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Excellent fits to the experimental data were achieved using simple correlations to predict εR and εD, as well as dimensionless correlations that incorporated the desired parameter (VLR).
Good fits to the tracer breakthrough data were achieved using a dual-porosity model, with immobile regions characterised by block diffusion timescales in the range of about one to ten years.
Additionally, to ensure consistent data were achieved using both miRBase versions 17 and 18, the miRNAs were manually checked for inconsistencies, and nomenclature was matched to miRBase version 17.
The proton CSI raw data were achieved using the multivoxel point-resolved spectroscopy sequence (PRESS, TR/TE 1,700/135 ms, 15-mm section thickness, 16 × 16 phase-encoding steps, FOV 120 mm × 120 mm) after obtaining the contrast-enhancing images.
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Modelling of solubility data is achieved using a simple model based on activity coefficients.
The process is repeated till the matching between the simulated and the measured pressure transient data is achieved using the most appropriate fracture realization.
Best agreement with the experimental data was achieved using a hydraulic conductivity vs. porosity relation derived from pore network simulation assuming the biomass to grow in colonies.
The problem is nonlinear and the numerical solution which minimizes the gap between the measured and the computed data is achieved using the Matlab toolbox routine lsqnonlin which is designed to minimize a sum of squares starting from an initial guess and with no gradient required to be supplied by the user.
Validation of the microarray data was achieved using a custom Taqman Low Density Array card (Applied Biosystems).
Quantitation of image data was achieved using the image-analysis programs ImageJ.
Validation of microarray data was achieved using cDNA synthesized from eschar specimens A/B, C and E and from R. conorii grown in Vero cells used as reference.
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CEO of Professional Science Editing for Scientists @ prosciediting.com