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All the contributions were obtained from calibration certificates and from statistical analysis of repeated measurements.
After 15 days of the recruitment (invitation), 291 valid contributions were obtained (de Mello et al. 2014b), composing the actual sample sizes presented in Table 12.
In this study, the binding affinity was estimated by using an implicit-solvent model in which the electrostatic contributions were obtained by solving the Poisson equation, and the hydrophobic effects were accounted for by using surface-area-dependent terms.
The contributions were obtained by multiplying the fractions by the measured light intensity.
The same contributions were obtained from the various mBPAP stimuli that simulated a back-propagating spike at different distances from the soma.
Standard free energies of binding and entropic contributions were obtained, respectively, as ΔG = −RT ln(Ka) and TΔS = ΔH − ΔG, from the Ka and ΔH values derived from ITC curve fitting.
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These solutions, presented as a sum of the Newtonian and non-Newtonian contributions, are obtained by using Fourier cosine and sine transforms, and the Laplace transform.
Therefore, two original contributions are obtained: the MR-Radix multi-relational algorithm proposal and the empiric presentation of advantages of the multi-relational approach for mining various tables.
The unconditional likelihood contributions are obtained by integrating V out: {l}_s=underset{V}{int}left[{l}_s V /{D}_0 V right]mathrm{d}G V kern1.5em mathrm{for} mathrm{s}=1,dots, 5 where G V) is the joint distribution of the unobserved heterogeneity terms.
In a computer simulation these contributions are obtained from molecular dynamics simulations.
The macrolocking contribution was obtained from the comparison between the ultimate strength achieved in the first half-cycle of loading τu1 and the ultimate strength achieved in the third half-cycle of loading τu3 of the non-lubricated specimens.
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