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: To date, an effective therapeutic treatment that confers strong attenuation toward coronaviruses (CoVs) remains elusive. Of all the potential drug targets, the helicase of CoVs is considered to be one of the most important. Here, we first present the structure of the full-length Nsp13 helicase of SARS-CoV (SARS-Nsp13) and investigate the structural coordination of its five domains and how these contribute to its translocation and unwinding activity. A translocation model is proposed for the Upf1like helicase members according to three different structural conditions in solution characterized through H/D exchange assay, including substrate state (SARS-Nsp13-dsDNA bound with AMPPNP), transition state (bound with ADP-AlF 4 − ) and product state (bound with ADP). We observed that the ␤19-␤20 loop on the 1A domain is involved in unwinding process directly. Furthermore, we have shown that the RNA dependent RNA polymerase (RdRp), SARS-Nsp12, can enhance the helicase activity of SARS-Nsp13 through interacting with it directly. The interacting regions were identified and can be considered common across CoVs, which provides new insights into the Replication and Transcription Complex (RTC) of CoVs. 
 Title: Delicate structural coordination of the Severe Acute Respiratory Syndrome coronavirus Nsp13 upon ATP hydrolysis
Example Output: True

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: The corona virus (SARS-CoV-2) or Covid-19 pandemic is growing alarmingly throughout the whole world. Using the power law scaling we analyze the data of different countries and three states of India up to 1st April, 2020 and explain in terms of power law exponent. We find significant reduction in growth of infections in China and Denmark ( reduced from approximately 2.18 to 0.05 and 11.41 to 6.95, respectively). Very slow reduction is also seen in Brazil and Germany ( reduced from approximately 6 to 4 and 11 to 7, respectively). Infection in India is growing (=9.23) though lesser in number than that in the USA (highest  of 16 approximately, studied so far), Italy and a few other countries. Among three Indian states the growth in West Bengal (=0.64) is much slower than other states like Maharashtra and Kerala (=3.23 and 3.32, respectively). Some future predictions, though not rigid, has also been incorporated in our analysis. The earlier lock-down and stricter measures from the Governments concerned are being thought to be the only possible solutions, in the present situation, to fight against this virus. All rights reserved. No reuse allowed without permission. the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. 
 Title: Search for trends of Covid-19 infection in Germany and the USA on the basis of power law scaling
Example Output:
True