IntroductionBreast cancer is associated with significant restructuring of the gut ecosystem. Gut microbial composition and function may influence cancer development and progression through immune modulation, metabolic regulation, and inflammation-related pathways.MethodsUsing shotgun metagenomic sequencing of fecal samples from 38 stage I–III breast cancer patients and 36 age- and body mass index-matched healthy controls. Machine learning models were constructed to evaluate the diagnostic potential of integrated microbial and metabolic features.ResultsSignificant alterations were observed in gut microbiota composition, including depletion of beneficial taxa (Limosilactobacillus fermentum, Blautia sp.) and enrichment of Prevotella copri. Pathways involved in short-chain fatty acid and purine metabolism were reduced. The gut phageome exhibited structural changes and altered correlations with bacterial hosts. Predictive analysis revealed depletion of short-chain fatty acids (butyrate, propionate), purine intermediates (hypoxanthine, xanthine), and nicotinate in patients. A machine learning model integrating microbial and predicted metabolic features achieved an area under the curve values of 0.78 in the discovery cohort and 0.73 (recall = 0.74) in an independent validation cohort.DiscussionCoordinated gut microbiome, phageome, and metabolome alterations characterize breast cancer, offering potential non-invasive biomarkers and mechanistic insights for disease detection and intervention.
ABSTRACT: Adipose tissue remodeling is essential for mammary gland development and functional restoration. The dry period represents a pivotal phase characterized by extensive tissue remodeling as the gland transitions from pregnancy to lactation. However, the morphological and molecular mechanisms underlying mammary adipose tissue remodeling during this stage remain poorly understood. In this study, we investigated adipose remodeling in the mammary gland of dairy goats during this transitional period. An integrative multi-omics approach combining transcriptomics, lipidomics, and metabolomics was employed to comprehensively characterize the molecular changes of mammary tissue. Histological analysis and transmission electron microscopy revealed a substantial increase in both the proportion and size of adipocytes at 4 wk before prepartum. RNA sequencing of isolated mammary adipose cells indicated that the p38 mitogen-activated protein kinase (MAPK) pathway may play a key regulatory role in this process. To further dissect its function, we established an immortalized goat mammary preadipocyte cell line. Pharmacological inhibition of p38 MAPK significantly impaired adipocyte differentiation and hypertrophy. Furthermore, our findings suggested that p38 MAPK modulates adipogenesis in mammary adipocytes through downstream effectors, including apolipoprotein E. These findings highlight a critical role for the p38 MAPK signaling pathway in orchestrating mammary adipose tissue remodeling during the dry period. This work provides novel insights into the molecular regulation of mammary gland renewal and offers potential targets to manipulate mammary gland development in ruminants.
Listeria monocytogenes (Lm) is a major human and animal foodborne pathogen. Here we show that hypervirulent Lm clones, particularly CC1, are strongly associated with dairy products, whereas hypovirulent clones, CC9 and CC121, are associated with meat products. Clone adaptation to distinct ecological niches and/or different food products contamination routes may account for this uneven distribution. Indeed, hypervirulent clones colonize better the intestinal lumen and invade more intestinal tissues than hypovirulent ones, reflecting their adaption to host environment. Conversely, hypovirulent clones are adapted to food processing environments, with a higher prevalence of stress resistance and benzalkonium chloride tolerance genes and a higher survival and biofilm formation capacity in presence of sub-lethal benzalkonium chloride concentrations. Lm virulence heterogeneity therefore reflects the diversity of the ecological niches in which it evolves. These results also have important public health implications and may help in reducing food contamination and improving food consumption recommendations to at-risk populations. Here, Maury et al. show that hypervirulent Listeria monocytogenes (Lm) clones associated to dairy products exhibit higher adaptation to the mammalian gut environment, while hypovirulent clones persist in food processing environment, suggesting a relationship between Lm pathogenic potential and niche adaptation.
This chapter explores the transformative potential of precision fermentation in the production of animal-identical dairy proteins. As consumer demand for sustainable and ethical food sources rises, traditional dairy production faces challenges related to environmental impact and animal welfare. This chapter delves into the innovative technologies that harness microbial fermentation to create dairy components like whey protein, casein, and lactoferrin, replicating their nutritional and functional properties without relying on animal agriculture. By examining the scientific principles, biotechnological advancements, and potential applications of precision fermentation, this chapter aims to provide insights into how these methods can revolutionize the dairy industry and contribute to a more sustainable food system. Through case studies and future projections, it highlights the role of precision fermentation in meeting global dietary needs while reducing the ecological footprint of dairy production.
Milk proteins, well-known for their nutritional properties, have also interesting techno-functional properties including foaming and emulsifying properties. Indeed, they play a crucial role in the creation and stabilization of foams and emulsions, making them one of the most essential components in manufacturing dairy products. First, major proteins in milk (αS-, β- and κ-caseins as well as α-lactalbumin and β-lactoglobulin) are presented and their biological and physicochemical characteristics are highlighted. Furthermore, this chapter explores the recent researches about foaming and emulsifying properties of milk proteins, focusing on their molecular interactions, mechanisms of stabilization, and the impact of processing conditions such as pH value and thermal treatments. Key factors influencing foam and emulsion stability, such as the impact of emerging technologies in dairy industry including high pressure homogenization, high pressure jet processing, ultrasound, and sonication, are also discussed. Understanding these properties is essential for optimizing product formulation and ensuring the quality and texture of dairy-based foods.
The ability to accurately identify key growth stages is critical for proper rice management, Since management practices are directly tied to plant developmental processes, a sound understanding of rice growth is essential for effective cultivation. This study was conducted to evaluate the growth and yield performance of seven rice varieties (BT, CUDH1, NU986, ST25, TBR225, TX111, and VNR20) during different growth stages (Root establishment and Greening up,Tillering, Panicle initiation, Flowering, and Dough stage) in Thanh Hoa province, Vietnam. Results indicated that CUDH1 and TX111 exhibited superior performance, with greater plant height, higher leaf area index (LAI) throughout most growth stages, and enhanced dry matter accumulation. These characteristics reflect strong photosynthetic capacity and robust growth potential, ultimately leading to the highest recorded yields: CUDH1 (8.25 t ha-1) and TX111 (8.02 t ha-1). The NU986 (7.73 t ha-1) and BT (7.32 t ha-1) also achieved relatively good yields, though improvements in cultivation techniques are recommended to fully exploit their potential. Conversely, ST25 (6.97 t ha-¹), VNR20 (7.05 t ha-¹), and TBR225 (7.18 t ha-¹) exhibited lower plant height and dry matter accumulation, thus making them more suitable for high-quality rice production or areas prone to lodging. the conclusion, varietal selection tailored to specific production goals and local ecological conditions is a critical factor in improving the efficiency and sustainability of rice production systems.
Farzana Siddique, Sana Imtiaz, Saima Noreen
et al.
Abstract Pulsed Electric Field (PEF) technology has emerged as a cutting-edge processing and preservation technique for milk and dairy products by utilizing high-voltage pulses to promote electroporation-mediated inactivation of microbes and enzymes without compromising the inherent nutritional and sensory properties. This review underscores the ability of PEF to maintain and improve the quality and safety of milk and dairy products. PEF-treated milk has a significant retention of major nutrients, such as proteins, minerals, and vitamins, with minimal effect on texture, taste, and color, as well as maintaining fat globule structural stability. This technology enhances the technological and functional properties of milk proteins like solubility, gelation, and emulsification without promoting significant conformational alterations. PEF has significant potential in mitigating antibiotic residues including sulfonamides, benzylpenicillin in dairy products. Moreover, this technology exhibits significant potential in dairy processing applications, such as production of yogurt, cheese, ice-cream and other dairy products, with a synergistic effect when combined with mild thermal treatment and other innovative approaches. Further research is necessary to optimize PEF processing parameters to fully understand the significant effects of this approach on the nutritional bioaccessibility, molecular stability, microbial parameters, and shelf-life of PEF treated dairy products. This could facilitate the development of precise and evidence-based PEF processing strategies for dairy technology. Graphical abstract
The article presents the authors’ position on the organization and methodology of waste accounting regulation as applied to dairy processing enterprises aiming to implement the lean production concept. The key principles of lean production and their integration into the waste accounting system of dairy processing plants are analysed. Methodological approaches to accounting for waste generation and its utilization in the milk processing technological cycle are examined. The relevance of optimizing waste accounting to minimize losses and enhance resource efficiency is substantiated.
The main groups and types of waste resulting from milk raw material processing are analysed, along with accounting methods that leverage lean production tools and digital technologies. The study demonstrates the impact of organizational and methodological support for waste accounting on the economic and environmental performance of dairy processing production systems. Recommendations are provided for implementing waste accounting based on lean production principles and the use of modern digital technologies.
The aim of the study is to develop an organizational and methodological framework for waste accounting within the lean production system of the dairy industry, a socially significant sector of the Russian economy.
The scientific novelty of the research lies in advancing the theory and methodology of waste accounting for dairy processing enterprises. This includes a scientifically grounded classification of milk processing by products, with criteria for designating them as waste, as well as methods for assessing and proposals to improve methodological approaches to waste accounting regulation from the perspective of implementing the lean production concept in this economic sector.
The practical value of the study consists in developing waste accounting recommendations that can be used by dairy processing enterprises to formulate new waste accounting policies. These recommendations will not only reduce the cost of dairy products and optimize the use of material resources overall, but also minimize the negative environmental impact.
Implementing these proposals in accounting and analytical practice will have a positive effect on the waste management system of dairy processing enterprises. This is particularly significant for the current stage of domestic economic development in terms of ensuring sustainable growth of the dairy industry and improving the environmental situation.
The authors’ approach to organizational and methodological regulation of waste accounting under lean production conditions in dairy processing can be applied in future research to develop and refine sector specific accounting standards for the dairy processing industry.
Prebiotic-probiotic synergy is vital to fermented milk's overall performance. This study investigates the effects of complex prebiotics (CPs) on the quality and functional properties of probiotic-fermented milk. Results indicated that CPs improved the physicochemical properties and sensory characteristics of fermented milk. Meanwhile, CPs supplementation improved the flavor profile of fermented milk by increasing the levels of key aroma compounds, including aldehydes (e.g., decanal increased by 18.80 % in the St-Lr + CP3 group) and ketones (e.g., 3-hydroxy-2-butanone increased by 16.37 % in the St-Lf-Lp-Lr + CP4 group). Moreover, CPs supplementation significantly enhanced the ACE inhibitory activity (up to 73.74 % in the St-Lp group) and α-amylase inhibition (with a 27.69 % improvement observed in the St-Lf group), while also strengthening the antimicrobial effects of fermented milk against Pseudomonas aeruginosa, Listeria monocytogenes, and Bacillus subtilis. These findings demonstrate the great potential of CPs in the development of health-promoting dairy products and their applications in the food industry.
Nutrition. Foods and food supply, Food processing and manufacture
ABSTRACT: In vitro meat cultivation, a cutting-edge innovation in food science, may represent a more sustainable and ethical source of animal proteins compared with conventionally grown meat. An important challenge for meat cultivation lies in eliminating the use of fetal bovine serum (FBS) in cell culture media due to ethical concerns. Milk whey is a nutrient-rich liquid portion of the milk, derived as a byproduct of dairy industry. Similar to FBS, whey contains proteins that are crucial for nutrition, cell adhesion, and biomolecular transport. In this study, we investigated whether whey proteins (WP) can replace FBS in supporting muscle cell cultivation, using the C2C12 myoblast model. Accordingly, under serum-free conditions, cells were treated with 2 WP mixtures, grouping high (β-LG 1.25%, α-LA 1.25%, BSA 1.25%) and low (β-LG 0.07%, α-LA 0.15%, BSA 0.15%) selected concentrations of individual proteins that positively affected cell growth in a preceding dose–response study. Cells cultured in only basal Dulbecco's Modified Eagle Medium were included as a negative control, and cells cultured in 10% FBS as a positive control. Cells were maintained in the treatment media for 48 h (d 1 and 2) to support myoblast proliferation. Subsequently, all the treatments were replaced with a standard low mitogenic 2% horse serum (HS) medium until full differentiation (d 6). The treatment effects on morphology, viability, and lactate dehydrogenase release were assessed after d 1, 2, and 6, respectively. The results showed that WP stimulated cell proliferation under serum-free culture conditions, similar to the FBS control, and subsequently facilitated myotube formation when the WP or FBS treatments were switched to HS medium. After differentiation, these cells also exhibited increased expression of cell differentiation markers such as creatine kinase and citrate synthase and underwent morphological changes from spindle-shaped cells to fused elongated myotubes, in contrast to the negative control. This study demonstrates that WP are a promising and sustainable alternative for considerably replacing FBS-based growth supplements for use in cultivated animal products.
Claudia F. Viana, Ana C.C. Lopes, Rosemary S. Conrrado
et al.
ABSTRACT: Despite buffalo milk being an important food worldwide, not much is known about factors that influence its quality on Brazilian buffalo milk farms. It ranks second in total volume of milk production, with cow milk in first place, but a lack of minimum legal parameters for buffalo milk is another problem faced by some countries as it happens in Brazil, where the buffalo population represents less than 1% of dairy cows, even though the Brazilian buffalo herd is one of the largest in Latin America, with a successful dairy processing chain. This study investigated the composition and SCC of buffalo milk produced in a high-altitude tropical region in the state of Minas Gerais, Brazil, where buffalo calving naturally happens from February to May. A total of 2,211 samples of raw milk were analyzed for compositional parameters (fat, protein, lactose, TS, and SNF) using Fourier-transform infrared spectra and for SCC using flow cytometry. Official climatological data were also collected. Descriptive, multivariate statistics and principal component analysis were used. The highest fat, protein, solids nonfat, and total solids concentrations were recorded during the summer and spring seasons, 6.01 and 6.31 g/100 g for fat, 4.23 and 4.18 g/100 g for protein, 9.93 and 9.92 g/100 g for SNF, and 15.95 and 16.23 g/100 g for TS, respectively. Lactose, however, had the lowest concentration observed in the same seasons, 4.76 and 4.75 g/100 g, respectively. The component with the highest correlation to SCC was lactose, with the highest SCC values during spring, which was similar to the SCC during the fall. The concentrations of buffalo milk components were influenced by the season, with an opposite trend when compared with cow milk. However, buffalo milk SCC was usually lower than the counts reported for cow milk in the same region. Upcoming studies must include data as well as weighted averages to estimate the financial effect of this oscillation during the year and for milk quality payment.
Gunvantsinh Rathod, Suresh Sutariya, Ram Kumar
et al.
ABSTRACT: Mozzarella cheese is widely used for pizza applications, and it is generally shredded either in conversion plants or in pizzerias. The shreddability of mozzarella cheese is influenced by a variety of factors, and it is critical to understand how different mozzarella cheese types and storage conditions (temperature and age) affect this property. Three batches each of 3 types of mozzarella cheese (low-moisture mozzarella [LMM], low-moisture part-skim mozzarella [LMPS], and reduced-fat mozzarella [RFM]) representing 3 different fat levels were procured directly from a commercial manufacturer and stored at 2 different temperatures (1.67 and 4.44°C) and evaluated for shreddability at 2- and 3-wk storage. Shreddability parameters such as stiction (peak force) and work of grating were significantly affected by fat content and storage time, whereas the weight of the shred obtained after each cycle was significantly affected by fat content and storage temperature. Along with shreddability, other tests, such as texture profile analysis (TPA), wire cutting, stretchability, and dynamic shear rheology (DSR) were performed to understand their relationship with shreddability. Textural parameters were significantly affected by fat content and storage time, whereas wire cutting parameters were significantly affected by fat content, storage time, and temperature. Stretchability and dynamic shear rheology parameters were significantly affected by fat content followed by storage time and temperature. Further shreddability parameters, such as stiction and work of shear had a positive significant correlation, whereas the weight of shreds had a negative significant correlation with unmelt (TPA) and melt parameters (stretchability and DSR). Overall, fat content had the greatest effect on shreddability followed by storage time and temperature. Considering the high correlation of shreddability with other textural parameters, textural parameters can be used to predict shreddability of mozzarella cheese.
Highly pathogenic avian influenza (HPAI) has expanded its host range with recent detections in dairy cattle, raising critical concerns regarding within-herd persistence and cross-species spillover. This study develops a stochastic $SEI_sI_aR-B$ compartmental model to analyse HPAI transmission, explicitly accounting for environmental pathogen reservoirs and noise intensities through Wiener processes. The positivity and boundedness of solutions are established, and the disease-free and endemic equilibria are analytically derived. The basic reproduction number is determined using the next-generation matrix method. Numerical simulations confirm that the model dynamics are consistent with theoretical analysis and illustrate how stochastic fluctuations significantly influence disease persistence. Furthermore, sensitivity analysis using Latin Hypercube Sampling (LHS) and Partial Rank Correlation Coefficients (PRCC) identifies the transmission rate from asymptomatic infectious cattle ($β_a$) as the primary driver of transmission. The model effectively captures the dynamics of environmental variability affecting HPAI spread, suggesting that effective control strategies must prioritise the early detection and isolation of asymptomatic carriers alongside environmental management.
Reinhold Haeb-Umbach, Tomohiro Nakatani, Marc Delcroix
et al.
Multi-channel acoustic signal processing is a well-established and powerful tool to exploit the spatial diversity between a target signal and non-target or noise sources for signal enhancement. However, the textbook solutions for optimal data-dependent spatial filtering rest on the knowledge of second-order statistical moments of the signals, which have traditionally been difficult to acquire. In this contribution, we compare model-based, purely data-driven, and hybrid approaches to parameter estimation and filtering, where the latter tries to combine the benefits of model-based signal processing and data-driven deep learning to overcome their individual deficiencies. We illustrate the underlying design principles with examples from noise reduction, source separation, and dereverberation.
Sibi Parivendan, Kashfia Sailunaz, Suresh Neethirajan
Precision livestock farming requires objective assessment of social behavior to support herd welfare monitoring, yet most existing approaches infer interactions using static proximity thresholds that cannot distinguish affiliative from agonistic behaviors in complex barn environments. This limitation constrains the interpretability of automated social network analysis in commercial settings. We present a pose-based computational framework for interaction classification that moves beyond proximity heuristics by modeling the spatiotemporal geometry of anatomical keypoints. Rather than relying on pixel-level appearance or simple distance measures, the proposed method encodes interaction-specific motion signatures from keypoint trajectories, enabling differentiation of social interaction valence. The framework is implemented as an end-to-end computer vision pipeline integrating YOLOv11 for object detection (mAP@0.50: 96.24%), supervised individual identification (98.24% accuracy), ByteTrack for multi-object tracking (81.96% accuracy), ZebraPose for 27-point anatomical keypoint estimation, and a support vector machine classifier trained on pose-derived distance dynamics. On annotated interaction clips collected from a commercial dairy barn, the classifier achieved 77.51% accuracy in distinguishing affiliative and agonistic behaviors using pose information alone. Comparative evaluation against a proximity-only baseline shows substantial gains in behavioral discrimination, particularly for affiliative interactions. The results establish a proof-of-concept for automated, vision-based inference of social interactions suitable for constructing interaction-aware social networks, with near-real-time performance on commodity hardware.
Heat stress is one of the main welfare and productivity problems faced by dairy cattle in Mediterranean climates. In this study, we approach the prediction of the daily shade-seeking count as a non-linear multivariate regression problem and evaluate two soft computing algorithms -- Random Forests and Neural Networks -- trained on high-resolution behavioral and micro-climatic data collected in a commercial farm in Titaguas (Valencia, Spain) during the 2023 summer season. The raw dataset (6907 daytime observations, 5-10 min resolution) includes the number of cows in the shade, ambient temperature and relative humidity. From these we derive three features: current Temperature--Humidity Index (THI), accumulated daytime THI, and mean night-time THI. To evaluate the models' performance a 5-fold cross-validation is also used. Results show that both soft computing models outperform a single Decision Tree baseline. The best Neural Network (3 hidden layers, 16 neurons each, learning rate = 10e-3) reaches an average RMSE of 14.78, while a Random Forest (10 trees, depth = 5) achieves 14.97 and offers best interpretability. Daily error distributions reveal a median RMSE of 13.84 and confirm that predictions deviate less than one hour from observed shade-seeking peaks. These results demonstrate the suitability of soft computing, data-driven approaches embedded in an applied-mathematical feature framework for modeling noisy biological phenomena, demonstrating their value as low-cost, real-time decision-support tools for precision livestock farming under heat-stress conditions.
Recent advances in deep neural networks (DNNs) have significantly improved various audio processing applications, including speech enhancement, synthesis, and hearing-aid algorithms. DNN-based closed-loop systems have gained popularity in these applications due to their robust performance and ability to adapt to diverse conditions. Despite their effectiveness, current DNN-based closed-loop systems often suffer from sound quality degradation caused by artifacts introduced by suboptimal sampling methods. To address this challenge, we introduce dCoNNear, a novel DNN architecture designed for seamless integration into closed-loop frameworks. This architecture specifically aims to prevent the generation of spurious artifacts-most notably tonal and aliasing artifacts arising from non-ideal sampling layers. We demonstrate the effectiveness of dCoNNear through a proof-of-principle example within a closed-loop framework that employs biophysically realistic models of auditory processing for both normal and hearing-impaired profiles to design personalized hearing-aid algorithms. We further validate the broader applicability and artifact-free performance of dCoNNear through speech-enhancement experiments, confirming its ability to improve perceptual sound quality without introducing architecture-induced artifacts. Our results show that dCoNNear not only accurately simulates all processing stages of existing non-DNN biophysical models but also significantly improves sound quality by eliminating audible artifacts in both hearing-aid and speech-enhancement applications. This study offers a robust, perceptually transparent closed-loop processing framework for high-fidelity audio applications.
Consumer interest and research in plant-based dairy analogues has been growing in recent years because of increasingly negative implications of animal-derived products on human health, animal wellbeing, and the environment. However, plant-based dairy analogues face many challenges in mimicking the organoleptic properties of dairy products due to their undesirable off-flavours and textures. This article thus reviews fermentation as a viable pathway to developing clean-label plant-based dairy analogues with satisfactory consumer acceptability. Discussions on complementary strategies such as raw material selection and extraction technologies are also included. An overview of plant raw materials with the potential to be applied in dairy analogues is first discussed, followed by a review of the processing steps and innovative techniques required to transform these plant raw materials into functional ingredients such as plant-based aqueous extracts or flours for subsequent fermentation. Finally, the various fermentation (bacterial, yeast, and fungal) methodologies applied for the improvement of texture and other sensory qualities of plant-based dairy analogues are covered. Concerted research efforts would be required in the future to tailor and optimise the presented wide diversity of options to produce plant-based fermented dairy analogues that are both delicious and nutritionally adequate.
Firsty Ainun Zalzabila Ansori, Fithri Choirun Nisa, Ahmad Zaki Mubarok
The high consumer interest in functional food products not only provides basic nutrition but also adds health benefits. One of the nutrient-rich products is milk. Milk is an important and nutrient-high livestock product. Besides that, milk is also widely consumed by the people of Indonesia, one of which is goat milk. Goat milk has advantages such as having protein like breast milk, having high digestibility, and being able to be consumed by individuals who are allergic to cow's milk. Goat milk derivative products are quite diverse, including powdered goat milk, goat milk yogurt, and goat milk kefir. With its rich nutrition, goat milk has excellent potential to be developed as a functional product, especially by adding natural antioxidants. One of the ingredients in milk that can bind to antioxidants such as phenolic compounds is protein. As long as there is processing, both drying for milk powder products and the fermentation process for yogurt and kefir products can increase the antioxidant content of the final product which shows a synergistic effect on increasing the antioxidant content of the added natural ingredients. Milk protein can effectively bind to antioxidant compounds and strengthen the interaction between antioxidant compounds and casein so that the antioxidant content in the final product can be maintained. During the fermentation process, there is an increase in antioxidant value due to the activity of microbial enzymes produced during fermentation in binding and stabilization, where proteins such as casein and whey interact with antioxidant molecules like polyphenols and vitamins, forming more stable complexes. Efforts to increase the functional value of dairy products can increase antioxidant activity to ward off free radicals. Therefore, it is essential to review and analyze the various studies that have been conducted regarding the addition of natural antioxidants in goat milk to provide a comprehensive picture of the potential and challenges in the development of this product.