Search Results (17,589)

NLRP1 is an inflammasome sensor protein expressed in barrier tissues of humans. Its activation in response to microbes or cellular stress triggers a cascade of molecular events, leading up to IL1β-driven inflammation and pyroptosis. Rare germline mutations of NLRP1 cause its persistent activation, resulting in autoinflammatory syndromes. Multiple self-healing palmoplantar carcinoma (MSPC) is one such syndrome, characterized by the appearance of recurrent keratoacanthomas (KAs) on the palms and soles. Here, we aimed to compare the subcellular localization of mutant NLRP1 in lesions from an MSPC patient to wild-type NLRP1 in non-MSPC-KAs and in skin from healthy donors. Using mass spectrometry, immunohistochemistry and immunoelectron tomography, we found that NLRP1 localized to mast cell granules in all MSPC lesions but also in healthy skin, a novel finding which implicates these cells in NLRP1-associated responses in human skin. Moreover, we found that mast cells expressing the A66V pathogenic variant of NLRP1 overpopulated MSPC-KAs, infiltrated the epidermis and degranulated, a behavior not seen in other lesions from this study. The released granules had the highest NLRP1 protein content and also contained NLRP3 and IL1β, suggesting the coexistence of inflammasome pathways within mast cells. Taken together, our findings propose cutaneous mast cells as a previously unrecognized NLRP1 reservoir in health and disease. Full article

COVID-19 was a prolonged public-health shock that disrupted mobility, access to services, and household spending. Although the official U.S. poverty rate declined to 11.1%, the Supplemental Poverty Measure rose to 12.9%, suggesting that material hardship persisted unevenly across places. This study asks whether pre-existing livelihood vulnerability and local epidemic burden translated into geographically concentrated consumption losses during 2020–2022. Because sustained consumption loss can erode households’ health-related spending, tracking where spending declines concentrate helps connect local social and environmental conditions to how communities withstand a health crisis. We analyze consumer expenditure, unlike prior research relying on aggregate retail sales, to capture fine-grained economic strains as a proxy for shock-absorption capacity. A Livelihood Vulnerability Index (LVI) was calculated for each U.S. county using 16 socio-economic variables, and counties were classified as high- or low-risk. A multilevel model then examined how socio-economic and COVID-19 factors at county and census tract levels shaped consumption changes. Higher-risk communities experienced greater consumption reductions. At the census tract level, the non-White ratio, vacancy rate, built year, per capita income, education level, and housing value were significant. At the county level, COVID-19 cases and deaths, crowding, public transportation use, and vehicle availability mattered most. These findings support place-targeted strategies that combine public-health response with socio-environmental interventions to reduce disparities rooted in pre-existing vulnerability. Full article

Background: Post-stroke upper-limb spasticity can cause pain, hinder passive care, and lead to secondary musculoskeletal complications. Current minimally invasive treatments have important limitations. Cryoneurolysis, which creates a controlled cold lesion of peripheral nerves, may offer a partially reversible focal denervation alternative. Methods: We conducted a feasibility case series in the outpatient department of a rehabilitation centre. Three adults with chronic post-stroke hemiparesis and a non-functional spastic upper limb underwent ultrasound- and nerve stimulation-guided cryoneurolysis of the musculocutaneous, median, and/or ulnar nerves. All had demonstrated a positive response to diagnostic nerve blocks beforehand. Feasibility outcomes included completion of planned nerve targets, tolerability under local anesthesia, absence of serious adverse events, and completion of 6-month follow-up. Secondary outcomes were Modified Ashworth Scale (MAS), qualitatively assessed passive joint mobility (video-documented), pain measured by visual analogue scale, sensory testing, and electroneuromyography (ENMG). Results: All procedures were completed as planned. Treatment was well tolerated under local anesthesia, and no serious adverse events occurred. MAS decreased by at least 2 points in targeted patterns, with immediate improvement in passive mobility; these effects persisted at 6 months. Pain remained unchanged in two participants and improved in one. Sensory testing at 6 weeks was stable. ENMG findings were heterogeneous, including reduced ulnar sensory action potential amplitude and biceps denervation activity in one participant. Conclusions: In this small series, cryoneurolysis for post-stroke upper-limb spasticity was feasible and associated with sustained tone reduction and improved passive mobility. Larger controlled studies are required to better define safety, optimize targeting strategies, and assess patient-centred outcomes. Full article

Machine learning interatomic potentials (MLIPs) are typically developed for globally ordered homogeneous systems (GOHomS), which exhibit only minor local deviations from equilibrium configurations. Consequently, most existing MLIPs trained on GOHomS often perform inadequately when applied to locally ordered heterogeneous systems (LOHetS), e.g., substitutional alloying elements in multicomponent alloys. To describe doping alloy systems, we develop a fine-tuned MLIP based on the MACE foundation model, specifically tailored for Mo-based dilute alloys containing one or two out of 20 substitutional elements: Cr, Fe, Mn, Nb, Re, Ta, Ti, V, W, Y, Zr, Al, Zn, Cu, Ag, Au, Hg, Co, Ni, and Hf. The model is built on more than 7000 equilibrium and non-equilibrium structures derived from first-principles density functional theory (DFT) calculations. The optimized large-scale fine-tuned model attains state-of-the-art accuracy, with a mean absolute error (MAE) and root-mean-square error (RMSE) of 2.27 meV/atom and 3.79 meV/atom for energy predictions, and 13.83 meV/Å and 24.26 meV/Å for force predictions, respectively. Systematic evaluation under different data-splitting protocols shows that unknown element extrapolation remains challenging under strict dopant hold-out, whereas substantially improved accuracy can be achieved in partial-exposure transfer settings. The fine-tuned models reduce the MAE by approximately 7–10 times compared to models trained from scratch, and by 10–20 times relative to zero-shot foundation models. This performance gain remains consistent across varying dataset sizes (equilibrium vs. non-equilibrium structures) and model scales. Our work illustrates the efficacy of transfer learning from globally ordered homogeneous systems to locally ordered heterogeneous multicomponent alloy environments. However, direct transfer to entirely unknown elements remains challenging, especially when proxy embeddings are employed without fine-tuning. Thus, to achieve high accuracy without incurring additional cost, it is essential to include unknown elements in the training dataset while minimizing the number of configurations containing known elements. Moreover, the current findings are primarily validated for dilute Mo-based alloy systems. Extending this approach to more compositionally complex alloy spaces may necessitate additional data and further fine-tuning. Full article

Multivariate time series forecasting presents a challenging problem in stochastic modeling, particularly under non-stationary conditions with low signal-to-noise ratios. While recent inverted architectures enhance cross-variable dependency modeling, the conventional point-wise inversion strategy often compromises local temporal patterns. To address this limitation, we propose PiTransformer, a gated patch-wise inverted framework for multivariate time series modeling. Specifically, a Patch-wise Inverted Embedding (PIE) mechanism is introduced to segment temporal sequences into regional patches prior to inversion, enabling the preservation of localized temporal structures. In addition, a Variable–Temporal Gating (VTG) module is incorporated to regulate feature interactions based on the information bottleneck principle, thereby suppressing spurious correlations in noisy environments. Empirical evaluations on diverse benchmarks—including financial and energy datasets—demonstrate that PiTransformer achieves consistent improvements in predictive accuracy and stability over competitive baselines. These results suggest that the proposed framework provides a robust and interpretable approach for modeling high-dimensional stochastic time series under non-stationary conditions. Full article

Aircraft brake torque tubes are safety-critical components subject to combined torsional and thermal loading. As such, in aging aircraft, fatigue cracks frequently occur at the side walls of the grooves near the fillet transitions. This study presents a detailed analysis of the stress–strain state of the torque tube support section using a thermo-mechanically coupled finite element model (FEM) developed in ANSYS 2023 R2 Workbench. The model parameters are based on operational and design data provided by Röder Component Service Center Ltd. Unlike previous studies using idealized models, this approach integrates real-world non-destructive testing (NDT) evidence to identify critical areas with high stress concentrations. The model evaluates stress distributions under normal and emergency braking. Results show that the baseline 1 mm groove fillet exhibits pronounced stress peaks, correlating with observed crack initiation sites. Increasing the fillet radius to 3 mm reduces peak equivalent stress and improves the safety-factor distribution, significantly lowering crack-initiation propensity. These findings demonstrate that even minor local geometric refinements can enhance the structural robustness of torque-transmitting components. This FE–inspection integration framework offers a transferable method for reliability assessment and design improvement in aging aircraft fleets. Full article

As urbanization continues to reshape societies, the concept of child-friendly cities (CFCs) has emerged as a rights-based approach to support the well-being of children in urban environments, particularly as increasing numbers of children grow up in rapidly expanding cities. While international frameworks provide general guidance, effective implementation requires contextual adaptation. Despite Jordan’s commitment to the Convention on the Rights of the Child, limited research has examined how CFC principles translate into urban policy and practice. This study explores the conceptualization of CFCs in the Jordanian context by identifying and prioritizing key stakeholders, urban features, barriers, and evaluation indicators. A structured Delphi methodology consisting of iterative rounds was used to gather cross-sectoral expert perspectives and establish areas of consensus. The findings reveal disparities in resources and efforts across governorates and cities while highlighting the recognized role of municipalities and local governments alongside a limited acknowledgement of non-traditional actors such as media. Prioritized features emphasize clean and climate-responsive environments, while funding limitations were identified as a major constraint and child safety as the most critical indicator. This study provides a consensus-based reference for understanding the key dimensions of CFCs in Jordan and contributes to the discussion on localizing CFC frameworks. Full article

Point cloud semantic segmentation is a pivotal technology for realizing non-contact body measurement and refined management of livestock. However, processing sheep point clouds in smart livestock scenarios presents specific challenges, primarily due to non-rigid posture deformations and severe background interference. To address these issues, this paper proposes a novel symmetric encoder–decoder architecture named Local–Global Aggregation Network (LGA-Net), which achieves high-precision parsing of sheep point clouds by constructing a dual-scale feature aggregation mechanism. First, a Dual Attention Aggregation (DAA) module is designed to jointly encode geometric and color features, significantly enhancing the network’s ability to capture fine-grained local boundaries, such as sheep ears and hooves. Second, a Global Semantic Relation (GSR) module is introduced, utilizing spatial occupancy ratios to establish long-range dependencies, thereby effectively resolving semantic ambiguity caused by posture variations. Furthermore, a plug-and-play Dual-domain Feature Enhancement (DFE) module is proposed. By fusing bilinear interactions between explicit 3D space and implicit feature space, the DFE module constructs a high-pass filtering mechanism to suppress low-frequency background noise. Extensive experiments on a self-constructed point cloud dataset involving two semantic classes (Sheep and Fence) demonstrate that LGA-Net achieves a mIoU of 97.3%, an OA of 99.0%, and a mAcc of 97.8%. These results indicate that the proposed method outperforms existing mainstream algorithms in both segmentation accuracy and robustness. This study not only proposes a feasible solution for precise sheep extraction under the tested experimental conditions, but also provides solid technical support for subsequent automated body measurement and behavior analysis. Full article

Continuous wrist angle estimation based on surface electromyography (sEMG) is often affected by signal variability and prediction instability. Although regression models provide instantaneous outputs, their predictions may exhibit temporal fluctuations and limited robustness due to the non-stationary nature of sEMG signals. To address this issue, we propose an uncertainty-aware probabilistic fusion post-processing framework for continuous wrist motion estimation. The proposed approach decouples regression and uncertainty modeling, enabling plug-in compatibility with feature-based regression models. A local Gaussian process regression (LGPR) model is employed to estimate predictive uncertainty from a sliding feature window. The instantaneous regression output is then fused with the LGPR prediction through a Bayesian-inspired Gaussian formulation, resulting in a closed-form adaptive gain that dynamically adjusts smoothing strength according to predictive variance. Experimental results from both open-loop wrist joint motion estimation and closed-loop myoelectric control tasks demonstrate that our method outperforms existing methods in key performance indicators, including task completion time, trajectory smoothness, and trajectory tracking error. Full article

Standard fringe-based structured-light processing estimates wrapped phase from phase-shifted sinusoidal images and commonly relies on phase unwrapping to obtain a globally consistent phase representation. In practical measurements, this approach may become unstable on reflective objects and under low or non-uniform illumination, where the recorded fringe signal is distorted and the recovered phase becomes unreliable. To address these limitations, we propose a correspondence extraction method based on subpixel peak localization performed directly on phase-domain images. The wrapped phase is transformed into absolute value phase profiles, $\Phi =|{\varphi }_w|$, whose local structure follows the projected fringe pattern and is less affected by object-dependent intensity variations. The proposed method reformulates correspondence extraction as a local signal-based estimation problem in the phase-domain, thereby reducing reliance on global phase-consistency constraints at the correspondence stage. A practical advantage observed in the evaluated examples is that the method remained usable in some regions where the phase became locally flat because of low modulation, saturation, or reflective surface effects. In such regions, conventional processing relies on sufficiently reliable phase gradients and subsequent unwrapping, whereas the proposed method uses local peak geometry in the transformed phase representation. In the implementation used here, Gray-code information is employed only for pixel-wise phase extension and reference indexing, not as a spatial phase-unwrapping mechanism. The method does not require machine learning models or training data and can be integrated as a correspondence analysis stage in practical structured-light systems. Full article

In this paper, we propose a spatiotemporal approach for binary classification of violent and non-violent behavior in real-world settings. The experimental pipeline includes video preprocessing, stratified data splitting, generation of temporally structured clips, and comparative evaluation of baseline models, including a convolutional neural network. We also developed a Residual Adaptive Motion Temporal Binary Heat Network model that combines frame color characteristics, residual motion descriptions, temporal feature fusion, an early risk assessment mechanism, and interpretable localization maps. Experiments were conducted on a balanced dataset of 2000 video clips. The proposed model demonstrated the best early warning performance: a supervision rate of 0.6, an F1 score of 0.9527, and a balanced accuracy of 0.9533. With full supervision, the F1 score was 0.9342, and the area under the receiver operating characteristic curve (AUC) was 0.9871. The practical significance of the work is that the proposed approach can be used as a decision support tool for the preliminary identification of potentially dangerous video fragments with subsequent manual verification, without the assumption of autonomous use in high-risk scenarios. Full article

Background: Actinic keratoses (AKs) are considered early in situ forms of cutaneous squamous cell carcinoma (cSCC). However reliable histopathological or molecular markers for predicting the risk of progression are still lacking. The aim of this study was to investigate the relationship between immunohistochemical markers and basal proliferation patterns of AKs in order to identify histopathological associations that may be relevant for malignant transformation. Methods: A total of 97 AK samples from facial and scalp areas were retrospectively analyzed and classified according to their basal proliferation pattern (Pro I: non-proliferative and Pro III: proliferative). Immunohistochemical staining was performed for Ki-67, p53, p16 and podoplanin. In addition, histopathological parameters such as Röwert-Huber grade, inflammatory infiltrate, parakeratosis, elastosis and the presence of acantholysis were evaluated. Results: Pro III lesions were significantly more frequently associated with higher Röwert-Huber grades (AK III: 47.9% vs. 14.3%; p = 0.0004) and with acantholysis (p = 0.0004). No significant differences between the groups were found for Ki-67, p53 and p16. Podoplanin expression, however, was significantly higher in Pro III lesions (93.7% vs. 57.1%, p < 0.0001) and was predominantly localized basally. The combination of a PRO III pattern and podoplanin positivity identified a distinct histopathological subgroup associated with features linked to progression. Conclusions: Podoplanin expression, especially in combination with PRO III pattern and acantholysis, characterizes a histologically and biologically distinct AK subgroup. In contrast, Ki-67, p53 and p16 showed no additional discriminative value in this cohort. Podoplanin could therefore be a useful addition to existing classification systems and in the future support risk-adapted treatment decision. However, prospective longitudinal studies are required to determine its prognostic value. Full article

We develop a structural framework for the three-dimensional incompressible Navier–Stokes equations in which the nonlinear dynamics are reorganized in terms of triadic interactions, dyadic shells, and helical modes. Within this formulation, all interactions are classified into Low–Low, Low–High, and High–High channels, and it is shown that the Low–Low and Low–High contributions are perturbatively controlled through scale-localized estimates without introducing external assumptions. Consequently, potentially non-perturbative contributions are confined, within the present framework, to a class of same-scale High–High interactions. This class is further reduced, through geometric and dynamical constraints, to a coherent core characterized by amplitude activity and low phase drift. The resulting reduced dynamics is expressed in terms of family-level phase variables and associated curvature quantities. The main result establishes a quantitative residence-time compression principle for this coherent regime. Specifically, it is shown that intervals on which both amplitude activity and low phase drift persist must have small total measures, due to an absolute-value coercivity property of the curvature combined with bounded-variation control of the phase dynamics. This implies that coherent same-scale interactions cannot occupy a macroscopic portion of any bounded time interval, even though local re-entry into low-drift configurations is not excluded. Consequently, the nonlinear transfer associated with coherent triads becomes temporally localized and admits a shellwise compressed representation. These results provide a structurally reduced description of a candidate mechanism for cumulative same-scale amplification within the present dyadic–triadic framework. They do not claim a framework-level structural exclusion of the global regularity problem. Rather, they identify and analyze, within an explicit structural setting, a minimal mechanism for non-perturbative amplification, and establish a quantitative constraint on its temporal persistence. Full article

Background: Migration from low- and middle-income to high-income settings is often accompanied by dietary and lifestyle changes that may increase long-term risk of non-communicable diseases. African migrants represent a growing but under-studied population in Australia, with limited evidence on post-migration nutrition transitions and associated chronic disease risk. This study examined changes in diet and lifestyle among Nigerian-born adults before and after migration to Australia and explored any association with chronic diseases. Methods: A pilot cross-sectional study was conducted among adults who migrated from Nigeria to New South Wales, Australia, between 1992 and 2019. Data were collected via a culturally adapted, self-administered online questionnaire assessing socio-demographic characteristics, dietary intake, lifestyle behaviours, and self-reported chronic conditions in the 12 months immediately before and after migration. Descriptive statistics (frequencies and proportions) and inferential analyses (Chi-square tests, McNemar test, and the Bowker test) were used to compare pre- and post-migration behaviours and examine associations with chronic disease outcomes. Results: Ninety-three participants completed the survey (mean age 37.0 ± 7.2 years; 50.5% male). Post-migration, regular breakfast consumption declined (−24.3%), while irregular eating (low and moderate) patterns increased (+7.6% and +16.7%). Regular vegetable intake improved (+5.4%), whereas fruit intake remained low (13.0%). Regular consumption of Nigerian local foods decreased markedly (−53.7%), while regular intake of meat (+18.5%), dairy foods, fats (+14.3%), and non-alcoholic beverages increased (+22.8%). Salt use shifted away from the highest-risk category (−22.2%), and smoking and alcohol consumption remained low and stable. Self-reported chronic conditions were uncommon; hypertension (6.5%) and obesity (5.4%) were the most frequently reported. Conclusions: Nigerian migrants in Australia experience substantial post-migration dietary and lifestyle transitions that may elevate long-term chronic disease risk despite a currently low reported disease burden. Early, culturally responsive nutrition and lifestyle interventions are needed to support healthy adaptation and prevent the progression of cardiometabolic conditions in this growing migrant population. Full article

Variational models describe deformation and stability through the first and second variations in an underlying functional, but the relationship between these responses is seldom expressed as an intrinsic equilibrium quantity of the model itself. A canonical curvature–strain representation for equilibrium ratios arising in variational field settings is developed. For a twice Fréchet differentiable functional and an admissible perturbation generator, strain is defined as normalized first-order response and curvature as normalized second-order response along the generator direction. Their quotient defines a curvature–strain ratio that measures proportional balance between deformation and curvature within the model. The main result shows that this curvature–strain ratio is a canonical representative of a response ratio already implicit in the variational data. Under canonical normalization, the curvature–strain ratio coincides with the quotient of second- and first-order response, and stationarity of the curvature–strain ratio is equivalent to proportional stationarity of that response quotient along the admissible flow. A further theorem establishes transfer of local isolation: when the second-variation operator satisfies standard hypotheses such as compact resolvent and non-degeneracy of the constrained extremum, isolated equilibrium ratios persist in the curvature–strain representation for the same operator-theoretic reasons. Quadratic scalar and Maxwell-type models illustrate the construction. The paper establishes a mathematically controlled curvature–strain representation of equilibrium ratios within ordinary variational theory, with emphasis on the analysis of variational response and equilibrium balance. Full article