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".
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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 CC-BY-NC-ND 4.0 International license is made available under a The copyright holder for this preprint (which was not peer-reviewed) is the author/funder.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: The World Health Organization (WHO) and other organizations report that the prevalence of human diseases during the past decade is rapidly increasing. Population growth and the pollution of water, air, and soil are contributing to the increasing number of human diseases worldwide. Currently an estimated 40% of world deaths are due to environmental degradation. The ecology of increasing diseases has complex factors of environmental degradation, population growth, and the current malnutrition of about 3.7 billion people in the world. 
 Title: Ecology of Increasing Diseases: Population Growth and Environmental Degradation

Answer: True


Question: Paragraph: Glycyrrhetinic acid 3-O-mono-b-D-glucuronide (GAMG), an important pharmaceutical intermediate and functional sweetener, has broad applications in the food and medical industries. A green and cost-effective method for its preparation is highly desired. Using sitedirected mutagenesis, we previously obtained a variant of β-glucuronidase from Aspergillus oryzae Li-3 (PGUS1), which can specifically transform glycyrrhizin (GL) into GAMG. In this study, a facile method was established to prepare a CaHPO 4 -PGUS1 hybrid nanoflower for enzyme immobilization, based on protein-inorganic hybrid selfassembly. Under optimal conditions, 1.2 mg of a CaHPO 4 -PGUS1 hybrid nanoflower precipitate with 71.2% immobilization efficiency, 35.60 mg$g -1 loading capacity, and 118% relative activity was obtained. Confocal laser scanning microscope and scanning electron microscope results showed that the enzyme was encapsulated in the CaHPO 4 -PGUS1 hybrid nanoflower. Moreover, the thermostability of the CaHPO 4 -PGUS1 hybrid nanoflower at 55°C was improved, and its half-life increased by 1.3 folds. Additionally, the CaHPO 4 -PGUS1 hybrid nanoflower was used for the preparation of GAMG through GL hydrolysis, with the conversion rate of 92% in 8 h, and after eight consecutive runs, it had 60% of its original activity. 
 Title: Construction of a CaHPO 4 -PGUS1 hybrid nanoflower through protein-inorganic self-assembly, and its application in glycyrrhetinic acid 3-O-mono-β-D-glucuronide preparation

Answer:
True