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Omer Weissbrod, Elior Rahmani, Regev Schweiger, Saharon Rosset, Eran Halperin
Motivation: Epigenome-wide association studies can provide novel insights into the regulation of genes involved in traits and diseases. The rapid emergence of bisulfite-sequencing technologies enables performing such genome-wide studies at the resolution of single nucleotides. However, analysis of data produced by bisulfite-sequencing poses statistical challenges owing to low and uneven sequencing depth, as well as the presence of confounding factors. The recently introduced Mixed model Association for Count data via data AUgmentation (MACAU) can address these challenges via a generalized linear mixed model when confounding can be encoded via a single variance component...
July 15, 2017: Bioinformatics
Peida Zhan, Hong Jiao, Dandan Liao
To provide more refined diagnostic feedback with collateral information in item response times (RTs), this study proposed joint modelling of attributes and response speed using item responses and RTs simultaneously for cognitive diagnosis. For illustration, an extended deterministic input, noisy 'and' gate (DINA) model was proposed for joint modelling of responses and RTs. Model parameter estimation was explored using the Bayesian Markov chain Monte Carlo (MCMC) method. The PISA 2012 computer-based mathematics data were analysed first...
September 5, 2017: British Journal of Mathematical and Statistical Psychology
Nasim Ejlali, Mohammad Reza Faghihi, Mehdi Sadeghi
An important topic in bioinformatics is the protein structure alignment. Some statistical methods have been proposed for this problem, but most of them align two protein structures based on the global geometric information without considering the effect of neighbourhood in the structures. In this paper, we provide a Bayesian model to align protein structures, by considering the effect of both local and global geometric information of protein structures. Local geometric information is incorporated to the model through the partial Procrustes distance of small substructures...
September 26, 2017: Statistical Applications in Genetics and Molecular Biology
Yaoguang Wang, Di Wu, Qin Wei, Dong Wei, Tao Yan, Liangguo Yan, Lihua Hu, Bin Du
In this study, branched polyethylenimine (PEI) enhanced magnetic carboxymethyl chitosan (MCMC-PEI) was synthesized and applied as adsorbent for the rapid removal of Pb(II) from aqueous solution. The successful synthesis of the adsorbent was proved by scanning electron microscope (SEM), Fourier transform infrared spectrum (FTIR) and X-ray powder diffraction (XRD). Simultaneously, the effect of the parameters such as initial concentration, adsorbent mass and pH of the solution on the removal of Pb(II) was studied by using response surface methodology (RSM)...
August 31, 2017: Scientific Reports
Jean-Paul Fox, Joris Mulder, Sandip Sinharay
Two marginal one-parameter item response theory models are introduced, by integrating out the latent variable or random item parameter. It is shown that both marginal response models are multivariate (probit) models with a compound symmetry covariance structure. Several common hypotheses concerning the underlying covariance structure are evaluated using (fractional) Bayes factor tests. The support for a unidimensional factor (i.e., assumption of local independence) and differential item functioning are evaluated by testing the covariance components...
August 29, 2017: Psychometrika
Mark G F Sun, Philip M Kim
Protein design remains an important problem in computational structural biology. Current computational protein design methods largely use physics-based methods, which make use of information from a single protein structure. This is despite the fact that multiple structures of many protein folds are now readily available in the PDB. While ensemble protein design methods can use multiple protein structures, they treat each structure independently. Here, we introduce a flexible backbone strategy, FlexiBaL-GP, which learns global protein backbone movements directly from multiple protein structures...
August 2017: PLoS Computational Biology
Oscar O M Iheshiulor, John A Woolliams, Morten Svendsen, Trygve Solberg, Theo H E Meuwissen
BACKGROUND: The rapid adoption of genomic selection is due to two key factors: availability of both high-throughput dense genotyping and statistical methods to estimate and predict breeding values. The development of such methods is still ongoing and, so far, there is no consensus on the best approach. Currently, the linear and non-linear methods for genomic prediction (GP) are treated as distinct approaches. The aim of this study was to evaluate the implementation of an iterative method (called GBC) that incorporates aspects of both linear [genomic-best linear unbiased prediction (G-BLUP)] and non-linear (Bayes-C) methods for GP...
August 24, 2017: Genetics, Selection, Evolution: GSE
Liang Xu, Bei Xu, Zhi-Yu Zhao, Hui-Ping Yang, Cheng Tang, Lin-Yi Dong, Kun Liu, Li Fu, Xian-Hua Wang
Cell membrane chromatography (CMC) is an effective tool in screening active compounds from natural products and studying membrane protein interactions. Nevertheless, it always consumes a large amount of cells (e.g. 10(7)-10(8)) for column preparation. To overcome this, micro-CMC (mCMC), that employs a silica capillary as membrane carrier, was developed. However, both CMC and mCMC suffer from short column life span (e.g. 3days), mainly due to the falling-off of cellular membranes (CMs). This has greatly limited further application of CMC and mCMC, especially when the cells are hard to obtain...
August 12, 2017: Journal of Chromatography. A
Tingting Wang, Yi-Ping Phoebe Chen, Iona M MacLeod, Jennie E Pryce, Michael E Goddard, Ben J Hayes
BACKGROUND: Using whole genome sequence data might improve genomic prediction accuracy, when compared with high-density SNP arrays, and could lead to identification of casual mutations affecting complex traits. For some traits, the most accurate genomic predictions are achieved with non-linear Bayesian methods. However, as the number of variants and the size of the reference population increase, the computational time required to implement these Bayesian methods (typically with Monte Carlo Markov Chain sampling) becomes unfeasibly long...
August 15, 2017: BMC Genomics
Tarang Sharma, Peter C Gøtzsche, Oliver Kuss
OBJECTIVE: To identify the validity of effect estimates for serious rare adverse events in clinical study reports of antidepressants trials, across different meta-analysis methods. STUDY DESIGN AND SETTING: Four serious rare adverse events (all-cause mortality, suicidality, aggressive behaviour and akathisia) were meta-analysed using different methods. The Yusuf-Peto odds ignores studies with no events, was compared with the alternative approaches of generalised linear mixed models (GLMM), conditional logistic regression, a Bayesian approach using Markov Chain Monte Carlo (MCMC) and a beta-binomial regression model...
August 9, 2017: Journal of Clinical Epidemiology
Jingyi Huang, Brendan P Malone, Budiman Minasny, Alex B McBratney, John Triantafilis
Understanding the uncertainty in spatial modelling of environmental variables is important because it provides the end-users with the reliability of the maps. Over the past decades, Bayesian statistics has been successfully used. However, the conventional simulation-based Markov Chain Monte Carlo (MCMC) approaches are often computationally intensive. In this study, the performance of a novel Bayesian inference approach called Integrated Nested Laplace Approximation with Stochastic Partial Differential Equation (INLA-SPDE) was evaluated using independent calibration and validation datasets of various skewed and non-skewed soil properties and was compared with a linear mixed model estimated by residual maximum likelihood (REML-LMM)...
December 31, 2017: Science of the Total Environment
Francisco Laguna, María Eugenia Grillet, José R León, Carenne Ludeña
The influence of climatic variables on the dynamics of human malaria has been widely highlighted. Also, it is known that this mosquito-borne infection varies in space and time. However, when the data is spatially incomplete most popular spatio-temporal methods of analysis cannot be applied directly. In this paper, we develop a two step methodology to model the spatio-temporal dependence of malaria incidence on local rainfall, temperature, and humidity as well as the regional sea surface temperatures (SST) in the northern coast of Venezuela...
August 2017: Spatial and Spatio-temporal Epidemiology
Ning Nan, Qi Chen, Yu Wang, Xu Zhai, Chuan-Ce Yang, Bin Cao, Tie Chong
OBJECTIVE: To explore the disturbed molecular functions and pathways in clear cell renal cell carcinoma (ccRCC) using Gibbs sampling. METHODS: Gene expression data of ccRCC samples and adjacent non-tumor renal tissues were recruited from public available database. Then, molecular functions of expression changed genes in ccRCC were classed to Gene Ontology (GO) project, and these molecular functions were converted into Markov chains. Markov chain Monte Carlo (MCMC) algorithm was implemented to perform posterior inference and identify probability distributions of molecular functions in Gibbs sampling...
July 13, 2017: Computational Biology and Chemistry
Harshana Rajakaruna, D Andrew R Drake, Farrah T Chan, Sarah A Bailey
Understanding the functional relationship between the sample size and the performance of species richness estimators is necessary to optimize limited sampling resources against estimation error. Nonparametric estimators such as Chao and Jackknife demonstrate strong performances, but consensus is lacking as to which estimator performs better under constrained sampling. We explore a method to improve the estimators under such scenario. The method we propose involves randomly splitting species-abundance data from a single sample into two equally sized samples, and using an appropriate incidence-based estimator to estimate richness...
October 2016: Ecology and Evolution
Lucas M Wijnands, Peter F M Teunis, Angelina F A Kuijpers, Ellen H M Delfgou-Van Asch, Annemarie Pielaat
Different techniques are available for assessing differences in virulence of bacterial foodborne pathogens. The use of animal models or human volunteers is not expedient for various reasons; the use of epidemiological data is often hampered by lack of crucial data. In this paper, we describe a static, sequential gastrointestinal tract (GIT) model system in which foodborne pathogens are exposed to simulated gastric and intestinal contents of the human digestive tract, including the interaction of pathogens with the intestinal epithelium...
2017: Frontiers in Microbiology
David Deruytter, Jan M Baert, Nancy Nevejan, Karel A C DE Schamphelaere, Colin R Janssen
Little is known about the effect of metal mixtures on marine organisms, especially when exposed to environmentally realistic concentrations. This information is, however, required to evaluate the need to include mixtures in future environmental risk assessment (ERA) procedures. Here, the effect of Cu-Ni binary mixtures on Mytilus edulis larval development was assessed using a full factorial design that included environmentally relevant metal concentrations and ratios. The reproducibility of the results was assessed by repeating this experiment 5 times...
July 15, 2017: Environmental Toxicology and Chemistry
Hussnain Mukhtar, Yu-Pin Lin, Oleg V Shipin, Joy R Petway
This study presents an approach for obtaining realization sets of parameters for nitrogen removal in a pilot-scale waste stabilization pond (WSP) system. The proposed approach was designed for optimal parameterization, local sensitivity analysis, and global uncertainty analysis of a dynamic simulation model for the WSP by using the R software package Flexible Modeling Environment (R-FME) with the Markov chain Monte Carlo (MCMC) method. Additionally, generalized likelihood uncertainty estimation (GLUE) was integrated into the FME to evaluate the major parameters that affect the simulation outputs in the study WSP...
July 12, 2017: International Journal of Environmental Research and Public Health
Xiaoliang Ji, Runting Xie, Yun Hao, Jun Lu
Quantitative identification of nitrate (NO3(-)-N) sources is critical to the control of nonpoint source nitrogen pollution in an agricultural watershed. Combined with water quality monitoring, we adopted the environmental isotope (δD-H2O, δ(18)O-H2O, δ(15)N-NO3(-), and δ(18)O-NO3(-)) analysis and the Markov Chain Monte Carlo (MCMC) mixing model to determine the proportions of riverine NO3(-)-N inputs from four potential NO3(-)-N sources, namely, atmospheric deposition (AD), chemical nitrogen fertilizer (NF), soil nitrogen (SN), and manure and sewage (M&S), in the ChangLe River watershed of eastern China...
October 2017: Environmental Pollution
Chao Du, Jun Zhu, Bo Zhang
Deep generative models (DGMs), which are often organized in a hierarchical manner, provide a principled framework of capturing the underlying causal factors of data. Recent work on DGMs focussed on the development of efficient and scalable variational inference methods that learn a single model under some mean-field or parameterization assumptions. However, little work has been done on extending Markov chain Monte Carlo (MCMC) methods to Bayesian DGMs, which enjoy many advantages compared with variational methods...
June 28, 2017: IEEE Transactions on Neural Networks and Learning Systems
Javier Revuelta, Carmen Ximénez
This article introduces Bayesian estimation and evaluation procedures for the multidimensional nominal response model. The utility of this model is to perform a nominal factor analysis of items that consist of a finite number of unordered response categories. The key aspect of the model, in comparison with traditional factorial model, is that there is a slope for each response category on the latent dimensions, instead of having slopes associated to the items. The extended parameterization of the multidimensional nominal response model requires large samples for estimation...
2017: Frontiers in Psychology
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