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| 1 | Late Paleozoic to Mesozoic Intrusions Distribution in the North Sanjiang Orogenic Belt,Southwest China:Evidence from Zircon U-Pb Dating and Geochemistry显示文摘A mosaic of terranes or blocks and associated Late Paleozoic to Mesozoic sutures are characteristics of the north Sanjiang orogenic belt(NSOB).A detailed field study and sampling across the three magmatic belts in north Sanjiang orogenic belt,which are the Jomda-Weixi magmatic belt,the Yidun magmatic belt and the Northeast Lhasa magmatic belt,yield abundant data that demonstrate multiphase magmatism took place during the late Paleozoic to early Mesozoic.9 new zircon LA-ICP-MS U-Pb ages and 160 published geochronological data have identified five continuous episodes of magma activities in the NSOB from the Late Paleozoic to Mesozoic:the Late Permian to Early Triassic(c.261-230 Ma);the Middle to Late Triassic(c.229-210 Ma);the Early to Middle Jurassic(c.206-165 Ma);the Early Cretaceous(c.138-110 Ma) and the Late Cretaceous(c.103-75 Ma).105 new and 830 published geochemical data reveal that the intrusive rocks in different episodes have distinct geochemical compositions.The Late Permian to Early Triassic intrusive rocks are all distributed in the Jomda-Weixi magmatic belt,showing arc-like characteristics;the Middle to Late Triassic intrusive rocks widely distributed in both Jomda-Weixi and Yidun magmatic belts,also demonstrating volcanic-arc granite features;the Early to Middle Jurassic intrusive rocks are mostly exposed in the easternmost Yidun magmatic belt and scattered in the westernmost Yangtza Block along the Garze-Litang suture,showing the properties of syn-collisional granite;nearly all the Early Cretaceous intrusive rocks distributed in the NE Lhasa magmatic belt along Bangong suture,exhibiting both arc-like and syn-collision-like characteristics;and the Late Cretaceous intrusive rocks mainly exposed in the westernmost Yidun magmatic belt,with A-type granite features.These suggest that the co-collision related magmatism in Indosinian period developed in the central and eastern parts of NSOB while the Yanshan period co-collision related magmatism mainly occurred in the west area.In detail,the earliest magmatism developed in late Permian to Triassic and formed the Jomda-Wei magmatic belt,then magmatic activity migrated eastwards and westwards,forming the Yidun magmatic bellt,the magmatism weakend at the end of late Triassic,until the explosure of the magmatic activity occurred in early Cretaceous in the west NSOB,forming the NE Lhasa magmatic belt.Then the magmatism migrated eastwards and made an impact on the within-plate magmatism in Yidun magmatic belt in late Cretaceous. | GONG Xuejing YANG Zhusen MENG Xiangjin PAN Xiaofei WANG Qian ZHANG Lejun | 2017 | Acta Geologica Sinica(English Edition)2017，91，3： | 4 |
| 2 | Analysis of nucleosome positioning in promoters of miRNA genes and protein-coding genes显示文摘Nucleosome positioning in promoters is important for gene transcription regulation. In this paper, with a nucleosome prediction model, curvature profile, the characteristics of nucleosome positioning in promoters are analyzed for miRNA genes and protein-coding genes. In the vicinity of transcription start site (TSS), there is a nucleosome-free region (NFR) followed by a positioned nucleosome at ~200 bp downstream of TSS. A similar characteristic is observed in independent intronic promoters and intergenic promoters, namely, both types of promoters have a longer NFR in 0—-400 bp upstream of TSS. Moreover, transcription factor binding sites (TFBSs) locate in the NFR with a high concentration. However, nucleosome pattern in dependent intronic promoters are like that in protein-coding promoters, with two nucleosomes positioned at -200—-400 bp and -400—-600 bp upstream of TSS. The results indicate nucleosome positioning is probably different in independent miRNA promoters and protein-coding promoters; and positioning seems to be an important factor not only in regulation of protein-coding gene, but also in that of miRNA gene. | LIU HongDe ZHANG DeJin XIE JianMing YUAN ZhiDong MA Xin LU ZhiYuan GONG LeJun SUN Xiao | 2010 | Chinese Science Bulletin2010，55，22： | 1 |
| 3 | Predicting Genotype Information Related to COVID-19 for Molecular Mechanism Based on Computational Methods显示文摘Novel coronavirus disease 2019(COVID-19)is an ongoing health emergency.Several studies are related to COVID-19.However,its molecular mechanism remains unclear.The rapid publication of COVID-19 provides a new way to elucidate its mechanism through computational methods.This paper proposes a prediction method for mining genotype information related to COVID-19 from the perspective of molecular mechanisms based on machine learning.The method obtains seed genes based on prior knowledge.Candidate genes are mined from biomedical literature.The candidate genes are scored by machine learning based on the similarities measured between the seed and candidate genes.Furthermore,the results of the scores are used to perform functional enrichment analyses,including KEGG,interaction network,and Gene Ontology,for exploring the molecular mechanism of COVID-19.Experimental results show that the method is promising for mining genotype information to explore the molecular mechanism related to COVID-19. | Lejun Gong Xingxing Zhang Li Zhang Zhihong Gao | 2021 | Computer Modeling in Engineering & Sciences2021，，10： | 0 |
| 4 | BDLR:lncRNA identification using ensemble learning显示文摘Long non-coding RNAs(lncRNAs)play an important role in many life activities such as epigenetic material regulation,cell cycle regulation,dosage compensation and cell differentiation regulation,and are associated with many human diseases.There are many limitations in identifying and annotating lncRNAs using traditional biological experimental methods.With the development of high-throughput sequencing technology,it is of great practical significance to identify the lncRNAs from massive RNA sequence data using machine learning method.Based on the Bagging method and Decision Tree algorithm in ensemble learning,this paper proposes a method of lncRNAs gene sequence identification called BDLR.The identification results of this classification method are compared with the identification results of several models including Byes,Support Vector Machine,Logical Regression,Decision Tree and Random Forest.The experimental results show that the lncRNAs identification method named BDLR proposed in this paper has an accuracy of 86.61%in the human test set and 90.34%in the mouse for lncRNAs,which is more than the identification results of the other methods.Moreover,the proposed method offers a reference for researchers to identify lncRNAs using the ensemble learning. | LEJUN GONG SHEHAI ZHOU JINGMEI CHEN YONGMIN LI LI ZHANG ZHIHONG GAO | 2022 | BIOCELL2022，46，4： | 0 |
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