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Summary Vibrio parahaemolyticus, the leading cause of seafood‐associated gastroenteritis worldwide, requires the two type‐III secretion systems (T3SS1 and T3SS2) and a thermostable direct hemolysin (encoded by tdh1 and tdh2) for full virulence. The tdh genes and the T3SS2 gene cluster constitute an 80 kb pathogenicity island known as Vp‐PAI located on the chromosome II. Expression of T3SS1 and Vp‐PAI is regulated in a quorum sensing (QS)‐dependent manner but its detailed mechanisms remain unknown. Herein, we show that three factors (QS regulators AphA and OpaR and an AraC‐type transcriptional regulator QsvR) form a complex regulatory network to control the expression of T3SS1 and Vp‐PAI genes. At low cell density (LCD), whereas Vp‐PAI expression is repressed, T3SS1 genes are induced by AphA, which directly binds (an operator region of) the exsBAD‐vscBCD operon. At high cell density (HCD), the bacterium turns off T3SS1 expression by replacing AphA with OpaR, triggering the induction of Vp‐PAI. Furthermore, QsvR binds to the regulatory regions of all the tested T3SS1 and Vp‐PAI genes to activate their transcription at HCD. Taken together, our data highlight how multiple QS regulators contribute to the pathogenicity of V. parahaemolyticus by precisely controlling the expression of major virulence determinants during different stages of growth.

Surrogate models are widely used in simulation-based engineering design and optimization to save the computing cost. The choice of sampling approach has a great impact on the metamodel accuracy. This article presents a robust error-pursuing sequential sampling approach called cross-validation (CV)-Voronoi for global metamodeling. During the sampling process, CV-Voronoi uses Voronoi diagram to partition the design space into a set of Voronoi cells according to existing points. The error behavior of each cell is estimated by leave-one-out (LOO) cross-validation approach. Large prediction error indicates that the constructed metamodel in this Voronoi cell has not been fitted well and, thus, new points should be sampled in this cell. In order to rapidly improve the metamodel accuracy, the proposed approach samples a Voronoi cell with the largest error value, which is marked as a sensitive region. The sampling approach exploits locally by the identification of sensitive region and explores globally with the shift of sensitive region. Comparative results with several sequential sampling approaches have demonstrated that the proposed approach is simple, robust, and achieves the desired metamodel accuracy with fewer samples, that is needed in simulation-based engineering design problems.

In situ XRD examinations demonstrate significant effects of a Li2MnO3 coating on suppressing structural degradation during charging/discharging of Ni-rich cathode materials for enhanced cycling stability.

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