BACKGROUND Chronic heart failure is a complex clinical syndrome.The Chinese herbal compound preparation Jianpi Huatan Quyu recipe has been used to treat chronic heart failure;however,the underlying molecular mechanism...BACKGROUND Chronic heart failure is a complex clinical syndrome.The Chinese herbal compound preparation Jianpi Huatan Quyu recipe has been used to treat chronic heart failure;however,the underlying molecular mechanism is still not clear.AIM To identify the effective active ingredients of Jianpi Huatan Quyu recipe and explore its molecular mechanism in the treatment of chronic heart failure.METHODS The effective active ingredients of eight herbs composing Jianpi Huatan Quyu recipe were identified using the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform.The target genes of chronic heart failure were searched in the Genecards database.The target proteins of active ingredients were mapped to chronic heart failure target genes to obtain the common drugdisease targets,which were then used to construct a key chemical componenttarget network using Cytoscape 3.7.2 software.The protein-protein interaction network was constructed using the String database.Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were performed through the Metascape database.Finally,our previously published relevant articles were searched to verify the results obtained via network pharmacology.RESULTS A total of 227 effective active ingredients for Jianpi Huatan Quyu recipe were identified,of which quercetin,kaempferol,7-methoxy-2-methyl isoflavone,formononetin,and isorhamnetin may be key active ingredients and involved in the therapeutic effects of TCM by acting on STAT3,MAPK3,AKT1,JUN,MAPK1,TP53,TNF,HSP90AA1,p65,MAPK8,MAPK14,IL6,EGFR,EDN1,FOS,and other proteins.The pathways identified by KEGG enrichment analysis include pathways in cancer,IL-17 signaling pathway,PI3K-Akt signaling pathway,HIF-1 signaling pathway,calcium signaling pathway,cAMP signaling pathway,NF-kappaB signaling pathway,AMPK signaling pathway,etc.Previous studies on Jianpi Huatan Quyu recipe suggested that this Chinese compound preparation can regulate the TNF-α,IL-6,MAPK,cAMP,and AMPK pathways to affect the mitochondrial structure of myocardial cells,oxidative stress,and energy metabolism,thus achieving the therapeutic effects on chronic heart failure.CONCLUSION The Chinese medicine compound preparation Jianpi Huatan Quyu recipe exerts therapeutic effects on chronic heart failure possibly by influencing the mitochondrial structure of cardiomyocytes,oxidative stress,energy metabolism,and other processes.Future studies are warranted to investigate the role of the IL-17 signaling pathway,PI3K-Akt signaling pathway,HIF-1 signaling pathway,and other pathways in mediating the therapeutic effects of Jianpi Huatan Quyu recipe on chronic heart failure.展开更多
连接顺序选择是查询优化领域中极具挑战性的研究方向,对于数据库管理系统获得良好的查询性能至关重要.然而,传统优化方法和现有智能优化方法均存在着不足,如规划时间过长、容易得到质量较差的连接计划、编码未考虑结构特征、依赖基数估...连接顺序选择是查询优化领域中极具挑战性的研究方向,对于数据库管理系统获得良好的查询性能至关重要.然而,传统优化方法和现有智能优化方法均存在着不足,如规划时间过长、容易得到质量较差的连接计划、编码未考虑结构特征、依赖基数估计和代价估计使得连接计划无法反映真实的执行时间等.针对上述问题,提出了一种新型基于异步Dueling DQN(Deep Q-network)和计划时间预测网络的连接优化器:ADP-Join(Asynchronous Dueling DQN and Plan Latency Prediction Network for Join Order Selection).ADP-Join集成了一种新的编码方法,能够区分不同结构的连接计划.ADP-Join设计了计划时间预测网络PLN(Plan Latency Prediction Network)来改善现有基于强化学习优化器的奖励机制.再者,提出异步更新机制改进Dueling DQN模型来提升训练性能和减少训练时间.大量的实验结果表明,在TPC-H和JOB真实数据集上ADP-Join的性能优于现有的智能优化器.展开更多
综合征监测作为公共医疗卫生政策的主要检测指标,拥有充足且及时的监测信息至关重要。传统流行病学指标监测的滞后和误导会影响病情严重地区的医疗实施方案。使用谷歌趋势搜索量、谷歌移动、电信运营商、英国国家医疗服务体系(National ...综合征监测作为公共医疗卫生政策的主要检测指标,拥有充足且及时的监测信息至关重要。传统流行病学指标监测的滞后和误导会影响病情严重地区的医疗实施方案。使用谷歌趋势搜索量、谷歌移动、电信运营商、英国国家医疗服务体系(National Health Service,NHS)电话119和线上新冠检测请求网站的空间数据,提出一种局部范围内SARS-CoV-2传播和临床风险的早期指标建模方法。利用浅层学习算法作为基准方法训练局部空间神经网络,提出空间集成长短期记忆(Spatio-Integrated Long Short-Term Memory,SI-LSTM)算法和空间集成卷积神经网络长短期记忆(Spatio-Integrated Convolutional Neural Network Long Short-Term Memory,SI-CNN-LSTM)算法。在规定的评估时间周期内,两种算法均能准确识别出疫情感染高风险区域。此外,在基本公共卫生服务项目中,该模型还原了2020年底阿尔法变体、2021年4月德尔塔变体和2021年11月奥密克戎变体在英国境内的局部增长指数,其空间分散性和增长指数得到了临床数据的证实。展开更多
针对传统方剂配伍规律分析方法的不足,提出一种面向复杂网络的新型中药(traditional Chinese medicine,TCM)方剂配伍规律挖掘算法。根据中药方剂特性并结合点式互信息构建TCM网络模型,结合TCM网络的小世界特性提出TCM网络的局部适应度模...针对传统方剂配伍规律分析方法的不足,提出一种面向复杂网络的新型中药(traditional Chinese medicine,TCM)方剂配伍规律挖掘算法。根据中药方剂特性并结合点式互信息构建TCM网络模型,结合TCM网络的小世界特性提出TCM网络的局部适应度模型,分析TCM网络的特性并挖掘TCM网络中配伍关系紧密、相似度较大的药物群。以4 000余首经典方剂作为实验对象,验证了所提方法具有较好的有效性,与经典LFM(local fitness measure)算法对比,平均模块度值提高了0.05,为中药方剂的配伍规律进行探索及新药研发提供了新思路。展开更多
基金Supported by 2021 Shenyang Science and Technology Program-Public Health R&D Special Project(Joint Project)of Shenyang Municipal Science and Technology Bureau,No.21-174-9-04.
文摘BACKGROUND Chronic heart failure is a complex clinical syndrome.The Chinese herbal compound preparation Jianpi Huatan Quyu recipe has been used to treat chronic heart failure;however,the underlying molecular mechanism is still not clear.AIM To identify the effective active ingredients of Jianpi Huatan Quyu recipe and explore its molecular mechanism in the treatment of chronic heart failure.METHODS The effective active ingredients of eight herbs composing Jianpi Huatan Quyu recipe were identified using the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform.The target genes of chronic heart failure were searched in the Genecards database.The target proteins of active ingredients were mapped to chronic heart failure target genes to obtain the common drugdisease targets,which were then used to construct a key chemical componenttarget network using Cytoscape 3.7.2 software.The protein-protein interaction network was constructed using the String database.Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were performed through the Metascape database.Finally,our previously published relevant articles were searched to verify the results obtained via network pharmacology.RESULTS A total of 227 effective active ingredients for Jianpi Huatan Quyu recipe were identified,of which quercetin,kaempferol,7-methoxy-2-methyl isoflavone,formononetin,and isorhamnetin may be key active ingredients and involved in the therapeutic effects of TCM by acting on STAT3,MAPK3,AKT1,JUN,MAPK1,TP53,TNF,HSP90AA1,p65,MAPK8,MAPK14,IL6,EGFR,EDN1,FOS,and other proteins.The pathways identified by KEGG enrichment analysis include pathways in cancer,IL-17 signaling pathway,PI3K-Akt signaling pathway,HIF-1 signaling pathway,calcium signaling pathway,cAMP signaling pathway,NF-kappaB signaling pathway,AMPK signaling pathway,etc.Previous studies on Jianpi Huatan Quyu recipe suggested that this Chinese compound preparation can regulate the TNF-α,IL-6,MAPK,cAMP,and AMPK pathways to affect the mitochondrial structure of myocardial cells,oxidative stress,and energy metabolism,thus achieving the therapeutic effects on chronic heart failure.CONCLUSION The Chinese medicine compound preparation Jianpi Huatan Quyu recipe exerts therapeutic effects on chronic heart failure possibly by influencing the mitochondrial structure of cardiomyocytes,oxidative stress,energy metabolism,and other processes.Future studies are warranted to investigate the role of the IL-17 signaling pathway,PI3K-Akt signaling pathway,HIF-1 signaling pathway,and other pathways in mediating the therapeutic effects of Jianpi Huatan Quyu recipe on chronic heart failure.
文摘连接顺序选择是查询优化领域中极具挑战性的研究方向,对于数据库管理系统获得良好的查询性能至关重要.然而,传统优化方法和现有智能优化方法均存在着不足,如规划时间过长、容易得到质量较差的连接计划、编码未考虑结构特征、依赖基数估计和代价估计使得连接计划无法反映真实的执行时间等.针对上述问题,提出了一种新型基于异步Dueling DQN(Deep Q-network)和计划时间预测网络的连接优化器:ADP-Join(Asynchronous Dueling DQN and Plan Latency Prediction Network for Join Order Selection).ADP-Join集成了一种新的编码方法,能够区分不同结构的连接计划.ADP-Join设计了计划时间预测网络PLN(Plan Latency Prediction Network)来改善现有基于强化学习优化器的奖励机制.再者,提出异步更新机制改进Dueling DQN模型来提升训练性能和减少训练时间.大量的实验结果表明,在TPC-H和JOB真实数据集上ADP-Join的性能优于现有的智能优化器.
文摘综合征监测作为公共医疗卫生政策的主要检测指标,拥有充足且及时的监测信息至关重要。传统流行病学指标监测的滞后和误导会影响病情严重地区的医疗实施方案。使用谷歌趋势搜索量、谷歌移动、电信运营商、英国国家医疗服务体系(National Health Service,NHS)电话119和线上新冠检测请求网站的空间数据,提出一种局部范围内SARS-CoV-2传播和临床风险的早期指标建模方法。利用浅层学习算法作为基准方法训练局部空间神经网络,提出空间集成长短期记忆(Spatio-Integrated Long Short-Term Memory,SI-LSTM)算法和空间集成卷积神经网络长短期记忆(Spatio-Integrated Convolutional Neural Network Long Short-Term Memory,SI-CNN-LSTM)算法。在规定的评估时间周期内,两种算法均能准确识别出疫情感染高风险区域。此外,在基本公共卫生服务项目中,该模型还原了2020年底阿尔法变体、2021年4月德尔塔变体和2021年11月奥密克戎变体在英国境内的局部增长指数,其空间分散性和增长指数得到了临床数据的证实。
