To assess the effects of season and genotype on fresh semen quality for freezing and subsequent use for AI, 32 healthy, mature (>4 to <6 years old) and habituated bulls (Bos indicus n = 14, Bos taurus n = 5, Crossbred taurus x indicus n = 6, and tropically adapted Bos taurus composites n = 7) were evaluated at a Venezuelan AI center for 12 months in which four distinct seasons (Hot-dry, Transition, Hot-humid, Cool-rainy) occur. Ejaculates were collected weekly from Bos taurus (n = 260), Bos indicus (n = 669), tropically adapted Bos taurus composites (n = 389), and crossbred Bos taurus x Bos indicus (n = 340) bulls. Routine AI Center assessments were conducted i.e., ejaculate volume (EV), sperm mass-motility (MM), total sperm number/ejaculate (TSE), sperm concentration/mL (SC), pre-freezing (PREF), and post-freezing minimum criteria rate for AI use (POSTF). Genotype affected EV (P < 0.0001), TSE (P < 0.0001), and SC (P < 0.0001) but not MM (P>0.05). Season affected EV (P < 0.001), TSE (P < 0.0001), SC (P < 0.01), and MM (P < 0.05). There were genotype x season interactions for EV, MM, TSE, and SC. The PREF averaged 74.0% during the study, although was less (P < 0.0001) during the hot-humid season than the other seasons. Even though, percent ejaculates considered unsuitable for freezing differed (P < 0.03) among the Hot-dry (20.2%), transition (30.9%), Hot-humid (32.4%), and Cool-rainy (24%) seasons. For POSTF, there were no seasonal differences (P>0.05). It is concluded that in tropical regions, season and genotype can affect bull semen variables, particularly those which affect the success of semen freezing and AI.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1016/j.anireprosci.2020.106592 | DOI Listing |
Front Artif Intell
December 2024
School of Industrial Engineering and Management, Oklahoma State University, Stillwater, OK, United States.
The ability to accurately predict the yields of different crop genotypes in response to weather variability is crucial for developing climate resilient crop cultivars. Genotype-environment interactions introduce large variations in crop-climate responses, and are hard to factor in to breeding programs. Data-driven approaches, particularly those based on machine learning, can help guide breeding efforts by factoring in genotype-environment interactions when making yield predictions.
View Article and Find Full Text PDFBMC Plant Biol
December 2024
Center of Excellence in Genomics & Systems Biology (CEGSB) and Centre for Pre-breeding Research (CPBR), International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, 502324, India.
Pre-harvest sprouting (PHS) in groundnut leads to substantial yield losses and reduced seed quality, resulting in reduced market value of groundnuts. Breeding cultivars with 14-21 days of fresh seed dormancy (FSD) holds promise for precisely mitigating the yield and quality deterioration. In view of this, six multi-locus genome-wide association study (ML-GWAS) models alongside a single-locus GWAS (SL-GWAS) model were employed on a groundnut mini-core collection using multi season phenotyping and 58 K "Axiom_Arachis" array genotyping data.
View Article and Find Full Text PDFJ Clin Virol
December 2024
Division of Microbiology, Kingston Health Sciences Centre, Kingston, ON, Canada; Department of Pathology and Molecular Medicine, Queen's University, Kingston, ON, Canada; Infectious Disease Sequencing Laboratory, Kingston Health Sciences Centre, Kingston, ON, Canada; Gastrointestinal Disease Research Unit, Department of Medicine, Queen's University, Kingston, ON, Canada.
Background: Respiratory Syncytial Virus (RSV) infections are a cause of significant morbidity and mortality in children and the elderly. Despite the clinical burden of disease, very little is known about the inter- and intra-seasonal genomic variability of RSV. Furthermore, the recent approval of vaccines and monoclonal antibody therapies will likely lead to higher selective pressure on RSV.
View Article and Find Full Text PDFFront Cell Infect Microbiol
December 2024
National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases (NITFID), National Health Commission Key Laboratory for Medical Virology and Viral Diseases, National Institute for Viral Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
Introduction: This study, conducted in China prior to RotaTeq's launch, examined the epidemiological, molecular, and evolutionary features of the G1P[8] genotype RVA in children admitted with diarrhea, to aid in evaluating its efficacy and impact on G1P[8] RVA in China.
Methods: Data from the Chinese viral diarrhea surveillance network were collected from January 2016 to December 2018. RVA strains identified as the G1P[8] genotype were subjected to whole-genome sequencing.
Tanaffos
January 2024
Virology Research Center, National Research Institute of Tuberculosis and Lung Diseases (NRITLD), Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Background: Rhinoviruses are known as the leading pathogens of respiratory diseases. Determining the prevalence and phylogeny of rhinoviruses plays a pivotal role in producing vaccines and medications and preventing virus complications. This study investigated the frequency, and genetic variation of rhinoviruses detected in patients referred to Masih Daneshvari Hospital.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!