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.

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.

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.

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.

Population divergence of Melipona scutellaris (Hymenoptera: Meliponina) in two restricted areas in Bahia, Brazil

Melipona scutellaris Latreille has great economic and ecological importance, especially because it is a pollinator of native plant species. Despite the importance of this species, there is little information about the conservation status of their populations. The objective of this study was to assess the diversity in populations of M. scutellaris coming from a Semideciduous Forest Fragment and an Atlantic Forest Fragment in the Northeast Brazil, through geometric morphometric analysis of wings in worker bees. In each area, worker bees were collected from 10 colonies, 10 workers per colony. To assess the diversity on the right wings of worker bees, 15 landmarks were plotted and the measures were used in analysis of variance and multivariate analysis, principal component analysis, discriminant analysis and clustering analysis. There were significant differences in the shape of the wing venation patterns between colonies of two sites (Wilk's lambda = 0.000006; p < 0.000001), which is probably due to the geographical distance between places of origin which impedes the gene flow between them. It indicates that inter and intrapopulation morphometric variability exists (p < 0.000001) in M. scutellaris coming from two different biomes, revealing the existence of diversity in these populations, which is necessary for the conservation of this bee species.

Four-gene expression model predictive of lymph node metastases in oral squamous cell carcinoma

Background. Previous knowledge of cervical lymph node compromise may be crucial to choose the best treatment strategy in oral squamous cell carcinoma (OSCC). Here we propose a set four genes, whose mRNA expression in the primary tumor predicts nodal status in OSCC, excluding tongue. Material and methods. We identified differentially expressed genes in OSCC with and without compromised lymph nodes using Differential Display RT-PCR. Known genes were chosen to be validated by means of Northern blotting or real time RT-PCR (qRT-PCR). Thereafter we constructed a Nodal Index (NI) using discriminant analysis in a learning set of 35 patients, which was further validated in a second independent group of 20 patients. Results. Of the 63 differentially expressed known genes identified comparing three lymph node positive (pN+) and three negative (pN0) primary tumors, 23 were analyzed by Northern analysis or RT-PCR in 49 primary tumors. Six genes confirmed as differentially expressed were used to construct a NI, as the best set predictive of lymph nodal status, with the final result including four genes. The NI was able to correctly classify 32 of 35 patients comprising the learning group (88.6%; p = 0.009). Casein kinase 1alpha1 and scavenger receptor class B, member 2 were found to be up regulated in pN + group in contrast to small proline-rich protein 2B and Ras-GTPase activating protein SH3 domain-binding protein 2 which were upregulated in the pN0 group. We validated further our NI in an independent set of 20 primary tumors, 11 of them pN0 and nine pN+ with an accuracy of 80.0% (p = 0.012). Conclusions. The NI was an independent predictor of compromised lymph nodes, taking into the consideration tumor size and histological grade. The genes identified here that integrate our "Nodal Index" model are predictive of lymph node metastasis in OSCC.

Electrospray Ionization Mass Spectrometry Fingerprinting of Extracts of the Leaves of Arrabidaea chica

Arrabidaea chica (crajiru) is an important Amazonian plant. Its extracts are used as red pigments, antimicrobial agents and astringents. Three different varieties of this species are cultivated in the Amazon region. In this work, direct infusions of A. chica extracts from these three varieties were analyzed via electrospray ionization mass spectrometry (ESI(+)-MS) fingerprinting. Derived data from the spectra were classified by using a multivariate method (PLS-DA, partial least squares-discriminant analysis). The direct method that is herein presented relies on extraction of dry, powdered leaves with acidified methanol/water solution with no further sample preparation. The resulting supernatants were analyzed by direct infusion ESI(+)-MS, which provides characteristic fingerprints of the sample composition. 3-Deoxyanthocyanidins are important substances in A. chica, their ions were used as markers in the PLS-DA data treatment. PLS-DA was able to differentiate the three varieties. ESI(+)-MS fingerprinting works as a simple and fast method to differentiate varieties of A. chica.

Comparison of INAA elemental composition data between Lago Grande and Osvaldo archaeological sites in the central Amazon: a first perspective

In this work, 50 ceramic fragments from the Lago Grande and 30 from the Osvaldo archaeological site were compared to assess elemental similarities. The aim is to perform a preliminary comparison between the sites, which are located in the central Amazon, Brazil. The analytical technique employed to obtain the ceramics elemental composition was instrumental neutron activation analysis (INAA). The data set obtained was explored by the multivariate statistical techniques of cluster, principal component and discriminant analysis. The analyzed elements were: Na, Lu, U, Yb, La, Th, Cr, Cs, Sc, Fe, Eu, Ce and Hf. The results showed the existence of at least two compositional groups for Lago Grande and Osvaldo. Each compositional group of Osvaldo archaeological site matches with one group of Lago Grande. Correlated with the archaeological background, the results suggest commercial or cultural exchange in the region, which is an indicative of socio-cultural interactions between those sites.

Morphometrical diagnosis of the malaria vectors Anopheles cruzii, An. homunculus and An. bellator

Background: Anopheles (Kerteszia) cruzii is a primary vector of Plasmodium parasites in Brazil's Atlantic Forest. Adult females of An. cruzii and An. homunculus, which is a secondary malaria vector, are morphologically similar and difficult to distinguish when using external morphological characteristics only. These two species may occur syntopically with An. bellator, which is also a potential vector of Plasmodium species and is morphologically similar to An. cruzii and An. homunculus. Identification of these species based on female specimens is often jeopardised by polymorphisms, overlapping morphological characteristics and damage caused to specimens during collection. Wing geometric morphometrics has been used to distinguish several insect species; however, this economical and powerful tool has not been applied to Kerteszia species. Our objective was to assess wing geometry to distinguish An. cruzii, An. homunculus and An. bellator. Methods: Specimens were collected in an area in the Serra do Mar hotspot biodiversity corridor of the Atlantic Forest biome (Cananeia municipality, State of Sao Paulo, Brazil). The right wings of females of An. cruzii (n= 40), An. homunculus (n= 50) and An. bellator (n= 27) were photographed. For each individual, 18 wing landmarks were subjected to standard geometric morphometrics. Discriminant analysis of Procrustean coordinates was performed to quantify wing shape variation. Results: Individuals clustered into three distinct groups according to species with a slight overlap between representatives of An. cruzii and An. homunculus. The Mahalanobis distance between An. cruzii and An. homunculus was consistently lower (3.50) than that between An. cruzii and An. bellator (4.58) or An. homunculus and An. bellator (4.32). Pairwise cross-validated reclassification showed that geometric morphometrics is an effective analytical method to distinguish between An. bellator, An. cruzii and An. homunculus with a reliability rate varying between 78-88%. Shape analysis revealed that the wings of An. homunculus are narrower than those of An. cruzii and that An. bellator is different from both of the congeneric species. Conclusion: It is possible to distinguish among the vectors An. cruzii, An. homunculus and An. bellator based on female wing characteristics.

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.

Lipid fingerprinting in women with early-onset preeclampsia: A first look

Objectives: The aim of this preliminary study was to characterize the plasma lipid profiling of women with preeclampsia. Design and methods: Plasma samples of 8 pregnant women with early-onset preeclampsia and 8 normal pregnant women were evaluated. Lipids were extracted from plasma using the Bligh-Dyer protocol. The extracts were subjected to MALDI-MS. Data matrix was exported for partial least squares discriminant analysis (PLS-DA) and a parameter VIP was employed to reflect the variable importance in the discriminant analysis. The major discriminant variables were selected and underwent to Mann-Whitney U test. Results: A total of 1290 ions were initially identified and twelve m/z signals were highlighted as the most important lipids for the discrimination of patients with preeclampsia. The identification of these differential lipids was carried out through Lipid Database Search. Conclusions: The main classes identified were glycerophosphocholines [GP01], glycerophosphoserines [GP03], glycerophosphoglycerols [GP04], glycosyldiradylglycerols [GL05] and glycerophosphates [GP10]. (C) 2012 The Canadian Society of Clinical Chemists. Published by Elsevier Inc. All rights reserved.

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.