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By utilizing multi-level context terms, our model shows better performance than the previous ABC model.
On physiological terms, our model of benign thyroid disease considers magnesium deficiency as a condition that leads to mitochondrial dysfunction by altering the functional integrity of Complex V of oxidative phosphorylation (OXPHOS).
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To test for such difference, we add interaction terms our models; this is not a GO enrichment test.
For this reason, we decided to term our model the "Drought and Flowering Genetic Module Hypothesis".
We term our model an "integrated-exposure response" model because its development requires the integration of exposures to PM2.5 from different combustion types (i.e., AAP, SHS, HAP, and AS).
We have tried to account for these floor effects by using squared terms and interaction terms in our model, but, as the figures illustrate, this does not resolve the problem.
Parameter extraction in terms of our model allows the testing of individual differences in terms of not only the RT index but also the psychological parameters involved in decision-making.
We then show how previous work can be described in terms of our model, and discuss extensions to the basic model to accommodate particular features of previous work.
The inherent semantic interpretation of common objects allows us to use rich sources of online data to populate the probabilistic terms of our model.
Such possibilities should be investigated in terms of our model, which makes predictions that can be tested with integrated paleontological, geochemical, and sedimentological data.
We can see that there are three terms in our model.
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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