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To compare different modeling approaches, we calculated V T values from the outcome parameters obtained with NLME modeling (V T-NLME as the ratio Q in/Q out) and compared them with the respective V T-2T4K values obtained with standard PK modeling [9, 30].
Using Bayesian coalescent approaches, we calculated a mean rate of nucleotide substitution for IHHNV that was unexpectedly high (1.39×10−4 substitutions/site/year) and comparable to that reported for RNA viruses.
Using single-sample and trio-based approaches, we calculated that 0.9% of DDD probands had large-scale mosaicism.
Using the likelihood- and graph-based reduction approaches, we calculated all 15 reduction steps from the full context-dependent model to the independent model and found that the optimal clustering scheme contains six clusters.
For both the [hsTnT] value and [hsTnT] thresholding approaches, we calculated sample size using Monte Carlo simulation and mixed effects modelling adjusting for baseline value (on the log scale).
To illustrate the difference of our model compared with previous approaches, we calculated the Shannon entropy for the subset of PAS clusters that showed significant tissue specificity for both the individual and overlap PAS, Figure 3a.
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Combining these approaches we calculate specific stimulus-secretion efficiencies for L-type, N-type, P/Q-type and R-type calcium channels under varied physiological activity levels.
Our analytical framework features the following characteristics and modification compared to previous approaches: We calculate the long-run marginal costs of each energy saving technology.
To further compare the performance of the stringent and the conventional DDI-based approaches, we calculate the percentage of PPIs that have coherent informative GO terms.
As last experiment for the by-hand approach, we calculated similarity rankings with the ECFP similarity and also with an extended version of the ECFP similarity.
At the same time and using the same approach, we calculated bootstrap based corrections for the lower and upper bound of the corresponding confidence interval.
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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