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: Community-ARI and inpatient-ARI was also combined with mild and severe cases of influenza from a historical prospective study as mild-ARI and severe-ARI respectively to evaluate the performance of clinical case definitions. We analysed 130 community-ARI and 140 inpatient-ARI episodes (5 inpatient-ARI excluded because multiple pathogens were detected), involving 138 and 207 samples respectively. Detection by PCR declined with days post-onset for influenza virus; decrease was faster for community-ARI than for inpatient-ARI. No such patterns were observed for noninfluenza respiratory virus infections. PathChip added substantially to viruses detected for community-ARI only. Clinical case definitions discriminated influenza from other mild-ARI but performed poorly for severe-ARI and for older participants. Rational strategies for diagnosis and surveillance of influenza and other respiratory virus must acknowledge the differences between ARIs presenting in community and hospital settings.
Title: Detection of viral respiratory pathogens in mild and severe acute respiratory infections in Singapore To investigate the performance of laboratory methods and clinical case definitions in detecting the viral pathogens for acute respiratory infections (ARIs) from a prospective community cohort and hospital inpatients, nasopharyngeal swabs from cohort members reporting ARIs (community-ARI) and inpatients admitted with ARIs (inpatient-ARI) were tested by Singleplex Real Time-Polymerase Chain Reaction (SRT-PCR), multiplex RT-PCR (MRT-PCR) and pathogen-chip system (PathChip) between OPEN

A: True
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Q: Paragraph: Fast simulation tools for the prediction of transient particle transport are critical in designing the air distribution indoors to reduce the exposure to indoor particles and associated health risks. This investigation proposed a combined fast fluid dynamics (FFD) and Markov chain model for fast predicting transient particle transport indoors. The solver for FFD-Markov-chain model was programmed in OpenFOAM, an open-source CFD toolbox. This study used two cases from the literature to validate the developed model and found well agreement between the transient particle concentrations predicted by the FFD-Markov-chain model and the experimental data. This investigation further compared the FFD-Markov-chain model with the CFD-Eulerian model and CFD-Lagrangian model in terms of accuracy and efficiency. The accuracy of the FFD-Markov-chain model was similar to that of the other two models. For the two studied cases, the FFD-Markovchain model was 4.7 and 6.8 times faster, respectively, than the CFD-Eulerian model, and it was 137.4 and 53.3 times faster than the CFD-Lagrangian model in predicting the steady-state airflow and transient particle transport. Therefore, the FFD-Markov-chain model is able to greatly reduce the computing cost for predicting transient particle transport in indoor environments. computational fluid dynamics, indoor environment, Eulerian model, Lagrangian model, particle dispersion, aerosol dynamics Article History
Title: Modeling transient particle transport by fast fluid dynamics with the Markov chain method

A: True
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Q: Paragraph: To warrant potential clinical testing, the equine anti-SARS-CoV F(ab) 2 requires evaluation in as many animal models as possible and a safety test in a primate model. In this study, we evaluated the pharmacokinetics, tolerance and immunity of this kind of antibody in macaques and rats. Results showed that the F(ab) 2 fragments had a normal metabolism in injected animals. The general physiological indexes did not differ between animals injected with anti-SARS-CoV F(ab) 2 or saline. However, a mild inflammatory response in local injection site and a moderate immune response against this antibody in the successively injected animals were observed, which however recovered 3 weeks after the last injection. The antibody titring from 1:100 to 400 against the equine anti-SARS-CoV F(ab) 2 in the inoculated hosts could be detected at week 2 during the successive injections of the equine F(ab) 2 . The considerable safety of this antibody used in primates and the fact that the immune system of the host can be motivated by post-injection of the F(ab) 2 indicate that this type of anti-SARS-CoV antibody can be used for prevention and treatment of SASR, especially at the early stage of this virus infection. In addition, it can also provide the precious time for the combined use of other anti-SARS-CoV agents such as antiviral drug and vaccine.
Title: Outstanding Youth Scientist Foundation of China (No. 30325020) and National Natural Science Foundation

A:
False
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