Given a passage, 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.

Example Input: The apolipoprotein B (apoB)/apoA-I ratio represents the balance of proatherogenic and antiatherogenic lipoproteins. The purpose of this study was to determine whether the apoB/apoA-I ratio was superior to any of the cholesterol ratios - total cholesterol/high-density lipoprotein cholesterol (TC/HDL-C), low-density lipoprotein cholesterol (LDL-C)/HDL-C and non-HDL-C/HDL-C - in predicting the risk of coronary disease. Moreover, we examined whether any lipids, lipoproteins or cholesterol ratios add significant predictive information beyond that provided by the apoB/apoA-I ratio.', 'Plasma lipids, lipoproteins, apoB, and apoA-I were measured in 69,030 men and 57,168 women above 40 years of age. After a mean follow-up of 98 months, 1183 men and 560 women had died from a myocardial infarction in this prospective apolipoprotein-related mortality risk (AMORIS) study.', 'High apoB and a high apoB/apoA-I ratio were strongly related to increased coronary risk, while high apoA-I was inversely related to risk. The apoB/apoA-I ratio was superior to any of the cholesterol ratios in predicting risk. This advantage was most pronounced in subjects with LDL-C levels <3.6 mmol/l. Addition of lipids, lipoproteins or any cholesterol ratio to apoB/apoA-I in risk models did not further improve the strong predictive value of apoB/apoA-I.
Example Output: Is the apoB/apoA-I ratio better than the cholesterol ratios to estimate the balance between plasma proatherogenic and antiatherogenic lipoproteins and to predict coronary risk?

Example Input: Crowdsourcing dietary ratings for food photographs, which uses the input of several users to provide feedback, has potential to assist with dietary self-monitoring.', "This study assessed how closely crowdsourced ratings of foods and beverages contained in 450 pictures from the Eatery mobile app as rated by peer users (fellow Eatery app users) (n = 5006 peers, mean 18.4 peer ratings/photo) using a simple 'healthiness' scale were related to the ratings of the same pictures by trained observers (raters). In addition, the foods and beverages present in each picture were categorized and the impact on the peer rating scale by food/beverage category was examined. Raters were trained to provide a 'healthiness' score using criteria from the 2010 US Dietary Guidelines.", "The average of all three raters' scores was highly correlated with the peer healthiness score for all photos (r = 0.88, p<0.001). Using a multivariate linear model (R(2) = 0.73) to examine the association of peer healthiness scores with foods and beverages present in photos, peer ratings were in the hypothesized direction for both foods/beverages to increase and ones to limit. Photos with fruit, vegetables, whole grains, and legumes, nuts, and seeds (borderline at p = 0.06) were all associated with higher peer healthiness scores, and processed foods (borderline at p = 0.06), food from fast food restaurants, refined grains, red meat, cheese, savory snacks, sweets/desserts, and sugar-sweetened beverages were associated with lower peer healthiness scores.
Example Output: Are the use of crowdsourcing for dietary self-monitoring : crowdsourced ratings of food pictures comparable to ratings by trained observers?

Example Input: Cholangiocarcinoma is a feared complication of primary sclerosing cholangitis (PSC). Neoplastic bile duct strictures may be difficult to differentiate cholangiographically from the non-neoplastic bile duct irregularities characteristic of this disorder, and the diagnosis of cholangiocarcinoma may be difficult to establish with certainty, even in tissue samples. Thus, new methods which can improve the diagnostic accuracy of cholangiocarcinoma in PSC are needed.', 'We investigated the occurrence of K-ras codon 12 and 13 mutations, p53 protein accumulation, and Ki-67 expression in tumor tissue from PSC patients (n=33) who had developed cholangiocarcinoma, using bile duct specimens exised at liver transplantation of PSC patients without cholangiocarcinoma (n=15) as controls', 'K-ras mutations were present in 11 (33%) of the cholangiocarcinoma samples and significantly more frequent in females. Nine tumors carried a codon 12 mutation, and 2 had a codon 13 mutation. The most frequent substitutions in codon 12 were GGT-->GAT (n=5) and GGT-->TGT (n=3). None of the control bile ducts had K-ras mutations. p53 protein was accumulated in 10 (31%) of the tumors, as opposed to negative findings in all the control samples. Sixteen (48%) tumors revealed either K-ras mutation or p53 accumulation. Ki-67 positivity was significantly higher in cholangiocarcinomas than in the non-neoplastic bile ducts (median 29% vs 12%, respectively; p=0.011).
Example Output:
Are cholangiocarcinoma in primary sclerosing cholangitis : K-ras mutations and Tp53 dysfunction implicated in the neoplastic development?