In this task, you're given a paragraph from the research paper and your task is to generate a suitable title for the research paper based on the given paper. Under 100 words is a good title length.

[Q]: 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.
[A]: 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


[Q]: Although single cell RNA sequencing studies have begun providing compendia of cell expression profiles, it has proven more difficult to systematically identify and localize all molecular cell types in individual organs to create a full molecular cell atlas. Here we describe droplet-and plate-based single cell RNA sequencing applied to ~75,000 human lung and blood cells, combined with a multi-pronged cell annotation approach, which have allowed us to define the gene expression profiles and anatomical locations of 58 cell populations in the human lung, including 41 of 45 previously known cell types or subtypes and 14 new ones. This comprehensive molecular atlas elucidates the biochemical functions of lung cell types and the cell-selective transcription factors and optimal markers for making and monitoring them; defines the cell targets of circulating hormones and predicts local signaling interactions including sources and targets of chemokines in immune cell trafficking and expression changes on lung homing; and identifies the cell types directly affected by lung disease genes and respiratory viruses. Comparison to mouse identified 17 molecular types that appear to have been gained or lost during lung evolution and others whose expression profiles have been substantially altered, revealing extensive plasticity of cell types and cell-type-specific gene expression during organ evolution including expression switches between cell types. This atlas provides the molecular foundation for investigating how lung cell identities, functions, and interactions are achieved in development and tissue engineering and altered in disease and evolution. All rights reserved. No reuse allowed without permission.
[A]: A molecular cell atlas of the human lung from single cell RNA sequencing


[Q]: Viruses in avian hosts can pose threats to avian health and some have zoonotic potential. Hospitals that provide veterinary care for avian patients may serve as a site of exposure of other birds and human staff in the facility to these viruses. They can also provide a useful location to collect samples from avian patients in order to examine the viruses present in wild birds. This study aimed to investigate viruses of biosecurity and/or zoonotic significance in Australian birds by screening samples collected from 409 birds presented to the Australian Wildlife Health Centre at Zoos Victoria's Healesville Sanctuary for veterinary care between December 2014 and December 2015. Samples were tested for avian influenza viruses, herpesviruses, paramyxoviruses and coronaviruses, using genus-or family-wide polymerase chain reaction methods coupled with sequencing and phylogenetic analyses for detection and identification of both known and novel viruses. A very low prevalence of viruses was detected. Columbid alphaherpesvirus 1 was detected from a powerful owl (Ninox strenua) with inclusion body hepatitis, and an avian paramyxovirus most similar to Avian avulavirus 5 was detected from a musk lorikeet (Glossopsitta concinna). Two distinct novel avian alphaherpesviruses were detected in samples from a sulphurcrested cockatoo (Cacatua galerita) and a tawny frogmouth (Podargus strigoides). Avian influenza viruses and avian coronaviruses were not detected. The clinical significance of the newly detected viruses remains undetermined. Further studies are needed to assess the host specificity, epidemiology, pathogenicity and host-pathogen relationships of these novel viruses. Further genome characterization is also indicated, and would be required before these viruses can be formally classified taxonomically. The detection of these viruses contributes to our knowledge on avian virodiversity. The low level of avian virus detection, and the absence of any viruses with zoonotic potential, suggests low risk to biosecurity and human health.
[A]:
Avian viral surveillance in Victoria, Australia, and detection of two novel avian herpesviruses