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Using human epidermal growth factor receptor 2 (HER2) as an example, we determined whether testing for a recently introduced biomarker was associated with the outcome of women with breast cancer.
As a first example, we determined the location of the N-terminus of two membrane-active amphipathic peptides, the 26-mer bee venom melittin and a de novo designed 15-mer D,L-amino acid amphipathic peptide (5D-L9K6C), bofh of which are antimicrobial and bind and act similarly on negatively charged membranes.
For example, we determined that although the total 18O composition of our synthesized compounds matched the predicted amount, the 18O composition of the individual species differed, indicating that the assumptions of either complete, or no P-O bond-breakage in POCl3 were not met.
For example, we determined the percentage of action potential trains (for trains of 1, 2, 3, 4, 5, 10 and 40 action potentials at 83 Hz) that could be detected at a 5% false positive rate during 1 second of data acquisition (i.e., such that when a time series is divided into subsequent intervals of 1 second length, 5% of these intervals will contain a false positive event).
As an example, we determined fold change gene expression differences for Neu initiated tumors.
For example, we determined a broad linear correlation of the bridging P O(R) bond length in monosubstituted dianionic phosphates and the p Ka of the substituent alcohol.
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As representative examples, we determined specificity as a function of ligand sequence-dependent affinity contributions by statistical analysis of peptide library screens for the AF6, ERBIN and SNA1 (α-1-syntrophin) PDZ domains.
To illustrate this property with concrete examples, we determined which FCSs in R89\R1 involved any of the 24 reactions occurring at scope distance 1 in Figure 4b.
In order to test if WDR66 expression correlates with prognostic markers in a separate validation set of ESCC examples, we determined WDR66 expression in an independent set of n = 25 ESCC examples using qRT-PCR (Additional file 1: Table S1).
For example, if we determined that author "Jones A" in one article was not the same person as author "Jones A" in another article, we marked one author as "Jones AA" and the other as "Jones AB".
For example, if we determined that author "Smith J" in one article and author "Smith JA" in another article were the same person, we marked both instances as "Smith JA" in the database.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com