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: 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 CC-BY-NC-ND 4.0 International license is made available under a The copyright holder for this preprint (which was not peer-reviewed) is the author/funder.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

Output: True


Input: Consider Input: Paragraph: As simple organisms with a parasite nature, viruses have become masters in manipulating and subvert cellular components, including host proteins and organelles, to improve viral replication. Therefore, the understanding of viral strategies to manipulate cell function disrupting plant defenses and enhancing viral infection cycles is fundamental to the production of virus-resistant plant lines. After invading susceptible plants, viruses create conditions that favor local and systemic infections by suppressing multiple layers of innate host defenses while use cellular machinery to own benefit. Viral interference in interlinked essential cellular functions results in phenotypic changes and disease symptoms, which debilitates plants favoring infection establishment. Herein in this review, the novelty it will be the discussion about the strategies used by (+) single strand RNA viruses to affect cellular processes and components to improve viral replication, in parallel to overcome plant defenses, favoring disease establishment by applying in one action using the same viral protein to coordinate viral replication and breaking down plant defense. This focus on plant-virus interaction was never done before, and this knowledge has the potential to help in the development of new strategies to produce resistant plants. 
 Title: Killing two birds with one stone: How do Plant Viruses Break Down Plant Defenses and Manipulate Cellular Processes to Replicate Themselves?

Output: True


Input: Consider Input: Paragraph: Transchromosomic bovines (Tc-bovines) adaptively produce fully human polyclonal immunoglobulin (Ig)G antibodies after exposure to immunogenic antigen(s). The National Interagency Confederation for Biological Research and collaborators rapidly produced and then evaluated anti-Ebola virus IgG immunoglobulins (collectively termed SAB-139) purified from Tc-bovine plasma after sequential hyperimmunization with an Ebola virus Makona isolate glycoprotein nanoparticle vaccine. SAB-139 was characterized by several in vitro production, research, and clinical level assays using wild-type Makona-C05 or recombinant virus/antigens from different Ebola virus variants. SAB-139 potently activates natural killer cells, monocytes, and peripheral blood mononuclear cells and has high-binding avidity demonstrated by surface plasmon resonance. SAB-139 has similar concentrations of galactose-α-1,3-galactose carbohydrates compared with human-derived intravenous Ig, and the IgG1 subclass antibody is predominant. All rhesus macaques infected with Ebola virus/H.sapiens-tc/GIN/2014/Makona-C05 and treated with sufficient SAB-139 at 1 day (n = 6) or 3 days (n = 6) postinfection survived versus 0% of controls. This study demonstrates that Tc-bovines can produce pathogen-specific human Ig to prevent and/or treat patients when an emerging infectious disease either threatens to or becomes an epidemic. 
 Title: The Journal of Infectious Diseases Fully Human Immunoglobulin G From Transchromosomic Bovines Treats Nonhuman Primates Infected With Ebola Virus Makona Isolate


Output: True