instruction:
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".
question:
Paragraph: Contact patterns among hosts are considered as one of the most critical factors contributing to unequal pathogen transmission. Consequently, networks have been widely applied in infectious disease modeling. However most studies assume static network structure due to lack of accurate observation and appropriate analytic tools. In this study we used high temporal and spatial resolution animal position data to construct a high-resolution contact network relevant to infectious disease transmission. The animal contact network aggregated at hourly level was highly variable and dynamic within and between days, for both network structure (network degree distribution) and individual rank of degree distribution in the network (degree order). We integrated network degree distribution and degree order heterogeneities with a commonly used contact-based, directly transmitted disease model to quantify the effect of these two sources of heterogeneity on the infectious disease dynamics. Four conditions were simulated based on the combination of these two heterogeneities. Simulation results indicated that disease dynamics and individual contribution to new infections varied substantially among these four conditions under both parameter settings. Changes in the contact network had a greater effect on disease dynamics for pathogens with smaller basic reproduction number (i.e. R 0 , 2).
Title: Highly dynamic animal contact network and implications on disease transmission
answer:
True

question:
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
answer:
True

question:
Paragraph: Since 2007, repeated outbreaks of Zika virus (ZIKV) has affected millions of people worldwide and created global health concern with major complications like microcephaly and Guillain Barre's syndrome. Generally, ZIKV transmits through mosquitoes (Aedes aegypti) like other flaviviruses, but reports show blood transfusion and sexual mode of ZIKV transmission which further makes the situation alarming. Till date, there is not a single Zika specific licensed drug or vaccine present in the market. However, in recent months, several antiviral molecules have been screened against viral and host proteins. Among those, ()-Epigallocatechin-3-gallate (EGCG), a green tea polyphenol has shown great virucidal potential against flaviviruses including ZIKV. However, the mechanistic understanding of EGCG targeting viral proteins is not yet entirely deciphered except little is known about its interaction with viral envelope protein and viral protease. Since literature has shown significant inhibitory interactions of EGCG against various kinases and bacterial DNA gyrases; we designed our study to find inhibitory actions of EGCG against ZIKV NS3 helicase. NS3 helicase is playing a significant role in viral replication by unwinding RNA after hydrolyzing NTP. We employed molecular docking and simulation approach and found significant interactions at ATPase site and also at RNA binding site. Further, the enzymatic assay has shown significant inhibition of NTPase activity with an IC50 value of 295.7 nM and Ki of 0.387 0.034 M. Our study suggests the possibility that EGCG could be considered as prime backbone molecule for further broadspectrum and multitargeted inhibitor development against ZIKV and other flaviviruses. author/funder. All rights reserved. No reuse allowed without permission.
Title: Mechanistic insights into Zika virus NS3 helicase inhibition by Epigallocatechin-3-gallate
answer:
False