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: There is an urgent need to project how transmission of the novel betacoronavirus SARS-CoV-2 will unfold in coming years. These dynamics will depend on seasonality, the duration of immunity, and the strength of cross-immunity to/from the other human coronaviruses. Using data from the United States, we measured how these factors affect transmission of human betacoronaviruses HCoV-OC43 and HCoV-HKU1. We then built a mathematical model to simulate transmission of SARS-CoV-2 through the year 2025. We project that recurrent wintertime outbreaks of SARS-CoV-2 will probably occur after an initial pandemic wave. We summarize the full range of plausible transmission scenarios and identify key data still needed to distinguish between them, most importantly longitudinal serological studies to determine the duration of immunity to SARS-CoV-2. Main text. The ongoing SARS-CoV-2 epidemic has caused nearly 80,000 detected cases of COVID-19 illness and claimed over 2,500 lives as of 24 Feb 2020 (1). With sustained transmission reported in China, Japan, Iran, Italy, and South Korea (1), the outbreak is on the verge of becoming a pandemic. The required intensity, duration, and urgency of public health responses will depend on how the initial pandemic wave unfolds and on the subsequent transmission dynamics of SARS-CoV-2. One possibility is that SARS-CoV-2 will follow its closest genetic relative, SARS-CoV, and be eradicated by intensive public health measures after causing a brief but intense epidemic (2). Increasingly, public health authorities consider this scenario unlikely (3). Alternatively, the transmission of SARS-CoV-2 could resemble that of pandemic influenza by circulating seasonally after causing an initial global wave of infection (4). Such a scenario could reflect the previous emergence of known human coronaviruses from zoonotic origins e.g. human coronavirus (HCoV) OC43 (5). This paper identifies viral, environmental, and immunologic factors which in combination will determine which scenarios in fact play out, and identifies key data needed to distinguish between them. The transmission dynamics of the SARS-CoV-2 epidemic will depend on factors including the degree of seasonal variation in transmission strength, the duration of immunity, and the degree of cross-immunity between SARS-CoV-2 and other coronaviruses. SARS-CoV-2 belongs to the betacoronavirus genus, which includes the SARS coronavirus, MERS coronavirus, and two other human coronaviruses, HCoV-OC43 and HCoV-HKU1. The SARS and MERS coronaviruses cause severe illness with case fatality rates of 9 and 36% respectively, but the transmission of both has remained limited (6). HCoV-OC43 and HCoV-HKU1 infections may be asymptomatic or associated with mild to moderate upper respiratory tract illness; these HCoVs are considered the second most common cause of the common cold (6). While investigations into the spectrum of illness caused by SARS-CoV-2 are ongoing, recent evidence indicates the majority of cases experience mild illness with more limited occurrence of severe lower respiratory infection (7). Current COVID-19 case fatality rates in China are estimated at 2.9% within Hubei province, and 0.4% outside (8), suggesting lower severity than SARS and MERS but higher severity than HCoV-OC43 and HCoV-HKU1. In terms of transmission, the ability of SARS-CoV-2 to cause widespread infection is more reflective of HCoV-OC43 and HCoV-HKU1 than of its more clinically severe relatives. HCoV-OC43 and HCoV-HKU1 cause annual wintertime outbreaks of respiratory illness in temperate regions (9, 10), suggesting that wintertime climate and host behaviors may facilitate transmission, as is true for influenza (11) (12) (13) . Immunity to HCoV-OC43 and HCoV-HKU1 appears to wane appreciably within one year (14), while SARS infection can induce longer-lasting immunity (15). The . 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 . 
 Title: Projecting the transmission dynamics of SARS-CoV-2 through the post-pandemic period
Example Output: False

Example Input: Paragraph: With concerns about the potential for the aerosol and airborne transmission of infectious agents, particularly influenza, more attention is being focused on the effectiveness of infection control procedures to prevent hospital-acquired infections by this route. More recently a number of different techniques have been applied to examine the temporalespatial information about the airflow patterns and the movement of related, suspended material within this air in a hospital setting. Closer collaboration with engineers has allowed clinical microbiologists, virologists and infection control teams to assess the effectiveness of hospital isolation and ventilation facilities. The characteristics of human respiratory activities have also been investigated using some familiar engineering techniques. Such studies aim to enhance the effectiveness of such preventive measures and have included experiments with humanlike mannequins using various tracer gas/particle techniques, real human volunteers with realtime non-invasive Schlieren imaging, numerical modelling using computational fluid dynamics, and small scale physical analogues with water. This article outlines each of these techniques in a non-technical manner, suitable for a clinical readership without specialist airflow or engineering knowledge. 
 Title: Observing and quantifying airflows in the infection control of aerosol-and airborne-transmitted diseases: an overview of approaches
Example Output: True

Example Input: Paragraph: Protein chips have emerged as a promising approach for a wide variety of applications including the identification of protein-protein interactions, protein-phospholipid interactions, small molecule targets, and substrates of proteins kinases. They can also be used for clinical diagnostics and monitoring disease states. This article reviews current methods in the generation and applications of protein microarrays. Understanding complex cellular systems will require the identification and analysis of each of its components and determining how they function together and are regulated. A critical step in this process is to determine the biochemical activities of the proteins and how these activities themselves are controlled and modified by other proteins. Traditionally, the biochemical activities of proteins have been elucidated by studying single molecules, one experiment at a time. This process is not optimal, as it is slow and labor intensive. In contrast to this traditional approach, high-throughput scientific methods have been developed in the last decade to optimize the study of large numbers of molecules, including DNA, proteins and metabolites. DNA microarrays in particular have proved valuable in genomic research (Schena et al., 1995) . They have been used to study gene expression patterns, to locate transcription factor binding sites, and to detect sequence mutations and deletions on a grand scale. However, DNA microarrays tell us only about the genes themselves and provide little information regarding the functions of the proteins they encode. More recently high throughput approaches have been developed for the study of proteins, including profiling of proteins using mass spectrometry (Gavin et al., 
 Title: Protein Microarray Technology Types of protein microarrays
Example Output:
False