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At least 631 records · Page 35Linked to original sources
PubMed2004

Ransacking the curve of cardiac isovolumic pressure decay by logistic-and-oscillation regression.

The decelerative part of the left ventricular isovolumic pressure decay is an important phase to make the heart ready for diastolic refill (lusitropy). Its widely used characterization by an exponential regression with zero pressure asymptote or coestimated asymptote provides empirically biased time constant estimates because of significant deviations of the pressure decay from exponentiality. We systematically analyzed the regression residua of these pressure decays in isolated ejecting rat, guinea pig, and ferret hearts. A four-parametric logistic (tangens hyperbolicus) function, together with a superimposed acustomechanic oscillation, yields normally distributed residua with standard regression error typically less than one per cent of the initial pressure; this is the first model with proved unbiased and statistically complete regressive extraction of the information provided by the time course of pressure decay. Equal values of the lusitropic parameters (logistic time constant and pressure asymptote) were estimated even after the oscillatory component was removed from the regression model. Reliable estimates of the frequency, but not of the amplitude, can be obtained by fitting the oscillation model to the residua provided by the logistic; this two-step method is statistically weaker than the full one-step model, but it reduces computational effort. In conclusion, the four-parametric logistic, but not a three-parametric exponential or logistic model, suffices to obtain unbiased lusitropic parameters characterizing the left ventricular isovolumic pressure decay of small animal hearts.

Animals↗
PubMed1993

PC program for estimating polynomial growth, velocity and acceleration curves when subjects may have missing data.

A stand-alone, menu-driven PC program, written in GAUSS386i, for estimating polynomial growth, velocity, and acceleration curves from longitudinal data is described, illustrated and made available to interested readers. Missing data are accommodated: we assume that the study is planned so that individuals will have common times of measurement, but allow some of the sequences to be incomplete. The degrees, Di, adequate to fit the growth profiles of the N individuals are determined and the corresponding polynomial regression coefficients are calculated and can be saved in ASCII files which may then be imported into a statistical computing package for further analysis. Examples of the use of the program are provided.

Achondroplasia↗
PubMedDate not supplied

The influence of spouses over each other's contraceptive attitudes in Ghana.

To what extent do spouses influence each other's reproductive goals? This question was investigated in Ghana with particular reference to family planning attitudes. Two mechanisms were identified as plausible explanations for why an individual's characteristics may affect a partner's beliefs and behavior. Quantitative evidence from the Ghana Demographic and Health Survey and qualitative information from focus-group research in Ghana were used in the analysis. Results from both data sources show that spousal influence, rather than being mutual or reciprocal, is an exclusive right exercised only by the husband. The study attributed the limited impact of family planning programs in Ghana and most of sub-Saharan Africa to the continued neglect of men as equal targets of such programs.

Adult↗
PubMed1993

A quantitative measure of nonlinearity.

Quantitative measures of the nonlinearity of an analytical method are defined as follows: the "(dimensional) nonlinearity" of a method is the square root of the mean of the square of the deviation of the response curve from a straight line, where the straight line is chosen to minimize the nonlinearity. The "relative nonlinearity" is defined as the dimensional nonlinearity divided by the difference between the maximum and minimum assayed values. These definitions may be used to develop practical criteria for linearity that are still objective. Calculation of the nonlinearity requires a method of curve-fitting. In this article, we use polynomial regression to demonstrate calculations, but the definition of nonlinearity also accommodates alternative nonlinear regression procedures.

Algorithms↗
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↗