bluuebunny/crossref_metadata_2025_split huggingface dataset:
Metabolic effects of static magnetic fields on streptococcus pyogenes
Authors: Morrow A.C. , Dunstan R.H. , King B.V. , Roberts T.K. 
Published: May 2007

AbstractThis study aimed to develop a simple experimental system utilising bacterial cells to investigate the dose responses resulting from exposures to static magnetic flux densities ranging from 0.05 to 0.5 T on viability, bacterial metabolism and levels of DNA damage in Streptococcus pyogenes. Exposure of S. pyogenes to a field of 0.3 T at 24 °C under anaerobic conditions resulted in a significant (P < 0.05) decrease in growth rate, with an increased mean generation time of 199 ± 6 min compared to the control cells at 165 ± 6 min (P < 0.05). Conversely, exposure to magnetic fields of 0.5 T significantly accelerated the growth rate at 24 °C compared to control cells, with a decreased mean generation time of 147 ± 4 min (P < 0.05). The patterns of metabolite release from cells incubated in phosphate buffered saline (PBS) at 24 °C and exposed to different magnetic flux densities (0.05–0.5 T) were significantly (P < 0.05) altered, compared to non‐exposed controls. Concentrations of metabolites, with the exception of aspartic acid (r = 0.44), were not linearly correlated with magnetic flux density, with all other r < 0.20. Instead, “window” effects were observed, with 0.25–0.3 T eliciting the maximal release of the majority of metabolites, suggesting that magnetic fields of these strengths had significant impacts on metabolic homeostasis in S. pyogenes. The exposure of cells to 0.3 T was also found to significantly reduce the yield of 8‐hydroxyguanine in extracted DNA compared to controls, suggesting some possible anti‐oxidant protection to S. pyogenes at this field strength. Bioelectromagnetics 28:439–445, 2007. © 2007 Wiley‐Liss, Inc.
Hayman's Graphical Analysis in Segregating Generations of a Diallel Cross
Authors: Singh M. , Singh R. K. 
Published: May 2007

AbstractThe expected values of average degree of dominance in F2 and later segregating generations of a diallel cross have been mathematically derived, and compared to its value in F1 generation. The interpretation of intercepts on Wr axis, a measure of average degree of dominance, differed in segregating generations from that of F1 at origin, and between origin and tangent in Wr, Vr‐graph.
Die praktische Belevanz des McNemar‐Tests
Authors: Zimmermann H. 
Published: May 2007

AbstractBy application of the McNemar‐Test the question of the practical relevance (practical significance) of the resulting test statistic generally arises. An analogy to the t‐Test for paired measurements gives some measure for the practical relevance. The evaluation of this relevance is based upon suggestions made by COHEN (1977).
Cluster Analysis via Density Functions: Amelioration of a Lower Bound of the Parameter
Authors: Kopp B. 
Published: May 2007

AbstractThis paper refers to an earlier investigation on Cluster Analysis procedures based on general empirical density functions, in which the number of classes is controled by a positive parameter λ: the number of clusters, m, increases as λ →∞. Since there is no functional relationship between m and λ, upper and lower bounds of the parameter are of interest (see KOPP, 1976a). We now give a better value for the lower bound of λ in the case of the multivariate normal distribution.
Local and Regional vs. Global Contingency Testing
Authors: Havránek T. , Lienert G. A. 
Published: May 2007

AbstractContingency analysis of r × c‐tables is systematized as follows: Global contingency testing based on the total chisquare under H0 of independent row and column variables is least favorable to exhaustive interpretation. Local contingency testing based on chi‐square components of the individual cells of an r × c‐table and realized in Configural frequency analysis (KRAUTH and LIENERT, 1973) is more favourable to substantive interpretation. Compromising between global and local contingency testing leads to so‐called generalized local and regional contingency testing. Every contingency testing (local, regional and naturally global) is including all N individuals of a sample supposed to have been drawn randomly from a defined population. Extension to 3‐and t‐dimensional contingency analysis is outlined, and biomedical applications are discussed.
Tests of Special Structures on the Dispersion Matrix Associated with a Two‐Way Mixed Effects Model
Authors: Patel H. I. , Tsai K. T. 
Published: May 2007

AbstractExact and approximate methods are available in the literature to compare the fixed effect levels in an unbalanced two‐way mixed model under the conventional distributional assumptions for random effects. However, as suggested by SHEFFÉ (1959) the conventional assumptions may not be justified in practice. Recently, KHATRI and PATEL (1992) have studied a model with an unstructured dispersion matrix associated with the random effects of which the conventional model is a special model. In this note we give likelihood ratio tests for testing some special structures on the dispersion matrix.
Estimation of Vaccine Efficacy in Non‐Randomly Mixing Populations
Authors: Haber Michael , Halloran M. Elizabeth , Longini Ira M. , Watelet Luc 
Published: May 2007

AbstractA deterministic model for the transmission of an acute infectious disease in a heterogeneous, nonrandomly mixing population is developed. This model facilitates the estimation of transmission probabilities from the observed attack rates. If some of the members of the population are vaccinated, then the vaccine efficacy (VE), defined as the relative reduction in the transmission probability due to vaccination, can be estimated. We provide several estimators of VE, depending on the amount of information available on the mixing pattern and on the action of the vaccine. We show that if vaccinated persons increase the frequency of their contacts with infectious persons, then estimators ignoring this change in behavior may substantially underestimate the VE.
Estimation of the Spirometric Residual Volume (RV) by a Regression Built from Gower Distances
Authors: Bartkowiak Anna , Liebhart Jerzy 
Published: May 2007

AbstractWe estimate the Residual Volume, a spirometric parameter, by use of four continuous and four categorical variables. The estimation is done using distance‐based regression, which allows to construct the predicting regression equation from mixed‐type explanatory variables. The additionally introduced categorical variables improve essentially the goodness of fit of the regression equation.
A Robust Test for Homoscedasticity in a Two‐Way Layout
Authors: Piepho Hans‐Peter 
Published: May 2007

AbstractThis paper discusses tests for homogeneity of row‐variances in a two‐way classification. Parametric tests for this problem are shown to be highly sensitive to departures from the normality assumption. Levene's robust tests tend to be rather liberal when the number of rows exceeds the number of columns. A modification is suggested which improves the approximation for this situation, while retaining the desirable robustness property.
On the PBB Designs for Multiresponse Experiments
Authors: Brzeskwiniewicz Henryk 
Published: May 2007

AbstractWe present the idea of using multiresponse incomplete block designs when not all responses can be observed in all experimental units. For a special class of such designs, in which partial designs are PBB designs, a method for estimating natural treatment contrast is given. We also consider the problem of testing the hypotheses concerning the natural and any estimable treatment contrasts. For testing this hypothesis the Wald statistics, being asymptotically chi‐square distributed, is proposed.
Further Study on the Statistical Test to Detect Spatial Pattern
Authors: Ping Sun 
Published: May 2007

AbstractIn the study of spatial patterns, the statistic I' = (n — 1)s2/x was commonly used. In this paper, we found that x — s2 has an approximated normal distribution with zero mean if the xi's (i = 1 to n) are independent identically distributed Poisson variables. Based on this conclusion, the hypothesis that a point pattern is completely random can be tested directly. And a method for the test of spatial patterns was proposed which can be sued as an alternative to the Chi‐square based dispersion index test.
A Genetic Basis Behind Combining Ability and Breeding Values in Mono‐ and Di‐genic Systems
Authors: Arunachalam V. 
Published: May 2007

AbstractCombining ability and breeding values are concepts of crucial importance in practical breeding. However, in early literature, methods of partitioning the genetic value into additive and dominance effects were described mostly in single gene and under random mating. Though average effect and average excess of gene substitution were defined by Fisher and a practial method of obtaining breeding values outlined by Falconer, concerted attempts are scarce in integrating the concepts of combining ability, breeding value and additive effects. Only a few workers have reported the values of those parameters for inbred populations while published reports on those effects in digenic systems with linkage appear to be not readily available. Practical breeders continue to use those concepts as if they were independent. This paper is therefore an attempt at filling the research gaps and setting those concepts in proper practical perspective.
On a Continuous Branching Process and Birth‐Death and Immigration Models for Testing the Efficacy of Some Control Programmes for AIDS
Authors: Biswas Suddhendu , Thomas Mariamma 
Published: May 2007

AbstractThe present communication is an attempt to describe the mode of propagation of AIDS epidemic and its control programme using a branching process as well as a birth‐death and immigration model. A comparison of the project of AIDS control programme on the basis of its propagation by a continuous branching process model with that of a linear birth and death process with immigration shows a remarkable contrast. Branching process model shows that it is possible to control the propagation of the disease by suitably increasing the detection rate and lowering the infection rate. However, the propagation of AIDS models by birth and death Process with or without immigration shows that it is increasingly difficult to control the invasion of AIDS merely by controlling the birth, death and immigration parameters.
Size and Power of Test Statistics for Gene Correlation in 2 × 2 Contingency Tables
Authors: Mannan M. , Nassar R. 
Published: May 2007

AbstractMonte Carlo simulation of size and power of two proposed tests for linkage disequilibrium between two genes each with two alleles were investigated. Results were compared with two commonly used statistics, the correlation coefficientrand the log‐odds ratio tests. Depending on the sign of the linkage disequilibrium, the new tests were found to be more powerful than either of the correlation or log‐odds ratio tests. However, on average (positive and negative linkage disequilibrium) the Chi‐square test using the correlation coefficient was to a small extent more powerful than the other tests.
Determination of Selection Gradients Using Multiple Regression versus Structural Equation Models (SEM)
Authors: Pugesek Bruce H. , Tomer Adrian 
Published: May 2007

AbstractSelection studies involving multiple intercorrelated independent variables have employed multiple regression analysis as a means to estimate and partition natural and sexual selection's direct and indirect effects. These statistical models assume that independent variables are measured without error. Most would conclude that such is not the case in the field studies for which these methods are employed. We demonstrate that the distortion of estimates resulting from error variance is not trivial. When independent variables are intercorrelated, extreme distortions may occur. We propose to use Structural Equation Models (SEM), to estimate error variance and produce highly accurate coefficients for formulation of selection gradients. This method is particularly appropriate when the selection is viewed as happening at the level of the latent variables.
Fish Shape Analysis Using Landmarks
Authors: Glasbey C. A. , Horgan G. W. , Gibson G. J. , Hitchcock D. 
Published: May 2007

AbstractLandmarks are used to summarize the shapes of ten haddock and nine whiting. Error variance models, after generalized Procrustes analysis, are compared using simulation tests. The power to discriminate between fish species is shown to be increased by permitting non‐affine transformations that correct for fish curvature.
Nested ANOVA for Repeated Surveys with Autocorrelated Errors
Authors: Prayag V. R. , Chiplonkar S. A. , Paranjpe S. A. 
Published: May 2007

AbstractDiet surveys in the science of human nutrition involve longitudinal observations on the same individuals repeated at distinct time points. Consequently a common feature of such surveys is inherent correlation between successive observations. In this paper, an autoregressive model of order 1 is assumed for the successive observations on the same individual in a survey. Further, observations on the same individual in two different surveys are assumed to be correlated. The technique of analysis of variance is modified suitably for this set up and applied to two nutritional surveys carried on army recruits.
A Simple, Flexible Failure Model
Authors: Yu Shuiyang , Voit Eberhard O. 
Published: May 2007

AbstractA new failure model is introduced in the form of a four‐parameter nonlinear differential equation, with failure probability as the dependent variable and failure time as the independent variable. The first parameter characterizes the location, the second the scale, and the other two the shape of the model. The type of the accompanying hazard function is immediately read off the shape parameters. The new model approximates the classical failure models with rather high precision, but also models cases where the failure density is skewed to the left. It can be used to analyze survival data objectively, based on the shape of the failure distribution. The computation of quantiles and moments is easy and fast. Nonlinear regression methods are used to estimate parameter values.
Accurate Confidence Intervals for the Ratio of Specific Occurrence/Exposure Rates in Risk and Survival Analysis
Authors: Tu Dongsheng , Gross Alan J. 
Published: May 2007

AbstractTransformation and computer intensive methods such as the jackknife and bootstrap are applied to construct accurate confidence intervals for the ratio of specific occurrence/exposure rates, which are used to compare the mortality (or survival) experience of individuals in two study populations. Monte Carlo simulations are employed to compare the performances of the proposed confidence intervals when sample sizes are small or moderate.
Hidden Markov Models and Animal Behaviour
Authors: Macdonald Iain L. , Raubenheimer David 
Published: May 2007

AbstractThis paper proposes the use of hidden Markov time series models for the analysis of the behaviour sequences of one or more animals under observation. These models have advantages over the Markov chain models commonly used for behaviour sequences, as they can allow for time‐trend or expansion to several subjects without sacrificing parsimony. Furthermore, they provide an alternative to higher‐order Markov chain models if a first‐order Markov chain is unsatisfactory as a model. To illustrate the use of such models, we fit multivariate and univariate hidden Markov models allowing for time‐trend to data from an experiment investigating the effects of feeding on the locomotory behaviour of locusts (Locusta migratoria).
Maximum Likelihood Neural Network Prediction Models
Authors: Faraggi David , Simon Richard 
Published: May 2007

AbstractNeural networks have received much attention in recent years mostly by non‐statisticians. The purpose of this paper is to incorporate neural networks in a non‐linear regression model and obtain maximum likelihood estimates of the network parameters using a standard Newton‐Raphson algorithm. We use maximum likelihood estimators instead of the usual back‐propagation technique and compare the neural network predictions with predictions of quadratic regression models and with non‐parametric nearest neighbor predictions. These comparisons are made using data generated from a variety of functions. Because of the number of parameters involved, neural network models can easily over‐fit the data, hence validation of results is crucial.
Estimation and Testing of Tumor Incidence Rates in Experiments Lacking Cause‐of‐Death Data
Authors: Ahn Hongshik , Kodell Ralph L. 
Published: May 2007

AbstractApproximate nonparametric maximum likelihood estimation of the tumor incidence rate and comparison of tumor incidence rates between treatment groups are examined in the context of animal carcinogenicity experiments that have interval sacrifice data but lack cause‐of‐death information. The estimation procedure introduced by MALANI and VAN RYZIN (1988), which can result in a negative estimate of the tumor incidence rate, is modified by employing a numerical method to maximize the likelihood function iteratively, under the constraint that the tumor incidence rate is nonnegative. With the new procedure, estimates can be obtained even if sacrifices occur anywhere within an interval. The resulting estimates have reduced standard error and give more power to the test of two heterogeneous groups. Furthermore, a linear contrast of more than two groups can be tested using our procedure. The proposed estimation and testing methods are illustrated with an experimental data set.
Estimating Location Parameters Using Censored Rank Statistics
Authors: Öhman Marie‐Louise 
Published: May 2007

AbstractIn this paper censored data rank location estimators are obtained by using censored one‐sample rank test statistics of the location parameter and censored two‐sample rank test statistics of the shift of location parameter. Also, methods for constructing censored small sample confidence intervals and asymptotic confidence intervals for the location are considered. Generalizations of the solutions from uncensored one‐sample and two‐sample rank tests are utilized.
Normality Testing in a Mixed Model of a Two‐way Nested Classification
Authors: Wagner Wieslaw , Brzeskwiniewicz H. 
Published: May 2007

AbstractThe paper presents a testing procedure verifying the hypothesis about the normal distribution of random components in a mixed model of a two‐way nested classification. The basis for this application is a simple sample obtained from orthogonal and the O'Reilly‐Quesenberry transformation to the residual vector from the method of least squares (MLS).
Additive and Non‐additive Genetic Correlations. II. Application to Data Obtained from a 12 × 12 Diallel Cross
Authors: Hohls Trevor , Clarke G. P. Y. , Shanahan P. E. , Gevers H. O. 
Published: May 2007

AbstractThe proposed genetic correlation analysis, that involves the partitioning of the overall genetic correlation into an additive and a non‐additive component, has been applied to data obtained from a diallel experiment involving 12 white modified opaque‐2 maize inbred lines. The correlation analysis provided an insight into possible indirect selection strategies for the improvement of inferior kernel quality traits associated with the opaque‐2 gene. Direct selection for high yield and low vitreousness rating would provide an efficient selection strategy for the development of high‐yielding modified opaque‐2 maize hybrids with desirable endosperm traits. It was concluded that it is not necessary to conduct the density, hardness and breakability determinations.
Analysis of a Generalized Linear Mixed Model: A Case Study and Simulation Results
Authors: Engel Bas , Buist Willem 
Published: May 2007

AbstractA class of generalized linear mixed models can be obtained by introducing random effects in the linear predictor of a generalized linear model, e.g. a split plot model for binary data or count data. Maximum likelihood estimation, for normally distributed random effects, involves high‐dimensional numerical integration, with severe limitations on the number and structure of the additional random effects. An alternative estimation procedure based on an extension of the iterative re‐weighted least squares procedure for generalized linear models will be illustrated on a practical data set involving carcass classification of cattle. The data is analysed as overdispersed binomial proportions with fixed and random effects and associated components of variance on the logit scale. Estimates are obtained with standard software for normal data mixed models. Numerical restrictions pertain to the size of matrices to be inverted. This can be dealt with by absorption techniques familiar from e.g. mixed models in animal breeding. The final model fitted to the classification data includes four components of variance and a multiplicative overdispersion factor. Basically the estimation procedure is a combination of iterated least squares procedures and no full distributional assumptions are needed. A simulation study based on the classification data is presented. This includes a study of procedures for constructing confidence intervals and significance tests for fixed effects and components of variance. The simulation results increase confidence in the usefulness of the estimation procedure.
Generalized Linear Mixed‐Effects Models with a Finite‐Support Random‐Effects Distribution: A Maximum‐Penalized‐Likelihood Approach
Authors: Leung Moon K. , Elashoff Robert M. 
Published: May 2007

AbstractWe discuss a method for simultaneously estimating the fixed parameters of a generalized linear mixed‐effects model and the random‐effects distribution of which no parametric assumption is made. In addition, classifying subjects into clusters according to the random regression coefficients is a natural by‐product of the proposed method. An alternative approach to maximum‐likelihood method, maximum‐penalized‐likelihood method, is used to avoid estimating “too many” clusters. Consistency and asymptotic normality properties of the estimators are presented. We also provide robust variance estimators of the fixed parameters estimators which remain consistent even in presence of misspecification. The methodology is illustrated by an application to a weight loss study.
A Bayesian Estimator of the Optimum for a Single Factor Quadratic Response Model
Authors: Fan T. H. , Karson M. J. , Wang H. S. 
Published: May 2007

AbstractEstimation of the location and magnitude of the optimum has long been considered an important problem in response surface methodology. In the industrial context, prior information accumulated by the subject matter specialist bears special significance. In this paper we use the Bayesian approach to estimating the optimum in a single factor quadratic regression model. Following the Bayesian general linear model development by Broemeling the normal/gamma conjugate prior is used. Explicit formulas for the generalized maximum likehood estimates of the characteristic parameters are obtained from the joint posterior distribution.
A Note on Testing Hypotheses about Trimmed Means
Authors: Wilcox Rand R. 
Published: May 2007

AbstractA well known result is that skewness can cause problems when testing hypotheses about measures of location, particulary when a one‐sided test is of interest. Wilcox (1994) reports both theoretical and simulation results showing that when testing hypotheses about trimmed means, control over Type I error probabilities can be substantially better than methods for means. However, at least in some situations, control over the probability of a Type I error might still be judged to be inadequate. One way of adressing this concern is to combine trimmed means with the bootsrap method advocated by Westfall and Yuong (1993). This note reports simulation results indicating that there are situations where substantial improvements over Type I error probabilities are indeed obtained.