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6,151 results on '"Zhang, JunFeng"'

1. One-step Structure Prediction and Screening for Protein-Ligand Complexes using Multi-Task Geometric Deep Learning

Author

He, Kelei, Dong, Tiejun, Wu, Jinhui, and Zhang, Junfeng

Subjects
Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Quantitative Biology - Biomolecules
Abstract

Understanding the structure of the protein-ligand complex is crucial to drug development. Existing virtual structure measurement and screening methods are dominated by docking and its derived methods combined with deep learning. However, the sampling and scoring methodology have largely restricted the accuracy and efficiency. Here, we show that these two fundamental tasks can be accurately tackled with a single model, namely LigPose, based on multi-task geometric deep learning. By representing the ligand and the protein pair as a graph, LigPose directly optimizes the three-dimensional structure of the complex, with the learning of binding strength and atomic interactions as auxiliary tasks, enabling its one-step prediction ability without docking tools. Extensive experiments show LigPose achieved state-of-the-art performance on major tasks in drug research. Its considerable improvements indicate a promising paradigm of AI-based pipeline for drug development.

Published
2024

4. Indoor Air Sources of Outdoor Air Pollution: Health Consequences, Policy, and Recommendations: An Official American Thoracic Society Workshop Report.

Author

Nassikas, Nicholas, McCormack, Meredith, Ewart, Gary, Bond, Tami, Brigham, Emily, Cromar, Kevin, Paulin, Laura, Rice, Mary, Thurston, George, Turpin, Barbara, Vance, Marina, Weschler, Charles, Zhang, Junfeng, Kipen, Howard, Hicks, Anne, Hopke, Philip, Meyer, Brittany, Balmes, John, Goldstein, Allen, and Nazaroff, William

Subjects
cooking, indoor air pollution, natural gas, volatile organic compounds, wood burning
Abstract

Indoor sources of air pollution worsen indoor and outdoor air quality. Thus, identifying and reducing indoor pollutant sources would decrease both indoor and outdoor air pollution, benefit public health, and help address the climate crisis. As outdoor sources come under regulatory control, unregulated indoor sources become a rising percentage of the problem. This American Thoracic Society workshop was convened in 2022 to evaluate this increasing proportion of indoor contributions to outdoor air quality. The workshop was conducted by physicians and scientists, including atmospheric and aerosol scientists, environmental engineers, toxicologists, epidemiologists, regulatory policy experts, and pediatric and adult pulmonologists. Presentations and discussion sessions were centered on 1) the generation and migration of pollutants from indoors to outdoors, 2) the sources and circumstances representing the greatest threat, and 3) effective remedies to reduce the health burden of indoor sources of air pollution. The scope of the workshop was residential and commercial sources of indoor air pollution in the United States. Topics included wood burning, natural gas, cooking, evaporative volatile organic compounds, source apportionment, and regulatory policy. The workshop concluded that indoor sources of air pollution are significant contributors to outdoor air quality and that source control and filtration are the most effective measures to reduce indoor contributions to outdoor air. Interventions should prioritize environmental justice: Households of lower socioeconomic status have higher concentrations of indoor air pollutants from both indoor and outdoor sources. We identify research priorities, potential health benefits, and mitigation actions to consider (e.g., switching from natural gas to electric stoves and transitioning to scent-free consumer products). The workshop committee emphasizes the benefits of combustion-free homes and businesses and recommends economic, legislative, and education strategies aimed at achieving this goal.

Published
2024

25. The Impact of Artificial Intelligence on Operational Command

Author

Liu, Shichao, Xiao, Shujun, Zhang, Yong, Liu, Yaofeng, Liu, Jiantao, Ju, Jinsheng, Guan, Weiwei, Zhang, Junfeng, Niu, Mingming, Zheng, Lei, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Tan, Kay Chen, Series Editor, Long, Shengzhao, editor, Dhillon, Balbir S., editor, and Ye, Long, editor

Published
2024
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26. Steady-State Stress Prediction of Engine Components Using the State Function Method

Author

Wang, Wei, Yue, Zhufeng, Zhang, Junfeng, Liu, Yixiong, Wu, Yunwu, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Tan, Kay Chen, Series Editor, and Fu, Song, editor

Published
2024
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33. Urinary exosomes: Potential diagnostic markers and application in bladder cancer

Author

Ji Liu, Zhang Zhijin, Wentao Zhang, Maskey Niraj, Fuhan Yang, Guo Changcheng, Liliang Shen, Tianyuan Xu, Shenghua Liu, Zhang Junfeng, Shiyu Mao, Wei Li, and Xudong Yao

Subjects
Bladder cancer diagnosis, Urine, Exosomes, Biomarker, Liquid biopsy, Science (General), Q1-390, Social sciences (General), H1-99
Abstract

Background: The exosome is a critical component of the intercellular communication., playing a vital role in regulating cell function. These small vesicles contain proteins, mRNAs, miRNAs, and lncRNAs, surrounded by lipid bilayer substances. Most cells in the human body can produce exosomes, released into various body fluids such as urine, blood, and cerebrospinal fluid. Bladder cancer is the most common tumor in the urinary system, with high recurrence and metastasis rates. Early diagnosis and treatment are crucial for improving patient outcomes. Methods: This study employed the PubMed search engine to retrieve publicly accessible data pertaining to urinary exosomes. Results: We summarize the origins and intricate biological characteristics of urinary exosomes, the introduction of research methodologies used in basic experiments to isolate and analyze these exosomes, the discussion of their applications and progress in the diagnosis and treatment of bladder cancer, and the exploration of the current limitations associated with using urinary exosomes as molecular biomarkers for diagnosing bladder cancer. Conclusion: Exosomes isolated from urine may be used as molecular biomarkers for early detection of bladder cancer.

Published
2024
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34. Transformers in Medical Image Analysis: A Review

Author

He, Kelei, Gan, Chen, Li, Zhuoyuan, Rekik, Islem, Yin, Zihao, Ji, Wen, Gao, Yang, Wang, Qian, Zhang, Junfeng, and Shen, Dinggang

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Transformers have dominated the field of natural language processing, and recently impacted the computer vision area. In the field of medical image analysis, Transformers have also been successfully applied to full-stack clinical applications, including image synthesis/reconstruction, registration, segmentation, detection, and diagnosis. Our paper aims to promote awareness and application of Transformers in the field of medical image analysis. Specifically, we first overview the core concepts of the attention mechanism built into Transformers and other basic components. Second, we review various Transformer architectures tailored for medical image applications and discuss their limitations. Within this review, we investigate key challenges revolving around the use of Transformers in different learning paradigms, improving the model efficiency, and their coupling with other techniques. We hope this review can give a comprehensive picture of Transformers to the readers in the field of medical image analysis.

Published
2022

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