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Structure–Property Relationships to Guide the Selection of Fluorinated Ethers for Li–S Batteries
Palm oil fuel ash blended cement for solidification stabilization of petroleum sludge and heavy metal immobilization and waste valorization
Anionic Passivation Enables Reconstruction-Free Seawater Electrolysis
Abstract Nonoxide electrocatalysts hold great promise for high-efficiency seawater oxidation, yet their practical application is hindered by the inevitable surface reconstruction and pronounced corrosion under high anodic potentials. Herein, we solve this longstanding challenge by developing a rational anionic ligand passivation strategy that stabilizes phosphide lattices against oxidative degradation while preserving inherent electrocatalytic activity. Leveraging Lewis acid–base interactions, we grow an ultrathin, undercoordinated TiOx overlayer on a FeNiP (FNP) support. This overlayer withdraws electrons from lattice phosphorus, downshifts the P p-band center, and thermodynamically stabilizes the entire anionic sublattice against oxidative leaching. The oxidation-resistant scaffold further enables the anchoring of atomically ordered Ir arrays with a well-defined interatomic spacing of ∼2.8 Å, promoting direct O–O radical coupling via the oxide pathway mechanism and effectively circumventing the corrosive lattice oxygen route. Operando spectroscopy and 18O isotope tracing confirm fully reconstruction-free OER behavior with negligible lattice oxygen participation. The as-developed TiIr@FNP catalyst achieves ultralow overpotentials (only 310 mV at 1 A cm–2), operates stably for over 1200 h in alkaline seawater, and maintains near-unity Faradaic efficiency. The work demonstrates a generalizable strategy for designing durable, high-performance nonoxide anodic electrocatalysts under industrially relevant conditions.
Usability and feasibility of Mai 20, a user-centered mHealth App for secondary stroke prevention in China: mixed methods study
Adaptive Fabrication of Porous Crystals through Multiple-Site Noncovalent Conjunction of [1,4]Diazocine-Embedded Three-Dimensionally Concave Nanographenes
Numerical investigation and optimization of structural and operating parameters for cuttings transport in a bidirectional eccentric micro-underreamer
Deep learning-enhanced graphene metasurface terahertz absorber for real-time multi-gas identification and quantification
Allosteric inhibition of EGFR triple-mutant (L858R/T790M/C797S) in non-small cell lung cancer: an integrative conformational dynamics framework
Blood DNA methylation at AMD candidate loci in discordant monozygotic twins
Abstract Age-related macular degeneration (AMD) is a leading cause of visual impairment in older adults, with both genetic and environmental factors contributing to disease risk. Epigenetic mechanisms, particularly DNA methylation, may mediate the effects of environmental exposures on disease-relevant genes, yet their role in AMD remains poorly understood. To investigate blood DNA methylation differences associated with AMD severity, we studied 29 monozygotic twin pairs discordant for AMD from the Finnish Twin Cohort. This design controls for genetic background, sex, age, and shared early-life environment. Genome-wide DNA methylation was measured in whole blood using the Illumina HumanMethylation EPIC BeadChip. Analyses were restricted to 263 AMD candidate genes identified through prior genetic, epigenetic, and transcriptomic studies. Of 9,694 analyzed CpG sites, one site within ESYT1 reached statistical significance after multiple testing correction, but the corresponding methylation difference was below our predefined threshold for biological relevance. No CpG sites met both statistical and biological significance criteria. These findings do not provide evidence for large, systemic DNA methylation differences in peripheral blood at established AMD loci, though subtle effects below the detection limit of the current sample size cannot be excluded.
A deep reinforcement learning account of competition and integration of visual and goal vector signals for spatial navigation
Abstract Integrating different sources of information is essential to successful spatial navigation. For instance, animals often rely on a combination of visual impressions, self-motion, olfaction, and other signals to navigate towards a goal. This is especially important when navigating in uncertain environments, where switching from one source of information to another or integrating multiple sources of information may be required to make navigation decisions. We propose a deep reinforcement learning model of the interaction of visual and goal-vector signals based on reinforcement learning and use it to study behavior and spatial representations. We show that the nature and degree of signal noise strongly influences how the signals drive behavior and spatial representations. Our model also demonstrates that the ability to navigate using each information source independently, in addition to integrating them, is crucial to successfully navigating in uncertain environments. Counterintuitively, our model shows that when one of the signals is removed, navigation may be improved if the remaining signal is reliable and sufficient to navigate. However, this improvement comes at the expense of robustness. Our modeling results demonstrate that combining redundant sources of information in biological systems is far more complex than suggested by sensor fusion in the engineering context.
Synthesis and characterization of polycitric acid-functionalized MXene nanofluid for enhanced oil recovery
Decoupling of expansive and structural growth induced by β-aminobutyric acid (BABA) is associated with metabolic reconfiguration in linseed (Linum usitatissimum L.)
Structural brain alterations in frailty among end-stage renal disease patients
Lattice-based post-quantum image encryption with NDFA-driven spin-matrix transformation
Abstract The increasing reliance on visual data transmission across distributed and cloud-based environments requires image encryption frameworks that provide both strong cryptographic guarantees and practical computational efficiency. Conventional image encryption schemes frequently rely on heuristic transformations whose security is primarily evaluated through statistical analyses, often lacking formal resistance against emerging post-quantum threats. A layered post-quantum image encryption framework is presented, integrating lattice-based key establishment, deterministic image scrambling, and authenticated encryption within a unified architecture. A shared secret is established using the sntrup761 key encapsulation mechanism, providing IND-CCA security under lattice-based assumptions. The derived session key is expanded using HKDF into independent keys for permutation scheduling and encryption, enabling separation between cryptographic security and image transformation layers. A nondeterministic finite-state scheduling mechanism drives spin-matrix-based scrambling to enhance spatial diffusion, while authenticated encryption with associated data ensures confidentiality and integrity through strict verification prior to image recovery. Security analysis shows that post-quantum security comes from the lattice-based key encapsulation mechanism rather than the image-transformation layer, and demonstrates robustness against adaptive adversarial models, while computational evaluation indicates linear scalability with image size. Statistical and robustness analyses further confirm strong diffusion properties and stable scrambling-layer behavior under noise and occlusion conditions. The proposed framework bridges standardized post-quantum cryptography with lightweight image-oriented transformations, providing a structured and practical approach for secure visual data transmission.
A cross-sectional analysis of the associations between dry eye disease and depression
Abstract Dry Eye Disease (DED) is a common condition worldwide that is often associated with mental health conditions such as depression. The study evaluates the associations between dry eye disease and depression in the Hong Kong population, based on a large population-based cohort. A total of 614 participants were enrolled, and 1228 eyes were evaluated. The Ocular Surface Disease Index (OSDI) questionnaire was administered to assess subjective dry eye symptoms. Lipid layer thickness (LLT) was measured using the IDRA ocular surface analyzer, while tear meniscus height (TMH), first non-invasive tear break-up time (NITBUT), average NITBUT, Meiboscore, and redness were assessed with the Keratograph 5 M. The osmolarity was measured by the TearLab. Dry eye and non-dry eye were categorized based on the Tear Film and Ocular Surface Society Dry Eye Workshop II (TFOS DEWS II) diagnostic criteria. Depression classification was based on participant self-report, together with concurrent antidepressant use. For statistical analysis, the Kolmogorov–Smirnov test was used to examine normality. Fisher’s exact test was applied to categorical variables, and the Mann–Whitney U test was used for continuous variables in two-group comparisons. A Generalized Estimating Equation (GEE) model was constructed to explore associations between dry eye parameters and depression. Among the 614 participants recruited from the general Hong Kong population, 596 were classified as not having depression (1192 eyes; median age, 46 years), and 18 were classified as having depression (36 eyes; median age, 51 years). The OSDI score was higher among participants with depression than among participants without depression [20 (9–36) vs. 14 (6–23), P = 0.004]. However, other parameters showed no significant differences between the two groups. In the multivariate model, the OSDI score was significantly associated with depression, showing an odds ratio (OR) of 1.03 (95% CI: 1.01–1.06; P = 0.015). Furthermore, the severe subjective dry eye (OSDI > 32) was significantly associated with depression, with an OR of 3.00 (95% CI: 1.14–7.86; P = 0.026). Severe DED symptom score was significantly associated with depression in the population of Hong Kong, highlighting the clinical relevance of the relationship between DED and mental health.
Normal inflammatory marker levels do not exclude metabolic disturbances in young male team sport athletes
Sickle cell disease premarital screening utilization and associated factors among youth corps members in Osun State, Nigeria
Modeling ultrafast laser excitation of fused silica with a hybrid Lorentz–Drude dielectric response and density-dependent two-temperature model
Abstract Ultrashort laser excitation of dielectric materials is governed by a complex interplay between nonlinear photoexcitation, transient optical response, and energy transfer to the lattice. A consistent physical description of these processes across different pulse durations remains challenging, as purely free-electron-based optical models and classical thermal approaches do not adequately capture the strongly dynamic and non-equilibrium nature of the excited electron system. In this work, a coupled modeling framework is developed for amorphous fused silica (SiO 2 ) that directly links the transient optical response to a density-dependent two-temperature description of the subsequent thermal evolution. The optical properties are described using a hybrid Lorentz–Drude formalism, enabling a consistent representation of bound, localized, and free electronic states during excitation. The resulting energy deposition is coupled to a modified thermal model incorporating electron-density-dependent material parameters derived from first-principles calculations. The model is evaluated by comparison with experimentally measured ablation geometries and single-pulse ablation thresholds for pulse durations in the femtosecond and picosecond range. The threshold analysis indicates that experimentally measurable material removal can occur below the model-internal phase-explosion criterion, particularly for longer pulse durations. In the femtosecond regime, both ablation depth and diameter are reproduced within a limited deviation range. In the picosecond regime, the ablation depth remains consistent with experimental observations, while systematic deviations in the lateral extent become apparent. At longer pulse durations, the experimentally observed structures are increasingly influenced by melt-mediated material redistribution, which is not captured by the present model and leads to pronounced deviations in the ablation geometry. These results demonstrate that the proposed framework provides an effective physical description of ultrafast laser–matter interaction in non-equilibrium and transition regimes, while also defining its limitations in thermally dominated regimes where hydrodynamic effects become significant.