In this task, you're given a paragraph from the research paper and your task is to generate a suitable title for the research paper based on the given paper. Under 100 words is a good title length.

[Q]: Influenza A virus (IAV) is a major cause of respiratory illness. Given the disease severity, associated economic costs, and recent appearance of novel IAV strains, there is a renewed interest in developing novel and efficacious "universal" IAV vaccination strategies. Recent studies have highlighted that immunizations capable of generating local (i.e., nasal mucosa and lung) tissue-resident memory T and B cells in addition to systemic immunity offer the greatest protection against future IAV encounters. Current IAV vaccines are designed to largely stimulate IAV-specific antibodies, but do not generate the lung-resident memory T and B cells induced during IAV infections. Herein, we report on an intranasally administered biocompatible polyanhydride nanoparticle-based IAV vaccine (IAV-nanovax) capable of providing protection against subsequent homologous and heterologous IAV infections in both inbred and outbred populations. Our findings also demonstrate that vaccination with IAV-nanovax promotes the induction of germinal center B cells within the lungs, both systemic and lung local IAV-specific antibodies, and IAV-specific lung-resident memory CD4 and CD8 T cells. Altogether our findings show that an intranasally administered nanovaccine can induce immunity within the lungs, similar to what occurs during IAV infections, and thus could prove useful as a strategy for providing "universal" protection against IAV.
[A]: a section of the journal Frontiers in Immunology Polyanhydride Nanovaccine Induces Robust Pulmonary B and T Cell Immunity and Confers Protection Against Homologous and Heterologous Influenza A Virus Infections

[Q]: Hepatitis C virus (HCV) infects more than 2 % of the world population with highest prevalence in parts of Africa and Asia. Past standard of care using interferon and ribavirin had adverse effects and showed modest efficacy for some HCV genotypes spurring the development of direct acting antivirals (DAAs). Such DAAs target viral proteins and are thus better tolerated but they suffer from emergence of vial resistance. Furthermore, DAAs are often HCV genotype specific. Novel drug candidates targeting host factors required for HCV propagation, so called host-targeting antivirals (HTAs), promise to overcome both caveats. The genetic barrier to resistance is usually considered to be high for HTAs and all HCV genotypes presumably use the same host factors. Recent data, however, challenge these assumptions, at least for some HTAs. Here, we highlight the most important hosttargeting strategies against hepatitis C and critically discuss their opportunities and risks.
[A]: Opportunities and Risks of Host-targeting Antiviral Strategies for Hepatitis C

[Q]: Although there are a large number of thesauri for the biomedical domain many of them lack coverage in terms and their variant forms. Automatic thesaurus construction based on patterns was first suggested by Hearst [1], but it is still not clear how to automatically construct such patterns for different semantic relations and domains. In particular it is not certain which patterns are useful for capturing synonymy. The assumption of extant resources such as parsers is also a limiting factor for many languages, so it is desirable to find patterns that do not use syntactical analysis. Finally to give a more consistent and applicable result it is desirable to use these patterns to form synonym sets in a sound way. We present a method that automatically generates regular expression patterns by expanding seed patterns in a heuristic search and then develops a feature vector based on the occurrence of term pairs in each developed pattern. This allows for a binary classifications of term pairs as synonymous or non-synonymous. We then model this result as a probability graph to find synonym sets, which is equivalent to the well-studied problem of finding an optimal set cover. We achieved 73.2% precision and 29.7% recall by our method, out-performing hand-made resources such as MeSH and Wikipedia. We conclude that automatic methods can play a practical role in developing new thesauri or expanding on existing ones, and this can be done with only a small amount of training data and no need for resources such as parsers. We also concluded that the accuracy can be improved by grouping into synonym sets.
[A]:
Synonym set extraction from the biomedical literature by lexical pattern discovery