Medical device clinical trial is a preliminary clinical human safety and effectiveness evaluation test. With the rapid development of science and technology, the research and development of medical devices is increasi...Medical device clinical trial is a preliminary clinical human safety and effectiveness evaluation test. With the rapid development of science and technology, the research and development of medical devices is increasing year by year, so it is particularly important to improve the clinical research level of medical devices. Medical statistics is one of the effective means to ensure scientific and reasonable clinical trial design and reliable test results. SAS software is important software for statistical analysis of clinical trials of medical equipment. Starting from the specific application of SAS software, this paper focuses on correctly understanding and selecting various results of SAS statistics, and provides some practical experience for those who learn to apply SAS, so as to make the clinical statistics of medical devices possible. The trial is more accurate and scientific.展开更多
Reliability is one of the most critical properties of software system.System deployment architecture is the allocation of system software components on host nodes.Software Architecture(SA) based software deployment mo...Reliability is one of the most critical properties of software system.System deployment architecture is the allocation of system software components on host nodes.Software Architecture(SA) based software deployment models help to analyze reliability of different deployments.Though many approaches for architecture-based reliability estimation exist,little work has incorporated the influence of system deployment and hardware resources into reliability estimation.There are many factors influencing system deployment.By translating the multi-dimension factors into degree matrix of component dependence,we provide the definition of component dependence and propose a method of calculating system reliability of deployments.Additionally,the parameters that influence the optimal deployment may change during system execution.The existing software deployment architecture may be ill-suited for the given environment,and the system needs to be redeployed to improve reliability.An approximate algorithm,A*_D,to increase system reliability is presented.When the number of components and host nodes is relative large,experimental results show that this algorithm can obtain better deployment than stochastic and greedy algorithms.展开更多
Software today often consists of a large number of components offering and requiring services. Such components should be deployed into embedded, pervasive environments, and several deployment architectures are typical...Software today often consists of a large number of components offering and requiring services. Such components should be deployed into embedded, pervasive environments, and several deployment architectures are typically possible. These deployment architectures can have significant impacts on system reliability. However, existing reliability estimation approaches are typically limited to certain classes or exclusively concentrate on software reliability, neglecting the influence of hardware resources, software deployment and architectural styles. The selection of an appropriate architectural style has a significant impact on system reliability of the target system. Therefore, we propose a novel software architecture （SA） based reliability estimation model incorporating software deployment and architectural style. On the basis of two architectural styles, we design influence factors and present a new approach to calculate system reliability. Experimental results show that influence factors provide an accurate and simple method of reflecting architectural styles and software deployment on system reliability. It is important for considering the influence of other architectural styles on system reliability in large scale deployment environment.展开更多
[Objective] The aim was to study the heat stress mechanism of differen tially expressed genes in rat jejunal mucosal.[Method] Variable cluster analysis and cluster analysis of samples on the differentially expressed h...[Objective] The aim was to study the heat stress mechanism of differen tially expressed genes in rat jejunal mucosal.[Method] Variable cluster analysis and cluster analysis of samples on the differentially expressed heat stress genes in rat jejunal mucosal were carried out with SAS software,and statistics of distribution of the differentially expressed genes on chromosomes were conducted.[Result] The differentially expressed genes were divided into seven categories,of which,the upregulated genes included three categories （i.e.：Category A：Hspa1a,Hspa1b,Hspb1,Hsph1,Dnaja4,Ahsa2 and P4ha1; Category B：Cyp1a2,Zbtb16,Gucy2g,Fgb,Cyp4a3 and Etv2; and Category C：Cyp1a2,Chac1 and Cyp4b1） and the down-regulated genes included four categories （i.e.：Category D：Tlr2,Noxo1,LOC286989 and Aspg; Category E：RGD1560395,Alb and BQ194726; Category F：Ccl4,Gzmk,Al228153,Anxa10,S100a9 and Ascl5; and Category G：Reg1a and Slc13a1）.The classification,function and reasons of differential expression for each gene category were analyzed.[Conclusion] Most of the three categories of up-regulated genes were related to the heat shock proteins; and most of the four categories of down-regulated genes were related to the immunity,providing reference for discussion of the heat stress mechanism.展开更多
文摘Medical device clinical trial is a preliminary clinical human safety and effectiveness evaluation test. With the rapid development of science and technology, the research and development of medical devices is increasing year by year, so it is particularly important to improve the clinical research level of medical devices. Medical statistics is one of the effective means to ensure scientific and reasonable clinical trial design and reliable test results. SAS software is important software for statistical analysis of clinical trials of medical equipment. Starting from the specific application of SAS software, this paper focuses on correctly understanding and selecting various results of SAS statistics, and provides some practical experience for those who learn to apply SAS, so as to make the clinical statistics of medical devices possible. The trial is more accurate and scientific.
基金Supported by the High Technology Research and Development Program of China(No.2008AA01A201)National High Technology Research,Development Plan of China (No.2006AA01A103)the High Technology Research and Development Program of China(No.2009AA01A404)
文摘Reliability is one of the most critical properties of software system.System deployment architecture is the allocation of system software components on host nodes.Software Architecture(SA) based software deployment models help to analyze reliability of different deployments.Though many approaches for architecture-based reliability estimation exist,little work has incorporated the influence of system deployment and hardware resources into reliability estimation.There are many factors influencing system deployment.By translating the multi-dimension factors into degree matrix of component dependence,we provide the definition of component dependence and propose a method of calculating system reliability of deployments.Additionally,the parameters that influence the optimal deployment may change during system execution.The existing software deployment architecture may be ill-suited for the given environment,and the system needs to be redeployed to improve reliability.An approximate algorithm,A*_D,to increase system reliability is presented.When the number of components and host nodes is relative large,experimental results show that this algorithm can obtain better deployment than stochastic and greedy algorithms.
文摘Software today often consists of a large number of components offering and requiring services. Such components should be deployed into embedded, pervasive environments, and several deployment architectures are typically possible. These deployment architectures can have significant impacts on system reliability. However, existing reliability estimation approaches are typically limited to certain classes or exclusively concentrate on software reliability, neglecting the influence of hardware resources, software deployment and architectural styles. The selection of an appropriate architectural style has a significant impact on system reliability of the target system. Therefore, we propose a novel software architecture （SA） based reliability estimation model incorporating software deployment and architectural style. On the basis of two architectural styles, we design influence factors and present a new approach to calculate system reliability. Experimental results show that influence factors provide an accurate and simple method of reflecting architectural styles and software deployment on system reliability. It is important for considering the influence of other architectural styles on system reliability in large scale deployment environment.
基金Supported by General Program of Science and Technology Development Project of Beijing Municipal Education(KM201110020010)Non-profit Industry Technology Projectof the Ministry of Agriculture funded by Funding Program for Academic Human Resources Development in Institutions of Higher Learning Under the Jurisdiction of Beijing Municipality of China[201003060-(9-10)]
文摘[Objective] The aim was to study the heat stress mechanism of differen tially expressed genes in rat jejunal mucosal.[Method] Variable cluster analysis and cluster analysis of samples on the differentially expressed heat stress genes in rat jejunal mucosal were carried out with SAS software,and statistics of distribution of the differentially expressed genes on chromosomes were conducted.[Result] The differentially expressed genes were divided into seven categories,of which,the upregulated genes included three categories （i.e.：Category A：Hspa1a,Hspa1b,Hspb1,Hsph1,Dnaja4,Ahsa2 and P4ha1; Category B：Cyp1a2,Zbtb16,Gucy2g,Fgb,Cyp4a3 and Etv2; and Category C：Cyp1a2,Chac1 and Cyp4b1） and the down-regulated genes included four categories （i.e.：Category D：Tlr2,Noxo1,LOC286989 and Aspg; Category E：RGD1560395,Alb and BQ194726; Category F：Ccl4,Gzmk,Al228153,Anxa10,S100a9 and Ascl5; and Category G：Reg1a and Slc13a1）.The classification,function and reasons of differential expression for each gene category were analyzed.[Conclusion] Most of the three categories of up-regulated genes were related to the heat shock proteins; and most of the four categories of down-regulated genes were related to the immunity,providing reference for discussion of the heat stress mechanism.
