Recent publications
In this work, a 3D FEM electro-thermal model of a slotted core HTS cable, developed at the ENEA Frascati Superconductivity Laboratory (Italy) in collaboration with the University of Bologna (Italy) and Eni S.p.A. (Italy), is presented. A benchmark of the model with experimental tests is provided, showing the temporal evolution of voltages and temperatures (monitored through fibre optic sensors) during quench. The model is also applied to compute the normal zone propagation velocity. The analysis finally provides critical information for the design of a quench protection system based on fiber optics.
The study evaluated the application of a novel high-pressure microbial inactivation method combining dense
carbon dioxide with modified atmosphere packaging on organic fresh-cut squash (Cucurbita moschata).
Approximately 4 g or 32 g of squash was packed in plastic pouches filled with CO2 to test two different gas-to-
product ratios and treated with the high-pressure method at previously optimized process conditions (45 ◦C, 6.0
MPa and 40 min). The products were then stored for 21 days at 4 ◦C and assessed for enzymatic activity, product
quality, sugar content, bioaccessibility (polyphenols, DPPH antioxidant activity, and carotenoids), and sensory
acceptance, with products packed in air and CO2 serving as controls. The high-pressure treatment effectively
inactivated inoculated E. coli to undetectable levels (inactivation >3.63 ±0.53 Log CFU/g) and reduced the
activity of the browning-responsible enzymes up to 50 %. During the shelf life, treated samples exhibited
significantly higher scavenging activity for DPPH, ABTS, OH, O2−
, and NO compared to non-treated samples, with
minor exceptions at a high gas-to-product ratio. Additionally, treated samples showed increased levels of glucose
and fructose and a comparable or higher bioaccessibility of antioxidants with respect to the products packed in
air or in CO2. Sensory evaluation indicated that the treatment enhanced color and smell appreciation among
panelists, demonstrating the potential of this method to improve both safety and quality of fresh-cut squash.
Background and Objectives
Several computational pipelines for biomedical data have been proposed to perform patients’ stratification and predict prognosis through survival analysis. However, these steps are usually performed independently, without exploiting and integrating the information derived from each step of the analysis. Clustering of survival data is an underexplored problem, and current approaches are limited for biomedical applications, whose data are usually heterogeneous and multimodal, with poor scalability for high-dimensionality.
Methods
We introduce VAE-Surv, a multimodal computational framework for patients’ stratification and prognosis prediction. VAE-Surv integrates a Variational Autoencoder (VAE), which reduces the high-dimensional space characterizing the molecular data, with a deep survival model, which combines the embedded information with the clinical features. The VAE embedding step prioritizes local coherence within the feature space to detect potential nonlinear relationships among the molecular markers. The latent representation is then exploited to perform K-means clustering. To test the clinical robustness of the algorithm, VAE-Surv was applied to the Genomed4all cohort of Myelodysplastic Syndromes (MDS), comparing the identified subtypes with the World Health Organization (WHO) classification. The survival outcome was compared with the state-of-the-art Cox model and its penalized versions. Finally, to assess the generalizability of the results, the method was also validated on an external MDS cohort.
Results
Tested on 2,043 patients in the GenomMed4All cohort, VAE-Surv achieved a median C-index of 0.78, outperforming classical approaches. In addition, the latent space enhances the clustering performance compared to a traditional approach that applies the clustering directly to the input data. The analysis of the identified clusters in relation to the WHO 2016 MDS subtypes indicates that the proposed framework can capture existing clinical categorizations while also suggesting novel, data-driven patient groups. Even tested in an external MDS cohort, VAE-Surv achieved a good prediction performance (median C-index=0.74), preserving the interpretability of the main clinical and genetic features.
Conclusions
VAE-Surv enables automatic identification of patients’ clusters, while outperforming the traditional CoxPH model in survival prediction tasks at the same time. Applied to MDS use case, the obtained genetic-based clusters exhibit a clear survival stratification, and the application of the clinical information allowed high performance in prognosis prediction.
Antimicrobial resistance (AMR) is a major global health threat, exacerbated by globalization which facilitates the spread of resistant bacteria. Addressing this issue requires a One Health perspective, involving humans, animals, and the environment. This study aims to compare the phenotypic resistance profiles of 69 clinical bacterial isolates (Enterobacteriaceae and Pseudomonaceae) from a Veterinary Teaching Hospital in Spain with their genotypic resistance profiles based on the presence of Extended-Spectrum Beta-Lactamases (ESBLs), AmpC and carbapenemases -enconding genes. For the genotypical analysis, whole genome sequencing (WGS) was used. Phenotypic characterization revealed that 37 isolates (53.6 %) grew on ESBL-selective medium. Phenotypic confirmatory tests showed that 12 strains (17.4 %) had some type of ESBL and 21 (30.4 %) could have an AmpC. Also, 24 isolates (34.8 %) grew in selective media for carbapenemases-producing bacteria, and 2 of these had a class A carbapenemase based on the KPC&MBL&OXA-48 disc kit. The genotypic analysis revealed 20 isolates (29 %) had blaTEM, 8 (11.6 %) had blaCTX-M and 7 (10.1 %) blaSHV. 27 (39.1 %) isolates had class C beta-lactamase genes. 35 isolates (50.7 %) had blaOXA, class D beta-lactamase. 37 strains (53.6 %) had an Inc. plasmid replicon associated with the spread of AMR genes, including beta-lactamases and carbapenemases. This study emphasizes the value of combining phenotypic and genomic analyses to better understand and address antibiotic resistance, especially in veterinary contexts. Integrating these approaches enhances diagnostic accuracy by identifying strains with resistance genes that may not show phenotypically, helping clinicians in anticipating resistance under selective pressure.
The wetting properties of a liquid in contact with a solid are commonly described by Young’s equation, which defines the relationship between the angle made by a fluid droplet onto the solid surface and the interfacial properties of the different interfaces involved. When modeling such interfacial systems, several assumptions are usually made to determine this angle of contact, such as a completely rigid solid or the use of the tension at the interface instead of the surface free energy. In this work, we perform molecular dynamics simulations of a Lennard-Jones liquid in contact with a Lennard-Jones crystal and compare the contact angles measured from a droplet simulation with those calculated using Young’s equation based on surface free energy or surface stress. We analyze cases where the solid atoms are kept frozen in their positions and where they are allowed to relax and simulate surfaces with different wettability and degrees of softness. Our results show that using either surface free energy or surface stress in Young’s equation leads to similar contact angles but different interfacial properties. We find that the approximation of keeping the solid atoms frozen must be done carefully, especially if the liquid can efficiently pack at the interface. Finally, we show that to correctly reproduce the measured contact angles when the solid becomes soft, the quantity to be used in Young’s equation is the surface free energy only and that the error committed in using the surface stress becomes larger as the softness of the solid increases.
Sunflower syndrome, a rare photosensitive epilepsy characterized by handwaving episodes and fixation on light sources, remains poorly understood, especially regarding its neuropsychological profile. This study provides a detailed cognitive evaluation of two patients, revealing a likely disruption in the visual dorsal stream, with particular involvement of the ventral attention network (VAN). Despite normal overall intellectual functioning, both patients exhibited significant deficits in Processing Speed Index and inhibitory control, indicating selective executive dysfunction. Coupled with EEG anomalies predominantly in the right frontal lobe, these findings suggest right hemisphere involvement, potentially along the VAN pathway. This study highlights the need for further research, particularly functional neuroimaging, to better understand the neurocognitive aspects of sunflower syndrome and its impact on patients' quality of life.
Light activated local stimulation and sensing of biological cells hold great promise for minimally invasive bioelectronic interfaces. Organic semiconductors are particularly appealing for these applications due to their optoelectronic properties and biocompatibility. This study examines the material properties necessary to localize the optical excitation and achieve optoelectronic transduction with high spatial resolution. Using photovoltage and photocurrent microscopy, we investigate spatial broadening of local optical excitation in Phthalocyanine/3,4,9,10‐Perylenetetracarboxylic diimide (H2PC/PTCDI) planar heterojunctions. Our measurements reveal that resolution losses are tied to the effective diffusion length of charge carriers at the heterojunction. For the H2PC/PTCDI heterojunction, the diffusion length is determined to be λd = 1.5 ± 0.1 µm, attributed to reduced carrier mobility. Covering the heterojunction with poly(3,4‐ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) improves the charge generation performance but increases the carrier diffusion length to λd = 7.0 ± 0.3 µm due to longer lifetime and higher carrier mobility. These findings elucidate the physical mechanisms underlying transduction and provide design principles for organic semiconductor devices aimed at achieving high efficiency and high spatial resolution for wireless and optically activated bioelectronics.
Despite the existence of several studies on collectibles, the rare coin market is still underexplored. This paper examines this market with a sample of 5553 Spanish columnarios (1732–1772) auctioned from 1992 to 2021, investigating the key factors influencing auction prices using a dataset with an extensive number of covariates. Traditional hedonic models face challenges with large datasets, including multicollinearity, overfitting, and parameter complexity, which compromise clear and reliable interpretation. To address these limitations, this study employs the cross-fit partialing-out LASSO regression to select key explanatory variables, resulting in unbiased estimates and insights for investors and researchers. An interrupted time series analysis is subsequently conducted to compare indices derived from the traditional and LASSO hedonic methods. Findings confirm that LASSO approach outperforms the traditional hedonic regression method in terms of estimation accuracy.
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