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International Symposium for Intelligent Transportation and Smart City (ITASC) 2019 Proceedings 1

丛书名： Smart Innovation, Systems and Technologies

2019年

作者： Xiaoqing Zeng Xiongyao Xie Jian Sun Limin Ma Yinong Chen

ISBN: (数字)9789811375422

ISBN: (纸本)9789811375415

This book presents research advances in intelligent transportation and smart cities in detail, mainly focusing on green traffic and urban utility tunnels, presented at the 4th International Symposium for Intelligent Transportation and Smart City (ITASC) held at Tongji University, Shanghai, on May 8–10, 2019. It discusses a number of hot topics, such as the 2BMW system (Bus, Bike, Metro and Walking), transportation safety and environmental protection, urban utility design and application, as well as the application of BIM (Building Information Modeling) in city design. By connecting the theory and applications of intelligent transportation in smart cities, it enhances traffic efficiency and quality. The book gathers numerous selected papers and lectures, including contributions from respected scholars and the latest engineering advances, to provide guidance to researchers in the field of transportation and urban planning at universities and in related industries.

关键词： Transportation Technology and Traffic Engineering Geotechnical Engineering and Applied Earth sciences Sustainability Communications Engineering, Networks

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Decoding the exposome: data science methodologies and implications in exposome-wide association studies (ExWASs)

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Exposome 2024年第1期4卷 osae001页

作者： Ming Kei Chung John S House Farida S Akhtari Konstantinos C Makris Michael A Langston Khandaker Talat Islam Philip Holmes Marc Chadeau-Hyam Alex I Smirnov Xiuxia Du Anne E Thessen Yuxia Cui Kai Zhang Arjun K Manrai Alison Motsinger-Reif Chirag J Patel Department of Biomedical Informatics Harvard Medical School Boston MA USA. School of Public Health and Primary Care The Chinese University of Hong Kong Hong Kong China. Institute of Environment Energy and Sustainability The Chinese University of Hong Kong Hong Kong China. Biostatistics and Computational Biology Branch Division of Intramural Research National Institute of Environmental Health Sciences Durham NC USA. Cyprus International Institute for Environmental and Public Health School of Health Sciences Cyprus University of Technology Limassol Cyprus. Department of Electrical Engineering and Computer Science University of TN Knoxville TN USA. Department of Population and Public Health Sciences Keck School of Medicine of the University of Southern CA Los Angeles CA USA. Department of Physics Villanova University Villanova Philadelphia USA. Department of Epidemiology and Biostatistics School of Public Health Imperial College London London UK. Department of Chemistry NC State University Raleigh NC USA. Department of Bioinformatics and Genomics College of Computing and Informatics University of NC at Charlotte Charlotte NC USA. Department of Biomedical Informatics University of CO Anschutz Medical Campus Aurora CO USA. Exposure Response and Technology Branch Division of Extramural Research and Training National Institute of Environmental Health Sciences Durham NC USA. Department of Environmental Health Sciences School of Public Health University at Albany State University of NY Rensselaer NY USA.

This paper explores the exposome concept and its role in elucidating the interplay between environmental exposures and human health. We introduce two key concepts critical for exposomics research. Firstly, we discuss the joint impact of genetics and environment on phenotypes, emphasizing the variance attributable to shared and nonshared environmental factors, underscoring the complexity of quantifying the exposome's influence on health outcomes. Secondly, we introduce the importance of advanced data-driven methods in large cohort studies for exposomic measurements. Here, we introduce the exposome-wide association study (ExWAS), an approach designed for systematic discovery of relationships between phenotypes and various exposures, identifying significant associations while controlling for multiple comparisons. We advocate for the standardized use of the term "exposome-wide association study, ExWAS," to facilitate clear communication and literature retrieval in this field. The paper aims to guide future health researchers in understanding and evaluating exposomic studies. Our discussion extends to emerging topics, such as FAIR Data Principles, biobanked healthcare datasets, and the functional exposome, outlining the future directions in exposomic research. This abstract provides a succinct overview of our comprehensive approach to understanding the complex dynamics of the exposome and its significant implications for human health.

关键词： Exposome-Wide Association Study (ExWAS) data science epidemiology exposome false discovery rate phenotype

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Artificial light at night outweighs temperature in lengthening urban growing seasons

Nature Cities

引用

Nature Cities 2025年 1-12页

作者： Lvlv Wang Jun Xia Dunxian She Lin Meng Huidong Li Andrew D. Richardson Franz Hölker Jiafu Mao Travis Longcore State Key Laboratory of Water Resources Engineering and Management Wuhan University Wuhan China Hubei Key Laboratory of Water System Science for Sponge City Construction Wuhan University Wuhan China Department of Earth and Environmental Sciences Vanderbilt University Nashville TN USA School of Informatics Computing and Cyber Systems Northern Arizona University Flagstaff AZ USA Center for Ecosystem Science and Society Northern Arizona University Flagstaff AZ USA Leibniz Institute of Freshwater Ecology and Inland Fisheries Berlin Germany Institute of Biology Freie Universität Berlin Berlin Germany Environmental Sciences Division Oak Ridge National Laboratory Oak Ridge TN USA Institute of the Environment and Sustainability University of California Los Angeles Los Angeles CA USA

Plant growing seasons are largely regulated by light and temperature. Cities are increasingly hot (higher air temperature), from the urban heat island effect, and bright (artificial light at night, ALAN). However, the relative effect of heat and light on the timing of plant growth events, called phenology, is unclear, limiting our understanding under climate change and urbanization. Here we used multiple satellite observations of 428 Northern Hemisphere cities from 2014 to 2020 to analyze phenological patterns along a gradient from rural to urban. We found that ALAN increased exponentially toward urban centers, and exerted stronger influence than air temperature in lengthening the urban growing season, especially by delaying its end, although the effects varied across climate zones. Our findings demonstrate that ALAN is a critical driver of vegetation dynamics in cities, one we should consider during urban management and development.

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Predicting Spring Phenology in Deciduous Broadleaf Forests: Neon Phenology Forecasting Community Challenge

SSRN

引用

SSRN 2023年

作者： Wheeler, Kathryn I. Dietze, Michael C. LeBauer, David Peters, Jody Richardson, Andrew D. Ross, Arun A. Thomas, R. Quinn Zhu, Kai Bhat, Uttam Munch, Stephan Buzbee, Raphaela Floreani Chen, Min Goldstein, Benjamin Guo, Jessica Hao, Dalei Jones, Chris Kelly-Fair, Mira Liu, Haoran Malmborg, Charlotte Neupane, Naresh Pal, Debasmita Shirey, Vaughn Song, Yiluan Steen, McKalee Vance, Eric A. Woelmer, Whitney M. Wynne, Jacob Zachmann, Luke Department of Earth and Environment Boston University 685 Commonwealth Avenue BostonMA02215 United States Cooperative Programs for the Advancement of Earth System Science University Corporation for Atmospheric Research Foothills Laboratory Bldg 4 3300 Mitchell Lane BoulderCO80301 United States National Oceanic and Atmospheric Administration Foothills Laboratory Bldg 4 3300 Mitchell Lane BoulderCO80301 United States Department of Civil and Environmental Engineering Massachusetts Institute of Technology 15 Vassar St CambridgeMA02142 United States Arizona Experiment Station University of Arizona 1230 N Cherry Ave TucsonAZ85721 United States Department of Biological Sciences University of Notre Dame 100 Galvin Notre DameIN46556 United States School of Informatics Computing and Cyber Systems Northern Arizona University 1295 Knoles Dr. FlagstaffAZ86011 United States Center for Ecosystem Science and Society Northern Arizona University PO Box 5620 FlagstaffAZ86011 United States Department of Computer Science and Engineering Michigan State University 428 South Shaw Lane East LansingMI48824 United States Department of Forest Resources and Environmental Conservation Virginia Polytechnic Instit 310 West Campus Drive BlacksburgVA24061 United States Department of Biological Sciences Virginia Tech 926 West Campus Drive BlacksburgVA24061 United States Institute for Global Change Biology University of Michigan 440 Church Street Ann ArborMI48109 United States School for Environment and Sustainability University of Michigan 440 Church Street Ann ArborMI48109 United States Institute of Marine Sciences University of California Santa Cruz 1156 High Street Santa CruzCA95060 United States Department of Applied Mathematics University of California Santa Cruz 1156 High Street Santa CruzCA95060 United States Department of Environmental Science Policy and Management University of California Berkeley 4004 Valley Life Sciences Building Universi

Accurate models are important to predict how global climate change will continue to alter plant phenology and near-term ecological forecasts can be used to iteratively improve models and evaluate predictions that are made a priori. The Ecological Forecasting Initiative’s National Ecological Observatory Network (NEON) Forecasting Challenge, is an open challenge to the community to forecast daily greenness values, measured through digital images collected by the PhenoCam Network at NEON sites before the data are collected. For the first round of the challenge, which is presented here, we forecasted canopy greenness throughout the spring at eight deciduous broadleaf sites to investigate what factors impact current predictive skill of phenology forecasts. A total of 192,536 predictions were submitted, representing eighteen models, including a persistence and a day of year mean null models. We found that overall forecast skill was highest when forecasting earlier in the greenup curve compared to the end, for shorter lead times, for sites that greened up earlier, and when submitting forecasts during times other than near budburst. The models based on day of year historical mean had the highest predictive skill across the challenge period. In this first round of the challenge, by synthesizing across forecasts, we started to elucidate what factors affect the predictive skill of near-term phenology forecasts. © 2023, The Authors. All rights reserved.

关键词： Forecasting

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Predicting Spring Phenology in Deciduous Broadleaf Forests: An Open Community Forecast Challenge

SSRN

引用

SSRN 2023年

作者： Wheeler, Kathryn I. Dietze, Michael C. LeBauer, David Peters, Jody Richardson, Andrew D. Thomas, R. Quinn Zhu, Kai Bhat, Uttam Munch, Stephan Buzbee, Raphaela Floreani Chenp, Min Goldstein, Benjamin Guo, Jessica S. Haoq, Dalei Jones, Chris Kelly-Fair, Mira Liup, Haoran Malmborg, Charlotte Neupane, Naresh Pal, Debasmita Ross, Arun A. Shirey, Vaughn Song, Yiluan Steen, McKalee Vance, Eric A. Woelmer, Whitney M. Wynne, Jacob Zachmann, Luke Department of Earth and Environment Boston University 685 Commonwealth Avenue BostonMA02215 United States Cooperative Programs for the Advancement of Earth System Science University Corporation for Atmospheric Research Foothills Laboratory Bldg 4 3300 Mitchell Lane BoulderCO80301 United States National Oceanic and Atmospheric Administration Foothills Laboratory Bldg 4 3300 Mitchell Lane BoulderCO80301 United States Department of Civil and Environmental Engineering Massachusetts Institute of Technology 15 Vassar St CambridgeMA02142 United States Arizona Experiment Station University of Arizona 1230 N Cherry Ave TucsonAZ85721 United States Department of Biological Sciences University of Notre Dame 100 Galvin Notre DameIN46556 United States School of Informatics Computing and Cyber Systems Northern Arizona University 1295 Knoles Dr. FlagstaffAZ86011 United States Center for Ecosystem Science and Society Northern Arizona University PO Box 5620 FlagstaffAZ86011 United States Department of Forest Resources and Environmental Conservation Virginia Polytechnic Institute State University 310 West Campus Drive BlacksburgVA24061 United States Department of Biological Sciences Virginia Polytechnic Institute State University 926 West Campus Drive BlacksburgVA24061 United States Institute for Global Change Biology University of Michigan 440 Church Street Ann ArborMI48109 United States School for Environment and Sustainability University of Michigan 440 Church Street Ann ArborMI48109 United States Institute of Marine Sciences University of California Santa Cruz 1156 High Street Santa CruzCA95060 United States Department of Applied Mathematics University of California Santa Cruz 1156 High Street Santa CruzCA95060 United States Department of Environmental Science Policy and Management University of California Berkeley 4004 Valley Life Sciences Building University of California Berkeley BerkeleyCA94720 United States Department of For

Accurate phenology models are important to predict how global climate change will continue to alter the timing of plant phenological events, such as spring greenup in deciduous broadleaf forests. While there is merit in long-term predictions, investigating how models can predict near-term (1– 35 days) canopy greenness throughout the spring allows us to validate performance and understanding now. The Ecological Forecasting Initiative’s NEON Forecasting Challenge, is an open challenge to the community to predict daily greenness values, measured through digital images collected by the PhenoCam Network at National Ecological Observatory Network (NEON) sites. For the first round of the challenge, which is presented here, teams were tasked to forecast phenology at eight deciduous broadleaf sites. A total of 192,536 predictions were submitted, representing eighteen models, including a persistence and a day of year mean null models. We found that forecast skill was highest when forecasting earlier in the greenup curve compared to the end, for shorter lead times, for sites that greened up earlier, and when submitting forecasts during times other than near budburst. The models based on day of year historical mean had the highest predictive skill across the challenge period. In this first round of the challenge, by synthesizing across forecasts, we started to elucidate what factors affect the predictive skill of near-term phenology forecasts, which future rounds will continue to investigate. © 2023, The Authors. All rights reserved.

关键词： Forecasting

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Foundation models and intelligent decision-making: Progress, challenges, and perspectives

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Innovation 2025年第6期6卷 100948页

作者： Huang, Jincai Xu, Yongjun Wang, Qi Wang, Qi [Cheems] Liang, Xingxing Wang, Fei Zhang, Zhao Wei, Wei Zhang, Boxuan Huang, Libo Chang, Jingru Ma, Liantao Ma, Ting Liang, Yuxuan Zhang, Jie Guo, Jian Jiang, Xuhui Fan, Xinxin An, Zhulin Li, Tingting Li, Xuefei Shao, Zezhi Qian, Tangwen Sun, Tao Diao, Boyu Yang, Chuanguang Yu, Chenqing Wu, Yiqing Li, Mengxian Zhang, Haifeng Zeng, Yongcheng Zhang, Zhicheng Zhu, Zhengqiu Lv, Yiqin Li, Aming Chen, Xu An, Bo Xiao, Wei Bai, Chenguang Mao, Yuxing Yin, Zhigang Gui, Sheng Su, Wentao Zhu, Yinghao Gao, Junyi He, Xinyu Li, Yizhou Jin, Guangyin Ao, Xiang Zhai, Xuehao Tan, Haoran Yun, Lijun Shi, Hongquan Li, Jun Fan, Changjun Huang, Kuihua Harrison, Ewen Leung, Victor C.M. Qiu, Sihang Dong, Yanjie Zheng, Xiaolong Wang, Gang Zheng, Yu Wang, Yuanzhuo Guo, Jiafeng Wang, Lizhe Cheng, Xueqi Wang, Yaonan Yang, Shanlin Fu, Mengyin Fei, Aiguo Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Department of Automation Tsinghua University Beijing100084 China Huazhong University of Science and Technology Wuhan430074 China School of Automation Beijing Institute of Technology Beijing100081 China School of Information Science and Engineering Dalian Polytechnic University Dalian116034 China National Engineering Research Center for Software Engineering Peking University Beijing100871 China Department of Oral Implantology Peking University School and Hospital of Stomatology Beijing100081 China Guangzhou511453 China College of Information and Electrical Engineering China Agricultural University Beijing100083 China IDEA Research International Digital Economy Academy Shenzhen518057 China Institute of Automation Chinese Academy of Sciences Beijing100190 China The State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China College of Science National University of Defense Technology Changsha410073 China Center for Systems and Control College of Engineering Peking University Beijing100871 China Gaoling School of Artificial Intelligence Renmin University of China Beijing100872 China College of Systems Engineering National University of Defense Technology Changsha410073 China Laboratory for Big Data and Decision National University of Defense Technology Changsha410073 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore School of Electrical Engineering Chongqing University Chongqing400044 China Department of Mathematics The University of Hong Kong Hong Kong SAR999077 China School of Food Science and Technology Dalian Polytechnic University Dalian116034 China Centre for Medical Informatics University of Edinburgh EdinburghEH16 4UX United Kingdom Department of Stomatology Peking Union Medical College Hospital Chinese Academ

Intelligent decision-making (IDM) is a cornerstone of artificial intelligence (AI) designed to automate or augment decision processes. Modern IDM paradigms integrate advanced frameworks to enable intelligent agents to make effective and adaptive choices and decompose complex tasks into manageable steps, such as AI agents and high-level reinforcement learning. Recent advances in multimodal foundation-based approaches unify diverse input modalities—such as vision, language, and sensory data—into a cohesive decision-making process. Foundation models (FMs) have become pivotal in science and industry, transforming decision-making and research capabilities. Their large-scale, multimodal data-processing abilities foster adaptability and interdisciplinary breakthroughs across fields such as healthcare, life sciences, and education. This survey examines IDM's evolution, advanced paradigms with FMs and their transformative impact on decision-making across diverse scientific and industrial domains, highlighting the challenges and opportunities in building efficient, adaptive, and ethical decision systems. © 2025 The Authors

关键词： Reinforcement learning

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Strategic priorities for transformative progress in advancing biology with proteomics and artificial intelligence

arXiv

引用

arXiv 2025年

作者： Sun, Yingying Jun, A. Liu, Zhiwei Sun, Rui Qian, Liujia Payne, Samuel H. Bittremieux, Wout Ralser, Markus Li, Chen Chen, Yi Dong, Zhen Perez-Riverol, Yasset Khan, Asif Sander, Chris Aebersold, Ruedi Vizcaíno, Juan Antonio Krieger, Jonathan R. Yao, Jianhua Wen, Han Zhang, Linfeng Zhu, Yunping Xuan, Yue Sun, Benjamin Boyang Qiao, Liang Hermjakob, Henning Tang, Haixu Gao, Huanhuan Deng, Yamin Zhong, Qing Chang, Cheng Bandeira, Nuno Li, Ming Weinan, E. Sun, Siqi Yang, Yuedong Omenn, Gilbert S. Zhang, Yue Xu, Ping Fu, Yan Liu, Xiaowen Overall, Christopher M. Wang, Yu Deutsch, Eric W. Chen, Luonan Cox, Jürgen Demichev, Vadim He, Fuchu Huang, Jiaxing Jin, Huilin Liu, Chao Li, Nan Luan, Zhongzhi Song, Jiangning Yu, Kaicheng Wan, Wanggen Wang, Tai Zhang, Kang Zhang, Le Bell, Peter A. Mann, Matthias Zhang, Bing Guo, Tiannan Affiliated Hangzhou First People’s Hospital State Key Laboratory of Medical Proteomics School of Medicine Westlake University Zhejiang Province Hangzhou China Westlake Center for Intelligent Proteomics Westlake Laboratory of Life Sciences and Biomedicine Zhejiang Province Hangzhou China Biology Department Brigham Young University ProvoUT84602 United States Department of Computer Science University of Antwerp Antwerp2020 Belgium Department of Biochemistry CharitéUniversitätsmedizin Berlin Berlin Germany Biomedicine Discovery Institute Department of Biochemistry and Molecular Biology Monash University MelbourneVICVIC 3800 Australia Wellcome Genome Campus Hinxton CambridgeCB10 1SD United Kingdom Harvard Medical School Ludwig Center at Harvard United States Harvard Medical School Broad Institute Ludwig Center at Harvard Dana-Farber Cancer Institute United States Department of Biology Institute of Molecular Systems Biology ETH Zürich Zürich Switzerland Bruker Ltd. MiltonONL9T 6P4 Canada AI for Life Sciences Lab Tencent Shenzhen518057 China State Key Laboratory of Medical Proteomics AI for Science Institute Beijing100080 China Beijing Institute of Lifeomics Beijing102206 China Thermo Fisher Scientific GmbH Hanna-Kunath Str. 11 Bremen28199 Germany Informatics and Predictive Sciences Research Bristol Myers Squibb United States Department of Chemistry Fudan University Songhu Road 2005 Shanghai200438 China Department of Computer Science Luddy School of Informatics Computing and Engineering Indiana University IN47408 United States ProCan® Children’s Medical Research Institute Faculty of Medicine and Health The University of Sydney WestmeadNSW Australia La Jolla CA United States Central China Institute of Artificial Intelligence University of Waterloo Canada AI for Science Institute Center for Machine Learning Research School of Mathematical Sciences Peking University China Research Institute of Intelligent Complex Systems Fudan U

Artificial intelligence (AI) is transforming scientific research, including proteomics. Advances in mass spectrometry (MS)-based proteomics data quality, diversity, and scale, combined with groundbreaking AI techniques, are unlocking new challenges and opportunities in biological discovery. Here, we highlight key areas where AI is driving innovation, from data analysis to new biological insights. These include developing an AI-friendly ecosystem for proteomics data generation, sharing, and analysis;improving peptide and protein identification and quantification;characterizing protein-protein interactions and protein complexes;advancing spatial and perturbation proteomics;integrating multi-omics data;and ultimately enabling AI-empowered virtual cells. © 2025, CC BY.

关键词： Biotic

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Preface

Lecture Notes in Electrical Engineering

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Lecture Notes in Electrical Engineering 2021年 724卷 v-vi页

作者： Alfred, Rayner Iida, Hiroyuki Haviluddin, Haviluddin Anthony, Patricia Faculty of Computing and Informatics Universiti Malaysia Sabah Kota Kinabalu Sabah Malaysia Research Center for Entertainment Science Japan Advanced Institute of Science and Technology Ishikawa Nomi Japan Faculty of Computer Science and Information Technology Department of Informatics Universitas Mulawarman Samarinda Kalimantan Timur Indonesia Department of Environmental Management Faculty of Environment Society and Design Lincoln University Christchurch New Zealand

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Correction: VER-Net: a hybrid transfer learning model for lung cancer detection using CT scan images

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BMC medical imaging 2024年第1期24卷 128页

作者： Anindita Saha Shahid Mohammad Ganie Pijush Kanti Dutta Pramanik Rakesh Kumar Yadav Saurav Mallik Zhongming Zhao Department of Computing Science and Engineering IFTM University Moradabad Uttar Pradesh India. AI Research Centre Department of Analytics School of Business Woxsen University Hyderabad Telangana 502345 India. School of Computer Applications and Technology Galgotias University Greater Noida Uttar Pradesh 203201 India. pijushjld@yahoo.co.in. Department of Computer Science & Engineering MSOET Maharishi University of Information Technology Lucknow Uttar Pradesh India. Department of Environmental Health Harvard T. H. Chan School of Public Health Boston MA USA. Center for Precision Health McWilliams School of Biomedical Informatics The University of Texas Health Science Center at Houston Houston TX 77030 USA. zhongming.zhao@uth.tmc.edu.

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SARS-CoV-2 Wastewater Genomic Surveillance: Approaches, Challenges, and Opportunities

arXiv

引用

arXiv 2023年

作者： Munteanu, Viorel Saldana, Michael A. Dreifuss, David Ouyang, Wenhao O. Ferdous, Jannatul Mohebbi, Fatemeh Schlueter, Jessica Ciorba, Dumitru Bostan, Viorel Gordeev, Victor Su, Justin Maine Kasianchuk, Nadiia Sharma, Nitesh Kumar Knyazev, Sergey Aßmann, Eva Lobiuc, Andrei Covasa, Mihai Crandall, Keith A. Wu, Nicholas C. Mason, Christopher E. Tierney, Braden T. Lucaci, Alexander G. Ophoff, Roel A. Gibas, Cynthia Rzymski, Piotr Skums, Pavel Solo-Gabriele, Helena Niko, Beerenwinkel Zelikovsky, Alex Hölzer, Martin Smith, Adam Mangul, Serghei Department of Computers Informatics and Microelectronics Technical University of Moldova Chisinau2045 Moldova Department of Biological and Morphofunctional Sciences College of Medicine and Biological Sciences Stefan cel Mare University of Suceava Suceava720229 Romania Astani Department of Civil and Environmental Engineering University of Southern California 3620 South Vermont Avenue Los AngelesCA90089 United States Department of Biosystems Science and Engineering ETH Zurich Basel Switzerland SIB Swiss Institute of Bioinformatics Lausanne Switzerland Department of Biochemistry University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Bioinformatics and Genomics University of North Carolina Charlotte United States Department of Clinical Pharmacy School of Pharmacy University of Southern California 1540 Alcazar Street Los AngelesCA90033 United States Department of Computers Informatics and Microelectronics Technical University of Moldova Chisinau2045 Moldova Titus Family Department of Clinical Pharmacy USC Alfred E. Mann School of Pharmacy and Pharmaceutical Sciences University of Southern California Los AngelesCA90089 United States Faculty of Biology Adam Mickiewicz University Poznan61-712 Poland Faculty of Pharmacy Bogomolets National University Kyiv01601 Ukraine Kyiv School of Economics Kyiv02000 Ukraine Titus Family Department of Clinical Pharmacy USC Alfred E. Mann School of Pharmacy and Pharmaceutical Sciences University of Southern California 1540 Alcazar Street Los AngelesCA90033 United States Department of Pathology and Laboratory Medicine David Geffen School of Medicine University of California Los Angeles Los AngelesCA United States Method Development and Research Infrastructure Robert Koch Institute Berlin13353 Germany Robert Koch Institute Berlin13353 Germany College of Medicine and Biological Sciences University of Suceava Suceava720229 Romania College of Osteopathic Medicine of the Pacific

During the SARS-CoV-2 pandemic, wastewater-based genomic surveillance (WWGS) emerged as an efficient virus surveillance tool capable of accounting for asymptomatic cases, identifying known and novel mutations, and assigning them to lineages and sublineages of the virus. WWGS also offers the potential to uncover novel or cryptic lineages, although identifying and defining these lineages solely from wastewater presents challenges. Besides the significant advantages of WWGS for monitoring SARS-CoV-2 viral spread, technical challenges remain, including coverage dropouts and poor quality of wastewater sequencing (WWS) data due to viral RNA fragmentation and degradation. Subsequently, WWGS analysis requires advanced computational tools that are yet to be developed and benchmarked. The existing bioinformatics tools used to analyze WWS data are often based on previously developed computational methods for variant calling, de-novo assembly, or quantifying the expression of transcripts. Those methods were not specifically developed for WWS data and must be optimized to address unique challenges associated with wastewater analysis. While specialized tools for analyzing WWS data have also been developed recently, it remains to be seen how they will perform given the ongoing evolution of SARS-CoV-2 and the decline in testing and patient-based genomic surveillance. Here, we explore opportunities and challenges associated with WWGS, including aspects such as sample preparation, sequencing technology, and bioinformatics methods. We also highlight its potential role in monitoring infectious diseases and informing public health responses by complementing other surveillance systems. We also critically examine the practical applications and ethical considerations of WWGS, emphasizing the need for a robust framework and its translation into sustainable wastewater surveillance programs to fully realize its potential for public health. © 2023, CC BY.

关键词： Coronavirus

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