Acute toxicity of total ammonia to Macrobrachium rosenbergii postlarvae at different salinity levels.

Nitrogen compounds, particularly ammonium, nitrite and nitrate, are a major problem in shrimp production systems. These compounds can accumulate in the aquatic environment and reach harmful or even lethal levels. Thus, monitoring the levels of nitrogenous compounds such as ammonia and studying their effects on the animals are essential. One tool used for this purpose is acute toxicity testing based on the evaluation of LC50 values. Furthermore, tools that can help improve the performance of aquatic organisms in culture are needed. The present study aimed to evaluate the effect of salinity on the toxicity of total ammonia to postlarvae of the freshwater prawn Macrobrachium rosenbergii. For this purpose, acute toxicity testing (LC50-96h) was performed using 540 postlarvae with a mean weight of 0.13 g and a mean total length of 2.47 cm, divided into 54 experimental units of two liters each. A completely randomized design in a 3×6 factorial scheme was used, combining three salinities (0, 5, and 10 g.L-1) and six total ammonia concentrations (0, 8, 16, 32, 64, and 128 mg.L-1), with three replicates per combination. The LC50 values for M. rosenbergii postlarvae at 24, 48, 72, and 96 h and their respective confidence intervals (95%) were estimated using the trimmed Spearman-Karber method. The results showed that salinities of 5 or 10 g.L-1 did not reduce the acute toxicity of total ammonia.

Effect of temperature on embryonic development and first exogenous feeding of goldfish Carassius auratus (Linnaeus, 1758).

Goldfish or Kinguio is a widely marketed species worldwide due to the ornamental market. There is some lack of acknowledgment of the production of the species under specific climatic conditions. To evaluate the effect of temperature on embryonic development and the first exogenous feeding of goldfish, an experiment was proposed. Fifteen incubators, organized in five treatments (18, 22, 26, 30, and 34 °C) with three replications each, were used to keep the fertilized goldfish eggs until the first exogenous feeding of the larvae. The main development events were observed to understand the possible effects of these temperatures on embryos and larvae of the species. Temperature influences embryo development and the time of first exogenous feeding of goldfish. The temperature of 34 °C was lethal to the species causing 100% of anomalies in the embryos and larvae. The experiment data allow us to conclude that the species presents a maximum thermal limit during embryogenesis, and these data are important to the aquaculture industry and to understand the effect of climate changes on goldfish. The data obtained in this experiment will assist in the management of invasive species and production of the species (aquaculture).

Avaliação do modelo NRC para predição do consumo de matéria seca por vacas leiteiras manejadas em pastos tropicais / NCR Model Evaluation for prediction of dry matter intake by dairy cows managed in tropical pastures

Objetivou-se avaliar o poder preditivo do modelo do National Research Council (NRC) para gado leiteiro em estimar o consumo de matéria seca (CMS) por vacas mestiças, em pastagens tropicais. Foi efetuada uma análise conjunta de cinco estudos, contemplando três forrageiras. Foram avaliadas 132 estimativas individuais do CMS observado (CMSObs), obtidas por meio do indicador externo Cr2O3. O CMS também foi predito por meio do software do NRC (CMSPred), que, por sua vez, foi abastecido com inputs referentes aos animais e ao ambiente de criação. Os valores de CMSPred (12,7±1,6kg/d) foram semelhantes aos de CMSObs (12,3±3,3kg/d). Foram obtidas as seguintes estimativas da avaliação do poder preditivo do modelo: viés médio (-0,419kg/d), coeficiente de determinação (0,029), coeficiente de correlação (0,17; P=,051), quadrado médio do erro de predição (11,844±20,034), fator de eficiência do modelo (-0,081), coeficiente de determinação do modelo (4,1032) e fator de correção do viés (0,767). A comparação entre CMSObs e CMSPred permitiu identificar a tendência de superestimação das predições se considerado o ajuste por meio de regressão robusta para o modelo linear simples sem intercepto. Nas condições avaliadas, o modelo produz predições de CMS com satisfatória exatidão, porém com baixa precisão.(AU)

The aim of the present study was to evaluate the predictive power of estimating the dry matter intake (DMI) of crossbred cows on tropical pastures by the National Research Council (NRC) equation for dairy cattle. A joint analysis of five studies covering three forages was performed in which 132 individual estimates of observed DMI obtained through Cr2O3 as a marker. DMI was also predicted from the NRC (DMIPred) software with inputs concerning animals and breeding environment of the studies used. Predicted DMIPred average values (12.7±1.6kg/d) were similar to the observed DMIObs ones (12.3±3.3kg/d). We obtained the following estimates of the evaluation of the predictive power of the model: average bias (- 0.419kg/d), coefficient of determination (0.029), Person's correlation coefficient (0.17, P= 0.051), mean square error of prediction (11,844±20,034), model efficiency factor (- 0.081), coefficient of determination (4.1032), and bias correction factor (0.767). The comparison between DMIObs and DMIPred values allowed the identification of the overestimating tendency of the predictions demonstrated by the robust regression fit of the simple linear no intercept model. Nevertheless, the model yields predictions with satisfactory accuracy, but with low precision.(AU)

The ontogenetic allometry of body morphology and chemical composition in dairy goat wethers.

We studied the ontogenetic growth of goat wethers (castrated male goats) of the Saanen and Swiss Alpine breeds based on a large range of intraspecific body mass (BM). The body parts and the chemical constituents of the empty body were described by the allometric function by using BM and the empty body mass (EBM) as the predictors for morphological traits and chemical composition, respectively. We fitted the allometric scaling function by applying the SAS NLMIXED procedure, but to evaluate assumptions regarding variances in morphological and compositional traits, we combined the scaling function with homoscedastic (MOD1), and the heteroscedastic exponential (MOD2) and power-of-the-mean (MOD3) variance functions. We also predicted the ontogenetic growth by using the traditional log-log transformation and back-transformed results into the arithmetic scale (MOD4). We obtained predictions from MOD4 in the arithmetic scale by a two-step process, and evaluated MOD1, MOD2 and MOD3 by a model selection framework, and compared MOD4 with MOD1, MOD2 and MOD3 based on goodness-of-fit measures. Based on information criteria for model selection, heterogeneous variance functions were more likely to describe 10 over 36 traits with a low level of model selection uncertainty. One trait was predicted by averaging the MOD1 and MOD2 variance functions; and nine traits were better described by averaging the MOD2 and MOD3 variance functions. The predictions for other 16 traits were averaged from MOD1, MOD2 and MOD3. However, MOD4 better described 11 traits according to the goodness-of-fit measures. Depending on the variable being analyzed, the body parts and the chemical amounts exhibited the three types of allometric behavior with respect to BM and EBM, that is, positive, negative and isometric ontogenetic growth. Reference BMs, that is, 20, 27, 35 and 45 kg, were used to compute the net protein and energy requirements based on the first derivative of the scaling function, and the results were presented in reference to the EBM and EBM0.75. Both the net protein and energy requirements scaled to EBM0.75 increased from 20 to 45 kg of BM.

Bayesian forecasting of temporal gene expression by using an autoregressive panel data approach.

We propose and evaluate a novel approach for forecasting gene expression over non-observed times in longitudinal trials under a Bayesian viewpoint. One of the aims is to cluster genes that share similar expression patterns over time and then use this similarity to predict relative expression at time points of interest. Expression values of 106 genes expressed during the cell cycle of Saccharomyces cerevisiae were used and genes were partitioned into five distinct clusters of sizes 33, 32, 21, 16, and 4. After removing the last observed time point, the agreements of signals (upregulated or downregulated) considering the predicted expression level were 72.7, 81.3, 76.2, 68.8, and 50.0%, respectively, for each cluster. The percentage of credibility intervals that contained the true values of gene expression for a future time was ~90%. The methodology performed well, providing a valid forecast of gene expression values by fitting an autoregressive panel data model. This approach is easily implemented with other time-series models and when Poisson and negative binomial probability distributions are assumed for the gene expression data.

Comparison of dimensionality reduction methods to predict genomic breeding values for carcass traits in pigs.

A significant contribution of molecular genetics is the direct use of DNA information to identify genetically superior individuals. With this approach, genome-wide selection (GWS) can be used for this purpose. GWS consists of analyzing a large number of single nucleotide polymorphism markers widely distributed in the genome; however, because the number of markers is much larger than the number of genotyped individuals, and such markers are highly correlated, special statistical methods are widely required. Among these methods, independent component regression, principal component regression, partial least squares, and partial principal components stand out. Thus, the aim of this study was to propose an application of the methods of dimensionality reduction to GWS of carcass traits in an F2 (Piau x commercial line) pig population. The results show similarities between the principal and the independent component methods and provided the most accurate genomic breeding estimates for most carcass traits in pigs.

Genomic selection for slaughter age in pigs using the Cox frailty model.

The aim of this study was to compare genomic selection methodologies using a linear mixed model and the Cox survival model. We used data from an F2 population of pigs, in which the response variable was the time in days from birth to the culling of the animal and the covariates were 238 markers [237 single nucleotide polymorphism (SNP) plus the halothane gene]. The data were corrected for fixed effects, and the accuracy of the method was determined based on the correlation of the ranks of predicted genomic breeding values (GBVs) in both models with the corrected phenotypic values. The analysis was repeated with a subset of SNP markers with largest absolute effects. The results were in agreement with the GBV prediction and the estimation of marker effects for both models for uncensored data and for normality. However, when considering censored data, the Cox model with a normal random effect (S1) was more appropriate. Since there was no agreement between the linear mixed model and the imputed data (L2) for the prediction of genomic values and the estimation of marker effects, the model S1 was considered superior as it took into account the latent variable and the censored data. Marker selection increased correlations between the ranks of predicted GBVs by the linear and Cox frailty models and the corrected phenotypic values, and 120 markers were required to increase the predictive ability for the characteristic analyzed.