Simple Summary Consumption of food products that are rich in natural antioxidants improves the antioxidant status of an organism through protection against oxidative stress and damage. Milk and dairy products (yogurt and cheese) accounting for approximately 25–30% of the average human diet are undoubtedly a rich source of compounds exhibiting antioxidant properties. The aim of the study was to present a review of literature data on the antioxidant potential of raw milk and dairy products (milk, fermented products, and cheese) and the possibility to modify its level at the milk production and processing stage. The antioxidant capacity of milk and dairy products is mainly related to the presence of sulfur amino acids, whey proteins (especially β-lactoglobulin), vitamins A, E, and C, or β-carotene. The processes of fermentation or cheese maturation are associated with the release of bioactive peptides, which are responsible for the level of the antioxidant status of the product. The use of probiotic strains significantly enhances the antioxidant status. The antioxidant status of milk and dairy products can be modified with the use of natural additives in animal nutrition or at the stage of milk processing. Herbal mixtures, seeds, fruits, and waste from the fruit and vegetable industry are used most commonly. It is worth emphasizing that regular consumption of natural dairy antioxidants minimizes the risk of development of civilization diseases (e.g., cardiovascular disease, cancer, or diabetes). It also slows down the aging process in the organism. Abstract The aim of the study was to present a review of literature data on the antioxidant potential of raw milk and dairy products (milk, fermented products, and cheese) and the possibility to modify its level at the milk production and processing stage. Based on the available reports, it can be concluded that the consumption of products that are a rich source of bioactive components improves the antioxidant status of the organism and reduces the risk of development of many civilization diseases. Milk and dairy products are undoubtedly rich sources of antioxidant compounds. Various methods, in particular, ABTS, FRAP, and DPPH assays, are used for the measurement of the overall antioxidant activity of milk and dairy products. Research indicates differences in the total antioxidant capacity of milk between animal species, which result from the differences in the chemical compositions of their milk. The content of antioxidant components in milk and the antioxidant potential can be modified through animal nutrition (e.g., supplementation of animal diets with various natural additives (herbal mixtures, waste from fruit and vegetable processing)). The antioxidant potential of dairy products is associated with the quality of the raw material as well as the bacterial cultures and natural plant additives used. Antioxidant peptides released during milk fermentation increase the antioxidant capacity of dairy products, and the use of probiotic strains contributes its enhancement. Investigations have shown that the antioxidant activity of dairy products can be enhanced by the addition of plant raw materials or their extracts in the production process. Natural plant additives should therefore be widely used in animal nutrition or as functional additives to dairy products.
ABSTRACT: Long-chain fatty acids such as essential fatty acids (EFA) and CLA have the potential to affect glucose metabolism and milk synthesis in the mammary gland. The present study tested the hypothesis that EFA, CLA, or a combined EFA and CLA treatment influences the formation of milk constituents from glucose and regulation of milk synthesis during early lactation. For this purpose, the carbon flow from glucose and important enzymes and regulatory factors of milk synthesis were measured in the mammary gland. Rumen-cannulated German Holstein cows (n = 38) were investigated from wk 9 antepartum until wk 9 postpartum. The cows were abomasally infused with coconut oil (CTRL, 76 g/d; n = 9), 78 g/d linseed and 4 g/d safflower oil (EFA treatment; n = 9), Lutalin (CLA treatment, isomers cis-9,trans-11 and trans-10,cis-12 CLA, each 10 g/d; n = 10), or the combination of EFA+CLA (n = 10). In wk 3 postpartum, [13C6]-glucose was infused intravenously for 4 h, and 13C-enrichment was measured in milk ingredients (lactose, casein, and fat) before and several times after tracer infusion. Tissue from the mammary gland was collected after tracer infusion in wk 4 by biopsy and in wk 9 after slaughtering the cows. Tissue was used for measuring gene and protein (only wk 9) expression of parameters related to milk synthesis. Milk FCM and ECM decreased with CLA treatment. After [13C6]-glucose infusion, 13C-enrichment increased about 30-fold higher in lactose than in casein and milk fat. 13C-Enrichment in milk fat increased more, but in casein increased less in CLA-treated than non-CLA-treated cows after infusion of [13C6]-glucose. In milk fat, 13C-enrichment of triglycerides was closely related to glycerol. The mRNA abundance of key factors and enzymes associated with milk fat synthesis, such as SREBF1, ACACA, FASN, ELOVL2, and EEF1A1, was reduced by CLA treatment. Protein expression of FAS and SREBP (68 kD) was reduced, but FABP4 was increased in cows infused with CLA. The reduced 13C-enrichment in casein of CLA-treated cows was partly combined with lower urea and protein concentrations in milk but no clear changes in gene and protein expression of parameters associated with milk protein synthesis. The EFA treatment did not affect 13C-enrichment of milk ingredients after [13C6]-glucose infusion, but protein expression of SREBP (68 kD) was reduced. Results indicated a coordinated inhibition of parameters related to milk fat synthesis in the mammary gland in CLA and EFA+CLA cows. The inhibitory effect on milk fat synthesis was associated with an elevated carbon flux of glucose in glycerol of the triglycerides in milk fat. Reduction of glucose flux in casein by CLA treatment and less milk urea pointed at less nonessential AA synthesized from glucose, but regulation of milk protein synthesis was barely affected by CLA and EFA+CLA. The EFA treatment had no influence on glucose flux into milk ingredients and seems to barely affect milk fat and protein synthesis in the present study.
Computer vision provides automated, non-invasive, and scalable tools for monitoring dairy cattle, thereby supporting management, health assessment, and phenotypic data collection. Although transfer learning is commonly used for predicting body weight from images, its effectiveness and optimal fine-tuning strategies remain poorly understood in livestock applications, particularly beyond the use of pretrained ImageNet or COCO weights. In addition, while both depth images and three-dimensional point-cloud data have been explored for body weight prediction, direct comparisons of these two modalities in dairy cattle are limited. Therefore, the objectives of this study were to 1) evaluate whether transfer learning from a large farm enhances body weight prediction on a small farm with limited data, and 2) compare the predictive performance of depth-image- and point-cloud-based approaches under three experimental designs. Top-view depth images and point-cloud data were collected from 1,201, 215, and 58 cows at large, medium, and small dairy farms, respectively. Four deep learning models were evaluated: ConvNeXt and MobileViT for depth images, and PointNet and DGCNN for point clouds. Transfer learning markedly improved body weight prediction on the small farm across all four models, outperforming single-source learning and achieving gains comparable to or greater than joint learning. These results indicate that pretrained representations generalize well across farms with differing imaging conditions and dairy cattle populations. No consistent performance difference was observed between depth-image- and point-cloud-based models. Overall, these findings suggest that transfer learning is well suited for small farm prediction scenarios where cross-farm data sharing is limited by privacy, logistical, or policy constraints, as it requires access only to pretrained model weights rather than raw data.
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.
Play behaviour serves as a positive welfare indicator in dairy calves, yet the influence of space allowance under commercial conditions remains poorly characterized, particularly at intermediate-to-high allowances (6-20 m2 per calf). This study investigated the relationship between space allowance and play behaviour in 60 group-housed dairy calves across 14 commercial farms in the Netherlands (space range: 2.66-17.98 m2 per calf), and developed an automated computer vision pipeline for scalable monitoring. Video observations were analyzed using a detailed ethogram, with play expressed as percentage of observation period (%OP). Statistical analysis employed linear mixed models with farm as a random effect. A computer vision pipeline was trained on manual annotations from 108 hours on 6 farms and validated on held-out test data. The computer vision classifier achieved 97.6% accuracy with 99.4% recall for active play detection. Calves spent on average 1.0% of OP playing reflecting around 10 minutes per 17-hour period. The space-play relationship was non-linear, with highest play levels at 8-10 m2 per calf (1.6% OP) and lowest at 6-8 m2 and 12-14 m2 (<0.6% OP). Space remained significant after controlling for age, health, and group size. In summary, these findings suggest that 8-10 m2 per calf represents a practical target balancing welfare benefits with economic feasibility, and demonstrate that automated monitoring can scale small annotation projects to continuous welfare assessment systems.
Milk and dairy products are naturally exposed to multiple microbiological, physical and biochemical degradations. Such products require one or more stabilization operations during processing and storing to ensure acceptable sanitary and sensory qualities to the consumer. Several stabilization treatments, such as drying, are commonly applied to dairy products. A successful drying operation requires an in-depth understanding of the physic-chemical changes that occur during the production and storage of dairy powders. Mastering these changes is mandatory to avoid dairy powder degradations, which mainly depend on water dynamics and thermodynamic characteristics of the produced powder. This chapter gives an overview of dairy powder production through the understanding of the powder production principals. A discussion of some essential powder characteristics will be provided. Finally, an overview of the production of camel milk powder will be provided and will be compared with that of cow milk powder.
Introduction & Background
Climate change represents one of the most pressing challenges of our time. Within this context, the food sector is a critical domain for intervention, accounting for approximately one-third of global greenhouse gas emissions. While consumers increasingly express concern about environmental issues, they often lack access to transparent, actionable information about the environmental footprint of their food choices.
Objectives & Approach
This study integrates environmental impact data of grocery products with digital footprint data from a major UK retailer to analyse sustainable food purchasing patterns. Through advanced text processing techniques including natural language processing (NLP) algorithms and machine learning classification models, we established comprehensive product mappings across retailers using product name similarity matching and category classification. Environmental impact scores were calculated based on life cycle assessment (LCA) data encompassing carbon footprint, water usage, and land use metrics, weighted by product-specific environmental intensity factors. These scores were merged with transactional shopping data for over 4 million loyalty card holders covering the period from July 2019 to December 2021. The analysis examines spatio-temporal variations in food-related environmental impacts across British neighbourhoods, at the Lower and Middle Super Output Area levels, incorporating area-level socioeconomic status (SES) data, such as the 2019 Indices of Deprivation, to explore demographic patterns in consumption behaviours and their associated environmental impact.
Relevance to Digital Footprints
Analysis of grocery transactions by loyalty card members demonstrates the ability of digital footprint data to be an effective method for visualising variations in the environmental footprint of food purchases at the local level and highlighting seasonal variations in environmental impacts.
Results
Mapping the normalised results shows wide divergences of environmental impact across neighbourhoods and seasons. Areas with above-average levels of animal-based products have higher environmental footprints, with a notable correlation between higher socio-economic status areas and increased consumption of environmentally intensive products per pound spent. Despite red meat having a significantly higher impact than other foods, it is often foods with lower environmental impact, like dairy products, that are responsible for much of the environmental footprint due to their higher levels of overall consumption. Seasonal analysis reveals distinct patterns: environmental impacts peak during winter months (December-February), potentially due to increased consumption of imported produce and processed foods, while summer months show reduced impacts likely coinciding with greater consumption of locally-sourced fresh produce.
Conclusions & Implications
The development of cross-retailer product datasets through text-based matching and machine learning techniques enables broader application of retailer-specific environmental impact data. This methodology mitigates single-source bias and enhances the generalizability of research findings. Our spatiotemporal analysis reveals that environmental footprints are primarily driven by consumption volume, with significant seasonal variations observed at the neighbourhood level and notable socio-economic disparities in consumption patterns of high-impact animal-based foods like red meat and dairy products.
The objective of this study was to derive the estimated breeding values (EBVs) of Holstein sires for colostrum and passive transfer of immunity traits to (1) estimate the genetic association between these new traits and established production, conformation, and function, and (2) explore whether sires can be classified in specific profiles regarding the new traits. For cow colostrum traits, the study included 699 daughters of 67 sires from 6 commercial dairy herds. The number of daughters per sire ranged from 5 to 49. Passive transfer of immunity was measured as the blood serum total protein content in 854 purebred Holstein calves of 61 sires from 8 commercial dairy herds. The number of calves per sire ranged from 5 to 44. Data were statistically analyzed using mixed models. Approximate genetic correlations of the derived sire EBVs for cow colostrum and calf serum total protein with EBVs for several other traits were estimated. Moreover, sires were classified into colostrum and calf serum total protein profile groups. Approximate genetic correlations of cow colostrum and calf serum traits with milk production, conformation, and functional traits were mostly unfavorable. Colostrum TS and protein contents were negatively correlated with milk yield, fat yield, protein yield, productive life, and livability. The only favorable correlation found was between colostrum TS/calf serum total protein and daughter pregnancy rate. However, several bulls had favorable profiles in both colostrum/passive transfer of immunity and production traits. Colostrum/passive immunity traits could be included in future genetic improvement programs after careful structure of relevant indexes.
Mengisti Berihu Girmay, Jakob Droste, Hannah Deters
et al.
Artificial Intelligence (AI) promises new opportunities across many domains, including agriculture. However, the adoption of AI systems in this sector faces several challenges. System complexity can impede trust, as farmers' livelihoods depend on their decision-making and they may reject opaque or hard-to-understand recommendations. Data privacy concerns also pose a barrier, especially when farmers lack transparency regarding who can access their data and for what purposes. This paper examines dairy farmers' explainability requirements for technical recommendations and data privacy, along with the influence of socio-demographic factors. Based on a mixed-methods study involving 40 German dairy farmers, we identify five user personas through k-means clustering. Our findings reveal varying requirements, with some farmers preferring little detail while others seek full transparency across different aspects. Age, technology experience, and confidence in using digital systems were found to correlate with these explainability requirements. The resulting user personas offer practical guidance for requirements engineers aiming to tailor digital systems more effectively to the diverse requirements of farmers.
Activity and behaviour correlate with dairy cow health and welfare, making continual and accurate monitoring crucial for disease identification and farm productivity. Manual observation and frequent assessments are laborious and inconsistent for activity monitoring. In this study, we developed a unique multi-camera, real-time tracking system for indoor-housed Holstein Friesian dairy cows. This technology uses cutting-edge computer vision techniques, including instance segmentation and tracking algorithms to monitor cow activity seamlessly and accurately. An integrated top-down barn panorama was created by geometrically aligning six camera feeds using homographic transformations. The detection phase used a refined YOLO11-m model trained on an overhead cow dataset, obtaining high accuracy (mAP\@0.50 = 0.97, F1 = 0.95). SAMURAI, an upgraded Segment Anything Model 2.1, generated pixel-precise cow masks for instance segmentation utilizing zero-shot learning and motion-aware memory. Even with occlusion and fluctuating posture, a motion-aware Linear Kalman filter and IoU-based data association reliably identified cows over time for object tracking. The proposed system significantly outperformed Deep SORT Realtime. Multi-Object Tracking Accuracy (MOTA) was 98.7% and 99.3% in two benchmark video sequences, with IDF1 scores above 99% and near-zero identity switches. This unified multi-camera system can track dairy cows in complex interior surroundings in real time, according to our data. The system reduces redundant detections across overlapping cameras, maintains continuity as cows move between viewpoints, with the aim of improving early sickness prediction through activity quantification and behavioural classification.
Italo Henrique Souza Mafra, Glauber da Rocha Balthazar
Dairy farming has great economic value in Brazil, however, during production, diseases such as mastitis can occur in animals, which can reduce productivity and, consequently, economic profitability. When mastitis is present in animals, it can cause physical and chemical changes in the milk, affecting its quality, market value and also compromising the health of the animal. MastiteApp is a tool to help producers prevent mastitis in their herds by checking the temperature taken from the four teats of the animal. To perform theanalysis, the temperature of all the animals' teats must be measured and, if there is a change in temperature, the system will display a message informing the producer of the possible presence of subclinical mastitis in their animal. The application has proven to be efficient in alerting producers to the possible presence of subclinical mastitis in the first few days of manifestation, thus initiating treatment and preventing the disease from worsening.
Social relations have been shown to impact individual and group success in farm animal populations. Fundamental to addressing these relationships is an understanding of the social network structure resulting from the co-habitation and co-movement of relationships between individuals in a group. Here, we investigate the social network of a group of around 210 lactating dairy cows on a dutch farm during a 14 days period. A positioning system called \emph{Cowview} collected positional data for the whole period. We make the assumption that spatial proximity can be used as a proxy for social interaction. The data is processed to get adjacency matrices. Then social networks are identified based on these matrices. Community detection techniques are applied to the networks. We measure metrics of different dimensions to test community structure, centralization, and similarity of network structure over time. Our study show that there is no evidence that cows are subdivided into stable social communities when looking at interaction in the whole barn. We, however, notice relatively clear communities when dividing the barn into areas with different activities. The social network is characterized by significant centralization, low connectivity, and a hierarchy.
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.
H. O. Fatoyinbo, P. Tiwari, P. O. Olanipekun
et al.
Highly pathogenic avian influenza (HPAI), especially the H5N1 strain, remains a major threat to animal health, food security, and public health. Recent spillover events in dairy cattle in the United States, linked to wild birds, highlight the critical importance of understanding transmission pathways at the cattle--wild bird--environment interface. In this work, we formulate and analyze a deterministic compartmental model that captures the transmission of HPAI between dairy cattle and wild birds, incorporating both direct and indirect (environmental) routes. The model combines an $SEIR$ framework for cattle with an $SIR$ structure for wild birds, coupled through an environmental compartment. We derive the basic reproduction number, $\mathcal{R}_{0}$, using the next-generation matrix approach, decomposing it into cattle-to-cattle, bird-to-bird, and environmental contributions. Qualitative analysis establishes positivity, boundedness, and global stability of equilibria through Lyapunov functions. Numerical simulations confirm the results of the theoretical analyses, illustrating outbreak trajectories, extinction thresholds, and persistence dynamics. A global sensitivity analysis, based on Latin hypercube sampling and partial rank correlation coefficients, identifies key parameters, particularly transmission among cattle, environmental contamination, and recovery rate as critical drivers of epidemic outcomes. Our results show that disease elimination is achievable when $\mathcal{R}_{0} < 1$, while persistence is inevitable for $\mathcal{R}_{0} > 1$. These findings provide a comprehensive mathematical framework for assessing HPAI risks and offer guidance for biosecurity strategies aimed at mitigating spillover and controlling outbreaks in livestock populations.
This study models milk product processing and sustainable of the shelf-life extension in a dairy factory using artificial intelligence. The Cappadocia dairy factory was used to study chemical processes and computational system modeling and simulation. Levenberg–Marquardt algorithm was used to create an artificial neural network model from real-time data. An AI-based method utilizing a Multilayer Perceptron (MLP) Artificial Neural Network (ANN) model was employed to precisely analyze productivity data in dairy factories. There are 9 product types and production quantities used as input parameters, and 90 datasets of actual dairy products used as output values. The model was trained using the Levenberg–Marquardt algorithm on 62 datasets for training, 14 for validation, and 14 for testing. The accuracy of the model is affected by the optimal data segmentation. The model showed how AI algorithms can improve processes and industrial production by increasing dairy production efficiency from 20 to 40%. Model efficiency values were compared to observed values to determine prediction accuracy. Model mean squared error was 4.02E-06, and coefficient of determination was 0.99984. Model efficiency predictions and observed values differed by −0.04% on average. This study investigated using artificial intelligence to optimize salvage processes and systems to increase energy efficiency and reduce environmental impact. The results show that a neural network model trained with real data can predict dairy plant productivity.
Abstract Probiotic products have long been recognized for their health benefits. Additionally, milk has held a longstanding reputation as a dairy product that offers high-quality proteins and essential micronutrients. As awareness of the impact of food on health grows, interest in functional products such as probiotic dairy products is on the rise. Fermentation, a time-honored technique used to enhance nutritional value and food preservation, has been used for centuries to increase nutritional value and is one of the oldest food processing methods. Historically, fermented dairy products have been used as convenient vehicle for the consumption of probiotics. However, addressing the potential drawbacks of fermentation has recently led to increase in research on probiotic dairy drinks prepared without fermentation. These non-fermented dairy drinks have the advantage of maintaining the original flavors of milk drinks, containing potential health functional probiotics, and being an alternative dairy product that is helpful for probiotics intake. Currently, research on plant-based dairy products is rapidly increasing in the market. These developments might suggest the potential for novel forms of non-fermented dairy beverages with substantial prospects in the food market. This review aims to provide an overview of milk-based dairy beverages, both fermented and non-fermented, and discuss the potential of non-fermented dairy products. This exploration paves the way for innovative approaches to deliver probiotics and nutrition to consumers.
Pranav Vashisht, Lovepreet Singh, Shikhadri Mahanta
et al.
Customer priority for consuming dairy products with intact natural characteristics is leading the food processing trends towards the extensive exploration of non‐thermal technologies. Pulsed electric field (PEF) technique has arisen as a potential and sustainable processing solution. This review discusses the microbial and enzyme inactivation potential of PEF technique in milk and milk products. Alongside its effect on their natural characteristics. Its potential for better probiotic retention in spray‐dried powders, dairy waste treatment and nutrient enrichment application in dairy products has also been highlighted. PEF has demonstrated its effectiveness in ensuring the dairy product's safety with minimal effects on quality parameters. However, these findings are limited to lab and pilot scale level. Further, extensive research is needed at a commercial scale for the implementation of this technology in the dairy industry.
S.J.R. Woodward, J.P. Edwards, K.J. Verhoek
et al.
Heat stress events in dairy cows are associated with behavioral and physiological changes such as seeking shade, increased respiration rate and body temperature, reduced milk production, and psychological distress. Knowledge of the relationship between weather and animal responses to heat stress enables automated alerts using forecast weather, aiding early provision of shade or other mitigation practices. While numerous heat stress indices for cattle have been developed, these have limitations for cows exposed to wind and solar radiation (i.e., predominantly grazing outdoors or managed on pasture). To develop a predictive model for heat stress events in pasture-based dairy systems, rumen temperature data from smaXtec (smaXtec animal care GmbH, Graz, Austria) rumen boluses in 443 cows on 3 dairy farms in Northland, New Zealand, were used to identify heat stress events and these were matched with automated weather station data collected on or near the farm. Heat stress rate (HSR) was defined as the percentage of cows within an age-breed group having a rumen temperature greater than 3 standard deviations above an individual cow's mean and heat stress events were defined as HSR >25%. Single and multiple linear regression models, including published heat stress indices, were generally able to predict a high proportion of heat stress events (sensitivity 34%–68%), but were insufficiently discriminating, predicting also a high number of false positives (precision only 9%–27%). A machine learning algorithm, cubist, was the best performing model, predicting 79% of heat stress events with a precision of 52% for this dataset. Our proof-of-concept study demonstrates the potential of this approach, using climate data to predict and forecast heat stress events in pasture-based dairy systems. Further work should test the cubist model using independent data, refine dataset construction, investigate the value of including known animal variables such as cow age or breed, and incorporate other measures of heat stress such as respiration rate.
ABSTRACT: This study investigated the potential of 2′-fucosyllactose (2′-FL) and galacto-oligosaccharides (GOS) combinations as a novel and cost-effective substitute for human milk oligosaccharides (HMO) in promoting gut health and reducing inflammation. In vitro studies using caco-2 cells showed that 2′-FL and GOS combinations (H1 = GOS:2′-FL ratio of 1.8:1; H2 = GOS:2′-FL ratio of 3.6:1) reduced LPS-induced inflammation by decreasing pro-inflammatory markers, whereas individual treatments had no significant effects. In a mouse model of dextran sulfate sodium (DSS)-induced colitis, combined 2′-FL and GOS supplementation alleviated symptoms, improved gut permeability, and enhanced intestinal structure, with the GH1 group (H1 combo with DSS) being the most effective. 2′-Fucosyllactose and GOS combinations also enhanced short-chain fatty acid production in infant fecal batch fermentation and mouse fecal analysis, with GH1 showing the most promising results. The GH1 supplementation altered gut microbiota in mice with DSS-induced colitis, promoting microbial diversity and a more balanced Firmicutes to Bacteroidota ratio. Infant formula products (IFP) containing 2′-FL and GOS combinations (IFP2 = 174 mg of GOS and 95 mg of 2′-FL per 14 g serving, 1.8:1 ratio; IFP3 = 174 mg of GOS and 48 mg of 2′-FL per 14 g serving, 3.6:1 ratio) demonstrated gastrointestinal protective and anti-inflammatory properties in a co-culture model of caco-2 and THP-1 cells. These findings suggest that 2′-FL and GOS combinations have potential applications in advanced infant formulas and supplements to promote gut health and reduce inflammation.