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".

Input: Consider Input: Paragraph: Background: Hirschsprung's disease (HSCR) is a type of megacolon induced by defi ciency or dysfunction of ganglion cells in the distal intestine and is associated with developmental disorders of the enteric nervous system. To explore the mechanisms of HSCR, we analyzed the RNAsequencing data of the expansion and the narrow segments of colon tissues separated from children with HSCR. Methods: RNA-sequencing of the expansion segments and the narrow segments of colon tissues isolated from children with HSCR was performed. After differentially expressed genes (DEGs) were identified using the edgeR package in R, functional and pathway enrichment analyses of DEGs were carried out using DAVID software. To further screen the key genes, protein-protein interaction (PPI) network and module analyses were conducted separately using Cytoscape software. Results: A total of 117 DEGs were identified in the expansion segment samples, including 47 up-regulated and 70 down-regulated genes. Functional enrichment analysis suggested that FOS and DUSP1 were implicated in response to endogenous stimulus. In the PPI network analysis, FOS (degree=20), EGR1 (degree=16), ATF3 (degree=9), NOS1 (degree=8), CCL5 (degree=8), DUSP1 (degree=7), CXCL3 (degree=6), VIP (degree=6), FOSB (degree=5), and NOS2 (degree=4) had higher degrees, which could interact with other genes. In addition, two significant modules (module 1 and module 2) were identifi ed from the PPI network. Conclusion: Several genes (including FOS, EGR1, ATF3, NOS1, CCL5, DUSP1, CXCL3, VIP, FOSB, and NOS2) might be involved in the development of HSCR through their effect on the nervous system. World J Pediatr 2017;13(3):267-273
Title: Genes involved in HSCR Identifying key genes associated with Hirschsprung's disease based on bioinformatics analysis of RNA-sequencing data

Output: False

Input: Consider 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

Output: False

Input: Consider Input: Paragraph: As infectious disease outbreaks emerge, public health agencies often enact vaccination and social distancing measures to slow transmission. Their success depends on not only strategies and resources, but also public adherence. Individual willingness to take precautions may be influenced by global factors, such as news media, or local factors, such as infected family members or friends. Here, we compare three modes of epidemiological decisionmaking in the midst of a growing outbreak using network-based mathematical models that capture plausible heterogeneity in human contact patterns. Individuals decide whether to adopt a recommended intervention based on overall disease prevalence, the proportion of social contacts infected, or the number of social contacts infected. While all strategies can substantially mitigate transmission, vaccinating (or self isolating) based on the number of infected acquaintances is expected to prevent the most infections while requiring the fewest intervention resources. Unlike the other strategies, it has a substantial herd effect, providing indirect protection to a large fraction of the population. misinformation from the "anti-vaxxer" movement [8, [11] [12] [13] . Recently, there have been calls for a special government commission on vaccine safety, despite overwhelming scientific consensus that vaccines are both safe and effective [12, [14] [15] [16] . As outbreaks unfold, people can take a variety precautionary measures to avoid infection, including immunization and social distancing [1, [17] [18] [19] . They often judge personal risk based on their impressions of overall disease prevalence and severity [2, [20] [21] [22] [23] . When infection risk appears low, small risks of adverse effects from the vaccine can seem relatively important and cause vaccine coverage to drop below levels required to control transmission. A variety of other factors can influence the perceived utility of disease prevention, including epidemiological news and rumors, costs of vaccination and other control measures, trust in health professionals, government agencies, media and non-official information sources, as well as societal pressure to ensure the health of one's children [24] [25] [26] [27] [28] [29] . Studies have shown that media reports about outbreaks that specify numbers of cases, hospitalizations or deaths, can influence avoidance behavior and contact patterns at both individual and community levels. In some cases, oversimplified or erroneous media reports regarding flawed vaccines can trigger panic and increases in vaccine hesitancy [30] [31] [32] [33] . For both seasonal and pandemic influenza, such interactions between vaccination decision-making and transmission dynamics can profoundly shape the course of epidemics [20, [34] [35] [36] . Individual intervention decisions can have far-reaching effects. For example, vaccination protects not only the immunized individual, but also social contacts who might have been infected by the individual. Social distancing decisions may break chains of transmission by protecting the decision-maker and more generally disrupting social dynamics. Following [37, 38] , we refer to this indirect protection as a herd effect. Although previously equated to the reduction in incidence in the unimmunized population [38], we quantify the herd effect of an intervention effort by estimating the number of infections averted per vaccine administered (or per individual social distancing action). The general phenomenon in which individual intervention actions reduce the risk of infection to others has also been called vaccination efficiency, vaccination effectiveness, herd immunity, and indirect protection [37] [38] [39] [40] . The magnitude of the herd effect will critically depend on contact patterns [41, 42]. Measures taken by gregarious individuals may have greater immediate benefits than those taken by solitary individuals, with downstream epidemiological consequences modulated by the full social network [43, 44] . Contact patterns may also influence the decision-making process itself, by constraining epidemiological perspectives. When gauging infection risk, individuals may consider global information (e.g., from news media) or local first-hand encounters with disease (e.g., infected acquaintances, friends or family members) [30, 31] . While traditional compartmental models assume homogeneity in both epidemiological risks and intervention benefits, network-based models provide a tractable framework for studying the complex interplay between contact networks, intervention decision making and disease transmission [34, [45] [46] [47] [48] [49] [50] [51] . Here, we investigate the epidemiological impacts of different decision paradigms using a network-based SIR epidemic model, in which individuals also make vaccination or social distancing choices based on their perceived epidemiological risks. Depending on the decision model, they estimate either overall disease prevalence, their number of infected social contacts, or their fraction of infected social contacts. When the perceived threat is sufficiently high, they take a measure that immediately affords full protection for the duration of the epidemic. We compare the efficacy of these three different paradigms across a range of diseases in a realistically heterogeneous network, and show that the most naive model-simply counting one's infected contacts-affords the most epidemiological protection using the least amount of resources (e.g., vaccinations or economic costs associated with social distancing). Local risk perception enhances epidemic control PLOS ONE | https://doi.
Title: Local risk perception enhances epidemic control
Output: True