Iodine-131 (131I) and fluorine-18 (18F) are widely used radionuclides in nuclear medicine; however, accidental localized contamination of the hands may occur during radiopharmaceutical preparation, dispensing, administration, or contaminated-material handling. This study presents a scenario-based computational assessment of hand skin dose from 131I and 18F contamination using the SkinDose module of VARSKIN/V+. Skin equivalent dose coefficients were calculated as a function of exposure time and normalized per unit spilled activity as mSv/MBq. For 18F, the cumulative dose increased rapidly during the first few hours and approached a plateau due to its short physical half-life. For 131I, dose accumulation continued over the evaluated period because of its longer physical half-life. The results provide practical reference dose coefficients for radiation-protection assessment of localized hand contamination, while emphasizing that they should be interpreted within the limits of the defined scenarios and realistic contamination-management conditions.

Particulate matter (PM) is a central concern in atmospheric environmental research and poses a serious threat to the long-term preservation of grotto heritage sites such as the Mogao Grottoes. Previous studies of PM in semi-enclosed grottoes have mainly focused on windblown sand intrusion, external meteorological variability, and human activities, while the internal driving role of earth-air vertical movement (EVM) has received little attention. Using the semi-enclosed basement of conservation department at the Mogao Grottoes as an ideal field site, we conducted a one-year in situ continuous monitoring campaign based on closed earth-air theoretical framework. Spearman correlation analysis and structural equation modeling (SEM) were applied to quantify the direct/indirect effects of EVM on PM and to clarify the mechanism by which EVM drives dynamic changes in surface PM. The results indicate that atmospheric pressure fluctuations drive EVM, which is a major natural factor influencing surface PM. Surface PM generally exhibited positive synchronous variations with earth-air flow (EF), CO2 and 222Rn concentrations in earth-air, as well as with temperature and relative humidity (RH). However, earth-air RH and 222Rn showed weak negative correlations with PM100, with correlation coefficients of -0.04 and -0.07. The SEM results further verify that CO2 acts as a characteristic tracer for PM dynamics driven by EVM. It exerts positive driving effects on PM2.5, PM1 and PM100 with corresponding path coefficients of 0.452, 0.470 and 0.386, respectively. Although 222Rn also imposes positive regulation on PM, its impact intensity is far weaker than that of CO2. By contrast, RH produces prominent inhibitory effects on PM, while AP and temperature only exert slight negative influences. PM2.5 and PM1 were strongly affected by EVM, whereas PM100 was only weakly affected. This study provides a novel theoretical perspective on matter exchange at the land-atmosphere interface for elucidating the transport mechanisms of surface PM.

Beach sands may contain elevated concentrations of naturally occurring radionuclides because of sediment provenance, hydraulic sorting and local accumulation of heavy minerals. This study establishes an HPGe-based radiological baseline for beach sands from Rio de Janeiro State, Brazil, and evaluates external gamma exposure through an uncertainty-propagated screening approach. Forty-seven surface sand samples were collected from recreational beach sectors and analysed by high-purity germanium gamma-ray spectrometry. Activity concentrations of ²²⁶Ra, ²²⁸Ra, ²²⁸Th and ⁴⁰K were determined, and their reported analytical uncertainties were propagated to derived exposure quantities. To avoid double counting when applying a conventional whole-series thorium dose coefficient, a central ²³²Th-series screening equivalent was calculated as the arithmetic mean of the ²²⁸Ra and ²²⁸Th indicators. Sensitivity calculations using ²²⁸Ra alone and ²²⁸Th alone were also performed. Activity concentrations ranged from 1.10 to 1408.10 Bq kg⁻¹ for ²²⁶Ra, 0.03 to 5449.93 Bq kg⁻¹ for ²²⁸Ra, 0.42 to 6957.21 Bq kg⁻¹ for ²²⁸Th and 2.07 to 1008.36 Bq kg⁻¹ for quantified ⁴⁰K results. Canto was the dominant point-specific anomaly, with a central calculated absorbed dose-rate estimate of 4420 ± 169 nGy h⁻¹. Excluding Canto, the mean absorbed dose rate was 38.6 nGy h⁻¹, whereas the complete-dataset median was 16.8 nGy h⁻¹. Under all three thorium-series assumptions, the same eight sampling points exceeded the comparative outdoor value of 55 nGy h⁻¹ and only Canto exceeded 1 mSv y⁻¹ under the frequent local-use scenario. All short-term tourist-use estimates remained below 0.3 mSv y⁻¹. The 55 nGy h⁻¹ value is used only for comparison and not as a regulatory or action level. Most sampling points therefore represent lower-priority external gamma exposure conditions, whereas a limited number of local anomalies require confirmatory sampling, in situ dose-rate mapping and mineralogical characterisation before any beach-wide radiological protection conclusion is drawn.

Molecule diffusion limits target detection in conventional homogeneous solution, typically resulting in inefficient target-probe collision efficiency, low reaction kinetics, and insufficient signal accumulation. Herein, a dual rolling circle amplification-based DNA hydrogel was constructed to establish a hydrogel confined digital imaging platform (HCDIP) for visual imaging. In HCDIP, the spatial confinement provided by the DNA hydrogel scaffold constrains target recognition, signal conversion, and Cas12a cleavage in a confined three-dimensional network. Kinetic analysis showed that HCDIP activated Cas12a trans-cleavage activity rapidly, resulting in faster signal accumulation and earlier maximum reaction rate. Furthermore, fluorescence recovery after photobleaching and finite element simulation demonstrated that the HCDIP exhibited mass-transfer characteristics of restricted diffusion and local enrichment, with the effective diffusion coefficients of 45 nt and 150 nt nucleic acids reduced by approximately 2.8-fold and 3.3-fold, respectively, while maintaining a higher local concentration at the reaction center. By integrating HCDIP with an aptamer-based strategy, the visual detection of ochratoxin A (OTA) was achieved in a range of 2 pg·mL-1-100 ng·mL-1 with a detection limit of 1.5 pg·mL-1. Owing to the programmability of the DNA hydrogel, HCDIP can be extended for orthogonal detection of multiple food contaminants, providing a general strategy for confined CRISPR-based ultrasensitive bioanalysis.

Driver fatigue is a leading contributor to fatal traffic accidents globally, and Electroencephalogram (EEG)-Electrooculogram (EOG) multi-modal systems are the gold standard for objective fatigue detection. However, existing Transformer-based methods face critical bottlenecks of high quadratic complexity and oversimplified feature fusion that neglects intrinsic brain-eye neurophysiological couplings. To address these issues, this paper proposes NTSM, a neurophysiology-guided tri-stream architecture integrating the Mamba-based selective State Space Model (SSM) with four specialized modules, i.e., Functional Connectivity Bridge (FC-Bridge), Spectral Recalibration Unit (SRU), Ocular-Guided Attention (OGA), Artifact Disentanglement Gate (AD-Gate). Equipped with a bidirectional Mamba encoder, NTSM achieves linear complexity (reducing computational cost to $5\%$ of Transformer) while preserving cross-modal interaction performance. Evaluations on the SEED-VIG dataset via Leave-One-Subject-Out (LOSO) validation yield a Pearson Correlation Coefficient (COR) of $\mathbf {0.8738} \pm 0.10$ and Root Mean Square Error (RMSE) of $\mathbf {0.1294} \pm 0.06$, delivering $\mathbf {8.19}\times$ inference acceleration over the state-of-the-art Transformer-based EEG-EOG Cross-modal Fusion (E2CF). Furthermore, cross-dataset validation on the clinical MPDDF dataset achieved an exceptional accuracy of $92.95\%$, proving its robust generalization capability. Gradient-weighted saliency analysis confirms its neuroscientifically plausibility, with significant parieto-occipital activation during alertness and frontal-temporal slow-wave dominance during fatigue.

Realizing high-throughput aberration-corrected Scanning Transmission Electron Microscopy (STEM) exploration of atomic structures requires rapid tuning of multipole probe correctors while compensating for the inevitable drift of the optical column. While automated alignment routines exist, conventional approaches rely on serial, gradient-free searches (e.g., Nelder-Mead) that are sample-inefficient and struggle to correct multiple interacting parameters simultaneously. Conversely, emerging deep learning methods offer speed but often lack the flexibility to adapt to varying sample conditions without extensive retraining. Here, we introduce a Multi-Objective Bayesian Optimization (MOBO) framework for rapid, data-efficient aberration correction. Importantly, this framework does not prescribe a single notion of image quality; instead, it enables user-defined, physically motivated reward formulations (e.g., symmetry-induced objectives) and uses Pareto fronts to expose the resulting tradeoffs between competing experimental priorities. We use Gaussian Process regression to build a probabilistic surrogate of the aberration-dependent image-quality landscape, and employ a multi-objective acquisition strategy to select aberration settings that improve the estimated Pareto front across competing physics-informed rewards. We demonstrate that this active learning loop is more robust than traditional optimization algorithms and effectively tunes focus, astigmatism, and higher-order aberrations. By balancing competing objectives, this approach enables "self-optimizing" microscopy by dynamically sustaining optimal performance during experiments.

Background: Primitive reflex (PR) assessments based on the Institute for Neuro-Physiological Psychology (INPP) framework are increasingly used in developmental and athletic populations. However, the reproducibility of these observer-based assessments remains insufficiently documented. This study investigated the intra- and inter-rater reliability of subjective PR assessments and examined the potential influence of evaluator expertise in elite youth football athletes. Methods: Sixty-nine male youth football players from an elite academy (age: 18 ± 2 years) participated in this study. PR assessments included the Moro reflex, asymmetrical tonic neck reflex, symmetrical tonic neck reflex, and tonic labyrinthine reflex, evaluated using standardized INPP procedures (0-4 ordinal scoring scale). Intra-rater reliability was assessed by an experienced examiner across three testing sessions performed over six months. Inter-rater reliability was evaluated using standardized video recordings independently scored by two novice raters. Relative reliability was assessed using intraclass correlation coefficients (ICCs) with corresponding 95% confidence intervals (95% CIs), while absolute reliability was quantified using the standard error of measurement (SEM) and the minimal detectable change at the 95% confidence level (MDC95). Results: Good to excellent intra-rater relative reliability (ICC = 0.86-0.98; 95% CI = [0.81-0.99]) was observed, with SEM values of 0.1-0.6 and MDC95 values of 0.3-1.7 points. Moderate to excellent video-based inter-rater reliability was found (ICC = 0.69-0.99; 95% CI [0.56-0.99]), with SEM values of 0.1-0.7 and MDC95 values of 0.3-2.0 points. Pairwise comparisons among raters showed moderate to excellent relative reliability (ICC = 0.57-0.99; 95% CI [0.35-0.99]), with SEM values of 0.1-0.5 and MDC95 values of 0.2-1.3 points. Conclusions: Subjective PR assessments performed using the INPP framework demonstrated good to excellent intra-rater relative reliability and moderate to excellent video-based inter-rater relative reliability, together with generally low absolute measurement error in elite youth football athletes. These findings provide preliminary psychometric support for the use of standardized PR assessments in research and applied sport settings.

Thin Bi2Se3 films were successfully electrodeposited from a non-aqueous ethylene glycol-based electrolyte and subsequently underwent thermal treatment in an Ar atmosphere to improve crystallinity. The phase and compositional properties of the films were characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS), Fourier-transform infrared spectroscopy (FTIR), and Raman spectroscopy. XRD and Raman analyses confirmed the formation of crystalline rhombohedral Bi2Se3, while FTIR measurements indicated minor surface oxidation. The electrical and semiconductor properties of the films were investigated through current-voltage and temperature-dependent conductivity measurements. The activation energy associated with intrinsic conduction was determined to be 0.25 eV, while the temperature sensitivity coefficient and temperature coefficient of resistance were found to be 2903 K and 0.032-0.013 K-1, respectively. Infrared photoconductivity measurements in the wavelength range of 2800-4000 nm revealed a pronounced photoresponse near the band-gap energy of Bi2Se3. The obtained results demonstrate the potential of electrodeposited Bi2Se3 thin films for infrared-sensitive semiconductor and optoelectronic applications.

Background: Creatinine normalization is the default adjustment for urine dilution in spot-urine monitoring of athletes, yet its appropriateness is seldom tested in field data. We tested whether it removes or introduces concentration dependence across a panel of urinary analytes in soccer players training in extreme heat and whether raw versus normalized values change the inferred response. Methods: Sixteen male soccer players (age 19.9 ± 0.8 y) provided spot urine at three daily time points (morning, pre-training, post-training) over six training days in Riyadh, Saudi Arabia. Sodium, potassium, chloride, calcium, magnesium, glucose, urea nitrogen, uric acid, total protein, and creatinine were measured. For each analyte we quantified its scaling exponent with creatinine, the correlation of raw and normalized values with creatinine, and the within-day pattern on raw versus normalized values using linear mixed-effects models. Results: Urinary creatinine varied 22.7-fold (coefficient of variation 39%). Only protein and glucose scaled proportionally to creatinine (exponents ≈1.0) and normalized cleanly (ratio-creatinine r = -0.06 and -0.21). The remaining seven analytes, including all electrolytes, scaled weakly (exponents 0.16-0.49), so normalization over-corrected them, producing strong negative ratio-creatinine correlations (uric acid -0.74, sodium -0.59, chloride -0.55). For four of nine a the inferred within-day response differed in direction or significance between raw and normalized values; restricting creatinine to the recommended 0.3-3 g·L-1 range attenuated but did not abolish the over-correction. Conclusions: Creatinine normalization suited protein and glucose but over-corrected every electrolyte and could reverse inferred responses. In this setting of hot-environment team-sport athletes, creatinine normalization should not be assumed adequate for electrolytes, which instead warrant interpretation against an independent index of urine concentration such as osmolality or specific gravity.

Background and Objective: Maximal oxygen uptake (VO2max) is a fundamental indicator of cardiorespiratory fitness in sports medicine, essential for athletic profiling and training prescription. However, traditional direct measurements require exhaustive physical testing, which induces considerable physiological stress, limits testing frequency, and increases the risk of injury. To address this problem, this study aimed to eliminate the need for exhaustive protocols by developing a highly precise, non-invasive digital prediction model for VO2max utilizing readily available anthropometric data and acute metabolic biomarkers (blood glucose and lactate kinetics). Methods: To overcome the limitations of a small initial empirical sample (n = 16) and prevent model overfitting, the original dataset was statistically augmented to create a robust synthetic cohort (n = 200) using Multivariate Normal Distribution and k-Nearest Neighbors (k-NN) algorithms. Three different machine learning models (Multiple Linear Regression [MLR], Random Forest [RF], and Support Vector Regression [SVR]) were trained using such parameters as sex, height, weight, baseline/pre-exercise, and net (Δ) glucose and lactate concentrations. Evaluation of the performance of the models included tenfold cross-validation and Bland-Altman analysis as a measure of clinical agreement. Results: Among the three algorithms used, the highest correlation coefficient (R2 = 0.939) was observed for SVR, along with the lowest error metrics (RMSE = 1.442 mL/kg/min, MAPE = 2.78%). Moreover, SVR showed remarkable performance in predicting VO2max of female (R2 = 0.890) and male (R2 = 0.702) athletes separately. Also, Bland-Altman analysis proved almost zero-bias estimation with 95% limits of agreement ranging between -4.40 and 4.43 mL/kg/min. In order to bypass the black-box problem of complex algorithms for practical application in the field, the MLR model was used for the development of the Beta-Weighted Lacto-Glycemic Equation (β-LGE). Conclusions: In this work, a unique dual-model approach was introduced, with the SVR algorithm serving as a high-performance backend of a digital tool for sport technologists and Beta-Weighted LGE providing a practical calculation formula for coaches. This innovative approach allowed for a completely non-exhaustive profiling of an athlete's VO2max using only minimally invasive metabolic measurements.

In recent years, there has been increased attention on classifying histopathological images through hybrid decision-level fusion, and the challenge of exploring data fusion to improve classification accuracy in colon cancer has become significant. This study introduces new elements by incorporating various deep learning (DL) architectures, including EfficientNetB0, DenseNet121, ResNet101V2, NASNetMobile, MobileNetV2, and VGG16 Convolutional Neural Networks (CNNs), as well as Random Forest (RF) and Histogram Gradient Boosting (HGB) Machine Learning (ML) algorithms, along with the original dataset. The proposed hybrid decision-level fusion approach analyzes the LC25000 dataset's colon histopathological images and handcraft features (HFs) to improve predictive performance. The HFs such as entropy, the Gini index, and the radius of gyration from adenocarcinoma and benign colon tissue (CC) were extracted. The prediction of the proposed models leveraging late fusion was conducted by classifying both deep and HFs. During the experiments, it was demonstrated that the combination of ResNet101V2 with RF classifier and all HFs yielded greater accuracy and consistent performance. The achieved performance metrics include accuracy of 94.1%, F1-score of 94%, Matthews Correlation Coefficient (MCC) of 88.3%, and an area under the curve (AUC) of 0.979. To explain and interpret the decisions made by the DL models, the explainable methods SHapley Additive exPlanations (SHAP) and Gradient-weighted Class Activation Mapping (Grad-CAM) were utilized.

Mass cytometers can record 40-50 parameters per single cell for millions of cells in a sample. Many methods have been developed to cluster phenotypically similar cells within cytometry data, but there are fewer methods to visualize activity and interactions of pairs of proteins across these populations. We have developed a workflow for analyzing correlations associated with predefined populations. By clustering blood samples from acute myeloid leukemia (AML) patients and normal controls using an established algorithm, we obtained a minimum spanning tree of clusters of single cells. Using surface marker expression, we identified clusters on the tree that belonged to phenotypes of interest. Next, we computed correlations between pairs of proteins in each cluster. We developed a novel, coherent, probability-based statistic to test differences between vectors of correlation coefficients. By comparing all combinations of the normal controls under the statistic, we created an empirical distribution that could provide a conservative threshold of differential correlation. Using this empirically derived distribution to define significance, we compared pooled samples from AML subtypes and normal controls to detect differential correlations. Given the structure present within this cytometry dataset, we found it natural to consider correlations in this manner versus aggregating all data and computing a single correlation. Our approach has the advantage that we can localize the statistical measure to determine contributions from particular phenotypic populations. Differentially correlated pairs of proteins can be further explored as possible testable hypotheses by considering a population's distribution of correlation coefficients or biaxially plotting protein expressions within individual cells in a given population.

Rapid hemostasis assessment is essential for managing acute coagulopathy, guiding anticoagulant therapy, and monitoring cardiac surgery patients. Although viscoelastic testing provides valuable guidance for early intervention, its widespread adoption is constrained by large blood usage (~2 mL), prolonged turnaround times (several hours), long assay durations (~25-40 min), and high costs. To overcome these limitations, we developed an innovative, all-in-one magnetoelastic (ME) sensing chip that enables systematic coagulation kinetics monitoring using only 46 μL of whole blood within 15 min. Featuring prepackaged lyophilized reagents and pumpless blood loading, this user-friendly chip is highly cost-effective for disposable use. The experimental results demonstrated good reproducibility for on-chip extrinsic coagulation activation, with clotting parameters maintaining coefficients of variation under 10%. Furthermore, clotting parameters derived from heparin monitoring results exhibited a strong linear correlation that compares favorably with the clinical standard (r = 0.986). Finally, sensitivity evaluation toward various blood components validated the sensor's dual-mode characterization strategy for identifying coagulation factors and fibrin-related coagulopathies. The proposed ME sensing chip holds promise for bedside testing and flexible, on-demand coagulation monitoring across diverse clinical scenarios.