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: 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: Sequence analysis revealed that rice dwarf phytoreovirus segment S12 is 1066 nucleotides long with a small out-ofphase, overlapping open reading frame (ORF) as well as a major ORF. The large ORF (positions 42 to 960) encodes 312 amino acids, while the small one (bases 313 to 591) encodes 92 amino acids with an additional in-frame AUG codon (positions 337-339) 24 nucleotides downstream from the first one. Transcripts from a full-length cDNA directed the in vitro synthesis of three polypeptides of 33 (considered to be translated from the long ORF), 8, and 7 kDa. Alteration of each of the two ATG codons on the small ORF demonstrated their involvement in the generation of the 8-and 7-kDa polypeptides. Although it is still unknown whether these proteins are expressed in t&o, the small ORF is shown to be conserved in S9s of two other members of the genus Phytoreovirus, rice gall dwarf virus and wound tumor virus, suggesting its common, important function. 
 Title: Rice Dwarf Phytoreovirus Segment S12 Transcript Is Tricistronic in Vim

A: False
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Q: Paragraph: Bovine respiratory disease (BRD) often occurs when active respiratory virus infections (BHV-1, etc.) impair resistance to Mannheimia haemolytica infection in the lower respiratory tract. The interactions that occur when the respiratory epithelium encounters these viral and bacterial pathogens are poorly understood. We used Agilent bovine gene microarray chips containing 44,000 transcripts to elucidate bovine bronchial epithelial cell (BBEC) responses following in vitro exposure to BHV-1 alone, M. haemolytica alone, or both BHV-1 and M. haemolytica. Microarray analysis revealed differential regulation (>2-fold) of 978 transcripts by BHV-1 alone, 2040 transcripts by M. haemolytica alone, and 2189 genes by BHV-1 and M. haemolytica in combination. M. haemolytica treatment produced significantly greater inductions (>10-fold) of several inflammation associated genes, such as CXCL2, IL-6, IL-1␣, e-selectin, and IL-8, than to BHV-1 alone. Functional analysis of the microarray data revealed a significant upregulation of genes involved in important biological processes such as inflammation (TNF-␣, IL-8, Tlr-2, IL-1, CXCL2, CSF2), vascular functions (VEGF, EDN2) and leukocyte migration (ICAM1, IL-16) during a co-infection with BHV-1 and M. haemolytica compared to either pathogen alone. This study provides evidence to support that lung epithelial cells are a source of mediators that may promote inflammatory changes observed during bovine respiratory disease. 
 Title: Gene expression profiling of bovine bronchial epithelial cells exposed in vitro to bovine herpesvirus 1 and Mannheimia haemolytica

A:
False
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