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https://www.readbyqxmd.com/read/28735111/screening-disrupted-molecular-functions-and-pathways-associated-with-clear-cell-renal-cell-carcinoma-using-gibbs-sampling
#1
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
https://www.readbyqxmd.com/read/28725399/optimizing-performance-of-nonparametric-species-richness-estimators-under-constrained-sampling
#2
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
https://www.readbyqxmd.com/read/28713334/quantification-of-salmonella-survival-and-infection-in-an-in-vitro-model-of-the-human-intestinal-tract-as-proxy-for-foodborne-pathogens
#3
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
https://www.readbyqxmd.com/read/28710852/mixture-toxicity-in-the-marine-environment-model-development-and-evidence-for-synergism-at-environmental-concentrations
#4
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
https://www.readbyqxmd.com/read/28704958/modeling-nitrogen-dynamics-in-a-waste-stabilization-pond-system-using-flexible-modeling-environment-with-mcmc
#5
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
https://www.readbyqxmd.com/read/28689147/quantitative-identification-of-nitrate-pollution-sources-and-uncertainty-analysis-based-on-dual-isotope-approach-in-an-agricultural-watershed
#6
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...
July 6, 2017: Environmental Pollution
https://www.readbyqxmd.com/read/28678716/learning-deep-generative-models-with-doubly-stochastic-gradient-mcmc
#7
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
https://www.readbyqxmd.com/read/28670291/bayesian-dimensionality-assessment-for-the-multidimensional-nominal-response-model
#8
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
https://www.readbyqxmd.com/read/28666320/inferring-transcriptional-logic-from-multiple-dynamic-experiments
#9
Giorgos Minas, Dafyd J Jenkins, David A Rand, Bärbel Finkenstädt
Motivation: The availability of more data of dynamic gene expression under multiple experimental conditions provides new information that makes the key goal of identifying not only the transcriptional regulators of a gene but also the underlying logical structure attainable. Results: We propose a novel method for inferring transcriptional regulation using a simple, yet biologically interpretable, model to find the logic by which a set of candidate genes and their associated transcription factors (TFs) regulate the transcriptional process of a gene of interest...
June 28, 2017: Bioinformatics
https://www.readbyqxmd.com/read/28664564/clustering-high-dimensional-mixed-data-to-uncover-sub-phenotypes-joint-analysis-of-phenotypic-and-genotypic-data
#10
D McParland, C M Phillips, L Brennan, H M Roche, I C Gormley
The LIPGENE-SU.VI.MAX study, like many others, recorded high-dimensional continuous phenotypic data and categorical genotypic data. LIPGENE-SU.VI.MAX focuses on the need to account for both phenotypic and genetic factors when studying the metabolic syndrome (MetS), a complex disorder that can lead to higher risk of type 2 diabetes and cardiovascular disease. Interest lies in clustering the LIPGENE-SU.VI.MAX participants into homogeneous groups or sub-phenotypes, by jointly considering their phenotypic and genotypic data, and in determining which variables are discriminatory...
June 30, 2017: Statistics in Medicine
https://www.readbyqxmd.com/read/28646868/comprehensive-benchmarking-of-markov-chain-monte-carlo-methods-for-dynamical-systems
#11
Benjamin Ballnus, Sabine Hug, Kathrin Hatz, Linus Görlitz, Jan Hasenauer, Fabian J Theis
BACKGROUND: In quantitative biology, mathematical models are used to describe and analyze biological processes. The parameters of these models are usually unknown and need to be estimated from experimental data using statistical methods. In particular, Markov chain Monte Carlo (MCMC) methods have become increasingly popular as they allow for a rigorous analysis of parameter and prediction uncertainties without the need for assuming parameter identifiability or removing non-identifiable parameters...
June 24, 2017: BMC Systems Biology
https://www.readbyqxmd.com/read/28645318/detecting-consistent-patterns-of-directional-adaptation-using-differential-selection-codon-models
#12
Sahar Parto, Nicolas Lartillot
BACKGROUND: Phylogenetic codon models are often used to characterize the selective regimes acting on protein-coding sequences. Recent methodological developments have led to models explicitly accounting for the interplay between mutation and selection, by modeling the amino acid fitness landscape along the sequence. However, thus far, most of these models have assumed that the fitness landscape is constant over time. Fluctuations of the fitness landscape may often be random or depend on complex and unknown factors...
June 23, 2017: BMC Evolutionary Biology
https://www.readbyqxmd.com/read/28626349/identifying-mixtures-of-mixtures-using-bayesian-estimation
#13
Gertraud Malsiner-Walli, Sylvia Frühwirth-Schnatter, Bettina Grün
The use of a finite mixture of normal distributions in model-based clustering allows us to capture non-Gaussian data clusters. However, identifying the clusters from the normal components is challenging and in general either achieved by imposing constraints on the model or by using post-processing procedures. Within the Bayesian framework, we propose a different approach based on sparse finite mixtures to achieve identifiability. We specify a hierarchical prior, where the hyperparameters are carefully selected such that they are reflective of the cluster structure aimed at...
April 3, 2017: Journal of Computational and Graphical Statistics
https://www.readbyqxmd.com/read/28620143/meta-analysis-of-differences-in-constant-murley-scores-for-three-mid-shaft-clavicular-fracture-treatments
#14
Wei Jiang, Hua Wang, Yu-Sheng Li, Tian-Jian Zhou, Xin-Jia Hu
There is no consensus on the optimal treatment for mid-shaft clavicular fracture. We conducted a meta-analysis to compare the effectiveness of non-operative treatment, plate fixation, and intramedullary pin fixation in terms of the Constant-Murley Score (CMS) for treatment of mid-shaft clavicular fracture. Comprehensive search of the Embase, Cochrane Library and PubMed was conducted to retrieve relevant randomized controlled trials (RCTs). A random-effect network meta-analysis was conducted within a Bayesian framework using Markov Chain Monte Carlo (MCMC) in OpenBUGS 3...
June 12, 2017: Oncotarget
https://www.readbyqxmd.com/read/28598193/sample-size-estimation-for-pilot-animal-experiments-by-using-a-markov-chain-monte-carlo-approach
#15
Andreas Allgoewer, Benjamin Mayer
The statistical determination of sample size is mandatory when planning animal experiments, but it is usually difficult to implement appropriately. The main reason is that prior information is hardly ever available, so the assumptions made cannot be verified reliably. This is especially true for pilot experiments. Statistical simulation might help in these situations. We used a Markov Chain Monte Carlo (MCMC) approach to verify the pragmatic assumptions made on different distribution parameters used for power and sample size calculations in animal experiments...
May 2017: Alternatives to Laboratory Animals: ATLA
https://www.readbyqxmd.com/read/28569547/phylodynamic-analysis-revealed-that-epidemic-of-crf07_bc-strain-in-msm-drove-its-second-spreading-wave-in-china
#16
Min Zhang, Dijing Jia, Hanping Li, Tao Gui, Lei Jia, Xiaolin Wang, Tianyi Li, Yongjian Liu, Zuoyi Bao, Siyang Liu, Daomin Zhuang, Jingyun Li, Lin Li
CRF07_BC was originally formed in Yunnan province of China in 1980s and spread quickly in IDUs. In recent years, it has been introduced into MSMs and become most dominant strain in China. In this study, we fulfilled comprehensively phylodynamic analysis on CRF07_BC epidemic in China. All CRF07_BC sequences identified in China were retrieved from database. More sequences obtained in our laboratory were added to make the dataset to be more representative. ML tree was constructed by using PhyML3.0. MCC tree and effective population size were predicted by using Markov Chains Monte Carlo (MCMC) sampling method with Beast software...
June 1, 2017: AIDS Research and Human Retroviruses
https://www.readbyqxmd.com/read/28567562/identification-and-comparative-analysis-of-hepatitis-b-virus-genotype-d-e-recombinants-in-africa
#17
Ceejay L Boyce, Lilia Ganova-Raeva, Timothy N A Archampong, Margaret Lartey, Kwamena W Sagoe, Adjoa Obo-Akwa, Ernest Kenu, Awewura Kwara, Jason T Blackard
Globally, there are approximately 240 million people chronically infected with hepatitis B virus (HBV)-a major cause of hepatocellular carcinoma. Ten different HBV genotypes (A-J) have been identified with distinct geographic distributions. Novel variants generated by recombination between different HBV genotypes have been documented worldwide and represent an important element of genetic variability with possible clinical implications. Here, the complete genome sequence of an HBV genotype D/E recombinant from Ghana is reported...
August 2017: Virus Genes
https://www.readbyqxmd.com/read/28545083/simultaneous-inference-of-phylogenetic-and-transmission-trees-in-infectious-disease-outbreaks
#18
Don Klinkenberg, Jantien A Backer, Xavier Didelot, Caroline Colijn, Jacco Wallinga
Whole-genome sequencing of pathogens from host samples becomes more and more routine during infectious disease outbreaks. These data provide information on possible transmission events which can be used for further epidemiologic analyses, such as identification of risk factors for infectivity and transmission. However, the relationship between transmission events and sequence data is obscured by uncertainty arising from four largely unobserved processes: transmission, case observation, within-host pathogen dynamics and mutation...
May 2017: PLoS Computational Biology
https://www.readbyqxmd.com/read/28534238/a-fast-algorithm-for-bayesian-multi-locus-model-in-genome-wide-association-studies
#19
Weiwei Duan, Yang Zhao, Yongyue Wei, Sheng Yang, Jianling Bai, Sipeng Shen, Mulong Du, Lihong Huang, Zhibin Hu, Feng Chen
Genome-wide association studies (GWAS) have identified a large amount of single-nucleotide polymorphisms (SNPs) associated with complex traits. A recently developed linear mixed model for estimating heritability by simultaneously fitting all SNPs suggests that common variants can explain a substantial fraction of heritability, which hints at the low power of single variant analysis typically used in GWAS. Consequently, many multi-locus shrinkage models have been proposed under a Bayesian framework. However, most use Markov Chain Monte Carlo (MCMC) algorithm, which are time-consuming and challenging to apply to GWAS data...
August 2017: Molecular Genetics and Genomics: MGG
https://www.readbyqxmd.com/read/28476865/accounting-for-sampling-error-in-genetic-eigenvalues-using-random-matrix-theory
#20
Jacqueline L Sztepanacz, Mark W Blows
The distribution of genetic variance in multivariate phenotypes is characterized by the empirical spectral distribution of the eigenvalues of the genetic covariance matrix. Empirical estimates of genetic eigenvalues from random effects linear models are known to be overdispersed by sampling error, where large eigenvalues are biased upward, and small eigenvalues are biased downward. The overdispersion of the leading eigenvalues of sample covariance matrices have been demonstrated to conform to the Tracy-Widom (TW) distribution...
July 2017: Genetics
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