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As the secondary structure prediction is one of the input parameters for the de novo modeling, we conducted a set of control modeling calculations, in which minimal secondary structure was provided (see the SSP-full-SERT prediction in Figure 1a).
Prior to the modeling, we conducted a 3D alignment of 3VRI, 3VRJ, and 3UPR in order to evaluate any significant deviations between the protein structures, bound ligand, and peptides.
For the stochastic modeling, we conducted a 1-year measurement program for NO2 and PM2.5 at 40 measurement sites.
Before the BH regression modeling, we conducted CV studies for our imputation method for the missing constituent levels.
In addition, to illustrate some of the uncertainties associated with our atmospheric modeling, we conducted sensitivity analyses for concentration estimates from one airport (PVD) using meteorological input from a different year and characterizing airport emissions as a volume source rather than as an area source.
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After building a model, we conducted flow simulations by TOUGH2/ECO2N.
To test these models, we conducted extensive studies in 1992, and again in 2002.
In order to test our conceptual model, we conducted an online survey among U.S. citizens.
To test the robustness of the model, we conducted three types of sensitivity analyses.
In order to evaluate the proposed model, we conducted an additional experiment.
To validate the model we conducted a study on an educational application.
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