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Due to using RS, we do not necessarily need to run the neutronics simulation for all the inputs generated from the GA module rather, run the simulations for a predefined set of inputs, build a multivariate regression fit to the input and the output parameters, and then use this fit to predict the output parameters for the inputs generated by GA.
Due to using rpart, we do not necessarily need to run the neutronics simulation for all the inputs generated from the GA module rather, run the simulations for a predefined set of inputs, build a regression fit to the input and the output parameters, and then use this fit to predict the output parameters for the inputs generated by GA.
We use this fit to normalize the data for each ligand accordingly, allowing for comparison between different cell lines expressing different ligands.
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By using this fit for a set of proteins with β-sheet topology we find that kF0≈kU0, the prefactor for unfolding.
Using this fit.
The temporal instrument response function (IRF) used for this fit was determined using the attenuated excitation laser to directly illuminate the gated intensifier.
Using this fitted normal distribution we determined the mean and standard deviation of the null distribution.
The equation used for this fit incorporates both specific binding (specific = Bmax*X/[X+Kd]) and non-specific binding (nonspecific = NS*X + Background).
Quantitative fit testing (QNFT) is the gold standard used to determine this fit objectively.
This allows using the fit to transform RSSI values into received power values when processing actual measurements in the w-iLab.t testbed network.
We can use this data to fit (alpha^) and (beta^).
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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