911 resultados para Multiple discriminant analysis


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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.

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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.

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Background: It is recognized that the growing epidemic of metabolic syndrome is related to dietary and lifestyle changes. Objective: The purpose of this study was to evaluate short-term application of nutritional counseling in women with metabolic syndrome. Methods: This follow-up study was conducted from September to November 2008 with thirty three women >= 35 years old screened clinically for nutritional counseling. Dietary intake was reported, and biochemical and body composition measures were taken at baseline and after three months of follow-up. Results: Of the 33 women evaluated, 29 patients completed the study. The prevalence of type 2 diabetes mellitus, hypertension, dyslipidemia, and obesity was high at 38%, 72.4%, 55.2%, and 75.8%, respectively. At the end of three-months of follow-up, a significant decline in body mass index, waist circumference, triceps skinfold, and triglycerides was observed, as was an increase in calcium and vitamin D intake. The multiple regression analysis showed that changes in body mass index, triceps skinfold, waist circumference and triglyceride levels after nutritional intervention were positively associated with changes in anthropometric (loss of body weight) and biochemical (decrease of TG/HDL-c ratio) parameters. Moreover, waist circumference changes were negatively associated with changes in calcium and vitamin D intake. Conclusion: Short-term nutritional counseling improved some factors of metabolic syndrome. Moreover, the increases in calcium and vitamin D consumption can be associated with the improvement in markers of metabolic syndrome.

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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]

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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.

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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.

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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.

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Performance of different immobilized lipases in palm oil biodiesel synthesis. Optimized conditions for palm oil and ethanol enzymatic biodiesel synthesis were determined with different immobilized lipases SiO(2)-PVA-immobilized lipase from Pseudomonas fluorescens and acrylic resin-immobilized lipase, Novozym (R) 435, from Candida antartica, in solvent-free medium. A full factorial design assessed the influence of temperature (42 - 58 degrees C) and ethanol: palm oil (6:1 - 18:1) molar ratio on the transesterification yield. Main effects were adjusted by multiple regression analysis to linear models and the maximum transesterification yield was obtained at 42 degrees C and 18:1 ethanol: palm oil molar ratio. Mathematical models featuring total yield for each immobilized lipase were suitable to describe the experimental results.

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Data from 9 studies were compiled to evaluate the effects of 20 yr of selection for postweaning weight (PWW) on carcass characteristics and meat quality in experimental herds of control Nellore (NeC) and selected Nellore (NeS), Caracu (CaS), Guzerah (GuS), and Gir (GiS) breeds. These studies were conducted with animals from a genetic selection program at the Experimental Station of Sertaozinho, Sao Paulo State, Brazil. After the performance test (168 d postweaning), bulls (n = 490) from the calf crops born between 1992 and 2000 were finished and slaughtered to evaluate carcass traits and meat quality. Treatments were different across studies. A meta-analysis was conducted with a random coefficients model in which herd was considered a fixed effect and treatments within year and year were considered as random effects. Either calculated maturity degree or initial BW was used interchangeably as the covariate, and least squares means were used in the multiple-comparison analysis. The CaS and NeS had heavier (P = 0.002) carcasses than the NeC and GiS; GuS were intermediate. The CaS had the longest carcass (P < 0.001) and heaviest spare ribs (P < 0.001), striploin (P < 0.001), and beef plate (P = 0.013). Although the body, carcass, and quarter weights of NeS were similar to those of CaS, NeS had more edible meat in the leg region than did CaS bulls. Selection for PWW increased rib-eye area in Nellore bulls. Selected Caracu had the lowest (most favorable) shear force values compared with the NeS (P = 0.003), NeC (P = 0.005), GuS (P = 0.003), and GiS (P = 0.008). Selection for PWW increased body, carcass, and meat retail weights in the Nellore without altering dressing percentage and body fat percentage.

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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.

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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.

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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.

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Multifrequency bioimpedance analysis has the potential to provide a non-invasive technique for determining body composition in live cattle. A bioimpedance meter developed for use in clinical medicine was adapted and evaluated in 2 experiments using a total of 31 cattle. Prediction equations were obtained for total body water, extracellular body water, intracellular body water, carcass water and carcass protein. There were strong correlations between the results obtained through chemical markers and bioimpedance analysis when determined in cattle that had a wide range of liveweights and conditions. The r(2) values obtained were 0.87 and 0.91 for total body water and extracellular body water respectively. Bioimpedance also correlated with carcass water, measured by chemical analysis (r(2) = 0.72), but less well with carcass protein (r(2) = 0.46). These correlations were improved by inclusion of liveweight and sex as variables in multiple regression analysis. However, the resultant equations were poor predictors of protein and water content in the carcasses of a group of small underfed beef cattle, that had a narrow range of liveweights. In this case, although there was no statistical difference between the predicted and measured values overall, bioimpedance analysis did not detect the differences in carcass protein between the 2 groups that were apparent following chemical analysis. Further work is required to determine the sensitivity of the technique in small underfed cattle, and its potential use in heavier well fed cattle close to slaughter weight.

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Back,ground To examine the role of long-term swimming exercise on regional and total body bone mineral density (BMD) in men. Methods. Experimental design: Cross-sectional. Setting: Musculoskeletal research laboratory at a medical center, Participants:We compared elite collegiate swimmers (n=11) to age-, weight-, and height-matched non-athletic controls (n=11), Measures: BMD (g/cm(2)) of the lumbar spine L2-4, proximal femur (femoral neck, trochanter, Ward's triangle), total body and various subregions of the total body, as well as regional and total body fat and bone mineral-free lean mass (LM) was assessed by dual-energy X-ray absorptiometry (DXA, Hologic QDR 1000/W). Results. Swimmers, who commenced training at 10.7+/-3.7 yrs (mean+/-SD) and trained for 24.7+/-4.2 hrs per week, had a greater amount of LM (p<0.05), lower fat mass (p<0.001) and percent body fat (9.5 vs 16.2 %, p<0.001) than controls. There was no significant difference between groups for regional or total body BRID, In stepwise multiple regression analysis, body weight was a consistent independent predictor of regional and total body BMD, Conclusions. These results suggest that long-term swimming is not an osteogenic mode of training in college-aged males. This supports our previous findings in young female swimmers who displayed no bone mass benefits despite long-standing athletic training.

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We previously described significant changes in GH-binding protein (GHBP) in pathological human pregnancy. There was a substantial elevation of GHBP in cases of noninsulin-dependent diabetes mellitus and a reduction in insulin-dependent diabetes mellitus. GHBP has the potential to modulate the proportion of free placental GH (PGH) and hence the impact on the maternal GH/insulin-like growth factor I (IGF-I) axis, fetal growth, and maternal glycemic status. The present study was undertaken to investigate the relationship among glycemia, GHBP, and PGH during pregnancy and to assess the impact of GHBP on the concentration of free PGH. We have extended the analysis of specimens to include measurements of GHBP, PGH, IGF-I, IGF-II, IGF-binding protein-1 (IGFBP-1), IGFSP-2, and IGFBP-3 and have related these to maternal characteristics, fetal growth, and glycemia. The simultaneous measurement of GHBP and PGH has for the first time allowed calculation of the free component of PGH and correlation of the free component to indexes of fetal growth and other endocrine markers. PGH, free PGH, IGF-I, and IGF-II were substantially decreased in IUGR at 28-30 weeks gestation (K28) and 36-38 weeks gestation (K36). The mean concentration (+/-SEM) of total PGH increased significantly from K28 to K36 (30.0 +/- 2.2 to 50.7 +/- 6.2 ng/mL; n = 40), as did the concentration of free PGH (23.4 +/- 2.3 to 43.7 +/- 6.0 ng/mL; n = 38). The mean percentage of free PGH was significantly less in IUGR than in normal subjects (67% vs. 79%; P < 0.01). Macrosomia was associated with an increase in these parameters that did not reach statistical significance. Multiple regression analysis revealed that PGH/IGF-I and IGFBP-5 account for 40% of the variance in birth weight. IGFBP-3 showed a significant correlation with IGF-I, IGF-II, and free and total PGK at K28 and K36. Noninsulin-dependent diabetes mellitus patients had a lower mean percentage of free PGH (65%; P < 0.01), and insulin-dependent diabetics had a higher mean percentage of free PGH (87%; P < 0.01) than normal subjects. Mean postprandial glucose at K28 correlated positively with PGH and free PGH (consistent with the hyperglycemic action of GH). GHBP correlated negatively with both postprandial and fasting glucose. Although GHBP correlated negatively with PGH (r = -0.52; P