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.

[EX Q]: Human coronavirus (HCoV) is one of the most common causes of respiratory tract infection throughout the world. To investigate the epidemiological and genetic variation of HCoV in Guangzhou, south China, we collected totally 13048 throat and nasal swab specimens from adults and children with fever and acute upper respiratory infection symptoms in Gunazhou, south China between July 2010 and June 2015, and the epidemiological features of HCoV and its species were studied. Specimens were screened for HCoV by real-time RT-PCR, and 7 other common respiratory viruses were tested simultaneously by PCR or real-time PCR. HCoV was detected in 294 cases (2.25%) of the 13048 samples, with most of them inpatients (251 cases, 85.4% of HCoV positive cases) and young children not in nursery (53.06%, 156 out of 294 HCoV positive cases). Four HCoVs, as OC43, 229E, NL63 and HKU1 were detected prevalent during 2010-2015 in Guangzhou, and among the HCoV positive cases, 60.20% were OC43, 16.67% were 229E, 14.97% were NL63 and 7.82% were HKU1. The month distribution showed that totally HCoV was prevalent in winter, but differences existed in different species. The 5 year distribution of HCoV showed a peak-valley distribution trend, with the detection rate higher in 2011 and 2013 whereas lower in 2010, 2012 and 2014. The age distribution revealed that children (especially those <3 years old) and old people (>50 years) were both high risk groups to be infected by HCoV. Of the 294 HCoV positive patients, 34.69% (101 cases) were co-infected by other common respiratory viruses, and influenza virus was the most common co-infecting virus (30/101, 29.70%). Fifteen HCoV-OC43 positive samples of 2013-2014 were selected for S gene sequencing PLOS ONE | https://doi., et al. (2018) Epidemiology characteristics of human coronaviruses in patients with respiratory infection symptoms and phylogenetic analysis of HCoV-OC43 during 2010-2015 in Guangzhou. PLoS ONE 13(1): e0191789. and phylogenetic analysis, and the results showed that the 15 strains could be divided into 2 clusters in the phylogenetic tree, 12 strains of which formed a separate cluster that was closer to genotype G found in Malaysia. It was revealed for the first time that genotype B and genotype G of HCoV-OC43 co-circulated and the newly defined genotype G was epidemic as a dominant genotype during 2013-2014 in Guanzhou, south China.
[EX A]: Epidemiology characteristics of human coronaviruses in patients with respiratory infection symptoms and phylogenetic analysis of HCoV-OC43 during 2010-2015 in Guangzhou

[EX 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.
[EX 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

[EX Q]: Acute undifferentiated fever (AUF) is frequently observed in tropical settings, but diagnosing the cause of AUF is often a challenge for local physicians and the physicians treating returning travellers. We conducted a case-control study in central Vietnam in 2016. A total of 378 febrile adult patients (AUFs) with a fever for ≤21 days, no evidence of localized infection and negative screening tests for dengue and malaria, and 384 afebrile adult patients (Controls) were prospectively enrolled. Whole blood, plasma, eschar swab, throat swab and urine specimens were collected and analysed. Quantitative PCR and RT-PCR were used to test for 55 bacteria, viruses and their subtypes. Serological tests were also used to test for rickettsial agents. The most common aetiology was influenza virus (20.9% in AUFs vs. 0% in Controls), followed by rickettsial agents (mainly Orientia tsutsugamushi and Rickettsia typhi) (10.8% vs. 0.3%), dengue virus (7.7% vs. 0.5%), Leptospira (4.8% vs. 0.8%), adenovirus (4.8% vs. 1.0%), and enterovirus (2.1% vs. 0%) (p < .05). The real proportion of dengue in AUF cases was underestimated because patients with denguepositive rapid diagnosis tests were excluded from the study. The emerging agent Rickettsia felis, which had not been previously observed in Vietnam, was detected in this study. In total, 216 patients (57.1%) were given causative diagnoses, comprising 143 (66.2%) monoinfections and 73 (33.8%) coinfections. The infections caused by these agents should be considered in clinical practice and further studies. Additionally, agents susceptible to doxycycline were detected in 15.6% of AUFs; thus, this drug should be included in the panel used to treat AUF patients.
[EX A]:
Prospective case-control analysis of the aetiologies of acute undifferentiated fever in Vietnam