Search Results (114,075)

This study examines the association between the formal (de jure) adoption of renewable energy source (RES) support instruments and observed RES deployment outcomes across 36 European countries. We assess whether broader legislative adoption—measured by a transparent breadth/coverage index (SIC/OIL) based on binary coding and equal sector weights—correlates with higher RES shares. The empirical design comprises three complementary steps: (i) hierarchical clustering (Ward’s method; Euclidean distance on standardised indicators) to classify countries by legislative adoption profiles; (ii) parallel clustering of countries by RES utilisation profiles using 10 z-score-standardised outcome indicators (total and sectoral RES shares and per capita RES use by source); and (iii) an integrated comparison of both typologies, followed by a cross-sectional regression test of the OIL–RES association. Legislative and utilisation clusters do not systematically coincide, and the baseline regression shows a weak, statistically insignificant association with very low explanatory power (R² = ≈ 0.015), supporting heterogeneity (H1) rather than a universal positive average relationship (H₂). Interpretation is conservative because SIC/OIL captures policy-mix coverage (not budgets, enforcement, or design stringency) and because some low/zero policy entries may reflect limited source coverage. Overall, the findings suggest that observed RES performance is primarily shaped by country-specific structural conditions (resource endowments, economic capacity, and sustained long-term investment), implying that context-sensitive instruments and stronger implementation capacities should complement formal policy adoption. Full article

Fairness-aware classification with respect to sensitive attributes, such as gender and race, is one of the most important topics in machine learning. Although numerous studies have made outstanding progress through various approaches, one key limitation is that they necessarily require additional labels of sensitive attributes for training. This poses a significant challenge since sensitive attributes typically correspond to personal information. To this end, we propose a novel reweighting method that dynamically gives more weights to underrepresented groups across potential sensitive attributes. Without auxiliary networks or strong assumptions about sensitive attributes, the proposed method significantly improves fairness under various scenarios on benchmark datasets, outperforming the existing state-of-the-art methods. Full article

Training load monitoring in women’s volleyball is a challenge for optimizing performance and mitigating injury risk. Non-invasive monitoring technologies and machine learning (ML) can support decision-making, but the evidence remains heterogeneous. This scoping review mapped and integrated the evidence on training load management, fatigue, and performance in women’s volleyball and identified gaps. The PRISMA Extension for Scoping Reviews (PRISMA-ScR) and the Joanna Briggs Institute (JBI) framework were followed. A systematic search was conducted in Scopus, Web of Science, and PubMed, covering January 2020 to September 2025. We included studies in female players at any competitive level, including mixed-sex studies meeting a minimum threshold of female participation, that evaluated external and/or internal load, neuromuscular or perceptual fatigue, and/or performance, using standardized data extraction and narrative/thematic synthesis. Fifty-three studies were included. Inertial measurement units (IMUs), force platforms, heart rate (HR) and heart rate variability (HRV), wellness questionnaires, and global/local positioning systems (GPSs/LPSs) were most prevalent. External-load intensity indicators (e.g., high-intensity jumps and accelerations) were reported as more sensitive to fatigue-related changes than accumulated volume. Machine learning models were less frequent and were mainly applied to multi-source integration and fatigue/readiness prediction, with recurring limitations in external validation and interpretability. Women-specific biological moderators, such as the menstrual cycle, were rarely addressed. Full article

Background and Objectives: Totally endoscopic aortic valve replacement (TE-AVR) is a minimally invasive technique offering potential benefits of reduced surgical trauma and faster recovery compared with median sternotomy or other minimally invasive access. While isolated aortic valve replacement (AVR) is well established through conventional and minimally invasive access, large-scale evidence for the totally endoscopic approach remains limited. This meta-analysis aimed to systematically assess the safety and feasibility of TE-AVR by aggregating perioperative outcomes, including mortality, stroke, conversion, bleeding, paravalvular leak (PVL), and atrial fibrillation (AF). Materials and Methods: A systematic search of PubMed, Embase, and the Cochrane Library was performed, following PRISMA 2020 guidelines. Observational studies and randomized controlled trials reporting outcomes of totally endoscopic or thoracoscopic AVR were eligible. After independent screening and selection, data were analyzed using a single-arm proportion model. Leave-one-out sensitivity analyses were performed to evaluate the influence of individual studies. The protocol was registered in PROSPERO (CRD42024610128). Results: A total of 11 studies comprising 1135 patients were included. The pooled perioperative mortality was 0.00% (95% CI 0.00–0.23; I² = 0.0%), indicating highly consistent results across cohorts. The stroke incidence was 0.69% (95% CI 0.00–2.07; I² = 42.7%), confirming the low cerebrovascular risk of this approach. Conversion to sternotomy occurred in 0.00% of cases (95% CI 0.00–0.17; I² = 0.0%), with no statistical heterogeneity observed. Reintervention for bleeding occurred in 1.75% (95% CI 0.34–3.85; I² = 43.4%), while PVL was reported in 1.24% (95% CI 0.00–4.22; I² = 64.0%). AF incidence was 10.54% (95% CI 3.79–19.70; I² = 90.5%), with substantial between-study heterogeneity, likely related to non-standardized definitions of new-onset AF and variability in postoperative rhythm monitoring and reporting across studies. Conclusions: TE-AVR is a safe and feasible technique associated with very low perioperative mortality, bleeding, and stroke rates, as well as low PVL incidence. The absent conversion rate in our pooled analysis highlights the technical reliability of the procedure. Variability in AF reporting underscores the need for future randomized studies with harmonized definitions. Overall, TE-AVR offers a promising minimally invasive alternative for aortic valve replacement, with potential advantages in recovery (pooled ICU stay 1.86 days), hospital stay (pooled 7.98 days), and aesthetic outcomes. Full article

This study investigates the effects of physical activity on serum cortisol levels and phagocytic capacity of the innate immune system in eight common bottlenose dolphins under human care. Analysis of 8 pairs (16 samples) revealed a significant increase in cortisol during periods of physical activity (mean increase of 1.27 µg/dL, 122% elevation), accompanied by decreased phagocytosis in granulocytes (92% reduction) and monocytes (52% reduction). Statistical analyses demonstrated consistent negative correlations between cortisol levels and phagocytic function, suggesting that physical activity influences hypothalamic–pituitary–adrenal axis activation and, consequently, innate immune system function. The male showed an attenuated cortisol response, but maintained monocyte sensitivity. These findings highlight the complex interplay between the neuroendocrine cortisol response and immune function in cetaceans, for the management and evaluation of animals under human care. A multi-method statistical framework incorporating Bayesian analysis, bootstrapping, and traditional approaches ensured robust inference despite limited sample size. Full article

Metabolic stress caused by lipid overload is a key driver of cellular dysfunction in aging and disease. Excess saturated fatty acids such as palmitate impair fatty acid oxidation (FAO), promote lipid accumulation, and increase reactive oxygen species (ROS), ultimately triggering premature senescence-like states. Senescence further amplifies vulnerability by worsening mitochondrial dysfunction, enhancing lipid imbalance, and sustaining pro-inflammatory signaling. Here, we investigated the role of the neuron-enriched RNA-binding protein HuD (ELAVL4) in protecting cells against lipotoxic stress. Using Neuro2a neuroblastoma cells, we found that HuD knockdown suppressed FAO, leading to increased lipid accumulation and elevated ROS following palmitate exposure. HuD-deficient cells also exhibited cytosolic mitochondrial DNA release, IRF phosphorylation, and upregulation of senescence markers. Mechanistically, RNA immunoprecipitation revealed that HuD binds directly to PPARα mRNA, sustaining its expression by competing with the PPARα-targeting microRNAs miR-9-5p and miR-22-3p. Loss of HuD reduced PPARα levels, thereby weakening the FAO capacity and sensitizing cells to palmitate-induced lipotoxic stress. These findings identify a previously unrecognized HuD–PPARα–FAO axis that restrains metabolic stress and senescence. By linking post-transcriptional regulation to lipid metabolism and inflammatory signaling, this work highlights stress-induced premature senescence as both an outcome and a propagator of metabolic dysfunction, providing insight into mechanisms of aging-related vulnerability. Full article

Background: Delayed bowel ischemia is a major cause of failure of nonoperative management in patients with blunt mesenteric injury. Although decreased bowel wall enhancement on computed tomography (CT) is a definitive sign of bowel ischemia, it is uncommon and may be absent on early imaging. This study aimed to identify specific CT findings that predict bowel ischemia to distinguish patients requiring surgery from those suitable for conservative management. Methods: We retrospectively reviewed 174 patients with blunt mesenteric injury treated at a Level 1 trauma center between January 2013 and December 2024. Initial CT findings were classified as mesenteric contrast extravasation freely extending into the peritoneal cavity (extravasation type 1), contrast extravasation tracking along the bowel contour (extravasation type 2), pseudoaneurysm, mesenteric haziness, mesenteric hematoma, interloop fluid, dependent portion fluid, and decreased bowel wall enhancement. Predictors of bowel ischemia were evaluated using univariate analysis and ridge-penalized multivariable logistic regression. Results: Bowel ischemia occurred in 30 patients (17.2%). Decreased bowel wall enhancement was rare (4.6%) but demonstrated perfect specificity and positive predictive value (both 100%), with low sensitivity (26.7%). Extravasation type 2 showed high specificity (97.2%) and remained an independent predictor of bowel ischemia. Dependent portion fluid showed relatively high sensitivity, whereas mesenteric haziness and mesenteric hematoma were inversely associated with ischemia. Conclusions: Contrast extravasation tracking along the bowel contour and decreased bowel wall enhancement on early CT are strong predictors of bowel ischemia in patients with blunt mesenteric injury. These findings should prompt consideration of early surgical exploration, even in patients who initially appear hemodynamically stable. Full article

Electronic cigarettes (e-cigarettes) have emerged as a prevalent substitute for conventional cigarettes, garnering perceptions of being a safer option for health. Nicotine addicts use e-cigarettes to cease smoking. These products have also become common among young people because of their taste, smell, and attractive appearance. However, accumulating experimental and clinical evidence indicates that e-cigarette use is not risk-free. The inhalation of e-cigarette aerosols exposes users and their non-using peers to a complex mixture of chemical compounds, including aldehydes, heavy metals, and flavoring agents, many of which possess pro-oxidative and pro-inflammatory properties. This review summarizes and critically analyzes current evidence on the molecular and cellular mechanisms underlying the biological effects of e-cigarette aerosols. Particular attention is given to excessive production of reactive oxygen species, mitochondrial dysfunction, DNA damage, and the activation of redox-sensitive signaling pathways, including NF-κB and NRF2. These molecular alterations may trigger acute and, with prolonged exposure, chronic oxidative stress and inflammation, which in turn can affect gene expression, protein function, and metabolic pathways. While molecular and experimental studies often demonstrate adverse biological responses to e-cigarette aerosols, the translation of these findings into long-term clinical outcomes remains an area of ongoing investigation. Full article

Acoustic logging tools, deployed thousands of meters underground to detect geological structures and evaluate reservoir fluids, are essential for oil and gas exploration and development. These tools generate acoustic signals through piezoelectric ceramic transducers. The material properties of piezoelectric ceramics are significantly affected by the high-temperature downhole environment, leading to a failure in impedance matching between the transducer and its excitation circuit. This results in a substantial degradation of the tool’s performance. This paper experimentally obtains the electrical parameters and excitation energy of commonly used monopole transducers at different temperatures. Based on this data, the optimal matching inductance values at various temperatures are calculated. A temperature-adaptive transducer excitation circuit is then designed and implemented. This circuit can adjust the excitation frequency according to the measured temperature to compensate for resonant frequency drift and select the optimal inductor tap via a programmable multiplexer. Experimental results demonstrate that this circuit significantly enhances the transducer’s excitation energy at high temperatures. This technology is expected to markedly improve the operational stability of acoustic logging tools and facilitate the exploration and development of deep and ultra-deep oil and gas resources. Full article

The all-d-enantiomeric-peptide RD2 was developed for the treatment of Alzheimer’s disease. This study aimed to develop a specific and highly sensitive liquid chromatography-mass-spectrometric (UHPLC-ESI-QTOF) method for quantifying RD2 in the mouse brain and to validate it according to the ICH M10 guideline to investigate the pharmacokinetic profile of RD2 in its target organ. Sample preparation, chromatographic separation and quantification were very challenging due to RD2’s highly hydrophilic properties, the complex matrix and the required lower limit of quantification (LLOQ). Chromatographic separation was performed on an Acquity UPLC BEH C18 column (2.1 × 100 mm, 1.7 μm particle size) within 5 min at 50 °C with a flow rate of 0.5 mL·min⁻¹. Mobile phases consisted of water and acetonitrile with 0.2% formic acid and 0.015% heptafluorobutyric acid. Ions were generated by electrospray ionization in the positive mode, and RD2 was quantified by QTOF-MS. The developed extraction method revealed complete recovery. The linearity of the calibration curve was in the range of 2 ng·mL⁻¹ to 500 ng·mL⁻¹ (R² > 0.99) with a LLOQ of 5 ng·mL⁻¹. The intraday and interday accuracy and precision ranged from 0.4% to 12.2% and from 1.0% to 12.0%. RD2 remained stable in the freshly homogenized brain even after several freeze–thaw cycles, but stability decreased over time during long-term storage at −80 °C. Using this validated method, RD2-spiked brain homogenate samples and samples of a pharmacokinetic study with RD2 in mice were analyzed. Full article

Advanced melanoma remains difficult to treat due to its intrinsic resistance to conventional therapies and the frequent development of acquired resistance to targeted agents, such as BRAF inhibitors. Onconase (ONC), an amphibian ribonuclease with established antitumor activity, had been previously shown to have selective cytotoxicity toward melanoma cells. In this study, we investigated the molecular mechanisms underlying ONC-induced cytotoxicity in BRAF-mutated melanoma cell lines that are either sensitive or resistant to the BRAF inhibitor dabrafenib. We focused on oxidative stress regulation, mitochondrial dynamics, and cell death-related signaling pathways. ONC treatment resulted in a marked increase in reactive oxygen species (ROS) levels, concomitant with a pronounced downregulation of NRF2 and multiple NRF2-dependent antioxidant proteins. These effects were particularly evident in dabrafenib-resistant melanoma cells. In parallel, ONC impaired mitochondrial plasticity by inhibiting mitochondrial biogenesis and fission, as evidenced by reduced PGC1α, DRP1, and FIS1 expression. Confocal analysis confirmed the presence of more enlarged mitochondria in ONC-treated cells. Mitophagy and autophagy are hindered by ONC due to the downregulation of PINK1, beclin1, ATG3 expression, as well as the lack of LC3B activation. These mitochondrial defects were associated with mitochondrial-dependent apoptosis, characterized by caspase-9 activation and strong downregulation of the antiapoptotic protein survivin. Lipid peroxidation was also induced by ONC, especially in the A375 cell line. Additionally, ONC inhibited key proliferation-related signaling pathways, including STAT3 and NF-κB, and reduced cyclin-dependent kinase 1, 2, and 4 activities. Collectively, these findings demonstrate that ONC disrupts redox homeostasis, mitochondrial function, and survival signaling in melanoma cells, exerting particularly potent effects in BRAF inhibitor-resistant populations. This study provides mechanistic insight into the anti-melanoma activity of ONC and supports its potential therapeutic application in drug-resistant melanoma. Full article

Flexible wearable pressure sensors still face the challenges of complex structure and high manufacturing costs. In this article, we present a simple method for preparing a highly sensitive, flexible wearable pressure sensor based on candle soot and porous PDMS foam. Meanwhile, to enhance the sensor’s robustness and practicality, a fully enclosed packaging design based on PDMS film was developed. The resulting sensor demonstrates excellent sensitivity, attributed to its porous structure, rough surface, and the unique properties of candle soot. Furthermore, the developed sensor can accurately detect movements in various parts of the human body and measure the force applied during finger pressing. This innovative porous PDMS/candle soot pressure sensor shows great potential for applications in wearable electronics. Full article

Precision nutrition has emerged as a promising strategy for the prevention of type 2 diabetes mellitus (T2DM) by targeting molecular pathways underlying insulin resistance and impaired glucose metabolism. Accumulating evidence indicates that dietary patterns, caloric intake, and specific nutrients can modulate gene expression and epigenetic mechanisms involved in insulin signaling, inflammation, and energy homeostasis. This narrative review synthesizes recent human and experimental studies (2025–2026) examining how dietary components influence transcriptional and epigenetic regulation of insulin signaling and glucose metabolism in the context of T2DM prevention. A total of 29 peer-reviewed studies were included, encompassing dietary patterns, macronutrient manipulation, micronutrient and bioactive supplementation, and gene–diet interactions. Very-low-calorie diets consistently induced coordinated modulation of key metabolic genes, including downregulation of glucose transporter type 4 (GLUT4) and upregulation of PDK4, CPT1, and AMPK, reflecting a metabolic shift toward enhanced fatty acid oxidation and improved insulin sensitivity. In contrast, high-fat and fructose-rich diets promoted proinflammatory gene expression and immune activation, contributing to insulin resistance. Plant-based and vegan dietary patterns were associated with reduced epigenetic aging and improved insulin sensitivity through DNA methylation changes. Targeted interventions, including vitamin D combined with probiotics, dietary fiber, nucleotides, and trace elements such as copper, further demonstrated favorable transcriptional and epigenetic effects linked to improved glycemic control. Collectively, these findings highlight diet-driven modulation of insulin signaling and glucose metabolism at the molecular level and support nutrigenomics-guided precision nutrition as a viable preventive approach for T2DM. Integrating genetic and epigenetic insights into dietary strategies may enable more personalized and effective interventions to curb the growing global burden of type 2 diabetes. Full article

This paper evaluates a data-driven classification approach of operational wind turbine blades based on consecutive tower-radar measurements that are each compressed in a two-dimensional slow-time to range representation (radargram). Like many real-world machine learning systems, installed tower-radar systems face some key challenges: (i) transferability to new operational contexts, (ii) impediments due to evolving environmental and operational conditions (EOCs), and (iii) limited explainability of their deep neural decisions. These challenges are addressed here with a set of structured machine learning studies. The unique field data comes from a sensor box equipped with a frequency-modulated continuous wave (FMCW) radar (33.4–36 GHz frequency range). Relevant parts of the radargram that contribute to a decision of the used convolutional neural networks were identified by a class-sensitive visualization technique named GuidedGradCAM (Guided Gradient-weighted Class Activation Mapping). The following main contributions are provided to the field of tower-radar monitoring (TRM) in the context of wind energy applications: (i) every individual rotor blade holds a number of characteristic structural features revealed by the radar sensor, which can be used to discriminate rotor blades from the same turbine via neural networks; (ii) those unique features are not agnostic to changing EOCs; and (iii) pixel-level distortions reveal the necessity of low-level information for a precise rotor blade classification. Full article

Background/Objectives: Electroencephalography (EEG) is a promising modality for fatigue detection because it directly reflects neural states; however, it is hindered by the need for subject-specific calibration and its reliance on unstable labeling. Moreover, classical EEG features are sensitive to intrinsic brain rhythm variations, causing pronounced domain shifts that degrade performance across sessions and subjects. Methods: Motivated by the biological fatigue rebound mechanism, we propose a robust cross-subject metric which we name Short-Term Second-Order Differential Entropy (ST-SODE). ST-SODE effectively suppresses the interference of background brain rhythms, enhancing robustness to cross-domain drift; consequently, its one-dimensional output can provide an indication of fatigue states without additional model training. Results: ST-SODE is validated on the public driving fatigue regression dataset SEED-VIG and on a private Vigilance classification dataset based on the N-Back task. ST-SODE achieves a correlation coefficient of 0.56 on SEED-VIG dataset (vs. 0.4 for differential entropy, DE) and a binary classification accuracy of 93.75% on the Vigilance dataset, outperforming other EEG-based fatigue metrics. Conclusions: ST-SODE offers a reliable solution for deployment in fields such as driving, manufacturing, and healthcare, where it could reduce safety incidents caused by fatigue. Full article