Pb-induced cellular defense system in the root meristematic cells of Allium sativum L

<p>Abstract</p> <p>Background</p> <p>Electron microscopy (EM) techniques enable identification of the main accumulations of lead (Pb) in cells and cellular organelles and observations of changes in cell ultrastructure. Although there is extensive literature relating to studies on the influence of heavy metals on plants, Pb tolerance strategies of plants have not yet been fully explained. <it>Allium sativum </it>L. is a potential plant for absorption and accumulation of heavy metals. In previous investigations the effects of different concentrations (10^-5to 10^-3M) of Pb were investigated in <it>A. sativum</it>, indicating a significant inhibitory effect on shoot and root growth at 10^-3to 10^-4M Pb. In the present study, we used EM and cytochemistry to investigate ultrastructural alterations, identify the synthesis and distribution of cysteine-rich proteins induced by Pb and explain the possible mechanisms of the Pb-induced cellular defense system in <it>A. sativum</it>.</p> <p>Results</p> <p>After 1 h of Pb treatment, dictyosomes were accompanied by numerous vesicles within cytoplasm. The endoplasm reticulum (ER) with swollen cisternae was arranged along the cell wall after 2 h. Some flattened cisternae were broken up into small closed vesicles and the nuclear envelope was generally more dilated after 4 h. During 24-36 h, phenomena appeared such as high vacuolization of cytoplasm and electron-dense granules in cell walls, vacuoles, cytoplasm and mitochondrial membranes. Other changes included mitochondrial swelling and loss of cristae, and vacuolization of ER and dictyosomes during 48-72 h. In the Pb-treatment groups, silver grains were observed in cell walls and in cytoplasm, suggesting the Gomori-Swift reaction can indirectly evaluate the Pb effects on plant cells.</p> <p>Conclusions</p> <p>Cell walls can immobilize some Pb ions. Cysteine-rich proteins in cell walls were confirmed by the Gomori-Swift reaction. The morphological alterations in plasma membrane, dictyosomes and ER reflect the features of detoxification and tolerance under Pb stress. Vacuoles are ultimately one of main storage sites of Pb. Root meristematic cells of <it>A. sativum </it>exposed to lower Pb have a rapid and effective defense system, but with the increased level of Pb in the cytosol, cells were seriously injured.</p

A New Method for Conflict Resoluton Based on Multi-Agent Reinforcement Learning Algorithms

Conflict resolution is a research topic for game theory (GT) and conflict analysis. A decision support system (DSS) is very helpful for conflict decision making. Reinforcement learning (RL) is an efficient method to learn knowledge by agents themselves. Although successful applications of RL have been reported in single-agent domain, a lot of work should be done in the case of multi-agent domain. Nash Q-learning is a famous learning algorithm for multi-agent RL. Based on the Nash Q-learning, a novel DSS: multi-agent RL based DSS (MRLDSS) is proposed in this paper and is tested by using several typical examples of conflict resolution. Experimental results show that the proposed architecture and algorithm can solve conflict resolution problems correctly and efficiently

A Survey on Physical Adversarial Attack in Computer Vision

Over the past decade, deep learning has revolutionized conventional tasks that rely on hand-craft feature extraction with its strong feature learning capability, leading to substantial enhancements in traditional tasks. However, deep neural networks (DNNs) have been demonstrated to be vulnerable to adversarial examples crafted by malicious tiny noise, which is imperceptible to human observers but can make DNNs output the wrong result. Existing adversarial attacks can be categorized into digital and physical adversarial attacks. The former is designed to pursue strong attack performance in lab environments while hardly remaining effective when applied to the physical world. In contrast, the latter focus on developing physical deployable attacks, thus exhibiting more robustness in complex physical environmental conditions. Recently, with the increasing deployment of the DNN-based system in the real world, strengthening the robustness of these systems is an emergency, while exploring physical adversarial attacks exhaustively is the precondition. To this end, this paper reviews the evolution of physical adversarial attacks against DNN-based computer vision tasks, expecting to provide beneficial information for developing stronger physical adversarial attacks. Specifically, we first proposed a taxonomy to categorize the current physical adversarial attacks and grouped them. Then, we discuss the existing physical attacks and focus on the technique for improving the robustness of physical attacks under complex physical environmental conditions. Finally, we discuss the issues of the current physical adversarial attacks to be solved and give promising directions

A Plug-and-Play Defensive Perturbation for Copyright Protection of DNN-based Applications

Wide deployment of deep neural networks (DNNs) based applications (e.g., style transfer, cartoonish), stimulating the requirement of copyright protection of such application's production. Although some traditional visible copyright techniques are available, they would introduce undesired traces and result in a poor user experience. In this paper, we propose a novel plug-and-play invisible copyright protection method based on defensive perturbation for DNN-based applications (i.e., style transfer). Rather than apply the perturbation to attack the DNNs model, we explore the potential utilization of perturbation in copyright protection. Specifically, we project the copyright information to the defensive perturbation with the designed copyright encoder, which is added to the image to be protected. Then, we extract the copyright information from the encoded copyrighted image with the devised copyright decoder. Furthermore, we use a robustness module to strengthen the decoding capability of the decoder toward images with various distortions (e.g., JPEG compression), which may be occurred when the user posts the image on social media. To ensure the image quality of encoded images and decoded copyright images, a loss function was elaborately devised. Objective and subjective experiment results demonstrate the effectiveness of the proposed method. We have also conducted physical world tests on social media (i.e., Wechat and Twitter) by posting encoded copyright images. The results show that the copyright information in the encoded image saved from social media can still be correctly extracted.Comment: 9 pages, 7 figure

Adversarial Examples in the Physical World: A Survey

Deep neural networks (DNNs) have demonstrated high vulnerability to adversarial examples. Besides the attacks in the digital world, the practical implications of adversarial examples in the physical world present significant challenges and safety concerns. However, current research on physical adversarial examples (PAEs) lacks a comprehensive understanding of their unique characteristics, leading to limited significance and understanding. In this paper, we address this gap by thoroughly examining the characteristics of PAEs within a practical workflow encompassing training, manufacturing, and re-sampling processes. By analyzing the links between physical adversarial attacks, we identify manufacturing and re-sampling as the primary sources of distinct attributes and particularities in PAEs. Leveraging this knowledge, we develop a comprehensive analysis and classification framework for PAEs based on their specific characteristics, covering over 100 studies on physical-world adversarial examples. Furthermore, we investigate defense strategies against PAEs and identify open challenges and opportunities for future research. We aim to provide a fresh, thorough, and systematic understanding of PAEs, thereby promoting the development of robust adversarial learning and its application in open-world scenarios.Comment: Adversarial examples, physical-world scenarios, attacks and defense

Multiobjective optimization algorithm for accurate MADYMO reconstruction of vehicle-pedestrian accidents

In vehicle–pedestrian accidents, the preimpact conditions of pedestrians and vehicles are frequently uncertain. The incident data for a crash, such as vehicle deformation, injury of the victim, distance of initial position and rest position of accident participants, are useful for verification in MAthematical DYnamic MOdels (MADYMO) simulations. The purpose of this study is to explore the use of an improved optimization algorithm combined with MADYMO multibody simulations and crash data to conduct accurate reconstructions of vehicle–pedestrian accidents. The objective function of the optimization problem was defined as the Euclidean distance between the known vehicle, human and ground contact points, and multiobjective optimization algorithms were employed to obtain the local minima of the objective function. Three common multiobjective optimization algorithms—nondominated sorting genetic algorithm-II (NSGA-II), neighbourhood cultivation genetic algorithm (NCGA), and multiobjective particle swarm optimization (MOPSO)—were compared. The effect of the number of objective functions, the choice of different objective functions and the optimal number of iterations were also considered. The final reconstructed results were compared with the process of a real accident. Based on the results of the reconstruction of a real-world accident, the present study indicated that NSGA-II had better convergence and generated more noninferior solutions and better final solutions than NCGA and MOPSO. In addition, when all vehicle-pedestrian-ground contacts were considered, the results showed a better match in terms of kinematic response. NSGA-II converged within 100 generations. This study indicated that multibody simulations coupled with optimization algorithms can be used to accurately reconstruct vehicle-pedestrian collisions

ASB-CS: Adaptive sparse basis compressive sensing model and its application to medical image encryption

Recent advances in intelligent wearable devices have brought tremendous chances for the development of healthcare monitoring system. However, the data collected by various sensors in it are user-privacy-related information. Once the individuals’ privacy is subjected to attacks, it can potentially cause serious hazards. For this reason, a feasible solution built upon the compression-encryption architecture is proposed. In this scheme, we design an Adaptive Sparse Basis Compressive Sensing (ASB-CS) model by leveraging Singular Value Decomposition (SVD) manipulation, while performing a rigorous proof of its effectiveness. Additionally, incorporating the Parametric Deformed Exponential Rectified Linear Unit (PDE-ReLU) memristor, a new fractional-order Hopfield neural network model is introduced as a pseudo-random number generator for the proposed cryptosystem, which has demonstrated superior properties in many aspects, such as hyperchaotic dynamics and multistability. To be specific, a plain medical image is subjected to the ASB-CS model and bidirectional diffusion manipulation under the guidance of the key-controlled cipher flows to yield the corresponding cipher image without visual semantic features. Ultimately, the simulation results and analysis demonstrate that the proposed scheme is capable of withstanding multiple security attacks and possesses balanced performance in terms of compressibility and robustness

Determination of heavy metals in soil by inductively coupled plasma mass spectrometry (ICP-MS) with internal standard method

Soil ,the carrier of agricultural production and important part of the ecological environment, is heavily contaminated with hazards heavy metals. Therefore, it is oblige to research analytical techniques that could efficiently determine the total content of heavy metals in soil. The determination of heavy metals in soil was disturbed by matrix elements or spectral interferences . In this study , this problem was solved by internal standard method . GBW07402、GBW07448、GBW07423、GBW07428、GBW074079 soil sample were chosen to be the Certified Reference Materials, soils was prepared by microwave digestion with mixed acid following analyzed for determination the content（Cr,Cu, Pb,Ba,Ni,Mn ） by Inductively coupled plasma mass spectrometric in 50ug/L internal standard concentration, the method was validated by compared with certified values 、method contrast(standard addition method versus internal standard method scan the same prepared solution ) and recovery check. The results of internal standard method are in excellent agreement with the indicative values and the date obtained from standard addition method, respectively. Recoveries were adequate being in the acceptable range of 90-99% and RSD of &lt;6.7 % for all the elements at three level of 5,20 and 50mg/kg with quantified by standard addition method and internal standard method .Finally, The graphy of quality control(n=100)were obtained to guide internal quality control in laborator