African swine fever virus(ASFV)is a lethal pathogen that causes severe threats to the global swine industry and it has already had catastrophic socio-economic effects.To date,no licensed prophylactic vaccine exists.Li...African swine fever virus(ASFV)is a lethal pathogen that causes severe threats to the global swine industry and it has already had catastrophic socio-economic effects.To date,no licensed prophylactic vaccine exists.Limited knowledge exists about the major immunogens of ASFV and the epitope mapping of the key antigens.As such,there is a considerable requirement to understand the functional monoclonal antibodies(mAbs)and the epitope mapping may be of utmost importance in our understanding of immune responses and designing improved vaccines,therapeutics,and diagnostics.In this study,we generated an ASFV antibody phage-display library from ASFV convalescent swine PBMCs,further screened a specific ASFV major capsid protein(p72)single-chain antibody and fused with an IgG Fc fragment(scFv-83-Fc),which is a specific recognition antibody against ASFV Pig/HLJ/2018 strain.Using the scFv-83-Fc mAb,we selected a conserved epitope peptide(221MTGYKH226)of p72 retrieved from a phage-displayed random peptide library.Moreover,flow cytometry and cell uptake experiments demonstrated that the epitope peptide can significantly promote BMDCs maturation in vitro and could be effectively uptaken by DCs,which indicated its potential application in vaccine and diagnostic reagent development.Overall,this study provided a valuable platform for identifying targets for ASFV vaccine development,as well as to facilitate the optimization design of subunit vaccine and diagnostic reagents.展开更多
交互式数据探索是一组多样的发现式应用程序的关键技术,着重于交互、探索和发现;在许多场景和领域中广泛应用.以海量的学术文献数据探索为背景,对交互式数据探索的特征自适应技术进行研究.首先,提出一种适用于面向学术文献数据探索的特...交互式数据探索是一组多样的发现式应用程序的关键技术,着重于交互、探索和发现;在许多场景和领域中广泛应用.以海量的学术文献数据探索为背景,对交互式数据探索的特征自适应技术进行研究.首先,提出一种适用于面向学术文献数据探索的特征自适应交互式数据探索框架FA-IDE(feature-adaptive interactive data exploration),在每次迭代过程中动态地调整特征子集,以满足用户兴趣多样性的需求.其次,针对该框架,提出特征子集的均匀度BFS(balance of feature subsets)评价准则,并给出了基于BFS的序列前向特征选择算法.再次,针对相关样本发现问题,提出划分等级建立方法,根据决策树模型对用户兴趣区域划分后,提出基于相似度的结果集排序策略.实验结果表明,所提出方法可有效提高用户探索效率和最终结果的准确性.展开更多
基金supported by the National Natural Science Foundation of China(31941001 and 32002292)the Major Science and Technology Project of Henan Province,China(221100110600)the Natural Science Foundation of Henan Province(202300410199).
文摘African swine fever virus(ASFV)is a lethal pathogen that causes severe threats to the global swine industry and it has already had catastrophic socio-economic effects.To date,no licensed prophylactic vaccine exists.Limited knowledge exists about the major immunogens of ASFV and the epitope mapping of the key antigens.As such,there is a considerable requirement to understand the functional monoclonal antibodies(mAbs)and the epitope mapping may be of utmost importance in our understanding of immune responses and designing improved vaccines,therapeutics,and diagnostics.In this study,we generated an ASFV antibody phage-display library from ASFV convalescent swine PBMCs,further screened a specific ASFV major capsid protein(p72)single-chain antibody and fused with an IgG Fc fragment(scFv-83-Fc),which is a specific recognition antibody against ASFV Pig/HLJ/2018 strain.Using the scFv-83-Fc mAb,we selected a conserved epitope peptide(221MTGYKH226)of p72 retrieved from a phage-displayed random peptide library.Moreover,flow cytometry and cell uptake experiments demonstrated that the epitope peptide can significantly promote BMDCs maturation in vitro and could be effectively uptaken by DCs,which indicated its potential application in vaccine and diagnostic reagent development.Overall,this study provided a valuable platform for identifying targets for ASFV vaccine development,as well as to facilitate the optimization design of subunit vaccine and diagnostic reagents.
文摘交互式数据探索是一组多样的发现式应用程序的关键技术,着重于交互、探索和发现;在许多场景和领域中广泛应用.以海量的学术文献数据探索为背景,对交互式数据探索的特征自适应技术进行研究.首先,提出一种适用于面向学术文献数据探索的特征自适应交互式数据探索框架FA-IDE(feature-adaptive interactive data exploration),在每次迭代过程中动态地调整特征子集,以满足用户兴趣多样性的需求.其次,针对该框架,提出特征子集的均匀度BFS(balance of feature subsets)评价准则,并给出了基于BFS的序列前向特征选择算法.再次,针对相关样本发现问题,提出划分等级建立方法,根据决策树模型对用户兴趣区域划分后,提出基于相似度的结果集排序策略.实验结果表明,所提出方法可有效提高用户探索效率和最终结果的准确性.
