Hasil untuk "Dairy processing. Dairy products"

M. Ghelichkhan, L.H.P. Silva, K.J. Soder et al.

ABSTRACT: Canola meal (CM) contains glucosinolates, which are metabolites known to inhibit the transfer of I from feed to milk. Therefore, we aimed to compare the effects of diets containing soybean meal (SBM) or CM, each supplemented with ethylenediamine dihydroiodide (EDDI) and the I-rich brown seaweed Ascophyllum nodosum (ASCO), on I metabolism, production performance, and nutrient utilization in dairy cows. Sixteen multiparous Jersey cows averaging (mean ± SD) 138 ± 58 DIM and 456 ± 62.1 kg of BW at the beginning of the study were used in a replicated 4 × 4 Latin square design with a 2 × 2 factorial arrangement of treatments. Each experimental period lasted 21 d, with 14 d for diet adaptation and 7 d for data and sample collection. Diets were formulated to yield similar concentrations of CP and I. Cows were fed (DM basis) the following experimental diets as TMR: (1) 10% SBM plus 110 mg/d of EDDI (SB+I), (2) 10% SBM plus 113 g/d of the brown seaweed ASCO (SB+SWD), (3) 12.5% CM plus 110 mg/d of EDDI (CAN+I), and (4) 12.5% CM plus 113 g/d of the brown seaweed ASCO (CAN+SWD). The I sources EDDI and ASCO meal were mixed with ground corn, placed inside rubber tubs, and offered to cows immediately before the delivery of the TMR. We observed a protein source by I source interaction for milk I concentration, which was similar between diets containing EDDI (SB+I and CAN+I), but it decreased by 27.8% when feeding CAN+SWD versus SB+SWD. We also detected a protein source by I source interaction for the serum concentration of free thyroxin, with cows fed CAN+I showing a tendency to have greater free thyroxin levels than those fed SB+I. Iodine intake, milk I yield, serum I concentration, and urinary excretion of I were lower in cows fed CAN+SWD and CAN+SWD than SB+I and CAN+I. In addition, milk I yield, milk I transfer efficiency (milk I yield/I intake), and the serum concentrations of thyroid-stimulating hormone and total triiodothyronine were lower in diets containing CM than SBM. Contrarily, the serum concentration of I and the urinary excretion of I increased in cows receiving CAN+I and CAN+SWD versus SB+I and SB+SWD. Whereas DMI and total glucosinolate intake were greater with feeding CAN+I and CAN+SWD versus SB+I and SB+SWD, milk yield, milk N efficiency, and the apparent total-tract digestibility of nutrients were lower in cows fed diets containing CM than SBM. The CM used in our study was likely overheated based on the high concentrations of neutral detergent insoluble CP (23.6%) and acid detergent insoluble CP (17.5%), possibly explaining the reduction in milk yield and nutrient utilization in cows fed CAN+I and CAN+SWD compared with SB+I and SB+SWD. In brief, our results revealed that feeding CM reduced milk I concentration, milk I yield, and milk I transfer efficiency, particularly when supplemented with ASCO meal.

J.L. Bermeo, L.C. Solórzano, A. Rico et al.

Accurate estimation of urine output is essential for assessing nutrient utilization, particularly for nutrients predominantly excreted through urine, such as minerals and protein-derived metabolites. An equation utilizing creatinine concentration and BW has been previously developed to estimate urinary volume (EUV) in lactating Holstein dairy cows, defined as EUV = [29 × BW (kg)]/[urinary creatinine (mg/L)]. Our objective was to evaluate whether this equation introduces bias when used for estimating urinary output in nonpregnant, nonlactating Holstein cows and to identify factors influencing its accuracy. For our study, we used 72 paired observations that included observed urinary volume (OUV) and urinary creatinine concentration. These observations were obtained from a prior research study that assessed the relative availability of various magnesium (Mg) sources using a duplicated 6 × 6 Latin square design, with cows (n = 12) grouped into squares based on lower (square 1) or higher (square 2) BW across 6 periods. Enrolled cows were of second parity (n = 8) and third or greater parity (n = 4), with BW ranging from 590 to 831 kg. To determine the agreement between EUV and OUV, we constructed 2 mixed-effects models. The first model evaluated slope bias (testing if the slope was significantly different from 1), whereas the second assessed mean bias (testing if the intercept was significantly different from 0) between EUV and mean-centered OUV. In our dataset, creatinine excretion per kilogram of BW ranged from 15.0 to 35.6 mg/kg BW with an average of 27.6 mg/kg BW. When assessing the agreement between OUV and EUV, we observed both slope and mean biases when applying the creatinine-based equation. Furthermore, there was a bias estimate across BW quartiles. Overall, 76.4% of observations fell within ±10% deviation range between EUV and OUV. These findings suggest that further research is needed to identify factors that can refine the creatinine and BW-based predictive equation specifically for nonpregnant, nonlactating Holstein dairy cows.

Giovanna Lomolino, Stefania Zannoni, Mara Vegro et al.

Consumer interest in vegetarian, ethical, and clean-label foods is reviving the use of plant-derived milk coagulants. Cardosins from <i>Cynara cardunculus</i> (“thistle”) are aspartic proteases with strong clotting activity, yet their technological impact in cheese remains under-explored. This study compared a commercial thistle extract (PC) with traditional bovine rennet rich in chymosin (AC) during manufacture and 60-day ripening of Caciotta cheese. Classical compositional assays (ripening index, texture profile, color, solubility) were integrated with scanning electron microscopy, three-dimensional surface reconstruction, and descriptive sensory analysis. AC cheeses displayed slower but sustained proteolysis, yielding a higher and more linear ripening index, softer body, greater solubility, and brighter, more yellow appearance. Imaging revealed a continuous protein matrix with uniformly distributed, larger pores, consistent with a dairy-like sensory profile dominated by milky and umami notes. Conversely, PC cheeses underwent rapid early proteolysis that plateaued, producing firmer, chewier curds with lower solubility and darker color. Micrographs showed a fragmented matrix with smaller, heterogeneous pores; sensory evaluation highlighted vegetal, bitter, and astringent attributes. The data demonstrate that thistle coagulant can successfully replace animal rennet but generates cheeses with distinct structural and sensory fingerprints. The optimization of process parameters is therefore required when targeting specific product styles.

T. Touil, F. Huot, S. Claveau et al.

ABSTRACT: Low reticuloruminal pH (rpH) for a prolonged period could lead to SARA. This disease negatively affects cow health and is associated with monetary losses for the dairy industry. The aim of this study was to predict rpH and SARA separately using different machine learning (ML) models applied to Fourier-transform infrared spectroscopy (FTIR) spectra obtained from routine DHI milk analysis of individual cows. A total of 107 primiparous and multiparous Holstein cows were selected from 12 commercial farms in Québec, Canada, and their rpH was continuously monitored for 150 d using wireless boluses. In parallel, 2,634 individual milk samples were collected in the morning and afternoon and analyzed to obtain FTIR spectra. After the cleaning process, 1,744 samples remained, evenly divided into 872 morning (a.m.) and 872 afternoon (p.m.) samples. The FTIR and rpH data were combined to create 3 equally balanced datasets for ML model development: one for a.m. samples, one for p.m. samples, and one composed of both a.m. and p.m. samples, with 872 samples in each dataset. Various spectra preprocessing methods were evaluated, including using the first derivative of the spectra and filtering with 3 different sets of spectra. Additionally, different ML algorithms, including partial least squares, random forest, and gradient boosting, were used to predict rpH and SARA. A total of 36 different models were developed and evaluated for both rpH and SARA prediction. All ML models were assessed using 3 different cross-validation (C-V) methods: nested 10-fold, nested leave-one-farm-out (LOFO), and nested leave-cows-out (LCO) C-V. For rpH prediction, the best performance was achieved using nested 10-fold C-V with median R2 values of 0.26, 0.26, and 0.22 for the a.m., p.m., and a.m./p.m. datasets, respectively. However, these performances were likely overoptimistic as none of the models evaluated using nested LOFO or nested LCO C-V obtained R2 higher than 0.12. Unlike rpH, SARA prediction accuracies evaluated using nested LOFO (a.m.: 59%, p.m.: 69%, a.m./p.m.: 64%), and nested LCO (a.m.: 67%, p.m.: 66%, a.m./p.m.: 64%) were closer to the nested 10-fold C-V. These results indicated that rpH was likely not predictable from FTIR, but SARA can be predicted separately and directly from FTIR with 69% accuracy from routine DHI milk samples of individual cows.

G. Ferreira, C.L. Teets, H. Galyon et al.

ABSTRACT: We hypothesized that, relative to harvesting small-grain grasses at the soft dough stage (SFT) of maturity, harvesting small-grain grasses at the boot stage (BT) of maturity would result in less expensive dairy rations when commodity prices are high but not when commodity prices are low. Small plots of small-grain grasses were planted during the fall of 2020 and 2021 in Blacksburg, Blackstone, and Orange, Virginia. In each year and location, 2 varieties of barley, 2 varieties of rye, and 4 varieties of triticale were planted in plots replicated 6 times, yielding 288 plots. Within each year and location, we harvested half of the plots at BT and the other half at SFT. For each of the 6 small-grain grasses, we formulated 8 rations according to 8 different scenarios using the least-cost optimizer. The scenarios included high and low commodity prices, high and low dietary forage (60% and 40% forage, respectively), and the inclusion of small-grain grasses harvested at BT or SFT. Harvesting at SFT yielded 107% to 205% more DM than harvesting at BT. Relative to BT, small-grain grasses harvested at SFT had greater concentrations of OM, NDF, ADF, ADL, and starch but lower concentrations of CP. Relative to BT, small-grain grasses harvested at SFT also had a greater concentration of undegraded NDF (NDF basis). Species had minimal influence on the nutritional quality of small-grain grasses for silage. Under a low-price scenario, the ration formulation system ignored all 6 small-grain grass silages and included corn silage as the only forage source when we did not limit its inclusion. Under a high-price scenario, the ration formulation system included all 6 small-grain grass silages when formulating low-forage diets with unlimited corn silage. However, a preference between BT and SFT stages did not exist, with the optimizer not consistently selecting a specific maturity stage. After evaluating the yields, the chemical composition, and the effects on ration formulation in this study, future studies should aim to evaluate the influence of maturity at harvest of small-grain grasses on cow performance and environmental impacts.

K.J. Alward, A.J. Duncan, A.D. Ealy et al.

ABSTRACT: Multiparous Holstein cows exposed to short-day photoperiod (SDPP) of 8 h of light per day during their dry period produced up to 3.2 kg more milk per day compared with cows exposed to long-day photoperiod (LDPP) of 16 h of light per day; it is unknown if a similar response would be observed for Jersey cow milk production. The objective of this study was to determine the effect of photoperiod during the dry period on subsequent colostrum and milk production in Holstein and Jersey cattle. Holstein and Jersey cows (n = 33) were dried off 60 d before their due date and randomly assigned to SDPP (Holstein, n = 9; Jersey, n = 8) or LDPP (Holstein, n = 8; Jersey, n = 8) until calving. Cows were weighed at the time of enrollment (d 0) and were housed in an enclosed barn at 20°C and exposed to 250 to 450 lx during periods of light and <10 lx during periods of darkness. At calving, colostrum volume was weighed and tested for relative protein concentration with a Brix refractometer and a sample was collected for component analysis (fat, protein, lactose, SNF) via infrared spectroscopy, as well as IgA, IgG, IgG1, IgM, lactoferrin, and SCS analysis. After calving, cows were returned to the freestall barn and exposed to ambient photoperiod and temperature. Milk production data were collected for 15 wk postcalving. Data were analyzed using PROC MIXED in SAS (SAS 9.4; SAS Institute Inc., Cary, NC) with treatment, breed, and d 0 weight as fixed effects. PROC MIXED with repeated measures was used to evaluate the relationship of day length and breed with mature milk volume, fat, and protein production. Random effects included replicate, lactation number, genetic inbreeding percentage, previous lactation mature equivalent 305-d protein production, and calf sex. For colostrum, Brix score, colostral protein, fat, IgA, and IgM were increased in Jersey cows compared with Holstein cows. Total colostrum weight, SNF, lactose, lactoferrin, IgG, IgG1, and SCS did not differ by breed or treatment. Postcalving, ECM production was increased in Holstein cows compared with Jersey cows but unaffected by photoperiod treatment. Conversely, milk protein percentage was increased for Jersey cows relative to Holstein cows but was unaffected by photoperiod treatment. Milk fat increased in LDPP Holstein cows compared with SDPP Jersey cows during the first week of lactation, which is likely due to the transition from colostrum to mature milk production. Overall, photoperiod did not affect colostrum production, but differences by breed were detected. Photoperiod during the dry period did not affect mature milk production or protein, but milk fat percentage was affected by photoperiod × breed. Therefore, altered lighting during the dry period does not unfavorably affect colostrum or milk production in Jersey or Holstein cows.

D. Zamudio, R.A. De Castro, A.P. Jimenez-Lagos et al.

ABSTRACT: We evaluated the effects of insufficient (WET) or extended (CUR) wilting on the concentration of phytoestrogens, nutritional value, microbial populations, in vitro ruminal methane emissions, and in situ degradability of red clover silage (DM: 294 and 453 g/kg) and hay (DM: 651 and 891 g/kg, respectively) across storage stages. Measurements were taken at the start of storage (STRT), after 14 d (early stage of storage), and once storage processes had stabilized for hay and silage (50 and 78 d, respectively; late stage). Only late samples of hay and silage were tested for the in situ procedure. Data were analyzed as a randomized complete block design (5 blocks) with a 2 (wilting extents) × 2 (conservation methods) × 3 (storage stages) factorial. Results showed that storage DM losses were greater for WET versus CUR hay, but no differences were observed within silage. The CUR hay and silage preserved better sugars during storage relative to WET. Due to microbial spoilage, the NH3-N of WET hay was greater than CUR hay after 14 d of storage, but the opposite was observed after 50 d. The NDF of WET hay increased across storage stages, whereas it remained stable for CUR hay. In contrast, the NDF levels of both WET and CUR silage decreased during the ensiling period. The WET hay favored the growth of molds during storage, whereas CUR hay reduced their counts after 50 d of storage. For silage, mold counts were lower in WET compared with CUR after 14 d of storage, but no differences were observed after 78 d. When the ensiling period is limited to 14 d, the aerobic exposure DM losses and heating were greater for CUR silage compared with WET. However, when the ensiling period was extended to 78 d, no differences were observed between WET and CUR silage in terms of aerobic exposure DM losses and heating degree days. The CUR hay preserved ruminal in vitro DM fermentation kinetics compared with WET, whereas the ruminal DM fermentation kinetics of silage were not affected by the wilting extent. For both conservation methods, WET reduced methane yield only at the end of storage. The in situ rumen degradability kinetics showed that ensiling decreased the potentially degradable DM and CP fractions compared with haymaking. Haymaking reduced the ruminal degradation rate of DM but not of CP, compared with ensiling. Wilting was more critical for silage than hay in decreasing the concentration of formononetin and biochanin A. Across storage stages, hay had lower formononetin and biochanin A than silage. Overall, wilting red clover further helps conserve the nutritional quality of hay and silage while reducing phytoestrogen levels.

Dandan Wang, Wusigale, Lu Li et al.

Lacticaseibacillus casei Zhang (L. casei Zhang) was used as an auxiliary starter culture to explore its application in camel milk fermentation. This study evaluated the effects of L. casei Zhang supplementation on viable cell count, acidity, texture, insulin-like growth factor 1 (IGF-1) retention, and metabolite profiles over a 21-day storage period. L. casei Zhang enhanced the retention rate of active IGF-1 from 52.95% to 59.13% and mitigated the progression of acidity (from 125 °T to 97.5 °T) compared with the control group. Additionally, L. casei Zhang significantly improved viscosity and promoted the formation of gel structures. Furthermore, its addition significantly influenced the production of key metabolites, including adenosine diphosphate, oleuropein, and threonine–tryptophan (P < 0.05). These findings highlight the potential of L. casei Zhang as an effective auxiliary starter culture for camel milk fermentation, enhancing its physicochemical properties and modulating its metabolomic profile.

Adrien Dorise, Marjorie Bellizzi, Adrien Girard et al.

With increasing processing power, deploying AI models for remote sensing directly onboard satellites is becoming feasible. However, new constraints arise, mainly when using raw, unprocessed sensor data instead of preprocessed ground-based products. While current solutions primarily rely on preprocessed sensor images, few approaches directly leverage raw data. This study investigates the effects of utilising raw data on deep learning models for object detection and classification tasks. We introduce a simulation workflow to generate raw-like products from high-resolution L1 imagery, enabling systemic evaluation. Two object detection models (YOLOv11n and YOLOX-S) are trained on both raw and L1 datasets, and their performance is compared using standard detection metrics and explainability tools. Results indicate that while both models perform similarly at low to medium confidence thresholds, the model trained on raw data struggles with object boundary identification at high confidence levels. It suggests that adapting AI architectures with improved contouring methods can enhance object detection on raw images, improving onboard AI for remote sensing.

Yisen Wang, Hanqin Cai, Longxiu Huang

The dynamical sampling problem is centered around reconstructing signals that evolve over time according to a dynamical process, from spatial-temporal samples that may be noisy. This topic has been thoroughly explored for one-dimensional signals. Multidimensional signal recovery has also been studied, but primarily in scenarios where the driving operator is a convolution operator. In this work, we shift our focus to the dynamical sampling problem in the context of three-dimensional signal recovery, where the evolution system can be characterized by tensor products. Specifically, we provide a necessary condition for the sampling set that ensures successful recovery of the three-dimensional signal. Furthermore, we reformulate the reconstruction problem as an optimization task, which can be solved efficiently. To demonstrate the effectiveness of our approach, we include some straightforward numerical simulations that showcase the reconstruction performance.

Jiawen Lyu, Manu Ramesh, Madison Simonds et al.

Few automated video systems are described in the open literature that enable hands-free cataloging and identification (ID) of cows in a dairy herd. In this work, we describe our system, composed of an AutoCattloger, which builds a Cattlog of dairy cows in a herd with a single input video clip per cow, an eidetic cow recognizer which uses no deep learning to ID cows, and a CowFinder, which IDs cows in a continuous stream of video. We demonstrate its value in finding individuals in unlabeled, unsegmented videos of cows walking unconstrained through the holding area of a milking parlor.

Mirco Vacca, Giuseppe Celano, Nadia Serale et al.

ABSTRACT: The microbiota of a cheese play a critical role in influencing its sensory and physicochemical properties. In this study, traditional Apulian Caciocavallo cheeses coming from 4 different dairies in the same area and produced following standardized procedures were examined, as well as the different bulk milks and natural whey starter (NWS) cultures used. Moreover, considering the cheese wheels as the blocks of Caciocavallo cheeses as whole, these were characterized at different layers (i.e., core, under-rind, and rind) of the block using a multi-omics approach. In addition to physical-chemical characterization, culturomics, quantitative PCR, metagenomics, and metabolomics analysis were carried out after salting and throughout the ripening time (2 mo) to investigate major shifts in the succession of the microbiota and flavor development. Culture-dependent and 16S rRNA metataxonomics results clearly clustered samples based on microbiota biodiversity related to the production dairy plant as a result of the use of different NWS or the intrinsic conditions of each production site. At the beginning of the ripening, cheeses were dominated by Lactobacillus, and in 2 dairies (Art and SdC), Streptococcus genera were associated with the NWS. The analysis allowed us to show that although the diversity of identified genera did not change significantly between the rind, under-rind, and core fractions of the same samples, there was an evolution in the relative abundance and absolute quantification, modifying and differentiating profiles during ripening. The real-time PCR, also known as quantitative or qPCR, mainly differentiated the temporal adaptation of those species originating from bulk milks and those provided by NWS. The primary starters detected in NWS and cheeses contributed to the high relative concentration of 1-butanol, 2-butanol, 2-heptanol, 2-butanone, acetoin, delta-dodecalactone, hexanoic acid ethyl ester, octanoic acid ethyl ester, and volatile free fatty acids during ripening, whereas cheeses displaying low abundances of Streptococcus and Lactococcus (dairy Del) had a lower total concentration of acetoin compared with Art and SdC. However, the subdominant strains and nonstarter lactic acid bacteria present in cheeses are responsible for the production of secondary metabolites belonging to the chemical classes of ketones, alcohols, and organic acids, reaffirming the importance and relevance of autochthonous strains of each dairy plant although only considering a delimited production area.

Giulio Giagnoni, Peter Lund, Marianne Johansen et al.

ABSTRACT: Dietary carbohydrate manipulation can be used to reduce enteric CH4 emission, but few studies have focused on the interaction of the different types of carbohydrates that can affect feed intake and ruminal fermentation. Understanding this interaction is necessary to make the most out of CH4 mitigation feeding strategies using different dietary carbohydrates. The aim of this study was to test the effect on enteric CH4 emission, feed intake, and milk production response when cows were fed either grass-clover silage (GCS) or corn silage (CS) as the sole forage source (55% of dry matter, DM), in combination with either barley (BAR) or dried beet pulp (DBP) as a concentrate (21.5% of DM). A total of 24 (half first-parity and half second-parity) cows were used in a crossover design with 2 periods of 21 d each, receiving 2 of 4 diets obtained from a 2 × 2 factorial arrangement of the experimental diet. Feed intake, CH4 emission metrics, and milk production were recorded at the end of the experimental periods. The diets had NDF concentrations between 258 and 340 g/kg of DM and starch concentrations between 340 and 7.45 g/kg of DM (CS-BAR and GCS-DBP, respectively). The effects of silage and concentrate on dry matter intake (DMI) were additive, with the highest feed intake in cows fed CS-BAR, followed by cows fed CS-DBP, GCS-BAR, and GCS-DBP (21.2, 19.9, 19.1, and 18.3 kg/d, respectively). Energy corrected milk (ECM) yield was not affected by silage source in first parity cows, but it was higher for cows fed CS than cows fed GCS in second parity. The effects of silage and concentrate on CH4 production (g/d), yield (g/kg of DMI), and intensity (g/kg of ECM) were not additive, as cows fed GCS had similar responses regardless of the concentrate used, but cows fed CS had lower CH4 production, yield, and intensity when fed BAR instead of DBP. The lower CH4 production, yield, and intensity in cows fed CS-BAR compared with other diets could be partially explained by the nonlinear relationship between ruminal VFA and carbohydrates (NDF and starch) concentration reported in the literature; however, we observed a linear relationship between the acetate/propionate ratio and CH4 yield, suggesting possible other effects. The effects of silage and concentrate on the ruminal VFA were additive in first parity cows, but not in second parity cows. The interaction between dietary carbohydrate type and parity might indicate an effect of feed intake or the energy balance of the cow. Feeding cows silage and concentrate both rich in starch can result in the lowest enteric CH4 emission.

Ye Tao, Qingcheng Wang, Min Xiao et al.

ABSTRACT: Breastfeeding by mothers with gestational diabetes mellitus (GDM) has been shown to reduce maternal insulin demands and diminish the risks of diabetes in infants, leading to improved long-term health outcomes. Milk fat globule membrane (MFGM) proteins play a crucial role in influencing the immunity and cognitive development of infants. Understanding the alterations in MFGM proteins in breast milk from mothers with GDM is essential for enhancing their self-efficacy and increase breastfeeding rates. The objective of this study is to investigate and compare MFGM proteins in milk from mothers with and without GDM based on tandem mass tag (TMT) labeling and liquid chromatography–tandem MS techniques. A total of 5,402 proteins were identified, including 4 upregulated proteins and 24 downregulated proteins. These significantly altered proteins were found to be associated with human diseases, cellular processes, and metabolism pathways. Additionally, the oxidative phosphorylation pathway emerged as the predominant pathway through Gene Set Enrichment Analysis involving all genes.

Kehan Sheng, Borbala Foris, Joseph Krahn et al.

ABSTRACT: Dominance hierarchies are known for mitigating conflicts and guiding priority of access to limited resources in gregarious animals. The dominance hierarchy of dairy cows is typically investigated using agonistic interactions, often monitored at the feed bunk right after fresh feed delivery when competition is high, resulting in frequent interactions. Yet, the outcome of agonistic interactions during times of high competition may be more influenced by cows' high valuation of fresh feed than their intrinsic attributes, such that the dominance hierarchy constructed using agonistic interactions under high versus low competition times might differ. We tested how the structure of the dominance hierarchy changes in relation to different levels of competition in a dynamic group of 48 lactating dairy cows over 10 mo, with 6 cows exchanged every 16 d, for a total of 159 cows. Using a validated algorithm, we continuously detected the actor and reactor of replacement behaviors in 30 feed bins as cows competed for feed. We also calculated the percentage of occupied feed bins to characterize competition at the moment of each replacement. These data were combined to create hierarchies using Elo ratings, separately for 25 occupancy levels ranging from 13% to 100%. For each 1% rise in feeder occupancy, hierarchy steepness fell by 2.41 × 10−3 ± 9.71 × 10−5 (SE), and the percentage of dyads where both cows replaced each other rose by 0.13% ± 0.01%. At the highest feeder occupancy level in comparison to the lowest one, we observed 7.57% more dyads in which the dominant individual (those that won more interactions at the lowest feeder occupancy) started to lose proportionally more. The magnitude of decrease in the winning rate of the dominant individual in those dyads also got amplified by 1.06 × 10−3% ± 1.37 × 10−4% (SE) for each 1% increase in feeder occupancy. These findings illustrate how inferred hierarchies vary with competition, with high competition flattening the hierarchy due to increased success of subordinate animals. We suggest that during heightened competition, increased valuation of resources can affect competitive success more than the individual's intrinsic dominance attributes. We recommend against calculating dominance hierarchies based on agonistic interactions during periods of high competition alone, and more generally urge researchers to differentiate agonistic interactions based on context when constructing dominance hierarchies.

Nikolett Szpisják-Gulyás, Zsuzsanna László, Szabolcs Kertész et al.

In this study, the integration of a 3D printed turbulence promoter into a stirred membrane separation cell during dairy wastewater ultrafiltration was investigated. Its effects, along with the effects of stirring, on the permeate flux and membrane fouling were examined. The experiments were carried out at different transmembrane pressures (0.1, 0.2, and 0.3 MPa) and stirring speeds (RPM: 100–400 min⁻¹), both with and without the application of the turbulence promoter. Various parameters were employed to characterize the membrane performance, such as the permeate flux, the flux decline ratio, and the fouling coefficient. To further investigate the membrane fouling mechanisms, mathematical models were used: the resistance-in-series model, the Makardij model, and the Hermia model. With the resistance-in-series model, we examined whether the membrane fouling was reversible (the deposit could be easily removed by washing operations) or irreversible (irreversible fouling) for each measurement, and with the Makardij model, we investigated whether the rate constant of the fouling or the rate constant of the deposit removal was the most important. In the case of the Hermia model, changes in the cake filtration rate constant were monitored. The results indicate that the combination of the 3D printed turbulence promoter and the stirring speed could effectively reduce membrane fouling during dairy wastewater ultrafiltration.

Wenhua Zhang, Majida Al-Wraikata, Linqiang Li et al.

ABSTRACT: There is growing interest in the origin, preparation, and application of bioactive peptides. This study investigated the effect of 6 enzymes on the structural, physicochemical properties, antioxidant activities, and antidiabetic potential of defatted fresh goat milk. Structural and functional changes resulting from enzymatic hydrolysis were assessed using gel electrophoresis, laser particle size analysis, multi-spectroscopy, and evaluations of foaming and emulsification properties. Antioxidant capacity was determined through free radical scavenging, Fe2+ chelation, and reducing ability experiments. Additionally, the inhibitory effects of the hydrolysates on α-glucosidase and α-amylase were measured to evaluate antidiabetic activity. Results showed that enzymatic hydrolysis disrupted the spatial structure of goat milk protein and reduced its molecular weight. Papain hydrolysate exhibited the highest degree of hydrolysis (32.87% ± 0.11%) and smallest particle size (294.75 ± 3.33 nm), followed by alcalase hydrolysate (29.12% ± 0.09%, 302.03 ± 7.28 nm). Alcalase hydrolysate showed the best foaming properties, and papain hydrolysate demonstrated the strongest 2,2-diphenyl-1-picrylhydrazyl and hydroxyl radical scavenging activity, Fe2+ chelation, and antidiabetic potential. These findings provide a solid theoretical basis for utilizing defatted goat milk as functional ingredients or excipients in the food, medical, and cosmetic industries.

Hui Liu, Xing Ji, Haichang Sun et al.

<i>Cronobacter</i> spp. are the most concerning foodborne pathogen in infant formula milk powder. Currently, there are many reports on the prevalence of <i>Cronobacter</i> spp. in infant formula milk and its processing environment, but there are few studies on the prevalence of <i>Cronobacter</i> spp. on dairy farms. We have, therefore, undertaken this study to investigate and track genomic epidemiology of <i>Cronobacter</i> spp. isolates from Chinese dairy farms in the provinces of Jiangsu and Shandong. In this study, forty <i>Cronobacter</i> spp. strains, consisting of thirty <i>Cronobacter sakazakii</i>, eight <i>Cronobacter malonaticus</i>, and two <i>Cronobacter dublinensis</i>, were obtained from 1115 dairy farm samples (raw milk, silage, bedding, and feces), with a prevalence rate of 3.57%. These isolates were classified into 10 <i>Cronobacter</i> serotypes and 31 sequence types (STs), including three novel STs which were isolated for the first time. Notably, pathogenic <i>Cronobacter</i> STs 7, 8, 17, 60, and 64, which are associated with clinical infections, were observed. Antimicrobial susceptibility testing showed that all the <i>Cronobacter</i> spp. were highly resistant to cephalothin and fosfomycin, which was consistent with the antimicrobial genotype. All isolates carried core virulence genes related to adherence, invasion, endotoxin, immune evasion, secretion system, and regulation. Approximately half the isolates were also able to produce a strong biofilm. Twenty-one prophages and eight plasmids were detected, with the most common prophage being <i>Cronobacter</i>_ENT47670 and the most common plasmid being IncFIB (pCTU1). In addition, two isolates harbored the transmissible locus of stress tolerance (tLST) which confers high environmental persistence. Phylogenetic analysis showed strong clustering by species level and sequence types. Isolates from different sources or regions with a similar genomic background suggests the cross-contamination of <i>Cronobacter</i> spp. The presence of diverse genotypes of <i>Cronobacter</i> spp. in dairy farms in Jiangsu and Shandong provinces indicates that surveillance of <i>Cronobacter</i> spp. on dairy farms should be strengthened, to prevent and control transmission and ensure the quality and safety of raw dairy products.

Nils L. Westhausen, Hendrik Kayser, Theresa Jansen et al.

Deep learning has the potential to enhance speech signals and increase their intelligibility for users of hearing aids. Deep models suited for real-world application should feature a low computational complexity and low processing delay of only a few milliseconds. In this paper, we explore deep speech enhancement that matches these requirements and contrast monaural and binaural processing algorithms in two complex acoustic scenes. Both algorithms are evaluated with objective metrics and in experiments with hearing-impaired listeners performing a speech-in-noise test. Results are compared to two traditional enhancement strategies, i.e., adaptive differential microphone processing and binaural beamforming. While in diffuse noise, all algorithms perform similarly, the binaural deep learning approach performs best in the presence of spatial interferers. Through a post-analysis, this can be attributed to improvements at low SNRs and to precise spatial filtering.

Minje Kim, Jan Skoglund

This paper explores the integration of model-based and data-driven approaches within the realm of neural speech and audio coding systems. It highlights the challenges posed by the subjective evaluation processes of speech and audio codecs and discusses the limitations of purely data-driven approaches, which often require inefficiently large architectures to match the performance of model-based methods. The study presents hybrid systems as a viable solution, offering significant improvements to the performance of conventional codecs through meticulously chosen design enhancements. Specifically, it introduces a neural network-based signal enhancer designed to post-process existing codecs' output, along with the autoencoder-based end-to-end models and LPCNet--hybrid systems that combine linear predictive coding (LPC) with neural networks. Furthermore, the paper delves into predictive models operating within custom feature spaces (TF-Codec) or predefined transform domains (MDCTNet) and examines the use of psychoacoustically calibrated loss functions to train end-to-end neural audio codecs. Through these investigations, the paper demonstrates the potential of hybrid systems to advance the field of speech and audio coding by bridging the gap between traditional model-based approaches and modern data-driven techniques.