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您的检索式：作者名="Lin Zhenghuan"
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| 1 | Ultrastable and colorful afterglow from organic luminophores in amorphous nanocomposites:Advanced anti-counterfeiting and in vivo imaging application显示文摘Special attention has been paid to the organic afterglow materials(OAM)for their fascinating properties.However,poor stability at air and complicated structure design hinder the development of OAM.Herein,sol-gel,a facile and simple technique,is employed to synthesize a series of organic/inorganic hybrid nanocomposites(N1/SiO2,N2/SiO2,and N3/SiO2)by covalently linking three common aryl imides with crosslinked silica skeleton.These nanomaterials show excitation wavelength-dependent and colorful(yellow,red and green)afterglow of organic imides with long lifetime up to 1.1 s at air.Interestingly,the ultralong phosphorescence is ultrastable under various conditions:water,high temperature,UV irradiation and in vivo,due to the protection of inorganic silica.In particularly,heating to 500℃does not quench the afterglow of organic luminophore in nanocomposites,but forms new ultralong phosphorescence originated from space-conjugation of silica and carbonyl.The afterglow nanomaterials display huge advantages in the applications of advanced anti-counterfeiting and bioimaging. | Qiuqin Huang Heqi Gao Shuming Yang Dan Ding Zhenghuan Lin Qidan Ling | 2020 | Nano Research2020，13，4： | 2 |
| 2 | Asymmetric indolylmaleimides as non-dopant type red color emitting dyes显示文摘 | Lee Ying-Shin Lin Zhenghuan Chen Yu-Yuan | 2010 | Org Electron2010，11，4： | 1 |
| 3 | White light-Emitting devices based on star-shape polymers with abisindolylmaleimide core显示文摘 | Zhenghuan Lin Yan-Duo Lin Cheng-Ya Wu | 2010 | Macromolecules2010，4314，14： | 1 |
| 4 | Performance Comparison of Computational Methods for the Prediction of the Function and Pathogenicity of Non-coding Variants显示文摘Non-coding variants in the human genome significantly influence human traits and complex diseases via their regulation and modification effects.Hence,an increasing number of computational methods are developed to predict the effects of variants in human non-coding sequences.However,it is difficult for inexperienced users to select appropriate computational methods from dozens of available methods.To solve this issue,we assessed 12 performance metrics of 24 methods on four independent non-coding variant benchmark datasets:(1)rare germline variants from clinical relevant sequence variants(ClinVar),(2)rare somatic variants from Catalogue Of Somatic Mutations In Cancer(COSMIC),(3)common regulatory variants from curated expression quantitative trait locus(eQTL)data,and(4)disease-associated common variants from curated genomewide association studies(GWAS).All 24 tested methods performed differently under various conditions,indicating varying strengths and weaknesses under different scenarios.Importantly,the performance of existing methods was acceptable for rare germline variants from ClinVar with the area under the receiver operating characteristic curve(AUROC)of 0.4481–0.8033 and poor for rare somatic variants from COSMIC(AUROC=0.4984–0.7131),common regulatory variants from curated eQTL data(AUROC=0.4837–0.6472),and disease-associated common variants from curated GWAS(AUROC=0.4766–0.5188).We also compared the prediction performance of 24 methods for non-coding de novo mutations in autism spectrum disorder,and found that the combined annotation-dependent depletion(CADD)and context-dependent tolerance score(CDTS)methods showed better performance.Summarily,we assessed the performance of 24 computational methods under diverse scenarios,providing preliminary advice for proper tool selection and guiding the development of new techniques in interpreting non-coding variants. | Zheng Wang Guihu Zhao Bin Li Zhenghuan Fang Qian Chen Xiaomeng Wang Tengfei Luo Yijing Wang Qiao Zhou Kuokuo Li Lu Xia Yi Zhang Xun Zhou Hongxu Pan Yuwen Zhao Yige Wang Lin Wang Jifeng Guo Beisha Tang Kun Xia Jinchen Li | 2023 | Genomics, Proteomics & Bioinformatics2023，21，3： | 0 |
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