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Shade affects all aspects of plant growth and development, including seed germination, hypocotyl elongation, petiole growth, leaf hyponasty, and flowering time. Here, we found that mutations in the key Arabidopsis karrikins signal perception-associated KARRIKIN INSENSITIVE 2 (KAI2) gene, encoding an α/β-fold hydrolase, and the MORE AXILLARY GROWTH 2 (MAX2) gene, encoding an F-box protein, led to greater hypocotyl elongation under shade avoidance conditions. We further verified that these phenotypes were caused by perception of the endogenous KAI2-ligands (KLs), and that this phenotype is independent of strigolactone biosynthetic or signaling pathways. Upon perception of a KL, it is probable that the target protein forms a complex with the KAI2/MAX2 proteins, which are degraded through the action of the 26S proteasome. We demonstrated that SUPPRESSOR OF MAX2-1 (SMAX1) is the degradation target for the KAI2/MAX2 complex in the context of shade avoidance. KAI2 and MAX2 require SMAX1 to limit the hypocotyl growth associated with shade avoidance. Treatment with l-kynurenine, an inhibitor of auxin accumulation, partially restored elongation of kai2 mutant hypocotyls under simulated shade. Furthermore, KAI2 is involved in regulating auxin accumulation and polar auxin transport, which may contribute to the hypocotyl shade response. In addition, SMAX1 gene overexpression promoted the hypocotyl shade response. RNA-sequencing analysis revealed that SMAX1-overexpression affected the expression of many auxin homeostasis genes, especially under simulated shade. Altogether, our data support the conclusion that KL signaling regulates shade avoidance by modulating auxin homeostasis in the hypocotyl.

Screening for primary aldosteronism on and off interfering medications Appendix

In this paper, we study the stability of nuclear magnetic resonance gyroscope (NMRG), which employs Xe nuclear spins to measure inertial rotation rate. The Xe spin polarization is sensed by an in-situ Rb-magnetometer. The Rb-magnetometer works in a parametric oscillation mode (henceforth referred to as the Rb parametric magnetometer, or Rb-PM), in which the Larmor frequency of the Rb spins is modulated and the transverse components of Xe nuclear spin polarization are measured. As the measurement output of the Rb-PM, the phase of the Xe nuclear spin precession is eventually converted to the Xe nuclear magnetic resonance (NMR) frequencies and the inertial rotation rate. Here we provide a comprehensive study of the NMR phase measured by the Rb-PM, and analyze the influence of various control parameters, including the DC magnetic field, the frequency and phase of the modulation field, and the Rb resonance linewidth, on the stability of the NMR phase. Based on these analysis, we propose and implement a self-calibrating method to compensate the NMR phase drift during the Rb-PM measurement. With the self-calibrating Rb-PM, we demonstrate a significant improvement of the bias stability of NMRG., Comment: 16 pages, 11 figures

A lightweight and high-performance electromagnetic wave absorbing material, microtubule-based hierarchical porous carbon (MHPC), was realized by a feasible pretreatment and thermal annealing process. The unique architecture not only effectively reduced the carbon density, but also significantly contributed to impedance matching, inner multi-scattering, conductive loss, and dielectric polarization, thereby achieving extraordinary microwave absorption performance. An exceptionally strong reflection loss (RL) of −63.5 dB at 14.5 GHz and a wide effective absorption bandwidth up to 7.3 GHz covering the whole Ku band and half X band were revealed. Simultaneously, a superior specific reflection loss was attained under a low absorber loading (15 wt%) and an ultralow density (0.170 g cm−3). The fundamental microwave absorbing mechanism of typical MHPC was discussed. This study provides an important strategy toward the mass production of a sustainable, lightweight, and strong broadband microwave absorption material.

Sound event localization and detection (SELD) is a joint task of sound event detection and direction-of-arrival estimation. In DCASE 2022 Task 3, types of data transform from computationally generated spatial recordings to recordings of real-sound scenes. Our system submitted to the DCASE 2022 Task 3 is based on our previous proposed Event-Independent Network V2 (EINV2) with a novel data augmentation method. Our method employs EINV2 with a track-wise output format, permutation-invariant training, and a soft parameter-sharing strategy, to detect different sound events of the same class but in different locations. The Conformer structure is used for extending EINV2 to learn local and global features. A data augmentation method, which contains several data augmentation chains composed of stochastic combinations of several different data augmentation operations, is utilized to generalize the model. To mitigate the lack of real-scene recordings in the development dataset and the presence of sound events being unbalanced, we exploit FSD50K, AudioSet, and TAU Spatial Room Impulse Response Database (TAU-SRIR DB) to generate simulated datasets for training. We present results on the validation set of Sony-TAu Realistic Spatial Soundscapes 2022 (STARSS22) in detail. Experimental results indicate that the ability to generalize to different environments and unbalanced performance among different classes are two main challenges. We evaluate our proposed method in Task 3 of the DCASE 2022 challenge and obtain the second rank in the teams ranking. Source code is released., Comment: Submitted to DCASE 2022 Workshop. Code is available at https://github.com/Jinbo-Hu/DCASE2022-TASK3

In order to solve the problems of complicated calculation and poor real-time performance in measuring the electrical parameters of the traditional smart meter, this paper proposed a real-time Synchronized Goertzel algorithm which can be well applied in the smart meter (SM) system. By processing the voltage and current data collected from the grid terminal, the algorithm can obtain the information of frequency, energy and waveform with time labels, so as to realize low cost, efficient measurement and transmission of grid monitoring information in the Internet of Things (IoT). At the same time, the algorithm can adapt to the change of power grid frequency, calculate the RMS values of voltage and current with high accuracy, avoiding the problem of inaccurate energy evaluation caused by the fixed frequency constant of smart meter. Furthermore, the efficacy of the proposed algorithm has been evaluated by theoretical calculation and validation in MATLAB/Simulink environment.

In this paper, the main circuit topology of the three-phase four-wire active power filter (APF) and its harmonic current detection and control algorithm are studied. An active power filter system combining LCLCL filter and a three-phase H-bridge structure is proposed. This paper has applied harmonic detection algorithm based on an improved synchronous phase-locked loop (SRF-PLL) and recursive discrete Fourier transform (RDFT) while using triangular carrier modulation control based on PI adjustment to realize the harmonic compensation of the power filter to the grid current. The system has a notch effect on the harmonic at the switching frequency. It can detect the fundamental component and each harmonic component of the load current separately with a small amount of calculation, good real-time performance. Finally, a 5KVA single-phase APF prototype was built to test the superiority of the main circuit topology and the algorithm of current detection and control.