instruction:
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".
question:
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
answer:
False


question:
Paragraph: Infectious diseases are a leading cause of death globally. Decisions surrounding how to control an infectious disease outbreak currently rely on a subjective process involving surveillance and expert opinion. However, there are many situations where neither may be available. Modeling can fill gaps in the decision making process by using available data to provide quantitative estimates of outbreak trajectories. Effective reduction of the spread of infectious diseases can be achieved through collaboration between the modeling community and public health policy community. However, such collaboration is rare, resulting in a lack of models that meet the needs of the public health community. Here we show a Susceptible-Infectious-Recovered (SIR) model modified to include control measures that allows parameter ranges, rather than parameter point estimates, and includes a web user interface for broad adoption. We apply the model to three diseases, measles, norovirus and influenza, to show the feasibility of its use and describe a research agenda to further promote interactions between decision makers and the modeling community. Despite substantial public health improvements in the last century, infectious diseases remain one of the leading causes of both morbidity and mortality 1,2 . When confronted with an infectious disease outbreak, public health officials typically work to control the outbreak by performing assessments, analyzing surveillance data, identifying resources and interacting with subject mater experts 2-4 . Control measures are then implemented based on the cumulative information collected. These approaches rely heavily on good surveillance systems, access to experts, and good intuition about which control measures to use. As such, they are largely subjective, time consuming, and the infrastructure required is often not present in high disease burden areas. Modeling is an attractive supplemental method because of the ability to estimate an outbreak's trajectory and the effects of possible control measures in a timely manner. Compartmental models are historically common; they divide individuals into categories based on their disease status. The most common variant is the SIR model, named after the categories used-"susceptible", "infectious" and "recovered". Models of this nature have small computational requirements, and are thus commonly used as first pass attempts to characterize outbreaks or infections quickly 5 . For example, after the sudden emergence of Severe Acute Respiratory Syndrome (SARS) in the early 2000s, researchers used modeling to characterize the virus' epidemiology. Several used compartmental models with control measures like quarantine or isolation in various settings (e.g., hospitals or cities) to describe effects of possible interventions 6-9 . Similar work exists for essentially all well known infectious diseases. For example, Mandal et al. provide a review of models used for malaria 10 and Bauch et al. explore model use with respect to SARS and other emerging infectious diseases 6 . Methods among these groups are often similar, but tend to focus on specific diseases and locations of interest. In contrast, agent-based models use a bottom-up approach where the agents (these are often people) interact with particular rules to simulate outbreaks 11 . This allows simulations at high resolutions, but requires large amounts of data to parameterize the models, as well as substantial computational power. It is thought that they may reflect real world scenarios more accurately, but the lack of available epidemiological data necessitates assumptions that are difficult or impossible to test 11 . These models further require computational resources inaccessible to an average health department. Both agent-based and compartmental models exhibit additional features that are problematic to wide-spread model adoption. The first is the focus on particular disease-location pairs. This emphasis precludes application to 
 Title: An approach to and web-based tool for infectious disease outbreak intervention analysis OPEN
answer:
False


question:
Paragraph: Canine infectious respiratory disease (CIRD) is a syndrome where multiple viral and bacterial pathogens are involved sequentially or synergistically to cause illness. There is limited information regarding the prevalence of pathogens related to CIRD in the United States as well as the role of co-infections in the pathogenesis of the syndrome. We aimed to conduct a comprehensive etiologic and epidemiologic study of multiple CIRD agents in a diverse dog population using molecular methods and statistical modeling analyses. In addition, a novel probe-based multiplex real-time PCR was developed to simultaneously detect and differentiate two species of Mycoplasma (M. canis and M. cynos). Canine adenovirus, canine distemper virus, canine parainfluenza virus, coronavirus, influenza A virus (H3N2 and H3N8), Bordetella bronchiseptica, M. canis, M. cynos and Streptococcus equi subsp. zooepidemicus were investigated in specimens from clinically ill and asymptomatic dogs received at the Athens Veterinary Diagnostic Laboratory. Results showed low occurrence of classical CIRD agents such as B. bronchiseptica, canine adenovirus and distemper virus, while highlighting the potential role of emerging bacteria such as M. canis and M. cynos. Statistical modeling analyses of CIRD pathogens emphasized the impact of co-infections on the severity of clinical presentation, and showed that host factors, such as animal age, are the most important predictors of disease severity. This study provides new insights into the current understanding of the prevalence and role of co-infections with selected viruses and bacteria in the etiology of CIRD, while underscoring the importance of molecular diagnosis and vaccination against this disease. 
 Title: Canine infectious respiratory disease: New insights into the etiology and epidemiology of associated pathogens
answer:

False