Search Results (6,511)

Background/Objectives: Sleep quality, anxiety symptoms, physical activity, musculoskeletal pain, and multimorbidity are interrelated health domains, but evidence examining these factors together among adults aged ≥50 years in Saudi Arabia remains limited. This study described their prevalence and assessed cross-sectional associations among these domains. Methods: A cross-sectional online survey using convenience sampling was conducted in Saudi Arabia between December 2024 and April 2025. Participants completed measures of musculoskeletal pain, self-reported morbidity, physical activity (Global Physical Activity Questionnaire), sleep quality (Brief Version of the Pittsburgh Sleep Quality Index), and anxiety symptoms (Generalized Anxiety Disorder-7). Associations were examined using Spearman rank correlations and logistic regression with core adjustment for age, sex, and BMI. The Benjamini–Hochberg false discovery rate correction was applied to eight focal comparisons; Firth penalized logistic regression was applied as a sensitivity analysis for models in which multimorbidity was the outcome. Results: The sample included 298 adults (mean age 58.2 +/− 6.3 years; 75.5% male). Musculoskeletal pain was reported by 73.8%, multimorbidity by 8.4%, poor sleep by 43.3%, and moderate-to-severe anxiety symptoms by 23.8%. Better sleep quality was moderately associated with lower anxiety severity (Spearman rho = −0.45, p < 0.001). Poor sleep was associated with higher odds of moderate-to-severe anxiety symptoms (OR 6.49, 95% CI 3.45–12.20), and low total physical activity was associated with higher odds of both moderate-to-severe anxiety symptoms (OR 7.87, 95% CI 2.68–23.08) and poor sleep (OR 5.09, 95% CI 2.40–10.83). These focal associations remained significant after FDR correction. In an exploratory sensitivity analysis, the poor sleep–multimorbidity association remained comparable when Firth regression was applied (OR 2.71, 95% CI 1.12–7.06; p = 0.027). Conclusions: In this online convenience sample of adults aged ≥50 years, sleep quality, anxiety symptoms, and total physical activity showed consistent cross-sectional associations. Findings involving multimorbidity were exploratory because of the limited number of events. These results require confirmation in representative longitudinal studies. Full article

Banana bunchy top virus (BBTV) is a devastating pathogen threatening global banana production. The plant ubiquitin–proteasome system (UPS) governs immune signaling and is frequently subverted by invading viruses, yet the molecular mechanism through which BBTV interferes with host UPS remains unclear. Here, we show that BBTV nuclear shuttle protein (NSP) serves as the core viral effector to disrupt banana ubiquitination homeostasis. RT-qPCR time-series assays confirmed that BBTV infection dynamically remodels the transcription of eight phylogenetically divergent RING-type E3 ubiquitin ligases: four subfamily I E3-SIS3 paralogs and E3-HIP1 are significantly upregulated at 14 dpi and 21 dpi, while E3-BOI and E3-RHA1B are suppressed at 21 dpi. Transient expression screening of all six BBTV-encoded proteins verified that only NSP reproduces the UPS perturbation signature triggered by viral infection. Cross-species sequence alignment identified an evolutionarily conserved FNGSF motif within NSP orthologs of all Nanoviridae members. Alanine substitution mutagenesis (NSP^AAAAA) completely abolished NSP’s capacity to alter E3 ligase transcription. Western blot assays further validated that wild-type NSP induces massive accumulation of ubiquitinated host proteins, whereas the FNGSF-deficient mutant does not disrupt cellular ubiquitination. Phylogenetic analysis revealed that NSP-targeted E3 ligases share low overall sequence similarity but retain conserved catalytic RING domains, indicating that NSP exerts broad-spectrum regulatory effects on host UPS via the FNGSF motif. Collectively, this study reveals a novel pathogenic strategy whereby BBTV NSP recruits diverse host RING E3 ligases via its conserved FNGSF motif to dysregulate plant ubiquitination and elicit plant pathogenicity. Our findings provide two promising targets—the NSP FNGSF motif and defense-associated E3-SIS3 ligases—for developing antiviral agents and breeding BBTV-resistant banana germplasm. Full article

To address the limited generalization of lithium-ion battery state-of-health (SOH) estimation under unseen aging conditions, this study proposes a degradation-feature-constrained domain-difference gated method (DFC-DGGate). Cycle-level features are constructed from capacity, voltage, local statistics, and first-order degradation variations. Three branches, namely Local ET, Trend Ridge, and Robust Huber, are used to characterize local nonlinear mapping, global degradation trends, and robust estimation, respectively. Their outputs are fused by a condition-aware domain-difference gate and further smoothed to obtain continuous SOH estimates. Cell-wise and condition-wise validations are conducted on the XJTU dataset, and external testing is performed on the NASA dataset. Under XJTU condition-wise validation, DFC-DGGate achieves an RMSE of 14.3895%, while the best-performing baseline, SVR, achieves 5.7623%. The proposed framework remains more accurate than the global Trend Ridge branch (22.3915%) but does not outperform the strongest nonlinear baselines under the substantial Sim_satellite shift. In XJTU-to-NASA validation, the anchor-corrected external extension achieves RMSEs of 20.45% and 19.10% on NASA core and NASA clean, respectively, slightly outperforming ExtraTrees on both subsets. Full article

Treat-to-target management in inflammatory bowel disease (IBD) combines symptoms, fecal and serum biomarkers, endoscopy, histology, and cross-sectional imaging, but these measures frequently diverge. Discordance may reflect analytical variation, timing, disease location, phenotype, comorbidity, or partially non-overlapping biological processes. We performed a critical narrative review using a structured PubMed/MEDLINE search, supplemented by citation chaining and publisher searches. Guidelines, systematic reviews, diagnostic studies, cohorts, randomized trials, and selected mechanistic studies were prioritized. Fecal calprotectin (FC) and lactoferrin primarily reflect intestinal neutrophilic inflammation, whereas C-reactive protein (CRP) and related serum indices reflect a nonlocalizing systemic response. The fecal immunochemical test (FIT) detects gastrointestinal bleeding, and leucine-rich alpha-2 glycoprotein (LRG) remains promising but insufficiently standardized. We distinguish five biological biomarker domains—namely, fecal–neutrophil; serum–systemic; epithelial/barrier; restitution/resolution; and fibrosis/extracellular matrix (ECM) remodeling—from symptoms and clinical indices, pharmacologic measurements, and phenotype-directed reference assessments. Circulating barrier, repair, and matrix-turnover markers remain investigational. Reactive therapeutic drug monitoring (TDM) for anti-tumor necrosis factor (anti-TNF) agents has the most mature evidence. Vedolizumab and ustekinumab show exposure–response associations, but actionable thresholds are unvalidated, and clinical TDM is not established for newer biologics or oral small molecules. After objective confirmation of disease activity, the framework may support phenotype-directed therapeutic decisions but is not a validated algorithm. Clinically important disagreement should prompt assessment of sampling, assay, timing, infection, medication-related confounding, and pretest probability before phenotype-directed endoscopy, histology, imaging, or reactive TDM is selected. A single discordant result should neither trigger treatment escalation nor exclude active or structural disease. Full article

Small object detection in UAV remote sensing imagery plays a crucial role in applications such as infrastructure inspection, disaster assessment, and precision agriculture, where targets of interest frequently occupy fewer than $32\times 32$ pixels under large ground sampling distance variation and complex cluttered backgrounds. Existing methods still face three main challenges in UAV small-object detection: fine-grained detail loss caused by repeated downsampling, feature inconsistency during cross-scale fusion, and unstable boundary regression in densely distributed aerial scenes. To address these issues, this paper proposes HSAR-DETR, a detection framework that jointly improves hierarchical feature representation, cross-scale refinement, and geometry-aware localization. Specifically, a Hierarchical Enhancement Network (HENet) is introduced to preserve shallow spatial details while strengthening deep semantic-context representation. A Dual-Stream Feature Refinement module (DSFR) is designed at the P4-to-P3 fusion stage, combining spatial-domain structural modeling with frequency-domain phase refinement to improve cross-scale feature consistency. A Coordinate-Guided Adaptive Convolution module (CGAC) is further deployed before the detection head, converting coordinate-guided offset magnitudes into modulation weights for adaptive feature recalibration and improved localization stability. In addition, a conventional high-resolution P2 detection branch is incorporated to enhance small-object representation. Experimental results on the VisDrone, RSOD, and TinyPerson datasets demonstrate improved detection performance. On the VisDrone validation set, HSAR-DETR achieves 50.8% $mA{P}_{50}$ and 31.4% $mA{P}_{50:95}$, outperforming the RT-DETR baseline by 4.2 and 3.0 percentage points, respectively. Full article

Digital identity and battery-passport systems can make electric-vehicle battery lifecycle records traceable, but traceability alone does not guarantee verifiable evidence or physical recycling compliance. We develop a normative two-stage Stackelberg model in which a regulator chooses audit intensity and verified incentives and a representative obligated firm jointly chooses pre-verification evidence quality and recycling compliance. The benchmark is stress-tested under reduced eligibility-confirmation effectiveness, alternative evidence–recycling interaction, decentralized manufacturer–recycler decisions, and implementation costs. Baseline traceability can leave evidence quality at the reporting floor and compliance incomplete, while additional responsibility exposure can conditionally increase recycling compliance while reducing evidence quality. Coordinated verified incentives improve both decisions and welfare in the benchmark complementarity domain. At zero eligibility-confirmation effectiveness, the fixed positive-rate package can become marginally welfare-inferior under finite capacity; adverse interaction can reverse a cross-effect, decentralization creates coordination underinvestment, and high payment or startup costs create single- or no-positive-payment regions. The model is therefore a forward-looking governance analysis rather than an empirical evaluation or legal calibration of current Chinese or European Union rules. Full article

Background/Objectives: Adults at high risk of type 2 diabetes frequently show concurrent dyslipidemia, insulin resistance, and central adiposity before overt diabetes develops. We evaluated cardiometabolic responses to standardized whole-grain staple replacement and examined grain-quality patterns in free-living and country-level settings. Methods: This secondary exploratory analysis included 144 participants (48 per group) from a 12-week, three-arm randomized dietary trial. Participants were analyzed according to their randomized assignments to 100 g/day, 50 g/day, or control. Lipid, anthropometric, body composition, and composite cardiometabolic phenotypes were evaluated. Longitudinal and time-weighted cumulative effects were estimated using linear mixed-effects and regression models, with Benjamini–Hochberg false discovery rate (FDR) correction applied within the corresponding multiplicity domains. Complementary analyses included 30,769 NHANES adults without diabetes, including 12,169 with prediabetes, and descriptive country-level data on whole-grain intake, high-FPG-attributable diabetes burden, and cereal supply. Results: Within the extended phenotype panel, HWG versus control time-weighted cumulative effects included triglycerides (β = −0.220, 95% CI −0.397 to −0.043; FDR-adjusted p = 0.047), the atherogenic index of plasma (β = −0.125, −0.191 to −0.060; FDR-adjusted p = 0.002), and the triglyceride–glucose index (β = −0.301, −0.429 to −0.173; FDR-adjusted p < 0.001). Conventional lipid components showed distinct temporal responses. In NHANES, higher whole-grain exposure and more favorable grain-quality ratios were associated with lower adiposity- and insulin resistance-related phenotypes, whereas refined-grain exposure generally showed the opposite pattern. Country-level analyses showed marked heterogeneity in whole-grain intake, diabetes burden, and cereal supply context. Conclusions: Standardized whole-grain staple replacement was associated with short-term changes across several cardiometabolic dimensions in adults at high risk of type 2 diabetes, with the clearest extended-phenotype signals involving triglyceride-related and glucose–triglyceride coupling measures. NHANES identified cross-sectional grain-quality associations in free-living adults; country-level analyses described heterogeneity in whole-grain intake, diabetes burden, and cereal supply context. Full article

Multiphase swirling flows in confined spaces induce highly destructive, nonlinear fluid–structure interaction (FSI) vibrations. Understanding the underlying physical mechanisms is critical for ensuring the safety of industrial operations. This study proposes a mesoscopic multiscale framework coupling the Multi-Relaxation Time Lattice Boltzmann Method with Large Eddy Simulation (MRT-LBM-LES) and the Flügge thin-walled cylindrical shell equations to analyze two-way FSI responses. Variational Mode Decomposition (VMD) and the Hilbert–Huang Transform (HHT) are employed to decouple non-stationary broadband excitation signals. The macroscopic topological evolution of the swirling air core—from initial depression to critical breakthrough—is accurately captured. Dynamic mapping reveals a strict time-domain phase-locking mechanism between macroscopic flow instability and microscopic high-frequency structural excitation caused by cavitation bubble collapse. Furthermore, a dimensionless cross-scale energy cascade index is defined to quantify energy transfer. Results indicate that while higher discharge flow rates delay the critical breakthrough, they trigger a delayed, high-amplitude step mutation in the energy cascade, amplifying the global cumulative excitation energy by nearly 75%. Notably, the dominant high-frequency excitation consistently converges within a narrow band of 760 Hz to 790 Hz, independent of flow rate variations. These findings provide a theoretical foundation for unsteady excitation source localization and targeted vibration reduction in complex industrial pipeline networks. Full article

Reliable crop-type mapping from satellite image time series is affected by distribution shifts across geographic regions, agricultural years, and heterogeneous label systems. To address this challenge, we propose Cross-Domain Mixup (CDMix), a supervised domain-adaptation method designed to leverage a larger labeled source dataset to improve performance on a smaller labeled target dataset under distribution shifts. We also introduce PixelSet-Slovakia, a new multi-year, parcel-level Sentinel-2 dataset covering three Slovak study regions and several growing seasons. Using a common backbone, we compare CDMix against three families of adaptation strategies: (i) no adaptation, (ii) weight transfer through fine-tuning and encoder freezing, and (iii) feature-space alignment using Maximum Mean Discrepancy (MMD) and Correlation Alignment (CORAL). All methods are evaluated in two scenarios: geographic supervised adaptation across datasets from two countries and temporal supervised adaptation across different growing seasons. Across both tested source–target settings, CDMix generally achieves competitive performance when initialized from pretrained representations. Under the region-held-out validation protocol, several pretrained adaptation strategies outperform training from scratch. Full article

Oilfield production forecasting serves as the decision-making basis for monthly production allocation and injection–production system optimization. Existing mainstream prediction methods face significant limitations: the Arps decline curve extrapolates historical production trends, yet its accuracy degrades rapidly following adjustments to injection–production regimes; machine learning methods such as XGBoost can leverage extensive dynamic data but rely heavily on manual feature engineering and offer limited decision interpretability. This paper proposes the FTT-Transformer prediction model, whose core innovation is the Feature-Time Tokenizer (FTT). The FTT projects every scalar pair (time step, feature) in a multivariate time series matrix into a token of uniform dimensionality, superimposing three types of positional information—time embedding, feature embedding, and global position encoding. On this foundation, a multi-head self-attention mechanism performs end-to-end, full-capacity learning of nonlinear interactions across both the temporal and feature dimensions. The model is lightweight, requiring only 35,361 parameters for 13 input features and is readily deployable. Validation was conducted using production data from two independent waterflooding oilfields. On Dataset 2 (60 wells, 2012–2026), the model achieved an R² of 0.819, achieving performance on par with XGBoost (0.813; DM test p = 0.620, indicating no statistically significant difference) and substantially outperforming temporal Transformer baselines PatchTST (R² = 0.683) and iTransformer (R² = 0.786). On Dataset 1 (96 wells), it reached an R² of 0.930, statistically indistinguishable from XGBoost’s 0.943 (DM test p = 0.611). Five-fold temporal cross-validation yielded a mean R² of 0.840 ± 0.036, confirming the model’s stability. Ablation experiments revealed that global position encoding contributed most significantly, with its removal causing a 3.9 percentage point reduction in R² on Dataset 2. Composite feature-importance analysis showed that monthly liquid production and water cut are identified by the model as the two most predictive features, contributing 31.93% and 30.17% of the total importance, respectively. Multi-step forecasting results demonstrated that the model retains an R² of 0.640 for predictions two months ahead, spanning one complete decision cycle of monthly production reallocation. The proposed architecture is not domain-specific; by adapting the feature embeddings and time encoding, it could potentially be extended to diverse multivariate time series forecasting applications. However, cross-domain validation remains future work. Full article

Cross-scene hyperspectral band selection must reduce spectral redundancy while retaining channels that remain useful beyond the source scene. We propose Adaptive Band Gating (ABG), a source-only selector that combines frequency-domain decoupling enhancement, global and sample-specific gating, source-side spectral, spatial, morphology-inspired, and sensor-noise perturbations, and a dual-head evaluator. The selector is trained with source data and frozen before downstream evaluation. Selected bands are assessed with a radial-basis-function support vector machine on Pavia Center and HyRANK under fixed band budgets and target-label fractions from 0% to 10%. At 5% target labels, 15 selected bands achieve 96.05% overall accuracy on Pavia Center, compared with 95.41% using all 102 bands; 20 selected bands achieve 81.49% on HyRANK, compared with 79.20% using all 176 bands. Across evaluated band budgets, ABG is comparable to XGBS on Pavia Center and provides stronger results on HyRANK. Ablation experiments show that adaptive gating, frequency-domain enhancement, and source-side expansion each contribute to performance. Together, these results demonstrate that ABG learns compact and traceable original-band subsets with strong downstream transfer utility across the evaluated cross-scene settings. Full article

Cross-domain data flows are commonplace in regulated inter-organizational environments, where durable audit evidence must be retained without publicly exposing sensitive flow metadata. This paper presents a privacy-preserving on-chain attestation framework for recorded cross-domain data transfers in a permissioned setting. Its core data structure, termed GBFPlus, extends the Garbled Bloom Filter (GBF) with explicit occupancy indicators, constrained payloads that encode a consistency prefix and an adjacent-domain identifier, and distinct pairing-derived positions. Each domain administrator records observed inbound and outbound transfers in directional GBFPlus instances and periodically commits signed filter attestations to an append-only ledger. An authorized regulator can reconstruct candidate transfer edges from available bilateral attestations, while light clients verify ledger inclusion through Merkle proofs. A traceable anonymous attestation signature conceals the uploader’s cryptographic identity from ordinary ledger observers while retaining regulator-assisted accountability. The security analysis establishes integrity, conditional anonymity, traceability, and metadata-privacy properties for committed attestations under the stated trust assumptions, and the prototype evaluation reports the measured costs of GBFPlus and the signature operations. Full article

Accurate brain tumor segmentation from multi-modal magnetic resonance imaging (MRI) is essential for clinical diagnosis and treatment planning. However, effectively capturing long-range contextual information and fine lesion boundaries under limited computational budgets remains challenging. In this work, we propose SDFR-Net, a lightweight stage-asymmetric Spectral Diffusion and Frequency–Spatial Refinement Network for efficient 2.5D brain tumor MRI segmentation. Instead of applying identical processing across all hierarchical stages, SDFR-Net adopts stage-dependent spectral diffusion, stage-selective conditional refinement, and asymmetric cross-stage frequency-grid allocation to accommodate the distinct semantic and frequency characteristics of shallow and deep representations. The network consists of a Spectral Diffusion Encoder for spectral-domain contextual propagation, a Frequency–Spatial Enhancement Module for adaptive refinement of multi-scale skip features, and a lightweight Conditional Refinement Decoder for lesion-aware reconstruction. Experiments on the BraTS 2019 and BraTS 2020 datasets demonstrate that SDFR-Net achieves whole-tumor Dice scores of 0.856 and 0.880, respectively, while requiring only 1.33 M parameters. Ablation comparisons of stage-selective FiLM injection and symmetric versus asymmetric frequency-grid schedules further support the stage-asymmetric design. These results indicate that SDFR-Net provides a favorable accuracy–efficiency trade-off for resource-constrained brain tumor MRI segmentation. Full article

Background/Objectives: Transcranial magnetic stimulation (TMS) initiates a cascade from intracranial electric-field exposure through neural recruitment and plasticity to distributed network responses. Neuronavigation improves the geometric reproducibility of delivery but does not guarantee equivalent cortical exposure or target engagement. This narrative review integrates these levels within an operational framework for precision TMS. Methods: Six domain-specific PubMed searches covering 1 January 1985 to 31 July 2026 were supplemented by Google Scholar and citation tracking. A documented rerun on 17 August 2026 yielded 6430 records (5617 unique after cross-query deduplication). Evidence was synthesized narratively; no quantitative synthesis or formal risk-of-bias assessment was performed. Results: Neuronavigation improves geometric precision by stabilizing target definition and coil pose, whereas individualized electric-field models estimate intracranial exposure. Neither establishes biological precision, which also depends on neuronal orientation, brain state, circuit architecture, medication, and behavior. Motor-system measures are not validated as universal biomarkers for nonmotor cortex, and no single validated biomarker captures TMS-induced plasticity. Convergent, controlled multimodal evidence may strengthen inference about target engagement; adaptive and closed-loop approaches remain experimental. Conclusions: Geometric delivery, modeled exposure, biological engagement, and durable functional or clinical benefit require separate validation. Spatial accuracy alone does not establish clinical value. Full article

Previously, food processing and nutrition sciences operated as distinct scientific domains; however, increasing health concerns have highlighted the need for integrated perspectives and practical food classifications. This study aimed to assess participants’ knowledge of food processing classification according to the NOVA system and investigate its association with ultra-processed food (UPF) consumption patterns and nutrient intake. A cross-sectional study of 403 healthy students (18–30 years) was conducted. Participants were asked to classify each food according to its level of processing. Dietary intake was assessed in a subsample of 190 participants who completed the 24-h dietary recall assessment and had complete dietary data available. Higher food processing knowledge was associated with lower UPF consumption (B = −0.241, 95% CI: −0.414 to −0.068; p = 0.006). Participants with lower knowledge exhibited higher mean consumption of bread and bakery products, breakfast cereals, savory snacks, dairy products, ready-to-eat meals, and processed meats. After adjustment for age, no significant differences in nutrient intake were observed between knowledge groups. Full article