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First, we have implemented here a standardized AgNOR staining, as recommended by the "International Committee on AgNOR Quantitation" [ 12].

The model we have implemented here is a leaky integrate and fire device, which receives inputs from exclusively high spontaneous rate (HSR) auditory nerve fibers.

Developed within the PhyloBayes-MPI package, the models we have implemented here inherit the ability to perform a variety of types of posterior predictive model assessments, cross-validation comparisons, ancestral sequence reconstruction, as well as phylogenetic inference per se.

We believe that for tandem duplications, the LD measurement method that we have implemented here would be able to detect rates of IGC between duplicates, especially if they are high.

We note that the approach we have implemented here, using a rate multiplier and a single set of molecular branch lengths, does not allow for heterotachy (variation in the pattern of rates among sites over time), although this is known to be an important source of variation in patterns of substitution [ 1, 11].

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Despite our best efforts to develop a method of providing real-time feedback about the search process (i.e., which areas have and have not been inspected) to improve rare target search, we conclude that these EMF manipulations, as we have implemented them here, are unlikely to improve rare target search performance and may even hinder it.

However, unlike previous investigators, we have implemented random methods here instead of heuristically making the input of training and target runs.

We believe that the methods and tools we have implemented and described here will allow for the efficient dynamic integration and analysis of diverse data in other disease systems.

Aiming at the latter approach we here have implemented an autoantibody profiling strategy which particularly focuses on proteins released by tumor cells in vitro: the so-called secretome.

Here, we have implemented the Adomian decomposition method coupled with the Laplace transformation and the Padé approximation on the Ricatti differential equation with fractional order.

Here, we have implemented a SVM kernel-based method to develop a robust method for identification of cyclins.

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