Mammary Gland Health and Identification of β-defensin Gene of Holstein-Friesian Cows

Parturition and post partum period are susceptible for antioxidative/oxidative imbalance as well as inflammatory processes related to either uterus or mammary gland. Fifteen Holstein-Friesian cows were used to examine the relationship between the antioxidant enzyme glutathione peroxidase (GSH-Px) and inflammatory enzyme N-acetyl-glucosaminidase (NAGase) in colostrum and milk during 12 days after parturition. Samples from each udder quarter were collected immediately after parturition, after 24 h and 48 h as well as after 6 and 12 days post partum. The activity of GSH-Px was measured spectrophotometrically and NAGase spectrofluorimetrically. NAGase activity generally remained constant during the examined period confirming no inflammation and healthy udder. Moreover, no significant differences in the activity among quarters were detected. The activity of GSH-Px rose significantly (P < 0.05) in the examined period suggesting the dynamic balance of antioxidative defence. No differences among quarters confirmed that in healthy quarters metabolic processes are on a similar level but positive correlations between the antioxidative and inflammatory enzyme in quarters and with regard to time may suggest a possible relationship during inflammation.

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The polymorphism in the ?4-defensin gene and its association with production and somatic cell count in Holstein-Friesian cows

Journal of Animal Breeding and Genetics ◽

10.1111/j.1439-0388.2007.00649.x ◽

2007 ◽

Vol 124 (3) ◽

pp. 150-156 ◽

Cited By ~ 17

Author(s):

E. Bagnicka ◽

N. Strzałkowska ◽

K. Flisikowski ◽

T. Szreder ◽

A. Jóźwik ◽

...

Keyword(s):

Somatic Cell ◽

Cell Count ◽

Somatic Cell Count ◽

Defensin Gene ◽

Holstein Friesian ◽

Friesian Cows

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Activities of N-acetyl-β-d-glucosaminidase and glutathione peroxidase in bovine colostrum and milk

Czech Journal of Animal Science ◽

10.17221/1716-cjas ◽

2010 ◽

Vol 55 (No. 11) ◽

pp. 488-495 ◽

Cited By ~ 1

Author(s):

M. Kankofer ◽

E. Albera ◽

M. Różańska-Boczula

Keyword(s):

Mammary Gland ◽

Glutathione Peroxidase ◽

Antioxidant Enzyme ◽

Post Partum ◽

Dynamic Balance ◽

Holstein Friesian ◽

Inflammatory Processes ◽

Positive Correlations ◽

The Relationship ◽

Friesian Cows

Parturition and post partum period are susceptible for antioxidative/oxidative imbalance as well as inflammatory processes related to either uterus or mammary gland. Fifteen Holstein-Friesian cows were used to examine the relationship between the antioxidant enzyme glutathione peroxidase (GSH-Px) and inflammatory enzyme N-acetyl-glucosaminidase (NAGase) in colostrum and milk during 12 days after parturition. Samples from each udder quarter were collected immediately after parturition, after 24 h and 48 h as well as after 6 and 12 days post partum. The activity of GSH-Px was measured spectrophotometrically and NAGase spectrofluorimetrically. NAGase activity generally remained constant during the examined period confirming no inflammation and healthy udder. Moreover, no significant differences in the activity among quarters were detected. The activity of GSH-Px rose significantly (P < 0.05) in the examined period suggesting the dynamic balance of antioxidative defence. No differences among quarters confirmed that in healthy quarters metabolic processes are on a similar level but positive correlations between the antioxidative and inflammatory enzyme in quarters and with regard to time may suggest a possible relationship during inflammation.

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The efficiency of amino acid utilisation by the mammary gland of holstein friesian cows fed three levels of dietary crude protein

Proceedings of the British Society of Animal Production (1972) ◽

10.1017/s0308229600023345 ◽

1993 ◽

Vol 1993 ◽

pp. 3-3

Author(s):

J A Metcalf ◽

D Wray-Cahen ◽

B J Bequette ◽

F R C Backwell ◽

D J Humphries ◽

...

Keyword(s):

Mammary Gland ◽

Crude Protein ◽

Milk Protein ◽

Sufficient Information ◽

Dietary Crude Protein ◽

Milk Output ◽

Milk Protein Content ◽

Holstein Friesian ◽

Different Levels ◽

Friesian Cows

Although milk output is known to be affected by dietary manipulation, studies have so far failed to provide sufficient information to successfully predict dietary effects on both milk protein content and yield (Thomas 1983). In a further effort to determine the metabolic control of milk protein synthesis three diets containing different levels of crude protein were fed to four Holstein-Friesian cows in early- to mid-lactation, and metabolite flow across the mammary gland was measured in relation to milk protein production.

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The effects of monensin on milk production and levels of metabolites in blood and rumen fluid of Holstein-Friesian cows in early lactation

Australian Journal of Experimental Agriculture ◽

10.1071/ea99033 ◽

1999 ◽

Vol 39 (8) ◽

pp. 933 ◽

Cited By ~ 7

Author(s):

B. C. Granzin ◽

G. McL. Dryden

Keyword(s):

Mammary Gland ◽

Milk Production ◽

Milk Yield ◽

Plasma Glucose ◽

Feed Intake ◽

Milk Fat ◽

Early Lactation ◽

Genetic Merit ◽

Holstein Friesian ◽

Friesian Cows

Summary. Monensin was fed to Holstein–Friesian cows in early lactation to study its effects on concentrations of blood metabolites, feed intake, body condition and milk production. In the first experiment, 18 Holstein–Friesian cows were randomly stratified into 6 similar groups of cows based on parity and previous milk yield. Cows within these groups were assigned randomly to be fed either 0, 150 or 300 mg of monensin per day. Monensin was fed as a component of a 1 kg grain supplement at 0600 h daily from 14 days prepartum to 84 days postpartum. Mean milk yields (kg/day) of cows supplemented with monensin at 150 mg/day (23.0) and 300 mg/day (23.7) were significantly higher (P<0.05) than those of unsupplemented cows (21.1). Milk fat and protein contents were not affected by monensin feeding but daily yields of milk fat and protein differed significantly (P<0.05) between treatments. Monensin supplemented at 300 mg per day significantly (P<0.05) reduced the molar proportion of rumen acetate and increased the molar proportion of rumen propionate. Monensin fed at either 150 or 300 mg/day significantly (P<0.05) increased the ratio of plasma glucose: β-hydroxybutyrate (BHB), but had no effect on concentrations of blood acetoacetate, serum nonesterified fatty acids (NEFA), plasma glucose or BHB. Monensin had no effect on liveweight change or condition score. In the second experiment, 12 Holstein–Friesian cows in early lactation were blocked on parity into 3 groups of 4 cows, and 2 cows within each block were of either high, or low genetic merit. Monensin (none or 320 mg per day) and genetic merit were assigned as a 2 by 2 factorial arrangement of treatments. Monensin supplementation commenced 28 days prepartum and ended 56 days postpartum. Monensin caused a significant (P<0.05) increase in feed intake (21.6 v. 23.2 kg/day) and significantly reduced plasma BHB concentration (64.0 v. 6.7 mg/dL). Cows with a higher genetic merit had a significantly (P<0.05) higher milk yield (27.2 kg/day) compared to cows of low genetic merit (26.3 kg/day). Low genetic merit cows fed monensin had significantly (P<0.05) lower daily milk fat yield, lower serum NEFA concentration and higher plasma glucose concentration compared to low genetic merit cows not fed monensin, or high genetic merit cows. Serum insulin and bovine somatotrophin concentrations were unaffected by the treatments. The results of these experiments suggest that with Holstein–Friesian cows in early lactation, the increase in hepatic propionate supply caused by monensin supplementation increases glucose synthesis and subsequent milk production when the requirements of the mammary gland for glucose are not otherwise being met. If the glucose requirements of the mammary gland are being satisfied, monensin decreases ketogenesis and the mobilisation of adipose tissue. Monensin had a positive effect on intake in this study.

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Associations between Mammary Gland Echotexture and Milk Composition in Cows

Animals ◽

10.3390/ani10112005 ◽

2020 ◽

Vol 10 (11) ◽

pp. 2005

Author(s):

Tomasz Schwarz ◽

Nelia Scheeres ◽

Martyna M. Małopolska ◽

Maciej Murawski ◽

Tristan D. Agustin ◽

...

Keyword(s):

Mammary Gland ◽

Crude Protein ◽

Freezing Point ◽

Milk Composition ◽

Computer Assisted ◽

Freezing Point Depression ◽

Milk Samples ◽

Holstein Friesian ◽

Citric Acid Content ◽

Friesian Cows

Thirty clinically healthy Holstein-Friesian cows underwent twice daily machine milking and ultrasonographic examinations of the udder just prior to and after milking. Digital ultrasonographic images of each udder quarter were subjected to computer-assisted echotextural analyses to obtain mean numerical pixel values (NPVs) and pixel heterogeneity (PSD) of the mammary gland parenchyma. The average milk yield and pH were higher (p < 0.05) in the morning, whereas crude fat, total solids, solids non-fat and citric acid content were higher (p < 0.05) during the evening milking period. Mean NPVs and PSDs of the mammary gland parenchyma were greater (p < 0.05) after than before milking. There were significant correlations among echotextural characteristics of the udder and protein percentage, lactose content and freezing point depression determined in the milk samples collected in the morning and crude protein, casein, lactose and solids non-fat in the evening. Our results can be interpreted to suggest that computerized analysis of the mammary gland ultrasonograms has the makings of a technique for estimating non-fat milk constituents in cows. However, future validating studies are necessary before this method can be employed in commercial settings and research. Moreover, significant inter-quarter differences in udder echogenicity may necessitate further echotextural studies of separate quarters.

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The Use of Artificial Neural Networks and a General Discriminant Analysis for Predicting Culling Reasons in Holstein-Friesian Cows Based on First-Lactation Performance Records

Animals ◽

10.3390/ani11030721 ◽

2021 ◽

Vol 11 (3) ◽

pp. 721

Author(s):

Krzysztof Adamczyk ◽

Wilhelm Grzesiak ◽

Daniel Zaborski

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Discriminant Analysis ◽

Multilayer Perceptrons ◽

Correct Classification ◽

Correct Classification Rate ◽

Classification Rate ◽

Holstein Friesian ◽

Artificial Neural ◽

Friesian Cows

The aim of the present study was to verify whether artificial neural networks (ANN) may be an effective tool for predicting the culling reasons in cows based on routinely collected first-lactation records. Data on Holstein-Friesian cows culled in Poland between 2017 and 2018 were used in the present study. A general discriminant analysis (GDA) was applied as a reference method for ANN. Considering all predictive performance measures, ANN were the most effective in predicting the culling of cows due to old age (99.76–99.88% of correctly classified cases). In addition, a very high correct classification rate (99.24–99.98%) was obtained for culling the animals due to reproductive problems. It is significant because infertility is one of the conditions that are the most difficult to eliminate in dairy herds. The correct classification rate for individual culling reasons obtained with GDA (0.00–97.63%) was, in general, lower than that for multilayer perceptrons (MLP). The obtained results indicated that, in order to effectively predict the previously mentioned culling reasons, the following first-lactation parameters should be used: calving age, calving difficulty, and the characteristics of the lactation curve based on Wood’s model parameters.

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Determination of Body Parts in Holstein Friesian Cows Comparing Neural Networks and k Nearest Neighbour Classification

Animals ◽

10.3390/ani11010050 ◽

2020 ◽

Vol 11 (1) ◽

pp. 50

Author(s):

Jennifer Salau ◽

Jan Henning Haas ◽

Wolfgang Junge ◽

Georg Thaller

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Body Parts ◽

Nearest Neighbour ◽

Data Set ◽

3D Data ◽

Holstein Friesian ◽

Knn Classification ◽

Friesian Cows

Machine learning methods have become increasingly important in animal science, and the success of an automated application using machine learning often depends on the right choice of method for the respective problem and data set. The recognition of objects in 3D data is still a widely studied topic and especially challenging when it comes to the partition of objects into predefined segments. In this study, two machine learning approaches were utilized for the recognition of body parts of dairy cows from 3D point clouds, i.e., sets of data points in space. The low cost off-the-shelf depth sensor Microsoft Kinect V1 has been used in various studies related to dairy cows. The 3D data were gathered from a multi-Kinect recording unit which was designed to record Holstein Friesian cows from both sides in free walking from three different camera positions. For the determination of the body parts head, rump, back, legs and udder, five properties of the pixels in the depth maps (row index, column index, depth value, variance, mean curvature) were used as features in the training data set. For each camera positions, a k nearest neighbour classifier and a neural network were trained and compared afterwards. Both methods showed small Hamming losses (between 0.007 and 0.027 for k nearest neighbour (kNN) classification and between 0.045 and 0.079 for neural networks) and could be considered successful regarding the classification of pixel to body parts. However, the kNN classifier was superior, reaching overall accuracies 0.888 to 0.976 varying with the camera position. Precision and recall values associated with individual body parts ranged from 0.84 to 1 and from 0.83 to 1, respectively. Once trained, kNN classification is at runtime prone to higher costs in terms of computational time and memory compared to the neural networks. The cost vs. accuracy ratio for each methodology needs to be taken into account in the decision of which method should be implemented in the application.

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