川芎嗪与冰片处方配比与组合物的巴布剂基质处方的正交设计优化研究

目的:考察优化川芎嗪与冰片处方配比与组合物的巴布剂基质处方。方法:采用家兔离体皮肤透皮试验考察川芎嗪与冰片处方配比,采用正交设计筛选CMC-Na、NP-700、二氧化钛和丙三醇的用量。结果:川芎嗪与冰片处方最佳配比50:1;最佳处方为NP-700 6.0 g、二氧化钛0.5g、丙三醇10.0g和CMC-Na 0.5g。结论:通过优化处方配比,可制得黏附性能良好的川芎嗪与冰片巴布剂。     

乳品废水中蛋白质降解的最佳工艺条件研究

对乳品加工废水处理过程中的蛋白质含量的降解和蛋白酶活力以及COD进行了测试,找出在厌氧发酵下的一些关键影响参数：温度、pH值、泥水比以及厌氧发酵时间。根据各单因素测验结果设计了四因素、三水平的正交试验。通过极差分析我们得出温度为30℃,pH为7．0,发酵时间为4天,泥水比为1：11．5的厌氧处理蛋白质的最优化工艺条件。     

EXCEL在天然产物提取工艺中的应用

正交试验设计是天然产物研究中重要的方法之一,本文介绍了利用Microsoft EXCEL的内部统计函数编制天然产物提取工艺统计分析程序,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、准确地进行直观分析和方差分析,并可根据极差值得出最佳工艺条件,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,可大大提高天然产物提取工艺的效率。     

天然配比油对家兔肝组织中脂肪酸的影响

目的　研究天然配比油对家兔肝组织中脂肪酸的影响。方法　用GC法测定 5 0只家兔喂天然配比油 90d肝组织脂肪酸组分和含量的变化。结果　猪油对照组中 14∶0、18∶1和MUFA明显增高 ;三个配比油组α18∶3水平明显增高 ,14∶0明显降低 ,配 1组的α18∶3含量最高 ,配 2和配 3组的γ18∶3和 18∶2含量明显增高、18∶1和MU FA无明显变化。结论　补充三种配比油均可提高体内n3系多烯脂酸水平 ;配比 2和配比 3油还可提高n6 系多烯脂酸质量、抑制 18∶1升高 ,治疗高脂血症效果较好     

正交试验设计法考察苏云金杆菌“140”－51培养基

我们运用了正变试验设计法对经过复壮、紫外线照射处理的苏云金杆菌“１４０”－５１菌株进行培养基的考察,采用Ｌ２７（３１３）的正交表来安排培养基中影响发酵单位的几个主要因子［１．２．３］,其它试验条件固定为;培养温度３０±１℃,１５ｍｌ装量／５００ｍｌ三角瓶,摇床转速２１０ｒ／ｍｉｎ,ｐＨ值控制在每一配比培养基灭菌后为ｐＨ６．８～７．２范围内。     

复合酶法提取黄秋葵嫩果多酚的工艺优化

对复合酶法提取黄秋葵嫩果多酚的工艺进行优化。通过单因素及正交试验,确定复合酶中纤维素酶、甘露聚糖酶和果胶酶的最佳配比浓度,分别是2%、3%、1%;在此基础上,以提取温度、料液比、提取时间为自变量,黄秋葵多酚提取含量为响应值,采用Design-Expert软件中的Central Composite Design模块设计3因素组合试验,建立多项式回归方程,经响应面回归分析优化组合条件,得到复合酶法提取黄秋葵嫩果多酚的最佳提取工艺为:提取温度44.97℃,液料比(mL:g)58.80∶1,提取时间3.99 h,预测多酚提取值为29.193 mg/g,验证值为29.156 mg/g。研究结果可为黄秋葵多酚的批量提取工艺提供参考。     

产纤维素酶菌株宇佐美曲霉Y-11产酶条件及酶性质的研究

通过正交试验,对宇佐美曲霉纤维素酶固态发酵条件进行了研究,其较适培养基为：麸皮与稻草配比为２：３,氮源：尿素,含水量：２００％（液固比）,ｐＨ：５．５。２８－３０℃培养７２小时,ＣＭＣ酶活力为５．０５（ｕ／ｍｌ）,滤纸酶活力达到１．０４（ｕ／ｍｌ）。ＣＭＣ酶及滤纸酶最适ｐＨ分别为４．４１、６．０６;最适温度分别为６５℃、５５℃。酶的热稳定性与ｐＨ稳定性较高。     

基于GET技术的混合厌氧发酵产甲烷研究

GET是一种国外新研发的在田间利用秸秆进行甲烷生产的技术。为了推动该技术在我国的实际应用,本研究以稻草与牛粪为发酵原料,水稻田土壤作为基底,采用正交试验法,测试了温度、发酵基质配比（稻草∶牛粪）和秸秆腐熟剂浓度对混合厌氧发酵产气量的影响。结果表明：各处理因子的产气动态呈“S”型增长模式,但在快速增长时间和增长幅度上有一定区别。30 ℃和40 ℃处理的累积产气量显著高于10 ℃和20 ℃处理的累积产气量;发酵基质配比以1∶1处理的累积产气量最大,分别比1∶0（对照）、1∶2和1∶3配比高34.12%、20.39%和21.78%;秸秆腐熟剂浓度的产气效果在实验范围内随着浓度的增加而增加。厌氧发酵过程中各实验因子下挥发性脂肪酸（VFAs）含量和pH在20 d左右分别有一个高峰和低谷值且都有较大差异,各因子主要是通过在这个时期对厌氧发酵进程的影响而对整个产气量产生影响。综合分析显示稻草厌氧发酵产生物甲烷的最优组合为：温度30 ℃、发酵基质配比1∶1、秸秆腐熟剂浓度8%。     

植物生长物质在胚培养法挽救陈年小麦种质中的作用

以陈年小麦离体胚为外植体,MS培养基为基本培养基,附加5种植物生长物质2,4-D、NAA、KT、6-BA、GA3进行单因子和正交试验,研究植物生长物质在陈年小麦离体胚萌发中的作用。单因子试验结果表明：单独使用6-BA 1．0mg／L、GA3 2．5mg/L均能在一定程度上促进陈年小麦离体胚出芽和生根,出芽率分别为70．0％和77．5％,比对照分别提高了21．7％和29．2％;生根率分别为63．3％和72．2％,比对照分别提高了21．3％和30．2％。正交试验结果显示GA3对陈年小麦离体胚的萌发影响显著,最佳植物生长物质组合为2,4-D2．0mg／L NAA 1．1mg／L 6-BA0．5mg/L GA3 4．0mg/L,出芽率为82．5％,生根率为60．0％,比对照分别提高了32．5％和17．5％。     

基于姬松茸深层发酵的Plackett-Burman筛选

对姬松茸液体深层发酵培养工艺条件进行单因素实验：应用Minitab15软件设计Plackett—Burman筛选实验,筛选出蔗糖及酵母膏质量浓度、培养温度、微量元素配比4个显著性影响因素,通过正交试验对其进行优化。确定最佳培养工艺条件：蔗糖质量浓度45g/L,酵母膏质量浓度3g／L,KH2PO4质量浓度2．5g／L,MgSO4质量浓度1．25g／L,培养温度27℃,培养时间7d,多糖产量可达4．64g／L。     

Bootstrap Restricted Likelihood Ratio Test for the Detection of Rare Variants

In this paper the detection of rare variants association with continuous phenotypes of interest is investigated via the likelihood-ratio based variance component test under the framework of linear mixed models. The hypothesis testing is challenging and nonstandard, since under the null the variance component is located on the boundary of its parameter space. In this situation the usual asymptotic chisquare distribution of the likelihood ratio statistic does not necessarily hold. To circumvent the derivation of the null distribution we resort to the bootstrap method due to its generic applicability and being easy to implement. Both parametric and nonparametric bootstrap likelihood ratio tests are studied. Numerical studies are implemented to evaluate the performance of the proposed bootstrap likelihood ratio test and compare to some existing methods for the identification of rare variants. To reduce the computational time of the bootstrap likelihood ratio test we propose an effective approximation mixture for the bootstrap null distribution. The GAW17 data is used to illustrate the proposed test.     

Permutation tests for random effects in linear mixed models

Inference regarding the inclusion or exclusion of random effects in linear mixed models is challenging because the variance components are located on the boundary of their parameter space under the usual null hypothesis. As a result, the asymptotic null distribution of the Wald, score, and likelihood ratio tests will not have the typical χ(2) distribution. Although it has been proved that the correct asymptotic distribution is a mixture of χ(2) distributions, the appropriate mixture distribution is rather cumbersome and nonintuitive when the null and alternative hypotheses differ by more than one random effect. As alternatives, we present two permutation tests, one that is based on the best linear unbiased predictors and one that is based on the restricted likelihood ratio test statistic. Both methods involve weighted residuals, with the weights determined by the among- and within-subject variance components. The null permutation distributions of our statistics are computed by permuting the residuals both within and among subjects and are valid both asymptotically and in small samples. We examine the size and power of our tests via simulation under a variety of settings and apply our test to a published data set of chronic myelogenous leukemia patients.     

A generalized nonparametric test for lattice-ordered means

Treatment means in factorial experiments are lattice ordered when there is an increase in mean response as the level of any factor is increased while holding the other factors fixed. Such means occur naturally in many experiments. A nonparametric test for lattice-ordered means involving a Kendall-type statistic will be summarized for k-factor factorial experiments. Specifically, the form of the test statistic and variance under the null hypothesis will be presented. In addition, a normalized version of the test statistic will be discussed and applied to relevant data.     

The use of score tests for inference on variance components

Whenever inference for variance components is required, the choice between one-sided and two-sided tests is crucial. This choice is usually driven by whether or not negative variance components are permitted. For two-sided tests, classical inferential procedures can be followed, based on likelihood ratios, score statistics, or Wald statistics. For one-sided tests, however, one-sided test statistics need to be developed, and their null distribution derived. While this has received considerable attention in the context of the likelihood ratio test, there appears to be much confusion about the related problem for the score test. The aim of this paper is to illustrate that classical (two-sided) score test statistics, frequently advocated in practice, cannot be used in this context, but that well-chosen one-sided counterparts could be used instead. The relation with likelihood ratio tests will be established, and all results are illustrated in an analysis of continuous longitudinal data using linear mixed models.     

On Testing an Unspecified Function Through a Linear Mixed Effects Model with Multiple Variance Components

Summary We examine a generalized F ‐test of a nonparametric function through penalized splines and a linear mixed effects model representation. With a mixed effects model representation of penalized splines, we imbed the test of an unspecified function into a test of some fixed effects and a variance component in a linear mixed effects model with nuisance variance components under the null. The procedure can be used to test a nonparametric function or varying‐coefficient with clustered data, compare two spline functions, test the significance of an unspecified function in an additive model with multiple components, and test a row or a column effect in a two‐way analysis of variance model. Through a spectral decomposition of the residual sum of squares, we provide a fast algorithm for computing the null distribution of the test, which significantly improves the computational efficiency over bootstrap. The spectral representation reveals a connection between the likelihood ratio test (LRT) in a multiple variance components model and a single component model. We examine our methods through simulations, where we show that the power of the generalized F ‐test may be higher than the LRT, depending on the hypothesis of interest and the true model under the alternative. We apply these methods to compute the genome‐wide critical value and p ‐value of a genetic association test in a genome‐wide association study (GWAS), where the usual bootstrap is computationally intensive (up to 10⁸ simulations) and asymptotic approximation may be unreliable and conservative.     

Null models of geographic range size evolution reaffirm its heritability

Most models of allopatric speciation predict that the two daughter species will have range sizes different from each other's and potentially from that of their common ancestor. However, I find that this difference is less than that expected under a variety of null models of range evolution. Sister species' range values may therefore become more similar in the time following speciation. Greater-than-expected similarity (symmetry) has also been treated as a form of range size heritability. I therefore compare the results of this symmetry approach to a test for phylogenetic signal, using the range sizes of North American birds. I find that range size is heritable under both tests. I suggest that null models for range size heritability should be informed by an explicit model of evolution. Comparative methods may give erroneous results if they fail to take the unusual form of inheritance of range size into account.     

Mapping quantitative trait loci using multiple phenotypes in general pedigrees

The use of correlated phenotypes may dramatically increase the power to detect the underlying quantitative trait loci (QTLs). Current approaches for multiple phenotypes include regression-based methods, the multivariate variance of components method, factor analysis and structural equations. Issues with these methods include: 1) They are computation intensive and are subject to problems of optimization algorithms; 2) Existing claims on the asymptotic distribution of the likelihood ratio statistic for the multivariate variance of components method are contradictory and erroneous; 3) The dimension reduction of the parameter space under the null hypothesis, a phenomenon that is unique to multivariate analyses, makes the asymptotic distribution of the likelihood ratio statistic more complicated than expected. In this article, three cases of varying complexity are considered. For each case, the efficient score statistic, which is asympotically equivalent to the likelihood ratio statistic, is derived, so is its asymptotic distribution [correction]. These methods are straightforward to calculate. Finite-sample properties of these score statistics are studied through extensive simulations. These score statistics are for use with general pedigrees.     

Plug‐in tests for nonequivalence of means of independent normal populations

In this paper, we consider the problem of testing nonequivalence of several independent normal population means. It is a well‐known problem to test the equality of several means using the analysis of variance (ANOVA). Instead of determining the equality, one may consider more flexible homogeneity, which allows a predetermined level of difference. This problem is known as testing nonequivalence of populations. We propose the plug‐in statistics for two different measures of variability: the sum of the absolute deviations and the maximum of the absolute deviations. For each test, the least favorable configuration (LFC) to ensure the maximum rejection probability under the null hypothesis is investigated. Furthermore, we demonstrate the numerical studies based on both simulation and real data to evaluate the plug‐in tests and compare these with the range test.     

A note on permutation tests for variance components in multilevel generalized linear mixed models

In many applications of generalized linear mixed models to multilevel data, it is of interest to test whether a random effects variance component is zero. It is well known that the usual asymptotic chi-square distribution of the likelihood ratio and score statistics under the null does not necessarily hold. In this note we propose a permutation test, based on randomly permuting the indices associated with a given level of the model, that has the correct Type I error rate under the null. Results from a simulation study suggest that it is more powerful than tests based on mixtures of chi-square distributions. The proposed test is illustrated using data on the familial aggregation of sleep disturbance.     

A Combined Invariant Test for a Null Variance Ratio

This paper investigates the properties of a test that combines the Wald and LMPI (locally most powerful invariant) tests of variance components in unbalanced mixed models. The combined test statistic is easily computed and its null distribution may be approximated by a central F distribution with the degrees of freedom of the numerator adjusted in accordance with the degree of imbalance of the design. Numerical methods are used to show that the approximation is accurate over a wide range of conditions and that the efficiency of the combined test, relative to the power envelope, is satisfactorily high overall.