检索结果-内蒙古大学图书馆

SSRN 2023年

作者： Peng, Siyuan Yang, Yue Luo, Dengnan Zeng, Xiang Liang, Minqi Tao, Long Zhang, Guangru Li, Pan Liao, Weijie Guo, Qun Cao, Ruochen Li, Yuzhe Zhang, Weirong Hu, Zhongmin School of Geography South China Normal University Shipai Campus Guangzhou510631 China Key Laboratory of Agro-Forestry Environmental Processes and Ecological Regulation School of Ecology and Environment Hainan University Haikou570228 China Center for Eco-Environment Restoration Engineering of Hainan Province Hainan University Haikou570228 China Facility of Geographical Science Beijing Normal University Beijing100875 China Zhuhai Branch of State Key Laboratory of Earth Surface Processes and Resource Ecology Beijing Normal University Zhuhai519087 China School of Atmospheric Sciences Guangdong Province Data Center of Terrestrial and Marine Ecosystems Carbon Cycle Sun Yat-sen University Guangdong Zhuhai510245 China Key Lab of Ecosystem Network Observation and Modeling Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing100101 China College of Resources and Environment University of Chinese Academy of Sciences Beijing101408 China International Institute for Earth System Sciences Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing University Jiangsu210023 China Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology Key Laboratory for Land Satellite Remote Sensing Applications of Ministry of Natural Resources School of Geography and Ocean Science Nanjing University Jiangsu210023 China Key Laboratory of Land Surface Pattern and Simulation Institute of Geographical Sciences and Natural Resources Research Chinese Academy of Sciences Beijing China

Accurately quantifying spatiotemporal variations in evapotranspiration (ET) and its components on Tibetan Plateau (TP) is crucial for understanding the regional water cycle and energy balance. However, the determinant of spatial variations in ET and its component processes in grassland ecosystems of TP remain unclear. In this study, we used 14 sites observation data and applied Shuttleworth-Wallace-Hu (SWH) model to investigate the spatial relationships between ET, its components, and influencing factors. Our results highlighted the good performance of SWH model. There were corresponding drops in ET on alpine meadow and on alpine steppe. And the ratio of ET and precipitation (ET/PPT) of most sites was less than 1 in alpine meadow. Furthermore, the ratio of transpiration (T) and ET (T/ET) in alpine meadow generally exceeded that in alpine steppe. PPT emerged as the primary driver of interannual spatial variations in ET, while LAI played a crucial role in regulating T/ET. Our results further demonstrated that in the grassland ecosystem of TP, as the aridity index increased, the ET gradually transitioned from being primarily influenced by water supply to energy demand. This transition occurred at an aridity index of 0.73 approximately. The results of this study imply that in the context of global warming, with aridity index in TP decreasing, the ET of more ecosystems will shift from energy-led to water-led. Such findings are of immense significance in enhancing our understanding of the intricate interactions of the water cycle between land and the atmosphere. © 2023, The Authors. All rights reserved.

关键词： Water supply

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HYBRID SATELLITE AND TERRESTRIAL networkS

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INTERNATIONAL JOURNAL OF SATELLITE COMMUNICATIONS 1994年第3期12卷 313-327页

作者： CHITRE, DM SHYY, DJ EPHREMIDES, A GUPTA, S COMSAT Laboratories 22300 Comsat Drive Clarksburg MD 20871–9475 USA. Received his B.Sc. from the University of Bombay India an M.A. in mathematics from the University of Cambridge U.K. and a Ph.D. in physics from the University of Maryland. He is currently an Associate Executive Director of the Network Technology Division at COMSAT Laboratories. He has been involved in research and development activities in ISDN VSAT networks data communications and network systems and architectures. Prior to his current positions Dr. Chitre was a Principal Scientist in the Network Technology Division at COMSAT Laboratories. Dr. Chitre joined COMSAT Laboratories in 1980. He has made major contributions to the analysis and architecture of data communication ISDN and BISDN via satellite. Dr. Chitre directs and participates in the international and national standards activities in ISDN BISDN and data communication as they apply to satellite communication. He was Chairman of the Working Group on Protocols and Network Timing Function of the CCIR/CCITT Joint Ad Hoc Group on ISDN/Satellite Matters during 1990–1992. Currently he is the Chairman of the Working Group on New Technologies in the ITU Intersector Coordinating Group (ICG) on Satellite Matters. Dr. Chitre was a programme manager during 1990 and 1991 on a contract from INTELSAT on systems studies on satellite communications systems architectures for ISDN and broadband ISDN systems. Currently he is the technical manager of the DoD Contract on ATM via satellite demonstration and the programme manager for the INTELSAT contract on analysis and top-level specification of INTELSAT ISDN subnetworks and SDH compatible transport network. Received the B.S. degree in electrical engineering from national Chiao-Tung University Hsin-Chu Taiwan in 1983 and the M.S. and Ph.D. degrees in electrical engineering from Georgia Institute of Technology Atlanta GA in 1986 and 1990 respectively. From June 1987 to October 1987 he worked for the Department of Neurology Emory Univers

The role of satellite communications in networks that provide new services, such as frame relay and multimedia, is investigated. Both passive and active (on-board switching/processing) satellite systems are considered. Novel techniques are developed for each system to demonstrate, via detailed analysis and simulation, how the communications bandwidth agility of multipoint/broadcast satellite channels, and the on-board switching/processing, makes it feasible to provide these new services via hybrid satellite and terrestrial networks in a resource-efficient manner.

关键词： HYBRID networkS ON-BOARD PROCESSING FRAME RELAY

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Computer-Aided System for the Detection of Multicategory Pulmonary Tuberculosis in Radiographs

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Journal of Healthcare engineering 2020年第1期2020卷

作者： Yilin Xie Zhuoyue Wu Xin Han Hongyu Wang Yifan Wu Lei Cui Jun Feng Zhaohui Zhu Zhongyuanlong Chen Department of Information Science and Technology Northwest University Xi’an Shaanxi 710127 *** School of Computer Science and Technology Xi’an University of Posts and Telecommunications Xi’an Shaanxi 710121 *** Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi’an University of Posts and Telecommunications Xi’an Shaanxi 710121 *** State-Province Joint Engineering and Research Center of Advanced Networking and Intelligent Information Services School of Information Science and Technology Northwest University Xi’an 710127 Shaanxi *** Chest Hospital of Xinjiang Uyghur Autonomous Region of the PRC Urumqi Xinjiang Uygur Autonomous Region 830049 China

The early screening and diagnosis of tuberculosis plays an important role in the control and treatment of tuberculosis infections. In this paper, an integrated computer-aided system based on deep learning is proposed for the detection of multiple categories of tuberculosis lesions in chest radiographs. In this system, the fully convolutional neural network method is used to segment the lung area from the entire chest radiograph for pulmonary tuberculosis detection. Different from the previous analysis of the whole chest radiograph, we focus on the specific tuberculosis lesion areas for the analysis and propose the first multicategory tuberculosis lesion detection method. In it, a learning scalable pyramid structure is introduced into the Faster Region-based Convolutional network (Faster RCNN), which effectively improves the detection of small-area lesions, mines indistinguishable samples during the training process, and uses reinforcement learning to reduce the detection of false-positive lesions. To compare our method with the current tuberculosis detection system, we propose a classification rule for whole chest X-rays using a multicategory tuberculosis lesion detection model and achieve good performance on two public datasets (Montgomery: AUC = 0.977 and accuracy = 0.926; Shenzhen: AUC = 0.941 and accuracy = 0.902). Our proposed computer-aided system is superior to current systems that can be used to assist radiologists in diagnoses and public health providers in screening for tuberculosis in areas where tuberculosis is endemic.

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Efficient MedSAMs: Segment Anything in Medical Images on Laptop

arXiv

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arXiv 2024年

作者： Ma, Jun Li, Feifei Kim, Sumin Asakereh, Reza Le, Bao-Hiep Nguyen-Vu, Dang-Khoa Pfefferle, Alexander Wei, Muxin Gao, Ruochen Lyu, Donghang Yang, Songxiao Purucker, Lennart Marinov, Zdravko Staring, Marius Lu, Haisheng Dao, Thuy Thanh Ye, Xincheng Li, Zhi Brugnara, Gianluca Vollmuth, Philipp Foltyn-Dumitru, Martha Cho, Jaeyoung Mahmutoglu, Mustafa Ahmed Bendszus, Martin Pflüger, Irada Rastogi, Aditya Ni, Dong Yang, Xin Zhou, Guang-Quan Wang, Kaini Heller, Nicholas Papanikolopoulos, Nikolaos Weight, Christopher Tong, Yubing Udupa, Jayaram K. Patrick, Cahill J. Wang, Yaqi Zhang, Yifan Contijoch, Francisco McVeigh, Elliot Ye, Xin He, Shucheng Haase, Robert Pinetz, Thomas Radbruch, Alexander Krause, Inga Kobler, Erich He, Jian Tang, Yucheng Yang, Haichun Huo, Yuankai Luo, Gongning Kushibar, Kaisar Amankulov, Jandos Toleshbayev, Dias Mukhamejan, Amangeldi Egger, Jan Pepe, Antonio Gsaxner, Christina Luijten, Gijs Fujita, Shohei Kikuchi, Tomohiro Wiestler, Benedikt Kirschke, Jan S. de la Rosa, Ezequiel Bolelli, Federico Lumetti, Luca Grana, Costantino Xie, Kunpeng Wu, Guomin Puladi, Behrus Martín-Isla, Carlos Lekadir, Karim Campello, Victor M. Shao, Wei Brisbane, Wayne Jiang, Hongxu Wei, Hao Yuan, Wu Li, Shuangle Zhou, Yuyin Wang, Bo AI Collaborative Centre University Health Network Department of Laboratory Medicine and Pathobiology University of Toronto Vector Institute Toronto Canada Peter Munk Cardiac Centre University Health Network Toronto Canada Toronto General Hospital Research Institute University Health Network Department of Computer Science University of Toronto University Health Network Vector Institute Toronto Canada University of Science Vietnam National University Ho Chi Minh City Viet Nam Institute of Computer Science University of Freiburg Freiburg Germany School of Medicine and Health Harbin Institute of Technology Harbin China Division of Image Processing Department of Radiology Leiden University Medical Center Leiden Netherlands Department of System and Control Engineering School of Engineering Institute of Science Tokyo Formerly Tokyo Institute of Technology Tokyo Japan Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Karlsruhe Germany School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China School of Electrical Engineering and Computer Science University of Queensland Brisbane Australia School of Cyberspace Hangzhou Dianzi University Hangzhou China Division for Computational Radiology and Clinical AI The Department of Neuroradiology University Hospital Bonn Germany Division for Computational Radiology and Clinical AI The Department of Neuroradiology University Hospital Bonn Germany Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany Division for Computational Radiology and Clinical AI Department of Neuroradiology University Hospital Bonn Germany School of Biomedical Engineering Shenzhen University Shenzhen China School of Biological Science and Medical Engineering Southeast University Nanjing China Department of Urology Cleveland Clinic Cleveland United States Department of Computer Science University of Minnesota Minneapolis United St

Promptable segmentation foundation models have emerged as a transformative approach to addressing the diverse needs in medical images, but most existing models require expensive computing, posing a big barrier to their adoption in clinical practice. In this work, we organized the first international competition dedicated to promptable medical image segmentation, featuring a large-scale dataset spanning nine common imaging modalities from over 20 different institutions. The top teams developed lightweight segmentation foundation models and implemented an efficient inference pipeline that substantially reduced computational requirements while maintaining state-of-the-art segmentation accuracy. Moreover, the post-challenge phase advanced the algorithms through the design of performance booster and reproducibility tasks, resulting in improved algorithms and validated reproducibility of the winning solution. Furthermore, the best-performing algorithms have been incorporated into the open-source software with a user-friendly interface to facilitate clinical adoption. The data and code are publicly available to foster the further development of medical image segmentation foundation models and pave the way for impactful real-world applications. © 2024, CC BY-NC-SA.

关键词： Open source software

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THE CALLING(SM) network - A GLOBAL WIRELESS COMMUNICATION-SYSTEM

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INTERNATIONAL JOURNAL OF SATELLITE COMMUNICATIONS 1994年第1期12卷 45-61页

作者： TUCK, EF PATTERSON, DP STUART, JR LAWRENCE, MH Calling Communications Corporation. 1900 West Garvey Ave South. Suite 200 West Covina CA 91790 USA. Chairman of Calling Communications Corporation. He is also the Managing Director of Kinship Venture Management Inc. the general partner of Kinship Partners 11 and a General Partner of Boundary the general partner of The Boundary Fund. As a venture capitalist he has founded or participated in founding several telecommunications companies including Calling Communications Corporation Magellan Systems Corporation manufactures of Global Positioning System receivers Applied Digital Access manufacturer of DS-3 test access and network performance monitoring equipment Endgate Technology Corporation specialists in satellite phased array antennas and Poynting Systems Corporation. now a division of Reliance Corporation manufacturers of fibre optic transport equipment. He was a founder of Kebby Microwave Corporation where he invented the first solid-state. frequency-modulated commercial microwave link system. The company was acquired by ITT Corporation where he rose to the position of V.P. and Technical Director of ITT North America Telecommunications Inc. Subsequently he was V.P. of Marketing and Engineering at American Telecommunications Inc. (ATC). He was founding Director of American Telecom Inc. a joint venture between ATC and Fujitsu and has served on more than 20 boards of directors including those of three public companies. He has authored articles on microwave engineering and telephone signalling and was a contributor to Reference Data For Radio Engineers. He is a graduate of the University of Missouri at Rolla where he was later awarded an honorary Professional degree and serves on its Academy of Electrical Engineering. Mr Tuck is a Senior Member of the IEEE a Fellow of the Institution of Engineers (Australia) a Professional Member of the AIAA and a registered professional engineer in three states. More than 25 years of experience in the telecommunications industry where he has been responsibl

There is a very large demand for basic telephone service in developing nations, and remote parts of industrialized nations, which cannot be met by conventional wireline and cellular systems. This is the world's largest unserved market. We describe a system which uses recent advances in active phased arrays, fast-packet switching technology, adaptive routeing, and light spacecraft technology, in part based on the work of the Jet Propulsion laboratory and on recently-declassified work done on the Strategic Defense Initiative, to make it possible to address this market with a global telephone network based on a large low-Earth-orbit constellation of identical satellites. A telephone utility can use such a network to provide the same modern basic and enhanced telephone services offered by telephone utilities in the urban centres of fully-industrialized nations. Economies of scale permit capital and operating costs per subscriber low enough to provide a service to all subscribers, regardless of location, at prices comparable to the same services in urban areas of industrialized nations, while generating operating profits great enough to attract the capital needed for its construction. The bandwidth needed to support the capacity needed to gain these economies of scale requires that the system use K(alpha)-band frequencies. This choice of frequencies places unusual constraints on the network design, and in particular forces the use of a large number of satellites. Global demand for basic and enhanced telephone service is great enough to support at least three networks of the size described herein. The volume of advanced components, and services such as launch services, required to construct and replace these networks is sufficient to propel certain industries to market leadership positions in the early 21st Century.

关键词： LOW EARTH ORBIT SATELLITE COMMUNICATIONS FAST PACKET SWITCHING EARTH FIXED CELLS ADAPTIVE ROUTEING LIGHT SPACECRAFT K(A) BAND PHASED-ARRAY ANTENNAS

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The Multi-modality Cell Segmentation Challenge: Towards Universal Solutions

arXiv

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arXiv 2023年

作者： Ma, Jun Xie, Ronald Ayyadhury, Shamini Ge, Cheng Gupta, Anubha Gupta, Ritu Gu, Song Zhang, Yao Lee, Gihun Kim, Joonkee Lou, Wei Li, Haofeng Upschulte, Eric Dickscheid, Timo de Almeida, José Guilherme Wang, Yixin Han, Lin Yang, Xin Labagnara, Marco Gligorovski, Vojislav Scheder, Maxime Rahi, Sahand Jamal Kempster, Carly Pollitt, Alice Espinosa, Leon Mignot, Tâm Middeke, Jan Moritz Eckardt, Jan-Niklas Li, Wangkai Li, Zhaoyang Cai, Xiaochen Bai, Bizhe Greenwald, Noah F. Van Valen, David Weisbart, Erin Cimini, Beth A. Cheung, Trevor Brück, Oscar Bader, Gary D. Wang, Bo Peter Munk Cardiac Centre University Health Network TorontoON Canada Department of Laboratory Medicine and Pathobiology University of Toronto TorontoON Canada Vector Institute TorontoON Canada Department of Molecular Genetics University of Toronto TorontoON Canada Donnelly Centre University of Toronto TorontoON Canada Princess Margaret Cancer Centre University Health Network TorontoON Canada School of Medicine and Pharmacy Ocean University of China Qingdao China New Delhi India Laboratory Oncology Dr. BRA-IRCH All India Institute of Medical Sciences New Delhi India Department of Image Reconstruction Nanjing Anke Medical Technology Co. Ltd. Nanjing China Shanghai Artificial Intelligence Laboratory Shanghai China Graduate School of AI KAIST Seoul Korea Republic of Shenzhen Research Institute of Big Data Shenzhen China Shenzhen China Helmholtz AI Research Center Jülich Jülich Germany Faculty of Mathematics and Natural Sciences Institute of Computer Science Heinrich Heine University Düsseldorf Düsseldorf Germany Hinxton United Kingdom Champalimaud Foundation - Centre for the Unknown Lisbon Portugal Department of Bioengineering Stanford University Palo AltoCA United States Tandon School of Engineering New York University New YorkNY United States School of Biomedical Engineering Health Science Center Shenzhen University Shenzhen China Lausanne Switzerland School of Biological Sciences University of Reading Reading United Kingdom Laboratoire de Chimie Bactérienne CNRS Université Aix Marseille UMR Institut de Microbiologie de la Méditerranée Marseille France Department of Internal Medicine I University Hospital Dresden Technical University Dresden Dresden Germany Else Kroener Fresenius Center for Digital Health Technical University Dresden Dresden Germany Department of Automation University of Science and Technology of China Hefei China Institute of Advanced Technology University of Science and Technology of China Hefei Chi

Cell segmentation is a critical step for quantitative single-cell analysis in microscopy images. Existing cell segmentation methods are often tailored to specific modalities or require manual interventions to specify hyper-parameters in different experimental settings. Here, we present a multi-modality cell segmentation benchmark, comprising over 1500 labeled images derived from more than 50 diverse biological experiments. The top participants developed a Transformer-based deep-learning algorithm that not only exceeds existing methods but can also be applied to diverse microscopy images across imaging platforms and tissue types without manual parameter adjustments. This benchmark and the improved algorithm offer promising avenues for more accurate and versatile cell analysis in microscopy imaging. © 2023, CC BY-NC-ND.

关键词： Deep learning

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Redefining pandemic preparedness:Multidisciplinary insights from the CERP modelling workshop in infectious diseases,workshop report

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Infectious Disease Modelling 2024年第2期9卷 501-518页

作者： Marta C.Nunes Edward Thommes Holger Fröhlich Antoine Flahault Julien Arino Marc Baguelin Matthew Biggerstaff Gaston Bizel-Bizellot Rebecca Borchering Giacomo Cacciapaglia Simon Cauchemez Alex Barbier-Chebbah Carsten Claussen Christine Choirat Monica Cojocaru Catherine Commaille-Chapus Chitin Hon Jude Kong Nicolas Lambert Katharina B.Lauer Thorsten Lehr Cédric Mahe Vincent Marechal Adel Mebarki Seyed Moghadas Rene Niehus Lulla Opatowski Francesco Parino Gery Pruvost Andreas Schuppert Rodolphe Thiébaut Andrea Thomas-Bachli Cecile Viboud Jianhong Wu Pascal Crépey Laurent Coudeville Center of Excellence in Respiratory Pathogens(CERP) Hospices Civils de Lyon(HCL)and Centre International de Recherche en Infectiologie(CIRI)Équipe SantéPubliqueÉpidémiologie etÉcologieÉvolutive des Maladies Infectieuses(PHE3ID)Inserm U1111CNRS UMR5308ENS de LyonUniversitéClaude Bernard Lyon 1LyonFrance South African Medical Research Council Vaccines&Infectious Diseases Analytics Research UnitFaculty of Health SciencesUniversity of the WitwatersrandJohannesburgSouth Africa New Products and Innovation(NPI) Sanofi Vaccines(Global)TorontoOntarioCanada Department of Mathematics and Statistics University of GuelphGuelphOntarioCanada Fraunhofer Institute for Algorithms and Scientific Computing(SCAI) Department of BioinformaticsSchloss BirlinghovenSankt AugustinGermany University of Bonn Bonn-Aachen International Center for IT(b-it)BonnGermany Institute of Global Health Faculty of MedicineUniversity of GenevaGenevaSwitzerland and Swiss School of Public HealthZürichSwitzerland Department of Mathematics University of ManitobaWinnipegManitobaCanada MRC Centre for Global Infectious Disease Analysis School of Public HealthImperial College LondonLondonUK Centre for Mathematical Modelling of Infectious Diseases Department of Infectious Disease EpidemiologyLondon School of Hygiene&Tropical MedicineLondonUK National Center for Immunization and Respiratory Diseases(NCIRD) US Centers for Disease Control and Prevention(CDC)AtlantaGAUSA Departement of Computational Biology Departement of Global HealthInstitut PasteurParisFrance Institut de Physique des Deux Infinis de Lyon(IP2I) UMR5822IN2P3/CNRSUniversitéClaude Bernard Lyon 1VilleurbanneFrance Mathematical Modelling of Infectious Diseases Unit Institut PasteurUniversitéParis CitéUMR2000 CNRSParisFrance Decision and Bayesian Computation Institut PasteurUniversitéParis CitéCNRS UMR 3571France Fraunhofer-Institute for Translational Medicine and Pharmacology HamburgGermany Institute of Global Health Faculty of MedicineUniversity of

In July 2023,the Center of Excellence in Respiratory Pathogens organized a two-day workshop on infectious diseases modelling and the lessons learnt from the Covid-19 *** report summarizes the rich discussions that occurred during the *** workshop participants discussed multisource data integration and highlighted the benefits of combining traditional surveillance with more novel data sources like mobility data,social media,and wastewater *** advancements were noted in the development of predictive models,with examples from various countries showcasing the use of machine learning and artificial intelligence in detecting and monitoring disease *** role of open collaboration between various stakeholders in modelling was stressed,advocating for the continuation of such partnerships beyond the pandemic.A major gap identified was the absence of a common international framework for data sharing,which is crucial for global pandemic ***,the workshop underscored the need for robust,adaptable modelling frameworks and the integration of different data sources and collaboration across sectors,as key elements in enhancing future pandemic response and preparedness.

关键词： Modelling Covid-19 Infectious diseases Pandemic preparedness Workshop

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INTRODUCTION

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Bulletin of the American Meteorological Society 2024年第8期105卷 S1-S11页

作者： Boyer, T. Blunden, J. Dunn, R.J.H. NOAA/NESDIS National Centers for Environmental Information Silver Spring Maryland NOAA/NESDIS National Centers for Environmental Information Asheville North Carolina Met Office Hadley Centre Exeter United Kingdom European Centre for Medium-Range Weather Forecasts Reading United Kingdom University of Maryland College Park Maryland Scripps Institution of Oceanography University of California San Diego La Jolla California NOAA/NWS National Centers for Environmental Prediction Climate Prediction Center College Park Maryland Brandenburg University of Technology (BTU) Cottbus-Senftenberg Germany Servicio Meteorológico Nacional Buenos Aires Argentina NOAA/OAR Physical Sciences Laboratory Boulder Colorado European Centre for Medium-Range Weather Forecasts Bonn Germany Center for Geophysical Research and School of Physics University of Costa Rica San José Costa Rica Department of Meteorology and National Centre for Earth Observation University of Reading Reading United Kingdom Centro Nacional de Monitoramento e Alertas de Desastres Naturais CEMADEN São Paulo Brazil Université Grenoble Alpes Institut des Géosciences de l’Environnement IRD CNRS Grenoble INP Grenoble France Hampton University Hampton Virginia Seychelles Meteorological Authority Mahe Seychelles Section for Glaciers Ice and Snow Norwegian Water Resources and Energy Directorate Oslo Norway National Research Institute for Agriculture Food and Environment (INRAE) CARRTEL Université Savoie Mont Blanc Thonon les Bains France Graduate School of Agriculture Osaka Metropolitan University Sakai Japan Science Faculty Universidad Nacional de Colombia Bogotá Colombia University of Bremen Bremen Germany NOAA Global Monitoring Laboratory Boulder Colorado Servicio Nacional de Meteorología e Hidrología del Perú Lima Perú Centro de Investigaciones sobre Desertificación – Spanish National Research Council (CSIC-UV-GVA) Valencia Spain Hydro-Climate Extremes Lab (H-CEL) Ghent University Ghent Belgium Department o

In 2023, La Niña conditions that generally prevailed in the eastern Pacific Ocean from mid-2020 into early 2023 gave way to a strong El Niño by October. Atmospheric concentrations of Earth’s major greenhouse gases—carbon dioxide, methane, and nitrous oxide—all increased to record-high levels. The annual global average carbon dioxide concentration in the atmosphere rose to 419.3±0.1 ppm, which is 50% greater than the pre-industrial level. The growth from 2022 to 2023 was 2.8 ppm, the fourth highest in the record since the 1960s. The combined short-term effects of El Niño and the long-term effects of increasing levels of heat-trapping gases in the atmosphere contributed to new records for many essential climate variables reported here. The annual global temperature across land and oceans was the highest in records dating as far back as 1850, with the last seven months (June–December) having each been record warm. Over land, the globally averaged temperature was also record high. Dozens of countries reported record or near-record warmth for the year, including China and continental Europe as a whole (warmest on record), India and Russia (second warmest), and Canada (third warmest). Intense and widespread heatwaves were reported around the world. In Vietnam, an all-time national maximum temperature record of 44.2°C was observed at Tuong Duong on 7 May, surpassing the previous record of 43.4°C at Huong Khe on 20 April 2019. In Brazil, the air temperature reached 44.8°C in Araçuaí in Minas Gerais on 20 November, potentially a new national record and 12.8°C above normal. The effect of rising temperatures was apparent in the cryosphere, where snow cover extent by June 2023 was the smallest in the 56-year record for North America and seventh smallest for the Northern Hemisphere overall. Heatwaves contributed to the greatest average mass balance loss for Alpine glaciers around the world since the start of the record in 1970. Due to rapid volume loss beginning in 2021, St. A

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26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

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BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics University of Aberdeen Aberdeen Scotland UK Department of Integrativ

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Corrigendum to "Dissemination in time and space in presymptomatic granulin mutation carriers: A spatial chronnectome study" [Neurobiology of Aging Volume 108, December 2021, Pages 155-167]

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Neurobiology of aging 2022年 119卷 140-144页

作者： Enrico Premi Marcello Giunta Armin Iraji Srinivas Rachakonda VinceD Calhoun Stefano Gazzina Alberto Benussi Roberto Gasparotti Silvana Archetti Martina Bocchetta Dave Cash Emily Todd Georgia Peakman Rhian Convery John C van Swieten Lize Jiskoot Raquel Sanchez-Valle Fermin Moreno Robert Laforce Caroline Graff Matthis Synofzik Daniela Galimberti JamesB Rowe Mario Masellis Carmela Tartaglia Elizabeth Finger Rik Vandenberghe Alexandre de Mendonça Fabrizio Tagliavini Chris R Butler Isabel Santana Alexander Gerhard Isabelle Le Ber Florence Pasquier Simon Ducharme Johannes Levin Adrian Danek Sandro Sorbi Markus Otto Jonathan D Rohrer Barbara Borroni Stroke Unit Azienda Socio Sanitaria Territoriale Spedali Civili Brescia Brescia Italy. Centre for Neurodegenerative Disorders Department of Clinical and Experimental Sciences University of Brescia Brescia Italy. Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University Georgia Institute of Technology Emory University Atlanta GA USA. Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University Georgia Institute of Technology Emory University Atlanta GA USA Departments of Psychology and Computer Science Georgia State University Atlanta GA USA Department of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA. Neurophysiology Unit Azienda Socio Sanitaria Territoriale Spedali Civili Brescia Brescia Italy. Neuroradiology Unit University of Brescia Brescia Italy. Laboratory Unit Azienda Socio Sanitaria Territoriale Spedali Civili Brescia Brescia Italy. Department of Neurodegenerative Disease Dementia Research Centre UCL Institute of Neurology Queen Square London UK. Department of Neurology Erasmus Medical Centre Rotterdam the Netherlands. Alzheimer's disease and Other Cognitive Disorders Unit Neurology Service Hospital Clinic University of Barcelona Barcelona Spain. Cognitive Disorders Unit Department of Neurology Donostia University Hospital San Sebastian Gipuzkoa Spain. Clinique Interdisciplinaire de Memoire Department des Sciences Neurologiques CHU de Quebec and Faculte de Medecine Universite Laval Quebec Canada. Center for Alzheimer Research Division of Neurogeriatrics Department of Neurobiology Care Sciences and Society Bioclinicum Karolinska Institutet Solna Sweden. Department of Neurodegenerative Diseases Hertie-Institute for Clinical Brain Research and Center of Neurology University of Tubingen Tubingen Germany. Fondazione Ca' Granda IRCCS Ospedale Policlinico Milan Italy University of Milan

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