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.

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.

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.

Shared and Unique Gene Expression in Systemic Lupus Erythematosus Depending on Disease Activity

Patients presenting with active Systemic lupus erythematosus (SLE) manifestations may exhibit distinct pathogenetic features in relation to inactive SLE. Also, cDNA microarrays may potentially discriminate the gene expression profile of a disease or disease variant. Therefore, we evaluated the expression profile of 4500 genes in peripheral blood lymphocytes (PBL) of SLE patients. We studied 11 patients with SLE (seven with active SLE and four with inactive SLE) and eight healthy controls. Total RNA was isolated from PBL, reverse transcribed into cDNA, and postlabeled with Cy3 fluorochrome. These probes were then hybridized to a glass slide cDNA microarray containing 4500 human IMAGE cDNA target sequences. An equimolar amount of total RNA from human cell lines served as reference. The microarray images were quantified, normalized, and analyzed using the R environment (ANOVA, significant analysis of microarrays, and cluster-tree view algorithms). Disease activity was assessed by the SLE disease activity index. Compared to the healthy controls, 104 genes in active SLE patients (80 repressed and 24 induced) and 52 genes in nonactive SLE patients (31 induced and 21 repressed) were differentially expressed. The modulation of 12 genes, either induced or repressed, was found in both disease variants; however, each disease variant had differential expression of different genes. Taken together, these results indicate that the two lupus variants studied have common and unique differentially expressed genes. Although the biological significance of the differentially expressed genes discussed above has not been completely understood, they may serve as a platform to further explore the molecular basis of immune deregulation in SLE.

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.

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.

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.

Distinção entre cachaças destiladas em alambiques e em colunas usando quimiometria

One hundred fifteen cachaça samples derived from distillation in copper stills (73) or in stainless steels (42) were analyzed for thirty five itens by chromatography and inductively coupled plasma optical emission spectrometry. The analytical data were treated through Factor Analysis (FA), Partial Least Square Discriminant Analysis (PLS-DA) and Quadratic Discriminant Analysis (QDA). The FA explained 66.0% of the database variance. PLS-DA showed that it is possible to distinguish between the two groups of cachaças with 52.8% of the database variance. QDA was used to build up a classification model using acetaldehyde, ethyl carbamate, isobutyl alcohol, benzaldehyde, acetic acid and formaldehyde as chemical descriptors. The model presented 91.7% of accuracy on predicting the apparatus in which unknown samples were distilled.

Morte neonatal precoce segundo complexidade hospitalar e rede SUS e não-SUS na Região Metropolitana de São Paulo, Brasil

O objetivo foi analisar o perfil dos recém-nascidos, mães e mortalidade neonatal precoce, segundo complexidade do hospital e vínculo com o Sistema Único de Saúde (SUS), na Região Metropolitana de São Paulo, Brasil. Estudo baseado em dados de nascidos vivos, óbitos e cadastro de hospitais. Para obter a tipologia de complexidade e o perfil da clientela, empregaram-se análise fatorial e de clusters. O SUS atende mais recém-nascidos de risco e mães com baixa escolaridade, pré-natal insuficiente e adolescentes. A probabilidade de morte neonatal precoce foi 5,6‰ nascidos vivos (65% maior no SUS), sem diferenças por nível de complexidade do hospital, exceto nos de altíssima (SUS) e média (não-SUS) complexidade. O diferencial de mortalidade neonatal precoce entre as duas redes é menor no grupo de recém-nascidos < 1.500g (22%), entretanto, a taxa é 131% mais elevada no SUS para os recém-nascidos > 2.500g. Há uma concentração de nascimentos de alto risco na rede SUS, contudo a diferença de mortalidade neonatal precoce entre a rede SUS e não-SUS é menor nesse grupo de recém-nascidos. Novos estudos são necessários para compreender melhor a elevada mortalidade de recém-nascidos > 2.500g no SUS.

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.

NEAR INFRARED SPECTROSCOPY FOR ESTIMATING SUGARCANE BAGASSE CONTENT IN MEDIUM DENSITY FIBERBOARD

Medium density fiberboard (MDF) is an engineered wood product formed by breaking down selected lignin-cellulosic material residuals into fibers, combining it with wax and a resin binder, and then forming panels by applying high temperature and pressure. Because the raw material in the industrial process is ever-changing, the panel industry requires methods for monitoring the composition of their products. The aim of this study was to estimate the ratio of sugarcane (SC) bagasse to Eucalyptus wood in MDF panels using near infrared (NIR) spectroscopy. Principal component analysis (PCA) and partial least square (PLS) regressions were performed. MDF panels having different bagasse contents were easily distinguished from each other by the PCA of their NIR spectra with clearly different patterns of response. The PLS-R models for SC content of these MDF samples presented a strong coefficient of determination (0.96) between the NIR-predicted and Lab-determined values and a low standard error of prediction (similar to 1.5%) in the cross-validations. A key role of resins (adhesives), cellulose, and lignin for such PLS-R calibrations was shown. PLS-DA model correctly classified ninety-four percent of MDF samples by cross-validations and ninety-eight percent of the panels by independent test set. These NIR-based models can be useful to quickly estimate sugarcane bagasse vs. Eucalyptus wood content ratio in unknown MDF samples and to verify the quality of these engineered wood products in an online process.

Comparison Between Single-Diode Low-Level Laser Therapy (LLLT) and LED Multi-Diode (Cluster) Therapy (LEDT) Applications Before High-Intensity Exercise

Background Data and Objective: There is anecdotal evidence that low-level laser therapy (LLLT) may affect the development of muscular fatigue, minor muscle damage, and recovery after heavy exercises. Although manufacturers claim that cluster probes (LEDT) maybe more effective than single-diode lasers in clinical settings, there is a lack of head-to-head comparisons in controlled trials. This study was designed to compare the effect of single-diode LLLT and cluster LEDT before heavy exercise. Materials and Methods: This was a randomized, placebo-controlled, double-blind cross-over study. Young male volleyball players (n = 8) were enrolled and asked to perform three Wingate cycle tests after 4 x 30 sec LLLT or LEDT pretreatment of the rectus femoris muscle with either (1) an active LEDT cluster-probe (660/850 nm, 10/30mW), (2) a placebo cluster-probe with no output, and (3) a single-diode 810-nm 200-mW laser. Results: The active LEDT group had significantly decreased post-exercise creatine kinase (CK) levels (-18.88 +/- 41.48U/L), compared to the placebo cluster group (26.88 +/- 15.18U/L) (p < 0.05) and the active single-diode laser group (43.38 +/- 32.90U/L) (p<0.01). None of the pre-exercise LLLT or LEDT protocols enhanced performance on the Wingate tests or reduced post-exercise blood lactate levels. However, a non-significant tendency toward lower post-exercise blood lactate levels in the treated groups should be explored further. Conclusion: In this experimental set-up, only the active LEDT probe decreased post-exercise CK levels after the Wingate cycle test. Neither performance nor blood lactate levels were significantly affected by this protocol of pre-exercise LEDT or LLLT.

Characterizing chaotic melodies in automatic music composition

In this paper, we initially present an algorithm for automatic composition of melodies using chaotic dynamical systems. Afterward, we characterize chaotic music in a comprehensive way as comprising three perspectives: musical discrimination, dynamical influence on musical features, and musical perception. With respect to the first perspective, the coherence between generated chaotic melodies (continuous as well as discrete chaotic melodies) and a set of classical reference melodies is characterized by statistical descriptors and melodic measures. The significant differences among the three types of melodies are determined by discriminant analysis. Regarding the second perspective, the influence of dynamical features of chaotic attractors, e.g., Lyapunov exponent, Hurst coefficient, and correlation dimension, on melodic features is determined by canonical correlation analysis. The last perspective is related to perception of originality, complexity, and degree of melodiousness (Euler's gradus suavitatis) of chaotic and classical melodies by nonparametric statistical tests. (c) 2010 American Institute of Physics. [doi: 10.1063/1.3487516]

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.