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Figure 3 shows validation set profiles for a different model (logP2-2).
Fig. 5 shows validation of these Abs as tools to detect target proteins in the rabbit heart, with rat brain or rat heart proteins as positive controls.
Figure 1 figure supplement 2 shows validation of an imprinted gene.
Figure 4 shows validation of all 3 exons of this predicted transcript.
Figure 3 shows validation of the protein protein interactions inferred from the results in figure 2 by use of a second reporter gene, β-galactosidase.
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Finally we show validation results for two different shape memory phenomena: unconstrained shape recovery and constrained stress recovery.
The novelty of this work lies in showing validation and understanding limitation of DRT for its effective use and accurate prediction of delamination damage in stitched composites.
You can also show validation with non-verbals, such as giving them a hug if they feel lonely, making them a cup of tea if they feel jittery, or giving them space if they need time to think.
The authors also provided preliminary data showing validation of the RoB tool: studies at high or unclear risk of bias had significantly greater treatment effects (effect size = 0.52) than those at low risk of bias (effect size = 0.23).
Only in Figures 3 and 4, using the neuroD reporter, do they show validation.
Though some of the primers showed validation up to 36.67% (Table 3).
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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