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By threading modeling, we generated the first theoretical models of LTR-RTE proteins in plants.
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The model we generated with a highly generalized set of formulations can be applied for any combination of a gas species and a catalytic adsorbent/absorbent.
To reliably validate our model, we generated three types of App-DDoS attacks.
For each 3D model, we generated a set of silhouettes corresponding to training viewpoints selected on the view manifold.
Using this T2 distribution model, we generated echo trains with 500 echoes, where echo spacing is 0.9 ms.
For each of the models we generated and compared, random test sets combined randomly against random test sets that had their values combined by the pairwise strategy.
For the BOT model, we generated 100 trajectories over 24 time steps based on the given state transition equations in [23] and, respectively, the observations.
We then recorded the responses of these same ORNs to a new set of chemicals to test whether the models we generated can be used to predict an odorant's activity.
The ERAP1 models we generated exhibit structural characteristics that appear highly relevant to the peptide library screen results.
After characterizing our cellular model, we generated Neuro2a cell lines stably expressing PrPC or the three PrD-related mutants.
To better characterize the neuroprotective activity of parkin in an animal model, we generated transgenic zebrafish stably overexpressing parkin.
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