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.

Example Input: Context: Material loss at the taper junction of metal-on-metal total hip arthroplasties has been implicated in their early failure. The mechanisms of material loss are not fully understood; analysis of the patterns of damage at the taper can help us better understand why material loss occurs at this junction.', 'We mapped the patterns of material loss in a series of 155 metal-on-metal total hip arthroplasties received at our center by scanning the taper surface using a roundness-measuring machine. We examined these material loss maps to develop a 5-tier classification system based on visual differences between different patterns. We correlated these patterns to surgical, implant, and patient factors known to be important for head-stem taper damage.', 'We found that 63 implants had "minimal damage" at the taper (material loss <1 mm
Answer: These material loss maps allow us to suggest different mechanisms that dominate the cause of the material loss in each pattern: (1) corrosion, (2) mechanically assisted corrosion, or (3) intraoperative damage or poor size tolerances leading to toggling of trunnion in taper.
Example Output: Does damage Patterns at the Head-Stem Taper Junction help Understand the Mechanisms of Material Loss?

Example Input: Context: Our previous studies suggested that brain-derived neurotrophic factor (BDNF)/tropomyosin-related kinase B (TrkB) axis inhibited cardiomyocyte apoptosis in myocardial infarction (MI). However, the relationship between BDNF and microRNA (miRNA) in cardiomyocytes are unclear. The present study was performed to investigate the role of miR-195 and the interplay between BDNF and miR-195 in ischemic cardiomyocyte apoptosis.', 'Male Wistar rats were subjected to coronary artery ligation, and primary neonatal rat ventricular myocytes were treated with hypoxia or hydrogen peroxide (H2O2). BDNF level in rat ventricles was measured by enzyme linked immunosorbent assay (ELISA). miR-195 mimic, inhibitor or negative control was transfected into the cardiomyocytes. Cell viability and apoptosis were detected by MTT assay and TdT-mediated dUTP nick end labeling (TUNEL) staining, respectively. Cardiac function and apoptosis were detected in MI rats intravenously injected with antagomiR-195. Luciferase assay, Western blot and Real-time RT-PCR were employed to clarify the interplay between miR-195 and BDNF.', 'miR-195 level was dynamically regulated in response to MI and significantly increased in ischemic regions 24 h post-MI as well as in hypoxic or H2O2-treated cardiomyocytes. Meanwhile, BDNF protein level was rapidly increased in MI rats and H2O2-treated cardiomyocytes. Apoptosis in both hypoxic and H2O2-treated cardiomyocytes were markedly reduced and cell viability was increased by miR-195 inhibitor. Moreover, inhibition of miR-195 significantly improved cardiac function of MI rats. Bcl-2 but not BDNF was validated as the direct target of miR-195. Furthermore, BDNF abolished the pro-apoptotic role of miR-195, which was reversed by its scavenger TrkB-Fc.
Answer: Up-regulation of miR-195 in ischemic cardiomyocytes promotes ischemic apoptosis by targeting Bcl-2. BDNF mitigated the pro-apoptotic effect of miR-195 in rat cardiomyocytes. These findings may provide better understanding of the pro-apoptotic role of miR-195 in MI and suggest that BDNF/miR-195/Bcl-2 axis may be beneficial for limiting myocardial ischemic injury.
Example Output: Does bDNF-mediates Down-regulation of MicroRNA-195 inhibit Ischemic Cardiac Apoptosis in Rats?

Example Input: Context: RNA editing is the process whereby an RNA sequence is modified from the sequence of the corresponding DNA template. In the mitochondria of land plants, some cytidines are converted to uridines before translation. Despite substantial study, the molecular biological mechanism by which C-to-U RNA editing proceeds remains relatively obscure, although several experimental studies have implicated a role for cis-recognition. A highly non-random distribution of nucleotides is observed in the immediate vicinity of edited sites (within 20 nucleotides 5' and 3'), but no precise consensus motif has been identified.", "Data for analysis were derived from the the complete mitochondrial genomes of Arabidopsis thaliana, Brassica napus, and Oryza sativa; additionally, a combined data set of observations across all three genomes was generated. We selected datasets based on the 20 nucleotides 5' and the 20 nucleotides 3' of edited sites and an equivalently sized and appropriately constructed null-set of non-edited sites. We used tree-based statistical methods and random forests to generate models of C-to-U RNA editing based on the nucleotides surrounding the edited/non-edited sites and on the estimated folding energies of those regions. Tree-based statistical methods based on primary sequence data surrounding edited/non-edited sites and estimates of free energy of folding yield models with optimistic re-substitution-based estimates of approximately 0.71 accuracy, approximately 0.64 sensitivity, and approximately 0.88 specificity. Random forest analysis yielded better models and more exact performance estimates with approximately 0.74 accuracy, approximately 0.72 sensitivity, and approximately 0.81 specificity for the combined observations.
Answer: Simple models do moderately well in predicting which cytidines will be edited to uridines, and provide the first quantitative predictive models for RNA edited sites in plant mitochondria. Our analysis shows that the identity of the nucleotide -1 to the edited C and the estimated free energy of folding for a 41 nt region surrounding the edited C are the most important variables that distinguish most edited from non-edited sites. However, the results suggest that primary sequence data and simple free energy of folding calculations alone are insufficient to make highly accurate predictions.
Example Output:
Do simple statistical models predict C-to-U edited sites in plant mitochondrial RNA?