Chlorophyll a spatial inference using artificial neural network from multispectral images and in situ measurements

Considering the importance of monitoring the water quality parameters, remote sensing is a practicable alternative to limnological variables detection, which interacts with electromagnetic radiation, called optically active components (OAC). Among these, the phytoplankton pigment chlorophyll a is the most representative pigment of photosynthetic activity in all classes of algae. In this sense, this work aims to develop a method of spatial inference of chlorophyll a concentration using Artificial Neural Networks (ANN). To achieve this purpose, a multispectral image and fluorometric measurements were used as input data. The multispectral image was processed and the net training and validation dataset were carefully chosen. From this, the neural net architecture and its parameters were defined to model the variable of interest. In the end of training phase, the trained network was applied to the image and a qualitative analysis was done. Thus, it was noticed that the integration of fluorometric and multispectral data provided good results in the chlorophyll a inference, when combined in a structure of artificial neural networks.

Genetic Parameters for Reproductive Traits of Crossbred Buffaloes from Brazil, Estimated by Bayesian Inference

The objective of the study was to estimate heritability for calving interval (CI) and age at first calving (AFC) and also calculate repeatability for CI in buffaloes using Bayesian inference. The Brazilian Buffaloes Genetic Improvement Program provided the database. Data consists on information from 628 females and four different herds, born between 1980 and 2003. In order to estimate the variance, univariate analyses were performed employing Gibbs sampler procedure included in the MTGSAM software. The model for CI included the random effects direct additive and permanent environment factors, and the fixed effects of contemporary groups and calving orders. The model for AFC included the direct additive random effect and contemporary groups as a fixed effect. The convergence diagnosis was obtained using Geweke that was implemented through the Bayesian Output Analysis package in R software. The estimated averages were 433.2 days and 36.7months for CI and AFC, respectively. The means, medians and modes for the calculated heritability coefficients were similar. The heritability coefficients were 0.10 and 0.42 for CI and AFC respectively, with a posteriori marginal density that follows a normal distribution for both traits. The repeatability for CI was 0.13. The low heritability estimated for CI indicates that the variation in this trait is, to a large extent, influenced by environmental factors such as herd management policies. The age at first calving has clear potential for yield improvement through direct selection in these animals.

Genetic Correlation for Pre-Weaning and Post-Weaning Traits of Mediterranean Buffaloes from Brazil, Estimated By Bayesian Inference

The aim of this study was to estimate genetic, environmental and phenotypic correlation between birth weight (BW) and weight at 205 days age (W205), BW and weight at 365 days age (W365) and W205-W365, using Bayesian inference. The Brazilian Program for Genetic Improvement of Buffaloes provided the data that included 3,883 observations from Mediterranean breed buffaloes. With the purpose to estimate variance and covariance, bivariate analyses were performed using Gibbs sampler that is included in the MTGSAM software. The model for BW, W205 and W365 included additive direct and maternal genetic random effects, maternal environmental random effect and contemporary group as fixed effect. The convergence diagnosis was achieved using Geweke, a method that uses an algorithm implemented in R software through the package Bayesian Output Analysis. The calculated direct genetic correlations were 0.34 (BW-W205), 0.25 (BW-W365) and 0.74 (W205-W365). The environmental correlations were 0.12, 0.11 and 0.72 between BW-W205, BW-W365 and W205-W365, respectively. The phenotypic correlations were low for BW-W205 (0.01) and BW-W365 (0.04), differently than the obtained for W205-W365 with a value of 0.67. The results indicate that BW trait have low genetic, environmental and phenotypic association with the two others traits. The genetic correlation between W205 and W365 was high and suggests that the selection for weight at around 205 days could be beneficial to accelerate the genetic gain.

Genetic Parameters for Growth Traits of Mediterranean Buffaloes from Brazil, Estimated By Bayesian Inference

Quantitative analysis of growth genetic parameters is not available for many breeds of buffaloes making selection and breeding decisions an empirical process that lacks robustness. The objective of this study was to estimate heritability for birth weight (BW), weight at 205 days (W205) and 365 days (W365) of age using Bayesian inference. The Brazilian Program for Genetic Improvement of Buffaloes provided the data. For the traits BW, W205 and W365 of Brazilian Mediterranean buffaloes 5169, 3792 and 3883 observations have been employed for the analysis, respectively. In order to obtain the estimates of variance, univariate analyses were conducted using the Gibbs sampler included in the MTGSAM software. The model for BW, W205 and W365 included additive direct and maternal genetic random effects, random maternal permanent environmental effect and contemporary group that was treated as a fixed effect. The convergence diagnosis was performed employing Geweke, a method that uses an algorithm from the Bayesian Output Analysis package that was implemented using R software environment. The average values for weight traits were 37.6 +/- 4.7 kg for BW, 192.7 +/- 40.3 kg for W205 and 298.6 +/- 67.4 kg for W365. The heritability posterior distributions for direct and maternal effects were symmetric and close to those expected in a normal distribution. Direct heritability estimates obtained using the modes were 0.30 (BW), 0.52 (W205) and 0.54 (W365). The maternal heritability coefficient estimates were 0.31, 0.19 and 0.21 for BW, W205 and W365, respectively. Our data suggests that all growth traits and mainly W205 and W365, have clear potential for yield improvement through direct genetic selection.

Genetic Parameters for Milk Yield and Lactation Length of Crossbred Buffaloes from Brazil by Bayesian Inference

The objective of the study was to estimate heritability and repeatability for milk yield (MY) and lactation length (LL) in buffaloes using Bayesian inference. The Brazilian genetic improvement program of buffalo provided the data that included 628 females, from four herds, born between 1980 and 2003. In order to obtain the estimates of variance, univariate analyses were performed with the Gibbs sampler, using the MTGSAM software. The model for MY and LL included direct genetic additive and permanent environment as random effects, and contemporary groups, milking frequency and calving number as fixed effects. The convergence diagnosis was performed with the Geweke method using an algorithm implemented in R software through the package Bayesian Output Analysis. Average for milk yield and lactation length was 1,546.1 +/- 483.8 kg and 252.3 +/- 42.5 days, respectively. The heritability coefficients were 0.31 (mode), 0.35 (mean) and 0.34 (median) for MY and 0.11 (mode), 0.10 (mean) and 0.10 (median) for LL. The repeatability coefficient (mode) were 0.50 and 0.15 for MY and LL, respectively. Milk yield is the only trait with clear potential for genetic improvement by direct genetic selection. The repeatability for MY indicates that selection based on the first lactation could contribute for an improvement in this trait.

Bayesian inference for two-parameter gamma distribution assuming different noninformative priors

In this paper distinct prior distributions are derived in a Bayesian inference of the two-parameters Gamma distribution. Noniformative priors, such as Jeffreys, reference, MDIP, Tibshirani and an innovative prior based on the copula approach are investigated. We show that the maximal data information prior provides in an improper posterior density and that the different choices of the parameter of interest lead to different reference priors in this case. Based on the simulated data sets, the Bayesian estimates and credible intervals for the unknown parameters are computed and the performance of the prior distributions are evaluated. The Bayesian analysis is conducted using the Markov Chain Monte Carlo (MCMC) methods to generate samples from the posterior distributions under the above priors.

The zero-inflated Conway-Maxwell-Poisson distribution: Bayesian inference, regression modeling and influence diagnostic

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Rhinolekos capetinga: a new cascudinho species (Loricariidae, Otothyrinae) from the rio Tocantins basin and comments on its ancestral dispersal route

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Genome-wide association study and ancestral origins of the slick-hair coat in tropically adapted cattle

The slick hair coat (SLICK) is a dominantly inherited trait typically associated with tropically adapted cattle that are from Criollo descent through Spanish colonization of cattle into the New World. The trait is of interest relative to climate change, due to its association with improved thermo-tolerance and subsequent increased productivity. Previous studies localized the SLICK locus to a 4 cM region on chromosome (BTA) 20 and identified signatures of selection in this region derived from Senepol cattle. The current study compares three slick-haired Criollo-derived breeds including Senepol, Carora, and Romosinuano and three additional slick-haired cross-bred lineages to non-slick ancestral breeds. Genome-wide association (GWA), haplotype analysis, signatures of selection, runs of homozygosity (ROH), and identity by state (IBS) calculations were used to identify a 0.8 Mb (37.7-38.5 Mb) consensus region for the SLICK locus on BTA20 in which contains SKP2 and SPEF2 as possible candidate genes. Three specific haplotype patterns are identified in slick individuals, all with zero frequency in non-slick individuals. Admixture analysis identified common genetic patterns between the three slick breeds at the SLICK locus. Principal component analysis (PCA) and admixture results show Senepol and Romosinuano sharing a higher degree of genetic similarity to one another with a much lesser degree of similarity to Carora. Variation in GWA, haplotype analysis, and IBS calculations with accompanying population structure information supports potentially two mutations, one common to Senepol and Romosinuano and another in Carora, effecting genes contained within our refined location for the SLICK locus.

Genome-wide association study of reproductive traits in Nellore heifers using Bayesian inference

An important goal of Zebu breeding programs is to improve reproductive performance. A major problem faced with the genetic improvement of reproductive traits is that recording the time for an animal to reach sexual maturity is costly. Another issue is that accurate estimates of breeding values are obtained only a long time after the young bulls have gone through selection. An alternative to overcome these problems is to use traits that are indicators of the reproductive efficiency of the herd and are easier to measure, such as age at first calving. Another problem is that heifers that have conceived once may fail to conceive in the next breeding season, which increases production costs. Thus, increasing heifer's rebreeding rates should improve the economic efficiency of the herd. Response to selection for these traits tends to be slow, since they have a low heritability and phenotypic information is provided only later in the life of the animal. Genome-wide association studies (GWAS) are useful to investigate the genetic mechanisms that underlie these traits by identifying the genes and metabolic pathways involved. Data from 1853 females belonging to the Agricultural Jacarezinho LTDA were used. Genotyping was performed using the BovineHD BeadChip (777 962 single nucleotide polymorphisms (SNPs)) according to the protocol of Illumina - Infinium Assay II ® Multi-Sample HiScan with the unit SQ ™ System. After quality control, 305 348 SNPs were used for GWAS. Forty-two and 19 SNPs had a Bayes factor greater than 150 for heifer rebreeding and age at first calving, respectively. All significant SNPs for age at first calving were significant for heifer rebreeding. These 42 SNPs were next or within 35 genes that were distributed over 18 chromosomes and comprised 27 protein-encoding genes, six pseudogenes and two miscellaneous noncoding RNAs. The use of Bayes factor to determine the significance of SNPs allowed us to identify two sets of 42 and 19 significant SNPs for heifer rebreeding and age at first calving, respectively, which explain 11.35 % and 6.42 % of their phenotypic variance, respectively. These SNPs provide relevant information to help elucidate which genes affect these traits.

A Model-Based Approach for Making Ecological Inference from Distance Sampling Data

We consider a fully model-based approach for the analysis of distance sampling data. Distance sampling has been widely used to estimate abundance (or density) of animals or plants in a spatially explicit study area. There is, however, no readily available method of making statistical inference on the relationships between abundance and environmental covariates. Spatial Poisson process likelihoods can be used to simultaneously estimate detection and intensity parameters by modeling distance sampling data as a thinned spatial point process. A model-based spatial approach to distance sampling data has three main benefits: it allows complex and opportunistic transect designs to be employed, it allows estimation of abundance in small subregions, and it provides a framework to assess the effects of habitat or experimental manipulation on density. We demonstrate the model-based methodology with a small simulation study and analysis of the Dubbo weed data set. In addition, a simple ad hoc method for handling overdispersion is also proposed. The simulation study showed that the model-based approach compared favorably to conventional distance sampling methods for abundance estimation. In addition, the overdispersion correction performed adequately when the number of transects was high. Analysis of the Dubbo data set indicated a transect effect on abundance via Akaike’s information criterion model selection. Further goodness-of-fit analysis, however, indicated some potential confounding of intensity with the detection function.

Patterns and processes of diversification in a widespread and ecologically diverse avian group, the buteonine hawks (Aves, Accipitridae)

Buteonine hawks represent one of the most diverse groups in the Accipitridae, with 58 species distributed in a variety of habitats on almost all continents. Variations in migratory behavior, remarkable dispersal capability, and unusual diversity in Central and South America make buteonine hawks an excellent model for studies in avian evolution. To evaluate the history of their global radiation, we used an integrative approach that coupled estimation of the phylogeny using a large sequence database (based on 6411 bp of mitochondrial markers and one nuclear intron from 54 species), divergence time estimates, and ancestral state reconstructions. Our findings suggest that Neotropical buteonines resulted from a long evolutionary process that began in the Miocene and extended to the Pleistocene. Colonization of the Nearctic, and eventually the Old World, occurred from South America, promoted by the evolution of seasonal movements and development of land bridges. Migratory behavior evolved several times and may have contributed not only to colonization of the Holarctic, but also derivation of insular species. In the Neotropics, diversification of the buteonines included four disjunction events across the Andes. Adaptation of monophyletic taxa to wet environments occurred more than once, and some relationships indicate an evolutionary connection among mangroves, coastal and varzea environments. On the other hand, groups occupying the same biome, forest, or open vegetation habitats are not monophyletic. Refuges or sea-level changes or a combination of both was responsible for recent speciation in Amazonian taxa. In view of the lack of concordance between phylogeny and classification, we propose numerous taxonomic changes. (C) 2009 Elsevier Inc. All rights reserved.

Assessing the gain of biological data integration in gene networks inference

Background: A current challenge in gene annotation is to define the gene function in the context of the network of relationships instead of using single genes. The inference of gene networks (GNs) has emerged as an approach to better understand the biology of the system and to study how several components of this network interact with each other and keep their functions stable. However, in general there is no sufficient data to accurately recover the GNs from their expression levels leading to the curse of dimensionality, in which the number of variables is higher than samples. One way to mitigate this problem is to integrate biological data instead of using only the expression profiles in the inference process. Nowadays, the use of several biological information in inference methods had a significant increase in order to better recover the connections between genes and reduce the false positives. What makes this strategy so interesting is the possibility of confirming the known connections through the included biological data, and the possibility of discovering new relationships between genes when observed the expression data. Although several works in data integration have increased the performance of the network inference methods, the real contribution of adding each type of biological information in the obtained improvement is not clear. Methods: We propose a methodology to include biological information into an inference algorithm in order to assess its prediction gain by using biological information and expression profile together. We also evaluated and compared the gain of adding four types of biological information: (a) protein-protein interaction, (b) Rosetta stone fusion proteins, (c) KEGG and (d) KEGG+GO. Results and conclusions: This work presents a first comparison of the gain in the use of prior biological information in the inference of GNs by considering the eukaryote (P. falciparum) organism. Our results indicates that information based on direct interaction can produce a higher improvement in the gain than data about a less specific relationship as GO or KEGG. Also, as expected, the results show that the use of biological information is a very important approach for the improvement of the inference. We also compared the gain in the inference of the global network and only the hubs. The results indicates that the use of biological information can improve the identification of the most connected proteins.