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Noor-Ul-Huda Ghori, Atif Shafique, Muhammad Qasim Hayat, Sadia Anjum
Hepatitis C Virus (HCV) is the most prevalent human pathogen in Pakistan and is the major cause of liver cirrhosis and hepatocellular carcinoma in infected patients. It has shifted from being hypo-endemic to being hyper-endemic. There was no information about the origin and evolution of the local variants. Here we use newly developed phyloinformatic methods of sequence analysis to conduct the first comprehensive investigation of the evolutionary and biogeographic history in unprecedented detail and breadth...
2016: PloS One
Lizhen Xu, Radu V Craiu, Lei Sun, Andrew D Paterson
Motivated by genetic association studies of pleiotropy, we propose a Bayesian latent variable approach to jointly study multiple outcomes. The models studied here can incorporate both continuous and binary responses, and can account for serial and cluster correlations. We consider Bayesian estimation for the model parameters, and we develop a novel MCMC algorithm that builds upon hierarchical centering and parameter expansion techniques to efficiently sample from the posterior distribution. We evaluate the proposed method via extensive simulations and demonstrate its utility with an application to aa association study of various complication outcomes related to type 1 diabetes...
2016: Journal of Computational and Graphical Statistics
Keisuke Yoshihara, Minh Nhat Le, Koo Nagasawa, Hiroyuki Tsukagoshi, Hien Anh Nguyen, Michiko Toizumi, Hiroyuki Moriuchi, Masahiro Hashizume, Koya Ariyoshi, Duc Anh Dang, Hirokazu Kimura, Lay-Myint Yoshida
We performed molecular evolutionary analyses of the G gene C-terminal 3rd hypervariable region of RSV-A genotypes NA1 and ON1 strains from the paediatric acute respiratory infection patients in central Vietnam during the 2010-2012 study period. Time-scaled phylogenetic analyses were performed using Bayesian Markov Chain Monte Carlo (MCMC) method, and pairwise distances (p-distances) were calculated. Bayesian Skyline Plot (BSP) was constructed to analyze the time-trend relative genetic diversity of central Vietnam RSV-A strains...
October 14, 2016: Infection, Genetics and Evolution
Prathiba Natesan, Ratna Nandakumar, Tom Minka, Jonathan D Rubright
This study investigated the impact of three prior distributions: matched, standard vague, and hierarchical in Bayesian estimation parameter recovery in two and one parameter models. Two Bayesian estimation methods were utilized: Markov chain Monte Carlo (MCMC) and the relatively new, Variational Bayesian (VB). Conditional (CML) and Marginal Maximum Likelihood (MML) estimates were used as baseline methods for comparison. Vague priors produced large errors or convergence issues and are not recommended. For both MCMC and VB, the hierarchical and matched priors showed the lowest root mean squared errors (RMSEs) for ability estimates; RMSEs of difficulty estimates were similar across estimation methods...
2016: Frontiers in Psychology
Valentina Clamer, Ilaria Dorigatti, Laura Fumanelli, Caterina Rizzo, Andrea Pugliese
BACKGROUND: Epidemic models are being extensively used to understand the main pathways of spread of infectious diseases, and thus to assess control methods. Schools are well known to represent hot spots for epidemic spread; hence, understanding typical patterns of infection transmission within schools is crucial for designing adequate control strategies. The attention that was given to the 2009 A/H1N1pdm09 flu pandemic has made it possible to collect detailed data on the occurrence of influenza-like illness (ILI) symptoms in two primary schools of Trento, Italy...
October 12, 2016: Theoretical Biology & Medical Modelling
Tayeb Mohammadi, Soleiman Kheiri, Morteza Sedehi
Recognizing the factors affecting the number of blood donation and blood deferral has a major impact on blood transfusion. There is a positive correlation between the variables "number of blood donation" and "number of blood deferral": as the number of return for donation increases, so does the number of blood deferral. On the other hand, due to the fact that many donors never return to donate, there is an extra zero frequency for both of the above-mentioned variables. In this study, in order to apply the correlation and to explain the frequency of the excessive zero, the bivariate zero-inflated Poisson regression model was used for joint modeling of the number of blood donation and number of blood deferral...
2016: Computational and Mathematical Methods in Medicine
Jiangan Xie, Lili Zhao, Shangbo Zhou, Yongqun He
Vaccinations often induce various adverse events (AEs), and sometimes serious AEs (SAEs). While many vaccines are used in combination, the effects of vaccine-vaccine interactions (VVIs) on vaccine AEs are rarely studied. In this study, AE profiles induced by hepatitis A vaccine (Havrix), hepatitis B vaccine (Engerix-B), and hepatitis A and B combination vaccine (Twinrix) were studied using the VAERS data. From May 2001 to January 2015, VAERS recorded 941, 3,885, and 1,624 AE case reports where patients aged at least 18 years old were vaccinated with only Havrix, Engerix-B, and Twinrix, respectively...
October 3, 2016: Scientific Reports
Pranjal Vachaspati, Tandy Warnow
MOTIVATION: The estimation of phylogenetic trees is a major part of many biological dataset analyses, but maximum likelihood approaches are NP-hard and Bayesian MCMC methods do not scale well to even moderate-sized datasets. Supertree methods, which are used to construct trees from trees computed on subsets, are critically important tools for enabling the statistical estimation of phylogenies for large and potentially heterogeneous datasets . Supertree estimation is itself NP-hard, and no current supertree method has sufficient accuracy and scalability to provide good accuracy on the large datasets that supertree methods were designed for, containing thousands of species and many subset trees...
September 23, 2016: Bioinformatics
Remco Bouckaert
BACKGROUND: Techniques for reconstructing geographical history along a phylogeny can answer many questions of interest about the geographical origins of species. Bayesian models based on the assumption that taxa move through a diffusion process have found many applications. However, these methods rely on diffusion processes on a plane, and do not take the spherical nature of our planet in account. Performing an analysis that covers the whole world thus does not take in account the distortions caused by projections like the Mercator projection...
2016: PeerJ
Facundo Costa, Hadj Batatia, Thomas Oberlin, Carlos D'Giano, Jean-Yves Tourneret
This paper deals with EEG source localization. The aim is to perform spatially coherent focal localization and recover temporal EEG waveforms, which can be useful in certain clinical applications. A new hierarchical Bayesian model is proposed with a multivariate Bernoulli Laplacian structured sparsity prior for brain activity. This distribution approximates a mixed ℓ20 pseudo norm regularization in a Bayesian framework. A partially collapsed Gibbs sampler is proposed to draw samples asymptotically distributed according to the posterior of the proposed Bayesian model...
September 15, 2016: NeuroImage
Mary M Conner, W Carl Saunders, Nicolaas Bouwes, Chris Jordan
Before-after-control-impact (BACI) designs are an effective method to evaluate natural and human-induced perturbations on ecological variables when treatment sites cannot be randomly chosen. While effect sizes of interest can be tested with frequentist methods, using Bayesian Markov chain Monte Carlo (MCMC) sampling methods, probabilities of effect sizes, such as a ≥20 % increase in density after restoration, can be directly estimated. Although BACI and Bayesian methods are used widely for assessing natural and human-induced impacts for field experiments, the application of hierarchal Bayesian modeling with MCMC sampling to BACI designs is less common...
October 2015: Environmental Monitoring and Assessment
Vinzent Boerner, Bruce Tier
BACKGROUND: The advent of genomic marker data has triggered the development of various Bayesian algorithms for estimation of marker effects, but software packages implementing these algorithms are not readily available, or are limited to a single algorithm, uni-variate analysis or a limited number of factors. Moreover, script based environments like R may not be able to handle large-scale genomic data or exploit model properties which save computing time or memory (RAM). RESULTS: BESSiE is a software designed for best linear unbiased prediction (BLUP) and Bayesian Markov chain Monte Carlo analysis of linear mixed models allowing for continuous and/or categorical multivariate, repeated and missing observations, various random and fixed factors and large-scale genomic marker data...
2016: Genetics, Selection, Evolution: GSE
Kirsty M Rhodes, Rebecca M Turner, Ian R White, Dan Jackson, David J Spiegelhalter, Julian P T Higgins
Many meta-analyses combine results from only a small number of studies, a situation in which the between-study variance is imprecisely estimated when standard methods are applied. Bayesian meta-analysis allows incorporation of external evidence on heterogeneity, providing the potential for more robust inference on the effect size of interest. We present a method for performing Bayesian meta-analysis using data augmentation, in which we represent an informative conjugate prior for between-study variance by pseudo data and use meta-regression for estimation...
August 30, 2016: Statistics in Medicine
Hua Zhang, Mingdong Huo, Jianqian Chao, Pei Liu
BACKGROUND: Hepatitis B virus (HBV) infection is a major problem for public health; timely antiviral treatment can significantly prevent the progression of liver damage from HBV by slowing down or stopping the virus from reproducing. In the study we applied Bayesian approach to cost-effectiveness analysis, using Markov Chain Monte Carlo (MCMC) simulation methods for the relevant evidence input into the model to evaluate cost-effectiveness of entecavir (ETV) and lamivudine (LVD) therapy for chronic hepatitis B (CHB) in Jiangsu, China, thus providing information to the public health system in the CHB therapy...
2016: PloS One
Alexandra Gavryushkina, Tracy A Heath, Daniel T Ksepka, Tanja Stadler, David Welch, Alexei J Drummond
The total-evidence approach to divergence time dating uses molecular and morphological data from extant and fossil species to infer phylogenetic relationships, species divergence times, and macroevolutionary parameters in a single coherent framework. Current model-based implementations of this approach lack an appropriate model for the tree describing the diversification and fossilization process and can produce estimates that lead to erroneous conclusions. We address this shortcoming by providing a total-evidence method implemented in a Bayesian framework...
August 24, 2016: Systematic Biology
Gie Ken-Dror, Ian M Hastings
BACKGROUND: Haplotypes are important in anti-malarial drug resistance because genes encoding drug resistance may accumulate mutations at several codons in the same gene, each mutation increasing the level of drug resistance and, possibly, reducing the metabolic costs of previous mutation. Patients often have two or more haplotypes in their blood sample which may make it impossible to identify exactly which haplotypes they carry, and hence to measure the type and frequency of resistant haplotypes in the malaria population...
2016: Malaria Journal
Camille Piponiot, Antoine Cabon, Laurent Descroix, Aurélie Dourdain, Lucas Mazzei, Benjamin Ouliac, Ervan Rutishauser, Plinio Sist, Bruno Hérault
BACKGROUND: Managed forests are a major component of tropical landscapes. Production forests as designated by national forest services cover up to 400 million ha, i.e. half of the forested area in the humid tropics. Forest management thus plays a major role in the global carbon budget, but with a lack of unified method to estimate carbon fluxes from tropical managed forests. In this study we propose a new time- and spatially-explicit methodology to estimate the above-ground carbon budget of selective logging at regional scale...
December 2016: Carbon Balance and Management
Zhou Lan, Yize Zhao, Jian Kang, Tianwei Yu
MOTIVATION: Network marker selection on genome-scale networks plays an important role in the understanding of biological mechanisms and disease pathologies. Recently, a Bayesian nonparametric mixture model [Zhao et al., 2014] has been developed and successfully applied for selecting genes and gene sub-networks. Hence, extending this method to a unified approach for network-based feature selection on general large-scale networks and creating an easy-to-use software package is on demand...
August 8, 2016: Bioinformatics
Brian Cloteaux
We examine the problem of creating random realizations of very large degree sequences. Although fast in practice, the Markov chain Monte Carlo (MCMC) method for selecting a realization has limited usefulness for creating large graphs because of memory constraints. Instead, we focus on sequential importance sampling (SIS) schemes for random graph creation. A difficulty with SIS schemes is assuring that they terminate in a reasonable amount of time. We introduce a new sampling method by which we guarantee termination while achieving speed comparable to the MCMC method...
2016: Internet Mathematics
Haiyong Wu, Geng Chen, Yan Jin, Dinggang Shen, Pew-Thian Yap
Global tractography estimates brain connectivity by organizing signal-generating fiber segments in an optimal configuration that best describes the measured diffusion-weighted data, promising better stability than local greedy methods with respect to imaging noise. However, global tractography is computationally very demanding and requires computation times that are often prohibitive for clinical applications. We present here a reformulation of the global tractography algorithm for fast parallel implementation amendable to acceleration using multi-core CPUs and general-purpose GPUs...
2016: Frontiers in Neuroinformatics
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