检索结果-内蒙古大学图书馆

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Chengyu Yuan Hao Xiong Guoqing Shangguan Hualei Shen Dong Liu Haojie Zhang Zhonghua Liu Kun Qian Bin Hu Björn W. Schuller Yoshiharu Yamamoto Shlomo Berkovsky College of Computer and Information Engineering Henan Normal University China Key Laboratory of Artificial Intelligence and Personalized Learning in Education Henan China Big Data Engineering Lab of Teaching Resources & Assessment of Education Quality Henan China Centre for Health Informatics Australian Institute of Health Innovation Macquarie University Australia Key Laboratory of Brain Health Intelligent Evaluation and Intervention (BIT) Ministry of Education China School of Medical Technology Beijing Institute of Technology China School of Information Engineering Zhejiang Ocean University China GLAM – Group on Language Audio & Music Imperial College London U.K. Chair of Embedded Intelligence for Health Care and Wellbeing University of Augsburg Germany Educational Physiology Laboratory Graduate School of Education The University of Tokyo Japan

Medical image segmentation is an important task in modern analysis of medical images. Current methods tend to extract either local features with convolutions or global features with Transformers. However, few of them are able to effectively fuse global and local features to facilitate segmentation. In this work, we propose a novel hybrid network that involves three main branches: the Multi-Layer Perception (MLP) branch, the Convolutional Neural Network (CNN) branch, and a Fusion branch. The MLP and CNN branches aim to learn global and local features, respectively. To fuse these, the fusion branch introduces a novel hierarchical fusion that performs multi-layered fusions that generate high-level representations to enhance segmentation. Our evaluation with two datasets shows strong performance of the proposed method compared to state-of-the-art baselines.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Correction to "Correlated Component Analysis for Enhancing the Performance of SSVEP-Based brain-computer interface"

引用

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society 2018年第8期26卷 1645-1646页

作者： Yangsong Zhang Daqing Guo Fali Li Erwei Yin Yu Zhang Peiyang Li Qibin Zhao Toshihisa Tanaka Dezhong Yao Peng Xu Masaki Nakanishi School of Computer Science and Technology Southwest University of Science and Technology Mianyang China MOE Key Laboratory for Neuroinformation Clinical Hospital of Chengdu Brain Science Institute University of Electronic Science and Technology of China Chengdu China Academy of Military Sciences China National Institute of Defense Technology Innovation Beijing China Department of Psychiatry and Behaviour Sciences Stanford University Stanford CA USA Tensor Learning Unit RIKEN Center for Advanced Intelligence Project Tokyo Japan Department of Electronic and Information Engineering Tokyo University of Agriculture and Technology Tokyo Japan Swartz Center for Computational Neuroscience Institute for Neural Computation University of California at San Diego La Jolla CA USA

In the above paper [1], a method has been proposed to use the correlated component analysis (CORCA) to learn spatial filters with multiple blocks of individual training data for steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) scenario. In order to evaluate the performance of CORCA, the task-related component analysis (TRCA)-based method was used as a baseline method [2]. For a fair and convincing comparison, the MATLAB codes on the website (https://***/mnakanishi/TRCA-SSVEP) for implementing TRCA method provided by Dr. Masaki Nakanishi, the first author of [2], were used to take the role of the TRCA method. At that time, the proposed CORCA-based method outperforms the TRCA-based method [1].

关键词： Bars Standards Analysis of variance brain-computer interfaces Task analysis Hospitals Electronic mail

来源：评论

学校读者我要写书评

暂无评论

Directly wireless communication of human minds via non-invasive brain-computer-metasurface platform

arXiv

引用

arXiv 2022年

作者： Ma, Qian Gao, Wei Xiao, Qiang Ding, Lingsong Gao, Tianyi Zhou, Yajun Gao, Xinxin Yan, Tao Liu, Che Gu, Ze Kong, Xianghong Abbasi, Qammer H. Li, Lianlin Qiu, Cheng-Wei Li, Yuanqing Cui, Tie Jun Institute of Electromagnetic Space Southeast University Nanjing210096 China School of Automation Science and Engineering South China University of Technology Guangzhou510641 China State Key Laboratory of Millimeter Wave Southeast University Nanjing210096 China Center of Intelligent Metamaterials Pazhou Laboratory Guangzhou510330 China Research Center for Brain-Computer Interface Pazhou Lab Guangzhou510330 China Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore University of Glasgow James Watt School of Engineering GlasgowG12 8QQ United Kingdom State Key Laboratory of Advanced Optical Communication Systems and Networks Department of Electronics Peking University Beijing100871 China

brain-computer interfaces (BCIs), invasive or non-invasive, have projected unparalleled vision and promise for assisting patients in need to better their interaction with the surroundings. Inspired by the BCI-based rehabilitation technologies for nerve-system impairments and amputation, we propose an electromagnetic brain-computer-metasurface (EBCM) paradigm, regulated by human's cognition by brain signals directly and non-invasively. We experimentally show that our EBCM platform can translate human's mind from evoked potentials of P300-based electroencephalography to digital coding information in the electromagnetic domain non-invasively, which can be further processed and transported by an information metasurface in automated and wireless fashions. Directly wireless communications of the human minds are performed between two EBCM operators with accurate text transmissions. Moreover, several other proof-of-concept mind-control schemes are presented using the same EBCM platform, exhibiting flexibly-customized capabilities of information processing and synthesis like visual-beam scanning, wave modulations, and pattern encoding. © 2022, CC BY.

关键词： Electrophysiology

来源：评论

学校读者我要写书评

暂无评论

多肽靶向嵌合体通过促进tau蛋白特异性去磷酸化治疗阿尔茨海默病和其他tau蛋白病

引用

science Bulletin 2024年第8期69卷 1137-1152页

作者：苏静芬肖越魏林郁雷慧杨孙飞王围霞尹君熊瑞李师宏张配周颖王小川郑杰王建枝 Department of Pathophysiology School of Basic MedicineKey Laboratory of Education Ministry of China/Hubei Province for Neurological DisordersTongji Medical CollegeHuazhong University of Science and TechnologyWuhan 430030China School of Artificial Intelligence and Automation Huazhong University of Science and TechnologyWuhan 430030China Sino-UK Joint Laboratory of Brain Function and Injury of Henan Province Department of Physiology and PathophysiologySchool of Basic Medical SciencesXinxiang Medical UniversityXinxiang 453003China Department of Pathophysiology School of Basic Medical SciencesWuhan UniversityWuhan 430030China Department of Anesthesiology The First Affiliated Hospital of Gannan Medical UniversityGanzhou 341000China The Core Facility and Technical Support Wuhan Institute of VirologyChinese Academy of SciencesWuhan 430030China Research Center for Medicine and Structural Biology Wuhan UniversityWuhan 430030China Neuroscience Research Institute and Department of Neurobiology School of Basic Medical SciencesPeking UniversityKey Laboratory for NeuroscienceMinistry of Education/National Health CommissionBeijing 100083China Co-innovation Center of Neuroregeneration Nantong UniversityNantong 226000China

Abnormal hyperphosphorylation and accumulation of tau protein play a pivotal role in neurodegeneration in Alzheimer’s disease(AD)and many other *** elimination of hyperphosphorylated tau is promising for the therapy of these *** have conceptualized a strategy,named dephosphorylation-targeting chimeras(DEPTACs),for specifically hijacking phosphatases to tau to debilitate its ***,we conducted the step-by-step optimization of each constituent motif to generate DEPTACs with reasonable effectiveness in facilitating the dephosphorylation and subsequent clearance of pathological ***,for one of the selected chimeras,D16,we demonstrated its significant efficiency in rescuing the neurodegeneration caused by neurotoxic K18-tau seeds in ***,intravenous administration of D16 also alleviated tau pathologies in the brain and improved memory deficits in AD *** results suggested DEPTACs as targeted modulators of tau phosphorylation,which hold therapeutic potential for AD and other tauopathies.

关键词： tau Dephosphorylation-targeting chimeras Therapeutic potential Tauopathy

来源：评论

学校读者我要写书评

暂无评论

A survey of deep learning-based classification methods for steady-state visual evoked potentials

引用

brain-Apparatus Communication: A Journal of Bacomics 2023年第1期2卷

作者： Yudong Pan Jianbo Chen Yangsong Zhang a School of Computer Science and Technology Laboratory for Brain Science and Medical Artificial Intelligence Southwest University of Science and Technology Mianyang P.R. China a School of Computer Science and Technology Laboratory for Brain Science and Medical Artificial Intelligence Southwest University of Science and Technology Mianyang P.R. Chinab MOE Key Lab for Neuroinformation University of Electronic Science and Technology of China Chengdu P.R. China

PurposeSteady-state visual evoked potential (SSVEP) based BCI has attracted great interests owing to the high information transfer rate (ITR) and little training requirement. The performance of SSVEP-based BCI heavily depends on the classification methods. Deep Learning (DL) technology provides an alternative avenue for the data classification in SSVEP-based BCI, and has received increasing interests in recent years. This review aimed to summarize the progress of DL-based classification methods for SSVEP data over the past *** and methodThe literature was searched and selected based on the research topics of DL and SSVEP. We categorized these methods into four classes, i.e., traditional neural network structures-based DL methods, traditional frequency recognition methods inspiring DL methods, attention mechanisms-based DL models, and transfer learning technology-based DL methods, and generative model-based recognition method. Moreover, we analyzed the current challenges and presented future research *** study provides a systematic description of the current development status on DL-based SSVEP classification methods, and sheds insight on future researches.

关键词： Deep learning (DL) brain-computer interface (BCI) steady-state visual evoked potential (SSVEP) frequency recognition

来源：评论

学校读者我要写书评

暂无评论

Gut microbiota links to TRANK1 overexpression in bipolar depression

Gut microbiota links to TRANK1 overexpression in bipolar dep...

引用

2020年浙江省医学会精神病学学术大会浙江省医师协会精神科医师分会第十三届年会

作者： Jianbo Lai Ang Li Xiuxia Yuan Jiajun Jiang Peifen Zhang Caixi Xi Yanli Du Yifan Li Tingting Huang Lingling Wu Ming Li Huijuan Li Xin Cai Zheng Wang Jingkai Chen Jing Lu Shaojia Lu Tingting Mou Hetong Zhou Dandan Wang Manli Huang Fengqin Dong Peng Zheng Ming D.Li Yi Xu Xueqin Song Shaohua Hu Department of Psychiatry The First Affiliated HospitalZhejiang University School of Medicine The Key Laboratory of Mental Disorder's Management in Zhejiang Province Brain Research Institute of Zhejiang University Gene Hospital of Henan Province Precision Medicine CenterThe First Affiliated Hospital of Zhengzhou University Department of Psychiatry First Affiliated Hospital of Zhengzhou University Department of Neurology The First Affiliated Hospital of Chongqing Medical University NHC Key Laboratory of Diagnosis and Treatment on Brain Functional Diseases The First Affiliated Hospital of Chongqing Medical University Hangzhou Geriatric Hospital Hangzhou First People's Hospital Group Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences and Yunnan Province Kunming Institute of ZoologyChinese Academy of Sciences CAS Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences Department of Endocrinology and Metabolism The First Affiliated HospitalZhejiang University School of Medicine State Key Laboratory for Diagnosis and Treatment of Infectious Diseases Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases The First Affiliated HospitalZhejiang University School of Medicine

Accumulating evidences have shown gut microbial dysbiosis in patients with bipolar disorder(BD),but little is known how the microbiota-gut-brain axis operates in *** this study,we aimed to characterize the gut microbiota with metagenomic sequencing and explored its linkage to tetratricopeptide repeat and ankyrin repeat containing 1(TRANK1),a risk gene of *** samples were obtained from 60 healthy individuals and 62 patients in acute depressive episodes,who received over one-month quetiapine treatment *** analysis of BD microbiota revealed specific alterations in microbial diversity and compositions and the impacts of quetiapine treatment on microbial *** neural oscillation in hippocampus was positively associated with the abundance of Clostridium *** transplanted with feces from depressed patients presented depression-like *** mRNA expressions of inflammatory cytokines,brain-derived neurotrophic factor,kynurenine aminotransferase Ⅱ,and TRANK1 were detected in mice hippocampus and prefrontal ***3.1(+) vector encoding human TRANK 1 was constructed to explore the impact of TRANK1 overexpression on neuronal morphology,which manifested as decreased dendritic spine density in primary cortical *** findings add new evidences to microbiota-gut-brain regulation in BD,indicating that microbiota is possibly involved in the neuropathogenesis of BD by modulating the expression of TRANK1.

关键词： bipolar disorder gut microbiota quetiapine metagenomics tetratricopeptide repeat and ankyrin repeat containing 1(TRANK1)

来源：评论

学校读者我要写书评

暂无评论

technology Roadmap for Flexible Sensors

引用

ACS NANO 2023年第6期17卷 5211-5295页

作者： Luo, Yifei Abidian, Mohammad Reza Ahn, Jong-Hyun Akinwande, Deji Andrews, Anne M. Antonietti, Markus Bao, Zhenan Berggren, Magnus Berkey, Christopher A. Bettinger, Christopher John Chen, Jun Chen, Peng Cheng, Wenlong Cheng, Xu Choi, Seon-Jin Chortos, Alex Dagdeviren, Canan Dauskardt, Reinhold H. Di, Chong-an Dickey, Michael D. Duan, Xiangfeng Facchetti, Antonio Fan, Zhiyong Fang, Yin Feng, Jianyou Feng, Xue Gao, Huajian Gao, Wei Gong, Xiwen Guo, Chuan Fei Guo, Xiaojun Hartel, Martin C. He, Zihan Ho, John S. Hu, Youfan Huang, Qiyao Huang, Yu Huo, Fengwei Hussain, Muhammad M. Javey, Ali Jeong, Unyong Jiang, Chen Jiang, Xingyu Kang, Jiheong Karnaushenko, Daniil Khademhosseini, Ali Kim, Dae-Hyeong Kim, Il-Doo Kireev, Dmitry Kong, Lingxuan Lee, Chengkuo Lee, Nae-Eung Lee, Pooi See Lee, Tae-Woo Li, Fengyu Li, Jinxing Liang, Cuiyuan Lim, Chwee Teck Lin, Yuanjing Lipomi, Darren J. Liu, Jia Liu, Kai Liu, Nan Liu, Ren Liu, Yuxin Liu, Yuxuan Liu, Zhiyuan Liu, Zhuangjian Loh, Xian Jun Lu, Nanshu Lv, Zhisheng Magdassi, Shlomo Malliaras, George G. Matsuhisa, Naoji Nathan, Arokia Niu, Simiao Pan, Jieming Pang, Changhyun Pei, Qibing Peng, Huisheng Qi, Dianpeng Ren, Huaying Rogers, John A. Rowe, Aaron Schmidt, Oliver G. Sekitani, Tsuyoshi Seo, Dae-Gyo Shen, Guozhen Sheng, Xing Shi, Qiongfeng Someya, Takao Song, Yanlin Stavrinidou, Eleni Su, Meng Sun, Xuemei Takei, Kuniharu Tao, Xiao-Ming Tee, Benjamin C. K. Thean, Aaron Voon-Yew Trung, Tran Quang Wan, Changjin Wang, Huiliang Wang, Joseph Wang, Ming Wang, Sihong Wang, Ting Wang, Zhong Lin Weiss, Paul S. Wen, Hanqi Xu, Sheng Xu, Tailin Yan, Hongping Yan, Xuzhou Yang, Hui Yang, Le Yang, Shuaijian Yin, Lan Yu, Cunjiang Yu, Guihua Yu, Jing Yu, Shu-Hong Yu, Xinge Zamburg, Evgeny Zhang, Haixia Zhang, Xiangyu Zhang, Xiaosheng Zhang, Xueji Zhang, Yihui Zhang, Yu Zhao, Siyuan Zhao, Xuanhe Zheng, Yuanjin Zheng, Yu-Qing Zheng, Zijian Zhou, Tao Zhu, Bowen Zhu, Ming Zhu, Rong Zhu, Yangzhi Zhu, Yong Zou, Guijin Chen, Xiaodong 08-03 Innovis Singapore 138634 Republic of Singapore Innovative Centre for Flexible Devices (iFLEX) School of Materials Science and Engineering Nanyang Technological University Singapore 639798 Singapore Department of Biomedical Engineering University of Houston Houston Texas 77024 United States School of Electrical and Electronic Engineering Yonsei University Seoul 03722 Republic of Korea Department of Electrical and Computer Engineering The University of Texas at Austin Austin Texas 78712 United States Microelectronics Research Center The University of Texas at Austin Austin Texas 78758 United States Department of Chemistry and Biochemistry California NanoSystems Institute and Department of Psychiatry and Biobehavioral Sciences Semel Institute for Neuroscience and Human Behavior and Hatos Center for Neuropharmacology University of California Los Angeles Los Angeles California 90095 United States Colloid Chemistry Department Max Planck Institute of Colloids and Interfaces 14476 Potsdam Germany Department of Chemical Engineering Stanford University Stanford California 94305 United States Laboratory of Organic Electronics Department of Science and Technology Campus Norrköping Linköping University 83 Linköping Sweden Wallenberg Initiative Materials Science for Sustainability (WISE) and Wallenberg Wood Science Center (WWSC) SE-100 44 Stockholm Sweden Department of Materials Science and Engineering Stanford University Stanford California 94301 United States Department of Biomedical Engineering and Department of Materials Science and Engineering Carnegie Mellon University Pittsburgh Pennsylvania 15213 United States Department of Bioengineering University of California Los Angeles Los Angeles California 90095 United States School of Chemistry Chemical Engineering and Biotechnology Nanyang Technological University Singapore 637457 Singapore Nanobionics Group Department of Chemical and Biological Engineering Monash University Clayton Australia 3800 Monash

Humans rely increasingly on sensors to address grand challenges and to improve quality of life in the era of digitalization and big data. For ubiquitous sensing, flexible sensors are developed to overcome the limitations of conventional rigid counterparts. Despite rapid advancement in bench-side research over the last decade, the market adoption of flexible sensors remains limited. To ease and to expedite their deployment, here, we identify bottlenecks hindering the maturation of flexible sensors and propose promising solutions. We first analyze continued...challenges in achieving satisfactory sensing performance for real-world applications and then summarize issues in compatible sensor-biology interfaces, followed by brief discussions on powering and connecting sensor networks. Issues en route to commercialization and for sustainable growth of the sector are also analyzed, highlighting environmental concerns and emphasizing nontechnical issues such as business, regulatory, and ethical considerations. Additionally, we look at future intelligent flexible sensors. In proposing a comprehensive roadmap, we hope to steer research efforts towards common goals and to guide coordinated development strategies from disparate communities. Through such collaborative

关键词： soft materials mechanics engineering flexible electronics conformable sensors bioelectronics human-machine interfaces body area sensor networks technology translation sustainable electronics

来源：评论

学校读者我要写书评

暂无评论

Author Correction: A novel interface for cortical columnar neuromodulation with multipoint infrared neural stimulation

引用

Nature communications 2024年第1期15卷 9742页

作者： Feiyan Tian Ying Zhang Kenneth E Schriver Jia Ming Hu Anna Wang Roe Department of Neurosurgery of the Second Affiliated Hospital Interdisciplinary Institute of Neuroscience and Technology School of Medicine Zhejiang University Hangzhou 310029 China. Key Laboratory for Biomedical Engineering of Ministry of Education College of Biomedical Engineering and Instrument Science Zhejiang University Hangzhou 310027 China. MOE Frontier Science Center for Brain Science and Brain-machine Integration School of Brain Science and Brain Medicine Zhejiang University Hangzhou 310012 China. Department of Neurosurgery of the Second Affiliated Hospital Interdisciplinary Institute of Neuroscience and Technology School of Medicine Zhejiang University Hangzhou 310029 China. hujiaming@***. MOE Frontier Science Center for Brain Science and Brain-machine Integration School of Brain Science and Brain Medicine Zhejiang University Hangzhou 310012 China. hujiaming@***. Department of Neurosurgery of the Second Affiliated Hospital Interdisciplinary Institute of Neuroscience and Technology School of Medicine Zhejiang University Hangzhou 310029 China. annawang@***. Key Laboratory for Biomedical Engineering of Ministry of Education College of Biomedical Engineering and Instrument Science Zhejiang University Hangzhou 310027 China. annawang@***. MOE Frontier Science Center for Brain Science and Brain-machine Integration School of Brain Science and Brain Medicine Zhejiang University Hangzhou 310012 China. annawang@***. National Key Laboratory of Brain and Computer Intelligence Zhejiang University Hangzhou 310058 China. annawang@***.

来源：评论

学校读者我要写书评

暂无评论

SYNAPSE: An international roadmap to large brain imaging

引用

Physics Reports 2023年第1期999卷 1-60页

作者： Stampfl, Anton P.J. Liu, Zhongdong Hu, Jun Sawada, Kei Takano, H. Kohmura, Yoshiki Ishikawa, Tetsuya Lim, Jae-Hong Je, Jung-Ho Low, Chian-Ming Teo, Alvin Tok, Eng Soon Tan, Tin Wee Ban, Kenneth Libedinsky, Camilo Tan, Francis Chee Kuan Chen, Kuan-Peng Yang, An-Cheng Chuang, Chao-Chun Chen, Nan-Yow Shih, Chi-Tin Lee, Ting-Kuo Yang, De-Nian Lai, Hsu-Chao Shuai, Hong-Han Cheng, Chang-Chieh Ching, Yu-Tai Li, Chia-Wei Charng, Ching-Che Lo, Chung-Chuan Chiang, Ann-Shyn Recur, Benoit Petibois, Cyril Cheng, Chia-Liang Chen, Hsiang-Hsin Yang, Shun-Min Hwu, Yeukuang Rojviriya, Catleya Rugmai, Supagorn Rujirawat, Saroj Margaritondo, Giorgio Australian Nuclear Science and Technology Organization Lucas Heights 2234 NSW Australia Henan university of Technology Zhengzhou China Shanghai Institute of Applied Physics Chinese Academy of Science Shanghai China RIKEN SPring-8 Center Hyogo 679-5148 Japan Pohang Accelerator Laboratory Pohang University of Science and Technology Pohang Gyeongbuk 37673 South Korea Department of Materials Science and Engineering Pohang University of Science and Technology Gyeongbuk Pohang 37673 South Korea Department of Pharmacology Department of Anaesthesia and Healthy Longevity Research Programme Yong Loo Lin School of Medicine National University of Singapore Singapore School of Chemical and Life Sciences Nanyang Polytechnic Singapore eMaGIC-Lab Department of Physics National University of Singapore Singapore National Supercomputing Center Singapore Department of Psychology The N1 Institute for Health National University of Singapore Singapore Institute of Molecular and Cell Biology A*STAR Singapore Department of Anaesthesia National University Hospital National University Health System Singapore National Center for High-Performance Computing Hsinchu Taiwan Department of Physics Tunghai University Taichung Taiwan Department of Physics National Tsing Hua University Hsinchu Taiwan Institute of Information Science Academia Sinica Taipei Taiwan Department of Computer Science National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Electronics and Electrical Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan Information Technology Service Center National Yang Ming Chiao Tung University Hsinchu Taiwan National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Life Science National Tsing Hua University Hsinchu Taiwan Institute of Systems Neuroscience and Brain Research Center National Tsing Hua University Hsinchu Taiwan Institute of Physics Academia Sinica Taipei Taiwan Department of Physics National Dong Hwa University

Since 2020, synchrotron radiation facilities in several Asia-Pacific countries have been collaborating in a major project called “SYNAPSE” (Synchrotrons for Neuroscience: an Asia-Pacific Scientific Enterprise). They use x-ray imaging to attack in a coordinated fashion one of the major issues in modern science: the structure of animal and human brains, including neurons and connections. The objective is to develop Google-like maps also including detailed structural and functional information for selected regions of interest. The sheer mass of data needed for the objective poses huge problems for the acquisition, processing, storage and use of images. In order to complete the task within a reasonable time, the key element of the SYNAPSE strategy is the parallel and coordinated work of several facilities on the same specimens. This article reviews different aspects of the enterprise, including the foundations of synchrotron radiation, coherence and of its role in advanced imaging, electron accelerators, x-ray optics and detectors. This will provide the foundation for an extensive presentation of the different components of SYNAPSE, with an overview of results already obtained within the consortium. © 2022

关键词： brain mapping Phase contrast Radiology Supercomputing Synchrotron X-rays

来源：评论

学校读者我要写书评

暂无评论

Mapping global lake dynamics reveals the emerging roles of small lakes (October, 10.1038/s41467-022-33239-3, 2022)

引用

NATURE COMMUNICATIONS 2022年第1期13卷 1-12页

作者： Pi, Xuehui Luo, Qiuqi Feng, Lian Xu, Yang Tang, Jing Liang, Xiuyu Ma, Enze Cheng, Ran Fensholt, Rasmus Brandt, Martin Cai, Xiaobin Gibson, Luke Liu, Junguo Zheng, Chunmiao Li, Weifeng Bryan, Brett A. Southern University of Science and Technology School of Environmental Science and Engineering Shenzhen China (GRID:grid.263817.9) (ISNI:0000 0004 1773 1790) The University of Hong Kong Department of Urban Planning and Design Hong Kong SAR China (GRID:grid.194645.b) (ISNI:***) The University of Hong Kong Urban Systems Institute Hong Kong SAR China (GRID:grid.194645.b) (ISNI:***) Southern University of Science and Technology School of Environmental Science and Engineering Shenzhen China (GRID:grid.263817.9) (ISNI:0000 0004 1773 1790) Southern University of Science and Technology School of Environmental Science and Engineering Shenzhen China (GRID:grid.263817.9) (ISNI:0000 0004 1773 1790) University of Copenhagen Department of Geosciences and Natural Resource Management Copenhagen Denmark (GRID:grid.5254.6) (ISNI:0000 0001 0674 042X) University of Copenhagen Department of Biology Copenhagen Denmark (GRID:grid.5254.6) (ISNI:0000 0001 0674 042X) Lund University Department of Physical Geography and Ecosystem Science Lund Sweden (GRID:grid.4514.4) (ISNI:0000 0001 0930 2361) Southern University of Science and Technology Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Shenzhen China (GRID:grid.263817.9) (ISNI:0000 0004 1773 1790) University of Copenhagen Department of Geosciences and Natural Resource Management Copenhagen Denmark (GRID:grid.5254.6) (ISNI:0000 0001 0674 042X) Chinese Academy of Sciences Institute of Geodesy and Geophysics Wuhan China (GRID:grid.9227.e) (ISNI:***) Southern University of Science and Technology School of Environmental Science and Engineering Shenzhen China (GRID:grid.263817.9) (ISNI:0000 0004 1773 1790) North China University of Water Resources and Electric Power School of Water Conservancy Zhengzhou China (GRID:grid.412224.3) (ISNI:0000 0004 1759 6955) Southern University of Science and Technology School of Environmental Science and Engineering Shenzhen China (GRID:grid.263

Lakes are important natural resources and carbon gas emitters and are undergoing rapid changes worldwide in response to climate change and human activities. A detailed global characterization of lakes and their long-term dynamics does not exist, which is however crucial for evaluating the associated impacts on water availability and carbon emissions. Here, we map 3.4 million lakes on a global scale, including their explicit maximum extents and probability-weighted area changes over the past four decades. From the beginning period (1984–1999) to the end (2010–2019), the lake area increased across all six continents analyzed, with a net change of +46,278 km2, and 56% of the expansion was attributed to reservoirs. Interestingly, although small lakes (<1 km2) accounted for just 15% of the global lake area, they dominated the variability in total lake size in half of the global inland lake regions. The identified lake area increase over time led to higher lacustrine carbon emissions, mostly attributed to small lakes. Our findings illustrate the emerging roles of small lakes in regulating not only local inland water variability, but also the global trends of surface water extent and carbon *** are essential components of the hydrological and biogeochemical cycles. Here, Pi et al develop a global lake dataset called GLAKES via high-resolution satellite images and deep learning to examine global lake changes over four decades.

关键词： Hydrology Limnology

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：