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: Human coronavirus OC43 (HCoV-OC43) is a causative agent of the common cold. The nucleocapsid (N) protein, which is a major structural protein of CoVs, binds to the viral RNA genome to form the virion core and results in the formation of the ribonucleoprotein (RNP) complex. We have solved the crystal structure of the N-terminal domain of HCoV-OC43 N protein (N-NTD) (residues 58 to 195) to a resolution of 2.0Å. The HCoV-OC43 N-NTD is a single domain protein composed of a five-stranded -sheet core and a long extended loop, similar to that observed in the structures of N-NTDs from other coronaviruses. The positively charged loop of the HCoV-OC43 N-NTD contains a structurally well-conserved positively charged residue, R106. To assess the role of R106 in RNA binding, we undertook a series of site-directed mutagenesis experiments and docking simulations to characterize the interaction between R106 and RNA. The results show that R106 plays an important role in the interaction between the N protein and RNA. In addition, we showed that, in cells transfected with plasmids that encoded the mutant (R106A) N protein and infected with virus, the level of the matrix protein gene was decreased by 7-fold compared to cells that were transfected with the wild-type N protein. This finding suggests that R106, by enhancing binding of the N protein to viral RNA plays a critical role in the viral replication. The results also indicate that the strength of N protein/RNA interactions is critical for HCoV-OC43 replication. 
 Title: Crystal structure-based exploration of the important role of Arg106 in the RNA-binding domain of human coronavirus OC43 nucleocapsid protein NIH Public Access Author Manuscript

A: True
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Q: Paragraph: Increasing numbers of H5N1 influenza viruses (IVs) are responsible for human deaths, especially in North Africa and Southeast Asian. The binding of hemagglutinin (HA) on the viral surface to host sialic acid (SA) receptors is a requisite step in the infection process. Phylogenetic analysis reveals that H5N1 viruses can be divided into 10 clades based on their HA sequences, with most human IVs centered from clade 1 and clade 2.1 to clade 2.3. Protein sequence alignment in various clades indicates the high conservation in the receptor-binding domains (RBDs) is essential for binding with the SA receptor. Two glycosylation sites, 158N and 169N, also participate in receptor recognition. In the present work, we attempted to construct a serial H5N1 HA models including diverse glycosylated HAs to simulate the binding process with various SA receptors in silico. As the SA-a-2,3-Gal and SA-a-2,6-Gal receptor adopted two distinctive topologies, straight and fishhooklike, respectively, the presence of N-glycans at 158N would decrease the affinity of HA for all of the receptors, particularly SA-a-2,6-Gal analogs. The steric clashes of the huge glycans shown at another glycosylation site, 169N, located on an adjacent HA monomer, would be more effective in preventing the binding of SA-a-2,3-Gal analogs. 
 Title: Two Glycosylation Sites in H5N1 Influenza Virus Hemagglutinin That Affect Binding Preference by Computer-Based Analysis

A: False
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Q: Paragraph: Background. The diagnosis of severe acute respiratory syndrome (SARS) is difficult early in the illness, because its presentation resembles that of other nonspecific viral fevers, such as dengue. Dengue fever is endemic in many of the countries in which the large SARS outbreaks occurred in early 2003. Misdiagnosis may have serious public health consequences. We aimed to determine simple laboratory features to differentiate SARS from dengue. Methods. We compared the laboratory features of 55 adult patients with SARS at presentation (who were all admitted before radiological changes had occurred) and 147 patients with dengue. Features independently predictive of dengue were modeled by multivariate logistic regression to create a diagnostic tool with 100% specificity for dengue. Results. Multivariate analysis identified 3 laboratory features that together are highly predictive of a diagnosis of dengue and able to rule out the possibility of SARS: platelet count of ! platelets/L, white blood cell 
 Title: Use of Simple Laboratory Features to Distinguish the Early Stage of Severe Acute Respiratory Syndrome from Dengue Fever

A:
False
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