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Bayesian inference - Top 30 Publications

Genetic parameters in female reproductive traits of Nile tilapia (Oreochromis niloticus).

Genetic parameters for reproductive traits in female Nile tilapia were estimated in this study using Bayesian inference method. The data set presented information from 451 Nile tilapia females that were evaluated at two different places in Maringá - Paraná - Brazil (hapas of 1 and 10 m³) and at one location in Alfenas - Minas Gerais - Brazil. A spawning examination was conducted once a week from October 2012 to March 2013 for a total of 23 weeks of evaluation. Single-trait analyses for spawning success, multiple spawning, spawning frequency, and volume of eggs/female were performed by using the software MTGSAM Threshold. The heritability estimates were 0.14, 0.16, 0.53, and 0.08 for spawning success, multiple spawning, spawning frequency and volume of eggs/female, respectively, indicating it is possible to achieve a substantial genetic gain using these reproductive traits as selection criteria.

Predicting Hepatotoxicity of Drug Metabolites via an Ensemble Approach Based on Support Cector Machine.

Drug-induced liver injury (DILI) is a major cause of drug withdrawal. The chemical properties of the drug, especially drug metabolites, play key roles in DILI. Our goal is to construct a QSAR model to predict drug hepatotoxicity based on drug metabolites.

BIBI: Bayesian inference of breed composition.

The aim of this paper was to develop statistical models to estimate individual breed composition based on the previously proposed idea of regressing discrete random variables corresponding to counts of reference alleles of biallelic molecular markers located across the genome on the allele frequencies of each marker in the pure (base) breeds. Some of the existing regression-based methods do not guarantee that estimators of breed composition will lie in the appropriate parameter space, and none of them account for uncertainty about allele frequencies in the pure breeds, that is, uncertainty about the design matrix. To overcome these limitations, we proposed two Bayesian generalized linear models. For each individual, both models assume that the counts of the reference allele at each marker locus follow independent Binomial distributions, use the logit link and pose a Dirichlet prior over the vector of regression coefficients (which corresponds to breed composition). This prior guarantees that point estimators of breed composition such as the posterior mean pertain to the appropriate space. The difference between these models is that model termed BIBI does not account for uncertainty about the design matrix, while model termed BIBI2 accounts for such an uncertainty by assigning independent Beta priors to the entries of this matrix. We implemented these models in a data set from the University of Florida's multibreed Angus-Brahman population. Posterior means were used as point estimators of breed composition. In addition, the ordinary least squares estimator proposed by Kuehn et al. () (OLSK) was also computed. BIBI and BIBI2 estimated breed composition more accurately than OLSK, and BIBI2 had a 7.69% improvement in accuracy as compared to BIBI.

Bayesian Occam's Razor Is a Razor of the People.

Occam's razor-the idea that all else being equal, we should pick the simpler hypothesis-plays a prominent role in ordinary and scientific inference. But why are simpler hypotheses better? One attractive hypothesis known as Bayesian Occam's razor (BOR) is that more complex hypotheses tend to be more flexible-they can accommodate a wider range of possible data-and that flexibility is automatically penalized by Bayesian inference. In two experiments, we provide evidence that people's intuitive probabilistic and explanatory judgments follow the prescriptions of BOR. In particular, people's judgments are consistent with the two most distinctive characteristics of BOR: They penalize hypotheses as a function not only of their numbers of free parameters but also as a function of the size of the parameter space, and they penalize those hypotheses even when their parameters can be "tuned" to fit the data better than comparatively simpler hypotheses.

Mitochondrial genomes of three Tetrigoidea species and phylogeny of Tetrigoidea.

The mitochondrial genomes (mitogenomes) of Formosatettix qinlingensis, Coptotettix longjiangensis and Thoradonta obtusilobata (Orthoptera: Caelifera: Tetrigoidea) were sequenced in this study, and almost the entire mitogenomes of these species were determined. The mitogenome sequences obtained for the three species were 15,180, 14,495 and 14,538 bp in length, respectively, and each sequence included 13 protein-coding genes (PCGs), partial sequences of rRNA genes (rRNAs), tRNA genes (tRNAs) and a A + T-rich region. The order and orientation of the gene arrangement pattern were identical to that of most Tetrigoidea species. Some conserved spacer sequences between trnS(UCN) and nad1 were useful to identify Tetrigoidea and Acridoidea. The Ka/Ks value of atp8 between Trachytettix bufo and other four Tetrigoidea species indicated that some varied sites in this gene might be related with the evolution of T. bufo. The three Tetrigoidea species were compared with other Caelifera. At the superfamily level, conserved sequences were observed in intergenic spacers, which can be used for superfamily level identification between Tetrigoidea and Acridoidea. Furthermore, a phylogenomic analysis was conducted based on the concatenated data sets from mitogenome sequences of 24 species of Orthoptera in the superorders Caelifera and Ensifera. Both maximum likelihood and bayesian inference analyses strongly supported Acridoidea and Tetrigoidea as forming monophyletic groups. The relationships among six Tetrigoidea species were (((((Tetrix japonica, Alulatettix yunnanensis), Formosatettix qinlingensis), Coptotettix longjiangensis), Trachytettix bufo), Thoradonta obtusilobata).

Fates of angiosperm species following long-distance dispersal: Examples from American amphitropical Polemoniaceae.

Following establishment after long-distance dispersal, species may experience stasis, accumulate changes leading to new species identity, diversify into multiple species, interact with related species to form novel species, and even become extirpated. We examined each species of temperate Polemoniaceae in South America via the literature and new analyses to better understand the fates of species in this family after their dispersal from North America.

Bayesian estimation of true prevalence, sensitivity and specificity of three diagnostic tests for detection of Escherichia coli O157 in cattle feces.

Cattle are a reservoir for Escherichia coli O157 and they shed the pathogen in their feces. Fecal contaminants on the hides can be transferred onto carcasses during processing at slaughter plants, thereby serving as a source of foodborne infection in humans. The detection of E. coli O157 in cattle feces is based on culture, immunological, and molecular methods We evaluated the diagnostic sensitivity and specificity of one culture- and two PCR-based tests for the detection of E. coli O157 in cattle feces, and its true prevalence using a Bayesian implementation of latent class models. A total of 576 fecal samples were collected from the floor of pens of finishing feedlot cattle in the central United States during summer 2013. Samples were enriched and subjected to detection of E. coli O157 by culture (immunomagnetic separation, plating on a selective medium, latex agglutination, and indole testing), conventional PCR (cPCR), and multiplex quantitative PCR (mqPCR). The statistical models assumed conditional dependence of the PCR tests and high specificity for culture (mode=99%; 5th percentile=97%). Prior estimates of test parameters were elicited from three experts. Estimated posterior sensitivity (posterior median and 95% highest posterior density intervals) of culture, cPCR, and mqPCR was 49.1% (44.8-53.4%), 59.7% (55.3-63.9%), and 97.3% (95.1-99.0%), respectively. Estimated posterior specificity of culture, cPCR, and mqPCR were 98.7% (96.8-99.8%), 94.1% (87.4-99.1%), and 94.8% (84.1-99.9%), respectively. True prevalence was estimated at 91.3% (88.1-94.2%). There was evidence of a weak conditional dependence between cPCR and mqPCR amongst test positive samples, but no evidence of conditional dependence amongst test negative samples. Sensitivity analyses showed that overall our posterior inference was rather robust to the choice of priors, except for inference on specificity of mqPCR, which was estimated with considerable uncertainty. Our study evaluates performance of three diagnostic tests for detection of E. coli O157 in feces of feedlot cattle which is important for quantifying true fecal prevalence and adjusting for test error in risk modeling.

Bayesian monotonic errors-in-variables models with applications to pathogen susceptibility testing.

Drug dilution (MIC) and disk diffusion (DIA) are the 2 most common antimicrobial susceptibility assays used by hospitals and clinics to determine an unknown pathogen's susceptibility to various antibiotics. Since only one assay is commonly used, it is important that the 2 assays give similar results. Calibration of the DIA assay to the MIC assay is typically done using the error-rate bounded method, which selects DIA breakpoints that minimize the observed discrepancies between the 2 assays. In 2000, Craig proposed a model-based approach that specifically models the measurement error and rounding processes of each assay, the underlying pathogen distribution, and the true monotonic relationship between the 2 assays. The 2 assays are then calibrated by focusing on matching the probabilities of correct classification (susceptible, indeterminant, and resistant). This approach results in greater precision and accuracy for estimating DIA breakpoints. In this paper, we expand the flexibility of the model-based method by introducing a Bayesian 4-parameter logistic model (extending Craig's original 3-parameter model) as well as a Bayesian nonparametric spline model to describe the relationship between the 2 assays. We propose 2 ways to handle spline knot selection, considering many equally spaced knots but restricting overfitting via a random walk prior and treating the number and location of knots as additional unknown parameters. We demonstrate the 2 approaches via a series of simulation studies and apply the methods to 2 real data sets.

Bayesian Word Learning in Multiple Language Environments.

Infant language learners are faced with the difficult inductive problem of determining how new words map to novel or known objects in their environment. Bayesian inference models have been successful at using the sparse information available in natural child-directed speech to build candidate lexicons and infer speakers' referential intentions. We begin by asking how a Bayesian model optimized for monolingual input (the Intentional Model; Frank et al., 2009) generalizes to new monolingual or bilingual corpora and find that, especially in the case of the bilingual input, the model shows a significant decrease in performance. In the next experiment, we propose the ME Model, a modified Bayesian model, which approximates infants' mutual exclusivity bias to support the differential demands of monolingual and bilingual learning situations. The extended model is assessed using the same corpora of real child-directed speech, showing that its performance is more robust against varying input and less dependent than the Intentional Model on optimization of its parsimony parameter. We argue that both monolingual and bilingual demands on word learning are important considerations for a computational model, as they can yield significantly different results than when only one such context is considered.

Evidence synthesis from aggregate recurrent event data for clinical trial design and analysis.

Information from historical trials is important for the design, interim monitoring, analysis, and interpretation of clinical trials. Meta-analytic models can be used to synthesize the evidence from historical data, which are often only available in aggregate form. We consider evidence synthesis methods for trials with recurrent event endpoints, which are common in many therapeutic areas. Such endpoints are typically analyzed by negative binomial regression. However, the individual patient data necessary to fit such a model are usually unavailable for historical trials reported in the medical literature. We describe approaches for back-calculating model parameter estimates and their standard errors from available summary statistics with various techniques, including approximate Bayesian computation. We propose to use a quadratic approximation to the log-likelihood for each historical trial based on 2 independent terms for the log mean rate and the log of the dispersion parameter. A Bayesian hierarchical meta-analysis model then provides the posterior predictive distribution for these parameters. Simulations show this approach with back-calculated parameter estimates results in very similar inference as using parameter estimates from individual patient data as an input. We illustrate how to design and analyze a new randomized placebo-controlled exacerbation trial in severe eosinophilic asthma using data from 11 historical trials.

State-space mark-recapture estimates reveal a recent decline in abundance of North Atlantic right whales.

North Atlantic right whales (Eubalaena glacialis Müller 1776) present an interesting problem for abundance and trend estimation in marine wildlife conservation. They are long lived, individually identifiable, highly mobile, and one of the rarest of cetaceans. Individuals are annually resighted at different rates, primarily due to varying stay durations among several principal habitats within a large geographic range. To date, characterizations of abundance have been produced that use simple accounting procedures with differing assumptions about mortality. To better characterize changing abundance of North Atlantic right whales between 1990 and 2015, we adapted a state-space formulation with Jolly-Seber assumptions about population entry (birth and immigration) to individual resighting histories and fit it using empirical Bayes methodology. This hierarchical model included accommodation for the effect of the substantial individual capture heterogeneity. Estimates from this approach were only slightly higher than published accounting procedures, except for the most recent years (when recapture rates had declined substantially). North Atlantic right whales' abundance increased at about 2.8% per annum from median point estimates of 270 individuals in 1990 to 483 in 2010, and then declined to 2015, when the final estimate was 458 individuals (95% credible intervals 444-471). The probability that the population's trajectory post-2010 was a decline was estimated at 99.99%. Of special concern was the finding that reduced survival rates of adult females relative to adult males have produced diverging abundance trends between sexes. Despite constraints in recent years, both biological (whales' distribution changing) and logistical (fewer resources available to collect individual photo-identifications), it is still possible to detect this relatively recent, small change in the population's trajectory. This is thanks to the massive dataset of individual North Atlantic right whale identifications accrued over the past three decades. Photo-identification data provide biological information that allows more informed inference on the status of this species.

Detecting Multiple Random Changepoints in Bayesian Piecewise Growth Mixture Models.

Piecewise growth mixture models are a flexible and useful class of methods for analyzing segmented trends in individual growth trajectory over time, where the individuals come from a mixture of two or more latent classes. These models allow each segment of the overall developmental process within each class to have a different functional form; examples include two linear phases of growth, or a quadratic phase followed by a linear phase. The changepoint (knot) is the time of transition from one developmental phase (segment) to another. Inferring the location of the changepoint(s) is often of practical interest, along with inference for other model parameters. A random changepoint allows for individual differences in the transition time within each class. The primary objectives of our study are as follows: (1) to develop a PGMM using a Bayesian inference approach that allows the estimation of multiple random changepoints within each class; (2) to develop a procedure to empirically detect the number of random changepoints within each class; and (3) to empirically investigate the bias and precision of the estimation of the model parameters, including the random changepoints, via a simulation study. We have developed the user-friendly package BayesianPGMM for R to facilitate the adoption of this methodology in practice, which is available at . We describe an application to mouse-tracking data for a visual recognition task.

Application of Bayesian Approach in Cancer Clinical Trial.

The application of Bayesian approach in clinical trials becomes more useful over classical method. It is beneficial from design to analysis phase. The straight forward statement is possible to obtain through Bayesian about the drug treatment effect. Complex computational problems are simple to handle with Bayesian techniques. The technique is only feasible to performing presence of prior information of the data. The inference is possible to establish through posterior estimates. However, some limitations are present in this method. The objective of this work was to explore the several merits and demerits of Bayesian approach in cancer research. The review of the technique will be helpful for the clinical researcher involved in the oncology to explore the limitation and power of Bayesian techniques.

The effect of non-reversibility on inferring rooted phylogenies.

Most phylogenetic models assume that the evolutionary process is stationary and reversible. In addition to being biologically improbable, these assumptions also impair inference by generating models under which the likelihood does not depend on the position of the root. Consequently, the root of the tree cannot be inferred as part of the analysis. Yet identifying the root position is a key component of phylogenetic inference because it provides a point of reference for polarising ancestor/descendant relationships and therefore interpreting the tree. In this paper we investigate the effect of relaxing the unrealistic reversibility assumption and allowing the position of the root to be another unknown. We propose two hierarchical models that are centred on a reversible model but perturbed to allow non-reversibility. The models differ in the degree of structure imposed on the perturbations. The analysis is performed in the Bayesian framework using Markov chain Monte Carlo methods for which software is provided. We illustrate the performance of the two non-reversible models in analyses of simulated data using two types of topological priors. We then apply the models to a real biological data set, the radiation of polyploid yeasts, for which there is robust biological opinion about the root position. Finally we apply the models to a second biological alignment for which the rooted tree is controversial: the ribosomal tree of life. We compare the two non-reversible models and conclude that both are useful in inferring the position of the root from real biological data.

Monorchis lewisi n. sp. (Trematoda: Monorchiidae) from the surf bream, Acanthopagrus australis (Sparidae), in Moreton Bay, Australia.

We describe Monorchis lewisi n. sp. (Monorchiidae) from the surf bream, Acanthopagrus australis (Günther, 1859) (Sparidae), in Moreton Bay, eastern Australia. The new species differs from most existing species of Monorchis Monticelli, 1893 in its possession of an elongate I-shaped excretory vesicle, and from other congeners in the relative configuration of the gut and suckers. Ovipusillus mayu Dove & Cribb, 1998 is re-reported from Gnathanodon speciosus (Forsskål, 1775) (Carangidae) from Moreton Bay. We report new second internal transcribed spacer (ITS2) and 28S rDNA sequence data for both species. Bayesian inference and Maximum Likelihood analyses of the 28S rDNA dataset suggest that existing subfamily and genus concepts within the family require substantial revision.

A Bayesian method for detecting pairwise associations in compositional data.

Compositional data consist of vectors of proportions normalized to a constant sum from a basis of unobserved counts. The sum constraint makes inference on correlations between unconstrained features challenging due to the information loss from normalization. However, such correlations are of long-standing interest in fields including ecology. We propose a novel Bayesian framework (BAnOCC: Bayesian Analysis of Compositional Covariance) to estimate a sparse precision matrix through a LASSO prior. The resulting posterior, generated by MCMC sampling, allows uncertainty quantification of any function of the precision matrix, including the correlation matrix. We also use a first-order Taylor expansion to approximate the transformation from the unobserved counts to the composition in order to investigate what characteristics of the unobserved counts can make the correlations more or less difficult to infer. On simulated datasets, we show that BAnOCC infers the true network as well as previous methods while offering the advantage of posterior inference. Larger and more realistic simulated datasets further showed that BAnOCC performs well as measured by type I and type II error rates. Finally, we apply BAnOCC to a microbial ecology dataset from the Human Microbiome Project, which in addition to reproducing established ecological results revealed unique, competition-based roles for Proteobacteria in multiple distinct habitats.

P3: Phylogenetic Posterior Prediction in RevBayes.

Tests of absolute model fit are crucial in model-based inference because poorly structured models can lead to biased parameter estimates. In Bayesian inference, posterior predictive simulations can be used to test absolute model fit. However such tests have not been commonly practiced in phylogenetic inference due to a lack of convenient and flexible software. Here we describe our newly implemented tests of model fit using posterior predictive testing, based on both data- and inference-based test statistics, in the phylogenetics software RevBayes. This new implementation makes a large spectrum of models available for use through a user-friendly and flexible interface.

Bayesian inference for psychology, part III: Parameter estimation in nonstandard models.

We demonstrate the use of three popular Bayesian software packages that enable researchers to estimate parameters in a broad class of models that are commonly used in psychological research. We focus on WinBUGS, JAGS, and Stan, and show how they can be interfaced from R and MATLAB. We illustrate the use of the packages through two fully worked examples; the examples involve a simple univariate linear regression and fitting a multinomial processing tree model to data from a classic false-memory experiment. We conclude with a comparison of the strengths and weaknesses of the packages. Our example code, data, and this text are available via .

Multiple optimality criteria support Ornithoscelida.

A recent study of early dinosaur evolution using equal-weights parsimony recovered a scheme of dinosaur interrelationships and classification that differed from historical consensus in a single, but significant, respect; Ornithischia and Saurischia were not recovered as monophyletic sister-taxa, but rather Ornithischia and Theropoda formed a novel clade named Ornithoscelida. However, these analyses only used maximum parsimony, and numerous recent simulation studies have questioned the accuracy of parsimony under equal weights. Here, we provide additional support for this alternative hypothesis using Bayesian implementation of the Mkv model, as well as through number of additional parsimony analyses, including implied weighting. Using Bayesian inference and implied weighting, we recover the same fundamental topology for Dinosauria as the original study, with a monophyletic Ornithoscelida, demonstrating that the main suite of methods used in morphological phylogenetics recover this novel hypothesis. This result was further scrutinized through the systematic exclusion of different character sets. Novel characters from the original study (those not taken or adapted from previous phylogenetic studies) were found to be more important for resolving the relationships within Dinosauromorpha than the relationships within Dinosauria. Reanalysis of a modified version of the character matrix that supports the Ornithischia-Saurischia dichotomy under maximum parsimony also supports this hypothesis under implied weighting, but not under the Mkv model, with both Theropoda and Sauropodomorpha becoming paraphyletic with respect to Ornithischia.

Revision of the West Palaearctic Polistes Latreille, with the descriptions of two species - an integrative approach using morphology and DNA barcodes (Hymenoptera, Vespidae).

The genus Polistes is revised for the West Palaearctic region based on morphology and DNA barcodes. The revision includes all known West Palaearctic species, raising the number of species in Europe to 14 and to 17 for the West Palaearctic realm. DNA barcodes were recovered from 15 species, 14 of which belong to the subgenus Polistes, and one, P. wattii, to the subgenus Gyrostoma. An integrative taxonomic approach combining morphology and molecular data (DNA barcoding) was employed to resolve longstanding taxonomic problems in this group. Two species, P. austroccidentalis van Achterberg & Neumeyer, sp. n. (= P. semenowi auctt.) from W and SW Europe and P. maroccanus Schmid-Egger, sp. n. from Morocco are described as new. Polistes bucharensis Erichson, 1849, and P. foederatus Kohl, 1898, were restored from synonymy. The following new synonyms are proposed: P. sulcifer Zimmermann, 1930, and Pseudopolistes sulcifer var. similator Zirngiebl, 1955, under P. semenowi Morawitz, 1889, syn. n.; Polistes iranus Guiglia, 1976, Polistes gallica var. ornata Weyrauch, 1938 and Polistes gallicus muchei Gusenleitner, 1976, under P. bucharensis Erichson, 1849, syn. n.; Polistes omissus var. ordubadensis Zirngiebl, 1955, and P. hellenicus Arens, 2011, under Polistes mongolicus du Buysson, 1911, syn. n. An illustrated key includes all species and additionally three species from the subgenera Aphanilopterus Meunier, 1888 and Gyrostoma Kirby, 1828 (including a Nearctic species recently introduced to Spain and two species occurring in Egypt, the Arabian Peninsula, and SW Asia). A phylogenetic analysis using Bayesian inference provides insights into phylogenetic relationships within the genus Polistes.

Why lipostatic set point systems are unlikely to evolve.

Body fatness is widely assumed to be regulated by a lipostatic set-point system, which has evolved in response to trade-offs in the risks of mortality. Increasing fatness makes the risk of starvation lower but increases the risk of predation. Yet other models are available. The aim of this work is to evaluate using mathematical modeling whether set-point systems are more likely to evolve than the alternatives.

Are Resting State Spectral Power Measures Related to Executive Functions in Healthy Young Adults?

Resting-state electroencephalogram (rsEEG) has been found to be associated with psychopathology, intelligence, problem solving, academic performance and is sometimes used as a supportive physiological indicator of enhancement in cognitive training interventions (e.g. neurofeedback, working memory training). In the current study, we measured rsEEG spectral power measures (relative power, between-band ratios and asymmetry) in one hundred sixty five young adults who were also tested on a battery of executive function (EF). We specifically focused on upper Alpha, Theta and Beta frequency bands given their putative role in EF. Our indices enabled finding correlations since they had decent-to-excellent internal and retest reliability and very little range restriction relative to a nation-wide representative large sample. Nonetheless, Bayesian statistical inference indicated support for the null hypothesis concerning lack of monotonic correlation between EF and rsEEG spectral power measures. Therefore, we conclude that, contrary to the quite common interpretation, these rsEEG spectral power measures do not indicate individual differences in the measured EF abilities.

Prediction of mortality risk in victims of violent crimes.

To predict mortality risk in victims of violent crimes based on individual injury diagnoses and other information available in health care registries.

Modeling coverage gaps in haplotype frequencies via Bayesian inference to improve stem cell donor selection.

Regardless of sampling depth, accurate genotype imputation is limited in regions of high polymorphism which often have a heavy-tailed haplotype frequency distribution. Many rare haplotypes are thus unobserved. Statistical methods to improve imputation by extending reference haplotype distributions using linkage disequilibrium patterns that relate allele and haplotype frequencies have not yet been explored. In the field of unrelated stem cell transplantation, imputation of highly polymorphic human leukocyte antigen (HLA) genes has an important application in identifying the best-matched stem cell donor when searching large registries totaling over 28,000,000 donors worldwide. Despite these large registry sizes, a significant proportion of searched patients present novel HLA haplotypes. Supporting this observation, HLA population genetic models have indicated that many extant HLA haplotypes remain unobserved. The absent haplotypes are a significant cause of error in haplotype matching. We have applied a Bayesian inference methodology for extending haplotype frequency distributions, using a model where new haplotypes are created by recombination of observed alleles. Applications of this joint probability model offer significant improvement in frequency distribution estimates over the best existing alternative methods, as we illustrate using five-locus HLA frequency data from the National Marrow Donor Program registry. Transplant matching algorithms and disease association studies involving phasing and imputation of rare variants may benefit from this statistical inference framework.

Assisting differential clinical diagnosis of cattle diseases using smartphone-based technology in low resource settings: a pilot study.

The recent rise in mobile phone use and increased signal coverage has created opportunities for growth of the mobile Health sector in many low resource settings. This pilot study explores the use of a smartphone-based application, VetAfrica-Ethiopia, in assisting diagnosis of cattle diseases. We used a modified Delphi protocol to select important diseases and Bayesian algorithms to estimate the related disease probabilities based on various clinical signs being present in Ethiopian cattle.

Characterization of the complete mitochondrial genome of Marshallagia marshalli and phylogenetic implications for the superfamily Trichostrongyloidea.

Marshallagia marshalli (Nematoda: Trichostrongylidae) infection can lead to serious parasitic gastroenteritis in sheep, goat, and wild ruminant, causing significant socioeconomic losses worldwide. Up to now, the study concerning the molecular biology of M. marshalli is limited. Herein, we sequenced the complete mitochondrial (mt) genome of M. marshalli and examined its phylogenetic relationship with selected members of the superfamily Trichostrongyloidea using Bayesian inference (BI) based on concatenated mt amino acid sequence datasets. The complete mt genome sequence of M. marshalli is 13,891 bp, including 12 protein-coding genes, 22 transfer RNA genes, and 2 ribosomal RNA genes. All protein-coding genes are transcribed in the same direction. Phylogenetic analyses based on concatenated amino acid sequences of the 12 protein-coding genes supported the monophylies of the families Haemonchidae, Molineidae, and Dictyocaulidae with strong statistical support, but rejected the monophyly of the family Trichostrongylidae. The determination of the complete mt genome sequence of M. marshalli provides novel genetic markers for studying the systematics, population genetics, and molecular epidemiology of M. marshalli and its congeners.

Inference of time-delayed gene regulatory networks based on dynamic Bayesian network hybrid learning method.

Gene regulatory networks (GRNs) research reveals complex life phenomena from the perspective of gene interaction, which is an important research field in systems biology. Traditional Bayesian networks have a high computational complexity, and the network structure scoring model has a single feature. Information-based approaches cannot identify the direction of regulation. In order to make up for the shortcomings of the above methods, this paper presents a novel hybrid learning method (DBNCS) based on dynamic Bayesian network (DBN) to construct the multiple time-delayed GRNs for the first time, combining the comprehensive score (CS) with the DBN model. DBNCS algorithm first uses CMI2NI (conditional mutual inclusive information-based network inference) algorithm for network structure profiles learning, namely the construction of search space. Then the redundant regulations are removed by using the recursive optimization algorithm (RO), thereby reduce the false positive rate. Secondly, the network structure profiles are decomposed into a set of cliques without loss, which can significantly reduce the computational complexity. Finally, DBN model is used to identify the direction of gene regulation within the cliques and search for the optimal network structure. The performance of DBNCS algorithm is evaluated by the benchmark GRN datasets from DREAM challenge as well as the SOS DNA repair network in Escherichia coli, and compared with other state-of-the-art methods. The experimental results show the rationality of the algorithm design and the outstanding performance of the GRNs.

Phylogenomics and evolution of floral traits in the Neotropical tribe Malmeeae (Annonaceae).

Androdioecy is the rarest sexual system among plants. The majority of androdioecious species are herbaceous plants that have evolved from dioecious ancestors. Nevertheless, some woody and androdioecious plants have hermaphrodite ancestors, as in the Annonaceae, where androdioecious genera have arisen several times in different lineages. The majority of androdioecious species of Annonaceae belong to the Neotropical tribe Malmeeae. In addition to these species, Pseudoxandra spiritus-sancti was recently confirmed to be androdioecious. Here, we describe the morphology of male and bisexual flowers of Pseudoxandra spiritus-sancti, and investigate the evolution of androdioecy in Malmeeae. The phylogeny of tribe Malmeeae was reconstructed using Bayesian inference, maximum parsimony and maximum likelihood of 32 taxa, using DNA sequences of 66 molecular markers of the chloroplast genome, sequenced by next generation sequencing. The reconstruction of ancestral states was performed for characters associated with sexual systems and floral morphology. The phylogenetic analyses reconstructed three main groups in Malmeeae, (Malmea (Cremastosperma, Pseudoxandra)) sister to the rest of the tribe, and (Unonopsis (Bocageopsis, Onychopetalum)) sister to (Mosannona, Ephedranthus, Klarobelia, Oxandra, Pseudephedranthus fragrans, Pseudomalmea, Ruizodendron ovale). Hermaphroditism is plesiomorphic in the tribe, with four independent evolutions of androdieocy, which represents a synapomorphy of two groups, one that includes three genera and 14 species, the other with a single genus of seven species. Male flowers are unisexual from inception and bisexual flowers possess staminodes and functional stamens. Pseudoxandra spiritus-sancti is structurally androdioecious.

A total-evidence phylogeny of the lady fern genus Athyrium Roth (Athyriaceae) with a new infrageneric classification.

The lady fern genus Athyrium represents one of the most diversified lineages in Athyriaceae with about 160-220 known species, and is notorious for its taxonomic difficulty. Despite progress in recent phylogenetic studies involving this genus, it still lacks a modern systematic and taxonomic update using integrative analyses of molecular and morphological evidence based on a broad species sampling. Here, we present, to our knowledge, the most comprehensive phylogenetic analysis of the genus to date based on a total-evidence approach, covering all formerly accepted segregates within the athyrioid ferns. We sampled up to eight plastid markers and 20 morphological characters for each species. Our analyses, including maximum parsimony, maximum likelihood and Bayesian inference, yield a robust phylogenetic framework. We find that Athyrium is not monophyletic by recovering Athyrium skinneri and A. alpestre nested with Anisocampium and Cornopteris respectively while Pseudocystopteris is included in Athyrium. Furthermore, eight well-resolved clades and two isolated species within Athyrium are found in the phylogenetic topology, which can be also characterized by morphological synapomorphies from traits of petioles, leaves, sori and spores. In the interest of recognizing monophyletic taxa with morphological synapomorphies, we agree with the inclusion of Pseudocystopteris in Athyrium as proposed in previous studies, but treat Anisocampium and Cornopteris as separate genera. We further propose to resurrect a monotypic Pseudathyrium to accommodate A. alpestre. Based on morphological characters and molecular phylogeny, a new infrageneric classification system of Athyrium is proposed which subdivided it into ten sections, and one New-World species A. skinneri is transferred into Anisocampium.

Bayesian intravoxel incoherent motion parameter mapping in the human heart.

Intravoxel incoherent motion (IVIM) imaging of diffusion and perfusion in the heart suffers from high parameter estimation error. The purpose of this work is to improve cardiac IVIM parameter mapping using Bayesian inference.