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".
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Question: Paragraph: Background: Household studies provide opportunities to understand influenza-like-illness (ILI) transmission, but data from (sub)tropical developing countries are scarce. Objective: To determine the viral etiology and epidemiology of ILI in households. Study design: ILI was detected by active case finding amongst a cohort of 263 northern Vietnam households between 2008 and 2013. Health workers collected nose and throat swabs for virus detection by multiplex real-time RT-PCR. Results: ILI was detected at least once in 219 (23.7%) of 945 household members. 271 (62.3%) of 435 nose/throat swabs were positive for at least one of the 15 viruses tested. Six viruses predominated amongst positive swabs: Rhinovirus (28%), Influenza virus (17%), Coronavirus (8%), Enterovirus (5%), Respiratory syncytial virus (3%), Metapneumovirus virus (2.5%) and Parainfluenza virus 3 (1.8%). There was no clear seasonality, but 78% of episodes occurred in Winter/Spring for Influenza compared to 32% for Rhinovirus. Participants, on average, suffered 0.49 ILI, and 0.29 virus-positive ILI episodes, with no significant effects of gender, age, or household size. In contrast to US and Australian community studies, the frequency of ILI decreased as the number of household members aged below 5 years increased (p = 0.006). Conclusion: The findings indicate the need for tailored ILI control strategies, and for better understanding of how local childcare practices and seasonality may influence transmission and the role of children. 
 Title: Epidemiology and etiology of influenza-like-illness in households in Vietnam; it's not all about the kids!

Answer: True


Question: Paragraph: Viral bronchiolitis is a common clinical syndrome aff ecting infants and young children. Concern about its associated morbidity and cost has led to a large body of research that has been summarised in systematic reviews and integrated into clinical practice guidelines in several countries. The evidence and guideline recommendations consistently support a clinical diagnosis with the limited role for diagnostic testing for children presenting with the typical clinical syndrome of viral upper respiratory infection progressing to the lower respiratory tract. Management is largely supportive, focusing on maintaining oxygenation and hydration of the patient. Evidence suggests no benefi t from bronchodilator or corticosteroid use in infants with a fi rst episode of bronchiolitis. Evidence for other treatments such as hypertonic saline is evolving but not clearly defi ned yet. For infants with severe disease, the insuffi cient available data suggest a role for high-fl ow nasal cannula and continuous positive airway pressure use in a monitored setting to prevent respiratory failure. 
 Title: Viral bronchiolitis

Answer: True


Question: Paragraph: Background: All polypeptide backbones have the potential to form amyloid fibrils, which are associated with a number of degenerative disorders. However, the likelihood that amyloidosis would actually occur under physiological conditions depends largely on the amino acid composition of a protein. We explore using a naive Bayesian classifier and a weighted decision tree for predicting the amyloidogenicity of immunoglobulin sequences. The average accuracy based on leave-one-out (LOO) cross validation of a Bayesian classifier generated from 143 amyloidogenic sequences is 60.84%. This is consistent with the average accuracy of 61.15% for a holdout test set comprised of 103 AM and 28 non-amyloidogenic sequences. The LOO cross validation accuracy increases to 81.08% when the training set is augmented by the holdout test set. In comparison, the average classification accuracy for the holdout test set obtained using a decision tree is 78.64%. Non-amyloidogenic sequences are predicted with average LOO cross validation accuracies between 74.05% and 77.24% using the Bayesian classifier, depending on the training set size. The accuracy for the holdout test set was 89%. For the decision tree, the nonamyloidogenic prediction accuracy is 75.00%. Conclusions: This exploratory study indicates that both classification methods may be promising in providing straightforward predictions on the amyloidogenicity of a sequence. Nevertheless, the number of available sequences that satisfy the premises of this study are limited, and are consequently smaller than the ideal training set size. Increasing the size of the training set clearly increases the accuracy, and the expansion of the training set to include not only more derivatives, but more alignments, would make the method more sound. The accuracy of the classifiers may also be improved when additional factors, such as structural and physico-chemical data, are considered. The development of this type of classifier has significant applications in evaluating engineered antibodies, and may be adapted for evaluating engineered proteins in general. 
 Title: Using simple artificial intelligence methods for predicting amyloidogenesis in antibodies

Answer:
False