运用双重组合设计建立黑龙江省玉米降水产量模型

本文运用旋转设计进一步发展的双重组合试验设计,建立黑龙江省关于降水、有机质、氮、磷、钾肥及有机肥的玉米产量模型,探讨了双重组合试验设计在建模时用可控因素去挂接不可控因素的应用,对模型进行了检验和频数分析,提出玉米产量大子3000公斤的高产在艺措施．     

Optimum experimental designs for multinomial logistic models

Multinomial responses frequently occur in dose level experiments. For example, in a study of the influence of gamma radiation on the emergence of house flies (Musca domestica L., 1758), three disjoint outcomes occurred: death before the pupae opened, death during emergence, and life after emergence. Although the flies are easy to breed, this sort of bioassay is, in general, very expensive since it requires the use of a gamma radiation source. Experiments therefore need to be designed to involve the minimum number of different doses. Here the theory of optimum experimental design is applied to provide efficient experiments to estimate the parameters of those multinomial logistic models that are a special case of the multivariate logistic models of Glonek and McCullagh (1995, Journal of the Royal Statistical Society, Series B 57, 533-546). The purpose is to reduce the overall experimental cost. The general equivalence theorem (Fedorov, 1972, Theory of Optimal Experiments) is adapted to this class of models, providing an effective method of generating and checking the optimality of designs. One example on flies demonstrates the method, which can be easily implemented.     

中国红豆杉细胞悬浮体系营养条件的优化

采用均匀设计的试验方法,对中国红豆杉细胞悬浮体系培养条件进行优化,获得了关于细胞生长与培养基内碳源,氮源,磷浓度的回归方程,表明碳氮是影响细胞生长的关键因素。采用中心组合实验设计对培养基成分继续优化,并通过回归分析,结果表明,较优的培养基为碳源33．9g/L,氮源58．2mmol/L,磷浓度为5mmol/L,此工艺在15L反应器中,也取得了良好效果。     

云梦泽圩田的生态模式设计研究

云梦泽圩田是儒家井田制文化与云梦泽地区水文环境相结合的产物,是人们在长期治田治水实践中创造的一种独特的农田开发形式,然而,圩田这种土地利用方式利弊并存,如何挖掘这种传统水利田的生态智慧使之继续发挥作用是一个亟待解决的现实问题。以湖北朱湖湿地公园内的圩田恢复区为研究对象,以生态学思想为指导,从单纯的治田治水理念拓展为水利、生态、经济、文化与社会发展并重的多重理念,充分挖掘蕴含圩田之中的生态智慧,提出了"湖-圩"共生复合生态系统的生态模式,主要包括：小微湿地群模式、水陆界面生态调控模式、柔性设计模式和"河流-湿地"复合体模式。这些模式有助于云梦泽地区蓄洪防旱、水质净化、景观优化,以及生物生境等综合生态服务功能的实现,并促进云梦泽地区圩田景观文化传承与生态环境的协同共生,从而实现了智慧圩田的生态模式。这些生态模式是对圩田系统生态结构设计思路与方法的全新探索,可以为长江中下游地区圩田的景观保护与生态模式设计提供工程示范与参考。     

Information in Observation Under the Correlated Linear Model

In this paper we propose a measure of information in a set of observations under a given design and the linear model with the observations being correlated. We then compare the different sets of observations with respect to the information measure in the situations where the underlying designs are nested and split-plot. AMS 1970 Subject Classification: Secondary 62J05, 62J10, 62K05, 62K10, 62K15, 62K99.     

Experimental design and model reduction in systems biology

Background: In systems biology, the dynamics of biological networks are often modeled with ordinary differential equations (ODEs) that encode interacting components in the systems, resulting in highly complex models. In contrast, the amount of experimentally available data is almost always limited, and insufficient to constrain the parameters. In this situation, parameter estimation is a very challenging problem. To address this challenge, two intuitive approaches are to perform experimental design to generate more data, and to perform model reduction to simplify the model. Experimental design and model reduction have been traditionally viewed as two distinct areas, and an extensive literature and excellent reviews exist on each of the two areas. Intriguingly, however, the intrinsic connections between the two areas have not been recognized.Results: Experimental design and model reduction are deeply related, and can be considered as one unified framework. There are two recent methods that can tackle both areas, one based on model manifold and the other based on profile likelihood. We use a simple sum-of-two-exponentials example to discuss the concepts and algorithmic details of both methods, and provide Matlab-based code and implementation which are useful resources for the dissemination and adoption of experimental design and model reduction in the biology community.Conclusions: From a geometric perspective, we consider the experimental data as a point in a high-dimensional data space and the mathematical model as a manifold living in this space. Parameter estimation can be viewed as a projection of the data point onto the manifold. By examining the singularity around the projected point on the manifold, we can perform both experimental design and model reduction. Experimental design identifies new experiments that expand the manifold and remove the singularity, whereas model reduction identifies the nearest boundary, which is the nearest singularity that suggests an appropriate form of a reduced model. This geometric interpretation represents one step toward the convergence of experimental design and model reduction as a unified framework.     

An accurate binding interaction model in de novo computational protein design of interactions: If you build it,they will bind

Computational protein design efforts aim to create novel proteins and functions in an automated manner and, in the process, these efforts shed light on the factors shaping natural proteins. The focus of these efforts has progressed from the interior of proteins to their surface and the design of functions, such as binding or catalysis. Here we examine progress in the development of robust methods for the computational design of non-natural interactions between proteins and molecular targets such as other proteins or small molecules. This problem is referred to as the de novo computational design of interactions. Recent successful efforts in de novo enzyme design and the de novo design of protein–protein interactions open a path towards solving this problem. We examine the common themes in these efforts, and review recent studies aimed at understanding the nature of successes and failures in the de novo computational design of interactions. While several approaches culminated in success, the use of a well-defined structural model for a specific binding interaction in particular has emerged as a key strategy for a successful design, and is therefore reviewed with special consideration.     

基于格子模型的蛋白质设计方法

提出一个简单有效的蛋白质设计方法,这一方法完全基于物理学原理. 与同类工作相比,该方法在很大程度上可节省对序列空间进行的搜索,是对同类工作的简化与发展. 对三个平面格子模型进行的检验表明该方法是成功的. 该方法可进一步用于真实蛋白质的三维非格子模型.     

Biometrical modeling of twin and family data using standard mixed model software

Summary .   Biometrical genetic modeling of twin or other family data can be used to decompose the variance of an observed response or 'phenotype' into genetic and environmental components. Convenient parameterizations requiring few random effects are proposed, which allow such models to be estimated using widely available software for linear mixed models (continuous phenotypes) or generalized linear mixed models (categorical phenotypes). We illustrate the proposed approach by modeling family data on the continuous phenotype birth weight and twin data on the dichotomous phenotype depression. The example data sets and commands for Stata and R/S-PLUS are available at the Biometrics website.     

面向药品决策支持系统的数据仓库模型设计

医院药品的业务庞杂,数据集成与分析不完善,缺少信息全面、集成的数据仓库系统。研究医院药品数据,运用“业务维度生命周期法”进行数据仓库项目的设计、开发和部署。解决的问题有：创建数据仓库总线结构,建立主题模型,使用维度建模来进行逻辑建模,数据存储的物理设计,数据转储与开发。总体逻辑结构模型设计清晰,构建方法新颖,给出一个较好的医院药品数据仓库的分析模型。     

Structure-based design of model proteins

A structure-based, sequence-design procedure is proposed in which one considers a set of decoy structures that compete significantly with the target structure in being low energy conformations. The decoy structures are chosen to have strong overlaps in contacts with the putative native state. The procedure allows the design of sequences with large and small stability gaps in a random-bond heteropolymer model in both two and three dimensions by an appropriate assignment of the contact energies to both the native and nonnative contacts. The design procedure is also successfully applied to the two-dimensional HP model. Proteins 31:10–20, 1998. © 1998 Wiley-Liss, Inc.     

Design for rectangular hyperbolic crop–weed competition

Fitting curves to data rather than doing multiple pairwise comparisons necessitates consideration of design. For the rectangular hyperbolic model of crop‐weed competition, emphasising extreme weed densities is shown to be efficient relative to experiments using a traditional design.     

Predicting the Geographic Distribution of a Species from Presence‐Only Data Subject to Detection Errors

Summary Several models have been developed to predict the geographic distribution of a species by combining measurements of covariates of occurrence at locations where the species is known to be present with measurements of the same covariates at other locations where species occurrence status (presence or absence) is unknown. In the absence of species detection errors, spatial point‐process models and binary‐regression models for case‐augmented surveys provide consistent estimators of a species’ geographic distribution without prior knowledge of species prevalence. In addition, these regression models can be modified to produce estimators of species abundance that are asymptotically equivalent to those of the spatial point‐process models. However, if species presence locations are subject to detection errors, neither class of models provides a consistent estimator of covariate effects unless the covariates of species abundance are distinct and independently distributed from the covariates of species detection probability. These analytical results are illustrated using simulation studies of data sets that contain a wide range of presence‐only sample sizes. Analyses of presence‐only data of three avian species observed in a survey of landbirds in western Montana and northern Idaho are compared with site‐occupancy analyses of detections and nondetections of these species.     

Analysis of Covariance in Split Block Design

In this paper the analysis of covariance in the split block design with many concomitant variables is presented. The problems concerning the estimation of parametric functions and testing hypotheses are discussed. In the presentation of the model three kinds of regression coefficients for individual sources of variation are taken into consideration. It is shown that for every estimable function of fixed effects, the best linear unbiased estimator under the assumed model is the same as the best linear unbiased estimator under the model with covariance matrix equal to identity matrix multiplied by a positive constant. A variance of this estimator can be calculated by the method presented here. Test functions for standard hypotheses concerning fixed effects are obtained.     

Sample size recalculation in multicenter randomized controlled clinical trials based on noncomparative data

Many late-phase clinical trials recruit subjects at multiple study sites. This introduces a hierarchical structure into the data that can result in a power-loss compared to a more homogeneous single-center trial. Building on a recently proposed approach to sample size determination, we suggest a sample size recalculation procedure for multicenter trials with continuous endpoints. The procedure estimates nuisance parameters at interim from noncomparative data and recalculates the sample size required based on these estimates. In contrast to other sample size calculation methods for multicenter trials, our approach assumes a mixed effects model and does not rely on balanced data within centers. It is therefore advantageous, especially for sample size recalculation at interim. We illustrate the proposed methodology by a study evaluating a diabetes management system. Monte Carlo simulations are carried out to evaluate operation characteristics of the sample size recalculation procedure using comparative as well as noncomparative data, assessing their dependence on parameters such as between-center heterogeneity, residual variance of observations, treatment effect size and number of centers. We compare two different estimators for between-center heterogeneity, an unadjusted and a bias-adjusted estimator, both based on quadratic forms. The type 1 error probability as well as statistical power are close to their nominal levels for all parameter combinations considered in our simulation study for the proposed unadjusted estimator, whereas the adjusted estimator exhibits some type 1 error rate inflation. Overall, the sample size recalculation procedure can be recommended to mitigate risks arising from misspecified nuisance parameters at the planning stage.