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.

Example Input: The most conspicuous event in the cell cycle is the alignment of chromosomes in metaphase. Chromosome alignment fosters faithful segregation through the formation of bi-oriented attachments of kinetochores to spindle microtubules. Notably, numerous kinetochore-microtubule (k-MT) attachment errors are present in early mitosis (prometaphase), and the persistence of those errors is the leading cause of chromosome mis-segregation in aneuploid human tumour cells that continually mis-segregate whole chromosomes and display chromosomal instability. How robust error correction is achieved in prometaphase to ensure error-free mitosis remains unknown. Here we show that k-MT attachments in prometaphase cells are considerably less stable than in metaphase cells. The switch to more stable k-MT attachments in metaphase requires the proteasome-dependent destruction of cyclin A in prometaphase. Persistent cyclin A expression prevents k-MT stabilization even in cells with aligned chromosomes. By contrast, k-MTs are prematurely stabilized in cyclin-A-deficient cells. Consequently, cells lacking cyclin A display higher rates of chromosome mis-segregation. Thus, the stability of k-MT attachments increases decisively in a coordinated fashion among all chromosomes as cells transit from prometaphase to metaphase. Cyclin A creates a cellular environment that promotes microtubule detachment from kinetochores in prometaphase to ensure efficient error correction and faithful chromosome segregation.
Example Output: False

Example Input: BACKGROUND Soy protein or its components may protect against the atherosclerotic cardiovascular disease (CVD) risk factors total homocysteine (tHcy), C-reactive protein (CRP), and excess body iron, which generally increase with menopause.
OBJECTIVE The primary objective of this study was to determine the independent effect of the soy protein components isoflavones and phytate on CVD risk factors in postmenopausal women. The secondary objective was to identify factors [blood lipids, oxidative stress indexes, serum ferritin, plasma folate, plasma vitamin B-12, and body mass index (BMI)] contributing to tHcy and CRP concentrations.
DESIGN In a double-blind, 6-wk study, 55 postmenopausal women aged 47-72 y were randomly assigned to 1 of 4 soy protein (40 g/d) isolate treatments: native phytate and native isoflavone (n = 14), native phytate and low isoflavone (n = 13), low phytate and native isoflavone (n = 14), or low phytate and low isoflavone (n = 14). We measured iron indexes, tHcy, CRP, and BMI.
RESULTS Soy protein with native phytate significantly reduced tHcy (P = 0.017), transferrin saturation (P = 0.027), and ferritin (P = 0.029), whereas soy protein with native isoflavones had no effect on any variables. At baseline, BMI was highly correlated with tHcy (r = 0.39, P = 0.003) and CRP (r = 0.55, P < 0.0001), whereas HDL cholesterol was correlated with CRP (r = -0.30, P = 0.02). Multiple regression analysis showed that LDL cholesterol and BMI contributed significantly (R2= 19.9%, P = 0.003) to the overall variance in tHcy.
CONCLUSION Consuming phytate-rich foods and maintaining a healthy weight may reduce atherosclerotic CVD risk factors in postmenopausal women.
Example Output: True

Example Input: Detecting and localizing selective sweeps on the basis of SNP data has recently received considerable attention. Here we introduce the use of hidden Markov models (HMMs) for the detection of selective sweeps in DNA sequences. Like previously published methods, our HMMs use the site frequency spectrum, and the spatial pattern of diversity along the sequence, to identify selection. In contrast to earlier approaches, our HMMs explicitly model the correlation structure between linked sites. The detection power of our methods, and their accuracy for estimating the selected site location, is similar to that of competing methods for constant size populations. In the case of population bottlenecks, however, our methods frequently showed fewer false positives.
Example Output:
False