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Suggested Methods (suggested + methods)

Distribution by Scientific Domains
Mathematics and Statistics40%

Selected Abstracts

Suggested Methods to Mitigate Bias from Nondissolved Petroleum in Ground Water Samples Collected from the Smear Zone

GROUND WATER MONITORING & REMEDIATION, Issue 3 2009
Dawn A. Zemo
This article provides actual site data that confirm that turbid ground water samples collected from within the smear zone at petroleum release sites can be significantly biased high by the inclusion of a nondissolved component that is an artifact of the sampling process. Side-by-side comparisons show that reducing sample turbidity can result in significant reductions of reported concentrations for the ground water samples and that the lower turbidity results are more representative of the petroleum actually dissolved in the ground water. Depending on site-specific factors, ground water sample turbidity can be reduced by four field-based and two laboratory-based methods. These methods should be used routinely at sites where turbid samples with a nondissolved component are being collected. [source]

New and fast statistical-thermodynamic method for computation of protein-ligand binding entropy substantially improves docking accuracy

JOURNAL OF COMPUTATIONAL CHEMISTRY, Issue 11 2005
A. M. Ruvinsky
Abstract We present a novel method to estimate the contributions of translational and rotational entropy to protein-ligand binding affinity. The method is based on estimates of the configurational integral through the sizes of clusters obtained from multiple docking positions. Cluster sizes are defined as the intervals of variation of center of ligand mass and Euler angles in the cluster. Then we suggest a method to consider the entropy of torsional motions. We validate the suggested methods on a set of 135 PDB protein-ligand complexes by comparing the averaged root-mean square deviations (RMSD) of the top-scored ligand docked positions, accounting and not accounting for entropy contributions, relative to the experimentally determined positions. We demonstrate that the method increases docking accuracy by 10,21% when used in conjunction with the AutoDock docking program, thus reducing the percent of incorrectly docked ligands by 1.4-fold to four-fold, so that in some cases the percent of ligands correctly docked to within an RMSD of 2 Ĺ is above 90%. We show that the suggested method to account for entropy of relative motions is identical to the method based on the Monte Carlo integration over intervals of variation of center of ligand mass and Euler angles in the cluster. © 2005 Wiley Periodicals, Inc. J Comput Chem 26: 1089,1095, 2005 [source]

Airway fire due to diathermy during tracheostomy in an intensive care patient

ANAESTHESIA, Issue 5 2001
S. A. Rogers
We describe a case of airway fire in an 83-year-old, critically ill patient. The fire occurred during a surgical tracheostomy under general anaesthesia, following ignition of the tracheal tube by diathermy. After debridement of the burnt tissue and treatment with intravenous antibiotics and glucocorticoids, the patient's respiratory function worsened initially. The patient eventually recovered without long-term sequelae and was discharged from the intensive care unit. The circumstances of this and other similar incidents are reviewed, as are the suggested methods for preventing this frightening occurrence. [source]

Shrinkage drift parameter estimation for multi-factor Ornstein,Uhlenbeck processes

APPLIED STOCHASTIC MODELS IN BUSINESS AND INDUSTRY, Issue 2 2010
Sévérien Nkurunziza
Abstract We consider some inference problems concerning the drift parameters of multi-factors Vasicek model (or multivariate Ornstein,Uhlebeck process). For example, in modeling for interest rates, the Vasicek model asserts that the term structure of interest rate is not just a single process, but rather a superposition of several analogous processes. This motivates us to develop an improved estimation theory for the drift parameters when homogeneity of several parameters may hold. However, the information regarding the equality of these parameters may be imprecise. In this context, we consider Stein-rule (or shrinkage) estimators that allow us to improve on the performance of the classical maximum likelihood estimator (MLE). Under an asymptotic distributional quadratic risk criterion, their relative dominance is explored and assessed. We illustrate the suggested methods by analyzing interbank interest rates of three European countries. Further, a simulation study illustrates the behavior of the suggested method for observation periods of small and moderate lengths of time. Our analytical and simulation results demonstrate that shrinkage estimators (SEs) provide excellent estimation accuracy and outperform the MLE uniformly. An over-ridding theme of this paper is that the SEs provide powerful extensions of their classical counterparts. Copyright © 2009 John Wiley & Sons, Ltd. [source]

Bayesian Methods for Examining Hardy,Weinberg Equilibrium

BIOMETRICS, Issue 1 2010
Jon Wakefield
Summary Testing for Hardy,Weinberg equilibrium is ubiquitous and has traditionally been carried out via frequentist approaches. However, the discreteness of the sample space means that uniformity of,p -values under the null cannot be assumed, with enumeration of all possible counts, conditional on the minor allele count, offering a computationally expensive way of,p -value calibration. In addition, the interpretation of the subsequent,p -values, and choice of significance threshold depends critically on sample size, because equilibrium will always be rejected at conventional levels with large sample sizes. We argue for a Bayesian approach using both Bayes factors, and the examination of posterior distributions. We describe simple conjugate approaches, and methods based on importance sampling Monte Carlo. The former are convenient because they yield closed-form expressions for Bayes factors, which allow their application to a large number of single nucleotide polymorphisms (SNPs), in particular in genome-wide contexts. We also describe straightforward direct sampling methods for examining posterior distributions of parameters of interest. For large numbers of alleles at a locus we resort to Markov chain Monte Carlo. We discuss a number of possibilities for prior specification, and apply the suggested methods to a number of real datasets. [source]