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.

Approximating a class of combinatorial problems with rational objective function

In the late seventies, Megiddo proposed a way to use an algorithm for the problem of minimizing a linear function a(0) + a(1)x(1) + ... + a(n)x(n) subject to certain constraints to solve the problem of minimizing a rational function of the form (a(0) + a(1)x(1) + ... + a(n)x(n))/(b(0) + b(1)x(1) + ... + b(n)x(n)) subject to the same set of constraints, assuming that the denominator is always positive. Using a rather strong assumption, Hashizume et al. extended Megiddo`s result to include approximation algorithms. Their assumption essentially asks for the existence of good approximation algorithms for optimization problems with possibly negative coefficients in the (linear) objective function, which is rather unusual for most combinatorial problems. In this paper, we present an alternative extension of Megiddo`s result for approximations that avoids this issue and applies to a large class of optimization problems. Specifically, we show that, if there is an alpha-approximation for the problem of minimizing a nonnegative linear function subject to constraints satisfying a certain increasing property then there is an alpha-approximation (1 1/alpha-approximation) for the problem of minimizing (maximizing) a nonnegative rational function subject to the same constraints. Our framework applies to covering problems and network design problems, among others.

Hidrocarbonetos policíclicos aromáticos (HPAS) em cachaça, rum, uísque e álcool combustível

The concentration of 15 polycyclic aromatic hydrocarbons (PAHs) in 57 samples of distillates (cachaça, rum, whiskey, and alcohol fuel) has been determined by HPLC-Fluorescence detection. The quantitative analytical profile of PAHs treated by Partial Least Square - Discriminant Analysis (PLS-DA) provided a good classification of the studied spirits based on their PAHs content. Additionally, the classification of the sugar cane derivatives according to the harvest practice was obtained treating the analytical data by Linear Discriminant Analysis (LDA), using naphthalene, acenaphthene, fluorene, phenanthrene, anthracene, fluoranthene, pyrene, benz[a]anthracene, benz[b]fluoranthene, and benz[g,h,i]perylene, as a chemical descriptors.

Classification of breast masses using a committee machine of artificial neural networks

This work proposes a new approach using a committee machine of artificial neural networks to classify masses found in mammograms as benign or malignant. Three shape factors, three edge-sharpness measures, and 14 texture measures are used for the classification of 20 regions of interest (ROIs) related to malignant tumors and 37 ROIs related to benign masses. A group of multilayer perceptrons (MLPs) is employed as a committee machine of neural network classifiers. The classification results are reached by combining the responses of the individual classifiers. Experiments involving changes in the learning algorithm of the committee machine are conducted. The classification accuracy is evaluated using the area A. under the receiver operating characteristics (ROC) curve. The A, result for the committee machine is compared with the A, results obtained using MLPs and single-layer perceptrons (SLPs), as well as a linear discriminant analysis (LDA) classifier Tests are carried out using the student's t-distribution. The committee machine classifier outperforms the MLP SLP, and LDA classifiers in the following cases: with the shape measure of spiculation index, the A, values of the four methods are, in order 0.93, 0.84, 0.75, and 0.76; and with the edge-sharpness measure of acutance, the values are 0.79, 0.70, 0.69, and 0.74. Although the features with which improvement is obtained with the committee machines are not the same as those that provided the maximal value of A(z) (A(z) = 0.99 with some shape features, with or without the committee machine), they correspond to features that are not critically dependent on the accuracy of the boundaries of the masses, which is an important result. (c) 2008 SPIE and IS&T.

Variations on a theme: diversification of cuticular hydrocarbons in a clade of cactophilic Drosophila

Background: We characterized variation and chemical composition of epicuticular hydrocarbons (CHCs) in the seven species of the Drosophila buzzatii cluster with gas chromatography/mass spectrometry. Despite the critical role of CHCs in providing resistance to desiccation and involvement in communication, such as courtship behavior, mating, and aggregation, few studies have investigated how CHC profiles evolve within and between species in a phylogenetic context. We analyzed quantitative differences in CHC profiles in populations of the D. buzzatii species cluster in order to assess the concordance of CHC differentiation with species divergence. Results: Thirty-six CHC components were scored in single fly extracts with carbon chain lengths ranging from C(29) to C(39), including methyl-branched alkanes, n alkenes, and alkadienes. Multivariate analysis of variance revealed that CHC amounts were significantly different among all species and canonical discriminant function (CDF) analysis resolved all species into distinct, non-overlapping groups. Significant intraspecific variation was found in different populations of D. serido suggesting that this taxon is comprised of at least two species. We summarized CHC variation using CDF analysis and mapped the first five CHC canonical variates (CVs) onto an independently derived period (per) gene + chromosome inversion + mtDNA COI gene for each sex. We found that the COI sequences were not phylogenetically informative due to introgression between some species, so only per + inversion data were used. Positive phylogenetic signal was observed mainly for CV1 when parsimony methods and the test for serial independence (TFSI) were used. These results changed when no outgroup species were included in the analysis and phylogenetic signal was then observed for female CV3 and/or CV4 and male CV4 and CV5. Finally, removal of divergent populations of D. serido significantly increased the amount of phylogenetic signal as up to four out of five CVs then displayed positive phylogenetic signal. Conclusions: CHCs were conserved among species while quantitative differences in CHC profiles between populations and species were statistically significant. Most CHCs were species-, population-, and sex-specific. Mapping CHCs onto an independently derived phylogeny revealed that a significant portion of CHC variation was explained by species' systematic affinities indicating phylogenetic conservatism in the evolution of these hydrocarbon arrays, presumptive waterproofing compounds and courtship signals as in many other drosophilid species.

Musical genres: beating to the rhythms of different drums

Online music databases have increased significantly as a consequence of the rapid growth of the Internet and digital audio, requiring the development of faster and more efficient tools for music content analysis. Musical genres are widely used to organize music collections. In this paper, the problem of automatic single and multi-label music genre classification is addressed by exploring rhythm-based features obtained from a respective complex network representation. A Markov model is built in order to analyse the temporal sequence of rhythmic notation events. Feature analysis is performed by using two multi-variate statistical approaches: principal components analysis (unsupervised) and linear discriminant analysis (supervised). Similarly, two classifiers are applied in order to identify the category of rhythms: parametric Bayesian classifier under the Gaussian hypothesis (supervised) and agglomerative hierarchical clustering (unsupervised). Qualitative results obtained by using the kappa coefficient and the obtained clusters corroborated the effectiveness of the proposed method.

A boundary element formulation for analysis of elastoplastic plates with geometrical nonlinearity

In this paper a new boundary element method formulation for elastoplastic analysis of plates with geometrical nonlinearities is presented. The von Mises criterion with linear isotropic hardening is considered to evaluate the plastic zone. Large deflections are assumed but within the context of small strain. To derive the boundary integral equations the von Karman`s hypothesis is taken into account. An initial stress field is applied to correct the true stresses according to the adopted criterion. Isoparametric linear elements are used to approximate the boundary unknown values while triangular internal cells with linear shape function are adopted to evaluate the domain value influences. The nonlinear system of equations is solved by using an implicit scheme together with the consistent tangent operator derived along the paper. Numerical examples are presented to demonstrate the accuracy and the validity of the proposed formulation.

A decision support system to facilitate management of patients with acute gastrointestinal bleeding

Objective: To develop a model to predict the bleeding source and identify the cohort amongst patients with acute gastrointestinal bleeding (GIB) who require urgent intervention, including endoscopy. Patients with acute GIB, an unpredictable event, are most commonly evaluated and managed by non-gastroenterologists. Rapid and consistently reliable risk stratification of patients with acute GIB for urgent endoscopy may potentially improve outcomes amongst such patients by targeting scarce health-care resources to those who need it the most. Design and methods: Using ICD-9 codes for acute GIB, 189 patients with acute GIB and all. available data variables required to develop and test models were identified from a hospital medical records database. Data on 122 patients was utilized for development of the model and on 67 patients utilized to perform comparative analysis of the models. Clinical data such as presenting signs and symptoms, demographic data, presence of co-morbidities, laboratory data and corresponding endoscopic diagnosis and outcomes were collected. Clinical data and endoscopic diagnosis collected for each patient was utilized to retrospectively ascertain optimal management for each patient. Clinical presentations and corresponding treatment was utilized as training examples. Eight mathematical models including artificial neural network (ANN), support vector machine (SVM), k-nearest neighbor, linear discriminant analysis (LDA), shrunken centroid (SC), random forest (RF), logistic regression, and boosting were trained and tested. The performance of these models was compared using standard statistical analysis and ROC curves. Results: Overall the random forest model best predicted the source, need for resuscitation, and disposition with accuracies of approximately 80% or higher (accuracy for endoscopy was greater than 75%). The area under ROC curve for RF was greater than 0.85, indicating excellent performance by the random forest model Conclusion: While most mathematical models are effective as a decision support system for evaluation and management of patients with acute GIB, in our testing, the RF model consistently demonstrated the best performance. Amongst patients presenting with acute GIB, mathematical models may facilitate the identification of the source of GIB, need for intervention and allow optimization of care and healthcare resource allocation; these however require further validation. (c) 2007 Elsevier B.V. All rights reserved.

Forest structure in a mosaic of rainforest sites: The effect of fragmentation and recovery after clear cut

A variety of human-induced disturbances such as forest fragmentation and recovery after deforestation for pasture or agricultural activities have resulted in a complex landscape mosaic in the Una region of northeastern Brazil. Using a set of vegetation descriptors, we investigated the main structural changes observed in forest categories that comprise the major components of the regional landscape and searched for potential key descriptors that could be used to discriminate among different forest categories. We assessed the forest structure of five habitat categories defined as (I) interiors and (2) edges of large fragments of old-growth forest (>1000 ha), (3) interiors and (4) edges of small forest fragments (<100 ha), and (5) early secondary forests. Forest descriptors used here were: frequency of herbaceous lianas and woody climbers, number of standing dead trees, number of fallen trunks, litter depth, number of pioneer plants (early secondary and shade-intolerant species), vertical foliage stratification profile and distribution Of trees in different diameter classes. Edges and interiors of forest fragments were significantly different only in the number of standing dead trees. Secondary forests and edges of fragments showed differences in litter depth, fallen trunks and number of pioneer trees, and secondary forests were significantly different from fragment interiors in the number of standing dead trees and the number of pioneer trees. Horizontal and vertical structure evaluated via ordination analysis showed that fragment interiors, compared to secondary forests, were characterized by a greater number of medium (25-35 cm) and large (35-50 cm) trees and smaller numbers of thin trees (5-10 cm). There was great heterogeneity at the edges of small and large fragments, as these sites were distributed along almost the entire gradient. Most interiors of large and small fragments presented higher values of foliage densities at higher strata ( 15-20 m and at 20-25 m height), and lower densities at 1-5 m. All secondary forests and some fragment edge sites showed an opposite tendency. A discriminant function highlighted differences among forest categories, with transects of large fragment interiors and secondary forests representing two extremes along a disturbance gradient determined by foliage structure (densities at 15-20 m and 20-25 m), with the edges of both large and small fragments and the interiors of small fragments scattered across the gradient. The major underlying processes determining patterns of forest disturbance in the study region are discussed, highlighting the importance of forest fragments, independently of its size, as forests recovery after clear cut show a greatly distinct structure, with profound implications on fauna movements. (C) 2009 Elsevier BY. All rights reserved.

New species of the bee genus Augochlorodes Moure (Hymenoptera, Apidae s. I., Halictinae)

Five new species of the bee genus Augochlorodes from Brazil are described and the type species, A. turrifaciens Moure, 1958, is redescribed. The new species are: A. clementis sp. nov. (from the states of Parana, Santa Catarina and Rio Grande do Sul), A. incomitatus sp. nov. (Rio Grande do Sul), A. politus sp. nov. (Parana), A. rostratus sp. nov. (Rio Grande do Sul) and A. vachali sp. nov. (Parana, Santa Catarina, Sao Paulo). A discriminant function analysis using head measurements is presented to help distinguish the species. An identification key for the species is provided.

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.

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.

Cold Nuclear Matter Effects on J/psi Yields as a Function of Rapidity and Nuclear Geometry in d plus A Collisions at root S(NN)=200 GeV

We present measurements of J/psi yields in d + Au collisions at root S(NN) = 200 GeV recorded by the PHENIX experiment and compare them with yields in p + p collisions at the same energy per nucleon-nucleon collision. The measurements cover a large kinematic range in J/psi rapidity (-2.2 < y < 2.4) with high statistical precision and are compared with two theoretical models: one with nuclear shadowing combined with final state breakup and one with coherent gluon saturation effects. In order to remove model dependent systematic uncertainties we also compare the data to a simple geometric model. The forward rapidity data are inconsistent with nuclear modifications that are linear or exponential in the density weighted longitudinal thickness, such as those from the final state breakup of the bound state.

GLOBAL WELL-POSEDNESS AND NON-LINEAR STABILITY OF PERIODIC TRAVELING WAVES FOR A SCHRODINGER-BENJAMIN-ONO SYSTEM

The objective of this paper is two-fold: firstly, we develop a local and global (in time) well-posedness theory for a system describing the motion of two fluids with different densities under capillary-gravity waves in a deep water flow (namely, a Schrodinger-Benjamin-Ono system) for low-regularity initial data in both periodic and continuous cases; secondly, a family of new periodic traveling waves for the Schrodinger-Benjamin-Ono system is given: by fixing a minimal period we obtain, via the implicit function theorem, a smooth branch of periodic solutions bifurcating a Jacobian elliptic function called dnoidal, and, moreover, we prove that all these periodic traveling waves are nonlinearly stable by perturbations with the same wavelength.

Double generalized linear model for tissue culture proportion data: a Bayesian perspective

Joint generalized linear models and double generalized linear models (DGLMs) were designed to model outcomes for which the variability can be explained using factors and/or covariates. When such factors operate, the usual normal regression models, which inherently exhibit constant variance, will under-represent variation in the data and hence may lead to erroneous inferences. For count and proportion data, such noise factors can generate a so-called overdispersion effect, and the use of binomial and Poisson models underestimates the variability and, consequently, incorrectly indicate significant effects. In this manuscript, we propose a DGLM from a Bayesian perspective, focusing on the case of proportion data, where the overdispersion can be modeled using a random effect that depends on some noise factors. The posterior joint density function was sampled using Monte Carlo Markov Chain algorithms, allowing inferences over the model parameters. An application to a data set on apple tissue culture is presented, for which it is shown that the Bayesian approach is quite feasible, even when limited prior information is available, thereby generating valuable insight for the researcher about its experimental results.