2D Materials-Based Static Random-Access Memory.

2D transition-metal dichalcogenide semiconductors, such as MoS2 and WSe2 , with adequate bandgaps are promising channel materials for ultrascaled logic transistors. This scalability study of 2D material (2DM)-based field-effect transistor (FET) and static random-access memory (SRAM) cells analyzing the impact of layer thickness reveals that the monolayer 2DM FET with superior electrostatics is beneficial for its ability to mitigate the read-write conflict in an SRAM cell at scaled technology nodes (1-2.1 nm). Moreover, the monolayer 2DM SRAM exhibits lower cell read access time and write time than the bilayer and trilayer 2DM SRAM cells at fixed leakage power. This simulation predicts that the optimization of 2DM SRAM designed with state-of-the-art contact resistance, mobility, and equivalent oxide thickness leads to excellent stability and operation speed at the 1-nm node. Applying the nanosheet (NS) gate-all-around (GAA) structure to 2DM further reduces cell read access time and write time and improves the area density of the SRAM cells, demonstrating a feasible scaling path beyond Si technology using 2DM NSFETs. In addition to the device design, the process challenges for 2DM NSFETs, including the cost-effective stacking of 2DM layers, formation of electrical contacts, suspended 2DM channels, and GAA structures, are also discussed.

LEAFY COTYLEDON 2: A Regulatory Factor of Plant Growth and Seed Development.

Transcription factors are key molecules in the regulation of gene expression in all organisms. The transcription factor LEAFY COTYLEDON 2 (LEC2), which belongs to the DNA-binding protein family, contains a B3 domain. The transcription factor is involved in the regulation of important plant biological processes such as embryogenesis, somatic embryo formation, seed storage protein synthesis, fatty acid metabolism, and other important biological processes. Recent studies have shown that LEC2 regulates the formation of lateral roots and influences the embryonic resetting of the parental vernalization state. The orthologs of LEC2 and their regulatory effects have also been identified in some crops; however, their regulatory mechanism requires further investigation. Here, we summarize the most recent findings concerning the effects of LEC2 on plant growth and seed development. In addition, we discuss the potential molecular mechanisms of the action of the LEC2 gene during plant development.

Seed-specific activity of the Arabidopsis ß-glucosidase 19 promoter in transgenic Arabidopsis and tobacco.

KEY MESSAGE: The promoter of the Arabidopsis thaliana ß-glucosidase 19 gene directs GUS expression in a seed-specific manner in transgenic Arabidopsis and tobacco. In the present study, an 898-bp putative promoter of the Arabidopsis ß-glucosidase 19 (AtBGLU19) gene was cloned. The bioinformatics analysis of the cis-acting elements indicated that this putative promoter contains many seed-specific elements, such as RY elements. The features of this promoter fragment were evaluated for the capacity to direct the ß-glucuronidase (GUS) reporter gene in transgenic Arabidopsis and tobacco. Histochemical and fluorometric GUS analyses of transgenic Arabidopsis plants revealed that the AtBGLU19 promoter directed strong GUS activity in late-maturing seeds and dry seeds, whereas no GUS expression was observed in other organs. The results indicated that the AtBGLU19 promoter was able to direct GUS expression in a seed-specific manner in transgenic Arabidopsis. In tobacco, the intensity of the staining and the level of GUS activity were considerably higher in the seeds than in the other tissues. These results further confirmed that the AtBGLU19 promoter is seed specific and can be used to control transgene expression in a heterologous plant system.

Sparse representation for infrared Dim target detection via a discriminative over-complete dictionary learned online.

It is difficult for structural over-complete dictionaries such as the Gabor function and discriminative over-complete dictionary, which are learned offline and classified manually, to represent natural images with the goal of ideal sparseness and to enhance the difference between background clutter and target signals. This paper proposes an infrared dim target detection approach based on sparse representation on a discriminative over-complete dictionary. An adaptive morphological over-complete dictionary is trained and constructed online according to the content of infrared image by K-singular value decomposition (K-SVD) algorithm. Then the adaptive morphological over-complete dictionary is divided automatically into a target over-complete dictionary describing target signals, and a background over-complete dictionary embedding background by the criteria that the atoms in the target over-complete dictionary could be decomposed more sparsely based on a Gaussian over-complete dictionary than the one in the background over-complete dictionary. This discriminative over-complete dictionary can not only capture significant features of background clutter and dim targets better than a structural over-complete dictionary, but also strengthens the sparse feature difference between background and target more efficiently than a discriminative over-complete dictionary learned offline and classified manually. The target and background clutter can be sparsely decomposed over their corresponding over-complete dictionaries, yet couldn't be sparsely decomposed based on their opposite over-complete dictionary, so their residuals after reconstruction by the prescribed number of target and background atoms differ very visibly. Some experiments are included and the results show that this proposed approach could not only improve the sparsity more efficiently, but also enhance the performance of small target detection more effectively.