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| 1 | Detection of serum IgM and IgG for COVID-19 diagnosis显示文摘Dear Editor,Infection with the novel coronavirus(SARS-CoV-2,which is the virus responsible for the coronavirus disease 2019(COVID-19))was first reported in Wuhan,China on December 31,2019.The outbreak of COVID-19 remains ongoing and was linked to more than 80,000 infected patients and more than 3,000 deaths in China as of March 7,2020(Holshue et al.,2020). | Ling Zhong Junlan Chuan Bo Gong Ping Shuai Yu Zhou Yi Zhang Zhilin Jiang Dingding Zhang Xiaoqi Liu Shi Ma Yi Huang He Lin Qingwei Wang Lulin Huang Dan Jiang Fang Hao Juan Tang Chunqi Zheng Hua Yu Zhibin Wang Qi Jiang Tao Zeng Mei Luo Fanwei Zeng Fanxin Zeng Jianghai Liu Junxi Tian Yu Xu Tengxiang Long Kaiju Xu Xingxiang Yang Yuping Liu Yi Shi Li Jiang Zhenglin Yang | 2020 | Science China(Life Sciences)2020，63，5： | 8 |
| 2 | Surface analysis of chemical stripping titanium alloy oxide films显示文摘 | Jianhua Liu Guolong Wu Songmei Li Mei Yu Junlan Yi Liang Wu | 2012 | Journal of Wuhan University of Technology-Mater. Sci. Ed2012，，3： | 1 |
| 3 | Effect of electrolyte concentration on morphology, microstructure and electrochemical impedance of anodic oxide film on titanium alloy Ti-10V–2Fe–3Al显示文摘 | Jianhua Liu Junlan Yi Songmei Li Mei Yu Guolong Wu Liang Wu | 2010 | Journal of Applied Electrochemistry2010，，8： | 1 |
| 4 | Fluorescent Aptamer-Polyethylene Glycol Functionalized Graphene Oxide Biosensor for Profenofos Detection in Food显示文摘A biosensor based on self-assembled ssDNA(aptamer)and polyethylene glycol functionalized graphene oxide(GO-PEG)has been designed for sensing profenofos in food.The sensor has employed the fluorescence'on/off'switching strategy in a single step iii homogeneous solution.Compared to traditional detection methods,the strategy proposed here is simple,convenient,fast and sensitive.Furthermore,compared with the general aptamer-GO structure,this aptamer-GO-PEG stmcture is in possession of a better detection performance,wliich is largely attributed to the improvement of the biocompatibility and the adjustment of the adsorption capacity of GO by grafting the blocking agent PEG onto the surface of GO.Additionally,the improved biocompatibility of GO shows better stability in salt solutions and physiological solutions,which is more conducive to its practical application in foods.In this project,profenofos had been detected with the proposed strategy,and the limit of detection has been controlled to be 0.21 ng/mL.This aptasensing assay has been applied to detemiining profenofos in(spiked)tap water,cabbage and milk with the recovery values ranging from 93.1% to 108.5%,from 90.8% to 113.2% and from 105.9% to 114.2%,respectively. | XIONG Jin'en LI Shuang LI Yi CHEN Yingli LIU Yu GAN Junlan JU Jiahui XIAN Yaoling XIONG Xiaohui | 2020 | Chemical Research in Chinese Universities2020，36，5： | 0 |
| 5 | Deep Learning for Medication Recommendation:A Systematic Survey显示文摘Making medication prescriptions in response to the patient's diagnosis is a challenging task.The number of pharmaceutical companies,their inventory of medicines,and the recommended dosage confront a doctor with the well-known problem of information and cognitive overload.To assist a medical practitioner in making informed decisions regarding a medical prescription to a patient,researchers have exploited electronic health records(EHRs)in automatically recommending medication.In recent years,medication recommendation using EHRs has been a salient research direction,which has attracted researchers to apply various deep learning(DL)models to the EHRs of patients in recommending prescriptions.Yet,in the absence of a holistic survey article,it needs a lot of effort and time to study these publications in order to understand the current state of research and identify the best-performing models along with the trends and challenges.To fill this research gap,this survey reports on state-of-the-art DL-based medication recommendation methods.It reviews the classification of DL-based medication recommendation(MR)models,compares their performance,and the unavoidable issues they face.It reports on the most common datasets and metrics used in evaluating MR models.The findings of this study have implications for researchers interested in MR models. | Zafar Ali Yi Huang Irfan Ullah Junlan Feng Chao Deng Nimbeshaho Thierry Asad Khan Asim Ullah Jan Xiaoli Shen Wu Ruil Guilin Qi | 2023 | Data Intelligence2023，5，2： | 0 |
| 6 | Origin of non-uniform plasticity in a high-strength Al-Mn-Sc based alloy produced by laser powder bed fusion显示文摘The Al-Mn-Sc-based alloys specific for additive manufacturing(AM)have been recently developed and can reach ultrahigh strength and adequate elongation.However,these alloys commonly exhibit nonuniform plasticity during tensile deformation,which is a critical issue hindering their wider application.In this work,the origin of this non-uniform plasticity of the alloys produced by laser powder bed fusion(LPBF)has been systematically investigated for the first time.The results show that the loss of uniform plasticity in the alloy originates from microstructural regions containing equiaxed fine-grains(FGs)(~650nm in size)at the bottom of the melt pools.In micro-tensile tests,the strength of these FG regions can reach a peak of~630 MPa.After this,an apparent yield drop occurs,followed by rapid strain softening.This FG behavior is associated with intermetallic particles along grain boundaries and a lack of uniform mobile dislocations during deformation.The columnar coarse-grain(CG)regions in the remaining melt pools show uniform plasticity and moderate work hardening.Furthermore,the quantitative calculations indicate that the solid solution strengthening in these two regions is similar.Nevertheless,secondary Al_(3)Sc precipitates contribute to~260 MPa strength in the FG,compared to 310 MPa in the CG due to their different number density.In addition,grain boundary strengthening can reach 230 MPa in the FG region;nearly double the CG region value. | Dina Bayoumy Kwangsik Kwak Torben Boll Stefan Dietrich Daniel Schliephake Jie Huang Junlan Yi Kazuki Takashima Xinhua Wu Yuman Zhu Aijun Huang | 2022 | Journal of Materials Science & Technology2022，，8： | 0 |
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