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The emerging event cameras are bio-inspired sensors that can output pixel-level brightness changes at extremely high rates, and event-based visual-inertial odometry (VIO) is widely studied and used in autonomous robots. In this paper, we propose an event-based stereo VIO system, namely ESVIO. Firstly, we present a novel direct event-based VIO method, which fuses events’ depth, Time-Surface images, and pre-integrated inertial measurement to estimate the camera motion and inertial measurement unit (IMU) biases in a sliding window non-linear optimization framework, effectively improving the state estimation accuracy and robustness. Secondly, we design an event-inertia semi-joint initialization method, through two steps of event-only initialization and event-inertia initial optimization, to rapidly and accurately solve the initialization parameters of the VIO system, thereby further improving the state estimation accuracy. Based on these two methods, we implement the ESVIO system and evaluate the effectiveness and robustness of ESVIO on various public datasets. The experimental results show that ESVIO achieves good performance in both accuracy and robustness when compared with other state-of-the-art event-based VIO and stereo visual odometry (VO) systems, and, at the same time, with no compromise to real-time performance.

Neuroinflammation has been proven to exert an important effect on brain injury after intracerebral hemorrhage (ICH). Previous studies reported that Didymin possessed anti-inflammatory properties after acute hepatic injury, hyperglycemia-induced endothelial dysfunction, and death. However, the role of Didymin in microglial pyroptosis and neuroinflammation after ICH is unclear. The current study aimed to investigate the effect of Didymin on neuroinflammation mediated by microglial pyroptosis in mouse models of ICH and shed some light on the underlying mechanisms. In this study, we observed that Didymin treatment remarkably improved neurobehavioral performance and decreased BBB disruption and brain water content. Microglial activation and neutrophil infiltration in the peri-hematoma tissue after ICH were strikingly mitigated by Didymin as well. At the molecular level, administration of Didymin significantly unregulated the expression of Rkip and downregulated the expression of pyroptotic molecules and inflammatory cytokines such as Nlrp3 inflammasome, GSDMD, caspase-1, and mature IL-1β, TNF-α, and MPO after ICH. Besides, Didymin treatment decreased the number of Caspase-1-positive microglia and GSDMD-positive microglia after ICH. Inversely, Locostatin, an Rkip-specific inhibitor, significantly abolished the anti-pyroptosis and anti-neuroinflammation effects of Didymin. Moreover, Rkip binding with Asc could interrupt the activation and assembly of the inflammasome. Mechanistically, inhibition of Caspase-1 by VX-765 attenuated brain injury and suppressed microglial pyroptosis and neuroinflammation by downregulation of GSDMD, mature IL-1β, TNF-α, and MPO based on Locostatin-treated ICH. Taken together, Didymin alleviated microglial pyroptosis and neuroinflammation, at least in part through the Asc/Caspase-1/GSDMD pathway via upregulating Rkip expression after ICH. Therefore, Didymin may be a potential agent to attenuate neuroinflammation via its anti-pyroptosis effect after ICH.

This study proposes a novel egg-crate honeycomb core sandwich panel (SP-EHC) that combines the structural advantages of conventional lattice and grid configurations while mitigating their limitations in stability and mechanical performance. The design employs chamfered intersecting grid walls to create a semi-enclosed honeycomb architecture, enhancing out-of-plane stiffness and buckling resistance and enabling ventilation and drainage. To facilitate efficient and accurate structural analysis, a two-dimensional equivalent plate model (2D-EPM) is developed using the variational asymptotic method (VAM). This model significantly reduces the complexity of three-dimensional elasticity problems while preserving essential microstructural characteristics. A Reissner–Mindlin-type formulation is derived, enabling local field reconstruction for detailed stress and displacement evaluation. Model validation is conducted through experimental testing and three-dimensional finite element simulations. The 2D-EPM demonstrates high accuracy, with static analysis errors in load–displacement response within 10% and a maximum modal frequency error of 10.23% in dynamic analysis. The buckling and bending analyses, with or without initial deformation, show strong agreement with the 3D-FEM results, with deviations in the critical buckling load not exceeding 5.23%. Local field reconstruction achieves stress and displacement prediction errors below 2.7%, confirming the model’s fidelity at both global and local scales. Overall, the VAM-based 2D-EPM provides a robust and computationally efficient framework for the structural analysis and optimization of advanced sandwich panels.

Characterizing the genetic diversity and population structure can determine whether there is gene flow of the natural population of Helicoverpa armigera (Hübner) under disparate climate and habitat conditions in Northwest China. In this paper, H. armigera was genotyped in various regions of Xinjiang using Genotyping-by-Sequencing (GBS). The samples were compared using the single nucleotide polymorphism (SNP) and insertion deletion (InDel) marker data. The SNPs were used to analyze the population structure and five subgroups were obtained, which was further confirmed by principal component analysis (PCA). The phylogenetic tree identified five cluster populations of H. armigera . The average values of polymorphic information content (PIC) and genetic differentiation index (Fst) are 0.1783 and 0.1293, respectively, which are at a high level. The phylogenetic tree differentiation also indicates that the genetic diversity of cotton bollworm populations in different regions of Xinjiang is low diversity, moderate differentiation, and widespread gene flow. According to correlation analysis of the source of feeding on host plants (Bt cotton and non Bt crops) of H. armigera, seven SNPs with significant differences were obtained. The most significant SNP sequence was compared with the whole genome of H. armigera , and 10 candidate genes were screened. Whether the candidate genes function are related to Bt resistance needs further verification. This study can provide scientific basis for screening Bt resistance genes and formulating refuge strategy of H. armigera in Northwest China.

In this paper, an enhanced deep reinforcement learning approach is presented for unmanned aerial vehicles (UAVs) operating in dynamic and potentially hazardous environments. Initially, the capability to discern obstacles from visual data is achieved through the application of the Yolov8-StrongSort technique. Concurrently, a novel data storage system for deep Q-networks (DQN), named dynamic data memory (DDM), is introduced to hasten the learning process and convergence for UAVs. Furthermore, addressing the issue of UAVs’ paths veering too close to obstacles, a novel strategy employing an artificial potential field to adjust the reward function is introduced, which effectively guides the UAVs away from proximate obstacles. Rigorous simulation tests in an AirSim-based environment confirm the effectiveness of these methods. Compared to DQN, dueling DQN, M-DQN, improved Q-learning, DDM-DQN, EPF (enhanced potential field), APF-DQN, and L1-MBRL, our algorithm achieves the highest success rate of 77.67%, while also having the lowest average number of moving steps. Additionally, we conducted obstacle avoidance experiments with UAVs with different densities of obstacles. These tests highlight fast learning convergence and real-time obstacle detection and avoidance, ensuring successful achievement of the target.

The investigation of electronic excited states in single-molecule junctions not only provides platforms to reveal the photophysical and photochemical processes at the molecular level, but also brings opportunities for the development of single-molecule optoelectronic devices. Understanding the interaction mechanisms between molecules and nanocavities is essential to obtain on-demand properties in devices by artificial design, since molecules in junctions exhibit unique behaviors of excited states benefited from the structures of metallic nanocavities. Here, we review the excitation mechanisms involved in the interplay between molecules and plasmonic nanocavities, and reveal the influence of nanostructures on excited-state properties by demonstrating the differences in excited state decay processes. Furthermore, vibronic transitions of molecules between nanoelectrodes are also discussed, offering a new single-molecule characterization method. Finally, we provide the potential applications and challenges in single-molecule optoelectronic devices and the possible directions in exploring the underlying mechanisms of photophysical and photochemical processes.

Intracerebral hemorrhage (ICH) is a major cerebrovascular illness that causes substantial neurological sequelae and dysfunction caused by secondary brain injury (SBI), and there are no effective therapies to mitigate the disability. Microglia, the brain-resident macrophage, participates in the primary inflammatory response, and activation of microglia to an M1-like phenotype largely takes place in the acute phase following ICH. A growing body of research suggests that the pathophysiology of SBI after ICH is mediated by an inflammatory response mediated by microglial-pyroptotic inflammasomes, while inhibiting the activation of microglial pyroptosis could suppress the inflammatory cascade reaction, thus attenuating the brain injury after ICH. Pyroptosis is characterized by rapid plasma membrane disruption, followed by the release of cellular contents and pro-inflammatory mediators. In this review, we outline the molecular mechanism of microglial pyroptosis and summarize the up-to-date evidence of its involvement in the pathological process of ICH, and highlight microglial pyroptosis-targeted strategies that have the potential to cure intracerebral hemorrhage. This review contributes to a better understanding of the function of microglial pyroptosis in ICH and assesses it as a possible therapeutic target.

The emerging event cameras have the potential to be an excellent complement for standard cameras within various visual tasks, especially in illumination‐changing environments or situations requiring high‐temporal resolution. Herein, an event‐based stereo visual odometry (VO) system via adaptive time‐surface (TS) and truncated signed distance function (TSDF), namely, T‐ESVO, is proposed . The system consists of three carefully designed components, including the event processing unit, the mapping unit, and the tracking unit. Specifically, the event processing unit adopts a novel spatial–temporal adaptive TS that can deal with different camera motions in various environments. The mapping unit introduces the TSDF to describe the 3D representation of environments and achieves depth estimation based on the global historical depth information contained in the environmental TSDF description. The tracking unit achieves the 6‐DoF pose estimation through an 3D–2D registration method based on the left/right TS selection mechanism and the depth point selection mechanism. The effectiveness and robustness of the proposed system are evaluated on various datasets, and the experimental results show that T‐ESVO achieves good performance in both accuracy and robustness when compared with other state‐of‐the‐art event‐based stereo VO systems.

Understanding the mechanisms and kinetics of enzymatic reactions is essential for studies of life science and for bioengineering. Here the different reaction states in the catalytic cycle of formate dehydrogenase have been distinguished by their characteristic conductances, using the scanning tunnelling microscope break-junction technique, and these conductances have been further exploited as markers to monitor the catalytic mechanism of formate dehydrogenase from Candida boidinii . Combined with multiscale simulations, we demonstrate that the bound reduced form of nicotinamide adenine dinucleotide (NADH) converts to nicotinamide adenine dinucleotide (NAD + ) directly via a hydride-transfer reaction in situ during the catalytic cycle of formate dehydrogenase. This conversion does not proceed via the apoenzyme state invoked in the conventional, generally accepted Theorell–Chance mechanism. This work provides intriguing insight into the mechanism of formate dehydrogenase and highlights the potential of the single-molecule technique in revealing the catalytic mechanism of NADH/NAD + -dependent oxidoreductases. The catalytic cycle of formate dehydrogenase is generally assumed to involve an apoenzyme state according to the Theorell–Chance mechanism. Now, based on single-molecule experiments and multiscale simulations of formate dehydrogenase from Candida boidinii , an alternative mechanism that bypasses the apoenzyme state is proposed.

Event cameras which transmit per-pixel intensity changes have emerged as a promising candidate in applications such as consumer electronics, industrial automation, and autonomous vehicles, owing to their efficiency and robustness. To maintain these inherent advantages, the trade-off between efficiency and accuracy stands as a priority in event-based algorithms. Thanks to the preponderance of deep learning techniques and the compatibility between bio-inspired spiking neural networks and event-based sensors, data-driven approaches have become a hot spot, which along with the dedicated hardware and datasets constitute an emerging field named event-based data-driven technology. Focusing on data-driven technology in event-based vision, this paper first explicates the operating principle, advantages, and intrinsic nature of event cameras, as well as background knowledge in event-based vision, presenting an overview of this research field. Then, we explain why event-based data-driven technology becomes a research focus, including reasons for the rise of event-based vision and the superiority of data-driven approaches over other event-based algorithms. Current status and future trends of event-based data-driven technology are presented successively in terms of hardware, datasets, and algorithms, providing guidance for future research. Generally, this paper reveals the great prospects of event-based data-driven technology and presents a comprehensive overview of this field, aiming at a more efficient and bio-inspired visual system to extract visual features from the external environment.