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As a critical signaling molecule, ABA plays an important role in plant growth, development and stresses response. However, tea plant [ Camellia sinensis (L.)], an important economical perennial woody plant, has not been systematically reported in response to ABA signal transduction in vivo. In this study, we mined and identified the gene structure of CsPYL/CsPP2C-A/CsSnRK gene families in the ABA signal transduction pathway through the genome-wide analysis of tea plants. Spatiotemporal expression and stress response (drought, salt, chilling) expression patterns were characterized. The results showed that most members of CsPYLs were conserved, and the gene structures of members of A-type CsPP2Cs were highly similar, whereas the gene structure of CsSnRK2s was highly variable. The transcription levels of different family members were differentially expressed with plant growth and development, and their response to stress signal patterns was highly correlated. The expression patterns of CsPYL/CsPP2C-A/CsSnRK2 gene family members in different tissues of tea plant cuttings after exogenous ABA treatment were detected by qRT-PCR, and the hierarchical model of ABA signaling was constructed by correlation analysis to preliminarily obtain three potential ABA-dependent signaling transduction pathways. Subsequently, the protein interaction of the CsPYL4/7-CsPP2C-A2-CsSnRK2.8 signaling pathway was verified by yeast two-hybrid and surface plasmon resonance experiments, indicating that there is specific selectivity in the ABA signaling pathway. Our results provided novel insights into the ABA-dependent signal transduction model in tea plant and information for future functional characterizations of stress tolerance genes in tea plant. [ABSTRACT FROM AUTHOR]

A bias‐compensated normalised least mean square (BCNLMS) algorithm with variable step size (denoted as BCVNLMS) is developed in this work to improve the accuracy and convergence speed for the three‐phase power system (TPPS) frequency estimation in circumstance of the noise and harmonic interference. Taking into account the problem of bias estimation caused by the mixed noises in the acquisition signal of the TPPS, the NLMS algorithm with an unbiased criterion (called BCNLMS) was employed to compensate the bias and improve the estimate accuracy. Furthermore, an adaptive variable step scheme is also incorporated into the BCNLMS algorithm to improve the convergence speed and stability during the process of adaptively frequency estimation. The experimental results show that the proposed BCVNLMS algorithm outperforms other well‐known methods for frequency estimation of the TPPS in several scenarios such as severe noise pollution, harmonic interference and frequency mutation. [ABSTRACT FROM AUTHOR]

This brief proposes a bias-compensated proportionate normalized least mean square (BCPNLMS) method for identifying sparse system when subjected to the noisy input. The proposed BCPNLMS algorithm, which combines the proportionate scheme and the unbiasedness criterion, is able to identify the system parameters with better steady-state accuracy and faster convergence speed than conventional NLMS, bias-compensated NLMS, and PNLMS algorithms. Robustness and high identification accuracy with noisy input can be achieved by introducing the bias-compensation term derived from the unbiasedness criterion. Simulation results on sparse system identification confirm the excellent performance of the proposed BCPNLMS in the presence of both input and output noises. [ABSTRACT FROM AUTHOR]

Proportionate-type adaptive filtering (PtAF) algorithms have been successfully applied to sparse system identification. The major drawback of the traditional PtAF algorithms based on the mean square error (MSE) criterion show poor robustness in the presence of impulsive noises or abrupt changes because MSE is only valid and rational under Gaussian assumption. However, this assumption is not satisfied in most real-world applications. To improve its robustness under non-Gaussian environments, we incorporate the maximum correntropy criterion (MCC) into the update equation of the PtAF to develop proportionate MCC (PMCC) algorithm. The mean and mean square convergence performance analysis are also performed. Simulation results in sparse system identification and echo cancellation applications are presented, which demonstrate that the proposed PMCC exhibits outstanding performance under the impulsive noise environments. [ABSTRACT FROM AUTHOR]

To address the sparse system identification problem under noisy input and non-Gaussian output measurement noise, two novel types of sparse bias-compensated normalized maximum correntropy criterion algorithms are developed, which are capable of eliminating the impact of non-Gaussian measurement noise and noisy input. The first is developed by using the correntropy-induced metric as the sparsity penalty constraint, which is a smoothed approximation of the ℓ 0 norm. The second is designed using the proportionate update scheme, which facilitates the close tracking of system parameter change. Simulation results confirm that the proposed algorithms can effectively improve the identification performance compared with other algorithms presented in the literature for the sparse system identification problem. [ABSTRACT FROM AUTHOR]

The multiplexing of fiber ring resonators (FRRs) for no crosstalk loss sensing is proposed and demonstrated experimentally. The difference between the parallel and series FRRs is theoretically elaborated to determine the multiplexing scheme. The frequency response properties of the cascaded FRRs at distinct radio frequency (RF) working points are compared and analyzed. The optical carrier-based microwave interferometry system is implemented to verify the numerical investigation and exhibit the multiplexing of phase-shift based demodulation at diverse RF working points. Enhanced by the phase-shift amplification and the series configuration, each FRR can be independently demodulated by recording the phase of frequency response at the specific RF working point. The experimental results indicate that the sensitivity of transmittance reaches -0.341 rad with the advantage of robustness and immunity to power fluctuation. Owing to the prominent contribution of insensitive points and the series strategy, the crosstalk of multiplexing for loss sensing between two FRRs is eliminated virtually, which matches well with the theory. The proposed scheme provides an innovative approach for multiplexing the phase-based FRRs sensors without additional expenditure.

It is a challenge to perform simple and rapid detection of substances due to their complex structure. Biochemical molecules play a vital role in human health and environmental testing. Surface-enhanced Raman scattering (SERS) detection has the characteristics of strong specificity and real-time performance. At present, most SERS systems are expensive and not portable. Here, we demonstrate a SERS detection system with all-fiber connection, combined with a microfluidic chip and micro-lenses. Compared with the conventional SERS system that uses the spatial optical path, the devices in our system are connected by optical fibers, making the system more stable and operable. Besides, the microfluidic chips are introduced to further improve the system integration and stability. Owing to the micro-lensed fiber probe, the detected Raman signal intensity is increased by 2-3 times. We anticipate that the presented work will lead toward a rapid and portable SERS system and corresponding detection system. It also lays the foundation for real-time recognition in various complex environments in the design of a future optical fiber system.