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Advancing the capability approach to well-being pathways from access and use of urban green space
Abstract Urban green spaces (UGS) are critical for well-being, yet greening efforts often prioritize physical proximity while assuming equal benefit. Challenging this assumption, we apply Amartya Sen’s Capability Approach by conceptualizing and empirically assessing residents’ access-use-benefit pathways that determine their ability to convert green space availability into realized well-being. Using participatory survey data from Jakarta, we evaluated how travel time, transport modes, and socioeconomic conditions shape individuals’ ability to convert spatial access into realized engagement. We operationalize this pathway through a novel time-based benefit–cost ratio approach that measures constrained functioning and reveals inequalities hidden by proximity-based planning. Our results showed that residents with lower socioeconomic status and those reliant on walking or public transport face greater time burdens and constrained access, limiting their ability to benefit from the park meaningfully. These findings demonstrate that spatial proximity alone is insufficient for equitable greening outcomes. To support just and inclusive cities, planners must address structural and temporal barriers hindering the meaningful green space use and the well-being opportunities it enables, particularly those stemming from socioeconomic disadvantage, time constraints, and mobility limitations.
Unlocking solar-derived CO2 conversion to hydrocarbons over plasmonic Cu-Fe tandem catalysts under industrially relevant conditions
Research on the roof hazard factors quantification based on Chinese word segmentation and TF-IDF algorithm
Quantitative Prediction of Exchangeable Proton Chemical Shifts
Temporal improvements in elephant longevity challenge the notion of compromised survival in zoos
Mechanism of actin thin filament pointed-end elongation by leiomodin
Piezoelectric Nanozymes With Acoustically Rejuvenated Catalytic Activities Rewire Lipid Metabolism for Two‐Pronged Ferroptotic Therapy
ABSTRACT Spatiotemporal activation of nanozyme activity remains a major hurdle for minimizing off‐target effects in nanocatalytic therapy. Concurrently, lipid metabolism‐driven ferroptosis resistance impairs the efficacy of reactive oxygen species‐based cancer treatments. Herein, we design an ultrathin, defective CuFe‐based piezoelectric nanozyme (SPzyme) that integrates acoustically activated enzymocatalytic effects and lipid metabolic rewiring ability for the precise induction of ferroptotic induction. Notably, the generated oxygen/metal vacancies endow SPzyme nanosheets (∼3.6 nm thick) with enhanced piezoelectricity and distinctively quiescent basal enzymocatalytic activity compared to their defect‐free counterparts. Upon ultrasound (US) stimulation, SPzymes exhibit markedly rejuvenated peroxidase‐ and glutathione peroxidase‐like activities. Theoretical calculations demonstrate that planar defects and strain‐induced polarization lower the energy barriers for catalytic ROS production via phonon‐electron coupling effects. Furthermore, after the loading of a phospholipase inhibitor, the obtained SPzyme‐D effectively remodels cellular lipid metabolism by enriching polyunsaturated phospholipids and inhibiting phospholipid detoxification, thereby sensitizing hepatocellular carcinoma cells to ferroptosis. In vivo evaluations corroborate that SPzyme‐D upon US irradiation achieves 86.2% tumor inhibition with negligible systemic toxicity. Our study establishes a paradigm for designing biocompatible piezoelectric nanozymes with spatiotemporally controlled piezocatalytic/enzymocatalytic activities for precise and two‐pronged ferroptotic therapy.
Dual synthetic jets controlled spray cooling: analysis and evaluation of heat transfer characteristics
Fluorine-free acetonitrile-based electrolytes with anion-reinforced solvation chemistry for sustainable sodium-ion full batteries
Physical vulnerability-oriented reconfiguration of microgrids: centralized vs. decentralized control
Hippocampal ripples initiate cortical dimensionality expansion for memory retrieval
Abstract How are past experiences reconstructed from memory? Learning is thought to compress external inputs into low-dimensional hippocampal representations, later expanded into high-dimensional cortical activity during recall. Hippocampal ripples, brief high-frequency bursts linked to retrieval, may initiate this expansion. Analysing intracranial EEG data from patients with pharmacoresistant epilepsy during an episodic memory task, we found that cortical dimensionality increased following ripple events during correct, but not incorrect, retrieval. This expansion correlated with faster reaction times and reinstatement of the target association. Crucially, hippocampal theta and cortical gamma phase-amplitude coupling emerged after ripples but before cortical expansion, suggesting a mechanism for ripple-driven communication. Ripple events also marked the separation of task-relevant variables in cortical state space, revealing how hippocampal output reshapes the geometry of memory representations to support successful recall.
Domain-independent PPG signal quality assessment framework via representation learning
Abstract Photoplethysmography (PPG) is a widely used non-invasive sensing technique for monitoring cardiovascular and respiratory parameters, however, its measurements are highly susceptible to artifacts, which can degrade signal quality and, in turn, the accuracy of derived vital signs. Reliable PPG signal quality assessment (SQA) is therefore critical, yet existing SQA methods often rely on handcrafted features or ad-hoc thresholds and tend to be dataset-specific, limiting their generalizability across different sensor locations and users. To address these gaps, this paper proposes a novel SQA approach for PPG that is real-time compatible and sensor-location invariant. The method leverages deep representation learning by fine-tuning a pre-trained PPG transformer (PPG-PT) with a triplet loss to learn an embedding space where PPG segments are meaningfully partitioned based on their morphology. Signal quality is then quantified by computing each window’s embedding distance to a set of reference embeddings representing high-quality PPG segments, yielding a flexible reference-based quality score without requiring any model retraining when adapting to new devices or domains. The proposed framework was evaluated on three PPG datasets: the Complex System Laboratory (CSL) dataset providing finger PPG, the Wearable and Clinical Devices (WCS) for wrist PPG signals, and the Apnea and Artifacts (AA) dataset representing neck PPG signals, encompassing a wide range of artifact types and recording conditions. The model achieved strong agreement with expert annotations on the CSL dataset, with an F1-score of $$\approx$$ 90%. It also demonstrated robust generalization on the WCS wearable dataset: using a generalized reference set drawn from multiple subjects, the approach attained a high F1-score of about $$\approx$$ 95% on wrist PPG data, highlighting the method’s potential for population-level deployment. Performance on the AA neck PPG dataset was moderately acceptable with $$\approx$$ 70% F1-score, likely due to the complexity and varying executions of the different artifacts presented in the dataset. The proposed methodology provides a deep learning SQA framework capable of generalizing across different PPG sensor locations and devices.
Viral syncytia evolve to resist interferon
Abstract SARS-CoV-2, like many viruses, generates syncytia but the role of syncytia formation in viral evolution remains unknown. Using SARS-CoV-2 and SARS-CoV-2 Spike (S) replacement vesicular stomatitis (VSV), we show that S-mediated syncytia impair the antiviral effects of interferons in cultured cells, human lung cell cultures, and hACE2 transgenic mice. Amino acid substitutions that modulate syncytia formation in Delta- and Omicron-encoded S have parallel effects on viral interferon resistance. S-mediated syncytia compromise antibody-mediated virus neutralization in cultured cells. We recapitulate interferon and neutralizing antibody resistance in syncytia generated by the orthoreovirus p14 fusion-associated small transmembrane (FAST) protein in VSV, influenza virus, and seasonal coronavirus OC43 infections. These findings explain selection of SARS-CoV-2 fusogenic variants in humans and, more generally, the evolution of fusogenic viruses driven by adaptive and innate immunity.
Fluorescence lifetime and intensity detection of water-soluble hypericin accumulation in ex-vivo glioma, meningioma, and brain metastasis tissue
Abstract Fluorescence-guided surgery improves intraoperative brain tumor visualization, but currently available agents remain unreliable for several common entities, particularly lower-grade gliomas and lesions without visible 5-aminolevulinic acid (5-ALA)-induced fluorescence. We prospectively analyzed 69 surgically obtained tumor specimens from 61 patients with WHO grade 4 gliomas, WHO grade 2/3 gliomas, meningiomas, and brain metastases before and after ex vivo incubation in 4 µM water-soluble high-load hypericin-polyvinylpyrrolidone complex (HHL-PVP). Fluorescence lifetime and intensity were quantified using a dual-tap CMOS camera system with a hypericin-specific 575–615 nm bandpass filter. HHL-PVP incubation significantly increased both fluorescence intensity and lifetime in all tumor entities, including specimens without visible 5-ALA fluorescence. An independently derived combined lifetime/intensity regression model discriminated pre- from post-incubation measurements with an area under the receiver operating characteristic curve of 0.975, sensitivity of 98.6%, and specificity of 82.6%; entity-specific areas under the curve ranged from 0.933 to 1.000. These findings support robust hypericin-associated signal detection across major brain tumor entities after ex vivo incubation and provide a rationale for future in vivo evaluation of HHL-PVP in fluorescence-guided neuro-oncological surgery.
Constructal theory of aerodynamic design in NACA airfoils for search and rescue UAVs using integrated CFD and ANN approaches
Correction: A satellite imagery-driven framework for rapid resource allocation in flood scenarios to enhance loss and damage fund effectiveness
Screening glioma and glioblastoma brain tumors using dual deep learning algorithm incorporated correlative GAN and BrainNet through the probability segmentation
An EfficientNet hybrid deep learning detection framework of network intrusion with an optimized mixture-of-experts architecture
The value of ultrasound semi-quantitative scoring in the clinical evaluation of acute gouty arthritis with normal and elevated serum uric acid
Abstract This study aimed to evaluate and compare the differences in musculoskeletal ultrasound semi-quantitative scores between acute gouty arthritis patients with normal and elevated serum uric acid levels. We prospectively evaluated 25 patients with acute gouty arthritis and normal serum uric acid, alongside 32 patients with elevated serum uric acid. Clinical data and ultrasound features (double-contour sign, tophi, aggregates, bone erosion, synovial hyperplasia, power Doppler signal, and joint effusion) were compared. Ultrasound successfully detected urate crystal deposition in 68.0% (17/25) of affected joints in the normal uric acid group. However, the elevated uric acid group showed significantly higher semi-quantitative scores for crystal deposition features, including double-contour sign, tophi, aggregates, and bone erosion ( P < 0.05). Additionally, pain scores, erythrocyte sedimentation rate, and C-reactive protein levels demonstrated strong positive correlations with the semi-quantitative scores of inflammatory ultrasound features (r s = 0.738–0.861, P < 0.01). Musculoskeletal ultrasound semi-quantitative scoring can effectively assess the urate crystal deposition burden and reflect acute-phase inflammatory activity, providing valuable imaging characteristics to inform clinical evaluation during the acute phase.