TASK DEFINITION: In this task, you're given a paragraph and title from the research paper. Your task is to classify whether the given title is suitable or not for the research paper based on the given paragraph. Return "True" if title is proper according to paragraph else "False".
PROBLEM: Paragraph: For more than 30 years X-ray crystallography has been by far the most powerful approach for determining the structures of viruses and viral proteins at atomic resolution. The information provided by these structures, which covers many important aspects of the viral life cycle such as cell-receptor recognition, viral entry, nucleic acid transfer and genome replication, has extensively enriched our vision of the virus world. Many of the structures available correspond to potential targets for antiviral drugs against important human pathogens. This article provides an overview of the current knowledge of different structural aspects of the above-mentioned processes. 
 Title: BIOLOGYjMEDICINE Viruses and viral proteins

SOLUTION: True

PROBLEM: Paragraph: 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. 
 Title: 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

SOLUTION: False

PROBLEM: Paragraph: Background: Allogeneic bone marrow transplantation (allo-BMT) is a potentially curative therapy for a variety of hematologic diseases, but benefits, including graft-versus-tumor (GVT) activity are limited by graft-versus-host-disease (GVHD). Carcinoembryonic antigen related cell adhesion molecule 1 (Ceacam1) is a transmembrane glycoprotein found on epithelium, T cells, and many tumors. It regulates a variety of physiologic and pathological processes such as tumor biology, leukocyte activation, and energy homeostasis. Previous studies suggest that Ceacam1 negatively regulates inflammation in inflammatory bowel disease models. Methods: We studied Ceacam1 as a regulator of GVHD and GVT after allogeneic bone marrow transplantation (allo-BMT) in mouse models. In vivo, Ceacam1 2/2 T cells caused increased GVHD mortality and GVHD of the colon, and greater numbers of donor T cells were positive for activation markers (CD25 hi , CD62L lo ). Additionally, Ceacam1 2/2 CD8 T cells had greater expression of the gut-trafficking integrin a 4 b 7 , though both CD4 and CD8 T cells were found increased numbers in the gut posttransplant. Ceacam1 2/2 recipients also experienced increased GVHD mortality and GVHD of the colon, and alloreactive T cells displayed increased activation. Additionally, Ceacam1 2/2 mice had increased mortality and decreased numbers of regenerating small intestinal crypts upon radiation exposure. Conversely, Ceacam1-overexpressing T cells caused attenuated target-organ and systemic GVHD, which correlated with decreased donor T cell numbers in target tissues, and mortality. Finally, graft-versus-tumor survival in a Ceacam1 + lymphoma model was improved in animals receiving Ceacam1 2/2 vs. control T cells. 
 Title: Ceacam1 Separates Graft-versus-Host-Disease from Graft-versus-Tumor Activity after Experimental Allogeneic Bone Marrow Transplantation

SOLUTION:
False