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".

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 CC-BY-NC-ND 4.0 International license is made available under a The copyright holder for this preprint (which was not peer-reviewed) is the author/funder.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

Paragraph: Background: Adverse events are associated with significant morbidity, mortality and cost in hospitalized patients. Measuring adverse events is necessary for quality improvement, but current detection methods are inaccurate, untimely and expensive. The advent of electronic health records and the development of automated methods for encoding and classifying electronic narrative data, such as natural language processing, offer an opportunity to identify potentially better methods. The objective of this study is to determine the accuracy of using automated methods for detecting three highly prevalent adverse events: a) hospital-acquired pneumonia, b) catheter-associated bloodstream infections, and c) in-hospital falls. Methods/design: This validation study will be conducted at two large Canadian academic health centres: the McGill University Health Centre (MUHC) and The Ottawa Hospital (TOH). The study population consists of all medical, surgical and intensive care unit patients admitted to these centres between 2008 and 2014. An automated detection algorithm will be developed and validated for each of the three adverse events using electronic data extracted from multiple clinical databases. A random sample of MUHC patients will be used to develop the automated detection algorithms (cohort 1, development set). The accuracy of these algorithms will be assessed using chart review as the reference standard. Then, receiver operating characteristic curves will be used to identify optimal cut points for each of the data sources. Multivariate logistic regression and the areas under curve (AUC) will be used to identify the optimal combination of data sources that maximize the accuracy of adverse event detection. The most accurate algorithms will then be validated on a second random sample of MUHC patients (cohort 1, validation set), and accuracy will be measured using chart review as the reference standard. The most accurate algorithms validated at the MUHC will then be applied to TOH data (cohort 2), and their accuracy will be assessed using a reference standard assessment of the medical chart. Discussion: There is a need for more accurate, timely and efficient measures of adverse events in acute care hospitals. This is a critical requirement for evaluating the effectiveness of preventive interventions and for tracking progress in patient safety through time. 
 Title: Accuracy of using automated methods for detecting adverse events from electronic health record data: a research protocol
True

Paragraph: A novel ultraviolet light irradiation (UV-C, 254 nm) process was designed as an additional safety feature for manufacturing of spray dried porcine plasma (SDPP). In Exp. 1, three 10-L batches of bovine plasma were inoculated with 10 5.2±0.12 tissue culture infectious dose 50 (TCID 50 ) of porcine parvovirus (PPV) per mL of plasma and subjected to UV-C ranging from 0 to 9180 J/L. No viable PPV was detected in bovine plasma by micro-titer assay in SK6 cell culture after UV-C at 2295 J/L. In Exp. 2, porcine plasma was subjected to UV-C (3672 J/L), then spray dried and mixed in complete mash diets. Diets were a control without SDPP (Control), UV-C SDPP either at 3% (UVSDPP3) or 6% (UVSDPP6) and non-UV-C SDPP at 3% (SDPP3) or 6% (SDPP6). Diets were fed ad libitum to 320 weaned pigs (26 d of age; 16 pens/diet; 4 pigs/pen) for 14 d after weaning and a common diet was fed d 15 to 28. During d 0 to 14, pigs fed UVSDPP3, UVSDPP6, or SDPP6 had higher (P < 0.05) weight gain and feed intake than control. During d 0 to 28, pigs fed UVSDPP3 and UVSDPP6 had higher (P < 0.05) weight gain and feed intake than control and SDPP3, and SDPP6 had higher (P < 0.05) feed intake than control. Also, pigs fed UVSDPP had higher (P < 0.05) weight gain than pigs fed SDPP. In conclusion, UV-C inactivated PPV in liquid plasma and UVSDPP used in pig feed had no detrimental effects on pig performance. 
 Title: Ultraviolet Light (UV) Inactivation of Porcine Parvovirus in Liquid Plasma and Effect of UV Irradiated Spray Dried Porcine Plasma on Performance of Weaned Pigs

False