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: 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.

A: Neutralizing antibodies against porcine epidemic diarrhea virus block virus attachment and internalization
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Q: The sudden death of three calves, one diarrheic calf, and one aborted fetus from four farms in southern Brazil was investigated. Two Histophilus somni-associated syndromes were identified: systemic histophilosis (n=4) and abortion (n=1). The principal pathological findings included vasculitis, meningoencephalitis with thrombosis, necrotizing myocarditis, renal infarctions, hepatic abscesses, and bronchopneumonia. PCR assays were used to amplify specific amplicons of the ovine herpesvirus 2, bovine herpesvirus 1 and −5, Listeria monocytogenes, H. somni, and pestivirus; bovine group A rotavirus (BoRV-A) and bovine coronavirus (BCoV) were investigated in calves with diarrhea. H. somni DNA was amplified in tissues from all calves and the brain of the aborted fetus with pathological alterations consistent with histophilosis. All other PCR assays were negative; BoRV-A and BCoV were not identified. These findings confirm the participation of H. somni in the pathological alterations observed in this study and represent the first description of histophilosis in cattle from Brazil.

A: Histophilus somni-induced infections in cattle from southern Brazil
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Q: Influenza pandemics have occurred throughout history and were associated with substantial excess mortality and morbidity. Mathematical models of infectious diseases permit quantitative description of epidemic processes based on the underlying biological mechanisms. Mathematical models have been widely used in the past decade to aid pandemic planning by allowing detailed predictions of the speed of spread of an influenza pandemic and the likely effectiveness of alternative control strategies. During the initial waves of the 2009 influenza pandemic, mathematical models were used to track the spread of the virus, predict the time course of the pandemic and assess the likely impact of large-scale vaccination. While mathematical modeling has made substantial contributions to influenza pandemic preparedness, its use as a real-time tool for pandemic control is currently limited by the lack of essential surveillance information such as serologic data. Mathematical modeling provided a useful framework for analyzing and interpreting surveillance data during the 2009 influenza pandemic, for highlighting limitations in existing pandemic surveillance systems, and for guiding how these systems should be strengthened in order to cope with future epidemics of influenza or other emerging infectious diseases.

A:
The use of mathematical models to inform influenza pandemic preparedness and response
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