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 CC-BY-NC-ND 4.0 International license is made available under a The copyright holder for this preprint (which was not peer-reviewed) is the author/funder.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

SOLUTION: True

PROBLEM: Paragraph: The goal of this study was to determine whether the use of nesting material or polycarbonate shelters, as enrichment devices would have an impact on endpoints commonly measured during the conduct of the National Toxicology Program (NTP) 13-week studies. The study design was consistent with the NTP 13-week toxicity studies. Harlan Sprague Dawley (HSD) rats and their offspring, and B6C3F1/N mice were assigned to control (unenriched) and enriched experimental groups. Body weight, food and water consumption, behavioral observations, fecal content, clinical pathology, gross pathology, organ weights, and histopathology were evaluated. Enriched male mice and male and female rats exhibited decreased feed intake without a subsequent decrease in body weight; this may have been the result of the nesting material reducing the effect of cold stress thereby allowing for more efficient use of feed. There were statistical differences in some hematological parameters, however these were not considered physiologically relevant since all values were within the normal range. Gross pathology and histopathological findings were background changes and were not considered enrichment-related. Nesting material and shelters were used frequently and consistently and allowed animals to display species typical behavior. There was no significant impact on commonly measured endpoints in HSD rats and B6C3F1/N mice given enrichment devices. 
 Title: Impact of Environmental Enrichment Devices on NTP In Vivo Studies

SOLUTION: True

PROBLEM: Paragraph: Background: Respiratory viral diagnosis of upper respiratory tract infections has largely developed through multiplex molecular techniques. Although the sensitivity of different types of upper respiratory tract samples seems to be correlated to the number of sampled cells, this link remains largely unexplored. Methods: Our study included 800 upper respiratory tract specimens of which 400 negative and 400 positive for viral detection in multiplex PCR. All samples were selected and matched for age in these 2 groups. For the positive group, samples were selected for the detected viral species. Results: Among the factors influencing the cellularity were the type of sample (p < 0.0001); patient age (p < 0.001); viral positive or negative nature of the sample (p = 0.002); and, for the positive samples, the number of viral targets detected (0.004 < p < 0.049) and viral species. Conclusion: The cellular load of upper respiratory samples is multifactorial and occurs for many in the sensitivity of molecular detection. However it was not possible to determine a minimum cellularity threshold allowing molecular viral detection. The differences according to the type of virus remain to be studied on a larger scale. 
 Title: Study and interest of cellular load in respiratory samples for the optimization of molecular virological diagnosis in clinical practice

SOLUTION:
True