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: The interferon-induced transmembrane protein 3 (IFITM3) is a widely expressed potent antiviral effector of the host innate immune system. It restricts a diverse group of pathogenic, enveloped viruses, by interfering with endosomal fusion. In this report, the swine IFITM3 (sIFITM3) gene was cloned. It shares the functionally conserved CD225 domain and multiple critical amino acid residues (Y19, F74, F77, R86 and Y98) with its human ortholog, which are essential for antiviral activity. Ectopic expression of sIFITM3 significantly inhibited non-enveloped foot-and-mouth disease virus (FMDV) infection in BHK-21 cells. Furthermore, sIFITM3 blocked FMDV infection at early steps in the virus life cycle by disrupting viral attachment to the host cell surface. Importantly, inoculation of 2-day-old suckling mice with a plasmid expressing sIFITM3 conferred protection against lethal challenge with FMDV. These results suggest that sIFITM3 is a promising antiviral agent and that can safeguard the host from infection with FMDV. 
 Title: Swine interferon-induced transmembrane protein, sIFITM3, inhibits foot-and-mouth disease virus infection in vitro and in vivo
[EX A]: True

[EX Q]: 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
[EX A]: False

[EX Q]: Paragraph: The origin of viruses remains mysterious because of their diverse and patchy molecular and functional makeup. Although numerous hypotheses have attempted to explain viral origins, none is backed by substantive data. We take full advantage of the wealth of available protein structural and functional data to explore the evolution of the proteomic makeup of thousands of cells and viruses. Despite the extremely reduced nature of viral proteomes, we established an ancient origin of the "viral supergroup" and the existence of widespread episodes of horizontal transfer of genetic information. Viruses harboring different replicon types and infecting distantly related hosts shared many metabolic and informational protein structural domains of ancient origin that were also widespread in cellular proteomes. Phylogenomic analysis uncovered a universal tree of life and revealed that modern viruses reduced from multiple ancient cells that harbored segmented RNA genomes and coexisted with the ancestors of modern cells. The model for the origin and evolution of viruses and cells is backed by strong genomic and structural evidence and can be reconciled with existing models of viral evolution if one considers viruses to have originated from ancient cells and not from modern counterparts. 
 Title: A phylogenomic data-driven exploration of viral origins and evolution
[EX A]:
False