Transformation and Weighting in Regression

Carroll, Raymond J.; Ruppert, David

doi:10.1007/978-1-4899-2873-3

Cited by 1,170 publications

(770 citation statements)

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“…Values were expressed as the weighted mean and standard error for three replicate experiments. The weight for the individual experiments was inversely proportional to the variance of the experimental results [26].…”

Section: Methodsmentioning

confidence: 99%

Probing the inhibitor‐binding site of aldose reductase with site‐directed mutagenesis

Hohman

El‐Kabbani

Malamas

et al. 1998

European Journal of Biochemistry

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Aldose reductase (AR) has been implicated in the etiology of the secondary complications of diabetes, and enzyme inhibitors have been proposed as therapeutic agents. While effectively preventing the development of diabetic complications in animals, results from clinical studies of AR inhibitors have been disappointing, possibly due to poor potency in man. To assist in the design of more potent and specific inhibitors, crystallographic studies have attempted to identify enzyme-inhibitor interactions. Resolution of crystal complexes has suggested that the inhibitors bind to the enzyme active site and are held in place through hydrogen bonding and van der Waals interactions formed within two hydrophobic pockets. To confirm and extend these findings we quantified inhibitor activity with single, site-directed, mutant, human AR enzymes in which the apolar active-site residues tryptophan 20, Ϫ79, Ϫ111 and phenylalanine 115 were replaced with alanine or tyrosine, decreasing the potential for van der Waals interactions.Consistent with molecular models, the inhibitory activity of Tolrestat, Sorbinil and Zopolrestat decreased 800Ϫ2000-fold when tested with the mutant enzyme in which Trp20 was replaced with alanine. Further, alanine substitution for Trp111 decreased Zopolrestat's activity 400-fold, while mutations to Trp79 and Phe115 had little effect on the activity of any of the inhibitors. The alanine mutation at Trp111 had no effect on Tolrestat's activity but decreased the activity of Sorbinil by about 1000-fold. These latter effects were unanticipated based on the number of non-bonded interactions between the inhibitors, Tolrestat and Sorbinil, and Trp20 and Trp111 that have been identified in the crystal structures. In spite of these unexpected findings, our results are consistent with the hypothesis that AR inhibitors occupy the enzyme active site and that hydrophobic interactions between the enzyme and inhibitor contribute to inhibitor binding stability.Keywords : site-directed mutagenesis ; human aldose reductase; enzyme inhibition; polyol pathway ; diabetes.The polyol pathway has been implicated in the etiology of the secondary complications of diabetes. There are two enzymes in this pathway. The first, aldose reductase (AR), catalyzes the reduction of glucose to sorbitol and the second, sorbitol dehydrogenase, catalyzes the oxidation of sorbitol to fructose. AR has a relatively low affinity for glucose and during conditions of normal glycemia (80Ϫ110 mg/dl), where cellular glucose rapidly becomes phosphorylated via hexokinase, little substrate enters the pathway. During hyperglycemia, however, the cellular level of glucose greatly increases in tissues where glucose entry is independent of insulin. In these tissues which include the lens, retina, kidney and peripheral nerves, all sites of diabetes-induced

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Section: Methodsmentioning

confidence: 99%

Probing the inhibitor‐binding site of aldose reductase with site‐directed mutagenesis

Hohman

El‐Kabbani

Malamas

et al. 1998

European Journal of Biochemistry

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“…(9). Although some caution has been expressed towards the use of WLSM for non-precisely estimated weights, 28 in the present study, uncertainties in the volumes of the attached particles and, consequently, in the critical retention concentrations were accurately calculated within the experimental uncertainty of PAMAS. Applying the WLSM to the quadratic function (9), the following expressions and values for coefficients a, b, and model parameter U W LSM min are obtained with their CSUs and RCSUs listed in Table III:…”

Section: A Evaluation Of Uncertainty For Extrapolated Resultsmentioning

confidence: 82%

Critical analysis of uncertainties during particle filtration

Badalyan

Carageorgos

Bedrikovetsky

et al. 2012

Review of Scientific Instruments

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Using the law of propagation of uncertainties we show how equipment- and measurement-related uncertainties contribute to the overall combined standard uncertainties (CSU) in filter permeability and in modelling the results for polystyrene latex microspheres filtration through a borosilicate glass filter at various injection velocities. Standard uncertainties in dynamic viscosity and volumetric flowrate of microspheres suspension have the greatest influence on the overall CSU in filter permeability which excellently agrees with results obtained from Monte Carlo simulations. Two model parameters “maximum critical retention concentration” and “minimum injection velocity” and their uncertainties were calculated by fitting two quadratic mathematical models to the experimental data using a weighted least squares approximation. Uncertainty in the internal cake porosity has the highest impact on modelling uncertainties in critical retention concentration. The model with the internal cake porosity reproduces experimental “critical retention concentration vs velocity”-data better than the second model which contains the total electrostatic force whose value and uncertainty have not been reliably calculated due to the lack of experimental dielectric data.

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“…The variance structure in this model is the same one used by Harvey (1976), Cook and Weisberg (1983), Carroll and Ruppert (1988), and others. It has been studied in the context of 2 k factorial models by Wang (1989), who discussed maximum likelihood estimation and derived properties of the estimates, as well as by Nair and Pregibon (1988), Engel and Huele (1996), and others.…”

Section: Effect Selection Using a Joint Location And Dispersion Modelmentioning

confidence: 99%

Joint Identification of Location and Dispersion Effects in Unreplicated Two-Level Factorials

Henrey

Loughin

2017

Technometrics

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Most procedures that have been proposed to identify dispersion effects in unreplicated factorial designs assume that location effects have been identified correctly. Incorrect identification of location effects may impair subsequent identification of dispersion effects. We develop a model for joint identification of location and dispersion effects that can reliably identify active effects of both types. The joint model is estimated using maximum likelihood, and hence effect selection is done using a specially derived information criterion. An exhaustive search through a limited version of the space of possible models is conducted.Both a single-model output and model averaging are considered. The method is shown to be capable of identifying sensible location-dispersion models that are missed by methods that rely on sequential estimation of location and dispersion effects.

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Transformation and Weighting in Regression

Cited by 1,170 publications

References 0 publications

Probing the inhibitor‐binding site of aldose reductase with site‐directed mutagenesis

Probing the inhibitor‐binding site of aldose reductase with site‐directed mutagenesis

Critical analysis of uncertainties during particle filtration

Joint Identification of Location and Dispersion Effects in Unreplicated Two-Level Factorials

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