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".
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Question: 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

Answer: False


Question: Paragraph: Background: Infectious laryngotracheitis virus (ILTV; gallid herpesvirus 1) infection causes high mortality and huge economic losses in the poultry industry. To protect chickens against ILTV infection, chicken-embryo origin (CEO) and tissue-culture origin (TCO) vaccines have been used. However, the transmission of vaccine ILTV from vaccinated-to unvaccinated chickens can cause severe respiratory disease. Previously, host cell responses against virulent ILTV infections were determined by microarray analysis. In this study, a microarray analysis was performed to understand host-vaccine ILTV interactions at the host gene transcription level. Results: The 44 K chicken oligo microarrays were used, and the results were compared to those found in virulent ILTV infection. Total RNAs extracted from vaccine ILTV infected chicken embryo lung cells at 1, 2, 3 and 4 days post infection (dpi), compared to 0 dpi, were subjected to microarray assay using the two color hybridization method. Data analysis using JMP Genomics 5.0 and the Ingenuity Pathway Analysis (IPA) program showed that 213 differentially expressed genes could be grouped into a number of functional categories including tissue development, cellular growth and proliferation, cellular movement, and inflammatory responses. Moreover, 10 possible gene networks were created by the IPA program to show intermolecular connections. Interestingly, of 213 differentially expressed genes, BMP2, C8orf79, F10, and NPY were expressed distinctly in vaccine ILTV infection when compared to virulent ILTV infection. Conclusions: Comprehensive knowledge of gene expression and biological functionalities of host factors during vaccine ILTV infection can provide insight into host cellular defense mechanisms compared to those of virulent ILTV. 
 Title: Genome-wide host responses against infectious laryngotracheitis virus vaccine infection in chicken embryo lung cells

Answer: False


Question: Paragraph: The year 2003 promises to begin a time of change for the Federal Depository Library program (FDLP). In January 2003 the Hon. Bruce James was appointed Public Printer of the United States and declared his intention to accelerate the modernization of the GPO. At the spring 2003 Depository Library Council meeting in Reno NV, he stated, ''The nineteenth century isn't coming back to the GPO.'' Depository librarians have responded enthusiastically to this idea, attending the meeting in record numbers for a Council meeting not held in Washington, DC, and offering their ideas for the future of the FDLP. The GPO is undergoing an internal reorganization and is engaged in several pilot projects, including one for an all-electronic depository at the University of Arizona. At the time of this writing, GPO is seeking partners for an all-electronic depository in a law library, virtual reference service, and web-based training. Of particular interest is a proposal to revamp the inspection process with locally based ''consultants'' who would visit libraries in their assigned geographic area once per quarter to offer advice and training. Progress continues toward a mostly electronic FDLP, with 60% of FDLP titles being distributed online this year. Earlier this year, GPO unveiled a redesigned home page for GPO Access. The GPO Access databases continue to grow, and staff members are engaged in efforts to improve the performance of the system in the face of an average of 32 million retrievals per month. Regardless of format, the Federal Government continues to produce an overwhelming amount of interesting and useful information. In keeping with the war on terrorism and the recent war with Iraq, this year's notable documents selections contain numerous titles relating 
 Title: US government (depository items)

Answer:
True