In this task, you're given a paragraph and title from the research paper. Your task is to classify whether the given title is suitable or not for the research paper based on the given paragraph. Return "True" if title is proper according to paragraph else "False".

Q: Paragraph: Infectious bronchitis (IB) generated by the infectious bronchitis virus (IBV) causes economic difficulties for livestock farmers. The 3D8 single chain variable fragment (scFv) protein is a recombinant antibody with nuclease activity that shows antiviral effects against various DNA and RNA viruses in mice and chickens. In this experiment, 3D8 scFv G 2 transgenic chickens produced by crossing 3D8 scFv G 1 transgenic rooster and wild type hens were screened by genomic PCR and immunohistochemistry analysis. 3D8 scFv transgenic chickens, wild type sibling chickens, and SPF chickens were directly infected with IBV (5 chickens per group) and indirectly infected by airborne propagation (15 chickens per group). The relative IBV shedding titers were measured by quantitative real-time PCR using oropharyngeal and cloacal swabs on days 3 and 5 after intraocular infection. The viral load was significantly decreased in the 3D8 scFv transgenic chickens from the contact transmission group. Additionally, blood was collected from each group on day 17 post-infection. The ELISA results showed a marked reduction of the antibody titer against IBV in the 3D8 scFv transgenic chickens from the contact transmission group. These results suggest that the 3D8 scFv protein potentially inhibits infectious bronchitis virus transmission in chickens. 
 Title: The 3D8 single chain variable fragment protein suppress infectious bronchitis virus transmission in the transgenic chickens

A: True
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Q: Paragraph: 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. 
 Title: Accuracy of using automated methods for detecting adverse events from electronic health record data: a research protocol

A: True
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Q: Paragraph: People in the Middle East have been affected by the Middle East Respiratory Syndrome CoronaVirus (MERS Co-V) since 2012. New cases are continuously reported especially in the Kingdom of Saudi Arabia, and the risk of exposure remains an issue. Data visualization plays a vital role in effective analysis of the data. In this paper, we introduce an interactive visualization application for MERS data collected from the Control and Command Centre, Ministry of Health website of Saudi Arabia. The data corresponding to the period from January 1, 2019 to February 28, 2019 was used in the present work. The attributes considered include gender, age, date of reporting, city, region, camel contact, description and status of the patient. The visualization tool has been developed using Shiny framework of R programming language. The application presents information in the form of interactive plots, maps and tables. The salient feature of the tool is that users can view and download data corresponding to the period of their choice. This tool can help decision makers in the detailed analysis of data and hence devise measures to prevent the spread of the disease. 
 Title: Shiny Framework Based Visualization and Analytics Tool for Middle East Respiratory Syndrome

A:
False
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