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".

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 CC-BY-NC-ND 4.0 International license is made available under a The copyright holder for this preprint (which was not peer-reviewed) is the author/funder.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
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Paragraph: A major entry route for the gammaretrovirus amphotropic murine leukemia virus (A-MLV) into NIH 3T3 fibroblasts is via caveola-dependent endocytosis. However, during the infection time, few viral particles can be observed intracellularly. Analyzing the dynamics of the A-MLV infection process by using total internal reflection fluorescence microscopy, we show that the majority of viruses are extracellular and bound to the fibronectin matrix. Moreover, the amounts of bound virus and of fibronectin correlated. Using confocal microscopy, nanoparticles targeted to fibronectin by a III1C-fibronectin fragment or anti-fibronectin antibody were detected intracellularly in NIH 3T3 cells; unconjugated nanoparticles neither bound to cells nor were detectable intracellularly. Furthermore, A-MLV colocalized intracellularly with the fibronectin-targeted nanoparticles, suggesting that they were taken up by the same cellular pathway. Both A-MLV entry and fibronectin turnover depend on caveolar endocytosis, and we found that inhibiting viral binding to the extracellular NIH 3T3 fibronectin-matrix dramatically reduced A-MLV infection, indeed, showing an active role of fibronectin in infection. We suggest that binding to the cellular fibronectin matrix provides a new mechanism by which viruses can enter cells. 
 Title: Matrix Fibronectin Binds Gammaretrovirus and Assists in Entry: New Light on Viral Infections ᰔ † Downloaded from
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Paragraph: Highlights d Viral infection leads to redox dysregulation including the downregulation of SOD1 d Sod1 À/À mice exhibit aggravated viral hepatitis, which is rescued by antioxidants d IFN-I signaling via STAT1 drives SOD1 downregulation and early liver damage d Ablation of IFN-I signaling ameliorates viral hepatitis in Sod1 À/À and WT mice Correspondence abergthaler@cemm.oeaw.ac.at Bergthaler and colleagues show that superoxide dismutase 1 protects the liver from type I interferon-driven oxidative damage in viral hepatitis. Liver damage was mediated by hepatocyte-intrinsic IFNAR1-STAT1 signaling. 
 Title: Superoxide Dismutase 1 Protects Hepatocytes from Type I Interferon-Driven Oxidative Damage Accession Number Article Superoxide Dismutase 1 Protects Hepatocytes from Type I Interferon-Driven Oxidative Damage

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