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".

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
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Paragraph: BACKGROUND: Study of human coronavirus and other virus-associated respiratory illnesses is needed to describe their clinical effects on chronically ill, older adults. METHODS: A prospective study during 2009 to 2013 clinically assessed acute respiratory illnesses soon after onset and 3 to 4 weeks later in patients aged !60 years with chronic lung and heart diseases (group 1, 100 subjects) and healthy adults aged 18 to 40 years (group 2, 101 subjects). Respiratory secretions were tested for nucleic acids of a panel of respiratory viruses. An increase in antibody titer was assessed for 4 coronavirus strains. : Virus-associated illnesses (29 [39.1%] of 74 illnesses in group 1 and 59 [48.7%] of 121 illnesses in group 2) occurred in all calendar quarters, most commonly in the first and fourth quarters. Coronaviruses (group 1: 14 [18.9%] illnesses; group 2: 26 [21.5%] illnesses) and enteroviruses/rhinoviruses (group 1: 14 [18.9%] illnesses; group 2: 37 [30.6%] illnesses) were most common. Virus co-infections occurred in 10 illnesses. Illnesses with 9 to 11 symptoms were more common in group 1 (17 [23.0%]) than in group 2 (15 [12.4%]) (P < .05). Compared with group 2, more group 1 subjects reported dyspnea, more severe disease of longer duration, and treatment for acute illness with prednisone and antibiotics. Coronavirusassociated illnesses (percent of illnesses, group 1 vs group 2) were characterized by myalgias (21% vs 68%, P < .01), chills (50% vs 52%), dyspnea (71% vs 24%, P < .01), headache (64% vs 72%), malaise (64% vs 84%), cough (86% vs 68%), sputum production (86% vs 60%), sore throat (64% vs 80%), and nasal congestion (93% vs 96%). CONCLUSIONS: Respiratory illnesses were commonly associated with coronaviruses and enteroviruses/ rhinoviruses affecting chronically ill, older patients more than healthy, young adults. Published by Elsevier Inc. 
 Title: Coronavirus and Other Respiratory Illnesses Comparing Older with Young Adults
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Paragraph: Purpose: Malignant melanoma's (MM) incidence is rising faster than that of any other cancer in the US and the overall survival at 5 years is less than 10%. B cell associated protein 31 (BAP31) is overexpressed in most MMs and might be a promising target for immunotherapy of this disease. Experimental design: Firstly, we investigated the expression profiles of human BAP31 (hBAP31) and mouse BAP31 (mBAP31) in human and mouse normal tissues, respectively. The expression level of hBAP31 in human MMs and mBAP31 in B16 melanoma cells was also analyzed. Then we constructed novel mBAP31 DNA vaccines and tested there ability to stimulate mBAP31-specific immune responses and antitumor immunity in B16 melanoma-bearing mice. Results: For the first time, we found that protein expression of hBAP31 were dramatically upregulated in human MMs when compared with human normal tissues. Predominant protein expression of mBAP31 was found in mouse B16 melanoma cells but not in mouse important organs. When mice were immunized with mBAP31 DNA vaccines, strong cellular response to mBAP31 was observed in the vaccinated mice. CTLs isolated from immunized mice could effectively kill mBAP31-positive target mouse B16 melanoma tumor cells in vitro and vaccination with mBAP31 DNA vaccines had potent anti-tumor activity in therapeutic model using B16 melanoma cells. Conclusions: These are the first data supporting a vaccine targeting BAP31 that is capable of inducing effective immunity against BAP31-expressing MMs and will be applicable to human MMs and hBAP31 DNA vaccine warrants investigation in human clinical trials. 
 Title: BAP31, a promising target for the immunotherapy of malignant melanomas

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