Tainara C. Michelotti, Alyssa Imbert, Arash Veshkini
et al.
Free fatty acid receptors (FFAR) are molecular sensors involved in the regulation of energy metabolism. Free fatty acid receptors are expressed in the bovine liver, although their biological functions are not fully understood. Our objectives were to study the expression of hepatic FFAR in periparturient dairy cows supplemented or not with a mixture of essential fatty acids (EFA) and CLA, and to investigate potential associations between FFAR and metabolic adaptation during the transition period. Multiparous Holstein cows received abomasal infusions of either coconut oil (control; n = 8) or a mixture of EFA and CLA (EFACLA; n = 8) from −9 to 9 wk relative to parturition. Liver samples were collected at −3, 0, 4, and 9 wk relative to parturition. We quantified the liver expression of FFAR (FFAR1–4 and GPR84) and peroxisome proliferator-activated receptor delta (PPARD) by real-time quantitative PCR. Repeated-measurement correlations and multilevel multiple factor analysis (MFA) were used to investigate the links between FFAR and other metabolic parameters (i.e., energy balance, blood metabolic indicators, liver proteomics, and liver gene expression). All targeted FFAR were expressed in the liver, except for FFAR4. We found no effects of EFACLA or interactions with time for the expressed FFAR. FFAR1, FFAR2, and GPR84 expression decreased from −3 to 9 wk relative to parturition, whereas FFAR3 remained constant from −3 to 4 wk, then decreased at 9 wk postpartum. We observed strong correlations between FFAR, and moderate correlations between FFAR and PPARD. Multivariate (MFA) and univariate (correlation) analyses revealed weak links between FFAR liver expression and other metabolic parameters (e.g., IGFBP3 liver expression and plasma IGFBP-2). Downregulation of FFAR in the liver from pre- to postpartum may prevent receptors hyperactivation during periods of high free fatty acid concentrations. Physiological relevance and individual contributions of FFAR to the hepatic metabolism require further investigation.
With advances in artificial intelligence, image processing has gained significant interest. Image super-resolution is a vital technology closely related to real-world applications, as it enhances the quality of existing images. Since enhancing fine details is crucial for the super-resolution task, pixels that contribute to high-frequency information should be emphasized. This paper proposes two methods to enhance high-frequency details in super-resolution images: a Laplacian pyramid-based detail loss and a repeated upscaling and downscaling process. Total loss with our detail loss guides a model by separately generating and controlling super-resolution and detail images. This approach allows the model to focus more effectively on high-frequency components, resulting in improved super-resolution images. Additionally, repeated upscaling and downscaling amplify the effectiveness of the detail loss by extracting diverse information from multiple low-resolution features. We conduct two types of experiments. First, we design a CNN-based model incorporating our methods. This model achieves state-of-the-art results, surpassing all currently available CNN-based and even some attention-based models. Second, we apply our methods to existing attention-based models on a small scale. In all our experiments, attention-based models adding our detail loss show improvements compared to the originals. These results demonstrate our approaches effectively enhance super-resolution images across different model structures.
The modern food industry is undergoing a rapid change with the trend of production of plant-based food products that are more sustainable and have less impact on nature. Plant-based dairy analogues have been increasingly popular due to their suitability for individuals with milk protein allergy or lactose intolerance and those preferring a plant-based diet. Nevertheless, plant-based products still have insufficient nutritional quality, undesirable structure, and earthy, green, and bean-like flavor compared to dairy products. In addition, most plant-based foods contain lesser amounts of essential nutrients, antinutrients limiting the bioavailability of some nutrients, and allergenic proteins. Novel processing technologies can be applied to have a homogeneous and stable structure. On the other hand, fermentation of plant-based matrix with lactic acid bacteria can provide a solution to most of these problems. Additional nutrients can be produced and antinutrients can be degraded by bacterial metabolism, thereby increasing nutritional value. Allergenic proteins can be hydrolyzed reducing their immunoreactivity. In addition, fermentation has been found to reduce undesired flavors and to enhance various bioactivities of plant foods. However, the main challenge in the production of fermented plant-based dairy analogues is to mimic familiar dairy-like flavors by producing the major flavor compounds other than organic acids, yielding a flavor profile similar to those of fermented dairy products. Further studies are required for the improvement of the flavor of fermented plant-based dairy analogues through the selection of special microbial cultures and formulations.
Natural butter possesses a mild aroma that can be enhanced through biotransformation into cheese-flavored bases. This study systematically optimized hydrolysis and fermentation conditions using four proteases, lipase, and Lactococcus lactis. Initial single-factor experiments suggested that the lipase-to-protease ratio, hydrolysis temperature and duration, and starter culture inoculum level significantly affected flavor development. Response surface methodology (RSM) was used to further refine the enzymatic hydrolysis and fermentation conditions for the four treatment groups. GC–MS analysis identified 218 volatile metabolites across all samples, with esters, acids, and ketones being the predominant volatile classes in enzymatically hydrolyzed and fermented butter. Notably, distinct flavor profiles were observed for each of the four processes studied. Metabolic pathway analysis confirmed that these characteristic flavors originated from the synergistic interaction of proteases, lipase, and L. lactis. These findings provide a theoretical basis for the development and application of natural cheese-flavored essences in the food industry.
Nutrition. Foods and food supply, Food processing and manufacture
ABSTRACT: Cronobacter (7 species) are prevalent foodborne pathogens with a remarkable capacity to adapt to acidic environments. This resilience enables them to persist in both food matrices and host organisms. Here we investigated the role of the 2-component system response regulator OmpR in the acid tolerance of Cronobacter. Under acid stress, Cronobacter malonaticus demonstrated significantly elevated expression of ompR and type VI secretion system (T6SS) genes, as well as a marked decrease in the survival of OmpR or T6SS structure gene mutants, indicating the pivotal role of OmpR and T6SS in acid tolerance. Notably, OmpR markedly enhanced the T6SS expression by binding specifically to its promoter, and the activated T6SS expedited adaptation to acidic environments and facilitated biofilm formation, thereby aiding Cronobacter's survival under acidic conditions. Moreover, knocking out ompR in 6 additional Cronobacter species resulted in decreased T6SS expression and tolerance to acid stress than their wild-type strains, which further solidifies the widespread nature of the acid tolerance mechanism predicated on the activation of T6SS by OmpR in Cronobacter spp. A comprehensive understanding of the adaptation mechanisms employed by Cronobacter spp. in acidic conditions will provide a theoretical foundation for managing their contamination in acidic food matrices and preventing infection outbreaks in the infant gastrointestinal tract.
ABSTRACT: In this study, the effect of luxS, a key gene involved in quorum sensing, on the characteristic flavor of yogurt and its molecular mechanisms during the cofermentation of yogurt with engineered probiotics was investigated. The luxS gene overexpression strain was constructed by the homologous recombination technique, and its effect on the expression of population sensing signaling molecules and luxS gene was determined by bioluminescence and quantitative real-time PCR, and finally, headspace solid-phase micro extraction-GC-MS (HS-SPME-GC-MS) and metabolomics were used to determine the mechanism of its effect on the characteristic flavor of yogurt. The results demonstrated that the overexpression strains of Lactobacillus acidophilus CICC 6074-pMG36e-luxS and Lactobacillus helveticus R0052-pMG36e-luxS were successfully constructed. The expression of the luxS gene was upregulated by 2.25-fold and 3.16-fold, respectively. Compared with the wild-type strains, yogurt fermented by the overexpression strains showed a significant increase in AI-2 content, acidity, viable bacterial count, and protein hydrolysis, whereas pH, water-holding capacity, and hardness were significantly reduced. The HS-SPME-GC-MS results revealed the presence of 31 volatile flavor substances in yogurt. Among them, benzaldehyde (almond and burned sugar flavors), 2,4-dimethyl- (almond, cherry, and naphthalene flavors), dibutyl phthalate (a faint aromatic odor), and n-decanoic acid (rancid and fatty notes) were identified as the key differential flavor substances mediated by the luxS gene. Metabolomics results showed that the luxS gene mediates the production of organic acids in yogurt through arginine and proline metabolism, phenylalanine metabolism, and tryptophan metabolism. This study provides a theoretical basis for a deeper understanding of the molecular mechanisms underlying yogurt flavor formation.
ABSTRACT: Somatic cell count is used as an indicator of milk quality and udder health in dairy goats, although its interpretation is complicated by noninfectious causes, including seasonality, farm-specific practices, and physiological factors. This study analyzed 868 milk samples from 9 Norwegian dairy goat farms to investigate the interplay between SCC, individual bacterial count (IBC), and milk composition. Samples were collected on 3 occasions during the lactation period (early, mid, and late lactation). The results showed that SCC peaked in the pasture period and then decreased but remained elevated in late lactation. Individual bacterial count showed a positive correlation with higher SCC levels, although this correlation varied significantly across different farms and time periods. The presence of intramammary infections only partially explained the varying correlation between SCC and bacterial counts. This indicates that the relationship between SCC and IBC is influenced not only by infections, but also by management practices, environmental conditions, and other farm-level factors. The study revealed a covariation between SCC and other milk components according to the lactation stage and season. Furthermore, the investigation of factors influencing the interplay between SCC and IBC provides a deeper understanding of SCC as a milk quality indicator in dairy goats.
Alejandro Belanche, André Bannink, Jan Dijkstra
et al.
ABSTRACT: This publication aims to provide guidelines of the knowledge required and the potential research to be conducted in order to understand the mode of action of antimethanogenic feed additives (AMFA). In the first part of the paper, we classify AMFA into 4 categories according to their mode of action: (1) lowering dihydrogen (H2) production; (2) inhibiting methanogens; (3) promoting alternative H2-incorporating pathways; and (4) oxidizing methane (CH4). The second part of the paper presents questions that guide the research to identify the mode of action of an AMFA on the rumen CH4 production from 5 different perspectives: (1) microbiology; (2) cell and molecular biochemistry; (3) microbial ecology; (4) animal metabolism; and (5) cross-cutting aspects. Recommendations are provided to address various research questions within each perspective, along with examples of how aspects of the mode of action of AMFA have been elucidated before. In summary, this paper offers timely and comprehensive guidelines to better understand and reveal the mode of action of current and emerging AMFA.
ABSTRACT: The objective of this retrospective descriptive study was to characterize housing and herd management practices of Quebec dairy farms. Pre-existent survey data (housing, husbandry, and herd health; 36 questions) collected in person by Lactanet technicians (n = 116; March 2020 to February 2021) from 1,965 herds were used. Results were segregated by facility type (freestall [FS] vs. tiestall herds [TS]) and summarized using descriptive statistics. The average herd size was 76 ± 56 cows, with 65 ± 47 milking cows, peaking at 40 ± 5 kg/d, with most herds housed their milking cows in TS facilities (80%). Of the FS herds (20%), 36% transitioned from TS after 2016. Based on the Canadian Code of Practice and peer-reviewed literature, management strengths included frequent bedding in TS, feed reduction before dry-off (>59% FS; >75% TS), routine hoof trimming (≥2×/yr in >80%), and adequate lighting (>85% maintained >200 lx for 14–17 h/d). Areas needed improvement included the adoption of secondary ventilation systems (observed in <45% FS and <20% TS), implementation of targeted dry-off protocols (>80% of herds applied a single dry-off protocol, regardless of milk yield) and greater adoption of teat sealant use (45% reported using intramammary antibiotics without teat sealants). Deep-bedded lying surfaces were uncommon (30% of far-off and lactating groups in FS; <20% of dry and lactating groups in TS). In FS herds, horizontal bars were most frequent in lactating groups (40%). In TS herds, 58% of herds calved cows in tiestalls and >80% lacked pasture or exercise pens access. Regarding hoof health, footbaths and sprays were largely absent in TS herds (90%), whereas FS herds more often used footbaths, especially for lactating cows (70%). These findings establish benchmarks for Quebec dairy herds, highlighting well-adopted practices and identifying opportunities to increase the uptake of specific management practices across the province to further enhance herd health and welfare.
Voraprapa Nakavachara, Chanon Thongtai, Thanarat Chalidabhongse
et al.
This study examines how consumer attitudes toward animal welfare influence food selection and pricing using real-world market data from a Swiss supermarket. Our findings indicate that higher animal welfare standards are consistently associated with higher prices, suggesting that ethical considerations play a significant role in generating price premiums based on consumer preferences. On average, a one-point increase in the animal welfare score (ranging from 1 to 5, with 5 being the highest) corresponds to a 16.4% price increase, with the effect being most pronounced in Dairy & Eggs (25.3%), compared to Meat & Fish (14.3%). These results highlight the psychological and behavioral factors underlying consumer preferences for ethically produced foods. Additionally, we find limited evidence of a price premium for climate-friendly food products, observed only in Yogurts & Desserts, a subcategory within Dairy & Eggs. Our findings contribute to the understanding of how ethical food attributes influence consumer decision-making and pricing in retail settings.
Brain foundation models (BFMs) have emerged as a transformative paradigm in computational neuroscience, offering a revolutionary framework for processing diverse neural signals across different brain-related tasks. These models leverage large-scale pre-training techniques, allowing them to generalize effectively across multiple scenarios, tasks, and modalities, thus overcoming the traditional limitations faced by conventional artificial intelligence (AI) approaches in understanding complex brain data. By tapping into the power of pretrained models, BFMs provide a means to process neural data in a more unified manner, enabling advanced analysis and discovery in the field of neuroscience. In this survey, we define BFMs for the first time, providing a clear and concise framework for constructing and utilizing these models in various applications. We also examine the key principles and methodologies for developing these models, shedding light on how they transform the landscape of neural signal processing. This survey presents a comprehensive review of the latest advancements in BFMs, covering the most recent methodological innovations, novel views of application areas, and challenges in the field. Notably, we highlight the future directions and key challenges that need to be addressed to fully realize the potential of BFMs. These challenges include improving the quality of brain data, optimizing model architecture for better generalization, increasing training efficiency, and enhancing the interpretability and robustness of BFMs in real-world applications.
Slozhenkina M.I. Gorlov I.F. Mosolov A.A. Abramov S.A. Natyrov A.K. Stifanko M.D., Gorlov I.F. , Mosolov A.A.
et al.
The article describes the results of a study of the effectiveness of the lecithomix feed additive on live weight gain, hematological and biochemical blood parameters of calves. The preparation contains lecithin as an active substance - 27-33% and excipients: mono- and diglycerides of fatty acids at least 5%, PEG-40 hydrogenated oil at least 0.5%, vitamin E-acetate at least 0.1%, silicon dioxide at least 27% and chalk up to 100%. The studies were conducted on red steppe calves from 7 to 180 days of age. The calves of the experimental group were given the studied feed additive from the age of 7 days as part of a mixture with whole milk replacer (WMR) for 120 days daily, but the livestock was monitored until the age of 6 months. The dosage of the test sample of the feed additive was carried out in accordance with the envisaged draft instructions for use - 2 kg of the test feed additive per 1 ton of milk replacer. Animals of the control group received milk replacer that did not contain the test feed additive. According to the results of the studies, it was established that the calves of the experimental group had an advantage over the control group in terms of body weight by 9.3 kg or 6.8% (PЈ0.01). In the experimental young animals, the blood hemoglobin level was higher by 2.9 g/l or 2.7%, the erythrocyte content was higher by 0.12ґ1012/l or 1.7%, the total protein by 2.24 g/l or 3.1%, respectively. The dry matter digestibility index of the experimental calves compared to the control group was higher by 2.9% (PЈ0.05), the organic matter content by 3.3% (PЈ0.01), crude protein by 3.5% (P?0.01), crude fat increased by 1.1% (PЈ0.01), crude fiber by 3.4% (PЈ0.05), and nitrogen-free extractive substances by 2.2% (PЈ0.01).
Wildfires are burning more acres annually, contributing to air pollution across the United States. Air pollutants, such as particulate matter (PM2.5), have health implications for humans and animals, and are known to alter behavior in several species, but effects of wildfire PM2.5 on dairy calf behavior are unknown. The present study aimed to understand how dairy calf standing and lying behavior is affected by wildfire PM2.5. Holstein heifer calves (n = 13) were monitored for the first 90 d of life, concurrent with the 2022 wildfire season. Hourly PM2.5 concentrations and meteorological conditions, which were used to calculate temperature-humidity index (THI), were recorded. Wildfire and wind trajectory mapping was used to determine the contribution of wildfires to spikes in PM2.5. Calf activity data were recorded every minute using accelerometers and analyzed as total hourly and daily standing and lying times, standing and lying bouts, and duration of bouts. Additionally, the responses of calves to the initial 24-h period of each of 2 separate exposures to wildfire smoke were assessed. Wildfire PM2.5 exposure was associated with reduced daily standing time and bout duration, increased daily total lying time, and increased, albeit not significantly, daily standing bouts. Percent of time standing hourly was increased, whereas percent of time lying hourly was decreased by wildfire PM2.5. The initial 24 h of each smoke exposure was characterized by decreased standing and increased lying time, but there was a greater change in behavior during the first event compared with the second event. These results indicate that exposure to wildfire PM2.5 induces a behavioral response, which may diminish with repeated exposures. Future research should aim to understand the health and welfare implications of the behavioral responses to wildfire PM2.5.
Causal inference seeks to identify cause-and-effect interactions in coupled systems. A recently proposed method by Liang detects causal relations by quantifying the direction and magnitude of information flow between time series. The theoretical formulation of information flow for stochastic dynamical systems provides a general expression and a data-driven statistic for the rate of entropy transfer between different system units. To advance understanding of information flow rate in terms of intuitive concepts and physically meaningful parameters, we investigate statistical properties of the data-driven information flow rate between coupled stochastic processes. We derive relations between the expectation of the information flow rate statistic and properties of the auto- and cross-correlation functions. Thus, we elucidate the dependence of the information flow rate on the analytical properties and characteristic times of the correlation functions. Our analysis provides insight into the influence of the sampling step, the strength of cross-correlations, and the temporal delay of correlations on information flow rate. We support the theoretical results with numerical simulations of correlated Gaussian processes.
Monique Mi Song Chung, April J. Arbour, Jen-Yi Huang
Membrane filtration is a key technology in dairy processing for the separation of dairy liquids to clarify, concentrate, and fractionate a variety of dairy products. Ultrafiltration (UF) is widely applied for whey separation, protein concentration and standardization, and lactose-free milk production, though its performance can be hindered by membrane fouling. As an automated cleaning process commonly used in the food and beverage industries, cleaning in place (CIP) uses large amounts of water, chemicals, and energy, resulting in significant environmental impacts. This study introduced micron-scale air-filled bubbles (microbubbles; MBs) with mean diameters smaller than 5 μm into cleaning liquids to clean a pilot-scale UF system. During the UF of model milk for concentration, cake formation was identified as the dominant membrane fouling mechanism. The MB-assisted CIP process was conducted at two bubble number densities (2021 and 10,569 bubbles per mL of cleaning liquid) and two flow rates (130 and 190 L/min). For all the cleaning conditions tested, MB addition largely increased the membrane flux recovery by 31–72%; however, the effects of bubble density and flow rate were insignificant. Alkaline wash was found to be the main step in removing proteinaceous foulant from the UF membrane, though MBs did not show a significant effect on the removal due to the operational uncertainty of the pilot-scale system. The environmental benefits of MB incorporation were quantified by a comparative life cycle assessment and the results indicated that MB-assisted CIP had up to 37% lower environmental impact than control CIP. This is the first study incorporating MBs into a full CIP cycle at the pilot scale and proving their effectiveness in enhancing membrane cleaning. This novel CIP process can help reduce water and energy use in dairy processing and improve the environmental sustainability of the dairy industry.