Overexpression of the Chrysanthemum lavandulifolium ROS1 gene promotes flowering in Arabidopsis thaliana by reducing the methylation level of CONSTANS.

DNA demethylation is involved in the regulation of flowering in plants, yet the underlying molecular mechanisms remain largely unexplored. The RELEASE OF SILENCING 1 (ROS1) gene, encoding a DNA demethyltransferase, plays key roles in many developmental processes. In this study, the ROS1 gene was isolated from Chrysanthemum lavandulifolium, where it was strongly expressed in the leaves, buds and flowers. Overexpression of the ClROS1 gene caused an early flowering phenotype in Arabidopsis thaliana. RNA-seq analysis of the transgenic plants revealed that differentially expressed genes (DEGs) were significantly enriched in the circadian rhythm pathway and that the positive regulator of flowering, CONSTANS (CO), was up-regulated. Additionally, whole-genome bisulphite sequencing (WGBS), PCR following methylation-dependent digestion with the enzyme McrBC, and bisulfite sequencing PCR (BSP) confirmed that the methylation level of the AtCO promoter was reduced, specifically in CG context. Overall, our results demonstrated that ClROS1 accelerates flowering by reducing the methylation level of the AtCO promoter. These findings clarify the epigenetic mechanism by which ClROS1-mediated DNA demethylation regulates flowering.

Synaptic transistor with multiple biological functions based on metal-organic frameworks combined with the LIF model of a spiking neural network to recognize temporal information.

Spiking neural networks (SNNs) have immense potential due to their utilization of synaptic plasticity and ability to take advantage of temporal correlation and low power consumption. The leaky integration and firing (LIF) model and spike-timing-dependent plasticity (STDP) are the fundamental components of SNNs. Here, a neural device is first demonstrated by zeolitic imidazolate frameworks (ZIFs) as an essential part of the synaptic transistor to simulate SNNs. Significantly, three kinds of typical functions between neurons, the memory function achieved through the hippocampus, synaptic weight regulation and membrane potential triggered by ion migration, are effectively described through short-term memory/long-term memory (STM/LTM), long-term depression/long-term potentiation (LTD/LTP) and LIF, respectively. Furthermore, the update rule of iteration weight in the backpropagation based on the time interval between presynaptic and postsynaptic pulses is extracted and fitted from the STDP. In addition, the postsynaptic currents of the channel directly connect to the very large scale integration (VLSI) implementation of the LIF mode that can convert high-frequency information into spare pulses based on the threshold of membrane potential. The leaky integrator block, firing/detector block and frequency adaptation block instantaneously release the accumulated voltage to form pulses. Finally, we recode the steady-state visual evoked potentials (SSVEPs) belonging to the electroencephalogram (EEG) with filter characteristics of LIF. SNNs deeply fused by synaptic transistors are designed to recognize the 40 different frequencies of EEG and improve accuracy to 95.1%. This work represents an advanced contribution to brain-like chips and promotes the systematization and diversification of artificial intelligence.

Light extraction from quantum dot light emitting diodes by multiscale nanostructures.

Improving the light extraction efficiency by introducing optical-functional structures outside of quantum dot light emitting diodes (QLEDs) for further enhancing the external quantum efficiency (EQE) is essential for their application in display and lighting industries. Although the efficiency of QLEDs has been optimized by controlling the synthesis of the quantum dots, the low outcoupling efficiency is indeed unresolved because of total internal reflections, waveguides and metal surface absorptions within the device. Here, we utilize multiscale nanostructures attached to the outer surface of the glass substrate to extract the trapped light from the emitting layers of QLEDs. The result indicates that both the EQE and luminance are improved from 12.29% to 17.94% and 122 400 cd m-2 to 178 700 cd m-2, respectively. The maximum EQE and current efficiency improve to 21.3% and 88.3 cd A-1, respectively, which are the best performances among reported green QLEDs with light outcoupling nanostructures. The improved performance is ascribed to the elimination of total internal reflection by multiscale nanostructures attached to the outer surface of the QLEDs. Additionally, the simulation results of the finite-difference time domain (FDTD) also demonstrate that the light trapping effect is reduced by the multiscale nanostructures. The design of novel light outcoupling nanostructures for further improving the efficiency of QLEDs can promote their application in display and lighting industries.