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".

Ex Input:
Paragraph: Cox, E., Pensaert, M.B., Callebaut, P. and van Deun, K., 1990. Intestinal replication of a porcine respiratory coronavirus closely related antigenically to transmissible gastroenteritis virus. Vet. Microbiol., One-week-old piglets were inoculated with the porcine respiratory coronavirus (PRCV) either intravenously or directly into the lumen of the gastrointestinal tract. Both inoculation routes resulted in the isolation of virus from the caudal small intestine. Viral replication, however, was only observed upon inoculation into the digestive tract in quantities of > 10 3 TCIDso. Replication remained limited to a few unidentified cells located in or underneath the epithelial layer at villus-or crypt-sites. Virus was excreted in the faeces for several days but infection of the respiratory tract occurred rarely in the same pigs. The results of this study indicate that small changes in molecular structure between PRCV and transmissible gastroenteritis virus have resulted in important changes in host cell tropism. 
 Title: Intestinal replication of a porcine respiratory coronavirus closely related antigenically to the enteric transmissible gastroenteritis virus

Ex Output:
False


Ex Input:
Paragraph: Background: The detrimental role of viruses has been well described in CF, although the pattern of virus infections has not been investigated in a longitudinal study. The primary aim was to determine the feasibility of fortnightly parent collected swabs in young children with CF. Methods: Children under three years with CF were recruited. Nasal swabs were collected by parents every fortnight and during periods of symptoms over 12 months. Nasal swabs were posted and virus detected using real-time PCR. Results: Only 27% of the patients completed the study to 10 months, although 98% of the swabs returned were adequate for analysis. Mould was observed growing on 23% of the returned swabs. There was no evidence to demonstrate relationships with symptoms and viruses, prolonged symptoms, prolonged shedding or patterns of virus infections. Conclusions: This study highlights the need to further investigate the role of viruses in children with CF using a robust method of frequent collection in children for a longitudinal study, with appropriate storage and shipping techniques to avoid mould growth or other potential contaminants. 
 Title: Original Article

Ex Output:
False


Ex Input:
Paragraph: Background: Recently, the outbreak of MERS-CoV infections caused worldwide attention to Saudi Arabia. The novel virus belongs to the coronaviruses family, which is responsible for causing mild to moderate colds. The control and command center of Saudi Ministry of Health issues a daily report on MERS-CoV infection cases. The infection with MERS-CoV can lead to fatal complications, however little information is known about this novel virus. In this paper, we apply two data mining techniques in order to better understand the stability and the possibility of recovery from MERS-CoV infections. Method: The Naive Bayes classifier and J48 decision tree algorithm were used to build our models. The dataset used consists of 1082 records of cases reported between 2013 and 2015. In order to build our prediction models, we split the dataset into two groups. The first group combined recovery and death records. A new attribute was created to indicate the record type, such that the dataset can be used to predict the recovery from MERS-CoV. The second group contained the new case records to be used to predict the stability of the infection based on the current status attribute. Results: The resulting recovery models indicate that healthcare workers are more likely to survive. This could be due to the vaccinations that healthcare workers are required to get on regular basis. As for the stability models using J48, two attributes were found to be important for predicting stability: symptomatic and age. Old patients are at high risk of developing MERS-CoV complications. Finally, the performance of all the models was evaluated using three measures: accuracy, precision, and recall. In general, the accuracy of the models is between 53.6% and 71.58%. We believe that the performance of the prediction models can be enhanced with the use of more patient data. As future work, we plan to directly contact hospitals in Riyadh in order to collect more information related to patients with MERS-CoV infections. 
 Title: Building predictive models for MERS-CoV infections using data mining techniques Building predictive models for MERS-CoV infections using data mining techniques 745

Ex Output:

True