Search Results (119)

Mitogen-Activated Protein Kinases (MAPKs) play crucial roles in plant stress signaling, but the mechanisms of MAPK genes in Portulaca oleracea remain functionally uncharacterized. In this study, transcriptomic screening of P. oleracea under salt stress identified PoMPK3 as a candidate gene, showing significant root-specific upregulation. Phylogenetic analysis classified it as a Group A MAPK protein, and subcellular localization confirmed its membrane association. Heterologous expression of PoMPK3 in Arabidopsis thaliana significantly enhanced salt tolerance, as evidenced by improved seed germination rates, longer primary roots, increased biomass, and reduced stress symptoms. Mechanistically, PoMPK3 expression activated ABA signaling, leading to increased ABA levels and upregulation of AtNCED3, AtPYR1, and AtABF3. Furthermore, it strengthened the antioxidant defense, as evidenced by elevated antioxidant enzyme activity, leading to a reduction in oxidative stress. The transgenic lines also demonstrated enhanced osmotic adjustment through osmolytes accumulation and ionic homeostasis, evidenced by tissue-specific Na⁺/K⁺ ratios (low in shoots, high in roots) resulting from the concerted upregulation of AtSOS1, AtNHX1, and AtHKT1. In addition, gene co-expression network analysis and molecular docking predicted phosphorylation of WRKY transcription factors, suggesting a novel mechanism for transcriptome reprogramming. Collectively, our findings not only advance the understanding of salt tolerance mechanisms in purslane but also identify PoMPK3 as a key genetic determinant, thereby laying the foundation for its use in breeding programs aimed at enhancing salt stress resilience in crops. Full article

The allosteric activation of antithrombin (AT) involves a conformational shift from a native, repressed (R) to a heparin-bound, activated (AH) state. Using computational structural analysis, we identified an evolutionarily conserved allosteric communication network (ACN) comprising the residues H120, Y131, and Y166, which undergo key structural displacements during this transition. Site-directed mutagenesis of these residues markedly enhanced AT native reactivity toward FXa and reduced thermal stability, indicating their role in stabilizing the R state. These findings support a three-step “slingshot” model in which the ACN functions as a molecular lock that restrains stored conformational energy, preventing premature activation. Heparin binding disengages this lock, triggering a cascade of structural changes that propagate from the heparin-binding site (HBS) to the reactive center loop (RCL). Additional mutational analyses of residues bridging the β-sheet A (βsA) and the RCL/exosite domains revealed a delicate energetic balance involving the S380 insertion and E381–R197 salt bridge, which collectively tune the activation threshold. Molecular dynamics simulations of ACN mutants further revealed increased flexibility at both HBS and RCL domains, consistent with concerted allosteric coupling. Together, these results provide new mechanistic insights into the structural basis of AT activation and suggest avenues for engineering heparin-independent AT variants. Full article

Many Time-Sensitive Networking (TSN) workloads require deterministic service across heterogeneous links, yet commodity WLANs are contention-based. Although IEEE 802.11be introduced Restricted Target Wake Time (r-TWT) for prioritized access, its ability to robustly guarantee determinism in mixed deployments with legacy devices remains unverified. We propose a standards-aligned scheme that composes r-TWT, Quiet Time Period (QTP), and an optional Randomized Enqueue (RE) policy. These three mechanisms act in concert to protect the Scheduled Traffic (ST) service period (SP) while minimizing the impact on Non-Scheduled Traffic (NST). To analyze how the proposed scheme impacts existing WLANs, we focus the analysis on how the scheme reshapes the contention period (CP)—where opportunistic capacity is realized—by modeling SP/CP timing with renewal theory and embedding it into an EDCA Markov chain. Simulation results confirm that the proposed scheme protects ST determinism: ST throughput remains pinned to the ceiling with zero observed outage and bounded delay across a wide range of station counts. The proposed scheme minimizes NST throughput degradation in the system-peak throughput range (8–12 stations). Full article

Network Music Performance (NMP) refers to network-based remote collaboration when applied to music performances, such as musical education, music production and live music concerts. In NMP, the most important parameter for the Quality of Experience (QoE) of the participants is low end-to-end audio delay. Increasing delays prevent musicians’ synchronization and lead to a suboptimal musical experience. Visual contact between the participants is also crucial for their experience but highly demanding in terms of bandwidth. Since audio compression induces additional coding and decoding delays on the signal path, most NMP systems rely on audio quality reduction when bandwidth is limited to avoid violating the stringent delay limitations of NMP. To assess the delay and quality tolerance limits for NMP and see if they can be satisfied by emerging 5G networks, we asked eleven pairs of musicians to perform musical pieces of their choice in a carefully controlled laboratory environment, which allowed us to set different end-to-end delays or audio sampling rates. To assess the QoE of these NMP sessions, each musician responded to a set of questions after each performance. The analysis of the musicians’ responses revealed that actual musicians in delay-controlled NMP scenarios can synchronize at delays of up to 40 ms, compared to the 25–30 ms reported in rhythmic hand-clapping experiments. Our analysis also shows that audio quality can be considerably reduced by sub-sampling, so as to save bandwidth without significant QoE loss. Finally, we find that musicians rely more on audio and less on video to synchronize during an NMP session. These results indicate that NMP can become feasible in advanced 5G networks. Full article

Bringing together the Internet of Things (IoT), LLMs, and Federated Learning (FL) offers exciting possibilities, creating a synergy to build smarter, privacy-preserving distributed systems. This review explores the merging of these technologies, particularly within edge computing environments. We examine current architectures and practical methods enabling this fusion, such as efficient low-rank adaptation (LoRA) for fine-tuning large models and memory-efficient Split Federated Learning (SFL) for collaborative edge training. However, this integration faces significant hurdles: the resource limitations of IoT devices, unreliable network communication, data heterogeneity, diverse security threats, fairness considerations, and regulatory demands. While other surveys cover pairwise combinations, this review distinctively analyzes the three-way synergy, highlighting how IoT, LLMs, and FL working in concert unlock capabilities unattainable otherwise. Our analysis compares various strategies proposed to tackle these issues (e.g., federated vs. centralized, SFL vs. standard FL, DP vs. cryptographic privacy), outlining their practical trade-offs. We showcase real-world progress and potential applications in domains like Industrial IoT and smart cities, considering both opportunities and limitations. Finally, this review identifies critical open questions and promising future research paths, including ultra-lightweight models, robust algorithms for heterogeneity, machine unlearning, standardized benchmarks, novel FL paradigms, and next-generation security. Addressing these areas is essential for responsibly harnessing this powerful technological blend. Full article

Metabolism, the network of biochemical reactions that powers life, arose under conditions radically different from those on Earth today. Investigating its origins reveals how initially simple chemical processes gradually integrated nucleic acid and then protein catalysts, becoming progressively more complex and regulated until they evolved into the enzyme-rich systems observed in modern organisms. Here, we integrate multiple perspectives on the origin of metabolism, focusing primarily on an evolutionary trajectory from an RNA-based world, where ribozymes, metal ions, coenzymes, small peptides, and other small organic molecules worked in concert, to enzyme-driven metabolic networks. We also address the longstanding debates on whether these early metabolic pathways were largely autotrophic or heterotrophic, and consider so-called “pre-metabolisms” (non-enzymatic networks) as an alternative conceptual framework. We discuss key examples such as the Wood–Ljungdahl (W–L) pathway and the reverse tricarboxylic acid (TCA) cycle, both posited to function under early Earth conditions. Finally, we examine how the environment (e.g., minerals, clays, hydrothermal vents) shaped early metabolism, describe unresolved questions about the Last Common Ancestor’s catalytic repertoire and propose future directions that link geochemical insights with molecular biology and synthetic approaches. Full article

Autonomous vessels are becoming paramount to ocean transportation, while they also face complex risks in dynamic marine environments. Machine learning plays a crucial role in enhancing maritime safety by leveraging its data analysis and predictive capabilities. However, there has been no review grounded in bibliometric analysis in this field. To explore the research evolution and knowledge frontier in the field of maritime safety for autonomous shipping, a bibliometric analysis was conducted using 719 publications from the Web of Science database, covering the period from 2000 up to May 2024. This study utilized VOSviewer, alongside traditional literature analysis methods, to construct a knowledge network map and perform cluster analysis, thereby identifying research hotspots, evolution trends, and emerging knowledge frontiers. The findings reveal a robust cooperative network among journals, researchers, research institutions, and countries or regions, underscoring the interdisciplinary nature of this research domain. Through the review, we found that maritime safety machine learning methods are evolving toward a systematic and comprehensive direction, and the integration with AI and human interaction may be the next bellwether. Future research will concentrate on three main areas: evolving safety objectives towards proactive management and autonomous coordination, developing advanced safety technologies, such as bio-inspired sensors, quantum machine learning, and self-healing systems, and enhancing decision-making with machine learning algorithms such as generative adversarial networks (GANs), hierarchical reinforcement learning (HRL), and federated learning. By visualizing collaborative networks, analyzing evolutionary trends, and identifying research hotspots, this study lays a groundwork for pioneering advancements and sets a visionary angle for the future of safety in autonomous shipping. Moreover, it also facilitates partnerships between industry and academia, making for concerted efforts in the domain of USVs. Full article

Sea-ice thickness (SIT) from satellites is an essential climate variable for characterizing the ice-covered ocean and evaluating numerical models. Although satellite altimetry is a promising option to obtain sustainable circum-Antarctic SIT estimates, its application in the Antarctic remains challenging due to the scarcity of systematic in situ observations for validation, and the most recent intercomparison exercise covered the period 2004 to 2008. In this study, we compared three empirical methods (ERM, BERM, and OLM) and one lidar-only method (ZIF) to determine SIT from lidar freeboard observations, one method combining lidar and radar freeboard observations (FDM), and one that uses both lidar freeboard observations and an independent snow depth dataset from passive microwaves (SICC). We first compared the methods in 2019, which is the only data-rich year during the overlapping period from 2019 to 2023. While the methods agreed on the broad spatial patterns of SIT, they clustered in two groups that have significant magnitude differences, with SICC and FDM estimating thicker ice and the lidar-based methods producing the thinnest estimates. Based on the limited set of available data, we did not find any single best performing method, and we recommend using the methods in a complementary way and to establish a network of concerted and continued field measurements for method assessments. Full article

This study investigates the application of network audio technology in performing arts and media art collaborations within virtual environments, analyzing its impact through four case studies. Employing a practice-based research methodology through using a variety of open-source software and communication protocols, it examines the cultural and social dynamics, creative workflows, and technical frameworks of ensembles leveraging network audio technology for remote recording and virtual production. These projects, recognized internationally within the electroacoustic music community, underscore the potential of network audio to transform virtual music performance, industry practices, and education. The research addresses challenges in internet-based production, particularly in real-time multichannel audio recording, mixing, and production with limited home setups. Insights into managing multiple audio networks effectively and capturing distinct tracks across virtual spaces are presented, offering both creative and technical strategies for virtual music performance and production in emerging digital environments. Full article

Traditional anaerobic digestion (AD) technology continues to have severe limitations in terms of complicated substrate degradation efficiency and methane production. This study optimizes the AD system using corn straw and cattle manure as substrates by introducing an exogenous N-Hexanoyl-L-Homoserine lactone (C6-HSL) signaling molecule in concert with an applied external voltage of 0.8 V, systematically investigating its impact on methanogenic performance and microbial community dynamics. The results show that the combined regulation significantly increased methane production (by 29.74%) and substrate utilization rate (by 74.73%) while preventing acid inhibition and ammonia nitrogen inhibition. Mechanistic analysis revealed that the external voltage enhanced the system’s electrocatalytic activity, while the C6-HSL signaling molecule further facilitated the electron transfer efficiency of the biofilm on the electrode. The combined regulation notably enriched hydrogenotrophic methanogens (with Methanobacterium predominating on the cathode and Methanobrevibacter in the digestate), establishing a stable metabolic cooperative network on both the electrode and in the digestate, optimizing the hydrogenotrophic methanogenesis pathway, and enhancing the synergistic effects among microbial communities and system robustness. This study uncovers the synergistic enhancement mechanism of C6-HSL and external voltage, providing new technological pathways and theoretical support for the efficient conversion of low-quality biomass resources and the production of clean energy. Full article

RNA plays important roles in the regulation of gene expression in response to environmental stimuli. SVALKA, a long noncoding cis-natural antisense RNA, is a key component of regulating the response to cold temperature in Arabidopsis thaliana. There are three mechanisms through which SVALKA fine tunes the transcriptional response to cold temperatures. SVALKA regulates the expression of the CBF1 (C-Repeat Dehydration Binding Factor 1) transcription factor through a collisional transcription mechanism and a dsRNA and DICER mediated mechanism. SVALKA also interacts with Polycomb Repressor Complex 2 to regulate the histone methylation of CBF3. Both CBF1 and CBF3 are key components of the COLD REGULATED (COR) regulon that direct the plant’s response to cold temperature over time, as well as plant drought adaptation, pathogen responses, and growth regulation. The different isoforms of SVALKA and its potential to form dynamic RNA conformations are important features in regulating a complex gene network in concert with several other noncoding RNA. This review will summarize the three mechanisms through which SVALKA participates in gene regulation, describe the ways that dynamic RNA structures support the function of regulatory noncoding RNA, and explore the potential for improving agricultural genetic engineering with a better understanding of the roles of noncoding RNA. Full article

Lake ecosystems are impacted by anthropogenic disturbances and have become vulnerable worldwide. Highly disturbed lake ecosystems are not well understood due to the lack of data on changes in the structures and functions of ecosystems. In this paper, we focus on Lake Baiyangdian (BYDL), the largest shallow lake in North China. Following the establishment of the Xiong’an New Area (XNA) in 2017, concerted efforts to restore BYDL’s aquatic environment have been undertaken, which has led to significant changes in the structures and functions of the ecosystems. We evaluated the biomass dynamics of main biological communities and detected the regime shifts of environmental factors in BYDL from 2016 to 2023. Further, we constructed a food web model for the BYDL ecosystem in 2023 by using Ecopath with Ecosim (EwE) and made a comparison with the reported results in 2018. The results showed significant changes in the ecosystem structure of BYDL over the last 6 years. In 2023, the submerged macrophytes biomass in the system increased by 4.2 times compared to 2018, leading to an increase in total system throughput. We found that BYDL changed from an algal-type lake to a macrophyte-dominated lake. In addition, we found TN, NH₄⁺-N, and COD_Mn were significantly decreased in BYDL during the restoration. TN and NH₄⁺-N had a change point in approximately 2021, indicating that a regime shift had occurred during restoration. Overall, the BYDL ecosystem was in an immature but developing state, as indicated by ecological network analysis indicators. Nutrient-loading reduction, hydrological regulation, and rational biomanipulation may be the potential driving factors of change in the BYDL ecosystem. We strongly recommend the timely harvesting of submerged macrophytes, the proliferation and release of herbivorous fishes, and the assessment of the ecological capacity of carnivorous fishes in the future ecological restoration of BYDL. Full article

At the Group of Seven (G7) summit held on 13–15 June in 2024, the Group’s leaders committed to establishing a collective cyber security framework and reinforcing the work of the cyber security working group to manage the risks targeting energy systems. Likewise, oil and electricity, and natural gas rely on complex and interdependent technologies and communication networks from production to consumption. The preparedness to handle cyber security threats in the energy infrastructures among decision makers, planners, and the industry in a concerted manner signifies that cyber security is becoming more appreciated. Therefore, considering the ambition and achievement of the G7 countries towards energy and cyber sovereignty, this paper’s focus and research question aims to explore the potential existence of the cyber governance alliance in the gas subsector within the G7. The objective of this paper is twofold. First, it explores the potential of the G7, the world’s seven largest advanced economies, to lead on a nascent cyber governance for risk management in the gas sector. The qualitative analysis conducted through the institutional analysis and design method examines up-to-date data involving mainly state actors. Second, by drawing on LNG, one of the world’s fastest growing energy types in the coming decades, the paper points out the need for further research on the transnational governance operating through public–private engagement to address the cyber risks to gas systems. While the paper makes an empirical contribution to the field of security governance and a practical contribution to security consulting, its limitations rely on the necessity to also conduct a quantitative enquiry, which would necessitate, among others, a review of the literature in the G7 countries, and a group of researchers from academia and practitioners to obtain a sense of the cyberspace in the energy reality. Full article

The advent of connected vehicles holds significant promise for enhancing existing traffic signal and vehicle speed control methods. Despite this potential, there has been a lack of concerted efforts to address issues related to vehicle fuel consumption and emissions during travel across multiple intersections controlled by traffic signals. To bridge this gap, this research introduces a novel technique aimed at optimizing both traffic signals and vehicle speeds within transportation networks. This approach is designed to contribute to the improvement of transportation networks by simultaneously addressing issues related to fuel consumption and pollutant emissions. Simulation results vividly illustrate the pronounced the effectiveness of the proposed traffic signal and vehicle speed control methods of alleviating vehicle delay, reducing stops, lowering fuel consumption, and minimizing CO₂ emissions. Notably, these benefits are particularly prominent in scenarios characterized by moderate traffic density, emphasizing the versatility and positive impact of the method across varied traffic conditions. Full article

The lack of a universally accepted definition of a pandemic hinders a comprehensive understanding of and effective response to these global health crises. Current definitions often lack quantitative criteria, rendering them vague and limiting their utility. Here, we propose a refined definition that considers the likelihood of susceptible individuals contracting an infectious disease that culminates in widespread global transmission, increased morbidity and mortality, and profound societal, economic, and political consequences. Applying this definition retrospectively, we identify 22 pandemics that occurred between 165 and 2024 AD and were caused by a variety of diseases, including smallpox (Antonine and American), plague (Justinian, Black Death, and Third Plague), cholera (seven pandemics), influenza (two Russian, Spanish, Asian, Hong Kong, and swine), AIDS, and coronaviruses (SARS, MERS, and COVID-19). This work presents a comprehensive analysis of past pandemics caused by both emerging and re-emerging pathogens, along with their epidemiological characteristics, societal impact, and evolution of public health responses. We also highlight the need for proactive measures to reduce the risk of future pandemics. These strategies include prioritizing surveillance of emerging zoonotic pathogens, conserving biodiversity to counter wildlife trafficking, and minimizing the potential for zoonotic spillover events. In addition, interventions such as promoting alternative protein sources, enforcing the closure of live animal markets in biodiversity-rich regions, and fostering global collaboration among diverse stakeholders are critical to preventing future pandemics. Crucially, improving wildlife surveillance systems will require the concerted efforts of local, national and international entities, including laboratories, field researchers, wildlife conservationists, government agencies and other stakeholders. By fostering collaborative networks and establishing robust biorepositories, we can strengthen our collective capacity to detect, monitor, and mitigate the emergence and transmission of zoonotic pathogens. Full article