68994 research outputs found
Sort by
FedSynthCT-Brain: A federated learning framework for multi-institutional brain MRI-to-CT synthesis
Get PDFThe generation of Synthetic Computed Tomography (sCT) images has become a pivotal methodology in modern clinical practice, particularly in the context of Radiotherapy (RT) treatment planning. The use of sCT enables the calculation of doses, pushing towards Magnetic Resonance Imaging (MRI) guided radiotherapy treatments. Moreover, with the introduction of MRI-Positron Emission Tomography (PET) hybrid scanners, the derivation of sCT from MRI can improve the attenuation correction of PET images. Deep learning methods for MRI-to-sCT have shown promising results, but their reliance on single-centre training dataset limits generalisation capabilities to diverse clinical settings. Moreover, creating centralised multi-centre datasets may pose privacy concerns. To address the aforementioned issues, we introduced FedSynthCT-Brain, an approach based on the Federated Learning (FL) paradigm for MRI-to-sCT in brain imaging. This is among the first applications of FL for MRI-to-sCT, employing a cross-silo horizontal FL approach that allows multiple centres to collaboratively train a U-Net-based deep learning model. We validated our method using real multicentre data from four European and American centres, simulating heterogeneous scanner types and acquisition modalities, and tested its performance on an independent dataset from a centre outside the federation. In the case of the unseen centre, the federated model achieved a median Mean Absolute Error (MAE) of 102.0 HU across 23 patients, with an interquartile range of 96.7–110.5 HU. The median (interquartile range) for the Structural Similarity Index (SSIM) and the Peak Signal to Noise Ratio (PNSR) were 0.89 (0.86–0.89) and 26.58 (25.52–27.42), respectively. The analysis of the results showed acceptable performances of the federated approach, thus highlighting the potential of FL to enhance MRI-to-sCT to improve generalisability and advancing safe and equitable clinical applications while fostering collaboration and preserving data privac
Core domain set for studies of acute calcium pyrophosphate crystal arthritis: OMERACT delphi survey to establish consensus
Get PDFObjective: To identify potential domains for the Outcome Measures in Rheumatology (OMERACT) core domain set for studies of an individual flare of acute calcium pyrophosphate (CPP) crystal arthritis. Methods: Patient research partners (PRPs) and other participants (mainly clinicians and researchers) completed three rounds of survey using Delphi methodology. Consensus was defined as ≥ 70 % of both PRP and other participants groups rated the domain as a ‘critically important domain to include’. In a subsequent ranking exercise, all participants were asked to rank and comment on up to 10 domains to include as core domains. Results: Fourteen domains reached consensus as critically important in the Delphi survey. In the Pathophysiological Manifestations area, the domains were joint pain, joint tenderness, joint swelling, joint inflammation on imaging tests and duration of acute CPP crystal arthritis flare. In the Life Impact area, the domains were overall function, ability to complete daily tasks, ability to work, health related quality of life, patient global assessment response to treatment, patient and physician global assessments of disease activity, and patient satisfaction with treatment. In the Societal/Resource Use area, use of rescue medications reached consensus. In the ranking exercise, joint pain, joint tenderness, joint swelling, overall function and ability to complete daily tasks ranked highest. Conclusion: Joint pain, joint swelling, joint tenderness, duration of acute CPP crystal arthritis flare, overall function, ability to complete daily tasks, and patient global assessment of disease activity received the strongest support to be included in the OMERACT core domain set for studies of acute CPP crystal arthritis
Modeling the filament of magnesium alloys
Get PDFThe realization of the required geometry when representing the complex part of an object is determined by discretization of the material amount to be applied by a filament. The FDM of metals could in this way be competitive with conventional technologies to net shape making. The possibility to realize smaller and smaller filaments whose characteristics can be given in the extruding tool-material system by using proper geometries and parameters in order to produce the temperature rise inside the container is investigated. The analytical modeling proposed describes the extrusion behaviour of two magnesium alloys, that are the AZ31 and the ZM21 ones. That to perform a filament section of 0.4-0.5 mm in radius, beginning by a one of about 1.5 mm, with which to apply metal ribs. The effect of friction, distortion and heating phenomena as well as the solid fraction are considered on the stress vs. displacement curves inside the extruder until submillimeter diameter. The material model updated to consider the higher strain rates evidences the different behaviour of the models in describing different strain rates conditions useful to realize the filament geometry
Mitochondrial respiratory complex II is altered in renal carcinoma
Get PDFBackground: Renal cell carcinoma (RCC) is a disease typified by anomalies in cell metabolism. The function of mitochondria, including subunits of mitochondrial respiratory complex II (CII), in particular SDHB, are often affected. Here we investigated the state and function of CII in RCC patients. Methods: We evaluated tumour tissue as well as the adjacent healthy kidney tissue of 78 patients with RCC of different histotypes, focusing on their mitochondrial function. As clear cell RCC (ccRCC) is by far the most frequent histotype of RCC, we focused on these patients, which were grouped based on the pathological WHO/ ISUP grading system to low- and high-grade patients, indicative of prognosis. We also evaluated mitochondrial function in organoids derived from tumour tissue of 7 patients. Results: ccRCC tumours were characterized by mutated von Hippel-Lindau gene and high expression of carbonic anhydrase IX. We found low levels of mitochondrial DNA, protein and function, together with CII function in ccRCC tumour tissue, but not in other RCC types and non-tumour tissues. Mitochondrial content increased in high-grade tumours, while the function of CII remained low. Tumour organoids from ccRCC patients recapitulated molecular characteristics of RCC tissue. Conclusions: Our findings suggest that the state of CII, epitomized by its assembly and SDHB levels, deteriorates with the progressive severity of ccRCC. These observations hold the potential for stratification of patients with worse prognosis and may guide the exploration of targeted therapeutic interventions
Mixed-Embeddings and Deep Learning Ensemble for DGA Classification With Limited Training Data
Get PDFRecent papers in the cybersecurity research field of Domain Generation Algorithms (DGAs) detection show the increase of performances associated with the introduction of unsupervised neural vectorized representation of domain names in the supervised classification process. In this paper we explore the effectiveness of this approach by proposing a novel mixed pre-trained neural embeddings model which integrates different vectorized representations of domain names: n-grams streams and words. We used the embeddings with two different classifiers, both based on ensemble architectures: a stacking model and an end-to-end multi-input neural architecture. We trained and tested the classifiers with two datasets, differing both in the distribution of domain names between real and DGAs and in the number and type of DGAs. The obtained results show that our solution provides considerable advantages with respect to state-of-the-art single classifiers both in classification accuracy and in the detection of challenging DGAs, such as those based on word dictionaries. The improvement of performance is significant in a particularly relevant operating condition, known as few-shot-learning, where only few examples of DGA-generated domain names are available for the classifier training
Robotic-Assisted Excision of Choledochal Cyst and Hepaticojejunostomy in Children
No full textCholedochal cyst represents a significant congenital malformation of the biliary tract in children, often requiring surgical management to prevent severe complications such as cholestasis, recurrent pancreatitis, malignancy and liver failure. An increasing number of high-volume centres have adopted minimally invasive techniques, especially robotic-assisted approaches, due to their enhanced precision, visualization and postoperative outcomes. The aim of this chapter is to describe in detail the robotic-assisted excision of choledochal cyst and hepaticojejunostomy, emphasizing preoperative preparation, trocar placement and the main surgical steps to ensure optimal surgical outcome. The patient is positioned supine, with a slight reverse Trendelenburg tilt. Surgical steps include aspiration and dissection of the cyst, resection of the hepatic duct, creation of the hepaticojejunostomy and placement of the drainage. Based on the results, robotic-assisted surgery improves outcomes in choledochal cyst management, making complex procedures safer and more precise. The patients showed a rapid recovery, with the drainage removed on day 4 and discharge occurring between days 4 and 6, depending on the patient’s clinical condition. The hepaticojejunostomy was stable and functional in all cases, offering excellent postoperative outcomes in paediatric patients. Robotic - assisted technology represents an advance for minimal invasive management of choledochal cyst in paediatric patients. The technique is safe, effective and advantageous. The conversion and complication rates are comparable favourably to conventional approaches. The authors prefer the use of three arms of the da Vinci system, a fixed liver retractor and an extracorporeal Roux-en-Y loop formation. The aim of this chapter is to describe step by step the technique of robotic assisted excision of choledochal cyst and hepaticojejunostomy in children. The importance of meticulous technique and attention to details throughout the entire management period are emphasized strongly
Assessment of Synchrony in Parent-Child Interaction in Autism: A Systematic Review of Observational Tools.
No full textProcedimento disciplinare e giudicato penale: pubblico e privato a confronto
No full textNonostante il consolidato principio di autonomia dei giudizi e i correttivi apportati dal legislatore, il coordinamento tra giudicato penale e procedimento disciplinare continua a presentare profili di criticità, soprattutto nel raffronto tra l’impiego privato e quello pubblico.Despite the consolidation of the principle of the autonomy of proceedings and successive legislative reforms, the relationship between criminal judgments and disciplinary proceedings continues to raise significant concerns, particularly in comparing public and private sector employment
An AIoT System for Earthquake Early Warning on Resource Constrained Devices
No full textSeismic wave picking is an essential task the implementation of earthquake early warning (EEW) systems. While artificial intelligence picking methods show excellent accuracy, most were designed for devices with high-computational resources. At the same time, distributed approaches for Early Warning systems show promise for the implementation of viable, widespread alert systems. This article introduces a complete AIoT system for earthquake picking on resource-constrained devices. An algorithm has been developed to enable AIoT devices to switch between a detection mode, in which inferences are run on the data measured by the device, and a transmission mode, in which the device transmits alarms or other event information. To reduce inference times and input window duration, a set of deep learning pickers, called Fast PNet, derived from the PhaseNet model, were developed, achieving 74.4% inference time reduction for the shortest input model, which also showed a significant decrease in computational and power consumption. Despite a slight reduction in picking precision compared to the baseline model (from 0.09 to 0.25 s), detection performance remains high (96% precision and 98% recall). The overall system has been tested on a real event from Central Italy, displaying a 0.16-s picking error for the selected event, besides also showing its ability to reduce the amount of data to be processed for transmission to about 26.5% of the total observed input data, a great benefit compared to fully centralized EEW systems
Munitions Mobility and Burial in a Micro-Tidal Estuary
No full textThe MINELAB project, funded by the Strategic Environmental Research and Development Program (SERDP), aims at investigating the fate of underwater unexploded ordnances (UXOs) displaced over or buried into the heterogenous sedimentary bed of a micro-tidal estuarine site, subjected to both fluvial and marine forcing. Our objectives are: 1) to observe directly the combined effect of river flow, water waves and tide on the mobility and burial of UXOs; 2) to improve the models for the prediction of the UXOs behavior. While studies have largely focused on UXO surrogate fate in underwater sandy environments (Calantoni et al., 2014; Traykovski and Austin, 2017; Puleo and Cristaudo, 2020), less knowledge exists on sites characterized by cohesive sediments or mixtures of cohesive and non-cohesive materials (Trembanis and DuVal, 2020). Furthermore, to the authors’ knowledge, no studies exist on UXO mobility and burial in estuarine environments, where riverine and marine forcing interact over a heterogeneous bottom