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The purpose of this study was to identify profile characteristics that make wells susceptible to, or provide natural protection against microbial contamination.
Here, we use deep sequencing and computational methods to identify, profile and analyze non-conserved MIRNA genes in Arabidopsis thaliana.
Health indicators are measured repeatedly over time to identify profile membership at each occasion.
To identify profile shape variations within each chromatin mark, we used the set of expressed RTSSs as profile anchor points and k-means clustering to identify distinct chromatin profiles over a ±3kbp extension around expressed RTSS center positions.
To identify profile of glioma-based miRNAs which regulate expression of MMP14 and are altered by TMZ/XRT treatment, we applied the miFinder SAbioscience platform to the total miRNAs isolated from U87 cells treated with TMZ/ XRT (100 μmol/L+2 Gy/daily) or DMSO for 48 h.
We used deep-sequencing, miRNA microarray assays and computational methods to identify, profile, and describe conserved and non-conserved miRNAs at four ear developmental stages, which resulted in identification of 22 conserved and 21-maize-specific miRNA families together with their corresponding miRNA*.
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STR analysis, which looks at specific repeating patterns in DNA, can help identify profiles to an incredibly narrow degree.
The aim of this study was to identify profiles of psychoactive substances consumers among workers according to their professional characteristics.
In the latent class analysis we omitted data on symptoms from the first year to identify profiles.
It may be possible not only to identify profiles predicting relapse (prognostic profiles), but also to predict which treatments are most likely to be effective (predictive profiles).
Latent Class Analyses were conducted to identify profiles of healthcare and regression analyses were used to analyse the characteristics of the profiles.
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