CIs cannot be calculated for the average rate of gains using this method because gain events occur exactly one time on the tree and there is no variance in gains among ortholog sets.
So, in order to better illustrate the gain of this method in terms of visual perception, we compared the displayed image in two cases: with and without ROI differentiation, with the channel conditions evolution of the previous simulation.
Data extraction techniques were used to interpret and illicit meaning and gain understanding – this method is based on the work of Corbin & Strauss [ 20] and Rapley [ 21].
Finally, insight into how specific disease-causing mutations within or outside of a kinase domain affect conformational preferences of the catalytic site can also be gained with this method.
We also show how to determine the controller gain matrix under this method.
It is also shown that the parallel gain obtainable with this method is very promising (close to an order of magnitude for the cases and implementations described), even when it scales with the number of processors quite differently to what is typical for spatial parallelization.
Therefore, the lack of gain seen with this method may have been due partially to a ceiling effect.
If this did not hold for HAPim, the gain from using this method rather than T2 would be low given that T2 is not affected by the haplotype phases.
The main shortcoming of this technique is uncertainty to locate the best sketch of dynamic compaction to gain maximum effect of this method of soil improvement.
A new method to handle the unknown time-varying behavior of the control gain is developed; with this method it is assured that the output tracks the desired reference with a user-defined tolerance and parameter drifting is avoided.
