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.     

尼雅遗址古代居民线粒体DNA研究

尼雅遗址是《汉书.西域传》中记载的"精绝"国故址,是丝绸之路沿线保存最完好的古代遗址之一,14C测定在距今1480~2635年之间。本实验对一例尼雅股骨样本线粒体高可变一区(364bp)进行扩增和测序,并同时做了编码区的限制性片段长度分析,结果显示这一个体属于U3亚型。U3亚型在现代人群中主要集中分布在近东和伊朗,与其他新疆古代人群对比显示:尼雅人群和山普拉人群可能存在一定的母系遗传联系,与潘其凤先生的体质人类学分析结果一致。     

植物分子育种

我国自70年代兴起植物分子育种,到80年代已取得很大的进展,在外源DNA导入方法探讨和抗性育种,品质育种、早熟与超高产育种等方面都获明显的成效。研究的进展和实践成果表明,它是植物多类型、多抗、优质、超高育种的有效途径,是植物品种改良技术的发展方向。     

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。     

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。     

从海鱼加工下脚料中提取核酸的试验(Ⅱ)--从鱼白中提取核酸及产物纯度分析和组分鉴定

对地产海鱼加工下脚料之一-鱼白中核酸的提取方法，产品纯度测定和组分鉴定方法进行一些探索，结果显示，DNA粗产物得率为4.62%，DNA质量分数为4.77%，RNA粗产物得率为1.98%，RNA质量分数4.06%，提取方法基本可行。     

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.     

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.