Abstract. As one of the most successful domesticated animals in the Neolithic age, sheep gradually migrated all over the world with human activities. During the domestication process, remarkable changes have taken place in morphology, physiology, and behavior, resulting in different breeds with different characters via artificial and natural selection. However, the genetic background responsible for these phenotypic variations remains largely unclear. Here, we used whole genome resequencing technology to compare and analyze the genome differences between Asiatic mouflon wild sheep (Ovis orientalis) and Hu sheep (Ovis aries). A total of 755 genes were positively selected in the process of domestication and selection, and the genes related to sensory perception had directional evolution in the autosomal region, such as OPRL1, LEF1, TAS1R3, ATF6, VSX2, MYO1A, RDH5, and some novel genes. A missense mutation of c.T722C/p.M241T in exon 4 of RDH5 existing in sheep were found, and the T allele was completely fixed in Hu sheep. In addition, the mutation with the C allele reduced the retinol dehydrogenase activity encoding by RDH5, which can impair retinoic acid metabolism and further influenced the visual cycle. Overall, our results showed significant enrichment for positively selected genes involved in sensory perception development during sheep domestication; RDH5 and its variants may be related to the retinal degeneration in sheep. We infer that the wild sheep ancestors with weaker visual sensitivity were weeded out by humans, and the mutation was selective, swept by the dual pressures of natural and artificial selection.
With the demand for standardized large-scale livestock farming and the development of artificial intelligence technology, a lot of research in the area of animal face detection and face identification was conducted. However, there are no specialized studies on livestock face normalization, which may significantly reduce the performance of face identification. The keypoint detection technology, which has been widely applied in human face normalization, is not suitable for animal face normalization due to the arbitrary directions of animal face images captured from uncooperative animals. It is necessary to develop a livestock face normalization method that can handle arbitrary face directions. In this study, a lightweight angle detection and region-based convolutional network (LAD-RCNN) was developed, which contains a new rotation angle coding method that can detect the rotation angle and the location of the animal’s face in one stage. LAD-RCNN also includes a series of image enhancement methods to improve its performance. LAD-RCNN has been evaluated on multiple datasets, including a goat dataset and infrared images of goats. Evaluation results show that the average precision of face detection was more than 97%, and the deviations between the detected rotation angle and the ground-truth rotation angle were less than 6.42° on all the test datasets. LAD-RCNN runs very fast and only takes 13.7 ms to process a picture on a single RTX 2080Ti GPU. This shows that LAD-RCNN has an excellent performance in livestock face recognition and direction detection, and therefore it is very suitable for livestock face detection and normalization.
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