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To identify trials with clear covert changes of mind, we used the following algorithm.
To define tests as screening indication or diagnostic indication, we used the following algorithm, similar to our previous study [ 16].
When representative features (R2) are (R4) are used, the following algorithm can better accommodate the second order terms.
Some specimens had more than 1 encounter on the day matched, so we used the following algorithm for selection of the ICD-9 code: if 1 of the ICD-9 codes present was from the ILI syndrome list, it was selected.
For population-burst detection and analysis, we used the following algorithm (see 'network_burst_analysis.m' in Source code 1).
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In the present study we used the following algorithms: linear model, Elastic Net, SVM with linear and radial kernels, and RF.
In order to model the nanoparticle deposition, we used the following algorithms and physical models: Nanoparticles were modeled as Lennard-Jones spheres, the surface by an integrated Lennard-Jones potential [25, 26].
Proteins for which GO information was unavailable, in order to filter out false positives, we used the following algorithms that predict the sub-cellular localization of a given protein: 1) Psort http://psort.ims.u-tokyo.ac.jp/[ 9], 2) TargetP http://www.cbs.dtu.dk/services/TargetP/[ 10], and 3) Predotar http://urgi.versailles.inra.fr/predotar/predotar.html[ 11].
We use the following algorithm for this purpose.
The RBF neural network is trained using the following algorithm [19].
We describe the key exchange technique using the following Algorithm 1.
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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