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".

Example Input: Paragraph: h i g h l i g h t s We obtained a spatial epidemic model with logistic growth. Using multiple-scale analysis, we present amplitude equations. There are different types of stationary patterns. Reaction diffusion epidemic systems have rich dynamics. Mathematical models are very useful in analyzing the spread and control of infectious diseases which can be used to predict the developing tendency of the infectious disease, determine the key factors and to seek the optimum strategies of disease control. As a result, we investigated the pattern dynamics of a spatial epidemic model with logistic growth. By using amplitude equation, we found that there were different types of stationary patterns including spotted, mixed, and stripe patterns, which mean that spatial motion of individuals can form high density of diseases. The obtained results can be extended in other related fields, such as vegetation patterns in ecosystems.
Title: Physica A Dynamics of an epidemic model with spatial diffusion
Example Output: True

Example Input: Paragraph: G-quadruplexes are non-canonical nucleic acid structures that control transcription, replication, and recombination in organisms. G-quadruplexes are present in eukaryotes, prokaryotes, and viruses. In the latter, mounting evidence indicates their key biological activity. Since data on viruses are scattered, we here present a comprehensive analysis of putative G-quadruplexes in the genome of all known viruses that can infect humans. We show that the presence, distribution, and location of G-quadruplexes are features characteristic of each virus class and family. Our statistical analysis proves that their presence within the viral genome is orderly arranged, as indicated by the possibility to correctly assign up to two-thirds of viruses to their exact class based on the G-quadruplex classification. For each virus we provide: i) the list of all G-quadruplexes formed by GG-, GGG-and GGGG-islands present in the genome (positive and negative strands), ii) their position in the viral genome along with the known function of that region, iii) the degree of conservation among strains of each G-quadruplex in its genome context, iv) the statistical significance of G-quadruplex formation. This information is accessible from a database
Title: G-quadruplex forming sequences in the genome of all known human viruses: a comprehensive guide Running title: Presence and conservation of G4s in human viruses
Example Output: True

Example Input: Paragraph: The seasonal outbreaks of influenza infection cause globally respiratory illness, or even death in all age groups. Given early-warning signals preceding the influenza outbreak, timely intervention such as vaccination and isolation management effectively decrease the morbidity. However, it is usually a difficult task to achieve the real-time prediction of influenza outbreak due to its complexity intertwining both biological systems and social systems. By exploring rich dynamical and high-dimensional information, our dynamic network marker/biomarker (DNM/DNB) method opens a new way to identify the tipping point prior to the catastrophic transition into an influenza pandemics. In order to detect the early-warning signals before the influenza outbreak by applying DNM method, the historical information of clinic hospitalization caused by influenza infection between years 2009 and 2016 were extracted and assembled from public records of Tokyo and Hokkaido, Japan. The early-warning signal, with an average of 4week window lead prior to each seasonal outbreak of influenza, was provided by DNM-based on the hospitalization records, providing an opportunity to apply proactive strategies to prevent or delay the onset of influenza outbreak. Moreover, the study on the dynamical changes of hospitalization in local district networks unveils the influenza transmission dynamics or landscape in network level. city network, critical transition, dynamic network biomarker (DNB), dynamic network marker (DNM), early-warning signal, influenza outbreak
Title: Detecting early-warning signals of influenza outbreak based on dynamic network marker
Example Output:
True