TASK DEFINITION: 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".
PROBLEM: Paragraph: Worldwide community-acquired pneumonia (CAP) is one of the most frequent infectious diseases and a leading cause of death. Several studies have shown that a pathogen could be identified only in 50 to 60% of all patients, although in children < 6 month infectious agents can be detected in about 90%. Viral infections are most frequent in children < 2 years (80%), whereas bacterial infections increase with age. RSV, influenzaviruses, rhinoviruses, parainfluenzaviruses and adenoviruses are the most common viruses associated with CAP in children. Among adenoviruses a predominance of adenovirus 7 has been reported in several countries with emergence of highly pathogenic variants with significant lethality in young children. Many childhood respiratory infections are caused by more than one pathogen and up to 30% mixed viral / bacterial infections can be observed. CAP in immunocompetent adults is rare, whereas persons with underlaying diseases have an increased incidence of CAP. In the elderly, RSV, influenzaviruses, parainfluenzaviruses and less frequent adenoviruses are predominant viruses causing pneumonia. Less frequently associated with CAP are the newly discovered human metapneumovirus and the coronaviruses NL63 and HKU1. Hantaviruses, involved in the hantavirus pulmonary syndrome, belong to the emerging pathogens to date in North, Middle and South America. For optimum diagnosis the whole spectrum of potential respiratory viral agents should be included and multiple diagnostic techniques have to be used. In view of the high relevance of influenzavirus for CAP influenza vaccination is highly advisable for prevention of CAP, especially in high-risk groups. 
 Title: Community-Acquired Pneumonia Viral pathogens and epidemiology, detection, therapy and resistance

SOLUTION: True

PROBLEM: 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

SOLUTION: False

PROBLEM: Paragraph: The death morphology commonly known as apoptosis results from a post-translational pathway driven largely by specific limited proteolysis. In the last decade the structural basis for apoptosis regulation has moved from nothing to 'quite good', and we now know the fundamental structures of examples from the initiator phase, the pre-mitochondrial regulator phase, the executioner phase, inhibitors and their antagonists, and even the structures of some substrates. The field is as well advanced as the best known of proteolytic pathways, the coagulation cascade. Fundamentally new mechanisms in protease regulation have been disclosed. Structural evidence suggests that caspases have an unusual catalytic mechanism, and that they are activated by apparently unrelated events, depending on which position in the apoptotic pathway they occupy. Some naturally occurring caspase inhibitors have adopted classic inhibition strategies, but other have revealed completely novel mechanisms. All of the structural and mechanistic information can, and is, being applied to drive therapeutic strategies to combat overactivation of apoptosis in degenerative disease, and underactivation in neoplasia. We present a comprehensive review of the caspases, their regulators and inhibitors from a structural and mechanistic point of view, and with an aim to consolidate the many threads that define the rapid growth of this field. 
 Title: The protein structures that shape caspase activity, specificity, activation and inhibition

SOLUTION:
True