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".

Ex Input:
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

Ex Output:
True


Ex Input:
Paragraph: The NIAID (National Institute for Allergy and Infectious Diseases) Biodefense Proteomics program aims to identify targets for potential vaccines, therapeutics, and diagnostics for agents of concern in bioterrorism, including bacterial, parasitic, and viral pathogens. The program includes seven Proteomics Research Centers, generating diverse types of pathogen-host data, including mass spectrometry, microarray transcriptional profiles, protein interactions, protein structures and biological reagents. The Biodefense Resource Center (www.proteomicsresource.org) has developed a bioinformatics framework, employing a protein-centric approach to integrate and support mining and analysis of the large and heterogeneous data. Underlying this approach is a data warehouse with comprehensive protein + gene identifier and name mappings and annotations extracted from over 100 molecular databases. Value-added annotations are provided for key proteins from experimental findings using controlled vocabulary. The availability of pathogen and host omics data in an integrated framework allows global analysis of the data and comparisons across different experiments and organisms, as illustrated in several case studies presented here. (1) The identification of a hypothetical protein with differential gene and protein expressions in two host systems (mouse macrophage and human HeLa cells) infected by different bacterial (Bacillus anthracis and Salmonella typhimurium) and viral (orthopox) pathogens suggesting that this protein can be prioritized for additional analysis and functional characterization. (2) The analysis of a vaccinia-human protein interaction network supplemented with protein accumulation levels led to the identification of human Keratin, type II cytoskeletal 4 protein as a potential therapeutic target. (3) Comparison of complete genomes from pathogenic variants coupled with experimental information on complete proteomes allowed the identification and prioritization of ten potential diagnostic targets from Bacillus anthracis. The integrative analysis across data sets from multiple centers can reveal potential functional significance and hidden relationships between pathogen and host proteins, thereby providing a systems approach to basic understanding of pathogenicity and target identification. 
 Title: Systems Integration of Biodefense Omics Data for Analysis of Pathogen-Host Interactions and Identification of Potential Targets

Ex Output:
False


Ex Input:
Paragraph: Interferon-induced transmembrane proteins (IFITMs) have been recognized as important antiviral effectors of the innate immune system, both in cell culture and in infected humans. In particular, polymorphisms of the human IFITM3 gene have been shown to affect disease severity and progression in influenza A virus (FLUAV) and human immunodeficiency virus (HIV) infection, respectively. Rhesus macaques (Macaca mulatta) are commonly used to model human infections and the experimental inoculation of these animals with simian immunodeficiency virus (SIV) is one of the best models for HIV/AIDS in humans. However, information on the role of IFITM3 in SIV infection of rhesus macaques is currently lacking. We show that rhesus macaque (rh) IFITM3 inhibits SIV and FLUAV entry in cell culture, although with moderately reduced efficiency as compared to its human counterpart. We further report the identification of 16 polymorphisms in the rhIFITM3 gene, three of which were exonic and synonymous while the remainder was located in non-coding regions. Employing previously characterized samples from two cohorts of SIV-infected rhesus macaques, we investigated the relationship between these rhIFITM3 polymorphisms and both AIDS-free survival time and virus load. In cohort 1, several intronic polymorphisms were significantly associated with virus load or survival. However, an association with both parameters was not observed and significance was lost in most cases when animals were stratified for the presence of MHC allele Mamu-A1*001. Moreover, no significant genotype-phenotype associations were detected in cohort 2. These results suggest that, although IFITM3 can inhibit SIV infection in cell culture, genetic variation in rhIFITM3 might have only a minor impact on the course of SIV infection in experimentally infected animals. 
 Title: Rhesus macaque IFITM3 gene polymorphisms and SIV infection

Ex Output:

False