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In each bootstrap sample, we ran multiple ordered logistic model to find which variables were significant and how many times (Table 1).
In each bootstrap sample, we ran all models to generate the c-statistic, AIC, and BIC.
For the entire sample, we ran a single multivariable model that included the three level cumulative exposure variable and two separate variables for HPEE and pesticide poisoning.
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To assess whether our sampling intensity effectively captured species composition in our samples, we ran a species accumulation curve.
After calibrating the instrument, and after each batch of 20 samples, we ran quality control nail samples with known As concentrations.
Because jActiveModules relies on random sampling, we ran several iterations of the data set to ensure the reproducibility of the identified modules.
For sampling, we ran 10 chains and selected for further analysis the one having its likelihood closest to the mean of non-outlier chains.
For quantifying the relative expression levels of ADIPOR2 mRNA in the various samples, we ran the target (AdipoR2) and the reference (β-actin) gene of each sample along with the calibrator cDNA, using the Second Derivative Maximum Method with the Arithmetic baseline adjustment for the determination of the various crossing points (Fig 3B).
Using these samples, we run simulations of the EWMA Surveillance Tree partitioning.
To quantify the relative expression levels of Adipo-Rs mRNA in the various samples, we run the target and the reference gene of each sample, along with the calibrator cDNA, and the final results were calculated using the LightCycler Relative Quantification 1.0.1 Software (ROCHE, Manheim, Germany).
Finally, to assess the effects of different time periods of sample collection, we ran separate models using the DAP concentrations in samples taken at the 16-week and 26-week visits.
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CEO of Professional Science Editing for Scientists @ prosciediting.com