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: Metagenomics is currently the only generic method for pathogen detection. Starting from RNA allows the assessment of the whole sample community including RNA viruses. Here we present our modular concerted protocol for sample processing for diagnostic metagenomics analysis of human, animal, and food samples. The workflow does not rely on dedicated amplification steps at any stage in the process and, in contrast to published methods, libraries prepared accordingly will yield only minute amounts of unclassifiable reads. We confirmed the performance of the approach using a spectrum of pathogen/ matrix-combinations showing it has the potential to become a commonly usable analytical framework. Diagnostic metagenomics with high-throughput sequencing (HTS) techniques continuously gains importance for broad and swift identification of pathogens in human, animal, and food samples 1 . While for known pathogens, highly sensitive and specific diagnostic methods like real-time quantitative PCR (qPCR) are in routine use and deliver reliable results, the identification of unrecognized pathogens, meaning unexpected or newly emerging pathogens or pathogens that are only distantly related with known ones, can be very difficult. In this respect, metagenomics using HTS are much more promising than routine diagnostics. Unrecognized pathogens, especially newly emerging zoonoses, may cause serious infectious diseases, and a delay of medical treatment or development of vaccines might have fatal consequences for the affected patients and animal stocks. Such delays can be caused by performing numerous laborious screening tests until the potential pathogen is found instead of a single comprehensive screening test. Prominent cases of emerging infectious diseases caused by novel or varying viruses for instance are the discovery of the Middle East respiratory syndrome (MERS) coronavirus in 2012 2 , the tremendous Ebola outbreak in 2014 3 , the report on a novel zoonotic bornavirus 4 , or the detection of the new Schmallenberg virus 5 affecting domestic and wild ruminants. However, infectious diseases with clinical signs like high fever, diarrhoea, or encephalitis − often life-threatening − can be caused by very different infectious agents 6,7 , not only viruses. In such puzzling cases, a generic approach that works likewise successful and efficient for all pathogen groups, as sketched in Fig. 1 , is essential. In a number of review articles 1,8-12 , valuable considerations for this approach have been summarized. In addition to the overall workflow outlined in Fig. 1 , after metagenomic analysis, it is desirable to confirm the initial sequencing-based suspicion by other methods and in the ideal case by fulfilling the Henle-Loeffler-Koch postulates 13 as for example done in case of the Schmallenberg virus 5 . Metagenomics for generic pathogen detection, so-called diagnostic metagenomics, in its pure form is a broad and undirected approach to find gene sequences or sequence fragments of infectious agents within sequence data sets of the whole community of a sample generated by high-throughput sequencing. A crucial point when handling diagnostic sequencing approaches is to explicitly distinguish between i) high-throughput well-standardisable routine diagnostics for expected known pathogens and ii) diagnostic metagenomics for all including unrecognized pathogens. The first approach is easier to design based on the spectrum of known sequences, testing with specific oligonucleotide primers for pathogens of known identity 14,15 , known tropism, and maybe also of a known proportion within a sample. The latter one − used for unrecognized pathogens like in the case of Schmallenberg virus 5 − should be a generic approach that can ideally be applied to all samples and pathogens. This approach can indeed be developed and tested using samples of known origin and pathogen content. In case of an emergency, however, there might be no information about nature and proportion of the pathogen and only clinical data could be available, and the sample is a closed book. For these cases, metagenomics Published: xx xx xxxx OPEN www.nature.com/scientificreports/ 
 Title: A Versatile Sample Processing Workflow for Metagenomic Pathogen Detection OPEN
Example Output: False

Example Input: Paragraph: Bluetongue virus (BTV) encodes a single capping protein, VP4, which catalyzes all reactions required to generate cap1 structures on nascent viral transcripts. Further, structural analysis by X-ray crystallography indicated each catalytic reaction is arranged as a discrete domain, including a nucleoside-2 0 -O-methyltransferase (2 0 -O MTase). In this study, we have exploited the structural information to identify the residues that are important for the catalytic activity of 2 0 -O MTase of VP4 and their influence on BTV replication. The effect of these mutations on GMP binding, guanylyltransferase (GTase) and methylase activities were analysed by a series of in vitro biochemical assays using recombinant mutant proteins; subsequently their effects on virus replication were assessed by introducing the same mutations in replicating viral genome using a reverse genetics system. Our data showed that single substitution mutations in the catalytic tetrad K-D-K-E were sufficient to abolish 2 0 -O MTase activity in vitro and to completely abrogate BTV replication in cells; although these mutants retained the upstream GMP binding, GTase and guanine-N7-methyltransferase activities. Mutations of the surrounding substrate-binding pocket (predicted to recruit cap0) had variable effects on in vitro VP4 capping activity. Only triple but not single substitution mutations of these residues in genome resulted in reduced virus replication kinetics. This is the first report investigating the importance of 2 0 -O MTase function for any member of the Reoviridae and highlights the significance of K-D-K-E tetrad and surrounding residues for the efficiency of 2 0 -O MTase activity and in turn, for virus fitness. 
 Title: Structure-based identification of functional residues in the nucleoside- 2 0 -O-methylase domain of Bluetongue virus VP4 capping enzyme-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Example Output: True

Example Input: Paragraph: Background: Detection of respiratory viruses by time-resolved fluoroimmunoassay based on monoclonal antibodies were developed in our laboratories in the late 1980s and they have been successfully used in daily diagnosis for more than seven years. Later, similar Biotin-EIAs were developed but the sensitivities were unsatisfactory. Objectives: Further optimization of monoclonal Biotin-EIAs and comparison of the optimized assays with TR-FIAs. Study design: Variations in test format, diluents, incubation times and temperatures, and different monoclonal antibodies were tested, and the final comparisons were made with TR-FIA using stored nasopharyngeal aspirates. Results: The improvements in Biotin-EIA featured four changes which increased sensitivity in the assay: (a) test diluent contained diethylenetriamino-pentaacetic acid; (b) antigen and biotinylated detector antibody were added simultaneously; (c) reaction time was extended from 1 h at 37°C to overnight at 4°C; (d) from the thirteen monoclonal antibodies used in TR-FIA, ten were optimal also in Biotin-EIA, but in the parainfluenza 1 and 2 assays other monoclonals proved more sensitive. Out of 257 originally positive specimens tested in the comparison studies, 192 (74.7%) were again positive and 54 (21.0%) were negative in both assays; nine were negative in TR-FIA but positive in Biotin-EIA, while two specimens were negative in Biotin-EIA but positive in TR-FIA. The overall agreement between the two assays was 95.7%. 
 Title: Clinical and Diagnostic Virology Comparison of monoclonal biotin-avidin enzyme immunoassay and monoclonal time-resolved fluoroimmunoassay in detection of respiratory virus antigens
Example Output:
False