instruction:
In this task, you're given a paragraph from the research paper and your task is to generate a suitable title for the research paper based on the given paper. Under 100 words is a good title length.
question:
An urgent demand of assessing passengers' exposure risks in airliner cabins was raised as commercial airliners are one of the major media that carrying and transmitting infectious disease worldwide. In this study, simulations were conducted using a Boeing 737 cabin model to study the transport characteristics of airborne droplets and the associated infection risks of passengers. The numerical results of the airflow field were firstly compared against the experimental data in the literature to validate the reliability of the simulations. Airborne droplets were assumed to be released by passengers through coughing and their transport characteristics were modelled using the Lagrangian approach. Numerical results found that the particle travel distance was very sensitive to the release locations, and the impact was more significant along the longitudinal and horizontal directions. Particles released by passengers sitting next to the windows could travel much further than the others. A quantifiable approach was then applied to assess the individual infection risks of passengers. The key particle transport information such as the particle residence time yielded from the Lagrangian tracking process was extracted and integrated into the Wells-Riley equation to estimate the risks of infection. Compared to the Eulerian-based approach, the Lagrangian-based approach presented in this study is more robust as it addresses both the particle concentration and particle residence time in the breathing zone of every individual passenger.
answer:
Evaluation of airborne disease infection risks in an airliner cabin using the Lagrangian-based Wells-Riley approach


question:
Current hemagglutinin (HA)-based seasonal influenza vaccines induce vaccine strain-specific neutralizing antibodies that usually fail to provide protection against mismatched circulating viruses. Inclusion in the vaccine of highly conserved internal proteins such as the nucleoprotein (NP) and the matrix protein 1 (M1) was shown previously to increase vaccine efficacy by eliciting cross-reactive T-cells. However, appropriate delivery systems are required for efficient priming of T-cell responses. In this study, we demonstrated that administration of novel self-amplifying mRNA (SAM 1 ) vectors expressing influenza NP (SAM (NP)), M1 (SAM(M1)), and NP and M1 (SAM(M1-NP)) delivered with lipid nanoparticles (LNP) induced robust polyfunctional CD4 T helper 1 cells, while NP-containing SAM also induced cytotoxic CD8 T cells. Robust expansions of central memory (T CM ) and effector memory (T EM ) CD4 and CD8 T cells were also measured. An enhanced recruitment of NPspecific cytotoxic CD8 T cells was observed in the lungs of SAM(NP)-immunized mice after influenza infection that paralleled with reduced lung viral titers and pathology, and increased survival after homologous and heterosubtypic influenza challenge. Finally, we demonstrated for the first time that the co-administration of RNA (SAM(M1-NP)) and protein (monovalent inactivated influenza vaccine (MIIV)) was feasible, induced simultaneously NP-, M1-and HA-specific T cells and HA-specific neutralizing antibodies, and enhanced MIIV efficacy against a heterologous challenge. In conclusion, systemic administration of SAM vectors expressing conserved internal influenza antigens induced protective immune responses in mice, supporting the SAM 1 platform as another promising strategy for the development of broad-spectrum universal influenza vaccines.
answer:
Self-Amplifying mRNA Vaccines Expressing Multiple Conserved Influenza Antigens Confer Protection against Homologous and Heterosubtypic Viral Challenge


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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.
answer:

Monitoring Influenza Activity in the United States: A Comparison of Traditional Surveillance Systems with Google Flu Trends