On estimation and identifiability issues of sex-linked inheritance with a case study of pigmentation in Swiss barn owl (Tyto alba)

Genetic evaluation using animal models or pedigree-based models generally assume only autosomal inheritance. Bayesian animal models provide a flexible framework for genetic evaluation, and we show how the model readily can accommodate situations where the trait of interest is influenced by both autosomal and sex-linked inheritance. This allows for simultaneous calculation of autosomal and sex-chromosomal additive genetic effects. Inferences were performed using integrated nested Laplace approximations (INLA), a nonsampling-based Bayesian inference methodology. We provide a detailed description of how to calculate the inverse of the X- or Z-chromosomal additive genetic relationship matrix, needed for inference. The case study of eumelanic spot diameter in a Swiss barn owl (Tyto alba) population shows that this trait is substantially influenced by variation in genes on the Z-chromosome (sigma(2)(z) = 0.2719 and sigma(2)(a) = 0.4405). Further, a simulation study for this study system shows that the animal model accounting for both autosomal and sex-chromosome-linked inheritance is identifiable, that is, the two effects can be distinguished, and provides accurate inference on the variance components.

Análise bayesiana univariada e bivariada para a conversão alimentar de suínos da raça Piau

O objetivo deste trabalho foi apresentar modelagens alternativas, uni e bivariadas, para avaliação da conversão alimentar (CA) de suínos da raça Piau, com uso de inferência bayesiana. Os efeitos de sexo e genótipo sobre a CA dos animais foram avaliados por meio de procedimentos de simulação de Monte Carlo via cadeias de Markov (MCMC) e de integração aproximada aninhada de Laplace (INLA). O modelo univariado foi avaliado com diferentes distribuições para o erro - normal (gaussiana), t de Student, gama, log-normal e skew-normal -, enquanto, para o modelo bivariado, considerou-se o erro normal. A distribuição skew-normal foi o modelo mais parcimonioso para inferir sobre a resposta direta (univariada) da CA aos efeitos de sexo e genótipo, os quais não foram significativos. O modelo bivariado foi capaz de identificar diferenças significativas no ganho de peso e no consumo de ração em níveis de significância não detectados pelo modelo univariado. Além disso, ele também foi capaz de detectar diferenças entre sexos, quando agrupados por genótipos NN (machos, 2,73±0,04; fêmeas, 2,68±0,04) e Nn (machos, 2,70±0,07; fêmeas, 2,64±0,07), e revelou maior acurácia e precisão nas inferências nutricionais. Em ambas as abordagens, o método bayesiano mostra-se flexível e eficiente para a avaliação do desempenho nutricional dos animais.

Production of biofilm by Listeria monocytogenes in different materials and temperatures

Listeria monocytogenes, considered as one of the most important foodborne pathogens, is easily found on surfaces, particularly in the form of a biofilm. Biofilms are aggregates of cells that facilitate the persistence of these pathogens in food processing environments conferring resistance to the processes of cleaning and may cause contamination of food during processing, thus, representing a danger to public health. Little is known about the dynamics of the formation and regulation of biofilm production in L.monocytogenes, but several authors reported that the luxS gene may be a precursor in this process. In addition, the product of the inlA gene is responsible for facilitating the entry of the microorganism into epithelial cells that express the receptor E-cadherin, also participates in surface attachment. Thus, 32 strains of L.monocytogenes isolated from different foods (milk and vegetables) and from food processing environments were analyzed for the presence of these genes and their ability to form biofilms on three different surfaces often used in the food industry and retail (polystyrene, glass and stainless steel) at different temperatures (4, 20 and 30°C). All strains had the ilnA gene and 25 out of 32 strains (78.1%) were positive for the presence of the luxS gene, but all strains produced biofilm in at least one of the temperatures and materials tested. This suggests that genes in addition to luxS may participate in this process, but were not the decisive factors for biofilm formation. The bacteria adhered better to hydrophilic surfaces (stainless steel and glass) than to hydrophobic ones (polystyrene), since at 20°C for 24h, 30 (93.8%) and 26 (81.3%) produced biofilm in stainless steel and glass, respectively, and just 2 (6.2%) in polystyrene. The incubation time seemed to be an important factor in the process of biofilm formation, mainly at 35°C for 48h, because the results showed a decrease from 30 (93.8%) to 20 (62.5%) and from 27 (84.4%) to 12 (37.5%), on stainless steel and glass, respectively, although this was not significant (. p=0.3847). We conclude that L.monocytogenes is capable of forming biofilm on different surfaces independent of temperature, but the surface composition may be important factor for a faster development of biofilm. © 2013 Elsevier Ltd.

Spatial distribution of dengue incidence and socio-environmental conditions in Campinas, São Paulo State, Brazil, 2007

This study aimed to analyze the spatial distribution of dengue risk and its association with socio-environmental conditions. This was an ecological study of the counts of autochthonous dengue cases in the municipality of Campinas, São Paulo State, Brazil, in the year 2007, aggregated according to 47 coverage areas of municipal health centers. Spatial models for mapping diseases were constructed with Bayesian hierarchical models, based on Integrated Nested Laplace Approximation (INLA). The analyses were stratified according to two age groups, 0 to 14 years and above 14 years. The results indicate that the spatial distribution of dengue risk is not associated with socio-environmental conditions in the 0 to 14 year age group. In the age group older than 14 years, the relative risk of dengue increases significantly as the level of socio-environmental deprivation increases. Mapping of socio-environmental deprivation and dengue cases proved to be a useful tool for data analysis in dengue surveillance systems.

Modelos lineares generalizados bayesianos para dados longitudinais

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Characterization of atypical Listeria innocua isolated from swine slaughterhouses and meat markets

Atypical Listeria innocua strains presenting phenotypic characteristics similar to those of Listeria monocyto genes were recently isolated from food and the environment. These isolates also tested positive for virulence genes specific to L. monocytogenes. Here we report the isolation of atypical hemolytic L. innocua strains from the environment of pork processing plants in Brazil. The strains were positive for L. monocytogenes virulence genes hly, inlA and inlB by PCR and presented genotypic similarities with human isolates of L. monocytogenes via the AFLP technique using HindIII single enzyme protocol. Phenotypic and genotypic similarities suggest that these atypical L. innocua may be pathogenic strains. (C) 2012 Institut Pasteur. Published by Elsevier Masson SAS. All rights reserved.

A Bayesian changepoint analysis on spatio-temporal point processes

Changepoint analysis is a well established area of statistical research, but in the context of spatio-temporal point processes it is as yet relatively unexplored. Some substantial differences with regard to standard changepoint analysis have to be taken into account: firstly, at every time point the datum is an irregular pattern of points; secondly, in real situations issues of spatial dependence between points and temporal dependence within time segments raise. Our motivating example consists of data concerning the monitoring and recovery of radioactive particles from Sandside beach, North of Scotland; there have been two major changes in the equipment used to detect the particles, representing known potential changepoints in the number of retrieved particles. In addition, offshore particle retrieval campaigns are believed may reduce the particle intensity onshore with an unknown temporal lag; in this latter case, the problem concerns multiple unknown changepoints. We therefore propose a Bayesian approach for detecting multiple changepoints in the intensity function of a spatio-temporal point process, allowing for spatial and temporal dependence within segments. We use Log-Gaussian Cox Processes, a very flexible class of models suitable for environmental applications that can be implemented using integrated nested Laplace approximation (INLA), a computationally efficient alternative to Monte Carlo Markov Chain methods for approximating the posterior distribution of the parameters. Once the posterior curve is obtained, we propose a few methods for detecting significant change points. We present a simulation study, which consists in generating spatio-temporal point pattern series under several scenarios; the performance of the methods is assessed in terms of type I and II errors, detected changepoint locations and accuracy of the segment intensity estimates. We finally apply the above methods to the motivating dataset and find good and sensible results about the presence and quality of changes in the process.

Ruminant rhombencephalitis-associated Listeria monocytogenes alleles linked to a multilocus variable-number tandem-repeat analysis complex

Listeria monocytogenes is among the most important food-borne pathogens and is well adapted to persist in the environment. To gain insight into the genetic relatedness and potential virulence of L. monocytogenes strains causing central nervous system (CNS) infections, we used multilocus variable-number tandem-repeat analysis (MLVA) to subtype 183 L. monocytogenes isolates, most from ruminant rhombencephalitis and some from human patients, food, and the environment. Allelic-profile-based comparisons grouped L. monocytogenes strains mainly into three clonal complexes and linked single-locus variants (SLVs). Clonal complex A essentially consisted of isolates from human and ruminant brain samples. All but one rhombencephalitis isolate from cattle were located in clonal complex A. In contrast, food and environmental isolates mainly clustered into clonal complex C, and none was classified as clonal complex A. Isolates of the two main clonal complexes (A and C) obtained by MLVA were analyzed by PCR for the presence of 11 virulence-associated genes (prfA, actA, inlA, inlB, inlC, inlD, inlE, inlF, inlG, inlJ, and inlC2H). Virulence gene analysis revealed significant differences in the actA, inlF, inlG, and inlJ allelic profiles between clinical isolates (complex A) and nonclinical isolates (complex C). The association of particular alleles of actA, inlF, and newly described alleles of inlJ with isolates from CNS infections (particularly rhombencephalitis) suggests that these virulence genes participate in neurovirulence of L. monocytogenes. The overall absence of inlG in clinical complex A and its presence in complex C isolates suggests that the InlG protein is more relevant for the survival of L. monocytogenes in the environment.

A framework for interpreting the leucine-rich repeats of the Listeria internalins

The surface protein InlB of the bacterial pathogen Listeria monocytogenes is required for inducing phagocytosis in various nonphagocytic mammalian cell types in vitro. InlB causes tyrosine phosphorylation of host cell adaptor proteins, activation of phosphoinositide 3-kinase, and rearrangements of the actin cytoskeleton. These events lead to phagocytic uptake of the bacterium by the host cell. InlB belongs to the internalin family of Listeria proteins, which also includes InlA, another surface protein involved in host cell invasion. The internalins are the largest class of bacterial proteins containing leucine-rich repeats (LRR), a motif associated with protein–protein interactions. The LRR motif is found in a functionally diverse array of proteins, including those involved in the plant immune system and in the mammalian innate immune response. Structural and functional interpretations of the sequences of internalin family members are presented in light of the recently determined x-ray crystal structure of the InlB LRR domain.

Lianas and soil nutrients predict fine-scale distribution of above-ground biomass in a tropical moist forest

Acknowledgements. This study was supported by the FP7-PEOPLE-2013-IEF Marie-Curie Action – SPATFOREST. Tree data from BCI were provided by the Center for Tropical Forest Science of the Smithsonian Tropical Research Institute and the primary granting agencies that have supported the BCI plot tree census. Data for the liana censuses were supported by the US National Science Foundation grants: DEB-0613666, DEB-0845071, and DEB-1019436 (to SAS). Soil data was funded by the National Science Foundation grants DEB021104, DEB021115, DEB0212284 and DEB0212818 supporting soils mapping in the BCI plot. We thank Helene Muller-Landau for providing some data on tree height for some BCI trees. We also thank all the people that contributed to obtain the data.

Modelling the influence of biotic factors on species distribution patterns

Biotic interactions can have large effects on species distributions yet their role in shaping species ranges is seldom explored due to historical difficulties in incorporating biotic factors into models without a priori knowledge on interspecific interactions. Improved SDMs, which account for biotic factors and do not require a priori knowledge on species interactions, are needed to fully understand species distributions. Here, we model the influence of abiotic and biotic factors on species distribution patterns and explore the robustness of distributions under future climate change. We fit hierarchical spatial models using Integrated Nested Laplace Approximation (INLA) for lagomorph species throughout Europe and test the predictive ability of models containing only abiotic factors against models containing abiotic and biotic factors. We account for residual spatial autocorrelation using a conditional autoregressive (CAR) model. Model outputs are used to estimate areas in which abiotic and biotic factors determine species’ ranges. INLA models containing both abiotic and biotic factors had substantially better predictive ability than models containing abiotic factors only, for all but one of the four species. In models containing abiotic and biotic factors, both appeared equally important as determinants of lagomorph ranges, but the influences were spatially heterogeneous. Parts of widespread lagomorph ranges highly influenced by biotic factors will be less robust to future changes in climate, whereas parts of more localised species ranges highly influenced by the environment may be less robust to future climate. SDMs that do not explicitly include biotic factors are potentially misleading and omit a very important source of variation. For the field of species distribution modelling to advance, biotic factors must be taken into account in order to improve the reliability of predicting species distribution patterns both presently and under future climate change.