Search Results (608)

Arsenic (As) is a ubiquitous and highly toxic metalloid with well-established carcinogenicity. Its accumulation and secondary release from lake sediments pose potential risks to lake ecosystem integrity and human health. Meanwhile, the ongoing intensification of lake eutrophication at the global scale has altered the sources, composition, and environmental behavior of internally derived dissolved organic matter (DOM). These changes have profoundly influenced As mobilization and transformation at the sediment-water interface (SWI). To advance understanding of the regulatory roles and underlying mechanisms of algal dissolved organic matter (ADOM) and submerged macrophyte dissolved organic matter (SMDOM) in As biogeochemical cycling under lake ecosystem regime shifts, extensive findings from the international literature were synthesized. The characteristic properties and environmental behaviors of ADOM and SMDOM were systematically compared, and their distinct regulatory pathways in lacustrine systems were further summarized. Results indicate that ADOM is typically characterized by low molecular weight, weak aromaticity, and high bioavailability. It can enhance As dissolution and mobilization from sediments through direct complexation, competition for adsorption sites, and stimulation of microbial metabolism and Fe(III) reduction. In contrast, SMDOM exhibits higher molecular weight, greater aromaticity, and a higher degree of humification. It tends to form stable complexes with mineral phases. Under the influence of radial oxygen loss (ROL) from submerged macrophyte roots during the growth phase, its capacity to promote mineral reduction is relatively limited. This process favors stable As retention in sediments. The regulatory effects of ADOM and SMDOM on As behavior are strongly modulated by environmental factors such as pH, redox potential (Eh), temperature, and light conditions, as well as by microbial communities. ADOM is more sensitive to reducing environments and photochemical processes. SMDOM, in contrast, exerts more persistent control under oxidizing conditions and at mineral-water interfaces. In addition, ADOM more readily drives microbial community shifts toward assemblages with enhanced capacities for Fe(III) reduction and As reduction or methylation. SMDOM is less likely to trigger strongly reducing processes. Based on these mechanisms, the outbreak and decay phases in algal-dominated lakes often correspond to critical periods of enhanced As mobilization and elevated ecological risk. In submerged macrophyte-dominated lakes, the decay phase may represent an important window for sedimentary As release. Finally, a conceptual framework describing the differential regulation of As biogeochemical cycling by ADOM and SMDOM is proposed. This framework provides a theoretical basis for As risk identification, the determination of critical risk periods, and the development of management strategies across lakes with different trophic states. Full article

The progressive decline in functional β-cell mass in Type 2 Diabetes (T2D) is increasingly recognized not as a simple apoptotic loss, but as a complex erosion of cellular identity termed “dedifferentiation.” Central to this phenotypic shift is the metabolo-epigenetic axis, where mitochondria act as the primary sensing hub, transducing nutrient flux into biochemical signals that govern the chromatin landscape. This review synthesizes current evidence on how mitochondrial metabolites—including Acetyl-CoA, α-ketoglutarate, and NAD+—serve as obligatory co-factors for the epigenetic machinery. We explore how chronic metabolic stress triggers a “Systemic epigenetic destabilization,” leading to the loss of lineage-specific markers and the formation of persistent “metabolic scars.” Furthermore, we discuss the clinical implications of these changes, specifically regarding the phenomenon of metabolic memory and the molecular limits of β-cell reversibility. By integrating foundational transcriptional studies with emerging epigenomic data, we propose that targeting the mitochondrial–epigenetic axis offers a strategic window for re-differentiating failing β-cells and restoring glycemic homeostasis. Full article

Task scheduling for vehicle–crane coordination is crucial for the operational efficiency of electrified automated container terminals (ACTs). However, under fully shared dispatching, existing studies rarely capture how charging-induced capacity fluctuations disrupt bidirectional service–arrival matching and propagate service-window shifts. To address this gap, this study proposes a comprehensive spatio-temporal optimization approach. Firstly, a bi-objective model is established to minimize service–arrival mismatch and vehicle energy consumption under state-of-charge (SOC) and charger-capacity constraints, explicitly quantifying vehicle–crane alignment at both handling interfaces. Secondly, an enhanced multi-objective algorithm (ST-NSGA-II) is developed, integrating a feasibility-preserving recursive decoding mechanism and a spatio-temporal variable neighborhood search (VNS) procedure. Finally, numerical experiments demonstrate that ST-NSGA-II significantly reduces mismatch and energy consumption compared to standard NSGA-II in large-scale scenarios. It also outperforms MOEA/D in Pareto-set quality, yielding a higher hypervolume (1.301 vs. 0.960) and a lower Spacing value (0.102 vs. 0.185). The results demonstrate that the proposed spatio-temporal optimization approach can effectively reduce handover mismatch compared to conventional scheduling modes, thereby achieving seamless task scheduling for vehicle–crane coordination. Full article

Cardiovascular emergencies most frequently arise from the sudden destabilization of atherosclerotic plaques. Conventional diagnostic strategies predominantly focus on luminal stenosis, despite the fact that most acute coronary events originate from non-obstructive lesions with high inflammatory activity. Recent advances in cardiac computed tomography (CCT) enable visualization of plaque morphology and surrounding perivascular fat, offering a unique window into coronary inflammation. The fat attenuation index (FAI), derived from pericoronary adipose tissue (PCAT) radiodensity, has emerged as a dynamic imaging biomarker capable of detecting vascular inflammation before clinical events occur. This review summarizes current evidence on the role of PCAT inflammation in plaque vulnerability, its implications for acute cardiovascular presentations, and recent technological innovations—including AI-enhanced analysis and photon-counting CT—that advance risk prediction. Inflammation-based imaging derived from CCT, including PCAT-FAI, has emerged as a promising research tool that may enhance risk stratification in patients presenting with chest pain. These developments signify a shift from purely anatomical assessment toward biological characterization of CAD, potentially transforming prevention and acute care. Full article

Decentralized agricultural gasification remains constrained by the thermochemical instability of high-alkali residues, such as straw and stalks. This operational bottleneck is defined by a narrow thermal window: oxidation core temperatures are typically targeted above 1000 °C for effective tar cracking, yet grate temperatures are constrained, often below 850 °C, depending on the specific ash fusion characteristics of the feedstock, to prevent viscous sintering and bed clinkering. This work proposes a conceptual framework for a control strategy designed to address these conflicting requirements through a unified framework integrating inferential soft-sensing, hierarchical Model Predictive Control (MPC), and sensor health monitoring. Machine learning architectures capture temporal dependencies and cumulative thermochemical transformations to reconstruct unobservable internal states. This enables real-time state estimation with reported accuracy levels (average test R² of 0.91–0.97) and 100% physical consistency through monotonicity constraints, effectively managing the critical thermal lag of densified pellets (400–600 s response time). High-fidelity CFD simulations anchor the soft-sensing layer, ensuring model robustness across the inherent variability of agricultural feedstocks. The architecture shifts control logic from reactive adjustments to anticipatory intervention through adaptive multi-mode operation that decouples high-intensity oxidation from grate integrity limits, while dynamic biochar management serves as a multifunctional control variable for tar cracking enhancement and alkali sequestration. Future work will focus on pilot-scale validation under transient feedstock conditions. Full article

‘Mollar de Elche’ pomegranate is highly valued for its sweet flavor but faces significant commercial hurdles due to pale coloration and sensitivity to postharvest disorders. This study investigates the impact of preharvest foliar applications of L-α-amino acids, applied alone (AA) or combined with 2.5% sorbitol (Sor–AA), on secondary metabolism and physiological resilience, defined here as the fruit’s capacity to maintain metabolic homeostasis and stabilize antioxidant pigments during cold storage (7 °C). Our results show that both treatments triggered a substantial shift in secondary metabolism, doubling anthocyanin concentrations at harvest and effectively overcoming the cultivar’s color deficit. While the AA treatment maximized fruit quantity per tree, the Sor–AA combination achieved the highest total yield (83.58 ± 6.82 kg) and individual fruit weight (469.00 ± 16.00 g) through a ‘metabolic bypass’ that optimizes energy use. Crucially, the physiological resilience of the fruit was uniquely bolstered by the Sor–AA treatment, which was the only strategy to stabilize anthocyanin levels (~108 mg L⁻¹) and maximize free ellagic acid in the husk (371.72 mg kg⁻¹) throughout 42 days of storage. Multivariate PCA (explaining 79.79% of variance) confirmed that the synergy of amino acids and sorbitol triggers systemic metabolic reprogramming. Consequently, this targeted agronomic approach could provide significant economic benefits by increasing the proportion of export-grade fruit and extending the commercial window for the pomegranate sector. Full article

This study provides sedimentological and stratigraphic insights into the Ediacaran fluviolacustrine successions of the Amane-n’Tourhart and Tifernine basins. The Amane-n’Tourhart Basin developed in a post-caldera volcanic setting along the margin of the Oued Dar’a Caldera, whereas the Tifernine Basin formed in a pre-caldera tectono-volcanic context associated with caldera development. The successions provide valuable information about the sedimentary processes operating in late Ediacaran continental environments. Field observations, facies analysis, and petrography reveal a variety of siliciclastic, carbonate, mixed siliciclastic–carbonate, and volcaniclastic facies. These facies form associations indicative of alluvial fan, floodplain, and shallow-water lacustrine settings. Alluvial fan deposits are dominated by conglomerates and sandstones forming braided systems. Fluviolacustrine successions show a transition from clay-rich siltstones with calcareous nodules to nodular and massive limestones, marking a gradual shift from fluvial to lacustrine conditions. Laminated limestones and stromatolites indicate intermittent microbial activity that contributed to carbonate precipitation. Sedimentation was strongly influenced by volcanic inputs and climatic fluctuations, alternating between humid and arid conditions. These factors drove cycles of channel incision, sediment infill, and lake expansion–contraction, illustrating the dynamic interplay of volcanism and climate that modulated deposition in these Ediacaran continental basins, with broad relevance to our understanding of this critical window in the Earth’s history. Full article

Historically, stage T4 non-small cell lung cancer (NSCLC) with direct spinal invasion was considered a definitive surgical contraindication due to the perceived inability to achieve negative margins without catastrophic morbidity. This paradigm has shifted through the advancement of specialized surgical techniques, which facilitate radical en-bloc resection in highly selected candidates by adhering to the en-bloc concept. This concept mandates the retrieval of the tumor and invaded vertebral segments as a single, contiguous unit to prevent intralesional transgression and local recurrence. Achieving microscopic negative margins (R0) stands as the most critical prognostic factor, as radical resection offers a significantly improved potential for long-term survival. Technical success requires a meticulously planned multidisciplinary approach encompassing varied surgical corridors—ranging from combined anterior–posterior windows to single-stage posterior-only approaches—tailored to the tumor’s anatomical level. Furthermore, preoperative hemostatic optimization using dual-energy computed tomography (DECT) for vascular assessment and transarterial embolization (TAE) has become indispensable for managing the hypervascularity of the invaded vertebral bone. This review synthesizes these evolving strategies, illustrated by a case of a 74-year-old male with stage T4 NSCLC where an R0 resection was achieved through a two-stage approach integrating uniportal video-assisted thoracoscopic surgery (VATS). Ultimately, en-bloc management provides a feasible and potential surgical strategy toward long-term survival for localized, spine-invasive lung cancer within a multidisciplinary treatment framework. Full article

Europe’s fossil-based electricity mix has shifted rapidly in recent years, raising a practical question: can we model competitive substitution among fuels with a framework that is both predictive and interpretable? We address this by combining a compact neural network (NN) with a three-dimensional Lotka–Volterra (LV) system to study monthly EU coal, natural gas, and oil-fired generation shares from the second semester of 2017 to 2023. After converting the series to row-wise shares that sum to one, we use the first $70\%$ of the sample to learn smooth trajectories and data-driven derivatives with the NN and then estimate the LV interaction coefficients through a constrained nonlinear fit. We advance the calibrated LV system over the final $30\%$ holdout with a fourth-order Runge–Kutta (RK4) scheme and evaluate forecasts using the RMSE and MAE for each fuel share series. For comparison, we report the results against both a neural network-only forecasting baseline and a classical ARIMA benchmark, both trained on the same $70\%$ window and evaluated on the same $30\%$ holdout. The hybrid NN–LV model achieves competitive forecast errors while yielding interpretable interaction patterns consistent with substitution pressures (for example, negative cross-effects between coal and gas). Finally, we run counterfactual shock experiments to illustrate how a change in one fuel’s share propagates through the mix under the learned LV dynamics, highlighting the usefulness of embedding a simple mechanistic structure within a data-driven estimator. Full article

Multilingual research offers a unique window into the diverse developmental trajectories of language and cognition; yet this research has largely been built on a monolingual framework. Here, we first describe how a monolingual bias has limited theory construction and research on the multilingual experience. We then apply a developmental systems framework to understand the multilingual experience, shifting the field away from a monolingual bias toward centering the lived language experiences of multilingual children. At the center of our framework are the moment-to-moment, multimodal, and dynamic interactions between children, their social partners, and environment. Contributing to interaction dynamics are child and social partner characteristics (cognition, motivation, and experiences), as well as contextual factors (activities, places, and policies) that can shape multilingual exposure. Cultural practices, values, and beliefs, as well as developmental time at the micro level (seconds, hours, days) and the macro level (weeks, months, and years), permeate all levels of the framework. Our proposal reveals important avenues of future research, including (1) understanding the dynamic coordination of multimodal behaviors and languages within interactions, (2) how experiences specific to minoritized communities (e.g., language discrimination) shape interaction dynamics, (3) how the temporal patterns of language experience at the micro level contribute to long-term multilingual exposure, and (4) understanding experiences of different multilingual communities within and across communities. Use of this framework can advance knowledge of the contexts enriching multilingual experiences and reconstruct multilingual development research for the benefit of multilingual learners. Full article

The hypothalamic–pituitary–adrenal (HPA) axis coordinates metabolic, immune, and behavioral responses to a changing environment. Its molecular effectors are the nuclear receptors for glucocorticoids and mineralocorticoids (the GRs/MRs), encoded by nr3c1/nr3c2. The MR serves as the high-affinity sensor of basal hormone concentrations, whereas the GR amplifies the stress response and mediates negative feedback. Despite their shared domain architecture, the receptors have diverged functionally: isoform composition, post-translational modifications, and the complement of co-regulators together determine which genes are activated or repressed in a given tissue at a given time. The regulation of the HPA axis activity is a major determinant of embryonic development. Pregnancy adds a placental control layer that meters maternal signals: 11β-hydroxysteroid dehydrogenase type 2 (11β-HSD2) in the syncytiotrophoblast inactivates cortisol, whereas 11β-hydroxysteroid dehydrogenase type 1 (11β-HSD1) can regenerate it, and systemic buffering by transcortin (cortisol-binding globulin, CBG) limits the free hormone fraction. Under stress, inflammation, or hypoxia, this barrier weakens, exposing the fetus to stronger glucocorticoid pulses during windows of heightened vulnerability for brain and immune development. Such overexposure not only reshapes ongoing transcription but is also epigenetically inscribed: the methylation of alternative nr3c1 promoters, the remodeling of histones, and the shifts in ncRNA profiles recalibrate the axis sensitivity for the long term. At the phenotypic level, this manifests as variability in stress reactivity, cognitive and affective trajectories, and an immune and metabolic risk across later ontogeny. In this review, we integrate evidence on the structure and functions of the GR, the mechanisms of its post-translational and epigenetic regulation, and the role of the placenta, to provide a coherent framework for understanding the multifaceted consequences of prenatal stress and to identify potential targets for early prevention. Full article

Background: Exacerbations of chronic obstructive pulmonary disease (COPD) are a leading cause of morbidity, mortality, and healthcare burden worldwide. Early detection and timely intervention remain important challenges in COPD management, given the unpredictable nature of acute deterioration and limitations of traditional spirometry-based risk assessment. Methods: This narrative review synthesizes artificial intelligence (AI)-driven approaches for predicting and detecting chronic obstructive pulmonary disease (COPD) exacerbations across electronic health records, wearable sensors, imaging, environmental data, and patient-reported outcomes, emphasizing novel discoveries and emerging relationships rather than predictive performance. Results: Three major discoveries have been made. First, measurable physiological and behavioral deterioration may precede symptom recognition by approximately 7–14 days, thereby establishing a potential intervention window for anticipatory care. Second, machine learning (ML) models integrating pollutant exposure, medication adherence, and clinical characteristics have identified phenotypes with differential environmental sensitivity, including unexpected exposure–adherence interactions. Third, deep neural network analysis of full spirometry curves has revealed structural phenotypes beyond traditional Forced Expiratory Volume (FEV₁)-based measures and novel imaging biomarkers. The predictive performance ranges from the Area Under the Curve (AUC) 0.72–0.95, with a pooled meta-analytic AUC of approximately 0.77. Conclusions: AI has uncovered hidden patterns in the progression of COPD, supporting a shift from reactive to anticipatory management. Translation to routine care requires prospective validation, improved interpretability, workflow integration, and generalizability and equity. Full article

Achieving the EU 2030 target of a 30% CO₂ reduction requires transitioning intercity buses to CNG- or fuel-cell-driven vehicles, and urban buses to electric vehicles. The increasing mass of roof-mounted energy systems, such as battery packs, creates additional loads on the body frame. This study investigates the integration of a welded handrail system into the bus body frame as an additional load-bearing element. A combined approach based on dynamic modeling and finite element analysis was applied to evaluate the structural body response under the UNECE R100 and R110 regulations. The results demonstrate that the structural concept significantly improves the stress–strain state of the body frame. Maximum roof displacements under 5g loading decreased by 34% for the gas-powered model and by 50% for the electric model, enhancing passive safety by reducing window-rack intrusion. Maximum stress decreased by 20%, shifting the stress state below the ultimate strength of S235 steel and preventing rupture. Uniform strength under vertical loading increased significantly (by 58%) due to a more favorable stress distribution within the structure. Overall, the results indicate that integrating a welded handrail truss into the bus body frame can effectively improve structural stiffness and redistribute loads within the frame. Full article

Traffic calming measures are widely used to reduce operating speeds and mitigate crash risk in urban corridors; however, the way drivers adapt their control strategy when traversing Berlin speed cushions is still poorly described from a multivariate behavioral perspective. This study proposes a behavior-oriented analysis to identify recurring speed-cushion traversal strategies using driving simulator telemetry. A fixed-base simulator reproduced a real urban corridor, and trajectories were segmented in device-centered spatial windows capturing approach, traversal, and immediate recovery. Each segment was summarized by three indicators describing longitudinal and lateral control: mean speed, peak braking demand, and average lane position deviation. Features were standardized and clustered using k-means. The number of clusters was selected primarily through mean silhouette evaluation, while resampling-based checks and a Gaussian mixture modeling comparison were used as supportive evidence rather than competing decision rules. Three traversal profiles emerged: smooth cautious, reactive cautious, and unmoderated fast. The introduction of speed cushions shifted the distribution of segments towards cautious profiles, while driver-level concentration within a single profile was moderate. Overall, results indicate that speed cushions influence the whole vehicle control strategy, offering a quantitative basis for behavior-oriented evaluation of local traffic calming interventions in smart-city contexts. Full article

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