In this task, you're given a paragraph from the research paper and your task is to generate a suitable title for the research paper based on the given paper. Under 100 words is a good title length.

Example Input: All coronaviruses (CoVs), including the causative agent of severe acute respiratory syndrome (SARS), encode a nucleocapsid (N) protein that harbors two independent RNA binding domains of known structure, but poorly characterized RNA binding properties. We show here that the Nterminal domain (NTD) of N protein from mouse hepatitis virus (MHV), a virus most closely related to SARS-CoV, employs aromatic amino acidnucleobase stacking interactions with a triple adenosine motif to mediate high-affinity binding to single-stranded RNAs containing the transcriptional regulatory sequence (TRS) or its complement (cTRS). Stoichiometric NTD fully unwinds a TRS-cTRS duplex that mimics a transiently formed transcription intermediate in viral subgenomic RNA synthesis. Mutation of the solvent-exposed Y127, positioned on the β-platform surface of our 1.75 Å structure, binds the TRS far less tightly and is severely crippled in its RNA unwinding activity. In contrast, the C-terminal domain (CTD) exhibits no RNA unwinding activity. Viruses harboring Y127A N mutation are strongly selected against and Y127A N does not support an accessory function in MHV replication. We propose that the helix melting activity of the coronavirus N protein NTD plays a critical accessory role in subgenomic RNA synthesis and other processes requiring RNA remodeling.
Example Output: Coronavirus N Protein N-Terminal Domain (NTD) Specifically Binds the Transcriptional Regulatory Sequence (TRS) and Melts TRS-cTRS RNA Duplexes

Example Input: Background: Adverse events are associated with significant morbidity, mortality and cost in hospitalized patients. Measuring adverse events is necessary for quality improvement, but current detection methods are inaccurate, untimely and expensive. The advent of electronic health records and the development of automated methods for encoding and classifying electronic narrative data, such as natural language processing, offer an opportunity to identify potentially better methods. The objective of this study is to determine the accuracy of using automated methods for detecting three highly prevalent adverse events: a) hospital-acquired pneumonia, b) catheter-associated bloodstream infections, and c) in-hospital falls. Methods/design: This validation study will be conducted at two large Canadian academic health centres: the McGill University Health Centre (MUHC) and The Ottawa Hospital (TOH). The study population consists of all medical, surgical and intensive care unit patients admitted to these centres between 2008 and 2014. An automated detection algorithm will be developed and validated for each of the three adverse events using electronic data extracted from multiple clinical databases. A random sample of MUHC patients will be used to develop the automated detection algorithms (cohort 1, development set). The accuracy of these algorithms will be assessed using chart review as the reference standard. Then, receiver operating characteristic curves will be used to identify optimal cut points for each of the data sources. Multivariate logistic regression and the areas under curve (AUC) will be used to identify the optimal combination of data sources that maximize the accuracy of adverse event detection. The most accurate algorithms will then be validated on a second random sample of MUHC patients (cohort 1, validation set), and accuracy will be measured using chart review as the reference standard. The most accurate algorithms validated at the MUHC will then be applied to TOH data (cohort 2), and their accuracy will be assessed using a reference standard assessment of the medical chart. Discussion: There is a need for more accurate, timely and efficient measures of adverse events in acute care hospitals. This is a critical requirement for evaluating the effectiveness of preventive interventions and for tracking progress in patient safety through time.
Example Output: Accuracy of using automated methods for detecting adverse events from electronic health record data: a research protocol

Example Input: Background: An ongoing outbreak of mystery pneumonia in Wuhan was caused by coronavirus disease 2019 . The infectious disease has spread globally and become a major threat to public health. We aim to investigate the ultra-high-resolution CT (UHR-CT) findings of imported COVID-19 related pneumonia from the initial diagnosis to early-phase follow-up. Methods: This retrospective study included confirmed cases with early-stage COVID-19 related pneumonia imported from the epicenter. Initial and early-phase follow-up UHR-CT scans (within 5 days) were reviewed for characterizing the radiological findings. The normalized total volumes of ground-glass opacities (GGOs) and consolidations were calculated and compared during the radiological follow-up by artificial-intelligence-based methods. Results: Eleven patients (3 males and 8 females, aged 32-74 years) with confirmed COVID-19 were evaluated. Subpleural GGOs with inter/intralobular septal thickening were typical imaging findings. Other diagnostic CT features included distinct margins (8/11, 73%), pleural retraction or thickening (7/11, 64%), intralesional vasodilatation (6/11, 55%). Normalized volumes of pulmonary GGOs (p=0.003) and consolidations (p=0.003) significantly increased during the CT follow-up. The abnormalities of GGOs with peripleural distribution, consolidated areas, septal thickening, pleural involvement and intralesional vasodilatation on UHR-CT indicate the diagnosis of COVID-19. COVID-19 cases could manifest significantly progressed GGOs and consolidations with increased volume during the early-phase CT follow-up.
Example Output:
Ultra-High-Resolution CT Follow-Up in Patients with Imported Early-Stage