Fungal endophytes associated with three South American Myrtae (Myrtaceae) exhibit preferences in the colonization at leaf level

Fungal endophytes associated with Myrtaceae from Brazil and Argentina were isolated at three levels of nesting: leaf, individual host trees, and site collection. The alternating logistic regression (ALR) was used to model the data because it offers a computationally convenient method for fitting regression structures involving large clusters. The objectives of this study were to determine: (i) whether the colonization pattern is influenced by environmental variables, (ii) if there is some leaf part they prefer to colonize; (iii) if there is some fungal endophyte aggregation between hierarchical levels; (iv) what the distance effect is on the fungal association. The environmental variables were statistically significant only for Xylaria, i.e., when the elevation and water precipitation increase and the temperature decreases, the odds ratio of finding another fungal endophyte of that genus previously found increases. Sordariomycetes, Xylariales, and Xylaria exhibited leaf fragment preference to petiole and tip. Fungal endophytes showed association within leaf. The horizontal transmission mode and the dispersal limitation may explain this association at the leaf level. Moreover, our results suggest that when a fungal endophyte infects a leaf or host tree individual, the odds ratio of dispersal inside them is greater.     

On power and sample size calculations for likelihood ratio tests in generalized linear models

A direct extension of the approach described in Self, Mauritsen, and Ohara (1992, Biometrics 48, 31-39) for power and sample size calculations in generalized linear models is presented. The major feature of the proposed approach is that the modification accommodates both a finite and an infinite number of covariate configurations. Furthermore, for the approximation of the noncentrality of the noncentral chi-square distribution for the likelihood ratio statistic, a simplification is provided that not only reduces substantial computation but also maintains the accuracy. Simulation studies are conducted to assess the accuracy for various model configurations and covariate distributions.     

Multiscale determinants of parasite abundance: A quantitative hierarchical approach for coral reef fishes

During recent decades, there have been numerous attempts to identify the key determinants of parasite communities and several influential variables have been clarified at either infra-, component or compound community scales. However, in view of the possible complexity of interactions among determinants, the commonly-used exploratory and statistical modelling techniques have often failed to find meaningful ecological patterns from such data. Moreover, quantitative assessments of factors structuring species richness, abundance, community structure and species associations in parasite communities remain elusive. Recently, because they are ideally suited for the analysis of complex and highly interactive data, there has been increasing interest in the use of classification and regression tree analyses in several ecological fields. To date, such approaches have never been used by parasitologists for field data. This study aims to both introduce and illustrate the use of multivariate regression trees in order to investigate the determinants of parasite abundance in a multi-scale quantitative context. To do this, we used new field epidemiological data from 1489 coral reef fishes collected around two islands in French Polynesia. We evaluated the relative effect and interactions of several host traits and environmental factors on the abundance of metazoan parasite assemblage at several scales and assessed the impact of major factors on each parasite taxon. Our results suggest that the islands sampled, the host species and host size are equal predictors of parasite abundance at a global scale, whereas other factors proved to be significant predictors of a local pattern, depending on host family. We also discuss the potential use of regression trees for parasitologists as both an explorative and a promising predictive tool.     

Evaluation and interpretation of results for three cross-over designs

In cross-over designs, individual sequences of treatments are applied to the animals. Within such designs it is possible that every treatment could modify the effect of the subsequent treatment applied to the same animal. We compared three cross-over designs each with three treatments, three periods, and two blocks. This comparison was done with respect to the variance of the estimations of the effects and its biases caused by the interactions between the treatment and the carry over effect of the foregoing treatment. Moreover, different methods of estimating variance components and calculating the degrees of freedom were compared by means of simulation. If the animal variance component is small, then the bias of the REML estimator of the variance components is greater than one of the widespread ANOVA-estimator called 'TYPE3'. But nevertheless, the mean squared error of this estimation is smaller in the case of REML in comparison to ANOVA. Therefore, the REML method should be preferred. For calculating the degrees of freedom, the Kenward-Roger method should be used. After applying this method, the true significance level is almost equal to its required value, but if the Satterthwaite method is used, the true significance level will be too high. If the interaction (treatment × carry over) is ignored in the model although it exists, the standard error of the treatment effect estimation is too great, and, therefore, the true significance level is too small. The methods which have been evaluated are available in the SAS-procedure MIXED (<citeref rid="b9">SAS Institute, 1999a</citeref>). To assist the investigation of cross-over designs by using this software, we developed programs for data management and data analysis. These programs are available from the first author.     

Antler and Body Weight Allometry in Red Deer: A Comparison of Statistical Estimators

In allometric expressions, both variables are measured with error. Such structural relationships require specific statistical techniques for precise and valid estimation of equation parameters. The relative growth pattern for antler weight and body weight was examined for 1700 red deer (Cervus elaphus L.) stags from the Harz mountain district in West Germany. Coefficients for the allometric equation were determined using several statistical estimation procedures and the results were compared and evaluated. There is evidence that the magnitude of the exponent in the allometric expression increases with the onset of sexual maturity.     

Assessment of Optimal Selected Prognostic Factors

The identification and assessment of prognostic factors is one of the major tasks in clinical research. The assessment of one single prognostic factor can be done by recently established methods for using optimal cutpoints. Here, we suggest a method to consider an optimal selected prognostic factor from a set of prognostic factors of interest. This can be viewed as a variable selection method and is the underlying decision problem at each node of various tree building algorithms. We propose to use maximally selected statistics where the selection is defined over the set of prognostic factors and over all cutpoints in each prognostic factor. We demonstrate that it is feasible to compute the approximate null distribution. We illustrate the new variable selection test with data of the German Breast Cancer Study Group and of a small study on patients with diffuse large B‐cell lymphoma. Using the null distribution for a p‐value adjusted regression trees algorithm, we adjust for the number of variables analysed at each node as well. (© 2004 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

Reducing variability of crossvalidation for smoothing-parameter choice

One of the attractions of crossvalidation, as a tool for smoothing-parameterchoice, is its applicability to a wide variety of estimatortypes and contexts. However, its detractors comment adverselyon the relatively high variance of crossvalidatory smoothingparameters, noting that this compromises the performance ofthe estimators in which those parameters are used. We show thatthe variability can be reduced simply, significantly and reliablyby employing bootstrap aggregation or bagging. We establishthat in theory, when bagging is implemented using an adaptivelychosen resample size, the variability of crossvalidation canbe reduced by an order of magnitude. However, it is arguablymore attractive to use a simpler approach, based for exampleon half-sample bagging, which can reduce variability by approximately50%.     

The dynamics of EEG entropy

The scaling properties of human EEG have so far been analyzed predominantly in the framework of detrended fluctuation analysis (DFA). In particular, these studies suggested the existence of power-law correlations in EEG. In DFA, EEG time series are tacitly assumed to be made up of fluctuations, whose scaling behavior reflects neurophysiologically important information and polynomial trends. Even though these trends are physiologically irrelevant, they must be eliminated (detrended) to reliably estimate such measures as Hurst exponent or fractal dimension. Here, we employ the diffusion entropy method to study the scaling behavior of EEG. Unlike DFA, this method does not rely on the assumption of trends superposed on EEG fluctuations. We find that the growth of diffusion entropy of EEG increments of awake subjects with closed eyes is arrested only after approximately 0.5 s. We demonstrate that the salient features of diffusion entropy dynamics of EEG, such as the existence of short-term scaling, asymptotic saturation, and alpha wave modulation, may be faithfully reproduced using a dissipative, first-order, stochastic differential equation—an extension of the Langevin equation. The structure of such a model is utterly different from the “noise+trend” paradigm of DFA. Consequently, we argue that the existence of scaling properties for EEG dynamics is an open question that necessitates further studies.     

Statistical analysis of data pertaining to complex state systems by stepwise regression with reformulated parameters; Application to spectroscopically monitored hemoglobin oxygen binding data

A method is described for the statistical analysis of data pertaining to complex state systems, based on the concept of reformulating the parameters describing the system as a hierarchy of interactions, and this method demonstrated on the analysis of spectroscopically monitored hemoglobin oxygen binding data [K. Imai, Biophys. Chem. 37 (1990) 197-210]. The concept of reformulation was first extended to state parameters other than ΔG°s, such as the extinction coefficients (εs) associated with different ligation states during hemoglobin oxygen binding. The reformulated parameters are incrementally allowed to vary in the data fitting procedure, and the statistical significance of the added parameters tested by F and Kolmogorov-Smirnov tests. The result of this method is the minimal set of statistically significant parameters required to describe the data. The hierarchical nature of reformulated parameters allows the physical significance of the subset of statistically significant parameters to be discussed even when all reformulated terms may not be statistically significant. Applying this method to hemoglobin oxygen binding data with the reformulated Adair model demonstrated that at least two, and at most three, of the four reformulated Adair constants are statistically significant. A reformulated square model was found to give a statistically indistinguishable fit from the Adair model, with the statistically significant thermodynamic terms essentially those proposed by Linus Pauling in 1935. A change in Δ ε with subsequent oxygen binding events was found to be significant in both models. These results are consistent with a model for hemoglobin oxygen binding where a subunit changes its conformation upon oxygen binding, and affects the conformation of adjacent subunits.     

A model for the origin of life

Summary A simple statistical model is constructed, describing the transition from disorder to order in a population of mutually catalytic molecules undergoing random mutations. The consequences of the model are calculated, and its possible relevance to the problem of the origin of life is discussed. The main conclusion of the analysis is that the model allows populations of several thousand molecular units to make the transition from disorder to order with reasonable probability.     

药物动力学中矩量计算的方法

本文给出了计算药物动力学的矩的一种方法,并给出了截断误差公式。最后,通过例题,将梯形法、对数梯形法及文[5]的方法以及本文的方法,作了比较,并可看出本文方法算出的结果最为精确。     

Parametric modal regression with varying precision

In this paper, we propose a simple parametric modal linear regression model where the response variable is gamma distributed using a new parameterization of this distribution that is indexed by mode and precision parameters, that is, in this new regression model, the modal and precision responses are related to a linear predictor through a link function and the linear predictor involves covariates and unknown regression parameters. The main advantage of our new parameterization is the straightforward interpretation of the regression coefficients in terms of the mode of the positive response variable, as is usual in the context of generalized linear models, and direct inference in parametric mode regression based on the likelihood paradigm. Furthermore, we discuss residuals and influence diagnostic tools. A Monte Carlo experiment is conducted to evaluate the performances of these estimators in finite samples with a discussion of the results. Finally, we illustrate the usefulness of the new model by two applications, to biology and demography.     

A multivariate analysis of temporal change in Arikara craniometrics: A methodological approach

We have examined temporal changes in the cranial architecture of Arikara Amerindians from five archaeological sites in South Dakota which span a time period of approximately 230 years (ca. A.D. 1600–1830). We have utilized a multivariate statistical method based on a principal components analysis of the pooled within-groups correlation matrix rather than the more traditional methods of ascertaining morphological relationships, e.g., discriminant functions, Mahalanobis' D², or Penrose's Size and Shape. Our component structure, based on a regional sample and the mathematically simpler principal components analysis, is very similar to the factor structure obtained by Howells (1973) using a world-wide sample and factor analysis proper. This supports the notion of the “universality” of cranial structure. An axis of temporal variation was introduced into the component space by means of multiple regression. This analysis indicates that a substantial portion of the intergroup variation is temporal in nature and that systematic temporal changes occur along the facial height, transverse frontal flatness, and frontal profile flatness components. Earlier analyses of the same material by more conventional methods either did not detect the temporal trends at all or failed to isolate the specific nature of the temporal changes. The success of the present analysis attests to the value of examining morphological relationships by means of principal components.     

Statistical shape modelling reveals large and distinct subchondral bony differences in osteoarthritic knees

Knee osteoarthritis (OA) results in changes such as joint-space narrowing and osteophyte formation. Radiographic classification systems group patients by the presence or absence of these gross anatomical features but are poorly correlated to function. Statistical-shape modelling (SSM) can detect subtle differences in 3D-bone geometry, providing an opportunity for accurate predictive models. The aim of this study was to describe and quantify the main modes of shape variation which distinguish end-stage OA from asymptomatic knees. Seventy-six patients with OA and 77 control participants received a CT of their knee. 3D models of the joint were created by manual segmentation. A template mesh was fitted to all meshes and rigidly aligned resulting in a set of correspondent meshes. Principal Component Analysis (PCA) was performed to create the SSM. Logistic regression was performed on the PCA weights to distinguish morphological features of the two groups. The first 7 modes of the SSM captured >90% shape variation with 6 modes best distinguishing between OA and asymptomatic knees. OA knees displayed sub-chondral bone expansion particularly in the condyles and posterior medial tibial plateau of up to 10 mm. The model classified the two groups with 95% accuracy, 96% sensitivity, 94% specificity, and 97% AUC. There were distinct features which differentiated OA from asymptomatic knees. Further research will elucidate how magnitude and location of shape changes in the knee influence clinical and functional outcomes.     

A model for thermal exchange in axons during action potential propagation

Several experiments have shown that during propagation of the action potential in axons, thermal energy is locally exchanged. In this paper, we use a simple model based on statistical physics to show that an important part of this exchange comes from the physics of the effusion. We evaluate, during the action potential propagation, the variation of internal energy and of the energy associated with the chemical potential of the effusion of water and ions to extract the thermal energy exchanged. The temperature exchanged is then evaluated on the area where the action potential is active. Results give a good correspondence between experimental work and this model, showing that an important part of the thermal energy exchange comes from the statistical cooling power of the effusion.