Visual Basic在药物配方优化中的应用

正交试验设计是药物配方优化中重要的方法之一,本文介绍了利用Visual Basic编制药物配方优化统计分析程序,并以实例说明了该程序的使用方法和应用技巧。结果表明,用户只需输入试验的原始数据,即可快速、准确地进行试验数据的统计分析,并可根据试验结果快速得出其优化配方,所得分析结果与专业统计分析软件DPS对数据进行正交分析相比,二者计算结果基本一致,且该方法不但快捷方便,操作简单,而且结果准确可靠,可大大提高药物配方优化的效率。     

EXCEL在农药毒力测定中的应用

根据机值分析法原理,利用EXCEL编制了农药毒力测定数据处理程序。用户只需输入药剂剂量（或浓度）、试验总虫数和死虫数,即可快速、准确地计算出毒力测定中毒力回归曲线、致死中量,95%置信限,相关系数和卡平方值等数据,并可进行卡平方检验和毒力曲线的绘制。     

利用Microsoft Excel软件对农业常用田间试验进行方差分析的探索

探索了利用MicrosoftExcel软件对农业常用田间试验进行方差分析的基本原理和具体操作方法,并提供了系统分组试验、裂区试验、拉丁方试验等方差分析的实例运算。     

国际规程与国家规程在高温烘干法测定玉米种子水分上的差异研究

国家种子检验规程与国际种子检验规程规定的高温烘干法测定玉米种子水分在烘干时间上存在着差异,利用不同水分含量的玉米种子样品进行对比试验,找出两种不同的方法对测定水分的差异,运用统计分析方法计算出二者的相关系数,求得回归方程,据此回此方程,可以利用一种规程测定出的种子水分,推算出另外一种规程测定出的该玉米种子水分,指出,这可为玉米种子进行国际贸易提供种子质量指标的依据。     

玉米种子DNA快速提取及纯度鉴定新方法

试验分别以玉米幼苗和干种子为材料，利用CTAB法对DNA提取程序进行了研究，建立了一种简单、快速的玉米DNA提取程序。该程序提取的DNA分子量大小与幼苗法相当，能够利用SSR引物进行扩增，可应用于玉米杂交种子纯度检测。     

裂颖与去壳对水稻种子萌发生理的初步研究

利用3个在四川生产应用比较普遍的杂交水稻品种为材料,将试验种子做裂颖、去壳处理,以正常颖壳种子为对照,研究颖壳对种子萌芽生长的影响.结果表明,萌芽速度、物质运转效率和贮藏物质消耗率为:去壳＞裂颖＞完整;随萌发时间的延长,α-淀粉酶活性下降速率为:去壳＞裂颖＞完整;根冠比为:去壳＜裂颖＜完整;叶绿素含量为:完整处理为最高,裂颖与去壳偏低.表明颖壳对水稻种子正常萌芽至关重要,是水稻幼苗健康生长的保障,是培育优质稻苗的基础.     

不同单株梭梭种子质量比较研究

收集14份不同单株梭梭种子，比较它们的外观、发芽及出苗等性状．并利用SPSS软件进行数据统计分析，研究比较不同单株梭梭种子的质量。结果是梭梭种子不同单株间质量性状存在显著差异。     

华山松种子区区划及贵州用种调拔区

我省华山松种源研究多点试验结果,将华山松分布区划分为3个种子区和4个种子亚区。贵州属云贵高原种子区的北部亚区,其生产用种的调拨,除本省威宁、赫章种子外,可调用本亚区内云南东川、会泽,四川会理、会东的种子;云贵高原南部亚区(云南保山、腾冲、梁河、昌宁)是贵州华山松生产用种优良供种区,比本省威宁种子有更大的增产潜力。但决不能调用秦巴山地生产的种子,以免造成损失。     

木节对轴心受压胶合木柱稳定承载力的影响

采用压杆挠曲线法分析了木节对轴心受压胶合木柱稳定承载力的影响，分析时考虑了初弯曲、初偏心以及木节和木材本构的弹塑性等因素的影响。在考虑木节的影响时，模型中将截面分为木节区、木节影响区和无影响区，3个区域分别采用不同的本构模型，木节区的位置由自编程序随机产生，以模拟实际工程中木节在构件中的随机分布。分别计算了木节率为2%、6%和9%时的稳定系数φ。计算结果表明，木节会在一定程度上降低轴心受压胶合木柱的稳定承载力。数值计算结果和试验数据的对比表明，数值计算结果与试验结果吻合较好。     

汉化区试数据管理和统计分析系统的功能及应用

汉化区试数据管理和统计分析系统是集试验设计、数据管理、统计计算功能于一体的应用软件。它适用于品种区域试验数据的管理和生物统计等工作。能在汉字环境下运行，使用方便；能大幅度提高工作效率和计算准确性。     

Statistical analysis of growth curves in plant breeding

Summary In the first part of this paper some experimental conditions necessary for a valid statistical analysis of differences in growth curves are discussed.Next the multivariate character of growth curves is considered. Data on the growth curves of three genotypes of an experiment with tomatoes of the IVT are used for illustrating the computational procedure and some aspects of interpretation.The growth curves of log dry leaf weight for each plot could be described by a quadratic function of time and were characterised by three components: mean level m, slope 1 (linear component) and curvature q (quadratic component).Univariate analyses of variance (ANOVA) showed significant differences between cultivars for each growth component: however, this is only part of the information as the correlation between the components is not involved. From a multivariate analysis of variance (MANOVA) it appeared that in this experiment the differences in growth between the cultivars were best expressed by using mean level m only.An estimate of the final (reduced) statistical model underlying the tomato example is derived.It is advised to split up growth into different phases of development each with its own analysis. At least four harvest dates for every phase seem desirable.     

运用广义线性混合模型分析随机区组重复测量的试验资料

重复测量试验对同一受试对象进行多次测量,各时间点数据间存在自相关性,进行方差分析和均值比较时需要进行特殊处理。虽然此方法在农业等研究领域运用十分广泛,但目前有效地相关统计方法鲜见。为了建立操作简单、实用性强、结果可靠的统计分析方法,本研究采用SAS的广义线性混合模型(Generalized Linear Mixed Models,GLIMMIX),以随机区组重复测量试验资料为例,说明了协方差结构筛选、方差分析和均值比较的具体方法。结果表明,用传统的裂区设计、多变量统计等方法会造成资料信息浪费,统计功效降低,缺区无法处理等问题,甚至会导致错误的结论。GLIMMIX能很好地处理自相关问题,功能强大,结果可靠,使用简单,允许缺区,是进行重复测量试验资料方差分析和均值比较的理想方法。目前在国内将其运用到农学类试验数据的统计分析的相关报道鲜见,该文在本领域具有很强的实用性和创新性。     

运用广义线性混合模型分析随机区组重复测量的试验资料

Multiple measurements of the same subject are conducted, and there is autocorrelations among the data at each time point. Some special treatment is required for statistical analysis of repeated measure data. Although the repeated measure is widely used in agricultural and other research fields, the relevant and effective statistical methods are rare. In order to establish a simple, easy to use, and reliable statistical method, generalized linear mixed models (GLIMMIX) of SAS was adapted. Selection of covariance structure, variance analysis, and means comparison processes were showed by using RCB data. Traditional split plot and MANOVA methods wasted large amounts of information, reduced the power of the test, and could not handle missing data effectively, even resulting in incorrect conclusions. GLIMMIX was the best choice for variance analysis and means comparison of repeated measure data, because it was easy to use, and had powerful function, high reliability, and ability to handle missing data. At present, there was few relevant report in China, and this method would be very practical and innovative in this field.     

A tutorial on the statistical analysis of factorial experiments with qualitative and quantitative treatment factor levels

Agronomic experiments are often complex and difficult to interpret, and the proper use of appropriate statistical methodology is essential for an efficient and reliable analysis. In this paper, the basics of the statistical analysis of designed experiments are discussed using real examples from agricultural field trials. Factorial designs allow for the study of two or more treatment factors in the same experiment, and here we discuss the analysis of factorial designs for both qualitative and quantitative level treatment factors. Where treatment factors have quantitative levels, models of treatment effects are essential for efficient analysis and in this paper we discuss the use of polynomials for empirical quantitative modelling of treatment effects. The example analyses cover experiments with a single quantitative level factor, experiments with mixtures of quantitative and qualitative level factors, polynomial regression designs with two quantitative level factors, split‐plot designs with quantitative level factors and repeated‐measures designs with correlated data and a quantitative treatment response over time. Modern mixed model computer software for routine analysis of experimental data is now readily available, and we demonstrate the use of two alternative software packages, the SAS package and the R language. The main purpose of the paper is to exemplify standard statistical methodology for routine analysis of designed experiments in agricultural research, but in our discussion we also provide some references for the study of more advanced methodology.     

Important considerations in the analysis of generation means

Summary The weighted least squares (WLS) procedure has been widely used in the analysis of generation means in biometrical genetics. Two methods to carry out the analysis have been presented in the literature (Mather & Jinks, 1971; Rowe & Alexander, 1980). Neither method identified the factors that need to be taken into account when deciding which procedure to use. This paper examines the fitting of the additive-dominance model using the general principles of least squares analysis and presents the statistical basis of each procedure. Mather & Jink's method, which is more realistic according to genetic theory, is based on the assumption that the populations have nonhomogeneous variances. Rowe & Alexander's method is based on the assumption that the populations have homogeneous variances. In the latter case, there is actually no need to do a WLS analysis. The ordinary least square analysis using the original observations rather than the means, will produce the regression parameter estimates.     

Analysis of a site x year experiment with timothy polycross progeny

Summary Results from a cooperative breeding programme with timothy for the northern areas of Scandinavia are presented. The main aim of the programme was to identify genotypes for synthetic populations that are high yielding, adapted over the whole area and stable over years. A polycross comprising 12 genotypes from each of five sites within the region was formed. The subsequent 60 half-sib families and four reference varieties were then compared under sward conditions at the same sites. The trials lasted for three years and results for total dry matter yields are presented.Significant differences in yield between lines were found. There were also significant two and three factor interactions between lines, sites and years. A new two-step procedure is presented to select the genotypes to make up a synthetic variety. Firstly, a superiority measure (Q-value) is used to select a group of genotypes that are high yielding and stable over sites and years. Secondly, the GxE part of the Q-value is split into two terms. One measures adaptation to predictable environmental conditions. The other one measures stability to unpredictable changes in the environment. A plot of these two measures provides a tool for discarding unstable or poorly adapted genotypes. The statistical properties of the selection parameters are discussed.     

Inheritance of the stay-green trait in tropical maize

Stay-green maize genotypes have been associated with tolerance to biotic and abiotic stresses, including tolerance to drought, and to stalk and root lodging, but there is limited information on its inheritance. Thus, this research was conducted to study the inheritance of the stay-green trait using both conventional analysis and QTL mapping of the Design III in a tropical maize population developed from two inbred lines genetically divergent for this trait. Two-hundred and fifty F₂ plants were genotyped with 177 microsatellite markers, and their backcrossed progenies to both parental inbreds were evaluated at three locations. Ten plants per plot were assessed 120 days after sowing and the plot means scores for stay-green, adjusted for days to silking emergence, were used for analysis. The additive variance was larger than the dominance variance, the genetic by location interaction variance presented a high magnitude, and the heritability coefficient on a plant-basis a low magnitude. Seventeen QTL were mapped, most of them were clustered on four chromosomes and accounted for by 73.08 % of the genetic variance. About half of the QTL interacted with location, and the average level of dominance was partial dominance. The additive effects were larger than the dominance effects; the latter were not unidirectional, so that heterosis could not be exploited in crosses. Procedures for marker-assisted selection to increase the level of stay-green are discussed and an approach is suggested for using both stable and non-stable QTL in a marker-assisted backcross program.     

Effects of gamma and electron beam irradiations on the triacylglycerol profile of fresh and stored Castanea sativa Miller samples

The present chestnut (Castanea sativa Miller) commercialization process, including distribution to novel markets, demands suitable preservation technologies. Irradiation has been considered a promising alternative to chemical fumigation (legally forbidden and harmful for human health and environment) or heat treatments (technological difficulties and low efficiency). Following previous studies on the effects of irradiation in different chemical parameters, the present work aimed to evaluate the effects of electron beam and γ-irradiation on the triacylglycerol profiles of fresh and stored chestnuts. An analysis of variance with type III sums of squares was performed using the general linear model procedure. As a classification technique, a linear discriminant analysis using the stepwise procedure was also applied. Independently of irradiation type, samples irradiated with higher doses showed higher modifications in triacylglycerol profiles. Samples irradiated with 1 and 3 kGy were clearly separated from the remaining groups in the linear discriminant analysis. The results highlight the potential of triacylglycerol profiles as indicators of chestnut irradiation. Irradiation might be recommended as a suitable method for chestnut preservation.     

Contributions to the Analysis of Genotype × Environment Interactions: Comparison of Different Parametric and Non-parametric Tests for Interactions with Emphasis on Crossover Interactions

The objective of this study was to compare five statistical procedures (analysis of variance, the Azzalini/Cox test, the Hildebrand procedure, the Kubinger approach, and the de Kroon/van der Laan technique) for the analysis of genotype × environment interactions in cross‐classified data sets from cultivar performance yield trials with rows = cultivars and columns = environments (locations and/or years). The procedures Hildebrand, Kubinger and de Kroon/van der Laan are non‐parametric methods based on ranks, while analysis of variance and the Azzalini/Cox test proceed from the original absolute yield data. These very different statistical techniques were applied to extensive data sets from German official registration trials (1985–1989) with winter oilseed rape (Brassica napus L.), faba bean (Vicia faba L.), oat (Avena sativa L.), fodder beet (Beta vulgaris L.) and sugar beet (Beta vulgaris L.). The Azzalini/Cox and de Kroon/van der Laan methods are based on the crossover concept of interaction (different rank orders) while the other methods are based on the usual concept of interaction (deviations from additivity of main effects). For an analysis of usual interactions the procedures Hildebrand, Kubinger and analysis of variance are approximately equivalent. For the crossover concept of interaction, the Azzalini/Cox approach is recommended, especially if one is particularly interested in rank changes between environments within genotypes.