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In-silico structural and functional analysis of nonsynonymous single nucleotide polymorphisms in human FOLH1 gene

In silico pharmacology 2025年第1期13卷 32页

作者： Abtin Tondar Muhammad Irfan Sergio Sánchez-Herrero Hafsa Athar Aleena Haqqi Asim Kumar Bepari Laura Calvet Liñán David Hervás Marin Department of Computer Science Multimedia and Telecommunication Interuniversity Doctoral Program in Bioinformatics Universitat Oberta de Catalunya Barcelona (UOC) Spain. Stanford Deep Data Research Center Stanford University Stanford USA. Atta-Ur-Rahman School of Applied Biosciences (ASAB) National University of Sciences and Technology (NUST) Islamabad Punjab Pakistan. School of Medical Laboratory Technology Minhaj University Lahore (MUL) Lahore Punjab Pakistan. Department of Pharmaceutical Sciences North South University (NSU) Dhaka Bangladesh. Telecommunications and Systems Engineering Department Universitat Autònoma de Barcelona (UAB) Sabadell Spain. Department of Applied Statistics and Operational Research and Quality Alcoy Universitat Politècnica de València (UPV) Alcoy Spain.

Non-synonymous single nucleotide polymorphisms (nsSNPs), also known as missense SNPs, can seriously affect an individual’s vulnerability to numerous diseases, including cancer. In this study, we conducted a comprehensive in-silico analysis to examine the structural and functional implications of nsSNPs within the Folate Hydrolase 1(FOLH1) gene, which encodes the Prostate-Specific Membrane Antigen (PSMA). A total of 504 SNPs were retrieved, and after filtering, 15 pathogenic nsSNPs were identified using five different in-silico tools. Three of these SNPs—R255H (rs375565491), R255C (rs201789325), and G168E (rs267602926)—were consistently predicted to be pathogenic across all in-silico tools. MutPred2 was used to predict the structural and functional consequences of the identified mutations. The analysis revealed multiple alterations in the PSMA protein, including changes in helical conformations, glycosylation patterns, transmembrane properties, and solvent accessibility. Furthermore, I-Mutant 2.0 analysis demonstrated a decrease in protein stability for most nsSNPs, except for rs267602926 (G168E), which was predicted to increase stability. Conservation analysis using ConSurf revealed varying degrees of amino acid conservation, with R255H and R255C identified as highly conserved residues, indicating their potential functional and structural significance. Additionally, post-translational modification (PTM) analysis indicated that while phosphorylation and methylation sites remained unchanged, specific glycosylation sites were lost in two pathogenic mutant variants (R255H and R255C), potentially affecting PSMA function and adversely impacting prostate cancer. Our findings highlight the importance of in silico studies to investigate the structural and functional impacts of FOLH1 nsSNPs on the PSMA protein. Such in silico studies can deepen our understanding of the roles of nsSNPs in prostate cancer onset, progression, and drug resistance.

关键词： FOLH1 In-silico structural and functional analyses PSMA PTM nsSNPs

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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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Self-Awareness for Autonomous Systems

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PROCEEDINGS OF THE IEEE 2020年第7期108卷 971-975页

作者： Dutt, Nikil Regazzoni, Carlo S. Rinner, Bernhard Yao, Xin Nikil Dutt (Fellow IEEE) received the Ph.D. degree from the University of Illinois at Urbana–Champaign Champaign IL USA in 1989.""He is currently a Distinguished Professor of computer science (CS) cognitive sciences and electrical engineering and computer sciences (EECS) with the University of California at Irvine Irvine CA USA. He is a coauthor of seven books. His research interests include embedded systems electronic design automation (EDA) computer architecture distributed systems healthcare Internet of Things (IoT) and brain-inspired architectures and computing.""Dr. Dutt is a Fellow of ACM. He was a recipient of the IFIP Silver Core Award. He has received numerous best paper awards. He serves as the Steering Committee Chair of the IEEE/ACM Embedded Systems Week (ESWEEK). He is also on the steering organizing and program committees of several premier EDA and embedded system design conferences and workshops. He has served on the Editorial Boards for the IEEE Transactions on Very Large Scale Integration (VLSI) Systems and the ACM Transactions on Embedded Computing Systems and also previously served as the Editor-in-Chief (EiC) for the ACM Transactions on Design Automation of Electronic Systems. He served on the Advisory Boards of the IEEE Embedded Systems Letters the ACM Special Interest Group on Embedded Systems the ACM Special Interest Group on Design Automationt and the ACM Transactions on Embedded Computing Systems. Carlo S. Regazzoni (Senior Member IEEE) received the M.S. and Ph.D. degrees in electronic and telecommunications engineering from the University of Genoa Genoa Italy in 1987 and 1992 respectively.""He is currently a Full Professor of cognitive telecommunications systems with the Department of Electrical Electronics and Telecommunication Engineering and Naval Architecture (DITEN) University of Genoa and a Co-Ordinator of the Joint Doctorate on Interactive and Cognitive Environments (JDICE) international Ph.D. course started initially as EU Erasmus Mundus Project and

Autonomous systems are able to make decisions and potentially take actions without direct human intervention, which requires some knowledge about the system and its environment as well as goal-oriented reasoning. In computer systems, one can derive such behavior from the concept of a rational agent with autonomy (“control over its own actions”), reactivity (“react to events from the environment”), proactivity (“act on its own initiative”), and sociality (“interact with other agents”) as fundamental properties \n[1]\n. Autonomous systems will undoubtedly pervade into our everyday lives, and we will find them in a variety of domains and applications including robotics, transportation, health care, communications, and entertainment to name a few. \nThe articles in this month’s special issue cover concepts and fundamentals, architectures and techniques, and applications and case studies in the exciting area of self-awareness in autonomous systems.

关键词： Special issues and sections Autonomous systems Self-aware Robot sensing systems Computational modeling

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A biometric based medical information system for the identification of comorbidity in Covid-19 patients

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AIP Conference Proceedings 2021年第1期2387卷

作者： V. Kumaresan K. Punitha S. Ragupathy B. Priestly Shan T. Gunasekaran 1Department of Electrical and Electronics Engineering Kongu Engineering College Perundurai Tamilnadu India 2School of Computer Science and Engineering Vellore Institute of Technology Chennai Tamil Nadu India 3Department of Electrical and Electronics Engineering SSM College of Engineering Komarapalayam Namakkal Tamil Nadu India 4Professor and Dean - Academic Affairs Chandigarh University Mohali Punjab India 5Program Director - Telecommunications University of Technology and Applied Sciences Higher College of Technology Muscat Sultanate of Oman

Medical information System provides the best way to collect patient’s medical information. It helps the doctors to retrieve the patient’s previous medical history very faster and able to provide suitable treatment quickly. To keep medical information of a patient securely is a difficult task. Many MQTT algorithms have been developed to provide security for the patients information. The sensitive medical information of a patient can also be protected by using biometric technology. Internet of Thing (IoT) technology used to send the medical information to the doctor like blood group, BP value, cholesterol level and heart rate level. The proposed system helps to provide the comorbidity details of the Covid-19 infected patient’s to doctors mobile phone. It also takes the current health details of the patients like temperature, respiratory rate and heart beat rate. By considering the past and present medical data of the Covid-19 patients , this system recommends the type of treatment to be given for Covid-19 infected patients.

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

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NATURE COMMUNICATIONS 2022年第1期13卷 1-1页

作者： Sieverling, Lina Hong, Chen Koser, Sandra D. Ginsbach, Philip Kleinheinz, Kortine Hutter, Barbara Braun, Delia M. Cortes-Ciriano, Isidro Xi, Ruibin Kabbe, Rolf Park, Peter J. Eils, Roland Schlesner, Matthias Brors, Benedikt Rippe, Karsten Jones, David T. W. Feuerbach, Lars Division of Applied Bioinformatics German Cancer Research Center (DKFZ) 69120 Heidelberg Germany Faculty of Biosciences Heidelberg University 69120 Heidelberg Germany Division of Applied Bioinformatics German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Biosciences Heidelberg University Heidelberg Germany Division of Theoretical Bioinformatics German Cancer Research Center (DKFZ) 69120 Heidelberg Germany Department for Bioinformatics and Functional Genomics Institute for Pharmacy and Molecular Biotechnology (IPMB) and BioQuant 69120 Heidelberg Germany Division of Theoretical Bioinformatics German Cancer Research Center (DKFZ) Heidelberg Germany Institute of Pharmacy and Molecular Biotechnology and BioQuant Heidelberg University Heidelberg Germany German Cancer Consortium (DKTK) Heidelberg Germany National Center for Tumor Diseases (NCT) Heidelberg Heidelberg Germany Heidelberg Center for Personalized Oncology (DKFZ-HIPO) German Cancer Research Center (DKFZ) Heidelberg Germany German Cancer Consortium (DKTK) German Cancer Research Center (DKFZ) Heidelberg Germany Division of Chromatin Networks German Cancer Research Center (DKFZ) and BioQuant 69120 Heidelberg Germany Department of Biomedical Informatics Harvard Medical School Boston Massachusetts 02115 USA Department of Chemistry Centre for Molecular Science Informatics University of Cambridge Cambridge CB2 1EW UK Ludwig Center at Harvard Medical School Boston Massachusetts 02115 USA Department of Biomedical Informatics Harvard Medical School Boston MA USA Department of Chemistry Centre for Molecular Science Informatics University of Cambridge Cambridge UK Ludwig Center at Harvard Medical School Boston MA USA School of Mathematical Sciences and Center for Statistical Science Peking University Beijing 100871 China Heidelberg University Heidelberg Germany New BIH Digital Health Center Berlin Institute of Health (BIH) and Charité - Universitätsmedizin Berlin Berlin Germany Bioi

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Supernova model discrimination with Hyper-Kamiokande

arXiv

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arXiv 2021年

作者： Abe, K. Adrich, P. Aihara, H. Akutsu, R. Alekseev, I. Ali, A. Ameli, F. Anghel, I. Anthony, L.H.V. Antonova, M. Araya, A. Asaoka, Y. Ashida, Y. Aushev, V. Ballester, F. Bandac, I. Barbi, M. Barker, G.J. Barr, G. Batkiewicz-Kwasniak, M. Bellato, M. Berardi, V. Bergevin, M. Bernard, L. Bernardini, E. Berns, L. Bhadra, S. Bian, J. Blanchet, A. Blaszczyk, F.D.M. Blondel, A. Boiano, A. Bolognesi, S. Bonavera, L. Booth, N. Borjabad, S. Boschi, T. Bose, D. Boyd, S.B. Bozza, C. Bravar, A. Bravo-Berguño, D. Bronner, C. Brown, L. Bubak, A. Buchowicz, A. Avanzini, M. Buizza Cafagna, F.S. Calabria, N.F. Calvo-Mozota, J.M. Cao, S. Cartwright, S.L. Carroll, A. Catanesi, M.G. Cebriàn, S. Chabera, M. Chakraborty, S. Checchia, C. Choi, J.H. Choubey, S. Cicerchia, M. Coleman, J. Collazuol, G. Cook, L. Cowan, G. Cuen-Rochin, S. Danilov, M. Lopez, G. Daz de la Fuente, E. de Perio, P. de Rosa, G. Dealtry, T. Densham, C.J. Dergacheva, A. Deshmukh, N. Devi, M.M. Di Lodovico, F. Di Meo, P. Di Palma, I. Doyle, T.A. Drakopoulou, E. Drapier, O. Dumarchez, J. Dunne, P. Dziewiecki, M. Eklund, L. El Hedri, S. Ellis, J. Emery, S. Esmaili, A. Esteve, R. Evangelisti, A. Feely, M. Fedotov, S. Feng, J. Fernandez, P. Fernández-Martinez, E. Ferrario, P. Ferrazzi, B. Feusels, T. Finch, A. Finley, C. Fiorentini, A. Fiorillo, G. Fitton, M. Frankiewicz, K. Friend, M. Fujii, Y. Fukuda, Y. Galinski, G. Gao, J. Garde, C. Garfagnini, A. Garode, S. Gialanella, L. Giganti, C. Gomez-Cadenas, J.J. Gonin, M. González-Nuevo, J. Gorin, A. Gornea, R. Gousy-Leblanc, V. Gramegna, F. Grassi, M. Grella, G. Guigue, M. Gumplinger, P. Hadley, D.R. Harada, M. Hartfiel, B. Hartz, M. Hassani, S. Hastings, N.C. Hayato, Y. Hernando-Morata, J.A. Herrero, V. Hill, J. Hiraide, K. Hirota, S. Holin, A. Horiuchi, S. Hoshina, K. Hultqvist, K. Iacob, F. Ichikawa, A.K. Ibnsalih, W. Idrissi Iijima, T. Ikeda, M. Inomoto, M. Inoue, K. Insler, J. Ioannisian, A. Ishida, T. Ishidoshiro, K. AGH University of Science and Technology Faculty of Computer Science Electronics and Telecommunications Krakow Poland Boston University Department of Physics BostonMA United States University of British Columbia Department of Physics and Astronomy VancouverBC Canada British Columbia Institute of Technology Physics Department BurnabyBC Canada Università della Campania L. Vanvitelli INFN Sezione di Napoli Napoli Italy Carleton University Department of Physics OttawaON Canada University of California Davis Department of Physics DavisCA United States University of California Irvine Department of Physics and Astronomy IrvineCA United States California State University Department of Physics CarsonCA United States Charles University IPNP FMF Prague Czech Republic Chonnam National University Department of Physics Gwangju Korea Republic of Dongshin University Laboratory for High Energy Physics Naju Korea Republic of Donostia International Physics Center Ikerbasque Foundation Basque Country Spain Ecole Polytechnique IN2P3-CNRS Laboratoire Leprince-Ringuet Palaiseau France University of Edinburgh School of Physics and Astronomy Edinburgh United Kingdom ETH Zurich Institute for Particle and Astroparticle Physics Zurich Switzerland University of Geneva Section de Physique DPNC Geneva Switzerland GIST College Gwangju Institute of Science and Technology Gwangju Korea Republic of University of Glasgow School of Physics and Astronomy Glasgow United Kingdom Universidad de Guadalajara CUCEI Departamento de Fisica Jal Guadalajara Mexico Universidad de Guadalajara CUCEA IT.Ph.D. program Jal Guadalajara Mexico H. Niewodniczá nski Institute of Nuclear Physics PAN Cracow Poland University of Hawaii Department of Physics and Astronomy HonoluluHI United States Imperial College London Department of Physics London United Kingdom Indian Institute of Technology Guwahati Guwahati India Indian Institute of Technology Jodhpur Department of Physics Raj

Core-collapse supernovae are among the most magnificent events in the observable universe. They produce many of the chemical elements necessary for life to exist and their remnants-neutron stars and black holes-are interesting astrophysical objects in their own right. However, despite millennia of observations and almost a century of astrophysical study, the explosion mechanism of core-collapse supernovae is not yet well understood. Hyper-Kamiokande is a next-generation neutrino detector that will be able to observe the neutrino flux from the next galactic core-collapse supernova in unprecedented detail. We focus on the first 500 ms of the neutrino burst, corresponding to the accretion phase, and use a newly-developed, high-precision supernova event generator to simulate Hyper-Kamiokande's response to five different supernova models. We show that Hyper-Kamiokande will be able to distinguish between these models with high accuracy for a supernova at a distance of up to 100 kpc. Once the next galactic supernova happens, this ability will be a powerful tool for guiding simulations towards a precise reproduction of the explosion mechanism observed in nature. Copyright © 2021, The Authors. All rights reserved.

关键词： Supernovae

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Characteristic Time based Routing in Information Centric Networks

Characteristic Time based Routing in Information Centric Net...

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International Conference on Computing, Networking and Communications

作者： Bitan Bandyopadhyay Anand Seetharam Amitava Mukherjee Mrinal Kanti Naskar Department of Electronics and Telecommunications Engineering Jadavpur University Computer Science Program California State University Monterey Bay IBM India Pvt. Limited

ISBN: (纸本)9781467385800

Information centric networking (ICN) aims to transform today's Internet from a host-centric model to a content-centric one by caching content internally within the network at storage-enabled nodes. Recently, multiple routing and cache management strategies have been proposed [1]-[6] to improve the user-level performance (e.g., content-download latency) in ICN. In this paper, we propose a simple routing strategy that leverages the concept of characteristic time to improve content-download latency. Characteristic time for a content in a cache indicates the amount of time in future a recently accessed content is likely to remain in that cache. Our proposed algorithm (CTR) uses characteristic time information to forward requests to caches where the content is likely to be found. CTR augments native routing strategies (e.g., Dijkstra's algorithm), works with existing cache management policies and thus can be implemented in ICN prototypes with minimal effort. We perform exhaustive simulations using realistic Internet topologies (e.g., GEANT, WIDE, TISCALI, ROCKETFUEL [7]) and demonstrate that the CTR algorithm provides approximately 10-50% improvement in latency over state-of-the-art routing and caching management strategies for ICN for a wide range of simulation parameters.

关键词： Routing Algorithm design and analysis Approximation algorithms Data structures Prototypes Internet topology

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The Hyper-Kamiokande experiment - snowmass LOI

arXiv

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arXiv 2020年

作者： Abe, K. Adrich, P. Aihara, H. Akutsu, R. Alekseev, I. Ali, A. Ameli, F. Anthony, L.H.V. Araya, A. Asaoka, Y. Aushev, V. Bandac, I. Barbi, M. Barr, G. Batkiewicz-Kwasniak, M. Bellato, M. Berardi, V. Bernard, L. Bernardini, E. Berns, L. Bhadra, S. Bian, J. Blanchet, A. Blondel, A. Boiano, A. Bolognesi, S. Bonavera, L. Borjabad, S. Boschi, T. Bose, D. Boyd, S.B. Bozza, C. Bravar, A. Bronner, C. Bubak, A. Buchowicz, A. Avanzini, M. Buizza Cafagna, F.S. Calabria, N.F. Calvo-Mozota, J.M. Cao, S. Catanesi, M.G. Chakraborty, S. Choi, J.H. Choubey, S. Cicerchia, M. Coleman, J. Collazuol, G. Cuen-Rochin, S. Danilov, M. Fuente, E. De la de Perio, P. de Rosa, G. Dealtry, T. Densham, C.J. Dergacheva, A. Deshmukh, N. Devi, M.M. Di Lodovico, F. Di Meo, P. Di Palma, I. Doyle, T.A. Drakopoulou, E. Drapier, O. Dumarchez, J. Eklund, L. El Hedri, S. Ellis, J. Emery, S. Esmaili, A. Fedotov, S. Feng, J. Fernández-Martinez, E. Ferrario, P. Ferrazzi, B. Finch, A. Finley, C. Fiorillo, G. Fitton, M. Friend, M. Fujii, Y. Fukuda, Y. Galinski, G. Gao, J. Garde, C. Garfagnini, A. Garode, S. Gialanella, L. Giganti, C. Gomez-Cadenas, J.J. Gonin, M. González-Nuevo, J. Gorin, A. Gornea, R. Gramegna, F. Grassi, M. Grella, G. Guigue, M. Hadley, D.R. Harada, M. Hartz, M. Hassani, S. Hastings, N.C. Hayato, Y. Hiraide, K. Hoshina, K. Hultqvist, K. Iacob, F. Ichikawa, A.K. Ibnsalih, W. Idrissi Ikeda, M. Inomoto, M. Ioannisian, A. Ishida, T. Ishidoshiro, K. Ishino, H. Ishitsuka, M. Ito, H. Ito, S. Itow, Y. Iwamoto, K. Izumi, N. Izumiyama, S. Jakkapu, M. Jamieson, B. Jang, J.S. Jo, H.S. Jonsson, P. Joo, K.K. Kajita, T. Kakuno, H. Kameda, J. Kano, Y. Karlen, D. Kataoka, Y. Kato, A. Katori, T. Kazarian, N. Khabibullin, M. Khotjantsev, A. Kikawa, T. Kim, J.Y. Kim, S.B. King, S. Kinoshita, T. Kisiel, J. Klekotko, A. Kobayashi, T. Koerich, L. Kolev, N. Konaka, A. Kormos, L.L. Koshio, Y. Kotsar, Y. Kouzakov, K.A. Kowalik, K.L. AGH University of Science and Technology Faculty of Computer Science Electronics and Telecommunications Krakow Poland British Columbia Institute of Technology Physics Department BurnabyBC Canada Università della Campania"L. Vanvitelli" INFN Sezione di Napoli Napoli Italy Carleton University Department of Physics OttawaON Canada University of California Irvine Department of Physics and Astronomy IrvineCA United States Charles University IPNP FMF Prague Czech Republic Chonnam National University Department of Physics Gwangju Korea Republic of Dongshin University Laboratory for High Energy Physics Naju Korea Republic of Donostia International Physics Center Ikerbasque Foundation Basque Country Spain Ecole Polytechnique IN2P3-CNRS Laboratoire Leprince-Ringuet Palaiseau France University of Edinburgh School of Physics and Astronomy Edinburgh United Kingdom ETH Zurich Institute for Particle and Astroparticle Physics Zurich Switzerland University of Geneva Section de Physique DPNC Geneva Switzerland GIST College Gwangju Institute of Science and Technology Gwangju Korea Republic of University of Glasgow School of Physics and Astronomy Glasgow United Kingdom Universidad de Guadalajara CUCEA IT.Ph.D. program GuadalajaraJal Mexico Universidad de Guadalajara CUCEI Departamento de Fisica GuadalajaraJal Mexico H. Niewodniczá nski Institute of Nuclear Physics PAN Cracow Poland Imperial College London Department of Physics London United Kingdom Indian Institute of Technology Guwahati Guwahati India Indian Institute of Technology Jodhpur Department of Physics Karwar Rajasthan India Indian Institute of Technology Kharagpur Department of Physics Kharagpur India INFN Gruppo Collegato di Salerno Fisciano Italy INFN Laboratori Nazionali di Legnaro LegnaroPD Italy INFN Sezione di Bari Università e Politecnico di Bari Bari Italy INFN Sezione di Napoli Università Federico II di Napoli Dipartimento di Fisica Napoli Italy INFN Sezione di Napoli Nap

Hyper-Kamiokande is the next generation underground water Cherenkov detector that builds on the highly successful Super-Kamiokande experiment. The detector which has an 8.4 times larger effective volume than its predecessor will be located along the T2K neutrino beamline and utilize an upgraded J-PARC beam with 2.6 times beam power. Hyper-K’s low energy threshold combined with the very large fiducial volume make the detector unique, that is expected to acquire an unprecedented exposure of 3.8 Mton·year over a period of 20 years of operation. Hyper-Kamiokande combines an extremely diverse science program including nucleon decays, long-baseline neutrino oscillations, atmospheric neutrinos, and neutrinos from astrophysical origins. The scientific scope of this program is highly complementary to liquid-argon detectors for example in sensitivity to nucleon decay channels or supernova detection modes. Hyper-Kamiokande construction has started in early 2020 and the experiment is expected to start operations in 2027. The Hyper-Kamiokande collaboration is presently being formed amongst groups from 19 countries including the United States, whose community has a long history of making significant contributions to the neutrino physics program in Japan. US physicists have played leading roles in the Kamiokande, Super-Kamiokande, EGADS, K2K, and T2K programs. Copyright © 2020, The Authors. All rights reserved.

关键词： Atomic physics

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Systems for spectrum forensics 2

Systems for spectrum forensics

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2nd ACM International Workshop on Hot Topics in Wireless, HotWireless 2015

作者： Anderson, Austin Wang, Xiang Baker, Kenneth R. Grunwald, Dirk Dept. of Aerospace Engineering Univ. of Colorado Boulder United States Dept. of Electrical Energy and Computer Engineering Univ. of Colorado Boulder United States Interdisciplinary Telecommunications Program Univ. of Colorado Boulder United States Dept. of Computer Science Univ. of Colorado Boulder United States

ISBN: (纸本)9781450336994

Historically, spectrum allocations were assigned to a single user by regulation and spectrum interference was relatively easy to identify. Enforcement was simplified by having a small set of known licensees. Spectrum regulation is increasingly moving toward dynamic spectrum access mediated by databases, exclusion zones, sensors and other methods intended to improve spectrum efficiency. To have meaning, these spectrum sharing mechanisms must have some form of enforcement. While the spectrum policy community has consider ex-post (preventative) and ex-ante (punitive) enforcement mechanisms, enforcement will rely on spectrum forensics - the scientific gathering of information to inform an enforcement action. In this paper, we initiate exploration of a system for spectrum forensics that combines crowd-sourced interference detection, fixed sensors and aerial measurement as well as a system for minimizing the data that needs to be collected. As part of a multi-tiered approach, the highest tier will be enabled by the equivalent of an "SDR in the sky" for self-directed localization and identification of interference sources.

关键词： Antennas

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Wave algorithm for recruitment in swarm robotics

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in computer science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2015年 9156卷 3-13页

作者： Silva, Luneque Nedjah, Nadia Systems Engineering and Computer Science Program Federal University of Rio de Janeiro Rio de Janeiro Brazil Department of Electronics Engineering and Telecommunications State University of Rio de Janeiro Rio de Janeiro Brazil

In the last decades, studies on swarm robotics have grown significantly, with different new aspects becoming of interest in research. Although the primary interest in swarm robotics should be the application, which is the task that robots should perform, other aspects are also studied. For instance, in a robotic swarm, it may be necessary to select the robots that would perform a given task, aiming at a division of labor among the members. A similar problem is that of robot recruitment, required when a specific robot needs assistance to execute some tasks and thus needs to recruit other robots of the swarm to do so. In this paper, a distributed algorithm, termed the wave algorithm, is exploited to perform the recruitment based mainly on robot message passing to neighboring robots. © Springer International Publishing Switzerland 2015.

关键词： Message passing

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