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: One-hundred sixty Escherichia coli isolates obtained from piglets with diarrhea from different parts of Slovakia were examined for the presence of genes coding for F4, F5, F6 and F41 fimbrial adhesins, and hemolytic activity. According to polymerase chain reaction tests 74 (46 %) E. coli isolates were positive for primers that detected genes coding for fimbrial adhesins. Of these 74 isolates, 64 were positive for genes encoding for F4 + , four for F5 + , five for F6 + , and one for both F41 + and F5 + adhesins.
Title: Genotyping of Fimbrial Adhesins in Escherichia coli Strains Isolated from Slovak Piglets Suffering from Diarrhea

Output: False

Input: Consider Input: Paragraph: Infectious disease outbreaks often have consequences beyond human health, including concern among the population, economic instability, and sometimes violence. A warning system capable of anticipating social disruptions resulting from disease outbreaks is urgently needed to help decision makers prepare appropriately. We designed a system that operates in near real-time to identify and predict social response. Over 150,000 Internet-based news articles related to outbreaks of 16 diseases in 72 countries and territories were provided by HealthMap. These articles were automatically tagged with indicators of the disease activity and population reaction. An anomaly detection algorithm was implemented on the population reaction indicators to identify periods of unusually severe social response. Then a model was developed to predict the probability of these periods of unusually severe social response occurring in the coming week, 2 and 3 weeks. This model exhibited remarkably strong performance for diseases with substantial media coverage. For country-disease pairs with a median of 20 or more articles per year, the onset of social response in the next week was correctly predicted over 60% of the time, and 87% of weeks were correctly predicted. Performance was weaker for diseases with little media coverage, and, for these diseases, the main utility of our system is in identifying social response when it occurs, rather than predicting when it will happen in the future. Overall, the developed near real-time prediction approach is a promising step toward developing predictive models to inform responders of the likely social consequences of disease spread. This research was funded by Defense Threat Reduction Agency (www.dtra.mil) contact HDTRA1-12-C-0061.
Title: Predicting social response to infectious disease outbreaks from internet-based news streams
Output: False