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"Untreated" data were used as reference to analyse effects of differences in species numbers and numerical scale between taxonomic groups.
The use of the ALiDAR data together with L-Architect allowed quantifying how significant object occlusion affects untreated data.
Instead, we trained our machine learning algorithm on the untreated data set, which was labelled with a DRAQ5 DNA stain (see a) and used the trained machine learning algorithm to predict the DNA stain of the blocked cells.
As expected, the simple combination of "untreated" data cannot be recommended, since the outcome is strongly dominated by the taxonomic group with highest numbers of species and individuals.
Taking into account the effects of combination ERT and SRT on urine volume (data not shown; see Figure 2), total uromodulin amounts excreted over a 24 h period were most effectively reduced by the combined ERT and SRT approaches (∼75% compared to untreated; data not shown).
We found no change in untreated (data not shown) or saline groups.
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Data are expressed as % reduction in metabolic activity that is, 100-((O.D.490 nm Untreated)–(O.D.490 nm Treated))/(O.D.490 nm Untreated)*100) versus the indicated concentrations of the drug.
Untreated and alkylated-only protein gave no signal at residue 2 (data not shown).
To test its robustness to such contamination, we deliberately mixed the Treated and Untreated datasets, and analyzed K a / K s from the mixed data.
Untreated or anti-CD16 antibody treated irradiated monocytes did not mediate cytotoxicity against hMSCs, (data not shown).
Untreated Kcnq1−/− mice had ABR thresholds around 90 dB SPL (n = 6, Fig 5B; data points shown by open triangles).
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