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.

Input: Consider Input: From 2002 to 2003, a global pandemic of severe acute respiratory syndrome (SARS) spread to 5 continents and caused 8000 respiratory infections and 800 deaths. To ameliorate the effects of future outbreaks as well as to prepare for biodefense, a process for the production of a recombinant protein vaccine candidate is under development. Previously, we reported the 5 L scale expression and purification of a promising recombinant SARS vaccine candidate, RBD219-N1, the 218eamino acid residue receptorbinding domain (RBD) of SARS coronavirus expressed in yeastePichia pastoris X-33. When adjuvanted with aluminum hydroxide, this protein elicited high neutralizing antibody titers and high RBD-specific antibody titers. However, the yield of RBD219-N1 (60 mg RBD219-N1 per liter of fermentation supernatant; 60 mg/L FS) still required improvement to reach our target of >100 mg/L FS. In this study, we optimized the 10 L scale production process and increased the fermentation yield 6-to 7-fold to 400 mg/ L FS with purification recovery >50%. A panel of characterization tests indicated that the process is reproducible and that the purified, tag-free RBD219-N1 protein has high purity and a well-defined structure and is therefore a suitable candidate for production under current Good Manufacturing Practice and future phase-1 clinical trials.

Output: Optimization of the Production Process and Characterization of the Yeast-Expressed SARS-CoV Recombinant Receptor-Binding Domain (RBD219-N1), a SARS Vaccine Candidate


Input: Consider 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. Ç. Ak, Clin Microbiol Infect 2020;26:123.e1e123.e7

Output: A prospective prediction tool for understanding CrimeaneCongo haemorrhagic fever dynamics in Turkey


Input: Consider Input: The One Health concept recognizes that the health of human beings, animals, plants and the environment is interconnected and interdependent. This idea has been shaped over the centuries and has gained momentum and traction as anatomy, physiology, microbiology and other disciplines have substantiated earlier theories. Here we recall major historical milestones which have contributed to shaping the One Health concept as it is today, and discuss the past and future drivers in view of future challenges in an evolving scenario.


Output: One Health (r)Evolution: Learning from the Past to Build a New Future