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  1. Article: Testing the hypothesis of an ancient Roman soldier origin of the Liqian people in northwest China: a Y-chromosome perspective.

    Zhou, Ruixia / An, Lizhe / Wang, Xunling / Shao, Wei / Lin, Gonghua / Yu, Weiping / Yi, Lin / Xu, Shijian / Xu, Jiujin / Xie, Xiaodong

    Journal of human genetics

    2007  Volume 52, Issue 7, Page(s) 584–591

    Abstract: The Liqian people in north China are well known because of the controversial hypothesis ... At the haplogroup levels, 77% Liqian Y chromosomes were restricted to East Asia. Principal component (PC) and ... to the Liqian gene pool. The Liqian and the Yugur people, regarded as kindred populations with common origins ...

    Abstract The Liqian people in north China are well known because of the controversial hypothesis of an ancient Roman mercenary origin. To test this hypothesis, 227 male individuals representing four Chinese populations were analyzed at 12 short tandem repeat (STR) loci and 12 single nucleotide polymorphisms (SNP). At the haplogroup levels, 77% Liqian Y chromosomes were restricted to East Asia. Principal component (PC) and multidimensional scaling (MDS) analysis suggests that the Liqians are closely related to Chinese populations, especially Han Chinese populations, whereas they greatly deviate from Central Asian and Western Eurasian populations. Further phylogenetic and admixture analysis confirmed that the Han Chinese contributed greatly to the Liqian gene pool. The Liqian and the Yugur people, regarded as kindred populations with common origins, present an underlying genetic difference in a median-joining network. Overall, a Roman mercenary origin could not be accepted as true according to paternal genetic variation, and the current Liqian population is more likely to be a subgroup of the Chinese majority Han.
    MeSH term(s) China ; Chromosomes, Human, Y/genetics ; Emigration and Immigration/history ; Haplotypes/genetics ; History, Ancient ; Humans ; Male ; Microsatellite Repeats/genetics ; Military Personnel/history ; Phylogeny ; Principal Component Analysis/history ; Rome
    Language English
    Publishing date 2007-06-20
    Publishing country England
    Document type Historical Article ; Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 1425192-9
    ISSN 1435-232X ; 1434-5161
    ISSN (online) 1435-232X
    ISSN 1434-5161
    DOI 10.1007/s10038-007-0155-0
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 201: Show issues Location:
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    Jg. 1995 - 2021: Lesesall (1.OG)
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  2. Article: Finding potential lncRNA-disease associations using a boosting-based ensemble learning model.

    Zhou, Liqian / Peng, Xinhuai / Zeng, Lijun / Peng, Lihong

    Frontiers in genetics

    2024  Volume 15, Page(s) 1356205

    Abstract: Introduction: ...

    Abstract Introduction:
    Language English
    Publishing date 2024-03-01
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2606823-0
    ISSN 1664-8021
    ISSN 1664-8021
    DOI 10.3389/fgene.2024.1356205
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article: Editorial: Machine Learning-Based Methods for RNA Data Analysis.

    Peng, Lihong / Yang, Jialiang / Wang, Minxian / Zhou, Liqian

    Frontiers in genetics

    2022  Volume 13, Page(s) 828575

    Language English
    Publishing date 2022-05-25
    Publishing country Switzerland
    Document type Editorial
    ZDB-ID 2606823-0
    ISSN 1664-8021
    ISSN 1664-8021
    DOI 10.3389/fgene.2022.828575
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article: Editorial: Machine learning-based methods for RNA data analysis-Volume II.

    Peng, Lihong / Yang, Jialiang / Wang, Minxian / Zhou, Liqian

    Frontiers in genetics

    2022  Volume 13, Page(s) 1010089

    Language English
    Publishing date 2022-11-29
    Publishing country Switzerland
    Document type Editorial
    ZDB-ID 2606823-0
    ISSN 1664-8021
    ISSN 1664-8021
    DOI 10.3389/fgene.2022.1010089
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article ; Online: Unlocking the effects and optimization path of financial support for improvement in environmental quality and rural revitalization development: an empirical analysis based on provincial data of Shaanxi province.

    Wei, Liqian / Wang, Yaping / Zhou, Zizhan / Luo, Jianchao

    Environmental science and pollution research international

    2023  Volume 30, Issue 16, Page(s) 46795–46812

    Abstract: This study establishes an economic financial support model for the improvement of environmental quality through the development rural revitalization based on the extended Cobb-Douglas production function. Using statistical data from Shaanxi Province, ... ...

    Abstract This study establishes an economic financial support model for the improvement of environmental quality through the development rural revitalization based on the extended Cobb-Douglas production function. Using statistical data from Shaanxi Province, China, from 2004 to 2019, a vector autoregressive (VAR) model is used to empirically analyze the development effect of financial support for rural revitalization and to give the focus points and optimization paths for financial support for environmental quality, rural revitalization and sustainable development. The research results show that financial support plays an active and long-term role in improving environmental quality and promoting rural revitalization and sustainable development. Specifically, the effect of financial instruments in supporting rural revitalization and sustainable development is continuous. In the insurance system, increasing the scale of agricultural insurance support and expanding the coverage of agricultural insurance are key to improving environmental quality and promoting rural revitalization and sustainable development. Therefore, financial policy makers should improve the targeting of financial instruments to provide the right guidance for improving the quality of rural environment and enhancing rural economy, so as to ultimately realize rural revitalization in China.
    MeSH term(s) Humans ; Environment ; Agriculture ; Sustainable Development ; Models, Economic ; Financial Support ; Rural Population ; China
    Language English
    Publishing date 2023-02-01
    Publishing country Germany
    Document type Journal Article
    ZDB-ID 1178791-0
    ISSN 1614-7499 ; 0944-1344
    ISSN (online) 1614-7499
    ISSN 0944-1344
    DOI 10.1007/s11356-023-25569-6
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    In stock of ZB MED Bonn / Germany

    Z 5915: Show issues

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  6. Article ; Online: An AQP-4-IgG-Positive Patient with Neuroimaging Findings Suggestive of Multiple Sclerosis.

    Li, Mingxia / Liu, Shuangxi / Zhou, Jun / Xiao, Liqian / Man, Rongyong / Yin, Junjie

    The American journal of case reports

    2024  Volume 25, Page(s) e942475

    Abstract: BACKGROUND Multiple sclerosis (MS) and neuromyelitis optica spectrum disorders (NMOSDs) are 2 similar but distinct diseases. These diseases were difficult to distinguish from each other until aquaporin-4-IgG (AQP-4-IgG) was discovered. The accurate ... ...

    Abstract BACKGROUND Multiple sclerosis (MS) and neuromyelitis optica spectrum disorders (NMOSDs) are 2 similar but distinct diseases. These diseases were difficult to distinguish from each other until aquaporin-4-IgG (AQP-4-IgG) was discovered. The accurate identification of these 2 diseases is crucial for appropriate drug treatment in clinical practice. Herein, we report a case of AQP-4-IgG seroconversion with magnetic resonance imaging (MRI) findings suggestive of MS. CASE REPORT A 54-year-old woman developed weakness in her right lower extremity that gradually returned to normal 4 years ago. Recently, she was admitted to the hospital for numbness and weakness of both lower limbs and the right upper limb for more than 10 days. The clinical and MRI features of the patient suggested a high susceptibility for misdiagnosis of MS. However, careful observation of the MRI revealed the presence of atypical MS lesions ("red flag" signs), indicating the possibility of other diagnoses in this patient. After further examination, serum AQP-4-IgG was detected, suggesting the potential presence of another disorder, NMOSD, in the patient. CONCLUSIONS Attention should be given to the identification of MS MRI "red flag" signs. Even for patients with a high suspicion of MS, it is necessary to conduct antibody tests for AQP-4-IgG, MOG-IgG and other relevant markers to screen for associated diseases because MS disease-modifying therapy approaches may lead to a deterioration in the state of NMOSD patients. Analyzing this case can help us to further distinguish the differences between these 2 types of diseases, which has important practical clinical value.
    MeSH term(s) Female ; Humans ; Middle Aged ; Multiple Sclerosis/diagnostic imaging ; Multiple Sclerosis/pathology ; Myelin-Oligodendrocyte Glycoprotein ; Autoantibodies ; Aquaporin 4 ; Neuroimaging ; Immunoglobulin G
    Chemical Substances Myelin-Oligodendrocyte Glycoprotein ; Autoantibodies ; Aquaporin 4 ; Immunoglobulin G
    Language English
    Publishing date 2024-02-02
    Publishing country United States
    Document type Case Reports ; Journal Article
    ZDB-ID 2517183-5
    ISSN 1941-5923 ; 1941-5923
    ISSN (online) 1941-5923
    ISSN 1941-5923
    DOI 10.12659/AJCR.942475
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  7. Article: Identifying potential drug-target interactions based on ensemble deep learning.

    Zhou, Liqian / Wang, Yuzhuang / Peng, Lihong / Li, Zejun / Luo, Xueming

    Frontiers in aging neuroscience

    2023  Volume 15, Page(s) 1176400

    Abstract: Introduction: Drug-target interaction prediction is one important step in drug research and development. Experimental methods are time consuming and laborious.: Methods: In this study, we developed a novel DTI prediction method called EnGDD by ... ...

    Abstract Introduction: Drug-target interaction prediction is one important step in drug research and development. Experimental methods are time consuming and laborious.
    Methods: In this study, we developed a novel DTI prediction method called EnGDD by combining initial feature acquisition, dimensional reduction, and DTI classification based on Gradient boosting neural network, Deep neural network, and Deep Forest.
    Results: EnGDD was compared with seven stat-of-the-art DTI prediction methods (BLM-NII, NRLMF, WNNGIP, NEDTP, DTi2Vec, RoFDT, and MolTrans) on the nuclear receptor, GPCR, ion channel, and enzyme datasets under cross validations on drugs, targets, and drug-target pairs, respectively. EnGDD computed the best recall, accuracy, F1-score, AUC, and AUPR under the majority of conditions, demonstrating its powerful DTI identification performance. EnGDD predicted that D00182 and hsa2099, D07871 and hsa1813, DB00599 and hsa2562, D00002 and hsa10935 have a higher interaction probabilities among unknown drug-target pairs and may be potential DTIs on the four datasets, respectively. In particular, D00002 (Nadide) was identified to interact with hsa10935 (Mitochondrial peroxiredoxin3) whose up-regulation might be used to treat neurodegenerative diseases. Finally, EnGDD was used to find possible drug targets for Parkinson's disease and Alzheimer's disease after confirming its DTI identification performance. The results show that D01277, D04641, and D08969 may be applied to the treatment of Parkinson's disease through targeting hsa1813 (dopamine receptor D2) and D02173, D02558, and D03822 may be the clues of treatment for patients with Alzheimer's disease through targeting hsa5743 (prostaglandinendoperoxide synthase 2). The above prediction results need further biomedical validation.
    Discussion: We anticipate that our proposed EnGDD model can help discover potential therapeutic clues for various diseases including neurodegenerative diseases.
    Language English
    Publishing date 2023-06-15
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2558898-9
    ISSN 1663-4365
    ISSN 1663-4365
    DOI 10.3389/fnagi.2023.1176400
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  8. Article ; Online: Role of SARS‑CoV‑2 nucleocapsid protein in affecting immune cells and insights on its molecular mechanisms.

    Lu, Yan / Ye, Ziyu / Liu, Xinlan / Zhou, Liqian / Ding, Xiang / Hou, Yiling

    Experimental and therapeutic medicine

    2023  Volume 26, Issue 5, Page(s) 504

    Abstract: The present study aimed to explore the immune regulatory function of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) nucleocapsid (N) protein and related mechanisms. In a series of protein activity experiments, SARS-CoV-2 N protein promoted ... ...

    Abstract The present study aimed to explore the immune regulatory function of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) nucleocapsid (N) protein and related mechanisms. In a series of protein activity experiments, SARS-CoV-2 N protein promoted proliferation of three immune cell lines: mouse Raw264.7, human Jurkat and human Raji in a dose-dependent manner. A total of 10 µg/ml N protein could significantly change cell cycle progression of the aforementioned three immune cell lines and could promote quick entry of Raw264.7 cells into G
    Language English
    Publishing date 2023-09-12
    Publishing country Greece
    Document type Journal Article
    ZDB-ID 2683844-8
    ISSN 1792-1015 ; 1792-0981
    ISSN (online) 1792-1015
    ISSN 1792-0981
    DOI 10.3892/etm.2023.12203
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  9. Article ; Online: EnANNDeep: An Ensemble-based lncRNA-protein Interaction Prediction Framework with Adaptive k-Nearest Neighbor Classifier and Deep Models.

    Peng, Lihong / Tan, Jingwei / Tian, Xiongfei / Zhou, Liqian

    Interdisciplinary sciences, computational life sciences

    2022  Volume 14, Issue 1, Page(s) 209–232

    Abstract: lncRNA-protein interactions (LPIs) prediction can deepen the understanding of many important biological processes. Artificial intelligence methods have reported many possible LPIs. However, most computational techniques were evaluated mainly on one ... ...

    Abstract lncRNA-protein interactions (LPIs) prediction can deepen the understanding of many important biological processes. Artificial intelligence methods have reported many possible LPIs. However, most computational techniques were evaluated mainly on one dataset, which may produce prediction bias. More importantly, they were validated only under cross validation on lncRNA-protein pairs, and did not consider the performance under cross validations on lncRNAs and proteins, thus fail to search related proteins/lncRNAs for a new lncRNA/protein. Under an ensemble learning framework (EnANNDeep) composed of adaptive k-nearest neighbor classifier and Deep models, this study focuses on systematically finding underlying linkages between lncRNAs and proteins. First, five LPI-related datasets are arranged. Second, multiple source features are integrated to depict an lncRNA-protein pair. Third, adaptive k-nearest neighbor classifier, deep neural network, and deep forest are designed to score unknown lncRNA-protein pairs, respectively. Finally, interaction probabilities from the three predictors are integrated based on a soft voting technique. In comparing to five classical LPI identification models (SFPEL, PMDKN, CatBoost, PLIPCOM, and LPI-SKF) under fivefold cross validations on lncRNAs, proteins, and LPIs, EnANNDeep computes the best average AUCs of 0.8660, 0.8775, and 0.9166, respectively, and the best average AUPRs of 0.8545, 0.8595, and 0.9054, respectively, indicating its superior LPI prediction ability. Case study analyses indicate that SNHG10 may have dense linkage with Q15717. In the ensemble framework, adaptive k-nearest neighbor classifier can separately pick the most appropriate k for each query lncRNA-protein pair. More importantly, deep models including deep neural network and deep forest can effectively learn the representative features of lncRNAs and proteins.
    MeSH term(s) Artificial Intelligence ; Computational Biology/methods ; Machine Learning ; Neural Networks, Computer ; RNA, Long Noncoding/genetics ; RNA, Long Noncoding/metabolism
    Chemical Substances RNA, Long Noncoding
    Language English
    Publishing date 2022-01-10
    Publishing country Germany
    Document type Journal Article
    ZDB-ID 2493085-4
    ISSN 1867-1462 ; 1913-2751
    ISSN (online) 1867-1462
    ISSN 1913-2751
    DOI 10.1007/s12539-021-00483-y
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  10. Article ; Online: Multivariate Strategy Preparation of Nanoscale Ru-Doped Metal-Organic Frameworks with Boosted Photoactivity for Bioimaging and Reactive Oxygen Species Generation.

    Zhou, Junli / Liu, Liqian / Li, Yite / Wang, Lei / Xie, Zhigang

    Inorganic chemistry

    2022  Volume 61, Issue 11, Page(s) 4647–4654

    Abstract: How to incorporate chromophores into MOFs is a key for the development of multifunctional photoactive systems. The poor internalization by cancer cells and low efficiency of ROS generation hamper the potential clinic application of Ru-based molecular ... ...

    Abstract How to incorporate chromophores into MOFs is a key for the development of multifunctional photoactive systems. The poor internalization by cancer cells and low efficiency of ROS generation hamper the potential clinic application of Ru-based molecular agents. In this work, a nanoscale Ru-doped metal-organic framework Hf-UiO-Ru (Hf-Ru) with framework-boosted photoactivities was prepared
    MeSH term(s) Metal-Organic Frameworks/chemistry ; Metal-Organic Frameworks/pharmacology ; Organometallic Compounds/pharmacology ; Phthalic Acids ; Reactive Oxygen Species
    Chemical Substances Metal-Organic Frameworks ; Organometallic Compounds ; Phthalic Acids ; Reactive Oxygen Species ; UiO-66
    Language English
    Publishing date 2022-03-10
    Publishing country United States
    Document type Journal Article
    ZDB-ID 1484438-2
    ISSN 1520-510X ; 0020-1669
    ISSN (online) 1520-510X
    ISSN 0020-1669
    DOI 10.1021/acs.inorgchem.1c03649
    Database MEDical Literature Analysis and Retrieval System OnLINE

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