Enzymatic differences between the endophyte Guignardia mangiferae (Botryosphaeriaceae) and the citrus pathogen G. citricarpa

The endophyte Guignardia mangiferae is closely related to G. citricarpa, the causal agent of citrus black spot; for many years these species had been confused with each other. The development of molecular analytical methods has allowed differentiation of the pathogen G. citricarpa from the endophyte G. mangiferae, but the physiological traits associated with pathogenicity were not described. We examined genetic and enzymatic characteristics of Guignardia spp strains; G. citricarpa produces significantly greater amounts of amylases, endoglucanases and pectinases, compared to G. mangiferae, suggesting that these enzymes could be key in the development of citrus black spot. Principal component analysis revealed pectinase production as the main enzymatic characteristic that distinguishes these Guignardia species. We quantified the activities of pectin lyase, pectin methylesterase and endopolygalacturonase; G. citricarpa and G. mangiferae were found to have significantly different pectin lyase and endopolygalacturonase activities. The pathogen G. citricarpa is more effective in pectin degradation. We concluded that there are significant physiological differences between the species G. citricarpa and G. mangiferae that could be associated with differences in pathogenicity for citrus plants.

Chemometric Studies on Natural Products as Potential Inhibitors of the NADH Oxidase from Trypanosoma cruzi Using the VolSurf Approach

Natural products have widespread biological activities, including inhibition of mitochondrial enzyme systems. Some of these activities, for example cytotoxicity, may be the result of alteration of cellular bioenergetics. Based on previous computer-aided drug design (CADD) studies and considering reported data on structure-activity relationships (SAR), an assumption regarding the mechanism of action of natural products against parasitic infections involves the NADH-oxidase inhibition. In this study, chemometric tools, such as: Principal Component Analysis (PCA), Consensus PCA (CPCA), and partial least squares regression (PLS), were applied to a set of forty natural compounds, acting as NADH-oxidase inhibitors. The calculations were performed using the VolSurf+ program. The formalisms employed generated good exploratory and predictive results. The independent variables or descriptors having a hydrophobic profile were strongly correlated to the biological data.

Characterizing suspended sediments from the Piracicaba River Basin by means of k(0)-INAA

The inorganic chemical characterization of suspended sediments is of utmost relevance for the knowledge of the dynamics and movement of chemical elements in the aquatic and wet ecosystems. Despite the complexity of the effective design for studying this ecological compartment, this work has tested a procedure for analyzing suspended sediments by instrumental neutron activation analysis, k(0) method (k(0)-INAA). The chemical elements As, Ba, Br, Ca, Ce, Co, Cr, Cs, Eu, Fe, Hf, Fig, K, La, Mo, Na, Ni, Rb, Sb, Sc, Se, Sm, Sr, Ta, Tb, Th, Yb and Zn were quantified in the suspended sediment compartment by means of k(0)-INAA. When compared with World Average for rivers, high mass fractions of Fe (222,900 mg/kg), Ba (4990 mg/kg), Zn (1350 mg/kg), Cr (646 mg/kg), Co (74.5 mg/kg), Br (113 mg/kg) and Mo (31.9 mg/kg) were quantified in suspended sediments from the Piracicaba River, the Piracicamirim Stream and the Marins Stream. Results of the principal component analysis for standardized chemical element mass fractions indicated an intricate correlation among chemical elements evaluated, as a response of the contribution of natural and anthropogenic sources of chemical elements for ecosystems. (C) 2010 Elsevier B.V. All rights reserved.

Improvements on ICA mixture models for image pre-processing and segmentation

Today several different unsupervised classification algorithms are commonly used to cluster similar patterns in a data set based only on its statistical properties. Specially in image data applications, self-organizing methods for unsupervised classification have been successfully applied for clustering pixels or group of pixels in order to perform segmentation tasks. The first important contribution of this paper refers to the development of a self-organizing method for data classification, named Enhanced Independent Component Analysis Mixture Model (EICAMM), which was built by proposing some modifications in the Independent Component Analysis Mixture Model (ICAMM). Such improvements were proposed by considering some of the model limitations as well as by analyzing how it should be improved in order to become more efficient. Moreover, a pre-processing methodology was also proposed, which is based on combining the Sparse Code Shrinkage (SCS) for image denoising and the Sobel edge detector. In the experiments of this work, the EICAMM and other self-organizing models were applied for segmenting images in their original and pre-processed versions. A comparative analysis showed satisfactory and competitive image segmentation results obtained by the proposals presented herein. (C) 2008 Published by Elsevier B.V.

Characterization of different fruit wines made from cacao, cupuassu, gabiroba, jaboticaba and umbu

The main aim of this work was to produce fruit wines from pulp of gabiroba, cacao, umbu, cupuassu and jaboticaba and characterize them using gas chromatography-mass spectrometry for determination of minor compounds and gas chromatography-flame ionization detection for major compounds. Ninety-nine compounds (C(6) compounds, alcohols, monoterpenic alcohols, monoterpenic oxides, ethyl esters, acetates, volatile phenols, acids, carbonyl compounds, sulfur compounds and sugars) were identified in fruit wines. The typical composition for each fruit wine was evidenced by principal component analysis and Tukey test. The yeast UFLA CA 1162 was efficient in the fermentation of the fruit pulp used in this work. The identification and quantification of the compounds allowed a good characterization of the fruit wines. With our results, we conclude that the use of tropical fruits in the production of fruit wines is a viable alternative that allows the use of harvest surpluses and other underused fruits, resulting in the introduction of new products into the market. (C) 2010 Elsevier Ltd. All rights reserved.

Human gait recognition using extraction and fusion of global motion features

This paper proposes a novel computer vision approach that processes video sequences of people walking and then recognises those people by their gait. Human motion carries different information that can be analysed in various ways. The skeleton carries motion information about human joints, and the silhouette carries information about boundary motion of the human body. Moreover, binary and gray-level images contain different information about human movements. This work proposes to recover these different kinds of information to interpret the global motion of the human body based on four different segmented image models, using a fusion model to improve classification. Our proposed method considers the set of the segmented frames of each individual as a distinct class and each frame as an object of this class. The methodology applies background extraction using the Gaussian Mixture Model (GMM), a scale reduction based on the Wavelet Transform (WT) and feature extraction by Principal Component Analysis (PCA). We propose four new schemas for motion information capture: the Silhouette-Gray-Wavelet model (SGW) captures motion based on grey level variations; the Silhouette-Binary-Wavelet model (SBW) captures motion based on binary information; the Silhouette-Edge-Binary model (SEW) captures motion based on edge information and the Silhouette Skeleton Wavelet model (SSW) captures motion based on skeleton movement. The classification rates obtained separately from these four different models are then merged using a new proposed fusion technique. The results suggest excellent performance in terms of recognising people by their gait.

Ultrasound, eddy current and magnetic Barkhausen noise as tools for sigma phase detection on a UNS S31803 duplex stainless steel

Sigma phase is a deleterious one which can be formed in duplex stainless steels during heat treatment or welding. Aiming to accompany this transformation, ferrite and sigma percentage and hardness were measured on samples of a UNS S31803 duplex stainless steel submitted to heat treatment. These results were compared to measurements obtained from ultrasound and eddy current techniques, i.e., velocity and impedance, respectively. Additionally, backscattered signals produced by wave propagation were acquired during ultrasonic inspection as well as magnetic Barkhausen noise during magnetic inspection. Both signal types were processed via a combination of detrended-fluctuation analysis (DFA) and principal component analysis (PCA). The techniques used were proven to be sensitive to changes in samples related to sigma phase formation due to heat treatment. Furthermore, there is an advantage using these methods since they are nondestructive. (C) 2010 Elsevier B.V. All rights reserved.

Bacterial community in the rhizosphere and rhizoplane of wild type and transgenic eucalyptus

The rhizosphere is a niche exploited by a wide variety of bacteria. The expression of heterologous genes by plants might become a factor affecting the structure of bacterial communities in the rhizosphere. In a greenhouse experiment, the bacterial community associated to transgenic eucalyptus, carrying the Lhcb1-2 genes from pea (responsible for a higher photosynthetic capacity), was evaluated. The culturable bacterial community associated to transgenic and wild type plants were not different in density, and the Amplified Ribosomal DNA Restriction Analysis (ARDRA) typing of 124 strains revealed dominant ribotypes representing the bacterial orders Burkholderiales, Rhizobiales, and Actinomycetales, the families Xanthomonadaceae, and Bacillaceae, and the genus Mycobacterium. Principal Component Analysis based on the fingerprints obtained by culture-independent Denaturing Gradient Gel Electrophoresis analysis revealed that Alphaproteobacteria, Betaproteobacteria and Actinobacteria communities responded differently to plant genotypes. Similar effects for the cultivation of transgenic eucalyptus to those observed when two genotype-distinct wild type plants are compared.

Characterization of fungal soil communities by F-RISA and arbuscular mycorrhizal fungi from Araucaria angustifolia forest soils after replanting and wildfire disturbances

The Fungal Ribosomal Intergenic Spacer Analysis (F-RISA) was used to characterize soil fungal communities from three ecosystems of Araucaria angustifolia from Brazil: a native forest and two replanted forest ecosystems, one of them with a past history of wildfire. The arbuscular mycorrhizal fungi (AMF) infection was evaluated in Araucaria roots of 18-month-old axenic plants previously inoculated with soils collected from those areas in a greenhouse experiment. The principal component analysis of F-RISA profiles showed different soil fungal community between the three studied areas. Sixty three percent of F-RISA fragments amplified in the soil and the substrate samples presented lengths between 500 and 700 bp. The number of Operational Taxonomic Units (OTUs) was 34 for soil and 38 for substrate, however, more fragments were detected in soil (214) than in substrate (163). An in silico F-RISA analysis to compare our data with ITS1-5.8S-ITS2 sequences from NCBI database showed the presence of Ascomycota, Basidiomycota and Glomeromycota among the soil and substrate fungal communities. AMF infection was higher in plants inoculated with soil from the native forest and the replanted forest with wildfire, both presenting similar chemical characteristics but with different disturbance levels. These results indicate that soil chemical composition may influence the soil fungal community structures rather than the anthropogenic or fire disturbances.

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.

Assessing the association between phenolic compounds and the antioxidant activity of Brazilian red wines using chemometrics

The objective of this study was to evaluate the association among chemical parameters, the commercial value, and the antioxidant activity of Brazilian red wines using chemometric techniques. Twenty-nine samples from five different varieties were assessed. Samples were separated into three groups using hierarchical cluster analysis: cluster 1 presented the highest antioxidant activity towards DPPH (68.51% of inhibition) and ORAC (30,918.64 mu mol Trolox Equivalents/L), followed by cluster 3 (DPPH = 59.36% of inhibition: ORAC = 25,255.02 mu mol Trolox Equivalents/L) and then cluster 2 (DPPH = 46.67% of inhibition; ORAC = 19,395.74 gmol Trolox Equivalents/L). Although the correlation between the commercial value and the antioxidant activity on DPPH and ORAC was not statistically significant (P = 0.13 and P = 0.06, respectively), cluster 1 grouped the samples with higher commercial values. Cluster analysis applied to the variables suggested that non-anthocyanin flavonoids were the main phenolic class exerting antioxidant activity on Brazilian red wines. (C) 2010 Elsevier Ltd. All rights reserved.

Monitoring the authenticity of Brazilian UHT milk: A chemometric approach

In this work, chemometric methods are reported as potential tools for monitoring the authenticity of Brazilian ultra-high temperature (UHT) milk processed in industrial plants located in different regions of the country. A total of 100 samples were submitted to the qualitative analysis of adulterants such as starch, chlorine, formal. hydrogen peroxide and urine. Except for starch, all the samples reported, at least, the presence of one adulterant. The use of chemometric methodologies such as the Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) enabled the verification of the occurrence of certain adulterations in specific regions. The proposed multivariate approaches may allow the sanitary agency authorities to optimise materials, human and financial resources, as they associate the occurrence of adulterations to the geographical location of the industrial plants. (c) 2010 Elsevier Ltd. All rights reserved.

A chemometric study on the analgesic activity of cannabinoid compounds using SDA, KNN and SIMCA methods

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.

Distribution of major, minor, and trace metals in lake environments of Antarctica

The concentrations of major, minor and trace metals were measured in water samples collected from five shallow Antarctic lakes (Carezza, Edmonson Point (No 14 and 15a), Inexpressible Island and Tarn Flat) found in Terra Nova Bay (northern Victoria Land, Antarctica) during the Italian Expeditions of 1993-2001. The total concentrations of a large suite of elements (Al, As, Ba, Ca, Cd, Ce, Co, Cr, Cs, Cu, Fe, Ga, Gd, K, La, Li, Mg, Mn, Mo, Na, Nd, Ni, Pb, Pr, Rb, Sc, Si, Sr, Ta, Ti, U, V, Y, W, Zn and Zr) were determined using spectroscopic techniques (ICP-AES, GF-AAS and ICP-MS). The results are similar to those obtained for the freshwater lakes of the Larsemann Hills, East Antarctica, and for the McMurdo Dry Valleys. Principal Component Analysis (PCA) and Cluster Analysis (CA) were performed to identify groups of samples with similar characteristics and to find correlations between the variables. The variability observed within the water samples is closely connected to the sea spray input; hence, it is primarily a consequence of geographical and meteorological factors, such as distance from the ocean and time of year. The trace element levels, in particular those of heavy metals, are very low, suggesting an origin from natural sources rather than from anthropogenic contamination.

Using biplots to interpret gene expression patterns in plants

Sum: Plant biologists in fields of ecology, evolution, genetics and breeding frequently use multivariate methods. This paper illustrates Principal Component Analysis (PCA) and Gabriel's biplot as applied to microarray expression data from plant pathology experiments. Availability: An example program in the publicly distributed statistical language R is available from the web site (www.tpp.uq.edu.au) and by e-mail from the contact. Contact: scott.chapman@csiro.au.