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International Conference on Control, Automation, Robotics and Vision

作者： Matheus Chaves Menezes Edison Pignaton de Freitas Sen Cheng Alexandre Cesar Muniz de Oliveira Paulo Rogerio de Almeida Ribeiro Graduate Program on Computer Science Federal University of Maranhão São Luís Brazil Graduate Program on Computer Science Federal University of Rio Grande do Sul Porto Alegre Brazil Computational Neuroscience Ruhr University Bochum Bochum Germany Department of Informatics Federal University of Maranhão São Luís Maranhão Department of Computer Engineering Federal University of Maranhão São Luís Maranhão

This paper presents a neuro-inspired mapping approach that uses partial information shared by multiple robots to reduce the time to create a map of an entire environment. Robots using a neurobiologically inspired algorithm, namely RatSLAM, map an environment sharing video information among themselves. RatSLAM, which is based on the navigation system present in the hippocampus of rodents' brain, has been widely used on simultaneous localization and mapping (SLAM) problem. This proposal has been able to generate suitable maps mainly when there is redundant information, e.g. a scene is seen more than once, since this fact activates local view cells that inject activity inside the pose cells via an excitatory link. The work here reported extends this approach by merging partial information acquired by multiple robots. The results from the performed experiments show that the final map built by two robots with shared information is similar to one built by two robots performing the same mapping task individually, i.e. without sharing information. However, the time spent to generate the whole map with the proposed shared approach was smaller than the one without the shared information. Thus, the current approach allows creating a complete map of an environment within a reduced time using multiple robots.

关键词： Navigation Simultaneous localization and mapping Feature extraction Robot vision systems Task analysis Hippocampus

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Author Correction: Pathway and network analysis of more than 2500 whole cancer genomes

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Nature communications 2022年第1期13卷 7566页

作者： Matthew A Reyna David Haan Marta Paczkowska Lieven P C Verbeke Miguel Vazquez Abdullah Kahraman Sergio Pulido-Tamayo Jonathan Barenboim Lina Wadi Priyanka Dhingra Raunak Shrestha Gad Getz Michael S Lawrence Jakob Skou Pedersen Mark A Rubin David A Wheeler Søren Brunak Jose M G Izarzugaza Ekta Khurana Kathleen Marchal Christian von Mering S Cenk Sahinalp Alfonso Valencia Jüri Reimand Joshua M Stuart Benjamin J Raphael Department of Computer Science Princeton University Princeton NJ 08540 USA. Department of Biomedical Informatics Emory University Atlanta GA 30322 USA. Department of Biomolecular Engineering and UC Santa Cruz Genomics Institute University of California Santa Cruz Santa Cruz CA 95060 USA. Computational Biology Program Ontario Institute for Cancer Research Toronto Ontario Canada. Department of Information Technology IDLab Ghent University IMEC Ghent the Netherlands. Department of Plant Biotechnology and Bioinformatics Ghent University Ghent the Netherlands. Barcelona Supercomputing Center (BSC) Barcelona 08034 Spain. Department of Clinical and Molecular Medicine Faculty of Medicine and Health Sciences Norwegian University of Science and Technology Trondheim Norway. Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics University of Zurich CH-8057 Zurich Switzerland. Department of Pathology and Molecular Pathology University Hospital Zurich CH-8091 Zurich Switzerland. Department of Physiology and Biophysics Weill Cornell Medicine New York NY 10065 USA. Vancouver Prostate Centre 2660 Oak Street Vancouver BC V6H 3Z6 Canada. The Broad Institute of MIT and Harvard Cambridge MA 02124 USA. Massachusetts General Hospital Center for Cancer Research Charlestown MA 02129 USA. Harvard Medical School 250 Longwood Avenue Boston MA 02115 USA. Massachusetts General Hospital Department of Pathology Boston MA 02114 USA. Department of Molecular Medicine (MOMA) Aarhus University Hospital Aarhus Denmark. Bioinformatics Research Centre (BiRC) Aarhus University Aarhus Denmark. Human Genome Sequencing Center Baylor College of Medicine Houston TX 77030 USA. DTU Bioinformatics Department of Bio and Health Informatics Technical University of Denmark Kemitorvet 2800 Kongens Lyngby Denmark. Novo Nordisk Foundation Center for Protein Research Faculty of Health and Medical Sciences University of Copenhagen 2200 Copenhagen Denmark. De

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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 1-1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 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 German Cancer Consortium (DKTK) Heidelberg Germany National Center for Tumor Diseases (NCT) Heidelberg Heidelberg Germany Heidelberg Center for Personalized Oncology (DKFZ-HIPO) German Cancer Research Center (DKFZ) Heidelberg Germany German Cancer Consortium (DKTK) German Cancer Research Center (DKFZ) Heidelberg Germany Division of Chromatin Networks German Cancer Research Center (DKFZ) and BioQuant 69120 Heidelberg Germany Department of Biomedical Informatics Harvard Medical School Boston Massachusetts 02115 USA Department of Chemistry Centre for Molecular Science Informatics University of Cambridge Cambridge CB2 1EW UK Ludwig Center at Harvard Medical School Boston Massachusetts 02115 USA Department of Biomedical Informatics Harvard Medical School Boston MA USA Department of Chemistry Centre for Molecular Science Informatics University of Cambridge Cambridge UK Ludwig Center at Harvard Medical School Boston MA USA School of Mathematical Sciences and Center for Statistical Science Peking University Beijing 100871 China Heidelberg University Heidelberg Germany New BIH Digital Health Center Berlin Institute of Health (BIH) and Charité - Universitätsmedizin Berlin Berlin Germany Bioi

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Retracted: LUC Algorithm in Visual Cryptography

Retracted: LUC Algorithm in Visual Cryptography

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2018 Workshop on Multidisciplinary and Its Applications: Applied Mathematics, computer science, Information Systems, and Information Technology, WMA-Mathcomtech 2018

作者： Achmad Daengs, G.S. Ratnadewi Alpha, Christian Dio Napitupulu, Darmawan Nurdiyanto, Heri Kurniasih, Nuning Hasibuan, Abdurrozaq Daengs, Achmad Manurung, Rosida Tiurma Mulyaningsih, Indrya Rahim, Robbi Sugianto, Castaka Agus Abdullah, Dahlan Department of Electrical Engineering Universitas Kristen Maranatha Bandung Indonesia Research Center for Quality System and Testing Technology Indonesian Institute of Sciences Banten Indonesia STMIK Dharma Wacana Indonesia Faculty of Communication Sciences Library and Information Science Program Universitas Padjadjaran Bandung Indonesia Universitas Islam Sumatera Utara Indonesia University of 45 Surabaya Indonesia Graduate Program in Scientific Psychology Universitas Kristen Maranatha Bandung Indonesia Department of Education Institut Agama Islam Negeri Syekh Nurjati Cirebon Indonesia School of Computer and Communication Engineering Universiti Malaysia Perlis Kubang Gajah Malaysia Politeknik TEDC Bandung Indonesia Department of Informatics Universitas Malikussaleh Aceh Utara Indonesia

Nowadays, messaging technology in digital data form more often used and not less messages that confidentially wanted. Then it should be modified so that can be understood only by the sender and the intended recipients. All of this system can be realization by using cryptography. LUC algorithms are introduced by Smith and Lennon in 1993. LUC algorithm is an algorithm that based on the use of Lucas sequence (specific arithmetic operations derived from Lucas row) that rarely used to enhance the security of the messages. In this research, LUC algorithm is combined with visual cryptography to process the encryption and the description of a colored image. Four different images were used in the trial here. The performance of the system is assessed using Structural similarity (SSIM) which has an assessment similar to human eye. If the image quality is the same as the original image, then the SSIM value is one. Whereas if the quality of decryption image is very different from the original image then the value of SSIM is zero. © Published under licence by IOP Publishing Ltd.

关键词： Information systems

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Author Correction: Federated learning enables big data for rare cancer boundary detection

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Nature communications 2023年第1期14卷 436页

作者： Sarthak Pati Ujjwal Baid Brandon Edwards Micah Sheller Shih-Han Wang G Anthony Reina Patrick Foley Alexey Gruzdev Deepthi Karkada Christos Davatzikos Chiharu Sako Satyam Ghodasara Michel Bilello Suyash Mohan Philipp Vollmuth Gianluca Brugnara Chandrakanth J Preetha Felix Sahm Klaus Maier-Hein Maximilian Zenk Martin Bendszus Wolfgang Wick Evan Calabrese Jeffrey Rudie Javier Villanueva-Meyer Soonmee Cha Madhura Ingalhalikar Manali Jadhav Umang Pandey Jitender Saini John Garrett Matthew Larson Robert Jeraj Stuart Currie Russell Frood Kavi Fatania Raymond Y Huang Ken Chang Carmen Balaña Jaume Capellades Josep Puig Johannes Trenkler Josef Pichler Georg Necker Andreas Haunschmidt Stephan Meckel Gaurav Shukla Spencer Liem Gregory S Alexander Joseph Lombardo Joshua D Palmer Adam E Flanders Adam P Dicker Haris I Sair Craig K Jones Archana Venkataraman Meirui Jiang Tiffany Y So Cheng Chen Pheng Ann Heng Qi Dou Michal Kozubek Filip Lux Jan Michálek Petr Matula Miloš Keřkovský Tereza Kopřivová Marek Dostál Václav Vybíhal Michael A Vogelbaum J Ross Mitchell Joaquim Farinhas Joseph A Maldjian Chandan Ganesh Bangalore Yogananda Marco C Pinho Divya Reddy James Holcomb Benjamin C Wagner Benjamin M Ellingson Timothy F Cloughesy Catalina Raymond Talia Oughourlian Akifumi Hagiwara Chencai Wang Minh-Son To Sargam Bhardwaj Chee Chong Marc Agzarian Alexandre Xavier Falcão Samuel B Martins Bernardo C A Teixeira Flávia Sprenger David Menotti Diego R Lucio Pamela LaMontagne Daniel Marcus Benedikt Wiestler Florian Kofler Ivan Ezhov Marie Metz Rajan Jain Matthew Lee Yvonne W Lui Richard McKinley Johannes Slotboom Piotr Radojewski Raphael Meier Roland Wiest Derrick Murcia Eric Fu Rourke Haas John Thompson David Ryan Ormond Chaitra Badve Andrew E Sloan Vachan Vadmal Kristin Waite Rivka R Colen Linmin Pei Murat Ak Ashok Srinivasan J Rajiv Bapuraj Arvind Rao Nicholas Wang Ota Yoshiaki Toshio Moritani Sevcan Turk Joonsang Lee Snehal Prabhudesai Fanny Morón Jacob Mandel Konstantinos Kamnitsas Ben Glocker Luke V M Dixon Matthew Williams Peter Zamp Center for Biomedical Image Computing and Analytics (CBICA) University of Pennsylvania Philadelphia PA USA. Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. Department of Informatics Technical University of Munich Munich Bavaria Germany. Intel Corporation Santa Clara CA USA. Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany. Clinical Cooperation Unit Neuropathology German Cancer Consortium (DKTK) within the German Cancer Research Center (DKFZ) Heidelberg Germany. Department of Neuropathology Heidelberg University Hospital Heidelberg Germany. Division of Medical Image Computing German Cancer Research Center Heidelberg Germany. Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany. Neurology Clinic Heidelberg University Hospital Heidelberg Germany. Department of Radiology & Biomedical Imaging University of California San Francisco San Francisco CA USA. Symbiosis Center for Medical Image Analysis Symbiosis International University Pune Maharashtra India. Department of Neuroimaging and Interventional Radiology National Institute of Mental Health and Neurosciences Bangalore Karnataka India. Department of Radiology School of Medicine and Public Health University of Wisconsin Madison WI USA. Department of Medical Physics School of Medicine and Public Health University of Wisconsin Madison WI USA. Leeds Teaching Hospitals Trust Department of Radiology Leeds UK. Department of Radiology Brigham and Women's Hospital Harvard Medical School Boston MA USA. Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital Charlestown MA USA. Catalan Institute of Oncology Badalona Spain. Consorci MAR Parc de Salut de Barcelona Catalonia Spain. Department of Radiology (IDI

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Methods for segmentation and classification of digital microscopy tissue images

arXiv

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arXiv 2018年

作者： Vu, Quoc Dang Graham, Simon To, Minh Nguyen Nhat Shaban, Muhammad Qaiser, Talha Koohbanani, Navid Alemi Khurram, Syed Ali Kurc, Tahsin Farahani, Keyvan Zhao, Tianhao Gupta, Rajarsi Kwak, Jin Tae Rajpoot, Nasir Saltz, Joel Department of Computer Science Engineering Sejong University Seoul Korea Republic of Department of Computer Science University of Warwick University of Sheffield Sheffield United Kingdom Department of Biomedical Informatics Stony Brook University Stony Brook United States Cancer Imaging Program National Cancer Institute National Institutes of Health United States Department of Pathology Stony Brook University Stony Brook United States

High-resolution microscopy images of tissue specimens provide detailed information about the morphology of normal and diseased tissue. Image analysis of tissue morphology can help cancer researchers develop a better understanding of cancer biology. Segmentation of nuclei and classification of tissue images are two common tasks in tissue image analysis. Development of accurate and efficient algorithms for these tasks is a challenging problem because of the complexity of tissue morphology and tumor heterogeneity. In this paper we present two computer algorithms;one designed for segmentation of nuclei and the other for classification of whole slide tissue images. The segmentation algorithm implements a multiscale deep residual aggregation network to accurately segment nuclear material and then separate clumped nuclei into individual nuclei. The classification algorithm initially carries out patch-level classification via a deep learning method, then patch-level statistical and morphological features are used as input to a random forest regression model for whole slide image classification. The segmentation and classification algorithms were evaluated in the MICCAI 2017 Digital Pathology challenge. The segmentation algorithm achieved an accuracy score of 0.78. The classification algorithm achieved an accuracy score of 0.81. These scores were the highest in the challenge. Copyright © 2018, The Authors. All rights reserved.

关键词： Image analysis

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Validation of MRI-VLFF for the non-invasive measurement of steatosis in children

GastroHep

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GastroHep 2020年第4期2卷

作者： Miriam B. Vos Eduardo Castillo-Leon Jack Knight-Scott Rebecca Cleeton Alvin Jay Freeman Jennifer K. Frediani Albert Hernandez Stephen F. Simoneaux Jun Kong Alton Brad Farris Michael J House Tim St. Pierre Division of Gastroenterology Hepatology and Nutrition Department of Pediatrics Emory University Atlanta GA USA Children's Healthcare of Atlanta Atlanta GA USA Nutrition and Health Sciences Program Laney Graduate School Emory University Atlanta GA USA Department of Radiology Children's Healthcare of Atlanta Atlanta GA USA Department of Computer Science and Biomedical Informatics Emory University Atlanta GA USA Department of Mathematics and Statistics and Computer Science Georgia State University Atlanta GA USA Department of Pathology and Laboratory Medicine Emory University Atlanta GA USA School of Physics Mathematics and Computing The University of Western Australia Crawley WA Australia Resonance Health Ltd Claremont WA Australia

Background & Aims Magnetic resonance-based steatosis measurement of the liver has become the non-invasive gold standard; however very few diagnostic thresholds have been independently validated and none in children. This study was designed to validate diagnostic thresholds, assess repeatability and the relationship to histology of a proprietary MRI volumetric liver fat fraction (VLFF) method (HepaFat-Scan ® ) and magnetic resonance spectroscopy (MRS) proton density fat fraction (PDFF) method. Methods This cross-sectional study assesses the diagnostic accuracy of MRI-VLFF and MRS-PDFF of 50 children who had a clinically indicated liver biopsy. Twenty-five children were randomly selected for intra-examination repeatability. The positive pixel count (PPC) algorithm was used to quantify the area of fat vesicles in the histology. The area under the receiver operating characteristic curve (AUROC) was used to assess the diagnostic accuracy. Bland-Altman plots were used to compare the methods and examine the repeatability. Results The MRI-VLFF ranged from 0.3% to 35.4% and the MRS-PDFF ranged from 0.5% to 36.9%. Based on histopathological analysis, the diagnostic accuracy of MRI-VLFF for steatosis detection (grade 0 vs 1-3) showed an AUROC of 0.95 (95% confidence interval [CI]: 0.89-1.00), whereas MRI-PDFF classified grade 0 vs 1-3 showed an AUROC of 0.96 (95% CI: 0.91-1.00). The repeatability coefficients were 1.8 (±0.5) % for MRI-VLFF and 2.1 (±0.6) % for MRS-PDFF. There was no bias between MRI-VLFF and PPC steatosis. To allow direct comparison, MRS-PDFF was converted to MRS-VLFF. Small, but significant biases were measured between PPC steatosis and MRS-VLFF (−1.38%) and between MRI-VLFF and MRS-VLFF (−1.39%). Conclusions Both MRI-VLFF and MRS-PDFF accurately estimated hepatic steatosis in children with excellent agreement with the histological assessment.

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Learning from learning machines: A new generation of AI technology to meet the needs of science

arXiv

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arXiv 2021年

作者： Pion-Tonachini, Luca Bouchard, Kristofer Martin, Hector Garcia Peisert, Sean Holtz, W. Bradley Aswani, Anil Dwivedi, Dipankar Wainwright, Haruko Pilania, Ghanshyam Nachman, Benjamin Marrone, Babetta L. Falco, Nicola Prabhat Arnold, Daniel Wolf-Yadlin, Alejandro Powers, Sarah Climer, Sharlee Jackson, Quinn Carlson, Ty Sohn, Michael Zwart, Petrus Kumar, Neeraj Justice, Amy Tomlin, Claire Jacobson, Daniel Micklem, Gos Gkoutos, Georgios V. Bickel, Peter J. Cazier, Jean-Baptiste Müller, Juliane Webb-Robertson, Bobbie-Jo Stevens, Rick Anderson, Mark Kreutz-Delgado, Ken Mahoney, Michael W. Brown, James B. Pattern Computer Inc. Friday HarborWA98250 United States Biosciences Area Lawrence Berkeley National Lab BerkeleyCA94803 United States Computational Research Division Lawrence Berkeley National Lab BerkeleyCA94720 United States Helen Wils Neuroscience Institute Redwood Center for Theoretical Neuroscience Uc Berkeley BerkeleyCA94720 United States Doe Agile BioFoundry Lawrence Berkeley National Laboratory BerkeleyCA94803 United States Joint BioEnergy Institute Lawrence Berkeley National Laboratory BerkeleyCA94803 United States Bcam Basque Center for Applied Mathematics Bilbao48009 Spain Computer Science University of California Davis DavisCA95616 United States Cenic La MiradaCA90638 United States Health Informatics University of California Davis School of Medicine SacramentoCA95817 United States Berkeley Institute for Data Science University of California Berkeley BerkeleyCA94720 United States Industrial Engineering and Operations Research University of California Berkeley BerkeleyCA94720 United States Environmental & Earth Sciences Area Lawrence Berkeley National Laboratory BerkeleyCA94803 United States Nuclear Engineering University of California Berkeley BerkeleyCA94720 United States Materials Science and Technology Division Los Alamos National Laboratory Los AlamosNM87545 United States Physics Division Lawrence Berkeley National Lab BerkeleyCA94720 United States Bioscience Division Los Alamos National Laboratory Los AlamosNM87545 United States Earth and Environmental Sciences Area Lawrence Berkeley National Lab BerkeleyCA94803 United States Energy Technologies Area Lawrence Berkeley National Lab BerkeleyCA94803 United States Computing and Computational Sciences Oak Ridge National Laboratory Oak RidgeTN37831 United States Industrial & Systems Engineering The University of Tennessee KnoxvilleTN37996 United States Department of Computer Science University of Missouri-Saint Louis St. LouisMO63121 United

We outline emerging opportunities and challenges to enhance the utility of AI for scientific discovery. The distinct goals of AI for industry versus the goals of AI for science create tension between identifying patterns in data versus discovering patterns in the world from data. If we address the fundamental challenges associated with "bridging the gap" between domain-driven scientific models and data-driven AI learning machines, then we expect that these AI models can transform hypothesis generation, scientific discovery, and the scientific process itself. © 2021, CC BY.

关键词： Learning systems

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Abstracts

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JOURNAL OF SPINAL CORD MEDICINE 2021年第SUPPL 1期44卷 S278-S324页

作者： [Anonymous] Department of Medicine Division of Physical Medicine and Rehabilitation University of Toronto Toronto ON CA KITE - Toronto Rehabilitation Institute University Health Network Toronto ON CA Institute for Health Policy Management and Evaluation University of Toronto Toronto ON CA Rehabilitation Sciences Institute University of Toronto Toronto ON CA Institute of Medical Sciences University of Toronto Toronto ON CA. Rehabilitation Sciences Institute University of Toronto ON CA KITE Research Institute-Toronto Rehab-University Health Network Toronto ON CA Physical Therapy Department University of Toronto ON CA Praxis Spinal Cord Institute Vancouver BC Université de Montréal (École de réadaptation) Montreal QC CA Institut de réadaptation Gingras-Linsay-de-Montréal Montreal QC CA Division of Physical Medicine & Rehabilitation Department of Medicine Faculty of Medicine & Dentistry University of Alberta Edmonton AB CA University of Calgary Calgary AB CA Alberta Health Services Centre for Family Medicine Kitchener ON CA McMaster University Department of Family Medicine Hamilton ON CA Dept. of Physical Therapy University of Toronto Toronto ON CA KITE-University Health Network Toronto ON CA Praxis Spinal Cord Institute Vancouver BC CA Center for Interdisciplinary Research in Rehabilitation and Social Integration (CIRRIS) Quebec City QC CA Dept. of Rehabilitation Laval University Quebec City QC CA Institute of Health Policy Management and Evaluation University of Toronto Toronto ON CA School of Rehabilitation Science University of Saskatchewan Saskatoon SK CA Ontario Neurotrauma Foundation Toronto ON CA Canadian Spinal Research Organization Toronto ON CA CIUSSS du Centre-Sud-de-l ‘Île-de-Montréal (Installation Gingras-Lindsay) Montréal QC CA École de réadaptation Université de Montréal Montréal QC CA First Steps Wellness Centre Regina SK CA Stan Cassidy Centre for Rehabilitation Horizon Health Network Fredericton NB CA Departmen

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Geographic and age variations in mutational processes in colorectal cancer

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Nature 2025年 1-11页

作者： Marcos Díaz-Gay Mariya Kazachkova Ammal Abbasi Christopher D. Steele Raviteja Vangara Erik N. Bergstrom Azhar Khandekar Burçak Otlu Ludmil B. Alexandrov Wellington dos Santos Sergey Senkin Behnoush Abedi-Ardekani Ana Carolina de Carvalho Thomas Cattiaux Ricardo Cortez Cardoso Penha Valérie Gaborieau Priscilia Chopard Sandra Perdomo Paul Brennan Sarah Moody Jingwei Wang Stephen Fitzgerald Saamin Cheema Calli Latimer Jon W. Teague Laura Humphreys Michael R. Stratton Christine Carreira Anush Mukeriya David Zaridze Riley Cox Monique Albert Larry Phouthavongsy Steven Gallinger Reza Malekzadeh Ahmadreza Niavarani Marko Miladinov Katarina Erić Sasa Milosavljevic Suleeporn Sangrajrang Maria Paula Curado Samuel Aguiar Rui Manuel Reis Monise Tadin Reis Luis Gustavo Romagnolo Denise Peixoto Guimarães Ivana Holcatova Jaroslav Kalvach Carlos Alberto Vaccaro Tamara Alejandra Piñero Beata Świątkowska Jolanta Lissowska Katarzyna Roszkowska-Purska Antonio Huertas-Salgado Tatsuhiro Shibata Satoshi Shiba Surasak Sangkhathat Taned Chitapanarux Gholamreza Roshandel Patricia Ashton-Prolla Daniel C. Damin Francine Hehn de Oliveira Trevor D. Lawley Department of Cellular and Molecular Medicine University of California San Diego La Jolla CA USA Department of Bioengineering University of California San Diego La Jolla CA USA Moores Cancer Center University of California San Diego La Jolla CA USA Digital Genomics Group Structural Biology Program Spanish National Cancer Research Center (CNIO) Madrid Spain Biomedical Sciences Graduate Program University of California San Diego La Jolla CA USA Division of Cancer Epidemiology and Genetics National Cancer Institute Bethesda MD USA Department of Health Informatics Graduate School of Informatics Middle East Technical University Ankara Turkey Sanford Stem Cell Institute University of California San Diego La Jolla CA USA Genomic Epidemiology Branch International Agency for Research on Cancer (IARC/WHO) Lyon France Cancer Ageing and Somatic Mutation Wellcome Sanger Institute Cambridge UK Evidence Synthesis and Classification Branch International Agency for Research on Cancer (IARC/WHO) Lyon France Clinical Epidemiology N. N. Blokhin National Medical Research Centre of Oncology Moscow Russia Ontario Tumour Bank Ontario Institute for Cancer Research Toronto Ontario Canada Centre for Biodiversity Genomics University of Guelph Guelph Ontario Canada Lunenfeld-Tanenbaum Research Institute Sinai Health System Toronto Ontario Canada Digestive Oncology Research Center Digestive Disease Research Institute Tehran University of Medical Sciences Tehran Iran Clinic for Digestive Surgery—First Surgical Clinic University Clinical Centre of Serbia Belgrade Serbia Department of Pathology University Clinical Centre of Serbia Belgrade Serbia International Organization for Cancer Prevention and Research Belgrade Serbia National Cancer Institute Bangkok Thailand Department of Epidemiology A. C. Camargo Cancer Center Sao Paulo Brazil Colon Cancer Reference Center A. C. Camargo Cancer Center Sao Paulo Brazil Molecular Oncology Research Center Barretos Cancer Hospital

Incidence rates of colorectal cancer vary geographically and have changed over time1. Notably, in the past two decades, the incidence of early-onset colorectal cancer, which affects individuals below 50 years of age, has doubled in many countries2,3,4,5. The reasons for this increase are unknown. Here we investigate whether mutational processes contribute to geographic and age-related differences by examining 981 colorectal cancer genomes from 11 countries. No major differences were found in microsatellite-unstable cancers, but variations in mutation burden and signatures were observed in the 802 microsatellite-stable cases. Multiple signatures, most with unknown aetiologies, exhibited varying prevalence in Argentina, Brazil, Colombia, Russia and Thailand, indicating geographically diverse levels of mutagenic exposure. Signatures SBS88 and ID18, caused by the bacteria-produced mutagen colibactin6,7, had higher mutation loads in countries with higher colorectal cancer incidence rates. SBS88 and ID18 were also enriched in early-onset colorectal cancers, being 3.3 times more common in individuals who were diagnosed before 40 years of age than in those over 70 years of age, and were imprinted early during colorectal cancer development. Colibactin exposure was further linked toAPCdriver mutations, with ID18 being responsible for about 25% ofAPCdriver indels in colibactin-positive cases. This study reveals geographic and age-related variations in colorectal cancer mutational processes, and suggests that mutagenic exposure to colibactin-producing bacteria in early life may contribute to the increasing incidence of early-onset colorectal cancer.

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