ABSTRACT: Protein-stabilized high internal phase Pickering emulsions (HIPPE) as edible 3-dimensional (3D) food printing materials have various applications within the food industry. Herein, whey protein–based nanoparticles with curcumin (Cur) and different amounts of proanthocyanins (PC) incorporated exerted reduced surface tension, enhanced particle size, Cur loading efficiency, 3-phase contact angle, interfacial protein adsorption, and surface load with increasing PC content, making them excellent Pickering stabilizers for HIPPE. The formed HIPPE also exhibited relatively uniform oil droplets distribution, increased centrifugation stability, enhanced oxidation stability, improved viscoelasticity, and good 3D printing performance (high resolution and shape fidelity). Moreover, the nanoparticle as an interfacial antioxidant reservoir showed a controlled-release behavior for Cur at oil-water interface and then migrated into the oil phase, which was modulated by PC content. This result provided new possibilities for producing whey protein–based HIPPE as 3D printing inks for nutrient delivery and future food manufacturing.
Aqeel A. Qraidi, Muslim Ashor Al-etby, Aqeel Alyousuf
et al.
A molecular diagnosis study was conducted on termite insect that invades palm trees. BLAST results showed that most of the samples were matched to the Microserotermes sp. with accession number KY224717.1, except one sample matched with Amitermes desertorum and Amitermes vilis with accession number KU523914.1, KU523912.1, respectively which is Garma-2 with query cover reaching 95.73% , 95.445 respectively. The results of the similarity and likelihood algorithm analysis showed the emergence of two main clusters, the first cluster includes taxa samples of the Microcerotermes diversus species with a taxa from the National Gene Bank (KY224717.1) for comparison, the second cluster includes the taxa from Gurma-2 region, which was morphologically classified as Amitermes vilis, with the comparison taxa from the National Gene Bank (KU523914.1) as out group (O.G) with a divergence branch length 0.06357. In general, the sequences of the cytochrome c oxidase (COXII) gene of the studied samples matched the sequences of the standard recorded samples in US GenBank with a matching rate of more than 97%, but some samples in phylogenetic tree showed a difference, as they gave a subcluster, which is an indicator of the development of a new subspecies or another species in the future perhaps.
ABSTRACT: This study evaluated the effects of an oral probiotic capsule containing a live culture of Megasphaera elsdenii NCIMB 41125 on performance, feeding behavior, rumen pH and VFA concentration, and development of dairy-beef crossbred calves. Thirty-one male dairy-beef crossbred calves (Holstein × Angus; mean ± SD; 45.3 ± 7.1 kg; 8.2 ± 2.0 d old) were enrolled in a blinded, 76-d randomized trial. Calves were randomly assigned to one of 3 treatments: placebo, probiotic capsule administration on d 15, or probiotic capsule on d 15 plus a second capsule on d 39 of the study. Calves were housed individually with ad libitum access to water and calf starter and were fed 7 L/d of milk replacer (1,050 g of powder/d) in 2 meals until d 41, then 3.5 L/d in 2 meals until weaning on d 56. Behavioral observations were recorded in 1-min intervals using a wall-mounted camera. Rumen fluid samples were collected on d 14, 35, 49, 58, and 70, and analyzed for pH and VFA. Upon euthanasia on d 77, forestomach weights were recorded and rumen papillae dimensions were measured. Mixed linear models were used for statistical analysis. Probiotic treatment resulted in greater daily solid feed DMI and ADG, particularly during weaning and postweaning periods. Additionally, probiotic-treated calves spent more time drinking water and tended to have lower rumen pH compared with control calves. Empty rumen weight and papillae area were greater in calves supplemented with the probiotic capsule on d 15 compared with the other treatments. These findings suggest that preweaning M. elsdenii supplementation enhances performance and rumen development in dairy-beef crossbred calves. However, the effects of timing and number of capsule applications on rumen development and calf performance should be further investigated. Further research should also investigate the probiotic's effect on the rumen microbiome and fermentation dynamics throughout the rearing period using detailed microbiome analysis.
Simon J.R. Woodward, Lydia J. Farrell, Chris R. Burke
et al.
ABSTRACT: The health, welfare, and production impacts of heat stress are of increasing concern to dairy farmers. For cows grazing pasture, solar radiation is a key driver of heat stress load, and provision of shade (e.g., trees) is a primary mitigation option. To date, research on shade use choices by dairy cows has been limited to small-scale observation-based studies, and analysis at a commercial scale using sensor-derived data using machine learning has not been attempted. This study quantified the availability and use of shade at a herd level using cow global positioning system location data from herds of lactating dairy cows grazing pasture for 1 month in summer on 4 farms in the North Island of New Zealand, along with 15-min weather data and software that predicts potential shaded areas from aerial light detection and ranging imagery. Farms ranged from 60 to 400 ha of flat to rolling terrain. Cow shade use was quantified as the percentage of cows in shade above what would be expected from independent random walking and was modeled using 9 contrasting machine learning approaches. Shade use was found to be predictable and dependent on several interacting variables. Shade use increased when solar radiation exceeded 2 MJ/m2 per hour, provided at least 1.3 m2 of shade was available per cow, especially when air temperature exceeded 24°C and wind speed was less than 4.2 m/s. These results can be applied to historic or forecast weather data to help farmers make informed decisions about investment in shade resources or to maximize the use of existing resources.
ABSTRACT: Interactions among metabolic, oxidative, and inflammatory states are expected to influence cow health and physiology during the transition period. However, the interrelation remains to be elucidated. In this study, 66 lactations from 57 individual multiparous Holstein Friesian cows with a complete profile of oxidative and inflammatory status-related variables at both −7 and 21 d relative to calving were involved under similar transition management. Blood samples were collected at −7 (7 d before the expected calving date) and 3, 6, 9, and 21 DIM to analyze metabolic markers, including BHB acid (BHBA), nonesterified fatty acids, and insulin. Additionally, oxidative variables (proportion of oxidized glutathione to total glutathione in red blood cells [GSSG {%}], the activity of glutathione peroxidase [GPx] and of superoxide dismutase [SOD], concentrations of malondialdehyde [MDA], and oxygen radical absorbance capacity [ORAC]) and acute phase proteins (APP), including Hp and SAA, and albumin-to-globulin ratio (A:G) were assessed in the blood of −7 and 21 d relative to calving. Initially, 5 oxidative variables (GSSG [%], GPx, SOD, MDA, and ORAC) at 21 DIM were subjected to fuzzy c-means clustering, delineating 2 groups: lower antioxidant ability (LAA80%, n = 27) and higher antioxidant ability (HAA80%, n = 14), with 80% referring to the cut-off value for cluster membership. Twenty-five lactations with membership values below this threshold were excluded. Subsequently, using k-medoids clustering on 3 APP (Hp, SAA, and A:G) at 21 DIM, clinically healthy cows were categorized into 2 groups: those exhibiting an acute phase response (APR, n = 13) and those without this response, termed non-APR (n = 41). Cows that developed one or more clinical diseases during the transition period (n = 12) were considered as a distinct group. A modest association was observed between compromised antioxidant ability and enhanced inflammatory status at a systemic level. Clinically diseased cases manifested increased GPx activities compared with clinically healthy cases, independent of their inflammation levels. A limited association was noticed between oxidative status at −7 and 21 d relative to calving. However, at −7 d relative to calving, the non-APR group exhibited reduced SAA levels in comparison to both the APR and diseased groups. Energy metabolic stress was more pronounced in the LAA80% group than in the HAA80% group, characterized by elevated BHBA concentrations during the transition period. Between 5 and 21 d of lactation, a decline in milk yield was observed in the LAA80% group. Moreover, the LAA80% group displayed increased milk fat percentages. Nevertheless, the detrimental effects of the transition period were relatively muted when clinical symptoms were absent in the APR group. Notably, the clinically healthy cows produced more milk than the diseased cows. During the transition period, the diseased cases exhibited elevated BHBA concentrations. These findings highlight the heterogeneity in the oxidative and inflammatory status of dairy cows during early lactation. Cows with impaired oxidative status require close monitoring, whereas those with higher antioxidant ability and no clinical symptoms appear capable of managing elevated inflammatory responses.
ABSTRACT: The objective of this study was to create a framework for training and selecting machine learning algorithms (MLA) to classify cow health status daily using data from multiple automated health monitoring systems (AHMS), including wearable and nonwearable sensors, combined with nonsensor data of potential value for predicting cow health. The work presented in this manuscript is part of a series of studies aimed at identifying a single candidate algorithm that, upon extensive refinement and further development, could be deployed in a commercial dairy operation to identify cows potentially affected by health disorders for clinical examination. Data from AHMS and other cow features and performance data, including the clinical health status of cows, were collected in a prospective cohort study including Holstein cows (n = 1,252). Data from AHMS used for MLA training included rumination, eating, and physical activity measured in the neck (neck sensor), temperature and physical activity measured in the reticulorumen (bolus sensor), physical activity and resting measured in the leg (leg sensor), and milk yield, milk electrical conductivity, and milk components (parlor sensors). Other non-AHMS data used were temperature and humidity index, cow and calving event features, and current and previous lactation performance and management indicators. The dataset included 22,415 cow-day records with 49 features. The dataset was split into training and testing sets in an 80:20 ratio, resulting in 17,887 and 4,528 cow-day records, respectively. Data imputation and standardization were applied automatically or manually. A diverse set of nondeep learning (n = 26) MLA were trained and compared using the open-source automated ML (AutoML) tool Lazy Predict Classifier (LZP). Upon selection of the best-performing nondeep learning algorithms (i.e., XGBoost, AdaBoost, Nearest Centroid, and Bernoulli Naive Bayes) from the pool tested with LZP, classifier algorithms were compared with more complex deep learning algorithms (multilayer perceptron, recurrent neural networks, long short-term memory networks, and gated recurrent unit models) not included in LZP. All algorithms underwent training and evaluation before selection of a single best-performing algorithm, using several metrics of performance. Ensemble learning models, particularly XGBoost, achieved the best performance and balanced results with a sensitivity of 82.4% and a precision of 42.6% combined with a specificity of 86.4% and a negative predictive value of 97.6%. This model also had the highest F1-score (0.56) and area under the curve (84.4%). The XGBoost algorithm also demonstrated robustness in handling missing data. Our comprehensive approach to MLA screening and selection enabled informed decisions in selecting a suitable algorithm for identifying cows for clinical examination based on the daily prediction of health disorder occurrence. The combination of the AutoML tool LZP and manual refinement and testing of multiple MLA provided a robust framework for comparing multiple ML models. Ensemble classification learner algorithms such as XGBoost and Adaboost might outperform other deep learning and nondeep learning algorithms for classifying cow health daily using AHMS and other cow management and performance indicators.
Abstract:: In the dairy cattle sector, the number of crossbred genotypes increased in the last years, and therefore, the need for accurate genomic evaluations for crossbred animals has also increased. Thus, this study aimed to investigate the feasibility of including crossbred genotypes in multibreed, single-step genomic BLUP (ssGBLUP) evaluations. The Council of Dairy Cattle Breeding provided more than 47 million lactation records registered between 2000 and 2021 in purebred Holstein and Jersey and their crosses. A total of 27 million animals were included in the analysis, of which 1.4 million were genotyped. Milk, fat, and protein yields were analyzed in a 3-trait repeatability model using BLUP or ssGBLUP. The 2 models were validated using prediction bias and accuracy computed for genotyped cows with no records in the truncated dataset and at least one lactation in the complete dataset. Bias and accuracy were better in the genomic model than in the pedigree-based one, with accuracies for crossbred cows higher than those of purebreds, except for fat yield in Holstein. Our study shows that genotypes for crossbred animals can be included in a ssGBLUP analysis with their purebred ancestors to estimate genomic estimated breeding values in a single run.
ABSTRACT: Due to its beneficial effects on human health, Bifidobacterium is commonly added to milk powder. Accurate quantification of viable Bifidobacterium is essential for assessing the therapeutic efficacy of milk powder. In this study, we introduced a novel propidium monoazide (PMA)-antibiotic fluorescence in situ hybridization (AFISH)-flow cytometry (FC) method to rapidly and accurately quantify viable Bifidobacterium cells in milk powder. Briefly, Bifidobacterium cells were treated with chloramphenicol (CM) to increase their rRNA content, followed by staining with RNA-binding oligonucleotide probes, based on the AFISH technique. Then, the DNA-binding dye PMA was used to differentiate between viable and nonviable cells. The PMA-AFISH-FC method, including sample pretreatment, CM treatment, dual staining, and FC analysis, required approximately 2 h and was found to be better than the current methods. This is the first study to implement FC combined with PMA and an oligonucleotide probe for detecting Bifidobacterium.
ABSTRACT: The contentious issue of cow-calf separation at birth is incongruent with many views on acceptable farming practices, and carries the risk of eroding public trust in the dairy industry if it is not addressed. The available evidence provides little support for the practice, but research on best practices for maintaining cow-calf contact in a way that enhances animal welfare while preserving farm profitability is nascent. In this article, the authors address the research questions that require answers to better inform producers and facilitate their decision-making and prepare the dairy industry to take another evolutionary step forward.
Christian Marinoni, Riccardo Fosco Gramaccioni, Changan Chen
et al.
The primary goal of the L3DAS23 Signal Processing Grand Challenge at ICASSP 2023 is to promote and support collaborative research on machine learning for 3D audio signal processing, with a specific emphasis on 3D speech enhancement and 3D Sound Event Localization and Detection in Extended Reality applications. As part of our latest competition, we provide a brand-new dataset, which maintains the same general characteristics of the L3DAS21 and L3DAS22 datasets, but with first-order Ambisonics recordings from multiple reverberant simulated environments. Moreover, we start exploring an audio-visual scenario by providing images of these environments, as perceived by the different microphone positions and orientations. We also propose updated baseline models for both tasks that can now support audio-image couples as input and a supporting API to replicate our results. Finally, we present the results of the participants. Further details about the challenge are available at https://www.l3das.com/icassp2023.
Understanding how the collective activity of neural populations relates to computation and ultimately behavior is a key goal in neuroscience. To this end, statistical methods which describe high-dimensional neural time series in terms of low-dimensional latent dynamics have played a fundamental role in characterizing neural systems. Yet, what constitutes a successful method involves two opposing criteria: (1) methods should be expressive enough to capture complex nonlinear dynamics, and (2) they should maintain a notion of interpretability often only warranted by simpler linear models. In this paper, we develop an approach that balances these two objectives: the Gaussian Process Switching Linear Dynamical System (gpSLDS). Our method builds on previous work modeling the latent state evolution via a stochastic differential equation whose nonlinear dynamics are described by a Gaussian process (GP-SDEs). We propose a novel kernel function which enforces smoothly interpolated locally linear dynamics, and therefore expresses flexible -- yet interpretable -- dynamics akin to those of recurrent switching linear dynamical systems (rSLDS). Our approach resolves key limitations of the rSLDS such as artifactual oscillations in dynamics near discrete state boundaries, while also providing posterior uncertainty estimates of the dynamics. To fit our models, we leverage a modified learning objective which improves the estimation accuracy of kernel hyperparameters compared to previous GP-SDE fitting approaches. We apply our method to synthetic data and data recorded in two neuroscience experiments and demonstrate favorable performance in comparison to the rSLDS.
This study investigates mask-based beamformers (BFs), which estimate filters for target sound extraction (TSE) using time-frequency masks. Although multiple mask-based BFs have been proposed, no consensus has been reached on which one offers the best target-extraction performance. Previously, we found that maximum signal-to-noise ratio and minimum mean square error (MSE) BFs can achieve the same extraction performance as the theoretical upper-bound performance, with each BF containing a different optimal mask. However, two issues remained unsolved: only two BFs were covered, excluding the minimum variance distortionless response BF, and ideal scaling (IS) was employed to ideally adjust the output scale, which is not applicable to realistic scenarios. To address these issues, this study proposes a unified framework for mask-based BFs comprising two processes: filter estimation that can cover all possible BFs and scaling applicable to realistic scenarios by employing a mask to generate a scaling reference. Based on the operators and covariance matrices used in BF formulas, all possible BFs can be classified into 12 variations, including two new ones. Optimal masks for both processes are obtained by minimizing the MSE between the target and BF output. The experimental results using the CHiME-4 dataset suggested that 1) all 12 variations can achieve the theoretical upper-bound performance, and 2) mask-based scaling can behave like IS, even when constraining the temporal mean of a non-negative mask to one. These results can be explained by considering the practical parameter count of the masks. These findings contribute to 1) designing a TSE system, 2) improving scaling accuracy through mask-based scaling, and 3) estimating the extraction performance of a BF.
Joice Pranata, Marshall Dunn, MaryAnne Drake
et al.
ABSTRACT: Liquid micellar casein concentrate (MCC) is an ideal milk-based protein ingredient for neutral-pH ready-to-drink beverages. The texture and mouthfeel of liquid MCC-based beverages depend on the beverage protein content, as well as the composition of soluble proteins in the aqueous phase around the casein micelle. The objective of this study was to determine the composition of soluble proteins in the aqueous phase around the casein micelles in skim milk and liquid MCC containing 7.0% and 11.6% protein content. Skim milk was pasteurized and concentrated to 7% protein content by microfiltration and then to 18% protein content by ultrafiltration. The 18% MCC was then serially diluted with distilled water to produce 11.6% and 7.0% protein MCC. Skim milk, 7.0% MCC, and 11.6% MCC representing starting materials with different protein concentrations were each ultracentrifuged at 100,605 × g for 2 h. The ultracentrifugation for each of the starting materials was performed at 3 different temperatures: 4°C, 20°C, and 37°C. The ultracentrifugation supernatants were collected to represent the aqueous phase around the casein micelle in MCC solutions. The supernatants were analyzed by Kjeldahl to determine the crude protein, casein, and casein as a percentage of crude protein content, and by sodium dodecyl sulfate PAGE to determine the composition of the individual proteins. Most of the proteins in MCC supernatant (about 45%) were casein proteolysis products. The remaining proteins in the MCC supernatant consisted of a combination of intact αS-, β-, and κ-caseins (about 40%) and serum proteins (14–18%). Concentrations of αS-casein and β-casein in the supernatant increased with decreasing temperature, especially at higher protein concentrations. Temperature and interaction between temperature and protein explained about 80% of the variation in concentration of supernatant αS- and β-caseins. Concentration of supernatant κ-casein, casein proteolysis products, and serum protein increased with increasing MCC protein concentration, and MCC protein concentration explained most of the variation in supernatant κ-casein, casein proteolysis products, and serum protein concentrations. Predicted MCC apparent viscosity was positively associated with the dissociation of αS- and β-caseins. Optimal beverage viscosity could be achieved by controlling the dissociation of these proteins in MCC.
Cheese and milk are stapled dairy products consumed globally. However, adulterants in these products pose significant health risks and compromise their quality. Analytical techniques are crucial in detecting and quantifying adulterants to combat adulteration. This opinion explores the problem of cheese and milk adulteration, highlights the role of spectroscopic techniques (fluorescence spectroscopy, nuclear magnetic resonance (NMR) spectroscopy, and infrared (IR) spectroscopy) in adulteration detection, and compares their effectiveness with the well-established high-performance liquid chromatography (HPLC) method. The advantages and limitations of each technique are discussed, providing valuable insights into their applications to ensure the authenticity and safety of cheese and milk products.
The development of computer vision and in-situ monitoring using visual sensors allows the collection of large datasets from the additive manufacturing (AM) process. Such datasets could be used with machine learning techniques to improve the quality of AM. This paper examines two scenarios: first, using convolutional neural networks (CNNs) to accurately classify defects in an image dataset from AM and second, applying active learning techniques to the developed classification model. This allows the construction of a human-in-the-loop mechanism to reduce the size of the data required to train and generate training data.
Vasyl Liasota, Nadiia Bohatko, Svitlana Tkachuk
et al.
The relevance of this study is conditioned upon the need to improve the hygiene of milking cows by selecting detergents and disinfectants with different component compositions that ensure the quality and safety of dairy products. The purpose of this paper was to investigate the effectiveness of solutions of modern chlorine-containing and chlorine- free detergents and disinfectants on the test cultures of microorganisms, the sanitary and hygienic condition of the milking parlour, parts of milking equipment, and the skin of the udder teats of cows. The total number of bacteria was determined according to the requirements of DSTU ISO 15214:2007, by inoculating 1 cm3 of the test material on meat-peptone agar followed by incubation at an average temperature of 36°C for 24-48 h. Cultivation was performed in a thermostat at 37°C. After incubation, colonies of grown microorganisms were counted, and the number of colony-forming units was determined. Detergents and disinfectants “Chloramine B”, “Sanalcalin” and “Neochlor” have a detrimental effect on the growth of test cultures of E. coli, S. aureus and P. aeruginosa. “Desmol” suppresses the growth of test cultures of E. coli and P. aeruginosa after 20 minutes. The number of colonies of microorganisms in the air of the milking parlour is minimal at the beginning and greatest at the end of milking. 0.5% solution has a detrimental effect on E. coli and S. aureus, and slightly affects the growth of Salmonella spp. At the same time, this product is effective against microbial contamination of the skin of the udder teats of cows for its use in pre-milking disinfection. During the treatment of milking equipment with a 0.5% “Gralan Gel” solution, the total bacterial inoculation of the milking gum is reduced by 94.1%, the collector – by 98.4%, the milk hose – by 96.5%, the tank of the milking machine – by 97.4% compared to the indicators before processing the milking equipment with detergents and disinfectants. According to the results of the study, the best efficiency in reducing the general bacterial contamination of milking equipment, the harmful effect on opportunistic microflora and microbial contamination of the skin of the udders of cows when used in pre-milking disinfection at milk production enterprises shows a 0.5% solution of the alkaline detergent and disinfectant “Gralan Gel”. Thus, it is advisable to use a 0.5% solution of alkaline detergent and disinfectant “Gralan Gel” for producers of cow’s raw milk to reduce the total bacterial contamination of milking equipment and cow udders
ABSTRACT: Biohydrogenation-induced milk fat depression (MFD) is a reduction in milk fat synthesis caused by bioactive fatty acids (FA) produced during altered ruminal microbial metabolism of unsaturated FA. The methionine analog 2-hydroxy-4-(methylthio)butanoate (HMTBa) has been shown to reduce the shift to the alternate biohydrogenation pathway and maintain higher milk fat yield in high-producing cows fed diets lower in fiber and higher in unsaturated FA. The objective of this experiment was to verify the effect of HMTBa on biohydrogenation-induced MFD and investigate associated changes in rumen environment and fermentation. Twenty-two rumen cannulated high-producing Holstein cows [168 ± 66 d in milk; 42 ± 7 kg of milk/d (mean ± standard deviation)] were used in a randomized design performed in 2 blocks (1 = 14 cows, 2 = 8 cows). Treatments were control (corn carrier) and HMTBa (0.1% of diet dry matter). The experiment included a 7-d covariate period followed by 3 phases that fed diets with increasing risk of MFD. The diet during the covariate and low-risk phase (7 d) was 32% neutral detergent fiber with no additional oil. The diet during the moderate-risk phase (17 d) was 29% neutral detergent fiber with 0.75% soybean oil. Soybean oil was increased to 1.5% for the last 4 d. The statistical model included the random effect of block and time course data were analyzed with repeated measures including the random effect of cow and tested the interaction of treatment and time. There was no effect of block or interaction of block and treatment or time. There was no overall effect of treatment or treatment by time interaction for dry matter intake, milk yield, and milk protein concentration and yield. Overall, HMTBa increased milk fat percent (3.2 vs. 3.6%) and yield (1,342 vs. 1,543 g/d) and there was no interaction of treatment and dietary phase. Additionally, HMTBa decreased the concentration of trans-10 18:1 in milk fat and rumen digesta. Average total ruminal concentration of volatile FA across the day and total-tract dry matter and fiber digestibility were not affected by HMTBa, but HMTBa increased average rumen butyrate and decreased propionate concentration and increased total protozoa abundance. Additionally, HMTBa increased the fractional rate of α-linoleic acid clearance from the rumen following a bolus predominantly driven by a difference in the first 30 min. Plasma insulin was decreased by HMTBa. In conclusion, HMTBa prevented the increase in trans FA in milk fat associated with MFD through a mechanism that is independent of total volatile FA concentration, but involves modification of rumen biohydrogenation. Decreased propionate and increased butyrate and ruminal protozoa may also have functional roles in the mechanism.