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".

Input: Consider Input: Paragraph: Background: Positive controls are an integral component of any sensitive molecular diagnostic tool, but this can be affected, if several mutations are being screened in a scenario of a pandemic or newly emerging disease where it can be difficult to acquire all the necessary positive controls from the host. This work describes the development of a synthetic oligo-cassette for positive controls for accurate and highly sensitive diagnosis of several mutations relevant to influenza virus drug resistance. Results: Using influenza antiviral drug resistance mutations as an example by employing the utility of synthetic paired long oligonucleotides containing complementary sequences at their 3' ends and utilizing the formation of oligonucleotide dimers and DNA polymerization, we generated~170bp dsDNA containing several known specific neuraminidase inhibitor (NAI) resistance mutations. These templates were further cloned and successfully applied as positive controls in downstream assays. Conclusion: This approach significantly improved the development of diagnosis of resistance mutations in terms of time, accuracy, efficiency and sensitivity, which are paramount to monitoring the emergence and spread of antiviral drug resistant influenza strains. Thus, this may have a significantly broader application in molecular diagnostics along with its application in rapid molecular testing of all relevant mutations in an event of pandemic.
Title: Synthetic long oligonucleotides to generate artificial templates for use as positive controls in molecular assays: drug resistance mutations in influenza virus as an example

Output: False

Input: Consider Input: Paragraph: Background. Experimental inoculation is an important tool for common cold and asthma research. Producing rhinovirus (RV) inocula from nasal secretions has required prolonged observation of the virus donor to exclude extraneous pathogens. We produced a RV-A16 inoculum using reverse genetics and determined the dose necessary to cause moderate colds in seronegative volunteers. Methods. The consensus sequence of RV-A16 from a previous inoculum was cloned, and inoculum virus was produced using reverse genetics techniques. After safety testing, volunteers were inoculated with either RV-A16 (n = 26) or placebo (n = 10), Jackson cold scores were recorded, and nasal secretions were tested for shedding of RV-A16 ribonucleic acid. Results. The reverse genetics process produced infectious virus that was neutralized by specific antisera and had a mutation rate similar to conventional virus growth techniques. The 1000 median tissue culture infectious dose (TCID 50 ) dose produced moderate colds in most individuals with effects similar to that of a previously tested conventional RV-A16 inoculum. Conclusions. Reverse genetics techniques produced a RV-A16 inoculum that can cause clinical colds in seronegative volunteers, and they also serve as a stable source of virus for laboratory use. The recombinant production procedures eliminate the need to derive seed virus from nasal secretions, thus precluding introduction of extraneous pathogens through this route.
Title: Development of a Rhinovirus Inoculum Using a Reverse Genetics Approach

Output: False

Input: Consider Input: Paragraph: SIR and SEIR models Bayesian inference Model selection Nonidentifiability Quarantine Peak time of epidemic a b s t r a c t Since the COVID-19 outbreak in Wuhan City in December of 2019, numerous model predictions on the COVID-19 epidemics in Wuhan and other parts of China have been reported. These model predictions have shown a wide range of variations. In our study, we demonstrate that nonidentifiability in model calibrations using the confirmed-case data is the main reason for such wide variations. Using the Akaike Information Criterion (AIC) for model selection, we show that an SIR model performs much better than an SEIR model in representing the information contained in the confirmed-case data. This indicates that predictions using more complex models may not be more reliable compared to using a simpler model. We present our model predictions for the COVID-19 epidemic in Wuhan after the lockdown and quarantine of the city on January 23, 2020. We also report our results of modeling the impacts of the strict quarantine measures undertaken in the city after February 7 on the time course of the epidemic, and modeling the potential of a second outbreak after the return-to-work in the city.
Title: -NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Output: True