葡萄糖耐受者空腹甘油三酯与重IGF-1启动子区的多态性研究

目的为了检测IGF-1基因启动子区的微卫星多态性中的等位基因与Ⅱ型糖尿病的相关性,并在葡萄糖耐受性人群中进行相关性状的定量分析。方法 IGF-1基因启动子多态性的研究通过对347名Ⅱ型糖尿病患者和109名葡萄糖耐受性者作为对照进行的研究。结果本研究没有发现启动子区缺少等位基因的与Ⅱ型糖尿病或是葡萄糖耐受性者中的葡萄糖耐受机制间存在相互关系(P =0．25)。在Ⅱ型葡萄糖耐受性糖尿病患者的空腹血清甘油三酯水平与野生型等位基因间存在相互关系(P=0．002)。结论本研究表明,IGF-1基因启动子多态性的野生型等位基因与葡萄糖耐受性人群空腹血清甘油三酯水平增加之间存在相互关系。     

Disease resistance modelled as first-passage times of genetically dependent stochastic processes

Summary.  Mastitis resistance data on dairy cattle are modelled as first-passage times of stochastic processes. Population heterogeneity is included by expressing process parameters as functions of shared random variables. We show how dependences between individuals, e.g. genetic relationships, can be exploited in the analyses. The method can be extended to handle situations with multiple hidden causes of failure. Markov chain Monte Carlo methods are used for estimation.     

基于遗传技术辅助设计的神经网络期货市场预测

本文在对期货市场的历史数据进行预分析的基础上,建立了神经网络期货市场预测模型．文中不仅对神经网络进行了改进研究,还利用遗传技术优化网络的结构和参数．运运实例对模型进行学习与测试的实验结果表明,利用遗传技术辅助设计的神经网络预测模型能较准确地预报期货价格的波动趋势.     

Significance and impotence: towards a balanced view of the null and the alternative hypotheses in marker selection for plant breeding

Summary.  The isolation of DNA markers that are linked to interesting genes helps plant breeders to select parent plants that transmit useful traits to future generations. Such 'marker-assisted breeding and selection' heavily leans on statistical testing of associations between markers and a well-chosen trait. Statistical association analysis is guided by classical p -values or the false discovery rate and thus relies predominantly on the null hypothesis. The main concern of plant breeders, however, is to avoid missing an important alternative. To judge evidence from this perspective, we complement the traditional p -value with a one-sided 'alternative p -value' which summarizes evidence against a target alternative in the direction of the null hypothesis. This p -value measures 'impotence' as opposed to significance: how likely is it to observe an outcome as extreme as or more extreme than the one that was observed when data stem from the alternative? We show how a graphical inspection of both p -values can guide marker selection when the null and the alternative hypotheses have a comparable importance. We derive formal decision tools with balanced properties yielding different rejection regions for different markers. We apply our approach to study rye-grass plants.     

现代伦理学视角与基因技术伦理原则的建构

以发展伦理和责任伦理的视角揭示了伦理学由关注个体伦理行为到社会伦理行为研究的现代转向,阐明了现代伦理学的核心是"正义、正当、规范"的观点,并深刻地剖析了"正义、正当、规范"的伦理意蕴。在肯定基因工程技术积极意义的同时,指出了基因工程技术引发的深刻的社会伦理问题,并以发展伦理和责任伦理的视角建构了基因工程技术的伦理原则及范式。     

A multivariate regression model for continuous genetic traits

Summary.  In many commonly used models for multivariate traits, the likelihood is specified as a mixture of nested sums of products over the unobserved genotypes of all the family members, in which the familial covariance matrices vary in size and structure for different families, and their sizes can be immense for large family units. These issues pose computational difficulties in many applications. Bonney's compound regressive model for univariate traits simplifies the familial covariance structure and reduces the mixture of nested sums only to the parent–offspring level, thus enhancing computation significantly. This model has been extended to the multivariate case in the absence of unobserved genotypes. Here, we further extend this model to incorporate major genes, covariates and multiple loci. As is typical in practice, this causes new computational difficulties. We study the computational issues and explore the behaviour of this extended model.     

ASSESSING FAMILIAL AGGREGATION WITH AN ORDINAL RESPONSE

A latent variable model is considered for the analysis of twin data with an ordinal response. The underlying latent multivariate normally distributed variable is expressed in terms of genetic and environmental effects, and the variance components associated with these effects are estimated. We illustrate this approach with analysis of the NHLBI Twin Study. Model assessment is ascertained by proposing a goodness-of-fit test for ordered categorical data. Extensions of this approach for the investigation of how genetic effects vary over time are discussed.     

改进的Wiener滤波在图像恢复中的应用简

在Wiener滤波进行图像恢复时,利用遗传算法（GA,Genetic Algorithm）全局和局部搜索力强的优势,搜索Wiener滤波的信噪比最优Г.为了得到最优Г值,利用峰值信噪比（PSNR,Peak Signal-to-Noise Ratio）作为图像恢复质量评价指标,对经过处理的LIVE数据库图像、IKONOS、ZY-3卫星图像进行实验.结果表明：无论图像噪声大小,该方法都可以找到Wiener滤波的最优Г值.     

基因工程及其应用

文章介绍了基因工程及其在农业、医学领域的应用。     

Block designs for random treatment effects

A Bayesian design criterion for selection experiments in plant breeding is derived using a utility function that minimizes the risk of an incorrect selection. A prior distribution on the heritability parameter is used to complete the definition of the design optimality criterion. An example is given with evaluations of the criterion for different prior distributions on the heritability. Though coming from a genetic motivation this criterion should prove useful for any other types of experiments with random treatment effects.