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The respondents were asked whether they could reason out their choices of figures for each trait.
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The respondents were instructed to choose only one figure for each trait and not to leave any trait without a choice.
Figure 2 shows for each trait and animal the relation between MGBV and SDGBV.
Here we propose a new analysis paradigm, depicted in Figure 1, whereby for each trait (i.e., any given expression level), all remaining cDNA levels are potential regressors that can be included in the model.
The genome-wide linkage results for the seven traits are illustrated in Figure 1 with the strongest linkage signals for each trait listed in Table 3.
Supplementary Material Figures S1 S4 show that the Q–Q plots for each trait approximate what would be expected in a homogeneous population.
Figure 5 shows the average predicted phenotypic values for each trait against different numbers of top crosses.
The profiles of p-values (in terms of –log10(p)) for all tested SNPs for each trait are illustrated in Figure 4.
QTL detected on the global design for all studied traits are reported in Table 6, Table S2, and Figure 2. Between 10 and 15 QTL were detected for each trait, explaining between 34 and 40% of the phenotypic variation.
This figure allows us to view the full range of significant changes for each trait, and how this range relates to a zero baseline.
Figure 2 shows phenotypic correlations as a function of additive relationship for each trait.
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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