The tea plant [Camellia sinensis (L.) O. Kuntze] is an industrial crop in China. The Anhui Province has a long history of tea cultivation and has a large resource of tea germplasm with abundant genetic diversity. To reduce the cost of conservation and utilization of germplasm resources, a core collection needs to be constructed. To this end, 573 representative tea accessions were collected from six major tea-producing areas in Anhui Province. Based on 60 pairs of simple sequence repeat (SSR) markers, phylogenetic relationships, population structure and principal coordinate analysis (PCoA) were conducted. Phylogenetic analysis indicated that the 573 tea individuals clustered into five groups were related to geographical location and were consistent with the results of the PCoA. Finally, we constructed a core collection consisting of 115 tea individuals, accounting for 20% of the whole collection. The 115 core collections were considered to have a 90.9% retention rate for the observed number of alleles (Na), and Shannon’s information index (I) of the core and whole collections were highly consistent. Of these, 39 individuals were preserved in the Huangshan area, accounting for 33.9% of the core collection, while only 10 individuals were reserved in the Jinzhai County, accounting for 8.9% of the core set. PCoA of the accessions in the tea plant core collection exhibited a pattern nearly identical to that of the accessions in the entire collection, further supporting the broad representation of the core germplasm in Anhui Province. The results demonstrated that the core collection could represent the genetic diversity of the original collection. Our present work is valuable for the high-efficiency conservation and utilization of tea plant germplasms in Anhui Province
Obesity presents a serious threat to human health and broiler performance. The expansion of adipose tissue is mainly regulated by the differentiation of preadipocytes. The differentiation of preadipocytes is a complex biological process regulated by a variety of transcription factors and signaling pathways. Previous studies have shown that the transcription factor HMG-box protein 1 (HBP1) can regulate the differentiation of mouse 3T3-L1 preadipocytes by activating the Wnt/β-catenin signaling pathway. However, it is unclear whether HBP1 involved in chicken preadipocyte differentiation and which signaling pathways it regulates. The aim of the current study was to explore the biological function and molecular regulatory mechanism of HBP1 in the differentiation of chicken preadipocytes. The expression patterns of chicken HBP1 in abdominal adipose tissue and during preadipocyte differentiation were analyzed by RT-qPCR and Western blot. The preadipocyte stably overexpressing HBP1 or knockout HBP1 and their control cell line were used to analyze the effect of HBP1 on preadipocyte differentiation by oil red O staining, RT-qPCR and Western blot. Cignal 45-Pathway Reporter Array was used to screen the signal pathways that HBP1 regulates in the differentiation of chicken preadipocytes. Chemical inhibitor and siRNA for signal transducer and activator of transcription 3 (STAT3) were used to analyze the effect of STAT3 on preadipocyte differentiation. The preadipocyte stably overexpressing HBP1 was transfected by the siRNA of STAT3 or treated with a chemical inhibitor of STAT3 for the rescue experiment. The results of gene expression analysis showed that the expression of HBP1 was related to abdominal fat deposition and preadipocyte differentiation in chickens. The results of function gain and loss experiments indicated that overexpression/knockout of HBP1 in chicken preadipocytes could inhibit/promote (P<0.05) lipid droplet deposition and the expression of adipogenesis-related genes. Mechanismlly, HBP1 activates (P<0.05) the signal transducer and activator of transcription 3 (STAT3) signaling pathway by targeting janus kinase 2 (JAK2) transcription. The results of functional rescue experiments indicated that STAT3 signaling mediated the regulation of HBP1 on chicken preadipocyte differentiation. In conclusion, HBP1 inhibits chicken preadipocyte differentiation by activating the STAT3 signaling pathway via directly enhancing JAK2 expression. Our findings provided new insights for further analysis of the molecular genetic basis of chicken adipose tissue growth and development.
Agromyzid leafminers cause significant economic losses in both vegetable and horticultural crops, and precise assessments of pesticide needs must be based on the extent of leaf damage. Traditionally, surveyors estimate the damage by visually comparing the proportion of damaged to intact leaf area, a method that lacks objectivity, precision, and reliable data traceability. To address these issues, an advanced survey system that combines augmented reality (AR) glasses with a camera and an artificial intelligence (AI) algorithm was developed in this study to objectively and accurately assess leafminer damage in the field. By wearing AR glasses equipped with a voice-controlled camera, surveyors can easily flatten damaged leaves by hand and capture images for analysis. This method can provide a precise and reliable diagnosis of leafminer damage levels, which in turn supports the implementation of scientifically grounded and targeted pest management strategies. To calculate the leafminer damage level, the DeepLab-Leafminer model was proposed to precisely segment the leafminer-damaged regions and the intact leaf region. The integration of an edge-aware module and a Canny loss function into the DeepLabv3+ model enhanced the DeepLab-Leafminer model's capability to accurately segment the edges of leafminer-damaged regions, which often exhibit irregular shapes. Compared with state-of-the-art segmentation models, the DeepLab-Leafminer model achieved superior segmentation performance with an Intersection over Union (IoU) of 81.23% and an F1 score of 87.92% on leafminer-damaged leaves. The test results revealed a 92.38% diagnosis accuracy of leafminer damage levels based on the DeepLab-Leafminer model. A mobile application and a web platform were developed to assist surveyors in displaying the diagnostic results of leafminer damage levels. This system provides surveyors with an advanced, user-friendly, and accurate tool for assessing agromyzid leafminer damage in agricultural fields using wearable AR glasses and an AI model. This method can also be utilized to automatically diagnose pest and disease damage levels in other crops based on leaf images.