The association of dental caries with blood lead in children when adjusted for IQ and neurobehavioral performance

Associations between childhood lead exposures and dental caries in children have been reported for over 30 years, with widely varying findings and conclusions, and using measures of lead exposure which ranged from food sources and water to tooth, hair or blood lead concentrations. OBJECTIVES: This study examined the relationship of lead exposure and dental caries in a population of normatively healthy children. METHODS: This cross-sectional study used a population of 507 children aged 8-12 who were participating in a clinical trial of dental materials to examine the relationship between lead and caries. Blood lead concentrations and dental caries were examined for association in both primary and permanent teeth. Because it is possible that neurobehavioral status could be associated with both lead exposure and dental caries prevalence, we also examined neurobehavioral status of the subjects. RESULTS: A gender-specific association (males only) between lead exposure and dental caries was found in primary teeth only. Neurobehavioral measures and IQ were not associated with caries status in this population. CONCLUSIONS: This study did not support neurobehavioral status as mediating any association between lead exposure and caries in a normatively healthy population. A gender-specific association between lead and caries not previously reported was found in primary teeth, and no biological explanation for this has been suggested. We conclude that this study provides only weak evidence, if any, for an association of low-level lead exposure with dental caries.     

Brazilian red propolis: unreported substances, antioxidant and antimicrobial activities

BACKGROUND: Chloroform, ethyl acetate and methanol extracts of a sample of red propolis from the state of Alagoas (northeast Brazil) were analyzed by gas chromatography–mass spectrometry and high‐performance liquid chromatography–diode array detection–electrospray ionization‐mass spectrometry. Antimicrobial and antioxidant activities were also obtained. RESULTS: The propolis sample contained low content of narigenin‐8‐C‐hexoside, this being the first report of a C‐glycoside in propolis. The main constituent found was characterized as 3,4,2′,3′‐tetrahydroxychalcone. Other important constituents were the chalcone isoliquiritigenin, the isoflavans (3S)‐vestitol, (3S)‐7‐O‐methylvestitol, the pterocarpan medicarpin, the phenylpropenes trans‐anethol, methyl eugenol, elimicin, methoxyeugenol and cis‐asarone, and the triterpenic alcohols lupeol and α‐ and β‐ amyrins. The methanol extract exhibited high antioxidant activities by 2,2‐diphenyl‐1‐picrylhydrazyl and β‐carotene/linoleic acid assay methods, and antimicrobial activity toward Gram‐positive and Gram‐negative bacteria. CONCLUSION: Structures are suggested for new substances never before seen in any kind of propolis. This is the first report of 3,4,2′,3′‐tetrahydroxychalcone and a flavone C‐glycoside in a propolis sample. Copyright © 2011 Society of Chemical Industry     

Chemometric Analysis of <Superscript>1</Superscript>H NMR Fingerprints of <Emphasis Type="Italic">Coffea arabica</Emphasis> Green Bean Extracts Cultivated under Different Planting Densities

High planting density has been used to increase coffee production but there are few studies related to the variations it provokes in metabolite compositions. The use of ¹H NMR data associated with chemometric techniques allows the determination of metabolic fingerprints and verification of metabolic changes when coffee is subjected to high planting densities. The aim of this work is to investigate ¹H NMR spectral data of green bean extracts of Coffea arabica cv. IAPAR 59 grown in a square pattern at two planting densities, 6000 and 10,000 plants ha^?1. Thirty extracts were obtained using a simplex centroid design with four solvents (ethanol, acetone, dichloromethane, and hexane). The lyophilized extracts were dissolved in DMSO-d₆ to obtain the ¹H NMR spectra. The spectral data were analyzed with principal component (PCA) and cluster analyses (CA). Significant differences between ethanolic and non-ethanolic extracts were found by PCA. Only the ethanolic mean spectrum showed characteristic chemical shifts of sugars and trigonelline. Acetone extracts were separated by cluster analysis.     

Arabinosylated phenolics obtained from SO2‐steam‐pretreated sugarcane bagasse

A pentose‐rich hydrolysate fraction obtained by extraction of steam‐pretreated sugarcane bagasse was analysed with regard to dissolved phenolics. The liquid obtained after steam pretreatment (2% SO₂ (w/w) at 190 °C for 5 min) was divided into two parts: one containing dissolved compounds originating from hemicellulose (with xylose as the dominating compound), and the other containing predominantly dissolved compounds originating from lignin. Using nuclear magnetic resonance, the main dissolved compounds originating from lignin were identified as the glycosylated aromatics, 5‐O‐(trans‐feruloyl)‐L‐Arabinofuranose and 5‐O‐(trans‐coumaroyl)‐L‐Arabinofuranose, together with p‐coumaric acid and small amounts of more common free phenolics such as p‐hydroxybenzaldehyde, p‐hydroxybenzoic acid and vanillin. The phenolic compounds were analysed and quantified using reversed‐phase high‐performance liquid chromatography. The findings show that SO₂ steam explosion opened up new degradation pathways during lignin degradation. Copyright © 2012 Society of Chemical Industry     

Identification of temperature and moisture content fields using a combined neural network and clustering method approach

Studies on the dynamics of temperature and moisture content distributions in porous soils have provided important insight on their effect on the building hygrothermal behavior, where the interaction between both building and soil can contribute to reduce building thermal gains or looses. Hygrothermal aspects can be related to many attributes such as energy consumption, occupants' thermal comfort and health, and material deterioration. Recently, a great variety of mathematical models to predict thermal and moisture content profiles in porous media have been presented in the literature. Most of those models are based on analysis of multilayer measurements or on Fourier analysis. The development and validation of such mathematical models facilitate the understanding of heat and moisture flows at different soil depths. In this research, a radial basis function neural network (RBF-NN) approach, combined with Gath–Geva clustering method in order to predict the temperature and moisture content profiles in soils, has been presented. A set of data obtained from the computation of the coupled heat and moisture transfer in porous soils for the Curitiba city (Paraná State, Brazil) weather data file has been used by the RBF-NN modeling method. Simulation results indicate the potentialities of the RBF-NNs to learn, for the one step ahead identification, the behavior of temperature and moisture content profiles in the media at various depths.     

Quantification of soluble solids in reconstituted açaí (Euterpe oleracea Mart.) pulp using near‐infrared spectroscopy

Açaí consumption is increasing worldwide because of the growing recognition of its nutritional and therapeutic properties. This product is classified based on its soluble solids content (SS), but the determination of SS in pulp is time consuming, tedious and not suitable for modern food processing plants. As near‐infrared (NIR) systems have been implemented to measure various quality attributes of food products, the objective of this study was to evaluate the feasibility of NIR diffuse reflectance spectroscopy to quantify the SS content of açaí pulp. Partial least squares (PLS) regression models were constructed to predict the SS. An optimum PLS model required one latent variable [principal component (PC)1 = 97%] with a root‐mean‐square error of calibration (RMSEC) of 1.06% for the calibration data set and the root‐mean‐square error of prediction (RMSEP) of 1.03% for internal cross‐validation. External validation using an independent data set showed good performance (RMSEP = 1.33% and Rp² = 0.82). NIR spectroscopy is a reliable method with which to determine SS in açaí pulp and thereby to classify açaí pulp according to established minimum quality standards.     

Determining the residual alcohol in biodiesel through its flash point

A simple low cost method is proposed for the quantitative analysis of residual alcohol in biodiesel through determination of the flash point, with which it is correlated. Methyl ester biodiesels from vegetable oils such as corn, soy and sunflower were prepared. The ethyl ester was obtained from soy oil and methyl biodiesel was also synthesized from bovine fat. In all cases it became very evident that there is a direct correlation between the flash point and the residual alcohol content in the prepared biodiesel. Therefore this parameter can be used to directly determine the residual alcohol content of the product.     

Antioxidant and antiglycation properties of Passiflora alata and Passiflora edulis extracts

The leaves of Passiflora alata Dryander and Passiflora edulis Sims, traditionally used in American countries to treat both anxiety and nervousness by folk medicine, are rich in polyphenols, which have been reported as natural antioxidants. In this study, the antioxidant activities of P. edulis and P. alata hydroalcoholic leaf extracts were verified in in vitro and ex vivo assays. P. alata showed a higher total reactive antioxidant potential than did P. edulis. The antioxidant activities of both extracts were significantly correlated with polyphenol contents. In addition, both extracts attenuated ex vivo iron-induced cell death, quantified by lactate dehydrogenase leakage, and effectively protected against protein damage induced by iron and glucose. These findings demonstrate that the P. alata and P. edulis leaf extracts have potent in vitro and ex vivo antioxidant properties and might be considered as possible new sources of natural antioxidants.     

Intrusion Detection System for Wireless Sensor Networks Using Danger Theory Immune-Inspired Techniques

An IDS framework inspired in the Human Immune System to be applied in the wireless sensor network context is proposed. It uses an improved decentralized and customized version of the Dendritic Cell Algorithm, which allows nodes to monitor their neighborhood and collaborate to identify an intruder. The work was implemented and tested both in simulation and in real sensor platform scenarios, comparing them to each other and was also compared to a Negative Selection Theory implementation in order to demonstrate its efficiency in detecting a denial-of-sleep attack and in energy consumption. Results demonstrated the success of the proposal.