Given a passage with a context and an answer, construct a question on the basis of the information present in the passage. Construct the question in such a way that (i) it is unambiguous, (ii) it is answerable from the passage, (iii) its answer is unique (iv) it answer uses text phrases from the passage. Avoid creating questions that (i) can't be answered correctly without actually understanding the passage and (ii) uses the same words or phrases given in the passage.

Ex Input:
Context: Nonasthmatic eosinophilic bronchitis (EB) has emerged as a useful tool to study the structural and inflammatory mechanisms of airway hyperresponsiveness (AHR) in asthma. We have previously shown that vascular remodeling and reticular basement membrane (RBM) thickening are present in EB. However, it is not known whether other features of structural remodeling including increased airway smooth muscle (ASM) mass, matrix deposition, and glandular hyperplasia are also present in EB.', 'We sought to determine whether structural remodeling occurs in EB and is associated with AHR and airflow limitation.', 'Forty-two patients with asthma, 21 patients with EB, and 19 healthy volunteers were recruited. ASM area, RBM thickness, collagen 3 deposition, glandular area, mast cells, and granulocytes were assessed in bronchial biopsy samples.', 'Nonasthmatic eosinophilic bronchitis and asthma were associated with a significant increase in ASM mass and RBM thickness compared with healthy subjects. In contrast, we did not observe any significant differences in collagen 3 deposition in the lamina propria and ASM or the % area of glands in the lamina propria. Univariate analysis demonstrated that mast cell numbers in the ASM were the only feature of remodeling associated with AHR (beta = -0.51; P = .004). Stepwise linear regression revealed that a combination of mast cell numbers in the ASM (beta = -0.43) and disease duration (beta = -0.25; model-adjusted R(2) = 0.26; P = .027) best modeled AHR.
Answer: Mast cell localization to the ASM bundle, but not structural remodeling of the airway wall, is associated with AHR in asthma.

Ex Output:
Is airway hyperresponsiveness dissociated from airway wall structural remodeling?


Ex Input:
Context: To investigate the recent incidence of T1D in a US Midwestern county to determine whether this increase has been sustained and compare it with the incidence of celiac disease (CD) and also investigate the prevalence of CD, an associated autoimmune disease, within the cohort.', 'A broad search strategy was used to identify all incident cases of T1D in Olmsted County, Minnesota, between January 1, 1994, and December 31, 2010, using the Rochester Epidemiology Project. Diagnosis and residency status were confirmed through the medical record. Incidence rates were directly standardized to the 2010 US population. Poisson regression was used to test for a change in incidence rate. Clinical charts were reviewed to confirm case status.', 'There were 233 incident cases of T1D. Directly adjusting for age and sex with respect to the 2010 US white population, the overall annual incidence of T1D was 9.2 (95% CI, 8.0-10.4) per 100,000 people per year among all ages and 19.9 (95% CI, 16.6-23.2) per 100,000 people per year for those younger than 20 years. There was no significant increase in the incidence of T1D over time (P=.45). Despite the overall stability in annual incidence, there was an initial increasing trend followed by a plateau. Of the 109 patients with T1D (47%) tested for CD, 12% (13) had biopsy-proven CD.
Answer: The incidence of T1D has stopped increasing in Olmsted County, Minnesota, in the most recent decade. Further studies are needed to confirm this finding and explore reasons for this plateau.

Ex Output:
Is incidence of Type 1 Diabetes Increasing in a Population-Based Cohort in Olmsted County , Minnesota , USA?


Ex Input:
Context: Developmental dyslexia is a specific cognitive disorder in reading acquisition that has genetic and neurological origins. Despite histological evidence for brain differences in dyslexia, we recently demonstrated that in large cohort of subjects, no differences between control and dyslexic readers can be found at the macroscopic level (MRI voxel), because of large variances in brain local volumes. In the present study, we aimed at finding brain areas that most discriminate dyslexic from control normal readers despite the large variance across subjects. After segmenting brain grey matter, normalizing brain size and shape and modulating the voxels' content, normal readers' brains were used to build a 'typical' brain via bootstrapped confidence intervals. Each dyslexic reader's brain was then classified independently at each voxel as being within or outside the normal range. We used this simple strategy to build a brain map showing regional percentages of differences between groups. The significance of this map was then assessed using a randomization technique.", "The right cerebellar declive and the right lentiform nucleus were the two areas that significantly differed the most between groups with 100% of the dyslexic subjects (N = 38) falling outside of the control group (N = 39) 95% confidence interval boundaries. The clinical relevance of this result was assessed by inquiring cognitive brain-based differences among dyslexic brain subgroups in comparison to normal readers' performances. The strongest difference between dyslexic subgroups was observed between subjects with lower cerebellar declive (LCD) grey matter volumes than controls and subjects with higher cerebellar declive (HCD) grey matter volumes than controls. Dyslexic subjects with LCD volumes performed worse than subjects with HCD volumes in phonologically and lexicon related tasks. Furthermore, cerebellar and lentiform grey matter volumes interacted in dyslexic subjects, so that lower and higher lentiform grey matter volumes compared to controls differently modulated the phonological and lexical performances. Best performances (observed in controls) corresponded to an optimal value of grey matter and they dropped for higher or lower volumes.
Answer: These results provide evidence for the existence of various subtypes of dyslexia characterized by different brain phenotypes. In addition, behavioural analyses suggest that these brain phenotypes relate to different deficits of automatization of language-based processes such as grapheme/phoneme correspondence and/or rapid access to lexicon entries.

Ex Output:

Does brain classification reveal the right cerebellum as the best biomarker of dyslexia?