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".

Ex Input:
Paragraph: Pathogens that spill over between species cause a significant human and animal health burden. Here, we describe characteristics of animal reservoirs that are required for pathogen spillover. We assembled and analyzed a database of 330 disease systems in which a pathogen spills over from a reservoir of one or more species. Three-quarters of reservoirs included wildlife, and 84% included mammals. Further, 65% of pathogens depended on a community of reservoir hosts, rather than a single species, for persistence. Among mammals, the most frequently identified reservoir hosts were rodents, artiodactyls, and carnivores. The distribution among orders of mammalian species identified as reservoirs did not differ from that expected by chance. Among disease systems with high priority pathogens and epidemic potential, we found birds, primates, and bats to be overrepresented. We also analyzed the life history traits of mammalian reservoir hosts and compared them to mammals as a whole. Reservoir species had faster life history characteristics than mammals overall, exhibiting traits associated with greater reproductive output rather than long-term survival. Thus, we find that in many respects, reservoirs of spillover pathogens are indeed special. The described patterns provide a useful resource for studying and managing emerging infectious diseases. 
 Title: Are disease reservoirs special? Taxonomic and life history characteristics

Ex Output:
False


Ex Input:
Paragraph: To analyze the course of seasonal viral infections of respiratory tract in patients hospitalized in pediatric intensive care units (PICU) of 16 centers in Turkey. Materials and methods: It is a retrospective, observational, and multicenter study conducted in 16 tertiary PICUs in Turkey includes a total of 302 children with viral cause in the nasal swab which required PICU admission with no interventions. Results: Median age of patients was 12 months. Respiratory syncytial virus (RSV) was more common in patients over one year of age whereas influenza, human Bocavirus in patients above a year of age was more common (p <0.05). Clinical presentations influencing mortality were neurologic symptoms, tachycardia, hypoxia, hypotension, elevated lactate, and acidosis. The critical pH value related with mortality was ≤7.10, and critical PCO 2 ≥60 mm Hg. Conclusion: Our findings demonstrate that patients with neurological symptoms, tachycardia, hypoxia, hypotension, acidosis, impaired liver, and renal function at the time of admission exhibit more severe mortal progressions. Presence of acidosis and multiorgan failure was found to be predictor for mortality. Knowledge of clinical presentation and age-related variations among seasonal viruses may give a clue about severe course and prognosis. By presenting the analyzed data of 302 PICU admissions, current study reveals severity of viral respiratory tract infections and release tips for handling them. 
 Title: Comprehensive Analysis of Severe Viral Infections of Respiratory Tract admitted to PICUs during the Winter Season in Turkey

Ex Output:
True


Ex 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

Ex Output:

True