Zero-shot stance detection based on multi-expert collaboration.

Zero-shot stance detection is pivotal for autonomously discerning user stances on novel emerging topics. This task hinges on effective feature alignment transfer from known to unseen targets. To address this, we introduce a zero-shot stance detection framework utilizing multi-expert cooperative learning. This framework comprises two core components: a multi-expert feature extraction module and a gating mechanism for stance feature selection. Our approach involves a unique learning strategy tailored to decompose complex semantic features. This strategy harnesses the expertise of multiple specialists to unravel and learn diverse, intrinsic textual features, enhancing transferability. Furthermore, we employ a gating-based mechanism to selectively filter and fuse these intricate features, optimizing them for stance classification. Extensive experiments on standard benchmark datasets demonstrate that our model significantly surpasses existing baseline models in performance.

Exploring the potential mechanism of Chinese herbal medicine Fuzi on aplastic anemia based on UHPLC-MS/MS method combined with network pharmacology and molecular docking.

To reveal the potential mechanism of the effect of Chinese Herbal Medicine Fuzi on Aplastic anaemia (AA) according to the network pharmacology approach and molecular docking. According to Ultra High Performance Liquid Chromatography Mass Spectrometry (UHPLC-MS/MS), 146 chemical ingredients of Fuzi were obtained. By SwissADME online system analysis, a total of 55 compounds such as Magnoflorine, Scutellarein, Luteolin and Gingerol may be the main active components of Fuzi and 145 common targets related to AA were predicted. 17 targets such as MAPK1, AKT1 and GRB2 were considered as hub targets. KEGG and GO enrichment analysis obtained 122 signalling pathways and 950 remarkable results. These results suggested that Fuzi exerted pharmacological effects on AA mainly by regulating PI3K-Akt, MAPK and JAK-STAT signalling pathways and epithelial cell proliferation, cell differentiation, regulate energy production and other biological processes. Meanwhile, molecular docking results showed that the hub targets had good binding ability with the main active ingredients.

Transformative skeletal motion analysis: optimization of exercise training and injury prevention through graph neural networks.

Introduction: Exercise is pivotal for maintaining physical health in contemporary society. However, improper postures and movements during exercise can result in sports injuries, underscoring the significance of skeletal motion analysis. This research aims to leverage advanced technologies such as Transformer, Graph Neural Networks (GNNs), and Generative Adversarial Networks (GANs) to optimize sports training and mitigate the risk of injuries. Methods: The study begins by employing a Transformer network to model skeletal motion sequences, facilitating the capture of global correlation information. Subsequently, a Graph Neural Network is utilized to delve into local motion features, enabling a deeper understanding of joint relationships. To enhance the model's robustness and adaptability, a Generative Adversarial Network is introduced, utilizing adversarial training to generate more realistic and diverse motion sequences. Results: In the experimental phase, skeletal motion datasets from various cohorts, including professional athletes and fitness enthusiasts, are utilized for validation. Comparative analysis against traditional methods demonstrates significant enhancements in specificity, accuracy, recall, and F1-score. Notably, specificity increases by ~5%, accuracy reaches around 90%, recall improves to around 91%, and the F1-score exceeds 89%. Discussion: The proposed skeletal motion analysis method, leveraging Transformer and Graph Neural Networks, proves successful in optimizing exercise training and preventing injuries. By effectively amalgamating global and local information and integrating Generative Adversarial Networks, the method excels in capturing motion features and enhancing precision and adaptability. Future research endeavors will focus on further advancing this methodology to provide more robust technological support for healthy exercise practices.