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您的检索式：作者名="Qiwei Lian"
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| 1 | Progress of Jinping Underground laboratory for Nuclear Astrophysics(JUNA)显示文摘Jinping Underground laboratory for Nuclear Astrophysics(JUNA) will take the advantage of the ultra-low background of CJPL lab and high current accelerator based on an ECR source and a highly sensitive detector to directly study for the first time a number of crucial reactions occurring at their relevant stellar energies during the evolution of hydrostatic stars. In its first phase, JUNA aims at the direct measurements of^(25)Mg(p,γ)^(26)Al,^(19)F(p,α)^(16)O,^(13)C(α,n)^(16)O and ^(12)C(α,γ)^(16)O reactions. The experimental setup,which includes an accelerator system with high stability and high intensity, a detector system, and a shielding material with low background, will be established during the above research. The current progress of JUNA will be given. | WeiPing Liu ZhiHong Li JiangJun He XiaoDong Tang Gang Lian Zhu An JianJun Chang Han Chen QingHao Chen XiongJun Chen ZhiJun Chen BaoQun Cui XianChao Du ChangBo Fu Lin Gan Bing Guo GuoZhu He Alexander Heger SuQing Hou HanXiong Huang Ning Huang BaoLu Jia LiYang Jiang Shigeru Kubono JianMin Li KuoAng Li Tao Li YunJu Li Maria Lugaro XiaoBing Luo HongYi Ma ShaoBo Ma DongMing Mei YongZhong Qian JiuChang Qin Jie Ren YangPing Shen Jun Su LiangTing Sun WanPeng Tan Isao Tanihata Shuo Wang Peng Wang YouBao Wang Qi Wu ShiWei Xu ShengQuan Yan LiTao Yang Yao Yang XiangQing Yu Qian Yue Sheng Zeng HuanYu Zhang Hui Zhang LiYong Zhang NingTao Zhang QiWei Zhang Tao Zhang XiaoPeng Zhang XueZhen Zhang ZiMing Zhang Wei Zhao Zuo Zhao Chao Zhou | 2016 | Science China(Physics,Mechanics & Astronomy)2016，59，4： | 3 |
| 2 | A Survey on Methods for Predicting Polyadenylation Sites from DNA Sequences,Bulk RNA-seq,and Single-cell RNA-seq显示文摘Alternative polyadenylation(APA)plays important roles in modulating mRNA stability,translation,and subcellular localization,and contributes extensively to shaping eukaryotic transcriptome complexity and proteome diversity.Identification of poly(A)sites(pAs)on a genomewide scale is a critical step toward understanding the underlying mechanism of APA-mediated gene regulation.A number of established computational tools have been proposed to predict pAs from diverse genomic data.Here we provided an exhaustive overview of computational approaches for predicting pAs from DNA sequences,bulk RNA sequencing(RNA-seq)data,and single-cell RNA sequencing(scRNA-seq)data.Particularly,we examined several representative tools using bulk RNA-seq and scRNA-seq data from peripheral blood mononuclear cells and put forward operable suggestions on how to assess the reliability of pAs predicted by different tools.We also proposed practical guidelines on choosing appropriate methods applicable to diverse scenarios.Moreover,we discussed in depth the challenges in improving the performance of pA prediction and benchmarking different methods.Additionally,we highlighted outstanding challenges and opportunities using new machine learning and integrative multi-omics techniques,and provided our perspective on how computational methodologies might evolve in the future for non-30 untranslated region,tissuespecific,cross-species,and single-cell pA prediction. | Wenbin Ye Qiwei Lian Congting Ye Xiaohui Wu | 2023 | Genomics, Proteomics & Bioinformatics2023，21，1： | 0 |
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