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: cDNA, synthesized on bovine coronavirus (BCV) genomic RNA templates, could be used to detect very small quantities (i .e . 1 pg) of viral RNA by hybridization with either radioisotopic-labelled or biotinylated recombinant plasmids . Virus was optimally attached to nitrocellulose membranes when spotted in 1 x SSC, whereas 20 x SSC was superior for viral RNA. Denaturation and RNA fixation of both RNA, still encapsidated in virus particles and isolated genomic RNA, was achieved by baking of the blots in vacuum . Virus detection in the supernatant of infected HRT-18 cells was feasible, but improved significantly after proteinase K treatment . No homology was observed between virus cDNA with either plasmid DNA or nucleic acid isolated from non-infected HRT-18 cells . Hybridization with radioisotopic-labelled probes in higher formamide concentrations (up to 60%) increased the detection signals, possibly by reducing reassociation of the probe . Significant detection amplification (30-50 times) was achieved in the case of biotinylated probes by stimulation of hyperpolymer formation on already hybridized target sequences, by additional hybridization with biotinylated pUC-19 . A detection amplification was also obtained when hybridization was done with two probes (pBC-52 and pBC-247), containing non-overlapping viral sequences . Although the detectability was surpassed by biotinylated probes, sensitivity was superior in radioisotopic virus detection .
Title: Biotinylated and radioactive cDNA probes in the detection by hybridization of bovine enteric coronavirus

A: True
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Q: 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

A: False
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Q: Paragraph: Background: Noncontact forehead temperature measurement by handheld infrared thermometer was used as a screening tool for fever. However, the accuracy data and normal range of forehead temperature determined by this method were not available. Methods: The temperature readings from 3 handheld infrared thermometers were validated against an electronic thermometer. Normal range of forehead temperature was determined by measuring the forehead temperature in 1000 apparently healthy subjects. Results: Significant differences were detected in readings obtained by the 3 different handheld infrared thermometers (analysis of covariance, P , .001) The most accurate one was chosen, and the normal range of forehead temperature in 1000 subjects detected by this method was 31.0C to 35.6C. Conclusions: Our study shows that commercially available, handheld infrared thermometers require individual validation. Forehead temperature in excess of 35.6C is suggestive of fever. Further studies are required to confirm accuracy of this value in detecting fever. (Am J Infect Control 2005;33:227-9.)
Title: A brief report on the normal range of forehead temperature as determined by noncontact, handheld, infrared thermometer

A:
False
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