TASK DEFINITION: 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.
PROBLEM: The development of high-throughput molecular technologies and associated bioinformatics has dramatically changed the capacities of scientists to produce, handle, and analyze large amounts of genomic, transcriptomic, and proteomic data. A clear example of this step-change is represented by the amount of DNA sequence data that can be now produced using next-generation sequencing (NGS) platforms. Similarly, recent improvements in protein and peptide separation effi ciencies and highly accurate mass spectrometry have promoted the identifi cation and quantifi cation of proteins in a given sample. These advancements in biotechnology have increasingly been applied to the study of animal infectious diseases and are beginning to revolutionize the way that biological and evolutionary processes can be studied at the molecular level. Studies have demonstrated the value of NGS technologies for molecular characterization, ranging from metagenomic characterization of unknown pathogens or microbial communities to molecular epidemiology and evolution of viral quasispecies. Moreover, high-throughput technologies now allow detailed studies of hostpathogen interactions at the level of their genomes (genomics), transcriptomes (transcriptomics), or proteomes (proteomics). Ultimately, the interaction between pathogen and host biological networks can be questioned by analytically integrating these levels (integrative OMICS and systems biology). The application of high-throughput biotechnology platforms in these fi elds and their typical low-cost per information content has revolutionized the resolution with which these processes can now be studied. The aim of this chapter is to provide a current and prospective view on the opportunities and challenges associated with the application of massive parallel sequencing technologies to veterinary medicine, with particular focus on applications that have a potential impact on disease control and management.

SOLUTION: Chapter 30 Next-Generation Sequencing in Veterinary Medicine: How Can the Massive Amount of Information Arising from High- Throughput Technologies Improve Diagnosis, Control, and Management of Infectious Diseases?

PROBLEM: All coronaviruses (CoVs), including the causative agent of severe acute respiratory syndrome (SARS), encode a nucleocapsid (N) protein that harbors two independent RNA binding domains of known structure, but poorly characterized RNA binding properties. We show here that the Nterminal domain (NTD) of N protein from mouse hepatitis virus (MHV), a virus most closely related to SARS-CoV, employs aromatic amino acidnucleobase stacking interactions with a triple adenosine motif to mediate high-affinity binding to single-stranded RNAs containing the transcriptional regulatory sequence (TRS) or its complement (cTRS). Stoichiometric NTD fully unwinds a TRS-cTRS duplex that mimics a transiently formed transcription intermediate in viral subgenomic RNA synthesis. Mutation of the solvent-exposed Y127, positioned on the -platform surface of our 1.75 A structure, binds the TRS far less tightly and is severely crippled in its RNA unwinding activity. In contrast, the C-terminal domain (CTD) exhibits no RNA unwinding activity. Viruses harboring Y127A N mutation are strongly selected against and Y127A N does not support an accessory function in MHV replication. We propose that the helix melting activity of the coronavirus N protein NTD plays a critical accessory role in subgenomic RNA synthesis and other processes requiring RNA remodeling.

SOLUTION: Coronavirus N Protein N-Terminal Domain (NTD) Specifically Binds the Transcriptional Regulatory Sequence (TRS) and Melts TRS-cTRS RNA Duplexes

PROBLEM: Background: The traditional Serfling-type approach for influenza-like illness surveillance requires long historical time-series. We retrospectively evaluated the use of recent, short, historical time-series for recognizing the onset of community outbreaks of respiratory tract infections (RTIs). The data used referred to the proportion of diagnoses for upper or lower RTIs to total diagnoses for house-call visits, performed by a private network of medical specialists (SOS Doctors) in the metropolitan area of Athens, Greece, between January 01, 2000 and October 12, 2008. The reference standard classification of the observations was obtained by generating epidemic thresholds after analyzing the full 9-year period. We evaluated two different alert generating methods [simple regression and cumulative sum (CUSUM), respectively], under a range of input parameters, using data for the previous running 4-6 week period. These methods were applied if the previous weeks contained non-aberrant observations. Results: We found that the CUSUM model with a specific set of parameters performed marginally better than simple regression for both groups. The best results (sensitivity, specificity) for simple regression and CUSUM models for upper RTIs were (1.00, 0.82) and (0.94, 0.93) respectively. Corresponding results for lower RTIs were (1.00, 0.80) and (0.93, 0.91) respectively. Conclusions: Short-term data for house-call visits can be used rather reliably to identify respiratory tract outbreaks in the community using simple regression and CUSUM methods. Such surveillance models could be particularly useful when a large historical database is either unavailable or inaccurate and, thus, traditional methods are not optimal.

SOLUTION:
Surveillance of Community Outbreaks of Respiratory Tract Infections Based on House-Call Visits in the Metropolitan Area of Athens, Greece