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 CC-BY-NC-ND 4.0 International license is made available under a The copyright holder for this preprint (which was not peer-reviewed) is the author/funder.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

[EX Q]: Paragraph: Some stem cell lines may contain an endogenous virus or can be contaminated with exogenous viruses (even of animal origin) and may secrete viral particles or express viral antigens on their surface. Moreover, certain biotechnological products (e.g. bovine fetal serum, murine feeder cells) may contain prion particles. Viral and prion contamination of cell cultures and ''feeder'' cells, which is a common risk in all biotechnological products derived from the cell lines, is the most challenging and potentially serious outcome to address, due to the difficulty involved in virus and prion detection and the potential to cause serious disease in recipients of these cell products. Stem cell banks should introduce adequate quality assurance programs like the microbiological control program and can provide researchers with valuable support in the standardization and safety of procedures and protocols used for the viral and prion testing and in validation programs to assure the quality and safety of the cells.
Title: Diagnostic approaches for viruses and prions in stem cell banks
[EX A]: True

[EX Q]: Paragraph: A susceptibility locus for tuberculosis, a re-emerging infectious disease throughout the world, was previously discovered to exist on chromosome 11p15. IFITM3 gene encoding for interferon inducible transmembrane protein 3, is located at 11p15. It acts as an effector molecule for interferon-gamma, which is essential for anti-tuberculosis immune response. In order to investigate the association between susceptibility to TB and genetic polymorphisms of the IFITM3 core promoter, a casecontrol study including 368 TB patients and 794 healthy controls was performed in Han Chinese children in northern China. The rs3888188 polymorphism showed significant association with susceptibility to TB. The rs3888188 G allele, acting recessively, was more frequent in TB patients (95% confidence interval: 1.08-1.56, Bonferroni P-value: 0.039). We further assessed the effect of rs3888188 polymorphism on IFITM3 transcription in vitro. As based on luciferase promoter assays, the promoter activity of haplotypes with rs3888188 G allele was lower than that of haplotypes with rs3888188 T allele. Moreover, peripheral-blood mononuclear cells carrying rs3888188 GG genotype showed a reduced IFITM3 mRNA level compared to cells carrying TT or GT genotype. In conclusion, rs3888188, a functional promoter polymorphism of IFITM3, was identified to influence the risk for pediatric TB in Han Chinese population. Citation: Shen C, Wu X-r, Jiao W-w, Sun L, Feng W-x, et al. (2013) A Functional Promoter Polymorphism of IFITM3 Is Associated with Susceptibility to Pediatric Tuberculosis in Han Chinese Population. PLoS ONE 8(7): e67816.
Title: A Functional Promoter Polymorphism of IFITM3 Is Associated with Susceptibility to Pediatric Tuberculosis in Han Chinese Population
[EX A]:
False