A fast sample preparation procedure for mercury speciation in hair samples by high-performance liquid chromatography coupled to ICP-MS

A simple method for mercury speciation in hair samples with a fast sample preparation procedure using high-performance liquid chromatography coupled to inductively coupled plasma mass spectrometry is proposed. Prior to analysis, 50 mg of hair samples were accurately weighed into 15 mL conical tubes. Then, an extractant solution containing mercaptoethanol, L-cysteine and HCl was added to the samples following sonication for 10 min. Quantitative mercury extraction was achieved with the proposed procedure. Separation of inorganic mercury (Ino-Hg), methylmercury (Met-Hg) and ethylmercury (Et-Hg) was accomplished in less than 8 min on a C18 reverse phase column with a mobile phase containing 0.05% v/v mercaptoethanol, 0.4% m/v L-cysteine, 0.06 mol L(-1) ammonium acetate and 5% v/v methanol. The method detection limits were found to be 15 ng g(-1), 10 ng g(-1) and 38 ng g(-1), for inorganic mercury, methylmercury and ethylmercury, respectively. Sample throughput is 4 samples h(-1) (duplicate). A considerable improvement in the time of analysis was achieved when compared to other published methods. Method accuracy is traceable to Certified Reference Materials (CRMs) 85 and 86 human hair from the International Atomic Energy Agency (IAEA). Finally, the proposed method was successfully applied to the speciation of mercury in hair samples collected from fish-eating communities of the Brazilian Amazon.

Mercury speciation in seafood samples by LC-ICP-MS with a rapid ultrasound-assisted extraction procedure: Application to the determination of mercury in Brazilian seafood samples

This paper describes a simple method for mercury speciation in seafood samples by LC-ICP-MS with a fast sample preparation procedure. Prior to analysis, mercury species were extracted from food samples with a solution containing mercaptoethanol, L-cysteine and HCl and sonication for 15 min. Separation of mercury species was accomplished in less than 5 min on a C8 reverse phase column with a mobile phase containing 0.05%-v/v mercaptoethanol, 0.4% m/v L-cysteine and 0.06 mol L(-1) ammonium acetate. The method detection limits were found to be 0.25, 0.20 and 0.1 ng g(-1) for inorganic mercury, ethylmercury and methylmercury, respectively. Method accuracy is traceable to Certified Reference Materials (DOLT-3 and DORM-3) from the National Research Council Canada (NRCC). With the proposed method there is a considerable reduction of the time of sample preparation. Finally, the method was applied for the speciation of mercury in seafood samples purchased from the Brazilian market. (C) 2010 Elsevier Ltd. All rights reserved.

Biotype stability of Candida albicans isolates after culture storage determined by randomly amplified polymorphic DNA and phenotypical methods

P>Typing methods to evaluate isolates in relation to their phenotypical and molecular characteristics are essential in epidemiological studies. In this study, Candida albicans biotypes were determined before and after storage in order to verify their stability. Twenty C. albicans isolates were typed by Randomly Amplified Polymorphic DNA (RAPD), production of phospholipase and proteinase exoenzymes (enzymotyping) and morphotyping before and after 180 days of storage in Sabouraud dextrose agar (SDA) and sterilised distilled water. Before the storage, 19 RAPD patterns, two enzymotypes and eight morphotypes were identified. The fragment patterns obtained by RAPD, on the one hand, were not significantly altered after storage. On the other hand, the majority of the isolates changed their enzymotype and morphotype after storage. RAPD typing provided the better discriminatory index (DI) among isolates (DI = 0.995) and maintained the profile identified, thereby confirming its utility in epidemiological surveys. Based on the low reproducibility observed after storage in SDA and distilled water by morphotyping (DI = 0.853) and enzymotyping (DI = 0.521), the use of these techniques is not recommended on stored isolates.

Trypanocidal, leislimanicidal and antifungal potential from marine red alga Bostrychia tenella J. Agardli (Rhodomelaceae, Ceraniiales)

Specimens of the red alga Bostrychia tenella J Agardh (Rhodomelaceae, Ceramiales) were collected from the Sao Paulo coast and submitted to loom temperature solvent extraction The resulting extract was fractionated by partitioning with organic solvent The n-hexane (BT-H) and dichloromethane (BT-D) fractions showed antiprotozoal potential in biological tests with Trypanosoma cruzi and Leishmania amazonensis and presented high activity in an antifungal assay with the phytopathogenic fungi Cladosporium cladosporioides and Cladosporium sphaerospermum Chromatography methods were used to generate subfractions from BT-H (H01 to H11) and from BT-D (D01 to 019) The subtractions were analyzed by gas chromatography-mass spectrometry (GC/MS). and the substances were identified by retention index (Kovats) and by comparison to databases of commercial mass spectra The volatile compounds found in marine algae were identified as fatty acids, low molecular mass hydrocarbons, esters and steroids, some of these have been previously described in the literature based on other biological activities Moreover, uncommon substances. such as neophytadiene were also identified In a trypanocidal assay, fractions BT-H and BT-D showed IC(50) values of 168 and 19 1 mu g/mL. respectively, and were mote active than the gentian violet standard (31 mu g/ml.); subfractions H02. H03, D01 and D02 were active against L amasonensis, exhibiting IC(50) values of 1 S. 2 7, 4 4. and 4 3 mu g/mL., respectively (standard amphotericin B IC(50) = 13 mu g/mL.) All fractions showed antifungal potential this work reports the biological activity and identification of compounds by GC/MS for the marine red alga B tenella for the first time (C) 2010 Elsevier B V All lights 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.

Assessing the competitiveness of loss allocation methods in a deregulated electricity market

This paper critically assesses several loss allocation methods based on the type of competition each method promotes. This understanding assists in determining which method will promote more efficient network operations when implemented in deregulated electricity industries. The methods addressed in this paper include the pro rata [1], proportional sharing [2], loss formula [3], incremental [4], and a new method proposed by the authors of this paper, which is loop-based [5]. These methods are tested on a modified Nordic 32-bus network, where different case studies of different operating points are investigated. The varying results obtained for each allocation method at different operating points make it possible to distinguish methods that promote unhealthy competition from those that encourage better system operation.

Calculation of line integrals in boundary integral methods

We propose quadrature rules for the approximation of line integrals possessing logarithmic singularities and show their convergence. In some instances a superconvergence rate is demonstrated.

Selection bias in gene extraction on the basis of microarray gene-expression data

In the context of cancer diagnosis and treatment, we consider the problem of constructing an accurate prediction rule on the basis of a relatively small number of tumor tissue samples of known type containing the expression data on very many (possibly thousands) genes. Recently, results have been presented in the literature suggesting that it is possible to construct a prediction rule from only a few genes such that it has a negligible prediction error rate. However, in these results the test error or the leave-one-out cross-validated error is calculated without allowance for the selection bias. There is no allowance because the rule is either tested on tissue samples that were used in the first instance to select the genes being used in the rule or because the cross-validation of the rule is not external to the selection process; that is, gene selection is not performed in training the rule at each stage of the cross-validation process. We describe how in practice the selection bias can be assessed and corrected for by either performing a cross-validation or applying the bootstrap external to the selection process. We recommend using 10-fold rather than leave-one-out cross-validation, and concerning the bootstrap, we suggest using the so-called. 632+ bootstrap error estimate designed to handle overfitted prediction rules. Using two published data sets, we demonstrate that when correction is made for the selection bias, the cross-validated error is no longer zero for a subset of only a few genes.

A framework for electricity price spike analysis with advanced data mining methods

There are many techniques for electricity market price forecasting. However, most of them are designed for expected price analysis rather than price spike forecasting. An effective method of predicting the occurrence of spikes has not yet been observed in the literature so far. In this paper, a data mining based approach is presented to give a reliable forecast of the occurrence of price spikes. Combined with the spike value prediction techniques developed by the same authors, the proposed approach aims at providing a comprehensive tool for price spike forecasting. In this paper, feature selection techniques are firstly described to identify the attributes relevant to the occurrence of spikes. A simple introduction to the classification techniques is given for completeness. Two algorithms: support vector machine and probability classifier are chosen to be the spike occurrence predictors and are discussed in details. Realistic market data are used to test the proposed model with promising results.

A demonstration of artificial dissipation effects in some finite volume solution methods for the Navier-Stokes equations

The artificial dissipation effects in some solutions obtained with a Navier-Stokes flow solver are demonstrated. The solvers were used to calculate the flow of an artificially dissipative fluid, which is a fluid having dissipative properties which arise entirely from the solution method itself. This was done by setting the viscosity and heat conduction coefficients in the Navier-Stokes solvers to zero everywhere inside the flow, while at the same time applying the usual no-slip and thermal conducting boundary conditions at solid boundaries. An artificially dissipative flow solution is found where the dissipation depends entirely on the solver itself. If the difference between the solutions obtained with the viscosity and thermal conductivity set to zero and their correct values is small, it is clear that the artificial dissipation is dominating and the solutions are unreliable.

A shift from passive teaching at medical conferences to more interactive methods improves physician learning

Conferences that deliver interactive sessions designed to enhance physician participation, such as role play, small discussion groups, workshops, hands-on training, problem- or case-based learning and individualised training sessions, are effective for physician education.

Appropriate surveying methods in the Philippines: Can modern GPS units help?

An investigation was undertaken to test the effectiveness of two procedures for recording boundaries and plot positions for scientific studies on farms on Leyte Island, the Philippines. The accuracy of a Garmin 76 Global Positioning System (GPS) unit and a compass and chain was checked under the same conditions. Tree canopies interfered with the ability of the satellite signal to reach the GPS and therefore the GPS survey was less accurate than the compass and chain survey. Where a high degree of accuracy is required, a compass and chain survey remains the most effective method of surveying land underneath tree canopies, providing operator error is minimised. For a large number of surveys and thus large amounts of data, a GPS is more appropriate than a compass and chain survey because data are easily up-loaded into a Geographic Information System (GIS). However, under dense canopies where satellite signals cannot reach the GPS, it may be necessary to revert to a compass survey or a combination of both methods.