近交系MIJ和HFJ大鼠部分生理数据测定和分析

目的测定近交系大鼠MIJ、HFJ和封闭群Wistar大鼠的脏器系数、体温、呼吸频率、肠管长度,观察两个近交系大鼠的生理表型。方法 采用常规方法对大鼠脏器系数、体温、呼吸频率及肠管长度进行测定,并对HFJ、MIJ、Wistar大鼠三者间进行比较。结果①体重:HFJ、MIJ、Wistar大鼠同性别间比较差异无显著性。②脏器系数:HFJ与Wistar大鼠雄性间心脏、肺,雌性间子宫、肝;MIJ与Wistar大鼠雄性间肺、脾,雌性间脑、脾,差异均有显著性。HFJ与MIJ大鼠雄性间脑,雌性间脑和脾差异有显著性。HFJ大鼠不同性别间肝、脑、眼球差异有显著性。MIJ大鼠不同性别间,脑、心脏、肾、肺、脾和眼球差异均有显著性。③体温:HFJ雌性大鼠明显高于Wistar雌性,雄性间差异无显著性。HFJ大鼠雌性高于雄性,差异有显著性。④呼吸频率:HFJ雌性大鼠低于同性Wistar大鼠,MIJ大鼠雌性和雄性均高于同性别Wistar大鼠,差异有显著性。⑤肠管长度:HFJ与Wistar同性别间比较,肠管总长和小肠差异有显著性。MIJ与Wistar大鼠雄性间肠管总长、大肠、小肠、盲肠差异均有显著性,雌性间肠管总长、小肠、盲肠差异有显著性。HFJ与MIJ大鼠同性别间差异均无显著性,不同性别的HFJ和MIJ的肠管总长、大肠和小肠差异有显著性。结论 MIJ和HFJ近交系大鼠的脏器系数、体温、呼吸频率、肠管长度都各有其独特的生理概貌。     

Marginal regression analysis of a multivariate binary response

We propose the use of the mean parameter for regression analysisof a multivariate binary response. We model the associationusing dependence ratios defined in terms of the mean parameter,the components of which are the joint success probabilitiesof all orders. This permits flexible modelling of higher-orderassociations, using maximum likelihood estimation. We reanalysetwo data sets, one with variable cluster size and the othera longitudinal data set with constant cluster size.     

Fuzzy clustering of fuzzy ecological data

Besides the problem of searching for effective methods for data analysis there are some additional problems with handling data of high uncertainty. Uncertainty problems often arise in an analysis of ecological data, e.g. in the cluster analysis of ecological data. Conventional clustering methods based on Boolean logic ignore the continuous nature of ecological variables and the uncertainty of ecological data. That can result in misclassification or misinterpretation of the data structure. Clusters with fuzzy boundaries reflect better the continuous character of ecological features. But the problem is, that the common clustering methods (like the fuzzy c-means method) are only designed for treating crisp data, that means they provide a fuzzy partition only for crisp data (e.g. exact measurement data). This paper presents the extension and implementation of the method of fuzzy clustering of fuzzy data proposed by Yang and Liu [Yang, M.-S. and Liu, H-H, 1999. Fuzzy clustering procedures for conical fuzzy vector data. Fuzzy Sets and Systems, 106, 189-200.]. The imprecise data can be defined as multidimensional fuzzy sets with not sharply formed boundaries (in the form of the so-called conical fuzzy vectors). They can then be used for the fuzzy clustering together with crisp data. That can be particularly useful when information is not available about the variances which describe the accuracy of the data and probabilistic approaches are impossible. The method proposed by Yang has been extended and implemented for the Fuzzy Clustering System EcoFucs developed at the University of Kiel. As an example, the paper presents the fuzzy cluster analysis of chemicals according to their ecotoxicological properties. The uncertainty and imprecision of ecotoxicological data are very high because of the use of various data sources, various investigation tests and the difficulty of comparing these data. The implemented method can be very helpful in searching for an adequate partition of ecological data into clusters with similar properties.     

Book reviews

Editing of community data matrices is complementary to analyzing data by multivariate techniques of classification and ordination in the overall task of data analysis. A computer program, DATAEDIT, is described that can perform numerous editing functions, including data transformation, deletion of certain species or samples, deletion of rare species, deletion of outliers, separation of disjunet sample groups, reordering of the species or samples of a data matrix, and the formation of composite samples or of sample subsets. DATAEDIT can use the information in a nonhierarchical or hierarchical classification, and includes its own internal routine for reciprocal averaging ordination.We appreciate valuable suggestions from the late Robert H. Whittaker, and from Philip Dixon, David Hieks, Laura Huenneke, Linda Olsvig-whittaker, and Mark Wilson. Mark O. Hill kindly supplied a fast subroutine for reciprocal averaging.     

Ovarian cancer detection by logical analysis of proteomic data

A new type of efficient and accurate proteomic ovarian cancer diagnosis systems is proposed. The system is developed using the combinatorics and optimization-based methodology of logical analysis of data (LAD) to the Ovarian Dataset 8-7-02 (http://clinicalproteomics.steem.com), which updates the one used by Petricoin et al. in The Lancet 2002, 359, 572-577. This mass spectroscopy-generated dataset contains expression profiles of 15 154 peptides defined by their mass/charge ratios (m/z) in serum of 162 ovarian cancer and 91 control cases. Several fully reproducible models using only 7-9 of the 15 154 peptides were constructed, and shown in multiple cross-validation tests (k-folding and leave-one-out) to provide sensitivities and specificities of up to 100%. A special diagnostic system for stage I ovarian cancer patients is shown to have similarly high accuracy. Other results: (i) expressions of peptides with relatively low m/z values in the dataset are shown to be better at distinguishing ovarian cancer cases from controls than those with higher m/z values; (ii) two large groups of patients with a high degree of similarities among their formal (mathematical) profiles are detected; (iii) several peptides with a blocking or promoting effect on ovarian cancer are identified.     

Temporal degradation of data limits biodiversity research

Spatial and/or temporal biases in biodiversity data can directly influence the utility, comparability, and reliability of ecological and evolutionary studies. While the effects of biased spatial coverage of biodiversity data are relatively well known, temporal variation in data quality (i.e., the congruence between recorded and actual information) has received much less attention. Here, we develop a conceptual framework for understanding the influence of time on biodiversity data quality based on three main processes: (1) the natural dynamics of ecological systems—such as species turnover or local extinction; (2) periodic taxonomic revisions, and; (3) the loss of physical and metadata due to inefficient curation, accidents, or funding shortfalls. Temporal decay in data quality driven by these three processes has fundamental consequences for the usage and comparability of data collected in different time periods. Data decay can be partly ameliorated by adopting standard protocols for generation, storage, and sharing data and metadata. However, some data degradation is unavoidable due to natural variations in ecological systems. Consequently, changes in biodiversity data quality over time need be carefully assessed and, if possible, taken into account when analyzing aging datasets.     

生物实验数据的两因素方差分析

结合实例详细地叙述了生物实验数据的两因素方差分析计算方法,具有较强的实用意义。并介绍了如何分析计算结果。     

Estimation of Box's ε for low‐ and high‐dimensional repeated measures designs with unequal covariance matrices

We present new inference methods for the analysis of low‐ and high‐dimensional repeated measures data from two‐sample designs that may be unbalanced, the number of repeated measures per subject may be larger than the number of subjects, covariance matrices are not assumed to be spherical, and they can differ between the two samples. In comparison, we demonstrate how crucial it is for the popular Huynh‐Feldt (HF) method to make the restrictive and often unrealistic or unjustifiable assumption of equal covariance matrices. The new method is shown to maintain desired α‐levels better than the well‐known HF correction, as demonstrated in several simulation studies. The proposed test gains power when the number of repeated measures is increased in a manner that is consistent with the alternative. Thus, even increasing the number of measurements on the same subject may lead to an increase in power. Application of the new method is illustrated in detail, using two different real data sets. In one of them, the number of repeated measures per subject is smaller than the sample size, while in the other one, it is larger.     

Methods for analysis of unbalanced,longitudinal, growth data

We describe an approach to analysis of growth that does not depend on assumptions about the underlying functional growth pattern and that allows for multiple observations arising from individual-specific, irregularly spaced data. We produce estimated growth curves for predefined subject groups by using LOWESS, a nonparametric smoothing algorithm. We describe how statistical significance of curve features may be evaluated by using the “jackknife,” a sample re-use method; this technique can be used to assess differences between subject groups. We then obtain residuals at each data point by reference to the estimated curve. Consistency of residuals is evaluated as a characteristic of individual subjects, and in the presence of individual consistency, relative size-for-age is then scored by the average residual for each individual. This allows study of relationships between relative size and other individual characteristics such as birth order, dominance rank, or age of maturation. Finally, we indicate flexibility of these methods and alternatives, propose uses related to other questions about growth, and suggest potential applications to variables other than body size. Appendices demonstrate application of the LOWESS and jackknife algorithms to the problem of testing sex differences in growth. © 1992 Wiley-Liss, Inc.     

A Test for the Equality of Variances in the Paired Case with Incomplete Data

A modification of the numbers of degrees of freedom which makes the F ratio test of the equality of two variances applicable also in the paired case with incomplete data is suggested. Monte Carlo simulation studies indicate that the suggested test is reasonably powerful in many cases.     

The Principles of Data Formalization for Building Biological Model of Shoot Development in Flowering Plants

Shoot system of a plant can be divided into elementary molecules composed of phyllome, internode, and meristem of the lateral bud. The capacity of plants for open growth is manifested as multiple reproductions of the modules. These main principles of plant structural organization can be used to formalize and integrate the data from various disciplines studying shoot development—genetics of development, morphology, etc. At the example of a model species Arabidopsis thaliana we show that the data on genetic control of shoot development can be considered in terms of rearrangement of individual modules. Several variants of the module structural reorganization are allowed: reduction or transformation of phyllome, change in the internode length, and triggering active/inactive status of the lateral shoot meristem. Each variant of module structure corresponds to specific pattern of genes activity. Such integration of the data on genetic and structural aspects of morphogenesis can form a basis for mathematical modeling of plant development.     

Logistic regression analysis of two-phase studies using generalized method of moments

Two-phase designs can reduce the cost of epidemiological studies by limiting the ascertainment of expensive covariates or/and exposures to an efficiently selected subset (phase-II) of a larger (phase-I) study. Efficient analysis of the resulting data set combining disparate information from phase-I and phase-II, however, can be complex. Most of the existing methods, including semiparametric maximum-likelihood estimator, require the information in phase-I to be summarized into a fixed number of strata. In this paper, we describe a novel method for the analysis of two-phase studies where information from phase-I is summarized by parameters associated with a reduced logistic regression model of the disease outcome on available covariates. We then setup estimating equations for parameters associated with the desired extended logistic regression model, based on information on the reduced model parameters from phase-I and complete data available at phase-II after accounting for nonrandom sampling design. We use generalized method of moments to solve overly identified estimating equations and develop the resulting asymptotic theory for the proposed estimator. Simulation studies show that the use of reduced parametric models, as opposed to summarizing data into strata, can lead to more efficient utilization of phase-I data. An application of the proposed method is illustrated using the data from the U.S. National Wilms Tumor Study.     

Test of marginal compatibility and smoothing methods for exchangeable binary data with unequal cluster sizes

Exchangeable binary data are often collected in developmental toxicity and other studies, and a whole host of parametric distributions for fitting this kind of data have been proposed in the literature. While these distributions can be matched to have the same marginal probability and intra-cluster correlation, they can be quite different in terms of shape and higher-order quantities of interest such as the litter-level risk of having at least one malformed fetus. A sensible alternative is to fit a saturated model (Bowman and George, 1995, Journal of the American Statistical Association 90, 871-879) using the expectation-maximization (EM) algorithm proposed by Stefanescu and Turnbull (2003, Biometrics 59, 18-24). The assumption of compatibility of marginal distributions is often made to link up the distributions for different cluster sizes so that estimation can be based on the combined data. Stefanescu and Turnbull proposed a modified trend test to test this assumption. Their test, however, fails to take into account the variability of an estimated null expectation and as a result leads to inaccurate p-values. This drawback is rectified in this article. When the data are sparse, the probability function estimated using a saturated model can be very jagged and some kind of smoothing is needed. We extend the penalized likelihood method (Simonoff, 1983, Annals of Statistics 11, 208-218) to the present case of unequal cluster sizes and implement the method using an EM-type algorithm. In the presence of covariate, we propose a penalized kernel method that performs smoothing in both the covariate and response space. The proposed methods are illustrated using several data sets and the sampling and robustness properties of the resulting estimators are evaluated by simulations.     

On Testing Equality of Means in the Paired Case with Incomplete Data on Both Responses

Several non iterative tests for the equality of two correlated means with missing data on both responses are compared by means of Monte Carlo studies. The main results are that a test due to LIN and STIVERS (1974) is to be preferred when the number of complete pairs is large, a WELCH type statistic with degrees of freedom modified in this paper when the correlation between the two responses is known to be low, and one of four suggested, one in this paper, maximum likelihood based tests when the number of complete pairs is small and there is no large variance inequality believed to exist.     

A new dimension in combining data? The use of morphology and phylogenomic data in metazoan systematics

Giribet, G. 2010. A new dimension in combining data? The use of morphology and phylogenomic data in metazoan systematics. —Acta Zoologica (Stockholm) 91 : 11–19 Animal phylogenies have been traditionally inferred by using the character state information derived from the observation of a diverse array of morphological and anatomical features, but the incorporation of molecular data into the toolkit of phylogenetic characters has shifted drastically the way researchers infer phylogenies. A main reason for this is the ease at which molecular data can be obtained, compared to, e.g., traditional histological and microscopical techniques. Researchers now routinely use genomic data for reconstructing relationships among animal phyla (using whole genomes or Expressed Sequence Tags) but the amount of morphological data available to study the same phylogenetic patterns has not grown accordingly. Given the disparity between the amounts of molecular and morphological data, some authors have questioned entire morphological programs. In this review I discuss issues related to the combinability of genomic and morphological data, the informativeness of each set of characters, and conclude with a discussion of how morphology could be made scalable by utilizing new techniques that allow for non‐intrusive examination of large amounts of preserved museum specimens. Morphology should therefore remains a strong field in evolutionary and comparative biology, as it continues to provide information for inferring phylogenetic patterns, is an important complement for the patterns derived from the molecular data, and it is the common nexus that allows studying fossil taxa with large data sets of molecular data.     

Estimation of Distances and Variances in Bookstein's Landmark Model

Planar random figures can be described by series of landmarks. Bookstein's model assumes that the landmarks result from independent individual fluctuations around fixed fictive landmark centres. The aim of this paper is to estimate the distances between the centers and the variances of fluctuations around them. Furthermore, two new shape variable estimators are suggested and compared with an estimator of Bookstein.     

Partial least squares for functional joint models with applications to the Alzheimer's disease neuroimaging initiative study

Many biomedical studies have identified important imaging biomarkers that are associated with both repeated clinical measures and a survival outcome. The functional joint model (FJM) framework, proposed by Li and Luo in 2017, investigates the association between repeated clinical measures and survival data, while adjusting for both high-dimensional images and low-dimensional covariates based on the functional principal component analysis (FPCA). In this paper, we propose a novel algorithm for the estimation of FJM based on the functional partial least squares (FPLS). Our numerical studies demonstrate that, compared to FPCA, the proposed FPLS algorithm can yield more accurate and robust estimation and prediction performance in many important scenarios. We apply the proposed FPLS algorithm to a neuroimaging study. Data used in preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database.