Journal of Dairy Science最新文献

Accurate local ancestry (LA) inference is important for genomic evaluations in admixed dairy cattle. This study had 2 main objectives: to compare the performance of 3 LA inference software: AllOr, BreedOrigin, and ChromoPainter, and to evaluate the impact of LA inference errors on genomic prediction accuracy. Two simulated admixed populations were used: a structured DairyCross population created through three-way rotational crossbreeding and a Red Dairy cattle (RDC) population representing historical admixture among multiple breeds. For LA software evaluation, true breed-of-origin labels were available to benchmark accuracy in LA inference. To assess robustness of genomic prediction, we varied heritability (h² = 0.1 or 0.4), QTL effect correlation (ρ = 1.0 or 0.3), and the proportion of LA assignment errors at 3 levels (1%, 5%, and 10%) across the genome, referring to the fraction of alleles misassigned. Two prediction methods that incorporate LA information were tested: (1) the breed-of-origin method (BOM), which uses purebred phenotypes, and (2) the breed-of-origin of alleles method (BOA), which uses both purebred and crossbred phenotypes, to estimate SNP effects. We also employed a joint SNP-BLUP method (Joint) as a baseline prediction method assuming the same SNP effects across breeds. Results based on simulated data showed that ChromoPainter achieved the highest LA inference accuracy (RDC 97.98%, DairyCross 99.93%) among the software but required substantially more computing time than acceptable in practice. BreedOrigin was the fastest. AllOr and BreedOrigin performed comparably well in LA inference accuracy (RDC up to 96.71% and 97.04%; DairyCross up to 99.45% and 99.81%, respectively). In genomic prediction, both BOM and BOA methods were robust to 1% to 10% LA assignment errors, with prediction accuracy declining by ~0.5% to 5.7% (BOM) and ~0.9% to 7.3% (BOA). The BOA consistently outperformed BOM (at h² = 0.1, +15.1%-18.0%; at h² = 0.4, ~+8.8%), especially when ρ = 0.3, reflecting BOA's use of crossbred phenotypes in the training set. However, BOA did not always exceed Joint: under ρ = 0.1, Joint was similar to or slightly better than BOA in prediction accuracy (BOA 0.754-0.804 vs. Joint 0.799-0.805 across 0-10% LA assignment error), whereas under ρ = 0.3, BOA was generally higher. Overall, the results provide software guidelines for LA inference and support LA-informed prediction, showing that the performance depends on genetic architecture and is only slightly affected by moderate LA inference errors.

Excessive lipolysis and inflammatory response are critically involved in the pathogenesis of ketosis in periparturient dairy cows. Evidence has been growing for participation of the growth hormone (GH) in the metabolic regulation of adipose tissue. However, the potential role of GH in promoting lipolysis and proinflammatory signaling activation in bovine adipocytes remains to be elucidated. The objective of this study was to investigate the regulatory effects of GH on the lipolysis and inflammatory response of bovine adipocytes. Subcutaneous adipose tissue and blood samples were collected from 10 healthy cows (blood BHB concentration <1.2 mM) and 10 cows with clinical ketosis (CK; blood BHB concentration >3.0 mM). For in vitro experiments, adipocytes were isolated from healthy Holstein cows. Differentiated adipocytes were used for (1) treatment with 0, 5, 10, or 15 ng/mL of GH for 8 h, or 15 ng/mL of GH for 0, 4, 8 or 12 h; (2) co-treatment with 15 ng/mL GH and 0.1 ng/mL tumor necrosis factor α (TNF-α); (3) pretreatment with 10 μM BAY 11-7082, a nuclear factor kappa B (NF-κB) inhibitor, and then treatment with 15 ng/mL GH. The CK cows displayed higher serum GH concentration. The protein abundance of phosphorylated lipolysis-limiting enzyme hormone sensitive lipase (HSL) was higher and mRNA abundance of lipid droplet coating proteins cell death-inducing DFFA-like effector c and perilipin 1 was lower in adipose tissue of CK cows versus healthy cows. The protein abundance of phosphorylated inhibitor of kappa B α (IκBα) and NF-κB, mRNA abundance of proinflammatory cytokines TNFA, NLR family pyrin domain containing 3 (NLRP3), IL-18 (IL18), caspase 1 (CASP1) and IL-1B (IL1B), and the activity of caspase 1 were greater in adipose tissue of CK cows, but protein abundance of IκBα was lower. In bovine adipocytes, GH induced lipolysis and inflammatory response, as evidenced by increased glycerol content in the supernatant and decreased cellular triglyceride content, as well as elevated phosphorylation levels of IκBα and NF-κB, decreased protein abundance of IκBα, upregulated mRNA abundance of TNFA, NLRP3, IL18, CASP1, and IL1B, and enhanced caspase 1 activity. Furthermore, TNF-α exacerbated GH-induced lipolysis and inflammation, whereas inhibition of NF-κB signaling pathway partially reverses these metabolic alterations of GH-treated adipocytes. These findings suggested that GH promote lipolysis in bovine adipocytes by activating inflammatory pathways.

The gastrointestinal tract (GIT) plays a crucial role in maintaining overall health and performance in ruminants, with the rumen functioning as fermentation chamber for plant biomass. Intestinal epithelial cells are exposed to billions of microorganisms that significantly affect the epithelial barrier by activating complex metabolic and immune pathways. The interaction between microbiota and host is mediated by signaling compounds such as metabolites and peptides, and also extracellular vesicles (EV). The EV are small lipid membrane-bound vesicles released by almost all kinds of prokaryotic and eukaryotic cells in the GIT ecosystem, thus playing a role in intra- and interorganismic communication. They are unique among biological messenger molecules because of their structural, functional, and delivery characteristics. The EV carry various functional cargo, such as proteins, lipids, and nucleic acids, that modulate biological processes, including metabolism and immune functions. Their separation requires adequate protocols optimized for each biological matrix and subsequent confirmation of their identity in terms of size and the presence of specific marker proteins. For gaining insights into their functional role, omics technologies such as proteomics and transcriptomics are commonly used. Recent studies have shown the presence of EV in GIT compartments, including ruminal fluid and feces. These EV are produced not only by the host but also by the various types of microbiota residing in the different GIT sections. The EV can influence feed digestion, nutrient absorption, and overall performance and may also affect other organs such as liver, mammary gland and brain. The EV from ingested colostrum and milk are attributed with a special significance for intestinal function and development in early life. This review underscores the importance of further research to understand the complex roles of EV in the ruminant GIT.

Mastitis represents one of the most formidable challenges in modern dairy farming, posing significant threats to individual cow health and causing substantial economic losses throughout the dairy production chain. Traditional disease diagnosis methods are often reactive and costly, creating an urgent need for advanced predictive technologies. To address these issues, we proposed a novel machine learning-based mastitis prediction system that breaks through conventional diagnostic paradigms by deeply integrating data science with veterinary medicine. We analyzed 177,493 dairy cow records from Nanjing Weigang Dairy Farm, implementing 9 distinct machine learning algorithms for model development and evaluation: Random forest, multilayer perceptron, support vector machine, decision tree, gradient boosting, AdaBoost, linear discriminant analysis, logistic regression, and Naive Bayes. The dataset included comprehensive production metrics, physiological parameters, and management variables, with models trained on both standardized and nonstandardized datasets using rigorous cross-validation techniques. Random forest demonstrated superior predictive performance, significantly outperforming other algorithms with the highest F₁-score (0.804) and area under the receiver operating characteristic curve value (0.884, 95% CI: 0.883-0.885), achieving a sensitivity of 80.6%, specificity of 80.0%, and overall accuracy of 80.3%. Feature importance analysis revealed month_age as the most critical predictor (relative importance: 0.165), followed by milk_yield (0.138), protein_percentage (0.138), and fat_percentage (0.135). The Z-standardization consistently enhanced model performance across all algorithms, with random forest maintaining optimal calibration between predicted probabilities and actual outcomes. Leveraging the predictive model, we quantified key risk factors and developed an early warning system capable of identifying high-risk animals, providing a robust foundation for precision dairy farming and improved mastitis management strategies. This research successfully develops a data-driven technological solution that substantially reduces disease spread risk and economic losses while driving the transformation of dairy farming toward digitalization and precision management.

Staphylococcus aureus, a prominent global foodborne pathogen, frequently triggers epidemics with severe public health impacts. Timely and reliable detection of S. aureus is crucial for mitigating the disease burden in low- and middle-income countries. However, conventional laboratory-based detection methods remain impractical in resource-limited settings, highlighting the urgent need for accessible point-of-care solutions. Here, we present an inner-outer-tube (IOT) assay that synergistically integrates the polymerase spiral amplification (PSR) technology for enhanced sensitivity with the clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated 12a (Cas12a) system for sequence-specific identification. Additionally, we have created a portable all-in-one mobile detection (PAMD) device that combines all the steps needed for testing in the field, allowing for quick visual detection of S. aureus in just 60 min. The PSR-CRISPR/Cas12a-IOT method implemented with the PAMD device achieves a detection limit of 10 cfu/mL without needing extra preparation or costly equipment. The detection platform developed in this work has advantages of ease of operation, manageable costs, and robust performance, making it highly ideal for low-resource contexts and on-site detection scenarios. Furthermore, the PSR-CRISPR/Cas12a-IOT-PAMD detection platform provides global versatility through the interchangeable use of primer sets, hence broadening its applicability to various infections.

This study investigated the effects of varying fat levels and ratios of coconut-to-palm fat in milk replacer on solid feed intake and performance of male Holstein calves fed a high milk replacer allowance. Calves were individually housed and enrolled in a 91-d (study 1; n = 128) and 84-d (study 2; n = 128) experiment at the same research facility divided into 3 phases: preweaning (1-42 d), weaning (43-63 d), and postweaning (64-study end). In study 1, calves were randomly assigned to 1 of the 3 treatments, consisting of a milk replacer containing a spray-dried blend of 20% coconut-to-80% palm fat with: 1) low fat (17%; LF-17%; n = 42; BW = 47.64 ± 2.62 kg), 2) moderate fat (23%; MF-23%; n = 43; BW = 47.69 ± 3.42 kg), and 3) high fat (29%; HF-29%; n = 41; BW = 47.04 ± 3.74 kg) in relation to 26% CP level. In study 2, calves were randomly assigned to 1 of the 3 treatments with the same fat-to-CP ratio (21% fat; 26% CP), varying the ratio of coconut-to-palm fat in the spray-dried fat blend with: 1) 20% to 80% (20C:80P; n = 42; BW = 47.63 ± 4.40 kg), 2) 35% to 65% (35C:65P; n = 42; BW = 49.07 ± 5.30 kg), and 3) 50% to 50% (50C:50P; n = 44; BW = 48.48 ± 4.09 kg), respectively. Milk replacer was fed twice daily (130 g/L) following the step-up, step-down program: from d 0 to 6, 520 g/d; d 7 to 13, 650 g/d; d 14 to 20, 910 g/d; d 21 to 41, 1,040 g/d; d 42 to 48, 910 g/d; d 49 to 63, 650 g/d. Body weight and calf starter intake were recorded weekly. Mixed linear models were used with treatment, phase, and their interaction included as fixed effects. Initial BW, serum total protein, calf source, and total number of disease interventions were tested as covariates. In study 1, ADG was greater for calves fed a low-fat milk replacer over the 91-d period (LF-17% = 1.24 ± 0.02, MF-23% = 1.09 ± 0.02, HF-29% = 1.12 ± 0.03 kg/d). At 91d, BW was greater for LF-17% (LF-17% = 160.80 ± 2.11, MF-23% = 147.22 ± 2.17, HF-29% = 149.59 ± 2.26 kg). Total DMI was greater for LF-17% calves across the postweaning phase, leading to greater ME intake (LF-17% = 14.15 ± 0.22, MF-23% = 11.98 ± 0.22, HF-29% = 12.31 ± 0.23 Mcal/kg). During preweaning, LF-17% calves had greater feed energy efficiency. In study 2, ADG was greater for 35C:65P calves over the total period (20C:80P = 1.04 ± 0.02, 35C:65P = 1.11 ± 0.02, 50C:50P = 1.04 ± 0.02 kg/d). At d 84, BW was greater for 35C:65P dairy calves (20C:80P = 135.82 ± 1.97 kg; 35C:65P = 141.63 ± 2.01 kg; 50C:50P = 136.25 ± 1.87 kg). These findings suggest that lower levels of fat in milk replacer, formulated with a spray-dried 20% coconut-to-80% palm fat blend, promote solid feed intake and overall performance. A spray-dried blend with 35% coconut-to-65% palm fat ratio, included at 21% DM, appears to be a more favorable ratio in supporting calf performance.

Dairy cows are highly motivated to use grooming brushes, and their use promotes expression of natural behavior. Although brushes are increasingly common on commercial dairy farms, little is known about how their mechanical properties, specifically swingability and rotation, influence cattle preferences for brush use. This study investigated adult dairy cow preferences for 3 brushes identical in physical and visual features but differing mechanically: (1) swinging and rotating, (2) swinging-only, and (3) stationary (neither swinging nor rotating). We predicted cows would prefer swinging brushes for their ability to reach multiple body regions and that rotation would further enhance engagement. Fourteen primiparous Holstein cows were habituated and individually exposed to all 3 brushes before testing. Over 9 d, each cow had simultaneous access to all brushes during 5-min sessions in a test arena. Brush interactions were video recorded and analyzed for first-choice preference, grooming duration, and body region contacted (head, neck, back, or rump); generalized linear mixed models accounted for zero inflation and repeated measures (back-transformed values with 95% CI). There was individual variation in brush preference; 10 of the 14 cows preferred the swinging-rotating brush, 3 preferred the swinging-only brush, and one preferred the stationary brush, based on total brush use duration. First-choice rates did not differ between swingintg-rotating and swinging-only brushes, but both were greater than first-choice rates for the stationary brush or no brush choice. Grooming time varied by brush type and body part. Cows used all brushes to groom the head for similar durations (approximately 113, 103, and 122 s for swinging-rotating, swinging-only, and stationary brushes, respectively), but the stationary brush was used almost exclusively for the head. Cows used the swinging-rotating and swinging-only brushes for similar durations to groom other body parts. To groom the neck, cows used the swinging-rotating brush more than the stationary brush (approximately 95 and 13 s, respectively), but not more than the swinging-only brush (approximately 59 s). To groom the back and rump areas, cows used the swinging-rotating brush (approximately 92 and 144 s, respectively) and swinging-only brush (approximately 31 and 81 s, respectively) more than the stationary brush (approximately 1 and 9 s, respectively). Overall, cows favored brushes that could swing, likely because this feature enabled grooming of multiple body regions. However, the stationary brush may offer more tactile precision for head grooming. Brush design, particularly swingability, strongly influences cow grooming behavior. Future research should explore how providing brush variety affects welfare and investigate the underlying motivations for individual preferences.

In dairy systems, β-LG, the predominant whey protein, is valued for its nutritional and techno-functional properties, yet it remains a major milk allergen. Modulating protein-ligand interactions presents a potential strategy to alter its characteristics. Although interactions between β-LG and polyphenols have been extensively studied, the binding mechanisms with other common food-grade molecules possessing diverse structural features remain less understood. Therefore, this study selected 3 such additives representing distinct chemical categories: galactooligosaccharides, sucrose fatty acid esters, and casein phosphopeptides, to explore how their unique functional group profiles (hydroxyl, amphiphilic, and phosphopeptide moieties, respectively) drive their interaction with β-LG. We employed a combined multispectroscopic and computational approach to build a qualitative, mechanistic portrait of these interactions. Our findings demonstrate that all 3 additives interact with β-LG, primarily via a static fluorescence quenching mechanism, which alters the protein's local microenvironment without significantly perturbing its secondary structure. These experimental observations are complemented by an in-depth computational analysis, including molecular docking, extensive molecular dynamics simulations, and binding free energy calculations. This integrated approach not only identified the specific binding modes and key interacting residues, but also quantitatively highlighted the crucial roles of hydrogen bonding and hydrophobic forces in stabilizing the complexes. Collectively, this work provides a theoretical basis for future efforts to modulate whey protein functionality and explore its potential impact on milk allergenicity.

Digital technologies and the internet determine our professional and private everyday life. This also applies to the dairy industry and veterinary practice. The objective of the presented study was to learn more about the perception of digital technologies by students of veterinary medicine (VetMed) and agricultural sciences (AgriSci) in Germany, Austria, and Switzerland. This next generation of farmers and veterinarians will have to deal with digital technologies in their later professional lives and they will have to face societal demands such as animal welfare and the reduced use of pharmaceuticals. We created an online survey comprising 6 sections: (1) demographic data, (2) questions about the participants' relationship to today's dairy industry, (3) participants' perception of digital technologies in everyday life and in the dairy industry, (4) associations based on the effects of images, (5) visions and expectations of the dairy industry in the future. Finally, the participants were asked whether they felt well prepared for the digital transformation in the dairy industry by their colleges. The survey link was sent to the students through their administration or student body of veterinary medicine and agricultural sciences colleges and faculties in Austria, Germany, and Switzerland. In total, 454 questionnaires were eligible for the final analysis, 318 from veterinary medicine students, and 136 from agricultural science students. In general students of both disciplines have a positive attitude toward the use of digital technologies as all participants of the study showed a high acceptance of cows being equipped with sensors. But the survey shows also areas in which the students are skeptical about the technological progress and especially VetMed students did not agree with some procedures in dairy cattle husbandry such as early cow-calf separation and an automatic feeding of calves. They also associated digital technologies with a reduction or even a loss of human-animal relationship. One reason for this can be seen in the preparation for the digital transformation in the dairy industry. Almost 50% of VetMed students were not "adequately prepared" for this transformation during their studies; among AgriSci students, it was one-third of the respondents. The current survey provides a fundament for discussing various topics against the background of digitalization in the dairy industry. Representative examples are veterinary education and the shortage of livestock veterinarians.