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The table of requirements used to model this procedure is available in [33].
Compared to previously described methods for obtaining an augmented three-dimensional virtual model, this procedure eliminates the need for dental impressions, simplifies the necessary technical steps and computational work, and reduces the patient's exposure to ionizing radiation.
In the HCT116 model, this procedure significantly increased the number and area of visible lymph node foci and enhanced colonization of the lung.
We eliminated the variable with the highest P value and repeated the model; this procedure was repeated until all variables were found to be significant.
For each model this procedure selects the parameter set for which both CV1 and CV2 are small, giving CVminmax as a measure of the highest robustness when both genes 1 and 2 are taken into account.
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In the present study, we emphasized both speed and accuracy to reflect the functional differences between the two internal models; this procedure may have resulted in the absence of an advantage for the right hand under the visual feedback condition because participants might not have consulted the visual feedback sufficiently when moving their hands.
Since the same deterministic formulation can be the limiting case of many individual based models, this procedure may be problematic.
For all models, this procedure was run from start to finish at least three times and the model with the lowest AIC was selected.
For sets with more than, say, 10,000 models this procedure is very time-consuming and alternative faster algorithms, restricting comparisons only to most similar structures would be useful.
For each of the K candidate models, this procedure involves generating Bw i replicate mortality time series using model M i as the basis for the bootstrap procedure described above, for each i = 1, 2,…, K.
In the case of several potential model structures, this procedure can be repeated for each model to identify experimental setups where candidate models yield qualitatively different predictions.
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