Low genetic diversity of Phytophthora infestans population in potato in north China

Late blight, caused by Phytophthora infestans is the most important disease of potato (Solanum tuberosum). This study reveals the genetic diversity of P. infestans population in north China. A total of 134 strains of P. infestans were isolated from different agricultural fields in Hebei, Liaoning, Jinlin and Heilongjiang Provinces in north China. The genetic variation among these strains were analyzed using 15 ‘simple-sequence repeat’ (SSR) markers. The results show that forty different SSR genotypes and an average of 3.8 (range 2 to 9) alleles per locus were found. Low genetic diversity (Shannon’s diversity index = 0.26) was found among these 134 strains from four provinces, revealing the presence of clonal populations of the pathogen in this region. The average heterozygosity was 0.162, indicating the low level of genetic variations of P. infestans populations. There was no correlation between population genetic diversity of P. infestans and geographical origin. These results provided a foundation for making integrated control measures in the future.Key words: Phytophthora infestans, population genetics, simple-sequence repeat (SSR), potato late blight

Item-side Fairness of Large Language Model-based Recommendation System

Recommendation systems for Web content distribution intricately connect to the information access and exposure opportunities for vulnerable populations. The emergence of Large Language Models-based Recommendation System (LRS) may introduce additional societal challenges to recommendation systems due to the inherent biases in Large Language Models (LLMs). From the perspective of item-side fairness, there remains a lack of comprehensive investigation into the item-side fairness of LRS given the unique characteristics of LRS compared to conventional recommendation systems. To bridge this gap, this study examines the property of LRS with respect to item-side fairness and reveals the influencing factors of both historical users' interactions and inherent semantic biases of LLMs, shedding light on the need to extend conventional item-side fairness methods for LRS. Towards this goal, we develop a concise and effective framework called IFairLRS to enhance the item-side fairness of an LRS. IFairLRS covers the main stages of building an LRS with specifically adapted strategies to calibrate the recommendations of LRS. We utilize IFairLRS to fine-tune LLaMA, a representative LLM, on \textit{MovieLens} and \textit{Steam} datasets, and observe significant item-side fairness improvements. The code can be found in https://github.com/JiangM-C/IFairLRS.git.Comment: Accepted by the Proceedings of the ACM Web Conference 202

The Impact of the Digital Economy on Agricultural Green Development: Evidence from China

Whether the digital economy can effectively promote agricultural green development is crucial to the realization of agricultural rural modernization. This study empirically analyzes the impact of the digital economy on agricultural green development and the mechanism of action based on panel data of 30 Chinese provinces from 2011 to 2020. The results reveal that (1) the digital economy can significantly improve the green development level of China’s agriculture; the dividends in the eastern region and central region are significantly higher than that in the western region, and there is regional heterogeneity. (2) The role of the digital economy in promoting agricultural green development has a nonlinear characteristic of increasing “marginal effect.” (3) The digital economy has a significant spatial spillover effect, which can have a positive impact on agricultural green development in the surrounding areas. (4) The construction of “Broadband Countryside” can improve the development of the rural digital economy and indirectly promote agricultural green development. This study deepens our understanding of the internal effect and interval relationship of how the digital economy enables agricultural green development and provides the theoretical basis and practical suggestions for optimizing digital facility construction and high-quality agricultural development