scholarly journals Short Communication Sequence analysis of the regulatory region of the TNF-RII gene in Polish Holstein-Friesian cows

2013 ◽  
Vol 12 (2) ◽  
pp. 1028-1034 ◽  
Author(s):  
A. Stachura ◽  
E. Kaczmarczyk ◽  
B. Bojarojć-Nosowicz
2019 ◽  
Vol 67 (2) ◽  
pp. 241-245
Author(s):  
Baukje G. Andela ◽  
Frank J. C. M. Van Eerdenburg ◽  
Ali Choukeir ◽  
Dávid Buják ◽  
Zoltán Szelényi ◽  
...  

Activities of alkaline phosphatase, aspartate aminotransferase and alanine aminotransferase, and concentrations of serum metabolites [beta-hydroxybutyrate (BHB) and non-esterified fatty acids (NEFA)] of primiparous (n = 83) and multiparous (n = 213) Holstein cows were studied as possible predictors of retained fetal membranes (RFM), grade 2 clinical metritis (CM) and clinical endometritis (CEM). A logistic regression model was used to calculate odds ratios (OR) for the prevalence of CM diagnosed between 0–5, 6–10 and 11–20 days in milk (DIM) and for the prevalence of CEM diagnosed between 22–28 and 42–49 DIM. The activities of the examined serum enzymes did not show significant associations either with CM or with CEM. For NEFA sampled on days 0 and 5, an OR of 2.38 for CM 0–20 DIM and an OR of 2.58 for CM 11–20 DIM was found. For BHB sampled on days 0 and 5, an OR of 8.20 for CEM 22–28 and 42–49 DIM and an OR of 1.98 for CM 6–10 DIM were found. The prevalence of RFM was higher in ≥ 4 parity cows compared to primiparous cows (46.3% vs. 26.5%). BHB and NEFA levels measured between 0 and 5 DIM could have a predictive ability for postpartum uterine disorders such as RFM, CM and CEM.


Animals ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 721
Author(s):  
Krzysztof Adamczyk ◽  
Wilhelm Grzesiak ◽  
Daniel Zaborski

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.


Animals ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 50
Author(s):  
Jennifer Salau ◽  
Jan Henning Haas ◽  
Wolfgang Junge ◽  
Georg Thaller

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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