Sampling Strategies for Evaluating the Rate of Adventitious Transgene Presence in Non‐Genetically Modified Crop Fields

According to E.U. regulations, the maximum allowable rate of adventitious transgene presence in non‐genetically modified (GM) crops is 0.9%. We compared four sampling methods for the detection of transgenic material in agricultural non‐GM maize fields: random sampling, stratified sampling, random sampling + ratio reweighting, random sampling + regression reweighting. Random sampling involves simply sampling maize grains from different locations selected at random from the field concerned. The stratified and reweighting sampling methods make use of an auxiliary variable corresponding to the output of a gene‐flow model (a zero‐inflated Poisson model) simulating cross‐pollination as a function of wind speed, wind direction, and distance to the closest GM maize field. With the stratified sampling method, an auxiliary variable is used to define several strata with contrasting transgene presence rates, and grains are then sampled at random from each stratum. With the two methods involving reweighting, grains are first sampled at random from various locations within the field, and the observations are then reweighted according to the auxiliary variable. Data collected from three maize fields were used to compare the four sampling methods, and the results were used to determine the extent to which transgene presence rate estimation was improved by the use of stratified and reweighting sampling methods. We found that transgene rate estimates were more accurate and that substantially smaller samples could be used with sampling strategies based on an auxiliary variable derived from a gene‐flow model.     

On selection biases with prediction rules formed from gene expression data

There has been ever increasing interest in the use of microarray experiments as a basis for the provision of prediction (discriminant) rules for improved diagnosis of cancer and other diseases. Typically, the microarray cancer studies provide only a limited number of tissue samples from the specified classes of tumours or patients, whereas each tissue sample may contain the expression levels of thousands of genes. Thus researchers are faced with the problem of forming a prediction rule on the basis of a small number of classified tissue samples, which are of very high dimension. Usually, some form of feature (gene) selection is adopted in the formation of the prediction rule. As the subset of genes used in the final form of the rule have not been randomly selected but rather chosen according to some criterion designed to reflect the predictive power of the rule, there will be a selection bias inherent in estimates of the error rates of the rules if care is not taken. We shall present various situations where selection bias arises in the formation of a prediction rule and where there is a consequent need for the correction of this bias. We describe the design of cross-validation schemes that are able to correct for the various selection biases.     

荧光假单胞菌BIT-18磷脂酶B基因的克隆及生物信息学分析

利用PCR从Pseudomonas fluorescens BIT-18总DNA中成功扩增到编码Pf-PLB的全长基因,并进行测序.通过生物信息学分析,可知Pf-PLB基因全长1272 bp,编码423个氨基酸,理论分子量为45.8kDa,等电点为5.53,在N末端有一个包含23个氨基酸的信号肽.进化树及序列分析结果显示,pfplb是PLBs新基因家族的成员.采用模建软件Modeller进行人工建模,结果表明Pf-PLB为由14股Strand组成的β-桶状蛋白,本研究为Pf-PLB进一步高效表达及其脱胶机理研究奠定基础.     

Bayesian Weibull tree models for survival analysis of clinico-genomic data

An important goal of research involving gene expression data for outcome prediction is to establish the ability of genomic data to define clinically relevant risk factors. Recent studies have demonstrated that microarray data can successfully cluster patients into low- and high-risk categories. However, the need exists for models which examine how genomic predictors interact with existing clinical factors and provide personalized outcome predictions. We have developed clinico-genomic tree models for survival outcomes which use recursive partitioning to subdivide the current data set into homogeneous subgroups of patients, each with a specific Weibull survival distribution. These trees can provide personalized predictive distributions of the probability of survival for individuals of interest. Our strategy is to fit multiple models; within each model we adopt a prior on the Weibull scale parameter and update this prior via Empirical Bayes whenever the sample is split at a given node. The decision to split is based on a Bayes factor criterion. The resulting trees are weighted according to their relative likelihood values and predictions are made by averaging over models. In a pilot study of survival in advanced stage ovarian cancer we demonstrate that clinical and genomic data are complementary sources of information relevant to survival, and we use the exploratory nature of the trees to identify potential genomic biomarkers worthy of further study.     

Sparsity and smoothness via the fused lasso

Summary.  The lasso penalizes a least squares regression by the sum of the absolute values ( L ₁-norm) of the coefficients. The form of this penalty encourages sparse solutions (with many coefficients equal to 0). We propose the 'fused lasso', a generalization that is designed for problems with features that can be ordered in some meaningful way. The fused lasso penalizes the L ₁-norm of both the coefficients and their successive differences. Thus it encourages sparsity of the coefficients and also sparsity of their differences—i.e. local constancy of the coefficient profile. The fused lasso is especially useful when the number of features p is much greater than N , the sample size. The technique is also extended to the 'hinge' loss function that underlies the support vector classifier. We illustrate the methods on examples from protein mass spectroscopy and gene expression data.     

Incorporating gene functional annotations in detecting differential gene expression

Summary.  The importance of incorporating existing biological knowledge, such as gene functional annotations in gene ontology, in analysing high throughput genomic and proteomic data is being increasingly recognized. In the context of detecting differential gene expression, however, the current practice of using gene annotations is limited primarily to validations. Here we take a direct approach to incorporating gene annotations into mixture models for analysis. First, in contrast with a standard mixture model assuming that each gene of the genome has the same distribution, we study stratified mixture models allowing genes with different annotations to have different distributions, such as prior probabilities. Second, rather than treating parameters in stratified mixture models independently, we propose a hierarchical model to take advantage of the hierarchical structure of most gene annotation systems, such as gene ontology. We consider a simplified implementation for the proof of concept. An application to a mouse microarray data set and a simulation study demonstrate the improvement of the two new approaches over the standard mixture model.     

Mixture model on the variance for the differential analysis of gene expression data

Summary.  In microarray experiments, accurate estimation of the gene variance is a key step in the identification of differentially expressed genes. Variance models go from the too stringent homoscedastic assumption to the overparameterized model assuming a specific variance for each gene. Between these two extremes there is some room for intermediate models. We propose a method that identifies clusters of genes with equal variance. We use a mixture model on the gene variance distribution. A test statistic for ranking and detecting differentially expressed genes is proposed. The method is illustrated with publicly available complementary deoxyribonucleic acid microarray experiments, an unpublished data set and further simulation studies.     

基因技术的趋向及道德哲学的反思——与樊浩教授商榷

基因技术、生物医学技术虽然会干预、介入人的生命体与生命活动,改变某种自然性状,但很难从根本上颠覆人和家庭的自然本质。传统道德哲学的基石是“自然人”和“自然家庭”,但自近代以来,道德哲学理论随着历史的变迁已发生了很大的变化,是以现代性的重大价值理念作为理论的奠基,而远离了自然人、自然家庭的起始点。基因技术引发的伦理问题的背后,实际上存在着深层、宽广的社会背景,涉及科技与社会、科技与现代价值理念的关系等等更具挑战性的重大问题,而基因伦理学并不能解答这些深层而又复杂的问题。必须把基因伦理学、生命伦理学、科技伦理学、发展伦理学等学科协同起来思考,进而在现代性理念的观照下进行道德哲学的深层反思。     

潜伏在人类基因组计划内的巨大商机

人类基因组计划(HGP)是当前生物医学领域乃至医学领域最大的科学工程,它对人类未来发展的推动力不亚于"人类蹬月计划".当人们对HGP的伦理学、社会学、乃至医学、军事科学诸方面评头论足的时候,笔者从经济学角度分析了HGP在基因诊断、基因治疗、克隆技术、基因工程药物、生物信息产业等方面给商家带来的商业契机,使人们更能清晰的看到HGP的经济前景,这对于各相关产业、企业的投资、转轨提供了极具价值的参考依据.     

古希腊命运观念的现代阐释

"命运观念"是古希腊文学和哲学的核心概念。古希腊悲剧被称之为命运悲剧,它以神谕这一感性的意象形式抒写着命运的神秘可畏,以及人与命运冲突的悲剧必然性;在希腊哲学中,毕达哥拉斯的"数",赫拉克利特的"逻各斯"以及柏拉图的"理念",其实都是"命运"这一意象的哲学表达。现代基因理论从分子这一微观水平上较为科学地解释了什么是命运,基因从某种意义上说即人类的现代命运。从古希腊文学的命运悲剧到现代基因理论对命运观念的科学阐释,西方文学似乎完成了一个有意味的循环。