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: Similarity in time series is an important feature of dynamical systems such as financial systems, with potential use for clustering of series in system. Here, we mainly introduce a novel method: the reconstructed phase space information clustering method to analyze the financial markets. The method is used to examine the similarity of different sequences by calculating the distances between them, which the main difference from previous method is the way to map the original time series to symbolic sequences. Here we make use of the state space reconstruction to construct the symbolic sequences and quantify the similarity of different stock markets and exchange rate markets considering the chaotic behavior between the complex time series. And we compare the results of similarity of artificial and real data using the modified method, information categorization method and system clustering method. We conclude that the reconstructed phase space information clustering method is effective to research the close relationship in time series and for short time series especially. Besides, we report the results of similarity of different exchange rate time series in different periods and find the effect of the exchange rate regime in 2008 on the time series. Also we acquire some characteristics of exchange rate time
Title: The similarity analysis of financial stocks based on information clustering

A: False
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Q: Paragraph: To assess the viability of isolation and contact tracing to control onwards transmission from imported cases of 2019-nCoV. We developed a stochastic transmission model, parameterised to the 2019-nCoV outbreak. We used the model to quantify the potential effectiveness of contact tracing and isolation of cases at controlling a 2019 nCoV-like pathogen. We considered scenarios that varied in: the number of initial cases; the basic reproduction number R0; the delay from symptom onset to isolation; the probability contacts were traced; the proportion of transmission that occurred before symptom onset, and the proportion of subclinical infections. We assumed isolation prevented all further transmission in the model. Outbreaks were deemed controlled if transmission ended within 12 weeks or before 5000 cases in total. We measured the success of controlling outbreaks using isolation and contact tracing, and quantified the weekly maximum number of cases traced to measure feasibility of public health effort. Findings: While simulated outbreaks starting with only 5 initial cases, R0 of 1.5 and little transmission before symptom onset could be controlled even with low contact tracing probability, the prospects of controlling an outbreak dramatically dropped with the number of initial cases, with higher R0, and with more transmission before symptom onset. Across different initial numbers of cases, the majority of scenarios with an R0 of 1.5 were controllable with under 50% of contacts successfully traced. For R0 of 2.5 and 3.5, more than 70% and 90% of contacts respectively had to be traced to control the majority of outbreaks. The delay between symptom onset and isolation played the largest role in determining whether an outbreak was controllable for lower values of R0. For higher values of R0 and a large initial number of cases, contact tracing and isolation was only potentially feasible when less than 1% of transmission occurred before symptom onset. Interpretation: We found that in most scenarios contact tracing and case isolation alone is unlikely to control a new outbreak of 2019-nCov within three months. The probability of control decreases with longer delays from symptom onset to isolation, fewer cases ascertained by contact tracing, and increasing transmission before symptoms. This model can be modified to reflect updated transmission characteristics and more specific definitions of outbreak control to assess the potential success of local response efforts.
Title: Title: Feasibility of controlling 2019-nCoV outbreaks by isolation of cases and contacts

A: True
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Q: Paragraph: The cellular serine protease TMPRSS2, a member of the type II transmembrane serine protease (TTSP) family, cleaves and activates the hemagglutinin of influenza A viruses (FLUAV) in cell culture and is essential for spread of diverse FLUAV in mice. Non-human primates (NHP), in particular rhesus and cynomolgus macaques, serve as animal models for influenza and experimental FLUAV infection of common marmosets has recently also been reported. However, it is currently unknown whether the NHP orthologues of human TMPRSS2 cleave and activate FLUAV hemagglutinin and contribute to viral spread in respiratory tissue. Here, we cloned and functionally analyzed the macaque and marmoset orthologues of human TMPRSS2. In addition, we analyzed the macaque orthologues of human TMPRSS4 and HAT, which also belong to the TTSP family. We found that all NHP orthologues of human TMPRSS2, TMPRSS4 and HAT cleave and activate HA upon directed expression and provide evidence that endogenous TMPRSS2 is expressed in the respiratory epithelium of rhesus macaques. Finally, we demonstrate that a serine protease inhibitor active against TMPRSS2 suppresses FLUAV spread in precision-cut lung slices of human, macaque and marmoset origin. These results indicate that FLUAV depends on serine protease activity for spread in diverse NHP and in humans. Moreover, our findings suggest that macaques and marmosets may serve as models to study FLUAV activation by TMPRSS2 in human patients. Conceptualization: PZ SP. Formal analysis: PZ SP. Funding acquisition: SP. Investigation: PZ PMB SB KWG IN AH ASM SK SD KS CB CC JG KMR FD. Methodology: AB. Project administration: SP. Resources: SK AB. Supervision: PZ SB SK AB. Validation: PZ SP. Visualization: PZ SP.
Title: Non-human primate orthologues of TMPRSS2 cleave and activate the influenza virus hemagglutinin

A:
False
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