Vasodilator activity of venom involves activation of the NO/cGMP pathway and inhibition of calcium influx to vascular smooth muscle cells.

Tarantula venoms may be a natural source of new vasodilator components useful in pharmacological research. Moreover, biological function data of the venoms are important to enhance the knowledge about the biodiversity and evolution of these species. The present study aims to describe the vasodilatory activity induced by the venom of on isolated rat aortic rings.

An R Package for Bayesian Analysis of Multi-environment and Multi-trait Multi-environment Data for Genome-Based Prediction.

Evidence that genomic selection (GS) is a technology that is revolutionizing plant breeding continues to grow. However, it is very well documented that its success strongly depends on statistical models, which are used by GS to perform predictions of candidate genotypes that were not phenotyped. Because there is no universally better model for prediction and models for each type of response variable are needed (continuous, binary, ordinal, count, etc.

An R Package for Multitrait and Multienvironment Data with the Item-Based Collaborative Filtering Algorithm.

The Item-Based Collaborative Filtering for Multitrait and Multienvironment Data (IBCF.MTME) package was developed to implement the item-based collaborative filtering (IBCF) algorithm for continuous phenotypic data in the context of plant breeding where data are collected for various traits and environments. The main difference between this package and the other available packages that can implement IBCF is that this one was developed for continuous phenotypic data, which cannot be implemented in the current packages because they can implement IBCF only for binary and ordinary phenotypes.

Correction to: Bayesian functional regression as an alternative statistical analysis of high-throughput phenotyping data of modern agriculture.

[This corrects the article DOI: 10.1186/s13007-018-0314-7.].

Bayesian functional regression as an alternative statistical analysis of high-throughput phenotyping data of modern agriculture.

Background: Modern agriculture uses hyperspectral cameras with hundreds of reflectance data at discrete narrow bands measured in several environments. Recently, Montesinos-López et al. (Plant Methods 13(4):1-23, 2017a.

A Variational Bayes Genomic-Enabled Prediction Model with Genotype × Environment Interaction.

There are Bayesian and non-Bayesian genomic models that take into account G×E interactions. However, the computational cost of implementing Bayesian models is high, and becomes almost impossible when the number of genotypes, environments, and traits is very large, while, in non-Bayesian models, there are often important and unsolved convergence problems. The variational Bayes method is popular in machine learning, and, by approximating the probability distributions through optimization, it tends to be faster than Markov Chain Monte Carlo methods.