Search Results (1,292)

Background: Plant-based protein supplementation in supporting muscle recovery following resistance exercise remains an area of growing interest, particularly among vegan athletes, as a potential alternative to animal-based proteins. This systematic review aimed to evaluate the effectiveness of plant-based proteins on recovery from resistance exercise-induced muscle damage in healthy young adults. Methods: A systematic and comprehensive search was administered in eight databases up to 1 May 2025, identifying 1407 articles. Following deduplication and screening, 24 studies met the eligibility criteria, including 22 randomized controlled trials and 2 non-randomized studies, with the majority from high income western countries. Results: Interventions primarily involved soy, pea, rice, hemp, potato, and blended plant protein sources, with doses ranging from 15 to 50 g, typically administered post resistance exercise. Outcomes assessed included muscle protein synthesis (MPS), delayed-onset muscle soreness (DOMS), inflammatory biomarkers, muscle function, and fatigue. The review findings reaffirm that single-source plant proteins generally offer limited benefits compared to animal proteins such as whey, particularly in acute recovery settings, a limitation well-documented consistently in the literature. However, our synthesis highlights that well-formulated plant protein blends (e.g., combinations of pea, rice, and canola) can stimulate MPS at levels comparable to whey when consumed at adequate doses (≥30 g with ~2.5 g leucine). Some studies also reported improvements in subjective recovery outcomes and reductions in muscle damage biomarkers with soy or pea protein. However, overall evidence remains limited by small sample sizes, moderate to high risk of bias, and heterogeneity in intervention protocols, protein formulations, and outcome measures. Risk of bias assessments revealed concerns related to detection and reporting bias in nearly half the studies. Due to clinical and methodological variability, a meta-analysis was not conducted. Conclusion: plant-based proteins particularly in the form of protein blends and when dosed appropriately, may support muscle recovery in resistance-trained individuals and offer a viable alternative to animal-based proteins. However, further high-quality, long-term trials in vegan populations are needed to establish definitive recommendations for plant protein use in sports nutrition. Full article

The ability to quantify equine kinematics is essential for clinical evaluation, research, and performance feedback. However, current methods are challenging to implement. This study presents a motion capture methodology for horses, where three-dimensional, full-body kinematics are calculated without instrumentation on the animal, offering a more scalable and labor-efficient approach when compared with traditional techniques. Kinematic trajectories are calculated from multi-camera video data. First, a neural network identifies skeletal landmarks (markers) in each camera view and the 3D location of each marker is triangulated. An equine biomechanics model is scaled to match the subject’s shape, using segment lengths defined by markers. Finally, inverse kinematics (IK) produces full kinematic trajectories. We test this methodology on a horse at three gaits. Multiple neural networks (NNs), trained on different equine datasets, were evaluated. All networks predicted over 78% of the markers within 25% of the length of the radius bone on test data. Root-mean-square-error (RMSE) between joint angles predicted via IK using ground truth marker-based motion capture data and network-predicted data was less than 10 degrees for 25 to 32 of 35 degrees of freedom, depending on the gait and data used for network training. NNs trained over a larger variety of data improved joint angle RMSE and curve similarity. Marker prediction error, the average distance between ground truth and predicted marker locations, and IK marker error, the distance between experimental and model markers, were used to assess network, scaling, and registration errors. The results demonstrate the potential of markerless motion capture for full-body equine kinematic analysis. Full article

Monitoring the activity index of animals is crucial for assessing their welfare and behavior patterns. However, traditional methods for calculating the activity index, such as pixel intensity differencing of entire frames, are found to suffer from significant interference and noise, leading to inaccurate results. These classical approaches also do not support group or individual tracking in a user-friendly way, and no open-access platform exists for non-technical researchers. This study introduces an open-source web-based platform that allows researchers to calculate the activity index from top-view videos by selecting individual or group animals. It integrates Segment Anything Model2 (SAM2), a promptable deep learning segmentation model, to track animals without additional training or annotation. The platform accurately tracked Cobb 500 male broilers from weeks 1 to 7 with a 100% success rate, IoU of 92.21% ± 0.012, precision of 93.87% ± 0.019, recall of 98.15% ± 0.011, and F1 score of 95.94% ± 0.006, based on 1157 chickens. Statistical analysis showed that tracking 80% of birds in week 1, 60% in week 4, and 40% in week 7 was sufficient (r ≥ 0.90; p ≤ 0.048) to represent the group activity in respective ages. This platform offers a practical, accessible solution for activity tracking, supporting animal behavior analytics with minimal effort. Full article

Stress in pigs poses significant challenges to animal welfare and productivity in modern pig farming, contributing to increased antimicrobial use and the rise of antimicrobial resistance (AMR). This study involves stress classification in pregnant sows by exploring five deep learning models: ConvNeXt, EfficientNet_V2, MobileNet_V3, RegNet, and Vision Transformer (ViT). These models are used for stress detection from facial images, leveraging an expanded dataset. A facial image dataset of sows was collected at Scotland’s Rural College (SRUC) and the images were categorized into primiparous Low-Stressed (LS) and High-Stress (HS) groups based on expert behavioural assessments and cortisol level analysis. The selected deep learning models were then trained on this enriched dataset and their performance was evaluated using cross-validation on unseen data. The Vision Transformer (ViT) model outperformed the others across the dataset of annotated facial images, achieving an average accuracy of 0.75, an F1 score of 0.78 for high-stress detection, and consistent batch-level performance (up to 0.88 F1 score). These findings highlight the efficacy of transformer-based models for automated stress detection in sows, supporting early intervention strategies to enhance welfare, optimize productivity, and mitigate AMR risks in livestock production. Full article

The transgenic animals have been yielding invaluable insights into amyloid pathology by replicating the key features of Alzheimer’s disease (AD). However, there is no clear relationship between senile plaques and memory deficits. Instead, cognitive impairment and synaptic dysfunction are particularly linked to a rise in Aβ_1-42 oligomer level. Thus, injection of Aβ_1-42 oligomers into a specific brain region is considered an alternative approach to investigate the effects of increased soluble Aβ species without any plaques, offering higher controllability, credibility and validity compared to the transgenic model. The hippocampal CA1 (cornu ammonis 1) region is selectively affected in the early stage of AD and specific targeting of CA1 region directly links Aβ oligomer-related pathology with memory impairment in early AD. Next, the inhibitory avoidance (IA) task, a learning paradigm to assess the synaptic basis of CA1-dependent contextual learning, triggers training-dependent synaptic plasticity similar to in vitro HFS (high-frequency stimulation). Given its reliability in assessing contextual memory and synaptic plasticity, this task provides an effective framework for studying early stage AD-related memory deficit. Therefore, in this review, we will focus on why Aβ_1-42 oligomer injection is a valid in vivo model to investigate the early stage of AD and why dorsal CA1 region serves as a target area to understand the adverse effects of Aβ_1-42 oligomers on contextual learning through the IA task. Full article

Forecasting the energy consumption of different consumers became an important procedure with the creation of the European Electricity Market. This study presents a methodology for 24-hour ahead prediction of the energy consumption, which is suitable for application in animal husbandry facilities, such as pig farms. To achieve this, 24 individual models are trained using artificial neural networks that forecast the energy production 1 to 24 h ahead. The selected features include power consumption over the last 72 h, time-based data, average, minimum, and maximum daily temperatures, relative humidities, and wind speeds. The models’ Normalized mean absolute error (NMAE), Normalized root mean square error (NRMSE), and Mean absolute percentage error (MAPE) vary between 16.59% and 19.00%, 22.19% and 24.73%, and 9.49% and 11.49%, respectively. Furthermore, the case studies showed that in most situations, the forecasting error does not exceed 10% with several cases up to 25%. The proposed methodology can be useful for energy managers of animal farm facilities, and help them provide a better prognosis of their energy consumption for the Energy Market. The proposed methodology could be improved by selecting additional features, such as the variation of the controlled meteorological parameters over the last couple of days and the schedule of technological processes. Full article

Equine-Assisted Services (EAS), which incorporate horses in a variety of ways in an effort to improve human wellbeing, have grown in popularity in recent years. Although much research has been conducted regarding the benefits that horses may provide for human health and wellbeing, little attention has been paid to practitioners’ experiences and perspectives of the field, despite the fact they are uniquely positioned to advance our understanding of this area. This study aimed to explore practitioners’ lived experiences of EAS, focusing on the benefits they observed, possible underlying mechanisms for any health benefits witnessed, and challenges faced in the area. Fifteen EAS practitioners from the UK/Ireland took part in qualitative semi-structured interviews, analysed using reflexive thematic analysis. Five themes were identified, three of which related to the horse’s influence on building connections, relationships, and enriching the process, whilst the remainder explored challenges within the field of EAS. These themes were explored through the practitioners’ lens, where possible linking them to our current understanding of human–animal interactions and related fields in the literature. Findings showed that horses, through EAS, were considered invaluable for building relationships, relational skills, and motivation to engage in whichever service was being provided. However, EAS was also viewed as complex. Concerns regarding competencies to practice, training, and lack of governance were expressed. These areas need further exploration and progress if EAS is to grow in efficacy and attain professional status. Full article

Foraging behavior in hens is an important indicator of animal welfare. It involves both the search for food and exploration of the environment, which provides necessary enrichment. In addition, it has been inversely linked to damaging behaviors such as severe feather pecking. Conventional studies rely on manual observation to investigate foraging location, duration, timing, and frequency. However, this approach is labor-intensive, time-consuming, and subject to human bias. Our study developed computer vision-based methods to automatically detect foraging hens in a cage-free research environment and compared their performance. A cage-free room was divided into four pens, two larger pens measuring 2.9 m × 2.3 m with 30 hens each and two smaller pens measuring 2.3 m × 1.8 m with 18 hens each. Cameras were positioned vertically, 2.75 m above the floor, recording the videos at 15 frames per second. Out of 4886 images, 70% were used for model training, 20% for validation, and 10% for testing. We trained multiple You Only Look Once (YOLO) object detection models from YOLOv9, YOLOv10, and YOLO11 series for 100 epochs each. All the models achieved precision, recall, and mean average precision at 0.5 intersection over union (mAP@0.5) above 75%. YOLOv9c achieved the highest precision (83.9%), YOLO11x achieved the highest recall (86.7%), and YOLO11m achieved the highest mAP@0.5 (89.5%). These results demonstrate the use of computer vision to automatically detect complex poultry behavior, such as foraging, making it more efficient. Full article

Brachycephalic head shape in dogs has been associated with behavioural traits that may enhance their appeal as companion animals, contributing to their popularity. However, it remains unclear whether these behavioural differences are directly linked to head shape or are mediated by factors such as body size, demographics, and dog-keeping practices. Drawing on two large-scale owner surveys (N = 5613) and cephalic index estimates for 90 breeds, we investigated the relationship between head shape and eight behavioural variables (four personality traits and four behavioural problems), while controlling for 20 demographic and dog-keeping characteristics, as well as body size. Our results show that behavioural differences among head shapes are only partly attributable to head shape itself; some are explained by confounding variables. Specifically, brachycephalic dogs appeared predisposed to positive behaviours (e.g., calmness, fewer behavioural problems), but these traits were often obscured by their small body size and low training experience. These findings highlight the complex interplay between morphology, behaviour, and environment, and emphasize the role of training and management in supporting the behavioural well-being of popular brachycephalic breeds. This has important implications for owners, breeders, and welfare professionals aiming to align aesthetic preferences with behavioural and welfare outcomes. Full article

Q Fever is a zoonosis caused by Coxiella burnetii that affects domestic and wild ruminants, leading to reproductive disorders. In humans, the disease can manifest with acute and chronic clinical manifestations. Veterinarians, as healthcare professionals in close contact with animals, serve both as the first line of defence in preventing infection at the animal–human interface and as an important sentinel group for the rapid detection of outbreaks. The aim of this study was to assess the knowledge, perception, and attitude of veterinarians in Southern Spain regarding Q Fever. To this end, an online survey was designed, validated, and conducted among veterinarians in the province of Malaga, with a final participation of 97 individuals, predominantly from the private sector (clinic, livestock, agri-food, etc.). The data obtained reflected a general lack of knowledge about the disease, particularly concerning its epidemiology and infection prevention. Regarding perception and attitude, a significant percentage of respondents stated they did not use protective equipment when handling susceptible animals and only sought information about the disease in response to outbreak declarations. The study emphasised the significance of promoting training in zoonotic diseases during and after graduation, the relevance of official channels in occupational risk prevention, and the utility of epidemiological surveys as a tool to identify and address potential gaps in knowledge related to this disease. Full article

Brucellosis is caused by Brucella spp.; it can result in fetal loss and abortion, resulting in economic losses and negative effects on human health. Herein, a cross-sectional study on the epidemiology of Brucella spp. in aborted livestock in Ningxia from 2022 to 2023 was conducted. A total of 749 aborted tissue samples from 215 cattle and 534 sheep were collected from farmers who reported abortions that were supported by veterinarians trained in biosecurity. The samples were analyzed using qPCR and were cultured for Brucella spp. when a positive result was obtained; the samples were speciated using AMOS-PCR. MLST and MLVA were employed for genotype identification. The results demonstrated that 8.68% of the samples were identified as being positive for Brucella spp. based on qPCR results. In total, 14 field strains of Brucella spp. were subsequently isolated, resulting in 11 B. melitensis, 2 B. abortus, and 1 B. suis. being identified via AMOS-PCR. Four sequence types were identified via MLST—ST7 and ST8 (B. melitensis), ST2 (B. abortus), and ST14 (B. suis)—with ST8 predominating. Five MLVA-8 genotypes and seven MLVA-11 genotypes were identified, with MLVA-11 GT116 predominating in livestock. Thus, at least three Brucella species are circulating in aborted livestock in Ningxia. This suggests a significant risk of transmission to other animals and humans. Therefore, disinfection and safe treatment procedures for aborted livestock and their products should be carried out to interrupt the transmission pathway; aborted livestock should be examined to determine zoonotic causes and targeted surveillance should be strengthened to improve the early detection of infectious causes, which will be of benefit to the breeding industry and public health security. Full article

The present study explores the use of Instagram as an innovative strategy in the teaching–learning process in the context of animal reproduction topics. In the current era, with digital technology and social media transforming how information is accessed and consumed, it is essential for teachers to adapt and harness the potential of these tools for educational purposes. This article delves into the need for teachers to stay updated with current trends and the importance of promoting digital competences among teachers. This research aims to provide insights into the benefits of integrating social media into the educational landscape. Students of Veterinary Science degrees, Master’s degrees in Equine Sport Medicine as well as vocational education and training (VET) were involved in this study. An Instagram account named “UCOREPRO” was created for educational use, and it was openly available to all users. Instagram usage metrics were consistently tracked. A voluntary survey comprising 35 questions was conducted to collect feedback regarding the educational use of smartphone technology, social media habits and the UCOREPRO Instagram account. The integration of Instagram as an educational tool was positively received by veterinary students. Survey data revealed that 92.3% of respondents found the content engaging, with 79.5% reporting improved understanding of the subject and 71.8% acquiring new knowledge. Students suggested improvements such as more frequent posting and inclusion of academic incentives. Concerns about privacy and digital distraction were present but did not outweigh the perceived benefits. The use of short videos and microlearning strategies proved particularly effective in capturing students’ attention. Overall, Instagram was found to be a promising platform to enhance motivation, engagement, and informal learning in veterinary education, provided that thoughtful integration and clear educational objectives are maintained. In general, students expressed positive opinions about the initiative, and suggested some ways in which it could be improved as an educational tool. Full article

Virtual fencing (VF) offers a promising alternative to conventional or electrified fences for managing livestock grazing distribution. This study evaluated the behavioral responses of 25 Rarámuri Criollo cows fitted with Nofence^® collars in Pine Valley, CA, USA. The VF system was deployed in chaparral rangeland pastures. The study included a 14-day training phase followed by an 18-day testing phase. The collar-recorded variables, including audio warnings and electric pulses, animal movement, and daily typical behavior patterns of cows classified into a High or Low virtual fence response group, were compared using repeated-measure analyses with mixed models. During training, High-response cows (i.e., resistant responders) received more audio warnings and electric pulses, while Low-response cows (i.e., active responders) had fewer audio warnings and electric pulses, explored smaller areas, and exhibited lower mobility. Despite these differences, both groups showed a time-dependent decrease in the pulse-to-warning ratio, indicating increased reliance on audio cues and reduced need for electrical stimulation to achieve similar containment rates. In the testing phase, both groups maintained high containment with minimal reinforcement. The study found that Rarámuri Criollo cows can effectively adapt to virtual fencing technology, achieving over 99% containment rate while displaying typical diurnal patterns for grazing, resting, or traveling behavior. These findings support the technical feasibility of using virtual fencing in chaparral rangelands and underscore the importance of accounting for individual behavioral variability in behavior-based containment systems. Full article

Capturing the interactions between moving objects is vital in traffic analysis, sports, and animal behavior, but remains challenging because of subtle spatiotemporal dynamics. This paper introduces HAIMO (Hybrid Analysis of the Interaction of Moving Objects), a conceptual framework that combines knowledge-driven AI for interpretable, symbolic interaction representations with data-driven models trained through self-supervised learning (SSL) on large sets of unlabeled trajectory data. In HAIMO, we propose using transformer architectures to model complex spatiotemporal dependencies while maintaining interpretability through symbolic reasoning. To illustrate the feasibility of this hybrid approach, we present a basic proof-of-concept using elite tennis rallies, where the knowledge-driven component identifies interaction patterns between players and the ball, and we outline how SSL-enhanced transformer models could support and strengthen movement prediction. By bridging symbolic reasoning and self-supervised data-driven learning, HAIMO provides a conceptual foundation for future GeoAI and spatiotemporal analytics, especially in applications where both pattern discovery and explainability are crucial. Full article

The relationships between farmers and livestock are multifaceted. The aim of this study was to describe the sociodemographic, biogeographic, and institutional factors that influence the relationships between humans and animals in the natural savanna. Visits were made to 65 farms, followed by interviews (n = 13) and three focus group interviews (n = 24) directed at farmers and institutional representatives. The results were triangulated to extract the key findings. The following findings were obtained: (a) cultural gender transitions and the lack of generational succession have transformed livestock farming; (b) the relationships between farmers and livestock have favored the implementation of new productive practices and innovations, as well as improvements in animal welfare practices; (c) conditioning factors affecting these relationships include gender discriminatory norms, low profitability and credit access, poor sanitation, animal handling infrastructure, security, and resistance to change; and (d) improvement opportunities include the inclusion of young people and women in livestock farming, education for work practices, credit facilitation, access to technologies, governance, and improvement in the cattle logistics chain. The results are useful for enhancing the relationships between farmers and livestock, guiding training activities, and responsible governance. Full article