The Adjuvanticity of an O. volvulus-Derived rOv-ASP-1 Protein in Mice Using Sequential Vaccinations and in Non-Human Primates

Adjuvants potentiate antigen-specific protective immune responses and can be key elements promoting vaccine effectiveness. We previously reported that the Onchocerca volvulus recombinant protein rOv-ASP-1 can induce activation and maturation of naïve human DCs and therefore could be used as an innate adjuvant to promote balanced Th1 and Th2 responses to bystander vaccine antigens in mice. With a few vaccine antigens, it also promoted a Th1-biased response based on pronounced induction of Th1-associated IgG2a and IgG2b antibody responses and the upregulated production of Th1 cytokines, including IL-2, IFN-γ, TNF-α and IL-6. However, because it is a protein, the rOv-ASP-1 adjuvant may also induce anti-self-antibodies. Therefore, it was important to verify that the host responses to self will not affect the adjuvanticity of rOv-ASP-1 when it is used in subsequent vaccinations with the same or different vaccine antigens. In this study, we have established rOv-ASP-1's adjuvanticity in mice during the course of two sequential vaccinations using two vaccine model systems: the receptor-binding domain (RBD) of SARS-CoV spike protein and a commercial influenza virus hemagglutinin (HA) vaccine comprised of three virus strains. Moreover, the adjuvanticity of rOv-ASP-1 was retained with an efficacy similar to that obtained when it was used for a first vaccination, even though a high level of anti-rOv-ASP-1 antibodies was present in the sera of mice before the administration of the second vaccine. To further demonstrate its utility as an adjuvant for human use, we also immunized non-human primates (NHPs) with RBD plus rOv-ASP-1 and showed that rOv-ASP-1 could induce high titres of functional and protective anti-RBD antibody responses in NHPs. Notably, the rOv-ASP-1 adjuvant did not induce high titer antibodies against self in NHPs. Thus, the present study provided a sound scientific foundation for future strategies in the development of this novel protein adjuvant

Mixed Kernel Functions for Multivariate Statistical Monitoring of Nonlinear Processes

Machine learning techniques have now become pervasive in the field of process condition monitoring. In particular, kernel methods are those that use kernel functions to allow for the efficient nonlinear analysis of process data by projecting them onto high-dimensional spaces. A widely used kernel machine in multivariate process monitoring is kernel principal components analysis (KPCA). Many choices of kernel functions were used in previous KPCA studies. However, the use of single kernels alone was recently shown to give only limited expressive ability. In this work, we explored the impact of combining various kernel functions to the performance of KPCA for condition monitoring. Fault detection performance is defined by percent correct detection of faulty states and non-detection of normal states. Optimal kernel parameters were obtained using the genetic algorithm (GA). Visualizations of the boundary between normal and faulty states are provided for demonstration in a chemical process case study. This work can inform the development of mixed kernels for nonlinear process monitoring, not only in KPCA, but also in other kernel machines