In this task, you are given a passage which has a question and the context. You have to generate an answer to the question based on the information present in the context.

[EX Q]: Context: Natural disasters expose entire communities to stress and trauma, leading to increased risk for psychiatric symptoms. Yet, the majority of exposed individuals are resilient, highlighting the importance of identifying underlying factors that contribute to outcomes.', "The current study was part of a larger prospective study of children in Long Island, New York (n = 260). At age 9, children viewed unpleasant and pleasant images while the late positive potential (LPP), an event-related potential component that reflects sustained attention toward salient information, was measured. Following the event-related potential assessment, Hurricane Sandy, the second costliest hurricane in United States history, hit the region. Eight weeks after the hurricane, mothers reported on exposure to hurricane-related stress and children's internalizing and externalizing symptoms. Symptoms were reassessed 8 months after the hurricane.", 'The LPP predicted both internalizing and externalizing symptoms after accounting for prehurricane symptomatology and interacted with stress to predict externalizing symptoms. Among children exposed to higher levels of hurricane-related stress, enhanced neural reactivity to unpleasant images predicted greater externalizing symptoms 8 weeks after the disaster, while greater neural reactivity to pleasant images predicted lower externalizing symptoms. Moreover, interactions between the LPP and stress continued to predict externalizing symptoms 8 months after the hurricane.\Question: Does neural Reactivity to Emotional Stimuli Prospectively predict the Impact of a Natural Disaster on Psychiatric Symptoms in Children?
[EX A]: Results indicate that heightened neural reactivity and attention toward unpleasant information, as measured by the LPP, predispose children to psychiatric symptoms when exposed to higher levels of stress related to natural disasters, while greater reactivity to and processing of pleasant information may be a protective factor.

[EX Q]: Context: Both obesity and type 2 diabetes are among the risk factors for breast cancer development. Combined effect of these metabolic abnormalities on breast cancer risk however, has not been examined in premenopausal women. We tested this association in type 2 diabetic women, categorized as obese, overweight and normal body weight groups based on BMI.', 'A total of 101 subjects were included in this study. Serum levels of IL-6, TNF-alpha, C reactive protein, leptin, TGF-alpha, adiponectin and insulin were measured by ELISA. Data were logarithmically transformed for variables not normally distributed. Analysis of variance with post-hoc Bonferroni was applied to compare the data between the groups. Simple and partial correlation coefficients between the variables were determined and a stepwise multiple linear regression analysis was performed to determine the relationships between the variables of interest.', 'Significantly increased levels of IL-6, C reactive protein, leptin and significantly decreased levels of adiponectin were found in obese group, while the levels of TNF-alpha and TGF-alpha were unaltered. A positive correlation between waist circumference and IL-6 was found in obese group. Similarly, C reactive protein, waist and hip circumferences were linearly correlated with BMI in obese group. Stepwise multiple linear regression analysis revealed several significant predictors for breast cancer risk.\Question: Do combined effects of obesity and type 2 diabetes contribute to increased breast cancer risk in premenopausal women?
[EX A]: Obesity and type 2 diabetes, owing to their effects on adipocytokines and inflammatory mediators, contribute to increased breast cancer risk in premenopausal women. This study emphasizes healthy life style and better management of these metabolic disorders to avoid the pathogenesis of breast cancer and of other chronic diseases.

[EX Q]: Context: Obesity is a well-known risk factor for type 2 diabetes. Genome-wide association studies have identified a number of genetic loci associated with obesity. The aim of this study is to examine the contribution of obesity-related genomic loci to type 2 diabetes in a Chinese population.', 'We successfully genotyped 18 obesity-related single nucleotide polymorphisms among 5338 type 2 diabetic patients and 4663 controls. Both individual and joint effects of these single nucleotide polymorphisms on type 2 diabetes and quantitative glycemic traits (assessing β-cell function and insulin resistance) were analyzed using logistic and linear regression models, respectively.', 'Two single nucleotide polymorphisms near MC4R and GNPDA2 genes were significantly associated with type 2 diabetes before adjusting for body mass index and waist circumference (OR (95% CI)\u200a=\u200a1.14 (1.06, 1.22) for the A allele of rs12970134, P\u200a=\u200a4.75×10(-4); OR (95% CI)\u200a=\u200a1.10 (1.03, 1.17) for the G allele of rs10938397, P\u200a=\u200a4.54×10(-3)). When body mass index and waist circumference were further adjusted, the association of MC4R with type 2 diabetes remained significant (P\u200a=\u200a1.81×10(-2)) and that of GNPDA2 was attenuated (P\u200a=\u200a1.26×10(-1)), suggesting the effect of the locus including GNPDA2 on type 2 diabetes may be mediated through obesity. Single nucleotide polymorphism rs2260000 within BAT2 was significantly associated with type 2 diabetes after adjusting for body mass index and waist circumference (P\u200a=\u200a1.04×10(-2)). In addition, four single nucleotide polymorphisms (near or within SEC16B, BDNF, MAF and PRL genes) showed significant associations with quantitative glycemic traits in controls even after adjusting for body mass index and waist circumference (all P values<0.05).\Question: Are obesity-related genomic loci associated with type 2 diabetes in a Han Chinese population?
[EX A]:
This study indicates that obesity-related genomic loci were associated with type 2 diabetes and glycemic traits in the Han Chinese population.