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.

Example Input: 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
Example Output: Highly Sensitive Assay for Measurement of Arenavirus-cell Attachment

Example Input: | The ancient biological 'arms race' between microbial pathogens and humans has shaped genetic variation in modern populations, and this has important implications for the growing field of medical genomics. As humans migrated throughout the world, populations encountered distinct pathogens, and natural selection increased the prevalence of alleles that are advantageous in the new ecosystems in both host and pathogens. This ancient history now influences human infectious disease susceptibility and microbiome homeostasis, and contributes to common diseases that show geographical disparities, such as autoimmune and metabolic disorders. Using new high-throughput technologies, analytical methods and expanding public data resources, the investigation of natural selection is leading to new insights into the function and dysfunction of human biology. www.nature.com/reviews/genetics
Example Output: Natural selection and infectious disease in human populations

Example Input: Identifying recombinant sequences in an era of large genomic databases is challenging as it requires an efficient algorithm to identify candidate recombinants and parents, as well as appropriate statistical methods to correct for the large number of comparisons performed. In 2007, a computation was introduced for an exact nonparametric mosaicism statistic that gave high-precision P values for putative recombinants. This exact computation meant that multiplecomparisons corrected P values also had high precision, which is crucial when performing millions or billions of tests in large databases. Here, we introduce an improvement to the algorithmic complexity of this computation from O(mn 3 ) to O(mn 2 ), where m and n are the numbers of recombination-informative sites in the candidate recombinant. This new computation allows for recombination analysis to be performed in alignments with thousands of polymorphic sites. Benchmark runs are presented on viral genome sequence alignments, new features are introduced, and applications outside recombination analysis are discussed.
Example Output:
Improved Algorithmic Complexity for the 3SEQ Recombination Detection Algorithm