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: This study numerically investigated the thermal effect of human body on the time-dependent dispersion of cough droplets with evaporation process. The thermal flow of human body was imitated using a 3D thermal manikin with real body features, while a recent developed multi-component Eulerian-Lagrangian approach was used to address the effects of inhomogeneous temperature and humidity fields on droplet evaporation. By comparing the results yielded without and with the human body heat, the outcomes demonstrated strong impact of human body heat on the droplets mass fraction and local air velocity distributions. Inspirable droplets could potentially drop into respirable droplets by evaporation, although the evaporation rate was not significantly affected by body heat. The thermal effect of human body revealed its vital impacts on the time-dependent droplets dispersion. Due to the buoyancy driven thermal flow, both the vertical velocity and displacement of small droplets (≤20 μm) were completely reversed from descending to ascending, while the deposition time of large droplets (≥50 μm) were significantly delayed. With the reduced droplet size by evaporation and droplets lifted into breathing zone by human thermal effect, the inhalability and infection risks of cough droplets would be much higher in real occupied indoor spaces. 
 Title: Thermal effect of human body on cough droplets evaporation and dispersion in an enclosed space

Answer: True


Question: Paragraph: The binding between peptide epitopes and major histocompatibility complex proteins (MHCs) is an important event in the cellular immune response. Accurate prediction of the binding between short peptides and the MHC molecules has long been a principal challenge for immunoinformatics. Recently, the modeling of MHC-peptide binding has come to emphasize quantitative predictions: instead of categorizing peptides as "binders" or "non-binders" or as "strong binders" and "weak binders", recent methods seek to make predictions about precise binding affinities. We developed a quantitative support vector machine regression (SVR) approach, called SVRMHC, to model peptide-MHC binding affinities. As a non-linear method, SVRMHC was able to generate models that out-performed existing linear models, such as the "additive method". By adopting a new "11-factor encoding" scheme, SVRMHC takes into account similarities in the physicochemical properties of the amino acids constituting the input peptides. When applied to MHC-peptide binding data for three mouse class I MHC alleles, the SVRMHC models produced more accurate predictions than those produced previously. Furthermore, comparisons based on Receiver Operating Characteristic (ROC) analysis indicated that SVRMHC was able to outperform several prominent methods in identifying strongly binding peptides. As a method with demonstrated performance in the quantitative modeling of MHCpeptide binding and in identifying strong binders, SVRMHC is a promising immunoinformatics tool with not inconsiderable future potential. 
 Title: Quantitative prediction of mouse class I MHC peptide binding affinity using support vector machine regression (SVR) models

Answer: False


Question: Paragraph: In December 2019, a cluster of patients with emerging pneumonia of unknown etiology were discovered in Wuhan city, China. Laboratory examination identified a novel coronavirus which was tentatively named as 2019 novel coronavirus (2019-nCoV) by the World Health Organization (WHO) in January 2020. The WHO has more recently formally named the 2019-nCoV as coronavirus Diseases 2019 (COVID-19). Within one month, the novel coronavirus rapidly spread across the whole of China through droplet transmission and contact transmission, and the reported number of confirmed COVID-19 cases demonstrated a rising trend. Radiological examinations, especially computed tomography CT, were confirmed as effective methods for the screening and diagnosis of infected cases. Reports stated that many radiologists and technologists were being infected when examining patients with COVID-19. In order to clarify the imaging features of COVID-19 and reduce the infection risk of medical personnel in radiology departments, we summarized the experience on radiological examinations and infection prevention for COVID-19, aiming to guide virus prevention, diagnosis and control within radiology departments. 
 Title: Journal Pre-proof Experience on radiological examinations and infection prevention for COVID-19 in radiology department Experience on radiological examinations and infection prevention for COVID-19 in radiology department Corresponding authors

Answer:
False