Most EEG dementia classifiers rely on absolute feature magnitudes. We assessed whether pairwise relative-ordering methods, widely used in omics, support compact, interpretable EEG classifiers. We analysed eyes-closed (EC) and eyes-open with photic stimulation (EO/photo) recordings from the same Alzheimer’s disease (AD), frontotemporal dementia (FTD), and cognitively normal (CN) OpenNeuro cohort (N = 88). Top Scoring Pair and k-Top Scoring Pair models were evaluated against standard machine-learning classifiers and fixed EEG-ratio controls across amplitude-decile and PSD/bandpower representations using repeated subject-wise cross-validation with training-fold-only model selection. Spectral representations most consistently supported relative-ordering models. Across six prespecified disease-versus-control task–condition settings, the main k-TSP models were broadly competitive with the strongest setting-specific standard machine-learning comparators (macro-F1 differences −0.030 to +0.035). Age and sex were examined in complementary sensitivity analyses; results were moderately sensitive to demographic adjustment, whereas repeated matching produced no consistent directional loss. In regional spectral models, slow–fast restriction gave positive macro-F1 point differences in all six settings (mean +0.029), whereas removing slow–fast or theta–alpha candidates gave lower macro-F1 point estimates in all six. Participant-level permutation localised the strongest recurrence evidence at the level of broader prespecified spectral–regional patterns (motifs): these motifs exceeded corrected nulls in specific AD-related settings, although no individual exact pair did so. In a limited no-refit test on the independent ADSZ dataset (AD versus healthy controls), AD-versus-CN-trained k-TSP models reached balanced accuracy up to 0.846. These results support compact relative-ordering models as a transparent complement and favour interpretation at the broader spectral–regional-pattern level rather than from individual recurrent pairs.
This study investigates the influence of disinfection procedures on the surface chemistry, mechanical integrity and in vitro behavior of Trogamid® CX-based orthodontic hybrid brackets. Brackets were fabricated by injection molding onto micro-structured nickel free stainless-steel inserts and subjected to three disinfection procedures: UV-C irradiation, 70% ethanol immersion or steam autoclaving prior to surface analysis and in vitro testing. Fourier-transform infrared (FTIR) spectroscopy, scanning electron microscopy (SEM), and atomic force microscopy (AFM) with force spectroscopy were employed to assess changes in the polymer's chemical structure, morphology, and mechanical response. In vitro biocompatibility and bacterial adhesion were also investigated. FTIR spectra indicated that UV irradiation weakened the amide hydrogen-bond network without forming new degradation bands, whereas ethanol disinfection and autoclaving were associated with a reorganization of the hydrogen-bond network. AFM showed a progressive reduction in surface roughness from UV irradiation to ethanol disinfection to autoclaving. Force spectroscopy revealed a lower elastic modulus after UV irradiation (1.2 vs 1.9 GPa for untreated brackets) and higher moduli after ethanol disinfection and autoclaving (2.3 and 2.4 GPa). Streptococcus mutans colonization did not differ significantly between untreated and treated brackets, and UV-disinfected hybrid brackets showed no cytotoxic effect on human gingival cells, comparable to stainless-steel brackets. Overall, none of the treatments left the bracket surface unchanged: UV disinfection best preserved the original topography but softened the surface layer, whereas ethanol and autoclave disinfection smoothed and stiffened the surface, with ethanol causing the smallest change in surface chemistry. The choice of procedure for in vitro studies should therefore be guided by the surface property most relevant to the intended investigation.
Machine-learning representations of gait waveforms are increasingly used in movement analysis, but their added value over conventional biomechanical descriptors remains uncertain, particularly in pediatric cohorts. We evaluated whether implicit neural representations (INRs) provide useful encodings of pediatric gait waveforms for reconstruction and developmental modeling. The full cohort comprised 78 healthy children and adolescents, of whom 73 had waveform files conforming to the predefined data structure required for the repeated cross-validation analyses. Selected waveforms were modeled using several INR architectures, and reconstruction quality was assessed using R2, RMSE, and MAE. For the INR feature-ablation analyses, one architecture was assigned to each waveform class based exclusively on reconstruction performance, independently of the age targets. Side-specific, bilateral-asymmetry, signal-specific, and descriptor-family feature sets were evaluated using ten repeats of five-fold cross-validation. A separate matched benchmark in the same 73 participants compared compact Fourier-MLP-derived features with conventional biomechanical descriptors and a fold-wise raw-waveform PCA baseline. In the INR ablation analyses, combining side-specific and bilateral-asymmetry features achieved R2 = 0.700 +/- 0.017 for chronological-age regression, and Energy/AUC descriptors were the strongest individual feature family (R2 = 0.696 +/- 0.031). For exploratory three-class age-group classification (4-7, 8-12, and 13-18 years), the combined INR representation reached a balanced accuracy of 0.749 +/- 0.034. In the matched representation benchmark, configurations containing conventional biomechanical descriptors performed best. The biomechanics-only baseline achieved regression R2 = 0.837 +/- 0.070 and balanced accuracy = 0.798 +/- 0.109, whereas biomechanics+PCA achieved the highest mean regression performance (R2 = 0.842 +/- 0.066); this difference was not statistically significant. Adding INRderived features did not improve regression performance and significantly reduced the evaluated classification metrics. INRs should therefore be considered complementary continuous waveform representations rather than replacements for established biomechanical descriptors.
The development of effective antioxidants has evolved from descriptive analysis toward a precise, mechanism-driven discipline targeting the molecular “redox switch”. This review synthesizes the critical advances reported since 2021, focusing on how the interplay between polyphenolic architecture and electronic descriptors, such as bond dissociation enthalpy and ionization potential, governs radical scavenging through the HAT, SET, and SPLET pathways. We evaluate the dual influence of metal coordination, where interactions can either enhance antioxidant stability through σ bond polarization or trigger pro-oxidant transitions via ligand-to-metal charge transfer. Central to this progress is the integration of computational models (DFT, QSAR) with advanced synchrotron methodologies (XAS, STXM, SR-FTIR, and SAXS), which provide element-specific validation of antioxidant behavior and subcellular oxidative mapping within complex matrices. Furthermore, we highlight how these molecular insights inform formulation engineering, specifically the development of organic nanocarriers and hybrid delivery systems, such as metal–phenolic networks, that shield therapeutic cargo from degradation and govern release in challenging physiological environments. These fundamental studies provide an essential physicochemical basis for medicine by enabling a better understanding and the rational design of antioxidant drugs, dietary supplements, and antioxidant strategies.
Motivated by the imperative for enhanced wear resistance in high-temperature applications such as aerospace, this paper presents a comprehensive investigation into the temperature-dependent wear mechanisms of sparkplasma-sintered CoCrFeMnNi high-entropy alloy (HEA) composites reinforced with titanium carbide (TiC). Five compositions containing 0-50 wt% TiC were evaluated by X-ray diffraction, scanning electron microscopy, electron backscatter diffraction, mechanical testing, and elemental mapping. The 15 wt% TiC composite (H15) exhibited the optimal combination of hardness and compressive yield strength. Ball-on-plate wear tests against Si3N4 counterbodies were performed from room temperature (RT) to 900 degrees C, demonstrating superior wear resistance across this broad temperature range with wear rates of 10-6 to 10-5 mm3/Nm. At RT-300 degrees C, mild abrasive and oxidative wear prevailed, controlled by uniform dispersion of TiC and Cr7C3 particles. At 600 degrees C, a discontinuous oxide-carbide layer forms on the wear surface, while a fine-grained layer develops in the subsurface region. This dual structure effectively suppresses delamination, enabling moderate oxide adhesion to the Si3N4 ball while maintaining a low wear rate. At 900 degrees C, a dense, continuous oxide glaze, comprising Mn-rich and Cr-rich sublayers, acted as a self-lubricating barrier, reducing wear-track dimensions by over 60 % compared to non-TiC HEA and lowering the coefficient of friction. These findings provide the first end-to-end mapping of tribological regimes in TiC reinforced HEAs, offering design guidelines for oxide-glaze engineering in advanced wear-resistant alloys.
