The Probiotic Lactobacillus sakei Subsp. Sakei and Hawthorn Extract Supplements Improved Growth Performance, Digestive Enzymes, Immunity, and Resistance to the Pesticide Acetamiprid in Common Carp (Cyprinus carpio).

This study evaluated the impacts of the probiotic, Lactobacillus sakei (L. sakei), and the extract of hawthorn, Crataegus elbursensis, on growth and immunity of the common carp exposed to acetamiprid. Fish (mean ± SE: 11.48 ± 0.1 g) feeding was done with formulated diets (T 1 (control): no supplementation, T 2: 1 × 106 CFU/g LS (Lactobacillus sakei), T3: 1 × 108 CFU/g LS, T 4: 0.5% hawthorn extract (HWE), and T 5: 1% HWE) for 60 days and then exposed to acetamiprid for 14 days. The growth performance improved in the fish fed LS at dietary level of 1 × 108 CFU/g, even after exposure to acetamiprid (P < 0.05). Intestinal Lactobacillus sakei (CFU/g) load increased (P < 0.05), following supplementation with the probiotic-enriched diet. The LS-treated fish had increases in the activity of digestive enzymes (P < 0.05). Both LS and HWE stimulated antioxidant enzymes and immune system components in serum and mucus (alkaline phosphatase (ALP), protease, total Ig, and lysozyme) (P < 0.05). However, the changes were different depending on the kind of the supplement. The malondialdehyde (MDA) levels decreased in HWE-treated fish after acetamiprid exposure (P < 0.05). Both LS and HWE reduced the liver metabolic enzymes (LDH, ALP, AST, ALT, and LDH) in serum both before and after exposure to the pesticide (P < 0.05). However, each enzyme exhibited a different change trend depending on the type of the supplement. HWE showed a stress-ameliorating effect, as glucose and cortisol levels declined in the HWE-treated fish (P < 0.05). This study indicated the immunomodulatory impacts of LS (1 × 108 CFU/g) and HWE (at dietary levels of 0.5-1%). The probiotic showed more performance compared to HWE. However, the HWE mitigated oxidative stress more efficiently than the probiotic.

The reciprocal Bayesian LASSO.

A reciprocal LASSO (rLASSO) regularization employs a decreasing penalty function as opposed to conventional penalization approaches that use increasing penalties on the coefficients, leading to stronger parsimony and superior model selection relative to traditional shrinkage methods. Here we consider a fully Bayesian formulation of the rLASSO problem, which is based on the observation that the rLASSO estimate for linear regression parameters can be interpreted as a Bayesian posterior mode estimate when the regression parameters are assigned independent inverse Laplace priors. Bayesian inference from this posterior is possible using an expanded hierarchy motivated by a scale mixture of double Pareto or truncated normal distributions. On simulated and real datasets, we show that the Bayesian formulation outperforms its classical cousin in estimation, prediction, and variable selection across a wide range of scenarios while offering the advantage of posterior inference. Finally, we discuss other variants of this new approach and provide a unified framework for variable selection using flexible reciprocal penalties. All methods described in this article are publicly available as an R package at: https://github.com/himelmallick/BayesRecipe.

New Gibbs sampling methods for bayesian regularized quantile regression.

In this paper, we propose new Bayesian hierarchical representations of lasso, adaptive lasso and elastic net quantile regression models. We explore these representations by observing that the lasso penalty function corresponds to a scale mixture of truncated normal distribution (with exponential mixing densities). We consider fully Bayesian treatments that lead to new Gibbs sampler methods with tractable full conditional posteriors. The new methods are then illustrated with both simulated and real data. Results show that the new methods perform very well under a variety of simulations, such as the presence of a moderately large number of predictors, collinearity and heterogeneity.

The Bayesian adaptive lasso regression.

Classical adaptive lasso regression is known to possess the oracle properties; namely, it performs as well as if the correct submodel were known in advance. However, it requires consistent initial estimates of the regression coefficients, which are generally not available in high dimensional settings. In addition, none of the algorithms used to obtain the adaptive lasso estimators provide a valid measure of standard error. To overcome these drawbacks, some Bayesian approaches have been proposed to obtain the adaptive lasso and related estimators. In this paper, we consider a fully Bayesian treatment for the adaptive lasso that leads to a new Gibbs sampler with tractable full conditional posteriors. Through simulations and real data analyses, we compare the performance of the new Gibbs sampler with some of the existing Bayesian and non-Bayesian methods. Results show that the new approach performs well in comparison to the existing Bayesian and non-Bayesian approaches.

Gene selection for microarray gene expression classification using Bayesian Lasso quantile regression.

Gene selection has been proven to be an effective way to improve the results of many classification methods. However, existing gene selection techniques in binary classification regression are sensitive to outliers of the data, heteroskedasticity or other anomalies of the latent response. In this paper, we propose a new Bayesian hierarchical model to overcome these problems in a relatively straightforward way. In particular, we propose a new Bayesian Lasso method that employs a skewed Laplace distribution for the errors and a scaled mixture of uniform distribution for the regression parameters, together with Bayesian MCMC estimation. Comprehensive comparisons between our proposed gene selection method and other competitor methods are performed experimentally, depending on four benchmark gene expression datasets. The experimental results prove that the proposed method is very effective for selecting the most relevant genes with high classification accuracy.

Statistical methods for body mass index: A selective review.

Obesity rates have been increasing over recent decades, causing significant concern among policy makers. Excess body fat, commonly measured by body mass index, is a major risk factor for several common disorders including diabetes and cardiovascular disease, placing a substantial burden on health care systems. To guide effective public health action, we need to understand the complex system of intercorrelated influences on body mass index. This paper, based on all eligible articles searched from Global health, Medline and Web of Science databases, reviews both classical and modern statistical methods for body mass index analysis. We give a description of each of these methods, exploring the classification, links and differences between them and the reasons for choosing one over the others in different settings. We aim to provide a key resource and statistical library for researchers in public health and medicine to deal with obesity and body mass index data analysis.