新疆褐梅衣属地衣研究

使用光学显微镜、扫描电子显微镜等手段,研究了新疆褐梅衣属7种地衣的解剖学特征;利用显色反应(CT)、微量结晶( MCT)和薄层层析(TLC)等方法,对化学成分做了检测,并利用这些结果对主要分类学特征做了归纳。     

野生豆腐柴营养成分分析

对豆腐柴叶水分、灰分、粗蛋白、粗纤维、总糖、Vcβc、矿物元素的含量及氨基酸的组成和含量进行分析,为合理地开发与利用野生资源植物豆腐柴提供了科学的依据。     

基于WWW与UNIX的核酸序列分析实用软件的开发

讨论了计算机序列分析的工作,介绍了基于WWW和UNIX的核酸序列分析实用软件,其特点是快速,易用,提高了工作效率。     

基于PC/Linux的核酸序列分析系统的构建及其应用

基于PC机和Linux操作系统, 利用Phred/Phrap/Consed软件和Blast软件, 构建了核酸序列大规模自动分析系统. 该套系统可自动完成从测序峰图向核酸序列的转化、载体序列去除、序列自动拼接、重复序列鉴定以及序列的相似性分析, 可加速对大规模测序数据的分析和利用.     

多花含笑叶的挥发油成分研究

运用气相色谱－质谱－计算机联用技术,结合标准谱库,对昆明产多花含笑叶的挥发油成分进行了研究,分离鉴定了42个化合物,其中单萜16个,倍半萜21个,分别占精油总含量的21＝21％和49．08％,主要成分为γ－木罗烯（12．04％）,十六碳三烯酸甲酸（7．45％）,兰桉醇（7．38％）,1,8－桉叶素（5．76％）,α－愈创木烯（5．64％）,香桧烯（4．26％）,十六碳二烯酸甲酯（4．26％）,β－槛香烯（3．76％）,β－蒎烯（3．20％）等。     

Alternative implementations of Monte Carlo EM algorithms for likelihood inferences

Two methods of computing Monte Carlo estimators of variance components using restricted maximum likelihood via the expectation-maximisation algorithm are reviewed. A third approach is suggested and the performance of the methods is compared using simulated data.     

Multivariate statistics in analysis of data from the in vitro motility assay

A novel approach is described for classification of filaments as stationary or moving and for extraction of velocity data for smooth actin filament sliding in vitro. Moving and stationary filaments were effectively classified using four discriminating variables in a multivariate statistical analysis. The variables were (1) two different measures of the average filament distance from its starting point, (2) a measure of the variability in sliding direction, and (3) the coefficient of variation (CV) of the frame-to-frame sliding velocity (v(mean)). On the basis of this multivariate analysis we obtained correct classification of 98% of the stationary filaments and 94% of the moving filaments in a cross-validation data set. The same classification functions were useful throughout despite a 10-fold variation in the average sliding velocity in the cross-validation data. Further analysis of motile filaments suggested that the velocity of smooth sliding should, ideally, be obtained from the intercept on the velocity axis of a plot of v(mean) against CV. The velocity, so obtained, was between 10 and 30% (mean 20+/-3%; n=7; p<0.001) higher than if average sliding velocity was obtained for all moving filaments with CV<0.5.     

Biological database design and implementation

We present our experience of building biological databases. Such databases have most aspects in common with other complex databases in other fields. We do not believe that biological data are that different from complex data in other fields. Our experience has led us to emphasise simplicity and conservative technology choices when building these databases. This is a short paper of advice that we hope is useful to people designing their own biological database.     

A general maximum likelihood analysis of variance components in generalized linear models

This paper describes an EM algorithm for nonparametric maximum likelihood (ML) estimation in generalized linear models with variance component structure. The algorithm provides an alternative analysis to approximate MQL and PQL analyses (McGilchrist and Aisbett, 1991, Biometrical Journal 33, 131-141; Breslow and Clayton, 1993; Journal of the American Statistical Association 88, 9-25; McGilchrist, 1994, Journal of the Royal Statistical Society, Series B 56, 61-69; Goldstein, 1995, Multilevel Statistical Models) and to GEE analyses (Liang and Zeger, 1986, Biometrika 73, 13-22). The algorithm, first given by Hinde and Wood (1987, in Longitudinal Data Analysis, 110-126), is a generalization of that for random effect models for overdispersion in generalized linear models, described in Aitkin (1996, Statistics and Computing 6, 251-262). The algorithm is initially derived as a form of Gaussian quadrature assuming a normal mixing distribution, but with only slight variation it can be used for a completely unknown mixing distribution, giving a straightforward method for the fully nonparametric ML estimation of this distribution. This is of value because the ML estimates of the GLM parameters can be sensitive to the specification of a parametric form for the mixing distribution. The nonparametric analysis can be extended straightforwardly to general random parameter models, with full NPML estimation of the joint distribution of the random parameters. This can produce substantial computational saving compared with full numerical integration over a specified parametric distribution for the random parameters. A simple method is described for obtaining correct standard errors for parameter estimates when using the EM algorithm. Several examples are discussed involving simple variance component and longitudinal models, and small-area estimation.     

Multivariate elliptically contoured distributions for repeated measurements

The multivariate power exponential distribution, a member of the multivariate elliptically contoured family, provides a useful generalization of the multivariate normal distribution for the modeling of repeated measurements. Both light and heavy tailed distributions are included. The covariance matrix retains its interpretation so that it can easily be structured for serial dependence and several levels of variance components. A crossover trial on insulin applied to rabbits, with a series of repeated measurements within each period, is analyzed by means of this distribution using autocorrelation and two levels of variance components.