Response Surface Analysis for Continous Regressors Applied to Neurotoxic Organophosphorus Pesticides

Usually, the purpose of the response surface optimization is to be able to locate the optimum operating levels of the regressors. In quantitative structure-activity relationship (QSAR) studies, the predicting variable reflects any biological property, and the regressors are structural and physico-chemical terms. By contrast to the Box-Wilson approach, the regressors are continously distributed. The working technique is demonstrated on an example adapted from organophosphorus pesticide research. It was found that the maximum neurotoxicity depends on lipophilic and steric substituent properties.     

Estimation of the Proportion of Differentially Expressed Genes Using Hellinger Distance

We consider the problem of estimating the proportion of differentially expressed genes from the distribution of P-values arising from statistical tests in a microarray experiment. A two-component mixture model is studied in which one component is uniform on [0,1] and corresponds to equally expressed genes and the other component corresponds to differentially expressed genes. Two different parametric families are explicitly investigated for modeling the nonuniform component—the Beta family and a mixture of uniform densities whose supports form a partition of [0,1]. The Minimum Hellinger discrepancy is used to estimate the mixture proportions which are then used to estimate the proportion of differentially expressed genes in the microarray study. The performance of the proposed methods are investigated using simulation studies in which they are also compared with selected other published procedures. The methods are illustrated through a case study involving data from a published microarray experiment.     

Further evaluation of physical fitness age versus physiological age in women

The present study was conducted to examine further whether adult women who are in a state of high physical fitness possess high physiological functions, and also to investigate whether those who exercise regularly are able to maintain a high quality of various physiological functions. The subjects of this study were 249 healthy Japanese adult women (aged 20–70 years). Of these subjects 30 had jogged or walked regularly for more than 3 years. The physiological ages (PA) and physical fitness ages (FA) of the individuals were estimated from 17 physiological function tests and 5 physical fitness tests, respectively, by principal components analyses. The results indicated that there was a significant correlation between PA and FA (r = 0.76, P < 0.01). To examine this relationship in more detail, the subjects were classified into three physical fitness groups (high, normal and low) based on the deviation from the regression line of FA. Comparison of the mean PA among three physical fitness groups revealed that the high physical fitness groups demonstrated a much lower PA (physiologically younger), while the low physical fitness groups showed a relatively higher PA (physiologically older) in spite of their equivalent chronological ages. From this series of studies, a new concept is proposed where different individuals have different peak physiological capacities, but that these capacities change with age at similar rates. It is suggested that interventions such as exercise and a proper diet for promoting health could increase peak functional capacity but have little effect on the rate of decline. Accepted: 3 March 1998     

Growth Based Morphogenesis of Vertebrate Limb Bud

Many genes and their regulatory relationships are involved in developmental phenomena. However, by chemical information alone, we cannot fully understand changing organ morphologies through tissue growth because deformation and growth of the organ are essentially mechanical processes. Here, we develop a mathematical model to describe the change of organ morphologies through cell proliferation. Our basic idea is that the proper specification of localized volume source (e.g., cell proliferation) is able to guide organ morphogenesis, and that the specification is given by chemical gradients. We call this idea “growth-based morphogenesis.” We find that this morphogenetic mechanism works if the tissue is elastic for small deformation and plastic for large deformation. To illustrate our concept, we study the development of vertebrate limb buds, in which a limb bud protrudes from a flat lateral plate and extends distally in a self-organized manner. We show how the proportion of limb bud shape depends on different parameters and also show the conditions needed for normal morphogenesis, which can explain abnormal morphology of some mutants. We believe that the ideas shown in the present paper are useful for the morphogenesis of other organs.     

A rapid and cost-effective tool for managing habitats of the European Natura 2000 network: a case study in the Italian Alps

“Habitats” Directive 92/43/EEC is the pivotal European law for building a continental network of sites of community importance (SCIs) for nature conservation. Article 6 of such directive underlines the importance of biodiversity conservation through the realization of proper management plans. As a result, such plans are increasingly common. A management plan based on intensive field studies and monitoring activities requires time and financial resources, which are generally limited. The aim of this paper is to offer a rapid, cost-effective and scientifically based decision tool aimed to achieve GIS-based conservation strategies for habitats of EU interest within SCIs and, in general, within protected areas. As a case study, we considered species-rich Nardus grasslands (threatened by natural reconversion and intensive cattle grazing), and transition mires (threatened by pasturing and human disturbance) in a SCI in the Alps. Through a multi-criteria evaluation, we selected indicators and weights based upon our knowledge of the study area. As a result, we were able to: (a) quantify the level of existing threats, (b) suggest urgent conservation strategies, and (c) suggest future monitoring activities. Since the study area is representative of many protected areas in the Alps and the conservation topics under evaluation are frequent threats impacting habitats of EU interest, our decision model might be transferable to further areas given proper adaptation of weights to the intensity and the frequency of current threats.     

Adjusting for confounding due to population admixture when estimating the effect of candidate genes on quantitative traits

When analyzing the relationship between allelic variability and traits, a potential source of confounding is population admixture. An approach to adjusting for potential confounding due to population admixture when estimating the influence of allelic variability at a candidate gene is presented. The approach involves augmenting linear regression models with additional regressors. Family genotype data are used to define the regressors, and inclusion of the regressors ensures that, even in the presence of population admixture, the estimates of the regression coefficients that parameterize the influence of allelic variability on the trait are unbiased. The approach is illustrated through an analysis of the influence of apolipoprotein E genotype on plasma low density lipoprotein cholesterol concentrations.     

A power analysis of a model of an irreversible chronic disease

Some clinical investigations of irreversible chronic diseases begin with a sample of healthy subjects and follow them through the process of disease onset and death. Researchers often ask if the presence of the disease diminishes a person's life expectancy. When parametric models are used for the analysis, hypotheses about the death rates for those people with and without the disease are usually tested by means of standard normal tests on the difference in the parameter estimates or by likelihood-ratio chi-square tests. This paper uses Monte Carlo simulation techniques to examine the precision of parameter estimates and the power of these tests for a model used for a previous study of senile dementia, Alzheimer's type.     

Parametric and non-parametric modeling of short-term synaptic plasticity. Part II: Experimental study

This paper presents a synergistic parametric and non-parametric modeling study of short-term plasticity (STP) in the Schaffer collateral to hippocampal CA1 pyramidal neuron (SC) synapse. Parametric models in the form of sets of differential and algebraic equations have been proposed on the basis of the current understanding of biological mechanisms active within the system. Non-parametric Poisson–Volterra models are obtained herein from broadband experimental input–output data. The non-parametric model is shown to provide better prediction of the experimental output than a parametric model with a single set of facilitation/depression (FD) process. The parametric model is then validated in terms of its input–output transformational properties using the non-parametric model since the latter constitutes a canonical and more complete representation of the synaptic nonlinear dynamics. Furthermore, discrepancies between the experimentally-derived non-parametric model and the equivalent non-parametric model of the parametric model suggest the presence of multiple FD processes in the SC synapses. Inclusion of an additional set of FD process in the parametric model makes it replicate better the characteristics of the experimentally-derived non-parametric model. This improved parametric model in turn provides the requisite biological interpretability that the non-parametric model lacks.     

Estimating and controlling for spatial structure in the study of ecological communities

Aim Variation partitioning based on canonical analysis is the most commonly used analysis to investigate community patterns according to environmental and spatial predictors. Ecologists use this method in order to understand the pure contribution of the environment independent of space, and vice versa, as well as to control for inflated type I error in assessing the environmental component under spatial autocorrelation. Our goal is to use numerical simulations to compare how different spatial predictors and model selection procedures perform in assessing the importance of the spatial component and in controlling for type I error while testing environmental predictors. Innovation We determine for the first time how the ability of commonly used (polynomial regressors) and novel methods based on eigenvector maps compare in the realm of spatial variation partitioning. We introduce a novel forward selection procedure to select spatial regressors for community analysis. Finally, we point out a number of issues that have not been previously considered about the joint explained variation between environment and space, which should be taken into account when reporting and testing the unique contributions of environment and space in patterning ecological communities. Main conclusions In tests of species‐environment relationships, spatial autocorrelation is known to inflate the level of type I error and make the tests of significance invalid. First, one must determine if the spatial component is significant using all spatial predictors (Moran's eigenvector maps). If it is, consider a model selection for the set of spatial predictors (an individual‐species forward selection procedure is to be preferred) and use the environmental and selected spatial predictors in a partial regression or partial canonical analysis scheme. This is an effective way of controlling for type I error in such tests. Polynomial regressors do not provide tests with a correct level of type I error.     

Independent contrasts and regression through the origin

Following the pioneering work of Felsenstein and Garland, phylogeneticists have been using regression through the origin to analyze comparative data using independent contrasts. The reason why regression through the origin must be used with such data was revisited. The demonstration led to the formulation of a permutation test for the coefficient of determination and the regression coefficient estimates in regression through the origin. Simulations were carried out to measure type I error and power of the parametric and permutation tests under two models of data generation: regression models I and II (correlation model). Although regression through the origin assumes model I data, in independent contrast data error is present in the explanatory as well as the response variables. Two forms of permutations were investigated to test the regression coefficients: permutation of the values of the response variable y, and permutation of the residuals of the regression model. The simulations showed that the parametric tests or any of the permutation tests can be used when the error is normal, which is the usual assumption in independent contrast studies; only the test by permutation of y should be used when the error is highly asymmetric; and the parametric tests should be used when extreme values are present in covariables. Two examples are presented. The first one concerns non-specificity in fish parasites of the genus Lamellodiscus, the second the richness in parasites in 78 species of mammals.     

Evaluation of genetic tests for susceptibility to common complex diseases: why, when and how?

Recent research into the human genome has generated a wealth of scientific knowledge and increased both public and professional interest in the concept of personalised medicine. Somewhat unexpectedly, in addition to increasing our understanding about the genetic basis for numerous diseases, these new discoveries have also spawned a burgeoning new industry of ‘consumer genetic testing’. In this paper, we present the principles learnt though the evaluation of tests for single gene disorders and suggest a comparable framework for the evaluation of genetic tests for susceptibility to common complex diseases. Both physicians and the general public will need to be able to assess the claims made by providers of genetic testing services, and ultimately policy-makers will need to decide if and when such tests should be offered through state funded healthcare systems.     

A novel population balance model to investigate the kinetics of in vitro cell proliferation: part II. Numerical solution, parameters' determination, and model outcomes

Based on the general theoretical model developed in Part I of this work, a series of numerical simulations related to the in vitro proliferation kinetics of adherent cells is here presented. First the complex task of assigning a specific value to all the parameters of the proposed population balance (PB) model is addressed, by also highlighting the difficulties arising when performing proper comparisons with experimental data. Then, a parametric sensitivity analysis is performed, thus identifying the more relevant parameters from a kinetics perspective. The proposed PB model can be adapted to describe cell growth under various conditions, by properly changing the value of the adjustable parameters. For this reason, model parameters able to mimic cell culture behavior under microgravity conditions are identified by means of a suitable parametric sensitivity analysis. Specifically, it is found that, as the volume growth parameter is reduced, proliferation slows down while cells arrest in G0/G1 or G2/M depending on the initial distribution of cell population. On the basis of this result, model capabilities have been tested by means of a proper comparison with literature experimental data related to the behavior of synchronized and not-synchronized cells under micro- and standard gravity levels.     

Second-Order Designs in Two Stages

This study examines the comparative information intrinsic to each stage of a two-stage experiment. Aliasing at the first stage, possible confounding of effects between stages, and efficiency comparisons for the two stages are determined directly from the analysis. A practical advantage is that runs to be assigned prospectively to the first and second stages may be evaluated beforehand, independently of empirical data. Motivated by studies in the digestion of seafood wastes, a case study develops a central composite design in k = 3 regressors, first on augmenting a 2³ design with center run, then on augmenting a small composite design with center run. Other augmentations are considered as well. The study concludes with some minimal augmentations to a 2³ design with center run, so as to ensure full estimability in a second-order model in three regressors.     

Computing parametric beta diversity with unequal plot weights: a solution based on resampling methods

Jost (Ecology, 88:2427–2439, 2007) recently showed that the Shannon diversity is the only standard diversity measure that can be partitioned into meaningful independent alpha and beta components when plot weights are unequal. This conclusion is very disappointing if one wants to calculate the beta diversity of unequal weighted plots using a parametric measure with varying sensitivities to the occurrence of rare and abundant species. To overcome this impasse, at least partially, in this paper, I propose a parametric measure of beta diversity that is based on the combination of Shannon’s entropy with Hurlbert’s ‘expected species diversity’. Unlike most parametric measures of diversity, the proposed index has a clear probabilistic interpretation, allowing at the same time a multiplicative partition of diversity into independent alpha and beta components for unequally weighted plots.     

Parametric and non-parametric modeling of short-term synaptic plasticity. Part I: Computational study

Parametric and non-parametric modeling methods are combined to study the short-term plasticity (STP) of synapses in the central nervous system (CNS). The nonlinear dynamics of STP are modeled by means: (1) previously proposed parametric models based on mechanistic hypotheses and/or specific dynamical processes, and (2) non-parametric models (in the form of Volterra kernels) that transforms the presynaptic signals into postsynaptic signals. In order to synergistically use the two approaches, we estimate the Volterra kernels of the parametric models of STP for four types of synapses using synthetic broadband input–output data. Results show that the non-parametric models accurately and efficiently replicate the input–output transformations of the parametric models. Volterra kernels provide a general and quantitative representation of the STP.     

Power comparison of parametric and nonparametric linkage tests in small pedigrees

When the mode of inheritance of a disease is unknown, the LOD-score method of linkage analysis must take into account uncertainties in model parameters. We have previously proposed a parametric linkage test called "MFLOD," which does not require specification of disease model parameters. In the present study, we introduce two new model-free parametric linkage tests, known as "MLOD" and "MALOD." These tests are defined, respectively, as the LOD score and the admixture LOD score, maximized (subject to the same constraints as MFLOD) over disease-model parameters. We compared the power of these three parametric linkage tests and that of two nonparametric linkage tests, NPLall and NPLpairs, which are implemented in GENEHUNTER. With the use of small pedigrees and a fully informative marker, we found the powers of MLOD, NPLall, and NPLpairs to be almost equivalent to each other and not far below that of a LOD-score analysis performed under the assumption the correct genetic parameters. Thus, linkage analysis is not much hindered by uncertain mode of inheritance. The results also suggest that both parametric and nonparametric methods are suitable for linkage analysis of complex disorders in small pedigrees. However, whether these results apply to large pedigrees remains to be answered.     

A likelihood ratio test for equality of deviations from randomness

The use of the sample variance-to-mean ratio as a measure of deviation from randomness in spatial pattern is reviewed. The likelihood ratio method of constructing a statistical test for the equality of several population variance-to-mean ratios is described, and details are provided for the special case where counts are modelled as arising from a negative binomial distribution. This test is illustrated by application to example data sets in ecology. Likelihood ratio tests represent a general methodology whereby relationships among several indices of aggregation can be systematically investigated, provided one is able to specify a suitable parametric form for the underlying distributions.     

Multiple augmentation with partial missing regressors

In large cohort studies, it is common that a subset of the regressors may be missing for some study subjects by design or happenstance. In this article, we apply the multiple data augmentation techniques to semiparametric models for epidemiologic data when a subset of the regressors are missing for some subjects, under the assumption that the data are missing at random in the sense of Rubin (2004) and that the missingness probabilities depend jointly on the observable subset of regressors, on a set of observable extraneous variables and on the outcome. Computational algorithms for the Poor Man's and the Asymptotic Normal data augmentations are investigated. Simulation studies show that the data augmentation approach generates satisfactory estimates and is computationally affordable. Under certain simulation scenarios, the proposed approach can achieve asymptotic efficiency similar to the maximum likelihood approach. We apply the proposed technique to the Multi-Ethic Study of Atherosclerosis (MESA) data and the South Wales Nickel Worker Study data.     

Efficient modelling protocols for oligosaccharides: from vacuum to solvent

The determination of conformational preferences of oligosaccharides is best approached by describing their preferred conformations on potential energy surfaces as a function of the glycosidic linkage φ, ψ torsional angles. For proper molecular mechanics modelling the flexibility of the rotatable pendant groups must also be considered. The so called adiabatic maps partially mimic the flexibility within the 10 dimensional conformational space of the pendant groups of the given disaccharide. These molecular mechanics maps are considered to be the state-of-the art of the φ, ψ potential energy surface of disaccharides recently calculated. The RAMM (RAndom Molecular Mechanics) method was shown to be able to calculate such profiles automatically. Additionally, based on the continuum solvent approach, RAMM allows the calculation of the effects of solvent on conformational energy profiles. Molecular dynamics simulations are also useful tools to study the influence of solvent on conformational behaviour of oligosaccharides. The capability of the RAMM calculational protocol to locate low-energy conformers on the multidimensional potential energy hypersurfaces of disaccharides is illustrated and compared with molecular dynamics simulations with and without inclusion of the solvent. This revised version was published online in August 2006 with corrections to the Cover Date.