An image-based modeling framework for patient-specific computational hemodynamics

We present a modeling framework designed for patient-specific computational hemodynamics to be performed in the context of large-scale studies. The framework takes advantage of the integration of image processing, geometric analysis and mesh generation techniques, with an accent on full automation and high-level interaction. Image segmentation is performed using implicit deformable models taking advantage of a novel approach for selective initialization of vascular branches, as well as of a strategy for the segmentation of small vessels. A robust definition of centerlines provides objective geometric criteria for the automation of surface editing and mesh generation. The framework is available as part of an open-source effort, the Vascular Modeling Toolkit, a first step towards the sharing of tools and data which will be necessary for computational hemodynamics to play a role in evidence-based medicine

Veno-occlusive disease nurse management: Development of a dynamic monitoring tool by the GITMO nursing group

Veno-occlusive disease (VOD) is a complication arising from the toxicity of conditioning regimens that have a significant impact on the survival of patients who undergo stem cell transplantation. There are several known risk factors for developing VOD and their assessment before the start of conditioning regimens could improve the quality of care. Equally important are early identification of signs and symptoms ascribable to VOD, rapid diagnosis, and timely adjustment of support therapy and treatment. Nurses have a fundamental role at the stages of assessment and monitoring for signs and symptoms; therefore, they should have documented skills and training. The literature defines nurses' areas of competence in managing VOD, but in the actual clinical practice, this is not so clear. Moreover, there is an intrinsic difficulty in managing VOD due to its rapid and often dramatic evolution, together with a lack of care tools to guide nurses. Through a complex evidence-based process, the Gruppo Italiano per il Trapianto di Midollo Osseo (GITMO), cellule staminali emopoietiche e terapia cellulare nursing board has developed an operational flowchart and a dynamic monitoring tool applicable to haematopoietic stem cell transplantation patients, whether they develop this complication or not

A search for ultra-high-energy photons at the Pierre Auger Observatory exploiting air-shower universality

The Pierre Auger Observatory is the most sensitive detector to primary photons with energies above ∼0.2 EeV. It measures extensive air showers using a hybrid technique that combines a fluorescence detector (FD) with a ground array of particle detectors (SD). The signatures of a photon-induced air shower are a larger atmospheric depth at the shower maximum (X$_{max}$) and a steeper lateral distribution function, along with a lower number of muons with respect to the bulk of hadron-induced background. Using observables measured by the FD and SD, three photon searches in different energy bands are performed. In particular, between threshold energies of 1-10 EeV, a new analysis technique has been developed by combining the FD-based measurement of X$_{max}$ with the SD signal through a parameter related to its muon content, derived from the universality of the air showers. This technique has led to a better photon/hadron separation and, consequently, to a higher search sensitivity, resulting in a tighter upper limit than before. The outcome of this new analysis is presented here, along with previous results in the energy ranges below 1 EeV and above 10 EeV. From the data collected by the Pierre Auger Observatory in about 15 years of operation, the most stringent constraints on the fraction of photons in the cosmic flux are set over almost three decades in energy

Study on multi-ELVES in the Pierre Auger Observatory

Since 2013, the four sites of the Fluorescence Detector (FD) of the Pierre Auger Observatory record ELVES with a dedicated trigger. These UV light emissions are correlated to distant lightning strikes. The length of recorded traces has been increased from 100 μs (2013), to 300 μs (2014-16), to 900 μs (2017-present), to progressively extend the observation of the light emission towards the vertical of the causative lightning and beyond. A large fraction of the observed events shows double ELVES within the time window, and, in some cases, even more complex structures are observed. The nature of the multi-ELVES is not completely understood but may be related to the different types of lightning in which they are originated. For example, it is known that Narrow Bipolar Events can produce double ELVES, and Energetic In-cloud Pulses, occurring between the main negative and upper positive charge layer of clouds, can induce double and even quadruple ELVES in the ionosphere. This report shows the seasonal and daily dependence of the time gap, amplitude ratio, and correlation between the pulse widths of the peaks in a sample of 1000+ multi-ELVES events recorded during the period 2014-20. The events have been compared with data from other satellite and ground-based sensing devices to study the correlation of their properties with lightning observables such as altitude and polarity

Extraction of the Muon Signals Recorded with the Surface Detector of the Pierre Auger Observatory Using Recurrent Neural Networks

We present a method based on the use of Recurrent Neural Networks to extract the muon component from the time traces registered with water-Cherenkov detector (WCD) stations of the Surface Detector of the Pierre Auger Observatory. The design of the WCDs does not allow to separate the contribution of muons to the time traces obtained from the WCDs from those of photons, electrons and positrons for all events. Separating the muon and electromagnetic components is crucial for the determination of the nature of the primary cosmic rays and properties of the hadronic interactions at ultra-high energies. We trained a neural network to extract the muon and the electromagnetic components from the WCD traces using a large set of simulated air showers, with around 450 000 simulated events. For training and evaluating the performance of the neural network, simulated events with energies between 1018.5, eV and 1020 eV and zenith angles below 60 degrees were used. We also study the performance of this method on experimental data of the Pierre Auger Observatory and show that our predicted muon lateral distributions agree with the parameterizations obtained by the AGASA collaboration