Hasil untuk "Cattle"

Axiu Mao, Meilu Zhu, Lei Shen et al.

With the rapid advancements in deep learning techniques, wearable sensor-aided animal activity recognition (AAR) has demonstrated promising performance, thereby improving livestock management efficiency as well as animal health and welfare monitoring. However, existing research often prioritizes overall performance, overlooking the fact that classification accuracies for specific animal behavioral categories may remain unsatisfactory. This issue typically stems from suboptimal sampling rates or class imbalance problems. To address these challenges and achieve high classification accuracy across all individual behaviors in farm animals, we propose a novel Individual-Behavior-Aware Network (IBA-Net). This network enhances the recognition of each specific behavior by simultaneously customizing features and calibrating the classifier. Specifically, considering that different behaviors require varying sampling rates to achieve optimal performance, we design a Mixture-of-Experts (MoE)-based Feature Customization (MFC) module. This module adaptively fuses data from multiple sampling rates, capturing customized features tailored to various animal behaviors. Additionally, to mitigate classifier bias toward majority classes caused by class imbalance, we develop a Neural Collapse-driven Classifier Calibration (NC3) module. This module introduces a fixed equiangular tight frame (ETF) classifier during the classification stage, maximizing the angles between pair-wise classifier vectors and thereby improving the classification performance for minority classes. To validate the effectiveness of IBA-Net, we conducted experiments on three public datasets covering goat, cattle, and horse activity recognition. The results demonstrate that our method consistently outperforms existing approaches across all datasets.

Riasad Alvi, Mohaimenul Azam Khan Raiaan, Sadia Sultana Chowa et al.

Precision livestock farming requires accurate and timely heat stress prediction to ensure animal welfare and optimize farm management. This study presents a physics-informed digital twin (DT) framework combined with an uncertainty-aware, expert-weighted stacked ensemble for multimodal forecasting of Core Body Temperature (CBT) in dairy cattle. Using the high-frequency, heterogeneous MmCows dataset, the DT integrates an ordinary differential equation (ODE)-based thermoregulation model that simulates metabolic heat production and dissipation, a Gaussian process for capturing cow-specific deviations, a Kalman filter for aligning predictions with real-time sensor data, and a behavioral Markov chain that models activity-state transitions under varying environmental conditions. The DT outputs key physiological indicators, such as predicted CBT, heat stress probability, and behavioral state distributions are fused with raw sensor data and enriched through multi-scale temporal analysis and cross-modal feature engineering to form a comprehensive feature set. The predictive methodology is designed in a three-stage stacked ensemble, where stage 1 trains modality-specific LightGBM 'expert' models on distinct feature groups, stage 2 collects their predictions as meta-features, and at stage 3 Optuna-tuned LightGBM meta-model yields the final CBT forecast. Predictive uncertainty is quantified via bootstrapping and validated using Prediction Interval Coverage Probability (PICP). Ablation analysis confirms that incorporating DT-derived features and multimodal fusion substantially enhances performance. The proposed framework achieves a cross-validated R2 of 0.783, F1 score of 84.25% and PICP of 92.38% for 2-hour ahead forecasting, providing a robust, uncertainty-aware, and physically principled system for early heat stress detection and precision livestock management.

Henning Schulte, Christian Ammon, Frauke Hagenkamp-Korth et al.

Previous studies have shown that ammonia emissions can be continuously reduced through the application of a urease inhibitor (UI) in cattle and pig farming. However, there is no information on whether the use of these inhibitors also has an effect on other emissions, and whether it leads to an increase or decrease in these emissions. In this study, carbon dioxide, ammonia, methane, nitrous oxide and odour emissions were measured in three mechanically ventilated, fully slatted pig fattening houses in Germany during 2019–2020. The UI was applied daily to compartments, and effects on emission values were comparatively analysed using four different calculation approaches: linear mixed model, direct case-control, case-control in time and a novel ratio-difference approach. As expected, a significant reduction in ammonia emissions of 22–24 % was observed across all four calculation approaches and all three farms, confirming the effectiveness of the UI; no decisive effects on carbon dioxide, methane or odour emissions were found. Effects on nitrous oxide emissions could not be reliably analysed due to low concentrations which were below the Fourier-transform infrared spectroscopy (FTIR) quantification limit. It is recommended to calculate the reduction effect using a combined approach so that over- and underestimation of the effect can be avoided. Two approaches are available for this purpose: the ratio-difference and linear mixed model. The ratio-difference approach has a simplicity of calculation and the ability to achieve results very similar to those of the linear mixed model.

Bettina A. Weber, Aline Zimmermann, Irene M. Häfliger et al.

Genetic neuromuscular disorders (NMDs) are characterized by progressive skeletal muscle degeneration and weakness. In Brown Swiss (BS) cattle, three recessive variants have been associated with NMDs. This study aimed to (1) describe the phenotype of BS cattle affected by a novel form of postural proprioceptive deficits, (2) identify a candidate genetic variant using whole-genome sequencing (WGS), and (3) estimate its prevalence in the Swiss BS population. A first BS heifer (case 1) showing ataxia underwent clinical and hematological examination, followed by WGS of the heifer and its sire. Variants were filtered and compared against 5577 controls. Candidate variants were evaluated in silico for predicted pathogenicity, genotyped in national breeding cohorts, and clinically assessed in additional homozygous animals. A rare homozygous missense variant in LIPC (chr10:51715800G>C; NM_001035410.1:c.924C>G; p.Phe308Leu; omia.variant:1842) was identified and in-silico predictions classified this variant as deleterious. Population-level genotyping of over 20.000 BS cattle revealed a variant allele frequency of 17% with significant deviation from the Hardy–Weinberg equilibrium (p-value=1.14 × 10⁻20), suggesting possible lethality in homozygotes. Case 1, as well as 11 additional LIPC-homozygous BS females (mean age 2.4 ± 0.2 years), exhibited consistent neurological signs, including postural deficits and subconscious proprioceptive ataxia. Biochemical profiles revealed hypercholesterolemia, hypertriglyceridemia, and decreased HDL cholesterol, suggesting dyslipidemia. Pedigree analysis identified a shared ancestor and inbreeding loops, supporting recessive inheritance. This study describes a novel autosomal recessive LIPC-related juvenile-onset NMD in BS cattle characterized by postural proprioceptive deficits with a tendency towards dyslipidemia. More research is needed to better understand the effects of the identified variant and prove that it is the cause.

Yubo Chen, Di Zhao, Yun Sing Koh et al.

Camera-based animal re-identification (Animal Re-ID) can support wildlife monitoring and precision livestock management in large outdoor environments with limited wireless connectivity. In these settings, inference must run directly on collar tags or low-power edge nodes built around microcontrollers (MCUs), yet most Animal Re-ID models are designed for workstations or servers and are too large for devices with small memory and low-resolution inputs. We propose an on-device framework. First, we characterise the gap between state-of-the-art Animal Re-ID models and MCU-class hardware, showing that straightforward knowledge distillation from large teachers offers limited benefit once memory and input resolution are constrained. Second, guided by this analysis, we design a high-accuracy Animal Re-ID architecture by systematically scaling a CNN-based MobileNetV2 backbone for low-resolution inputs. Third, we evaluate the framework with a real-world dataset and introduce a data-efficient fine-tuning strategy to enable fast adaptation with just three images per animal identity at a new site. Across six public Animal Re-ID datasets, our compact model achieves competitive retrieval accuracy while reducing model size by over two orders of magnitude. On a self-collected cattle dataset, the deployed model performs fully on-device inference with only a small accuracy drop and unchanged Top-1 accuracy relative to its cluster version. We demonstrate that practical, adaptable Animal Re-ID is achievable on MCU-class devices, paving the way for scalable deployment in real field environments.

Wenwen Fang, Yang Cao, Yu Liu et al.

Abstract Yanbian cattle are a native cattle breed originating from the Yanbian Korean Autonomous Prefecture in Jilin Province, northeastern China. Developed over centuries through natural and artificial selection under cold climatic conditions, this breed is highly adapted to harsh environments, including low temperatures, rugged terrain, and sparse forage. Here, we constructed a high-quality chromosome-level genome assembly for Yanbian cattle using HiFi, Hi-C and RNA-seq data. The genome sequence was anchored to 30 chromosomes, with a total genome length of 2.8 Gb, a contig N50 of 86.41 Mb and a scaffold N50 of 111.08 Mb. Short-read data showed the average 99.56% mapping ratio to the assembly, validating base-level accuracy. Also, 93% complete BUSCOs verified the integrity of the assembled genome. 51.94% repetitive elements of the genome and 20,421 protein-coding genes were annotated. This Yanbian cattle genome serves as an indispensable resource for bovine genomic studies and local breed conservation, enabling both evolutionary insights and genetic characteristic analyses.

Gul Sahika Gökdemir, Sümeyye Çakmak, Berjan Demirtas et al.

In order to investigate the effects of acute CO poisoning and subsequent oxygen therapy on cardiac necrosis in rats, with a specific focus on adiponectin levels, twenty–one male Wistar albino rats were divided into three groups (Control, CO, CO+O2). The Control group was placed in a container and exposed to room air for 30 min. Acute CO poisoning was induced in the CO group and CO+O2 group by exposing the rats to CO gas for 30 min. Following CO exposure, the CO+O2 group received oxygen therapy for 30 min, while the CO group did not receive any additional intervention. The animals were euthanized by cardiac puncture under anesthesia, following the approved ethical procedures. Carboxyhemoglobin (COHb), serum levels of creatine kinase (CK), creatine kinase myocardial band (CK–MB), C–reactive protein (CRP) and lactate dehydrogenase (LDH), as well as cardiac and serum adiponectin levels were measured. CO poisoning caused necrosis in cardiac tissue however, oxygen therapy alleviated the negative effect of CO on cardiac injury. COHb and LDH levels in CO group were increased, whereas both cardiac and serum adiponectin levels were decreased (all, P<0.05). There were no changes in CK, CK–MB, CRP levels among groups (all, P>0.05). Oxygen therapy decreased COHb, but increased both cardiac and serum adiponectin levels (all, P<0.05). Adiponectin and LDH may serve as potential biomarkers for early diagnosis of cardiac necrosis caused by acute CO poisoning. The assessment or quantification of adiponectin can also be useful for the early prognosis of cardiac necrosis after oxygen therapy.

Klementina Fon Tacer, Gregor Majdič

Reproduction is one of the fundamental biological imperatives shared by all living beings. Organisms must reproduce to pass on their genes to the next generation, ensuring the survival and continuation of their species. In pursuit of this goal, nature has evolved a remarkable diversity of reproductive methods and behaviors, including external fertilization in aquatic species, internal fertilization in terrestrial animals, oviparity, viviparity, complex hormonal regulation, and diverse strategies of parental investment. In the Slovenian Veterinary Research journal, we welcome articles addressing various aspects of veterinary and comparative reproductive research and medicine. In this issue, we have placed particular emphasis on this topic. With this editorial, we would also like to bring attention to these articles, including a review of the phenomics evaluation and research on hormone GnRH injection in cattle and sheep breeding, the effect of food additives and environmental enrichment on fertility protection against toxicity  and egg production  and a case report on canine idiopathic oligoasthenoteratozoospermia. Razmnoževanje skozi prizmo evolucije – pomen za sodobno veterinarsko medicino Izvleček: Razmnoževanje je temelj življenja. Omogoča prenos genov na naslednje generacije in s tem ohranjanje vrst. Narava je skozi evolucijo razvila osupljivo paleto strategij, od zunanje oploditve pri vodnih organizmih do notranje oploditve pri kopenskih živalih, jajcerodnosti, živorodnosti, kompleksnega hormonskega uravnavanja in različnih oblik starševske skrbi. V reviji Slovenian Veterinary Research z veseljem objavljamo prispevke, ki obravnavajo različne vidike veterinarskih in primerjalnih raziskav ter medicine razmnoževanja. V tej številki smo temu področju namenili poseben poudarek. Predstavljamo prispevke, ki osvetljujejo fenomsko evalvacijo in raziskave vpliva hormona GnRH na plodnost goveda in ovc, vpliv prehranskih dodatkov in obogatitve okolja na zaščito plodnosti, in proizvodnjo jajc ter primer idiopatske oligoasteno-teratozoospermije pri psu.

Chang-Xiao Shi, Shun-Ran Yang, Ying-Qi Li et al.

<i>Saccharomyces cerevisiae</i> (SC) can be incorporated into ruminant diets as a postbiotic product. This study aimed to explore the effects of supplementing different levels of SC in the diets of mid-fattening Angus steers under heat stress conditions. A total of twenty-seven steers were randomly allocated into 3 groups: control, 30 g SC addition and 60 g SC addition groups. After a 7-day adaptation period followed by a 120-day experimental period, including respiratory rate, rectal temperature, growth performance, apparent digestibility of nutrients, rumen fermentation parameters, urine metabolites, serum biochemistry and antioxidant were measured. The results showed that the rectal temperature and respiratory rate of cattle decreased upon the addition of SC during heat stress. Meanwhile, the growth performance of cattle was improved in the 30 g SC addition group. The serum energy metabolism related indexes, such as non-esterified fatty acids, glucose, and β-hydroxybutyric acid, were altered. Additionally, the activity of catalase was significantly enhanced with the addition of SC. Overall, the addition of SC to the diets of mid-fattening Angus steer did not negatively affect rumen fermentation and nutrient apparent digestibility. Instead, it was capable of improving physiological performance under heat stress by modifying the energy metabolism and augmenting antioxidant capacity, which ultimately led to an improvement in growth performance. In conclusion, the most suitable level of SC to be added to the diet of mid-fattening Angus steers is 30 g/steer/d.

Ramya S. Barre, Ruby A. Escobedo, Esteban M. Castro et al.

Summary: A multistate outbreak of highly pathogenic avian influenza virus (HPAIV) H5N1 in the United States dairy cattle was first reported on March 2024, followed by a zoonotic cattle-to-human virus transmission to a dairy farm worker in Texas. To facilitate real-time tracking of HPAIV H5N1, we generated a recombinant nanoluciferase (Nluc)-expressing H5N1 virus, HPhTX NSs-Nluc, by introducing an Nluc reporter into the non-structural gene of human A/Texas/37/2024 H5N1 (HPhTX). HPhTX NSs-Nluc replicated with kinetics and plaque morphology comparable to wild-type virus in vitro. In vivo and ex vivo, HPhTX NSs-Nluc allowed tracking viral infection in the living animals and their necropsied organs using in vivo imaging systems (IVISs). Treatment with baloxavir effectively inhibited HPhTX NSs-Nluc replication, comparable to wild-type virus, validating its applicability for high-throughput screening of potential antiviral therapeutics. These results demonstrate that HPhTX NSs-Nluc is a robust tool for studying H5N1 pathogenesis and assessing antiviral efficacy against HPAIV H5N1.

L.A. Jiménez-Maroto, S. Govindasamy-Lucey, J.J. Jaeggi et al.

ABSTRACT: High-pressure processing (HPP) and low-temperature storage (0°C) were explored as alternatives to freezing for extending the performance shelf life of low-moisture, part-skim (LMPS) mozzarella intended for export. Batches (n = 5) of reduced Na LMPS mozzarella were manufactured using camel chymosin as a lower proteolytic type of rennet. Cheeses were stored for 2 wk at 4°C, divided into control (non-HPP) and HPP (600 MPa for 3 min) groups, and stored at 3 different temperatures (4, 0, and −18°C) for 365 d. Analyses were performed at 0, 90, 150, 210, 270, and 365 d of storage. Frozen and 0°C samples (∼2.3 kg) were thawed/tempered at 4°C for 1 wk before analysis. Urea PAGE and quantification of the pH 4.6 soluble N over time were used to monitor primary proteolysis. Body and rheological properties were monitored using texture profile analysis (TPA) and dynamic low-amplitude oscillatory rheology. Changes in flavor, body, shred properties, and pizza performance were evaluated using quantitative descriptive analysis with 12 trained panelists using a 15-point scale. High-pressure processing treatment caused ∼5 log cfu/mL reduction in starter counts, partial solubilization of the insoluble Ca, and a small pH increase (from ∼5.2 to 5.3). The rate of primary proteolysis was reduced by HPP and low-temperature storage. High-pressure processing treatment reduced initial cheese hardness, but no further significant decrease was observed over storage time, whereas the hardness of non-HPP samples decreased over the 365 d of storage, apart from the frozen samples. In pizza applications, blister quantity development and loss of strand thickness were limited by storage at −18°C. Freezing LMPS mozzarella to −18°C gave the least changes in proteolysis and pizza performance over the 365 d of study, storage of cheese at 0°C slowed the loss of hardness and the deterioration of pizza performance attributes. The combination of HPP and 0°C storage of cheese resulted in little change in blistering quantity of pizza during the 365 d of study, whereas cheese stored at 0°C had blisters covering much of the pizza after this extended storage time. Combining HPP with low-temperature storage is a promising alternative approach to freezing for the extension of the functionality shelf life of LMPS mozzarella.

Azadeh Hassanpour, Johannes Geibel, Henner Simianer et al.

The effective planning and allocation of resources in modern breeding programs is a complex task. Breeding program design and operational management have a major impact on the success of a breeding program and changing parameters such as the number of selected/phenotyped/genotyped individuals will impact genetic gain, genetic diversity, and costs. As a result, careful assessment and balancing of design parameters is crucial, considering the trade-offs between different breeding goals and associated costs. In a previous study, we optimized the resource allocation strategy in a dairy cattle breeding scheme via the combination of stochastic simulations and kernel regression, aiming to maximize a target function containing genetic gain and the inbreeding rate under a given budget. However, the high number of simulations required when using the proposed kernel regression method to optimize a breeding program with many parameters weakens the effectiveness of such a method. In this work, we are proposing an optimization framework that builds on the concepts of kernel regression but additionally makes use of an evolutionary algorithm to allow for a more effective and general optimization. The key idea is to consider a set of potential parameterizations of the breeding program, evaluate their performance based on stochastic simulations, and use these outputs to derive new parametrization to test in an iterative procedure. The evolutionary algorithm was implemented in a Snakemake pipeline to allow for efficient scaling on large distributed computing platforms. The algorithm achieved convergence to the same optimum with a massively reduced number of simulations. Thereby, the incorporation of class variables and accounting for a higher number of parameters in the optimization pipeline leads to substantially reduced computing time and better scaling for the desired optimization of a breeding program.

Steven Reece, Emma O'Donnell, Felicia Liu et al.

There is growing recognition among financial institutions, financial regulators and policy makers of the importance of addressing nature-related risks and opportunities. Evaluating and assessing nature-related risks for financial institutions is challenging due to the large volume of heterogeneous data available on nature and the complexity of investment value chains and the various components' relationship to nature. The dual problem of scaling data analytics and analysing complex systems can be addressed using Artificial Intelligence (AI). We address issues such as plugging existing data gaps with discovered data, data estimation under uncertainty, time series analysis and (near) real-time updates. This report presents potential AI solutions for models of two distinct use cases, the Brazil Beef Supply Use Case and the Water Utility Use Case. Our two use cases cover a broad perspective within sustainable finance. The Brazilian cattle farming use case is an example of greening finance - integrating nature-related considerations into mainstream financial decision-making to transition investments away from sectors with poor historical track records and unsustainable operations. The deployment of nature-based solutions in the UK water utility use case is an example of financing green - driving investment to nature-positive outcomes. The two use cases also cover different sectors, geographies, financial assets and AI modelling techniques, providing an overview on how AI could be applied to different challenges relating to nature's integration into finance. This report is primarily aimed at financial institutions but is also of interest to ESG data providers, TNFD, systems modellers, and, of course, AI practitioners.

Kathleen R. Mullen, Imke Tammen, Nicolas A. Matentzoglu et al.

Background: Limited universally-adopted data standards in veterinary medicine hinder data interoperability and therefore integration and comparison; this ultimately impedes the application of existing information-based tools to support advancement in diagnostics, treatments, and precision medicine. Objectives: A single, coherent, logic-based standard for documenting breed names in health, production, and research-related records will improve data use capabilities in veterinary and comparative medicine. Methods: The Vertebrate Breed Ontology (VBO) was created from breed names and related information compiled from the Food and Agriculture Organization of the United Nations, breed registries, communities, and experts, using manual and computational approaches. Each breed is represented by a VBO term that includes breed information and provenance as metadata. VBO terms are classified using description logic to allow computational applications and Artificial Intelligence-readiness. Results: VBO is an open, community-driven ontology representing over 19,500 livestock and companion animal breed concepts covering 49 species. Breeds are classified based on community and expert conventions (e.g., cattle breed) and supported by relations to the breed's genus and species indicated by National Center for Biotechnology Information (NCBI) Taxonomy terms. Relationships between VBO terms (e.g., relating breeds to their foundation stock) provide additional context to support advanced data analytics. VBO term metadata includes synonyms, breed identifiers/codes, and attributed cross-references to other databases. Conclusion and clinical importance: The adoption of VBO as a source of standard breed names in databases and veterinary electronic health records can enhance veterinary data interoperability and computability.

Hanbing Li, Ruixue Zhang, Haijing Li et al.

Intensive farming can reduce production costs and maximize animal production efficiency; however, it also causes many adverse effects on the welfare of dairy cows. A mechanical brush is an automated grooming device that promotes the grooming behavior of dairy cattle, thereby helping to alleviate stress. In the present study, we evaluated the effects of using mechanical brushes on the production performance of dairy cows by comprehensively analyzing their milk production, health status, and reproductive performance. The cows were assigned to 6 groups: 109 lactating dairy cows (brush treatment) and 105 controls (without brush treatment), 64 dry milk dairy cows (brush treatment) and 49 controls (without brush treatment), and 198 perinatal cows (brush treatment) and 65 controls (without brush treatment). We found an increasing trend in the daily utility time and usage frequency of mechanical brushes for each cow during the lactating period (7.73 ± 4.02 min/d and 2.90 ± 1.22 times/d, respectively), dry period (15.97 ± 14.16 min/d and 4.21 ± 2.91 times/d, respectively), and perinatal period (25.15 ± 19.05 min/d and 5.45 ± 3.83 times/d, respectively) (<i>p</i> < 0.01 and <i>p</i> < 0.05, respectively). The installation location of the mechanical brush significantly affected the frequency of its usage during the different periods. The head was the preferred body part for using the mechanical brush during the lactation and dry periods (59.32% and 44.54%, respectively), while the hip was the main preferred grooming part during the perinatal period (40.17%). Overall, the time, frequency, and preferred body part of dairy cows that used mechanical brushes varied across different physiological stages. Additionally, mechanical brush use in lactating and dry dairy cows significantly improved cleanliness of the body’s surface (<i>p</i> < 0.05) and enhanced milk production of lactating cows (<i>p</i> < 0.01), particularly for cows with four and five parities. Thus, the use of mechanical brushes could improve the production performance of dairy cows and enhance sustainability of large-scale farms.

Mengya Wang, Lei Zhang, Xingwei Jiang et al.

ABSTRACT: The nutritional components and quality of milk are influenced by the rumen microbiota and its metabolites at different lactation stages. Hence, rumen fluid and milk samples from 6 dairy cows fed the same diet were collected during peak lactation, early mid-lactation, and later mid-lactation. Untargeted metabolomics and 16S rRNA sequencing were applied for analyzing milk and rumen metabolites, as well as rumen microbial composition, respectively. The levels of lipid-related metabolites, l-glutamate, glucose-1-phosphate, and acetylphosphate in milk exhibited lactation-dependent attenuation. Maltol, N-acetyl-d-glucosamine, and choline, which are associated with milk flavor or coagulation properties, as well as l-valine, lansioside A, clitocine, and ginsenoside La, increased significantly in early mid-lactation and later mid-lactation, especially in later mid-lactation. The obvious increase in rumen microbial diversities (ACE and Shannon indices) were observed in early mid-lactation compared with peak lactation. Twenty-one differential bacterial genera of the rumen were identified, with Succinivibrionaceae_UCG-001, Candidatus Saccharimonas, Fibrobacter, and SP3-e08 being significantly enriched in peak lactation. Rikenellaceae_RC9_gut_group, Eubacterium_ruminantium_group, Lachnospira, Butyrivibrio, Eubacterium_hallii_group, and Schwartzia were most significantly enriched in early mid-lactation. In comparison, only 2 bacteria (unclassified_f__Prevotellaceae and Prevotellaceae_UCG-001) were enriched in later mid-lactation. For rumen metabolites, LysoPE(16:0), l-glutamate, and l-tyrosine had higher levels in peak lactation, whereas PE(17:0/0:0), PE(16:0/0:0), PS(18:1(9Z)/0:0), l-phenylalanine, dulcitol, 2-(methoxymethyl)furan, and 3-phenylpropyl acetate showed higher levels in early mid-lactation and later mid-lactation. Multiomics-integrated analysis revealed that a greater abundance of Fibrobacter contributed to phospholipid content in milk by increasing ruminal acetate, l-glutamate, and LysoPE(16:0). Prevotellaceae_UCG-001 and unclassified_f_Prevotellaceae provide substrates for milk metabolites of the same category by increasing ruminal l-phenylalanine and dulcitol contents. These results demonstrated that milk metabolomic fingerprints and critical functional metabolites during lactation, and the key bacteria in rumen related to them. These findings provide new insights into the development of functional dairy products.

Kasatkina Ekaterina, Vavilova Daiana, Faizullin Rinat

The article discusses the importance of animal husbandry in ensuring food security and maintaining a high quality of life. In the current study, statistical monthly data on animal husbandry in the Udmurt Republic from 2018 to 2023 is analyzed to create models for forecasting key indicators: the average daily milk yield, the number of cows, and the total volume of milk production. The model of the average daily milk yield takes into account seasonal fluctuations, temperature, and time trends, with an average relative error of just 1.55%. The autoregressive model for predicting the number of cattle with a lag of 12 months has shown high accuracy with an average relative approximation error of 0.19%. The econometric model of total milk production takes into account the average daily milk yield and other factors, demonstrating high accuracy in its forecasts. These results are important to support decision-making on the development of animal husbandry and the agricultural sector in general.

Stephen Coombes, Mustafa Sayli, Rüdiger Thul et al.

There is enormous interest -- both mathematically and in diverse applications -- in understanding the dynamics of coupled oscillator networks. The real-world motivation of such networks arises from studies of the brain, the heart, ecology, and more. It is common to describe the rich emergent behavior in these systems in terms of complex patterns of network activity that reflect both the connectivity and the nonlinear dynamics of the network components. Such behavior is often organized around phase-locked periodic states and their instabilities. However, the explicit calculation of periodic orbits in nonlinear systems (even in low dimensions) is notoriously hard, so network-level insights often require the numerical construction of some underlying periodic component. In this paper, we review powerful techniques for studying coupled oscillator networks. We discuss phase reductions, phase-amplitude reductions, and the master stability function for smooth dynamical systems. We then focus in particular on the augmentation of these methods to analyze piecewise-linear systems, for which one can readily construct periodic orbits. This yields useful insights into network behavior, but the cost is that one needs to study nonsmooth dynamical systems. The study of nonsmooth systems is well-developed when focusing on the interacting units (i.e., at the node level) of a system, and we give a detailed presentation of how to use \textit{saltation operators}, which can treat the propagation of perturbations through switching manifolds, to understand dynamics and bifurcations at the network level. We illustrate this merger of tools and techniques from network science and nonsmooth dynamical systems with applications to neural systems, cardiac systems, networks of electro-mechanical oscillators, and cooperation in cattle herds.

Yaniv Altshuler, Tzruya Calvao Chebach, Shalom Cohen

In an era of increasing pressure to achieve sustainable agriculture, the optimization of livestock feed for enhancing yield and minimizing environmental impact is a paramount objective. This study presents a pioneering approach towards this goal, using rumen microbiome data to predict the efficacy of feed additives in dairy cattle. We collected an extensive dataset that includes methane emissions from 2,190 Holstein cows distributed across 34 distinct sites. The cows were divided into control and experimental groups in a double-blind, unbiased manner, accounting for variables such as age, days in lactation, and average milk yield. The experimental groups were administered one of four leading commercial feed additives: Agolin, Kexxtone, Allimax, and Relyon. Methane emissions were measured individually both before the administration of additives and over a subsequent 12-week period. To develop our predictive model for additive efficacy, rumen microbiome samples were collected from 510 cows from the same herds prior to the study's onset. These samples underwent deep metagenomic shotgun sequencing, yielding an average of 15.7 million reads per sample. Utilizing innovative artificial intelligence techniques we successfully estimated the efficacy of these feed additives across different farms. The model's robustness was further confirmed through validation with independent cohorts, affirming its generalizability and reliability. Our results underscore the transformative capability of using targeted feed additive strategies to both optimize dairy yield and milk composition, and to significantly reduce methane emissions. Specifically, our predictive model demonstrates a scenario where its application could guide the assignment of additives to farms where they are most effective. In doing so, we could achieve an average potential reduction of over 27\% in overall emissions.

Gaël Beaunée, Pauline Ezanno, Alain Joly et al.

Spatio-temporal pathogen spread is often partially observed at the metapopulation scale. Available data correspond to proxies and are incomplete, censored and heterogeneous. Moreover, representing such biological systems often leads to complex stochastic models. Such complexity together with data characteristics make the analysis of these systems a challenge. Our objective was to develop a new inference procedure to estimate key parameters of stochastic metapopulation models of animal disease spread from longitudinal and spatial datasets, while accurately accounting for characteristics of census data. We applied our procedure to provide new knowledge on the regional spread of \emph{Mycobacterium avium} subsp. \emph{paratuberculosis} (\emph{Map}), which causes bovine paratuberculosis, a worldwide endemic disease. \emph{Map} spread between herds through trade movements was modeled with a stochastic mechanistic model. Comprehensive data from 2005 to 2013 on cattle movements in 12,857 dairy herds in Brittany (western France) and partial data on animal infection status in 2,278 herds sampled from 2007 to 2013 were used. Inference was performed using a new criterion based on a Monte-Carlo approximation of a composite likelihood, coupled to a numerical optimization algorithm (Nelder-Mead Simplex-like). Our criterion showed a clear superiority to alternative ones in identifying the right parameter values, as assessed by an empirical identifiability on simulated data. Point estimates and profile likelihoods allowed us to establish the initial state of the system, identify the risk of pathogen introduction from outside the metapopulation, and confirm the assumption of the low sensitivity of the diagnostic test. Our inference procedure could easily be applied to other spatio-temporal infection dynamics, especially when ABC-like methods face challenges in defining relevant summary statistics.