Combined multi-omics approach to analyze the flavor characteristics and formation mechanism of gabaron green tea.

Gabaron green tea (GAGT) has unique flavor and health benefits through the special anaerobic treatment. However, how this composite processing affects the aroma formation of GAGT and the regulatory mechanism was rarely reported. This study used nontargeted metabolomics and molecular sensory science to overlay screen differential metabolites and key aroma contributors. The potential regulatory mechanism of anaerobic treatment on the aroma formation of GAGT was investigated by transcriptomics and correlation analyses. Five volatiles: benzeneacetaldehyde, nonanal, geraniol, linalool, and linalool oxide III, were screened as target metabolites. Through the transcriptional-level differential genes screening and analysis, some CsERF transcription factors in the ethylene signaling pathway were proposed might participate the response to the anaerobic treatment. They might regulate the expression of related genes in the metabolic pathway of the target metabolites thus affecting the GAGT flavor. The findings of this study provide novel information on the flavor and its formation of GAGT.

A hierarchical model of ABA-mediated signal transduction in tea plant revealed by systematic genome mining analysis and interaction validation.

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.

Multiplexing of RF-assisted fiber ring resonators based on phase-shift amplification.

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.

All-fiber surface-enhanced Raman scattering detection system combining an integrated microfluidic chip and micro-lensed fiber.

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.

Sparse-Aware Bias-Compensated Adaptive Filtering Algorithms Using the Maximum Correntropy Criterion for Sparse System Identification with Noisy Input.

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.