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Mutants of the Arabidopsis thaliana genes, EDM2 (Enhanced Downy Mildew 2) , EDM3 (Enhanced Downy Mildew 3) and IBM2 ( Increase in Bonsai Methylation 2 ) are known to show defects in a diverse set of defense and developmental processes. For example, they jointly exhibit enhanced levels of basal defense and stunted growth. Here we show that these two phenotypes are functionally connected by their dependency on the salicylic acid biosynthesis gene SID2 and the basal defense regulatory gene PAD4 . Stunted growth of edm2 , edm3 and ibm2 plants is a consequence of up-regulated basal defense. Constitutively enhanced activity of reactive oxygen species-generating peroxidases, we observed in these mutants, appears also to contribute to both, their enhanced basal defense and their growth retardation phenotypes. Furthermore, we found the histone H3 demethylase gene IBM1 , a direct regulatory target of EDM2, EDM3 and IBM2, to be at least partially required for the basal defense and growth-related effects observed in these mutants. We recently reported that EDM2 , EDM3 and IBM2 coordinate basal immunity with the timing of the floral transition by gradually reducing the extent of this defense mechanism prior to flowering. Together with these observations, data presented here show that at least some of the diverse phenotypic effects in edm2 , edm3 and ibm2 mutants are genetically interlinked and functionally connected. Our new results show that repression of basal immunity by EDM2 , EDM3 and IBM2 limits negative impact on growth and development.

Substation equipment defect detection has always played an important role in equipment operation and maintenance. However, the task scenarios of substation equipment defect detection are complex and different. Recent studies have revealed issues such as a significant missed detection rate for small-sized targets and diminished detection precision. At the same time, the current mainstream detection algorithms are highly complex, which is not conducive to deployment on resource-constrained devices. In view of the above problems, a small target and lightweight substation main scene equipment defect detection algorithm is proposed: Efficient Attentional Lightweight-YOLO (EAL-YOLO), which detection accuracy exceeds the current mainstream model, and the number of parameters and floating point operations (FLOPs) are also advantageous. Firstly, the EfficientFormerV2 is used to optimize the model backbone, and the Large Separable Kernel Attention (LSKA) mechanism has been incorporated into the Spatial Pyramid Pooling Fast (SPPF) to enhance the model’s feature extraction capabilities; secondly, a small target neck network Attentional scale Sequence Fusion P2-Neck (ASF2-Neck) is proposed to enhance the model’s ability to detect small target defects; finally, in order to facilitate deployment on resource-constrained devices, a lightweight shared convolution detection head module Lightweight Shared Convolutional Head (LSCHead) is proposed. Experiments show that compared with YOLOv8n, EAL-YOLO has improved its accuracy by 2.93 percentage points, and the mAP50 of 12 types of typical equipment defects has reached 92.26%. Concurrently, the quantity of FLOPs and parameters has diminished by 46.5% and 61.17% respectively, in comparison with YOLOv8s, meeting the needs of substation defect detection.

Metal-organic frameworks (MOFs) based on zirconium phosphonates exhibit superior chemical stability suitable for applications under harsh conditions. These compounds mostly exist as poorly crystallized precipitates, and precise structural information has therefore remained elusive. Furthermore, a zero-dimensional zirconium phosphonate cluster acting as secondary building unit has been lacking, leading to poor designability in this system. Herein, we overcome these challenges and obtain single crystals of three zirconium phosphonates that are suitable for structural analysis. These compounds are built by previously unknown isolated zirconium phosphonate clusters and exhibit combined high porosity and ultrastability even in fuming acids. SZ-2 possesses the largest void volume recorded in zirconium phosphonates and SZ-3 represents the most porous crystalline zirconium phosphonate and the only porous MOF material reported to survive in aqua regia. SZ-2 and SZ-3 can effectively remove uranyl ions from aqueous solutions over a wide pH range, and we have elucidated the removal mechanism. Zirconium phosphonate based metal-organic frameworks often exhibit superior chemical stabilities, but typically exist as poorly crystalline or amorphous materials. Here the authors exploit an ionothermal method to obtain highly porous and remarkably stable single crystalline zirconium phosphonate frameworks that can efficiently remove uranyl ions from aqueous solutions.

Despite the maximized metal dispersion offered by single-atom catalysts, further improvement of intrinsic activity can be hindered by the lack of neighboring metal atoms in these systems. Here we report the use of isolated Pt 1 atoms on ceria as “seeds” to develop a Pt-O-Pt ensemble, which is well-represented by a Pt 8 O 14 model cluster that retains 100% metal dispersion. The Pt atom in the ensemble is 100–1000 times more active than their single-atom Pt 1 /CeO 2 parent in catalyzing the low-temperature CO oxidation under oxygen-rich conditions. Rather than the Pt-O-Ce interfacial catalysis, the stable catalytic unit is the Pt-O-Pt site itself without participation of oxygen from the 10–30 nm-size ceria support. Similar Pt-O-Pt sites can be built on various ceria and even alumina, distinguishable by facile activation of oxygen through the paired Pt-O-Pt atoms. Extending this design to other reaction systems is a likely outcome of the findings reported here. Single-atom metal catalysts offer maximized material efficiency, but there is large room to improve the intrinsic activity per metal atom for many reactions. Here, the authors demonstrate that the solution for CO oxidation is to tackle the issue of lacking neighboring Pt atoms in the single-atom Pt1/CeO2 system.

Dynamic vehicle detection is of great significance for the safety of autonomous vehicles and the formulation of subsequent driving strategies. A pose-estimation algorithm, namely, the pose estimation with convex-hull model (PE-CHM), is proposed in this paper, and introduced in the dynamic vehicle detection system. In PE-CHM, the convex hull of the object’s point-clouds is first extracted and the most fitted bounding box is determined by a multifactor objective function. Next, the center position of the target is inferred according to the location and direction of the target. With the obtained bounding box and the position inference, the pose of the target is determined, which reduces the interference of the missing contour on pose estimation. Finally, three experiments were performed to validate the performance of the proposed PE-CHM method. Compared with several typical model-based methods, PE-CHM can implement dynamic vehicle detection faster, which reduces the amount of calculation on the basis of ensuring detection efficiency.

Communication disruptions in connected and autonomous vehicle (CAV) platoons induce critical stability degradation. This study proposes a dual-layer framework that combines adaptive spacing policy switching and distributed exponential sliding mode control (DESMC). The hybrid policy dynamically switches between constant spacing (CS) and constant time headway (CTH) policies based on real-time metrics, such as packet loss exceeding 5% or RSSI dropping below − 90 dBm. This approach reduces emergency braking by 85% compared to pure CS. Additionally, the DESMC controller utilizes an exponential approaching law, resulting in a 1.16% reduction in velocity RMSE and a 21.71% decrease in acceleration oscillations compared to conventional methods. Stability is verified using the Lyapunov theory, and string stability is validated under time delays via infinity-norm analysis. Simulations conducted on six CAVs show that, during failures, the system achieves a safe spacing of 8.7 m, exceeding ETC standards by 8.75%. Furthermore, platoon synchronization occurs in 35 s, 16.7% faster than conventional approaches, with steady-state errors of ± 0.15 m. Finally, the DESMC method proposed here quantitatively demonstrates the superior smoothness and stability compared to the conventional controller. The proposed framework enhances robustness in dynamic communication environments, supporting 5G-V2X-enabled transportation systems.

With the development of high-integration and high-power electronics, the lack of matching chip connecting materials that can withstand high temperatures has been a challenge. In this manuscript, a Cu@Cu 6 Sn 5 core–shell bimetallic particles (approx. 1 μm in diameter) are successfully prepared and introduced as a new solder material for the packaging of power devices to obtain a Cu 3 Sn all-IMC solder joint. The joint consisted mainly of equiaxed Cu 3 Sn grains, and a small portion of columnar Cu 3 Sn grains. In columnar-type growth, Sn is the dominant diffusing species, which comes from the depletion of Sn in Cu 6 Sn 5 . The depleted Cu 6 Sn 5 is transformed into columnar Cu 3 Sn. In equiaxed-type growth, Cu is the dominant diffusing species. Cu reacts with Cu 6 Sn 5 to grow a Cu 3 Sn layer. This conclusion was confirmed by the orientation relationship. The equiaxed Cu 3 Sn grain nucleates at the Cu/Cu 3 Sn interface have an orientation relationship with the Cu substrate. Columnar Cu 3 Sn grains at the Cu 6 Sn 5 /Cu 3 Sn interface have an orientation relationship with Cu 6 Sn 5 .

Searching for actinide decorporation agents with advantages of high decorporation efficiency, minimal biological toxicity, and high oral efficiency is crucial for nuclear safety and the sustainable development of nuclear energy. Removing actinides deposited in bones after intake is one of the most significant challenges remaining in this field because of the instantaneous formation of highly stable actinide phosphate complexes upon contact with hydroxyapatite. Here we report a hydroxypyridinone-based ligand (5LIO-1-Cm-3,2-HOPO) exhibiting stronger affinity for U(VI) compared with the reported tetradentate hydroxypyridinone ligands. This is further revealed by the first principles calculation analysis on bonding between the ligand and uranium. Both in vitro uranium removal assay and in vivo decorporation experiments with mice show that 5LIO-1-Cm-3,2-HOPO can remove uranium from kidneys and bones with high efficiencies, while the decorporation efficiency is nearly independent of the treatment time. Moreover, this ligand shows a high oral decorporation efficiency, making it attractive for practical applications. In vivo decorporation of U(VI) from bones is an unsolved challenge because of the formation of stable uranium phosphate complexes. Here, the authors develop a hydroxypyridonone-based ligand with strong uranium complexation and low cytotoxicity. They find this ligand effectively removes uranium from kidney and bones in mice, and is suitable for oral administration.

Remote sensing images contain a wealth of Earth-observation information. Efficient extraction and application of hidden knowledge from these images will greatly promote the development of resource and environment monitoring, urban planning and other related fields. Remote sensing image caption (RSIC) involves obtaining textual descriptions from remote sensing images through accurately capturing and describing the semantic-level relationships between objects and attributes in the images. However, there is currently no comprehensive review summarizing the progress in RSIC based on deep learning. After defining the scope of the papers to be discussed and summarizing them all, the paper begins by providing a comprehensive review of the recent advancements in RSIC, covering six key aspects: encoder–decoder framework, attention mechanism, reinforcement learning, learning with auxiliary task, large visual language models and few-shot learning. Subsequently a brief explanation on the datasets and evaluation metrics for RSIC is given. Furthermore, we compare and analyze the results of the latest models and the pros and cons of different deep learning methods. Lastly, future directions of RSIC are suggested. The primary objective of this review is to offer researchers a more profound understanding of RSIC.