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SQUINT (SQN) regulates plant maturation by promoting the activity of miR156, which functions primarily in the miR156–SQUAMOSA PROMOTER BINDING PROTEIN-LIKE9 (SPL9) module regulating plant growth and development. Here, we show that SQN acts in the jasmonic acid (JA) pathway, a major signaling pathway regulating plant responses to insect herbivory and pathogen infection. Arabidopsis thaliana sqn mutants showed elevated sensitivity to the necrotrophic fungus Botrytis cinerea compared with wild type. However, SQN is not involved in the early pattern-triggered immunity (PTI) response often triggered by fungal attack. Rather, SQN positively regulates the JA pathway, as sqn loss-of-function mutants treated with B. cinerea showed reduced JA accumulation, JA response and sensitivity to JA. Furthermore, the miR156–SPL9 module regulates plant resistance to B. cinerea: mir156 mutant and SPL9 overexpression plants displayed elevated sensitivity to B. cinerea. Moreover, constitutively expressing miR156a or reducing SPL9 expression in the sqn-1 mutant restored sensitivity of Arabidopsis to B. cinerea and JA responses. These results suggest that SQN positively modulate plant resistance to B. cinerea through the JA pathway, and the miR156–SPL9 module functions as a bridge between SQN and JA to mediate plant resistance to this pathogen.

In a wireless sensor network, the signal received by the terminal processor is usually a complex single channel hybrid chaotic signal. The engineering needs to separate the useful signal from the mixed signal to perform the next transmission analysis. Since chaotic signals are nonlinear and unpredictable, traditional blind separation algorithms cannot effectively separate chaotic signals. Aiming to correct these problems-based on the particle filter estimation algorithm-an extended Kalman particle filter algorithm (EPF) and an unscented Kalman particle filter algorithm (UPF) are proposed to solve the single channel blind separation problem of chaotic signals. Mixing chaotic signals of different intensities performs blind source separation. Using different evaluation indexes carries out the experiment and performance can be analyzed. The results show that the proposed algorithm effectively separates the mixed chaotic signals.

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