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.

Q: We summarize herein the results of various virologic studies of acute otitis media (AOM) conducted at our site over a ten -year period. Among 566 children with AOM, respiratory syncytial virus (RSV) was the most common virus identified in either middle ear fluid or nasal wash; it was found in 16% of all children and 38% of virus-positive children. Seventy-one percent of the children with RSV were one year of age or older, which was significantly older than all other viruses combined (P = 0.045). RSV infection was associated with the common bacterial pathogens causing AOM. Past efforts to develop vaccines for RSV have emphasized prevention of lower respiratory tract infection in infants, which is a more serious problem but less common than AOM. Our results suggest that RSV vaccines that work only against infection in older children may have value in preventing AOM, the most common pediatric disease.

A: ROLE OF RESPIRATORY SYNCYTIAL VIRUS IN ACUTE OTITIS MEDIA: IMPLICATIONS FOR VACCINE DEVELOPMENT
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Q: Linear motifs are short, evolutionarily plastic components of regulatory proteins and provide low-affinity interaction interfaces. These compact modules play central roles in mediating every aspect of the regulatory functionality of the cell. They are particularly prominent in mediating cell signaling, controlling protein turnover and directing protein localization. Given their importance, our understanding of motifs is surprisingly limited, largely as a result of the difficulty of discovery, both experimentally and computationally. The Eukaryotic Linear Motif (ELM) resource at http:// elm.eu.org provides the biological community with a comprehensive database of known experimentally validated motifs, and an exploratory tool to discover putative linear motifs in user-submitted protein sequences. The current update of the ELM database comprises 1800 annotated motif instances representing 170 distinct functional classes, including approximately 500 novel instances and 24 novel classes. Several older motif class entries have been also revisited, improving annotation and adding novel instances. Furthermore, addition of full-text search capabilities, an enhanced interface and simplified batch download has improved the overall accessibility of the ELM data. The motif discovery portion of the ELM resource has added conservation, and structural attributes have been incorporated to aid users to discriminate biologically relevant motifs from stochastically occurring non-functional instances. Figure 5 . Schema of the ELM resource and data life cycle. Annotated ELM classes, and instances thereof, can be searched by database query. Via sequence search by the motif detection pipeline, annotated ELM classes yield putative instances in query sequences. By adding experimental evidence and references, these putative instances become candidate instances for annotation, and, with further curation, ultimately become fully annotated instances.

A: ELM-the database of eukaryotic linear motifs
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Q: Novel infectious diseases continue to emerge within human populations. Predictive studies have begun to identify pathogen traits associated with emergence. However, emerging pathogens vary widely in virulence, a key determinant of their ultimate risk to public health. Here, we use structured literature searches to review the virulence of each of the 214 known human-infective RNA virus species. We then use a machine learning framework to determine whether viral virulence can be predicted by ecological traits, including human-tohuman transmissibility, transmission routes, tissue tropisms, and host range. Using severity of clinical disease as a measurement of virulence, we identified potential risk factors using predictive classification tree and random forest ensemble models. The random forest approach predicted literature-assigned disease severity of test data with mean accuracy of 89.4% compared to a null accuracy of 74.2%. In addition to viral taxonomy, the ability to cause systemic infection was the strongest predictor of severe disease. Further notable predictors of severe disease included having neural and/or renal tropism, direct contact or respiratory transmission, and limited (0 < R 0 � 1) human-to-human transmissibility. We present a novel, to our knowledge, comparative perspective on the virulence of all currently known human RNA virus species. The risk factors identified may provide novel perspectives in understanding the evolution of virulence and elucidating molecular virulence mechanisms. These risk factors could also improve planning and preparedness in public health strategies as part of a predictive framework for novel human infections.

A:
Tissue tropism and transmission ecology predict virulence of human RNA viruses
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