Three signal transduction pathways, the two-component systems CpxRA and BaeSR and the alternative sigma factor E , respond to extracytoplasmic stress that facilitates bacterial adaptation to changing environments. At least the CpxRA and E pathways control the production of protein-folding and degradation factors that counter the effects of protein misfolding in the periplasm. This function also influences the biogenesis of multicomponent extracellular appendages that span the bacterial envelope, such as various forms of pili. Herein, we investigated whether any of these regulatory pathways in the enteropathogen Yersinia pseudotuberculosis affect the functionality of the Ysc-Yop type III secretion system. This is a multicomponent molecular syringe spanning the bacterial envelope used to inject effector proteins directly into eukaryotic cells. Disruption of individual components revealed that the Cpx and E pathways are important for Y. pseudotuberculosis type III secretion of Yops (Yersinia outer proteins). In particular, a loss of CpxA, a sensor kinase, reduced levels of structural Ysc (Yersinia secretion) components in bacterial membranes, suggesting that these mutant bacteria are less able to assemble a functional secretion apparatus. Moreover, these bacteria were no longer capable of localizing Yops into the eukaryotic cell interior. In addition, a cpxA lcrQ double mutant engineered to overproduce and secrete Yops was still impaired in intoxicating cells. Thus, the Cpx pathway might mediate multiple influences on bacterium-target cell contact that modulate Yersinia type III secretion-dependent host cell cytotoxicity.

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The extracytoplasmic-stress-responsive CpxRA two-component signal transduction pathway allows bacteria to adapt to growth in extreme environments. It controls the production of periplasmic protein folding and degradation factors, which aids in the biogenesis of multicomponent virulence determinants that span the bacterial envelope. This is true of the Yersinia pseudotuberculosis Ysc-Yop type III secretion system. However, despite using a second-site suppressor mutation to restore Yop effector secretion by yersiniae defective in the CpxA sensor kinase, these bacteria poorly translocated Yops into target eukaryotic cells. Investigation of this phenotype herein revealed that the expression of genes which encode several surface-located adhesins is also influenced by the Cpx pathway. In particular, the expression and surface localization of invasin, an adhesin that engages ␤1-integrins on the eukaryotic cell surface, are severely restricted by the removal of CpxA. This reduces bacterial association with eukaryotic cells, which could be suppressed by the ectopic production of CpxA, invasin, or RovA, a positive activator of inv expression. In turn, these infected eukaryotic cells then became susceptible to intoxication by translocated Yop effectors. In contrast, bacteria harboring an in-frame deletion of cpxR, which encodes the cognate response regulator, displayed an enhanced ability to interact with cell monolayers, as well as elevated inv and rovA transcription. This phenotype could be drastically suppressed by providing a wild-type copy of cpxR in trans. We propose a mechanism of inv regulation influenced by the direct negative effects of phosphorylated CpxR on inv and rovA transcription. In this fashion, sensing of extracytoplasmic stress by CpxAR contributes to productive Yersinia sp.-eukaryotic cell interactions.

This special issue includes eight original works that detail the further developments of ELMs in theories, applications, and hardware implementation. In "Representational Learning with ELMs for Big Data," Liyanaarachchi Lekamalage Chamara Kasun, Hongming Zhou, Guang-Bin Huang, and Chi Man Vong propose using the ELM as an auto-encoder for learning feature representations using singular values. In "A Secure and Practical Mechanism for Outsourcing ELMs in Cloud Computing," Jiarun Lin, Jianping Yin, Zhiping Cai, Qiang Liu, Kuan Li, and Victor C.M. Leung propose a method for handling large data applications by outsourcing to the cloud that would dramatically reduce ELM training time. In "ELM-Guided Memetic Computation for Vehicle Routing," Liang Feng, Yew-Soon Ong, and Meng-Hiot Lim consider the ELM as an engine for automating the encapsulation of knowledge memes from past problem-solving experiences. In "ELMVIS: A Nonlinear Visualization Technique Using Random Permutations and ELMs," Anton Akusok, Amaury Lendasse, Rui Nian, and Yoan Miche propose an ELM method for data visualization based on random permutations to map original data and their corresponding visualization points. In "Combining ELMs with Random Projections," Paolo Gastaldo, Rodolfo Zunino, Erik Cambria, and Sergio Decherchi analyze the relationships between ELM feature-mapping schemas and the paradigm of random projections. In "Reduced ELMs for Causal Relation Extraction from Unstructured Text," Xuefeng Yang and Kezhi Mao propose combining ELMs with neuron selection to optimize the neural network architecture and improve the ELM ensemble's computational efficiency. In "A System for Signature Verification Based on Horizontal and Vertical Components in Hand Gestures," Beom-Seok Oh, Jehyoung Jeon, Kar-Ann Toh, Andrew Beng Jin Teoh, and Jaihie Kim propose a novel paradigm for hand signature biometry- for touchless applications without the need for handheld devices. Finally, in "An Adaptive and Iterative Online Sequential ELM-Based Multi-Degree-of-Freedom Gesture Recognition System," Hanchao Yu, Yiqiang Chen, Junfa Liu, and Guang-Bin Huang propose an online sequential ELM-based efficient gesture recognition algorithm for touchless human-machine interaction