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 Input:
Most acute respiratory infections (ARIs) in children are due to viral etiology, and represent an important cause of mortality and morbidity in children <5 years old in developing countries. The pathogens that cause ARIs vary geographically and by season, and viruses serve a major role. In the present study, the distribution of the seven respiratory viruses that are more prevalent in Southern European countries were retrospectively analyzed in a Southern Italy Hospital, that centralizes pediatric diseases from the Naples province. Viruses were categorized by a FilmArray Respiratory Panel, and demonstrated no substantial differences in sex, age and seasonal viruses distribution. However, all the investigated viruses had a higher detection rate in the surrounding municipalities than in the metropolitan area of Naples. In recent years, the association between air pollution and respiratory infections has become an increasing public health concern. The data in this study support this association in the surrounding areas of Naples extensively contaminated by environmental toxic agents. In these areas, characterization of the epidemiology of ARIs is required to implement a prevention and control program.

Ex Output:
Characterization of respiratory infection viruses in hospitalized children from Naples province in Southern Italy


Ex Input:
Background: While it is known that acute respiratory illness (ARI) is caused by an array of viruses, less is known about co-detections and the resultant comparative symptoms and illness burden. This study examined the co-detections, the distribution of viruses, symptoms, and illness burden associated with ARI between December 2012 and March 2013. Methods: Outpatients with ARI were assayed for presence of 18 viruses using multiplex reverse transcriptase polymerase chain reaction (MRT-PCR) to simultaneously detect multiple viruses. Results: Among 935 patients, 60% tested positive for a single virus, 9% tested positive for ≥1 virus and 287 (31%) tested negative. Among children (<18 years), the respective distributions were 63%, 14%, and 23%; whereas for younger adults (18-49 years), the distributions were 58%, 8%, and 34% and for older adults (≥50 years) the distributions were 61%, 5%, and 32% (P < 0.001). Co-detections were more common in children than older adults (P = 0.01), and less frequent in households without children (P = 0.003). Most frequently co-detected viruses were coronavirus, respiratory syncytial virus, and influenza A virus. Compared with single viral infections, those with co-detections less frequently reported sore throat (P = 0.01), missed fewer days of school (1.1 vs. 2 days; P = 0.04), or work (2 vs. 3 days; P = 0.03); other measures of illness severity did not vary. Conclusions: Among outpatients with ARI, 69% of visits were associated with a viral etiology. Co-detections of specific clusters of viruses were observed in 9% of ARI cases particularly in children, were less frequent in households without children, and were less symptomatic (e.g., lower fever) than single infections.

Ex Output:
influenza season


Ex Input:
Background 3 In silico bacterial, viral, and human truth datasets were generated to evaluate available metagenomics 4 algorithms. Sequenced datasets include background organisms, creating ambiguity in the true 5 source organism for each read. Bacterial and viral datasets were created with even and staggered 6 coverage to evaluate organism identification, read mapping, and gene identification capabilities of 7 available algorithms. These truth datasets are provided as a resource for the development and 8 refinement of metagenomic algorithms. Algorithm performance on these truth datasets can inform 9 decision makers on strengths and weaknesses of available algorithms and how the results may be best 10 leveraged for bacterial and viral organism identification and characterization. 11 12 Source organisms were selected to mirror communities described in the Human Microbiome Project as 13 well as the emerging pathogens listed by the National Institute of Allergy and Infectious Diseases. The 14 six in silico datasets were used to evaluate the performance of six leading metagenomics algorithms: 15 MetaScope, Kraken, LMAT, MetaPhlAn, MetaCV, and MetaPhyler. 16 Results Algorithms were evaluated on runtime, true positive organisms identified to the genus and species 19 levels, false positive organisms identified to genus and species level, read mapping, relative abundance 20 estimation, and gene calling. No algorithm out performed the others in all categories, and the 21 algorithm or algorithms of choice strongly depends on analysis goals. MetaPhlAn excels for bacteria 22 and LMAT for viruses. The algorithms were ranked by overall performance using a normalized 23 weighted sum of the above metrics, and MetaScope emerged as the overall winner, followed by Kraken 24 and LMAT. 25 Conclusions 27 Simulated FASTQ datasets with well--characterized truth data about microbial community composition 28 reveal numerous insights about the relative strengths and weaknesses of the metagenomics algorithms 29 evaluated. The simulated datasets are available to download from the Sequence Read Archive 30 (SRP062063). 31 32

Ex Output:

Evaluating performance of metagenomic characterization algorithms using in silico datasets generated with FASTQSim