Communication Mechanism Research Articles

A Study on the Evolution of Public Sentiment and Emotional Communication Mechanisms in Women's Issues on microblog: Case Study of "Quan XiXi' s Dialogue with Chizuko Ueno"

The research utilizes Sina microblog as its platform, analyzing textual data associated with trending topics featuring "Peking University Dormitory and Ueno Chat", to explore patterns of sentiment evolution and emotional expression. Findings reveal that public sentiment follows a four-phase trajectory: inception, peak, fluctuation, and decline. Negative sentiments dominate among users discussing the topic. This analysis elucidates microblog's role in disseminating women's issues, identifies key characteristics and challenges of such discussions on the platform, delineates major disagreements and focal points in user discourse, and endeavors to uncover underlying societal symptoms. The insights contribute valuable data to studies on the forms and impacts of women's issue dissemination on microblog.

Machine learning-driven discovery of novel therapeutic targets in diabetic foot ulcers.

To utilize machine learning for identifying treatment response genes in diabetic foot ulcers (DFU). Transcriptome data from patients with DFU were collected and subjected to comprehensive analysis. Initially, differential expression analysis was conducted to identify genes with significant changes in expression levels between DFU patients and healthy controls. Following this, enrichment analyses were performed to uncover biological pathways and processes associated with these differentially expressed genes. Machine learning algorithms, including feature selection and classification techniques, were then applied to the data to pinpoint key genes that play crucial roles in the pathogenesis of DFU. An independent transcriptome dataset was used to validate the key genes identified in our study. Further analysis of single-cell datasets was conducted to investigate changes in key genes at the single-cell level. Through this integrated approach, SCUBE1 and RNF103-CHMP3 were identified as key genes significantly associated with DFU. SCUBE1 was found to be involved in immune regulation, playing a role in the body's response to inflammation and infection, which are common in DFU. RNF103-CHMP3 was linked to extracellular interactions, suggesting its involvement in cellular communication and tissue repair mechanisms essential for wound healing. The reliability of our analysis results was confirmed in the independent transcriptome dataset. Additionally, the expression of SCUBE1 and RNF103-CHMP3 was examined in single-cell transcriptome data, showing that these genes were significantly downregulated in the cured DFU patient group, particularly in NK cells and macrophages. The identification of SCUBE1 and RNF103-CHMP3 as potential biomarkers for DFU marks a significant step forward in understanding the molecular basis of the disease. These genes offer new directions for both diagnosis and treatment, with the potential for developing targeted therapies that could enhance patient outcomes. This study underscores the value of integrating computational methods with biological data to uncover novel insights into complex diseases like DFU. Future research should focus on validating these findings in larger cohorts and exploring the therapeutic potential of targeting SCUBE1 and RNF103-CHMP3 in clinical settings.

Demystifying React Native Android Apps for Static Analysis

React Native, an open-source framework, simplifies cross-platform app development by allowing JavaScript-side code to interact with native-side code. Previous studies disregarded React Native, resulting in insufficient static analysis of React Native app code. This study initiates the investigation of challenges when statically analyzing React Native apps. We propose ReuNify to improve Soot-based static analysis coverage for JavaScript-side and native-side code. ReuNify converts Hermes bytecode to Soot’s intermediate representation. Hermes bytecode, compiled from JavaScript code and integrated into React Native apps, possesses a unique syntax that eludes current JavaScript analyzers. Additionally, we investigate opcode distribution and conduct in-depth analyses of the usage of opcode between popular apps and malware. We also propose a benchmark consisting of 97 control-flow-related cases to validate the control-flow recovery of the generated intermediate representation. Furthermore, we model the cross-language communication mechanisms of React Native to expand the static analysis coverage for native-side code. Our evaluation demonstrates that ReuNify enables an average increase of 84% in reached nodes within the call graph and further identifies an average of two additional privacy leaks in taint analysis. In summary, this paper demonstrates that ReuNify significantly improves the static analysis for the React Native Android apps

PUBLIC ANTI-CRISIS MANAGEMENT AND COMMUNICATIONS WHEN PROVIDING EMERGENCY MEDICAL AID IN THE MEDICAL INDUSTRY OF UKRAINE

In this article, the authors focus on several key problems that arise in the process of management and communication in crisis situations related to the provision of emergency medical care. The article examines approaches to managing crisis situations in the sphere of health care of Ukraine in conditions of increased uncertainty and instability. The authors emphasize the importance of effective communication between medical institutions, government structures and the public to ensure prompt response to emergency situations. The article examines various aspects of public crisis management, including planning, organization of resources, and coordination of actions during the provision of emergency medical care. The article analyzes the tools of public management, which allow to minimize various consequences of incoherence of actions between state institutions and medical institutions. Special attention is paid to the importance of prompt decision-making and establishment of communication links between state bodies, medical workers and the population. The possibilities of applying international experience in the field of anti-crisis management and its adaptation to Ukrainian realities are also discussed. The conclusions of the article emphasize the need to reform the health care system in the direction of strengthening its resilience to crises, improving communication mechanisms, and implementing innovative management solutions to increase the effectiveness of providing emergency medical care in emergency situations. The article emphasizes the need for quick decision-making, building effective communication chains between medical staff, patients and other stakeholders. Special attention is paid to the challenges associated with the lack of resources, the limited capabilities of the health care system in the context of crises and the role of leadership in implementing changes. The article also offers recommendations for improving communication processes and management approaches that will contribute to increasing the effectiveness of medical care during emergency situations, particularly in wartime.

TMIC-35. MITOCHONDRIAL TRANSFER PROMOTES ASTROCYTE REACTIVITY IN GLIOBLASTOMA

Abstract Astrocytes are the most abundant glial cell in the brain and have been shown to adopt reactive phenotypes upon interaction with glioma cells. While tumor-associated astrocyte (TAA) reactivity is linked to increased inflammation, proliferation, invasion, and treatment resistance of glioblastoma (GBM), the mechanisms by which cancer cells reprogram TAA are poorly defined. We have previously shown that mitochondrial transfer from astrocytes to GBM cells via GAP43-dependent microtubes alters GBM cellular metabolism and increases tumorigenicity. However, the potential role of this communication mechanism in TAA function remains unknown. Patient-derived GBM cells (PDCs) were transduced with a mito-GFP lentiviral vector and were co-cultured with hTERT-immortalized, mito-mCherry human astrocytes. Using flow cytometry, we observed that the frequency of mitochondrial transfer to hTERT astrocytes was dependent on GBM cell type and noted transfer rates as high as ~40% in co-cultures with L1 PDCs. Utilizing high-resolution confocal microscopy, we subsequently confirmed co-localization of fluorescent protein signal with MitoTracker Deep Red and observed increases in mitochondrial biomass in transfer-positive astrocytes. To evaluate whether mitochondrial transfer occurs in the tumor microenvironment, we implanted immunocompromised mice with L1 mito-mCherry PDCs. Approximately 17.7% of mouse TAAs in the tumor core were positive for GBM mitochondria, whereas long-distance transfer was not observed in the contralateral hemisphere. Functionally, acquisition of tumor mitochondria promoted astrocyte proliferation as measured by cells in G2/M phase and significantly increased total cellular reactive oxygen species (ROS) levels. These findings highlight a process by which astrocytes may become reactive in response to increased ROS from GBM mitochondrial transfer. Future studies will investigate the cellular response to increased ROS in astrocytes and investigate potential links to inflammatory and immunosuppressive signaling. Elucidating how GBM-to-astrocyte mitochondrial transfer reprograms TAA would help identify therapeutic vulnerabilities that can be exploited by blocking mitochondrial transfer.

The brain’s first “traffic map” through Unified Structural and Functional Connectivity (USFC) modeling

The brain’s white matter connections are thought to provide the structural basis for its functional connections between distant brain regions but how our brain selects the best structural routes for functional communications remains poorly understood. In this study, we propose a Unified Structural and Functional Connectivity (USFC) model and use an “economical assumption” to create the brain’s first “traffic map” reflecting how frequently each segment of the brain structural connection is used to achieve the global functional communication system. The resulting USFC map highlights regions in the subcortical, default-mode, and salience networks as the most heavily traversed nodes and a midline frontal-caudate-thalamus-posterior cingulate-visual cortex corridor as the backbone of the whole brain connectivity system. Our results further revealed a striking negative association between structural and functional connectivity strengths in routes supporting negative functional connections, as well as significantly higher efficiency metrics and better predictive performance for cognition in the USFC connectome when compared to structural and functional ones alone. Overall, the proposed USFC model opens up a new window for integrated brain connectome modeling and provides a major leap forward in brain mapping efforts for a better understanding of the brain’s fundamental communication mechanisms.

Human Adaption to Climate Change: Marine Disaster Risk Reduction in the Era of Intelligence

With the intensification of global warming and sea level rise, extreme weather and climate events occur frequently, increasing the probability and destructive power of marine disasters. The purpose of this paper is to propose the specific application of artificial intelligence (AI) in marine disaster risk reduction. First, this paper uses computer vision to assess the vulnerability of the target and then uses CNN-LSTM to forecast tropical cyclones. Second, this paper proposes a social media communication mechanism based on deep learning and a psychological crisis intervention mechanism based on AIGC. In addition, the rescue response system based on an intelligent unmanned platform is also the focus of this research. Third, this paper also attempts to discuss disaster loss assessment and reconstruction based on machine learning and smart city concepts. After proposing specific application measures, this paper proposes three policy recommendations. The first one is improving legislation to break the technological trap of AI. The second one is promoting scientific and technological innovation to break through key technologies of AI. The third one is strengthening coordination and cooperation to build a disaster reduction system that integrates man and machine. The purpose of this paper is to reduce the risk of marine disasters by applying AI. Furthermore, we hope to provide scientific references for sustainability and human adaptation to climate change.

An optimized RDMA QP communication mechanism for hyperscale AI infrastructure

An optimized RDMA QP communication mechanism for hyperscale AI infrastructure

Astrocytic determinant of the fate of long-term memory.

While some vivid memories are unyielding and unforgettable, others fade with time. Astrocytes are recognized for their role in modulating the brain's environment and have recently been considered integral to the brain's information processing and memory formation. This suggests their potential roles in emotional perception and memory formation. In this study, we delve into the impact of amygdala astrocytes on fear behaviors and memory, employing astrocyte-specific optogenetic manipulations in mice. Our findings reveal that astrocytic photoactivation with channelrhodopsin-2 (ChR2) provokes aversive behavioral responses, while archaerhodopsin-T (ArchT) photoactivation diminishes fear perception. ChR2 photoactivation amplifies fear perception and fear memory encoding but obstructs its consolidation. On the other hand, ArchT photoactivation inhibits memory formation during intense aversive stimuli, possibly due to weakened fear perception. However, it prevents the decay of remote fear memory over three weeks. Crucially, these memory effects were observed when optogenetic manipulations coincided with the aversive experience, indicating a deterministic role of astrocytic states at the exact moment of fear experiences in shaping long-term memory. This research underscores the significant and multifaceted role of astrocytes in emotional perception, fear memory formation, and modulation, suggesting a sophisticated astrocyte-neuron communication mechanism underlying basic emotional state transitions of information processing in the brain.

Numerical investigation on hydraulic fracture propagation mechanism in fault-karst carbonate reservoir

The fault-karst reservoir takes the fault-controlled fracture-cavity system as storage space, which is surrounded by a large number of high permeability zones. The distribution of reservoir seepage field has an important influence on fracture propagation behavior during the hydraulic fracturing process. Based on statistical damage mechanics theory and finite element method, this paper established a seepage-stress coupling numerical model of fracture propagation under the complex medium condition of matrix-cave-natural fracture in fault-karst carbonate reservoir. This new model innovatively considers the effects of matrix seepage, which is generally ignored by the traditional numerical models, for simulating the fluid–solid coupled fracture propagation behavior. The communication mechanism between fracture and cave is first revealed. The influence of geologic and engineering factors, e.g., permeability in high permeability zone around cave and injection rate etc., on hydraulic fracture propagation in fault-karst reservoir is studied. The results show that the high permeability zone around cave can provide sufficient infiltration capacity to communicate fracture with cave in the form of fluid pressure through fluid flow and fluid pressure conduction, even if hydraulic fracture does not directly communicate the cave. Hydraulic fracture is deflected to the direction of cave under the influence of seepage field, but the deflection angle is mostly within 10°. The high permeability of high permeability zone around cave and the short cave offset distance are conducive to the virtual communication between fracture and cave. Communicating fracture with cave in the non-principal stress direction can be accomplished by fluid flow and fluid pressure conduction, optimizing hydraulic fracturing treatment parameters should be performed by choosing lower injection rates and lower fracturing fluid viscosities. This study can provide key technical support and theoretical guidance for oil and gas development in fault-karst reservoir.

Awareness support in collaborative programming tools: An evaluation based on programmer's perception and eye tracking

Groupware technology is an essential asset for organizations and a successful medium to support social meetings and teacher-learning processes when people are geographically distributed. Computer programming is a domain that can take advantage of the Collaborative Work paradigm and of this technology. However, to be truly effective, groupware systems must provide suitable awareness support, i.e., be capable of informing users of the activity taking place and of the team outcome, so that they can build a setting for their work. The study of awareness mechanisms and their influence on collaborative programming processes is an open research question. In this context, this article contributes an experimental study to evaluate the mechanisms to support coordination, communication, and awareness of COLLECE, a distributed group programming system rich in awareness support. This study brings as a novel approach the combination of subjective and objective information sources, specifically eye tracking techniques, and incorporates a heuristic evaluation of awareness support based on a well-known framework. Thanks to this experiment, we have been able to verify that this blended approach, in which users participate intensively, provides a more comprehensive and deeper assessment of awareness and other coordination and communication mechanisms, and thus a better understanding of how their use can influence the collaborative programming process and outcome (program or piece of software). The study has revealed strengths, weaknesses and opportunities for improvement of the evaluated system, and has yielded concrete results as to which are the most useful awareness components of the user interface not related to the activity supported by the system (programming), these being the traffic lights; the easiest to use, such as the session panel; or the components that impose a greater cognitive load, such as the multi-scrollbar. Regarding the relationship between the supported awareness dimensions and the quality of the collaboration outcome (computer program), the results point to the need to adequately support the What-Artifact dimension, i.e., highlighting in which part of the shared workspace (the source code) the user with floor control is working.

Unveiling Quorum Sensing Mechanisms: Computational Docking and Dynamics of Bacterial Receptors and Ligands

The bacterial cell-to-cell communication mechanism known as quorum sensing (QS) uses chemical cues called autoinducers (AIs) to control several processes, including pathogenicity, antibiotic resistance, and biofilm formation. This study investigates the QS receptor-ligand interactions and QS compatibility within and between bacterial species using computational molecular docking. Receptor proteins including LuxS, LuxP, AgrC, LuxN, SdiA, LasR, esaR, YenR, LamC, PlcR, and TraR were docked with their respective AIs (AHL, AI-1, AI-2, AIP1, LamD, PapR7) in both biofilm and non-biofilm producing bacteria. Our findings indicate that QS receptors exhibit high affinity for their cognate ligands, with binding affinities ≥ -4.5 Kcal/mol. Additionally, Zinc Pharmar-derived similar chemical structures demonstrated binding affinities ≥ -5.3 Kcal/mol. Density Functional Theory (DFT) analysis revealed AI-2 as the most reactive autoinducer. Molecular Dynamics (MD) simulations confirmed the stability of LasR-AHL and LuxP-AI-2 complexes. These insights pave the way for further in vitro and in vivo investigations of QS mechanisms.

The service trade with AI and energy efficiency: Multiplier effect of the digital economy in a green city by using quantum computation based on QUBO modeling

This research examines the energy efficiency of city districts through the Malmquist–DEA model and investigates the spatial effects of the service trade and the digital economy on energy efficiency in urban green development. The study also delves into the specific context of the AI service trade to gain insights into and align with the emerging digital intelligence industry. The interplay of the service trade with the digital economy, alongside the AI service trade with innovation, significantly enhances urban energy efficiency and demonstrates positive externalities. Building on the empirical findings, this research employs cluster analysis to explore the green development of city districts and uses AI technology to program green communication and cooperation mechanisms across district clusters, employing quantum computation based on QUBO modeling. This study contributes to a deeper understanding of the cofunction of the service trade and the digital economy in terms of energy efficiency and aids in developing new quality productivities for green cities through quantum-based AI advancements. This research has clear implications for cutting-edge interdisciplinary applications of green AI technologies in social computing science.

Intelligent optimization of horizontal wellbore trajectory based on reinforcement learning

Ultra-long horizontal wells are crucial for improving the production and developmental benefits of shale oil and gas wells. Currently, advanced downhole semi-closed-loop steering drilling technology depends on a dual communication mechanism between the surface and downhole, as well as the empirical decisions of human experts. However, it is difficult to acutely control the trajectory of ultra-long horizontal open hole drilling in a reservoir owing to geological uncertainties related to shale oil and gas, the uncertainty of the drilling tool's building capacity, and the lag of measurement information while drilling. This report proposes an adaptive target detection and design method for ultra-long horizontal well trajectories based on reinforcement learning. The developed approach enables dynamic identification of reservoir sequences according to logging-while-drilling data and prediction of horizontal targets in real-time, with a recognition accuracy of 90.1%. Additionally, measurement-while-drilling data are used to accurately characterize the depth, inclination, and azimuth of the target-entering process. The dynamic interaction mechanism between the bit and the target environment is simulated by defining the bit action, reward function, and an update mechanism. The drilling engineering conditions restrict the interactions, such that the bit automatically decides the direction in which to drill the targets, demonstrating 100% accuracy for the wellbore trajectory toward the target. The experimental application results indicate that the developed method can be applied to determine the dynamic target area of a horizontal well in real time and make intelligent downhole decisions regarding ultra-long horizontal section borehole trajectory adjustments while drilling and measuring. The drilling rate of high-quality reservoirs is therefore significantly improved. The results discussed herein provide insights to support further developments of downhole full-closed-loop intelligent autonomous decision-making borehole trajectory control.

A Literature Review on Digital Creativity in Higher Education—Toward a Conceptual Model

The present study relies on the assumption that in today’s knowledge society and the use of online knowledge, learning, and communication mechanisms, pre-university and university teachers, students of all ages, those preparing for teaching careers or other careers, parents, managers, and administrators have become increasingly interested in acquiring proficiency, capitalization, and stimulation of digital creativity. As such, research must provide, primarily for teachers and those preparing for this career, systematic results on digital creativity. This study aims to identify the current state-of-the-art in scientific knowledge on the comprehensive topic of digital creativity: conceptual frameworks and theoretical highlights, elaborating a model of digital creativity, and identifying and formulating future research opportunities. The investigation relies on the key concept of digital creativity and aims to cover two aspects: 1. basic meanings of the concept of digital creativity, specifying its applicability in the educational process at pre-university and university levels, and 2. identifying a set of concepts that are complementary to digital creativity. The research is a conceptual literature review, implying the use of literature review and bibliographic analysis methods. The study relies on a comprehensive literature review of papers selected from the three international databases WEB of SCIENCE, Science Direct, and Scopus from 2020 to 2024. The literature search enabled us to identify an initial sample of 1848 studies in the three databases and finally select 31 relevant articles. The analysis of the relevant papers allowed us to formulate conclusions related to the two investigated aspects: 1. The concept of digital creativity has two types of definitions—explicit and implicit; 2. The concept of digital creativity is correlated with other complementary concepts: digital competence, mobile networking creativity, multi-team digital creativity, the use of artificial intelligence, and digital innovation. Our findings support us in proposing ways to effectively use and stimulate digital creativity in the educational process and build 21st-century skills and competences for students of all ages and at all levels of education.

Continuous and discrete decoding of overt speech with scalp electroencephalography (EEG).

Neurological disorders affecting speech production adversely impact quality of
life for over 7 million individuals in the US. Traditional speech interfaces like eyetracking
devices and P300 spellers are slow and unnatural for these patients. An
alternative solution, speech Brain-Computer Interfaces (BCIs), directly decodes speech
characteristics, offering a more natural communication mechanism. This research
explores the feasibility of decoding speech features using non-invasive EEG. Nine
neurologically intact participants were equipped with a 63-channel EEG system
with additional sensors to eliminate eye artifacts. Participants read aloud sentences
displayed on a screen selected for phonetic similarity to the English language. Deep
learning models, including Convolutional Neural Networks and Recurrent Neural
Networks with and without attention modules, were optimized with a focus on
minimizing trainable parameters and utilizing small input window sizes for real-time
application. These models were employed for discrete and continuous speech decoding
tasks, achieving statistically significant participant-independent decoding performance
for discrete classes and continuous characteristics of the produced audio signal. A
frequency sub-band analysis highlighted the significance of certain frequency bands
(delta, theta, and gamma) for decoding performance, and a perturbation analysis
was used to identify crucial channels. Assessed channel selection methods did not
significantly improve performance, suggesting a distributed representation of speech
information encoded in the EEG signals. Leave-One-Out training demonstrated
the feasibility of utilizing common speech neural correlates, reducing data collection
requirements from individual participants.

Light‐Based Juxtacrine Signaling Between Synthetic Cells

Cell signaling through direct physical cell–cell contacts plays vital roles in biology during development, angiogenesis, and immune response. Intercellular communication mechanisms between synthetic cells constructed from the bottom up are majorly reliant on diffusible chemical signals, thus limiting the range of responses in receiver cells. Engineering contact‐dependent signaling between synthetic cells promises to unlock more complicated signaling schemes with spatial responses. Herein, a light‐activated contact‐dependent communication scheme for synthetic cells is designed and demonstrated. A split luminescent protein is utilized to limit signal generation exclusively to contact interfaces of synthetic cells, driving the recruitment of a photoswitchable protein in receiver cells, akin to juxtacrine signaling in living cells. The modular design not only demonstrates contact‐dependent communication between synthetic cells but also provides a platform for engineering orthogonal contact‐dependent signaling mechanisms.

Environmental Stress Drives Transgenerational Epigenetic Inheritance through miR-212/132 Expression Alterations

Transgenerational Epigenetic Inheritance (TEI) is a phenomenon that is deeply affected by the presence of environmental stress and disease. This phenomenon has been proposed to accelerate the evolution of many animal species. However, most current studies commonly focus on the C. elegantly which is frequently presented as deviating a lot from humans and the studies on mice still fail to propose a plausible explanation for the detailed mechanism of TEI in mammals. In this study, we primarily focused on the effect of environmental stress on mice and the possible TEI characteristics it may perform. The miR212/132 was chosen as the main target for studying and the role it play in the TEI is verified. The hippocampus-sperm communication mechanism of miR212/132 will also be focused and verified which revealed why the differential expression happen in the germ-line cell.

Increased endothelial cell uptake of erythrocyte-derived extracellular vesicles carrying arginase-1 induces endothelial dysfunction in type 2 diabetes

Abstract Background Recently, we have demonstrated that red blood cells (RBCs) from individuals with type 2 diabetes (T2D-RBCs) induce endothelial dysfunction. However, the mechanism by which RBCs communicate with the vessel is unknown. Extracellular vesicles (EVs) are actively secreted by practically all cell types, including RBCs, and represent a novel mechanism of intercellular communication. However, the involvement of EVs from RBC in the development of endothelial dysfunction remains to be elucidated. Purpose This study was designed to test the hypothesis that EVs are key players in the communication and the transfer of signalling between RBCs and the vascular endothelium to induce endothelial dysfunction in T2D. Methods RBCs from T2D patients and age-matched healthy controls (H-RBCs) were incubated for 18h with Krebs-Henseleit buffer (20% haematocrit) for EV release. RBC-derived EVs in the conditioned medium were isolated using a membrane affinity column. The EVs were co-incubated with mouse aortae to evaluate endothelium-dependent relaxation and with endothelial cells for expression analysis. The uptake of the EVs by endothelial cells and their content of arginase-1 were determined. The functional involvement of arginase was investigated using pharmacological interventions and expression analyses. All animal experiments were performed according to the principles of laboratory animal care (NIH Publication no. 85-23 revised 1985) and human procedures according to the declaration of Helsinki with approval by the Swedish Ethical Review Authority. Results The uptake of EVs derived from T2D-RBCs by endothelial cells was 2-fold greater than that of EVs from H-RBCs (Fig. 1A-B). Inhibiting the uptake of EVs derived from T2D-RBCs by the addition of heparin during the co-incubation rescued the endothelial function (Fig. 1C). Arginase-1 was detected in RBC-derived EVs (Fig. 2A). Arginase-1 mRNA and protein levels were increased in endothelial cells following co-incubation with EVs derived from T2D-RBCs (Fig. 2B-D). Additionally, the increase in arginase-1 protein induced by EVs derived from T2D-RBCs in endothelial cells was observed also following mRNA silencing for arginase-1 (Fig. 2E-F). Finally, mouse aortae co-incubated with EVs derived from T2D-RBCs in the presence or absence of the arginase inhibitor 2(S)-amino-6-boronohexanoic acid significantly attenuated the impairment in endothelial function induced by EVs derived from T2D-RBCs (Fig. 2G). Conclusion Increased uptake of RBC-derived EVs by the endothelial cells is an important feature of the endothelial dysfunction induced by these EVs in T2D. In addition, these EVs carry arginase-1 protein to induce endothelial dysfunction. The mechanism underlying the increased uptake of EVs in target cells is of importance to identify in future studies, as it could lead to new treatment strategies.

Analysis of the Main Contradictions and Solutions in Achieving Educational Equity under China's "Double Reduction" Policy

This article aims to analyze the obstacles encountered by Chinese society in implementing the "double reduction" policy and the main contradictions in achieving educational equity, explore the new contradictions and problems brought by the "double reduction" policy to the education sector, and provide corresponding countermeasures for Chinese society, communities, families, and schools. It is hoped that this can improve the "double reduction" policy while eradicating the remaining irrationality in the Chinese education sector, enhance the level and quality of Chinese education, and develop an educational powerhouse. A comprehensive review was conducted through literature review, combined with knowledge in the fields of educational equity, educational policies, and comparative education. This article explores the four major contradictions behind the "double reduction policy" and proposes four guiding suggestions: balancing educational resources, optimizing the rationality of education and training, clarifying the responsibilities of families and schools, establishing a communication mechanism between families and schools, implementing a comprehensive evaluation system, strengthening the enrollment form of higher education, enhancing community resources, and improving community after-school services.