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".

[Q]: Paragraph: A B S T R A C T Acute hepatopancreatic necrosis disease (AHPND), also known as Early mortality syndrome (EMS), is a recently emerged lethal disease that has caused major economic losses in shrimp aquaculture. The etiologic agents are Vibrio spp. that carry Photorhabdus Insect-Related (Pir) toxin genes pirA and pirB. A multiplex SYBR Green realtime PCR was developed that detects pirA, pirB, and two internal control genes, the shrimp 18S rRNA and the bacterial 16S rRNA genes in a single reaction. The pirB primers amplify the 3'-end of the pirB gene allowing the detection of Vibrio spp. mutants that contain a complete deletion of pirA and the partial deletion of pirB. The assay also detects mutants that contain the entire pirA gene and the deletion of the pirB gene. Since both toxin genes are needed for disease development, this assays can distinguish between pathogenic strains of Vibrio spp. that cause AHPND in shrimp and mutants that do not cause disease. The amplicons for pirA, pirB, 18S rRNA and 16S rRNA showed easily distinguishable melting temperatures of 78.21 0.18, 75.20 0.20, 82.28 0.34 and 85.41 0.21C respectively. Additionally, a duplex real-time PCR assay was carried out by designing TaqMan probes for the pirA and pirB primers. The diagnostic sensitivity and specificity was compared between the SYBR Green and TaqMan assays. Both assays showed similar sensitivity with a limit of detection being 10 copies for pirA and pirB, and neither assays showed any cross reaction with other known bacterial and viral pathogens in shrimp. The high sensitivity of both assays make them suitable for the detection of low copies of the pirA and pirB genes in AHPND causing Vibrio spp. as well as for detecting non-pathogenic mutants.
Title: Multiplex SYBR Green and duplex TaqMan real-time PCR assays for the detection of Photorhabdus Insect-Related (Pir) toxin genes pirA and pirB
[A]: False

[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
[A]: False

[Q]: Paragraph: Human intestinal maltase (HMA) is an glucosidase responsible for the hydrolysis of -1,4-linkages from the non-reducing end of malto-oligosaccharides. HMA has become an important target in the treatment of type-2 diabetes. In this study, epigallocatechin gallate (EGCG) and EGCG glucoside (EGCG-G1) were identified as inhibitors of HMA by an in vitro assay with IC 50 of 20 1.0 and 31.5 1.0 M, respectively. A Lineweaver-Burk plot confirmed that EGCG and EGCG-G1 were competitive inhibitors of maltose substrate against HMA and inhibition kinetic constants (K i ) calculated from a Dixon plot were 5.93 0.26 and 7.88 0.57 M, respectively. Both EGCG and EGCG-G1 bound to the active site of HMA with numerous hydrophobic and hydrogen bond interactions.
Title: Inhibitory Effects of Epigallocatechin Gallate and Its Glucoside on the Human Intestinal Maltase Inhibition
[A]:
True