Inhibition of the LH Surge by Restraint Stress in Cyclic Rats: Studies on the Role of GABAA and GABAB Receptors

There is evidence that stress can alter the activity in the brain of gamma-aminobutyricacid (GABA), a neurotransmitter that has been implicated in the regulation of LH secretion. In the present study the role of GABA in the restraint stress-induced inhibition of the LH surge was investigated in the intact cyclic rat. Intracerebroventricular (icv) administration of the GABAA receptor agonist muscimol (0.1, 0.5 or 1 μg) 5 min before the presumed onset of the pro-oestrous LH surge (at 0900 h) caused a dose dependent suppression of the surge. A single dose of the GABAB receptor agonist baclofen (1 μg; icv) injected at 0855 h postponed the onset of the LH surge, and repeated injections at 0855 and 1130 h suppressed the surge. These data indicate that GABA-ergic activity in the brain can inhibit the LH surge in the cyclic rat via GABAA and GABAB receptors. Pro-oestrous rats were subjected to 5 hrs of restraint starting at 0855 h. Pretreatment with the GABAA receptor antagonist bicuculine (1 μg; icv) at 0840, 0940 and 1040 h or pretreatment with the GABAB receptor antagonist phaclofen (10 μg; icv) at 0840 h were ineffective in preventing the restraint-induced inhibition of the LH surge. The results suggest that GABAA and GABAB receptors are not involved in the inhibitory effect of restraint stress on the LH surge.     

Nonlinear principal components analysis: introduction and application

The authors provide a didactic treatment of nonlinear (categorical) principal components analysis (PCA). This method is the nonlinear equivalent of standard PCA and reduces the observed variables to a number of uncorrelated principal components. The most important advantages of nonlinear over linear PCA are that it incorporates nominal and ordinal variables and that it can handle and discover nonlinear relationships between variables. Also, nonlinear PCA can deal with variables at their appropriate measurement level; for example, it can treat Likert-type scales ordinally instead of numerically. Every observed value of a variable can be referred to as a category. While performing PCA, nonlinear PCA converts every category to a numeric value, in accordance with the variable's analysis level, using optimal quantification. The authors discuss how optimal quantification is carried out, what analysis levels are, which decisions have to be made when applying nonlinear PCA, and how the results can be interpreted. The strengths and limitations of the method are discussed. An example applying nonlinear PCA to empirical data using the program CATPCA (J. J. Meulman, W. J. Heiser, & SPSS, 2004) is provided.     

The integration of multidimensional scaling and multivariate analysis with optimal transformations

The recent history of multidimensional data analysis suggests two distinct traditions that have developed along quite different lines. In multidimensional scaling (MDS), the available data typically describe the relationships among a set of objects in terms of similarity/dissimilarity (or (pseudo-)distances). In multivariate analysis (MVA), data usually result from observation on a collection of variables over a common set of objects. This paper starts from a very general multidimensional scaling task, defined on distances between objects derived from one or more sets of multivariate data. Particular special cases of the general problem, following familiar notions from MVA, will be discussed that encompass a variety of analysis techniques, including the possible use of optimal variable transformation. Throughout, it will be noted how certain data analysis approaches are equivalent to familiar MVA solutions when particular problem specifications are combined with particular distance approximations.This research was supported by the Royal Netherlands Academy of Arts and Sciences (KNAW). An earlier version of this paper was written during a stay at McGill University in Montréal; this visit was supported by a travel grant from the Netherlands Organization for Scientific Research (NWO). I am grateful to Jim Ramsay and Willem Heiser for their encouragement and helpful suggestions, and to the Editor and referees for their constructive comments.     

Optimal scaling by alternating length-constrained nonnegative least squares, with application to distance-based analysis

An important feature of distance-based principal components analysis, is that the variables can be optimally transformed. For monotone spline transformation, a nonnegative least-squares problem with a length constraint has to be solved in each iteration. As an alternative algorithm to Lawson and Hanson (1974), we propose the Alternating Length-Constrained Non-Negative Least-Squares (ALC-NNLS) algorithm, which minimizes the nonnegative least-squares loss function over the parameters under a length constraint, by alternatingly minimizing over one parameter while keeping the others fixed. Several properties of the new algorithm are discussed. A Monte Carlo study is presented which shows that for most cases in distance-based principal components analysis, ALC-NNLS performs as good as the method of Lawson and Hanson or sometimes even better in terms of the quality of the solution. Supported by The Netherlands Organization for Scientific Research (NWO) by grant nr. 030-56403 for the “PIONEER” project “Subject Oriented Multivariate Analysis” to the third author. We would like to thank the anonymous referees for their valuable remarks that have improved the quality of this paper.     

Stability of nonlinear principal components analysis: an empirical study using the balanced bootstrap

Principal components analysis (PCA) is used to explore the structure of data sets containing linearly related numeric variables. Alternatively, nonlinear PCA can handle possibly nonlinearly related numeric as well as nonnumeric variables. For linear PCA, the stability of its solution can be established under the assumption of multivariate normality. For nonlinear PCA, however, standard options for establishing stability are not provided. The authors use the nonparametric bootstrap procedure to assess the stability of nonlinear PCA results, applied to empirical data. They use confidence intervals for the variable transformations and confidence ellipses for the eigenvalues, the component loadings, and the person scores. They discuss the balanced version of the bootstrap, bias estimation, and Procrustes rotation. To provide a benchmark, the same bootstrap procedure is applied to linear PCA on the same data. On the basis of the results, the authors advise using at least 1,000 bootstrap samples, using Procrustes rotation on the bootstrap results, examining the bootstrap distributions along with the confidence regions, and merging categories with small marginal frequencies to reduce the variance of the bootstrap results.     

Empirical support for the gender‐as‐culture hypothesis.

This investigation provided a test of the gender‐as‐culture, or ‘two cultures’, hypothesis proposed by Maltz and Borker (1982) to explain male/female differences in language use. Analysis of previous empirical investigations located 16 language features that had consistently been shown to indicate communicator gender and these were tested within the framework of the four dimensions of intercultural style proposed by Gudykunst and Ting‐Toomey (1998): direct versus indirect, succinct versus elaborate, personal versus contextual and instrumental versus affective. Study 1 provided preliminary evidence supporting the hypothesized language‐feature‐by‐dimension relationships (e.g., male directives were rated more direct and female uncertainty verbs more indirect). In Study 2, respondents rated multiple exemplars of the 16 language features, as well as 16 contrasting foil sentences, on all four dimensions, finding that nearly all of the variables fell on the hypothesized intercultural dimensions. In Study 3, respondents rated four sets of naturally occurring target sentences and matching foil sentences, representing all language variables, on their appropriate intercultural dimensions in order to establish dimensional polarity. Results across the three studies supported the hypothesized language feature‐by‐stylistic dimension relationship for 15 of the 16 variables: The 6 male language features were rated as more direct, succinct, personal, and instrumental, whereas 9 of the 10 female features were perceived as more indirect, elaborate, and affective. The findings demonstrate that gender preferences for language use function in ways that are consistent with stylistic preferences that distinguish national cultures.     

Anticipatory Processing and Post‐Event Processing in Social Anxiety Disorder: An Update on the Literature

Two repetitive thinking processes that have been proposed in prominent maintenance models of social anxiety disorder (SAD) are anticipatory processing and post‐event processing. Research into these two processes has steadily increased over the last 20 years. This review highlights the main lines of existing research on anticipatory processing and post‐event processing, including studies on the nature of these processes, their association with social anxiety, the predictors, and consequences of these processes, as well as how these processes respond to treatments for SAD. The review also highlights some of the conceptual and methodological issues that have prevented the literature on anticipatory processing and post‐event processing from being more integrated and focused. Finally, the review draws together some new directions in terms of theory and research to further advance the field.     

A distance-based variety of nonlinear multivariate data analysis, including weights for objects and variables

In the distance approach to nonlinear multivariate data analysis the focus is on the optimal representation of the relationships between the objects in the analysis. In this paper two methods are presented for including weights in distance-based nonlinear multivariate data analysis. In the first method, weights are assigned to the objects while the second method is concerned with differential weighting of groups of variables. When each analysis variable defines a group the latter method becomes a variable weighting method. For objects the weights are assumed to be given; for groups of variables they may be given, or estimated. These weighting schemes can also be combined and have several important applications. For example, they make it possible to perform efficient analyses of large data sets, to use the distance-based variety of nonlinear multivariate data analysis as an addition to loglinear analysis of multiway contingency tables, and to do stability studies of the solutions by applying the bootstrap on the objects or the variables in the analysis. These and other applications are discussed, and an efficient algorithm is proposed to minimize the corresponding loss function.This study is funded by The Netherlands Organization for Scientific Research (NWO) by grant nr. 030-56403 for the PIONEER project Subject Oriented Multivariate Analysis to the third author.     

Graph-theoretic representations for proximity matrices through strongly-anti-Robinson or circular strongly-anti-Robinson matrices

There are various optimization strategies for approximating, through the minimization of a least-squares loss function, a given symmetric proximity matrix by a sum of matrices each subject to some collection of order constraints on its entries. We extend these approaches to include components in the approximating sum that satisfy what are called the strongly-anti-Robinson (SAR) or circular strongly-anti-Robinson (CSAR) restrictions. A matrix that is SAR or CSAR is representable by a particular graph-theoretic structure, where each matrix entry is reproducible from certain minimum path lengths in the graph. One published proximity matrix is used extensively to illustrate the types of approximation that ensue when the SAR or CSAR constraints are imposed.The authors are indebted to Boris Mirkin who first noted in a personal communication to one of us (LH, April 22, 1996) that the optimization method for fitting anti-Robinson matrices in Hubert and Arabie (1994) should be extendable to the fitting of strongly anti-Robinson matrices as well.