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We have generated a time series of 8 earthquakes in which the observed relationship between the origin time and focal depth of the earthquakes were preserved.
This generated a time vector of coefficients (βcoh, βt1 and βt2 and βchoice) for each neuron describing the influence of each factor on the mean firing rate at successive time points.
We generated a time course of whole-genome expression data covering the pre-symptomatic situation, the onset of hyperglycemia, the onset of the DPN and finally the symptomatic period in Ins2Akita/+ mice.
Synchronized single-channel SD-tVEP waves were recorded and generated a time series of 240 data points per analysis window.
Changing the ERR from linear to U-shaped generated a time series with a 6 month periodicity (i.e. double the frequency of the parent 12 month periodicity).
Based on date of birth in the CRS we generated a time varying covariate age (14 –49, 50 64, and ≥65 years).
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We then generated a time-to-event outcome for each subject using a data-generating process for time-to-event outcomes described by Bender et al. [ 16].
For this, we generated a time-course series of SNV stocks in Vero E6 cells and assayed both their viral titer on fresh Vero E6 cells and their ISG-inducing potential on Huh7 cells (Figure 1B).
We first generated a time-calibrated phylogeny using a subset of conserved single-copy protein-coding genes [ 37] and time of origin estimates from fossil records [ 38].
Thus, each of the 1000 permutations generated a time-by-time map of classification accuracies like those in Figures 4A and 4B.
Using the optimal rate smoothing parameter of 0.001 derived from the cross-validation, r8s generated a time-calibrated ultrametric tree that was utilized in the subsequent analyses.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com