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We assessed the accuracy of our proposed method using example data obtained in previous studies (Nakata et al. 2012 20144) (Table A1, see Additional file 1 for what was done in each study).
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We conclude with some recommendations, and illustrate our rec- ommended method using examples that compare them to existing ap- proaches.
Like in other previous work [ 4, 5], our method uses example data (manually labeled scans) to learn from.
For example, hallucination and Markov random field (MRF) methods use examples drawn from the same domain as the image being enhanced to determine what the missing high-frequency information is likely to be.
We demonstrate the method using an example of Johne's disease.
We will exemplify in the following the stages of the proposed forecasting method, using an example of a particular trace (corresponding to BS1).
We explain the method using an example (fig. 1) and then introduce our new method.
We illustrated our pre-validation method using the example of the AGQ; a new glaucoma-specific PRO instrument for use as the primary patient reported outcome measure for a RCT evaluating the effectiveness of glaucoma screening.
Finally, the proposed method was evaluated using examples and methods available in the literature.
We then illustrate the method using two examples, and conclusions are given in final section.
We illustrate the method using two examples (one simple, one complex) based on sanitation and nutritional interventions to improve child growth.
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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