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Due to the long resting state acquisition, we were able to also investigate the temporal variation of ECPs by splitting the datasets into five parts where each is still of sufficient length.
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It will be shown that this could be done by splitting the dataset in two.
Figure 8 shows the spread of GIC data for the same nodes by splitting the dataset into values from the 2D conductivity models.
We then examined each putative recombinant by splitting the dataset at each identified recombination breakpoint.
The resolution of 21Å was calculated by splitting the dataset in two after alignment.
The phylogenetic resolution of the σ70 family appeared to be more robustly determined by splitting the dataset into two subsets corresponding to ECF and non-ECF (primary, stationary, and stress response/cellular differentiation) deduced σ70 AA sequences.
As you point out, classical sub-tomogram averaging may produce such overfitting, while the "gold standard" procedure reduces this risk by splitting the dataset into two sub-sets and processes these completely independently.
This was done by splitting the dataset up into the subsets "root-specific", "shoot-specific", "differential", "similar", "uncategorized", and "not regulated" using an algorithm outlined in Additional file 1: Figure S1.
Finally we examined the temporal stability of the ECPs within individual subjects by splitting the individual datasets into five equal segments.
The CV was performed by splitting the reference dataset by country (stratified CV), or by splitting it randomly in a 5-fold CV.
A comparison of the functional content of cDNA-AFLP records was also performed by splitting the sequence dataset into monocots and dicots and by comparing them to all annotated ESTs of Arabidopsis and rice, respectively.
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