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]: publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. factors and the gene regulatory network in Escherichia coli. Nucleic Acids Res. 31, 1234-1244 15 Richter, G. et al. (1997 Biosynthesis of riboflavin: characterization of the bifunctional deaminase-reductase of Escherichia coli and Bacillus subtilis.
[EX A]: Biosynthesis of riboflavin. Lumazine synthase of Escherichia coli

[EX Q]: . The role of interferon in spontaneous cell-mediated cytotoxicity in pigs. Vet. Microbiol., I i : 69 -77. Specific release of StCr and the production of interferon (IFN) increased in parallel in a spontaneous cell-mediated cytotoxicity (SCMC) assay in which uninfected PK-15 cells or PK-15 cells persistently infected with transmissible gastroenteritis virus (PK-15-TGE cells) were used as targets, and peripheral blood lymphocytes (PBL) from a young adult pig were used as effector cells. Higher levels of both specific S~Cr release and IFN were obtained in the assays containing PK-15-TGE cells. Co-cultivation of PBL from newborn piglets with PK-15-TGE cells yielded similar levels of IFN to those produced by co-cultivation of adult PBL and PK-15-TGE cells, but lower levels of IFN were produced by co-cultivation with uninfected PK-15 cells. Pretreatment of adult PBL with IFN augmented their SCMC effector activity for both PK-15 and PK-15-TGE cells in the S~Cr release assay. Pretreatment of the PK-15-TGE target cells with IFN did not affect their release of S*Cr in the SCMC reaction, while IFN pretreatment of PK-15 targets protected them against SCMC. In a single cell cytotoxicity assay the effects of IFN pretreatment on the effector adult PBL and on the PK-15 and PK-15-TGE target cells were confirmed, and SCMC incompetent PBL from neonatal piglets were rendered cytotoxic by pretreatment with IFN. PBL from newborn piglets bound to either target cell with the same frequency as PBL from SCMC competent adult pigs, and IFN pretreatment of either effector or target cells had no effect on target-binding frequency.
[EX A]: THE ROLE OF INTERFERON IN SPONTANEOUS CELL-MEDIATED CYTOTOXICITY IN PIGS

[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