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 open reading frame 3 of the severe acute respiratory syndrome coronavirus (SARS-CoV) genome encodes a predicted protein 3a, consisting of 274 amino acids, that lacks any significant similarities to any known protein. We generated specific antibodies against SARS protein 3a by using a synthetic peptide (P2) corresponding to amino acids 261-274 of the putative protein. Anti-P2 antibodies and the sera from SARS patients could specifically detect the recombinant SARS protein 3a expressed in Escherichia coli and in Vero E6 cells. Expression of SARS protein 3a was detected at 8-12 h after infection and reached a higher level after $24 h in SARS-CoV-infected Vero E6 cells. Protein 3a was also detected in the alveolar lining pneumocytes and some intra-alveolar cells of a SARS-CoVinfected patient's lung specimen. Recombinant protein 3a expressed in Vero E6 cells and protein 3a in the SARS-CoVinfected cells was distributed over the cytoplasm in a fine punctate pattern with partly concentrated staining in the Golgi apparatus. Our study demonstrates that SARS-CoV indeed expresses a novel protein 3a, which is present only in SARS-CoV and not in other known CoVs.
Title: Identification of a novel protein 3a from severe acute respiratory syndrome coronavirus

A: False
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Q: Paragraph: Rapid and accurate detection and identification of viruses causing respiratory tract infections is important for patient care and disease control. Despite the fact that several assays are available, identification of an etiological agent is not possible in ,30% of patients suffering from respiratory tract diseases. Therefore, the aim of the current study was to develop a diagnostic set for the detection of respiratory viruses with sensitivity as low as 1-10 copies per reaction. Evaluation of the assay using a training clinical sample set showed that viral nucleic acids were identified in ,76% of cases. To improve assay performance and facilitate the identification of novel species or emerging strains, cultures of fully differentiated human airway epithelium were used to pre-amplify infectious viruses. This additional step resulted in the detection of pathogens in all samples tested. Based on these results it can be hypothesized that the lack of an etiological agent in some clinical samples, both reported previously and observed in the present study, may result not only from the presence of unknown viral species, but also from imperfections in the detection methods used.
Title: Use of Sensitive, Broad-Spectrum Molecular Assays and Human Airway Epithelium Cultures for Detection of Respiratory Pathogens

A: True
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Q: Paragraph: Motivation: Accurately determining the atomic structure of proteins represents a fundamental problem in the field of structural bioinformatics. A solution would be significant as protein structure information could be utilized in the medical field, e.g. in the development of vaccines for new viruses. This paper focuses on predicting the protein structure based on 3D images of the proteins captured through cryogenic electron microscopes (cryo-EM). A fully automated computationally efficient protein structure prediction method would be particularly beneficial in the field of cryo-EM as the technology allows researchers to photograph multiple large protein complexes in a single study, which means that a fast prediction method could allow for a high throughput of derived protein structures. We present a deep learning approach, DeepTracer, for predicting locations of the backbone atoms, secondary structure elements, and the amino acid types. In order to connect the predicted amino acids into chains, we applied a modified traveling salesman algorithm. Results: We trained our deep learning model on experimental cryo-EM density maps and tested it on a set of 50 density maps. We found that our new approach predicted protein structures with an average RMSD value of 1.18 and a coverage of 87.5%. Furthermore, we detected secondary structure information for 87.2% of amino acids correctly. We also showed preliminarily that 25.2% of amino acid types could be predicted directly from the 3D cryo-EM density map, considering 20 different types in total. Finally, we noted that the prediction runtime of DeepTracer is significantly improved compared to other methods. It predicts a large protein complex structure of more than 30,000 amino acids in only 2 hours. Availability: The repository of this project will be published. There are two main approaches to predict protein structures: homology modeling and de novo structure prediction. They differ in such that homology modeling requires structural knowledge about a homologous protein for its prediction while de novo methods do not. This limits the
Title: DeepTracer: Predicting Backbone Atomic Structure from High Resolution Cryo-EM Den- sity Maps of Protein Complexes XXXXXXX Received on XXXXX; revised on XXXXX; accepted on XXXXX

A:
True
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