Subtypes of adolescents with substance use disorders and psychiatric comorbidity using cluster and discriminant analysis of MMPI-A profiles

The main aim of this study was to replicate and extend previous results on subtypes of adolescents with substance use disorders (SUD), according to their Minnesota Multiphasic Personality Inventory for adolescents (MMPI-A) profiles. Sixty patients with SUD and psychiatric comorbidity (41.7% male, mean age = 15.9 years old) completed the MMPI-A, the Teen Addiction Severity Index (T-ASI), the Child Behaviour Checklist (CBCL), and were interviewed in order to determine DSMIV diagnoses and level of substance use. Mean MMPI-A personality profile showed moderate peaks in Psychopathic Deviate, Depression and Hysteria scales. Hierarchical cluster analysis revealed four profiles (acting-out, 35% of the sample; disorganized-conflictive, 15%; normative-impulsive, 15%; and deceptive-concealed, 35%). External correlates were found between cluster 1, CBCL externalizing symptoms at a clinical level and conduct disorders, and between cluster 2 and mixed CBCL internalized/externalized symptoms at a clinical level. Discriminant analysis showed that Depression, Psychopathic Deviate and Psychasthenia MMPI-A scales correctly classified 90% of the patients into the clusters obtained.

Subtypes of adolescents with substance use disorders and psychiatric comorbidity using cluster and discriminant analysis of MMPI-A profiles

The main aim of this study was to replicate and extend previous results on subtypes of adolescents with substance use disorders (SUD), according to their Minnesota Multiphasic Personality Inventory for adolescents (MMPI-A) profiles. Sixty patients with SUD and psychiatric comorbidity (41.7% male, mean age = 15.9 years old) completed the MMPI-A, the Teen Addiction Severity Index (T-ASI), the Child Behaviour Checklist (CBCL), and were interviewed in order to determine DSMIV diagnoses and level of substance use. Mean MMPI-A personality profile showed moderate peaks in Psychopathic Deviate, Depression and Hysteria scales. Hierarchical cluster analysis revealed four profiles (acting-out, 35% of the sample; disorganized-conflictive, 15%; normative-impulsive, 15%; and deceptive-concealed, 35%). External correlates were found between cluster 1, CBCL externalizing symptoms at a clinical level and conduct disorders, and between cluster 2 and mixed CBCL internalized/externalized symptoms at a clinical level. Discriminant analysis showed that Depression, Psychopathic Deviate and Psychasthenia MMPI-A scales correctly classified 90% of the patients into the clusters obtained.

Discriminant analysis of trace elements in normal, benign and malignant breast tissues measured by total reflection X-ray fluorescence

In this work total reflection X-ray fluorescence spectrometry has been employed to determine trace element concentrations in different human breast tissues (normal, normal adjacent, benign and malignant). A multivariate discriminant analysis of observed levels was performed in order to build a predictive model and perform tissue-type classifications. A total of 83 breast tissue samples were studied. Results showed the presence of Ca, Ti, Fe, Cu and Zn in all analyzed samples. All trace elements, except Ti, were found in higher concentrations in both malignant and benign tissues, when compared to normal tissues and normal adjacent tissues. In addition, the concentration of Fe was higher in malignant tissues than in benign neoplastic tissues. An opposite behavior was observed for Ca, Cu and Zn. Results have shown that discriminant analysis was able to successfully identify differences between trace element distributions from normal and malignant tissues with an overall accuracy of 80% and 65% for independent and paired breast samples respectively, and of 87% for benign and malignant tissues. (C) 2009 Elsevier B.V. All rights reserved.

Exploring the knowledge contained in neuroimages: Statistical discriminant analysis and automatic segmentation of the most significant changes

Objective: The aim of this article is to propose an integrated framework for extracting and describing patterns of disorders from medical images using a combination of linear discriminant analysis and active contour models. Methods: A multivariate statistical methodology was first used to identify the most discriminating hyperplane separating two groups of images (from healthy controls and patients with schizophrenia) contained in the input data. After this, the present work makes explicit the differences found by the multivariate statistical method by subtracting the discriminant models of controls and patients, weighted by the pooled variance between the two groups. A variational level-set technique was used to segment clusters of these differences. We obtain a label of each anatomical change using the Talairach atlas. Results: In this work all the data was analysed simultaneously rather than assuming a priori regions of interest. As a consequence of this, by using active contour models, we were able to obtain regions of interest that were emergent from the data. The results were evaluated using, as gold standard, well-known facts about the neuroanatomical changes related to schizophrenia. Most of the items in the gold standard was covered in our result set. Conclusions: We argue that such investigation provides a suitable framework for characterising the high complexity of magnetic resonance images in schizophrenia as the results obtained indicate a high sensitivity rate with respect to the gold standard. (C) 2010 Elsevier B.V. All rights reserved.

Discriminant analysis of skull morphometric characters in Apodemus sylvaticus, A-flavicollis, and A-alpicola (Mammalia; Rodentia) from the Alps

A total of 108 Apodemus skulls from Switzerland, Austria, Italy, France and Germany was studied to determine morphological characteristics useful in identifying individuals as Apodemus sylvaticus (Linnaeus, 1758), A. flavicollis (Melchior, 1834) or A. alpicola Heinrich, 1952. The original assignment of the samples to the three species was based on molar cusp morphology, body proportions, pelage coloration, and allozyme analysis. The 24 measured cranial characters used together accurately discriminated between the three species and correctly classified 100% of the individuals to species. A stepwise discriminant function analysis showed that 6 cranial characters are sufficient to differentiate between the three species, with a correct classification above 97%. Fisher's linear discriminant function coefficients can be used directly for classification of unknown specimens.

HETEROTIC GROUP FORMATION IN PSIDIUM GUAJAVA L. BY ARTIFICIAL NEURAL NETWORK AND DISCRIMINANT ANALYSIS

ABSTRACT The present study aimed at evaluating the heterotic group formation in guava based on quantitative descriptors and using artificial neural network (ANN). For such, we evaluated eight quantitative descriptors. Large genetic variability was found for the eight quantitative traits in the 138 genotypes of guava. The artificial neural network technique determined that the optimal number of groups was three. The grouping consistency was determined by linear discriminant analysis, which obtained classification percentage of the groups, with a value of 86 %. It was concluded that the artificial neural network method is effective to detect genetic divergence and heterotic group formation.

Identification of species of the Euterpe genus by rare earth elements using inductively coupled plasma mass spectrometry and linear discriminant analysis

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Copper ore type definition from Sossego Mine using X-ray diffraction and cluster analysis technique

This paper presents the classification of 110 copper ore samples from Sossego Mine, based on X-ray diffraction and cluster analysis. The comparison based on the position and the intensity of the diffracted peaks allowed the distinction of seven ore types, whose differences refer to the proportion of major minerals: quartz, feldspar, actinolite, iron oxides, mica and chlorite. There was a strong correlation between the grouping and the location of the samples in Sequeirinho and Sossego orebodies. This relationship is due to different types and intensities of hydrothermal alteration prevailing in each body, which reflect the mineralogical composition and thus the X-ray diffractograms of samples.

Business Clusters in Eastern Europe: Policy Analysis and Cluster Performance

Clusters have increasingly become an essential part of policy discourses at all levels, EU, national, regional, dealing with regional development, competitiveness, innovation, entrepreneurship, SMEs. These impressive efforts in promoting the concept of clusters on the policy-making arena have been accompanied by much less academic and scientific research work investigating the actual economic performance of firms in clusters, the design and execution of cluster policies and going beyond singular case studies to a more methodologically integrated and comparative approach to the study of clusters and their real-world impact. The theoretical background is far from being consolidated and there is a variety of methodologies and approaches for studying and interpreting this phenomenon while at the same time little comparability among studies on actual cluster performances. The conceptual framework of clustering suggests that they affect performance but theory makes little prediction as to the ultimate distribution of the value being created by clusters. This thesis takes the case of Eastern European countries for two reasons. One is that clusters, as coopetitive environments, are a new phenomenon as the previous centrally-based system did not allow for such types of firm organizations. The other is that, as new EU member states, they have been subject to the increased popularization of the cluster policy approach by the European Commission, especially in the framework of the National Reform Programmes related to the Lisbon objectives. The originality of the work lays in the fact that starting from an overview of theoretical contributions on clustering, it offers a comparative empirical study of clusters in transition countries. There have been very few examples in the literature that attempt to examine cluster performance in a comparative cross-country perspective. It adds to this an analysis of cluster policies and their implementation or lack of such as a way to analyse the way the cluster concept has been introduced to transition economies. Our findings show that the implementation of cluster policies does vary across countries with some countries which have embraced it more than others. The specific modes of implementation, however, are very similar, based mostly on soft measures such as funding for cluster initiatives, usually directed towards the creation of cluster management structures or cluster facilitators. They are essentially founded on a common assumption that the added values of clusters is in the creation of linkages among firms, human capital, skills and knowledge at the local level, most often perceived as the regional level. Often times geographical proximity is not a necessary element in the application process and cluster application are very similar to network membership. Cluster mapping is rarely a factor in the selection of cluster initiatives for funding and the relative question about critical mass and expected outcomes is not considered. In fact, monitoring and evaluation are not elements of the cluster policy cycle which have received a lot of attention. Bulgaria and the Czech Republic are the countries which have implemented cluster policies most decisively, Hungary and Poland have made significant efforts, while Slovakia and Romania have only sporadically and not systematically used cluster initiatives. When examining whether, in fact, firms located within regional clusters perform better and are more efficient than similar firms outside clusters, we do find positive results across countries and across sectors. The only country with negative impact from being located in a cluster is the Czech Republic.