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".

[EX 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
[EX A]: False

[EX Q]: Paragraph: Background: Mechanisms influencing severity of acute lower respiratory infection (ALRI) in children are not established. We aimed to assess the role of inflammatory markers and respiratory viruses in ALRI severity. Methods: Concentrations of interleukin(IL)-33, soluble suppression of tumorigenicity (sST)2, IL-1ß, tumor necrosis factor α, IL-4, IL-6 and IL-8 and types of respiratory viruses were evaluated in children at the first and fifth days after hospital admission. Disease severity was defined as need for mechanical ventilation. Results: Seventy-nine children < 5 years-old were included; 33(41.8%) received mechanical ventilation. No associations between virus type, viral load or co-detections and severity of disease were observed. Detection of IL-33 and sST2 in nasopharyngeal aspirates (NPA) on admission were associated with higher risk for mechanical ventilation (RR = 2.89 and RR = 4.57, respectively). IL-6 and IL-8 concentrations were higher on Day 5 in mechanically ventilated children. IL-6 NPA concentrations decreased from Day 1 to Day 5 in children who did not receive mechanical ventilation. Increase in sST2 NPA concentrations from Day 1 to Day 5 was associated with longer hospital length of stay (p < 0.01). Conclusions: An exacerbated local activation of the IL-33/ST2 axis and persistently high sST2 concentrations over time were associated with severity of viral ALRI in children. 
 Title: IL-33 and ST2 as predictors of disease severity in children with viral acute lower respiratory infection
[EX A]: False

[EX Q]: Paragraph: The oligoadenylate synthetase 1 (OAS1) enzyme acts as an innate sensor of viral infection and plays a major role in the defense against a wide diversity of viruses. Polymorphisms at OAS1 have been shown to correlate with differential susceptibility to several infections of great public health significance, including hepatitis C virus, SARS coronavirus, and West Nile virus. Population genetics analyses in hominoids have revealed interesting evolutionary patterns. In Central African chimpanzee, OAS1 has evolved under longterm balancing selection, resulting in the persistence of polymorphisms since the origin of hominoids, whereas human populations have acquired and retained OAS1 alleles from Neanderthal and Denisovan origin. We decided to further investigate the evolution of OAS1 in primates by characterizing intra-specific variation in four species commonly used as models in infectious disease research: the rhesus macaque, the cynomolgus macaque, the olive baboon, and the Guinea baboon. In baboons, OAS1 harbors a very low level of variation. In contrast, OAS1 in macaques exhibits a level of polymorphism far greater than the genomic average, which is consistent with the action of balancing selection. The region of the enzyme that directly interacts with viral RNA, the RNAbinding domain, contains a number of polymorphisms likely to affect the RNA-binding affinity of OAS1. This strongly suggests that pathogen-driven balancing selection acting on the RNA-binding domain of OAS1 is maintaining variation at this locus. Interestingly, we found that a number of polymorphisms involved in RNA-binding were shared between macaques and chimpanzees. This represents an unusual case of convergent polymorphism. 
 Title: Contrasted patterns of variation and evolutionary convergence at the antiviral OAS1 gene in old world primates
[EX A]:
False