Zhejiang Modern Architectural Design & Research Institute

Genetic diversity and kinship information of cotton germplasm resources are fundamental to breeding, providing a theoretical basis for the rational selection of hybrid parents and further breeding of new varieties with high yield, high quality, and multi-resistance. This study utilized cotton varieties that have been used for variety improvement or are widely planted in the Northern Xinjiang cotton region as materials. Genotyping was performed using the ZJU CottonSNP40K chip to analyze genetic diversity and kinship relationships. A total of 26,852 high-quality SNP markers were obtained, including 15,222 SNPs in subgenome A and 11,630 SNPs in subgenome D. The number of SNPs per chromosome ranged from 547 (A04) to 2168 (A08). Based on phylogenetic tree and principal component analysis, the 83 materials were clustered into 3 major subgroups. Group I contained varieties introduced from the former Soviet Union and the United States, which have become important parents for cotton breeding in Northern Xinjiang. Among them, as many as 27 varieties were derived and selected from the introduced US variety 'Beiersinuo' as a parent. While playing an important role in cotton breeding in Northern Xinjiang, this has also led to the current situation where the genetic base of Northern Xinjiang varieties is relatively narrow (average kinship coefficient 0.72). It clarifies the significant role of introduced American variety 'Beiersinuo' in the breeding of Northern Xinjiang cultivars and provides theoretical guidance for broadening the genetic base of Northern Xinjiang cotton varieties.

BackgroundOperating a mouse for long periods of time increases the risk of muscle fatigue in the upper limbs of office workers.ObjectiveThis study aimed to contribute to the field of knowledge by providing guidance on the selection of a suitable computer mouse control-display gain (CDG) for office workers, and to reduce muscle fatigue and fatigue accumulation in office workers who use computer mice.MethodThirty healthy office workers used five different control-display gain settings to complete five sessions of a 30-min Fitts' task. Muscle activity in the extensor digitorum, extensor carpi ulnaris and flexor digitorum superficialis were measured by surface electromyography (sEMG). Subjective muscle fatigue was assessed using the Borg Rating Scale and user performance was recorded using the GoFitts program.ResultsIt was found that when the control-display gain was set to 12.97, the highest effective frequency of operation was reached by the users. Additionally, the subjects reported more subjective muscle fatigue at a control-display gain of 12.97, and the sEMG data showed that manipulation led to more upper limb muscle fatigue when the control-display gain was set to 8.64 and 12.97. Upper limb muscle fatigue, subjective muscle fatigue and user performance also varied between genders. Overall, female subjects had lower error rates while male subjects reported higher levels of subjective muscle fatigue and were more adaptable to the low control-display gain setting.ConclusionThis study found a correlation between muscle fatigue and subjective fatigue with control-display gain. Similar to the subjective fatigue for control-display gain settings, the control-display gain settings that are commonly used by office workers may lead to higher levels of fatigue in the forearm muscles. This study could provide guidance for the selection of computer mouse control-display gain to reduce muscle fatigue and fatigue accumulation in office workers who use computer mice.

Cinnamomi cortex (CC), a traditional Chinese herbal medicine, exhibits antidiabetic properties, yet the underlying mechanisms are not fully understood. Our study combined network pharmacology, molecular docking, and experimental validation to elucidate the antidiabetic mechanisms of CC. Active components of CC and their potential antidiabetic targets were identified through TCMSP, DisGeNET, and GeneCards. The PPI networks were constructed with STRING and analyzed with Cytoscape, while GO and KEGG analyses utilized the DAVID database. Molecular docking with core targets was performed using Autodock Vina. The efficacy of CC in diabetes mellitus was evaluated through H&E staining, qPCR, and Western blot in the T2DM mouse. Eleven active components and sixty-six potential antidiabetic targets of CC were identified. The enrichment analysis revealed 288 GO terms and 37 pathways. The molecular docking showed high affinity for PPAR-γ and IL-6 receptors. In vivo studies further confirmed CC's ability to modulate PPAR-γ and IL-6, contributing to its antidiabetic effects. CC manages diabetes by regulating the PPAR-γ pathway and suppressing associated inflammation, providing a multi-pathway therapeutic approach.

Abstract The rapid development of information and artificial intelligence technology has brought new opportunities for innovation and transformation in higher education, leading to innovative applications of data-driven-assisted teaching decisions. This paper analyzes the equilibrium point in predicting students’ performance using nonlinear differential equations, solves the expected value of students’ performance, and constructs a prediction model for students’ innovation and entrepreneurship performance. Student achievement data and characteristics are preprocessed to improve the accuracy of the prediction model. Based on Pearson’s coefficient, students’ innovation and entrepreneurship achievement are significantly correlated with teaching ability (P<0.05). The results show that, after implementing innovation and entrepreneurship teaching based on this prediction model, the predicted achievement of students in the intervention group is significantly higher than that of students in the control group (P<0.05). In the actual achievement test, the average total grade for the innovative entrepreneurship course of students in the intervention group was about 4.68-8.78 points higher than that of students in the control group. The achievement prediction model developed in this paper, along with the proposed optimization path for building innovation and entrepreneurship teaching capacity, offers significant guidance for the teaching research department in implementing effective teaching interventions and in the development of innovation and entrepreneurship education.

Abstract Changes in the waxing and waning of carbon pools in forest ecosystems have a profound impact on the global carbon cycle. The study takes forests in a city as an example, uses sample plot survey data and Sentinel-2 and GF-1 satellite remote sensing data as data sources, and after multiple feature extraction, the RBF neural network optimized by the PSO algorithm is used for modeling the dynamic estimation of carbon storage, assessing the accuracy of the model and inverting the estimation of carbon storage in the sample area. The results show that the PSO-RBF model in this paper has the best fit and prediction accuracy for carbon storage estimation, with the highest R² of 0.644 and the smallest RRMSE and MAE of 0.287 and 15.368 in the combination of the two data sources, and the estimated value of forest carbon storage in 2022 in the sample area is 6809451.80t, which differs from the actual value by 0.04% and the difference of predicted and actual values in each subarea is 0.04%. The predicted and measured values in subregions had a difference of less than 0.34%. The forest carbon storage model that utilizes PSO-RBF has a good estimation effect overall. The technical support provided by this study enables accurate monitoring of carbon storage in forest ecosystems.

People with motor and speech impairments has limited communication abilities, resulting in a heavy reliance on Augmentative and Alternative Communication (AAC) tools. Existing commercial AAC tools provide simple combination of fixed words, satisfying basic physiological needs while encounter huge challenges in social communication. This social communication is significant for users’ mental health, especially for those with limited motor abilities. This article thus developed SocializeChat, an AAC tool that employs LLM technology to boost social chat with gaze inputs. Specifically, SocializeChat generates multiple sentences of conversation in real time, offers suggestions tailoring to users' preferences of topics, and phrases sentences in a style in accord with the relationship of people in conversation. This is achieved through a user dataset containing content preferences and social closeness, and through dedicated design of prompts and procedures. In a brief testing, SocializeChat was rated as effectively embodying subjects' own content preferences and communication style.