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The visual predictive check was generated using 1,000 simulations from the final model, for all dose levels in our study, to assess the predictive performance and to verify if the performance is consistent among the dose levels.
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Automatic checks were generated for missing or incoherent data.
A Spearman correlation matrix was generated to check for collinearity between the remaining variables, but all correlation values were below 0.7 [63].
A melting curve was generated to check for presence of nonspecific PCR products.
Cluster analysis was generated to check the expression of miRNAs in different samples.
The Spearman coefficient was used to determine correlations, and a Bland-Altman plot was generated to check for possible bias.
A melting curve (61 cycles at 65°C for 10 s) was generated to check for specific amplification.
A preliminary view of the ICPA 2039 assembly was generated to check the connections between the scaffolds and for the removal of contaminants (Fig. 1).
After PCR amplification, a melting curve was generated to check the specificity of each PCR reaction (absence of primer dimers and other non-specific amplification products).
An initial structural model was generated and checked for recognition of errors in 3D structures using ProSA (http://prosa.services.came.sbg.ac.at/prosa.php), and for a first overall quality estimation of the model with QMEAN (http://swissmodel.expasy.org/qmean/cgi/index.cgi).org/qmean/cgi/index.cgi
The runs test can be used to check if the given data set was generated by a random process or not (Bradley, 1968).
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