Xiangsheng Huang

2 months ago | digitalcommons.library.tmc.edu | Suzanne G. Mays |Emma D'Agostino |Autumn R. Flynn |Xiangsheng Huang

KeywordsAnimals, Colitis, Ligands, Mice, Phospholipids, Receptors, Cytoplasmic and Nuclear, Phospholipid, LRH-1, nuclear receptor, agonist, ulcerative colitisAbstractPhospholipids are ligands for nuclear hormone receptors (NRs) that regulate transcriptional programs relevant to normal physiology and disease. Here, we demonstrate that mimicking phospholipid-NR interactions is a robust strategy to improve agonists of liver receptor homolog-1 (LRH-1), a therapeutic target for colitis.

Nov 19, 2024 | mdpi.com | Xiaogang Li |Xiangsheng Huang |Peng Ding |Xiaohu Chen

1. IntroductionThe implementation of the transportation power strategy has facilitated the rapid development of urban rail transit, with the construction of track bridges now well underway. Cable-stayed bridges are widely used in track bridges because of their mature construction technology and beautiful shape [1].

Aug 12, 2024 | mdpi.com | Peng Ding |Xiaogang Li |Sheng Chen |Xiangsheng Huang

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Jun 3, 2024 | nature.com | Xiangsheng Huang |Fang Wang |Yilong Liao |Wenjing Zhang |Li Zhang |Zhenrong Xu | +1 more

The early screening of depression is highly beneficial for patients to obtain better diagnosis and treatment. While the effectiveness of utilizing voice data for depression detection has been demonstrated, the issue of insufficient dataset size remains unresolved. Therefore, we propose an artificial intelligence method to effectively identify depression. The wav2vec 2.0 voice-based pre-training model was used as a feature extractor to automatically extract high-quality voice features from raw audio. Additionally, a small fine-tuning network was used as a classification model to output depression classification results. Subsequently, the proposed model was fine-tuned on the DAIC-WOZ dataset and achieved excellent classification results. Notably, the model demonstrated outstanding performance in binary classification, attaining an accuracy of 0.9649 and an RMSE of 0.1875 on the test set. Similarly, impressive results were obtained in multi-classification, with an accuracy of 0.9481 and an RMSE of 0.3810. The wav2vec 2.0 model was first used for depression recognition and showed strong generalization ability. The method is simple, practical, and applicable, which can assist doctors in the early screening of depression.

Feb 20, 2024 | mdpi.com | Xiaogang Li |Xiangsheng Huang |Peng Ding |Qiansong Wang

All articles published by MDPI are made immediately available worldwide under an open access license. No specialpermission is required to reuse all or part of the article published by MDPI, including figures and tables. Forarticles published under an open access Creative Common CC BY license, any part of the article may be reused withoutpermission provided that the original article is clearly cited. For more information, please refer tohttps://www.mdpi.com/openaccess.