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Our pipeline method generated similar results to the published studies with significantly less manual intervention.
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The method generates similar quality SR plans in distribution systems of significantly different sizes (from 3860 to 30,880 buses).
Both methods generated similar results for the release efficiencies, therefore immunoblots from both types of purification were combined to calculate the average release efficiencies with standard deviation and to perform the statistics.
Phylogenetic analyses performed using maximum parsimony, neighbor-joining, and distance-matrix methods generated similar tree topologies.
Both maximum likelihood (ML) and distance methods generated similar tree topologies.
Consensus trees were constructed following a similar strategy to that described for the PTHRs and both maximum likelihood and distance methods generated similar tree topologies.
Both methods generated similar binding profiles, with all of the ETS factors binding to the previously described core GGA(A/T) motif.
The analysis demonstrated that the methods generated similar estimates across the different sized LGAs and supported the observations of Eayres and Williams, which also showed good agreement between the two methods.
These results indicate that the two amplification methods generate similar amplified DNA samples despite the procedural differences.
The strong correlation of 0.94 between pfGNM and GNM implies that these two structure-based methods generate similar results.
These methods all generated similar rankings of the methods (Supplementary Fig. S2); however, we present area under the survival ROC curve for direct comparability to real datasets.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com