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".

Q: Paragraph: The rapid improvements in DNA synthesis technology hold the potential to revolutionize biosciences in the near future. Traditional genetic engineering methods are template dependent and make extensive but laborious use of site-directed mutagenesis to explore the impact of small variations on an existing sequence ''theme.'' De novo gene and genome synthesis frees the investigator from the restrictions of the pre-existing template and allows for the rational design of any conceivable new sequence theme. Viruses, being among the simplest replicating entities, have been at the forefront of the advancing biosciences since the dawn of molecular biology. Viral genomes, especially those of RNA viruses, are relatively short, often less than 10,000 bases long, making them amenable to whole genome synthesis with the currently available technology. For this reason viruses are once again poised to lead the way in the budding field of synthetic biology-for better or worse. 
 Title: Putting Synthesis into Biology: A Viral View of Genetic Engineering through De Novo Gene and Genome Synthesis

A: True
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Q: 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

A: False
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Q: Paragraph: microRNAs (miRNAs) represent a newly identified class of nonprotein-coding *22 nt small RNA that plays important roles in multiple biological processes by degrading targeted mRNA or repressing mRNA translation. This study observed the morphology of swine testicular tissue at different developmental stages (including 1-day old, 1-7 month old) by Hematoxylin-eosin staining. We also examined the expression of miR-499 and its target genes (CYLC1, DMRT1, QKI, XRN2, ZNF313) in samples of tissue slices using quantitative reversetranscription polymerase chain reaction, which showed that miR-499 had a significant negative correlation with QKI gene. Then, the proteins of QKI gene expression were determined by western blot, which were consistent with results of quantitative polymerase chain reaction (qPCR) detection. Therefore, the luciferase reporter gene system was used to verify correlation between miR-499 and QKI gene. Activity of luciferase was significantly lower in miR-499 co-transfected with pmiR-RB-REPORT-QKI-WT group than the miR-499 co-transfected with pmiR-RB-REPORT-QKI-mut/si groups, indicating that target sequence of miR-499 existed in 3¢UTR of QKI gene. Furthermore, the expressions of miR-499 and QKI were detected in testicular cells that were transfected with miR-499, miR-499 negative control and untreated. The results showed that the diameter of convoluted seminiferous tubule growth increased with age. Significantly different expressions of miR-499 and its target genes were found in swine testicular tissue at different developmental stages ( p < 0.05), overexpressing miR-499 analysis, suggesting that miR-499 was negatively correlated to the expression of QKI ( p < 0.05). In conclusion, QKI is a target gene of miR-499. 
 Title: Differential Expression of miR-499 and Validation of Predicted Target Genes in the Testicular Tissue of Swine at Different Developmental Stages

A:
False
****