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: Research on respiratory protection against biologic agents is important to address major concerns such as occupational safety and terrorist attack. This review describes the literature on respiratory protection against bioaerosols and identifies research gaps. Respiratory protection is a complex field involving a number of factors, such as the efficiency of respirator filter material; facepiece fitting; and maintenance, storage, and reuse of respirators. Several studies used nonpathogenic microorganisms having physical characteristics similar to that of Mycobacterium tuberculosis to analyze microbial penetration through respirators. Some studies showed that high-efficiency particulate air (HEPA) and N95 filters provided a higher level of protection than dust/mist (DM) and dust/mist/fume (DMF) filters. Flow rate and relative humidity appear to alter the level of penetration of microorganisms through respirator filters. The relationship between microbial penetration through respirator filters and the aerodynamic diameter, length, or other physical characteristics of microorganisms remains controversial. Whether reaerosolization of bioaerosol particles should be a concern is unclear, given the fact that one study has demonstrated significant reaerosolization of 1-to 5-mm particles loaded onto respirator filters. Respirator maintenance, storage, and decontamination are important factors to be considered when reusing respirators. The respiratory protection against biologic warfare agents such as anthrax in military and civilian situations is described. (Am J Infect Control 2004;32:345-54.)  
 Title: Respiratory protection against bioaerosols: Literature review and research needs
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: The coronavirus disease 2019 outbreak is an unprecedented global public health challenge, leading to thousands of deaths every day worldwide. Despite the epidemiological importance, clinical patterns of children with COVID-19 remain unclear. Objective: To describe the clinical, laboratorial and radiological characteristics of children with COVID-19. Data Sources: The Medline database was searched between December 1 st 2019 and March 30 th 2020. Study Selection: Inclusion criteria were: (1) studied patients younger than 18 years old; (2) presented original data from cases of COVID-19 confirmed by reverse-transcription polymerase chain reaction; and (3) contained descriptions of clinical manifestations, laboratory tests or radiological examinations. Data Extraction: Number of cases, gender, age, clinical manifestations, laboratory tests, radiological examinations and outcomes. Results: A total of 34 studies (1,118 cases) were included. From all the cases, 1,111 had their severity classified: 14.3% were asymptomatic, 36.4% were mild, 46.0% were moderate, 2.2% were severe and 1.2% were critical. The most prevalent symptom was fever (16.3%), followed by cough (14.4%), nasal symptoms (3.6%), diarrhea (2.7%) and nausea/vomiting (2.5%). One hundred forty-five (12.9%) children were diagnosed with pneumonia and 43 (3.8%) upper airway infections were reported. Reduced lymphocyte count were reported in 13.1% of cases. Abnormalities on computed tomography was reported in 62.7% of cases. The most prevalent abnormalities reported were ground glass opacities, patchy shadows and consolidations. Only one death was reported. 
 Title: Clinical Manifestations of Children with COVID-19: a Systematic Review

True