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.

Input: Consider Input: Background. In April 2012, the Jordan Ministry of Health investigated an outbreak of lower respiratory illnesses at a hospital in Jordan; 2 fatal cases were retrospectively confirmed by real-time reverse transcription polymerase chain reaction (rRT-PCR) to be the first detected cases of Middle East respiratory syndrome (MERS-CoV). Methods. Epidemiologic and clinical characteristics of selected potential cases were assessed through serum blood specimens, medical record reviews, and interviews with surviving outbreak members, household contacts, and healthcare personnel. Cases of MERS-CoV infection were identified using 3 US Centers for Disease Control and Prevention serologic tests for detection of anti-MERS-CoV antibodies. Results. Specimens and interviews were obtained from 124 subjects. Seven previously unconfirmed individuals tested positive for anti-MERS-CoV antibodies by at least 2 of 3 serologic tests, in addition to 2 fatal cases identified by rRT-PCR. The case-fatality rate among the 9 total cases was 22%. Six subjects were healthcare workers at the outbreak hospital, yielding an attack rate of 10% among potentially exposed outbreak hospital personnel. There was no evidence of MERS-CoV transmission at 2 transfer hospitals having acceptable infection control practices. Conclusions. Novel serologic tests allowed for the detection of otherwise unrecognized cases of MERS-CoV infection among contacts in a Jordanian hospital-associated respiratory illness outbreak in April 2012, resulting in a total of 9 test-positive cases. Serologic results suggest that further spread of this outbreak to transfer hospitals did not occur. Most subjects had no major, underlying medical conditions; none were on hemodialysis. Our observed casefatality rate was lower than has been reported from outbreaks elsewhere.

Output: Eastern Mediterranean Public Health Network, and 6 Field Epidemiology Training Program

Input: Consider Input: Motivation: Deep sequencing of clinical samples is now an established tool for the detection of infectious pathogens, with direct medical applications. The large amount of data generated produces an opportunity to detect species even at very low levels, provided that computational tools can effectively profile the relevant metagenomic communities. Data interpretation is complicated by the fact that short sequencing reads can match multiple organisms and by the lack of completeness of existing databases, in particular for viral pathogens. Here we present metaMix, a Bayesian mixture model framework for resolving complex metagenomic mixtures. We show that the use of parallel Monte Carlo Markov chains for the exploration of the species space enables the identification of the set of species most likely to contribute to the mixture. Results: We demonstrate the greater accuracy of metaMix compared with relevant methods, particularly for profiling complex communities consisting of several related species. We designed metaMix specifically for the analysis of deep transcriptome sequencing datasets, with a focus on viral pathogen detection; however, the principles are generally applicable to all types of metagenomic mixtures. Availability and implementation: metaMix is implemented as a user friendly R package, freely available on CRAN: http://cran.r-project.org/web/packages/metaMix Contact: sofia.morfopoulou.10@ucl.ac.uk Supplementary information: Supplementary data are available at Bionformatics online.

Output: Bayesian mixture analysis for metagenomic community profiling

Input: Consider Input: Recent studies point to the prevalence of the evolutionary phenomenon of drastic structural transformation of protein domains while continuing to preserve their basic biochemical function. These transformations span a wide spectrum, including simple domains incorporated into larger structural scaffolds, changes in the structural core, major active site shifts, topological rewiring and extensive structural transmogrifications. Proteins from biological conflict systems, such as toxinantitoxin, restriction-modification, CRISPR/Cas, polymorphic toxin and secondary metabolism systems commonly display such transformations. These include endoDNases, metalindependent RNases, deaminases, ADP ribosyltransferases, immunity proteins, kinases and E1like enzymes. In eukaryotes such transformations are seen in domains involved in chromatinrelated peptide recognition and protein/DNA-modification. Intense selective pressures from "armrace"-like situations in conflict and macromolecular modification systems could favor drastic structural divergence while preserving function. Drastic structural transformation of protein domains while retaining their basic biochemical function is a notable phenomenon in protein evolution. These transformations span a wide spectrum of structural changes including complex modifications of domain structure. Domains displaying these transformations are most commonly observed in systems involved in biological conflicts, eukaryotic chromatin-related protein/DNA modification and peptide recognition. These systems show "arm-race"-like situations that could favor drastic structural divergence while preserving function Zhang et al.
Output: Resilience of biochemical activity in protein domains in the face of structural divergence