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: Providing information about single virus particles has for a long time been mainly the domain of electron microscopy. More recently, technologies have been developed-or adapted from other fields, such as nanotechnology-to allow for the real-time quantification of physical virion particles, while supplying additional information such as particle diameter concomitantly. These technologies have progressed to the stage of commercialization increasing the speed of viral titer measurements from hours to minutes, thus providing a significant advantage for many aspects of virology research and biotechnology applications. Additional advantages lie in the broad spectrum of virus species that may be measured and the possibility to determine the ratio of infectious to total particles. A series of disadvantages remain associated with these technologies, such as a low specificity for viral particles. In this review we will discuss these technologies by comparing four systems for real-time single virus particle analysis and quantification. 
 Title: Quantitative real-time single particle analysis of virions
Example Output: False

Example Input: Paragraph: In February 2014, health officials from around the world announced the Global Health Security Agenda, a critical effort to strengthen national and global systems to prevent, detect, and respond to infectious disease threats and to foster stronger collaboration across borders. With its increasing global roles and broad range of regulatory responsibilities in ensuring the availability, safety, and security of medical and food products, the US Food and Drug Administration (FDA) is engaged in a range of efforts in support of global health security. This article provides an overview of FDA's global health security roles, focusing on its responsibilities related to the development and use of medical countermeasures (MCMs) for preventing, detecting, and responding to global infectious disease and other public health emergency threats. The article also discusses several areas-antimicrobial resistance, food safety, and supply chain integrity-in which FDA's global health security roles continue to evolve and extend beyond MCMs and, in some cases, beyond traditional infectious disease threats. Brooke Courtney, JD, MPH, is Senior Regulatory Counsel, Office of Counterterrorism and Emerging Threats, Office of the Chief Scientist, Office of the Commissioner; Katherine C. Bond, ScD, is Director, Office 
 Title: Regulatory Underpinnings of Global Health Security: FDA's Roles in Preventing, Detecting, and Responding to Global Health Threats
Example Output: True

Example Input: Paragraph: BACKGROUND The outbreak of the new coronavirus infection in Wuhan City, Hubei Province in December 2019, poses a huge threat to China and even global public health security. Respiratory droplets and contact transmission are the main routes of transmission of new coronaviruses. Compared with SARS and Ebola viruses, new coronavirus infections are infectious during the incubation period. Traditional SEIR (susceptibility-exposure-infection-Removal) There are some differences in conditions for the prediction of the epidemic trend of new coronavirus infection. The outbreak of the new coronavirus infection coincided with the Spring Festival before and after the Chinese Spring Festival.It is necessary to make appropriate optimization and amendments to the traditional model to meet the actual evolution of the epidemic situation. The traditional SEIR model assumes that the virus-infected person is not infectious during the incubation period and that the infected person did not take isolation measures during the illness. The transmission of the new coronavirus no longer meets the basic assumptions of the classical kinetic system. Therefore, this article first establishes a modified SEIR model. Predict and analyze the changing trend of the epidemic situation, then estimate the parameters involved in the infection dynamics model, and then use Matlab to simulate the established dynamic equations based on public data and analyze the results. Recommendations for universal prevention and control of infectious diseases. . CC-BY-NC-ND 4.0 International license It is made available under a author/funder, who has granted medRxiv a license to display the preprint in perpetuity. is the (which was not peer-reviewed) The copyright holder for this preprint . : medRxiv preprint 2 Wuhan City took no action, assuming the average daily number of contacts per infected person k = 5, the number of infected persons will reach about 2,384,803 people; If wuhan adopts the measures of sealing the city on January 22, 2020, under the premise of k=2, the number of infected people decreases by 19,773 compared with that on January 23, and there is no significant change in the time when the number of infected people reaches the peak. Under the premise of k = 1, the number of infected persons was reduced by 14,330 compared with the closure on January 23, and the time to reach the peak of the number of infected persons was reduced by 2 days. If Wuhan City is closed for one day, the number of infected persons will increase from 106,145 to 130,626 under the premise of k = 2; the number of infected persons will increase from 74,369 to 92,010 under the premise of k = 1. Comparing the number of confirmed diagnoses actually notified by the department with the number of infected people obtained from the simulation of the model, it can be seen that the city closure measures adopted by the Wuhan Municipal Government on January 23 and the first-level response measures adopted by the country are effective for the epidemic Prevention and control play a vital role. Wearing a mask when going out and avoiding close contact with people can effectively reduce the infection rate. 
 Title: Prediction of New Coronavirus Infection Based on a Modified SEIR Model
Example Output:
False