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2022 Interoperability for Enterprise Systems and Applications Workshops, I-ESA Workshops 2022

作者： Agostinho, Carlos Pimenta, Andreia Marques, Maria Tsiouris, Kostas M. Kalatzis, Fanis Nikitas, Christos Iliadou, Eleftheria Occhipinti, Mattia Kouris, Ioannis Koutsouris, Dimitrios Basdekis, Ioannis Koloutsou, Konstantina Gallo, Luigi de Pietro, Giueppe Jardim-Goncalves, Ricardo FCT-Campus Caparica2829-516 Portugal NOVA School of Science and Technology Caparica2829-516 Portugal Secretaria Regional de Saúde e Ptroteção Civil Rua da Carreira nº107 Funchal9000-042 Portugal Unit of Medical Technology and Intelligent Information Systems Dept. of Material Science and Engineering University of Ioannina IoanninaGR45110 Greece National Kapodistrian University of Athens First Department of Otolaryngology-Head and Neck Surgery ‘Hippokrateio’ General Hospital Athens Greece Università degli Studi di Milano Computer Science Department Milan Italy Biomedical Engineering Laboratory Institute of Communication and Computer Systems National Technical University of Athens Heroon Polytechneiou 9 15780 Greece SPHYNX Technology Solutions AG Zug Switzerland National Research Council of Italy Via P. Bucci cubo 8/9C Rende87036 Italy

The ageing of the population creates new heterogeneous challenges for age-friendly living. The progressive decline in physical and cognitive skills tends to prevent elderly people from performing basic instrumental activities of daily living and there is a growing interest in technology for aging support. Digital health today can be exercised by anyone owning a smartphone and parameters such as heart rate, step counts, calorie intake, sleep quality, can be collected and used not only to monitor and improve the individual’s health condition but also to prevent illnesses. However, for the benefits of e-health to take place, digital health data, either Electronic Health Records (EHR) or sensor data from the IoMT, must be shared, maintaining privacy and security requirements but unlocking the potential for research an innovation throughout EU. This paper demonstrates the added value of such interoperability requirements, and a form of accomplishing them through a cross-project pilot. © 2022 CEUR-WS. All rights reserved.

关键词： Interoperability

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An overview of deep learning techniques for epileptic seizures detection and prediction based on neuroimaging modalities: Methods, challenges, and future works

arXiv

引用

arXiv 2021年

作者： Shoeibi, Afshin Moridian, Parisa Khodatars, Marjane Ghassemi, Navid Jafari, Mahboobeh Alizadehsani, Roohallah Kong, Yinan Gorriz, Juan Manuel Ramírez, Javier Khosravi, Abbas Nahavandi, Saeid Acharya, U. Rajendra Data Science and Computational Intelligence Institute University of Granada Spain Faculty of Engineering Science and Research Branch Islamic Azad University Tehran Iran Department of Medical Engineering Mashhad Branch Islamic Azad University Mashhad Iran Computer Engineering department Ferdowsi University of Mashhad Mashhad Iran Electrical and Computer Engineering Faculty Semnan University Semnan Iran Deakin University VIC3217 Australia School of Engineering Macquarie University Sydney2109 Australia Department of Signal Theory Networking and Communications Universidad de Granada Spain Ngee Ann Polytechnic Singapore599489 Singapore Dept. of Biomedical Informatics and Medical Engineering Asia University Taichung Taiwan Dept. of Biomedical Engineering School of Science and Technology Singapore University of Social Sciences Singapore

Epilepsy is a disorder of the brain denoted by frequent seizures. The symptoms of seizure include confusion, abnormal staring, and rapid, sudden, and uncontrollable hand movements. Epileptic seizure detection methods involve neurological exams, blood tests, neuropsychological tests, and neuroimaging modalities. Among these, neuroimaging modalities have received considerable attention from specialist physicians. One method to facilitate the accurate and fast diagnosis of epileptic seizures is to employ computer-aided diagnosis systems (CADS) based on deep learning (DL) and neuroimaging modalities. This paper has studied a comprehensive overview of DL methods employed for epileptic seizures detection and prediction using neuroimaging modalities. First, DL-based CADS for epileptic seizures detection and prediction using neuroimaging modalities are discussed. Also, descriptions of various datasets, preprocessing algorithms, and DL models which have been used for epileptic seizures detection and prediction have been included. Then, research on rehabilitation tools has been presented, which contains brain-computer interface (BCI), cloud computing, internet of things (IoT), hardware implementation of DL techniques on field-programmable gate array (FPGA), etc. In the discussion section, a comparison has been carried out between research on epileptic seizure detection and prediction. The challenges in epileptic seizures detection and prediction using neuroimaging modalities and DL models have been described. In addition, possible directions for future works in this field, specifically for solving challenges in datasets, DL, rehabilitation, and hardware models, have been proposed. The final section is dedicated to the conclusion which summarizes the significant findings of the paper. © 2021, CC BY-NC-ND.

关键词： Forecasting

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Security enhancement of big data in cloud application using block-chain

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AIP Conference Proceedings 2023年第1期2587卷

作者： Avein Jabar Al-Asadi J. V. S. Arundathi Omdev Dahiya Abdul Wahab Hashmi Ankur Gupta Malik Bader Alazzam Dept. of Information Technology Technical College of Informatics TCI Sulaimani Polytechnic University Sulaymaniyah Iraq Dept. of Computer Science SR & BGNR Government Arts and Science Degree & PG College (Autonomous) Khammam Telangana India School of Computer Science and Engineering Lovely Professional University Phagwara Punjab India Advanced Manufacturing and Mechatronics Lab Mechanical Engineering Department Malaviya National Institute of Technology Jaipur India Dept. of Computer Science and Engineering Vaish College of Engineering Rohtak Haryana India Faculty of Computer Science and Informatics Amman Arab University Amman Jordan

Organizations are inundated on a daily basis with vast, difficult-to-manage volumes of structured and unstructured data, which is known as “big data.” Analysis of large amounts of data may provide useful information that can be used to improve decision-making and boost employee morale. This kind of data storage makes it almost hard to change, hack, or otherwise deceive. Every computer in a block-chain network has a copy of the block-transaction chain’s log, which is called a “block-chain.” In previous researches it has been observed that security of big data is a major issue. Block chain has been found suitable mechanism to deal with such issues. Block-chain has also been considered a service in the context of cloud computing, termed Block-chain as a Service (BaaS). By using virtual storage nodes, block-chain technology may create whole new cloud storage services that are very resistant to data tampering. The proposed research has focused on the enhancement of security of big data from SQL injection attack that is transferred over cloud application. Research is making use of block chain technology in order to resolve the issue of security. The major objective of research is to propose block chain based security model for big data that should be more secure and efficient in case of SQL injection attack. Research work has proposed modified algorithm enhances the security of big data in cloud application against SQL injection, cross script.

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Heart Attack Classification System using Neural Network Trained with Particle Swarm Optimization

arXiv

引用

arXiv 2022年

作者： Amin, Askandar H. Ahmed, Botan K. Maaroof, Bestan B. Rashid, Tarik A. Technical College of Informatics Sulaimani Polytechnic University KRG Sulaimani Iraq Administration and Economics University of Sulaimani KRG Sulaimani Iraq IT Department College of Commerce University of Sulaimani KRG Sulaimani Iraq School of Information Technology Fanshawe College London Canada Computer Science and Engineering Dept. University of Kurdistan Hewler KRG Erbil Iraq

The prior detection of a heart attack could lead to the saving of one’s life. Putting specific criteria into a system that provides an early warning of an imminent attack will be advantageous to a better prevention plan for an upcoming heart attack. Some studies have been conducted for this purpose, but yet the goal has not been reached to prevent a patient from getting such a disease. In this paper, Neural Network trained with Particle Swarm Optimization (PSONN) is used to analyze the input criteria and enhance heart attack anticipation. A real and novel dataset that has been recorded on the disease is used. After preprocessing the data, the features are fed into the system. As a result, the outcomes from PSONN have been evaluated against those from other algorithms. Decision Tree, Random Forest, Neural network trained with Backpropagation (BPNN), and Naive Bayes were among those employed. Then the results of 100%, 99.2424%, 99.2323%, 81.3131%, and 66.4141% are produced concerning the mentioned algorithms, which show that PSONN has recorded the highest accuracy rate among all other tested algorithms. © 2022, CC BY.

关键词： Swarm intelligence

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Computational Approaches to Explainable Artificial Intelligence: Advances Intheory, Applications and Trends

SSRN

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SSRN 2023年

作者： Górriz, Juan Manuel Álvarez-Illán, I. Álvarez-Marquina, A. Arco, J.E. Atzmueller, M. Ballarini, F. Barakova, E. Bologna, G. Bonomini, P. Castellanos-Dominguez, G. Castillo-Barnes, D. Cho, S.B. Contreras, R. Cuadra, J.M. Domínguez, E. Domínguez-Mateos, F. Duro, R.J. Elizondo, D. Fernández-Caballero, A. Fernandez-Jover, E. Formoso, M.A. Gallego-Molina, N.J. Gamazo, J. García González, J. Garcia-Rodriguez, J. Garre, C. Garrigós, J. Gómez-Rodellar, A. Gómez-Vilda, P. Graña, M. Guerrero-Rodriguez, B. Hendrikse, S.C.F. Jimenez-Mesa, C. Jodra-Chuan, M. Julian, V. Kotz, G. Kutt, K. Leming, M. de Lope, J. Macas, B. Marrero-Aguiar, V. Martinez, J.J. Martinez-Murcia, F.J. Martínez-Tomás, R. Mekyska, J. Nalepa, G.J. Novais, P. Orellana, D. Ortiz, A. Palacios-Alonso, D. Palma, J. Pereira, A. Pinacho-Davidson, P. Pinninghoff, M.A. Ponticorvo, M. Psarrou, A. Ramírez, J. Rincón, M. Rodellar-Biarge, V. Rodríguez-Rodríguez, I. Roelofsma, P.H.M.P. Santos, J. Salas-Gonzalez, D. Salcedo-Lagos, P. Segovia, F. Shoeibi, A. Silva, M. Simic, D. Suckling, J. Treur, J. Tsanas, A. Varela, R. Wang, S.H. Wang, W. Zhang, Y.D. Zhu, H. Zhu, Z. Ferrández-Vicente, J.M. SiPBA at Data Science and Computational Intelligence Institute University of Granada Granada Spain University of Granada Granada Spain Dept. of Psychiatry University of Cambridge Cambridge United Kingdom Dept. of Communications Engineering University of Malaga Malaga Spain School of Computing and Mathematical Sciences University of Leicester Leicester United Kingdom Semantic Information Systems Group Osnabrück University Osnabrück Germany Osnabrück Germany Dept. of Electronics Computer Technology and Projects Universidad Politécnica de Cartagena Cartagena Spain Universitat Politècnica de València València Spain Institute of Applied Computer Science Jagiellonian University Kraków Poland Dept. of Information and Communication Engineering University of Murcia Murcia Spain Dept. of Computer Science and Information Technologies University of A Coruña A Coruña Spain Dept. of Computer Science University of Oviedo Oviedo Spain Buenos Aires Argentina Buenos Aires Argentina Eindhoven University of Technology Eindhoven5612 AZ Netherlands Dept. of Computer Science University of Malaga Malaga Spain De Montfort University Leicester United Kingdom Dept. of Computer Science Faculty of Engineering Universidad de Concepción Concepción Chile Neurocognition and Emotion Research Unit Albacete Research Institute of Informatics University of Castilla-La Mancha Albacete Spain Dept. of Educational Informatics Universidad de Concepción Concepción Chile Madrid Spain Dept. of Humanistic Studies University of Naples Federico II Napoli Italy Escuela Técnica Superior de Ingeniería Informática Universidad Rey Juan Carlos Campus de Móstoles Móstoles Madrid28933 Spain Neuromorphic Speech Processing Lab Center for Biomedical Technology Universidad Politécnica de Madrid Campus de Montegancedo Pozuelo de Alarcón Madrid28223 Spain Biomedical Research Networking Center in Mental Health Instituto de Salud Carlos III Madrid28016 Spain Usher Institute Faculty o

Groundbreaking advances in theoretical and applied Artificial Intelligence (AI). DL algorithms are groundedin non-linear and complex artificial neural systems that progressively extract higher-level features from *** is frequently compared with human-level performance in real-world tasks, such as clinical diagnostics. Itis also a turning point in solving otherwise intractable problems in the design of virtual agents or robotics;orin providing novel insight into disease progress in genomics and Neuroimaging (NI), understanding humanbrain function, the automation of industrial processes based on computer vision, and so on. In this paper, themost relevant advances from the last few years in AI and several applications to neuroscience, neuroimaging,computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-ofthe-art in AI methods, models and applications within a collection of works presented at the 9th InternationalConference on the Interplay between Natural and Artificial Computation (IWINAC). The works presentedin this paper are excellent examples of new scientific discoveries made in laboratories that are successfullytransferred to real-life applications. © 2023, The Authors. All rights reserved.

关键词： Neuroimaging

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Assessing responsible innovation training

引用

Journal of Responsible Technology 2023年 16卷

作者： Stahl, Bernd Carsten Aicardi, Christine Brooks, Laurence Craigon, Peter J. Cunden, Mayen Burton, Saheli Datta Heaver, Martin De Saille, Stevienna De Dolby, Serena Dowthwaite, Liz Eke, Damian Hughes, Stephen Keene, Paul Kuh, Vivienne Portillo, Virginia Shanley, Danielle Smallman, Melanie Smith, Michael Stilgoe, Jack Ulnicane, Inga Wagner, Christian Webb, Helena School of Computer Science University of Nottingham United Kingdom Dept. of Informatics and School of Biomedical Engineering & Imaging Sciences King's College London United Kingdom Information School University of Sheffield Sheffield United Kingdom Horizon Digital Economy Research School of Computer Science University of Nottingham United Kingdom Independent Researcher Dept of Science and Technology Studies University College London United Kingdom ORBIT RRI limited Leicester United Kingdom Dept of Sociological Studies University of Sheffield Sheffield United Kingdom Centre for Computing and Social Responsibility De Montfort University Leicester United Kingdom School of Chemistry University of Bristol United Kingdom Department of Philosophy Faculty of Arts and Social Sciences Maastricht University Netherlands Biodiversity and Conservation Science Department of Biodiversity Conservation and Attractions Wildlife Research Centre Western Australia De Montfort University United Kingdom Lab for Uncertainty in Data and Decision Making (LUCID) School of Computer Science University of Nottingham United Kingdom

There is broad agreement that one important aspect of responsible innovation (RI) is to provide training on its principles and practices to current and future researchers and innovators, notably including doctoral students. Much less agreement can be observed concerning the question of what this training should consist of, how it should be delivered and how it could be assessed. The increasing institutional embedding of RI leads to calls for the alignment of RI training with training in other subjects. One can therefore observe a push towards the official assessment of RI training, for example in the recent call for proposals for centres for doctoral training by UK Research and Innovation. This editorial article takes its point of departure from the recognition that the RI community will need to react to the call for assessment of RI training. It provides an overview of the background and open questions around RI training and assessment as a background of examples of RI training assessment at doctoral level. There is unlikely to be one right way of assessing RI training across institutions and disciplines, but we expect that the examples provided in this article can help RI scholars and practitioners orient their training and its assessment in ways that are academically viable as well as supportive of the overall aims of RI. © 2023

关键词： Assessment Doctoral training Education Responsible innovation

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Bounds on the Spectral Radius of Nonnegative Matrices

Bounds on the Spectral Radius of Nonnegative Matrices

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International Conference on Mathematics and computers in science and engineering (MACISE)

作者： Fotis Babouklis Maria Adam Nicholas Assimakis Dept. of Computer Science & Biomedical Informatics University of Thessaly Lamia Greece General Department National and Kapodistrian University of Athens Athens Greece

In this paper, we determine some new bounds for the spectral radius of a nonnegative matrix with respect to the upper and the lower bounds for the spectral radius given by Frobenius' bounds, by using similarity tr... 详细信息

ISBN: (数字)9781728166957

ISBN: (纸本)9781728166964

关键词： Eigenvalues and eigenfunctions computer science biomedical informatics Laplace equations Symmetric matrices Upper bound Linear matrix inequalities

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Generative Adversarial Networks Based Cognitive Feedback Analytics System for Integrated Cyber-Physical System and Industrial Iot Networks

Research Square

引用

Research Square 2021年

作者： Upreti, Kamal Syed, Mohammad Haider Alam, Mohammad Shabbir Alhudhaif, Adi Shuaib, Mohammed Sharma, Arvind K. Department of Information & Technology Inderprastha Engineering College Ghaziabad India College of Computing and Informatics Saudi Electronic University Saudi Arabia Department of Computer Science College of computer science and Information Technology Jazan University Saudi Arabia Department of Computer Science College of Computer Engineering and Sciences in Al-kharj Prince Sattam bin Abdulaziz University P.O. Box 151 al-Kharj11942 Saudi Arabia College of Computer Science & IT Jazan University Saudi Arabia Dept. of CSI University of Kota Rajasthan India

In the modern era of technologies, the internet grows in the advancement of our day-to-day life like automation devices. The devices to set up industries with integrated cyber-physical systems and industrial IoT applications. Generative adversarial networks (GAN) can generate Cognitive feedback analysis with various data for both generator and discriminator in a supervised model. Neural networks are used for artificial intelligence algorithms, but in adversarial networks, feedback analytics is analyzed with the significance of data. The modern age of intelligent manufacturing will indeed be ushered in by Cyber-Physical Production Systems (CPPS). However, because of the connections between the virtual and physical worlds, CPPS would be subject to cross-domain assaults. Against Denial-of-Service (DoS) threats, this paper concentrates on complex performance feedback management of Cyber-Physical Systems (CPS). To begin, a swapping system modelling approach for the complex response feedback CPS is provided by analyzing the distinct effects of DoS assaults on the sensor-controller (SC) and controller-to-actuator (C-A) channels, accordingly. Given the difference in bandwidth between the dual channels and the accused's energy cap, it is reasonable to conclude that an offender can only jam a single communication stream at a point and also that the possible number of successive DoS attacks is limited. Second, using a packet-based transfer scheme, a nested switching paradigm is built on the foundation of the switching mechanism, considering both the spatial heterogeneity and the temporal durability of DoS attacks. The probability of discriminator gets analyzed feedback data to check whether actual data or fake data is sampled, and it is generated. Cognitive feedback supports genetic algorithms to sample the feedback data in a system for advanced technologies. © 2021, CC BY.

关键词： Cyber Physical System

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FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

arXiv

引用

arXiv 2023年

作者： Lekadir, Karim Feragen, Aasa Fofanah, Abdul Joseph Frangi, Alejandro F. Buyx, Alena Emelie, Anais Lara, Andrea Porras, Antonio R. Chan, An-Wen Navarro, Arcadi Glocker, Ben Botwe, Benard O. Khanal, Bishesh Beger, Brigit Wu, Carol C. Cintas, Celia Langlotz, Curtis P. Rueckert, Daniel Mzurikwao, Deogratias Fotiadis, Dimitrios I. Zhussupov, Doszhan Ferrante, Enzo Meijering, Erik Weicken, Eva González, Fabio A. Asselbergs, Folkert W. Prior, Fred Krestin, Gabriel P. Collins, Gary S. Tegenaw, Geletaw S. Kaissis, Georgios Misuraca, Gianluca Tsakou, Gianna Dwivedi, Girish Kondylakis, Haridimos Jayakody, Harsha Woodruf, Henry C. Mayer, Horst Joachim Aerts, Hugo JWL Walsh, Ian Chouvarda, Ioanna Buvat, Irène Tributsch, Isabell Rekik, Islem Duncan, James Kalpathy-Cramer, Jayashree Zahir, Jihad Park, Jinah Mongan, John Gichoya, Judy W. Schnabel, Julia A. Kushibar, Kaisar Riklund, Katrine Mori, Kensaku Marias, Kostas Amugongo, Lameck M. Fromont, Lauren A. Maier-Hein, Lena Alberich, Leonor Cerdá Rittner, Leticia Phiri, Lighton Marrakchi-Kacem, Linda Donoso-Bach, Lluís Martí-Bonmatí, Luis Cardoso, M. Jorge Bobowicz, Maciej Shabani, Mahsa Tsiknakis, Manolis Zuluaga, Maria A. Bielikova, Maria Fritzsche, Marie-Christine Camacho, Marina Linguraru, Marius George Wenzel, Markus De Bruijne, Marleen Tolsgaard, Martin G. Ghassemi, Marzyeh Ashrafuzzaman, Md Goisauf, Melanie Yaqub, Mohammad Abadía, Mónica Cano Mahmoud, Mukhtar M.E. Elattar, Mustafa Rieke, Nicola Papanikolaou, Nikolaos Lazrak, Noussair Díaz, Oliver Salvado, Olivier Pujol, Oriol Sall, Ousmane Guevara, Pamela Gordebeke, Peter Lambin, Philippe Brown, Pieta Abolmaesumi, Purang Dou, Qi Lu, Qinghua Osuala, Richard Nakasi, Rose Zhou, S. Kevin Napel, Sandy Colantonio, Sara Albarqouni, Shadi Joshi, Smriti Carter, Stacy Klein, Stefan Petersen, Steffen E. Aussó, Susanna Awate, Suyash Raviv, Tammy Riklin Cook, Tessa Mutsvangwa, Tinashe E.M. Rogers, Wendy A. Niessen, Wiro J. Puig-Bosch, Xènia Zeng, Yi Mohammed, Yunusa G. Aquino, Yves Saint James Salahuddin, Zohaib Starmans, Martijn P.A. Department de Matemàtiques i Informàtica Universitat de Barcelona Barcelona Spain Barcelona Spain DTU Compute Technical University of Denmark Kgs Lyngby Denmark Department of Mathematics and Computer Science Faculty of Science and Technology Milton Margai Technical University Freetown Sierra Leone Center for Computational Imaging & Simulation Technologies in Biomedicine Schools of Computing and Medicine University of Leeds Leeds United Kingdom Cardiovascular Science and Electronic Engineering Departments KU Leuven Leuven Belgium Institute of History and Ethics in Medicine Technical University of Munich Munich Germany Faculty of Engineering of Systems Informatics and Sciences of Computing Galileo University Guatemala City Guatemala Department of Biostatistics and Informatics Colorado School of Public Health University of Colorado Anschutz Medical Campus AuroraCO United States Department of Medicine Women’s College Research Institute University of Toronto Toronto Canada Universitat Pompeu Fabra BarcelonaBeta Brain Research Center Barcelona Spain Department of Computing Imperial College London London United Kingdom School of Biomedical & Allied Health Sciences University of Ghana Accra Ghana Department of Midwifery & Radiography School of Health & Psychological Sciences City University of London United Kingdom Kathmandu Nepal European Heart Network Brussels Belgium Department of Thoracic Imaging University of Texas MD Anderson Cancer Center Houston United States IBM Research Africa Nairobi Kenya Departments of Radiology Medicine and Biomedical Data Science Stanford University School of Medicine Stanford United States Institute for AI and Informatics in Medicine Klinikum rechts der Isar Technical University Munich Munich Germany Department of Computing Imperial College London London United Kingdom Muhimbili University of Health and Allied Sciences Dar es Salaam Tanzania United Republic of Ioannina Greece Almaty AI Lab Almaty Kazakhstan

Background: Despite major advances in artificial intelligence (AI) research for healthcare, the deployment and adoption of AI technologies remain limited in clinical practice. In recent years, concerns have been raised about the clinical, technical, ethical and legal risks associated with healthcare AI. To increase adoption in the real world, it is essential that AI technologies are trusted and accepted by patients, clinicians, health organisations and authorities. This paper describes the FUTURE-AI framework as the first international consensus guideline for trustworthy AI in healthcare. Methods: The FUTURE-AI consortium was founded in 2021 and now comprises 117 interdisciplinary experts from 50 countries representing all continents, including AI scientists, clinical researchers, biomedical ethicists, and social scientists. Over a two-year period, the consortium established guiding principles and best practices for trustworthy and deployable AI through an iterative process comprising an in-dept. literature review, a modified Delphi survey, and online consensus meetings. Findings: The FUTURE-AI framework was established based on six guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 30 best practices were defined, addressing technical, clinical, socio-ethical and legal dimensions of trustworthy AI. The recommendations cover the entire lifecycle of healthcare AI, from design, development and validation to regulation, deployment, and monitoring. Interpretation: FUTURE-AI is a structured, risk-informed framework which provides guidance for constructing healthcare AI tools that will be trusted, deployed and adopted in real-world clinical practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of healthcare AI. Copyright © 2023, The Authors. All rights

关键词： Clinical research

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Centenarian clocks: epigenetic clocks for validating claims of exceptional longevity

引用

GEROscience 2023年第3期45卷 1817-1835页

作者： Dec, Eric Clement, James Cheng, Kaiyang Church., George M. Fossel., Michael B. Rehkopf, David. H. Rosero-Bixby, Luis Kobor, Michael S. Lin, David TS. Lu, Ake T. Fei, Zhe Guo, Wei Chew, Yap Ching Yang, Xiaojing Putra, Sulistyo E. Dwi Reiner, Alex P. Correa, Adolfo Vilalta, Adrian Pirazzini, Chiara Passarino, Giuseppe Monti, Daniela Arosio, Beatrice Garagnani, Paolo Franceschi, Claudio Horvath, Steve Department of Pediatrics Division of Genetics and Genomic Medicine University of California Irvine USA Betterhumans Inc. Gainesville USA Department of Chemical Pharmaceutical and Agricultural Sciences University of Ferrara Ferrara Italy Medical Informatics David Geffen School of Medicine University of California Los Angeles Los Angeles USA Department of Genetics Harvard Medical School Boston USA Wyss Institute for Biologically Inspired Engineering Harvard University Cambridge USA Telocyte LLC Grand Rapids USA Epidemiology & Population Health and Medicine School of Medicine Stanford University Stanford USA Centro Centroamericano de Población Universidad de Costa Rica San Pedro Costa Rica Edwin S.H. Leong Healthy Aging Program Centre for Molecular Medicine and Therapeutics University of British Columbia Vancouver Canada Dept. of Human Genetics David Geffen School of Medicine University of California Los Angeles Los Angeles USA Altos Labs San Diego USA Dept. of Biostatistics Fielding School of Public Health University of California Los Angeles Los Angeles USA Zymo Research Corp Irvine USA Department of Biology Faculty of Biotechnology University of Surabaya Surabaya Indonesia Public Health Sciences Division Fred Hutchinson Cancer Research Center Seattle USA Medicine and Population Health Science University of Mississippi Medical Center Jackson USA Outcomes Metrics LLC San Diego USA IRCCS Istituto Delle Scienze Neurologiche Di Bologna Bologna Italy Department of Biology Ecology and Earth Science University of Calabria Rende Italy Department of Experimental and Clinical Biomedical Sciences “Mario Serio” University of Florence Florence Italy Department of Clinical Sciences and Community Health University of Milan Milan Italy Department of Medical and Surgical Sciences University of Bologna Bologna Italy Alma Mater Research Institute On Global Challenges and Climate Change (Alma Climate) University of Bologna Bologna Italy Department of Laboratory

Claims surrounding exceptional longevity are sometimes disputed or dismissed for lack of credible evidence. Here, we present three DNA methylation-based age estimators (epigenetic clocks) for verifying age claims of centenarians. The three centenarian clocks were developed based on n = 7039 blood and saliva samples from individuals older than 40, including n = 184 samples from centenarians, 122 samples from semi-supercentenarians (aged 105 +), and 25 samples from supercentenarians (aged 110 +). The oldest individual was 115 years old. Our most accurate centenarian clock resulted from applying a neural network model to a training set composed of individuals older than 40. An epigenome-wide association study of age in different age groups revealed that age effects in young individuals (age < 40) are correlated (r = 0.55) with age effects in old individuals (age > 90). We present a chromatin state analysis of age effects in centenarians. The centenarian clocks are expected to be useful for validating claims surrounding exceptional old age.

关键词： Centenarian clocks Epigenetic clocks Longevity

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