Since a sensor node handles wireless communication in data transmission and reception and is installed in poor environment, it is easily exposed to certain attacks such as data transformation and sniffing. Therefore, ...Since a sensor node handles wireless communication in data transmission and reception and is installed in poor environment, it is easily exposed to certain attacks such as data transformation and sniffing. Therefore, it is necessary to verify data integrity to properly respond to an adversary's ill-intentioned data modification. In sensor network environment, the data integrity verification method verifies the final data only, requesting multiple communications. An energy-efficient private information retrieval(PIR)-based data integrity verification method is proposed. Because the proposed method verifies the integrity of data between parent and child nodes, it is more efficient than the existing method which verifies data integrity after receiving data from the entire network or in a cluster. Since the number of messages for verification is reduced, in addition, energy could be used more efficiently. Lastly, the excellence of the proposed method is verified through performance evaluation.展开更多
基于网络药理学、分子对接及实验验证探究中药药对黄芪-党参(Astragali Radix-Codonopsis Radix,AR-CR)治疗动脉粥样硬化(atherosclerosis,AS)的分子作用机制。首先通过检索TCMSP、PubChem、SwissTargetPrediction、UniProt、GeneCards...基于网络药理学、分子对接及实验验证探究中药药对黄芪-党参(Astragali Radix-Codonopsis Radix,AR-CR)治疗动脉粥样硬化(atherosclerosis,AS)的分子作用机制。首先通过检索TCMSP、PubChem、SwissTargetPrediction、UniProt、GeneCards等数据库,获取中药药对AR-CR的活性成分,预测该药对的作用靶点,筛选动脉粥样硬化的相关靶点基因,然后利用Venny平台、STRING数据库、Cytoscape(Version 3.8.2)软件进行拓扑分析获取AR-CR治疗动脉粥样硬化的关键靶点,使用DAVID数据库对所获得的关键靶点进行GO和KEGG富集分析,并借助Auto Dock tools、Auto Dock cina对核心蛋白与活性成分完成分子对接,最后,利用氧化型低密度脂蛋白(oxidized low density lipoprotein,ox-LDL)诱导人脐静脉内皮细胞,建立AS细胞模型进行体外生物学验证。AR-CR共检索到34个活性化合物,并预测潜在化合物靶点426个,通过与875个AS靶点进行交际映射,获得AR-CR治疗AS关键靶点69个,筛选出3,9-二邻甲基苯胺、7-甲氧基-2-甲基异黄酮、5α-豆甾烷-3,6-二酮、木犀草素4个主要活性化合物,丝氨酸/苏氨酸蛋白激酶(serine/threonine-protein kinase,AKT1)、肿瘤蛋白p53(tumor protein p53,TP53)、丝裂原激活蛋白激酶3(mitogen-activated protein kinase 3,MAPK3)等25个核心靶点。KEGG通路富集分析得到关键通路为糖基化终末产物(advanced glycation end-product,AGE)/糖基化终末产物受体(receptor for advanced glycation endproduct,RAGE)信号通路和脂质与动脉粥样硬化信号通路等。分子对接结果显示4个主要活性化合物与核心靶点蛋白均能连接,其中与TP53的结合活性最强。体外实验结果表明低、中、高剂量的AR-CR能够促进动脉粥样硬化模型细胞增殖,抑制其凋亡,并促进TP53 mRNA及TP53蛋白表达。综上,该研究初步揭示AR-CR治疗AS的作用机制,与网络药理学及分子对接预测的TP53因子相关,通过调控TP53因子促进AS模型细胞增殖,抑制其凋亡,为临床治疗AS提供了理论基础。展开更多
作为自然语言处理领域的一项关键任务,事实验证要求能够从大量的纯文本中根据给定的声明检索相关的证据,并使用这些证据推理验证声明。以往的研究通常利用证据句子拼接或图结构表示证据之间的关系,而不能清晰地表示各证据之间的内在关...作为自然语言处理领域的一项关键任务,事实验证要求能够从大量的纯文本中根据给定的声明检索相关的证据,并使用这些证据推理验证声明。以往的研究通常利用证据句子拼接或图结构表示证据之间的关系,而不能清晰地表示各证据之间的内在关联。因此,设计一种基于图谱和文本融合的协同推理网络模型CNGT(Co-attention Network with Graph and Text fusion),以通过构建证据知识图谱和证据句子进行语义融合。首先,根据证据句子构建证据知识图谱,并利用图变换编码器学习图谱表示;其次,利用BERT(Bidirectional Encoder Representations from Transformers)模型对声明和证据编码;最后,通过双层协同推理网络有效地融合推理图谱信息和文本特征。实验结果表明,相较于先进模型KGAT(KnowledgeGraphAttentionneTwork),所提模型在FEVER(FactExtractionand VERification)数据集上的标签准确率(LA)提高了0.84个百分点,FEVER得分提高了1.51个百分点。可见,所提模型更关注证据句子之间的关系,并且通过证据图谱展示出模型对证据句子关系的可解释性。展开更多
基金supported by the Sharing and Diffusion of National R&D Outcome funded by the Korea Institute of Science and Technology Information
文摘Since a sensor node handles wireless communication in data transmission and reception and is installed in poor environment, it is easily exposed to certain attacks such as data transformation and sniffing. Therefore, it is necessary to verify data integrity to properly respond to an adversary's ill-intentioned data modification. In sensor network environment, the data integrity verification method verifies the final data only, requesting multiple communications. An energy-efficient private information retrieval(PIR)-based data integrity verification method is proposed. Because the proposed method verifies the integrity of data between parent and child nodes, it is more efficient than the existing method which verifies data integrity after receiving data from the entire network or in a cluster. Since the number of messages for verification is reduced, in addition, energy could be used more efficiently. Lastly, the excellence of the proposed method is verified through performance evaluation.
文摘基于网络药理学、分子对接及实验验证探究中药药对黄芪-党参(Astragali Radix-Codonopsis Radix,AR-CR)治疗动脉粥样硬化(atherosclerosis,AS)的分子作用机制。首先通过检索TCMSP、PubChem、SwissTargetPrediction、UniProt、GeneCards等数据库,获取中药药对AR-CR的活性成分,预测该药对的作用靶点,筛选动脉粥样硬化的相关靶点基因,然后利用Venny平台、STRING数据库、Cytoscape(Version 3.8.2)软件进行拓扑分析获取AR-CR治疗动脉粥样硬化的关键靶点,使用DAVID数据库对所获得的关键靶点进行GO和KEGG富集分析,并借助Auto Dock tools、Auto Dock cina对核心蛋白与活性成分完成分子对接,最后,利用氧化型低密度脂蛋白(oxidized low density lipoprotein,ox-LDL)诱导人脐静脉内皮细胞,建立AS细胞模型进行体外生物学验证。AR-CR共检索到34个活性化合物,并预测潜在化合物靶点426个,通过与875个AS靶点进行交际映射,获得AR-CR治疗AS关键靶点69个,筛选出3,9-二邻甲基苯胺、7-甲氧基-2-甲基异黄酮、5α-豆甾烷-3,6-二酮、木犀草素4个主要活性化合物,丝氨酸/苏氨酸蛋白激酶(serine/threonine-protein kinase,AKT1)、肿瘤蛋白p53(tumor protein p53,TP53)、丝裂原激活蛋白激酶3(mitogen-activated protein kinase 3,MAPK3)等25个核心靶点。KEGG通路富集分析得到关键通路为糖基化终末产物(advanced glycation end-product,AGE)/糖基化终末产物受体(receptor for advanced glycation endproduct,RAGE)信号通路和脂质与动脉粥样硬化信号通路等。分子对接结果显示4个主要活性化合物与核心靶点蛋白均能连接,其中与TP53的结合活性最强。体外实验结果表明低、中、高剂量的AR-CR能够促进动脉粥样硬化模型细胞增殖,抑制其凋亡,并促进TP53 mRNA及TP53蛋白表达。综上,该研究初步揭示AR-CR治疗AS的作用机制,与网络药理学及分子对接预测的TP53因子相关,通过调控TP53因子促进AS模型细胞增殖,抑制其凋亡,为临床治疗AS提供了理论基础。
文摘作为自然语言处理领域的一项关键任务,事实验证要求能够从大量的纯文本中根据给定的声明检索相关的证据,并使用这些证据推理验证声明。以往的研究通常利用证据句子拼接或图结构表示证据之间的关系,而不能清晰地表示各证据之间的内在关联。因此,设计一种基于图谱和文本融合的协同推理网络模型CNGT(Co-attention Network with Graph and Text fusion),以通过构建证据知识图谱和证据句子进行语义融合。首先,根据证据句子构建证据知识图谱,并利用图变换编码器学习图谱表示;其次,利用BERT(Bidirectional Encoder Representations from Transformers)模型对声明和证据编码;最后,通过双层协同推理网络有效地融合推理图谱信息和文本特征。实验结果表明,相较于先进模型KGAT(KnowledgeGraphAttentionneTwork),所提模型在FEVER(FactExtractionand VERification)数据集上的标签准确率(LA)提高了0.84个百分点,FEVER得分提高了1.51个百分点。可见,所提模型更关注证据句子之间的关系,并且通过证据图谱展示出模型对证据句子关系的可解释性。
