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The value we reduce by is of great importance.
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To assess the importance of this value we reduced it by half in the sensitivity analysis.
The results indicate that reducing the amount of nucleating agent from the original value we used reduces the chances of successful nucleation.
Since mismatches in low probability sequences are less likely to account for real SNPs, by increasing the read_1_MinPrb threshold value, we can reduce the size of the P1_Reads table without significantly affecting SNPs prediction.
Since we compute the uncertainty bound, rather than assuming its value in the trial domain, we reduce the conservatism of the robust design.
As shown in Figure 1A, the modes of the prior distribution will move toward zero when we reduce the value of τ.
If the value was reduced to 0.8, we found it significantly influenced by the particle distribution.
In each iteration we reduce the p value cutoff by a small amount, until the target p value cutoff is reached.
(In the simulation, we reduced the initial value of [BECN1]T by 50%.) As expected, the model predicts significantly higher percentages of apoptotic cells at 24 hours in all cases.
When we reduce the global Infernal E-value cutoff, we gradually reduce the number of families that have matches in the shuffled sequence to 161 at an Infernal E-value cutoff of 1 where the worst family is Histone-3-prime-UTR with 47 hits.
That is to underestimate the value of reducing misery.
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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