instruction:
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".
question:
Paragraph: The present study was undertaken to evaluate chikungunya virus (CHIKV) proteins for RNAi suppressor activity. We systematically analyzed all nine CHIKV proteins for RNAi suppressor activity using Sf21 RNAi sensor cell line based assay. Two non-structural proteins, namely, nsP2 and nsP3 were found to exhibit RNAi suppressor activity. We further validated the findings in natural hosts, namely in Aedes and in mammalian cell lines and further through EMSA and Agrobacterium infiltration in GFP silenced transgenic tobacco plants. Domains responsible for maximum RNAi suppressor activity were also identified within these proteins. RNA binding motifs in these domains were identified and their participation in RNAi suppression evaluated using site directed mutagenesis. Sequence alignment of these motifs across all species of known alphaviruses revealed conservation of these motifs emphasizing on a similar role of action in other species of alphaviruses as well. Further validation of RNAi suppressor activity of these proteins awaits establishment of specific virus infection models. RNA interference (RNAi) is a conserved evolutionary mechanism designed to impart an antiviral defence system across organisms ranging from plants, fungi, arthropods and nematodes 1-4 . The phenomenon, triggered by the production of double stranded RNA (dsRNA) by the pathogen upon infection, leads to the activation of the RNA interference silencing complex (RISC) by loading of dsRNA/siRNA into the complex ultimately leading to cleavage of the viral RNA 5,6,7 . Several components of RISC participate in the cleavage, most important being ribonuclease III Dicer (DCR) and Argonaute protein (AGO). Dicer acts on dsRNA to generate small RNA (sRNA) that are loaded into the AGO, resulting in cleavage 8,9 ; however, some RNAi pathways are independent of Dicer and other components like RNA-dependent RNA polymerase (RdRP) produce short RNA transcripts that directly bind to AGO 10 . Similarly, much is now known about the Piwi pathway that is specifically active in the germline 11 . In response to RNAi in plants and insects, viruses infecting these systems utilise some of their own proteins/sequence elements that counteract the antiviral RNAi pathway 12 . These viral suppressors of RNAi (VSRs) act through different mechanisms, either by binding to long dsRNA and protecting them from Dicer 7,13 or short RNAs 14 or through multi-mode mechanisms 15,16 . Recently it has been shown that some viruses encode host-specific VSRs due to co-evolution of the virus and the host, emphasising on the importance of analysing suppressor activity in relevant host 17 . Amongst medically important pathogenic viruses, arboviruses form an important subset of viruses due to the complexity of their host preferences 18 . These viruses, mainly constituted by genera flavivirus and alphavirus, 
 Title: Analysis of chikungunya virus proteins reveals that non-structural proteins nsP2 and nsP3 exhibit RNA interference (RNAi) suppressor activity OPEN
answer:
False


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: Human influenza viruses cause regular epidemics and occasional pandemics with a substantial public health burden. Household transmission studies have provided valuable information on the dynamics of influenza transmission. We reviewed published studies and found that once one household member is infected with influenza, the risk of infection in a household contact can be up to 38%, and the delay between onset in index and secondary cases is around 3 days. Younger age was associated with higher susceptibility. In the future, household transmission studies will provide information on transmission dynamics including the correlation of virus shedding and symptoms with transmission, and the correlation of new measures of immunity with protection against infection. 
 Title: Household transmission of influenza virus
answer:

True