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.

[EX Q]: After decades of low personal risk for contracting lethal diseases, physicians are suddenly facing the possibility of a substantial increase in occupational risk during an influenza pandemic. If they are not confronted before the onset of an influenza pandemic, feelings of unease and fear or ignorance about physicians' professional obligations could profoundly hinder individual physicians in fulfilling their professional duties. Such feelings could therefore undermine institutional and societal preparations. In their review published in Critical Care, Anantham and coworkers outline the ethical framework that forms the basis of the professional obligations of physicians who respond to health care emergencies, such as an influenza pandemic.
[EX A]: Pandemic influenza: are we prepared to face our obligations?

[EX Q]: Most acute respiratory infections (ARIs) in children are due to viral etiology, and represent an important cause of mortality and morbidity in children <5 years old in developing countries. The pathogens that cause ARIs vary geographically and by season, and viruses serve a major role. In the present study, the distribution of the seven respiratory viruses that are more prevalent in Southern European countries were retrospectively analyzed in a Southern Italy Hospital, that centralizes pediatric diseases from the Naples province. Viruses were categorized by a FilmArray Respiratory Panel, and demonstrated no substantial differences in sex, age and seasonal viruses distribution. However, all the investigated viruses had a higher detection rate in the surrounding municipalities than in the metropolitan area of Naples. In recent years, the association between air pollution and respiratory infections has become an increasing public health concern. The data in this study support this association in the surrounding areas of Naples extensively contaminated by environmental toxic agents. In these areas, characterization of the epidemiology of ARIs is required to implement a prevention and control program.
[EX A]: Characterization of respiratory infection viruses in hospitalized children from Naples province in Southern Italy

[EX Q]: 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.
[EX A]:
Using simple artificial intelligence methods for predicting amyloidogenesis in antibodies