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Magnetic Resonance Imaging (MRI) exams provide imaging data of high importance to various medical fields including that of neuro-oncology [1]. Beyond the technological aspect of this imaging technique, the assessment of this data is crucial and specialists are necessary. Recent trends aim at developing software systems for the automation of certain tasks [2]. The advances of artificial intelligence and in specific that of neural networks allowed for the automation of processes like that of segmentation based [3] on modern mathematical instruments, their software implementations and deployment in suitable hardware. Due to their complexity such systems are hard to be accessed by groups of specialists not belonging to the original research team and affiliated institutes. Even at premature stages such systems could provide useful insights to the broader medical community. This is especially the case when cross-facility experiments are necessary and novel approaches to research are implemented like virtual trials [ref]. This short manuscript communicates the design and development of a web-based system [Fig.1 and Fig.2] that allows for easier access to advanced methods of automated MRI assessment [4] while it allows for data management and feedback collection for further research. The system permits the inclusion of multiple imaging methods including systems that provide quantitative information [5] for the automatically segmented data. Last but not least, the outcome of this project suggests that such systems can be prototyped fast and render important radiomic advances [6] available to a larger audience. While funds allow it a publicly available beta is in www.rmn.ai

This ExPaNDS project deliverable describes a FAIR self-assessment undertaken by the ten ExPaNDS partner Photon and Neutron Research Infrastructures (PaN RIs) over the three-month period July – September 2022. After reviewing selected examples of existing FAIR evaluation frameworks designed to enable assessment at different levels (dataset, repository, and organisation), the report describes the evaluation approach adopted for the ExPaNDS FAIR self-assessment. As no existing framework met our specific need to focus on FAIR workflows and processes in PaN RIs, it was necessary to select, combine, and adapt existing frameworks. Supported by four underlying guiding principles, our approach drew heavily on the FAIR Principles, the RDA FAIR Data Maturity Model, and FAIRsFAIR’s CoreTrustSeal+FAIRenabling framework. Post-evaluation feedback from ExPaNDS partners indicated that they found the FAIR self-assessment a useful and valuable exercise for understanding current levels of FAIRness at their facilities and for articulating what implementations they have in progress or planned to support FAIR in future. A key output of the ExPaNDS FAIR evaluation is the collected self-assessment reports from the ten partner facilities. These reports are published openly and in full as part of the deliverable. In addition, the self-assessments are supplemented with some high-level observations on the state of the FAIR journey across the ExPaNDS facilities.

n this work, we propose the software library PyPore3D, an open source solution for data processing of large 3D/4D tomographic data sets. PyPore3D is based on the Pore3D core library, developed thanks to the collaboration between Elettra Sincrotrone (Trieste) and the University of Trieste (Italy). The Pore3D core library is built with a distinction between the User Interface and the backend filtering, segmentation, morphological processing, skeletonisation and analysis functions. The current Pore3D version relies on the closed source IDL framework to call the backend functions and enables simple scripting procedures for streamlined data processing. PyPore3D addresses this limitation by proposing a full open source solution which provides Python wrappers to the the Pore3D C library functions. The PyPore3D library allows the users to fully use the Pore3D Core Library as an open source solution under Python and Jupyter Notebooks PyPore3D is both getting rid of all the intrinsic limitations of licensed platforms (e.g., closed source and export restrictions) and adding, when needed, the flexibility of being able to integrate scientific libraries available for Python (SciPy, TensorFlow, etc.) Opis vira z dne 13. 9. 2022. Nasl. z nasl. zaslona. Št. članka: 187. Bibliografija: str. 13-15. Abstract.

In this work, we propose the software library PyPore3D, an open source solution for data processing of large 3D/4D tomographic data sets. PyPore3D is based on the Pore3D core library, developed thanks to the collaboration between Elettra Sincrotrone (Trieste) and the University of Trieste (Italy). The Pore3D core library is built with a distinction between the User Interface and the backend filtering, segmentation, morphological processing, skeletonisation and analysis functions. The current Pore3D version relies on the closed source IDL framework to call the backend functions and enables simple scripting procedures for streamlined data processing. PyPore3D addresses this limitation by proposing a full open source solution which provides Python wrappers to the the Pore3D C library functions. The PyPore3D library allows the users to fully use the Pore3D Core Library as an open source solution under Python and Jupyter Notebooks PyPore3D is both getting rid of all the intrinsic limitations of licensed platforms (e.g., closed source and export restrictions) and adding, when needed, the flexibility of being able to integrate scientific libraries available for Python (SciPy, TensorFlow, etc.).

The advancement of quantum technologies is expected to pave the way for what we refer to as practical Quantum Computing (pQC). pQC should provide solutions to certain problems, superior to those offered by the classical computational paradigm. The QC field is in development but has not taken its final form yet. In an attempt to define requirements for its practical use, this research reviews the current state of QC services as provided by IBM, Google, Microsoft , Rigetti and D-Wave, and proposes potential applications in the synchrotron radiation facility Elettra Sincrotrone Trieste. This research has concentrated its focus on potential applications that may benefit from using QC for Optimization problems even if it acknowledges other uses in fields like those of simulation modelling and information theory. In specific, this work starts by hinting on the optimisation needs for accelerator control till beamline end-station uses in scientific applications like X-ray Fluorescence data fitting and Ptychography reconstructions. The connection of QC to specific practical optimisation problems in a synchrotron and a Free Electron Laser may serve as a partial guideline for the future development of the field.

International audience; Synchrotron X-ray µ-and nano-probes are increasingly affirming their relevance in cultural heritage applications, especially in material characterization of tiny and complex micro-sam-ples which are typical from archaeological and artistic artifacts. For such purposes, synchrotron radiation facilities are tailoring and optimizing beamlines and set-ups for CH, taking also ad-vantages from the challenges offered by the third-generation radiation sources. In ancient ceramics studies, relevant information for the identification of production centers and manufacture technology can be obtained in a non-invasive and non-destructive way at the micro-sample level by combining different SR based methods. However, the selection of appropriate beamlines, techniques and set-ups are critical for the success of the experiments. Fine and varnished wares (e.g., Attic and western-Greek colonial products) are an excellent case study for exploring challenges offered by synchrotron X-ray microprobes optimized to collect microchemical and phase-distribution maps. The determination of provenance and/or technological tracers is relevant in correctly classifying productions, often based only on ceramic paste, gloss macroscopic features or style. In addition, when these vessels are preserved in Museums as masterpieces or intact pieces the application of non-invasive approach at the micro sample is strictly required. Well-designed synchrotron µXRF and µXANES mapping experiments are able providing relevant clues for discriminating workshops and exploring technological aspects, which are fundamental in answering the current archaeological questions on varnished Greek or western-Greek colonial products.

Code and data for a publication on a fast tomography alignment algorithm. Raw data have been kindly provided by Prof. Dr. Francesco Brun (University of Trieste, Elettra Sincrotrone Trieste)

Two dataset for XRF alignment experiments. Contents: dataset 1 (raw signals, manual alignment, automatic alignment) dataset 2 (raw signals , manual alignment, automatic alignment) &nbsp