An ecological and comparative analysis of parasites in juvenile Mugil liza (Pisces, Mugilidae) from two sites in Samborombón bay, Argentina

ABSTRACT Mugil liza Valenciennes, 1836 is an economically important food fish and has been recommended for aquaculture in South America. A total of 278 fishes were collected in the spring and summer of 2009 and 2010. These fish were sorted into sample groups according to their size class. We used Bayesian statistics and 95% credible intervals for each parameter tested were calculated. Fish studied harbored a total of 15 different species of parasites. Diversity of parasite species found on Mugil liza was greatest at the S.R.C. collection site, but evidenced a lower species richness than at A.R. site. The 1st size fishes of both sites evidenced greater parasite diversity than either 2nd or 3rd size fish. Differences observed could be explained by the different use of habitat types at the two sites or differential susceptibility to infection by parasites. The dominance of D. fastigatainfluenced observed results of lower community diversity indexes. New works elucidating different parasite life cycles within juvenile and adults ofM. liza in Argentina, promise to be important for determining the risk of the parasitism by zoonotic metacercariae A. (P.) longa and use of this fish as food and an economic resource, and the possible use of mullet parasites in other promising fields as indicators of biodiversity, and/ or water contamination.

Recherche d’étoiles jeunes de faible masse dans le voisinage solaire

Formées lors de l’effondrement gravitationnel d’un nuage de gaz moléculaire, les étoiles naissantes auront différentes masses variant entre 0.08 et environ 100M . La majorité de la population stellaire de la Galaxie est constituée d’étoiles dont la masse est inférieure à environ 0.6 M . Le dernier évènement de formation stellaire dans le voisinage solaire s’est produit dans la bulle locale il y a au plus 100 millions d’années, vraisemblablement provoqué par le passage d’une onde de choc dans le bras local de la Galaxie. C’est ainsi que se formèrent de jeunes associations d’étoiles dont les membres se caractérisent en particulier par une vitesse spatiale et une position commune dans la Galaxie. Les associations jeunes étant peu densément peuplées et relativement proches du Soleil, leurs membres se font plutôt rares et dispersés sur toute la voûte céleste. Jusqu’à présent, surtout les étoiles les plus massives (brillantes) ont été répertoriées. Les étoiles jeunes de faible masse, constituant la majorité de la population, restent pour la plupart à être identifiées. Les étoiles jeunes de faible masse représentent une population clef pour contraindre les modèles évolutifs des étoiles M et des naines brunes. Elles sont également d’excellentes candidates pour chercher des exoplanètes via les techniques d’imagerie directe. Ce mémoire présente une nouvelle méthode utilisant un modèle cinématique enrichi d’une analyse statistique Bayesienne pour identifier des étoiles jeunes de faible masse dans les associations beta Pictoris, Tucana-Horologium et AB Doradus. À partir d’un échantillon de 1080 étoiles K et M, toutes comportant des indicateurs de jeunesse tels l’émission Halpha et une forte luminosité dans les rayons X, leurs propriétés cinématiques (mouvement propre) et photométriques sont analysées pour en extraire 98 candidates hautement probables membres d’une des trois associations. Une confirmation de leur statut comme membre nécessitera en particulier une mesure de leur vitesse radiale (prédit par notre analyse) et une mesure de la largeur équivalente du lithium à 6708 Å pour mieux contraindre leur âge.

Fatores genéticos e ambientais sobre a probabilidade de prenhez precoce em bovinos Caracu

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Análise bayesiana na estimação de correlações genéticas entre escores visuais e características reprodutivas de bovinos Nelore utilizando modelos linear-limiar

Estimaram-se as correlações genéticas entre os escores visuais e as características reprodutivas, utilizando a estatística bayesiana sob modelo animal linear-limiar, em bovinos da raça Nelore. Foram estudadas características categóricas morfológicas, avaliadas visualmente aos oito, 15 e 22 meses de idade; e características contínuas de perímetro escrotal padronizado aos 365 e 450 dias de idade, além da idade ao primeiro parto. As estimativas de correlações genéticas foram de sentido favorável à seleção, apresentando magnitudes moderadas, sugerindo que a seleção de animais para um biótipo desejável pode levar a animais com maior fertilidade e precocidade sexual. As estimativas de correlação genética para o perímetro escrotal padronizado aos 450 dias e a idade ao primeiro parto com as características morfológicas avaliadas aos 22 meses de idade foram maiores do que as obtidas entre as características de escores visuais avaliadas aos oito e 15 meses de idade. A utilização de escores visuais como critério de seleção trará progresso genético também para as características reprodutivas.

Hierarchical modelling of timber reference properties using probabilistic methods: Maximum Likelihood Method, Bayesian Methods and Probability Networks

In recent decades, an increased interest has been evidenced in the research on multi-scale hierarchical modelling in the field of mechanics, and also in the field of wood products and timber engineering. One of the main motivations for hierar-chical modelling is to understand how properties, composition and structure at lower scale levels may influence and be used to predict the material properties on a macroscopic and structural engineering scale. This chapter presents the applicability of statistic and probabilistic methods, such as the Maximum Likelihood method and Bayesian methods, in the representation of timber’s mechanical properties and its inference accounting to prior information obtained in different importance scales. These methods allow to analyse distinct timber’s reference properties, such as density, bending stiffness and strength, and hierarchically consider information obtained through different non, semi or destructive tests. The basis and fundaments of the methods are described and also recommendations and limitations are discussed. The methods may be used in several contexts, however require an expert’s knowledge to assess the correct statistic fitting and define the correlation arrangement between properties.

Bayesian sample size for exploratory clinical trials incorporating historical data

This paper presents a simple Bayesian approach to sample size determination in clinical trials. It is required that the trial should be large enough to ensure that the data collected will provide convincing evidence either that an experimental treatment is better than a control or that it fails to improve upon control by some clinically relevant difference. The method resembles standard frequentist formulations of the problem, and indeed in certain circumstances involving 'non-informative' prior information it leads to identical answers. In particular, unlike many Bayesian approaches to sample size determination, use is made of an alternative hypothesis that an experimental treatment is better than a control treatment by some specified magnitude. The approach is introduced in the context of testing whether a single stream of binary observations are consistent with a given success rate p(0). Next the case of comparing two independent streams of normally distributed responses is considered, first under the assumption that their common variance is known and then for unknown variance. Finally, the more general situation in which a large sample is to be collected and analysed according to the asymptotic properties of the score statistic is explored. Copyright (C) 2007 John Wiley & Sons, Ltd.

Comparative evaluation of a new effective population size estimator based on approximate Bayesian computation

We describe and evaluate a new estimator of the effective population size (N-e), a critical parameter in evolutionary and conservation biology. This new "SummStat" N-e. estimator is based upon the use of summary statistics in an approximate Bayesian computation framework to infer N-e. Simulations of a Wright-Fisher population with known N-e show that the SummStat estimator is useful across a realistic range of individuals and loci sampled, generations between samples, and N-e values. We also address the paucity of information about the relative performance of N-e estimators by comparing the SUMMStat estimator to two recently developed likelihood-based estimators and a traditional moment-based estimator. The SummStat estimator is the least biased of the four estimators compared. In 32 of 36 parameter combinations investigated rising initial allele frequencies drawn from a Dirichlet distribution, it has the lowest bias. The relative mean square error (RMSE) of the SummStat estimator was generally intermediate to the others. All of the estimators had RMSE > 1 when small samples (n = 20, five loci) were collected a generation apart. In contrast, when samples were separated by three or more generations and Ne less than or equal to 50, the SummStat and likelihood-based estimators all had greatly reduced RMSE. Under the conditions simulated, SummStat confidence intervals were more conservative than the likelihood-based estimators and more likely to include true N-e. The greatest strength of the SummStat estimator is its flexible structure. This flexibility allows it to incorporate any, potentially informative summary statistic from Population genetic data.

A Bayesian Approach for Decision Making on the Identification of Genes with Different Expression Levels: An Application to Escherichia coli Bacterium Data

A common interest in gene expression data analysis is to identify from a large pool of candidate genes the genes that present significant changes in expression levels between a treatment and a control biological condition. Usually, it is done using a statistic value and a cutoff value that are used to separate the genes differentially and nondifferentially expressed. In this paper, we propose a Bayesian approach to identify genes differentially expressed calculating sequentially credibility intervals from predictive densities which are constructed using the sampled mean treatment effect from all genes in study excluding the treatment effect of genes previously identified with statistical evidence for difference. We compare our Bayesian approach with the standard ones based on the use of the t-test and modified t-tests via a simulation study, using small sample sizes which are common in gene expression data analysis. Results obtained report evidence that the proposed approach performs better than standard ones, especially for cases with mean differences and increases in treatment variance in relation to control variance. We also apply the methodologies to a well-known publicly available data set on Escherichia coli bacterium.

Bayesian invariant measurements of generalisation

The problem of evaluating different learning rules and other statistical estimators is analysed. A new general theory of statistical inference is developed by combining Bayesian decision theory with information geometry. It is coherent and invariant. For each sample a unique ideal estimate exists and is given by an average over the posterior. An optimal estimate within a model is given by a projection of the ideal estimate. The ideal estimate is a sufficient statistic of the posterior, so practical learning rules are functions of the ideal estimator. If the sole purpose of learning is to extract information from the data, the learning rule must also approximate the ideal estimator. This framework is applicable to both Bayesian and non-Bayesian methods, with arbitrary statistical models, and to supervised, unsupervised and reinforcement learning schemes.

The full Bayesian significance test for mixture models: results in gene expression clustering

Gene clustering is a useful exploratory technique to group together genes with similar expression levels under distinct cell cycle phases or distinct conditions. It helps the biologist to identify potentially meaningful relationships between genes. In this study, we propose a clustering method based on multivariate normal mixture models, where the number of clusters is predicted via sequential hypothesis tests: at each step, the method considers a mixture model of m components (m = 2 in the first step) and tests if in fact it should be m - 1. If the hypothesis is rejected, m is increased and a new test is carried out. The method continues (increasing m) until the hypothesis is accepted. The theoretical core of the method is the full Bayesian significance test, an intuitive Bayesian approach, which needs no model complexity penalization nor positive probabilities for sharp hypotheses. Numerical experiments were based on a cDNA microarray dataset consisting of expression levels of 205 genes belonging to four functional categories, for 10 distinct strains of Saccharomyces cerevisiae. To analyze the method's sensitivity to data dimension, we performed principal components analysis on the original dataset and predicted the number of classes using 2 to 10 principal components. Compared to Mclust (model-based clustering), our method shows more consistent results.

Fluorescence images combined to statistic test for fingerprinting of citrus plants after bacterial infection

The citrus greening (or huanglongbing) disease has caused serious problems in citrus crops around the world. An early diagnostic method to detect this malady is needed due to the rapid dissemination of Candidatus Liberibacter asiaticus (CLas) in the field. This analytical study investigated the fluorescence responses of leaves from healthy citrus plants and those inoculated with CLas by images from a stereomicroscope and also evaluated their potential for the early diagnosis of the infection caused by this bacterium. The plants were measured monthly, and the evolution of the bacteria on inoculated plants was monitored by real-time quantitative polymerase chain reaction (RT-qPCR) amplification of CLas sequences. A statistical method was used to analyse the data. The selection of variables from histograms of colours (colourgrams) of the images was optimized using a paired Student's t-test. The intensity of counts for green colours from images of fluorescence had clearly minor variations for healthy plants than diseased ones. The darker green colours were the indicators of healthy plants and the light colours for the diseased. The method of fluorescence images is novel for fingerprinting healthy and diseased plants and provides an alternative to the current method represented by PCR and visual inspection. A new, non-subjective pattern of analysis and a non-destructive method has been introduced that can minimize the time and costs of analyses.

Disequilibrium Coefficient: A Bayesian Perspective

Hardy-Weinberg Equilibrium (HWE) is an important genetic property that populations should have whenever they are not observing adverse situations as complete lack of panmixia, excess of mutations, excess of selection pressure, etc. HWE for decades has been evaluated; both frequentist and Bayesian methods are in use today. While historically the HWE formula was developed to examine the transmission of alleles in a population from one generation to the next, use of HWE concepts has expanded in human diseases studies to detect genotyping error and disease susceptibility (association); Ryckman and Williams (2008). Most analyses focus on trying to answer the question of whether a population is in HWE. They do not try to quantify how far from the equilibrium the population is. In this paper, we propose the use of a simple disequilibrium coefficient to a locus with two alleles. Based on the posterior density of this disequilibrium coefficient, we show how one can conduct a Bayesian analysis to verify how far from HWE a population is. There are other coefficients introduced in the literature and the advantage of the one introduced in this paper is the fact that, just like the standard correlation coefficients, its range is bounded and it is symmetric around zero (equilibrium) when comparing the positive and the negative values. To test the hypothesis of equilibrium, we use a simple Bayesian significance test, the Full Bayesian Significance Test (FBST); see Pereira, Stern andWechsler (2008) for a complete review. The disequilibrium coefficient proposed provides an easy and efficient way to make the analyses, especially if one uses Bayesian statistics. A routine in R programs (R Development Core Team, 2009) that implements the calculations is provided for the readers.

Bayesian online algorithms for learning in discrete Hidden Markov Models

We propose and analyze two different Bayesian online algorithms for learning in discrete Hidden Markov Models and compare their performance with the already known Baldi-Chauvin Algorithm. Using the Kullback-Leibler divergence as a measure of generalization we draw learning curves in simplified situations for these algorithms and compare their performances.

Haplotype distribution of five nuclear genes based on network genealogies and Bayesian inference indicates that Trypanosoma cruzi hybrid strains are polyphyletic

Chagas disease is still a major public health problem in Latin America. Its causative agent, Trypanosoma cruzi, can be typed into three major groups, T. cruzi I, T. cruzi II and hybrids. These groups each have specific genetic characteristics and epidemiological distributions. Several highly virulent strains are found in the hybrid group; their origin is still a matter of debate. The null hypothesis is that the hybrids are of polyphyletic origin, evolving independently from various hybridization events. The alternative hypothesis is that all extant hybrid strains originated from a single hybridization event. We sequenced both alleles of genes encoding EF-1 alpha, actin and SSU rDNA of 26 T. cruzi strains and DHFR-TS and TR of 12 strains. This information was used for network genealogy analysis and Bayesian phylogenies. We found T. cruzi I and T. cruzi II to be monophyletic and that all hybrids had different combinations of T. cruzi I and T. cruzi II haplotypes plus hybrid-specific haplotypes. Bootstrap values (networks) and posterior probabilities (Bayesian phylogenies) of clades supporting the monophyly of hybrids were far below the 95% confidence interval, indicating that the hybrid group is polyphyletic. We hypothesize that T. cruzi I and T. cruzi II are two different species and that the hybrids are extant representatives of independent events of genome hybridization, which sporadically have sufficient fitness to impact on the epidemiology of Chagas disease.

Bayesian updating rules in continuous opinion dynamics models

Here, I investigate the use of Bayesian updating rules applied to modeling how social agents change their minds in the case of continuous opinion models. Given another agent statement about the continuous value of a variable, we will see that interesting dynamics emerge when an agent assigns a likelihood to that value that is a mixture of a Gaussian and a uniform distribution. This represents the idea that the other agent might have no idea about what is being talked about. The effect of updating only the first moments of the distribution will be studied, and we will see that this generates results similar to those of the bounded confidence models. On also updating the second moment, several different opinions always survive in the long run, as agents become more stubborn with time. However, depending on the probability of error and initial uncertainty, those opinions might be clustered around a central value.