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| 1 | An infrastructure with user-centered presentation data model for integrated management of materials data and services显示文摘With scientific research in materials science becoming more data intensive and collaborative after the announcement of the Materials Genome Initiative,the need for modern data infrastructures that facilitate the sharing of materials data and analysis tools is compelling in the materials community.In this paper,we describe the challenges of developing such infrastructure and introduce an emerging architecture with high usability.We call this architecture the Materials Genome Engineering Databases(MGED).MGED provides cloud-hosted services with features to simplify the process of collecting datasets from diverse data providers,unify data representation forms with user-centered presentation data model,and accelerate data discovery with advanced search capabilities.MGED also provides a standard service management framework to enable finding and sharing of tools for analyzing and processing data.We describe MGED’s design,current status,and how MGED supports integrated management of shared data and services. | Shilong Liu Yanjing Su Haiqing Yin Dawei Zhang Jie He Haiyou Huang Xue Jiang Xuan Wang Haiyan Gong Zhuang Li Hao Xiu Jiawang Wan Xiaotong Zhang | 2021 | npj Computational Materials2021，，1： | 4 |
| 2 | Genetic Analysis and Mapping of the Purple Gene in Purple Heading Chinese Cabbage显示文摘To analyze genetic linkage and map purple gene(Br Pur) controlling purple inner leaves trait of Chinese cabbage, a F2 population was constructed by selfing a F1 plant from homozygous purple heading line ‘14S839' and homozygous orange heading line ‘14S162'. The phenotype investigation of head color showed that the segregation ratio of purple to non-purple individuals was consistent with the expected ratio of 3:1,which indicated that purple inner leaves trait is controlled by a single dominant gene. A total of 297 SSRs in whole genome of Chinese cabbage were tested by modified Bulked Segregant Analysis(BSA) method. Two linked markers flanking Br Pur, A710 and A714, were obtained and Br Pur was mapped on linkage group A07 based on the sequence of these two markers. Subsequently, two new markers flanking Br Pur, CL-12 and B214-87, were developed based on two known anthocyanins-related genes, Br4CL3 and Bra004214. Genetic mapping of all markers in the F2 mapping population showed CL-12 and B214-87 linked to Br Pur with the genetic distance of 3.1 c M and 3.5 c M, respectively. | WU Junqing ZHAO Jing QIN Meiling REN Yanjing ZHANG Huamin DAI Zihui HAO Lingyu ZHANG Lugang | 2016 | Horticultural Plant Journal2016，2，6： | 3 |
| 3 | 查看详情显示文摘 | Hao Yanjing Lai Qiongyu Lu Jizheng | | 0，，02： | 1 |
| 4 | Systhesis and characterization of spinel Li4 Ti5 O12 anode material by oxalic acid-assisted sol-gel method 显示文摘 | HAO Yanjing LAI Qiongyu LU Jizheng | 2006 | Journal of Power Sources2006，158，： | 1 |
| 5 | Systhesis and characterization of spinel Li4Ti5O anode material by oxalic acid-assisted sol-gel method 显示文摘 | HAO Yanjing LAI Qiongyu LU Jizheng | 2006 | Journal of Power Sources2006，158，： | 1 |
| 6 | Synthesis and Characterization of Spinel Li4Ti5O12 Anode Material By Oxalic Acid-Assisted Sol-Gel Method 显示文摘 | HAO Yanjing LAI Qiongyu LU Jizheng | 2006 | Journal of Power Sources2006，158，： | 1 |
| 7 | Combining radiation with autophagy inhibition enhances suppression oftumor growth and angiogenesis in esophageal cancer显示文摘 | Yongshun Chen Xiaohong Li Leiming Guo Xiaoyuan Wu Chunyu He Song Zhang Yanjing Xiao Yuanyuan Yang Daxuan Hao | 2015 | Molecular Medicine Reports2015，，： | 1 |
| 8 | Synthesis by TEA sol gel method and electrochemical properties of Li4TiS012 an- ode material for lithium-ion battery显示文摘 | Hao Yanjing Lai Qiongyu Xu Zhihui | 2005 | Sold State Ionics2005，176，1314： | 1 |
| 9 | Synthesis and characterization of spinel Li4 Ti5O12 anode material by oxalic acid-assisted sol-gel method 显示文摘 | Hao Yanjing Lai Qiongyu Lu Jizheng | 2006 | J Power Sources2006，158，： | 1 |
| 10 | Synthesis by citric acid sol-gel method and electrochemical properties of Li4Ti5O12 anode material for lithium-ion battery显示文摘 | Hao Yanjing Lai Qiongyu Liu Dongqiang | 2005 | Mater Chem Phys2005，94，： | 1 |
| 11 | Synthesis by TEA sol-gel method and electrochemical properties of Li4Ti5O12 anode material for lithium-ion battery显示文摘 | Hao Yanjing Lai Qiongyu Lu Jizheng | 2005 | Solid State Ionics2005，176，： | 1 |
| 12 | Influence of various complex agents on electrochemical property of Li4Ti5O12 anode material 显示文摘 | Hao Yanjing Lai Qiongyu Lu J izheng | 2007 | J Alloys Compd2007，439，： | 1 |
| 13 | Synthesis by TEA Sol-gel Method and Electrochemical Properties of Li4Ti5O12 Anode Material for Lithium-ion Battery 显示文摘 | Yanjing Hao Qiongyu Lai Zhihui Xu | 2005 | Solid State Ionics2005，176，： | 1 |
| 14 | 显示文摘 | Hao Yanjing Lai Qiongyu Xu Zhihui | 2005 | Solid State Ionics2005，176，： | 1 |
| 15 | Data augmentation in microscopic images for material data mining显示文摘Recent progress in material data mining has been driven by high-capacity models trained on large datasets.However,collecting experimental data(real data)has been extremely costly owing to the amount of human effort and expertise required.Here,we develop a novel transfer learning strategy to address problems of small or insufficient data.This strategy realizes the fusion of real and simulated data and the augmentation of training data in a data mining procedure.For a specific task of grain instance image segmentation,this strategy aims to generate synthetic data by fusing the images obtained from simulating the physical mechanism of grain formation and the“image style”information in real images.The results show that the model trained with the acquired synthetic data and only 35%of the real data can already achieve competitive segmentation performance of a model trained on all of the real data.Because the time required to perform grain simulation and to generate synthetic data are almost negligible as compared to the effort for obtaining real data,our proposed strategy is able to exploit the strong prediction power of deep learning without significantly increasing the experimental burden of training data preparation. | Boyuan Ma Xiaoyan Wei Chuni Liu Xiaojuan Ban Haiyou Huang Hao Wang Weihua Xue Stephen Wu Mingfei Gao Qing Shen Michele Mukeshimana Adnan Omer Abuassba Haokai Shen Yanjing Su | 2020 | npj Computational Materials2020，，1： | 1 |
| 16 | Author Correction:Data augmentation in microscopic images for material data mining显示文摘The original version of this article omitted the following from the Acknowledgements:“This work was supported by Beijing Top Discipline for Artificial Intelligent Science and Engineering,University of Science and Technology Beijing”.This has now been corrected in both the PDF and HTML versions of the article. | Boyuan Ma Xiaoyan Wei Chuni Liu Xiaojuan Ban Haiyou Huang Hao Wang Weihua Xue Stephen Wu Mingfei Gao Qing Shen Michele Mukeshimana Adnan Omer Abuassba Haokai Shen Yanjing Su | 2020 | npj Computational Materials2020，，1： | 0 |
| 17 | High-throughput screening and evaluation of repurposed drugs targeting the SARS-CoV-2 main protease显示文摘Dear editor,To date,a number of clinically approved drugs have been evaluated for potential to treat coronavirus disease 2019(COVID-19),such as lopinavir/ritonavir,hydroxychloroquine,cobicistat,and darunavir.Some of these drugs have been proven to be effective in vitro;however,clinical trials showed that none of these compounds led to a significant improvement in symptoms or length of hospitalization.Thus,it is essential and more reliable to start from a defined target to ide ntify can didate drugs. | Yan Li Jinyong Zhang Zilei Duan Ning Wang Xiangcheng Sun Yanjing Zhang Li Fu Kaiyun Liu Yongjun Yang Shulei Pan Yun Shi Hao Zeng Gang Guo Ren Lai Quanming Zou | 2021 | Signal Transduction and Targeted Therapy2021，6，10： | 0 |
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