Search Results (631)

Background/Objectives: Biological ageing is accompanied by progressive alterations in mitochondrial metabolism, microvascular function, and thermoregulation. These processes collectively influence tissue heat production and dissipation, reflecting integrated metabolic, vascular, and thermoregulatory activity measurable at the physiological level. Passive microwave radiometry (MWR) provides a non-invasive, radiation-free method for detecting deep-tissue bioenergy emissions, complementing surface infrared thermography. To evaluate a thermophysiological Bioenergetic Index (BEI), derived from deep-tissue microwave emission, surface temperature, and their spatial and deep–surface relationships, as a candidate age-referenced thermophysiological marker associated with chronological ageing. Methods: Breast thermophysiology measurements from 36,391 women aged 20–80 years were analysed using data collected during routine clinical assessments. Supervised machine-learning models were trained exclusively on thermal features, with chronological age used only as the prediction target. Model performance was assessed using mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R²). In addition, data were aggregated into 5-year age bins to evaluate population-level ageing trajectories. Results: At the individual level, models predicted chronological age with MAE ≈ 3.5 years, RMSE ≈ 5.4 years, and R² ≈ 0.76. Aggregation into 5-year age bins revealed a robust nonlinear ageing trajectory characterised by midlife decline and late-life stabilisation. The increased correspondence at the grouped level reflects reconstruction of the population-level ageing trajectory rather than improved individual-level prediction accuracy, as averaging reduces inter-individual variability. Conclusions: These findings demonstrate a strong ageing-related signal in female breast thermophysiology and support thermophysiology as a candidate age-referenced physiological marker, pending longitudinal and outcome-based validation. The present analysis is cross-sectional and requires longitudinal validation before diagnostic or prognostic interpretation. Full article

Background: The click-evoked Auditory Brainstem Response (ABR) is the gold standard electrophysiological tool for assessing auditory pathway integrity in infants and young children. As normative data are inherently equipment-specific, the absence of pediatric reference values for the NeuroAudio system (Neurosoft, Ivanovo, Russia) represents a significant gap in clinical practice, given that existing normative datasets for this system are restricted to adult populations. Objective: To establish normative data for click ABR recorded with the NeuroAudio system in children under three years of age, stratified by age group according to auditory maturation patterns. Methods: A prospective, cross-sectional study was conducted at the Electrophysiology Laboratory of the Department of Speech Therapy, Paulista School of Medicine, Federal University of São Paulo (UNIFESP/EPM), under the approval of the Research Ethics Committee (protocol 7.939.564). A total of 203 children (121 males, 82 females; age range: 2 weeks to 36 months) with confirmed normal peripheral auditory function were included. Click stimuli (0.1 ms, rarefaction polarity) were delivered monaurally via ER-3A insert earphones at 80 dB nHL and a repetition rate of 19.3/s. Two average runs of 2000 artifact-free sweeps were recorded per ear. Absolute latencies of waves I, III, and V, interpeak intervals I–III, III–V, and I–V, and amplitudes of waves I and V were analyzed. Results: Statistical modeling supported the consolidation of 12 initial age bins into three clinically and statistically validated categories: 0–3, 4–12, and 13–36 months. Wave I latency remained stable across age groups, whereas waves III and V and all interpeak intervals showed progressive shortening with increasing age. Wave V amplitude increased progressively with age, while wave I amplitude remained unchanged. Females presented shorter latencies than males for waves III and V and for all interpeak intervals. The right ear exhibited a shorter III–V interpeak interval than the left ear, with a significant ear × age interaction indicating that this asymmetry is modulated during early maturation. Age, sex, and ear-stratified normative values (two SD and three SD reference limits) are reported. Conclusion: This study provides the first pediatric normative dataset for click-evoked ABR acquired with the NeuroAudio system in children under three years of age. The proposed three age stratifications, together with sex- and ear-specific reference values for the III–V interpeak interval, offer a clinically actionable framework for the accurate interpretation of pediatric ABR recordings and for the early identification of auditory pathway abnormalities. Full article

High-resolution surface soil moisture (SM) is needed for local hydrological and agricultural applications, but reliable retrieval at 100 m remains challenging. Within this broader methodological context, radiometer-constrained SAR change detection remains a practical and interpretable option for high-resolution soil moisture retrieval. It uses SAR-derived temporal changes to describe fine-scale wetting and drying processes, while passive microwave observations provide volumetric moisture references. This study proposes an improved SMAP-anchored Sentinel-1 change-detection framework (ISSF) for 100 m SM mapping. ISSF addresses these limitations by fitting NDVI-binned upper-envelope samples with a nonlinear quadratic function to normalize the vegetation-dependent backscatter-change range and by using multi-year SMAP dry/wet quantiles to scale the normalized relative wetness into volumetric SM. ISSF was evaluated using in situ measurements, a near-concurrent airborne reference, SMAP-based products, and direct transfer to OzNet. In the Shandian River Basin, ISSF achieved R = 0.549 and ubRMSE = 0.062 m³ m⁻³ at the point scale. Relative to three benchmark change-detection methods, ISSF increased R by 11–53% and reduced ubRMSE by 7–15%. For the airborne-referenced event, ISSF showed R = 0.635 and ubRMSE = 0.027 m³ m⁻³. Under direct transfer to OzNet, ISSF achieved mean R = 0.55 and mean ubRMSE = 0.05 m³ m⁻³. These results indicate that ISSF provides a practical and interpretable approach for 100 m soil moisture mapping in semi-arid regions with sparse to moderate vegetation. Full article

Radiomics has the potential to improve risk stratification in head and neck squamous cell carcinoma (HNSCC), but clinical adoption is limited by inconsistent performance across institutions. A key source of variability is how radiomic features are generated, preprocessed, and selected prior to model development. This multicenter study evaluated how radiomics parameterization and feature selection strategies affect external model performance, feature stability, and time-to-event risk stratification. We studied pre-treatment CT scans from 752 patients with primary HNSCC from three hospitals. For each scan, 1648 radiomic features were computed using 20 different preparation methods that varied in scaling, outlier removal, and gray-level bin width. We compared five feature selection methods: Graph-FS with connected components, Boruta, Lasso, RFE-RF, and mRMR. The classification models used were Random Forest, XGBoost, CatBoost, and Logistic Regression. We measured performance using external ROC-AUC, bootstrap confidence intervals, Brier score, and RobustScore. Stability of feature selection was assessed using the Kuncheva and Jaccard indices. Cox proportional hazards models confirmed time-to-event results, and consensus SHAP analysis helped explain the models. Radiomics parameterization influenced model performance, and no single configuration was optimal across all analyses. Radiomics-only models outperformed clinical-only models, while clinical–radiomics models achieved the highest overall performance. mRMR and Lasso produced the highest average external AUCs, while Graph-FS showed the greatest stability. The best classification model achieved an external AUC of 0.817. In Cox validation, the best clinical–radiomics configuration achieved an external C-index of 0.662 and separated high- and low-risk patients in the external cohort. Full article

Biomimetic design has been used to reduce the high operating resistance of agricultural soil-engaging components, thereby lowering energy consumption. However, most existing contour-based structural biomimetic designs rely on a single or a few biological samples, making the resulting designs susceptible to individual variation and randomness in sample selection. To address this issue, this study used the abdomen of antlion larvae as a biological prototype. Abdominal contours of 85 antlion larvae were extracted from the front, top, and side views, and elliptic Fourier descriptors (EFDs) were used for contour normalization, averaging, and reconstruction to obtain population mean contours. Seven biomimetic wedge specimens were designed based on the population mean contours, and vertical penetration and horizontal cutting tests were conducted in two different media. The results showed that in the vertical penetration tests, the B-FT specimen, which integrated contour features from the front and top views, exhibited the best drag-reduction performance. Its average penetration resistance decreased by 44.26% and 32.81% in quartz sand and loam soil, respectively. In the horizontal cutting tests, the B-FTS specimen, which integrated contour features from all three views, showed the lowest average cutting resistance, with reductions of 17.62% and 36.47%, respectively. The FTS contour features were further applied to the biomimetic design of a subsoiler tine and validated by discrete element method (DEM) simulation and soil bin tests. Compared with the standard subsoiler tine, the biomimetic subsoiler tine reduced draft force by 11.57% in the simulation and by 12.61% in the soil bin test. These results demonstrate the drag-reduction effectiveness of population mean contours and provide a statistically grounded geometric reference for the biomimetic low-resistance design of agricultural soil-engaging components. Full article

Accurate fault classification of electro-hydrostatic actuators (EHAs) remains challenging because multivariate fault signals contain local transient variations, inter-variable coupling, and dynamic temporal dependencies that are difficult to capture simultaneously using a single model. To address this problem, this paper proposes a multi-view temporal feature collaborative heterogeneous ensemble learning model (MTF-HEM) for EHA multivariate time series fault classification. MTF-HEM integrates a representative subsequence-guided time series forest (RSG-TSF), XGBoost, and a lightweight LSTM to extract local morphological, global statistical, and temporal dependency features, respectively. The outputs of these heterogeneous base learners are fused using a bootstrap-driven out-of-bag probability binning stacking (BOPB-stacking) strategy. The proposed method was evaluated on an AMESim-based simulated EHA plunger pump fault dataset containing one normal condition and six fault conditions. Under the present simulation setting, MTF-HEM achieved an accuracy of 99.52% and outperformed the tested deep time series classification models, ensemble models, and individual base learners. These results suggest that multi-view heterogeneous feature fusion can improve the classification of simulated EHA fault time series and provide a methodological reference for intelligent actuator fault diagnosis. However, the current validation is based on data generated from a single AMESim simulation model, and further evaluation on real EHA systems is needed to assess the practical applicability and generalizability of the proposed approach. Full article

Mechanistic interpretability seeks to reverse-engineer the computational circuits within large language models, but current methods rely on exhaustive or heuristic search over exponentially many feature interactions. This paper introduces Active Circuit Discovery (ACD), a framework that combines attribution-graph analysis with active inference to select interventions efficiently. ACD uses Anthropic’s circuit-tracer library as its attributiongraph backend, applying Edge Attribution Patching with transcoders to identify the active transcoder features for each prompt. A partially observable Markov decision process (POMDP) agent, implemented with pymdp, maintains a multi-factor generative model of feature importance, layer role, and causal influence. At each step, the agent selects both a target feature and an intervention type (ablation, activation patching, or feature steering) by minimising Expected Free Energy over the joint feature–action space, and it learns its observation model online through Dirichlet parameter updates. ACD is an interventionselection layer over existing attribution-graph tools; it is not a whole-circuit discovery method, and no claim of state-of-the-art circuit discovery is made. The framework is evaluated on Gemma-2-2B (26 layers) and Llama-3.2-1B (16 layers) across four settings: Indirect Object Identification (IOI), multi-step reasoning, feature steering, and a multidomain benchmark spanning geography, mathematics, science, logic, and history. With a budget of 20 interventions per prompt, an ablation-only agent scored by bounded oracle efficiency against the ablation oracle reaches 82.0% efficiency on Gemma IOI and 73.0% on Gemma multi-step. It exceeds random selection by 43.5% (relative) on Gemma IOI (paired permutation p = 0.031) and is competitive with greedy ranking, a heuristic UCB bandit, and a plain UCB baseline. A direct Edge-Attribution-Patching ranking is itself a strong baseline that the agent does not consistently surpass, and on Llama multi-step the agent reaches 9.3% efficiency (37.8% with finer layer-role bins). All comparisons report bootstrap 95% confidence intervals. The full multi-action agent is characterised separately by a Relative Cumulative KL, a steering-driven amplification factor reported apart from the bounded efficiency. Feature steering changes the top-1 prediction in a dose-dependent manner, but a matched random-feature control shows that circuit-selected features are only marginally, and not significantly, more steerable than random active features at large multipliers, indicating that part of the effect is generic activation scaling. Multi-domain analysis shows task-dependent circuit structure, with IOI circuits concentrated in late layers and reasoning and scientific knowledge recruiting early and middle layers. Code, notebooks (free T4), AMD64/aarch64 Docker images, and raw results are publicly available. Full article

Wideband direction-of-arrival (DOA) estimation is often formulated as a sparse signal recovery problem with multiple dictionaries, where the commonly adopted ℓ_2,1-norm minimization framework exploits the joint sparsity shared across different frequency bins. However, the resulting optimization problem involves a large number of variables and becomes computationally expensive as the problem scale increases. In this paper, a compact reformulation of the multi-dictionary ℓ_2,1-norm minimization problem is derived, which significantly reduces the number of optimization variables by introducing an equivalent diagonal representation. Under the special case of uniform linear arrays and harmonic sources, the proposed formulation is further extended to a gridless form, and its equivalence to wideband atomic norm minimization is discussed. For the grid-based compact formulation, an efficient block coordinate descent algorithm is developed, where each update admits a closed-form expression. For the gridless formulation, a first-order solver based on the alternating direction method of multipliers is employed to handle large-scale problems. Numerical simulations demonstrate that the proposed methods achieve substantial reductions in computational complexity, thereby enabling efficient wideband DOA estimation in large-scale scenarios. Full article

To address the limitations of the traditional cross-scanning method in absolute calibration of weather radars using metal spheres, including insufficient spatial coverage, limited target acquisition efficiency, and echo underestimation in inter-range bins, this study proposes a sector scanning field calibration method. In this approach, standard metal spheres are suspended from UAVs, and a three-dimensional scanning volume around their theoretical positions is constructed to enable high-density echo sampling. By applying drive backlash correction, quadratic Gaussian surface fitting, and three-dimensional ellipsoid model inversion, key radar parameters can be retrieved. Experimental results show that the improved sector scanning method enhances automation, accuracy, and robustness in field environments and minor target drifts. The experiments were conducted under low-wind and low-clutter conditions. The average calibration error of antenna pointing is 0.08°, the average error of echo intensity calibration is 0.3 dB, the average beamwidth error is 0.07°, the range resolution is 6.6 m, and the average radial ranging error is 14 m. These results indicate that the proposed method can meet the main calibration requirements of weather radars in the present experiments. Full article

The gut microbiome has emerged as one of the most promising sources of non-invasive biomarkers for colorectal cancer (CRC). Over the past decade, fecal metagenomic studies have consistently identified a core CRC-associated signature enriched with oral-typical, biofilm-forming species, most notably Fusobacterium nucleatum, Parvimonas micra, Peptostreptococcus stomatis, and Bacteroides fragilis. The recent landmark pooled analysis by Piccinno et al., which combined 3741 metagenomes from 18 international cohorts, offers the most methodologically solid confirmation of this signature to date. It achieved a leave-one-dataset-out area under the curve (AUC) of around 0.85 and expanded resolution to previously unclassified species-level genome bins (SGBs) and strain-level phylogenies. In this narrative review, we critically evaluate the evidence supporting current universal CRC microbiome signatures, explore the mechanistic basis of the oral-to-gut microbial axis and the immunometabolic tumor microenvironment, and argue that increasing evidence indicates the field is nearing a point where investigating patient-level heterogeneity could be the most valuable next step. Because a strong average CRC signal has been convincingly established, an important next direction is to examine how much these signatures’ impact varies among individual patients, considering tumor molecular subtype, immune environment, metabolic profile, and host genetics. We review emerging evidence of such patient-level heterogeneity, outline analytical methods to assess it, and discuss its importance for developing microbiome-based screening, prognostics, and therapeutic strategies in CRC. Full article

Background: The primary objective of this study was to compare the in vivo placenta volume across gestation in fetuses with congenital heart disease (CHD) and healthy controls. The second objective was to determine the relationship between placental volume and both CHD characteristics and neonatal birth anthropometrics. Methods: Pregnant women with a fetal diagnosis of CHD and healthy pregnancies were enrolled in a longitudinal observational study at Children’s National Hospital. A total of 451 fetal MRIs were analyzed from 284 pregnant women (112 mothers/182 scans with CHD; 172 controls/261 scans). In vivo placentas were manually segmented to derive volumes and z-scores. Z-scores were computed from placental volume data derived from control participants for weekly GA bins using means and standard deviations. Z-scores were then assigned to the CHD cohort. A linear mixed effects model with random intercepts clustered by subject was applied to examine the associations between placental volumes and CHD characteristics, including comparing placental volumes between groups according to gestational windows. Results: Overall, placental volumes in CHD were not significantly different than placental volumes from controls. However, in infants delivered at term age, CHD placental volume plateaued in the final four weeks of gestation. Smaller in vivo CHD placental volume z-scores were associated with decreased weight at delivery (p ≤ 0.0001). Conclusions: This study identifies that in vivo CHD placentas are abnormal in the final four weeks of gestation. Smaller CHD placentas were associated with decreased birth weight, underscoring the importance of placental development in neonatal anthropometrics. Full article

Background: Non-invasive prenatal testing (NIPT) based on analysis of Cell-Free Fetal DNA has transformed screening for common aneuploidies and is increasingly extended to genome-wide detection of copy number variants (CNVs). However, CNV detection remains constrained by analytical limitations and biological signal complexity. Methods: This review evaluates the analytical validity, biological constraints, and clinical interpretation challenges of CNV detection by NIPT, framing it as a probabilistic genomic inference rather than a direct measure of fetal copy number. Results: Performance depends on sequencing depth, bin resolution, fetal fraction, guanine–cytosine correction, and reference modeling, leading to variable detection thresholds. The predominantly placental origin of cfDNA introduces discordance through Confined Placental Mosaicism, post-zygotic events, and clonal variation. Maternal CNVs, mosaicism, vanishing twin, and occult malignancy further complicate interpretation and may cause false positives. Clinical validity is heterogeneous, with positive predictive value dependent on CNV size, genomic context, and prevalence. Reporting practices remain inconsistent. Conclusions: CNV detection by NIPT is fundamentally limited by interpretation of a composite maternal–placental signal. Progress requires improved tissue-of-origin discrimination, multi-omic integration, and standardized reporting to ensure responsible clinical implementation. Full article

Background/Objectives: Traditionally, Sanger sequencing was used to characterize reference materials and confirm discordant allele calls from different STR typing kits at the National Institute of Standards and Technology (NIST). Sequencing can also identify genomic variations within polymerase chain reaction (PCR) amplicons containing STRs, particularly variants that result in null alleles and alleles that do not migrate within allele sizing bins provided by kit manufacturers. Methods: Sanger methods are low-throughput, time- and labor-intensive, and require additional procedures for analysis of heterozygous alleles. To address these limitations, a quicker, more straightforward protocol that uses next-generation sequencing (NGS) was developed. Results: This research provides the criteria used to individually sequence thirty-five autosomal STR loci, with PCR primer locations chosen to increase amplicon length and maximize the likelihood of detecting variants in the flanking region. The list of targeted sequences, associated primers, and chromosomal coordinates is also included. Conclusions: By applying NGS technology to forensic samples containing variant alleles, additional information can be obtained about their molecular basis, and this information can be published and shared across the forensic community. The development of this protocol can increase awareness and encourage the integration of NGS technology into forensic laboratories to improve forensic DNA typing for human identification. Full article

Federated Learning (FL) is a machine learning technique that preserves data privacy and security by training models directly on decentralized edge network devices. This generates substantial communication overhead due to the repeated exchange of model updates across numerous edge network devices. Quantization has tackled this challenge by reducing communication overhead and computational costs by quantizing model updates. Although selecting the most suitable quantization level to balance communication efficiency and model accuracy is challenging, failing to achieve this balance results in excessive compression, leading to accuracy degradation due to the lossy nature of the quantization technique. This challenge was tackled in this paper via a Quantile-based lossless compression method named Pcodec, which implements lossless compression in the FL context. Pcodec is a Quantile-based lossless compression algorithm designed for numerical data that utilizes mode identification with delta encoding and binning, where binning groups similar values into entropy-coded bins and stores the exact offset within each bin, thus achieving high compression ratios and efficient processing speed. Using MNIST and CIFAR-10 datasets and models such as CNN and ResNet18, we demonstrate that Pcodec achieves up to 58.19% size reduction with no accuracy loss compared to standard quantization methods. The experiments showed that the proposed Quantile-based compression approach in FL reduces up to 2.81× the communication overhead between each server and edge network device while maintaining the accuracy. In comparison to quantization, the Quantile approach reduced the communication overhead by 2.74×, tackling the main challenge of FL context by reducing communication overhead with a remarkably high compression ratio while maintaining the model’s accuracy. Full article

Cross-condition remaining useful life (RUL) prediction of rolling bearings is affected by distribution shifts between operating conditions, limited labeled target-domain degradation samples, and interference from long stationary healthy stages. Under an offline full-life retrospective analysis protocol, this paper proposes a Degradation-Stage-Aware Transformer-GRU (DSA-TGRU) method. First, a health indicator is constructed from selected multidimensional degradation features by principal component analysis (PCA-HI), and an adaptive threshold moving rate of change (ATMROC) criterion is used to identify the transition from the healthy stage to the degradation stage, defined as the first prognostic time (FPT), i.e., the degradation-start time. Only post-FPT windows are then used to construct RUL labels for model training and evaluation. The prediction model combines a Transformer encoder for long-range sequence dependencies with gated recurrent units for temporal degradation evolution. The model is pretrained on source-domain bearings and then fine-tuned using a small number of labeled target-domain degradation samples available under the offline protocol. Stage-binned sampling and late-stage linear weighting are treated as auxiliary training strategies rather than universally effective modules. Experiments on the XJTU-SY and PHM2012 datasets show that post-FPT degradation modeling and target-domain fine-tuning play major roles in reducing cross-condition errors. The proposed method achieves average normalized MAE values of 0.0492 and 0.0738 and average normalized RMSE values of 0.0626 and 0.0928 on the two datasets, respectively, and generally outperforms several transfer-learning baselines in normalized error metrics. Ablation results further indicate that the benefits of stage-binned sampling and late-stage weighting are dataset- and task-dependent. The current version is not designed for online RUL prediction from incomplete target-bearing trajectories. Full article