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: Flu response centre Public health Helpline Emerging disease s u m m a r y Objective: To assess the operational experience and knowledge of staff who worked in the West Midlands Flu Response Centre (FRC) during the 'containment' phase of influenza A(H1N1)pdm09. using an online self-administered questionnaire. The questionnaire included sections related to the respondents' FRC experience, knowledge about influenza A(H1N1)pdm09 and the containment phase. Results: This study had a 51% (89/176) response rate. Of the respondents, 59% were female, 64% were of White ethnicity, 55% were clinicians, 46% were aged 25e44 years, and 67% had no previous call centre experience. A significant proportion (P < 0.001) of respondents believed that the FRC had made a positive contribution to the public health management of the containment phase. Nearly all respondents indicated that they were familiar with the terms 'pandemic flu' or 'influenza pandemic' (99%), understood the aim of the containment phase (90%), and knew the severity of the illness caused by the virus (97%). However, specific knowledge was lacking regarding a number of public health interventions, and overall knowledge scores for clinicians and non-clinicians were similar. Conclusion: This study is the first to report on the characteristics of FRC staff during the containment phase of influenza A(H1N1)pdm09 pandemic. Although overall, staff evaluation of the West Midlands FRC was very positive, a number of knowledge gaps were identified. This study provides valuable insights which can contribute to preparedness planning. 
 Title: Characteristics of flu response centre staff in the West Midlands

Ex Output:
False


Ex Input:
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 Output:
False


Ex Input:
Paragraph: Cut-off Cut-off Supplementary Figure 1 | Setting cut-off to identify links and hidden source. (a) Coarse-grained distribution of element values ln(1 − λ i )a ij times −1 in the reconstructed vector X of all nodes in a Newman-Watts small-world network (NW), where bin accumulation is implemented for the original distribution. The peak centered at zero corresponds to null connections and the other corresponds to actual links. A cut-off value can be set within the gap between the two peaks. (b) The values of ln(1 − λ i )a ij times -1 in the reconstructed vector X for Erdös-Rényi random networks (ER), Watts-Strogatz small-world networks (WS), NW and Barabási-Albert scale-free networks (BA), where green and yellow points correspond to existent links and null connections, respectively. All the networks share the same cut-off value of about 0.13. (c) Coarse-grained distribution of the structural variance σ in the NW network, where bin accumulation is exercised on the original distribution. The largest peak centered close zero corresponds to nodes of indirect neighbors of the hidden source and the large positive values of σ are associated with the immediate neighbors of the hidden source. A cut-off can be set within the largest gap. (d) The variance σ * i of element values in the reconstructed vector x of all nodes in the presence of a hidden source for ER, WS, NW and BA networks, where green and yellow points correspond to immediate neighbors and indirect neighbors of the hidden source, respectively. All the networks share the same cut-off values of about σ * i = 0.9. The network size in a,b is 200 and in c,d is 50, Θ = 0.25, ∆ = 0.45, λ i and δ i are uniformly distributed in the ranges (0.2, 0.4) and (0.4, 0.6), respectively. SIS dynamics are used for the illustration. Panels a and c correspond to Fig. 2a with nt = 0.4 and Fig. 5 in the main text, respectively. 
 Title: Supplementary Figures

Ex Output:

False