凝胶色谱图象计算机分析技术用于DNA检测的实验研究

用计算机图象分析技术对凝胶色谱图象进行分析从而获得DNA分子量和其它参数 构成了数字化凝胶色谱图象计算机系统分析系统。本文介绍了该项技术的实验仪器构成、工作原理及国内外有关的最新发展情况 并结合临床实验给出了实验分析结果。     

古DNA技术:民族考古学研究的新视角

古DNA技术使考古学对古人遗骸的研究得以进入分子水平 ,为民族考古学对过去人群的研究 ,如性别与家系鉴定、族属分辨、群体内部关系及群体迁徙等 ,提供了直接的证据 ,为高新技术与人文学科的交叉研究开辟了全新的领域。     

Extreme shape analysis

Summary.  We consider the analysis of extreme shapes rather than the more usual mean- and variance-based shape analysis. In particular, we consider extreme shape analysis in two applications: human muscle fibre images, where we compare healthy and diseased muscles, and temporal sequences of DNA shapes from molecular dynamics simulations. One feature of the shape space is that it is bounded, so we consider estimators which use prior knowledge of the upper bound when present. Peaks-over-threshold methods and maximum-likelihood-based inference are used. We introduce fixed end point and constrained maximum likelihood estimators, and we discuss their asymptotic properties for large samples. It is shown that in some cases the constrained estimators have half the mean-square error of the unconstrained maximum likelihood estimators. The new estimators are applied to the muscle and DNA data, and practical conclusions are given.     

美国已分离DNA分子可专利性之辩及启示

美国Myriad案是引发已分离DNA分子是否具有可专利性论争的重要案件。2011年,美国联邦巡回法院推翻纽约南区法院作出的一审判决,认定已分离DNA分子具有可专利性,这一裁判的根本性逆转更是激起利益各方的广泛关注和持久争辩。争辩中,对立双方往往忽略一个事实,即基因、天然DNA和已分离DNA三者内涵和外延不尽相同,应予以区分。因而,认为所有基因及DNA分子都不具有可专利性是不可取的,但不加区分地一律赋予授权资格同样毫无依据。判断已分离DNA分子的可专利性资格,重点应考量与天然DAN分子的组分差异,并采用多重标准综合判断。同时,应严格将科学发现排除在可专利性资格范围之外。     

结构主义、自然科学与经济学的变革

经济学与自然科学的元素结构具有惊人的相似性。运用结构主义方法,可以改进经济学的基因结构。与生物DNA的基本片段相比较,《资本论》的基因片段尚未健全。由于历史限制,马克思尚未建立起多维对称的基因结构。但《资本论》的基因片段可以自我复制、自我完善。《资本论》基因片段的自我完善,对于建立经济学元素周期率和经济学DNA结构,对于21世纪经济学的变革意义重大。     

企业成长能力的演进机理——以建筑企业为例

对企业成长能力形成及演进机理开展了研究,从企业的成长演化进程分析入手,研究了建筑企业应对市场环境变化,沿着经验环和创新环,通过企业层、项目层和企业间3个层面的组织学习与互动,将知识整合成市场开拓流程、组织管理流程、技术创新流程和网络合作流程,进而复合形成建筑企业成长能力的演进过程.运用基因(DNA)模型对各子流程形成的子能力之间的复合进行了分析,对建筑企业成长能力的复合过程进行了诠释,进而解释了建筑企业在各成长阶段中的能力特征.     

Clustering Binary Oligonucleotide Fingerprint Vectors for DNA Clone Classification Analysis

We considered the problem of clustering binarized oligonucleotide fingerprints that attempts to identify clusters. Oligonucleotide fingerprinting is a powerful DNA array based method to characterize cDNA and rRNA libraries and has many applications including gene expression profiling and DNA clone classification. DNA clone classification is the main application for the problem considered in this paper. Most of the existing approaches for clustering use normalized real intensity values and thus do not treat positive and negative hybridization signals equally. This is demonstrated in a series of recent publications where a discrete approach typically useful in the classification of microbial rRNA clones has been proposed. In the discrete approach, hybridization intensities are normalized and thresholds are set such that a value of 1 represents hybridization, a value of 0 represents no hybridization, and an N represents unknown, which is also called a missing value. A combinatorial optimization problem is then formulated attempting to cluster the fingerprints and resolve the missing values simultaneously. It has been examined that missing values cause much difficulty in clustering analysis and most clustering methods are very sensitive to them. In this paper, we turned a little back to the traditional clustering problem, which takes in no missing values but with the revised goal to stabilize the number of clusters and maintain the clustering quality. We adopted the binarizing scheme used in the discrete approach as it is shown to be typically useful for the clone classifications. We formulated such a problem into another combinatorial optimization problem. The computational complexity of this new clustering problem and its relationships to the discrete approach and the traditional clustering problem were studied. We have designed an exact algorithm for the new clustering problem, which is an A* search algorithm for finding a minimum number of clusters. The experimental results on two commonly tested real datasets demonstrated that the A* search algorithm runs fast and performs better than some popular hierarchical clustering methods, in terms of separating clones that have different characteristics with respect to the given oligonucleotide probes.Supported by NSERC and CFI.Supported by NSERC.Supported partially by NSERC, CFI, and NNSF Grant 60373012.     

Assessing the missing data problem in criminal network analysis using forensic DNA data

Missing data is pertinent to criminal networks due to the hidden nature of crime. Generally, researchers evaluate the impact of incomplete network data by extracting or adding nodes and/or edges from a known network. Statistics on this reduced or completed network are then compared with statistics from the known network. In this study, we integrate police data on known offenders with DNA data on unknown offenders. Statistics from the integrated dataset (‘known network’) are compared with statistics from the police data (‘reduced network’). Networks with both known and unknown offenders are bigger but also have a different structure to networks with only known offenders.     

股市波动的标度无关性算法及应用研究

标度无关性是广泛存在于自然界系统甚至于经济金融系统可观测量的幂函数关系 ,它揭示了股票市场波动的金融复杂性 .在分析股市波动标度无关性及其非趋势波动分析 DFA算法基础上进行了应用研究 ,( i)将 DFA推广为动态递推算法 ;( ii)对国内若干股价指数进行标度指数实际计算 .结果表明 ,递推 DFA算法与 DFA算法、重标极差 R/S分析方法相比具有计算速度快、内存容量小的优点 ,更能适应股市波动分析的实际需要 .国内股市波动的标度无关性分析表明 ,沪深股市具有持久性 ,在重大金融事件的作用下 ,对于股市的影响是长期的