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".
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Question: Paragraph: The chemokine CXCL10 is produced during infection and inflammation to activate the chemokine receptor CXCR3, an important regulator of lymphocyte trafficking and activation. The goal of this study was to assess the contributions of CXCL10 to the pathogenesis of experimental septic shock in mice. Methods: Septic shock was induced by cecal ligation and puncture (CLP) in mice resuscitated with lactated Ringer's solution and, in some cases, the broad spectrum antibiotic Primaxin. Studies were performed in CXCL10 knockout mice and mice treated with anti-CXCL10 immunoglobulin G (IgG). Endpoints included leukocyte trafficking and activation, core body temperature, plasma cytokine concentrations, bacterial clearance and survival. Results: CXCL10 was present at high concentrations in plasma and peritoneal cavity during CLP-induced septic shock. Survival was significantly improved in CXCL10 knockout (CXCL10KO) mice and mice treated with anti-CXCL10 IgG compared to controls. CXCL10KO mice and mice treated with anti-CXCL10 IgG showed attenuated hypothermia, lower concentrations of interleukin-6 (IL-6) and macrophage inhibitory protein-2 (MIP-2) in plasma and lessened natural killer (NK) cell activation compared to control mice. Compared to control mice, bacterial burden in blood and lungs was lower in CXCL10-deficient mice but not in mice treated with anti-CXCL10 IgG. Treatment of mice with anti-CXCL10 IgG plus fluids and Primaxin at 2 or 6 hours after CLP significantly improved survival compared to mice treated with non-specific IgG under the same conditions. Conclusions: CXCL10 plays a role in the pathogenesis of CLP-induced septic shock and could serve as a therapeutic target during the acute phase of septic shock. 
 Title: The role of CXCL10 in the pathogenesis of experimental septic shock

Answer: False


Question: Paragraph: We characterized DBD by measuring concentrations of reactive species and ions. The inactivation efficiency was deduced using aerosol-and colony-counting methods. Susceptibility constant (Z) was introduced for performance evaluation of DBD. Modified Z was suggested for evaluation of DBD reactors of different sizes. Our methodology will be used for design optimization and performance evaluation. Dielectric barrier discharge (DBD) is a promising method to remove contaminant bioaerosols. The collection efficiency of a DBD reactor is an important factor for determining a reactor's removal efficiency. Without considering collection, simply defining the inactivation efficiency based on colony counting numbers for DBD as on and off may lead to overestimation of the inactivation efficiency of the DBD reactor. One-pass removal tests of bioaerosols were carried out to deduce the inactivation efficiency of the DBD reactor using both aerosol-and colony-counting methods. Our DBD reactor showed good performance for removing test bioaerosols for an applied voltage of 7.5 kV and a residence time of 0.24 s, with Á CFU , Á Number , and Á Inactivation values of 94%, 64%, and 83%, respectively. Additionally, we introduce the susceptibility constant of bioaerosols to DBD as a quantitative parameter for the performance evaluation of a DBD reactor. The modified susceptibility constant, which is the ratio of the susceptibility constant to the volume of the plasma reactor, has been successfully demonstrated for the performance evaluation of different sized DBD reactors under different DBD operating conditions. Our methodology will be used for design optimization, performance evaluation, and prediction of power consumption of DBD for industrial applications. 
 Title: Susceptibility constants of airborne bacteria to dielectric barrier discharge for antibacterial performance evaluation

Answer: False


Question: Paragraph: Background: Google Flu Trends was developed to estimate US influenza-like illness (ILI) rates from internet searches; however ILI does not necessarily correlate with actual influenza virus infections. Findings: Influenza activity data from 2003-04 through 2007-08 were obtained from three US surveillance systems: Google Flu Trends, CDC Outpatient ILI Surveillance Network (CDC ILI Surveillance), and US Influenza Virologic Surveillance System (CDC Virus Surveillance). Pearson's correlation coefficients with 95% confidence intervals (95% CI) were calculated to compare surveillance data. An analysis was performed to investigate outlier observations and determine the extent to which they affected the correlations between surveillance data. Pearson's correlation coefficient describing Google Flu Trends and CDC Virus Surveillance over the study period was 0.72 (95% CI: 0.64, 0.79). The correlation between CDC ILI Surveillance and CDC Virus Surveillance over the same period was 0.85 (95% CI: 0.81, 0.89). Most of the outlier observations in both comparisons were from the 2003-04 influenza season. Exclusion of the outlier observations did not substantially improve the correlation between Google Flu Trends and CDC Virus Surveillance (0.82; 95% CI: 0.76, 0.87) or CDC ILI Surveillance and CDC Virus Surveillance (0.86; 95%CI: 0.82, 0.90). Conclusions: This analysis demonstrates that while Google Flu Trends is highly correlated with rates of ILI, it has a lower correlation with surveillance for laboratory-confirmed influenza. Most of the outlier observations occurred during the 2003-04 influenza season that was characterized by early and intense influenza activity, which potentially altered health care seeking behavior, physician testing practices, and internet search behavior. 
 Title: Monitoring Influenza Activity in the United States: A Comparison of Traditional Surveillance Systems with Google Flu Trends

Answer:
False