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".
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Question: Paragraph: 1. Predicting infectious disease dynamics is a central challenge in disease ecology. Models 36 that can assess which individuals are most at risk of being exposed to a pathogen not only 37 provide valuable insights into disease transmission and dynamics but can also guide 38 management interventions. Constructing such models for wild animal populations, 39 however, is particularly challenging; often only serological data is available on a subset 40 of individuals and non-linear relationships between variables are common. 41 42 2. Here we take advantage of the latest advances in statistical machine learning to construct 43 pathogen-risk models that automatically incorporate complex non-linear relationships 44 with minimal statistical assumptions from ecological data with missing values. Our 45 approach compares multiple machine learning algorithms in a unified environment to 46 find the model with the best predictive performance and uses game theory to better 47 interpret results. We apply this framework on two major pathogens that infect African 48 lions: canine distemper virus (CDV) and feline parvovirus. 49 50 3. Our modelling approach provided enhanced predictive performance compared to more 51 traditional approaches, as well as new insights into disease risks in a wild population. We 52 were able to efficiently capture and visualise strong non-linear patterns, as well as model 53 complex interactions between variables in shaping exposure risk from CDV and feline 54 parvovirus. For example, we found that lions were more likely to be exposed to CDV at a 55 young age but only in low rainfall years. 56 57 . CC-BY-NC-ND 4.0 International license is made available under a The copyright holder for this preprint (which was not peer-reviewed) is the author/funder. It . https://doi.org/10.1101/569012 doi: bioRxiv preprint 3 4. When combined with our data calibration approach, our framework helped us to answer 58 questions about risk of pathogen exposure which are difficult to address with previous 59 methods. Our framework not only has the potential to aid in predicting disease risk in 60 animal populations, but also can be used to build robust predictive models suitable for 61 other ecological applications such as modelling species distribution or diversity patterns. 
 Title: How to make more from exposure data? An integrated machine Author Contributions Data Accessibility Statement Running Title Machine learning and pathogen exposure risk

Answer: True


Question: Paragraph: Fusion of MHV-3-immune splenocytes from MHV-3-resistant A/J murine strain, with NS myeloma cells produced several hybridomas. Among eight hybridoma clones, the 1E7A4H1 clone secreted kappa IgG2a apparently directed against the nucleoprotein of the MHV-3 virion. The monoclonal antibody was able to neutralize tile in vitro cytopathic effect of MHV-3 on cultured L2 cells, and was detected by indirect immunofluorescence on MHV-3-infected cultured Y AC cells. In addition, it conferred a significant protection against MHV-3-induced acute disease, if injected intraperitoneally to C57BL/6 mice before inoculation with MHV-3. , Mouse hepatitis viruses (MHVs), members of the coronavirus family, produce various diseases i n t h e mouse and other animal species [16] . They exhibit different tissue tropisms and patterns of infection [1]. The susceptibility of the animals to the virus-induced diseases appears to be genetically controlled at different levels [16] . Serological neutralization patterns of several of the MHVs are very complex [11, 15] and the three structural proteins demonstrated variable degrees of conservation [4, 13] . Coronaviruses are frequently endemic in laboratory mouse colonies [8] . Neutralizing monoclonal antibodies have been recently prepared against the J H M (MHV-4) [2, 4, 17] and MHV-2 [9] strains. In this brief note, we report on a monoclonaI antibody, apparently specific for the nueleo-* Present address: Instituto Butantan, Laboratoria Especial de Immunologia Viral, S~o Paulo, Brazil. 
 Title: Archives of Virology Protection from mouse hepatitis virus type 3-induced acute disease by an anti-nucleoprotein monoclonal antibody Brief Report

Answer: True


Question: Paragraph: Background: Since December 2019, a pneumonia caused by the 2019 novel coronavirus (2019-nCoV) has broken out in Wuhan, Hubei province, China. The continuous rising of infected cases has imposed overwhelming pressure on public health decision and medical resource allocation in China. We managed to forecast the infection peak time in Hubei province and the severe and critical case distribution. 
 Title: Utilize State Transition Matrix Model to Predict the Novel Corona Virus Infection Peak and Patient Distribution

Answer:
True