TASK DEFINITION: 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".
PROBLEM: Paragraph: Cox, E., Pensaert, M.B., Callebaut, P. and van Deun, K., 1990. Intestinal replication of a porcine respiratory coronavirus closely related antigenically to transmissible gastroenteritis virus. Vet. Microbiol., One-week-old piglets were inoculated with the porcine respiratory coronavirus (PRCV) either intravenously or directly into the lumen of the gastrointestinal tract. Both inoculation routes resulted in the isolation of virus from the caudal small intestine. Viral replication, however, was only observed upon inoculation into the digestive tract in quantities of > 10 3 TCIDso. Replication remained limited to a few unidentified cells located in or underneath the epithelial layer at villus-or crypt-sites. Virus was excreted in the faeces for several days but infection of the respiratory tract occurred rarely in the same pigs. The results of this study indicate that small changes in molecular structure between PRCV and transmissible gastroenteritis virus have resulted in important changes in host cell tropism. 
 Title: Intestinal replication of a porcine respiratory coronavirus closely related antigenically to the enteric transmissible gastroenteritis virus

SOLUTION: False

PROBLEM: Paragraph: Using sequence data to infer population dynamics is playing an increasing role in the analysis of outbreaks. The most common methods in use, based on coalescent inference, have been widely used but not extensively tested against simulated epidemics. Here, we use simulated data to test the ability of both parametric and non-parametric methods for inference of effective population size (coded in the popular BEAST package) to reconstruct epidemic dynamics. We consider a range of simulations centred on scenarios considered plausible for pandemic influenza, but our conclusions are generic for any exponentially growing epidemic. We highlight systematic biases in non-parametric effective population size estimation. The most prominent such bias leads to the false inference of slowing of epidemic spread in the recent past even when the real epidemic is growing exponentially. We suggest some sampling strategies that could reduce (but not eliminate) some of the biases. Parametric methods can correct for these biases if the infected population size is large. We also explore how some poor sampling strategies (e.g. that over-represent epidemiologically linked clusters of cases) could dramatically exacerbate bias in an uncontrolled manner. Finally, we present a simple diagnostic indicator, based on coalescent density and which can easily be applied to reconstructed phylogenies, that identifies time-periods for which effective population size estimates are less likely to be biased. We illustrate this with an application to the 2009 H1N1 pandemic. Pandemic growth rates from sequences E. de Silva et al. 1799 Pandemic growth rates from sequences E. de Silva et al. 1807  
 Title: Inferring pandemic growth rates from sequence data

SOLUTION: True

PROBLEM: Paragraph: Background. Human coronavirus NL63 (HCoV-NL63) is a globally endemic pathogen causing mild and severe respiratory tract infections with reinfections occurring repeatedly throughout a lifetime. Methods. Nasal samples were collected in coastal Kenya through community-based and hospital-based surveillance. HCoV-NL63 was detected with multiplex real-time reverse transcription PCR, and positive samples were targeted for nucleotide sequencing of the spike (S) protein. Additionally, paired samples from 25 individuals with evidence of repeat HCoV-NL63 infection were selected for whole-genome virus sequencing. Results. HCoV-NL63 was detected in 1.3% (75/5573) of child pneumonia admissions. Two HCoV-NL63 genotypes circulated in Kilifi between 2008 and 2014. Full genome sequences formed a monophyletic clade closely related to contemporary HCoV-NL63 from other global locations. An unexpected pattern of repeat infections was observed with some individuals showing higher viral titers during their second infection. Similar patterns for 2 other endemic coronaviruses, HCoV-229E and HCoV-OC43, were observed. Repeat infections by HCoV-NL63 were not accompanied by detectable genotype switching. Conclusions. In this coastal Kenya setting, HCoV-NL63 exhibited low prevalence in hospital pediatric pneumonia admissions. Clade persistence with low genetic diversity suggest limited immune selection, and absence of detectable clade switching in reinfections indicates initial exposure was insufficient to elicit a protective immune response. 
 Title: Human Coronavirus NL63 Molecular Epidemiology and Evolutionary Patterns in Rural Coastal Kenya

SOLUTION:
True