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Then, we selected the condition (position) having the highest likelihood from the likelihood calculation result and used its position-specific NN sets to do feature mapping on the input.
This was repeated 16 times and the tree with the highest likelihood from each iteration was tested for congruence using the Consense function of the Phylip software package.
A minimum of two log-likelihood units separating a single state of highest likelihood from alternative states was used as a threshold for determining the state optimisation of each internal node.
To further illustrate the diversity of possible competition outcomes, representative patterns with the highest likelihood from random initial conditions are shown in Figure 3B G, where the inhibition strength is varied across the phase diagram (c1 = 1.7, 1.8, 1.9, 2.0, 2.3, 2.3) for a fixed growth advantage (α / β = 3.5) and diffusion constant (D = 0.001).
We then chose the tree with the highest likelihood from all 50 maximum likelihood searches and both Bayesian trees and applied both maximum likelihood and Bayesian consensus support values to the most likely tree, which was arranged and visualized using FigTree v1.3.1 [ 106].
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Optimisations at nodes for which this threshold is not reached, or for which two log-likelihood units separate more than one state of high likelihood from others, were considered undetermined.
Bootstrap proportions were drawn on the tree with highest likelihood score from the 10 independent searches.
The tree with the highest likelihood resulting from the last round of analyses was selected as the ML tree.
Ancestral reconstruction relied on ML as implemented in Mesquite using the Markov 1- or 2- parameter models [ 36] and the highest likelihood tree from RAxML.
The highest likelihood tree from each An partition and its corresponding Bn partition were then compared using the branch score metric [ 79] and partition metric [ 80] as implemented in the treedist executable of Phylip v.3.69 [ 81].
We would suggest that a good starting point would be to use the cutpoints with the highest likelihood ratios from our study (QRS > 120 ms, QTc > 500 and R/S ratio in aVR > 0.7) at defined times (e.g. on admission and 2 hours later).
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