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arXiv 2018年

作者： Jia, Chen Qian, Hong Chen, Min Zhang, Michael Q. Department of Mathematical Sciences University of Texas at Dallas RichardsonTX75080 United States Department of Applied Mathematics University of Washington SeattleWA98195 United States Department of Biological Sciences Center for Systems Biology University of Texas at Dallas RichardsonTX75080 United States MOE Key Lab Division of Bioinformatics CSSB TNLIST Tsinghua University Beijing100084 China

The transient response to a stimulus and subsequent recovery to a steady state are the fundamental characteristics of a living organism. Here we study the relaxation kinetics of autoregulatory gene networks based on the chemical master equation model of single-cell stochastic gene expression with nonlinear feedback regulation. We report a novel relation between the rate of relaxation, characterized by the spectral gap of the Markov model, and the feedback sign of the underlying gene circuit. When a network has no feedback, the relaxation rate is exactly the decaying rate of the protein. We further show that positive feedback always slows down the relaxation kinetics while negative feedback always speeds it up. Numerical simulations demonstrate that this relation provides a possible method to infer the feedback topology of autoregulatory gene networks by using time-series data of gene expression. Copyright © 2018, The Authors. All rights reserved.

关键词： Eigenvalues and eigenfunctions

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Super Resolution of Pathology Images with Hierarchical Feature Integration and Local Image Patterns

SSRN

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SSRN 2024年

作者： Gao, Chaofei Xu, Feng Li, Lei Wang, Shuyang Ling, Ren Zhang, Xie Deng, Xi Zhou, Mengzhe Ling, Jin Department of Gastroenterology The Affiliated Lihuili Hospital of Ningbo University Zhejiang Ningbo315040 China Pathological Diagnosis Department Meikang Medicalsystem Laboratory. Ningbo Zhejiang 315105 China Department of Pathology School of Basic Medical Sciences Fudan University Xuhui District Shanghai200032 China Shanghai Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention Xuhui District Shanghai200032 China Department of Research and Development Shanghai Laizi Software Technology Co. LTD. Fengxian District Shanghai201499 China Ningbo Clinical Pathology Diagnosis Center Zhejiang Ningbo315020 China MOE Key Laboratory of Bioinformatics Bioinformatics Division BNRist Department of Automation Tsinghua University Beijing100084 China

The rapid development of artificial intelligence and digital pathology has demonstrated promise in precision diagnosis in clinical. However, pathology analysis at single cell and sub-cellular level is still limited by the image resolution as the high resolution image acquirement is expensive and time-consuming. Additionally, most super resolution models ignored the intrinsic characteristics of pathology images without reaching satisfactory performance. In this paper, we developed a hierarchical super resolution deep learning framework based on local image patterns (HLIP) to reconstruct super resolution images from the low resolution ones with arbitrary input size and magnification. HLIP integrates hierarchical feature maps and conducts reconstruction by considering the local image patterns of neighboring pixels. Benchmark analysis shows HLIP achieves the best performance on both the internal and external validation datasets and the generated super resolution images approximate high resolution ones, which have reached clinical quality. It is revealed that HLIP can be applied in multiple clinical scenes as a versatile tool to improve the performance of other models on the tasks including the gland segmentation, cell segmentation and Helicobacter pylori detection. Moreover, HLIP can be integrated directly into the existing computer-aided pathology diagnostic systems for real-time super resolution. © 2024, The Authors. All rights reserved.

关键词： Deep learning

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Comparison of Jinzhen oral liquid and ambroxol hydrochloride and clenbuterol hydrochloride oral solution in the treatment of acute bronchitis in children:A multicenter,non-inferiority,prospective,randomized controlled trial

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Acta Pharmaceutica Sinica B 2024年第12期14卷 5186-5200页

作者： Qinhua Fan Chongming Wu Yawei Du Boyang Wang Yanming Xie Zeling Zhang Wenquan Su Zizhuo Wang Changchang Xu Xueke Li Ying Ding Xinjiang An Jing Chen Yunying Xiao Rong Yu Nan Li Juan Wang Yiqun Teng Hongfen Lv Nian Yang Yuling Wen Xiaoli Huang Wei Pan Yufeng Liu Xueqin Xi Qianye Zhao Changshan Liu Jian Xu Haitao Zhang Lie Zhuo Qiangquan Rong Yu Xia Qin Shen Shao Li Junhong Wang Shengxian Wu Dongzhimen Hospital Beijing University of Chinese MedicineBeijing 100700China School of Chinese Materia Medica Tianjin University of Traditional Chinese MedicineTianjin 301617China Institute of Basic Research in Clinical Medicine China Academy of Chinese Medical SciencesBeijing 100700China The First Affiliated Hospital of Henan University of CM Zhengzhou 450099China Xuzhou Children’s Hospital Xuzhou 221002China Women and Children’s Health Care Hospital of Linyi Linyi 276016China Taian Maternity and Child Health Hospital Taian 271001China Wuxi No.8 People’s Hospital Group Wuxi 214011China Shi Jia Zhuang Maternity&Child Healthcare Hospital Shijiazhuang 050006China The First People’s Hospital of Lianyungang Lianyungang 222002China Jiaxing Second Hospital Jiaxing 314001China Jiangyan Hospital of Traditional Chinese Medicine Taizhou 225599China Linshu County People’s Hospital Linyi 276799China Qiqihar Hospital of Traditional Chinese Medicine Qiqihar 161005China Liuzhou Maternal and Child Health Care Hospital Liuzhou 545001China Wuxi Hospital of Traditional Chinese Medicine Wuxi 214071China The Fourth Affiliated Hospital of Liaoning University of Traditional Chinese Medicine Shenyang 110103China Shandong Maternal and Child Health Hospital Jinan 250014China Lianyungang Maternal and Child Health Hospital Lianyungang 222062China The Second Hospital of Tianjin Medical University Tianjin 300211China Qidong Maternal and Child Health Hospital Qidong 226299China Suzhou Integrated Traditional and Western Medicine Hospital Suzhou 215101China Nanjing Drum Tower Hospital Group Suqian Hospital Nanjing 210008China Nanjing Gaochun People’s Hospital Nanjing 211302China Nanjing Lishui People’s Hospital Nanjing 211299China The Affiliated Suqian First People’s Hospital of Nanjing Medical University Suqian 223812China Institute for TCM-X MOE Key Laboratory of BioinformaticsBioinformatics DivisionBNRistDepartment of AutomationTsinghua UniversityBeijing 100084China State Key Laboratory on Technologies for

The comparison between traditional Chinese medicine Jinzhen oral liquid(JZOL)and West-ern medicine in treating children with acute bronchitis(AB)showed encouraging *** trial eval-uated the efficacy and safety of the JZOL for improving cough and expectoration in children with AB.480 children were randomly assigned to take JZOL or ambroxol hydrochloride and clenbuterol hydrochloride oral solution for 7 *** primary outcome was time-to-cough *** median time-to-cough resolution in both groups was 5.0 days and the antitussive onset median time was only 1 *** random-ized controlled trial showed that JZOL was not inferior to cough suppressant and phlegm resolving western medicine in treating cough and sputum and could comprehensively treat respiratory and systemic discom-fort *** with clinical trials,the mechanism of JZOL against AB was uncovered by network target analysis,it was found that the pathways in TRP channels like IL-1b/IL1R/TRPV1/TRPA1,NGF/TrkA/TRPV1/TRPA1,and PGE2/EP/PKA/TRPV1/TRPA1 might play important *** ex-periments further confirmed that inflammation and the immune regulatory effect of JZOL in the treatment of AB were of vital importance and TRP channels were the key mechanism of action.

关键词： Acute bronchitis Jinzhen oral liquid Complementary and alternative medicine Clinical trial Network target analysis

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Publisher Correction: scCASE: accurate and interpretable enhancement for single-cell chromatin accessibility sequencing data

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Nature communications 2024年第1期15卷 2212页

作者： Songming Tang Xuejian Cui Rongxiang Wang Sijie Li Siyu Li Xin Huang Shengquan Chen School of Mathematical Sciences and LPMC Nankai University Tianjin 300071 China. MOE Key Laboratory of Bioinformatics and Bioinformatics Division of BNRIST Department of Automation Tsinghua University 100084 Beijing China. Department of Computer Science University of Virginia Charlottesville VA 22903 USA. School of Statistics and Data Science Nankai University Tianjin 300071 China. Beijing Key Laboratory for Radiobiology Department of Radiation Biology Beijing Institute of Radiation Medicine 100850 Beijing China. School of Mathematical Sciences and LPMC Nankai University Tianjin 300071 China. chenshengquan@***.

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Deep learning radiomics for assessment of gastroesophageal varices in people with compensated advanced chronic liver disease

arXiv

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arXiv 2023年

作者： Wang, Lan He, Ruiling Zhao, Lili Wang, Jia Geng, Zhengzi Ren, Tao Zhang, Guo Zhang, Peng Tang, Kaiqiang Gao, Chaofei Chen, Fei Zhang, Liting Zhou, Yonghe Li, Xin He, Fanbin Huan, Hui Wang, Wenjuan Liang, Yunxiao Tang, Juan Ai, Fang Wang, Tingyu Zheng, Liyun Zhao, Zhongwei Ji, Jiansong Liu, Wei Xu, Jiaojiao Liu, Bo Wang, Xuemei Zhang, Yao Yan, Qiong Lv, Muhan Chen, Xiaomei Zhang, Shuhua Wang, Yihua Liu, Yang Yin, Li Liu, Yanni Huang, Yanqing Liu, Yunfang Wang, Kun Su, Meiqin Bian, Li An, Ping Zhang, Xin Qian, Linxue Li, Shao Qi, Xiaolong Institute for TCM-X MOE Key Laboratory of Bioinformatics Bioinformatics Division BNRIST Department of Automation Tsinghua University Beijing China Department of Ultrasound Donggang Branch the First Hospital of Lanzhou University Lanzhou China The First Clinical Medical College of Lanzhou University Lanzhou China Department of Gastroenterology and Hepatology Tianjin Second People's Hospital Tianjin China Department of Ultrasound Beijing Friendship Hospital Capital Medical University Beijing China Department of Ultrasound The People's Hospital of Linxia Linxia China Department of Gastroenterology Hospital of Chengdu Office of People's Government of Tibetan Autonomous Region Sichuan China Department of Gastroenterology The People's Hospital of Guangxi Zhuang Autonomous Region Nanning China Department of Control and Systems Engineering Nanjing University Nanjing China Department of Ultrasound The First Hospital of Lanzhou University Lanzhou China Department of Infectious Diseases The First Hospital of Lanzhou University Lanzhou China Department of Ultrasonography Tianjin Second People's Hospital Tianjin China Department of Infectious Disease Zigong First People's Hospital Zigong China Department of Ultrasound Zigong First People's Hospital Zigong China Department of Gastroscopy Zigong First People's Hospital Zigong China Department of Radiology Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research Fifth Affiliated Hospital of Wenzhou Medical University Lishui China Ultrasound Diagnosis Center Shaanxi Provincial People's Hospital Xi'An China Department of Ultrasound Ditan Hospital Capital Medical University Beijing China Department of Gastroenterology The Affiliated Hospital of Southwest Medical University Luzhou China Department of Ultrasound The Affiliated Hospital of Southwest Medical University Luzhou China Department of Ultrasound North China University of Science and Technology Affiliated Hospital Tangshan China Depar

Objective: Bleeding from gastroesophageal varices (GEV) is a medical emergency associated with high mortality. We aim to construct an artificial intelligence-based model of two-dimensional shear wave elastography (2D-SWE) of the liver and spleen to precisely assess the risk of GEV and high-risk gastroesophageal varices (HRV). Design: A prospective multicenter study was conducted in patients with compensated advanced chronic liver disease. 305 patients were enrolled from 12 hospitals, and finally 265 patients were included, with 1136 liver stiffness measurement (LSM) images and 1042 spleen stiffness measurement (SSM) images generated by 2D-SWE. We leveraged deep learning methods to uncover associations between image features and patient risk, and thus conducted models to predict GEV and HRV. Results: A multi-modality Deep Learning Risk Prediction model (DLRP) was constructed to assess GEV and HRV, based on LSM and SSM images, and clinical information. Validation analysis revealed that the AUCs of DLRP were 0.91 for GEV (95% CI 0.90 to 0.93, p Copyright © 2023, The Authors. All rights reserved.

关键词： Deep learning

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Author Correction: π-HuB: the proteomic navigator of the human body

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Nature 2025年第8046期637卷 E22页

作者： Fuchu He Ruedi Aebersold Mark S Baker Xiuwu Bian Xiaochen Bo Daniel W Chan Cheng Chang Luonan Chen Xiangmei Chen Yu-Ju Chen Heping Cheng Ben C Collins Fernando Corrales Jürgen Cox Weinan E Jennifer E Van Eyk Jia Fan Pouya Faridi Daniel Figeys George Fu Gao Wen Gao Zu-Hua Gao Keisuke Goda Wilson Wen Bin Goh Dongfeng Gu Changjiang Guo Tiannan Guo Yuezhong He Albert J R Heck Henning Hermjakob Tony Hunter Narayanan Gopalakrishna Iyer Ying Jiang Connie R Jimenez Lokesh Joshi Neil L Kelleher Ming Li Yang Li Qingsong Lin Cui Hua Liu Fan Liu Guang-Hui Liu Yansheng Liu Zhihua Liu Teck Yew Low Ben Lu Matthias Mann Anming Meng Robert L Moritz Edouard Nice Guang Ning Gilbert S Omenn Christopher M Overall Giuseppe Palmisano Yaojin Peng Charles Pineau Terence Chuen Wai Poon Anthony W Purcell Jie Qiao Roger R Reddel Phillip J Robinson Paola Roncada Chris Sander Jiahao Sha Erwei Song Sanjeeva Srivastava Aihua Sun Siu Kwan Sze Chao Tang Liujun Tang Ruijun Tian Juan Antonio Vizcaíno Chanjuan Wang Chen Wang Xiaowen Wang Xinxing Wang Yan Wang Tobias Weiss Mathias Wilhelm Robert Winkler Bernd Wollscheid Limsoon Wong Linhai Xie Wei Xie Tao Xu Tianhao Xu Liying Yan Jing Yang Xiao Yang John Yates Tao Yun Qiwei Zhai Bing Zhang Hui Zhang Lihua Zhang Lingqiang Zhang Pingwen Zhang Yukui Zhang Yu Zi Zheng Qing Zhong Yunping Zhu State Key Laboratory of Medical Proteomics Beijing Proteome Research Center National Center for Protein Sciences (Beijing) Beijing Institute of Lifeomics Beijing China. hefc@***. International Academy of Phronesis Medicine (Guangdong) Guangdong China. hefc@***. Department of Biology Institute of Molecular Systems Biology ETH Zurich Zurich Switzerland. aebersold@imsb.biol.ethz.ch. Macquarie Medical School Macquarie University Sydney New South Wales Australia. Institute of Pathology and Southwest Cancer Center Southwest Hospital Third Military Medical University (Army Medical University) and Key Laboratory of Tumor Immunopathology Ministry of Education of China Chongqing China. Institute of Health Service and Transfusion Medicine Beijing China. Department of Pathology and The Sidney Kimmel Comprehensive Cancer Center Johns Hopkins University Baltimore MD USA. State Key Laboratory of Medical Proteomics Beijing Proteome Research Center National Center for Protein Sciences (Beijing) Beijing Institute of Lifeomics Beijing China. Key Laboratory of Systems Biology Center for Excellence in Molecular Cell Science Shanghai Institute of Biochemistry and Cell Biology Chinese Academy of Sciences Shanghai China. Department of Nephrology First Medical Center of Chinese PLA General Hospital Nephrology Institute of the Chinese People's Liberation Army State Key Laboratory of Kidney Diseases National Clinical Research Center for Kidney Diseases Beijing Key Laboratory of Kidney Disease Research Beijing China. Institute of Chemistry Academia Sinica Taipei China. National Biomedical Imaging Center State Key Laboratory of Membrane Biology Institute of Molecular Medicine Peking-Tsinghua Center for Life Sciences College of Future Technology Peking University Beijing China. School of Biological Sciences Queen's University of Belfast Belfast UK. Functional Proteomics Laboratory Centro Nacional de Biotecnología-CSIC Madrid Spain. Computational Systems Biochemistry Research Group Ma

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Dense sampling of bird diversity increases power of comparative genomics (vol 587, pg 252, 2020)

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NATURE 2021年第7856期592卷 E24-E24页

作者： Feng, Shaohong Stiller, Josefin Deng, Yuan Armstrong, Joel Fang, Qi Reeve, Andrew Hart Xie, Duo Chen, Guangji Guo, Chunxue Faircloth, Brant C. Petersen, Bent Wang, Zongji Zhou, Qi Diekhans, Mark Chen, Wanjun Andreu-Sanchez, Sergio Margaryan, Ashot Howard, Jason Travis Parent, Carole Pacheco, George Sinding, Mikkel-Holger S. Puetz, Lara Cavill, Emily Ribeiro, Angela M. Eckhart, Leopold Fjeldsa, Jon Hosner, Peter A. Brumfield, Robb T. Christidis, Les Bertelsen, Mads F. Sicheritz-Ponten, Thomas Tietze, Dieter Thomas Robertson, Bruce C. Song, Gang Borgia, Gerald Claramunt, Santiago Lovette, Irby J. Cowen, Saul J. Njoroge, Peter Dumbacher, John Philip Ryder, Oliver A. Fuchs, Jerome Bunce, Michael Burt, David W. Cracraft, Joel Meng, Guanliang Hackett, Shannon J. Ryan, Peter G. Jonsson, Knud Andreas Jamieson, Ian G. da Fonseca, Rute R. Braun, Edward L. Houde, Peter Mirarab, Siavash Suh, Alexander Hansson, Bengt Ponnikas, Suvi Sigeman, Hanna Stervander, Martin Frandsen, Paul B. van der Zwan, Henriette van der Sluis, Rencia Visser, Carina Balakrishnan, Christopher N. Clark, Andrew G. Fitzpatrick, John W. Bowman, Reed Chen, Nancy Cloutier, Alison Sackton, Timothy B. Edwards, Scott V. Foote, Dustin J. Shakya, Subir B. Sheldon, Frederick H. Vignal, Alain Soares, Andre E. R. Shapiro, Beth Gonzalez-Solis, Jacob Ferrer-Obiol, Joan Rozas, Julio Riutort, Marta Tigano, Anna Friesen, Vicki Dalen, Love Urrutia, Araxi O. Szekely, Tamas Liu, Yang Campana, Michael G. Corvelo, Andre Fleischer, Robert C. Rutherford, Kim M. Gemmell, Neil J. Dussex, Nicolas Mouritsen, Henrik Thiele, Nadine Delmore, Kira Liedvogel, Miriam Franke, Andre Hoeppner, Marc P. Krone, Oliver Fudickar, Adam M. Mila, Borja Ketterson, Ellen D. Fidler, Andrew Eric Friis, Guillermo Parody-Merino, Angela M. Battley, Phil F. Cox, Murray P. Lima, Nicholas Costa Barroso Prosdocimi, Francisco Parchman, Thomas Lee Schlinger, Barney A. Loiselle, Bette A. Blake, John G. Lim, Haw Chuan Day, Lainy B. Fuxjager, Matthew J. Baldwin, Maude W. Braun, Michael J. Wirthlin, Morgan Dikow, China National GeneBank BGI-Shenzhen Shenzhen China State Key Laboratory of Genetic Resources and Evolution Kunming Institute of Zoology Chinese Academy of Sciences Kunming China BGI-Shenzhen Shenzhen China Villum Centre for Biodiversity Genomics Section for Ecology and Evolution Department of Biology University of Copenhagen Copenhagen Denmark BGI Education Center University of Chinese Academy of Sciences Shenzhen China MOE Laboratory of Biosystems Homeostasis and Protection Life Sciences Institute Zhejiang University Hangzhou China Department of Neuroscience and Developmental Biology University of Vienna Vienna Austria Center for Excellence in Animal Evolution and Genetics Chinese Academy of Sciences Kunming China James D. Watson Institute of Genome Sciences Hangzhou China UC Santa Cruz Genomics Institute UC Santa Cruz Santa Cruz CA USA Natural History Museum of Denmark University of Copenhagen Copenhagen Denmark Center for Macroecology Evolution and Climate GLOBE Institute University of Copenhagen Copenhagen Denmark Department of Biological Sciences Louisiana State University Baton Rouge LA USA Museum of Natural Science Louisiana State University Baton Rouge LA USA Centre of Excellence for Omics-Driven Computational Biodiscovery (COMBio) Faculty of Applied Sciences AIMST University Kedah Malaysia Section for Evolutionary Genomics The GLOBE Institute Faculty of Health and Medical Sciences University of Copenhagen Copenhagen Denmark Institute of Molecular Biology National Academy of Sciences Yerevan Armenia University Museum Norwegian University of Science and Technology Trondheim Norway Center for Reproductive Medicine The 2nd Affiliated Hospital School of Medicine Zhejiang University Hangzhou China Novogene Durham NC USA Duke University Medical Center Durham NC USA The Rockefeller University New York NY USA Howard Hughes Medical Institute Chevy Chase MD USA Department of Dermatology Medical University of Vienna Vienna Austria

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