Search Results (238)

The timing of HIV-1 protease (PR) activation and the determinants of orderly Gag and Gag-Pol polyprotein cleavage, leading to structural maturation of virions, are not well-understood. Here, we employed total internal reflection microscopy to visualize PR activation and cleavage of a FRET-based fluorescent substrate in single HIV-1 particles assembled on the cell’s ventral membrane. PR activity manifested as a reduction in FRET signal in a relatively small fraction of nascent viral particles. By contrast, most virions released from cells contained a processed FRET substrate, suggesting that PR activation may be delayed or suppressed in most virions assembled at the ventral membrane. A combination of single-particle tracking and FRET measurements detected PR activity, on average, within ~20 min after the onset of particle assembly, while cleavage of a FRET substrate in single virions was completed within a few minutes. Importantly, substrates containing distinct PR cleavage sequences derived from Gag and Gag-Pol polyproteins were processed with different rates and efficiency. Our findings validate the use of surrogate FRET substrates to assess the timing of PR activation in single virions and elucidate the determinants of the tightly orchestrated order of Gag and Gag-Pol cleavage required for the formation of infectious virions. Full article

The rapid expansion of the cloud–edge continuum requires containerized applications to scale dynamically across highly heterogeneous and resource-constrained environments. As the de facto standard for container orchestration, Kubernetes (K8s for short) relies heavily on its scheduling subsystem to manage these complex distributed environments. However, default scheduling methodologies are inherently designed for homogeneous cloud data centers and bring substantial deployment challenges when used in edge topologies. While numerous custom schedulers, plugins, and extensions have been put forward to bridge this gap, a review of the state of the art is needed to evaluate existing approaches and capture recent trends. In this survey, we present a comprehensive review of Kubernetes scheduling strategies published between January 2023 and January 2026. We establish a multi-dimensional taxonomy that categorizes scheduling approaches based on common objectives, modification methods, optimization methodologies, targeted workloads, evaluation methods, scheduling scopes, and performance metrics. We investigate open challenges arising across different computing paradigms and highlight recent trends and possible directions for future research in Kubernetes scheduling. Full article

Background: Deep learning segmentation of coronary CT angiography (CCTA) can support visualization, quantitative analysis, and patient-specific research workflows, but local deployment is often limited by the GPU configuration, dependency drift, heterogeneous operating systems, and limited DevOps resources. Objective: This study aimed to develop and evaluate a cross-platform orchestrator for the reproducible execution of containerized cardiovascular segmentation models without the need for Kubernetes. The segmentation models were used as representative demonstration workloads. Materials and Methods: A lightweight container orchestrator was developed using Podman and Podman Desktop Machine. The system provides a standardized REST API, asynchronous job execution, parallel and cascaded inference modes, persistent logging, and an external evaluation module. Two containerized nnU-Net v2 services were implemented for coronary artery and aortic segmentation from CCTA. Models were trained on the ImageCAS data and evaluated on an independent external cohort of 200 CCTA examinations. The performance was assessed using the Dice similarity coefficient (DSC) and intersection-over-union (IoU). System latency, throughput, and resource utilization were also measured. Results: The mean end-to-end latency on Linux was 52.3 ± 7.1 s with GPU acceleration and 74.1 ± 8.3 s in CPU fallback mode. Parallel execution increased throughput from 44.4 to 62.1 cases/hour, with the expected latency increase due to resource contention. As workload validation, the coronary and aortic nnU-Net services achieved DSC values of 0.93 and 0.95, respectively. Conclusions: The proposed orchestrator enables reproducible, standardized, portable deployment of containerized CCTA segmentation models in on-premises research environments, reducing operational barriers while supporting visualization, validation, and downstream applications. Full article

2,4-Dichlorophenoxyacetic acid (2,4-D), originally developed as a synthetic auxinic herbicide, is the most widely used chemical inducer of somatic embryogenesis (SE) in plants. Despite extensive use of 2,4-D in plant regeneration, the systems-level regulatory mechanisms connecting hormonal signaling, metabolic reprogramming, translational control, and embryogenic competence remain poorly resolved. Here, we hypothesize that TOR signaling functions as an integrative molecular hub coordinating transcriptional, metabolic, and developmental reprogramming during somatic embryogenesis induction. To investigate the molecular regulatory landscape associated with 2,4-D-induced SE, we performed a systems-level analysis integrating publicly available transcriptomic data from Arabidopsis thaliana with high-confidence protein–protein interaction (PPI) network analyses using STRING v12.0 (confidence score ≥ 0.900). Using a previously published transcriptomic dataset, we identified 1927 upregulated genes associated with SE induction, which were organized into 34 functional modules related to transcriptional regulation, translation metabolism, hormone signaling and cellular homeostasis. Within this interactome, TARGET OF RAPAMYCIN (TOR) kinase emerged as an integrative regulatory hub associated with multiple pathways involved in embryogenic reprogramming. Network analyses revealed three major TOR-associated regulatory axes: (1) the TOR–FKBP12–RPS6A axis, associated with ribosome biogenesis and translational regulation; (2) the TOR–CBP20 axis, connected with transcriptional reprogramming; SE master regulators (LEC1, LEC2, and FUS3); and lipid, sterol, brassinosteroid (BR), and auxin-associated pathways; and (3) the TOR–TAP46 axis, linked with one-carbon metabolism, nucleotide biosynthesis, DNA replication and repair, and genome-stability pathways. Additionally, the network contained 411 embryo-lethal (EMBL) genes distributed across multiple regulatory modules, reinforcing the biological relevance of the identified interactome and highlighting the importance of coordinated developmental, metabolic, and transcriptional regulation during embryogenesis induction. These findings support a systems-level TOR-associated regulatory framework involved in the integration of transcriptional, translational, metabolic, hormonal, and genome-maintenance pathways during embryogenesis. This interactome model provides a foundation for functional studies aimed at dissecting the molecular mechanisms underlying SE and identifying candidate targets to improve regeneration and biotechnological application and crop genetic engineering. Collectively, this study proposes a mechanistic framework in which TOR signaling integrates developmental, metabolic, translational, and genome-stability pathways to orchestrate embryogenic competence, providing candidate molecular targets for improving plant regeneration and genome engineering platforms. Full article

Background: High-technology supply chains are interdependent logistics infrastructures in which digital, energy, manufacturing, cloud and orbital layers are tightly coupled. This paper examines how such layering changes supply chain resilience and systemic vulnerability. Methods: The paper develops an analytical–conceptual framework linking supply chain resilience, interdependent infrastructure theory and network topology. It introduces Topological Phase Vulnerability (TPV), capturing proximity to structural fragility thresholds, and the Shock Amplification Coefficient (SAC), conceptualizing disruption amplification as a function of centrality concentration, cross-layer coupling and reconfiguration capacity. The framework is supported by a fuzzy-inspired diagnostic scorecard and stylized assessment of alternative infrastructure configurations. Orbital computing is an extreme illustrative context because it combines dependence on advanced semiconductor fabrication, hyperscale cloud orchestration, energy systems, launch capacity and logistics coordination. Results: Highly centralized configurations are more likely to transform local disruptions into cross-layer cascades, whereas modular and distributed configurations are more likely to contain disruption through redundancy, substitutability and rerouting. Conclusions: Resilience in next-generation logistics infrastructure depends not only on capacity or component reliability, but also on topology. Centrality dispersion, modularity and reconfiguration capacity are critical design principles for reducing shock amplification in high-technology supply chains. Full article

Small and medium enterprises (SMEs) in the entertainment sector face significant challenges managing seat assignments through manual processes that are error-prone and time-consuming. This paper presents an intelligent agent-based system that automates seat assignment, while providing natural language support for operational staff. The system integrates a large language model (Gemini 2.5 Flash) for conversational interaction with a constraint-based optimization algorithm that considers capacity, accessibility, revenue, and business priorities. A fuzzy matching engine combining spaCywith the fuzzy string matching library FuzzyWuzzy consolidates duplicate reservations from multiple channels. The cloud-based architecture leverages AWS managed serverless services (ECS Fargate for container orchestration and Lambda for event-driven pipelines) with PostgreSQL for data management. Technology Readiness Level 4 (TRL4) validation demonstrated 94% precision in duplicate detection, successful assignment of 87% of reservations with 82% average capacity utilization, and effective natural language query handling. The system reduces manual processing time by 65%, while improving assignment quality through systematic enforcement of constraints. This work demonstrates the feasibility of AI-powered operations management for resource-constrained SMEs, offering a practical reference architecture combining conversational AI with algorithmic optimization. Full article

Aircraft digital twins are increasingly used for diagnostics, prognostics, and predictive maintenance, but their role as lifecycle-oriented, multi-stakeholder decision-support ecosystems remains insufficiently developed. This paper addresses this gap by proposing a conceptual systems-engineering framework for an aircraft digital twin ecosystem supporting sustainable aviation transport management. The framework integrates physics-based, data-driven, hybrid, probabilistic, and federated modelling approaches and includes a three-layer ecosystem model, formal mathematical representation of aircraft and digital twin lifecycle evolution, federated model updating, lifecycle decision-support scenarios, reference architecture, validation and trustworthiness principles, and a five-level maturity model. Representative aviation industrial cases are used to interpret the framework. The analysis shows that current industrial practice already contains elements of predictive maintenance, fleet analytics, engine health monitoring, and cloud-enabled MRO optimization, but full aircraft-level lifecycle governance, sustainability trade-off analysis, federated validation, and multi-stakeholder decision orchestration remain underdeveloped. The proposed framework positions aircraft digital twins as asset-level instruments for lifecycle planning, coordinated governance, and sustainability-oriented decision support. Full article

Future Internet applications such as intelligent transportation, immersive services, and edge-assisted artificial intelligence require latency-sensitive service provisioning at the network edge. In containerized mobile edge computing (MEC), service orchestration is not only a task-offloading problem, but also a task–container–image constrained decision problem: an offloaded task can be executed only when the required runtime container is active, and a newly activated container must be supported by a locally cached service image. This dependency couples task placement, runtime container caching, and persistent image caching under limited RAM and ROM resources. To address this challenge, this paper proposes HAM-MADDPG, a dependency-aware hierarchical action-masked multi-agent reinforcement learning algorithm for joint task offloading and image–container caching in containerized MEC networks. HAM-MADDPG decomposes the monolithic orchestration decision into three causally ordered policy layers: task offloading, runtime container caching, and persistent image caching. Each layer learns a structured subproblem conditioned on upstream realized decisions, while dynamic action masking and feasibility-aware action realization guide the learned policies toward executable decisions satisfying task–container and container–image constraints. Extensive simulations under dynamic service demands and heterogeneous edge resources show that HAM-MADDPG achieves more stable convergence than non-hierarchical reinforcement learning baselines and reduces long-term system latency by approximately 14–25% compared with representative heuristic and flat DRL baselines. Full article

Evaluating intrusion detection methods at the level of individual MITRE Adversarial Tactics, Techniques, and Common Knowledge (ATT&CK) for Industrial Control System techniques requires Operational Technology traffic in which each attack sequence carries its MITRE technique identifier as ground truth. Publicly available Industrial Control System datasets either provide coarse attack-versus-benign labels (SWaT, WADI, CIC-APT-IIoT) or require ex-post technique reconstruction from CALDERA operation logs, and therefore do not support per-technique benchmarking. We describe one primary contribution and two supporting contributions, demonstrated on one Modbus/Raspberry-Pi programmable logic controller/CALDERA/convolutional bidirectional Long Short-Term Memory autoencoder (CNN-BiLSTM-AE) use case. The primary contribution is an in-orchestrator labelling methodology for per-technique-labelled Industrial Control System attack capture. Its single load-bearing property is that the campaign orchestrator owns the label primitive and writes each per-sequence technique identifier into the capture artefact at injection time, eliminating ex-post log-to-packet alignment. The first supporting contribution is a protocol-aware detection pipeline. Its load-bearing architectural choice is a priority-ordered protocol router that dispatches each labelled flow to a per-protocol detector plug-in (protocol-aware features here, with generic-flow features admissible as an alternative plug-in policy on the same router). The second supporting contribution is a suite of four reproducible CALDERA chains (three Information-Technology-to-Operational-Technology kill chains plus one enterprise-side control) that exercise the labelling methodology end-to-end and the detection pipeline along complementary detection paths. All three contributions are platform-independent: any ATT&CK-aligned emulator and any fieldbus protocol can host the labelling methodology, and any detector trained on an admissible feature space can plug into the router. The dataset contains 40,000 benign and 9997 attack Modbus sequences spanning four ATT&CK techniques (T0802 Automated Collection, T0831 Manipulation of Control, T0836 Modify Parameter, T0846 Remote System Discovery). On this dataset, the CNN-BiLSTM-AE reaches a 100% true-positive rate (TPR) at the 98th-percentile benign threshold across all four techniques and a 99.7% overall TPR at the tighter 99.5th-percentile threshold, with per-technique TPR between 96.1% (T0836 Modify Parameter) and 100% (T0802 Automated Collection, T0846 Remote System Discovery). Across the four CALDERA chains, the Modbus autoencoder produces 234 protocol-layer detections and the Security Information and Event Management (SIEM) rule set produces 30 alerts, with per-chain tactic coverage between 0.714 and 0.786 and CALDERA-ability success rates between 0.800 and 0.857. Full article

Recent improvements in hardware and software have enabled a paradigm shift in satellite computing, moving from purpose-built satellites running a single application to platforms capable of executing and even receiving new workloads on orbit. This evolution has allowed image processing to migrate from ground stations to single- or multi-node satellite clusters, with only processed results transmitted, significantly reducing end-to-end latency. This paper proposes ORCHIDE, an orchestration solution built on cloud-native technologies such as Kubernetes and Argo, purpose built for space edge computing. A key capability of ORCHIDE is its support for unikernels—minimal, single-application virtual machines—alongside containers. Compared to traditional containerized deployments, unikernels substantially reduce CPU and memory footprint, achieve short boot times, and produce smaller binary images. ORCHIDE further enables unikernel workloads to leverage heterogeneous accelerator hardware, including FPGAs, through a dedicated accelerator management library. We describe the system architecture, the scheduling model, and the minimum target hardware required for deployment. Three clusters of varying topology were used to evaluate ORCHIDE, demonstrating that it operates effectively on both single- and multi-node heterogeneous configurations. Preliminary results show the ORCHIDE platform being able to run in heterogeneous and single-node environments with as low as 4 cores and 8 GB of memory, offering potential users the flexibility to compose satellite hardware to best match their mission requirements. Full article

Hybrid quantum-classical learning pipelines combine conventional accelerators, quantum runtimes, and quantum processing units (QPUs), creating scheduling, memory, isolation, encoding, and deployment challenges that are not captured by application-level quantum machine learning surveys alone. This paper presents a systematic review of runtime and systems mechanisms for hybrid quantum-classical workloads, with medical imaging used as a translation lens rather than as an exclusive inclusion boundary. Following a PRISMA-aligned review process, we screened 364 records and synthesized 40 studies published between 2020 and 2025. Each study was coded by systems layer, application grounding, noisy-label relevance, and evaluation maturity. The coding shows that the corpus combines direct medical evidence with broader transferable systems evidence: 8 studies directly evaluated medical data, 12 were medically motivated, and 20 were generic systems studies. Across the corpus, the strongest support concerns hybrid orchestration, qubit/resource allocation, classical–quantum data movement, and container-based reproducibility, whereas evidence remains limited for realistic clinical operation, end-to-end remote-QPU workflows, multi-tenant isolation, and noisy-label retraining loops. We contribute an evidence map, a direct/indirect/interpretive evidence distinction, and cross-layer design guidelines for future hybrid quantum-classical imaging pipelines in regulated settings. Full article

Aging is a significant risk factor for cardiovascular diseases. The prevalence of heart failure increases with age, making it a leading cause of morbidity and mortality. We investigated age-associated changes in expression of Nuclear Factor (Erythroid-derived 2)-Like 2 (NFE2L2 or NRF2) in the myocardium of humans, rhesus monkeys, Fischer rats, and C57BL/6 mice. NRF2 is a transcription factor that orchestrates the expression of genes involved in antioxidant and detoxification responses. Analyses of RNA-seq data from the Genotype-Tissue Expression (GTEx) project, which contains left ventricular samples from 294 male donors, revealed a trend of age-associated declines in NRF2 transcripts and several of its downstream genes (SOD1, SOD2, CAT, GCLM, and AKR1B). Age-dependent decreases in NRF2 protein expression were observed in the myocardium of Rhesus monkeys and Fischer rats. To determine whether NRF2 loss contributes to myocardial aging, we evaluated cardiac function of NRF2 knockout mice (KO) at 19 and 24 months of age. At 19 months, the NRF2 KO mice exhibited diastolic dysfunction, characterized by an increased end-diastolic volume (EDV) and end-systolic volume (ESV), accompanied by a reduced ejection fraction (EF) and fractional shortening (FS), indicative of early onset of heart failure. The NRF2 KO mice displayed premature aging phenotypes and had reduced lifespans. Our findings support the trend of NRF2 signaling decline with age, and that loss of NRF2 accelerates the maladaptive cardiac remodeling and functional deterioration associated with aging. Full article

New-energy plants and battery energy-storage stations increasingly depend on software-defined supervision, remote maintenance, and event-driven control, which makes cyber protection inseparable from operational responsiveness. This study presents a calibrated context-aware Security-as-a-Service orchestration framework, denoted SECaaS-CARO, for station-oriented adaptive risk control. The framework separates field assets, control services, security services, and an adaptive decision layer, and it uses a monotone nine-indicator risk score whose weights are calibrated from the training split rather than fixed heuristically. A validation-based threshold search maps that score to low-, medium-, and high-intensity service chains so that protection strength changes with session context instead of remaining static. A reproducible semi-synthetic dataset containing 17,000 station sessions was used to emulate operator login, remote maintenance, gateway misuse, and malicious command scenarios. Across 10 independently resampled 5000-session test streams, SECaaS-CARO achieved an F1 score of 0.973, a blocking success of 0.965, and the highest deployment utility of 1.173 while reducing mean latency to 21.28 ms compared with 27.06 ms for Logistic-Fixed and 28.15 ms for RandomForest-Fixed. The results indicate that an interpretable calibrated service-orchestration policy can preserve near-supervised detection quality while materially improving deployment-oriented efficiency for new-energy and energy-storage stations. Full article

The innate immune system serves as the primary barrier against viral invasion, utilizing pattern recognition receptors (PRRs) to orchestrate a rapid defense. Among these, the nucleotide-binding domain and leucine-rich repeat (NLR) containing proteins function as central signaling scaffolds, assembling into multiprotein complexes known as inflammasomes. These complexes drive the maturation of pro-inflammatory cytokines IL-1β and IL-18, and initiate gasdermin D (GSDMD)-mediated pyroptosis, a lytic cell death pathway that eliminates intracellular replication niches. This comprehensive review synthesizes the diversified landscape of inflammasome activation during viral infections, extending beyond the canonical NLRP3 inflammasome to include specialized sensors such as NLRP6, NLRP9, NLRP1, NLRP12, and NLRC4. We critically evaluate the evolutionary “arms race” between host defenses and viral pathogens, detailing the sophisticated immune evasion strategies employed by viruses—ranging from the expression of decoy proteins and direct proteolytic cleavage of immune sensors to the manipulation of post-translational modifications (PTMs). Furthermore, we discuss the dual nature of inflammasome activation, which balances protective viral clearance against pathological hyperinflammation, and provide an exhaustive analysis of novel therapeutic strategies, including direct NLR inhibitors and downstream cytokine blockers, currently navigating clinical transition. Full article

Soybean (Glycine max L.) yield is critically influenced by the number of nodes on the main stem (MSN), which serves as the primary site for pods and seeds. To elucidate the genetic mechanisms underlying MSN, we conducted a multi-omics analysis integrating bulk segregant analysis sequencing (BSA-seq), phytohormone, and transcriptome profilings in a soybean mutant, LSD914, which exhibits a significantly increased MSN number compared to its wild-type parent, HN48. BSA-seq of an F2 population identified 27 candidate genomic regions spanning 2.92 Mb, primarily on chromosome 18. Within these regions, 149 genes harbored non-synonymous SNPs and 26 genes contained frameshift InDels, with functional enrichment pointing to pathways in plant hormone signal transduction and developmental regulation. Phytohormone profiling revealed a distinct shift in LSD914, characterized by down-regulation of jasmonates, salicylates, and auxins, alongside specific accumulation of cis-zeatin. Integrative transcriptome analysis identified Glyma.18G259400, a gene encoding a gibberellin-regulated protein (GmGASA32), which was consistently and significantly down-regulated in LSD914 across all developmental stages and tissues. This finding contrasts with previous reports of its overexpression promoting plant height, suggesting a nuanced, context-dependent regulatory role. Our integrated approach identifies a key set of candidate genes and highlights GmGASA32 as a pivotal node in a hormone signaling network that orchestrates soybean node number, providing valuable targets for breeding high-yield soybean varieties with optimized plant architecture. Full article