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.

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

Feline infectious peritonitis virus (FIPV) (Coronaviridae) causes the most lethal viral infection in cats: FIP. The related feline enteric coronavirus (FECV) causes mild enteritis. Why these feline coronaviruses manifest so differently in vivo is not known. In this study, infection kinetics (titres and antigen expression) of FIPV 79-1146, and FECV 79-1683, were determined in peripheral blood monocytes from 3 donor cats and compared to those in Crandell feline kidney (CrFK) cells. The infection kinetics in monocytes were host dependent. Monocytes from 1 cat were resistant to both FIPV-and FECV-infection. Monocytes from the other 2 cats could initially be infected by both FIPV and FECV but FIPV infection was sustained in monocytes of only one cat. FECV-infection was never sustained and viral production was up to 100 times lower than in FIPVinfected monocytes. In CrFK cells, FIPV and FECV infection kinetics did not differ. In monocytes of a larger cat population (n = 19) the 3 infection patterns were also found. Considering all 22 investigated cats, 3/22 were not susceptible for FIPV and FECV. The rest could be infected with FECV and FIPV but 10/22 cats had monocytes that only sustained FIPV infection and 9/22 sustained neither FIPV nor FECV infection. * Both authors contributed equally.
Replication of feline coronaviruses in peripheral blood monocytes

This study investigates the impacts of high-speed rail (HSR) on domestic air transportation in China using a new comprehensive modeling framework utilizing both demand and supply perspectives. For the first time the assessment was conducted using an improved panel regression model by taking into account of the detailed opening schedules of various HSR services during the period 2001-2014. The research findings reveal that the deployed HSR services have a significant substitutional effect on domestic air transportation in China, but the effect varies across different HSR routes, travel distance and city type. Specifically, the research found a decrease in domestic passengers of 28.2%, in flights of 24.6% and in seat capacity of 27.9% after the introduction of HSR services. The impacts are found much stronger among those air routes that connect major hub within a distance range of 500 to 800 km. The uneven nature of the impact can be seen in the different experiences of selected cities. For example, air travel declined approximately 45% after commencement of the Wuhan-Guangzhou HSR, whereas it fell by 34% after the opening of the Beijing-Shanghai HSR. http://dx.

Impacts of high-speed rail on domestic air transportation in China