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Revealing cryptic lineages: genetic structure and conservation units in the Eurasian Stone-curlew (Burhinus oedicnemus)
No full textBirds with parapatric distribution often develop distinct subspecies over time, which exhibit morphological, behavioral,
or genetic diferences in response to local environmental pressures. The Eurasian Stone-curlew (Burhinus oedicnemus) is
a migratory steppe bird species of conservation concern in Europe with fve subspecies traditionally recognized by morphological and ecological features. Previous genetic studies have only partially clarifed their diferentiation. To further investigate the genetic structure of this species and assess the molecular support for subspecies divisions, we genotyped 274 individuals sampled across the full distribution range of all fve traditionally recognized subspecies using 22 polymorphic microsatellite (STR) loci. The inclusion of samples from previously unsampled areas (Morocco, Portugal, Jordan, France, and Kazakhstan) revealed new patterns of genetic variation and ancestry. Clustering methods applied on STR data indicated that the saharae subspecies, previously considered a single taxon across the southern Mediterranean basin, including North Africa, is genetically divisible into two distinct units. The eastern subspecies harterti, never investigated before, showed a clear distinct genetic identity. Overall, our results indicate the existence of six genetically diferentiated units, some of which are not aligned with current subspecies delineation. These fndings highlight complex patterns of ancestry and gene fow within the species. Given the expected impact of climate change and ongoing biodiversity loss on (pseudo-)steppe habitats, the study provides critical information for refning conservation strategies for the Stone-curlew inhabiting these threatened ecosystems
The process of addressing fundamental needs by nursing students during internship: A qualitative study
No full textAim: This study explores how nursing students address patients' fundamental needs during their internships, identifying key processes and challenges. Background: The Fundamentals of Care framework emphasizes a holistic, person-centered approach to nursing by addressing patients' physical, psychosocial and relational needs. However, its integration into nursing education remains limited, with a stronger focus on technical competencies rather than fundamental care. Design: Qualitative descriptive study. Methods: The study was conducted within the FoC-Form project in northern Italy. Semi-structured interviews were carried out with first- and second-year nursing students following their internships. Thematic analysis was applied to identify emerging themes and patterns. Results: Four themes emerged: personal resources, care dynamics, contextual features and characteristics of the internship program. Students highlighted the importance of time management, relational aspects and mentoring in delivering fundamental care. Differences between first- and second-year students wereobserved in their clinical reasoning and approach to patient-centered care. Organizational factors such as workload and staffing also influenced their ability to meet fundamental needs. Conclusion: This study highlighted the critical role of mentorship and organizational support in fostering the integration of the Fundamentals of Care framework in nursing practice. Findings suggest the need for curriculum enhancements that balance technical training with the relational and holistic aspects of patient care. Nursing leadership should prioritize creating supportive environments that facilitate fundamental care practices
Black Hole Spectroscopy and Tests of General Relativity with GW250114
No full textThe binary black hole signal GW250114, the loudest gravitational wave detected to date, offers a unique opportunity to test Einstein’s general relativity (GR) in the high-velocity, strong-gravity regime and probe whether the remnant conforms to the Kerr metric. Upon perturbation, black holes emit a spectrum of damped sinusoids with specific, complex frequencies. Our analysis of the postmerger signal shows that at least two quasinormal modes are required to explain the data, with the most damped remaining statistically significant for about one cycle. We probe the remnant’s Kerr nature by constraining the spectroscopic pattern of the dominant quadrupolar (l = m =2) mode and its first overtone to match the Kerr prediction to tens of percent at multiple postpeak times. The measured mode amplitudes and phases agree with a numerical-relativity simulation having parameters close to GW250114. By fitting a parametrized waveform that incorporates the full inspiral-merger-ringdown sequence, we constrain the fundamental (l = m =4) mode to tens of percent and bound the quadrupolar frequency to within a few percent of the GR prediction. We perform a suite of tests-spanning inspiral, merger, and ringdown-finding constraints that are comparable to, and in some cases 2-3 times more stringent than those obtained by combining dozens of events in the fourth Gravitational-Wave Transient Catalog. These results constitute the most stringent single-event verification of GR and the Kerr nature of black holes to date, and outline the power of black-hole spectroscopy for future gravitational-wave observations
Enhancing upper limb motor recovery prediction after acute stroke using EEG and subacute data
No full text: Electroencephalography (EEG) has shown promise in assessing and monitoring functional recovery in stroke survivors, but its utility in predicting upper limb motor recovery in a data-driven framework remains underexplored. This study presents a novel EEG-based machine-learning model, StrokeRecovNet, developed to predict motor recovery outcomes based on the upper extremity subscale of the Fugl-Meyer Assessment (FMAUE). StrokeRecovNet is a feed-forward neural network optimized for regression tasks, leveraging 221 candidate EEG biomarkers, spanning spectral and functional connectivity domains, along with baseline clinical information. These inputs are used to predict follow-up FMAUE scores in stroke survivors who underwent standard rehabilitative protocols. We validated our pipeline on two independent datasets of patients in the acute and subacute post-stroke phases. StrokeRecovNet consistently outperformed the proportional recovery rule (PRR), a standard benchmark based on initial impairment, in predicting FMAUE scores in the subacute stage (median absolute error, MAE: StrokeRecovNet = 5.85, PRR = 19.00). Incorporating support data from the subacute dataset led to improved predictive performance in the acute sample (MAE: StrokeRecovNet = 5.87, PRR = 8.80), whereas the model trained solely on the acute data did not (MAE: 13.74). Key features contributing to the model's success included brain symmetry indices and functional connectivity measures, evolving across recovery stages. These findings demonstrate the potential of EEG-based biomarkers to predict individual recovery trajectories. This work introduces a novel, data-driven approach to forecasting upper limb recovery using EEG and suggests that EEG data from the subacute stage, which is more readily available in clinical settings, can enhance early predictions, paving the way for personalized post-stroke rehabilitation strategies
Magmatic to Subsolidus Evolution of the Variscan Kastoria Pluton (NW Greece): Constraints from Mineral Chemistry and Textures
No full textThis study focuses on the mineralogy and mineral chemistry of the accessory minerals occurring in the Kastoria pluton situated in NW Greece, which intrudes the Pelagonian nappe having crystallized during the Late Paleozoic (~300 Ma). The pluton consists of porphyritic granite (GR) that hosts mafic microgranular enclaves (MME) of monzonitic composition. Both lithologies contain quartz, microcline, plagioclase, biotite, secondary white mica, hornblende, and actinolite along with accessory minerals including titanite, epidote, allanite, apatite, zircon, and magnetite. Compared to the granite, the enclaves are richer in biotite, amphibole, and plagioclase but poorer in quartz and microcline. Mineral chemistry indicates a calc–alkaline affinity, consistent with the observed magmatic trends. Crystallization pressure, estimated at 3 kbar from Al in a hornblende barometer, suggests emplacement at mid-crustal levels. During the Alpine deformation, the pluton underwent low-grade greenschist to amphibolite-facies metamorphism, which partially overprinted the primary mineral assemblages. Magmatic titanite and allanite crystals are well preserved, showing only recrystallization features. Metamorphism produced tiny titanite needles and epidote replacing primary minerals (plagioclase, amphibole, and biotite). Later, hydrothermal alteration produced another generation of secondary epidote. Only a couple of epidote crystals preserve potential magmatic relict characteristics (euhedral habit, zircon inclusions, positive Eu anomaly, and sharp contact with primary minerals). These results provide insights into both the primary magmatic features and the subsequent metamorphic modification of the I-type Kastoria pluton within the Pelagonian domain
Mapping flashback velocity in hydrogen-fueled perforated burners over a broad geometric design space
No full text"Arthur in medieval Italy", in "The Cambridge History of Arthurian Literature and Culture", a cura di R. Radulescu e A. Lynch
No full textFederated SHAP: Privacy-Preserving and Consistent Post-hoc Explainability in Federated Learning
No full textThe widespread adoption of Artificial Intelligence in everyday activities highlights a growing and urgent need for trustworthiness. Designing trustworthy AI systems requires addressing key technical challenges, including ensuring data privacy and model explainability. Federated Learning (FL) is a widely adopted paradigm to preserve data privacy in collaborative learning scenarios, while post-hoc methods are commonly applied to enhance the explainability of opaque AI-based models. In this paper, we propose a novel approach, called Federated SHAP, to simultaneously address privacy and explainability. Specifically, we leverage the SHapley Additive exPlanations (SHAP) method to provide post-hoc explanations of Neural Networks trained through FL. SHAP relies on a representative background dataset; however, constructing such a dataset in the FL setting is particularly challenging since raw data distributed across multiple clients cannot be shared directly due to strict privacy requirements. To address this challenge, we propose two tailored strategies depending on the data type: for tabular data, we adopt a Federated Fuzzy C-Means clustering algorithm to collaboratively summarize the distributed datasets into a suitable background dataset; for image data, we introduce a Federated Generative Adversarial Network (GAN) to synthesize representative background instances. A comprehensive experimental evaluation demonstrates the effectiveness and robustness of our proposed approaches, comparing them against several baseline and alternative strategies in terms of both representativeness and quality of generated explanations. Compared to baselines employing randomly generated representative background datasets, our approach reduces the discrepancy of SHAP explanations by up to three times on tabular data and two times on image data (depending on the test case involved), when measured against the centralized SHAP values computed using the full training set as background dataset
ECLYPSE: A Python Framework for Simulation and Emulation of the Cloud‐Edge Continuum
No full textThe Cloud-Edge continuum enhances application performance by bringing computation closer to data sources. However, it presents considerable challenges in managing resources and determining application service placement, as these tasks require analyzing diverse, dynamic environments characterized by fluctuating network conditions. Addressing these challenges calls for tools combining simulation and emulation of Cloud-Edge systems to rigorously assess novel application and resource management strategies. In this paper, we introduce ECLYPSE, a Python-based framework that enables the simulation and emulation of the Cloud-Edge continuum via adaptable resource allocation and service placement models. ECLYPSE features an event-driven architecture for dynamically adapting network configurations and resources. It also supports seamless transitions between simulated and emulated setups, thus enabling the execution of experiments in simulated, emulated, and hybrid settings. In this work, we illustrate and assess ECLYPSE capabilities over three use cases, demonstrating the framework's effectiveness in rapid prototyping across diverse scenarios
