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: With concerns about the potential for the aerosol and airborne transmission of infectious agents, particularly influenza, more attention is being focused on the effectiveness of infection control procedures to prevent hospital-acquired infections by this route. More recently a number of different techniques have been applied to examine the temporalespatial information about the airflow patterns and the movement of related, suspended material within this air in a hospital setting. Closer collaboration with engineers has allowed clinical microbiologists, virologists and infection control teams to assess the effectiveness of hospital isolation and ventilation facilities. The characteristics of human respiratory activities have also been investigated using some familiar engineering techniques. Such studies aim to enhance the effectiveness of such preventive measures and have included experiments with humanlike mannequins using various tracer gas/particle techniques, real human volunteers with realtime non-invasive Schlieren imaging, numerical modelling using computational fluid dynamics, and small scale physical analogues with water. This article outlines each of these techniques in a non-technical manner, suitable for a clinical readership without specialist airflow or engineering knowledge. 
 Title: Observing and quantifying airflows in the infection control of aerosol-and airborne-transmitted diseases: an overview of approaches

Answer: True


Question: Paragraph: Newcastle disease (ND) is a contagious disease that affects most species of birds. Its causative pathogen, Newcastle disease virus (NDV), also exhibits considerable oncolytic activity against mammalian cancers. A better understanding of the pathogenesis of NDV will help us design efficient vaccines and novel anticancer strategies. GW3965, a widely used synthetic ligand of liver X receptor (LXR), induces the expression of LXRs and its downstream genes, including ATP-binding cassette transporter A1 (ABCA1). ABCA1 regulates cellular cholesterol homeostasis. Here, we found that GW3965 inhibited NDV infection in DF-1 cells. It also inhibited NF-jB activation and reduced the upregulation of proinflammatory cytokines induced by the infection. Further studies showed that GW3965 exerted its inhibitory effects on virus entry and replication. NDV infection increased the mRNA levels of several lipogenic genes but decreased the ABCA1 mRNA level. Overexpression of ABCA1 inhibited NDV infection and reduced the cholesterol content in DF-1 cells, but when the cholesterol was replenished, NDV infection was restored. GW3965 treatment prevented cholesterol accumulation in the perinuclear area of the infected cells. In summary, our studies suggest that GW3965 inhibits NDV infection, probably by affecting cholesterol homeostasis. 
 Title: The LXR ligand GW3965 inhibits Newcastle disease virus infection by affecting cholesterol homeostasis

Answer: True


Question: Paragraph: The development of the complex network graphs permits us to describe any real system such as social, neural, computer or genetic networks by transforming real properties in topological indices (TIs). This work uses Randic's star networks in order to convert the protein primary structure data in specific topological indices that are used to construct a natural/random protein classification model. The set of natural proteins contains 1046 protein chains selected from the pre-compiled CulledPDB list from PISCES Dunbrack's Web Lab. This set is characterized by a protein homology of 20%, a structure resolution of 1.6 Å and R-factor lower than 25%. The set of random amino acid chains contains 1046 sequences which were generated by Python script according to the same type of residues and average chain length found in the natural set. A new Sequence to Star Networks (S2SNet) wxPython GUI application (with a Graphviz graphics back-end) was designed by our group in order to transform any character sequence in the following star network topological indices: Shannon entropy of Markov matrices, trace of connectivity matrices, Harary number, Wiener index, Gutman index, Schultz index, Moreau-Broto indices, Balaban distance connectivity index, Kier-Hall connectivity indices and Randic connectivity index. The model was constructed with the General Discriminant Analysis methods from STATISTICA package and gave training/predicting set accuracies of 90.77% for the forward stepwise model type. In conclusion, this study extends for the first time the classical TIs to protein star network TIs by proposing a model that can predict if a protein/fragment of protein is natural or random using only the amino acid sequence data. This classification can be used in the studies of the protein functions by changing some fragments with random amino acid sequences or to detect the fake amino acid sequences or the errors in proteins. These results promote the use of the S2SNet application not only for protein structure analysis but also for mass spectroscopy, clinical proteomics and imaging, or DNA/RNA structure analysis. 
 Title: Natural/random protein classification models based on star network topological indices

Answer:
False