Heat Stress in Japanese Quails (): Benefits of Phytase Supplementation.

In tropical and subtropical climate regions, heat stress is one of the main causes of production losses in laying quails, aggravated by the antinutritional effects of the phytate in diet ingredients, which negatively affect the bioavailability of minerals, especially calcium and phosphorus. This situation results in a reduction in production and the quality of eggs from commercial laying quails. Several nutritional strategies are utilized to reduce the adverse effects of high temperatures and antinutritional factors such as phytate.

How does the adoption of nutritional plans influence the performance of meat quail during the growth phase in hot environments?

The present study aimed to evaluate the effects of different nutritional plans on meat quails subjected to heat stress. A total of 324 quails male European quails () were used, with an average initial weight of 121.48 g ± 3.

Platelet-neutrophil aggregate formation induces NLRP3 inflammasome activation in vaccine-induced thrombotic thrombocytopenia.

Background: Although rare, vaccine-induced thrombotic thrombocytopenia (VITT) following adenoviral vector COVID-19 vaccination is a concerning and often severe adverse effect of vaccination. The generation of high antiplatelet factor 4 antibody titers promotes the formation of immune complexes capable of activating platelets and neutrophils through FcγRIIa.

Objectives: Given that platelet-leukocyte aggregate formation and inflammasome activation are common features of thromboinflammatory diseases, we aimed to evaluate if these are also features of VITT.

A Bayesian Approach for Modeling and Forecasting Solar Photovoltaic Power Generation.

In this paper, we propose a Bayesian approach to estimate the curve of a function f(·) that models the solar power generated at moments per day for days and to forecast the curve for the (n+1)th day by using the history of recorded values. We assume that f(·) is an unknown function and adopt a Bayesian model with a Gaussian-process prior on the vector of values f(t)=f(1),…, f(k). An advantage of this approach is that we may estimate the curves of f(·) and fn+1(·) as "smooth functions" obtained by interpolating between the points generated from a -variate normal distribution with appropriate mean vector and covariance matrix.