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We characterized each isolated unit's SRF by presenting sounds from 290 virtual locations.
We characterized each mRNA by its mean fold change and a x-dimensional binary vector (Fig. 1B).
Further, we characterized each vector for its viral titer production as well as its efficiency in expressing or depleting proteins of interest.
To investigate in more detail the factors affecting the performance of each program, we characterized each alignment using a number of 'global' attributes describing the overall full-length alignment, including the number of sequences to be aligned, their length, an MSA objective function (norMD) [40] and the percentage of the alignment covered by the blocks.
To evaluate this possibility, we characterized each mouse phenotype using a microarray-based immunophenotyping algorithm, which estimates overall inflammation intensity and also identifies leukocyte subsets underlying an inflammation signature within microarray data (Figure 9) [31] (see also Haider et al. [37] for a similar approach).
We characterized each heat wave by its intensity, duration, and timing in season.
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We characterize each pixel in terms of the average density and standard deviation of its pixel vector and exploit that information to normalize its output.
From these measurements we characterize each instrument's sounding frequencies (fundamental and 1st overtone where possible), radiation pattern, and impedance, and we estimate the bore area function of each shell.
We characterize each ramet by some microsatellite markers [7].
We characterize each community by checking the majority attributes (e.g., country and industry sector) of the firms in the community.
Here we characterize each patch using three channels of local visual descriptors: vector quantized SIFT [25], color histograms, and Gabor textons.
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