Reduced gray matter volume in ventral prefrontal cortex but not amygdala in bipolar disorder: Significant effects of gender and trait anxiety

Neuroimaging studies in bipolar disorder report gray matter volume (GMV) abnormalities in neural regions implicated in emotion regulation. This includes a reduction in ventral/orbital medial prefrontal cortex (OMPFC) GMV and, inconsistently, increases in amygdala GMV. We aimed to examine OMPFC and amygdala GMV in bipolar disorder type 1 patients (BPI) versus healthy control participants (HC), and the potential confounding effects of gender, clinical and illness history variables and psychotropic medication upon any group differences that were demonstrated in OMPFC and amygdala GMV Images were acquired from 27 BPI (17 euthymic, 10 depressed) and 28 age- and gender-matched HC in a 3T Siemens scanner. Data were analyzed with SPM5 using voxel-based morphometry (VBM) to assess main effects of diagnostic group and gender upon whole brain (WB) GMV. Post-hoc analyses were subsequently performed using SPSS to examine the extent to which clinical and illness history variables and psychotropic medication contributed to GMV abnormalities in BPI in a priori and non-a priori regions has demonstrated by the above VBM analyses. BPI showed reduced GMV in bilateral posteromedial rectal gyrus (PMRG), but no abnormalities in amygdala GMV. BPI also showed reduced GMV in two non-a priori regions: left parahippocampal gyrus and left putamen. For left PMRG GMV, there was a significant group by gender by trait anxiety interaction. GMV was significantly reduced in male low-trait anxiety BPI versus male low-trait anxiety HC, and in high-versus low-trait anxiety male BPI. Our results show that in BPI there were significant effects of gender and trait-anxiety, with male BPI and those high in trait-anxiety showing reduced left PMRG GMV. PMRG is part of medial prefrontal network implicated in visceromotor and emotion regulation. (C) 2008 Elsevier Ireland Ltd. All rights reserved.

Older adults' trait ratings of three age groups around the Pacific rim

In this paper, we assess the traits that older adultsassociate with younger, middle-aged, and older adults in fivePacific Rim nations from Western and Eastern cultural traditions(Australia, People's Republic of China (PRC), Hong Kong,Philippines, Thailand). We find cross-cultural trends whichreplicate patterns found in the US context. In most cultures,attractiveness, strength, activity, liberalism, health, andflexibility are seen to decline with increasing age. Kindnessassessments are positively associated with age across cultures. Mixed patterns are found with assessments of wisdom andgenerosity, with respondents from the PRC and Hong Kong beingnotably more negative about increasing age than otherrespondents. Implications for the aging process across culturesare discussed, and suggestions made for future research.

Fitting genetic models to twin data with binary and ordered categorical responses: A comparison of structural equation modelling and Bayesian hierarchical models

We compare Bayesian methodology utilizing free-ware BUGS (Bayesian Inference Using Gibbs Sampling) with the traditional structural equation modelling approach based on another free-ware package, Mx. Dichotomous and ordinal (three category) twin data were simulated according to different additive genetic and common environment models for phenotypic variation. Practical issues are discussed in using Gibbs sampling as implemented by BUGS to fit subject-specific Bayesian generalized linear models, where the components of variation may be estimated directly. The simulation study (based on 2000 twin pairs) indicated that there is a consistent advantage in using the Bayesian method to detect a correct model under certain specifications of additive genetics and common environmental effects. For binary data, both methods had difficulty in detecting the correct model when the additive genetic effect was low (between 10 and 20%) or of moderate range (between 20 and 40%). Furthermore, neither method could adequately detect a correct model that included a modest common environmental effect (20%) even when the additive genetic effect was large (50%). Power was significantly improved with ordinal data for most scenarios, except for the case of low heritability under a true ACE model. We illustrate and compare both methods using data from 1239 twin pairs over the age of 50 years, who were registered with the Australian National Health and Medical Research Council Twin Registry (ATR) and presented symptoms associated with osteoarthritis occurring in joints of the hand.

Quantitative trait loci for agronomic and seed quality traits in an F-2 and F-4 : 6 soybean population

Molecular breeding is becoming more practical as better technology emerges. The use of molecular markers in plant breeding for indirect selection of important traits can favorably impact breeding efficiency. The purpose of this research is to identify quantitative trait loci (QTL) on molecular linkage groups (MLG) which are associated with seed protein concentration, seed oil concentration, seed size, plant height, lodging, and maturity, in a population from a cross between the soybean cultivars 'Essex' and 'Williams.' DNA was extracted from F-2 generation soybean leaves and amplified via polymerase chain reaction (PCR) using simple sequence repeat (SSR) markers. Markers that were polymorphic between the parents were analyzed against phenotypic trait data from the F-2 and F-4:6 generation. For the F-2 population, significant additive QTL were Satt540 (MLG M, maturity, r(2)=0.11; height, r(2)=0.04, seed size, r(2)=0.061, Satt373 (MLG L, seed size, r(2)=0.04; height, r(2)=0.14), Satt50 (MLG A1, maturity r(2)=0.07), Satt14 (MLG D2, oil, r(2)=0.05), and Satt251 (protein r(2)=0.03, oil, r(2)=0.04). Significant dominant QTL for the F-2 population were Satt540 (MLG M, height, r(2)=0.04; seed size, r(2)=0.06) and Satt14 (MLG D2, oil, r(2)=0.05). In the F-4:6 generation significant additive QTL were Satt239 (MLG I, height, r(2)=0.02 at Knoxville, TN and r(2)=0.03 at Springfield, TN), Satt14 (MLG D2, seed size, r(2)=0.14 at Knoxville, TN), Satt373 (MLG L, protein, r(2)=0.04 at Knoxville, TN) and Satt251 (MLG B I, lodging r(2)=0.04 at Springfield, TN). Averaged over both environments in the F-4:6 generation, significant additive QTL were identified as Satt251 (MLG B 1, protein, r(2)=0.03), and Satt239 (MLG 1, height, r(2)=0.03). The results found in this study indicate that selections based solely on these QTL would produce limited gains (based on low r(2) values). Few QTL were detected to be stable across environments. Further research to identify stable QTL over environments is needed to make marker-assisted approaches more widely adopted by soybean breeders.

Two-locus Linkage Analysis Applied to Putative Quantitative Trait Loci for Lipoprotein(a) Levels

Plasma levels of lipoprotein(a) _ Lp(a) _ are associated with cardiovascular risk (Danesh et al., 2000) and were long believed to be influenced by the LPA locus on chromosome 6q27 only. However, a recent report of Broeckel et al. (2002) suggested the presence of a second quantitative trait locus on chromosome 1 influencing Lp(a) levels. Using a two-locus model, we found no evidence for an additional Lp(a) locus on chromosome 1 in a linkage study among 483 dizygotic twin pairs.

Signal trait sexual dimorphism and mutual sexual selection in Drosophila serrata

The evolution of sexual dimorphism may occur when natural and sexual selection result in different optimum trait values for males and females. Perhaps the most prominent examples of sexual dimorphism occur in sexually selected traits, for which males usually display exaggerated trait levels, while females may show reduced expression of the trait. In some species, females also exhibit secondary sexual traits that may either be a consequence of a correlated response to sexual selection on males or direct sexual selection for female secondary sexual traits. In this experiment, we simultaneously measure the intersex genetic correlations and the relative strength of sexual selection on males and females for a set of cuticular hydrocarbons in Drosophila serrata. There was significant directional sexual selection on both male and female cuticular hydrocarbons: the strength of sexual selection did not differ among the sexes but males and females preferred different cuticular hydrocarbons. In contrast with many previous studies of sexual dimorphism, intersex genetic correlations were low. The evolution of sexual dimorphism in D. serrata appears to have been achieved by sex-limited expression of traits controlled by genes on the X chromosome and is likely to be in its final stages.

Feature selection for clustering categorical data with an embedded modelling approach

Research on the problem of feature selection for clustering continues to develop. This is a challenging task, mainly due to the absence of class labels to guide the search for relevant features. Categorical feature selection for clustering has rarely been addressed in the literature, with most of the proposed approaches having focused on numerical data. In this work, we propose an approach to simultaneously cluster categorical data and select a subset of relevant features. Our approach is based on a modification of a finite mixture model (of multinomial distributions), where a set of latent variables indicate the relevance of each feature. To estimate the model parameters, we implement a variant of the expectation-maximization algorithm that simultaneously selects the subset of relevant features, using a minimum message length criterion. The proposed approach compares favourably with two baseline methods: a filter based on an entropy measure and a wrapper based on mutual information. The results obtained on synthetic data illustrate the ability of the proposed expectation-maximization method to recover ground truth. An application to real data, referred to official statistics, shows its usefulness.

Categorical data clustering using a minimum message length criterion

Research on cluster analysis for categorical data continues to develop, new clustering algorithms being proposed. However, in this context, the determination of the number of clusters is rarely addressed. We propose a new approach in which clustering and the estimation of the number of clusters is done simultaneously for categorical data. We assume that the data originate from a finite mixture of multinomial distributions and use a minimum message length criterion (MML) to select the number of clusters (Wallace and Bolton, 1986). For this purpose, we implement an EM-type algorithm (Silvestre et al., 2008) based on the (Figueiredo and Jain, 2002) approach. The novelty of the approach rests on the integration of the model estimation and selection of the number of clusters in a single algorithm, rather than selecting this number based on a set of pre-estimated candidate models. The performance of our approach is compared with the use of Bayesian Information Criterion (BIC) (Schwarz, 1978) and Integrated Completed Likelihood (ICL) (Biernacki et al., 2000) using synthetic data. The obtained results illustrate the capacity of the proposed algorithm to attain the true number of cluster while outperforming BIC and ICL since it is faster, which is especially relevant when dealing with large data sets.

Determining the number of clusters in categorical data

Cluster analysis for categorical data has been an active area of research. A well-known problem in this area is the determination of the number of clusters, which is unknown and must be inferred from the data. In order to estimate the number of clusters, one often resorts to information criteria, such as BIC (Bayesian information criterion), MML (minimum message length, proposed by Wallace and Boulton, 1968), and ICL (integrated classification likelihood). In this work, we adopt the approach developed by Figueiredo and Jain (2002) for clustering continuous data. They use an MML criterion to select the number of clusters and a variant of the EM algorithm to estimate the model parameters. This EM variant seamlessly integrates model estimation and selection in a single algorithm. For clustering categorical data, we assume a finite mixture of multinomial distributions and implement a new EM algorithm, following a previous version (Silvestre et al., 2008). Results obtained with synthetic datasets are encouraging. The main advantage of the proposed approach, when compared to the above referred criteria, is the speed of execution, which is especially relevant when dealing with large data sets.

Clustering and selecting categorical features

In data clustering, the problem of selecting the subset of most relevant features from the data has been an active research topic. Feature selection for clustering is a challenging task due to the absence of class labels for guiding the search for relevant features. Most methods proposed for this goal are focused on numerical data. In this work, we propose an approach for clustering and selecting categorical features simultaneously. We assume that the data originate from a finite mixture of multinomial distributions and implement an integrated expectation-maximization (EM) algorithm that estimates all the parameters of the model and selects the subset of relevant features simultaneously. The results obtained on synthetic data illustrate the performance of the proposed approach. An application to real data, referred to official statistics, shows its usefulness.

Enhancing the selection of a model-based clustering with external categorical variables

In cluster analysis, it can be useful to interpret the partition built from the data in the light of external categorical variables which are not directly involved to cluster the data. An approach is proposed in the model-based clustering context to select a number of clusters which both fits the data well and takes advantage of the potential illustrative ability of the external variables. This approach makes use of the integrated joint likelihood of the data and the partitions at hand, namely the model-based partition and the partitions associated to the external variables. It is noteworthy that each mixture model is fitted by the maximum likelihood methodology to the data, excluding the external variables which are used to select a relevant mixture model only. Numerical experiments illustrate the promising behaviour of the derived criterion. © 2014 Springer-Verlag Berlin Heidelberg.

Enhancing the selection of a model-based clustering with external categorical variables

In cluster analysis, it can be useful to interpret the partition built from the data in the light of external categorical variables which are not directly involved to cluster the data. An approach is proposed in the model-based clustering context to select a number of clusters which both fits the data well and takes advantage of the potential illustrative ability of the external variables. This approach makes use of the integrated joint likelihood of the data and the partitions at hand, namely the model-based partition and the partitions associated to the external variables. It is noteworthy that each mixture model is fitted by the maximum likelihood methodology to the data, excluding the external variables which are used to select a relevant mixture model only. Numerical experiments illustrate the promising behaviour of the derived criterion.

Massive ocular hemorrhage resulting in blindness in a patient with the sickle cell trait who developed leptospirosis. Case report

This case report describes the findings of a 18 year-old black male from Bahia, a Northeastern state in Brazil, with the sickle cell trait, who developed bilateral hyphema and vitreous hemorrhage with blindness in the course of leptospirosis. The patient started to complain of blurred vision four days after the start of fever and muscular pain and approximately twelve hours after the introduction of penicillin. The severity of the leptospirosis in conjunction with sickle cell trait was considered to be the most likely explanation for this ocular complication.

Manifesto para uma nova química: o discurso preliminar do Traité Elémentaire de Chimie de Antoine Laurent Lavoisier

Em 2011, ano internacional da química, parece ser pertinente a publicação de um dos textos que marcou o esforço de renovação desta disciplina nos finais do século xviii. O Discurso Preliminar do Tratado Elementar de Química (1789) de Antoine Laurent Lavoisier constituiu-se como um verdadeiro manifesto para uma nova forma de ensinar, entender e praticar esta disciplina. No âmbito de um processo com cariz revolucionário era imperativo mobilizar as jovens gerações de futuros químicos. Era ainda necessária a própria refundição da linguagem química. São estes os principais propósitos que Lavoisier apresenta neste documento inaugural, num modo claro e metódico característico do seu estilo.