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Background: LipL32 induces a renal cell inflammatory response through the TLR2-signaling pathway. Results: Ca 2ϩ -binding LipL32 mutants showed attenuated TLR2-mediated inflammatory responses. Conclusion:The Ca 2ϩ -binding cluster of LipL32 is essential in regulating its interaction with TLR2 for subsequent inflammatory response induction. Significance: This investigation provides significant evidence for crucial roles of the Ca 2ϩ -binding cluster of LipL32 for pathogenesis via association with TLR2.

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This paper aims to propose a more efficient control algorithm for chaos time-series prediction and synchronization. A novel type-2 fuzzy cerebellar model articulation controller (T2FCMAC) is proposed. In some special cases, this T2FCMAC can be reduced to an interval type-2 fuzzy neural network, a fuzzy neural network, and a fuzzy cerebellar model articulation controller (CMAC). So, this T2FCMAC is a more generalized network with better learning ability, thus, it is used for the chaos time-series prediction and synchronization. Moreover, this T2FCMAC realizes the un-normalized interval type-2 fuzzy logic system based on the structure of the CMAC. It can provide better capabilities for handling uncertainty and more design degree of freedom than traditional type-1 fuzzy CMAC. Unlike most of the interval type-2 fuzzy system, the type-reduction of T2FCMAC is bypassed due to the property of un-normalized interval type-2 fuzzy logic system. This causes T2FCMAC to have lower computational complexity and is more practical. For chaos time-series prediction and synchronization applications, the training architectures with corresponding convergence analyses and optimal learning rates based on Lyapunov stability approach are introduced. Finally, two illustrated examples are presented to demonstrate the performance of the proposed T2FCMAC.