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: Background: On the 30th September 2009, the pandemic (H1N1) 2009 influenza vaccine was made available to adults and children aged 10 years and over, in Australia. Acceptance of a novel vaccine is influenced by perceptions of risk including risk of infection, risk of death or severe illness and risk of serious vaccine side-effects. We surveyed a sample of residents from Sydney, Australia to ascertain their risk perception, attitudes towards the pandemic and willingness to accept the pandemic (H1N1) 2009 influenza vaccine. We sampled residents using a cross-sectional intercept design during the WHO Phase 6. Members of the public were approached in shopping and pedestrian malls to undertake the survey during September and October 2009. The survey measured perceived risk, seriousness of disease, recent behavioural changes, likely acceptance of the pandemic (H1N1) 2009 vaccine and issues relating to uptake and perceived safety. 
 Title: Why do I need it? I am not at risk! Public perceptions towards the pandemic (H1N1) 2009 vaccine
[A]: True


[Q]: Paragraph: Candida auris has emerged in the past decade as a multi-drug resistant public health threat causing health care outbreaks. Here we report epidemiological, clinical, and microbiological investigations of a C. auris outbreak in a regional Omani hospital between April 2018 and April 2019. The outbreak started in the intensive care areas (intensive care unit (ICU), coronary care unit (CCU), and high dependency unit) but cases were subsequently diagnosed in other medical and surgical units. In addition to the patients' clinical and screening samples, environmental swabs from high touch areas and from the hands of 35 staff were collected. All the positive samples from patients and environmental screening were confirmed using MALDI-TOF, and additional ITS-rDNA sequencing was done for ten clinical and two environmental isolates. There were 32 patients positive for C. auris of which 14 (43.8%) had urinary tract infection, 11 (34.4%) had candidemia, and 7 (21.8%) had asymptomatic skin colonization. The median age was 64 years (14-88) with 17 (53.1%) male and 15 (46.9%) female patients. Prior to diagnosis, 21 (65.6%) had been admitted to the intensive care unit, and 11 (34.4%) had been nursed in medical or surgical wards. The crude mortality rate in our patient's cohort was 53.1. Two swabs collected from a ventilator in two different beds in the ICU were positive for C. auris. None of the health care worker samples were positive. Molecular typing showed that clinical and environmental isolates were genetically similar and all belonged to the South Asian C. auris clade I. Most isolates had non-susceptible fluconazole (100%) and amphotericin B (33%) 
 Title: Ongoing Challenges with Healthcare-Associated Candida auris Outbreaks in Oman
[A]: False


[Q]: Paragraph: Unbiased next-generation sequencing (NGS) approaches enable comprehensive pathogen detection in the clinical microbiology laboratory and have numerous applications for public health surveillance, outbreak investigation, and the diagnosis of infectious diseases. However, practical deployment of the technology is hindered by the bioinformatics challenge of analyzing results accurately and in a clinically relevant timeframe. Here we describe SURPI (''sequence-based ultrarapid pathogen identification''), a computational pipeline for pathogen identification from complex metagenomic NGS data generated from clinical samples, and demonstrate use of the pipeline in the analysis of 237 clinical samples comprising more than 1.1 billion sequences. Deployable on both cloud-based and standalone servers, SURPI leverages two state-of-the-art aligners for accelerated analyses, SNAP and RAPSearch, which are as accurate as existing bioinformatics tools but orders of magnitude faster in performance. In fast mode, SURPI detects viruses and bacteria by scanning data sets of 7-500 million reads in 11 min to 5 h, while in comprehensive mode, all known microorganisms are identified, followed by de novo assembly and protein homology searches for divergent viruses in 50 min to 16 h. SURPI has also directly contributed to real-time microbial diagnosis in acutely ill patients, underscoring its potential key role in the development of unbiased NGS-based clinical assays in infectious diseases that demand rapid turnaround times. 
 Title: A cloud-compatible bioinformatics pipeline for ultrarapid pathogen identification from next-generation sequencing of clinical samples
[A]:
True