Log-Multiplicative Association Models as Item Response Models

ERIC Educational Resources Information Center

Anderson, Carolyn J.; Yu, Hsiu-Ting

2007-01-01

Log-multiplicative association (LMA) models, which are special cases of log-linear models, have interpretations in terms of latent continuous variables. Two theoretical derivations of LMA models based on item response theory (IRT) arguments are presented. First, we show that Anderson and colleagues (Anderson & Vermunt, 2000; Anderson & Bockenholt,…

A Risk Stratification Model for Lung Cancer Based on Gene Coexpression Network and Deep Learning

PubMed Central

2018-01-01

Risk stratification model for lung cancer with gene expression profile is of great interest. Instead of previous models based on individual prognostic genes, we aimed to develop a novel system-level risk stratification model for lung adenocarcinoma based on gene coexpression network. Using multiple microarray, gene coexpression network analysis was performed to identify survival-related networks. A deep learning based risk stratification model was constructed with representative genes of these networks. The model was validated in two test sets. Survival analysis was performed using the output of the model to evaluate whether it could predict patients' survival independent of clinicopathological variables. Five networks were significantly associated with patients' survival. Considering prognostic significance and representativeness, genes of the two survival-related networks were selected for input of the model. The output of the model was significantly associated with patients' survival in two test sets and training set (p < 0.00001, p < 0.0001 and p = 0.02 for training and test sets 1 and 2, resp.). In multivariate analyses, the model was associated with patients' prognosis independent of other clinicopathological features. Our study presents a new perspective on incorporating gene coexpression networks into the gene expression signature and clinical application of deep learning in genomic data science for prognosis prediction. PMID:29581968

Genotype-Based Association Mapping of Complex Diseases: Gene-Environment Interactions with Multiple Genetic Markers and Measurement Error in Environmental Exposures

PubMed Central

Lobach, Irvna; Fan, Ruzone; Carroll, Raymond T.

2011-01-01

With the advent of dense single nucleotide polymorphism genotyping, population-based association studies have become the major tools for identifying human disease genes and for fine gene mapping of complex traits. We develop a genotype-based approach for association analysis of case-control studies of gene-environment interactions in the case when environmental factors are measured with error and genotype data are available on multiple genetic markers. To directly use the observed genotype data, we propose two genotype-based models: genotype effect and additive effect models. Our approach offers several advantages. First, the proposed risk functions can directly incorporate the observed genotype data while modeling the linkage disequihbrium information in the regression coefficients, thus eliminating the need to infer haplotype phase. Compared with the haplotype-based approach, an estimating procedure based on the proposed methods can be much simpler and significantly faster. In addition, there is no potential risk due to haplotype phase estimation. Further, by fitting the proposed models, it is possible to analyze the risk alleles/variants of complex diseases, including their dominant or additive effects. To model measurement error, we adopt the pseudo-likelihood method by Lobach et al. [2008]. Performance of the proposed method is examined using simulation experiments. An application of our method is illustrated using a population-based case-control study of association between calcium intake with the risk of colorectal adenoma development. PMID:21031455

A Model of Compound Heterozygous, Loss-of-Function Alleles Is Broadly Consistent with Observations from Complex-Disease GWAS Datasets

PubMed Central

Sanjak, Jaleal S.; Long, Anthony D.; Thornton, Kevin R.

2017-01-01

The genetic component of complex disease risk in humans remains largely unexplained. A corollary is that the allelic spectrum of genetic variants contributing to complex disease risk is unknown. Theoretical models that relate population genetic processes to the maintenance of genetic variation for quantitative traits may suggest profitable avenues for future experimental design. Here we use forward simulation to model a genomic region evolving under a balance between recurrent deleterious mutation and Gaussian stabilizing selection. We consider multiple genetic and demographic models, and several different methods for identifying genomic regions harboring variants associated with complex disease risk. We demonstrate that the model of gene action, relating genotype to phenotype, has a qualitative effect on several relevant aspects of the population genetic architecture of a complex trait. In particular, the genetic model impacts genetic variance component partitioning across the allele frequency spectrum and the power of statistical tests. Models with partial recessivity closely match the minor allele frequency distribution of significant hits from empirical genome-wide association studies without requiring homozygous effect sizes to be small. We highlight a particular gene-based model of incomplete recessivity that is appealing from first principles. Under that model, deleterious mutations in a genomic region partially fail to complement one another. This model of gene-based recessivity predicts the empirically observed inconsistency between twin and SNP based estimated of dominance heritability. Furthermore, this model predicts considerable levels of unexplained variance associated with intralocus epistasis. Our results suggest a need for improved statistical tools for region based genetic association and heritability estimation. PMID:28103232

Weighted functional linear regression models for gene-based association analysis.

PubMed

Belonogova, Nadezhda M; Svishcheva, Gulnara R; Wilson, James F; Campbell, Harry; Axenovich, Tatiana I

2018-01-01

Functional linear regression models are effectively used in gene-based association analysis of complex traits. These models combine information about individual genetic variants, taking into account their positions and reducing the influence of noise and/or observation errors. To increase the power of methods, where several differently informative components are combined, weights are introduced to give the advantage to more informative components. Allele-specific weights have been introduced to collapsing and kernel-based approaches to gene-based association analysis. Here we have for the first time introduced weights to functional linear regression models adapted for both independent and family samples. Using data simulated on the basis of GAW17 genotypes and weights defined by allele frequencies via the beta distribution, we demonstrated that type I errors correspond to declared values and that increasing the weights of causal variants allows the power of functional linear models to be increased. We applied the new method to real data on blood pressure from the ORCADES sample. Five of the six known genes with P < 0.1 in at least one analysis had lower P values with weighted models. Moreover, we found an association between diastolic blood pressure and the VMP1 gene (P = 8.18×10-6), when we used a weighted functional model. For this gene, the unweighted functional and weighted kernel-based models had P = 0.004 and 0.006, respectively. The new method has been implemented in the program package FREGAT, which is freely available at https://cran.r-project.org/web/packages/FREGAT/index.html.

The role of within-compound associations in learning about absent cues.

PubMed

Witnauer, James E; Miller, Ralph R

2011-05-01

When two cues are reinforced together (in compound), most associative models assume that animals learn an associative network that includes direct cue-outcome associations and a within-compound association. All models of associative learning subscribe to the importance of cue-outcome associations, but most models assume that within-compound associations are irrelevant to each cue's subsequent behavioral control. In the present article, we present an extension of Van Hamme and Wasserman's (Learning and Motivation 25:127-151, 1994) model of retrospective revaluation based on learning about absent cues that are retrieved through within-compound associations. The model was compared with a model lacking retrieval through within-compound associations. Simulations showed that within-compound associations are necessary for the model to explain higher-order retrospective revaluation and the observed greater retrospective revaluation after partial reinforcement than after continuous reinforcement alone. These simulations suggest that the associability of an absent stimulus is determined by the extent to which the stimulus is activated through the within-compound association.

The role of within-compound associations in learning about absent cues

PubMed Central

Witnauer, James E.

2011-01-01

When two cues are reinforced together (in compound), most associative models assume that animals learn an associative network that includes direct cue–outcome associations and a within-compound association. All models of associative learning subscribe to the importance of cue–outcome associations, but most models assume that within-compound associations are irrelevant to each cue's subsequent behavioral control. In the present article, we present an extension of Van Hamme and Wasserman's (Learning and Motivation 25:127–151, 1994) model of retrospective revaluation based on learning about absent cues that are retrieved through within-compound associations. The model was compared with a model lacking retrieval through within-compound associations. Simulations showed that within-compound associations are necessary for the model to explain higher-order retrospective revaluation and the observed greater retrospective revaluation after partial reinforcement than after continuous reinforcement alone. These simulations suggest that the associability of an absent stimulus is determined by the extent to which the stimulus is activated through the within-compound association. PMID:21264569

A prediction model-based algorithm for computer-assisted database screening of adverse drug reactions in the Netherlands.

PubMed

Scholl, Joep H G; van Hunsel, Florence P A M; Hak, Eelko; van Puijenbroek, Eugène P

2018-02-01

The statistical screening of pharmacovigilance databases containing spontaneously reported adverse drug reactions (ADRs) is mainly based on disproportionality analysis. The aim of this study was to improve the efficiency of full database screening using a prediction model-based approach. A logistic regression-based prediction model containing 5 candidate predictors was developed and internally validated using the Summary of Product Characteristics as the gold standard for the outcome. All drug-ADR associations, with the exception of those related to vaccines, with a minimum of 3 reports formed the training data for the model. Performance was based on the area under the receiver operating characteristic curve (AUC). Results were compared with the current method of database screening based on the number of previously analyzed associations. A total of 25 026 unique drug-ADR associations formed the training data for the model. The final model contained all 5 candidate predictors (number of reports, disproportionality, reports from healthcare professionals, reports from marketing authorization holders, Naranjo score). The AUC for the full model was 0.740 (95% CI; 0.734-0.747). The internal validity was good based on the calibration curve and bootstrapping analysis (AUC after bootstrapping = 0.739). Compared with the old method, the AUC increased from 0.649 to 0.740, and the proportion of potential signals increased by approximately 50% (from 12.3% to 19.4%). A prediction model-based approach can be a useful tool to create priority-based listings for signal detection in databases consisting of spontaneous ADRs. © 2017 The Authors. Pharmacoepidemiology & Drug Safety Published by John Wiley & Sons Ltd.

GRace: a MATLAB-based application for fitting the discrimination-association model.

PubMed

Stefanutti, Luca; Vianello, Michelangelo; Anselmi, Pasquale; Robusto, Egidio

2014-10-28

The Implicit Association Test (IAT) is a computerized two-choice discrimination task in which stimuli have to be categorized as belonging to target categories or attribute categories by pressing, as quickly and accurately as possible, one of two response keys. The discrimination association model has been recently proposed for the analysis of reaction time and accuracy of an individual respondent to the IAT. The model disentangles the influences of three qualitatively different components on the responses to the IAT: stimuli discrimination, automatic association, and termination criterion. The article presents General Race (GRace), a MATLAB-based application for fitting the discrimination association model to IAT data. GRace has been developed for Windows as a standalone application. It is user-friendly and does not require any programming experience. The use of GRace is illustrated on the data of a Coca Cola-Pepsi Cola IAT, and the results of the analysis are interpreted and discussed.

Mitigating circumstances: A model-based analysis of associations between risk environment and infrequent condom use among Chinese street-based sex workers.

PubMed

Chang, Ruth C; Hail-Jares, Katie; Zheng, Huang; He, Na; Bouey, Jennifer Z H

2018-01-01

Little is known about how freelance street-based sex workers navigate condom use while soliciting. Traditional behavioural model may fail to account for the complex risk environment that most street-based sex workers work within. We examine first the association of self-efficacy and the infrequent condom use, then we investigated the roles of clients and venues frequented on this association. Using a purposive chain-referral sampling method, we surveyed 248 street-based sex workers in Shanghai. The survey focused on sex workers HIV risk factors, sex work patterns, HIV knowledge, and related HIV self-efficacy. Clients types and behaviours, and characteristics of the venues frequented by these commercial sex workers were also collected. We conducted a series of multiple logistic regression models to explore how the association between a sex worker's self-efficacy with infrequent condom use change as client and venue characteristics were added to the models. We find that within the basic model, low self-efficacy was marginally associated with infrequent condom use (54.9% vs. 45.1%, AOR = 1.70, 95% CI = 0.95-3.03). As client- and venue- characteristics were added, the associations between self-efficacy and condom use were strengthened (AOR = 2.10 95% CI = 1.12-3.91 and 2.54 95% CI = 1.24-5.19 respectively). Those who reported middle-tiered income were more likely to report infrequent condom use compared to their peers of high income (AOR = 3.92 95% CI = 1.32-11.70) whereas such difference was not found between low income and high income sex workers. Visiting multiple venues and having migrant workers as clients were also associated with infrequent condom use. Our findings suggest sex worker's self-efficacy matters in their HIV risk behaviours only when environment characteristics were adjusted. Risk environment for street-based sex workers are complex. Programming addressing behavioural changes among female sex workers should adopt holistic, multilevel models with the consideration of risk environments.

All-Atom Polarizable Force Field for DNA Based on the Classical Drude Oscillator Model

PubMed Central

Savelyev, Alexey; MacKerell, Alexander D.

2014-01-01

Presented is a first generation atomistic force field for DNA in which electronic polarization is modeled based on the classical Drude oscillator formalism. The DNA model is based on parameters for small molecules representative of nucleic acids, including alkanes, ethers, dimethylphosphate, and the nucleic acid bases and empirical adjustment of key dihedral parameters associated with the phosphodiester backbone, glycosidic linkages and sugar moiety of DNA. Our optimization strategy is based on achieving a compromise between satisfying the properties of the underlying model compounds in the gas phase targeting QM data and reproducing a number of experimental properties of DNA duplexes in the condensed phase. The resulting Drude force field yields stable DNA duplexes on the 100 ns time scale and satisfactorily reproduces (1) the equilibrium between A and B forms of DNA and (2) transitions between the BI and BII sub-states of B form DNA. Consistency with the gas phase QM data for the model compounds is significantly better for the Drude model as compared to the CHARMM36 additive force field, which is suggested to be due to the improved response of the model to changes in the environment associated with the explicit inclusion of polarizability. Analysis of dipole moments associated with the nucleic acid bases shows the Drude model to have significantly larger values than those present in CHARMM36, with the dipoles of individual bases undergoing significant variations during the MD simulations. Additionally, the dipole moment of water was observed to be perturbed in the grooves of DNA. PMID:24752978

LIFETIME LUNG CANCER RISKS ASSOCIATED WITH INDOOR RADON EXPOSURE BASED ON VARIOUS RADON RISK MODELS FOR CANADIAN POPULATION.

PubMed

Chen, Jing

2017-04-01

This study calculates and compares the lifetime lung cancer risks associated with indoor radon exposure based on well-known risk models in the literature; two risk models are from joint studies among miners and the other three models were developed from pooling studies on residential radon exposure from China, Europe and North America respectively. The aim of this article is to make clear that the various models are mathematical descriptions of epidemiologically observed real risks in different environmental settings. The risk from exposure to indoor radon is real and it is normal that variations could exist among different risk models even when they were applied to the same dataset. The results show that lifetime risk estimates vary significantly between the various risk models considered here: the model based on the European residential data provides the lowest risk estimates, while models based on the European miners and Chinese residential pooling with complete dosimetry give the highest values. The lifetime risk estimates based on the EPA/BEIR-VI model lie within this range and agree reasonably well with the averages of risk estimates from the five risk models considered in this study. © Crown copyright 2016.

Association of Brain-Derived Neurotrophic Factor and Vitamin D with Depression and Obesity: A Population-Based Study.

PubMed

Goltz, Annemarie; Janowitz, Deborah; Hannemann, Anke; Nauck, Matthias; Hoffmann, Johanna; Seyfart, Tom; Völzke, Henry; Terock, Jan; Grabe, Hans Jörgen

2018-06-19

Depression and obesity are widespread and closely linked. Brain-derived neurotrophic factor (BDNF) and vitamin D are both assumed to be associated with depression and obesity. Little is known about the interplay between vitamin D and BDNF. We explored the putative associations and interactions between serum BDNF and vitamin D levels with depressive symptoms and abdominal obesity in a large population-based cohort. Data were obtained from the population-based Study of Health in Pomerania (SHIP)-Trend (n = 3,926). The associations of serum BDNF and vitamin D levels with depressive symptoms (measured using the Patient Health Questionnaire) were assessed with binary and multinomial logistic regression models. The associations of serum BDNF and vitamin D levels with obesity (measured by the waist-to-hip ratio [WHR]) were assessed with binary logistic and linear regression models with restricted cubic splines. Logistic regression models revealed inverse associations of vitamin D with depression (OR = 0.966; 95% CI 0.951-0.981) and obesity (OR = 0.976; 95% CI 0.967-0.985). No linear association of serum BDNF with depression or obesity was found. However, linear regression models revealed a U-shaped association of BDNF with WHR (p < 0.001). Vitamin D was inversely associated with depression and obesity. BDNF was associated with abdominal obesity, but not with depression. At the population level, our results support the relevant roles of vitamin D and BDNF in mental and physical health-related outcomes. © 2018 S. Karger AG, Basel.

A new pattern associative memory model for image recognition based on Hebb rules and dot product

NASA Astrophysics Data System (ADS)

Gao, Mingyue; Deng, Limiao; Wang, Yanjiang

2018-04-01

A great number of associative memory models have been proposed to realize information storage and retrieval inspired by human brain in the last few years. However, there is still much room for improvement for those models. In this paper, we extend a binary pattern associative memory model to accomplish real-world image recognition. The learning process is based on the fundamental Hebb rules and the retrieval is implemented by a normalized dot product operation. Our proposed model can not only fulfill rapid memory storage and retrieval for visual information but also have the ability on incremental learning without destroying the previous learned information. Experimental results demonstrate that our model outperforms the existing Self-Organizing Incremental Neural Network (SOINN) and Back Propagation Neuron Network (BPNN) on recognition accuracy and time efficiency.

Application for managing model-based material properties for simulation-based engineering

DOEpatents

Hoffman, Edward L [Alameda, CA

2009-03-03

An application for generating a property set associated with a constitutive model of a material includes a first program module adapted to receive test data associated with the material and to extract loading conditions from the test data. A material model driver is adapted to receive the loading conditions and a property set and operable in response to the loading conditions and the property set to generate a model response for the material. A numerical optimization module is adapted to receive the test data and the model response and operable in response to the test data and the model response to generate the property set.

Simulating Runoff from a Grid Based Mercury Model: Flow Comparisons

EPA Science Inventory

Several mercury cycling models, including general mass balance approaches, mixed-batch reactors in streams or lakes, or regional process-based models, exist to assess the ecological exposure risks associated with anthropogenically increased atmospheric mercury (Hg) deposition, so...

Characterizations of Social-Based and Self-Based Contexts Associated with Students' Awareness, Evaluation, and Regulation of Their Thinking during Small-Group Mathematical Modeling

ERIC Educational Resources Information Center

Magiera, Marta T.; Zawojewski, Judith S.

2011-01-01

This exploratory study focused on characterizing problem-solving situations associated with spontaneous metacognitive activity. The results came from connected case studies of a group of 3 purposefully selected 9th-grade students working collaboratively on a series of 5 modeling problems. Students' descriptions of their own thinking during…

A Gaussian random field model for similarity-based smoothing in Bayesian disease mapping.

PubMed

Baptista, Helena; Mendes, Jorge M; MacNab, Ying C; Xavier, Miguel; Caldas-de-Almeida, José

2016-08-01

Conditionally specified Gaussian Markov random field (GMRF) models with adjacency-based neighbourhood weight matrix, commonly known as neighbourhood-based GMRF models, have been the mainstream approach to spatial smoothing in Bayesian disease mapping. In the present paper, we propose a conditionally specified Gaussian random field (GRF) model with a similarity-based non-spatial weight matrix to facilitate non-spatial smoothing in Bayesian disease mapping. The model, named similarity-based GRF, is motivated for modelling disease mapping data in situations where the underlying small area relative risks and the associated determinant factors do not vary systematically in space, and the similarity is defined by "similarity" with respect to the associated disease determinant factors. The neighbourhood-based GMRF and the similarity-based GRF are compared and accessed via a simulation study and by two case studies, using new data on alcohol abuse in Portugal collected by the World Mental Health Survey Initiative and the well-known lip cancer data in Scotland. In the presence of disease data with no evidence of positive spatial correlation, the simulation study showed a consistent gain in efficiency from the similarity-based GRF, compared with the adjacency-based GMRF with the determinant risk factors as covariate. This new approach broadens the scope of the existing conditional autocorrelation models. © The Author(s) 2016.

Gene-Based Association Analysis for Censored Traits Via Fixed Effect Functional Regressions.

PubMed

Fan, Ruzong; Wang, Yifan; Yan, Qi; Ding, Ying; Weeks, Daniel E; Lu, Zhaohui; Ren, Haobo; Cook, Richard J; Xiong, Momiao; Swaroop, Anand; Chew, Emily Y; Chen, Wei

2016-02-01

Genetic studies of survival outcomes have been proposed and conducted recently, but statistical methods for identifying genetic variants that affect disease progression are rarely developed. Motivated by our ongoing real studies, here we develop Cox proportional hazard models using functional regression (FR) to perform gene-based association analysis of survival traits while adjusting for covariates. The proposed Cox models are fixed effect models where the genetic effects of multiple genetic variants are assumed to be fixed. We introduce likelihood ratio test (LRT) statistics to test for associations between the survival traits and multiple genetic variants in a genetic region. Extensive simulation studies demonstrate that the proposed Cox RF LRT statistics have well-controlled type I error rates. To evaluate power, we compare the Cox FR LRT with the previously developed burden test (BT) in a Cox model and sequence kernel association test (SKAT), which is based on mixed effect Cox models. The Cox FR LRT statistics have higher power than or similar power as Cox SKAT LRT except when 50%/50% causal variants had negative/positive effects and all causal variants are rare. In addition, the Cox FR LRT statistics have higher power than Cox BT LRT. The models and related test statistics can be useful in the whole genome and whole exome association studies. An age-related macular degeneration dataset was analyzed as an example. © 2016 WILEY PERIODICALS, INC.

Gene-based Association Analysis for Censored Traits Via Fixed Effect Functional Regressions

PubMed Central

Fan, Ruzong; Wang, Yifan; Yan, Qi; Ding, Ying; Weeks, Daniel E.; Lu, Zhaohui; Ren, Haobo; Cook, Richard J; Xiong, Momiao; Swaroop, Anand; Chew, Emily Y.; Chen, Wei

2015-01-01

Summary Genetic studies of survival outcomes have been proposed and conducted recently, but statistical methods for identifying genetic variants that affect disease progression are rarely developed. Motivated by our ongoing real studies, we develop here Cox proportional hazard models using functional regression (FR) to perform gene-based association analysis of survival traits while adjusting for covariates. The proposed Cox models are fixed effect models where the genetic effects of multiple genetic variants are assumed to be fixed. We introduce likelihood ratio test (LRT) statistics to test for associations between the survival traits and multiple genetic variants in a genetic region. Extensive simulation studies demonstrate that the proposed Cox RF LRT statistics have well-controlled type I error rates. To evaluate power, we compare the Cox FR LRT with the previously developed burden test (BT) in a Cox model and sequence kernel association test (SKAT) which is based on mixed effect Cox models. The Cox FR LRT statistics have higher power than or similar power as Cox SKAT LRT except when 50%/50% causal variants had negative/positive effects and all causal variants are rare. In addition, the Cox FR LRT statistics have higher power than Cox BT LRT. The models and related test statistics can be useful in the whole genome and whole exome association studies. An age-related macular degeneration dataset was analyzed as an example. PMID:26782979

HYDROLOGIC MODEL UNCERTAINTY ASSOCIATED WITH SIMULATING FUTURE LAND-COVER/USE SCENARIOS: A RETROSPECTIVE ANALYSIS

EPA Science Inventory

GIS-based hydrologic modeling offers a convenient means of assessing the impacts associated with land-cover/use change for environmental planning efforts. Alternative future scenarios can be used as input to hydrologic models and compared with existing conditions to evaluate pot...

Processing speed enhances model-based over model-free reinforcement learning in the presence of high working memory functioning

PubMed Central

Schad, Daniel J.; Jünger, Elisabeth; Sebold, Miriam; Garbusow, Maria; Bernhardt, Nadine; Javadi, Amir-Homayoun; Zimmermann, Ulrich S.; Smolka, Michael N.; Heinz, Andreas; Rapp, Michael A.; Huys, Quentin J. M.

2014-01-01

Theories of decision-making and its neural substrates have long assumed the existence of two distinct and competing valuation systems, variously described as goal-directed vs. habitual, or, more recently and based on statistical arguments, as model-free vs. model-based reinforcement-learning. Though both have been shown to control choices, the cognitive abilities associated with these systems are under ongoing investigation. Here we examine the link to cognitive abilities, and find that individual differences in processing speed covary with a shift from model-free to model-based choice control in the presence of above-average working memory function. This suggests shared cognitive and neural processes; provides a bridge between literatures on intelligence and valuation; and may guide the development of process models of different valuation components. Furthermore, it provides a rationale for individual differences in the tendency to deploy valuation systems, which may be important for understanding the manifold neuropsychiatric diseases associated with malfunctions of valuation. PMID:25566131

Evaluation of procedures for prediction of unconventional gas in the presence of geologic trends

USGS Publications Warehouse

Attanasi, E.D.; Coburn, T.C.

2009-01-01

This study extends the application of local spatial nonparametric prediction models to the estimation of recoverable gas volumes in continuous-type gas plays to regimes where there is a single geologic trend. A transformation is presented, originally proposed by Tomczak, that offsets the distortions caused by the trend. This article reports on numerical experiments that compare predictive and classification performance of the local nonparametric prediction models based on the transformation with models based on Euclidean distance. The transformation offers improvement in average root mean square error when the trend is not severely misspecified. Because of the local nature of the models, even those based on Euclidean distance in the presence of trends are reasonably robust. The tests based on other model performance metrics such as prediction error associated with the high-grade tracts and the ability of the models to identify sites with the largest gas volumes also demonstrate the robustness of both local modeling approaches. ?? International Association for Mathematical Geology 2009.

A Model-Based Joint Identification of Differentially Expressed Genes and Phenotype-Associated Genes

PubMed Central

Seo, Minseok; Shin, Su-kyung; Kwon, Eun-Young; Kim, Sung-Eun; Bae, Yun-Jung; Lee, Seungyeoun; Sung, Mi-Kyung; Choi, Myung-Sook; Park, Taesung

2016-01-01

Over the last decade, many analytical methods and tools have been developed for microarray data. The detection of differentially expressed genes (DEGs) among different treatment groups is often a primary purpose of microarray data analysis. In addition, association studies investigating the relationship between genes and a phenotype of interest such as survival time are also popular in microarray data analysis. Phenotype association analysis provides a list of phenotype-associated genes (PAGs). However, it is sometimes necessary to identify genes that are both DEGs and PAGs. We consider the joint identification of DEGs and PAGs in microarray data analyses. The first approach we used was a naïve approach that detects DEGs and PAGs separately and then identifies the genes in an intersection of the list of PAGs and DEGs. The second approach we considered was a hierarchical approach that detects DEGs first and then chooses PAGs from among the DEGs or vice versa. In this study, we propose a new model-based approach for the joint identification of DEGs and PAGs. Unlike the previous two-step approaches, the proposed method identifies genes simultaneously that are DEGs and PAGs. This method uses standard regression models but adopts different null hypothesis from ordinary regression models, which allows us to perform joint identification in one-step. The proposed model-based methods were evaluated using experimental data and simulation studies. The proposed methods were used to analyze a microarray experiment in which the main interest lies in detecting genes that are both DEGs and PAGs, where DEGs are identified between two diet groups and PAGs are associated with four phenotypes reflecting the expression of leptin, adiponectin, insulin-like growth factor 1, and insulin. Model-based approaches provided a larger number of genes, which are both DEGs and PAGs, than other methods. Simulation studies showed that they have more power than other methods. Through analysis of data from experimental microarrays and simulation studies, the proposed model-based approach was shown to provide a more powerful result than the naïve approach and the hierarchical approach. Since our approach is model-based, it is very flexible and can easily handle different types of covariates. PMID:26964035

Matrix factorization-based data fusion for the prediction of lncRNA-disease associations.

PubMed

Fu, Guangyuan; Wang, Jun; Domeniconi, Carlotta; Yu, Guoxian

2018-05-01

Long non-coding RNAs (lncRNAs) play crucial roles in complex disease diagnosis, prognosis, prevention and treatment, but only a small portion of lncRNA-disease associations have been experimentally verified. Various computational models have been proposed to identify lncRNA-disease associations by integrating heterogeneous data sources. However, existing models generally ignore the intrinsic structure of data sources or treat them as equally relevant, while they may not be. To accurately identify lncRNA-disease associations, we propose a Matrix Factorization based LncRNA-Disease Association prediction model (MFLDA in short). MFLDA decomposes data matrices of heterogeneous data sources into low-rank matrices via matrix tri-factorization to explore and exploit their intrinsic and shared structure. MFLDA can select and integrate the data sources by assigning different weights to them. An iterative solution is further introduced to simultaneously optimize the weights and low-rank matrices. Next, MFLDA uses the optimized low-rank matrices to reconstruct the lncRNA-disease association matrix and thus to identify potential associations. In 5-fold cross validation experiments to identify verified lncRNA-disease associations, MFLDA achieves an area under the receiver operating characteristic curve (AUC) of 0.7408, at least 3% higher than those given by state-of-the-art data fusion based computational models. An empirical study on identifying masked lncRNA-disease associations again shows that MFLDA can identify potential associations more accurately than competing models. A case study on identifying lncRNAs associated with breast, lung and stomach cancers show that 38 out of 45 (84%) associations predicted by MFLDA are supported by recent biomedical literature and further proves the capability of MFLDA in identifying novel lncRNA-disease associations. MFLDA is a general data fusion framework, and as such it can be adopted to predict associations between other biological entities. The source code for MFLDA is available at: http://mlda.swu.edu.cn/codes.php? name = MFLDA. gxyu@swu.edu.cn. Supplementary data are available at Bioinformatics online.

Adequacy Model for School Funding

ERIC Educational Resources Information Center

Banicki, Guy; Murphy, Gregg

2014-01-01

This study considers the effectiveness of the Evidence-Based Adequacy model of school funding. In looking at the Evidence-Based Adequacy model for school funding, one researcher has been centrally associated with the development and study of this model. Allen Odden is currently a professor in the Department of Educational Leadership and Policy…

The Open Access Association? EAHIL's new model for sustainability.

PubMed

McSean, Tony; Jakobsson, Arne

2009-12-01

To discover a governance structure and a business model for the European Association for Health Information and Libraries (EAHIL) which will be economically sustainable in the medium term, arresting a long-term gradual decline in membership numbers and implementing new revenue streams to sustain association activity. Reviewed survival strategies of other professional associations, investigated potential of emerging interactive web technologies, investigated alternative revenue streams based around the 'franchise' of the annual EAHIL conferences and workshops. A fully worked-through and costed alternative structure was produced, based on abolition of the subscription, web-based procedures and functions, increased income from advertising and sponsorship and a large measure of member participation and engagement. Statutes and Rules of Procedure were rewritten to reflect the changes. This plan was put through the Association's approval cycle and implemented in 2005. The new financial model has proved itself sustainable on the basis of the first 2 years' operations. The long-term gradual decline in membership was reversed, with membership numbers trebling across the EAHIL region. The software worked with minimal problems, including the online electoral process. With no identified precedent from other professional associations, the changes represented a considerable risk, which was justifiable because long-term projections made it clear that continuing the traditional model was not viable. The result is a larger, healthier association with a stronger link to its membership. Long-term risks include the high level of member commitment and expertise. There are also important questions about scalability-diseconomies of scale probably limit the applicability of the overall open access model to larger associations.

Introducing memory and association mechanism into a biologically inspired visual model.

PubMed

Qiao, Hong; Li, Yinlin; Tang, Tang; Wang, Peng

2014-09-01

A famous biologically inspired hierarchical model (HMAX model), which was proposed recently and corresponds to V1 to V4 of the ventral pathway in primate visual cortex, has been successfully applied to multiple visual recognition tasks. The model is able to achieve a set of position- and scale-tolerant recognition, which is a central problem in pattern recognition. In this paper, based on some other biological experimental evidence, we introduce the memory and association mechanism into the HMAX model. The main contributions of the work are: 1) mimicking the active memory and association mechanism and adding the top down adjustment to the HMAX model, which is the first try to add the active adjustment to this famous model and 2) from the perspective of information, algorithms based on the new model can reduce the computation storage and have a good recognition performance. The new model is also applied to object recognition processes. The primary experimental results show that our method is efficient with a much lower memory requirement.

[Cognitive Functions in the Prefrontal Association Cortex; Transitive Inference and the Lateral Prefrontal Cortex].

PubMed

Tanaka, Shingo; Oguchi, Mineki; Sakagami, Masamichi

2016-11-01

To behave appropriately in a complex and uncertain world, the brain makes use of several distinct learning systems. One such system is called the "model-free process", via which conditioning allows the association between a stimulus or response and a given reward to be learned. Another system is called the "model-based process". Via this process, the state transition between a stimulus and a response is learned so that the brain is able to plan actions prior to their execution. Several studies have tried to relate the difference between model-based and model-free processes to the difference in functions of the lateral prefrontal cortex (LPFC) and the striatum. Here, we describe a series of studies that demonstrate the ability of LPFC neurons to categorize visual stimuli by their associated behavioral responses and to generate abstract information. If LPFC neurons utilize abstract code to associate a stimulus with a reward, they should be able to infer similar relationships between other stimuli of the same category and their rewards without direct experience of these stimulus-reward contingencies. We propose that this ability of LPFC neurons to utilize abstract information can contribute to the model-based learning process.

Analysis of Parametric Adaptive Signal Detection with Applications to Radars and Hyperspectral Imaging

DTIC Science & Technology

2010-02-01

98 8.4.5 Training Screening ............................. .................................................................99 8.5 Experimental...associated with the proposed parametric model. Several im- portant issues are discussed, including model order selection, training screening , and time...parameters associated with the NS-AR model. In addition, we develop model order selection, training screening , and time-series based whitening and

Retrospective Binary-Trait Association Test Elucidates Genetic Architecture of Crohn Disease

PubMed Central

Jiang, Duo; Zhong, Sheng; McPeek, Mary Sara

2016-01-01

In genetic association testing, failure to properly control for population structure can lead to severely inflated type 1 error and power loss. Meanwhile, adjustment for relevant covariates is often desirable and sometimes necessary to protect against spurious association and to improve power. Many recent methods to account for population structure and covariates are based on linear mixed models (LMMs), which are primarily designed for quantitative traits. For binary traits, however, LMM is a misspecified model and can lead to deteriorated performance. We propose CARAT, a binary-trait association testing approach based on a mixed-effects quasi-likelihood framework, which exploits the dichotomous nature of the trait and achieves computational efficiency through estimating equations. We show in simulation studies that CARAT consistently outperforms existing methods and maintains high power in a wide range of population structure settings and trait models. Furthermore, CARAT is based on a retrospective approach, which is robust to misspecification of the phenotype model. We apply our approach to a genome-wide analysis of Crohn disease, in which we replicate association with 17 previously identified regions. Moreover, our analysis on 5p13.1, an extensively reported region of association, shows evidence for the presence of multiple independent association signals in the region. This example shows how CARAT can leverage known disease risk factors to shed light on the genetic architecture of complex traits. PMID:26833331

Audio visual speech source separation via improved context dependent association model

NASA Astrophysics Data System (ADS)

Kazemi, Alireza; Boostani, Reza; Sobhanmanesh, Fariborz

2014-12-01

In this paper, we exploit the non-linear relation between a speech source and its associated lip video as a source of extra information to propose an improved audio-visual speech source separation (AVSS) algorithm. The audio-visual association is modeled using a neural associator which estimates the visual lip parameters from a temporal context of acoustic observation frames. We define an objective function based on mean square error (MSE) measure between estimated and target visual parameters. This function is minimized for estimation of the de-mixing vector/filters to separate the relevant source from linear instantaneous or time-domain convolutive mixtures. We have also proposed a hybrid criterion which uses AV coherency together with kurtosis as a non-Gaussianity measure. Experimental results are presented and compared in terms of visually relevant speech detection accuracy and output signal-to-interference ratio (SIR) of source separation. The suggested audio-visual model significantly improves relevant speech classification accuracy compared to existing GMM-based model and the proposed AVSS algorithm improves the speech separation quality compared to reference ICA- and AVSS-based methods.

A method for assigning species into groups based on generalized Mahalanobis distance between habitat model coefficients

USGS Publications Warehouse

Williams, C.J.; Heglund, P.J.

2009-01-01

Habitat association models are commonly developed for individual animal species using generalized linear modeling methods such as logistic regression. We considered the issue of grouping species based on their habitat use so that management decisions can be based on sets of species rather than individual species. This research was motivated by a study of western landbirds in northern Idaho forests. The method we examined was to separately fit models to each species and to use a generalized Mahalanobis distance between coefficient vectors to create a distance matrix among species. Clustering methods were used to group species from the distance matrix, and multidimensional scaling methods were used to visualize the relations among species groups. Methods were also discussed for evaluating the sensitivity of the conclusions because of outliers or influential data points. We illustrate these methods with data from the landbird study conducted in northern Idaho. Simulation results are presented to compare the success of this method to alternative methods using Euclidean distance between coefficient vectors and to methods that do not use habitat association models. These simulations demonstrate that our Mahalanobis-distance- based method was nearly always better than Euclidean-distance-based methods or methods not based on habitat association models. The methods used to develop candidate species groups are easily explained to other scientists and resource managers since they mainly rely on classical multivariate statistical methods. ?? 2008 Springer Science+Business Media, LLC.

Challenges Associated With Applying Physiologically Based Pharmacokinetic Modeling for Public Health Decision-Making

EPA Science Inventory

The development and application of physiologically based pharmacokinetic (PBPK) models in chemical toxicology have grown steadily since their emergence in the 1980s. However, critical evaluation of PBPK models to support public health decision-making across federal agencies has t...

How to compare cross-lagged associations in a multilevel autoregressive model.

PubMed

Schuurman, Noémi K; Ferrer, Emilio; de Boer-Sonnenschein, Mieke; Hamaker, Ellen L

2016-06-01

By modeling variables over time it is possible to investigate the Granger-causal cross-lagged associations between variables. By comparing the standardized cross-lagged coefficients, the relative strength of these associations can be evaluated in order to determine important driving forces in the dynamic system. The aim of this study was twofold: first, to illustrate the added value of a multilevel multivariate autoregressive modeling approach for investigating these associations over more traditional techniques; and second, to discuss how the coefficients of the multilevel autoregressive model should be standardized for comparing the strength of the cross-lagged associations. The hierarchical structure of multilevel multivariate autoregressive models complicates standardization, because subject-based statistics or group-based statistics can be used to standardize the coefficients, and each method may result in different conclusions. We argue that in order to make a meaningful comparison of the strength of the cross-lagged associations, the coefficients should be standardized within persons. We further illustrate the bivariate multilevel autoregressive model and the standardization of the coefficients, and we show that disregarding individual differences in dynamics can prove misleading, by means of an empirical example on experienced competence and exhaustion in persons diagnosed with burnout. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Modeling Time-Dependent Association in Longitudinal Data: A Lag as Moderator Approach

ERIC Educational Resources Information Center

Selig, James P.; Preacher, Kristopher J.; Little, Todd D.

2012-01-01

We describe a straightforward, yet novel, approach to examine time-dependent association between variables. The approach relies on a measurement-lag research design in conjunction with statistical interaction models. We base arguments in favor of this approach on the potential for better understanding the associations between variables by…

A Standardized Generalized Dimensionality Discrepancy Measure and a Standardized Model-Based Covariance for Dimensionality Assessment for Multidimensional Models

ERIC Educational Resources Information Center

Levy, Roy; Xu, Yuning; Yel, Nedim; Svetina, Dubravka

2015-01-01

The standardized generalized dimensionality discrepancy measure and the standardized model-based covariance are introduced as tools to critique dimensionality assumptions in multidimensional item response models. These tools are grounded in a covariance theory perspective and associated connections between dimensionality and local independence.…

Investigating the state of physiologically based kinetic modelling practices and challenges associated with gaining regulatory acceptance of model applications

EPA Science Inventory

Physiologically based kinetic (PBK) models are used widely throughout a number of working sectors, including academia and industry, to provide insight into the dosimetry related to observed adverse health effects in humans and other species. Use of these models has increased over...

Examining a conceptual model of parental nurturance, parenting practices and physical activity among 5-6 year olds.

PubMed

Sebire, Simon J; Jago, Russell; Wood, Lesley; Thompson, Janice L; Zahra, Jezmond; Lawlor, Deborah A

2016-01-01

Parenting is an often-studied correlate of children's physical activity, however there is little research examining the associations between parenting styles, practices and the physical activity of younger children. This study aimed to investigate whether physical activity-based parenting practices mediate the association between parenting styles and 5-6 year-old children's objectively-assessed physical activity. 770 parents self-reported parenting style (nurturance and control) and physical activity-based parenting practices (logistic and modeling support). Their 5-6 year old child wore an accelerometer for five days to measure moderate-to-vigorous physical activity (MVPA). Linear regression was used to examine direct and indirect (mediation) associations. Data were collected in the United Kingdom in 2012/13 and analyzed in 2014. Parent nurturance was positively associated with provision of modeling (adjusted unstandardized coefficient, β = 0.11; 95% CI = 0.02, 0.21) and logistic support (β = 0.14; 0.07, 0.21). Modeling support was associated with greater child MVPA (β = 2.41; 0.23, 4.60) and a small indirect path from parent nurturance to child's MVPA was identified (β = 0.27; 0.04, 0.70). Physical activity-based parenting practices are more strongly associated with 5-6 year old children's MVPA than parenting styles. Further research examining conceptual models of parenting is needed to understand in more depth the possible antecedents to adaptive parenting practices beyond parenting styles. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

Predicting microRNA-disease associations using label propagation based on linear neighborhood similarity.

PubMed

Li, Guanghui; Luo, Jiawei; Xiao, Qiu; Liang, Cheng; Ding, Pingjian

2018-05-12

Interactions between microRNAs (miRNAs) and diseases can yield important information for uncovering novel prognostic markers. Since experimental determination of disease-miRNA associations is time-consuming and costly, attention has been given to designing efficient and robust computational techniques for identifying undiscovered interactions. In this study, we present a label propagation model with linear neighborhood similarity, called LPLNS, to predict unobserved miRNA-disease associations. Additionally, a preprocessing step is performed to derive new interaction likelihood profiles that will contribute to the prediction since new miRNAs and diseases lack known associations. Our results demonstrate that the LPLNS model based on the known disease-miRNA associations could achieve impressive performance with an AUC of 0.9034. Furthermore, we observed that the LPLNS model based on new interaction likelihood profiles could improve the performance to an AUC of 0.9127. This was better than other comparable methods. In addition, case studies also demonstrated our method's outstanding performance for inferring undiscovered interactions between miRNAs and diseases, especially for novel diseases. Copyright © 2018. Published by Elsevier Inc.

The Cognitive, Perceptual, and Neural Bases of Skilled Performance

DTIC Science & Technology

1988-09-01

shunting, masking field, bidirectional associative memory, Volterra - Lotka , Gilpin-Ayala, ani Eigen-Schuster models. The Cohen-Grossberg model thus...field, bidirectional associative memory, Volterra - Lotka , Gilpin-Ayala, and Eigen-Schuster models. A Liapunov functional method is described for...storage by neural networks: A general model and global Liapunov method. In E.L. Schwartz (Ed.), Computational neuroscience. Cambridge, MA: MIT Press

Exploring the relationships among performance-based functional ability, self-rated disability, perceived instrumental support, and depression: a structural equation model analysis.

PubMed

Weil, Joyce; Hutchinson, Susan R; Traxler, Karen

2014-11-01

Data from the Women's Health and Aging Study were used to test a model of factors explaining depressive symptomology. The primary purpose of the study was to explore the association between performance-based measures of functional ability and depression and to examine the role of self-rated physical difficulties and perceived instrumental support in mediating the relationship between performance-based functioning and depression. The inclusion of performance-based measures allows for the testing of functional ability as a clinical precursor to disability and depression: a critical, but rarely examined, association in the disablement process. Structural equation modeling supported the overall fit of the model and found an indirect relationship between performance-based functioning and depression, with perceived physical difficulties serving as a significant mediator. Our results highlight the complementary nature of performance-based and self-rated measures and the importance of including perception of self-rated physical difficulties when examining depression in older persons. © The Author(s) 2014.

Intent inferencing with a model-based operator's associate

NASA Technical Reports Server (NTRS)

Jones, Patricia M.; Mitchell, Christine M.; Rubin, Kenneth S.

1989-01-01

A portion of the Operator Function Model Expert System (OFMspert) research project is described. OFMspert is an architecture for an intelligent operator's associate or assistant that can aid the human operator of a complex, dynamic system. Intelligent aiding requires both understanding and control. The understanding (i.e., intent inferencing) ability of the operator's associate is discussed. Understanding or intent inferencing requires a model of the human operator; the usefulness of an intelligent aid depends directly on the fidelity and completeness of its underlying model. The model chosen for this research is the operator function model (OFM). The OFM represents operator functions, subfunctions, tasks, and actions as a heterarchic-hierarchic network of finite state automata, where the arcs in the network are system triggering events. The OFM provides the structure for intent inferencing in that operator functions and subfunctions correspond to likely operator goals and plans. A blackboard system similar to that of Human Associative Processor (HASP) is proposed as the implementation of intent inferencing function. This system postulates operator intentions based on current system state and attempts to interpret observed operator actions in light of these hypothesized intentions.

Variability in Dopamine Genes Dissociates Model-Based and Model-Free Reinforcement Learning

PubMed Central

Bath, Kevin G.; Daw, Nathaniel D.; Frank, Michael J.

2016-01-01

Considerable evidence suggests that multiple learning systems can drive behavior. Choice can proceed reflexively from previous actions and their associated outcomes, as captured by “model-free” learning algorithms, or flexibly from prospective consideration of outcomes that might occur, as captured by “model-based” learning algorithms. However, differential contributions of dopamine to these systems are poorly understood. Dopamine is widely thought to support model-free learning by modulating plasticity in striatum. Model-based learning may also be affected by these striatal effects, or by other dopaminergic effects elsewhere, notably on prefrontal working memory function. Indeed, prominent demonstrations linking striatal dopamine to putatively model-free learning did not rule out model-based effects, whereas other studies have reported dopaminergic modulation of verifiably model-based learning, but without distinguishing a prefrontal versus striatal locus. To clarify the relationships between dopamine, neural systems, and learning strategies, we combine a genetic association approach in humans with two well-studied reinforcement learning tasks: one isolating model-based from model-free behavior and the other sensitive to key aspects of striatal plasticity. Prefrontal function was indexed by a polymorphism in the COMT gene, differences of which reflect dopamine levels in the prefrontal cortex. This polymorphism has been associated with differences in prefrontal activity and working memory. Striatal function was indexed by a gene coding for DARPP-32, which is densely expressed in the striatum where it is necessary for synaptic plasticity. We found evidence for our hypothesis that variations in prefrontal dopamine relate to model-based learning, whereas variations in striatal dopamine function relate to model-free learning. SIGNIFICANCE STATEMENT Decisions can stem reflexively from their previously associated outcomes or flexibly from deliberative consideration of potential choice outcomes. Research implicates a dopamine-dependent striatal learning mechanism in the former type of choice. Although recent work has indicated that dopamine is also involved in flexible, goal-directed decision-making, it remains unclear whether it also contributes via striatum or via the dopamine-dependent working memory function of prefrontal cortex. We examined genetic indices of dopamine function in these regions and their relation to the two choice strategies. We found that striatal dopamine function related most clearly to the reflexive strategy, as previously shown, and that prefrontal dopamine related most clearly to the flexible strategy. These findings suggest that dissociable brain regions support dissociable choice strategies. PMID:26818509

Identification of Putative Cardiovascular System Developmental Toxicants using a Classification Model based on Signaling Pathway-Adverse Outcome Pathways

EPA Science Inventory

An important challenge for an integrative approach to developmental systems toxicology is associating putative molecular initiating events (MIEs), cell signaling pathways, cell function and modeled fetal exposure kinetics. We have developed a chemical classification model based o...

CARSVM: a class association rule-based classification framework and its application to gene expression data.

PubMed

Kianmehr, Keivan; Alhajj, Reda

2008-09-01

In this study, we aim at building a classification framework, namely the CARSVM model, which integrates association rule mining and support vector machine (SVM). The goal is to benefit from advantages of both, the discriminative knowledge represented by class association rules and the classification power of the SVM algorithm, to construct an efficient and accurate classifier model that improves the interpretability problem of SVM as a traditional machine learning technique and overcomes the efficiency issues of associative classification algorithms. In our proposed framework: instead of using the original training set, a set of rule-based feature vectors, which are generated based on the discriminative ability of class association rules over the training samples, are presented to the learning component of the SVM algorithm. We show that rule-based feature vectors present a high-qualified source of discrimination knowledge that can impact substantially the prediction power of SVM and associative classification techniques. They provide users with more conveniences in terms of understandability and interpretability as well. We have used four datasets from UCI ML repository to evaluate the performance of the developed system in comparison with five well-known existing classification methods. Because of the importance and popularity of gene expression analysis as real world application of the classification model, we present an extension of CARSVM combined with feature selection to be applied to gene expression data. Then, we describe how this combination will provide biologists with an efficient and understandable classifier model. The reported test results and their biological interpretation demonstrate the applicability, efficiency and effectiveness of the proposed model. From the results, it can be concluded that a considerable increase in classification accuracy can be obtained when the rule-based feature vectors are integrated in the learning process of the SVM algorithm. In the context of applicability, according to the results obtained from gene expression analysis, we can conclude that the CARSVM system can be utilized in a variety of real world applications with some adjustments.

Comparing two remote video survey methods for spatial predictions of the distribution and environmental niche suitability of demersal fishes.

PubMed

Galaiduk, Ronen; Radford, Ben T; Wilson, Shaun K; Harvey, Euan S

2017-12-15

Information on habitat associations from survey data, combined with spatial modelling, allow the development of more refined species distribution modelling which may identify areas of high conservation/fisheries value and consequentially improve conservation efforts. Generalised additive models were used to model the probability of occurrence of six focal species after surveys that utilised two remote underwater video sampling methods (i.e. baited and towed video). Models developed for the towed video method had consistently better predictive performance for all but one study species although only three models had a good to fair fit, and the rest were poor fits, highlighting the challenges associated with modelling habitat associations of marine species in highly homogenous, low relief environments. Models based on baited video dataset regularly included large-scale measures of structural complexity, suggesting fish attraction to a single focus point by bait. Conversely, models based on the towed video data often incorporated small-scale measures of habitat complexity and were more likely to reflect true species-habitat relationships. The cost associated with use of the towed video systems for surveying low-relief seascapes was also relatively low providing additional support for considering this method for marine spatial ecological modelling.

Model Based Analysis and Test Generation for Flight Software

NASA Technical Reports Server (NTRS)

Pasareanu, Corina S.; Schumann, Johann M.; Mehlitz, Peter C.; Lowry, Mike R.; Karsai, Gabor; Nine, Harmon; Neema, Sandeep

2009-01-01

We describe a framework for model-based analysis and test case generation in the context of a heterogeneous model-based development paradigm that uses and combines Math- Works and UML 2.0 models and the associated code generation tools. This paradigm poses novel challenges to analysis and test case generation that, to the best of our knowledge, have not been addressed before. The framework is based on a common intermediate representation for different modeling formalisms and leverages and extends model checking and symbolic execution tools for model analysis and test case generation, respectively. We discuss the application of our framework to software models for a NASA flight mission.

A powerful score-based test statistic for detecting gene-gene co-association.

PubMed

Xu, Jing; Yuan, Zhongshang; Ji, Jiadong; Zhang, Xiaoshuai; Li, Hongkai; Wu, Xuesen; Xue, Fuzhong; Liu, Yanxun

2016-01-29

The genetic variants identified by Genome-wide association study (GWAS) can only account for a small proportion of the total heritability for complex disease. The existence of gene-gene joint effects which contains the main effects and their co-association is one of the possible explanations for the "missing heritability" problems. Gene-gene co-association refers to the extent to which the joint effects of two genes differ from the main effects, not only due to the traditional interaction under nearly independent condition but the correlation between genes. Generally, genes tend to work collaboratively within specific pathway or network contributing to the disease and the specific disease-associated locus will often be highly correlated (e.g. single nucleotide polymorphisms (SNPs) in linkage disequilibrium). Therefore, we proposed a novel score-based statistic (SBS) as a gene-based method for detecting gene-gene co-association. Various simulations illustrate that, under different sample sizes, marginal effects of causal SNPs and co-association levels, the proposed SBS has the better performance than other existed methods including single SNP-based and principle component analysis (PCA)-based logistic regression model, the statistics based on canonical correlations (CCU), kernel canonical correlation analysis (KCCU), partial least squares path modeling (PLSPM) and delta-square (δ (2)) statistic. The real data analysis of rheumatoid arthritis (RA) further confirmed its advantages in practice. SBS is a powerful and efficient gene-based method for detecting gene-gene co-association.

Models for Theory-Based M.A. and Ph.D. Programs.

ERIC Educational Resources Information Center

Botan, Carl; Vasquez, Gabriel

1999-01-01

Presents work accomplished at the 1998 National Communication Association Summer Conference. Outlines reasons for theory-based education in public relations. Presents an integrated model of student outcomes, curriculum, pedagogy, and assessment for theory-based master's and doctoral programs, including assumptions made and rationale for such…

Associative memory in phasing neuron networks

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

Nair, Niketh S; Bochove, Erik J.; Braiman, Yehuda

2014-01-01

We studied pattern formation in a network of coupled Hindmarsh-Rose model neurons and introduced a new model for associative memory retrieval using networks of Kuramoto oscillators. Hindmarsh-Rose Neural Networks can exhibit a rich set of collective dynamics that can be controlled by their connectivity. Specifically, we showed an instance of Hebb's rule where spiking was correlated with network topology. Based on this, we presented a simple model of associative memory in coupled phase oscillators.

Evaluating Exposure-Response Associations for Non-Hodgkin Lymphoma with Varying Methods of Assigning Cumulative Benzene Exposure in the Shanghai Women's Health Study.

PubMed

Friesen, Melissa C; Bassig, Bryan A; Vermeulen, Roel; Shu, Xiao-Ou; Purdue, Mark P; Stewart, Patricia A; Xiang, Yong-Bing; Chow, Wong-Ho; Ji, Bu-Tian; Yang, Gong; Linet, Martha S; Hu, Wei; Gao, Yu-Tang; Zheng, Wei; Rothman, Nathaniel; Lan, Qing

2017-01-01

To provide insight into the contributions of exposure measurements to job exposure matrices (JEMs), we examined the robustness of an association between occupational benzene exposure and non-Hodgkin lymphoma (NHL) to varying exposure assessment methods. NHL risk was examined in a prospective population-based cohort of 73087 women in Shanghai. A mixed-effects model that combined a benzene JEM with >60000 short-term, area benzene inspection measurements was used to derive two sets of measurement-based benzene estimates: 'job/industry-specific' estimates (our presumed best approach) were derived from the model's fixed effects (year, JEM intensity rating) and random effects (occupation, industry); 'calibrated JEM' estimates were derived using only the fixed effects. 'Uncalibrated JEM' (using the ordinal JEM ratings) and exposure duration estimates were also calculated. Cumulative exposure for each subject was calculated for each approach based on varying exposure definitions defined using the JEM's probability ratings. We examined the agreement between the cumulative metrics and evaluated changes in the benzene-NHL associations. For our primary exposure definition, the job/industry-specific estimates were moderately to highly correlated with all other approaches (Pearson correlation 0.61-0.89; Spearman correlation > 0.99). All these metrics resulted in statistically significant exposure-response associations for NHL, with negligible gain in model fit from using measurement-based estimates. Using more sensitive or specific exposure definitions resulted in elevated but non-significant associations. The robust associations observed here with varying benzene assessment methods provide support for a benzene-NHL association. While incorporating exposure measurements did not improve model fit, the measurements allowed us to derive quantitative exposure-response curves. Published by Oxford University Press on behalf of the British Occupational Hygiene Society 2017.

A Memory-Based Model of Hick's Law

ERIC Educational Resources Information Center

Schneider, Darryl W.; Anderson, John R.

2011-01-01

We propose and evaluate a memory-based model of Hick's law, the approximately linear increase in choice reaction time with the logarithm of set size (the number of stimulus-response alternatives). According to the model, Hick's law reflects a combination of associative interference during retrieval from declarative memory and occasional savings…

Hetero-association for pattern translation

NASA Astrophysics Data System (ADS)

Yu, Francis T. S.; Lu, Thomas T.; Yang, Xiangyang

1991-09-01

A hetero-association neural network using an interpattern association algorithm is presented. By using simple logical rules, hetero-association memory can be constructed based on the association between the input-output reference patterns. For optical implementation, a compact size liquid crystal television neural network is used. Translations between the English letters and the Chinese characters as well as Arabic and Chinese numerics are demonstrated. The authors have shown that the hetero-association model can perform more effectively in comparison to the Hopfield model in retrieving large numbers of similar patterns.

Variability in Dopamine Genes Dissociates Model-Based and Model-Free Reinforcement Learning.

PubMed

Doll, Bradley B; Bath, Kevin G; Daw, Nathaniel D; Frank, Michael J

2016-01-27

Considerable evidence suggests that multiple learning systems can drive behavior. Choice can proceed reflexively from previous actions and their associated outcomes, as captured by "model-free" learning algorithms, or flexibly from prospective consideration of outcomes that might occur, as captured by "model-based" learning algorithms. However, differential contributions of dopamine to these systems are poorly understood. Dopamine is widely thought to support model-free learning by modulating plasticity in striatum. Model-based learning may also be affected by these striatal effects, or by other dopaminergic effects elsewhere, notably on prefrontal working memory function. Indeed, prominent demonstrations linking striatal dopamine to putatively model-free learning did not rule out model-based effects, whereas other studies have reported dopaminergic modulation of verifiably model-based learning, but without distinguishing a prefrontal versus striatal locus. To clarify the relationships between dopamine, neural systems, and learning strategies, we combine a genetic association approach in humans with two well-studied reinforcement learning tasks: one isolating model-based from model-free behavior and the other sensitive to key aspects of striatal plasticity. Prefrontal function was indexed by a polymorphism in the COMT gene, differences of which reflect dopamine levels in the prefrontal cortex. This polymorphism has been associated with differences in prefrontal activity and working memory. Striatal function was indexed by a gene coding for DARPP-32, which is densely expressed in the striatum where it is necessary for synaptic plasticity. We found evidence for our hypothesis that variations in prefrontal dopamine relate to model-based learning, whereas variations in striatal dopamine function relate to model-free learning. Decisions can stem reflexively from their previously associated outcomes or flexibly from deliberative consideration of potential choice outcomes. Research implicates a dopamine-dependent striatal learning mechanism in the former type of choice. Although recent work has indicated that dopamine is also involved in flexible, goal-directed decision-making, it remains unclear whether it also contributes via striatum or via the dopamine-dependent working memory function of prefrontal cortex. We examined genetic indices of dopamine function in these regions and their relation to the two choice strategies. We found that striatal dopamine function related most clearly to the reflexive strategy, as previously shown, and that prefrontal dopamine related most clearly to the flexible strategy. These findings suggest that dissociable brain regions support dissociable choice strategies. Copyright © 2016 the authors 0270-6474/16/361211-12$15.00/0.

FlyBase portals to human disease research using Drosophila models

PubMed Central

Millburn, Gillian H.; Crosby, Madeline A.; Gramates, L. Sian; Tweedie, Susan

2016-01-01

ABSTRACT The use of Drosophila melanogaster as a model for studying human disease is well established, reflected by the steady increase in both the number and proportion of fly papers describing human disease models in recent years. In this article, we highlight recent efforts to improve the availability and accessibility of the disease model information in FlyBase (http://flybase.org), the model organism database for Drosophila. FlyBase has recently introduced Human Disease Model Reports, each of which presents background information on a specific disease, a tabulation of related disease subtypes, and summaries of experimental data and results using fruit flies. Integrated presentations of relevant data and reagents described in other sections of FlyBase are incorporated into these reports, which are specifically designed to be accessible to non-fly researchers in order to promote collaboration across model organism communities working in translational science. Another key component of disease model information in FlyBase is that data are collected in a consistent format ­­– using the evolving Disease Ontology (an open-source standardized ontology for human-disease-associated biomedical data) – to allow robust and intuitive searches. To facilitate this, FlyBase has developed a dedicated tool for querying and navigating relevant data, which include mutations that model a disease and any associated interacting modifiers. In this article, we describe how data related to fly models of human disease are presented in individual Gene Reports and in the Human Disease Model Reports. Finally, we discuss search strategies and new query tools that are available to access the disease model data in FlyBase. PMID:26935103

Model-based Acceleration Control of Turbofan Engines with a Hammerstein-Wiener Representation

NASA Astrophysics Data System (ADS)

Wang, Jiqiang; Ye, Zhifeng; Hu, Zhongzhi; Wu, Xin; Dimirovsky, Georgi; Yue, Hong

2017-05-01

Acceleration control of turbofan engines is conventionally designed through either schedule-based or acceleration-based approach. With the widespread acceptance of model-based design in aviation industry, it becomes necessary to investigate the issues associated with model-based design for acceleration control. In this paper, the challenges for implementing model-based acceleration control are explained; a novel Hammerstein-Wiener representation of engine models is introduced; based on the Hammerstein-Wiener model, a nonlinear generalized minimum variance type of optimal control law is derived; the feature of the proposed approach is that it does not require the inversion operation that usually upsets those nonlinear control techniques. The effectiveness of the proposed control design method is validated through a detailed numerical study.

Comparison of CME/Shock Propagation Models with Heliospheric Imaging and In Situ Observations

NASA Astrophysics Data System (ADS)

Zhao, Xinhua; Liu, Ying D.; Inhester, Bernd; Feng, Xueshang; Wiegelmann, Thomas; Lu, Lei

2016-10-01

The prediction of the arrival time for fast coronal mass ejections (CMEs) and their associated shocks is highly desirable in space weather studies. In this paper, we use two shock propagation models, I.e., Data Guided Shock Time Of Arrival (DGSTOA) and Data Guided Shock Propagation Model (DGSPM), to predict the kinematical evolution of interplanetary shocks associated with fast CMEs. DGSTOA is based on the similarity theory of shock waves in the solar wind reference frame, and DGSPM is based on the non-similarity theory in the stationary reference frame. The inputs are the kinematics of the CME front at the maximum speed moment obtained from the geometric triangulation method applied to STEREO imaging observations together with the Harmonic Mean approximation. The outputs provide the subsequent propagation of the associated shock. We apply these models to the CMEs on 2012 January 19, January 23, and March 7. We find that the shock models predict reasonably well the shock’s propagation after the impulsive acceleration. The shock’s arrival time and local propagation speed at Earth predicted by these models are consistent with in situ measurements of WIND. We also employ the Drag-Based Model (DBM) as a comparison, and find that it predicts a steeper deceleration than the shock models after the rapid deceleration phase. The predictions of DBM at 1 au agree with the following ICME or sheath structure, not the preceding shock. These results demonstrate the applicability of the shock models used here for future arrival time prediction of interplanetary shocks associated with fast CMEs.

Applying inventory methods to estimate aboveground biomass from satellite light detection and ranging (LiDAR) forest height data

Treesearch

Sean P. Healey; Paul L. Patterson; Sassan Saatchi; Michael A. Lefsky; Andrew J. Lister; Elizabeth A. Freeman; Gretchen G. Moisen

2012-01-01

Light Detection and Ranging (LiDAR) returns from the spaceborne Geoscience Laser Altimeter (GLAS) sensor may offer an alternative to solely field-based forest biomass sampling. Such an approach would rely upon model-based inference, which can account for the uncertainty associated with using modeled, instead of field-collected, measurements. Model-based methods have...

Predicting Risk of Type 2 Diabetes Mellitus with Genetic Risk Models on the Basis of Established Genome-wide Association Markers: A Systematic Review

PubMed Central

Bao, Wei; Hu, Frank B.; Rong, Shuang; Rong, Ying; Bowers, Katherine; Schisterman, Enrique F.; Liu, Liegang; Zhang, Cuilin

2013-01-01

This study aimed to evaluate the predictive performance of genetic risk models based on risk loci identified and/or confirmed in genome-wide association studies for type 2 diabetes mellitus. A systematic literature search was conducted in the PubMed/MEDLINE and EMBASE databases through April 13, 2012, and published data relevant to the prediction of type 2 diabetes based on genome-wide association marker–based risk models (GRMs) were included. Of the 1,234 potentially relevant articles, 21 articles representing 23 studies were eligible for inclusion. The median area under the receiver operating characteristic curve (AUC) among eligible studies was 0.60 (range, 0.55–0.68), which did not differ appreciably by study design, sample size, participants’ race/ethnicity, or the number of genetic markers included in the GRMs. In addition, the AUCs for type 2 diabetes did not improve appreciably with the addition of genetic markers into conventional risk factor–based models (median AUC, 0.79 (range, 0.63–0.91) vs. median AUC, 0.78 (range, 0.63–0.90), respectively). A limited number of included studies used reclassification measures and yielded inconsistent results. In conclusion, GRMs showed a low predictive performance for risk of type 2 diabetes, irrespective of study design, participants’ race/ethnicity, and the number of genetic markers included. Moreover, the addition of genome-wide association markers into conventional risk models produced little improvement in predictive performance. PMID:24008910

Association between glutathione S-transferase P1 Ile (105) Val gene polymorphism and chronic obstructive pulmonary disease: A meta-analysis based on seventeen case-control studies.

PubMed

Yang, Lingjing; Li, Xixia; Tong, Xiang; Fan, Hong

2015-12-01

Previous studies have shown that glutathione S-transferase P1 (GSTP1) was associated with chronic obstructive pulmonary disease (COPD). However, the association between GSTP1 Ile (105) Val gene polymorphism and COPD remains controversial. To drive a more precise estimation, we performed a meta-analysis based on published case-control studies. An electronic search of PubMed, EMBASE, Cochrane library, Web of Science and China Knowledge Resource Integrated (CNKI) Database for papers on GSTP1 Ile (105) Val gene polymorphism and COPD risk was performed. The pooled odds ratios (ORs) with 95% confidence intervals (CIs) were used to assess the strength of association in the homozygote model, heterozygote model, dominant model, recessive model and an additive mode. Statistical heterogeneity, test of publication bias and sensitivity analysis was performed. The software STATA (Version 13.0) was used data analysis. Overall, seventeen studies with 1892 cases and 2012 controls were included in this meta-analysis. The GSTP1 Ile (105) Val polymorphism showed pooled odds ratios for the homozygote comparison (OR = 1.501, 95%CI [0.862, 2.614]), heterozygote comparison (OR = 0.924, 95%CI [0.733, 1.165]), dominant model (OR = 1.003, 95%CI [0.756, 1.331]), recessive model (OR = 1.510, 95%CI [0.934, 2.439]), and an additive model (OR = 1.072, 95%CI [0.822, 1.398]). In conclusion, the current meta-analysis, based on the most updated information, showed no significant association between GSTP1 Ile (105) Val gene polymorphism and COPD risk in any genetic models. The results of subgroup analysis also showed no significant association between GSTP1 Ile (105) Val gene polymorphism and COPD risk in Asian population and Caucasian population. Further studies involving large populations and careful control with age, sex, ethnicity, and cigarette smoking are greatly needed.

Association between glutathione S-transferase P1 Ile (105) Val gene polymorphism and chronic obstructive pulmonary disease: A meta-analysis based on seventeen case–control studies

PubMed Central

Yang, Lingjing; Li, Xixia; Tong, Xiang; Fan, Hong

2015-01-01

Introduction Previous studies have shown that glutathione S-transferase P1 (GSTP1) was associated with chronic obstructive pulmonary disease (COPD). However, the association between GSTP1 Ile (105) Val gene polymorphism and COPD remains controversial. To drive a more precise estimation, we performed a meta-analysis based on published case–control studies. Methods An electronic search of PubMed, EMBASE, Cochrane library, Web of Science and China Knowledge Resource Integrated (CNKI) Database for papers on GSTP1 Ile (105) Val gene polymorphism and COPD risk was performed. The pooled odds ratios (ORs) with 95% confidence intervals (CIs) were used to assess the strength of association in the homozygote model, heterozygote model, dominant model, recessive model and an additive mode. Statistical heterogeneity, test of publication bias and sensitivity analysis was performed. The software STATA (Version 13.0) was used data analysis. Results Overall, seventeen studies with 1892 cases and 2012 controls were included in this meta-analysis. The GSTP1 Ile (105) Val polymorphism showed pooled odds ratios for the homozygote comparison (OR = 1.501, 95%CI [0.862, 2.614]), heterozygote comparison (OR = 0.924, 95%CI [0.733, 1.165]), dominant model (OR = 1.003, 95%CI [0.756, 1.331]), recessive model (OR = 1.510, 95%CI [0.934, 2.439]), and an additive model (OR = 1.072, 95%CI [0.822, 1.398]). Conclusions In conclusion, the current meta-analysis, based on the most updated information, showed no significant association between GSTP1 Ile (105) Val gene polymorphism and COPD risk in any genetic models. The results of subgroup analysis also showed no significant association between GSTP1 Ile (105) Val gene polymorphism and COPD risk in Asian population and Caucasian population. Further studies involving large populations and careful control with age, sex, ethnicity, and cigarette smoking are greatly needed. PMID:26504746

Examining a conceptual model of parental nurturance, parenting practices and physical activity among 5–6 year olds

PubMed Central

Sebire, Simon J.; Jago, Russell; Wood, Lesley; Thompson, Janice L.; Zahra, Jezmond; Lawlor, Deborah A.

2016-01-01

Rationale Parenting is an often-studied correlate of children's physical activity, however there is little research examining the associations between parenting styles, practices and the physical activity of younger children. Objective This study aimed to investigate whether physical activity-based parenting practices mediate the association between parenting styles and 5–6 year-old children's objectively-assessed physical activity. Methods 770 parents self-reported parenting style (nurturance and control) and physical activity-based parenting practices (logistic and modeling support). Their 5–6 year old child wore an accelerometer for five days to measure moderate-to-vigorous physical activity (MVPA). Linear regression was used to examine direct and indirect (mediation) associations. Data were collected in the United Kingdom in 2012/13 and analyzed in 2014. Results Parent nurturance was positively associated with provision of modeling (adjusted unstandardized coefficient, β = 0.11; 95% CI = 0.02, 0.21) and logistic support (β = 0.14; 0.07, 0.21). Modeling support was associated with greater child MVPA (β = 2.41; 0.23, 4.60) and a small indirect path from parent nurturance to child's MVPA was identified (β = 0.27; 0.04, 0.70). Conclusions Physical activity-based parenting practices are more strongly associated with 5–6 year old children's MVPA than parenting styles. Further research examining conceptual models of parenting is needed to understand in more depth the possible antecedents to adaptive parenting practices beyond parenting styles. PMID:26647364

Ventral striatal dopamine reflects behavioral and neural signatures of model-based control during sequential decision making.

PubMed

Deserno, Lorenz; Huys, Quentin J M; Boehme, Rebecca; Buchert, Ralph; Heinze, Hans-Jochen; Grace, Anthony A; Dolan, Raymond J; Heinz, Andreas; Schlagenhauf, Florian

2015-02-03

Dual system theories suggest that behavioral control is parsed between a deliberative "model-based" and a more reflexive "model-free" system. A balance of control exerted by these systems is thought to be related to dopamine neurotransmission. However, in the absence of direct measures of human dopamine, it remains unknown whether this reflects a quantitative relation with dopamine either in the striatum or other brain areas. Using a sequential decision task performed during functional magnetic resonance imaging, combined with striatal measures of dopamine using [(18)F]DOPA positron emission tomography, we show that higher presynaptic ventral striatal dopamine levels were associated with a behavioral bias toward more model-based control. Higher presynaptic dopamine in ventral striatum was associated with greater coding of model-based signatures in lateral prefrontal cortex and diminished coding of model-free prediction errors in ventral striatum. Thus, interindividual variability in ventral striatal presynaptic dopamine reflects a balance in the behavioral expression and the neural signatures of model-free and model-based control. Our data provide a novel perspective on how alterations in presynaptic dopamine levels might be accompanied by a disruption of behavioral control as observed in aging or neuropsychiatric diseases such as schizophrenia and addiction.

Applying quantitative adiposity feature analysis models to predict benefit of bevacizumab-based chemotherapy in ovarian cancer patients

NASA Astrophysics Data System (ADS)

Wang, Yunzhi; Qiu, Yuchen; Thai, Theresa; More, Kathleen; Ding, Kai; Liu, Hong; Zheng, Bin

2016-03-01

How to rationally identify epithelial ovarian cancer (EOC) patients who will benefit from bevacizumab or other antiangiogenic therapies is a critical issue in EOC treatments. The motivation of this study is to quantitatively measure adiposity features from CT images and investigate the feasibility of predicting potential benefit of EOC patients with or without receiving bevacizumab-based chemotherapy treatment using multivariate statistical models built based on quantitative adiposity image features. A dataset involving CT images from 59 advanced EOC patients were included. Among them, 32 patients received maintenance bevacizumab after primary chemotherapy and the remaining 27 patients did not. We developed a computer-aided detection (CAD) scheme to automatically segment subcutaneous fat areas (VFA) and visceral fat areas (SFA) and then extracted 7 adiposity-related quantitative features. Three multivariate data analysis models (linear regression, logistic regression and Cox proportional hazards regression) were performed respectively to investigate the potential association between the model-generated prediction results and the patients' progression-free survival (PFS) and overall survival (OS). The results show that using all 3 statistical models, a statistically significant association was detected between the model-generated results and both of the two clinical outcomes in the group of patients receiving maintenance bevacizumab (p<0.01), while there were no significant association for both PFS and OS in the group of patients without receiving maintenance bevacizumab. Therefore, this study demonstrated the feasibility of using quantitative adiposity-related CT image features based statistical prediction models to generate a new clinical marker and predict the clinical outcome of EOC patients receiving maintenance bevacizumab-based chemotherapy.

Geographically-based discrimination is a social determinant of mental health in a deprived or stigmatized area in Japan: a cross-sectional study.

PubMed

Tabuchi, Takahiro; Fukuhara, Hiroyuki; Iso, Hiroyasu

2012-09-01

Perceived discrimination has been shown to be associated with health. However, it is uncertain whether discrimination based on geographical place of residence (geographically-based discrimination), such as Buraku or Nishinari discrimination in Japan, is associated with health. We conducted a cross-sectional study (response rate = 52.3%) from February to March 2009 in a Buraku district of Nishinari ward in Osaka city, one of the most deprived areas in Japan. We implemented sex-stratified and education-stratified multivariate regression models to examine the association between geographically-based discrimination and two mental health outcomes (depressive symptoms and diagnosis of mental illness) with adjustment for age, socioeconomic status, social relationships and lifestyle factors. A total of 1994 persons aged 25-79 years (928 men and 1066 women) living in the district were analyzed. In the fully-adjusted model, perceived geographically-based discrimination was significantly associated with depressive symptoms and diagnosis of mental illness. It was more strongly associated among men or highly educated people than among women or among less educated people. The effect of geographically-based discrimination on mental health is independent of socioeconomic status, social relationship and lifestyle factors. Geographically-based discrimination may be one of the social determinants of mental health. Copyright © 2012. Published by Elsevier Ltd.

Architecture with GIDEON, A Program for Design in Structural DNA Nanotechnology

PubMed Central

Birac, Jeffrey J.; Sherman, William B.; Kopatsch, Jens; Constantinou, Pamela E.; Seeman, Nadrian C.

2012-01-01

We present geometry based design strategies for DNA nanostructures. The strategies have been implemented with GIDEON – a Graphical Integrated Development Environment for OligoNucleotides. GIDEON has a highly flexible graphical user interface that facilitates the development of simple yet precise models, and the evaluation of strains therein. Models are built on a simple model of undistorted B-DNA double-helical domains. Simple point and click manipulations of the model allow the minimization of strain in the phosphate-backbone linkages between these domains and the identification of any steric clashes that might occur as a result. Detailed analysis of 3D triangles yields clear predictions of the strains associated with triangles of different sizes. We have carried out experiments that confirm that 3D triangles form well only when their geometrical strain is less than 4% deviation from the estimated relaxed structure. Thus geometry-based techniques alone, without energetic considerations, can be used to explain general trends in DNA structure formation. We have used GIDEON to build detailed models of double crossover and triple crossover molecules, evaluating the non-planarity associated with base tilt and junction mis-alignments. Computer modeling using a graphical user interface overcomes the limited precision of physical models for larger systems, and the limited interaction rate associated with earlier, command-line driven software. PMID:16630733

Applying Model Analysis to a Resource-Based Analysis of the Force and Motion Conceptual Evaluation

ERIC Educational Resources Information Center

Smith, Trevor I.; Wittmann, Michael C.; Carter, Tom

2014-01-01

Previously, we analyzed the Force and Motion Conceptual Evaluation in terms of a resources-based model that allows for clustering of questions so as to provide useful information on how students correctly or incorrectly reason about physics. In this paper, we apply model analysis to show that the associated model plots provide more information…

Leveraging Strengths Assessment and Intervention Model (LeStAIM): A Theoretical Strength-Based Assessment Framework

ERIC Educational Resources Information Center

Laija-Rodriguez, Wilda; Grites, Karen; Bouman, Doug; Pohlman, Craig; Goldman, Richard L.

2013-01-01

Current assessments in the schools are based on a deficit model (Epstein, 1998). "The National Association of School Psychologists (NASP) Model for Comprehensive and Integrated School Psychological Services" (2010), federal initiatives and mandates, and experts in the field of assessment have highlighted the need for the comprehensive…

Prediction of gestational age based on genome-wide differentially methylated regions.

PubMed

Bohlin, J; Håberg, S E; Magnus, P; Reese, S E; Gjessing, H K; Magnus, M C; Parr, C L; Page, C M; London, S J; Nystad, W

2016-10-07

We explored the association between gestational age and cord blood DNA methylation at birth and whether DNA methylation could be effective in predicting gestational age due to limitations with the presently used methods. We used data from the Norwegian Mother and Child Birth Cohort study (MoBa) with Illumina HumanMethylation450 data measured for 1753 newborns in two batches: MoBa 1, n = 1068; and MoBa 2, n = 685. Gestational age was computed using both ultrasound and the last menstrual period. We evaluated associations between DNA methylation and gestational age and developed a statistical model for predicting gestational age using MoBa 1 for training and MoBa 2 for predictions. The prediction model was additionally used to compare ultrasound and last menstrual period-based gestational age predictions. Furthermore, both CpGs and associated genes detected in the training models were compared to those detected in a published prediction model for chronological age. There were 5474 CpGs associated with ultrasound gestational age after adjustment for a set of covariates, including estimated cell type proportions, and Bonferroni-correction for multiple testing. Our model predicted ultrasound gestational age more accurately than it predicted last menstrual period gestational age. DNA methylation at birth appears to be a good predictor of gestational age. Ultrasound gestational age is more strongly associated with methylation than last menstrual period gestational age. The CpGs linked with our gestational age prediction model, and their associated genes, differed substantially from the corresponding CpGs and genes associated with a chronological age prediction model.

A mixed model for the relationship between climate and human cranial form.

PubMed

Katz, David C; Grote, Mark N; Weaver, Timothy D

2016-08-01

We expand upon a multivariate mixed model from quantitative genetics in order to estimate the magnitude of climate effects in a global sample of recent human crania. In humans, genetic distances are correlated with distances based on cranial form, suggesting that population structure influences both genetic and quantitative trait variation. Studies controlling for this structure have demonstrated significant underlying associations of cranial distances with ecological distances derived from climate variables. However, to assess the biological importance of an ecological predictor, estimates of effect size and uncertainty in the original units of measurement are clearly preferable to significance claims based on units of distance. Unfortunately, the magnitudes of ecological effects are difficult to obtain with distance-based methods, while models that produce estimates of effect size generally do not scale to high-dimensional data like cranial shape and form. Using recent innovations that extend quantitative genetics mixed models to highly multivariate observations, we estimate morphological effects associated with a climate predictor for a subset of the Howells craniometric dataset. Several measurements, particularly those associated with cranial vault breadth, show a substantial linear association with climate, and the multivariate model incorporating a climate predictor is preferred in model comparison. Previous studies demonstrated the existence of a relationship between climate and cranial form. The mixed model quantifies this relationship concretely. Evolutionary questions that require population structure and phylogeny to be disentangled from potential drivers of selection may be particularly well addressed by mixed models. Am J Phys Anthropol 160:593-603, 2016. © 2015 Wiley Periodicals, Inc. © 2015 Wiley Periodicals, Inc.

Literature Review on Systems of Systems (SoS): A Methodology With Preliminary Results

DTIC Science & Technology

2013-11-01

Appendix H. The Enhanced ISAAC Neural Simulation Toolkit (EINSTein) 73  Appendix I. The Map Aware Nonuniform Automata (MANA) Agent-Based Model 81...83  Figure I-3. Quadrant chart addressing SoS and associated SoSA designs for the Map Aware Nonuniform Automata (MANA) agent...Map Aware Nonuniform Automata (MANA) agent-based model. 85  Table I-2. SoS and SoSA software component maturation scores associated with the Map

CoNVaQ: a web tool for copy number variation-based association studies.

PubMed

Larsen, Simon Jonas; do Canto, Luisa Matos; Rogatto, Silvia Regina; Baumbach, Jan

2018-05-18

Copy number variations (CNVs) are large segments of the genome that are duplicated or deleted. Structural variations in the genome have been linked to many complex diseases. Similar to how genome-wide association studies (GWAS) have helped discover single-nucleotide polymorphisms linked to disease phenotypes, the extension of GWAS to CNVs has aided the discovery of structural variants associated with human traits and diseases. We present CoNVaQ, an easy-to-use web-based tool for CNV-based association studies. The web service allows users to upload two sets of CNV segments and search for genomic regions where the occurrence of CNVs is significantly associated with the phenotype. CoNVaQ provides two models: a simple statistical model using Fisher's exact test and a novel query-based model matching regions to user-defined queries. For each region, the method computes a global q-value statistic by repeated permutation of samples among the populations. We demonstrate our platform by using it to analyze a data set of HPV-positive and HPV-negative penile cancer patients. CoNVaQ provides a simple workflow for performing CNV-based association studies. It is made available as a web platform in order to provide a user-friendly workflow for biologists and clinicians to carry out CNV data analysis without installing any software. Through the web interface, users are also able to analyze their results to find overrepresented GO terms and pathways. In addition, our method is also available as a package for the R programming language. CoNVaQ is available at https://convaq.compbio.sdu.dk .

Socioeconomic Inequalities in Statin Adherence Under Universal Coverage: Does Sex Matter?

PubMed

Aarnio, Emma; Martikainen, Janne; Winn, Aaron N; Huupponen, Risto; Vahtera, Jussi; Korhonen, Maarit J

2016-11-01

Previous research shows that low socioeconomic position (SEP; especially low income) is associated with statin nonadherence. We investigated the relationship between SEP and statin adherence in a country with universal coverage using group-based trajectory modeling in addition to the proportion of days covered. Using data from Finnish healthcare registers, we identified 116 846 individuals, aged 45 to 75 years, who initiated statin therapy for primary prevention of cardiovascular disease. We measured adherence as proportion of days covered over an 18-month period since initiation and identified different adherence patterns based on monthly adherence with group-based trajectory modeling. When adjusted for age, marital status, residential area, clinical characteristics, and copayment, low SEP was associated with statin nonadherence (proportion of days covered <80%) among men (eg, lowest versus highest income quintile: odds ratio, 1.41; 95% confidence interval, 1.32-1.50; basic versus higher-degree education: odds ratio, 1.18; 95% confidence interval, 1.13-1.24; unemployment versus employment: odds ratio, 1.17; 95% confidence interval, 1.10-1.25). Among women, the corresponding associations were different ( P <0.001 for sex-by-income quintile, sex-by-education level, and sex-by-labor market status interactions) and mainly nonsignificant. Results based on adherence trajectories showed that men in low SEP were likely to belong to trajectories presenting a fast decline in adherence. Low SEP was associated with overall and rapidly increasing statin nonadherence among men. Conversely, in women, associations between SEP and nonadherence were weak and inconsistent. Group-based trajectory modeling provided insight into the dynamics of statin adherence and its association with SEP. © 2016 American Heart Association, Inc.

Median Hetero-Associative Memories Applied to the Categorization of True-Color Patterns

NASA Astrophysics Data System (ADS)

Vázquez, Roberto A.; Sossa, Humberto

Median associative memories (MED-AMs) are a special type of associative memory based on the median operator. This type of associative model has been applied to the restoration of gray scale images and provides better performance than other models, such as morphological associative memories, when the patterns are altered with mixed noise. Despite of his power, MED-AMs have not been applied in problems involving true-color patterns. In this paper we describe how a median hetero-associative memory (MED-HAM) could be applied in problems that involve true-color patterns. A complete study of the behavior of this associative model in the restoration of true-color images is performed using a benchmark of 14400 images altered by different type of noises. Furthermore, we describe how this model can be applied to an image categorization problem.

Mixed Model Association with Family-Biased Case-Control Ascertainment.

PubMed

Hayeck, Tristan J; Loh, Po-Ru; Pollack, Samuela; Gusev, Alexander; Patterson, Nick; Zaitlen, Noah A; Price, Alkes L

2017-01-05

Mixed models have become the tool of choice for genetic association studies; however, standard mixed model methods may be poorly calibrated or underpowered under family sampling bias and/or case-control ascertainment. Previously, we introduced a liability threshold-based mixed model association statistic (LTMLM) to address case-control ascertainment in unrelated samples. Here, we consider family-biased case-control ascertainment, where case and control subjects are ascertained non-randomly with respect to family relatedness. Previous work has shown that this type of ascertainment can severely bias heritability estimates; we show here that it also impacts mixed model association statistics. We introduce a family-based association statistic (LT-Fam) that is robust to this problem. Similar to LTMLM, LT-Fam is computed from posterior mean liabilities (PML) under a liability threshold model; however, LT-Fam uses published narrow-sense heritability estimates to avoid the problem of biased heritability estimation, enabling correct calibration. In simulations with family-biased case-control ascertainment, LT-Fam was correctly calibrated (average χ 2 = 1.00-1.02 for null SNPs), whereas the Armitage trend test (ATT), standard mixed model association (MLM), and case-control retrospective association test (CARAT) were mis-calibrated (e.g., average χ 2 = 0.50-1.22 for MLM, 0.89-2.65 for CARAT). LT-Fam also attained higher power than other methods in some settings. In 1,259 type 2 diabetes-affected case subjects and 5,765 control subjects from the CARe cohort, downsampled to induce family-biased ascertainment, LT-Fam was correctly calibrated whereas ATT, MLM, and CARAT were again mis-calibrated. Our results highlight the importance of modeling family sampling bias in case-control datasets with related samples. Copyright © 2017 American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.

Costs of providing infusion therapy for patients with inflammatory bowel disease in a hospital-based infusion center setting.

PubMed

Afzali, Anita; Ogden, Kristine; Friedman, Michael L; Chao, Jingdong; Wang, Anthony

2017-04-01

Inflammatory bowel disease (IBD) (e.g. ulcerative colitis [UC] and Crohn's disease [CD]) severely impacts patient quality-of-life. Moderate-to-severe disease is often treated with biologics requiring infusion therapy, adding incremental costs beyond drug costs. This study evaluates US hospital-based infusion services costs for treatment of UC or CD patients receiving infliximab or vedolizumab therapy. A model was developed, estimating annual costs of providing monitored infusions using an activity-based costing framework approach. Multiple sources (published literature, treatment product inserts) informed base-case model input estimates. The total modeled per patient infusion therapy costs in Year 1 with infliximab and vedolizumab was $38,782 and $41,320, respectively, and Year 2+, $49,897 and $36,197, respectively. Drug acquisition cost was the largest total costs driver (90-93%), followed by costs associated with hospital-based infusion provision: labor (53-56%, non-drug costs), allocated overhead (23%, non-drug costs), non-labor (23%, non-drug costs), and laboratory (7-10%, non-drug costs). Limitations included reliance on published estimates, base-case cost estimates infusion drug, and supplies, not accounting for volume pricing, assumption of a small hospital infusion center, and that, given the model adopts the hospital perspective, costs to the patient were not included in infusion administration cost base-case estimates. This model is an early step towards a framework to fully analyze infusion therapies' associated costs. Given the lack of published data, it would be beneficial for hospital administrators to assess total costs and trade-offs with alternative means of providing biologic therapies. This analysis highlights the value to hospital administrators of assessing cost associated with infusion patient mix to make more informed resource allocation decisions. As the landscape for reimbursement changes, tools for evaluating the costs of infusion therapy may help hospital administrators make informed choices and weigh trade-offs associated with providing infusion services for IBD patients.

FlyBase portals to human disease research using Drosophila models.

PubMed

Millburn, Gillian H; Crosby, Madeline A; Gramates, L Sian; Tweedie, Susan

2016-03-01

The use of Drosophila melanogaster as a model for studying human disease is well established, reflected by the steady increase in both the number and proportion of fly papers describing human disease models in recent years. In this article, we highlight recent efforts to improve the availability and accessibility of the disease model information in FlyBase (http://flybase.org), the model organism database for Drosophila. FlyBase has recently introduced Human Disease Model Reports, each of which presents background information on a specific disease, a tabulation of related disease subtypes, and summaries of experimental data and results using fruit flies. Integrated presentations of relevant data and reagents described in other sections of FlyBase are incorporated into these reports, which are specifically designed to be accessible to non-fly researchers in order to promote collaboration across model organism communities working in translational science. Another key component of disease model information in FlyBase is that data are collected in a consistent format --- using the evolving Disease Ontology (an open-source standardized ontology for human-disease-associated biomedical data) - to allow robust and intuitive searches. To facilitate this, FlyBase has developed a dedicated tool for querying and navigating relevant data, which include mutations that model a disease and any associated interacting modifiers. In this article, we describe how data related to fly models of human disease are presented in individual Gene Reports and in the Human Disease Model Reports. Finally, we discuss search strategies and new query tools that are available to access the disease model data in FlyBase. © 2016. Published by The Company of Biologists Ltd.

Pain, pain intensity and pain disability in high school students are differently associated with physical activity, screening hours and sleep.

PubMed

Silva, Anabela G; Sa-Couto, Pedro; Queirós, Alexandra; Neto, Maritza; Rocha, Nelson P

2017-05-16

Studies exploring the association between physical activity, screen time and sleep and pain usually focus on a limited number of painful body sites. Nevertheless, pain at different body sites is likely to be of different nature. Therefore, this study aims to explore and compare the association between time spent in self-reported physical activity, in screen based activities and sleeping and i) pain presence in the last 7-days for 9 different body sites; ii) pain intensity at 9 different body sites and iii) global disability. Nine hundred sixty nine students completed a questionnaire on pain, time spent in moderate and vigorous physical activity, screen based time watching TV/DVD, playing, using mobile phones and computers and sleeping hours. Univariate and multivariate associations between pain presence, pain intensity and disability and physical activity, screen based time and sleeping hours were investigated. Pain presence: sleeping remained in the multivariable model for the neck, mid back, wrists, knees and ankles/feet (OR 1.17 to 2.11); moderate physical activity remained in the multivariate model for the neck, shoulders, wrists, hips and ankles/feet (OR 1.06 to 1.08); vigorous physical activity remained in the multivariate model for mid back, knees and ankles/feet (OR 1.05 to 1.09) and screen time remained in the multivariate model for the low back (OR = 2.34. Pain intensity: screen time and moderate physical activity remained in the multivariable model for pain intensity at the neck, mid back, low back, shoulder, knees and ankles/feet (Rp 2 0.02 to 0.04) and at the wrists (Rp 2  = 0.04), respectively. Disability showed no association with sleeping, screen time or physical activity. This study suggests both similarities and differences in the patterns of association between time spent in physical activity, sleeping and in screen based activities and pain presence at 8 different body sites. In addition, they also suggest that the factors associated with the presence of pain, pain intensity and pain associated disability are different.

Launch Vehicle Debris Models and Crew Vehicle Ascent Abort Risk

NASA Technical Reports Server (NTRS)

Gee, Ken; Lawrence, Scott

2013-01-01

For manned space launch systems, a reliable abort system is required to reduce the risks associated with a launch vehicle failure during ascent. Understanding the risks associated with failure environments can be achieved through the use of physics-based models of these environments. Debris fields due to destruction of the launch vehicle is one such environment. To better analyze the risk posed by debris, a physics-based model for generating launch vehicle debris catalogs has been developed. The model predicts the mass distribution of the debris field based on formulae developed from analysis of explosions. Imparted velocity distributions are computed using a shock-physics code to model the explosions within the launch vehicle. A comparison of the debris catalog with an existing catalog for the Shuttle external tank show good comparison in the debris characteristics and the predicted debris strike probability. The model is used to analyze the effects of number of debris pieces and velocity distributions on the strike probability and risk.

12 CFR Appendix C to Part 567 - Risk-Based Capital Requirements-Internal-Ratings-Based and Advanced Measurement Approaches

Code of Federal Regulations, 2013 CFR

2013-01-01

... the savings association for one or more exposures is not commensurate with the risks associated with... a provision of this appendix to one or more exposures, provided that: (1) The savings association... sample period not used in model development. In this context, backtesting is one form of out-of-sample...

12 CFR Appendix C to Part 567 - Risk-Based Capital Requirements-Internal-Ratings-Based and Advanced Measurement Approaches

Code of Federal Regulations, 2011 CFR

2011-01-01

... the savings association for one or more exposures is not commensurate with the risks associated with... a provision of this appendix to one or more exposures, provided that: (1) The savings association... sample period not used in model development. In this context, backtesting is one form of out-of-sample...

12 CFR Appendix C to Part 567 - Risk-Based Capital Requirements-Internal-Ratings-Based and Advanced Measurement Approaches

Code of Federal Regulations, 2014 CFR

2014-01-01

... the savings association for one or more exposures is not commensurate with the risks associated with... a provision of this appendix to one or more exposures, provided that: (1) The savings association... sample period not used in model development. In this context, backtesting is one form of out-of-sample...

12 CFR Appendix C to Part 567 - Risk-Based Capital Requirements-Internal-Ratings-Based and Advanced Measurement Approaches

Code of Federal Regulations, 2012 CFR

2012-01-01

... the savings association for one or more exposures is not commensurate with the risks associated with... a provision of this appendix to one or more exposures, provided that: (1) The savings association... sample period not used in model development. In this context, backtesting is one form of out-of-sample...

The economic impact of improving phosphate binder therapy adherence and attainment of guideline phosphorus goals in hemodialysis patients: a Medicare cost-offset model.

PubMed

Ramakrishnan, Karthik; Braunhofer, Peter; Newsome, Britt; Lubeck, Deborah; Wang, Steven; Deuson, Jennifer; Claxton, Ami J

2014-12-01

Hyperphosphatemia (serum phosphorus >5.5 mg/dL) in hemodialysis patients is a key factor in mineral and bone disorders and is associated with increased hospitalization and mortality risks. Treatment with oral phosphate binders offers limited benefit in achieving target serum phosphorus concentrations due to high daily pill burden (7-10 pills/day) and associated poor medication adherence. The economic value of improving phosphate binder adherence and increasing percent time in range (PTR) for target phosphorus concentrations has not been previously assessed in dialysis patients. The current retrospective analysis was conducted to summarize health care cost savings to United States (US) payers associated with improved phosphate binder adherence and increased PTR for target phosphorus concentrations in adult end-stage renal disease (ESRD) patients receiving hemodialysis therapy. Phosphate binder adherence and PTR were derived from hemodialysis patients who were treated at a large dialysis organization between January 2007 and December 2011. Cost model inputs were derived from US Renal Data System data between July 2007 and December 2009. A cost-offset model was constructed to estimate monthly and annual incremental health care costs (total Medicare; inpatient, outpatient, and Medicare Part B) associated with different levels of phosphate binder adherence and PTR. Model inputs included number of ESRD patients, population adherence to phosphate binders, PTR associated with adherence to phosphate binders, and per-patient per-month cost associated with PTR. A base case model estimated monthly and annual costs of phosphate binder therapy in the population using estimated model inputs. The estimated adherence rate was used to determine number of patients in compliant and noncompliant groups. Monthly costs were calculated as the sum of per-patient per-month cost times the number of patients in adherent and nonadherent groups. Annual costs were monthly costs times 12 and assumed the same level of adherence, PTR, and per-patient per-month costs over time. To study the impact of improving phosphate binder adherence and PTR on cost outcomes, we hypothetically and simultaneously increased both base phosphate binders adherence and PTR for adherent patients (adherence/PTR: 10/20%, 20/40%, 30/60%). Monthly and annual costs were derived for each scenario and compared against the results of the base case model. One-way sensitivity analysis was performed to test model robustness. The base case model estimated total Medicare and inpatient costs of $5,152,342 and $1,435,644, respectively (N = 1,000). When base case model costs were compared to results of each extended model scenario, overall Medicare cost savings (range 0.3-1.9%) and inpatient cost savings (range 1.2-5.7%) were observed. The one-way sensitivity analysis indicated that results were sensitive to PTR for adherent and nonadherent patients and the factor used to increase adherence rate and PTR associated with adherence in the hypothetical scenarios. However, cost savings in overall Medicare costs and inpatient costs were still noted. Increasing phosphate binder adherence and improving phosphorus control were associated with increased cost savings in total Medicare costs and inpatient costs.

Estimation of biological parameters of marine organisms using linear and nonlinear acoustic scattering model-based inversion methods.

PubMed

Chu, Dezhang; Lawson, Gareth L; Wiebe, Peter H

2016-05-01

The linear inversion commonly used in fisheries and zooplankton acoustics assumes a constant inversion kernel and ignores the uncertainties associated with the shape and behavior of the scattering targets, as well as other relevant animal parameters. Here, errors of the linear inversion due to uncertainty associated with the inversion kernel are quantified. A scattering model-based nonlinear inversion method is presented that takes into account the nonlinearity of the inverse problem and is able to estimate simultaneously animal abundance and the parameters associated with the scattering model inherent to the kernel. It uses sophisticated scattering models to estimate first, the abundance, and second, the relevant shape and behavioral parameters of the target organisms. Numerical simulations demonstrate that the abundance, size, and behavior (tilt angle) parameters of marine animals (fish or zooplankton) can be accurately inferred from the inversion by using multi-frequency acoustic data. The influence of the singularity and uncertainty in the inversion kernel on the inversion results can be mitigated by examining the singular values for linear inverse problems and employing a non-linear inversion involving a scattering model-based kernel.

Do trust-based beliefs mediate the associations of frequency of private prayer with mental health? A cross-sectional study.

PubMed

Pössel, Patrick; Winkeljohn Black, Stephanie; Bjerg, Annie C; Jeppsen, Benjamin D; Wooldridge, Don T

2014-06-01

Significant associations of private prayer with mental health have been found, while mechanisms underlying these associations are largely unknown. This cross-sectional online study (N = 325, age 35.74, SD 18.50, 77.5 % females) used path modeling to test if trust-based beliefs (whether, when, and how prayers are answered) mediated the associations of prayer frequency with the Anxiety, Confusion, and Depression Profile of Mood States-Short Form scales. The association of prayer and depression was fully mediated by trust-based beliefs; associations with anxiety and confusion were partially mediated. Further, the interaction of prayer frequency by stress was associated with anxiety.

Progression of regional grey matter atrophy in multiple sclerosis

PubMed Central

Marinescu, Razvan V; Young, Alexandra L; Firth, Nicholas C; Jorge Cardoso, M; Tur, Carmen; De Angelis, Floriana; Cawley, Niamh; Brownlee, Wallace J; De Stefano, Nicola; Laura Stromillo, M; Battaglini, Marco; Ruggieri, Serena; Gasperini, Claudio; Filippi, Massimo; Rocca, Maria A; Rovira, Alex; Sastre-Garriga, Jaume; Geurts, Jeroen J G; Vrenken, Hugo; Wottschel, Viktor; Leurs, Cyra E; Uitdehaag, Bernard; Pirpamer, Lukas; Enzinger, Christian; Ourselin, Sebastien; Gandini Wheeler-Kingshott, Claudia A; Chard, Declan; Thompson, Alan J; Barkhof, Frederik; Alexander, Daniel C; Ciccarelli, Olga

2018-01-01

Abstract See Stankoff and Louapre (doi:10.1093/brain/awy114) for a scientific commentary on this article. Grey matter atrophy is present from the earliest stages of multiple sclerosis, but its temporal ordering is poorly understood. We aimed to determine the sequence in which grey matter regions become atrophic in multiple sclerosis and its association with disability accumulation. In this longitudinal study, we included 1417 subjects: 253 with clinically isolated syndrome, 708 with relapsing-remitting multiple sclerosis, 128 with secondary-progressive multiple sclerosis, 125 with primary-progressive multiple sclerosis, and 203 healthy control subjects from seven European centres. Subjects underwent repeated MRI (total number of scans 3604); the mean follow-up for patients was 2.41 years (standard deviation = 1.97). Disability was scored using the Expanded Disability Status Scale. We calculated the volume of brain grey matter regions and brainstem using an unbiased within-subject template and used an established data-driven event-based model to determine the sequence of occurrence of atrophy and its uncertainty. We assigned each subject to a specific event-based model stage, based on the number of their atrophic regions. Linear mixed-effects models were used to explore associations between the rate of increase in event-based model stages, and T2 lesion load, disease-modifying treatments, comorbidity, disease duration and disability accumulation. The first regions to become atrophic in patients with clinically isolated syndrome and relapse-onset multiple sclerosis were the posterior cingulate cortex and precuneus, followed by the middle cingulate cortex, brainstem and thalamus. A similar sequence of atrophy was detected in primary-progressive multiple sclerosis with the involvement of the thalamus, cuneus, precuneus, and pallidum, followed by the brainstem and posterior cingulate cortex. The cerebellum, caudate and putamen showed early atrophy in relapse-onset multiple sclerosis and late atrophy in primary-progressive multiple sclerosis. Patients with secondary-progressive multiple sclerosis showed the highest event-based model stage (the highest number of atrophic regions, P < 0.001) at the study entry. All multiple sclerosis phenotypes, but clinically isolated syndrome, showed a faster rate of increase in the event-based model stage than healthy controls. T2 lesion load and disease duration in all patients were associated with increased event-based model stage, but no effects of disease-modifying treatments and comorbidity on event-based model stage were observed. The annualized rate of event-based model stage was associated with the disability accumulation in relapsing-remitting multiple sclerosis, independent of disease duration (P < 0.0001). The data-driven staging of atrophy progression in a large multiple sclerosis sample demonstrates that grey matter atrophy spreads to involve more regions over time. The sequence in which regions become atrophic is reasonably consistent across multiple sclerosis phenotypes. The spread of atrophy was associated with disease duration and with disability accumulation over time in relapsing-remitting multiple sclerosis. PMID:29741648

Progression of regional grey matter atrophy in multiple sclerosis.

PubMed

Eshaghi, Arman; Marinescu, Razvan V; Young, Alexandra L; Firth, Nicholas C; Prados, Ferran; Jorge Cardoso, M; Tur, Carmen; De Angelis, Floriana; Cawley, Niamh; Brownlee, Wallace J; De Stefano, Nicola; Laura Stromillo, M; Battaglini, Marco; Ruggieri, Serena; Gasperini, Claudio; Filippi, Massimo; Rocca, Maria A; Rovira, Alex; Sastre-Garriga, Jaume; Geurts, Jeroen J G; Vrenken, Hugo; Wottschel, Viktor; Leurs, Cyra E; Uitdehaag, Bernard; Pirpamer, Lukas; Enzinger, Christian; Ourselin, Sebastien; Gandini Wheeler-Kingshott, Claudia A; Chard, Declan; Thompson, Alan J; Barkhof, Frederik; Alexander, Daniel C; Ciccarelli, Olga

2018-06-01

See Stankoff and Louapre (doi:10.1093/brain/awy114) for a scientific commentary on this article.Grey matter atrophy is present from the earliest stages of multiple sclerosis, but its temporal ordering is poorly understood. We aimed to determine the sequence in which grey matter regions become atrophic in multiple sclerosis and its association with disability accumulation. In this longitudinal study, we included 1417 subjects: 253 with clinically isolated syndrome, 708 with relapsing-remitting multiple sclerosis, 128 with secondary-progressive multiple sclerosis, 125 with primary-progressive multiple sclerosis, and 203 healthy control subjects from seven European centres. Subjects underwent repeated MRI (total number of scans 3604); the mean follow-up for patients was 2.41 years (standard deviation = 1.97). Disability was scored using the Expanded Disability Status Scale. We calculated the volume of brain grey matter regions and brainstem using an unbiased within-subject template and used an established data-driven event-based model to determine the sequence of occurrence of atrophy and its uncertainty. We assigned each subject to a specific event-based model stage, based on the number of their atrophic regions. Linear mixed-effects models were used to explore associations between the rate of increase in event-based model stages, and T2 lesion load, disease-modifying treatments, comorbidity, disease duration and disability accumulation. The first regions to become atrophic in patients with clinically isolated syndrome and relapse-onset multiple sclerosis were the posterior cingulate cortex and precuneus, followed by the middle cingulate cortex, brainstem and thalamus. A similar sequence of atrophy was detected in primary-progressive multiple sclerosis with the involvement of the thalamus, cuneus, precuneus, and pallidum, followed by the brainstem and posterior cingulate cortex. The cerebellum, caudate and putamen showed early atrophy in relapse-onset multiple sclerosis and late atrophy in primary-progressive multiple sclerosis. Patients with secondary-progressive multiple sclerosis showed the highest event-based model stage (the highest number of atrophic regions, P < 0.001) at the study entry. All multiple sclerosis phenotypes, but clinically isolated syndrome, showed a faster rate of increase in the event-based model stage than healthy controls. T2 lesion load and disease duration in all patients were associated with increased event-based model stage, but no effects of disease-modifying treatments and comorbidity on event-based model stage were observed. The annualized rate of event-based model stage was associated with the disability accumulation in relapsing-remitting multiple sclerosis, independent of disease duration (P < 0.0001). The data-driven staging of atrophy progression in a large multiple sclerosis sample demonstrates that grey matter atrophy spreads to involve more regions over time. The sequence in which regions become atrophic is reasonably consistent across multiple sclerosis phenotypes. The spread of atrophy was associated with disease duration and with disability accumulation over time in relapsing-remitting multiple sclerosis.

Development of Assays for Detecting Significant Prostate Cancer Based on Molecular Alterations Associated with Cancer in Non-Neoplastic Prostate Tissue

DTIC Science & Technology

2016-12-01

Award Number: W81XWH-11-1-0744 TITLE: Development of Assays for Detecting Significant Prostate Cancer Based on Molecular Alterations Associated...Significant Prostate Cancer Based on Molecular Alterations Associated with Cancer in Non- Neoplastic Prostate Tissue 5b. GRANT NUMBER 10623678 5c...Public Release; Distribution Unlimited 13. SUPPLEMENTARY NOTES 14. ABSTRACT The goal of this project was to develop molecular models to

Model weights and the foundations of multimodel inference

USGS Publications Warehouse

Link, W.A.; Barker, R.J.

2006-01-01

Statistical thinking in wildlife biology and ecology has been profoundly influenced by the introduction of AIC (Akaike?s information criterion) as a tool for model selection and as a basis for model averaging. In this paper, we advocate the Bayesian paradigm as a broader framework for multimodel inference, one in which model averaging and model selection are naturally linked, and in which the performance of AIC-based tools is naturally evaluated. Prior model weights implicitly associated with the use of AIC are seen to highly favor complex models: in some cases, all but the most highly parameterized models in the model set are virtually ignored a priori. We suggest the usefulness of the weighted BIC (Bayesian information criterion) as a computationally simple alternative to AIC, based on explicit selection of prior model probabilities rather than acceptance of default priors associated with AIC. We note, however, that both procedures are only approximate to the use of exact Bayes factors. We discuss and illustrate technical difficulties associated with Bayes factors, and suggest approaches to avoiding these difficulties in the context of model selection for a logistic regression. Our example highlights the predisposition of AIC weighting to favor complex models and suggests a need for caution in using the BIC for computing approximate posterior model weights.

Beliefs Held by Associate Degree Nursing Students about Role Models.

ERIC Educational Resources Information Center

Bellinger, Kathleen; And Others

1985-01-01

Reports on a study of the professional socialization of associate degree nursing (ADN) students. Reviews previous research on the process of nursing socialization. Presents study findings based on responses from 1,877 nursing students in 20 ADN programs, focusing on students' characteristics and ideal and actual role models. (DMM)

Bayes factors based on robust TDT-type tests for family trio design.

PubMed

Yuan, Min; Pan, Xiaoqing; Yang, Yaning

2015-06-01

Adaptive transmission disequilibrium test (aTDT) and MAX3 test are two robust-efficient association tests for case-parent family trio data. Both tests incorporate information of common genetic models including recessive, additive and dominant models and are efficient in power and robust to genetic model specifications. The aTDT uses information of departure from Hardy-Weinberg disequilibrium to identify the potential genetic model underlying the data and then applies the corresponding TDT-type test, and the MAX3 test is defined as the maximum of the absolute value of three TDT-type tests under the three common genetic models. In this article, we propose three robust Bayes procedures, the aTDT based Bayes factor, MAX3 based Bayes factor and Bayes model averaging (BMA), for association analysis with case-parent trio design. The asymptotic distributions of aTDT under the null and alternative hypothesis are derived in order to calculate its Bayes factor. Extensive simulations show that the Bayes factors and the p-values of the corresponding tests are generally consistent and these Bayes factors are robust to genetic model specifications, especially so when the priors on the genetic models are equal. When equal priors are used for the underlying genetic models, the Bayes factor method based on aTDT is more powerful than those based on MAX3 and Bayes model averaging. When the prior placed a small (large) probability on the true model, the Bayes factor based on aTDT (BMA) is more powerful. Analysis of a simulation data about RA from GAW15 is presented to illustrate applications of the proposed methods.

Circulating Adipokines and Vascular Function: Cross-Sectional Associations in a Community-Based Cohort.

PubMed

Zachariah, Justin P; Hwang, Susan; Hamburg, Naomi M; Benjamin, Emelia J; Larson, Martin G; Levy, Daniel; Vita, Joseph A; Sullivan, Lisa M; Mitchell, Gary F; Vasan, Ramachandran S

2016-02-01

Adipokines may be potential mediators of the association between excess adiposity and vascular dysfunction. We assessed the cross-sectional associations of circulating adipokines with vascular stiffness in a community-based cohort of younger adults. We related circulating concentrations of leptin and leptin receptor, adiponectin, retinol-binding protein 4, and fatty acid-binding protein 4 to vascular stiffness measured by arterial tonometry in 3505 Framingham Third Generation cohort participants free of cardiovascular disease (mean age 40 years, 53% women). Separate regression models estimated the relations of each adipokine to mean arterial pressure and aortic stiffness, as carotid femoral pulse wave velocity, adjusting for age, sex, smoking, heart rate, height, antihypertensive treatment, total and high-density lipoprotein cholesterol, diabetes mellitus, alcohol consumption, estimated glomerular filtration rate, glucose, and C-reactive protein. Models evaluating aortic stiffness also were adjusted for mean arterial pressure. Mean arterial pressure was positively associated with blood retinol-binding protein 4, fatty acid-binding protein 4, and leptin concentrations (all P<0.001) and inversely with adiponectin (P=0.002). In fully adjusted models, mean arterial pressure was positively associated with retinol-binding protein 4 and leptin receptor levels (P<0.002 both). In fully adjusted models, aortic stiffness was positively associated with fatty acid-binding protein 4 concentrations (P=0.02), but inversely with leptin and leptin receptor levels (P≤0.03 both). In our large community-based sample, circulating concentrations of select adipokines were associated with vascular stiffness measures, consistent with the hypothesis that adipokines may influence vascular function and may contribute to the relation between obesity and hypertension. © 2015 American Heart Association, Inc.

Stream Discharge and Evapotranspiration Responses to Climate Change and Their Associated Uncertainties in a Large Semi-Arid Basin

NASA Astrophysics Data System (ADS)

Bassam, S.; Ren, J.

2017-12-01

Predicting future water availability in watersheds is very important for proper water resources management, especially in semi-arid regions with scarce water resources. Hydrological models have been considered as powerful tools in predicting future hydrological conditions in watershed systems in the past two decades. Streamflow and evapotranspiration are the two important components in watershed water balance estimation as the former is the most commonly-used indicator of the overall water budget estimation, and the latter is the second biggest component of water budget (biggest outflow from the system). One of the main concerns in watershed scale hydrological modeling is the uncertainties associated with model prediction, which could arise from errors in model parameters and input meteorological data, or errors in model representation of the physics of hydrological processes. Understanding and quantifying these uncertainties are vital to water resources managers for proper decision making based on model predictions. In this study, we evaluated the impacts of different climate change scenarios on the future stream discharge and evapotranspiration, and their associated uncertainties, throughout a large semi-arid basin using a stochastically-calibrated, physically-based, semi-distributed hydrological model. The results of this study could provide valuable insights in applying hydrological models in large scale watersheds, understanding the associated sensitivity and uncertainties in model parameters, and estimating the corresponding impacts on interested hydrological process variables under different climate change scenarios.

Design and Implementation of C-iLearning: A Cloud-Based Intelligent Learning System

ERIC Educational Resources Information Center

Xiao, Jun; Wang, Minjuan; Wang, Lamei; Zhu, Xiaoxiao

2013-01-01

The gradual development of intelligent learning (iLearning) systems has prompted the changes of teaching and learning. This paper presents the architecture of an intelligent learning (iLearning) system built upon the recursive iLearning model and the key technologies associated with this model. Based on this model and the technical structure of a…

The practice of quality-associated costing: application to transfusion manufacturing processes.

PubMed

Trenchard, P M; Dixon, R

1997-01-01

This article applies the new method of quality-associated costing (QAC) to the mixture of processes that create red cell and plasma products from whole blood donations. The article compares QAC with two commonly encountered but arbitrary models and illustrates the invalidity of clinical cost-benefit analysis based on these models. The first, an "isolated" cost model, seeks to allocate each whole process cost to only one product class. The other is a "shared" cost model, and it seeks to allocate an approximately equal share of all process costs to all associated products.

Modeling and mining term association for improving biomedical information retrieval performance.

PubMed

Hu, Qinmin; Huang, Jimmy Xiangji; Hu, Xiaohua

2012-06-11

The growth of the biomedical information requires most information retrieval systems to provide short and specific answers in response to complex user queries. Semantic information in the form of free text that is structured in a way makes it straightforward for humans to read but more difficult for computers to interpret automatically and search efficiently. One of the reasons is that most traditional information retrieval models assume terms are conditionally independent given a document/passage. Therefore, we are motivated to consider term associations within different contexts to help the models understand semantic information and use it for improving biomedical information retrieval performance. We propose a term association approach to discover term associations among the keywords from a query. The experiments are conducted on the TREC 2004-2007 Genomics data sets and the TREC 2004 HARD data set. The proposed approach is promising and achieves superiority over the baselines and the GSP results. The parameter settings and different indices are investigated that the sentence-based index produces the best results in terms of the document-level, the word-based index for the best results in terms of the passage-level and the paragraph-based index for the best results in terms of the passage2-level. Furthermore, the best term association results always come from the best baseline. The tuning number k in the proposed recursive re-ranking algorithm is discussed and locally optimized to be 10. First, modelling term association for improving biomedical information retrieval using factor analysis, is one of the major contributions in our work. Second, the experiments confirm that term association considering co-occurrence and dependency among the keywords can produce better results than the baselines treating the keywords independently. Third, the baselines are re-ranked according to the importance and reliance of latent factors behind term associations. These latent factors are decided by the proposed model and their term appearances in the first round retrieved passages.

Modeling and mining term association for improving biomedical information retrieval performance

PubMed Central

2012-01-01

Background The growth of the biomedical information requires most information retrieval systems to provide short and specific answers in response to complex user queries. Semantic information in the form of free text that is structured in a way makes it straightforward for humans to read but more difficult for computers to interpret automatically and search efficiently. One of the reasons is that most traditional information retrieval models assume terms are conditionally independent given a document/passage. Therefore, we are motivated to consider term associations within different contexts to help the models understand semantic information and use it for improving biomedical information retrieval performance. Results We propose a term association approach to discover term associations among the keywords from a query. The experiments are conducted on the TREC 2004-2007 Genomics data sets and the TREC 2004 HARD data set. The proposed approach is promising and achieves superiority over the baselines and the GSP results. The parameter settings and different indices are investigated that the sentence-based index produces the best results in terms of the document-level, the word-based index for the best results in terms of the passage-level and the paragraph-based index for the best results in terms of the passage2-level. Furthermore, the best term association results always come from the best baseline. The tuning number k in the proposed recursive re-ranking algorithm is discussed and locally optimized to be 10. Conclusions First, modelling term association for improving biomedical information retrieval using factor analysis, is one of the major contributions in our work. Second, the experiments confirm that term association considering co-occurrence and dependency among the keywords can produce better results than the baselines treating the keywords independently. Third, the baselines are re-ranked according to the importance and reliance of latent factors behind term associations. These latent factors are decided by the proposed model and their term appearances in the first round retrieved passages. PMID:22901087

Extending Geographic Weights of Evidence Models for Use in Location Based Services

ERIC Educational Resources Information Center

Sonwalkar, Mukul Dinkar

2012-01-01

This dissertation addresses the use and modeling of spatio-temporal data for the purposes of providing applications for location based services. One of the major issues in dealing with spatio-temporal data for location based services is the availability and sparseness of such data. Other than the hardware costs associated with collecting movement…

Temporal validation for landsat-based volume estimation model

Treesearch

Renaldo J. Arroyo; Emily B. Schultz; Thomas G. Matney; David L. Evans; Zhaofei Fan

2015-01-01

Satellite imagery can potentially reduce the costs and time associated with ground-based forest inventories; however, for satellite imagery to provide reliable forest inventory data, it must produce consistent results from one time period to the next. The objective of this study was to temporally validate a Landsat-based volume estimation model in a four county study...

Career Education: Papers Presented at the 1972 Annual Meeting of the American Educational Research Association (Chicago, Illinois, April 4, 1972).

ERIC Educational Resources Information Center

American Educational Research Association, Washington, DC.

Three of the papers in this collection present the separate models--school based, employer based, and home-community based. Titles of the five papers are: (1) "Facts and Fantasies of Career Education" by Gorden I. Swanson, (2) "Strategies for Implementing Career Education: A School Based Model" by Aaron J. Miller, (3)…

20180312 - Structure-based QSAR Models to Predict Systemic Toxicity Points of Departure (SOT)

EPA Science Inventory

Human health risk assessment associated with environmental chemical exposure is limited by the tens of thousands of chemicals with little or no experimental in vivo toxicity data. Data gap filling techniques, such as quantitative structure activity relationship (QSAR) models base...

Global Distribution of Outbreaks of Water-Associated Infectious Diseases

PubMed Central

Yang, Kun; LeJeune, Jeffrey; Alsdorf, Doug; Lu, Bo; Shum, C. K.; Liang, Song

2012-01-01

Background Water plays an important role in the transmission of many infectious diseases, which pose a great burden on global public health. However, the global distribution of these water-associated infectious diseases and underlying factors remain largely unexplored. Methods and Findings Based on the Global Infectious Disease and Epidemiology Network (GIDEON), a global database including water-associated pathogens and diseases was developed. In this study, reported outbreak events associated with corresponding water-associated infectious diseases from 1991 to 2008 were extracted from the database. The location of each reported outbreak event was identified and geocoded into a GIS database. Also collected in the GIS database included geo-referenced socio-environmental information including population density (2000), annual accumulated temperature, surface water area, and average annual precipitation. Poisson models with Bayesian inference were developed to explore the association between these socio-environmental factors and distribution of the reported outbreak events. Based on model predictions a global relative risk map was generated. A total of 1,428 reported outbreak events were retrieved from the database. The analysis suggested that outbreaks of water-associated diseases are significantly correlated with socio-environmental factors. Population density is a significant risk factor for all categories of reported outbreaks of water-associated diseases; water-related diseases (e.g., vector-borne diseases) are associated with accumulated temperature; water-washed diseases (e.g., conjunctivitis) are inversely related to surface water area; both water-borne and water-related diseases are inversely related to average annual rainfall. Based on the model predictions, “hotspots” of risks for all categories of water-associated diseases were explored. Conclusions At the global scale, water-associated infectious diseases are significantly correlated with socio-environmental factors, impacting all regions which are affected disproportionately by different categories of water-associated infectious diseases. PMID:22348158

Expert models and modeling processes associated with a computer-modeling tool

NASA Astrophysics Data System (ADS)

Zhang, Baohui; Liu, Xiufeng; Krajcik, Joseph S.

2006-07-01

Holding the premise that the development of expertise is a continuous process, this study concerns expert models and modeling processes associated with a modeling tool called Model-It. Five advanced Ph.D. students in environmental engineering and public health used Model-It to create and test models of water quality. Using think aloud technique and video recording, we captured their computer screen modeling activities and thinking processes. We also interviewed them the day following their modeling sessions to further probe the rationale of their modeling practices. We analyzed both the audio-video transcripts and the experts' models. We found the experts' modeling processes followed the linear sequence built in the modeling program with few instances of moving back and forth. They specified their goals up front and spent a long time thinking through an entire model before acting. They specified relationships with accurate and convincing evidence. Factors (i.e., variables) in expert models were clustered, and represented by specialized technical terms. Based on the above findings, we made suggestions for improving model-based science teaching and learning using Model-It.

A novel co-occurrence-based approach to predict pure associative and semantic priming.

PubMed

Roelke, Andre; Franke, Nicole; Biemann, Chris; Radach, Ralph; Jacobs, Arthur M; Hofmann, Markus J

2018-03-15

The theoretical "difficulty in separating association strength from [semantic] feature overlap" has resulted in inconsistent findings of either the presence or absence of "pure" associative priming in recent literature (Hutchison, 2003, Psychonomic Bulletin & Review, 10(4), p. 787). The present study used co-occurrence statistics of words in sentences to provide a full factorial manipulation of direct association (strong/no) and the number of common associates (many/no) of the prime and target words. These common associates were proposed to serve as semantic features for a recent interactive activation model of semantic processing (i.e., the associative read-out model; Hofmann & Jacobs, 2014). With stimulus onset asynchrony (SOA) as an additional factor, our findings indicate that associative and semantic priming are indeed dissociable. Moreover, the effect of direct association was strongest at a long SOA (1,000 ms), while many common associates facilitated lexical decisions primarily at a short SOA (200 ms). This response pattern is consistent with previous performance-based accounts and suggests that associative and semantic priming can be evoked by computationally determined direct and common associations.

Effect of quality chronic disease management for alcohol and drug dependence on addiction outcomes.

PubMed

Kim, Theresa W; Saitz, Richard; Cheng, Debbie M; Winter, Michael R; Witas, Julie; Samet, Jeffrey H

2012-12-01

We examined the effect of the quality of primary care-based chronic disease management (CDM) for alcohol and/or other drug (AOD) dependence on addiction outcomes. We assessed quality using (1) a visit frequency based measure and (2) a self-reported assessment measuring alignment with the chronic care model. The visit frequency based measure had no significant association with addiction outcomes. The self-reported measure of care-when care was at a CDM clinic-was associated with lower drug addiction severity. The self-reported assessment of care from any healthcare source (CDM clinic or elsewhere) was associated with lower alcohol addiction severity and abstinence. These findings suggest that high quality CDM for AOD dependence may improve addiction outcomes. Quality measures based upon alignment with the chronic care model may better capture features of effective CDM care than a visit frequency measure. Copyright © 2012 Elsevier Inc. All rights reserved.

Prediction of microbe-disease association from the integration of neighbor and graph with collaborative recommendation model.

PubMed

Huang, Yu-An; You, Zhu-Hong; Chen, Xing; Huang, Zhi-An; Zhang, Shanwen; Yan, Gui-Ying

2017-10-16

Accumulating clinical researches have shown that specific microbes with abnormal levels are closely associated with the development of various human diseases. Knowledge of microbe-disease associations can provide valuable insights for complex disease mechanism understanding as well as the prevention, diagnosis and treatment of various diseases. However, little effort has been made to predict microbial candidates for human complex diseases on a large scale. In this work, we developed a new computational model for predicting microbe-disease associations by combining two single recommendation methods. Based on the assumption that functionally similar microbes tend to get involved in the mechanism of similar disease, we adopted neighbor-based collaborative filtering and a graph-based scoring method to compute association possibility of microbe-disease pairs. The promising prediction performance could be attributed to the use of hybrid approach based on two single recommendation methods as well as the introduction of Gaussian kernel-based similarity and symptom-based disease similarity. To evaluate the performance of the proposed model, we implemented leave-one-out and fivefold cross validations on the HMDAD database, which is recently built as the first database collecting experimentally-confirmed microbe-disease associations. As a result, NGRHMDA achieved reliable results with AUCs of 0.9023 ± 0.0031 and 0.9111 in the validation frameworks of fivefold CV and LOOCV. In addition, 78.2% microbe samples and 66.7% disease samples are found to be consistent with the basic assumption of our work that microbes tend to get involved in the similar disease clusters, and vice versa. Compared with other methods, the prediction results yielded by NGRHMDA demonstrate its effective prediction performance for microbe-disease associations. It is anticipated that NGRHMDA can be used as a useful tool to search the most potential microbial candidates for various diseases, and therefore boosts the medical knowledge and drug development. The codes and dataset of our work can be downloaded from https://github.com/yahuang1991/NGRHMDA .

Description of a New Predictive Modeling Approach That Correlates the Risk and Associated Cost of Well-Defined Diabetes-Related Complications With Changes in Glycated Hemoglobin (HbA1c)

PubMed Central

Fortwaengler, Kurt; Parkin, Christopher G.; Neeser, Kurt; Neumann, Monika; Mast, Oliver

2017-01-01

The modeling approach described here is designed to support the development of spreadsheet-based simple predictive models. It is based on 3 pillars: association of the complications with HbA1c changes, incidence of the complications, and average cost per event of the complication. For each pillar, the goal of the analysis was (1) to find results for a large diversity of populations with a focus on countries/regions, diabetes type, age, diabetes duration, baseline HbA1c value, and gender; (2) to assess the range of incidences and associations previously reported. Unlike simple predictive models, which mostly are based on only 1 source of information for each of the pillars, we conducted a comprehensive, systematic literature review. Each source found was thoroughly reviewed and only sources meeting quality expectations were considered. The approach allows avoidance of unintended use of extreme data. The user can utilize (1) one of the found sources, (2) the found range as validation for the found figures, or (3) the average of all found publications for an expedited estimate. The modeling approach is intended for use in average insulin-treated diabetes populations in which the baseline HbA1c values are within an average range (6.5% to 11.5%); it is not intended for use in individuals or unique diabetes populations (eg, gestational diabetes). Because the modeling approach only considers diabetes-related complications that are positively associated with HbA1c decreases, the costs of negatively associated complications (eg, severe hypoglycemic events) must be calculated separately. PMID:27510441

A rapid generalized least squares model for a genome-wide quantitative trait association analysis in families.

PubMed

Li, Xiang; Basu, Saonli; Miller, Michael B; Iacono, William G; McGue, Matt

2011-01-01

Genome-wide association studies (GWAS) using family data involve association analyses between hundreds of thousands of markers and a trait for a large number of related individuals. The correlations among relatives bring statistical and computational challenges when performing these large-scale association analyses. Recently, several rapid methods accounting for both within- and between-family variation have been proposed. However, these techniques mostly model the phenotypic similarities in terms of genetic relatedness. The familial resemblances in many family-based studies such as twin studies are not only due to the genetic relatedness, but also derive from shared environmental effects and assortative mating. In this paper, we propose 2 generalized least squares (GLS) models for rapid association analysis of family-based GWAS, which accommodate both genetic and environmental contributions to familial resemblance. In our first model, we estimated the joint genetic and environmental variations. In our second model, we estimated the genetic and environmental components separately. Through simulation studies, we demonstrated that our proposed approaches are more powerful and computationally efficient than a number of existing methods are. We show that estimating the residual variance-covariance matrix in the GLS models without SNP effects does not lead to an appreciable bias in the p values as long as the SNP effect is small (i.e. accounting for no more than 1% of trait variance). Copyright © 2011 S. Karger AG, Basel.

Generalized neurofuzzy network modeling algorithms using Bézier-Bernstein polynomial functions and additive decomposition.

PubMed

Hong, X; Harris, C J

2000-01-01

This paper introduces a new neurofuzzy model construction algorithm for nonlinear dynamic systems based upon basis functions that are Bézier-Bernstein polynomial functions. This paper is generalized in that it copes with n-dimensional inputs by utilising an additive decomposition construction to overcome the curse of dimensionality associated with high n. This new construction algorithm also introduces univariate Bézier-Bernstein polynomial functions for the completeness of the generalized procedure. Like the B-spline expansion based neurofuzzy systems, Bézier-Bernstein polynomial function based neurofuzzy networks hold desirable properties such as nonnegativity of the basis functions, unity of support, and interpretability of basis function as fuzzy membership functions, moreover with the additional advantages of structural parsimony and Delaunay input space partition, essentially overcoming the curse of dimensionality associated with conventional fuzzy and RBF networks. This new modeling network is based on additive decomposition approach together with two separate basis function formation approaches for both univariate and bivariate Bézier-Bernstein polynomial functions used in model construction. The overall network weights are then learnt using conventional least squares methods. Numerical examples are included to demonstrate the effectiveness of this new data based modeling approach.

Identity related to living situation in six individuals with congenital quadriplegia.

PubMed

Robey, Kenneth L

2008-01-01

This study was a preliminary examination of structural aspects of identity, particularly identity associated with living situation, in individuals who have quadriplegia due to cerebral palsy. A hierarchical classes algorithm (HICLAS) was used to construct idiographic 'identity structure' models for three individuals who are living in an inpatient hospital setting and for three individuals living in community-based group residences. Indices derived from the models indicate that the identity 'myself as one who has a disability' was structurally superordinate (i.e., resided at a high hierarchical level) for all six participants, suggesting a high level of importance of this identity in participants' sense of self. The models also indicate that while identity associated with one's particular living situation was superordinate for persons living in the hospital, it was not for persons living in community residences. While conclusions based on this small sample are necessarily limited, the data suggest that identity associated with living situation might differ in structural centrality, and presumably subjective importance, for persons living in inpatient versus community-based settings.

Neural networks with multiple general neuron models: a hybrid computational intelligence approach using Genetic Programming.

PubMed

Barton, Alan J; Valdés, Julio J; Orchard, Robert

2009-01-01

Classical neural networks are composed of neurons whose nature is determined by a certain function (the neuron model), usually pre-specified. In this paper, a type of neural network (NN-GP) is presented in which: (i) each neuron may have its own neuron model in the form of a general function, (ii) any layout (i.e network interconnection) is possible, and (iii) no bias nodes or weights are associated to the connections, neurons or layers. The general functions associated to a neuron are learned by searching a function space. They are not provided a priori, but are rather built as part of an Evolutionary Computation process based on Genetic Programming. The resulting network solutions are evaluated based on a fitness measure, which may, for example, be based on classification or regression errors. Two real-world examples are presented to illustrate the promising behaviour on classification problems via construction of a low-dimensional representation of a high-dimensional parameter space associated to the set of all network solutions.

Predictors of Short- and Long-Term Attrition From the Parents as Agents of Change Randomized Controlled Trial for Managing Pediatric Obesity.

PubMed

Spence, Nicholas D; Newton, Amanda S; Keaschuk, Rachel A; Ambler, Kathryn A; Jetha, Mary M; Holt, Nicholas L; Rosychuk, Rhonda J; Spence, John C; Sharma, Arya M; Ball, Geoff D C

Attrition in pediatric weight management is a substantial problem. This study examined factors associated with short- and long-term attrition from a lifestyle and behavioral intervention for parents of children with overweight or obesity. Fifty-two families with children ages 6 to 12 years old and body mass index at or above the 85th percentile participated in a randomized controlled trial focused on parents, comparing parent-based cognitive behavioral therapy with parent-based psychoeducation for pediatric weight management. We examined program attrition using two clinical phases of the intervention: short-term and long-term attrition, modeled using the general linear model. Predictors included intervention type, child/parent weight status, sociodemographic factors, and health of the family system. Higher self-assessed health of the family system was associated with lower short-term attrition; higher percentage of intervention sessions attended by parents was associated with lower long-term attrition. Different variables were significant in our short- and long-term models. Attrition might best be conceptualized based on short- and long-term phases of clinical, parent-based interventions for pediatric weight management. Copyright © 2016 National Association of Pediatric Nurse Practitioners. Published by Elsevier Inc. All rights reserved.

Psychosocial Modeling of Insider Threat Risk Based on Behavioral and Word Use Analysis

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

Greitzer, Frank L.; Kangas, Lars J.; Noonan, Christine F.

In many insider crimes, managers and other coworkers observed that the offenders had exhibited signs of stress, disgruntlement, or other issues, but no alarms were raised. Barriers to using such psychosocial indicators include the inability to recognize the signs and the failure to record the behaviors so that they can be assessed. A psychosocial model was developed to assess an employee’s behavior associated with an increased risk of insider abuse. The model is based on case studies and research literature on factors/correlates associated with precursor behavioral manifestations of individuals committing insider crimes. A complementary Personality Factor modeling approach was developedmore » based on analysis to derive relevant personality characteristics from word use. Several implementations of the psychosocial model were evaluated by comparing their agreement with judgments of human resources and management professionals; the personality factor modeling approach was examined using email samples. If implemented in an operational setting, these models should be part of a set of management tools for employee assessment to identify employees who pose a greater insider threat.« less

Paying for Primary Care: The Factors Associated with Physician Self-selection into Payment Models.

PubMed

Rudoler, David; Deber, Raisa; Barnsley, Janet; Glazier, Richard H; Dass, Adrian Rohit; Laporte, Audrey

2015-09-01

To determine the factors associated with primary care physician self-selection into different payment models, we used a panel of eight waves of administrative data for all primary care physicians who practiced in Ontario between 2003/2004 and 2010/2011. We used a mixed effects logistic regression model to estimate physicians' choice of three alternative payment models: fee for service, enhanced fee for service, and blended capitation. We found that primary care physicians self-selected into payment models based on existing practice characteristics. Physicians with more complex patient populations were less likely to switch into capitation-based payment models where higher levels of effort were not financially rewarded. These findings suggested that investigations aimed at assessing the impact of different primary care reimbursement models on outcomes, including costs and access, should first account for potential selection effects. Copyright © 2015 John Wiley & Sons, Ltd.

ARL and Association 3.0: Ten Management Challenges

ERIC Educational Resources Information Center

Funk, Carla J.

2009-01-01

Association management in today's "association 3.0" environment presents some new challenges and new perspectives on old ones. This paper summarizes 10 such challenges including collaboration, diversity, innovation, transparency, financial stability, member benefits, knowledge-based decision-making, a demand-driven association model, pro-activity…

Raman spectroscopy-based screening of hepatitis C and associated molecular changes

NASA Astrophysics Data System (ADS)

Bilal, Maria; Bilal, M.; Saleem, M.; Khan, Saranjam; Ullah, Rahat; Fatima, Kiran; Ahmed, M.; Hayat, Abbas; Shahzada, Shaista; Ullah Khan, Ehsan

2017-09-01

This study presents the optical screening of hepatitis C and its associated molecular changes in human blood sera using a partial least-squares regression model based on their Raman spectra. In total, 152 samples were tested through enzyme-linked immunosorbent assay for confirmation. This model utilizes minor spectral variations in the Raman spectra of the positive and control groups. Regression coefficients of this model were analyzed with reference to the variations in concentration of associated molecules in these two groups. It was found that trehalose, chitin, ammonia, and cytokines are positively correlated while lipids, beta structures of proteins, and carbohydrate-binding proteins are negatively correlated with hepatitis C. The regression vector yielded by this model is utilized to predict hepatitis C in unknown samples. This model has been evaluated by a cross-validation method, which yielded a correlation coefficient of 0.91. Moreover, 30 unknown samples were screened for hepatitis C infection using this model to test its performance. Sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve from these predictions were found to be 93.3%, 100%, 96.7%, and 1, respectively.

A Practical Guide to Calibration of a GSSHA Hydrologic Model Using ERDC Automated Model Calibration Software - Effective and Efficient Stochastic Global Optimization

DTIC Science & Technology

2012-02-01

parameter estimation method, but rather to carefully describe how to use the ERDC software implementation of MLSL that accommodates the PEST model...model independent LM method based parameter estimation software PEST (Doherty, 2004, 2007a, 2007b), which quantifies model to measure- ment misfit...et al. (2011) focused on one drawback associated with LM-based model independent parameter estimation as implemented in PEST ; viz., that it requires

XML-Based SHINE Knowledge Base Interchange Language

NASA Technical Reports Server (NTRS)

James, Mark; Mackey, Ryan; Tikidjian, Raffi

2008-01-01

The SHINE Knowledge Base Interchange Language software has been designed to more efficiently send new knowledge bases to spacecraft that have been embedded with the Spacecraft Health Inference Engine (SHINE) tool. The intention of the behavioral model is to capture most of the information generally associated with a spacecraft functional model, while specifically addressing the needs of execution within SHINE and Livingstone. As such, it has some constructs that are based on one or the other.

Characterizing the spatial structure of endangered species habitat using geostatistical analysis of IKONOS imagery

USGS Publications Warehouse

Wallace, C.S.A.; Marsh, S.E.

2005-01-01

Our study used geostatistics to extract measures that characterize the spatial structure of vegetated landscapes from satellite imagery for mapping endangered Sonoran pronghorn habitat. Fine spatial resolution IKONOS data provided information at the scale of individual trees or shrubs that permitted analysis of vegetation structure and pattern. We derived images of landscape structure by calculating local estimates of the nugget, sill, and range variogram parameters within 25 ?? 25-m image windows. These variogram parameters, which describe the spatial autocorrelation of the 1-m image pixels, are shown in previous studies to discriminate between different species-specific vegetation associations. We constructed two independent models of pronghorn landscape preference by coupling the derived measures with Sonoran pronghorn sighting data: a distribution-based model and a cluster-based model. The distribution-based model used the descriptive statistics for variogram measures at pronghorn sightings, whereas the cluster-based model used the distribution of pronghorn sightings within clusters of an unsupervised classification of derived images. Both models define similar landscapes, and validation results confirm they effectively predict the locations of an independent set of pronghorn sightings. Such information, although not a substitute for field-based knowledge of the landscape and associated ecological processes, can provide valuable reconnaissance information to guide natural resource management efforts. ?? 2005 Taylor & Francis Group Ltd.

A Complex Network Approach to Distributional Semantic Models

PubMed Central

Utsumi, Akira

2015-01-01

A number of studies on network analysis have focused on language networks based on free word association, which reflects human lexical knowledge, and have demonstrated the small-world and scale-free properties in the word association network. Nevertheless, there have been very few attempts at applying network analysis to distributional semantic models, despite the fact that these models have been studied extensively as computational or cognitive models of human lexical knowledge. In this paper, we analyze three network properties, namely, small-world, scale-free, and hierarchical properties, of semantic networks created by distributional semantic models. We demonstrate that the created networks generally exhibit the same properties as word association networks. In particular, we show that the distribution of the number of connections in these networks follows the truncated power law, which is also observed in an association network. This indicates that distributional semantic models can provide a plausible model of lexical knowledge. Additionally, the observed differences in the network properties of various implementations of distributional semantic models are consistently explained or predicted by considering the intrinsic semantic features of a word-context matrix and the functions of matrix weighting and smoothing. Furthermore, to simulate a semantic network with the observed network properties, we propose a new growing network model based on the model of Steyvers and Tenenbaum. The idea underlying the proposed model is that both preferential and random attachments are required to reflect different types of semantic relations in network growth process. We demonstrate that this model provides a better explanation of network behaviors generated by distributional semantic models. PMID:26295940

A Four-Part Model of Autonomy during Emerging Adulthood: Associations with Adjustment

ERIC Educational Resources Information Center

Lamborn, Susie D.; Groh, Kelly

2009-01-01

We found support for a four-part model of autonomy that links connectedness, separation, detachment, and agency to adjustment during emerging adulthood. Based on self-report surveys of 285 American college students, expected associations among the autonomy variables were found. In addition, agency, as measured by self-reliance, predicted lower…

Climate-based species distribution models for Armillaria solidipes in Wyoming: A preliminary assessment

Treesearch

John W. Hanna; James T. Blodgett; Eric W. I. Pitman; Sarah M. Ashiglar; John E. Lundquist; Mee-Sook Kim; Amy L. Ross-Davis; Ned B. Klopfenstein

2014-01-01

As part of an ongoing project to predict Armillaria root disease in the Rocky Mountain zone, this project predicts suitable climate space (potential distribution) for A. solidipes in Wyoming and associated forest areas at risk to disease caused by this pathogen. Two bioclimatic models are being developed. One model is based solely on verified locations of A. solidipes...

A model for memory systems based on processing modes rather than consciousness.

PubMed

Henke, Katharina

2010-07-01

Prominent models of human long-term memory distinguish between memory systems on the basis of whether learning and retrieval occur consciously or unconsciously. Episodic memory formation requires the rapid encoding of associations between different aspects of an event which, according to these models, depends on the hippocampus and on consciousness. However, recent evidence indicates that the hippocampus mediates rapid associative learning with and without consciousness in humans and animals, for long-term and short-term retention. Consciousness seems to be a poor criterion for differentiating between declarative (or explicit) and non declarative (or implicit) types of memory. A new model is therefore required in which memory systems are distinguished based on the processing operations involved rather than by consciousness.

Ecposure Related Dose Estimating Model

EPA Science Inventory

ERDEM is a physiologically based pharmacokinetic (PBPK) modeling system consisting of a general model and an associated front end. An actual model is defined when the user prepares an input command file. Such a command file defines the chemicals, compartments and processes that...

Application of regular associated solution model to the liquidus curves of the Sn-Te and Sn-SnS systems

NASA Astrophysics Data System (ADS)

Eric, H.

1982-12-01

The liquidus curves of the Sn-Te and Sn-SnS systems were evaluated by the regular associated solution model (RAS). The main assumption of this theory is the existence of species A, B and associated complexes AB in the liquid phase. Thermodynamic properties of the binary A-B system are derived by ternary regular solution equations. Calculations based on this model for the Sn-Te and Sn-SnS systems are in agreement with published data.

A high-capacity model for one shot association learning in the brain

PubMed Central

Einarsson, Hafsteinn; Lengler, Johannes; Steger, Angelika

2014-01-01

We present a high-capacity model for one-shot association learning (hetero-associative memory) in sparse networks. We assume that basic patterns are pre-learned in networks and associations between two patterns are presented only once and have to be learned immediately. The model is a combination of an Amit-Fusi like network sparsely connected to a Willshaw type network. The learning procedure is palimpsest and comes from earlier work on one-shot pattern learning. However, in our setup we can enhance the capacity of the network by iterative retrieval. This yields a model for sparse brain-like networks in which populations of a few thousand neurons are capable of learning hundreds of associations even if they are presented only once. The analysis of the model is based on a novel result by Janson et al. on bootstrap percolation in random graphs. PMID:25426060

A high-capacity model for one shot association learning in the brain.

PubMed

Einarsson, Hafsteinn; Lengler, Johannes; Steger, Angelika

2014-01-01

We present a high-capacity model for one-shot association learning (hetero-associative memory) in sparse networks. We assume that basic patterns are pre-learned in networks and associations between two patterns are presented only once and have to be learned immediately. The model is a combination of an Amit-Fusi like network sparsely connected to a Willshaw type network. The learning procedure is palimpsest and comes from earlier work on one-shot pattern learning. However, in our setup we can enhance the capacity of the network by iterative retrieval. This yields a model for sparse brain-like networks in which populations of a few thousand neurons are capable of learning hundreds of associations even if they are presented only once. The analysis of the model is based on a novel result by Janson et al. on bootstrap percolation in random graphs.

Power of data mining methods to detect genetic associations and interactions.

PubMed

Molinaro, Annette M; Carriero, Nicholas; Bjornson, Robert; Hartge, Patricia; Rothman, Nathaniel; Chatterjee, Nilanjan

2011-01-01

Genetic association studies, thus far, have focused on the analysis of individual main effects of SNP markers. Nonetheless, there is a clear need for modeling epistasis or gene-gene interactions to better understand the biologic basis of existing associations. Tree-based methods have been widely studied as tools for building prediction models based on complex variable interactions. An understanding of the power of such methods for the discovery of genetic associations in the presence of complex interactions is of great importance. Here, we systematically evaluate the power of three leading algorithms: random forests (RF), Monte Carlo logic regression (MCLR), and multifactor dimensionality reduction (MDR). We use the algorithm-specific variable importance measures (VIMs) as statistics and employ permutation-based resampling to generate the null distribution and associated p values. The power of the three is assessed via simulation studies. Additionally, in a data analysis, we evaluate the associations between individual SNPs in pro-inflammatory and immunoregulatory genes and the risk of non-Hodgkin lymphoma. The power of RF is highest in all simulation models, that of MCLR is similar to RF in half, and that of MDR is consistently the lowest. Our study indicates that the power of RF VIMs is most reliable. However, in addition to tuning parameters, the power of RF is notably influenced by the type of variable (continuous vs. categorical) and the chosen VIM. Copyright © 2011 S. Karger AG, Basel.

Family-Based Association Testing of OCD-Associated SNPs of SLC1A1 in an Autism Sample

PubMed Central

Brune, Camille W.; Kim, Soo-Jeong; Hanna, Gregory L.; Courchesne, Eric; Lord, Catherine; Leventhal, Bennett L.; Cook, Edwin H.

2009-01-01

Reports identified the neuronal glutamate transporter gene, SLC1A1 (OMIM 133550, chromosome 9p24), as a positional and functional candidate gene for obsessive–compulsive disorder (OCD). The presence of obsessions and compulsions similar to OCD in autism, the identification of this region in a genome-wide linkage analysis of individuals with autism spectrum disorders (ASDs), and the hypothesized role of glutamate in ASDs make SLC1A1 a candidate gene for ASD as well. To test for association between SLC1A1 and autism, we typed three single nucleotide polymorphisms (SNPs, rs301430, rs301979, rs301434) previously associated with OCD in 86 strictly defined trios with autism. Family-Based Association Tests (FBAT) with additive and recessive models were used to check for association. Additionally, an rs301430–rs301979 haplotype identified for OCD was investigated. FBAT revealed nominally significant association between autism and one SNP under a recessive model. The G allele of rs301979 was undertransmitted (equivalent to overtransmission of the C allele under a dominant model) to individuals with autism (Z = −2.47, P = 0.01). The G allele was also undertransmitted in the T–G haplotype under the recessive model (Z = −2.41, P = 0.02). Both findings were also observed in the male-only sample. However, they did not withstand correction for multiple comparisons. PMID:19360657

Plasma amino acid profile associated with fatty liver disease and co-occurrence of metabolic risk factors.

PubMed

Yamakado, Minoru; Tanaka, Takayuki; Nagao, Kenji; Imaizumi, Akira; Komatsu, Michiharu; Daimon, Takashi; Miyano, Hiroshi; Tani, Mizuki; Toda, Akiko; Yamamoto, Hiroshi; Horimoto, Katsuhisa; Ishizaka, Yuko

2017-11-03

Fatty liver disease (FLD) increases the risk of diabetes, cardiovascular disease, and steatohepatitis, which leads to fibrosis, cirrhosis, and hepatocellular carcinoma. Thus, the early detection of FLD is necessary. We aimed to find a quantitative and feasible model for discriminating the FLD, based on plasma free amino acid (PFAA) profiles. We constructed models of the relationship between PFAA levels in 2,000 generally healthy Japanese subjects and the diagnosis of FLD by abdominal ultrasound scan by multiple logistic regression analysis with variable selection. The performance of these models for FLD discrimination was validated using an independent data set of 2,160 subjects. The generated PFAA-based model was able to identify FLD patients. The area under the receiver operating characteristic curve for the model was 0.83, which was higher than those of other existing liver function-associated markers ranging from 0.53 to 0.80. The value of the linear discriminant in the model yielded the adjusted odds ratio (with 95% confidence intervals) for a 1 standard deviation increase of 2.63 (2.14-3.25) in the multiple logistic regression analysis with known liver function-associated covariates. Interestingly, the linear discriminant values were significantly associated with the progression of FLD, and patients with nonalcoholic steatohepatitis also exhibited higher values.

Determining Barriers and Facilitators Associated With Willingness to Use a Personal Health Information Management System to Support Worksite Wellness Programs.

PubMed

Neyens, David M; Childers, Ashley Kay

2017-07-01

To determine the barriers and facilitators associated with willingness to use personal health information management (PHIM) systems to support an existing worksite wellness program (WWP). The study design involved a Web-based survey. The study setting was a regional hospital. Hospital employees comprised the study subjects. Willingness, barriers, and facilitators associated with PHIM were measured. Bivariate logit models were used to model two binary dependent variables. One model predicted the likelihood of believing PHIM systems would positively affect overall health and willingness to use. Another predicted the likelihood of worrying about online security and not believing PHIM systems would benefit health goals. Based on 333 responses, believing PHIM systems would positively affect health was highly associated with willingness to use PHIM systems (p < .01). Those comfortable online were 7.22 times more willing to use PHIM systems. Participants in exercise-based components of WWPs were 3.03 times more likely to be willing to use PHIM systems. Those who worried about online security were 5.03 times more likely to believe PHIM systems would not help obtain health goals. Comfort with personal health information online and exercise-based WWP experience was associated with willingness to use PHIM systems. However, nutrition-based WWPs did not have similar effects. Implementation barriers relate to technology anxiety and trust in security, as well as experience with specific WWP activities. Identifying differences between WWP components and addressing technology concerns before implementation of PHIM systems into WWPs may facilitate improved adoption and usage.

Conditional Random Fields for Fast, Large-Scale Genome-Wide Association Studies

PubMed Central

Huang, Jim C.; Meek, Christopher; Kadie, Carl; Heckerman, David

2011-01-01

Understanding the role of genetic variation in human diseases remains an important problem to be solved in genomics. An important component of such variation consist of variations at single sites in DNA, or single nucleotide polymorphisms (SNPs). Typically, the problem of associating particular SNPs to phenotypes has been confounded by hidden factors such as the presence of population structure, family structure or cryptic relatedness in the sample of individuals being analyzed. Such confounding factors lead to a large number of spurious associations and missed associations. Various statistical methods have been proposed to account for such confounding factors such as linear mixed-effect models (LMMs) or methods that adjust data based on a principal components analysis (PCA), but these methods either suffer from low power or cease to be tractable for larger numbers of individuals in the sample. Here we present a statistical model for conducting genome-wide association studies (GWAS) that accounts for such confounding factors. Our method scales in runtime quadratic in the number of individuals being studied with only a modest loss in statistical power as compared to LMM-based and PCA-based methods when testing on synthetic data that was generated from a generalized LMM. Applying our method to both real and synthetic human genotype/phenotype data, we demonstrate the ability of our model to correct for confounding factors while requiring significantly less runtime relative to LMMs. We have implemented methods for fitting these models, which are available at http://www.microsoft.com/science. PMID:21765897

A New Search Paradigm for Correlated Neutrino Emission from Discrete GRBs using Antarctic Cherenkov Telescopes in the Swift Era

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

Stamatikos, Michael; Band, David L.; JCA/UMBC, Baltimore, MD 21250

2006-05-19

We describe the theoretical modeling and analysis techniques associated with a preliminary search for correlated neutrino emission from GRB980703a, which triggered the Burst and Transient Source Experiment (BATSE GRB trigger 6891), using archived data from the Antarctic Muon and Neutrino Detector Array (AMANDA-B10). Under the assumption of associated hadronic acceleration, the expected observed neutrino energy flux is directly derived, based upon confronting the fireball phenomenology with the discrete set of observed electromagnetic parameters of GRB980703a, gleaned from ground-based and satellite observations, for four models, corrected for oscillations. Models 1 and 2, based upon spectral analysis featuring a prompt photon energymore » fit to the Band function, utilize an observed spectroscopic redshift, for isotropic and anisotropic emission geometry, respectively. Model 3 is based upon averaged burst parameters, assuming isotropic emission. Model 4 based upon a Band fit, features an estimated redshift from the lag-luminosity relation, with isotropic emission. Consistent with our AMANDA-II analysis of GRB030329, which resulted in a flux upper limit of {approx} 0.150GeV /cm2/s for model 1, we find differences in excess of an order of magnitude in the response of AMANDA-B10, among the various models for GRB980703a. Implications for future searches in the era of Swift and IceCube are discussed.« less

Professional Learning: A Fuzzy Logic-Based Modelling Approach

ERIC Educational Resources Information Center

Gravani, M. N.; Hadjileontiadou, S. J.; Nikolaidou, G. N.; Hadjileontiadis, L. J.

2007-01-01

Studies have suggested that professional learning is influenced by two key parameters, i.e., climate and planning, and their associated variables (mutual respect, collaboration, mutual trust, supportiveness, openness). In this paper, we applied analysis of the relationships between the proposed quantitative, fuzzy logic-based model and a series of…

A process-based emission model for volatile organic compounds from silage sources on farms

USDA-ARS?s Scientific Manuscript database

Silage on dairy farms can emit large amounts of volatile organic compounds (VOCs), a precursor in the formation of tropospheric ozone. Because of the challenges associated with direct measurements, process-based modeling is another approach for estimating emissions of air pollutants from sources suc...

Mathematical Modelling in Engineering: An Alternative Way to Teach Linear Algebra

ERIC Educational Resources Information Center

Domínguez-García, S.; García-Planas, M. I.; Taberna, J.

2016-01-01

Technological advances require that basic science courses for engineering, including Linear Algebra, emphasize the development of mathematical strengths associated with modelling and interpretation of results, which are not limited only to calculus abilities. Based on this consideration, we have proposed a project-based learning, giving a dynamic…

DIFFERENTIAL CROSS SECTION ANALYSIS IN KAON PHOTOPRODUCTION USING ASSOCIATED LEGENDRE POLYNOMIALS

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

P. T. P. HUTAURUK, D. G. IRELAND, G. ROSNER

2009-04-01

Angular distributions of differential cross sections from the latest CLAS data sets,6 for the reaction γ + p→K+ + Λ have been analyzed using associated Legendre polynomials. This analysis is based upon theoretical calculations in Ref. 1 where all sixteen observables in kaon photoproduction can be classified into four Legendre classes. Each observable can be described by an expansion of associated Legendre polynomial functions. One of the questions to be addressed is how many associated Legendre polynomials are required to describe the data. In this preliminary analysis, we used data models with different numbers of associated Legendre polynomials. We thenmore » compared these models by calculating posterior probabilities of the models. We found that the CLAS data set needs no more than four associated Legendre polynomials to describe the differential cross section data. In addition, we also show the extracted coefficients of the best model.« less

Analysis of a Shock-Associated Noise Prediction Model Using Measured Jet Far-Field Noise Data

NASA Technical Reports Server (NTRS)

Dahl, Milo D.; Sharpe, Jacob A.

2014-01-01

A code for predicting supersonic jet broadband shock-associated noise was assessed using a database containing noise measurements of a jet issuing from a convergent nozzle. The jet was operated at 24 conditions covering six fully expanded Mach numbers with four total temperature ratios. To enable comparisons of the predicted shock-associated noise component spectra with data, the measured total jet noise spectra were separated into mixing noise and shock-associated noise component spectra. Comparisons between predicted and measured shock-associated noise component spectra were used to identify deficiencies in the prediction model. Proposed revisions to the model, based on a study of the overall sound pressure levels for the shock-associated noise component of the measured data, a sensitivity analysis of the model parameters with emphasis on the definition of the convection velocity parameter, and a least-squares fit of the predicted to the measured shock-associated noise component spectra, resulted in a new definition for the source strength spectrum in the model. An error analysis showed that the average error in the predicted spectra was reduced by as much as 3.5 dB for the revised model relative to the average error for the original model.

Group Membership, Group Change, and Intergroup Attitudes: A Recategorization Model Based on Cognitive Consistency Principles.

PubMed

Roth, Jenny; Steffens, Melanie C; Vignoles, Vivian L

2018-01-01

The present article introduces a model based on cognitive consistency principles to predict how new identities become integrated into the self-concept, with consequences for intergroup attitudes. The model specifies four concepts (self-concept, stereotypes, identification, and group compatibility) as associative connections. The model builds on two cognitive principles, balance-congruity and imbalance-dissonance, to predict identification with social groups that people currently belong to, belonged to in the past, or newly belong to. More precisely, the model suggests that the relative strength of self-group associations (i.e., identification) depends in part on the (in)compatibility of the different social groups. Combining insights into cognitive representation of knowledge, intergroup bias, and explicit/implicit attitude change, we further derive predictions for intergroup attitudes. We suggest that intergroup attitudes alter depending on the relative associative strength between the social groups and the self, which in turn is determined by the (in)compatibility between social groups. This model unifies existing models on the integration of social identities into the self-concept by suggesting that basic cognitive mechanisms play an important role in facilitating or hindering identity integration and thus contribute to reducing or increasing intergroup bias.

Group Membership, Group Change, and Intergroup Attitudes: A Recategorization Model Based on Cognitive Consistency Principles

PubMed Central

Roth, Jenny; Steffens, Melanie C.; Vignoles, Vivian L.

2018-01-01

The present article introduces a model based on cognitive consistency principles to predict how new identities become integrated into the self-concept, with consequences for intergroup attitudes. The model specifies four concepts (self-concept, stereotypes, identification, and group compatibility) as associative connections. The model builds on two cognitive principles, balance–congruity and imbalance–dissonance, to predict identification with social groups that people currently belong to, belonged to in the past, or newly belong to. More precisely, the model suggests that the relative strength of self-group associations (i.e., identification) depends in part on the (in)compatibility of the different social groups. Combining insights into cognitive representation of knowledge, intergroup bias, and explicit/implicit attitude change, we further derive predictions for intergroup attitudes. We suggest that intergroup attitudes alter depending on the relative associative strength between the social groups and the self, which in turn is determined by the (in)compatibility between social groups. This model unifies existing models on the integration of social identities into the self-concept by suggesting that basic cognitive mechanisms play an important role in facilitating or hindering identity integration and thus contribute to reducing or increasing intergroup bias. PMID:29681878

Associability-modulated loss learning is increased in posttraumatic stress disorder

PubMed Central

Brown, Vanessa M; Zhu, Lusha; Wang, John M; Frueh, B Christopher

2018-01-01

Disproportionate reactions to unexpected stimuli in the environment are a cardinal symptom of posttraumatic stress disorder (PTSD). Here, we test whether these heightened responses are associated with disruptions in distinct components of reinforcement learning. Specifically, using functional neuroimaging, a loss-learning task, and a computational model-based approach, we assessed the mechanistic hypothesis that overreactions to stimuli in PTSD arise from anomalous gating of attention during learning (i.e., associability). Behavioral choices of combat-deployed veterans with and without PTSD were fit to a reinforcement learning model, generating trial-by-trial prediction errors (signaling unexpected outcomes) and associability values (signaling attention allocation to the unexpected outcomes). Neural substrates of associability value and behavioral parameter estimates of associability updating, but not prediction error, increased with PTSD during loss learning. Moreover, the interaction of PTSD severity with neural markers of associability value predicted behavioral choices. These results indicate that increased attention-based learning may underlie aspects of PTSD and suggest potential neuromechanistic treatment targets. PMID:29313489

Inner and outer coronary vessel wall segmentation from CCTA using an active contour model with machine learning-based 3D voxel context-aware image force

NASA Astrophysics Data System (ADS)

Sivalingam, Udhayaraj; Wels, Michael; Rempfler, Markus; Grosskopf, Stefan; Suehling, Michael; Menze, Bjoern H.

2016-03-01

In this paper, we present a fully automated approach to coronary vessel segmentation, which involves calcification or soft plaque delineation in addition to accurate lumen delineation, from 3D Cardiac Computed Tomography Angiography data. Adequately virtualizing the coronary lumen plays a crucial role for simulating blood ow by means of fluid dynamics while additionally identifying the outer vessel wall in the case of arteriosclerosis is a prerequisite for further plaque compartment analysis. Our method is a hybrid approach complementing Active Contour Model-based segmentation with an external image force that relies on a Random Forest Regression model generated off-line. The regression model provides a strong estimate of the distance to the true vessel surface for every surface candidate point taking into account 3D wavelet-encoded contextual image features, which are aligned with the current surface hypothesis. The associated external image force is integrated in the objective function of the active contour model, such that the overall segmentation approach benefits from the advantages associated with snakes and from the ones associated with machine learning-based regression alike. This yields an integrated approach achieving competitive results on a publicly available benchmark data collection (Rotterdam segmentation challenge).

The emergence of the Activity Reduces Conflict Associated Strain (ARCAS) model: a test of a conditional mediation model of workplace conflict and employee strain.

PubMed

Dijkstra, Maria T M; Beersma, Bianca; Cornelissen, Roosmarijn A W M

2012-07-01

To test and extend the emerging Activity Reduces Conflict-Associated Strain (ARCAS) model, we predicted that the relationship between task conflict and employee strain would be weakened to the extent that people experience high organization-based self-esteem (OBSE). A survey among Dutch employees demonstrated that, consistent with the model, the conflict-employee strain relationship was weaker the higher employees' OBSE and the more they engaged in active problem-solving conflict management. Our data also revealed that higher levels of OBSE were related to more problem-solving conflict management. Moreover, consistent with the ARCAS model, we could confirm a conditional mediation model in which organization-based self-esteem through its relationship with problem-solving conflict management weakened the relationship between task conflict and employee strain. Potential applications of the results are discussed.

Deletion Diagnostics for Alternating Logistic Regressions

PubMed Central

Preisser, John S.; By, Kunthel; Perin, Jamie; Qaqish, Bahjat F.

2013-01-01

Deletion diagnostics are introduced for the regression analysis of clustered binary outcomes estimated with alternating logistic regressions, an implementation of generalized estimating equations (GEE) that estimates regression coefficients in a marginal mean model and in a model for the intracluster association given by the log odds ratio. The diagnostics are developed within an estimating equations framework that recasts the estimating functions for association parameters based upon conditional residuals into equivalent functions based upon marginal residuals. Extensions of earlier work on GEE diagnostics follow directly, including computational formulae for one-step deletion diagnostics that measure the influence of a cluster of observations on the estimated regression parameters and on the overall marginal mean or association model fit. The diagnostic formulae are evaluated with simulations studies and with an application concerning an assessment of factors associated with health maintenance visits in primary care medical practices. The application and the simulations demonstrate that the proposed cluster-deletion diagnostics for alternating logistic regressions are good approximations of their exact fully iterated counterparts. PMID:22777960

Multiscale Modeling of Angiogenesis and Predictive Capacity

NASA Astrophysics Data System (ADS)

Pillay, Samara; Byrne, Helen; Maini, Philip

Tumors induce the growth of new blood vessels from existing vasculature through angiogenesis. Using an agent-based approach, we model the behavior of individual endothelial cells during angiogenesis. We incorporate crowding effects through volume exclusion, motility of cells through biased random walks, and include birth and death-like processes. We use the transition probabilities associated with the discrete model and a discrete conservation equation for cell occupancy to determine collective cell behavior, in terms of partial differential equations (PDEs). We derive three PDE models incorporating single, multi-species and no volume exclusion. By fitting the parameters in our PDE models and other well-established continuum models to agent-based simulations during a specific time period, and then comparing the outputs from the PDE models and agent-based model at later times, we aim to determine how well the PDE models predict the future behavior of the agent-based model. We also determine whether predictions differ across PDE models and the significance of those differences. This may impact drug development strategies based on PDE models.

Bayesian analysis of physiologically based toxicokinetic and toxicodynamic models.

PubMed

Hack, C Eric

2006-04-17

Physiologically based toxicokinetic (PBTK) and toxicodynamic (TD) models of bromate in animals and humans would improve our ability to accurately estimate the toxic doses in humans based on available animal studies. These mathematical models are often highly parameterized and must be calibrated in order for the model predictions of internal dose to adequately fit the experimentally measured doses. Highly parameterized models are difficult to calibrate and it is difficult to obtain accurate estimates of uncertainty or variability in model parameters with commonly used frequentist calibration methods, such as maximum likelihood estimation (MLE) or least squared error approaches. The Bayesian approach called Markov chain Monte Carlo (MCMC) analysis can be used to successfully calibrate these complex models. Prior knowledge about the biological system and associated model parameters is easily incorporated in this approach in the form of prior parameter distributions, and the distributions are refined or updated using experimental data to generate posterior distributions of parameter estimates. The goal of this paper is to give the non-mathematician a brief description of the Bayesian approach and Markov chain Monte Carlo analysis, how this technique is used in risk assessment, and the issues associated with this approach.

Petrologic model of the northern Mississippi Embayment based on satellite magnetic and ground-based geophysical data

NASA Technical Reports Server (NTRS)

Thomas, H. H.

1984-01-01

A petrologic model of the northern Mississippi Embayment, derived from gravity, seismic and rift data, is evaluated by converting the model to a magnetization model which is compared with satellite magnetic anomaly models. A magnetization contrast of approximately -0.54 A/m, determined from the petrologic model of the embayment compares favorably to values of -0.62 A/m and -0.45 A/m from a Magsat United States Apparent Magnetization Contrast Map and a published POGO magnetization contrast model, respectively. The petrologic model suggests that the magnetic anomaly low associated with the Mississippi Embayment may be largely due to the intrusion under non-oxidizing conditions of low Curie temperature gabbroic material at the base of the crust of the embayment. Near-surface mafic plutons, bordering the Mississippi Valley Graben, appear from aeromagnetic data to have higher magnetizations than the deeper gabbroic material; however, it is impossible to ascertain if this is due to compositional differences or similar material at shallower (lower temperature) depths. These results indicate that variations in the Curie temperatures of intrusions accompanying rifting may account for a large part of the wide range of magnetic anomalies associated with presently inactive rifts with normal heat flow.

Semantic Boost on Episodic Associations: An Empirically-Based Computational Model

ERIC Educational Resources Information Center

Silberman, Yaron; Bentin, Shlomo; Miikkulainen, Risto

2007-01-01

Words become associated following repeated co-occurrence episodes. This process might be further determined by the semantic characteristics of the words. The present study focused on how semantic and episodic factors interact in incidental formation of word associations. First, we found that human participants associate semantically related words…

Population Structure, Diversity and Trait Association Analysis in Rice (Oryza sativa L.) Germplasm for Early Seedling Vigor (ESV) Using Trait Linked SSR Markers

PubMed Central

Anandan, Annamalai; Anumalla, Mahender; Pradhan, Sharat Kumar; Ali, Jauhar

2016-01-01

Early seedling vigor (ESV) is the essential trait for direct seeded rice to dominate and smother the weed growth. In this regard, 629 rice genotypes were studied for their morphological and physiological responses in the field under direct seeded aerobic situation on 14th, 28th and 56th days after sowing (DAS). It was determined that the early observations taken on 14th and 28th DAS were reliable estimators to study ESV as compared to56th DAS. Further, 96 were selected from 629 genotypes by principal component (PCA) and discriminate function analyses. The selected genotypes were subjected to decipher the pattern of genetic diversity in terms of both phenotypic and genotypic by using ESV QTL linked simple sequence repeat (SSR) markers. To assess the genetic structure, model and distance based approaches were used. Genotyping of 96 rice lines using 39 polymorphic SSRs produced a total of 128 alleles with the phenotypic information content (PIC) value of 0.24. The model based population structure approach grouped the accession into two distinct populations, whereas unrooted tree grouped the genotypes into three clusters. Both model based and structure based approach had clearly distinguished the early vigor genotypes from non-early vigor genotypes. Association analysis revealed that 16 and 10 SSRs showed significant association with ESV traits by general linear model (GLM) and mixed linear model (MLM) approaches respectively. Marker alleles on chromosome 2 were associated with shoot dry weight on 28 DAS, vigor index on 14 and 28 DAS. Improvement in the rate of seedling growth will be useful for identifying rice genotypes acquiescent to direct seeded conditions through marker-assisted selection. PMID:27031620

Associations of Perfluoroalkyl Substances (PFAS) with Lower Birth Weight: An Evaluation of Potential Confounding by Glomerular Filtration Rate Using a Physiologically Based Pharmacokinetic Model (PBPK).

PubMed

Verner, Marc-André; Loccisano, Anne E; Morken, Nils-Halvdan; Yoon, Miyoung; Wu, Huali; McDougall, Robin; Maisonet, Mildred; Marcus, Michele; Kishi, Reiko; Miyashita, Chihiro; Chen, Mei-Huei; Hsieh, Wu-Shiun; Andersen, Melvin E; Clewell, Harvey J; Longnecker, Matthew P

2015-12-01

Prenatal exposure to perfluoroalkyl substances (PFAS) has been associated with lower birth weight in epidemiologic studies. This association could be attributable to glomerular filtration rate (GFR), which is related to PFAS concentration and birth weight. We used a physiologically based pharmacokinetic (PBPK) model of pregnancy to assess how much of the PFAS-birth weight association observed in epidemiologic studies might be attributable to GFR. We modified a PBPK model to reflect the association of GFR with birth weight (estimated from three studies of GFR and birth weight) and used it to simulate PFAS concentrations in maternal and cord plasma. The model was run 250,000 times, with variation in parameters, to simulate a population. Simulated data were analyzed to evaluate the association between PFAS levels and birth weight due to GFR. We compared simulated estimates with those from a meta-analysis of epidemiologic data. The reduction in birth weight for each 1-ng/mL increase in simulated cord plasma for perfluorooctane sulfonate (PFOS) was 2.72 g (95% CI: -3.40, -2.04), and for perfluorooctanoic acid (PFOA) was 7.13 g (95% CI: -8.46, -5.80); results based on maternal plasma at term were similar. Results were sensitive to variations in PFAS level distributions and the strength of the GFR-birth weight association. In comparison, our meta-analysis of epidemiologic studies suggested that each 1-ng/mL increase in prenatal PFOS and PFOA levels was associated with 5.00 g (95% CI: -21.66, -7.78) and 14.72 g (95% CI: -8.92, -1.09) reductions in birth weight, respectively. Results of our simulations suggest that a substantial proportion of the association between prenatal PFAS and birth weight may be attributable to confounding by GFR and that confounding by GFR may be more important in studies with sample collection later in pregnancy.

Elemental representation and configural mappings: combining elemental and configural theories of associative learning.

PubMed

McLaren, I P L; Forrest, C L; McLaren, R P

2012-09-01

In this article, we present our first attempt at combining an elemental theory designed to model representation development in an associative system (based on McLaren, Kaye, & Mackintosh, 1989) with a configural theory that models associative learning and memory (McLaren, 1993). After considering the possible advantages of such a combination (and some possible pitfalls), we offer a hybrid model that allows both components to produce the phenomena that they are capable of without introducing unwanted interactions. We then successfully apply the model to a range of phenomena, including latent inhibition, perceptual learning, the Espinet effect, and first- and second-order retrospective revaluation. In some cases, we present new data for comparison with our model's predictions. In all cases, the model replicates the pattern observed in our experimental results. We conclude that this line of development is a promising one for arriving at general theories of associative learning and memory.

Can metric-based approaches really improve multi-model climate projections? A perfect model framework applied to summer temperature change in France.

NASA Astrophysics Data System (ADS)

Boé, Julien; Terray, Laurent

2014-05-01

Ensemble approaches for climate change projections have become ubiquitous. Because of large model-to-model variations and, generally, lack of rationale for the choice of a particular climate model against others, it is widely accepted that future climate change and its impacts should not be estimated based on a single climate model. Generally, as a default approach, the multi-model ensemble mean (MMEM) is considered to provide the best estimate of climate change signals. The MMEM approach is based on the implicit hypothesis that all the models provide equally credible projections of future climate change. This hypothesis is unlikely to be true and ideally one would want to give more weight to more realistic models. A major issue with this alternative approach lies in the assessment of the relative credibility of future climate projections from different climate models, as they can only be evaluated against present-day observations: which present-day metric(s) should be used to decide which models are "good" and which models are "bad" in the future climate? Once a supposedly informative metric has been found, other issues arise. What is the best statistical method to combine multiple models results taking into account their relative credibility measured by a given metric? How to be sure in the end that the metric-based estimate of future climate change is not in fact less realistic than the MMEM? It is impossible to provide strict answers to those questions in the climate change context. Yet, in this presentation, we propose a methodological approach based on a perfect model framework that could bring some useful elements of answer to the questions previously mentioned. The basic idea is to take a random climate model in the ensemble and treat it as if it were the truth (results of this model, in both past and future climate, are called "synthetic observations"). Then, all the other members from the multi-model ensemble are used to derive thanks to a metric-based approach a posterior estimate of climate change, based on the synthetic observation of the metric. Finally, it is possible to compare the posterior estimate to the synthetic observation of future climate change to evaluate the skill of the method. The main objective of this presentation is to describe and apply this perfect model framework to test different methodological issues associated with non-uniform model weighting and similar metric-based approaches. The methodology presented is general, but will be applied to the specific case of summer temperature change in France, for which previous works have suggested potentially useful metrics associated with soil-atmosphere and cloud-temperature interactions. The relative performances of different simple statistical approaches to combine multiple model results based on metrics will be tested. The impact of ensemble size, observational errors, internal variability, and model similarity will be characterized. The potential improvements associated with metric-based approaches compared to the MMEM is terms of errors and uncertainties will be quantified.

Using Data-Driven Model-Brain Mappings to Constrain Formal Models of Cognition

PubMed Central

Borst, Jelmer P.; Nijboer, Menno; Taatgen, Niels A.; van Rijn, Hedderik; Anderson, John R.

2015-01-01

In this paper we propose a method to create data-driven mappings from components of cognitive models to brain regions. Cognitive models are notoriously hard to evaluate, especially based on behavioral measures alone. Neuroimaging data can provide additional constraints, but this requires a mapping from model components to brain regions. Although such mappings can be based on the experience of the modeler or on a reading of the literature, a formal method is preferred to prevent researcher-based biases. In this paper we used model-based fMRI analysis to create a data-driven model-brain mapping for five modules of the ACT-R cognitive architecture. We then validated this mapping by applying it to two new datasets with associated models. The new mapping was at least as powerful as an existing mapping that was based on the literature, and indicated where the models were supported by the data and where they have to be improved. We conclude that data-driven model-brain mappings can provide strong constraints on cognitive models, and that model-based fMRI is a suitable way to create such mappings. PMID:25747601

Urinary Concentrations of Parabens and Other Antimicrobial Chemicals and Their Association with Couples’ Fecundity

PubMed Central

Smarr, Melissa M.; Sundaram, Rajeshwari; Honda, Masato; Kannan, Kurunthachalam; Louis, Germaine M. Buck

2016-01-01

Background: Human exposure to parabens and other antimicrobial chemicals is continual and pervasive. The hormone-disrupting properties of these environmental chemicals may adversely affect human reproduction. Objective: We aimed to prospectively assess couples’ urinary concentrations of antimicrobial chemicals in the context of fecundity, measured as time to pregnancy (TTP). Methods: In a prospective cohort of 501 couples, we examined preconception urinary chemical concentrations of parabens, triclosan and triclorcarban in relation to TTP; chemical concentrations were modeled both continuously and in quartiles. Cox’s proportional odds models for discrete survival time were used to estimate fecundability odds ratios (FORs) and 95% confidence intervals (CIs) adjusting for a priori–defined confounders. In light of TTP being a couple-dependent outcome, both partner and couple-based exposure models were analyzed. In all models, FOR estimates < 1.0 denote diminished fecundity (longer TTP). Results: Overall, 347 (69%) couples became pregnant. The highest quartile of female urinary methyl paraben (MP) concentrations relative to the lowest reflected a 34% reduction in fecundity (aFOR = 0.66; 95% CI: 0.45, 0.97) and remained so when accounting for couples’ concentrations (aFOR = 0.63; 95% CI: 0.41, 0.96). Similar associations were observed between ethyl paraben (EP) and couple fecundity for both partner and couple-based models (p-trend = 0.02 and p-trend = 0.05, respectively). No associations were observed with couple fecundity when chemicals were modeled continuously. Conclusions: Higher quartiles of preconception urinary concentrations of MP and EP among female partners were associated with reduced couple fecundity in partner-specific and couple-based exposure models. Citation: Smarr MM, Sundaram R, Honda M, Kannan K, Buck Louis GM. 2016. Urinary concentrations of parabens and other antimicrobial chemicals and their association with couples’ fecundity. Environ Health Perspect 124:730–736; http://dx.doi.org/10.1289/EHP189 PMID:27286252

Multi-Fidelity Framework for Modeling Combustion Instability

DTIC Science & Technology

2016-07-27

generated from the reduced-domain dataset. Evaluations of the framework are performed based on simplified test problems for a model rocket combustor showing...generated from the reduced-domain dataset. Evaluations of the framework are performed based on simplified test problems for a model rocket combustor...of Aeronautics and Astronautics and Associate Fellow AIAA. ‡ Professor Emeritus. § Senior Scientist, Rocket Propulsion Division and Senior Member

The Relationship between Implementation of the American School Counselor Association National Model and Secondary School Counselor Burnout

ERIC Educational Resources Information Center

Camelford, Kellie Giorgio; Ebrahim, Christine H.; Herlihy, Barbara

2017-01-01

The purpose of this study was to examine the relationships between the implementation of the American School Counselor Association (ASCA) National Model and burnout in secondary school counselors who were ASCA members (n = 494). An inverse relationship was discovered between implementation and burnout based on survey results. Results indicated…

Predicting Ideological Prejudice

PubMed Central

Brandt, Mark J.

2017-01-01

A major shortcoming of current models of ideological prejudice is that although they can anticipate the direction of the association between participants’ ideology and their prejudice against a range of target groups, they cannot predict the size of this association. I developed and tested models that can make specific size predictions for this association. A quantitative model that used the perceived ideology of the target group as the primary predictor of the ideology-prejudice relationship was developed with a representative sample of Americans (N = 4,940) and tested against models using the perceived status of and choice to belong to the target group as predictors. In four studies (total N = 2,093), ideology-prejudice associations were estimated, and these observed estimates were compared with the models’ predictions. The model that was based only on perceived ideology was the most parsimonious with the smallest errors. PMID:28394693

Distributed Lag Models: Examining Associations between the Built Environment and Health

PubMed Central

Baek, Jonggyu; Sánchez, Brisa N.; Berrocal, Veronica J.; Sanchez-Vaznaugh, Emma V.

2016-01-01

Built environment factors constrain individual level behaviors and choices, and thus are receiving increasing attention to assess their influence on health. Traditional regression methods have been widely used to examine associations between built environment measures and health outcomes, where a fixed, pre-specified spatial scale (e.g., 1 mile buffer) is used to construct environment measures. However, the spatial scale for these associations remains largely unknown and misspecifying it introduces bias. We propose the use of distributed lag models (DLMs) to describe the association between built environment features and health as a function of distance from the locations of interest and circumvent a-priori selection of a spatial scale. Based on simulation studies, we demonstrate that traditional regression models produce associations biased away from the null when there is spatial correlation among the built environment features. Inference based on DLMs is robust under a range of scenarios of the built environment. We use this innovative application of DLMs to examine the association between the availability of convenience stores near California public schools, which may affect children’s dietary choices both through direct access to junk food and exposure to advertisement, and children’s body mass index z-scores (BMIz). PMID:26414942

Inverse Association Between Gluteofemoral Obesity and Risk of Barrett's Esophagus in a Pooled Analysis.

PubMed

Kendall, Bradley J; Rubenstein, Joel H; Cook, Michael B; Vaughan, Thomas L; Anderson, Lesley A; Murray, Liam J; Shaheen, Nicholas J; Corley, Douglas A; Chandar, Apoorva K; Li, Li; Greer, Katarina B; Chak, Amitabh; El-Serag, Hashem B; Whiteman, David C; Thrift, Aaron P

2016-10-01

Gluteofemoral obesity (determined by measurement of subcutaneous fat in the hip and thigh regions) could reduce risks of cardiovascular and diabetic disorders associated with abdominal obesity. We evaluated whether gluteofemoral obesity also reduces the risk of Barrett's esophagus (BE), a premalignant lesion associated with abdominal obesity. We collected data from non-Hispanic white participants in 8 studies in the Barrett's and Esophageal Adenocarcinoma Consortium. We compared measures of hip circumference (as a proxy for gluteofemoral obesity) from cases of BE (n = 1559) separately with 2 control groups: 2557 population-based controls and 2064 individuals with gastroesophageal reflux disease (GERD controls). Study-specific odds ratios (ORs) and 95% confidence intervals (95% CIs) were estimated using individual participant data and multivariable logistic regression and combined using a random-effects meta-analysis. We found an inverse relationship between hip circumference and BE (OR per 5-cm increase, 0.88; 95% CI, 0.81-0.96), compared with population-based controls in a multivariable model that included waist circumference. This association was not observed in models that did not include waist circumference. Similar results were observed in analyses stratified by frequency of GERD symptoms. The inverse association with hip circumference was statistically significant only among men (vs population-based controls: OR, 0.85; 95% CI, 0.76-0.96 for men; OR, 0.93; 95% CI, 0.74-1.16 for women). For men, within each category of waist circumference, a larger hip circumference was associated with a decreased risk of BE. Increasing waist circumference was associated with an increased risk of BE in the mutually adjusted population-based and GERD control models. Although abdominal obesity is associated with an increased risk of BE, there is an inverse association between gluteofemoral obesity and BE, particularly among men. Copyright © 2016 AGA Institute. Published by Elsevier Inc. All rights reserved.

Associations of Perfluoroalkyl Substances (PFAS) with Lower Birth Weight: An Evaluation of Potential Confounding by Glomerular Filtration Rate Using a Physiologically Based Pharmacokinetic Model (PBPK)

PubMed Central

Loccisano, Anne E.; Morken, Nils-Halvdan; Yoon, Miyoung; Wu, Huali; McDougall, Robin; Maisonet, Mildred; Marcus, Michele; Kishi, Reiko; Miyashita, Chihiro; Chen, Mei-Huei; Hsieh, Wu-Shiun; Andersen, Melvin E.; Clewell, Harvey J.; Longnecker, Matthew P.

2015-01-01

Background Prenatal exposure to perfluoroalkyl substances (PFAS) has been associated with lower birth weight in epidemiologic studies. This association could be attributable to glomerular filtration rate (GFR), which is related to PFAS concentration and birth weight. Objectives We used a physiologically based pharmacokinetic (PBPK) model of pregnancy to assess how much of the PFAS–birth weight association observed in epidemiologic studies might be attributable to GFR. Methods We modified a PBPK model to reflect the association of GFR with birth weight (estimated from three studies of GFR and birth weight) and used it to simulate PFAS concentrations in maternal and cord plasma. The model was run 250,000 times, with variation in parameters, to simulate a population. Simulated data were analyzed to evaluate the association between PFAS levels and birth weight due to GFR. We compared simulated estimates with those from a meta-analysis of epidemiologic data. Results The reduction in birth weight for each 1-ng/mL increase in simulated cord plasma for perfluorooctane sulfonate (PFOS) was 2.72 g (95% CI: –3.40, –2.04), and for perfluorooctanoic acid (PFOA) was 7.13 g (95% CI: –8.46, –5.80); results based on maternal plasma at term were similar. Results were sensitive to variations in PFAS level distributions and the strength of the GFR–birth weight association. In comparison, our meta-analysis of epidemiologic studies suggested that each 1-ng/mL increase in prenatal PFOS and PFOA levels was associated with 5.00 g (95% CI: –21.66, –7.78) and 14.72 g (95% CI: –8.92, –1.09) reductions in birth weight, respectively. Conclusion Results of our simulations suggest that a substantial proportion of the association between prenatal PFAS and birth weight may be attributable to confounding by GFR and that confounding by GFR may be more important in studies with sample collection later in pregnancy. Citation Verner MA, Loccisano AE, Morken NH, Yoon M, Wu H, McDougall R, Maisonet M, Marcus M, Kishi R, Miyashita C, Chen MH, Hsieh WS, Andersen ME, Clewell HJ III, Longnecker MP. 2015. Associations of perfluoroalkyl substances (PFAS) with lower birth weight: an evaluation of potential confounding by glomerular filtration rate using a physiologically based pharmacokinetic model (PBPK). Environ Health Perspect 123:1317–1324; http://dx.doi.org/10.1289/ehp.1408837 PMID:26008903

Study of the Gray Scale, Polychromatic, Distortion Invariant Neural Networks Using the Ipa Model.

NASA Astrophysics Data System (ADS)

Uang, Chii-Maw

Research in the optical neural network field is primarily motivated by the fact that humans recognize objects better than the conventional digital computers and the massively parallel inherent nature of optics. This research represents a continuous effort during the past several years in the exploitation of using neurocomputing for pattern recognition. Based on the interpattern association (IPA) model and Hamming net model, many new systems and applications are introduced. A gray level discrete associative memory that is based on object decomposition/composition is proposed for recognizing gray-level patterns. This technique extends the processing ability from the binary mode to gray-level mode, and thus the information capacity is increased. Two polychromatic optical neural networks using color liquid crystal television (LCTV) panels for color pattern recognition are introduced. By introducing a color encoding technique in conjunction with the interpattern associative algorithm, a color associative memory was realized. Based on the color decomposition and composition technique, a color exemplar-based Hamming net was built for color image classification. A shift-invariant neural network is presented through use of the translation invariant property of the modulus of the Fourier transformation and the hetero-associative interpattern association (IPA) memory. To extract the main features, a quadrantal sampling method is used to sampled data and then replace the training patterns. Using the concept of hetero-associative memory to recall the distorted object. A shift and rotation invariant neural network using an interpattern hetero-association (IHA) model is presented. To preserve the shift and rotation invariant properties, a set of binarized-encoded circular harmonic expansion (CHE) functions at the Fourier domain is used as the training set. We use the shift and symmetric properties of the modulus of the Fourier spectrum to avoid the problem of centering the CHE functions. Almost all neural networks have the positive and negative weights, which increases the difficulty of optical implementation. A method to construct a unipolar IPA IWM is discussed. By searching the redundant interconnection links, an effective way that removes all negative links is discussed.

Diagnosing and dealing with multicollinearity.

PubMed

Schroeder, M A

1990-04-01

The purpose of this article was to increase nurse researchers' awareness of the effects of collinear data in developing theoretical models for nursing practice. Collinear data distort the true value of the estimates generated from ordinary least-squares analysis. Theoretical models developed to provide the underpinnings of nursing practice need not be abandoned, however, because they fail to produce consistent estimates over repeated applications. It is also important to realize that multicollinearity is a data problem, not a problem associated with misspecification of a theorectical model. An investigator must first be aware of the problem, and then it is possible to develop an educated solution based on the degree of multicollinearity, theoretical considerations, and sources of error associated with alternative, biased, least-square regression techniques. Decisions based on theoretical and statistical considerations will further the development of theory-based nursing practice.

HMI Data Driven Magnetohydrodynamic Model Predicted Active Region Photospheric Heating Rates: Their Scale Invariant, Flare Like Power Law Distributions, and Their Possible Association With Flares

NASA Technical Reports Server (NTRS)

Goodman, Michael L.; Kwan, Chiman; Ayhan, Bulent; Shang, Eric L.

2017-01-01

There are many flare forecasting models. For an excellent review and comparison of some of them see Barnes et al. (2016). All these models are successful to some degree, but there is a need for better models. We claim the most successful models explicitly or implicitly base their forecasts on various estimates of components of the photospheric current density J, based on observations of the photospheric magnetic field B. However, none of the models we are aware of compute the complete J. We seek to develop a better model based on computing the complete photospheric J. Initial results from this model are presented in this talk. We present a data driven, near photospheric, 3 D, non-force free magnetohydrodynamic (MHD) model that computes time series of the total J, and associated resistive heating rate in each pixel at the photosphere in the neutral line regions (NLRs) of 14 active regions (ARs). The model is driven by time series of B measured by the Helioseismic & Magnetic Imager (HMI) on the Solar Dynamics Observatory (SDO) satellite. Spurious Doppler periods due to SDO orbital motion are filtered out of the time series of B in every AR pixel. Errors in B due to these periods can be significant.

Sensation Seeking and Online Gaming Addiction in Adolescents: A Moderated Mediation Model of Positive Affective Associations and Impulsivity.

PubMed

Hu, Jianping; Zhen, Shuangju; Yu, Chengfu; Zhang, Qiuyan; Zhang, Wei

2017-01-01

Based on the Dual Systems Model (Somerville et al., 2010; Steinberg, 2010a) and the biosocial-affect model (Romer and Hennessy, 2007) of adolescent sensation seeking and problem behaviors, the present study examined how (affective associations with online games as a mediator) and when (impulsivity as a moderator) did sensation seeking influence online gaming addiction in adolescence. A total of 375 Chinese male adolescents (mean age = 16.02 years, SD = 0.85) from southern China completed anonymous questionnaires regarding sensation seeking, positive affective associations with online games, impulsivity, and online gaming addiction. Our findings revealed that sensation seeking, positive affective associations with online games and impulsivity were each significantly and positively associated with online gaming addiction in adolescents. Positive affective associations mediated the relationship between sensation seeking and online gaming addiction. Further, impulsivity moderated the relationship between positive affective associations and online gaming addiction, such that the association between positive affective association and online gaming addiction was stronger for high than for low impulsivity adolescents. These findings underscore the importance of integrating the biosocial-affect model and the Dual Systems Model to understand how and when sensation seeking impacts adolescent online gaming addiction.

Sensation Seeking and Online Gaming Addiction in Adolescents: A Moderated Mediation Model of Positive Affective Associations and Impulsivity

PubMed Central

Hu, Jianping; Zhen, Shuangju; Yu, Chengfu; Zhang, Qiuyan; Zhang, Wei

2017-01-01

Based on the Dual Systems Model (Somerville et al., 2010; Steinberg, 2010a) and the biosocial-affect model (Romer and Hennessy, 2007) of adolescent sensation seeking and problem behaviors, the present study examined how (affective associations with online games as a mediator) and when (impulsivity as a moderator) did sensation seeking influence online gaming addiction in adolescence. A total of 375 Chinese male adolescents (mean age = 16.02 years, SD = 0.85) from southern China completed anonymous questionnaires regarding sensation seeking, positive affective associations with online games, impulsivity, and online gaming addiction. Our findings revealed that sensation seeking, positive affective associations with online games and impulsivity were each significantly and positively associated with online gaming addiction in adolescents. Positive affective associations mediated the relationship between sensation seeking and online gaming addiction. Further, impulsivity moderated the relationship between positive affective associations and online gaming addiction, such that the association between positive affective association and online gaming addiction was stronger for high than for low impulsivity adolescents. These findings underscore the importance of integrating the biosocial-affect model and the Dual Systems Model to understand how and when sensation seeking impacts adolescent online gaming addiction. PMID:28529494

Interconnected growing self-organizing maps for auditory and semantic acquisition modeling.

PubMed

Cao, Mengxue; Li, Aijun; Fang, Qiang; Kaufmann, Emily; Kröger, Bernd J

2014-01-01

Based on the incremental nature of knowledge acquisition, in this study we propose a growing self-organizing neural network approach for modeling the acquisition of auditory and semantic categories. We introduce an Interconnected Growing Self-Organizing Maps (I-GSOM) algorithm, which takes associations between auditory information and semantic information into consideration, in this paper. Direct phonetic-semantic association is simulated in order to model the language acquisition in early phases, such as the babbling and imitation stages, in which no phonological representations exist. Based on the I-GSOM algorithm, we conducted experiments using paired acoustic and semantic training data. We use a cyclical reinforcing and reviewing training procedure to model the teaching and learning process between children and their communication partners. A reinforcing-by-link training procedure and a link-forgetting procedure are introduced to model the acquisition of associative relations between auditory and semantic information. Experimental results indicate that (1) I-GSOM has good ability to learn auditory and semantic categories presented within the training data; (2) clear auditory and semantic boundaries can be found in the network representation; (3) cyclical reinforcing and reviewing training leads to a detailed categorization as well as to a detailed clustering, while keeping the clusters that have already been learned and the network structure that has already been developed stable; and (4) reinforcing-by-link training leads to well-perceived auditory-semantic associations. Our I-GSOM model suggests that it is important to associate auditory information with semantic information during language acquisition. Despite its high level of abstraction, our I-GSOM approach can be interpreted as a biologically-inspired neurocomputational model.

From Models to Measurements: Comparing Downed Dead Wood Carbon Stock Estimates in the U.S. Forest Inventory

PubMed Central

Domke, Grant M.; Woodall, Christopher W.; Walters, Brian F.; Smith, James E.

2013-01-01

The inventory and monitoring of coarse woody debris (CWD) carbon (C) stocks is an essential component of any comprehensive National Greenhouse Gas Inventory (NGHGI). Due to the expense and difficulty associated with conducting field inventories of CWD pools, CWD C stocks are often modeled as a function of more commonly measured stand attributes such as live tree C density. In order to assess potential benefits of adopting a field-based inventory of CWD C stocks in lieu of the current model-based approach, a national inventory of downed dead wood C across the U.S. was compared to estimates calculated from models associated with the U.S.’s NGHGI and used in the USDA Forest Service, Forest Inventory and Analysis program. The model-based population estimate of C stocks for CWD (i.e., pieces and slash piles) in the conterminous U.S. was 9 percent (145.1 Tg) greater than the field-based estimate. The relatively small absolute difference was driven by contrasting results for each CWD component. The model-based population estimate of C stocks from CWD pieces was 17 percent (230.3 Tg) greater than the field-based estimate, while the model-based estimate of C stocks from CWD slash piles was 27 percent (85.2 Tg) smaller than the field-based estimate. In general, models overestimated the C density per-unit-area from slash piles early in stand development and underestimated the C density from CWD pieces in young stands. This resulted in significant differences in CWD C stocks by region and ownership. The disparity in estimates across spatial scales illustrates the complexity in estimating CWD C in a NGHGI. Based on the results of this study, it is suggested that the U.S. adopt field-based estimates of CWD C stocks as a component of its NGHGI to both reduce the uncertainty within the inventory and improve the sensitivity to potential management and climate change events. PMID:23544112

From models to measurements: comparing downed dead wood carbon stock estimates in the U.S. forest inventory.

PubMed

Domke, Grant M; Woodall, Christopher W; Walters, Brian F; Smith, James E

2013-01-01

The inventory and monitoring of coarse woody debris (CWD) carbon (C) stocks is an essential component of any comprehensive National Greenhouse Gas Inventory (NGHGI). Due to the expense and difficulty associated with conducting field inventories of CWD pools, CWD C stocks are often modeled as a function of more commonly measured stand attributes such as live tree C density. In order to assess potential benefits of adopting a field-based inventory of CWD C stocks in lieu of the current model-based approach, a national inventory of downed dead wood C across the U.S. was compared to estimates calculated from models associated with the U.S.'s NGHGI and used in the USDA Forest Service, Forest Inventory and Analysis program. The model-based population estimate of C stocks for CWD (i.e., pieces and slash piles) in the conterminous U.S. was 9 percent (145.1 Tg) greater than the field-based estimate. The relatively small absolute difference was driven by contrasting results for each CWD component. The model-based population estimate of C stocks from CWD pieces was 17 percent (230.3 Tg) greater than the field-based estimate, while the model-based estimate of C stocks from CWD slash piles was 27 percent (85.2 Tg) smaller than the field-based estimate. In general, models overestimated the C density per-unit-area from slash piles early in stand development and underestimated the C density from CWD pieces in young stands. This resulted in significant differences in CWD C stocks by region and ownership. The disparity in estimates across spatial scales illustrates the complexity in estimating CWD C in a NGHGI. Based on the results of this study, it is suggested that the U.S. adopt field-based estimates of CWD C stocks as a component of its NGHGI to both reduce the uncertainty within the inventory and improve the sensitivity to potential management and climate change events.

Associating clinical archetypes through UMLS Metathesaurus term clusters.

PubMed

Lezcano, Leonardo; Sánchez-Alonso, Salvador; Sicilia, Miguel-Angel

2012-06-01

Clinical archetypes are modular definitions of clinical data, expressed using standard or open constraint-based data models as the CEN EN13606 and openEHR. There is an increasing archetype specification activity that raises the need for techniques to associate archetypes to support better management and user navigation in archetype repositories. This paper reports on a computational technique to generate tentative archetype associations by mapping them through term clusters obtained from the UMLS Metathesaurus. The terms are used to build a bipartite graph model and graph connectivity measures can be used for deriving associations.

Connected Component Model for Multi-Object Tracking.

PubMed

He, Zhenyu; Li, Xin; You, Xinge; Tao, Dacheng; Tang, Yuan Yan

2016-08-01

In multi-object tracking, it is critical to explore the data associations by exploiting the temporal information from a sequence of frames rather than the information from the adjacent two frames. Since straightforwardly obtaining data associations from multi-frames is an NP-hard multi-dimensional assignment (MDA) problem, most existing methods solve this MDA problem by either developing complicated approximate algorithms, or simplifying MDA as a 2D assignment problem based upon the information extracted only from adjacent frames. In this paper, we show that the relation between associations of two observations is the equivalence relation in the data association problem, based on the spatial-temporal constraint that the trajectories of different objects must be disjoint. Therefore, the MDA problem can be equivalently divided into independent subproblems by equivalence partitioning. In contrast to existing works for solving the MDA problem, we develop a connected component model (CCM) by exploiting the constraints of the data association and the equivalence relation on the constraints. Based upon CCM, we can efficiently obtain the global solution of the MDA problem for multi-object tracking by optimizing a sequence of independent data association subproblems. Experiments on challenging public data sets demonstrate that our algorithm outperforms the state-of-the-art approaches.

College Students Coping with Interpersonal Stress: Examining a Control-Based Model of Coping

ERIC Educational Resources Information Center

Coiro, Mary Jo; Bettis, Alexandra H.; Compas, Bruce E.

2017-01-01

Objective: The ways that college students cope with stress, particularly interpersonal stress, may be a critical factor in determining which students are at risk for impairing mental health disorders. Using a control-based model of coping, the present study examined associations between interpersonal stress, coping strategies, and symptoms.…

Understanding women's mammography intentions: a theory-based investigation.

PubMed

Naito, Mikako; O'Callaghan, Frances V; Morrissey, Shirley

2009-01-01

The present study compared the utility of two models (the Theory of Planned Behavior and Protection Motivation Theory) in identifying factors associated with intentions to undertake screening mammography, before and after an intervention. The comparison was made between the unique components of the two models. The effect of including implementation intentions was also investigated. Two hundred and fifty-one women aged 37 to 69 years completed questionnaires at baseline and following the delivery of a standard (control) or a protection motivation theory-based informational intervention. Hierarchical multiple regressions indicated that theory of planned behavior variables were associated with mammography intentions. Results also showed that inclusion of implementation intention in the model significantly increased the association with mammography intentions. The findings suggest that future interventions aiming to increase screening mammography participation should focus on the theory of planned behavior variables and that implementation intention should also be targeted.

MO-C-18A-01: Advances in Model-Based 3D Image Reconstruction

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

Chen, G; Pan, X; Stayman, J

2014-06-15

Recent years have seen the emergence of CT image reconstruction techniques that exploit physical models of the imaging system, photon statistics, and even the patient to achieve improved 3D image quality and/or reduction of radiation dose. With numerous advantages in comparison to conventional 3D filtered backprojection, such techniques bring a variety of challenges as well, including: a demanding computational load associated with sophisticated forward models and iterative optimization methods; nonlinearity and nonstationarity in image quality characteristics; a complex dependency on multiple free parameters; and the need to understand how best to incorporate prior information (including patient-specific prior images) within themore » reconstruction process. The advantages, however, are even greater – for example: improved image quality; reduced dose; robustness to noise and artifacts; task-specific reconstruction protocols; suitability to novel CT imaging platforms and noncircular orbits; and incorporation of known characteristics of the imager and patient that are conventionally discarded. This symposium features experts in 3D image reconstruction, image quality assessment, and the translation of such methods to emerging clinical applications. Dr. Chen will address novel methods for the incorporation of prior information in 3D and 4D CT reconstruction techniques. Dr. Pan will show recent advances in optimization-based reconstruction that enable potential reduction of dose and sampling requirements. Dr. Stayman will describe a “task-based imaging” approach that leverages models of the imaging system and patient in combination with a specification of the imaging task to optimize both the acquisition and reconstruction process. Dr. Samei will describe the development of methods for image quality assessment in such nonlinear reconstruction techniques and the use of these methods to characterize and optimize image quality and dose in a spectrum of clinical applications. Learning Objectives: Learn the general methodologies associated with model-based 3D image reconstruction. Learn the potential advantages in image quality and dose associated with model-based image reconstruction. Learn the challenges associated with computational load and image quality assessment for such reconstruction methods. Learn how imaging task can be incorporated as a means to drive optimal image acquisition and reconstruction techniques. Learn how model-based reconstruction methods can incorporate prior information to improve image quality, ease sampling requirements, and reduce dose.« less

Towards Modeling False Memory With Computational Knowledge Bases.

PubMed

Li, Justin; Kohanyi, Emma

2017-01-01

One challenge to creating realistic cognitive models of memory is the inability to account for the vast common-sense knowledge of human participants. Large computational knowledge bases such as WordNet and DBpedia may offer a solution to this problem but may pose other challenges. This paper explores some of these difficulties through a semantic network spreading activation model of the Deese-Roediger-McDermott false memory task. In three experiments, we show that these knowledge bases only capture a subset of human associations, while irrelevant information introduces noise and makes efficient modeling difficult. We conclude that the contents of these knowledge bases must be augmented and, more important, that the algorithms must be refined and optimized, before large knowledge bases can be widely used for cognitive modeling. Copyright © 2016 Cognitive Science Society, Inc.

An overview of TOUGH-based geomechanics models

DOE PAGES

Rutqvist, Jonny

2016-09-22

After the initial development of the first TOUGH-based geomechanics model 15 years ago based on linking TOUGH2 multiphase flow simulator to the FLAC3D geomechanics simulator, at least 15 additional TOUGH-based geomechanics models have appeared in the literature. This development has been fueled by a growing demand and interest for modeling coupled multiphase flow and geomechanical processes related to a number of geoengineering applications, such as in geologic CO 2 sequestration, enhanced geothermal systems, unconventional hydrocarbon production, and most recently, related to reservoir stimulation and injection-induced seismicity. This paper provides a short overview of these TOUGH-based geomechanics models, focusing on somemore » of the most frequently applied to a diverse set of problems associated with geomechanics and its couplings to hydraulic, thermal and chemical processes.« less

Model-based choices involve prospective neural activity

PubMed Central

Doll, Bradley B.; Duncan, Katherine D.; Simon, Dylan A.; Shohamy, Daphna; Daw, Nathaniel D.

2015-01-01

Decisions may arise via “model-free” repetition of previously reinforced actions, or by “model-based” evaluation, which is widely thought to follow from prospective anticipation of action consequences using a learned map or model. While choices and neural correlates of decision variables sometimes reflect knowledge of their consequences, it remains unclear whether this actually arises from prospective evaluation. Using functional MRI and a sequential reward-learning task in which paths contained decodable object categories, we found that humans’ model-based choices were associated with neural signatures of future paths observed at decision time, suggesting a prospective mechanism for choice. Prospection also covaried with the degree of model-based influences on neural correlates of decision variables, and was inversely related to prediction error signals thought to underlie model-free learning. These results dissociate separate mechanisms underlying model-based and model-free evaluation and support the hypothesis that model-based influences on choices and neural decision variables result from prospection. PMID:25799041

Fully Associative, Nonisothermal, Potential-Based Unified Viscoplastic Model for Titanium-Based Matrices

NASA Technical Reports Server (NTRS)

2005-01-01

A number of titanium matrix composite (TMC) systems are currently being investigated for high-temperature air frame and propulsion system applications. As a result, numerous computational methodologies for predicting both deformation and life for this class of materials are under development. An integral part of these methodologies is an accurate and computationally efficient constitutive model for the metallic matrix constituent. Furthermore, because these systems are designed to operate at elevated temperatures, the required constitutive models must account for both time-dependent and time-independent deformations. To accomplish this, the NASA Lewis Research Center is employing a recently developed, complete, potential-based framework. This framework, which utilizes internal state variables, was put forth for the derivation of reversible and irreversible constitutive equations. The framework, and consequently the resulting constitutive model, is termed complete because the existence of the total (integrated) form of the Gibbs complementary free energy and complementary dissipation potentials are assumed a priori. The specific forms selected here for both the Gibbs and complementary dissipation potentials result in a fully associative, multiaxial, nonisothermal, unified viscoplastic model with nonlinear kinematic hardening. This model constitutes one of many models in the Generalized Viscoplasticity with Potential Structure (GVIPS) class of inelastic constitutive equations.

Predicting exposure-response associations of ambient particulate matter with mortality in 73 Chinese cities.

PubMed

Madaniyazi, Lina; Guo, Yuming; Chen, Renjie; Kan, Haidong; Tong, Shilu

2016-01-01

Estimating the burden of mortality associated with particulates requires knowledge of exposure-response associations. However, the evidence on exposure-response associations is limited in many cities, especially in developing countries. In this study, we predicted associations of particulates smaller than 10 μm in aerodynamic diameter (PM10) with mortality in 73 Chinese cities. The meta-regression model was used to test and quantify which city-specific characteristics contributed significantly to the heterogeneity of PM10-mortality associations for 16 Chinese cities. Then, those city-specific characteristics with statistically significant regression coefficients were treated as independent variables to build multivariate meta-regression models. The model with the best fitness was used to predict PM10-mortality associations in 73 Chinese cities in 2010. Mean temperature, PM10 concentration and green space per capita could best explain the heterogeneity in PM10-mortality associations. Based on city-specific characteristics, we were able to develop multivariate meta-regression models to predict associations between air pollutants and health outcomes reasonably well. Copyright © 2015 Elsevier Ltd. All rights reserved.

Air Pollution and Lung Function in Dutch Children: A Comparison of Exposure Estimates and Associations Based on Land Use Regression and Dispersion Exposure Modeling Approaches

PubMed Central

Gehring, Ulrike; Hoek, Gerard; Keuken, Menno; Jonkers, Sander; Beelen, Rob; Eeftens, Marloes; Postma, Dirkje S.; Brunekreef, Bert

2015-01-01

Background There is limited knowledge about the extent to which estimates of air pollution effects on health are affected by the choice for a specific exposure model. Objectives We aimed to evaluate the correlation between long-term air pollution exposure estimates using two commonly used exposure modeling techniques [dispersion and land use regression (LUR) models] and, in addition, to compare the estimates of the association between long-term exposure to air pollution and lung function in children using these exposure modeling techniques. Methods We used data of 1,058 participants of a Dutch birth cohort study with measured forced expiratory volume in 1 sec (FEV1), forced vital capacity (FVC), and peak expiratory flow (PEF) measurements at 8 years of age. For each child, annual average outdoor air pollution exposure [nitrogen dioxide (NO2), mass concentration of particulate matter with diameters ≤ 2.5 and ≤ 10 μm (PM2.5, PM10), and PM2.5 soot] was estimated for the current addresses of the participants by a dispersion and a LUR model. Associations between exposures to air pollution and lung function parameters were estimated using linear regression analysis with confounder adjustment. Results Correlations between LUR- and dispersion-modeled pollution concentrations were high for NO2, PM2.5, and PM2.5 soot (R = 0.86–0.90) but low for PM10 (R = 0.57). Associations with lung function were similar for air pollutant exposures estimated using LUR and dispersion modeling, except for associations of PM2.5 with FEV1 and FVC, which were stronger but less precise for exposures based on LUR compared with dispersion model. Conclusions Predictions from LUR and dispersion models correlated very well for PM2.5, NO2, and PM2.5 soot but not for PM10. Health effect estimates did not depend on the type of model used to estimate exposure in a population of Dutch children. Citation Wang M, Gehring U, Hoek G, Keuken M, Jonkers S, Beelen R, Eeftens M, Postma DS, Brunekreef B. 2015. Air pollution and lung function in Dutch children: a comparison of exposure estimates and associations based on land use regression and dispersion exposure modeling approaches. Environ Health Perspect 123:847–851; http://dx.doi.org/10.1289/ehp.1408541 PMID:25839747

A processing architecture for associative short-term memory in electronic noses

NASA Astrophysics Data System (ADS)

Pioggia, G.; Ferro, M.; Di Francesco, F.; DeRossi, D.

2006-11-01

Electronic nose (e-nose) architectures usually consist of several modules that process various tasks such as control, data acquisition, data filtering, feature selection and pattern analysis. Heterogeneous techniques derived from chemometrics, neural networks, and fuzzy rules used to implement such tasks may lead to issues concerning module interconnection and cooperation. Moreover, a new learning phase is mandatory once new measurements have been added to the dataset, thus causing changes in the previously derived model. Consequently, if a loss in the previous learning occurs (catastrophic interference), real-time applications of e-noses are limited. To overcome these problems this paper presents an architecture for dynamic and efficient management of multi-transducer data processing techniques and for saving an associative short-term memory of the previously learned model. The architecture implements an artificial model of a hippocampus-based working memory, enabling the system to be ready for real-time applications. Starting from the base models available in the architecture core, dedicated models for neurons, maps and connections were tailored to an artificial olfactory system devoted to analysing olive oil. In order to verify the ability of the processing architecture in associative and short-term memory, a paired-associate learning test was applied. The avoidance of catastrophic interference was observed.

Empirically Based Composite Fracture Prediction Model From the Global Longitudinal Study of Osteoporosis in Postmenopausal Women (GLOW)

PubMed Central

Compston, Juliet E.; Chapurlat, Roland D.; Pfeilschifter, Johannes; Cooper, Cyrus; Hosmer, David W.; Adachi, Jonathan D.; Anderson, Frederick A.; Díez-Pérez, Adolfo; Greenspan, Susan L.; Netelenbos, J. Coen; Nieves, Jeri W.; Rossini, Maurizio; Watts, Nelson B.; Hooven, Frederick H.; LaCroix, Andrea Z.; March, Lyn; Roux, Christian; Saag, Kenneth G.; Siris, Ethel S.; Silverman, Stuart; Gehlbach, Stephen H.

2014-01-01

Context: Several fracture prediction models that combine fractures at different sites into a composite outcome are in current use. However, to the extent individual fracture sites have differing risk factor profiles, model discrimination is impaired. Objective: The objective of the study was to improve model discrimination by developing a 5-year composite fracture prediction model for fracture sites that display similar risk profiles. Design: This was a prospective, observational cohort study. Setting: The study was conducted at primary care practices in 10 countries. Patients: Women aged 55 years or older participated in the study. Intervention: Self-administered questionnaires collected data on patient characteristics, fracture risk factors, and previous fractures. Main Outcome Measure: The main outcome is time to first clinical fracture of hip, pelvis, upper leg, clavicle, or spine, each of which exhibits a strong association with advanced age. Results: Of four composite fracture models considered, model discrimination (c index) is highest for an age-related fracture model (c index of 0.75, 47 066 women), and lowest for Fracture Risk Assessment Tool (FRAX) major fracture and a 10-site model (c indices of 0.67 and 0.65). The unadjusted increase in fracture risk for an additional 10 years of age ranges from 80% to 180% for the individual bones in the age-associated model. Five other fracture sites not considered for the age-associated model (upper arm/shoulder, rib, wrist, lower leg, and ankle) have age associations for an additional 10 years of age from a 10% decrease to a 60% increase. Conclusions: After examining results for 10 different bone fracture sites, advanced age appeared the single best possibility for uniting several different sites, resulting in an empirically based composite fracture risk model. PMID:24423345

A Permeability-Limited Physiologically Based Pharmacokinetic (PBPK) Model for Perfluorooctanoic acid (PFOA) in Male Rats.

PubMed

Cheng, Weixiao; Ng, Carla A

2017-09-05

Physiologically based pharmacokinetic (PBPK) modeling is a powerful in silico tool that can be used to simulate the toxicokinetics and tissue distribution of xenobiotic substances, such as perfluorooctanoic acid (PFOA), in organisms. However, most existing PBPK models have been based on the flow-limited assumption and largely rely on in vivo data for parametrization. In this study, we propose a permeability-limited PBPK model to estimate the toxicokinetics and tissue distribution of PFOA in male rats. Our model considers the cellular uptake and efflux of PFOA via both passive diffusion and transport facilitated by various membrane transporters, association with serum albumin in circulatory and extracellular spaces, and association with intracellular proteins in liver and kidney. Model performance is assessed using seven experimental data sets extracted from three different studies. Comparing model predictions with these experimental data, our model successfully predicts the toxicokinetics and tissue distribution of PFOA in rats following exposure via both IV and oral routes. More importantly, rather than requiring in vivo data fitting, all PFOA-related parameters were obtained from in vitro assays. Our model thus provides an effective framework to test in vitro-in vivo extrapolation and holds great promise for predicting toxicokinetics of per- and polyfluorinated alkyl substances in humans.

Family-Based Rare Variant Association Analysis: A Fast and Efficient Method of Multivariate Phenotype Association Analysis.

PubMed

Wang, Longfei; Lee, Sungyoung; Gim, Jungsoo; Qiao, Dandi; Cho, Michael; Elston, Robert C; Silverman, Edwin K; Won, Sungho

2016-09-01

Family-based designs have been repeatedly shown to be powerful in detecting the significant rare variants associated with human diseases. Furthermore, human diseases are often defined by the outcomes of multiple phenotypes, and thus we expect multivariate family-based analyses may be very efficient in detecting associations with rare variants. However, few statistical methods implementing this strategy have been developed for family-based designs. In this report, we describe one such implementation: the multivariate family-based rare variant association tool (mFARVAT). mFARVAT is a quasi-likelihood-based score test for rare variant association analysis with multiple phenotypes, and tests both homogeneous and heterogeneous effects of each variant on multiple phenotypes. Simulation results show that the proposed method is generally robust and efficient for various disease models, and we identify some promising candidate genes associated with chronic obstructive pulmonary disease. The software of mFARVAT is freely available at http://healthstat.snu.ac.kr/software/mfarvat/, implemented in C++ and supported on Linux and MS Windows. © 2016 WILEY PERIODICALS, INC.

The Effect of Reform-Based Science Teaching on SES-Associated Achievement Gap on PISA 2006: A Comparative Study of the United States and Taiwan

NASA Astrophysics Data System (ADS)

Tang, Nai-En

The goal of this study is to examine how reform-based science teaching has been implemented and whether reform-based science teaching has promoted education equity through being available and beneficial for students from different socioeconomic status (SES) family backgrounds in the U.S. and Taiwan. No existing study used large-scale assessment to investigate the implementation and outcomes of the science reform movement in the U.S. and Taiwan. This study was developed to fill this gap using the Program of International Student Assessment (PISA) 2006 data including 5,611 students in the United States and 5995 students in Taiwan. A Latent Profile Analysis (LPA) was used to classify students into different science learning subgroups to understand how broadly reform-based science learning has been implemented in classrooms. The results showed that students in the U.S. had more opportunity to learn science through the reform-based learning activities than students in Taiwan. Latent Class Regression (LCR) and Structural Equation Modeling (SEM) were used for examining the availability of reform-based science teaching in both countries. The results showed that in the U.S., higher SES students had more opportunity to learn science reform-based learning activities. On the other hand, students' SES had no association with reform-based science learning in Taiwan. Regression Mixture Modeling and SEM were used to examine whether there was an association between reform-based science teaching and SES-associated achievement gaps. The results found no evidence to support the claim that reform-based science teaching helps to minimize SES-associated achievement gaps in both countries.

A model-based economic evaluation of improved primary care management of patients with type 2 diabetes in Australia.

PubMed

Haji Ali Afzali, Hossein; Gray, Jodi; Beilby, Justin; Holton, Christine; Karnon, Jonathan

2013-12-01

There are few studies investigating the economic value of the Australian practice nurse workforce on the management of chronic conditions. This is particularly important in Australia, where the government needs evidence to inform decisions on whether to maintain or redirect current financial incentives that encourage practices to recruit practice nurses. The objective of this study was to estimate the lifetime costs and quality-adjusted life-years (QALYs) associated with two models of practice nurse involvement in clinical-based activities (high and low level) in the management of type 2 diabetes within the primary care setting. A previously validated state transition model (the United Kingdom Prospective Diabetes Study Outcomes Model) was adapted, which uses baseline prognostic factors (e.g. gender, haemoglobin A1c [HbA1c]) to predict the risk of occurrence of diabetes-related complications (e.g. stroke). The model was populated by data from Australian and UK observational studies. Costs and utility values associated with complications were summed over patients' lifetimes to estimate costs and QALY gains from the perspective of the health care system. All costs were expressed in 2011 Australian dollars (AU$). The base-case analysis assumed a 40-year time horizon with an annual discount rate of 5 %. Relative to low-level involvement of practice nurses in the provision of clinical-based activities, the high-level model was associated with lower mean lifetime costs of management of complications (-AU$8,738; 95 % confidence interval [CI] -AU$12,522 to -AU$4,954), and a greater average gain in QALYs (0.3; 95 % CI 0.2-0.4). A range of sensitivity analyses were performed, in which the high-level model was dominant in all cases. Our results suggest that the high-level model is a dominant management strategy over the low-level model in all modelled scenarios. These findings indicate the need for effective primary care-based incentives to encourage general practices not only to employ practice nurses, but to better integrate them into the provision of clinical services.

A Web-Based Computer-Aided Learning Module for an Anatomy Course Using Open Source Image Mapping Software

ERIC Educational Resources Information Center

Carleton, Renee E.

2012-01-01

Computer-aided learning (CAL) is used increasingly to teach anatomy in post-secondary programs. Studies show that augmentation of traditional cadaver dissection and model examination by CAL can be associated with positive student learning outcomes. In order to reduce costs associated with the purchase of skeletons and models and to encourage study…

A Fully Associative, Non-Linear Kinematic, Unified Viscoplastic Model for Titanium Based Matrices

NASA Technical Reports Server (NTRS)

Arnold, S. M.; Saleeb, A. F.; Castelli, M. G.

1994-01-01

Specific forms for both the Gibb's and complementary dissipation potentials are chosen such that a complete (i.e., fully associative) potential based multiaxial unified viscoplastic model is obtained. This model possesses one tensorial internal state variable that is associated with dislocation substructure, with an evolutionary law that has nonlinear kinematic hardening and both thermal and strain induced recovery mechanisms. A unique aspect of the present model is the inclusion of non-linear hardening through the use of a compliance operator, derived from the Gibb's potential, in the evolution law for the back stress. This non-linear tensorial operator is significant in that it allows both the flow and evolutionary laws to be fully associative (and therefore easily integrated) and greatly influences the multiaxial response under non-proportional loading paths. In addition to this nonlinear compliance operator, a new consistent, potential preserving, internal strain unloading criterion has been introduced to prevent abnormalities in the predicted stress-strain curves, which are present with nonlinear hardening formulations, during unloading and reversed loading of the external variables. Specification of an experimental program for the complete determination of the material functions and parameters for characterizing a metallic matrix, e.g., TIMETAL 21S, is given. The experiments utilized are tensile, creep, and step creep tests. Finally, a comparison of this model and a commonly used Bodner-Partom model is made on the basis of predictive accuracy and numerical efficiency.

Costs of Providing Infusion Therapy for Rheumatoid Arthritis in a Hospital-based Infusion Center Setting.

PubMed

Schmier, Jordana; Ogden, Kristine; Nickman, Nancy; Halpern, Michael T; Cifaldi, Mary; Ganguli, Arijit; Bao, Yanjun; Garg, Vishvas

2017-08-01

Many hospital-based infusion centers treat patients with rheumatoid arthritis (RA) with intravenous biologic agents, yet may have a limited understanding of the overall costs of infusion in this setting. The purposes of this study were to conduct a microcosting analysis from a hospital perspective and to develop a model using an activity-based costing approach for estimating costs associated with the provision of hospital-based infusion services (preparation, administration, and follow-up) in the United States for maintenance treatment of moderate to severe RA. A spreadsheet-based model was developed. Inputs included hourly wages, time spent providing care, supply/overhead costs, laboratory testing, infusion center size, and practice pattern information. Base-case values were derived from data from surveys, published studies, standard cost sources, and expert opinion. Costs are presented in year-2017 US dollars. The base case modeled a hospital infusion center serving patients with RA treated with abatacept, tocilizumab, infliximab, or rituximab. Estimated overall costs of infusions per patient per year were $36,663 (rituximab), $36,821 (tocilizumab), $44,973 (infliximab), and $46,532 (abatacept). Of all therapies, the biologic agents represented the greatest share of overall costs, ranging from 87% to $91% of overall costs per year. Excluding infusion drug costs, labor accounted for 53% to 57% of infusion costs. Biologic agents represented the highest single cost associated with RA infusion care; however, personnel, supplies, and overhead costs also contributed substantially to overall costs (8%-16%). This model may provide a helpful and adaptable framework for use by hospitals in informing decision making about services offered and their associated financial implications. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Mentalization-based treatment for psychosis: linking an attachment-based model to the psychotherapy for impaired mental state understanding in people with psychotic disorders.

PubMed

Brent, Benjamin K; Holt, Daphne J; Keshavan, Matcheri S; Seidman, Larry J; Fonagy, Peter

2014-01-01

Disturbances of mentalization have been increasingly associated with the symptoms and functional impairment of people with psychotic disorders. it has been proposed that psychotherapy designed to foster self and other understanding, such as mentalization-based treatment (mBt), may play an important part in facilitating recovery from psychosis. Here, we present an attachment-based understanding of mentalization impairments. We then outline a neuropsychological model that links disruptions of mentalization associated with disturbances in the caregiving environment to the pathophysiology of psychosis in genetically at-risk individuals. this is followed by an illustration of some of the core mBt techniques for the rehabilitation of the capacity to mentalize as applied to the treatment of a patient with a psychotic disorder.

First Principles Modeling of RFQ Cooling System and Resonant Frequency Responses for Fermilab’s PIP-II Injector Test

DOE PAGES

Edelen, J. P.; Edelen, A. L.; Bowring, D.; ...

2016-12-23

In this study we develop an a priori method for simulating dynamic resonant frequency and temperature responses in a radio frequency quadrupole (RFQ) and its associated water-based cooling system respectively. Our model provides a computationally efficient means to evaluate the transient response of the RFQ over a large range of system parameters. The model was constructed prior to the delivery of the PIP-II Injector Test RFQ and was used to aid in the design of the water-based cooling system, data acquisition system, and resonance control system. Now that the model has been validated with experimental data, it can confidently bemore » used to aid in the design of future RFQ resonance controllers and their associated water-based cooling systems. Finally, without any empirical fitting, it has demonstrated the ability to predict absolute temperature and frequency changes to 11% accuracy on average, and relative changes to 7% accuracy.« less

Log-Linear Models for Gene Association

PubMed Central

Hu, Jianhua; Joshi, Adarsh; Johnson, Valen E.

2009-01-01

We describe a class of log-linear models for the detection of interactions in high-dimensional genomic data. This class of models leads to a Bayesian model selection algorithm that can be applied to data that have been reduced to contingency tables using ranks of observations within subjects, and discretization of these ranks within gene/network components. Many normalization issues associated with the analysis of genomic data are thereby avoided. A prior density based on Ewens’ sampling distribution is used to restrict the number of interacting components assigned high posterior probability, and the calculation of posterior model probabilities is expedited by approximations based on the likelihood ratio statistic. Simulation studies are used to evaluate the efficiency of the resulting algorithm for known interaction structures. Finally, the algorithm is validated in a microarray study for which it was possible to obtain biological confirmation of detected interactions. PMID:19655032

MGAS: a powerful tool for multivariate gene-based genome-wide association analysis.

PubMed

Van der Sluis, Sophie; Dolan, Conor V; Li, Jiang; Song, Youqiang; Sham, Pak; Posthuma, Danielle; Li, Miao-Xin

2015-04-01

Standard genome-wide association studies, testing the association between one phenotype and a large number of single nucleotide polymorphisms (SNPs), are limited in two ways: (i) traits are often multivariate, and analysis of composite scores entails loss in statistical power and (ii) gene-based analyses may be preferred, e.g. to decrease the multiple testing problem. Here we present a new method, multivariate gene-based association test by extended Simes procedure (MGAS), that allows gene-based testing of multivariate phenotypes in unrelated individuals. Through extensive simulation, we show that under most trait-generating genotype-phenotype models MGAS has superior statistical power to detect associated genes compared with gene-based analyses of univariate phenotypic composite scores (i.e. GATES, multiple regression), and multivariate analysis of variance (MANOVA). Re-analysis of metabolic data revealed 32 False Discovery Rate controlled genome-wide significant genes, and 12 regions harboring multiple genes; of these 44 regions, 30 were not reported in the original analysis. MGAS allows researchers to conduct their multivariate gene-based analyses efficiently, and without the loss of power that is often associated with an incorrectly specified genotype-phenotype models. MGAS is freely available in KGG v3.0 (http://statgenpro.psychiatry.hku.hk/limx/kgg/download.php). Access to the metabolic dataset can be requested at dbGaP (https://dbgap.ncbi.nlm.nih.gov/). The R-simulation code is available from http://ctglab.nl/people/sophie_van_der_sluis. Supplementary data are available at Bioinformatics online. © The Author 2014. Published by Oxford University Press.

Gender-Based Power and Couples' HIV Risk in Uttar Pradesh and Uttarakhand, North India

PubMed Central

Agrawal, Alpna; Bloom, Shelah S.; Suchindran, Chirayath; Curtis, Sian; Angeles, Gustavo

2015-01-01

Context Gender inequality is a long-recognized driver of the HIV epidemic. However, few studies have investigated the association between gender-based power and HIV risk in India, which has the world's third largest HIV epidemic. Methods Population-based data collected in 2003 from 3,385 couples residing in Uttar Pradesh and Uttarakhand, North India, were used to examine associations between gender-based power (wife's autonomy and husband's inequitable gender attitudes) and indicators of couples' HIV risk (whether the husband had had premarital sex with someone other than his eventual spouse, extramarital sex in the past year or STI symptoms in the past year). Structural equation modeling was used to create composite variables for the gender-based power measures and test their associations with HIV risk measures. Results Twenty-four percent of husbands had had premarital sex, 7% had had extramarital sex in the past year and 6% had had STI symptoms in the past year. Structural equation models indicated that wives who reported higher levels of autonomy were less likely than other wives to have husbands who had had extramarital sex in the past year (direct association) and STI symptoms in the past year (indirect association). Moreover, husbands who endorsed more inequitable gender attitudes were more likely than others to report having had premarital sex with someone other than their spouse, which in turn was associated with having had extramarital sex and STI symptoms in the past year. Conclusions If the associations identified in this study reflect a causal relationship between gender-based power and HIV risk behavior, then HIV prevention programs that successfully address inequitable gender roles may reduce HIV risks in North India. PMID:25565347

Gender-based power and couples' HIV risk in Uttar Pradesh and Uttarakhand, north India.

PubMed

Agrawal, Alpna; Bloom, Shelah S; Suchindran, Chirayath; Curtis, Siân; Angeles, Gustavo

2014-12-01

Gender inequality is a long-recognized driver of the HIV epidemic. However, few studies have investigated the association between gender-based power and HIV risk in India, which has the world's third largest HIV epidemic. Population-based data collected in 2003 from 3,385 couples residing in Uttar Pradesh and Uttarakhand, North India, were used to examine associations between gender-based power (wife's autonomy and husband's inequitable gender attitudes) and indicators of couples' HIV risk (whether the husband had had premarital sex with someone other than his eventual spouse, extramarital sex in the past year or STI symptoms in the past year). Structural equation modeling was used to create composite variables for the gender-based power measures and test their associations with HIV risk measures. Twenty-four percent of husbands had had premarital sex, 7% had had extramarital sex in the past year and 6% had had STI symptoms in the past year. Structural equation models indicated that wives who reported higher levels of autonomy were less likely than other wives to have husbands who had had extramarital sex in the past year (direct association) or STI symptoms in the past year (indirect association). Moreover, husbands who endorsed more inequitable gender attitudes were more likely than others to report having had premarital sex with someone other than their spouse, which in turn was associated with having had extramarital sex and STI symptoms in the past year. If the associations identified in this study reflect a causal relationship between gender-based power and HIV risk behavior, then HIV prevention programs that successfully address inequitable gender roles may reduce HIV risks in North India.

A home-school-doctor model to break the barriers for uptake of human papillomavirus vaccine.

PubMed

Lee, Albert; Wong, Martin C S; Chan, Tracy T; Chan, Paul K S

2015-09-21

A high coverage of human papillomavirus (HPV) vaccination is required to achieve a clinically significant reduction in disease burden. Countries implementing free-of-charge national vaccination program for adolescent girls are still challenged by the sub-optimal uptake rate. Voluntary on-site school-based mass vaccination programs have demonstrated high coverage. Here, we tested whether this could be an option for countries without a government-supported vaccination program as in Hong Kong. A Home-School-Doctor model was evolved based on extensive literature review of various health promotion models together with studies on HPV vaccination among adolescent girls. The outcome measure was uptake of vaccination. Factors associated with the outcome were measured by validated surveys in which 4,631 students from 24 school territory wide participated. Chi-square test was used to analyze association between the categorical variables and the outcome. Multivariate analysis was performed to identify independent variables associated with the outcome with vaccine group as case and non-vaccine group as control. In multivariate analysis, parental perception of usefulness of the Home-School-Doctor model had a very high odds ratio for uptake of HPV vaccination (OR 26.6, 95% CI 16.4, 41.9). Paying a reasonable price was another independent factor associated with increased uptake (OR 1.71, 95% CI 1.39, 2.1 for those with parents willing to pay US$125-250 for vaccination). For parents and adolescents who were not sure where to get vaccination, this model was significantly associated with improved uptake rate (OR 1.66, 95% CI 1.23, 2.23). Concerns with side effects of vaccine (OR 0.70, 95% CI 0.55, 0.88), allowing daughters to make their own decisions (OR 0.49, 95% CI 0.38, 0.64) and not caring much about daughters' social life (95% CI 0.45, 0.92) were factors associated with a lower uptake. The findings of this study have added knowledge on how a school-based vaccination program would improve vaccine uptake rate even when the users need to pay. Our findings are consistent with other study that the most acceptable way to achieve high uptake of HPV vaccine is to offer voluntary school-based vaccination. A model of care incorporating the efforts and expertise of academics and health professionals working closely with school can be applied to improve the uptake of vaccine among adolescent girls. Subsidized voluntary school-based vaccination scheme can be an option.

ADAM33 polymorphisms are associated with asthma and a distinctive palm dermatoglyphic pattern

PubMed Central

XUE, WEILIN; HAN, WEI; ZHOU, ZHAO-SHAN

2013-01-01

A close correlation between asthma and palm dermatoglyphic patterns has been observed in previous studies, but the underlying genetic mechanisms have not been investigated. A disintegrin and metalloprotein-33 (ADAM33) polymorphisms are important in the development of asthma and other atopic diseases. To investigate the underlying mechanisms of the association between asthma and distinctive palm dermatoglyphic patterns, thirteen ADAM33 single-nucleotide polymorphisms (SNPs) were analyzed for the association between asthma and palm dermatoglyphic patterns in a population of 400 asthmatic patients and 200 healthy controls. Based on the results, five SNPs, rs44707 (codominant model, P=0.031; log-additive model, P=0.0084), rs2787094 (overdominant model, P=0.049), rs678881 (codominant model, P=0.028; overdominant model, P=0.0083), rs677044 (codominant model, P=0.013; log-additive model, P=0.0033) and rs512625 (dominant model, P=0.033), were associated with asthma in this population. Two SNPs, rs44707 (dominant model, P=0.042) and rs2787094 (codominant model, P=0.014; recessive model, P=0.0038), were observed in the asthma patients with the distinctive palm pattern. As rs44707 and rs2787094 are associated with asthma and a distinctive palm pattern, the data suggest that ADAM33 polymorphisms are correlated with asthma and may be the underlying genetic basis of the association between asthma and palm dermatoglyphic patterns. PMID:24141861

A Modeling Framework for Optimal Computational Resource Allocation Estimation: Considering the Trade-offs between Physical Resolutions, Uncertainty and Computational Costs

NASA Astrophysics Data System (ADS)

Moslehi, M.; de Barros, F.; Rajagopal, R.

2014-12-01

Hydrogeological models that represent flow and transport in subsurface domains are usually large-scale with excessive computational complexity and uncertain characteristics. Uncertainty quantification for predicting flow and transport in heterogeneous formations often entails utilizing a numerical Monte Carlo framework, which repeatedly simulates the model according to a random field representing hydrogeological characteristics of the field. The physical resolution (e.g. grid resolution associated with the physical space) for the simulation is customarily chosen based on recommendations in the literature, independent of the number of Monte Carlo realizations. This practice may lead to either excessive computational burden or inaccurate solutions. We propose an optimization-based methodology that considers the trade-off between the following conflicting objectives: time associated with computational costs, statistical convergence of the model predictions and physical errors corresponding to numerical grid resolution. In this research, we optimally allocate computational resources by developing a modeling framework for the overall error based on a joint statistical and numerical analysis and optimizing the error model subject to a given computational constraint. The derived expression for the overall error explicitly takes into account the joint dependence between the discretization error of the physical space and the statistical error associated with Monte Carlo realizations. The accuracy of the proposed framework is verified in this study by applying it to several computationally extensive examples. Having this framework at hand aims hydrogeologists to achieve the optimum physical and statistical resolutions to minimize the error with a given computational budget. Moreover, the influence of the available computational resources and the geometric properties of the contaminant source zone on the optimum resolutions are investigated. We conclude that the computational cost associated with optimal allocation can be substantially reduced compared with prevalent recommendations in the literature.

Linkage and related analyses of Barrett's esophagus and its associated adenocarcinomas.

PubMed

Sun, Xiangqing; Elston, Robert; Falk, Gary W; Grady, William M; Faulx, Ashley; Mittal, Sumeet K; Canto, Marcia I; Shaheen, Nicholas J; Wang, Jean S; Iyer, Prasad G; Abrams, Julian A; Willis, Joseph E; Guda, Kishore; Markowitz, Sanford; Barnholtz-Sloan, Jill S; Chandar, Apoorva; Brock, Wendy; Chak, Amitabh

2016-07-01

Familial aggregation and segregation analysis studies have provided evidence of a genetic basis for esophageal adenocarcinoma (EAC) and its premalignant precursor, Barrett's esophagus (BE). We aim to demonstrate the utility of linkage analysis to identify the genomic regions that might contain the genetic variants that predispose individuals to this complex trait (BE and EAC). We genotyped 144 individuals in 42 multiplex pedigrees chosen from 1000 singly ascertained BE/EAC pedigrees, and performed both model-based and model-free linkage analyses, using S.A.G.E. and other software. Segregation models were fitted, from the data on both the 42 pedigrees and the 1000 pedigrees, to determine parameters for performing model-based linkage analysis. Model-based and model-free linkage analyses were conducted in two sets of pedigrees: the 42 pedigrees and a subset of 18 pedigrees with female affected members that are expected to be more genetically homogeneous. Genome-wide associations were also tested in these families. Linkage analyses on the 42 pedigrees identified several regions consistently suggestive of linkage by different linkage analysis methods on chromosomes 2q31, 12q23, and 4p14. A linkage on 15q26 is the only consistent linkage region identified in the 18 female-affected pedigrees, in which the linkage signal is higher than in the 42 pedigrees. Other tentative linkage signals are also reported. Our linkage study of BE/EAC pedigrees identified linkage regions on chromosomes 2, 4, 12, and 15, with some reported associations located within our linkage peaks. Our linkage results can help prioritize association tests to delineate the genetic determinants underlying susceptibility to BE and EAC.

Analysis of a Shock-Associated Noise Prediction Model Using Measured Jet Far-Field Noise Data

NASA Technical Reports Server (NTRS)

Dahl, Milo D.; Sharpe, Jacob A.

2014-01-01

A code for predicting supersonic jet broadband shock-associated noise was assessed us- ing a database containing noise measurements of a jet issuing from a convergent nozzle. The jet was operated at 24 conditions covering six fully expanded Mach numbers with four total temperature ratios. To enable comparisons of the predicted shock-associated noise component spectra with data, the measured total jet noise spectra were separated into mixing noise and shock-associated noise component spectra. Comparisons between predicted and measured shock-associated noise component spectra were used to identify de ciencies in the prediction model. Proposed revisions to the model, based on a study of the overall sound pressure levels for the shock-associated noise component of the mea- sured data, a sensitivity analysis of the model parameters with emphasis on the de nition of the convection velocity parameter, and a least-squares t of the predicted to the mea- sured shock-associated noise component spectra, resulted in a new de nition for the source strength spectrum in the model. An error analysis showed that the average error in the predicted spectra was reduced by as much as 3.5 dB for the revised model relative to the average error for the original model.

Testing in Microbiome-Profiling Studies with MiRKAT, the Microbiome Regression-Based Kernel Association Test

PubMed Central

Zhao, Ni; Chen, Jun; Carroll, Ian M.; Ringel-Kulka, Tamar; Epstein, Michael P.; Zhou, Hua; Zhou, Jin J.; Ringel, Yehuda; Li, Hongzhe; Wu, Michael C.

2015-01-01

High-throughput sequencing technology has enabled population-based studies of the role of the human microbiome in disease etiology and exposure response. Distance-based analysis is a popular strategy for evaluating the overall association between microbiome diversity and outcome, wherein the phylogenetic distance between individuals’ microbiome profiles is computed and tested for association via permutation. Despite their practical popularity, distance-based approaches suffer from important challenges, especially in selecting the best distance and extending the methods to alternative outcomes, such as survival outcomes. We propose the microbiome regression-based kernel association test (MiRKAT), which directly regresses the outcome on the microbiome profiles via the semi-parametric kernel machine regression framework. MiRKAT allows for easy covariate adjustment and extension to alternative outcomes while non-parametrically modeling the microbiome through a kernel that incorporates phylogenetic distance. It uses a variance-component score statistic to test for the association with analytical p value calculation. The model also allows simultaneous examination of multiple distances, alleviating the problem of choosing the best distance. Our simulations demonstrated that MiRKAT provides correctly controlled type I error and adequate power in detecting overall association. “Optimal” MiRKAT, which considers multiple candidate distances, is robust in that it suffers from little power loss in comparison to when the best distance is used and can achieve tremendous power gain in comparison to when a poor distance is chosen. Finally, we applied MiRKAT to real microbiome datasets to show that microbial communities are associated with smoking and with fecal protease levels after confounders are controlled for. PMID:25957468

P-KIMMO: A Prolog Implementation of the Two Level Model.

ERIC Educational Resources Information Center

Lee, Kang-Hyuk

Implementation of a computer-based model for morphological analysis and synthesis of language, entitled P-KIMMO, is discussed. The model was implemented in Quintus Prolog on a Sun Workstation and exported to a Macintosh computer. This model has two levels of morphophonological representation, lexical and surface levels, associated by…

Community Organizing Practices in Academia: A Model, and Stories of Partnerships

ERIC Educational Resources Information Center

Avila, Maria

2010-01-01

This article describes a model of civic engagement based on four key community organizing practices, created at Occidental College and implemented since 2001. The foundations of this model do not include confrontation, mass mobilization, or demonstrations--tactics commonly associated with the term community organizing. This model, instead,…

Clinical errors that can occur in the treatment decision-making process in psychotherapy.

PubMed

Park, Jake; Goode, Jonathan; Tompkins, Kelley A; Swift, Joshua K

2016-09-01

Clinical errors occur in the psychotherapy decision-making process whenever a less-than-optimal treatment or approach is chosen when working with clients. A less-than-optimal approach may be one that a client is unwilling to try or fully invest in based on his/her expectations and preferences, or one that may have little chance of success based on contraindications and/or limited research support. The doctor knows best and the independent choice models are two decision-making models that are frequently used within psychology, but both are associated with an increased likelihood of errors in the treatment decision-making process. In particular, these models fail to integrate all three components of the definition of evidence-based practice in psychology (American Psychological Association, 2006). In this article we describe both models and provide examples of clinical errors that can occur in each. We then introduce the shared decision-making model as an alternative that is less prone to clinical errors. PsycINFO Database Record (c) 2016 APA, all rights reserved

Induced pluripotent stem cell technology for modelling and therapy of cerebellar ataxia

PubMed Central

Watson, Lauren M.; Wong, Maggie M. K.; Becker, Esther B. E.

2015-01-01

Induced pluripotent stem cell (iPSC) technology has emerged as an important tool in understanding, and potentially reversing, disease pathology. This is particularly true in the case of neurodegenerative diseases, in which the affected cell types are not readily accessible for study. Since the first descriptions of iPSC-based disease modelling, considerable advances have been made in understanding the aetiology and progression of a diverse array of neurodegenerative conditions, including Parkinson's disease and Alzheimer's disease. To date, however, relatively few studies have succeeded in using iPSCs to model the neurodegeneration observed in cerebellar ataxia. Given the distinct neurodevelopmental phenotypes associated with certain types of ataxia, iPSC-based models are likely to provide significant insights, not only into disease progression, but also to the development of early-intervention therapies. In this review, we describe the existing iPSC-based disease models of this heterogeneous group of conditions and explore the challenges associated with generating cerebellar neurons from iPSCs, which have thus far hindered the expansion of this research. PMID:26136256

Team-based versus traditional primary care models and short-term outcomes after hospital discharge.

PubMed

Riverin, Bruno D; Li, Patricia; Naimi, Ashley I; Strumpf, Erin

2017-04-24

Strategies to reduce hospital readmission have been studied mainly at the local level. We assessed associations between population-wide policies supporting team-based primary care delivery models and short-term outcomes after hospital discharge. We extracted claims data on hospital admissions for any cause from 2002 to 2009 in the province of Quebec. We included older or chronically ill patients enrolled in team-based or traditional primary care practices. Outcomes were rates of readmission, emergency department visits and mortality in the 90 days following hospital discharge. We used inverse probability weighting to balance exposure groups on covariates and used marginal structural survival models to estimate rate differences and hazard ratios. We included 620 656 index admissions involving 312 377 patients. Readmission rates at any point in the 90-day post-discharge period were similar between primary care models. Patients enrolled in team-based primary care practices had lower 30-day rates of emergency department visits not associated with readmission (adjusted difference 7.5 per 1000 discharges, 95% confidence interval [CI] 4.2 to 10.8) and lower 30-day mortality (adjusted difference 3.8 deaths per 1000 discharges, 95% CI 1.7 to 5.9). The 30-day difference for mortality differed according to morbidity level (moderate morbidity: 1.0 fewer deaths per 1000 discharges in team-based practices, 95% CI 0.3 more to 2.3 fewer deaths; very high morbidity: 4.2 fewer deaths per 1000 discharges, 95% CI 3.0 to 5.3; p < 0.001). Our study showed that enrolment in the newer team-based primary care practices was associated with lower rates of postdischarge emergency department visits and death. We did not observe differences in readmission rates, which suggests that more targeted or intensive efforts may be needed to affect this outcome. © 2017 Canadian Medical Association or its licensors.

Associations between Screen-Based Sedentary Behaviour and Anxiety Symptoms in Mothers with Young Children

PubMed Central

Teychenne, Megan; Hinkley, Trina

2016-01-01

Objectives Anxiety is a serious illness and women (including mothers with young children) are at particular risk. Although physical activity (PA) may reduce anxiety risk, little research has investigated the link between sedentary behaviour and anxiety risk. The aim of this study was to examine the association between screen-based sedentary behaviour and anxiety symptoms, independent of PA, amongst mothers with young children. Methods During 2013–2014, 528 mothers with children aged 2–5 years completed self-report measures of recreational screen-based sedentary behaviour (TV/DVD/video viewing, computer/e-games/hand held device use) and anxiety symptoms (using the Hospital Anxiety and Depression Scale, HADS-A). Linear regression analyses examined the cross-sectional association between screen-based sedentary behaviour and anxiety symptoms. Results In models that adjusted for key demographic and behavioural covariates (including moderate- to vigorous-intensity PA, MVPA), computer/device use (B = 0.212; 95% CI = 0.048, 0.377) and total screen time (B = 0.109; 95% CI = 0.014, 0.205) were positively associated with heightened anxiety symptoms. TV viewing was not associated with anxiety symptoms in either model. Conclusions Higher levels of recreational computer or handheld device use and overall screen time may be linked to higher risk of anxiety symptoms in mothers with young children, independent of MVPA. Further longitudinal and intervention research is required to determine temporal associations. PMID:27191953

Testing a self-determination theory model of children's physical activity motivation: a cross-sectional study.

PubMed

Sebire, Simon J; Jago, Russell; Fox, Kenneth R; Edwards, Mark J; Thompson, Janice L

2013-09-26

Understanding children's physical activity motivation, its antecedents and associations with behavior is important and can be advanced by using self-determination theory. However, research among youth is largely restricted to adolescents and studies of motivation within certain contexts (e.g., physical education). There are no measures of self-determination theory constructs (physical activity motivation or psychological need satisfaction) for use among children and no previous studies have tested a self-determination theory-based model of children's physical activity motivation. The purpose of this study was to test the reliability and validity of scores derived from scales adapted to measure self-determination theory constructs among children and test a motivational model predicting accelerometer-derived physical activity. Cross-sectional data from 462 children aged 7 to 11 years from 20 primary schools in Bristol, UK were analysed. Confirmatory factor analysis was used to examine the construct validity of adapted behavioral regulation and psychological need satisfaction scales. Structural equation modelling was used to test cross-sectional associations between psychological need satisfaction, motivation types and physical activity assessed by accelerometer. The construct validity and reliability of the motivation and psychological need satisfaction measures were supported. Structural equation modelling provided evidence for a motivational model in which psychological need satisfaction was positively associated with intrinsic and identified motivation types and intrinsic motivation was positively associated with children's minutes in moderate-to-vigorous physical activity. The study provides evidence for the psychometric properties of measures of motivation aligned with self-determination theory among children. Children's motivation that is based on enjoyment and inherent satisfaction of physical activity is associated with their objectively-assessed physical activity and such motivation is positively associated with perceptions of psychological need satisfaction. These psychological factors represent potential malleable targets for interventions to increase children's physical activity.

A RETROSPECTIVE ANALYSIS OF MODEL UNCERTAINTY FOR FORECASTING HYDROLOGIC CHANGE

EPA Science Inventory

GIS-based hydrologic modeling offers a convenient means of assessing the impacts associated with land-cover/use change for environmental planning efforts. Alternative future scenarios can be used as input to hydrologic models and compared with existing conditions to evaluate pot...

Transition from 'model-based' to 'model-free' behavioral control in addiction: Involvement of the orbitofrontal cortex and dorsolateral striatum.

PubMed

Lucantonio, Federica; Caprioli, Daniele; Schoenbaum, Geoffrey

2014-01-01

Cocaine addiction is a complex and multidimensional process involving a number of behavioral and neural forms of plasticity. The behavioral transition from voluntary drug use to compulsive drug taking may be explained at the neural level by drug-induced changes in function or interaction between a flexible planning system, associated with prefrontal cortical regions, and a rigid habit system, associated with the striatum. The dichotomy between these two systems is operationalized in computational theory by positing model-based and model-free learning mechanisms, the former relying on an "internal model" of the environment and the latter on pre-computed or cached values to control behavior. In this review, we will suggest that model-free and model-based learning mechanisms appear to be differentially affected, at least in the case of psychostimulants such as cocaine, with the former being enhanced while the latter are disrupted. As a result, the behavior of long-term drug users becomes less flexible and responsive to the desirability of expected outcomes and more habitual, based on the long history of reinforcement. To support our specific proposal, we will review recent neural and behavioral evidence on the effect of psychostimulant exposure on orbitofrontal and dorsolateral striatum structure and function. This article is part of a Special Issue entitled 'NIDA 40th Anniversary Issue'. Published by Elsevier Ltd.

Family-centred care delivery: comparing models of primary care service delivery in Ontario.

PubMed

Mayo-Bruinsma, Liesha; Hogg, William; Taljaard, Monica; Dahrouge, Simone

2013-11-01

To determine whether models of primary care service delivery differ in their provision of family-centred care (FCC) and to identify practice characteristics associated with FCC. Cross-sectional study. Primary care practices in Ontario (ie, 35 salaried community health centres, 35 fee-for-service practices, 32 capitation-based health service organizations, and 35 blended remuneration family health networks) that belong to 4 models of primary care service delivery. A total of 137 practices, 363 providers, and 5144 patients. Measures of FCC in patient and provider surveys were based on the Primary Care Assessment Tool. Statistical analyses were conducted using linear mixed regression models and generalized estimating equations. Patient-reported FCC scores were high and did not vary significantly by primary care model. Larger panel size in a practice was associated with lower odds of patients reporting FCC. Provider-reported FCC scores were significantly higher in community health centres than in family health networks (P = .035). A larger number of nurse practitioners and clinical services on-site were both associated with higher FCC scores, while scores decreased as the number of family physicians in a practice increased and if practices were more rural. Based on provider and patient reports, primary care reform strategies that encourage larger practices and more patients per family physician might compromise the provision of FCC, while strategies that encourage multidisciplinary practices and a range of services might increase FCC.

Models to study gravitational biology of Mammalian reproduction

NASA Technical Reports Server (NTRS)

Tou, Janet; Ronca, April; Grindeland, Richard; Wade, Charles

2002-01-01

Mammalian reproduction evolved within Earth's 1-g gravitational field. As we move closer to the reality of space habitation, there is growing scientific interest in how different gravitational states influence reproduction in mammals. Habitation of space and extended spaceflight missions require prolonged exposure to decreased gravity (hypogravity, i.e., weightlessness). Lift-off and re-entry of the spacecraft are associated with exposure to increased gravity (hypergravity). Existing data suggest that spaceflight is associated with a constellation of changes in reproductive physiology and function. However, limited spaceflight opportunities and confounding effects of various nongravitational factors associated with spaceflight (i.e., radiation, stress) have led to the development of ground-based models for studying the effects of altered gravity on biological systems. Human bed rest and rodent hindlimb unloading paradigms are used to study exposure to hypogravity. Centrifugation is used to study hypergravity. Here, we review the results of spaceflight and ground-based models of altered gravity on reproductive physiology. Studies utilizing ground-based models that simulate hyper- and hypogravity have produced reproductive results similar to those obtained from spaceflight and are contributing new information on biological responses across the gravity continuum, thereby confirming the appropriateness of these models for studying reproductive responses to altered gravity and the underlying mechanisms of these responses. Together, these unique tools are yielding new insights into the gravitational biology of reproduction in mammals.

Modeling exposure–lag–response associations with distributed lag non-linear models

PubMed Central

Gasparrini, Antonio

2014-01-01

In biomedical research, a health effect is frequently associated with protracted exposures of varying intensity sustained in the past. The main complexity of modeling and interpreting such phenomena lies in the additional temporal dimension needed to express the association, as the risk depends on both intensity and timing of past exposures. This type of dependency is defined here as exposure–lag–response association. In this contribution, I illustrate a general statistical framework for such associations, established through the extension of distributed lag non-linear models, originally developed in time series analysis. This modeling class is based on the definition of a cross-basis, obtained by the combination of two functions to flexibly model linear or nonlinear exposure-responses and the lag structure of the relationship, respectively. The methodology is illustrated with an example application to cohort data and validated through a simulation study. This modeling framework generalizes to various study designs and regression models, and can be applied to study the health effects of protracted exposures to environmental factors, drugs or carcinogenic agents, among others. © 2013 The Authors. Statistics in Medicine published by John Wiley & Sons, Ltd. PMID:24027094

An assessment of the demographic and clinical correlates of the dimensions of alcohol use behaviour.

PubMed

Smith, Gillian W; Shevlin, Mark; Murphy, Jamie; Houston, James E

2010-01-01

To identify population-based clinical and demographic correlates of alcohol use dimensions. Using data from a population-based sample of Great Britain (n = 7849), structural equation modelling (SEM) was used to identify associations between demographic and clinical variables and two competing dimensional models of the Alcohol Use Disorders Identification Test (AUDIT). A two-factor SEM fit best. In this model, Factor 1, alcohol consumption, was associated with male sex, younger age, lower educational attainment, generalized anxiety disorder (GAD) and suicide attempts. Factor 2, alcohol-related problems, was associated with the demographic variables (to a lesser extent) and to a wider range of clinical variables, including depressive episode, GAD, mixed anxiety and depressive disorder, obsessive compulsive disorder, phobia, suicidal thoughts and suicide attempts. The one-factor SEM was associated with demographic and all assessed clinical correlates; however, this model did not fit the data well. Two main conclusions justify the two-factor approach to alcohol use classification. First, the model fit was considerably superior and, second, the dimensions of alcohol consumption and alcohol-related problems vary considerably in their associations with measures of demographic and clinical risk. A one-factor representation of alcohol use, for instance, would fail to recognize that measures of affective/anxiety disorders are more consistently related to alcohol-related problems than to alcohol consumption. It is suggested therefore that to fully understand the complexity of alcohol use behaviour and its associated risk, future research should acknowledge the basic underlying dimensional structure of the construct.

Projecting School Psychology Staffing Needs Using a Risk-Adjusted Model.

ERIC Educational Resources Information Center

Stellwagen, Kurt

A model is proposed to project optimal school psychology service ratios based upon the percentages of at risk students enrolled within a given school population. Using the standard 1:1,000 service ratio advocated by The National Association of School Psychologists (NASP) as a starting point, ratios are then adjusted based upon the size of three…

Detection of abrupt changes in dynamic systems

NASA Technical Reports Server (NTRS)

Willsky, A. S.

1984-01-01

Some of the basic ideas associated with the detection of abrupt changes in dynamic systems are presented. Multiple filter-based techniques and residual-based method and the multiple model and generalized likelihood ratio methods are considered. Issues such as the effect of unknown onset time on algorithm complexity and structure and robustness to model uncertainty are discussed.

Assessing the Reliability of Curriculum-Based Measurement: An Application of Latent Growth Modeling

ERIC Educational Resources Information Center

Yeo, Seungsoo; Kim, Dong-Il; Branum-Martin, Lee; Wayman, Miya Miura; Espin, Christine A.

2012-01-01

The purpose of this study was to demonstrate the use of Latent Growth Modeling (LGM) as a method for estimating reliability of Curriculum-Based Measurement (CBM) progress-monitoring data. The LGM approach permits the error associated with each measure to differ at each time point, thus providing an alternative method for examining of the…

Some Useful Cost-Benefit Criteria for Evaluating Computer-Based Test Delivery Models and Systems

ERIC Educational Resources Information Center

Luecht, Richard M.

2005-01-01

Computer-based testing (CBT) is typically implemented using one of three general test delivery models: (1) multiple fixed testing (MFT); (2) computer-adaptive testing (CAT); or (3) multistage testing (MSTs). This article reviews some of the real cost drivers associated with CBT implementation--focusing on item production costs, the costs…

Adaptive transmission disequilibrium test for family trio design.

PubMed

Yuan, Min; Tian, Xin; Zheng, Gang; Yang, Yaning

2009-01-01

The transmission disequilibrium test (TDT) is a standard method to detect association using family trio design. It is optimal for an additive genetic model. Other TDT-type tests optimal for recessive and dominant models have also been developed. Association tests using family data, including the TDT-type statistics, have been unified to a class of more comprehensive and flexable family-based association tests (FBAT). TDT-type tests have high efficiency when the genetic model is known or correctly specified, but may lose power if the model is mis-specified. Hence tests that are robust to genetic model mis-specification yet efficient are preferred. Constrained likelihood ratio test (CLRT) and MAX-type test have been shown to be efficiency robust. In this paper we propose a new efficiency robust procedure, referred to as adaptive TDT (aTDT). It uses the Hardy-Weinberg disequilibrium coefficient to identify the potential genetic model underlying the data and then applies the TDT-type test (or FBAT for general applications) corresponding to the selected model. Simulation demonstrates that aTDT is efficiency robust to model mis-specifications and generally outperforms the MAX test and CLRT in terms of power. We also show that aTDT has power close to, but much more robust, than the optimal TDT-type test based on a single genetic model. Applications to real and simulated data from Genetic Analysis Workshop (GAW) illustrate the use of our adaptive TDT.

Association of educational status with cardiovascular disease: Teheran Lipid and Glucose Study.

PubMed

Hajsheikholeslami, Farhad; Hatami, Masumeh; Hadaegh, Farzad; Ghanbarian, Arash; Azizi, Fereidoun

2011-06-01

The aim of this study was to evaluate the associations between educational level and cardiovascular disease (CVD) in an older Iranian population. To estimate the odds ratio (OR) of educational level in a cross-sectional study, logistic regression analysis was used on 1,788 men and 2,204 women (222 men and 204 women positive based on their CVD status) aged ≥ 45 years. In men, educational levels of college degree and literacy level below diploma were inversely associated with CVD in the multivariate model [0.52 (0.28-0.94), 0.61 (0.40-0.92), respectively], but diploma level did not show any significant association with CVD, neither in the crude model nor in the multivariate model. In women, increase in educational level was inversely associated with risk of CVD in the crude model, but in the multivariate adjusted model, literacy level below diploma decreased risk of CVD by 39%, compared with illiteracy. Our findings support those of developed countries that, along with other CVD risk factors, educational status has an inverse association with CVD among a representative Iranian population of older men and women.

A Geo-referenced 3D model of the Juan de Fuca Slab and associated seismicity

USGS Publications Warehouse

Blair, J.L.; McCrory, P.A.; Oppenheimer, D.H.; Waldhauser, F.

2011-01-01

We present a Geographic Information System (GIS) of a new 3-dimensional (3D) model of the subducted Juan de Fuca Plate beneath western North America and associated seismicity of the Cascadia subduction system. The geo-referenced 3D model was constructed from weighted control points that integrate depth information from hypocenter locations and regional seismic velocity studies. We used the 3D model to differentiate earthquakes that occur above the Juan de Fuca Plate surface from earthquakes that occur below the plate surface. This GIS project of the Cascadia subduction system supersedes the one previously published by McCrory and others (2006). Our new slab model updates the model with new constraints. The most significant updates to the model include: (1) weighted control points to incorporate spatial uncertainty, (2) an additional gridded slab surface based on the Generic Mapping Tools (GMT) Surface program which constructs surfaces based on splines in tension (see expanded description below), (3) double-differenced hypocenter locations in northern California to better constrain slab location there, and (4) revised slab shape based on new hypocenter profiles that incorporate routine depth uncertainties as well as data from new seismic-reflection and seismic-refraction studies. We also provide a 3D fly-through animation of the model for use as a visualization tool.

A Partial Least Square Approach for Modeling Gene-gene and Gene-environment Interactions When Multiple Markers Are Genotyped

PubMed Central

Wang, Tao; Ho, Gloria; Ye, Kenny; Strickler, Howard; Elston, Robert C.

2008-01-01

Genetic association studies achieve an unprecedented level of resolution in mapping disease genes by genotyping dense SNPs in a gene region. Meanwhile, these studies require new powerful statistical tools that can optimally handle a large amount of information provided by genotype data. A question that arises is how to model interactions between two genes. Simply modeling all possible interactions between the SNPs in two gene regions is not desirable because a greatly increased number of degrees of freedom can be involved in the test statistic. We introduce an approach to reduce the genotype dimension in modeling interactions. The genotype compression of this approach is built upon the information on both the trait and the cross-locus gametic disequilibrium between SNPs in two interacting genes, in such a way as to parsimoniously model the interactions without loss of useful information in the process of dimension reduction. As a result, it improves power to detect association in the presence of gene-gene interactions. This approach can be similarly applied for modeling gene-environment interactions. We compare this method with other approaches: the corresponding test without modeling any interaction, that based on a saturated interaction model, that based on principal component analysis, and that based on Tukey’s 1-df model. Our simulations suggest that this new approach has superior power to that of the other methods. In an application to endometrial cancer case-control data from the Women’s Health Initiative (WHI), this approach detected AKT1 and AKT2 as being significantly associated with endometrial cancer susceptibility by taking into account their interactions with BMI. PMID:18615621

A partial least-square approach for modeling gene-gene and gene-environment interactions when multiple markers are genotyped.

PubMed

Wang, Tao; Ho, Gloria; Ye, Kenny; Strickler, Howard; Elston, Robert C

2009-01-01

Genetic association studies achieve an unprecedented level of resolution in mapping disease genes by genotyping dense single nucleotype polymorphisms (SNPs) in a gene region. Meanwhile, these studies require new powerful statistical tools that can optimally handle a large amount of information provided by genotype data. A question that arises is how to model interactions between two genes. Simply modeling all possible interactions between the SNPs in two gene regions is not desirable because a greatly increased number of degrees of freedom can be involved in the test statistic. We introduce an approach to reduce the genotype dimension in modeling interactions. The genotype compression of this approach is built upon the information on both the trait and the cross-locus gametic disequilibrium between SNPs in two interacting genes, in such a way as to parsimoniously model the interactions without loss of useful information in the process of dimension reduction. As a result, it improves power to detect association in the presence of gene-gene interactions. This approach can be similarly applied for modeling gene-environment interactions. We compare this method with other approaches, the corresponding test without modeling any interaction, that based on a saturated interaction model, that based on principal component analysis, and that based on Tukey's one-degree-of-freedom model. Our simulations suggest that this new approach has superior power to that of the other methods. In an application to endometrial cancer case-control data from the Women's Health Initiative, this approach detected AKT1 and AKT2 as being significantly associated with endometrial cancer susceptibility by taking into account their interactions with body mass index.

12 CFR Appendix C to Part 167 - Risk-Based Capital Requirements-Internal-Ratings-Based and Advanced Measurement Approaches

Code of Federal Regulations, 2013 CFR

2013-01-01

... the savings association for one or more exposures is not commensurate with the risks associated with... to apply a provision of this appendix to one or more exposures, provided that: (1) The savings... during a sample period not used in model development. In this context, backtesting is one form of out-of...

12 CFR Appendix C to Part 167 - Risk-Based Capital Requirements-Internal-Ratings-Based and Advanced Measurement Approaches

Code of Federal Regulations, 2012 CFR

2012-01-01

... the savings association for one or more exposures is not commensurate with the risks associated with... to apply a provision of this appendix to one or more exposures, provided that: (1) The savings... during a sample period not used in model development. In this context, backtesting is one form of out-of...

A hybrid training approach for leaf area index estimation via Cubist and random forests machine-learning

NASA Astrophysics Data System (ADS)

Houborg, Rasmus; McCabe, Matthew F.

2018-01-01

With an increasing volume and dimensionality of Earth observation data, enhanced integration of machine-learning methodologies is needed to effectively analyze and utilize these information rich datasets. In machine-learning, a training dataset is required to establish explicit associations between a suite of explanatory 'predictor' variables and the target property. The specifics of this learning process can significantly influence model validity and portability, with a higher generalization level expected with an increasing number of observable conditions being reflected in the training dataset. Here we propose a hybrid training approach for leaf area index (LAI) estimation, which harnesses synergistic attributes of scattered in-situ measurements and systematically distributed physically based model inversion results to enhance the information content and spatial representativeness of the training data. To do this, a complimentary training dataset of independent LAI was derived from a regularized model inversion of RapidEye surface reflectances and subsequently used to guide the development of LAI regression models via Cubist and random forests (RF) decision tree methods. The application of the hybrid training approach to a broad set of Landsat 8 vegetation index (VI) predictor variables resulted in significantly improved LAI prediction accuracies and spatial consistencies, relative to results relying on in-situ measurements alone for model training. In comparing the prediction capacity and portability of the two machine-learning algorithms, a pair of relatively simple multi-variate regression models established by Cubist performed best, with an overall relative mean absolute deviation (rMAD) of ∼11%, determined based on a stringent scene-specific cross-validation approach. In comparison, the portability of RF regression models was less effective (i.e., an overall rMAD of ∼15%), which was attributed partly to model saturation at high LAI in association with inherent extrapolation and transferability limitations. Explanatory VIs formed from bands in the near-infrared (NIR) and shortwave infrared domains (e.g., NDWI) were associated with the highest predictive ability, whereas Cubist models relying entirely on VIs based on NIR and red band combinations (e.g., NDVI) were associated with comparatively high uncertainties (i.e., rMAD ∼ 21%). The most transferable and best performing models were based on combinations of several predictor variables, which included both NDWI- and NDVI-like variables. In this process, prior screening of input VIs based on an assessment of variable relevance served as an effective mechanism for optimizing prediction accuracies from both Cubist and RF. While this study demonstrated benefit in combining data mining operations with physically based constraints via a hybrid training approach, the concept of transferability and portability warrants further investigations in order to realize the full potential of emerging machine-learning techniques for regression purposes.

Recollection can be Weak and Familiarity can be Strong

PubMed Central

Ingram, Katherine M.; Mickes, Laura; Wixted, John T.

2012-01-01

The Remember/Know procedure is widely used to investigate recollection and familiarity in recognition memory, but almost all of the results obtained using that procedure can be readily accommodated by a unidimensional model based on signal-detection theory. The unidimensional model holds that Remember judgments reflect strong memories (associated with high confidence, high accuracy, and fast reaction times), whereas Know judgments reflect weaker memories (associated with lower confidence, lower accuracy, and slower reaction times). Although this is invariably true on average, a new two-dimensional account (the Continuous Dual-Process model) suggests that Remember judgments made with low confidence should be associated with lower old/new accuracy, but higher source accuracy, than Know judgments made with high confidence. We tested this prediction – and found evidence to support it – using a modified Remember/Know procedure in which participants were first asked to indicate a degree of recollection-based or familiarity-based confidence for each word presented on a recognition test and were then asked to recollect the color (red or blue) and screen location (top or bottom) associated with the word at study. For familiarity-based decisions, old/new accuracy increased with old/new confidence, but source accuracy did not (suggesting that stronger old/new memory was supported by higher degrees of familiarity). For recollection-based decisions, both old/new accuracy and source accuracy increased with old/new confidence (suggesting that stronger old/new memory was supported by higher degrees of recollection). These findings suggest that recollection and familiarity are continuous processes and that participants can indicate which process mainly contributed to their recognition decisions. PMID:21967320

Privacy-preserving periodical publishing for medical information

NASA Astrophysics Data System (ADS)

Jin, Hua; Ju, Shi-guang; Liu, Shan-cheng

2013-07-01

Existing privacy-preserving publishing models can not meet the requirement of periodical publishing for medical information whether these models are static or dynamic. This paper presents a (k,l)-anonymity model with keeping individual association and a principle based on (Epsilon)-invariance group for subsequent periodical publishing, and then, the PKIA and PSIGI algorithms are designed for them. The proposed methods can reserve more individual association with privacy-preserving and have better publishing quality. Experiments confirm our theoretical results and its practicability.

A process model of the implications of spillover from coparenting conflicts into the parent–child attachment relationship in adolescence

PubMed Central

Martin, Meredith J.; Sturge-Apple, Melissa L.; Davies, Patrick T.; Romero, Christine V.; Buckholz, Abigail

2017-01-01

Drawing on a two-wave, multimethod, multi-informant design, this study provides the first test of a process model of spillover specifying why and how disruptions in the coparenting relationship influence the parent–adolescent attachment relationship. One hundred ninety-four families with an adolescent aged 12–14 (M age = 12.4) were followed for 1 year. Mothers and adolescents participated in two experimental tasks designed to elicit behavioral expressions of parent and adolescent functioning within the attachment relationship. Using a novel observational approach, maternal safe haven, secure base, and harshness (i.e., hostility and control) were compared as potential unique mediators of the association between conflict in the coparenting relationship and adolescent problems. Path models indicated that, although coparenting conflicts were broadly associated with maternal parenting difficulties, only secure base explained the link to adolescent adjustment. Adding further specificity to the process model, maternal secure base support was uniquely associated with adolescent adjustment through deficits in adolescents’ secure exploration. Results support the hypothesis that coparenting disagreements undermine adolescent adjustment in multiple domains specifically by disrupting mothers’ ability to provide a caregiving environment that supports adolescent exploration during a developmental period in which developing autonomy is a crucial stage-salient task. PMID:28401834

A predictive framework for evaluating models of semantic organization in free recall

PubMed Central

Morton, Neal W; Polyn, Sean M.

2016-01-01

Research in free recall has demonstrated that semantic associations reliably influence the organization of search through episodic memory. However, the specific structure of these associations and the mechanisms by which they influence memory search remain unclear. We introduce a likelihood-based model-comparison technique, which embeds a model of semantic structure within the context maintenance and retrieval (CMR) model of human memory search. Within this framework, model variants are evaluated in terms of their ability to predict the specific sequence in which items are recalled. We compare three models of semantic structure, latent semantic analysis (LSA), global vectors (GloVe), and word association spaces (WAS), and find that models using WAS have the greatest predictive power. Furthermore, we find evidence that semantic and temporal organization is driven by distinct item and context cues, rather than a single context cue. This finding provides important constraint for theories of memory search. PMID:28331243

Paradigm Change: Alternate Approaches to Constitutive and Necking Models for Sheet Metal Forming

NASA Astrophysics Data System (ADS)

Stoughton, Thomas B.; Yoon, Jeong Whan

2011-08-01

This paper reviews recent work proposing paradigm changes for the currently popular approach to constitutive and failure modeling, focusing on the use of non-associated flow rules to enable greater flexibility to capture the anisotropic yield and flow behavior of metals using less complex functions than those needed under associated flow to achieve that same level of fidelity to experiment, and on the use of stress-based metrics to more reliably predict necking limits under complex conditions of non-linear forming. The paper discusses motivating factors and benefits in favor of both associated and non-associated flow models for metal forming, including experimental, theoretical, and practical aspects. This review is followed by a discussion of the topic of the forming limits, the limitations of strain analysis, the evidence in favor of stress analysis, the effects of curvature, bending/unbending cycles, triaxial stress conditions, and the motivation for the development of a new type of forming limit diagram based on the effective plastic strain or equivalent plastic work in combination with a directional parameter that accounts for the current stress condition.

Interconnected growing self-organizing maps for auditory and semantic acquisition modeling

PubMed Central

Cao, Mengxue; Li, Aijun; Fang, Qiang; Kaufmann, Emily; Kröger, Bernd J.

2014-01-01

Based on the incremental nature of knowledge acquisition, in this study we propose a growing self-organizing neural network approach for modeling the acquisition of auditory and semantic categories. We introduce an Interconnected Growing Self-Organizing Maps (I-GSOM) algorithm, which takes associations between auditory information and semantic information into consideration, in this paper. Direct phonetic–semantic association is simulated in order to model the language acquisition in early phases, such as the babbling and imitation stages, in which no phonological representations exist. Based on the I-GSOM algorithm, we conducted experiments using paired acoustic and semantic training data. We use a cyclical reinforcing and reviewing training procedure to model the teaching and learning process between children and their communication partners. A reinforcing-by-link training procedure and a link-forgetting procedure are introduced to model the acquisition of associative relations between auditory and semantic information. Experimental results indicate that (1) I-GSOM has good ability to learn auditory and semantic categories presented within the training data; (2) clear auditory and semantic boundaries can be found in the network representation; (3) cyclical reinforcing and reviewing training leads to a detailed categorization as well as to a detailed clustering, while keeping the clusters that have already been learned and the network structure that has already been developed stable; and (4) reinforcing-by-link training leads to well-perceived auditory–semantic associations. Our I-GSOM model suggests that it is important to associate auditory information with semantic information during language acquisition. Despite its high level of abstraction, our I-GSOM approach can be interpreted as a biologically-inspired neurocomputational model. PMID:24688478

Sensorimotor Learning Biases Choice Behavior: A Learning Neural Field Model for Decision Making

PubMed Central

Schöner, Gregor; Gail, Alexander

2012-01-01

According to a prominent view of sensorimotor processing in primates, selection and specification of possible actions are not sequential operations. Rather, a decision for an action emerges from competition between different movement plans, which are specified and selected in parallel. For action choices which are based on ambiguous sensory input, the frontoparietal sensorimotor areas are considered part of the common underlying neural substrate for selection and specification of action. These areas have been shown capable of encoding alternative spatial motor goals in parallel during movement planning, and show signatures of competitive value-based selection among these goals. Since the same network is also involved in learning sensorimotor associations, competitive action selection (decision making) should not only be driven by the sensory evidence and expected reward in favor of either action, but also by the subject's learning history of different sensorimotor associations. Previous computational models of competitive neural decision making used predefined associations between sensory input and corresponding motor output. Such hard-wiring does not allow modeling of how decisions are influenced by sensorimotor learning or by changing reward contingencies. We present a dynamic neural field model which learns arbitrary sensorimotor associations with a reward-driven Hebbian learning algorithm. We show that the model accurately simulates the dynamics of action selection with different reward contingencies, as observed in monkey cortical recordings, and that it correctly predicted the pattern of choice errors in a control experiment. With our adaptive model we demonstrate how network plasticity, which is required for association learning and adaptation to new reward contingencies, can influence choice behavior. The field model provides an integrated and dynamic account for the operations of sensorimotor integration, working memory and action selection required for decision making in ambiguous choice situations. PMID:23166483

Development of a Logic Model to Guide Evaluations of the ASCA National Model for School Counseling Programs

ERIC Educational Resources Information Center

Martin, Ian; Carey, John

2014-01-01

A logic model was developed based on an analysis of the 2012 American School Counselor Association (ASCA) National Model in order to provide direction for program evaluation initiatives. The logic model identified three outcomes (increased student achievement/gap reduction, increased school counseling program resources, and systemic change and…

Coiled-Coil Hydrogels. Effect of Grafted Copolymer Composition and Cyclization on Gelation

PubMed Central

Dušek, Karel; Dušková-Smrčková, Miroslava; Yang, Jiyuan; Kopeček, Jindřich

2009-01-01

A mean-field theoretical approach was developed to model gelation of solutions of hydrophilic polymers with grafted peptide motifs capable of forming associates of coiled-coil type. The model addresses the competition between associates engaged in branching and cyclization. It results in relative concentrations of intra- and intermolecular associates in dependence on associate strength and motif concentration. The cyclization probability is derived from the model of equivalent Gaussian chain and takes into account all possible paths connecting the interacting motifs. Examination of the association-dissociation equilibria, controlled by the equilibrium constant for association taken as input information, determines the fractions of inter- and intramolecularly associated motifs. The gelation model is based on the statistical theory of branching processes and in combination with the cyclization model predicts the critical concentration delimiting the regions of gelled and liquid states of the system. A comparison between predictions of the model and experimental data available for aqueous solutions of poly[N-(2-hydroxypropyl)methacrylamide] grafted with oppositely charged pentaheptad peptides, CCE and CCK, indicates that the association constant of grafted motifs by four orders of magnitude lower than that of free motifs. It is predicted that at the critical concentration of each motif of about 6×10−7 mol/cm3, about half of motifs in associated state is engaged in intramolecular bonds. PMID:20160932

Air pollution and survival within the Washington University-EPRI veterans cohort: risks based on modeled estimates of ambient levels of hazardous and criteria air pollutants.

PubMed

Lipfert, Frederick W; Wyzga, Ronald E; Baty, Jack D; Miller, J Philip

2009-04-01

For this paper, we considered relationships between mortality, vehicular traffic density, and ambient levels of 12 hazardous air pollutants, elemental carbon (EC), oxides of nitrogen (NOx), sulfur dioxide (SO2), and sulfate (SO4(2-)). These pollutant species were selected as markers for specific types of emission sources, including vehicular traffic, coal combustion, smelters, and metal-working industries. Pollutant exposures were estimated using emissions inventories and atmospheric dispersion models. We analyzed associations between county ambient levels of these pollutants and survival patterns among approximately 70,000 U.S. male veterans by mortality period (1976-2001 and subsets), type of exposure model, and traffic density level. We found significant associations between all-cause mortality and traffic-related air quality indicators and with traffic density per se, with stronger associations for benzene, formaldehyde, diesel particulate, NOx, and EC. The maximum effect on mortality for all cohort subjects during the 26-yr follow-up period is approximately 10%, but most of the pollution-related deaths in this cohort occurred in the higher-traffic counties, where excess risks approach 20%. However, mortality associations with diesel particulates are similar in high- and low-traffic counties. Sensitivity analyses show risks decreasing slightly over time and minor differences between linear and logarithmic exposure models. Two-pollutant models show stronger risks associated with specific traffic-related pollutants than with traffic density per se, although traffic density retains statistical significance in most cases. We conclude that tailpipe emissions of both gases and particles are among the most significant and robust predictors of mortality in this cohort and that most of those associations have weakened over time. However, we have not evaluated possible contributions from road dust or traffic noise. Stratification by traffic density level suggests the presence of response thresholds, especially for gaseous pollutants. Because of their wider distributions of estimated exposures, risk estimates based on emissions and atmospheric dispersion models tend to be more precise than those based on local ambient measurements.

A flexible framework for process-based hydraulic and water quality modeling of stormwater green infrastructure performance

EPA Science Inventory

Background Models that allow for design considerations of green infrastructure (GI) practices to control stormwater runoff and associated contaminants have received considerable attention in recent years. While popular, generally, the GI models are relatively simplistic. However,...

An RES-Based Model for Risk Assessment and Prediction of Backbreak in Bench Blasting

NASA Astrophysics Data System (ADS)

Faramarzi, F.; Ebrahimi Farsangi, M. A.; Mansouri, H.

2013-07-01

Most blasting operations are associated with various forms of energy loss, emerging as environmental side effects of rock blasting, such as flyrock, vibration, airblast, and backbreak. Backbreak is an adverse phenomenon in rock blasting operations, which imposes risk and increases operation expenses because of safety reduction due to the instability of walls, poor fragmentation, and uneven burden in subsequent blasts. In this paper, based on the basic concepts of a rock engineering systems (RES) approach, a new model for the prediction of backbreak and the risk associated with a blast is presented. The newly suggested model involves 16 effective parameters on backbreak due to blasting, while retaining simplicity as well. The data for 30 blasts, carried out at Sungun copper mine, western Iran, were used to predict backbreak and the level of risk corresponding to each blast by the RES-based model. The results obtained were compared with the backbreak measured for each blast, which showed that the level of risk achieved is in consistence with the backbreak measured. The maximum level of risk [vulnerability index (VI) = 60] was associated with blast No. 2, for which the corresponding average backbreak was the highest achieved (9.25 m). Also, for blasts with levels of risk under 40, the minimum average backbreaks (<4 m) were observed. Furthermore, to evaluate the model performance for backbreak prediction, the coefficient of correlation ( R 2) and root mean square error (RMSE) of the model were calculated ( R 2 = 0.8; RMSE = 1.07), indicating the good performance of the model.

MODTRAN Radiance Modeling of Multi-Angle Worldview-2 Imagery

DTIC Science & Technology

2013-09-01

this thesis, multi-angle CHRIS data has been used to validate canopy BRDF models generated using PROSPECT and SAILH radiative transfer models (D’Urso...67 1. MODTRAN Modeling using BRDF Algorithms .............................67 2. MODTRAN Modeling of Hyperspectral Data...associated with BRDF , and (2) develop software- 2 based atmospheric models , using parameters similar to those found in the imagery, for comparison to

FHSA-SED: Two-Locus Model Detection for Genome-Wide Association Study with Harmony Search Algorithm.

PubMed

Tuo, Shouheng; Zhang, Junying; Yuan, Xiguo; Zhang, Yuanyuan; Liu, Zhaowen

2016-01-01

Two-locus model is a typical significant disease model to be identified in genome-wide association study (GWAS). Due to intensive computational burden and diversity of disease models, existing methods have drawbacks on low detection power, high computation cost, and preference for some types of disease models. In this study, two scoring functions (Bayesian network based K2-score and Gini-score) are used for characterizing two SNP locus as a candidate model, the two criteria are adopted simultaneously for improving identification power and tackling the preference problem to disease models. Harmony search algorithm (HSA) is improved for quickly finding the most likely candidate models among all two-locus models, in which a local search algorithm with two-dimensional tabu table is presented to avoid repeatedly evaluating some disease models that have strong marginal effect. Finally G-test statistic is used to further test the candidate models. We investigate our method named FHSA-SED on 82 simulated datasets and a real AMD dataset, and compare it with two typical methods (MACOED and CSE) which have been developed recently based on swarm intelligent search algorithm. The results of simulation experiments indicate that our method outperforms the two compared algorithms in terms of detection power, computation time, evaluation times, sensitivity (TPR), specificity (SPC), positive predictive value (PPV) and accuracy (ACC). Our method has identified two SNPs (rs3775652 and rs10511467) that may be also associated with disease in AMD dataset.

FHSA-SED: Two-Locus Model Detection for Genome-Wide Association Study with Harmony Search Algorithm

PubMed Central

Tuo, Shouheng; Zhang, Junying; Yuan, Xiguo; Zhang, Yuanyuan; Liu, Zhaowen

2016-01-01

Motivation Two-locus model is a typical significant disease model to be identified in genome-wide association study (GWAS). Due to intensive computational burden and diversity of disease models, existing methods have drawbacks on low detection power, high computation cost, and preference for some types of disease models. Method In this study, two scoring functions (Bayesian network based K2-score and Gini-score) are used for characterizing two SNP locus as a candidate model, the two criteria are adopted simultaneously for improving identification power and tackling the preference problem to disease models. Harmony search algorithm (HSA) is improved for quickly finding the most likely candidate models among all two-locus models, in which a local search algorithm with two-dimensional tabu table is presented to avoid repeatedly evaluating some disease models that have strong marginal effect. Finally G-test statistic is used to further test the candidate models. Results We investigate our method named FHSA-SED on 82 simulated datasets and a real AMD dataset, and compare it with two typical methods (MACOED and CSE) which have been developed recently based on swarm intelligent search algorithm. The results of simulation experiments indicate that our method outperforms the two compared algorithms in terms of detection power, computation time, evaluation times, sensitivity (TPR), specificity (SPC), positive predictive value (PPV) and accuracy (ACC). Our method has identified two SNPs (rs3775652 and rs10511467) that may be also associated with disease in AMD dataset. PMID:27014873

ALC: automated reduction of rule-based models

PubMed Central

Koschorreck, Markus; Gilles, Ernst Dieter

2008-01-01

Background Combinatorial complexity is a challenging problem for the modeling of cellular signal transduction since the association of a few proteins can give rise to an enormous amount of feasible protein complexes. The layer-based approach is an approximative, but accurate method for the mathematical modeling of signaling systems with inherent combinatorial complexity. The number of variables in the simulation equations is highly reduced and the resulting dynamic models show a pronounced modularity. Layer-based modeling allows for the modeling of systems not accessible previously. Results ALC (Automated Layer Construction) is a computer program that highly simplifies the building of reduced modular models, according to the layer-based approach. The model is defined using a simple but powerful rule-based syntax that supports the concepts of modularity and macrostates. ALC performs consistency checks on the model definition and provides the model output in different formats (C MEX, MATLAB, Mathematica and SBML) as ready-to-run simulation files. ALC also provides additional documentation files that simplify the publication or presentation of the models. The tool can be used offline or via a form on the ALC website. Conclusion ALC allows for a simple rule-based generation of layer-based reduced models. The model files are given in different formats as ready-to-run simulation files. PMID:18973705

Timescale analysis of rule-based biochemical reaction networks

PubMed Central

Klinke, David J.; Finley, Stacey D.

2012-01-01

The flow of information within a cell is governed by a series of protein-protein interactions that can be described as a reaction network. Mathematical models of biochemical reaction networks can be constructed by repetitively applying specific rules that define how reactants interact and what new species are formed upon reaction. To aid in understanding the underlying biochemistry, timescale analysis is one method developed to prune the size of the reaction network. In this work, we extend the methods associated with timescale analysis to reaction rules instead of the species contained within the network. To illustrate this approach, we applied timescale analysis to a simple receptor-ligand binding model and a rule-based model of Interleukin-12 (IL-12) signaling in näive CD4+ T cells. The IL-12 signaling pathway includes multiple protein-protein interactions that collectively transmit information; however, the level of mechanistic detail sufficient to capture the observed dynamics has not been justified based upon the available data. The analysis correctly predicted that reactions associated with JAK2 and TYK2 binding to their corresponding receptor exist at a pseudo-equilibrium. In contrast, reactions associated with ligand binding and receptor turnover regulate cellular response to IL-12. An empirical Bayesian approach was used to estimate the uncertainty in the timescales. This approach complements existing rank- and flux-based methods that can be used to interrogate complex reaction networks. Ultimately, timescale analysis of rule-based models is a computational tool that can be used to reveal the biochemical steps that regulate signaling dynamics. PMID:21954150

A Path Analysis of Latino Parental, Teenager and Cultural Variables in Teenagers' Sexual Attitudes, Norms, Self-Efficacy, and Sexual Intentions.

PubMed

Gaioso, Vanessa Pirani; Villarruel, Antonia Maria; Wilson, Lynda Anne; Azuero, Andres; Childs, Gwendolyn Denice; Davies, Susan Lane

2015-01-01

to test a theoretical model based on the Parent-Based Expansion of the Theory of Planned Behavior examining relation between selected parental, teenager and cultural variables and Latino teenagers' intentions to engage in sexual behavior. a cross-sectional correlational design based on a secondary data analysis of 130 Latino parent and teenager dyads. regression and path analysis procedures were used to test seven hypotheses and the results demonstrated partial support for the model. Parent familism and knowledge about sex were significantly associated with parents' attitudes toward sexual communication with their teenagers. Parent Latino acculturation was negatively associated with parents' self-efficacy toward sexual communication with their teenagers and positevely associated with parents' subjective norms toward sexual communication with their teenagers. Teenager knowledge about sex was significantly associated with higher levels of teenagers' attitudes and subjective norms about sexual communication with parents. Only the predictor of teenagers' attitudes toward having sex in the next 3 months was significantly associated with teenagers' intentions to have sex in the next 3 months. the results of this study provide important information to guide future research that can inform development of interventions to prevent risky teenager sexual behavior among Latinos.

The demand control model and circadian saliva cortisol variations in a Swedish population based sample (The PART study)

PubMed Central

Alderling, Magnus; Theorell, Töres; de la Torre, Bartolomé; Lundberg, Ingvar

2006-01-01

Background Previous studies of the relationship between job strain and blood or saliva cortisol levels have been small and based on selected occupational groups. Our aim was to examine the association between job strain and saliva cortisol levels in a population-based study in which a number of potential confounders could be adjusted for. Methods The material derives from a population-based study in Stockholm on mental health and its potential determinants. Two data collections were performed three years apart with more than 8500 subjects responding to a questionnaire in both waves. In this paper our analyses are based on 529 individuals who held a job, participated in both waves as well as in an interview linked to the second wave. They gave saliva samples at awakening, half an hour later, at lunchtime and before going to bed on a weekday in close connection with the interview. Job control and job demands were assessed from the questionnaire in the second wave. Mixed models were used to analyse the association between the demand control model and saliva cortisol. Results Women in low strain jobs (high control and low demands) had significantly lower cortisol levels half an hour after awakening than women in high strain (low control and high demands), active (high control and high demands) or passive jobs (low control and low demands). There were no significant differences between the groups during other parts of the day and furthermore there was no difference between the job strain, active and passive groups. For men, no differences were found between demand control groups. Conclusion This population-based study, on a relatively large sample, weakly support the hypothesis that the demand control model is associated with saliva cortisol concentrations. PMID:17129377

Assessing alternative measures of wealth in health research.

PubMed

Cubbin, Catherine; Pollack, Craig; Flaherty, Brian; Hayward, Mark; Sania, Ayesha; Vallone, Donna; Braveman, Paula

2011-05-01

We assessed whether it would be feasible to replace the standard measure of net worth with simpler measures of wealth in population-based studies examining associations between wealth and health. We used data from the 2004 Survey of Consumer Finances (respondents aged 25-64 years) and the 2004 Health and Retirement Survey (respondents aged 50 years or older) to construct logistic regression models relating wealth to health status and smoking. For our wealth measure, we used the standard measure of net worth as well as 9 simpler measures of wealth, and we compared results among the 10 models. In both data sets and for both health indicators, models using simpler wealth measures generated conclusions about the association between wealth and health that were similar to the conclusions generated by models using net worth. The magnitude and significance of the odds ratios were similar for the covariates in multivariate models, and the model-fit statistics for models using these simpler measures were similar to those for models using net worth. Our findings suggest that simpler measures of wealth may be acceptable in population-based studies of health.

Development of Rock Engineering Systems-Based Models for Flyrock Risk Analysis and Prediction of Flyrock Distance in Surface Blasting

NASA Astrophysics Data System (ADS)

Faramarzi, Farhad; Mansouri, Hamid; Farsangi, Mohammad Ali Ebrahimi

2014-07-01

The environmental effects of blasting must be controlled in order to comply with regulatory limits. Because of safety concerns and risk of damage to infrastructures, equipment, and property, and also having a good fragmentation, flyrock control is crucial in blasting operations. If measures to decrease flyrock are taken, then the flyrock distance would be limited, and, in return, the risk of damage can be reduced or eliminated. This paper deals with modeling the level of risk associated with flyrock and, also, flyrock distance prediction based on the rock engineering systems (RES) methodology. In the proposed models, 13 effective parameters on flyrock due to blasting are considered as inputs, and the flyrock distance and associated level of risks as outputs. In selecting input data, the simplicity of measuring input data was taken into account as well. The data for 47 blasts, carried out at the Sungun copper mine, western Iran, were used to predict the level of risk and flyrock distance corresponding to each blast. The obtained results showed that, for the 47 blasts carried out at the Sungun copper mine, the level of estimated risks are mostly in accordance with the measured flyrock distances. Furthermore, a comparison was made between the results of the flyrock distance predictive RES-based model, the multivariate regression analysis model (MVRM), and, also, the dimensional analysis model. For the RES-based model, R 2 and root mean square error (RMSE) are equal to 0.86 and 10.01, respectively, whereas for the MVRM and dimensional analysis, R 2 and RMSE are equal to (0.84 and 12.20) and (0.76 and 13.75), respectively. These achievements confirm the better performance of the RES-based model over the other proposed models.

Science anxiety and social cognitive factors predicting STEM career aspirations of high school freshmen in general science class

NASA Astrophysics Data System (ADS)

Skells, Kristin Marie

Extant data was used to consider the association between science anxiety, social cognitive factors and STEM career aspirations of high school freshmen in general science classes. An adapted model based on social cognitive career theory (SCCT) was used to consider these relationships, with science anxiety functioning as a barrier in the model. The study assessed the following research questions: (1) Do social cognitive variables relate in the expected way to STEM career aspirations based on SCCT for ninth graders taking general science classes? (2) Is there an association between science anxiety and outcomes and processes identified in the SCCT model for ninth graders taking general science classes? (3) Does gender moderate these relationships? Results indicated that support was found for many of the central tenants of the SCCT model. Science anxiety was associated with prior achievement, self-efficacy, and science interest, although it did not relate directly to STEM career goals. Gender was found to moderate only the relationship between prior achievement and science self-efficacy.

Human Gut Microbiota Predicts Susceptibility to Vibrio cholerae Infection.

PubMed

Midani, Firas S; Weil, Ana A; Chowdhury, Fahima; Begum, Yasmin A; Khan, Ashraful I; Debela, Meti D; Durand, Heather K; Reese, Aspen T; Nimmagadda, Sai N; Silverman, Justin D; Ellis, Crystal N; Ryan, Edward T; Calderwood, Stephen B; Harris, Jason B; Qadri, Firdausi; David, Lawrence A; LaRocque, Regina C

2018-04-12

Cholera is a public health problem worldwide and the risk factors for infection are only partially understood. We prospectively studied household contacts of cholera patients to compare those who were infected with those who were not. We constructed predictive machine learning models of susceptibility using baseline gut microbiota data. We identified bacterial taxa associated with susceptibility to Vibrio cholerae infection and tested these taxa for interactions with V. cholerae in vitro. We found that machine learning models based on gut microbiota predicted V. cholerae infection as well as models based on known clinical and epidemiological risk factors. A 'predictive gut microbiota' of roughly 100 bacterial taxa discriminated between contacts who developed infection and those who did not. Susceptibility to cholera was associated with depleted levels of microbes from the phylum Bacteroidetes. By contrast, a microbe associated with cholera by our modeling framework, Paracoccus aminovorans, promoted the in vitro growth of V. cholerae. Gut microbiota structure, clinical outcome, and age were also linked. These findings support the hypothesis that abnormal gut microbial communities are a host factor related to V. cholerae susceptibility.

Statistical shape model-based reconstruction of a scaled, patient-specific surface model of the pelvis from a single standard AP x-ray radiograph

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

Zheng Guoyan

2010-04-15

Purpose: The aim of this article is to investigate the feasibility of using a statistical shape model (SSM)-based reconstruction technique to derive a scaled, patient-specific surface model of the pelvis from a single standard anteroposterior (AP) x-ray radiograph and the feasibility of estimating the scale of the reconstructed surface model by performing a surface-based 3D/3D matching. Methods: Data sets of 14 pelvises (one plastic bone, 12 cadavers, and one patient) were used to validate the single-image based reconstruction technique. This reconstruction technique is based on a hybrid 2D/3D deformable registration process combining a landmark-to-ray registration with a SSM-based 2D/3D reconstruction.more » The landmark-to-ray registration was used to find an initial scale and an initial rigid transformation between the x-ray image and the SSM. The estimated scale and rigid transformation were used to initialize the SSM-based 2D/3D reconstruction. The optimal reconstruction was then achieved in three stages by iteratively matching the projections of the apparent contours extracted from a 3D model derived from the SSM to the image contours extracted from the x-ray radiograph: Iterative affine registration, statistical instantiation, and iterative regularized shape deformation. The image contours are first detected by using a semiautomatic segmentation tool based on the Livewire algorithm and then approximated by a set of sparse dominant points that are adaptively sampled from the detected contours. The unknown scales of the reconstructed models were estimated by performing a surface-based 3D/3D matching between the reconstructed models and the associated ground truth models that were derived from a CT-based reconstruction method. Such a matching also allowed for computing the errors between the reconstructed models and the associated ground truth models. Results: The technique could reconstruct the surface models of all 14 pelvises directly from the landmark-based initialization. Depending on the surface-based matching techniques, the reconstruction errors were slightly different. When a surface-based iterative affine registration was used, an average reconstruction error of 1.6 mm was observed. This error was increased to 1.9 mm, when a surface-based iterative scaled rigid registration was used. Conclusions: It is feasible to reconstruct a scaled, patient-specific surface model of the pelvis from single standard AP x-ray radiograph using the present approach. The unknown scale of the reconstructed model can be estimated by performing a surface-based 3D/3D matching.« less

Integrating Agent Models of Subsistence Farming With Dynamic Models of Water Distribution

NASA Astrophysics Data System (ADS)

Bithell, M.; Brasington, J.

2004-12-01

Subsistence farming communities are dependent on the landscape to provide the resource base upon which their societies can be built. A key component of this is the role of climate, and the feedback between rainfall, crop growth and land clearance, and their coupling to the hydrological cycle. Temporal fluctuations in rainfall on timescales from annual through to decadal and longer, and the associated changes in in the spatial distribution of water availability mediated by the soil-type, slope and landcover determine the locations within the landscape that can support agriculture, and control sustainability of farming practices. We seek to make an integrated modelling system to represent land use change by coupling an agent based model of subsistence farming, and the associated exploitation of natural resources, to a realistic representation of the hydrology at the catchment scale, using TOPMODEL to map the spatial distribution of crop water stress for given time-series of rainfall. In this way we can, for example, investigate how demographic changes and associated removal of forest cover influence the possibilities for field locations within the catchment, through changes in ground water availability. The framework for this modelling exercise will be presented and preliminary results from this system will be discussed.

Patient or physician preferences for decision analysis: the prenatal genetic testing decision.

PubMed

Heckerling, P S; Verp, M S; Albert, N

1999-01-01

The choice between amniocentesis and chorionic villus sampling for prenatal genetic testing involves tradeoffs of the benefits and risks of the tests. Decision analysis is a method of explicitly weighing such tradeoffs. The authors examined the relationship between prenatal test choices made by patients and the choices prescribed by decision-analytic models based on their preferences, and separate models based on the preferences of their physicians. Preferences were assessed using written scenarios describing prenatal testing outcomes, and were recorded on linear rating scales. After adjustment for sociodemographic and obstetric confounders, test choice was significantly associated with the choice of decision models based on patient preferences (odds ratio 4.44; Cl, 2.53 to 7.78), but not with the choice of models based on the preferences of the physicians (odds ratio 1.60; Cl, 0.79 to 3.26). Agreement between decision analyses based on patient preferences and on physician preferences was little better than chance (kappa = 0.085+/-0.063). These results were robust both to changes in the decision-analytic probabilities and to changes in the model structure itself to simulate non-expected utility decision rules. The authors conclude that patient but not physician preferences, incorporated in decision models, correspond to the choice of amniocentesis or chorionic villus sampling made by the patient. Nevertheless, because patient preferences were assessed after referral for genetic testing, prospective preference-assessment studies will be necessary to confirm this association.

Solution influence on biomolecular equilibria - Nucleic acid base associations

NASA Technical Reports Server (NTRS)

Pohorille, A.; Pratt, L. R.; Burt, S. K.; Macelroy, R. D.

1984-01-01

Various attempts to construct an understanding of the influence of solution environment on biomolecular equilibria at the molecular level using computer simulation are discussed. First, the application of the formal statistical thermodynamic program for investigating biomolecular equilibria in solution is presented, addressing modeling and conceptual simplications such as perturbative methods, long-range interaction approximations, surface thermodynamics, and hydration shell. Then, Monte Carlo calculations on the associations of nucleic acid bases in both polar and nonpolar solvents such as water and carbon tetrachloride are carried out. The solvent contribution to the enthalpy of base association is positive (destabilizing) in both polar and nonpolar solvents while negative enthalpies for stacked complexes are obtained only when the solute-solute in vacuo energy is added to the total energy. The release upon association of solvent molecules from the first hydration layer around a solute to the bulk is accompanied by an increase in solute-solvent energy and decrease in solvent-solvent energy. The techniques presented are expectd to displace less molecular and more heuristic modeling of biomolecular equilibria in solution.

A fast boosting-based screening method for large-scale association study in complex traits with genetic heterogeneity.

PubMed

Wang, Lu-Yong; Fasulo, D

2006-01-01

Genome-wide association study for complex diseases will generate massive amount of single nucleotide polymorphisms (SNPs) data. Univariate statistical test (i.e. Fisher exact test) was used to single out non-associated SNPs. However, the disease-susceptible SNPs may have little marginal effects in population and are unlikely to retain after the univariate tests. Also, model-based methods are impractical for large-scale dataset. Moreover, genetic heterogeneity makes the traditional methods harder to identify the genetic causes of diseases. A more recent random forest method provides a more robust method for screening the SNPs in thousands scale. However, for more large-scale data, i.e., Affymetrix Human Mapping 100K GeneChip data, a faster screening method is required to screening SNPs in whole-genome large scale association analysis with genetic heterogeneity. We propose a boosting-based method for rapid screening in large-scale analysis of complex traits in the presence of genetic heterogeneity. It provides a relatively fast and fairly good tool for screening and limiting the candidate SNPs for further more complex computational modeling task.

Dose-Dependent Associations between Wine Drinking and Breast Cancer Risk - Meta-Analysis Findings.

PubMed

Chen, Jia-Yan; Zhu, Hong-Cheng; Guo, Qing; Shu, Zheng; Bao, Xu-Hui; Sun, Feng; Qin, Qin; Yang, Xi; Zhang, Chi; Cheng, Hong-Yan; Sun, Xin-Chen

2016-01-01

To investigate any potential association between wine and breast cancer risk. We quantitatively assessed associations by conducting a meta-analysis based on evidence from observational studies. In May 2014, we performed electronic searches in PubMed, EmBase and the Cochrane Library to identify studies examining the effect of wine drinking on breast cancer incidence. The relative risk (RR) or odds ratio (OR) were used to measure any such association. The analysis was further stratified by confounding factors that could influence the results. A total of twenty-six studies (eight case-control and eighteen cohort studies) involving 21,149 cases were included in our meta-analysis. Our study demonstrated that wine drinking was associated with breast cancer risk. A 36% increase in breast cancer risk was observed across overall studies based on the highest versus lowest model, with a combined RR of 1.0059 (95%CI 0.97-1.05) in dose-response analysis. However, 5 g/d ethanol from wine seemed to have protective value from our non-linear model. Our findings indicate that wine drinking is associated with breast cancer risk in a dose-dependent manner. High consumption of wine contributes to breast cancer risk with protection exerted by low doses. Further investigations are needed for clarification.

Identifying prebariatric subtypes based on temperament traits, emotion dysregulation, and disinhibited eating: A latent profile analysis.

PubMed

Schäfer, Lisa; Hübner, Claudia; Carus, Thomas; Herbig, Beate; Seyfried, Florian; Kaiser, Stefan; Schütz, Tatjana; Dietrich, Arne; Hilbert, Anja

2017-10-01

The efficacy of bariatric surgery has been proven; however, a subset of patients fails to achieve expected long-term weight loss postoperatively. As differences in surgery outcome may be influenced by heterogeneous psychological profiles in prebariatric patients, previous subtyping models differentiated patients based on temperament traits. The objective of this study was to expand these models by additionally considering emotion dysregulation and disinhibited eating behaviors for subtyping, as these factors were associated with maladaptive eating behaviors and poor postbariatric weight loss outcome. Within a prospective multicenter registry, N = 370 prebariatric patients were examined using interview and self-report questionnaires. A latent profile analysis was performed to identify subtypes based on temperament traits, emotion dysregulation, and disinhibited eating behaviors. Five prebariatric subtypes were identified with specific profiles regarding self-control, emotion dysregulation, and disinhibited eating behaviors. Subtypes were associated with different levels of eating disorder psychopathology, depression, and quality of life. The expanded model increased variance explanation compared to temperament-based models. By adding emotion dysregulation and disinhibited eating behaviors to previous subtyping models, specific prebariatric subtypes emerged with distinct psychological deficit patterns. Future investigations should test the predictive value of these subtypes for postbariatric weight loss and health-related outcomes. © 2017 Wiley Periodicals, Inc.

Improved evidence-based genome-scale metabolic models for maize leaf, embryo, and endosperm

PubMed Central

Seaver, Samuel M. D.; Bradbury, Louis M. T.; Frelin, Océane; Zarecki, Raphy; Ruppin, Eytan; Hanson, Andrew D.; Henry, Christopher S.

2015-01-01

There is a growing demand for genome-scale metabolic reconstructions for plants, fueled by the need to understand the metabolic basis of crop yield and by progress in genome and transcriptome sequencing. Methods are also required to enable the interpretation of plant transcriptome data to study how cellular metabolic activity varies under different growth conditions or even within different organs, tissues, and developmental stages. Such methods depend extensively on the accuracy with which genes have been mapped to the biochemical reactions in the plant metabolic pathways. Errors in these mappings lead to metabolic reconstructions with an inflated number of reactions and possible generation of unreliable metabolic phenotype predictions. Here we introduce a new evidence-based genome-scale metabolic reconstruction of maize, with significant improvements in the quality of the gene-reaction associations included within our model. We also present a new approach for applying our model to predict active metabolic genes based on transcriptome data. This method includes a minimal set of reactions associated with low expression genes to enable activity of a maximum number of reactions associated with high expression genes. We apply this method to construct an organ-specific model for the maize leaf, and tissue specific models for maize embryo and endosperm cells. We validate our models using fluxomics data for the endosperm and embryo, demonstrating an improved capacity of our models to fit the available fluxomics data. All models are publicly available via the DOE Systems Biology Knowledgebase and PlantSEED, and our new method is generally applicable for analysis transcript profiles from any plant, paving the way for further in silico studies with a wide variety of plant genomes. PMID:25806041

Improved evidence-based genome-scale metabolic models for maize leaf, embryo, and endosperm

DOE PAGES

Seaver, Samuel M.D.; Bradbury, Louis M.T.; Frelin, Océane; ...

2015-03-10

There is a growing demand for genome-scale metabolic reconstructions for plants, fueled by the need to understand the metabolic basis of crop yield and by progress in genome and transcriptome sequencing. Methods are also required to enable the interpretation of plant transcriptome data to study how cellular metabolic activity varies under different growth conditions or even within different organs, tissues, and developmental stages. Such methods depend extensively on the accuracy with which genes have been mapped to the biochemical reactions in the plant metabolic pathways. Errors in these mappings lead to metabolic reconstructions with an inflated number of reactions andmore » possible generation of unreliable metabolic phenotype predictions. Here we introduce a new evidence-based genome-scale metabolic reconstruction of maize, with significant improvements in the quality of the gene-reaction associations included within our model. We also present a new approach for applying our model to predict active metabolic genes based on transcriptome data. This method includes a minimal set of reactions associated with low expression genes to enable activity of a maximum number of reactions associated with high expression genes. We apply this method to construct an organ-specific model for the maize leaf, and tissue specific models for maize embryo and endosperm cells. We validate our models using fluxomics data for the endosperm and embryo, demonstrating an improved capacity of our models to fit the available fluxomics data. All models are publicly available via the DOE Systems Biology Knowledgebase and PlantSEED, and our new method is generally applicable for analysis transcript profiles from any plant, paving the way for further in silico studies with a wide variety of plant genomes.« less

Verification of the tumor volume delineation method using a fixed threshold of peak standardized uptake value.

PubMed

Koyama, Kazuya; Mitsumoto, Takuya; Shiraishi, Takahiro; Tsuda, Keisuke; Nishiyama, Atsushi; Inoue, Kazumasa; Yoshikawa, Kyosan; Hatano, Kazuo; Kubota, Kazuo; Fukushi, Masahiro

2017-09-01

We aimed to determine the difference in tumor volume associated with the reconstruction model in positron-emission tomography (PET). To reduce the influence of the reconstruction model, we suggested a method to measure the tumor volume using the relative threshold method with a fixed threshold based on peak standardized uptake value (SUV peak ). The efficacy of our method was verified using 18 F-2-fluoro-2-deoxy-D-glucose PET/computed tomography images of 20 patients with lung cancer. The tumor volume was determined using the relative threshold method with a fixed threshold based on the SUV peak . The PET data were reconstructed using the ordered-subset expectation maximization (OSEM) model, the OSEM + time-of-flight (TOF) model, and the OSEM + TOF + point-spread function (PSF) model. The volume differences associated with the reconstruction algorithm (%VD) were compared. For comparison, the tumor volume was measured using the relative threshold method based on the maximum SUV (SUV max ). For the OSEM and TOF models, the mean %VD values were -0.06 ± 8.07 and -2.04 ± 4.23% for the fixed 40% threshold according to the SUV max and the SUV peak, respectively. The effect of our method in this case seemed to be minor. For the OSEM and PSF models, the mean %VD values were -20.41 ± 14.47 and -13.87 ± 6.59% for the fixed 40% threshold according to the SUV max and SUV peak , respectively. Our new method enabled the measurement of tumor volume with a fixed threshold and reduced the influence of the changes in tumor volume associated with the reconstruction model.

Multilocus Association Mapping Using Variable-Length Markov Chains

PubMed Central

Browning, Sharon R.

2006-01-01

I propose a new method for association-based gene mapping that makes powerful use of multilocus data, is computationally efficient, and is straightforward to apply over large genomic regions. The approach is based on the fitting of variable-length Markov chain models, which automatically adapt to the degree of linkage disequilibrium (LD) between markers to create a parsimonious model for the LD structure. Edges of the fitted graph are tested for association with trait status. This approach can be thought of as haplotype testing with sophisticated windowing that accounts for extent of LD to reduce degrees of freedom and number of tests while maximizing information. I present analyses of two published data sets that show that this approach can have better power than single-marker tests or sliding-window haplotypic tests. PMID:16685642

Multilocus association mapping using variable-length Markov chains.

PubMed

Browning, Sharon R

2006-06-01

I propose a new method for association-based gene mapping that makes powerful use of multilocus data, is computationally efficient, and is straightforward to apply over large genomic regions. The approach is based on the fitting of variable-length Markov chain models, which automatically adapt to the degree of linkage disequilibrium (LD) between markers to create a parsimonious model for the LD structure. Edges of the fitted graph are tested for association with trait status. This approach can be thought of as haplotype testing with sophisticated windowing that accounts for extent of LD to reduce degrees of freedom and number of tests while maximizing information. I present analyses of two published data sets that show that this approach can have better power than single-marker tests or sliding-window haplotypic tests.

A rough set-based association rule approach implemented on a brand trust evaluation model

NASA Astrophysics Data System (ADS)

Liao, Shu-Hsien; Chen, Yin-Ju

2017-09-01

In commerce, businesses use branding to differentiate their product and service offerings from those of their competitors. The brand incorporates a set of product or service features that are associated with that particular brand name and identifies the product/service segmentation in the market. This study proposes a new data mining approach, a rough set-based association rule induction, implemented on a brand trust evaluation model. In addition, it presents as one way to deal with data uncertainty to analyse ratio scale data, while creating predictive if-then rules that generalise data values to the retail region. As such, this study uses the analysis of algorithms to find alcoholic beverages brand trust recall. Finally, discussions and conclusion are presented for further managerial implications.

Predictive representations can link model-based reinforcement learning to model-free mechanisms.

PubMed

Russek, Evan M; Momennejad, Ida; Botvinick, Matthew M; Gershman, Samuel J; Daw, Nathaniel D

2017-09-01

Humans and animals are capable of evaluating actions by considering their long-run future rewards through a process described using model-based reinforcement learning (RL) algorithms. The mechanisms by which neural circuits perform the computations prescribed by model-based RL remain largely unknown; however, multiple lines of evidence suggest that neural circuits supporting model-based behavior are structurally homologous to and overlapping with those thought to carry out model-free temporal difference (TD) learning. Here, we lay out a family of approaches by which model-based computation may be built upon a core of TD learning. The foundation of this framework is the successor representation, a predictive state representation that, when combined with TD learning of value predictions, can produce a subset of the behaviors associated with model-based learning, while requiring less decision-time computation than dynamic programming. Using simulations, we delineate the precise behavioral capabilities enabled by evaluating actions using this approach, and compare them to those demonstrated by biological organisms. We then introduce two new algorithms that build upon the successor representation while progressively mitigating its limitations. Because this framework can account for the full range of observed putatively model-based behaviors while still utilizing a core TD framework, we suggest that it represents a neurally plausible family of mechanisms for model-based evaluation.

Predictive representations can link model-based reinforcement learning to model-free mechanisms

PubMed Central

Botvinick, Matthew M.

2017-01-01

Humans and animals are capable of evaluating actions by considering their long-run future rewards through a process described using model-based reinforcement learning (RL) algorithms. The mechanisms by which neural circuits perform the computations prescribed by model-based RL remain largely unknown; however, multiple lines of evidence suggest that neural circuits supporting model-based behavior are structurally homologous to and overlapping with those thought to carry out model-free temporal difference (TD) learning. Here, we lay out a family of approaches by which model-based computation may be built upon a core of TD learning. The foundation of this framework is the successor representation, a predictive state representation that, when combined with TD learning of value predictions, can produce a subset of the behaviors associated with model-based learning, while requiring less decision-time computation than dynamic programming. Using simulations, we delineate the precise behavioral capabilities enabled by evaluating actions using this approach, and compare them to those demonstrated by biological organisms. We then introduce two new algorithms that build upon the successor representation while progressively mitigating its limitations. Because this framework can account for the full range of observed putatively model-based behaviors while still utilizing a core TD framework, we suggest that it represents a neurally plausible family of mechanisms for model-based evaluation. PMID:28945743

Mathematical Modelling in Engineering: A Proposal to Introduce Linear Algebra Concepts

ERIC Educational Resources Information Center

Cárcamo Bahamonde, Andrea; Gómez Urgelles, Joan; Fortuny Aymemí, Josep

2016-01-01

The modern dynamic world requires that basic science courses for engineering, including linear algebra, emphasise the development of mathematical abilities primarily associated with modelling and interpreting, which are not exclusively calculus abilities. Considering this, an instructional design was created based on mathematical modelling and…

Invited commentary: Lost in estimation--searching for alternatives to markov chains to fit complex Bayesian models.

PubMed

Molitor, John

2012-03-01

Bayesian methods have seen an increase in popularity in a wide variety of scientific fields, including epidemiology. One of the main reasons for their widespread application is the power of the Markov chain Monte Carlo (MCMC) techniques generally used to fit these models. As a result, researchers often implicitly associate Bayesian models with MCMC estimation procedures. However, Bayesian models do not always require Markov-chain-based methods for parameter estimation. This is important, as MCMC estimation methods, while generally quite powerful, are complex and computationally expensive and suffer from convergence problems related to the manner in which they generate correlated samples used to estimate probability distributions for parameters of interest. In this issue of the Journal, Cole et al. (Am J Epidemiol. 2012;175(5):368-375) present an interesting paper that discusses non-Markov-chain-based approaches to fitting Bayesian models. These methods, though limited, can overcome some of the problems associated with MCMC techniques and promise to provide simpler approaches to fitting Bayesian models. Applied researchers will find these estimation approaches intuitively appealing and will gain a deeper understanding of Bayesian models through their use. However, readers should be aware that other non-Markov-chain-based methods are currently in active development and have been widely published in other fields.

Comparison of the CME-associated shock arrival times at the earth using the WSA-ENLIL model with three cone models

NASA Astrophysics Data System (ADS)

Jang, S.; Moon, Y.; Na, H.

2012-12-01

We have made a comparison of CME-associated shock arrival times at the earth based on the WSA-ENLIL model with three cone models using 29 halo CMEs from 2001 to 2002. These halo CMEs have cone model parameters from Michalek et al. (2007) as well as their associated interplanetary (IP) shocks. For this study we consider three different cone models (an asymmetric cone model, an ice-cream cone model and an elliptical cone model) to determine CME cone parameters (radial velocity, angular width and source location), which are used for input parameters of the WSA-ENLIL model. The mean absolute error (MAE) of the arrival times for the elliptical cone model is 10 hours, which is about 2 hours smaller than those of the other models. However, this value is still larger than that (8.7 hours) of an empirical model by Kim et al. (2007). We are investigating several possibilities on relatively large errors of the WSA-ENLIL cone model, which may be caused by CME-CME interaction, background solar wind speed, and/or CME density enhancement.

Dietary Iron Bioavailability: Agreement between Estimation Methods and Association with Serum Ferritin Concentrations in Women of Childbearing Age

PubMed Central

Dias, Gisele Cristina; Morimoto, Juliana Massami; Marchioni, Dirce Maria Lobo; Colli, Célia

2018-01-01

Predictive iron bioavailability (FeBio) methods aimed at evaluating the association between diet and body iron have been proposed, but few studies explored their validity and practical usefulness in epidemiological studies. In this cross-sectional study involving 127 women (18–42 years) with presumably steady-state body iron balance, correlations were checked among various FeBio estimates (probabilistic approach and meal-based and diet-based algorithms) and serum ferritin (SF) concentrations. Iron deficiency was defined as SF < 15 µg/L. Pearson correlation, Friedman test, and linear regression were employed. Iron intake and prevalence of iron deficiency were 10.9 mg/day and 12.6%. Algorithm estimates were strongly correlated (0.69≤ r ≥0.85; p < 0.001), although diet-based models (8.5–8.9%) diverged from meal-based models (11.6–12.8%; p < 0.001). Still, all algorithms underestimated the probabilistic approach (17.2%). No significant association was found between SF and FeBio from Monsen (1978), Reddy (2000), and Armah (2013) algorithms. Nevertheless, there was a 30–37% difference in SF concentrations between women stratified at extreme tertiles of FeBio from Hallberg and Hulthén (2000) and Collings’ (2013) models. The results demonstrate discordance of FeBio from probabilistic approach and algorithm methods while suggesting two models with best performances to rank individuals according to their bioavailable iron intakes. PMID:29883384

Climate and dengue transmission: evidence and implications.

PubMed

Morin, Cory W; Comrie, Andrew C; Ernst, Kacey

2013-01-01

Climate influences dengue ecology by affecting vector dynamics, agent development, and mosquito/human interactions. Although these relationships are known, the impact climate change will have on transmission is unclear. Climate-driven statistical and process-based models are being used to refine our knowledge of these relationships and predict the effects of projected climate change on dengue fever occurrence, but results have been inconsistent. We sought to identify major climatic influences on dengue virus ecology and to evaluate the ability of climate-based dengue models to describe associations between climate and dengue, simulate outbreaks, and project the impacts of climate change. We reviewed the evidence for direct and indirect relationships between climate and dengue generated from laboratory studies, field studies, and statistical analyses of associations between vectors, dengue fever incidence, and climate conditions. We assessed the potential contribution of climate-driven, process-based dengue models and provide suggestions to improve their performance. Relationships between climate variables and factors that influence dengue transmission are complex. A climate variable may increase dengue transmission potential through one aspect of the system while simultaneously decreasing transmission potential through another. This complexity may at least partly explain inconsistencies in statistical associations between dengue and climate. Process-based models can account for the complex dynamics but often omit important aspects of dengue ecology, notably virus development and host-species interactions. Synthesizing and applying current knowledge of climatic effects on all aspects of dengue virus ecology will help direct future research and enable better projections of climate change effects on dengue incidence.

Self-Associations Influence Task-Performance through Bayesian Inference

PubMed Central

Bengtsson, Sara L.; Penny, Will D.

2013-01-01

The way we think about ourselves impacts greatly on our behavior. This paper describes a behavioral study and a computational model that shed new light on this important area. Participants were primed “clever” and “stupid” using a scrambled sentence task, and we measured the effect on response time and error-rate on a rule-association task. First, we observed a confirmation bias effect in that associations to being “stupid” led to a gradual decrease in performance, whereas associations to being “clever” did not. Second, we observed that the activated self-concepts selectively modified attention toward one’s performance. There was an early to late double dissociation in RTs in that primed “clever” resulted in RT increase following error responses, whereas primed “stupid” resulted in RT increase following correct responses. We propose a computational model of subjects’ behavior based on the logic of the experimental task that involves two processes; memory for rules and the integration of rules with subsequent visual cues. The model incorporates an adaptive decision threshold based on Bayes rule, whereby decision thresholds are increased if integration was inferred to be faulty. Fitting the computational model to experimental data confirmed our hypothesis that priming affects the memory process. This model explains both the confirmation bias and double dissociation effects and demonstrates that Bayesian inferential principles can be used to study the effect of self-concepts on behavior. PMID:23966937

Using a high-dimensional graph of semantic space to model relationships among words

PubMed Central

Jackson, Alice F.; Bolger, Donald J.

2014-01-01

The GOLD model (Graph Of Language Distribution) is a network model constructed based on co-occurrence in a large corpus of natural language that may be used to explore what information may be present in a graph-structured model of language, and what information may be extracted through theoretically-driven algorithms as well as standard graph analysis methods. The present study will employ GOLD to examine two types of relationship between words: semantic similarity and associative relatedness. Semantic similarity refers to the degree of overlap in meaning between words, while associative relatedness refers to the degree to which two words occur in the same schematic context. It is expected that a graph structured model of language constructed based on co-occurrence should easily capture associative relatedness, because this type of relationship is thought to be present directly in lexical co-occurrence. However, it is hypothesized that semantic similarity may be extracted from the intersection of the set of first-order connections, because two words that are semantically similar may occupy similar thematic or syntactic roles across contexts and thus would co-occur lexically with the same set of nodes. Two versions the GOLD model that differed in terms of the co-occurence window, bigGOLD at the paragraph level and smallGOLD at the adjacent word level, were directly compared to the performance of a well-established distributional model, Latent Semantic Analysis (LSA). The superior performance of the GOLD models (big and small) suggest that a single acquisition and storage mechanism, namely co-occurrence, can account for associative and conceptual relationships between words and is more psychologically plausible than models using singular value decomposition (SVD). PMID:24860525

Using a high-dimensional graph of semantic space to model relationships among words.

PubMed

Jackson, Alice F; Bolger, Donald J

2014-01-01

The GOLD model (Graph Of Language Distribution) is a network model constructed based on co-occurrence in a large corpus of natural language that may be used to explore what information may be present in a graph-structured model of language, and what information may be extracted through theoretically-driven algorithms as well as standard graph analysis methods. The present study will employ GOLD to examine two types of relationship between words: semantic similarity and associative relatedness. Semantic similarity refers to the degree of overlap in meaning between words, while associative relatedness refers to the degree to which two words occur in the same schematic context. It is expected that a graph structured model of language constructed based on co-occurrence should easily capture associative relatedness, because this type of relationship is thought to be present directly in lexical co-occurrence. However, it is hypothesized that semantic similarity may be extracted from the intersection of the set of first-order connections, because two words that are semantically similar may occupy similar thematic or syntactic roles across contexts and thus would co-occur lexically with the same set of nodes. Two versions the GOLD model that differed in terms of the co-occurence window, bigGOLD at the paragraph level and smallGOLD at the adjacent word level, were directly compared to the performance of a well-established distributional model, Latent Semantic Analysis (LSA). The superior performance of the GOLD models (big and small) suggest that a single acquisition and storage mechanism, namely co-occurrence, can account for associative and conceptual relationships between words and is more psychologically plausible than models using singular value decomposition (SVD).

Illustrative visualization of 3D city models

NASA Astrophysics Data System (ADS)

Doellner, Juergen; Buchholz, Henrik; Nienhaus, Marc; Kirsch, Florian

2005-03-01

This paper presents an illustrative visualization technique that provides expressive representations of large-scale 3D city models, inspired by the tradition of artistic and cartographic visualizations typically found in bird"s-eye view and panoramic maps. We define a collection of city model components and a real-time multi-pass rendering algorithm that achieves comprehensible, abstract 3D city model depictions based on edge enhancement, color-based and shadow-based depth cues, and procedural facade texturing. Illustrative visualization provides an effective visual interface to urban spatial information and associated thematic information complementing visual interfaces based on the Virtual Reality paradigm, offering a huge potential for graphics design. Primary application areas include city and landscape planning, cartoon worlds in computer games, and tourist information systems.

Changes in travel-related carbon emissions associated with modernization of services for patients with acute myocardial infarction: a case study.

PubMed

Zander, Alexis; Niggebrugge, Aphrodite; Pencheon, David; Lyratzopoulos, Georgios

2011-06-01

Little attention has been paid on the carbon footprint of different healthcare service models. We examined this question for service models for patients with acute ST elevation myocardial infarction (STEMI). We estimated carbon emissions associated with ambulance (patient) transport under a primary percutaneous coronary intervention (pPCI) care model based in tertiary centres, compared with historical emissions under a thrombolysis model based in general hospitals. We used geographical information on 41,449 hospitalizations, and published UK government fuel to carbon emissions conversion factors. The average ambulance journey required for transporting a STEMI patient to its closest care point was 13.0 km under the thrombolysis model and 42.2 km under the pPCI model, producing 3.46 and 11.2 kg of CO(2) emissions, respectively. Thus, introducing pPCI will more than triple ambulance journey associated carbon emissions (by a factor of 3.24). This ratio was robust to sensitivity analysis varying assumptions on conversion factor values; and the number of patients treated. Introducing pPCI to manage STEMI patients results in substantial carbon emissions increase. Environmental profiling of service modernization projects could motivate carbon control strategies, and care pathways design that will reduce patient transport need. Healthcare planners should consider the environmental legacy of quality improvement initiatives.

Systematic identification of latent disease-gene associations from PubMed articles.

PubMed

Zhang, Yuji; Shen, Feichen; Mojarad, Majid Rastegar; Li, Dingcheng; Liu, Sijia; Tao, Cui; Yu, Yue; Liu, Hongfang

2018-01-01

Recent scientific advances have accumulated a tremendous amount of biomedical knowledge providing novel insights into the relationship between molecular and cellular processes and diseases. Literature mining is one of the commonly used methods to retrieve and extract information from scientific publications for understanding these associations. However, due to large data volume and complicated associations with noises, the interpretability of such association data for semantic knowledge discovery is challenging. In this study, we describe an integrative computational framework aiming to expedite the discovery of latent disease mechanisms by dissecting 146,245 disease-gene associations from over 25 million of PubMed indexed articles. We take advantage of both Latent Dirichlet Allocation (LDA) modeling and network-based analysis for their capabilities of detecting latent associations and reducing noises for large volume data respectively. Our results demonstrate that (1) the LDA-based modeling is able to group similar diseases into disease topics; (2) the disease-specific association networks follow the scale-free network property; (3) certain subnetwork patterns were enriched in the disease-specific association networks; and (4) genes were enriched in topic-specific biological processes. Our approach offers promising opportunities for latent disease-gene knowledge discovery in biomedical research.

Systematic identification of latent disease-gene associations from PubMed articles

PubMed Central

Mojarad, Majid Rastegar; Li, Dingcheng; Liu, Sijia; Tao, Cui; Yu, Yue; Liu, Hongfang

2018-01-01

Recent scientific advances have accumulated a tremendous amount of biomedical knowledge providing novel insights into the relationship between molecular and cellular processes and diseases. Literature mining is one of the commonly used methods to retrieve and extract information from scientific publications for understanding these associations. However, due to large data volume and complicated associations with noises, the interpretability of such association data for semantic knowledge discovery is challenging. In this study, we describe an integrative computational framework aiming to expedite the discovery of latent disease mechanisms by dissecting 146,245 disease-gene associations from over 25 million of PubMed indexed articles. We take advantage of both Latent Dirichlet Allocation (LDA) modeling and network-based analysis for their capabilities of detecting latent associations and reducing noises for large volume data respectively. Our results demonstrate that (1) the LDA-based modeling is able to group similar diseases into disease topics; (2) the disease-specific association networks follow the scale-free network property; (3) certain subnetwork patterns were enriched in the disease-specific association networks; and (4) genes were enriched in topic-specific biological processes. Our approach offers promising opportunities for latent disease-gene knowledge discovery in biomedical research. PMID:29373609

A comparison between conventional and LANDSAT based hydrologic modeling: The Four Mile Run case study

NASA Technical Reports Server (NTRS)

Ragan, R. M.; Jackson, T. J.; Fitch, W. N.; Shubinski, R. P.

1976-01-01

Models designed to support the hydrologic studies associated with urban water resources planning require input parameters that are defined in terms of land cover. Estimating the land cover is a difficult and expensive task when drainage areas larger than a few sq. km are involved. Conventional and LANDSAT based methods for estimating the land cover based input parameters required by hydrologic planning models were compared in a case study of the 50.5 sq. km (19.5 sq. mi) Four Mile Run Watershed in Virginia. Results of the study indicate that the LANDSAT based approach is highly cost effective for planning model studies. The conventional approach to define inputs was based on 1:3600 aerial photos, required 110 man-days and a total cost of $14,000. The LANDSAT based approach required 6.9 man-days and cost $2,350. The conventional and LANDSAT based models gave similar results relative to discharges and estimated annual damages expected from no flood control, channelization, and detention storage alternatives.

Sleep duration and sleep quality are associated differently with alterations of glucose homeostasis.

PubMed

Byberg, S; Hansen, A-L S; Christensen, D L; Vistisen, D; Aadahl, M; Linneberg, A; Witte, D R

2012-09-01

Studies suggest that inadequate sleep duration and poor sleep quality increase the risk of impaired glucose regulation and diabetes. However, associations with specific markers of glucose homeostasis are less well explained. The objective of this study was to explore possible associations of sleep duration and sleep quality with markers of glucose homeostasis and glucose tolerance status in a healthy population-based study sample. The study comprised 771 participants from the Danish, population-based cross-sectional 'Health2008' study. Sleep duration and sleep quality were measured by self-report. Markers of glucose homeostasis were derived from a 3-point oral glucose tolerance test and included fasting plasma glucose, 2-h plasma glucose, HbA(1c), two measures of insulin sensitivity (the insulin sensitivity index(0,120) and homeostasis model assessment of insulin sensitivity), the homeostasis model assessment of β-cell function and glucose tolerance status. Associations of sleep duration and sleep quality with markers of glucose homeostasis and tolerance were analysed by multiple linear and logistic regression. A 1-h increment in sleep duration was associated with a 0.3 mmol/mol (0.3%) decrement in HbA(1c) and a 25% reduction in the risk of having impaired glucose regulation. Further, a 1-point increment in sleep quality was associated with a 2% increase in both the insulin sensitivity index(0,120) and homeostasis model assessment of insulin sensitivity, as well as a 1% decrease in homeostasis model assessment of β-cell function. In the present study, shorter sleep duration was mainly associated with later alterations in glucose homeostasis, whereas poorer sleep quality was mainly associated with earlier alterations in glucose homeostasis. Thus, adopting healthy sleep habits may benefit glucose metabolism in healthy populations. © 2012 The Authors. Diabetic Medicine © 2012 Diabetes UK.

Comparing and combining process-based crop models and statistical models with some implications for climate change

NASA Astrophysics Data System (ADS)

Roberts, Michael J.; Braun, Noah O.; Sinclair, Thomas R.; Lobell, David B.; Schlenker, Wolfram

2017-09-01

We compare predictions of a simple process-based crop model (Soltani and Sinclair 2012), a simple statistical model (Schlenker and Roberts 2009), and a combination of both models to actual maize yields on a large, representative sample of farmer-managed fields in the Corn Belt region of the United States. After statistical post-model calibration, the process model (Simple Simulation Model, or SSM) predicts actual outcomes slightly better than the statistical model, but the combined model performs significantly better than either model. The SSM, statistical model and combined model all show similar relationships with precipitation, while the SSM better accounts for temporal patterns of precipitation, vapor pressure deficit and solar radiation. The statistical and combined models show a more negative impact associated with extreme heat for which the process model does not account. Due to the extreme heat effect, predicted impacts under uniform climate change scenarios are considerably more severe for the statistical and combined models than for the process-based model.

Fasting Glucose, Obesity, and Coronary Artery Calcification in Community-Based People Without Diabetes

PubMed Central

Rutter, Martin K.; Massaro, Joseph M.; Hoffmann, Udo; O’Donnell, Christopher J.; Fox, Caroline S.

2012-01-01

OBJECTIVE Our objective was to assess whether impaired fasting glucose (IFG) and obesity are independently related to coronary artery calcification (CAC) in a community-based population. RESEARCH DESIGN AND METHODS We assessed CAC using multidetector computed tomography in 3,054 Framingham Heart Study participants (mean [SD] age was 50 [10] years, 49% were women, 29% had IFG, and 25% were obese) free from known vascular disease or diabetes. We tested the hypothesis that IFG (5.6–6.9 mmol/L) and obesity (BMI ≥30 kg/m2) were independently associated with high CAC (>90th percentile for age and sex) after adjusting for hypertension, lipids, smoking, and medication. RESULTS High CAC was significantly related to IFG in an age- and sex-adjusted model (odds ratio 1.4 [95% CI 1.1–1.7], P = 0.002; referent: normal fasting glucose) and after further adjustment for obesity (1.3 [1.0–1.6], P = 0.045). However, IFG was not associated with high CAC in multivariable-adjusted models before (1.2 [0.9–1.4], P = 0.20) or after adjustment for obesity. Obesity was associated with high CAC in age- and sex-adjusted models (1.6 [1.3–2.0], P < 0.001) and in multivariable models that included IFG (1.4 [1.1–1.7], P = 0.005). Multivariable-adjusted spline regression models suggested nonlinear relationships linking high CAC with BMI (J-shaped), waist circumference (J-shaped), and fasting glucose. CONCLUSIONS In this community-based cohort, CAC was associated with obesity, but not IFG, after adjusting for important confounders. With the increasing worldwide prevalence of obesity and nondiabetic hyperglycemia, these data underscore the importance of obesity in the pathogenesis of CAC. PMID:22773705

Fasting glucose, obesity, and coronary artery calcification in community-based people without diabetes.

PubMed

Rutter, Martin K; Massaro, Joseph M; Hoffmann, Udo; O'Donnell, Christopher J; Fox, Caroline S

2012-09-01

Our objective was to assess whether impaired fasting glucose (IFG) and obesity are independently related to coronary artery calcification (CAC) in a community-based population. We assessed CAC using multidetector computed tomography in 3,054 Framingham Heart Study participants (mean [SD] age was 50 [10] years, 49% were women, 29% had IFG, and 25% were obese) free from known vascular disease or diabetes. We tested the hypothesis that IFG (5.6-6.9 mmol/L) and obesity (BMI ≥30 kg/m(2)) were independently associated with high CAC (>90th percentile for age and sex) after adjusting for hypertension, lipids, smoking, and medication. High CAC was significantly related to IFG in an age- and sex-adjusted model (odds ratio 1.4 [95% CI 1.1-1.7], P = 0.002; referent: normal fasting glucose) and after further adjustment for obesity (1.3 [1.0-1.6], P = 0.045). However, IFG was not associated with high CAC in multivariable-adjusted models before (1.2 [0.9-1.4], P = 0.20) or after adjustment for obesity. Obesity was associated with high CAC in age- and sex-adjusted models (1.6 [1.3-2.0], P < 0.001) and in multivariable models that included IFG (1.4 [1.1-1.7], P = 0.005). Multivariable-adjusted spline regression models suggested nonlinear relationships linking high CAC with BMI (J-shaped), waist circumference (J-shaped), and fasting glucose. In this community-based cohort, CAC was associated with obesity, but not IFG, after adjusting for important confounders. With the increasing worldwide prevalence of obesity and nondiabetic hyperglycemia, these data underscore the importance of obesity in the pathogenesis of CAC.

A model for the solution structure of the rod arrestin tetramer.

PubMed

Hanson, Susan M; Dawson, Eric S; Francis, Derek J; Van Eps, Ned; Klug, Candice S; Hubbell, Wayne L; Meiler, Jens; Gurevich, Vsevolod V

2008-06-01

Visual rod arrestin has the ability to self-associate at physiological concentrations. We previously demonstrated that only monomeric arrestin can bind the receptor and that the arrestin tetramer in solution differs from that in the crystal. We employed the Rosetta docking software to generate molecular models of the physiologically relevant solution tetramer based on the monomeric arrestin crystal structure. The resulting models were filtered using the Rosetta energy function, experimental intersubunit distances measured with DEER spectroscopy, and intersubunit contact sites identified by mutagenesis and site-directed spin labeling. This resulted in a unique model for subsequent evaluation. The validity of the model is strongly supported by model-directed crosslinking and targeted mutagenesis that yields arrestin variants deficient in self-association. The structure of the solution tetramer explains its inability to bind rhodopsin and paves the way for experimental studies of the physiological role of rod arrestin self-association.

Complex Networks in Psychological Models

NASA Astrophysics Data System (ADS)

Wedemann, R. S.; Carvalho, L. S. A. V. D.; Donangelo, R.

We develop schematic, self-organizing, neural-network models to describe mechanisms associated with mental processes, by a neurocomputational substrate. These models are examples of real world complex networks with interesting general topological structures. Considering dopaminergic signal-to-noise neuronal modulation in the central nervous system, we propose neural network models to explain development of cortical map structure and dynamics of memory access, and unify different mental processes into a single neurocomputational substrate. Based on our neural network models, neurotic behavior may be understood as an associative memory process in the brain, and the linguistic, symbolic associative process involved in psychoanalytic working-through can be mapped onto a corresponding process of reconfiguration of the neural network. The models are illustrated through computer simulations, where we varied dopaminergic modulation and observed the self-organizing emergent patterns at the resulting semantic map, interpreting them as different manifestations of mental functioning, from psychotic through to normal and neurotic behavior, and creativity.

Fast Identification of Biological Pathways Associated with a Quantitative Trait Using Group Lasso with Overlaps

PubMed Central

Silver, Matt; Montana, Giovanni

2012-01-01

Where causal SNPs (single nucleotide polymorphisms) tend to accumulate within biological pathways, the incorporation of prior pathways information into a statistical model is expected to increase the power to detect true associations in a genetic association study. Most existing pathways-based methods rely on marginal SNP statistics and do not fully exploit the dependence patterns among SNPs within pathways. We use a sparse regression model, with SNPs grouped into pathways, to identify causal pathways associated with a quantitative trait. Notable features of our “pathways group lasso with adaptive weights” (P-GLAW) algorithm include the incorporation of all pathways in a single regression model, an adaptive pathway weighting procedure that accounts for factors biasing pathway selection, and the use of a bootstrap sampling procedure for the ranking of important pathways. P-GLAW takes account of the presence of overlapping pathways and uses a novel combination of techniques to optimise model estimation, making it fast to run, even on whole genome datasets. In a comparison study with an alternative pathways method based on univariate SNP statistics, our method demonstrates high sensitivity and specificity for the detection of important pathways, showing the greatest relative gains in performance where marginal SNP effect sizes are small. PMID:22499682

Uncovering hidden heterogeneity: Geo-statistical models illuminate the fine scale effects of boating infrastructure on sediment characteristics and contaminants.

PubMed

Hedge, L H; Dafforn, K A; Simpson, S L; Johnston, E L

2017-06-30

Infrastructure associated with coastal communities is likely to not only directly displace natural systems, but also leave environmental footprints' that stretch over multiple scales. Some coastal infrastructure will, there- fore, generate a hidden layer of habitat heterogeneity in sediment systems that is not immediately observable in classical impact assessment frameworks. We examine the hidden heterogeneity associated with one of the most ubiquitous coastal modifications; dense swing moorings fields. Using a model based geo-statistical framework we highlight the variation in sedimentology throughout mooring fields and reference locations. Moorings were correlated with patches of sediment with larger particle sizes, and associated metal(loid) concentrations in these patches were depressed. Our work highlights two important ideas i) mooring fields create a mosaic of habitat in which contamination decreases and grain sizes increase close to moorings, and ii) model- based frameworks provide an information rich, easy-to-interpret way to communicate complex analyses to stakeholders. Crown Copyright © 2017. Published by Elsevier Ltd. All rights reserved.

Real-time investigation of cytochrome c release profiles in living neuronal cells undergoing amyloid beta oligomer-induced apoptosis

NASA Astrophysics Data System (ADS)

Lee, Jae Young; Park, Younggeun; Pun, San; Lee, Sung Sik; Lo, Joe F.; Lee, Luke P.

2015-06-01

Intracellular Cyt c release profiles in living human neuroblastoma undergoing amyloid β oligomer (AβO)-induced apoptosis, as a model Alzheimer's disease-associated pathogenic molecule, were analysed in a real-time manner using plasmon resonance energy transfer (PRET)-based spectroscopy.Intracellular Cyt c release profiles in living human neuroblastoma undergoing amyloid β oligomer (AβO)-induced apoptosis, as a model Alzheimer's disease-associated pathogenic molecule, were analysed in a real-time manner using plasmon resonance energy transfer (PRET)-based spectroscopy. Electronic supplementary information (ESI) available. See DOI: 10.1039/c5nr02390d

Finding Common Ground: Interprofessional Collaborative Practice Competencies in Patient-Centered Medical Homes.

PubMed

Swihart, Diana

2016-01-01

The patient-centered medical home model is predicated on interprofessional collaborative practice and team-based care. While information on the roles of various providers is increasingly woven into the literature, the competencies of those providers have been generally profession-specific. In 2011, the Interprofessional Education Collaborative comprising the American Association of Colleges of Nursing, the American Association of Colleges of Osteopathic Medicine, the American Association of Colleges of Pharmacy, the American Dental Education Association, the Association of American Medical Colleges, and the Association of Schools of Public Health sponsored an expert panel of their members to identify and develop 4 domains of core competencies needed for a successful interprofessional collaborative practice: (1) Values/Ethics for Interprofessional Practice; (2) Roles/Responsibilities; (3) Interprofessional Communication; and (4) Teams and Teamwork. Their findings and recommendations were recorded in their Core Competencies for Interprofessional Collaborative Practice: Report of an Expert Panel. This article explores these 4 domains and how they provide common ground for team-based care within the context of the medical home model approach to patient-centered primary care.

Application of multiple modelling to hyperthermia estimation: reducing the effects of model mismatch.

PubMed

Potocki, J K; Tharp, H S

1993-01-01

Multiple model estimation is a viable technique for dealing with the spatial perfusion model mismatch associated with hyperthermia dosimetry. Using multiple models, spatial discrimination can be obtained without increasing the number of unknown perfusion zones. Two multiple model estimators based on the extended Kalman filter (EKF) are designed and compared with two EKFs based on single models having greater perfusion zone segmentation. Results given here indicate that multiple modelling is advantageous when the number of thermal sensors is insufficient for convergence of single model estimators having greater perfusion zone segmentation. In situations where sufficient measured outputs exist for greater unknown perfusion parameter estimation, the multiple model estimators and the single model estimators yield equivalent results.

76 FR 53137 - Bundled Payments for Care Improvement Initiative: Request for Applications

Federal Register 2010, 2011, 2012, 2013, 2014

2011-08-25

... (RFA) will test episode-based payment for acute care and associated post-acute care, using both retrospective and prospective bundled payment methods. The RFA requests applications to test models centered around acute care; these models will inform the design of future models, including care improvement for...

Economic Modeling as a Component of Academic Strategic Planning.

ERIC Educational Resources Information Center

MacKinnon, Joyce; Sothmann, Mark; Johnson, James

2001-01-01

Computer-based economic modeling was used to enable a school of allied health to define outcomes, identify associated costs, develop cost and revenue models, and create a financial planning system. As a strategic planning tool, it assisted realistic budgeting and improved efficiency and effectiveness. (Contains 18 references.) (SK)

Student Modeling Based on Problem Solving Times

ERIC Educational Resources Information Center

Pelánek, Radek; Jarušek, Petr

2015-01-01

Student modeling in intelligent tutoring systems is mostly concerned with modeling correctness of students' answers. As interactive problem solving activities become increasingly common in educational systems, it is useful to focus also on timing information associated with problem solving. We argue that the focus on timing is natural for certain…

Program Assessment: Getting to a Practical How-To Model

ERIC Educational Resources Information Center

Gardiner, Lorraine R.; Corbitt, Gail; Adams, Steven J.

2010-01-01

The Association to Advance Collegiate Schools of Business (AACSB) International's assurance of learning (AoL) standards require that schools develop a sophisticated continuous-improvement process. The authors review various assessment models and develop a practical, 6-step AoL model based on the literature and the authors' AoL-implementation…

Numerical modelling of glacial lake outburst floods using physically based dam-breach models

NASA Astrophysics Data System (ADS)

Westoby, M. J.; Brasington, J.; Glasser, N. F.; Hambrey, M. J.; Reynolds, J. M.; Hassan, M. A. A. M.; Lowe, A.

2015-03-01

The instability of moraine-dammed proglacial lakes creates the potential for catastrophic glacial lake outburst floods (GLOFs) in high-mountain regions. In this research, we use a unique combination of numerical dam-breach and two-dimensional hydrodynamic modelling, employed within a generalised likelihood uncertainty estimation (GLUE) framework, to quantify predictive uncertainty in model outputs associated with a reconstruction of the Dig Tsho failure in Nepal. Monte Carlo analysis was used to sample the model parameter space, and morphological descriptors of the moraine breach were used to evaluate model performance. Multiple breach scenarios were produced by differing parameter ensembles associated with a range of breach initiation mechanisms, including overtopping waves and mechanical failure of the dam face. The material roughness coefficient was found to exert a dominant influence over model performance. The downstream routing of scenario-specific breach hydrographs revealed significant differences in the timing and extent of inundation. A GLUE-based methodology for constructing probabilistic maps of inundation extent, flow depth, and hazard is presented and provides a useful tool for communicating uncertainty in GLOF hazard assessment.

Advancing reservoir operation description in physically based hydrological models

NASA Astrophysics Data System (ADS)

Anghileri, Daniela; Giudici, Federico; Castelletti, Andrea; Burlando, Paolo

2016-04-01

Last decades have seen significant advances in our capacity of characterizing and reproducing hydrological processes within physically based models. Yet, when the human component is considered (e.g. reservoirs, water distribution systems), the associated decisions are generally modeled with very simplistic rules, which might underperform in reproducing the actual operators' behaviour on a daily or sub-daily basis. For example, reservoir operations are usually described by a target-level rule curve, which represents the level that the reservoir should track during normal operating conditions. The associated release decision is determined by the current state of the reservoir relative to the rule curve. This modeling approach can reasonably reproduce the seasonal water volume shift due to reservoir operation. Still, it cannot capture more complex decision making processes in response, e.g., to the fluctuations of energy prices and demands, the temporal unavailability of power plants or varying amount of snow accumulated in the basin. In this work, we link a physically explicit hydrological model with detailed hydropower behavioural models describing the decision making process by the dam operator. In particular, we consider two categories of behavioural models: explicit or rule-based behavioural models, where reservoir operating rules are empirically inferred from observational data, and implicit or optimization based behavioural models, where, following a normative economic approach, the decision maker is represented as a rational agent maximising a utility function. We compare these two alternate modelling approaches on the real-world water system of Lake Como catchment in the Italian Alps. The water system is characterized by the presence of 18 artificial hydropower reservoirs generating almost 13% of the Italian hydropower production. Results show to which extent the hydrological regime in the catchment is affected by different behavioural models and reservoir operating strategies.

Volume-translated cubic EoS and PC-SAFT density models and a free volume-based viscosity model for hydrocarbons at extreme temperature and pressure conditions

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

Burgess, Ward A.; Tapriyal, Deepak; Morreale, Bryan D.

2013-12-01

This research focuses on providing the petroleum reservoir engineering community with robust models of hydrocarbon density and viscosity at the extreme temperature and pressure conditions (up to 533 K and 276 MPa, respectively) characteristic of ultra-deep reservoirs, such as those associated with the deepwater wells in the Gulf of Mexico. Our strategy is to base the volume-translated (VT) Peng–Robinson (PR) and Soave–Redlich–Kwong (SRK) cubic equations of state (EoSs) and perturbed-chain, statistical associating fluid theory (PC-SAFT) on an extensive data base of high temperature (278–533 K), high pressure (6.9–276 MPa) density rather than fitting the models to low pressure saturated liquidmore » density data. This high-temperature, high-pressure (HTHP) data base consists of literature data for hydrocarbons ranging from methane to C{sub 40}. The three new models developed in this work, HTHP VT-PR EoS, HTHP VT-SRK EoS, and hybrid PC-SAFT, yield mean absolute percent deviation values (MAPD) for HTHP hydrocarbon density of ~2.0%, ~1.5%, and <1.0%, respectively. An effort was also made to provide accurate hydrocarbon viscosity models based on literature data. Viscosity values are estimated with the frictional theory (f-theory) and free volume (FV) theory of viscosity. The best results were obtained when the PC-SAFT equation was used to obtain both the attractive and repulsive pressure inputs to f-theory, and the density input to FV theory. Both viscosity models provide accurate results at pressures to 100 MPa but experimental and model results can deviate by more than 25% at pressures above 200 MPa.« less

Association between ADAM metallopeptidase domain 33 gene polymorphism and risk of childhood asthma: a meta-analysis.

PubMed

Sun, F J; Zou, L Y; Tong, D M; Lu, X Y; Li, J; Deng, C B

2017-08-31

This study aimed to investigate the association between ADAM metallopeptidase domain 33 (ADAM33) gene polymorphisms and the risk of childhood asthma. The relevant studies about the relationship between ADAM33 gene polymorphisms and childhood asthma were searched from electronic databases and the deadline of retrieval was May 2016. The single nucleotide polymorphisms (SNPs) of ADAM33 (rs511898, rs2280092, rs3918396, rs528557, rs2853209, rs44707, rs2280091 and rs2280089) were analyzed based on several models including the allele, codominant, recessive and dominant models. The results showed that the ADAM33 rs2280091 polymorphism in all four genetic models was associated with an increased risk of childhood asthma. Positive associations were also found between the polymorphisms rs2280090, rs2787094, rs44707 and rs528557 and childhood asthma in some genetic models. This meta-analysis suggested that ADAM33 polymorphisms rs2280091, rs2280090, rs2787094, rs44707 and rs528557 were significantly associated with a high risk of childhood asthma.

Comparing lagged linear correlation, lagged regression, Granger causality, and vector autoregression for uncovering associations in EHR data.

PubMed

Levine, Matthew E; Albers, David J; Hripcsak, George

2016-01-01

Time series analysis methods have been shown to reveal clinical and biological associations in data collected in the electronic health record. We wish to develop reliable high-throughput methods for identifying adverse drug effects that are easy to implement and produce readily interpretable results. To move toward this goal, we used univariate and multivariate lagged regression models to investigate associations between twenty pairs of drug orders and laboratory measurements. Multivariate lagged regression models exhibited higher sensitivity and specificity than univariate lagged regression in the 20 examples, and incorporating autoregressive terms for labs and drugs produced more robust signals in cases of known associations among the 20 example pairings. Moreover, including inpatient admission terms in the model attenuated the signals for some cases of unlikely associations, demonstrating how multivariate lagged regression models' explicit handling of context-based variables can provide a simple way to probe for health-care processes that confound analyses of EHR data.

Implementing a GPU-based numerical algorithm for modelling dynamics of a high-speed train

NASA Astrophysics Data System (ADS)

Sytov, E. S.; Bratus, A. S.; Yurchenko, D.

2018-04-01

This paper discusses the initiative of implementing a GPU-based numerical algorithm for studying various phenomena associated with dynamics of a high-speed railway transport. The proposed numerical algorithm for calculating a critical speed of the bogie is based on the first Lyapunov number. Numerical algorithm is validated by analytical results, derived for a simple model. A dynamic model of a carriage connected to a new dual-wheelset flexible bogie is studied for linear and dry friction damping. Numerical results obtained by CPU, MPU and GPU approaches are compared and appropriateness of these methods is discussed.

A new approach for modeling patient overall radiosensitivity and predicting multiple toxicity endpoints for breast cancer patients.

PubMed

Mbah, Chamberlain; De Ruyck, Kim; De Schrijver, Silke; De Sutter, Charlotte; Schiettecatte, Kimberly; Monten, Chris; Paelinck, Leen; De Neve, Wilfried; Thierens, Hubert; West, Catharine; Amorim, Gustavo; Thas, Olivier; Veldeman, Liv

2018-05-01

Evaluation of patient characteristics inducing toxicity in breast radiotherapy, using simultaneous modeling of multiple endpoints. In 269 early-stage breast cancer patients treated with whole-breast irradiation (WBI) after breast-conserving surgery, toxicity was scored, based on five dichotomized endpoints. Five logistic regression models were fitted, one for each endpoint and the effect sizes of all variables were estimated using maximum likelihood (MLE). The MLEs are improved with James-Stein estimates (JSEs). The method combines all the MLEs, obtained for the same variable but from different endpoints. Misclassification errors were computed using MLE- and JSE-based prediction models. For associations, p-values from the sum of squares of MLEs were compared with p-values from the Standardized Total Average Toxicity (STAT) Score. With JSEs, 19 highest ranked variables were predictive of the five different endpoints. Important variables increasing radiation-induced toxicity were chemotherapy, age, SATB2 rs2881208 SNP and nodal irradiation. Treatment position (prone position) was most protective and ranked eighth. Overall, the misclassification errors were 45% and 34% for the MLE- and JSE-based models, respectively. p-Values from the sum of squares of MLEs and p-values from STAT score led to very similar conclusions, except for the variables nodal irradiation and treatment position, for which STAT p-values suggested an association with radiosensitivity, whereas p-values from the sum of squares indicated no association. Breast volume was ranked as the most significant variable in both strategies. The James-Stein estimator was used for selecting variables that are predictive for multiple toxicity endpoints. With this estimator, 19 variables were predictive for all toxicities of which four were significantly associated with overall radiosensitivity. JSEs led to almost 25% reduction in the misclassification error rate compared to conventional MLEs. Finally, patient characteristics that are associated with radiosensitivity were identified without explicitly quantifying radiosensitivity.

Using Satellite-Based Spatiotemporal Resolved Air Temperature Exposure to Study the Association between Ambient Air Temperature and Birth Outcomes in Massachusetts

PubMed Central

Melly, Steven J.; Coull, Brent A.; Nordio, Francesco; Schwartz, Joel D.

2015-01-01

Background Studies looking at air temperature (Ta) and birth outcomes are rare. Objectives We investigated the association between birth outcomes and daily Ta during various prenatal exposure periods in Massachusetts (USA) using both traditional Ta stations and modeled addresses. Methods We evaluated birth outcomes and average daily Ta during various prenatal exposure periods in Massachusetts (USA) using both traditional Ta stations and modeled address Ta. We used linear and logistic mixed models and accelerated failure time models to estimate associations between Ta and the following outcomes among live births > 22 weeks: term birth weight (≥ 37 weeks), low birth weight (LBW; < 2,500 g at term), gestational age, and preterm delivery (PT; < 37 weeks). Models were adjusted for individual-level socioeconomic status, traffic density, particulate matter ≤ 2.5 μm (PM2.5), random intercept for census tract, and mother’s health. Results Predicted Ta during multiple time windows before birth was negatively associated with birth weight: Average birth weight was 16.7 g lower (95% CI: –29.7, –3.7) in association with an interquartile range increase (8.4°C) in Ta during the last trimester. Ta over the entire pregnancy was positively associated with PT [odds ratio (OR) = 1.02; 95% CI: 1.00, 1.05] and LBW (OR = 1.04; 95% CI: 0.96, 1.13). Conclusions Ta during pregnancy was associated with lower birth weight and shorter gestational age in our study population. Citation Kloog I, Melly SJ, Coull BA, Nordio F, Schwartz JD. 2015. Using satellite-based spatiotemporal resolved air temperature exposure to study the association between ambient air temperature and birth outcomes in Massachusetts. Environ Health Perspect 123:1053–1058; http://dx.doi.org/10.1289/ehp.1308075 PMID:25850104

Joint genotype- and ancestry-based genome-wide association studies in admixed populations.

PubMed

Szulc, Piotr; Bogdan, Malgorzata; Frommlet, Florian; Tang, Hua

2017-09-01

In genome-wide association studies (GWAS) genetic loci that influence complex traits are localized by inspecting associations between genotypes of genetic markers and the values of the trait of interest. On the other hand, admixture mapping, which is performed in case of populations consisting of a recent mix of two ancestral groups, relies on the ancestry information at each locus (locus-specific ancestry). Recently it has been proposed to jointly model genotype and locus-specific ancestry within the framework of single marker tests. Here, we extend this approach for population-based GWAS in the direction of multimarker models. A modified version of the Bayesian information criterion is developed for building a multilocus model that accounts for the differential correlation structure due to linkage disequilibrium (LD) and admixture LD. Simulation studies and a real data example illustrate the advantages of this new approach compared to single-marker analysis or modern model selection strategies based on separately analyzing genotype and ancestry data, as well as to single-marker analysis combining genotypic and ancestry information. Depending on the signal strength, our procedure automatically chooses whether genotypic or locus-specific ancestry markers are added to the model. This results in a good compromise between the power to detect causal mutations and the precision of their localization. The proposed method has been implemented in R and is available at http://www.math.uni.wroc.pl/~mbogdan/admixtures/. © 2017 WILEY PERIODICALS, INC.

Alternate forms of the associated Legendre functions for use in geomagnetic modeling.

USGS Publications Warehouse

Alldredge, L.R.; Benton, E.R.

1986-01-01

An inconvenience attending traditional use of associated Legendre functions in global modeling is that the functions are not separable with respect to the 2 indices (order and degree). In 1973 Merilees suggested a way to avoid the problem by showing that associated Legendre functions of order m and degree m+k can be expressed in terms of elementary functions. This note calls attention to some possible gains in time savings and accuracy in geomagnetic modeling based upon this form. For this purpose, expansions of associated Legendre polynomials in terms of sines and cosines of multiple angles are displayed up to degree and order 10. Examples are also given explaining how some surface spherical harmonics can be transformed into true Fourier series for selected polar great circle paths. -from Authors

Nonlinear system modeling based on bilinear Laguerre orthonormal bases.

PubMed

Garna, Tarek; Bouzrara, Kais; Ragot, José; Messaoud, Hassani

2013-05-01

This paper proposes a new representation of discrete bilinear model by developing its coefficients associated to the input, to the output and to the crossed product on three independent Laguerre orthonormal bases. Compared to classical bilinear model, the resulting model entitled bilinear-Laguerre model ensures a significant parameter number reduction as well as simple recursive representation. However, such reduction still constrained by an optimal choice of Laguerre pole characterizing each basis. To do so, we develop a pole optimization algorithm which constitutes an extension of that proposed by Tanguy et al.. The bilinear-Laguerre model as well as the proposed pole optimization algorithm are illustrated and tested on a numerical simulations and validated on the Continuous Stirred Tank Reactor (CSTR) System. Copyright © 2012 ISA. Published by Elsevier Ltd. All rights reserved.

Physically based estimation of soil water retention from textural data: General framework, new models, and streamlined existing models

USGS Publications Warehouse

Nimmo, J.R.; Herkelrath, W.N.; Laguna, Luna A.M.

2007-01-01

Numerous models are in widespread use for the estimation of soil water retention from more easily measured textural data. Improved models are needed for better prediction and wider applicability. We developed a basic framework from which new and existing models can be derived to facilitate improvements. Starting from the assumption that every particle has a characteristic dimension R associated uniquely with a matric pressure ?? and that the form of the ??-R relation is the defining characteristic of each model, this framework leads to particular models by specification of geometric relationships between pores and particles. Typical assumptions are that particles are spheres, pores are cylinders with volume equal to the associated particle volume times the void ratio, and that the capillary inverse proportionality between radius and matric pressure is valid. Examples include fixed-pore-shape and fixed-pore-length models. We also developed alternative versions of the model of Arya and Paris that eliminate its interval-size dependence and other problems. The alternative models are calculable by direct application of algebraic formulas rather than manipulation of data tables and intermediate results, and they easily combine with other models (e.g., incorporating structural effects) that are formulated on a continuous basis. Additionally, we developed a family of models based on the same pore geometry as the widely used unsaturated hydraulic conductivity model of Mualem. Predictions of measurements for different suitable media show that some of the models provide consistently good results and can be chosen based on ease of calculations and other factors. ?? Soil Science Society of America. All rights reserved.

Reducing youth internalizing symptoms: Effects of a family-based preventive intervention on parental guilt induction and youth cognitive style

PubMed Central

McKEE, LAURA G.; PARENT, JUSTIN; FOREHAND, REX; RAKOW, AARON; WATSON, KELLY H.; DUNBAR, JENNIFER P.; REISING, MICHELLE M.; HARDCASTLE, EMILY; COMPAS, BRUCE E.

2014-01-01

This study utilized structural equation modeling to examine the associations among parental guilt induction (a form of psychological control), youth cognitive style, and youth internalizing symptoms, with parents and youth participating in a randomized controlled trial of a family-based group cognitive–behavioral preventive intervention targeting families with a history of caregiver depression. The authors present separate models utilizing parent report and youth report of internalizing symptoms. Findings suggest that families in the active condition (family-based group cognitive–behavioral group) relative to the comparison condition showed a significant decline in parent use of guilt induction at the conclusion of the intervention (6 months postbaseline). Furthermore, reductions in parental guilt induction at 6 months were associated with significantly lower levels of youth negative cognitive style at 12 months. Finally, reductions in parental use of guilt induction were associated with lower youth internalizing symptoms 1 year following the conclusion of the intervention (18 months postbaseline). PMID:24438999

Compositional schedulability analysis of real-time actor-based systems.

PubMed

Jaghoori, Mohammad Mahdi; de Boer, Frank; Longuet, Delphine; Chothia, Tom; Sirjani, Marjan

2017-01-01

We present an extension of the actor model with real-time, including deadlines associated with messages, and explicit application-level scheduling policies, e.g.,"earliest deadline first" which can be associated with individual actors. Schedulability analysis in this setting amounts to checking whether, given a scheduling policy for each actor, every task is processed within its designated deadline. To check schedulability, we introduce a compositional automata-theoretic approach, based on maximal use of model checking combined with testing. Behavioral interfaces define what an actor expects from the environment, and the deadlines for messages given these assumptions. We use model checking to verify that actors match their behavioral interfaces. We extend timed automata refinement with the notion of deadlines and use it to define compatibility of actor environments with the behavioral interfaces. Model checking of compatibility is computationally hard, so we propose a special testing process. We show that the analyses are decidable and automate the process using the Uppaal model checker.

An evaluation of Computational Fluid dynamics model for flood risk analysis

NASA Astrophysics Data System (ADS)

Di Francesco, Silvia; Biscarini, Chiara; Montesarchio, Valeria

2014-05-01

This work presents an analysis of the hydrological-hydraulic engineering requisites for Risk evaluation and efficient flood damage reduction plans. Most of the research efforts have been dedicated to the scientific and technical aspects of risk assessment, providing estimates of possible alternatives and of the risk associated. In the decision making process for mitigation plan, the contribute of scientist is crucial, due to the fact that Risk-Damage analysis is based on evaluation of flow field ,of Hydraulic Risk and on economical and societal considerations. The present paper will focus on the first part of process, the mathematical modelling of flood events which is the base for all further considerations. The evaluation of potential catastrophic damage consequent to a flood event and in particular to dam failure requires modelling of the flood with sufficient detail so to capture the spatial and temporal evolutions of the event, as well of the velocity field. Thus, the selection of an appropriate mathematical model to correctly simulate flood routing is an essential step. In this work we present the application of two 3D Computational fluid dynamics models to a synthetic and real case study in order to evaluate the correct evolution of flow field and the associated flood Risk . The first model is based on a opensource CFD platform called openFoam. Water flow is schematized with a classical continuum approach based on Navier-Stokes equation coupled with Volume of fluid (VOF) method to take in account the multiphase character of river bottom-water- air systems. The second model instead is based on the Lattice Boltzmann method, an innovative numerical fluid dynamics scheme based on Boltzmann's kinetic equation that represents the flow dynamics at the macroscopic level by incorporating a microscopic kinetic approach. Fluid is seen as composed by particles that can move and collide among them. Simulation results from both models are promising and congruent to experimental results available in literature, thought the LBM model requires less computational effort respect to the NS one.

TA [B] Predicting Microstructure-Creep Resistance Correlation in High Temperature Alloys over Multiple Time Scales

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

Tomar, Vikas

2017-03-06

DoE-NETL partnered with Purdue University to predict the creep and associated microstructure evolution of tungsten-based refractory alloys. Researchers use grain boundary (GB) diagrams, a new concept, to establish time-dependent creep resistance and associated microstructure evolution of grain boundaries/intergranular films GB/IGF controlled creep as a function of load, environment, and temperature. The goal was to conduct a systematic study that includes the development of a theoretical framework, multiscale modeling, and experimental validation using W-based body-centered-cubic alloys, doped/alloyed with one or two of the following elements: nickel, palladium, cobalt, iron, and copper—typical refractory alloys. Prior work has already established and validated amore » basic theory for W-based binary and ternary alloys; the study conducted under this project extended this proven work. Based on interface diagrams phase field models were developed to predict long term microstructural evolution. In order to validate the models nanoindentation creep data was used to elucidate the role played by the interface properties in predicting long term creep strength and microstructure evolution.« less

Common IED exploitation target set ontology

NASA Astrophysics Data System (ADS)

Russomanno, David J.; Qualls, Joseph; Wowczuk, Zenovy; Franken, Paul; Robinson, William

2010-04-01

The Common IED Exploitation Target Set (CIEDETS) ontology provides a comprehensive semantic data model for capturing knowledge about sensors, platforms, missions, environments, and other aspects of systems under test. The ontology also includes representative IEDs; modeled as explosives, camouflage, concealment objects, and other background objects, which comprise an overall threat scene. The ontology is represented using the Web Ontology Language and the SPARQL Protocol and RDF Query Language, which ensures portability of the acquired knowledge base across applications. The resulting knowledge base is a component of the CIEDETS application, which is intended to support the end user sensor test and evaluation community. CIEDETS associates a system under test to a subset of cataloged threats based on the probability that the system will detect the threat. The associations between systems under test, threats, and the detection probabilities are established based on a hybrid reasoning strategy, which applies a combination of heuristics and simplified modeling techniques. Besides supporting the CIEDETS application, which is focused on efficient and consistent system testing, the ontology can be leveraged in a myriad of other applications, including serving as a knowledge source for mission planning tools.

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

PubMed

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

2017-12-05

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

Delineating Hydrofacies Spatial Distribution by Integrating Ensemble Data Assimilation and Indicator Geostatistics

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

Song, Xuehang; Chen, Xingyuan; Ye, Ming

2015-07-01

This study develops a new framework of facies-based data assimilation for characterizing spatial distribution of hydrofacies and estimating their associated hydraulic properties. This framework couples ensemble data assimilation with transition probability-based geostatistical model via a parameterization based on a level set function. The nature of ensemble data assimilation makes the framework efficient and flexible to be integrated with various types of observation data. The transition probability-based geostatistical model keeps the updated hydrofacies distributions under geological constrains. The framework is illustrated by using a two-dimensional synthetic study that estimates hydrofacies spatial distribution and permeability in each hydrofacies from transient head data.more » Our results show that the proposed framework can characterize hydrofacies distribution and associated permeability with adequate accuracy even with limited direct measurements of hydrofacies. Our study provides a promising starting point for hydrofacies delineation in complex real problems.« less

Novel health economic evaluation of a vaccination strategy to prevent HPV-related diseases: the BEST study.

PubMed

Favato, Giampiero; Baio, Gianluca; Capone, Alessandro; Marcellusi, Andrea; Costa, Silvano; Garganese, Giorgia; Picardo, Mauro; Drummond, Mike; Jonsson, Bengt; Scambia, Giovanni; Zweifel, Peter; Mennini, Francesco S

2012-12-01

The development of human papillomavirus (HPV)-related diseases is not understood perfectly and uncertainties associated with commonly utilized probabilistic models must be considered. The study assessed the cost-effectiveness of a quadrivalent-based multicohort HPV vaccination strategy within a Bayesian framework. A full Bayesian multicohort Markov model was used, in which all unknown quantities were associated with suitable probability distributions reflecting the state of currently available knowledge. These distributions were informed by observed data or expert opinion. The model cycle lasted 1 year, whereas the follow-up time horizon was 90 years. Precancerous cervical lesions, cervical cancers, and anogenital warts were considered as outcomes. The base case scenario (2 cohorts of girls aged 12 and 15 y) and other multicohort vaccination strategies (additional cohorts aged 18 and 25 y) were cost-effective, with a discounted cost per quality-adjusted life-year gained that corresponded to €12,013, €13,232, and €15,890 for vaccination programs based on 2, 3, and 4 cohorts, respectively. With multicohort vaccination strategies, the reduction in the number of HPV-related events occurred earlier (range, 3.8-6.4 y) when compared with a single cohort. The analysis of the expected value of information showed that the results of the model were subject to limited uncertainty (cost per patient = €12.6). This methodological approach is designed to incorporate the uncertainty associated with HPV vaccination. Modeling the cost-effectiveness of a multicohort vaccination program with Bayesian statistics confirmed the value for money of quadrivalent-based HPV vaccination. The expected value of information gave the most appropriate and feasible representation of the true value of this program.

Association between addiction treatment staff professional and educational levels and perceptions of organizational climate and resources.

PubMed

Krull, Ivy; Lundgren, Lena; Beltrame, Clelia

2014-01-01

Research studies have identified addiction treatment staff who have higher levels of education as having more positive attitudes about evidence-based treatment practices, science-based training, and the usefulness of evidence-based practices. This study examined associations between addiction treatment staff level of education and their perceptions of 3 measures of organizational change: organizational stress, training resources and staffing resources in their treatment unit. The sample included 588 clinical staff from community-based substance abuse treatment organizations who received Substance Abuse and Mental Health Services Administration (SAMHSA) funding (2003-2008) to implement evidence-based practices (EBPs). Bivariate analysis and regression modeling methods examined the relationship between staff education level (no high school education, high school education, some college, associate's degree, bachelor's degree, master's degree, doctoral degree, and other type of degree such as medical assistant, registered nurse [RN], or postdoctoral) and attitudes about organizational climate (stress), training resources, and staffing resources while controlling for staff and treatment unit characteristics. Multivariable models identified staff with lower levels of education as having significantly more positive attitudes about their unit's organizational capacity. These results contradict findings that addiction treatment staff with higher levels of education work in units with greater levels of organizational readiness for change. It cannot be inferred that higher levels of education among treatment staff is necessarily associated with high levels of organizational readiness for change.

REDUCING UNCERTAINTY IN AIR TOXICS RISK ASSESSMENT: A MECHANISTIC EXPOSURE-DOSE-RESPONSE (EDR) MODEL FOR ASSESSING THE ACUTE NEUROTOXICITY OF VOLATILE ORGANIC COMPOUNDS (VOCS) BASED UPON A RECEPTOR-MEDIATED MODE OF ACTION

EPA Science Inventory

SUMMARY: The major accomplishment of NTD’s air toxics program is the development of an exposure-dose- response model for acute exposure to volatile organic compounds (VOCs), based on momentary brain concentration as the dose metric associated with acute neurological impairments...

A hydrothermal after-ripening time model for seed dormancy loss in Bromus tectorum L.

Treesearch

Necia B. Bair; Susan E. Meyer; Phil S. Allen

2006-01-01

After-ripening, the loss of dormancy under dry conditions, is associated with a decrease in mean base water potential for germination of Bromus tectorum L. seeds. After-ripening rate is a linear function of temperature above a base temperature, so that dormancy loss can be quantified using a thermal after-ripening time (TAR) model. To incorporate storage water...

Are parents’ motivations to exercise and intention to engage in regular family-based activity associated with both adult and child physical activity?

PubMed Central

Solomon-Moore, Emma; Sebire, Simon J; Thompson, Janice L; Zahra, Jesmond; Lawlor, Debbie A; Jago, Russ

2016-01-01

Background/aim To examine the associations between parents’ motivation to exercise and intention to engage in family-based activity with their own and their child’s physical activity. Methods Cross-sectional data from 1067 parent–child pairs (76.1% mother–child); children were aged 5–6 years. Parents reported their exercise motivation (ie, intrinsic motivation, identified regulation, introjected regulation, external regulation and amotivation) as described in self-determination theory and their intention to engage in family-based activity. Parents’ and children’s mean minutes of moderate-to-vigorous-intensity physical activity (MVPA) and mean counts per minute were derived from ActiGraph accelerometers worn for 3 to 5 days (including a mixture of weekdays and weekend days). Multivariable linear regression models, adjusted for parent sex, number of children, indices of multiple deprivation and clustering of children in schools were used to examine associations (total of 24 associations tested). Results In fully adjusted models, each unit increase in identified regulation was associated with a 6.08 (95% CI 3.27 to 8.89, p<0.001) min-per-day increase in parents’ MVPA. Parents’ external regulation was associated with children performing 2.93 (95% CI −5.83 to −0.03, p=0.05) fewer minutes of MVPA per day and a 29.3 (95% CI −53.8 to −4.7, p=0.02) accelerometer count-per-minute reduction. There was no evidence of association for the other 21 associations tested. Conclusions Future family-based physical activity interventions may benefit from helping parents identify personal value in exercise while avoiding the use of external control or coercion to motivate behaviour. PMID:28879025

Comparison of the WSA-ENLIL model with three CME cone types

NASA Astrophysics Data System (ADS)

Jang, Soojeong; Moon, Y.; Na, H.

2013-07-01

We have made a comparison of the CME-associated shock propagation based on the WSA-ENLIL model with three cone types using 29 halo CMEs from 2001 to 2002. These halo CMEs have cone model parameters as well as their associated interplanetary (IP) shocks. For this study we consider three different cone types (an asymmetric cone model, an ice-cream cone model and an elliptical cone model) to determine 3-D CME parameters (radial velocity, angular width and source location), which are the input values of the WSA-ENLIL model. The mean absolute error (MAE) of the arrival times for the asymmetric cone model is 10.6 hours, which is about 1 hour smaller than those of the other models. Their ensemble average of MAE is 9.5 hours. However, this value is still larger than that (8.7 hours) of the empirical model of Kim et al. (2007). We will compare their IP shock velocities and densities with those from ACE in-situ measurements and discuss them in terms of the prediction of geomagnetic storms.Abstract (2,250 Maximum Characters): We have made a comparison of the CME-associated shock propagation based on the WSA-ENLIL model with three cone types using 29 halo CMEs from 2001 to 2002. These halo CMEs have cone model parameters as well as their associated interplanetary (IP) shocks. For this study we consider three different cone types (an asymmetric cone model, an ice-cream cone model and an elliptical cone model) to determine 3-D CME parameters (radial velocity, angular width and source location), which are the input values of the WSA-ENLIL model. The mean absolute error (MAE) of the arrival times for the asymmetric cone model is 10.6 hours, which is about 1 hour smaller than those of the other models. Their ensemble average of MAE is 9.5 hours. However, this value is still larger than that (8.7 hours) of the empirical model of Kim et al. (2007). We will compare their IP shock velocities and densities with those from ACE in-situ measurements and discuss them in terms of the prediction of geomagnetic storms.

Practical Application of Model-based Programming and State-based Architecture to Space Missions

NASA Technical Reports Server (NTRS)

Horvath, Gregory A.; Ingham, Michel D.; Chung, Seung; Martin, Oliver; Williams, Brian

2006-01-01

Innovative systems and software engineering solutions are required to meet the increasingly challenging demands of deep-space robotic missions. While recent advances in the development of an integrated systems and software engineering approach have begun to address some of these issues, they are still at the core highly manual and, therefore, error-prone. This paper describes a task aimed at infusing MIT's model-based executive, Titan, into JPL's Mission Data System (MDS), a unified state-based architecture, systems engineering process, and supporting software framework. Results of the task are presented, including a discussion of the benefits and challenges associated with integrating mature model-based programming techniques and technologies into a rigorously-defined domain specific architecture.

Physical frailty is associated with incident mild cognitive impairment in community-based older persons.

PubMed

Boyle, Patricia A; Buchman, Aron S; Wilson, Robert S; Leurgans, Sue E; Bennett, David A

2010-02-01

To test the hypothesis that physical frailty is associated with risk of mild cognitive impairment (MCI). Prospective, observational cohort study. Approximately 40 retirement communities across the Chicago metropolitan area. More than 750 older persons without cognitive impairment at baseline. Physical frailty, based on four components (grip strength, timed walk, body composition, and fatigue), was assessed at baseline, and cognitive function was assessed annually. Proportional hazards models adjusted for age, sex, and education were used to examine the association between physical frailty and the risk of incident MCI, and mixed effect models were used to examine the association between frailty and the rate of change in cognition. During up to 12 years of annual follow-up, 305 of 761 (40%) persons developed MCI. In a proportional hazards model adjusted for age, sex, and education, physical frailty was associated with a high risk of incident MCI, such that each one-unit increase in physical frailty was associated with a 63% increase in the risk of MCI (hazard ratio=1.63; 95% confidence interval=1.27-2.08). This association persisted in analyses that required MCI to persist for at least 1 year and after controlling for depressive symptoms, disability, vascular risk factors, and vascular diseases. Furthermore, a higher level of physical frailty was associated with a faster rate of decline in global cognition and five cognitive systems (episodic memory, semantic memory, working memory, perceptual speed, and visuospatial abilities). Physical frailty is associated with risk of MCI and a rapid rate of cognitive decline in aging.

A review on machine learning principles for multi-view biological data integration.

PubMed

Li, Yifeng; Wu, Fang-Xiang; Ngom, Alioune

2018-03-01

Driven by high-throughput sequencing techniques, modern genomic and clinical studies are in a strong need of integrative machine learning models for better use of vast volumes of heterogeneous information in the deep understanding of biological systems and the development of predictive models. How data from multiple sources (called multi-view data) are incorporated in a learning system is a key step for successful analysis. In this article, we provide a comprehensive review on omics and clinical data integration techniques, from a machine learning perspective, for various analyses such as prediction, clustering, dimension reduction and association. We shall show that Bayesian models are able to use prior information and model measurements with various distributions; tree-based methods can either build a tree with all features or collectively make a final decision based on trees learned from each view; kernel methods fuse the similarity matrices learned from individual views together for a final similarity matrix or learning model; network-based fusion methods are capable of inferring direct and indirect associations in a heterogeneous network; matrix factorization models have potential to learn interactions among features from different views; and a range of deep neural networks can be integrated in multi-modal learning for capturing the complex mechanism of biological systems.

Hierarchical calibration and validation of computational fluid dynamics models for solid sorbent-based carbon capture

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

Lai, Canhai; Xu, Zhijie; Pan, Wenxiao

2016-01-01

To quantify the predictive confidence of a solid sorbent-based carbon capture design, a hierarchical validation methodology—consisting of basic unit problems with increasing physical complexity coupled with filtered model-based geometric upscaling has been developed and implemented. This paper describes the computational fluid dynamics (CFD) multi-phase reactive flow simulations and the associated data flows among different unit problems performed within the said hierarchical validation approach. The bench-top experiments used in this calibration and validation effort were carefully designed to follow the desired simple-to-complex unit problem hierarchy, with corresponding data acquisition to support model parameters calibrations at each unit problem level. A Bayesianmore » calibration procedure is employed and the posterior model parameter distributions obtained at one unit-problem level are used as prior distributions for the same parameters in the next-tier simulations. Overall, the results have demonstrated that the multiphase reactive flow models within MFIX can be used to capture the bed pressure, temperature, CO2 capture capacity, and kinetics with quantitative accuracy. The CFD modeling methodology and associated uncertainty quantification techniques presented herein offer a solid framework for estimating the predictive confidence in the virtual scale up of a larger carbon capture device.« less

Roadmap for Lean implementation in Indian automotive component manufacturing industry: comparative study of UNIDO Model and ISM Model

NASA Astrophysics Data System (ADS)

Jadhav, J. R.; Mantha, S. S.; Rane, S. B.

2015-06-01

The demands for automobiles increased drastically in last two and half decades in India. Many global automobile manufacturers and Tier-1 suppliers have already set up research, development and manufacturing facilities in India. The Indian automotive component industry started implementing Lean practices to fulfill the demand of these customers. United Nations Industrial Development Organization (UNIDO) has taken proactive approach in association with Automotive Component Manufacturers Association of India (ACMA) and the Government of India to assist Indian SMEs in various clusters since 1999 to make them globally competitive. The primary objectives of this research are to study the UNIDO-ACMA Model as well as ISM Model of Lean implementation and validate the ISM Model by comparing with UNIDO-ACMA Model. It also aims at presenting a roadmap for Lean implementation in Indian automotive component industry. This paper is based on secondary data which include the research articles, web articles, doctoral thesis, survey reports and books on automotive industry in the field of Lean, JIT and ISM. ISM Model for Lean practice bundles was developed by authors in consultation with Lean practitioners. The UNIDO-ACMA Model has six stages whereas ISM Model has eight phases for Lean implementation. The ISM-based Lean implementation model is validated through high degree of similarity with UNIDO-ACMA Model. The major contribution of this paper is the proposed ISM Model for sustainable Lean implementation. The ISM-based Lean implementation framework presents greater insight of implementation process at more microlevel as compared to UNIDO-ACMA Model.

Descriptive vs. mechanistic network models in plant development in the post-genomic era.

PubMed

Davila-Velderrain, J; Martinez-Garcia, J C; Alvarez-Buylla, E R

2015-01-01

Network modeling is now a widespread practice in systems biology, as well as in integrative genomics, and it constitutes a rich and diverse scientific research field. A conceptually clear understanding of the reasoning behind the main existing modeling approaches, and their associated technical terminologies, is required to avoid confusions and accelerate the transition towards an undeniable necessary more quantitative, multidisciplinary approach to biology. Herein, we focus on two main network-based modeling approaches that are commonly used depending on the information available and the intended goals: inference-based methods and system dynamics approaches. As far as data-based network inference methods are concerned, they enable the discovery of potential functional influences among molecular components. On the other hand, experimentally grounded network dynamical models have been shown to be perfectly suited for the mechanistic study of developmental processes. How do these two perspectives relate to each other? In this chapter, we describe and compare both approaches and then apply them to a given specific developmental module. Along with the step-by-step practical implementation of each approach, we also focus on discussing their respective goals, utility, assumptions, and associated limitations. We use the gene regulatory network (GRN) involved in Arabidopsis thaliana Root Stem Cell Niche patterning as our illustrative example. We show that descriptive models based on functional genomics data can provide important background information consistent with experimentally supported functional relationships integrated in mechanistic GRN models. The rationale of analysis and modeling can be applied to any other well-characterized functional developmental module in multicellular organisms, like plants and animals.

The stability of gadolinium-based contrast agents in human serum: A reanalysis of literature data and association with clinical outcomes.

PubMed

Prybylski, John P; Semelka, Richard C; Jay, Michael

2017-05-01

To reanalyze literature data of gadolinium (Gd)-based contrast agents (GBCAs) in plasma with a kinetic model of dissociation to provide a comprehensive assessment of equilibrium conditions for linear GBCAs. Data for the release of Gd from GBCAs in human serum was extracted from a previous report in the literature and fit to a kinetic dissociation/association model. The conditional stabilities (logK cond ) and percent intact over time were calculated using the model rate constants. The correlations between clinical outcomes and logK cond or other stability indices were determined. The release curves for Omniscan®, gadodiamide, OptiMARK®, gadoversetamide Magnevist® and Multihance® were extracted and all fit well to the kinetic model. The logK cond s calculated from the rate constants were on the order of ~4-6, and were not significantly altered by excess ligand or phosphate. The stability constant based on the amount intact by the initial elimination half-life of GBCAs in plasma provided good correlation with outcomes observed in patients. Estimation of the kinetic constants for GBCA dissociation/association revealed that their stability in physiological fluid is much lower than previous approaches would suggest, which correlates well with deposition and pharmacokinetic observations of GBCAs in human patients. Copyright © 2017 Elsevier Inc. All rights reserved.

Are needs to manage uncertainty and threat associated with political conservatism or ideological extremity?

PubMed

Jost, John T; Napier, Jaime L; Thorisdottir, Hulda; Gosling, Samuel D; Palfai, Tibor P; Ostafin, Brian

2007-07-01

Three studies are conducted to assess the uncertainty- threat model of political conservatism, which posits that psychological needs to manage uncertainty and threat are associated with political orientation. Results from structural equation models provide consistent support for the hypothesis that uncertainty avoidance (e.g., need for order, intolerance of ambiguity, and lack of openness to experience) and threat management (e.g., death anxiety, system threat, and perceptions of a dangerous world) each contributes independently to conservatism (vs. liberalism). No support is obtained for alternative models, which predict that uncertainty and threat management are associated with ideological extremism or extreme forms of conservatism only. Study 3 also reveals that resistance to change fully mediates the association between uncertainty avoidance and conservatism, whereas opposition to equality partially mediates the association between threat and conservatism. Implications for understanding the epistemic and existential bases of political orientation are discussed.

Testing association and linkage using affected-sib-parent study designs.

PubMed

Millstein, Joshua; Siegmund, Kimberly D; Conti, David V; Gauderman, W James

2005-11-01

We have developed a method for jointly testing linkage and association using data from affected sib pairs and their parents. We specify a conditional logistic regression model with two covariates, one that quantifies association (either direct association or indirect association via linkage disequilibrium), and a second that quantifies linkage. The latter covariate is computed based on expected identity-by-descend (ibd) sharing of marker alleles between siblings. In addition to a joint test of linkage and association, our general framework can be used to obtain a linkage test comparable to the mean test (Blackwelder and Elston [1985] Genet. Epidemiol. 2:85-97), and an association test comparable to the Family-Based Association Test (FBAT; Rabinowitz and Laird [2000] Hum. Hered. 50:211-223). We present simulation results demonstrating that our joint test can be more powerful than some standard tests of linkage or association. For example, with a relative risk of 2.7 per variant allele at a disease locus, the estimated power to detect a nearby marker with a modest level of LD was 58.1% by the mean test (linkage only), 69.8% by FBAT, and 82.5% by our joint test of linkage and association. Our model can also be used to obtain tests of linkage conditional on association and association conditional on linkage, which can be helpful in fine mapping. Copyright 2005 Wiley-Liss, Inc.

Capture mechanism in Palaeotropical pitcher plants (Nepenthaceae) is constrained by climate

PubMed Central

Moran, Jonathan A.; Gray, Laura K.; Clarke, Charles; Chin, Lijin

2013-01-01

Background and Aims Nepenthes (Nepenthaceae, approx. 120 species) are carnivorous pitcher plants with a centre of diversity comprising the Philippines, Borneo, Sumatra and Sulawesi. Nepenthes pitchers use three main mechanisms for capturing prey: epicuticular waxes inside the pitcher; a wettable peristome (a collar-shaped structure around the opening); and viscoelastic fluid. Previous studies have provided evidence suggesting that the first mechanism may be more suited to seasonal climates, whereas the latter two might be more suited to perhumid environments. In this study, this idea was tested using climate envelope modelling. Methods A total of 94 species, comprising 1978 populations, were grouped by prey capture mechanism (large peristome, small peristome, waxy, waxless, viscoelastic, non-viscoelastic, ‘wet’ syndrome and ‘dry’ syndrome). Nineteen bioclimatic variables were used to model habitat suitability at approx. 1 km resolution for each group, using Maxent, a presence-only species distribution modelling program. Key Results Prey capture groups putatively associated with perhumid conditions (large peristome, waxless, viscoelastic and ‘wet’ syndrome) had more restricted areas of probable habitat suitability than those associated putatively with less humid conditions (small peristome, waxy, non-viscoelastic and‘dry’ syndrome). Overall, the viscoelastic group showed the most restricted area of modelled suitable habitat. Conclusions The current study is the first to demonstrate that the prey capture mechanism in a carnivorous plant is constrained by climate. Nepenthes species employing peristome-based and viscoelastic fluid-based capture are largely restricted to perhumid regions; in contrast, the wax-based mechanism allows successful capture in both perhumid and more seasonal areas. Possible reasons for the maintenance of peristome-based and viscoelastic fluid-based capture mechanisms in Nepenthes are discussed in relation to the costs and benefits associated with a given prey capture strategy. PMID:23975653

Capture mechanism in Palaeotropical pitcher plants (Nepenthaceae) is constrained by climate.

PubMed

Moran, Jonathan A; Gray, Laura K; Clarke, Charles; Chin, Lijin

2013-11-01

Nepenthes (Nepenthaceae, approx. 120 species) are carnivorous pitcher plants with a centre of diversity comprising the Philippines, Borneo, Sumatra and Sulawesi. Nepenthes pitchers use three main mechanisms for capturing prey: epicuticular waxes inside the pitcher; a wettable peristome (a collar-shaped structure around the opening); and viscoelastic fluid. Previous studies have provided evidence suggesting that the first mechanism may be more suited to seasonal climates, whereas the latter two might be more suited to perhumid environments. In this study, this idea was tested using climate envelope modelling. A total of 94 species, comprising 1978 populations, were grouped by prey capture mechanism (large peristome, small peristome, waxy, waxless, viscoelastic, non-viscoelastic, 'wet' syndrome and 'dry' syndrome). Nineteen bioclimatic variables were used to model habitat suitability at approx. 1 km resolution for each group, using Maxent, a presence-only species distribution modelling program. Prey capture groups putatively associated with perhumid conditions (large peristome, waxless, viscoelastic and 'wet' syndrome) had more restricted areas of probable habitat suitability than those associated putatively with less humid conditions (small peristome, waxy, non-viscoelastic and'dry' syndrome). Overall, the viscoelastic group showed the most restricted area of modelled suitable habitat. The current study is the first to demonstrate that the prey capture mechanism in a carnivorous plant is constrained by climate. Nepenthes species employing peristome-based and viscoelastic fluid-based capture are largely restricted to perhumid regions; in contrast, the wax-based mechanism allows successful capture in both perhumid and more seasonal areas. Possible reasons for the maintenance of peristome-based and viscoelastic fluid-based capture mechanisms in Nepenthes are discussed in relation to the costs and benefits associated with a given prey capture strategy.

Quantum Associative Neural Network with Nonlinear Search Algorithm

NASA Astrophysics Data System (ADS)

Zhou, Rigui; Wang, Huian; Wu, Qian; Shi, Yang

2012-03-01

Based on analysis on properties of quantum linear superposition, to overcome the complexity of existing quantum associative memory which was proposed by Ventura, a new storage method for multiply patterns is proposed in this paper by constructing the quantum array with the binary decision diagrams. Also, the adoption of the nonlinear search algorithm increases the pattern recalling speed of this model which has multiply patterns to O( {log2}^{2^{n -t}} ) = O( n - t ) time complexity, where n is the number of quantum bit and t is the quantum information of the t quantum bit. Results of case analysis show that the associative neural network model proposed in this paper based on quantum learning is much better and optimized than other researchers' counterparts both in terms of avoiding the additional qubits or extraordinary initial operators, storing pattern and improving the recalling speed.

Enlarged leukocyte referent libraries can explain additional variance in blood-based epigenome-wide association studies.

PubMed

Kim, Stephanie; Eliot, Melissa; Koestler, Devin C; Houseman, Eugene A; Wetmur, James G; Wiencke, John K; Kelsey, Karl T

2016-09-01

We examined whether variation in blood-based epigenome-wide association studies could be more completely explained by augmenting existing reference DNA methylation libraries. We compared existing and enhanced libraries in predicting variability in three publicly available 450K methylation datasets that collected whole-blood samples. Models were fit separately to each CpG site and used to estimate the additional variability when adjustments for cell composition were made with each library. Calculation of the mean difference in the CpG-specific residual sums of squares error between models for an arthritis, aging and metabolic syndrome dataset, indicated that an enhanced library explained significantly more variation across all three datasets (p < 10(-3)). Pathologically important immune cell subtypes can explain important variability in epigenome-wide association studies done in blood.

Development of the diabetes typology model for discerning Type 2 diabetes mellitus with national survey data.

PubMed

Bellatorre, Anna; Jackson, Sharon H; Choi, Kelvin

2017-01-01

To classify individuals with diabetes mellitus (DM) into DM subtypes using population-based studies. Population-based survey. Individuals participated in 2003-2004, 2005-2006, or 2009-2010 the National Health and Nutrition Examination Survey (NHANES), and 2010 Coronary Artery Risk Development in Young Adults (CARDIA) survey (research materials obtained from the National Heart, Lung, and Blood Institute Biologic Specimen and Data Repository Information Coordinating Center). 3084, 3040 and 3318 US adults from the 2003-2004, 2005-2006 and 2009-2010 NHANES samples respectively, and 5,115 US adults in the CARDIA cohort. We proposed the Diabetes Typology Model (DTM) through the use of six composite measures based on the Homeostatic Model Assessment (HOMA-IR, HOMA-%β, high HOMA-%S), insulin and glucose levels, and body mass index and conducted latent class analyses to empirically classify individuals into different classes. Three empirical latent classes consistently emerged across studies (entropy = 0.81-0.998). These three classes were likely Type 1 DM, likely Type 2 DM, and atypical DM. The classification has high sensitivity (75.5%), specificity (83.3%), and positive predictive value (97.4%) when validated against C-peptide level. Correlates of Type 2 DM were significantly associated with model-identified Type 2 DM. Compared to regression analysis on known correlates of Type 2 DM using all diabetes cases as outcomes, using DTM to remove likely Type 1 DM and atypical DM cases results in a 2.5-5.3% r-square improvement in the regression analysis, as well as model fits as indicated by significant improvement in -2 log likelihood (p<0.01). Lastly, model-defined likely Type 2 DM was significantly associated with known correlates of Type 2 DM (e.g., age, waist circumference), which provide additional validation of the DTM-defined classes. Our Diabetes Typology Model reflects a promising first step toward discerning likely DM types from population-based data. This novel tool will improve how large population-based studies can be used to examine behavioral and environmental factors associated with different types of DM.

Novel associative-memory-based self-learning neurocontrol model

NASA Astrophysics Data System (ADS)

Chen, Ke

1992-09-01

Intelligent control is an important field of AI application, which is closely related to machine learning, and the neurocontrol is a kind of intelligent control that controls actions of a physical system or a plant. Linear associative memory model is a good analytic tool for artificial neural networks. In this paper, we present a novel self-learning neurocontrol on the basis of the linear associative memory model to support intelligent control. Using our self-learning neurocontrol model, the learning process is viewed as an extension of one of J. Piaget's developmental stages. After a particular linear associative model developed by us is presented, a brief introduction to J. Piaget's cognitive theory is described as the basis of our self-learning style control. It follows that the neurocontrol model is presented, which usually includes two learning stages, viz. primary learning and high-level learning. As a demonstration of our neurocontrol model, an example is also presented with simulation techniques, called that `bird' catches an aim. The tentative experimental results show that the learning and controlling performance of this approach is surprisingly good. In conclusion, future research is pointed out to improve our self-learning neurocontrol model and explore other areas of application.

Computing Models for FPGA-Based Accelerators

PubMed Central

Herbordt, Martin C.; Gu, Yongfeng; VanCourt, Tom; Model, Josh; Sukhwani, Bharat; Chiu, Matt

2011-01-01

Field-programmable gate arrays are widely considered as accelerators for compute-intensive applications. A critical phase of FPGA application development is finding and mapping to the appropriate computing model. FPGA computing enables models with highly flexible fine-grained parallelism and associative operations such as broadcast and collective response. Several case studies demonstrate the effectiveness of using these computing models in developing FPGA applications for molecular modeling. PMID:21603152

Goal-Proximity Decision-Making

ERIC Educational Resources Information Center

Veksler, Vladislav D.; Gray, Wayne D.; Schoelles, Michael J.

2013-01-01

Reinforcement learning (RL) models of decision-making cannot account for human decisions in the absence of prior reward or punishment. We propose a mechanism for choosing among available options based on goal-option association strengths, where association strengths between objects represent previously experienced object proximity. The proposed…

TREAT (TREe-based Association Test)

Cancer.gov

TREAT is an R package for detecting complex joint effects in case-control studies. The test statistic is derived from a tree-structure model by recursive partitioning the data. Ultra-fast algorithm is designed to evaluate the significance of association between candidate gene and disease outcome

Verbal Bullying Changes Among Students Following an Educational Intervention Using the Integrated Model for Behavior Change.

PubMed

Naidoo, Saloshni; Satorius, Benn K; de Vries, Hein; Taylor, Myra

2016-11-01

Bullying behavior in schools can lead to psychosocial problems. School-based interventions are important in raising student awareness, developing their skills and in planning to reduce bullying behavior. A randomized controlled trial, using a school-based educational intervention to reduce verbal bullying, was conducted among grade 10 students in 16 urban and rural schools in KwaZulu-Natal, South Africa in 2013. Baseline and postintervention questionnaires, developed using the Integrated Model for Behavior Change theoretical model, were used to assess changes in verbal bullying. Postintervention there were reduced verbal bullying experiences. Improved social norms and awareness of verbal bullying were associated with reduced verbal bullying experiences and behavior. Although less likely to bully others verbally, girls were more likely to experience verbal bullying. Students with no living father were more likely to bully others verbally. The study findings indicate that a school-based intervention can positively impact on verbal bullying experiences and behavior. © 2016, American School Health Association.

Financial incentives for quality in breast cancer care.

PubMed

Tisnado, Diana M; Rose-Ash, Danielle E; Malin, Jennifer L; Adams, John L; Ganz, Patricia A; Kahn, Katherine L

2008-07-01

To examine the use of financial incentives related to performance on quality measures reported by oncologists and surgeons associated with a population-based cohort of patients with breast cancer in Los Angeles County, California, and to explore the physician and practice characteristics associated with the use of these incentives among breast cancer care providers. Cross-sectional observational study. Physician self-reported financial arrangements from a survey of 348 medical oncologists, radiation oncologists, and surgeons caring for patients with breast cancer in Los Angeles County (response rate, 76%). Physicians were asked whether they were subject to financial incentives for quality (ie, patient satisfaction surveys and adherence to practice guidelines). We examined the prevalence and correlates of incentives and performed multivariate logistic regression analyses to assess predictors of incentives, controlling for other covariates. Twenty percent of respondents reported incentives based on patient satisfaction, and 15% reported incentives based on guideline adherence. The use of incentives for quality in this cohort of oncologists and surgeons was modest and was primarily associated with staff- or group-model health maintenance organization (HMO) settings. In other settings, important predictors were partial physician ownership interest, large practice size, and capitation. Most cancer care providers in Los Angeles County outside of staff- or group-model HMOs are not subject to explicit financial incentives based on quality-of-care measures. Those who are, seem more likely to be associated with large practice settings. New approaches are needed to direct financial incentives for quality toward specialists outside of staff- or group-model HMOs if pay-for-performance programs are to succeed in influencing care.

Neural-Based Compensation of Nonlinearities in an Airplane Longitudinal Model with Dynamic-Inversion Control

PubMed Central

Li, YuHui; Jin, FeiTeng

2017-01-01

The inversion design approach is a very useful tool for the complex multiple-input-multiple-output nonlinear systems to implement the decoupling control goal, such as the airplane model and spacecraft model. In this work, the flight control law is proposed using the neural-based inversion design method associated with the nonlinear compensation for a general longitudinal model of the airplane. First, the nonlinear mathematic model is converted to the equivalent linear model based on the feedback linearization theory. Then, the flight control law integrated with this inversion model is developed to stabilize the nonlinear system and relieve the coupling effect. Afterwards, the inversion control combined with the neural network and nonlinear portion is presented to improve the transient performance and attenuate the uncertain effects on both external disturbances and model errors. Finally, the simulation results demonstrate the effectiveness of this controller. PMID:29410680

Influence of Methylenetetrahydrofolate Reductase C677T Polymorphism on the Risk of Lung Cancer and the Clinical Response to Platinum-Based Chemotherapy for Advanced Non-Small Cell Lung Cancer: An Updated Meta-Analysis

PubMed Central

Zhu, Ning; Gong, Yi; He, Jian; Xia, Jingwen

2013-01-01

Purpose Methylenetetrahydrofolate reductase (MTHFR) has been implicated in lung cancer risk and response to platinum-based chemotherapy in advanced non-small cell lung cancer (NSCLC). However, the results are controversial. We performed meta-analysis to investigate the effect of MTHFR C677T polymorphism on lung cancer risk and response to platinum-based chemotherapy in advanced NSCLC. Materials and Methods The databases of PubMed, Ovid, Wanfang and Chinese Biomedicine were searched for eligible studies. Nineteen studies on MTHFR C677T polymorphism and lung cancer risk and three articles on C677T polymorphism and response to platinum-based chemotherapy in advanced NSCLC, were identified. Results The results indicated that the allelic contrast, homozygous contrast and recessive model of the MTHFR C677T polymorphism were associated significantly with increased lung cancer risk. In the subgroup analysis, the C677T polymorphism was significantly correlated with an increased risk of NSCLC, with the exception of the recessive model. The dominant model and the variant T allele showed a significant association with lung cancer susceptibility of ever smokers. Male TT homozygote carriers had a higher susceptibility, but the allelic contrast and homozygote model had a protective effect in females. No relationship was observed for SCLC in any comparison model. In addition, MTHFR 677TT homozygote carriers had a better response to platinum-based chemotherapy in advanced NSCLC in the recessive model. Conclusion The MTHFR C677T polymorphism might be a genetic marker for lung cancer risk or response to platinum-based chemotherapy in advanced NSCLC. However, our results require further verification. PMID:24142642

First-Year College Student Affect and Alcohol Use: Paradoxical within-and between-Person Associations

ERIC Educational Resources Information Center

Rankin, Lela A.; Maggs, Jennifer L.

2006-01-01

Based on 10 weekly telephone interviews with first-year college students (N=202; 63% women; M=18.8 years, SD=0.4), within- and between-person associations of positive and negative affect with alcohol use were examined. Multi-level models confirmed hypothesized within-person associations between weekly positive affect and alcohol use: Higher…

Stochastic model search with binary outcomes for genome-wide association studies.

PubMed

Russu, Alberto; Malovini, Alberto; Puca, Annibale A; Bellazzi, Riccardo

2012-06-01

The spread of case-control genome-wide association studies (GWASs) has stimulated the development of new variable selection methods and predictive models. We introduce a novel Bayesian model search algorithm, Binary Outcome Stochastic Search (BOSS), which addresses the model selection problem when the number of predictors far exceeds the number of binary responses. Our method is based on a latent variable model that links the observed outcomes to the underlying genetic variables. A Markov Chain Monte Carlo approach is used for model search and to evaluate the posterior probability of each predictor. BOSS is compared with three established methods (stepwise regression, logistic lasso, and elastic net) in a simulated benchmark. Two real case studies are also investigated: a GWAS on the genetic bases of longevity, and the type 2 diabetes study from the Wellcome Trust Case Control Consortium. Simulations show that BOSS achieves higher precisions than the reference methods while preserving good recall rates. In both experimental studies, BOSS successfully detects genetic polymorphisms previously reported to be associated with the analyzed phenotypes. BOSS outperforms the other methods in terms of F-measure on simulated data. In the two real studies, BOSS successfully detects biologically relevant features, some of which are missed by univariate analysis and the three reference techniques. The proposed algorithm is an advance in the methodology for model selection with a large number of features. Our simulated and experimental results showed that BOSS proves effective in detecting relevant markers while providing a parsimonious model.

A Rescorla-Wagner drift-diffusion model of conditioning and timing

PubMed Central

Alonso, Eduardo

2017-01-01

Computational models of classical conditioning have made significant contributions to the theoretic understanding of associative learning, yet they still struggle when the temporal aspects of conditioning are taken into account. Interval timing models have contributed a rich variety of time representations and provided accurate predictions for the timing of responses, but they usually have little to say about associative learning. In this article we present a unified model of conditioning and timing that is based on the influential Rescorla-Wagner conditioning model and the more recently developed Timing Drift-Diffusion model. We test the model by simulating 10 experimental phenomena and show that it can provide an adequate account for 8, and a partial account for the other 2. We argue that the model can account for more phenomena in the chosen set than these other similar in scope models: CSC-TD, MS-TD, Learning to Time and Modular Theory. A comparison and analysis of the mechanisms in these models is provided, with a focus on the types of time representation and associative learning rule used. PMID:29095819

Exploring the Spatial Association between Social Deprivation and Cardiovascular Disease Mortality at the Neighborhood Level.

PubMed

Ford, Mary Margaret; Highfield, Linda D

2016-01-01

Cardiovascular disease (CVD), the leading cause of death in the United States, is impacted by neighborhood-level factors including social deprivation. To measure the association between social deprivation and CVD mortality in Harris County, Texas, global (Ordinary Least Squares (OLS) and local (Geographically Weighted Regression (GWR)) models were built. The models explored the spatial variation in the relationship at a census-tract level while controlling for age, income by race, and education. A significant and spatially varying association (p < .01) was found between social deprivation and CVD mortality, when controlling for all other factors in the model. The GWR model provided a better model fit over the analogous OLS model (R2 = .65 vs. .57), reinforcing the importance of geography and neighborhood of residence in the relationship between social deprivation and CVD mortality. Findings from the GWR model can be used to identify neighborhoods at greatest risk for poor health outcomes and to inform the placement of community-based interventions.

Using Necessary Information to Identify Item Dependence in Passage-Based Reading Comprehension Tests

ERIC Educational Resources Information Center

Baldonado, Angela Argo; Svetina, Dubravka; Gorin, Joanna

2015-01-01

Applications of traditional unidimensional item response theory models to passage-based reading comprehension assessment data have been criticized based on potential violations of local independence. However, simple rules for determining dependency, such as including all items associated with a particular passage, may overestimate the dependency…

Mindfulness as a Coping Strategy for Bias-Based School Victimization among Latina/o Sexual Minority Youth

PubMed Central

Toomey, Russell B.; Anhalt, Karla

2016-01-01

This study examined whether mindfulness strategies (e.g., acting non-judgmentally with awareness and attention to present events) were effective in mitigating the associations among school-based victimization related to ethnicity and sexual orientation, well-being (i.e., depressive symptoms and self-esteem), and grade-point average (GPA). The U.S.-based sample included 236 Latina/o sexual minority students, ranging in age from 14 to 24 years (47% were enrolled in secondary schools, 53% in postsecondary schools). Results from structural equation modeling revealed that ethnicity-based school victimization was negatively associated with GPA but not well-being. However, sexual orientation-based victimization was not associated with well-being or GPA. Mindfulness was positively associated with well-being but not GPA. High levels of mindfulness coping were protective when the stressor was sexual orientation-based victimization but not ethnicity-based school victimization. These findings contribute to a growing literature documenting the unique school barriers experienced by Latina/o sexual minority youth and highlight the promising utility of mindfulness-based intervention strategies for coping with minority stress. PMID:28018933

A Path Analysis of Latino Parental, Teenager and Cultural Variables in Teenagers' Sexual Attitudes, Norms, Self-Efficacy, and Sexual Intentions1

PubMed Central

Gaioso, Vanessa Pirani; Villarruel, Antonia Maria; Wilson, Lynda Anne; Azuero, Andres; Childs, Gwendolyn Denice; Davies, Susan Lane

2015-01-01

OBJECTIVE: to test a theoretical model based on the Parent-Based Expansion of the Theory of Planned Behavior examining relation between selected parental, teenager and cultural variables and Latino teenagers' intentions to engage in sexual behavior. METHOD: a cross-sectional correlational design based on a secondary data analysis of 130 Latino parent and teenager dyads. RESULTS: regression and path analysis procedures were used to test seven hypotheses and the results demonstrated partial support for the model. Parent familism and knowledge about sex were significantly associated with parents' attitudes toward sexual communication with their teenagers. Parent Latino acculturation was negatively associated with parents' self-efficacy toward sexual communication with their teenagers and positevely associated with parents' subjective norms toward sexual communication with their teenagers. Teenager knowledge about sex was significantly associated with higher levels of teenagers' attitudes and subjective norms about sexual communication with parents. Only the predictor of teenagers' attitudes toward having sex in the next 3 months was significantly associated with teenagers' intentions to have sex in the next 3 months. CONCLUSION: the results of this study provide important information to guide future research that can inform development of interventions to prevent risky teenager sexual behavior among Latinos. PMID:26312635

SCREENING MODEL FOR NONAQUEOUS PHASE-LIQUID TRANSPORT IN THE VADOSE ZONE USING GREEN-AMPT AND KINEMATIC WAVE THEORY

EPA Science Inventory

In this paper, a screening model for flow of a nonaqueous phase liquid (NAPL) and associated chemical transport in the vadose zone is developed. he model is based on kinematic approximation of the governing equations for both the NAPL and a partitionable chemical constituent. he ...

Developing an Adequately Specified Model of State Level Student Achievement with Multilevel Data.

ERIC Educational Resources Information Center

Bernstein, Lawrence

Limitations of using linear, unilevel regression procedures in modeling student achievement are discussed. This study is a part of a broader study that is developing an empirically-based predictive model of variables associated with academic achievement from a multilevel perspective and examining the differences by which parameters are estimated…

An Investigation of Mathematical Modeling with Pre-Service Secondary Mathematics Teachers

ERIC Educational Resources Information Center

Thrasher, Emily Plunkett

2016-01-01

The goal of this thesis was to investigate and enhance our understanding of what occurs while pre-service mathematics teachers engage in a mathematical modeling unit that is broadly based upon mathematical modeling as defined by the Common Core State Standards for Mathematics (National Governors Association Center for Best Practices & Council…

Flatness-based control in successive loops for stabilization of heart's electrical activity

NASA Astrophysics Data System (ADS)

Rigatos, Gerasimos; Melkikh, Alexey

2016-12-01

The article proposes a new flatness-based control method implemented in successive loops which allows for stabilization of the heart's electrical activity. Heart's pacemaking function is modeled as a set of coupled oscillators which potentially can exhibit chaotic behavior. It is shown that this model satisfies differential flatness properties. Next, the control and stabilization of this model is performed with the use of flatness-based control implemented in cascading loops. By applying a per-row decomposition of the state-space model of the coupled oscillators a set of nonlinear differential equations is obtained. Differential flatness properties are shown to hold for the subsystems associated with the each one of the aforementioned differential equations and next a local flatness-based controller is designed for each subsystem. For the i-th subsystem, state variable xi is chosen to be the flat output and state variable xi+1 is taken to be a virtual control input. Then the value of the virtual control input which eliminates the output tracking error for the i-th subsystem becomes reference setpoint for the i + 1-th subsystem. In this manner the control of the entire state-space model is performed by successive flatness-based control loops. By arriving at the n-th row of the state-space model one computes the control input that can be actually exerted on the aforementioned biosystem. This real control input of the coupled oscillators' system, contains recursively all virtual control inputs associated with the previous n - 1 rows of the state-space model. This control approach achieves asymptotically the elimination of the chaotic oscillation effects and the stabilization of the heart's pulsation rhythm. The stability of the proposed control scheme is proven with the use of Lyapunov analysis.

Replicating Health Economic Models: Firm Foundations or a House of Cards?

PubMed

Bermejo, Inigo; Tappenden, Paul; Youn, Ji-Hee

2017-11-01

Health economic evaluation is a framework for the comparative analysis of the incremental health gains and costs associated with competing decision alternatives. The process of developing health economic models is usually complex, financially expensive and time-consuming. For these reasons, model development is sometimes based on previous model-based analyses; this endeavour is usually referred to as model replication. Such model replication activity may involve the comprehensive reproduction of an existing model or 'borrowing' all or part of a previously developed model structure. Generally speaking, the replication of an existing model may require substantially less effort than developing a new de novo model by bypassing, or undertaking in only a perfunctory manner, certain aspects of model development such as the development of a complete conceptual model and/or comprehensive literature searching for model parameters. A further motivation for model replication may be to draw on the credibility or prestige of previous analyses that have been published and/or used to inform decision making. The acceptability and appropriateness of replicating models depends on the decision-making context: there exists a trade-off between the 'savings' afforded by model replication and the potential 'costs' associated with reduced model credibility due to the omission of certain stages of model development. This paper provides an overview of the different levels of, and motivations for, replicating health economic models, and discusses the advantages, disadvantages and caveats associated with this type of modelling activity. Irrespective of whether replicated models should be considered appropriate or not, complete replicability is generally accepted as a desirable property of health economic models, as reflected in critical appraisal checklists and good practice guidelines. To this end, the feasibility of comprehensive model replication is explored empirically across a small number of recent case studies. Recommendations are put forward for improving reporting standards to enhance comprehensive model replicability.

Testing a self-determination theory model of children’s physical activity motivation: a cross-sectional study

PubMed Central

2013-01-01

Background Understanding children’s physical activity motivation, its antecedents and associations with behavior is important and can be advanced by using self-determination theory. However, research among youth is largely restricted to adolescents and studies of motivation within certain contexts (e.g., physical education). There are no measures of self-determination theory constructs (physical activity motivation or psychological need satisfaction) for use among children and no previous studies have tested a self-determination theory-based model of children’s physical activity motivation. The purpose of this study was to test the reliability and validity of scores derived from scales adapted to measure self-determination theory constructs among children and test a motivational model predicting accelerometer-derived physical activity. Methods Cross-sectional data from 462 children aged 7 to 11 years from 20 primary schools in Bristol, UK were analysed. Confirmatory factor analysis was used to examine the construct validity of adapted behavioral regulation and psychological need satisfaction scales. Structural equation modelling was used to test cross-sectional associations between psychological need satisfaction, motivation types and physical activity assessed by accelerometer. Results The construct validity and reliability of the motivation and psychological need satisfaction measures were supported. Structural equation modelling provided evidence for a motivational model in which psychological need satisfaction was positively associated with intrinsic and identified motivation types and intrinsic motivation was positively associated with children’s minutes in moderate-to-vigorous physical activity. Conclusions The study provides evidence for the psychometric properties of measures of motivation aligned with self-determination theory among children. Children’s motivation that is based on enjoyment and inherent satisfaction of physical activity is associated with their objectively-assessed physical activity and such motivation is positively associated with perceptions of psychological need satisfaction. These psychological factors represent potential malleable targets for interventions to increase children’s physical activity. PMID:24067078

Factors influencing behavior and transferability of habitat models for a benthic stream fish

Treesearch

Kevin N. Leftwich; Paul L. Angermeier; C. Andrew Dolloff

1997-01-01

The authors examined the predictive power and transferability of habitat-based models by comparing associations of tangerine darter Percina aurantiaca and stream habitat at local and regional scales in North Fork Holston River (NFHR) and Little River, VA. The models correctly predicted the presence or absence of tangerine darters in NFHR for 64 percent (local model)...

Split-Ring Springback Simulations with the Non-associated Flow Rule and Evolutionary Elastic-Plasticity Models

NASA Astrophysics Data System (ADS)

Lee, K. J.; Choi, Y.; Choi, H. J.; Lee, J. Y.; Lee, M. G.

2018-03-01

Finite element simulations and experiments for the split-ring test were conducted to investigate the effect of anisotropic constitutive models on the predictive capability of sheet springback. As an alternative to the commonly employed associated flow rule, a non-associated flow rule for Hill1948 yield function was implemented in the simulations. Moreover, the evolution of anisotropy with plastic deformation was efficiently modeled by identifying equivalent plastic strain-dependent anisotropic coefficients. Comparative study with different yield surfaces and elasticity models showed that the split-ring springback could be best predicted when the anisotropy in both the R value and yield stress, their evolution and variable apparent elastic modulus were taken into account in the simulations. Detailed analyses based on deformation paths superimposed on the anisotropic yield functions predicted by different constitutive models were provided to understand the complex springback response in the split-ring test.

Multivariate Boosting for Integrative Analysis of High-Dimensional Cancer Genomic Data

PubMed Central

Xiong, Lie; Kuan, Pei-Fen; Tian, Jianan; Keles, Sunduz; Wang, Sijian

2015-01-01

In this paper, we propose a novel multivariate component-wise boosting method for fitting multivariate response regression models under the high-dimension, low sample size setting. Our method is motivated by modeling the association among different biological molecules based on multiple types of high-dimensional genomic data. Particularly, we are interested in two applications: studying the influence of DNA copy number alterations on RNA transcript levels and investigating the association between DNA methylation and gene expression. For this purpose, we model the dependence of the RNA expression levels on DNA copy number alterations and the dependence of gene expression on DNA methylation through multivariate regression models and utilize boosting-type method to handle the high dimensionality as well as model the possible nonlinear associations. The performance of the proposed method is demonstrated through simulation studies. Finally, our multivariate boosting method is applied to two breast cancer studies. PMID:26609213

Split-Ring Springback Simulations with the Non-associated Flow Rule and Evolutionary Elastic-Plasticity Models

NASA Astrophysics Data System (ADS)

Lee, K. J.; Choi, Y.; Choi, H. J.; Lee, J. Y.; Lee, M. G.

2018-06-01

Finite element simulations and experiments for the split-ring test were conducted to investigate the effect of anisotropic constitutive models on the predictive capability of sheet springback. As an alternative to the commonly employed associated flow rule, a non-associated flow rule for Hill1948 yield function was implemented in the simulations. Moreover, the evolution of anisotropy with plastic deformation was efficiently modeled by identifying equivalent plastic strain-dependent anisotropic coefficients. Comparative study with different yield surfaces and elasticity models showed that the split-ring springback could be best predicted when the anisotropy in both the R value and yield stress, their evolution and variable apparent elastic modulus were taken into account in the simulations. Detailed analyses based on deformation paths superimposed on the anisotropic yield functions predicted by different constitutive models were provided to understand the complex springback response in the split-ring test.

Nonassociative plasticity model for cohesionless materials and its implementation in soil-structure interaction

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

Hashmi, Q.S.E.

A constitutive model based on rate-independent elastoplasticity concepts is developed and used to simulate the behavior of geologic materials under arbitrary three-dimensional stress paths. The model accounts for various factors such as friction, stress path, and stress history that influence the behavior of geologic materials. A hierarchical approach is adopted whereby models of progressively increasing sophistication are developed from a basic isotropic-hardening associate model. Nonassociativeness is introduced as correction or perturbation to the basic model. Deviation of normality of the plastic-strain increments to the yield surface F is captured through nonassociativeness. The plastic potential Q is obtained by applying amore » correction to F. This simplified approach restricts the number of extra parameters required to define the plastic potential Q. The material constants associated with the model are identified, and they are evaluated for three different sands (Leighton Buzzard, Munich and McCormick Ranch). The model is then verified by comparing predictions with laboratory tests from which the constants were found, and typical tests not used for finding the constants. Based on the above findings, a soil-footing system is analyzed using finite-element techniques.« less

SBRML: a markup language for associating systems biology data with models.

PubMed

Dada, Joseph O; Spasić, Irena; Paton, Norman W; Mendes, Pedro

2010-04-01

Research in systems biology is carried out through a combination of experiments and models. Several data standards have been adopted for representing models (Systems Biology Markup Language) and various types of relevant experimental data (such as FuGE and those of the Proteomics Standards Initiative). However, until now, there has been no standard way to associate a model and its entities to the corresponding datasets, or vice versa. Such a standard would provide a means to represent computational simulation results as well as to frame experimental data in the context of a particular model. Target applications include model-driven data analysis, parameter estimation, and sharing and archiving model simulations. We propose the Systems Biology Results Markup Language (SBRML), an XML-based language that associates a model with several datasets. Each dataset is represented as a series of values associated with model variables, and their corresponding parameter values. SBRML provides a flexible way of indexing the results to model parameter values, which supports both spreadsheet-like data and multidimensional data cubes. We present and discuss several examples of SBRML usage in applications such as enzyme kinetics, microarray gene expression and various types of simulation results. The XML Schema file for SBRML is available at http://www.comp-sys-bio.org/SBRML under the Academic Free License (AFL) v3.0.

A Modeling Investigation of Thermal and Strain Induced Recovery and Nonlinear Hardening in Potential Based Viscoplasticity

NASA Technical Reports Server (NTRS)

Arnold, S. M.; Saleeb, A. F.; Wilt, T. E.

1993-01-01

Specific forms for both the Gibb's and the complementary dissipation potentials were chosen such that a complete potential based multiaxial, isothermal, viscoplastic model was obtained. This model in general possesses three internal state variables (two scalars associated with dislocation density and one tensor associated with dislocation motion) both thermal and dynamic recovery mechanisms, and nonlinear kinematic hardening. This general model, although possessing associated flow and evolutionary laws, is shown to emulate three distinct classes of theories found in the literature, by modification of the driving threshold function F. A parametric study was performed on a specialized nondimensional multiaxial form containing only a single tensorial internal state variable (i.e., internal stress). The study was conducted with the idea of examining the impact of including a strain-induced recovery mechanism and the compliance operator, derived from the Gibb's potential, on the uniaxial and multiaxial response. One important finding was that inclusion of strain recovery provided the needed flexibility in modeling stress-strain and creep response of metals at low homologous temperatures, without adversely affecting the high temperature response. Furthermore, for nonproportional loading paths, the inclusion of the compliance operator had a significant influence on the multiaxial response, but had no influence on either uniaxial or proportional load histories.

Assessing the Influence of Traffic-related Air Pollution on Risk of Term Low Birth Weight on the Basis of Land-Use-based Regression Models and Measures of Air Toxics

PubMed Central

Ghosh, Jo Kay C.; Wilhelm, Michelle; Su, Jason; Goldberg, Daniel; Cockburn, Myles; Jerrett, Michael; Ritz, Beate

2012-01-01

Few studies have examined associations of birth outcomes with toxic air pollutants (air toxics) in traffic exhaust. This study included 8,181 term low birth weight (LBW) children and 370,922 term normal-weight children born between January 1, 1995, and December 31, 2006, to women residing within 5 miles (8 km) of an air toxics monitoring station in Los Angeles County, California. Additionally, land-use-based regression (LUR)-modeled estimates of levels of nitric oxide, nitrogen dioxide, and nitrogen oxides were used to assess the influence of small-area variations in traffic pollution. The authors examined associations with term LBW (≥37 weeks’ completed gestation and birth weight <2,500 g) using logistic regression adjusted for maternal age, race/ethnicity, education, parity, infant gestational age, and gestational age squared. Odds of term LBW increased 2%–5% (95% confidence intervals ranged from 1.00 to 1.09) per interquartile-range increase in LUR-modeled estimates and monitoring-based air toxics exposure estimates in the entire pregnancy, the third trimester, and the last month of pregnancy. Models stratified by monitoring station (to investigate air toxics associations based solely on temporal variations) resulted in 2%–5% increased odds per interquartile-range increase in third-trimester benzene, toluene, ethyl benzene, and xylene exposures, with some confidence intervals containing the null value. This analysis highlights the importance of both spatial and temporal contributions to air pollution in epidemiologic birth outcome studies. PMID:22586068

Assessing the influence of traffic-related air pollution on risk of term low birth weight on the basis of land-use-based regression models and measures of air toxics.

PubMed

Ghosh, Jo Kay C; Wilhelm, Michelle; Su, Jason; Goldberg, Daniel; Cockburn, Myles; Jerrett, Michael; Ritz, Beate

2012-06-15

Few studies have examined associations of birth outcomes with toxic air pollutants (air toxics) in traffic exhaust. This study included 8,181 term low birth weight (LBW) children and 370,922 term normal-weight children born between January 1, 1995, and December 31, 2006, to women residing within 5 miles (8 km) of an air toxics monitoring station in Los Angeles County, California. Additionally, land-use-based regression (LUR)-modeled estimates of levels of nitric oxide, nitrogen dioxide, and nitrogen oxides were used to assess the influence of small-area variations in traffic pollution. The authors examined associations with term LBW (≥37 weeks' completed gestation and birth weight <2,500 g) using logistic regression adjusted for maternal age, race/ethnicity, education, parity, infant gestational age, and gestational age squared. Odds of term LBW increased 2%-5% (95% confidence intervals ranged from 1.00 to 1.09) per interquartile-range increase in LUR-modeled estimates and monitoring-based air toxics exposure estimates in the entire pregnancy, the third trimester, and the last month of pregnancy. Models stratified by monitoring station (to investigate air toxics associations based solely on temporal variations) resulted in 2%-5% increased odds per interquartile-range increase in third-trimester benzene, toluene, ethyl benzene, and xylene exposures, with some confidence intervals containing the null value. This analysis highlights the importance of both spatial and temporal contributions to air pollution in epidemiologic birth outcome studies.

Buses of Cuernavaca—an agent-based model for universal random matrix behavior minimizing mutual information

NASA Astrophysics Data System (ADS)

Warchoł, Piotr

2018-06-01

The public transportation system of Cuernavaca, Mexico, exhibits random matrix theory statistics. In particular, the fluctuation of times between the arrival of buses on a given bus stop, follows the Wigner surmise for the Gaussian unitary ensemble. To model this, we propose an agent-based approach in which each bus driver tries to optimize his arrival time to the next stop with respect to an estimated arrival time of his predecessor. We choose a particular form of the associated utility function and recover the appropriate distribution in numerical experiments for a certain value of the only parameter of the model. We then investigate whether this value of the parameter is otherwise distinguished within an information theoretic approach and give numerical evidence that indeed it is associated with a minimum of averaged pairwise mutual information.

Model of areas for identifying risks influencing the compliance of technological processes and products

NASA Astrophysics Data System (ADS)

Misztal, A.; Belu, N.

2016-08-01

Operation of every company is associated with the risk of interfering with proper performance of its fundamental processes. This risk is associated with various internal areas of the company, as well as the environment in which it operates. From the point of view of ensuring compliance of the course of specific technological processes and, consequently, product conformity with requirements, it is important to identify these threats and eliminate or reduce the risk of their occurrence. The purpose of this article is to present a model of areas of identifying risk affecting the compliance of processes and products, which is based on multiregional targeted monitoring of typical places of interference and risk management methods. The model is based on the verification of risk analyses carried out in small and medium-sized manufacturing companies in various industries..

Estimating the global public health implications of electricity and coal consumption.

PubMed

Gohlke, Julia M; Thomas, Reuben; Woodward, Alistair; Campbell-Lendrum, Diarmid; Prüss-Üstün, Annette; Hales, Simon; Portier, Christopher J

2011-06-01

The growing health risks associated with greenhouse gas emissions highlight the need for new energy policies that emphasize efficiency and low-carbon energy intensity. We assessed the relationships among electricity use, coal consumption, and health outcomes. Using time-series data sets from 41 countries with varying development trajectories between 1965 and 2005, we developed an autoregressive model of life expectancy (LE) and infant mortality (IM) based on electricity consumption, coal consumption, and previous year's LE or IM. Prediction of health impacts from the Greenhouse Gas and Air Pollution Interactions and Synergies (GAINS) integrated air pollution emissions health impact model for coal-fired power plants was compared with the time-series model results. The time-series model predicted that increased electricity consumption was associated with reduced IM for countries that started with relatively high IM (> 100/1,000 live births) and low LE (< 57 years) in 1965, whereas LE was not significantly associated with electricity consumption regardless of IM and LE in 1965. Increasing coal consumption was associated with increased IM and reduced LE after accounting for electricity consumption. These results are consistent with results based on the GAINS model and previously published estimates of disease burdens attributable to energy-related environmental factors, including indoor and outdoor air pollution and water and sanitation. Increased electricity consumption in countries with IM < 100/1,000 live births does not lead to greater health benefits, whereas coal consumption has significant detrimental health impacts.

Estimating the Global Public Health Implications of Electricity and Coal Consumption

PubMed Central

Thomas, Reuben; Woodward, Alistair; Campbell-Lendrum, Diarmid; Prüss-üstün, Annette; Hales, Simon; Portier, Christopher J.

2011-01-01

Background: The growing health risks associated with greenhouse gas emissions highlight the need for new energy policies that emphasize efficiency and low-carbon energy intensity. Objectives: We assessed the relationships among electricity use, coal consumption, and health outcomes. Methods: Using time-series data sets from 41 countries with varying development trajectories between 1965 and 2005, we developed an autoregressive model of life expectancy (LE) and infant mortality (IM) based on electricity consumption, coal consumption, and previous year’s LE or IM. Prediction of health impacts from the Greenhouse Gas and Air Pollution Interactions and Synergies (GAINS) integrated air pollution emissions health impact model for coal-fired power plants was compared with the time-series model results. Results: The time-series model predicted that increased electricity consumption was associated with reduced IM for countries that started with relatively high IM (> 100/1,000 live births) and low LE (< 57 years) in 1965, whereas LE was not significantly associated with electricity consumption regardless of IM and LE in 1965. Increasing coal consumption was associated with increased IM and reduced LE after accounting for electricity consumption. These results are consistent with results based on the GAINS model and previously published estimates of disease burdens attributable to energy-related environmental factors, including indoor and outdoor air pollution and water and sanitation. Conclusions: Increased electricity consumption in countries with IM < 100/1,000 live births does not lead to greater health benefits, whereas coal consumption has significant detrimental health impacts. PMID:21339091

Bounding the electrostatic free energies associated with linear continuum models of molecular solvation.

PubMed

Bardhan, Jaydeep P; Knepley, Matthew G; Anitescu, Mihai

2009-03-14

The importance of electrostatic interactions in molecular biology has driven extensive research toward the development of accurate and efficient theoretical and computational models. Linear continuum electrostatic theory has been surprisingly successful, but the computational costs associated with solving the associated partial differential equations (PDEs) preclude the theory's use in most dynamical simulations. Modern generalized-Born models for electrostatics can reproduce PDE-based calculations to within a few percent and are extremely computationally efficient but do not always faithfully reproduce interactions between chemical groups. Recent work has shown that a boundary-integral-equation formulation of the PDE problem leads naturally to a new approach called boundary-integral-based electrostatics estimation (BIBEE) to approximate electrostatic interactions. In the present paper, we prove that the BIBEE method can be used to rigorously bound the actual continuum-theory electrostatic free energy. The bounds are validated using a set of more than 600 proteins. Detailed numerical results are presented for structures of the peptide met-enkephalin taken from a molecular-dynamics simulation. These bounds, in combination with our demonstration that the BIBEE methods accurately reproduce pairwise interactions, suggest a new approach toward building a highly accurate yet computationally tractable electrostatic model.

Bounding the electrostatic free energies associated with linear continuum models of molecular solvation

NASA Astrophysics Data System (ADS)

Bardhan, Jaydeep P.; Knepley, Matthew G.; Anitescu, Mihai

2009-03-01

The importance of electrostatic interactions in molecular biology has driven extensive research toward the development of accurate and efficient theoretical and computational models. Linear continuum electrostatic theory has been surprisingly successful, but the computational costs associated with solving the associated partial differential equations (PDEs) preclude the theory's use in most dynamical simulations. Modern generalized-Born models for electrostatics can reproduce PDE-based calculations to within a few percent and are extremely computationally efficient but do not always faithfully reproduce interactions between chemical groups. Recent work has shown that a boundary-integral-equation formulation of the PDE problem leads naturally to a new approach called boundary-integral-based electrostatics estimation (BIBEE) to approximate electrostatic interactions. In the present paper, we prove that the BIBEE method can be used to rigorously bound the actual continuum-theory electrostatic free energy. The bounds are validated using a set of more than 600 proteins. Detailed numerical results are presented for structures of the peptide met-enkephalin taken from a molecular-dynamics simulation. These bounds, in combination with our demonstration that the BIBEE methods accurately reproduce pairwise interactions, suggest a new approach toward building a highly accurate yet computationally tractable electrostatic model.

A simple methodology to produce flood risk maps consistent with FEMA's base flood elevation maps: Implementation and validation over the entire contiguous United States

NASA Astrophysics Data System (ADS)

Goteti, G.; Kaheil, Y. H.; Katz, B. G.; Li, S.; Lohmann, D.

2011-12-01

In the United States, government agencies as well as the National Flood Insurance Program (NFIP) use flood inundation maps associated with the 100-year return period (base flood elevation, BFE), produced by the Federal Emergency Management Agency (FEMA), as the basis for flood insurance. A credibility check of the flood risk hydraulic models, often employed by insurance companies, is their ability to reasonably reproduce FEMA's BFE maps. We present results from the implementation of a flood modeling methodology aimed towards reproducing FEMA's BFE maps at a very fine spatial resolution using a computationally parsimonious, yet robust, hydraulic model. The hydraulic model used in this study has two components: one for simulating flooding of the river channel and adjacent floodplain, and the other for simulating flooding in the remainder of the catchment. The first component is based on a 1-D wave propagation model, while the second component is based on a 2-D diffusive wave model. The 1-D component captures the flooding from large-scale river transport (including upstream effects), while the 2-D component captures the flooding from local rainfall. The study domain consists of the contiguous United States, hydrologically subdivided into catchments averaging about 500 km2 in area, at a spatial resolution of 30 meters. Using historical daily precipitation data from the Climate Prediction Center (CPC), the precipitation associated with the 100-year return period event was computed for each catchment and was input to the hydraulic model. Flood extent from the FEMA BFE maps is reasonably replicated by the 1-D component of the model (riverine flooding). FEMA's BFE maps only represent the riverine flooding component and are unavailable for many regions of the USA. However, this modeling methodology (1-D and 2-D components together) covers the entire contiguous USA. This study is part of a larger modeling effort from Risk Management Solutions° (RMS) to estimate flood risk associated with extreme precipitation events in the USA. Towards this greater objective, state-of-the-art models of flood hazard and stochastic precipitation are being implemented over the contiguous United States. Results from the successful implementation of the modeling methodology will be presented.

Chemical-specific screening criteria for interpretation of biomonitoring data for volatile organic compounds (VOCs)--application of steady-state PBPK model solutions.

PubMed

Aylward, Lesa L; Kirman, Chris R; Blount, Ben C; Hays, Sean M

2010-10-01

The National Health and Nutrition Examination Survey (NHANES) generates population-representative biomonitoring data for many chemicals including volatile organic compounds (VOCs) in blood. However, no health or risk-based screening values are available to evaluate these data from a health safety perspective or to use in prioritizing among chemicals for possible risk management actions. We gathered existing risk assessment-based chronic exposure reference values such as reference doses (RfDs), reference concentrations (RfCs), tolerable daily intakes (TDIs), cancer slope factors, etc. and key pharmacokinetic model parameters for 47 VOCs. Using steady-state solutions to a generic physiologically-based pharmacokinetic (PBPK) model structure, we estimated chemical-specific steady-state venous blood concentrations across chemicals associated with unit oral and inhalation exposure rates and with chronic exposure at the identified exposure reference values. The geometric means of the slopes relating modeled steady-state blood concentrations to steady-state exposure to a unit oral dose or unit inhalation concentration among 38 compounds with available pharmacokinetic parameters were 12.0 microg/L per mg/kg-d (geometric standard deviation [GSD] of 3.2) and 3.2 microg/L per mg/m(3) (GSD=1.7), respectively. Chemical-specific blood concentration screening values based on non-cancer reference values for both oral and inhalation exposure range from 0.0005 to 100 microg/L; blood concentrations associated with cancer risk-specific doses at the 1E-05 risk level ranged from 5E-06 to 6E-02 microg/L. The distribution of modeled steady-state blood concentrations associated with unit exposure levels across VOCs may provide a basis for estimating blood concentration screening values for VOCs that lack chemical-specific pharmacokinetic data. The screening blood concentrations presented here provide a tool for risk assessment-based evaluation of population biomonitoring data for VOCs and are most appropriately applied to central tendency estimates for such datasets. Copyright (c) 2010 Elsevier Inc. All rights reserved.

ADHD bifactor model based on parent and teacher ratings of Malaysian children.

PubMed

Gomez, Rapson

2014-04-01

The study used confirmatory factor analysis to ascertain support for the bifactor model of the Attention Deficit/Hyperactivity Disorder (ADHD) symptoms, based on parent and teacher ratings for a group of Malaysian children. Malaysian parents and teachers completed ratings of ADHD and Opposition Defiant Disorder (ODD) symptoms for 934 children. For both sets of ratings, the findings indicating good fit for the bifactor model, and the factors in this model showed differential associations with ODD, thereby supporting the internal and external validity of this model. The theoretical and clinical implications of the findings are discussed. Copyright © 2013 Elsevier B.V. All rights reserved.

Standard solar model

NASA Technical Reports Server (NTRS)

Guenther, D. B.; Demarque, P.; Kim, Y.-C.; Pinsonneault, M. H.

1992-01-01

A set of solar models have been constructed, each based on a single modification to the physics of a reference solar model. In addition, a model combining several of the improvements has been calculated to provide a best solar model. Improvements were made to the nuclear reaction rates, the equation of state, the opacities, and the treatment of the atmosphere. The impact on both the structure and the frequencies of the low-l p-modes of the model to these improvements are discussed. It is found that the combined solar model, which is based on the best physics available (and does not contain any ad hoc assumptions), reproduces the observed oscillation spectrum (for low-l) within the errors associated with the uncertainties in the model physics (primarily opacities).

Association between intake of less-healthy foods defined by the United Kingdom's nutrient profile model and cardiovascular disease: A population-based cohort study

PubMed Central

Scarborough, Peter; Lentjes, Marleen; Khaw, Kay Tee

2018-01-01

Background In the United Kingdom, the Food Standards Agency-Ofcom nutrient profiling model (FSA-Ofcom model) is used to define less-healthy foods that cannot be advertised to children. However, there has been limited investigation of whether less-healthy foods defined by this model are associated with prospective health outcomes. The objective of this study was to test whether consumption of less-healthy food as defined by the FSA-Ofcom model is associated with cardiovascular disease (CVD). Methods and findings We used data from the European Prospective Investigation of Cancer (EPIC)-Norfolk cohort study in adults (n = 25,639) aged 40–79 years who completed a 7-day diet diary between 1993 and 1997. Incident CVD (primary outcome), cardiovascular mortality, and all-cause mortality (secondary outcomes) were identified using record linkage to hospital admissions data and death certificates up to 31 March 2015. Each food and beverage item reported was coded and given a continuous score, using the FSA-Ofcom model, based on the consumption of energy; saturated fat; total sugar; sodium; nonsoluble fibre; protein; and fruits, vegetables, and nuts. Items were classified as less-healthy using Ofcom regulation thresholds. We used Cox proportional hazards regression to test for an association between consumption of less-healthy food and incident CVD. Sensitivity analyses explored whether the results differed based on the definition of the exposure. Analyses were adjusted for age, sex, behavioural risk factors, clinical risk factors, and socioeconomic status. Participants were followed up for a mean of 16.4 years. During follow-up, there were 4,965 incident cases of CVD (1,524 fatal within 30 days). In the unadjusted analyses, we observed an association between consumption of less-healthy food and incident CVD (test for linear trend over quintile groups, p < 0.01). After adjustment for covariates (sociodemographic, behavioural, and indices of cardiovascular risk), we found no association between consumption of less-healthy food and incident CVD (p = 0.84) or cardiovascular mortality (p = 0.90), but there was an association between consumption of less-healthy food and all-cause mortality (test for linear trend, p = 0.006; quintile group 5, highest consumption of less-healthy food, versus quintile group 1, HR = 1.11, 95% CI 1.02–1.20). Sensitivity analyses produced similar results. The study is observational and relies on self-report of dietary consumption. Despite adjustment for known and reported confounders, residual confounding is possible. Conclusions After adjustment for potential confounding factors, no significant association between consumption of less-healthy food (as classified by the FSA-Ofcom model) and CVD was observed in this study. This suggests, in the UK setting, that the FSA-Ofcom model is not consistently discriminating among foods with respect to their association with CVD. More studies are needed to understand better the relationship between consumption of less-healthy food, defined by the FSA-Ofcom model, and indices of health. PMID:29300725

A Cyclic-Plasticity-Based Mechanistic Approach for Fatigue Evaluation of 316 Stainless Steel Under Arbitrary Loading

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

Barua, Bipul; Mohanty, Subhasish; Listwan, Joseph T.

In this paper, a cyclic-plasticity based fully mechanistic fatigue modeling approach is presented. This is based on time-dependent stress-strain evolution of the material over the entire fatigue life rather than just based on the end of live information typically used for empirical S~N curve based fatigue evaluation approaches. Previously we presented constant amplitude fatigue test based related material models for 316 SS base, 508 LAS base and 316 SS- 316 SS weld which are used in nuclear reactor components such as pressure vessels, nozzles, and surge line pipes. However, we found that constant amplitude fatigue data based models have limitationmore » in capturing the stress-strain evolution under arbitrary fatigue loading. To address the above mentioned limitation, in this paper, we present a more advanced approach that can be used for modeling the cyclic stress-strain evolution and fatigue life not only under constant amplitude but also under any arbitrary (random/variable) fatigue loading. The related material model and analytical model results are presented for 316 SS base metal. Two methodologies (either based on time/cycle or based on accumulated plastic strain energy) to track the material parameters at a given time/cycle are discussed and associated analytical model results are presented. From the material model and analytical cyclic plasticity model results, it is found that the proposed cyclic plasticity model can predict all the important stages of material behavior during the entire fatigue life of the specimens with more than 90% accuracy« less

A Cyclic-Plasticity-Based Mechanistic Approach for Fatigue Evaluation of 316 Stainless Steel Under Arbitrary Loading

DOE PAGES

Barua, Bipul; Mohanty, Subhasish; Listwan, Joseph T.; ...

2017-12-05

In this paper, a cyclic-plasticity based fully mechanistic fatigue modeling approach is presented. This is based on time-dependent stress-strain evolution of the material over the entire fatigue life rather than just based on the end of live information typically used for empirical S~N curve based fatigue evaluation approaches. Previously we presented constant amplitude fatigue test based related material models for 316 SS base, 508 LAS base and 316 SS- 316 SS weld which are used in nuclear reactor components such as pressure vessels, nozzles, and surge line pipes. However, we found that constant amplitude fatigue data based models have limitationmore » in capturing the stress-strain evolution under arbitrary fatigue loading. To address the above mentioned limitation, in this paper, we present a more advanced approach that can be used for modeling the cyclic stress-strain evolution and fatigue life not only under constant amplitude but also under any arbitrary (random/variable) fatigue loading. The related material model and analytical model results are presented for 316 SS base metal. Two methodologies (either based on time/cycle or based on accumulated plastic strain energy) to track the material parameters at a given time/cycle are discussed and associated analytical model results are presented. From the material model and analytical cyclic plasticity model results, it is found that the proposed cyclic plasticity model can predict all the important stages of material behavior during the entire fatigue life of the specimens with more than 90% accuracy« less

Defeaturing CAD models using a geometry-based size field and facet-based reduction operators.

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

Quadros, William Roshan; Owen, Steven James

2010-04-01

We propose a method to automatically defeature a CAD model by detecting irrelevant features using a geometry-based size field and a method to remove the irrelevant features via facet-based operations on a discrete representation. A discrete B-Rep model is first created by obtaining a faceted representation of the CAD entities. The candidate facet entities are then marked for reduction by using a geometry-based size field. This is accomplished by estimating local mesh sizes based on geometric criteria. If the field value at a facet entity goes below a user specified threshold value then it is identified as an irrelevant featuremore » and is marked for reduction. The reduction of marked facet entities is primarily performed using an edge collapse operator. Care is taken to retain a valid geometry and topology of the discrete model throughout the procedure. The original model is not altered as the defeaturing is performed on a separate discrete model. Associativity between the entities of the discrete model and that of original CAD model is maintained in order to decode the attributes and boundary conditions applied on the original CAD entities onto the mesh via the entities of the discrete model. Example models are presented to illustrate the effectiveness of the proposed approach.« less

Effect of the microtubule-associated protein tau on dynamics of single-headed motor proteins KIF1A

NASA Astrophysics Data System (ADS)

Sparacino, J.; Farías, M. G.; Lamberti, P. W.

2014-02-01

Intracellular transport based on molecular motors and its regulation are crucial to the functioning of cells. Filamentary tracks of the cells are abundantly decorated with nonmotile microtubule-associated proteins, such as tau. Motivated by experiments on kinesin-tau interactions [Dixit et al., Science 319, 1086 (2008), 10.1126/science.1152993] we developed a stochastic model of interacting single-headed motor proteins KIF1A that also takes into account the interactions between motor proteins and tau molecules. Our model reproduces experimental observations and predicts significant effects of tau on bound time and run length which suggest an important role of tau in regulation of kinesin-based transport.

Value-Based Payment Reform and the Medicare Access and Children's Health Insurance Program Reauthorization Act of 2015: A Primer for Plastic Surgeons.

PubMed

Squitieri, Lee; Chung, Kevin C

2017-07-01

In 2015, the U.S. Congress passed the Medicare Access and Children's Health Insurance Program Reauthorization Act, which effectively repealed the Centers for Medicare and Medicaid Services sustainable growth rate formula and established the Centers for Medicare and Medicaid Services Quality Payment Program. The Medicare Access and Children's Health Insurance Program Reauthorization Act represents an unparalleled acceleration toward value-based payment models and a departure from traditional volume-driven fee-for-service reimbursement. The Quality Payment Program includes two paths for provider participation: the Merit-Based Incentive Payment System and Advanced Alternative Payment Models. The Merit-Based Incentive Payment System pathway replaces existing quality reporting programs and adds several new measures to create a composite performance score for each provider (or provider group) that will be used to adjust reimbursed payment. The advanced alternative payment model pathway is available to providers who participate in qualifying Advanced Alternative Payment Models and is associated with an initial 5 percent payment incentive. The first performance period for the Merit-Based Incentive Payment System opens January 1, 2017, and closes on December 31, 2017, and is associated with payment adjustments in January of 2019. The Centers for Medicare and Medicaid Services estimates that the majority of providers will begin participation in 2017 through the Merit-Based Incentive Payment System pathway, but aims to have 50 percent of payments tied to quality or value through Advanced Alternative Payment Models by 2018. In this article, the authors describe key components of the Medicare Access and Children's Health Insurance Program Reauthorization Act to providers navigating through the Quality Payment Program and discuss how plastic surgeons may optimize their performance in this new value-based payment program.

In Silico Analysis of the Association Relationship between Neuroprotection and Flavors of Traditional Chinese Medicine Based on the mGluRs

PubMed Central

Qiao, Liansheng; Chen, Yankun; Zhao, Bowen; Gu, Yu; Huo, Xiaoqian; Zhang, Yanling; Li, Gongyu

2018-01-01

The metabotropic glutamate receptors (mGluRs) are known as both synaptic receptors and taste receptors. This feature is highly similar to the Property and Flavor theory of Traditional Chinese medicine (TCM), which has the pharmacological effect and flavor. In this study, six ligand based pharmacophore (LBP) models, seven homology modeling models, and fourteen molecular docking models of mGluRs were built based on orthosteric and allosteric sites to screening potential compounds from Traditional Chinese Medicine Database (TCMD). Based on the Pharmacopoeia of the People’s Republic of China, TCMs of compounds and their flavors were traced and listed. According to the tracing result, we found that the TCMs of the compounds which bound to orthosteric sites of mGluRs are highly correlated to a sweet flavor, while the allosteric site corresponds to a bitter flavor. Meanwhile, the pharmacological effects of TCMs with highly frequent flavors were further analyzed. We found that those TCMs play a neuroprotective role through the efficiencies of detumescence, promoting blood circulation, analgesic effect, and so on. This study provides a guide for developing new neuroprotective drugs from TCMs which target mGluRs. Moreover, it is the first study to present a novel approach to discuss the association relationship between flavor and the neuroprotective mechanism of TCM based on mGluRs. PMID:29320397

Relationship between prenatal care and maternal complications in women with preeclampsia: implications for continuity and discontinuity of prenatal care.

PubMed

Liu, Ching-Ming; Chang, Shuenn-Dyh; Cheng, Po-Jen

2012-12-01

Prenatal care is associated with better pregnancy outcome and may be a patient safety issue. However, no studies have investigated the types and quality of prenatal care provided in northern Taiwan. This retrospective study assessed whether the hospital-based continuous prenatal care model at tertiary hospitals reduced the risk of perinatal morbidity and maternal complications in pre-eclampsia patients. Of 385 pre-eclampsia patients recruited from among 23,665 deliveries, 198 were classified as patients with little or no prenatal care who received traditional, individualized, and physician-based discontinuous prenatal care (community-based model), and 187 were classified as control patients who received tertiary hospital-based continuous prenatal care. The effects on perinatal outcome were significantly different between the two groups. The cases in the hospital-based care group were less likely to be associated with preterm delivery, low birth weight, very low birth weight, and intrauterine growth restriction. After adjustment of confounding factors, the factors associated with pregnant women who received little or no prenatal care by individualized physician groups were diastolic blood pressure ≥ 105 mmHg, serum aspartate transaminase level ≥ 150 IU/L, and low-birth-weight deliveries. This study also demonstrated the dose-response effect of inadequate, intermediate, adequate, and intensive prenatal care status on fetal birth weight and gestational periods (weeks to delivery). The types of prenatal care may be associated with different pregnancy outcomes and neonatal morbidity. Factors associated with inadequate prenatal care may be predictors of pregnancy outcome in pregnant women with pre-eclampsia. Copyright © 2012. Published by Elsevier B.V.

Association of cutaneous melanoma incidence with area-based socioeconomic indicators-United States, 2004-2006.

PubMed

Singh, Simple D; Ajani, Umed A; Johnson, Christopher J; Roland, Katherine B; Eide, Melody; Jemal, Ahmedin; Negoita, Serban; Bayakly, Rana A; Ekwueme, Donatus U

2011-11-01

Socioeconomic status (SES) has been associated with melanoma incidence and outcomes. Examination of the relationship between melanoma and SES at the national level in the United States is limited. Expanding knowledge of this association is needed to improve early detection and eliminate disparities. We sought to provide a detailed description of cutaneous melanoma incidence and stage of disease in relationship to area-based socioeconomic measures including poverty level, education, income, and unemployment in the United States. Invasive cutaneous melanoma data reported by 44 population-based central cancer registries for 2004 to 2006 were merged with county-level SES estimates from the US Census Bureau. Age-adjusted incidence rates were calculated by gender, race/ethnicity, poverty, education, income, unemployment, and metro/urban/rural status using software. Poisson multilevel mixed models were fitted, and incidence density ratios were calculated by stage for area-based SES measures, controlling for age, gender, and state random effects. Counties with lower poverty, higher education, higher income, and lower unemployment had higher age-adjusted melanoma incidence rates for both early and late stage. In multivariate models, SES effects persisted for early-stage but not late-stage melanoma incidence. Individual-level measures of SES were unavailable, and estimates were based on county-level SES measures. Our findings show that melanoma incidence in the United States is associated with aggregate county-level measures of high SES. Analyses using finer-level SES measures, such as individual or census tract level, are needed to provide more precise estimates of these associations. Copyright © 2011 American Academy of Dermatology, Inc. Published by Mosby, Inc. All rights reserved.

A new engineering approach to reveal correlation of physiological change and spontaneous expression from video images

NASA Astrophysics Data System (ADS)

Yang, Fenglei; Hu, Sijung; Ma, Xiaoyun; Hassan, Harnani; Wei, Dongqing

2015-03-01

Spontaneous expression is associated with physiological states, i.e., heart rate, respiration, oxygen saturation (SpO2%), and heart rate variability (HRV). There have yet not sufficient efforts to explore correlation of physiological change and spontaneous expression. This study aims to study how spontaneous expression is associated with physiological changes with an approved protocol or through the videos provided from Denver Intensity of Spontaneous Facial Action Database. Not like a posed expression, motion artefact in spontaneous expression is one of evitable challenges to be overcome in the study. To obtain a physiological signs from a region of interest (ROI), a new engineering approach is being developed with an artefact-reduction method consolidated 3D active appearance model (AAM) based track, affine transformation based alignment with opto-physiological mode based imaging photoplethysmography. Also, a statistical association spaces is being used to interpret correlation of spontaneous expressions and physiological states including their probability densities by means of Gaussian Mixture Model. The present work is revealing a new avenue of study associations of spontaneous expressions and physiological states with its prospect of applications on physiological and psychological assessment.

Parenting and risk for mood, anxiety and substance use disorders: a study in population-based male twins.

PubMed

Otowa, Takeshi; Gardner, Charles O; Kendler, Kenneth S; Hettema, John M

2013-11-01

Previous studies consistently identified a relationship between parenting behavior and psychopathology. In this study, we extended prior analyses performed in female twins to a large sample of twins from male-male pairs. We used interview data on 2,609 adult male twins from a population-based twin registry. We examined the association between three retrospectively reported parenting dimensions (coldness, protectiveness, and authoritarianism) and lifetime history of seven common psychiatric and substance use disorders. Using univariate structural equation modeling, we also examined the influence of the genetic and environmental factors on parenting. Examined individually, coldness was consistently associated with risk for a broad range of adult psychopathology. Averaged odds of psychiatric disorders associated with parenting were increased between 26 and 36 %. When the three parenting dimensions were examined together, coldness remained significant for major depression, phobia, and generalized anxiety disorder. Controlling for other disorders, the associations between the parenting dimensions and psychopathology were non-specific. Twin fitting model demonstrated that modest heritability accounted for parenting, whereas most variance resulted from the non-shared environment. Based on our current and prior findings, there is broad similarity in the impact of parenting on adult psychopathology between men and women.

Adaptive MPC based on MIMO ARX-Laguerre model.

PubMed

Ben Abdelwahed, Imen; Mbarek, Abdelkader; Bouzrara, Kais

2017-03-01

This paper proposes a method for synthesizing an adaptive predictive controller using a reduced complexity model. This latter is given by the projection of the ARX model on Laguerre bases. The resulting model is entitled MIMO ARX-Laguerre and it is characterized by an easy recursive representation. The adaptive predictive control law is computed based on multi-step-ahead finite-element predictors, identified directly from experimental input/output data. The model is tuned in each iteration by an online identification algorithms of both model parameters and Laguerre poles. The proposed approach avoids time consuming numerical optimization algorithms associated with most common linear predictive control strategies, which makes it suitable for real-time implementation. The method is used to synthesize and test in numerical simulations adaptive predictive controllers for the CSTR process benchmark. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Transgenic animal models of neurodegeneration based on human genetic studies

PubMed Central

Richie, Christopher T.; Hoffer, Barry J.; Airavaara, Mikko

2011-01-01

The identification of genes linked to neurodegenerative diseases such as Alzheimer's disease (AD), amyotrophic lateral sclerosis (ALS), Huntington's disease (HD) and Parkinson's disease (PD) has led to the development of animal models for studying mechanism and evaluating potential therapies. None of the transgenic models developed based on disease-associated genes have been able to fully recapitulate the behavioral and pathological features of the corresponding disease. However, there has been enormous progress made in identifying potential therapeutic targets and understanding some of the common mechanisms of neurodegeneration. In this review, we will discuss transgenic animal models for AD, ALS, HD and PD that are based on human genetic studies. All of the diseases discussed have active or complete clinical trials for experimental treatments that benefited from transgenic models of the disease. PMID:20931247

Water-based adhesives with tailored hydrophobic association: dilution resistance and improved setting behavior.

PubMed

Dundua, Alexander; Landfester, Katharina; Taden, Andreas

2014-11-01

Hydrophobic association and stimuli-responsiveness is a powerful tool towards water-based adhesives with strongly improved properties, which is demonstrated based on the example of hydrophobically modified alkali-soluble latexes (HASE) with modulated association. Their rheological properties are highly tunable due to the hydrophobic domains that act as physical crosslinking sites of adjustable interaction strength. Ethanol, propanol, and butanol are used as water-soluble model additives with different hydrophobicity in order to specifically target the association sites and impact the viscoelastic properties and stimuli-responsiveness. The rheological and mechanical property response upon dilution with water can be tailored, and dilution-resistant or even dilution-thickening systems are obtained. The investigations are of high importance for water-based adhesives, as our findings provide insight into general structure-property relationships to improve their setting behavior, especially upon contact with wet substrates. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

GEO Collisional Risk Assessment Based on Analysis of NASA-WISE Data and Modeling

DTIC Science & Technology

2015-10-18

GEO Collisional Risk Assessment Based on Analysis of NASA -WISE Data and Modeling Jeremy Murray Krezan1, Samantha Howard1, Phan D. Dao1, Derek...Surka2 1AFRL Space Vehicles Directorate,2Applied Technology Associates Incorporated From December 2009 through 2011 the NASA Wide-Field Infrared...of known debris. The NASA -WISE GEO belt debris population adds potentially thousands previously uncataloged objects. This paper describes

util_2comp: Planck-based two-component dust model utilities

NASA Astrophysics Data System (ADS)

Meisner, Aaron

2014-11-01

The util_2comp software utilities generate predictions of far-infrared Galactic dust emission and reddening based on a two-component dust emission model fit to Planck HFI, DIRBE and IRAS data from 100 GHz to 3000 GHz. These predictions and the associated dust temperature map have angular resolution of 6.1 arcminutes and are available over the entire sky. Implementations in IDL and Python are included.

SCADA-based Operator Support System for Power Plant Equipment Fault Forecasting

NASA Astrophysics Data System (ADS)

Mayadevi, N.; Ushakumari, S. S.; Vinodchandra, S. S.

2014-12-01

Power plant equipment must be monitored closely to prevent failures from disrupting plant availability. Online monitoring technology integrated with hybrid forecasting techniques can be used to prevent plant equipment faults. A self learning rule-based expert system is proposed in this paper for fault forecasting in power plants controlled by supervisory control and data acquisition (SCADA) system. Self-learning utilizes associative data mining algorithms on the SCADA history database to form new rules that can dynamically update the knowledge base of the rule-based expert system. In this study, a number of popular associative learning algorithms are considered for rule formation. Data mining results show that the Tertius algorithm is best suited for developing a learning engine for power plants. For real-time monitoring of the plant condition, graphical models are constructed by K-means clustering. To build a time-series forecasting model, a multi layer preceptron (MLP) is used. Once created, the models are updated in the model library to provide an adaptive environment for the proposed system. Graphical user interface (GUI) illustrates the variation of all sensor values affecting a particular alarm/fault, as well as the step-by-step procedure for avoiding critical situations and consequent plant shutdown. The forecasting performance is evaluated by computing the mean absolute error and root mean square error of the predictions.

Real-Time Model-Based Leak-Through Detection within Cryogenic Flow Systems

NASA Technical Reports Server (NTRS)

Walker, M.; Figueroa, F.

2015-01-01

The timely detection of leaks within cryogenic fuel replenishment systems is of significant importance to operators on account of the safety and economic impacts associated with material loss and operational inefficiencies. Associated loss in control of pressure also effects the stability and ability to control the phase of cryogenic fluids during replenishment operations. Current research dedicated to providing Prognostics and Health Management (PHM) coverage of such cryogenic replenishment systems has focused on the detection of leaks to atmosphere involving relatively simple model-based diagnostic approaches that, while effective, are unable to isolate the fault to specific piping system components. The authors have extended this research to focus on the detection of leaks through closed valves that are intended to isolate sections of the piping system from the flow and pressurization of cryogenic fluids. The described approach employs model-based detection of leak-through conditions based on correlations of pressure changes across isolation valves and attempts to isolate the faults to specific valves. Implementation of this capability is enabled by knowledge and information embedded in the domain model of the system. The approach has been used effectively to detect such leak-through faults during cryogenic operational testing at the Cryogenic Testbed at NASA's Kennedy Space Center.

lme4qtl: linear mixed models with flexible covariance structure for genetic studies of related individuals.

PubMed

Ziyatdinov, Andrey; Vázquez-Santiago, Miquel; Brunel, Helena; Martinez-Perez, Angel; Aschard, Hugues; Soria, Jose Manuel

2018-02-27

Quantitative trait locus (QTL) mapping in genetic data often involves analysis of correlated observations, which need to be accounted for to avoid false association signals. This is commonly performed by modeling such correlations as random effects in linear mixed models (LMMs). The R package lme4 is a well-established tool that implements major LMM features using sparse matrix methods; however, it is not fully adapted for QTL mapping association and linkage studies. In particular, two LMM features are lacking in the base version of lme4: the definition of random effects by custom covariance matrices; and parameter constraints, which are essential in advanced QTL models. Apart from applications in linkage studies of related individuals, such functionalities are of high interest for association studies in situations where multiple covariance matrices need to be modeled, a scenario not covered by many genome-wide association study (GWAS) software. To address the aforementioned limitations, we developed a new R package lme4qtl as an extension of lme4. First, lme4qtl contributes new models for genetic studies within a single tool integrated with lme4 and its companion packages. Second, lme4qtl offers a flexible framework for scenarios with multiple levels of relatedness and becomes efficient when covariance matrices are sparse. We showed the value of our package using real family-based data in the Genetic Analysis of Idiopathic Thrombophilia 2 (GAIT2) project. Our software lme4qtl enables QTL mapping models with a versatile structure of random effects and efficient computation for sparse covariances. lme4qtl is available at https://github.com/variani/lme4qtl .

Association between participation in life situations of children with cerebral palsy and their physical, social, and attitudinal environment: a cross-sectional multicenter European study.

PubMed

Colver, Allan; Thyen, Ute; Arnaud, Catherine; Beckung, Eva; Fauconnier, Jerome; Marcelli, Marco; McManus, Vicki; Michelsen, Susan I; Parkes, Jackie; Parkinson, Kathryn; Dickinson, Heather O

2012-12-01

To evaluate how participation of children with cerebral palsy (CP) varied with their environment. Home visits to children. Administration of Assessment of Life Habits and European Child Environment Questionnaires. Structural equation modeling of putative associations between specific domains of participation and environment, while allowing for severity of child's impairments and pain. European regions with population-based registries of children with CP. Children (n=1174) aged 8 to 12 years were randomly selected from 8 population-based registries of children with CP in 6 European countries. Of these, 743 (63%) agreed to participate; 1 further region recruited 75 children from multiple sources. Thus, there were 818 children in the study. Not applicable. Participation in life situations. For the hypothesized associations, the models confirmed that higher participation was associated with better availability of environmental items. Higher participation in daily activities-mealtimes, health hygiene, personal care, and home life-was significantly associated with a better physical environment at home (P<.01). Mobility was associated with transport and physical environment in the community. Participation in social roles (responsibilities, relationships, recreation) was associated with attitudes of classmates and social support at home. School participation was associated with attitudes of teachers and therapists. Environment explained between 14% and 52% of the variation in participation. The findings confirmed the social model of disability. The physical, social, and attitudinal environment of disabled children influences their participation in everyday activities and social roles. Copyright © 2012 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.

Association Between Participation in Life Situations of Children With Cerebral Palsy and Their Physical, Social, and Attitudinal Environment: A Cross-Sectional Multicenter European Study

PubMed Central

Colver, Allan; Thyen, Ute; Arnaud, Catherine; Beckung, Eva; Fauconnier, Jerome; Marcelli, Marco; McManus, Vicki; Michelsen, Susan I.; Parkes, Jackie; Parkinson, Kathryn; Dickinson, Heather O.

2013-01-01

Objective To evaluate how participation of children with cerebral palsy (CP) varied with their environment. Design Home visits to children. Administration of Assessment of Life Habits and European Child Environment Questionnaires. Structural equation modeling of putative associations between specific domains of participation and environment, while allowing for severity of child’s impairments and pain. Setting European regions with population-based registries of children with CP. Participants Children (n=1174) aged 8 to 12 years were randomly selected from 8 population-based registries of children with CP in 6 European countries. Of these, 743 (63%) agreed to participate; 1 further region recruited 75 children from multiple sources. Thus, there were 818 children in the study. Interventions Not applicable. Main Outcome Measure Participation in life situations. Results For the hypothesized associations, the models confirmed that higher participation was associated with better availability of environmental items. Higher participation in daily activities—mealtimes, health hygiene, personal care, and home life—was significantly associated with a better physical environment at home (P<.01). Mobility was associated with transport and physical environment in the community. Participation in social roles (responsibilities, relationships, recreation) was associated with attitudes of classmates and social support at home. School participation was associated with attitudes of teachers and therapists. Environment explained between 14% and 52% of the variation in participation. Conclusions The findings confirmed the social model of disability. The physical, social, and attitudinal environment of disabled children influences their participation in everyday activities and social roles. PMID:22846455

Intelligent Evaluation Method of Tank Bottom Corrosion Status Based on Improved BP Artificial Neural Network

NASA Astrophysics Data System (ADS)

Qiu, Feng; Dai, Guang; Zhang, Ying

According to the acoustic emission information and the appearance inspection information of tank bottom online testing, the external factors associated with tank bottom corrosion status are confirmed. Applying artificial neural network intelligent evaluation method, three tank bottom corrosion status evaluation models based on appearance inspection information, acoustic emission information, and online testing information are established. Comparing with the result of acoustic emission online testing through the evaluation of test sample, the accuracy of the evaluation model based on online testing information is 94 %. The evaluation model can evaluate tank bottom corrosion accurately and realize acoustic emission online testing intelligent evaluation of tank bottom.

QUANTIFYING AN UNCERTAIN FUTURE: HYDROLOGIC MODEL PERFORMANCE FOR A SERIES OF REALIZED "/FUTURE" CONDITIONS

EPA Science Inventory

A systematic analysis of model performance during simulations based on observed landcover/use change is used to quantify errors associated with simulations of known "future" conditions. Calibrated and uncalibrated assessments of relative change over different lengths of...

Retrosynthetic Reaction Prediction Using Neural Sequence-to-Sequence Models

PubMed Central

2017-01-01

We describe a fully data driven model that learns to perform a retrosynthetic reaction prediction task, which is treated as a sequence-to-sequence mapping problem. The end-to-end trained model has an encoder–decoder architecture that consists of two recurrent neural networks, which has previously shown great success in solving other sequence-to-sequence prediction tasks such as machine translation. The model is trained on 50,000 experimental reaction examples from the United States patent literature, which span 10 broad reaction types that are commonly used by medicinal chemists. We find that our model performs comparably with a rule-based expert system baseline model, and also overcomes certain limitations associated with rule-based expert systems and with any machine learning approach that contains a rule-based expert system component. Our model provides an important first step toward solving the challenging problem of computational retrosynthetic analysis. PMID:29104927

Of goals and habits: age-related and individual differences in goal-directed decision-making.

PubMed

Eppinger, Ben; Walter, Maik; Heekeren, Hauke R; Li, Shu-Chen

2013-01-01

In this study we investigated age-related and individual differences in habitual (model-free) and goal-directed (model-based) decision-making. Specifically, we were interested in three questions. First, does age affect the balance between model-based and model-free decision mechanisms? Second, are these age-related changes due to age differences in working memory (WM) capacity? Third, can model-based behavior be affected by manipulating the distinctiveness of the reward value of choice options? To answer these questions we used a two-stage Markov decision task in in combination with computational modeling to dissociate model-based and model-free decision mechanisms. To affect model-based behavior in this task we manipulated the distinctiveness of reward probabilities of choice options. The results show age-related deficits in model-based decision-making, which are particularly pronounced if unexpected reward indicates the need for a shift in decision strategy. In this situation younger adults explore the task structure, whereas older adults show perseverative behavior. Consistent with previous findings, these results indicate that older adults have deficits in the representation and updating of expected reward value. We also observed substantial individual differences in model-based behavior. In younger adults high WM capacity is associated with greater model-based behavior and this effect is further elevated when reward probabilities are more distinct. However, in older adults we found no effect of WM capacity. Moreover, age differences in model-based behavior remained statistically significant, even after controlling for WM capacity. Thus, factors other than decline in WM, such as deficits in the in the integration of expected reward value into strategic decisions may contribute to the observed impairments in model-based behavior in older adults.

Of goals and habits: age-related and individual differences in goal-directed decision-making

PubMed Central

Eppinger, Ben; Walter, Maik; Heekeren, Hauke R.; Li, Shu-Chen

2013-01-01

In this study we investigated age-related and individual differences in habitual (model-free) and goal-directed (model-based) decision-making. Specifically, we were interested in three questions. First, does age affect the balance between model-based and model-free decision mechanisms? Second, are these age-related changes due to age differences in working memory (WM) capacity? Third, can model-based behavior be affected by manipulating the distinctiveness of the reward value of choice options? To answer these questions we used a two-stage Markov decision task in in combination with computational modeling to dissociate model-based and model-free decision mechanisms. To affect model-based behavior in this task we manipulated the distinctiveness of reward probabilities of choice options. The results show age-related deficits in model-based decision-making, which are particularly pronounced if unexpected reward indicates the need for a shift in decision strategy. In this situation younger adults explore the task structure, whereas older adults show perseverative behavior. Consistent with previous findings, these results indicate that older adults have deficits in the representation and updating of expected reward value. We also observed substantial individual differences in model-based behavior. In younger adults high WM capacity is associated with greater model-based behavior and this effect is further elevated when reward probabilities are more distinct. However, in older adults we found no effect of WM capacity. Moreover, age differences in model-based behavior remained statistically significant, even after controlling for WM capacity. Thus, factors other than decline in WM, such as deficits in the in the integration of expected reward value into strategic decisions may contribute to the observed impairments in model-based behavior in older adults. PMID:24399925

A prenatal nicotine exposure mouse model of methylphenidate responsive ADHD-associated cognitive phenotypes.

PubMed

Zhu, Jinmin; Fan, Fangfang; McCarthy, Deirdre M; Zhang, Lin; Cannon, Elisa N; Spencer, Thomas J; Biederman, Joseph; Bhide, Pradeep G

2017-05-01

Prenatal exposure to nicotine via cigarette smoke or other forms of tobacco use is a significant environmental risk factor for attention deficit hyperactivity disorder (ADHD). The neurobiological mechanisms underlying the link between prenatal nicotine exposure (PNE) and ADHD are not well understood. Animal models, especially rodent models, are beginning to bridge this gap in knowledge. Although ADHD is characterized by hyperactivity, inattention, impulsivity and working memory deficits, the majority of the animal models are based on only one or two ADHD associated phenotypes, in particular, hyperactivity or inattention. We report a PNE mouse model that displays the full range of ADHD associated behavioral phenotypes including working memory deficit, attention deficit and impulsive-like behavior. All of the ADHD-associated phenotypes respond to a single administration of a therapeutic equivalent dose of methylphenidate. In an earlier study, we showed that PNE produces hyperactivity, frontal cortical hypodopaminergic state and thinning of the cingulate cortex. Collectively, these data suggest that the PNE mouse model recapitulates key features of ADHD and may be a suitable preclinical model for ADHD research. Copyright © 2017 ISDN. Published by Elsevier Ltd. All rights reserved.

Underlying Mechanisms of Cooperativity, Input Specificity, and Associativity of Long-Term Potentiation Through a Positive Feedback of Local Protein Synthesis.

PubMed

Hao, Lijie; Yang, Zhuoqin; Lei, Jinzhi

2018-01-01

Long-term potentiation (LTP) is a specific form of activity-dependent synaptic plasticity that is a leading mechanism of learning and memory in mammals. The properties of cooperativity, input specificity, and associativity are essential for LTP; however, the underlying mechanisms are unclear. Here, based on experimentally observed phenomena, we introduce a computational model of synaptic plasticity in a pyramidal cell to explore the mechanisms responsible for the cooperativity, input specificity, and associativity of LTP. The model is based on molecular processes involved in synaptic plasticity and integrates gene expression involved in the regulation of neuronal activity. In the model, we introduce a local positive feedback loop of protein synthesis at each synapse, which is essential for bimodal response and synapse specificity. Bifurcation analysis of the local positive feedback loop of brain-derived neurotrophic factor (BDNF) signaling illustrates the existence of bistability, which is the basis of LTP induction. The local bifurcation diagram provides guidance for the realization of LTP, and the projection of whole system trajectories onto the two-parameter bifurcation diagram confirms the predictions obtained from bifurcation analysis. Moreover, model analysis shows that pre- and postsynaptic components are required to achieve the three properties of LTP. This study provides insights into the mechanisms underlying the cooperativity, input specificity, and associativity of LTP, and the further construction of neural networks for learning and memory.

An empirical study of race times in recreational endurance runners.

PubMed

Vickers, Andrew J; Vertosick, Emily A

2016-01-01

Studies of endurance running have typically involved elite athletes, small sample sizes and measures that require special expertise or equipment. We examined factors associated with race performance and explored methods for race time prediction using information routinely available to a recreational runner. An Internet survey was used to collect data from recreational endurance runners (N = 2303). The cohort was split 2:1 into a training set and validation set to create models to predict race time. Sex, age, BMI and race training were associated with mean race velocity for all race distances. The difference in velocity between males and females decreased with increasing distance. Tempo runs were more strongly associated with velocity for shorter distances, while typical weekly training mileage and interval training had similar associations with velocity for all race distances. The commonly used Riegel formula for race time prediction was well-calibrated for races up to a half-marathon, but dramatically underestimated marathon time, giving times at least 10 min too fast for half of runners. We built two models to predict marathon time. The mean squared error for Riegel was 381 compared to 228 (model based on one prior race) and 208 (model based on two prior races). Our findings can be used to inform race training and to provide more accurate race time predictions for better pacing.

Going beyond the unitary curve: incorporating richer cognition into agent-based water resources models

NASA Astrophysics Data System (ADS)

Kock, B. E.

2008-12-01

The increased availability and understanding of agent-based modeling technology and techniques provides a unique opportunity for water resources modelers, allowing them to go beyond traditional behavioral approaches from neoclassical economics, and add rich cognition to social-hydrological models. Agent-based models provide for an individual focus, and the easier and more realistic incorporation of learning, memory and other mechanisms for increased cognitive sophistication. We are in an age of global change impacting complex water resources systems, and social responses are increasingly recognized as fundamentally adaptive and emergent. In consideration of this, water resources models and modelers need to better address social dynamics in a manner beyond the capabilities of neoclassical economics theory and practice. However, going beyond the unitary curve requires unique levels of engagement with stakeholders, both to elicit the richer knowledge necessary for structuring and parameterizing agent-based models, but also to make sure such models are appropriately used. With the aim of encouraging epistemological and methodological convergence in the agent-based modeling of water resources, we have developed a water resources-specific cognitive model and an associated collaborative modeling process. Our cognitive model emphasizes efficiency in architecture and operation, and capacity to adapt to different application contexts. We describe a current application of this cognitive model and modeling process in the Arkansas Basin of Colorado. In particular, we highlight the potential benefits of, and challenges to, using more sophisticated cognitive models in agent-based water resources models.

Development and Characterization of a Rate-Dependent Three-Dimensional Macroscopic Plasticity Model Suitable for Use in Composite Impact Problems

NASA Technical Reports Server (NTRS)

Goldberg, Robert K.; Carney, Kelly S.; DuBois, Paul; Hoffarth, Canio; Rajan, Subramaniam; Blankenhorn, Gunther

2015-01-01

Several key capabilities have been identified by the aerospace community as lacking in the material/models for composite materials currently available within commercial transient dynamic finite element codes such as LS-DYNA. Some of the specific desired features that have been identified include the incorporation of both plasticity and damage within the material model, the capability of using the material model to analyze the response of both three-dimensional solid elements and two dimensional shell elements, and the ability to simulate the response of composites composed with a variety of composite architectures, including laminates, weaves and braids. In addition, a need has been expressed to have a material model that utilizes tabulated experimentally based input to define the evolution of plasticity and damage as opposed to utilizing discrete input parameters (such as modulus and strength) and analytical functions based on curve fitting. To begin to address these needs, an orthotropic macroscopic plasticity based model suitable for implementation within LS-DYNA has been developed. Specifically, the Tsai-Wu composite failure model has been generalized and extended to a strain-hardening based orthotropic plasticity model with a non-associative flow rule. The coefficients in the yield function are determined based on tabulated stress-strain curves in the various normal and shear directions, along with selected off-axis curves. Incorporating rate dependence into the yield function is achieved by using a series of tabluated input curves, each at a different constant strain rate. The non-associative flow-rule is used to compute the evolution of the effective plastic strain. Systematic procedures have been developed to determine the values of the various coefficients in the yield function and the flow rule based on the tabulated input data. An algorithm based on the radial return method has been developed to facilitate the numerical implementation of the material model. The presented paper will present in detail the development of the orthotropic plasticity model and the procedures used to obtain the required material parameters. Methods in which a combination of actual testing and selective numerical testing can be combined to yield the appropriate input data for the model will be described. A specific laminated polymer matrix composite will be examined to demonstrate the application of the model.

A novel approach for predicting microRNA-disease associations by unbalanced bi-random walk on heterogeneous network.

PubMed

Luo, Jiawei; Xiao, Qiu

2017-02-01

MicroRNAs (miRNAs) play a critical role by regulating their targets in post-transcriptional level. Identification of potential miRNA-disease associations will aid in deciphering the pathogenesis of human polygenic diseases. Several computational models have been developed to uncover novel miRNA-disease associations based on the predicted target genes. However, due to the insufficient number of experimentally validated miRNA-target interactions as well as the relatively high false-positive and false-negative rates of predicted target genes, it is still challenging for these prediction models to obtain remarkable performances. The purpose of this study is to prioritize miRNA candidates for diseases. We first construct a heterogeneous network, which consists of a disease similarity network, a miRNA functional similarity network and a known miRNA-disease association network. Then, an unbalanced bi-random walk-based algorithm on the heterogeneous network (BRWH) is adopted to discover potential associations by exploiting bipartite subgraphs. Based on 5-fold cross validation, the proposed network-based method achieves AUC values ranging from 0.782 to 0.907 for the 22 human diseases and an average AUC of almost 0.846. The experiments indicated that BRWH can achieve better performances compared with several popular methods. In addition, case studies of some common diseases further demonstrated the superior performance of our proposed method on prioritizing disease-related miRNA candidates. Copyright © 2017 Elsevier Inc. All rights reserved.

Gardening and age-related weight gain: Results from a cross-sectional survey of Denver residents.

PubMed

Litt, Jill S; Lambert, Jeffrey Richard; Glueck, Deborah H

2017-12-01

This study examined whether gardening modifies the association between age and body mass index (BMI). We used data from the Neighborhood Environments and Health Survey, which was conducted in Denver (N = 469) between 2006 and 2007. We fit two general linear mixed models. The base model had BMI in kg/m 2 as the outcome, and age, an indicator variable for non-gardening status and the age-by-non-gardening status interaction as predictors. The adjusted model included as covariates the potential confounders of education, ethnicity and self-reported health. We assessed self-selection bias and confounding. BMI was 27.18 kg/m 2 for non-gardeners, 25.62 kg/m 2 for home gardeners, and 24.17 kg/m 2 for community gardeners. In the base model, a statistically significant association was observed between age and BMI for non-gardeners but not for the combined community and home gardening group (F = 9.27, ndf = 1, ddf = 441, p = 0.0025). In the adjusted model, the association between age and BMI in non-gardeners was not statistically significant (F = 1.72, ndf = 1, ddf = 431, p = 0.1908). Gardeners differed on social and demographic factors when compared to non-gardeners. The results from the base model are consistent with the hypothesis that gardening might offset age-related weight gain. However, the cross-sectional design does not permit differentiation of true causal effects from the possible effects of bias and confounding. As a follow-up study, to remove bias and confounding, we are conducting a randomized clinical trial of community gardening in Denver.

Interaction of ACE genotype and salt intake on hypertension among Chinese Kazakhs: results from a population-based cross-sectional study.

PubMed

Wang, Yuyan; Zhang, Biao; Hou, Lei; Han, Wei; Xue, Fang; Wang, Yanhong; Tang, Yong; Liang, Shaohua; Wang, Weizhi; Asaiti, Kuliqian; Wang, Zixing; Hu, Yaoda; Wang, Lei; Qiu, Changchun; Zhang, Mingtao; Jiang, Jingmei

2017-05-17

To explore the effect of interaction between ACE genotype and salt intake on hypertension among Chinese Kazakhs, and to compare applications of interactions between logistic model and generalised partially linear tree-based regression (GPLTR) model. Population-based cross-sectional study. Hong Dun, North Xinjiang, China. Non-consanguineous Chinese Kazakh participants (n=916, 342 men and 574 women) aged ≥30 years. Association between ACE genotype and hypertension, association between salt intake and hypertension, and interaction of ACE genotype and salt intake on hypertension in two models. Associations between salt intake and hypertension were different in ACE genotype of II and ID+DD. Under the logistic models, main and interaction effects were not observed for men, but effects were present in opposite directions for women (main effect of ACE: OR=0.20, p=0.003; interaction effect: OR=1.07, p=0.027). Under the GPLTR model, Bayesian information criterion trees included both salt intake and ACE genotype as split variables. Individuals with a salt intake ≥19.5 g/day and ID+DD genotypes had a 3.99-fold (p=0.004) higher risk of hypertension compared with the II genotype for men, whereas salt intake <20.1 g/day and ID+DD genotypes had an OR=0.55 (p=0.014) compared with the II genotype for women. An interaction of ACE genotype and salt intake on hypertension was observed among Chinese Kazakhs but in different ways according to sex. The GPLTR model appears to be more suitable for an exploration of interactions in complex diseases. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Entropy Based Genetic Association Tests and Gene-Gene Interaction Tests

PubMed Central

de Andrade, Mariza; Wang, Xin

2011-01-01

In the past few years, several entropy-based tests have been proposed for testing either single SNP association or gene-gene interaction. These tests are mainly based on Shannon entropy and have higher statistical power when compared to standard χ2 tests. In this paper, we extend some of these tests using a more generalized entropy definition, Rényi entropy, where Shannon entropy is a special case of order 1. The order λ (>0) of Rényi entropy weights the events (genotype/haplotype) according to their probabilities (frequencies). Higher λ places more emphasis on higher probability events while smaller λ (close to 0) tends to assign weights more equally. Thus, by properly choosing the λ, one can potentially increase the power of the tests or the p-value level of significance. We conducted simulation as well as real data analyses to assess the impact of the order λ and the performance of these generalized tests. The results showed that for dominant model the order 2 test was more powerful and for multiplicative model the order 1 or 2 had similar power. The analyses indicate that the choice of λ depends on the underlying genetic model and Shannon entropy is not necessarily the most powerful entropy measure for constructing genetic association or interaction tests. PMID:23089811

Need satisfaction, motivational regulations and exercise: moderation and mediation effects.

PubMed

Weman-Josefsson, Karin; Lindwall, Magnus; Ivarsson, Andreas

2015-05-20

Based on the Self-determination theory process model, this study aimed to explore relationships between the latent constructs of psychological need satisfaction, autonomous motivation and exercise behaviour; the mediational role of autonomous motivation in the association of psychological need satisfaction with exercise behaviour; as well as gender and age differences in the aforementioned associations. Adult active members of an Internet-based exercise program (n = 1091) between 18 and 78 years of age completed a test battery on motivational aspects based on Self-determination theory. The Basic Psychological Needs in Exercise Scale and the Behavioural Regulation in Exercise Questionnaire-2 were used to measure need satisfaction and type of motivation and the Leisure Time Exercise Questionnaire to measure self-reported exercise. Need satisfaction predicted autonomous motivation, which in turn predicted exercise, especially for women. Autonomous motivation was found to mediate the association between need satisfaction and exercise. Age and gender moderated several of the paths in the model linking need satisfaction with motivation and exercise. The results demonstrated gender and age differences in the proposed sequential mechanisms between autonomous motivation and exercise in the process model. This study thus highlights a potential value in considering moderating factors and the need to further examine the underlying mechanisms between needs, autonomous motivation, and exercise behaviour.

A critical issue in model-based inference for studying trait-based community assembly and a solution.

PubMed

Ter Braak, Cajo J F; Peres-Neto, Pedro; Dray, Stéphane

2017-01-01

Statistical testing of trait-environment association from data is a challenge as there is no common unit of observation: the trait is observed on species, the environment on sites and the mediating abundance on species-site combinations. A number of correlation-based methods, such as the community weighted trait means method (CWM), the fourth-corner correlation method and the multivariate method RLQ, have been proposed to estimate such trait-environment associations. In these methods, valid statistical testing proceeds by performing two separate resampling tests, one site-based and the other species-based and by assessing significance by the largest of the two p -values (the p max test). Recently, regression-based methods using generalized linear models (GLM) have been proposed as a promising alternative with statistical inference via site-based resampling. We investigated the performance of this new approach along with approaches that mimicked the p max test using GLM instead of fourth-corner. By simulation using models with additional random variation in the species response to the environment, the site-based resampling tests using GLM are shown to have severely inflated type I error, of up to 90%, when the nominal level is set as 5%. In addition, predictive modelling of such data using site-based cross-validation very often identified trait-environment interactions that had no predictive value. The problem that we identify is not an "omitted variable bias" problem as it occurs even when the additional random variation is independent of the observed trait and environment data. Instead, it is a problem of ignoring a random effect. In the same simulations, the GLM-based p max test controlled the type I error in all models proposed so far in this context, but still gave slightly inflated error in more complex models that included both missing (but important) traits and missing (but important) environmental variables. For screening the importance of single trait-environment combinations, the fourth-corner test is shown to give almost the same results as the GLM-based tests in far less computing time.

A Statistical Framework for Protein Quantitation in Bottom-Up MS-Based Proteomics

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

Karpievitch, Yuliya; Stanley, Jeffrey R.; Taverner, Thomas

2009-08-15

Motivation: Quantitative mass spectrometry-based proteomics requires protein-level estimates and associated confidence measures. Challenges include the presence of low quality or incorrectly identified peptides and informative missingness. Furthermore, models are required for rolling peptide-level information up to the protein level. Results: We present a statistical model that carefully accounts for informative missingness in peak intensities and allows unbiased, model-based, protein-level estimation and inference. The model is applicable to both label-based and label-free quantitation experiments. We also provide automated, model-based, algorithms for filtering of proteins and peptides as well as imputation of missing values. Two LC/MS datasets are used to illustrate themore » methods. In simulation studies, our methods are shown to achieve substantially more discoveries than standard alternatives. Availability: The software has been made available in the opensource proteomics platform DAnTE (http://omics.pnl.gov/software/). Contact: adabney@stat.tamu.edu Supplementary information: Supplementary data are available at Bioinformatics online.« less

Modeling avian exposures to perfluoroalkyl substances in aquatic habitats impacted by historical aqueous film forming foam releases.

PubMed

Larson, Emily S; Conder, Jason M; Arblaster, Jennifer A

2018-06-01

Releases of Perfluoroalkyl and Polyfluoroalkyl Substances (PFASs) associated with Aqueous Film Forming Foams (AFFFs) have the potential to impact on-site and downgradient aquatic habitats. Dietary exposures of aquatic-dependent birds were modeled for seven PFASs (PFHxA, PFOA, PFNA, PFDA, PFHxS, PFOS, and PFDS) using five different scenarios based on measurements of PFASs obtained from five investigations of sites historically-impacted by AFFF. Exposure modeling was conducted for four avian receptors representing various avian feeding guilds: lesser scaup (Aythya affinis), spotted sandpiper (Actitis macularia), great blue heron (Ardea herodias), and osprey (Pandion haliaetus). For the receptor predicted to receive the highest PFAS exposure (spotted sandpiper), model-predicted exposure to PFOS exceeded a laboratory-based, No Observed Adverse Effect Level exposure benchmark in three of the five model scenarios, confirming that risks to aquatic-dependent avian wildlife should be considered for investigations of historic AFFF releases. Perfluoroalkyl sulfonic acids (PFHxS, PFOS, and PFDS) represented 94% (on average) of total PFAS exposures due to their prevalence in historical AFFF formulations, and increased bioaccumulation in aquatic prey items and partitioning to aquatic sediment relative to perfluoroalkyl carboxylic acids. Sediment-associated PFASs (rather than water-associated PFASs) were the source of the highest predicted PFAS exposures, and are likely to be very important for understanding and managing AFFF site-specific ecological risks. Additional considerations for research needs and site-specific ecological risk assessments are discussed with the goal of optimizing ecological risk-based decision making at AFFF sites and prioritizing research needs. Copyright © 2018 Elsevier Ltd. All rights reserved.

Sensory Processing Subtypes in Autism: Association with Adaptive Behavior

ERIC Educational Resources Information Center

Lane, Alison E.; Young, Robyn L.; Baker, Amy E. Z.; Angley, Manya T.

2010-01-01

Children with autism are frequently observed to experience difficulties in sensory processing. This study examined specific patterns of sensory processing in 54 children with autistic disorder and their association with adaptive behavior. Model-based cluster analysis revealed three distinct sensory processing subtypes in autism. These subtypes…

Evaluation of bone insertion level of support teeth in class I mandibular removable partial denture associated with an osseointegrated implant: a study using finite element analysis.

PubMed

Verri, Fellippo Ramos; Pellizzer, Eduardo Piza; Pereira, João Antônio; Zuim, Paulo Renato Junqueira; Santiago Júnior, Joel Ferreira

2011-06-01

: This study evaluated the influence of distal extension removable partial denture associated with implant in cases of different bone level of abutment tooth, using 2D finite element analysis. : Eight hemiarch models were simulated: model A-presenting tooth 33 and distal extension removable partial denture replacing others teeth, using distal rest connection and no bone lost; model B-similar to model A but presenting distal guide plate connection; model C- similar to model A but presenting osseointegrated implant with ERA retention system associated under prosthetic base; model D-similar to model B but presenting osseointegrated implant as described in model C; models E, F, G, and H were similar to models A, B, C, and D but presenting reduced periodontal support around tooth 33. Using ANSYS 9.0 software, the models were loaded vertically with 50 N on each cusp tip. For results, von Mises Stress Maps were plotted. : Maximum stress value was encountered in model G (201.023 MPa). Stress distribution was concentrated on implant and retention system. The implant/removable partial denture association decreases stress levels on alveolar mucosa for all models. : Use of implant and ERA system decreased stress concentrations on supporting structures in all models. Use of distal guide plate decreased stress levels on abutment tooth and cortical and trabecular bone. Tooth apex of models with reduced periodontal support presented increased stress when using distal rest.

Potential clinical and economic outcomes of active beta-D-glucan surveillance with preemptive therapy for invasive candidiasis at intensive care units: a decision model analysis.

PubMed

Pang, Y-K; Ip, M; You, J H S

2017-01-01

Early initiation of antifungal treatment for invasive candidiasis is associated with change in mortality. Beta-D-glucan (BDG) is a fungal cell wall component and a serum diagnostic biomarker of fungal infection. Clinical findings suggested an association between reduced invasive candidiasis incidence in intensive care units (ICUs) and BDG-guided preemptive antifungal therapy. We evaluated the potential cost-effectiveness of active BDG surveillance with preemptive antifungal therapy in patients admitted to adult ICUs from the perspective of Hong Kong healthcare providers. A Markov model was designed to simulate the outcomes of active BDG surveillance with preemptive therapy (surveillance group) and no surveillance (standard care group). Candidiasis-associated outcome measures included mortality rate, quality-adjusted life year (QALY) loss, and direct medical cost. Model inputs were derived from the literature. Sensitivity analyses were conducted to evaluate the robustness of model results. In base-case analysis, the surveillance group was more costly (1387 USD versus 664 USD) (1 USD = 7.8 HKD), with lower candidiasis-associated mortality rate (0.653 versus 1.426 per 100 ICU admissions) and QALY loss (0.116 versus 0.254) than the standard care group. The incremental cost per QALY saved by the surveillance group was 5239 USD/QALY. One-way sensitivity analyses found base-case results to be robust to variations of all model inputs. In probabilistic sensitivity analysis, the surveillance group was cost-effective in 50 % and 100 % of 10,000 Monte Carlo simulations at willingness-to-pay (WTP) thresholds of 7200 USD/QALY and ≥27,800 USD/QALY, respectively. Active BDG surveillance with preemptive therapy appears to be highly cost-effective to reduce the candidiasis-associated mortality rate and save QALYs in the ICU setting.

Novel probabilistic models of spatial genetic ancestry with applications to stratification correction in genome-wide association studies.

PubMed

Bhaskar, Anand; Javanmard, Adel; Courtade, Thomas A; Tse, David

2017-03-15

Genetic variation in human populations is influenced by geographic ancestry due to spatial locality in historical mating and migration patterns. Spatial population structure in genetic datasets has been traditionally analyzed using either model-free algorithms, such as principal components analysis (PCA) and multidimensional scaling, or using explicit spatial probabilistic models of allele frequency evolution. We develop a general probabilistic model and an associated inference algorithm that unify the model-based and data-driven approaches to visualizing and inferring population structure. Our spatial inference algorithm can also be effectively applied to the problem of population stratification in genome-wide association studies (GWAS), where hidden population structure can create fictitious associations when population ancestry is correlated with both the genotype and the trait. Our algorithm Geographic Ancestry Positioning (GAP) relates local genetic distances between samples to their spatial distances, and can be used for visually discerning population structure as well as accurately inferring the spatial origin of individuals on a two-dimensional continuum. On both simulated and several real datasets from diverse human populations, GAP exhibits substantially lower error in reconstructing spatial ancestry coordinates compared to PCA. We also develop an association test that uses the ancestry coordinates inferred by GAP to accurately account for ancestry-induced correlations in GWAS. Based on simulations and analysis of a dataset of 10 metabolic traits measured in a Northern Finland cohort, which is known to exhibit significant population structure, we find that our method has superior power to current approaches. Our software is available at https://github.com/anand-bhaskar/gap . abhaskar@stanford.edu or ajavanma@usc.edu. Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

A nonparametric test for Markovianity in the illness-death model.

PubMed

Rodríguez-Girondo, Mar; de Uña-Álvarez, Jacobo

2012-12-30

Multistate models are useful tools for modeling disease progression when survival is the main outcome, but several intermediate events of interest are observed during the follow-up time. The illness-death model is a special multistate model with important applications in the biomedical literature. It provides a suitable representation of the individual's history when a unique intermediate event can be experienced before the main event of interest. Nonparametric estimation of transition probabilities in this and other multistate models is usually performed through the Aalen-Johansen estimator under a Markov assumption. The Markov assumption claims that given the present state, the future evolution of the illness is independent of the states previously visited and the transition times among them. However, this assumption fails in some applications, leading to inconsistent estimates. In this paper, we provide a new approach for testing Markovianity in the illness-death model. The new method is based on measuring the future-past association along time. This results in a detailed inspection of the process, which often reveals a non-Markovian behavior with different trends in the association measure. A test of significance for zero future-past association at each time point is introduced, and a significance trace is proposed accordingly. Besides, we propose a global test for Markovianity based on a supremum-type test statistic. The finite sample performance of the test is investigated through simulations. We illustrate the new method through the analysis of two biomedical data analysis. Copyright © 2012 John Wiley & Sons, Ltd.

Better and Worse: A Dual-Process Model of the Relationship between Core Self-evaluation and Work-Family Conflict.

PubMed

Yu, Kun

2016-01-01

Based on both resource allocation theory (Becker, 1965; Bergeron, 2007) and role theory (Katz and Kahn, 1978), the current study aims to uncover the relationship between core self-evaluation (CSE) and three dimensions of work interference with family (WIF). A dual-process model was proposed, in which both work stress and career resilience mediate the CSE-WIF relationship. The mediation model was tested with a sample of employees from various organizations ( N = 561). The results first showed that CSE was negatively related to time-based and strain-based WIF and positively related to behavior-based WIF via the mediation of work stress. Moreover, CSE was positively associated with behavior-based and strain-based WIF via the mediation of career resilience, suggesting that CSE may also have its "dark-side."

Better and Worse: A Dual-Process Model of the Relationship between Core Self-evaluation and Work-Family Conflict

PubMed Central

Yu, Kun

2016-01-01

Based on both resource allocation theory (Becker, 1965; Bergeron, 2007) and role theory (Katz and Kahn, 1978), the current study aims to uncover the relationship between core self-evaluation (CSE) and three dimensions of work interference with family (WIF). A dual-process model was proposed, in which both work stress and career resilience mediate the CSE-WIF relationship. The mediation model was tested with a sample of employees from various organizations (N = 561). The results first showed that CSE was negatively related to time-based and strain-based WIF and positively related to behavior-based WIF via the mediation of work stress. Moreover, CSE was positively associated with behavior-based and strain-based WIF via the mediation of career resilience, suggesting that CSE may also have its “dark-side.” PMID:27790177

Model-assisted template extraction SRAF application to contact holes patterns in high-end flash memory device fabrication

NASA Astrophysics Data System (ADS)

Seoud, Ahmed; Kim, Juhwan; Ma, Yuansheng; Jayaram, Srividya; Hong, Le; Chae, Gyu-Yeol; Lee, Jeong-Woo; Park, Dae-Jin; Yune, Hyoung-Soon; Oh, Se-Young; Park, Chan-Ha

2018-03-01

Sub-resolution assist feature (SRAF) insertion techniques have been effectively used for a long time now to increase process latitude in the lithography patterning process. Rule-based SRAF and model-based SRAF are complementary solutions, and each has its own benefits, depending on the objectives of applications and the criticality of the impact on manufacturing yield, efficiency, and productivity. Rule-based SRAF provides superior geometric output consistency and faster runtime performance, but the associated recipe development time can be of concern. Model-based SRAF provides better coverage for more complicated pattern structures in terms of shapes and sizes, with considerably less time required for recipe development, although consistency and performance may be impacted. In this paper, we introduce a new model-assisted template extraction (MATE) SRAF solution, which employs decision tree learning in a model-based solution to provide the benefits of both rule-based and model-based SRAF insertion approaches. The MATE solution is designed to automate the creation of rules/templates for SRAF insertion, and is based on the SRAF placement predicted by model-based solutions. The MATE SRAF recipe provides optimum lithographic quality in relation to various manufacturing aspects in a very short time, compared to traditional methods of rule optimization. Experiments were done using memory device pattern layouts to compare the MATE solution to existing model-based SRAF and pixelated SRAF approaches, based on lithographic process window quality, runtime performance, and geometric output consistency.

Unipolar Terminal-Attractor Based Neural Associative Memory with Adaptive Threshold

NASA Technical Reports Server (NTRS)

Liu, Hua-Kuang (Inventor); Barhen, Jacob (Inventor); Farhat, Nabil H. (Inventor); Wu, Chwan-Hwa (Inventor)

1996-01-01

A unipolar terminal-attractor based neural associative memory (TABAM) system with adaptive threshold for perfect convergence is presented. By adaptively setting the threshold values for the dynamic iteration for the unipolar binary neuron states with terminal-attractors for the purpose of reducing the spurious states in a Hopfield neural network for associative memory and using the inner-product approach, perfect convergence and correct retrieval is achieved. Simulation is completed with a small number of stored states (M) and a small number of neurons (N) but a large M/N ratio. An experiment with optical exclusive-OR logic operation using LCTV SLMs shows the feasibility of optoelectronic implementation of the models. A complete inner-product TABAM is implemented using a PC for calculation of adaptive threshold values to achieve a unipolar TABAM (UIT) in the case where there is no crosstalk, and a crosstalk model (CRIT) in the case where crosstalk corrupts the desired state.

Unipolar terminal-attractor based neural associative memory with adaptive threshold

NASA Technical Reports Server (NTRS)

Liu, Hua-Kuang (Inventor); Barhen, Jacob (Inventor); Farhat, Nabil H. (Inventor); Wu, Chwan-Hwa (Inventor)

1993-01-01

A unipolar terminal-attractor based neural associative memory (TABAM) system with adaptive threshold for perfect convergence is presented. By adaptively setting the threshold values for the dynamic iteration for the unipolar binary neuron states with terminal-attractors for the purpose of reducing the spurious states in a Hopfield neural network for associative memory and using the inner product approach, perfect convergence and correct retrieval is achieved. Simulation is completed with a small number of stored states (M) and a small number of neurons (N) but a large M/N ratio. An experiment with optical exclusive-OR logic operation using LCTV SLMs shows the feasibility of optoelectronic implementation of the models. A complete inner-product TABAM is implemented using a PC for calculation of adaptive threshold values to achieve a unipolar TABAM (UIT) in the case where there is no crosstalk, and a crosstalk model (CRIT) in the case where crosstalk corrupts the desired state.

College students coping with interpersonal stress: Examining a control-based model of coping.

PubMed

Coiro, Mary Jo; Bettis, Alexandra H; Compas, Bruce E

2017-04-01

The ways that college students cope with stress, particularly interpersonal stress, may be a critical factor in determining which students are at risk for impairing mental health disorders. Using a control-based model of coping, the present study examined associations between interpersonal stress, coping strategies, and symptoms. A total of 135 undergraduate students from 2 universities. Interpersonal stress, coping strategies, depression, anxiety, and somatization were assessed via self-report. Students reporting more interpersonal stress reported more depression, anxiety, and somatization, and they reported less use of engagement coping strategies and greater use of disengagement coping strategies. Engagement coping strategies accounted for a significant portion of the association between interpersonal stress and mental health symptoms. Unexpectedly, coping strategies did not moderate the association between stress and mental health symptoms. Interventions designed to improve students' coping strategies may be an effective way to reduce mental health problems on college campuses.

OFMspert: An architecture for an operator's associate that evolves to an intelligent tutor

NASA Technical Reports Server (NTRS)

Mitchell, Christine M.

1991-01-01

With the emergence of new technology for both human-computer interaction and knowledge-based systems, a range of opportunities exist which enhance the effectiveness and efficiency of controllers of high-risk engineering systems. The design of an architecture for an operator's associate is described. This associate is a stand-alone model-based system designed to interact with operators of complex dynamic systems, such as airplanes, manned space systems, and satellite ground control systems in ways comparable to that of a human assistant. The operator function model expert system (OFMspert) architecture and the design and empirical validation of OFMspert's understanding component are described. The design and validation of OFMspert's interactive and control components are also described. A description of current work in which OFMspert provides the foundation in the development of an intelligent tutor that evolves to an assistant, as operator expertise evolves from novice to expert, is provided.

Ancestral haplotype-based association mapping with generalized linear mixed models accounting for stratification.

PubMed

Zhang, Z; Guillaume, F; Sartelet, A; Charlier, C; Georges, M; Farnir, F; Druet, T

2012-10-01

In many situations, genome-wide association studies are performed in populations presenting stratification. Mixed models including a kinship matrix accounting for genetic relatedness among individuals have been shown to correct for population and/or family structure. Here we extend this methodology to generalized linear mixed models which properly model data under various distributions. In addition we perform association with ancestral haplotypes inferred using a hidden Markov model. The method was shown to properly account for stratification under various simulated scenari presenting population and/or family structure. Use of ancestral haplotypes resulted in higher power than SNPs on simulated datasets. Application to real data demonstrates the usefulness of the developed model. Full analysis of a dataset with 4600 individuals and 500 000 SNPs was performed in 2 h 36 min and required 2.28 Gb of RAM. The software GLASCOW can be freely downloaded from www.giga.ulg.ac.be/jcms/prod_381171/software. francois.guillaume@jouy.inra.fr Supplementary data are available at Bioinformatics online.

Mirror Neurons Modeled Through Spike-Timing-Dependent Plasticity are Affected by Channelopathies Associated with Autism Spectrum Disorder.

PubMed

Antunes, Gabriela; Faria da Silva, Samuel F; Simoes de Souza, Fabio M

2018-06-01

Mirror neurons fire action potentials both when the agent performs a certain behavior and watches someone performing a similar action. Here, we present an original mirror neuron model based on the spike-timing-dependent plasticity (STDP) between two morpho-electrical models of neocortical pyramidal neurons. Both neurons fired spontaneously with basal firing rate that follows a Poisson distribution, and the STDP between them was modeled by the triplet algorithm. Our simulation results demonstrated that STDP is sufficient for the rise of mirror neuron function between the pairs of neocortical neurons. This is a proof of concept that pairs of neocortical neurons associating sensory inputs to motor outputs could operate like mirror neurons. In addition, we used the mirror neuron model to investigate whether channelopathies associated with autism spectrum disorder could impair the modeled mirror function. Our simulation results showed that impaired hyperpolarization-activated cationic currents (Ih) affected the mirror function between the pairs of neocortical neurons coupled by STDP.

A physics-based crystallographic modeling framework for describing the thermal creep behavior of Fe-Cr alloys

DOE PAGES

Wen, Wei; Capolungo, Laurent; Patra, Anirban; ...

2017-02-23

In this work, a physics-based thermal creep model is developed based on the understanding of the microstructure in Fe-Cr alloys. This model is associated with a transition state theory based framework that considers the distribution of internal stresses at sub-material point level. The thermally activated dislocation glide and climb mechanisms are coupled in the obstacle-bypass processes for both dislocation and precipitate-type barriers. A kinetic law is proposed to track the dislocation densities evolution in the subgrain interior and in the cell wall. The predicted results show that this model, embedded in the visco-plastic self-consistent (VPSC) framework, captures well the creepmore » behaviors for primary and steady-state stages under various loading conditions. We also discuss the roles of the mechanisms involved.« less

Conceptual information processing: A robust approach to KBS-DBMS integration

NASA Technical Reports Server (NTRS)

Lazzara, Allen V.; Tepfenhart, William; White, Richard C.; Liuzzi, Raymond

1987-01-01

Integrating the respective functionality and architectural features of knowledge base and data base management systems is a topic of considerable interest. Several aspects of this topic and associated issues are addressed. The significance of integration and the problems associated with accomplishing that integration are discussed. The shortcomings of current approaches to integration and the need to fuse the capabilities of both knowledge base and data base management systems motivates the investigation of information processing paradigms. One such paradigm is concept based processing, i.e., processing based on concepts and conceptual relations. An approach to robust knowledge and data base system integration is discussed by addressing progress made in the development of an experimental model for conceptual information processing.

An improved Pearson's correlation proximity-based hierarchical clustering for mining biological association between genes.

PubMed

Booma, P M; Prabhakaran, S; Dhanalakshmi, R

2014-01-01

Microarray gene expression datasets has concerned great awareness among molecular biologist, statisticians, and computer scientists. Data mining that extracts the hidden and usual information from datasets fails to identify the most significant biological associations between genes. A search made with heuristic for standard biological process measures only the gene expression level, threshold, and response time. Heuristic search identifies and mines the best biological solution, but the association process was not efficiently addressed. To monitor higher rate of expression levels between genes, a hierarchical clustering model was proposed, where the biological association between genes is measured simultaneously using proximity measure of improved Pearson's correlation (PCPHC). Additionally, the Seed Augment algorithm adopts average linkage methods on rows and columns in order to expand a seed PCPHC model into a maximal global PCPHC (GL-PCPHC) model and to identify association between the clusters. Moreover, a GL-PCPHC applies pattern growing method to mine the PCPHC patterns. Compared to existing gene expression analysis, the PCPHC model achieves better performance. Experimental evaluations are conducted for GL-PCPHC model with standard benchmark gene expression datasets extracted from UCI repository and GenBank database in terms of execution time, size of pattern, significance level, biological association efficiency, and pattern quality.

An Improved Pearson's Correlation Proximity-Based Hierarchical Clustering for Mining Biological Association between Genes

PubMed Central

Booma, P. M.; Prabhakaran, S.; Dhanalakshmi, R.

2014-01-01

Microarray gene expression datasets has concerned great awareness among molecular biologist, statisticians, and computer scientists. Data mining that extracts the hidden and usual information from datasets fails to identify the most significant biological associations between genes. A search made with heuristic for standard biological process measures only the gene expression level, threshold, and response time. Heuristic search identifies and mines the best biological solution, but the association process was not efficiently addressed. To monitor higher rate of expression levels between genes, a hierarchical clustering model was proposed, where the biological association between genes is measured simultaneously using proximity measure of improved Pearson's correlation (PCPHC). Additionally, the Seed Augment algorithm adopts average linkage methods on rows and columns in order to expand a seed PCPHC model into a maximal global PCPHC (GL-PCPHC) model and to identify association between the clusters. Moreover, a GL-PCPHC applies pattern growing method to mine the PCPHC patterns. Compared to existing gene expression analysis, the PCPHC model achieves better performance. Experimental evaluations are conducted for GL-PCPHC model with standard benchmark gene expression datasets extracted from UCI repository and GenBank database in terms of execution time, size of pattern, significance level, biological association efficiency, and pattern quality. PMID:25136661

Mental Health Referrals: A Survey of Practicing School Psychologists

ERIC Educational Resources Information Center

Villarreal, Victor

2018-01-01

Schools are the most common entry point for accessing mental health services for school-age children. Children may access school-based services as part of a multitiered system of supports or other service models, such as school-based health centers. However, limitations associated with school-based services (e.g., lack of time, inability to…

Maternal Management of Social Relationships as a Correlate of Children's School-Based Experiences

ERIC Educational Resources Information Center

Fletcher, Anne C.; Walls, Jill K.; Eanes, Angella Y.; Troutman, David R.

2010-01-01

We tested a model considering the manner in which mothers' use of their own social relationships and efforts to facilitate their children's school-based social relationships were associated with two distinct types of school-based competence: academic achievement and levels of stress experienced within the school environment. Fourth grade children…

SCREENING MODEL FOR NONAQUEOUS PHASE LIQUID TRANS- PORT IN THE VADOSE ZONE USING GREEN-AMPT AND KINEMATIC WAVE THEORY

EPA Science Inventory

In this paper, a screening model for flow of a nonaqueous phase liquid (NAPL) and associated chemical transport in the vadose zone is developed. The model is based on kinematic approximation of the governing equations for both the NAPL and a partitionable chemical constituent. Th...

Design of an Information Technology Undergraduate Program to Produce IT Versatilists

ERIC Educational Resources Information Center

Koohang, Alex; Riley, Liz; Smith, Terry; Floyd, Kevin

2010-01-01

This paper attempts to present a model for designing an IT undergraduate program that is based on the recommendations of the Association for Computer Machinery/Institute of Electrical and Electronics Engineers--Information Technology (ACM/IEEE--IT) Curriculum Model. The main intent is to use the ACM/IEEE--IT Curriculum Model's recommendations as a…

A Rotating Plug Model of Friction Stir Welding Heat Transfer

NASA Technical Reports Server (NTRS)

Raghulapadu J. K.; Peddieson, J.; Buchanan, G. R.; Nunes, A. C.

2006-01-01

A simplified rotating plug model is employed to study the heat transfer phenomena associated with the fiction stir welding process. An approximate analytical solution is obtained based on this idealized model and used both to demonstrate the qualitative influence of process parameters on predictions and to estimate temperatures produced in typical fiction stir welding situations.

Mining gene link information for survival pathway hunting.

PubMed

Jing, Gao-Jian; Zhang, Zirui; Wang, Hong-Qiang; Zheng, Hong-Mei

2015-08-01

This study proposes a gene link-based method for survival time-related pathway hunting. In this method, the authors incorporate gene link information to estimate how a pathway is associated with cancer patient's survival time. Specifically, a gene link-based Cox proportional hazard model (Link-Cox) is established, in which two linked genes are considered together to represent a link variable and the association of the link with survival time is assessed using Cox proportional hazard model. On the basis of the Link-Cox model, the authors formulate a new statistic for measuring the association of a pathway with survival time of cancer patients, referred to as pathway survival score (PSS), by summarising survival significance over all the gene links in the pathway, and devise a permutation test to test the significance of an observed PSS. To evaluate the proposed method, the authors applied it to simulation data and two publicly available real-world gene expression data sets. Extensive comparisons with previous methods show the effectiveness and efficiency of the proposed method for survival pathway hunting.

Analyzing Association Mapping in Pedigree-Based GWAS Using a Penalized Multitrait Mixed Model

PubMed Central

Liu, Jin; Yang, Can; Shi, Xingjie; Li, Cong; Huang, Jian; Zhao, Hongyu; Ma, Shuangge

2017-01-01

Genome-wide association studies (GWAS) have led to the identification of many genetic variants associated with complex diseases in the past 10 years. Penalization methods, with significant numerical and statistical advantages, have been extensively adopted in analyzing GWAS. This study has been partly motivated by the analysis of Genetic Analysis Workshop (GAW) 18 data, which have two notable characteristics. First, the subjects are from a small number of pedigrees and hence related. Second, for each subject, multiple correlated traits have been measured. Most of the existing penalization methods assume independence between subjects and traits and can be suboptimal. There are a few methods in the literature based on mixed modeling that can accommodate correlations. However, they cannot fully accommodate the two types of correlations while conducting effective marker selection. In this study, we develop a penalized multitrait mixed modeling approach. It accommodates the two different types of correlations and includes several existing methods as special cases. Effective penalization is adopted for marker selection. Simulation demonstrates its satisfactory performance. The GAW 18 data are analyzed using the proposed method. PMID:27247027

Real-time solution of linear computational problems using databases of parametric reduced-order models with arbitrary underlying meshes

NASA Astrophysics Data System (ADS)

Amsallem, David; Tezaur, Radek; Farhat, Charbel

2016-12-01

A comprehensive approach for real-time computations using a database of parametric, linear, projection-based reduced-order models (ROMs) based on arbitrary underlying meshes is proposed. In the offline phase of this approach, the parameter space is sampled and linear ROMs defined by linear reduced operators are pre-computed at the sampled parameter points and stored. Then, these operators and associated ROMs are transformed into counterparts that satisfy a certain notion of consistency. In the online phase of this approach, a linear ROM is constructed in real-time at a queried but unsampled parameter point by interpolating the pre-computed linear reduced operators on matrix manifolds and therefore computing an interpolated linear ROM. The proposed overall model reduction framework is illustrated with two applications: a parametric inverse acoustic scattering problem associated with a mockup submarine, and a parametric flutter prediction problem associated with a wing-tank system. The second application is implemented on a mobile device, illustrating the capability of the proposed computational framework to operate in real-time.

Mathematical and computational modelling of skin biophysics: a review

PubMed Central

2017-01-01

The objective of this paper is to provide a review on some aspects of the mathematical and computational modelling of skin biophysics, with special focus on constitutive theories based on nonlinear continuum mechanics from elasticity, through anelasticity, including growth, to thermoelasticity. Microstructural and phenomenological approaches combining imaging techniques are also discussed. Finally, recent research applications on skin wrinkles will be presented to highlight the potential of physics-based modelling of skin in tackling global challenges such as ageing of the population and the associated skin degradation, diseases and traumas. PMID:28804267

Mathematical and computational modelling of skin biophysics: a review

NASA Astrophysics Data System (ADS)

Limbert, Georges

2017-07-01

The objective of this paper is to provide a review on some aspects of the mathematical and computational modelling of skin biophysics, with special focus on constitutive theories based on nonlinear continuum mechanics from elasticity, through anelasticity, including growth, to thermoelasticity. Microstructural and phenomenological approaches combining imaging techniques are also discussed. Finally, recent research applications on skin wrinkles will be presented to highlight the potential of physics-based modelling of skin in tackling global challenges such as ageing of the population and the associated skin degradation, diseases and traumas.

Development and validation of a new population-based simulation model of osteoarthritis in New Zealand.

PubMed

Wilson, R; Abbott, J H

2018-04-01

To describe the construction and preliminary validation of a new population-based microsimulation model developed to analyse the health and economic burden and cost-effectiveness of treatments for knee osteoarthritis (OA) in New Zealand (NZ). We developed the New Zealand Management of Osteoarthritis (NZ-MOA) model, a discrete-time state-transition microsimulation model of the natural history of radiographic knee OA. In this article, we report on the model structure, derivation of input data, validation of baseline model parameters against external data sources, and validation of model outputs by comparison of the predicted population health loss with previous estimates. The NZ-MOA model simulates both the structural progression of radiographic knee OA and the stochastic development of multiple disease symptoms. Input parameters were sourced from NZ population-based data where possible, and from international sources where NZ-specific data were not available. The predicted distributions of structural OA severity and health utility detriments associated with OA were externally validated against other sources of evidence, and uncertainty resulting from key input parameters was quantified. The resulting lifetime and current population health-loss burden was consistent with estimates of previous studies. The new NZ-MOA model provides reliable estimates of the health loss associated with knee OA in the NZ population. The model structure is suitable for analysis of the effects of a range of potential treatments, and will be used in future work to evaluate the cost-effectiveness of recommended interventions within the NZ healthcare system. Copyright © 2018 Osteoarthritis Research Society International. Published by Elsevier Ltd. All rights reserved.

Cultural consensus modeling to measure transactional sex in Swaziland: Scale building and validation.

PubMed

Fielding-Miller, Rebecca; Dunkle, Kristin L; Cooper, Hannah L F; Windle, Michael; Hadley, Craig

2016-01-01

Transactional sex is associated with increased risk of HIV and gender based violence in southern Africa and around the world. However the typical quantitative operationalization, "the exchange of gifts or money for sex," can be at odds with a wide array of relationship types and motivations described in qualitative explorations. To build on the strengths of both qualitative and quantitative research streams, we used cultural consensus models to identify distinct models of transactional sex in Swaziland. The process allowed us to build and validate emic scales of transactional sex, while identifying key informants for qualitative interviews within each model to contextualize women's experiences and risk perceptions. We used logistic and multinomial logistic regression models to measure associations with condom use and social status outcomes. Fieldwork was conducted between November 2013 and December 2014 in the Hhohho and Manzini regions. We identified three distinct models of transactional sex in Swaziland based on 124 Swazi women's emic valuation of what they hoped to receive in exchange for sex with their partners. In a clinic-based survey (n = 406), consensus model scales were more sensitive to condom use than the etic definition. Model consonance had distinct effects on social status for the three different models. Transactional sex is better measured as an emic spectrum of expectations within a relationship, rather than an etic binary relationship type. Cultural consensus models allowed us to blend qualitative and quantitative approaches to create an emicly valid quantitative scale grounded in qualitative context. Copyright © 2015 Elsevier Ltd. All rights reserved.

Cortical and Hippocampal Correlates of Deliberation during Model-Based Decisions for Rewards in Humans

PubMed Central

Bornstein, Aaron M.; Daw, Nathaniel D.

2013-01-01

How do we use our memories of the past to guide decisions we've never had to make before? Although extensive work describes how the brain learns to repeat rewarded actions, decisions can also be influenced by associations between stimuli or events not directly involving reward — such as when planning routes using a cognitive map or chess moves using predicted countermoves — and these sorts of associations are critical when deciding among novel options. This process is known as model-based decision making. While the learning of environmental relations that might support model-based decisions is well studied, and separately this sort of information has been inferred to impact decisions, there is little evidence concerning the full cycle by which such associations are acquired and drive choices. Of particular interest is whether decisions are directly supported by the same mnemonic systems characterized for relational learning more generally, or instead rely on other, specialized representations. Here, building on our previous work, which isolated dual representations underlying sequential predictive learning, we directly demonstrate that one such representation, encoded by the hippocampal memory system and adjacent cortical structures, supports goal-directed decisions. Using interleaved learning and decision tasks, we monitor predictive learning directly and also trace its influence on decisions for reward. We quantitatively compare the learning processes underlying multiple behavioral and fMRI observables using computational model fits. Across both tasks, a quantitatively consistent learning process explains reaction times, choices, and both expectation- and surprise-related neural activity. The same hippocampal and ventral stream regions engaged in anticipating stimuli during learning are also engaged in proportion to the difficulty of decisions. These results support a role for predictive associations learned by the hippocampal memory system to be recalled during choice formation. PMID:24339770

Air pollution and acute respiratory response in a panel of asthmatic children along the U.S.-Mexico border.

PubMed

Sarnat, Stefanie Ebelt; Raysoni, Amit U; Li, Wen-Whai; Holguin, Fernando; Johnson, Brent A; Flores Luevano, Silvia; Garcia, Jose Humberto; Sarnat, Jeremy A

2012-03-01

Concerns regarding the health impact of urban air pollution on asthmatic children are pronounced along the U.S.-Mexico border because of rapid population growth near busy border highways and roads. We conducted the first binational study of the impacts of air pollution on asthmatic children in Ciudad Juarez, Mexico, and El Paso, Texas, USA, and compared different exposure metrics to assess acute respiratory response. We recruited 58 asthmatic children from two schools in Ciudad Juarez and two schools in El Paso. A marker of airway inflammation [exhaled nitric oxide (eNO)], respiratory symptom surveys, and pollutant measurements (indoor and outdoor 48-hr size-fractionated particulate matter, 48-hr black carbon, and 96-hr nitrogen dioxide) were collected at each school for 16 weeks. We examined associations between the pollutants and respiratory response using generalized linear mixed models. We observed small but consistent associations between eNO and numerous pollutant metrics, with estimated increases in eNO ranging from 1% to 3% per interquartile range increase in pollutant concentrations. Effect estimates from models using school-based concentrations were generally stronger than corresponding estimates based on concentrations from ambient air monitors. Both traffic-related and non-traffic-related particles were typically more robust predictors of eNO than was nitrogen dioxide, for which associations were highly sensitive to model specification. Associations differed significantly across the four school-based cohorts, consistent with heterogeneity in pollutant concentrations and cohort characteristics. Models examining respiratory symptoms were consistent with the null. The results indicate adverse effects of air pollution on the subclinical respiratory health of asthmatic children in this region and provide preliminary support for the use of air pollution monitors close to schools to track exposure and potential health risk in this population.

Dynamically supported geoid highs over hotspots: Observation and theory

NASA Technical Reports Server (NTRS)

Richards, M. A.; Hager, B. H.; Sleep, N. H.

1986-01-01

Hotspots are associated with long wavelength geoid highs, an association that is even stronger when the geoid highs associated with subduction zones are removed. These associations are quantified by expanding the hotspot distribution in spherical harmonics and calculating correlation coefficients as a function of harmonic degree. The hotspot distribution spectrum is essentially white, with peaks at degrees 2 and 6. It is correlated positively with the slab residual geoid for degrees 2 to 6, with low seismic velocity in the lower mantle at degree 2, and with low seismic velocity in the upper mantle at degree 6. A variety of fluid mechanical models were tested for hotspots, including lithospheric delamination and hot plumes, by calculating their predicted dynamic geoid responses and comparing them to the observations. These models include the effects of temperature dependent rheology. The preferred hotspot model, based on observations of the geoid and seismic tomography, has plumes preferentially occurring in regions of large scale background temperature highs in a mantle with substantial viscosity increase with depth, although other models are possible.

A unified account of tilt illusions, association fields, and contour detection based on elastica.

PubMed

Keemink, Sander W; van Rossum, Mark C W

2016-09-01

As expressed in the Gestalt law of good continuation, human perception tends to associate stimuli that form smooth continuations. Contextual modulation in primary visual cortex, in the form of association fields, is believed to play an important role in this process. Yet a unified and principled account of the good continuation law on the neural level is lacking. In this study we introduce a population model of primary visual cortex. Its contextual interactions depend on the elastica curvature energy of the smoothest contour connecting oriented bars. As expected, this model leads to association fields consistent with data. However, in addition the model displays tilt-illusions for stimulus configurations with grating and single bars that closely match psychophysics. Furthermore, the model explains not only pop-out of contours amid a variety of backgrounds, but also pop-out of single targets amid a uniform background. We thus propose that elastica is a unifying principle of the visual cortical network. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

Dynamically supported geoid highs over hotspots - Observation and theory

NASA Technical Reports Server (NTRS)

Richards, Mark A.; Hager, Bradford H.; Sleep, Norman H.

1988-01-01

Hotspots are associated with long wavelength geoid highs, an association that is even stronger when the geoid highs associated with subduction zones are removed. These associations are quantified by expanding the hotspot distribution in spherical harmonics and calculating correlation coefficients as a function of harmonic degree. The hotspot distribution spectrum is essentially white, with peaks at degrees 2 and 6. It is correlated positively with the slab residual geoid for degrees 2 to 6, with low seismic velocity in the lower mantle at degree 2, and with low seismic velocity in the upper mantle at degree 6. A variety of fluid mechanical models were tested for hotspots, including lithospheric delamination and hot plumes, by calculating their predicted dynamic geoid responses and comparing them to the observations. These models include the effects of temperature dependent rheology. The preferred hotspot model, based on observations of the geoid and seismic tomography, has plumes preferentially occurring in regions of large scale background temperature highs in a mantle with substantial viscosity increase with depth, although other models are possible.

Quality of workplace social relationships and perceived health.

PubMed

Rydstedt, Leif W; Head, Jenny; Stansfeld, Stephen A; Woodley-Jones, Davina

2012-06-01

Associations between the quality of social relationships at work and mental and self-reported health were examined to assess whether these associations were independent of job strain. The study was based on cross-sectional survey data from 728 employees (response rate 58%) and included the Demand-Control-(Support) (DC-S) model, six items on the quality of social relationships at the workplace, the General Health Questionnaire (30), and an item on self-reported physical health. Logistic regression analyses were used. A first set of models were run with adjustment for age, sex, and socioeconomic group. A second set of models were run adjusted for the dimensions of the DC-S model. Positive associations were found between the quality of social relationships and mental health as well as self-rated physical health, and these associations remained significant even after adjustment for the dimensions. The findings add support to the Health and Safety Executive stress management standards on social relationships at the workplace.

Effects of seasonal variability in across- and alongshore transport of anchoveta ( Engraulis ringens) larvae on model-based pre-recruitment indices off central Chile

NASA Astrophysics Data System (ADS)

Parada, Carolina; Colas, Francois; Soto-Mendoza, Samuel; Castro, Leonardo

2012-01-01

An individual-based model (IBM) of anchoveta ( Engraulis ringens) larvae was coupled to a climatological hydrodynamic (Regional Oceanic Modeling System, ROMS) model for central-southern Chile to answer the question as to whether or not across- and alongshore transport off central-southern Chile enhances retention in the spawning areas during the winter and summer reproductive periods, using model-based pre-recruitment indices (simulated transport success to nursery areas). The hydrodynamic model validation showed that ROMS captures the mean Seas Surface Temperature and Eddie Kinetic Energy observed in satellite-based data over the entire region. The IBM was used to simulate the transport of eggs and larvae from spawning zones in central Chile (Constitución, Dichato, Gulf of Arauco and Lebu-Corral) to historical nursery areas (HRZ, region between 35°S and 37°S). Model results corroborated HRZ as the most successful pre-recruitment zone (particles originated in the Dichato and Gulf of Arauco spawning areas), as well as identifying Lebu-Corral as a zone of high retention with a high associated pre-recruitment index (particles originated in the Lebu-Corral spawning zone). The highest pre-recruitment values were mainly found in winter. The Constitución and Dichato spawning zones displayed a typical summer upwelling velocity pattern, while the Gulf of Arauco in summertime showed strong offshore and alongshore velocity components. The Lebu-Corral region in winter presented important near-surface cross-shore transport towards the coast (associated with downwelling events), this might be one of the major mechanisms leading to high retention levels and a high pre-recruitment index for Lebu-Corral spawning zone. The limitations of the modeling approach are discussed and put into perspective for future work.

On the transient dynamics of piezoelectric-based, state-switched systems

NASA Astrophysics Data System (ADS)

Lopp, Garrett K.; Kelley, Christopher R.; Kauffman, Jeffrey L.

2018-01-01

This letter reports on the induced mechanical transients for piezoelectric-based, state-switching approaches utilizing both experimental tests and a numerical model that more accurately captures the dynamics associated with a switch between stiffness states. Currently, switching models instantaneously dissipate the stored piezoelectric voltage, resulting in a discrete change in effective stiffness states and a discontinuity in the system dynamics during the switching event. The proposed model allows for a rapid but continuous voltage dissipation and the corresponding variation between stiffness states, as one sees in physical implementations. This rapid variation in system stiffness when switching at a point of non-zero strain leads to high-frequency, large-amplitude transients in the system acceleration response. Utilizing a fundamental piezoelectric bimorph, a comparison between the numerical and experimental results reveals that these mechanical transients are much stronger than originally anticipated and masked by measurement hardware limitations, thus highlighting the significance of an appropriate system model governing the switch dynamics. Such a model enables designers to analyze systems that incorporate piezoelectric-based state switching with greater accuracy to ensure that these transients do not degrade the intended performance. Finally, if the switching does create unacceptable transients, controlling the duration of voltage dissipation enables control over the frequency content and peak amplitudes associated with the switch-induced acceleration transients.

Thriving in Partnership: Models for Continuing Education

ERIC Educational Resources Information Center

Moroney, Peter; Boeck, Deena

2012-01-01

This article, based on a presentation at the University Professional and Continuing Education Association Annual Conference, March 29, 2012, provides concepts, terminology, and financial models for establishing and maintaining successful institutional partnerships. The authors offer it as a contribution to developing a wider understanding of the…

Clinical Assessment of Family Caregivers in Dementia.

ERIC Educational Resources Information Center

Rankin, Eric D.; And Others

1992-01-01

Evaluated development of integrated family assessment inventory based on Double ABCX and Circumplex models of family functioning and its clinical utility with 121 primary family caregivers from cognitive disorders program. Proposed model predicted significant proportion of variance associated with caregiver stress and strain. Several aspects of…

Application of Signaling Pathway-Based Adverse Outcome Pathways and High Throughput Toxicokinetic-PBPK for Developmental Cardiac Malformations

EPA Science Inventory

Associating putative molecular initiating events (MIE) with downstream cell signaling pathways and modeling fetal exposure kinetics is an important challenge for integration in developmental systems toxicology. Here, we describe an integrative systems toxicology model for develop...

Explaining reported puma-related behaviors and behavioral intentions among northern Arizona residents

USGS Publications Warehouse

Mattson, David J.; Ruther, Elizabeth J.

2012-01-01

Management of pumas in the American West is typified by conflict among stakeholders plausibly rooted in life experiences and worldviews. We used a mail questionnaire to assess demographics, nature-views, puma-related life experiences and behaviors, and support for puma-related policies among residents of northern Arizona. Data from the questionnaire (n = 693 respondents) were used to model behaviors and support for policies. Compared to models based on nature-views and life experiences, those based on demographics had virtually no support from the data. The Utilitarian/Dominionistic nature-view had the strongest effect of any variable in six of seven models, and was associated with firearms and opposition to policies that would limit killing pumas. The Humanistic/Moralistic nature-view was positively associated with non-lethal behaviors and policies in five models. Gender had the strongest effect of any demographic variable. Compared to demographics alone, our results suggest that worldviews provide a more meaningful explanation of reported human behaviors and behavioral intentions regarding pumas.

Forgiveness and Suicidal Behavior: Cynicism and Psychache as Serial Mediators.

PubMed

Dangel, Trever J; Webb, Jon R; Hirsch, Jameson K

2018-02-17

Research is burgeoning regarding the beneficial association of forgiveness with numerous health-related outcomes; however, its particular relationship to suicidal behavior has received relatively little attention. Both cynicism and psychache, or agonizing psychological pain, have displayed deleterious associations with suicidal behavior, but have rarely been incorporated into more comprehensive models of suicidal behavior. Consistent with the recent development of a theoretical model regarding the forgiveness-suicidal behavior association, the present study utilized an undergraduate sample of college students (N = 312) to test a mediation-based model of the cross-sectional association of forgiveness with suicidal behavior, as serially mediated by cynicism and psychache. Dispositional forgiveness of self and forgiveness of uncontrollable situations were each indirectly associated with less suicidal behavior via less psychache. Also, dispositional forgiveness of others was indirectly associated with less suicidal behavior via less cynicism and less psychache, in a serial fashion. The present results are consistent with the extent literature on the forgiveness-suicidal behavior association, cynicism, and psychache, and pending future studies, may be utilized to inform further treatment efforts for individuals at a high risk of attempting suicide.

A cross-sectional study to estimate associations between education level and osteoporosis in a Chinese postmenopausal women sample.

PubMed

Piao, Hui-Hong; He, Jiajia; Zhang, Keqin; Tang, Zihui

2015-01-01

Our research aims to investigate the associations between education level and osteoporosis (OP) in Chinese postmenopausal women. A large-scale, community-based, cross-sectional study was conducted to examine the associations between education level and OP. A self-reported questionnaire was used to access the demographical information and medical history of the participants. A total of 1905 postmenopausal women were available for data analysis in this study. Multiple regression models controlling for confounding factors to include education level were performed to investigate the relationship with OP. The prevalence of OP was 28.29% in our study sample. Multivariate linear regression analyses adjusted for relevant potential confounding factors detected significant associations between education level and T-score (β = 0.025, P-value = 0.095, 95% CI: -0.004-0.055 for model 1; and β = 0.092, P-value = 0.032, 95% CI: 0.008-0.175 for model 2). Multivariate logistic regression analyses detected significant associations between education level and OP in model 1 (P-value = 0.070 for model 1, Table 5), while no significant associations was reported in model 2 (P value = 0.131). In participants with high education levels, the OR for OP was 0.914 (95% CI: 0.830-1.007). The findings indicated that education level was independently and significantly associated with OP. The prevalence of OP was more frequent in Chinese postmenopausal women with low educational status.

Pancreatic β-Cell Function and Prognosis of Nondiabetic Patients With Ischemic Stroke.

PubMed

Pan, Yuesong; Chen, Weiqi; Jing, Jing; Zheng, Huaguang; Jia, Qian; Li, Hao; Zhao, Xingquan; Liu, Liping; Wang, Yongjun; He, Yan; Wang, Yilong

2017-11-01

Pancreatic β-cell dysfunction is an important factor in the development of type 2 diabetes mellitus. This study aimed to estimate the association between β-cell dysfunction and prognosis of nondiabetic patients with ischemic stroke. Patients with ischemic stroke without a history of diabetes mellitus in the ACROSS-China (Abnormal Glucose Regulation in Patients with Acute Stroke across China) registry were included. Disposition index was estimated as computer-based model of homeostatic model assessment 2-β%/homeostatic model assessment 2-insulin resistance based on fasting C-peptide level. Outcomes included stroke recurrence, all-cause death, and dependency (modified Rankin Scale, 3-5) at 12 months after onset. Among 1171 patients, 37.2% were women with a mean age of 62.4 years. At 12 months, 167 (14.8%) patients had recurrent stroke, 110 (9.4%) died, and 184 (16.0%) had a dependency. The first quartile of the disposition index was associated with an increased risk of stroke recurrence (adjusted hazard ratio, 3.57; 95% confidence interval, 2.13-5.99) and dependency (adjusted hazard ratio, 2.30; 95% confidence interval, 1.21-4.38); both the first and second quartiles of the disposition index were associated with an increased risk of death (adjusted hazard ratio, 5.09; 95% confidence interval, 2.51-10.33; adjusted hazard ratio, 2.42; 95% confidence interval, 1.17-5.03) compared with the fourth quartile. Using a multivariable regression model with restricted cubic spline, we observed an L-shaped association between the disposition index and the risk of each end point. In this large-scale registry, β-cell dysfunction was associated with an increased risk of 12-month poor prognosis in nondiabetic patients with ischemic stroke. © 2017 American Heart Association, Inc.

Evaluation of Gene-Based Family-Based Methods to Detect Novel Genes Associated With Familial Late Onset Alzheimer Disease

PubMed Central

Fernández, Maria V.; Budde, John; Del-Aguila, Jorge L.; Ibañez, Laura; Deming, Yuetiva; Harari, Oscar; Norton, Joanne; Morris, John C.; Goate, Alison M.; Cruchaga, Carlos

2018-01-01

Gene-based tests to study the combined effect of rare variants on a particular phenotype have been widely developed for case-control studies, but their evolution and adaptation for family-based studies, especially studies of complex incomplete families, has been slower. In this study, we have performed a practical examination of all the latest gene-based methods available for family-based study designs using both simulated and real datasets. We examined the performance of several collapsing, variance-component, and transmission disequilibrium tests across eight different software packages and 22 models utilizing a cohort of 285 families (N = 1,235) with late-onset Alzheimer disease (LOAD). After a thorough examination of each of these tests, we propose a methodological approach to identify, with high confidence, genes associated with the tested phenotype and we provide recommendations to select the best software and model for family-based gene-based analyses. Additionally, in our dataset, we identified PTK2B, a GWAS candidate gene for sporadic AD, along with six novel genes (CHRD, CLCN2, HDLBP, CPAMD8, NLRP9, and MAS1L) as candidate genes for familial LOAD. PMID:29670507

Evaluation of Gene-Based Family-Based Methods to Detect Novel Genes Associated With Familial Late Onset Alzheimer Disease.

PubMed

Fernández, Maria V; Budde, John; Del-Aguila, Jorge L; Ibañez, Laura; Deming, Yuetiva; Harari, Oscar; Norton, Joanne; Morris, John C; Goate, Alison M; Cruchaga, Carlos

2018-01-01

Gene-based tests to study the combined effect of rare variants on a particular phenotype have been widely developed for case-control studies, but their evolution and adaptation for family-based studies, especially studies of complex incomplete families, has been slower. In this study, we have performed a practical examination of all the latest gene-based methods available for family-based study designs using both simulated and real datasets. We examined the performance of several collapsing, variance-component, and transmission disequilibrium tests across eight different software packages and 22 models utilizing a cohort of 285 families ( N = 1,235) with late-onset Alzheimer disease (LOAD). After a thorough examination of each of these tests, we propose a methodological approach to identify, with high confidence, genes associated with the tested phenotype and we provide recommendations to select the best software and model for family-based gene-based analyses. Additionally, in our dataset, we identified PTK2B , a GWAS candidate gene for sporadic AD, along with six novel genes ( CHRD, CLCN2, HDLBP, CPAMD8, NLRP9 , and MAS1L ) as candidate genes for familial LOAD.

How To Implement a Tech Prep Program Based on the Rhode Island Model. Tech Prep Associate Degree Program. Technical Programs. Business/Office Administration Programs. Allied Health/Dental Health Programs.

ERIC Educational Resources Information Center

Community Coll. of Rhode Island, Warwick.

This implementation guide contains information based on experiences that occurred during the development and implementation of the Rhode Island Tech Prep Model. It is intended to assist educators in addressing challenges and obstacles faced by the program early in the planning process. It begins with a rationale for tech prep. Rhode Island…

Family-centred care delivery

PubMed Central

Mayo-Bruinsma, Liesha; Hogg, William; Taljaard, Monica; Dahrouge, Simone

2013-01-01

Abstract Objective To determine whether models of primary care service delivery differ in their provision of family-centred care (FCC) and to identify practice characteristics associated with FCC. Design Cross-sectional study. Setting Primary care practices in Ontario (ie, 35 salaried community health centres, 35 fee-for-service practices, 32 capitation-based health service organizations, and 35 blended remuneration family health networks) that belong to 4 models of primary care service delivery. Participants A total of 137 practices, 363 providers, and 5144 patients. Main outcome measures Measures of FCC in patient and provider surveys were based on the Primary Care Assessment Tool. Statistical analyses were conducted using linear mixed regression models and generalized estimating equations. Results Patient-reported FCC scores were high and did not vary significantly by primary care model. Larger panel size in a practice was associated with lower odds of patients reporting FCC. Provider-reported FCC scores were significantly higher in community health centres than in family health networks (P = .035). A larger number of nurse practitioners and clinical services on-site were both associated with higher FCC scores, while scores decreased as the number of family physicians in a practice increased and if practices were more rural. Conclusion Based on provider and patient reports, primary care reform strategies that encourage larger practices and more patients per family physician might compromise the provision of FCC, while strategies that encourage multidisciplinary practices and a range of services might increase FCC. PMID:24235195

Probability Models Based on Soil Properties for Predicting Presence-Absence of Pythium in Soybean Roots.

PubMed

Zitnick-Anderson, Kimberly K; Norland, Jack E; Del Río Mendoza, Luis E; Fortuna, Ann-Marie; Nelson, Berlin D

2017-10-01

Associations between soil properties and Pythium groups on soybean roots were investigated in 83 commercial soybean fields in North Dakota. A data set containing 2877 isolates of Pythium which included 26 known spp. and 1 unknown spp. and 13 soil properties from each field were analyzed. A Pearson correlation analysis was performed with all soil properties to observe any significant correlation between properties. Hierarchical clustering, indicator spp., and multi-response permutation procedures were used to identify groups of Pythium. Logistic regression analysis using stepwise selection was employed to calculate probability models for presence of groups based on soil properties. Three major Pythium groups were identified and three soil properties were associated with these groups. Group 1, characterized by P. ultimum, was associated with zinc levels; as zinc increased, the probability of group 1 being present increased (α = 0.05). Pythium group 2, characterized by Pythium kashmirense and an unknown Pythium sp., was associated with cation exchange capacity (CEC) (α < 0.05); as CEC increased, these spp. increased. Group 3, characterized by Pythium heterothallicum and Pythium irregulare, were associated with CEC and calcium carbonate exchange (CCE); as CCE increased and CEC decreased, these spp. increased (α = 0.05). The regression models may have value in predicting pathogenic Pythium spp. in soybean fields in North Dakota and adjacent states.

Perceived Discrimination and Longitudinal Change in Kidney Function Among Urban Adults.

PubMed

Beydoun, May A; Poggi-Burke, Angedith; Zonderman, Alan B; Rostant, Ola S; Evans, Michele K; Crews, Deidra C

2017-09-01

Perceived discrimination has been associated with psychosocial distress and adverse health outcomes. We examined associations of perceived discrimination measures with changes in kidney function in a prospective cohort study, the Healthy Aging in Neighborhoods of Diversity across the Life Span. Our study included 1620 participants with preserved baseline kidney function (estimated glomerular filtration rate [eGFR] ≥ 60 mL/min/1.73 m) (662 whites and 958 African Americans, aged 30-64 years). Self-reported perceived racial discrimination and perceived gender discrimination (PGD) and a general measure of experience of discrimination (EOD) ("medium versus low," "high versus low") were examined in relation to baseline, follow-up, and annual rate of change in eGFR using multiple mixed-effects regression (γbase, γrate) and ordinary least square models (γfollow). Perceived gender discrimination "high versus low PGD" was associated with a lower baseline eGFR in all models (γbase = -3.51 (1.34), p = .009 for total sample). Among white women, high EOD was associated with lower baseline eGFR, an effect that was strengthened in the full model (γbase = -5.86 [2.52], p = .020). Overall, "high versus low" PGD was associated with lower follow-up eGFR (γfollow = -3.03 [1.45], p = .036). Among African American women, both perceived racial discrimination and PGD were linked to lower follow-up kidney function, an effect that was attenuated with covariate adjustment, indicating mediation through health-related, psychosocial, and lifestyle factors. In contrast, EOD was not linked to follow-up eGFR in any of the sex by race groups. Perceived racial and gender discrimination are associated with lower kidney function assessed by glomerular filtration rate and the strength of associations differ by sex and race groups. Perceived discrimination deserves further investigation as a psychosocial risk factors for kidney disease.

An Ellipsoidal Particle-Finite Element Method for Hypervelocity Impact Simulation. Chapter 1

NASA Technical Reports Server (NTRS)

Shivarama, Ravishankar; Fahrenthold, Eric P.

2004-01-01

A number of coupled particle-element and hybrid particle-element methods have been developed for the simulation of hypervelocity impact problems, to avoid certain disadvantages associated with the use of pure continuum based or pure particle based methods. To date these methods have employed spherical particles. In recent work a hybrid formulation has been extended to the ellipsoidal particle case. A model formulation approach based on Lagrange's equations, with particles entropies serving as generalized coordinates, avoids the angular momentum conservation problems which have been reported with ellipsoidal smooth particle hydrodynamics models.

From guideline modeling to guideline execution: defining guideline-based decision-support services.

PubMed Central

Tu, S. W.; Musen, M. A.

2000-01-01

We describe our task-based approach to defining the guideline-based decision-support services that the EON system provides. We categorize uses of guidelines in patient-specific decision support into a set of generic tasks--making of decisions, specification of work to be performed, interpretation of data, setting of goals, and issuance of alert and reminders--that can be solved using various techniques. Our model includes constructs required for representing the knowledge used by these techniques. These constructs form a toolkit from which developers can select modeling solutions for guideline task. Based on the tasks and the guideline model, we define a guideline-execution architecture and a model of interactions between a decision-support server and clients that invoke services provided by the server. These services use generic interfaces derived from guideline tasks and their associated modeling constructs. We describe two implementations of these decision-support services and discuss how this work can be generalized. We argue that a well-defined specification of guideline-based decision-support services will facilitate sharing of tools that implement computable clinical guidelines. PMID:11080007

Parents and friends both matter: simultaneous and interactive influences of parents and friends on European schoolchildren's energy balance-related behaviours - the ENERGY cross-sectional study.

PubMed

te Velde, Saskia J; ChinAPaw, Mai J M; De Bourdeaudhuij, Ilse; Bere, Elling; Maes, Lea; Moreno, Luis; Jan, Nataša; Kovacs, Eva; Manios, Yannis; Brug, Johannes

2014-07-08

The family, and parents in particular, are considered the most important influencers regarding children's energy-balance related behaviours (EBRBs). When children become older and gain more behavioural autonomy regarding different behaviours, the parental influences may become less important and peer influences may gain importance. Therefore the current study aims to investigate simultaneous and interactive associations of family rules, parent and friend norms and modelling with soft drink intake, TV viewing, daily breakfast consumption and sport participation among schoolchildren across Europe. A school-based cross-sectional survey in eight countries across Europe among 10-12 year old schoolchildren. Child questionnaires were used to assess EBRBs (soft drink intake, TV viewing, breakfast consumption, sport participation), and potential determinants of these behaviours as perceived by the child, including family rules, parental and friend norms and modelling. Linear and logistic regression analyses (n = 7811) were applied to study the association of parental (norms, modelling and rules) and friend influences (norm and modelling) with the EBRBs. In addition, potential moderating effects of parental influences on the associations of friend influences with the EBRBs were studied by including interaction terms. Children reported more unfavourable friend norms and modelling regarding soft drink intake and TV viewing, while they reported more favourable friend and parental norms and modelling for breakfast consumption and physical activity. Perceived friend and parental norms and modelling were significantly positively associated with soft drink intake, breakfast consumption, physical activity (only modelling) and TV time. Across the different behaviours, ten significant interactions between parental and friend influencing variables were found and suggested a weaker association of friend norms and modelling when rules were in place. Parental and friends norm and modelling are associated with schoolchildren's energy balance-related behaviours. Having family rules or showing favourable parental modelling and norms seems to reduce the potential unfavourable associations of friends' norms and modelling with the EBRBs.

Parasitic nematode-induced modulation of body weight and associated metabolic dysfunction in mouse models of obesity

USDA-ARS?s Scientific Manuscript database

Obesity is associated with a chronic low grade inflammation characterized by high level of pro-inflammatory cytokines and mediators implicated in disrupted metabolic homeostasis. Parasitic nematode infection induces a polarized Th2 cytokine response and has been shown to modulate immune-based pathol...

Genome wide association analyses based on a multiple trait approach for modeling feed efficiency

USDA-ARS?s Scientific Manuscript database

Genome wide association (GWA) of feed efficiency (FE) could help target important genomic regions influencing FE. Data provided by an international dairy FE research consortium consisted of phenotypic records on dry matter intakes (DMI), milk energy (MILKE), and metabolic body weight (MBW) on 6,937 ...

Decision-Making Accuracy of CBM Progress-Monitoring Data

ERIC Educational Resources Information Center

Hintze, John M.; Wells, Craig S.; Marcotte, Amanda M.; Solomon, Benjamin G.

2018-01-01

This study examined the diagnostic accuracy associated with decision making as is typically conducted with curriculum-based measurement (CBM) approaches to progress monitoring. Using previously published estimates of the standard errors of estimate associated with CBM, 20,000 progress-monitoring data sets were simulated to model student reading…

Recommended documentation of evapotranspiration measurements and associated weather data and a review of requirements for accuracy

USDA-ARS?s Scientific Manuscript database

More and more evapotranspiration (ET) models, ET crop coefficients, and associated measurements of ET are reported in the literature. These measurements base from a range of measurement systems including lysimeters, eddy covariance, Bowen ratio, water balance (gravimetric, neutron meter, other soil ...

Nutritional metabolomics: Progress in addressing complexity in diet and health

PubMed Central

Jones, Dean P.; Park, Youngja; Ziegler, Thomas R.

2013-01-01

Nutritional metabolomics is rapidly maturing to use small molecule chemical profiling to support integration of diet and nutrition in complex biosystems research. These developments are critical to facilitate transition of nutritional sciences from population-based to individual-based criteria for nutritional research, assessment and management. This review addresses progress in making these approaches manageable for nutrition research. Important concept developments concerning the exposome, predictive health and complex pathobiology, serve to emphasize the central role of diet and nutrition in integrated biosystems models of health and disease. Improved analytic tools and databases for targeted and non-targeted metabolic profiling, along with bioinformatics, pathway mapping and computational modeling, are now used for nutrition research on diet, metabolism, microbiome and health associations. These new developments enable metabolome-wide association studies (MWAS) and provide a foundation for nutritional metabolomics, along with genomics, epigenomics and health phenotyping, to support integrated models required for personalized diet and nutrition forecasting. PMID:22540256

A queuing-theory-based interval-fuzzy robust two-stage programming model for environmental management under uncertainty

NASA Astrophysics Data System (ADS)

Sun, Y.; Li, Y. P.; Huang, G. H.

2012-06-01

In this study, a queuing-theory-based interval-fuzzy robust two-stage programming (QB-IRTP) model is developed through introducing queuing theory into an interval-fuzzy robust two-stage (IRTP) optimization framework. The developed QB-IRTP model can not only address highly uncertain information for the lower and upper bounds of interval parameters but also be used for analysing a variety of policy scenarios that are associated with different levels of economic penalties when the promised targets are violated. Moreover, it can reflect uncertainties in queuing theory problems. The developed method has been applied to a case of long-term municipal solid waste (MSW) management planning. Interval solutions associated with different waste-generation rates, different waiting costs and different arriving rates have been obtained. They can be used for generating decision alternatives and thus help managers to identify desired MSW management policies under various economic objectives and system reliability constraints.

Reducing attenuation in exposure-response relationships by exposure modeling and grouping: the relationship between wood dust exposure and lung function.

PubMed

Teschke, Kay; Spierings, Judith; Marion, Stephen A; Demers, Paul A; Davies, Hugh W; Kennedy, Susan M

2004-12-01

In a study of wood dust exposure and lung function, we tested the effect on the exposure-response relationship of six different exposure metrics using the mean measured exposure of each subject versus the mean exposure based on various methods of grouping subjects, including job-based groups and groups based on an empirical model of the determinants of exposure. Multiple linear regression was used to examine the association between wood dust concentration and forced expiratory volume in 1s (FEV(1)), adjusting for age, sex, height, race, pediatric asthma, and smoking. Stronger point estimates of the exposure-response relationships were observed when exposures were based on increasing levels of aggregation, allowing the relationships to be found statistically significant in four of the six metrics. The strongest point estimates were found when exposures were based on the determinants of exposure model. Determinants of exposure modeling offers the potential for improvement in risk estimation equivalent to or beyond that from job-based exposure grouping.

A prognostic index for natural killer cell lymphoma after non-anthracycline-based treatment: a multicentre, retrospective analysis.

PubMed

Kim, Seok Jin; Yoon, Dok Hyun; Jaccard, Arnaud; Chng, Wee Joo; Lim, Soon Thye; Hong, Huangming; Park, Yong; Chang, Kian Meng; Maeda, Yoshinobu; Ishida, Fumihiro; Shin, Dong-Yeop; Kim, Jin Seok; Jeong, Seong Hyun; Yang, Deok-Hwan; Jo, Jae-Cheol; Lee, Gyeong-Won; Choi, Chul Won; Lee, Won-Sik; Chen, Tsai-Yun; Kim, Kiyeun; Jung, Sin-Ho; Murayama, Tohru; Oki, Yasuhiro; Advani, Ranjana; d'Amore, Francesco; Schmitz, Norbert; Suh, Cheolwon; Suzuki, Ritsuro; Kwong, Yok Lam; Lin, Tong-Yu; Kim, Won Seog

2016-03-01

The clinical outcome of extranodal natural killer T-cell lymphoma (ENKTL) has improved substantially as a result of new treatment strategies with non-anthracycline-based chemotherapies and upfront use of concurrent chemoradiotherapy or radiotherapy. A new prognostic model based on the outcomes obtained with these contemporary treatments was warranted. We did a retrospective study of patients with newly diagnosed ENKTL without any previous treatment history for the disease who were given non-anthracycline-based chemotherapies with or without upfront concurrent chemoradiotherapy or radiotherapy with curative intent. A prognostic model to predict overall survival and progression-free survival on the basis of pretreatment clinical and laboratory characteristics was developed by filling a multivariable model on the basis of the dataset with complete data for the selected risk factors for an unbiased prediction model. The final model was applied to the patients who had complete data for the selected risk factors. We did a validation analysis of the prognostic model in an independent cohort. We did multivariate analyses of 527 patients who were included from 38 hospitals in 11 countries in the training cohort. Analyses showed that age greater than 60 years, stage III or IV disease, distant lymph-node involvement, and non-nasal type disease were significantly associated with overall survival and progression-free survival. We used these data as the basis for the prognostic index of natural killer lymphoma (PINK), in which patients are stratified into low-risk (no risk factors), intermediate-risk (one risk factor), or high-risk (two or more risk factors) groups, which were associated with 3-year overall survival of 81% (95% CI 75-86), 62% (55-70), and 25% (20-34), respectively. In the 328 patients with data for Epstein-Barr virus DNA, a detectable viral DNA titre was an independent prognostic factor for overall survival. When these data were added to PINK as the basis for another prognostic index (PINK-E)-which had similar low-risk (zero or one risk factor), intermediate-risk (two risk factors), and high-risk (three or more risk factors) categories-significant associations with overall survival were noted (81% [95% CI 75-87%], 55% (44-66), and 28% (18-40%), respectively). These results were validated and confirmed in an independent cohort, although the PINK-E model was only significantly associated with the high-risk group compared with the low-risk group. PINK and PINK-E are new prognostic models that can be used to develop risk-adapted treatment approaches for patients with ENKTL being treated in the contemporary era of non-anthracycline-based therapy. Samsung Biomedical Research Institute. Copyright © 2016 Elsevier Ltd. All rights reserved.

A Public/Private Extension of Conway's Accessor Model

NASA Technical Reports Server (NTRS)

McCann, Karen M.; Yarrow, Maurice

2000-01-01

We present a new object-oriented model for a Perl package, based on Damien Conway's 'accessor' model. Our model includes both public and private data; it uses strategies to reduce a package namespace, but still maintains a robust and error-trapped approach. With this extended model we can make any package data or functions 'private', as well as 'public'. (Note: 'namespace' in this context means all the names, variables and subs, associated with a package.)

Discovery of potent NEK2 inhibitors as potential anticancer agents using structure-based exploration of NEK2 pharmacophoric space coupled with QSAR analyses.

PubMed

Khanfar, Mohammad A; Banat, Fahmy; Alabed, Shada; Alqtaishat, Saja

2017-02-01

High expression of Nek2 has been detected in several types of cancer and it represents a novel target for human cancer. In the current study, structure-based pharmacophore modeling combined with multiple linear regression (MLR)-based QSAR analyses was applied to disclose the structural requirements for NEK2 inhibition. Generated pharmacophoric models were initially validated with receiver operating characteristic (ROC) curve, and optimum models were subsequently implemented in QSAR modeling with other physiochemical descriptors. QSAR-selected models were implied as 3D search filters to mine the National Cancer Institute (NCI) database for novel NEK2 inhibitors, whereas the associated QSAR model prioritized the bioactivities of captured hits for in vitro evaluation. Experimental validation identified several potent NEK2 inhibitors of novel structural scaffolds. The most potent captured hit exhibited an [Formula: see text] value of 237 nM.

Discovering functional modules by topic modeling RNA-Seq based toxicogenomic data.

PubMed

Yu, Ke; Gong, Binsheng; Lee, Mikyung; Liu, Zhichao; Xu, Joshua; Perkins, Roger; Tong, Weida

2014-09-15

Toxicogenomics (TGx) endeavors to elucidate the underlying molecular mechanisms through exploring gene expression profiles in response to toxic substances. Recently, RNA-Seq is increasingly regarded as a more powerful alternative to microarrays in TGx studies. However, realizing RNA-Seq's full potential requires novel approaches to extracting information from the complex TGx data. Considering read counts as the number of times a word occurs in a document, gene expression profiles from RNA-Seq are analogous to a word by document matrix used in text mining. Topic modeling aiming at to discover the latent structures in text corpora would be helpful to explore RNA-Seq based TGx data. In this study, topic modeling was applied on a typical RNA-Seq based TGx data set to discover hidden functional modules. The RNA-Seq based gene expression profiles were transformed into "documents", on which latent Dirichlet allocation (LDA) was used to build a topic model. We found samples treated by the compounds with the same modes of actions (MoAs) could be clustered based on topic similarities. The topic most relevant to each cluster was identified as a "marker" topic, which was interpreted by gene enrichment analysis with MoAs then confirmed by compound and pathways associations mined from literature. To further validate the "marker" topics, we tested topic transferability from RNA-Seq to microarrays. The RNA-Seq based gene expression profile of a topic specifically associated with peroxisome proliferator-activated receptors (PPAR) signaling pathway was used to query samples with similar expression profiles in two different microarray data sets, yielding accuracy of about 85%. This proof-of-concept study demonstrates the applicability of topic modeling to discover functional modules in RNA-Seq data and suggests a valuable computational tool for leveraging information within TGx data in RNA-Seq era.

Stochastic model search with binary outcomes for genome-wide association studies

PubMed Central

Malovini, Alberto; Puca, Annibale A; Bellazzi, Riccardo

2012-01-01

Objective The spread of case–control genome-wide association studies (GWASs) has stimulated the development of new variable selection methods and predictive models. We introduce a novel Bayesian model search algorithm, Binary Outcome Stochastic Search (BOSS), which addresses the model selection problem when the number of predictors far exceeds the number of binary responses. Materials and methods Our method is based on a latent variable model that links the observed outcomes to the underlying genetic variables. A Markov Chain Monte Carlo approach is used for model search and to evaluate the posterior probability of each predictor. Results BOSS is compared with three established methods (stepwise regression, logistic lasso, and elastic net) in a simulated benchmark. Two real case studies are also investigated: a GWAS on the genetic bases of longevity, and the type 2 diabetes study from the Wellcome Trust Case Control Consortium. Simulations show that BOSS achieves higher precisions than the reference methods while preserving good recall rates. In both experimental studies, BOSS successfully detects genetic polymorphisms previously reported to be associated with the analyzed phenotypes. Discussion BOSS outperforms the other methods in terms of F-measure on simulated data. In the two real studies, BOSS successfully detects biologically relevant features, some of which are missed by univariate analysis and the three reference techniques. Conclusion The proposed algorithm is an advance in the methodology for model selection with a large number of features. Our simulated and experimental results showed that BOSS proves effective in detecting relevant markers while providing a parsimonious model. PMID:22534080

Validation of a Clinical Scoring System for Outcome Prediction in Dogs with Acute Kidney Injury Managed by Hemodialysis.

PubMed

Segev, G; Langston, C; Takada, K; Kass, P H; Cowgill, L D

2016-05-01

A scoring system for outcome prediction in dogs with acute kidney injury (AKI) recently has been developed but has not been validated. The scoring system previously developed for outcome prediction will accurately predict outcome in a validation cohort of dogs with AKI managed with hemodialysis. One hundred fifteen client-owned dogs with AKI. Medical records of dogs with AKI treated by hemodialysis between 2011 and 2015 were reviewed. Dogs were included only if all variables required to calculate the final predictive score were available, and the 30-day outcome was known. A predictive score for 3 models was calculated for each dog. Logistic regression was used to evaluate the association of the final predictive score with each model's outcome. Receiver operating curve (ROC) analyses were performed to determine sensitivity and specificity for each model based on previously established cut-off values. Higher scores for each model were associated with decreased survival probability (P < .001). Based on previously established cut-off values, 3 models (models A, B, C) were associated with sensitivities/specificities of 73/75%, 71/80%, and 75/86%, respectively, and correctly classified 74-80% of the dogs. All models were simple to apply and allowed outcome prediction that closely corresponded with actual outcome in an independent cohort. As expected, accuracies were slightly lower compared with those from the previously reported cohort used initially to develop the models. Copyright © 2016 The Authors. Journal of Veterinary Internal Medicine published by Wiley Periodicals, Inc. on behalf of the American College of Veterinary Internal Medicine.

How Accumulated Real Life Stress Experience and Cognitive Speed Interact on Decision-Making Processes

PubMed Central

Friedel, Eva; Sebold, Miriam; Kuitunen-Paul, Sören; Nebe, Stephan; Veer, Ilya M.; Zimmermann, Ulrich S.; Schlagenhauf, Florian; Smolka, Michael N.; Rapp, Michael; Walter, Henrik; Heinz, Andreas

2017-01-01

Rationale: Advances in neurocomputational modeling suggest that valuation systems for goal-directed (deliberative) on one side, and habitual (automatic) decision-making on the other side may rely on distinct computational strategies for reinforcement learning, namely model-free vs. model-based learning. As a key theoretical difference, the model-based system strongly demands cognitive functions to plan actions prospectively based on an internal cognitive model of the environment, whereas valuation in the model-free system relies on rather simple learning rules from operant conditioning to retrospectively associate actions with their outcomes and is thus cognitively less demanding. Acute stress reactivity is known to impair model-based but not model-free choice behavior, with higher working memory capacity protecting the model-based system from acute stress. However, it is not clear which impact accumulated real life stress has on model-free and model-based decision systems and how this influence interacts with cognitive abilities. Methods: We used a sequential decision-making task distinguishing relative contributions of both learning strategies to choice behavior, the Social Readjustment Rating Scale questionnaire to assess accumulated real life stress, and the Digit Symbol Substitution Test to test cognitive speed in 95 healthy subjects. Results: Individuals reporting high stress exposure who had low cognitive speed showed reduced model-based but increased model-free behavioral control. In contrast, subjects exposed to accumulated real life stress with high cognitive speed displayed increased model-based performance but reduced model-free control. Conclusion: These findings suggest that accumulated real life stress exposure can enhance reliance on cognitive speed for model-based computations, which may ultimately protect the model-based system from the detrimental influences of accumulated real life stress. The combination of accumulated real life stress exposure and slower information processing capacities, however, might favor model-free strategies. Thus, the valence and preference of either system strongly depends on stressful experiences and individual cognitive capacities. PMID:28642696

How Accumulated Real Life Stress Experience and Cognitive Speed Interact on Decision-Making Processes.

PubMed

Friedel, Eva; Sebold, Miriam; Kuitunen-Paul, Sören; Nebe, Stephan; Veer, Ilya M; Zimmermann, Ulrich S; Schlagenhauf, Florian; Smolka, Michael N; Rapp, Michael; Walter, Henrik; Heinz, Andreas

2017-01-01

Rationale: Advances in neurocomputational modeling suggest that valuation systems for goal-directed (deliberative) on one side, and habitual (automatic) decision-making on the other side may rely on distinct computational strategies for reinforcement learning, namely model-free vs. model-based learning. As a key theoretical difference, the model-based system strongly demands cognitive functions to plan actions prospectively based on an internal cognitive model of the environment, whereas valuation in the model-free system relies on rather simple learning rules from operant conditioning to retrospectively associate actions with their outcomes and is thus cognitively less demanding. Acute stress reactivity is known to impair model-based but not model-free choice behavior, with higher working memory capacity protecting the model-based system from acute stress. However, it is not clear which impact accumulated real life stress has on model-free and model-based decision systems and how this influence interacts with cognitive abilities. Methods: We used a sequential decision-making task distinguishing relative contributions of both learning strategies to choice behavior, the Social Readjustment Rating Scale questionnaire to assess accumulated real life stress, and the Digit Symbol Substitution Test to test cognitive speed in 95 healthy subjects. Results: Individuals reporting high stress exposure who had low cognitive speed showed reduced model-based but increased model-free behavioral control. In contrast, subjects exposed to accumulated real life stress with high cognitive speed displayed increased model-based performance but reduced model-free control. Conclusion: These findings suggest that accumulated real life stress exposure can enhance reliance on cognitive speed for model-based computations, which may ultimately protect the model-based system from the detrimental influences of accumulated real life stress. The combination of accumulated real life stress exposure and slower information processing capacities, however, might favor model-free strategies. Thus, the valence and preference of either system strongly depends on stressful experiences and individual cognitive capacities.