EXECUTER2 modulates the EXECUTER1 signalosome through its singlet oxygen-dependent oxidation

Oxidative post-translational modifications of specific chloroplast proteins contribute to the initiation of retrograde signaling.The Arabidopsis thaliana EXECUTER1(EX1)protein,a chloroplast-localized singlet oxygen(^(1)O_(2))sensor,undergoes tryptophan(Trp)643 oxidation by^(1)O_(2),a chloroplast-derived and light-dependent reactive oxygen species.The indole side chain of Trp is vulnerable to^(1)O_(2),leading to the generation of oxidized Trp variants and priming EX1 for degradation by a membrane-bound FtsH protease.The perception of^(1)O_(2)via Trp643 oxidation and subsequent EX1 proteolysis facilitate chloroplast-to-nucleus retrograde signaling.In this study,we discovered that the EX1-like protein EX2 also undergoes^(1)O_(2)-dependent Trp530 oxidation and FtsH-dependent turnover,which attenuates^(1)O_(2)signaling by decelerating EX1-Trp643 oxidation and subsequent EX1 degradation.Consistent with this finding,the loss of EX2 function reinforces EX1-dependent retrograde signaling by accelerating EX1-Trp643 oxidation and subsequent EX1 proteolysis,whereas overexpression of EX2 produces molecular phenotypes opposite to those observed in the loss-of-function mutants of EX2.Intriguingly,phylogenetic analysis suggests that EX2 may have emerged evolutionarily to attenuate the sensitivity of EX1 toward^(1)O_(2).Collectively,these results suggest that EX2 functions as a negative regulator of the EX1 signalosome through its own^(1)O_(2)-dependent oxidation,providing a new mechanistic insight into the regulation of EX1-mediated^(1)O_(2)signaling.

1O2-Mediated and EXECUTER-Dependent Retrograde Plastid-to-Nucleus Signaling in Norflurazon-Treated Seedlings of Arabidopsis thaliana

Chloroplast development depends on the synthesis and import of a large number of nuclear-encoded pro- teins. The synthesis of some of these proteins is affected by the functional state of the plastid via a process known as retrograde signaling. Retrograde plastid-to-nucleus signaling has been often characterized in seedlings of Arabidopsis thaliana exposed to norflurazon （NF）, an inhibitor of carotenoid biosynthesis. Results of this work suggested that, throughout seedling development, a factor is released from the plastid to the cytoplasm that indicates a perturbation of plastid homeostasis and represses nuclear genes required for normal chloroplast development. The identity of this factor is still under debate. Reactive oxygen species （ROS） were among the candidates discussed as possible retrograde signals in NF-treated plants. In the present work, this proposed role of ROS has been analyzed. In seedlings grown from the very beginning in the presence of NF, ROS-dependent signaling was not detectable, whereas, in seedlings first exposed to NF after light-dependent chloroplast formation had been completed, enhanced ROS production occurred and, among oth- ers, 1O2-mediated and EXECUTER-dependent retrograde signaling was induced. Hence, depending on the developmental stage at which plants are exposed to NF, different retrograde signaling pathways may be activated, some of which are also active in non-treated plants under light stress.

Dopamine in the prefrontal cortex plays multiple roles in the executive function of patients with Parkinson's disease

Parkinson’s disease can affect not only motor functions but also cognitive abilities,leading to cognitive impairment.One common issue in Parkinson’s disease with cognitive dysfunction is the difficulty in executive functioning.Executive functions help us plan,organize,and control our actions based on our goals.The brain area responsible for executive functions is called the prefrontal co rtex.It acts as the command center for the brain,especially when it comes to regulating executive functions.The role of the prefrontal cortex in cognitive processes is influenced by a chemical messenger called dopamine.However,little is known about how dopamine affects the cognitive functions of patients with Parkinson’s disease.In this article,the authors review the latest research on this topic.They start by looking at how the dopaminergic syste m,is alte red in Parkinson’s disease with executive dysfunction.Then,they explore how these changes in dopamine impact the synaptic structure,electrical activity,and connection components of the prefrontal cortex.The authors also summarize the relationship between Parkinson’s disease and dopamine-related cognitive issues.This information may offer valuable insights and directions for further research and improvement in the clinical treatment of cognitive impairment in Parkinson’s disease.

Cerebral arterial blood flow,attention,and executive and cognitive functions in depressed patients after acute hypertensive cerebral hemorrhage

BACKGROUND Intracerebral hemorrhage mainly occurs in middle-aged and elderly patients with hypertension,and surgery is currently the main treatment for hypertensive cerebral hemorrhage,but the bleeding caused by surgery will cause damage to the patient's nerve cells,resulting in cognitive and motor dysfunction,resulting in a decline in the patient's quality of life.AIM To investigate associations between cerebral arterial blood flow and executive and cognitive functions in depressed patients after acute hypertensive cerebral hemorrhage.METHODS Eighty-nine patients with depression after acute hypertensive cerebral hemorrhage who were admitted to our hospital between January 2019 and July 2021 were selected as the observation group,while 100 patients without depression who had acute hypertensive cerebral hemorrhage were selected as the control group.The attention span of the patients was assessed using the Paddle Pin Test while executive function was assessed using the Wisconsin Card Sorting Test(WCST)and cognitive function was assessed using the Montreal Cognitive Assessment Scale(MoCA).The Hamilton Depression Rating Scale(HAMD-24)was used to evaluate the severity of depression of involved patients.Cerebral arterial blood flow was measured in both groups.RESULTS The MoCA score,net scores I,II,III,IV,and the total net score of the scratch test in the observation group were significantly lower than those in the control group(P<0.05).Concurrently,the total number of responses,number of incorrect responses,number of persistent errors,and number of completed responses of the first classification in the WCST test were significantly higher in the observation group than those in the control group(P<0.05).Blood flow in the basilar artery,left middle cerebral artery,right middle cerebral artery,left anterior cerebral artery,and right anterior cerebral artery was significantly lower in the observation group than in the control group(P<0.05).The basilar artery,left middle cerebral artery,right middle cerebral artery,left anterior cerebral artery,and right anterior cerebral artery were positively correlated with the net and total net scores of each part of the Paddle Pin test and the MoCA score(P<0.05),and negatively correlated with each part of the WCST test(P<0.05).In the observation group,the post-treatment improvement was more prominent in the Paddle Pin test,WCST test,HAMD-24 score,and MoCA score compared with those in the pre-treatment period(P<0.05).Blood flow in the basilar artery,left middle cerebral artery,right middle cerebral artery,left anterior cerebral artery,and right anterior cerebral artery significantly improved in the observation group after treatment(P<0.05).CONCLUSION Impaired attention,and executive and cognitive functions are correlated with cerebral artery blood flow in patients with depression after acute hypertensive cerebral hemorrhage and warrant further study.

Binary Program Vulnerability Mining Based on Neural Network

Software security analysts typically only have access to the executable program and cannot directly access the source code of the program.This poses significant challenges to security analysis.While it is crucial to identify vulnerabilities in such non-source code programs,there exists a limited set of generalized tools due to the low versatility of current vulnerability mining methods.However,these tools suffer from some shortcomings.In terms of targeted fuzzing,the path searching for target points is not streamlined enough,and the completely random testing leads to an excessively large search space.Additionally,when it comes to code similarity analysis,there are issues with incomplete code feature extraction,which may result in information loss.In this paper,we propose a cross-platform and cross-architecture approach to exploit vulnerabilities using neural network obfuscation techniques.By leveraging the Angr framework,a deobfuscation technique is introduced,along with the adoption of a VEX-IR-based intermediate language conversion method.This combination allows for the unified handling of binary programs across various architectures,compilers,and compilation options.Subsequently,binary programs are processed to extract multi-level spatial features using a combination of a skip-gram model with self-attention mechanism and a bidirectional Long Short-Term Memory(LSTM)network.Finally,the graph embedding network is utilized to evaluate the similarity of program functionalities.Based on these similarity scores,a target function is determined,and symbolic execution is applied to solve the target function.The solved content serves as the initial seed for targeted fuzzing.The binary program is processed by using the de-obfuscation technique and intermediate language transformation method,and then the similarity of program functions is evaluated by using a graph embedding network,and symbolic execution is performed based on these similarity scores.This approach facilitates cross-architecture analysis of executable programs without their source codes and concurrently reduces the risk of symbolic execution path explosion.

Planning,monitoring and replanning techniques for handling abnormity in HTN-based planning and execution

A framework that integrates planning,monitoring and replanning techniques is proposed.It can devise the best solution based on the current state according to specific objectives and properly deal with the influence of abnormity on the plan execution.The framework consists of three parts:the hierarchical task network(HTN)planner based on Monte Carlo tree search(MCTS),hybrid plan monitoring based on forward and backward and norm-based replanning method selection.The HTN planner based on MCTS selects the optimal method for HTN compound task through pre-exploration.Based on specific objectives,it can identify the best solution to the current problem.The hybrid plan monitoring has the capability to detect the influence of abnormity on the effect of an executed action and the premise of an unexecuted action,thus trigger the replanning.The norm-based replanning selection method can measure the difference between the expected state and the actual state,and then select the best replanning algorithm.The experimental results reveal that our method can effectively deal with the influence of abnormity on the implementation of the plan and achieve the target task in an optimal way.

Robust Malicious Executable Detection Using Host-Based Machine Learning Classifier

The continuous development of cyberattacks is threatening digital transformation endeavors worldwide and leadsto wide losses for various organizations. These dangers have proven that signature-based approaches are insufficientto prevent emerging and polymorphic attacks. Therefore, this paper is proposing a Robust Malicious ExecutableDetection (RMED) using Host-based Machine Learning Classifier to discover malicious Portable Executable (PE)files in hosts using Windows operating systems through collecting PE headers and applying machine learningmechanisms to detect unknown infected files. The authors have collected a novel reliable dataset containing 116,031benign files and 179,071 malware samples from diverse sources to ensure the efficiency of RMED approach.The most effective PE headers that can highly differentiate between benign and malware files were selected totrain the model on 15 PE features to speed up the classification process and achieve real-time detection formalicious executables. The evaluation results showed that RMED succeeded in shrinking the classification timeto 91 milliseconds for each file while reaching an accuracy of 98.42% with a false positive rate equal to 1.58. Inconclusion, this paper contributes to the field of cybersecurity by presenting a comprehensive framework thatleverages Artificial Intelligence (AI) methods to proactively detect and prevent cyber-attacks.

Improving Prediction Efficiency of Machine Learning Models for Cardiovascular Disease in IoST-Based Systems through Hyperparameter Optimization

This study explores the impact of hyperparameter optimization on machine learning models for predicting cardiovascular disease using data from an IoST(Internet of Sensing Things)device.Ten distinct machine learning approaches were implemented and systematically evaluated before and after hyperparameter tuning.Significant improvements were observed across various models,with SVM and Neural Networks consistently showing enhanced performance metrics such as F1-Score,recall,and precision.The study underscores the critical role of tailored hyperparameter tuning in optimizing these models,revealing diverse outcomes among algorithms.Decision Trees and Random Forests exhibited stable performance throughout the evaluation.While enhancing accuracy,hyperparameter optimization also led to increased execution time.Visual representations and comprehensive results support the findings,confirming the hypothesis that optimizing parameters can effectively enhance predictive capabilities in cardiovascular disease.This research contributes to advancing the understanding and application of machine learning in healthcare,particularly in improving predictive accuracy for cardiovascular disease management and intervention strategies.

The Mediating Effect of Digital Transformation on the Link Between Executive Overconfidence and Corporate Green Innovation

Green technological innovation is crucial for the manufacturing industry’s green transformation and sustainable development.This study examines the impact of executive overconfidence on corporate green innovation,focusing on the internal drivers of corporate innovation and using a sample of Shanghai and Shenzhen A-share listed manufacturing companies from 2013 to 2020.We further examine the mediating role of digital transformation and the moderating role of external attention.The findings indicate that executive overconfidence promotes corporate green technological innovation.Overconfident executives enhance green innovation by accelerating digital transformation.Moreover,external attention from analysts and media positively moderates the relationship between executive overconfidence and corporate green innovation.Heterogeneity analysis reveals that the positive impact of executive overconfidence on green innovation is more significant in non-state-owned enterprises,high-tech firms,and enterprises with lower pollution levels.

Bibliographical Review on Rehabilitation of Executive Functions in Patients With Developmental Coordination Disorder(DCD)

The present study aims to establish a literature review on intervention programs for executive functions(EFs)through the use of fundamental motor skills,from a neuropsychopedagogical perspective in subjects with Developmental Coordination Disorder(DCD).An exploratory study was carried out through an integrative literature review.The research was carried out in the Scientific databases Electronic Library Online(SciELO),Latin American and Caribbean Literature in Health Sciences(LILACS),Virtual Health Library-Psychology Brazil(BVSPSI),Electronic Journals of Psychology(PePSIC),in the periodicals available in the Brazilian Digital Library of Theses and Dissertations(BDTD)and on the website of the Coordination for the Improvement of Higher Education Personnel(CAPES).The covering publications took place from 2018 to 2023,14 articles were selected for analysis.This literature review made it possible to create strategies for stimulating EF and Visuomotor Functions so that educators and other professionals can better deal with students with DCD.It was perceived the need to carry out and develop more empirical research regarding the intervention of EFs and Visuomotor Functions by educators and professionals,with a greater sampling amplitude,to increase the number of studies that enable interventions both in children and in teenagers with DCD.

Perspectives and Experiences of Education Stakeholders: A Quantitative Study on the Adoption of Artificial Intelligence in Executive Training Using Structural Equation Modeling

The recent increase in the use of artificial intelligence has led to fundamental changes in the development of training and teaching methods for executive education. However, the success of artificial intelligence in regional centers for teaching and training professions will depend on the acceptance of this technology by young executive trainees. This article discusses the potential benefits of adopting AI in executive training institutions in Morocco, specifically focusing on CRMEF Casablanca Settat. Based on the Unified Theory of Acceptance and Use of Technology (UTAUT) (Venkatesh et al., 2003), this study proposes a model to identify the factors influencing the acceptance of artificial intelligence in regional centers for teaching professions and training in Morocco. To achieve this, a structural equation modeling approach was used to quantitatively describe the impact of each factor on AI adoption, utilizing data collected from 173 young executive trainees. The results indicate that perceived ease of use, perceived usefulness, trainer influence, and personal innovativeness influence the intention to use artificial intelligence. Our research provides managers of CRMEFs with a set of practical recommendations to enhance the implementation conditions of an artificial intelligence system. It aims to understand which factors should be considered in designing an artificial intelligence system within regional centers for teaching professions and training (CRMEFs).

Research on Multi-Core Processor Analysis for WCET Estimation

Real-time system timing analysis is crucial for estimating the worst-case execution time(WCET)of a program.To achieve this,static or dynamic analysis methods are used,along with targeted modeling of the actual hardware system.This literature review focuses on calculating WCET for multi-core processors,providing a survey of traditional methods used for static and dynamic analysis and highlighting the major challenges that arise from different program execution scenarios on multi-core platforms.This paper outlines the strengths and weaknesses of current methodologies and offers insights into prospective areas of research on multi-core analysis.By presenting a comprehensive analysis of the current state of research on multi-core processor analysis for WCET estimation,this review aims to serve as a valuable resource for researchers and practitioners in the field.

Review of Arbitral Award of Network Loan and Analysis of Construction of Implementation Review Rules

The network arbitration cases arising from the network lending disputes are pouring into the courts in large numbers.It is reported that the network arbitration system of some arbitration institutions even“can accept more than 10,000 cases every day,”while online lending is booming,it has also caused a lot of contradictions and disputes,and traditional dispute resolution methods have failed to effectively respond to the need for efficient and convenient resolution of online lending disputes.This paper tries to study the arbitral award of online loans and proposes the construction of implementation review rules.

面向WCET估计的Cache分析研究综述

实时系统时间分析的首要任务是估计程序的最坏情况执行时间(worst-case execution time,简称WCET).程序的WCET通常受到硬件体系结构的影响,Cache则是其中最为突出的因素之一.对面向WCET计算的Cache分析研究进行了综述,介绍了经典Cache分析框架与Cache分析核心技术,并从循环结构分析、数据Cache分析、多级Cache分析、多核共享Cache分析、非LRU替换策略分析等角度介绍了Cache分析在不同维度上的研究问题与主要挑战,总结了现有技术的优缺点,展望了Cache分析研究的未来发展方向.

Development of a distributed and integratable manufacturing execution system framework

Using remote method invocation (RMI) and a distributed object-oriented technique, this paper presents a systematic approach to developing a manufacturing execution system (MES) framework, which is open, modularized, distributed, configurable, interoperable and maintainable. Moreover, the design patterns for the framework .are developed and a variety of functional components are designed by inheriting appropriate patterns. And then an application is constructed by invoking corresponding methods of related components. An MES system implementing the framework and design patterns can be facilely integrated with other manufacturing applications, such as enterprise resource planning (ERP) and floor control system (FCS) .

一种基于CPN的BPEL异常处理逻辑的开发方法

针对WS-BPEL在面向服务软件异常处理方面不够完善的问题,提出了一种基于着色Petri网描述的BPEL异常处理逻辑开发方法。该方法利用着色Petri网(CPN)形式化地描述BPEL的异常处理机制,建立BPEL的异常处理CPN模型,指导对BPEL的异常处理逻辑开发,并依据此建模思想,提出了一个BPEL的异常处理CPN模型的转换工具,用以实现将异常处理的BPEL着色Petri网模型转换成对应的带异常处理的BPEL代码。该工具可在输入的原始的没有异常处理功能的BPEL代码基础上,通过动态地添加BPEL的异常处理语言成份,形成带有异常处理功能的BPEL流程。最后通过引入供应商流程案例,阐述了该方法的具体使用过程。

对“行政”概念的历史考察

"行政"一词有着悠久的历史,经历了一个漫长的历史演化过程,在不同的时期以及不同的地区,有着不同的用法与含义。在古代希腊和罗马,人们谈论行政问题时,所关注的是公职或行政官职。到了中世纪,马西利乌斯开始关注到"行政职能"的问题,从而引发人们对法律与行政关系的思考,并促成了"立法与执行"的二分。沿着"立法与执行"二分的传统前行,就走到了孟德斯鸠的三权分立。在三权分立的理论确立起来之后,行政权虽然被作为一项单列出来的权力而加以认识,但是,它依然属于政治权力的范畴。威尔逊的贡献则是把抽象意义上的"executive power"与具体行动层面上的"administration"加以区分,形成了"政治—行政二分"原则,并建立了行政学。

基于WCET分析的实时系统轨迹获取技术

时序约束是判断实时系统运行是否正确的重要规约.为了减小测试时由于对系统进行插装而产生的对实时系统行为的影响,提出了一种混合式监控方法.它对系统的时间干扰比纯软件方式小,并支持对系统的完全测试.此外,还提出一种基于WCET(worst-caseexecutiontime)分析技术的目标系统时间补偿方法,在精确地计算插入断言对目标系统的时间影响基础上,给出时间补偿.

OSEK/VDX OS服务调用的规范一致性检测方法

OSEK/VDX(open systems and the corresponding interfaces for automobile electronic/vehicle distributed execu-tive)OS规范描述了一个嵌入式实时操作系统,对系统服务调用的上下文做了严格的限制。违反系统服务调用规范是一种很隐蔽的错误,不会被编译器发现。提出一种OSEK/VDX OS系统服务调用规范一致性的检测方法,利用森林图描述用户自定义代码中的函数及其调用关系,采用矩阵描述OSEK/VDX OS系统服务调用的规则,遍历该森林图的函数调用路径,对于每条路径,结合该规则矩阵判断其规范一致性。实验和分析表明该方法能有效地检测用户自定义代码中隐藏的违反OSEK/VDX OS系统服务调用规范的错误。

基于供应链环境下的执行信息管理系统

主要提出了基于供应链环境的执行信息管理系统的结构框架模型，以及具体的系统功能结构图，并阐述了系统所采用的信息支持技术，最后总结了系统的特点。