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For genes belonging to the 30 clans present in the LCA based on the gain model, our analyses revealed that overall loss of ancestral genes was much more prevalent than expansion.

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When PRRE = PRRLL = ']0.8,1.2]', model 3 estimates higher probabilities than does model 2. Our analyses have shown that the logistic regression can be used to predict ARFI based on the combination of several predictive causality criteria at the population level.

To validate the results of the MCMC method by incorporating genetic intra-locus recombination (not allowed in the MCMC-IM model) in our analyses, we used ABC [ 28, 43] as a more general model framework, to infer the same seven demographic parameters of the MCMC model.

Results were similar between models so we used the average of CpGs and a β model for our analyses.

We fitted a variety of proportional hazard models to data from first episodes and used these results to select a suitable model for our analyses of both first and subsequent episodes (Additional file 1: Appendix A).

Therefore, we primarily used the random effects model for our analyses.

Nonetheless, we will use this form of country-specific categorization based on Bulle's model in our analyses.

In order to reduce the complexity of the proposed optimization model, in our analyses we neglected the influence of transients from the off to the on state of BSSs.

First, we used an irradiated bone model for our analyses.

We utilized a random-effect repeated-measurement model for our analyses.

Although the I index was low (0%), the type and number of respiratory adverse events reported varied considerably between the studies so we chose a random effects model for our analyses.

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