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15 pages, 1621 KiB  
Article
Effects of Supplementing Rumen-Protected Glutathione on Lactation Performance, Nutrients, Oxidative Stress, Inflammation, and Health in Dairy Cows During the Transition Period
by Yu Hao, Xuejie Jiang, Rui Sun, Yunlong Bai, Chuang Xu, Yuxi Song and Cheng Xia
Vet. Sci. 2025, 12(2), 84; https://doi.org/10.3390/vetsci12020084 - 23 Jan 2025
Viewed by 3
Abstract
Glutathione (GSH), widely present in plant and animal cells and crucial for combating oxidative stress and inflammation, has not been evaluated in dairy cows. This study aims to evaluate the effects of rumen-protected glutathione (RPGSH) supplementation on lactation, nutrient metabolism, oxidative stress, inflammation, [...] Read more.
Glutathione (GSH), widely present in plant and animal cells and crucial for combating oxidative stress and inflammation, has not been evaluated in dairy cows. This study aims to evaluate the effects of rumen-protected glutathione (RPGSH) supplementation on lactation, nutrient metabolism, oxidative stress, inflammation, and health in transition dairy cows. Forty Holstein dairy cows (2.65 ± 0.78 of parity, 2.81 ± 0.24 of body condition score, 9207.56 ± 1139.18 kg of previous 305-day milk yield, 657.53 ± 55.52 kg of body weight, mean ± SD) were selected from a large cohort of 3215 cows on day 21 before expected calving (day −21 ± 3 d). Cows were randomly stratified into four dietary treatment groups (n = 10 per group): control (basal diet + 0 g/d RPGSH); T1 (basal diet + 1.5 g/d RPGSH); T2 (basal diet + 2 g/d RPGSH); and T3 (basal diet + 3 g/d RPGSH). Supplementation commenced approximately 21 days (±3) prepartum and continued through 21 days postpartum. Blood samples were collected at −21 ± 3, −14 ± 3, −7 ± 3, 0, 7, 14, and 21 d for analysis of serum metabolic parameters related to oxidative stress and inflammation. Milk composition was analyzed from samples collected on days 3, 7, 14, and 21 postpartum. Compared with the control group, supplementation with 2 g/d of RPGSH reduced somatic cell count (p < 0.05) and the incidence of postpartum diseases in dairy cows. No differences were observed among the groups in milk yield, milk fat, protein, lactose, total solids, dry matter intake, or energy-corrected milk. However, fat-corrected milk and feed efficiency were higher in the T2 group compared to the control (p < 0.05). Calcium and phosphorus levels did not differ among the groups. Compared to the control group, cows supplemented with 2 g/d RPGSH had lower β-hydroxybutyrate levels and higher glucose levels on days 14 and 21 postpartum (p < 0.05). From days 14 to 21 postpartum, RPGSH supplementation increased blood GSH, serum catalase, and total antioxidant capacity while reducing malondialdehyde, reactive oxygen species, haptoglobin, cortisol, C-reactive protein, and interleukin−6 levels compared with the control group (p < 0.05). The supplementation of 2 g/d RPGSH showed relatively better effects. RPGSH supplementation at 2 g/d improved lactation performance, nutrient metabolism, oxidative stress, and inflammation status in dairy cows, playing a crucial role in maintaining their health. To our knowledge, this is the first report on the effects of supplementing RPGSH additive in Holstein cows. Full article
(This article belongs to the Section Veterinary Internal Medicine)
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Figure 1
<p>Effects of rumen-protected glutathione (RPGSH) on glutathione (GSH, (<b>A</b>)), catalase (CAT, (<b>B</b>)), total antioxidant capacity (T-AOC, (<b>C</b>)), malondialdehyde (MDA, (<b>D</b>)), and reactive oxygen species (ROS, (<b>E</b>)). Treatment: T1 = basal diet + RPGSH 1.5 g/d (shown as ●), T2 = basal diet + RPGSH 2 g/d (shown as ■), T3 = basal diet + RPGSH 3 g/d (shown as ▲), and control = basal diet (shown as ⯁); Lin = linear; Quad = quadratic. Error bars indicate the SEM. Different lowercase letters indicate significant differences between peers (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Effects of rumen-protected glutathione (RPGSH) on haptoglobin (HP, (<b>A</b>)), cortisol (COR, (<b>B</b>)), C-reactive protein (CRP, (<b>C</b>)), and interleukin-6 (IL-6, (<b>D</b>)). Treatments: T1 = basal diet + RPGSH 1.5 g/d (shown as ●), T2 = basal diet + RPGSH 2 g/d (shown as ■), T3 = basal diet + RPGSH 3 g/d (shown as ▲), and control = basal diet (shown as ⯁); Lin = linear; Quad = quadratic. Error bars indicate the SEM. Different lowercase letters indicate significant differences between peers (<span class="html-italic">p</span> &lt; 0.05).</p>
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14 pages, 1930 KiB  
Article
The Significance of Selected Collagens and Their Connection with Relevant Extracellular Matrix Proteins in Bovine Early-Mid-Pregnancy and Parturition with and Without Retained Foetal Membranes
by Jacek Wawrzykowski, Monika Jamioł and Marta Kankofer
Biomolecules 2025, 15(2), 167; https://doi.org/10.3390/biom15020167 - 23 Jan 2025
Viewed by 154
Abstract
Appropriate placental structure and function assure foetal development, delivery of nutrients, and removal of waste. Collagens, as structural proteins, are crucial for the maintenance of placental growth and function. The aim of this study was to describe the profile of collagen 1 and [...] Read more.
Appropriate placental structure and function assure foetal development, delivery of nutrients, and removal of waste. Collagens, as structural proteins, are crucial for the maintenance of placental growth and function. The aim of this study was to describe the profile of collagen 1 and 4 in the placental tissues of cows and to correlate it to previously described activities of collagenases and adhesive proteins. Placental samples were collected from pregnant cows in the slaughterhouse (2nd, 4th, and 6th month; n = 12) and during parturition after caesarean section. Samples taken during caesarean section were retrospectively divided into retained (R; n = 6) and not retained foetal membranes (NR; n = 6). Determinations were performed of maternal and foetal parts separately after tissue homogenisation. Supernatants were used for the determination of COL1 and COL4 concentrations by ELISA and WB analysis. Significant differences were detected between pregnancy months and parturient samples in COL1 concentrations and between retained and released foetal membranes. The concentrations of COL4 were higher in the foetal as compared to the maternal part of the placenta. Significant differences were detected between retained and released foetal membranes, and, similarly to Col1, values were lower in retained than released foetal membranes. WB analysis showed the presence of examined collagen molecules and their molecular weights. The analysis of collagen profile together with the enzymes of their degradation and other adhesive proteins (glycodelin, decorin, and thrombospondins) in bovine placenta either during pregnancy and parturition showed a close relationship. Either attachment or detachment of the maternal and foetal parts of the bovine placenta requires actions in concert between all these adhesive proteins under the influence of pregnancy hormones. Full article
(This article belongs to the Special Issue Placental-Related Disorders of Pregnancy: 2nd Edition)
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<p>COL1A1 Western blot analysis in the placenta (maternal and foetal parts) of cows during pregnancy and parturition (2nd, 4th, and 6th month pregnancy period, NR, foetal membranes released up to 8–12 h; R, foetal membranes not released up to 8–12 h, ST—mass standard). Βeta-actin was used as loading control. The picture represents one of membranes with 2 randomly selected samples from each examined group. Original images can be found in <a href="#app1-biomolecules-15-00167" class="html-app">supplementary materials Figure S2</a>.</p>
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<p>COL4A4 Western blot analysis in the placenta (maternal and foetal parts) of cows during pregnancy and parturition (2nd, 4th, and 6th month pregnancy period, NR, foetal membranes released up to 8–12 h; R, foetal membranes not released up to 8–12 h, ST—mass standard). Βeta-actin was used as loading control. The picture represents one of the membranes with 2 randomly selected samples from each examined group. Original images can be found in <a href="#app1-biomolecules-15-00167" class="html-app">supplementary materials Figure S3</a>.</p>
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<p>COL1A1 concentrations in the placentas (maternal and foetal parts) of cows during pregnancy (2nd, 4th and 6th month) and parturition (NR, foetal membranes released up to 8–12 h; R, foetal membranes not released up to 8–12 h). The horizontal line inside each box indicates the median. The box plot shades the lower and upper quartiles of the data. Whiskers represent the maximum and minimum values. The <span class="html-italic">p</span>-value from the Mann–Whitney U test, dependent variable: COL1A1 maternal and foetal, grouping variable: month; only statistically significant results were marked (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>COL4A4 concentrations in the placentas (maternal and foetal parts) of cows during pregnancy (2nd, 4th, and 6th month) and parturition (NR, foetal membranes released up to 8–12 h; R, foetal membranes not released up to 8–12 h). The horizontal line inside each box indicates the median. The box plot shades the lower and upper quartiles of the data. Whiskers represent the maximum and minimum values. The <span class="html-italic">p</span>-value from the Mann–Whitney U test, dependent variable: COL4A4 maternal and foetal, grouping variable: month; only statistically significant results were marked (<span class="html-italic">p</span> &lt; 0.05).</p>
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20 pages, 813 KiB  
Review
Mycotoxins in Cheese: Assessing Risks, Fungal Contaminants, and Control Strategies for Food Safety
by Camila Aranda, Rodrigo Rodriguez, Martín A. Fernández-Baldo and Paola Durán
Foods 2025, 14(3), 351; https://doi.org/10.3390/foods14030351 - 22 Jan 2025
Viewed by 397
Abstract
According to the scientific information reviewed, cheese is highly susceptible to contamination by mycotoxin-producing fungi, primarily species from the genera Aspergillus (A. niger, A. flavus) and Penicillium (P. commune, P. solitum, P. palitans, and P. crustosum [...] Read more.
According to the scientific information reviewed, cheese is highly susceptible to contamination by mycotoxin-producing fungi, primarily species from the genera Aspergillus (A. niger, A. flavus) and Penicillium (P. commune, P. solitum, P. palitans, and P. crustosum). Studies on various types of cheese made from cow’s milk report an average concentration of Aflatoxin M1 (AFM1) at 13,000 ng kg−1, which is alarming since the regulatory limits for AFM1 in cheese range from 250 to 500 ng kg−1. For instance, limits set by Codex Alimentarius, the European Commission (EC), Turkey, and Iran are 250 ng kg−1. In the Netherlands, the limit is 200 ng kg−1, and in Italy, it is 450 ng kg−1. However, the concentration of mycotoxins frequently exceeds these regulatory limits, including critical mycotoxins such as ochratoxin A, citrinin, and cyclopiazonic acid, which pose significant global health concerns. Therefore, this study aims to review the mycobiota responsible for producing key mycotoxins in cheese and to assess the influence of physicochemical factors on fungal growth and mycotoxin production. By incorporating control strategies such as hygiene practices, pasteurization, and the use of preservatives, this study seeks to improve methodologies in the cheese production chain and mitigate contamination by fungi and mycotoxins. Full article
(This article belongs to the Section Food Microbiology)
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<p>The key physicochemical parameters that affect fungal growth and mycotoxin production in cheese. These include temperature, water activity (a<sub>w</sub>), pH, NaCl content, moisture, carbon and nitrogen sources, C/N ratio, and redox potential (E°). The interaction of these factors determines fungal spore germination, colony growth, and toxin synthesis, highlighting their critical roles in cheese contamination dynamics.</p>
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15 pages, 2565 KiB  
Article
Enhancement of Nitrogen Retention in Cow Manure Composting with Biochar: An Investigation into Migration and Regulation Mechanisms
by Zixi Han, Jianfei Zeng, Xu Zhao, Yanyan Dong, Ziyu Han and Tiezhu Yan
Agronomy 2025, 15(2), 265; https://doi.org/10.3390/agronomy15020265 - 22 Jan 2025
Viewed by 308
Abstract
Context: Biochar can affect the storage and forms of nitrogen; thus, it may also play a role in altering the nitrogen cycle during the fermentation process of cow dung into organic fertilizer. Objective: To elucidate the mechanism and process of nitrogen transformation during [...] Read more.
Context: Biochar can affect the storage and forms of nitrogen; thus, it may also play a role in altering the nitrogen cycle during the fermentation process of cow dung into organic fertilizer. Objective: To elucidate the mechanism and process of nitrogen transformation during the composting of cow manure with biochar, a comparative experiment was conducted. Method: This study investigates the use of biochar as a medium to enhance nitrogen storage during the aerobic composting of cow manure. The effectiveness was verified through a rapid composting experiment. Result and Conclusions: The results demonstrated that adding 5% biochar to the compost pile increased the total nitrogen content in manure by 12%. Specifically, the pyrrolic nitrogen in the composted cow manure increased from 38% to 44%, and the carbon-nitrogen ratio improved from 35% to 37%. Analysis of surface functional groups indicated that the C=O and C=C bonds in biochar played a key role in modifying nitrogen storage. Microbial analysis showed that biochar could significantly enhance the regional competitiveness of microorganisms, such as Cellvibrio, thereby boosting the expression of functional genes involved in the nitrification process, including amoABC, hao, and nxrAB. Therefore, adding 5% biochar not only enhances nitrogen storage in organic fertilizer but also changes the microbial population structure. Significance: This study carries substantial implications for the application of Biochar in the field, as well as for the development of microbial fertilizers based on cow manure. Full article
(This article belongs to the Section Agricultural Biosystem and Biological Engineering)
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Graphical abstract

Graphical abstract
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<p>The overall experiment flowchart.</p>
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<p>(<b>a</b>) Nitrogen distribution with and without biochar. (<b>b</b>) Nitrate N and ammonium nitrogen content in CMWB and CMNB.</p>
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<p>(<b>a</b>) Wide-scan XPS spectra of CW. (<b>b</b>) Wide-scan XPS spectra of CMWB. (<b>c</b>) Wide-scan XPS spectra of CMNB. (<b>d</b>) The deconvoluted N1s spectra of the XPS results of CW. (<b>e</b>) The deconvoluted N1s spectra of the XPS results of CMWB. (<b>f</b>) The deconvoluted N1s spectra of the XPS results of CMNB. (<b>g</b>) Wide-scan XPS spectra of primitive biochar. (<b>h</b>) Wide-scan XPS spectra of primitive BC.</p>
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<p>FTIR spectra result of CM, CMWB, CMNV, primitive biochar, and BC.</p>
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<p>(<b>a</b>) Species composition analysis of CM, CMNB, CMWB, and BC. (<b>b</b>) Correlation analysis of the various microorganisms in CM, CMNB, CMWB, and BC. (<b>c</b>) Principal component analysis of the microorganisms in CM, CMNB, CMWB, and BC.</p>
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<p>The relative abundances of the pathways involved in the nitrogen cycle of CM, CMNB, CMWB, and BC.</p>
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20 pages, 2818 KiB  
Article
Evaluating Rumination Time Changes During Estrus in Dairy Cows
by Mária Mičiaková, Peter Strapák, Eva Strapáková and Iveta Szencziová
Dairy 2025, 6(1), 5; https://doi.org/10.3390/dairy6010005 - 22 Jan 2025
Viewed by 259
Abstract
This study evaluated the impact of estrus on changes in rumination over 24 h using data from the DataFlow™ II program and the Heatime RuminAct device, encompassing 634 estrous cycles of dairy cows. During the reference period, three days before estrus, cows spent [...] Read more.
This study evaluated the impact of estrus on changes in rumination over 24 h using data from the DataFlow™ II program and the Heatime RuminAct device, encompassing 634 estrous cycles of dairy cows. During the reference period, three days before estrus, cows spent an average of 511 min per day ruminating. One day before estrus, the total rumination time decreased to 503 min per day. During estrus, rumination time further decreased to 481 min, reflecting a reduction of 31 min per day (6.2%) compared to the pre-estrus reference period. After estrus ended, we observed an immediate increase in rumination time, with post-estrus levels comparable to pre-estrus values. Using a linear model, we assessed the influence of the herd and individual cows on changes in rumination time during estrus compared to the reference period. Our findings confirm the notable impact of estrus on rumination in dairy cows. The reduction in rumination time was most pronounced in heifers (−66 min, −13%), followed by first-lactation cows (−36 min, −7%) and multiparous cows (−16 min, −4%). The influence of the lactation stage was significant, with cows in early lactation showing a greater reduction in rumination compared to cows in later stages. Additionally, high-milk-yielding cows exhibited slightly lower rumination times during estrus, reflecting the interplay between diet composition and energy demands. These results underscore the role of parity, lactation stage, milk yield, and individual differences in shaping rumination behavior during estrus. Behavior-monitoring systems proved valuable for detecting estrus and managing reproduction in dairy herds. Our results showed a notable 6.2% reduction in rumination during estrus, highlighting its potential as a reliable indicator in regions like Slovakia, where economic challenges impact dairy farming sustainability. Full article
(This article belongs to the Section Reproduction)
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Figure 1
<p>Testing differences in rumination time between herds during estrus and the reference period (n = 634). Data presented are based on 634 estrus cycles monitored from 180 Holstein cows (32 heifers and 148 lactating cows) across two herds; Herd 1 contributed 340 estrus cycles from lactating cows and 78 cycles from heifers, while Herd 2 contributed 294 estrus cycles from lactating cows, collected between July 2019 and November 2022. Variations in the results are represented by standard deviations (SD), and statistically significant differences are indicated by different letters (a, b) at a level of <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Changes in rumination time of dairy cows during the reference period and estrus according to parity for Herd 1 (heifers: n = 78; primiparous cows: n = 141; multiparous cows: n = 121). Values marked with different letters (a; b; c) indicate differences within the same color line.</p>
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<p>Changes in rumination time of dairy cows during the reference period and estrus according to parity for Herd 2 (primiparous cows: n = 96; multiparous cows: n = 198). Values marked with different letters (a; b) indicate differences within the same color line.</p>
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<p>Changes in rumination time of dairy cows during the reference period and estrus based on lactation stage for Herd 1 (≤80 days: n = 79; 81–150 days: n = 76; ≥151 days: n = 107). Values marked with different letters (a; b) indicate differences within the same color line.</p>
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<p>Changes in rumination time of dairy cows during the reference period and estrus based on lactation stage for Herd 2 (≤80 days: n = 158; 81–150 days: n = 10; ≥151 days: n = 32). Values marked with different letters (a; b) indicate differences within the same color line.</p>
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<p>Changes in rumination time of dairy cows during the reference period and estrus based on milk yield for Herd 1 (&lt;34.29 kg·day<sup>−1</sup>, n = 218; ≥34.29 kg·day<sup>−1</sup>, n = 122). Values marked with different letters (a; b; c) indicate differences within the same color line.</p>
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<p>Changes in rumination time of dairy cows during the reference period and estrus based on milk yield for Herd 2 (&lt;34.29 kg·day<sup>−1</sup>: n = 138; ≥34.29 kg·day<sup>−1</sup>: n = 156). Values marked with different letters (a; b) indicate differences within the same color line.</p>
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13 pages, 258 KiB  
Article
Circular Economy on a Small Scale: The Sustainable Use of Olive Tree Biomass Residues as Feed for Lactating Cows in the Sorrento Peninsula
by Felicia Masucci, Francesco Serrapica, Lucia De Luca, Raffaele Romano, Francesca Garofalo and Antonio Di Francia
Sustainability 2025, 17(3), 845; https://doi.org/10.3390/su17030845 - 21 Jan 2025
Viewed by 389
Abstract
To enhance the sustainability of marginal olive and dairy farms in the Sorrento peninsula, two separate crossover trials were conducted on two farms in the area to evaluate olive pruning residue (OlPr) and olive mill leaves (OlLes) as forage sources for lactating cows. [...] Read more.
To enhance the sustainability of marginal olive and dairy farms in the Sorrento peninsula, two separate crossover trials were conducted on two farms in the area to evaluate olive pruning residue (OlPr) and olive mill leaves (OlLes) as forage sources for lactating cows. Each trial lasted six weeks and consisted of two treatment periods, each including a 15-day adaptation phase followed by a 6-day measurement phase. During the measurement phase, milk production, feed intake, and olive residue consumption were assessed for two homogeneous cow groups: one receiving a ration supplemented with olive by-products and the other receiving a control diet. The olive-supplemented groups exhibited higher dry matter intake and roughage consumption (hay + olive residue) compared to the control groups. The intake of OlLes was about 30% higher than that of OlPr. Compared to the respective control, milk from OlLe-fed cows a had higher fat content and a higher fat-to-protein ratio, a more favorable fatty acid composition in terms of higher monounsaturated and polyunsaturated fatty acids and conjugated linoleic acid contents, a reduced atherogenic index, and a saturated-to-unsaturated ratio. Likely due to the lower level of olive by-product ingestion, only marginal differences were observed in milk fatty acid composition of cows fed OlPr compared to the control. We conclude that the use of OlLes in dairy cow diets may represent a promising strategy for improving milk quality, promoting a more circular agricultural system, reducing reliance on external feed inputs, and mitigating the environmental impact of both olive and milk production. Full article
14 pages, 5097 KiB  
Article
Pig and Cow Blood During Cold Storage in CPDA-1 Solution: Hematology and Fluid Behavior
by Ursula Windberger and Andreas Sparer
Biophysica 2025, 5(1), 3; https://doi.org/10.3390/biophysica5010003 - 21 Jan 2025
Viewed by 442
Abstract
Nature equipped red blood cells (RBCs) with diverse mechanical properties, which makes it possible to examine blood with different RBC properties (size, shape, aggregability, deformability). We investigated whether the shelf life of cow blood (stiff RBCs, low aggregability) is longer compared with pig [...] Read more.
Nature equipped red blood cells (RBCs) with diverse mechanical properties, which makes it possible to examine blood with different RBC properties (size, shape, aggregability, deformability). We investigated whether the shelf life of cow blood (stiff RBCs, low aggregability) is longer compared with pig blood (deformability/aggregability comparable to human) due to a delay in RBC clustering and decomposition. Blood was drawn from conscious pigs and cows in their familiar environment to reduce stress and stored 30 days at +7 °C. RBCs remained intact in cow samples whereas pig samples became hemolytic after day 20. White blood cells and platelets decreased with similar percentages in both species. Hematocrit (HCT) decreased due to RBC shrinking in bovine samples and due to RBC decay in porcine samples. Blood viscosity increased in both species although HCT decreased. In porcine samples, shear thinning decreased progressively, indicating a gradual loss of sample cohesion with storage. Yield stress and storage modulus decreased with hemolysis. In HCT-native cow samples, shear thinning, yield stress, and storage modulus showed high intraindividual variability, but the mean values did not change over the time course. In HCT-adjusted (38%) cow samples, solidification occurred after day 7, followed by a reduction in cohesion and shear thinning until the end of storage. Full article
(This article belongs to the Collection Feature Papers in Biophysics)
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Figure 1
<p>Change in the hemograms of porcine (n = 11) and bovine (n = 6) whole blood samples with storage time. (<b>a</b>,<b>b</b>): RBC count and Hb; (<b>c</b>,<b>d</b>): MCV and MCHC; (<b>e</b>,<b>f</b>) WBC and PLT count. Data present mean ± standard deviation of changes relative to baseline.</p>
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<p>Change in the rheological behavior and HCT of porcine (n = 11) and bovine (n = 6) whole blood samples with storage time. Rheological data present mean ± standard deviation of changes relative to baseline. (<b>a</b>,<b>b</b>): Shear viscosity at low and high shear rates. High shear rate viscosity increased continuously in all samples, but the low shear rate viscosity value peaked at day 14 in HCT-adjusted bovine samples, representing blood thickening; (<b>c</b>): shear thinning (η<sub>10</sub>/η<sub>1000</sub>) decreased in porcine and HCT-adjusted bovine samples towards the end of the observation period as an indicator of a deteriorating suspension but not in HCT-native bovine samples. The significant thickening of HCT-adjusted bovine samples is also reflected in the rise of shear thinning on day 14. Due to the species-specific difference, the HCT is displayed as absolute values. Technical problems prevented rheometry of HCT-native bovine samples on day 0. (<b>d</b>): Change in HCT in the form of absolute values.</p>
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<p>Blood smears of porcine and three selected bovine blood samples at the beginning and the end of storage. After 30 days of storage: pig 1: ghosts (red arrows) and cell debris; pig 2: crenated cells (green arrows); pig 3: ghosts. After 30 days of storage: cow 1 and 3: regular round shapes; cow 2: crenated cells. Scale bar: 20 μm.</p>
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<p>Yield points of bovine (n = 6, blue) and porcine (n = 11, green) whole blood obtained by amplitude sweep tests. (<b>a</b>,<b>b</b>): change in yield point with storage duration; the boxes represent median and interquartile range, asterisks show the mean value. (<b>c</b>,<b>d</b>): intraindividual variability of yield points during the time course; (<b>e</b>,<b>f</b>): yield point of fresh blood and aged blood on the 30th storage day: a quadratic regression curve interpolates the G′-values. The yield stress was obtained from the crossing point of the tangent that was drawn on the inflection point of this regression curve and crossed with a horizontal line through the first G′-values, which was extrapolated to the x-axis (method described in [<a href="#B12-biophysica-05-00003" class="html-bibr">12</a>]).</p>
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<p>(<b>a</b>,<b>b</b>): Frequency spectrum of G′ and G″ of cow (n = 6, blue) and pig (n = 11, green) whole blood at start and end of storage. Pig blood fluidified, as indicated by the decrease in G′, whereas the shear moduli of HCT-native bovine blood hardly altered. (<b>c</b>,<b>d</b>): The intraindividual variability of loss factor (G″/G′) during the time course. The inset in <a href="#biophysica-05-00003-f002" class="html-fig">Figure 2</a>c shows the HCT-adjusted bovine sample. (<b>e</b>,<b>f</b>): The change in loss factor with storage duration. In porcine samples, loss factor values increased beyond day 22 due to hemolysis. In HCT-native bovine samples, loss factor values showed large errors but did not change with storage time (except one outlier at +24 days, cow E). In HCT-adjusted bovine samples, the loss factor decreased transiently around day 14 and returned afterwards. Boxes represent median and interquartile range; asterisks show the mean value. Values below the torque limit of the rheometer (1 μNm) are deleted from the spectrum in <a href="#biophysica-05-00003-f005" class="html-fig">Figure 5</a>a.</p>
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<p>Blood smear from pig 1 (a different window of this smear is also shown in <a href="#biophysica-05-00003-f003" class="html-fig">Figure 3</a>) showing the clusters of cell debris and free hemoglobin.</p>
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18 pages, 1319 KiB  
Article
In a Changing World—An Economical Comparison Between Traditional and Wet-And-Drought-Resistant Grasses in Swedish Cattle Production Under Different Weather Scenarios
by Kristina Holmström, Karl-Ivar Kumm, Hans Andersson, Mikaela Jardstedt, Dannylo Sousa and Anna Hessle
Animals 2025, 15(3), 295; https://doi.org/10.3390/ani15030295 - 21 Jan 2025
Viewed by 272
Abstract
This study compared the profitability when feeding silages of different grass species in enterprises with either dairy cows, beef breed bulls, or beef suckler cows. Traditional (TR) grasses timothy and meadow fescue was compared to the alternative wet-and-drought-resistant (WD) grasses tall fescue, festulolium, [...] Read more.
This study compared the profitability when feeding silages of different grass species in enterprises with either dairy cows, beef breed bulls, or beef suckler cows. Traditional (TR) grasses timothy and meadow fescue was compared to the alternative wet-and-drought-resistant (WD) grasses tall fescue, festulolium, and reed canary grass in three different weather scenarios with either normal conditions (Ref), delayed late harvest time due to wet weather conditions (Wet), or decreased grass yield due to dry weather conditions (Dry). Contribution margin calculation was conducted for three geographical regions in Sweden. In the Ref and Wet scenarios, TR was more competitive than WD for dairy cows and beef bulls in all regions. Also in the Dry scenario, TR was more competitive than WD for dairy cows, as the lower production cost of the WD was outweighed by a lower milk yield of cows fed WD compared to cows fed TR. Contrary, for beef bulls, WD gave a higher contribution margin than TR did in the Dry scenario, where the break-even for WD being superior over TR occurred when more than every second year was dry. WD reed canary grass was always more competitive than TR and WD festulolium for beef cows. Full article
(This article belongs to the Section Animal System and Management)
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<p>Localization of three geographical regions in Sweden; the forest districts in Götaland (Gsk), the plain districts in northern Götaland (Gns), and the lower parts of Norrland (Nn) [<a href="#B25-animals-15-00295" class="html-bibr">25</a>]. Gsk and Gns are situated in the southern part with humid, warm temperate climate and Nn is situated in the north with cool summers.</p>
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<p>Basic calculation of contribution margin (CM = contribution to common cost, risk, and profit) in three different cattle production systems; (<b>a</b>) dairy cow, (<b>b</b>) beef breed bulls, and (<b>c</b>) beef cow, fed silage of traditional (TR) or wet-and-drought-resistant (WD) grasses in forest districts Gsk, plain districts Gns, and northern districts Nn in Sweden, in three scenarios: reference (Ref), wet (Wet), and dry (Dry). TR was timothy (dairy cow), meadow fescue (bull), and meadow fescue-timothy (beef cow), WD was tall fescue (dairy cow and bull), WD-f festulolium (beef cow), and WD-r reed canary grass (beef cow). Expressed as Euro per cow and year or per reared bull.</p>
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<p>Basic calculation of contribution margin (CM = contribution to common cost, risk, and profit) in three different cattle production systems; (<b>a</b>) dairy cow, (<b>b</b>) beef breed bulls, and (<b>c</b>) beef cow, fed silage of traditional (TR) or wet-and-drought-resistant (WD) grasses in forest districts Gsk, plain districts Gns, and northern districts Nn in Sweden, in three scenarios: reference (Ref), wet (Wet), and dry (Dry). TR was timothy (dairy cow), meadow fescue (bull), and meadow fescue-timothy (beef cow), WD was tall fescue (dairy cow and bull), WD-f festulolium (beef cow), and WD-r reed canary grass (beef cow). Expressed as Euro per cow and year or per reared bull.</p>
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15 pages, 283 KiB  
Article
Cow Culling Rates and Causes in 12 Pasture-Based Dairy Herds in Southern Uruguay, a Pilot Study
by Benjamín Doncel-Díaz, Santiago Fariña, Rubén D. Caffarena, Federico Giannitti and Franklin Riet-Correa
Dairy 2025, 6(1), 3; https://doi.org/10.3390/dairy6010003 - 20 Jan 2025
Viewed by 297
Abstract
The reasons for culling dairy cows in Uruguay are largely unknown. This study aimed to describe the culling rates of dairy cows and identify the causes of cow culling in 12 commercial herds in Uruguay. We conducted a prospective longitudinal observational study from [...] Read more.
The reasons for culling dairy cows in Uruguay are largely unknown. This study aimed to describe the culling rates of dairy cows and identify the causes of cow culling in 12 commercial herds in Uruguay. We conducted a prospective longitudinal observational study from June 2019 to May 2020 on 12 dairy farms stratified by herd size. Six farms with 51–199 cows, five with 200–500 cows, and one farm with more than 500 cows in the departments of Colonia and San José were included. The cows were pure Holstein and Holstein–Jersey crossbreeds. The overall dairy cow population on these 12 farms was 3126 cows (range: 74–740 cows per farm). The data were analyzed using descriptive statistics. The total annual culling rate was 23.1% (721/3126), including sales to slaughter (18.1%; 565/3126), on-farm mortality (4.5%; 141/3126), and dairy sales (0.5%; 15/3126). Cow culling for slaughter because of health (including reproductive) problems represented 70.7% (510/721) of the overall culling rate, most of which were due to reproductive failure (29.3%, 211/721), mastitis (25.9%, 187/721), poor udder conformation (6.2%, 45/721), lameness (4.6%, 33/721), and other diseases (4.7%, 34/721). Mortality represented 19.6% (141/721) of the overall culling rate. Cow culling for slaughter due to health (including reproductive) problems and mortality constituted 90.3% (651/721) of the total culled cows. In conclusion, dairy cows were culled mainly due to illnesses that lead to slaughter or death. Implementing effective measures to improve reproductive rates, reduce mastitis and lameness, and prevent other diseases, such as leukosis, paratuberculosis, and digestive disorders in the studied population would reduce cow culling, increasing cow longevity, animal welfare, and farm profitability. Full article
(This article belongs to the Section Dairy Animal Health)
25 pages, 2737 KiB  
Review
Common Biases, Difficulties, and Errors in Clinical Reasoning in Veterinary Medical Encounters with a Case Example
by Kiro Risto Petrovski and Roy Neville Kirkwood
Encyclopedia 2025, 5(1), 14; https://doi.org/10.3390/encyclopedia5010014 - 20 Jan 2025
Viewed by 533
Abstract
Clinical reasoning is an essential competence of veterinary graduands. Unfortunately, clinical reasoning and, therefore, the quality of provided veterinary medical services are prone to bias, difficulties, and errors. The literature on biases, difficulties, and errors in clinical reasoning in veterinary medical education is [...] Read more.
Clinical reasoning is an essential competence of veterinary graduands. Unfortunately, clinical reasoning and, therefore, the quality of provided veterinary medical services are prone to bias, difficulties, and errors. The literature on biases, difficulties, and errors in clinical reasoning in veterinary medical education is scarce or focused on theoretical rather than practical application. In this review, we address the practicality of learning and teaching biases, difficulties, and errors in clinical reasoning to veterinary learners utilizing a practical example of a cow with a prolapsed uterus complicated by hypocalcemia and hypomagnesemia. Learners should be guided through all of the stages of clinical reasoning as much as possible under direct supervision. The common clinical biases, difficulties, or errors in veterinary medical encounters may differ between stages of development of the learner, with more difficulties occurring in earlier stages (Observer, Reporter, ±Interpreter) but more heuristic biases occurring at later stages (Manager, Educator, ±Interpreter). However, clinical errors may occur at any learner development stage. Therefore, remediation of clinical biases, difficulties, and errors in veterinary medical encounters should use strategies that are tailored to the level of development of the learner, but also to the specific encounter (e.g., client, patient, and context). Full article
(This article belongs to the Section Biology & Life Sciences)
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<p>The prevention of veterinary medical clinical reasoning bias, difficulty, or errors is possible by being aware of them, as well as having awareness of strategies that can minimize or prevent them.</p>
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<p>Factors affecting clinical reasoning and outcomes in veterinary clinical encounters.</p>
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<p>Clinical biases, difficulties, and errors in clinical reasoning should be prevented and remediated early and “on-the-go” using clinical teaching models (e.g., the Five Microskills [<a href="#B62-encyclopedia-05-00014" class="html-bibr">62</a>]) and throughout the learners’ development (ORIME model [<a href="#B54-encyclopedia-05-00014" class="html-bibr">54</a>]), possible only with direct and immediate supervision.</p>
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<p>Difficulties and errors in clinical reasoning (grey-shaded shapes) related to the veterinary medical clinical reasoning cycle (golden-shaded shapes). The centrally positioned difficulties and errors apply to adjacent, multiple stages of the clinical reasoning cycle. Peripherally positioned difficulties and errors apply only to the adjacent stage of the clinical reasoning cycle.</p>
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15 pages, 255 KiB  
Article
Prepartum Dietary Supplementation of Potassium Humate Improves Postpartum Lactation Performance, Metabolic Profile of Multiparous Cows, and Immune Response of Their Calves
by Cangir Uyarlar, Abdur Rahman, Eyup Eren Gultepe, İbrahim Sadi Cetingul, Muhammad Uzair Akhtar and Ismail Bayram
Animals 2025, 15(2), 279; https://doi.org/10.3390/ani15020279 - 20 Jan 2025
Viewed by 441
Abstract
This research was carried out to determine the effects of potassium humate on the lactation performance and metabolic parameters of dairy cows during the transition period. Potassium humate was added to the concentrate feed at the following levels: (a) control (0%), (b) 0.5%, [...] Read more.
This research was carried out to determine the effects of potassium humate on the lactation performance and metabolic parameters of dairy cows during the transition period. Potassium humate was added to the concentrate feed at the following levels: (a) control (0%), (b) 0.5%, (c) 1%, (d) 1.5%, and (e) 2% humas, during the dry period from −60 to 0 days until calving. The results indicated that the total milk yield after 305 days was higher in the 0.5% group than in the 2% humic acid group. The average daily milk yield from lactation was also greater in the 0.5% group than in the 2% humic acid group. In terms of metabolic health and blood biochemistry, lymphocytes, neutrophils, monocytes, NEFAs, and BHBA were different among the treatment groups. No effects were detected on the blood physiology parameters of the calves. The IgG concentration in the colostrum and serum of calves on day 1 and 2 were higher in the 0.5% and 1% humic acid groups, respectively, than in the other groups. Overall, adding humic acid, especially at the dose of 0.5%, to the concentrate feed of dairy cows during the dry period resulted in an increased postpartum milk yield for the cows and increased serum IgG in both the cows and calves, with decreased NEFAs on the calving day and decreased postpartum BHBA for cows. Full article
(This article belongs to the Section Animal Nutrition)
24 pages, 2364 KiB  
Article
Characterization of Cantal and Salers Protected Designation of Origin Cheeses Based on Sensory Analysis, Physicochemical Characteristics and Volatile Compounds
by Cécile Bord, Louis Lenoir, Julie Benoit, Delphine Guérinon, Gilles Dechambre, Christophe Chassard and Christian Coelho
Appl. Sci. 2025, 15(2), 961; https://doi.org/10.3390/app15020961 - 19 Jan 2025
Viewed by 448
Abstract
In this work, the aim was to characterize and differentiate two Protected Designation of Origin (PDO) semi-hard French cheese categories (Salers and Cantal cheeses) by focusing on their sensory, biochemical and volatile characteristics. A total of twelve cheeses, including six Cantal and six [...] Read more.
In this work, the aim was to characterize and differentiate two Protected Designation of Origin (PDO) semi-hard French cheese categories (Salers and Cantal cheeses) by focusing on their sensory, biochemical and volatile characteristics. A total of twelve cheeses, including six Cantal and six Salers cheeses, were analyzed. The provenance of milk from two dairy cow breeds (Salers and non-Salers) was discussed sensorially and chemically for each cheese sample and for each cheese category. Despite very few significant differences in biochemical parameters, differences were observed concerning the volatile composition and sensory profiles between each cheese category. Salers cheeses were clearly differentiated by their appearance and their more intense aromatic characteristics compared to Cantal cheeses. A large number of volatile compounds (VOCs) belonging to acids, alcohols, aldehydes, ketones and esters were detected in each cheese category (n = 78). The relative quantity of each compound varied depending on the cheese category but was lowly impacted by the origin of the breed’s milk. The results suggest that the provenance of milk (Salers vs. non-Salers) have a low impact on the chemical and sensory differentiation of cheeses regardless of the PDO cheese category. However, the PDO cheese categories (Salers vs. Cantal) were clearly differentiated by their volatile and sensory characteristics. The PDO Salers cheeses presented the highest flavor variability compared to the PDO Cantal cheeses due to compounds belonging to alcohols, acids, aldehydes and ester conferring ammonia, vegetal and animal flavors compared to the PDO Cantal cheeses that were perceived as more pungent and bitter. Full article
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<p>Average percentages of classes of volatile compounds identified for each condition of cheese category: (<b>A</b>) Cantal cheeses vs. (<b>B</b>) Salers cheeses and cow breeds’ milk: (<b>C</b>) OB_M vs. (<b>D</b>) Salers_M (OB_M = other breeds’ milk; Salers_M = <span class="html-italic">Salers</span> milk).</p>
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<p>A principal component analysis performed on the volatile compounds and on the cheese samples (<span class="html-italic">n</span> = 24). A correlation circle from the PCA (F1-F2) realized on volatile compounds was used as the loading for the PCA (<b>A</b>). Score plots or cheese sample variables were represented with the 95% confidence ellipse for each milk’s origin (<b>B</b>) and cheese category (<b>C</b>) (OB_M = other breeds’ milk; Salers_M = <span class="html-italic">Salers</span> milk).</p>
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<p>Principal component analysis performed on sensory data and on cheese samples (<span class="html-italic">n</span> = 36). Correlation circle from PCA (F1–F2) performed on significant sensory attributes used as loadings of PCA (<b>A</b>). Score plots or cheese sample variables were represented with 95% confidence ellipse for each milk’s origin (<b>B</b>) and cheese category (<b>C</b>) (OB_M = other breeds’ milk; Salers_M = <span class="html-italic">Salers</span> milk).</p>
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<p>Principal component analysis performed on sensory data and on cheese samples (<span class="html-italic">n</span> = 36). Correlation circle from PCA (F1–F2) performed on significant sensory attributes used as loadings of PCA (<b>A</b>). Score plots or cheese sample variables were represented with 95% confidence ellipse for each milk’s origin (<b>B</b>) and cheese category (<b>C</b>) (OB_M = other breeds’ milk; Salers_M = <span class="html-italic">Salers</span> milk).</p>
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<p>Partial least squares regression loading for t1 and t2 performed on significant volatile compounds (X; <span class="html-italic">n</span> = 45; red point) and sensory flavor attributes (Y; <span class="html-italic">n</span> = 11; blue point) and cheese samples (green capital letters, <span class="html-italic">n</span> = 12). Blue circle includes samples from Salers category and orange circle includes samples from Cantal category. Number for variables (VOCs) refers to opposite table.</p>
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20 pages, 5288 KiB  
Article
A Study on Multi-Scale Behavior Recognition of Dairy Cows in Complex Background Based on Improved YOLOv5
by Zheying Zong, Zeyu Ban, Chunguang Wang, Shuai Wang, Wenbo Yuan, Chunhui Zhang, Lide Su and Ze Yuan
Agriculture 2025, 15(2), 213; https://doi.org/10.3390/agriculture15020213 - 19 Jan 2025
Viewed by 365
Abstract
The daily behaviors of dairy cows, including standing, drinking, eating, and lying down, are closely associated with their physical health. Efficient and accurate recognition of dairy cow behaviors is crucial for timely monitoring of their health status and enhancing the economic efficiency of [...] Read more.
The daily behaviors of dairy cows, including standing, drinking, eating, and lying down, are closely associated with their physical health. Efficient and accurate recognition of dairy cow behaviors is crucial for timely monitoring of their health status and enhancing the economic efficiency of farms. To address the challenges posed by complex scenarios and significant variations in target scales in dairy cow behavior recognition within group farming environments, this study proposes an enhanced recognition method based on YOLOv5. Four Shuffle Attention (SA) modules are integrated into the upsampling and downsampling processes of the YOLOv5 model’s neck network to enhance deep feature extraction of small-scale cow targets and focus on feature information, while maintaining network complexity and real-time performance. The C3 module of the model was enhanced by incorporating Deformable convolution (DCNv3), which improves the accuracy of cow behavior characteristic identification. Finally, the original detection head was replaced with a Dynamic Detection Head (DyHead) to improve the efficiency and accuracy of cow behavior detection across different scales in complex environments. An experimental dataset comprising complex backgrounds, multiple behavior categories, and multi-scale targets was constructed for comprehensive validation. The experimental results demonstrate that the improved YOLOv5 model achieved a mean Average Precision (mAP) of 97.7%, representing a 3.7% improvement over the original YOLOv5 model. Moreover, it outperformed comparison models, including YOLOv4, YOLOv3, and Faster R-CNN, in complex background scenarios, multi-scale behavior detection, and behavior type discrimination. Ablation experiments further validate the effectiveness of the SA, DCNv3, and DyHead modules. The research findings offer a valuable reference for real-time monitoring of cow behavior in complex environments throughout the day. Full article
(This article belongs to the Section Digital Agriculture)
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<p>Plan of the dairy cow test site. Note: Camera 1 (Dahua P40A20-WT-1) captures the outdoor activity areas I and II of the cows; camera 2 (Xiaomi Smart Camera 3 Pan and Tilt Version) captures the indoor feeding area of the cows.</p>
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<p>Dairy cow test site. (<b>a</b>) Diagram of camera in cow feeding area; (<b>b</b>) diagram of cow activity area camera.</p>
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<p>Example of a cow behavior shot. (<b>a</b>) Surveillance video scene; (<b>b</b>) example of cow behavior.</p>
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<p>Behavioral labeling analysis of cows.</p>
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<p>Improved network structure of YOLOv5 model.</p>
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<p>Shuffle Attention module structure.</p>
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<p>Principle of transformable convolution. (<b>a</b>) Conventional convolution; (<b>b</b>) deformable convolution; (<b>c</b>) special deformable convolution; (<b>d</b>) special deformable convolution. Green indicates the sampling points of the regular convolution, and blue indicates the dynamically sampled points of the deformable convolution.</p>
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<p>DCNv3 convolutional implementation process.</p>
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<p>DyHead structure.</p>
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<p>Loss function varies during training.</p>
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<p>Model accuracy changes during training.</p>
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<p>Improved recognition effect of the YOLOv5 model. (<b>a</b>) Standing behavior of outdoor dairy cows; (<b>b</b>) drinking behavior of outdoor dairy cows; (<b>c</b>) lying behavior of outdoor dairy cows; (<b>d</b>) feeding behavior of indoor dairy cows.</p>
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<p>Visual comparison of original model and attention model. (<b>a</b>) No attention mechanism; (<b>b</b>) SE attention mechanism; (<b>c</b>) EMA attention mechanism; (<b>d</b>) SA attention mechanism. Red indicates the highest attention weight; yellow indicates a medium-high attention weight; green indicates a medium attention weight; blue indicates a low attention weight; dark blue indicates the lowest attention weight.</p>
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17 pages, 7221 KiB  
Article
Effects of Replacing Inorganic Sources of Copper, Manganese, and Zinc with Different Organic Forms on Mineral Status, Immune Biomarkers, and Lameness of Lactating Cows
by Manqian Cha, Xingjun Ma, Yunlong Liu, Shengyang Xu, Qiyu Diao and Yan Tu
Animals 2025, 15(2), 271; https://doi.org/10.3390/ani15020271 - 19 Jan 2025
Viewed by 252
Abstract
(Objectives) The objectives of this study were to evaluate the effect of half-replacement of the supplementary sulfate sources of Cu, Mn, and Zn with methionine-hydroxy-analog-chelated (MHAC) mineral or amino-acid-complexed (AAC) mineral forms in diets on the mineral status, blood immune biomarkers, and lameness [...] Read more.
(Objectives) The objectives of this study were to evaluate the effect of half-replacement of the supplementary sulfate sources of Cu, Mn, and Zn with methionine-hydroxy-analog-chelated (MHAC) mineral or amino-acid-complexed (AAC) mineral forms in diets on the mineral status, blood immune biomarkers, and lameness of lactating cows. (Methods) Sixty multiparous Holstein cows (158 ± 26 days in milk; body weight: 665 ± 52 kg; milk yield: 32 ± 7 kg/day) were randomly assigned into one of three dietary treatments (n = 20 per group): (1) MHAC: 50% replacement of sulfate minerals with MHAC forms. (2) AAC: 50% replacement of sulfate minerals with AAC forms. (3) S: 100% sulfate minerals (control). Their Cu, Mn, and Zn concentrations, blood immune biomarkers, and lameness were measured monthly. Repeated-measure mixed models were used to evaluate the effects on trace mineral status over time. As the responses with the MHAC and AAC forms were similar, the treatments were also analyzed as organic trace minerals (OTMs, combining the MHAC and AAC groups, n = 40) versus inorganic trace minerals (ITMs, the S group, n = 20). (Results) Cows supplemented with OTMs had higher concentrations of Cu and Mn in their serum (p ≤ 0.05), a higher hoof hardness (p ≤ 0.05), and a lower incidence of lameness compared to those with ITMs on d 90. There were no statistical differences (p > 0.10) in the concentrations of IgA, IgG, or ceruloplasmin, but there were significant differences (p = 0.03) in the concentrations of IgM in the serum as fixed effects of the diet treatments during the whole trial. On d 30 and 90, the serum IgA concentrations of the cows supplemented with OTMs tended to be higher (0.05 < p ≤ 0.10) than those in the cows supplemented with ITMs. (Conclusions) The half-replacement strategy showed that the MHAC and AAC sources of Cu, Mn, and Zn additives had similar effects on the production performance, blood immune biomarkers, and lameness of the lactating cows. The long-term replacement strategy with OTMs led to the enhancement of the trace mineral concentrations in their body fluids, blood immune biomarkers, and hoof health. Full article
(This article belongs to the Section Cattle)
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<p>Schematic representation of experimental treatments. Treatments included 3 sources of supplemental Cu, Mn, and Zn. MHAC = replacing 50% of the sulfate form with 50% organic salts of trace minerals in methionine hydroxyl analog chelate form; AAC = replacing 50% of the sulfate form with 50% organic salts of trace minerals in amino acid complex form; S = 100% inorganic salts of trace minerals in sulfate form.</p>
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<p>Effects of trace mineral sources on DMI (<b>A</b>), milk yield (<b>B</b>), milk fat content (<b>C</b>), somatic cell count (<b>D</b>), milk solids (<b>E</b>), milk protein (<b>F</b>), milk lactose (<b>G</b>), and milk nitrogen (<b>H</b>) of lactating cows. The DMI was shown as the mean of the group DMI ± SD weekly. Probability values for independent variables of interest: Trt = fixed effect of diet treatments; Time = fixed effect of sampling time; Trt × Time = interaction effect of diet treatments and sampling time. Within one day or one sampling, significant differences (<span class="html-italic">p</span> ≤ 0.05) were represented as follows: * ITM vs. OTM groups (contrast differences between inorganic trace mineral treatment (S, <span class="html-italic">n</span> = 20) and organic trace mineral treatments (MHAC and AAC, <span class="html-italic">n</span> = 40)); meanwhile, tendencies (0.05 &lt; <span class="html-italic">p</span> ≤ 0.10) were represented by ‡.</p>
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<p>Effects of trace mineral sources on the concentration of Cu (<b>A</b>), Mn (<b>B</b>), and Zn (<b>C</b>) in the serum of lactating cows. Probability values for independent variables of interest: Trt = fixed effect of diet treatments; Time = fixed effect of sampling time; Trt × Time = interaction effect of diet treatments and sampling time. Within one day or one sampling, significant differences (<span class="html-italic">p</span> ≤ 0.05) were represented as follows: * ITM vs. OTM groups (contrast differences between inorganic trace mineral treatment (S, <span class="html-italic">n</span> = 20) and organic trace mineral treatments (MHAC and AAC, <span class="html-italic">n</span> = 40)); meanwhile, tendencies (0.05 &lt; <span class="html-italic">p</span> ≤ 0.10) were represented by ‡. # = MHAC vs. S; † = AAC vs. S.</p>
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<p>Effect of replacing inorganic trace minerals with organic trace minerals on blood IgA (<b>A</b>), IgG (<b>B</b>), IgM (<b>C</b>), ceruloplasmin (<b>D</b>), IL-4 (<b>E</b>), IL-6 (<b>F</b>), TNF-α (<b>G</b>), and T-AOC (<b>H</b>) of lactating cows. Probability values for independent variables of interest: Trt = fixed effect of diet treatments; Time = fixed effect of sampling time; Trt × Time = interaction effect of diet treatments and sampling time. Within one day or one sampling, significant differences (<span class="html-italic">p</span> ≤ 0.05) were represented as follows: * ITM vs. OTM groups (contrast differences between inorganic trace mineral treatment (S, <span class="html-italic">n</span> = 20) and organic trace mineral treatments (MHAC and AAC, <span class="html-italic">n</span> = 40)); meanwhile, tendencies (0.05 &lt; <span class="html-italic">p</span> ≤ 0.10) were represented by ‡. # = MHAC vs. S; † = AAC vs. S.</p>
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<p>Effects of trace mineral sources on hoof hardness (<b>A</b>) and incidence of lameness (<b>B</b>) in lactating cows. Incidence of lameness (%) was calculated according to the number of lame cows divided by the total numbers in each group (<span class="html-italic">n</span> = 20). Cows were considered lame when their score was ≥3. Probability values for independent variables of interest: Trt = fixed effect of diet treatments; Time = fixed effect of sampling time; Trt × Time = interaction effect of diet treatments and sampling time. Within one day or one sampling, significant differences (<span class="html-italic">p</span> ≤ 0.05) were represented as follows: * ITM vs. OTM groups (contrast differences between inorganic trace mineral treatment (S, <span class="html-italic">n</span> = 20) and organic trace mineral treatments (MHAC and AAC, <span class="html-italic">n</span> = 40. # = MHAC vs. S; † = AAC vs. S.</p>
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20 pages, 998 KiB  
Article
Effects of Dietary Starch Concentration on Milk Production, Nutrient Digestibility, and Methane Emissions in Mid-Lactation Dairy Cows
by Rebecca L. Culbertson, Fabian A. Gutiérrez-Oviedo, Pinar Uzun, Nirosh Seneviratne, Ananda B. P. Fontoura, Brianna K. Yau, Josie L. Judge, Amanda N. Davis, Diana C. Reyes and Joseph W. McFadden
Agriculture 2025, 15(2), 211; https://doi.org/10.3390/agriculture15020211 - 19 Jan 2025
Viewed by 404
Abstract
Our objective was to evaluate the effects of dietary starch concentration on milk production, nutrient digestibility, and methane emissions in lactating dairy cows. Thirty mid-lactation cows were randomly assigned to either a high-neutral-detergent-fiber, low-starch diet (LS; 20.2% starch) or a low-neutral-detergent-fiber, high-starch diet [...] Read more.
Our objective was to evaluate the effects of dietary starch concentration on milk production, nutrient digestibility, and methane emissions in lactating dairy cows. Thirty mid-lactation cows were randomly assigned to either a high-neutral-detergent-fiber, low-starch diet (LS; 20.2% starch) or a low-neutral-detergent-fiber, high-starch diet (HS; 25.2% starch) following a 3-week acclimation. The study lasted 8 weeks, with milk sampling and gas measurements conducted weekly during acclimation and at weeks 2, 4, 6, and 8. Blood and fecal samples were collected during acclimation and week 8. Compared with LS cows, HS cows produced 1.9 kg/d more energy-corrected milk (4.45% increase), with higher yields of true protein (+0.13 kg/day), lactose (+0.10 kg/day), and total solids (+0.24 kg/day). Dry matter and organic matter digestibility was 4.2 and 4.3% higher, respectively, in the HS group. The milk fatty acid (FA) profile differed, with LS cows having greater mixed FA content and HS cows showing higher de novo FA content and yield. Although methane production tended to be higher in HS cows (+25 g/day), methane yield decreased by 8.8%. Overall, the HS diet improved milk production, nutrient digestibility, and environmental efficiency by reducing methane yield in dairy cows. Full article
(This article belongs to the Section Farm Animal Production)
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<p>Effects of dietary starch concentration on (<b>A</b>) milk yield (MY), (<b>B</b>) dry matter intake (DMI), and (<b>C</b>) efficiency (MY/DMI). * Indicates a significant interaction (<span class="html-italic">p</span> ≤ 0.05) between week and treatment.</p>
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<p>Effects of dietary starch concentration on (<b>A</b>) methane production (g CH<sub>4</sub>/d) and (<b>B</b>) methane yield (g CH<sub>4</sub>/kg DMI). * Indicates a significant interaction (<span class="html-italic">p</span> ≤ 0.05) between week and treatment.</p>
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