910 resultados para Automated quantification
Resumo:
Petasis and Ugi reactions are used successively without intermediate purification, effectively accomplishing a six-component reaction. The examined reactions are transferred from traditional batch reactors to an automated continuous flow microreactor setup, where optimization and kinetic analyses are performed, proposed mechanisms evaluated, and rate-limiting steps determined.
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Background: The electroencephalogram (EEG) may be described by a large number of different feature types and automated feature selection methods are needed in order to reliably identify features which correlate with continuous independent variables. New method: A method is presented for the automated identification of features that differentiate two or more groups inneurologicaldatasets basedupona spectraldecompositionofthe feature set. Furthermore, the method is able to identify features that relate to continuous independent variables. Results: The proposed method is first evaluated on synthetic EEG datasets and observed to reliably identify the correct features. The method is then applied to EEG recorded during a music listening task and is observed to automatically identify neural correlates of music tempo changes similar to neural correlates identified in a previous study. Finally,the method is applied to identify neural correlates of music-induced affective states. The identified neural correlates reside primarily over the frontal cortex and are consistent with widely reported neural correlates of emotions. Comparison with existing methods: The proposed method is compared to the state-of-the-art methods of canonical correlation analysis and common spatial patterns, in order to identify features differentiating synthetic event-related potentials of different amplitudes and is observed to exhibit greater performance as the number of unique groups in the dataset increases. Conclusions: The proposed method is able to identify neural correlates of continuous variables in EEG datasets and is shown to outperform canonical correlation analysis and common spatial patterns.
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Contamination of the electroencephalogram (EEG) by artifacts greatly reduces the quality of the recorded signals. There is a need for automated artifact removal methods. However, such methods are rarely evaluated against one another via rigorous criteria, with results often presented based upon visual inspection alone. This work presents a comparative study of automatic methods for removing blink, electrocardiographic, and electromyographic artifacts from the EEG. Three methods are considered; wavelet, blind source separation (BSS), and multivariate singular spectrum analysis (MSSA)-based correction. These are applied to data sets containing mixtures of artifacts. Metrics are devised to measure the performance of each method. The BSS method is seen to be the best approach for artifacts of high signal to noise ratio (SNR). By contrast, MSSA performs well at low SNRs but at the expense of a large number of false positive corrections.
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A fully automated and online artifact removal method for the electroencephalogram (EEG) is developed for use in brain-computer interfacing. The method (FORCe) is based upon a novel combination of wavelet decomposition, independent component analysis, and thresholding. FORCe is able to operate on a small channel set during online EEG acquisition and does not require additional signals (e.g. electrooculogram signals). Evaluation of FORCe is performed offline on EEG recorded from 13 BCI particpants with cerebral palsy (CP) and online with three healthy participants. The method outperforms the state-of the-art automated artifact removal methods Lagged auto-mutual information clustering (LAMIC) and Fully automated statistical thresholding (FASTER), and is able to remove a wide range of artifact types including blink, electromyogram (EMG), and electrooculogram (EOG) artifacts.
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Of the many sources of urban greenhouse gas (GHG) emissions, solid waste is the only one for which management decisions are undertaken primarily by municipal governments themselves and is hence often the largest component of cities’ corporate inventories. It is essential that decision-makers select an appropriate quantification methodology and have an appreciation of methodological strengths and shortcomings. This work compares four different waste emissions quantification methods, including Intergovernmental Panel on Climate Change (IPCC) 1996 guidelines, IPCC 2006 guidelines, U.S. Environmental Protection Agency (EPA) Waste Reduction Model (WARM), and the Federation of Canadian Municipalities- Partners for Climate Protection (FCM-PCP) quantification tool. Waste disposal data for the greater Toronto area (GTA) in 2005 are used for all methodologies; treatment options (including landfill, incineration, compost, and anaerobic digestion) are examined where available in methodologies. Landfill was shown to be the greatest source of GHG emissions, contributing more than three-quarters of total emissions associated with waste management. Results from the different landfill gas (LFG) quantification approaches ranged from an emissions source of 557 kt carbon dioxide equivalents (CO2e) (FCM-PCP) to a carbon sink of −53 kt CO2e (EPA WARM). Similar values were obtained between IPCC approaches. The IPCC 2006 method was found to be more appropriate for inventorying applications because it uses a waste-in-place (WIP) approach, rather than a methane commitment (MC) approach, despite perceived onerous data requirements for WIP. MC approaches were found to be useful from a planning standpoint; however, uncertainty associated with their projections of future parameter values limits their applicability for GHG inventorying. MC and WIP methods provided similar results in this case study; however, this is case specific because of similarity in assumptions of present and future landfill parameters and quantities of annual waste deposited in recent years being relatively consistent.
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Observations from the Heliospheric Imager (HI) instruments aboard the twin STEREO spacecraft have enabled the compilation of several catalogues of coronal mass ejections (CMEs), each characterizing the propagation of CMEs through the inner heliosphere. Three such catalogues are the Rutherford Appleton Laboratory (RAL)-HI event list, the Solar Stormwatch CME catalogue, and, presented here, the J-tracker catalogue. Each catalogue uses a different method to characterize the location of CME fronts in the HI images: manual identification by an expert, the statistical reduction of the manual identifications of many citizen scientists, and an automated algorithm. We provide a quantitative comparison of the differences between these catalogues and techniques, using 51 CMEs common to each catalogue. The time-elongation profiles of these CME fronts are compared, as are the estimates of the CME kinematics derived from application of three widely used single-spacecraft-fitting techniques. The J-tracker and RAL-HI profiles are most similar, while the Solar Stormwatch profiles display a small systematic offset. Evidence is presented that these differences arise because the RAL-HI and J-tracker profiles follow the sunward edge of CME density enhancements, while Solar Stormwatch profiles track closer to the antisunward (leading) edge. We demonstrate that the method used to produce the time-elongation profile typically introduces more variability into the kinematic estimates than differences between the various single-spacecraft-fitting techniques. This has implications for the repeatability and robustness of these types of analyses, arguably especially so in the context of space weather forecasting, where it could make the results strongly dependent on the methods used by the forecaster.
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The components of many signaling pathways have been identified and there is now a need to conduct quantitative data-rich temporal experiments for systems biology and modeling approaches to better understand pathway dynamics and regulation. Here we present a modified Western blotting method that allows the rapid and reproducible quantification and analysis of hundreds of data points per day on proteins and their phosphorylation state at individual sites. The approach is of particular use where samples show a high degree of sample-to-sample variability such as primary cells from multiple donors. We present a case study on the analysis of >800 phosphorylation data points from three phosphorylation sites in three signaling proteins over multiple time points from platelets isolated from ten donors, demonstrating the technique's potential to determine kinetic and regulatory information from limited cell numbers and to investigate signaling variation within a population. We envisage the approach being of use in the analysis of many cellular processes such as signaling pathway dynamics to identify regulatory feedback loops and the investigation of potential drug/inhibitor responses, using primary cells and tissues, to generate information about how a cell's physiological state changes over time.
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Proteins from dromedary camel milk (CM) produced in Europe were separated and quantified by capillary electrophoresis (CE). CE analysis showed that camel milk lacks b-lactoglobulin and consists of high concentration of a-lactalbumin (2.01 ± 0.02 mg mL-1), lactoferrin (1.74 ± 0.06 mg mL-1) and serum albumin (0.46 ± 0.01 mg mL-1 ). Among caseins, the concentration of b-casein (12.78 ± 0.92 mg mL-1) was found the highest followed by a-casein (2.89 ± 0.29 mg mL-1) while k-casein represented only minor amount (1.67 ± 0.01 mg mL-1). These results were in agreement with sodium dodecyl sulphatepolyacrylamide gel electrophoresis patterns. Overall, CE offers a quick and reliable method for the determination of major CM proteins, which may be responsible for the many nutritional and health properties of CM.
Resumo:
Temperature, pressure, gas stoichiometry, and residence time were varied to control the yield and product distribution of the palladium-catalyzed aminocarbonylation of aromatic bromides in both a silicon microreactor and a packed-bed tubular reactor. Automation of the system set points and product sampling enabled facile and repeatable reaction analysis with minimal operator supervision. It was observed that the reaction was divided into two temperature regimes. An automated system was used to screen steady-state conditions for offline analysis by gas chromatography to fit a reaction rate model. Additionally, a transient temperature ramp method utilizing online infrared analysis was used, leading to more rapid determination of the reaction activation energy of the lower temperature regimes. The entire reaction spanning both regimes was modeled in good agreement with the experimental data.
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The preference for ready-to-eat sliced foods may pose an increased risk for food-borne diseases and a major concern is the presence of Listeria monocytogenes L monocytogenes was assessed in two types of products cooked ham and salami One hundred and thirty samples of each product were acquired in retail shops in the city of Sao Paulo and submitted to laboratory analysis The rate of positives was significantly higher in salami samples than in ham samples (62% and 0 8% respectively) L. monocytogenes counts in salami samples varied between <10 and 1900 colony-forming units per gram (CFU/g) The serotypes found in both products were as follows according to incidence 4b (37 5%) 1/2b (25%) 3b (25%) and 1/2c (12 5%) Based on the results of the present study the authors suggest that the risk of listeriosis resulting from the consumption of salami is higher than that associated with the consumption of cooked ham (C) 2010 Elsevier Ltd All rights reserved
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A rapid, sensitive and specific LC-MS/MS method was developed and validated for quantifying chlordesmethyldiazepam (CDDZ or delorazepam), the active metabolite of cloxazolam, in human plasma. In the analytical assay, bromazepam (internal standard) and CDDZ were extracted using a liquid-liquid extraction (diethyl-ether/hexane, 80/20, v/v) procedure. The LC-MS/MS method on a RP-C18 column had an overall run time of 5.0 min and was linear (1/x weighted) over the range 0.5-50 ng/mL (R > 0.999). The between-run precision was 8.0% (1.5 ng/mL), 7.6% (9 ng/mL), 7.4% (40 ng/mL), and 10.9% at the low limit of quantification-LLOQ (0.500 ng/mL). The between-run accuracies were 0.1, -1.5, -2.7 and 8.7% for the above mentioned concentrations, respectively. All current bioanalytical method validation requirements (FDA and ANVISA) were achieved and it was applied to the bioequivalence study (Cloxazolam-test, Eurofarma Lab. Ltda and Olcadil (R)-reference, Novartis Biociencias S/A). The relative bioavailability between both formulations was assessed by calculating individual test/reference ratios for Cmax, AUClast and AUCO-inf. The pharmacokinetic profiles indicated bioequivalence since all ratios were as proposed by FDA and ANVISA. Copyright (C) 2009 John Wiley & Sons, Ltd.
Resumo:
A rapid, sensitive and specific method for quantifying ciprofibrate in human plasma using bezafibrate as the internal standard (IS) is described. The sample was acidified prior extraction with formic acid (88%). The analyte and the IS were extracted from plasma by liquid-liquid extraction using an organic solvent (diethyl ether/dichloromethane 70/30 (v/v)). The extracts were analyzed by high performance liquid chromatography coupled with electrospray tandem mass spectrometry (HPLC-MS/MS). Chromatography was performed using Genesis C18 4 mu m analytical column (4.6 x 150 mm i.d.) and a mobile phase consisting of acetonitrile/water (70/30, v/v) and 1 mM acetic acid. The method had a chromatographic run time of 3.4 min and a linear calibration curve over the range 0.1-60 mu g/mL (r > 0.99). The limit of quantification was 0.1 mu g/mL. The intra- and interday accuracy and precision values of the assay were less than 13.5%. The stability tests indicated no significant degradation. The recovery of ciprofibrate was 81.2%, 73.3% and 76.2% for the 0.3, 5.0 and 48.0 ng/mL standard concentrations, respectively. For ciprofibrate, the optimized parameters of the declustering potential, collision energy and collision exit potential were -51 V, -16 eV and -5 V, respectively. The method was also validated without the use of the internal standard. This HPLC-MS/MS procedure was used to assess the bioequivalence of two ciprofibrate 100 mg tablet formulations in healthy volunteers of both sexes. The following pharmacokinetic parameters were obtained from the ciprofibrate plasma concentration vs. time curves: AUC(last), AUC(0-168 h), C(max) and T(max). The geometric mean with corresponding 90% confidence interval (CI) for test/reference percent ratios were 93.80% (90% CI = 88.16-99.79%) for C(max), 98.31% (90% CI = 94.91-101.83%) for AUC(last) and 97.67% (90% CI = 94.45-101.01%) for AUC(0-168 h). Since the 90% Cl for AUC(last), AUC(0-168 h) and C(max) ratios were within the 80-125% interval proposed by the US FDA, it was concluded that ciprofibrate (Lipless (R) 100 mg tablet) formulation manufactured by Biolab Sanus Farmaceutica Ltda. is bioequivalent to the Oroxadin (R) (100 mg tablet) formulation for both the rate and the extent of absorption. (C) 2011 Published by Elsevier B.V.
Resumo:
Teixeira SRL, Mattarazo F, Feres M, Figueiredo LC, de Faveri M, Simionato MRL, Mayer MPA. Quantification of Porphyromonas gingivalis and fimA genotypes in smoker chronic periodontitis. J Clin Periodontol 2009; 36: 482-487. doi: 10.1111/j.1600-051X.2009.01411.x. Porphyromonas gingivalis fimA genotypes were associated with virulence factors in vitro, but little evidence of an association with disease severity were shown in humans. We aimed to correlate levels of P. gingivalis fimA genotypes II and IV and probing depth in smoker-chronic periodontitis subjects. One hundred and sixty eight subgingival samples of 20 smokers non-treated chronic periodontitis subjects obtained from sites with different probing depths [shallow (<= 3 mm), intermediate (4-6 mm), deep (>= 7 mm)] were analysed by real-time PCR for P. gingivalis and genotypes fimA II and IV. P. gingivalis and fimA IV were detected in all subjects, whereas fimA II was detected in 18 subjects (90%). One hundred and fifty two sites (90.5%) harboured P. gingivalis. Genotypes II and IV were detected in 28% and 69.6% of sites, respectively. The proportions of genotypes II and IV in relation to P. gingivalis levels were similar in shallow, intermediate and deep probing sites (2.4%, 4.6%, 1.4% for genotype II and 15.5%, 17.7%, 11.7% for genotype IV, respectively), indicating that other non-tested genotypes were more abundant. Increased levels of genotype IV were associated with increasing probing depth, but not of genotype II. The data suggested an association between P. gingivalis genotype fimA IV and disease severity in smoker-chronic periodontitis subjects.