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- 저자 : Guo Wenbo (검색결과 3 건)
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Wenbo Zhou,Huiyan Wang,Yuqi Yang,Fang Guo,Bin Yu,Zhaoliang Su 한국분자세포생물학회 2022 Molecules and cells Vol.45 No.5
Trophoblasts, important functional cells in the placenta, play a critical role in maintaining placental function. The heterogeneity of trophoblasts has been reported, but little is known about the trophoblast subtypes and distinctive functions during preeclampsia (PE). In this study, we aimed to gain insight into the cell type-specific transcriptomic changes by performing unbiased single-cell RNA sequencing (scRNA-seq) of placental tissue samples, including those of patients diagnosed with PE and matched healthy controls. A total of 29,006 cells were identified in 11 cell types, including trophoblasts and immune cells, and the functions of the trophoblast subtypes in the PE group and the control group were also analyzed. As an important trophoblast subtype, extravillous trophoblasts (EVTs) were further divided into 4 subgroups, and their functions were preliminarily analyzed. We found that some biological processes related to pregnancy, hormone secretion and immunity changed in the PE group. We also identified and analyzed the regulatory network of transcription factors (TFs) identified in the EVTs, among which 3 modules were decreased in the PE group. Then, through in vitro cell experiments, we found that in one of the modules, CEBPB and GTF2B may be involved in EVT dysfunction in PE. In conclusion, our study showed the different transcriptional profiles and regulatory modules in trophoblasts between placentas in the control and PE groups at the single-cell level; these changes may be involved in the pathological process of PE, providing a new molecular theoretical basis for preeclamptic trophoblast dysfunction.
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GCNXSS: An Attack Detection Approach for Cross-Site Scripting Based on Graph Convolutional Networks
Hongyu Pan,Yong Fang,Cheng Huang,Wenbo Guo,Xuelin Wan 한국인터넷정보학회 2022 KSII Transactions on Internet and Information Syst Vol.16 No.12
Since machine learning was introduced into cross-site scripting (XSS) attack detection, many researchers have conducted related studies and achieved significant results, such as saving time and labor costs by not maintaining a rule database, which is required by traditional XSS attack detection methods. However, this topic came across some problems, such as poor generalization ability, significant false negative rate (FNR) and false positive rate (FPR). Moreover, the automatic clustering property of graph convolutional networks (GCN) has attracted the attention of researchers. In the field of natural language process (NLP), the results of graph embedding based on GCN are automatically clustered in space without any training, which means that text data can be classified just by the embedding process based on GCN. Previously, other methods required training with the help of labeled data after embedding to complete data classification. With the help of the GCN auto-clustering feature and labeled data, this research proposes an approach to detect XSS attacks (called GCNXSS) to mine the dependencies between the units that constitute an XSS payload. First, GCNXSS transforms a URL into a word homogeneous graph based on word co-occurrence relationships. Then, GCNXSS inputs the graph into the GCN model for graph embedding and gets the classification results. Experimental results show that GCNXSS achieved successful results with accuracy, precision, recall, F1-score, FNR, FPR, and predicted time scores of 99.97%, 99.75%, 99.97%, 99.86%, 0.03%, 0.03%, and 0.0461ms. Compared with existing methods, GCNXSS has a lower FNR and FPR with stronger generalization ability.
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Jin Yuhan,Ouyang Yina,Fan Xinyang,Huang Jing,Guo Wenbo,Miao Yongwang 아세아·태평양축산학회 2022 Animal Bioscience Vol.35 No.11
Objective: Water buffalo, an important domestic animal in tropical and subtropical regions, play an important role in agricultural economy. It is an important source for milk, meat, horns, skin, and draft power, especially its rich milk that is the great source of cream, butter, yogurt, and many cheeses. In recent years, long noncoding RNAs (lncRNAs) have been reported to play pivotal roles in many biological processes. Previous studies for the mammary gland development of water buffalo mainly focus on protein coding genes. However, lncRNAs of water buffalo remain poorly understood, and the regulation relationship between mammary gland development/milk production traits and lncRNA expression is also unclear. Methods: Here, we sequenced 22 samples of the milk somatic cells from three lactation stages and integrated the current annotation and identified 7,962 lncRNA genes. Results: By comparing the lncRNA genes of the water buffalo in the early, peak, and late different lactation stages, we found that lncRNA gene lnc-bbug14207 displayed significantly different expression between early and late lactation stages. And lnc-bbug14207 may regulate neighboring milk fat globule-EGF factor 8 (MFG-E8) and hyaluronan and proteoglycan link protein 3 (HAPLN3) protein coding genes, which are vital for mammary gland development. Conclusion: This study provides the first genome-wide identification of water buffalo lncRNAs and unveils the potential lncRNAs that impact mammary gland development. Objective: Water buffalo, an important domestic animal in tropical and subtropical regions, play an important role in agricultural economy. It is an important source for milk, meat, horns, skin, and draft power, especially its rich milk that is the great source of cream, butter, yogurt, and many cheeses. In recent years, long noncoding RNAs (lncRNAs) have been reported to play pivotal roles in many biological processes. Previous studies for the mammary gland development of water buffalo mainly focus on protein coding genes. However, lncRNAs of water buffalo remain poorly understood, and the regulation relationship between mammary gland development/milk production traits and lncRNA expression is also unclear.Methods: Here, we sequenced 22 samples of the milk somatic cells from three lactation stages and integrated the current annotation and identified 7,962 lncRNA genes.Results: By comparing the lncRNA genes of the water buffalo in the early, peak, and late different lactation stages, we found that lncRNA gene <i>lnc-bbug14207</i> displayed significantly different expression between early and late lactation stages. And <i>lnc-bbug14207</i> may regulate neighboring milk fat globule-EGF factor 8 (<i>MFG-E8</i>) and hyaluronan and proteoglycan link protein 3 (<i>HAPLN3</i>) protein coding genes, which are vital for mammary gland development.Conclusion: This study provides the first genome-wide identification of water buffalo lncRNAs and unveils the potential lncRNAs that impact mammary gland development.
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