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.

We evaluated the safety and immunogenicity of a chimeric alphavirus vaccine candidate in mice with selective immunodeficiencies. This vaccine candidate was highly attenuated in mice with deficiencies in the B and T cell compartments, as well as in mice with deficient gamma-interferon responsiveness. However, the level of protection varied among the strains tested. Wild type mice were protected against lethal VEEV challenge. In contrast, alpha/beta (αβ) TCR-deficient mice developed lethal encephalitis following VEEV challenge, while mice deficient in gamma/delta ( γδ) T cells were protected. Surprisingly, the vaccine potency was diminished by 50% in animals lacking interferon-gamma receptor alpha chain (R1)-chain and a minority of vaccinated immunoglobulin heavy chain-deficient (μMT) mice survived challenge, which suggests that neutralizing antibody may not be absolutely required for protection. Prolonged replication of encephalitic VEEV in the brain of pre-immunized mice is not lethal and adoptive transfer experiments indicate that CD3 + T cells are required for protection.
Alpha-beta T cells provide protection against lethal encephalitis in the murine model of VEEV infection

Background: Prevention of infection with airborne pathogens and exposure to airborne particulates and aerosols (environmental pollutants and allergens) can be facilitated through use of disposable face masks. The effectiveness of such masks for excluding pathogens and pollutants is dependent on the intrinsic ability of the masks to resist penetration by airborne contaminants. This study evaluated the relative contributions of a mask, valve, and Micro Ventilator on aerosol filtration efficiency of a new N95 respiratory face mask. Methods: The test mask was challenged, using standardized methods, with influenza A and rhinovirus type 14, bacteriophage ΦΧ174, Staphylococcus aureus (S. aureus), and model pollutants. The statistical significance of results obtained for different challenge microbial agents and for different mask configurations (masks with operational or nonoperational ventilation fans and masks with sealed Smart Valves) was assessed. Results: The results demonstrate >99.7% efficiency of each test mask configuration for exclusion of influenza A virus, rhinovirus 14, and S. aureus and >99.3% efficiency for paraffin oil and sodium chloride (surrogates for PM 2.5 ). Statistically significant differences in effectiveness of the different mask configurations were not identified. The efficiencies of the masks for excluding smaller-size (i.e., rhinovirus and bacteriophage ΦΧ174) vs. larger-size microbial agents (influenza virus, S. aureus) were not significantly different. Conclusions: The masks, with or without features intended for enhancing comfort, provide protection against both small-and large-size pathogens. Importantly, the mask appears to be highly efficient for filtration of pathogens, including influenza and rhinoviruses, as well as the fine particulates (PM 2.5 ) present in aerosols that represent a greater challenge for many types of dental and surgical masks. This renders this individual-use N95 respiratory mask an improvement over the former types of masks for protection against a variety of environmental contaminants including PM 2.5 and pathogens such as influenza and rhinoviruses.
Assessment of a respiratory face mask for capturing air pollutants and pathogens including human influenza and rhinoviruses

In this paper, we have proposed a two-phase procedure (combining discrete graphs and wavelets) for constructing true epidemic growth. In the first phase, a graph-theory-based approach was developed to update partial data available and in the second phase, we used this partial data to generate plausible complete data through wavelets. We have provided two numerical examples. This procedure is novel and implementable and adaptable to machine learning modeling framework. (P. Polyakov), arrao@augusta.edu (A.S.R.S. Rao). 1 Order of the authors listed is based on the alphabetical order of the last names. Here μ (infection related death rate), ν (immunization coefficient = m / n ), λ (infectivity coefficient = n / μ) and m = ν/μ are all

True epidemic growth construction through harmonic analysis