Visualization of metabolite distribution based on matrix-assisted laser desorption/ionization-mass spectrometry imaging of tea seedlings ().

Tea seedlings () have a well-developed root system with a strong taproot and lateral roots. Compared with ordinary cuttings, tea has stronger vitality and environmental adaptability, thus facilitating the promotion of good varieties. However, there is less of detailed research on the rooting and germination process of tea seeds.

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.

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.

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.

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.