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.

[EX Q]: Porcine blood mononuclear cells (PBMC) were shown to produce interferon-a (IFNa) following incubation with cells infected by a coronavirus, transmissible gastroenteritis virus. Monoclonal antibodies (mAb) with specificities for leukocyte subsets and major histocompatibility complex (MHC) antigens were used to characterize IFNa producer cells. The production of IFNa was found to be a function of non-phagocytic, non-adherent, non-T, non-B, CD4 + (and to a lesser extent CD8 +) MHC-class-II-positive ceils. Furthermore, addition of anti-MHC (class II) mAb during PBMC incubation with virus-infected cells reduced IFN yields, suggesting that masking of these surface antigens alters PBMC responsiveness to IFN induction.
[EX A]: CHARACTERIZATION OF BLOOD MONONUCLEAR CELLS PRODUC- ING IFNa FOLLOWING INDUCTION BY CORONAVIRUS-INFECTED CELLS (PORCINE TRANSMISSIBLE GASTROENTERITIS VIRUS)

[EX Q]: Structured RNA elements may control virus replication, transcription and translation, and their distinct features are being exploited by novel antiviral strategies. Viral RNA elements continue to be discovered using combinations of experimental and computational analyses. However, the wealth of sequence data, notably from deep viral RNA sequencing, viromes, and metagenomes, necessitates computational approaches being used as an essential discovery tool. In this review, we describe practical approaches being used to discover functional RNA elements in viral genomes. In addition to success stories in new and emerging viruses, these approaches have revealed some surprising new features of well-studied viruses e.g., human immunodeficiency virus, hepatitis C virus, influenza, and dengue viruses. Some notable discoveries were facilitated by new comparative analyses of diverse viral genome alignments. Importantly, comparative approaches for finding RNA elements embedded in coding and non-coding regions differ. With the exponential growth of computer power we have progressed from stem-loop prediction on single sequences to cutting edge 3D prediction, and from command line to user friendly web interfaces. Despite these advances, many powerful, user friendly prediction tools and resources are underutilized by the virology community.
[EX A]: Know Your Enemy: Successful Bioinformatic Approaches to Predict Functional RNA Structures in Viral RNAs

[EX Q]: Mannheimia haemolytica is the most important bacterial agent associated with the bovine respiratory disease complex (BRDC), which causes worldwide economic losses to the cattle industry. M. haemolytica cells initially colonize the tonsillar crypts in the upper respiratory tract of cattle, from where they can subsequently descend into the lungs to cause disease. Many bacteria exist as biofilms inside their hosts. We hypothesize that M. haemolytica colonization of cattle during its commensal state may include biofilm formation. To begin to assess this possibility, we developed an in vitro system to study biofilm formation directly on bovine respiratory epithelial cells. Using fixed primary bovine bronchial epithelial cells, we observed M. haemolytica biofilm formation after a 48 h incubation period at 37 C. Addition of mucin, the main component of mucus present in the upper respiratory tract, decreased M. haemolytica biofilm formation on bovine epithelial cells. We investigated the effects of prior viral infection of the epithelial cells on subsequent biofilm formation by M. haemolytica and found negligible effects. Utilization of this model system will provide new insights into the potential role of biofilm formation by M. haemolytica in the pathogenesis of BRDC.
[EX A]:
Mannheimia haemolytica biofilm formation on bovine respiratory epithelial cells