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.

A newly identified betacoronavirus, human coronavirus EMC (HCoV-EMC), has been isolated from several patients with respiratory and renal disease in the Middle East. While only a few infected patients have been identified, the mortality of the infection is greater than 50%. Like its better-known cousin severe acute respiratory syndrome coronavirus (SARS-CoV), HCoV-EMC appears to have originated from bats. In a recent article in mBio, Müller et al. described several important differences between the two viruses [M. A. Müller et al., mBio 3(6):e00515-12, 2012, doi:10.1128/mBio.00515-12]. Unlike SARS-CoV, HCoV-EMC can directly infect bat cells. As important, HCoV-EMC does not enter cells using the SARS-CoV receptor, human angiotensin-converting receptor -2 (hACE2) . These results provide a strong incentive for identifying the host cell receptor used by HCoV-EMC. Identification of the receptor will provide insight into the pathogenesis of pulmonary and renal disease and may also suggest novel therapeutic interventions. Citation Perlman S, Zhao J. 2013. Human coronavirus EMC is not the same as severe acute respiratory syndrome coronavirus. mBio 4(1):e00002-13.
Human Coronavirus EMC Is Not the Same as Severe Acute Respiratory Syndrome Coronavirus

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.
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

This investigation enrolled 570 healthy young males gathered from all over the country for military service at the Republic of Korea Air Force boot camp. It confirmed RSV IgG seroprevalence by utilizing the enzyme immunoassay method just prior to undergoing basic training. The mean age of this study was 20.25 ± 1.34 yr old. The results of their immunoassay seroprofiles showed that 561 men (98.4%) were positive, 2 (0.4%) were negative and 7 (1.2%) were equivocal belonging to the grey zone. It was confirmed that RSV is a common respiratory virus and RSV infection was encountered by almost all people before reaching adulthood in Korea. Nine basic trainees belonging to the RSV IgG negative and equivocal grey zone categories were prospectively observed for any particular vulnerability to respiratory infection during the training period of two months. However, these nine men completed their basic training without developing any specific respiratory illness.

Seroprevalence of Respiratory Syncytial Virus IgG among Healthy Young Adults in Basic Training for the Republic of Korea Air Force