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The selective threshold of the cluster heads is ρ r =1.5, through calculation of algorithm that we proposed in Subsection 3.1.
Both pre-existing and de novo mutations substantially contribute to fitness, and the relative fitness of pre-existing variants sets a selective threshold for new adaptive mutations.
To fine tune the selective threshold for activity, we investigated the frequency distribution of all significant miRNAs from T-REX (Supplementary Fig. 2).
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In contrast, highly selective thresholds (p < 10− 4 to 10− 6) yielded the lowest consistency values with less than 25% overlap of the voxels active in both scans.
Consistency was greatest when minimally selective thresholds were used to define "active" voxels (p < 0.05 uncorrected), revealing that approximately 65% of the voxels were commonly activated by both scans.
Here we tested whether core concepts of action selection models (decision and non-decision times, integration of automatic and selective inputs to threshold, interference across response options, noise, etc).
For the proposed AS (adaptive selective) scheme, the threshold value ρ is set to 0.4 regardless of the iteration number and the other parameters.
These nanofillers i.e. graphene oxide (GO), chemically reduced graphene oxide (rGO-C) and thermally reduced graphene oxide (rGO-Th), with selective low percolation threshold in ABS, brought erratic outcome.
Overall, to identify only the most cartilage-selective genes, a threshold of 50% coefficient of variation was used across all three platforms, yielding 161 cartilage-selective genes.
For this, a denoising method based on the DT-QWT with single-channel and multichannel selective neighboring coefficient thresholding and linear phase smoothing is presented.
A consequence of quasispecies is the existence of an error threshold for selective competence.
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