Browse Articles

Electrochemical Fluorescence Switching in Rhodamine–Ferrocene Dyads: Spatiotemporal Control in Biomimetic Membranes

Abstract The electrochemical control of fluorescence has been extensively developed in homogeneous media, yet its implementation within electrically insulating lipid bilayers remains largely unexplored. Here we establish that an electrochemically gated fluorescence switch can be implemented in individual giant unilamellar vesicles using a rhodamine–ferrocene dyad that modulates emission through redox-controlled photoinduced electron transfer. A membrane-anchored derivative enables direct visualization of reversible fluorescence activation under electrochemical bias. Remarkably, the switching is strictly leaflet-selective and occurs only for dyads exposed to the electrode interface, highlighting the insulating nature of lipid bilayers. Furthermore, membrane surface charge critically governs the switching efficiency and induces pronounced kinetic asymmetry between oxidation and reduction processes, revealing the key role of interfacial electrostatic interactions in redox-controlled emission. These results establish electrochemical fluorescence modulation in membranes as a spatially and electrostatically gated interfacial process and define general principles for redox-responsive probes operating in soft interfaces, such as lipid membranes.

Supporting recovery: Intersections of health, belonging, and help seeking after a campus mass casualty event

Mass casualty events (MCE), such as campus shootings, can severely impact the mental health of students, faculty, and staff. This study examined the psychological effects of an MCE at a minority-serving institution and explored how sense of belonging, cardiorespiratory fitness, and treatment preferences influenced recovery. To measure the impact of the MCE on mental health an anonymous survey was administered four to five months post-MCE to university students, faculty and staff. Within the survey, participants were asked to retrospectively report their depression (PHQ-9) and anxiety (GAD-7) symptoms as they recalled them two weeks prior to the MCE , and as they experienced them in the two weeks preceding survey completion. The survey also included current measures of sense of belonging (SBS), perceived discrimination (PEDQ), estimated cardiorespiratory fitness (eCRF), and intervention preference to cope with the impacts of the MCE. Participants reported significant perceived increases in PHQ-9 and GAD-7 scores from the pre-MCE recall to current time-periods. Increases in depression (PHQ-9 scores) from pre-MCE to recent recall were larger among respondents with lower pre-MCE anxiety (GAD-7 scores), higher perceived discrimination (PEDQ), and those currently receiving treatment for anxiety or depression; respondents not receiving treatment showed no significant change. Among students, lower belonging scores were associated with greater increases in depression. Neither belonging nor perceived discrimination were strongly linked to individual variables, indicating their multifactorial nature. Most participants reported a preference to learning about alternative interventions such as exercise or meditation over pursuing university counseling services.

Sex-stratified polygenic scores for 47 inflammation and vascular stress biomarkers provide a resource for profiling chronic disease risk

Assessing the Impacts of Conformational Fluxionality on Copper(II/I) Electron Transfer Self-Exchange

Abstract Typical Cu(II/I) complexes exhibit hallmark structural changes during their electron transfer (ET) reactions that result from the (pseudo) Jahn-Teller distortions and changes in polarizability inherent to their d9/d10 configurations. Given that such structural changes incur large reorganization energy penalties, the slow rates of ET characteristic of these compounds are unsuprising. However, we recently reported a set of Cu(II/I) complexes that undergo significant and well-defined structural changes during their redox reactions yet exhibit rapid (>105 M–1 s–1) ET self-exchange rate constants (k11). To explain these results, we proposed a pre-equilibrium model in which inherent conformational fluxionality in one of the two oxidation states provides access to pathways involving lower reorganization energies during the ET event. Herein, we report our results testing this hypothesis through the preparation and study of a homologous series of compounds exhibiting varying extents of conformational fluxionality in the Cu(I) state. We characterize these compounds electrochemically, structurally, and by variable temperature NMR spectroscopy to provide experimental evidence for increased fluxionality across the series. We then correlate the trend with increasing k11 through NMR linewidth broadening experiments, further taking care to define the impacts of solvent impurities therein. Finally, the nature of the conformational rearrangements and their relation to increased k11 are explored computationally to reveal differential Boltzmann populations of conformers across the series. The cumulative results of these studies support a previously underappreciated strategy for overcoming barriers to slow ET kinetics: namely through the incorporation of conformational fluxionality.

iNaturalist mammal observations classified by evidence type

Ecologists show growing interest in observational data generated by citizen scientists. For mammals, the largest citizen science platform is iNaturalist, which has more than 5 million Research Grade observations globally represented through images of living animals, dead animals, tracks, and scat. These different types of evidence could give insight into the underlying sampling paradigm for an observation (e.g., dead animals might be more likely to be reported near roads) and thus may be useful for scientific applications of these data. However, while iNaturalist allows users to annotate observations by evidence type, many observations are not annotated. We use machine learning to classify the evidence types associated with observations of North American mammals in iNaturalist, adding metadata that can be used to subset data or to model multiple observation processes. Here, we present a dataset containing metadata augmenting 1.33 million North American mammal iNaturalist observations with evidence type. Each observation is categorized as either live animal, dead animal, tracks, scat, or other sign, and an associated confidence score is provided.

Effects of street greenery and seasonality on health and wellbeing of cyclists: a Virtual Reality experiment

Abstract Street vegetation can play an important multifunctional role in increasing green spaces for more just, healthy and sustainable cities. However, there is still limited knowledge on how greenery exposure and seasonal conditions affect health and wellbeing during everyday active travel. This study integrates Virtual Reality, biosensors and visual semantic segmentation to test and compare different green infrastructure scenarios: only built, low greenery, high greenery and a winter condition. Using a between-subjects design, 138 participants were randomised to cycle in one of the four conditions, while gathering data on various physiological (skin conductance, blink rate, pupil diameter) and psychological outcomes (self-reported pre and post measures of restoration, affective states, subjective vitality). The results provide important empirical evidence that street greenery can decrease negative emotions and provide momentary stress reduction even under rapid exposure during a cycling commute. The findings also demonstrate high reactivity to natural elements in virtual environments.

Surface-Confinement Effect Enables Bioorthogonal Drug Release in Tumors

Abstract A critical challenge in the bench-to-bedside translation of controlled drug release strategies is the sharp decline in reaction efficiency as biological complexity increases. A platform capable of maintaining bioorthogonal-like drug release─remaining minimally perturbed by physiological environments─would address an unmet clinical need. This is particularly relevant for radiotherapy-mediated drug release, where the oxidative activation of prodrugs is often compromised by the rapid quenching of reactive intermediates in vivo. Herein, we engineer a hafnium-based nanoscale metal–organic layer platform that leverages a unique “surface-confinement effect” to overcome this challenge. By covalently tethering prodrugs to the Hf-nMOLs surface, we constructed two-dimensional nanoreactors that spatially localize the activation process within an interface enriched with reactive species. This design effectively insulates the activation step from biological scavengers, ensuring efficient payload release efficiency across increasing biological complexity. When loaded with the topoisomerase I inhibitor Exatecan, the Hf-nMOLs system achieved an intratumoral drug-release G-value of 568 nM·Gy−1, resulting in potent radiosensitization and significant tumor-growth suppression under low-dose X-ray irradiation. This work presents a versatile strategy for robust radio-chemotherapeutic combinations, achieving the simultaneous release of diverse payloads activated by radiotherapy. Our findings also suggest that engineering nanoscale surface confinement may provide a generalizable materials strategy to help confer bioorthogonality to otherwise labile activation reactions.

Healthcare providers' perspectives and needs related to the management of Pediatric Feeding Disorder: A focus group study

Pediatric feeding disorder is a prevalent, impactful diagnosis for children and their families. This diagnosis is heterogenous in presentation and requires the care of a multidisciplinary team of providers. Existing research suggests providers are underprepared to assess and treat pediatric feeding disorder, therefore more information on training and clinical practice is needed. This study conducted focus groups to describe the training journey of providers across all four pediatric feeding disorder domains (medical, nutrition, feeding skill, psychosocial). Seven focus groups (total of 25 providers) were conducted and analyzed using thematic analysis. Four themes were identified: differences in academic preparation, workplace infrastructure and access, desire for comprehensiveness and feasibility, and value of the family perspective. Overall, results point to opportunities to improve provider training and therefore patient care including academic exposure to pediatric feeding disorder and multi-disciplinary collaboration practices, increased access to mentorship, training, and evidenced-based resources, and enrichment of the research to practice pipeline with a focus on family-centered care.

Machine learning-based anomaly detection in surface water quality data using ensemble models with residual analysis

Abstract This study addresses the challenge of detecting anomalies in water-quality monitoring data, where traditional approaches often lack sufficient temporal and spatial resolution. A data-driven framework integrating ensemble machine-learning models (Random Forest and XGBoost) with residual-based anomaly detection was developed and evaluated using open surface-water monitoring data from Ukraine for 2022. The models were trained to predict key hydrochemical indicators, including nitrogen concentration and dissolved oxygen, using chemical, spatial, and seasonal features. Model robustness was assessed through 5-fold cross-validation, which demonstrated stable predictive performance for both indicators. Anomalies were identified as statistically significant deviations between observed and predicted values. Recurrent anomaly hotspots were subsequently analyzed and spatially mapped, with hotspot locations independently confirmed by both Random Forest and XGBoost models. The results demonstrate that the proposed framework can effectively identify statistically unusual observations and reveal spatially persistent anomaly hotspots across multiple river basins. However, the detected anomalies should be interpreted as statistical deviations rather than confirmed pollution events, as independent pollution-source information was not available. The study highlights the potential of combining ensemble learning, residual analysis, and spatial hotspot identification to support data-driven environmental assessment and monitoring activities.

Controlled Precursor Differentiation Enables Palladium-Catalyzed Divergent Carbonylation of Cyclobutenols

Abstract Controlling reaction selectivity is a central challenge in synthetic chemistry, particularly when multiple competing reactivity modes coexist within a single substrate. Existing strategies generally rely either on selectivity control during substrate activation or on downstream divergence from a common intermediate. However, these paradigms are less effective when distinct precursor states can independently evolve into distinct reaction manifolds. Herein, we introduce precursor differentiation as a distinct strategy for achieving divergent carbonylation. Through condition-controlled modulation of substrate evolution, a common cyclobutenol substrate can be selectively diverted into two distinct reactive precursors prior to catalytic engagement, thereby enabling access to two different carbonylation pathways. Under palladium catalysis, this strategy enables the highly selective synthesis of either hydroxyl-retained cyclobutanecarboxamides or cyclobutenamides from the same cyclobutenol platform. The method exhibits broad substrate scope (99 examples), consistently high selectivity (>20:1), and compatibility with pharmaceuticals and biologically relevant molecules. Furthermore, the resulting cyclobutenamide products serve as versatile platform intermediates for the selective synthesis of structurally distinct 3-azabicyclo[3.2.0]heptane and 3-azabicyclo[3.1.1]heptane frameworks. Mechanistic studies support a condition-controlled precursor differentiation process prior to carbonylation, providing a conceptual basis for achieving divergent carbonylation through selective control of precursor evolution.

Event-triggered MPC-PID based trajectory tracking control for differential drive mobile robots

Periodic model predictive control (MPC) for differential-drive mobile robots requires online optimization at every sampling instant, which can be redundant during near-steady tracking. This study develops an event-triggered hierarchical MPC–PID framework, termed ET–MPC–PID, to reduce the outer-loop optimization burden while accounting for actuator-side execution effects. The outer MPC updates the velocity setpoint only when a normalized event condition is satisfied or the maximum holding interval is reached. A fixed-period PID-form velocity servo with conditional integration regulates the actuator channel under lag and saturation. The formulation distinguishes holding-induced decision discrepancy from actuator-side mismatch and provides a local practical boundedness characterization. Simulations on double lane-change and figure-eight trajectories compare periodic PID, periodic MPC, periodic MPC–PID, an external variable-horizon ET–MPC reference, and event-triggered ablations. ET–MPC–PID reduces the QP call rate to 16.9% and 22.3% of periodic MPC while outperforming the no-PID event-triggered variant. Sensitivity, Pareto, runtime, and 100-trial Monte Carlo analyses further demonstrate the accuracy–computation trade-off and empirical robustness under sampled execution uncertainty.

Multi-objective optimization of tunnel field effect transistor based low noise amplifiers for energy efficient Zigbee receivers using QPSO

Numerical investigation on the mechanical response of steel-frame polyethylene pipelines subjected to strike-slip faulting

This study investigates the failure behavior of buried steel-reinforced polyethylene (SRPE) pipelines crossing strike-slip faults. A high-fidelity three-dimensional pipe-soil interaction model is established using the finite element method, and layered modeling with tie constraints is adopted to replicate the synergistic mechanical characteristics between the PE matrix and steel frame. The effects of pipe-fault intersection angle, steel wire diameter, and soil type on the mechanical response, buckling morphology, and critical strain of the pipeline are systematically examined, and the applicability of three design codes (CSA Z662-2023, GB 50470−2017, EN 13476−3) is quantitatively evaluated. The results show that SRPE pipelines exhibit a three-stage mechanical behavior under fault displacement: elastic bending at small displacement, plastic buckling propagation at moderate displacement, and sectional distortion with global instability at large displacement. The steel frame and PE matrix form an efficient synergistic mechanism featured by “matrix energy dissipation and frame load-bearing”, where failure initiates from plastic deformation of the PE matrix and further induces steel frame yielding and pipeline leakage. The pipe-fault angle dominates the loading pattern: tension is dominant at 30°, while transverse compression at 150° represents the most hazardous condition. The optimal wire diameter ranges from 2.0 to 2.5 mm ; loess provides the strongest constraint, whereas sand is the weakest. Conventional constant strain criteria neglect the angle effect and show obvious limitations in engineering practice. The findings provide significant theoretical support for the seismic design and safety assessment of SRPE pipelines crossing active fault zones.

A lightweight and configurable blockchain framework for vaccine certification

Abstract Global health crises and cross-border travel requirements have highlighted the need for vaccine certification systems that are secure, lightweight, and practical across diverse deployment settings. Traditional paper-based certificates are vulnerable to tampering and forgery, while centralized digital solutions may suffer from single points of failure, limited interoperability, and deployment constraints in low-resource environments. In this paper, we present a blockchain-based framework for vaccine certification with emphasis on lightweight user-side operation, QR-based certificate presentation, and implementation feasibility in heterogeneous deployment settings. Unlike smartphone-only approaches, the proposed framework supports both digital and QR-based certificates and provides online verification through blockchain access, together with a bounded offline verification mode based on locally cached trust material, certificate signatures, and freshness assumptions. The framework implements core certification operations, including certificate issuance, certificate verification, and vaccine-lot validation, while limiting direct on-chain exposure of user-related data through hashed and signed records. To support efficient retrieval and verification, we design and implement Ethereum smart contracts with indexing structures for certification data. The evaluation reports prototype-level implementation results in terms of gas consumption and certificate retrieval performance. These results demonstrate the feasibility of the indexing-based smart-contract design under the evaluated setting, while broader claims regarding large-scale deployment, regulatory compliance, biometric robustness, and offline revocation guarantees remain dependent on deployment-specific infrastructure and policies.

Enhanced C–F Activation and Proton Supply over Al2O3-Embedded RuO <i>x</i> Clusters Boost Perfluorocarbon Hydrolysis

Abstract The catalytic hydrolysis of per- and polyfluoroalkyl substances (PFAS) holds significant potential for environmental remediation; however, the chemically inert C–F bond poses substantial challenges for achieving efficient low-temperature activity. Herein, we demonstrate that RuOx clusters embedded in mesoporous Al2O3 nanosheet (RuOx/Al2O3) catalysts achieved the complete decomposition of CF4, one of the most chemically inert PFAS, at an unprecedented low temperature of 450 °C, outperforming all catalysts reported to date. The extraordinary CF4 hydrolysis performance is attributed to synergistically enhanced C–F bond activation and proton supply. Specifically, the strong electronic interaction between RuOx clusters and Al2O3 promotes the CF4 adsorption and C–F cleavage on Al sites in RuOx/Al2O3. Furthermore, the Ru sites in RuOx/Al2O3 promote H2O dissociation into *H and *OH, which enables the continuous regeneration of adjacent Al–OH groups and supplies sufficient protons for defluorination in the CF4 hydrolysis reaction. This study paves the way for designing and developing highly efficient catalysts for low-temperature catalytic hydrolysis of PFAS.

Longitudinal changes in laboratory parameters and QTc during isavuconazole therapy in Japanese patients with hematologic malignancies

Background Isavuconazole (ISCZ) is an azole antifungal with a low risk of QTc prolongation and no requirement for renal dose adjustment. However, real-world data in Asian patients remain limited. Methods We retrospectively evaluated 45 patients who received ISCZ between April 2023 and March 2025. Laboratory parameters and QTc values (QTcB/QTcF) were extracted from medical records. Longitudinal changes were assessed using linear mixed-effects models. In patients receiving arsenic trioxide (ATO), QTc trajectories were compared among ATO alone, ATO with ISCZ (ATO-ISCZ), and ATO with other antifungals. Results Grade ≥2 elevations in AST and ALT occurred in 11.1% and 13.3% of patients, respectively, and were transient. No significant deterioration in laboratory parameters was observed. In the ATO subgroup, QTc values in the ATO-ISCZ group were lower than those in the ATO group from Day 30, with the largest difference at Day 70 (−45.5 ms for QTcB and −42.3 ms for QTcF; both p &lt; 0.001). The ATO–other antifungals group showed similar trends to the ATO group. No clinically significant cardiac events were observed. Conclusions ISCZ showed a favorable safety profile in Japanese patients with hematologic malignancies and may be a safe option during QT-prolonging therapies.

Efficient real-time acupoint detection using a hybrid model combining mamba and transformer architectures

3,5-Bis(2-hexafluoro-isopropoxyl)phenyl: A Super-Fluorinated Moiety with Favorable Water-Solubility and Modifiability for 19F Magnetic Resonance Imaging

Abstract Among molecular imaging techniques, 19F magnetic resonance imaging (19F MRI) is particularly attractive due to deep penetration and multiplexed molecular imaging. However, the further development of 19F MRI remains constrained by its limited sensitivity, which largely depends on fluorinated probe design and more fundamentally on the availability of high-performance fluorinated moieties that define probe signal intensity. Here we systematically establish 3,5-bis(2-hexafluoro-isopropoxy)phenyl (BHFIP) as a structurally simple yet highly capable fluorinated moiety for 19F MRI probe design. Starting from 1,3-bis(2-hexafluoro-isopropoxy)benzene, a simple two-step nitration-reduction procedure afforded 3,5-bis(2-hexafluoro-isopropoxy)aniline (BHFIP-NH2), a modifiable BHFIP-based building block. Besides its high fluorine loading of 12 chemically equivalent fluorine atoms, BHFIP was found to possess an intrinsically short 19F longitudinal relaxation time (T1), together enabling exceptionally high 19F MRI sensitivity. Its 19F chemical shift at approximately – 75 ppm is clearly distinguishable from those of established highly fluorinated moieties, supporting multiplexed 19F MRI. The two hydroxyl groups of BHFIP contribute to water solubility and enable O-modification, while BHFIP-NH2 further provides an additional amino handle for N-modification. Representative incorporation of BHFIP-NH2 into 19F MRI probes further verified that the advantages of BHFIP can be retained in functional probe molecules. This work establishes BHFIP as a promising fluorinated moiety for high-sensitive and multifunctional 19F MRI probes.

Expert-guided optimization for load transfer in distribution networks assisted by virtual power plants

The rapid expansion of distribution networks and the increasing complexity of their topological structures pose significant challenges to fast and reliable post-fault service restoration. Meanwhile, driven by carbon neutrality goals, the large-scale integration of distributed energy resources (DERs) enhances operational flexibility but also introduces pronounced intermittency and uncertainty, further complicating post-fault load transfer decision-making. To address these challenges, this paper proposes an expert-guided and virtual power plant (VPP)-assisted load transfer optimization framework based on hierarchical graph reinforcement learning. A topology-aware graph neural network (GNN)–based state representation is developed, in which buses are modeled as nodes and switches as controllable edges, enabling explicit modeling of network connectivity and electrical coupling. On this basis, a hierarchical decision-making architecture is constructed: the upper-level agent, guided by expert knowledge, dynamically selects the restoration task type to coordinate the timing of network reconfiguration and VPP-assisted DER regulation; driven by this high-level directive, two specialized lower-level agents respectively execute the specific switch operations and stepwise DER power adjustments, ensuring power balance and voltage security. Simulation results on a practical distribution network demonstrate that, under high DER penetration, the proposed method achieves faster service restoration, higher load recovery ratios, and significantly fewer voltage violation events than conventional reinforcement learning approaches, exhibiting improved operational safety and scheduling stability.

Reliability of short-term measurements of heart rate variability (HRV) in patients with non-permanent atrial fibrillation

Abstract Short-term measurements of heart rate variability (HRV) are practical, noninvasive markers of autonomic activity, and reduced HRV has been associated with onset and recurrence of non-permanent atrial fibrillation. However, clinical use is limited by strict standardization requirements and uncertain reliability in clinical populations. We evaluated the reliability of short-term HRV derived from 48-h Holter recordings during sinus rhythm in patients with non-permanent AF. Fifty participants underwent two standardized outpatient recordings at Holter attachment and removal, and additional, manually selected, out-of-hospital daytime and night-time presumed-sleep segments, with low editing burden, were extracted. HRV was assessed using SDNN and RMSSD. Reliability was evaluated using Bland-Altman limits of agreement (LoA) and intraclass correlation coefficients (ICC). For standardized outpatient recordings, LoA ranged from 43 to 242% for SDNN and 38% to 280% for RMSSD, with ICC values of 0.67 and 0.43, respectively. Consecutive night-time presumed-sleep segments showed higher relative reliability, with ICC values of 0.79 for SDNN and 0.89 for RMSSD, but absolute agreement remained limited (LoA 53%-173% and 59%-170%). These findings indicate poor absolute reliability in this workflow, limiting individual-level clinical interpretation, and likely reflecting a combination of measurement-process variability, inherent to a clinically pragmatic workflow and biological within-person variability in HRV.