Teacher: In this task, you are given an abstract of article. Your task is to generate label "True" if abstract is structured, otherwise generate "False". A structured abstract is composed of a topic sentence (or key sentence), relevant supporting sentences, and a closing (or transition) sentence. This structure is key to keeping your abstract focused on the main idea and creating a clear and concise image.
Teacher: Now, understand the problem? If you are still confused, see the following example:
RanBP type proteins have been reported to increase the catalytic efficiency of the RanGAP-mediated GTPase reaction on Ran. Since the structure of the Ran-RanBP1-RanGAP complex showed RanBP1 to be located away from the active site, we reinvestigated the reaction using fluorescence spectroscopy under pre-steady-state conditions. We can show that RanBP1 indeed does not influence the rate-limiting step of the reaction, which is the cleavage of GTP and/or the release of product P(i). It does, however, influence the dynamics of the Ran-RanGAP interaction, its most dramatic effect being the 20-fold stimulation of the already very fast association reaction such that it is under diffusion control (4.5 x 10(8) M(-1) s(-1)). Having established a valuable kinetic system for the interaction analysis, we also found, in contrast to previous findings, that the highly conserved acidic C-terminal end of RanGAP is not required for the switch-off reaction. Rather, genetic experiments in Saccharomyces cerevisiae demonstrate a profound effect of the acidic tail on microtubule organization during mitosis. We propose that the acidic tail of RanGAP is required for a process during mitosis.
Solution: False
Reason: Output is appropriate for the given abstract because it is not structured.

Now, solve this instance: Purpose. To demonstrate how correlations among predictor variables in a population pharmacokinetic model affect the ability to discern which covariates should enter into the structural pharmacokinetic model. Methods. Monte Carlo simulation was used to generate multiple-dose concentration-time data similar to that seen in a Phase III clinical trial. The drugs' pharmacokinetics were dependent on two covariates. Five data sets were simulated with increasing correlation between the two covariates. All data sets were analyzed using NONMEM both with and without inclusion of the covariates in the structural pharmacokinetic model. Summary measures for ill-conditioning and sensitivity analysis were used to examine how increasing correlation among covariates affects the accuracy and precision of the parameter estimates. Results. When covariates were included in the structural pharmacokinetic model and the correlation between covariates increased, the standard error of the parameter estimates increased and the value of parameter estimates themselves became increasingly biased. When the correlation between predictor variables was 0.75, the standard errors of the parameter estimates were too large to declare statistical significance. Conclusions. Correlations among predictor variables greater than 0.5 when entered into the model simultaneously should be a warning to researchers because the (1) the accuracy of the parameter estimates themselves may be biased and (2) the precision of the estimates may be inflated due to ill-conditioning.
Student:
False