instruction:
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.
question:
Background: Members of the Baculoviridae encode two types of proteins that mediate virus:cell membrane fusion and penetration into the host cell. Alignments of primary amino acid sequences indicate that baculovirus fusion proteins of group I nucleopolyhedroviruses (NPV) form the GP64 superfamily. The structure of these viral penetrenes has not been determined. The GP64 superfamily includes the glycoprotein (GP) encoded by members of the Thogotovirus genus of the Orthomyxoviridae. The entry proteins of other baculoviruses, group II NPV and granuloviruses, are class I penetrenes. Results: Class III penetrenes encoded by members of the Rhabdoviridae and Herpesviridae have an internal fusion domain comprised of beta sheets, other beta sheet domains, an extended alpha helical domain, a membrane proximal stem domain and a carboxyl terminal anchor. Similar sequences and structural/functional motifs that characterize class III penetrenes are located collinearly in GP64 of group I baculoviruses and related glycoproteins encoded by thogotoviruses. Structural models based on a prototypic class III penetrene, vesicular stomatitis virus glycoprotein (VSV G), were established for Thogoto virus (THOV) GP and Autographa california multiple NPV (AcMNPV) GP64 demonstrating feasible cysteine linkages. Glycosylation sites in THOV GP and AcMNPV GP64 appear in similar model locations to the two glycosylation sites of VSV G. These results suggest that proteins in the GP64 superfamily are class III penetrenes.
answer:
Virology Journal Proteomics computational analyses suggest that baculovirus GP64 superfamily proteins are class III penetrenes

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Arenaviruses are a family of enveloped RNA viruses that cause severe human disease. The first step in the arenavirus life cycle is attachment of viral particles to host cells. While virus-cell attachment can be measured through the use of virions labeled with biotin, radioactive isotopes, or fluorescent dyes, these approaches typically require high multiplicities of infection (MOI) to enable detection of bound virus. We describe a quantitative (q)RT-PCR-based assay that measures Junin virus strain Candid 1 attachment via quantitation of virion-packaged viral genomic RNA. This assay has several advantages including its extreme sensitivity and ability to measure attachment over a large dynamic range of MOIs without the need to purify or label input virus. Importantly, this approach can be easily tailored for use with other viruses through the use of virus-specific qRT-PCR reagents. Further, this assay can be modified to permit measurement of particle endocytosis and genome uncoating. In conclusion, we describe a simple, yet robust assay for highly sensitive measurement of arenavirus-cell attachment. The video component of this article can be found at http://www.jove.com/video/53682/ 15, 23 . The qRT-PCR-based assay described here can detect attachment events using as few as 20 plaque forming units (PFUs) of virus, which translates into an MOI of 0.0004 for a 48-well plate. Therefore, the strong sensitivity of the assay overcomes the requirement for high MOI as well as any virus labeling or concentration steps. Further, the assay can be i) adapted to measure virion endocytosis Journal of Visualized Experiments
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Highly Sensitive Assay for Measurement of Arenavirus-cell Attachment

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The increasing availability and complexity of data has led to new opportunities and challenges in veterinary epidemiology around how to translate abundant, diverse, and rapidly growing "big" data into meaningful insights for animal health. Big data analytics are used to understand health risks and minimize the impact of adverse animal health issues through identifying high-risk populations, combining data or processes acting at multiple scales through epidemiological modeling approaches, and harnessing high velocity data to monitor animal health trends and detect emerging health threats. The advent of big data requires the incorporation of new skills into veterinary epidemiology training, including, for example, machine learning and coding, to prepare a new generation of scientists and practitioners to engage with big data. Establishing pipelines to analyze big data in near real-time is the next step for progressing from simply having "big data" to create "smart data," with the objective of improving understanding of health risks, effectiveness of management and policy decisions, and ultimately preventing or at least minimizing the impact of adverse animal health issues.
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Translating Big Data into Smart Data for Veterinary Epidemiology