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Deliveries to transgender and gender-expansive individuals and associated delivery outcomes: a cross-sectional analysis from the National Inpatient Sample
No full textWhat Course Should I Enroll In Next? Guiding Students Toward Academic Success with Sequential Recommendations
No full textPersonalized academic support plays a critical role in improving student engagement, reducing dropout rates, and enhancing the institutional reputation of universities. Although tutoring and mentoring services are available, students still lack access to an effective and timely course recommender system to guide their academic planning. This study aims to address that need by proposing a sequential recommender system trained on historical academic records from a prominent European university’s student portal. We develop and evaluate various state-of-the-art sequential recommendation models and enhance them by integrating exam grades as explicit feedback. Additionally, we introduce two re-ranking strategies that (i) increase recommendation diversity and (ii) amplify the influence of successful academic trajectories. Results from both offline and online experiments show that our best-performing method effectively anticipates relevant course choices, demonstrating the value of incorporating temporal patterns into the recommendation model
CT Radiomics machine learning for predicting treatment response in unresectable stage III-IV NSCLC: insights from dual chemo-immunotherapy and immunotherapy cohorts.
No full textBackground: early prediction of treatment response in advanced-stage unresectable non-small cell lung cancer (NSCLC) is essential for optimizing therapeutic decisions. Radiomics combined with machine learning provides a promising non-invasive approach for quantifying tumor heterogeneity and improving early response assessment.
Objective: the aim of this study is to develop a machine learning model based on radiomic features extracted from baseline contrast-enhanced CT scans for predicting early treatment responses in two cohorts of patients with stage III-IV NSCLC treated with distinct therapeutic regimens (first-line combined chemo-immunotherapy or immunotherapy alone).
Methods: in this prospective, bicentric, two-cohort study, patients with confirmed diagnosis of advanced non–small cell lung cancer (stage III or IV) were prospectively enrolled. At Center 1, after applying exclusion criteria, 90 eligible patients (52 CHT/IT and 38 IT) were included for model development and internal validation. An independent external cohort from Center 2 included 40 additional patients, 20 treated with CHT/IT and 20 with IT—used for external validation. Radiomic features were extracted from segmented tumor volumes using the Trace4ResearchTM platform and multiple machine learning models (Random Forest, Support Vector Machine, K-Nearest Neighbors, Multi-Layer Perceptron, Logistic Regression) were trained and compared.
Results: in the CHT/IT cohort (Group A), seven radiomic features were retained after outlier removal. The support vector machine (SVM) classifier achieved the highest performance (ROC-AUC = 0.90 [0.82–0.97]; accuracy = 82%; sensitivity = 82%; specificity = 83%; PPV = 67%; NPV = 94%; p < 0.005). For the IT cohort (Group B), 295 features were analyzed, and the SVM again provided optimal performance (ROC-AUC = 0.84 [0.73–0.95]; accuracy = 77% ; sensitivity = 82%; specificity = 70%; PPV = 80%; NPV = 83% ; p < 0.05).External validation using 20 independent cases per group confirmed the robustness and generalizability of both models.
Conclusions: Machine learning models leveraging CT-derived radiomic features enable accurate, noninvasive early prediction of treatment response in patients with advanced NSCLC receiving chemoimmunotherapy or immunotherapy alone. These findings support radiomics as a promising imaging biomarker for personalized treatment planning and response assessment in lung cancer
Understanding the consequences of reduced expression of CNBP in myotonic dystrophy type 2 pathogenesis
No full textMyotonic dystrophy type 2 (DM2) is an autosomal dominant multisystemic disorder that primarily affects skeletal muscle, leading to progressive muscle fiber degeneration and dysfunction. DM2 is caused by a CCTG repeat expansion within intron 1 of the CNBP (Cellular Nucleic acid-Binding Protein) gene, although the precise pathogenic mechanisms remain incompletely understood. Specifically, the relationship between the dystrophic phenotype and CNBP loss of function induced by the DNA expansion remains unclear.
This project aims to determine whether CNBP expression is reduced in the context of DM2 and to explore the functional consequences of CNBP depletion on muscle physiology and performance. To address these points, we employed complementary in vitro and in vivo models, including myoblasts cell line, DM2 patient-derived cells, Drosophila, and mice.
We demonstrate that CCTG expansion correlates with reduced CNBP expression in both DM2 patient-derived cells and in a novel Drosophila DM2 model that we have generated. Constitutive ablation of CNBP in Drosophila and mice causes severe locomotor defects and muscle atrophy. Molecular analysis conducted in CNBP-silenced myoblast cell lines highlights the role of CNBP in regulating critical pathways of muscle cell activity, including myogenesis and metabolism. In particular, we show that CNBP depletion induces inactivation of the PI3K-Akt-mTOR pathway, with consequent aberrant activation of autophagy in all our models. Importantly, we demonstrate that genetic blockade of autophagic activity through interference with ATG7, a key factor in autophagosome formation, rescues the locomotor defects caused by CNBP depletion in Drosophila. Together, our data identify PI3K-Akt-mTOR deregulation and the consequent activation of autophagy as a novel contributing mechanism to DM2 pathogenesis, suggesting new potential therapeutic approaches based on autophagy inhibition
Expanding the Stage: A Preliminary Discussion of the Impact of Extended Reality on the Performing Arts
No full textIn the last decades, performing arts have undergone a profound transformation, driven by the integration of new forms of participation and emerging technologies such as eXtended Reality (XR). By merging real and virtual spaces, XR challenges traditional notions of presence, embodiment, and spectatorship, transforming the audience from observers into participants and co-creators. This paper explores how XR is contributing to the reconfiguration of the language of the performing arts, with a particular focus on theatre. Through a theoretical discussion supported by selected case studies, this work highlights the main conceptual nodes emerging from the encounter between corporeality, performance and digital elements. The paper concludes by reflecting on the necessity of maintaining a balance between technological innovation and artistic integrity, and points to future possibilities for XR in performance, particularly in conjunction with Artificial Intelligence to further personalise and expand the interactive experience
REMOTE SENSING AND PASSIVE ACOUSTIC MONITORING IN RIPARIAN FOREST: LINKING HABITAT STRUCTURE TO BIRD DIVERSITY
No full textRiparian forests play a crucial role in maintaining biodiversity, providing essential habitats for
many species, including birds. This study investigates bird biodiversity in riparian forests of
South Tyrol, aiming to understand how variations in habitat structure influence species
diversity. Data collection was conducted using autonomous acoustic recorders, strategically
placed within these forested areas to capture bird activity over extended periods. Additionally,
high resolution lidar and multispectral data were utilized to map the structural characteristics
of the study areas. By analyzing the relationship between habitat features – such as canopy
volume, vegetation density, and landscape complexity – and bird species richness, this
research seeks to identify key habitat variables that either promote or limit avian biodiversity.
The study findings indicate that structural attributes – such as vegetation greenness, tree
density, canopy and understory layering, and vertical heterogeneity – emerge as key
determinants of avian presence and community composition in riparian forests. Sites
characterized by high canopy volume and multi-layered vegetation supported richer and more
specialized bird assemblages, underscoring the importance of mature stand features and
structurally diverse habitats for forest-dependent species.
From a management perspective, these results highlight the value of maintaining
heterogeneous riparian stands through strategies that preserve mature tree cohorts, retain
natural understory development, and avoid excessive canopy simplification. Ensuring the
structural continuity of riparian corridors may be particularly relevant in fragmented Alpine
landscapes, where these systems function as biodiversity refugia and dispersal routes.
By linking high-resolution structural metrics with acoustic biodiversity assessments, this work
provides an operational framework for integrating remote-sensing indicators into riparian
conservation planning and monitoring, with implications for evidence-based habitat
restoration and climate-adaptation strategies
Ulcerative colitis in adult patients: assessment of disease activity and severity and detection of neoplastic complications
No full textUlcerative colitis (UC) is a chronic, relapsing inflammatory bowel disease characterized by continuous mucosal inflammation of the colon, which not only impairs patients’ quality of life but also leads to long-term complications, including an increased risk of colorectal neoplasia. Over recent decades, therapeutic goals in UC have evolved from mere symptom control to the pursuit of objective, multidimensional treatment endpoints. From the attempt to achieve increasingly deeper remission, the concept of disease clearance (DC) has recently emerged as a more stringent and comprehensive therapeutic target. This composite outcome, defined by the simultaneous achievement of clinical, endoscopic, and histological remission, has been increasingly recognized as a marker of favorable long-term prognosis. Within this framework, the present PhD project aimed to investigate the determinants and therapeutic strategies associated with the attainment of DC in adult UC patients, as well as to evaluate the occurrence and characteristics of neoplastic complications within this population. A retrospective observational study was conducted on adult UC patients followed at the Digestive Disease Unit of the Sant’Andrea University Hospital in Rome between 2012 and 2024. Clinical, endoscopic, and histological disease activity were evaluated using validated indices, and potential predictors of DC were identified through multivariate statistical analysis. In parallel, a multicentre prospective investigation—the SVEDO Study—was carried out to assess the efficacy and safety of switching from intravenous to subcutaneous vedolizumab in UC patients in stable clinical remission. Furthermore, the long-term incidence, characteristics, and associated factors of colorectal dysplasia and carcinoma were analyzed in a separate cohort of UC patients with extensive follow-up. Among the 279 patients included in the DC analysis, 37% achieved DC after a median of 10 years from diagnosis. The absence of corticosteroid use emerged as an independent positive predictor, confirming that steroid-sparing therapeutic strategies are crucial for achieving sustained remission. Patients who reached DC experienced significantly fewer relapses, further emphasizing the clinical relevance of this comprehensive therapeutic endpoint. Interestingly, dysplastic lesions were more frequently identified among patients who achieved DC, a finding likely attributable to longer follow-up duration and more rigorous endoscopic surveillance. In the SVEDO Study, which included 168 UC patients in stable clinical remission, switching vedolizumab from the intravenous to the subcutaneous formulation proved both safe and effective. Nearly 80% of patients maintained remission at six months, with no significant biochemical or clinical deterioration observed. This treatment transition offered tangible benefits in terms of patient convenience and optimization of healthcare resources. In a separate cohort of 338 UC patients, the cumulative incidence of colorectal dysplasia and carcinoma was 11.2% and 2.7%, respectively, corresponding to annual incidence rates of 9.4 and 2.2 per 1,000 person-years. Most dysplastic lesions were small, polypoid, and endoscopically resectable, reflecting improved detection capabilities with modern high-definition endoscopy. Notably, more than half of the affected patients were in deep remission at the time of diagnosis, suggesting that neoplastic transformation may still occur in quiescent mucosa, possibly as a consequence of cumulative past inflammation. Age above 40 years was identified as the only independent risk factor associated with the development of neoplastic complications. Overall, this PhD project demonstrates that deep remission, as defined by the concept of DC, is both an achievable and clinically meaningful therapeutic goal in UC. Its attainment is associated with sustained disease control and a reduced risk of relapse, confirming its prognostic relevance in long-term management. Nevertheless, the persistence of colorectal neoplasia risk—even among patients in durable remission—underscores the importance of ongoing, individualized endoscopic surveillance. The integration of remission-oriented therapeutic strategies with lifelong, risk-adapted cancer prevention programs therefore remains fundamental to improving long-term outcomes and enhancing the quality of life of adults living with UC
Performance and low-carbon assessment of polymer modification processes for emulsified asphalt based on grey rational analysis
No full textThe preparation of emulsified asphalt is often challenged by initial aging and high energy consumption, particularly during high-temperature modification and inefficient emulsification. This study evaluates the performance and environmental impacts of emulsified asphalt modified with innovative waterborne polymers, featuring self-crosslinking properties. Four modifiers-epoxy resin, acrylate, nitrile rubber, and polyurethane-were incorporated at 3%, 6%, and 9% dosages. Adhesion tests revealed that all self-crosslinking polymers significantly improved bonding strength, with polyurethane achieving the highest increase (over 30% compared to the control). Rheological analyses explored using Multiple Stress Creep and Recovery tests showed that acrylate, epoxy resin, and polyurethane enhanced rutting resistance by increasing zero Viscosity and ηM values, while nitrile rubber had a softening effect. Fatigue resistance, assessed through dissipated energy calculations, also improved with crosslinking polymers but declined with nitrile rubber. Conversely, Glover–Rowe parameters at 180 kPa and 450 kPa indicated a moderate increase in low-temperature cracking susceptibility for crosslinking polymers, whereas nitrile rubber reduced cracking potential. Life-cycle assessment demonstrated that the use of waterborne polymers reduced total energy consumption by 16.55%-17.94% and carbon emissions by 15.64%-16.88% compared to traditional hot-mix processes. Finally, Grey Relational Analysis ranked 9% polyurethane-modified asphalt as the optimal formulation considering the high-temperature environment, balancing mechanical performance and environmental benefits. Waterborne nitrile rubber showed deteriorated high-temperature rutting resistance but excellent fatigue resistance, which were suitable for anti-cracking at low temperature. These findings confirm that self-crossilinking waterborne polymers can enhance asphalt emulsion performance while reducing lifecycle energy use and emissions
The Effect of Colored Noise on Doppler Measurements for Planetary Geodesy: Application to the VERITAS Gravity Science Experiment
No full textMeasurements of the Doppler shift of a carrier signal have always been the basis of radio science experiments, particularly for planetary gravity field recovery. Doppler observables are affected by correlated (colored) noise; however, standard orbit estimation filters assume that the noise is white and Gaussian. This assumption simplifies the code and reduces the runtime, as the weight matrix is diagonal. In this work, we present a method for incorporating colored noise into least-squares estimation filters by constructing a full noise covariance matrix that reflects the spectral characteristics of all contributing error sources, guaranteeing the proper estimation of parameters and their associated uncertainties. We apply this methodology to the gravity science experiment of NASA’s upcoming Venus Emissivity, Radio Science, InSAR, Topography, And Spectroscopy (VERITAS) mission. Our analysis finds that the white noise assumption is valid for VERITAS—thus supporting the robustness of earlier simulation results. The approach, however, is general and particularly valuable for future missions in which colored noise, such as plasma-induced red noise, dominates the Doppler error budget
Nuclear analyses in support of the water-cooled lithium lead breeding blanket design development. A prospective strategy to achieve the tritium self-sufficiency
No full textThe development of a closed tritium fuel cycle is essential for the sustainable operation of future fusion power plants. Within the EUROfusion roadmap, the Water-Cooled Lithium Lead (WCLL) breeding blanket is a key candidate for the Demonstration Fusion Power Reactor (DEMO). It uses pressurized water as coolant and a liquid lithium-lead (LiPb) eutectic alloy as both breeder and neutron multiplier. The current WCLL design features modular inboard and outboard Breeding Units (BUs) composed of helical Double Wall Tubes (DWTs) immersed in LiPb. This configuration improves heat removal and reduces the water inventory in the Breeding Zone, thereby potentially increasing tritium generation. This work presents an assessment of the WCLL DEMO nuclear performances in terms of shielding effectiveness and tritium self-sufficiency, based on 3D radiation transport simulations performed using the Monte Carlo MCNP code and JEFF nuclear data libraries. A detailed MCNP model has been developed, including a fully heterogeneous representation of the Breeding Zone and first wall (FW) water channels. Radial profiles of fast and total neutron fluxes, along with nuclear heating, are evaluated at the equatorial level, providing 3D maps of these quantities as well. The total Tritium Breeding Ratio (TBR) has been evaluated, accounting for contributions from both the BZ and LiPb manifolds. To further enhance tritium generation and support the long-term viability of the WCLL concept, design modifications to the latest layout have been explored through a prospective approach. The effectiveness of these modifications has been assessed by means of parametric studies and discussed in light of their potential impact on overall reactor performance