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".

Paragraph: Dickeya species are high consequence plant pathogenic bacteria listed among the quarantine pathogens of the European Union; associated with potato disease outbreaks and subsequent economic losses worldwide. Early, accurate, and reliable detection of Dickeya spp. is needed to prevent establishment and further dissemination of this pathogen. Therefore, a multiplex TaqMan qPCR was developed for sensitive detection of Dickeya spp. and specifically, D. dianthicola. A signature genomic region for the genus Dickeya (mglA/mglC) and unique genomic region for D. dianthicola (alcohol dehydrogenase) were identified using a whole genome based comparative genomics approach. The developed multiplex TaqMan qPCR was validated using extensive inclusivity and exclusivity panels, and naturally/artificially infected samples to confirm broad range detection capability and specificity. Both sensitivity and spiked assays showed detection limit of 10 fg DNA. The developed multiplex assay is sensitive and reliable to detect Dickeya spp. and D. dianthicola with no false positives or false negatives. It was able to detect mixed infection from naturally and artificially infected plant materials. The developed assay will serve as a practical tool for screening of propagative material, monitoring the presence and distribution, and quantification of target pathogens in a breeding program. The assay also has applications in routine diagnostics, biosecurity and microbial forensics.
Title: Comparative genomics reveals signature regions used to develop a robust and sensitive multiplex TaqMan real-time qPCR assay to detect the genus Dickeya and Dickeya dianthicola
False

Paragraph: The recent coronavirus pandemic follows in its early stages an almost exponential expansion, with the number of cases as a function of time reasonably well fit by N (t) e t , in many countries. We analyze the rate in different countries, choosing as a starting point in each country the first day with 30 cases and fitting for the following 12 days, capturing thus the early exponential growth in a rather homogeneous way. We look for a link between the rate and the average temperature T of each country, in the month of the epidemic growth. We analyze a base set of 42 countries, which developed the epidemic earlier, and an extended set of 88 countries, which developed the epidemic more recently. Fitting with a linear behavior (T ), we find evidence in both datasets for a decreasing growth rate as a function of T , at 99.66%C.L. and 99.86%C.L. in the base and extended dataset, respectively. In the base set, going beyond a linear model, a peak at about (7.7 3.6) C seems to be present in the data. Our findings give hope that, for northern hemisphere countries, the growth rate should significantly decrease as a result of both warmer weather and lockdown policies. In general the propagation should be hopefully stopped by strong lockdown, testing and tracking policies, before the arrival of the next cold season. I.
Title: Temperature dependence of COVID-19 transmission
True

Paragraph: A number of features were included in the final models, including article counts, indicator counts and slopes. In Table 2 -1, the ten features with highest Gini importance are listed for Model 0 1 and Model 1 0, predicting Y 1 i jt and training on all available weeks. There is substantial overlap between the lists for the two models. For both models, the article count and the mortality, outbreak worsening and novelty indicators were among the most important features. The mortality indicator describes whether or not fatalities were reported during the week, the outbreak worsening indicator captures mentions of increasing cases or worsening conditions, and the novelty indicator measures whether the disease is described as new or unfamiliar. The finding that the novelty indicator was among the most important features is consistent with previous research that has shown that outbreaks of novel diseases are more likely to have associated social response (Fast et al, 2015; McGrath, 1991) . Table 2-1: The ten features with highest Gini importance for Model 0 1 and Model 1 0 Model 0 1 Model 1 0 Social Response Indicator EWMA as a Percentage of UCL In this section, we present results for the 30 countries used for model specification and tuning and compare with the overall results in order to access the extent to which tuning and model specification may have affected results for these countries. We experimented with different model specifications and combinations of features on a subset of data consisting of 30 countries, prior to applying our models to the entire collection of countries. Table 2 -2 presents the model performance metrics for both the entire collection of 72 countries and for the subset of 30 countries used for model specification and tuning. Model performance was slightly diminished on the entire collection of countries compared with the 30 countries used for model specification and tuning. In part, the poorer performance can be explained by the fact that the average number of articles per week in the entire collection of countries was lower than in the 30 countries used for model specification -0.14 articles per week versus 3.50 articles per week. We have shown that in general our models perform best on country-disease pairs that receive substantial news coverage.
Title: Predicting Social Response to Infectious Disease Outbreaks from Internet-based News Streams Online Resource 2: Supplemental Model Results Article Count without Duplicates 3 Week Slope of EWMA Article Count with Duplicates Disease Name Mortality Indicator Article Count without Duplicates 3 Week Slope of Mortality Indicator Article Count with Duplicates Outbreak Worsening Indicator Mortality Indicator 3 Week Slope of Outbreak Worsening Indicator Outbreak Worsening Indicator Population Fear Indicator Novelty Indicator Novelty Indicator Officials Fear Indicator 2 Results for the 30 Countries Used for Model Specification and Tuning
False