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At least 1,369 records · Page 76Linked to original sources
PubMed2003

Three-dimensional quantitative structure-activity relationship studies on selected MT1 and MT2 melatonin receptor ligands: requirements for subtype selectivity and intrinsic activity modulation.

The three-dimensional quantitative structure-activity relationship comparative molecular field analysis (3D-QSAR CoMFA) approach was applied to some classes of melatonin (MLT) membrane receptor ligands, with the principal aim of exploring the correlation between their steric features and MT(2)-selective antagonism. Binding data obtained from cloned MT(1) and MT(2) receptor subtypes were used to develop 3D-QSAR models for agonists and for antagonists at the two receptor subtypes, looking for the structural requirements for receptor subtype selectivity. In particular, we superposed the compounds showing antagonist activity, or very low intrinsic activity at the GTPgammaS test, following the hypothesis that the occupation of an additional pocket positioned out of the plane of MLT is one of the major determinants for MT(2) selectivity; the statistical models obtained confirmed this hypothesis. Structure-intrinsic activity relationship studies, applied to a set of compounds homogeneously tested, allowed the identification of the structural features whose modulation shifts the behavior from that of the agonist to that of the antagonist. The pocket out of the plane of MLT was identified as one of the key features for obtaining selective MT(2) antagonists. The reliability of our statistical models was further confirmed by the correct prediction of the pharmacological behavior of some N-substituted melatonin derivatives, which were prepared and tested on cloned receptor subtypes.

Ligands↗
PubMedDate not supplied

A chemometric approach to the detection of milk adulteration based on protein profiles determined by capillary electrophoresis.

The general objective of this study was to utilize chemometrics in the interpretation of capillary electrophoresis milk protein profiles, for the detection of pasteurized milk adulteration with rehydrated milk powder or a rehydrated dairy-based milk substitute. The specific objectives were 1) to collect quantitative data on major casein and whey proteins in authentic and adulterated milks in a single CE analysis; and 2) to apply a pattern recognition procedure, Soft Independent Modeling of Class Analogies (SIMCA), on collected CE protein data, for the development of a statistical model useful in the detection of pasteurized milk adulteration. Authentic samples were fresh milk collected from various farms over a period of six months. Adulterated samples were authentic fresh milk partially or totally substituted with rehydrated milk powder or a rehydrated commercial milk substitute at different levels. Quantitative protein data obtained by capillary free zone electrophoresis for beta-lactoglobulin, alpha-lactalbumin, beta-casein, and alpha-casein of 86 samples, authentic and adulterated samples, were used as a training set to build a SIMCA multivariate statistical model. The detection of sample outliers was useful for the elimination of unusual samples and optimization of the multivariate model. From the 35 commercial pasteurized milks tested, which were treated as unknowns, a total of 14 samples (40%) were not assigned to the authentic or fresh milk group, meaning that these samples had some type of adulteration at the levels included in the training set (> 15%). Decision-making on detecting adulteration of unknown commercial pasteurized milk samples was eased since predictions were based on statistical probabilities.

Animals↗
PubMed1996

A neural network model forecasting for prediction of daily maximum ozone concentration in an industrialized urban area.

Prediction of ambient ozone concentrations in urban areas would allow evaluation of such factors as compliance and noncompliance with EPA requirements. Though ozone prediction models exist, there is still a need for more accurate models. Development of these models is difficult because the meteorological variables and photochemical reactions involved in ozone formation are complex. In this study, we developed a neural network model for forecasting daily maximum ozone levels. We then compared the neural network's performance with those of two traditional statistical models, regression, and Box-Jenkins ARIMA. The neural network model for forecasting daily maximum ozone levels is different from the two statistical models because it employs a pattern recognition approach. Such an approach does not require specification of the structural form of the model. The results show that the neural network model is superior to the regression and Box-Jenkins ARIMA models we tested.

Journal Article↗
PubMed2003

A model to select regimens for phase III trials for patients with advanced-stage non-small cell lung cancer.

PURPOSE: Historical data from pilot, Phase II, and Phase III studies for patients with advanced-stage non-small cell lung cancer (NSCLC) were used to evaluate a statistical model developed to provide assistance in selecting regimens from pilot studies for subsequent use in larger Phase III randomized studies. EXPERIMENTAL DESIGN: Information from 33 Phase III trials for patients with advanced-stage NSCLC performed from 1973 and 1994 in the United States and Canada was collected. The data from antecedent pilot or Phase II and subsequent Phase III trials were analyzed using a predictive statistical model. This model uses the number of patients in the pilot/Phase II study, the median survival of patients in the pilot, and the number of deaths observed, to estimate the statistical likelihood that the pilot regimen will be shown superior to standard therapy in a subsequent Phase III trial. RESULTS: Ten pilot/Phase II studies were identified that preceded eleven subsequent Phase III studies. The three pilot regimens associated with Phase III trials, revealing statistically significant longer survival, had an expected power of 0.69, 0.85, and 0.94 respectively. The regimens from the seven other pilot studies for which the median power expected was 0.38 (range, 0.07-0.80) showed no difference when compared with standard treatment in a Phase III trial. CONCLUSION: The use of the expected power model provides an important enhancement to the screening of new therapies. Regimens with an expected power of >0.55 may be good candidates for testing in Phase III trials.

Carcinoma, Non-Small-Cell Lung↗
PubMed2001

Modeling the electron field emission from carbon nanotube films.

A theoretical framework for the electron field emission from carbon nanotubes (CNTs) is discussed. Using the tunneling theory, the influence of the detailed electron energy dispersion is proven to be of little importance for the electron field emission. By means of numerical computations in a simplified model, the influence of the environment on the local field on a CNT is discussed for an aligned CNT film. In a simple triangular model for the potential energy barrier at the tube end, a tunneling probability was obtained. A statistical model was developed for the structural and functional parameters of aligned CNT films. Practical CNT films of excellent alignment, obtained directly on a tungsten wire by plasma-enhanced chemical vapor deposition, were analyzed by this statistical model. Their distribution in the enhancement factors was thus deduced. An indirect method to get the average electrical parameters of the film using only a limited amount of experimental data was thus established.

Journal Article↗
PubMed1997

Using Medicare claims data to assess provider quality for CABG surgery: does it work well enough?

OBJECTIVES: To assess the relative abilities of clinical and administrative data to predict mortality and to assess hospital quality of care for CABG surgery patients. DATA SOURCES/STUDY SETTING: 1991-1992 data from New York's Cardiac Surgery Reporting System (clinical data) and HCFA's MEDPAR (administrative data). STUDY DESIGN/SETTING/SAMPLE: This is an observational study that identifies significant risk factors for in-hospital mortality and that risk-adjusts hospital mortality rates using these variables. Setting was all 31 hospitals in New York State in which CABG surgery was performed in 1991-1992. A total of 13,577 patients undergoing isolated CABG surgery who could be matched in the two databases made up the sample. MAIN OUTCOME MEASURES: Hospital risk-adjusted mortality rates, identification of "outlier" hospitals, and discrimination and calibration of statistical models were the main outcome measures. PRINCIPAL FINDINGS: Part of the discriminatory power of administrative statistical models resulted from the miscoding of postoperative complications as comorbidities. Removal of these complications led to deterioration in the model's C index (from C = .78 to C = .71 and C = .73). Also, provider performance assessments changed considerably when complications of care were distinguished from comorbidities. The addition of a couple of clinical data elements considerably improved the fit of administrative models. Further, a clinical model based on Medicare CABG patients yielded only three outliers, whereas eight were identified using a clinical model for all CABG patients. CONCLUSIONS: If administrative databases are used in outcomes research, (1) efforts to distinguish complications of care from comorbidities should be undertaken, (2) much more accurate assessments may be obtained by appending a limited number of clinical data elements to administrative data before assessing outcomes, and (3) Medicare data may be misleading because they do not reflect outcomes for all patients.

Bias↗
PubMed2005

Bayesian mapping of QTL in outbred F2 families allowing inference about whether F0 grandparents are homozygous or heterozygous at QTL.

In this paper, we propose a new Bayesian method for QTL analysis in outbred F2 families based on Markov chain Monte Carlo (MCMC) estimation allowing inference about whether each of F0 founders (grandparents) is homozygous or heterozygous at QTL. This, in turn, allows us to select a model accurately explaining observations of phenotypes for F2 individuals. The proposed method performs the fitting a statistical model of the two possible QTL states in each F0 grandparent, that is, homozygous and heterozygous at QTL, and gives a posterior distribution for the QTL states in each F0 grandparent. We confine ourselves to the discrimination of two QTL states, homozygous or heterozygous, for each of the F0 grandparents without taking into consideration whether common alleles are shared by F0 grandparents. The statistical model includes allelic effects and dominance effects for each QTL. The number of parameters representing allelic effects and dominance effects is therefore changed depending on the QTL states. A Reversible Jump MCMC technique is used for transition between the models of different dimensions. The effectiveness of the proposed method was investigated using simulation experiments. It was practicable to estimate the QTL states of F0 grandparents as well as the number, the locations and the effects of QTL segregating in an outbred F2 family.

Alleles↗
PubMed1989

Ordinal regression models for epidemiologic data.

Health status is often measured in epidemiologic studies on an ordinal scale, but data of this type are generally reduced for analysis to a single dichotomy. Several statistical models have been developed to make full use of information in ordinal response data, but have not been much used in analyzing epidemiologic studies. The authors discuss two of these statistical models--the cumulative odds model and the continuation ratio model. They may be interpreted in terms of odds ratios, can account for confounding variables, have clear and testable assumptions, and have parameters that may be estimated and hypotheses that may be tested using available statistical packages. However, calculations of asymptotic relative efficiency and results of simulations showed that simple logistic regression applied to dichotomized responses can in some realistic situations have more than 75% of the efficiency of ordinal regression models, but only if the ordinal scale is collapsed into a dichotomy close to the optimal point. The application of the proposed models to data from a study of chest x-rays of workers exposed to mineral fibers confirmed that they are easy to use and interpret, but gave results quite similar to those obtained using simple logistic regression after dichotomizing outcome in the conventional way.

Asbestos, Amphibole↗
PubMed2000

A model to select chemotherapy regimens for phase III trials for extensive-stage small-cell lung cancer.

BACKGROUND: Many more phase II studies have favorable outcomes than the subsequent phase III trials. We used historical data from phase II and phase III studies for patients with extensive-stage small-cell lung cancer (SCLC) to generate a statistical model to provide assistance in selecting chemotherapy regimens from phase II studies for subsequent use in phase III randomized studies. METHODS: Information from 21 phase III trials for patients with extensive-stage SCLC initiated during the period from 1972 through 1990 was reviewed to identify those that were preceded by phase II studies of the same regimen. We used data from all the trial pairs to develop a statistical model in which the number of patients, the median survival of patients, and the number of deaths observed in the phase II trial are used to estimate the statistical power of the subsequent phase III trial. All statistical tests were two-sided. RESULTS: Nine phase II studies were identified that preceded phase III trials of the same regimen. The regimens from two phase II studies with the greatest expected power in the phase III trial (0. 62 and 0.58) both demonstrated significantly prolonged survival when compared with standard treatment in subsequent phase III trials (P<. 001 and P =.002, respectively). The regimens from six of the other phase II studies, for which the median power expected in the phase III trial was 0.28 (range, 0.19-0.52), showed no difference when compared with standard treatment in a phase III trial. CONCLUSIONS: Phase II studies for particular regimens that have an expected power of greater than 0.55 provide a reasonable basis for proceeding with a phase III trial.

Antineoplastic Agents↗
PubMed2006

Face recognition from a single training image under arbitrary unknown lighting using spherical harmonics.

In this paper, we propose two novel methods for face recognition under arbitrary unknown lighting by using spherical harmonics illumination representation, which require only one training image per subject and no 3D shape information. Our methods are based on the recent result which demonstrated that the set of images of a convex Lambertian object obtained under a wide variety of lighting conditions can be approximated accurately by a low-dimensional linear subspace. We provide two methods to estimate the spherical harmonic basis images spanning this space from just one image. Our first method builds the statistical model based on a collection of 2D basis images. We demonstrate that, by using the learned statistics, we can estimate the spherical harmonic basis images from just one image taken under arbitrary illumination conditions if there is no pose variation. Compared to the first method, the second method builds the statistical models directly in 3D spaces by combining the spherical harmonic illumination representation and a 3D morphable model of human faces to recover basis images from images across both poses and illuminations. After estimating the basis images, we use the same recognition scheme for both methods: we recognize the face for which there exists a weighted combination of basis images that is the closest to the test face image. We provide a series of experiments that achieve high recognition rates, under a wide range of illumination conditions, including multiple sources of illumination. Our methods achieve comparable levels of accuracy with methods that have much more onerous training data requirements. Comparison of the two methods is also provided.

Algorithms↗
PubMed2005

A comparison of six analytical disease mapping techniques as applied to West Nile Virus in the coterminous United States.

West Nile Virus has quickly become a serious problem in the United States (US). Its extremely rapid diffusion throughout the country argues for a better understanding of its geographic dimensions. Both 2003 and 2004 percentages of deaths by numbers of reported human cases, for the 48 coterminous US states, are analyzed with a range of spatial statistical models, seeking to furnish a fuller appreciation of the variety of models available to researchers interested in analytical disease mapping. Comparative results indicate that no single spatial statistical model specification furnishes a preferred description of these data, although normal approximations appear to furnish some questionable implications. Findings also suggest several possible future research topics.

Journal Article↗
PubMed2004

Evaluation of optimization techniques for an extractive alcoholic fermentation process.

The mathematical optimization of a continuous alcoholic fermentation process combined with a flash column under vacuum was studied. The objective was to maximize % yield and productivity in the fermentor. The results using surface response analysis combined with modeling and simulation were compared withy those obtained when the problem was written as a nonlinear programming problem and was solved with a successive quadratic programming (SQP) technique. Two process models were evaluated when the process was optimized using the SQP technique. The first one is a deterministic model, whose kinetic parameters were experimentally determined as functions of the temperature, and the second is a statistical model obtained using the factorial design technique combined with simulation. Although the best result was the one obtained using the rigorous model, the values for productivity and % yield obtained using the simplified model are acceptable, and these models can be used when the development of a rigorous model is excessively difficult, slow, or expensive.

Alcohols↗
PubMed1999

Modeling daily and subdaily cycles in rat sleep data.

We developed a statistical model for rat sleep-wake behavior over the 24-hour day; this model could be used for animals exposed to shorter cycles of light and dark conditions. Rat behavior was classified either as "wake" or "sleep," as determined by analysis of electrophysiological data. The proposed model consists of three parts: the first two explain cyclic effects relating to the lighting conditions, whereas the last part reflects any acyclic effect. Hypothesis tests were conducted on the magnitude of the parts. The model also accounts for correlated errors.

Activity Cycles↗
PubMed1983

Contextual constraints on synonymous codon choice.

We have studied the statistical constraints on synonymous codon choice to evaluate various proposals regarding the origin of the bias in synonymous codon usage observed by Fiers et al. (1975), Air et al. (1976), Grantham et al. (1980) and others. We have determined the statistical dependence of the degenerate third base on either of its nearest neighbors in mitochondrial, prokaryotic, and eukaryotic coding sequences. We noted an increasing dependence of the third base on its nearest neighbors in moving from mitochondria to prokaryotes to eukaryotes. A statistical model assuming random equiprobable selection of synonymous codons was found grossly adequate for the mitochondria, but totally inadequate for prokaryotes and eukaryotes. A model assuming selection of synonymous codons reflecting a genomic strategy, i.e. the genome hypothesis of Grantham et al. (1980), gave a good approximation of the mitochondrial sequences. A statistical model which exactly maintains codon frequency, but allows the position of corresponding synonymous codons to vary was only grossly adequate for prokaryotes and totally inadequate for eukaryotes. The results of these simulations are consistent with the measures on experimental sequences and suggest that a "frequency constraint" model such as that of Grantham et al. (1980) may be an adequate explanation of the codon usage in mitochondria. However, in addition to this frequency constraint, there may be constraints on synonymous codon choice in prokaryotes due to codon context. Furthermore, any proposal to explain codon usage in eukaryotes must involve a constraint on the context of a codon in the sequence.

Amino Acid Sequence↗