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".

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 CC-BY-NC-ND 4.0 International license is made available under a The copyright holder for this preprint (which was not peer-reviewed) is the author/funder.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


[Q]: Paragraph: Background 3 In silico bacterial, viral, and human truth datasets were generated to evaluate available metagenomics 4 algorithms. Sequenced datasets include background organisms, creating ambiguity in the true 5 source organism for each read. Bacterial and viral datasets were created with even and staggered 6 coverage to evaluate organism identification, read mapping, and gene identification capabilities of 7 available algorithms. These truth datasets are provided as a resource for the development and 8 refinement of metagenomic algorithms. Algorithm performance on these truth datasets can inform 9 decision makers on strengths and weaknesses of available algorithms and how the results may be best 10 leveraged for bacterial and viral organism identification and characterization. 11 12 Source organisms were selected to mirror communities described in the Human Microbiome Project as 13 well as the emerging pathogens listed by the National Institute of Allergy and Infectious Diseases. The 14 six in silico datasets were used to evaluate the performance of six leading metagenomics algorithms: 15 MetaScope, Kraken, LMAT, MetaPhlAn, MetaCV, and MetaPhyler. 16 Results Algorithms were evaluated on runtime, true positive organisms identified to the genus and species 19 levels, false positive organisms identified to genus and species level, read mapping, relative abundance 20 estimation, and gene calling. No algorithm out performed the others in all categories, and the 21 algorithm or algorithms of choice strongly depends on analysis goals. MetaPhlAn excels for bacteria 22 and LMAT for viruses. The algorithms were ranked by overall performance using a normalized 23 weighted sum of the above metrics, and MetaScope emerged as the overall winner, followed by Kraken 24 and LMAT. 25 Conclusions 27 Simulated FASTQ datasets with well--characterized truth data about microbial community composition 28 reveal numerous insights about the relative strengths and weaknesses of the metagenomics algorithms 29 evaluated. The simulated datasets are available to download from the Sequence Read Archive 30 (SRP062063). 31 32 
 Title: Evaluating performance of metagenomic characterization algorithms using in silico datasets generated with FASTQSim
[A]: False


[Q]: Paragraph: Background: Chikungunya Virus (ChikV) surprised by a massive re-emerging outbreak in Indian Ocean in 2006, reaching Europe in 2007 and exhibited exceptional severe physiopathology in infants and elderly patients. In this context, it is important to analyze the innate immune host responses triggered against ChikV. Autophagy has been shown to be an important component of the innate immune response and is involved in host defense elimination of different pathogens. However, the autophagic process was recently observed to be hijacked by virus for their own replication. Here we provide the first evidence that hallmarks of autophagy are specifically found in HEK.293 infected cells and are involved in ChikV replication. Methods: To test the capacity of ChikV to mobilize the autophagic machinery, we performed fluorescence microscopy experiments on HEK.GFP.LC3 stable cells, and followed the LC3 distribution during the time course of ChikV infection. To confirm this, we performed electron microscopy on HEK.293 infected cells. To test the effect of ChikV-induced-autophagy on viral replication, we blocked the autophagic process, either by pharmacological (3-MA) or genetic inhibition (siRNA against the transcript of Beclin 1, an autophagic protein), and analyzed the percentage of infected cells and the viral RNA load released in the supernatant. Moreover, the effect of induction of autophagy by Rapamycin on viral replication was tested. Results: The increasing number of GFP-LC3 positive cells with a punctate staining together with the enhanced number of GFP-LC3 dots per cell showed that ChikV triggered an autophagic process in HEK.293 infected cells. Those results were confirmed by electron microscopy analysis since numerous membrane-bound vacuoles characteristic of autophagosomes were observed in infected cells. Moreover, we found that inhibition of autophagy, either by biochemical reagent and RNA interference, dramatically decreases ChikV replication. Conclusions: Taken together, our results suggest that autophagy may play a promoting role in ChikV replication. Investigating in details the relationship between autophagy and viral replication will greatly improve our knowledge of the pathogenesis of ChikV and provide insight for the design of candidate antiviral therapeutics. 
 Title: Chikungunya triggers an autophagic process which promotes viral replication
[A]:
False