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3. Inferring cell-specific lncRNA regulation with single-cell RNA-sequencing data in the developing human neocortex

Author

Huang, Meng, Ma, Jiangtao, Long, Changzhou, Zhang, Junpeng, Ye, Xiucai, and Sakurai, Tetsuya

Subjects
Quantitative Biology - Molecular Networks
Abstract

Long non-coding RNAs (lncRNAs) are important regulators to modulate gene expression and cell proliferation in the developing human brain. Previous methods mainly use bulk lncRNA and mRNA expression data to study lncRNA regulation. However, to analyze lncRNA regulation regarding individual cells, we focus on single-cell RNA-sequencing (scRNA-seq) data instead of bulk data. Recent advance in scRNA-seq has provided a way to investigate lncRNA regulation at single-cell level. We will propose a novel computational method, CSlncR (cell-specific lncRNA regulation), which combines putative lncRNA-mRNA binding information with scRNA-seq data including lncRNAs and mRNAs to identify cell-specific lncRNA-mRNA regulation networks at individual cells. To understand lncRNA regulation at different development stages, we apply CSlncR to the scRNA-seq data of human neocortex. Network analysis shows that the lncRNA regulation is unique in each cell from the different human neocortex development stages. The comparison results indicate that CSlncR is also an effective tool for predicting cell-specific lncRNA targets and clustering single cells, which helps understand cell-cell communication.

Published
2022

13. A Survey on Multimodal Named Entity Recognition

Author

Qian, Shenyi, Jin, Wenduo, Chen, Yonggang, Ma, Jiangtao, Qiao, Yaqiong, Lu, Jinyu, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Huang, De-Shuang, editor, Premaratne, Prashan, editor, Jin, Baohua, editor, Qu, Boyang, editor, Jo, Kang-Hyun, editor, and Hussain, Abir, editor

Published
2023
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41. A fusion protein based pneumococcal vaccine

Author

Ma, Jiangtao

Subjects
616.9, QH301 Biology
Abstract

The Gram-positive bacterium Streptococcus pneumoniae(pneumococcus) is a major human pathogen, causing Otitis media, pneumonia, bacteraemia and meningitis worldwide. There are over 90 different serotypes of pneumococcus and current pneumococcal vaccines are somewhat limited in their protection against pneumococcal diseases. Currently available vaccines are based on the capsular polysaccharide. The 23-valent polysaccharide (PSP) vaccine can protect most of adults and children older than five, but it cannot protect children under age 2, immunodeficiency patients and cannot protect the elderly very well. The polysaccharide/ protein conjugated vaccines which are complementary to the 23-valent PS vaccine have been introduced. The PSP conjugate vaccine is highly immunogenic and protects children under the age of 2 and immunodeficiency patients against invasive pneumococcal diseases, but its efficacy is threatened by strain replacement and serotype switching since it only protects against the limited number of serotypes contained within the vaccine. There is therefore a need to develop improved vaccines against pneumococcal diseases. Pneumolysin (PLY), a cholesterol dependent cytolysin and an important virulence factor of pneumococcus, can act as a powerful mucosal adjuvant to induce both systemic and mucosal immunity to proteins genetically fused to PLY. Which regions of PLY are required for the novel adjuvant activity is not currently known. The model antigen eGFP was fused to domain 1-3 of PLY (D123PLY) and domain 4 of PLY (D4PLY) by ligation-dependent DNA recombinant technology during my master project. Balb/c mice were intranasally immunised with purified eGFPD4PLY or eGFPD123PLY fusion protein and immune response were then monitored by enzyme linked immunosorbent assay (ELISA). The ELISA data shows truncated PLY lost the adjuvant property and this adjuvant property was restored in the presence of free PLY or A6PLY when antigen was fused with truncated PLY. The aim of the current work is to determine whether a fusion protein based pneumococcal vaccine can be developed to provide protection against three pneumococcal strains in animal models of colonisation and invasive diseases. For this purpose, pneumococcal virulence factors, PsaA, PspA, PspC and PhtD were genetically fused to PLY or Δ6PLY separately by In-fusion cloning technology. Δ6PLY is a toxoid that lacks haemolytic activity but retains the immunogenic and adjuvant activity of PLY. MF1 mice were then vaccinated either intranasally or subcutanously by purified fusion protein antigens and immune responses were also monitored by ELISA; vaccinated mice were finally challenged intranasally by three strains of pneumococcus and monitored for colonisation. The immunity elicited by pneumococcal antigens fused to PLY/Δ6 PLY is protective in a colonisation model.

Published
2012

47. Multi-Head Self-Attention-Enhanced Prototype Network with Contrastive–Center Loss for Few-Shot Relation Extraction.

Author

Ma, Jiangtao, Cheng, Jia, Chen, Yonggang, Li, Kunlin, Zhang, Fan, and Shang, Zhanlei

Subjects
NATURAL language processing, PROTOTYPES, MULTICASTING (Computer networks)
Abstract

Few-shot relation extraction (FSRE) constitutes a critical task in natural language processing (NLP), involving learning relationship characteristics from limited instances to enable the accurate classification of new relations. The existing research primarily concentrates on using prototype networks for FSRE and enhancing their performance by incorporating external knowledge. However, these methods disregard the potential interactions among different prototype networks, and each prototype network can only learn and infer from its limited instances, which may limit the robustness and reliability of the prototype representations. To tackle the concerns outlined above, this paper introduces a novel prototype network called SACT (multi-head self-attention and contrastive-center loss), aimed at obtaining more comprehensive and precise interaction information from other prototype networks to bolster the reliability of the prototype network. Firstly, SACT employs a multi-head self-attention mechanism for capturing interaction information among different prototypes from traditional prototype networks, reducing the noise introduced by unknown categories with a small sample through information aggregation. Furthermore, SACT introduces a new loss function, the contrastive–center loss function, aimed at tightly clustering samples from a similar relationship category in the center of the feature space while dispersing samples from different relationship categories. Through extensive experiments on FSRE datasets, this paper demonstrates the outstanding performance of SACT, providing strong evidence for the effectiveness and practicality of SACT. [ABSTRACT FROM AUTHOR]

Published
2024
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