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: Long non-coding RNAs (lncRNAs) are found not only in mammals but also in other organisms, including viruses. Recent findings suggest that lncRNAs play various regulatory roles in multiple major biological and pathological processes. During viral life cycles, lncRNAs are involved in a series of steps, including enhancing viral gene expression, promoting viral replication and genome packaging, boosting virion release, maintaining viral latency and assisting viral transformation; additionally, lncRNAs antagonize host antiviral innate immune responses. In contrast to proteins that function in viral infection, lncRNAs are expected to be novel targets for the modulation of all types of biochemical processes due to their broad characteristics and profound influence. This review highlights our current understanding of the regulatory roles of lncRNAs during viral infection processes with an emphasis on the potential usefulness of lncRNAs as a target for viral intervention strategies, which could have therapeutic implications for the application of a clinical approach for the treatment of viral diseases. 
 Title: Viral lncRNA: A regulatory molecule for controlling virus life cycle

A: False
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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: We analyzed the virulence of pandemic H1N1 2009 influenza A viruses in vivo and in vitro. Selected viruses isolated in 2009Selected viruses isolated in , 2010Selected viruses isolated in , 2014Selected viruses isolated in , and 2015 were assessed using an aerosol-mediated high-dose infection model for pigs as well as air-liquid interface cultures of differentiated airway epithelial cells. Using a dyspnea score, rectal temperature, lung lesions, and viral load in the lung as parameters, the strains from 2014-2015 were significantly less virulent than the strains isolated in 2009-2010. In vitro, the viruses from 2009-2010 also differed from the 2014-2015 viruses by increased release of infectious virus, a more pronounced loss of ciliated cells, and a reduced thickness of the epithelial cell layer. Our in vivo and in vitro results reveal an evolution of A(H1N1)pdm09 viruses toward lower virulence. Our in vitro culture system can be used to predict the virulence of influenza viruses. 
 Title: The Journal of Infectious Diseases Infection Studies in Pigs and Porcine Airway Epithelial Cells Reveal an Evolution of A(H1N1)pdm09 Influenza A Viruses Toward Lower Virulence

A:
True
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