Search Results (6,362)

Cystic fibrosis (CF) is caused by loss-of-function variants in the cystic fibrosis transmembrane conductance regulator (CFTR) chloride and bicarbonate channel and affects multiple organs, with pancreatic involvement showing very high penetrance. In pancreatic ducts, CFTR drives secretion of alkaline, bicarbonate-rich fluid that maintains intraductal patency, neutralises gastric acid and permits safe delivery of digestive enzymes. Selective impairment of CFTR-dependent bicarbonate transport, even in the presence of residual chloride conductance, is strongly associated with exocrine pancreatic insufficiency, recurrent pancreatitis and cystic-fibrosis-related diabetes. These clinical manifestations are captured by pharmacodynamic anchors such as faecal elastase-1, steatorrhoea, pancreatitis burden and glycaemic control, providing clinically meaningful benchmarks for CFTR-targeted therapies. In this review, we summarise the principal mechanisms underlying pancreatic pathophysiology and the current approaches to clinical management. We then examine in vitro pancreatic duct models that are used to evaluate small molecules and emerging therapeutics targeting CFTR. These experimental systems include native tissue, primary cultures, organoids, co-cultures and microfluidic devices, each of which has its own advantages and limitations. Intact micro-perfused ducts provide the physiological benchmark for studying luminal pH control and bicarbonate (HCO₃⁻) secretion. Primary pancreatic duct epithelial cells (PDECs) and pancreatic ductal organoids (PDO) preserve ductal identity, patient-specific genotype and key regulatory networks. Immortalised ductal cell lines grown on permeable supports enable scalable screening and structure activity analyses. Co-culture models and organ-on-chip devices incorporate inflammatory, stromal and endocrine components together with flow and shear and provide system-level readouts, including duct-islet communication. Across this complementary toolkit, we prioritise bicarbonate-relevant endpoints, including luminal and intracellular pH and direct measures of HCO₃⁻ flux, to improve alignment between in vitro pharmacology and clinical pancreatic outcomes. The systematic use of complementary models should facilitate the discovery of next-generation CFTR modulators and adjunctive strategies with the greatest potential to protect both exocrine and endocrine pancreatic function in people with CF. Full article

Inflammatory bowel disease (IBD) is associated with an increased risk of venous thromboembolic events (VTEs) and a moderate risk of arterial cardiovascular events. This varies with inflammatory activity and acute-care exposure, with pathophysiological data supporting a thromboinflammatory phenotype in which intestinal inflammation influences systemic vascular homeostasis through innate immune activation, coagulation–platelet crosstalk, endothelial dysfunction, impaired fibrinolysis, and immunothrombosis. Clinically, prevention and management should be integrated into routine care and anchored in sustained, steroid-sparing disease control, combined with guideline-based in-hospital thromboprophylaxis and standard cardiovascular prevention. Decisions regarding anticoagulant therapy after VTEs should follow established principles while recognizing that recurrence prevention depends not only on anticoagulant choice but also on minimizing repeated inflammatory and treatment-related risk exposures. Cardiovascular risk assessment and optimization of modifiable factors should be considered before therapy escalation or treatment switching. Future advances will likely come from more personalized risk assessment across dynamic high-risk windows and from adjunctive, mechanism-informed strategies targeting key nodes of the gut–vascular interface and immunothrombosis. Full article

In digital marketplaces, trust in e-commerce platforms has evolved from a protective heuristic into a powerful mechanism of behavioral conditioning. This review interrogates how trust cues such as star ratings, fulfillment badges, and platform reputation shape consumer cognition, systematically displace critical evaluation, and create asymmetries in perceived quality. Drawing on over 47 high-quality studies across experimental, survey, and modeling methodologies, we identify seven interlocking dynamics: (1) cognitive outsourcing via platform trust, (2) reputational arbitrage by low-quality sellers, (3) consumer loyalty despite disappointment, (4) heuristic conditioning through trust signals, (5) trust inflation through ratings saturation, (6) false security masking structural risks, and (7) the shift in consumer trust from brands to platforms. Anchored in dual process theory, this synthesis positions trust not merely as a transactional enabler but as a socio-technical artifact engineered by platforms to guide attention, reduce scrutiny, and manage decision-making at scale. Eventually, platform trust functions as both lubricant and leash: streamlining choice while subtly constraining agency, with profound implications for digital commerce, platform governance, and consumer autonomy. Full article

Aspect Sentiment Triplet Extraction (ASTE) aims to jointly identify aspect terms, opinion terms, and their associated sentiment polarities. Existing approaches, such as tagging or span-based modeling, often struggle with complex aspect–opinion interactions and long-distance dependencies. We propose a Knowledge-Augmented Graph Convolutional Network (KMG-GCN) that represents a sentence as a multi-channel graph integrating syntactic dependencies, part-of-speech tags, and positional relations. An adjacency tensor is constructed via a biaffine attention mechanism, while a multi-anchor triplet learning strategy with orthogonal projection enhances representation disentanglement. Furthermore, a pairwise refinement module explicitly models aspect–opinion associations, improving robustness against overlapping triplets. Experiments on multiple benchmarks demonstrate that KMG-GCN achieves state-of-the-art performance with improved efficiency and generalization. Full article

High-precision autonomous localization remains a critical challenge for intelligent mining vehicles in GNSS-denied and unstructured coal mine roadways, where traditional odometry-based methods suffer from severe cumulative drift and perceptual aliasing. Inspired by the synergy between mammalian visual cues and cognitive neural mechanisms, this paper proposes a robust biomimetic localization framework that integrates multi-source perception with a prior cognitive map. The core contributions are three-fold: First, a semantic-enhanced biomimetic localization method is developed, leveraging roadway sign data as absolute spatial anchors to suppress long-distance cumulative errors. Second, an optimized head direction (HD) cell model is formulated by incorporating a speed balance factor, kinematic constraints, and a drift correction influence factor, significantly improving the precision of angular perception. Third, boundary-adaptive and sign-based semantic constraint terms are integrated into a continuous attractor network (CAN)-based path integration model, effectively preventing trajectory deviation into non-navigable regions. Comprehensive evaluations conducted in large-scale underground scenarios demonstrate that the proposed framework consistently outperforms conventional IMU-odometry fusion, representative 3D SLAM solutions, and baseline biomimetic algorithms. By effectively integrating semantic landmarks as spatial anchors, the system exhibits superior resilience against cumulative drift, maintaining high localization precision where standard methods typically diverge. The results confirm that our approach significantly enhances both trajectory consistency and heading stability across extensive distances, validating its robustness and scalability in handling the inherent complexities of unstructured coal mine environments for enhanced intrinsic safety. Full article

Halide perovskites have many advantages in environmental remediation. The photocatalytic performance of halide perovskites is often hindered by low specific surface area and rapid photogenerated carrier recombination. The aim of this work is to prepare a green, novel photocatalyst in the form of biochar-anchored Cs₃Bi₂Br₉ perovskite composites. The rose-petal-derived biomass carbon (RC) provides adsorption sites and high electrical conductivity, while the perovskite Cs₃Bi₂Br₉ can efficiently capture visible right and degrade pollutants, and the reciprocal effect can enhance the photocatalytic efficiency of the composite. The results of scanning electron microscopy (SEM) showed the Cs₃Bi₂Br₉ particles were loaded on the surface of RC. Compared with bare Cs₃Bi₂Br₉, Cs₃Bi₂Br₉/RC composite has a more perfect structure, higher specific surface area, enhanced ability to absorb visible light, and reduced bandgap value. As visible-light-driven photocatalysts, the prepared Cs₃Bi₂Br₉/RC composites can enhance the removal efficiency of Rhodamine B. The Cs₃Bi₂Br₉/RC–0.2 composite displays the highest degradation efficiencies for RhB (10 mg/L), reaching 98% within 60 min. And the rate constant (k) is 1.9 times that of bare Cs₃Bi₂Br₉. The results of electrochemical impedance spectroscopy (EIS) show that the interaction between RC and Cs₃Bi₂Br₉ speeds up charge carrier separation and transfer. During photocatalytic process, holes (h⁺) and superoxide radicals (·O₂⁻) played major roles. The composites also showed excellent stability. It is meaningful to deal with a large number of withered rose petals to make them high-value products. This work not only provides a guideline for the construction of perovskite composites materials but also shows the promising prospects of biochar composites in deep treatment for contaminated water. Full article

Activity space represents the spatiotemporal interaction between individuals and their environment. While most studies measure potential activity space using short-term data, few have defined or measured its actual internal structure. This study introduces “anchor regions” as the core areas where daily activities are concentrated, and conceptualizes the structure of an individual’s activity space by incorporating the concept of regular locations, anchor regions, potential regular activity space, and potential activity space. Using three months of mobile signaling data from 10,848 residents in Shenzhen, we detected anchor regions via a weighted density-based spatial clustering for applications with noise (DBSCAN) method and categorized individuals into six typical activity space structures based on a rule-based taxonomy. We also figured out the intra- and inter-anchor region mobility pattern of each type. Our results show the following: (1) A total of 80% of activities and 87% of time are concentrated in just 26% of locations, forming anchor regions—with 95% of individuals having no more than five such regions. (2) The total area of anchor regions is merely 0.1% of the potential activity space. (3) Six typical structures of activity space are derived with different combinations of several functional anchor regions, including home, weekday anchors, and daily activity anchors. (4) The spatial patterns of the six types are different, while intra-anchor region mobilities dominate daily movement in all six types. This study provides a region-based, instead of a point-based, perspective interpretation of the anchor points theory, helping to better understand the regularities and internal structure of human activity space. Our conceptual framework and methodology have the potential to help urban and transportation planning practice and policy making. Full article

Forecasting stock market crashes is difficult because such events are rare, highly nonlinear, and shaped by latent structural and behavioral forces. This study introduces a calibrated and interpretable Random Forest framework for detecting pre-crash conditions through structural feature engineering, early-warning calibration, and model explainability. Using daily data on global equity indices and major large-cap stocks from the U.S., Europe, and Asia, we construct a feature set that captures volatility expansion, moving-average deterioration, Bollinger Band width, and short-horizon return dynamics. Probability-threshold optimization significantly improves sensitivity to rare events and yields an operating point at a crash-probability threshold of 0.33. Compared with econometric and machine learning benchmarks, the calibrated model attains higher precision while maintaining competitive F1 and MCC scores, and it delivers meaningful early-warning signals with an average lead-time of around 60 days. SHAP analysis indicates that predictions are anchored in theoretically consistent indicators, particularly volatility clustering and weakening trends, while robustness checks show resilience to noise, structural perturbations, and simulated flash crashes. Taken together, these results provide a transparent and reproducible blueprint for building operational early-warning systems in financial markets. Full article

As a cornerstone of China’s energy infrastructure, the coal mining industry relies heavily on the stability of its underground roadways, where the support of soft rock formations presents a critical and persistent technological challenge. This challenge arises primarily from the high content of expansive clay minerals and well-developed micro-fractures within soft rock, which collectively undermine the effectiveness of conventional support methods. To address the soft rock control problem in China’s Longdong Mining Area, an integrated material–structure control approach is developed and validated in this study. Based on the engineering context of the 3205 material gateway in Xin’an Coal Mine, the research employs a combined methodology of micro-mesoscopic characterization (SEM, XRD), theoretical analysis, and field testing. The results identify the intrinsic instability mechanism, which stems from micron-scale fractures (0.89–20.41 μm) and a high clay mineral content (kaolinite and illite totaling 58.1%) that promote water infiltration, swelling, and strength degradation. In response, a novel synergistic technology was developed, featuring a high-performance grouting material modified with redispersible latex powder and a tiered thick anchoring system. This technology achieves microscale fracture sealing and self-stress cementation while constructing a continuous macroscopic load-bearing structure. Field verification confirms its superior performance: roof subsidence and rib convergence in the test section were reduced to approximately 10 mm and 52 mm, respectively, with grouting effectively sealing fractures to depths of 1.71–3.92 m, as validated by multi-parameter monitoring. By integrating microscale material modification with macroscale structural optimization, this study provides a systematic and replicable solution for enhancing the stability of soft rock roadways under demanding geo-environmental conditions. Soft rock roadways, due to their characteristics of being rich in expansive clay minerals and having well-developed microfractures, make traditional support difficult to ensure roadway stability, so there is an urgent need to develop new active control technologies. This paper takes the 3205 Material Drift in Xin’an Coal Mine as the engineering background and adopts an integrated method combining micro-mesoscopic experiments, theoretical analysis, and field tests. The soft rock instability mechanism is revealed through micro-mesoscopic experiments; a high-performance grouting material added with redispersible latex powder is developed, and a “material–structure” synergistic tiered thick anchoring reinforced load-bearing technology is proposed; the technical effectiveness is verified through roadway surface displacement monitoring, anchor cable axial force monitoring, and borehole televiewer. The study found that micron-scale fractures of 0.89–20.41 μm develop inside the soft rock, and the total content of kaolinite and illite reaches 58.1%, which is the intrinsic root cause of macroscopic instability. In the test area of the new support scheme, the roof subsidence is about 10 mm and the rib convergence is about 52 mm, which are significantly reduced compared with traditional support; grouting effectively seals rock mass fractures in the range of 1.71–3.92 m. This synergistic control technology achieves systematic control from micro-mesoscopic improvement to macroscopic stability by actively modifying the surrounding rock and optimizing the support structure, significantly improving the stability of soft rock roadways. Full article

The growing global demand for critical raw materials has highlighted the need for autonomous systems in abandoned underground mines. We propose a multi-robot coordination architecture using Hierarchical Finite State Machines (HFSMs) for sequential task execution in GPS-denied, infrastructure-less environments. Unlike existing centralized approaches, our system enables each robot to execute its own HFSM behavior triggered through inter-robot communication, eliminating dependency on persistent connectivity. We implemented and validated this architecture using a Deployer robot and a Stinger robot within the EU Horizon PERSEPHONE project. Experimental validation demonstrated successful coordination both with persistent connectivity and during network interruptions, proving the system’s fault tolerance capabilities. The system successfully executed sequential deployment and anchoring tasks, demonstrating that this coordination approach enables multi-robot coordination without requiring persistent connectivity, thereby addressing critical limitations for autonomous operations in underground environments. Full article

Coastal port cities depend on global seafood flows, yet their food security is increasingly exposed to price volatility and supply disruptions. This study examines Busan citizens’ perceptions of seafood-related food security and seafood supply chain stability, and derives actionable municipal policy priorities for a trade-dependent port city. Anchored in the FAO four-dimensional framework—availability, access, utilization, and stability—we developed 20 seafood-related attributes and surveyed adult residents in Busan (n = 297). The measurement structure was assessed through reliability checks and exploratory factor analysis, and Importance–Performance Analysis (IPA) was used to map attribute-level priorities and identify the largest importance–performance gaps. Overall, respondents regard seafood food security as highly important but only moderately satisfactory. Availability and utilization perform relatively well, indicating perceived strengths in basic supply conditions and safe consumption, whereas access and stability show lower performance relative to importance, reflecting concerns about affordability, uneven physical access for vulnerable groups, price volatility, and exposure to external shocks. Notably, several stability-related attributes emerge as “Concentrate Here” priorities, highlighting the need for strengthened risk management, early warning communication, and resilience-oriented logistics planning at the city level. By integrating the FAO framework with attribute-level IPA, this study demonstrates how citizen perception data can translate macro food security debates into locally implementable priorities for building sustainable food systems in coastal cities. Full article

Oriented object detection constitutes a fundamental yet challenging task in Artificial Intelligence of Things (AIoT)-enabled maritime surveillance, where real-time processing of dense visual streams is imperative. However, existing detectors suffer from three critical limitations: sequential attention mechanisms that fail to capture coupled spatial–channel dependencies, unconstrained deformable convolutions that yield unstable predictions for elongated vessels, and center-based distance metrics that ignore angular alignment in sample assignment. To address these challenges, we propose JAOSD (Joint Attention-based Oriented Ship Detection), an anchor-free framework incorporating three novel components: (1) a joint attention module that processes spatial and channel branches in parallel with coupled fusion, (2) an adaptive geometric convolution with two-stage offset refinement and spatial consistency regularization, and (3) an orientation-aware Adaptive Sample Selection strategy based on corner-aware distance metrics. Extensive experiments on three benchmarks demonstrate that JAOSD achieves state-of-the-art performance—94.74% mAP on HRSC2016, 92.43% AP₅₀ on FGSD2021, and 80.44% mAP on DOTA v1.0—while maintaining real-time inference at 42.6 FPS. Cross-domain evaluation on the Singapore Maritime Dataset further confirms robust generalization capability from aerial to shore-based surveillance scenarios without domain adaptation. Full article

Precise identification of early peanut leaf spot is strategically significant for safeguarding oilseed supplies and reducing pesticide reliance. However, general-purpose detectors face severe domain adaptation bottlenecks in unstructured field environments due to small feature dissipation, physical occlusion, and class imbalance. To address this, this study constructs a dataset spanning two phenological cycles and proposes POD-YOLO, a physics-aware and dynamics-optimized lightweight framework. Anchored on the YOLOv10n architecture and adhering to a “data-centric” philosophy, the framework optimizes the parameter convergence path via a synergistic “Augmentation-Loss-Optimization” mechanism: (1) Input Stage: A Physical Domain Reconstruction (PDR) module is introduced to simulate physical occlusion, blocking shortcut learning and constructing a robust feature space; (2) Loss Stage: A Loss Manifold Reshaping (LMR) mechanism is established utilizing dual-branch constraints to suppress background gradients and enhance small target localization; and (3) Optimization Stage: A Decoupled Dynamic Scheduling (DDS) strategy is implemented, integrating AdamW with cosine annealing to ensure smooth convergence on small-sample data. Experimental results demonstrate that POD-YOLO achieves a 9.7% precision gain over the baseline and 83.08% recall, all while maintaining a low computational cost of 8.4 GFLOPs. This study validates the feasibility of exploiting the potential of lightweight architectures through optimization dynamics, offering an efficient paradigm for edge-based intelligent plant protection. Full article

The recent review by Veron et al. (2025) posits that quantitative genomic evidence used to understand coral evolution should be secondary to species hypotheses derived from expert opinion based on field experience. The authors argue that morphological “biological entities” should take precedence over molecular evidence when conflicts arise. This perspective required the rejection of extensive, independent molecular datasets that have progressively converged on a robust evolutionary framework for reef corals. Here, we reaffirm how prioritising subjective visual assessments over quantitative genetic and genomic data is methodologically unsound and scientifically regressive. We reject the framing of this perspective as “morphology versus molecules”. Rather, it is a fundamental divergence between two opposing philosophies: a static system anchored in non-reproducible expert judgement, and an integrative framework where genetic data provide the necessary independent test of morphological hypotheses. We show how a reliance on “field entities” obscures true morphological patterns by failing to distinguish between phenotypic plasticity, convergence, and evolutionary divergence. Effective taxonomy requires species hypotheses to be testable, and to stand or fall on the strength of reproducible evidence. Such a framework does not replace morphology; it validates it by providing an explicit, testable basis for evaluating morphological hypotheses. The integration of testable, reproducible molecular analysis with other lines of evidence including morphology is the benchmark of modern taxonomy across all Kingdoms of Life. We address the logical inconsistencies in the general arguments put forward by Veron et al. (2025) and refute their specific rejection of recent Acropora species-level revision with reproducible data. Full article

This study systematically verified the applicability and accuracy of the Probability Density Evolution Method (PDEM) in the probabilistic modeling of the dynamic response of anchored rock slopes under random seismic action through large-scale shaking table model tests. Across 144 sets of non-stationary random ground motions and 7 sets of white noise excitations, key response data such as acceleration, displacement, and changes in anchor axial force were collected. The PDEM was used to model the instantaneous probability density function (PDF) and cumulative distribution function (CDF), which were then compared with the results of normal distribution, Gumbel distribution, and direct sample statistics from multiple dimensions. The results show that the PDEM does not require a preset distribution form and can accurately reproduce the non-Gaussian, multi-modal, and time evolution characteristics of the response; in the reliability assessment of peak responses, its prediction deviation is much smaller than that of traditional parametric models; the three-dimensional probability density evolution cloud map further reveals the law governing the entire process of the response PDF from “narrow and high” in the early stage of the earthquake, “wide and flat” in the main shock stage, to “re-convergence” after the earthquake. The study confirms that the PDEM has significant advantages and engineering application value in the analysis of random seismic responses and the dynamic reliability assessment of anchored slopes. Full article