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]: Human parainfluenza viruses (HPIVs) are common causes of severe pediatric respiratory viral disease. We characterized wild-type HPIV2 infection in an in vitro model of human airway epithelium (HAE) and found that the virus replicates to high titer, sheds apically, targets ciliated cells, and induces minimal cytopathology. Replication of an experimental, live attenuated HPIV2 vaccine strain, containing both temperature sensitive (ts) and non-ts attenuating mutations, was restricted >30-fold compared to rHPIV2-WT in HAE at 32°C and exhibited little productive replication at 37°C. This restriction paralleled attenuation in the upper and lower respiratory tract of African green monkeys, supporting the HAE model as an appropriate and convenient system for characterizing HPIV2 vaccine candidates.
[EX A]: Growth restriction of an experimental live attenuated human parainfluenza virus type 2 vaccine in human ciliated airway epithelium in vitro parallels attenuation in African green monkeys

[EX Q]: The novel coronavirus, COVID-19, has sparked an outflow of scientific research seeking to understand the virus, its spread, and best practices in prevention and treatment. If this international research effort is going to be as swift and effective as possible, it will need to rely on a principle of open science. When researchers share data, code, and software and generally make their work as transparent as possible, it allows other researchers to verify and expand upon their work. Furthermore, it allows public officials to make informed decisions. In this study, we analyzed 535 preprint articles related to COVID-19 for eight transparency criteria and recorded study location and funding information. We found that individual researchers have lined up to help during this crisis, quickly tackling important public health questions, often without funding or support from outside organizations. However, most authors could improve their data sharing and scientific reporting practices. The contrast between researchers' commitment to doing important research and their reporting practices reveals underlying weaknesses in the research community's reporting habits, but not necessarily their science.
[EX A]: Reproducibility and reporting practices in COVID-19 preprint manuscripts

[EX Q]: The recent growth in publicly available sequence data has introduced new opportunities for studying microbial evolution and spread. Because the pace of sequence accumulation tends to exceed the pace of experimental studies of protein function and the roles of individual amino acids, statistical tools to identify meaningful patterns in protein diversity are essential. Large sequence alignments from fast-evolving micro-organisms are particularly challenging to dissect using standard tools from phylogenetics and multivariate statistics because biologically relevant functional signals are easily masked by neutral variation and noise. To meet this need, a novel computational method is introduced that is easily executed in parallel using a cluster environment and can handle thousands of sequences with minimal subjective input from the user. The usefulness of this kind of machine learning is demonstrated by applying it to nearly 5000 haemagglutinin sequences of influenza A/H3N2.Antigenic and 3D structural mapping of the results show that the method can recover the major jumps in antigenic phenotype that occurred between 1968 and 2013 and identify specific amino acids associated with these changes. The method is expected to provide a useful tool to uncover patterns of protein evolution.
[EX A]:
K-Pax2: Bayesian identification of cluster-defining amino acid positions in large sequence datasets