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.
One 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 is here: False
Explanation: Output is appropriate for the given abstract because it is not structured.

Now, solve this: The recent success of genome-wide association studies (GWAS) is now followed by the challenge to determine how the reported susceptibility variants mediate complex traits and diseases. Expression quantitative trait loci (eQTLs) have been implicated in disease associations through overlaps between eQTLs and GWAS signals. However, the abundance of eQTLs and the strong correlation structure (LD) in the genome make it likely that some of these overlaps are coincidental and not driven by the same functional variants. In the present study, we propose an empirical methodology, which we call Regulatory Trait Concordance (RTC) that accounts for local LD structure and integrates eQTLs and GWAS results in order to reveal the subset of association signals that are due to cis eQTLs. We simulate genomic regions of various LD patterns with both a single or two causal variants and show that our score outperforms SNP correlation metrics, be they statistical (r(2)) or historical (D'). Following the observation of a significant abundance of regulatory signals among currently published GWAS loci, we apply our method with the goal to prioritize relevant genes for each of the respective complex traits. We detect several potential disease-causing regulatory effects, with a strong enrichment for immunity-related conditions, consistent with the nature of the cell line tested (LCLs). Furthermore, we present an extension of the method in trans, where interrogating the whole genome for downstream effects of the disease variant can be informative regarding its unknown primary biological effect. We conclude that integrating cellular phenotype associations with organismal complex traits will facilitate the biological interpretation of the genetic effects on these traits.
Solution:
False