In this study we profiled vaccine-induced polyclonal antibodies as well as plasmablast derived mAbs from subjects who received SARS-CoV-2 spike mRNA vaccine. Polyclonal antibody responses in vaccinees were robust and comparable to or exceeded those seen after natural infection. However, that the ratio of binding to neutralizing antibodies after vaccination was greater than that after natural infection and, at the monoclonal level, we found that the majority of vaccine-induced antibodies did not have neutralizing activity. We also found a co-dominance of mAbs targeting the NTD and RBD of SARS-CoV-2 spike and an original antigenic-sin like backboost to seasonal human coronaviruses OC43 and HKU1 spike proteins. Neutralizing activity of NTD mAbs but not RBD mAbs against a clinical viral isolate carrying E484K as well as extensive changes in the NTD was abolished, suggesting that a proportion of vaccine induced RBD binding antibodies may provide substantial protection against viral variants carrying E484K.

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Failure Modes and Effects Analysis (FMEA) is a widely used system and software safety analysis technique that systematically identifies failure modes of system components and explores whether these failure modes might lead to potential hazards. In practice, FMEA is typically a labor‐intensive team‐based exercise, with little tool support. This article presents our experience with automating parts of the FMEA process, using a model checker to automate the search for system‐level consequences of component failures. The idea is to inject runtime faults into a model based on the system specification and check if the resulting model violates safety requirements, specified as temporal logical formulas. This enables the safety engineer to identify if a component failure, or combination of multiple failures, can lead to a specified hazard condition. If so, the model checker produces an example of the events leading up to the hazard occurrence which the analyst can use to identify the relevant failure propagation pathways and co‐effectors. The process is applied on three medium‐sized case studies modeled with Behavior Trees. Performance metrics for SAL model checking are presented. Copyright © 2011 John Wiley & Sons, Ltd.

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