How the tubular network of the endoplasmic reticulum (ER) is generated is not well understood, but a class of membrane proteins, the reticulons and DP1/Yop1p, are known
The integrated absorption cross section Σ abs, peak emis sion cross section σ emi, Judd-Ofeld intensity parameters Ω t(t=2,4,6), and spontaneous emission probability A R of Er 3+ ions were determined fo r...The integrated absorption cross section Σ abs, peak emis sion cross section σ emi, Judd-Ofeld intensity parameters Ω t(t=2,4,6), and spontaneous emission probability A R of Er 3+ ions were determined fo r Erbium doped alkali and alkaline earth phosphate glasses. It is found the comp ositional dependence of σ emi is almost similar to that of Σ abs, wh ich is determined by the sum of Ω t (3Ω 2+10Ω 4+21Ω 6). In addition, the compositional dependence of Ω t was studied in these glass systems. As a resu lt, compared with Ω 4 and Ω 6, the Ω 2 has a stronger compositional depend ence on the ionic radius and content of modifiers. The covalency of Er-O bonds in phosphate glass is weaker than that in silicate glass, germanate glass, alumi nate glass, and tellurate glass, since Ω 6 of phosphate glass is relatively la rge. A R is affected by the covalency of the Er 3+ ion sites and correspon ds to the Ω 6 value.展开更多
目的基于网络药理学和分子对接技术探究苍耳子散(CRZS)治疗异质性鼻炎的作用机制以及“多成分-多靶点-多通路”的整体药理作用特征。方法利用中药系统药理学数据库与分析平台(TCMSP)检索CRZS的活性成分及靶点;从GeneCards、DisGeNET、Ph...目的基于网络药理学和分子对接技术探究苍耳子散(CRZS)治疗异质性鼻炎的作用机制以及“多成分-多靶点-多通路”的整体药理作用特征。方法利用中药系统药理学数据库与分析平台(TCMSP)检索CRZS的活性成分及靶点;从GeneCards、DisGeNET、Phenopedia、TTD和PharmGKB数据库获取相关鼻炎的基因。通过STRING数据库与Cytoscape软件构建蛋白质-蛋白质相互作用(PPI)网络;利用STRING数据库对核心基因进行基因本体论(GO)富集分析和京都基因与基因组百科全书(KEGG)通路富集分析。通过Cytoscape软件构建“中药成分-靶点-信号通路”网络并筛选核心靶点;采用AutoDock软件对筛选得到的关键通路靶点进行分子对接。结果CRZS有131个核心基因,鼻炎相关靶点756个,交集靶点41个。GO生物富集条目2005条,KEGG通路富集条目151条,HIF-1、Lipid and atherosclerosis、Fluid shear stress and atherosclerosis等通路最具连接可能。分子对接显示,CEZS靶点的有效成分与3个核心基因连接紧密。结论CEZS可通过多成分、多靶点,多信号通路发挥治疗鼻炎的作用,其中最有可能通过HIF-1、lipid and atherosclerosis、Fluid shear stress and atherosclerosis等3条通路进行调控。展开更多
Crop yield has been predicted using environmental,land,water,and crop characteristics in a prospective research design.When it comes to predicting crop production,there are a number of factors to consider,including we...Crop yield has been predicted using environmental,land,water,and crop characteristics in a prospective research design.When it comes to predicting crop production,there are a number of factors to consider,including weather con-ditions,soil qualities,water levels and the location of the farm.A broad variety of algorithms based on deep learning are used to extract useful crops for forecasting.The combination of data mining and deep learning creates a whole crop yield pre-diction system that is able to connect raw data to predicted crop yields.The sug-gested study uses a Discrete Deep belief network with Visual Geometry Group(VGG)Net classification method over the tweak chick swarm optimization approach to estimate agricultural production.The Network’s successively stacked layers were fed the data parameters.Based on the input parameters,a crop produc-tion prediction environment is constructed using the network architecture.Using the tweak chick swarm optimization technique,the best characteristics of input data are preprocessed,and the optimal output is used as input for the classification process.Discrete Deep belief network with the Visual Geometry Group Net clas-sifier is used to classify the data and forecast agricultural production.The sug-gested model correctly predicts crop output with 97 percent accuracy,exceeding existing models by maintaining the baseline data distribution.展开更多
文摘How the tubular network of the endoplasmic reticulum (ER) is generated is not well understood, but a class of membrane proteins, the reticulons and DP1/Yop1p, are known
基金Funded by the Natural Science Foundation of Guangdong Prov ince(013013) and the Science and Technology Plan of Guangdong Province(2002B11604)
文摘The integrated absorption cross section Σ abs, peak emis sion cross section σ emi, Judd-Ofeld intensity parameters Ω t(t=2,4,6), and spontaneous emission probability A R of Er 3+ ions were determined fo r Erbium doped alkali and alkaline earth phosphate glasses. It is found the comp ositional dependence of σ emi is almost similar to that of Σ abs, wh ich is determined by the sum of Ω t (3Ω 2+10Ω 4+21Ω 6). In addition, the compositional dependence of Ω t was studied in these glass systems. As a resu lt, compared with Ω 4 and Ω 6, the Ω 2 has a stronger compositional depend ence on the ionic radius and content of modifiers. The covalency of Er-O bonds in phosphate glass is weaker than that in silicate glass, germanate glass, alumi nate glass, and tellurate glass, since Ω 6 of phosphate glass is relatively la rge. A R is affected by the covalency of the Er 3+ ion sites and correspon ds to the Ω 6 value.
文摘目的基于网络药理学和分子对接技术探究苍耳子散(CRZS)治疗异质性鼻炎的作用机制以及“多成分-多靶点-多通路”的整体药理作用特征。方法利用中药系统药理学数据库与分析平台(TCMSP)检索CRZS的活性成分及靶点;从GeneCards、DisGeNET、Phenopedia、TTD和PharmGKB数据库获取相关鼻炎的基因。通过STRING数据库与Cytoscape软件构建蛋白质-蛋白质相互作用(PPI)网络;利用STRING数据库对核心基因进行基因本体论(GO)富集分析和京都基因与基因组百科全书(KEGG)通路富集分析。通过Cytoscape软件构建“中药成分-靶点-信号通路”网络并筛选核心靶点;采用AutoDock软件对筛选得到的关键通路靶点进行分子对接。结果CRZS有131个核心基因,鼻炎相关靶点756个,交集靶点41个。GO生物富集条目2005条,KEGG通路富集条目151条,HIF-1、Lipid and atherosclerosis、Fluid shear stress and atherosclerosis等通路最具连接可能。分子对接显示,CEZS靶点的有效成分与3个核心基因连接紧密。结论CEZS可通过多成分、多靶点,多信号通路发挥治疗鼻炎的作用,其中最有可能通过HIF-1、lipid and atherosclerosis、Fluid shear stress and atherosclerosis等3条通路进行调控。
文摘提出了一种2.5维(2.5D)系统封装高速输入/输出(I/O)全链路的信号/电源完整性(Signal integrity/power integrity,SI/PI)协同仿真方法。首先通过电磁全波仿真分析SiP内部“芯片I/O引脚-有源转接板-印刷电路板(即封装基板)-封装体I/O引脚”这一主要高速信号链路及相应的转接板/印刷电路板电源分配网络(Power distribution network,PDN)的结构特征和电学特性,在此基础上分别搭建对应有源转接板和印刷电路板两种组装层级的“信号链路+PDN”模型,并分别进行SI/PI协同仿真,提取出反映信号链路/PDN耦合特性的模块化集总电路模型,从而在电路仿真器中以级联模型实现快速的SI/PI协同仿真。与全链路的全波仿真结果的对比表明,模块化后的协同仿真有很好的可信度,而且仿真时间与资源开销大幅缩减,效率明显提升。同时总结了去耦电容的大小与布局密度对PDN电源完整性的影响及对信号完整性的潜在影响,提出了去耦电容布局优化的建议。
文摘Crop yield has been predicted using environmental,land,water,and crop characteristics in a prospective research design.When it comes to predicting crop production,there are a number of factors to consider,including weather con-ditions,soil qualities,water levels and the location of the farm.A broad variety of algorithms based on deep learning are used to extract useful crops for forecasting.The combination of data mining and deep learning creates a whole crop yield pre-diction system that is able to connect raw data to predicted crop yields.The sug-gested study uses a Discrete Deep belief network with Visual Geometry Group(VGG)Net classification method over the tweak chick swarm optimization approach to estimate agricultural production.The Network’s successively stacked layers were fed the data parameters.Based on the input parameters,a crop produc-tion prediction environment is constructed using the network architecture.Using the tweak chick swarm optimization technique,the best characteristics of input data are preprocessed,and the optimal output is used as input for the classification process.Discrete Deep belief network with the Visual Geometry Group Net clas-sifier is used to classify the data and forecast agricultural production.The sug-gested model correctly predicts crop output with 97 percent accuracy,exceeding existing models by maintaining the baseline data distribution.
