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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, Liang Feng, 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 19, 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
Osval A Montesinos-López, Abelardo Montesinos-López, José Crossa, Fernando H Toledo, José C Montesinos-López, Pawan Singh, Philomin Juliana, Josefhat Salinas-Ruiz
When a plant scientist wishes to make genomic-enabled predictions of multiple traits measured in multiple individuals in multiple environments, the most common strategy for performing the analysis is to use a single trait at a time taking into account genotype x environment interaction (GxE), because there is a lack of comprehensive models that simultaneously take into account the correlated counting traits and G×E. For this reason, in this study we propose a multiple-trait and multiple-environment model for count data...
March 31, 2017: G3: Genes—Genomes—Genetics
Javier Nuñez-Garcia, Sara H Downs, Jessica E Parry, Darrell A Abernethy, Jennifer M Broughan, Angus R Cameron, Alasdair J Cook, Ricardo de la Rua-Domenech, Anthony V Goodchild, Jane Gunn, Simon J More, Shelley Rhodes, Simon Rolfe, Michael Sharp, Paul A Upton, H Martin Vordermeier, Eamon Watson, Michael Welsh, Adam O Whelan, John A Woolliams, Richard S Clifton-Hadley, Matthias Greiner
Bovine Tuberculosis (bTB) in cattle is a global health problem and eradication of the disease requires accurate estimates of diagnostic test performance to optimize their efficiency. The objective of this study was, through statistical meta-analyses, to obtain estimates of sensitivity (Se) and specificity (Sp), for 14 different ante-mortem and post-mortem diagnostic tests for bTB in cattle. Using data from a systematic review of the scientific literature (published 1934-2009) diagnostic Se and Sp were estimated using Bayesian logistic regression models adjusting for confounding factors...
March 6, 2017: Preventive Veterinary Medicine
Helene Ruffieux, Anthony C Davison, Jorg Hager, Irina Irincheeva
Combined inference for heterogeneous high-dimensional data is critical in modern biology, where clinical and various kinds of molecular data may be available from a single study. Classical genetic association studies regress a single clinical outcome on many genetic variants one by one, but there is an increasing demand for joint analysis of many molecular outcomes and genetic variants in order to unravel functional interactions. Unfortunately, most existing approaches to joint modeling are either too simplistic to be powerful or are impracticable for computational reasons...
March 16, 2017: Biostatistics
Daniel L Rabosky, Jonathan S Mitchell, Jonathan Chang
BAMM (Bayesian Analysis of Macroevolutionary Mixtures) is a statistical framework that uses reversible jump MCMC to infer complex macroevolutionary dynamics of diversification and phenotypic evolution on phylogenetic trees. A recent article by Moore and coauthors (MEA) reported a number of theoretical and practical concerns with BAMM. Major claims from MEA are that (1) BAMM's likelihood function is incorrect, because it does not account for unobserved rate shifts; (2) the posterior distribution on the number of rate shifts is overly sensitive to the prior; and (3) diversification rate estimates from BAMM are unreliable...
February 21, 2017: Systematic Biology
Yujin Chung, Jody Hey
We present a new Bayesian method for estimating demographic and phylogenetic history using population genomic data. Several key innovations are introduced that allow the study of diverse models within an Isolation with Migration framework. The new method implements a 2-step analysis, with an initial Markov chain Monte Carlo (MCMC) phase that samples simple coalescent trees, followed by the calculation of the joint posterior density for the parameters of a demographic model. In step 1, the MCMC sampling phase, the method uses a reduced state space, consisting of coalescent trees without migration paths, and a simple importance sampling distribution without the demography of interest...
February 25, 2017: Molecular Biology and Evolution
Daiane Aparecida Zuanetti, Luis Aparecido Milan
We present a generalization of the usual (independent) mixture model to accommodate a Markovian first-order mixing distribution. We propose the data-driven reversible jump, a Markov chain Monte Carlo (MCMC) procedure, for estimating the a posteriori probability for each model in a model selection procedure and estimating the corresponding parameters. Simulated datasets show excellent performance of the proposed method in the convergence, model selection, and precision of parameters estimates. Finally, we apply the proposed method to analyze USA diabetes incidence datasets...
March 21, 2017: Biometrical Journal. Biometrische Zeitschrift
Radu Herbei, Rajib Paul, L Mark Berliner
We examine the performance of a strategy for Markov chain Monte Carlo (MCMC) developed by simulating a discrete approximation to a stochastic differential equation (SDE). We refer to the approach as diffusion MCMC. A variety of motivations for the approach are reviewed in the context of Bayesian analysis. In particular, implementation of diffusion MCMC is very simple to set-up, even in the presence of nonlinear models and non-conjugate priors. Also, it requires comparatively little problem-specific tuning. We implement the algorithm and assess its performance for both a test case and a glaciological application...
2017: PloS One
Alberto Garre, Pablo S Fernández, Roland Lindqvist, Jose A Egea
This contribution presents the bioinactivation software, which implements functions for the modelling of isothermal and non-isothermal microbial inactivation. This software offers features such as user-friendliness, modelling of dynamic conditions, possibility to choose the fitting algorithm and generation of prediction intervals. The software is offered in two different formats: Bioinactivation core and Bioinactivation SE. Bioinactivation core is a package for the R programming language, which includes features for the generation of predictions and for the fitting of models to inactivation experiments using non-linear regression or a Markov Chain Monte Carlo algorithm (MCMC)...
March 2017: Food Research International
Bethlyn Vergo Houlihan, Miriam Brody, Sarah Everhart-Skeels, Diana Pernigotti, Sam Burnett, Judi Zazula, Christa Green, Stathis Hasiotis, Timothy Belliveau, Subramani Seetharama, David Rosenblum, Alan Jette
OBJECTIVE: To evaluate the impact of "My Care My Call" (MCMC), a peer-led, telephone-based health self-management intervention in adults with chronic spinal cord injury (SCI). DESIGN: Single-blinded randomized controlled trial SETTING: General community PARTICIPANTS: Convenience sample of 84 adults with SCI (X= 9.9 years post-SCI); X=46 years old; 73.8% male; 44% with paraplegia; and 58% White. INTERVENTIONS: Trained Peer Health Coaches (PHC) applied the person-centered health self-management intervention with 42 experimental subjects over 6 months on a tapered call schedule...
March 8, 2017: Archives of Physical Medicine and Rehabilitation
Evan S Childress, Benjamin H Letcher
Estimating thermal performance of organisms is critical for understanding population distributions and dynamics and predicting responses to climate change. Typically, performance curves are estimated using laboratory studies to isolate temperature effects, but other abiotic and biotic factors influence temperature-performance relationships in nature reducing these models' predictive ability. We present a model for estimating thermal performance curves from repeated field observations that includes environmental and individual variation...
March 8, 2017: Ecology
Niannan Xue, Wei Pan, Yike Guo
Genetic regulatory networks have emerged as a useful way to elucidate the biochemical pathways for biological functions. Yet, determination of the exact parametric forms for these models remain a major challenge. In this paper, we present a novel computational approach implemented in C++ to solve this inverse problem. This takes the form of an optimization stage first after which Bayesian filtering takes place. The key advantage of such a flexible, general and robust approach is that it provides us with a joint probability distribution of the model parameters instead of single estimates, which we can propagate to final predictions...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Emma Saulnier, Olivier Gascuel, Samuel Alizon
Inferring epidemiological parameters such as the R0 from time-scaled phylogenies is a timely challenge. Most current approaches rely on likelihood functions, which raise specific issues that range from computing these functions to finding their maxima numerically. Here, we present a new regression-based Approximate Bayesian Computation (ABC) approach, which we base on a large variety of summary statistics intended to capture the information contained in the phylogeny and its corresponding lineage-through-time plot...
March 2017: PLoS Computational Biology
Xin-Peng Pan, Guang-Zhi Zhang, Jia-Jia Zhang, Xing-Yao Yin
The conventional Markov chain Monte Carlo (MCMC) method is limited to the selected shape and size of proposal distribution and is not easy to start when the initial proposal distribution is far away from the target distribution. To overcome these drawbacks of the conventional MCMC method, two useful improvements in MCMC method, adaptive Metropolis (AM) algorithm and delayed rejection (DR) algorithm, are attempted to be combined. The AM algorithm aims at adapting the proposal distribution by using the generated estimators, and the DR algorithm aims at enhancing the efficiency of the improved MCMC method...
2017: Petroleum Science
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