When these methods are applied for published real datasets, both static and dynamic methods performed well for most temporal genes; however, dynamic methods had particularly a slight edge at low and moderate expression levels.
Among the aforementioned methods, the Hirota method has proved particularly powerful.
This method has been particularly effective in demonstrating to students that evolution is the factual, unintended consequence of three independent phenomena (R, V, S).
Although the method has been particularly fruitful for low-order ordinary differential equations (cf. [3 8, 14, 18, 31, 32]), Kelley [33] and Klaasen [3] did obtain early applications to higher-order ODEs.
The adaptivity of the coupling between a global search by a population-based method (Genetic Algorithms or Evolution Strategies) and the local search by a descent method has been particularly emphasized.
This method has proven particularly effective in dissociating responses (reflected in both activation and deactivation) produced by our WM task with emotional distraction, in both healthy and clinical groups [17], [18], [37].
No one has particularly sparkled.
The method has been designed particularly for use in microfluidics applications.
This method has wider applications particularly for flexure systems, which will be the subject of a future article.
However, this method has numerous limitations, particularly in the case of C. suis, which displays limited growth in commercial cell lines [ 21].
