Application of discriminant analysis-based model for prediction of risk of low back disorders due to workplace design in industrial jobs

The occupational exposure limits of different risk factors for development of low back disorders (LBDs) have not yet been established. One of the main problems in setting such guidelines is the limited understanding of how different risk factors for LBDs interact in causing injury, since the nature and mechanism of these disorders are relatively unknown phenomena. Industrial ergonomists' role becomes further complicated because the potential risk factors that may contribute towards the onset of LBDs interact in a complex manner, which makes it difficult to discriminate in detail among the jobs that place workers at high or low risk of LBDs. The purpose of this paper was to develop a comparative study between predictions based on the neural network-based model proposed by Zurada, Karwowski & Marras (1997) and a linear discriminant analysis model, for making predictions about industrial jobs according to their potential risk of low back disorders due to workplace design. The results obtained through applying the discriminant analysis-based model proved that it is as effective as the neural network-based model. Moreover, the discriminant analysis-based model proved to be more advantageous regarding cost and time savings for future data gathering.

Physiological effects of glyphosate over amino acid profile in conventional and transgenic soybean (Glycine max)

This paper compares the responses of conventional and transgenic soybean to glyphosate application in terms of the contents of 17 detectable soluble amino acids in leaves, analyzed by HPLC and fluorescence detection. Glutamate, histidine, asparagine, arginine + alanine, glycine + threonine and isoleucine increased in conventional soybean leaves when compared to transgenic soybean leaves, whereas for other amino acids, no significant differences were recorded. Univariate analysis allowed us to make an approximate differentiation between conventional and transgenic lines, observing the changes of some variables by glyphosate application. In addition, by means of the multivariate analysis, using principal components analysis (PCA), cluster analysis (CA) and linear discriminant analysis (LDA) it was possible to identify and discriminate different groups based on the soybean genetic origin. (C) 2011 Elsevier Inc. All rights reserved.

Quantitative Chemical Profile and Multivariate Statistical Analysis of Alembic Distilled Sugarcane Spirit Fractions

Concentrations of 39 organic compounds were determined in three fractions (head, heart and tail) obtained from the pot still distillation of fermented sugarcane juice. The results were evaluated using analysis of variance (ANOVA), Tukey's test, principal component analysis (PCA), hierarchical cluster analysis (HCA) and linear discriminant analysis (LDA). According to PCA and HCA, the experimental data lead to the formation of three clusters. The head fractions give rise to a more defined group. The heart and tail fractions showed some overlap consistent with its acid composition. The predictive ability of calibration and validation of the model generated by LDA for the three fractions classification were 90.5 and 100%, respectively. This model recognized as the heart twelve of the thirteen commercial cachacas (92.3%) with good sensory characteristics, thus showing potential for guiding the process of cuts.

Validation of the Brazilian Portuguese version of the Beck Depression Inventory-II in a community sample

Background: The Beck Depression Inventory (BDI) is used worldwide for detecting depressive symptoms. This questionnaire has been revised (1996) to match the DSM-IV criteria for a major depressive episode. We assessed the reliability and the validity of the Brazilian Portuguese version of the BDI-II for non-clinical adults. Methods: The questionnaire was applied to 60 college students on two occasions. Afterwards, 182 community-dwelling adults completed the BDI-II, the Self-Report Questionnaire, and the K10 Scale. Trained psychiatrists performed face-to-face interviews with the respondents using the Structured Clinical Interview (SCID-I), the Montgomery-angstrom sberg Depression Scale, and the Hamilton Anxiety Scale. Descriptive analysis, signal detection analysis (Receiver Operating Characteristics), correlation analysis, and discriminant function analysis were performed to investigate the psychometric properties of the BDI-II. Results: The intraclass correlation coefficient of the BDI-II was 0.89, and the Cronbach's alpha coefficient of internal consistency was 0.93. Taking the SCID as the gold standard, the cut-off point of 10/11 was the best threshold for detecting depression, yielding a sensitivity of 70% and a specificity of 87%. The concurrent validity (a correlation of 0.63-0.93 with scales applied simultaneously) and the predictive ability of the severity level (over 65% correct classification) were acceptable. Conclusion: The BDI-II is reliable and valid for measuring depressive symptomatology among Portuguese-speaking Brazilian non-clinical populations.

Multivariate analyses of UV-Vis absorption spectral data from cachaca wood extracts: a model to classify aged Brazilian cachacas according to the wood species used

Multivariate analyses of UV-Vis spectral data from cachaca wood extracts provide a simple and robust model to classify aged Brazilian cachacas according to the wood species used in the maturation barrels. The model is based on inspection of 93 extracts of oak and different Brazilian wood species by a non-aged cachaca used as an extraction solvent. Application of PCA (Principal Components Analysis) and HCA (Hierarchical Cluster Analysis) leads to identification of 6 clusters of cachaca wood extracts (amburana, amendoim, balsamo, castanheira, jatoba, and oak). LDA (Linear Discriminant Analysis) affords classification of 10 different wood species used in the cachaca extracts (amburana, amendoim, balsamo, cabreuva-parda, canela-sassafras, castanheira, jatoba, jequitiba-rosa, louro-canela, and oak) with an accuracy ranging from 80% (amendoim and castanheira) to 100% (balsamo and jequitiba-rosa). The methodology provides a low-cost alternative to methods based on liquid chromatography and mass spectrometry to classify cachacas aged in barrels that are composed of different wood species.

A comparative study on multiscale fractal dimension descriptors

Fractal theory presents a large number of applications to image and signal analysis. Although the fractal dimension can be used as an image object descriptor, a multiscale approach, such as multiscale fractal dimension (MFD), increases the amount of information extracted from an object. MFD provides a curve which describes object complexity along the scale. However, this curve presents much redundant information, which could be discarded without loss in performance. Thus, it is necessary the use of a descriptor technique to analyze this curve and also to reduce the dimensionality of these data by selecting its meaningful descriptors. This paper shows a comparative study among different techniques for MFD descriptors generation. It compares the use of well-known and state-of-the-art descriptors, such as Fourier, Wavelet, Polynomial Approximation (PA), Functional Data Analysis (FDA), Principal Component Analysis (PCA), Symbolic Aggregate Approximation (SAX), kernel PCA, Independent Component Analysis (ICA), geometrical and statistical features. The descriptors are evaluated in a classification experiment using Linear Discriminant Analysis over the descriptors computed from MFD curves from two data sets: generic shapes and rotated fish contours. Results indicate that PCA, FDA, PA and Wavelet Approximation provide the best MFD descriptors for recognition and classification tasks. (C) 2012 Elsevier B.V. All rights reserved.

Multivariate gene expression analysis reveals functional connectivity changes between normal/tumoral prostates

Abstract Background Prostate cancer is a leading cause of death in the male population, therefore, a comprehensive study about the genes and the molecular networks involved in the tumoral prostate process becomes necessary. In order to understand the biological process behind potential biomarkers, we have analyzed a set of 57 cDNA microarrays containing ~25,000 genes. Results Principal Component Analysis (PCA) combined with the Maximum-entropy Linear Discriminant Analysis (MLDA) were applied in order to identify genes with the most discriminative information between normal and tumoral prostatic tissues. Data analysis was carried out using three different approaches, namely: (i) differences in gene expression levels between normal and tumoral conditions from an univariate point of view; (ii) in a multivariate fashion using MLDA; and (iii) with a dependence network approach. Our results show that malignant transformation in the prostatic tissue is more related to functional connectivity changes in their dependence networks than to differential gene expression. The MYLK, KLK2, KLK3, HAN11, LTF, CSRP1 and TGM4 genes presented significant changes in their functional connectivity between normal and tumoral conditions and were also classified as the top seven most informative genes for the prostate cancer genesis process by our discriminant analysis. Moreover, among the identified genes we found classically known biomarkers and genes which are closely related to tumoral prostate, such as KLK3 and KLK2 and several other potential ones. Conclusion We have demonstrated that changes in functional connectivity may be implicit in the biological process which renders some genes more informative to discriminate between normal and tumoral conditions. Using the proposed method, namely, MLDA, in order to analyze the multivariate characteristic of genes, it was possible to capture the changes in dependence networks which are related to cell transformation.

On the energy conversion efficiency in magnetic hyperthermia applications: A new perspective to analyze the departure from the linear regime

The success of magnetic hyperthermia cancer treatments rely strongly on the magnetic properties of the nanoparticles and their intricate dependence on the externally applied field. This is particularly more so as the response departs from the low field linear regime. In this paper we introduce a new parameter, referred to as the efficiency in converting electromagnetic energy into thermal energy, which is shown to be remarkably useful in the analysis of the system response, especially when the power loss is investigated as a function of the applied field amplitude. Using numerical simulations of dynamic hysteresis, through the stochastic Landau-Lifshitz model, we map in detail the efficiency as a function of all relevant parameters of the system and compare the results with simple-yet powerful-predictions based on heuristic arguments about the relaxation time. (C) 2012 American Institute of Physics. [http://dx.doi.org/10.1063/1.4705392]

Dry matter production of Tanzania grass as a function of agrometeorological variables

The objective of this work was to develop and validate linear regression models to estimate the production of dry matter by Tanzania grass (Megathyrsus maximus, cultivar Tanzania) as a function of agrometeorological variables. For this purpose, data on the growth of this forage grass from 2000 to 2005, under dry-field conditions in Sao Carlos, SP, Brazil, were correlated to the following climatic parameters: minimum and mean temperatures, degree-days, and potential and actual evapotranspiration. Simple linear regressions were performed between agrometeorological variables (independent) and the dry matter accumulation rate (dependent). The estimates were validated with independent data obtained in Sao Carlos and Piracicaba, SP, Brazil. The best statistical results in the development and validation of the models were obtained with the agrometeorological parameters that consider thermal and water availability effects together, such as actual evapotranspiration, accumulation of degree-days corrected by water availability, and the climatic growth index, based on average temperature, solar radiation, and water availability. These variables can be used in simulations and models to predict the production of Tanzania grass.

Linear parameter varying control design for rotating systems supported by journal bearings

Linear parameter varying (LPV) control is a model-based control technique that takes into account time-varying parameters of the plant. In the case of rotating systems supported by lubricated bearings, the dynamic characteristics of the bearings change in time as a function of the rotating speed. Hence, LPV control can tackle the problem of run-up and run-down operational conditions when dynamic characteristics of the rotating system change significantly in time due to the bearings and high vibration levels occur. In this work, the LPV control design for a flexible shaft supported by plain journal bearings is presented. The model used in the LPV control design is updated from unbalance response experimental results and dynamic coefficients for the entire range of rotating speeds are obtained by numerical optimization. Experimental implementation of the designed LPV control resulted in strong reduction of vibration amplitudes when crossing the critical speed, without affecting system behavior in sub- or supercritical speeds. (C) 2012 Elsevier Ltd. All rights reserved.

Cognitive function in patients with chronic pain treated with opioids: characteristics and associated factors

Background The paucity of studies regarding cognitive function in patients with chronic pain, and growing evidence regarding the cognitive effects of pain and opioids on cognitive function prompted us to assess cognition via neuropsychological measurement in patients with chronic non-cancer pain treated with opioids. Methods In this cross-sectional study, 49 patients were assessed by Continuous Reaction Time, Finger Tapping, Digit Span, Trail Making Test-B and Mini-mental State Examination tests. Linear regressions were applied. Results Patients scored poorly in the Trail Making Test-B (mean?=?107.6?s, SD?=?61.0, cut-off?=?91?s); and adequately on all other tests. Several associations among independent variables and cognitive tests were observed. In the multiple regression analyses, the variables associated with statistically significant poor cognitive performance were female sex, higher age, lower annual income, lower schooling, anxiety, depression, tiredness, lower opioid dose, and more than 5?h of sleep the night before assessment (P?<?0.05). Conclusions Patients with chronic pain may have cognitive dysfunction related to some reversible factors, which can be optimized by therapeutic interventions.

Sleep desaturation and its relationship to lung function, exercise and quality of life in LAM

Background: Lymphangioleiomyomatosis (LAM) is characterised by progressive airway obstruction and hypoxaemia in young women. Although sleep may trigger hypoxaemia in patients with airway obstruction, it has not been previously investigated in patients with LAM. Methods: Consecutive women with lung biopsy proven LAM and absence of hypoxaemia while awake were evaluated with pulmonary function test, echocardiography, 6-min walk test, overnight full polysomnography, and Short Form 36 health-related quality-of-life questionnaire. Results: Twenty-five patients with (mean +/- SD) age 45 +/- 10 years, SpO(2) awake 95% +/- 2, forced expiratory volume in the first second (median-interquartile) FEV1 (% predicted) 77 (47-90) and carbonic monoxide diffusion capacity, DLCO (%) 55 (34-74) were evaluated. Six-minute walk test distance and minimum SpO(2) (median-interquartile) were, respectively, 447 m (411 -503) and 90% (82-94). Median interquartile apnoea-hypopnoea index was in the normal range 2 (1-5). Fourteen patients (56%) had nocturnal hypoxaemia (10% total sleep time with SpO(2) <90%), and the median sleep time spent with SpO(2) <90% was 136 (13-201) min. Sleep time spent with SpO(2) <90% correlated with the residual volume/total lung capacity ratio (r(s) = 0.5, p: 0.02), DLCO (r(s) = -0.7, p: 0.001), FEV1 (r(s) = -0.6, p: 0.002). Multivariate linear regression model showed that RV/TLC ratio was the most important functional variable related to sleep hypoxaemia. Conclusion: Significant hypoxaemia during sleep is common in LAM patients with normal SpO(2) while awake, especially among those with some degree of hyperinflation in lung function tests. (C) 2011 Published by Elsevier Ltd.

Fuzzy Connectedness Image Segmentation in Graph Cut Formulation: A Linear-Time Algorithm and a Comparative Analysis

A deep theoretical analysis of the graph cut image segmentation framework presented in this paper simultaneously translates into important contributions in several directions. The most important practical contribution of this work is a full theoretical description, and implementation, of a novel powerful segmentation algorithm, GC(max). The output of GC(max) coincides with a version of a segmentation algorithm known as Iterative Relative Fuzzy Connectedness, IRFC. However, GC(max) is considerably faster than the classic IRFC algorithm, which we prove theoretically and show experimentally. Specifically, we prove that, in the worst case scenario, the GC(max) algorithm runs in linear time with respect to the variable M=|C|+|Z|, where |C| is the image scene size and |Z| is the size of the allowable range, Z, of the associated weight/affinity function. For most implementations, Z is identical to the set of allowable image intensity values, and its size can be treated as small with respect to |C|, meaning that O(M)=O(|C|). In such a situation, GC(max) runs in linear time with respect to the image size |C|. We show that the output of GC(max) constitutes a solution of a graph cut energy minimization problem, in which the energy is defined as the a"" (a) norm ayenF (P) ayen(a) of the map F (P) that associates, with every element e from the boundary of an object P, its weight w(e). This formulation brings IRFC algorithms to the realm of the graph cut energy minimizers, with energy functions ayenF (P) ayen (q) for qa[1,a]. Of these, the best known minimization problem is for the energy ayenF (P) ayen(1), which is solved by the classic min-cut/max-flow algorithm, referred to often as the Graph Cut algorithm. We notice that a minimization problem for ayenF (P) ayen (q) , qa[1,a), is identical to that for ayenF (P) ayen(1), when the original weight function w is replaced by w (q) . Thus, any algorithm GC(sum) solving the ayenF (P) ayen(1) minimization problem, solves also one for ayenF (P) ayen (q) with qa[1,a), so just two algorithms, GC(sum) and GC(max), are enough to solve all ayenF (P) ayen (q) -minimization problems. We also show that, for any fixed weight assignment, the solutions of the ayenF (P) ayen (q) -minimization problems converge to a solution of the ayenF (P) ayen(a)-minimization problem (ayenF (P) ayen(a)=lim (q -> a)ayenF (P) ayen (q) is not enough to deduce that). An experimental comparison of the performance of GC(max) and GC(sum) algorithms is included. This concentrates on comparing the actual (as opposed to provable worst scenario) algorithms' running time, as well as the influence of the choice of the seeds on the output.

Linear viscoelasticity of immiscible blends: The application of creep

A new method to characterize the long-time linear relaxation mechanisms of immiscible blends based on creep experiment was developed. Small-amplitude oscillatory shear and incomplete creep/recovery experiments were combined to characterize immiscible blends of polypropylene with dispersed droplets of polystyrene. An experimental protocol was defined such that the full creep compliance function could be obtained while minimizing morphological changes. Dynamic experiments were performed to characterize the shorter time relaxation processes, and creep and recovery measurements were used to detect the longer time portions of the relaxation spectra. Extended retardation and relaxation spectra were constructed by combining these data. It was found that using this technique, very long-time relaxation peaks which were inaccessible with dynamic experiments alone could be detected. (C) 2012 The Society of Rheology. [http://dx.doi.org/10.1122/1.4720081]

On Wald Residuals in Generalized Linear Models

A rigorous asymptotic theory for Wald residuals in generalized linear models is not yet available. The authors provide matrix formulae of order O(n(-1)), where n is the sample size, for the first two moments of these residuals. The formulae can be applied to many regression models widely used in practice. The authors suggest adjusted Wald residuals to these models with approximately zero mean and unit variance. The expressions were used to analyze a real dataset. Some simulation results indicate that the adjusted Wald residuals are better approximated by the standard normal distribution than the Wald residuals.