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".

Q: Paragraph: Marine sponges and their associated bacteria have been proven to be a rich source of novel secondary metabolites with therapeutic usefulness in cancer, infection, and autoimmunity. In this study, 79 strains belonging to 20 genera of the order Actinomycetales and seven strains belonging to two genera of the order Sphingomonadales were cultivated from 18 different Caribbean sponges and identified by 16S rRNA gene sequencing. Seven of these strains are likely to represent novel species. Crude extracts from selected strains were found to exhibit protease inhibition against cathepsins B and L, rhodesain, and falcipain-2 as well as immunomodulatory activities such as induction of cytokine release by human peripheral blood mononuclear cells. These results highlight the significance of marine sponge-associated bacteria to produce bioactive secondary metabolites with therapeutic potential in the treatment of infectious diseases and disorders of the immune system. 
 Title: Anti-protease and Immunomodulatory Activities of Bacteria Associated with Caribbean Sponges

A: False
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Q: Paragraph: Background: Porcine epidemic diarrhea virus (PEDV) is emerging as a pathogenic coronavirus that causes a huge economic burden to the swine industry. Interaction of the viral spike (S) surface glycoprotein with the host cell receptor is recognized as the first step of infection and is the main determinant of virus tropism. The mechanisms by which neutralizing antibodies inhibit PEDV have not been defined. Isolating PEDV neutralizing antibodies are crucial to identifying the receptor-binding domains of the viral spike and elucidating the mechanism of protection against PEDV infection. Methods: B cell hybridoma technique was used to generate hybridoma cells that secrete specific antibodies. E.coli prokaryotic expression system and Bac-to-Bac expression system were used to identify the target protein of each monoclonal antibody. qPCR was performed to analyze PEDV binding to Vero E6 cells with neutralizing antibody. Results: We identified 10 monoclonal antibodies using hybridoma technology. Remarkably, 4 mAbs (designed 2G8, 2B11, 3D9, 1E3) neutralized virus infection potently, of which 2B11 and 1E3 targeted the conformational epitope of the PEDV S protein. qPCR results showed that both 2B11 and 2G8 blocked virus entry into Vero cells. Conclusion: The data suggested that PEDV neutralizing antibody inhibited virus infection by binding to infectious virions, which could work as a tool to find the receptor-binding domains. 
 Title: Neutralizing antibodies against porcine epidemic diarrhea virus block virus attachment and internalization

A: False
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Q: Paragraph: The recent growth in publicly available sequence data has introduced new opportunities for studying microbial evolution and spread. Because the pace of sequence accumulation tends to exceed the pace of experimental studies of protein function and the roles of individual amino acids, statistical tools to identify meaningful patterns in protein diversity are essential. Large sequence alignments from fast-evolving micro-organisms are particularly challenging to dissect using standard tools from phylogenetics and multivariate statistics because biologically relevant functional signals are easily masked by neutral variation and noise. To meet this need, a novel computational method is introduced that is easily executed in parallel using a cluster environment and can handle thousands of sequences with minimal subjective input from the user. The usefulness of this kind of machine learning is demonstrated by applying it to nearly 5000 haemagglutinin sequences of influenza A/H3N2.Antigenic and 3D structural mapping of the results show that the method can recover the major jumps in antigenic phenotype that occurred between 1968 and 2013 and identify specific amino acids associated with these changes. The method is expected to provide a useful tool to uncover patterns of protein evolution. 
 Title: K-Pax2: Bayesian identification of cluster-defining amino acid positions in large sequence datasets

A:
False
****