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".

Paragraph: Bovine viral diarrhea virus (BVDV) is a pestivirus member of the Flaviviridae family, closely related to, and used as a surrogate model for the hepatitis C virus. Its envelope contains the E1 and E2 glycoproteins, disulfide linked into homo-and heterodimers. In this study, we investigate the role of disulfide bond formation in the folding, assembly, and stability of BVDV glycoproteins. We provide molecular evidence that intact disulfide bonds are critical for the acquirement of a stable conformation of E2 monomers. Forcing the E2 glycoproteins to adopt a reduced conformation either co-or post-translationally before assembly into dimers, determines their misfolding and degradation by proteasome. In contrast, dimerization of E2 glycoproteins results in a conformation resistant to reducing agents and degradation. Furthermore, inhibition of the ER-a-mannosidase activity leads to impairment of misfolded E2 degradation, demonstrating the involvement of this enzyme in targeting viral proteins towards proteasomal degradation. Ó 
 Title: Role of disulfide bond formation in the folding and assembly of the envelope glycoproteins of a pestivirus
False

Paragraph: Ebola virus is the causative agent of Ebola virus disease in humans. The lethality of Ebola virus infection is about 50%, supporting the urgent need to develop anti-Ebola drugs. Glycoprotein (GP) is the only surface protein of the Ebola virus, which is functionally critical for the virus to attach and enter the host cells, and is a promising target for anti-Ebola virus drug development. In this study, using the recombinant HIV-1/Ebola pseudovirus platform we previously established, we evaluated a small molecule library containing various quinoline compounds for anti-Ebola virus entry inhibitors. Some of the quinoline compounds specifically inhibited the entry of the Ebola virus. Among them, compound SYL1712 was the most potent Ebola virus entry inhibitor with an IC 50 of~1 µM. The binding of SYL1712 to the vial glycoprotein was computationally modeled and was predicted to interact with specific residues of GP. We used the time of the addition assay to show that compound SYL1712 blocks Ebola GP-mediated entry. Finally, consistent with being an Ebola virus entry inhibitor, compound SYL1712 inhibited infectious Ebola virus replication in tissue culture under biosafety level 4 containment, with an IC 50 of 2 µM. In conclusion, we identified several related molecules with a diaryl-quinoline scaffold as potential anti-EBOV entry inhibitors, which can be further optimized for anti-Ebola drug development. 
 Title: Identification of Diaryl-Quinoline Compounds as Entry Inhibitors of Ebola Virus
True

Paragraph: The seasonal outbreaks of influenza infection cause globally respiratory illness, or even death in all age groups. Given early-warning signals preceding the influenza outbreak, timely intervention such as vaccination and isolation management effectively decrease the morbidity. However, it is usually a difficult task to achieve the real-time prediction of influenza outbreak due to its complexity intertwining both biological systems and social systems. By exploring rich dynamical and high-dimensional information, our dynamic network marker/biomarker (DNM/DNB) method opens a new way to identify the tipping point prior to the catastrophic transition into an influenza pandemics. In order to detect the early-warning signals before the influenza outbreak by applying DNM method, the historical information of clinic hospitalization caused by influenza infection between years 2009 and 2016 were extracted and assembled from public records of Tokyo and Hokkaido, Japan. The early-warning signal, with an average of 4week window lead prior to each seasonal outbreak of influenza, was provided by DNM-based on the hospitalization records, providing an opportunity to apply proactive strategies to prevent or delay the onset of influenza outbreak. Moreover, the study on the dynamical changes of hospitalization in local district networks unveils the influenza transmission dynamics or landscape in network level. city network, critical transition, dynamic network biomarker (DNB), dynamic network marker (DNM), early-warning signal, influenza outbreak 
 Title: Detecting early-warning signals of influenza outbreak based on dynamic network marker

True