Landscape genetics across the Andes mountains: Environmental variation drives genetic divergence in the leaf-cutting ant Atta cephalotes.

Distinguishing among the mechanisms underlying the spatial distribution of genetic variation resulting from the environmental or physical barriers from those arising due to simple geographic distance is challenging in complex landscapes. The Andean uplift represents one of the most heterogeneous habitats where multiple mechanisms may interact, confounding their relative roles. We explore this broad question in the leaf-cutting ant Atta cephalotes, a species that is distributed across the Andes mountains, using nuclear microsatellite markers and mtCOI gene sequences. We investigate spatial genetic divergence across the western range of the northern Andes in Colombia by testing the relative role of alternative scenarios of population divergence, including isolation by geographic distance (IBD), climatic conditions (IBE), and the physical barriers presented by the Andes mountains (IBB). Our results reveal substantial genetic differentiation among A. cephalotes populations for both types of markers, but only nuclear divergence followed a hierarchical pattern with multiple models of genetic divergence imposed by the western range. Model selection showed that the IBD, IBE (temperature and precipitation), and IBB (Andes mountains) models, often proposed as individual drivers of genetic divergence, interact, and explain up to 33% of the genetic divergence in A. cephalotes. The IBE model remained significant after accounting for IBD, suggesting that environmental factors play a more prominent role than IBB. These factors, in combination with the idiosyncratic dispersal patterns of ants, appear to determine the hierarchical patterns of gene flow. This study enriches our understanding of the forces shaping population divergence in complex habitat landscapes.

AgroReg: main regression models in agricultural sciences implemented as an R Package

Regression analysis is highly relevant to agricultural sciences since many of the factors studied are quantitative. Researchers have generally used polynomial models to explain their experimental results, mainly because much of the existing software perform this analysis and a lack of knowledge of other models. On the other hand, many of the natural phenomena do not present such behavior; nevertheless, the use of non-linear models is costly and requires advanced knowledge of language programming such as R. Thus, this work presents several regression models found in scientific studies, implementing them in the form of an R package called AgroReg. The package comprises 44 analysis functions with 66 regression models such as polynomial, non-parametric (loess), segmented, logistic, exponential, and logarithmic, among others. The functions provide the coefficient of determination (R2), model coefficients and the respective p-values from the t-test, root mean square error (RMSE), Akaike's information criterion (AIC), Bayesian information criterion (BIC), maximum and minimum predicted values, and the regression plot. Furthermore, other measures of model quality and graphical analysis of residuals are also included. The package can be downloaded from the CRAN repository using the command: install.packages("AgroReg"). AgroReg is a promising analysis tool in agricultural research on account of its user-friendly and straightforward functions that allow for fast and efficient data processing with greater reliability and relevant information.

Nonlinear mixed models for characterization of growth trajectory of New Zealand rabbits raised in tropical climate.

OBJECTIVE: The identification of nonlinear mixed models that describe the growth trajectory of New Zealand rabbits was performed based on weight records and carcass measures obtained using ultrasonography. METHODS: Phenotypic records of body weight (BW) and loin eye area (LEA) were collected from 66 animals raised in a didactic-productive module of cuniculture located in the southern Piauí state, Brazil. The following nonlinear models were tested considering fixed parameters: Brody, Gompertz, Logistic, Richards, Meloun 1, modified Michaelis-Menten, Santana, and von Bertalanffy. The coefficient of determination (R2), mean squared error, percentage of convergence of each model (%C), mean absolute deviation of residuals, Akaike information criterion (AIC), and Bayesian information criterion (BIC) were used to determine the best model. The model that best described the growth trajectory for each trait was also used under the context of mixed models, considering two parameters that admit biological interpretation (A and k) with random effects. RESULTS: The von Bertalanffy model was the best fitting model for BW according to the highest value of R2 (0.98) and lowest values of AIC (6,675.30) and BIC (6,691.90). For LEA, the Logistic model was the most appropriate due to the results of R2 (0.52), AIC (783.90), and BIC (798.40) obtained using this model. The absolute growth rates estimated using the von Bertalanffy and Logistic models for BW and LEA were 21.51g/d and 3.16 cm2, respectively. The relative growth rates at the inflection point were 0.028 for BW (von Bertalanffy) and 0.014 for LEA (Logistic). CONCLUSION: The von Bertalanffy and Logistic models with random effect at the asymptotic weight are recommended for analysis of ponderal and carcass growth trajectories in New Zealand rabbits. The inclusion of random effects in the asymptotic weight and maturity rate improves the quality of fit in comparison to fixed models.

Sexual dimorphism and morphometrics in two populations of the neotropical freshwater turtle Mesoclemmys vanderhaegei (Testudines, Chelidae) / Dimorfismo sexual e morfometria em duas populações do quelônio aquático neotropical Mesoclemmys vanderhaegei (Testudines, Chelidae)

As commonly observed in turtles, sexual size dimorphism (SSD) is pronounced in the Neotropical freshwater turtle Mesoclemmys vanderhaegei (Bour, 1973), a species in which females are usually larger than males. We studied SSD in two populations of M. vanderhaegei from the Brazilian Cerrado savannah, based on 245 specimens captured between November 2010 and August 2013. The carapace length of the largest male was 201 mm (9.15% shorter than that of the largest female, 220 mm). The mean sizes of males and females did not differ in the two populations. However, a comparison of eight selected morphological variables revealed that the size distribution pattern differed between the populations. Using model selection, seven out of 34 morphometric variables - from the head, plastron, bridge, and tail - were selected as the most suitable ones to distinguish between males and females. The pattern of SSD found in M. vanderhaegei is similar to that found in other chelonian species and may be the result of natural selection rather than ecological factors, since individuals of both sexes use the same habitats.

Como comumente observado em quelônios, dimorfismo sexual em tamanho (SSD) é pronunciado em Mesoclemmys vanderhaegei (Bour, 1973), uma espécie de quelônio Neotropical de água doce onde as fêmeas são geralmente maiores que os machos. Nós estudamos SSD em duas populações de M. vanderhaegei no Cerrado brasileiro, com base em 245 espécimes capturados entre novembro de 2010 e agosto de 2013. O comprimento da carapaça do maior macho foi de 201 mm (9,15% menor que o comprimento da maior fêmea, 220 mm). Os tamanhos médios de fêmeas e machos não diferiram nas duas populações. No entanto, uma comparação de oito variáveis morfológicas revelou que o padrão de distribuição de tamanhos diferiu entre as populações. Usando a seleção de modelos, sete das 34 variáveis morfométricas - incluindo medidas da cabeça, plastrão, ponte e cauda - foram selecionadas como as mais adequadas para distinguir fêmeas e machos. O padrão de SSD encontrado em M. vanderhaegei é similar ao encontrado em outras espécies de quelônios e pode ser o resultado de seleção natural ao invés de fatores ecológicos, uma vez que indivíduos de ambos os sexos usam os mesmos habitats.

Fit and genetic parameters of the growth curve of Polled Nellore cattle / Ajuste e parâmetros genéticos da curva de crescimento em bovinos da raça Nelore Mocho

This study was undertaken to compare different non-linear models for fitting growth curves of Polled Nellore animals as well as to estimate genetic parameters for the components of the growth curve. The study involved body weight-age data of 6,717 Polled Nellore cattle from birth to 650 days of age, which belonged to the Brazilian Association of Zebu Breeders (ABCZ), corresponding to the period from 1980 to 2011. Four non-linear models (Brody, Bertalanffy, Logistic, and Gompertz) were fitted and compared by the adjusted coefficient of determination (R2adj), mean absolute deviation of residuals (MAD), root mean square error (RMSE), Akaike information criterion (AIC), and Bayesian information criterion (BIC). To estimate the genetic parameters and genetic values of asymptotic weight (A), integration constant (B), and maturation rate (K), the Bayesian inference method was adopted. The Brody model showed the lowest values of MAD, RMSE, AIC, and BIC and the highest R2adj. Heritability estimates for parameters A, B, and K were 0.11, 0.16, and 0.30, respectively, whereas genetic correlations were 0.01 (A-B), -0.91 (A-K), and 0.24 (B-K). The Brody model provided the best fit. The K parameter shows enough genetic variability for selection in the herd. Heavier animals in adulthood tend to exhibit lower growth rates. Despite the low heritability estimate of parameter A, there were genetic gains, indicating that selection is being efficient on asymptotic weight.(AU)

O objetivo deste estudo foi comparar diferentes modelos não lineares para o ajuste das curvas de crescimento de animais da raça Nelore Mocho e estimar os parâmetros genéticos para os componentes da curva de crescimento. Foram utilizados dados de peso corporal-idade do nascimento aos 650 dias de idades de 6.717 bovinos da raça Nelore Mocho, pertencentes à Associação Brasileira de Criadores de Zebu (ABCZ), referentes ao período de 1980 e 2011. Quatro modelos não lineares (Brody, Bertalanffy, Logístico e Gompertz) foram ajustados e comparados pelo coeficiente de determinação ajustado (R2adj), desvio médio absoluto dos resíduos (DMA), raiz quadrada do quadrado médio do resíduo (RMSE), critério de informação de Akaike (AIC) e o critério de informação bayesiano (BIC). Para estimativas dos parâmetros genéticos e valores genéticos do peso assintótico (A), constante de integração (B) e taxa de maturação (K), utilizou-se o método de inferência Bayesiana. O modelo Brody apresentou os menores valores de DMA, RMSE, AIC e BIC e o maior R2adj. As estimativas de herdabilidade foram 0,11; 0,16 e 0,30 para os parâmetros A, B e K, respectivamente, enquanto as correlações genéticas foram de 0,01 (A-B), -0,91 (A-K) e 0,24 (B-K). Constatou-se que o modelo Brody forneceu o melhor ajuste. O parâmetro K apresenta variabilidade genética suficiente para seleção no rebanho. Animais com maior peso na idade adulta tendem a apresentar menores taxas de crescimento. Apesar da baixa estimativa de herdabilidade do parâmetro A, observou-se ganhos genéticos, indicando que a seleção está sendo eficiente sobre o peso assintótico.(AU)

Nonlinear models for soil moisture sensor calibration in tropical mountainous soils

Electromagnetic sensors are widely used to monitor soil water content (θ); however, site-specific calibrations are necessary for accurate measurements. This study compares regression models used for calibration of soil moisture sensors and investigates the relation between soil attributes and the adjusted parameters of the specific calibration equations. Undisturbed soil samples were collected in the A and B horizons of two Ultisols and two Inceptisols from the Mantiqueira Range in Southeastern Brazil. After saturation, the Theta Probe ML2X was used to obtain the soil dielectric constant (ε). Several readings were made, ranging from saturation to oven-dry. After each reading, the samples were weighted to calculate θ (m³ m-³). Fourteen regression models (linear, linearized, and nonlinear) were adjusted to the calibration data and checked for their residue distribution. Only the exponential model with three parameters met the regression assumptions regarding residue distribution. The stepwise regression was used to obtain multiple linear equations to estimate the adjusted parameters of the calibration model from soil attributes, with silt and clay contents providing the best relations. Both the specific and the general calibrations performed well, with RMSE values of 0.02 and 0.03 m³ m-³, respectively. Manufacturer calibration and equations from the literature were much less accurate, reinforcing the need to develop specific calibrations.

Sexual dimorphism and morphometrics in two populations of the Neotropical freshwater turtle Mesoclemmys vanderhaegei (Testudines, Chelidae)

ABSTRACT As commonly observed in turtles, sexual size dimorphism (SSD) is pronounced in the Neotropical freshwater turtle Mesoclemmys vanderhaegei (Bour, 1973), a species in which females are usually larger than males. We studied SSD in two populations of M. vanderhaegei from the Brazilian Cerrado savannah, based on 245 specimens captured between November 2010 and August 2013. The carapace length of the largest male was 201 mm (9.15% shorter than that of the largest female, 220 mm). The mean sizes of males and females did not differ in the two populations. However, a comparison of eight selected morphological variables revealed that the size distribution pattern differed between the populations. Using model selection, seven out of 34 morphometric variables - from the head, plastron, bridge, and tail - were selected as the most suitable ones to distinguish between males and females. The pattern of SSD found in M. vanderhaegei is similar to that found in other chelonian species and may be the result of natural selection rather than ecological factors, since individuals of both sexes use the same habitats.

RESUMO Como comumente observado em quelônios, dimorfismo sexual em tamanho (SSD) é pronunciado em Mesoclemmys vanderhaegei (Bour, 1973), uma espécie de quelônio Neotropical de água doce onde as fêmeas são geralmente maiores que os machos. Nós estudamos SSD em duas populações de M. vanderhaegei no Cerrado brasileiro, com base em 245 espécimes capturados entre novembro de 2010 e agosto de 2013. O comprimento da carapaça do maior macho foi de 201 mm (9,15% menor que o comprimento da maior fêmea, 220 mm). Os tamanhos médios de fêmeas e machos não diferiram nas duas populações. No entanto, uma comparação de oito variáveis morfológicas revelou que o padrão de distribuição de tamanhos diferiu entre as populações. Usando a seleção de modelos, sete das 34 variáveis morfométricas - incluindo medidas da cabeça, plastrão, ponte e cauda - foram selecionadas como as mais adequadas para distinguir fêmeas e machos. O padrão de SSD encontrado em M. vanderhaegei é similar ao encontrado em outras espécies de quelônios e pode ser o resultado de seleção natural ao invés de fatores ecológicos, uma vez que indivíduos de ambos os sexos usam os mesmos habitats.

Comparing Genomic Prediction Models by Means of Cross Validation.

In the two decades of continuous development of genomic selection, a great variety of models have been proposed to make predictions from the information available in dense marker panels. Besides deciding which particular model to use, practitioners also need to make many minor choices for those parameters in the model which are not typically estimated by the data (so called "hyper-parameters"). When the focus is placed on predictions, most of these decisions are made in a direction sought to optimize predictive accuracy. Here we discuss and illustrate using publicly available crop datasets the use of cross validation to make many such decisions. In particular, we emphasize the importance of paired comparisons to achieve high power in the comparison between candidate models, as well as the need to define notions of relevance in the difference between their performances. Regarding the latter, we borrow the idea of equivalence margins from clinical research and introduce new statistical tests. We conclude that most hyper-parameters can be learnt from the data by either minimizing REML or by using weakly-informative priors, with good predictive results. In particular, the default options in a popular software are generally competitive with the optimal values. With regard to the performance assessments themselves, we conclude that the paired k-fold cross validation is a generally applicable and statistically powerful methodology to assess differences in model accuracies. Coupled with the definition of equivalence margins based on expected genetic gain, it becomes a useful tool for breeders.

Modeling Mortality Based on Pollution and Temperature Using a New Birnbaum-Saunders Autoregressive Moving Average Structure with Regressors and Related-Sensors Data.

Environmental agencies are interested in relating mortality to pollutants and possible environmental contributors such as temperature. The Gaussianity assumption is often violated when modeling this relationship due to asymmetry and then other regression models should be considered. The class of Birnbaum-Saunders models, especially their regression formulations, has received considerable attention in the statistical literature. These models have been applied successfully in different areas with an emphasis on engineering, environment, and medicine. A common simplification of these models is that statistical dependence is often not considered. In this paper, we propose and derive a time-dependent model based on a reparameterized Birnbaum-Saunders (RBS) asymmetric distribution that allows us to analyze data in terms of a time-varying conditional mean. In particular, it is a dynamic class of autoregressive moving average (ARMA) models with regressors and a conditional RBS distribution (RBSARMAX). By means of a Monte Carlo simulation study, the statistical performance of the new methodology is assessed, showing good results. The asymmetric RBSARMAX structure is applied to the modeling of mortality as a function of pollution and temperature over time with sensor-related data. This modeling provides strong evidence that the new ARMA formulation is a good alternative for dealing with temporal data, particularly related to mortality with regressors of environmental temperature and pollution.

Modeling the association between Aedes aegypti ovitrap egg counts, multi-scale remotely sensed environmental data and arboviral cases at Puntarenas, Costa Rica (2017-2018).

Problems with vector surveillance are a major barrier for the effective control of vector-borne disease transmission through Latin America. Here, we present results from a 80-week longitudinal study where Aedes aegypti (L.) (Diptera: Culicidae) ovitraps were monitored weekly at 92 locations in Puntarenas, a coastal city in Costa Rica with syndemic Zika, chikungunya and dengue transmission. We used separate models to investigate the association of either Ae. aegypti-borne arboviral cases or Ae. aegypti egg counts with remotely sensed environmental variables. We also evaluated whether Ae. aegypti-borne arboviral cases were associated with Ae. aegypti egg counts. Using cross-correlation and time series modeling, we found that arboviral cases were not significantly associated with Ae. aegypti egg counts. Through model selection we found that cases had a non-linear response to multi-scale (1-km and 30-m resolution) measurements of temperature standard deviation (SD) with a lag of up to 4 weeks, while simultaneously increasing with finely-grained NDVI (30-m resolution). Meanwhile, median ovitrap Ae. aegypti egg counts increased, and respectively decreased, with temperature SD (1-km resolution) and EVI (30-m resolution) with a lag of 6 weeks. A synchrony analysis showed that egg counts had a travelling wave pattern, with synchrony showing cyclic changes with distance, a pattern not observed in remotely sensed data with 30-m and 10-m resolution. Spatially, using generalized additive models, we found that eggs were more abundant at locations with higher temperatures and where EVI was leptokurtic during the study period. Our results suggest that, in Puntarenas, remotely sensed environmental variables are associated with both Ae. aegypti-borne arbovirus transmission and Ae. aegypti egg counts from ovitraps.

Inference of COVID-19 epidemiological distributions from Brazilian hospital data.

Knowing COVID-19 epidemiological distributions, such as the time from patient admission to death, is directly relevant to effective primary and secondary care planning, and moreover, the mathematical modelling of the pandemic generally. We determine epidemiological distributions for patients hospitalized with COVID-19 using a large dataset (N = 21 000 - 157 000) from the Brazilian Sistema de Informação de Vigilância Epidemiológica da Gripe database. A joint Bayesian subnational model with partial pooling is used to simultaneously describe the 26 states and one federal district of Brazil, and shows significant variation in the mean of the symptom-onset-to-death time, with ranges between 11.2 and 17.8 days across the different states, and a mean of 15.2 days for Brazil. We find strong evidence in favour of specific probability density function choices: for example, the gamma distribution gives the best fit for onset-to-death and the generalized lognormal for onset-to-hospital-admission. Our results show that epidemiological distributions have considerable geographical variation, and provide the first estimates of these distributions in a low and middle-income setting. At the subnational level, variation in COVID-19 outcome timings are found to be correlated with poverty, deprivation and segregation levels, and weaker correlation is observed for mean age, wealth and urbanicity.

A New Method to Reconstruct Quantitative Food Webs and Nutrient Flows from Isotope Tracer Addition Experiments.

Understanding how nutrients flow through food webs is central in ecosystem ecology. Tracer addition experiments are powerful tools to reconstruct nutrient flows by adding an isotopically enriched element into an ecosystem and tracking its fate through time. Historically, the design and analysis of tracer studies have varied widely, ranging from descriptive studies to modeling approaches of varying complexity. Increasingly, isotope tracer data are being used to compare ecosystems and analyze experimental manipulations. Currently, a formal statistical framework for analyzing such experiments is lacking, making it impossible to calculate the estimation errors associated with the model fit, the interdependence of compartments, and the uncertainty in the diet of consumers. In this article we develop a method based on Bayesian hidden Markov models and apply it to the analysis of N15-NH4+ tracer additions in two Trinidadian streams in which light was experimentally manipulated. Through this case study, we illustrate how to estimate N fluxes between ecosystem compartments, turnover rates of N within those compartments, and the associated uncertainty. We also show how the method can be used to compare alternative models of food web structure, calculate the error around derived parameters, and make statistical comparisons between sites or treatments.

A Bayesian multi-risks survival (MRS) model in the presence of double censorings.

Semi-competing risks data include the time to a nonterminating event and the time to a terminating event, while competing risks data include the time to more than one terminating event. Our work is motivated by a prostate cancer study, which has one nonterminating event and two terminating events with both semi-competing risks and competing risks present as well as two censoring times. In this paper, we propose a new multi-risks survival (MRS) model for this type of data. In addition, the proposed MRS model can accommodate noninformative right-censoring times for nonterminating and terminating events. Properties of the proposed MRS model are examined in detail. Theoretical and empirical results show that the estimates of the cumulative incidence function for a nonterminating event may be biased if the information on a terminating event is ignored. A Markov chain Monte Carlo sampling algorithm is also developed. Our methodology is further assessed using simulations and also an analysis of the real data from a prostate cancer study. As a result, a prostate-specific antigen velocity greater than 2.0 ng/mL per year and higher biopsy Gleason scores are positively associated with a shorter time to death due to prostate cancer.

Population pharmacokinetics of orally administrated bromopride: Focus on the absorption process.

Bromopride is a prokinetic and antiemetic drug used to treat nausea and vomiting. Although its prescription is common in Brazil, there is a lack of studies about bromopride pharmacokinetics. Therefore, the aims of this study were to investigate the population pharmacokinetics of bromopride and to evaluate the influence of covariates on its absorption. This study is a retrospective analysis of data collected from bioequivalence studies. The data was modeled using MONOLIX 2018R2. Assuming one-compartment and linear elimination, the absorption phase was evaluated with different structural models. The model of sequential first- and zero-order with combined error and exponential inter-individual variability in all parameters best described the atypical absorption profile of bromopride. Population estimates were first-order absorption rate (ka) of 0.08 h - 1, fraction of dose absorbed by first-order (Fr) of 32.60%, duration of the zero-order absorption (Tk0) of 0.88 h with latency time (Tlag) of 0.47 h, volume of distribution of 230 l and clearance of 46.80 l h - 1. Bodyweight affects Tk0, dosage form was found to correlate with Tk0 and Tlag, while gender affects Tlag. However, simulations evaluating the clinical importance of these covariates on steady-state indicated minimal changes on bromopride exposure. The mixed absorption model was reasonable to describe the absorption process of bromopride because it had the flexibility to fit multiple-peaks profile and shows good agreement with physicochemical properties of drug.

The unit-Weibull distribution as an alternative to the Kumaraswamy distribution for the modeling of quantiles conditional on covariates.

The Beta distribution is the standard model for quantifying the influence of covariates on the mean of a response variable on the unit interval. However, this well-known distribution is no longer useful when we are interested in quantifying the influence of such covariates on the quantiles of the response variable. Unlike Beta, the Kumaraswamy distribution has a closed-form expression for its quantile and can be useful for the modeling of quantiles in the absence/presence of covariates. As an alternative to the Kumaraswamy distribution for the modeling of quantiles, in this paper the unit-Weibull distribution was considered. This distribution was obtained by the transformation of a random variable with Weibull distribution. The same transformation applied to a random variable with Exponentiated Exponential distribution generates the Kumaraswamy distribution. The suitability of our proposal was demonstrated to model quantiles, conditional on covariates, with two simulated examples and three real applications with datasets from health, accounting and social science. For such data sets, the obtained fits of the proposed regression model were compared with those provided by the Beta and Kumaraswamy regression models.

Agricultural Landscape Heterogeneity Matter: Responses of Neutral Genetic Diversity and Adaptive Traits in a Neotropical Savanna Tree.

Plants are one of the most vulnerable groups to fragmentation and habitat loss, that may affect community richness, abundance, functional traits, and genetic diversity. Here, we address the effects of landscape features on adaptive quantitative traits and evolutionary potential, and on neutral genetic diversity in populations of the Neotropical savanna tree Caryocar brasiliense. We sampled adults and juveniles in 10 savanna remnants within five landscapes. To obtain neutral genetic variation, we genotyped all individuals from each site using nine microsatellite loci. For adaptive traits we measured seed size and mass and grown seeds in nursery in completely randomized experimental design. We obtained mean, additive genetic variance (V a ) and coefficient of variation (CV a %), which measures evolvability, for 17 traits in seedlings. We found that landscapes with higher compositional heterogeneity (SHDI) had lower evolutionary potential (CV a %) in leaf length (LL) and lower aboveground dry mass (ADM) genetic differentiation (Q ST ). We also found that landscapes with higher SHDI had higher genetic diversity (He) and allelic richness (AR) in adults, and lower genetic differentiation (F ST ). In juveniles, SHDI was also positively related to AR. These results are most likely due to longer dispersal distance of pollen in landscapes with lower density of flowering individuals. Agricultural landscapes with low quality mosaic may be more stressful for plant species, due to the lower habitat cover (%), higher cover of monocropping (%) and other land covers, and edge effects. However, in landscapes with higher SHDI with high quality mosaic, forest nearby savanna habitat and the other environments may facilitate the movement or provide additional habitat and resources for seed disperses and pollinators, increasing gene flow and genetic diversity. Finally, despite the very recent agriculture expansion in Central Brazil, we found no time lag in response to habitat loss, because both adults and juveniles were affected by landscape changes.

Evaluation of Bayesian models for analysis of crude protein requirement for pigs of Brazilian Piau breed

We evaluated the inclusion of information on genetic relationship into the analysis of crude protein requirement in diets for pigs of Brazilian Piau breed, using Bayesian inference. The animals were assigned to treatments in a completely randomized design in factorial scheme 4 × 2 (crude protein levels × sex) with 12 repetitions per treatment. The evaluations were carried out in the initial, growing and finishing phases, and after slaughter. The traits evaluated were feed conversion (FC), backfat thickness (BF), daily weight gain (DWG), daily feed intake (DFI) and some carcass cuts. Three models were considered to evaluate the inclusion of information on genetic relationship into the analysis: Model I, a simple linear model; Model II, the same effects of Model I with addition of the independent random effect of animal; and Model III, the same effects of Model II, but including the genetic relationship between the animals. Model III presented the best fit and was considered for later inferences. Crude protein (CP) levels did not significantly influence any of the evaluated traits. The effect of sex was significant only for the growing phase, while its interaction with protein levels presented an opposite result for all evaluated traits. Additionally, CP levels of 10.2 %, 9.6 % and 9.0 % can be used in diets for pigs of Brazilian Piau breed in the initial, growing and finishing phases, respectively.(AU)

Evaluation of Bayesian models for analysis of crude protein requirement for pigs of Brazilian Piau breed

We evaluated the inclusion of information on genetic relationship into the analysis of crude protein requirement in diets for pigs of Brazilian Piau breed, using Bayesian inference. The animals were assigned to treatments in a completely randomized design in factorial scheme 4 × 2 (crude protein levels × sex) with 12 repetitions per treatment. The evaluations were carried out in the initial, growing and finishing phases, and after slaughter. The traits evaluated were feed conversion (FC), backfat thickness (BF), daily weight gain (DWG), daily feed intake (DFI) and some carcass cuts. Three models were considered to evaluate the inclusion of information on genetic relationship into the analysis: Model I, a simple linear model; Model II, the same effects of Model I with addition of the independent random effect of animal; and Model III, the same effects of Model II, but including the genetic relationship between the animals. Model III presented the best fit and was considered for later inferences. Crude protein (CP) levels did not significantly influence any of the evaluated traits. The effect of sex was significant only for the growing phase, while its interaction with protein levels presented an opposite result for all evaluated traits. Additionally, CP levels of 10.2 %, 9.6 % and 9.0 % can be used in diets for pigs of Brazilian Piau breed in the initial, growing and finishing phases, respectively.

Heritability and Sex-Specific Genetic Effects of Self-Reported Physical Activity in a Brazilian Highly Admixed Population.

INTRODUCTION: The engagement in sports or habitual physical activity (PA) has shown an extensive protective role against multiple diseases such as cancer, obesity, and many others. Additionally, PA has also a significant impact on life quality, since it aids with managing stress, preserving cognitive function and memory, and preventing fractures in the elderly. OBJECTIVE: Considering there has been multiple evidence showing that genetic variation underpins variation of PA-related traits, we aimed to estimate the heritability (h2) of these phenotypes in a sample from the Brazilian population and assess whether males and females differ in relation to those estimates. METHODS: 2,027 participants from a highly admixed population from Baependi, MG, Brazil, had information regarding their PA and sedentary behavior (SB) phenotypes collected through a questionnaire (IPAQ-SF). After data cleaning and transformation procedures, we obtained four variables to be evaluated: total PA (TPA MET), walking time, (WK MET), moderate-vigorous PA (MVPA MET), and SB. A model selection procedure was performed using a single-step covariate inclusion approach. We tested for BMI, waist, hip and neck circumferences, smoking, and depression separately, and performed correlation tests among covariates. Linear mixed models, selection procedure, and the variance components approach to estimate h2 were implemented using SOLAR-Eclipse 8.3.1. RESULTS: We obtained estimates of 0.221, 0.109, 0.226, and 0 for TPA MET, WK MET, MVPA MET, and SB, respectively. We found evidence for gene-sex interactions, with males having higher sex-specific heritabilities than females for TPA MET and MVPA MET. In addition, we found higher estimates of the genetic variance component in males than females for most phenotypes. DISCUSSION/CONCLUSION: The heritability estimates presented in this work show a moderate heritable set of genetic factors affecting PA in a sample from the Brazilian population. The evaluation of the genetic variance component suggests segregating genetic factors in male individuals are more heterogeneous, which can explain why men globally tend to need to practice more intense PA than women to achieve similar health benefits. Hence, these findings have significant implications for the understanding of the genetic architecture of PA and might aid to promote health in the future.

A Comparison Study on Criteria to Select the Most Adequate Weighting Matrix.

The practice of spatial econometrics revolves around a weighting matrix, which is often supplied by the user on previous knowledge. This is the so-called W issue. Probably, the aprioristic approach is not the best solution although, presently, there are few alternatives for the user. Our contribution focuses on the problem of selecting a W matrix from among a finite set of matrices, all of them considered appropriate for the case. We develop a new and simple method based on the entropy corresponding to the distribution of probability estimated for the data. Other alternatives, which are common in current applied work, are also reviewed. The paper includes a large study of Monte Carlo to calibrate the effectiveness of our approach compared to others. A well-known case study is also included.