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: Middle East respiratory syndrome (MERS) is a highly lethal respiratory disease caused by a novel single-stranded, positive-sense RNA betacoronavirus (MERS-CoV). Dromedary camels, hosts for MERS-CoV, are implicated in direct or indirect transmission to human beings, although the exact mode of transmission is unknown. The virus was fi rst isolated from a patient who died from a severe respiratory illness in June, 2012, in Jeddah, Saudi Arabia. As of May 31, 2015, 1180 laboratory-confi rmed cases (483 deaths; 40% mortality) have been reported to WHO. Both community-acquired and hospital-acquired cases have been reported with little human-to-human transmission reported in the community. Although most cases of MERS have occurred in Saudi Arabia and the United Arab Emirates, cases have been reported in Europe, the USA, and Asia in people who travelled from the Middle East or their contacts. Clinical features of MERS range from asymptomatic or mild disease to acute respiratory distress syndrome and multiorgan failure resulting in death, especially in individuals with underlying comorbidities. No specifi c drug treatment exists for MERS and infection prevention and control measures are crucial to prevent spread in health-care facilities. MERS-CoV continues to be an endemic, low-level public health threat. However, the virus could mutate to have increased interhuman transmissibility, increasing its pandemic potential.
Example Output: Seminar Middle East respiratory syndrome

Example Input: Genome structural annotation, i.e., identification and demarcation of the boundaries for all the functional elements in a genome (e.g., genes, non-coding RNAs, proteins and regulatory elements), is a prerequisite for systems level analysis. Current genome annotation programs do not identify all of the functional elements of the genome, especially small non-coding RNAs (sRNAs). Whole genome transcriptome analysis is a complementary method to identify ''novel'' genes, small RNAs, regulatory regions, and operon structures, thus improving the structural annotation in bacteria. In particular, the identification of non-coding RNAs has revealed their widespread occurrence and functional importance in gene regulation, stress and virulence. However, very little is known about non-coding transcripts in Histophilus somni, one of the causative agents of Bovine Respiratory Disease (BRD) as well as bovine infertility, abortion, septicemia, arthritis, myocarditis, and thrombotic meningoencephalitis. In this study, we report a single nucleotide resolution transcriptome map of H. somni strain 2336 using RNA-Seq method. The RNA-Seq based transcriptome map identified 94 sRNAs in the H. somni genome of which 82 sRNAs were never predicted or reported in earlier studies. We also identified 38 novel potential protein coding open reading frames that were absent in the current genome annotation. The transcriptome map allowed the identification of 278 operon (total 730 genes) structures in the genome. When compared with the genome sequence of a non-virulent strain 129Pt, a disproportionate number of sRNAs (,30%) were located in genomic region unique to strain 2336 (,18% of the total genome). This observation suggests that a number of the newly identified sRNAs in strain 2336 may be involved in strain-specific adaptations.
Example Output: RNA-Seq Based Transcriptional Map of Bovine Respiratory Disease Pathogen ''Histophilus somni 2336

Example Input: The majority of human emerging infectious diseases (EIDs) are zoonotic, with viruses originating in wild mammals of particular concern (e.g. HIV, Ebola, SARS) 1-3 . Understanding patterns of viral diversity in wildlife and determinants of successful cross-species transmission, or spillover, are therefore key goals for pandemic surveillance programs 4 . However, few analytical tools exist to identify which host species likely harbor the next human virus, or which viruses can cross species boundaries 5-7 . Here we conduct the most comprehensive analysis yet of mammalian hostvirus relationships and show that both the total number of viruses that infect a given species, and the proportion likely to be zoonotic are predictable. After controlling for research effort, the proportion of zoonotic viruses per species is predicted by phylogenetic relatedness to humans, host taxonomy, and human population within a species range -which may reflect human-wildlife contact. We demonstrate for the first time that bats harbor a significantly higher proportion of zoonotic viruses than all other mammalian orders. We identify the taxa and geographic regions with the largest estimated number of 'missing viruses' and 'missing zoonoses' and therefore of highest value for future surveillance. We then show that phylogenetic host breadth and other viral traits are significant predictors of zoonotic potential, providing a novel framework to assess if a newly discovered mammalian virus could infect people. Viral zoonoses are a serious threat to public health and global security, and have caused the majority of recent pandemics in people 4 , yet our understanding of the factors driving viral diversity in mammals, viral host range, and cross-species transmission to humans remain nascent. Recent studies have described broad patterns of pathogen host range 1,3 and various host or microbial factors that facilitate cross-species transmission 5,7,8 , or have focused on Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use: http://www.nature. All datasets (host traits, viral traits, full list of host-virus associations and associated references, phylogenetic trees, and phylogenetic distance matrices) along with the R code and R package dependencies needed to fully replicate and evaluate these analyses are provided at http://doi.org/10.5281/zenodo.569079. The top-level README.txt file in the directory details the file structure and metadata provided.
Example Output:
Host and viral traits predict zoonotic spillover from mammals HHS Public Access