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 baculovirus expression vector system (BEVS) is a versatile and powerful platform for protein expression in insect cells. With the ability to approach similar post-translational modifications as in mammalian cells, the BEVS offers a number of advantages including high levels of expression as well as an inherent safety during manufacture and of the final product. Many BEVS products include proteins and protein complexes that require expression from more than one gene. This review examines the expression strategies that have been used to this end and focuses on the distinguishing features between those that make use of single polycistronic baculovirus (co-expression) and those that use multiple monocistronic baculoviruses (co-infection). Three major areas in which researchers have been able to take advantage of co-expression/co-infection are addressed, including compound structure-function studies, insect cell functionality augmentation, and VLP production. The core of the review discusses the parameters of interest for co-infection and co-expression with time of infection (TOI) and multiplicity of infection (MOI) highlighted for the former and the choice of promoter for the latter. In addition, an overview of modeling approaches is presented, with a suggested trajectory for future exploration. The review concludes with an examination of the gaps that still remain in co-expression/co-infection knowledge and practice.

SOLUTION: Co-expression vs. co-infection using baculovirus expression vectors in insect cell culture: Benefits and drawbacks

PROBLEM: Background: Adverse events are associated with significant morbidity, mortality and cost in hospitalized patients. Measuring adverse events is necessary for quality improvement, but current detection methods are inaccurate, untimely and expensive. The advent of electronic health records and the development of automated methods for encoding and classifying electronic narrative data, such as natural language processing, offer an opportunity to identify potentially better methods. The objective of this study is to determine the accuracy of using automated methods for detecting three highly prevalent adverse events: a) hospital-acquired pneumonia, b) catheter-associated bloodstream infections, and c) in-hospital falls. Methods/design: This validation study will be conducted at two large Canadian academic health centres: the McGill University Health Centre (MUHC) and The Ottawa Hospital (TOH). The study population consists of all medical, surgical and intensive care unit patients admitted to these centres between 2008 and 2014. An automated detection algorithm will be developed and validated for each of the three adverse events using electronic data extracted from multiple clinical databases. A random sample of MUHC patients will be used to develop the automated detection algorithms (cohort 1, development set). The accuracy of these algorithms will be assessed using chart review as the reference standard. Then, receiver operating characteristic curves will be used to identify optimal cut points for each of the data sources. Multivariate logistic regression and the areas under curve (AUC) will be used to identify the optimal combination of data sources that maximize the accuracy of adverse event detection. The most accurate algorithms will then be validated on a second random sample of MUHC patients (cohort 1, validation set), and accuracy will be measured using chart review as the reference standard. The most accurate algorithms validated at the MUHC will then be applied to TOH data (cohort 2), and their accuracy will be assessed using a reference standard assessment of the medical chart. Discussion: There is a need for more accurate, timely and efficient measures of adverse events in acute care hospitals. This is a critical requirement for evaluating the effectiveness of preventive interventions and for tracking progress in patient safety through time.

SOLUTION: Accuracy of using automated methods for detecting adverse events from electronic health record data: a research protocol

PROBLEM: The genus Coronavirus contains about 25 species of coronaviruses (CoVs), which are important pathogens causing highly prevalent diseases and often severe or fatal in humans and animals. No licensed specific drugs are available to prevent their infection. Different host receptors for cellular entry, poorly conserved structural proteins (antigens), and the high mutation and recombination rates of CoVs pose a significant problem in the development of wide-spectrum anti-CoV drugs and vaccines. CoV main proteases (M pro s), which are key enzymes in viral gene expression and replication, were revealed to share a highly conservative substrate-recognition pocket by comparison of four crystal structures and a homology model representing all three genetic clusters of the genus Coronavirus. This conclusion was further supported by enzyme activity assays. Mechanism-based irreversible inhibitors were designed, based on this conserved structural region, and a uniform inhibition mechanism was elucidated from the structures of M pro -inhibitor complexes from severe acute respiratory syndrome-CoV and porcine transmissible gastroenteritis virus. A structureassisted optimization program has yielded compounds with fast in vitro inactivation of multiple CoV M pro s, potent antiviral activity, and extremely low cellular toxicity in cell-based assays. Further modification could rapidly lead to the discovery of a single agent with clinical potential against existing and possible future emerging CoV-related diseases. Citation: Yang H, Xie W, Xue X, Yang K, Ma J, et al. (2005) Design of wide-spectrum inhibitors targeting coronavirus main proteases. PLoS Biol 3(10): e324.

SOLUTION:
Design of Wide-Spectrum Inhibitors Targeting Coronavirus Main Proteases