Population genetic information can provide valuable insight for the conservation and management of threatened and endangered plant species. Tamarix taklamakanensis is an endangered shrub endemic to arid basins of northwestern China. This species serves to stabilize soils in this region, but has seen substantial loss in its abundance due to depletion of ground water. The populations of this species have become small and fragmented, warranting conservation. Seven microsatellite loci were used to assess the genetic diversity and structure of 15 populations in the Tarim Basin, China. Among populations, the expected heterozygosity and total gene diversity were both moderate (H_E = 0.392, h_T = 0.432), however the allelic diversity was low (A = 2.4). Eleven populations were detected to have experienced recent bottlenecks using Wilcoxon’s test and a model-shift test. Most populations of T. taklamakanensis in the centre of Tarim Basin showed low levels of genetic differentiation, but higher levels in geographically outlying populations. Genetic structure based on Bayesian assignment, the neighbour-joining network and principal coordinates analyses produced similar results, supporting five groups in the Tarim Basin. Gene flow was high among Bayesian groups based on historical gene flow estimated by private alleles. The genetic structure of T. taklamakanensis supports a pattern where gene flow principally occurs along river corridors through hydrochory of seeds and insect-mediated pollination. Populations upstream have contributed to a more diverse mixture of populations near the confluence of several rivers near the centre of Tarim Basin. This pattern of genetic structure could be influenced by the flow of water from different river systems. Conservation efforts should focus on fostering the regeneration of this species, maintaining genetic diversity and preserving the extant genetic structure. Conservation efforts are contingent upon maintaining ground water and streamflows in this arid basin.

Bombyx mori nucleopolyhedrovirus (BmNPV) is a serious threat to sericulture. Nevertheless, no effective control strategy is currently available. The innate immunity of silkworm is critical in the antiviral process. Exploring its molecular mechanism provides theoretical support for the prevention and treatment of BmNPV. Insect hormone receptors play an essential role in regulating host immunity. We found a correlation between Bombyx mori ecdysone receptor B1 (BmEcR-B1) and BmNPV infection, whereas the underlying mechanism remains unclear. In this study, the expression patterns and sequence characteristics of BmEcR-B1 and its isoform, BmEcR-A, were initially analyzed. BmEcR-B1 was found to be more critical than BmEcR-A in silkworm development and responses to BmNPV. Moreover, RNAi and an overexpression in BmN cells showed BmEcR-B1 had antiviral effects in the presence of 20-hydroxyecdysone (20E); Otherwise, it had no antiviral activity. Furthermore, BmEcR-B1 was required for 20E-induced apoptosis, which significantly suppressed virus infection. Finally, feeding 20E had no significant negative impacts on larval growth and the cocoon shell, suggesting the regulation of this pathway has practical value in controlling BmNPV in sericulture. The findings of this study provide important theoretical support for understanding the mechanism of the silkworm innate immune system in response to BmNPV infection.

Intelligent management system centered on artificial intelligence algorithms, cloud computing technology, mobile Internet technology, and big data applications have made remarkable achievements in various industries. The intelligent management system designed in this project is to apply these technologies to explore a vocational education development model that adapts to the current Internet society. Vocational education needs to be oriented to the professional needs of the society, it needs to be oriented to students' vocational choices, and it needs to be oriented to the skill range of teachers. This project collects students' learning data and teaches students in accordance with their aptitude in the course of students' daily classes, matches students with appropriate vocational and technical elective courses and teachers, and records students' feedback during their studies, and in the future career development of students, to help students make better career choices. At the same time, the needs of students, companies, and teachers in vocational education will be collected, and artificial intelligence algorithms will be used to process student data and corporate recruitment data, automatically matching the most suitable combination of students and companies. The biggest core innovation of this system is the latest deep learning algorithm, and according to the actual situation of vocational education, the residual neural network is improved to create an improved residual neural network suitable for students' professional development recommendations. The final result of this algorithm, the auxiliary role in the process of student vocational education and development is far greater than the traditional vocational education management system, and in the C University student education and professional development database, there is about 90% satisfaction rate and the 85% occupational matching accuracy rate is far higher than the performance of the traditional vocational education management system.