The identification of genomic regions associated with reproductive traits as well as their biological processes allows a better understanding of the phenotypic variability of these traits. This information could be applied to animal breeding programs to accelerate genetic gain. The aim of this study was to evaluate the association between single nucleotide polymorphisms (SNP) with a scrotal circumference at 365 days of age (SC365) and at 450 days of age (SC450), gestation length (GL) as a calf trait, age at first calving (AFC), accumulated productivity (ACP), heifer early calving until 30 months (HC30), and stayability (STAY) traits, in order to identify candidate genes and biological pathways associated with reproductive traits in Nelore cattle. The data set consisted of pedigree, phenotypes, and genotypes of Nelore cattle from the “Associação Nacional de Criadores e Pesquisadores” (ANCP). The association analyses were performed using the Weighted Single-Step Genome-Wide Association method; the regions, consisting of 10 consecutive SNP, which explained more than 0.5% of additive genetic variance, were considered as a significant association. A total of 3, 6, 7, 5, 10, 25, and 12 windows were associated with SC355, SC450, GL, AFC, ACP, HC30, and STAY, respectively. The results revealed genes with important functions for reproductive traits, such as fertility and precocity. Some genes were associated with more than one trait, among them CAMK1D, TASP1, ACOXL, RAB11FIP5, and SFXN5. Moreover, the genes were enriched in functional terms, like negative regulation of fat cell differentiation, fatty acid alpha-oxidation, and sphingolipids signaling pathway. The identification of the genes associated with the traits, as well as genes enriched in the terms and pathway mentioned above, should contribute to future biological validation studies and may be used as candidate genes in Nelore breeding programs.

Knowledge of animal behavior can be indicative of the well-being, health, productivity, and reproduction of animals. The use of accelerometers to classify and predict animal behavior can be a tool for continuous animal monitoring. Therefore, the aim of this study was to provide strategies for predicting more and less frequent beef cattle grazing behaviors. The behavior activities observed were grazing, ruminating, idle, water consumption frequency (WCF), feeding (supplementation) and walking. Three Machine Learning algorithms: Random Forest (RF), Support Vector Machine (SVM) and Naïve Bayes Classifier (NBC) and two resample methods: under and over-sampling, were tested. Overall accuracy was higher for RF models trained with the over-sampled dataset. The greatest sensitivity (0.808) for the less frequent behavior (WCF) was observed in the RF algorithm trained with the under-sampled data. The SVM models only performed efficiently when classifying the most frequent behavior (idle). The greatest predictor in the NBC algorithm was for ruminating behavior, with the over-sampled training dataset. The results showed that the behaviors of the studied animals were classified with high accuracy and specificity when the RF algorithm trained with the resampling methods was used. Resampling training datasets is a strategy to be considered, especially when less frequent behaviors are of interest.

Natural and artificial selection leave footprints on the genome, known as selection signatures, that can indicate regions related to adaptive and economically important traits. The recurrent use of a limited number of sires and increased selection pressure may affect genetic diversity, potentially affecting long-term breeding programs. Among dairy cattle, the Holstein breed has been intensively selected to maximize productivity, particularly in Canada and the United States. In the dairy industry, the Gir breed plays an important role in milk production in tropical regions such as India and Latin America. Gir cattle were introduced into Brazil in the 19th and 20th centuries, and since 1985, this breed has been intensively selected for milk production. This study aimed to assess the genetic diversity and characterize the selection signatures in Holstein cattle from the United States and Canada (HOL), Gir cattle from India (GIR_IN), and Dairy Gir cattle from Brazil (GIR_BR). Genetic diversity was assessed by nucleotide diversity, single nucleotide variant density analysis, minor allele frequency, observed and expected heterozygosity, and the inbreeding coefficient. Selection signatures were identified via Tajima's D, the integrated haplotype score (iHS), the fixation index, and the cross-population extended haplotype homozygosity test for autosomes. Additionally, the analysis of selection signatures using Tajima's D and iHS was conducted for the X chromosome. Lower genetic diversity was observed in the HOL population, whereas the GIR_IN and GIR_BR populations presented greater diversity. Several genes previously related to economically important traits were identified as being under selection, including DNAJC18, FSHR, HELB, HMGA2, PLAG1, GAB3, and PTEN. In conclusion, the genes identified within the selection signatures were linked to several traits, including growth, reproduction, mastitis, milk production, heat tolerance, health, and adaptation.

The rapid evolution of high-throughput technologies in biosciences has generated diverse and voluminous datasets, requiring bioscientists to develop data manipulation and analysis skills. Python, known for its versatility and powerful libraries, has become a crucial tool for managing these datasets. However, there is a significant lack of programming training for bioscientists in many countries. To address this knowledge gap among scientists in Brazil, the Brazilian Python Workshop for Biological Data was introduced several years ago, focusing on basic programming concepts and data handling techniques using popular Python libraries. Despite the progress and positive feedback from earlier editions, challenges persisted, necessitating continuous adaptation and improvement to meet the evolving needs of bioscientists.This work describes the advancements made in the 2021 and 2022 editions of the workshop and discusses new suggestions for its ongoing enhancement. Key innovations were introduced in the workshop structure and coordination, including the creation of new committees and the establishment of a code of conduct. Feedback forms were updated to enable real-time adjustments during the event, improving its overall effectiveness. The workshop also expanded its reach by increasing geographical diversity among participants. New didactic strategies, such as pair-teaching, code clubs, and the integration of information and communication technologies (ICTs), were implemented to enhance learning outcomes. Programming best practices and scientific reproducibility were emphasized through talks and hands-on activities, guided by PEP8 conventions. Furthermore, efforts to enhance scientific dissemination were intensified, with an increased presence on social media and participation in international scientific events and communication networks. Finally, we present updated recommendations for students, researchers, and educators interested in organizing and promoting similar events, building on those previously described.

The rapid evolution of high‐throughput technologies in biosciences generates vast and diverse datasets, demanding that bioscientists develop advanced data manipulation and analysis skills. Python, with its versatility and powerful libraries, has become a crucial tool for managing these datasets. However, a significant lack of programming training for bioscientists persists in many countries. To address this knowledge gap in Brazil, the Brazilian Python Workshop for Biological Data was introduced several years ago, focusing on fundamental programming concepts and data handling techniques using popular Python libraries. Despite positive feedback from earlier editions, persistent challenges necessitated continuous adaptation to meet the evolving needs of bioscientists. This work describes the advancements implemented in the 2021 and 2022 editions of the workshop and discusses suggestions for its ongoing enhancement. Key innovations were introduced in the workshop's structure and coordination, including new committees and a code of conduct. Feedback forms were updated for real‐time adjustments, and the event's reach was expanded to increase geographical diversity. New didactic strategies, such as pair‐teaching, code clubs, and the integration of ICTs, were implemented to enhance learning outcomes. Programming best practices and scientific reproducibility were emphasized through talks and hands‐on activities guided by PEP8 conventions. Furthermore, scientific dissemination was intensified through an increased social media presence and participation in international events. Finally, we present updated recommendations for students, researchers, and educators interested in organizing similar initiatives.