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The cosolvent 2,2,2‐trifluoroethanol (TFE) is often used to mimic protein desiccation. We assessed the effects of TFE on cytosolic abundant heat soluble protein D (CAHS D) from tardigrades. CAHS D is a member of a unique protein class that is necessary and sufficient for tardigrades to survive desiccation. We find that the response of CAHS D to TFE depends on the concentration of both species. Dilute CAHS D remains soluble and, like most proteins exposed to TFE, gains α‐helix. More concentrated solutions of CAHS D in TFE accumulate β‐sheet, driving both gel formation and aggregation. At even higher TFE and CAHS D concentrations, samples phase separate without aggregation or increases in helix. Our observations show the importance of considering protein concentration when using TFE.

The statistical practice of modeling interaction with two linear main effects and a product term is ubiquitous in the statistical and epidemiological literature. Most data modelers are aware that the misspecification of main effects can potentially cause severe type I error inflation in tests for interactions, leading to spurious detection of interactions. However, modeling practice has not changed. In this article, we focus on the specific situation where the main effects in the model are misspecified as linear terms and characterize its impact on common tests for statistical interaction. We then propose some simple alternatives that fix the issue of potential type I error inflation in testing interaction due to main effect misspecification. We show that when using the sandwich variance estimator for a linear regression model with a quantitative outcome and two independent factors, both the Wald and score tests asymptotically maintain the correct type I error rate. However, if the independence assumption does not hold or the outcome is binary, using the sandwich estimator does not fix the problem. We further demonstrate that flexibly modeling the main effect under a generalized additive model can largely reduce or often remove bias in the estimates and maintain the correct type I error rate for both quantitative and binary outcomes regardless of the independence assumption. We show, under the independence assumption and for a continuous outcome, overfitting and flexibly modeling the main effects does not lead to power loss asymptotically relative to a correctly specified main effect model. Our simulation study further demonstrates the empirical fact that using flexible models for the main effects does not result in a significant loss of power for testing interaction in general. Our results provide an improved understanding of the strengths and limitations for tests of interaction in the presence of main effect misspecification. Using data from a large biobank study “The Michigan Genomics Initiative”, we present two examples of interaction analysis in support of our results.