Identification of the target genes of AhTWRKY24 and AhTWRKY106 transcription factors reveals their regulatory network in Arachis hypogaea cv. Tifrunner using DAP-seq

WRKY transcription factors (TFs) have been identified as important core regulators in the responses of plants to biotic and abiotic stresses. Cultivated peanut (Arachis hypogaea) is an important oil and protein crop. Previous studies have identified hundreds of WRKY TFs in peanut. However, their functions and regulatory networks remain unclear. Simultaneously, the AdWRKY40 TF is involved in drought tolerance in Arachis duranensis and has an orthologous relationship with the AhTWRKY24 TF, which has a homoeologous relationship with AhTWRKY106 TF in A. hypogaea cv. Tifrunner. To reveal how the homoeologous AhTWRKY24 and AhTWRKY106 TFs regulate the downstream genes, DNA affinity purification sequencing (DAP-seq) was performed to detect the binding sites of TFs at the genome-wide level. A total of 3486 downstream genes were identified that were collectively regulated by the AhTWRKY24 and AhTWRKY106 TFs. The results revealed that W-box elements were the binding sites for regulation of the downstream genes by AhTWRKY24 and AhTWRKY106 TFs. A gene ontology enrichment analysis indicated that these downstream genes were enriched in protein modification and reproduction in the biological process. In addition, RNA-seq data showed that the AhTWRKY24 and AhTWRKY106 TFs regulate differentially expressed genes involved in the response to drought stress. The AhTWRKY24 and AhTWRKY106 TFs can specifically regulate downstream genes, and they nearly equal the numbers of downstream genes from the two A. hypogaea cv. Tifrunner subgenomes. These results provide a theoretical basis to study the functions and regulatory networks of AhTWRKY24 and AhTWRKY106 TFs

Carbon conduction effect and multi-scenario carbon emission responses of land use patterns transfer: a case study of the Baiyangdian basin in China

Carbon pooling and release occur all the time in all corners of the earth, where the land use factor is key to influencing the realization of carbon peaking and neutrality. Land use patterns and carbon emissions change under different scenarios and analyzing the correlation will help formulate scientific land use policies for the future. In this study, through remote sensing data, we investigated the changes in land use patterns and carbon emissions in the Baiyangdian basin in China from 2000 to 2020 and analyzed the carbon conduction effect with the help of a land transfer matrix. The geographical simulation and optimization system-future land use simulation (GeoSOS-FLUS) and Markov models were used to predict the land use changes and carbon emissions under the four different scenarios for the region in 2035. The results indicated that 1) the net land use carbon emissions increased from 52,163.03 × 103 to 260,754.91.28 × 103 t from 2000 to 2020, and the carbon source-sink ratio exhibited a general uptrend; 2) the net carbon emissions due to terrestrial transfers increased over time. The carbon conduction effects due to the transfer of forests, grasslands, water areas, and unused lands to built-up lands also showed a rising trend, albeit the latter two exhibited only small changes; 3) in 2035, the net carbon uptake under the four development scenarios was predicted to be 404,238.04 × 103, 402,009.45 × 103, 404,231.64 × 103, and 404,202.87×103 t, respectively, with all values much higher than that of the study area in 2020. The maximum carbon sink capacity was 817.88 × 103 t under the double-carbon target scenario, and the maximum carbon source emission was 405,033.61 × 103 t under the natural development scenario. The above results provide an essential reference for low carbon-based urban land use regulations for the Baiyangdian basin and other similar projects in the future

Multiresponsive Supramolecular Gel Based on Pillararene-Containing Polymers

A multiresponsive supramolecular gel was constructed based on a bis­(pyridinium) dication guest and a copolymer with pillararenes as the pendant groups, which was synthesized by free radical copolymerization of methacrylate-functionalized pillararenes and methyl methacrylate. The mechanism of gel formation was explored by the intensive study. Upon addition of competitive host or guest molecules, pillararene-based gel could be transferred into sol due to the competition of host–guest complexation. Surprisingly, the ordered stacking of pillararenes was indispensable to obtain the supramolecular gel, which endowed the system with response to temperature change

A Threshold Switching Selector Based on Highly Ordered Ag Nanodots for X‐Point Memory Applications

Abstract Leakage interference between memory cells is the primary obstacle for enlarging X‐point memory arrays. Metal‐filament threshold switches, possessing excellent selectivity and low leakage current, are developed in series with memory cells to reduce sneak path current and lower power consumption. However, these selectors typically have limited on‐state currents (≤10 µA), which are insufficient for memory RESET operations. Here, a strategy is proposed to achieve sufficiently large RESET current (≈2.3 mA) by introducing highly ordered Ag nanodots to the threshold switch. Compared to the Ag thin film case, Ag nanodots as active electrode could avoid excessive Ag atoms migration into solid electrolyte during operations, which causes stable conductive filament growth. Furthermore, Ag nanodots with rapid thermal processing contribute to forming multiple weak Ag filaments at a lower voltage and then spontaneous rupture as the applied voltage reduced, according to quantized conductance and simulation analysis. Impressively, the Ag nanodots based threshold switch, which is bidirectional and truly electroforming‐free, demonstrates extremely high selectivity >109, ultralow leakage current <1 pA, very steep slope of 0.65 mV dec−1, and good thermal stability up to 200 °C, and further represents significant suppression of leakage currents and excellent performances for SET/RESET operations in the one‐selector‐one‐resistor configuration

Application of mathematical morphology operation with memristor-based computation-in-memory architecture for detecting manufacturing defects

Mathematical morphology operations are widely used in image processing such as defect analysis in semiconductor manufacturing and medical image analysis. These data-intensive applications have high requirements during hardware implementation that are challenging for conventional hardware platforms such as central processing units (CPUs) and graphics processing units (GPUs). Computation-in-memory (CIM) provides a possible solution for highly efficient morphology operations. In this study, we demonstrate the application of morphology operation with a novel memristor-based auto-detection architecture and demonstrate non-neuromorphic computation on a multi-array-based memristor system. Pixel-by-pixel logic computations with low parallelism are converted to parallel operations using memristors. Moreover, hardware-implemented computer-integrated manufacturing was used to experimentally demonstrate typical defect detection tasks in integrated circuit (IC) manufacturing and medical image analysis. In addition, we developed a new implementation scheme employing a four-layer network to realize small-object detection with high parallelism. The system benchmark based on the hardware measurement results showed significant improvement in the energy efficiency by approximately 358 times and 32 times more than when a CPU and GPU were employed, respectively, exhibiting the advantage of the proposed memristor-based morphology operation