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Data Transformation First, we transformed the data of some columns from type text to appropriate types.
First, we transformed a previously characterized E. coli transposon library27 with a plasmid encoding a candidate IMP with an in-frame superfolder green fluorescent protein (GFP) fusion joined with a linker (IMP-GFP, Table 1).
First, we transformed the Col-0 near-isogenic ttg1-13 line, which was obtained by three backcrosses of the deletion mutant ttg1-13 in the Rschew (RLD) background10 into Col-0 wild-type plants, with a 4.6-kb TTG1 genomic fragment including the 2.5-kb upstream sequence, the 1.0-kb coding sequence harboring a 4HA tag fused immediately after ATG, and the 1.1-kb downstream sequence.
First, we transformed the cost-efficiency (two-dimensional) representation into a one-dimensional one.
First, we transformed (Lu=0) to (L'u'=0) by making the change of variables (y=varphi (x)) of the form (2.15) and gauge transformation with the gauge (e^{-id s,tau,y')}) be^{-id s to the group (G_0) (cf. (1.22).
First, we transformed each position count matrix into the corresponding position frequency matrix (PFM) by adding position-dependent pseudo-counts [34].
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First we transform signal to time-frequency domain using spectrogram.
First, we transform the physical parameters into mutually independent principal components.
First we transform the MCNLS to the stationary state ones by using separation of variables.
First, we transform the TMI problem into a BMI (Bilinear Matrix Inequality) one using the successive over-bounding technique.
First, we transform the observation signal vector sequence, through a semi-orthogonal matrix, into a sequence of transformed signal vectors with a reduced dimension.
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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