|
|
|
题名
|
作者
|
年代
|
出处
|
被引量
|
| 1 | Single-cell analysis reveals bronchoalveolar epithelial dysfunction in COVID-19 patients显示文摘Dear Editor,In 2019,a zoonotic coronavirus named severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)was identified as the causative agent of Coronavirus Disease 2019(COVID-19).As of 8 June 2020,the World Health Organization(WHO)has reported 6,912,751 globally confirmed cases with 400,469 deaths.Although generally causes mild disease,SARS-CoV-2 infection can result in serious outcomes,including acute lung injury(ALI)and acute respiratory distress syndrome(ARDS),the leading cause of mortality in patients with comorbidities.Recent autopsy studies of COVID-19 patients revealed mononuclear infiltration and excessive production of mucus in the infected lung,especially in the damaged small airways and alveoli(Bian and Team,2020;Liu et al.,2020). | Jiangping He Shuijiang Cai Huijian Feng Baomei Cai Lihui Lin Yuanbang Mai Yinqiang Fan Airu Zhu Huang Huang Junjie Shi Dingxin Li' Yuanjie Wei Yueping Li Yingying Zhao’ Yuejun Pan He Liu Xiaoneng Mo Xi He Shangtao Cao FengYu Hu Jincun Zhao Jie Wang Nanshan Zhong Xinwen Chen Xilong Deng Jiekai Chen | 2020 | Protein & Cell2020，11，9： | 7 |
| 2 | Applying deep neural networks to the detection and space parameter estimation of compact binary coalescence with a network of gravitational wave detectors显示文摘In this paper, we study an application of deep learning to the advanced laser interferometer gravitational wave observatory(LIGO)and advanced Virgo coincident detection of gravitational waves(GWs) from compact binary star mergers. This deep learning method is an extension of the Deep Filtering method used by George and Huerta(2017) for multi-inputs of network detectors.Simulated coincident time series data sets in advanced LIGO and advanced Virgo detectors are analyzed for estimating source luminosity distance and sky location. As a classifier, our deep neural network(DNN) can effectively recognize the presence of GW signals when the optimal signal-to-noise ratio(SNR) of network detectors ≥ 9. As a predictor, it can also effectively estimate the corresponding source space parameters, including the luminosity distance D, right ascension α, and declination δ of the compact binary star mergers. When the SNR of the network detectors is greater than 8, their relative errors are all less than 23%.Our results demonstrate that Deep Filtering can process coincident GW time series inputs and perform effective classification and multiple space parameter estimation. Furthermore, we compare the results obtained from one, two, and three network detectors;these results reveal that a larger number of network detectors results in a better source location. | XiLong Fan Jin Li Xin Li YuanHong Zhong JunWei Cao | 2019 | Science China(Physics,Mechanics & Astronomy)2019，62，6： | 1 |
| 3 | The detection of gravitational waves and the new era of multi-messenger astronomy显示文摘For the first time,gravitational waves(GWs),a major prediction of Einstein’s 1915 general theory of relativity(GR),has been detected directly by the two detectors of the Laser Interferometer Gravitational-Wave Observatory(LIGO)[1, | XiLong Fan | 2016 | Science China(Physics,Mechanics & Astronomy)2016，59，4： | 1 |
| 4 | Understanding the Predication Mechanism of Deep Learning through Error Propagation among Parameters in Strong Lensing Case显示文摘The error propagation among estimated parameters reflects the correlation among the parameters.We study the capability of machine learning of'learning'the correlation of estimated parameters.We show that machine learning can recover the relation between the uncertainties of different parameters,especially,as predicted by the error propagation formula.Gravitational lensing can be used to probe both astrophysics and cosmology.As a practical application,we show that the machine learning is able to intelligently find the error propagation among the gravitational lens parameters(effective lens mass ML and Einstein radiusθ_(E))in accordance with the theoretical formula for the singular isothermal ellipse(SIE)lens model.The relation of errors of lens mass and Einstein radius,(e.g.,the ratio of standard deviations F=σ_(ML)/σ_(θ_(E)))predicted by the deep convolution neural network are consistent with the error propagation formula of the SIE lens model.As a proof-of-principle test,a toy model of linear relation with Gaussian noise is presented.We found that the predictions obtained by machine learning indeed indicate the information about the law of error propagation and the distribution of noise.Error propagation plays a crucial role in identifying the physical relation among parameters,rather than a coincidence relation,therefore we anticipate our case study on the error propagation of machine learning predictions could extend to other physical systems on searching the correlation among parameters. | Xilong Fan Peizheng Wang Jin Li Nan Yang | 2023 | Research in Astronomy and Astrophysics2023，23，12： | 0 |
| 5 | COVID-19 induces new-onset insulin resistance and lipid metabolic dysregulation via regulation of secreted metabolic factors显示文摘Abnormal glucose and lipid metabolism in COVID-19 patients were recently reported with unclear mechanism.In this study,we retrospectively investigated a cohort of COVID-19 patients without pre-existing metabolic-related diseases,and found new-onset in suli n resista nee,hyperglycemia,and decreased HDL-C in these patie nts.Mecha nistically,SARS-CoV-2 infecti on in creased the expression of RE1-silencing transcription factor(REST),which modulated the expression of secreted metabolic factors including myeloperoxidase,apelin,and myostatin at the transcriptional level,resulting in the perturbation of glucose and lipid metabolism.Furthermore,several lipids,including(±)5-HETE,(±)12-HETE,propionic acid,and isobutyric acid were identified as the potential biomarkers of COVID-19-induced metabolic dysregulation,especially in insulin resistance.Taken together,our study revealed insulin resistance as the direct cause of hyperglycemia upon COVID-19,and further illustrated the underlying mechanisms,providing potential therapeutic targets for COVID-19-induced metabolic complications. | Xi He Chenshu Liu Jiangyun Peng Zilun Li Fang Li Jian Wang Ao Hu Meixiu Peng Kan Huang Dongxiao Fan Na Li Fuchun Zhang Weiping Cai Xinghua Tan Zhongwei Hu Xilong Deng Yueping Li Xiaoneng Mo Linghua Li Yaling Shi Li Yang Yuanyuan Zhu Yanrong Wu Huichao Liang Baolin Liao Wenxin Hong Ruiying He Jiaojiao Li Pengle Guo Youguang Zhuo Lingzhai Zhao Fengyu Hu Wenxue Li Wei Zhu Zefeng Zhang Zeling Guo Wei Zhang Xiqiang Hong Wei kang Cai Lei Gu Ziming Du Yang Zhang Jin Xu Tao Zuo Kai Deng Li Yan Xinwen Chen Sifan Chen Chunliang Lei | 2022 | Signal Transduction and Targeted Therapy2022，7，1： | 0 |
| 6 | The first confirmed gravitational wave detection in LIGO's second observational run显示文摘In this article,we describe the results concerning the third coincident signal GW170104 from the coalescence of binary black holes(BBHs)during the second observation run(O2).The result was obtained from the LIGO Scientific Collaboration and the Virgo Collaboration.Following the first and second gravitational waves(GWs)detections in the first observation run(O1)[1],recently LIGO has observed a third coincident signal GW170104 from the coalescence of | Jin Li XiLong Fan | 2017 | Science China(Physics,Mechanics & Astronomy)2017，60，12： | 0 |
| 7 | Probing the Large-scale Structure of the Universe Through Gravitational Wave Observations显示文摘The improvements in the sensitivity of the gravitational wave(GW) network enable the detection of several large redshift GW sources by third-generation GW detectors. These advancements provide an independent method to probe the large-scale structure of the universe by using the clustering of the binary black holes(BBHs). The black hole catalogs are complementary to the galaxy catalogs because of large redshifts of GW events, which may imply that BBHs are a better choice than galaxies to probe the large-scale structure of the universe and cosmic evolution over a large redshift range. To probe the large-scale structure, we used the sky position of the BBHs observed by third-generation GW detectors to calculate the angular correlation function and the bias factor of the population of BBHs. This method is also statistically significant as 5000 BBHs are simulated. Moreover, for the third-generation GW detectors, we found that the bias factor can be recovered to within 33% with an observational time of ten years. This method only depends on the GW source-location posteriors;hence, it can be an independent method to reveal the formation mechanisms and origin of the BBH mergers compared to the electromagnetic method. | Xiaoyun Shao Zhoujian Cao Xilong Fan Shichao Wu | 2022 | Research in Astronomy and Astrophysics2022，22，1： | 0 |
| 8 | Poisson-Arago spot for gravitational waves显示文摘For the observer at infinity, a Schwarzschild black hole serves as an attractive opaque disk with a radius of 3√3 M that will produce the diffraction pattern of gravitational waves(GWs). In this study, we demonstrate that a bright spot, which is a diffraction effect analogous to the Poisson-Arago spot in optics, will appear when an ingoing(quasi-)plane GW is diffracted by a Schwarzschild black hole. Here, we propose the diffraction effect of the GWs described by the exact diffraction solution of the GWs using the Heun function. For the first time, the Fresnel half-wave zone method is proposed to calculate the angular part of the GW scattering stripes for the observer at infinity. The prospect of observing the diffraction bright spot is discussed with an eikonal approximation. For normal incidence(quasi)-plane waves with 100 Hz(0.1 Hz) frequency diffracted by the central black hole of the Milky Way, the time delay between the Earth bathed in a bright spot and the minimum of the first dark stripe is 3.86(3860) d. We will witness the second bright fringe(40% amplitude of the central bright spot) after 6.2(6200) d. This new diffraction pattern involving the early phase of inspirals and pulsars as continuous gravitational wave sources is a potential scientific target for future space-and ground-based gravitational wave detectors, respectively. | HongSheng Zhang XiLong Fan | 2021 | Science China(Physics,Mechanics & Astronomy)2021，64，12： | 0 |
| 9 | Observatory science with eXTP显示文摘In this White Paper we present the potential of the enhanced X-ray Timing and Polarimetry(eXTP) mission for studies related to Observatory Science targets. These include flaring stars, supernova remnants, accreting white dwarfs, low and high mass X-ray binaries, radio quiet and radio loud active galactic nuclei, tidal disruption events, and gamma-ray bursts. eXTP will be excellently suited to study one common aspect of these objects: their often transient nature. Developed by an international Consortium led by the Institute of High Energy Physics of the Chinese Academy of Science, the eXTP mission is expected to be launched in the mid 2020s. | Jean J.M.in 't Zand Enrico Bozzo JinLu Qu Xiang-Dong Li Lorenzo Amati Yang Chen Immacolata Donnarumma Victor Doroshenko Stephen A.Drake Margarita Hernanz Peter A.Jenke Thomas J.Maccarone Simin Mahmoodifar Domitilla de Martino Alessandra De Rosa Elena M.Rossi Antonia Rowlinson Gloria Sala Giulia Stratta Thomas M.Tauris Joern Wilms XueFeng Wu Ping Zhou Iván Agudo Diego Altamirano Jean-Luc Atteia Nils A.andersson M.Cristina Baglio David R.Ballantyne Altan Baykal Ehud Behar Tomaso Belloni Sudip Bhattacharyya Stefano Bianchi Anna Bilous Pere Blay Joao Braga Sφren Brandt Edward F.Brown Niccolo Bucciantini Luciano Burderi Edward M.Cackett Riccardo Campana Sergio Campana Piergiorgio Casella Yuri Cavecchi Frank Chambers Liang Chen Yu-Peng Chen Jér?me Chenevez Maria Chernyakova ChiChuan Jin Riccardo Ciolfi Elisa Costantini Andrew Cumming Antonino D'Aì Zi-Gao Dai Filippo D'Ammando Massimiliano De Pasquale Nathalie Degenaar Melania Del Santo Valerio D'Elia Tiziana Di Salvo Gerry Doyle Maurizio Falanga XiLong Fan Robert D.Ferdman Marco Feroci Federico Fraschetti Duncan K.Galloway Angelo F.Gambino Poshak Gandhi MingYu Ge Bruce Gendre Ramandeep Gill Diego G?tz Christian Gouiffès Paola Grandi Jonathan Granot Manuel Güdel Alexander Heger Craig O.Heinke Jeroen Homan Rosario Iaria Kazushi Iwasawa Luca Izzo Long Ji Peter G.Jonker Jordi José Jelle S.Kaastra Emrah Kalemci Oleg Kargaltsev Nobuyuki Kawai Laurens Keek Stefanie Komossa Ingo Kreykenbohm Lucien Kuiper Devaky Kunneriath Gang Li En-Wei Liang Manuel Linares Francesco Longo FangJun Lu Alexander A.Lutovinov Denys Malyshev Julien Malzac Antonios Manousakis Ian McHardy Missagh Mehdipour YunPeng Men Mariano Méndez Roberto P.Mignani Romana Mikusincova M.Coleman Miller Giovanni Miniutti Christian Motch Joonas Nättilä Emanuele Nardini Torsten Neubert Paul T.O'Brien Mauro Orlandini Julian P.Osborne Luigi Pacciani Stéphane Paltani Maurizio Paolillo Iossif E.Papadakis Biswajit Paul Alberto Pellizzoni Uria Peretz Miguel A.Pérez Torres Emanuele Perinati Chanda Prescod-Weinstein Pablo Reig Alessandro Riggio Jerome Rodriguez Pablo Rodríguez-Gil Patrizia Romano Agata Rózańska Takanori Sakamoto Tuomo Salmi Ruben Salvaterra andrea Sanna andrea Santangelo Tuomas Savolainen Stéphane Schanne Hendrik Schatz LiJing Shao andy Shearer Steven N.Shore Ben W.Stappers Tod E.Strohmayer Valery F.Suleimanov Jirí Svoboda F.-K.Thielemann Francesco Tombesi Diego F.Torres Eleonora Torresi Sara Turriziani andrea Vacchi Stefano Vercellone Jacco Vink Jian-Min Wang JunFeng Wang Anna L.Watts ShanShan Weng Nevin N.Weinberg Peter J.Wheatley Rudy Wijnands Tyrone E.Woods Stan E.Woosley ShaoLin Xiong YuPeng Xu Zhen Yan George Younes WenFei Yu Feng Yuan Luca Zampieri Silvia Zane andrzej A.Zdziarski Shuang-Nan Zhang Shu Zhang Shuo Zhang Xiao Zhang Michael Zingale | 2019 | Science China(Physics,Mechanics & Astronomy)2019，62，2： | 0 |
您所在的机构暂未开通此部分的功能，请联系管理员