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A genome-wide association study with 1,126,563 individuals identifies new risk loci for Alzheimer's disease (Sep, 10.1038/s41588-021-00921-z, 2021)

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NATURE GENETICS 2022年第7期54卷 1062-1062页

作者： Wightman, Douglas P. Jansen, Iris E. Savage, Jeanne E. Shadrin, Alexey A. Bahrami, Shahram Holland, Dominic Rongve, Arvid Borte, Sigrid Winsvold, Bendik S. Drange, Ole Kristian Martinsen, Amy E. Skogholt, Anne Heidi Willer, Cristen Brathen, Geir Bosnes, Ingunn Nielsen, Jonas Bille Fritsche, Lars G. Thomas, Laurent F. Pedersen, Linda M. Gabrielsen, Maiken E. Johnsen, Marianne Bakke Meisingset, Tore Wergeland Zhou, Wei Proitsi, Petroula Hodges, Angela Dobson, Richard Velayudhan, Latha Heilbron, Karl Auton, Adam Sealock, Julia M. Davis, Lea K. Pedersen, Nancy L. Reynolds, Chandra A. Karlsson, Ida K. Magnusson, Sigurdur Stefansson, Hreinn Thordardottir, Steinunn Jonsson, Palmi V. Snaedal, Jon Zettergren, Anna Skoog, Ingmar Kern, Silke Waern, Margda Zetterberg, Henrik Blennow, Kaj Stordal, Eystein Hveem, Kristian Zwart, John-Anker Athanasiu, Lavinia Selnes, Per Saltvedt, Ingvild Sando, Sigrid B. Ulstein, Ingun Djurovic, Srdjan Fladby, Tormod Aarsland, Dag Selbaek, Geir Ripke, Stephan Stefansson, Kari Andreassen, Ole A. Posthuma, Danielle Department of Complex Trait Genetics Center for Neurogenomics and Cognitive Research Amsterdam Neuroscience VU Amsterdam Amsterdam the Netherlands Department of Child and Adolescent Psychiatry and Pediatric Psychology Section Complex Trait Genetics Amsterdam Neuroscience Vrije Universiteit Medical Center Amsterdam University Medical Center Amsterdam the Netherlands NORMENT Centre University of Oslo Oslo Norway Institute of Clinical Medicine University of Oslo Oslo Norway Division of Mental Health and Addiction Oslo University Hospital Oslo Norway Research and Communication Unit for Musculoskeletal Health (FORMI) Department of Research Innovation and Education Division of Clinical Neuroscience Oslo University Hospital Oslo Norway K. G. Jebsen Center for Genetic Epidemiology Department of Public Health and Nursing Faculty of Medicine and Health Sciences Norwegian University of Science and Technology Trondheim Norway Department of Research Innovation and Education Division of Clinical Neuroscience Oslo University Hospital Oslo Norway Department of Neurology Akershus University Hospital Lørenskog Norway Department of Geriatric Medicine Oslo University Hospital Oslo Norway Norwegian National Advisory Unit on Ageing and Health Vestfold Hospital Trust Tønsberg Norway Department of Neurosciences University of California San Diego La Jolla CA USA Department of Research and Innovation Helse Fonna Haugesund Hospital Haugesund Norway The University of Bergen Institute of Clinical Medicine (K1) Bergen Norway Department of Neurology Oslo University Hospital Oslo Norway Department of Clinical and Molecular Medicine Norwegian University of Science and Technology Trondheim Norway Department of Internal Medicine Division of Cardiovascular Medicine University of Michigan Ann Arbor MI USA Department of Epidemiology Research Statens Serum Institut Copenhagen Denmark HUNT Research Center Department of Public Health and Nursing Faculty of Medicine and Health Scien

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867MO Epigenetic therapy modulates the tumor microenvironment to sensitize anti-PD-1 refractory head and neck cancers to immunotherapy

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Annals of Oncology 2024年 35卷 S617-S617页

作者： S. Pai T. Qin A. Mattox J. Campbell J.C. Park K-Y. Shin P. Sadow W. Faquin R. Haddad M. Pittet C. Garris T. Mempel A. Oneill M. Sartor Surgery Division of Otolaryngology-Head and Neck Surgery Yale University School of Medicine New Heaven CT USA Computational Medicine and Bioinformatics University of Michigan Ann Arbor MI USA Laboratory Medicine and Pathology University of Washington Seattle WA USA Medicine Department MGH - Massachusetts General Hospital Boston MA USA Data Science Dana-Farber Cancer Institute Boston MA USA Pathology MGH - Massachusetts General Hospital Boston MA USA Medical Oncology Dana-Farber Cancer Institute Boston MA USA Immunology University of Geneva - Faculty of Medicine Geneva Switzerland Radiology MGH - Massachusetts General Hospital Boston MA USA Immunology Harvard Medical School Boston MA USA Biostatistics University of Michigan Hospital Ann Arbor MI USA

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Fusion of convolution neural network, support vector machine and Sobel filter for accurate detection of COVID-19 patients using X-ray images

arXiv

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arXiv 2021年

作者： Sharifrazi, Danial Alizadehsani, Roohallah Roshanzamir, Mohamad Joloudari, Javad Hassannataj Shoeibi, Afshin Jafari, Mahboobeh Hussain, Sadiq Sani, Zahra Alizadeh Hasanzadeh, Fereshteh Khozeimeh, Fahime Khosravi, Abbas Nahavandi, Saeid Panahiazar, Maryam Zare, Assef Shariful Islam, Sheikh Mohammed Rajendra, U. Acharya Department of Computer Engineering School of Technical and Engineering Shiraz Branch Islamic Azad University Shiraz Iran Deakin University Geelong Australia Department of Engineering Fasa Branch Islamic Azad University Post Box No 364 Fasa Fars*** Iran Department of Computer Engineering Faculty of Engineering University of Birjand Birjand Iran Computer Engineering Department Ferdowsi University of Mashhad Mashhad Iran Faculty of Electrical and Computer Engineering Biomedical Data Acquisition Lab K. N. Toosi University of Technology Tehran Iran Electrical and Computer Engineering Faculty Semnan University Semnan Iran System Administrator Dibrugarh University Assam786004 India Rajaie Cardiovascular Medical and Research Center Iran University of Medical Sciences Tehran Iran Omid hospital Iran University of Medical Sciences Tehran Iran Institute for Computational Health Sciences University of California San Francisco United States Faculty of Electrical Engineering Gonabad Branch Islamic Azad University Gonabad Iran Institute for Physical Activity and Nutrition Deakin University Melbourne Australia Cardiovascular Division The George Institute for Global Health Australia Sydney Medical School University of Sydney Australia Department of Electronics and Computer Engineering Ngee Ann Polytechnic Singapore Department of Biomedical Engineering School of Science and Technology Singapore University of Social Sciences Singapore Department of Bioinformatics and Medical Engineering Asia University Taiwan

The coronavirus (COVID-19) is currently the most common contagious disease which is prevalent all over the world. The main challenge of this disease is the primary diagnosis to prevent secondary infections and its spread from one person to another. Therefore, it is essential to use an automatic diagnosis system along with clinical procedures for the rapid diagnosis of COVID-19 to prevent its spread. Artificial intelligence techniques using computed tomography (CT) images of the lungs and chest radiography have the potential to obtain high diagnostic performance for Covid-19 diagnosis. In this study, a fusion of convolutional neural network (CNN), support vector machine (SVM), and Sobel filter is proposed to detect COVID-19 using X-ray images. A new X-ray image dataset was collected and subjected to high pass filter using a Sobel filter to obtain the edges of the images. Then these images are fed to CNN deep learning model followed by SVM classifier with ten-fold cross validation strategy. This method is designed so that it can learn with not many data. Our results show that the proposed CNN-SVM with Sobel filtering (CNN-SVM+Sobel) achieved the highest classification accuracy of 99.02% in accurate detection of COVID-19. It showed that using Sobel filter can improve the performance of CNN. Unlike most of the other researches, this method does not use a pre-trained network. We have also validated our developed model using six public databases and obtained the highest performance. Hence, our developed model is ready for clinical application. Copyright © 2021, The Authors. All rights reserved.

关键词： data mining

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Sustaining public plant breeding programs across generations

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Crop science 2025年第3期65卷

作者： Iago Hale Jenny Koebernick Jenna Hershberger Trevor Rife Juan-David Arbelaez Neil Anderson Anton Bekkerman Martin Bohn Fred Bourland Timothy Burke Peng Chee Kate Evans Nathan Fumia Mitchell Feldmann Ksenija Gasic Steve Hague Ana María Heilman-Morales Amanda Hulse Kemp Carlos Iglesias Lukas Mueller Jim Luby Richard Pratt Addie Thompson Richard Vierling Margaret Worthington Margaret Smith Gayle Volk Marnin Wolfe Michael Kantar Department of Agriculture Nutrition and Food Systems University of New Hampshire Durham New Hampshire USA Department of Crop Soil and Environmental Sciences Auburn University Auburn Alabama USA Plant and Environmental Sciences Department Clemson University Clemson South Carolina USA Department of Crop Sciences University of Illinois Urbana Illinois USA Department of Horticultural Science University of Minnesota Saint Paul Minnesota USA Northeast Research and Extension Center (NEREC) University of Arkansas Keiser Arkansas USA Bayer CropScience LLC St. Louis Missouri USA Department of Soil and Crop Sciences University of Georgia Tifton Georgia USA Horticulture Tree Fruit Research and Extension Center Washington State University Wenatchee Washington USA Hawaii Agriculture Research Center Kunia Hawaiʻi USA Department of Plant Sciences University of California Davis Davis California USA Big Data Pipeline Unit North Dakota State University AES Fargo North Dakota USA USDA-ARS Genomics and Bioinformatics Research Unit Raleigh North Carolina USA Department of Horticultural Science North Carolina State University Raleigh North Carolina USA Boyce Thompson Institute Cornell University Ithaca New York USA Department of Plant and Environmental Sciences New Mexico State University Las Cruces New Mexico USA Department of Plant Soil and Microbial Sciences Michigan State University East Lansing Michigan USA AgriLife Research and Extension Center at Vernon Texas A&M University College Station Texas USA Department of Horticulture University of Arkansas System Division of Agriculture Fayetteville Arkansas USA School of Integrative Plant Science Plant Breeding and Genetics Section Cornell University Ithaca New York USA USDA-ARS National Laboratory for Genetic Resources Preservation Fort Collins Colorado USA Department of Tropical Plant & Soil Sciences University of Hawaii at Manoa Honolulu Hawaiʻi USA

Plant breeding in the public sector is a multigenerational process that creates new plant varieties intended to meet current and future needs of society. Many public sector plant breeding programs are over a century old, and they continue to curate plant genetic resources that are far older still. While individual breeders serve as temporary leaders of these programs and the plant genetic resources they maintain, it is only their institutions that have the capacity to provide the necessary generational glue, enabling the accrual of long-term value to both breeders and society. Identifying best practices to ensure mutual benefit to both public sector breeders and their institutions is critical to achieving the smooth leadership transitions necessary for the sustainability and long-term impact of public breeding programs. The findings presented here suggest that the successful passing of the torch in such programs depends not only on strategic institutional support but also, critically, on the routine actions and mindset of the breeders entrusted with their leadership. Plant breeding has long been considered an essential public good. Despite many public breeding programs being over a century old, there are no standard procedures for dealing with inevitable leadership transitions. Here, we report on the findings of a workshop convened to explore the practices required for successful transitions and what current plant breeders and their host institutions can do to prevent germplasm loss and ensure program continuity.

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CellMemory: hierarchical interpretation of out-of-distribution cells using bottlenecked transformer

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Genome biology 2025年第1期26卷 178页

作者： Qifei Wang He Zhu Yiwen Hu Yanjie Chen Yuwei Wang Guochao Li Yun Li Jinfeng Chen Xuegong Zhang James Zou Manolis Kellis Yue Li Dianbo Liu Lan Jiang China National Center for Bioinformation Beijing 100101 China. Beijing Institute of Genomics Chinese Academy of Sciences Beijing 100101 China. University of Chinese Academy of Sciences Beijing 100049 China. School of Computer Science McGill University Montreal QC H3A 0C6 Canada. Mila - Quebec AI Institute Montreal QC H2S 3H1 Canada. Sino-Danish College University of Chinese Academy of Sciences Beijing 100049 China. CAS Key Laboratory of Behavioral Science Institute of Psychology Beijing 100101 China. Department of Psychology University of Chinese Academy of Sciences Beijing 100049 China. International Big-Data Center for Depression Research Institute of Psychology Chinese Academy of Sciences Beijing 100101 China. Department of Automation MOE Key Laboratory of Bioinformatics and Bioinformatics Division BNRIST Tsinghua University Beijing 100084 China. School of Life Sciences and School of Medicine Center for Synthetic and Systems Biology Tsinghua University Beijing 100084 China. Department of Computer Science Stanford University Stanford CA 94305 USA. Department of Biomedical Data Science Stanford University School of Medicine Stanford CA 94305 USA. The Broad Institute of MIT and Harvard Cambridge MA 02142 USA. Computer Science and Artificial Intelligence Lab Massachusetts Institute of Technology Cambridge MA 02139 USA. School of Computer Science McGill University Montreal QC H3A 0C6 Canada. yueli@cs.mcgill.ca. Mila - Quebec AI Institute Montreal QC H2S 3H1 Canada. yueli@cs.mcgill.ca. Quantitative Life Science McGill University Montreal QC H3A 1E3 Canada. yueli@cs.mcgill.ca. School of Medicine and College of Design and Engineering National University of Singapore Singapore Singapore. dianbo@nus.edu.sg. China National Center for Bioinformation Beijing 100101 China. jiangl@***. Beijing Institute of Genomics Chinese Academy of Sciences Beijing 100101 China. jiangl@***. University of Chinese Academy of Sciences Beijing 100049 C

Machine learning methods, especially Transformer architectures, have been widely employed in single-cell omics studies. However, interpretability and accurate representation of out-of-distribution (OOD) cells remains challenging. Inspired by the global workspace theory in cognitive neuroscience, we introduce CellMemory, a bottlenecked Transformer with improved generalizability designed for the hierarchical interpretation of OOD cells. Without pre-training, CellMemory outperforms existing single-cell foundation models and accurately deciphers spatial transcriptomics at high resolution. Leveraging its robust representations, we further elucidate malignant cells and their founder cells across patients, providing reliable characterizations of the cellular changes caused by the disease.

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The risk of herpes zoster is positively associated with obesity, especially morbid obesity

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Scientific reports 2024年第1期14卷 14330页

作者： Hsiao-Lan Chen Chia-Hung Chen Wen-Che Hsieh Yu-Han Huang Tzu-Ju Hsu Fuu-Jen Tsai Yung-Chi Cheng Chao-Yu Hsu Division of Respiratory Therapy Ditmanson Medical Foundation Chia-Yi Christian Hospital Chia-Yi Taiwan. Department of Medical Education Ditmanson Medical Foundation Chia-Yi Christian Hospital Chia-Yi Taiwan. Department of Medical Imaging Ditmanson Medical Foundation Chia-Yi Christian Hospital Chia-Yi Taiwan. Department of Chinese Medicine Ditmanson Medical Foundation Chia-Yi Christian Hospital Chia-Yi Taiwan. Management Office for Health Data Clinical Trial Research Center China Medical University Hospital Taichung Taiwan. School of Chinese Medicine College of Chinese Medicine China Medical University Taichung Taiwan. Department of Medical Research China Medical University Hospital Taichung Taiwan. Division of Medical Genetics China Medical University Children's Hospital Taichung Taiwan. Department of Biotechnology and Bioinformatics Asia University Taichung Taiwan. Department of Rehabilitation Ditmanson Medical Foundation Chia-Yi Christian Hospital Chia-Yi Taiwan. Department of Medical Education Ditmanson Medical Foundation Chia-Yi Christian Hospital Chia-Yi Taiwan. 07692@cych.org.tw. Department of Artificial Intelligence and Healthcare Management Central Taiwan University of Science and Technology Taichung Taiwan. 07692@cych.org.tw. Center for General Education National Taichung University of Science and Technology Taichung Taiwan. 07692@cych.org.tw. Department of General Education National Chin-Yi University of Technology Taichung Taiwan. 07692@cych.org.tw.

This study aimed to investigate the association between obesity and herpes zoster (HZ) occurrence. This study used data covering 2 million people in Taiwan in 2000, which were obtained from the National Health Insurance Research database. The cohort study observed aged 20-100 years with obesity from 2000 to 2017 (tracking to 2018). Obesity was indicated by the presence of two or more outpatient diagnoses or at least one admission record. And, obesity was categorized into non-morbid obesity and morbid obesity. Patients with HZ before the index date were excluded. The obesity cohort and control cohort were matched 1:1 according to age, sex, comorbidities, and index year. There were 18,855 patients in both the obesity and control cohorts. The obesity cohort [adjusted hazard ratio (aHR) 1.09] had a higher risk of HZ than the control cohort. Further analysis, the morbid obesity group (aHR 1.47), had a significantly higher risk of HZ than the non-morbid obesity group. Among the patients without any comorbidities, the patients with obesity had a significantly higher risk of developing HZ than the patients without obesity (aHR 1.18). Obese patients are at a higher risk of HZ development, especially in the patients with morbid obesity. Weight reduction is critical for preventing the onset of chronic diseases and decreasing the risk of HZ in patients with obesity.

关键词： Herpes zoster Morbid obesity Obesity Weight reduction

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Author Correction: Genomic footprints of activated telomere maintenance mechanisms in cancer (Dec, 10.1038/s41467-019-13824-9, 2022)

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NATURE COMMUNICATIONS 2022年第1期13卷 1页

作者： Sieverling, Lina Hong, Chen Koser, Sandra D. Ginsbach, Philip Kleinheinz, Kortine Hutter, Barbara Braun, Delia M. Cortes-Ciriano, Isidro Xi, Ruibin Kabbe, Rolf Park, Peter J. Eils, Roland Schlesner, Matthias Brors, Benedikt Rippe, Karsten Jones, David T. W. Feuerbach, Lars Division of Applied Bioinformatics German Cancer Research Center (DKFZ) 69120 Heidelberg Germany Faculty of Biosciences Heidelberg University 69120 Heidelberg Germany Division of Applied Bioinformatics German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Biosciences Heidelberg University Heidelberg Germany German Cancer Consortium (DKTK) Heidelberg Germany National Center for Tumor Diseases (NCT) Heidelberg Heidelberg Germany German Cancer Consortium (DKTK) German Cancer Research Center (DKFZ) Heidelberg Germany Wellcome Sanger Institute Wellcome Genome Campus Hinxton Cambridge CB10 1SA UK Korea Advanced Institute of Science and Technology Daejeon 34141 South Korea Wellcome Sanger Institute Wellcome Genome Campus Hinxton UK Korea Advanced Institute of Science and Technology Daejeon South Korea Division of Chromatin Networks German Cancer Research Center (DKFZ) and BioQuant 69120 Heidelberg Germany Division of Theoretical Bioinformatics German Cancer Research Center (DKFZ) 69120 Heidelberg Germany Department for Bioinformatics and Functional Genomics Institute for Pharmacy and Molecular Biotechnology (IPMB) and BioQuant 69120 Heidelberg Germany Division of Theoretical Bioinformatics German Cancer Research Center (DKFZ) Heidelberg Germany Institute of Pharmacy and Molecular Biotechnology and BioQuant Heidelberg University Heidelberg Germany Heidelberg University Heidelberg Germany New BIH Digital Health Center Berlin Institute of Health (BIH) and Charité - Universitätsmedizin Berlin Berlin Germany Heidelberg Center for Personalized Oncology (DKFZ-HIPO) German Cancer Research Center (DKFZ) Heidelberg Germany Department of Biomedical Informatics Harvard Medical School Boston MA USA Ludwig Center at Harvard Medical School Boston MA USA Department of Biomedical Informatics Harvard Medical School Boston Massachusetts 02115 USA Ludwig Center at Harvard Medical School Boston Massachusetts 02115 USA Broad Institute of MIT and Harvard

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JS08.6.A A connectivity signature for glioblastoma

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Neuro-Oncology 2022年第SUPPLEMENT_2期24卷 ii8-ii9页

作者： Hoffmann, D C Hai, L Wagener, R Mandelbaum, H Xie, R Hausmann, D Dominguez Azorín, D Weil, S Sievers, P Cebulla, G Ito, J Warnken, U Venkataramani, V Ernst, K Reibold, D Will, R Suvà, M L Herold-Mende, C Sahm, F Winkler, F Schlesner, M Wick, W Kessler, T Clinical Cooperation Unit Neurooncology German Cancer Consortium (DKTK) German Cancer Research Center (DKFZ) Heidelberg Germany Department of Neurology and Neurooncology Program National Center for Tumor Diseases Heidelberg University Hospital Heidelberg Germany Faculty of Biosciences Heidelberg University Heidelberg Germany Bioinformatics and Omics Data Analytics DKFZ Heidelberg Germany Department of Neuropathology Institute of Pathology University Hospital Heidelberg Heidelberg Germany Clinical Cooperation Unit Neuropathology DKTK DKFZ Heidelberg Germany Department of Neuroanatomy Institute for Anatomy and Cell Biology Heidelberg University Heidelberg Germany Hopp Children's Cancer Center at the NCT Heidelberg (KiTZ) Heidelberg Germany Division of Pediatric Neurooncology DKTK DKFZ Heidelberg Germany Genomics and Proteomics Core Facility DKTK DKFZ Heidelberg Germany Broad Institute of Harvard and MIT Cambridge MA United States Department of Pathology and Center for Cancer Research Massachusetts General Hospital and Harvard Medical School Boston MA United States Department of Neurosurgery Heidelberg University Hospital Heidelberg Germany Biomedical Informatics Data Mining and Data Analytics Faculty of Applied Computer Science and Medical Faculty University of Augsburg Augsburg Germany

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Author Correction: The landscape of viral associations in human cancers

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Nature genetics 2023年第6期55卷 1077页

作者： Marc Zapatka Ivan Borozan Daniel S Brewer Murat Iskar Adam Grundhoff Malik Alawi Nikita Desai Holger Sültmann Holger Moch Colin S Cooper Roland Eils Vincent Ferretti Peter Lichter Division of Molecular Genetics German Cancer Research Center (DKFZ) Heidelberg Germany. Informatics and Bio-computing Program Ontario Institute for Cancer Research Toronto Ontario Canada. Norwich Medical School University of East Anglia Norwich UK. Earlham Institute Norwich UK. Heinrich-Pette-Institute Leibniz Institute for Experimental Virology Hamburg Germany. German Center for Infection Research (DZIF) Partner Site Hamburg-Borstel-Lübeck-Riems Hamburg Germany. Bioinformatics Core University Medical Center Hamburg-Eppendorf Hamburg Germany. Bioinformatics Group Department of Computer Science University College London London UK. Biomedical Data Science Laboratory Francis Crick Institute London UK. National Center for Tumor Diseases (NCT) Heidelberg Heidelberg Germany. Division of Cancer Genome Research German Cancer Research Center (DKFZ) Heidelberg Germany. German Cancer Consortium (DKTK) Heidelberg Germany. Department of Pathology and Molecular Pathology University and University Hospital Zürich Zurich Switzerland. Institute of Cancer Research London UK. University of East Anglia Norwich UK. Division of Theoretical Bioinformatics German Cancer Research Center (DKFZ) Heidelberg Germany. Department of Bioinformatics and Functional Genomics Institute of Pharmacy and Molecular Biotechnology Heidelberg University and BioQuant Center Heidelberg Germany. Center for Digital Health Berlin Institute of Health and Charité Universitätsmedizin Berlin Berlin Germany. Ontario Institute for Cancer Research MaRS Centre Toronto Ontario Canada. Department of Biochemistry and Molecular Medicine University of Montreal Montreal Québec Canada. Division of Molecular Genetics German Cancer Research Center (DKFZ) Heidelberg Germany. peter.lichter@dkfz-heidelberg.de. German Cancer Consortium (DKTK) Heidelberg Germany. peter.lichter@dkfz-heidelberg.de.

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Blockchain-Based Incentive Announcement System for Internet of Vehicles

Blockchain-Based Incentive Announcement System for Internet ...

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IEEE International Conference on Big data and Cloud Computing (BdCloud)

作者： Yang Yang Jialiang Chen Xianghan Zheng Ximeng Liu Wenzhong Guo Hairong Lv College of Mathematics and Computer Science Fuzhou University Fuzhou China Guangxi Key Laboratory of Cryptography and Information Security Guangnxi China Guangdong Provincial Key Laboratory of Data Security and Privacy Protection Guangzhou China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control (Minjiang University) Fuzhou China Ministry of Education Key Laboratory of Bioinformatics Bioinformatics Division and Center for Synthetic and Systems Biology Beijing National Research Center for Information Science and TechnologyDepartment of Automation Tsinghua University Beijing China

ISBN: (数字)9781728143286

ISBN: (纸本)9781728143293

With the rapid development of computation and communication technologies, the traditional vehicle ad hoc networks (VANETs) are changing to Internet of vehicle (IoV). Vehicular announcement networks in IoV have been widely used in the communication of vehicles. Generally, we need to solve two problems while establishing a vehicular announcement system. First, we need to protect user's privacy when broadcasting the message. Second, participants usually lack the enthusiasm to reply to the announcement. To solve these two problems, we propose a novel blockchain-based incentive announcement system that not only allows participants to anonymously announce their message on the blockchain in a non-trusted environment, but also motivates witnesses to respond to the request of the traffic information with incentive mechanism. Meanwhile, traffic messages and signatures in our system are tamper-resistant, which are recorded on the blockchain. According to the security and performance analysis, it shows that our system is privacy-preserving and efficient in computation cost.

关键词： Blockchain Privacy Smart contracts Authentication Internet Remuneration Hash functions

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