Case report: Investigation of genetic mutations in a case of schistosomus reflexus in a Holstein dairy cattle fetus in Korea

Schistosomus reflexus (SR) is one of the most common congenital anomalies found in cases of cattle dystocia; this disorder occurs mostly in cattle. Congenital anomalies such as SR are caused by various genetic and environmental factors, but no specific cause has been elucidated for SR. This study reports a case of SR in a Holstein dairy cattle fetus with congenital anomalies in Korea. Grossly, a distinct spine curvature was observed between the thoracic and lumbar vertebrae, accompanied by a consequential malformation from the sacrum to the occipital bone. Furthermore, the thoracic and abdominal organs were exposed. In computed tomography (CT) images, mild and severe kyphoscoliosis was observed in T1~11 and L1~6, respectively. Additionally, vertebral dysplasia was observed in S1~5 and Cd 1~5. To pinpoint the causal genes and mutations, we leveraged a custom 50K Hanwoo SNP-Chip and the Online Mendelian Inheritance in Animals (OMIA) database. As a result, we identified a nonsense mutation in apoptotic protease activating factor 1 (APAF1) within HH1 that was associated with a decrease in conception rate and an increase in abortion in Holstein dairy cattle. The genotype of the SR case was A/A, and most of the 1,142 normal Holstein dairy cattle tested as a control group had the genotype G/G. In addition, the A/A genotype did not exist in the control group. Based on the pathological, genetic, and radiological findings, the congenital abnormalities observed were diagnosed as SR

Hymenoscyphus fraxineus and two new Hymenoscyphus species identified in Korea

Hymenoscyphus fraxineus is an invasive fungal pathogen that causes ash dieback in Europe. Recent investigations have identified H. fraxineus on herbarium specimens in Korea. In this paper, these specimens, plus five additional collections, were studied by internal transcribed spacer (ITS) screening and subsequent phylogenetic analysis using three additional sequence markers (actin, calmodulin, EF1-α). Using the concept of genealogical concordance phylogenetic species recognition (GCPSR), H. fraxineus was confirmed in five of the collections on petioles of Fraxinus mandshurica and F. chinensis subsp. rhynchophylla. The remaining collections revealed two novel species, both occurring on petioles of F. chinensis subsp. rhynchophylla. They are described as Hymenoscyphus occultus sp. nov. and Hymenoscyphus koreanus sp. nov., based on morphological and molecular data. Both develop a Chalara-like anamorph similar to that of H. fraxineus. Together with the newly described H. albidoides from China and H. linearis from Japan, the clade containing H. fraxineus now consists of six species. Within this clade, H. koreanus forms a sister species to H. albidus and both share highly similar morphological and molecular features. Hymenoscyphus occultus is more distantly related to H. fraxineus and shows proximity to H. linearis. Ascocarp production on ash leaf malt-extract agar could be shown for the two new species, and for H. linearis and H. albidus. The experiment demonstrated these species’ ability to self-fertilize. Our findings suggest the diversity of Hymenoscyphus species on Fraxinus sp. might be higher than currently known, calling for further investigations on petioles of other Fraxinus species.ISSN:1617-416XISSN:1861-895

Machine Learning-Based Live Weight Estimation for Hanwoo Cow

Live weight monitoring is an important step in Hanwoo (Korean cow) livestock farming. Direct and indirect methods are two available approaches for measuring live weight of cows in husbandry. Recently, thanks to the advances of sensor technology, data processing, and Machine Learning algorithms, the indirect weight measurement has been become more popular. This study was conducted to explore and evaluate the feasibility of machine learning algorithms in estimating the body live weight of Hanwoo cow using ten body measurements as input features. Various supervised Machine Learning algorithms, including Multilayer Perceptron, k-Nearest Neighbor, Light Gradient Boosting Machine, TabNet, and FT-Transformer, are employed to develop the models that estimate the body live weight using body measurement data. Data analysis is exploited to explore the correlation between the body size measurements (the features) and the weights (target values that need to be estimated) of cows. Data analysis results show that ten body measurements have a high correlation with the body live weight. High performance of all applied Machine Learning models was obtained. It can be concluded that estimating the body live weight of Hanwoo cow is feasible by utilizing Machine Learning algorithms. Among all of the tested algorithms, LightGBM regression demonstrates not only the best model in terms of performance, model complexity and development time

Machine Learning-Based Live Weight Estimation for Hanwoo Cow

Live weight monitoring is an important step in Hanwoo (Korean cow) livestock farming. Direct and indirect methods are two available approaches for measuring live weight of cows in husbandry. Recently, thanks to the advances of sensor technology, data processing, and Machine Learning algorithms, the indirect weight measurement has been become more popular. This study was conducted to explore and evaluate the feasibility of machine learning algorithms in estimating the body live weight of Hanwoo cow using ten body measurements as input features. Various supervised Machine Learning algorithms, including Multilayer Perceptron, k-Nearest Neighbor, Light Gradient Boosting Machine, TabNet, and FT-Transformer, are employed to develop the models that estimate the body live weight using body measurement data. Data analysis is exploited to explore the correlation between the body size measurements (the features) and the weights (target values that need to be estimated) of cows. Data analysis results show that ten body measurements have a high correlation with the body live weight. High performance of all applied Machine Learning models was obtained. It can be concluded that estimating the body live weight of Hanwoo cow is feasible by utilizing Machine Learning algorithms. Among all of the tested algorithms, LightGBM regression demonstrates not only the best model in terms of performance, model complexity and development time

Case Study: Improving the Quality of Dairy Cow Reconstruction with a Deep Learning-Based Framework

Three-dimensional point cloud generation systems from scanning data of a moving camera provide extra information about an object in addition to color. They give access to various prospective study fields for researchers. With applications in animal husbandry, we can analyze the characteristics of the body parts of a dairy cow to improve its fertility and milk production efficiency. However, in the depth image generation from stereo data, previous solutions using traditional stereo matching algorithms have several drawbacks, such as poor-quality depth images and missing information in overexposed regions. Additionally, the use of one camera to reconstruct a comprehensive 3D point cloud of the dairy cow has several challenges. One of these issues is point cloud misalignment when combining two adjacent point clouds with the small overlapping area between them. In addition, another drawback is the difficulty of point cloud generation from objects which have little motion. Therefore, we proposed an integrated system using two cameras to overcome the above disadvantages. Specifically, our framework includes two main parts: data recording part applies state-of-the-art convolutional neural networks to improve the depth image quality, and dairy cow 3D reconstruction part utilizes the simultaneous localization and calibration framework in order to reduce drift and provide a better-quality reconstruction. The experimental results showed that our approach improved the quality of the generated point cloud to some extent. This work provides the input data for dairy cow characteristics analysis with a deep learning approach