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643 results for “cattle”
Assessment of animal diseases caused by bacteria resistant to antimicrobials: Cattle- Appendix B: Excel file with all data extracted
<p>Information on all the full-text studies that were assessed, including the reason for exclusion for those that were excluded at the full-text screening and the data extracted from the included studies, can be consulted here. </p> <p>The extensive literature review was carried out by the University of Copenhagen under the contract OC/EFSA/ALPHA/2020/02 – LOT 1 (https://ted.europa.eu/udl?uri=TED:NOTICE:457654-2020:TEXT:EN:HTML)</p>
New Zealand Cattle Detection
<p>Remote sensing dataset for detecting cows from high resolution aerial images.</p> <p>Images: Aerial RGB images with spatial resolution of 0.1m. 500 x 500 pixels corresponding to 50m x 50m<br> Label: point annotation of each visible cattle in the image<br> Image Sources: Land Information New Zealand 2016-2019<br> Total number of images: 655<br> Total number of cows in all images: 29803</p> <p><em>Contains data sourced from the <a href="https://data.linz.govt.nz/">LINZ Data Service</a> licensed for reuse under <a href="https://creativecommons.org/licenses/by/4.0/">CC BY 4.0</a></em></p>
Beef Cattle Muzzle/Noseprint database for individual identification
<p>This dataset contains muzzle/noseprint images for beef cattle. A total of 4923 muzzle images for 268 feedyard yearlings in the Midwest US were collected from March to July 2021, using a mirrorless digital camera (26 MP maximum resolution) and a 70-300 mm F4-5.6 focal lens. All images were taken outside of the pen did not create any contact or interference with the animals. These images covered three common US feedyard cattle breeds, including Angus, Angus x Hereford, and Continental x British cross). This database only contains the clean and cropped images showing the cattle muzzle area. </p> <p>All images are housed in individual folders in the <strong>.zip file: </strong>“BeefCattle_Muzzle_database.zip”. Each folder contains pictures from the same animal. On average, there were more than 12 images collected for each animal. </p>
Low-intensity cattle grazing is better than cattle exclusion to drive secondary savannas towards the features of native Cerrado vegetation
<p><span>Although livestock have been historically associated with land conversion and biodiversity loss, well-managed cattle grazing has been reported to contribute to conservation of open ecosystems. Knowing the balance between positive and negative effects of livestock (presence or exclusion) on different ecosystems is, therefore, crucial to support management decisions. </span><span>We conducted an experiment </span><span>in a secondary savanna with exotic grasses, used as pasture, to assess the effect of cattle presence in low density and cattle exclusion (in paired plots) on the trajectory of these ecosystems. Richness, composition and structure of the woody community, and exotic grass cover and biomass were compared between treatments in the beginning of the experiment and after seven years. At the end of the experiment, we also compared composition, richness, and density of the native ground layer. We verified that (a) cattle exclusion accelerates the undesirable woody encroachment, changes the species composition and leads to huge grass fuel accumulation, while (b) cattle grazing/browsing hinders changes in savanna structure and composition and reduces the exotic grass cover and biomass, thus favoring native herbaceous plants. By decreasing the grass biomass, cattle grazing also reduces the system flammability and, therefore, the risk and intensity of wildfires. Together, the positive effects of cattle presence and the negative effects of cattle exclusion lead to the conclusion that cattle should be maintained in these systems. Low-intensity cattle grazing limits woody and exotic grass invasion, improves native forb biodiversity, and help maintain </span><span>composition and structural features of secondary savannas of the Cerrado.</span></p>
Evolution of allele frequencies in the cattle breed Asturiana de los Valles
<p>Genotype data for 153 animals from the Asturiana de los Valles bovine breed, with birth dates from 1980 to 2013. These genotypes were obtained from 3 different sources: </p> <p>- 88 sires were genotyped using the Illumina’s BovineSNP50 v.2 chip; the resulting data are provided at plink ped/map format (asturiana_50K.tar.xz).</p> <p>- 50 animals (25 sires and 25 dams) were genotyped using the Illumina’s Bovine High Density BeadChip 770K SNP; the resulting data are provided at plink ped/map format (asturiana_800K.tar.xz).</p> <p>- 15 sires were sequenced on a HiSeq 3,000; the resulting genotype calls are provided at vcf format (asturiana_WGS_SNP.vcf.gz).</p> <p>Boitard et al (2021) combined these 3 datasets in order to detect recent and historical selection signatures in this breed. The scripts used for this analysis can be found at https://github.com/sboitard/Asturiana_analysis.</p>
Effect of using mycotoxin-detoxifying agents in dairy cattle feed on natural whey starter biodiversity
<p><strong>Supplemental Figure S1</strong>: p-values for alpha diversity index in the milk whey microbiota between treatments and controls. The control group for BD1 (ctr_BD1) was taken as reference class in the model. </p>
Whole genome sequence variant discovery from Ethiopian Boran, N'Dama and Holstein cattle
<p>Whole genome sequence variants (SNPs) from forty samples of each Ethiopian Boran, N'Dama and Holstein cattle breeds which were utilised in the Assessment of genotyping array performance for genome-wide association studies and imputation in African cattle study by Riggio et al., 2022, (in review). The NDama sequences included samples from Guinea (n =21), Nigeria (n =10) and Senegal (n = 9); and the Boran samples from Ethiopia (n = 30) and Kenya (n = 10). Variants discovery followed the protocol described in the Material and Method section of the paper.</p>
Illumina HD genotypes for 3,092 cattle from Burkina Faso, Ghana, Nigeria and Tanzania for: "Assessment of genotyping array performance for genome-wide association studies and imputation in African cattle"
<p>Raw HD data for Riggio et al. 2022: Assessment of genotyping array performance for genome-wide association studies and imputation in African cattle</p> <p>This repository contains the raw Illumina HD genotypes (i.e., 777,962 SNPs) mapped to the bovine UMD3.1 genome assembly for 3,092 animals from four African countries (namely Burkina Faso, Ghana, Nigeria and Tanzania). </p>
Lambeth Road Cattle Trough
A London Metropolitan Drinking Fountain Association cattle trough on the junction of Lambeth Road and Kennington Road, London. 119 photos taken in July 2020 with a Sony a6000 and processed in Reality Capture. Source: Objaverse 1.0 / Sketchfab
Cattle Trough Woolwich Market
A London Metropolitan Drinking Fountain Association cattle trough at the north west corner of Woolwich Market (this is the central one of 3 here), London. 179 photos taken in May 2022 with a Sony a7R III and processed in Reality Capture. Source: Objaverse 1.0 / Sketchfab
Cattle Trough Finsbury Park
A London Metropolitan Drinking Fountain Association cattle trough at the west gate of the service area of the Bowls Club at the centre of Finsbury Park, London. 209 photos taken in September 2022 with a Sony a7R III and processed in Reality Capture. Source: Objaverse 1.0 / Sketchfab
Cattle Trough Finsbury Square
One of two London Metropolitan Drinking Fountain Association cattle troughs on the south side of Finsbury Square, London. This one is the furthest west. 193 photos taken in June 2020 with a Sony a6000 and processed in Reality Capture. Source: Objaverse 1.0 / Sketchfab
Cattle Trough Albion Road
A London Metropolitan Drinking Fountain Association cattle trough on Albion Road and Clissold Crescent, London. Photos taken in February 2020 with a Sony a6000 and processed in Reality Capture. Source: Objaverse 1.0 / Sketchfab
Cattle Trough Stamford Hill
A London Metropolitan Drinking Fountain Association cattle trough outside the Stamford Hill Library on the A10 near the junction with Portland Avenue, London. 278 photos taken in June 2022 with a Sony a7R III and processed in Reality Capture. Source: Objaverse 1.0 / Sketchfab
Cattle Trough Bexley Road
A London Metropolitan Drinking Fountain Association cattle trough on Bexley Road at the junction with Gravel Pit Lane, London. 221 photos taken in June 2022 with a Sony a7R III and processed in Reality Capture. Source: Objaverse 1.0 / Sketchfab
Cattle Trough Clapham The Pavement
A London Metropolitan Drinking Fountain Association cattle trough on the middle of the junction of The Pavement and Clapham Common South Side, London. 398 photos taken in May 2021 with a Sony a6000 and processed in Reality Capture. Source: Objaverse 1.0 / Sketchfab
Cattle Trough London Wall
A cattle drinking trough on London Wall near the Museum of London. 193 photos taken in October 2019 with a Sony a6000 and processed in Reality Capture. Source: Objaverse 1.0 / Sketchfab
Data from: Genome-wide scans reveal selection signatures and cross-population variation in South African and European beef cattle breeds
<p>In genetics and evolutionary biology, the concept of selection signatures is used to describe specific patterns in the genome that are associated with the process of natural selection. These selection signatures provide insights into how evolutionary forces have shaped a population over time.In this study, a total of 96 samples were collected in several farms from four different cattle breeds, namely South African indigenous Nguni (n = 28) and Bonsmara (n = 21), Scottish Angus (n = 22), and Swedish Simmental (n = 25). Genotyped samples were subjected to quality control, and a total of 105,675 SNPs from 78 individuals remained for further analysis. Genomic signatures of positive selection within each breed were identified using the Integrated Haplotype Score (iHS) method, and cross-population comparison analysis using cross-population extended haplotype homozygosity ( XP-EHH), relative extended haplotype homozygosity (Rsb), and fixation index (Fst) methods, to assess the genetic differences between breeds. The results from the iHS method revealed selection signatures in two genomic regions for Bonsmara, six for Simmental, four for Nguni, and one for Angus cattle. Ten regions were found to be under selection, with BTA 12 being shared between Nguni and Bonsmara. Comparisons across populations using Rsb, and Fst methods performed better and revealed the most specific genomic regions that varied in selection between breeds. Gene annotation analyses linked candidate genes to several Quantitative Trait Loci (QTL). For example, in Simmental cattle's FAM110B gene was linked to carcass weight and body confirmation score. Bonsmara showed fewer candidate genes, such as CDK8 and FLT1, whereas Angus had none on BTA 18. Nguni identified potential genes such as CRB1, PLAG2GA, and VASH2, with CDK8 shared by Bonsmara and Nguni on BTA 12. Further cross-population studies revealed candidate genes associated with certain traits, genes including as PLCXD3, FAM149B1, and GRIK2 for Bonsmara versus Nguni, and SLIT2 and TSPAN9 for Simmental vs Angus. The study also emphasised gene related to meat quality, reproduction, health, illnesses, fertility, and body conformation score. Gene interaction study with the STRING database revealed a network of 63 candidate genes, demonstrating the structure of genetic connections, some biological processes. The study found that iHS performed well in population analysis with Nguni cattle, having exhibited the highest number of signatures across the genome, and significant signatures were also seen in comparisons between Nguni and Bonsmara using the Fst and Rsb methods. Furthermore, the study discovered that a bigger number of genes were connected with various traits, including sperm count and insemination per conception, sensitivity to bovine respiratory disease, and ease of calving. This genomic analysis underlined the relevance of the genetic relying which distinguishes distinct breeds. This understanding has the potential to significantly enhance selective breeding and increase desirable traits in cattle herds. This genomic analysis underlined the significance of the genetic basis for breed-specific traits. This understanding has the potential to drastically improve selective breeding and increase desirable traits in cattle herds.</p>
Data from: Consequences of gene editing of PRLR on thermotolerance, growth, and male reproduction in cattle
<p>Global warming is a major challenge to the sustainable and humane production of food because of the increased risk of livestock to heat stress. Here, the example of the prolactin receptor (<em>PRLR</em>) gene is used to demonstrate how gene editing can increase the resistance of cattle to heat stress by the introduction of mutations conferring thermotolerance. Several cattle populations in South and Central America possess natural mutations in <em>PRLR</em> that result in affected animals having short hair and being thermotolerant. CRISPR/Cas9 technology was used to introduce variants of <em>PRLR</em> in two thermosensitive breeds of cattle – Angus and Jersey. Gene-edited animals exhibited superior ability to regulate vaginal temperature (heifers) and rectal temperature (bulls) compared to animals that were not gene-edited. Moreover, gene-edited animals exhibited superior growth characteristics. There was no evidence for deleterious effects of the mutation on carcass characteristics or male reproductive function. These results indicate the potential for reducing heat stress in relevant environments to enhance cattle productivity. <strong><br></strong></p>
A high coverage Mesolithic aurochs genome and effective leveraging of ancient cattle genomes using whole genome imputation. -- VCF file
<p>This is the open-access VCF file that was created in the article: "<strong>A high coverage Mesolithic aurochs genome and effective leveraging of ancient cattle genomes using whole genome imputation."</strong></p> <p><strong>Information about the filtering steps can be found in the method section.</strong></p> <p>Extra information on the sample IDs can be found in the Supplementary tables.</p>
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