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 Q]: Paragraph: Background: Hirschsprung's disease (HSCR) is a type of megacolon induced by defi ciency or dysfunction of ganglion cells in the distal intestine and is associated with developmental disorders of the enteric nervous system. To explore the mechanisms of HSCR, we analyzed the RNAsequencing data of the expansion and the narrow segments of colon tissues separated from children with HSCR. Methods: RNA-sequencing of the expansion segments and the narrow segments of colon tissues isolated from children with HSCR was performed. After differentially expressed genes (DEGs) were identified using the edgeR package in R, functional and pathway enrichment analyses of DEGs were carried out using DAVID software. To further screen the key genes, protein-protein interaction (PPI) network and module analyses were conducted separately using Cytoscape software. Results: A total of 117 DEGs were identified in the expansion segment samples, including 47 up-regulated and 70 down-regulated genes. Functional enrichment analysis suggested that FOS and DUSP1 were implicated in response to endogenous stimulus. In the PPI network analysis, FOS (degree=20), EGR1 (degree=16), ATF3 (degree=9), NOS1 (degree=8), CCL5 (degree=8), DUSP1 (degree=7), CXCL3 (degree=6), VIP (degree=6), FOSB (degree=5), and NOS2 (degree=4) had higher degrees, which could interact with other genes. In addition, two significant modules (module 1 and module 2) were identifi ed from the PPI network. Conclusion: Several genes (including FOS, EGR1, ATF3, NOS1, CCL5, DUSP1, CXCL3, VIP, FOSB, and NOS2) might be involved in the development of HSCR through their effect on the nervous system. World J Pediatr 2017;13(3):267-273 
 Title: Genes involved in HSCR Identifying key genes associated with Hirschsprung's disease based on bioinformatics analysis of RNA-sequencing data
[EX A]: False

[EX Q]: 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
[EX A]: False

[EX Q]: Paragraph: This study investigates property securities bubbles and crashes by using a dynamic mathematical methodology developed from the previous research (Watanabe et al. 2007a, b [31,32]). The improved model is used to detect the bubble and crash periods in five international countries/cities (namely, . By this model definition, we are able to detect the beginning of each bubble period even before it bursts. Meanwhile, the empirical results show that most of property securities markets experienced bubble periods between 2003 and 2007, and crashes happened in Apr 2008 triggered by the Subprime Mortgage Crisis of US. In contrast, Japan suffered the shortest bubble period and no evidence has documented the existence of crash there. 
 Title: Physica A A dynamic mathematical test of international property securities bubbles and crashes
[EX A]:
True