Search Results (4,200)

Background: Uterus transplantation (UTx) is a proven treatment for individuals affected by absolute uterine factor infertility (AUFI) who desire biological motherhood. Despite the fact that over 130 procedures have been performed worldwide in the past decade, UTx remains relatively unfamiliar, even among healthcare professionals. This study aimed to identify knowledge gaps regarding and evaluate attitudes toward UTx among Hungarian obstetricians/gynecologists and transplantation providers, in anticipation of the first procedure to be performed in the country. Methods: A Microsoft Forms^® questionnaire was distributed electronically among Hungarian medical professionals via e-mail, including members of the Hungarian Society of Obstetrics and Gynaecology and the Hungarian Transplantation Society. Additionally, participants of the “Update 2024” OB/GYN conference (held 28–29 November 2024, in Visegrád, Hungary) were invited to complete the survey through a QR code displayed during the event. Results: A total of 290 medical professionals completed the survey (response rate: 27.6%, 290/1050). Most of the respondents specialized in obstetrics and gynecology (81.7%, n = 237), with the remainder representing transplantation fields (18.3%, n = 53). Over half (56.6%, n = 161) reported they would recommend UTx to patients with AUFI, and 64.1% (n = 186) agreed that UTx should be available as a treatment option. The medical risks associated with the procedure were deemed acceptable for both living donors (58.0%, n = 168) and recipients (54.8%, n = 159). Conclusions: This is the first study to explore perceptions of UTx among Hungarian medical professionals. The findings suggest there is a generally favorable professional attitude toward its future clinical implementation. Full article

Introduction: Long-term heart failure and mortality after catheter ablation for premature ventricular contraction (PVC) remain underexplored. Methods: We retrospectively analyzed 4195 adults who underwent PVC ablation in a nationwide claims database. To address class imbalance, we used synthetic minority over-sampling technique (SMOTE) and random over-sampling examples (ROSE). Five supervised algorithms were compared: logistic regression, decision tree, random forest, XGBoost, and LightGBM. Discrimination was assessed by stratified five-fold cross-validation using the area under the receiver operating characteristic curve (ROC AUC). Because rare events can bias ROC, we also examined precision–recall (PR) curves. Results: For predicting three-year heart failure, LightGBM with ROSE achieved the highest ROC AUC at 0.822. For three-year mortality, logistic regression with ROSE and LightGBM with ROSE showed balanced performance with ROC AUCs of 0.886 and 0.882. Pairwise DeLong tests indicated that these leading models formed a high-performing cluster without significant differences in ROC AUC. Age, prior heart failure, malignancy, and end-stage renal disease were the most influential predictors by model explainability analysis. Discussion: Addressing class imbalance and benchmarking modern learners against a transparent logistic baseline yielded robust, clinically interpretable risk stratification after PVC ablation. These models are suitable for integration into electronic health record dashboards, with external validation and local threshold optimization as next steps. Full article

Introduction: Patellofemoral osteoarthritis (PFOA) remains a therapeutic challenge with few effective non-surgical options. Objective: The aim of this study was to evaluate the feasibility, safety, and preliminary outcomes of ultrasound (US)-guided tibial endplate aspiration and intra-articular injection of bone marrow concentrate (BMC) in patients with isolated PFOA. Methods: In this retrospective case series, seven consecutive patients with symptomatic PFOA unresponsive to conservative therapy were treated with US-guided tibial endplate aspiration followed by intra-articular BMC injection. Clinical outcomes were assessed with the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) at baseline and 12 months. MRI with T2 mapping was performed to evaluate cartilage structure. BMC composition was analyzed, including colony-forming unit fibroblast (CFU-F) assays. Results: The procedures were feasible in all cases, and no adverse events occurred. WOMAC scores improved significantly from 21.7 ± 17.3 at baseline to 9.0 ± 9.3 at 12 months (p = 0.030). MRI showed a mean relative increase of 25.4% ± 43.5% in healthy cartilage volume, though this was not statistically significant (p = 0.49). Correlation analyses revealed no consistent association between clinical response and cellular composition, including estimated MSC dose. Conclusions: This small retrospective series suggests that US-guided tibial endplate aspiration and intra-articular BMC injection are safe, technically feasible, and may provide clinical benefit in isolated PFOA. Larger controlled studies are needed to confirm these preliminary findings. Full article

The sulfo-Embden–Meyerhof–Parnas (sulfo-EMP) pathway enables Escherichia coli to utilize sulfoquinovose, (SQ) a sulfonated sugar derived from plant sulfolipids, as a carbon source. This pathway is encoded by the yih gene cassette. However, structural and functional diversity of this cassette across E. coli strains has not been fully characterized. We identified two structural variants of the yih cassette across E. coli and Shigella strains: a long form (ompL-yihOPQRSTUVW) and a truncated short form (yihTUVW). Both forms occupy the same genomic location but differ in orientation and are scattered across the phylogenetic tree, suggesting frequent recombination events. Transcriptome analyses revealed that only the long cassette, as found in E. coli K-12 MG1655, is transcriptionally induced during growth on sulfoquinovose. The short cassette, found in E. coli Nissle 1917 and other host-adapted strains, showed no differential expression. Despite this, both strains grew comparably on sulfoquinovose, indicating different metabolic adaptation strategies. Gene expression profiling revealed shared stress responses but distinct central metabolic patterns. Electrophoretic mobility shift assays further demonstrated that the transcription factor YihW from Nissle 1917 binds its DNA targets with lower affinity than the homolog from K-12 and shows weaker sulfoquinovose-dependent dissociation. Full article

The recent years have seen a significantly increased representation of African artists at major recurring shows of contemporary art. This paper looks at works featured in the past few editions of La Biennale di Venezia, Kassel’s documenta, Berlin Biennale, and the European Nomadic Biennial Manifesta—events that once stemmed from civilisational transformations and now function as influential art institutions. The way these are organised leaves room for art which deals with pressing, difficult matters; especially our relationship with the Global South is becoming a major concern. Africa’s output is unlike all traditional forms of Western culture, and its most interesting instances are participatory, socially contextualised, and utilitarian; colonial crimes and trauma count among vital subjects. At the same time, various uncompromising approaches challenge our notions about how to conceive of an exhibition and how an art institution should operate: works of art are no longer fetishised simply as appealing manifestations of an unfamiliar aesthetic. Consequently, the art world has no other choice but to adjust the programming of its initiatives, shows, and organisations so that space is made for endeavours firmly rooted in the present day, actually facing its challenges. Full article

The widespread integration of renewable energy sources (RESs) into the grid through inertia-less power converters is reducing the overall system inertia leading to large frequency variations. To mitigate this issue, grid-forming (GFM) control strategies in bidirectional battery chargers have emerged as a promising solution, since the inertial response of synchronous generators (SGs) can be emulated by power converters. However, unlike SGs, which can withstand currents above their rated values, the output current of a power converter is limited to its nominal design value. Therefore, the estimation of the power delivered by the GFM power converter during frequency events, called Virtual Inertia (VI) support, is essential to prevent exceeding the rated current. This article analyzes the VI response of GFM power converters, classifying the dynamic behavior as underdamped, critically damped, or overdamped according to the selected inertia constant and damping coefficient, parameters of the GFM control strategy. Subsequently, the transient power response under step-shaped and ramp-shaped frequency deviations is quantified. The proposed analysis is validated using a 1.2 KW single-phase power converter. The simulation and experimental results confirm that the overdamped response under a ramp-shaped frequency event shows higher fidelity to the theorical model. Full article

Deep learning has been broadly applied in many fields and has greatly improved efficiency compared to traditional approaches. However, it cannot resolve issues well when there are a lack of training samples, or in some varying cases, it cannot give a clear output. Human beings and machines that work in a collaborative and equal mode to address complicated streaming data-driven tasks can achieve higher accuracy and clearer explanations. A novel framework is proposed which integrates human intelligence and machine intelligent computing, taking advantage of both strengths to work out complex tasks. Human beings are responsible for the highly decisive aspects of the task and provide empirical feedback to the model, whereas the machines undertake the repetitive computing aspects of the task. The framework will be executed in a flexible way through interactive human–machine cooperation mode, while it will be more robust for some hard samples recognition. We tested the framework using video anomaly detection, person re-identification, and sound event detection application scenarios, and we found that the human–machine collaborative learning mechanism obtained much better accuracy. After fusing human knowledge with deep learning processing, the final decision making is confirmed. In addition, we conducted abundant experiments to verify the effectiveness of the framework and obtained the competitive performance at the cost of a small amount of human intervention. The approach is a new form of machine learning, especially in dynamic and untrustworthy conditions. Full article

This article develops a method for detecting malware based on the multi-scale recurrent architecture (time-aware multi-scale LSTM) with salience gating, multi-headed attention, and a sequential statistical change detector (CUSUM) integration. The research aim is to create an algorithm capable of effectively detecting malicious activities in behavioural data streams of executable files with minimal delay and ensuring interpretability of the results for subsequent use in forensic audit and cyber defence systems. To implement the task, deep learning methods (training LSTM models with dynamic consideration of time intervals and adaptive attention mechanisms) and sequence statistical analysis (CUSUM, Kulback–Leibler divergence, and Wasserstein distances), as well as regularisation approaches to improve the model stability and explainability, were used. Experimental evaluation demonstrates the proposed approaches’ high efficiency, with the neural network model achieving competitive indicators of accuracy, recall, and classification balance with a low level of false positives and an acceptable detection delay. Attention and salience profile analysis confirmed the possibility of interpreting signals and early detection of abnormal events, which reduces the experts’ workload and reduces the number of false positives. This study introduces the new hybrid architecture development that combines the advantages of recurrent and statistical methods, the theoretical properties formalisation of gated cells for long-term memory, and the proposal of a practical approach to the model solutions’ explainability. The developed method implementation, implemented in the specialised software product form, is shown in a forensic audit. Full article

The frequency content of strong ground motion significantly affects the response of engineered systems under seismic excitation. Among some scalar parameters which exist in the literature, the mean period T_m has proved to be the most efficient. Ground Motion Predictive Equations (GMPEs) are usually developed for ground motion parameters through the calibration of coefficients of predefined functional forms, via linear or nonlinear regression, and based on recorded ground motion data. Such expressions of T_m are rare in the literature. Recently, the use of machine learning (ML) algorithms in earthquake engineering and engineering seismology has increased. The Artificial Neural Network (ANN) is an effective ML algorithm which has already been explored for the development of GMPEs for amplitude-based ground motion parameters. Within the work presented herein, multiple nonlinear regression (NLR)- and ANN-based GMPEs are developed for T_m using the latest strong motion database for shallow earthquakes in Greece. To the author’s knowledge, the implementation of ANN for producing GMPEs for T_m for shallow earthquake events has not been explored. Direct comparison between the NLR- and ANN-based GMPEs is performed, in terms of performance indexes, aleatory uncertainty, and working examples, as well as testing against earthquake events not included in the original dataset. The results reveal that the ANN-based GMPEs are useful in reducing aleatory uncertainty, although care should be taken in their implementation to avoid overfitting issues. Full article

This study introduces an approximate solution for the M/G/1 queueing model in scenarios where the service time distribution follows a pure mixture distribution. The derivation of the proposed approximation leverages the analytical tractability of the variance for certain mixture distributions. By incorporating this variance into the Pollaczek–Khinchine equation, an approximate closed-form expression for the M/G/1 queue is obtained. The formulation is extended to service-time distributions composed of two or more components, specifically Gamma, Gaussian, and Beta mixtures. To assess the accuracy of the proposed approach, a discrete-event simulation of an M/G/1 system was conducted using random variates generated from these mixture distributions. The comparative analysis reveals that the approximation yields results in close agreement with simulation outputs, with particularly high accuracy observed for Gaussian mixture cases. Full article

Background/Objectives: Fabry disease (FD) is a rare X-linked disease caused by the deficient activity of the enzyme α-galactosidase A. Cardiac involvement is particularly critical, often determining the disease prognosis. Epidemiological data on FD in Portugal are limited and inconsistent, highlighting the need for targeted screening. The F-CHECK study aimed to determine the prevalence of FD through the systematic screening of a Portuguese cohort of patients with unexplained cardiomyopathies. Methods: This multicenter observational study (NCT05409846) assessed the prevalence and clinical characteristics of FD in a Portuguese cohort (n = 409) of patients from 10 central hospitals who presented with unexplained cardiomyopathies, including idiopathic hypertrophic cardiomyopathy (HCM), left ventricular hypertrophy, dilated-phase HCM, and dilated cardiomyopathy with late gadolinium enhancement in the inferolateral segment. Screening was performed using dried blood spot assays to measure α-galactosidase A activity and/or by GLA gene sequencing in whole-blood samples. Results: FD was diagnosed in 14 patients, corresponding to a prevalence of 3.4%. FD diagnosis was significantly associated with systemic manifestations such as acroparesthesias (p = 0.027) and angiokeratomas (p = 0.003), as well as an increased risk of prior arrhythmic events (p = 0.021) and cerebrovascular disease (p = 0.016). Most FD patients (57%) presented a non-founder mutation in the GLA gene; however, they were pathogenically relevant. Conclusions: The observed 3.4% prevalence highlights the importance of systematic FD screening among Portuguese patients with unexplained cardiomyopathy, extending beyond classic hypertrophic presentations to dilated forms. Specific clinical signs, electrocardiogram findings, and cardiac imaging features can serve as valuable indicators to guide targeted genetic testing for FD. Full article

The bloom-forming dinoflagellates and euglenophyceae were observed in the coastal waters of Hammam-Lif (Southern Mediterranean), during a green tide event on 3 June 2023. The bloom was dominated by Lepidodinium chlorophorum, identified through ribotyping with densities reaching 2.3 × 10⁷ cells·L⁻¹. Euglena spp. and Eutrepsiella spp. contributed to the discoloration, with abundances up to 2.9 × 10⁷ cells·L⁻¹. Environmental data revealed significant depletion of nitrite and nitrate, coinciding with a rapid increase in sunlight duration, likely promoting the proliferation of L. chlorophorum and euglenophyceae. By 5 June, two days after the bloom, nutrient stocks were exhausted. Diatoms appeared limited by low silicate concentrations (<0.05 µmol·L⁻¹), while dissolved inorganic phosphate and Nitrogen-ammonia were elevated during the bloom (0.88 and 4.8 µmol·L⁻¹, respectively), then decreased significantly afterward (0.23 and 1.06 µmol·L⁻¹, respectively). Low salinity (34.0) indicated substantial freshwater input from the Meliane River, likely contributing to nutrient enrichment and bloom initiation. After the event, phytoplankton abundance and chlorophyll levels declined, with a shift from dinoflagellates to diatoms. The accumulation of pigments (chlorophyll b and carotenoids) and the presence of Mycosporine-like amino acids (MAAs) during and after the bloom suggest that UV radiation and Nitrogen-ammonia were key drivers of this green tide. Full article

To improve the physical consistency and interpretability of traditional drought indices, this study proposes a drought assessment model that couples physically based constraints with data-driven approaches, leading to the development of a Multivariate Drought Index (MDI). The model employs convolutional neural networks to achieve physically consistent downscaling, thereby obtaining a high-resolution Normalized Difference Water Index (NDWI), Temperature Vegetation Dryness Index (TVDI), Vegetation Condition Index (VCI), and Temperature Condition Index (TCI). Objective weights are determined using the Criteria Importance Through Intercriteria Correlation method, while random forest and Shapley Additive Explanations are integrated for nonlinear interpretation and physics-guided calibration, forming an ensemble framework that incorporates multi-source and multi-scale factors. Validation with multi-source data from 2000 to 2024 in the major maize-growing areas of Heilongjiang Province demonstrates that MDI outperforms single indices and the Vegetation Health Index (VHI), achieving a correlation coefficient (r = 0.87), coefficient of determination (R² = 0.87), RMSE (0.08), and classification accuracy (87.4%). During representative drought events, MDI identifies signals 16–20 days earlier than the Standardized Precipitation Evapotranspiration Index (SPEI) and the Soil Moisture Condition Index (SMCI), and effectively captures localized drought patches at a 250 m scale. Feature importance analysis indicates that the NDWI and TVDI are consistently identified as dominant factors across all three methods, aligning physically interpretable analysis with statistical contribution. Long-term risk zoning reveals that the central–western region of the study area constitutes a high-risk zone, accounting for 42.6% of the total. This study overcomes the limitations of single indices by integrating physical consistency with the advantages of data-driven methods, achieving refined spatiotemporal characterization and enhanced overall performance, while also demonstrating potential for application across different crops and regions. Full article

Climate change in Mediterranean regions is projected to cause declines in rainfall and higher temperatures and evaporation, which will enhance the formation of barriers at the mouth of low-inflow estuaries and potentially also in the riverine reaches. This review uses data from estuaries in south-western Australia across a rainfall gradient to describe how these barriers form and the effects they have on environmental conditions and biotic communities. The formation of barriers disconnects the estuary from adjacent freshwater and marine environments, prohibiting the movements of fauna and lowering taxonomic and functional diversity. Moreover, the longer periods of bar closure can result in increased frequency and magnitude of hypersalinity, hypoxia and nutrient enrichment. These conditions, in turn, act as stressors, often synergistically, on the floral and faunal communities. In some cases, mass mortality events occur, and some estuaries dry completely. To ensure the functioning of such systems in the future, regular monitoring across a wide range of estuaries is needed to understand how climate change is impacting different types of estuaries. A range of management options are discussed that may help mitigate the effects of increased barrier formation but should be employed as part of a whole-of-catchment approach and regularly evaluated. Full article

The integration of distributed energy resources into distribution networks creates operational challenges, including voltage instability and power quality issues. While battery energy storage systems (BESSs) can address these challenges, research has focused primarily on transmission-level applications or single services. This paper bridges this gap through a comprehensive review of BESS technologies and control strategies for multi-service ancillary support in distribution networks. Real-world case studies demonstrate BESS effectiveness: Hydro-Québec’s $1.2$ MW system maintained voltage within 5% and responded to frequency events in under 10 ms; Germany’s hybrid 5 MW M5BAT project optimized multiple battery chemistries for different services; and South Africa’s Eskom deployment improved renewable hosting capacity by 15–70% using modular BESS units. The analysis reveals grid-forming inverters and hierarchical control architectures as critical enablers, with model predictive control optimizing performance and droop control ensuring robustness. However, challenges like battery degradation, regulatory barriers, and high costs persist. This paper identifies future research directions in degradation-aware dispatch, cyber-resilient control, and market-based valuation of BESS flexibility services. By combining theoretical analysis with empirical results from international deployments, this study provides utilities and policymakers with actionable insights for implementing BESS in modern distribution grids. Full article