975 resultados para Matrix-Analytic Methods
Resumo:
Mercury (Hg) exposure causes health problems that may result from increased oxidative stress and matrix metalloproteinase (MMP) levels. We investigated whether there is an association between the circulating levels of MMP-2, MMP-9, their endogenous inhibitors (the tissue inhibitors of metalloproteinases; TIMPs) and the circulating Hg levels in 159 subjects environmentally exposed to Hg. Blood and plasma Hg were determined by inductively coupled plasma-mass spectrometry (ICP-MS). MMP and TIMP concentrations were measured in plasma samples by gelatin zymography and ELISA respectively. Thiobarbituric acid-reactive species (TBARS) were measured in plasma to assess oxidative stress. Selenium (Se) levels were determined by ICP-MS because it is an antioxidant. The relations between bioindicators of Hg and the metalloproteinases levels were examined using multivariate regression models. While we found no relation between blood or plasma Hg and MMP-9, plasma Hg levels were negatively associated with TIMP-1 and TIMP-2 levels, and thereby with increasing MMP-9/TIMP-1 and MMP-2/TIMP-2 ratios, thus indicating a positive association between plasma Hg and circulating net MMP-9 and MMP-2 activities. These findings provide a new insight into the possible biological mechanisms of Hg toxicity, particularly in cardiovascular diseases.
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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.
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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.
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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.
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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.
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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.
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The BR algorithm is a novel and efficient method to find all eigenvalues of upper Hessenberg matrices and has never been applied to eigenanalysis for power system small signal stability. This paper analyzes differences between the BR and the QR algorithms with performance comparison in terms of CPU time based on stopping criteria and storage requirement. The BR algorithm utilizes accelerating strategies to improve its performance when computing eigenvalues of narrowly banded, nearly tridiagonal upper Hessenberg matrices. These strategies significantly reduce the computation time at a reasonable level of precision. Compared with the QR algorithm, the BR algorithm requires fewer iteration steps and less storage space without depriving of appropriate precision in solving eigenvalue problems of large-scale power systems. Numerical examples demonstrate the efficiency of the BR algorithm in pursuing eigenanalysis tasks of 39-, 68-, 115-, 300-, and 600-bus systems. Experiment results suggest that the BR algorithm is a more efficient algorithm for large-scale power system small signal stability eigenanalysis.
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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.
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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.
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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.