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In order to interpret the maps, we performed flexible fitting of suilysin domains from the crystal structure.
With eigenvector centrality maps we performed paired t tests between the placebo and exenatide conditions for each scanning block.
Visualization of and Docking into 3D Maps We performed 3D structure analysis and image rendering using the UCSF Chimera package (Pettersen et al., 2004 ).
To lend face validity of the experiment and reduce effects of a putative interaction between DWI and multiparameter maps, we performed the structural covariance analysis of multiparameter maps in a larger dataset including different participants than those who underwent probabilistic diffusion tractography.
To establish evolutionary differences and similarities between human and mouse co-expression maps, we performed our analysis using the fraction of genes that have a homolog in both humans and mice, corresponding to 16,080 unique genes in humans and 16,463 unique genes in mice.
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To distinguish between correctly and incorrectly biased saliency maps, we perform a sub- image analysis (see Fig. 8).
To construct the linkage map, we performed re-sequencing of the parents and designed BeadArray markers using detected single nucleotide polymorphisms (SNPs).
To test the temporal stability (i.e., repeatability) of S-maps, we performed the following three-step analysis.
Then, for each noisy contact map we performed 10 different reconstructions.
After the mutational mapping, we performed in silico mutagenesis to observe the effects of the mutations on the interactions.
Difference Mapping We performed 2D difference mapping between class averages by first aligning, rotating, and shifting them in IMAGIC (van Heel et al., 1996 ) using the MSA-ALIGN command.
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