TASK DEFINITION: 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".
PROBLEM: Paragraph: The majority of human emerging infectious diseases (EIDs) are zoonotic, with viruses originating in wild mammals of particular concern (e.g. HIV, Ebola, SARS) 1-3 . Understanding patterns of viral diversity in wildlife and determinants of successful cross-species transmission, or spillover, are therefore key goals for pandemic surveillance programs 4 . However, few analytical tools exist to identify which host species likely harbor the next human virus, or which viruses can cross species boundaries 5-7 . Here we conduct the most comprehensive analysis yet of mammalian hostvirus relationships and show that both the total number of viruses that infect a given species, and the proportion likely to be zoonotic are predictable. After controlling for research effort, the proportion of zoonotic viruses per species is predicted by phylogenetic relatedness to humans, host taxonomy, and human population within a species range -which may reflect human-wildlife contact. We demonstrate for the first time that bats harbor a significantly higher proportion of zoonotic viruses than all other mammalian orders. We identify the taxa and geographic regions with the largest estimated number of 'missing viruses' and 'missing zoonoses' and therefore of highest value for future surveillance. We then show that phylogenetic host breadth and other viral traits are significant predictors of zoonotic potential, providing a novel framework to assess if a newly discovered mammalian virus could infect people. Viral zoonoses are a serious threat to public health and global security, and have caused the majority of recent pandemics in people 4 , yet our understanding of the factors driving viral diversity in mammals, viral host range, and cross-species transmission to humans remain nascent. Recent studies have described broad patterns of pathogen host range 1,3 and various host or microbial factors that facilitate cross-species transmission 5,7,8 , or have focused on Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use: http://www.nature. All datasets (host traits, viral traits, full list of host-virus associations and associated references, phylogenetic trees, and phylogenetic distance matrices) along with the R code and R package dependencies needed to fully replicate and evaluate these analyses are provided at http://doi.org/10.5281/zenodo.569079. The top-level README.txt file in the directory details the file structure and metadata provided. 
 Title: Host and viral traits predict zoonotic spillover from mammals HHS Public Access

SOLUTION: False

PROBLEM: Paragraph: Purpose: COVID-19 has become global threaten. CT acts as an important method of 60 diagnosis. However, human-based interpretation of CT imaging is time consuming. 61 More than that, substantial inter-observer-variation cannot be ignored. We aim at 62 developing a diagnostic tool for artificial intelligence (AI)-based classification of CT 63 images for recognizing COVID-19 and other common infectious diseases of the lung. 64 Experimental Design: In this study, images were retrospectively collected and 65 prospectively analyzed using machine learning. CT scan images of the lung that show 66 or do not show COVID-19 were used to train and validate a classification framework 67 based on convolutional neural network. Five conditions including COVID-19 68 pneumonia, non-COVID-19 viral pneumonia, bacterial pneumonia, pulmonary 69 tuberculosis, and normal lung were evaluated. Training and validation set of images 70 were collected from Wuhan Jin Yin-Tan Hospital whereas test set of images were 71 collected from Zhongshan Hospital Xiamen University and the fifth Hospital of 72 Wuhan. 73 Results: Accuracy, sensitivity, and specificity of the AI framework were reported. For 74 test dataset, accuracies for recognizing normal lung, COVID-19 pneumonia, 75 non-COVID-19 viral pneumonia, bacterial pneumonia, and pulmonary tuberculosis 76 were 99.4%, 98.8%, 98.5%, 98.3%, and 98.6%, respectively. For the test dataset, 77 accuracy, sensitivity, specificity, PPV, and NPV of recognizing COVID-19 were 78 98.8%, 98.2%, 98.9%, 94.5%, and 99.7%, respectively. 79 All rights reserved. No reuse allowed without permission. the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. 
 Title: Deep Learning-Based Recognizing COVID-19 Deep Learning-Based Recognizing COVID-19 and other Common Infectious Diseases of the Lung by Chest CT 2 Scan Images

SOLUTION: False

PROBLEM: Paragraph: Parasitic agents are a common cause of diarrhea in dogs and cats and, thus, determining their prevalence is essential to establish preventive and control measures. This retrospective study examined the fecal tests records from 1111 dogs and 203 cats with diarrhea submitted to a diagnostic laboratory in the city of Medellin between January and May 2018. The detection of parasites was carried out by direct smears and simple flotation methods. Parasitic organisms were detected in feces from 464 (41.7%) dogs and 96 (47.3%) cats. In order of decreasing prevalence, the parasites detected in dogs were: Giardia intestinalis (13%), ancylostomids (12.6%), Entamoeba spp. (6.1%), coccidian oocysts (5.8%), Toxocara spp. (5.6%) and Dipylidium caninum (1.3%). In cats, the prevalence was: Giardia intestinalis (20%), coccidian oocysts (8.9%), Entamoeba spp. (7.9%), ancylostomids (6.4%), Toxocara spp. (2.5%) and Dipylidium caninum (2%). Age, but not gender, was a predisposing factor, as puppies and kittens had significantly higher infection rates that older age categories. The majority of Giardia intestinalis positive cases occurred in puppies (109/145, 75.2%) and kittens (19/36, 52.8%), making this parasite the most prevalent in amongst animals with diarrhea. Out of 117 positive infections in the adult dog population, ancylostomids accounted for 56 cases (47.9%) and was the most common parasite in this age group. In conclusion, although these results do not imply a cause and effect relationship, they are an estimate of the type of parasites that may be most commonly associated with diarrhea in dogs and cats. The lower diagnostic sensitivity of the traditional methods used here as compared to more contemporary techniques like fecal flotation with centrifugation and PCR, may have underestimated the actual prevalence and diminished the detection of coinfections. Future studies should aim to have diagnostic panels that also screen for other enteric pathogens, including bacterial and viral agents. 
 Title: Giardia is the most prevalent parasitic infection in dogs and cats with diarrhea in the city of Medellín, Colombia

SOLUTION:
False