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Among the six networks identified by the core analysis using the IPA software, the top network (with 13 genes) confirmed previous data on the role of AR in CRPC.
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A related motif is reported by Molina and Grotewold from Arabidopsis core promoter analysis using the Gibbs-sampling method (Motif 1 with a typical sequence, TTCTTCTTC, [ 29]).
To test our hypothesis, we performed pan-genome (as well as core genome) analysis using the PGAP analysis pipeline [ 61] for L. monocytogenes lineages I, II and III by extrapolating the complete genome data of each lineage.
The baseline CT scan was provided to the CT core lab for analysis using a modification of the system used in the NETT study, a system adapted from prior work by Goddard et al[ 13], Bergin et al[ 14], and Bankier at al[ 12].
It was concluded that multi-locus sequence analysis using core gene set might represent the first consensus and valid approach for investigating the bacterial identification, phylogeny and taxonomy.
RNA samples were submitted to Stanford Protein and Nucleic Acid core facility for microarray analysis using Affymetrix GeneChip Human Genome U133 Plus 2.0 arrays.
To confirm our findings, we repeated the entire analysis using core promoters determined by DeepCAGE, and H3K9ac and RNA polymerase II ChIP-chip of THP-1 cells 96 hours after PMA treatment.
Although we could not use our computational analysis to estimate the pan-genome sizes of B. parapertussis and B. pertussis due to limited number of fully annotated genomes, we speculate that the pan-genome of these two species would be smaller than that of B. bronchiseptica due to restricted host range, consistent with our core and pan-genome analysis using eleven genomes.
A contiguous spreadsheet for all the usable cores (n = 301 malignant cores and n = 299 benign cores) was constructed and statistical analysis using Student's t-test was performed.
According to particle size analysis, using well core data acquired from Hei120, the particle size probability curve is mainly composed of two parts: a suspended part and a jump part.
Webster (A: Bio-objectification: definitions and tools unpublished internal document. COST Bio-objects action, unpublished) develops a bio-objects conceptual 'toolkit' that entails four core dimensions that can underpin analysis using the approach.
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