Data Science in Stata 16: Frames, Lasso, and Python Integration

Stata is one of the most widely used software for data analysis, statistics, and model fitting by economists, public policy researchers, epidemiologists, among others. Stata's recent release of version 16 in June 2019 includes an up-to-date methodological library and a user-friendly version of various cutting edge techniques. In the newest release, Stata has implemented several changes and additions that include:• Lasso• Multiple data sets in memory• Meta-analysis• Choice models• Python integration• Bayes-multiple chains• Panel-data ERMs• Sample-size analysis for CIs• Panel-data mixed logit• Nonlinear DSGE models• Numerical integrationThis review covers the most salient innovations in Stata 16. It is the first release that brings along an implementation of machine-learning tools. The three innovations we considered are: (1) Multiple data sets in Memory, (2) Lasso for causal inference, and (3) Python integration

Solucionando necesidades específicas con gnu/linux “aplicando funcionalidades de nethserver”

El presente articulo tiene como fin demostrar la implementación del servidor Nethserver el cual permite configurar hasta 4 zonas de red, sin embargo, para el desarrollo de las temáticas se tendrán en cuenta hasta 3 zonas identificadas como roja, verde y naranja; teniendo en cuenta la configuración del segmento de red de cada una de ellas, se procederá a demostrar el funcionamiento de algunos de los servicios que se pueden administrar en Nethserver.The purpose of this article is to demonstrate the implementation of the Nethserver server, which allows configuring up to 4 network zones. However, for the development of the topics, consideration will be given to up to 3 zones identified as red, green, and orange. Taking into account the configuration of the network segment for each of them, the demonstration will proceed to showcase the operation of some of the services that can be managed in Nethserver

Extracellular Vesicles Secreted by Pre-Hatching Bovine Embryos Produced In Vitro and In Vivo Alter the Expression of IFNtau-Stimulated Genes in Bovine Endometrial Cells

The embryo-maternal interaction occurs during the early stages of embryo development and is essential for the implantation and full-term development of the embryo. In bovines, the secretion of interferon Tau (IFNT) during elongation is the main signal for pregnancy recognition, but its expression starts around the blastocyst stage. Embryos release extracellular vesicles (EVs) as an alternative mechanism of embryo-maternal communication. The aim of the study was to determine whether EVs secreted by bovine embryos during blastulation (D5-D7) could induce transcriptomic modifications, activating IFNT signaling in endometrial cells. Additionally, it aims to assess whether the EVs secreted by embryos produced in vivo (EVs-IVV) or in vitro (EVs-IVP) have different effects on the transcriptomic profiles of the endometrial cells. In vitro- and in vivo-produced bovine morulae were selected and individually cultured for 48 h to collect embryonic EVs (E-EVs) secreted during blastulation. E-EVs stained with PKH67 were added to in vitro-cultured bovine endometrial cells to assess EV internalization. The effect of EVs on the transcriptomic profile of endometrial cells was determined by RNA sequencing. EVs from both types of embryos induced several classical and non-classical IFNT-stimulated genes (ISGs) and other pathways related to endometrial function in epithelial endometrial cells. Higher numbers of differentially expressed genes (3552) were induced by EVs released by IVP embryos compared to EVs from IVV (1838). Gene ontology analysis showed that EVs-IVP/IVV induced the upregulation of the extracellular exosome pathway, the cellular response to stimulus, and the protein modification processes. This work provides evidence regarding the effect of embryo origin (in vivo or in vitro) on the early embryo-maternal interaction mediated by extracellular vesicles