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".

[EX Q]: Paragraph: A number of emerging and re-emerging viruses have caused epidemics or pandemics of infectious diseases leading to major devastations throughout human history. Therefore, developing effective and safe vaccines against these viruses is clearly important for the protection of at-risk populations. Our previous studies have shown that the receptor-binding domain (RBD) in the spike protein of severe acute respiratory syndrome (SARS)-associated coronavirus (SARS-CoV) is a key target for the development of SARS vaccines. In this review, we highlight some key advances in the development of antiviral vaccines targeting the RBDs of spike proteins of emerging and re-emerging viruses, using SARS-CoV, influenza virus, Hendra virus (HeV) and Nipah virus (NiV) as examples. Figure 1 The interaction between the receptor-binding domain in the spike of an emerging virus and the corresponding receptor on the target cell.
Title: Emerging Microbes and Infections (2012) 1, e13
[EX A]: True

[EX 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
[EX A]: False

[EX Q]: Paragraph: The yeast Kre2p/Mntlp cxl,2-mannosyltransferase is a type II membrane protein with a short cytoplasmic amino terminus, a membrane-spanning region, and a large catalytic luminal domain containing one N-glycosylation site. Anti-Kre2p/Mntlp antibodies identify a 60-kD integral membrane protein that is progressively N-glycosylated in an MNNl-dependent manner. Kre2p/Mntlp is localized in a Golgi compartment that overlaps with that containing the medial-Golgi mannosyltransferase Mnnlp, and distinct from that including the late Golgi protein Kexlp. To determine which regions of Kre2p/Mntlp are required for Golgi localization, Kre2p/Mntlp mutant proteins were assem-bled by substitution of Kre2p domains with equivalent sequences from the vacuolar proteins DPAP B and Pho8p. Chimeric proteins were tested for correct topology, in vitro and in vivo activity, and were localized intracellularly by indirect immunofluorescence. The resuits demonstrate that the NH2-terminal cytoplasmic domain is necessary for correct Kre2p Golgi localization whereas, the membrane-spanning and stem domains are dispensable. However, in a test of targeting sufficiency, the presence of the entire Kre2p cytoplasmic tail, plus the transmembrane domain and a 36-amino acid residue luminal stem region was required to localize a Pho8p reporter protein to the yeast Golgi. T HE Golgi apparatus plays a fundamental role in glycan processing and sorting of newly synthesized proteins in the secretory pathway of eukaryotic cells. The Golgi apparatus of a typical mammalian cell is composed of a polarized stack of membranous saccules that are differentiated into functionally distinct subcompartments. After the addition of core N-linked sugar moieties in the endoplasmic reticulum, glycoproteins proceed through the cis-Golgi network, the cis-, medial-, and transcisternae, and the trans-Golgi network, where further modifications take place. These include the addition of O-linked sugars and the elaboration, in a protein-specific manner, of complex N-linked carbohydrate structures. The Golgi apparatus also constitutes a major organelle responsible for protein trafficking where particular proteins are directed to precise cellular locations. Two specific Golgi subcompartments have been found to be involved in protein sorting. The cis-Golgi network constitutes the site from which many resident ER proteins are retrieved, and the main function of the TGN is to direct glycoproteins exiting the Golgi complex to either the lysosome or the cell surface (
Title: Localization and Targeting of the Saccharomyces cerevisiae Kre2p/Mnt lp oL 1,2-Mannosyltransferase to a medial.Gol Compartment
[EX A]:
False