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.

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.

Referral to post-acute care following traumatic brain injury (TBI) in the Australian context

The study aimed to describe the types of care allocated at the end of acute care to people diagnosed with TBI and to identify the factors associated with variations in referral to care. A retrospective analysis of medical records of 61 patients was conducted based on a sample from two hospitals. While 60.7% of the study sample were referred to formal rehabilitation care, this was primarily non-inpatient rehabilitation care (32.8%). Discriminant analysis was used to determine medical and non-medical predictors of referral. Results indicated that place of treatment and age contribute to group differences and were significant in separating the inpatient rehabilitation group from the non-inpatient and no rehabilitation groups. Review by a rehabilitation physician was associated with referral to inpatient rehabilitation but was not adequate to explain referral to non-inpatient rehabilitation. An in-depth exploration of post-acute referral is warranted to improve policy and practice in relation to continuity of care following TBI.

Atypical enteropathogenic Escherichia coli genomic background allows the acquisition of non-EPEC virulence factors

Atypical enteropathogenic Escherichia coli (aEPEC) has been associated with infantile diarrhea in many countries. The clonal structure of aEPEC is the object of active investigation but few works have dealt with its genetic relationship with other diarrheagenic E. coli (DEC). This study aimed to evaluate the genetic relationship of aEPEC with other DEC pathotypes. The phylogenetic relationships of DEC strains were evaluated by multilocus sequence typing. Genetic diversity was assessed by pulsed-field gel electrophoresis (PFGE). The phylogram showed that aEPEC strains were distributed in four major phylogenetic groups (A, B1, B2 and D). Cluster I ( group B1) contains the majority of the strains and other pathotypes [enteroaggregative, enterotoxigenic and enterohemorrhagic E. coli ( EHEC)]; cluster II ( group A) also contains enteroaggregative and diffusely adherent E. coli; cluster III ( group B2) has atypical and typical EPEC possessing H6 or H34 antigen; and cluster IV ( group D) contains aEPEC O55:H7 strains and EHEC O157:H7 strains. PFGE analysis confirmed that these strains encompass a great genetic diversity. These results indicate that aEPEC clonal groups have a particular genomic background - especially the strains of phylogenetic group B1 that probably made possible the acquisition and expression of virulence factors derived from non-EPEC pathotypes.

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

Análise ecológica dos acidentes e da violência letal em Vitória, ES

Objetivo: Analisar o contexto socioeconômico e sua relação com a incidência espacial da mortalidade devido à violência. Métodos: Foi realizado estudo do tipo ecológico no município de Vitória, ES, de 2000 a 2003, sobre a distribuição espacial da mortalidade por acidentes e violência, com base nas informações populacionais e socioeconômicas. Os dados sobre mortalidade foram relacionados a informações como local de residência da vítima, tipo de ocorrência, sexo e raça/cor. A análise das informações utilizou a média espacial, odds ratio e análise de cluster. Resultados: Ocorreram 828 óbitos por violência no período estudado, representando 17% do total de óbitos do município. Destes, 72% eram homicídios, 21,8% acidentes de transporte e 6% suicídios. O padrão das vítimas dos homicídios foi ser jovem, negro, do sexo masculino e residente em regiões mais pobres da cidade. Suicídios e acidentes de transporte acometeram vítimas mais velhas, brancas, do sexo feminino e residentes na área mais rica da cidade. Conclusões: O resultados mostram que a violência é um fenômeno que atinge todas as classes sociais, com destaque para as pessoas da raça negra e baixo nível socieconômico que têm maior chance de morte por homicídio; e brancos de nível socioeconômico mais elevado, suicídios e acidentes de transporte se sobressaem.

Towards Emotional Interaction: Using Movies to Automatically Learn Users' Emotional States

The HCI community is actively seeking novel methodologies to gain insight into the user’s experience during interaction with both the application and the content. We propose an emotional recognition engine capable of automatically recognizing a set of human emotional states using psychophysiological measures of the autonomous nervous system, including galvanic skin response, respiration, and heart rate. A novel pattern recognition system, based on discriminant analysis and support vector machine classifiers is trained using movies’ scenes selected to induce emotions ranging from the positive to the negative valence dimension, including happiness, anger, disgust, sadness, and fear. In this paper we introduce an emotion recognition system and evaluate its accuracy by presenting the results of an experiment conducted with three physiologic sensors.