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For testing both models, we used 1% of the total n-grams from each bacterial genome, which also contain plasmid sequences as expected in the true metagenomic samples.
For both models, we used principal components to derive an overall courtship score for each male in each trial based on the intensity of sexual displays (time spent courting the female and number of courtship bouts).
For both models, we used a backward stepwise procedure by selecting all variables associated with a p <0.20 at univariate and kept in the model all those significantly associated with the outcome (p <0.05).
Based on the results from the correlations, in both models we used as independent variables HAQ, DAS28, CRP and ESR in an initial forward entry step, followed by overall physical activity and 'vigorous' physical activity on a final step.
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For both models we use the occupation number basis in real space in which the interaction part is diagonal and has a value N κ V, where N κ is either the number of occupied nearest-neighbor sites (N n n ) in case of the spinless fermion model, or the number of double-occupancies (N d ) in case of the Hubbard model.
For the evaluation of the predicted models, we used both the RMSD and TM-score [ 22].
For both bluefin and yellowfin models, we used a stepwise forward-backward model selection process with Akaike's Information Criterion with small sample correction (AICc) as the selection criterion [18].
For statistical purposes (both comparison and regression models), we used the HbA1c levels measured by HPLC.
Therefore, the average log-likelihood score levels off when the number of states in the model surpasses 3. To further validate our models, we used both A3 and M6 to compute MFPTs between the α R and β/ C5 regions of the conformation space.
We don't talk about the specifics of how we manage information but there are a lot of tools and capabilities and models we use for both preventive and detective approaches.
For both the genotype and haplotype effect models we use
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both models we confirmed
both models we tracked
both assays we used
both conditions we used
both models we checked
both models we classified
both tools we used
both tasks we used
both studies we used
both models we postulated
both algorithms we used
both cues we used
both models we evaluated
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both mechanisms we used
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