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]: RSV infection is typically associated with secondary bacterial infection. We hypothesise that the local airway immune response to RSV has incidental antibacterial effects. Using coordinated proteomics and metagenomics analysis we simultaneously analysed the microbiota and proteomes of the upper airway and determined direct antibacterial activity in airway secretions of RSV-infected children. Here, we report that the airway abundance of Streptococcus was higher in samples collected at the time of RSV infection compared with samples collected one month later. RSV infection is associated with neutrophil influx into the airway and degranulation and is marked by overexpression of proteins with known antibacterial activity including BPI, EPX, MPO and AZU1. Airway secretions of children infected with RSV, have significantly greater antibacterial activity compared to RSV-negative controls. This RSVassociated, neutrophil-mediated antibacterial response in the airway appears to act as a regulatory mechanism that modulates bacterial growth in the airways of RSV-infected children.
[EX A]: Airway response to respiratory syncytial virus has incidental antibacterial effects

[EX Q]: A novel betacoronavirus associated with lethal respiratory and renal complications was recently identified in patients from several countries in the Middle East. We report the deep genome sequencing of the virus directly from a patient's sputum sample. Our high-throughput sequencing yielded a substantial depth of genome sequence assembly and showed the minority viral variants in the specimen. Detailed phylogenetic analysis of the virus genome (England/ Qatar/2012) revealed its close relationship to European bat coronaviruses circulating among the bat species of the Vespertilionidae family. Molecular clock analysis showed that the 2 human infections of this betacoronavirus in June 2012 (EMC/2012) and September 2012 (England/Qatar/2012) share a common virus ancestor most likely considerably before early 2012, suggesting the human diversity is the result of multiple zoonotic events. T he ability of coronaviruses (CoVs) to infect multiple species and to rapidly change through recombination presents a continuing human health threat. The epidemic of severe acute respiratory syndrome (SARS) during 2003-2004 during which a CoV transmitted from bats to civet cats and then to humans demonstrated this potential (reviewed in [1, 2] ). Recently, a novel human betacoronavirus (betaCoV) was found to be associated with at least 13 human infections, 7 of which were fatal (3-7). One of the viruses, EMC/2012, has been sequenced, and its sequence similarity to several bat CoVs suggested an animal origin, but a definitive bat species of origin has not yet been identified (3). Additional genome sequences from this virus are needed to aid diagnostics, monitor population dynamics, identify the animal source, and characterize mechanisms of pathogenesis. The large size (30,000 nt) and high variability of CoV RNA genomes present a challenge for sequencing. We describe a strategy for rapidly designing the primers necessary for reverse transcription and cDNA amplification of such diverse RNA viruses and report the full-genome determination of the novel CoV directly from patient sputum using next-generation short-read sequencing. Full genomes from 2 epidemiologically unlinked novel CoV infections separated in time by >2 months were analyzed to gain clues to 2 major questions: what are the precursors of the virus, and how long has the virus been circulating in its current form? Materials and Methods Fifteen betaCoV full genomes with the closest homology to EMC/2012 (GenBank accession no. JX869059) were analyzed to identify all possible primer-like sequences (melting temperature 58°C-60°C, guanine plus cytosine content 35%-60%) yielding 357,198 potential primers. The 30-kb JX869059 CoV genome was divided into fifteen 2.5-kb overlapping amplicons, and the 2 highest frequency sequences mapping in the 5′ and 3′ 250 bp of each amplicon were selected. Reverse complements of the 3′ mapping sequences were prepared, resulting in a set of 60 primers for the 15 amplicons (4 primers/amplicon). Three additional primers were added for the extreme termini of the genome. The algorithm also prepared a virtual PCR map of the predicted binding and amplicon size for the primer set on CoV genomes. A map of the primer mapping positions
[EX A]: Full-Genome Deep Sequencing and Phylogenetic Analysis of Novel Human Betacoronavirus

[EX Q]: Estimating key infectious disease parameters from the COVID-19 outbreak is quintessential for modelling studies and guiding intervention strategies. Whereas different estimates for the incubation period distribution and the serial interval distribution have been reported, estimates of the generation interval for COVID-19 have not been provided. Methods: We used outbreak data from clusters in Singapore and Tianjin, China to estimate the generation interval from symptom onset data while acknowledging uncertainty about the incubation period distribution and the underlying transmission network. From those estimates we obtained the proportions pre-symptomatic transmission and reproduction numbers. Results: The mean generation interval was 5.20 (95%CI 3.78-6.78) days for Singapore and 3.95 (95%CI 3.01-4.91) days for Tianjin, China when relying on a previously reported incubation period with mean 5.2 and SD 2.8 days. The proportion of pre-symptomatic transmission was 48% (95%CI 32-67%) for Singapore and 62% (95%CI 50-76%) for Tianjin, China. Estimates of the reproduction number based on the generation interval distribution were slightly higher than those based on the serial interval distribution. Conclusions: Estimating generation and serial interval distributions from outbreak 1 . CC-BY-NC-ND 4.0 International license It is made available under a author/funder, who has granted medRxiv a license to display the preprint in perpetuity. is the (which was not peer-reviewed) The copyright holder for this preprint .
[EX A]:
Estimating the generation interval for COVID-19 based on symptom onset data