检索结果-内蒙古大学图书馆

arXiv 2020年

作者： Chen, Wei-Kun Liu, Ya-Feng de Domenico, Antonio Luo, Zhi-Quan Dai, Yu-Hong The School of Mathematics and Statistics Beijing Key Laboratory on MCAACI Beijing Institute of Technology Beijing100081 China The State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China The Huawei Technologies Co. Ltd France Research Center Boulogne-Billancourt92100 France The Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen518172 China

Network function virtualization is a promising technology to simultaneously support multiple services with diverse characteristics and requirements in the 5G and beyond networks. In particular, each service consists of a predetermined sequence of functions, called service function chain (SFC), running on a cloud environment. To make different service slices work properly in harmony, it is crucial to appropriately select the cloud nodes to deploy the functions in the SFC and flexibly route the flow of the services such that these functions are processed in the order defined in the corresponding SFC, the end-to-end (E2E) latency constraints of all services are guaranteed, and all cloud and communication resource budget constraints are respected. In this paper, we first propose a new mixed binary linear program (MBLP) formulation of the above network slicing problem that optimizes the system energy efficiency while jointly considers the E2E latency requirement, resource budget, flow routing, and functional instantiation. Then, we develop another MBLP formulation and show that the two formulations are equivalent in the sense that they share the same optimal solution. However, since the numbers of variables and constraints in the second problem formulation are significantly smaller than those in the first one, solving the second problem formulation is more computationally efficient especially when the dimension of the corresponding network is large. Numerical results demonstrate the advantage of the proposed formulations compared with the existing ones. Copyright © 2020, The Authors. All rights reserved.

关键词： Network function virtualization

来源：评论

学校读者我要写书评

暂无评论

Memory footprint reduction for the FFT-based volume integral equation method via tensor decompositions

arXiv

引用

arXiv 2018年

作者： Giannakopoulos, Ilias I. Litsarev, Mikhail S. Polimeridis, Athanasios G. Skoltech Center for Computational Data-Intensive Science and Engineering Skolkovo Institute of Science and Technology Moscow143026 Russia Q Bio CA94063 United States

We present a method of memory footprint reduction for FFT-based, electromagnetic (EM) volume integral equation (VIE) formulations. The arising Green's function tensors have low multilinear rank, which allows Tucker decomposition to be employed for their compression, thereby greatly reducing the required memory storage for numerical simulations. Consequently, the compressed components are able to fit inside a graphical processing unit (GPU) on which highly parallelized computations can vastly accelerate the iterative solution of the arising linear system. In addition, the element-wise products throughout the iterative solver's process require additional flops, thus, we provide a variety of novel and efficient methods that maintain the linear complexity of the classic element-wise product with an additional multiplicative small constant. We demonstrate the utility of our approach via its application to VIE simulations for the Magnetic Resonance Imaging (MRI) of a human head. For these simulations we report an order of magnitude acceleration over standard techniques. Copyright © 2018, The Authors. All rights reserved.

关键词： Fast Fourier transforms

来源：评论

学校读者我要写书评

暂无评论

Author Correction: Large-scale benchmarking of circRNA detection tools reveals large differences in sensitivity but not in precision

引用

Nature methods 2025年第2期22卷 448页

作者： Marieke Vromman Jasper Anckaert Stefania Bortoluzzi Alessia Buratin Chia-Ying Chen Qinjie Chu Trees-Juen Chuang Roozbeh Dehghannasiri Christoph Dieterich Xin Dong Paul Flicek Enrico Gaffo Wanjun Gu Chunjiang He Steve Hoffmann Osagie Izuogu Michael S Jackson Tobias Jakobi Eric C Lai Justine Nuytens Julia Salzman Mauro Santibanez-Koref Peter Stadler Olivier Thas Eveline Vanden Eynde Kimberly Verniers Guoxia Wen Jakub Westholm Li Yang Chu-Yu Ye Nurten Yigit Guo-Hua Yuan Jinyang Zhang Fangqing Zhao Jo Vandesompele Pieter-Jan Volders OncoRNALab Cancer Research Institute Ghent (CRIG) Department of Biomolecular Medicine Ghent University Ghent Belgium. Department of Molecular Medicine University of Padova Padova Italy. Genomics Research Center Academia Sinica Taipei City Taiwan. Institute of Crop Science and Institute of Bioinformatics Zhejiang University Zhejiang China. Department of Biomedical Data Science and of Biochemistry Stanford University Stanford CA USA. Klaus Tschira Institute for Integrative Computational Cardiology Department of Internal Medicine III University Hospital Heidelberg German Center for Cardiovascular Research (DZHK) Heidelberg Germany. School of Basic Medical Science Department of Medical Genetics Wuhan University Wuhan China. EMBL-EBI Hinxton UK. Collaborative Innovation Center of Jiangsu Province of Cancer Prevention and Treatment of Chinese Medicine School of Artificial Intelligence and Information Technology Nanjing University of Chinese Medicine Nanjing China. Computational Biology Group Leibniz Institute on Aging - Fritz Lipmann Institute (FLI) Jena Germany. Biosciences Institute Faculty of Medical Sciences Newcastle University Newcastle UK. Translational Cardiovascular Research Center University of Arizona - College of Medicine Phoenix Phoenix AZ USA. Sloan Kettering Institute New York NY USA. Bioinformatics Group Department of Computer Science and Interdisciplinary Center for Bioinformatics Universität Leipzig Leipzig Germany. Data Science Institute I-Biostat Hasselt University Hasselt Belgium. State Key Laboratory of Bioelectronics School of Biological Sciences and Medical Engineering Southeast University Nanjing China. Department of Biochemistry and Biophysics National Bioinformatics Infrastructure Sweden Science for Life Laboratory Stockholm University Stockholm Sweden. Center for Molecular Medicine Children's Hospital Fudan University and Shanghai Key Laboratory of Medical Epigenetics International Laboratory of Medical Epigenetics and Metab

来源：评论

学校读者我要写书评

暂无评论

Do cities have a unique magnetic pulse?

arXiv

引用

arXiv 2022年

作者： Dumont, Vincent Bowen, Trevor A. Roglans, Roger Dobler, Gregory Sharma, Mohit S. Karpf, Andy Bale, Stuart D. Wickenbrock, Arne Zhivun, Elena Kornack, Tom Wurtele, Jonathan S. Budker, Dmitry Computational Research Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Department Of Physics University Of California BerkeleyCA94720-7300 United States Space Sciences Laboratory University Of California BerkeleyCA94720-7300 United States Biden School Of Public Policy And Administration University Of Delaware NewarkDE19716 United States Department Of Physics And Astronomy University Of Delaware NewarkDE19716 United States Data Science Institute University Of Delaware NewarkDE19713 United States Center For Urban Science And Progress New York University BrooklynNY11201 United States Civil And Urban Engineering Tandon School Of Engineering New York University BrooklynNY11201 United States Institut Für Physik Universität Mainz Staudingerweg 7 Mainz55128 Germany Helmholtz Institut Mainz Staudingerweg 18 Mainz55128 Germany Twinleaf LLC 300 Deer Creek Drive PlainsboroNJ08536 United States Johannes Gutenberg-Universität Mainz Mainz55128 Germany Helmholtz Institut Mainz Mainz55128 Germany

We present a comparative analysis of urban magnetic fields between two American cities: Berkeley (California) and Brooklyn Borough of New York City (New York). Our analysis uses data taken over a four-week period during which magnetic field data were continuously recorded using a fluxgate magnetometer of 70 pT/√ Hz sensitivity. We identified significant differences in the magnetic signatures. In particular, we noticed that Berkeley reaches a near-zero magnetic field activity at night whereas magnetic activity in Brooklyn continues during nighttime. We also present auxiliary measurements acquired using magnetoresistive vector magnetometers (VMR), with sensitivity of 300 pT/√ Hz, and demonstrate how cross-correlation, and frequency-domain analysis, combined with data filtering can be used to extract urban magnetometry signals and study local anthropogenic activities. Finally, we discuss the potential of using magnetometer networks to characterize the global magnetic field of cities and give directions for future development. Copyright © 2022, The Authors. All rights reserved.

关键词： Magnetometers

来源：评论

学校读者我要写书评

暂无评论

The need to implement FAIR principles in biomolecular simulations

arXiv

引用

arXiv 2024年

作者： Amaro, R. Åqvist, J. Bahar, I. Battistini, F. Bellaiche, A. Beltran, D. Biggin, P.C. Bonomi, M. Bowman, G.R. Bryce, R. Bussi, G. Carloni, P. Case, D. Cavalli, A. Chang, C.A. Cheatham, T.E. Cheung, M.S. Chipot, C. Chong, L.T. Choudhary, P. Cisneros, G.A. Clementi, C. Collepardo-Guevara, R. Coveney, P. Covino, R. Crawford, T.D. Dal Peraro, M. de Groot, B. Delemotte, L. De Vivo, M. Essex, J. Fraternali, F. Gao, J. Gelpí, J.L. Gervasio, F.L. Gonzalez-Nilo, F.D. Grubmüller, H. Guenza, M.G. Guzman, H.V. Harris, S. Head-Gordon, T. Hernandez, R. Hospital, A. Huang, N. Huang, X. Hummer, G. Iglesias-Fernández, J. Jensen, J.H. Jha, S. Jiao, W. Jorgensen, W.L. Kamerlin, S.C.L. Khalid, S. Laughton, C. Levitt, M. Limongelli, V. Lindahl, E. Lindorff-Larsen, K. Loverde, S. Lundborg, M. Luo, Y.L. Luque, F.J. Lynch, C.I. MacKerell, A. Magistrato, A. Marrink, S.J. Martin, H. McCammon, J.A. Merz, K. Moliner, V. Mulholland, A. Murad, S. Naganathan, A.N. Nangia, S. Noe, F. Noy, A. Oláh, J. O'Mara, M. Ondrechen, M.J. Onuchic, J.N. Onufriev, A. Osuna, S. Palermo, G. Panchenko, A.R. Pantano, S. Parish, C. Parrinello, M. Perez, A. Perez-Acle, T. Perilla, J.R. Pettitt, B.M. Pietropaolo, A. Piquemal, J.-P. Poma, A. Praprotnik, M. Ramos, M.J. Ren, P. Reuter, N. Roitberg, A. Rosta, E. Rovira, C. Roux, B. Röthlisberger, U. Sanbonmatsu, K. Schlick, T. Shaytan, A.K. Simmerling, C. Smith, J.C. Sugita, Y. Świderek, K. Taiji, M. Tao, P. Tikhonova, I.G. Tirado-Rives, J. Tuñón, I. Van Der Kamp, M.W. Van der Spoel, D. Velankar, S. Voth, G.A. Wade, R. Warshel, A. Welborn, V.V. Wetmore, S. Wong, C.F. Yang, L.-W. Zacharias, M. Orozco, M. Department of Molecular Biology University of California San DiegoCA United States Department of Cell and Molecular Biology Uppsala University Uppsala Sweden Laufer Center for Physical and Quantitative Biology Renaissance School of Medicine Stony Brook University Stony BrookNY United States Department of Biochemistry and Cell Biology Renaissance School of Medicine Stony Brook University Stony BrookNY United States Barcelona Spain European Molecular Biology Laboratory European Bioinformatics Institute Hinxton United Kingdom Structural Bioinformatics and Computational Biochemistry Department of Biochemistry University of Oxford Oxford United Kingdom Institut Pasteur Université Paris Cité CNRS UMR 3528 Computational Structural Biology Unit Paris France Department of Biochemistry and Biophysics University of Pennsylvania PhiladelphiaPA United States Division of Pharmacy and Optometry University of Manchester Manchester United Kingdom Scuola Internazionale Superiore di Studi Avanzati-SISSA Trieste Italy Computational Biomedicine Institute of Advanced Simulations IAS-5 Institute for Neuroscience and Medicine INM-9 Forschungszentrum Jülich GmbH Jülich Germany Department of Physics and Universitätsklinikum RWTH Aachen University Aachen Germany Department of Chemistry & Chemical Biology Rutgers University PiscatawayNJ United States Istituto Italiano di Tecnologia Drug Discovery and Development Bologna Italy Lausanne Switzerland Department of Chemistry University of California RiversideCA United States Department of Medicinal Chemistry College of Pharmacy University of Utah Salt Lake CityUT United States Department of Physics University of Washington SeattleWA United States Pacific Northwest National Laboratory RichlandWA United States LIA CNRS-UIUC UMR n°7019 Université de Lorraine Vandœuvre-lès-Nancy France Department of Physics University of Illinois at Urbana-Champaign UrbanaIL United States Department of Biochemistry and Molecular Biol

The communities that embraced data archiving efforts decades ago are now, in the era of data-driven biology, those gaining the most from the AI revolution. The structural biology community was a pioneer in this regard by establishing the Protein data Bank (PDB) in 1971 and making data accessible using the FAIR (Findable, Accessible, Interoperable and Reusable) principles even before these were articulated [1, 2]. The genomics/bioinformatics community has followed the example, establishing many widely used databases [3, 4]. By contrast, molecular simulation has been anchored in usage paradigms dating back to the seventies, when molecular dynamics (MD) simulation was first applied to study biomacromolecules [5]. At that time, MD was used by theoretical physicists and chemists in "proof of concept" simulations. Still, fifty years later, MD has evolved into a cornerstone molecular biology technique that can provide accurate quantitative analysis and property prediction. MD is now employed by tens of thousands of researchers worldwide, accounting for roughly 15% of global supercomputer usage. Unfortunately, these rich and costly data are not systematically maintained, and when further analyses are required, simulations have to be rerun-an unacceptable situation from scientific, environmental and sustainability standpoints. In this letter, we argue for a collaborative endeavor to archive MD simulation data and describe ongoing efforts to establish cost-effective and sustainable data archiving strategies. © 2024, CC BY-NC-SA.

关键词： Biotic

来源：评论

学校读者我要写书评

暂无评论

Fusion of convolution neural network, support vector machine and Sobel filter for accurate detection of COVID-19 patients using X-ray images

arXiv

引用

arXiv 2021年

作者： Sharifrazi, Danial Alizadehsani, Roohallah Roshanzamir, Mohamad Joloudari, Javad Hassannataj Shoeibi, Afshin Jafari, Mahboobeh Hussain, Sadiq Sani, Zahra Alizadeh Hasanzadeh, Fereshteh Khozeimeh, Fahime Khosravi, Abbas Nahavandi, Saeid Panahiazar, Maryam Zare, Assef Shariful Islam, Sheikh Mohammed Rajendra, U. Acharya Department of Computer Engineering School of Technical and Engineering Shiraz Branch Islamic Azad University Shiraz Iran Deakin University Geelong Australia Department of Engineering Fasa Branch Islamic Azad University Post Box No 364 Fasa Fars*** Iran Department of Computer Engineering Faculty of Engineering University of Birjand Birjand Iran Computer Engineering Department Ferdowsi University of Mashhad Mashhad Iran Faculty of Electrical and Computer Engineering Biomedical Data Acquisition Lab K. N. Toosi University of Technology Tehran Iran Electrical and Computer Engineering Faculty Semnan University Semnan Iran System Administrator Dibrugarh University Assam786004 India Rajaie Cardiovascular Medical and Research Center Iran University of Medical Sciences Tehran Iran Omid hospital Iran University of Medical Sciences Tehran Iran Institute for Computational Health Sciences University of California San Francisco United States Faculty of Electrical Engineering Gonabad Branch Islamic Azad University Gonabad Iran Institute for Physical Activity and Nutrition Deakin University Melbourne Australia Cardiovascular Division The George Institute for Global Health Australia Sydney Medical School University of Sydney Australia Department of Electronics and Computer Engineering Ngee Ann Polytechnic Singapore Department of Biomedical Engineering School of Science and Technology Singapore University of Social Sciences Singapore Department of Bioinformatics and Medical Engineering Asia University Taiwan

The coronavirus (COVID-19) is currently the most common contagious disease which is prevalent all over the world. The main challenge of this disease is the primary diagnosis to prevent secondary infections and its spread from one person to another. Therefore, it is essential to use an automatic diagnosis system along with clinical procedures for the rapid diagnosis of COVID-19 to prevent its spread. Artificial intelligence techniques using computed tomography (CT) images of the lungs and chest radiography have the potential to obtain high diagnostic performance for Covid-19 diagnosis. In this study, a fusion of convolutional neural network (CNN), support vector machine (SVM), and Sobel filter is proposed to detect COVID-19 using X-ray images. A new X-ray image dataset was collected and subjected to high pass filter using a Sobel filter to obtain the edges of the images. Then these images are fed to CNN deep learning model followed by SVM classifier with ten-fold cross validation strategy. This method is designed so that it can learn with not many data. Our results show that the proposed CNN-SVM with Sobel filtering (CNN-SVM+Sobel) achieved the highest classification accuracy of 99.02% in accurate detection of COVID-19. It showed that using Sobel filter can improve the performance of CNN. Unlike most of the other researches, this method does not use a pre-trained network. We have also validated our developed model using six public databases and obtained the highest performance. Hence, our developed model is ready for clinical application. Copyright © 2021, The Authors. All rights reserved.

关键词： data mining

来源：评论

学校读者我要写书评

暂无评论

The status of the human gene catalogue

arXiv

引用

arXiv 2023年

作者： Amaral, Paulo Carbonell-Sala, Silvia De La Vega, Francisco M. Faial, Tiago Frankish, Adam Gingeras, Thomas Guigo, Roderic Harrow, Jennifer L. Hatzigeorgiou, Artemis G. Johnson, Rory Murphy, Terence D. Pertea, Mihaela Pruitt, Kim D. Pujar, Shashikant Takahashi, Hazuki Ulitsky, Igor Varabyou, Ales Wells, Christine A. Yandell, Mark Carninci, Piero Salzberg, Steven L. INSPER Institute of Education and Research SP São Paulo Brazil Dr. Aiguader 88 Catalonia Barcelona08003 Spain Department of Biomedical Data Science Stanford University School of Medicine StanfordCA United States Tempus Labs Inc. ChicagoIL United States Nature Genetics San FranciscoCA United States European Molecular Biology Laboratory European Bioinformatics Institute Wellcome Genome Campus Hinxton CambridgeCB10 1SD United Kingdom Department of Functional Genomics Cold Spring Harbor Laboratory Cold Spring HarborNY United States Catalonia Barcelona Spain Centre for Genomics Research Discovery Sciences AstraZeneca Da Vinci Building. Melbourn Science Park RoystonSG8 6HB United Kingdom Universithy of Thessaly Department of Computer Science and Biomedical Informatics Lamia Greece Hellenic Pasteur Institute Athens Greece School of Biology and Environmental Science University College Dublin DublinD04 V1W8 Ireland Conway Institute of Biomedical and Biomolecular Research University College Dublin DublinD04 V1W8 Ireland Department of Medical Oncology Inselspital Bern University Hospital University of Bern Bern3010 Switzerland Department for BioMedical Research University of Bern Bern3008 Switzerland National Center for Biotechnology Information National Library of Medicine National Institutes of Health BethesdaMD20894 United States Center for Computational Biology Johns Hopkins University BaltimoreMD United States Department of Biomedical Engineering Johns Hopkins University BaltimoreMD United States Laboratory for Transcriptome Technology RIKEN Center for Integrative Medical Sciences Kanagawa Yokohama230-0045 Japan Department of Immunology and Regenerative Biology Department of Molecular Neuroscience Weizmann Institute of Science Rehovot76100 Israel Department of Computer Science Johns Hopkins University BaltimoreMD United States Stem Cell Systems Department of Anatomy and Physiology Faculty of Medicine Dentistry and Health Scien

Scientists have been trying to identify all of the genes in the human genome since the initial draft of the genome was published in 2001. Over the intervening years, much progress has been made in identifying protein-coding genes, and the estimated number has shrunk to fewer than 20,000, although the number of distinct protein-coding isoforms has expanded dramatically. The invention of high-throughput RNA sequencing and other technological breakthroughs have led to an explosion in the number of reported non-coding RNA genes, although most of them do not yet have any known function. A combination of recent advances offers a path forward to identifying these functions and towards eventually completing the human gene catalogue. However, much work remains to be done before we have a universal annotation standard that includes all medically significant genes, maintains their relationships with different reference genomes, and describes clinically relevant genetic variants. © 2023, CC BY.

关键词： Genes

来源：评论

学校读者我要写书评

暂无评论

Inclusion of PH Improves Associations Between SARS-CoV-2 RNA Levels in Wastewater and Covid-19 Cases Within Smaller Sewersheds

SSRN

引用

SSRN 2024年

作者： Lamm, Erik D. Babler, Kristina M. Sharkey, Mark E. Amirali, Ayaaz Beaver, Cynthia Boone, Melinda M. Comerford, Samuel Cooper, Daniel Currall, Benjamin B. Grills, George S. Kobetz, Erin Kumar, Naresh Laine, Jennifer Lamar, Walter E. Lyu, Jiangnan Kennedy, Althea Perritano, Stefan Mason, Christopher E. Reding, Brian D. Roca, Matthew A. Schürer, Stephan C. Shukla, Bhavarth S. Solle, Natasha Schaefer Tallon, John J. Thomas, Collette Tierney, Braden T. Torres, Belkis Venkatpuram, Sreeharsha Vidovic, Dusica Williams, Sion L. Yin, Xue Zarnegarnia, Yalda Solo-Gabriele, Helena M. Department of Chemical Environmental and Materials Engineering University of Miami Coral GablesFL United States Department of Medicine University of Miami Miller School of Medicine MiamiFL United States Sylvester Comprehensive Cancer Center University of Miami Miller School of Medicine MiamiFL United States DataGrade Solutions LLC MiamiFL United States Department of Public Health Sciences University of Miami Miller School of Medicine MiamiFL United States Environmental Health and Safety University of Miami MiamiFL United States Division of Occupational Health Safety & Compliance University of Miami Health System MiamiFL United States Department of Physiology and Biophysics Weill Cornell Medical College New York CityNY United States The HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine Weill Cornell Medicine New YorkNY United States The WorldQuant Initiative for Quantitative Prediction Weill Cornell Medicine New YorkNY United States Department of Molecular & Cellular Pharmacology University of Miami Miller School of Medicines MiamiFL United States Institute for Data Science & Computing University of Miami Coral GablesFL United States Facilities and Operations University of Miami Coral GablesFL United States

Measurements of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) in wastewater can be used to understand the prevalence of COVID-19 cases within a community. Environmental conditions inclusive of water quality are known to impact wastewater SARS-CoV-2 signals, but they are rarely measured in areas upstream of wastewater treatment plants. The objectives of this study were to report on continuous measurements of environmental parameters (water quality and flow) in the upper reaches of a sewershed and to evaluate whether the inclusion of these environmental parameters improves associations between SARS-CoV-2 RNA levels in wastewater, and COVID-19 prevalence in the sewershed community. Measurements of environmental parameters and SARS-CoV-2 RNA in wastewater spanned different time scales (minutes, hours and weeks) and population scales (building, campus, community). Spearman correlations and stepwise regressions were used to evaluate associations. When comparing SARS-CoV-2 RNA levels directly to environmental parameters from instantaneous measurements or grab samples, results showed inconsistent correlations likely due to short term (minute-scale) variations observed in flow and water quality. For the analysis involving COVID-19 prevalence data which required averaging over weekly time scales, regressions showed that inclusion of environmental parameters, in particular pH, improved correlations between RNA and COVID-19 prevalence. The significance of pH in improving correlations of SARS-CoV-2 with prevalence data was greater for the cluster sewershed scale (populations of a few thousand) than at the community scale (populations of several 100,000). The significance of pH is believed to be related to the properties of the tap water characterized by high pH and low alkalinity, with pH serving as a proxy of fecal waste allowing for an adjustment of the SARS-CoV-2 signal to account for human waste dilution within the sewer system. © 2024, The Authors. All rights

关键词： Coronavirus

来源：评论

学校读者我要写书评

暂无评论

The Obscured Fraction of Quasars at Cosmic Noon

arXiv

引用

arXiv 2022年

作者： Vijarnwannaluk, Bovornpratch Akiyama, Masayuki Schramm, Malte Ueda, Yoshihiro Matsuoka, Yoshiki Toba, Yoshiki Sawicki, Marcin Gwyn, Stephen Pflugradt, Janek Astronomical Institute Tohoku University Aoba-ku Aramaki Miyagi Sendai980-8578 Japan Graduate School of Science and Engineering Saitama University 255 Shimo-Okubo Sakura-ku Saitama Saitama City338-8570 Japan Department of Astronomy Kyoto University Kitashirakawa-Oiwake-cho Sakyo-ku Kyoto606-8502 Japan Research Center for Space and Cosmic Evolution Ehime University 2-5 Bunkyo-cho Ehime Matsuyama790-8577 Japan National Astronomical Observatory of Japan 2-21-1 Osawa Tokyo Mitaka181-8588 Japan Institute for Computational Astrophysics Department of Astronomy and Physics Saint Mary's University HalifaxNSB3H 3C3 Canada Canadian Astronomy Data Centre NRC-Herzberg 5071 West Saanich Road VictoriaBCV9E 2E7 Canada

Statistical studies of X-ray selected Active Galactic Nuclei (AGN) indicate that the fraction of obscured AGN increases with increasing redshift, and the results suggest that a significant part of the accretion growth occurs behind obscuring material in the early universe. We investigate the obscured fraction of highly accreting X-ray AGN at around the peak epoch of supermassive black hole growth utilizing the wide and deep X-ray and optical/IR imaging datasets. A unique sample of luminous X-ray selected AGNs above z > 2 was constructed by matching the XMM-SERVS X-ray point-source catalog with a PSF-convolved photometric catalog covering from u∗to 4.5μm bands. Photometric redshift, hydrogen column density, and 2-10 keV AGN luminosity of the X-ray selected AGN candidates were estimated. Using the sample of 306 2-10 keV detected AGN at above redshift 2, we estimate the fraction of AGN with log NH(cm-2) > 22, assuming parametric X-ray luminosity and absorption functions. The results suggest that 76-3+4% of luminous quasars (log LX(erg s-1) > 44.5) above redshift 2 are obscured. The fraction indicates an increased contribution of obscured accretion at high redshift than that in the local universe. We discuss the implications of the increasing obscured fraction with increasing redshift based on the AGN obscuration scenarios, which describe obscuration properties in the local universe. Both the obscured and unobscured z > 2 AGN show a broad range of SEDs and morphology, which may reflect the broad variety of host galaxy properties and physical processes associated with the obscuration. © 2022, CC BY.

关键词： Active Galactic Nuclei

来源：评论

学校读者我要写书评

暂无评论

The leap to ordinal: Detailed functional prognosis after traumatic brain injury with a flexible modelling approach

arXiv

引用

arXiv 2022年

作者： Bhattacharyay, Shubhayu Milosevic, Ioan Wilson, Lindsay Menon, David K. Stevens, Robert D. Steyerberg, Ewout W. Nelson, David W. Ercole, Ari Åkerlund, Cecilia Amrein, Krisztina Andelic, Nada Andreassen, Lasse Anke, Audny Antoni, Anna Audibert, Gérard Azouvi, Philippe Azzolini, Maria Luisa Bartels, Ronald Barzó, Pál Beauvais, Romuald Beer, Ronny Bellander, Bo-Michael Belli, Antonio Benali, Habib Berardino, Maurizio Beretta, Luigi Blaabjerg, Morten Bragge, Peter Brazinova, Alexandra Brinck, Vibeke Brooker, Joanne Brorsson, Camilla Buki, Andras Bullinger, Monika Cabeleira, Manuel Caccioppola, Alessio Calappi, Emiliana Calvi, Maria Rosa Cameron, Peter Lozano, Guillermo Carbayo Carbonara, Marco Cavallo, Simona Chevallard, Giorgio Chieregato, Arturo Citerio, Giuseppe Clusmann, Hans Coburn, Mark Coles, Jonathan Cooper, Jamie D. Correia, Marta Čović, Amra Curry, Nicola Czeiter, Endre Czosnyka, Marek Dahyot-Fizelier, Claire Dark, Paul Dawes, Helen De Keyser, Véronique Degos, Vincent Della Corte, Francesco den Boogert, Hugo Depreitere, Bart Đilvesi, Đula Dixit, Abhishek Donoghue, Emma Dreier, Jens Dulière, Guy-Loup Esser, Patrick Ezer, Erzsébet Fabricius, Martin Feigin, Valery L. Foks, Kelly Frisvold, Shirin Furmanov, Alex Gagliardo, Pablo Galanaud, Damien Gantner, Dashiell Gao, Guoyi George, Pradeep Ghuysen, Alexandre Giga, Lelde Glocker, Ben Golubovic, Jagoš Gomez, Pedro A. Gratz, Johannes Gravesteijn, Benjamin Grossi, Francesca Gruen, Russell L. Gupta, Deepak Haagsma, Juanita A. Haitsma, Iain Helbok, Raimund Helseth, Eirik Horton, Lindsay Huijben, Jilske Hutchinson, Peter J. Jacobs, Bram Jankowski, Stefan Jarrett, Mike Jiang, Ji-Yao Johnson, Faye Jones, Kelly Karan, Mladen Kolias, Angelos G. Kompanje, Erwin Kondziella, Daniel Kornaropoulos, Evgenios Koskinen, Lars-Owe Kovács, Noémi Kowark, Ana Lagares, Alfonso Lanyon, Linda Laureys, Steven Lecky, Fiona Ledoux, Didier Lefering, Rolf Legrand, Valerie Lejeune, Aurelie Levi, Leon Lightfoot, Roger Lingsma, Hester Maas, Andrew I.R. Castaño-León, Ana M. Maegele, Marc Majdan, M Division of Anaesthesia University of Cambridge Addenbrooke's Hospital Cambridge United Kingdom Division of Neurosurgery Department of Clinical Neurosciences Addenbrooke's Hospital University of Cambridge Cambridge United Kingdom Laboratory of Computational Intensive Care Medicine Johns Hopkins University BaltimoreMD United States Division of Psychology University of Stirling Stirling United Kingdom Department of Anesthesiology and Critical Care Medicine Johns Hopkins University BaltimoreMD United States Department of Biomedical Data Sciences Leiden University Medical Center Leiden Netherlands Department of Physiology and Pharmacology Section for Perioperative Medicine and Intensive Care Karolinska Institutet Stockholm Sweden Cambridge Centre for Artificial Intelligence in Medicine Cambridge United Kingdom Division of Surgery and Clinical Neuroscience Department of Physical Medicine and Rehabilitation Oslo University Hospital University of Oslo Oslo Norway Department of Neurosurgery University Hospital Northern Norway Tromso Norway Department of Physical Medicine and Rehabilitation University Hospital Northern Norway Tromso Norway Trauma Surgery Medical University Vienna Vienna Austria Department of Anesthesiology & Intensive Care University Hospital Nancy Nancy France Raymond Poincare Hospital Assistance Publique - Hopitaux de Paris Paris France Department of Anesthesiology & Intensive Care S Raffaele University Hospital Milan Italy Department of Neurosurgery Radboud University Medical Center Nijmegen Netherlands Department of Neurosurgery University of Szeged Szeged Hungary International Projects Management ARTTIC Munchen Germany Department of Neurology Neurological Intensive Care Unit Medical University of Innsbruck Innsbruck Austria Department of Neurosurgery & Anesthesia & Intensive Care Medicine Karolinska University Hospital Stockholm Sweden NIHR Surgical Reconstruction and Microbiology Research Centre Birmingham United Kingdom Anesthesi

When a patient is admitted to the intensive care unit (ICU) after a traumatic brain injury (TBI), an early prognosis is essential for baseline risk adjustment and shared decision making. TBI outcomes are commonly categorised by the Glasgow Outcome Scale - Extended (GOSE) into eight, ordered levels of functional recovery at 6 months after injury. Existing ICU prognostic models predict binary outcomes at a certain threshold of GOSE (e.g., prediction of survival [GOSE > 1]). We aimed to develop ordinal prediction models that concurrently predict probabilities of each GOSE score. From a prospective cohort (n = 1,550, 65 centres) in the ICU stratum of the Collaborative European NeuroTrauma Effectiveness Research in TBI (center-TBI) patient dataset, we extracted all clinical information within 24 hours of ICU admission (1,151 predictors) and 6-month GOSE scores. We analysed the effect of two design elements on ordinal model performance: (1) the baseline predictor set, ranging from a concise set of ten validated predictors to a token-embedded representation of all possible predictors, and (2) the modelling strategy, from ordinal logistic regression to multinomial deep learning. With repeated k-fold cross-validation, we found that expanding the baseline predictor set significantly improved ordinal prediction performance while increasing analytical complexity did not. Half of these gains could be achieved with the addition of eight high-impact predictors to the concise set. At best, ordinal models achieved 0.76 (95% CI: 0.74 - 0.77) ordinal discrimination ability (ordinal c-index) and 57% (95% CI: 54% - 60%) explanation of ordinal variation in 6-month GOSE (Somers' Dxy). Model performance and the effect of expanding the predictor set decreased at higher GOSE thresholds, indicating the difficulty of predicting better functional outcomes shortly after ICU admission. Our results motivate the search for informative predictors that improve confidence in prognosis of higher GOSE a

关键词： Neural network models

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：