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Our survival analysis included a candidate model that distinguished no natal agriculture effect (model A7, Table S2A), but this received substantially less support than the most parsimonious model that distinguished a natal agriculture effect in a single age class (1- to 2-year-old birds) (model A6, Table S2A).
The experimental data were reduced with Hino's lattice model that distinguished the interactions due to London dispersion forces and those due to hydrogen bonding.
The model development was guided by our previous results obtained with a model that distinguished three fixed compartments between which the surface receptors were considered as partitioned [5].
For VEGF in particular, we approximated from Eppler et al.'s compartmental model that distinguished between receptor-specific tissue uptake and non-specific elimination components of plasma clearance [120].
In addition, we characterized the Alu distribution with a mixture model that distinguished the genes with no Alu elements from those containing Alus.
Due to the substantial existence (N = 5578) of multi-exon genes of such kind in human genome, we characterized the distribution of the intron Alu density with a mixture model that distinguished genes without Alu elements from those containing Alus.
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Two mathematical models were examined to explore this issue: a random mixing model and a compartmental model that distinguishes risk groups associated with sex work.
The first three columns of coefficients contain the results for the models that distinguish between caregiving at home and caregiving elsewhere.
Our case study illustrates the advantages of models that distinguish between an underlying latent condition and its measurements, and of a continuing dialogue between the modeller and the domain experts as the model is developed using knowledge as well as data.
Figure 4 shows the results of generalised estimating equation models that distinguish effects for friends, spouses, siblings, coworkers, and neighbours.
1, 2 Consequently, it would be highly desirable to establish models that distinguish responders from nonresponders (NRs) and predict the possible outcome of the IFN-alfa/RBV treatment.
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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