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Artificial Intelligence for the Electron Ion Collider (AI4EIC)

computing and Software for Big science 2024年第1期8卷 1-21页

作者： Allaire, C. Ammendola, R. Aschenauer, E.-C. Balandat, M. Battaglieri, M. Bernauer, J. Bondì, M. Branson, N. Britton, T. Butter, A. Chahrour, I. Chatagnon, P. Cisbani, E. Cline, E.W. Dash, S. Dean, C. Deconinck, W. Deshpande, A. Diefenthaler, M. Ent, R. Fanelli, C. Finger, M. Fol, E. Furletov, S. Gao, Y. Giroux, J. Waduge, N. C. Gunawardhana Hassan, O. Hegde, P.L. Hernández-Pinto, R.J. Blin, A. Hiller Horn, T. Huang, J. Jalotra, A. Jayakodige, D. Joo, B. Junaid, M. Kalantarians, N. Karande, P. Kriesten, B. Elayavalli, R. Kunnawalkam Li, Y. Lin, M. Liu, F. Liuti, S. Matousek, G. McEneaney, M. McSpadden, D. Menzo, T. Miceli, T. Mikuni, V. Montgomery, R. Nachman, B. Nair, R.R. Niestroy, J. Oregon, S. A. Ochoa Oleniacz, J. Osborn, J.D. Paudel, C. Pecar, C. Peng, C. Perdue, G.N. Phelps, W. Purschke, M.L. Rajendran, H. Rajput, K. Ren, Y. Renteria-Estrada, D.F. Richford, D. Roy, B.J. Roy, D. Saini, A. Sato, N. Satogata, T. Sborlini, G. Schram, M. Shih, D. Singh, J. Singh, R. Siodmok, A. Stevens, J. Stone, P. Suarez, L. Suresh, K. Tawfik, A.-N. Acosta, F. Torales Tran, N. Trotta, R. Twagirayezu, F.J. Tyson, R. Volkova, S. Vossen, A. Walter, E. Whiteson, D. Williams, M. Wu, S. Zachariou, N. Zurita, P. Université Paris-Saclay CNRS/IN2P3 IJCLAB Orsay 91400 France Sezione di Roma Tor Vergata INFN Rome 00133 Italy Brookhaven National Lab Upton 11973 NY United States Meta Menlo Park 935025 CA United States National Institute for Nuclear Physics Genoa 16146 Italy Center for Frontiers in Nuclear Science Stony Brook University Stony Brook 11790-3800 NY United States Stony Brook University Stony Brook 11794 NY United States National Institute for Nuclear Physics Catania 95125 Italy Drexel University Philadelphia 19104 PA United States Messiah University Mechanicsburg 17055 PA United States Jefferson Lab Newport News 293606 VA United States Laboratory Nuclear and High-Energy Physics CNRS Paris 75005 France University of Michigan Ann Arbor 48104 MI United States National Institute for Nuclear Physics Roma Rome 00185 Italy Indian Institute of Technology Bombay Maharashtra Mumbai 400076 India Massachusetts Institute of Technology Cambridge 021339 MA United States Department of Physics University of Manitoba Winnipeg R3T 2N2 MB Canada William & amp Charles University Faculty of Mathematics and Physics Prague 18000 Czech Republic Accelerators and Beam Physics Group CERN Geneva 1211 Switzerland University of Regina Regina S4S0A2 SK Canada University of Virginia Charlottesville 22904 VA United States University of Victoria Victoria V8P 5C2 Canada Central University of Karnataka Kalaburagi 585367 India Facultad de Ciencias Físico-Matemáticas Universidad Autónoma de Sinaloa Ciudad Universitaria Sinaloa Culiacán 80000 Mexico Institute for Theoretical Physics TübingenUniversity Tübingen 72076 Germany The Catholic University of America Washington 20064 DC United States University of Jammu Jammu and Kashmir Jammu 180006 India Hampton University Hampton 23668 VA United States Oak Ridge National Laboratory Oak Ridge 37830 TN United States Virginia Union University Richmond 23220 VA United States Lawrence Livermore N

The Electron-Ion Collider (EIC), a state-of-the-art facility for studying the strong force, is expected to begin commissioning its first experiments in 2028. This is an opportune time for artificial intelligence (AI) to be included from the start at this facility and in all phases that lead up to the experiments. The second annual workshop organized by the AI4EIC working group, which recently took place, centered on exploring all current and prospective application areas of AI for the EIC. This workshop is not only beneficial for the EIC, but also provides valuable insights for the newly established ePIC collaboration at EIC. This paper summarizes the different activities and R&D projects covered across the sessions of the workshop and provides an overview of the goals, approaches and strategies regarding AI/ML in the EIC community, as well as cutting-edge techniques currently studied in other experiments. © The Author(s) 2024.

关键词： Artificial Intelligence Deep learning EIC ePIC Machine learning Physics QCD

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MultiplEYE: Creating a multilingual eye-tracking-while-reading corpus 25

MultiplEYE: Creating a multilingual eye-tracking-while-readi...

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Proceedings of the 2025 Symposium on Eye Tracking research and Applications

作者： Deborah Noemie Jakobi Maja Stegenwallner-Schütz Nora Hollenstein Cui Ding Ramune Kaspere Ana Matić Škorić Eva Pavlinusic Vilus Stefan Frank Marie-Luise Müller Kristine M Jensen de López Nik Kharlamov Hanne B. Søndergaard Knudsen Yevgeni Berzak Ella Lion Irina A. Sekerina Cengiz Acarturk Mohd Faizan Ansari Katarzyna Harezlak Pawel Kasprowski Ana Bautista Lisa Beinborn Anna Bondar Antonia Boznou Leah Bradshaw Jana Mara Hofmann Thyra Krosness Not Battesta Soliva Anila Çepani Kristina Cergol Ana Došen Marijan Palmovic Adelina Çerpja Dalí Chirino Jan Chromý Vera Demberg Iza Škrjanec Nazik Dinçtopal Deniz Dr. Inmaculada Fajardo Mariola Giménez-Salvador Xavier Mínguez-López Maroš Filip Zigmunds Freibergs Jéssica Gomes Andreia Janeiro Paula Luegi João Veríssimo Sasho Gramatikov Jana Hasenäcker Alba Haveriku Nelda Kote Muhammad M. Kamal Hanna Kędzierska Dorota Klimek-Jankowska Sara Kosutar Daniel G. Krakowczyk Izabela Krejtz Marta Łockiewicz Kaidi Lõo Jurgita Motiejūnienė Jamal A. Nasir Johanne Sofie Krog Nedergård Ayşegül Özkan Mikuláš Preininger Loredana Pungă David Robert Reich Chiara Tschirner Špela Rot Andreas Säuberli Jordi Solé-Casals Ekaterina Strati Igor Svoboda Evis Trandafili Spyridoula Varlokosta Mila Vulchanova Lena A. Jäger Department of Computational Linguistics University of Zurich Zurich Switzerland Humboldt-University of Berlin Berlin Germany and University of Koblenz Koblenz Germany Kaunas University of Technology Kaunas Lithuania Faculty of Education and Rehabilitation Sciences Dept of Speech and Language pathology Laboratory for Psycholinguistic Research University of Zagreb Zagreb Croatia Centre for Language Studies Radboud University Nijmegen Netherlands ZPID Leibnitz Institute for Psychology Trier Germany Aalborg University Aalborg Denmark Data and Decision Sciences Technion Haifa Israel Department of Psychology College of Staten Island Staten Island New York USA Cognitive Science Department Jagiellonian University Krakow Poland Silesian University of Technology Gliwice Poland Institute of Informatics Silesian University of Technology Gliwice Poland Basque Center on Cognition Brain & Language Donostia Spain Human-Centered Data Science University of Göttingen Göttingen Germany Department of Computational Linguistics University of Zurich Zurich Switzerland and Digital Society Initiative University of Zurich Zurich Switzerland Lab of Psycholinguistics & Neurolinguistics National and Kapodistrian University of Athens Athens Greece Department of Computational Linguistics University of Zurich University of Zurich Switzerland University of Zurich Zurich Switzerland University of Tirana Tirana Albania Faculty of Teacher Education University of Zagreb Zagreb Croatia Hrvatska Croatia and Laboratory for Psycholinguistic Research University of Zagreb Zagreb Croatia Hrvatska Croatia University of Zagreb Zagreb Croatia Dept. of Speech & Lang. Path. Laboratory for Psycholinguistic Research University of Zagreb Zagreb Croatia Hrvatska Croatia Academy of Sciences of Albania Tirana Albania Radboud University Nijmegen Netherlands Institute of Czech Language and Theory of Communication Faculty of Arts Charles University Prague Czech Republic Saarland Universit

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A Robust Ensemble Algorithm for Ischemic Stroke Lesion Segmentation: Generalizability and Clinical Utility Beyond the ISLES Challenge

arXiv

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arXiv 2024年

作者： de la Rosa, Ezequiel Reyes, Mauricio Liew, Sook-Lei Hutton, Alexandre Wiest, Roland Kaesmacher, Johannes Hanning, Uta Hakim, Arsany Zubal, Richard Valenzuela, Waldo Robben, David Sima, Diana M. Anania, Vincenzo Brys, Arne Meakin, James A. Mickan, Anne Broocks, Gabriel Heitkamp, Christian Gao, Shengbo Liang, Kongming Zhang, Ziji Siddiquee, Md Mahfuzur Rahman Myronenko, Andriy Ashtari, Pooya Van Huffel, Sabine Jeong, Hyun-Su Yoon, Chi-Ho Kim, Chulhong Huo, Jiayu Ourselin, Sebastien Sparks, Rachel Clèrigues, Albert Oliver, Arnau Lladó, Xavier Chalcroft, Liam Pappas, Ioannis Bertels, Jeroen Heylen, Ewout Moreau, Juliette Hatami, Nima Frindel, Carole Qayyum, Abdul Mazher, Moona Puig, Domenec Lin, Shao-Chieh Juan, Chun-Jung Hu, Tianxi Boone, Lyndon Goubran, Maged Liu, Yi-Jui Wegener, Susanne Kofler, Florian Ezhov, Ivan Shit, Suprosanna Hernandez Petzsche, Moritz R. Menze, Bjoern Kirschke, Jan S. Wiestler, Benedikt Department of Quantitative Biomedicine University of Zurich Zurich Switzerland Department of Informatics Technical University Munich Germany Icometrix Leuven Belgium ARTORG Center for Biomedical Research University of Bern Bern Switzerland Department of Radiation Oncology University Hospital Bern University of Bern Switzerland University of Bern Bern Switzerland Chan Division of Occupational Science and Occupational Therapy University of Southern California Los AngelesCA United States Stevens Neuroimaging and Informatics Institute Department of Neurology Keck School of Medicine University of Southern California United States University Institute of Diagnostic and Interventional Neuroradiology Inselspital Bern Switzerland University Institute of Diagnostic and Interventional Neuroradiology University Hospital Bern Inselspital University of Bern Bern Switzerland Department of Diagnostic and Interventional neuroradiology University Medical Center Hamburg-Eppendorf Hamburg Germany Department of Medical Imaging Radboud University Medical Center Institute for Health Sciences Nijmegen Netherlands Deepwise AI Lab Beijing China Beijing University of Posts and Telecommunications Bejing China School of Computing and Augmented Intelligence Arizona State University TempeAZ United States NVIDIA Santa ClaraCA United States STADIUS Center for Dynamical Systems Signal Processing and Data Analytics KU Leuven Leuven Belgium Pohang Korea Republic of School of Biomedical Engineering & Imaging Sciences King’s College London United Kingdom Institute of Computer Vision and Robotics University of Girona Spain Wellcome Centre for Human Neuroimaging University College London London United Kingdom Laboratory of Neuro Imaging Stevens Institute for Neuroimaging and Informatics Keck School of Medicine University of Southern California Los Angeles United States KU Leuven Leuven Belgium CREATIS Université Lyon1 CNRS UMR5220 INSERM U1206 INSA-Lyon Villeurbanne696

Diffusion-weighted MRI (DWI) is essential for stroke diagnosis, treatment decisions, and prognosis. However, image and disease variability hinder the development of generalizable AI algorithms with clinical value. We address this gap by presenting a novel ensemble algorithm derived from the 2022 Ischemic Stroke Lesion Segmentation (ISLES) challenge. ISLES’22 provided 400 patient scans with ischemic stroke from various medical centers, facilitating the development of a wide range of cutting-edge segmentation algorithms by the research community. By assessing them against a hidden test set, we identified strengths, weaknesses, and potential biases. Through collaboration with leading teams, we combined top-performing algorithms into an ensemble model that overcomes the limitations of individual solutions. Our ensemble model combines the individual algorithms’ strengths and achieved superior ischemic lesion detection and segmentation accuracy (median Dice score: 0.82, median lesion-wise F1 score: 0.86) on our internal test set compared to individual algorithms. This accuracy generalized well across diverse image and disease variables. Furthermore, the model excelled in extracting clinical biomarkers like lesion types and affected vascular territories. Notably, in a Turing-like test, neuroradiologists consistently preferred the algorithm’s segmentations over manual expert efforts, highlighting increased comprehensiveness and precision. Validation using a real-world external dataset (N=1686) confirmed the model’s generalizability (median Dice score: 0.82, median lesion-wise F1 score: 0.86). The algorithm’s outputs also demonstrated strong correlations with clinical scores (admission NIHSS and 90-day mRS) on par with or exceeding expert-derived results, underlining its clinical relevance. This study offers two key findings. First, we present an ensemble algorithm that detects and segments ischemic stroke lesions on DWI across diverse scenarios on par with expert (neuro)rad

关键词： Diagnosis

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EEG data Augmentation for Emotion Recognition Using a Conditional Wasserstein GAN 40

EEG Data Augmentation for Emotion Recognition Using a Condit...

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40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018

作者： Luo, Yun Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Brain Science and Technology Research Center Shanghai Jiao Tong University 800 Dong Chuan Road Shanghai200240 China

ISBN: (纸本)9781538636466

Due to the lack of electroencephalography (EEG) data, it is hard to build an emotion recognition model with high accuracy from EEG signals using machine learning approach. Inspired by generative adversarial networks (GANs), we introduce a Conditional Wasserstein GAN (CWGAN) framework for EEG data augmentation to enhance EEG-based emotion recognition. A Wasserstein GAN with gradient penalty is adopted to generate realistic-like EEG data in differential entropy (DE) form. Three indicators are used to judge the qualities of the generated high-dimensional EEG data, and only high quality data are appended to supplement the data manifold, which leads to better classification of different emotions. We evaluate the proposed CWGAN framework on two public EEG datasets for emotion recognition, namely SEED and DEAP. The experimental results demonstrate that using the EEG data generated by CWGAN significantly improves the accuracies of emotion recognition models. © 2018 IEEE.

关键词： Electroencephalography Gallium nitride Training Generators Emotion recognition Generative adversarial networks Brain modeling

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Gut microbiota and diabetes: From correlation to causality and mechanism

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World Journal of Diabetes 2020年第7期11卷 293-308页

作者： Wei-Zheng Li Kyle Stirling Jun-Jie Yang Lei Zhang Microbiome-X Beijing Advanced Innovation Center for Big Data-Based Precision MedicineSchool of Medicine and EngineeringKey Laboratory of Big Data-Based Precision MedicineBeihang UniversityBeijing 100191China School of Biological Science and Medical Engineering Beihang UniversityBeijing 100191China Luddy School of Informatics Computing and EngineeringIndiana UniversityBloomingtonIN 47408United States The Crisis Technologies Innovation Lab Indiana UniversityThe Information Technology Services and the Pervasive Technology InstituteBloomingtonIN 47408United States Shandong Institute of Industrial Technology for Health Sciences and Precision Medicine Jinan 250000Shandong ProvinceChina College of Life Science Qilu Normal UniversityJinan 250000Shandong ProvinceChina Microbiome Research Center Shandong Institutes for Food and Drug ControlJinan 250000Shandong ProvinceChina Shandong Children’s Microbiome Center Qilu Children's Hospital of Shandong UniversityJinan 250000Shandong ProvinceChina Microbiological Laboratory Lin Yi People’s HospitalLinyi 276000Shandong ProvinceChina Qingdao Human Microbiome Center The Affiliated Central Hospital of Qingdao UniversityQingdao 266000Shandong ProvinceChina Institute for Medical Dataology Cheeloo College of MedicineShandong UniversityJinan 250000Shandong ProvinceChina Department of Biostatistics School of Public HealthCheeloo College of MedicineShandong UniversityJinan 250000Shandong ProvinceChina Shandong Children’s Microbiome Center Research Institute of PediatricsQilu Children's HospitalCheeloo College of MedicineShandong Universityand Jinan Children's HospitalJinan 250022Shandong ProvinceChina

In this review,we summarize the recent microbiome studies related to diabetes disease and discuss the key findings that show the early emerging potential causal roles for *** a global scale,diabetes causes a significant negative impact to the health status of human *** review covers type 1 diabetes and type 2 *** examine promising studies which lead to a better understanding of the potential mechanism of microbiota in diabetes *** appears that the human oral and gut microbiota are deeply interdigitated with *** is that *** studies of the human microbiome are capturing the attention of scientists and healthcare practitioners worldwide by focusing on the interplay of gut microbiome and *** studies focus on the role and the potential impact of intestinal microflora in *** paint a clear picture of how strongly microbes are linked and associated,both positively and negatively,with the fundamental and essential parts of diabetes in *** microflora seems to have an endless capacity to impact and transform *** conclude that there is clear and growing evidence of a close relationship between the microbiota and diabetes and this is worthy of future investments and research efforts.

关键词： Diabetes Microbiota Causality Mechanism Type 1 diabetes Type 2 diabetes Insulin resistance Inflammation Metabolites

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Trust Repair in Human-Swarm Teams+

Trust Repair in Human-Swarm Teams+

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Rui Liu Zekun Cai Michael Lewis Joseph Lyons Katia Sycara Cognitive Robotics and AI Lab College of Aeronautics and Engineering Kent State University OH USA School of Computing and Information University of Pittsburgh Air Force Research Laboratory Wright-Patterson AFB Dayton OH USA Robotics Institute School of Computer Science Carnegie Mellon University PA USA

ISBN: (纸本)9781728126234

Swarm robots are coordinated via simple control laws to generate emergent behaviors such as flocking, rendezvous, and deployment. Human-swarm teaming has been widely proposed for scenarios, such as human-supervised teams of unmanned aerial vehicles (UAV) for disaster rescue, UAV and ground vehicle cooperation for building security, and soldier-UAV teaming in combat. Effective cooperation requires an appropriate level of trust, between a human and a swarm. When an UAV swarm is deployed in a real-world environment, its performance is subject to real-world factors, such as system reliability and wind disturbances. Degraded performance of a robot can cause undesired swarm behaviors, decreasing human trust. This loss of trust, in turn, can trigger human intervention in UAVs' task executions, decreasing cooperation effectiveness if inappropriate. Therefore, to promote effective cooperation we propose and test a trust-repairing method (Trust-repair) restoring performance and human trust in the swarm to an appropriate level by correcting undesired swarm behaviors. Faulty swarms caused by both external and internal factors were simulated to evaluate the performance of the Trust-repair algorithm in repairing swarm performance and restoring human trust. Results show that Trust-repair is effective in restoring trust to a level intermediate between normal and faulty conditions.

关键词： dike swarms Nonprofit Organizations trust Trust Unmanned Aerial Vehicle drone honey bees control law Human Robot Swarming behavior

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Natural Language Processing Markers for Psychosis and Other Psychiatric Disorders: Emerging Themes and research Agenda From a Cross-Linguistic Workshop

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SCHIZOPHRENIA BULLETIN 2023年第SUPP2期49卷 S86-S92页

作者： Corona Hernandez, Hugo Corcoran, Cheryl Achim, Amelie M. de Boer, Janna N. Boerma, Tessel Brederoo, Sanne G. Cecchi, Guillermo A. Ciampelli, Silvia Elvevag, Brita Fusaroli, Riccardo Giordano, Silvia Hauglid, Mathias van Hessen, Arjan Hinzen, Wolfram Homan, Philipp de Kloet, Sybren F. Koops, Sanne Kuperberg, Gina R. Maheshwari, Kritika Mota, Natalia B. Parola, Alberto Rocca, Roberta Sommer, Iris E. C. Truong, Khiet Voppel, Alban E. van Vugt, Marieke Wijnen, Frank Palaniyappan, Lena Department of Biomedical Sciences of Cells and Systems University Medical Center Groningen University of Groningen Groningen Netherlands Department of Psychiatry Icahn School of Medicine at Mount Sinai NY USA Département de Psychiatrie et Neurosciences VITAM Centre de Recherche en Santé Durable Cervo Brain Research Centre Université Laval Québec Canada Department of Psychiatry University Medical Center Utrecht Utrecht University Utrecht Netherlands Department of Languages Literature and Communication Institute for Language Sciences Utrecht University Utrecht Netherlands University Center of Psychiatry University Medical Center Groningen Groningen Netherlands IBM T. J. Watson Research Center Yorktown Heights NY USA Department of Clinical Medicine University of Tromsø—the Arctic University of Norway Tromsø Norway Department of Linguistics Cognitive Science and Semiotics School of Communication and Culture Aarhus University Aarhus Denmark Department of Culture Interacting Minds Center Cognition and Computation Communication School of Culture and Society Aarhus University Aarhus Denmark Linguistic Data Consortium University of Pennsylvania PA USA Department of Innovative Technologies University of Applied Sciences and Arts of Southern Switzerland Lugano Switzerland Faculty of Law University of Tromsø—the Arctic University of Norway Tromsø Norway Department of Human Media Interaction University of Twente Enschede Netherlands Institució Catalana de Recerca i Estudis Avançats (ICREA) Universitat Pompeu Fabra Barcelona Spain Department of Psychiatry Psychiatric Hospital of the University of Zurich Psychotherapy and Psychosomatics Zurich Switzerland Neurocast BV Amsterdam Netherlands Department of Psychology Tufts University Medford MA USA Department of Psychiatry Harvard Medical School Boston MA USA The Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital Harvard Medical School Boston MA USA Department of Genetics U

This workshop summary on natural language processing (NLP) markers for psychosis and other psychiatric disorders presents some of the clinical and research issues that NLP markers might address and some of the activities needed to move in that direction. We propose that the optimal development of NLP markers would occur in the context of research efforts to map out the underlying mechanisms of psychosis and other disorders. In this workshop, we identified some of the challenges to be addressed in developing and implementing NLP markers-based Clinical Decision Support Systems (CDSSs) in psychiatric practice, especially with respect to psychosis. Of note, a CDSS is meant to enhance decision-making by clinicians by providing additional relevant information primarily through software (although CDSSs are not without risks). In psychiatry, a field that relies on subjective clinical ratings that condense rich temporal behavioral information, the inclusion of computational quantitative NLP markers can plausibly lead to operationalized decision models in place of idiosyncratic ones, although ethical issues must always be paramount.

关键词： speech technology implementation digital markers psychiatric practice pathophysiology

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Multi-organ metabolome biological age implicates cardiometabolic conditions and mortality risk

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Nature Communications 2025年第1期16卷 1-14页

作者： Aisen, Paul Rafii, Michael S. Bai, Wenjia Walker, Keenan A. Ferrucci, Luigi Tian, Ye Ella Zalesky, Andrew Anagnostakis, Filippos Ko, Sarah Saadatinia, Mehrshad Wang, Jingyue Davatzikos, Christos Wen, Junhao Laboratory of AI and Biomedical Science (LABS) Columbia University New York NY United States Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL) Center for AI and Data Science for Integrated Diagnostics (AI2D) Perelman School of Medicine University of Pennsylvania Philadelphia PA United States Department of Radiology Columbia University New York NY United States New York Genome Center (NYGC) New York NY United States Department of Biomedical Engineering (BME) Columbia University New York NY United States Data Science Institute (DSI) Columbia University New York NY United States Zuckerman Institute Columbia University New York NY United States Center for Innovation in Imaging Biomarkers and Integrated Diagnostics (CIMBID) Department of Radiology Columbia University New York NY United States Systems Lab Department of Psychiatry Melbourne Medical School The University of Melbourne Melbourne VIC Australia National Institute on Aging National Institutes of Health Baltimore MD United States Laboratory of Behavioral Neuroscience National Institute on Aging National Institutes of Health Baltimore MD United States Department of Brain Sciences and Department of Computing Imperial College London London United Kingdom Alzheimer’s Therapeutic Research Institute Keck School of Medicine of the University of Southern California San Diego 92121 CA United States

Multi-organ biological aging clocks across different organ systems have been shown to predict human disease and mortality. Here, we extend this multi-organ framework to plasma metabolomics, developing five organ-specific metabolome-based biological age gaps (MetBAGs) using 107 plasma non-derivatized metabolites from 274,247 UK Biobank participants. Our age prediction models achieve a mean absolute error of approximately 6 years (0.25data due to multicollinearity. Genome-wide associations identify 405 MetBAG-locus pairs (P < 5 × 10⁻⁸/5). Using SBayesS, we estimate the SNP-based heritability (0.09lable at https://***/medicine/. © The Author(s) 2025.

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An automatic biomedical ontology meta-matching technique

Journal of Network Intelligence

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Journal of Network Intelligence 2019年第3期4卷 109-113页

作者： Xue, Xingsi Yang, Haiyan Zhang, Jie Zhang, Jing Chen, Dongxu College of Information Science and Engineering Intelligent Information Processing Research Center Fujian Key Lab for Automotive Electronics and Electric Drive Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology No.3 Xueyuan Road University Town Minhou Fuzhou CityFujian Province350118 China College of Information Science and Engineering Fujian University of Technology No.33 Xuefu South Road University Town Minhou Fuzhou CityFujian Province350118 China School of Computer Science and Engineering Yulin Normal University No.299 Education Middle Road Yulin CityGuanxi Province537000 China School of Computing Ulster University Jordanstown BT370QB United Kingdom Fujian Medical University Union Hospital No.29 Xinquan Road Gulou Fuzhou CityFujian Province350001 China

Biomedical ontology matching aims at determining the heterogeneous biomed-ical concepts, and bridging the semantic gap between heterogeneous biomedical ontologies. The foundation of a biomedical ontology matching technique is the Biomedical Concept Similarity Measure (BCSM), which calculates the similarity value between two biomed-ical concepts. Since various BCSMs have different advantages, usually several BCSMs are aggregated together to improve the result’s confidence. How to tune the aggregating weights to ensure the quality of the alignment is called biomedical ontology meta-matching problem, which is a challenge in the ontology matching domain. Currently, researchers mainly focus on how to tune the aggregating weights for various similarity measures to improve the quality of the ontology alignments. However, the ignorance of the effects brought about by different biomedical concept mapping’s preference on different similarity measures significantly reduces the alignment’s quality. To overcome this drawback, in this work, we formally define the biomedical ontology meta-matching problem, and then present an Ordered Weighted Average (OWA) based approach to automatically aggregate various biomedical concept similarity measures. In our method, the aggregating weights determined for each concept mapping is associated with the ordered position of the similarity value instead of a particular concept similarity measure. The experiment utilizes the large biomed track provided by Ontology Alignment Evaluation Initiative (OAEI) to test our proposal’s performance, and the experimental results show the effectiveness of our proposal. © 2019, Taiwan Ubiquitous Information. All rights reserved.

关键词： Semantics

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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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