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".

Ex Input:
Paragraph: Very limited evidence has been reported on host T cell responses to the pandemic H1N1 swine-origin influenza A virus (S-OIV) infection in humans. Therefore, we investigated the proportions of peripheral T cell subsets and analyzed the relationship of T helper subset changes with T cell activation during this infection. We found that these S-OIV-infected patients exhibited rapid lymphopenia, T cell activation and preferential loss of Th17 subset at the early stage of acute infection. Statistical analysis indicated that CD4 depletion and loss of Th17 cells, rather than Th1 or Treg cells, were correlated with CD4 T cell activation. More importantly, upregulated IFN-α likely contributed to the functional loss of Th17 cells. Thus, rapidly generalized lymphopenia, preferential loss of Th17 population and T cell activation presented as characteristics of the early immune response in S-OIV-infected patients. These findings, therefore, may be helpful for an earlier diagnosis and further studies of immune pathogenesis of S-OIV infection. 
 Title: Preferential loss of Th17 cells is associated with CD4 T cell activation in patients with 2009 pandemic H1N1 swine-origin influenza A infection

Ex Output:
True


Ex Input:
Paragraph: Diseases caused by pathogenic microorganisms including bacteria and viruses can cause serious medical issues including death and result in huge economic losses. Despite the myriad of recent advances in the rapid and accurate detection of pathogens, large volume clinical samples with a low concentration of pathogens continue to present challenges for diagnosis and surveillance. We here report a simple and label-free approach via homobifunctional imidoesters (HIs) with a microfluidic platform (SLIM) to efficiently enrich and extract pathogens at low concentrations from clinical samples. The SLIM system consists of an assembled double microfluidic chip for streamlining large volume processing and HIs for capturing pathogens and isolating nucleic acids by both electrostatic and covalent interaction without a chaotropic detergent or bulky instruments. The SLIM system significantly increases the enrichment and extraction rate of pathogens (up to 80% at 10 CFU (colony forming unit) in a 1 mL volume within 50 min). We demonstrated its clinical utility in large sample volumes from 46 clinical specimens including environmental swabs, saliva, and blood plasma. The SLIM system showed higher sensitivity with these samples and could detect pathogens that were below the threshold of detection with other methods. Finally, by combining our SLIM approach with an isothermal optical sensor, pathogens could be detected at a very high sensitivity in blood plasma samples within 80 min via enrichment, extraction and detection steps. Our SLIM system thus provides a simple, reliable, cost-effective and ultrasensitive pathogen diagnosis platform for use with large volume clinical samples and would thus have significant utility for various infectious diseases. 
 Title: Simple and label-free pathogen enrichment via homobifunctional imidoesters using a microfluidic (SLIM) system for ultrasensitive pathogen detection in various clinical specimens

Ex Output:
True


Ex Input:
Paragraph: 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. 
 Title: Synonym set extraction from the biomedical literature by lexical pattern discovery

Ex Output:

True