships during ascent based on simulation

    NASA Astrophysics Data System (ADS)

    Yang, Xixiang

    2016-06-01

    For designers, operators and users, the ability to accurately predict thermal behavior and trajectory of stratospheric airships is very important. Thermal models and dynamic models of stratospheric airships during ascent are developed, including solar radiation, infrared radiation, convection heat transfer and gas expulsion equation. Based on the model, performance parameters of a stratospheric airship during ascent are obtained, including film temperature, helium gas temperature, air temperature, pressure differential, altitude and ascent velocity, changing regulation for these parameters are discussed, and influence of initial helium gas volume and film radiation properties on thermal behavior is analyzed. Simulation results show that, (1) stratospheric airships experience supercooling during ascent, the maximum value is about 30 K, supercooling causes loss of net buoyancy, and affects ascent velocity and trajectory in the end, (2) stratospheric airships experience superheating at the floating altitude, and the maximum value is about 51 K, (3) initial volume ratio of helium gas and the solar radiation absorptivity of film have important effect on thermal behavior and trajectory during ascent, the larger the initial volume ratio is, the faster the ascent velocity will be, and the bigger the solar radiation absorptivity of film is, the smaller the temperature differential between helium gas and outside atmosphere will be.

  11. 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. PMID:25903243

  12. Accurate and efficient prediction of fine-resolution hydrologic and carbon dynamic simulations from coarse-resolution models

    NASA Astrophysics Data System (ADS)

    Pau, George Shu Heng; Shen, Chaopeng; Riley, William J.; Liu, Yaning

    2016-02-01

    The topography, and the biotic and abiotic parameters are typically upscaled to make watershed-scale hydrologic-biogeochemical models computationally tractable. However, upscaling procedure can produce biases when nonlinear interactions between different processes are not fully captured at coarse resolutions. Here we applied the Proper Orthogonal Decomposition Mapping Method (PODMM) to downscale the field solutions from a coarse (7 km) resolution grid to a fine (220 m) resolution grid. PODMM trains a reduced-order model (ROM) with coarse-resolution and fine-resolution solutions, here obtained using PAWS+CLM, a quasi-3-D watershed processes model that has been validated for many temperate watersheds. Subsequent fine-resolution solutions were approximated based only on coarse-resolution solutions and the ROM. The approximation errors were efficiently quantified using an error estimator. By jointly estimating correlated variables and temporally varying the ROM parameters, we further reduced the approximation errors by up to 20%. We also improved the method's robustness by constructing multiple ROMs using different set of variables, and selecting the best approximation based on the error estimator. The ROMs produced accurate downscaling of soil moisture, latent heat flux, and net primary production with O(1000) reduction in computational cost. The subgrid distributions were also nearly indistinguishable from the ones obtained using the fine-resolution model. Compared to coarse-resolution solutions, biases in upscaled ROM solutions were reduced by up to 80%. This method has the potential to help address the long-standing spatial scaling problem in hydrology and enable long-time integration, parameter estimation, and stochastic uncertainty analysis while accurately representing the heterogeneities.

  13. How accurately can subject-specific finite element models predict strains and strength of human femora? Investigation using full-field measurements.

    PubMed

    Grassi, Lorenzo; Väänänen, Sami P; Ristinmaa, Matti; Jurvelin, Jukka S; Isaksson, Hanna

    2016-03-21

    Subject-specific finite element models have been proposed as a tool to improve fracture risk assessment in individuals. A thorough laboratory validation against experimental data is required before introducing such models in clinical practice. Results from digital image correlation can provide full-field strain distribution over the specimen surface during in vitro test, instead of at a few pre-defined locations as with strain gauges. The aim of this study was to validate finite element models of human femora against experimental data from three cadaver femora, both in terms of femoral strength and of the full-field strain distribution collected with digital image correlation. The results showed a high accuracy between predicted and measured principal strains (R(2)=0.93, RMSE=10%, 1600 validated data points per specimen). Femoral strength was predicted using a rate dependent material model with specific strain limit values for yield and failure. This provided an accurate prediction (<2% error) for two out of three specimens. In the third specimen, an accidental change in the boundary conditions occurred during the experiment, which compromised the femoral strength validation. The achieved strain accuracy was comparable to that obtained in state-of-the-art studies which validated their prediction accuracy against 10-16 strain gauge measurements. Fracture force was accurately predicted, with the predicted failure location being very close to the experimental fracture rim. Despite the low sample size and the single loading condition tested, the present combined numerical-experimental method showed that finite element models can predict femoral strength by providing a thorough description of the local bone mechanical response. PMID:26944687

  14. 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

  15. 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. PMID:24597438

  16. Can the Gibbs free energy of adsorption be predicted efficiently and accurately: an M05-2X DFT study.

    PubMed

    Michalkova, A; Gorb, L; Hill, F; Leszczynski, J

    2011-03-24

    This study presents new insight into the prediction of partitioning of organic compounds between a carbon surface (soot) and water, and it also sheds light on the sluggish desorption of interacting molecules from activated and nonactivated carbon surfaces. This paper provides details about the structure and interactions of benzene, polycyclic aromatic hydrocarbons, and aromatic nitrocompounds with a carbon surface modeled by coronene using a density functional theory approach along with the M05-2X functional. The adsorption was studied in vacuum and from water solution. The molecules studied are physisorbed on the carbon surface. While the intermolecular interactions of benzene and hydrocarbons are governed by dispersion forces, nitrocompounds are adsorbed also due to quite strong electrostatic interactions with all types of carbon surfaces. On the basis of these results, we conclude that the method of prediction presented in this study allows one to approach the experimental level of accuracy in predicting thermodynamic parameters of adsorption on a carbon surface from the gas phase. The empirical modification of the polarized continuum model leads also to a quantitative agreement with the experimental data for the Gibbs free energy values of the adsorption from water solution. PMID:21361266

  17. A highly accurate protein structural class prediction approach using auto cross covariance transformation and recursive feature elimination.

    PubMed

    Li, Xiaowei; Liu, Taigang; Tao, Peiying; Wang, Chunhua; Chen, Lanming

    2015-12-01

    Structural class characterizes the overall folding type of a protein or its domain. Many methods have been proposed to improve the prediction accuracy of protein structural class in recent years, but it is still a challenge for the low-similarity sequences. In this study, we introduce a feature extraction technique based on auto cross covariance (ACC) transformation of position-specific score matrix (PSSM) to represent a protein sequence. Then support vector machine-recursive feature elimination (SVM-RFE) is adopted to select top K features according to their importance and these features are input to a support vector machine (SVM) to conduct the prediction. Performance evaluation of the proposed method is performed using the jackknife test on three low-similarity datasets, i.e., D640, 1189 and 25PDB. By means of this method, the overall accuracies of 97.2%, 96.2%, and 93.3% are achieved on these three datasets, which are higher than those of most existing methods. This suggests that the proposed method could serve as a very cost-effective tool for predicting protein structural class especially for low-similarity datasets. PMID:26460680

  18. An Optimized Method for Accurate Fetal Sex Prediction and Sex Chromosome Aneuploidy Detection in Non-Invasive Prenatal Testing

    PubMed Central

    Li, Haibo; Ding, Jie; Wen, Ping; Zhang, Qin; Xiang, Jingjing; Li, Qiong; Xuan, Liming; Kong, Lingyin; Mao, Yan; Zhu, Yijun; Shen, Jingjing; Liang, Bo; Li, Hong

    2016-01-01

    Massively parallel sequencing (MPS) combined with bioinformatic analysis has been widely applied to detect fetal chromosomal aneuploidies such as trisomy 21, 18, 13 and sex chromosome aneuploidies (SCAs) by sequencing cell-free fetal DNA (cffDNA) from maternal plasma, so-called non-invasive prenatal testing (NIPT). However, many technical challenges, such as dependency on correct fetal sex prediction, large variations of chromosome Y measurement and high sensitivity to random reads mapping, may result in higher false negative rate (FNR) and false positive rate (FPR) in fetal sex prediction as well as in SCAs detection. Here, we developed an optimized method to improve the accuracy of the current method by filtering out randomly mapped reads in six specific regions of the Y chromosome. The method reduces the FNR and FPR of fetal sex prediction from nearly 1% to 0.01% and 0.06%, respectively and works robustly under conditions of low fetal DNA concentration (1%) in testing and simulation of 92 samples. The optimized method was further confirmed by large scale testing (1590 samples), suggesting that it is reliable and robust enough for clinical testing. PMID:27441628

  19. An Optimized Method for Accurate Fetal Sex Prediction and Sex Chromosome Aneuploidy Detection in Non-Invasive Prenatal Testing.

    PubMed

    Wang, Ting; He, Quanze; Li, Haibo; Ding, Jie; Wen, Ping; Zhang, Qin; Xiang, Jingjing; Li, Qiong; Xuan, Liming; Kong, Lingyin; Mao, Yan; Zhu, Yijun; Shen, Jingjing; Liang, Bo; Li, Hong

    2016-01-01

    Massively parallel sequencing (MPS) combined with bioinformatic analysis has been widely applied to detect fetal chromosomal aneuploidies such as trisomy 21, 18, 13 and sex chromosome aneuploidies (SCAs) by sequencing cell-free fetal DNA (cffDNA) from maternal plasma, so-called non-invasive prenatal testing (NIPT). However, many technical challenges, such as dependency on correct fetal sex prediction, large variations of chromosome Y measurement and high sensitivity to random reads mapping, may result in higher false negative rate (FNR) and false positive rate (FPR) in fetal sex prediction as well as in SCAs detection. Here, we developed an optimized method to improve the accuracy of the current method by filtering out randomly mapped reads in six specific regions of the Y chromosome. The method reduces the FNR and FPR of fetal sex prediction from nearly 1% to 0.01% and 0.06%, respectively and works robustly under conditions of low fetal DNA concentration (1%) in testing and simulation of 92 samples. The optimized method was further confirmed by large scale testing (1590 samples), suggesting that it is reliable and robust enough for clinical testing. PMID:27441628

  20. Comparative study of exchange-correlation functionals for accurate predictions of structural and magnetic properties of multiferroic oxides

    NASA Astrophysics Data System (ADS)

    Chen, Hanghui; Millis, Andrew J.

    2016-05-01

    We systematically compare predictions of various exchange correlation functionals for the structural and magnetic properties of perovskite Sr1 -xBaxMnO3 (0 ≤x ≤1 )—a representative class of multiferroic oxides. The local spin density approximation (LSDA) and spin-dependent generalized gradient approximation with Perdew-Burke-Ernzerhof parametrization (sPBE) make substantial different predictions for ferroelectric atomic distortions, tetragonality, and ground state magnetic ordering. Neither approximation quantitatively reproduces all the measured structural and magnetic properties of perovskite Sr0.5Ba0.5MnO3 . The spin-dependent generalized gradient approximation with Perdew-Burke-Ernzerhof revised for solids parametrization (sPBEsol) and the charge-only Perdew-Burke-Ernzerhof parametrized generalized gradient approximation with Hubbard U and Hund's J extensions both provide overall better agreement with measured structural and magnetic properties of Sr0.5Ba0.5MnO3 , compared to LSDA and sPBE. Using these two methods, we find that different from previous predictions, perovskite BaMnO3 has large Mn off-center displacements and is close to a ferromagnetic-to-antiferromagnetic phase boundary, making it a promising candidate to induce effective giant magnetoelectric effects and to achieve cross-field control of polarization and magnetism.

  1. Accurate Prediction of Advanced Liver Fibrosis Using the Decision Tree Learning Algorithm in Chronic Hepatitis C Egyptian Patients

    PubMed Central

    Hashem, Somaya; Esmat, Gamal; Elakel, Wafaa; Habashy, Shahira; Abdel Raouf, Safaa; Darweesh, Samar; Soliman, Mohamad; Elhefnawi, Mohamed; El-Adawy, Mohamed; ElHefnawi, Mahmoud

    2016-01-01

    Background/Aim. Respectively with the prevalence of chronic hepatitis C in the world, using noninvasive methods as an alternative method in staging chronic liver diseases for avoiding the drawbacks of biopsy is significantly increasing. The aim of this study is to combine the serum biomarkers and clinical information to develop a classification model that can predict advanced liver fibrosis. Methods. 39,567 patients with chronic hepatitis C were included and randomly divided into two separate sets. Liver fibrosis was assessed via METAVIR score; patients were categorized as mild to moderate (F0–F2) or advanced (F3-F4) fibrosis stages. Two models were developed using alternating decision tree algorithm. Model 1 uses six parameters, while model 2 uses four, which are similar to FIB-4 features except alpha-fetoprotein instead of alanine aminotransferase. Sensitivity and receiver operating characteristic curve were performed to evaluate the performance of the proposed models. Results. The best model achieved 86.2% negative predictive value and 0.78 ROC with 84.8% accuracy which is better than FIB-4. Conclusions. The risk of advanced liver fibrosis, due to chronic hepatitis C, could be predicted with high accuracy using decision tree learning algorithm that could be used to reduce the need to assess the liver biopsy. PMID:26880886

  2. Accurate Prediction of Advanced Liver Fibrosis Using the Decision Tree Learning Algorithm in Chronic Hepatitis C Egyptian Patients.

    PubMed

    Hashem, Somaya; Esmat, Gamal; Elakel, Wafaa; Habashy, Shahira; Abdel Raouf, Safaa; Darweesh, Samar; Soliman, Mohamad; Elhefnawi, Mohamed; El-Adawy, Mohamed; ElHefnawi, Mahmoud

    2016-01-01

    Background/Aim. Respectively with the prevalence of chronic hepatitis C in the world, using noninvasive methods as an alternative method in staging chronic liver diseases for avoiding the drawbacks of biopsy is significantly increasing. The aim of this study is to combine the serum biomarkers and clinical information to develop a classification model that can predict advanced liver fibrosis. Methods. 39,567 patients with chronic hepatitis C were included and randomly divided into two separate sets. Liver fibrosis was assessed via METAVIR score; patients were categorized as mild to moderate (F0-F2) or advanced (F3-F4) fibrosis stages. Two models were developed using alternating decision tree algorithm. Model 1 uses six parameters, while model 2 uses four, which are similar to FIB-4 features except alpha-fetoprotein instead of alanine aminotransferase. Sensitivity and receiver operating characteristic curve were performed to evaluate the performance of the proposed models. Results. The best model achieved 86.2% negative predictive value and 0.78 ROC with 84.8% accuracy which is better than FIB-4. Conclusions. The risk of advanced liver fibrosis, due to chronic hepatitis C, could be predicted with high accuracy using decision tree learning algorithm that could be used to reduce the need to assess the liver biopsy. PMID:26880886

  3. Accurate prediction of protein structural classes by incorporating predicted secondary structure information into the general form of Chou's pseudo amino acid composition.

    PubMed

    Kong, Liang; Zhang, Lichao; Lv, Jinfeng

    2014-03-01

    Extracting good representation from protein sequence is fundamental for protein structural classes prediction tasks. In this paper, we propose a novel and powerful method to predict protein structural classes based on the predicted secondary structure information. At the feature extraction stage, a 13-dimensional feature vector is extracted to characterize general contents and spatial arrangements of the secondary structural elements of a given protein sequence. Specially, four segment-level features are designed to elevate discriminative ability for proteins from the α/β and α+β classes. After the features are extracted, a multi-class non-linear support vector machine classifier is used to implement protein structural classes prediction. We report extensive experiments comparing the proposed method to the state-of-the-art in protein structural classes prediction on three widely used low-similarity benchmark datasets: FC699, 1189 and 640. Our method achieves competitive performance on prediction accuracies, especially for the overall prediction accuracies which have exceeded the best reported results on all of the three datasets. PMID:24316044

  4. Predicting Adolescent Deviant Behaviors through Data Mining Techniques

    ERIC Educational Resources Information Center

    Liu, Yu-Chin; Hsu, Yung-Chieh

    2013-01-01

    Adolescence is the time during which people develop and form their crucial values, personality traits, and beliefs. Hence, as deviant behaviors occur during adolescence, it is important to guide adolescents away from such behaviors and back to normal behaviors. Moreover, although there are various kinds of deviant behavior, most of them would…

  5. 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

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

    PubMed

    Kosinski, Michal; Stillwell, David; Graepel, Thore

    2013-04-01

    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

  7. Predicting the behavior of microfluidic circuits made from discrete elements

    PubMed Central

    Bhargava, Krisna C.; Thompson, Bryant; Iqbal, Danish; Malmstadt, Noah

    2015-01-01

    Microfluidic devices can be used to execute a variety of continuous flow analytical and synthetic chemistry protocols with a great degree of precision. The growing availability of additive manufacturing has enabled the design of microfluidic devices with new functionality and complexity. However, these devices are prone to larger manufacturing variation than is typical of those made with micromachining or soft lithography. In this report, we demonstrate a design-for-manufacturing workflow that addresses performance variation at the microfluidic element and circuit level, in context of mass-manufacturing and additive manufacturing. Our approach relies on discrete microfluidic elements that are characterized by their terminal hydraulic resistance and associated tolerance. Network analysis is employed to construct simple analytical design rules for model microfluidic circuits. Monte Carlo analysis is employed at both the individual element and circuit level to establish expected performance metrics for several specific circuit configurations. A protocol based on osmometry is used to experimentally probe mixing behavior in circuits in order to validate these approaches. The overall workflow is applied to two application circuits with immediate use at on the bench-top: series and parallel mixing circuits that are modularly programmable, virtually predictable, highly precise, and operable by hand. PMID:26516059

  8. Prediction of projectile ricochet behavior after water impact.

    PubMed

    Baillargeon, Yves; Bergeron, Guy

    2012-11-01

    Although not very common, forensic investigation related to projectile ricochet on water can be required when undesirable collateral damage occurs. Predicting the ricochet behavior of a projectile is challenging owing to numerous parameters involved: impact velocity, incident angle, projectile stability, angular velocity, etc. Ricochet characteristics of different projectiles (K50 BMG, 0.5-cal Ball M2, 0.5-cal AP-T C44, 7.62-mm Ball C21, and 5.56-mm Ball C77) were studied in a pool. The results are presented to assess projectile velocity after ricochet, ricochet angle, and projectile azimuth angle based on impact velocity or incident angle for each projectile type. The azimuth ranges show the highest variability at low postricochet velocity. The critical ricochet angles were ranging from 15 to 30°. The average ricochet angles for all projectiles were pretty close for all projectiles at 2.5 and 10° incident angles for the range of velocities studied. PMID:22536929

  9. Predicting the behavior of microfluidic circuits made from discrete elements

    NASA Astrophysics Data System (ADS)

    Bhargava, Krisna C.; Thompson, Bryant; Iqbal, Danish; Malmstadt, Noah

    2015-10-01

    Microfluidic devices can be used to execute a variety of continuous flow analytical and synthetic chemistry protocols with a great degree of precision. The growing availability of additive manufacturing has enabled the design of microfluidic devices with new functionality and complexity. However, these devices are prone to larger manufacturing variation than is typical of those made with micromachining or soft lithography. In this report, we demonstrate a design-for-manufacturing workflow that addresses performance variation at the microfluidic element and circuit level, in context of mass-manufacturing and additive manufacturing. Our approach relies on discrete microfluidic elements that are characterized by their terminal hydraulic resistance and associated tolerance. Network analysis is employed to construct simple analytical design rules for model microfluidic circuits. Monte Carlo analysis is employed at both the individual element and circuit level to establish expected performance metrics for several specific circuit configurations. A protocol based on osmometry is used to experimentally probe mixing behavior in circuits in order to validate these approaches. The overall workflow is applied to two application circuits with immediate use at on the bench-top: series and parallel mixing circuits that are modularly programmable, virtually predictable, highly precise, and operable by hand.

  10. Predicting the behavior of microfluidic circuits made from discrete elements.

    PubMed

    Bhargava, Krisna C; Thompson, Bryant; Iqbal, Danish; Malmstadt, Noah

    2015-01-01

    Microfluidic devices can be used to execute a variety of continuous flow analytical and synthetic chemistry protocols with a great degree of precision. The growing availability of additive manufacturing has enabled the design of microfluidic devices with new functionality and complexity. However, these devices are prone to larger manufacturing variation than is typical of those made with micromachining or soft lithography. In this report, we demonstrate a design-for-manufacturing workflow that addresses performance variation at the microfluidic element and circuit level, in context of mass-manufacturing and additive manufacturing. Our approach relies on discrete microfluidic elements that are characterized by their terminal hydraulic resistance and associated tolerance. Network analysis is employed to construct simple analytical design rules for model microfluidic circuits. Monte Carlo analysis is employed at both the individual element and circuit level to establish expected performance metrics for several specific circuit configurations. A protocol based on osmometry is used to experimentally probe mixing behavior in circuits in order to validate these approaches. The overall workflow is applied to two application circuits with immediate use at on the bench-top: series and parallel mixing circuits that are modularly programmable, virtually predictable, highly precise, and operable by hand. PMID:26516059

  11. The Potential for Accurately Measuring Behavioral and Economic Dimensions of Consumption, Prices, and Markets for Illegal Drugs

    PubMed Central

    Johnson, Bruce D.; Golub, Andrew

    2007-01-01

    There are numerous analytic and methodological limitations to current measures of drug market activity. This paper explores the structure of markets and individual user behavior to provide an integrated understanding of behavioral and economic (and market) aspects of illegal drug use with an aim toward developing improved procedures for measurement. This involves understanding the social processes that structure illegal distribution networks and drug users’ interactions with them. These networks are where and how social behaviors, prices, and markets for illegal drugs intersect. Our focus is upon getting an up close measurement of these activities. Building better measures of consumption behaviors necessitates building better rapport with subjects than typically achieved with one-time surveys in order to overcome withholding and underreporting and to get a comprehensive understanding of the processes involved. This can be achieved through repeated interviews and observations of behaviors. This paper also describes analytic advances that could be adopted to direct this inquiry including behavioral templates, and insights into the economic valuation of labor inputs and cash expenditures for various illegal drugs. Additionally, the paper makes recommendations to funding organizations for developing the mechanisms that would support behavioral scientists to weigh specimens and to collect small samples for laboratory analysis—by providing protection from the potential for arrest. The primary focus is upon U.S. markets. The implications for other countries are discussed. PMID:16978801

  12. Stratified neutrophil-to-lymphocyte ratio accurately predict mortality risk in hepatocellular carcinoma patients following curative liver resection

    PubMed Central

    Huang, Gui-Qian; Zhu, Gui-Qi; Liu, Yan-Long; Wang, Li-Ren; Braddock, Martin; Zheng, Ming-Hua; Zhou, Meng-Tao

    2016-01-01

    Objectives Neutrophil lymphocyte ratio (NLR) has been shown to predict prognosis of cancers in several studies. This study was designed to evaluate the impact of stratified NLR in patients who have received curative liver resection (CLR) for hepatocellular carcinoma (HCC). Methods A total of 1659 patients who underwent CLR for suspected HCC between 2007 and 2014 were reviewed. The preoperative NLR was categorized into quartiles based on the quantity of the study population and the distribution of NLR. Hazard ratios (HRs) and 95% confidence intervals (CIs) were significantly associated with overall survival (OS) and derived by Cox proportional hazard regression analyses. Univariate and multivariate Cox proportional hazard regression analyses were evaluated for association of all independent parameters with disease prognosis. Results Multivariable Cox proportional hazards models showed that the level of NLR (HR = 1.031, 95%CI: 1.002-1.060, P = 0.033), number of nodules (HR = 1.679, 95%CI: 1.285-2.194, P<0.001), portal vein thrombosis (HR = 4.329, 95%CI: 1.968-9.521, P<0.001), microvascular invasion (HR = 2.527, 95%CI: 1.726-3.700, P<0.001) and CTP score (HR = 1.675, 95%CI: 1.153-2.433, P = 0.007) were significant predictors of mortality. From the Kaplan-Meier analysis of overall survival (OS), each NLR quartile showed a progressively worse OS and apparent separation (log-rank P=0.008). The highest 5-year OS rate following CLR (60%) in HCC patients was observed in quartile 1. In contrast, the lowest 5-year OS rate (27%) was obtained in quartile 4. Conclusions Stratified NLR may predict significantly improved outcomes and strengthen the predictive power for patient responses to therapeutic intervention. PMID:26716411

  13. 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.

  14. aPPRove: An HMM-Based Method for Accurate Prediction of RNA-Pentatricopeptide Repeat Protein Binding Events.

    PubMed

    Harrison, Thomas; Ruiz, Jaime; Sloan, Daniel B; Ben-Hur, Asa; Boucher, Christina

    2016-01-01

    Pentatricopeptide repeat containing proteins (PPRs) bind to RNA transcripts originating from mitochondria and plastids. There are two classes of PPR proteins. The [Formula: see text] class contains tandem [Formula: see text]-type motif sequences, and the [Formula: see text] class contains alternating [Formula: see text], [Formula: see text] and [Formula: see text] type sequences. In this paper, we describe a novel tool that predicts PPR-RNA interaction; specifically, our method, which we call aPPRove, determines where and how a [Formula: see text]-class PPR protein will bind to RNA when given a PPR and one or more RNA transcripts by using a combinatorial binding code for site specificity proposed by Barkan et al. Our results demonstrate that aPPRove successfully locates how and where a PPR protein belonging to the [Formula: see text] class can bind to RNA. For each binding event it outputs the binding site, the amino-acid-nucleotide interaction, and its statistical significance. Furthermore, we show that our method can be used to predict binding events for [Formula: see text]-class proteins using a known edit site and the statistical significance of aligning the PPR protein to that site. In particular, we use our method to make a conjecture regarding an interaction between CLB19 and the second intronic region of ycf3. The aPPRove web server can be found at www.cs.colostate.edu/~approve. PMID:27560805

  15. A 3D-CFD code for accurate prediction of fluid flows and fluid forces in seals

    NASA Astrophysics Data System (ADS)

    Athavale, M. M.; Przekwas, A. J.; Hendricks, R. C.

    1994-01-01

    Current and future turbomachinery requires advanced seal configurations to control leakage, inhibit mixing of incompatible fluids and to control the rotodynamic response. In recognition of a deficiency in the existing predictive methodology for seals, a seven year effort was established in 1990 by NASA's Office of Aeronautics Exploration and Technology, under the Earth-to-Orbit Propulsion program, to develop validated Computational Fluid Dynamics (CFD) concepts, codes and analyses for seals. The effort will provide NASA and the U.S. Aerospace Industry with advanced CFD scientific codes and industrial codes for analyzing and designing turbomachinery seals. An advanced 3D CFD cylindrical seal code has been developed, incorporating state-of-the-art computational methodology for flow analysis in straight, tapered and stepped seals. Relevant computational features of the code include: stationary/rotating coordinates, cylindrical and general Body Fitted Coordinates (BFC) systems, high order differencing schemes, colocated variable arrangement, advanced turbulence models, incompressible/compressible flows, and moving grids. This paper presents the current status of code development, code demonstration for predicting rotordynamic coefficients, numerical parametric study of entrance loss coefficients for generic annular seals, and plans for code extensions to labyrinth, damping, and other seal configurations.

  16. aPPRove: An HMM-Based Method for Accurate Prediction of RNA-Pentatricopeptide Repeat Protein Binding Events

    PubMed Central

    Harrison, Thomas; Ruiz, Jaime; Sloan, Daniel B.; Ben-Hur, Asa; Boucher, Christina

    2016-01-01

    Pentatricopeptide repeat containing proteins (PPRs) bind to RNA transcripts originating from mitochondria and plastids. There are two classes of PPR proteins. The P class contains tandem P-type motif sequences, and the PLS class contains alternating P, L and S type sequences. In this paper, we describe a novel tool that predicts PPR-RNA interaction; specifically, our method, which we call aPPRove, determines where and how a PLS-class PPR protein will bind to RNA when given a PPR and one or more RNA transcripts by using a combinatorial binding code for site specificity proposed by Barkan et al. Our results demonstrate that aPPRove successfully locates how and where a PPR protein belonging to the PLS class can bind to RNA. For each binding event it outputs the binding site, the amino-acid-nucleotide interaction, and its statistical significance. Furthermore, we show that our method can be used to predict binding events for PLS-class proteins using a known edit site and the statistical significance of aligning the PPR protein to that site. In particular, we use our method to make a conjecture regarding an interaction between CLB19 and the second intronic region of ycf3. The aPPRove web server can be found at www.cs.colostate.edu/~approve. PMID:27560805

  17. Prediction of Total Lightning Behavior in Colorado Thunderstorms from Storm Dynamical and Microphysical Variables

    NASA Astrophysics Data System (ADS)

    Basarab, B.; Rutledge, S. A.; Fuchs, B.

    2014-12-01

    Accurate prediction of lightning flash rate is essential to successfully parameterize the production of nitrogen oxides by lightning (LNOx). In this study, new flash rate parameterization schemes are developed using observations of 10 Colorado thunderstorms from the Deep Convective Clouds and Chemistry (DC3) and the CHILL Microphysical Investigation of Electrification (CHILL-MIE) field projects. Storm total flash rates were determined by automated clustering of lightning mapping array (LMA)-detected radiation sources. Storm parameters, including hydrometeor and reflectivity echo volumes, ice masses, and measurements of updraft strength were obtained from polarimetric radar retrievals and dual-Doppler derived wind fields. Echo volumes exhibited a particularly strong correlation to flash rate (R2 = 0.78 for 30-dBZ echo volume). It is shown that existing flash rate schemes tend to underestimate flash rate for the storms in this study. New parameterizations developed based on the graupel echo volume, the precipitating ice mass (graupel and hail), and the 30-dBZ echo volume within the mixed-phase region of storms predicted flash rate trends reasonably well. However, there were sometimes large errors in the prediction of flash rate magnitudes, possibly due to fluctuations in storm updraft intensity. Updraft-based flash schemes were developed but significantly underestimated flash rate for the storms studied. It is shown that very high flash rates correlate differently to updraft strength than low flash rates. We hypothesize why this behavior is observed. The use of multiple storm parameters to predict flash rate was also investigated, and the results are improved somewhat compared to single-parameter schemes. New flash schemes were tested for storms outside of Colorado to examine their potential regional dependence. Finally, observations of the relationship between flash rate and flash size are discussed, with implications for the improved prediction of LNOx.

  18. BgN-Score and BsN-Score: Bagging and boosting based ensemble neural networks scoring functions for accurate binding affinity prediction of protein-ligand complexes

    PubMed Central

    2015-01-01

    Background Accurately predicting the binding affinities of large sets of protein-ligand complexes is a key challenge in computational biomolecular science, with applications in drug discovery, chemical biology, and structural biology. Since a scoring function (SF) is used to score, rank, and identify drug leads, the fidelity with which it predicts the affinity of a ligand candidate for a protein's binding site has a significant bearing on the accuracy of virtual screening. Despite intense efforts in developing conventional SFs, which are either force-field based, knowledge-based, or empirical, their limited predictive power has been a major roadblock toward cost-effective drug discovery. Therefore, in this work, we present novel SFs employing a large ensemble of neural networks (NN) in conjunction with a diverse set of physicochemical and geometrical features characterizing protein-ligand complexes to predict binding affinity. Results We assess the scoring accuracies of two new ensemble NN SFs based on bagging (BgN-Score) and boosting (BsN-Score), as well as those of conventional SFs in the context of the 2007 PDBbind benchmark that encompasses a diverse set of high-quality protein families. We find that BgN-Score and BsN-Score have more than 25% better Pearson's correlation coefficient (0.804 and 0.816 vs. 0.644) between predicted and measured binding affinities compared to that achieved by a state-of-the-art conventional SF. In addition, these ensemble NN SFs are also at least 19% more accurate (0.804 and 0.816 vs. 0.675) than SFs based on a single neural network that has been traditionally used in drug discovery applications. We further find that ensemble models based on NNs surpass SFs based on the decision-tree ensemble technique Random Forests. Conclusions Ensemble neural networks SFs, BgN-Score and BsN-Score, are the most accurate in predicting binding affinity of protein-ligand complexes among the considered SFs. Moreover, their accuracies are even higher

  19. Accurate prediction of the toxicity of benzoic acid compounds in mice via oral without using any computer codes.

    PubMed

    Keshavarz, Mohammad Hossein; Gharagheizi, Farhad; Shokrolahi, Arash; Zakinejad, Sajjad

    2012-10-30

    Most of benzoic acid derivatives are toxic, which may cause serious public health and environmental problems. Two novel simple and reliable models are introduced for desk calculations of the toxicity of benzoic acid compounds in mice via oral LD(50) with more reliance on their answers as one could attach to the more complex outputs. They require only elemental composition and molecular fragments without using any computer codes. The first model is based on only the number of carbon and hydrogen atoms, which can be improved by several molecular fragments in the second model. For 57 benzoic compounds, where the computed results of quantitative structure-toxicity relationship (QSTR) were recently reported, the predicted results of two simple models of present method are more reliable than QSTR computations. The present simple method is also tested with further 324 benzoic acid compounds including complex molecular structures, which confirm good forecasting ability of the second model. PMID:22959133

  20. Predicting the Operating Behavior of Ceramic Filters from Thermo-Mechanical Ash Properties

    SciTech Connect

    Hemmer, G.; Kasper, G.

    2002-09-19

    Stable operation, in other words the achievement of a succession of uniform filtration cycles of reasonable length is a key issue in high-temperature gas filtration with ceramic media. Its importance has rather grown in recent years, as these media gain in acceptance due to their excellent particle retention capabilities. Ash properties have been known for some time to affect the maximum operating temperature of filters. However, softening and consequently ''stickiness'' of the ash particles generally depend on composition in a complex way. Simple and accurate prediction of critical temperature ranges from ash analysis--and even more so from coal analysis--is still difficult without practical and costly trials. In general, our understanding of what exactly happens during break-down of filtration stability is still rather crude and general. Early work was based on the concept that ash particles begin to soften and sinter near the melting temperatures of low-melting, often alkaline components. This softening coincides with a fairly abrupt increase of stickiness, that can be detected with powder mechanical methods in a Jenicke shear cell as first shown by Pilz (1996) and recently confirmed by others (Kamiya et al. 2001 and 2002, Kanaoka et al. 2001). However, recording {sigma}-{tau}-diagrams is very time consuming and not the only off-line method of analyzing or predicting changes in thermo-mechanical ash behavior. Pilz found that the increase in ash stickiness near melting was accompanied by shrinkage attributed to sintering. Recent work at the University of Karlsruhe has expanded the use of such thermo-analytical methods for predicting filtration behavior (Hemmer 2001). Demonstrating their effectiveness is one objective of this paper. Finally, our intent is to show that ash softening at near melting temperatures is apparently not the only phenomenon causing problems with filtration, although its impact is certainly the ''final catastrophe''. There are other

  1. Accurate prediction of secreted substrates and identification of a conserved putative secretion signal for type III secretion systems

    SciTech Connect

    Samudrala, Ram; Heffron, Fred; McDermott, Jason E.

    2009-04-24

    The type III secretion system is an essential component for virulence in many Gram-negative bacteria. Though components of the secretion system apparatus are conserved, its substrates, effector proteins, are not. We have used a machine learning approach to identify new secreted effectors. The method integrates evolutionary measures, such as the pattern of homologs in a range of other organisms, and sequence-based features, such as G+C content, amino acid composition and the N-terminal 30 residues of the protein sequence. The method was trained on known effectors from Salmonella typhimurium and validated on a corresponding set of effectors from Pseudomonas syringae, after eliminating effectors with detectable sequence similarity. The method was able to identify all of the known effectors in P. syringae with a specificity of 84% and sensitivity of 82%. The reciprocal validation, training on P. syringae and validating on S. typhimurium, gave similar results with a specificity of 86% when the sensitivity level was 87%. These results show that type III effectors in disparate organisms share common features. We found that maximal performance is attained by including an N-terminal sequence of only 30 residues, which agrees with previous studies indicating that this region contains the secretion signal. We then used the method to define the most important residues in this putative secretion signal. Finally, we present novel predictions of secreted effectors in S. typhimurium, some of which have been experimentally validated, and apply the method to predict secreted effectors in the genetically intractable human pathogen Chlamydia trachomatis. This approach is a novel and effective way to identify secreted effectors in a broad range of pathogenic bacteria for further experimental characterization and provides insight into the nature of the type III secretion signal.

  2. 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

  3. Predicting College Students' First Year Success: Should Soft Skills Be Taken into Consideration to More Accurately Predict the Academic Achievement of College Freshmen?

    ERIC Educational Resources Information Center

    Powell, Erica Dion

    2013-01-01

    This study presents a survey developed to measure the skills of entering college freshmen in the areas of responsibility, motivation, study habits, literacy, and stress management, and explores the predictive power of this survey as a measure of academic performance during the first semester of college. The survey was completed by 334 incoming…

  4. Predicting Antimicrobial Resistance Prevalence and Incidence from Indicators of Antimicrobial Use: What Is the Most Accurate Indicator for Surveillance in Intensive Care Units?

    PubMed Central

    Fortin, Élise; Platt, Robert W.; Fontela, Patricia S.; Buckeridge, David L.; Quach, Caroline

    2015-01-01

    Objective The optimal way to measure antimicrobial use in hospital populations, as a complement to surveillance of resistance is still unclear. Using respiratory isolates and antimicrobial prescriptions of nine intensive care units (ICUs), this study aimed to identify the indicator of antimicrobial use that predicted prevalence and incidence rates of resistance with the best accuracy. Methods Retrospective cohort study including all patients admitted to three neonatal (NICU), two pediatric (PICU) and four adult ICUs between April 2006 and March 2010. Ten different resistance / antimicrobial use combinations were studied. After adjustment for ICU type, indicators of antimicrobial use were successively tested in regression models, to predict resistance prevalence and incidence rates, per 4-week time period, per ICU. Binomial regression and Poisson regression were used to model prevalence and incidence rates, respectively. Multiplicative and additive models were tested, as well as no time lag and a one 4-week-period time lag. For each model, the mean absolute error (MAE) in prediction of resistance was computed. The most accurate indicator was compared to other indicators using t-tests. Results Results for all indicators were equivalent, except for 1/20 scenarios studied. In this scenario, where prevalence of carbapenem-resistant Pseudomonas sp. was predicted with carbapenem use, recommended daily doses per 100 admissions were less accurate than courses per 100 patient-days (p = 0.0006). Conclusions A single best indicator to predict antimicrobial resistance might not exist. Feasibility considerations such as ease of computation or potential external comparisons could be decisive in the choice of an indicator for surveillance of healthcare antimicrobial use. PMID:26710322

  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. Microdosing of a Carbon-14 Labeled Protein in Healthy Volunteers Accurately Predicts Its Pharmacokinetics at Therapeutic Dosages.

    PubMed

    Vlaming, M L H; van Duijn, E; Dillingh, M R; Brands, R; Windhorst, A D; Hendrikse, N H; Bosgra, S; Burggraaf, J; de Koning, M C; Fidder, A; Mocking, J A J; Sandman, H; de Ligt, R A F; Fabriek, B O; Pasman, W J; Seinen, W; Alves, T; Carrondo, M; Peixoto, C; Peeters, P A M; Vaes, W H J

    2015-08-01

    Preclinical development of new biological entities (NBEs), such as human protein therapeutics, requires considerable expenditure of time and costs. Poor prediction of pharmacokinetics in humans further reduces net efficiency. In this study, we show for the first time that pharmacokinetic data of NBEs in humans can be successfully obtained early in the drug development process by the use of microdosing in a small group of healthy subjects combined with ultrasensitive accelerator mass spectrometry (AMS). After only minimal preclinical testing, we performed a first-in-human phase 0/phase 1 trial with a human recombinant therapeutic protein (RESCuing Alkaline Phosphatase, human recombinant placental alkaline phosphatase [hRESCAP]) to assess its safety and kinetics. Pharmacokinetic analysis showed dose linearity from microdose (53 μg) [(14) C]-hRESCAP to therapeutic doses (up to 5.3 mg) of the protein in healthy volunteers. This study demonstrates the value of a microdosing approach in a very small cohort for accelerating the clinical development of NBEs. PMID:25869840

  7. A new accurate ground-state potential energy surface of ethylene and predictions for rotational and vibrational energy levels

    NASA Astrophysics Data System (ADS)

    Delahaye, Thibault; Nikitin, Andrei; Rey, Michaël; Szalay, Péter G.; Tyuterev, Vladimir G.

    2014-09-01

    In this paper we report a new ground state potential energy surface for ethylene (ethene) C2H4 obtained from extended ab initio calculations. The coupled-cluster approach with the perturbative inclusion of the connected triple excitations CCSD(T) and correlation consistent polarized valence basis set cc-pVQZ was employed for computations of electronic ground state energies. The fit of the surface included 82 542 nuclear configurations using sixth order expansion in curvilinear symmetry-adapted coordinates involving 2236 parameters. A good convergence for variationally computed vibrational levels of the C2H4 molecule was obtained with a RMS(Obs.-Calc.) deviation of 2.7 cm-1 for fundamental bands centers and 5.9 cm-1 for vibrational bands up to 7800 cm-1. Large scale vibrational and rotational calculations for 12C2H4, 13C2H4, and 12C2D4 isotopologues were performed using this new surface. Energy levels for J = 20 up to 6000 cm-1 are in a good agreement with observations. This represents a considerable improvement with respect to available global predictions of vibrational levels of 13C2H4 and 12C2D4 and rovibrational levels of 12C2H4.

  8. Physical and Behavioral Measures that Predict Cats’ Socialization in an Animal Shelter Environment during a Three Day Period

    PubMed Central

    Slater, Margaret; Garrison, Laurie; Miller, Katherine; Weiss, Emily; Drain, Natasha; Makolinski, Kathleen

    2013-01-01

    Simple Summary Information from surveys completed by the cats’ caregivers provided a score for the level of socialization of cats. We examined the effectiveness of structured assessments and measures in their ability to distinguish More and Less Socialized cats in a shelter-like setting over a three day period. Statistical models were developed that best predicted More and Less Socialized cats. Measures from these models were used to calculate a point system where more points indicated more socialization. In combination with key socialized behaviors, these points were able to fairly accurately distinguish More Socialized from Less Socialized cats. Abstract Animal welfare organizations typically take in cats with unknown levels of socialization towards humans, ranging from unsocialized cats well-socialized but lost pets. Agencies typically determine the socialization status and disposition options of cats within three days, when even a well-socialized pet may be too frightened of the unfamiliar surroundings to display its typical behavior. This is the third part of a three-phase project to develop and evaluate a reliable and valid tool to predict cats’ socialization levels. We recruited cats from the full spectrum of socialization and, using information from the cats’ caregivers regarding typical behavior toward familiar and unfamiliar people, assigned each cat to a Socialization Category. This information was compared to the cats’ behavior during three days of structured assessments conducted in a shelter-like setting. The results of logistic regression modeling generated two models using assessments from the mornings of the second and third day, focusing on predicting shyer or more aloof but socialized cats. Using the coefficients from each of these models, two sets of points were calculated which were useful in differentiating More and Less Socialized cats. In combination with key socialized behaviors, these points were able to fairly accurately identify

  9. Exploring the intention-behavior relationship in the prediction of sexual risk behaviors: can it be strengthened?

    PubMed

    Turchik, Jessica A; Gidycz, Christine A

    2012-01-01

    Many theoretical models examining health risk behaviors, such as sexual risk taking, assume intentions directly predict behavior, and intentions are sometimes measured as a proxy for behavior. Given that there is often a discrepancy between intentions and behaviors (e.g., Sheeran, 2002 ), this study addressed factors that predict intention-behavior congruence. Specifically, utilizing a prospective design, the goal of the study was to determine if characteristics of university students' last sexual encounter predicted whether those students who intended to use condoms, contraception, or dual methods did so in their last sexual encounter with both relationship and casual partners. The seven tested variables were condom planning and preparatory behaviors, mood, sexual arousal, substance use, perceived partner attractiveness, intention certainty, and intention stability. Data were collected from 520 American undergraduate students at a medium-sized university. The six discriminant function analyses examining the intention-behavior relationship were able to correctly classify between 74% and 92% of the participants concerning whether intenders engaged in safe sex behaviors. The variables that best discriminated between behavior engagement over the six analyses were intention stability, intention certainty, and engagement in condom preparatory behaviors. The implications of these results for sexual risk prevention and intervention research are discussed. PMID:21660837

  10. Determination of test methods for the prediction of the behavior of mass concrete

    NASA Astrophysics Data System (ADS)

    Ferraro, Christopher C.

    Hydration at early ages results from chemical and physical processes that take place between Portland cement and water, and is an exothermic process. The resultant heat evolution and temperature rise for massive concrete placements can be so great that the temperature differentials between the internal concrete core and outer concrete stratum can cause cracking due to thermal gradients. Accurate prediction of temperature distribution and stresses in mass concrete is needed to determine if a given concrete mixture design may have problems in the field, so that adjustments to the design can be made prior to its use. This research examines calorimetric, strength, and physical testing methods in an effort to predict the thermal and physical behavior of mass concrete. Four groups of concrete mixture types containing different cementitious materials are examined. One group contains Portland cement, while the other three groups incorporate large replacements of supplementary cementitious materials: granulated blast furnace slag, fly ash, and a ternary blend (combining Portland cement, fly ash, and slag).

  11. Parental corporal punishment predicts behavior problems in early childhood.

    PubMed

    Mulvaney, Matthew K; Mebert, Carolyn J

    2007-09-01

    Using data from the National Institute of Child Health and Human Development Study of Early Child Care and Youth Development (Research Triangle Institute, 2002), this study examined the impact of corporal punishment (CP) on children's behavior problems. Longitudinal analyses were specified that controlled for covarying contextual and parenting variables and that partialed child effects. The results indicate that parental CP uniquely contributes to negative behavioral adjustment in children at both 36 months and at 1st grade, with the effects at the earlier age more pronounced in children with difficult temperaments. Parents and mental health professionals who work to modify children's negative behavior should be aware of the unique impact that CP likely plays in triggering and maintaining children's behavior problems. Broad-based family policies that reduce the use of this parenting behavior would potentially increase children's mental health and decrease the incidence of children's behavior problems. PMID:17874924

  12. 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

    Ryan, Natalia; Chorley, Brian; Tice, Raymond R; Judson, Richard; Corton, J Christopher

    2016-05-01

    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

  13. 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…

  14. 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. PMID:25820129

  15. 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

  16. 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

    2016-01-01

    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

  17. 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

  18. Accurate Ab Initio and Template-Based Prediction of Short Intrinsically-Disordered Regions by Bidirectional Recurrent Neural Networks Trained on Large-Scale Datasets

    PubMed Central

    Volpato, Viola; Alshomrani, Badr; Pollastri, Gianluca

    2015-01-01

    Intrinsically-disordered regions lack a well-defined 3D structure, but play key roles in determining the function of many proteins. Although predictors of disorder have been shown to achieve relatively high rates of correct classification of these segments, improvements over the the years have been slow, and accurate methods are needed that are capable of accommodating the ever-increasing amount of structurally-determined protein sequences to try to boost predictive performances. In this paper, we propose a predictor for short disordered regions based on bidirectional recurrent neural networks and tested by rigorous five-fold cross-validation on a large, non-redundant dataset collected from MobiDB, a new comprehensive source of protein disorder annotations. The system exploits sequence and structural information in the forms of frequency profiles, predicted secondary structure and solvent accessibility and direct disorder annotations from homologous protein structures (templates) deposited in the Protein Data Bank. The contributions of sequence, structure and homology information result in large improvements in predictive accuracy. Additionally, the large scale of the training set leads to low false positive rates, making our systems a robust and efficient way to address high-throughput disorder prediction. PMID:26307973

  19. Work Ethic and Academic Performance: Predicting Citizenship and Counterproductive Behavior

    ERIC Educational Resources Information Center

    Meriac, John P.

    2012-01-01

    In this study, work ethic was examined as a predictor of academic performance, compared with standardized test scores and high school grade point average (GPA). Academic performance was expanded to include student organizational citizenship behavior (OCB) and student counterproductive behavior, comprised of cheating and disengagement, in addition…

  20. Litter Size Predicts Adult Stereotypic Behavior in Female Laboratory Mice

    PubMed Central

    Bechard, Allison; Nicholson, Anthony; Mason, Georgia

    2012-01-01

    Stereotypic behaviors are repetitive invariant behaviors that are common in many captive species and potentially indicate compromised welfare and suitability as research subjects. Adult laboratory mice commonly perform stereotypic bar-gnawing, route-tracing, and back-flipping, although great individual variation in frequency occurs. Early life factors (for example, level of maternal care received) have lasting effects on CNS functioning and abilities to cope with stress and therefore may also affect stereotypic behavior in offspring. Access to maternal resources and care are influenced by the number of pups in a litter; therefore, we examined both litter size and its potential correlate, weight at weaning, as early environmental predictors of adult stereotypic behavior in laboratory mice. Further, we assessed the effects on offspring stereotypic behavior of delaying the separation of mother and pups (weaning) beyond the standard 21 d of age. Analyzing stereotypic behavior in 3 different mouse colonies composed of 2 inbred strains (C57BL/6N and C57BL/6J) and an outbred stock (CD1[ICR]) revealed significant positive correlation between litter size and stereotypic behavior in female, but not male, mice. Weight and age at weaning did not significantly affect levels of stereotypy in either sex. Litter size therefore may be a useful indicator of individual predisposition to stereotypic behavior in female laboratory mice. PMID:23043805

  1. Advancing Prediction of Foster Placement Disruption Using Brief Behavioral Screening

    ERIC Educational Resources Information Center

    Hurlburt, Michael S.; Chamberlain, Patricia; DeGarmo, David; Zhang, Jinjin; Price, Joe M.

    2010-01-01

    Objective: Behavioral difficulties increase the risk that children will experience negative placement disruptions while in foster care. Chamberlain et al. (2006) found that the Parent Daily Report (PDR), a brief measure of parent-reported child behaviors, was a strong predictor of negative placement changes over 1 year among children receiving…

  2. Predicting Preschool Effortful Control from Toddler Temperament and Parenting Behavior

    ERIC Educational Resources Information Center

    Cipriano, Elizabeth A.; Stifter, Cynthia A.

    2010-01-01

    This longitudinal study assessed whether maternal behavior and emotional tone moderated the relationship between toddler temperament and preschooler's effortful control. Maternal behavior and emotional tone were observed during a parent-child competing demands task when children were 2 years of age. Child temperament was also assessed at 2 years…

  3. Predicting Outcome in Behavioral Parent Training: Expected and Unexpected Results

    ERIC Educational Resources Information Center

    MacKenzie, Elizabeth P.; Fite, Paula J.; Bates, John E.

    2004-01-01

    This study examined the relationships among clinical utility and treatment outcome variables in Behavioral Parent Training (BPT). The sample included 21 mothers with 3-8 year-old children with significant externalizing behavior problems who received treatment for Oppositional Defiant Disorder. The primary aim was to relate two treatment…

  4. 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…

  5. Behavioral insights on big data: using social media for predicting biomedical outcomes.

    PubMed

    Young, Sean D

    2014-11-01

    Social media 'big data' can provide valuable insights about people's behaviors, such as their likelihood of engaging in risk behaviors or contracting a disease. Although in its infancy, advancing this research provides the promise of predicting health-related behaviors to promptly prepare for and respond to public health emergencies and epidemics. PMID:25438614

  6. Using SWPBS Expectations as a Screening Tool to Predict Behavioral Risk in Middle School

    ERIC Educational Resources Information Center

    Burke, Mack D.; Davis, John L.; Hagan-Burke, Shanna; Lee, Yuan-Hsuan; Fogarty, Melissa Shea

    2014-01-01

    School-wide positive behavior support (SWPBS) focuses on promoting social competence through the establishment of behavior expectations that are explicitly taught and reinforced by all teachers across all settings. This study investigated the validity of using adherence to SWPBS behavior expectations as a screening tool for predicting behavior…

  7. Early noninvasive measurement of the indocyanine green plasma disappearance rate accurately predicts early graft dysfunction and mortality after deceased donor liver transplantation.

    PubMed

    Olmedilla, Luis; Pérez-Peña, José María; Ripoll, Cristina; Garutti, Ignacio; de Diego, Roberto; Salcedo, Magdalena; Jiménez, Consuelo; Bañares, Rafael

    2009-10-01

    Early diagnosis of graft dysfunction in liver transplantation is essential for taking appropriate action. Indocyanine green clearance is closely related to liver function and can be measured noninvasively by spectrophotometry. The objectives of this study were to prospectively analyze the relationship between the indocyanine green plasma disappearance rate (ICGPDR) and early graft function after liver transplantation and to evaluate the role of ICGPDR in the prediction of severe graft dysfunction (SGD). One hundred seventy-two liver transplants from deceased donors were analyzed. Ten patients had SGD: 6 were retransplanted, and 4 died while waiting for a new graft. The plasma disappearance rate was measured 1 hour (PDRr60) and within the first 24 hours (PDR1) after reperfusion, and it was significantly lower in the SGD group. PDRr60 and PDR1 were excellent predictors of SGD. A threshold PDRr60 value of 10.8%/minute and a PDR1 value of 10%/minute accurately predicted SGD with areas under the receiver operating curve of 0.94 (95% confidence interval, 0.89-0.97) and 0.96 (95% confidence interval, 0.92-0.98), respectively. In addition, survival was significantly lower in patients with PDRr60 values below 10.8%/minute (53%, 47%, and 47% versus 95%, 94%, and 90% at 3, 6, and 12 months, respectively) and with PDR1 values below 10%/minute (62%, 62%, and 62% versus 94%, 92%, and 88%). In conclusion, very early noninvasive measurement of ICGPDR can accurately predict early severe graft dysfunction and mortality after liver transplantation. PMID:19790138

  8. The Role of Sexual Orientation in Predicting Outcome Value and Communication Behaviors.

    ERIC Educational Resources Information Center

    Mottet, Timothy P.

    The purpose of this study was to first, examine the role of sexual orientation in determining positive or negative predicted outcome value during initial interactions and second, to test predicted-outcome-value (POV) theory which posits that predicted outcome values are related to future communication behaviors. Participants, 284 students enrolled…

  9. A Theory of Planned Behavior research model for predicting the sleep intentions and behaviors of undergraduate college students.

    PubMed

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

    2012-02-01

    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 Flesch-Kincaid, face and content validity by a panel of six experts. The second phase included stability reliability by test–retest (n = 37). The final phase included construct validation applying confirmatory factor analysis, internal consistency by Cronbach’s alpha, and predictive validity (n = 197) employing multiple regression analysis. The majority of the participants reported receiving insufficient sleep (M = 407.3 min, SD = 100.75). Multiple regression modeled perceived behavioral control, subjective norm, and attitude toward adequate sleep behavior on behavioral intention. Collectively, the significant predictors produced an R(2)(adjusted) value of .362. Further specification of the model identified behavioral intention as a significant predictor of sleep behavior (R(2)(adjusted) = .185). As a population, undergraduate college students are not achieving adequate sleep. The TPB was found to be a useful framework for predicting the sleep intentions and behaviors of undergraduate students. Practical implications and recommendations for future research are discussed. PMID:22293980

  10. Comparing three attitude-behavior theories for predicting science teachers' intentions

    NASA Astrophysics Data System (ADS)

    Zint, Michaela

    2002-11-01

    Social psychologists' attitude-behavior theories can contribute to understanding science teachers' behaviors. Such understanding can, in turn, be used to improve professional development. This article describes leading attitude-behavior theories and summarizes results from past tests of these theories. A study predicting science teachers' intention to incorporate environmental risk education based on these theories is also reported. Data for that study were collected through a mail questionnaire (n = 1336, radjusted = 80%) and analyzed using confirmatory factor and multiple regression analysis. All determinants of intention to act in the Theory of Reasoned Action and Theory of Planned Behavior and some determinants in the Theory of Trying predicted science teachers' environmental risk education intentions. Given the consistency of results across studies, the Theory of Planned Behavior augmented with past behavior is concluded to provide the best attitude-behavior model for predicting science teachers' intention to act. Thus, science teachers' attitude toward the behavior, perceived behavioral control, and subjective norm need to be enhanced to modify their behavior. Based on the Theory of Trying, improving their attitude toward the process and toward success, and expectations of success may also result in changes. Future research should focus on identifying determinants that can further enhance the ability of these theories to predict and explain science teachers' behaviors.

  11. Identification of fidgety movements and prediction of CP by the use of computer-based video analysis is more accurate when based on two video recordings.

    PubMed

    Adde, Lars; Helbostad, Jorunn; Jensenius, Alexander R; Langaas, Mette; Støen, Ragnhild

    2013-08-01

    This study evaluates the role of postterm age at assessment and the use of one or two video recordings for the detection of fidgety movements (FMs) and prediction of cerebral palsy (CP) using computer vision software. Recordings between 9 and 17 weeks postterm age from 52 preterm and term infants (24 boys, 28 girls; 26 born preterm) were used. Recordings were analyzed using computer vision software. Movement variables, derived from differences between subsequent video frames, were used for quantitative analysis. Sensitivities, specificities, and area under curve were estimated for the first and second recording, or a mean of both. FMs were classified based on the Prechtl approach of general movement assessment. CP status was reported at 2 years. Nine children developed CP of whom all recordings had absent FMs. The mean variability of the centroid of motion (CSD) from two recordings was more accurate than using only one recording, and identified all children who were diagnosed with CP at 2 years. Age at assessment did not influence the detection of FMs or prediction of CP. The accuracy of computer vision techniques in identifying FMs and predicting CP based on two recordings should be confirmed in future studies. PMID:23343036

  12. Prediction of chirality- and size-dependent elastic properties of single-walled boron nitride nanotubes based on an accurate molecular mechanics model

    NASA Astrophysics Data System (ADS)

    Ansari, R.; Mirnezhad, M.; Sahmani, S.

    2015-04-01

    Molecular mechanics theory has been widely used to investigate the mechanical properties of nanostructures analytically. However, there is a limited number of research in which molecular mechanics model is utilized to predict the elastic properties of boron nitride nanotubes (BNNTs). In the current study, the mechanical properties of chiral single-walled BNNTs are predicted analytically based on an accurate molecular mechanics model. For this purpose, based upon the density functional theory (DFT) within the framework of the generalized gradient approximation (GGA), the exchange correlation of Perdew-Burke-Ernzerhof is adopted to evaluate force constants used in the molecular mechanics model. Afterwards, based on the principle of molecular mechanics, explicit expressions are given to calculate surface Young's modulus and Poisson's ratio of the single-walled BNNTs for different values of tube diameter and types of chirality. Moreover, the values of surface Young's modulus, Poisson's ratio and bending stiffness of boron nitride sheets are obtained via the DFT as byproducts. The results predicted by the present model are in reasonable agreement with those reported by other models in the literature.

  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. Lexical Stress and Linguistic Predictability Influence Proofreading Behavior

    PubMed Central

    Harris, Lindsay N.; Perfetti, Charles A.

    2016-01-01

    There is extensive evidence that the segmental (i.e., phonemic) layer of phonology is routinely activated during reading, but little is known about whether phonological activation extends beyond phonemes to subsegmental layers (which include articulatory information, such as voicing) and suprasegmental layers (which include prosodic information, such as lexical stress). In three proofreading experiments, we show that spelling errors are detected more reliably in syllables that are stressed than in syllables that are unstressed if comprehension is a goal of the reader, indicating that suprasegmental phonology is both active during silent reading and can influence orthographic processes. In Experiment 1, participants received instructions to read for both errors and comprehension, and we found that the effect of lexical stress interacted with linguistic predictability, such that detection of errors in more predictable words was aided by stress but detection of errors in less predictable words was not. This finding suggests that lexical stress patterns can be accessed prelexically if an upcoming word is sufficiently predictable from context. Participants with stronger vocabularies showed decreased effects of stress on task performance, which is consistent with previous findings that more skilled readers are less swayed by phonological information in decisions about orthographic form. In two subsequent experiments, participants were instructed to read only for errors (Experiment 2) or only for comprehension (Experiment 3); the effect of stress disappeared when participants read for errors and reappeared when participants read for comprehension, reconfirming our hypothesis that predictability is a driver of lexical stress effects. In all experiments, errors were detected more reliably in words that were difficult to predict from context than in words that were highly predictable. Taken together, this series of experiments contributes two important findings to the field

  15. Lexical Stress and Linguistic Predictability Influence Proofreading Behavior.

    PubMed

    Harris, Lindsay N; Perfetti, Charles A

    2016-01-01

    There is extensive evidence that the segmental (i.e., phonemic) layer of phonology is routinely activated during reading, but little is known about whether phonological activation extends beyond phonemes to subsegmental layers (which include articulatory information, such as voicing) and suprasegmental layers (which include prosodic information, such as lexical stress). In three proofreading experiments, we show that spelling errors are detected more reliably in syllables that are stressed than in syllables that are unstressed if comprehension is a goal of the reader, indicating that suprasegmental phonology is both active during silent reading and can influence orthographic processes. In Experiment 1, participants received instructions to read for both errors and comprehension, and we found that the effect of lexical stress interacted with linguistic predictability, such that detection of errors in more predictable words was aided by stress but detection of errors in less predictable words was not. This finding suggests that lexical stress patterns can be accessed prelexically if an upcoming word is sufficiently predictable from context. Participants with stronger vocabularies showed decreased effects of stress on task performance, which is consistent with previous findings that more skilled readers are less swayed by phonological information in decisions about orthographic form. In two subsequent experiments, participants were instructed to read only for errors (Experiment 2) or only for comprehension (Experiment 3); the effect of stress disappeared when participants read for errors and reappeared when participants read for comprehension, reconfirming our hypothesis that predictability is a driver of lexical stress effects. In all experiments, errors were detected more reliably in words that were difficult to predict from context than in words that were highly predictable. Taken together, this series of experiments contributes two important findings to the field

  16. Predicting preschool effortful control from toddler temperament and parenting behavior

    PubMed Central

    Cipriano, Elizabeth A.; Stifter, Cynthia A.

    2010-01-01

    This longitudinal study assessed whether maternal behavior and emotional tone moderated the relationship between toddler temperament and preschooler's effortful control. Maternal behavior and emotional tone were observed during a parent-child competing demands task when children were 2 years of age. Child temperament was also assessed at 2 years of age, and three temperament groups were formed: inhibited, exuberant, and low reactive. At 4.5 years of age, children's effortful control was measured from parent-report and observational measures. Results indicated that parental behavior and emotional tone appear to be especially influential on exuberant children's effortful control development. Exuberant children whose mothers used commands and prohibitive statements with a positive emotional tone were more likely to be rated higher on parent-reported effortful control 2.5 years later. When mothers conveyed redirections and reasoning-explanations in a neutral tone, their exuberant children showed poorer effortful control at 4.5 years. PMID:23814350

  17. Physical and Behavioral Measures that Predict Cats' Socialization in an Animal Shelter Environment during a Three Day Period.

    PubMed

    Slater, Margaret; Garrison, Laurie; Miller, Katherine; Weiss, Emily; Drain, Natasha; Makolinski, Kathleen

    2013-01-01

    Animal welfare organizations typically take in cats with unknown levels of socialization towards humans, ranging from unsocialized cats well-socialized but lost pets. Agencies typically determine the socialization status and disposition options of cats within three days, when even a well-socialized pet may be too frightened of the unfamiliar surroundings to display its typical behavior. This is the third part of a three-phase project to develop and evaluate a reliable and valid tool to predict cats' socialization levels. We recruited cats from the full spectrum of socialization and, using information from the cats' caregivers regarding typical behavior toward familiar and unfamiliar people, assigned each cat to a Socialization Category. This information was compared to the cats' behavior during three days of structured assessments conducted in a shelter-like setting. The results of logistic regression modeling generated two models using assessments from the mornings of the second and third day, focusing on predicting shyer or more aloof but socialized cats. Using the coefficients from each of these models, two sets of points were calculated which were useful in differentiating More and Less Socialized cats. In combination with key socialized behaviors, these points were able to fairly accurately identify More and Less Socialized cats. PMID:26479759

  18. Can single empirical algorithms accurately predict inland shallow water quality status from high resolution, multi-sensor, multi-temporal satellite data?

    NASA Astrophysics Data System (ADS)

    Theologou, I.; Patelaki, M.; Karantzalos, K.

    2015-04-01

    Assessing and monitoring water quality status through timely, cost effective and accurate manner is of fundamental importance for numerous environmental management and policy making purposes. Therefore, there is a current need for validated methodologies which can effectively exploit, in an unsupervised way, the enormous amount of earth observation imaging datasets from various high-resolution satellite multispectral sensors. To this end, many research efforts are based on building concrete relationships and empirical algorithms from concurrent satellite and in-situ data collection campaigns. We have experimented with Landsat 7 and Landsat 8 multi-temporal satellite data, coupled with hyperspectral data from a field spectroradiometer and in-situ ground truth data with several physico-chemical and other key monitoring indicators. All available datasets, covering a 4 years period, in our case study Lake Karla in Greece, were processed and fused under a quantitative evaluation framework. The performed comprehensive analysis posed certain questions regarding the applicability of single empirical models across multi-temporal, multi-sensor datasets towards the accurate prediction of key water quality indicators for shallow inland systems. Single linear regression models didn't establish concrete relations across multi-temporal, multi-sensor observations. Moreover, the shallower parts of the inland system followed, in accordance with the literature, different regression patterns. Landsat 7 and 8 resulted in quite promising results indicating that from the recreation of the lake and onward consistent per-sensor, per-depth prediction models can be successfully established. The highest rates were for chl-a (r2=89.80%), dissolved oxygen (r2=88.53%), conductivity (r2=88.18%), ammonium (r2=87.2%) and pH (r2=86.35%), while the total phosphorus (r2=70.55%) and nitrates (r2=55.50%) resulted in lower correlation rates.

  19. State-of-the-art in permeability determination from well log data: Part 2- verifiable, accurate permeability predictions, the touch-stone of all models

    SciTech Connect

    Mohaghegh, S.; Balan, B.; Ameri, S.

    1995-12-31

    The ultimate test for any technique that bears the claim of permeability prediction from well log data, is accurate and verifiable prediction of permeability for wells from which only the well log data is available. So far all the available models and techniques have been tried on data that includes both well logs and the corresponding permeability values. This approach at best is nothing more than linear or nonlinear curve fitting. The objective of this paper is to test the capability of the most promising of these techniques in independent (where corresponding permeability values are not available or have not been used in development of the model) prediction of permeability in a heterogeneous formation. These techniques are {open_quotes}Multiple Regression{close_quotes} and {open_quotes}Virtual Measurements using Artificial Neural Networks.{close_quotes} For the purposes of this study several wells from a heterogeneous formation in West Virginia were selected. Well log data and corresponding permeability values for these wells were available. The techniques were applied to the remaining data and a permeability model for the field was developed. The model was then applied to the well that was separated from the rest of the data earlier and the results were compared. This approach will test the generalization power of each technique. The result will show that although Multiple Regression provides acceptable results for wells that were used during model development, (good curve fitting,) it lacks a consistent generalization capability, meaning that it does not perform as well with data it has not been exposed to (the data from well that has been put aside). On the other hand, Virtual Measurement technique provides a steady generalization power. This technique is able to perform the permeability prediction task even for the entire wells with no prior exposure to their permeability profile.

  20. PredictSNP2: A Unified Platform for Accurately Evaluating SNP Effects by Exploiting the Different Characteristics of Variants in Distinct Genomic Regions

    PubMed Central

    Brezovský, Jan

    2016-01-01

    An important message taken from human genome sequencing projects is that the human population exhibits approximately 99.9% genetic similarity. Variations in the remaining parts of the genome determine our identity, trace our history and reveal our heritage. The precise delineation of phenotypically causal variants plays a key role in providing accurate personalized diagnosis, prognosis, and treatment of inherited diseases. Several computational methods for achieving such delineation have been reported recently. However, their ability to pinpoint potentially deleterious variants is limited by the fact that their mechanisms of prediction do not account for the existence of different categories of variants. Consequently, their output is biased towards the variant categories that are most strongly represented in the variant databases. Moreover, most such methods provide numeric scores but not binary predictions of the deleteriousness of variants or confidence scores that would be more easily understood by users. We have constructed three datasets covering different types of disease-related variants, which were divided across five categories: (i) regulatory, (ii) splicing, (iii) missense, (iv) synonymous, and (v) nonsense variants. These datasets were used to develop category-optimal decision thresholds and to evaluate six tools for variant prioritization: CADD, DANN, FATHMM, FitCons, FunSeq2 and GWAVA. This evaluation revealed some important advantages of the category-based approach. The results obtained with the five best-performing tools were then combined into a consensus score. Additional comparative analyses showed that in the case of missense variations, protein-based predictors perform better than DNA sequence-based predictors. A user-friendly web interface was developed that provides easy access to the five tools’ predictions, and their consensus scores, in a user-understandable format tailored to the specific features of different categories of variations

  1. PredictSNP2: A Unified Platform for Accurately Evaluating SNP Effects by Exploiting the Different Characteristics of Variants in Distinct Genomic Regions.

    PubMed

    Bendl, Jaroslav; Musil, Miloš; Štourač, Jan; Zendulka, Jaroslav; Damborský, Jiří; Brezovský, Jan

    2016-05-01

    An important message taken from human genome sequencing projects is that the human population exhibits approximately 99.9% genetic similarity. Variations in the remaining parts of the genome determine our identity, trace our history and reveal our heritage. The precise delineation of phenotypically causal variants plays a key role in providing accurate personalized diagnosis, prognosis, and treatment of inherited diseases. Several computational methods for achieving such delineation have been reported recently. However, their ability to pinpoint potentially deleterious variants is limited by the fact that their mechanisms of prediction do not account for the existence of different categories of variants. Consequently, their output is biased towards the variant categories that are most strongly represented in the variant databases. Moreover, most such methods provide numeric scores but not binary predictions of the deleteriousness of variants or confidence scores that would be more easily understood by users. We have constructed three datasets covering different types of disease-related variants, which were divided across five categories: (i) regulatory, (ii) splicing, (iii) missense, (iv) synonymous, and (v) nonsense variants. These datasets were used to develop category-optimal decision thresholds and to evaluate six tools for variant prioritization: CADD, DANN, FATHMM, FitCons, FunSeq2 and GWAVA. This evaluation revealed some important advantages of the category-based approach. The results obtained with the five best-performing tools were then combined into a consensus score. Additional comparative analyses showed that in the case of missense variations, protein-based predictors perform better than DNA sequence-based predictors. A user-friendly web interface was developed that provides easy access to the five tools' predictions, and their consensus scores, in a user-understandable format tailored to the specific features of different categories of variations. To

  2. Finite Element Analysis of 2.5D Woven Composites, Part II: Damage Behavior Simulation and Strength Prediction

    NASA Astrophysics Data System (ADS)

    Song, Jian; Wen, Weidong; Cui, Haitao; Zhang, Hongjian; Xu, Ying

    2016-02-01

    In the first part of the work, a new 2.5D woven composites finite element model (2.5D WCFEM) which took into consideration the impact of face structures and can accurately predict the main elastic performances has been established. In this part, the stress-strain behavior and the damage characteristic of this material under uniaxial tension are simulated using nonlinear progressive damage analysis based on damage mechanics. Meanwhile, experimental investigation and fracture analysis are conducted to evaluate the validity of the proposed method. Finally, the influence of woven parameters on the mechanical behavior is discussed. Compared with the test results, a good agreement between the computational and experimental results has been obtained. The progressive damage characteristic and main failure modes are also revealed.

  3. Disorganized Attachment and Inhibitory Capacity: Predicting Externalizing Problem Behaviors

    ERIC Educational Resources Information Center

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

    2012-01-01

    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…

  4. Aggressive Attitudes Predict Aggressive Behavior in Middle School Students.

    ERIC Educational Resources Information Center

    McConville, David W.; Cornell, Dewey G.

    2003-01-01

    This prospective study found that self-reported attitudes toward peer aggression among 403 middle school students were both internally consistent and stable over time (7 months). Aggressive attitudes were correlated with four outcome criteria for aggressive behavior: student self-report of peer aggression; peer and teacher nominations of bullying;…

  5. Predicting Adolescent and Adult Antisocial Behavior among Adjudicated Delinquent Females

    ERIC Educational Resources Information Center

    Cernkovich, Stephen A.; Lanctot, Nadine; Giordano, Peggy C.

    2008-01-01

    Studies identifying the mechanisms underlying the causes and consequences of antisocial behavior among female delinquents as they transit to adulthood are scarce and have important limitations: Most are based on official statistics, they typically are restricted to normative samples, and rarely do they gather prospective data from samples of…

  6. Resting high frequency heart rate variability selectively predicts cooperative behavior.

    PubMed

    Beffara, Brice; Bret, Amélie G; Vermeulen, Nicolas; Mermillod, Martial

    2016-10-01

    This study explores whether the vagal connection between the heart and the brain is involved in prosocial behaviors. The Polyvagal Theory postulates that vagal activity underlies prosocial tendencies. Even if several results suggest that vagal activity is associated with prosocial behaviors, none of them used behavioral measures of prosociality to establish this relationship. We recorded the resting state vagal activity (reflected by High Frequency Heart Rate Variability, HF-HRV) of 48 (42 suitale for analysis) healthy human adults and measured their level of cooperation during a hawk-dove game. We also manipulated the consequence of mutual defection in the hawk-dove game (severe vs. moderate). Results show that HF-HRV is positively and linearly related to cooperation level, but only when the consequence of mutual defection is severe (compared to moderate). This supports that i) prosocial behaviors are likely to be underpinned by vagal functioning ii) physiological disposition to cooperate interacts with environmental context. We discuss these results within the theoretical framework of the Polyvagal Theory. PMID:27343804

  7. Role of Behavioral Addictions in Predicting Reactivity in Bipolar Mood Disorder Patients

    PubMed Central

    Abolghasemi, Abbas; Sadeghi, Hasan; Kiamarsi, Azar; Abbasi, Moslem

    2014-01-01

    Background: Behavioral addictions (BAs) can be understood as disorders characterized by repetitive occurrence of reactivity and uncontrolled behaviors. Very few studies have investigated their association with bipolar mood disorders. Objectives: The present study aimed to determine the role of behavioral addictions in predicting interpersonal behavioral addictions in bipolar mood disorder patients. Materials and Methods: This study had a cross-sectional correlation design. The statistical population was composed of all outpatients with bipolar mood disorders referring to clinical centers in Ardabil. The sample included 60 bipolar mood patients selected from patients referring to clinical centers using the available sampling method. A researcher-made behavioral addiction checklist, Interpersonal Behavioral Addictions Index, and exercise, sexual, and work addiction questionnaires, were used for data collection. The data were analyzed with a Pearson’s correlation coefficient and multivariate regression analysis. Results: The results showed a significant negative relationship between behavioral addictions and interpersonal behavioral addictions (P ≥ 0.01). Multivariate regression analysis results also showed that behavioral addictions are significant and can explain 61% of the variance of interpersonal behavioral addictions in bipolar mood patients. Conclusions: These results suggest that addictive behaviors can affect behavioral addictions in bipolar mood patients. Behavioral addictions lead to negative emotional regulation strategies and result in increased behavioral addictions in these patients. People with high levels of arousal or those who cannot control their behavioral addictions are probably more prone to addictive behaviors. PMID:24971298

  8. Discovery of a general method of solving the Schrödinger and dirac equations that opens a way to accurately predictive quantum chemistry.

    PubMed

    Nakatsuji, Hiroshi

    2012-09-18

    Just as Newtonian law governs classical physics, the Schrödinger equation (SE) and the relativistic Dirac equation (DE) rule the world of chemistry. So, if we can solve these equations accurately, we can use computation to predict chemistry precisely. However, for approximately 80 years after the discovery of these equations, chemists believed that they could not solve SE and DE for atoms and molecules that included many electrons. This Account reviews ideas developed over the past decade to further the goal of predictive quantum chemistry. Between 2000 and 2005, I discovered a general method of solving the SE and DE accurately. As a first inspiration, I formulated the structure of the exact wave function of the SE in a compact mathematical form. The explicit inclusion of the exact wave function's structure within the variational space allows for the calculation of the exact wave function as a solution of the variational method. Although this process sounds almost impossible, it is indeed possible, and I have published several formulations and applied them to solve the full configuration interaction (CI) with a very small number of variables. However, when I examined analytical solutions for atoms and molecules, the Hamiltonian integrals in their secular equations diverged. This singularity problem occurred in all atoms and molecules because it originates from the singularity of the Coulomb potential in their Hamiltonians. To overcome this problem, I first introduced the inverse SE and then the scaled SE. The latter simpler idea led to immediate and surprisingly accurate solution for the SEs of the hydrogen atom, helium atom, and hydrogen molecule. The free complement (FC) method, also called the free iterative CI (free ICI) method, was efficient for solving the SEs. In the FC method, the basis functions that span the exact wave function are produced by the Hamiltonian of the system and the zeroth-order wave function. These basis functions are called complement

  9. Predicting Admissions Committee Behavior in a Medical School.

    ERIC Educational Resources Information Center

    Wergin, Jon F.

    The decisions made by admissions committee members of the Medical College of Virginia were studied to determine the criteria used to arrive at value judgments and to analyze variations in predicted ratings based on these criteria. All 983 applicants to the 1980-81 entering class of the medical school who underwent file review evaluations (the…

  10. Stage of Motivational Readiness: Predictive Ability for Exercise Behavior.

    ERIC Educational Resources Information Center

    Young, Deborah Rohm; King, Abby C.; Sheehan, Mary; Stefanick, Marcia L.

    2002-01-01

    Investigated whether stage of motivational readiness for exercise predicted adherence to an exercise intervention. Adults randomized into a trial had the exercise goal of completing or adding at least 10 miles of weekly brisk walking or jogging. Baseline exercise motivational readiness was assessed. Adherence was determined from logs. Overall, 64…

  11. What Predicts Method Effects in Child Behavior Ratings

    ERIC Educational Resources Information Center

    Low, Justin A.; Keith, Timothy Z.; Jensen, Megan

    2015-01-01

    The purpose of this research was to determine whether child, parent, and teacher characteristics such as sex, socioeconomic status (SES), parental depressive symptoms, the number of years of teaching experience, number of children in the classroom, and teachers' disciplinary self-efficacy predict deviations from maternal ratings in a…

  12. Predicting Adaptive Behavior from the Bayley Scales of Infant Development.

    ERIC Educational Resources Information Center

    Hotard, Stephen; McWhirter, Richard

    To examine the proportion of variance in adaptive functioning predictable from mental ability, chronological age, I.Q., evidence of brain malfunction, seizure medication, and receptive and expressive language scores, 25 severely and profoundly retarded institutionalized persons (2-19 years old) were administered the Bayley Infant Scale Mental…

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

    ERIC Educational Resources Information Center

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

    2010-01-01

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

  14. 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. PMID:24446927

  15. Sensation Seeking Predicting Growth in Adolescent Problem Behaviors

    PubMed Central

    Byck, Gayle R.; Swann, Greg; Schalet, Benjamin; Bolland, John; Mustanski, Brian

    2014-01-01

    There is limited literature on the relationship between sensation seeking and adolescent risk behaviors, particularly among African Americans. We tested the association between psychometrically-derived subscales of the Zuckerman Sensation Seeking Scale and the intercepts and slopes of individual growth curves of conduct problems, sexual risk taking, and substance use from ages 13-18 years by sex. Boys and girls had different associations between sensation seeking and baseline levels and growth of risk behaviors. The Pleasure Seeking scale was associated with baseline levels of conduct problems in boys and girls, baseline substance use in boys, and growth in sexual risk taking and substance use by girls. Girls had the same pattern of associations with the Danger/Novelty scale as the Pleasure Seeking scale. Knowledge about the relationships between adolescent risk taking and sensation seeking can help in the targeted design of prevention and intervention programs for the understudied population of very low-income, African American adolescents. PMID:25112599

  16. Can Humanized Mice Predict Drug "Behavior" in Humans?

    PubMed

    Xu, Dan; Peltz, Gary

    2016-01-01

    Most of what we know about a drug prior to human clinical studies is derived from animal testing. Because animals and humans have substantial differences in their physiology and in their drug metabolism pathways, we do not know very much about the pharmacokinetic and pharmacodynamic behavior of a drug in humans until after it is administered to many people. Hence, drug-induced liver injury has become a significant public health problem, and we have a very inefficient drug development process with a high failure rate. Because the human liver is at the heart of these problems, chimeric mice with humanized livers could be used to address these issues. We examine recent evidence indicating that drug testing in chimeric mice could provide better information about a drug's metabolism, disposition, and toxicity (i.e., its "behavior") in humans and could aid in developing personalized medicine strategies, which would improve drug efficacy and safety. PMID:26514208

  17. Sensation seeking predicting growth in adolescent problem behaviors.

    PubMed

    Byck, Gayle R; Swann, Gregory; Schalet, Benjamin; Bolland, John; Mustanski, Brian

    2015-06-01

    There is limited literature on the relationship between sensation seeking and adolescent risk behaviors, particularly among African Americans. We tested the association between psychometrically-derived subscales of the Zuckerman Sensation Seeking Scale and the intercepts and slopes of individual growth curves of conduct problems, sexual risk taking, and substance use from ages 13 to 18 years by sex. Boys and girls had different associations between sensation seeking and baseline levels and growth of risk behaviors. The Pleasure Seeking scale was associated with baseline levels of conduct problems in boys and girls, baseline substance use in boys, and growth in sexual risk taking and substance use by girls. Girls had the same pattern of associations with the Danger/Novelty scale as the Pleasure Seeking scale. Knowledge about the relationships between adolescent risk taking and sensation seeking can help in the targeted design of prevention and intervention programs for the understudied population of very low-income, African American adolescents. PMID:25112599

  18. Tuning of Strouhal number for high propulsive efficiency accurately predicts how wingbeat frequency and stroke amplitude relate and scale with size and flight speed in birds.

    PubMed Central

    Nudds, Robert L.; Taylor, Graham K.; Thomas, Adrian L. R.

    2004-01-01

    The wing kinematics of birds vary systematically with body size, but we still, after several decades of research, lack a clear mechanistic understanding of the aerodynamic selection pressures that shape them. Swimming and flying animals have recently been shown to cruise at Strouhal numbers (St) corresponding to a regime of vortex growth and shedding in which the propulsive efficiency of flapping foils peaks (St approximately fA/U, where f is wingbeat frequency, U is cruising speed and A approximately bsin(theta/2) is stroke amplitude, in which b is wingspan and theta is stroke angle). We show that St is a simple and accurate predictor of wingbeat frequency in birds. The Strouhal numbers of cruising birds have converged on the lower end of the range 0.2 < St < 0.4 associated with high propulsive efficiency. Stroke angle scales as theta approximately 67b-0.24, so wingbeat frequency can be predicted as f approximately St.U/bsin(33.5b-0.24), with St0.21 and St0.25 for direct and intermittent fliers, respectively. This simple aerodynamic model predicts wingbeat frequency better than any other relationship proposed to date, explaining 90% of the observed variance in a sample of 60 bird species. Avian wing kinematics therefore appear to have been tuned by natural selection for high aerodynamic efficiency: physical and physiological constraints upon wing kinematics must be reconsidered in this light. PMID:15451698

  19. Genome-Scale Metabolic Model for the Green Alga Chlorella vulgaris UTEX 395 Accurately Predicts Phenotypes under Autotrophic, Heterotrophic, and Mixotrophic Growth Conditions.

    PubMed

    Zuñiga, Cristal; Li, Chien-Ting; Huelsman, Tyler; Levering, Jennifer; Zielinski, Daniel C; McConnell, Brian O; Long, Christopher P; Knoshaug, Eric P; Guarnieri, Michael T; Antoniewicz, Maciek R; Betenbaugh, Michael J; Zengler, Karsten

    2016-09-01

    The green microalga Chlorella vulgaris has been widely recognized as a promising candidate for biofuel production due to its ability to store high lipid content and its natural metabolic versatility. Compartmentalized genome-scale metabolic models constructed from genome sequences enable quantitative insight into the transport and metabolism of compounds within a target organism. These metabolic models have long been utilized to generate optimized design strategies for an improved production process. Here, we describe the reconstruction, validation, and application of a genome-scale metabolic model for C. vulgaris UTEX 395, iCZ843. The reconstruction represents the most comprehensive model for any eukaryotic photosynthetic organism to date, based on the genome size and number of genes in the reconstruction. The highly curated model accurately predicts phenotypes under photoautotrophic, heterotrophic, and mixotrophic conditions. The model was validated against experimental data and lays the foundation for model-driven strain design and medium alteration to improve yield. Calculated flux distributions under different trophic conditions show that a number of key pathways are affected by nitrogen starvation conditions, including central carbon metabolism and amino acid, nucleotide, and pigment biosynthetic pathways. Furthermore, model prediction of growth rates under various medium compositions and subsequent experimental validation showed an increased growth rate with the addition of tryptophan and methionine. PMID:27372244

  20. Improved predictive modeling of white LEDs with accurate luminescence simulation and practical inputs with TracePro opto-mechanical design software

    NASA Astrophysics Data System (ADS)

    Tsao, Chao-hsi; Freniere, Edward R.; Smith, Linda

    2009-02-01

    The use of white LEDs for solid-state lighting to address applications in the automotive, architectural and general illumination markets is just emerging. LEDs promise greater energy efficiency and lower maintenance costs. However, there is a significant amount of design and cost optimization to be done while companies continue to improve semiconductor manufacturing processes and begin to apply more efficient and better color rendering luminescent materials such as phosphor and quantum dot nanomaterials. In the last decade, accurate and predictive opto-mechanical software modeling has enabled adherence to performance, consistency, cost, and aesthetic criteria without the cost and time associated with iterative hardware prototyping. More sophisticated models that include simulation of optical phenomenon, such as luminescence, promise to yield designs that are more predictive - giving design engineers and materials scientists more control over the design process to quickly reach optimum performance, manufacturability, and cost criteria. A design case study is presented where first, a phosphor formulation and excitation source are optimized for a white light. The phosphor formulation, the excitation source and other LED components are optically and mechanically modeled and ray traced. Finally, its performance is analyzed. A blue LED source is characterized by its relative spectral power distribution and angular intensity distribution. YAG:Ce phosphor is characterized by relative absorption, excitation and emission spectra, quantum efficiency and bulk absorption coefficient. Bulk scatter properties are characterized by wavelength dependent scatter coefficients, anisotropy and bulk absorption coefficient.

  1. Against matching theory: predictions of an evolutionary theory of behavior dynamics.

    PubMed

    McDowell, J J; Calvin, Nicholas T

    2015-05-01

    A selectionist theory of adaptive behavior dynamics instantiates the idea that behavior evolves in response to selection pressure from the environment in the form of resource acquisition or threat escape or avoidance. The theory is implemented by a computer program that creates an artificial organism and animates it with a population of potential behaviors. The population undergoes selection, recombination, and mutation across generations, or ticks of time, which produces a continuous stream of behavior that can be studied as if it were the behavior of a live organism. Novel predictions of the evolutionary theory can be compared to predictions of matching theory in a critical experiment that arranges concurrent schedules with reinforcer magnitudes that vary across conditions in one component of the schedules but not the other. Matching theory and the evolutionary theory make conflicting predictions about the outcome of this critical experiment, such that the results must disconfirm at least one of the theories. PMID:25680328

  2. Objective assessment of covert antisocial behavior: predictive validity and ethical considerations.

    PubMed

    Hinshaw, Stephen P

    2005-01-01

    Although less observable than the overt actions of fighting and assault, covert antisocial behaviors such as stealing and property destruction comprise an important subclass of externalizing behavior patterns, displaying considerable predictive power toward delinquency in adolescence. I discuss a laboratory paradigm for objective observation of such behaviors in children that has shown impressive concurrent and predictive validity among samples of boys with and without attention deficit hyperactivity disorder. Addressed herein are crucial questions regarding the ethics of tempting children to steal objects and small amounts of money and to deface property as well as the types of informed consent and debriefing procedures utilized in research with this paradigm. Weighing ethical considerations alongside the ability to predict delinquent behavior presents provocative issues for those interested in understanding the development of antisocial behavior. PMID:16523562

  3. Craving Predicts Within Session Drinking Behavior Following Placebo

    PubMed Central

    Leeman, Robert F.; Corbin, William R.; Fromme, Kim

    2009-01-01

    Tiffany’s (1990) cognitive processing model postulates that craving will only occur when access to alcohol is blocked. To test a hypothesis based on this model, we analyzed data from a naturalistic laboratory alcohol challenge study involving moderate-to-heavy drinking young adults (N = 174) with a focus on the placebo beverage condition of this study. Our hypothesis was that self-reports of “wanting more alcohol” (i.e., craving) in the lab, following placebo, would predict subsequent ad libitum consumption because placebo administration would constitute partial blocking of access to alcohol. We also tested the possibility that craving might mediate associations between personality traits and ad libitum consumption. Both trait disinhibition and reports of craving following the placebo beverage significantly predicted ad libitum consumption. Further, craving partially mediated the association between trait disinhibition and ad libitum consumption. Potential implications of these findings are discussed. PMID:20161258

  4. The Model for End-stage Liver Disease accurately predicts 90-day liver transplant wait-list mortality in Atlantic Canada

    PubMed Central

    Renfrew, Paul Douglas; Quan, Hude; Doig, Christopher James; Dixon, Elijah; Molinari, Michele

    2011-01-01

    OBJECTIVE: To determine the generalizability of the predictions for 90-day mortality generated by Model for End-stage Liver Disease (MELD) and the serum sodium augmented MELD (MELDNa) to Atlantic Canadian adults with end-stage liver disease awaiting liver transplantation (LT). METHODS: The predictive accuracy of the MELD and the MELDNa was evaluated by measurement of the discrimination and calibration of the respective models’ estimates for the occurrence of 90-day mortality in a consecutive cohort of LT candidates accrued over a five-year period. Accuracy of discrimination was measured by the area under the ROC curves. Calibration accuracy was evaluated by comparing the observed and model-estimated incidences of 90-day wait-list failure for the total cohort and within quantiles of risk. RESULTS: The area under the ROC curve for the MELD was 0.887 (95% CI 0.705 to 0.978) – consistent with very good accuracy of discrimination. The area under the ROC curve for the MELDNa was 0.848 (95% CI 0.681 to 0.965). The observed incidence of 90-day wait-list mortality in the validation cohort was 7.9%, which was not significantly different from the MELD estimate of 6.6% (95% CI 4.9% to 8.4%; P=0.177) or the MELDNa estimate of 5.8% (95% CI 3.5% to 8.0%; P=0.065). Global goodness-of-fit testing found no evidence of significant lack of fit for either model (Hosmer-Lemeshow χ2 [df=3] for MELD 2.941, P=0.401; for MELDNa 2.895, P=0.414). CONCLUSION: Both the MELD and the MELDNa accurately predicted the occurrence of 90-day wait-list mortality in the study cohort and, therefore, are generalizable to Atlantic Canadians with end-stage liver disease awaiting LT. PMID:21876856

  5. The VACS Index Accurately Predicts Mortality and Treatment Response among Multi-Drug Resistant HIV Infected Patients Participating in the Options in Management with Antiretrovirals (OPTIMA) Study

    PubMed Central

    Brown, Sheldon T.; Tate, Janet P.; Kyriakides, Tassos C.; Kirkwood, Katherine A.; Holodniy, Mark; Goulet, Joseph L.; Angus, Brian J.; Cameron, D. William; Justice, Amy C.

    2014-01-01

    Objectives The VACS Index is highly predictive of all-cause mortality among HIV infected individuals within the first few years of combination antiretroviral therapy (cART). However, its accuracy among highly treatment experienced individuals and its responsiveness to treatment interventions have yet to be evaluated. We compared the accuracy and responsiveness of the VACS Index with a Restricted Index of age and traditional HIV biomarkers among patients enrolled in the OPTIMA study. Methods Using data from 324/339 (96%) patients in OPTIMA, we evaluated associations between indices and mortality using Kaplan-Meier estimates, proportional hazards models, Harrel’s C-statistic and net reclassification improvement (NRI). We also determined the association between study interventions and risk scores over time, and change in score and mortality. Results Both the Restricted Index (c = 0.70) and VACS Index (c = 0.74) predicted mortality from baseline, but discrimination was improved with the VACS Index (NRI = 23%). Change in score from baseline to 48 weeks was more strongly associated with survival for the VACS Index than the Restricted Index with respective hazard ratios of 0.26 (95% CI 0.14–0.49) and 0.39(95% CI 0.22–0.70) among the 25% most improved scores, and 2.08 (95% CI 1.27–3.38) and 1.51 (95%CI 0.90–2.53) for the 25% least improved scores. Conclusions The VACS Index predicts all-cause mortality more accurately among multi-drug resistant, treatment experienced individuals and is more responsive to changes in risk associated with treatment intervention than an index restricted to age and HIV biomarkers. The VACS Index holds promise as an intermediate outcome for intervention research. PMID:24667813

  6. Change in ST segment elevation 60 minutes after thrombolytic initiation predicts clinical outcome as accurately as later electrocardiographic changes

    PubMed Central

    Purcell, I; Newall, N; Farrer, M

    1997-01-01

    Objective—To compare prospectively the prognostic accuracy of a 50% decrease in ST segment elevation on standard 12-lead electrocardiograms (ECGs) recorded at 60, 90, and 180 minutes after thrombolysis initiation in acute myocardial infarction.
Design—Consecutive sample prospective cohort study.
Setting—A single coronary care unit in the north of England.
Patients—190 consecutive patients receiving thrombolysis for first acute myocardial infarction.
Interventions—Thrombolysis at baseline.
Main outcome measures—Cardiac mortality and left ventricular size and function assessed 36 days later.
Results—Failure of ST segment elevation to resolve by 50% in the single lead of maximum ST elevation or the sum ST elevation of all infarct related ECG leads at each of the times studied was associated with a significantly higher mortality, larger left ventricular volume, and lower ejection fraction. There was some variation according to infarct site with only the 60 minute ECG predicting mortality after inferior myocardial infarction and only in anterior myocardial infarction was persistent ST elevation associated with worse left ventricular function. The analysis of the lead of maximum ST elevation at 60 minutes from thrombolysis performed as well as later ECGs in receiver operating characteristic curves for predicting clinical outcome.
Conclusion—The standard 12-lead ECG at 60 minutes predicts clinical outcome as accurately as later ECGs after thrombolysis for first acute myocardial infarction.

 Keywords: myocardial infarction;  thrombolysis;  ST segment elevation PMID:9415005

  7. Fecal Calprotectin is an Accurate Tool and Correlated to Seo Index in Prediction of Relapse in Iranian Patients With Ulcerative Colitis

    PubMed Central

    Hosseini, Seyed Vahid; Jafari, Peyman; Taghavi, Seyed Alireza; Safarpour, Ali Reza; Rezaianzadeh, Abbas; Moini, Maryam; Mehrabi, Manoosh

    2015-01-01

    . Besides, FC level of 341 μg/g was identified as the cut-off point with 11.2% and 79.7% relapse rate below and above this point, respectively. Additionally, Pearson correlation coefficient (r) between FC and the Seo index was significant in prediction of relapse (r = 0.63, P < 0.001). Conclusions: As a simple and noninvasive marker, FC is highly accurate and significantly correlated to the Seo activity index in prediction of relapse in the course of quiescent UC in Iranian patients. PMID:25793117

  8. Reliance on functional resting-state network for stable task control predicts behavioral tendency for cooperation.

    PubMed

    Hahn, Tim; Notebaert, Karolien; Anderl, Christine; Reicherts, Philipp; Wieser, Matthias; Kopf, Juliane; Reif, Andreas; Fehl, Katrin; Semmann, Dirk; Windmann, Sabine

    2015-09-01

    Humans display individual variability in cooperative behavior. While an ever-growing body of research has investigated the neural correlates of task-specific cooperation, the mechanisms by which situation-independent, stable differences in cooperation render behavior consistent across a wide range of situations remain elusive. Addressing this issue, we show that the individual tendency to behave in a prosocial or individualistic manner can be predicted from the functional resting-state connectome. More specifically, connections of the cinguloopercular network which supports goal-directed behavior encode cooperative tendency. Effects of virtual lesions to this network on the efficacy of information exchange throughout the brain corroborate our findings. These results shed light on the neural mechanisms underlying individualists' and prosocials' habitual social decisions by showing that reliance on the cinguloopercular task-control network predicts stable cooperative behavior. Based on this evidence, we provide a unifying framework for the interpretation of functional imaging and behavioral studies of cooperative behavior. PMID:26070266

  9. 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,…

  10. Predicting Residential Treatment Outcomes for Emotionally and Behaviorally Disordered Youth: The Role of Pretreatment Factors

    ERIC Educational Resources Information Center

    den Dunnen, Wendy; St. Pierre, Jeff; Stewart, Shannon L.; Johnson, Andrew; Cook, Steven; Leschied, Alan W.

    2012-01-01

    This study examined outcomes with 170 children and youth admitted to residential treatment with complex mental health problems. Overall, outcomes at 2 years post-treatment was predicted by children and youth's behavioral pretreatment status reflected in lower internalizing and externalizing behavior at admission. These findings recognize a cluster…

  11. 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)…

  12. Predicting Individuals' Turnover and Internal Transfer Behavior. Wisconsin Working Paper 6-82-31.

    ERIC Educational Resources Information Center

    Taylor, M. Susan; Covaleski, Mark A.

    Researchers have spent considerable time and effort in the last 5 years developing process models to explain individuals' job turnover behavior. To examine the predictability of internal job transfers and turnover behavior from individuals' work values, career plans, and job satisfaction, nurses (N=160) completed questionnaires on demographic…

  13. Interactions of Team Mental Models and Monitoring Behaviors Predict Team Performance in Simulated Anesthesia Inductions

    ERIC Educational Resources Information Center

    Burtscher, Michael J.; Kolbe, Michaela; Wacker, Johannes; Manser, Tanja

    2011-01-01

    In the present study, we investigated how two team mental model properties (similarity vs. accuracy) and two forms of monitoring behavior (team vs. systems) interacted to predict team performance in anesthesia. In particular, we were interested in whether the relationship between monitoring behavior and team performance was moderated by team…

  14. Prediction of Participation in Continuing Professional Education: A Test of Two Behavioral Intention Models.

    ERIC Educational Resources Information Center

    Yang, Baiyn; And Others

    1994-01-01

    Analysis of 551 Alberta veterinarians' intention to participate in continuing education revealed that the Triandis model of behavioral intention had greater predictive utility than the Fishbein-Azjen. Participation was largely determined by behavioral intention, which was influenced by attitude toward the program. (SK)

  15. 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…

  16. 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…

  17. An Evaluation of the Predictive Validity of Confidence Ratings in Identifying Functional Behavioral Assessment Hypothesis Statements

    ERIC Educational Resources Information Center

    Borgmeier, Chris; Horner, Robert H.

    2006-01-01

    Faced with limited resources, schools require tools that increase the accuracy and efficiency of functional behavioral assessment. Yarbrough and Carr (2000) provided evidence that informant confidence ratings of the likelihood of problem behavior in specific situations offered a promising tool for predicting the accuracy of function-based…

  18. The Influence of Differential "Power" and "Solidarity" upon the Predictability of Behavior: A Peruvian Example

    ERIC Educational Resources Information Center

    Moles, Jerry A.

    1978-01-01

    The usage of Spanish address terms is investigated to explore the predictability and variability in the behavior of non-Indians and Quechua Indians in Peru. The behavior variations are related to differential "power" and "solidarity" between the two ethnic groups and differential "solidarity" within the Quecha group. (Author/SW)

  19. 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

  20. Individual variability in behavioral flexibility predicts sign-tracking tendency

    PubMed Central

    Nasser, Helen M.; Chen, Yu-Wei; Fiscella, Kimberly; Calu, Donna J.

    2015-01-01

    Sign-tracking rats show heightened sensitivity to food- and drug-associated cues, which serve as strong incentives for driving reward seeking. We hypothesized that this enhanced incentive drive is accompanied by an inflexibility when incentive value changes. To examine this we tested rats in Pavlovian outcome devaluation or second-order conditioning prior to the assessment of sign-tracking tendency. To assess behavioral flexibility we trained rats to associate a light with a food outcome. After the food was devalued by pairing with illness, we measured conditioned responding (CR) to the light during an outcome devaluation probe test. The level of CR during outcome devaluation probe test correlated with the rats' subsequent tracking tendency, with sign-tracking rats failing to suppress CR to the light after outcome devaluation. To assess Pavlovian incentive learning, we trained rats on first-order (CS+, CS−) and second-order (SOCS+, SOCS−) discriminations. After second-order conditioning, we measured CR to the second-order cues during a probe test. Second-order conditioning was observed across all rats regardless of tracking tendency. The behavioral inflexibility of sign-trackers has potential relevance for understanding individual variation in vulnerability to drug addiction. PMID:26578917

  1. Predicting aggressive behavior with the aggressiveness-IAT.

    PubMed

    Banse, Rainer; Messer, Mario; Fischer, Ilka

    2015-01-01

    The Implicit Association Test (IAT, Greenwald, McGhee, & Schwartz, 1998) was adapted to assess the automatically activated (implicit) self-concept of aggressiveness. In three studies the validity of the Aggressiveness-IAT (Agg-IAT) was supported by substantial correlations with self-report measures of aggressiveness. After controlling for self-report measures of aggressiveness, the Agg-IAT accounted for 9-15% of the variance of three different indicators of aggressive behavior across three studies. To further explore the nomological network around the Agg-IAT we investigated its correlations with measures of social desirability (SD). Although not fully conclusive, the results across four studies provided some support for a weak negative correlation between impression management SD and aggressive behavior as well as the Agg-IAT. This result is in line with an interpersonally oriented self-control account of impression management SD. Individuals with high SD scores seem to behave less aggressively, and to show lower Agg-IAT scores. The one-week stability of the Agg-IAT was r = .58 in Study 4. Aggr. Behav. 41:65-83 2015. © 2014 Wiley Periodicals, Inc. PMID:27539875

  2. Understanding and Predicting Human Behavior for Social Communities

    NASA Astrophysics Data System (ADS)

    Simoes, Jose; Magedanz, Thomas

    Over the last years, with the rapid advance in technology, it is becoming increasingly feasible for people to take advantage of the devices and services in the surrounding environment to remain "connected" and continuously enjoy the activity they are engaged in, be it sports, entertainment, or work. Such a ubiquitous computing environment will allow everyone permanent access to the Internet anytime, anywhere and anyhow [1]. Nevertheless, despite the evolution of services, social aspects remain in the roots of every human behavior and activities. Great examples of such phenomena are online social networks, which engage users in a way never seen before in the online world. At the same time, being aware and communicating context is a key part of human interaction and is a particularly powerful concept when applied to a community of users where services can be made more personalized and useful. Altogether, harvesting context to reason and learn about user behavior will further enhance the future multimedia vision where services can be composed and customized according to user context. Moreover, it will help us to understand users in a better way.

  3. Corticosteroid-induced neural remodeling predicts behavioral vulnerability and resilience

    PubMed Central

    Gourley, Shannon L.; Swanson, Andrew M.; Koleske, Anthony J.

    2013-01-01

    Neurons in distinct brain regions remodel in response to postnatal stressor exposure, and structural plasticity may underlie stress-related modifications in behavioral outcomes. Given the persistence of stress-related diseases such as depression, a critical next step in identifying the contributions of neural structure to psychopathology will be to identify brain circuits and cell types that fail to recover from stressor exposure. We enumerated dendritic spines during and after chronic stress hormone exposure in hippocampal CA1, deep-layer prefrontal cortex, and the basal amygdala and also reconstructed dendritic arbors of CA1 pyramidal neurons. Corticosterone modified dendritic spine density in these regions, but with the exception of the orbitofrontal cortex, densities normalized with a recovery period. Dendritic retraction of hippocampal CA1 neurons and anhedonic-like insensitivity to a sucrose solution also persisted despite a recovery period. Using mice with reduced gene dosage of p190rhogap, a cytoskeletal regulatory protein localized to dendritic spines, we next isolated structural correlates of both behavioral vulnerability (spine elimination) and resilience (spine proliferation) to corticosterone within the orbital cortex. Our findings provide novel empirical support for the perspective that stress-related structural reorganization of certain neuron populations can persist despite a “recovery” period from stressor exposure, and that these modifications may lay a structural foundation for stressor vulnerability—or resiliency—across the lifespan. PMID:23407965

  4. Artificial Neural Networks: A New Approach for Predicting Application Behavior. AIR 2001 Annual Forum Paper.

    ERIC Educational Resources Information Center

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

    This paper examines how predictive modeling can be used to study application behavior. A relatively new technique, artificial neural networks (ANNs), was applied to help predict which students were likely to get into a large Research I university. Data were obtained from a university in Iowa. Two cohorts were used, each containing approximately…

  5. 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…

  6. Validation of a novel behavior prediction scale: A two-center trial

    PubMed Central

    Asokan, Sharath; Surendran, Sharmila; Punugoti, Dedeepya; Nuvvula, Sivakumar; Geetha Priya, P. R.

    2014-01-01

    Context: Prediction of the child's behavior can adequately equip the dentist in rendering effective and efficient dental treatment. Aim: This study was planned to evaluate and validate a specially prepared questionnaire as a child behavior prediction scale. Design: A two-center cross-sectional study was done to validate the new scale. Materials and Methods: Children aged 3–12 years (n = 296), from two different centers participated in this study. The questionnaire used was a 10-point observational scale. Observations involved perceiving overt and subtle behavioral characteristics of a child, to assess the child's behavior in the dental office before treatment. An independent observer approached the children and their parents in the waiting room. The child's behavior was then evaluated by the dentist using Frankl behavior rating scale during and after treatment. The prediction of behavior compared to the Frankl scale was assessed and validated. Statistical Analysis: Sensitivity, specificity tests, and receiver operating curve analysis were used to validate the new scale and calculate the cut-off score for positive and negative behavior. All data were processed by SPSS software (16.0, SPSS Inc., Chicago, Ill, USA). Results: The best cut-off score to predict a positive Frankl rating was ≥ 8.0 in both the centers. The sensitivity and specificity scores were 93.4% and 62.5% in center 1; 83.1% and 59.6% in center 2 respectively. Conclusion: This novel prediction scale can be of great importance in predicting children's behavior in the dental environment. PMID:25395769

  7. Prediction of Indentation Behavior of Superelastic TiNi

    NASA Astrophysics Data System (ADS)

    Neupane, Rabin; Farhat, Zoheir

    2014-09-01

    Superelastic TiNi shape memory alloys have been extensively used in various applications. The great interest in TiNi alloys is due to its unique shape memory and superelastic effects, along with its superior wear and dent resistance. Assessment of mechanical properties and dent resistance of superelastic TiNi is commonly performed using indentation techniques. However, the coupling of deformation and reversible martensitic transformation of TiNi under indentation conditions makes the interpretation of results challenging. An attempt is made to enhance current interpretation of indentation data. A load-depth curve is predicted that takes into consideration the reversible martensitic transformation. The predicted curve is in good agreement with experimental results. It is found in this study that the elastic modulus is a function of indentation depth. At shallow depths, the elastic modulus is high due to austenite dominance, while at high depths, the elastic modulus drops as the depth increases due to austenite to martensite transition, i.e., martensite dominance. It is also found that TiNi exhibits superior dent resistance compared to AISI 304 steel. There is two orders of magnitude improvement in dent resistance of TiNi in comparison to AISI 304 steel.

  8. A mathematical model to predict extraction behavior of metal ions between a polymer gel and an aqueous phase

    SciTech Connect

    Takeshita, Kenji

    1999-03-01

    A mathematical model to predict the extraction behavior of metal ion between a polymer gel and an aqueous solution was proposed. It consists of the Flory-Huggins formula for evaluating thermodynamically the physico-chemical properties of polymer gel, the modified Stokes-Einstein equation to evaluate the mass transfer rate of metal ion into polymer gel and the equation to evaluate the extraction equilibrium. The extraction of lanthanide elements, Nd(III), Sm(III) and Gd(III), from an aqueous solution containing nitrate ion was carried out by the use of SDB (styrene-divinylbenzene copolymer) gel swollen with a bidentate organophosphorus compound, CMP (dihexyl-N,N-diethylcarbamoylmethylpohosphonate). The binary extraction and the effect of the crosslinking degree of SDB gel on the extraction rate were examined. These experimental results were in agreement with the predictions calculated by the proposed model. It was confirmed that the extraction behavior of lanthanide ions into the SDB gel was predicted accurately, when the physico-chemical properties of SDB gel, such as the affinity between SDB and CMP ({chi}) and the crosslinking degree ({nu}{sub e}), and a coefficient defined in the modified Stokes-Einstein equation (K{sub 0}) were known. This model is available as a tool to design an extraction chromatographic process using polymer gel.

  9. Calibrating transition-metal energy levels and oxygen bands in first-principles calculations: Accurate prediction of redox potentials and charge transfer in lithium transition-metal oxides

    NASA Astrophysics Data System (ADS)

    Seo, Dong-Hwa; Urban, Alexander; Ceder, Gerbrand

    2015-09-01

    Transition-metal (TM) oxides play an increasingly important role in technology today, including applications such as catalysis, solar energy harvesting, and energy storage. In many of these applications, the details of their electronic structure near the Fermi level are critically important for their properties. We propose a first-principles-based computational methodology for the accurate prediction of oxygen charge transfer in TM oxides and lithium TM (Li-TM) oxides. To obtain accurate electronic structures, the Heyd-Scuseria-Ernzerhof (HSE06) hybrid functional is adopted, and the amount of exact Hartree-Fock exchange (mixing parameter) is adjusted to reproduce reference band gaps. We show that the HSE06 functional with optimal mixing parameter yields not only improved electronic densities of states, but also better energetics (Li-intercalation voltages) for LiCo O2 and LiNi O2 as compared to the generalized gradient approximation (GGA), Hubbard U corrected GGA (GGA +U ), and standard HSE06. We find that the optimal mixing parameters for TM oxides are system specific and correlate with the covalency (ionicity) of the TM species. The strong covalent (ionic) nature of TM-O bonding leads to lower (higher) optimal mixing parameters. We find that optimized HSE06 functionals predict stronger hybridization of the Co 3 d and O 2 p orbitals as compared to GGA, resulting in a greater contribution from oxygen states to charge compensation upon delithiation in LiCo O2 . We also find that the band gaps of Li-TM oxides increase linearly with the mixing parameter, enabling the straightforward determination of optimal mixing parameters based on GGA (α =0.0 ) and HSE06 (α =0.25 ) calculations. Our results also show that G0W0@GGA +U band gaps of TM oxides (M O ,M =Mn ,Co ,Ni ) and LiCo O2 agree well with experimental references, suggesting that G0W0 calculations can be used as a reference for the calibration of the mixing parameter in cases when no experimental band gap has been

  10. Approach behavior and sympathetic nervous system reactivity predict substance use in young adults.

    PubMed

    Hinnant, J Benjamin; Forman-Alberti, Alissa B; Freedman, Anna; Byrnes, Lindsay; Degnan, Kathryn A

    2016-07-01

    A behavioral measure of approach (performance on a resource gathering task) in combination with sympathetic nervous system (SNS) reactivity was used to predict substance use in a sample of young adults (n=93). Pre-ejection period reactivity (PEP-R), a cardiac index of SNS reactivity, was recorded during the resource gathering task (task PEP - resting PEP). Higher levels of approach behaviors on the task in combination with less PEP-R (blunted SNS reactivity) predicted the highest levels of substance use. Findings are discussed in the context of behavioral and physiological systems of approach and avoidance. PMID:27178723

  11. 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

  12. Forming Attitudes that Predict Future Behavior: A Meta-Analysis of the Attitude-Behavior Relation

    ERIC Educational Resources Information Center

    Glasman, Laura R.; Albarracin, Dolores

    2006-01-01

    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…

  13. Using the Theory of Planned Behavior to Explain and Predict Behavior Intentions in Taiwan

    ERIC Educational Resources Information Center

    Wu, Cheng-Lung

    2015-01-01

    This study aims to use the theory of planned behavior to verify undergraduates' behavioral intentions regarding their participation in aquatic sports. Undergraduates in Taiwan serve as the research subjects and a survey method employs questionnaires. A total of 200 valid questionnaires were received out of 230, thus giving a valid response rate of…

  14. Parents' Reports on the Child Adaptive Behavior Inventory Predict 4-Year-Olds' Playground Behavior.

    ERIC Educational Resources Information Center

    McHale, James P.; Neugebauer, Alyson

    1998-01-01

    Examined the effectiveness of parental reports of their preschool children's social adaptation outside the home as an indicator of children's behavior. Parent responses on the Child Adaptive Behavior Inventory, which assesses both competencies and difficulties with adaptation, were compared to evaluations by trained observers. Parents were found…

  15. A comprehensive model and method for model parameterization for predicting pattern collapse behavior in photoresist nanostructures

    NASA Astrophysics Data System (ADS)

    Yeh, Wei-Ming; Lawson, Richard A.; Henderson, Clifford L.

    2011-04-01

    Pattern collapse has become an issue of increasing importance in semiconductor lithography as the size of critical features continues to shrink. Although models have been proposed to explain the observed pattern collapse behavior, the ability of such models to quantitatively predict the collapse behavior has been limited without significant model fitting to experimental pattern collapse behavior. Such a need to collect extensive collapse data before these models can provide any predictive capability limits their use and in general does not provide further insight into the underlying root causes of the observed behavior in many cases. This is particularly true at small feature sizes for resist lines smaller than approximately 70 nm in width. In this work, a comprehensive pattern collapse model that accounts for both adhesion based pattern failure and elastoplastic deformation-based failure is used. Furthermore, the required model parameters are extracted from basic experiments on the resist materials and substrates themselves without the need for actual patterning experiments. For example, the resist mechanical modulus behavior is determined from simple thin film buckling experiments. The results of these simple tests are quantitatively predictive pattern collapse models for a particular resist-substrate combination that capture complex effects such as the dependence of the collapse behavior on resist film thickness and feature size due to feature size dependent polymer modulus behavior. Application of these models and experimental methods to an experimental resist and comparisons of the model predictions versus actual experimental pattern collapse data are shown and discussed to validate the methodology.

  16. 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

  17. Toward Relatively General and Accurate Quantum Chemical Predictions of Solid-State 17O NMR Chemical Shifts in Various Biologically Relevant Oxygen-containing Compounds

    PubMed Central

    Rorick, Amber; Michael, Matthew A.; Yang, Liu; Zhang, Yong

    2015-01-01

    Oxygen is an important element in most biologically significant molecules and experimental solid-state 17O NMR studies have provided numerous useful structural probes to study these systems. However, computational predictions of solid-state 17O NMR chemical shift tensor properties are still challenging in many cases and in particular each of the prior computational work is basically limited to one type of oxygen-containing systems. This work provides the first systematic study of the effects of geometry refinement, method and basis sets for metal and non-metal elements in both geometry optimization and NMR property calculations of some biologically relevant oxygen-containing compounds with a good variety of XO bonding groups, X= H, C, N, P, and metal. The experimental range studied is of 1455 ppm, a major part of the reported 17O NMR chemical shifts in organic and organometallic compounds. A number of computational factors towards relatively general and accurate predictions of 17O NMR chemical shifts were studied to provide helpful and detailed suggestions for future work. For the studied various kinds of oxygen-containing compounds, the best computational approach results in a theory-versus-experiment correlation coefficient R2 of 0.9880 and mean absolute deviation of 13 ppm (1.9% of the experimental range) for isotropic NMR shifts and R2 of 0.9926 for all shift tensor properties. These results shall facilitate future computational studies of 17O NMR chemical shifts in many biologically relevant systems, and the high accuracy may also help refinement and determination of active-site structures of some oxygen-containing substrate bound proteins. PMID:26274812

  18. Neuronal Prediction of Opponent’s Behavior during Cooperative Social Interchange in Primates

    PubMed Central

    Haroush, Keren; Williams, Ziv M.

    2015-01-01

    SUMMARY 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. PMID:25728667

  19. Predicting Outcome of Community-Based Early Intensive Behavioral Intervention for Children with Autism.

    PubMed

    Smith, Tristram; Klorman, Rafael; Mruzek, Daniel W

    2015-10-01

    We examined predictors of outcome (IQ, adaptive behavior, and ASD severity) after 12 and 24 months of early intensive behavioral intervention (EIBI) in 71, 20-59 months old children with autism spectrum disorder (ASD) who were enrolled in publicly-funded, community-based agencies. Predictors included social engagement (combining variables loading onto a single factor: social approach, joint attention, and imitation) and sensorimotor rituals. Younger age and higher IQ at intake predicted favorable outcomes at both 12 and 24 months. Adjusting for age, IQ, baseline predictor scores, EIBI hours, treatment site, and sensorimotor rituals, social engagement predicted superior later IQ and adaptive behavior. In contrast, sensorimotor rituals did not predict outcome. Although limited by the absence of a control group, the study indicates social engagement predicts some EIBI outcomes. PMID:25778537

  20. Contemporaneous and 1-year longitudinal prediction of children's prosocial behavior from sympathy and moral motivation.

    PubMed

    Malti, Tina; Gummerum, Michaela; Buchmann, Marlis

    2007-09-01

    The authors investigated the contemporaneous and longitudinal relations of children's (M age = 6.4 years) prosocial behavior to sympathy and moral motivation. Mothers and kindergarten teachers rated children's prosocial behavior. The authors measured sympathy via self- and adult reports. Moral motivation was assessed by children's attribution of emotions to hypothetical victimizers and self-as-victimizers and by moral reasoning after rule violations. Mother-rated prosocial behavior was contemporaneously and longitudinally related to sympathy. Moral motivation moderated the relation of sympathy to mother-rated prosocial behavior. Furthermore, boys' level of mother-rated prosocial behavior increased with level of moral motivation, whereas girls were high in mother-rated prosocial behavior, regardless of their level of moral motivation. Sympathy contemporaneously predicted kindergarten teacher-rated prosocial behavior. PMID:18200890

  1. Common Factors Predicting Long-term Changes in Multiple Health Behaviors

    PubMed Central

    BLISSMER, BRYAN; PROCHASKA, JAMES O.; VELICER, WAYNE F.; REDDING, COLLEEN A.; ROSSI, JOSEPH S.; GREENE, GEOFFREY W.; PAIVA, ANDREA; ROBBINS, MARK

    2010-01-01

    This study was designed to assess if there are consistent treatment, stage, severity, effort and demographic effects which predict long-term changes across the multiple behaviors of smoking, diet and sun exposure. A secondary data analysis integrated data from four studies on smoking cessation (N = 3927), three studies on diet (N = 4824) and four studies on sun exposure (N = 6465). Across all three behaviors, behavior change at 24 months was related to treatment, stage of change, problem severity and effort effects measured at baseline. There were no consistent demographic effects. Across multiple behaviors, long-term behavior changes are consistently related to four effects that are dynamic and open to change. Behavior changes were not consistently related to static demographic variables. Future intervention research can target the four effects to determine if breakthroughs can be produced in changing single and multiple behaviors. PMID:20207664

  2. Deep vein thrombosis is accurately predicted by comprehensive analysis of the levels of microRNA-96 and plasma D-dimer

    PubMed Central

    Xie, Xuesheng; Liu, Changpeng; Lin, Wei; Zhan, Baoming; Dong, Changjun; Song, Zhen; Wang, Shilei; Qi, Yingguo; Wang, Jiali; Gu, Zengquan

    2016-01-01

    The aim of the present study was to investigate the association between platelet microRNA-96 (miR-96) expression levels and the occurrence of deep vein thrombosis (DVT) in orthopedic patients. A total of consecutive 69 orthopedic patients with DVT and 30 healthy individuals were enrolled. Ultrasonic color Doppler imaging was performed on lower limb veins after orthopedic surgery to determine the occurrence of DVT. An enzyme-linked fluorescent assay was performed to detect the levels of D-dimer in plasma. A quantitative polymerase chain reaction assay was performed to determine the expression levels of miR-96. Expression levels of platelet miR-96 were significantly increased in orthopedic patients after orthopedic surgery. miR-96 expression levels in orthopedic patients with DVT at days 1, 3 and 7 after orthopedic surgery were significantly increased when compared with those in the control group. The increased miR-96 expression levels were correlated with plasma D-dimer levels in orthopedic patients with DVT. However, for the orthopedic patients in the non-DVT group following surgery, miR-96 expression levels were correlated with plasma D-dimer levels. In summary, the present results suggest that the expression levels of miR-96 may be associated with the occurrence of DVT. The occurrence of DVT may be accurately predicted by comprehensive analysis of the levels of miR-96 and plasma D-dimer. PMID:27588107

  3. 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. PMID:26282242

  4. TMC Behavior Modeling and Life Prediction Under Multiaxial Stresses

    NASA Technical Reports Server (NTRS)

    Merrick, H. F.; Aksoy, S. Z.; Costen, M.; Ahmad, J.

    1998-01-01

    The goal of this program was to manufacture and burst test small diameter SCS-6/Ti-6Al-4V composite rings for use in the design of an advanced titanium matrix composite (TMC) impeller. The Textron Specialty Metals grooved foil-fiber process was successfully used to make high quality TMC rings. A novel spin test arbor with "soft touch" fingers to retain the TMC ring was designed and manufactured. The design of the arbor took into account its use for cyclic experiments as well as ring burst tests. Spin testing of the instrumented ring was performed at ambient, 149C (300F), and 316C (600F) temperatures. Assembly vibration was encountered during spin testing but this was overcome through simple modification of the arbor. A spin-to-burst test was successfully completed at 316C (600F). The rotational speed of the TMC ring at burst was close to that predicted. In addition to the spin test program, a number of SCS-6/Ti-6Al-4V test panels were made. Neat Ti-6Al-4V panels also were made.

  5. Neural oscillatory deficits in schizophrenia predict behavioral and neurocognitive impairments

    PubMed Central

    Martínez, Antígona; Gaspar, Pablo A.; Hillyard, Steven A.; Bickel, Stephan; Lakatos, Peter; Dias, Elisa C.; Javitt, Daniel C.

    2015-01-01

    Paying attention to visual stimuli is typically accompanied by event-related desynchronizations (ERD) of ongoing alpha (7–14 Hz) activity in visual cortex. The present study used time-frequency based analyses to investigate the role of impaired alpha ERD in visual processing deficits in schizophrenia (Sz). Subjects viewed sinusoidal gratings of high (HSF) and low (LSF) spatial frequency (SF) designed to test functioning of the parvo- vs. magnocellular pathways, respectively. Patients with Sz and healthy controls paid attention selectively to either the LSF or HSF gratings which were presented in random order. Event-related brain potentials (ERPs) were recorded to all stimuli. As in our previous study, it was found that Sz patients were selectively impaired at detecting LSF target stimuli and that ERP amplitudes to LSF stimuli were diminished, both for the early sensory-evoked components and for the attend minus unattend difference component (the Selection Negativity), which is generally regarded as a specific index of feature-selective attention. In the time-frequency domain, the differential ERP deficits to LSF stimuli were echoed in a virtually absent theta-band phase locked response to both unattended and attended LSF stimuli (along with relatively intact theta-band activity for HSF stimuli). In contrast to the theta-band evoked responses which were tightly stimulus locked, stimulus-induced desynchronizations of ongoing alpha activity were not tightly stimulus locked and were apparent only in induced power analyses. Sz patients were significantly impaired in the attention-related modulation of ongoing alpha activity for both HSF and LSF stimuli. These deficits correlated with patients’ behavioral deficits in visual information processing as well as with visually based neurocognitive deficits. These findings suggest an additional, pathway-independent, mechanism by which deficits in early visual processing contribute to overall cognitive impairment in Sz. PMID

  6. Neural oscillatory deficits in schizophrenia predict behavioral and neurocognitive impairments.

    PubMed

    Martínez, Antígona; Gaspar, Pablo A; Hillyard, Steven A; Bickel, Stephan; Lakatos, Peter; Dias, Elisa C; Javitt, Daniel C

    2015-01-01

    Paying attention to visual stimuli is typically accompanied by event-related desynchronizations (ERD) of ongoing alpha (7-14 Hz) activity in visual cortex. The present study used time-frequency based analyses to investigate the role of impaired alpha ERD in visual processing deficits in schizophrenia (Sz). Subjects viewed sinusoidal gratings of high (HSF) and low (LSF) spatial frequency (SF) designed to test functioning of the parvo- vs. magnocellular pathways, respectively. Patients with Sz and healthy controls paid attention selectively to either the LSF or HSF gratings which were presented in random order. Event-related brain potentials (ERPs) were recorded to all stimuli. As in our previous study, it was found that Sz patients were selectively impaired at detecting LSF target stimuli and that ERP amplitudes to LSF stimuli were diminished, both for the early sensory-evoked components and for the attend minus unattend difference component (the Selection Negativity), which is generally regarded as a specific index of feature-selective attention. In the time-frequency domain, the differential ERP deficits to LSF stimuli were echoed in a virtually absent theta-band phase locked response to both unattended and attended LSF stimuli (along with relatively intact theta-band activity for HSF stimuli). In contrast to the theta-band evoked responses which were tightly stimulus locked, stimulus-induced desynchronizations of ongoing alpha activity were not tightly stimulus locked and were apparent only in induced power analyses. Sz patients were significantly impaired in the attention-related modulation of ongoing alpha activity for both HSF and LSF stimuli. These deficits correlated with patients' behavioral deficits in visual information processing as well as with visually based neurocognitive deficits. These findings suggest an additional, pathway-independent, mechanism by which deficits in early visual processing contribute to overall cognitive impairment in Sz. PMID

  7. Predicting maternal behaviors during pregnancy: does intention status matter?

    PubMed

    Kost, K; Landry, D J; Darroch, J E

    1998-01-01

    The hypothesis that women's behaviors during pregnancy that have the potential to influence their baby's health are influenced by their attitudes toward the pregnancy was examined through use of data from two US surveys: the 1988 National Maternal and Infant Health Survey (n = 9122 births) and the 1988 National Survey of Family Growth (n = 2586 births). Specifically, it was explored whether women with unintended (mistimed or unwanted) pregnancies make less use of prenatal care services and conform less closely to recommended practices such as those related to smoking and weight gain than women with planned pregnancies, independent of their social and demographic characteristics. Multivariate analysis indicated women with planned pregnancies were 12 percentage points more likely than women with unintended births to recognize their pregnancy in the first 6 weeks, 16 percentage points more likely to have initiated prenatal care in the first 8 weeks, and 8 percentage points more likely to have adhered to medical advice to quit smoking. Once the effects of social and demographic factors were controlled, these differences were reduced by 49%, 46%, and 32%, respectively. Contrary to expectations, once prenatal care was initiated, women with an unintended pregnancy were as likely to meet the recommended number of visits, reduce alcohol consumption, take vitamins, and gain weight per advice as women with a planned pregnancy. These results indicate a need to pay attention to social and demographic factors that contribute to late recognition of pregnancy, delayed entry into prenatal care, and continued smoking during pregnancy as well as the wantedness of the pregnancy in the design of maternal-child health programs. PMID:9561873

  8. An Extended Theory of Planned Behavior (TPB) Used to Predict Smoking Behavior Among a Sample of Iranian Medical Students

    PubMed Central

    Karimy, Mahmood; Zareban, Iraj; Araban, Marzieh; Montazeri, Ali

    2015-01-01

    Background: Smoking among the youth is an important public health concern. Although several studies have investigated the correlates of smoking behavior, no theory-based study has particularly assessed this problem among medical students. Objectives: This study aimed to evaluate the efficacy of the extended theory of planned behavior (TPB) to predict smoking behavior among a sample of Iranian medical students. Patients and Methods: This is a cross-sectional study carried out in Ahvaz, Iran, 2014. The data were collected through a self-administered questionnaire, which included items on demographics, smoking behavior, and components of the TPB model (attitude, subjective norms, perceived behavior control, and intention), and an added construct on smoking refusal skill. Data were analyzed using descriptive correlation, and linear regression statistics by SPSS, version 16. Results: One hundred and seventy medical students with a mean age of 21.25 (SD = 2.9) years were enrolled in the study. Of them, 24 (13.5%) students were smokers. All components of the TPB model and smoking refusal skill were statistically significant as to intention to smoke (P < 0.001). The TPB constructs with and without smoking refusal skill accounted for 77% (adjusted R2) and 78% of the variance observed for intention to smoke, respectively. The results also revealed the highest weight for perceived behavior control (β= -0.40). Conclusions: The findings of this study indicated that all TPB variables are useful tools for prediction of the smoking behaviors among students. Particularly, students’ perceived behavioral control and attitudes towards smoking were found to be important determinants of smoking intentions. Thus, the findings could be used for planning effective tobacco control programs targeting University students. PMID:26495261

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

    PubMed

    Siegel, Joshua Sarfaty; Ramsey, Lenny E; Snyder, Abraham Z; Metcalf, Nicholas V; Chacko, Ravi V; Weinberger, Kilian; Baldassarre, Antonello; Hacker, Carl D; Shulman, Gordon L; Corbetta, Maurizio

    2016-07-26

    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

  11. Neurophysiological processing of emotion and parenting interact to predict inhibited behavior: an affective-motivational framework

    PubMed Central

    Kessel, Ellen M.; Huselid, Rebecca F.; DeCicco, Jennifer M.; Dennis, Tracy A.

    2013-01-01

    Although inhibited behavior problems are prevalent in childhood, relatively little is known about the intrinsic and extrinsic factors that predict a child's ability to regulate inhibited behavior during fear- and anxiety-provoking tasks. Inhibited behavior may be linked to both disruptions in avoidance-related processing of aversive stimuli and in approach-related processing of appetitive stimuli, but previous findings are contradictory and rarely integrate consideration of the socialization context. The current exploratory study used a novel combination of neurophysiological and observation-based methods to examine whether a neurophysiological measure sensitive to approach- and avoidance-oriented emotional processing, the late positive potential (LPP), interacted with observed approach- (promotion) and avoidance- (prevention) oriented parenting practices to predict children's observed inhibited behavior. Participants were 5- to 7-year-old (N = 32) typically-developing children (M = 75.72 months, SD = 6.01). Electroencephalography was continuously recorded while children viewed aversive, appetitive, or neutral images, and the LPP was generated to each picture type separately. Promotion and prevention parenting were observed during an emotional challenge with the child. Child inhibited behavior was observed during a fear and a social evaluation task. As predicted, larger LPPs to aversive images predicted more inhibited behavior during both tasks, but only when parents demonstrated low promotion. In contrast, larger LPPs to appetitive images predicted less inhibited behavior during the social evaluative task, but only when parents demonstrated high promotion; children of high promotion parents showing smaller LPPs to appetitive images showed the greatest inhibition. Parent-child goodness-of-fit and the LPP as a neural biomarker for emotional processes related to inhibited behavior are discussed. PMID:23847499

  12. Neurophysiological processing of emotion and parenting interact to predict inhibited behavior: an affective-motivational framework.

    PubMed

    Kessel, Ellen M; Huselid, Rebecca F; Decicco, Jennifer M; Dennis, Tracy A

    2013-01-01

    Although inhibited behavior problems are prevalent in childhood, relatively little is known about the intrinsic and extrinsic factors that predict a child's ability to regulate inhibited behavior during fear- and anxiety-provoking tasks. Inhibited behavior may be linked to both disruptions in avoidance-related processing of aversive stimuli and in approach-related processing of appetitive stimuli, but previous findings are contradictory and rarely integrate consideration of the socialization context. The current exploratory study used a novel combination of neurophysiological and observation-based methods to examine whether a neurophysiological measure sensitive to approach- and avoidance-oriented emotional processing, the late positive potential (LPP), interacted with observed approach- (promotion) and avoidance- (prevention) oriented parenting practices to predict children's observed inhibited behavior. Participants were 5- to 7-year-old (N = 32) typically-developing children (M = 75.72 months, SD = 6.01). Electroencephalography was continuously recorded while children viewed aversive, appetitive, or neutral images, and the LPP was generated to each picture type separately. Promotion and prevention parenting were observed during an emotional challenge with the child. Child inhibited behavior was observed during a fear and a social evaluation task. As predicted, larger LPPs to aversive images predicted more inhibited behavior during both tasks, but only when parents demonstrated low promotion. In contrast, larger LPPs to appetitive images predicted less inhibited behavior during the social evaluative task, but only when parents demonstrated high promotion; children of high promotion parents showing smaller LPPs to appetitive images showed the greatest inhibition. Parent-child goodness-of-fit and the LPP as a neural biomarker for emotional processes related to inhibited behavior are discussed. PMID:23847499

  13. Anxiety Sensitivity Uniquely Predicts Exercise Behaviors in Young Adults Seeking to Increase Physical Activity.

    PubMed

    Moshier, Samantha J; Szuhany, Kristin L; Hearon, Bridget A; Smits, Jasper A J; Otto, Michael W

    2016-01-01

    Individuals with elevated levels of anxiety sensitivity (AS) may be motivated to avoid aversive emotional or physical states, and therefore may have greater difficulty achieving healthy behavioral change. This may be particularly true for exercise, which produces many of the somatic sensations within the domain of AS concerns. Cross-sectional studies show a negative association between AS and exercise. However, little is known about how AS may prospectively affect attempts at behavior change in individuals who are motivated to increase their exercise. We recruited 145 young adults who self-identified as having a desire to increase their exercise behavior. Participants completed a web survey assessing AS and additional variables identified as important for behavior change-impulsivity, grit, perceived behavioral control, and action planning-and set a specific goal for exercising in the next week. One week later, a second survey assessed participants' success in meeting their exercise goals. We hypothesized that individuals with higher AS would choose lower exercise goals and would complete less exercise at the second survey. AS was not significantly associated with exercise goal level, but significantly and negatively predicted exercise at Time 2 and was the only variable to offer significant prediction beyond consideration of baseline exercise levels. These results underscore the importance of considering AS in relation to health behavior intentions. This is particularly apt given the absence of prediction offered by other traditional predictors of behavior change. PMID:26342011

  14. Health Belief Model and Reasoned Action Theory in Predicting Water Saving Behaviors in Yazd, Iran

    PubMed Central

    Morowatisharifabad, Mohammad Ali; Momayyezi, Mahdieh; Ghaneian, Mohammad Taghi

    2012-01-01

    Background: People's behaviors and intentions about healthy behaviors depend on their beliefs, values, and knowledge about the issue. Various models of health education are used in deter¬mining predictors of different healthy behaviors but their efficacy in cultural behaviors, such as water saving behaviors, are not studied. The study was conducted to explain water saving beha¬viors in Yazd, Iran on the basis of Health Belief Model and Reasoned Action Theory. Methods: The cross-sectional study used random cluster sampling to recruit 200 heads of households to collect the data. The survey questionnaire was tested for its content validity and reliability. Analysis of data included descriptive statistics, simple correlation, hierarchical multiple regression. Results: Simple correlations between water saving behaviors and Reasoned Action Theory and Health Belief Model constructs were statistically significant. Health Belief Model and Reasoned Action Theory constructs explained 20.80% and 8.40% of the variances in water saving beha-viors, respectively. Perceived barriers were the strongest Predictor. Additionally, there was a sta¬tistically positive correlation between water saving behaviors and intention. Conclusion: In designing interventions aimed at water waste prevention, barriers of water saving behaviors should be addressed first, followed by people's attitude towards water saving. Health Belief Model constructs, with the exception of perceived severity and benefits, is more powerful than is Reasoned Action Theory in predicting water saving behavior and may be used as a framework for educational interventions aimed at improving water saving behaviors. PMID:24688927

  15. Peak Pc Prediction in Conjunction Analysis: Conjunction Assessment Risk Analysis. Pc Behavior Prediction Models

    NASA Technical Reports Server (NTRS)

    Vallejo, J.J.; Hejduk, M.D.; Stamey, J. D.

    2015-01-01

    Satellite conjunction risk typically evaluated through the probability of collision (Pc). Considers both conjunction geometry and uncertainties in both state estimates. Conjunction events initially discovered through Joint Space Operations Center (JSpOC) screenings, usually seven days before Time of Closest Approach (TCA). However, JSpOC continues to track objects and issue conjunction updates. Changes in state estimate and reduced propagation time cause Pc to change as event develops. These changes a combination of potentially predictable development and unpredictable changes in state estimate covariance. Operationally useful datum: the peak Pc. If it can reasonably be inferred that the peak Pc value has passed, then risk assessment can be conducted against this peak value. If this value is below remediation level, then event intensity can be relaxed. Can the peak Pc location be reasonably predicted?

  16. Adaptive Prediction Error Coding in the Human Midbrain and Striatum Facilitates Behavioral Adaptation and Learning Efficiency.

    PubMed

    Diederen, Kelly M J; Spencer, Tom; Vestergaard, Martin D; Fletcher, Paul C; Schultz, Wolfram

    2016-06-01

    Effective error-driven learning benefits from scaling of prediction errors to reward variability. Such behavioral adaptation may be facilitated by neurons coding prediction errors relative to the standard deviation (SD) of reward distributions. To investigate this hypothesis, we required participants to predict the magnitude of upcoming reward drawn from distributions with different SDs. After each prediction, participants received a reward, yielding trial-by-trial prediction errors. In line with the notion of adaptive coding, BOLD response slopes in the Substantia Nigra/Ventral Tegmental Area (SN/VTA) and ventral striatum were steeper for prediction errors occurring in distributions with smaller SDs. SN/VTA adaptation was not instantaneous but developed across trials. Adaptive prediction error coding was paralleled by behavioral adaptation, as reflected by SD-dependent changes in learning rate. Crucially, increased SN/VTA and ventral striatal adaptation was related to improved task performance. These results suggest that adaptive coding facilitates behavioral adaptation and supports efficient learning. PMID:27181060

  17. Spouses' attachment pairings predict neuroendocrine, behavioral, and psychological responses to marital conflict.

    PubMed

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

    2013-09-01

    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 1 anxious and 1 avoidant partner or 2 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 2 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

  18. “Any Condomless Anal Intercourse” is No Longer an Accurate Measure of HIV Sexual risk Behavior in Gay and Other Men Who have Sex with Men

    PubMed Central

    Jin, Fengyi; Prestage, Garrett P.; Mao, Limin; Poynten, I. Mary; Templeton, David J.; Grulich, Andrew E.; Zablotska, Iryna

    2015-01-01

    Background: Condomless anal intercourse (CLAI) has long been recognized as the primary mode of sexual transmission of HIV in gay and other men who have sex with men (MSM). A variety of measures of CLAI have been commonly used in behavioral surveillance for HIV risk and to forecast trends in HIV infection. However, gay and other MSM’s sexual practices changed as the understanding of disease and treatment options advance. In the present paper, we argue that summary measures such as “any CLAI” do not accurately measure HIV sexual risk behavior. Methods: Participants were 1,427 HIV-negative men from the Health in Men cohort study run from 2001 to 2007 in Sydney, Australia, with six-monthly interviews. At each interview, detailed quantitative data on the number of episodes of insertive and receptive CLAI in the last 6 months were collected, separated by partner type (regular vs. casual) and partners’ HIV status (negative, positive, and HIV status unknown). Results: A total of 228,064 episodes of CLAI were reported during the study period with a mean of 44 episodes per year per participant (median: 14). The great majority of CLAI episodes were with a regular partner (92.6%), most of them with HIV-negative regular partners (84.8%). Participants were more likely to engage in insertive CLAI with casual than with regular partners (66.7 vs. 55.3% of all acts of CLAI with each partner type, p < 0.001). Men were more likely to report CLAI in the receptive position with HIV-negative and HIV status unknown partners than with HIV-positive partners (p < 0.001 for both regular and casual partners). Conclusion: Gay and other MSM engaging in CLAI demonstrate clear patterns of HIV risk reduction behavior. As HIV prevention enters the era of antiretroviral-based biomedical approach, using all forms of CLAI indiscriminately as a measure of HIV behavioral risk is not helpful in understanding the current drivers of HIV transmission in the community. PMID:25774158

  19. 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. PMID:20303580

  20. 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. PMID:21949599

  1. Parent Attachment, Childrearing Behavior, and Child Attachment: Mediated Effects Predicting Preschoolers' Externalizing Behavior

    ERIC Educational Resources Information Center

    Roskam, Isabelle; Meunier, Jean-Christophe; Stievenart, Marie

    2011-01-01

    Attachment theory provides an interesting background for thinking about externalizing behavior (EB) in early childhood and for understanding how parenting influences the child's outcomes. The study examined how attachment and parenting could be combined to explain preschoolers' EB. Data were collected from 117 preschoolers aged from 4 to 6…

  2. Predicting Premeditation: Future Behavior Is Seen as More Intentional than Past Behavior

    ERIC Educational Resources Information Center

    Burns, Zachary C.; Caruso, Eugene M.; Bartels, Daniel M.

    2012-01-01

    People's intuitions about the underlying causes of past and future actions might not be the same. In 3 studies, we demonstrate that people judge the same behavior as more intentional when it will be performed in the future than when it has been performed in the past. We found this temporal asymmetry in perceptions of both the strength of an…

  3. Personality dimensions in adult male rhesus macaques: prediction of behaviors across time and situation.

    PubMed

    Capitanio, J P

    1999-01-01

    The idea that consistencies in behavior exist over time and across situations underlies human personality research. Although several studies have examined personality in nonhuman primates, there are very few data showing the predictive power of personality factors. The goal of the present study was to determine whether personality dimensions, identified in adult male rhesus monkeys living in half-acre cages, predicted behavior in situations different from the one from which the dimensions were originally derived and at time points of up to 4.5 years after the original assessments. Four personality dimensions (Sociability, Confidence, Excitability, and Equability) were identified using psychometric procedures and were correlated with behaviors recorded in several situations: the animals' natal groups, during tests of behavioral responsiveness while in individual cages, in small stable and unstable social groups, while viewing stimulus videotapes, and during stable social dyads. Results indicated substantial predictability. Sociability reflected a greater tendency to engage in affiliative interactions. Confidence correlated with more aggressive behaviors and with behaviors that suggest less attractiveness. Animals high in Excitability were somewhat inconsistent in their social behavior, perhaps reflecting hyper-responsiveness to novel circumstances and thwarted opportunities for escape. Equability appeared to be related to a less aggressive, more passive, style of interaction. Excitability and Equability appear to reflect more stylistic components of social behavior, whereas Sociability and Confidence may be more content-based dimensions. Sociability was strongly related to size of kin network in the animals' natal groups, suggesting an important role for ontogeny in this dimension. These data suggest that a limited number of personality dimensions exist in adult male rhesus macaques, and that these dimensions have predictive power that is both long-term and cross

  4. Incorporating Communication into the Theory of Planned Behavior to Predict Condom Use Among African American Women.

    PubMed

    Guan, Mengfei; Coles, Valerie B; Samp, Jennifer A; Sales, Jessica McDermott; DiClemente, Ralph J; Monahan, Jennifer L

    2016-09-01

    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 3 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 sexually transmitted infection prevention intervention design are discussed. PMID:27565192

  5. 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. PMID:26536145

  6. Cortical delta activity reflects reward prediction error and related behavioral adjustments, but at different times.

    PubMed

    Cavanagh, James F

    2015-04-15

    Recent work has suggested that reward prediction errors elicit a positive voltage deflection in the scalp-recorded electroencephalogram (EEG); an event sometimes termed a reward positivity. However, a strong test of this proposed relationship remains to be defined. Other important questions remain unaddressed: such as the role of the reward positivity in predicting future behavioral adjustments that maximize reward. To answer these questions, a three-armed bandit task was used to investigate the role of positive prediction errors during trial-by-trial exploration and task-set based exploitation. The feedback-locked reward positivity was characterized by delta band activities, and these related EEG features scaled with the degree of a computationally derived positive prediction error. However, these phenomena were also dissociated: the computational model predicted exploitative action selection and related response time speeding whereas the feedback-locked EEG features did not. Compellingly, delta band dynamics time-locked to the subsequent bandit (the P3) successfully predicted these behaviors. These bandit-locked findings included an enhanced parietal to motor cortex delta phase lag that correlated with the degree of response time speeding, suggesting a mechanistic role for delta band activities in motivating action selection. This dissociation in feedback vs. bandit locked EEG signals is interpreted as a differentiation in hierarchically distinct types of prediction error, yielding novel predictions about these dissociable delta band phenomena during reinforcement learning and decision making. PMID:25676913

  7. Observed parenting behavior with teens: measurement invariance and predictive validity across race.

    PubMed

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

    2011-07-01

    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 four 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

  8. 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

  9. 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

  10. Visual choice behavior by bumblebees (Bombus impatiens) confirms unsupervised neural network's predictions.

    PubMed

    Orbán, Levente L; Plowright, Catherine M S; Chartier, Sylvain; Thompson, Emma; Xu, Vicki

    2015-08-01

    The behavioral experiment herein tests the computational load hypothesis generated by an unsupervised neural network to examine bumblebee (Bombus impatiens) behavior at 2 visual properties: spatial frequency and symmetry. Untrained "flower-naïve" bumblebees were hypothesized to prefer symmetry only when the spatial frequency of artificial flowers is high and therefore places great information-processing demands on the bumblebees' visual system. Bumblebee choice behavior was recorded using high-definition motion-sensitive camcorders. The results support the computational model's prediction: 1-axis symmetry influenced bumblebees' preference behavior at low and high spatial frequency patterns. Additionally, increasing the level of symmetry from 1 axis to 4 axes amplified preference toward the symmetric patterns of both low and high spatial frequency patterns. The results are discussed in the context of the artificial neural network model and other hypotheses generated from the behavioral literature. PMID:25984936

  11. The orbitofrontal oracle: cortical mechanisms for the prediction and evaluation of specific behavioral outcomes.

    PubMed

    Rudebeck, Peter H; Murray, Elisabeth A

    2014-12-17

    The orbitofrontal cortex (OFC) has long been associated with the flexible control of behavior and concepts such as behavioral inhibition, self-control, and emotional regulation. These ideas emphasize the suppression of behaviors and emotions, but OFC's affirmative functions have remained enigmatic. Here we review recent work that has advanced our understanding of this prefrontal area and how its functions are shaped through interaction with subcortical structures such as the amygdala. Recent findings have overturned theories emphasizing behavioral inhibition as OFC's fundamental function. Instead, new findings indicate that OFC provides predictions about specific outcomes associated with stimuli, choices, and actions, especially their moment-to-moment value based on current internal states. OFC function thereby encompasses a broad representation or model of an individual's sensory milieu and potential actions, along with their relationship to likely behavioral outcomes. PMID:25521376

  12. Predicting adolescent eating and activity behaviors: the role of social norms and personal agency.

    PubMed

    Baker, Christina Wood; Little, Todd D; Brownell, Kelly D

    2003-03-01

    Guided by the theory of planned behavior, this 2-week longitudinal study examined health behaviors in a sample of 279 adolescents. Social norms and perceived behavioral control (PBC) were tested as predictors of self-reported intentions and behaviors in 2 domains, eating and physical activity. Differentiating, as opposed to aggregating, parent and peer norms provided unique information. For PBC, the authors distinguished global causality beliefs from self-related agency beliefs and intraself (effort, ability) from extraself (parents, teachers) means. Intraself agency beliefs strongly predicted healthy intentions, whereas intraself causality beliefs had a negative influence. Patterns differed somewhat across behaviors and gender. Results highlight theoretical issues and provide potential targets for research on health promotion programs for youth. PMID:12683739

  13. The orbitofrontal oracle: cortical mechanisms for the prediction and evaluation of specific behavioral outcomes

    PubMed Central

    Rudebeck, Peter H.; Murray, Elisabeth A.

    2014-01-01

    The orbitofrontal cortex (OFC) has long been associated with the flexible control of behavior and concepts such as behavioral inhibition, self-control and emotional regulation. These ideas emphasize the suppression of behaviors and emotions, but OFC’s affirmative functions have remained enigmatic. Here we review recent work that has advanced our understanding of this prefrontal area and how its functions are shaped through interaction with subcortical structures such as the amygdala. Recent findings have overturned theories emphasizing behavioral inhibition as OFC’s fundamental function. Instead, new findings indicate that OFC provides predictions about specific outcomes associated with stimuli, choices and actions, especially their moment-to-moment value based on current internal states. OFC function thereby encompasses a broad representation or model of an individual’s sensory milieu and potential actions, along with their relationship to likely behavioral outcomes. PMID:25521376

  14. Conflict and expectancies interact to predict sexual behavior under the influence among gay and bisexual men

    PubMed Central

    Wells, Brooke E; Starks, Tyrel J; Parsons, Jeffrey T; Golub, Sarit

    2013-01-01

    As the mechanisms of the associations between substance use and risky sex remain unclear, this study investigates the interactive roles of conflicts about casual sex and condom use and expectancies of the sexual effects of substances in those associations among gay men. Conflict interacted with expectancies to predict sexual behavior under the influence; low casual sex conflict coupled with high expectancies predicted the highest number of casual partners, and high condom use conflict and high expectancies predicted the highest number of unprotected sex acts. Results have implications for intervention efforts that aim to improve sexual decision-making and reduce sexual expectancies. PMID:23584507

  15. Threat Interference Biases Predict Socially Anxious Behavior: The Role of Inhibitory Control and Minute of Stressor.

    PubMed

    Gorlin, Eugenia I; Teachman, Bethany A

    2015-07-01

    The current study brings together two typically distinct lines of research. First, social anxiety is inconsistently associated with behavioral deficits in social performance, and the factors accounting for these deficits remain poorly understood. Second, research on selective processing of threat cues, termed cognitive biases, suggests these biases typically predict negative outcomes, but may sometimes be adaptive, depending on the context. Integrating these research areas, the current study examined whether conscious and/or unconscious threat interference biases (indexed by the unmasked and masked emotional Stroop) can explain unique variance, beyond self-reported anxiety measures, in behavioral avoidance and observer-rated anxious behavior during a public speaking task. Minute of speech and general inhibitory control (indexed by the color-word Stroop) were examined as within-subject and between-subject moderators, respectively. Highly socially anxious participants (N=135) completed the emotional and color-word Stroop blocks prior to completing a 4-minute videotaped speech task, which was later coded for anxious behaviors (e.g., speech dysfluency). Mixed-effects regression analyses revealed that general inhibitory control moderated the relationship between both conscious and unconscious threat interference bias and anxious behavior (though not avoidance), such that lower threat interference predicted higher levels of anxious behavior, but only among those with relatively weaker (versus stronger) inhibitory control. Minute of speech further moderated this relationship for unconscious (but not conscious) social-threat interference, such that lower social-threat interference predicted a steeper increase in anxious behaviors over the course of the speech (but only among those with weaker inhibitory control). Thus, both trait and state differences in inhibitory control resources may influence the behavioral impact of threat biases in social anxiety. PMID:26163713

  16. Predicting Bullying: Exploring the Contributions of Childhood Negative Life Experiences in Predicting Adolescent Bullying Behavior.

    PubMed

    Connell, Nadine M; Morris, Robert G; Piquero, Alex R

    2016-07-01

    Although there has been much interest in research on aggression and in particular bullying, a relatively less charted area of research has centered on articulating a better understanding of the mechanisms and processes by which persons are at increased risk for bullying. Furthermore, those studies that have investigated the linkages between childhood experiences and bullying perpetration have been limited with respect to definitional and operational issues, reliance on cross-sectional data, and the lack of assessing competing explanations of bullying perpetration. Using five waves of data from a community-based longitudinal sample of children followed through age 18 (N = 763), the current study examines the extent to which childhood negative life events in a variety of domains predict adolescent bullying. Results show that early childhood experiences, particularly those within the family and school domains, may alter life trajectories and can act as predictors for later adolescent bullying, thereby underscoring the potential importance that relatively minor experiences can have over the long term. Implications for future research based on these analyses are examined. PMID:25759430

  17. 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

  18. Predicting Social Support for Grieving Persons: A Theory of Planned Behavior Perspective

    ERIC Educational Resources Information Center

    Bath, Debra M.

    2009-01-01

    Research has consistently reported that social support from family, friends, and colleagues is an important factor in the bereaved person's ability to cope after the loss of a loved one. This study used a Theory of Planned Behavior framework to identify those factors that predict a person's intention to interact with, and support, a grieving…

  19. Predicting Behavior Problems and Social Competence in Children of Adolescent Mothers.

    ERIC Educational Resources Information Center

    Hubbs-Tait, Laura; And Others

    1994-01-01

    Describes longitudinal investigation evaluating additive model of impact of adolescent mothers' parenting practices on children. Suggests infant-mother attachment predicted behavior problems among preschool children of adolescent mothers and adolescent mothers' depression explained significant additional variance in those problems. States mothers'…

  20. Critical Features Predicting Sustained Implementation of School-Wide Positive Behavioral Interventions and Supports

    ERIC Educational Resources Information Center

    Mathews, Susanna; McIntosh, Kent; Frank, Jennifer L.; May, Seth L.

    2014-01-01

    The current study explored the extent to which a common measure of perceived implementation of critical features of Positive Behavioral Interventions and Supports (PBIS) predicted fidelity of implementation 3 years later. Respondents included school personnel from 261 schools across the United States implementing PBIS. School teams completed the…

  1. Critical Features Predicting Sustained Implementation of School-Wide Positive Behavior Support

    ERIC Educational Resources Information Center

    Mathews, Susanna; McIntosh, Kent; Frank, Jennifer; May, Seth

    2014-01-01

    The current study explored the extent to which a common measure of perceived implementation of critical features of School-wide Positive Behavior Support (SWPBS) predicted fidelity of implementation 3 years later. Respondents included school personnel from 261 schools across the United States implementing SWPBS. School teams completed the…

  2. Development and Predictive Validity of a Teacher Screener for Child Behavioral and Emotional Problems at School

    ERIC Educational Resources Information Center

    Kamphaus, Randy W.; Thorpe, Jennifer S.; Winsor, Anne Pierce; Kroncke, Anna P.; Dowdy, Erin T.; VanDeventer, Meghan C.

    2007-01-01

    A principal components analysis of the Teacher Rating Scale-Child (TRS-C) of the Behavior Assessment System for Children was conducted with a cross-sectional cohort of 659 children in Grades 1 to 5. A predictive validity study was then conducted with a 2-year longitudinal sample of 206 children. The results suggested that scores from the resulting…

  3. Attributions for Smoking Behavior: Comparing Smokers with Nonsmokers and Predicting Smokers' Cigarette Consumption.

    ERIC Educational Resources Information Center

    Kleinke, Chris L.; And Others

    1983-01-01

    Compared smokers' (214) and nonsmokers' (220) explanations for cigarette smoking behavior to determine predictors of cigarette consumption. Results showed addiction and affective smoking were the most important motives predicting consumption. Presented at the meeting of the Southeastern Psychological Association, Washington, DC, 1980. (WAS)

  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. Elemental Solubility Tendency for the Phases of Uranium by Classical Models Used to Predict Alloy Behavior

    SciTech Connect

    Van Blackwood; Travis Koenig; Saleem Drera; Brajenda Mishra; Davis Olson; Doug Porter; Robert Mariani

    2012-03-01

    Traditional alloy theory models, specifically Darken-Gurry and Miedema’s analyses, that characterize solutes in solid solvents relative to physical properties of the elements have been used to assist in predicting alloy behavior. These models will be applied relative to the three solid phases of uranium: alpha (orthorhombic), beta (tetragonal), and gamma (bcc). These phases have different solubilities for specific alloy additions as a function of temperature. The Darken-Gurry and Miedema models, with modifications based on concepts of Waber, Gschneider, and Brewer will be used to predict the behavior of four types of solutes: 1) Transition metals that are used for various purposes associated with the containment as alloy additions in the uranium fuel 2) Transuranic elements in the uranium 3) Rare earth fission products (lanthanides) 4) Transition metals and other fission products Using these solute map criteria, elemental behavior will be predicted as highly soluble, marginally soluble, or immiscible (compound formers) and will be used to compare solute effects during uranium phase transformations. The overlapping of these solute maps are convenient first approximation tools for predicting alloy behavior.

  6. Can Heterosexism Harm Organizations? Predicting the Perceived Organizational Citizenship Behaviors of Gay and Lesbian Employees

    ERIC Educational Resources Information Center

    Brenner, Bradley R.; Lyons, Heather Z.; Fassinger, Ruth E.

    2010-01-01

    An initial test and validation of a model predicting perceived organizational citizenship behaviors (OCBs) of lesbian and gay employees were conducted using structural equation modeling. The proposed structural model demonstrated acceptable goodness of ft and structural invariance across 2 samples (ns = 311 and 295), which suggested that…

  7. The Missing Link: Delayed Emotional Development Predicts Challenging Behavior in Adults with Intellectual Disability

    ERIC Educational Resources Information Center

    Sappok, Tanja; Budczies, Jan; Dziobek, Isabel; Bölte, Sven; Dosen, Anton; Diefenbacher, Albert

    2014-01-01

    Individuals with intellectual disability (ID) show high rates of challenging behavior (CB). The aim of this retrospective study was to assess the factors underlying CB in an adult, clinical ID sample (n = 203). Low levels of emotional development (ED), as measured by the "Scheme of Appraisal of ED," predicted overall CB, specifically…

  8. The Role of Life Satisfaction and Parenting Styles in Predicting Delinquent Behaviors among High School Students

    ERIC Educational Resources Information Center

    Onder, Fulya Cenkseven; Yilmaz, Yasin

    2012-01-01

    The purpose of this study is to determine whether the parenting styles and life satisfaction predict delinquent behaviors frequently or not. Firstly the data were collected from 471 girls and 410 boys, a total of 881 high school students. Then the research was carried out with 502 students showing low (n = 262, 52.2%) and high level of delinquent…

  9. 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…

  10. Significance of a Behavioral Economic Index of Reward Value in Predicting Drinking Problem Resolution

    ERIC Educational Resources Information Center

    Tucker, Jalie A.; Vuchinich, Rudy E.; Black, Bethany C.; Rippens, Paula D.

    2006-01-01

    This study investigated whether a behavioral economic index of the value of rewards available over different time horizons improved prediction of drinking outcomes beyond established biopsychosocial predictors. Preferences for immediate drinking versus more delayed rewards made possible by saving money were determined from expenditures prior to…

  11. Extension of a Theory of Predictive Behavior to Immediate Recall by Preschool Children.

    ERIC Educational Resources Information Center

    Bogartz, Richard S.

    This paper is concerned with memory functions in sequentially structured behavior. Twenty-five 4- and 5-year-old preschool children participated in a prediction experiment in which a stack of cards (each card alternately having a patch of red or green tape on it) was displayed to the child. The child was presented with a card and asked to predict…

  12. 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. PMID:23127300

  13. High-throughput characterization of chemical-associated embryonic behavioral changes predicts teratogenic outcomes.

    PubMed

    Reif, David M; Truong, Lisa; Mandrell, David; Marvel, Skylar; Zhang, Guozhu; Tanguay, Robert L

    2016-06-01

    New strategies are needed to address the data gap between the bioactivity of chemicals in the environment versus existing hazard information. We address whether a high-throughput screening (HTS) system using a vertebrate organism (embryonic zebrafish) can characterize chemical-elicited behavioral responses at an early, 24 hours post-fertilization (hpf) stage that predict teratogenic consequences at a later developmental stage. The system was used to generate full concentration-response behavioral profiles at 24 hpf across 1060 ToxCast™ chemicals. Detailed, morphological evaluation of all individuals was performed as experimental follow-up at 5 days post-fertilization (dpf). Chemicals eliciting behavioral responses were also mapped against external HTS in vitro results to identify specific molecular targets and neurosignalling pathways. We found that, as an integrative measure of normal development, significant alterations in movement highlighted active chemicals representing several modes of action. These early behavioral responses were predictive for 17 specific developmental abnormalities and mortality measured at 5 dpf, often at lower (i.e., more potent) concentrations than those at which morphological effects were observed. Therefore, this system can provide rapid characterization of chemical-elicited behavioral responses at an early developmental stage that are predictive of observable adverse effects later in life. PMID:26126630

  14. The generalization of attachment representations to new social situations: predicting behavior during initial interactions with strangers.

    PubMed

    Feeney, Brooke C; Cassidy, Jude; Ramos-Marcuse, Fatima

    2008-12-01

    The idea that attachment representations are generalized to new social situations and guide behavior with unfamiliar others is central to attachment theory. However, research regarding this important theoretical postulate has been lacking in adolescence and adulthood, as most research has focused on establishing the influence of attachment representations on close relationship dynamics. Thus, the goal of this investigation was to examine the extent to which attachment representations are predictive of adolescents' initial behavior when meeting and interacting with new peers. High school adolescents (N=135) participated with unfamiliar peers from another school in 2 social support interactions that were videotaped and coded by independent observers. Results indicated that attachment representations (assessed through interview and self-report measures) were predictive of behaviors exhibited during the discussions. Theoretical implications of the results and contributions to the existing literature are discussed. PMID:19025297

  15. Pain in context: Cues predicting a reward decrease fear of movement related pain and avoidance behavior.

    PubMed

    Claes, Nathalie; Vlaeyen, Johan W S; Crombez, Geert

    2016-09-01

    Previous research shows that goal-directed behavior might be modulated by cues that predict (dis)similar outcomes. However, the literature investigating this modulation with pain outcomes is scarce. Therefore, this experiment investigated whether environmental cues predicting pain or reward modulate defensive pain responding. Forty-eight healthy participants completed a joystick movement task with two different movement orientations. Performing one movement was associated with a painful stimulus, whereas performance of another movement was associated with reward, i.e. lottery tickets. In a subsequent task, participants learned to associate three different cues withpain, reward, or neither of the two. Next, these cues were integrated in the movement task. This study demonstrates that in general, aversive cues enhance and appetitive cues reduce pain-related fear. Furthermore, we found that incongruence between the outcomes predicted by the movement and the cue results in more oscillatory behavior, i.e., participants were more willing to perform a painful movement when a cue predicting reward was simultaneously presented, and vice versa. Similarly, when given a choice, participants preferred to perform the reward movement, unless there was an incongruence between the outcomes predicted by the movements and cues. Taken together, these results provide experimental evidence that environmental cues are capable of modulating pain-related fear and avoidance behavior. PMID:27475876

  16. 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. PMID:26075351

  17. Changes in intentions, planning, and self-efficacy predict changes in behaviors: an application of latent true change modeling.

    PubMed

    Reuter, Tabea; Ziegelmann, Jochen P; Wiedemann, Amelie U; Geiser, Christian; Lippke, Sonia; Schüz, Benjamin; Schwarzer, Ralf

    2010-09-01

    Can latent true changes in intention, planning, and self-efficacy account for latent change in two health behaviors (physical activity as well as fruit and vegetable intake)? Baseline data on predictors and behaviors and corresponding follow-up data four weeks later were collected from 853 participants. Interindividual differences in change and change-change associations were analyzed using structural equation modeling. For both behaviors, similar prediction patterns were found: changes in intention and self-efficacy predicted changes in planning, which in turn corresponded to changes in behavior. This evidence confirms that change predicts change, which is an inherent precondition in behavior change theories. PMID:20453049

  18. Perceived extrinsic mortality risk and reported effort in looking after health: testing a behavioral ecological prediction.

    PubMed

    Pepper, Gillian V; Nettle, Daniel

    2014-09-01

    Socioeconomic gradients in health behavior are pervasive and well documented. Yet, there is little consensus on their causes. Behavioral ecological theory predicts that, if people of lower socioeconomic position (SEP) perceive greater personal extrinsic mortality risk than those of higher SEP, they should disinvest in their future health. We surveyed North American adults for reported effort in looking after health, perceived extrinsic and intrinsic mortality risks, and measures of SEP. We examined the relationships between these variables and found that lower subjective SEP predicted lower reported health effort. Lower subjective SEP was also associated with higher perceived extrinsic mortality risk, which in turn predicted lower reported health effort. The effect of subjective SEP on reported health effort was completely mediated by perceived extrinsic mortality risk. Our findings indicate that perceived extrinsic mortality risk may be a key factor underlying SEP gradients in motivation to invest in future health. PMID:24990431

  19. Using the Theory of Planned Behavior to predict intention to comply with a food recall message.

    PubMed

    Freberg, Karen

    2013-01-01

    The Theory of Planned Behavior (TPB) has provided considerable insight into the public's intention to comply with many different health-related messages, but has not been applied previously to intention to comply with food safety recommendations and recalls ( Hallman & Cuite, 2010 ). Because food recalls can differ from other health messages in their urgency, timing, and cessation, the applicability of the TPB in this domain is unknown. The research reported here attempted to address this gap using a nationally representative consumer panel. Results showed that, consistent with the theory's predictions, attitudes and subjective norms were predictive of the intention to comply with a food recall message, with attitudes having a much greater impact on intent to comply than subjective norms. Perceived behavioral control failed to predict intention to comply. Implications of these results for health public relations and crisis communications and recommendations for future research were discussed. PMID:22746283

  20. Behavioral and electrophysiological indices of negative affect predict cocaine self-administration.

    PubMed

    Wheeler, Robert A; Twining, Robert C; Jones, Joshua L; Slater, Jennifer M; Grigson, Patricia S; Carelli, Regina M

    2008-03-13

    The motivation to seek cocaine comes in part from a dysregulation of reward processing manifested in dysphoria, or affective withdrawal. Learning is a critical aspect of drug abuse; however, it remains unclear whether drug-associated cues can elicit the emotional withdrawal symptoms that promote cocaine use. Here we report that a cocaine-associated taste cue elicited a conditioned aversive state that was behaviorally and neurophysiologically quantifiable and predicted subsequent cocaine self-administration behavior. Specifically, brief intraoral infusions of a cocaine-predictive flavored saccharin solution elicited aversive orofacial responses that predicted early-session cocaine taking in rats. The expression of aversive taste reactivity also was associated with a shift in the predominant pattern of electrophysiological activity of nucleus accumbens (NAc) neurons from inhibitory to excitatory. The dynamic nature of this conditioned switch in affect and the neural code reveals a mechanism by which cues may exert control over drug self-administration. PMID:18341996

  1. Do in-vivo behaviors predict early response in family-based treatment for anorexia nervosa?

    PubMed Central

    Darcy, Alison M; Bryson, Susan W.; Agras, W. Stewart; Fitzpatrick, Kathleen Kara; Le Grange, Daniel; Lock, James

    2014-01-01

    The aim of the study is to explore whether identified parental and patient behaviors observed in the first few sessions of family-based treatment (FBT) predict early response (weight gain of 1.8 kg by session four) to treatment. Therapy film recordings from 21 adolescent participants recruited into the FBT arm of a multi-site randomized clinical trial were coded for the presence of behaviors (length of observed behavior divided by length of session recording) in the first, second and fourth sessions. Behaviors that differed between early responders and non-early responders on univariate analysis were entered into discriminant class analyses. Participants with fewer negative verbal behaviors in the first session and were away from table during the meal session less had the greatest rates of early response. Parents who made fewer critical statements and who did not repeatedly present food during the meal session had children who had the greatest rates of early response. In-vivo behaviors in early sessions of FBT may predict early response to FBT. Adaptations to address participant resistance and to decrease the numbers of critical comments made by parents while encouraging their children to eat might improve early response to FBT. PMID:24091274

  2. Do in-vivo behaviors predict early response in family-based treatment for anorexia nervosa?

    PubMed

    Darcy, Alison M; Bryson, Susan W; Agras, W Stewart; Fitzpatrick, Kathleen Kara; Le Grange, Daniel; Lock, James

    2013-11-01

    The aim of the study is to explore whether identified parental and patient behaviors observed in the first few sessions of family-based treatment (FBT) predict early response (weight gain of 1.8 kg by session four) to treatment. Therapy film recordings from 21 adolescent participants recruited into the FBT arm of a multi-site randomized clinical trial were coded for the presence of behaviors (length of observed behavior divided by length of session recording) in the first, second and fourth sessions. Behaviors that differed between early responders and non-early responders on univariate analysis were entered into discriminant class analyses. Participants with fewer negative verbal behaviors in the first session and were away from table during the meal session less had the greatest rates of early response. Parents who made fewer critical statements and who did not repeatedly present food during the meal session had children who had the greatest rates of early response. In-vivo behaviors in early sessions of FBT may predict early response to FBT. Adaptations to address participant resistance and to decrease the numbers of critical comments made by parents while encouraging their children to eat might improve early response to FBT. PMID:24091274

  3. COMPARING SAFE VS. AT-RISK BEHAVIORAL DATA TO PREDICT ACCIDENTS

    SciTech Connect

    Jeffrey C. Joe

    2001-11-01

    The Safety Observations Achieve Results (SOAR) program at the Idaho National Laboratory (INL) encourages employees to perform in-field observations of each other’s behaviors. One purpose for performing these observations is that it gives the observers the opportunity to correct, if needed, their co-worker’s at-risk work practices and habits (i.e., behaviors). The underlying premise of doing this is that major injuries (e.g., OSHA-recordable events) are prevented from occurring because the lower level at-risk behaviors are identified and corrected before they can propagate into culturally accepted unsafe behaviors that result in injuries or fatalities. However, unlike other observation programs, SOAR also emphasizes positive reinforcement for safe behaviors observed. The underlying premise of doing this is that positive reinforcement of safe behaviors helps establish a strong positive safety culture. Since the SOAR program collects both safe and at-risk leading indicator data, this provides a unique opportunity to assess and compare the two kinds of data in terms of their ability to predict future adverse safety events. This paper describes the results of analyses performed on SOAR data to assess their relative predictive ability. Implications are discussed.

  4. Heart Rate during Conflicts Predicts Post-Conflict Stress-Related Behavior in Greylag Geese

    PubMed Central

    Wascher, Claudia A. F.; Fraser, Orlaith N.; Kotrschal, Kurt

    2010-01-01

    Background Social stressors are known to be among the most potent stressors in group-living animals. This is not only manifested in individual physiology (heart rate, glucocorticoids), but also in how individuals behave directly after a conflict. Certain ‘stress-related behaviors’ such as autopreening, body shaking, scratching and vigilance have been suggested to indicate an individual's emotional state. Such behaviors may also alleviate stress, but the behavioral context and physiological basis of those behaviors is still poorly understood. Methodology/Principal Findings We recorded beat-to-beat heart rates (HR) of 22 greylag geese in response to agonistic encounters using fully implanted sensor-transmitter packages. Additionally, for 143 major events we analyzed the behavior shown by our focal animals in the first two minutes after an interaction. Our results show that the HR during encounters and characteristics of the interaction predicted the frequency and duration of behaviors shown after a conflict. Conclusions/Significance To our knowledge this is the first study to quantify the physiological and behavioral responses to single agonistic encounters and to link this to post conflict behavior. Our results demonstrate that ‘stress-related behaviors’ are flexibly modulated by the characteristics of the preceding aggressive interaction and reflect the individual's emotional strain, which is linked to autonomic arousal. We found no support for the stress-alleviating hypothesis, but we propose that stress-related behaviors may play a role in communication with other group members, particularly with pair-partners. PMID:21187927

  5. 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

  6. 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.

  7. Properties of a Formal Method for Prediction of Emergent Behaviors in Swarm-based Systems

    NASA Technical Reports Server (NTRS)

    Rouff, Christopher; Vanderbilt, Amy; Hinchey, Mike; Truszkowski, Walt; Rash, James

    2004-01-01

    Autonomous intelligent swarms of satellites are being proposed for NASA missions that have complex behaviors and interactions. The emergent properties of swarms make these missions powerful, but at the same time more difficult to design and assure that proper behaviors will emerge. This paper gives the results of research into formal methods techniques for verification and validation of NASA swarm-based missions. Multiple formal methods were evaluated to determine their effectiveness in modeling and assuring the behavior of swarms of spacecraft. The NASA ANTS mission was used as an example of swarm intelligence for which to apply the formal methods. This paper will give the evaluation of these formal methods and give partial specifications of the ANTS mission using four selected methods. We then give an evaluation of the methods and the needed properties of a formal method for effective specification and prediction of emergent behavior in swarm-based systems.

  8. The roles of past behavior and health beliefs in predicting medication adherence to a statin regimen

    PubMed Central

    Molfenter, Todd D; Bhattacharya, Abhik; Gustafson, David H

    2012-01-01

    Purpose: Current medication-adherence predictive tools are based on patient medication-taking beliefs, but studying past behavior may now be a more explanatory and accessible method. This study will evaluate if past medication-refill behavior for a statin regimen is more predictive of medication adherence than patient medication-taking health beliefs. Patients and methods: This prospective longitudinal study was implemented in a national managed care plan in the United States. A group of 1433 statin patients were identified and followed for 6 months. Medication-taking health beliefs, collected from self-reported mail questionnaires, and past medication-refill behavior, using proportion of days covered (PDC), were collected prior to 6-month follow-up. Outcomes were measured using categorical PDC variable (of adherence, PDC ≥ 85%, versus nonadherence, PDC < 85%), with model fit estimated using receiver operator characteristic analysis. Results: The area under the receiver operator characteristic curve for past behavior (Az = 0.78) was significantly greater (P < 0.05) than for patient health beliefs (Az = 0.69), indicating that past prescription-refill behavior is a better predictor of medication adherence than prospective health beliefs. Among health beliefs, the factor most related to medication adherence was behavioral intent (odds ratio, 5.12; 95% confidence interval, 1.84 to 15.06). The factor most strongly related to behavioral intent was impact of regimen on daily routine (odds ratio, 3.3; 95% confidence interval, 1.41 to 7.74). Conclusion: Electronic medical records and community health-information networks may make past prescription-refill rates more accessible and assist physicians with managing medication-regimen adherence. Health beliefs, however, may still play an important role in influencing medication-taking behaviors. PMID:23055697

  9. Hemoglobin and Hematocrit Levels in the Prediction of Complicated Crohn's Disease Behavior – A Cohort Study

    PubMed Central

    Rieder, Florian; Paul, Gisela; Schnoy, Elisabeth; Schleder, Stephan; Wolf, Alexandra; Kamm, Florian; Dirmeier, Andrea; Strauch, Ulrike; Obermeier, Florian; Lopez, Rocio; Achkar, Jean-Paul; Rogler, Gerhard; Klebl, Frank

    2014-01-01

    Background Markers that predict the occurrence of a complicated disease behavior in patients with Crohn's disease (CD) can permit a more aggressive therapeutic regimen for patients at risk. The aim of this cohort study was to test the blood levels of hemoglobin (Hgb) and hematocrit (Hct) for the prediction of complicated CD behavior and CD related surgery in an adult patient population. Methods Blood samples of 62 CD patients of the German Inflammatory Bowel Disease-network “Kompetenznetz CED” were tested for the levels of Hgb and Hct prior to the occurrence of complicated disease behavior or CD related surgery. The relation of these markers and clinical events was studied using Kaplan-Meier survival analysis and adjusted COX-proportional hazard regression models. Results The median follow-up time was 55.8 months. Of the 62 CD patients without any previous complication or surgery 34% developed a complication and/or underwent CD related surgery. Low Hgb or Hct levels were independent predictors of a shorter time to occurrence of the first complication or CD related surgery. This was true for early as well as late occurring complications. Stable low Hgb or Hct during serial follow-up measurements had a higher frequency of complications compared to patients with a stable normal Hgb or Hct, respectively. Conclusions Determination of Hgb or Hct in complication and surgery naïve CD patients might serve as an additional tool for the prediction of complicated disease behavior. PMID:25116048

  10. Calibration of DFT Functionals for the Prediction of 57Fe Mössbauer Spectral Parameters in Iron-Nitrosyl and Iron-Sulfur Complexes: Accurate Geometries Prove Essential

    PubMed Central

    Sandala, Gregory M.; Hopmann, Kathrin H.; Ghosh, Abhik

    2011-01-01

    Six popular density functionals in conjunction with the conductor-like screening (COSMO) solvation model have been used to obtain linear Mössbauer isomer shift (IS) and quadrupole splitting (QS) parameters for a test set of 20 complexes (with 24 sites) comprised of nonheme nitrosyls (Fe–NO) and non-nitrosyl (Fe–S) complexes. For the first time in an IS analysis, the Fe electron density was calculated both directly at the nucleus, ρ(0)N, which is the typical procedure, and on a small sphere surrounding the nucleus, ρ(0)S, which is the new standard algorithm implemented in the ADF software package. We find that both methods yield (near) identical slopes from each linear regression analysis but are shifted with respect to ρ(0) along the x-axis. Therefore, the calculation of the Fe electron density with either method gives calibration fits with equal predictive value. Calibration parameters obtained from the complete test set for OLYP, OPBE, PW91, and BP86 yield correlation coefficients (r2) of approximately 0.90, indicating that the calibration fit is of good quality. However, fits obtained from B3LYP and B3LYP* with both Slater-type and Gaussian-type orbitals are generally found to be of poorer quality. For several of the complexes examined in this study, we find that B3LYP and B3LYP* give geometries that possess significantly larger deviations from the experimental structures than OLYP, OPBE, PW91 or BP86. This phenomenon is particularly true for the di- and tetranuclear Fe complexes examined in this study. Previous Mössbauer calibration fit studies using these functionals have usually included mononuclear Fe complexes alone, where these discrepancies are less pronounced. An examination of spin expectation values reveals B3LYP and B3LYP* approach the weak-coupling limit more closely than the GGA exchange-correlation functionals. The high degree of variability in our calculated S2 values for the Fe–NO complexes highlights their challenging electronic

  11. 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

  12. Lead effects on the predictability of reproductive behavior in fathead minnows (Pimephales promelas): A mathematical model

    SciTech Connect

    Alados, C.L.; Weber, D.N.

    1999-10-01

    Lead (Pb) has been shown to affect the behavior of a wide variety of vertebrates, including fish, amphibians, and mammals. This article re-examines previous data on the effect of short-term, sublethal levels of waterborne Pb on the reproductive behavior of fathead minnows (Pimephales promelas). Previous research has found that Pb decreased the time spent in displaying specific reproductive behaviors in male minnows. Because each activity performed within a sequence depends upon previous parts of the sequence, the reproductive behavior of fish is not randomly distributed but is presented as a long-range self-similar correlation. By treating these data as a fractal dimension, it is now possible to determine changes in the long-term correlation of different behavioral sequences involved in nest maintenance owing to Pb exposure, both before and after adult males attain reproductive maturity. The authors hypothesized that the scaling exponent of this fluctuation varies in relation with environmental contaminants. Known Pb-induced changes in hormonal activity may account for changes in observed reproductive and nest maintenance behaviors. Pb-exposed fish exhibited higher levels of predictability in their behavioral sequences, i.e., they demonstrated an increase in the scaling parameter of the fluctuation {alpha}. However, if Pb was introduced after sexual maturity was observed, there was no significant difference in the scaling component {alpha}. Thus, the use of fractal dimension may provide a useful tool to analyze the effects of environmental contaminants and other stresses.

  13. 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

  14. 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. PMID:25258524

  15. Facets and mechanisms of adaptive pain behavior: predictive regulation and action

    PubMed Central

    Morrison, India; Perini, Irene; Dunham, James

    2013-01-01

    Neural mechanisms underlying nociception and pain perception are considered to serve the ultimate goal of limiting tissue damage. However, since pain usually occurs in complex environments and situations that call for elaborate control over behavior, simple avoidance is insufficient to explain a range of mammalian pain responses, especially in the presence of competing goals. In this integrative review we propose a Predictive Regulation and Action (PRA) model of acute pain processing. It emphasizes evidence that the nervous system is organized to anticipate potential pain and to adjust behavior before the risk of tissue damage becomes critical. Regulatory processes occur on many levels, and can be dynamically influenced by local interactions or by modulation from other brain areas in the network. The PRA model centers on neural substrates supporting the predictive nature of pain processing, as well as on finely-calibrated yet versatile regulatory processes that ultimately affect behavior. We outline several operational categories of pain behavior, from spinally-mediated reflexes to adaptive voluntary action, situated at various neural levels. An implication is that neural processes that track potential tissue damage in terms of behavioral consequences are an integral part of pain perception. PMID:24348358

  16. A stochastic neuronal model predicts random search behaviors at multiple spatial scales in C. elegans

    PubMed Central

    Roberts, William M; Augustine, Steven B; Lawton, Kristy J; Lindsay, Theodore H; Thiele, Tod R; Izquierdo, Eduardo J; Faumont, Serge; Lindsay, Rebecca A; Britton, Matthew Cale; Pokala, Navin; Bargmann, Cornelia I; Lockery, Shawn R

    2016-01-01

    Random search is a behavioral strategy used by organisms from bacteria to humans to locate food that is randomly distributed and undetectable at a distance. We investigated this behavior in the nematode Caenorhabditis elegans, an organism with a small, well-described nervous system. Here we formulate a mathematical model of random search abstracted from the C. elegans connectome and fit to a large-scale kinematic analysis of C. elegans behavior at submicron resolution. The model predicts behavioral effects of neuronal ablations and genetic perturbations, as well as unexpected aspects of wild type behavior. The predictive success of the model indicates that random search in C. elegans can be understood in terms of a neuronal flip-flop circuit involving reciprocal inhibition between two populations of stochastic neurons. Our findings establish a unified theoretical framework for understanding C. elegans locomotion and a testable neuronal model of random search that can be applied to other organisms. DOI: http://dx.doi.org/10.7554/eLife.12572.001 PMID:26824391

  17. Predicting adolescent perpetration in cyberbullying: an application of the theory of planned behavior.

    PubMed

    Heirman, Wannes; Walrave, Michel

    2012-11-01

    This study aims to contribute to the research field on cyberbullying by offering a comprehensive theoretical framework that helps to predict adolescents' perpetration of cyberbullying. One thousand forty-two pupils from 12 to 18 years old in 30 different Belgian secondary schools participated in two surveys within a three-month interval. Structural equation modeling was used to test whether the overall model of theory of planned behavior (TPB) helps to predict adolescents' self-reported perpetration in cyberbullying. Overall, the present study provides strong support for the theoretical utility of the TPB in cyberbullying research. The model accounted for 44.8% of the variance in adolescents' behavioral intention to cyberbully and 33.2% of the variance in self-reported cyberbullying perpetration. We found a strong positive relationship between adolescents' attitude towards cyberbullying and their behavioral intention to perpetrate it. Perceived behavioral control and subjective norm, the other two TPB-constructs, were also significant albeit relatively less important predictors of adolescents' intention to cyberbully. The finding that adolescents' attitude is the most important predictor of perpetration, entails that prevention and intervention strategies should aim at reducing the perceived acceptability of cyberbullying among adolescents by converting neutral or positive attitudes towards this anti-social behavior into negative evaluations. PMID:23079360