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".

Example Input: Paragraph: To date, an effective therapeutic treatment that confers strong attenuation toward coronaviruses (CoVs) remains elusive. Of all the potential drug targets, the helicase of CoVs is considered to be one of the most important. Here, we first present the structure of the full-length Nsp13 helicase of SARS-CoV (SARS-Nsp13) and investigate the structural coordination of its five domains and how these contribute to its translocation and unwinding activity. A translocation model is proposed for the Upf1like helicase members according to three different structural conditions in solution characterized through H/D exchange assay, including substrate state (SARS-Nsp13-dsDNA bound with AMPPNP), transition state (bound with ADP-AlF 4 − ) and product state (bound with ADP). We observed that the ␤19-␤20 loop on the 1A domain is involved in unwinding process directly. Furthermore, we have shown that the RNA dependent RNA polymerase (RdRp), SARS-Nsp12, can enhance the helicase activity of SARS-Nsp13 through interacting with it directly. The interacting regions were identified and can be considered common across CoVs, which provides new insights into the Replication and Transcription Complex (RTC) of CoVs. 
 Title: Delicate structural coordination of the Severe Acute Respiratory Syndrome coronavirus Nsp13 upon ATP hydrolysis
Example Output: True

Example Input: Paragraph: OBJECTIVE: To determine the frequency, etiology and impact of respiratory viral infection (RVI) on infants evaluated for late-onset sepsis (LOS), defined as sepsis occurring 472 h of life, in the neonatal intensive care unit. STUDY DESIGN: Prospective observational study conducted from 6 March 2014 to 3 May 2016 on infants evaluated for LOS. PCR viral panel performed on nasopharyngeal specimens among infants with clinical suspicion for RVI. Sequence analysis was performed to determine viral subtypes. Fisher's exact or χ 2 tests were done to determine the impact of RVI. RESULTS: During the 26-month study, there were 357 blood cultures obtained for LOS evaluations, 29 (8%) had a respiratory virus detected. Only 88 (25%) of infants evaluated for LOS also had clinical suspicion for a respiratory viral infection. RSV (14 of 29; 48%) was the predominant virus detected. Almost all infants (13 of 14; 93%) with RSV required increased respiratory support. Antimicrobial therapy was withheld or discontinued on most infants with a virus detected (18 of 29; 62%) and in the majority where there was no confirmed bacterial co-infection (18 of 20; 90%). CONCLUSION: The incidence of RVI in infants being evaluated for LOS is about 8%. RVI should be considered in LOS evaluation to prevent unnecessary antibiotic therapy. 
 Title: Incidence of respiratory viral infection in infants with respiratory symptoms evaluated for late-onset sepsis
Example Output: False

Example Input: Paragraph: With the advent of deep sequencing, genomic surveillance has become a popular method for detection of infectious disease, supplementing information gathered by classic clinical or serological techniques to identify host-determinant markers and trace the origin of transmission. However, two main factors complicate genomic surveillance. First, pathogens exhibiting high genetic diversity demand higher levels of scrutiny to obtain an accurate representation of the entire population. Second, current systems of detection are nonuniform, with significant gaps in certain geographic locations and animal reservoirs. Despite past unforeseen pandemics like the 2009 swine-origin H1N1 influenza virus, there is no standardized way of evaluating surveillance. A more complete surveillance system should capture a greater proportion of pathogen diversity. Here we present a novel quantitative method of assessing the completeness of genomic surveillance that incorporates the time of sequence collection, as well as the pathogen's evolutionary rate. We propose the q2 coefficient, which measures the proportion of sequenced isolates whose closest neighbor in the past is within a genetic distance equivalent to 2 years of evolution, roughly the median time of changing strain selection for influenza A vaccines. Easily interpretable and significantly faster than other methods, the q2 coefficient requires no full phylogenetic characterization or use of arbitrary clade definitions. Application of the q2 coefficient to influenza A virus confirmed poor sampling of swine and avian populations and identified regions with deficient surveillance. We demonstrate that the q2 coefficient can not only be applied to other pathogens, including dengue and West Nile viruses, but also used to describe surveillance dynamics, particularly the effects of different public health policies. IMPORTANCE Surveillance programs have become key assets in determining the emergence or prevalence of pathogens circulating in human and animal populations. Genomic surveillance, in particular, provides comprehensive information on the history of isolates and potential molecular markers for infectivity and pathogenicity. Current techniques for evaluating genomic surveillance are inaccurate, ignoring the pathogen's evolutionary rate and biodiversity, as well as the timing of sequence collection. Using sequence data, we propose the q2 coefficient as a quantitative measure of surveillance completeness that combines elements of time and evolution without defining arbitrary criteria for clades or species. Through several case studies of influenza A, dengue, and West Nile viruses, we employed the q2 coefficient to identify sampling deficiencies in different host species and locations, as well as examine the effects of different public health policies through historical records of the q2 coefficient. These results can guide public health agencies to focus resource allocation and virus collection to bolster specific problems in surveillance. Citation Chan JM, Rabadan R. 2013. Quantifying pathogen surveillance using temporal genomic data. mBio 4(1):e00524-12. 
 Title: Quantifying Pathogen Surveillance Using Temporal Genomic Data
Example Output:
True