Use of Injectable Progesterone as a Pre-Synchronisation Strategy in a Timed Artificial Insemination Protocol Based on Gonadotropin-Releasing Hormone and Progesterone in Bos indicus Beef Cows in Anoestrous.

This study evaluated the effects of pre-synchronisation with injectable progesterone (P4) on the ovarian follicular dynamics of Bos taurus indicus cows in anoestrous treated with a timed artificial insemination (TAI) protocol. Multiparous Nelore females (n = 47) at 30-60 days postpartum were used in this study. 10 days before (D-10) the TAI protocol, antral follicle count (AFC; follicles ≥ 3 mm), ovarian condition and body condition score (BCS; 1-5) were assessed and were randomly allocated into two groups: Pre-sync (n = 25), which underwent pre-synchronisation with 150 mg of injectable P4 intramuscularly (i.

Predicting the pulse of the Amazon: Machine learning insights into deforestation dynamics.

This study aims to analyze deforestation in the Brazilian Amazon from 1999 to 2020 using machine learning techniques to assess 16 critical factors. Our approach leverages the capabilities of machine learning, particularly Random Forest, which proved to be the most accurate model in terms of determination coefficient, mean squared error, and mean absolute error. The analysis revealed that the harvested area of permanent crops is the most influential variable in predicting deforestation, followed by the area of temporary crops.

Application of Artificial Intelligence to Automate the Reconstruction of Muscle Cross-Sectional Area Obtained by Ultrasound.

Purpose: Manual reconstruction (MR) of the vastus lateralis (VL) muscle cross-sectional area (CSA) from sequential ultrasound (US) images is accessible, is reproducible, and has concurrent validity with magnetic resonance imaging. However, this technique requires numerous controls and procedures during image acquisition and reconstruction, making it laborious and time-consuming. The aim of this study was to determine the concurrent validity of VL CSA assessments between MR and computer vision-based automated reconstruction (AR) of CSA from sequential images of the VL obtained by US.

Comparative study of oncology patients' quality of life.

Background: A cancer diagnosis has a significant impact on a person's life, both physically and emotionally. However, the oncology patients' QoL (QoL) at different stages of the disease has been under investigated.

Aim: To assess and compare the QoL in three groups of oncology patients.