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Franklin Open 2024年 9卷

作者： Xingyue Liu Kaibo Shi Kun Zhou Shiping Wen Yiqian Tang Lin Tang School of Electronic Information and Electrical Engineering Chengdu University Chengdu 610106 PR China Key Laboratory of Intelligent Manufacturing Quality Big Data Tracing and Analysis of Zhejiang Province China Jiliang University Hangzhou 310018 PR China Engineering Research Center of Power Quality of Ministry of Education Anhui University Hefei 230601 PR China Institute of Electronic and Information Engineering of University of Electronic Science and Technology of China in Guangdong 523808 PR China Australian AI Institute Faculty of Engineering and Information Technology University of Technology Sydney Sydney NSW 2007 Australia

Distributed Computation Offloading and Power Control for UAV-Enabled Internet of Medical Things

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ACM Transactions on Internet Technology 1000年

作者： Jiakun Gao Xiaolong Xu Lianyong Qi Wanchun Dou Xiaoyu Xia Xiaokang Zhou School of Software Nanjing University of Information Science and Technology NanJing China School of Software Nanjing University of Information Science and Technology NanJing China and Jiangsu Province Engineering Research Center of Advanced Computing and Intelligent Services Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET) Nanjing University of Information Science and Technology Nanjing China College of Computer Science and Technology China University of Petroleum (East China) QingDao China and State Key Laboratory for Novel Software Technology Nanjing University Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China School of Computer Technology RMIT University Victoria Australia Faculty of Data Science Shiga University Hikone Japan

The advancement of the Internet of Medical Things (IoMT) has led to the emergence of various health and emotion care services, e.g., health monitoring. To cater to increasing computational requirements of IoMT services, Mobile Edge computing (MEC) has emerged as an indispensable technology in smart health. Benefiting from the cost-effectiveness of deployment, unmanned aerial vehicles (UAVs) equipped with MEC servers in Non-Orthogonal Multiple Access (NOMA) have emerged as a promising solution for providing smart health services in proximity to medical devices (MDs). However, the escalating number of MDs and the limited availability of communication resources of UAVs give rise to a significant increase in transmission latency. Moreover, due to the limited communication range of UAVs, the geographically-distributed MDs lead to workload imbalance of UAVs, which deteriorates the service response delay. To this end, this paper proposes a UAV-enabled Distributed computation Offloading and Power control method with Multi-Agent, named DOPMA, for NOMA-based IoMT environment. Specifically, this paper introduces computation and transmission queue models to analyze the dynamic characteristics of task execution latency and energy consumption. Moreover, a credit assignment scheme-based reward function is designed considering both system-level rewards and rewards tailored to each MD, and an improved multi-agent deep deterministic policy gradient algorithm is developed to derive offloading and power control decisions independently. Extensive simulations demonstrate that the proposed method outperforms existing schemes, achieving \(7.1\%\) reduction in energy consumption and \(16\%\) decrease in average delay.

关键词： Internet of Medical Things (IoMT) Mobile Edge computing (MEC) Unmanned Aerial Vehicles (UAV) Non-Orthogonal Multiple Access (NOMA)

Comparative analysis of SIMILAR to RCD ONE(SRO) family from tetraploid cotton species and their diploid progenitors depict their significance in cotton growth and development

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Journal of Cotton Research 2024年第4期 438页

作者： SHABAN Muhammad TABASSUM Riaz RANA Iqrar Ahmad ATIF Rana Muhammad AZMAT Muhammad Abubakkar IQBAL Zubair MAJEED Sajid AZHAR Muhammad Tehseen Department of Plant Breeding and Genetics University of Agriculture Faisalabad Sub-Campus Burewala Department of Chemistry and Biochemistry University of AgricultureFaisalabad Centre of Agricultural Biochemistry and Biotechnology University of AgricultureFaisalabad Department of Plant Breeding and Genetics University of AgricultureFaisalabad Precision Agriculture and Analytics Lab National Centre in Big Data and Cloud Computing Centre for Advanced Studies in Agriculture and Food Security University of Agriculture Faisalabad Department of Plant Pathology University of California State Key Laboratory of Cotton Biology Institute of Cotton ResearchChinese Academy of Agricultural Sciences Federal Seed Certification and Registration Department Ministry of National Food Security and Research School of Agriculture Sciences Zhengzhou University

Background SRO(Similar to RCD1) genes family is largely recognized for their importance in the growth,development,and in responding to environmental ***,genome-wide identification and functional characterization of SRO genes from cotton species have not been reported so *** A total of 36 SRO genes were identified from four cotton *** analysis divided these genes into three groups with distinct *** and chromosomal distribution analysis indicated uneven distribution of GaSRO,GrSRO,GhSRO,and GbSRO genes on A2,D5 genomes,Gh-At,Gh-Dt,Gb-At,and Gb-Dt subgenomes,*** duplication analysis revealed the presence of six duplicated gene pairs among GhSRO *** promoter analysis,several elements responsive to the growth,development and hormones were found in GhSRO genes,implying gene induction during cotton growth and *** miRNAs responsive to plant growth and abiotic stress were predicted to target12 GhSRO ***-specific expression profiling demonstrated the roles of GhSRO genes in one or more *** addition,specific expression pattern of some GhSRO genes during ovule development depicted their involvement in these developmental *** The data presented in this report laid a foundation for understanding the classification and functions of SRO genes in cotton.

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Insight-HXMT and GECAM-C observations of the brightest-of-all-time GRB 221009A

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arXiv 2023年

作者： An, Zheng-Hua Antier, S. Bi, Xing-Zi Bu, Qing-Cui Cai, Ce Cao, Xue-Lei Camisasca, Anna-Elisa Chang, Zhi Chen, Gang Chen, Li Chen, Tian-Xiang Chen, Wen Chen, Yi-Bao Chen, Yong Chen, Yu-Peng Coughlin, Michael W. Cui, Wei-Wei Dai, Zi-Gao Hussenot-Desenonges, T. Du, Yan-Qi Du, Yuan-Yuan Du, Yun-Fei Fan, Cheng-Cheng Frontera, Filippo Gao, He Gao, Min Ge, Ming-Yu Gong, Ke Gu, Yu-Dong Guan, Ju Guo, Dong-Ya Guo, Zhi-Wei Guidorzi, Cristiano Han, Da-Wei He, Jian-Jian He, Jun-Wang Hou, Dong-Jie Huang, Yue Huo, Jia Ji, Zhen Jia, Shu-Mei Jiang, Wei-Chun Kann, David Alexander Klotz, A. Kong, Ling-Da Lan, Lin Li, An Li, Bing Li, Chao-Yang Li, Cheng-Kui Li, Gang Li, Mao-Shun Li, Ti-Pei Li, Wei Li, Xiao-Bo Li, Xin-Qiao Li, Xu-Fang Li, Yan-Guo Li, Zheng-Wei Liang, Jing Liang, Xiao-Hua Liao, Jin-Yuan Lin, Lin Liu, Cong-Zhan Liu, He-Xin Liu, Hong-Wei Liu, Jia-Cong Liu, Xiao-Jing Liu, Ya-Qing Liu, Yu-Rong Lu, Fang-Jun Lu, Hong Lu, Xue-Feng Luo, Qi Luo, Tao Ma, Bin-Yuan Ma, Fu-Li Ma, Rui-Can Ma, Xiang Maccary, Romain Mao, Ji-Rong Meng, Bin Nie, Jian-Yin Orlandini, Mauro Ou, Ge Peng, Jing-Qiang Peng, Wen-Xi Qiao, Rui Qu, Jin-Lu Ren, Xiao-Qin Shi, Jing-Yan Shi, Qi Song, Li-Ming Song, Xin-Ying Su, Ju Sun, Gong-Xing Sun, Liang Sun, Xi-Lei Tan, Wen-Jun Tan, Ying Tao, Lian Tuo, You-Li Turpin, Damien Wang, Jin-Zhou Wang, Chen Wang, Chen-Wei Wang, Hong-Jun Wang, Hui Wang, Jin Wang, Ling-Jun Wang, Peng-Ju Wang, Ping Wang, Wen-Shuai Wang, Xiang-Yu Wang, Xi-Lu Wang, Yu-Sa Wang, Yue Wen, Xiang-Yang Wu, Bo-Bing Wu, Bai-Yang Wu, Hong Xiao, Sheng-Hui Xiao, Shuo Xiao, Yun-Xiang Xie, Sheng-Lun Xiong, Shao-Lin Xiong, Sen-Lin Xu, Dong Xu, He Xu, Yan-Jun Xu, Yan-Bing Xu, Ying-Chen Xu, Yu-Peng Xue, Wang-Chen Yang, Sheng Yang, Yan-Ji Yang, Zi-Xu Ye, Wen-Tao Yi, Qi-Bin Yi, Shu-Xu Yin, Qian-Qing You, Yuan Yu, Yun-Wei Yu, Wei Yu, Wen-Hui Zeng, Ming Zhang, Bing Zhang, Bin-Bin Zhang, Da-Li Zhang, Fan Zhang, Hong-Mei Zhang, Juan Key Laboratory of Particle Astrophysics Institute of High Energy Physics Chinese Academy of Sciences 19B Yuquan Road Beijing100049 China Institut für Astronomie und Astrophysik Kepler Center for Astro and Particle Physics Eberhard Karls Universität Sand 1 Tübingen72076 Germany College of Physics Hebei Key Laboratory of Photophysics Research and Application Hebei Normal University Hebei Shijiazhuang050024 China Department of Physics and Earth Science University of Ferrara Via Saragat 1 Ferrara44122 Italy Department of Astronomy Beijing Normal University Beijing100875 China CAS Key Laboratory for Research in Galaxies and Cosmology Department of Astronomy University of Science and Technology of China Hefei230026 China Department of Astronomy Tsinghua University Beijing100084 China Southwest Jiaotong University Chengdu610092 China INAF Osservatorio di Astrofisica e Scienza dello Spazio di Bologna Via Piero Gobetti 101 Bologna40129 Italy University of Chinese Academy of Sciences Chinese Academy of Sciences Beijing100049 China College of physics Sciences Technology Hebei University No. 180 Wusi Dong Road Lian Chi District Hebei Baoding071002 China INFN - Sezione di Ferrara Via Saragat 1 Ferrara44122 Italy Physics and Space Science College China West Normal University China Yunnan Observatories Chinese Academy of Sciences Yunnan Province Kunming650011 China Key Laboratory of Space Astronomy and Technology National Astronomical Observatories Chinese Academy of Sciences Beijing100012 China School of Astronomy and Space Science Nanjing University Nanjing210093 China School of Physics and Optoelectronics Xiangtan University Yuhu District Hunan Xiangtan411105 China Guizhou Provincial Key Laboratory of Radio Astronomy and Data Processing Guizhou Normal University Guiyang550001 China Institute of Astrophysics Central China Normal University Wuhan430079 China Nevada Center for Astrophysics Department of Physics and Astronomy University of

GRB 221009A is the brightest gamma-ray burst ever detected since the discovery of this kind of energetic explosions. However, an accurate measurement of the prompt emission properties of this burst is very challenging due to its exceptional brightness. With joint observations of Insight-HXMT and GECAM-C, we made an unprecedentedly accurate measurement of the emission during the first ∼1800 s of GRB 221009A, including its precursor, main emission (ME, which dominates the burst in flux), flaring emission and early afterglow, in the hard X-ray to soft gamma-ray band from ∼ 10 keV to ∼ 6 MeV. Based on the GECAM-C unsaturated data of the ME, we measure a record-breaking isotropic equivalent energy (Eiso) of ∼ 1.5 × 1055 erg, which is about eight times the total rest-mass energy of the Sun. The early afterglow data require a significant jet break between 650 s and 1100 s, most likely at ∼ 950 s from the afterglow starting time TAG, which corresponds to a jet opening angle of ∼ 0.7◦ (ηγn)1/8, where n is the ambient medium density in units of cm−3 and ηγ is the ratio between γ-ray energy and afterglow kinetic energy. The beaming-corrected total γ-ray energy Eγ is ∼ 1.15 × 1051 (ηγn)1/4 erg, which is typical for long GRBs. These results suggest that this GRB may have a special central engine, which could launch and collimate a very narrowly beamed jet with an ordinary energy budget, leading to exceptionally luminous gamma-ray radiation per unit solid angle. Alternatively, more GRBs might have such a narrow and bright beam, which are missed by an unfavorable viewing angle or have been detected without distance measurement. Copyright © 2023, The Authors. All rights reserved.

关键词： Gamma rays

The chemical evolution of the solar neighbourhood for planet-hosting stars

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arXiv 2023年

作者： Pignatari, Marco Trueman, Thomas C.L. Womack, Kate A. Gibson, Brad K. Côté, Benoit Turrini, Diego Sneden, Christopher Mojzsis, Stephen J. Stancliffe, Richard J. Fong, Paul Lawson, Thomas V. Keegans, James D. Pilkington, Kate Passy, Jean-Claude Beers, Timothy C. Lugaro, Maria ELKH Konkoly Thege M. út 15–17 BudapestH-1121 Hungary CSFK MTA Centre of Excellence Konkoly Thege Miklós út 15-17. BudapestH-1121 Hungary E. A. Milne Centre for Astrophysics Department of Physics and Mathematics University of Hull HU6 7RX United Kingdom INAF - Turin Astrophysical Observatory Via Osservatorio 20 Pino Torinese10025 Italy INAF - Institute of Space Astrophysics and Planetology Via Fosso del Cavaliere 100 Rome00133 Italy ICSC National Research Centre for High Performance Computing Big Data and Quantum Computing Via Magnanelli 2 Casalecchio di Reno40033 Italy Department of Astronomy and McDonald Observatory The University of Texas AustinTX78712 United States NuGrid Collaboration Joint Institute for Nuclear Astrophysics Center for the Evolution of the Elements United States Department of Physics and Astronomy University of Victoria VictoriaBCV8P 5C2 Canada Pázmány Péter sétány 1/c BudapestH-1117 Hungary H.H. Wills Physics Laboratory University of Bristol Tyndall Avenue BristolBS8 1TL United Kingdom Centre of Excellence for Data Science Artificial Intelligence and Modelling University of Hull HU6 7RX United Kingdom Astrophysics Group Lennard-Jones Laboratories Keele University KeeleST5 5BG United Kingdom School of Physical Sciences The Open University Milton KeynesMK7 6AA United Kingdom Max Planck Institute for Intelligent Systems Tübingen Germany Department of Physics and Astronomy University of Notre Dame Notre DameIN46556 United States ELTE Eötvös Loránd University Institute of Physics Pázmány Péter sétány 1/A Budapest1117 Hungary School of Physics and Astronomy Monash University VIC3800 Australia

Theoretical physical-chemical models for the formation of planetary systems depend on data quality for the Sun’s composition, that of stars in the solar neighbourhood, and of the estimated "pristine" compositions for stellar systems. The effective scatter and the observational uncertainties of elements within a few hundred parsecs from the Sun, even for the most abundant metals like carbon, oxygen and silicon, are still controversial. Here we analyse the stellar production and the chemical evolution of key elements that underpin the formation of rocky (C, O, Mg, Si) and gas/ice giant planets (C, N, O, S). We calculate 198 galactic chemical evolution (GCE) models of the solar neighbourhood to analyse the impact of different sets of stellar yields, of the upper mass limit for massive stars contributing to GCE (Mup) and of supernovae from massive-star progenitors which do not eject the bulk of the iron-peak elements (faint supernovae). Even considering the GCE variation produced via different sets of stellar yields, the observed dispersion of elements reported for stars in the Milky Way disk is not reproduced. Among others, the observed range of super-solar [Mg/Si] ratios, sub-solar [S/N], and the dispersion of up to 0.5 dex for [S/Si] challenge our models. The impact of varying Mup depends on the adopted supernova yields. Thus, observations do not provide a constraint on the Mup parametrization. When including the impact of faint supernova models in GCE calculations, elemental ratios vary by up to 0.1-0.2 dex in the Milky Way disk;this modification better reproduces observations. Copyright © 2023, The Authors. All rights reserved.

关键词： Dispersions

Corrigendum to “Numerical analysis of the magnetic field impact on hydrothermal characteristics of a microchannel heatsink with Fe 3 O 4 ferrofluid and various pin-fin shapes” [J. Magn. Magn. Mater. 585 (2023) 171102]

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Journal of Magnetism and Magnetic Materials 2024年 610卷

作者： Tao Hai Kamal Sharma Riyam K. Marjan Babak Farhang Mohamed H. Mahmoud Hassan Fouad Walid El-Shafai School of Computer and Information Qiannan Normal University for Nationalities Duyun Guizhou 558000 China School of Electronics and Information Engineering Ankang University China State Key Laboratory of Public Big Data Guizhou University Guizhou Guiyang 550025 China Institute of Engineering and Technology GLA University Mathura UP 281406 India Department of Intelligent Medical Systems Al-Mustaqbal University College Babylon 51001 Iraq Department of Energy Technology Aalborg University Denmark Department of Biochemistry College of Science King Saud University Riyadh Saudi Arabia Biomedical Engineering Department Faculty of Engineering Helwan University Cairo P.O. Box 11795 Egypt Department Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf 32952 Egypt

Correction: RPTOR blockade suppresses brain metastases of NSCLC by interfering the ceramide metabolism via hijacking YY1 binding

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Journal of experimental & clinical cancer research : CR 2024年第1期43卷 37页

作者： Ying Lin Yun Wu Qiangzu Zhang Xunwei Tu Sufang Chen Junfan Pan Nengluan Xu Ming Lin Peiwei She Gang Niu Yusheng Chen Hongru Li Department of Respiratory and Critical Care Medicine Shengli Clinical Medical College Fujian Medical University Fujian Provincial Hospital Fuzhou 350001 Fujian China. Department of General Practice Medicine Fujian Provincial Hospital Fuzhou 350001 China. The High Performance Computing Research Center Institute of Computing Technology Chinese Academy of Sciences Beijing 100095 China. The Centre for Experimental Research in Clinical Medicine Fujian Provincial Hospital Fuzhou 350001 Fujian China. The High Performance Computing Research Center Institute of Computing Technology Chinese Academy of Sciences Beijing 100095 China. g.niu@***. Department of Respiratory and Critical Care Medicine Shengli Clinical Medical College Fujian Medical University Fujian Provincial Hospital Fuzhou 350001 Fujian China. cysktz@***. Department of Respiratory and Critical Care Medicine Shengli Clinical Medical College Fujian Medical University Fujian Provincial Hospital Fuzhou 350001 Fujian China. muzi131122@***. Fujian Provincial Key Laboratory of Medical Big Data Engineering Fujian Provincial Hospital Shengli Clinical College of Fujian Medical University Fuzhou 350001 Fujian China. muzi131122@***.

CMRxRecon2024: A Multi-Modality, Multi-View K-Space dataset Boosting Universal Machine Learning for Accelerated Cardiac MRI

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arXiv 2024年

作者： Wang, Zi Wang, Fanwen Qin, Chen Lyu, Jun Ouyang, Cheng Wang, Shuo Li, Yan Yu, Mengyao Zhang, Haoyu Guo, Kunyuan Shi, Zhang Li, Qirong Xu, Ziqiang Zhang, Yajing Li, Hao Hua, Sha Chen, Binghua Sun, Longyu Sun, Mengting Li, Qin Chu, Ying-Hua Bai, Wenjia Qin, Jing Zhuang, Xiahai Prieto, Claudia Young, Alistair Markl, Michael Wang, He Wu, Lianming Yang, Guang Qu, Xiaobo Wang, Chengyan Department of Electronic Science Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance National Institute for Data Science in Health and Medicine Xiamen University Xiamen China Department of Bioengineering and Imperial-X Imperial College London London United Kingdom Cardiovascular Research Centre Royal Brompton Hospital London United Kingdom Department of Electrical and Electronic Engineering & Imperial-X Imperial College London London United Kingdom Psychiatry Neuroimaging Laboratory Brigham and Women’s Hospital Harvard Medical School Boston United States Department of Computing Department of Brain Sciences Imperial College London London United Kingdom Digital Medical Research Center School of Basic Medical Sciences Fudan University Shanghai China Department of Radiology Ruijin Hospital Shanghai Jiao Tong University School of Medicine Shanghai China Human Phenome Institute Fudan University Shanghai China Department of Radiology Zhongshan Hospital Fudan University Shanghai China School of Health Science and Engineering University of Shanghai for Science and Technology Shanghai China GE Healthcare Beijing China Institute of Science and Technology for Brain-Inspired Intelligence Fudan University Shanghai China Department of Cardiovascular Medicine Ruijin Hospital Lu Wan Branch Shanghai Jiao Tong University School of Medicine Shanghai China Department of Radiology Ren Ji Hospital School of Medicine Shanghai Jiao Tong University Shanghai China Siemens Healthineers Ltd. Shanghai China School of Nursing The Hong Kong Polytechnic University Hong Kong School of Data Science Fudan University Shanghai China School of Engineering Pontificia Universidad Católica de Chile Santiago Chile School of Biomedical Engineering and Imaging Sciences King’s College London London United Kingdom Millenium Institute for Intelligent Healthcare Engineering Santiago Chile Department of Radiology Feinberg School of Medicine Northwestern University Chicago Unit

The released CMRxRecon2024 dataset is currently the largest and most protocol-diverse publicly available k-space dataset including multi-modality and multi-view cardiac MRI data from 330 healthy volunteers, and each o... 详细信息

关键词： Adversarial machine learning

Harmonized-Multinational qEEG norms (HarMNqEEG)

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NEUROIMAGE 2022年 256卷 119190-119190页

作者： Li, Min Wang, Ying Lopez-Naranjo, Carlos Hu, Shiang Reyes, Ronaldo Cesar Garcia Paz-Linares, Deirel Areces-Gonzalez, Ariosky Hamid, Aini Ismafairus Abd Evans, Alan C. Savostyanov, Alexander N. Calzada-Reyes, Ana Villringer, Arno Tobon-Quintero, Carlos A. Garcia-Agustin, Daysi Yao, Dezhong Dong, Li Aubert-Vazquez, Eduardo Reza, Faruque Razzaq, Fuleah Abdul Omar, Hazim Abdullah, Jafri Malin Galler, Janina R. Ochoa-Gomez, John F. Prichep, Leslie S. Galan-Garcia, Lidice Morales-Chacon, Lilia Valdes-Sosa, Mitchell J. Trondle, Marius Zulkifly, Mohd Faizal Mohd Rahman, Muhammad Riddha Bin Abdul Milakhina, Natalya S. Langer, Nicolas Rudych, Pavel Koenig, Thomas Virues-Alba, Trinidad A. Lei, Xu Bringas-Vega, Maria L. Bosch-Bayard, Jorge F. Valdes-Sosa, Pedro Antonio [a]The Clinical Hospital of Chengdu Brain Science Institute MOE Key Lab for Neuroinformation School of Life Science and Technology University of Electronic Science and Technology of China Chengdu China [b]Cuban Center for Neurocience La Habana Cuba [c]McGill Centre for Integrative Neuroscience Ludmer Centre for Neuroinformatics and Mental Health Montreal Neurological Institute Canada [d]Department of Neurosciences School of Medical Sciences Universiti Sains Malaysia Universiti Sains Malaysia Health Campus Kota Bharu Kelantan 16150 Malaysia [e]Brain and Behaviour Cluster School of Medical Sciences Universiti Sains Malaysia Health Campus Kota Bharu Kelantan 16150 Malaysia [f]Hospital Universiti Sains Malaysia Universiti Sains Malaysia Health Campus Kota Bharu Kelantan 16150 Malaysia [g]Humanitarian Institute Novosibirsk State University Novosibirsk 630090 Russia [h]Laboratory of Psychophysiology of Individual Differences Federal State Budgetary Scientific Institution Scientific Research Institute of Neurosciences and Medicine Novosibirsk 630117 Russia [i]Laboratory of Psychological Genetics at the Institute of Cytology and Genetics Siberian Branch of the Russian Academy of Sciences Novosibirsk 630090 Russia [j]University of Pinar del Río “Hermanos Saiz Montes de Oca” Pinar del Río Cuba [k]Department of Neurology Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany [l]Department of Cognitive Neurology University Hospital Leipzig Leipzig Germany [m]Center for Stroke Research Charité-Universitätsmedizin Berlin Berlin Germany [n]Grupo Neuropsicología y Conducta - GRUNECO Faculty of Medicine Universidad de Antioquia Colombia [o]Research Department Institución Prestadora de Servicios de Salud IPS Universitaria Colombia [p]The Cuban center aging longevity and health Havana Cuba [q]Research Unit of NeuroInformation Chinese Academy of Medical Sciences Chengdu 2019RU035 China [r]School of Electrical Engineering Zhengzhou University Zhengzhou 4500

This paper extends frequency domain quantitative electroencephalography (qEEG) methods pursuing higher sensitivity to detect Brain Developmental Disorders. Prior qEEG work lacked integration of cross-spectral information omitting important functional connectivity descriptors. Lack of geographical diversity precluded accounting for site-specific variance, increasing qEEG nuisance variance. We ameliorate these weaknesses. (i) Create lifespan Riemannian multinational qEEG norms for cross-spectral tensors. These norms result from the HarMNqEEG project fostered by the Global Brain Consortium. We calculate the norms with data from 9 countries, 12 devices, and 14 studies, including 1564 subjects. Instead of raw data, only anonymized metadata and EEG cross spectral tensors were shared. After visual and automatic quality control, developmental equations for the mean and standard deviation of qEEG traditional and Riemannian DPs were calculated using additive mixed-effects models. We demonstrate qEEG "batch effects " and provide methods to calculate harmonized z-scores. (ii) We also show that harmonized Riemannian norms produce z-scores with increased diagnostic accuracy predicting brain dysfunction produced by malnutrition in the first year of life and detecting COVID induced brain dysfunction. (iii) We offer open code and data to calculate different individual z-scores from the HarMNqEEG dataset. These results contribute to developing bias-free, low-cost neuroimaging technologies applicable in various health settings.

关键词： Quantitative EEG EEG cross-spectrum Riemannian geometry Batch effects Z-score Harmonization Malnutrition Covid induced brain dysfunction Developmental Brain Chart

Radio detection of an elusive millisecond pulsar in the Globular Cluster NGC 6397

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arXiv 2022年

作者： Zhang, Lei Ridolfi, Alessandro Blumer, Harsha Freire, Paulo C.C. Manchester, Richard N. McLaughlin, Maura Kremer, Kyle Cameron, Andrew D. Zhang, Zhiyu Behrend, Jan Burgay, Marta Buchner, Sarah Champion, David J. Chen, Weiwei Dai, Shi Feng, Yi Fu, Xiaoting Guo, Meng Hobbs, George Keane, Evan F. Kramer, Michael Levin, Lina Li, Xiangdong Ni, Mengmeng Pan, Jingshan Padmanabh, Prajwal V. Possenti, Andrea Ransom, Scott M. Tsai, Chao-Wei Krishnan, Vivek Venkatraman Wang, Pei Zhang, Jie Zhi, Qijun Zhang, Yongkun Li, Di National Astronomical Observatories Chinese Academy of Sciences A20 Datun Road Chaoyang District Beijing100101 China Centre for Astrophysics and Supercomputing Swinburne University of Technology P.O. Box 218 HawthornVIC3122 Australia SelargiusI-09047 Italy Department of Physics and Astronomy West Virginia University MorgantownWV26506 United States Max-Planck Institut für Radioastronomie Auf dem Hügel 69 BonnD-53121 Germany CSIRO Space and Astronomy Australia Telescope National Facility PO Box 76 EppingNSW1710 Australia TAPIR California Institute of Technology PasadenaCA91125 United States The Observatories of the Carnegie Institution for Science PasadenaCA91101 United States Swinburne University of Technology Mail H74 PO Box 218 VIC3122 Australia School of Astronomy and Space Science Nanjing University Nanjing210023 China Ministry of Education Nanjing210093 China South African Radio Astronomy Observatory Fir Street Black River Park Cape Town7925 South Africa School of Science Western Sydney University Locked Bag 1797 PenrithNSW2751 Australia Research Center for Intelligent Computing Platforms Zhejiang Laboratory Hangzhou311100 China Purple Mountain Observatory Chinese Academy of Sciences Nanjing210023 China National Supercomputing Center in Jinan Qilu University of Technology 28666 East Jingshi Road Licheng Distrist Jinan250103 China Jinan Institute of Supercomputing Technology 28666 East Jingshi Road Licheng Distrist Jinan250103 China School of Physics Trinity College Dublin College Green Dublin Ireland Jodrell Bank Centre for Astrophysics Department of Physics and Astronomy The University of Manchester ManchesterM13 9PL United Kingdom HannoverD-30167 Germany MonserratoI-09042 Italy NRAO 520 Edgemont Road CharlottesvilleVA22903 United States College of Physics and Electronic Engineering Qilu Normal University 2 Wenbo Road Zhangqiu District Jinan250200 China Guizhou Provincial Key Laboratory of Radio

We report the discovery of a new 5.78 ms-period millisecond pulsar (MSP), PSR J1740−5340B (NGC 6397B), in an eclipsing binary system discovered with the Parkes radio telescope (now also known as Murriyanga), Australia, and confirmed with the MeerKAT radio telescope in South Africa. The measured orbital period, 1.97 days, is the longest among all eclipsing binaries in globular clusters (GCs) and consistent with that of the coincident X-ray source U18, previously suggested to be a ‘hidden MSP’. Our XMM-Newton observations during NGC 6397B’s radio quiescent epochs detected no X-ray flares. NGC 6397B is either a transitional MSP or an eclipsing binary in its initial stage of mass transfer after the companion star left the main sequence. The discovery of NGC 6397B potentially reveals a subgroup of extremely faint and heavily obscured binary pulsars, thus providing a plausible explanation to the apparent dearth of binary neutron stars in core-collapsed GCs as well as a critical constraint on the evolution of GCs. Copyright © 2022, The Authors. All rights reserved.

关键词： Mass transfer