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: Interferon lambda 4 (IFN4) has been recently known and studied for its role in hepatitis C virus (HCV) infection, but its clinical potential is significantly hampered due to its poor expression in vitro. Our study reports the successful production of IFN4 from a mammalian cell line through a glycoengineering and structure-based approach. We introduced de novo N-glycosylation of IFN4, guided by structural analysis, and produced IFN4 variants in Expi293F that displayed improved expression and potency. To preserve the structure and functionality of IFN4, the model structure of the IFN4 signaling complex was analyzed and the N-glycosylation candidate sites were selected. The receptor binding activity of engineered IFN4 variants and their receptormediated signaling pathway were similar to the E. coli version of IFN4 (eIFN4), while the antiviral activity and induction levels of interferon-stimulated gene (ISG) were all more robust in our variants. Our engineered IFN4 variants may be further developed for clinical applications and utilized in basic research to decipher the immunological roles of IFN4. Previously, the transient expression of wild-type IFN4 in mammalian cells failed to produce significant amounts of recombinant
Example Output: Structure-based glycoengineering of interferon lambda 4 enhances its productivity and anti-viral potency

Example Input: Objectives: We aimed to develop a prospective prediction tool on CrimeaneCongo haemorrhagic fever (CCHF) to identify geographic regions at risk. The tool could support public health decision-makers in implementation of an effective control strategy in a timely manner. Methods: We used monthly surveillance data between 2004 and 2015 to predict case counts between 2016 and 2017 prospectively. The Turkish nationwide surveillance data set collected by the Ministry of Health contained 10 411 confirmed CCHF cases. We collected potential explanatory covariates about climate, land use, and animal and human populations at risk to capture spatiotemporal transmission dynamics. We developed a structured Gaussian process algorithm and prospectively tested this tool predicting the future year's cases given past years' cases. Results: We predicted the annual cases in 2016 and 2017 as 438 and 341, whereas the observed cases were 432 and 343, respectively. Pearson's correlation coefficient and normalized root mean squared error values for 2016 and 2017 predictions were (0.83; 0.58) and (0.87; 0.52), respectively. The most important covariates were found to be the number of settlements with fewer than 25 000 inhabitants, latitude, longitude and potential evapotranspiration (evaporation and transpiration). Conclusions: Main driving factors of CCHF dynamics were human population at risk in rural areas, geographical dependency and climate effect on ticks. Our model was able to prospectively predict the numbers of CCHF cases. Our proof-of-concept study also provided insight for understanding possible mechanisms of infectious diseases and found important directions for practice and policy to combat against emerging infectious diseases. C. Ak, Clin Microbiol Infect 2020;26:123.e1e123.e7
Example Output: A prospective prediction tool for understanding CrimeaneCongo haemorrhagic fever dynamics in Turkey

Example Input: Severe acute respiratory syndrome coronavirus (SARS-CoV) encodes eight accessory proteins, the functions of which are not yet fully understood. SARS-CoV protein 6 (P6) is one of the previously studied accessory proteins that have been documented to enhance viral replication and suppress host interferon (IFN) signaling pathways. Through yeast two-hybrid screening, we identified eight potential cellular P6-interacting proteins from a human spleen cDNA library. KEYWORDS severe acute respiratory syndrome coronavirus (SARS-CoV); P6; N-Myc (and STAT) interactor (Nmi); interferon (IFN) signaling pathway; ubiquitination; proteosomal degradation
Example Output:
Severe acute respiratory syndrome coronavirus protein 6 mediates ubiquitin-dependent proteosomal degradation of N-Myc (and STAT) interactor