Search Results (115)

On 12 July 2016, the ruling on the South China Sea Arbitration was announced and rapidly drew worldwide attention, turning the event into a major international hotspot. Quantifying the dynamics of such hotspot events and understanding the evolution of media-based inter-actor networks during major shocks are of substantial research interest. Viewing these interactions as dynamic networks, we analyze the time-varying actor interaction structure surrounding the arbitration using the Global Database of Events, Location and Tone (GDELT), a large-scale media-based event database with global coverage since 1979. We extract nearly 30,000 events related to the arbitration from 5 July to 25 July 2016, constructing daily cooperation and conflict networks to quantify structural changes via network size and degree-entropy dynamics. To further reveal actor-level structural roles, we learn node embeddings on each daily network via an entropy-driven graph representation learning scheme and perform embedding-based clustering with automatically selected cluster numbers, visualized via t-SNE. The results show that key dates in the event window are associated with pronounced structural shifts in the networks, including changes in participation breadth, degree-distribution heterogeneity, and clearer differentiation and reconfiguration of actor roles, with distinct patterns between cooperation and conflict networks. These findings demonstrate the potential of massive media event data for characterizing structural responses and actor-role evolution in event-driven inter-actor networks. Full article

Vaccinia-Related Kinase 3 (VRK3) is increasingly recognized as a crucial signaling modulator in both normal and pathological processes. This kinase was long thought of as a catalytically inactive pseudokinase, until recently it was established to phosphorylate Barrier to Autointegration Factor (BAF) proteins through its extracatalytic domain. VRK3 regulates diverse cellular pathways through scaffold interactions and context-dependent phosphorylation. This review is centered around the phosphoregulatory network that modulates VRK3 phosphorylation with implications in its abundance and function. A large-scale phosphoproteomic data integration was performed by combining phosphoproteomics profiling and differential phosphorylation from 115 mass spectrometry studies, identifying 32 high-confidence phosphorylation sites on VRK3. Notably, VRK3 (S59), (S82), and (S83) were predominantly observed highlighting plausible functional significance. These phosphorylation sites share 33 potential upstream kinases, and multiple interactor proteins, which in combination are known to regulate ERK, Hippo, and GPCR pathways. These insights advance the understanding of phosphorylation control by kinases and highlight opportunities to target VRK3-associated networks for therapeutic intervention in diseases such as glioma and liver cancer. Full article

The governance of Intangible Cultural Heritage (ICH) has emerged as a critical issue for sustainable local development, particularly in cities where cultural vitality is largely community-driven but institutionally under-supported. This study examines the case of Kalamata, Greece, a medium-sized city with a dense network of community-based cultural associations, in order to analyse how ICH is governed in practice and how it contributes to social capital formation and sustainability outcomes. The research is based on 49 semi-structured interviews with representatives of 25 cultural associations and public or municipal bodies and employs qualitative thematic analysis. The findings demonstrate that cultural associations function as key governance actors at the community level, generating strong bonding social capital through participation, informal education, and collective memory. At the same time, limited bridging and linking social capital constrain inter-organisational cooperation, institutional coordination, and the integration of ICH into long-term development strategies. The study identifies significant governance challenges, including fragmented policy frameworks, unstable funding mechanisms, limited professional support, and weak participatory decision-making structures. By explicitly linking empirical findings to the Sustainable Development Goals, particularly SDGs 4.7, 11.4, 16.7, and 17, the paper highlights the importance of participatory cultural governance and co-governance models for enhancing the sustainability of local cultural ecosystems. The article contributes to policy-oriented debates on cultural sustainability by providing evidence from a Mediterranean medium-sized city and by proposing governance-relevant directions for integrating community-based ICH into sustainable local development planning. The findings offer practical guidance for local authorities and cultural organizations seeking to integrate community-based ICH into sustainable urban development strategies. Full article

Protein Kinase N1 (PKN1) is a PKC-related serine/threonine kinase of the AGC group within the eukaryotic protein kinase superfamily (ePK) that orchestrates oncogenic, metabolic, and cytoskeletal signaling. Despite these critical roles, the phosphorylation-dependent regulatory network of PKN1 remains largely undefined. We performed a large-scale phosphoproteomic data integration of publicly available human datasets (892 profiling datasets and 191 differential datasets) to identify recurrent PKN1 phosphorylation sites. This analysis identified two predominant PKN1 phosphosites, S562 and S916, that were frequently observed and differentially regulated across studies. The S916 maps to a turn motif (TM) in the AGC group of kinases, which is evolutionarily conserved among PKN paralogs, while S562 is non-conserved and appears to be PKN1-specific. Co-regulation and enrichment analyses suggest that S916 is associated with insulin/AMPK signaling and metabolic pathways, whereas S562 co-occurs with phosphosites involved in cell division, cytoskeletal regulation, and microtubule cytoskeleton organization. Integrating predicted and experimentally validated kinases, substrates, and interactors, we reconstructed a phospho-regulatory network that positions PKN1 at the crossroads of cytoskeleton organization and metabolic signaling. To assess the disease relevance of these phosphorylation events, we integrated transcriptomic and phosphoproteomic data from the hepatocellular carcinoma database (HCCDB). PKN1 was markedly up-regulated in HCC, and its phosphorylation at S916 was positively co-regulated with multiple oncogenic and proliferation-associated protein phosphosites. These results predict S562 and S916 as potential sites for targeted biochemical validation and functional experiments. The identification of S562 and S916 as key regulatory sites provides new mechanistic insight into PKN1 activation and highlights potential avenues for therapeutic targeting. Full article

Multi-agent trajectory prediction is essential for autonomous driving systems, as its performance heavily depends on the quality of feature representations. This paper proposes PFR-HiVT, a lightweight and effective approach for multi-agent trajectory prediction, and evaluates it on the Argoverse 1.1 motion forecasting dataset. Although existing methods such as the Hierarchical Vector Transformer (HiVT) have achieved strong performance, they still exhibit limitations in feature extraction and feature transition across different stages of the network. To address these limitations, a collaborative feature enhancement framework is introduced, consisting of two encoder-side modules and a Progressive Feature Refinement Global Interactor (PFR-Global Interactor). Specifically, the Feature Enhancement Module (FEM) and the Attention Enhancement Module (AEM) are employed to refine local spatiotemporal features before global interaction. In addition, the PFR-Global Interactor integrates three lightweight components—the Simple Feature Refinement Module (SFR), the Lightweight Gate Module (LG), and the Residual Connection Module (RC)—to progressively refine globally interacted features prior to trajectory decoding. All proposed modules adopt lightweight designs, introducing only 230.5 k additional parameters (approximately 8.7% of the total parameters of HiVT-128). Experiments on the Argoverse 1.1 dataset show that PFR-HiVT achieves a minADE of 0.703, a minFDE of 1.041, and an MR of 0.112, outperforming the baseline HiVT model. Ablation studies further validate the effectiveness and synergy of the proposed modules. Full article

Against the backdrop of China’s commitment to achieving carbon peaking by 2030 and carbon neutrality by 2060, inter-provincial carbon emissions form a complex interconnected spatial network—clarifying its operational mechanisms is crucial for optimizing regional carbon reduction strategies. Based on 2006–2021 data from 30 Chinese provinces, this study constructs the China Provincial Carbon Emission Spatial Correlation Network (CPCESCN) using a modified gravity model. Social Network Analysis (SNA) explores its structural characteristics, while motif and QAP correlation analyses identify endogenous structural and attribute variables. Innovatively integrating Exponential Random Graph Models (ERGM) and Stochastic Actor-Oriented Models (SAOM), it investigates the network’s static formation mechanisms and dynamic evolution drivers. Results show CPCESCN has a stable multi-threaded structure without isolated nodes, with Jiangsu, Guangdong, Shandong, Zhejiang, Henan, and Sichuan as high-centrality core nodes with high centrality. GDP, green technology innovation, urbanization rate, industrialization rate, energy consumption intensity, and environmental regulations significantly influence network dynamics, with reciprocal relationships as key endogenous drivers. While geographic proximity still facilitates network formation, its impact has weakened notably, and functional complementarity has become the dominant evolutionary driver—based on the findings, policy suggestions are proposed, including deepening inter-provincial functional cooperation, implementing differentiated carbon reduction policies, and optimizing multi-dimensional low-carbon transformation systems. Full article

Multi-agent path planning for Unmanned Aerial Vehicles (UAVs) in agricultural data collection tasks presents a significant challenge, requiring sophisticated coordination to ensure efficiency and avoid conflicts. Existing multi-agent reinforcement learning (MARL) algorithms often struggle with high-dimensional state spaces, continuous action domains, and complex inter-agent dependencies. To address these issues, we propose a novel algorithm, Multi-Agent Transformer-based Soft Actor–Critic (MATRS). Operating on the Centralized Training with Decentralized Execution (CTDE) paradigm, MATRS enables safe and efficient collaborative data collection and trajectory optimization. By integrating a Transformer encoder into its centralized critic network, our approach leverages the self-attention mechanism to explicitly model the intricate relationships between agents, thereby enabling a more accurate evaluation of the joint action–value function. Through comprehensive simulation experiments, we evaluated the performance of MATRS against established baseline algorithms (MADDPG, MATD3, and MASAC) in scenarios with varying data loads and problem scales. The results demonstrate that MATRS consistently achieves faster convergence and shorter task completion times. Furthermore, in scalability experiments, MATRS learned an efficient “task-space partitioning” strategy, where the UAV swarm autonomously divides the operational area for conflict-free coverage. These findings indicate that combining attention-based architectures with Soft Actor–Critic learning offers a potent and scalable solution for high-performance multi-UAV coordination in IoT data collection tasks. Full article

Porcine reproductive and respiratory syndrome virus (PRRSV) nonstructural protein 9 (NSP9), the viral RNA-dependent RNA polymerase (RdRp), is essential for viral replication but its comprehensive host interactome remains uncharacterized. This study employed co-immunoprecipitation coupled with liquid chromatography-tandem mass spectrometry (LC-MS/MS) to systematically identify NSP9-associated host proteins. We identified 222 high-confidence host interactors, with Gene Ontology and KEGG pathway analyses revealing significant enrichment in RNA/DNA-binding proteins, ubiquitin-proteasome pathways, metabolic regulators (amino acid/lipid biosynthesis), endoplasmic reticulum processing, and cell cycle components. Protein-protein interaction network analysis further delineated six functional modules involved in RNA processing, vesicular transport, and innate immunity. Crucially, validation studies confirmed direct binding between NSP9 and key candidates (CAPZ1, PSMA3, CDK1, USP48). Functional assessment demonstrated that CDK1 overexpression significantly inhibited PRRSV replication, implicating CDK1 as a host restriction factor. These findings collectively unveil the multifaceted role of NSP9 in subverting host machinery while identifying novel host defense mechanisms and potential targets for antiviral development against PRRSV. Full article

Grassroots community governance has gained increasing attention for its vital role in resource integration and multi-actor collaboration. As an innovative governance model, the “Five-Sector Linkage” (FSL) mechanism enhances service efficiency by aligning the efforts of communities, social organizations, social workers, volunteers, and philanthropic actors. However, quantitative research on interaction dynamics within such mechanisms remains insufficient, particularly regarding the optimization of collaborative networks for improved governance outcomes. This study applies Social Network Analysis (SNA) to the “After-School Program” project in Community B, Chengdu, to examine the structural features and interrelations of multi-actor cooperation under the FSL framework. The collaboration network consists of 39 nodes and 1482 links, with a density of 0.370 and an average path length of 1.632, indicating efficient communication and moderate cohesion. Degree and betweenness centrality analyses identify social workers (C1–C3) as key hubs, with C2 holding the highest bridging role (B_C = 81.401). The overall network shows low centralization (4.19%) and limited heterogeneity (2.74%), reflecting a polycentric and resilient structure. Inter-sectoral analysis showed that all nodes interacted with at least one social worker, while community actors (A1, A2) engaged broadly across 18 nodes. Volunteers maintained extensive grassroots connections, while philanthropic resources formed selective but strategic links with 13 nodes. These findings provide empirical insights into the coordination logic of the FSL mechanism and offer guidance for building adaptive, decentralized community governance networks. Future research should explore longitudinal dynamics and cross-community comparisons to further enhance the applicability of the model. Full article

To support the 6G vision of seamless “space–air–ground-integrated” global coverage, optical satellite networks must enable high-speed, low-latency, and intelligent data transmission. However, conventional inter-satellite laser link-based optical transport networks suffer from inefficient bandwidth utilization and nonlinear latency accumulation caused by multi-hop routing, which severely limits their ability to support ultra-low-latency and real-time applications. To address the critical challenges of high topological complexity and stringent real-time requirements in satellite elastic optical networks, we propose an asynchronous advantage actor–critic-based quality of service-aware routing optimization mechanism for the optical inter-satellite link (OISL-AQROM). By establishing a quantitative model that correlates the optical service unit (OSU) C value with node hop count, the algorithm enhances the performance of latency-sensitive services in dynamic satellite environments. Simulation results conducted on a Walker-type low Earth orbit (LEO) constellation comprising 1152 satellites demonstrate that OISL-AQROM reduces end-to-end latency by 76.3% to 37.6% compared to the traditional heuristic multi-constrained shortest path first (MCSPF) algorithm, while supporting fine-grained dynamic bandwidth adjustment down to a minimum granularity of 2.6 Mbps. Furthermore, OISL-AQROM exhibits strong convergence and robust stability across diverse traffic loads, consistently outperforming MCSPF and deep deterministic policy gradient (DDPG) algorithm in overall efficiency, load adaptability, and operational reliability. The proposed algorithm significantly improves service quality and transmission efficiency in commercial mega-constellation optical satellite networks, demonstrating engineering applicability and potential for practical deployment in future 6G infrastructure. Full article

Cooperative pursuit using multi-UAV systems presents significant challenges in dynamic task allocation, real-time coordination, and trajectory optimization within complex environments. To address these issues, this paper proposes a reinforcement learning-based task planning framework that employs a distributed Actor–Critic architecture enhanced with bidirectional recurrent neural networks (BRNN). The pursuit–evasion scenario is modeled as a multi-agent Markov decision process, enabling each UAV to make informed decisions based on shared observations and coordinated strategies. A multi-stage reward function and a BRNN-driven communication mechanism are introduced to improve inter-agent collaboration and learning stability. Extensive simulations across various deployment scenarios, including 3-vs-1 and 5-vs-2 configurations, demonstrate that the proposed method achieves a success rate of at least 90% and reduces the average capture time by at least 19% compared to rule-based baselines, confirming its superior effectiveness, robustness, and scalability in cooperative pursuit missions. Full article

In this paper, we investigate the Adaptive Service Migration (ASM) problem in dynamic satellite edge computing networks, focusing on Low Earth Orbit satellites with time-varying inter-satellite links. We formulate the ASM problem as a constrained optimization problem, aiming to minimize overall service cost, which includes both interruption cost and processing cost. To address this problem, we propose ASM-DRL, a deep reinforcement learning approach based on the soft Actor-Critic framework. ASM-DRL introduces an adaptive entropy adjustment mechanism to dynamically balance exploration and exploitation, and adopts a dual-Critic architecture with soft target updates to enhance training stability and reduce Q-value overestimation. Extensive simulations show that ASM-DRL significantly outperforms baseline approaches in reducing service cost. Full article

With the rapid advancement of mobile edge computing and Internet of Things (IoT) technologies, device-to-device (D2D) cooperative computing has garnered significant attention due to its low latency and high resource utilization efficiency. However, workflow task scheduling in D2D networks poses considerable challenges, such as severe heterogeneity in device resources and complex inter-task dependencies, which may result in low resource utilization and inefficient scheduling, ultimately breaking the computational symmetry—a balanced state of computational resource allocation among terminal devices and load balance across the network. To address these challenges and restore system-level symmetry, a novel workflow task scheduling method tailored for D2D cooperative environments is proposed. First, a Non-dominated Sorting Genetic Algorithm (NSGA) is employed to optimize the allocation of computational resources across terminal devices, maximizing the overall computing capacity while achieving a symmetrical and balanced resource distribution. A scoring mechanism and a normalization strategy are introduced to accurately assess the compatibility between tasks and processors, thereby enhancing resource utilization during scheduling. Subsequently, task priorities are determined based on the calculation of each task’s Shapley value, ensuring that critical tasks are scheduled preferentially. Finally, a hybrid algorithm integrating Q-learning with Asynchronous Advantage Actor–Critic (A3C) is developed to perform precise and adaptive task scheduling, improving system load balancing and execution efficiency. Extensive simulation results demonstrate that the proposed method outperforms state-of-art methods in both energy consumption and response time, with improvements of 26.34% and 29.98%, respectively, underscoring the robustness and superiority of the proposed method. Full article

Goose astrovirus (GAstV) infection has emerged as a prevalent cause of urate deposition and viral gout in major goose farming across China, leading to high mortality and substantial economic losses. However, the molecular mechanisms linking GAstV to gout pathogenesis remain elusive. Here, a total of 10 five-day-old Jiangnan white goslings were selected, and tissue damage and kidney gene expression profiles were investigated. The results showed multi-organ damage in GAstV-infected gosling, including kidney, liver, spleen, and lung. Also, 342 differentially expressed genes were identified in infected kidney tissues after 10 days post-infection using transcriptomic sequencing, including 185 upregulated and 157 downregulated genes. In addition, gene set enrichment analysis revealed significant positive correlations between GAstV infection and bile acid metabolism and fatty acid metabolism pathways. Notably, bile acid metabolism was implicated in uric acid regulation and gout progression. Protein–protein interaction network analysis identified AGXT as a central hub gene within the bile acid metabolic pathway, with key upregulated interactors including PIPOX, ALDH1A1, and CAT. AGXT, a critical enzyme in glyoxylate detoxification, directly modulates uric acid biosynthesis. Our findings propose that GAstV-induced activation of bile acid metabolism, particularly AGXT upregulation, drives hyperuricemia and subsequent gout pathology. This study elucidates a novel mechanism of GAstV-associated metabolic dysregulation and provides actionable genetic targets for antiviral breeding strategies in waterfowl. Full article

Inherited retinal dystrophies (IRDs) are clinically and genetically heterogeneous disorders. Most IRDs follow a monogenic inheritance pattern. However, an increasing number of unresolved cases suggest the possible contribution of oligogenic or digenic mechanisms. Here, we report two ultra-rare missense variants—AIPL1 R302L and BBS2 P134R—that co-segregate with early-onset nonsyndromic retinal degeneration in affected individuals from a non-consanguineous family. We performed a multi-level computational investigation to assess whether these variants may act through a convergent pathogenic mechanism. Using AlphaFold2-predicted structures, we modeled both wild-type and mutant proteins, introduced point mutations, and performed energy minimization and validation. FoldX, DynaMut2, and DUET all predicted destabilizing effects at the variant sites, corroborated by local disruption of secondary structure and altered surface electrostatics. Comparative docking (via HDOCK and ClusPro) identified a putative interaction interface between the TPR domain of AIPL1 and the β-sheet face of BBS2. This interface was destabilized in the double-mutant model. At the systems level, transcriptomic profiling confirmed co-expression of AIPL1 and BBS2 in human retina and fetal eye, while functional enrichment analysis highlighted overlapping involvement in ciliary and proteostasis pathways. Network propagation suggested that the two proteins may converge on shared interactors relevant to photoreceptor maintenance. Collectively, these in silico results provide structural and systems-level support for a candidate digenic mechanism involving AIPL1 and BBS2. While experimental validation remains necessary, our study proposes a testable mechanistic hypothesis and underscores the value of computational approaches in uncovering complex genetic contributions to IRDs. Full article