胶囊内镜绒毛图像自动检测方法

人体小肠绒毛特征与多种疾病有重要关系,使用胶囊内镜分析小肠绒毛是医学界新出现的研究方法。为了帮助医生检索胶囊内镜图像序列中的绒毛图像,提出了一种基于纹理的自动检测方法。分割出图像中可能包含绒毛的带状区域,利用几何变换把带状区域转化成方便计算的矩形区域,使用边缘、颜色、条纹频率等特征构建一个分层算法判断该图像是否为绒毛图像。实验部分比较了提出的方法与传统纹理描述算子的检测效果,结果表明提出的方法刻画了绒毛带的本质特征,能更好地检查出序列中的绒毛图像。     

Inapproximability of maximal strip recovery

In comparative genomics, the first step of sequence analysis is usually to decompose two or more genomes into syntenic blocks that are segments of homologous chromosomes. For the reliable recovery of syntenic blocks, noise and ambiguities in the genomic maps need to be removed first. Maximal Strip Recovery (MSR) is an optimization problem proposed by Zheng, Zhu, and Sankoff for reliably recovering syntenic blocks from genomic maps in the midst of noise and ambiguities. Given d genomic maps as sequences of gene markers, the objective of MSR-d is to find d subsequences, one subsequence of each genomic map, such that the total length of syntenic blocks in these subsequences is maximized. For any constant d≥2, a polynomial-time 2d-approximation for MSR-d was previously known. In this paper, we show that for any d≥2, MSR-d is APX-hard, even for the most basic version of the problem in which all gene markers are distinct and appear in positive orientation in each genomic map. Moreover, we provide the first explicit lower bounds on approximating MSR-d for all d≥2. In particular, we show that MSR-d is NP-hard to approximate within Ω(d/logd). From the other direction, we show that the previous 2d-approximation for MSR-d can be optimized into a polynomial-time algorithm even if d is not a constant but is part of the input. We then extend our inapproximability results to several related problems including CMSR-d, δ-gap-MSR-d, and δ-gap-CMSR-d.     

Interactive visual analysis of time-series microarray data

Estimating dynamic regulatory pathways using DNA microarray time-series can provide invaluable information about the dynamic interactions among genes and result in new methods of rational drug design. Even though several purely computational methods have been introduced for DNA pathway analysis, most of these techniques do not provide a fully interactive method to explore and analyze these dynamic interactions in detail, which is necessary to obtain a full understanding. In this paper, we present a unified modeling and visual approach focusing on visual analysis of gene regulatory pathways over time. As a preliminary step in analyzing the gene interactions, the method applies two different techniques, a clustering algorithm and an auto regressive (AR) model. This approach provides a successful prediction of the dynamic pathways involved in the biological process under study. At this level, these pure computational techniques lack the transparency required for analysis and understanding of the gene interactions. To overcome the limitations, we have designed a visual analysis method that applies several visualization techniques, including pixel-based gene representation, animation, and multi-dimensional scaling (MDS), in a new way. This visual analysis framework allows the user to quickly and thoroughly search for and find the dynamic interactions among genes, highlight interesting gene information, show the detailed annotations of the selected genes, compare regulatory behaviors for different genes, and support gene sequence analysis for the interesting genes. In order to enhance these analysis capabilities, several methods are enabled, providing a simple graph display, a pixel-based gene visualization technique, and a relation-displaying technique among gene expressions and gene regulatory pathways.     

用MATLAB生物信息学工具箱分析人类线粒体基因序列

利用MATLAB生物信息学工具箱搜索人类线粒体及其核苷酸序列的相关信息,查看其整个核苷酸序列的内容,对核苷酸和密码子成分进行统计。结果表明：用Matlab进行生物信息学方面的研究比用常规的生物信息学软件更具有方便性和灵活性。     

生物序列模式分析中神经网络的并行训练策略

神经网络作为模式识别、数据挖掘等方面的有效工具,已被广泛应用到生物序列的模式分析中,而生物序列的超大规模、超长同时也给神经网络提出了挑战,即必须解决训练时间过长、效率低下的问题。本文提出了若干适合生物应用的神经网络并行训练策略,并按其神经网络粒度进行分类,同时分析和比较了各种策略的代价。     

GeneGrid: Architecture, Implementation and Application

The emergence of Grid computing technology has opened up an unprecedented opportunity for biologists to share and access data, resources and tools in an integrated environment leading to a greater chance of knowledge discovery. GeneGrid is a Grid computing framework that seamlessly integrates a myriad of heterogeneous resources spanning multiple administrative domains and locations. It provides scientists an integrated environment for the streamlined access of a number of bioinformatics programs and databases through a simple and intuitive interface. It acts as a virtual bioinformatics laboratory by allowing scientists to create, execute and manage workflows that represent bioinformatics experiments. A number of cooperating Grid services interact in an orchestrated manner to provide this functionality. This paper gives insight into the details of the architecture, components and implementation of GeneGrid.     

Integrative proteomics for the future US HUPO Fifth Annual Conference from genes to function 22-25 February 2009, San Diego, CA, USA

This report reviews the 5th US HUPO annual conference which was held in San Diego, California, from 22th to 25th February, 2009. The major goal of this year's meeting was to discuss future prospects within the field of proteomics and to push it towards integration with other synergizing technologies. Each day's sessions were guided by three broad themes: The Interface of Proteomics and Genomics, Systems Medicine, and Protein Structure and Modifications. As a summary this meeting has shown, that integration of multiple disciplines and high performance proteomics is needed to meet the demands of future proteomics.     

燃煤火电机组节能分析与优化调整

本文对机组变工况下运行指标应达值进行了剖析，提出了适于现场应用的锅炉效率计算方法和机组节能的对策；通过运行调整，减少了各个环节的损失，使电厂的热经济性得以提高，这对提高电厂运行的经济性和安全性起到了重要作用。