Hasil untuk "Cattle"

C. Elsik, R. Tellam, K. Worley

A survey of genetic diversity of cattle suggests two domestication events in Asia and selection by husbandry. Not Just Dinner on Legs Several thousand years ago, human beings realized the virtues of domesticating wild animals as easy meat. Soon other possibilities became apparent, and as revealed in a series of papers in this issue, early pastoralists became selective about breeding for wool, leather, milk, and muscle power. In two papers, Gibbs et al. report on the bovine genome sequence (p. 522; see the cover, the Perspective by Lewin, and the Policy Forum by Roberts) and trace the diversity and genetic history of cattle (p. 528), while Chessa et al. (p. 532) survey the occurrence of endogenous retroviruses in sheep and map their distribution to historical waves of human selection and dispersal across Europe. Finally, Ludwig et al. (p. 485) note the origins of variation in the coat-color of horses and suggest that it is most likely to have been selected for by humans in need of good-looking transport. To understand the biology and evolution of ruminants, the cattle genome was sequenced to about sevenfold coverage. The cattle genome contains a minimum of 22,000 genes, with a core set of 14,345 orthologs shared among seven mammalian species of which 1217 are absent or undetected in noneutherian (marsupial or monotreme) genomes. Cattle-specific evolutionary breakpoint regions in chromosomes have a higher density of segmental duplications, enrichment of repetitive elements, and species-specific variations in genes associated with lactation and immune responsiveness. Genes involved in metabolism are generally highly conserved, although five metabolic genes are deleted or extensively diverged from their human orthologs. The cattle genome sequence thus provides a resource for understanding mammalian evolution and accelerating livestock genetic improvement for milk and meat production.

L. Matukumalli, C. Lawley, R. Schnabel et al.

The success of genome-wide association (GWA) studies for the detection of sequence variation affecting complex traits in human has spurred interest in the use of large-scale high-density single nucleotide polymorphism (SNP) genotyping for the identification of quantitative trait loci (QTL) and for marker-assisted selection in model and agricultural species. A cost-effective and efficient approach for the development of a custom genotyping assay interrogating 54,001 SNP loci to support GWA applications in cattle is described. A novel algorithm for achieving a compressed inter-marker interval distribution proved remarkably successful, with median interval of 37 kb and maximum predicted gap of <350 kb. The assay was tested on a panel of 576 animals from 21 cattle breeds and six outgroup species and revealed that from 39,765 to 46,492 SNP are polymorphic within individual breeds (average minor allele frequency (MAF) ranging from 0.24 to 0.27). The assay also identified 79 putative copy number variants in cattle. Utility for GWA was demonstrated by localizing known variation for coat color and the presence/absence of horns to their correct genomic locations. The combination of SNP selection and the novel spacing algorithm allows an efficient approach for the development of high-density genotyping platforms in species having full or even moderate quality draft sequence. Aspects of the approach can be exploited in species which lack an available genome sequence. The BovineSNP50 assay described here is commercially available from Illumina and provides a robust platform for mapping disease genes and QTL in cattle.

S. Dowd, T. Callaway, R. Wolcott et al.

BackgroundThe microbiota of an animal's intestinal tract plays important roles in the animal's overall health, productivity and well-being. There is still a scarcity of information on the microbial diversity in the gut of livestock species such as cattle. The primary reason for this lack of data relates to the expense of methods needed to generate such data. Here we have utilized a bacterial tag-encoded FLX 16s rDNA amplicon pyrosequencing (bTEFAP) approach that is able to perform diversity analyses of gastrointestinal populations. bTEFAP is relatively inexpensive in terms of both time and labor due to the implementation of a novel tag priming method and an efficient bioinformatics pipeline. We have evaluated the microbiome from the feces of 20 commercial, lactating dairy cows.ResultsUbiquitous bacteria detected from the cattle feces included Clostridium, Bacteroides, Porpyhyromonas, Ruminococcus, Alistipes, Lachnospiraceae, Prevotella, Lachnospira, Enterococcus, Oscillospira, Cytophage, Anaerotruncus, and Acidaminococcus spp. Foodborne pathogenic bacteria were detected in several of the cattle, a total of 4 cows were found to be positive for Salmonella spp (tentative enterica) and 6 cows were positive for Campylobacter spp. (tentative lanienae).ConclusionUsing bTEFAP we have examined the microbiota in the feces of cattle. As these methods continue to mature we will better understand the ecology of the major populations of bacteria the lower intestinal tract. This in turn will allow for a better understanding of ways in which the intestinal microbiome contributes to animal health, productivity and wellbeing.

R. Kambadur, Mridula Sharma, T. Smith et al.

A visibly distinct muscular hypertrophy (mh), commonly known as double muscling, occurs with high frequency in the Belgian Blue and Piedmontese cattle breeds. The autosomal recessive mh locus causing double-muscling condition in these cattle maps to bovine chromosome 2 within the same interval as myostatin, a member of the TGF-beta superfamily of genes. Because targeted disruption of myostatin in mice results in a muscular phenotype very similar to that seen in double-muscled cattle, we have evaluated this gene as a candidate gene for double-muscling condition by cloning the bovine myostatin cDNA and examining the expression pattern and sequence of the gene in normal and double-muscled cattle. The analysis demonstrates that the levels and timing of expression do not appear to differ between Belgian Blue and normal animals, as both classes show expression initiating during fetal development and being maintained in adult muscle. Moreover, sequence analysis reveals mutations in heavy-muscled cattle of both breeds. Belgian Blue cattle are homozygous for an 11-bp deletion in the coding region that is not detected in cDNA of any normal animals examined. This deletion results in a frame-shift mutation that removes the portion of the Myostatin protein that is most highly conserved among TGF-beta family members and that is the portion targeted for disruption in the mouse study. Piedmontese animals tested have a G-A transition in the same region that changes a cysteine residue to a tyrosine. This mutation alters one of the residues that are hallmarks of the TGF-beta family and are highly conserved during evolution and among members of the gene family. It therefore appears likely that the mh allele in these breeds involves mutation within the myostatin gene and that myostatin is a negative regulator of muscle growth in cattle as well as mice.

L. Grisi, R. C. Leite, J. R. Martins et al.

The profitability of livestock activities can be diminished significantly by the effects of parasites. Economic losses caused by cattle parasites in Brazil were estimated on an annual basis, considering the total number of animals at risk and the potential detrimental effects of parasitism on cattle productivity. Estimates in U.S. dollars (USD) were based on reported yield losses among untreated animals and reflected some of the effects of parasitic diseases. Relevant parasites that affect cattle productivity in Brazil, and their economic impact in USD billions include: gastrointestinal nematodes - $7.11; cattle tick (Rhipicephalus (Boophilus) microplus) - $3.24; horn fly (Haematobia irritans) - $2.56; cattle grub (Dermatobia hominis) - $0.38; New World screwworm fly (Cochliomyia hominivorax) - $0.34; and stable fly (Stomoxys calcitrans) - $0.34. The combined annual economic loss due to internal and external parasites of cattle in Brazil considered here was estimated to be at least USD 13.96 billion. These findings are discussed in the context of methodologies and research that are required in order to improve the accuracy of these economic impact assessments. This information needs to be taken into consideration when developing sustainable policies for mitigating the impact of parasitism on the profitability of Brazilian cattle producers.

S. Mao, Mengling Zhang, Junhua Liu et al.

The bacterial community composition and function in the gastrointestinal tracts (GITs) of dairy cattle is very important, since it can influence milk production and host health. However, our understanding of bacterial communities in the GITs of dairy cattle is still very limited. This study analysed bacterial communities in ten distinct GIT sites (the digesta and mucosa of the rumen, reticulum, omasum, abomasum, duodenum, jejunum, ileum, cecum, colon and rectum) in six dairy cattle. The study observed 542 genera belonging to 23 phyla distributed throughout the cattle GITs, with the Firmicutes, Bacteroidetes and Proteobacteria predominating. In addition, data revealed significant spatial heterogeneity in composition, diversity and species abundance distributions of GIT microbiota. Furthermore, the study inferred significant differences in the predicted metagenomic profiles among GIT regions. In particular, the relative abundances of the genes involved in carbohydrate metabolism were overrepresented in the digesta samples of forestomaches and the genes related to amino acid metabolism were mainly enriched in the mucosal samples. In general, this study provides the first deep insights into the composition of GIT microbiota in dairy cattle and it may serve as a foundation for future studies in this area.

Nazibul Basar Ayon, Abdul Hasib, Md. Faishal Ahmed et al.

Lumpy Skin Disease (LSD) and Foot-and-Mouth Disease (FMD) are highly contagious viral diseases affecting cattle, causing significant economic losses and welfare challenges. Their visual diagnosis is complicated by significant symptom overlap with each other and with benign conditions like insect bites or chemical burns, hindering timely control measures. Leveraging a comprehensive dataset of 10,516 expert-annotated images from 18 farms across India, Brazil, and the USA, this study presents a novel Ensemble Deep Learning framework integrating VGG16, ResNet50, and InceptionV3 with optimized weighted averaging for simultaneous LSD and FMD detection. The model achieves a state-of-the-art accuracy of 98.2\%, with macro-averaged precision of 98.2\%, recall of 98.1\%, F1-score of 98.1\%, and an AUC-ROC of 99.5\%. This approach uniquely addresses the critical challenge of symptom overlap in multi-disease detection, enabling early, precise, and automated diagnosis. This tool has the potential to enhance disease management, support global agricultural sustainability, and is designed for future deployment in resource-limited settings.

Zhou Tang, Jin Wang, Angelo De Castro et al.

Body condition score (BCS) is a widely used indicator of body energy status and is closely associated with metabolic status, reproductive performance, and health in dairy cattle; however, conventional visual scoring is subjective and labor-intensive. Computer vision approaches have been applied to BCS prediction, with depth images widely used because they capture geometric information independent of coat color and texture. More recently, three-dimensional point cloud data have attracted increasing interest due to their ability to represent richer geometric characteristics of animal morphology, but direct head-to-head comparisons with depth image-based approaches remain limited. In this study, we compared top-view depth image and point cloud data for BCS prediction under four settings: 1) unsegmented raw data, 2) segmented full-body data, 3) segmented hindquarter data, and 4) handcrafted feature data. Prediction models were evaluated using data from 1,020 dairy cows collected on a commercial farm, with cow-level cross-validation to prevent data leakage. Depth image-based models consistently achieved higher accuracy than point cloud-based models when unsegmented raw data and segmented full-body data were used, whereas comparable performance was observed when segmented hindquarter data were used. Both depth image and point cloud approaches showed reduced accuracy when handcrafted feature data were employed compared with the other settings. Overall, point cloud-based predictions were more sensitive to noise and model architecture than depth image-based predictions. Taken together, these results indicate that three-dimensional point clouds do not provide a consistent advantage over depth images for BCS prediction in dairy cattle under the evaluated conditions.

Druva Dhakshinamoorthy, Avikshit Jha, Sabyasachi Majumdar et al.

This paper presents a novel system for monitoring cattle behavior and detecting estrus (heat) periods using sensor data and machine learning. We designed and deployed a low-cost Bluetooth-based neck collar equipped with accelerometer and gyroscope sensors to capture real-time behavioral data from real cows, which was synced to the cloud. A labeled dataset was created using synchronized CCTV footage to annotate behaviors such as feeding, rumination, lying, and others. We evaluated multiple machine learning models -- Support Vector Machines (SVM), Random Forests (RF), and Convolutional Neural Networks (CNN) -- for behavior classification. Additionally, we implemented a Long Short-Term Memory (LSTM) model for estrus detection using behavioral patterns and anomaly detection. Our system achieved over 93% behavior classification accuracy and 96% estrus detection accuracy on a limited test set. The approach offers a scalable and accessible solution for precision livestock monitoring, especially in resource-constrained environments.

Maarten Perneel, Ines Adriaens, Ben Aernouts et al.

Over the past decade, studying animal behaviour with the help of computer vision has become more popular. Replacing human observers by computer vision lowers the cost of data collection and therefore allows to collect more extensive datasets. However, the majority of available computer vision algorithms to study animal behaviour is highly tailored towards a single research objective, limiting possibilities for data reuse. In this perspective, pose-estimation in combination with animal tracking offers opportunities to yield a higher level representation capturing both the spatial and temporal component of animal behaviour. Such a higher level representation allows to answer a wide variety of research questions simultaneously, without the need to develop repeatedly tailored computer vision algorithms. In this paper, we therefore first cope with several weaknesses of current pose-estimation algorithms and thereafter introduce KeySORT (Keypoint Simple and Online Realtime Tracking). KeySORT deploys an adaptive Kalman filter to construct tracklets in a bounding-box free manner, significantly improving the temporal consistency of detected keypoints. In this paper, we focus on pose estimation in cattle, but our methodology can easily be generalised to any other animal species. Our test results indicate our algorithm is able to detect up to 80% of the ground truth keypoints with high accuracy, with only a limited drop in performance when daylight recordings are compared to nightvision recordings. Moreover, by using KeySORT to construct skeletons, the temporal consistency of generated keypoint coordinates was largely improved, offering opportunities with regard to automated behaviour monitoring of animals.

Mariano Ferrero, José Omar Chelotti, Luciano Sebastián Martinez-Rau et al.

Monitoring feeding behaviour is a relevant task for efficient herd management and the effective use of available resources in grazing cattle. The ability to automatically recognise animals' feeding activities through the identification of specific jaw movements allows for the improvement of diet formulation, as well as early detection of metabolic problems and symptoms of animal discomfort, among other benefits. The use of sensors to obtain signals for such monitoring has become popular in the last two decades. The most frequently employed sensors include accelerometers, microphones, and cameras, each with its own set of advantages and drawbacks. An unexplored aspect is the simultaneous use of multiple sensors with the aim of combining signals in order to enhance the precision of the estimations. In this direction, this work introduces a deep neural network based on the fusion of acoustic and inertial signals, composed of convolutional, recurrent, and dense layers. The main advantage of this model is the combination of signals through the automatic extraction of features independently from each of them. The model has emerged from an exploration and comparison of different neural network architectures proposed in this work, which carry out information fusion at different levels. Feature-level fusion has outperformed data and decision-level fusion by at least a 0.14 based on the F1-score metric. Moreover, a comparison with state-of-the-art machine learning methods is presented, including traditional and deep learning approaches. The proposed model yielded an F1-score value of 0.802, representing a 14% increase compared to previous methods. Finally, results from an ablation study and post-training quantization evaluation are also reported.

Mackenzie Tapp, Sibi Chakravarthy Parivendan, Kashfia Sailunaz et al.

Pose estimation serves as a cornerstone of computer vision for understanding animal posture, behavior, and welfare. Yet, agricultural applications remain constrained by the scarcity of large, annotated datasets for livestock, especially dairy cattle. This study evaluates the potential and limitations of cross-species transfer learning by adapting ZebraPose - a vision transformer-based model trained on synthetic zebra imagery - for 27-keypoint detection in dairy cows under real barn conditions. Using three configurations - a custom on-farm dataset (375 images, Sussex, New Brunswick, Canada), a subset of the APT-36K benchmark dataset, and their combination, we systematically assessed model accuracy and generalization across environments. While the combined model achieved promising performance (AP = 0.86, AR = 0.87, PCK 0.5 = 0.869) on in-distribution data, substantial generalization failures occurred when applied to unseen barns and cow populations. These findings expose the synthetic-to-real domain gap as a major obstacle to agricultural AI deployment and emphasize that morphological similarity between species is insufficient for cross-domain transfer. The study provides practical insights into dataset diversity, environmental variability, and computational constraints that influence real-world deployment of livestock monitoring systems. We conclude with a call for agriculture-first AI design, prioritizing farm-level realism, cross-environment robustness, and open benchmark datasets to advance trustworthy and scalable animal-centric technologies.

S. Sanjuan, D. A. Méndez, R. Arnau et al.

Heat stress is one of the main welfare and productivity problems faced by dairy cattle in Mediterranean climates. In this study, we approach the prediction of the daily shade-seeking count as a non-linear multivariate regression problem and evaluate two soft computing algorithms -- Random Forests and Neural Networks -- trained on high-resolution behavioral and micro-climatic data collected in a commercial farm in Titaguas (Valencia, Spain) during the 2023 summer season. The raw dataset (6907 daytime observations, 5-10 min resolution) includes the number of cows in the shade, ambient temperature and relative humidity. From these we derive three features: current Temperature--Humidity Index (THI), accumulated daytime THI, and mean night-time THI. To evaluate the models' performance a 5-fold cross-validation is also used. Results show that both soft computing models outperform a single Decision Tree baseline. The best Neural Network (3 hidden layers, 16 neurons each, learning rate = 10e-3) reaches an average RMSE of 14.78, while a Random Forest (10 trees, depth = 5) achieves 14.97 and offers best interpretability. Daily error distributions reveal a median RMSE of 13.84 and confirm that predictions deviate less than one hour from observed shade-seeking peaks. These results demonstrate the suitability of soft computing, data-driven approaches embedded in an applied-mathematical feature framework for modeling noisy biological phenomena, demonstrating their value as low-cost, real-time decision-support tools for precision livestock farming under heat-stress conditions.

André L. A. Neves, Ricardo Augusto Mendonça Vieira, Einar Vargas-Bello-Pérez et al.

The rumen microbiome is central to feed digestion and host performance, making it an important target for improving ruminant productivity and sustainability. This study investigated how feed composition influences rumen microbial abundance and phenotypic traits in beef cattle. Fifty-nine Angus bulls were assigned to forage- and grain-based diets in a randomized block design, evaluating microbial dynamics, methane emissions, and feed efficiency. Quantitative PCR (qPCR) quantified bacterial, archaeal, fungal, and protozoal populations. Grain-based diets reduced bacterial and fungal counts compared to forage diets (1.1 × 10¹¹ vs. 2.8 × 10¹¹ copies of 16S rRNA genes and 1.5 × 10³ vs. 3.5 × 10⁴ copies of 18S rRNA genes/mL, respectively), while protozoan and methanogen populations remained stable. Microbial abundance correlated with feed intake metrics, including dry matter and neutral detergent fiber intakes. Methane emissions were lower in grain-fed bulls (14.8 vs. 18.0 L CH₄/kg DMI), though feed efficiency metrics showed no direct association with microbial abundance. Comparative analysis revealed adaptive microbial shifts in response to dietary changes, with functional redundancy maintaining rumen stability and supporting host performance. These findings provide insights into how feed composition shapes rumen microbial dynamics and host phenotypes, highlighting the functional adaptability of the rumen microbiome during dietary transitions.

Jiaqi Li, Weitao Huang, Qunhua Ke et al.

Abstract Lumpy Skin Disease (LSD), caused by the Lumpy Skin Disease Virus (LSDV), is a legally reportable disease recognized by the World Organization for Animal Health (WOAH) and has resulted in significant economic losses for the global cattle industry. Although several commercial LSDV vaccines are currently available, safer and more effective gene-deleted versions remain lacking. Therefore, screening key functional genes and developing gene-deleted live-attenuated vaccine strains hold substantial research value. In this study, we focused on ORF131, a gene whose function remains unclear. We successfully constructed an rLSDV-ΔORF131-EGFP gene deletion strain utilizing a homologous recombination, followed by purification using limiting dilution and single-cell subcloning techniques. Polymerase Chain Reaction (PCR) and Sanger sequencing validation confirmed that the deletion strain was successfully purified and free from wild-type virus contamination. Biological characterization indicated that the strain was genetically stable, with the optimal viral harvesting time in Madin-Darby Bovine Kidney (MDBK) cells being 72 h. Furthermore, RNA sequencing analysis of virus-infected cells revealed that rLSDV-ΔORF131-EGFP enhanced the immune and inflammatory responses of host cells compared to wild-type LSDV. This study not only provides a potential candidate strain for the development of an LSDV attenuated vaccine but also offers a theoretical foundation for the prevention and control strategies of LSD.

Qing-Ao Xiao, Lin Chen, Xiao-Long Li et al.

ABSTRACT: There is currently a lack of research examining the association between the consumption of different dairy products and type 2 diabetes (T2D) in East Asian populations. To address this gap, the present study employs Mendelian randomization to investigate the potential effects of 3 different types of milk consumption (including whole milk, semi-skim milk, and skim milk) on the risk of developing T2D. The results indicate that both whole milk and skim milk are associated with an increased risk of T2D (whole milk: odds ratio [OR] = 1.022, 95% CI: 1.001–1.044; skim milk: OR = 1.023, 95% CI: 1.007–1.039). Mediation analysis revealed that asparagine acts as a mediator between skim milk consumption and T2D, with a mediation effect of 0.003 (95% CI: 0.000 to 0.008), accounting for 14.269% of the total effect.

Yihang Zhou

Fescue toxicity causes reduced growth and reproductive issues in cattle grazing endophyte-infected tall fescue. To characterize the gut microbiota and its response to fescue toxicosis, we collected fecal samples before and after a 30-days toxic fescue seeds supplementation from eight Angus Simmental pregnant cows and heifers. We sequenced the 16 metagenomes using the whole-genome shotgun approach and generated 157 Gbp of metagenomic sequences. Through de novo assembly and annotation, we obtained a 13.1 Gbp reference contig assembly and identified 22 million microbial genes for cattle rectum microbiota. We discovered a significant reduction of microbial diversity after toxic seed treatment (P<0.01), suggesting dysbiosis of the microbiome. Six bacterial families and 31 species are significantly increased in the fecal microbiota (P-adj<0.05), including members of the top abundant rumen core taxa. This global elevation of rumen microbes in the rectum microbiota suggests a potential impairment of rumen microbiota under fescue toxicosis. Among these, Ruminococcaceae bacterium P7, an important species accounting for ~2% of rumen microbiota, was the most impacted with a 16-fold increase from 0.17% to 2.8% in feces (P<0.01). We hypothesized that rumen Ruminococcaceae bacterium P7 re-adapted to the large intestine environment under toxic fescue stress, causing this dramatic increase in abundance. Functional enrichment analysis revealed that the overrepresented pathways shifted from energy metabolism to antimicrobial resistance and DNA replication. In conclusion, we discovered dramatic microbiota alterations in composition, abundance, and functional capacities under fescue toxicosis, and our results suggest Ruminococcaceae bacterium P7 as a potential biomarker for fescue toxicosis management.

Phoenix Yu, Tilo Burghardt, Andrew W Dowsey et al.

We present MultiCamCows2024, a farm-scale image dataset filmed across multiple cameras for the biometric identification of individual Holstein-Friesian cattle exploiting their unique black and white coat-patterns. Captured by three ceiling-mounted visual sensors covering adjacent barn areas over seven days on a working dairy farm, the dataset comprises 101,329 images of 90 cows, plus underlying original CCTV footage. The dataset is provided with full computer vision recognition baselines, that is both a supervised and self-supervised learning framework for individual cow identification trained on cattle tracklets. We report a performance above 96% single image identification accuracy from the dataset and demonstrate that combining data from multiple cameras during learning enhances self-supervised identification. We show that our framework enables automatic cattle identification, barring only the simple human verification of tracklet integrity during data collection. Crucially, our study highlights that multi-camera, supervised and self-supervised components in tandem not only deliver highly accurate individual cow identification, but also achieve this efficiently with no labelling of cattle identities by humans. We argue that this improvement in efficacy has practical implications for livestock management, behaviour analysis, and agricultural monitoring. For reproducibility and practical ease of use, we publish all key software and code including re-identification components and the species detector with this paper, available at https://tinyurl.com/MultiCamCows2024.

S. Yüksel, A. Karaçuhalilar, B. Balta et al.

<p>This study was fictionalized as a prototype for other studies. The effects of breed and sex on the slaughter characteristics, carcass traits, meat quality and fatty acid composition of young animals, which were formed based on the enteric emission (<span class="inline-formula">CH₄</span>) level and animal protein production potential of different geographical regions were investigated. The region where the study was conducted consists of plateaus, and 13.7 % of the population lives in this area. A total of 36 animals, consisting of six males and six females from each of the Brown Swiss <span class="inline-formula">×</span> Eastern Anatolian Red (BSEAR), Holstein Friesian <span class="inline-formula">×</span> Eastern Anatolian Red (HFEAR) and Brown Swiss <span class="inline-formula">×</span> Holstein Friesian (BSHF) genotypes, were used to investigate animal protein production in this study. They were dispatched to be slaughtered at the age of 20 months. The data were subjected to analysis of variance (ANOVA), and differences between groups were compared with the Duncan test. Enteric <span class="inline-formula">CH₄</span> estimated among regions varied from 30.34 to 36.50 kg head<span class="inline-formula">⁻¹</span> yr<span class="inline-formula">⁻¹</span>. It was 0.215, 0.194, 0.183, 0.195 and 0.198 kg, respectively, per kilogram of edible meat of BSEAR, HFEAR, BSHF, male cattle and female cattle. The results indicated that slaughter traits, carcass characteristics and carcass measurements (<span class="inline-formula"><i>P</i>&lt;0.05</span> to <span class="inline-formula"><i>P</i>&lt;0.01</span>) were associated with breed. Slaughter traits, carcass characteristics and carcass measurements were affected by sex (<span class="inline-formula"><i>P</i>&lt;0.05</span> to <span class="inline-formula"><i>P</i>&lt;0.01</span>). Breed and sex interaction had no effect on carcass characteristics in subgroups (<span class="inline-formula"><i>P</i>&lt;0.05</span>). DM, CP and ash were significantly affected by breed (<span class="inline-formula"><i>P</i>&lt;0.05</span>). pH and <span class="inline-formula"><i>a</i>^∗</span> were also significantly affected by breed (<span class="inline-formula"><i>P</i>&lt;0.05</span>). Sex influenced pH (<span class="inline-formula"><i>P</i>&lt;0.05</span>), <span class="inline-formula"><i>L</i>^∗</span> (<span class="inline-formula"><i>P</i>&lt;0.001</span>), <span class="inline-formula"><i>a</i>^∗</span> (<span class="inline-formula"><i>P</i>&lt;0.01</span>) and <span class="inline-formula"><i>b</i>^∗</span> (<span class="inline-formula"><i>P</i>&lt;0.001</span>). Monounsaturated fatty acid (MUFA) and polyunsaturated fatty acid (PUFA) levels were found to be significant in different breeds (<span class="inline-formula"><i>P</i>&lt;0.01</span>), and PUFA levels were significant in different sexes (<span class="inline-formula"><i>P</i>&lt;0.05</span>).</p>

Sibel KIZIL, Fatma Esin AYDIN, Aziz Utku ÖNEL et al.

In the study, rectal swabs taken from 300 ruminant animals including cattle (100), sheep (100), and goats (100) were inoculated into Mac Conkey Agar and incubated for 18 h at 37°C. Escherichia coli isolates were confirmed by biochemical tests and the BBL Crystal rapid diagnosis system. O26, O45, O103, O111, O121, O145, and O157 serotypes by PCR test following DNA isolation; ETEC (elt, Stla); EPEC (eaeA,bfpA); STEC (stx1, stx2, eaeA); EHEC (EhlyA); EAEC (CVD432) tested for virulence and/or toxigenic genes. As a result of the isolation studies, 50 E. coli from cattle feces, 92 from sheep feces, and 80 from goat feces were isolated and identified. Apart from the first 5 serotypes frequently seen in studies (O157, O26, O103, O111, and O145), higher rates were found in serogroups such as O45 and O121, and subtypes such as STECs (stx1 and stx2), EPEC (eaeA and bfpA) and EAEC (CVD432) types compared to other studies. The EAEC (CVD432) subtype was found to be very high in this study. It has been determined that serotypes and subtypes detected at high rates in cattle, sheep, and goat feces in our region may cause an increase in the incidence of some critical food-borne infections in humans. Within the framework of the concept of one health, taking the necessary precautions is important for public health.