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".
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Question: 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

Answer: True

Question: Paragraph: Background: Human rhinoviruses (HRVs) are the most frequently detected pathogens in acute respiratory tract infections (ARTIs) and yet little is known about the prevalence, recurrence, structure and clinical impact of individual members. During 2007, the complete coding sequences of six previously unknown and highly divergent HRV strains were reported. To catalogue the molecular and clinical features distinguishing the divergent HRV strains, we undertook, for the first time, in silico analyses of all available polyprotein sequences and performed retrospective reviews of the medical records of cases in which variants of the prototype strain, HRV-QPM, had been detected. Methodology/Principle Findings: Genomic analyses revealed that the six divergent strains, residing within a clade we previously called HRV A2, had the shortest polyprotein of all picornaviruses investigated. Structure-based amino acid alignments identified conserved motifs shared among members of the genus Rhinovirus as well as substantive deletions and insertions unique to the divergent strains. Deletions mostly affected regions encoding proteins traditionally involved in antigenicity and serving as HRV and HEV receptor footprints. Because the HRV A2 strains cannot yet be cultured, we created homology models of predicted HRV-QPM structural proteins. In silico comparisons confirmed that HRV-QPM was most closely related to the major group HRVs. HRV-QPM was most frequently detected in infants with expiratory wheezing or persistent cough who had been admitted to hospital and required supplemental oxygen. It was the only virus detected in 65% of positive individuals. These observations contributed to an objective clinical impact ranging from mild to severe. The divergent strains did not meet classification requirements for any existing species of the genus Rhinovirus or Enterovirus. HRV A2 strains should be partitioned into at least one new species, putatively called Human rhinovirus C, populated by members detected with high frequency, from individuals with respiratory symptoms requiring hospital admission.
Title: Distinguishing Molecular Features and Clinical Characteristics of a Putative New Rhinovirus Species, Human Rhinovirus C (HRV C)

Answer: False

Question: Paragraph: Bats are natural reservoirs of coronaviruses and other viruses with zoonotic potential. Florida has indigenous non-migratory populations of Brazilian free-tailed bats (Tadarida brasiliensis) that mostly roost in colonies in artificial structures. Unlike their counterparts in Brazil and Mexico, the viruses harbored by the Florida bats have been underexplored. We report the detection of an alphacoronavirus RNA-dependent RNA polymerase (RdRp) gene sequence in the feces of two of 19 different T. brasiliensis that were capture/release bats that had been evaluated for overall health. The RdRp sequence is similar but not identical to previously detected sequences in the feces of two different species of bats (T. brasiliensis and Molossus molossus) in Brazil. In common with the experience of others doing similar work, attempts to isolate the virus in cell cultures were unsuccessful. We surmise that this and highly related alphacoronavirus are carried by Brazilian free-tailed bats living in a wide eco-spatial region. As various coronaviruses (CoVs) that affect humans emerged from bats, our study raises the question whether CoVs such as the one detected in our work are yet-to-be-detected pathogens of humans and animals other than bats.
Title: Detection of Alphacoronavirus vRNA in the Feces of Brazilian Free-Tailed Bats (Tadarida brasiliensis) from a Colony in Florida, USA

Answer:
False