Mitochondrial DNA content and its control mechanism of Cynoglossus semilaevis

半滑舌鳎(Cynoglossussemilaevis)是我国重要的名贵海水养殖对象，其雌雄个体大小和生长速度差异极为悬殊，这一特殊的发育模式为线粒体能量代谢及其相关生物学过程研究提供了良好的实验材料。线粒体是细胞的产能工厂，含有独立的遗传物质线粒体DNA（mtDNA）。mtDNA在细胞中呈多拷贝，不同类型组织细胞、不同发育阶段存在差异。本文以半滑舌鳎为研究对象，首先建立了准确特异的mtDNA含量测定方法，在此基础上，测定了半滑舌鳎早期发育各阶段及成鱼不同组织器官的mtDNA含量变化；结合ATP含量测定和mtDNA复制与转录关键基因表达分析，探讨了mtDNA含量同能量需求的相关性及mtDNA含量...The half-smooth tongue sole (Cynoglossus semilaevis) is an important marine flatfish of potentially great aquacultural value in China. This species exhibits a typical sexual dimorphism in which females grow significantly faster and bigger than males, which provides an excellent model system for investigating the mechanisms of mitochondrial metabolism. Mitochondria are organelles present in most eu...学位：理学博士院系专业：海洋与地球学院_海洋生物学学号：2242011015361

13-甲基十四烷酸对大鼠脑缺血后脑水肿的影响

目的研究13-甲基十四烷酸（13-methyltetradecanoic acid, 13-MTD）对脑缺血后脑水肿的影响及其机制。方法线栓法制备大鼠大脑中动脉闭塞模型,于插入栓线前30 min分别尾静脉注射13-MTD 40、80、120 mg·kg-1（M40、M80、M120）,阴性对照组给予等体积脂质体。于缺血6、12、24 h,以Longa神经功能缺失评分观察神经功能缺失症状;TTC染色观察脑梗死体积大小;AutoCAD图像分析软件计算脑水肿程度;脑干湿重测定脑含水量;伊文思蓝（EB）检测血脑屏障通透性;RT-PCR检测损伤侧脑组织AQP4 mRNA表达;免疫组化法检测损伤侧脑组织AQP4蛋白表达。结果 13-MTD可明显改善脑缺血大鼠神经功能缺失症状,使脑梗死体积明显缩小,使脑含水量和脑水肿程度明显减轻,EB渗出减少,明显改善缺血侧脑组织AQP4表达。结论 13-MTD能通过调控AQP4表达,减轻大鼠脑缺血后脑水肿。福建省教育厅中青年教师教育科研资助项目(No JA15861

Dynamic Materialized View Algorithm Based on Rough Set Clustering

摘 要：根据用户查询多样性的特点，提出了基于粗糙集聚类的物化视图的动态调整算法(RSCDMV)。该算法在对物化视图进行粗糙集聚 类的基础上进行动态调整，这不仅满足了用户查询多样性需求，而且兼顾了维的层次关系因素。实验结果证明，随着用户查询集合的增大， 查询集的动态性和多样性更加明显，因此，RSCDMV 算法更具有优势。 【 Abstract 】 Because of user’s various inquires, a new algorithm, named rough set clustering-based dynamic materialized view algorithm(RSCDMV) is presented. Based on rough set clustering on materialized view, the algorithm can execute dynamic adjustment which both satisfies the variety of the queries and take the hierarchy of dimension into consideration. Experimental results show, as the queries set increase, RSCDMV will show more advantages as inquires change.基金项目：福建省自然科学基金资助项目(A0310008)；福建省高新 技术研究开放计划基金资助项目(2003H043

Blackboard Model on Parallel Distributed Environments

摘要:黑板模型支持并行性 ,它是分布式和并行编程可用的强有力的模型之一。在一个需要并行性和分布式编程的系统中,黑板模型有助于组织和概念化并发性及通信。本文着重分析了黑板模型的结构、构造方法、控制策略。基于CORBA(Common Object Request Broker Architecture)对象和全局对象研究了黑板和知识库的实现。最后,通过一个具体实例的实现方法和过程 ,说明了黑板模型解决分布式和并行编程问题的可行性。 Abstract : The blackboard model supports parallelism. It is one of the most powerful model for distributed and parallel programming.In parallel and distributed programming system , the blackboard model is helpful to organizing and concept ualization concurrency and communicating. This paper researches the structure of the blackboard model, construction method, control strategies. Based on CORBA (Common Object Request Broker Architecture) objects and global objects,the implementing of blackboard and knowledge source are studied. At last,by using implementation method and process of a typical example,this paper illust rates the feasibility that blackboard models are used to solve dist ributed and parallel programming.福建省自然科学基金项目(A0310008) ; 福建省高新技术研究开放计划重点项目(2003H043

An Improved DBSCAN Clustering Algorithm

针对"基于密度的带有噪声的空间聚类"(DBSCAN)算法存在的不足,提出"分而治之"和高效的并行方法对DBSCAN算法进行改进.通过对数据进行划分,利用"分而治之"思想减少全局变量Eps值的影响;利用并行处理方法和降维技术提高聚类效率,降低DBSCAN算法对内存的较高要求;采用增量式处理方式解决数据对象的增加和删除对聚类的影响.结果表明:新方法有效地解决了DBSCAN算法存在的问题,其聚类效率和聚类效果明显优于传统DBSCAN聚类算法.An improved density based spatial clustering of applications with noise(DBSCAN) algorithm,which can considerably improve cluster quality,is proposed.The algorithm is based on two ideas: dividing and ruling,and;high performance parallel methods.The idea of dividing and ruling was used to reduce the effect of the global variable Eps by data partition.Parallel processing methods and the technique of reducing dimensionality were used to improve the efficiency of clustering and to reduce the large memory space requirements of the DBSCAN algorithm.Finally,an incremental processing method was applied to determine the influence on clustering of inserting or deleting data objects.The results show that an implementation of the new method solves existing problems treated by the DBSCAN algorithm: Both the efficiency and the cluster quality are better than for the original DBSCAN algorithm.福建省自然科学基金项目(A0310008);; 福建省高新技术研究开放计划重点项目(2003H043

New method to improve DBSCAN clustering algorithm quality

摘要: 针对data partition基于密度带有“噪声”的空间聚类应用(DBSCAN) 聚类算法存在的3 个主要问题: 输入参数 敏感、对内存要求高、数据分布不均匀时影响聚类效果,提出了一种基于遗传方法的DBSCAN 算法改进 方案数据分区中使用遗传思想的DBSCAN 算法(DPDGA) 来提高聚类质量. 利用遗传算法改进K2 means 算法来获取初始聚类中心;对数据进行划分,在此基础上对划分的每一部分使用DBSCAN 算法 进行聚类;合并聚类的结果. 仿真实验表明,新方法较好解决了传统DBSCAN 聚类算法存在的问题,在 聚类效率和聚类效果方面均优于传统DBSCAN 聚类算法.Abstract : 　There are three problems along with the Density Based Spatial Clustering of Applications with Noise (DBSCAN) Clustering Algorithm: input sensitivity , desire for too much memory space and the effect of nonuniform data. To solve these problems , a fast Data Partition DBSCAN using Genetic Algorithm(DPDGA) Algorithm is developed which considerably improves the cluster quality. First , the Genetic Algorithm is used to improve the K2means Algorithm to get the initial clustering center. Second , data is partitioned and the DBSCAN Algorithm is applied to cluster partitions. Finally , all clustered result set s are merged. Simulation experiment s indicate that the DPDGA Algorithm works well to solve these problems and that both the efficiency and the cluster quality are better than those of the original DBSCAN Algorithm.基金项目:国家自然科学基金资助(50474033

New method to improve DBSCAN clustering algorithm quality

针对基于密度带有"噪声"的空间聚类应用(DBSCAN)聚类算法存在的3个主要问题:输入参数敏感、对内存要求高、数据分布不均匀时影响聚类效果,提出了一种基于遗传方法的DBSCAN算法改进方案数据分区中使用遗传思想的DBSCAN算法(DPDGA)来提高聚类质量.利用遗传算法改进K-means算法来获取初始聚类中心;对数据进行划分,在此基础上对划分的每一部分使用DBSCAN算法进行聚类;合并聚类的结果.仿真实验表明,新方法较好解决了传统DBSCAN聚类算法存在的问题,在聚类效率和聚类效果方面均优于传统DBSCAN聚类算法.There are three problems along with the Density Based Spatial Clustering of Applications with Noise(DBSCAN) Clustering Algorithm: input sensitivity,desire for too much memory space and the effect of nonuniform data.To solve these problems,a fast Data Partition DBSCAN using Genetic Algorithm(DPDGA) Algorithm is developed which considerably improves the cluster quality.First,the Genetic Algorithm is used to improve the K-means Algorithm to get the initial clustering center.Second,data is partitioned and the DBSCAN Algorithm is applied to cluster partitions.Finally,all clustered result sets are merged.Simulation experiments indicate that the DPDGA Algorithm works well to solve these problems and that both the efficiency and the cluster quality are better than those of the original DBSCAN Algorithm.国家自然科学基金资助(50474033

点击流数据仓库的构建与多维分析

介绍点击流数据仓库的多维建模技术,在此基础上以“平和网“的日志数据为例,利用SQl SErVEr2008构建点击流数据仓库,并对其进行多维分析研究

Research and application of DBSCAN clustering algorithm based on density

摘要：首先对 D BSCA N（D ensity Based Spatial Clustering of A pplications with N oise）聚类算法进行了深入研究，分析了它的特 点、存在的问题及改进思想，提出了基于 D BSCA N 方法的交通事故多发点段的排查方法及其改进思路，并且给出了实例以说明处理过程及可行性。实验结果表明本文提出的方法可以大大提高交通事故黑点排查效率。A bstract：This paper first researches D BSCA N clustering algorithm，and analyzes characteristics and existing problem s of the D B- SCA N algorithm and im proved idea.Evaluation m ethod of the traffic accident black spots and an im proved thought based on D B- SCA N are proposed.In order to illum inate course of processing and feasibility，an exam ple is presented.The experim ental result dem onstrates that this paper m ethod can greatly enhance the working efficiency of evaluation of the traffic accident black spots.金项目：福建省自然科学基金（the N atural Science Foundation of Fujian Province of China under G rant N o.A 0310008）；福建省高新技术研究开 放计划重点项目（N o.2003H 043）

Improved Decision Tree Algorithm Based on Samples Selection

为提高决策树分类算法的精度,通过比较几种经典的决策树分类算法,提出了基于样本选取的改进的决策树分类算法.改进算法基于决策树精度与样本的相关性较大以及决策树只能得到局部最优解的事实,通过反复迭代寻找较优样本,从而在不改变决策树分类算法的前提下,得到较好的决策树分类算法.该算法不针对某个决策树,只利用输入和输出的反馈信息进行迭代,因此通用性较好.实验证明,该改进算法与Id3,C4.5算法平均错误率的比值约为0.82∶1.22∶0.92.To raise the accuracy of decision tree classification algorithms,an improved decision tree classification algorithm based on samples selection was proposed by comparing several classical decision tree classification algorithms.This improved algorithm searches better samples through a constantly iterative process based on the facts that the correlation between decision trees' accuracy and samples is large and decision trees can only get a local optimal solution.As a result,a better decision tree classification algorithm can be obtained under the condition of not changing the decision tree classification algorithm.The improved algorithm is not aiming at a decision tree and it carries through iteration only based on some feedback information of input and output,so its universality is better.Experimental results show that the ratio of the average error rates of the improved algorithm and the ID3,C4.5 algorithms is about 0.82 to 1.22 to 0.92.福建省自然科学基金资助项目(A0310008);福建省高新技术研究开放计划重点项目(2003H043