G-protein-coupled receptor 41 (GPR41) and G-protein-coupled receptor 43 (GPR43) are important short-chain fatty acids (SCFAs) receptors. Previous studies indicated that GPR41 and GPR43 are involved in the secretion of gastrointestinal peptides, and glucose and lipid metabolism, and are closely related to obesity and type II diabetes, and other diseases. The purpose of the study was to explore the relationship between the GPR41 and GPR43 and seasonal breeding, and provide new prospects for further exploring the nutritional needs of breeding. We identified the localization and expression levels of GPR41 and GPR43 in the colon of the wild ground squirrels (Spermophilus dauricus) both in the breeding season and non-breeding season. The histological results revealed that the lumen diameter of the colon had obvious seasonal changes, and the diameter of the colonic lumen in the non-breeding season was larger than that in the breeding season. Immunohistochemical staining suggested GPR41 and GPR43 have expressed in the simple layer columnar epithelium. In addition, compared with the breeding season, the mRNA and protein expression levels of GPR41 and GPR43 in the colon were higher during the non-breeding season. In general, these results indicated GPR41 and GPR43 might play a certain role in regulating seasonal breeding.

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The use of neural networks for plant disease identification is a hot topic of current research. However, unlike the classification of ordinary objects, the features of plant diseases frequently vary, resulting in substantial intra-class variation; in addition, the complex environmental noise makes it more challenging for the model to categorize the diseases. In this paper, an attention and multidimensional feature fusion neural network (AMDFNet) is proposed for Camellia oleifera disease classification network based on multidimensional feature fusion and attentional mechanism, which improves the classification ability of the model by fusing features to each layer of the Inception structure and enhancing the fused features with attentional enhancement. The model was compared with the classical convolutional neural networks GoogLeNet, Inception V3, ResNet50, and DenseNet121 and the latest disease image classification network DICNN in a self-built camellia disease dataset. The experimental results show that the recognition accuracy of the new model reaches 86.78% under the same experimental conditions, which is 2.3% higher than that of GoogLeNet with a simple Inception structure, and the number of parameters is reduced to one-fourth compared to large models such as ResNet50. The method proposed in this paper can be run on mobile with higher identification accuracy and a smaller model parameter number.