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 is below.
Q: 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.
A: False
Rationale: Output is appropriate for the given abstract because it is not structured.
Q: BACKGROUND It is not yet known whether DNA methylation levels can be used to accurately predict age across a broad spectrum of human tissues and cell types, nor whether the resulting age prediction is a biologically meaningful measure.
RESULTS I developed a multi-tissue predictor of age that allows one to estimate the DNA methylation age of most tissues and cell types. The predictor, which is freely available, was developed using 8,000 samples from 82 Illumina DNA methylation array datasets, encompassing 51 healthy tissues and cell types. I found that DNA methylation age has the following properties: first, it is close to zero for embryonic and induced pluripotent stem cells; second, it correlates with cell passage number; third, it gives rise to a highly heritable measure of age acceleration; and, fourth, it is applicable to chimpanzee tissues. Analysis of 6,000 cancer samples from 32 datasets showed that all of the considered 20 cancer types exhibit significant age acceleration, with an average of 36 years. Low age-acceleration of cancer tissue is associated with a high number of somatic mutations and TP53 mutations, while mutations in steroid receptors greatly accelerate DNA methylation age in breast cancer. Finally, I characterize the 353 CpG sites that together form an aging clock in terms of chromatin states and tissue variance.
CONCLUSIONS I propose that DNA methylation age measures the cumulative effect of an epigenetic maintenance system. This novel epigenetic clock can be used to address a host of questions in developmental biology, cancer and aging research.
A:
True