Search Results (166)

Background: Tumor cells can reprogram their metabolism, constituting a hallmark of cancer that plays a crucial role in tumor progression. As tumor cells exhibit an increased demand for nutrients, e.g., amino acids, they rely on extracellular sources and show deregulation of transport proteins. Among these, SNAT1 (SLC38A1) is described as the loader for glutamine that is responsible for the main influx of this amino acid. The aim of this study was to assess the molecular function of SNAT1 in melanoma regarding its role in amino acid transport and regulation of cellular metabolism. Methods: siPool-mediated downregulation of SNAT1 expression in melanoma cell lines was used to investigate the molecular function of this protein. Glutamine transport was assessed by measuring the intracellular and extracellular concentrations of glutamine. Regulation of downstream effectors was evaluated with qRT-PCR and Western Blot. Metabolism was investigated by performing Seahorse flux analysis. Mitochondrial staining was examined via flow cytometry. Protein interaction was assessed with Co-IP, and in silico modeling of protein interaction was performed with AlphaFold3. Results: In this study, we uncovered the new finding that SNAT1 is not primarily implicated in glutamine influx into melanoma cells but in signaling in response to extracellular glutamine. We identified P62 and cMYC as downstream effectors of SNAT1. By activating the P62/cMYC-axis and target genes of cMYC, SNAT1 modulates the metabolism of melanoma cells depending on the glutamine level. SNAT1 and P62 are interaction partners. Conclusions: This finding newly suggests that SNAT1 may function as a sensor or receptor (“transceptor”) for glutamine rather than being a direct and primary glutamine transporter, and could open up new therapeutic options targeting melanoma cells. Full article

The development and validation of a comprehensive five-phase methodology for creating a functional digital twin of complex educational infrastructures are presented, implemented through the IPN Hidalgo campus as a case study. Unlike conventional approaches that focus on isolated aspects of digital twin development, this integrated methodology systematically addresses the complete lifecycle from physical characterization to operational synchronization. The implementation resulted in an interactive digital twin integrating 15 buildings and over 200 network components, deployed across multiple platforms, including: desktop, mobile, and mixed reality devices. The validation results demonstrated a 30% reduction in fault identification time for technical teams and 85% user satisfaction regarding interface intuitiveness, with instrument reliability confirmed by a Cronbach’s alpha coefficient of 0.78. The methodological framework establishes a reproducible standard for developing educational digital twins that combine geometric accuracy with dynamic operational capabilities, offering significant advantages over fragmented approaches reported in the literature. Furthermore, the digital twin serves as a foundational platform for future integration of Internet of Things (IoT) sensors and predictive analytics, aligning with emerging trends in educational infrastructure management through immersive technologies. Full article

Decision-making relies on coordinated neural dynamics that integrate sensory evidence with top-down control. In this EEG study, we examined sensor (scalp)-level theta and alpha-band oscillations, as well as fronto-parietal network connectivity, during a tactile spatial discrimination task. Blindfolded participants judged the lateral offset of the central dot in a three-dot array delivered to the right index finger while an EEG was recorded. Time–frequency analyses revealed that both theta and alpha power were greater for correct than incorrect decision trials during pre-stimulus and post-stimulus intervals, suggesting enhanced preparatory and mnemonic engagement during accurate decisions. Directional connectivity assessed using block (multivariate) Granger causality demonstrated significantly stronger frontal-to-parietal influence in the theta band during both pre- and post-stimulus periods for correct decisions, supporting the role of long-range theta communication for top-down control in guiding tactile judgment. These findings highlight theta-band fronto-parietal communication as a key mechanism supporting successful tactile decision-making. Full article

This study compares a set of 43 AlphaSensor units produced by RadonTec GmbH, Wittislingen, Germany against the AlphaGUARD 1000PF (Bertin Technologies, Montigny-le-Bretonneux, France), which is used as a reference monitor. During this study, around 16 k integrated measurements were conducted. The concentration range varied between 10 and 20 k Bq/m³. Multiple key performance indicators, such as sensitivity, uncertainty, background, linearity, and temporal response, were evaluated using a variety of statistical approaches. The results confirm the manufacturer’s claim of 10% or lower uncertainty in comparison with AlphaGUARD. We tentatively suggest individual calibration factors and methodologies. Our conclusion is that the AlphaSensor and commercial devices based on it, such as AlphaTracer, are affordable and applicable for home use. With modest additional hardware, AlphaSensors are also a good option for scientific studies involving the deployment of large monitoring networks. Full article

The maintenance of endoplasmic reticulum (ER) Ca²⁺ homeostasis is intrinsically linked to the fidelity of protein folding, forming a functional tether that, when disrupted, triggers the Unfolded Protein Response (UPR). This bidirectional axis serves as a critical rheostat for cellular viability, yet its chronic dysregulation underpins the molecular etiology of numerous pathologies, including neurodegeneration, heart failure, and malignant transformation. This review provides a comprehensive interrogation of the Ca²⁺-ER Stress–UPR network, delineating how primary stress sensors—PERK, IRE1alpha, and ATF6—engage in complex feedback loops that either reinstate equilibrium or commit the cell to apoptosis. We specifically examine the PERK-CHOP-SERCA2b inhibitory circuit as a central driver of persistent Ca²⁺ depletion and discuss the role of Mitochondria-Associated Membranes (MAMs) in governing lethal Ca²⁺ transfer. Notably, we move beyond the classical paradigm of CHOP as a terminal apoptotic executioner, incorporating emerging evidence of its context-dependent adaptive functions. By synthesizing mechanistic insights across diverse disease models, this work highlights the transition from adaptive to maladaptive UPR as a universal pathological checkpoint. Ultimately, we evaluate the therapeutic potential of ‘axis-targeted’ interventions, such as SERCA activators and selective UPR modulators, aimed at resolving the underlying Ca²⁺ signaling defects in ER stress-related disorders. Full article

Spatial predictions of forest biomass at regional scale in forests are critical to evaluate the effects of management practices across environmental gradients. Although multi-source remote sensing combined with machine learning has been widely applied to estimate forest biomass, these approaches often rely on complex data acquisition and processing workflows that limit their scalability for large-area assessments. To improve the efficiency, this study evaluates the potential of annual multi-sensor satellite embeddings derived from the AlphaEarth Foundations model for forest biomass prediction. Using field inventory data from 89 forest plots at the Yunhe Forestry Station in Zhejiang Province, China, we assessed and compared the performance of four machine learning algorithms: Random Forest (RF), Support Vector Regression (SVR), Multi-Layer Perceptron Neural Networks (MLPNN), and Gaussian Process Regression (GPR). Model evaluation was conducted using repeated 5-fold cross-validation. The results show that SVR achieved the highest predictive accuracy in broad-leaved and mixed forests, whereas RF performed best in coniferous forests. When all forest types were modeled together, predictive performance was consistently limited across algorithms, indicating substantial heterogeneity (e.g., structure, environment, and topography) among forest types. Spatial prediction maps across Yunhe Forestry Station revealed ecologically coherent patterns, with higher biomass values concentrated in intact forests with less human disturbance and lower biomass primarily occurring in fragmented forests and near urban regions. Overall, this study highlights the potential of embedding-based remote sensing for regional forest biomass estimation and suggests its utility for large-scale forest monitoring and management. Full article

Objective: to clarify differences in the intermuscular coherence of core muscles during side kicks among male Sanda athletes at varying skill levels, particularly in critical frequency bands; to reveal the association between neuromuscular coordination mechanisms and technical proficiency; and to provide methodological references for quantitative analysis of combat sports techniques. Methods: Thirty-six male Sanda athletes were divided into professional (n = 18) and amateur (n = 18) groups based on athletic ranking and training duration. Surface electromyographic (EMG) signals from 15 core muscles and kinematic data were synchronously recorded using a wireless EMG system and a high-speed camera. Signal processing extracted root mean square amplitude (RMS) and integral EMG (iEMG). Muscle coordination was quantified via time-frequency coherence analysis across alpha (8–15 Hz), beta (15–30 Hz), and gamma (30–50 Hz) bands. Results: The professional group exhibited significantly higher RMS and iEMG values in most core muscles (e.g., rectus femoris RMS: 0.298 ± 0.072 vs. 0.214 ± 0.077 mV, p < 0.001). Regarding intermuscular coherence, the professional group demonstrated significantly superior coherence in the α, β, and γ bands for key muscle pairs, including upper limb–swing leg, support leg–swing leg, and upper limb–support leg. Notable differences were observed in pairs such as external oblique–rectus femoris (alpha band: 0.039 ± 0.012 vs. 0.032 ± 0.011, p < 0.01) and right rectus femoris–biceps femoris (beta band: 0.033 ± 0.010 vs. 0.023 ± 0.007, p < 0.01). Conclusions: The fundamental difference in side kick technique among Sanda athletes lies in neuromuscular control strategies and muscle coordination efficiency. Sensor-based intermuscular coherence analysis provides an objective quantitative indicator for distinguishing technical proficiency, offering a scientific basis for optimizing training and extending the methodological framework for technique assessment in combat sports. Full article

Accurate mapping of wetland vegetation is essential for ecosystem monitoring and conservation planning. Traditional workflows combining Sentinel-1 SAR, Sentinel-2 optical imagery, and topographic data have advanced vegetation classification but require extensive preprocessing and often yield fragmented boundaries and “salt-and-pepper” noise. In this study, we compare a conventional multi-sensor classification framework with a novel embedding-based approach derived from the AlphaEarth foundation model, using a cluster-guided Random Forest classifier applied to the dynamic wetland system of Narran Lake, New South Wales. Both approaches achieved high accuracy ac with test performance typically in the ranges: OA = 0.985–0.991, Cohen’s κ = 0.977–0.990, weighted F1 = 0.986–0.991, and MCC = 0.977–0.990. Embedding based maps showed markedly improved spatial coherence (lower edge density, local entropy, and patch fragmentation), producing smoother, ecologically consistent boundaries while requiring minimal preprocessing. Differences in class delineation were most evident in fire-affected and agricultural areas, where embeddings demonstrated greater resilience to spectral disturbance and post-fire variability. Although overall accuracies exceeded 0.98, these high values reflect the use of spectrally pure, homogeneous training samples rather than overfitting. The results highlight that embedding-driven methods can deliver cleaner, more interpretable vegetation maps with far less data preparation, underscoring their potential to streamline large-scale ecological monitoring and enhance the spatial realism of wetland mapping. Full article

Breast cancer is the leading cause of cancer-related mortality in African American (AA) women. In this study we evaluated the serum proteomic profile of AA women with breast cancer using an integrated proteomic framework with multivariate pattern analysis. Using 2D-DIGE, thousands of serum protein spots were detected across 33 gels; 46 spots met criteria for presence, statistical significance, and differential expression. Proteins from the spots were identified by MALDI-TOF/TOF and matched in curated databases, highlighting serum biomarkers including ceruloplasmin, alpha-2-macroglobulin, complement component C3 and C6, alpha-1-antitrypsin, alpha-1B-glycoprotein, alpha-2-HS-glycoprotein and haptoglobin-related protein. LC–MS/MS analysis revealed 163 differentiating peptides after imputing and filtering 286 peptides. These were evaluated using cumulative distribution function (CDF) analysis, a nonparametric method suited for limited sample sizes. Peptide patterns were explored with Random Forest, showing concordance with CDF. The model achieved an AUC of 0.85 at the peptide level. This workflow identified differentiating proteins (CERU, A2MG, CO3, VTDB, HEMO, APOB, APOA4, CFAH, CO4A, AACT, K1C10, ITIH2, ITIH4), highlighting CERU, A2MG, and CO3 with overexpression and reproducible identification across platforms. We present an integrated, non-invasive serum protein biomarker signature panel specific to AA women, through reproducible proteomic sensor framework to support early detection and breast cancer prevention. Full article

Synthetic Aperture Radar (SAR) has been widely applied for rice monitoring, especially in cloud-prone areas, due to its ability to penetrate clouds. However, only limited methods were developed to monitor separately irrigated rice and rainfed rice ecosystems. This study demonstrated the use of multi-temporal polarimetric dual-polarization (dual-pol) SAR (Sentinel-1B and ALOS PALSAR-2) data to monitor and discriminate the irrigated and favorable rainfed rice ecosystems in the province of Iloilo, Philippines. Key polarimetric parameters derived from H–A–α and model-based dual-pol decomposition were analyzed to characterize the rice phenology of both ecosystems. Segmented regression was performed to detect breakpoints corresponding to changes in rice phenology within each ecosystem and used to identify the parameters to use for classification. Based on the results, Sentinel-1B polarimetric parameters (entropy, anisotropy, and alpha) can capture the phenological dynamics, whereas ALOS2 polarimetric parameters were more sensitive to water conditions, as reflected in span and volume scattering. Furthermore, irrigated rice exhibited more stable and predictable scattering patterns than favorable rainfed rice. Using the Random Forest classifier, various combinations of backscatter and polarimetric parameters from Sentinel-1B and ALOS2 were tested to discriminate between the two ecosystems. The highest classification accuracy (81.81% overall accuracy; Kappa = 0.6345) was achieved using the combined backscatter (S1B VH, ALOS2 HH, and HV) and polarimetric parameters from both sensors. The results demonstrated that polarimetric parameters effectively capture phenological stages and associated scattering mechanisms, with the integration of Sentinel-1B and ALOS2 data improving the discrimination of irrigated and favorable rainfed rice systems. Full article

For many years, Warsaw has been one of the European cities with the worst air quality, mainly due to harmful pollutants emitted by the residential sector and street traffic. This has led to high concentrations of particulate matter (PM), nitrogen oxides (NO_x), and also benzo alpha pyrene (BaP), often exceeding WHO standards. However, since 2010, there have been significant changes in the Polish energy mix, with a trend towards a decrease in the share of coal, with a simultaneous increase in the share of renewable energy sources and natural gas. The article presents the related effects of the relevant central government’s policy during the last decade, further supported by the pro-environment decisions of the Warsaw authorities. We also present trends in the concentration of harmful pollutants over the 2012–2023 decade as recorded by the air quality monitoring system. Complete pollution records for 2023 come from two air quality monitoring systems recently operating in the city (GIOŚ official stationary and AIRLY IoT sensor systems). Since the sensors of these systems are located at different sites, the average annual records of both systems were compared indirectly, using the computer simulation results of key pollutant propagation in 2023. Based on the tests conducted, the hypothesis of equality of the annual means for the results from both the monitoring systems and the modeling is not rejected, despite a seemingly clear underestimation of the IoT sensors’ recordings versus the official ones. The reasons for these differences are investigated through a direct comparison and analysis of the average monthly recordings from the monitoring systems. Full article

This paper presents an integrated three-dimensional (3D) quality inspection system for mold manufacturing that addresses critical industrial constraints, including zero-shot generalization without retraining, complete decision traceability for regulatory compliance, and robustness under severe data shortages (<2% defect rate). Dual optical sensors (Photoneo MotionCam 3D and SICK Ruler) are integrated via affine transformation-based registration, followed by computer-aided design (CAD)-based classification using geometric feature matching to CAD specifications. Unsupervised defect detection combines density-based spatial clustering of applications with noise (DBSCAN) clustering, curvature analysis, and alpha shape boundary estimation to identify surface anomalies without labeled training data. Industrial validation on 38 product classes (3000 samples) yielded 99.00% classification accuracy and 99.12% macroscopic precision, outperforming Point-MAE (93.24%) trained under the same limited-data conditions. The CAD-based architecture enables immediate deployment via CAD reference registration, eliminating the five-day retraining cycle required for deep learning, essential for agile manufacturing. Processing time stability (0.47 s compared to 43.68 s for Point-MAE) ensures predictable production throughput. Defect detection achieved 98.00% accuracy on a synthetic validation dataset (scratches: 97.25% F1; dents: 98.15% F1). Full article

Accurate food mass estimation is a key component of automated calorie estimation tools, and there is growing interest in leveraging image analysis for this purpose due to its ease of use and scalability. However, current methods face important limitations. Some rely on 3D sensors for depth estimation, which are not widely accessible to all users, while others depend on camera intrinsic parameters to estimate volume, reducing their adaptability across different devices. Furthermore, AI-based approaches that bypass these parameters often struggle with generalizability when applied to images captured using diverse sensors or camera settings. To overcome these challenges, we introduce a three-stage, transformer-based method for estimating food mass from RGB images, balancing accuracy, computational efficiency, and scalability. The first stage applies the Segment Anything Model (SAM 2) to segment food items in images from the SUECFood dataset. Next, we use the Global-Local Path Network (GLPN) to perform monocular depth estimation (MDE) on the Nutrition5k dataset, inferring depth information from a single image. These outputs are then combined through alpha compositing to generate enhanced composite images with precise object boundaries. Finally, a Vision Transformer (ViT) model processes the composite images to estimate food mass by extracting relevant visual and spatial features. Our method achieves notable improvements in accuracy compared to previous approaches, with a mean squared error (MSE) of 5.61 and a mean absolute error (MAE) of 1.07. Notably, this pipeline does not require specialized hardware like depth sensors or multi-view imaging, making it well-suited for practical deployment. Future work will explore the integration of ingredient recognition to support a more comprehensive dietary assessment system. Full article

Accurate and early detection of alpha-fetoprotein (AFP) in human serum is essential for the diagnosis and monitoring of hepatocellular carcinoma and related diseases. In this study, we present a highly sensitive and reproducible quartz crystal microbalance (QCM) immunosensor for the quantitative detection of AFP. The detection strategy is based on a sandwich-type immunoassay coupled with a signal amplification method utilizing photocatalytic silver deposition on tungsten(IV) oxide (WO₃) nanoparticles. Since QCM detects resonance frequency shifts induced by mass changes on the sensor surface, the silver-enhanced growth of WO₃ nanoparticles enables significant signal amplification, allowing for precise mass-based quantification. Without amplification, the limit of detection (LOD) for AFP using the QCM immunosensor was 286 pg/mL, which was significantly improved to 43.7 pg/mL with photocatalytic silver staining. This approach markedly improves both sensitivity and reproducibility of the assay, offering a robust and efficient platform for clinical biomarker detection and early cancer diagnostics. Full article

Wearable devices (WDs) are increasingly integrated into clinical workflows to enable continuous, non-invasive vital signs monitoring. Combined with Artificial Intelligence (AI), these systems can shift clinical monitoring from being reactive to predictive, allowing for earlier detection of deterioration and more personalized interventions. The value of these technologies lies not in absolute measurements, but in detecting physiological parameters trends relative to each patient’s baseline. Such a trend-based approach enables real-time prediction of deterioration, enhancing patient safety and continuity of care. However, despite their shared multiparametric capabilities, WDs are not interchangeable. This narrative review analyzes nine clinically validated devices, Radius VSM^® (Masimo Corporation, Irvine, CA, USA), BioButton^® (BioIntelliSense Inc., Redwood City, CA, USA. Distributed by Medtronic), Portrait Mobile^® (GE HealthCare, Chicago, IL, USA), VitalPatch^® (VitalConnect Inc., San Jose, CA, USA), CardioWatch 287-2^® (Corsano Health B.V., The Hague, The Netherlands. Distributed by Medtronic), Cosinuss C-Med Alpha^® (Cosinuss Gmb, Munich, Germany), SensiumVitals^® (Sensium Healthcare Limited, Abingdon, Oxfordshire, UK), Isansys Lifetouch^® (Isansys Lifecare Ltd., Abingdon, Oxfordshire, UK), and CheckPoint Cardio^® (CheckPoint R&D LTD., Kazanlak, Bulgaria), highlighting how differences in sensor configurations, battery life, connectivity, and validation contexts influence their suitability across various clinical environments. Rather than establishing a hierarchy of technical superiority, this review emphasizes the importance of context-driven selection, considering care setting, patient profile, infrastructure requirements, and interoperability. Each device demonstrates strengths and limitations depending on patient population and operational demands, ranging from perioperative, post-operative, emergency, or post-Intensive Care Unit (ICU) settings. The findings support a tailored approach to WD implementation, where matching device capabilities to clinical needs is key to maximizing utility, safety, and efficiency. Full article