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Leveraging the Copernicus high‐resolution multi‐year Mediterranean Sea Surface Temperature (SST) dataset, 15 selected tropical‐like cyclones (TLCs) are analyzed with the objective of elucidating the anomalies of SST at the time of cyclogenesis and the connection between the change in SST during the cyclone lifetime and its characteristics. The long‐term SST increase associated with climate change is identified by comparing detrended and original anomalies. Detrending removes the effect of the intensification of SST anomaly over time, revealing that no significant anomalies generally emerge in the early stages of the TLC lifetimes. Conversely, winter events exhibit early‐stage positive SST anomalies. Also, high SST values were observed during the intensification of the most intense cyclones. A cold SST anomaly is left after the passage of the cyclones, due to the intense sea surface fluxes extracting heat from the sea. Plain Language Summary Mediterranean tropical‐like cyclones are severe weather events able to produce large socio‐economic and environmental impact, as well as considerable damage. On average, 1.5 such events affect the Mediterranean each year. In this study we assess the Sea Surface Temperature conditions related to the events reported in the literature of the past four decades. We use a high‐resolution multi‐year SST dataset over the Mediterranean Sea, to reveal the potential relationship between the change in SST before and during the cyclone's lifetime and their features. Our results show that SST plays an important role in the intensification phases of the events, while no significant SST anomaly emerges in the early stages for most of the cyclones. Key Points No significant SST anomalies are found at the early stage of medicanes, except for the winter cases that exhibit a positive anomaly SST value is particularly high for the most intense cyclones (September cases) The passage of the cyclones over the sea induces a cooling mainly during their mature phase

Aims Non-Alcoholic-Fatty-Liver-Disease (NAFLD) is the most common cause of chronic liver disease in Western countries; closely linked to obesity and type 2 diabetes (T2DM), it is an additional cardiovascular risk factor. The aim of this study is to investigate the prevalence of NAFLD at T2DM onset. Methods 122 newly diagnosed T2DM patients were enroled; NAFLD was diagnosed using ultrasound and fibrosis risk calculated with an FIB4-score. Intermediate and high-risk patients were referred to a hepatologist and underwent transient elastography (TE). Results At T2DM diagnosis, 25% of patients were overweight, 47% were obese; ultrasound steatosis was present in 79% of patients; the average FIB-4 score was 1.4 (0.7). The NAFLD population was characterised by higher presence of obesity (60%, p 0.06); hypertension (56%, p 0.00); AST (26.3 (23.6) UI/L; p 0.00); ALT (49.3(41.0) UI/L p 0.00); FIB-4 score (1.6 (0.8); p 0.00). Among patients referred to a hepatologist, at TE, 65% had severe steatosis, 22% significant fibrosis and 25% advanced fibrosis. Conclusion This is the first proposal of a NAFLD screening model at T2DM diagnosis. The high prevalence of fibrosis found at the early stage T2DM confirms the compelling need for early management of NAFLD through cost-effective screening and long-term monitoring algorithms.

We introduce the first method of uncertainty quantification in the domain of Kolmogorov–Arnold Networks, specifically focusing on (Higher Order) ReLU-KANs to enhance computational efficiency given the computational demands of Bayesian methods. The method we propose is general in nature, providing access to both epistemic and aleatoric uncertainties. It is also capable of generalization to other various basis functions. We validate our method through a series of closure tests, commonly found in the KAN literature, including simple one-dimensional functions and application to the domain of (stochastic) partial differential equations. Referring to the latter, we demonstrate the method’s ability to correctly identify functional dependencies introduced through the inclusion of a stochastic term.

The role of aflatoxins (AFs) in the biology of producing strains, Aspergillus sect. Flavi, is still a matter of debate. Over recent years, research has pointed to how environmental factors altering the redox balance in the fungal cell can switch on the synthesis of AF. Notably, it has been known for decades that oxidants promote AF synthesis. More recent evidence has indicated that AF synthesis is controlled at the transcriptional level: reactive species that accumulate in fungal cells in the stationary growth phase modulate the expression of aflR, the main regulator of AF synthesis—through the oxidative stress related transcription factor AP-1. Thus, AFs are largely synthesized and secreted when (i) the fungus has exploited most nutritional resources; (ii) the hyphal density is high; and (iii) reactive species are abundant in the environment. In this study, we show that AFs efficiently scavenge peroxides and extend the lifespan of E. coli grown under oxidative stress conditions. We hypothesize a novel role for AF as an antioxidant and suggest its biological purpose is to extend the lifespan of AFs-producing strains of Aspergillus sect. Flavi under highly oxidizing conditions such as when substrate resources are depleted, or within a host.

We introduce a physics-informed Bayesian neural network with flow-approximated posteriors using multiplicative normalizing flows for detailed uncertainty quantification (UQ) at the physics event-level. Our method is capable of identifying both heteroskedastic aleatoric and epistemic uncertainties, providing granular physical insights. Applied to deep inelastic scattering (DIS) events, our model effectively extracts the kinematic variables x , Q 2 , and y , matching the performance of recent deep learning regression techniques but with the critical enhancement of event-level UQ. This detailed description of the underlying uncertainty proves invaluable for decision-making, especially in tasks like event filtering. It also allows for the reduction of true inaccuracies without directly accessing the ground truth. A thorough DIS simulation using the H1 detector at HERA indicates possible applications for the future electron–ion collider. Additionally, this paves the way for related tasks such as data quality monitoring and anomaly detection. Remarkably, our approach effectively processes large samples at high rates.

The integration of deep learning (DL) into experimental nuclear and particle physics has driven significant progress in simulation and reconstruction workflows. However, traditional simulation frameworks such as Geant4 remain computationally intensive, especially for Cherenkov detectors, where simulating optical photon transport through complex geometries and reflective surfaces introduces a major bottleneck. To address this, we present an open, standalone fast simulation tool for detection of internally reflected Cherenkov light (DIRC) detectors, with a focus on the high-performance DIRC at the future electron–ion collider. Our framework incorporates a suite of generative models tailored to accelerate particle identification (PID) tasks by offering a scalable, graphical processing unit-accelerated alternative to full Geant4 -based simulations. Designed with accessibility in mind, our simulation package enables both DL researchers and physicists to efficiently generate high-fidelity large-scale datasets on demand, without relying on complex traditional simulation stacks. This flexibility supports the development and benchmarking of novel DL-driven PID methods.

Imaging Cherenkov detectors are crucial for particle identification (PID) in nuclear and particle physics experiments. Fast reconstruction algorithms are essential for near real-time alignment, calibration, data quality control, and efficient analysis. At the future electron–ion collider (EIC), the ePIC detector will feature a dual Ring Imaging Cherenkov (RICH) detector in the hadron direction, a Detector of Internally Reflected Cherenkov (DIRC) in the barrel, and a proximity focus RICH in the electron direction. This paper focuses on the DIRC detector, which presents complex hit patterns and is also used for PID of pions and kaons in the experiment at JLab. We present Deep(er)RICH, an extension of the seminal DeepRICH work, offering improved and faster PID compared to traditional methods and, for the first time, fast and accurate simulation. This advancement addresses a major bottleneck in Cherenkov detector simulations involving photon tracking through complex optical elements. Our results leverage advancements in Vision Transformers, specifically hierarchical Swin Transformer and normalizing flows. These methods enable direct learning from real data and the reconstruction of complex topologies. We conclude by discussing the implications and future extensions of this work, which can offer capabilities for PID for multiple cutting-edge experiments like the future EIC.

Anomaly Detection is becoming increasingly popular within the experimental physics community. At experiments such as the Large Hadron Collider, anomaly detection is growing in interest for finding new physics beyond the Standard Model. This paper details the implementation of a novel Machine Learning architecture, called Flux+Mutability, which combines cutting-edge conditional generative models with clustering algorithms. In the ‘flux’ stage we learn the distribution of a reference class. The ‘mutability’ stage at inference addresses if data significantly deviates from the reference class. We demonstrate the validity of our approach and its connection to multiple problems spanning from one-class classification to anomaly detection. In particular, we apply our method to the isolation of neutral showers in an electromagnetic calorimeter and show its performance in detecting anomalous dijets events from standard QCD background. This approach limits assumptions on the reference sample and remains agnostic to the complementary class of objects of a given problem. We describe the possibility of dynamically generating a reference population and defining selection criteria via quantile cuts. Remarkably this flexible architecture can be deployed for a wide range of problems, and applications like multi-class classification or data quality control are left for further exploration.

Background About 5% of Wilms tumors present with vascular extension, which sometimes extends to the right atrium. Vascular extension does not affect the prognosis, but impacts the surgical strategy, which is complex and not fully standardized. Our goal is to identify elements of successful surgical management of Wilms tumors with vascular extensions. Patients and Methods A retrospective study of pediatric Wilms tumors treated at three sites (January 1999–June 2019) was conducted. The inclusion criterion was the presence of a renal vein and vena cava thrombus at diagnosis. Tumor stage, pre and postoperative treatment, preoperative imaging, operative report, pathology, operative complications, and follow-up data were reviewed. Results Of the 696 pediatric patients with Wilms tumors, 69 (9.9%) met the inclusion criterion. In total, 24 patients (37.5%) had a right atrial extension and two presented with Budd–Chiari syndrome at diagnosis. Two died at diagnosis owing to pulmonary embolism. All patients received neoadjuvant chemotherapy and thrombus regressed in 35.6% of cases. Overall, 14 patients had persistent intra-atrial thrombus extension (58%) and underwent cardiopulmonary bypass. Most thrombi (72%) were removed intact with nephrectomy. Massive intraoperative bleeding occurred during three procedures. Postoperative renal insufficiency was identified as a risk factor for patient survival ( p = 0.01). With a median follow-up of 9 years (range: 0.5–20 years), overall survival was 89% and event-free survival was 78%. Conclusions Neoadjuvant chemotherapy with proper surgical strategy resulted in a survival rate comparable to that of children with Wilms tumors without intravascular extension. Clinicians should be aware that postoperative renal insufficiency is associated with worse survival outcomes.