检索结果-内蒙古大学图书馆

arXiv 2021年

作者： Rando, Halie M. MacLean, Adam L. Lee, Alexandra J. Lordan, Ronan Ray, Sandipan Bansal, Vikas Skelly, Ashwin N. Sell, Elizabeth Dziak, John J. Shinholster, Lamonica D'Agostino McGowan, Lucy Guebila, Marouen Ben Wellhausen, Nils Knyazev, Sergey Boca, Simina M. Capone, Stephen Qi, Yanjun Park, Yoson Sun, Yuchen Mai, David Boerckel, Joel D. Brueffer, Christian Byrd, James Brian Kamil, Jeremy P. Wang, Jinhui Velazquez, Ryan Szeto, Gregory L. Barton, John P. Goel, Rishi Raj Mangul, Serghei Lubiana, Tiago Gitter, Anthony Greene, Casey S. Department of Systems Pharmacology and Translational Therapeutics University of Pennsylvania PhiladelphiaPA United States Department of Biochemistry and Molecular Genetics University of Colorado School of Medicine AuroraCO United States University of Colorado School of Medicine AuroraCO United States Department of Quantitative and Computational Biology University of Southern California Los AngelesCA United States Institute for Translational Medicine and Therapeutics Perelman School of Medicine University of Pennsylvania PhiladelphiaPA19104-5158 United States Department of Biotechnology Indian Institute of Technology Hyderabad Kandi Sangareddy Telangana 502285 India Biomedical Data Science and Machine Learning Group German Center for Neurodegenerative Diseases Tubingen72076 Germany Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Institute for Immunology University of Pennsylvania Perelman School of Medicine Philadelphia United States Edna Bennett Pierce Prevention Research Center The Pennsylvania State University University ParkPA United States LSH2126 Mercer University MaconGA United States Department of Mathematics and Statistics Wake Forest University Winston-SalemNC United States Department of Biostatistics Harvard School of Public Health BostonMA United States Georgia State University AtlantaGA United States Innovation Center for Biomedical Informatics Georgetown University Medical Center WashingtonDC United States St. George's University School of Medicine Saint George's Grenada Department of Computer Science University of Virginia CharlottesvilleVA United States Department of Bioengineering University of Pennsylvania PhiladelphiaPA United States Department of Orthopaedic Surgery Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Bioengineering University of Pennsylvania Philadelphia PhiladelphiaPA United States Department of Clinical Sciences L

The novel coronavirus SARS-CoV-2, which emerged in late 2019, has since spread around the world and infected hundreds of millions of people with coronavirus disease 2019 (COVID-19). While this viral species was unknown prior to January 2020, its similarity to other coronaviruses that infect humans has allowed for rapid insight into the mechanisms that it uses to infect human hosts, as well as the ways in which the human immune system can respond. Here, we contextualize SARS-CoV-2 among other coronaviruses and identify what is known and what can be inferred about its behavior once inside a human host. Because the genomic content of coronaviruses, which specifies the virus's structure, is highly conserved, early genomic analysis provided a significant head start in predicting viral pathogenesis and in understanding potential differences among variants. The pathogenesis of the virus offers insights into symptomatology, transmission, and individual susceptibility. Additionally, prior research into interactions between the human immune system and coronaviruses has identified how these viruses can evade the immune system's protective mechanisms. We also explore systems-level research into the regulatory and proteomic effects of SARS-CoV-2 infection and the immune response. Understanding the structure and behavior of the virus serves to contextualize the many facets of the COVID-19 pandemic and can influence efforts to control the virus and treat the disease. © 2021, CC BY.

关键词： Coronavirus

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Gradient descent finds global minima of deep neural networks

arXiv

引用

arXiv 2018年

作者： Du, Simon S. Lee, Jason D. Li, Haochuan Wang, Liwei Zhai, Xiyu Machine Learning Department Carnegie Mellon University Data Science and Operations Department University of Southern California School of Physics Peking University Center for Data Science Peking University Beijing Institute of Big Data Research Key Laboratory of Machine Perception Moe School of Eecs Peking University Department of Eecs Massachusetts Institute of Technology

Gradient descent finds a global minimum in training deep neural networks despite the objective function being non-convex. The current paper proves gradient descent achieves zero training loss in polynomial time for a deep over-parameterized neural network with residual connections (ResNet). Our analysis relies on the particular structure of the Gram matrix induced by the neural network architecture. This structure allows us to show the Gram matrix is stable throughout the training process and this stability implies the global optimality of the gradient descent algorithm. We further extend our analysis to deep residual convolutional neural networks and obtain a similar convergence result. Copyright © 2018, The Authors. All rights reserved.

关键词： Gradient methods

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Automatic Extraction of Medication Mentions from Tweets-Overview of the BioCreative VII Shared Task 3 Competition

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database : the journal of biological databases and curation 2023年第2023期2023卷 baac108页

作者： Davy Weissenbacher Karen O'Connor Siddharth Rawal Yu Zhang Richard Tzong-Han Tsai Timothy Miller Dongfang Xu Carol Anderson Bo Liu Qing Han Jinfeng Zhang Igor Kulev Berkay Köprü Raul Rodriguez-Esteban Elif Ozkirimli Ammer Ayach Roland Roller Stephen Piccolo Peijin Han V G Vinod Vydiswaran Ramya Tekumalla Juan M Banda Parsa Bagherzadeh Sabine Bergler João F Silva Tiago Almeida Paloma Martinez Renzo Rivera-Zavala Chen-Kai Wang Hong-Jie Dai Luis Alberto Robles Hernandez Graciela Gonzalez-Hernandez Department of Computational Biomedicine Cedars-Sinai Medical Center Los Angeles CA USA. DBEI The Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. Department of Computer Science and Information Engineering National Central University No. 300 Zhongda Rd Zhongli District Taoyuan 320 Taiwan. IoX Center National Taiwan University Da'an District Section 4 Roosevelt Rd No. 1 Barry Lam Hall Taipei 106 Taiwan. Research Center for Humanities and Social Sciences Academia Sinica No. 128 Section 2 Academia Rd Nangang District Taipei 115 Taiwan. Computational Health Informatics Program Boston Children's Hospital Boston MA USA. Department of Pediatrics Harvard Medical School Boston MA USA. NVIDIA Santa Clara CA USA. Department of Statistics Florida State University Tallahassee FL USA. Data and Analytics Chapter F. Hoffmann-La Roche Ltd Switzerland. Pharmaceutical Research and Early Development Roche Innovation Center Basel Switzerland. Speech and Language Technology Lab DFKI Berlin Germany. Department of Biology Brigham Young University Provo UT USA. Department of Computational Medicine and Bioinformatics Medical School University of Michigan Ann Arbor MI USA. Department of Learning Health Sciences Medical School University of Michigan Ann Arbor MI USA. School of Information University of Michigan Ann Arbor MI USA. Department of Computer Science Georgia State University Atlanta GA USA. CLaC Labs Concordia University Montreal Canada. DETI Institute of Electronics and Informatics Engineering of Aveiro University of Aveiro Portugal. Department of Computation University of A Coruña Spain. Computer Science and Engineering Department Universidad Carlos III de Madrid Madrid Spain. Big Data Laboratory Chunghwa Telecom Laboratories Taoyuan Taiwan. Department of Computer Science National Yang Ming Chiao Tung University Hsinchu Taiwan. Department of Electrical Engineering College of Electrical Engineering and Computer Sci

This study presents the outcomes of the shared task competition BioCreative VII (Task 3) focusing on the extraction of medication names from a Twitter user's publicly available tweets (the user's 'timeline'). In general, detecting health-related tweets is notoriously challenging for natural language processing tools. The main challenge, aside from the informality of the language used, is that people tweet about any and all topics, and most of their tweets are not related to health. Thus, finding those tweets in a user's timeline that mention specific health-related concepts such as medications requires addressing extreme imbalance. Task 3 called for detecting tweets in a user's timeline that mentions a medication name and, for each detected mention, extracting its span. The organizers made available a corpus consisting of 182 049 tweets publicly posted by 212 Twitter users with all medication mentions manually annotated. The corpus exhibits the natural distribution of positive tweets, with only 442 tweets (0.2%) mentioning a medication. This task was an opportunity for participants to evaluate methods that are robust to class imbalance beyond the simple lexical match. A total of 65 teams registered, and 16 teams submitted a system run. This study summarizes the corpus created by the organizers and the approaches taken by the participating teams for this challenge. The corpus is freely available at https://***/tasks/biocreative-vii/track-3/. The methods and the results of the competing systems are analyzed with a focus on the approaches taken for learning from class-imbalanced data.

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Open and Sustainable AI: challenges, opportunities and the road ahead in the life sciences

arXiv

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arXiv 2025年

作者： Farrell, Gavin Adamidi, Eleni Buono, Rafael Andrade Anton, Mihail Attafi, Omar Abdelghani Gutierrez, Salvador Capella Capriotti, Emidio Castro, Leyla Jael Cirillo, Davide Crossman, Lisa Dessimoz, Christophe Dimopoulos, Alexandros Fernández-Díaz, Raúl Fragkouli, Styliani-Christina Goble, Carole Gu, Wei Hancock, John M. Khanteymoori, Alireza Lenaerts, Tom Liberante, Fabio G. Maccallum, Peter Monzon, Alexander Miguel Palmblad, Magnus Poveda, Lucy Radulescu, Ovidiu Shields, Denis C. Sufi, Shoaib Vergoulis, Thanasis Psomopoulos, Fotis Tosatto, Silvio C.E. Institute of Applied Biosciences Centre for Research and Technology Hellas Thessaloniki Greece 1Department of Biomedical Sciences University of Padova Padova35121 Italy Department of Biomedical Sciences University of Padova Via Ugo Bassi 58/B Padova35131 Italy Athena Research & Innovation Center Aigialias & Chalepa Marousi15125 Greece VIB Data Core VIB.AI Center for AI & Computational Biology Technologiepark-Zwijnaarde 75 Ghent9052 Belgium ELIXIR Europe Hub EMBL-EBI South Building Wellcome Genome HinxtonCB10 1SD United Kingdom Plaça Eusebi Güell 1-3 Barcelona08034 Spain Department of Pharmacy and Biotechnology University of Bologna Bologna40126 Italy Computational Genomics Platform IRCCS University Hospital of Bologna Bologna40138 Italy ZB MED Information Centre for Life Sciences Gleueler Straße 60 Cologne50931 Germany SequenceAnalysis.co.uk United Kingdom University of East Anglia Research Park NorwichNR4 7TJ United Kingdom Department of Computational Biology University of Lausanne Genopode 2024.3 Lausanne1015 Switzerland Swiss Institute of Bioinformatics Amphipôle Quartier UNIL-Sorge Lausanne1015 Switzerland Institute for Fundamental Biomedical Science Biomedical Sciences Research Center Alexander Fleming Vari Greece Department of Informatics & Telematics School of Digital Technology Harokopio University Athens Greece School of Medicine University College Dublin Belfield Dublin 4 Ireland Conway Institute of Biomolecular and Biomedical Research University College Dublin Belfield Dublin 4 Ireland IBM Research Dublin DublinD15 HN66 Ireland Institute of Applied Biosciences Centre for Research and Technology Hellas Thessaloniki57001 Greece Department of Biology National & Kapodistrian University of Athens Athens15772 Greece Department of Computer Science University of Manchester Oxford Road ManchesterM19 3PL United Kingdom Luxembourg National Data Service 6 avenue des Hauts-Fourneaux Esch-sur-AlzetteL-4362 Luxembourg Insti

Artificial intelligence (AI) has recently seen transformative breakthroughs in the life sciences, expanding possibilities for researchers to interpret biological information at an unprecedented capacity, with novel applications and advances being made almost daily. In order to maximise return on the growing investments in AI-based life science research and accelerate this progress, it has become urgent to address the exacerbation of long-standing research challenges arising from the rapid adoption of AI methods. We review the increased erosion of trust in AI research outputs, driven by the issues of poor reusability and reproducibility, and highlight their consequent impact on environmental sustainability. Furthermore, we discuss the fragmented components of the AI ecosystem and lack of guiding pathways to best support Open and Sustainable AI (OSAI) model development. In response, this perspective introduces a practical set of OSAI recommendations directly mapped to over 300 components of the AI ecosystem. Our work connects researchers with relevant AI resources, facilitating the implementation of sustainable, reusable and transparent AI. Built upon life science community consensus and aligned to existing efforts, the outputs of this perspective are designed to aid the future development of policy and structured pathways for guiding AI *** Codes 92 © 2025, CC BY-NC-ND.

关键词： Reusability

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Aggregation Signature for Small Object Tracking

arXiv

引用

arXiv 2019年

作者： Liu, Chunlei Ding, Wenrui Yang, Jinyu Murino, Vittorio Zhang, Baochang Han, Jungong Guo, Guodong School of Electrical and Information Engineering Beihang University Beijing China Unmanned System Research Institute Beihang University Beijing China School of Computer Science University of Birmingham British United Kingdom University of Verona Verona Italy Pattern Analysis and Computer Vision department Istituto Italiano di Tecnologia Genoa Italy School of Automation Science and Electrical Engineering Beihang University Beijing China Shenzhen Academy of Aerospace Technology Shenzhen China WMG Data Science Group University of Warwick CoventryCV4 7AL United Kingdom Institute of Deep Learning Baidu Research and National Engineering Laboratory for Deep Learning Technology and Application

—Small object tracking becomes an increasingly important task, which however has been largely unexplored in computer vision. The great challenges stem from the facts that: 1) small objects show extreme vague and variable appearances, and 2) they tend to be lost easier as compared to normal-sized ones due to the shaking of lens. In this paper, we propose a novel aggregation signature suitable for small object tracking, especially aiming for the challenge of sudden and large drift. We make three-fold contributions in this work. First, technically, we propose a new descriptor, named aggregation signature, based on saliency, able to represent highly distinctive features for small objects. Second, theoretically, we prove that the proposed signature matches the foreground object more accurately with a high probability. Third, experimentally, the aggregation signature achieves a high performance on multiple datasets, outperforming the state-of-the-art methods by large margins. Moreover, we contribute with two newly collected benchmark datasets, i.e., small90 and small112, for visually small object tracking. The datasets will be available in https://***/bczhangbczhang/. Copyright © 2019, The Authors. All rights reserved.

关键词： Large dataset

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Illinois Express Quantum Network (IEQNET): Metropolitan-scale experimental quantum networking over deployed optical fiber

arXiv

引用

arXiv 2021年

作者： Chung, Joaquin Kanter, Gregory Lauk, Nikolai Valivarthi, Raju Wu, Wenji Ceballos, Russell R. Peña, Cristián Sinclair, Neil Thomas, Jordan Xie, Si Kettimuthu, Rajkumar Kumar, Prem Spentzouris, Panagiotis Spiropulu, Maria Data Science and Learning Division Argonne National Laboratory 9700 S Cass Ave. LemontIL60439 United States University of Chicago Consortium for Advanced Science and Engineering 924 E 57th St ChicagoIL60637 United States NuCrypt LLC 1840 Oak Ave EvanstonIL60201 United States Center for Photonic Communication and Computing Department of Electrical and Computer Engineering McCormick School of Engineering and Applied Science Northwestern University 2145 Sheridan Road EvanstonIL60208-3118 United States Division of Physics Mathematics and Astronomy California Institute of Technology 1200 E. California Blvd. PasadenaCA91125 United States California Institute of Technology 1200 E. California Blvd. PasadenaCA91125 United States Fermi National Accelerator Laboratory Kirk Rd and Pine St BataviaIL60510 United States Department of Chemistry Physics and Engineering Studies Chicago State University 9501 S. King Dr. ChicagoIL60628 United States Pittsburgh Quantum Institute B4 Thaw Hall 4061 O'Hara Street PittsburghPA15260 United States John A. Paulson School of Engineering and Applied Sciences Harvard University 29 Oxford St. CambridgeMA02138 United States 2205 Tech Drive Suite 1-160 EvanstonIL60208 United States Department of Physics and Astronomy Northwestern University 2145 Sheridan Road EvanstonIL60208-3112 United States

The Illinois Express Quantum Network (IEQNET) is a program to realize metro-scale quantum networking over deployed optical fiber using currently available technology. IEQNET consists of multiple sites that are geographically dispersed in the Chicago metropolitan area. Each site has one or more quantum nodes (Q-nodes) representing the communication parties in a quantum network. Q-nodes generate or measure quantum signals such as entangled photons and communicate the results via standard, classical, means. The entangled photons in IEQNET nodes are generated at multiple wavelengths, and are selectively distributed to the desired users via optical switches. Here we describe the network architecture of IEQNET, including the Internet-inspired layered hierarchy that leverages software-defined-networking (SDN) technology to perform traditional wavelength routing and assignment between the Q-nodes. Specifically, SDN decouples the control and data planes, with the control plane being entirely classical. Issues associated with synchronization, calibration, network monitoring, and scheduling will be discussed. An important goal of IEQNET is demonstrating the extent to which the control plane can coexist with the data plane using the same fiber lines. This goal is furthered by the use of tunable narrow-band optical filtering at the receivers and, at least in some cases, a wide wavelength separation between the quantum and classical channels. We envision IEQNET to aid in developing robust and practical quantum networks by demonstrating metro-scale quantum communication tasks such as entanglement distribution and quantum-state teleportation. © 2021, CC BY.

关键词： Optical fibers

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Global investments in pandemic preparedness and COVID-19: development assistance and domestic spending on health between 1990 and 2026

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The Lancet Global Health 2023年第3期11卷 e385-e413页

作者： Micah, Angela E. Bhangdia, Kayleigh Cogswell, Ian E. Lasher, Dylan Lidral-Porter, Brendan Maddison, Emilie R. Nguyen, Trang Nhu Ngoc Patel, Nishali Pedroza, Paola Solorio, Juan Stutzman, Hayley Tsakalos, Golsum Wang, Yifeng Warriner, Wesley Zhao, Yingxi Zlavog, Bianca S. Abbafati, Cristiana Abbas, Jaffar Abbasi-Kangevari, Mohsen Abbasi-Kangevari, Zeinab Abdelmasseh, Michael Abdulah, Deldar Morad Abedi, Aidin Abegaz, Kedir Hussein Abhilash, E.S. Aboagye, Richard Gyan Abolhassani, Hassan Abrigo, Michael R.M. Abubaker Ali, Hiwa Abu-Gharbieh, Eman Adem, Mohammed Hussien Afzal, Muhammad Sohail Ahmadi, Ali Ahmed, Haroon Ahmed Rashid, Tarik Aji, Budi Akbarialiabad, Hossein Akelew, Yibeltal Al Hamad, Hanadi Alam, Khurshid Alanezi, Fahad Mashhour Alanzi, Turki M. Al-Hanawi, Mohammed Khaled Alhassan, Robert Kaba Aljunid, Syed Mohamed Almustanyir, Sami Al-Raddadi, Rajaa M. Alvis-Guzman, Nelson Alvis-Zakzuk, Nelson J. Amare, Azmeraw T. Ameyaw, Edward Kwabena Amini-Rarani, Mostafa Amu, Hubert Ancuceanu, Robert Andrei, Tudorel Anwar, Sumadi Lukman Appiah, Francis Aqeel, Muhammad Arabloo, Jalal Arab-Zozani, Morteza Aravkin, Aleksandr Y. Aremu, Olatunde Aruleba, Raphael Taiwo Athari, Seyyed Shamsadin Avila-Burgos, Leticia Ayanore, Martin Amogre Azari, Samad Baig, Atif Amin Bantie, Abere Tilahun Barrow, Amadou Baskaran, Pritish Basu, Sanjay Batiha, Abdul-Monim Mohammad Baune, Bernhard T. Berezvai, Zombor Bhardwaj, Nikha Bhardwaj, Pankaj Bhaskar, Sonu Boachie, Micheal Kofi Bodolica, Virginia Botelho, João Silva Botelho Braithwaite, Dejana Breitborde, Nicholas J.K. Busse, Reinhard Cahuana-Hurtado, Lucero Catalá-López, Ferrán Chansa, Collins Charan, Jaykaran Chattu, Vijay Kumar Chen, Simiao Chukwu, Isaac Sunday Dadras, Omid Dandona, Lalit Dandona, Rakhi Dargahi, Abdollah Debela, Sisay Abebe Denova-Gutiérrez, Edgar Desye, Belay Dharmaratne, Samath Dhamminda Diao, Nancy Doan, Linh Phuong Dodangeh, Milad dos Santos, Wendel Mombaque Doshmangir, Leila Dube, John Eini, Ebrahim El Sayed Zaki, Maysaa El Tantawi, Maha Enyew, Daniel Berhanie Institute for Health Metrics and Evaluation University of Washington Seattle WA United States Department of Applied Mathematics University of Washington Seattle WA United States Department of Health Metrics Sciences School of Medicine University of Washington Seattle WA United States Nuffield Department of Medicine University of Oxford Oxford United Kingdom Department of Juridical and Economic Studies La Sapienza University Rome Italy Antai College of Economics Shanghai Jiao Tong University Shanghai China Non-communicable Diseases Research Center Tehran University of Medical Sciences Tehran Iran Research Center for Immunodeficiencies Tehran University of Medical Sciences Tehran Iran Multiple Sclerosis Research Center Tehran University of Medical Sciences Tehran Iran Children’s Medical Center Tehran University of Medical Sciences Tehran Iran Department of Epidemiology and Biostatistics Tehran University of Medical Sciences Tehran Iran Tehran Heart Center Tehran University of Medical Sciences Tehran Iran Faculty of Medicine Tehran University of Medical Sciences Tehran Iran National Institute for Health Research Tehran University of Medical Sciences Tehran Iran Department of Pharmacology Tehran University of Medical Sciences Tehran Iran Department of Cardiology Tehran University of Medical Sciences Tehran Iran Sina Trauma and Surgery Research Center Tehran University of Medical Sciences Tehran Iran Digestive Diseases Research Institute Tehran University of Medical Sciences Tehran Iran Department of Neurology Tehran University of Medical Sciences Tehran Iran Social Determinants of Health Research Center Shahid Beheshti University of Medical Sciences Tehran Iran Department of Epidemiology Shahid Beheshti University of Medical Sciences Tehran Iran Department of Health Economics and Statistics Shahid Beheshti University of Medical Sciences Tehran Iran Department of Neurosurgery Shahid Beheshti University of Medical Sciences Tehran Iran Ophthalmic R

Background: The COVID-19 pandemic highlighted gaps in health surveillance systems, disease prevention, and treatment globally. Among the many factors that might have led to these gaps is the issue of the financing of national health systems, especially in low-income and middle-income countries (LMICs), as well as a robust global system for pandemic preparedness. We aimed to provide a comparative assessment of global health spending at the onset of the pandemic;characterise the amount of development assistance for pandemic preparedness and response disbursed in the first 2 years of the COVID-19 pandemic;and examine expectations for future health spending and put into context the expected need for investment in pandemic preparedness. Methods: In this analysis of global health spending between 1990 and 2021, and prediction from 2021 to 2026, we estimated four sources of health spending: development assistance for health (DAH), government spending, out-of-pocket spending, and prepaid private spending across 204 countries and territories. We used the Organisation for Economic Co-operation and Development (OECD)'s Creditor Reporting System (CRS) and the WHO Global Health Expenditure database (GHED) to estimate spending. We estimated development assistance for general health, COVID-19 response, and pandemic preparedness and response using a keyword search. Health spending estimates were combined with estimates of resources needed for pandemic prevention and preparedness to analyse future health spending patterns, relative to need. Findings: In 2019, at the onset of the COVID-19 pandemic, US$9·2 trillion (95% uncertainty interval [UI] 9·1–9·3) was spent on health worldwide. We found great disparities in the amount of resources devoted to health, with high-income countries spending $7·3 trillion (95% UI 7·2–7·4) in 2019;293·7 times the $24·8 billion (95% UI 24·3–25·3) spent by low-income countries in 2019. That same year, $43·1 billion in development assistance was provided

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Comprehensive proteomics metadata and integrative web portals facilitate sharing and integration of LINCS multiomics data

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Molecular & cellular proteomics : MCP 2025年 100947页

作者： Dušica Vidović Behrouz Shamsaei Stephan C Schürer Phillip Kogan Szymon Chojnacki Michal Kouril Mario Medvedovic Wen Niu Evren U Azeloglu Marc R Birtwistle Yibang Chen Tong Chen Jens Hansen Bin Hu Ravi Iyengar Gomathi Jayaraman Hong Li Tong Liu Eric A Sobie Yuguang Xiong Matthew J Berberich Gary Bradshaw Mirra Chung Robert A Everley Ben Gaudio Marc Hafner Marian Kalocsay Caitlin E Mills Maulik K Nariya Peter K Sorger Kartik Subramanian Chiara Victor Maria Banuelos Victoria Dardov Ronald Holewinski Danica-Mae Manalo Berhan Mandefro Andrea D Matlock Loren Ornelas Dhruv Sareen Clive N Svendsen Vineet Vaibhav Jennifer E Van Eyk Vidya Venkatraman Steve Finkbiener Ernest Fraenkel Jeffrey Rothstein Leslie Thompson Jacob Asiedu Steven A Carr Karen E Christianson Desiree Davison Deborah O Dele-Oni Katherine C DeRuff Shawn B Egri Alvaro Sebastian Vaca Jacome Jacob D Jaffe Daniel Lam Lev Litichevskiy Xiaodong Lu James Mullahoo Adam Officer Malvina Papanastasiou Ryan Peckner Caidin Toder Joel Blanchard Michael Bula Tak Ko Li-Huei Tsai Jennie Z Young Vagisha Sharma Ajay Pillai Jarek Meller Michael J MacCoss BD2K-LINCS DCIC Department of Molecular and Cellular Pharmacology University of Miami Miami FL 33146 Sylvester Comprehensive Cancer Center Miller School of Medicine University of Miami FL 33146. BD2K-LINCS DCIC Department of Environmental and Public Health Sciences University of Cincinnati Cincinnati OH 45220. Sylvester Comprehensive Cancer Center Miller School of Medicine University of Miami FL 33146 BD2K-LINCS DCIC Institute for Data Science and Computing University of Miami FL 33146. DToxS Department of Pharmacological Sciences Icahn School of Medicine at Mount Sinai New York NY 10029. DToxS Department of Pharmacological Sciences Icahn School of Medicine at Mount Sinai New York NY 10029 DToxS Center for Advanced Proteomics Research Rutgers University New Jersey Medical School Newark NJ 07103. HMS LINCS Center Harvard Medical School Boston MA 02115. NeuroLINCS Cedars-Sinai Medical Center Los Angeles CA 90048. NeuroLINCS Gladstone Institute of Neurological Disease and the Departments of Neurology and Physiology University of California San Francisco San Francisco CA 94158. NeuroLINCS Department of Biological Engineering MIT Cambridge MA 02142. NeuroLINCS Department of Neuroscience Johns Hopkins University Baltimore MD 21205. NeuroLINCS Departments of Psychiatry and Human Behavior and Neurobiology and Behavior University of California Irvine Irvine CA 92697. PCCSE The Broad Institute of Harvard and MIT Cambridge MA 02142. PCCSE Picower Institute for Learning and Memory Department of Brain and Cognitive Sciences Massachusetts Institute of Technology Cambridge MA 02139. PCCSE Department of Genome Sciences University of Washington Seattle WA 98195. NIH Bethesda MD 20892. BD2K-LINCS DCIC Department of Environmental and Public Health Sciences University of Cincinnati Cincinnati OH 45220. Electronic address: mellerj@ucmail.uc.edu. PCCSE Department of Genome Sciences University of Washington Seattle WA 98195. Electronic address: maccoss@uw.edu.

The Library of Integrated Network-based Cellular Signatures (LINCS), an NIH Common Fund program, has cataloged and analyzed cellular function and molecular activity profiles in response to >80,000 perturbing agents that are potentially disruptive to cells. Because of the importance of proteins and their modifications to the response of specific cellular perturbations, four of the six LINCS centers have included significant proteomics efforts in the characterization of the resulting phenotype. This manuscript aims to describe this effort and the data harmonization and integration of the LINCS proteomics data discussed in recent LINCS papers.

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On ADMM in deep learning: Convergence and saturation-avoidance

arXiv

引用

arXiv 2019年

作者： Zeng, Jinshan Lin, Shao-Bo Yao, Yuan Zhou, Ding-Xuan School of Computer and Information Engineering Jiangxi Normal University Nanchang China Liu Bie Ju Centre for Mathematical Sciences City University of Hong Kong Hong Kong Hong Kong Department of Mathematics Hong Kong University of Science and Technology Hong Kong Hong Kong Center of Intelligent Decision-Making and Machine Learning School of Management Xi’an Jiaotong University Xi’an China School of Data Science Department of Mathematics City University of Hong Kong Hong Kong Hong Kong

In this paper, we develop an alternating direction method of multipliers (ADMM) for deep neural networks training with sigmoid-type activation functions (called sigmoid-ADMM pair), mainly motivated by the gradient-free nature of ADMM in avoiding the saturation of sigmoid-type activations and the advantages of deep neural networks with sigmoid-type activations (called deep sigmoid nets) over their rectified linear unit (ReLU) counterparts (called deep ReLU nets) in terms of approximation. In particular, we prove that the approximation capability of deep sigmoid nets is not worse than that of deep ReLU nets by showing that ReLU activation function can be well approximated by deep sigmoid nets with two hidden layers and finitely many free parameters but not vice-verse. We also establish the global convergence of the proposed ADMM for the nonlinearly constrained formulation of the deep sigmoid nets training from arbitrary initial points to a Karush-Kuhn-Tucker (KKT) point at a rate of order O(1/k). Besides sigmoid activation, such a convergence theorem holds for a general class of smooth activations. Compared with the widely used stochastic gradient descent (SGD) algorithm for the deep ReLU nets training (called ReLU-SGD pair), the proposed sigmoid-ADMM pair is practically stable with respect to the algorithmic hyperparameters including the learning rate, initial schemes and the pro-processing of the input data. Moreover, we find that to approximate and learn simple but important functions the proposed sigmoid-ADMM pair numerically outperforms the ReLU-SGD pair. Copyright © 2019, The Authors. All rights reserved.

关键词： Chemical activation

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Genome-wide association study of long COVID

引用

Nature Genetics 2025年 1-16页

作者： Lammi, Vilma Nakanishi, Tomoko Jones, Samuel E. Andrews, Shea J. Karjalainen, Juha Cortés, Beatriz O’Brien, Heath E. Ochoa-Guzman, Ana Fulton-Howard, Brian E. Broberg, Martin Haapaniemi, Hele H. Kanai, Masahiro Pirinen, Matti Schmidt, Axel Mitchell, Ruth E. Mousas, Abdou Mangino, Massimo Huerta-Chagoya, Alicia Sinnott-Armstrong, Nasa Cirulli, Elizabeth T. Vaudel, Marc Kwong, Alex S. F. Maiti, Amit K. Marttila, Minttu M. Posner, Daniel C. Rodriguez, Alexis A. Batini, Chiara Minnai, Francesca Dearman, Anna R. Warmerdam, C. A. Robert Sequeros, Celia B. Winkler, Thomas W. Jordan, Daniel M. Rešcenko, Raimonds Miano, Lorenzo Lane, Jacqueline M. Chung, Ryan K. Guillen-Guio, Beatriz Leavy, Olivia C. Carvajal-Silva, Laura Aguilar-Valdés, Kevin Frangione, Erika Guare, Lindsay Vergasova, Ekaterina Marouli, Eirini Striano, Pasquale Zainulabid, Ummu Afeera Kumar, Ashutosh Ahmad, Hajar Fauzan Edahiro, Ryuya Azekawa, Shuhei Maj, Carlo Pensel, Max C. Miller, Abigail Nöthen, Markus M. Behzad, Pari Schultz, Sonja Heggemann, Julia Balla, Daniella Morrison, David R. Mooser, Vincent Perley, Danielle Ragoussis, Jiannis Rousseau, Simon Shi, Fangyi Lathrop, G. Mark Yanishevsky, Solomia Laurent, Laetitia Auld, Daniel Bertrand, Mylene Boisclair, Ariane Bourque, Guillaume Bujold, David Durand, Madeleine Adra, Darin St-Cyr, Janick Niemi, Mari E. K. Iyengar, Sudha K. Cai, Tianxi Cho, Kelly Sarvadhavabhatla, Sannidhi Donaire, Maria Sophia Valenzuela-Jorquera, Héctor Lamoza, Eduardo Arévalo, Tamara V. Retamales-Ortega, Rocío Sanhueza, Sergio Selman, Carolina S. Silva, Andrea X. Alarcon, Teresa A. Martínez, Matías F. Quiroga, Romina Zuñiga-Pacheco, Paula Zapata-Contreras, Daniela Yáñez, Cristian E. Quiñones, Luis A. Tobar-Calfucoy, Eduardo A. Monardes-Ramírez, Virginia A. Signore, Iskra A. Guajardo, Lissette G. Figueroa, Alvaro Oróstica, Karen Y. Muñoz, Christian A. Fuentes-Guajardo, Macarena Bocchieri, Pamela Cabrera, Camilo Echeverria, Cesar A. Cerpa, Leslie C. Donoso, Gerardo Gutiérrez-Richards, Scarlett Nova-Lamperti, Estefania Saez Hidalgo Institute for Molecular Medicine Finland (FIMM) Helsinki Institute of Life Science (HiLIFE) University of Helsinki Helsinki Finland Department of Human Genetics McGill University Montreal QC Canada Centre for Clinical Epidemiology Department of Medicine Lady Davis Institute Jewish General Hospital McGill University Montreal QC Canada Kyoto-McGill International Collaborative Program in Genomic Medicine Graduate School of Medicine Kyoto University Kyoto Japan Department of Genome Informatics Graduate School of Medicine the University of Tokyo Tokyo Japan Research Fellow Japan Society for the Promotion of Science Tokyo Japan Department of Psychiatry and Behavioral Sciences University of California San Francisco San Francisco CA United States Program in Medical and Population Genetics Broad Institute of Harvard and MIT Cambridge MA United States Stanley Center for Psychiatric Research Broad Institute of Harvard and MIT Cambridge MA United States Analytic and Translational Genetics Unit Massachusetts General Hospital Boston MA United States Genomes for Life-GCAT Lab CORE Program Germans Trias i Pujol Research Institute (IGTP) Badalona Spain Grup de REcerca en Impacte de les Malalties Cròniques i les seves Trajectòries (GRIMTra) Barcelona Spain Sano Genetics Limited London United Kingdom Unidad de Biología Molecular y Medicina Genómica Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán Mexico City Mexico Genetics and Genomic Sciences Icahn School of Medicine at Mount Sinai New York City NY United States Broad Institute Cambridge MA United States Analytical and Translational Genetics Unit Massachusetts General Hospital Harvard Medical School Boston MA United States Department of Mathematics and Statistics University of Helsinki Helsinki Finland Department of Public Health University of Helsinki Helsinki Finland Institute of Human Genetics University of Bonn School of Medicine and University Hospital Bonn Bonn Germany MRC Integrat

Infections can lead to persistent symptoms and diseases such as shingles after varicella zoster or rheumatic fever after streptococcal infections. Similarly, severe acute respiratory syndrome coronavirus 2 (SARS‑CoV‑2) infection can result in long coronavirus disease (COVID), typically manifesting as fatigue, pulmonary symptoms and cognitive dysfunction. The biological mechanisms behind long COVID remain unclear. We performed a genome-wide association study for long COVID including up to 6,450 long COVID cases and 1,093,995 population controls from 24 studies across 16 countries. We discovered an association of FOXP4 with long COVID, independent of its previously identified association with severe COVID-19. The signal was replicated in 9,500 long COVID cases and 798,835 population controls. Given the transcription factor FOXP4’s role in lung physiology and pathology, our findings highlight the importance of lung function in the pathophysiology of long COVID. © The Author(s) 2025.

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