Sentence examples for nonlinear exposure from inspiring English sources

There were no adverse events.Sildenafil demonstrated nonlinear exposure with high interindividual variability but was well tolerated and effectively lowered pulmonary vascular resistance index in all subjects.

BME analysis can incorporate nonlinear exposure estimators and non-Gaussian probability laws, and it can integrate core knowledge (epidemiologic laws, scientific models, theoretical space time dependence models, etc).

The authors note that plots of average inhaled doses of PM2.5 and associated cardiovascular health risks in the literature reveal a highly nonlinear exposure response.

We adjusted the weekly averaged mean temperature using a cross-basis framework (Gasparrini et al. 2010) to account for the combined effects of a 10-week maximum lag structure (stratified at 4 weeks for mean temperature and polynomial for rainfall) and a nonlinear exposure response represented by using natural cubic splines with 5 df for the effect of rainfall and temperature.

Secondly, a nonlinear exposure-risk relationship between maternal vitamin D levels and neonatal anthropometry has been found [ 22].

For instance, several found a nonlinear exposure-dependent formation of DNA damage in rats and nonhuman primates exposed to inhaled FA, with other studies showing that long-term FA exposures >6 ppm substantially increase squamous cell carcinoma in rats.

Third, it has been argued that a review of epidemiological evidence shows linear or no-threshold effects at low exposures in humans, despite nonlinear exposure-response in the experimental dose range in animal testing for similar endpoints.

In the first part of the results we will present the data from the Danish Medical Birth register [ 23], and in the second part we will present various models based on linear versus nonlinear exposure-risk relationships.

The study that best illustrates the effects of measurement error on assessing thresholds was that by Brauer et al. (2002), who conducted simulations in which a strict population threshold at various levels of exposure was assumed, and then the effect of measurement error on the ability to detect that threshold, or even a nonlinear exposure-response pattern, was assessed.

For these types of environmental variables, nonlinear risk exposure relationships might have to be checked.

Since associations with type 2 diabetes seemed to be nonlinear, all exposure variables were analyzed in quartiles.