Hasil untuk "Dairy processing. Dairy products"

H. Westhoek, J. Lesschen, T. Rood et al.

Western diets are characterised by a high intake of meat, dairy products and eggs, causing an intake of saturated fat and red meat in quantities that exceed dietary recommendations. The associated livestock production requires large areas of land and lead to high nitrogen and greenhouse gas emission levels. Although several studies have examined the potential impact of dietary changes on greenhouse gas emissions and land use, those on health, the agricultural system and other environmental aspects (such as nitrogen emissions) have only been studied to a limited extent. By using biophysical models and methods, we examined the large-scale consequences in the European Union of replacing 25–50% of animal-derived foods with plant-based foods on a dietary energy basis, assuming corresponding changes in production. We tested the effects of these alternative diets and found that halving the consumption of meat, dairy products and eggs in the European Union would achieve a 40% reduction in nitrogen emissions, 25–40% reduction in greenhouse gas emissions and 23% per capita less use of cropland for food production. In addition, the dietary changes would also lower health risks. The European Union would become a net exporter of cereals, while the use of soymeal would be reduced by 75%. The nitrogen use efficiency (NUE) of the food system would increase from the current 18% to between 41% and 47%, depending on choices made regarding land use. As agriculture is the major source of nitrogen pollution, this is expected to result in a significant improvement in both air and water quality in the EU. The resulting 40% reduction in the intake of saturated fat would lead to a reduction in cardiovascular mortality. These diet-led changes in food production patterns would have a large economic impact on livestock farmers and associated supply-chain actors, such as the feed industry and meat-processing sector.

B. Fessenden, D.J. Weigel, D. Liang et al.

ABSTRACT: Reproductive performance affects the profitability of a dairy herd. The ability to understand the reproductive capabilities of individual cows and the use of targeted reproductive management could optimize reproductive performance of dairy herds. To address this need, the early reproductive success prediction was developed using a light gradient-boosting machine algorithm, which included herd-level reproduction data, weather data, genomic-enhanced predicted transmitting ability, individual cow information, milk production, health events, and previous lactation performance data. The objective of this retrospective study was to evaluate the ability of the early reproductive success algorithm to predict pregnancy probability by 110 DIM at 43 DIM, which was before the end of the voluntary waiting period for enrolled herds. The study included 9,969 Holstein and 9,464 Jersey multiparous cows that calved in 2022 from 7 US commercial herds. Cows were ranked by their predicted probability within their own herds and then assigned to deciles based upon this ranking. Cows' reproductive and herd exit data were collected for 18 mo following calving from on-farm management software. Data were analyzed using a generalized linear mixed model. The worst 10% and best 10% early reproductive success prediction deciles were different for pregnancy at first insemination (25.3% vs. 44.2%), proportion pregnant at 110 DIM (35.6% vs. 64.8%), and proportion of cows that gave birth to a live calf to initiate the following lactation (49.1% vs. 77.8%), with percentage improvements in performance of 75%, 82%, and 58%, respectively. The predicted worst 10% and best 10% deciles were different for abortion incidence (20.9% vs. 6.8%) and whether cows were sold within enrollment lactation (43.8% vs. 17.2%), with percentage improvements in performance of 67% and 61% for these 2 metrics. These results demonstrated the ability of the early reproductive success algorithm to predict differences in pregnancy per insemination for all services, abortion incidence, proportion of cows sold in enrollment lactation, and proportion of cows producing a live calf. Further research is needed to determine whether the early reproductive success prediction has potential to be used to help dairy producers develop targeted reproductive management strategies.

N.I. Carvalho, I.C.R. Oliveira, A.F. Toledo et al.

ABSTRACT: Water quality plays a crucial role in calf health and performance, mainly when it is used to dilute milk replacers, which can affect the incidence of diarrhea. This study assessed the effects of municipal tap water (TW) compared with purified water (PW) on calf performance and health. Thirty Holstein calves were randomly assigned to 2 treatment groups in a randomized block design. Calves received 6 L/d of milk replacer (14% solids) diluted with the respective water treatment until gradual weaning (49–53 d). A pelleted starter was offered from birth, and chopped hay was provided ad libitum from 49 to 70 d. Dry matter intake, water intake, and health scores were recorded daily. Average daily gain was measured weekly, whereas body measurements and blood samples were collected biweekly. The PW tended to improve feed efficiency and increased ADG in the third week of life, but the final BW did not differ between treatments. Calves that received PW consumed more hay and tended to have greater water intake. Moreover, PW reduced day with diarrhea and the number of antibiotic treatments. Blood metabolites varied primarily with age, except for higher albumin concentrations in TW calves at wk 10. In summary, the PW tended to enhance performance during preweaning, particularly after diarrhea episodes. These findings highlight the importance of water quality during early life, but further studies are needed to determine the effect on dairy calf health.

Chunxiao Dong, Peipei Ma, Yongjie Tang et al.

ABSTRACT: Chinese Simmental cattle serve as an important dual-purpose breed in sustainable livestock systems. Despite their economic value, the genetic architecture underlying milk-production traits in this breed under temperate conditions remains poorly characterized. In this study, we estimated genetic parameters and identified associated genomic loci for 9 milk-production traits in a Chinese Simmental population. Our dataset consisted of 17,556 test-day records from 1,788 cows (parities 1 to 3), including whole-genome sequencing data for 781 individuals. Using a random regression test-day model with Legendre polynomials, we estimated variance components, heritabilities, breeding values (EBVs), and genetic correlations between different DIM. Heritability estimates ranged from 0.09 (fat-to-protein ratio) to 0.52 (protein percentage), with intermediate values for fat percentage (0.28), lactose percentage (0.35), and total milk solids (0.35). Within-lactation genetic correlations varied by trait, ranging from −0.88 to 0.99, with the strongest correlations between adjacent DIM, which weakened as intervals widened. Genome-wide association analysis (using the mixed linear model in SLEMM version 0.90.1) and Bayesian fine-mapping analysis identified significant SNPs near known candidate genes (BOP1, MROH1, NEGR1). These analyses also revealed putative novel associations with CACNB4, MTHFD2L, and SGMS1. Overall, the results enhance our understanding of the genetic regulation of milk production and provide practical targets for genomic selection to improve dairy performance in dual-purpose breeding programs.

C. Saro, C. Martin, G. Cantalapiedra-Hijar et al.

ABSTRACT: The aim of this study was to test the effect of 3-nitrooxypropanol (3-NOP) on methane emissions, animal performance, milk composition, and rumen fermentation in early-lactation dairy cows fed a forage-rich diet. A second objective was to assess the 13C isotopic signature of milk as a potential proxy for methane-emission status. Twenty-seven cows with similar BW and age in early lactation (7–11 DIM) and fed a 75% forage-based diet were selected and distributed to 2 balanced groups in a randomized block design. The treatment group (n = 13) received 3-NOP (60 mg/kg DM basis) in a TMR for 105 d and the control group (n = 14) received a placebo. The 3-NOP additive was included in a supplement formulated with propylene glycol and adsorbed on silicon dioxide. The control group received the same supplement without the additive. Individual daily methane emissions were quantified using the GreenFeed system throughout the study. Intake and milk production were recorded daily, and milk composition (fat, protein, lactose, urea) twice a week. Natural 13C abundance (δ13C) in milk and feed samples was determined using isotope-ratio MS coupled with elemental analysis for each cow at wk 3, 7, 11, and 15. Methane emissions, methane yield, and methane intensity were lower in the treatment group throughout the 105 d (on average −31% g/d, −24% g/kg DMI, and −30% g/kg ECM). By monitoring methane emissions throughout the day, we found that the main effect occurred after feeding, preventing the postprandial peak in methane emissions. Intake was reduced by ∼7% with 3-NOP, whereas milk production was similar between groups (34.7 kg ECM/d). Consequently, feed conversion efficiency in animals tended to increase with 3-NOP (1.39 vs. 1.48 kg milk/kg DMI on average). Milk composition did not vary between groups. Although no differences were found in dietary δ13C across the treatments, milk from the treatment group had lower δ13C than the control group throughout the experimental period. The results of this work with medium-producing dairy cows confirm the efficacy of the additive on diets relatively high in NDF, which broadens its applicability to less intensive production systems. The results for milk δ13C, suggesting its potential as a qualitative proxy for methane emissions, merit further investigation, as it could be used in breeding and in monitoring, reporting, and validation systems.

Ziyang Ma, Guanrou Yang, Wenxi Chen et al.

The recent surge in open-source Multimodal Large Language Models (MLLM) frameworks, such as LLaVA, provides a convenient kickoff for artificial intelligence developers and researchers. However, most of the MLLM frameworks take vision as the main input modality, and provide limited in-depth support for the modality of speech, audio, and music. This situation hinders the development of audio-language models, and forces researchers to spend a lot of effort on code writing and hyperparameter tuning. We present SLAM-LLM, an open-source deep learning framework designed to train customized MLLMs, focused on speech, language, audio, and music processing. SLAM-LLM provides a modular configuration of different encoders, projectors, LLMs, and parameter-efficient fine-tuning plugins. SLAM-LLM also includes detailed training and inference recipes for mainstream tasks, along with high-performance checkpoints like LLM-based Automatic Speech Recognition (ASR), Automated Audio Captioning (AAC), and Music Captioning (MC). Some of these recipes have already reached or are nearing state-of-the-art performance, and some relevant techniques have also been accepted by academic papers. We hope SLAM-LLM will accelerate iteration, development, data engineering, and model training for researchers. We are committed to continually pushing forward audio-based MLLMs through this open-source framework, and call on the community to contribute to the LLM-based speech, audio and music processing.

Xiaojun Wang, Linlin Wang, Xinyao Wei et al.

ABSTRACT: Yogurt, as a globally prevalent fermented dairy product, is renowned for its substantial nutritional value and a myriad of health benefits, particularly pertaining to the digestive system. This narrative review elucidates the latest advancements in yogurt development from 2019 to 2024, addressing aspects of microbiological safety, quality, functionality, sensory evaluation, and consumer perceptions across diverse protein sources. The intrinsic quality of yogurt is notably influenced by its primary ingredient, milk, traditionally derived from animals such as cows, goats, and sheep. In recent years, plant-based yogurt (PBY) have emerged as a popular alternative to traditional dairy yogurts, that are made from plant sources and offer similar textures and flavors, catering to those seeking nondairy options. This discussion encompasses the advantages and limitations of various sources and explores methodologies to enhance yogurt quality using these diverse sources. Ensuring the microbiological safety of yogurt is thus paramount to its quality, as it involves both preventing the presence of harmful pathogens and managing spoilage to maintain freshness. This article encapsulates the potential hazards and corresponding antibacterial strategies that safeguard yogurt consumption. These strategies include the use of natural preservatives, advancements in packaging technologies, and the implementation of stringent hygiene practices throughout the production process. Moreover, the quality of yogurt is dependent not only on the source but also on the fermentation process and additional ingredients used. By addressing both the prevention of pathogen contamination and the control of spoilage organisms, this article explores comprehensive approaches but also examines the use of high-quality starter cultures, the role of prebiotics in enhancing probiotic efficacy, and genetic advancements, as well as improvements in the overall nutritional profile and shelf life of yogurt. Techniques to improve texture, flavor, and nutrient content are also discussed, providing a comprehensive overview of current quality enhancement methods. This analysis delves into the intricate mechanisms underpinning probiotic development, including the roles of prebiotics, supplementary starter cultures, and genetic factors that facilitate probiotic proliferation. These benefits include improved digestive health, enhanced immune function, and potential reductions in the risk of certain chronic diseases. Beyond quality and functionality, the sensory evaluation of yogurt remains crucial for consumer acceptance. In recent years, the incorporation of diverse additional ingredients into yogurt has been observed, aimed at augmenting its sensory attributes. This examination reveals these ingredients and their respective functions, such as natural flavorings, sweeteners, and texturizing agents, with the ultimate goal of enhancing overall consumer satisfaction. Consumer preferences exert a profound influence on yogurt production, rendering the understanding of customer opinions essential for devising competitive industry strategies. This article consolidates consumer feedback and preferences, striving to elevate yogurt quality and promote dietary diversity. The analysis includes trends such as the growing demand for organic and nondairy yogurts, the importance of sustainable practices, and the impact of marketing and packaging on consumer choices. This comprehensive overview serves as a valuable reference for the dairy industry and researchers dedicated to the advancement of yogurt development.

Tarariko Yurii, Knysh Vladyslav, Sapaev Ibrokhim

The article examines the prospects for the introduction of a bioenergy system of agricultural production in Western Polesie using the example of a reclaimed area of 10 thousand hectares, under the jurisdiction of the Shatsk Department of Reclamation Systems (Volyn region, Ukraine). A multi-option simulation of the potential sectoral structure of this region was conducted using the “Agroecosystem” software package. The study assessed the key components of the agri-resource potential of the region, identified conditions for improving its efficiency, and proposed sustainable models for sectoral development based on bioenergy principles. A comparative computer analysis showed that the most promising scenario includes organic farming, high-yield dairy production, and the processing of raw materials into dairy and meat products, oil, and flax fiber. Energy needs are met through biogas production from all agricultural waste, which is fully sanitized. The application of biological crop protection systems and energy self-sufficiency reduces production costs by 30%, while the quality and market value of high-end food products increase accordingly. As a result, agricultural profitability rises significantly, and the local population benefits from affordable, high- quality food and products made from natural raw materials. In the future, an important part of the profit could come from reducing greenhouse gas emissions.

B.J. Sepulveda, O. González-Recio, A.J. Chamberlain et al.

ABSTRACT: Ruminant digestion emits methane, a potent greenhouse gas contributing to global warming and reducing feed efficiency. Reducing enteric methane emissions (EME) through breeding decisions is theoretically possible, yet measuring these emissions on commercial farms is currently challenging and costly. It is common for EME to be measured using different technologies, which may show weak correlations between them, complicating the combination of reference populations, especially between countries. Here, using the same sequencing strategy, we identified a group of ruminant metagenomic features (a core) present in at least 90% of 410 dairy cows in Australia and 434 in Spain. With subsets of this core (the breeding core subsets) we estimated larger reductions on EME than using direct selection on EME. A combination of direct selection on EME and indirect selection on the breeding core subsets was estimated to produce even larger reductions. Combining the principal components of the core with some genera, Kyoto Encyclopedia of Genes and Genomes ontology and Clusters of Orthologous Groups could enhance EME reductions in breeding programs. We estimated an EME reduction of 0.41 phenotypic standard deviations per generation by selecting the top 30% of individuals with desirable ruminal microbiota profiles. An R Shiny application to estimate those reductions is provided. Additionally, the breeding core subsets could predict EME irrespective of each population's EME trait (sulfur hexafluoride in Australia and sniffers in Spain). These results suggest that rumen metagenome features could be used as selection criteria for genomic selection programs to reduce EME, as many of these features are heritable and correlated with EME. Features in the core could connect EME from different cattle populations, irrespective of the methane phenotype used in those populations. We propose that our methodology should be applied to much larger datasets to improve the accuracy of identifying a breeding core. Therefore, we propose a global effort to validate a common core of EME-associated ruminal features.

Ege Keskin, İhsan Ozan Yıldırım

Fermented dairy products, including yogurt, are widely consumed for their nutritional and health benefits. While numerous methods exist to monitor and understand yogurt fermentation, the literature lacks an integrated evaluation of diverse sensing approaches within a single experimental framework. To address this gap, this study systematically examines and compares multiple measurement techniques--electrical impedance, DC resistance, pH, optical transparency, carbon dioxide concentration, ambient temperature, and relative humidity--in tracking the yogurt fermentation process. By presenting a unified set of experimental results and assessing each method's observational characteristics, this work offers an encompassing reference point for researchers seeking to understand the relative merits and limitations of different sensing modalities. Rather than establishing definitive guidelines or practical recommendations, the findings provide a foundation for subsequent investigations into sensor-based fermentation monitoring, thereby contributing to a more comprehensive understanding of yogurt fermentation dynamics.

Sean C. Borneman, Julia Krebs, Ronnie B. Wilbur et al.

Human language processing relies on the brain's capacity for predictive inference. We present a machine learning framework for decoding neural (EEG) responses to dynamic visual language stimuli in Deaf signers. Using coherence between neural signals and optical flow-derived motion features, we construct spatiotemporal representations of predictive neural dynamics. Through entropy-based feature selection, we identify frequency-specific neural signatures that differentiate interpretable linguistic input from linguistically disrupted (time-reversed) stimuli. Our results reveal distributed left-hemispheric and frontal low-frequency coherence as key features in language comprehension, with experience-dependent neural signatures correlating with age. This work demonstrates a novel multimodal approach for probing experience-driven generative models of perception in the brain.

Yue Jiang, Jichu Li, Yang Liu et al.

We introduce DanmakuTPPBench, a comprehensive benchmark designed to advance multi-modal Temporal Point Process (TPP) modeling in the era of Large Language Models (LLMs). While TPPs have been widely studied for modeling temporal event sequences, existing datasets are predominantly unimodal, hindering progress in models that require joint reasoning over temporal, textual, and visual information. To address this gap, DanmakuTPPBench comprises two complementary components: (1) DanmakuTPP-Events, a novel dataset derived from the Bilibili video platform, where user-generated bullet comments (Danmaku) naturally form multi-modal events annotated with precise timestamps, rich textual content, and corresponding video frames; (2) DanmakuTPP-QA, a challenging question-answering dataset constructed via a novel multi-agent pipeline powered by state-of-the-art LLMs and multi-modal LLMs (MLLMs), targeting complex temporal-textual-visual reasoning. We conduct extensive evaluations using both classical TPP models and recent MLLMs, revealing significant performance gaps and limitations in current methods' ability to model multi-modal event dynamics. Our benchmark establishes strong baselines and calls for further integration of TPP modeling into the multi-modal language modeling landscape. Project page: https://github.com/FRENKIE-CHIANG/DanmakuTPPBench

Amornyos Horprasert, Esa Apriaskar, Xingyu Liu et al.

One of the key challenges that Reinforcement Learning (RL) faces is its limited capability to adapt to a change of data distribution caused by uncertainties. This challenge arises especially in RL systems using deep neural networks as decision makers or policies, which are prone to overfitting after prolonged training on fixed environments. To address this challenge, this paper proposes Gaussian Process Diffusion Policy (GPDP), a new algorithm that integrates diffusion models and Gaussian Process Regression (GPR) to represent the policy. GPR guides diffusion models to generate actions that maximize learned Q-function, resembling the policy improvement in RL. Furthermore, the kernel-based nature of GPR enhances the policy's exploration efficiency under distribution shifts at test time, increasing the chance of discovering new behaviors and mitigating overfitting. Simulation results on the Walker2d benchmark show that our approach outperforms state-of-the-art algorithms under distribution shift condition by achieving around 67.74% to 123.18% improvement in the RL's objective function while maintaining comparable performance under normal conditions.

A. Jezequel, L. Delaby, Z.C. McKay et al.

ABSTRACT: The objective of this study was to evaluate the effect of sward diversification combined with a reduction of chemical nitrogen (N) fertilizer on the performance of spring-calving grazing dairy cows within a farm systems experiment. Three farmlets were created: a monoculture of perennial ryegrass (Lolium perenne L.; PRG) fertilized with 250 kg N/ha (PRG-250N), a PRG–white clover (Trifolium repens L; WC) sward fertilized with 125 kg N/ha (PRGWC-125N), and a multispecies sward (MSS) comprising grasses, legumes, and herbs, also fertilized with 125 kg N/ha (MSS-125N). Each farmlet had its own herd of dairy cows on a total area of 18.7 ha divided into 20 paddocks. Each herd comprised pure Holstein-Friesian (HF) and HF–Jersey crossbred (JFX) animals, and cows were randomly assigned through the 3 treatments. For 3 years (2021 to 2023), the performances of both swards (grass yield, botanical composition, nutritive value) and grazing animals (milk production and composition, BW, and BCS) were recorded. We found no significant differences in pasture production or sward nutritive value between sward systems, and grazing season length was also similar (264 d). On average over the 3 years, PRGWC-125N contained 150 g/kg DM of legumes, and MSS-125N contained 160 g/kg DM legumes, 130 g/kg DM plantain, and 40 g/kg DM chicory. Both individual cow milk and fat plus protein (milk solids) yield were lower for PRG-250N (5,018 and 452 kg, respectively), intermediate for PRGWC-125N (5,139 and 463 kg, respectively), and highest for MSS-125N (5,297 and 476 kg, respectively) whereas milk and milk solids production per hectare from grazing were similar during the study period (11,523 and 1,016 kg/ha, respectively). Breed also had a significant effect, with the JFX having lower milk yield but higher fat and protein concentration compared with HF. This resulted in higher milk solids production per kilogram of BW for the JFX compared with HF (0.96 and 0.87 kg milk solids/kg BW, respectively). The results of this study highlight the possibility for more diverse pastures to reduce chemical N fertilizer input requirements and maintain pasture productivity while increasing animal performance within pasture-based spring-calving systems.

A.J. Schwanke, K.M. Dancy, H.W. Neave et al.

ABSTRACT: The objectives of this study were to determine: (1) if dairy cow personality traits and concentrate allowance are associated with the behavior and performance of cows during training to use an automated milking system (AMS); and (2) if these factors were associated with the behavior and performance of cows after AMS training. Twenty-nine mid- to late-lactation Holstein cows (218 ± 49 DIM), who were milking on a rotary parlor and had never previously been milked in an AMS, were enrolled in this study. Cows were assigned to 1 of 2 dietary treatments, consisting of a basal partial mixed ration (PMR) common to both treatment groups, with a concentrate allowance (on a DM basis) of (1) 2.0 kg/d in the AMS (L-Tx), or (2) 6.0 kg/d in the AMS (H-Tx). Cows were trained to use the free-traffic AMS, with supervised milkings, over 72 h and were milked in this system for 63 d after training was complete. Variables relating to feeding behavior, milking activity, and production were measured from the start of AMS training until the end of the study. Between 42 and 63 d after AMS introduction, each cow was assessed for personality traits using a combined arena test consisting of exposure to a novel environment, novel object, and novel human. Principal components analysis of behaviors observed during the personality assessment revealed 2 factors (interpreted as boldness and activeness traits) that together explained 85% of the variance; each cow received a score for each trait. Associations between dietary treatment and personality traits with feeding behavior, milking activity, and production were analyzed using mixed-effect linear and logistic regression models. Cows with greater scores for the active trait produced less milk during the 3 d of AMS training compared with cows with lower scores. Within the H-Tx, more active cows had a 3.92 times greater risk of kicking off teat cups during AMS training than less active cows. However, during the 8 wk after training, more active cows had a 1.37 times lesser risk of teat cup kickoffs than those that were less active. Cows on the H-Tx produced 4.4 kg/d more ECM compared with cows on the L-Tx in the 8 wk after training. During the 8 wk after AMS training the cows on the H-Tx consumed an average of 21.4 kg/d of PMR and were delivered 4.6 kg/d of AMS concentrate, whereas the L-Tx cows consumed 23.4 kg/d PMR and were delivered 2.0 kg/d of AMS concentrate. The results indicate that both dairy cow personality traits and AMS concentrate allocation influence their response to AMS training and subsequent feeding and milking behavior and production.

Remus Floricel, Peter Wadel

This paper investigates the structure of product systems of Hilbert spaces derived from Banach space-valued Lévy processes. We establish conditions under which these product systems are completely spatial and show that Gaussian Lévy processes with non-degenerate covariance always give rise to product systems of type I. Furthermore, we construct a continuum of non-isomorphic product systems of type \(\rm{II}\sb\infty\) from pure jump Lévy processes.

Mengru Chen, Wan Xie, Shendong Zhou et al.

ABSTRACT: Long-term feeding of a high-concentrate diet can induce subacute ruminal acidosis (SARA) in ruminants, which further leads to systemic inflammatory response. However, few studies have examined the effects of feeding a high-concentrate diet on the hindgut of ruminants. The purpose of this study was to investigate the effects of a high-concentrate diet on the composition of gut microbiota in colonic contents, inflammatory response, and barrier damage in the colon tissue of ruminants. A total of 12 healthy multiparous lactating Hu sheep were randomly allotted into the following 2 groups: a high-concentrate (HC) group (concentrate:forage = 7:3) and a low-concentrate (LC) group (concentrate:forage = 3:7). All sheep were fitted with ruminal fistulas. The formal feeding experiment lasted for 8 wk. After the feeding experiment, rumen fluid, portal vein blood, hepatic vein blood, colonic contents, and colon tissue samples were collected. The results showed that feeding the HC diet induced SARA in Hu sheep and significantly reduced pH in the colonic contents. The abundances of Firmicutes, Verrucomicrobiota, and Actinobacteriota decreased significantly, whereas those of Bacteroidota, Spirochaetota, and Fibrobacterota significantly increased in colonic contents. At the genus level, the relative abundances of 29 genera were significantly altered depending on the different type of diets. Analysis of the 10 bacterial genera with high relative abundance revealed that feeding the HC diet significantly reduced the abundance of UCG-005, Christensenellaceae R-7 group, UCG-010-norank, Monoglobus, [Eubacterium] coprostanoligenes group_norank, and Alistipes, whereas the abundances of Rikenellaceae RC9 gut group, Treponema, Bacteroides, and Prevotella increased. Compared with the LC group, feeding the HC diet significantly increased the concentration of LPS in rumen fluid, portal vein blood, hepatic vein blood, and colonic contents, and significantly upregulated the mRNA expression levels of proinflammatory cytokines in colon tissue, including TNF-α, IL-1β, IL-6, and IL-8, indicating the occurrence of inflammatory response in the colon tissue. In addition, the structure of colonic epithelial cells was loose, the intercellular space became larger, epithelial cells were exfoliated, and the mRNA and protein abundances of ZO-1, occludin, claudin-1, claudin-3, and claudin-4 were significantly decreased in the HC group, which was consistent with the results of immunohistochemistry. Furthermore, feeding the HC diet increased the ratios of DNA methylation and chromatin compaction in the promoter regions of occludin and claudin-1, which in turn inhibited their transcriptional expression. Therefore, the present study demonstrated that feeding an HC diet induced SARA in Hu sheep, altered the composition and structure of the microbial community in the colonic contents, induced an inflammatory response, and disrupted the intestinal mucosal barrier in the colonic tissue.

Luciano S. Martinez-Rau, José O. Chelotti, Mariano Ferrero et al.

Farmers must continuously improve their livestock production systems to remain competitive in the growing dairy market. Precision livestock farming technologies provide individualized monitoring of animals on commercial farms, optimizing livestock production. Continuous acoustic monitoring is a widely accepted sensing technique used to estimate the daily rumination and grazing time budget of free-ranging cattle. However, typical environmental and natural noises on pastures noticeably affect the performance limiting the practical application of current acoustic methods. In this study, we present the operating principle and generalization capability of an acoustic method called Noise-Robust Foraging Activity Recognizer (NRFAR). The proposed method determines foraging activity bouts by analyzing fixed-length segments of identified jaw movement events produced during grazing and rumination. The additive noise robustness of the NRFAR was evaluated for several signal-to-noise ratios using stationary Gaussian white noise and four different nonstationary natural noise sources. In noiseless conditions, NRFAR reached an average balanced accuracy of 86.4%, outperforming two previous acoustic methods by more than 7.5%. Furthermore, NRFAR performed better than previous acoustic methods in 77 of 80 evaluated noisy scenarios (53 cases with p<0.05). NRFAR has been shown to be effective in harsh free-ranging environments and could be used as a reliable solution to improve pasture management and monitor the health and welfare of dairy cows. The instrumentation and computational algorithms presented in this publication are protected by a pending patent application: AR P20220100910. Web demo available at: https://sinc.unl.edu.ar/web-demo/nrfar

Mengke Song, Linfeng Li, Dunquan Wu et al.

Existing salient object detection methods are capable of predicting binary maps that highlight visually salient regions. However, these methods are limited in their ability to differentiate the relative importance of multiple objects and the relationships among them, which can lead to errors and reduced accuracy in downstream tasks that depend on the relative importance of multiple objects. To conquer, this paper proposes a new paradigm for saliency ranking, which aims to completely focus on ranking salient objects by their "importance order". While previous works have shown promising performance, they still face ill-posed problems. First, the saliency ranking ground truth (GT) orders generation methods are unreasonable since determining the correct ranking order is not well-defined, resulting in false alarms. Second, training a ranking model remains challenging because most saliency ranking methods follow the multi-task paradigm, leading to conflicts and trade-offs among different tasks. Third, existing regression-based saliency ranking methods are complex for saliency ranking models due to their reliance on instance mask-based saliency ranking orders. These methods require a significant amount of data to perform accurately and can be challenging to implement effectively. To solve these problems, this paper conducts an in-depth analysis of the causes and proposes a whole-flow processing paradigm of saliency ranking task from the perspective of "GT data generation", "network structure design" and "training protocol". The proposed approach outperforms existing state-of-the-art methods on the widely-used SALICON set, as demonstrated by extensive experiments with fair and reasonable comparisons. The saliency ranking task is still in its infancy, and our proposed unified framework can serve as a fundamental strategy to guide future work.

J.A. Johnson, K.S. Paddick, M. Gardner et al.

ABSTRACT: Provision of a palatable feed in automated milking systems (AMS) is considered an essential motivating factor to encourage voluntary visits to the milking stall. Although the quantity and composition of AMS concentrates have been previously investigated, the form of the concentrate has not been extensively evaluated. The objective of this study was to evaluate the effects of feeding pelleted (PB; 132.9 ± 56 DIM, 47.4 ± 9.51 kg/d milk yield) versus steam-flaked barley (SFB; 133.0 ± 63 DIM, 40.5 ± 8.23 kg/d milk yield) in an AMS on dry matter intake, AMS visits, milk and milk component yield, and partial mixed ration (PMR) feeding behavior. Twenty-nine Holstein cows of varying parities were enrolled in this study. Cows were housed in freestall housing with a feed-first guided-flow barn design; 7 cows were housed in a separate freestall pen to enable individual PMR intake and feeding behavior monitoring. This study was conducted as a 2-way crossover, with two 21-d periods in which each cow received the same basal PMR but was offered 2 kg/d (dry matter basis) of PB or SFB in the AMS. Cows receiving the SFB had fewer voluntary AMS visits (2.71 vs. 2.90 ± 0.051, no./d), tended to have a longer interval between milkings (541.7 vs. 505.8 ± 21.02 min), spent more time in the holding pen before entering the AMS (139.9 vs. 81.2 ± 11.68 min/d), and had lower total box time (19.7 vs. 21.4 ± 0.35 min/d) than cows fed PB. Despite changes in AMS attendance, there were no differences for average milk (44.0 kg/d), fat (1.62 kg/d), and protein (1.47 kg/d) yields or AMS concentrate intake (2.02 kg/d). These behavioral changes indicate that offering SFB as an alternative to PB may reduce motivation for cows to voluntarily enter the AMS.