Identification of SDG gene family members and exploration of flowering related genes in different cultivars of chrysanthemums and their wild ancestors.

The SET domain genes (SDGs) are significant contributors to various aspects of plant growth and development, mainly includes flowering, pollen development, root growth, regulation of the biological clock and branching patterns. To clarify the biological functions of the chrysanthemum SDG family, the SDG family members of four chrysanthemum cultivars and three related wild species were identified; their physical and chemical properties, protein domains and conserved motifs were predicted and analyzed. The results showed that 59, 67, 67, 102, 106, 114, and 123 SDGs were identified from Chrysanthemum nankingense, Chrysanthemum lavandulifolium, Chrysanthemum seticuspe, Chrysanthemum × morifolium cv.

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.

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.

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.