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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
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
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
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
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...
May 31, 2017: Virus Genes
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
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...
May 22, 2017: Molecular Genetics and Genomics: MGG
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 over-dispersed by sampling error, where large eigenvalues are biased upwards, and small eigenvalues are biased downwards. The overdispersion of the leading eigenvalues of sample covariance matrices have been demonstrated to conform to the Tracy-Widom (TW) distribution...
May 5, 2017: Genetics
Anders Eklund, Martin A Lindquist, Mattias Villani
We propose a voxel-wise general linear model with autoregressive noise and heteroscedastic noise innovations (GLMH) for analyzing functional magnetic resonance imaging (fMRI) data. The model is analyzed from a Bayesian perspective and has the benefit of automatically down-weighting time points close to motion spikes in a data-driven manner. We develop a highly efficient Markov Chain Monte Carlo (MCMC) algorithm that allows for Bayesian variable selection among the regressors to model both the mean (i.e., the design matrix) and variance...
May 1, 2017: NeuroImage
Jolene Atia, Emma Monaghan, Jasmeet Kaler, Kevin Purdy, Laura Green, Matt Keeling
Dichelobacter nodosus is a virulent, invasive, anaerobic bacterium that is believed to be the causative agent of ovine footrot, an infectious bacterial disease of sheep that causes lameness. Another anaerobe, Fusobacterium necrophorum, has been intimately linked with the disease occurrence and severity. Here we examine data from a longitudinal study of footrot on one UK farm, including quantitative PCR (qPCR) estimates of bacterial load of D. nodosus and F. necrophorum. The data is at foot level; all feet were monitored for five weeks assessing disease severity (healthy, interdigital dermatitis (ID), or severe footrot (SFR)) and bacterial load (number of bacteria/swab)...
April 12, 2017: Epidemics
Raimundo Castro-Orozco, Lyda R Castro-García, Doris E Gómez-Camargo
Objective The objective of this in silico study was to compare nucleotide and amino acids DENV-2-NS1 sequences isolated from febrile patients, with and without disease severity, from different South American countries. Matherials and Methods A bayesian MCMC phylogenetic analysis was carried out using 28 complete sequences of the gene NS1 of the DENV-2 serotype (1 056 bp), using MrBayes v.3.2.0 software, with the model SYM+G (2.5 million generations). We also carried out a phylogenetic analysis with Neighbor-Joining method (Jukes-Cantor model)...
June 2016: Revista de Salud Pública
Lars A Bratholm, Jan H Jensen
The accurate prediction of protein chemical shifts using a quantum mechanics (QM)-based method has been the subject of intense research for more than 20 years but so far empirical methods for chemical shift prediction have proven more accurate. In this paper we show that a QM-based predictor of a protein backbone and CB chemical shifts (ProCS15, PeerJ, 2016, 3, e1344) is of comparable accuracy to empirical chemical shift predictors after chemical shift-based structural refinement that removes small structural errors...
March 1, 2017: Chemical Science
Jesus Lozano-Fernandez, Mario Dos Reis, Philip C J Donoghue, Davide Pisani
Establishing an accurate timescale for the history of life is crucial to understand evolutionary processes. For this purpose, relaxed molecular clock models implemented in a Bayesian MCMC framework are generally used. However, these methods are time consuming. RelTime, a non-Bayesian method implementing a fast, ad hoc, algorithm for relative dating, was developed to overcome the computational inefficiencies of Bayesian software. RelTime was recently used to investigate the timing of origin of animals, yielding results consistent with early strict clock studies from the 1980s and 1990s, estimating metazoans to have a Mesoproterozoic origin-over a billion years ago...
May 1, 2017: Genome Biology and Evolution
Stuart Serdoz, Attila Egri-Nagy, Jeremy Sumner, Barbara R Holland, Peter D Jarvis, Mark M Tanaka, Andrew R Francis
Accurate estimation of evolutionary distances between taxa is important for many phylogenetic reconstruction methods. Distances can be estimated using a range of different evolutionary models, from single nucleotide polymorphisms to large-scale genome rearrangements. Corresponding corrections for genome rearrangement distances fall into 3 categories: Empirical computational studies, Bayesian/MCMC approaches, and combinatorial approaches. Here, we introduce a maximum likelihood estimator for the inversion distance between a pair of genomes, using a group-theoretic approach to modelling inversions introduced recently...
April 20, 2017: Journal of Theoretical Biology
Huw A Ogilvie, Remco R Bouckaert, Alexei J Drummond
Fully Bayesian multispecies coalescent (MSC) methods like *BEAST estimate species trees from multiple sequence alignments. Today thousands of genes can be sequenced for a given study, but using that many genes with *BEAST is intractably slow. An alternative is to use heuristic methods which compromise accuracy or completeness in return for speed. A common heuristic is concatenation, which assumes that the evolutionary history of each gene tree is identical to the species tree. This is an inconsistent estimator of species tree topology, a worse estimator of divergence times, and induces spurious substitution rate variation when incomplete lineage sorting is present...
April 14, 2017: Molecular Biology and Evolution
Victor A Alegana, Jim Wright, Carla Pezzulo, Andrew J Tatem, Peter M Atkinson
BACKGROUND: Seeking treatment in formal healthcare for uncomplicated infections is vital to combating disease in low- and middle-income countries (LMICs). Healthcare treatment-seeking behaviour varies within and between communities and is modified by socio-economic, demographic, and physical factors. As a result, it remains a challenge to quantify healthcare treatment-seeking behaviour using a metric that is comparable across communities. Here, we present an application for transforming individual categorical responses (actions related to fever) to a continuous probabilistic estimate of fever treatment for one country in Sub-Saharan Africa (SSA)...
April 20, 2017: BMC Medical Research Methodology
Xueqing Zou, Pengpeng Deng, Changjiang Zhou, Yulin Hou, Rongsheng Chen, Feng Liang, Liqiong Liao
Cryogel was synthesized through cryogelation of methacrylated carboxymethyl chitosan (mCMC) and poly(ethylene glycol) diacrylate (PEGDA) precursors by photopolymerization. Due to its excellent properties, such as fast swelling behavior, inter-connective porous structure, high water absorbing capacity, especially the presence of abundant carboxylmethyl groups on its backbone, the cryogel not only favored the absorption of silver ions but also was proved to be a good matrix for the incorporation of silver nanoparticles (AgNPs) by in situ chemical reduction...
April 26, 2017: Journal of Biomaterials Science. Polymer Edition
Peter Wittek, Christian Gogolin
Markov logic networks (MLNs) reconcile two opposing schools in machine learning and artificial intelligence: causal networks, which account for uncertainty extremely well, and first-order logic, which allows for formal deduction. An MLN is essentially a first-order logic template to generate Markov networks. Inference in MLNs is probabilistic and it is often performed by approximate methods such as Markov chain Monte Carlo (MCMC) Gibbs sampling. An MLN has many regular, symmetric structures that can be exploited at both first-order level and in the generated Markov network...
April 19, 2017: Scientific Reports
Hirokazu Kimura, Koo Nagasawa, Ryusuke Kimura, Hiroyuki Tsukagoshi, Yuki Matsushima, Kiyotaka Fujita, Eiko Hirano, Naruhiko Ishiwada, Takako Misaki, Kazunori Oishi, Makoto Kuroda, Akihide Ryo
In this study, we examined the molecular evolution of the fusion protein (F) gene in human respiratory syncytial virus subgroup B (HRSV-B). First, we performed time-scale evolution analyses using the Bayesian Markov chain Monte Carlo (MCMC) method. Next, we performed genetic distance, linear B-cell epitope prediction, N-glycosylation, positive/negative selection site, and Bayesian skyline plot analyses. We also constructed a structural model of the F protein and mapped the amino acid substitutions and the predicted B-cell epitopes...
April 14, 2017: Infection, Genetics and Evolution
Grigorios Mingas, Leonardo Bottolo, Christos-Savvas Bouganis
Particle Markov Chain Monte Carlo (pMCMC) is a stochastic algorithm designed to generate samples from a probability distribution, when the density of the distribution does not admit a closed form expression. pMCMC is most commonly used to sample from the Bayesian posterior distribution in State-Space Models (SSMs), a class of probabilistic models used in numerous scientific applications. Nevertheless, this task is prohibitive when dealing with complex SSMs with massive data, due to the high computational cost of pMCMC and its poor performance when the posterior exhibits multi-modality...
April 2017: International Journal of Approximate Reasoning
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