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| 1 | Upconversion nanoparticles for super-resolution quantification of single small extracellular vesicles显示文摘Although small EVs(sEVs)have been used widely as biomarkers in disease diagnosis,their heterogeneity at single EV level has rarely been revealed.This is because high-resolution characterization of sEV presents a major challenge,as their sizes are below the optical diffraction limit.Here,we report that upconversion nanoparticles(UCNPs)can be used for super-resolution profiling the molecular heterogeneity of sEVs.We show that Er3+-doped UCNPs has better brightness and Tm3+-doped UCNPs resulting in better resolution beyond diffraction limit.Through an orthogonal experimental design,the specific targeting of UCNPs to the tumour epitope on single EV has been cross validated,resulting in the Pearson’s R-value of 0.83 for large EVs and~65%co-localization double-positive spots for sEVs.Furthermore,super-resolution nanoscopy can distinguish adjacent UCNPs on single sEV with a resolution of as high as 41.9 nm.When decreasing the size of UCNPs from 40 to 27 nm and 18 nm,we observed that the maximum UCNPs number on single sEV increased from 3 to 9 and 21,respectively.This work suggests the great potentials of UCNPs approach“digitally”quantify the surface antigens on single EVs,therefore providing a solution to monitor the EV heterogeneity changes along with the tumour progression progress. | Guan Huang Yongtao Liu Dejiang Wang Ying Zhu Shihui Wen Juanfang Ruan Dayong Jin | 2022 | eLight2022，2，1： | 2 |
| 2 | Iwasawa decomposi- tion: a new approach to 2D affine registration problem显示文摘 | Ying Shihui PengYaxin Wen Zhijie | 2011 | Pattern Analysis and Applications2011，14，2： | 1 |
| 3 | Lie group frame- work for the iterative closest point algorithm in n-D data registration显示文摘 | Ying Shihui Peng Jigen Du Shaoyi | 2009 | International Journal of Pattern Recognition and Artificial Intelligence2009，23，6： | 1 |
| 4 | A scale stretch method based on ICP for 3D data registration 显示文摘 | Ying Shihui Peng Jigen Du Shaoyi | 2009 | IEEE Transactions on Automation Science and Engineering2009，6，3： | 1 |
| 5 | Super rigid tris-spirobifluorenes:Syntheses and properties显示文摘In this work,a blue emitter with a 3 D rigid structure composed of multiple spirobifluorene(3-Spiro) has been synthesized and characterized.Through a detailed study of the electrochemical and photophysical properties of 3-Spiro,we have evidenced that 3-Spiro can be applied as an active component of organic light-emitting diodes(OLEDs).The device with 5% doping rate of 4 CzPNPh exhibits high external quantum efficiency(EQE) of 11%,which proves the potential of 3 D rigid structure emitters for OLEDs. | Luyao Zhao Chunbo Duan Dongxue Ding Shihui Liu Debin Xia Ying Guo Hui Xu Martin Baumgartend | 2021 | Chinese Chemical Letters2021，32，1： | 1 |
| 6 | Self-face recognition in attended and unattended conditions: an event-related brain potential study显示文摘 | Jie Sui Ying Zhu Shihui Han | 2006 | NeuroReport2006，，4： | 1 |
| 7 | Self-face Recognition in Attended and Unattended Conditions: An Event-related Brain Potential Study 显示文摘 | Jie Sui Zhu Ying Han Shihui | 2006 | NeuroReport2006，17，4： | 1 |
| 8 | Neural basis of cultural influence on self-representation显示文摘 | Ying Zhu Li Zhang Jin Fan Shihui Han | 2006 | Neuroimage2006，，3： | 1 |
| 9 | The ability of NT-proBNP to detect chronic heart failure and predict all-cause mortality is higher in elderly Chinese coronary artery disease patients with chronic kidney disease显示文摘 | Shihui Fu Ying Ye Yongyi Bai Tiehui Xiao Liang Wang Bing Zhu Yuan Liu Shuangyan Yi Leiming Luo | 2013 | 2013 (defa)2013，，： | 1 |
| 10 | Affine iterative closest point algorithm for point set registration显示文摘 | Du Shaoyi Zheng Nanning Ying Shihui Liu Jianyi | | 0，，09： | 1 |
| 11 | Neural basis of cultural influence on self-representation显示文摘 | Ying Zhu Li Zhang Jin Fan Shihui Han | 2006 | Neuroimage2006，，3： | 1 |
| 12 | Affine itera- rive closest point algorithm for point set registration显示文摘 | Du Shaoyi Zheng Naning Ying Shihui | 2010 | Pattern Recognition Letters2010，31，9： | 1 |
| 13 | Fast MRI Reconstruction via Edge Attention显示文摘Fast and accurate MRI reconstruction is a key concern in modern clinical practice.Recently,numerous Deep-Learning methods have been proposed for MRI reconstruction,however,they usually fail to reconstruct sharp details from the subsampled k-space data.To solve this problem,we propose a lightweight and accurate Edge Attention MRI Reconstruction Network(EAMRI)to reconstruct images with edge guidance.Specifically,we design an efficient Edge Prediction Network to directly predict accurate edges from the blurred image.Meanwhile,we propose a novel Edge Attention Module(EAM)to guide the image reconstruction utilizing the extracted edge priors,as inspired by the popular self-attention mechanism.EAM first projects the input image and edges into Q_(image),K_(edge),and V_(image),respectively.Then EAM pairs the Q_(image)with K_(edge)along the channel dimension,such that 1)it can search globally for the high-frequency image features that are activated by the edge priors;2)the overall computation burdens are largely reduced compared with the traditional spatial-wise attention.With the help of EAM,the predicted edge priors can effectively guide the model to reconstruct high-quality MR images with accurate edges.Extensive experiments show that our proposed EAMRI outperforms other methods with fewer parameters and can recover more accurate edges. | Hanhui Yang Juncheng Li Lok Ming Lui Shihui Ying Jun Shi Tieyong Zeng | 2023 | Communications in Computational Physics2023，33，5： | 0 |
| 14 | A cytoprotective graphene oxide-polyelectrolytes nanoshell for single-cell encapsulation显示文摘Graphene oxide(GO)has been increasingly utilized in the fields of food,biomedicine,environment and other fields because of its benign biocompatible.We encapsulated two kinds of GO with different sizes on yeast cells with the assistance of polyelectrolytes poly(styrene sulfonic acid)sodium salt(PSS)and polyglutamic acid(PGA)(termed as Y@GO).The result does not show a significant difference between the properties of the two types of Y@GO(namely Y@GO1 and Y@GO2).The encapsulation layers are optimized as Yeast/PGA/PSS/PGA/GO/PGA/PSS based on the morphology,dispersity,colony-forming unit,and zeta potential.The encapsulation of GO increases the roughness of the yeast.It is proved that the Y@GO increases the survival time and enhance the activity of yeast cells.The GO shell improves the resistance of yeast cells against pH and salt stresses and extends the storage time of yeast cells. | Luanying He Yulin Chang Junhao Zhu Ying Bi Wenlin An Yiyang Dong Jia-Hui Liu Shihui Wang | 2021 | Frontiers of Chemical Science and Engineering2021，15，2： | 0 |
| 15 | LaNets:Hybrid Lagrange Neural Networks for Solving Partial Differential Equations显示文摘We propose new hybrid Lagrange neural networks called LaNets to predict the numerical solutions of partial differential equations.That is,we embed Lagrange interpolation and small sample learning into deep neural network frameworks.Concretely,we first perform Lagrange interpolation in front of the deep feedforward neural network.The Lagrange basis function has a neat structure and a strong expression ability,which is suitable to be a preprocessing tool for pre-fitting and feature extraction.Second,we introduce small sample learning into training,which is beneficial to guide themodel to be corrected quickly.Taking advantages of the theoretical support of traditional numerical method and the efficient allocation of modern machine learning,LaNets achieve higher predictive accuracy compared to the state-of-the-artwork.The stability and accuracy of the proposed algorithmare demonstrated through a series of classical numerical examples,including one-dimensional Burgers equation,onedimensional carburizing diffusion equations,two-dimensional Helmholtz equation and two-dimensional Burgers equation.Experimental results validate the robustness,effectiveness and flexibility of the proposed algorithm. | Ying Li Longxiang Xu Fangjun Mei Shihui Ying | 2023 | Computer Modeling in Engineering & Sciences2023，，1： | 0 |
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