Cryo-electron microscopy(cryo-EM)has become one of the mainstream techniques for determining the structures of proteins andmacromolecular complexes,with prospects for development and significance.Researchers must sele...Cryo-electron microscopy(cryo-EM)has become one of the mainstream techniques for determining the structures of proteins andmacromolecular complexes,with prospects for development and significance.Researchers must select hundreds of thousands of particles from micrographs to acquire the database for single-particle cryo-EM reconstruction.However,existing particle picking methods cannot ensure that the particles are in the center of the bounding box because the signal-to-noise ratio(SNR)of micrographs is extremely low,thereby directly affecting the efficiency and accuracy of 3D reconstruction.We propose an automated particle-picking method(CenterPicker)based on particle center point detection to automatically select a large number of high-quality particles from low signal-to-noise,low-contrast refrigerated microscopy images.The method uses a fully convolutional neural network to generate a keypoint heatmap.The heatmap value represents the probability that a micrograph pixel belongs to a particle center area.CenterPicker can process images of any size and can directly predict the center point and size of the particle.The network implements multiscale feature fusion and introduces an attention mechanism to improve the feature fusion part to obtain more accurate selection results.We have conducted a detailed evaluation of CenterPicker on a range of datasets,and results indicate that it excels in single-particle picking tasks.展开更多
Given the differences between cities in the United States and China,and the changing structure of Chinese cities,this paper examines the viability of some basic predictions of the Muth-Mills model of urban spatial str...Given the differences between cities in the United States and China,and the changing structure of Chinese cities,this paper examines the viability of some basic predictions of the Muth-Mills model of urban spatial structure for modern cities in China,using data for the year 2010. Chinese cities are experiencing rapid motorization and road construction over the last two decades,so we have tried to use different measurements for commuting cost in these cities to estimate the empirical model,and concluded that most results support the Muth-Mills assertion. The elasticities of the urban spatial size with respect to the signifi cant variables are discussed,comparing to those for cities in the United States. The results we obtain from both theoretical and empirical analyses can further increase our understanding on the model as a tool for a policy analysis on cities in China.展开更多
基金supported by Key Projects of the Ministry of Science and Technology of the People Republic of China(2018AAA0102301).
文摘Cryo-electron microscopy(cryo-EM)has become one of the mainstream techniques for determining the structures of proteins andmacromolecular complexes,with prospects for development and significance.Researchers must select hundreds of thousands of particles from micrographs to acquire the database for single-particle cryo-EM reconstruction.However,existing particle picking methods cannot ensure that the particles are in the center of the bounding box because the signal-to-noise ratio(SNR)of micrographs is extremely low,thereby directly affecting the efficiency and accuracy of 3D reconstruction.We propose an automated particle-picking method(CenterPicker)based on particle center point detection to automatically select a large number of high-quality particles from low signal-to-noise,low-contrast refrigerated microscopy images.The method uses a fully convolutional neural network to generate a keypoint heatmap.The heatmap value represents the probability that a micrograph pixel belongs to a particle center area.CenterPicker can process images of any size and can directly predict the center point and size of the particle.The network implements multiscale feature fusion and introduces an attention mechanism to improve the feature fusion part to obtain more accurate selection results.We have conducted a detailed evaluation of CenterPicker on a range of datasets,and results indicate that it excels in single-particle picking tasks.
基金partly supported by the National Natural Science Foundation of China(Grant 51008002)the Natural Science Foundation of Guangdong Province(S2012040007176)+2 种基金the Research Fund for the State Key Laboratory of Subtropical Building Science(Grant 2011KB20)the Research Fund for the Harbin Institute of Technology(Grants HIT.NSRIF.2013100 and HIT.NSFIR.2011126)the Peking University-Lincoln Institute Center for Urban Development and Land Policy(DS20120901).
文摘Given the differences between cities in the United States and China,and the changing structure of Chinese cities,this paper examines the viability of some basic predictions of the Muth-Mills model of urban spatial structure for modern cities in China,using data for the year 2010. Chinese cities are experiencing rapid motorization and road construction over the last two decades,so we have tried to use different measurements for commuting cost in these cities to estimate the empirical model,and concluded that most results support the Muth-Mills assertion. The elasticities of the urban spatial size with respect to the signifi cant variables are discussed,comparing to those for cities in the United States. The results we obtain from both theoretical and empirical analyses can further increase our understanding on the model as a tool for a policy analysis on cities in China.