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Correction: Association of regular glucosamine use with incident dementia: evidence from a longitudinal cohort and Mendelian randomization study

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BMC medicine 2023年第1期21卷 175页

作者： Jiazhen Zheng Can Ni Yingchai Zhang Jinghan Huang Daniel Nyarko Hukportie buwen liang Shaojun Tang Bioscience and Biomedical Engineering Thrust Systems Hub The Hong Kong University of Science and Technology (Guangzhou) Guangzhou Guangdong China. Department of Medicine and Therapeutics Prince of Wales Hospital The Chinese University of Hong Kong Sha Tin New Territories Hong Kong SAR China. Biomedical Genetics Section School of Medicine Boston University Boston USA. Department of Chemical Pathology Faculty of Medicine The Chinese University of Hong Kong Hong Kong SAR China. Department of Epidemiology School of Public Health (Guangdong Provincial Key Laboratory of Tropical Disease Research) Southern Medical University Guangzhou China. Bioscience and Biomedical Engineering Thrust Systems Hub The Hong Kong University of Science and Technology (Guangzhou) Guangzhou Guangdong China. shaojuntang@ust.hk. Division of Emerging Interdisciplinary Areas The Hong Kong University of Science and Technology Clear Water Bay Hong Kong SAR China. shaojuntang@ust.hk.

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Effect of global and regional SST biases on the East Asian Summer Monsoon in the MetUM GA7 and GC3 configurations

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CLIMATE DYNAMICS 2024年第2期62卷 1535-1553页

作者： Franco-Diaz, Armenia Klingaman, Nicholas P. Turner, Andrew G. Dong, buwen Guo, liang Karlsruhe Inst Technol Inst Meteorol & Climate Res D-76131 Karlsruhe Germany Univ Reading Natl Ctr Atmospher Sci Dept Meteorol Reading RG6 5ES England Univ Reading Dept Meteorol POB 243 Reading RG6 6ET England Citadel Investment Grp Moor House120 London Wall London EC2Y 5ET England

Climate-length experiments of the Met Office Unified Model Global Atmosphere 7.0 (GA7) and Global Coupled 3.0 (GC3) configurations are evaluated against observations and reanalyses for the simulation of the East Asian summer monsoon (EASM). The results show systematic model biases, such as overestimated rainfall over southern China and underestimated rainfall over northern China, suggesting a monsoon that does not penetrate northward enough. We evaluate the effects on the EASM of regional errors in sea-surface temperature (SST) conditions in three regions: the Pacific, the Indian, and the Atlantic Oceans. The global SST biases in GC3 configuration substantially shift the EASM seasonal cycle: a late northward progression of the EASM in the early/mid-monsoon season, and an early retreat of the monsoon that also reduces rainfall over most of northern China. The EASM seasonal rainfall bias in the EASM region is linked to changes in the locations and strength of the western North Pacific subtropical high, which is associated with biases in local evaporation and moisture transport towards South China. GC3 biases in the El Nino-Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD) teleconnection pathways also influence the EASM biases. GC3 biases weaken the ENSO teleconnection to the EASM and cause a strong dry bias in southeast China during developing El Nino.

关键词： MetUM ENSO IOD Interannual variability

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Anthropogenic Influence on 2022 Extreme January-February Precipitation in Southern China

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BULLETIN OF THE AMERICAN METEOROLOGICAL SOCIETY 2023年第11期104卷 E1935-E1940页

作者： Hu, Yamin Dong, buwen Xie, Jiehong Tan, Haobo Zhou, Baiquan Lin, Shuheng He, Jian Zhao, liang CMA Guangdong Climate Ctr Guangzhou 510641 Peoples R China Univ Reading Natl Ctr Atmospher Sci Dept Meteorol Reading England CMA Jieyang Meteorol Bur Jieyang 522031 Peoples R China CMA Guangdong Meteorol Serv Guangzhou Peoples R China Chinese Acad Meteorol Sci State Key Lab Severe Weather CMA Beijing Peoples R China Sun Yat Sen Univ Sch Atmospher Sci Guangzhou Peoples R China Chinese Acad Sci Inst Atmospher Phys State Key Lab Numer Modeling Atmosphere Sci & Geop Beijing Peoples R China

The precipitation in January-February 2022 in southern China was the second-largest amount since 1961. Anthropogenic influence reduced the likelihood of extreme events like 2022 by about 50% (55%) in HadGEM3 (CMIP6).

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保健食品中非法添加西地那非衍生物的确证

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中国药事 2019年第4期33卷 429-434页

作者：王珂董培智申国华张禄梁卜文郭景文山西中医药大学晋中030619 山西省食品药品检验所太原030001

目的:对在保健食品中发现的一种非法添加的西地那非衍生物进行确证。方法:采用高效液相色谱-二极管阵列检测器(HPLC-DAD)技术在日常检测保健食品非法添加时发现一种新型西地那非衍生物。经制备液相法分离纯化得到该化合物,采用高效液相... 详细信息

目的:对在保健食品中发现的一种非法添加的西地那非衍生物进行确证。方法:采用高效液相色谱-二极管阵列检测器(HPLC-DAD)技术在日常检测保健食品非法添加时发现一种新型西地那非衍生物。经制备液相法分离纯化得到该化合物,采用高效液相色谱-二级质谱联用(LC-MS/MS)技术获得该化合物准分子离子和碎片离子,并进一步通过核磁共振氢谱确认,结合文献分析,最终确证该化合物。结果:该化合物分子式C_(23)H_(30)N_6O_3,分子量438,结构为去甲基卡波地那非。结论:去甲基卡波地那非在国内未被报道,是一种新型PDE-5抑制剂。

关键词：保健食品非法添加西地那非衍生物 PDE-5抑制剂去甲基卡波地那非液质联用核磁共振

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Human disease MiRNA inference by combining target information based on heterogeneous manifolds

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JOURNAL OF BIOMEDICAL INFORMATICS 2018年 80卷 26-36页

作者： Ding, Pingjian Luo, Jiawei liang, Cheng Xiao, Qiu Cao, buwen Hunan Univ Coll Comp Sci & Elect Engn Changsha 410083 Hunan Peoples R China Shandong Normal Univ Sch Informat Sci & Engn Jinan 250014 Shandong Peoples R China

The emergence of network medicine has provided great insight into the identification of disease-related molecules, which could help with the development of personalized medicine. However, the state-of-the-art methods could neither simultaneously consider target information and the known miRNA-disease associations nor effectively explore novel gene-disease associations as a by-product during the process of inferring disease-related miRNAs. Computational methods incorporating multiple sources of information offer more opportunities to infer disease-related molecules, including miRNAs and genes in heterogeneous networks at a system level. In this study, we developed a novel algorithm, named inference of Disease-related MiRNAs based on Heterogeneous Manifold (DMHM), to accurately and efficiently identify miRNA-disease associations by integrating multi-omits data. Graph-based regularization was utilized to obtain a smooth function on the data manifold, which constitutes the main principle of DMHM. The novelty of this framework lies in the relatedness between diseases and miRNAs, which are measured via heterogeneous manifolds on heterogeneous networks integrating target information. To demonstrate the effectiveness of DMHM, we conducted comprehensive experiments based on HMDD datasets and compared DMHM with six state-of-the-art methods. Experimental results indicated that DMHM significantly outperformed the other six methods under fivefold cross validation and de novo prediction tests. Case studies have further confirmed the practical usefulness of DMHM.

关键词： miRNA-disease associations Heterogeneousnetwork Manifold learning Graph regularization Target information

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CeModule: an integrative framework for discovering regulatory patterns from genomic data in cancer

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BMC BIOINFORMATICS 2019年第1期20卷 67-67页

作者： Xiao, Qiu Luo, Jiawei liang, Cheng Cai, Jie Li, Guanghui Cao, buwen Hunan Univ Coll Comp Sci & Elect Engn Changsha 410082 Hunan Peoples R China Hunan Normal Univ Hunan Prov Key Lab Intelligent Comp & Language In Changsha 410081 Hunan Peoples R China Shandong Normal Univ Coll Informat Sci & Engn Jinan 250000 Shandong Peoples R China

BackgroundNon-coding RNAs (ncRNAs) are emerging as key regulators and play critical roles in a wide range of tumorigenesis. Recent studies have suggested that long non-coding RNAs (lncRNAs) could interact with microRNAs (miRNAs) and indirectly regulate miRNA targets through competing interactions. Therefore, uncovering the competing endogenous RNA (ceRNA) regulatory mechanism of lncRNAs, miRNAs and mRNAs in post-transcriptional level will aid in deciphering the underlying pathogenesis of human polygenic diseases and may unveil new diagnostic and therapeutic opportunities. However, the functional roles of vast majority of cancer specific ncRNAs and their combinational regulation patterns are still insufficiently *** we develop an integrative framework called CeModule to discover lncRNA, miRNA and mRNA-associated regulatory modules. We fully utilize the matched expression profiles of lncRNAs, miRNAs and mRNAs and establish a model based on joint orthogonality non-negative matrix factorization for identifying modules. Meanwhile, we impose the experimentally verified miRNA-lncRNA interactions, the validated miRNA-mRNA interactions and the weighted gene-gene network into this framework to improve the module accuracy through the network-based penalties. The sparse regularizations are also used to help this model obtain modular sparse solutions. Finally, an iterative multiplicative updating algorithm is adopted to solve the optimization *** applied CeModule to two cancer datasets including ovarian cancer (OV) and uterine corpus endometrial carcinoma (UCEC) obtained from TCGA. The modular analysis indicated that the identified modules involving lncRNAs, miRNAs and mRNAs are significantly associated and functionally enriched in cancer-related biological processes and pathways, which may provide new insights into the complex regulatory mechanism of human diseases at the system level.

关键词： Regulatory pattern Module discovery microRNA lncRNA function ceRNA Cancer Machine learning

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Low-Temperature Fabrication of Wafer-Bonded Ge/Si p-i-n Photodiodes by Layer Exfoliation and Nanosecond-Pulse Laser Annealing

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IEEE TRANSACTIONS ON ELECTRON DEVICES 2019年第3期66卷 1353-1360页

作者： Ke, Shaoying Ye, Yujie Wu, Jinyong liang, Dongxue Cheng, buwen Li, Zhiyong Ruan, Yujiao Zhang, Xiaoying Huang, Wei Wang, Jianyuan Xu, Jianfang Li, Cheng Chen, Songyan Xiamen Univ Dept Phys Collaborat Innovat Ctr Optoelect Semicond & Effic Fujian Prov Key Lab Semicond & Applicat Xiamen 361005 Peoples R China Chinese Acad Sci Inst Semicond State Key Lab Integrated Optoelect Beijing 100083 Peoples R China Xiamen Inst Measurement & Testing Xiamen 361005 Peoples R China Xiamen Univ Technol Sch Optoelect & Commun Engn Fujian Key Lab Optoelect Technol & Devices Xiamen 361024 Peoples R China

We report a potential method for the fabrication of Si-based Ge film with low threading dislocation (TD) density (<10(5) cm(-2)) based on a low-temperature Ge/Si wafer bonding and smart cut technique. This method may replace the traditionalepitaxial growth to solve the problems related to the TDs, high temperature, and high vacuum. A near-bubble-free Ge/Si bonded interface is achieved for the fabrication of Ge/Si p-i-n photodiodes. The insertion of amorphous Ge (a-Ge) has crystallized after Ge layer exfoliation, achieving a small FWHMXRD (96 arcsec) of the Ge peak. This determines the low dark current density (5.97 mA/cm(2)) and low ideality factor (1.19) of the Ge/Si p-i-n photodiode. The exfoliated Ge film suffers from in-plane compression strain due to the expansion of the H-2 gas during the blistering process. Interestingly, the in-plane compression strain turns into tensile strain after high-temperature postannealing due to the faster shrinkage rate of Ge during cooling. The in-plane tensile strain leads to the increase in the responsivity of the photodiode. The responsivity of the photodiode at 1310 nm increases from 0.505 to 0.71 A/W and that at 1550 nm increases from 0.244 to 0.524 A/W after postannealing. More importantly, the redshift of the absorption edge of the Ge/Si photodiode (0.1 A/W at 1630 nm) is also observed. This implies that the wafer-bonded Ge/Si p-i-n photodiode can be comparable with the epitaxial one and can be used for optical communication in all wavelength division multiplexing bands, including the L-band (1565-1625 nm) and entire C-band (1530-1565 nm).

关键词： Ge/Si photodiodes Ge/Si wafer bonding layer exfoliation smart cut threading dislocation (TD)

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Collective Prediction of Disease-Associated miRNAs Based on Transduction Learning

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2017年第6期14卷 1468-1475页

作者： Luo, Jiawei Ding, Pingjian liang, Cheng Cao, buwen Chen, Xiangtao Hunan Univ Coll Comp Sci & Elect Engn Changsha 410083 Hunan Peoples R China Shandong Normal Univ Coll Informat Sci & Engn Jinan 250000 Shandong Peoples R China

The discovery of human disease-related miRNA is a challenging problem for complex disease biology research. For existing computational methods, it is difficult to achieve excellent performance with sparse known miRNA-disease association verified by biological experiment. Here, we develop CPTL, a Collective Prediction based on Transduction Learning, to systematically prioritize miRNAs related to disease. By combining disease similarity, miRNA similarity with known miRNA-disease association, we construct a miRNA-disease network for predicting miRNA-disease association. Then, CPTL calculates relevance score and updates the network structure iteratively, until a convergence criterion is reached. The relevance score of node including miRNA and disease is calculated by the use of transduction learning based on its neighbors. The network structure is updated using relevance score, which increases the weight of important links. To show the effectiveness of our method, we compared CPTL with existing methods based on HMDD datasets. Experimental results indicate that CPTL outperforms existing approaches in terms of AUC, precision, recall, and F1-score. Moreover, experiments performed with different number of iterations verify that CPTL has good convergence. Besides, it is analyzed that the varying of weighted parameters affect predicted results. Case study on breast cancer has further confirmed the identification ability of CPTL.

关键词： Collective prediction miRNA disease transduction learning miRNA-disease network

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Predicting MicroRNA-Disease Associations Using Network Topological Similarity Based on DeepWalk

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IEEE ACCESS 2017年 5卷 24032-24039页

作者： Li, Guanghui Luo, Jiawei Xiao, Qiu liang, Cheng Ding, Pingjian Cao, buwen East China Jiaotong Univ Sch Informat Engn Nanchang 330013 Jiangxi Peoples R China Hunan Univ Coll Comp Sci & Elect Engn Changsha 410082 Hunan Peoples R China Shandong Normal Univ Coll Informat Sci & Engn Jinan 250000 Shandong Peoples R China Hunan City Univ Coll Informat & Elect Engn Yiyang 413000 Peoples R China

Recently, increasing experimental studies have shown that microRNAs (miRNAs) involved in multiple physiological processes are connected with several complex human diseases. Identifying human disease-related miRNAs will be useful in uncovering novel prognostic markers for cancer. Currently, several computational approaches have been developed for miRNA-disease association prediction based on the integration of additional biological information of diseases and miRNAs, such as disease semantic similarity and miRNA functional similarity. However, these methods do not work well when this information is unavailable. In this paper, we present a similarity-based miRNA-disease prediction method that enhances the existing association discovery methods through a topology-based similarity measure. DeepWalk, a deep learning method, is utilized in this paper to calculate similarities within a miRNA-disease association network. It shows superior predictive performance for 22 complex diseases, with area under the ROC curve scores ranging from 0.805 to 0.937 by using five-fold cross-validation. In addition, case studies on breast cancer, lung cancer, and prostatic cancer further justify the use of our method to discover latent miRNA-disease pairs.

关键词： Deep learning disease-related microRNAs microRNA-disease association similarity measure

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PCE-FR: A Novel Method for Identifying Overlapping Protein Complexes in Weighted Protein-Protein Interaction Networks Using Pseudo-Clique Extension Based on Fuzzy Relation

IEEE TRANSACTIONS ON NANOBIOSCIENCE

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IEEE TRANSACTIONS ON NANOBIOSCIENCE 2016年第7期15卷 728-738页

作者： Cao, buwen Luo, Jiawei liang, Cheng Wang, Shulin Ding, Pingjian Hunan Univ Coll Comp Sci & Elect Engn Changsha 410082 Hunan Peoples R China Hunan City Univ Sch Informat Sci & Engn Yiyang 413000 Peoples R China Shandong Normal Univ Sch Informat Sci & Engn Jinan 250014 Peoples R China

Identifying overlapping protein complexes in protein-protein interaction (PPI) networks can provide insight into cellular functional organization and thus elucidate underlying cellular mechanisms. Recently, various algorithms for protein complexes detection have been developed for PPI networks. However, majority of algorithms primarily depend on network topological feature and/or gene expression profile, failing to consider the inherent biological meanings between protein pairs. In this paper, we propose a novel method to detect protein complexes using pseudo-clique extension based on fuzzy relation (PCE-FR). Our algorithm operates in three stages: it first forms the nonoverlapping protein substructure based on fuzzy relation and then expands each substructure by adding neighbor proteins to maximize the cohesive score. Finally, highly overlapped candidate protein complexes are merged to form the final protein complex set. Particularly, our algorithm employs the biological significance hidden in protein pairs to construct edge weight for protein interaction networks. The experiment results show that our method can not only outperform classical algorithms such as CFinder, ClusterONE, CMC, RRW, HC-PIN, and ProRank +, but also achieve ideal overall performance in most of the yeast PPI datasets in terms of composite score consisting of precision, accuracy, and separation. We further apply our method to a human PPI network from the HPRD dataset and demonstrate it is very effective in detecting protein complexes compared to other algorithms.

关键词： PPI Protein complex Pseudo-clique extension Fuzzy relation

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