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The intermolecular interaction is robust enough to really act as a 'supramolecular glue' to assemble the binuclear coordination complex and picratol into three-dimensional arrays.
If this interaction is robust, it should be straightforward to demonstrate this for Irgb2-b1.
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Interestingly, whereas the PP6R1-Sit4 interaction was robust in the sap185 sap190 double mutant, which still expresses Sap155 and Sap4, the PP6R2 and PP6R3-Sit4 inthisctionstrainhis strain were reproducibly weaker than those observed in the quadruple sap mutant (Figure 2, lanes 6 8).
This gene-diet interaction was robust regardless of adjustment for BMI.
Taken together, these results demonstrate that in vitro the RTN1A/RyR2 interaction was robust and concentration-dependent as GST-RTN1523 was increased.
The estimated coefficients of the EITC-low-skill interactions are robust to including state-specific linear trends, or state-year interactions Source: Neumark and Wascher (2011), using CPS Annual Demographic Files.
The estimated coefficients of the EITC-kids interactions are robust to including state-specific linear trends, or state-year interactions; in the latter specifications, the main EITC effect drops out.
All of the above interactions are robust and deemed strong, as none of the negative controls reveal positive interactions under low stringency and many bona fide positive interactions may not survive as low as 1 mM 3-AT concentration under the same experimental conditions (data not shown).
More generally, attention to distinguishing processes related to illness onset from illness progression or exacerbation will be critical in assessing whether interactions are robust or should be expected in healthy populations.
Furthermore, these gene nutrient interactions were robust to other methods of classifying nutrient intake such as above and below median intake, with the lowest risks consistently occurring in individuals with high-nutrient intakes and del/del genotype.
Thus, we tested whether or not the predicted interactions are robust against changes in the set of prior knowledge by iterating the modelling approach 1000 times while randomly skipping 10% of all interactions in the set of prior knowledge in each run.
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