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To test the limits of our system on dynamic samples, we created light sheets behind living drosophila pupae.
For all simulated metagenomic samples, we created small-insert clone libraries that were end-sequenced using Sanger chain termination sequencing [24] and capillary gel electrophoresis.
To create an easy-to-use tool for others to categorize survey respondents in new, independent samples, we created a linear discriminant function using the output from the Latent Class Analysis.
To enable identification of segment status with new, independent samples, we created an identification tool based on a linear discriminant function of all 36 variables used in the segmentation analysis.
By merging the protein sets from the individual and pooled Pap test samples, we created a "Normal Pap test Core Proteome" consisting of 153 proteins.
To visualize the expression of top hits (listed in Table 3 and Table 4) in individual patient tumor samples, we created heatmaps using unsupervised clustering.
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To specify the measurement model for each sample, we created parcels by calculating the mean of all of the items within a particular subscale and using these means as indicators of the higher-order EALS constructs.
To simulate variation arising from random sampling, we created sub-data sets from arrays in two different experiment groups: amygdala and hippocampus samples harvested from the low fear strain, where each group contained six replicate arrays.
For each sample, we created an adjacency matrix A such that an element aij is equal to 1 when a flight exists between cities i and j and is equal to 0 otherwise.
From this sample, we created a subset of 1,055 unrelated adults (≥ 20 years of age).
For each DNA sample, we created two sequences using both AR11 and AF05 primers.
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CEO of Professional Science Editing for Scientists @ prosciediting.com