The Strength of Weak Parties

The Strength of Weak Parties The aim of this article is to fill some gaps in research on the Brazilian electoral arena. The current literature, by neglecting the study of party organization, ends up overlooking fundamental questions for understanding how the electoral process works. This study addressed two questions: How do Brazilian parties work? What is the impact of party organization on a party`s decision to launch or withhold a candidate in a given election? We intend to show that the parties have more life than many studies on our political system tend to show. This partisan life helps understand one of the central aspects of the electoral arena, that is, how pre-election coordination occurs.

Evaluating SVM and MLDA in the extraction of discriminant regions for mental state prediction

Pattern recognition methods have been successfully applied in several functional neuroimaging studies. These methods can be used to infer cognitive states, so-called brain decoding. Using such approaches, it is possible to predict the mental state of a subject or a stimulus class by analyzing the spatial distribution of neural responses. In addition it is possible to identify the regions of the brain containing the information that underlies the classification. The Support Vector Machine (SVM) is one of the most popular methods used to carry out this type of analysis. The aim of the current study is the evaluation of SVM and Maximum uncertainty Linear Discrimination Analysis (MLDA) in extracting the voxels containing discriminative information for the prediction of mental states. The comparison has been carried out using fMRI data from 41 healthy control subjects who participated in two experiments, one involving visual-auditory stimulation and the other based on bimanual fingertapping sequences. The results suggest that MLDA uses significantly more voxels containing discriminative information (related to different experimental conditions) to classify the data. On the other hand, SVM is more parsimonious and uses less voxels to achieve similar classification accuracies. In conclusion, MLDA is mostly focused on extracting all discriminative information available, while SVM extracts the information which is sufficient for classification. (C) 2009 Elsevier Inc. All rights reserved.

Hyperplane navigation: A method to set individual scores in fMRI group datasets

Recent studies have demonstrated that spatial patterns of fMRI BOLD activity distribution over the brain may be used to classify different groups or mental states. These studies are based on the application of advanced pattern recognition approaches and multivariate statistical classifiers. Most published articles in this field are focused on improving the accuracy rates and many approaches have been proposed to accomplish this task. Nevertheless, a point inherent to most machine learning methods (and still relatively unexplored in neuroimaging) is how the discriminative information can be used to characterize groups and their differences. In this work, we introduce the Maximum Uncertainty Linear Discrimination Analysis (MLDA) and show how it can be applied to infer groups` patterns by discriminant hyperplane navigation. In addition, we show that it naturally defines a behavioral score, i.e., an index quantifying the distance between the states of a subject from predefined groups. We validate and illustrate this approach using a motor block design fMRI experiment data with 35 subjects. (C) 2008 Elsevier Inc. All rights reserved.

Classification System of Raman Spectra using Cluster Analysis to Diagnose Coronary Artery Lesions

The traditional methods employed to detect atherosclerotic lesions allow for the identification of lesions; however, they do not provide specific characterization of the lesion`s biochemistry. Currently, Raman spectroscopy techniques are widely used as a characterization method for unknown substances, which makes this technique very important for detecting atherosclerotic lesions. The spectral interpretation is based on the analysis of frequency peaks present in the signal; however, spectra obtained from the same substance can show peaks slightly different and these differences make difficult the creation of an automatic method for spectral signal analysis. This paper presents a signal analysis method based on a clustering technique that allows for the classification of spectra as well as the inference of a diagnosis about the arterial wall condition. The objective is to develop a computational tool that is able to create clusters of spectra according to the arterial wall state and, after data collection, to allow for the classification of a specific spectrum into its correct cluster.

Phenotypic and functional characterization of pulmonary macrophages subpopulations after intratracheal injection of Paracoccidioides brasiliensis cell wall components

A shift in the activation of pulmonary macrophages characterized by an increase of IL-1, INF-alpha and IL-6 production has been induced in mice infected with Paracoccidioides brasiliensis. It is still unclear whether a functional shift in the resident alveolar macrophage population would be responsible for these observations due to the expression of cell surface molecules. We investigated pulmonary macrophages by flow cytometry from mice treated with P. brasiliensis derivatives by intratracheal route. In vivo labeling with the dye PKH26GL was applied to characterize newly recruited pulmonary macrophages from the bloodstream. Pulmonary macrophages from mice inflamed with P. brasiliensis derivatives showed a high expression of the surface antigens CD11b/CD18 and CD23 among several cellular markers. The expression of these markers indicated a pattern of activation of a subpopulation characterized as CD11b(+) or CD23(+), which was modulated in vitro by IFN-gamma and IL-4. Analysis of monocytes labelled with PKH26GL demonstrated that CD11b(+) cells did infiltrate the lung exhibiting a proinflammatoni pattern of activation, whereas CD23(+) cells were considered to be resident in the lung. These findings may contribute to better understand the pathology of lung inflammation caused by P. brasiliensis infection. (C) 2010 Elsevier GmbH. All rights reserved.