Reactive oxygen species promote chloroplast dysfunction and salicylic acid accumulation in fumonisin B1-induced cell death

AbstractWe report a novel regulatory mechanism by which reactive oxygen species (ROS) regulate fumonisin B1 (FB1)-induced cell death. We found that FB1 induction of light-dependent ROS production promoted the degradation of GFP-labeled chloroplast proteins and increased phenylalanine ammonia lyase (PAL) activity, PAL1 gene expression and SA content, while pretreatment with ROS manipulators reversed these trends. Moreover, treatment with H2O2 or 3-amino-1,2,4-triazole increased PAL activity, PAL1 gene expression and SA content. PAL inhibitor significantly blocked FB1-induced lesion formation and SA increase. Our results demonstrate that light-dependent ROS accumulation stimulates the degradation of chloroplastic proteins and up-regulates PAL-mediated SA synthesis, thus promoting FB1-induced light-dependent cell death

Image Source Identification Using Convolutional Neural Networks in IoT Environment

Digital image forensics is a key branch of digital forensics that based on forensic analysis of image authenticity and image content. The advances in new techniques, such as smart devices, Internet of Things (IoT), artificial images, and social networks, make forensic image analysis play an increasing role in a wide range of criminal case investigation. This work focuses on image source identification by analysing both the fingerprints of digital devices and images in IoT environment. A new convolutional neural network (CNN) method is proposed to identify the source devices that token an image in social IoT environment. The experimental results show that the proposed method can effectively identify the source devices with high accuracy

Prokineticin 2 Is a Target Gene of Proneural Basic Helix-Loop-Helix Factors for Olfactory Bulb Neurogenesis

Prokineticin 2, a cysteine-rich secreted protein, regulates diverse biological functions including the neurogenesis of olfactory bulb. Here we show that the PK2 gene is a functional target gene of proneural basic helix-loop-helix (bHLH) factors. Neurogenin 1 and MASH1 activate PK2 transcription by binding to E-box motifs on the PK2 promoter with the same set of E-boxes critical for another pair of bHLH factors, CLOCK and BMAL1, in the regulation of circadian clock. Our results establish PK2 as a common functional target gene for different bHLH transcriptional factors in mediating their respective functions

Uncertainty-Aware Decision Transformer for Stochastic Driving Environments

Offline Reinforcement Learning (RL) has emerged as a promising framework for learning policies without active interactions, making it especially appealing for autonomous driving tasks. Recent successes of Transformers inspire casting offline RL as sequence modeling, which performs well in long-horizon tasks. However, they are overly optimistic in stochastic environments with incorrect assumptions that the same goal can be consistently achieved by identical actions. In this paper, we introduce an UNcertainty-awaRE deciSion Transformer (UNREST) for planning in stochastic driving environments without introducing additional transition or complex generative models. Specifically, UNREST estimates state uncertainties by the conditional mutual information between transitions and returns, and segments sequences accordingly. Discovering the `uncertainty accumulation' and `temporal locality' properties of driving environments, UNREST replaces the global returns in decision transformers with less uncertain truncated returns, to learn from true outcomes of agent actions rather than environment transitions. We also dynamically evaluate environmental uncertainty during inference for cautious planning. Extensive experimental results demonstrate UNREST's superior performance in various driving scenarios and the power of our uncertainty estimation strategy

Analysis of the Hydroelastic Performance of Very Large Floating Structures Based on Multimodules Beam Theory

The hydroelastic behavior of very large floating structures (VLFSs) is investigated based on the proposed multimodules beam theory (MBT). To carry out the analysis, the VLFS is first divided into multiple submodules that are connected through their gravity center by a spatial beam with specific stiffness. The external force exerted on the submodules includes the wave hydrodynamic force as well as the beam bending force due to the relative displacements of different submodules. The wave hydrodynamic force is computed based on three-dimensional potential theory. The beam bending force is expressed in the form of a stiffness matrix. The motion response defined at the gravity center of the submodules is solved by the multibody hydrodynamic control equations; then both the displacement and the structure bending moment of the VLFS are determined from the stiffness matrix equations. To account for the moving point mass effects, the proposed method is extended to the time domain based on impulse response function (IRF) theory. The method is verified by comparison with existing results. Detailed results through the displacement and bending moment of the VLFS are provided to show the influence of the number of the submodules and the influence of the moving point mass

Boosting Offline Reinforcement Learning for Autonomous Driving with Hierarchical Latent Skills

Learning-based vehicle planning is receiving increasing attention with the emergence of diverse driving simulators and large-scale driving datasets. While offline reinforcement learning (RL) is well suited for these safety-critical tasks, it still struggles to plan over extended periods. In this work, we present a skill-based framework that enhances offline RL to overcome the long-horizon vehicle planning challenge. Specifically, we design a variational autoencoder (VAE) to learn skills from offline demonstrations. To mitigate posterior collapse of common VAEs, we introduce a two-branch sequence encoder to capture both discrete options and continuous variations of the complex driving skills. The final policy treats learned skills as actions and can be trained by any off-the-shelf offline RL algorithms. This facilitates a shift in focus from per-step actions to temporally extended skills, thereby enabling long-term reasoning into the future. Extensive results on CARLA prove that our model consistently outperforms strong baselines at both training and new scenarios. Additional visualizations and experiments demonstrate the interpretability and transferability of extracted skills

Fabrication and mechanical properties of Ti₂AlN/TiAl composite with continuous network structure

Nitrogen was introduced into TiAl pre-alloyed powder using high-temperature gas nitriding, and Ti2AlN/TiAl composites with a continuous network structure of the reinforced phase were prepared via spark plasma sintering. The results show that the hardness of the composite is significantly higher than that of TiAl alloy, and increases with the increase of nitriding time. The strengthening effect is originated from the synergistic effect of the solid-solution strengthening caused by the nitriding of the powder, the continuous network of the Ti2AlN phase with high hardness and elastic modulus, and the increase of dislocation density. Additionally, the compressive strength of the Ti2AlN/TiAl composites is lower than that of the TiAl alloy, which is related to a part of Ti2AlN particles that are directly formed after nitriding and excessive reinforcement content.</p