Key PointsQuestionWhat is the effect of convalescent plasma therapy added to standard treatment, compared with standard treatment alone, on clinical outcomes in patients with severe or life-threatening coronavirus disease 2019 (COVID-19)?FindingIn this randomized clinical trial that included 103 patients and was terminated early, the hazard ratio for time to clinical improvement within 28 days in the convalescent plasma group vs the standard treatment group was 1.40 and was not statistically significant.MeaningAmong patients with severe or life-threatening COVID-19, convalescent plasma therapy added to standard treatment did not significantly improve the time to clinical improvement within 28 days, although the trial was terminated early and may have been underpowered to detect a clinically important difference.

Supercapacitors (also known as ultracapacitors) are considered to be the most promising approach to meet the pressing requirements of energy storage. Supercapacitive electrode materials, which are closely related to the high-efficiency storage of energy, have provoked more interest. Herein, we present a high-capacity supercapacitor material based on the nitrogen-doped porous carbon nanofibers synthesized by carbonization of macroscopic-scale carbonaceous nanofibers (CNFs) coated with polypyrrole (CNFs@polypyrrole) at an appropriate temperature. The composite nanofibers exhibit a reversible specific capacitance of 202.0 F g(-1) at the current density of 1.0 A g(-1) in 6.0 mol L(-1) aqueous KOH electrolyte, meanwhile maintaining a high-class capacitance retention capability and a maximum power density of 89.57 kW kg(-1). This kind of nitrogen-doped carbon nanofiber represents an alternative promising candidate for an efficient electrode material for supercapacitors.

The linearly constrained matrix rank minimization problem is widely applicable in many fields such as control, signal processing and system identification. The tightest convex relaxation of this problem is the linearly constrained nuclear norm minimization. Although the latter can be cast as a semidefinite programming problem, such an approach is computationally expensive to solve when the matrices are large. In this paper, we propose fixed point and Bregman iterative algorithms for solving the nuclear norm minimization problem and prove convergence of the first of these algorithms. By using a homotopy approach together with an approximate singular value decomposition procedure, we get a very fast, robust and powerful algorithm, which we call FPCA (Fixed Point Continuation with Approximate SVD), that can solve very large matrix rank minimization problems 1 . Our numerical results on randomly generated and real matrix completion problems demonstrate that this algorithm is much faster and provides much better recoverability than semidefinite programming solvers such as SDPT3. For example, our algorithm can recover 1000 × 1000 matrices of rank 50 with a relative error of 10 −5 in about 3 minutes by sampling only 20 percent of the elements. We know of no other method that achieves as good recoverability. Numerical experiments on online recommendation, DNA microarray data set and image inpainting problems demonstrate the effectiveness of our algorithms.

Exploring low-cost and high-performance nonprecious metal catalysts (NPMCs) for oxygen reduction reaction (ORR) in fuel cells and metal-air batteries is crucial for the commercialization of these energy conversion and storage devices. Here we report a novel NPMC consisting of Fe3 C nanoparticles encapsulated in mesoporous Fe-N-doped carbon nanofibers, which is synthesized by a cost-effective method using carbonaceous nanofibers, pyrrole, and FeCl3 as precursors. The electrocatalyst exhibits outstanding ORR activity (onset potential of -0.02 V and half-wave potential of -0.140 V) closely comparable to the state-of-the-art Pt/C catalyst in alkaline media, and good ORR activity in acidic media, which is among the highest reported activities of NPMCs.