Morphometric analysis and taxonomic revision of the Vriesea paraibica complex (Bromeliaceae)

The species related to Vriesea paraibica (Bromeliaceae, Tillandsioideae) have controversial taxonomic limits. For several decades, this group has been identified in herbarium collections as V. x morreniana, an artificial hybrid that does not grow in natural habitats. The aim of this study was to assess the morphological variation in the V. paraibica complex through morphometric analyses of natural populations. Two sets of analyses were performed: the first involved six natural populations (G1) and the second was carried out on taxa that emerged from the first analysis, but using material from herbarium collections (G2). Univariate ANOVA was used, as well as discriminant analysis of 16 morphometric variables in G1 and 18 in G2. The results of the analyses of the two groups were similar and led to the selection of diagnostic traits of four species. Lengths of the lower and median floral bracts were significant for the separation of red and yellow floral bracts. Vriesea paraibica and V. interrogatoria have red bracts; these two species are differentiated by the widths of the lower and median portions of the inflorescence and by scape length. These structures are larger in the former and smaller in the latter. Of the species with yellow floral bracts, V. eltoniana is distinguished by longer leaf blades and scapes and V. flava is characterized by its shorter sepal lengths. (C) 2009 The Linnean Society of London, Botanical Journal of the Linnean Society, 2009, 159, 163-181.

ENHANCING MULTISCALE FRACTAL DESCRIPTORS USING FUNCTIONAL DATA ANALYSIS

This work presents a novel approach in order to increase the recognition power of Multiscale Fractal Dimension (MFD) techniques, when applied to image classification. The proposal uses Functional Data Analysis (FDA) with the aim of enhancing the MFD technique precision achieving a more representative descriptors vector, capable of recognizing and characterizing more precisely objects in an image. FDA is applied to signatures extracted by using the Bouligand-Minkowsky MFD technique in the generation of a descriptors vector from them. For the evaluation of the obtained improvement, an experiment using two datasets of objects was carried out. A dataset was used of characters shapes (26 characters of the Latin alphabet) carrying different levels of controlled noise and a dataset of fish images contours. A comparison with the use of the well-known methods of Fourier and wavelets descriptors was performed with the aim of verifying the performance of FDA method. The descriptor vectors were submitted to Linear Discriminant Analysis (LDA) classification method and we compared the correctness rate in the classification process among the descriptors methods. The results demonstrate that FDA overcomes the literature methods (Fourier and wavelets) in the processing of information extracted from the MFD signature. In this way, the proposed method can be considered as an interesting choice for pattern recognition and image classification using fractal analysis.

Multiscale Curvature Analysis of Asphaltic Aggregate Particles

A new method for characterization and analysis of asphaltic mixtures aggregate particles is reported. By relying on multiscale representation of the particles, curvature estimation, and discriminant analysis for optimal separation of the categories of mixtures, a particularly effective and comprehensive methodology is obtained. The potential of the methodology is illustrated with respect to three important types of particles used in asphaltic mixtures, namely basalt, gabbro, and gravel. The obtained results show that gravel particles are markedly distinct from the other two types of particles, with the gabbro category resulting with intermediate geometrical properties. The importance of each considered measurement in the discrimination between the three categories of particles was also quantified in terms of the adopted discriminant analysis.

Supercritical fluid extracts from the Brazilian cherry (Eugenia uniflora L.): Relationship between the extracted compounds and the characteristic flavour intensity of the fruit

Supercritical carbon dioxide (SC-CO(2)) extractions of Brazilian cherry (Eugenia uniflora L.) were carried out under varied conditions of pressure and temperature, according to a central composite 2(2) experimental design, in order to produce flavour-rich extracts. The composition of the extracts was evaluated by gas chromatography coupled with mass spectrometry (GC/MS). The abundance of the extracted compounds was then related to sensory analysis results, assisted by principal component and factorial discriminant analysis (PCA and FDA, respectively). The identified sesquiterpenes and ketones were found to strongly contribute to the characteristic flavour of the Brazilian cherry. The extracts also contained a variety of other volatile compounds, and part of the fruit wax contained long-chain hydrocarbons that according to multivariate analysis, contributed to the yield of the extracts, but not the flavour. Volatile phenolic compounds, to which antioxidant properties are attributed, were also present in the extracts in high proportion, regardless of the extraction conditions. (C) 2010 Elsevier Ltd. All rights reserved.

Symptomatic expression of depression among Jewish adolescents: effects of gender and age

Background Depression symptomatology was assessed with the Beck Depression Inventory (BDI) in a sample of Jewish adolescents, in order to compare the frequency and severity of depression with non-Jewish adolescents as well as examine gender difference of the expression of depressive symptomatology. Method Subjects comprised 475 students from Jewish private schools, aged 13-17 years, who were compared with an age-matched non-Jewish sample (n = 899). Kendall`s definition was adopted to classify these adolescents according to level of depressive symptoms. The frequency of depression was calculated for ethnicity, gender and age strata. Discriminant analysis and principal component analysis were performed to assess the importance of depression-specific and non-specific items, along with the factor structure of the BDI, respectively. Results The overall mean score on the BDI in the Jewish and the non-Jewish sample was 9.0 (SD = 6.4) and 8.6 (SD = 7.2), respectively. Jewish girls and boys had comparable mean BDI scores, contrasting with non-Jewish sample, where girls complained more of depressive symptoms than boys (p < 0.001). The frequency of depression, adopting a BDI cutoff of 20, was 5.1% for the Jewish sample and 6.3% for the non-Jewish sample. The frequency of depression for Jewish girls and boys was 5.5% (SE = 1.4) and 4.6% (SE = 1.5), respectively. On the other hand, the frequency of depression for non-Jewish girls and boys was 8.4% (SE = 1.2) and 4.0% (SE = 1.0), respectively. The female/male ratio of frequency of BDI-depression was 1.2 in the Jewish sample, but non-Jewish girls were twice (2.1) as likely to report depression as boys. Discriminant analysis showed that the BDI highly discriminates depressive symptomatology among Jewish adolescents, and measured specific aspects of depression. Factor analysis revealed two meaningful factors for the total sample and each gender (cognitive-affective dimension and somatic dimension), evidencing a difference between Jewish boys and Jewish girls in the symptomatic expression of depression akin to non-Jewish counterparts. Conclusions Ethnic-cultural factor might play a role in the frequency, severity and symptomatic expression of depressive symptoms in Jewish adolescents. The lack of gender effect on depression, which might persist from adolescence to adulthood among Jewish people, should be investigated in prospective studies.

Comparing time-varying autoregressive structures of locally stationary processes

In this paper, a novel statistical test is introduced to compare two locally stationary time series. The proposed approach is a Wald test considering time-varying autoregressive modeling and function projections in adequate spaces. The covariance structure of the innovations may be also time- varying. In order to obtain function estimators for the time- varying autoregressive parameters, we consider function expansions in splines and wavelet bases. Simulation studies provide evidence that the proposed test has a good performance. We also assess its usefulness when applied to a financial time series.

Comparison for three Classification Techniques

In this paper we show the results of a comparison simulation study for three classification techniques: Multinomial Logistic Regression (MLR), No Metric Discriminant Analysis (NDA) and Linear Discriminant Analysis (LDA). The measure used to compare the performance of the three techniques was the Error Classification Rate (ECR). We found that MLR and LDA techniques have similar performance and that they are better than DNA when the population multivariate distribution is Normal or Logit-Normal. For the case of log-normal and Sinh(-1)-normal multivariate distributions we found that MLR had the better performance.

Designing Novel Antitrypanosomal Agents from a Mixed Graph-Theoretical Substructural Approach

Chagas disease is nowadays the most serious parasitic health problem. This disease is caused by Trypanosoma cruzi. The great number of deaths and the insufficient effectiveness of drugs against this parasite have alarmed the scientific community worldwide. In an attempt to overcome this problem, a model for the design and prediction of new antitrypanosomal agents was obtained. This used a mixed approach, containing simple descriptors based on fragments and topological substructural molecular design descriptors. A data set was made up of 188 compounds, 99 of them characterized an antitrypanosomal activity and 88 compounds that belong to other pharmaceutical categories. The model showed sensitivity, specificity and accuracy values above 85%. Quantitative fragmental contributions were also calculated. Then, and to confirm the quality of the model, 15 structures of molecules tested as antitrypanosomal compounds (that we did not include in this study) were predicted, taking into account the information on the abovementioned calculated fragmental contributions. The model showed an accuracy of 100% which means that the ""in silico"" methodology developed by our team is promising for the rational design of new antitrypanosomal drugs. (C) 2009 Wiley Periodicals, Inc. J Comput Chem 31: 882-894. 2010

Design of novel antituberculosis compounds using graph-theoretical and substructural approaches

The increasing resistance of Mycobacterium tuberculosis to the existing drugs has alarmed the worldwide scientific community. In an attempt to overcome this problem, two models for the design and prediction of new antituberculosis agents were obtained. The first used a mixed approach, containing descriptors based on fragments and the topological substructural molecular design approach (TOPS-MODE) descriptors. The other model used a combination of two-dimensional (2D) and three-dimensional (3D) descriptors. A data set of 167 compounds with great structural variability, 72 of them antituberculosis agents and 95 compounds belonging to other pharmaceutical categories, was analyzed. The first model showed sensitivity, specificity, and accuracy values above 80% and the second one showed values higher than 75% for these statistical indices. Subsequently, 12 structures of imidazoles not included in this study were designed, taking into account the two models. In both cases accuracy was 100%, showing that the methodology in silico developed by us is promising for the rational design of antituberculosis drugs.