In this task, you're given a paragraph and title from the research paper. Your task is to classify whether the given title is suitable or not for the research paper based on the given paragraph. Return "True" if title is proper according to paragraph else "False".

Q: Paragraph: In Sindbis, Venezuelan equine encephalitis and related alphaviruses, the polymerase is translated as a fusion with other non-structural proteins via readthrough of a UGA stop codon. Surprisingly, earlier work reported that the signal for efficient readthrough comprises a single cytidine residue 3 0 -adjacent to the UGA. However, analysis of variability at synonymous sites revealed strikingly enhanced conservation within the $150 nt 3 0 -adjacent to the UGA, and RNA folding algorithms revealed the potential for a phylogenetically conserved stem-loop structure in the same region. Mutational analysis of the predicted structure demonstrated that the stem-loop increases readthrough by up to 10-fold. The same computational analysis indicated that similar RNA structures are likely to be relevant to readthrough in certain plant virus genera, notably Furovirus, Pomovirus, Tobravirus, Pecluvirus and Benyvirus, as well as the Drosophilia gene kelch. These results suggest that 3 0 RNA stimulatory structures feature in a much larger proportion of readthrough cases than previously anticipated, and provide a new criterion for assessing the large number of cellular readthrough candidates that are currently being revealed by comparative sequence analysis. 
 Title: Stimulation of stop codon readthrough: frequent presence of an extended 3 0 RNA structural element

A: False
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Q: Paragraph: COVID-19 threatens to overwhelm hospital facilities throughout the United States. We created an interactive, quantitative model that forecasts demand for COVID-19 related hospitalization based on county-level population characteristics, data from the literature on COVID-19, and data from online repositories. Using this information as well as user inputs, the model estimates a time series of demand for intensive care beds and acute care beds as well as the availability of those beds. The online model is designed to be intuitive and interactive so that local leaders with limited technical or epidemiological expertise may make decisions based on a variety of scenarios. This complements high-level models designed for public consumption and technically sophisticated models designed for use by epidemiologists. The model is actively being used by several academic medical centers and policy makers, and we believe that broader access will continue to aid community and hospital leaders in their response to COVID-19. 
 Title: A model to forecast regional demand for COVID-19 related hospital beds

A: True
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Q: Paragraph: Emerging and re-emerging pathogens and bioterror threats require an organized and coherent response from the worldwide research community to maximize available resources and competencies with the primary goals to understand the pathogen and enable intervention. In 2001, the Structural Proteomics In Europe project prototyped the pan-viral structural genomic approach, and the SARS outbreak in 2003 accelerated the concept of structural characterization of all proteins from a viral proteome and the interaction with their host partners. Following that approach, in 2004 the center for Functional and Structural Proteomics for SARS-CoV related proteins was initiated as part of the US NIH NIAID proteomics resource centers. Across worldwide efforts in Asia, Europe and America, the international research teams working on SARS-CoV have now determined experimental structural information for 45% of the SARS-CoV proteins and 53% of all its soluble proteins. This data is fully available to the scientific community and is providing an unprecedented level of insight to this class of RNA viruses. The efforts and results by the international scientific community to the SARS outbreak are serving as an example and roadmap of a rapid response using modern research methods. 
 Title: International Research Networks in Viral Structural Proteomics: again, lessons from SARS

A:
True
****