小麦品种联合试验效应及理论依据──Ⅲ.重复数品种数小区大小与效率

本文结合确定试验设计规则的统计方法介绍,展示了一个国家级小麦品种试验设计中的重复数,品种数和小区大小确定的情况．通过改变设计后对试验效率的影响,探讨了提高效率的途径以及在不明显降低精确度的前提下如何使试验更为经济．     

动物杂交试验的正反交循环设计法

为了提高配合力等参数估测的有效性、准确性和经济性。本文从动物自身的和试验的具体特点出发,提出了一种适于(1)客观条件仅允许试验部分组合、(2)试验目的仅为确定杂交育种的种群选配方案、(3)完全双列杂交试验分地分批进行等三种情况的新杂交试验设计法——正反交循环法,并且探讨了由这种设计法所设计的试验,即正反交循环杂交试验的最小二乘分析。     

均匀设计法快速确定超声场对纤维素酶活力影响的初探

本文应用均匀设计原理,在计算机上利用所编软件进行试验方案的设计,通过对试验数据的回归分析,考察了超声场中各因素对纤维素酶活力的影响,并得出结论。由于均匀设计法试验次数少,因此快速确定了超声场影响纤维素酶活力的主要因素。     

均匀设计试验布点方案在苗圃地土壤施肥研究中应用初探

本文通过对最优混合设计和均匀设计两个肥效试验的分析,讨论了均匀设计试验布点方案在苗圃地土壤施肥研究中应用的可行性及其条件。     

均匀设计在氨基酸分析中的应用——过甲酸氧化法测定胱、蛋氨酸的液相色谱条件筛选

本文介绍了均匀设计用于过甲酸氧化水解法测定胱、蛋氨酸液相色谱分析条件的筛选过程。均匀设计是中国科学院应用数学研究所方开泰先生提出的一种供多因素多水平而试验次数又比较少的试验设计法。本试验对液相色谱分析半胱磺酸、蛋氨酸砜的均匀试验结果用均匀设计的直观法和计算机逐步回归法进行了数据处理,总结了液相色谱条件对分析结果的影响规律,找出了过甲酸氧化水解法分析胱氨酸、蛋氨酸较为理想的液相色谱分析条件。用本试验筛选出的分析条件,对辽宁省饲料资源普查的49个样品进行分析,结果表明,所确定的分析条件是可靠的。本实验也为其它分析条件确定提供了一种可以借鉴的试验设计方法。     

基于药物毒性反应等级的Up-and-Down设计

Ⅰ期临床试验主要关心毒性,通常划分毒性为五个水平．简单起见,同时兼顾伦理问题,Ⅰ期临床试验通常采用up-and-down序贯设计(例如BCDⅠ,BCDⅡ,K-in-a-row,Narayana,Improved Narayana)．然而,在分配剂量水平时,该设计没有区分已经试验了的病人的严重毒性水平等级,从而有可能分配给病人更高毒性的剂量水平．因此,本文提出了基于药物毒性等级确定最大耐受剂量的up-and-down设计方法,并进一步研究了该设计方法在各种变化的剂量—毒性关系下的运作特征,并且和标准的up-and-down设计作模拟比较,结果表明该设计方法对Ⅰ期临床试验设计的剂量建议具有重要意义．     

试验设计和优化及其在发酵培养基优化中的应用

在发酵工业中,发酵培养基的优化对发酵水平的提高起着举足轻重的作用。而在寻求最佳发酵培养基的过程中,试验设计及统计优化发挥着重要作用。对近年来常用的试验设计及优化方法进行了综述,内容包括单因素轮换法、析因设计、均匀设计、响应面设计、人工神经网络和遗传算法等多种试验设计和优化方法,并对其进行分析和比较。     

单形格子和单形重心设计统计模型的优化分析方法

单形格子和单形重心设计是两种非常实用的配方试验设计方法,但其统计模型的优化分析却很困难．本文通过对单形格子和单形重心设计基本原理的分析,根据数学规划理论,构建了专门对这两种试验设计的统计模型进行优化分析的方法,同时给出了应用实例．     

海金沙草总黄酮提取工艺的响应面优化

为了提高海金沙草总黄酮提取效率,运用了Plackett-Burman试验设计、爬陡坡试验和Box-Behnken设计对提取工艺进行了响应面优化试验。利用Plackett-Burman对影响总黄酮提取的诸多相关因素进行了评价并成功筛选出主效应因子,即提取时间、提取温度和乙醇浓度。在Plackett-Burman设计基础上,根据主效应因子作用大小与方向进行了爬陡坡试验。最后用Box-Behnken响应面技术优化了总黄酮提取工艺并建立了关键影响产量的二次多项式数学模型,解模型逆矩阵得最优解(优化方案),即提取温度X1=45.45℃,乙醇体积分数X2=47.1%、提取时间X3=84.8 min。模型预测结果为0.433 mg.L-1,验证试验结果为0.428±0.004 mg.L-1(n=3)。     

基于药物毒性反应等级的up-and-down自适应设计

Ⅰ期临床试验主要关心药物的毒性,目的是在给定的剂量水平中寻找最大耐受剂量（MTD）．本文提出了基于药物毒性反应等级的up-and-down自适应设计,调查了该设计在各种变化的剂量一毒性关系下的运作特征．结果表明：该设计方法对提高建议MTD的精确度,以及保护病人,防止病人暴露在较高毒性剂量下方面,对Ⅰ期临床试验的设计实现了有意义的改善．     

基于蛋白质受体的药物分子计算机辅助设计策略

药物分子计算机辅助设计是一种在计算机或者理论上通过构建具有一定潜在药理活性的新化学实体的分子模拟方法。近十几年来,高通量组学技术的快速发展为生物和化学药物分子设计提供了良好的数据支撑和研究契机。另外,现代社会对生物制药合理性以及作用机理理解的要求越来越高,行业普遍要求药物需要有高效、无毒或者低毒以及靶向性强等特点。随着越来越多与药物靶点相关的蛋白质结构通过实验方法解析出来,基于蛋白质受体的药物分子设计方法可行性进一步提高,其方法也变得越来越重要。基于蛋白质受体的药物分子设计方法,一般是以蛋白质以及配体的三维结构出发进行分析,这让药物分子先导物的发现更加理性化。随着相关实验数据的积累以及深度学习等算法的发展,从而可以进行更加科学的药物分子设计,这在一定程度上加快了新药研发的进程,并更有利于探索相应的分子机理。本文对基于蛋白质受体的药物分子设计方法的常用策略进行系统的回顾、总结和展望。     

Application of iterative robust model‐based optimal experimental design for the calibration of biocatalytic models

The aim of model calibration is to estimate unique parameter values from available experimental data, here applied to a biocatalytic process. The traditional approach of first gathering data followed by performing a model calibration is inefficient, since the information gathered during experimentation is not actively used to optimize the experimental design. By applying an iterative robust model‐based optimal experimental design, the limited amount of data collected is used to design additional informative experiments. The algorithm is used here to calibrate the initial reaction rate of an ω‐transaminase catalyzed reaction in a more accurate way. The parameter confidence region estimated from the Fisher Information Matrix is compared with the likelihood confidence region, which is not only more accurate but also a computationally more expensive method. As a result, an important deviation between both approaches is found, confirming that linearization methods should be applied with care for nonlinear models. © 2017 American Institute of Chemical Engineers Biotechnol. Prog., 33:1278–1293, 2017     

改进支持向量机在棉铃虫人工饲料配方优化中的应用

发展新的实验设计与分析方法,通过实施尽可能少的实验而获得满意配方对动植物营养、发酵工程等复杂多因素多水平寻优问题极为重要。本研究结合均匀设计（uniform design, UD）与支持向量回归（support vector regression, SVR）,提出了一种新的配方优化实验设计与分析方法UD-SVR,将其应用于棉铃虫Helicoverpa armigera （Hübner）幼虫人工饲料配方优化。结果表明：在考虑6因素时仅通过2轮22个实验,表征配方优劣的指标--平均蛹重即由初始的0.2436 g高效提升至0.3044 g,明显优于二次多项式偏最小二乘回归等经验风险最小参比模型。UD-SVR预测精度高、指导性强、可解释性好、优化高效,有望在多因素多水平配方优化中得到广泛应用。     

Designing biomedical proteomics experiments: state-of-the-art and future perspectives

With the current expanded technical capabilities to perform mass spectrometry-based biomedical proteomics experiments, an improved focus on the design of experiments is crucial. As it is clear that ignoring the importance of a good design leads to an unprecedented rate of false discoveries which would poison our results, more and more tools are developed to help researchers designing proteomic experiments. In this review, we apply statistical thinking to go through the entire proteomics workflow for biomarker discovery and validation and relate the considerations that should be made at the level of hypothesis building, technology selection, experimental design and the optimization of the experimental parameters.     

Determining Parameters of the Numerical Response

The numerical response, the change in specific growth rate with food concentration, is a fundamental component of many aquatic microbial studies. Accurately and precisely determining the parameters of this response is essential to obtain useful data for both aut- and synecological studies. In this work we emphasize four points that are often ignored in designing numerical response experiments: (1) the inclusion of subthreshold concentrations (i.e., where growth rate is negative) in the experimental design; (2) an appropriate allocation of effort, i.e., the superiority of choosing more individual prey concentrations rather than replicating fewer; (3) the potential superiority of replicating experiments rather than simply replicating treatment in a single experiment; and (4) the placement of most measurements near the lower end of the concentration gradient, well below the asymptote, possibly following a geometric progression. We illustrate the first point by examining a small subset of published data on planktonic oligotrich ciliates and then, using a Monte Carlo simulation, rigorously evaluate the experimental design, supporting the remaining points.     

Optimal replication and the importance of experimental design for gel-based quantitative proteomics

Quantitative proteomic studies, based on two-dimensional gel electrophoresis, are commonly used to find proteins that are differentially expressed between samples or groups of samples. These proteins are of interest as potential diagnostic or prognostic biomarkers, or as proteins associated with a trait. The complexity of proteomic data poses many challenges, so while experiments may reveal proteins that are differentially expressed, these are often not significant when subjected to rigorous statistical analysis. However, this can be addressed through appropriate experimental design. A good experimental design considers the impact of different sources of variation, both analytical and biological, on the statistical importance of the results. The design should address the number of samples that must be analyzed and the number of replicate gels per sample, in the context of a particular minimum difference that one is seeking to achieve. In this study, we explore the ways to improve the quality of protein expression data from 2-DE gels, and describe an approach for defining the number of samples required and the number of gels per sample. It has been developed for the simplest of situations, two groups of samples with variation at two levels: between samples and between gels. This approach will also be useful as a guide for more complex designs involving more than two groups of samples. We describe some Internet-accessible tools that can assist in the design of proteomic studies.     

Statistical design and the analysis of gene expression microarray data

Gene expression microarrays are an innovative technology with enormous promise to help geneticists explore and understand the genome. Although the potential of this technology has been clearly demonstrated, many important and interesting statistical questions persist. We relate certain features of microarrays to other kinds of experimental data and argue that classical statistical techniques are appropriate and useful. We advocate greater attention to experimental design issues and a more prominent role for the ideas of statistical inference in microarray studies.     

Phylogenetic information and experimental design in molecular systematics.

Despite the widespread perception that evolutionary inference from molecular sequences is a statistical problem, there has been very little attention paid to questions of experimental design. Previous consideration of this topic has led to little more than an empirical folklore regarding the choice of suitable genes for analysis, and to dispute over the best choice of taxa for inclusion in data sets. I introduce what I believe are new methods that permit the quantification of phylogenetic information in a sequence alignment. The methods use likelihood calculations based on Markov-process models of nucleotide substitution allied with phylogenetic trees, and allow a general approach to optimal experimental design. Two examples are given, illustrating realistic problems in experimental design in molecular phylogenetics and suggesting more general conclusions about the choice of genomic regions, sequence lengths and taxa for evolutionary studies.     

Power studies in the estimation of genetic parameters and the localization of quantitative trait loci for backcross and doubled haploid populations

A statistical model for doubled haploids and backcrosses based on the interval-mapping methodology has been used to carry out power studies to investigate the effects of different experimental designs, heritabilities of the quantitative trait, and types of gene action, using two test statistics, the F of Fisher-Snedecor and the LOD score. The doubled haploid experimental design is more powerful than backcrosses while keeping actual type I errors similar to nominal ones. For the doubled haploid design, individual QTLs, showing heritabilities as low as 5% were detected in about 90% of the cases using only 250 individuals. The power to detect a given QTL is related to its contribution to the heritability of the trait. For a given nominal type I error, tests using F values are more powerful than with LOD scores. It seems that more conservative levels should be used for the LOD score in order to increase the power and obtain type I errors similar to nominal ones.     

Evaluation of the Total Carbonaceous Biochemical Oxygen Demand Method, Using a Compensated Recording Respirometer

The total carbonaceous biochemical oxygen demand test is described, and experimental data demonstrating its stoichiometry, precision, and accuracy are presented. The test is more reproducible and faster than the current 5-day biochemical oxygen demand test procedure, and if a respirometer is used, the effects of toxic chemicals, pH changes, and nutrient imbalances can be routinely monitored. The design principles of a multichannel compensated recording respirometer suitable for this test are described.