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| 1 | SynBioEcoli: a comprehensive metabolism network of engineered E. coil in three dimensional visualization显示文摘设计 E 的一个全面新陈代谢网络。coli 在系统生物学是很重要的, metabolomics 学习。许多工具集中于二维的空格在新陈代谢的网络显示小径。然而,三维的可视化的用法可以帮助更好理解新陈代谢、规章的网络的复杂拓扑学。方法我们手工地从文学的大量试验性的数据(包括的小径,反应和代谢物) 与设计 E 的不同类型联系了的 curated。coli 然后利用了三维的可视化的一种新奇技术开发一个全面新陈代谢的网络命名 SynBioEcoli。结果 SynBioEoli 包含 740 条 biosynthetic 小径, 3,889 新陈代谢的反应, 2,255 化学药品手工地从与设计 E 的不同类型联系的大约 11,000 份新陈代谢出版物加重 curated。coli。而且, SynBioEcoli 与各种各样的信息科学技术集成。结论 SynBioEcoli 能被认为是设计 E 的全面 knowledgebase。coli 并且代表下一代细胞的新陈代谢网络可视化技术。它能经由浏览器被存取(例如 Google 铬) 支持 WebGL,在 http://gffzz849681930f04473chq6vf5xq09xbk660w.ffgz.tsg.suse.edu.cn/synbioecoli/ 。 | Weizhong Tu Shaozhen Ding Ling Wu Zhe Deng Hui Zhu Xiaotong Xu Chen Lin Chaonan Ye Minlu Han Mengna Zhao Juan Liu Zixin Deng Junni Chen Dong-Qing Wei Qian-Nan Hu | 2017 | Frontiers of Electrical and Electronic Engineering in China2017，5，1： | 2 |
| 2 | AI-aided on-chip nucleic acid assay for smart diagnosis of infectious disease显示文摘Global pandemics such as COVID-19 have resulted in significant global social and economic disruption.Although polymerase chain reaction(PCR)is recommended as the standard test for identifying the SARS-CoV-2,conventional assays are time-consuming.In parallel,although artificial intelligence(AI)has been employed to contain the disease,the implementation of AI in PCR analytics,which may enhance the cognition of diagnostics,is quite rare.The information that the amplification curve reveals can reflect the dynamics of reactions.Here,we present a novel AI-aided on-chip approach by integrating deep learning with microfluidic paper-based analytical devices(μPADs)to detect synthetic RNA templates of the SARS-CoV-2 ORF1ab gene.TheμPADs feature a multilayer structure by which the devices are compatible with conventional PCR instruments.During analysis,real-time PCR data were synchronously fed to three unsupervised learning models with deep neural networks,including RNN,LSTM,and GRU.Of these,the GRU is found to be most effective and accurate.Based on the experimentally obtained datasets,qualitative forecasting can be made as early as 13 cycles,which significantly enhances the efficiency of the PCR tests by 67.5%(∼40 min).Also,an accurate prediction of the end-point value of PCR curves can be obtained by GRU around 20 cycles.To further improve PCR testing efficiency,we also propose AI-aided dynamic evaluation criteria for determining critical cycle numbers,which enables real-time quantitative analysis of PCR tests.The presented approach is the first to integrate AI for on-chip PCR data analysis.It is capable of forecasting the final output and the trend of qPCR in addition to the conventional end-point Cq calculation.It is also capable of fully exploring the dynamics and intrinsic features of each reaction.This work leverages methodologies from diverse disciplines to provide perspectives and insights beyond the scope of a single scientific field.It is universally applicable and can be extended to multiple areas of fundamental research. | Hao Sun Linghu Xiong Yi Huang Xinkai Chen Yongjian Yu Shaozhen Ye Hui Dong Yuan Jia Wenwei Zhang | 2022 | Fundamental Research2022，2，3： | 0 |
| 3 | Development of the Scale of Quality of Life for Diseases with Visual Impairment显示文摘Purpose :To describe the development of the of scale quality of life which can measure the quality of life of Chinese patients with visual impairment. Methods: Based on a thorough literature search and consultation with ophthalmologists and public health professionals, 20 items were selected to create a scale. Fifty-seven cataract patients with vision impairment and 60 glaucoma patients with vision impairment and visual field loss were measured by the scale to evaluate the validity, reliability and responsiveness of the scale.Results: The scale covered four domains of the quality of life(QOL). The criterion related validity of the scale: r = 0. 6865 (P=0. 001). The test-retest reliability of the scale: r = 0. 8959(P = 0. 001). Coronback’s alpha was 0. 9359. The variance ratio (VR) of intra-individual variance to inter- individual variance was 0. 0551 for overall scores. The correlation coefficient of split-half method was 0. 9553. The responsiveness; T-test, T = 5. 95 (P = 0. 001), effect size | Qiang Yu, Shaozhen Li, Henian Chen, Tiancai Ye, Jingjing AoZhongzhan Ophthalmic center, Sun Yat-sen University of Medical Sciences, Guangzhou 510060, China The First Millitary Medical University, Guangzhou 510515,China. | 1996 | Eye Science1996，16，1： | 0 |
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