Severity of SARS-CoV-2 infection is linked to double-negative (CD27- IgD-) B cell subset numbers.

OBJECTIVES: The role of B cells in COVID-19, beyond the production of specific antibodies against SARS-CoV-2, is still not well understood. Here, we describe the novel landscape of circulating double-negative (DN) CD27- IgD- B cells in COVID-19 patients, representing a group of atypical and neglected subpopulations of this cell lineage. METHODS: Using multiparametric flow cytometry, we determined DN B cell subset amounts from 91 COVID-19 patients, correlated those with cytokines, clinical and laboratory parameters, and segregated them by principal components analysis. RESULTS: We detected significant increments in the DN2 and DN3 B cell subsets, while we found a relevant decrease in the DN1 B cell subpopulation, according to disease severity and patient outcomes. These DN cell numbers also appeared to correlate with pro- or anti-inflammatory signatures, respectively, and contributed to the segregation of the patients into disease severity groups. CONCLUSION: This study provides insights into DN B cell subsets' potential role in immune responses against SARS-CoV-2, particularly linked to the severity of COVID-19.

The Set-Based Hypervolume Newton Method for Bi-Objective Optimization.

In this paper, we propagate the use of a set-based Newton method that enables computing a finite size approximation of the Pareto front (PF) of a given twice continuously differentiable bi-objective optimization problem (BOP). To this end, we first derive analytically the Hessian matrix of the hypervolume indicator, a widely used performance indicator for PF approximation sets. Based on this, we propose the hypervolume Newton method (HNM) for hypervolume maximization of a given set of candidate solutions. We first address unconstrained BOPs and focus further on first attempts for the treatment of inequality constrained problems. The resulting method may even converge quadratically to the optimal solution, however, this property is-as for all Newton methods-of local nature. We hence propose as a next step a hybrid of HNM and an evolutionary strategy in order to obtain a fast and reliable algorithm for the treatment of such problems. The strengths of both HNM and hybrid are tested on several benchmark problems and comparisons of the hybrid to state-of-the-art evolutionary algorithms for hypervolume maximization are presented.