Sentence examples for generating age from inspiring English sources

The first step in generating age- and species-specific prevalence data at all schools in coastal and western regions involved modeling the relationship between infection status and age, using mixed effects logistic regression.

Notably, senescent cells accumulate in various tissues and organs with aging and have been causally implicated in generating age-related phenotypes (Baker et al., 2011).

The green-up rules contained in the California, Oregon, and Washington forest practices laws were applied to two hypothetical forests with randomly generated age classes.

Our models, based upon simple assumptions and whole genome synonymous SNP surveys, allowed us to generate age estimates for the major events in the evolutionary history of B. anthracis (Methods).

Nonetheless, there was insufficient sample size within each age group to generate age group–specific disease incidence estimates.

We then generated age and gender specific population attributable fractions (PAF) (Appendix with spreadsheet of calculations is available upon request).

To generate age at infarction, which defined time-to-event T i, we used data from a Spanish study [ 16].

The relationships between motor unit properties and CNTF genotype were examined using mixed effects models adjusted for percent of MVC, force generated, age and gender.

18 Data were simulated by first using coefficients for each species-specific model to generate age and region, school-specific log odds of infection for each species.

To provide normative data for the PHQ-15, we generated age- and gender specific percentiles for the PHQ-15 total score.

To explore whether any ethnic differences had different magnitudes between the different anthropometric measurements, we also generated age and sex internally standardised z-scores for each measurement and regressed these z-scores on ethnicity.