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5th International Conference on Variable Neighborhood Search (ICVNS'17)

Electronic Notes in Discrete Mathematics 2018年 66卷 1-5页

作者： Vitor Nazário Coelho Haroldo Gambini Santos Igor Machado Coelho Puca Huachi Vaz Penna Thays A. Oliveira Marcone Jamilson Freitas Souza Angelo Sifaleras Institute of Computer Science Universidade Federal Fluminense Niterói Brazil Grupo da Causa Humana Ouro Preto Brazil Department of Computing Universidade Federal de Ouro Preto Ouro Preto Brazil Institute of Mathematics Statitics Universidade Estadual do Rio de Janeiro Rio de Janeiro Brazil Graduate Program of the Department of Engineering and Information and Communication Technologies Universitat Pompeu Fabra Barcelona Spain Department of Applied Informatics School of Information Sciences University of Macedonia 156 Egnatia Str. Thessaloniki 54636 Greece

This volume presents selected, peer-reviewed, short papers that were accepted for presentation in the 5th International Conference on Variable Neighborhood Search (ICVNS'17) which was held in Ouro Preto, Brazil, d... 详细信息

关键词： Metaheuristics Variable Neighborhood Search Discrete Optimization Nonlinear optimization

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Perspectives on risk prioritization of data center vulnerabilities using rank aggregation and multi-objective optimization

arXiv

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arXiv 2022年

作者： Grisci, Bruno Iochins Kuhn, Gabriela Colombelli, Felipe Matter, Vítor Kehl Lima, Leomar Heinen, Karine Pegoraro, Mauricio Borges, Marcio Rigo, Sandro José Barbosa, Jorge Luis Victória da Rosa Righi, Rodrigo da Costa, Cristiano André de Oliveira Ramos, Gabriel Software Innovation Laboratory - Softwarelab Graduate Program in Applied Computing Universidade do Vale do Rio dos Sinos Av. Unisinos 950 RS São Leopoldo93022-750 Brazil Institute of Informatics Federal University of Rio Grande do Sul Av. Bento Gonçalves 9500 RS Porto Alegre91501-970 Brazil Dell Technologies Brazil Av. Industrial Belgraf 400 Eldorado do Sul RS 92990-000 Brazil

Nowadays, data has become an invaluable asset to entities and companies, and keeping it secure represents a major challenge. Data centers are responsible for storing data provided by software applications. Nevertheless, the number of vulnerabilities has been increasing every day. Managing such vulnerabilities is essential for building a reliable and secure network environment. Releasing patches to fix security flaws in software is a common practice to handle these vulnerabilities. However, prioritization becomes crucial for organizations with an increasing number of vulnerabilities since time and resources to fix them are usually limited. This review intends to present a survey of vulnerability ranking techniques and promote a discussion on how multi-objective optimization could benefit the management of vulnerabilities risk prioritization. The state-of-the-art approaches for risk prioritization were reviewed, intending to develop an effective model for ranking vulnerabilities in data centers. The main contribution of this work is to point out multi-objective optimization as a not commonly explored but promising strategy to prioritize vulnerabilities, enabling better time management and increasing security. Copyright © 2022, The Authors. All rights reserved.

关键词： Multiobjective optimization

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Is Attention All You Need for EEG to Predict Neurological Outcomes in Cardiac Arrest?

Is Attention All You Need for EEG to Predict Neurological Ou...

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IEEE EMBS International Conference on Information Technology Applications in Biomedicine (ITAB)

作者： Jefferson Dionisio Muhammad Rizwan Hidetaka Suzuki Kazi Mahmudul Hassan Ingon Chanpornpakdi Lian-Yu Lin Toshihisa Tanaka Che Lin Department of Smart Medicine and Health Informatics National Taiwan University Taipei Taiwan Artificial Intelligence of Things Taiwan International Graduate Program Academia Sinica Taipei Taiwan Tokyo University of Agriculture and Technology Tokyo Japan Department of Emergency and Critical Care Japanese Red Cross Musashino Hospital Tokyo Japan Department of Internal Medicine Division of Cardiology National Taiwan University College of Medicine and Hospital Taipei Taiwan Department of Electrical Engineering National Taiwan University Taipei Taiwan Graduate Institute of Communication Engineering National Taiwan University Taipei Taiwan Center for Advanced Computing and Imaging in Biomedicine National Taiwan University Taipei Taiwan

ISBN: (数字)9798350351552

ISBN: (纸本)9798350351569

Sudden cardiac arrest (SCA) poses a significant health challenge, necessitating accurate predictions of neurological outcomes in comatose patients, where good outcomes are defined as the recovery of most cognitive functions. Electroencephalogram (EEG) serves as a valuable biomarker for monitoring neurological states due to its rich, time-dependent information. This study aims to predict neurological outcomes using early EEG data by employing the Transformer model, which leverages multi-headed attention to identify patterns in lengthy sequences such as hour-long EEG recordings. Unlike traditional methods that use subsampled EEG epochs, we utilize the entire EEG sequences, subdivided into time steps, allowing our model to capture detailed temporal patterns via the attention mechanism. Moreover, we trained our proposed model using each EEG recording as an individual data sample but evaluated our model through aggregated patient-wise predictions. This approach allows us to boost the data sample size. Our results demonstrate promising predictive performance, achieving an AUROC of 0.82 and AUPRC of 0.90 on the holdout test set and an AUROC of 0.73 and AUPRC of 0.93 on an external test set with patient-wise predictions. This study highlights the potential of utilizing attention mechanisms to capture important time series progressions across EEG sequences for improving SCA prognosis.

关键词： Attention mechanisms Biological system modeling Time series analysis Cardiac arrest Predictive models Brain modeling Transformers Electroencephalography Data models Recording

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Inter-comparison of multiple Global Climate Model (GCM) data based on spatial pattern of rainfall over Indonesia

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IOP Conference Series: Earth and Environmental Science 2019年第1期284卷

作者： Elania Aflahah Arnida Lailatul Latifah Rahmat Hidayat Rini Hidayati Andi Ihwan Applied Climatology Study Program Graduate School Bogor Agricultural University Bogor 16680 Laboratorium of High-Performance Computing Research Center for Informatics Indonesian Institute of Sciences Bogor West Java 16916 Department of Geophysics and Meteorology Bogor Agricultural University Bogor 16680

The Coupled Model Inter-comparison Project Phase 5 (CMIP5) is the output of many coupled atmosphere-ocean of global climate models (GCMs) and widely used for climate research, especially for driving regional climate model. There are more than 40 CMIP5 GCMs data available, but no single model can be considered as the best for every region. The use of CMIP5 GCMs data for rainfall projection in Indonesia is important to improve the accuracy of the monthly and seasonal rainfall forecast. Then, this study evaluates the capability of the CMIP5 GCMs data for Indonesia region by quantitatively comparing the spatial pattern of the precipitation mean and standard deviation of the CMIP5 data against GPCP, GPCC, and CRU data in the period 1980-2005. Furthermore, the composite analysis is conducted to observe the model performance in reproducing the precipitation characteristic over some areas in Indonesia. In conclusion, the models NorESM1-M, NorESM1-ME, GFDL-ESM2M, CSIRO-MK3-6-0 perform the rainfall mean better than others, while the standard deviation of the rainfall show that the models NorESM1-M, BNU-ESM, CMCC-CMS are superior in which NorESM1-M gives the best performance. The annual precipitation pattern of the model NorESM1-M over various areas in Indonesia is also highly correlated with the observations. Thus, the most suitable model for Indonesia region is NorESM1-M.

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ASIS&T annual meeting pre-conference activities: Information behavior on the move: 2013 ASIS&T SIG/USE research symposium

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Bulletin of the Association for Information Science and Technology 2014年第3期40卷

作者： Denise E. Agosto Lorri Mon Rong Tang Associate Professor College of Computing & Informatics Drexel University and editor of the Journal of Research on Libraries & Young Adults Associate Professor and Chair The MS-IT program in the Florida State University College of Communication and Information Associate Professor and Director Simmons Usability Lab in the Graduate School of Library and Information Science Simmons College

EDITOR'S SUMMARY The 13th Annual SIG/USE Research Symposium focused on ways mobile technologies have influenced information needs, seeking and use. In her keynote address, Caroline Haythornthwaite framed movement as changing contributorship, interactions and participation in multiple knowledge communities. A series of Ignite talks on the common theme of mobility followed, with topics ranging from mobile phones as the preferred Internet access device, credibility of mobile applications and online resources and public libraries' use of social media and mobile applications to mobile apps promoting health information literacy and supporting teleworking. Small groups brainstormed on opportunities, challenges and research ideas inspired by the theme. Annual SIG/USE awards recognized scholars for innovative work on topics including refugees' information seeking, factors influencing scholars' data sharing and drawings as visual representations of information. The symposium closed with encouragement to consider how information on the move influences perspectives and research into information behavior.

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From laptops to supercomputers: a single highly scalable code base for spiking neuronal network simulations

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BMC Neuroscience 2013年第1期14卷 1-2页

作者： Susanne Kunkel Abigail Morrison Markus Diesmann Maximilian Schmidt Jochen M Eppler Moritz Helias Hans E Plesser Jun Igarashi Tomoki Fukai Gen Masumoto Shin Ishii Simulation Laboratory Neuroscience - Bernstein Facility Simulation and Database Technology Institute for Advanced Simulation Jülich Aachen Research Alliance Jülich Research Centre Germany Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) Jülich Research Centre and JARA Germany Bernstein Center Freiburg Albert-Ludwig University of Freiburg Germany Institute of Cognitive Neuroscience Faculty of Psychology Ruhr-University Bochum Germany Medical Faculty RWTH University Aachen Germany Laboratory for Neural Circuit Theory RIKEN Brain Science Institute Wako Japan Department of Mathematical Sciences and Technology Norwegian University of Life Sciences Aas Norway High-Performance Computing Team RIKEN Computational Science Research Program Kobe Japan Integrated Systems Biology Laboratory Department of Systems Science Graduate School of Informatics Kyoto University Japan

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Homoharringtonine as a PHGDH inhibitor: Unraveling metabolic dependencies and developing a potent therapeutic strategy for high-risk neuroblastoma

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Biomedicine and Pharmacotherapy 2023年第1期166卷 115429-115429页

作者： Hsieh, Chiao-Hui Huang, Chen-Tsung Cheng, Yi-Sheng Hsu, Chun-Hua Hsu, Wen-Ming Chung, Yun-Hsien Liu, Yen-Lin Yang, Tsai-Shan Chien, Chia-Yu Lee, Yu-Hsuan Huang, Hsuan-Cheng Juan, Hsueh-Fen Department of Life Science National Taiwan University Taipei Taiwan Center for Computational and Systems Biology National Taiwan University Taipei Taiwan Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei Taiwan Institute of Plant Biology National Taiwan University Taipei Taiwan Genome and Systems Biology Degree Program National Taiwan University and Academia Sinica Taipei Taiwan Department of Agricultural Chemistry National Taiwan University Taipei Taiwan Department of Surgery National Taiwan University Hospital and National Taiwan University College of Medicine Taipei Taiwan Department of Pediatrics School of Medicine College of Medicine Taipei Medical University Taipei Taiwan Institute of Biomedical Informatics National Yang Ming Chiao Tung University Taipei Taiwan Center for Advanced Computing and Imaging in Biomedicine Taipei Taiwan

Neuroblastoma, a childhood cancer affecting the sympathetic nervous system, continues to challenge the development of potent treatments due to the limited availability of druggable targets for this aggressive illness. Recent investigations have uncovered that phosphoglycerate dehydrogenase (PHGDH), an essential enzyme for de novo serine synthesis, serves as a non-oncogene dependency in high-risk neuroblastoma. In this study, we show that homoharringtonine (HHT) acts as a PHGDH inhibitor, inducing intricate alterations in cellular metabolism, and thus providing an efficient treatment for neuroblastoma. We have experimentally verified the reliance of neuroblastoma on PHGDH and employed molecular docking, thermodynamic evaluations, and X-ray crystallography techniques to determine the bond interactions between HHT and PHGDH. Administering HHT to treat neuroblastoma resulted in effective cell elimination in vitro and tumor reduction in vivo. Metabolite and functional assessments additionally disclosed that HHT treatment suppressed de novo serine synthesis, initiating intricate metabolic reconfiguration and oxidative stress in neuroblastoma. Collectively, these discoveries highlight the potential of targeting PHGDH using HHT as a potent approach for managing high-risk neuroblastoma. © 2023 The Authors

关键词： Computational molecular docking Drug discovery High-risk neuroblastoma Metabolic reprogramming Molecular target therapy Phosphoglycerate dehydrogenase

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

arXiv

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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-depth 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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Combined search in dwarf spheroidal galaxies for branon dark matter annihilation signatures with the MAGIC telescopes

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Journal of Cosmology and Astroparticle Physics 2025年第3期2025卷 020-020页

作者： Abe, S. Abhir, J. Abhishek, A. Acciari, V.A. Aguasca-Cabot, A. Agudo, I. Aniello, T. Ansoldi, S. Arbet Engels, A. Arcaro, C. Asano, K. Baack, D. Babić, A. Barres de Almeida, U. Barrio, J.A. Batković, I. Bautista, A. Baxter, J. Becerra González, J. Bednarek, W. Bernardini, E. Bernete, J. Berti, A. Besenrieder, J. Bigongiari, C. Biland, A. Blanch, O. Bonnoli, G. Bošnjak, Ž. Bronzini, E. Burelli, I. Campoy-Ordaz, A. Carosi, R. Carretero-Castrillo, M. Castro-Tirado, A.J. Cerasole, D. Ceribella, G. Chai, Y. Cifuentes, A. Colombo, E. Contreras, J.L. Cortina, J. Covino, S. Da Vela, P. Dazzi, F. De Angelis, A. De Lotto, B. de Menezes, R. Delgado, J. Delgado Mendez, C. Di Pierro, F. Di Tria, R. Di Venere, L. Dominis Prester, D. Donini, A. Dorner, D. Doro, M. Elsaesser, D. Escudero, J. Fariña, L. Fattorini, A. Foffano, L. Font, L. Fröse, S. Fukami, S. Fukazawa, Y. García López, R.J. Garczarczyk, M. Gasparyan, S. Gaug, M. Giesbrecht Paiva, J.G. Giglietto, N. Giordano, F. Gliwny, P. Gradetzke, T. Grau, R. Green, D. Green, J.G. Günther, P. Hadasch, D. Hahn, A. Hassan, T. Heckmann, L. Herrera Llorente, J. Hrupec, D. Imazawa, R. Ishio, K. Jiménez Martínez, I. Jormanainen, J. Kankkunen, S. Kayanoki, T. Kerszberg, D. Kluge, G.W. Kobayashi, Y. Kouch, P.M. Kubo, H. Kushida, J. Láinez, M. Lamastra, A. Leone, F. Lindfors, E. Lombardi, S. Longo, F. López-Coto, R. López-Moya, M. López-Oramas, A. Loporchio, S. Lorini, A. Lyard, E. Majumdar, P. Makariev, M. Maneva, G. Manganaro, M. Mangano, S. Mannheim, K. Mariotti, M. Martínez, M. Martínez-Chicharro, M. Mas-Aguilar, A. Mazin, D. Menchiari, S. Mender, S. Miceli, D. Miener, T. Miranda, J.M. Mirzoyan, R. Molero González, M. Molina, E. Mondal, H.A. Moralejo, A. Morcuende, D. Nakamori, T. Nanci, C. Neustroev, V. Nievas Rosillo, M. Nigro, C. Nikolić, L. Nishijima, K. Njoh Ekoume, T. Nozaki, S. Okumura, A. Otero-Santos, J. Paiano, S. Paneque, D. Paoletti, R. Paredes, J.M. Peresano, M. Persic, M. Pihet, M. Pirola, G. Podobnik, F. Prada Moroni, P.G. Prandini, E. Principe, G. Rhode, W. Ribó, M. R Japanese MAGIC Group Institute for Cosmic Ray Research (ICRR) The University of Tokyo Chiba Kashiwa 277-8582 Japan ETH Zürich Zürich CH-8093 Switzerland Università di Siena INFN Pisa Siena I-53100 Italy Institut de Física d'Altes Energies (IFAE) The Barcelona Institute of Science and Technology (BIST) Bellaterra Barcelona E-08193 Spain Universitat de Barcelona ICCUB IEEC-UB Barcelona E-08028 Spain Instituto de Astrofísica de Andalucía-CSIC Glorieta de la Astronomía s/n Granada 18008 Spain National Institute for Astrophysics (INAF) Rome I-00136 Italy Università di Udine INFN Trieste Udine I-33100 Italy Max-Planck-Institut für Physik Garching D-85748 Germany Università di Padova INFN Padova I-35131 Italy Technische Universität Dortmund Dortmund D-44221 Germany Croatian MAGIC Group University of Zagreb Faculty of Electrical Engineering and Computing (FER) Zagreb 10000 Croatia Centro Brasileiro de Pesquisas Físicas (CBPF) URCA RJ Rio de Janeiro 22290-180 Brazil IPARCOS Institute EMFTEL Department Universidad Complutense de Madrid Madrid E-28040 Spain Instituto de Astrofísica de Canarias Dpto. de Astrofísica Universidad de La Laguna La Laguna Tenerife E-38200 Spain University of Lodz Faculty of Physics and Applied Informatics Department of Astrophysics Lodz 90-236 Poland Centro de Investigaciones Energéticas Medioambientales y Tecnológicas Madrid E-28040 Spain Departament de Física CERES-IEEC Universitat Autònoma de Barcelona Bellaterra E-08193 Spain Università di Pisa INFN Pisa Pisa I-56126 Italy INFN MAGIC Group INFN Sezione di Bari Dipartimento Interateneo di Fisica dell'Università e del Politecnico di Bari Bari I-70125 Italy Department for Physics and Technology University of Bergen Norway INFN MAGIC Group INFN Sezione di Torino Università degli Studi di Torino Torino I-10125 Italy Croatian MAGIC Group University of Rijeka Faculty of Physics Rijeka 51000 Croatia Universität Würzburg Würzburg D-97074 Germany Japanes

Massive brane fluctuations, called branons, behave as weakly interacting massive particles, which is one of the most favored class of candidates to fulfill the role of the dark matter (DM), an elusive kind of matter beyond the Standard Model. We present a multi-target search in dwarf spheroidal galaxies for branon DM annihilation signatures with a total exposure of 354 hours with the ground-based gamma-ray telescope system MAGIC. This search led to the most constraining limits on branon DM in the sub-TeV and multi-TeV DM mass range. Our most stringent limit on the thermally-averaged annihilation cross-section (at 95% confidence level) corresponds to ⟨σv⟩ ≃ 1.9 × 10^-24 cm³s^-1 at a branon mass of ∼ 1.5 TeV. © 2025 The Author(s)

关键词： cosmology of theories beyond the SM dark matter experiments gamma ray experiments

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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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