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".

Input: Consider Input: 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

Output: False


Input: Consider Input: Paragraph: Gram-negative pathogen-induced nosocomial infections and resistance are a most serious menace to global public health. Qingfei Xiaoyan Wan (QF), a traditional Chinese medicine (TCM) formula, has been used clinically in China for the treatment of upper respiratory tract infections, acute or chronic bronchitis and pulmonary infection. In this study, the effects of QF on Pseudomonas aeruginosainduced acute pneumonia in mice were evaluated. The mechanisms by which four typical antiinflammatory ingredients from QF, arctigenin (ATG), cholic acid (CLA), chlorogenic acid (CGA) and sinapic acid (SPA), regulate anti-inflammatory signaling pathways and related targets were investigated using molecular biology and molecular docking techniques. The results showed that pretreatment with QF significantly inhibits the release of cytokines (TNF-α and IL-6) and chemokines (IL-8 and RANTES), reduces leukocytes recruitment into inflamed tissues and ameliorates pulmonary edema and necrosis. In ;6(3):212-221 addition, ATG was identified as the primary anti-inflammatory agent with action on the PI3K/AKT and Ras/MAPK pathways. CLA and CGA enhanced the actions of ATG and exhibited synergistic NF-κB inactivation effects possibly via the Ras/MAPK signaling pathway. Moreover, CLA is speculated to target FGFR and MEK firstly. Overall, QF regulated the PI3K/AKT and Ras/MAPK pathways to inhibit pathogenic bacterial infections effectively. 
 Title: Qingfei Xiaoyan Wan, a traditional Chinese medicine formula, ameliorates Pseudomonas aeruginosa-induced acute lung inflammation by regulation of PI3K/AKT and Ras/MAPK pathways-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Output: False


Input: Consider Input: Paragraph: For more than 30 years X-ray crystallography has been by far the most powerful approach for determining the structures of viruses and viral proteins at atomic resolution. The information provided by these structures, which covers many important aspects of the viral life cycle such as cell-receptor recognition, viral entry, nucleic acid transfer and genome replication, has extensively enriched our vision of the virus world. Many of the structures available correspond to potential targets for antiviral drugs against important human pathogens. This article provides an overview of the current knowledge of different structural aspects of the above-mentioned processes. 
 Title: BIOLOGYjMEDICINE Viruses and viral proteins


Output: True