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In Figure 8, we plot the absorption wavelength having the highest oscillator strength (among the 10 lowest excitations) for each constrained geometry.
We generated 10,000 bootstrap replicates of the site likelihoods for each constrained tree using CONSEL [ 63], and ranked alternative hypotheses using the weighted Shimodaira-Hasegawa test (WSH [ 63]), the approximately unbiased test (AU [ 64]), and the Bayesian Information Critierion approximation for posterior probability (BIC [ 63]).
Through introduction of a fuzzy decision variable, not only the highest membership degree in the objective function but also a satisfactory degree for each constrained resource can be quantified as fuzzy membership functions and solved simultaneously.
BFs were calculated in Tracer 1.4.1 (Rambaut and Drummond 2007) using, for each constrained analysis, the trace files from the run of highest harmonic mean.
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Twenty replicate searches were done for each analysis (constrained and unconstrained) and the tree with the highest log-likelihood from each was used for topology testing (below).
In this analysis, we estimated the position (yellow circle) of the centroid of the grouped transponders (green circles) for each epoch, constrained by the positional relationship of the grouped transponders (red diamonds) in all epochs.
The likelihood at each nucleotide position was calculated for each alternative topology (constrained monophyletic Opisthobranchia, constrained monophyletic Basommatophora) as well as the topology under scrutiny using PAUP 4.0b10 [ 51].
Lastly, the average rates for each epoch are constrained by the rate regions in Equations 1 and 2 for reliable decoding, which translate to the problem as Equation 3i for phase I and Equation 3j for phase II.
The deflection for each member is constrained to l/360 of the member's length and this upper limit is used for all three cases.
In addition, the coefficients for each basis are constrained by prior information in the Bayesian approach such as Akaike's Bayesian Information Criterion (ABIC) (e.g. Ide and Takeo, 1997; Sekiguchi et al., 2000), and the constraint results in the slip distribution being smoothed.
The marginals of the locality matrix for each swap are constrained to those of the original matrix, preserving the localities' sample frequencies.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com