Important PM2.5 predictors for the pre-1999 model included predicted PM10 (from Yanosky et al. 2008), extinction coefficient, wind speed, temperature, precipitation, monthly time trend, distance to nearest A1 road, urban land use, block group and county population density, and elevation.
This NimbleGen probe set targets the Vega [ 8], CCDS, and RefSeq gene models, including predicted genes within RefSeq, as well as microRNA (miRNA) [ 9] and regulatory regions for a total target size of 42 Mb.
Existing uses of models include predicting time and errors for users to perform tasks, acting as embedded assistants to help users perform their tasks, and serving as surrogate users.
The utilities of such modeling include predicting systems behavior, identifying crucial steps in systems regulation.
To compensate for sparse monitoring data, the pre-1999 model also included predicted PM10 (PM with aerodynamic diameter < 10 μm) and extinction coefficients (km−1).
Two other studies reported prediction models that include predicted RSA [ 42, 43].
Annual changes by age group and birth cohort were estimated by applying a linear regression model including the predicted BMI-values (y) based on the models in Table 1 and time (x) for each of the age groups and birth cohorts.
Several useful predictions have been obtained from such in-silico models [ 23, 24], including predicting the consequences of gene deletions, optimal growth patterns, outcomes of adaptive evolution and more.
The outputs of the model included age-specific predicted cost and rates of cytology test utilisation, colposcopies, biopsies, treatment for precancerous lesions, cancer incidence and cancer mortality.
The outputs of the model included age-specific predicted rates of cytology test utilisation, colposcopies, biopsies and treatments for high grade CIN, cervical cancer incident cases and cancer deaths.
B ^ 2, i = predicted stand basal area (m2/ha) of plot i at age A 2. The individual-tree model included equations for predicting tree survival probability and diameter growth as follows: p ^ i j = 1 + exp 1.3586 − 0.0010 N 1, i + 0.1042 B 1, i − 0.2902 d 1, i j − 1 (3).
