TASK DEFINITION: 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.
PROBLEM: Porcine blood mononuclear cells (PBMC) were shown to produce interferon-a (IFNa) following incubation with cells infected by a coronavirus, transmissible gastroenteritis virus. Monoclonal antibodies (mAb) with specificities for leukocyte subsets and major histocompatibility complex (MHC) antigens were used to characterize IFNa producer cells. The production of IFNa was found to be a function of non-phagocytic, non-adherent, non-T, non-B, CD4 + (and to a lesser extent CD8 +) MHC-class-II-positive ceils. Furthermore, addition of anti-MHC (class II) mAb during PBMC incubation with virus-infected cells reduced IFN yields, suggesting that masking of these surface antigens alters PBMC responsiveness to IFN induction.

SOLUTION: CHARACTERIZATION OF BLOOD MONONUCLEAR CELLS PRODUC- ING IFNa FOLLOWING INDUCTION BY CORONAVIRUS-INFECTED CELLS (PORCINE TRANSMISSIBLE GASTROENTERITIS VIRUS)

PROBLEM: 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.

SOLUTION: 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

PROBLEM: Tyrosinase inhibitors improve skin whitening by inhibiting the formation of melanin precursors in the skin. The inhibitory activity of seven phlorotannins (Ecklonia cava was tested against tyrosinase, which converts tyrosine into dihydroxyphenylalanine. Compounds 3 and 5 had IC 50 values of 7.0 ± 0.2 and 8.8 ± 0.1 µM, respectively, in competitive mode, with K i values of 8.2 ± 1.1 and 5.8 ± 0.8 µM. Both compounds showed the characteristics of slow-binding inhibitors over the time course of the enzyme reaction. Compound 3 had a single-step binding mechanism and compound 5 a two-step-binding mechanism. With stable AutoDock scores of −6.59 and −6.68 kcal/mol, respectively, compounds 3 and 5 both interacted with His85 and Asn260 at the active site.

SOLUTION:
Slow-Binding Inhibition of Tyrosinase by Ecklonia cava Phlorotannins