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.
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Question: We investigated the ability of monoclonal B cells to restore primary and secondary T-cell dependent antibody responses in adoptive immune-deficient hosts. Priming induced B cell activation and expansion, AID expression, antibody production and the generation of IgM + IgGand IgM -IgG + antigen-experienced B-cell subsets that persisted in the lymphopenic environment by cell division. Upon secondary transfer and recall the IgM -IgG + cells responded by the production of antigen-specific IgG while the IgM + memory cells secreted mainly IgM and little IgG, but generated new B cells expressing germinal center markers. The recall responses were more efficient if the antigenic boost was delayed suggesting that a period of adaptation is necessary before the transferred cells are able to respond. Overall these findings indicate that reconstitution of a functional and complete memory pool requires transfer of all different antigen-experienced B cell subsets. We also found that the size of the memory B cell pool did not rely on the number of the responding naive B cells, suggesting autonomous homeostatic controls for naive and memory B cells. By reconstituting a stable memory B cell pool in immune-deficient hosts using a monoclonal high-affinity B cell population we demonstrate the potential value of B cell adoptive immunotherapy.

Answer: Regulation and Maintenance of an Adoptive T-Cell Dependent Memory B Cell Pool

Question: Motivation: Deep sequencing of clinical samples is now an established tool for the detection of infectious pathogens, with direct medical applications. The large amount of data generated produces an opportunity to detect species even at very low levels, provided that computational tools can effectively profile the relevant metagenomic communities. Data interpretation is complicated by the fact that short sequencing reads can match multiple organisms and by the lack of completeness of existing databases, in particular for viral pathogens. Here we present metaMix, a Bayesian mixture model framework for resolving complex metagenomic mixtures. We show that the use of parallel Monte Carlo Markov chains for the exploration of the species space enables the identification of the set of species most likely to contribute to the mixture. Results: We demonstrate the greater accuracy of metaMix compared with relevant methods, particularly for profiling complex communities consisting of several related species. We designed metaMix specifically for the analysis of deep transcriptome sequencing datasets, with a focus on viral pathogen detection; however, the principles are generally applicable to all types of metagenomic mixtures. Availability and implementation: metaMix is implemented as a user friendly R package, freely available on CRAN: http://cran.r-project.org/web/packages/metaMix Contact: sofia.morfopoulou.10@ucl.ac.uk Supplementary information: Supplementary data are available at Bionformatics online.

Answer: Bayesian mixture analysis for metagenomic community profiling

Question: Most significant change in the evolution of the influenza virus is the rapid growth of the Concentrated Animal Feeding Operations (CAFOs) on a global scale. These industrial agricultural operations have the potential of housing thousands of animals in a relatively small area. Emerging Infectious Diseases (EIDs) event can be considered as a shift in the pathogen-host-environment interplay characteristics described by Engering et al. (2013) . These changes in the host-environment and the disease ecology are key to creating novel transmission patterns and selection of novel pathogens with a modification of genetic traits. With the development of CAFOs throughout the world, the need for training of animal caretakers to observe, identify, treat, vaccinate and cull if necessary is important to safeguard public health. The best defense against another pandemic of Emerging Infectious Diseases (EIDs) is the constant monitoring of the livestock and handlers of CAFOs and the live animal markets. These are the most likely epicenter of the next pandemic.

Answer:
Interaction of the role of Concentrated Animal Feeding Operations (CAFOs) in Emerging Infectious Diseases (EIDS)