HYL1 (HYPONASTIC LEAVES 1) in Arabidopsis thaliana encodes a double-stranded RNA-binding protein needed for proper miRNA maturation, and its null mutant hyl1 shows a typical leaf-incurvature phenotype. In Chinese cabbage, BcpLH (Brassica rapa ssp. pekinensis LEAFY HEADS), a close homolog of HYL1, is differentially expressed in juvenile leaves, which are flat, and in adult leaves, which display extreme incurvature. BcpLH lacks protein–protein interaction domains and is much shorter than HYL1. To test whether BcpLH is associated with defects in microRNA (miRNA) biogenesis and leaf flatness, we enhanced and repressed the activity of BcpLH by transgenics and investigated BcpLH-dependent miRNAs and plant morphology. BcpLH promoted miRNA biogenesis by the proper processing of primary miRNAs. BcpLH downregulation via antisense decreased a specific subset of miRNAs and increased the activities of their target genes, causing upward curvature of rosette leaves and early leaf incurvature, concurrent with the enlargement, earliness, and round-to-oval shape transition of leafy heads. Moreover, BcpLH-dependent miRNAs in Chinese cabbage are not the same as HYL1-dependent miRNAs in Arabidopsis. We suggest that BcpLH controls a specific subset of miRNAs in Chinese cabbage and coordinates the direction, extent, and timing of leaf curvature during head formation in Brassica rapa.

Compared with single-channel speech enhancement methods, multichannel methods can utilize spatial information to design optimal filters. Although some filters adaptively consider second-order signal statistics, the temporal evolution of the speech spectrum is usually neglected. By using linear prediction (LP) to model the inter-frame temporal evolution of speech, single-channel Kalman filtering (KF) based methods have been developed for speech enhancement. In this paper, we derive a multichannel KF (MKF) that jointly uses both interchannel spatial correlation and interframe temporal correlation for speech enhancement. We perform LP in the modulation domain, and by incorporating the spatial information, derive an optimal MKF gain in the short-time Fourier transform domain. We show that the proposed MKF reduces to the conventional multichannel Wiener filter if the LP information is discarded. Furthermore, we show that, under an appropriate assumption, the MKF is equivalent to a concatenation of the minimum variance distortion response beamformer and a single-channel modulation-domain KF and therefore present an alternative implementation of the MKF. Experiments conducted on a public head-related impulse response database demonstrate the effectiveness of the proposed method.

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In this paper, A Robust Kalman filter and a bank ofKalman filters are applied in fault detection and isolation (FDI) of sensor and actuator for aircraft gas turbine engine. A bank of Kalman filters are used to detect and isolate sensor fault, each Kalman filter is designed based on a specific hypothesis for detecting a specific sensor fault. In the event that a fault does occur, all filters except the one using the correct hypothesis will produce large estimation errors, from which a specific fault is isolated. When the Kalman filter is used, failures in the sensors and actuators affect the characteristics of the residual signals of the Kalman filter. While a Robust Kalman filter is used, the decision statistics changes regardless the faults in the sensors or in the actuators, because it is sensitive to sensor fault but insensitive to actuator fault. The proposed FDI approach above, is applied to a nonlinear engine simulation in this paper, and the evaluation results show that this approach to detect and isolate sensor and actuator faults is demonstrated.