In this task, you are given an abstract of article. Your task is to generate title for this article. Preferred titles are under thirty words.
One example: Alterations of the architecture of cerebral white matter in the developing human brain can affect cortical development and result in functional disabilities. A line scan diffusion-weighted magnetic resonance imaging (MRI) sequence with diffusion tensor analysis was applied to measure the apparent diffusion coefficient, to calculate relative anisotropy, and to delineate three-dimensional fiber architecture in cerebral white matter in preterm (n = 17) and full-term infants (n = 7). To assess effects of prematurity on cerebral white matter development, early gestation preterm infants (n = 10) were studied a second time at term. In the central white matter the mean apparent diffusion coefficient at 28 wk was high, 1.8 microm2/ms, and decreased toward term to 1.2 microm2/ms. In the posterior limb of the internal capsule, the mean apparent diffusion coefficients at both times were similar (1.2 versus 1.1 microm2/ms). Relative anisotropy was higher the closer birth was to term with greater absolute values in the internal capsule than in the central white matter. Preterm infants at term showed higher mean diffusion coefficients in the central white matter (1.4 +/- 0.24 versus 1.15 +/- 0.09 microm2/ms, p = 0.016) and lower relative anisotropy in both areas compared with full-term infants (white matter, 10.9 +/- 0.6 versus 22.9 +/- 3.0%, p = 0.001; internal capsule, 24.0 +/- 4.44 versus 33.1 +/- 0.6% p = 0.006). Nonmyelinated fibers in the corpus callosum were visible by diffusion tensor MRI as early as 28 wk; full-term and preterm infants at term showed marked differences in white matter fiber organization. The data indicate that quantitative assessment of water diffusion by diffusion tensor MRI provides insight into microstructural development in cerebral white matter in living infants.
Solution is here: Microstructural development of human newborn cerebral white matter assessed in vivo by diffusion tensor magnetic resonance imaging.
Explanation: This title is appropriate for the given abstract because the focus of this abstract is cerebral white matter assesment.

Now, solve this: BACKGROUND High body-mass index (BMI) predisposes to several site-specific cancers, but a large-scale systematic and detailed characterisation of patterns of risk across all common cancers adjusted for potential confounders has not previously been undertaken. We aimed to investigate the links between BMI and the most common site-specific cancers.
METHODS With primary care data from individuals in the Clinical Practice Research Datalink with BMI data, we fitted Cox models to investigate associations between BMI and 22 of the most common cancers, adjusting for potential confounders. We fitted linear then non-linear (spline) models; investigated effect modification by sex, menopausal status, smoking, and age; and calculated population effects.
FINDINGS 524 million individuals were included; 166,955 developed cancers of interest. BMI was associated with 17 of 22 cancers, but effects varied substantially by site. Each 5 kg/m(2) increase in BMI was roughly linearly associated with cancers of the uterus (hazard ratio [HR] 162, 99% CI 156-169; p<00001), gallbladder (131, 112-152; p<00001), kidney (125, 117-133; p<00001), cervix (110, 103-117; p=000035), thyroid (109, 100-119; p=00088), and leukaemia (109, 105-113; p00001). BMI was positively associated with liver (119, 112-127), colon (110, 107-113), ovarian (109, 1.04-1.14), and postmenopausal breast cancers (105, 103-107) overall (all p<00001), but these effects varied by underlying BMI or individual-level characteristics. We estimated inverse associations with prostate and premenopausal breast cancer risk, both overall (prostate 098, 095-100; premenopausal breast cancer 089, 086-092) and in never-smokers (prostate 096, 093-099; premenopausal breast cancer 089, 085-094). By contrast, for lung and oral cavity cancer, we observed no association in never smokers (lung 099, 093-105; oral cavity 107, 091-126): inverse associations overall were driven by current smokers and ex-smokers, probably because of residual confounding by smoking amount. Assuming causality, 41% of uterine and 10% or more of gallbladder, kidney, liver, and colon cancers could be attributable to excess weight. We estimated that a 1 kg/m(2) population-wide increase in BMI would result in 3790 additional annual UK patients developing one of the ten cancers positively associated with BMI.
INTERPRETATION BMI is associated with cancer risk, with substantial population-level effects. The heterogeneity in the effects suggests that different mechanisms are associated with different cancer sites and different patient subgroups.
FUNDING National Institute for Health Research, Wellcome Trust, and Medical Research Council.
Solution:
Body-mass index and risk of 22 specific cancers: a population-based cohort study of 524 million UK adults