Average Correlation Factor Research Articles

Monitoring Direct Thrombin Inhibitors With Calibrated Diluted Thrombin Time vs Activated Partial Thromboplastin Time in Pediatric Patients.

Activated partial thromboplastin time (aPTT) is the primary test used to monitor intravenous (IV) direct thrombin inhibitors (DTIs) but has many limitations. The plasma diluted thrombin time (dTT) has shown better correlation with DTI levels than aPTT. This study compared dose-response curves for dTT and aPTT in pediatric patients receiving argatroban and bivalirudin. A retrospective review of pediatric patients treated with argatroban (n = 45) or bivalirudin (n = 14) monitored with dTT and aPTT. The dTT assay was calibrated to report DTI concentrations in µg/mL for argatroban and bivalirudin with good analytic sensitivity and specificity. The dTT was fivefold more likely to show a stable dose-response slope than the aPTT (P < .0002; odds ratio, 4.9). For patients in whom both dTT and aPTT showed a significant correlation between dose and assay results, dTT had a higher average correlation factor compared with aPTT (P = .007). Argatroban dose-response slopes showed more inter- and intrapatient variation than bivalirudin (dose-response slope coefficient of variation, 132% vs 52%). The dTT assay was more likely to show a stable dose response and have a stronger correlation with DTI dose than aPTT. Argatroban shows more variation in dose response than bivalirudin.

DEVELOPMENT AND EVALUATION OF NOVEL ESTIMATION TECHNIQUES FOR IN VITRO DISSOLUTION STUDY AND VALIDATION PROTOCOL FOR ESCITALOPRAM AS ANTIDEPRESSANT DRUG AND THEIR FORMULATION

Objective: To develop and validate the RP-HPLC method and in vitro dissolution study for escitalopram as antidepressant drug and their formulation.&#x0D; Methods: The chromatographic separation was done by using a C-18, 150 mm column and a mobile phase consisting of phosphate buffer (40%) and acetonitrile HPLC grade (60%). Detection was carried out at 211 nm with a flow rate of 1 ml/min with an injection of 20 μl. The method was validated with different parameters such as linearity, precision, accuracy, robustness, and limit of detection (LOD), the limit of quantification (LOQ) according to ICH guidelines.&#x0D; Results: The linear calibration curve was obtained in the concentration range of 0-50 μg/ml and gave an average correlation factor 0.992. The retention time was observed at 2.96 min. The Minimum concentration level at which the analyte can be reliably detected (LOD) and quantified (LOQ) were found to be 0.03 and 0.09 µg/ml, respectively. The relative standard deviation of intra and the inter-day assay was found to be less than 2. The dissolution studies show moderate dissolution (23.4%) after 45 min, but it reaches a plateau after approximately 25 min.&#x0D; Conclusion: This method was found to be simple, rapid and economic with less run time. The validated parameters manifest the method is reliable, linear, accurate and precise as well as robust with minor variations in chromatographic parameters. Therefore, the developed method can be applied for both routine analysis and quality control assay and it could be a very powerful tool to investigate the stability of escitalopram.

A phenomenological model utilizing Voce’s strain-hardening model describing high strain rate behavior of alloy 800H during hot working

In this study, a simple mathematical model is introduced to predict the stress–strain curves under the occurrence of minor dynamic recrystallization. In this regard, first, with the assumption of nonlinear variation of strain-hardening rate versus strain, a mathematical equation was derived to obtain the strain-hardening rate. Then, using the Voce’s equation, a new mathematical model was developed to predict the hot deformation behavior of metallic alloys at high strain rates. The most important feature of the proposed model is its simplicity so that the only constant of the stress–strain equation can easily be interpolated at different temperatures and strain rates. Accordingly, it was found that the defined material constant varies linearly and exponentially with strain rate and temperature, respectively. The high correlation factors (average correlation factor, r2 = 0.85) obtained from regression analysis showed that the model can provide a precise estimation of flow stress curves for the defined deformation conditions.

Effective Average Correlation Factor and Excess Permittivity for Dielectric Studies on Binary Systems of Phthalates with Alcohols and Nitriles

Effective Average Correlation Factor and Excess Permittivity for Dielectric Studies on Binary Systems of Phthalates with Alcohols and Nitriles

The Information Content of Option Implied Moments and Co-Moments: Extended Abstract

We introduce a new tensor derived computation of higher dimensional average implied correlations determined from high frequency options panels. Using supersymmetric identities to decrease we can determine average moments and co-moments for equities and incorporate these forward looking measures to a standard asset pricing framework. To compute the metrics we use both a broad market index (S&P 500) and nine sector indices versus the underlying components. Our empirical analysis shows that the cross section stock returns have substantial exposure to risk captured by the market average correlation factor. The results are robust to various permutations of the estimation procedure. The risk premium of the market average correlation factor is statistically and economically significant when controlling the other common market risk factors or firm characteristics. Finally, we test the higher-order CAPM with the ex ante market beta, gamma, and theta approximated by the option-implied average correlations. In line with the evidence documented in previous literature, we find positive significant risk premiums for the ex ante market beta and theta but mixed results for the ex ante market gamma.

Application of a radial basis function neural network to estimate pressure gradient in water–oil pipelines

An accurate determination of the pressure gradient is required for efficient designing of oil and gas wells and pipe systems. Despite the recent improvements in accuracy of models and correlations developed for determining the pressure gradient, they are still incapable of estimating the pressure drop with desired accuracy. Therefore, a robust model is required to determine the pressure gradient precisely. Regarding high performance and great robustness of Artificial Neural Networks for solving science and engineering problems, this paper presents a Radial Basis Function Neural Network (RBF-NN) model to determine the pressure gradient. The model was developed over 994 experimental data sets which are covering a wide range of variables such as oil slip velocity, water slip velocity, pipe diameter, pipe roughness and oil viscosity.The model estimation indicated an average relative deviation of 0.92%, an average absolute relative deviation of 8.25% and an average correlation factor of 0.99. A comparison between the proposed model and the most prominent models and correlations illustrated that the RBF-NN model exclusively out-performs other models and correlations and the estimated values are in great agreement with the experimental data. At last, a sensitivity analysis was applied to clarify the effect of input parameters in estimated results.

Assessing element distribution and speciation in a stream at abandoned Pb–Zn mining site by combining classical, in-situ DGT and modelling approaches

The distribution and speciation of elements along a stream subjected to neutralised acid mine drainage (NAMD) effluent waters (Mátra Mountain, Hungary; Toka stream) were studied by a multi-methodological approach: dissolved and particulate fractions of elements were determined by HR-ICPMS, whereas speciation was carried out by DGT, supported by speciation modelling performed by Visual MINTEQ. Before the NAMD discharge, the Toka is considered as a pristine stream, with averages of dissolved concentrations of elements lower than world averages. A considerable increase of element concentrations caused by effluent water inflow is followed by a sharp or gradual concentration decrease. A large difference between total and dissolved concentrations was found for Fe, Al, Pb, Cu, Zn and As in effluent water and at the first downstream site, with high correlation factors between elements in particulate fraction, indicating their common behaviour, governed by the formation of ferri(hydr)oxides (co)precipitates. In-situ speciation by the DGT technique revealed that Zn, Cd, Ni, Co, Mn and U were predominantly present as a labile, potentially bioavailable fraction (>90%). The formation of strong complexes with dissolved organic matter (DOM) resulted in a relatively low DGT-labile concentration of Cu (42%), while low DGT-labile concentrations of Fe (5%) and Pb (12%) were presumably caused by their existence in colloidal (particulate) fraction which is not accessible to DGT. Except for Fe and Pb, a very good agreement between DGT-labile concentrations and those predicted by the applied speciation model was obtained, with an average correlation factor of 0.96. This study showed that the in-situ DGT technique in combination with model-predicted speciation and classical analysis of samples could provide a reasonable set of data for the assessment of the water quality status (WQS), as well as for the more general study of overall behaviour of the elements in natural waters subjected to high element loads.

Comparison of three artificial models of the magnetohydrodynamic effect on the electrocardiogram

The electrocardiogram (ECG) is often acquired during magnetic resonance imaging (MRI), but its analysis is restricted by the presence of a strong artefact, called magnetohydrodynamic (MHD) effect. MHD effect is induced by the flow of electrically charged particles in the blood perpendicular to the static magnetic field, which creates a potential of the order of magnitude of the ECG and temporally coincident with the repolarisation period. In this study, a new MHD model is proposed by using MRI-based 4D blood flow measurements made across the aortic arch. The model is extended to several cardiac cycles to allow the simulation of a realistic ECG acquisition during MRI examination and the quality assessment of MHD suppression techniques. A comparison of two existing models, based, respectively, on an analytical solution and on a numerical method-based solution of the fluids dynamics problem, is made with the proposed model and with an estimate of the MHD voltage observed during a real MRI scan. Results indicate a moderate agreement between the proposed model and the estimated MHD model for most leads, with an average correlation factor of 0.47. However, the results demonstrate that the proposed model provides a closer approximation to the observed MHD effects and a better depiction of the complexity of the MHD effect compared with the previously published models, with an improved correlation (), coefficient of determination () and fraction of energy () compared with the best previous model. The source code will be made freely available under an open source licence to facilitate collaboration and allow more rapid development of more accurate models of the MHD effect.

Attribute selection impact on linear and nonlinear regression models for crop yield prediction.

Efficient cropping requires yield estimation for each involved crop, where data-driven models are commonly applied. In recent years, some data-driven modeling technique comparisons have been made, looking for the best model to yield prediction. However, attributes are usually selected based on expertise assessment or in dimensionality reduction algorithms. A fairer comparison should include the best subset of features for each regression technique; an evaluation including several crops is preferred. This paper evaluates the most common data-driven modeling techniques applied to yield prediction, using a complete method to define the best attribute subset for each model. Multiple linear regression, stepwise linear regression, M5′ regression trees, and artificial neural networks (ANN) were ranked. The models were built using real data of eight crops sowed in an irrigation module of Mexico. To validate the models, three accuracy metrics were used: the root relative square error (RRSE), relative mean absolute error (RMAE), and correlation factor (R). The results show that ANNs are more consistent in the best attribute subset composition between the learning and the training stages, obtaining the lowest average RRSE (86.04%), lowest average RMAE (8.75%), and the highest average correlation factor (0.63).

Towards overcoming TOC increase in wastewater treated with Moringa oleifera seed extract

Moringa oleifera seed extract was purified for treating textile wastewater effluents. Whereas the classical problem of these kinds of coagulants is the increase of TOC, purified M. oleifera seed extract was efficient in the removal of several anionic dyes and did not present this drawback. Specifically, Moringa purified coagulant was able to remove Acid Red 88, an azo dye, with a maximum coagulant capacity of ca. 1.2mgmg−1. The dye–coagulant system was adequately studied under the perspective of the Response Surface Methodology and an optimum combination of 17mgL−1 of coagulant concentration and 78mgL−1 of initial dye content was established. This system was modeled according to Langmuir hypothesis and gave an average correlation factor (r2) of 0.89. M. oleifera coagulant, after purification, did not increase the TOC concentration in the treated water and the final levels of such parameter were comparable to those obtained with alum or other coagulants, such as tannin-derived ones.

The diaphragm as an anatomic surrogate for lung tumor motion

Lung tumor motion due to respiration poses a challenge in the application of modern three-dimensional conformal radiotherapy. Direct tracking of the lung tumor during radiation therapy is very difficult without implanted fiducial markers. Indirect tracking relies on the correlation of the tumor's motion and the surrogate's motion. The present paper presents an analysis of the correlation between tumor motion and diaphragm motion in order to evaluate the potential use of diaphragm as a surrogate for tumor motion. We have analyzed the correlation between diaphragm motion and superior–inferior lung tumor motion in 32 fluoroscopic image sequences from ten lung cancer patients. A simple linear model and a more complex linear model that accounts for phase delays between the two motions have been used. Results show that the diaphragm is a good surrogate for tumor motion prediction for most patients, resulting in an average correlation factor of 0.94 and 0.98 with each model respectively. The model that accounts for delays leads to an average localization prediction error of 0.8 mm and an error at the 95% confidence level of 2.1 mm. However, for one patient studied, the correlation is much weaker compared to other patients. This indicates that, before using diaphragm for lung tumor prediction, the correlation should be examined on a patient-by-patient basis.

Experience from retrospective radon exposure estimations for individuals in a radon epidemiological study using solid-state nuclear track detectors

The relation between increased risk of lung cancer and exposure to indoor radon is assessed in epidemiological studies. Both the quality and reliability of smoking data and the radon exposure data are of primary importance. Contemporary measurement of radon concentration in the dwellings of individuals in a case-control study is traditionally used to assess past history of radon exposure. These assessments are somewhat unreliable since presently measured radon concentration might not be representative for a given location long ago. The measurement of long-lived decay products from 222Rn remaining indoors on hard surfaces, such as glass, makes it possible to assess the exposure to indoor radon. At the Swedish Radiation Protection Institute, a combination of two different solid-state nuclear track detectors has been developed to assess the 210Pb activity implanted in glass surfaces by measuring 210Po alpha activity. This detector (a RETRO detector) is used in the Swedish radon epidemiological case-control study of non-smokers with the aim to provide an alternative estimate of individual radon exposure and to evaluate the usefulness of RETRO measurements. A total of 576 different objects were found and 568 were measured. For 225 individuals, we measured two personal objects that had been in the same person's possession for more than 20 years. The standard deviation of the average radon concentration obtained from these two objects had a median value of 13 Bq/m 3 indicating a precision of exposure of approximately 20%. The correlation between 210Po surface activity measured earlier and the mean values of radon concentrations in a number of Swedish dwellings is used to estimate the historical, average radon concentration. This average correlation factor seems also to be valid for measurements in the non-smoker epidemiological study.

Multiple‐wavelength Raman lidar measurements of atmospheric water vapor

Height profiles of atmospheric water vapor obtained using a multiple‐wavelength Raman lidar are examined. The water vapor profiles exhibit vertical structure with scales on the order of the resolution of the lidar (75 m). To determine whether such structure is atmospheric in origin, measurements obtained simultaneously in a common volume at two independent wavelengths were compared. Correlation of the gradients of the water vapor profiles obtained from these two wavelengths yielded an average correlation factor of 0.88. It was also observed that for the given meteorological conditions, the vertical structure decorrelated with a time constant of approximately three hours.

On-line monitoring of pullulanase production during continuous culture of Clostridium thermosulfurogenes

An automated turbidimetric immunoassay for pullulanase from Clostridium thermosulfurogenes was developed for on-line bioprocess monitoring. Measurements are based on the turbidity caused by specific aggregation between pullulanase molecules present in the cultivation medium and antibodies against these enzymes. The time for one assay cycle is 3.0 min. Pullulanase concentrations between 10 units (U)/l and 1000 U/l could be measured. Standard deviations of less than 2% were found. The analysis system could be successfully employed for on-line product monitoring during a 240-h cultivation of C. thermosulfurogenes. An average correlation factor of 0.975 was obtained for the pullulanase data from on-line turbidimetric and the off-line reference assay, demonstrating the accuracy of this on-line method.

On diffusion kinetics in ordered binary alloys

Abstract It is shown that for self–diffusion by means of vacancies, in an ordered binary alloy with the B2 (CsCl) structure the diffusion coefficients can be decomposed into three relevant factors : the average correlation factor, the average vacancy–availability factor and the average atom–vacancy exchange rate. Within a model of only nearest–neighbour interaction the full procedure to obtain reasonably reliable estimates of all these factors is described. In this treatment Monte Carlo computer simulation serves as a criterion. The pair approximation of Kikuchi and Sato (1969, 1972} is found to give good results for vacancy availability and exchange frequency. An interpretation is given of the theory of Manning (1967, 1971) for the random alloy and it is demonstrated how a modification can be applied to calculate correlation factors for diffusion in ordered structures. The results always turn out to be better than the values obtained by Kikuchi and Sato's path–probability calculations. In a large majority of cases the agreement with the results of computer simulation is satisfactory. Only under the condition of extreme ratios of exchange frequencies of both species, do discrepancies occur. However, those situations are traceable.