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.

Laser-induced breakdown spectroscopy and chemometrics for classification of toys relying on toxic elements

Quality control of toys for avoiding children exposure to potentially toxic elements is of utmost relevance and it is a common requirement in national and/or international norms for health and safety reasons. Laser-induced breakdown spectroscopy (LIBS) was recently evaluated at authors` laboratory for direct analysis of plastic toys and one of the main difficulties for the determination of Cd. Cr and Pb was the variety of mixtures and types of polymers. As most norms rely on migration (lixiviation) protocols, chemometric classification models from LIBS spectra were tested for sampling toys that present potential risk of Cd, Cr and Pb contamination. The classification models were generated from the emission spectra of 51 polymeric toys and by using Partial Least Squares - Discriminant Analysis (PLS-DA), Soft Independent Modeling of Class Analogy (SIMCA) and K-Nearest Neighbor (KNN). The classification models and validations were carried out with 40 and 11 test samples, respectively. Best results were obtained when KNN was used, with corrected predictions varying from 95% for Cd to 100% for Cr and Pb. (C) 2011 Elsevier B.V. All rights reserved.

Inter-individual variability in temporal structure of the basketball shoot

This study analyzed inter-individual variability of the temporal structure applied in basketball throwing. Ten experienced male athletes in basketball throwing were filmed and a number of kinematic movement parameters analyzed. A biomechanical model provided the relative timing of the shoulder, elbow and wrist joint movements. Inter-individual variability was analyzed using sequencing and relative timing of tem phases of the throw. To compare the variability of the movement phases between subjects a discriminant analysis and an ANOVA were applied. The Tukey test was applied to determine where differences occurred. The significance level was p = 0.05. Inter-individual variability was explained by three concomitant factors: (a) a precision control strategy, (b) a velocity control strategy and (c) intrinsic characteristics of the subjects. Therefore, despite the fact that some actions are common to the basketball throwing pattern each performed demonstrated particular and individual characteristics.

Greenhouse gases emission from soil contaminated with automobile industry residue in Brazil

Solid waste of the automobile industry containing large amounts of heavy metals might affect the emission of greenhouse gases (GHG) when applied to the soil. Accumulation of inorganic chemical elements in the environment generally occurs due to human activity (industry, agriculture, mining and waste landfills). Residues from human activities may release heavy metals to the soil solution, causing toxicity to plants and other soil organisms. Heavy metals may also be adsorbed to clay minerals and/or complexed by the soil organic matter, becoming a potential source of pollutants. Not much is known about the behavior of solid wastes in tropical soil as regarded as source of greenhouse gases (GHG). The emission of GHG (CO(2), CH(4) and N(2)O) was evaluated in incubated soil samples collected in an area contaminated with a solid residue from an automobile industry. Samples were randomly collected at 0 to 0.2 m (a mix of soil and residue), 0.2 to 0.4 m (only residue) and 0.4 to 0.6 m (only soil). A contiguous uncontaminated area, cultivated with sugarcane, was also sampled following the same protocol. Canonical Discriminant Analysis and Principal Component Analysis were applied to the data to evaluate the GHG emission rates. Emission rates of GHG were greater in the samples from the contaminated than the sugarcane area, particularly high during the first days of incubation. CO(2) emissions were greater in samples collected at the upper layer for both areas, while CH(4) and N(2)O emissions were similar in all samples. The emission rates of CH(4) were the most efficient variables to differentiate contaminated and uncontaminated areas.

Influence of magnesium status and magnesium intake on the blood glucose control in patients with type 2 diabetes

Background & aims: This study was undertaken to assess magnesium intake and magnesium status in patients with type 2 diabetes, and to identify the parameters that best predict alterations in fasting glucose and plasma magnesium. Methods: A cross-sectional study was carried out in patients with type 2 diabetes (n = 51; 53.6 +/- 10.5 y) selected within the inclusion factors, at the University Hospital Onofre Lopes. Magnesium intake was assessed by three 24-h recalls. Urine, plasma and erythrocytes magnesium, fasting and 2-h postprandial glucose, HbA1, microalbuminuria, proteinuria, and serum and urine creatinine were measured. Results: Mean magnesium intake (9.37 +/- 1.76 mmol/d), urine magnesium (2.80 +/- 1.51 mmol/d), plasma magnesium (0.71 +/- 0.08 mmol/L) and erythrocyte magnesium (1.92 +/- 0.23 mmol/L) levels were low. Seventy-seven percent of participants presented one or more magnesium status parameters below the cut-off points of 3.00 mmol/L for urine, 0.75 mmol/L for plasma and 1.65 mmol/L for erythrocytes. Subjects presented poor blood glucose control with fasting glucose of 8.1 +/- 3.7 mmol/L, 2-h postprandial glucose of 11.1 +/- 5.1 mmol/L, and HbA1 of 11.4 +/- 3.0%. The parameters that influenced fasting glucose were urine, plasma and dietary magnesium, while plasma magnesium was influenced by creatinine clearance. Conclusions: Magnesium status was influenced by kidney depuration and was altered in patients with type 2 diabetes, and magnesium showed to play an important role in blood glucose control. (C) 2011 Elsevier Ltd and European Society for Clinical Nutrition and Metabolism. All rights reserved.

A chemometric study on the analgesic activity of cannabinoid compounds using SDA, KNN and SIMCA methods

The supervised pattern recognition methods K-Nearest Neighbors (KNN), stepwise discriminant analysis (SDA), and soft independent modelling of class analogy (SIMCA) were employed in this work with the aim to investigate the relationship between the molecular structure of 27 cannabinoid compounds and their analgesic activity. Previous analyses using two unsupervised pattern recognition methods (PCA-principal component analysis and HCA-hierarchical cluster analysis) were performed and five descriptors were selected as the most relevants for the analgesic activity of the compounds studied: R (3) (charge density on substituent at position C(3)), Q (1) (charge on atom C(1)), A (surface area), log P (logarithm of the partition coefficient) and MR (molecular refractivity). The supervised pattern recognition methods (SDA, KNN, and SIMCA) were employed in order to construct a reliable model that can be able to predict the analgesic activity of new cannabinoid compounds and to validate our previous study. The results obtained using the SDA, KNN, and SIMCA methods agree perfectly with our previous model. Comparing the SDA, KNN, and SIMCA results with the PCA and HCA ones we could notice that all multivariate statistical methods classified the cannabinoid compounds studied in three groups exactly in the same way: active, moderately active, and inactive.

Assessment of vocational guidance: The Berufsbilder test

The object of this study is to assess informative possibilities of some technical indicators of the Test of Photos of Professions (BBT - Berufsbilder test), a projective method to clarify professional inclination, proposed by Martin Achtnich. This psychological evaluation technique is composed of 96 photos of professionals, performing various types of activities. The test subject classifies the photos into three groups: positive (agreeable), negative (disagreeable) and indifferent (neutral). Among those chosen positively, five preferences are chosen and a story is developed that includes them, an activity that is requested two times during the Vocational Guidance process: in the beginning (or middle) and at the end of the intervention. In this study, 160 stories were created by 80 youths, between 15 and 20 years of age, in public and private schools in a mid-sized Brazilian city. The stories were compared in three analytical categories: protagonist, professional conflict and resolution. The results were submitted to Wilcoxon nonparametric statistical analysis (p < .05), significant and relevant indicators of resolution being found in the process of occupational choice. This technical resource was shown, from this empirical evidence, to be promising for use in evaluation of intervention processes of Vocational Guidance.

Classification model based on Raman spectra of selected morphological and biochemical tissue constituents for identification of atherosclerosis in human coronary arteries

This study presents the results of Raman spectroscopy applied to the classification of arterial tissue based on a simplified model using basal morphological and biochemical information extracted from the Raman spectra of arteries. The Raman spectrograph uses an 830-nm diode laser, imaging spectrograph, and a CCD camera. A total of 111 Raman spectra from arterial fragments were used to develop the model, and those spectra were compared to the spectra of collagen, fat cells, smooth muscle cells, calcification, and cholesterol in a linear fit model. Non-atherosclerotic (NA), fatty and fibrous-fatty atherosclerotic plaques (A) and calcified (C) arteries exhibited different spectral signatures related to different morphological structures presented in each tissue type. Discriminant analysis based on Mahalanobis distance was employed to classify the tissue type with respect to the relative intensity of each compound. This model was subsequently tested prospectively in a set of 55 spectra. The simplified diagnostic model showed that cholesterol, collagen, and adipocytes were the tissue constituents that gave the best classification capability and that those changes were correlated to histopathology. The simplified model, using spectra obtained from a few tissue morphological and biochemical constituents, showed feasibility by using a small amount of variables, easily extracted from gross samples.

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.

Altered endothelial function following the Fontan procedure

Objective: Thrombosis has been widely described after the Fontan procedure. The vascular endothelium plays a central role in the control of coagulation and fibrinolysis. The aim of this study was to investigate if patients undergoing a modified Fontan procedure have impaired endothelial function and fibrinolysis in the late postoperative course. Patients and methods: We compared 23 patients aged from 7 to 26 years with age-matched healthy volunteers, collecting blood samples prior to and following standardized venous occlusion testing. Plasma levels of von Willebrand factor antigen, tissue-type plasminogen activator antigen, plasminogen activator inhibitor-1, and D-dimer were measured with enzyme-linked immunosorbent assay. Results: We found increased plasma levels of von Willebrand factor antigen in patients when compared to controls (p = 0.003). At the basal condition, concentrations of tissue-type plasminogen activator antigen and plasminogen activator inhibitor-1 antigen in the plasma, as well as their activity, were not significantly different between patients and controls. Following venous occlusion, concentrations of tissue-type plasminogen activator antigen in the plasma were significantly increased both in patients and controls, compared to pre-occlusion values. D-dimer was within the reference range. Multivariate discriminant analysis differentiated patients and their controls on the basis of differences for plasminogen activator inhibitor-1 and von Willebrand factor antigen (p = 0.0016). Conclusions: Our data suggest that patients with the Fontan circulation may have endothelial dysfunction, as indicated by raised levels of von Willebrand factor. Fibrinolysis seems to be relatively preserved, as suggested by appropriate response to venous occlusion.

Characteristics of women who frequently under report their energy intake: a doubly labelled water study

Background/Objectives: We applied three dietary assessment methods and aimed at obtaining a set of physical, social and psychological variables that can discriminate those individuals who did not underreport (`never under-reporters`), those who underreported in one dietary assessment method (`occasional under-reporters`) and those who underreported in two or three dietary assessment methods (`frequent under-reporters`). Participants/Methods: Sixty-five women aged 18-57 years were recruited for this study. Total energy expenditure was determined by doubly labelled water, and energy intake was estimated by three 24-h diet recalls, 3-day food records and a food frequency questionnaire. A multiple discriminant analysis was used to identify which of those variables better discriminated the three groups: body mass index (BMI), income, education, social desirability, nutritional knowledge, dietary restraint, physical activity practice, body dissatisfaction and binge-eating symptoms. Results: Twenty-three participants were `never under-reporters`. Twenty-four participants were `occasional under-reporters` and 18 were `frequent under-reporters`. Four variables entered the discriminant model: income, BMI, social desirability and body dissatisfaction. According to potency indices, income contributed the most to the total discriminant power, followed in decreasing order by social desirability score, BMI and body dissatisfaction. Income, social desirability and BMI were the characteristics that mainly separated the `never under-reporters` from the under-reporters (occasional or frequent). Body dissatisfaction better discriminated the `occasional under-reporters` from the `frequent under-reporters`. Conclusions: `Frequent under-reporters` have a greater BMI, social desirability score, body dissatisfaction score and lower income. These four variables seemed to be able to discriminate individuals who are more prone to systematic under reporting. European Journal of Clinical Nutrition (2009) 63, 1192-1199; doi:10.1038/ejcn.2009.54; published online 15 July 2009

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.

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