Medical data set classification using a new feature selection algorithm combined with twin-bounded support vector machine.

Early diagnosis and treatment are the most important strategies to prevent deaths from several diseases. In this regard, data mining and machine learning techniques have been useful tools to help minimize errors and to provide useful information for diagnosis. Our paper aims to present a new feature selection algorithm. In order to validate our study, we used eight benchmark data sets which are commonly used among researchers who developed machine learning methods for medical data classification. The experiment has shown that the performance of our proposed new feature selection method combined with twin-bounded support vector machine (FSTBSVM) is very efficient. The robustness of the FSTBSVM is examined using classification accuracy, analysis of sensitivity, and specificity. The proposed FSTBSVM is a very promising technique for classification, and the results show that the proposed method is capable of producing good results with fewer features than the original data sets. Graphical abstract Model using a new feature selection and grid search with 10-fold CV to optimize model parameters in our FSTBSVM.

Chemical typicality of South American red wines classified according to their volatile and phenolic compounds using multivariate analysis.

In this study, 83 wines representating four commercial categories: "Argentinean Malbec", "Brazilian Merlot", "Uruguayan Tannat" and "Chilean Carménère" were analyzed according to their phenolic and volatile compounds. The objective was to identify the chemical compounds that would typify each category. From approximately about 600 peaks obtained by chromatographic techniques, 169 were identified and 53 of them were selected for multivariate statistical analysis. Chilean Carménère was the best discriminated group by the methods applied in our study, followed by Argentinean Malbec. Brazilian Merlot mixed mainly with some Carménère, whileTannat mixed with all wines categories, especially Malbec. In general, Chilean Carménère wines can be characterized by a bluish color, higher amounts of sulphur dioxide, higher content of octanoic acid, isobutanol, ethyl isoamyl succinate and catechin and a smaller amount of quercetin. These data can contribute for further process of authenticity or typification of South American red wines.

Advanced data mining approaches in the assessment of urinary concentrations of bisphenols, chlorophenols, parabens and benzophenones in Brazilian children and their association to DNA damage.

Human exposure to endocrine disrupting chemicals (EDCs) has received considerable attention over the last three decades. However, little is known about the influence of co-exposure to multiple EDCs on effect-biomarkers such as oxidative stress in Brazilian children. In this study, concentrations of 40 EDCs were determined in urine samples collected from 300 Brazilian children of ages 6-14 years and data were analyzed by advanced data mining techniques. Oxidative DNA damage was evaluated from the urinary concentrations of 8-hydroxy-2'-deoxyguanosine (8OHDG). Fourteen EDCs, including bisphenol A (BPA), methyl paraben (MeP), ethyl paraben (EtP), propyl paraben (PrP), 3,4-dihydroxy benzoic acid (3,4-DHB), methyl-protocatechuic acid (OH-MeP), ethyl-protocatechuic acid (OH-EtP), triclosan (TCS), triclocarban (TCC), 2-hydroxy-4-methoxybenzophenone (BP3), 2,4-dihydroxybenzophenone (BP1), bisphenol A bis(2,3-dihydroxypropyl) glycidyl ether (BADGE·2H2O), 2,4-dichlorophenol (2,4-DCP), and 2,5-dichlorophenol (2,5-DCP) were found in >50% of the urine samples analyzed. The highest geometric mean concentrations were found for MeP (43.1 ng/mL), PrP (3.12 ng/mL), 3,4-DHB (42.2 ng/mL), TCS (8.26 ng/mL), BP3 (3.71 ng/mL), and BP1 (4.85 ng/mL), and exposures to most of which were associated with personal care product (PCP) use. Statistically significant associations were found between urinary concentrations of 8OHDG and BPA, MeP, 3,4-DHB, OH-MeP, OH-EtP, TCS, BP3, 2,4-DCP, and 2,5-DCP. After clustering the data on the basis of i) 14 EDCs (exposure levels), ii) demography (age, gender and geographic location), and iii) 8OHDG (effect), two distinct clusters of samples were identified. 8OHDG concentration was the most critical parameter that differentiated the two clusters, followed by OH-EtP. When 8OHDG was removed from the dataset, predictability of exposure variables increased in the order of: OH-EtP > OH-MeP > 3,4-DHB > BPA > 2,4-DCP > MeP > TCS > EtP > BP1 > 2,5-DCP. Our results showed that co-exposure to OH-EtP, OH-MeP, 3,4-DHB, BPA, 2,4-DCP, MeP, TCS, EtP, BP1, and 2,5-DCP was associated with DNA damage in children. This is the first study to report exposure of Brazilian children to a wide range of EDCs and the data mining approach further strengthened our findings of chemical co-exposures and biomarkers of effect.

Xyloglucan, galactomannan, glucuronoxylan, and rhamnogalacturonan I do not have identical structures in soybean root and root hair cell walls.

MAIN CONCLUSION: Chemical analyses and glycome profiling demonstrate differences in the structures of the xyloglucan, galactomannan, glucuronoxylan, and rhamnogalacturonan I isolated from soybean ( Glycine max ) roots and root hair cell walls. The root hair is a plant cell that extends only at its tip. All other root cells have the ability to grow in different directions (diffuse growth). Although both growth modes require controlled expansion of the cell wall, the types and structures of polysaccharides in the walls of diffuse and tip-growing cells from the same plant have not been determined. Soybean (Glycine max) is one of the few plants whose root hairs can be isolated in amounts sufficient for cell wall chemical characterization. Here, we describe the structural features of rhamnogalacturonan I, rhamnogalacturonan II, xyloglucan, glucomannan, and 4-O-methyl glucuronoxylan present in the cell walls of soybean root hairs and roots stripped of root hairs. Irrespective of cell type, rhamnogalacturonan II exists as a dimer that is cross-linked by a borate ester. Root hair rhamnogalacturonan I contains more neutral oligosaccharide side chains than its root counterpart. At least 90% of the glucuronic acid is 4-O-methylated in root glucuronoxylan. Only 50% of this glycose is 4-O-methylated in the root hair counterpart. Mono O-acetylated fucose-containing subunits account for at least 60% of the neutral xyloglucan from root and root hair walls. By contrast, a galacturonic acid-containing xyloglucan was detected only in root hair cell walls. Soybean homologs of the Arabidopsis xyloglucan-specific galacturonosyltransferase are highly expressed only in root hairs. A mannose-rich polysaccharide was also detected only in root hair cell walls. Our data demonstrate that the walls of tip-growing root hairs cells have structural features that distinguish them from the walls of other roots cells.

A simple and practical control of the authenticity of organic sugarcane samples based on the use of machine-learning algorithms and trace elements determination by inductively coupled plasma mass spectrometry.

A practical and easy control of the authenticity of organic sugarcane samples based on the use of machine-learning algorithms and trace elements determination by inductively coupled plasma mass spectrometry is proposed. Reference ranges for 32 chemical elements in 22 samples of sugarcane (13 organic and 9 non organic) were established and then two algorithms, Naive Bayes (NB) and Random Forest (RF), were evaluated to classify the samples. Accurate results (>90%) were obtained when using all variables (i.e., 32 elements). However, accuracy was improved (95.4% for NB) when only eight minerals (Rb, U, Al, Sr, Dy, Nb, Ta, Mo), chosen by a feature selection algorithm, were employed. Thus, the use of a fingerprint based on trace element levels associated with classification machine learning algorithms may be used as a simple alternative for authenticity evaluation of organic sugarcane samples.