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2,880 results on '"Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL)"'

51. Modeling complex particles phase space with GAN for Monte Carlo SPECT simulations: a proof of concept

Author

David Sarrut, A. Etxebeste, Jean Michel Létang, Nils Krah, Imagerie Tomographique et Radiothérapie, Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Institut de Physique des 2 Infinis de Lyon (IP2I Lyon), Université de Lyon-Université de Lyon-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS), HEP, INSPIRE, Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS), and Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL)

Subjects
Computer science, Computation, Monte Carlo method, Physics::Medical Physics, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Single-photon emission computed tomography, Tracking (particle physics), 030218 nuclear medicine & medical imaging, [PHYS] Physics [physics], 03 medical and health sciences, 0302 clinical medicine, Neoplasms, medicine, Humans, Radiology, Nuclear Medicine and imaging, Computer Simulation, [PHYS]Physics [physics], Tomography, Emission-Computed, Single-Photon, Radiological and Ultrasound Technology, Artificial neural network, medicine.diagnostic_test, Phantoms, Imaging, Detector, Proof of concept, 030220 oncology & carcinogenesis, Phase space, Particle, Tomography, Neural Networks, Computer, Tomography, X-Ray Computed, Algorithm, Monte Carlo Method
Abstract

A method is proposed to model by a generative adversarial network the distribution of particles exiting a patient during Monte Carlo simulation of emission tomography imaging devices. The resulting compact neural network is then able to generate particles exiting the patient, going towards the detectors, avoiding costly particle tracking within the patient. As a proof of concept, the method is evaluated for single photon emission computed tomography (SPECT) imaging and combined with another neural network modeling the detector response function (ARF-nn). A complete rotating SPECT acquisition can be simulated with reduced computation time compared to conventional Monte Carlo simulation. It also allows the user to perform simulations with several imaging systems or parameters, which is useful for imaging system design.

Published
2021

52. Convolutional Neural Network for Material Decomposition in Spectral CT Scans

Author

Nicolas Ducros, Françoise Peyrin, Simon R. Arridge, Andreas Hauptmann, S. Bussod, Christine Chappard, Juan F P J Abascal, Imagerie Tomographique et Radiothérapie, Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), Centre for Medical Image Computing (CMIC), University College of London [London] (UCL), Department of Mathematical Sciences - University of Oulu, University of Oulu, Biologie, Bioingénierie et Bioimagerie Ostéo-articulaires (B3OA (UMR_7052)), École nationale vétérinaire d'Alfort (ENVA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP), and European Synchroton Radiation Facility [Grenoble] (ESRF)

Subjects
Tomographic reconstruction, medicine.diagnostic_test, Computer science, Detector, Shot noise, 020206 networking & telecommunications, Computed tomography, 02 engineering and technology, Convolutional neural network, Noise, [SPI]Engineering Sciences [physics], 0202 electrical engineering, electronic engineering, information engineering, medicine, 020201 artificial intelligence & image processing, [INFO]Computer Science [cs], Projection (set theory), Algorithm, ComputingMilieux_MISCELLANEOUS
Abstract

Spectral computed tomography acquires energy-resolved data that allows recovery of densities of constituents of an object. This can be achieved by decomposing the measured spectral projection into material projections, and passing these decomposed projections through a tomographic reconstruction algorithm, to get the volumetric mass density of each material. Material decomposition is a nonlinear inverse problem that has been traditionally solved using model-based material decomposition algorithms. However, the forward model is difficult to estimate in real prototypes. Moreover, the traditional regularizers used to stabilized inversions are not fully relevant in the projection domain.In this study, we propose a deep-learning method for material decomposition in the projection domain. We validate our methodology with numerical phantoms of human knees that are created from synchrotron CT scans. We consider four different scans for training, and one for validation. The measurements are corrupted by Poisson noise, assuming that at most 105 photons hit the detector. Compared to a regularized Gauss-Newton algorithm, the proposed deep-learning approach provides a compromise between noise and resolution, which reduces the computation time by a factor of 100.

Published
2021

53. Yttrium-90 Quantitative Phantom Study Using Digital Photon Counting PET

Author

David Sarrut, Joey Labour, Philippe Boissard, David Kryza, Thomas Mognetti, Fouzi Khayi, Pascale Veyrat Durebex, Thomas Baudier, Jean-Noël Badel, Sandrine Parisse-Di Martino, Imagerie Tomographique et Radiothérapie, Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Centre Léon Bérard [Lyon], Rayet, Béatrice, Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), and Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)

Subjects
Image quality, Monte Carlo method, [INFO.INFO-IM] Computer Science [cs]/Medical Imaging, Biomedical Engineering, R895-920, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Imaging phantom, 030218 nuclear medicine & medical imaging, 03 medical and health sciences, symbols.namesake, Medical physics. Medical radiology. Nuclear medicine, 0302 clinical medicine, Radioembolisation, Dosimetry, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Image noise, Listmode reconstruction, Radiology, Nuclear Medicine and imaging, Instrumentation, Monte Carlo simulation, Original Research, Physics, [PHYS.PHYS.PHYS-MED-PH] Physics [physics]/Physics [physics]/Medical Physics [physics.med-ph], Radiation, Photon counting, Gaussian filter, PET, 030220 oncology & carcinogenesis, Absorbed dose, [PHYS.PHYS.PHYS-MED-PH]Physics [physics]/Physics [physics]/Medical Physics [physics.med-ph], symbols, Digital photon counting, Biomedical engineering
Abstract

Background PET imaging of 90Y-microsphere distribution following radioembolisation is challenging due to the count-starved statistics from the low branching ratio of e+/e− pair production during 90Y decay. PET systems using silicon photo-multipliers have shown better 90Y image quality compared to conventional photo-multiplier tubes. The main goal of the present study was to evaluate reconstruction parameters for different phantom configurations and varying listmode acquisition lengths to improve quantitative accuracy in 90Y dosimetry, using digital photon counting PET/CT. Methods Quantitative PET and dosimetry accuracy were evaluated using two uniform cylindrical phantoms specific for PET calibration validation. A third body phantom with a 9:1 hot sphere-to-background ratio was scanned at different activity concentrations of 90Y. Reconstructions were performed using OSEM algorithm with varying parameters. Time-of-flight and point-spread function modellings were included in all reconstructions. Absorbed dose calculations were carried out using voxel S-values convolution and were compared to reference Monte Carlo simulations. Dose-volume histograms and root-mean-square deviations were used to evaluate reconstruction parameter sets. Using listmode data, phantom and patient datasets were rebinned into various lengths of time to assess the influence of count statistics on the calculation of absorbed dose. Comparisons between the local energy deposition method and the absorbed dose calculations were performed. Results Using a 2-mm full width at half maximum post-reconstruction Gaussian filter, the dosimetric accuracy was found to be similar to that found with no filter applied but also reduced noise. Larger filter sizes should not be used. An acquisition length of more than 10 min/bed reduces image noise but has no significant impact in the quantification of phantom or patient data for the digital photon counting PET. 3 iterations with 10 subsets were found suitable for large spheres whereas 1 iteration with 30 subsets could improve dosimetry for smaller spheres. Conclusion The best choice of the combination of iterations and subsets depends on the size of the spheres. However, one should be careful on this choice, depending on the imaging conditions and setup. This study can be useful in this choice for future studies for more accurate 90Y post-dosimetry using a digital photon counting PET/CT.

Published
2021

54. Tensor Factorization of Brain Structural Graph for Unsupervised Classification in Multiple Sclerosis

Author

Dominique Sappey-Marinier, Françoise Durand-Dubief, Claudio Stamile, Beradino Barile, Aldo Marzullo, Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), Department of Mathematics and Computer Science, University of Calabria, Rende, Italy, R&D Department CGnal, Milan, Italy, Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), and Barile, Berardino

Subjects
Tensor factorization, Computer science, business.industry, Multiple sclerosis, Pattern recognition, 02 engineering and technology, [INFO] Computer Science [cs], medicine.disease, White matter, 03 medical and health sciences, Statistical classification, 0302 clinical medicine, medicine.anatomical_structure, Factorization, Pattern recognition (psychology), 0202 electrical engineering, electronic engineering, information engineering, medicine, Graph (abstract data type), 020201 artificial intelligence & image processing, [INFO]Computer Science [cs], Tensor, Artificial intelligence, business, 030217 neurology & neurosurgery
Abstract

International audience; Analysis of longitudinal changes in brain diseases is essential for a better characterization of pathological processes and evaluation of the prognosis. This is particularly important in Multiple Sclerosis (MS) which is the first traumatic disease in young adults, with unknown etiology and characterized by complex inflammatory and degenerative processes leading to different clinical courses. In this work, we propose a fully automated tensor-based algorithm for the classification of MS clinical forms based on the structural connectivity graph of the white matter (WM) network. Using non-negative tensor factorization (NTF), we first focused on the detection of pathological patterns of the brain WM network affected by significant longitudinal variations. Second, we performed unsupervised classification of different MS phenotypes based on these longitudinal patterns, and finally, we used the latent factors obtained by the factorization algorithm to identify the most affected brain regions.

Published
2021

55. Apprendre l'apprentissage automatique : un retour d'expérience

Author

Debs, Noëlie, Peignier, Sergio, Douarre, Clément, Jourdan, Théo, Rigotti, Christophe, Frindel, Carole, Rigotti, Christophe, Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Biologie Fonctionnelle, Insectes et Interactions (BF2I), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Extraction de Caractéristiques et Identification (imagine), Laboratoire d'InfoRmatique en Image et Systèmes d'information (LIRIS), Université Lumière - Lyon 2 (UL2)-École Centrale de Lyon (ECL), Université de Lyon-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Lumière - Lyon 2 (UL2)-École Centrale de Lyon (ECL), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS), Artificial Evolution and Computational Biology (BEAGLE), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire de Biométrie et Biologie Evolutive - UMR 5558 (LBBE), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS), Data Mining and Machine Learning (DM2L), Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École Centrale de Lyon (ECL), Université de Lyon-Université Lumière - Lyon 2 (UL2)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Université Lumière - Lyon 2 (UL2), Université de Lyon-Université Lumière - Lyon 2 (UL2)-Inria Grenoble - Rhône-Alpes, Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), and Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)

Subjects
apprentissage automatique, [SCCO.COMP] Cognitive science/Computer science, [SHS.EDU]Humanities and Social Sciences/Education, [SHS.EDU] Humanities and Social Sciences/Education, [INFO.INFO-DS]Computer Science [cs]/Data Structures and Algorithms [cs.DS], retour d'expérience, enseignement, [SCCO.COMP]Cognitive science/Computer science, prédiction, [INFO.INFO-DS] Computer Science [cs]/Data Structures and Algorithms [cs.DS], modélisation
Abstract

National audience; Dans cet article, nous présentons un retour d'expérience sur un module d'initiation à l'apprentissage automatique de 3 crédits ECTS que nous avons créé pour des élèves ingénieurs de l'INSA de Lyon. Nous présentons la structuration de ce module ainsi que les spécificités pédagogiques liées au faible nombre d'heures de face à face au regard de la complexité du sujet. Nous montrons des exemples de cas d'usage proposés aux étudiants et faisons état de leurs réactions. L'ensemble est complété par des liens bibliographiques vers les sites des outils numériques libres et références pédagogiques utilisées.

Published
2021

56. Influence of sub-nanosecond time of flight resolution for online range verification in proton therapy using the line-cone reconstruction in Compton imaging

Author

M.-L. Gallin-Martel, A. Etxebeste, David Sarrut, Brent Huisman, Denis Dauvergne, Jean Michel Létang, E. Testa, Jayde Livingstone, Mattia Fontana, Sara Marcatili, Rayet, Béatrice, Laboratoire de Physique Subatomique et de Cosmologie (LPSC), Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA), Imagerie Tomographique et Radiothérapie, Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Institut de Physique des 2 Infinis de Lyon (IP2I Lyon), Université de Lyon-Université de Lyon-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS), Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS), and Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL)

Subjects
Diagnostic Imaging, Iterative reconstruction, Scintillator, Imaging phantom, Collimated light, 030218 nuclear medicine & medical imaging, 03 medical and health sciences, 0302 clinical medicine, Optics, Image Processing, Computer-Assisted, Proton Therapy, Radiology, Nuclear Medicine and imaging, [PHYS.PHYS.PHYS-INS-DET]Physics [physics]/Physics [physics]/Instrumentation and Detectors [physics.ins-det], Proton therapy, ComputingMilieux_MISCELLANEOUS, Physics, [PHYS.PHYS.PHYS-MED-PH] Physics [physics]/Physics [physics]/Medical Physics [physics.med-ph], Radiological and Ultrasound Technology, Phantoms, Imaging, business.industry, Detector, Time of flight, Gamma Rays, [PHYS.PHYS.PHYS-INS-DET] Physics [physics]/Physics [physics]/Instrumentation and Detectors [physics.ins-det], 030220 oncology & carcinogenesis, Temporal resolution, [PHYS.PHYS.PHYS-MED-PH]Physics [physics]/Physics [physics]/Medical Physics [physics.med-ph], business, Monte Carlo Method
Abstract

Online ion range monitoring in hadron therapy can be performed via detection of secondary radiation, such as prompt γ-rays, emitted during treatment. The prompt γ emission profile is correlated with the ion depth-dose profile and can be reconstructed via Compton imaging. The line-cone reconstruction, using the intersection between the primary beam trajectory and the cone reconstructed via a Compton camera, requires negligible computation time compared to iterative algorithms. A recent report hypothesised that time of flight (TOF) based discrimination could improve the precision of the γ fall-off position measured via line-cone reconstruction, where TOF comprises both the proton transit time from the phantom entrance until γ emission, and the flight time of the γ-ray to the detector. The aim of this study was to implement such a method and investigate the influence of temporal resolution on the precision of the fall-off position. Monte Carlo simulations of a 160 MeV proton beam incident on a homogeneous PMMA phantom were performed using GATE. The Compton camera consisted of a silicon-based scatterer and CeBr3 scintillator absorber. The temporal resolution of the detection system (absorber + beam trigger) was varied between 0.1 and 1.3 ns RMS and a TOF-based discrimination method applied to eliminate unlikely solution(s) from the line-cone reconstruction. The fall-off position was obtained for varying temporal resolutions and its precision obtained from its shift across 100 independent γ emission profiles compared to a high statistics reference profile. The optimal temporal resolution for the given camera geometry and 108 primary protons was 0.2 ns where a precision of 2.30 ± 0.15 mm (1σ) on the fall-off position was found. This precision is comparable to current state of-the-art Compton imaging using iterative reconstruction methods or 1D imaging with mechanically collimated devices, and satisfies the requirement of being smaller than the clinical safety margins.

Published
2021

57. Comparison of segmentation algorithms for organs at risk delineation on head-and-neck CT images

Author

Costea, Madalina-Liana, Biston, Marie-Claude, Grégoire, Vincent, Sarrut, David, Rayet, Béatrice, Centre Léon Bérard [Lyon], Imagerie Tomographique et Radiothérapie, Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), and Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)

Subjects
[PHYS.PHYS.PHYS-MED-PH] Physics [physics]/Physics [physics]/Medical Physics [physics.med-ph], [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, [PHYS.PHYS.PHYS-MED-PH]Physics [physics]/Physics [physics]/Medical Physics [physics.med-ph], [INFO.INFO-IM] Computer Science [cs]/Medical Imaging
Abstract

International audience; Purpose or ObjectiveTo investigate the performance of head and neck organs-at-risk (OAR) contouring using several atlas-based (ABAS) vs deep learning (DL) segmentation methods.Material and MethodsTen heterogeneous head-and-neck (HN) patients, having body mass index ranging from 18.9 to 30.7, were selected for the atlas collection. On each of the CT patient data, 20 organs-at-risk (OARs) were manually delineated by expert physicians. Three different ABAS algorithms were tested on 15 HN patients using Advanced Medical Imaging Registration Engine (ADMIRE) v3.26 (Elekta AB, Stockholm, Sweden): Simultaneous Truth and Performance Level Estimation (STAPLE), Patch Fusion (PF) and Random Forest label fusion (RF). Their performance was evaluated against manually contoured OARs in terms of the dice similarity coefficient (DICE) and Hausdorff distances (HD and 95-percentileHD (HD95)). The results were compared with two commercially available solutions: one ABAS (MIM-Maestro 7.0.2, Cleveland, USA) based on majority vote algorithm for label fusion and one DL solution trained on multi-centric data (ART-plan Annotate, Therapanacea, Paris, France).ResultsAll solutions had superior results compared to MIM-Maestro ABAS software for all the OARs. Between the three fusion Elekta-ABAS algorithms, RF label fusion, which contains artificial intelligence learning features, resulted in the best results for the majority of structures but did not segment optical nerves and cochlea (Fig. 1). Compared to the DL solution, ADMIRE RF had equal, better and worse results for 3, 7 and 5 out of 15 common OARs, respectively. Better DICE were obtained for the eyes (0.91 vs 0.87), the larynx (0.79 vs 0.77), the oral cavity (0.87 vs 0.84), the mandible (0.92 vs 0.89), one submandibular gland (0.78 vs 0.77) and for the external contour (0.99 vs 0.76). The HD metrics confirmed the superiority of the DICE results with smaller distances for the mentioned OARs and particularly for the oral cavity (HD: 10.71 vs 13.93; HD95: 6.53 vs 9.70) and the mandible (HD: 8.10 vs 10.71; HD95: 2.22 vs 3.56). The DL model outperformed ADMIRE RF for one parotid gland (DICE: 0.82 vs 0.80; HD: 13.45 vs 13.33; HD95: 7.49 vs 6.98), one submandibular gland (DICE: 0.81 vs 0.75; HD: 8.47 vs 8.14; HD95: 4.76 vs 4.87), for the esophagus (DICE: 0.73 vs 0.61; HD: 30.44 vs 34.06; HD95: 23.62 vs 24.71) and for the thyroid (DICE: 0.85 vs 0.74; HD: 8.43 vs 13.73; HD95: 3.04 vs 8.00).ConclusionThe ADMIRE multi-ABAS was capable of segmenting OARs in HN region with comparable or superior accuracy to a DL model. Further work is in progress to evaluate the clinical value of these results by assessing the impact of contouring inaccuracies with respect to dose coverage, and by evaluating the time needed for manual correction of the automatically delineated contours.

Published
2021

58. Complete genome of Bradyrhizobium sp. strain BDV5419, representative of Australian genospecies L

Author

Oger-Desfeux, Christine, Briolay, Jerome, Oger, P., Lafay, Bénédicte, Pôle Rhône-Alpin de BioInformatique [Lyon] (PRABI), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon, Université de Lyon, Microbiology of Extreme Environments (M2E), Microbiologie, adaptation et pathogénie (MAP), Université de Lyon-Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS), Laboratoire de Biométrie et Biologie Evolutive - UMR 5558 (LBBE), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS), Ampère, Département Automatique pour l'Ingénierie des Systèmes (AIS), Ampère (AMPERE), École Centrale de Lyon (ECL), Université de Lyon-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-École Centrale de Lyon (ECL), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), and Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National des Sciences Appliquées de Lyon (INSA Lyon)

Subjects
inorganic chemicals, [SDV.BBM.GTP]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN], bacteria, food and beverages, biochemical phenomena, metabolism, and nutrition, [SDV.MP.BAC]Life Sciences [q-bio]/Microbiology and Parasitology/Bacteriology
Abstract

International audience; We report the complete genome sequence of Bradyrhizobium sp. strain BDV5419, representative of Bradyrhizobium genospecies L, which symbiotically associates with the Australian native legume Hardenbergia violaceae and is expected to represent a novel Bradyrhizobium species. The complete genome sequence provides a genetic reference for this Australian genospecies.

Published
2021

59. Traitement du signal sur les complexes simpliciaux

Author

Ji, Feng, Kahn, Giacomo, Tay, Wee Peng, Kahn, Giacomo, School of Electrical and Electronic EngineeringNanyang Technological UniversityNanyangSingapore, Décision et Information pour les Systèmes de Production (DISP), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université Lumière - Lyon 2 (UL2), Université Lumière - Lyon 2 (UL2)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), and Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)

Subjects
[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, ComputingMilieux_MISCELLANEOUS, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Abstract

International audience

Published
2021

60. Mid-p strategy versus ITV strategy in locally advanced lung cancer. A randomized phase II study

Author

Claude, L., Isnardi, V., Schiffler, C., Metzger, S., Martel-Lafay, Isabelle, Rit, Simon, Sarrut, David, Baudier, Thomas, Ayadi, M., Rayet, Béatrice, Centre Léon Bérard [Lyon], Imagerie Tomographique et Radiothérapie, Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), and Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)

Subjects
[PHYS.PHYS.PHYS-MED-PH] Physics [physics]/Physics [physics]/Medical Physics [physics.med-ph], [SDV.CAN] Life Sciences [q-bio]/Cancer, [PHYS.PHYS.PHYS-MED-PH]Physics [physics]/Physics [physics]/Medical Physics [physics.med-ph], [SDV.CAN]Life Sciences [q-bio]/Cancer, non-small-cell-lung-cancer, Mid-p strategy, radiotherapy
Abstract

International audience; Purpose/Objective The overall survival (OS) of patients (pts) with non-resectable locally advanced non-small cell lung carcinoma (LA-NSCLC) is poor, in part due to insufficient local control (LC) using conformal irradiation techniques (RT). The personalization of the RT margins may impact the LC and the outcome. Internal Target Volume strategy (ITV) versus "Mid-position" strategy (Mid-p), was compared in a prospective non-comparative randomized monocentric phase II trial in NSCLC patients treated by definitive radiotherapy. Planning Target volumes and mean lung dose were previously reported as significantly reduced using the Mid-p strategy (DOI: 10.1259/bjr.20190692). We report here the clinical results. Material and Methods Eligible patients were randomized (2:1) to be treated with Mid-p or ITV strategies. Patients with proven LA-NSCLC, non-resected, non-metastatic treated by definitive RT could be included. The main objective was to evaluate the 1-year progression-free-survival (PFS) rate in the two arms. 36 pts were planned in the Mid-p arm, Fleming single-stage design (1-sided =0.1, 80% power, P0=30%, P1=50%). Secondary objectives were to evaluate 1-y and 2-y LC, OS and acute/middle term toxicity (NCI-CTCAE v4).Results 54 pts were randomized from 09/12 to 05/18. 3 patients finally did not receive radiotherapy and were excluded from the analysis. Median age was 65.2 y, 2/3 of the patients were male and had IIIA NSCLC stages, 31% received concomitant chemotherapy. 34 pts and 17 pts were included in the analysis in the Mid-p arm and ITV arm respectively. Median RT dose was 66 Gy in the Mid-p arm and 62 Gy in the ITV arm. Median PFS were 9.3 months and 10.3 months in the Mid-p arm and ITV arms respectively. 1-year PFS rate were 38% (1-sided CI95% = 25-) and 47% (CI95% = [27;[) in the Mid-p/ITV arm respectively. Efficacy in Mid-p arm is below that expected (starting hypothesis p0=30%, p1=50%). 2-year PFS rates were 15% (Mid-p) and 12% (ITV). 2-years LC rates were 65% (CI95% [48;81]) and 76% (CI95% [53;94]) in the Mid-p/ITV arms respectively. The analysis of the type of local failures (in field versus border of fields) is under analysis and will be available for the congress.No grade 4 or toxic deaths related to RT were reported. Grade 3 acute lung toxicity were reported in 12% and 23% in Mid-p and in ITV arms respectively. Grade 2 and Grade 3 late radiation fibrosis were reported in 29% and 15% respectively in the Mid-p arm, versus 23% and 29% using ITV strategy. Conclusion Two-year LC and PFS in LA-NSCLC seems similar in this non comparative Phase II randomized study using Mid-p or ITV strategies. The details of local relapses regarding RT fields and margins are under analysis and will be presented during the congress.Conflict of interest: this study was granted by Elekta.

Published
2021

61. Impact of the reperfusion status for predicting the final stroke infarct using deep learning

Author

Debs, Noëlie, Cho, Tae-Hee, Rousseau, David, Berthezène, Yves, Buisson, Marielle, Eker, Omer, Mechtouff, Laura, Nighoghossian, Norbert, Ovize, Michel, Frindel, Carole, Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), Hospices Civils de Lyon, Departement de Neurologie (HCL), Institut de Recherche en Horticulture et Semences (IRHS), Université d'Angers (UA)-AGROCAMPUS OUEST, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Laboratoire Angevin de Recherche en Ingénierie des Systèmes (LARIS), Université d'Angers (UA), Service de Radiologie et IRM, Hospices Civils de Lyon, Centre Hospitalier Lyon Sud [CHU - HCL] (CHLS), Hospices Civils de Lyon (HCL)-Hospices Civils de Lyon (HCL), Hospices Civils de Lyon (HCL), Cardiovasculaire, métabolisme, diabétologie et nutrition (CarMeN), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Hospices Civils de Lyon (HCL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), ANR-16-RHUS-0009,MARVELOUS,MARVELOUS(2016), ANR-18-RHUS-0001,BOOSTER,Brain clOt persOnalized therapeutic Strategies for sTroke Emergent Reperfusion(2018), CCSD, Accord Elsevier, MARVELOUS - - MARVELOUS2016 - ANR-16-RHUS-0009 - RHUS - VALID, Brain clOt persOnalized therapeutic Strategies for sTroke Emergent Reperfusion - - BOOSTER2018 - ANR-18-RHUS-0001 - RHUS - VALID, Université de Lyon-Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Modeling & analysis for medical imaging and Diagnosis (MYRIAD), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université d'Angers (UA)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-INSTITUT AGRO Agrocampus Ouest, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), and Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université Claude Bernard Lyon 1 (UCBL)

Subjects
[SDV.IB] Life Sciences [q-bio]/Bioengineering, Reperfusion status, Regular Article, Convolutional neural network, lcsh:Computer applications to medicine. Medical informatics, lcsh:RC346-429, Brain Ischemia, Stroke, Deep Learning, Diffusion Magnetic Resonance Imaging, Magnetic resonance imaging, [INFO.EIAH] Computer Science [cs]/Technology for Human Learning, Infarction, Reperfusion, Humans, lcsh:R858-859.7, [INFO.EIAH]Computer Science [cs]/Technology for Human Learning, [SDV.IB]Life Sciences [q-bio]/Bioengineering, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], [SDV.NEU] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Prediction, lcsh:Neurology. Diseases of the nervous system, ComputingMethodologies_COMPUTERGRAPHICS
Abstract

Graphical abstract, Highlights • Deep learning predictive models of the final stroke infarct may have better performances when trained on reperfusion status specific subsets. • The proposed predictive models outperformed the standards threshold-based perfusion-diffusion mismatch models. • Comparing the predicted infarct in case of a successful vs failed reperfusion may help in estimating the treatment effect and guiding therapeutic decisions in selected patients., Background Predictive maps of the final infarct may help therapeutic decisions in acute ischemic stroke patients. Our objectives were to assess whether integrating the reperfusion status into deep learning models would improve their performance, and to compare them to current clinical prediction methods. Methods We trained and tested convolutional neural networks (CNNs) to predict the final infarct in acute ischemic stroke patients treated by thrombectomy in our center. When training the CNNs, non-reperfused patients from a non-thrombectomized cohort were added to the training set to increase the size of this group. Baseline diffusion and perfusion-weighted magnetic resonance imaging (MRI) were used as inputs, and the lesion segmented on day-6 MRI served as the ground truth for the final infarct. The cohort was dichotomized into two subsets, reperfused and non-reperfused patients, from which reperfusion status specific CNNs were developed and compared to one another, and to the clinically-used perfusion-diffusion mismatch model. Evaluation metrics included the Dice similarity coefficient (DSC), precision, recall, volumetric similarity, Hausdorff distance and area-under-the-curve (AUC). Results We analyzed 109 patients, including 35 without reperfusion. The highest DSC were achieved in both reperfused and non-reperfused patients (DSC = 0.44 ± 0.25 and 0.47 ± 0.17, respectively) when using the corresponding reperfusion status-specific CNN. CNN-based models achieved higher DSC and AUC values compared to those of perfusion-diffusion mismatch models (reperfused patients: AUC = 0.87 ± 0.13 vs 0.79 ± 0.17, P

Published
2021

62. Reappraisal of archaeal C20-C25 diether lipid (extended archaeol) origin and use as a biomarker of hypersalinity

Author

Maxime Tourte, Philippe Schaeffer, Flore Vandier, Vincent Grossi, Julien Plancq, Cara Doumbe-Kingue, Phil M. Oger, Laboratoire de Géologie de Lyon - Terre, Planètes, Environnement [Lyon] (LGL-TPE), Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-École normale supérieure - Lyon (ENS Lyon), Microbiologie, adaptation et pathogénie (MAP), Centre National de la Recherche Scientifique (CNRS)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon, Microbiology of Extreme Environments (M2E), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Strasbourg (UNISTRA), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), School of Geographical and Earth Sciences [Univ Glasgow], University of Glasgow, Institut de Chimie de Strasbourg, Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS)-Institut de Chimie du CNRS (INC), École normale supérieure - Lyon (ENS Lyon)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Laboratoire de Géologie de Lyon - Terre, Planètes, Environnement (LGL-TPE), École normale supérieure de Lyon (ENS de Lyon)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut national des sciences de l'Univers (INSU - CNRS)-Université Jean Monnet - Saint-Étienne (UJM)-Centre National de la Recherche Scientifique (CNRS), and Université de Strasbourg (UNISTRA)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)

Subjects
[SDE.MCG]Environmental Sciences/Global Changes, Zoology, 010502 geochemistry & geophysics, [SDV.BID.SPT]Life Sciences [q-bio]/Biodiversity/Systematics, Phylogenetics and taxonomy, 01 natural sciences, Natrialbales, 03 medical and health sciences, chemistry.chemical_compound, Geochemistry and Petrology, [SDU.STU.GC]Sciences of the Universe [physics]/Earth Sciences/Geochemistry, adaptation to pH and salinity, Statistical analysis, Archaeol, 0105 earth and related environmental sciences, 0303 health sciences, biology, 030306 microbiology, Saturation (genetic), biology.organism_classification, extended archaeol, [SDV.MP.BAC]Life Sciences [q-bio]/Microbiology and Parasitology/Bacteriology, Halophilic archaea, Salinity, Biomarker (petroleum), chemistry, Haloarchaea, lipids (amino acids, peptides, and proteins), Archaea
Abstract

International audience; The diether core membrane lipid sesterterpanyl-phytanyl-glycerol (so-called extended archaeol and often abbreviated C 20-C 25) is considered as a hallmark of Haloarchaea, a clade of archaea thriving under extreme high salinities. We here report about extended archaeol occurrence in different saline aquatic settings with salinity ranging from ca. 50 psu (5 % NaCl w/v) to saturation (ca. 350 psu). This demonstrates that this lipid is not restricted to extreme saline environments but suggests a minimum salinity threshold of ca. 50 psu above which C 20-C 25 is most commonly produced. The proportion of C 20-C 25 relative to that of archaeol (C 20-C 20) did not appear linearly dependent on the salinity of the site and was potentially also influenced by pH and temperature, preventing its direct use as a quantitative salinity proxy based on the present data set. An extensive literature review of archaeal membrane lipid compositions further highlighted that taxonomy also contributes to the distribution of this lipid in the environment and identifies Natrialbales (one of the three orders of Haloarchaea) as the main source. Statistical analysis showed that, among Haloarchaea, C 20-C 25 producers display pH and salinity growth optima slightly higher than non-producers and are distributed within two distinct groups, one composed mostly of neutrophiles and one of alkaliphiles. In contrast, the presence of C 20-C 25 was not correlated to the optimal growth temperature of the strains. This suggests that two confounding parameters, i.e., taxonomy and adaptation to changes in salinity and/or pH, contribute to the distribution of C 20-C 25 within Haloarchaea.

Published
2021

63. Reversible Edema in the Penumbra Correlates With Severity of Hypoperfusion

Author

Claus Z Simonsen, Ángel Chamorro, Salvador Pedraza, Vincent Thijs, Götz Thomalla, Martin Ebinger, Keith W. Muir, Florent Boutitie, Armin Schneider, Anke Wouters, Bastian Cheng, Lauranne Scheldeman, Erich Bernd Ringelstein, Matthias Endres, Jochen B. Fiebach, Christian Gerloff, Soren Christensen, Rico Laage, Norbert Nighoghossian, Patrick Dupont, Martin Grond, Robin Lemmens, University Hospitals Leuven [Leuven], Catholic University of Leuven - Katholieke Universiteit Leuven (KU Leuven), Flanders Make [Leuven], Flanders Make, Leuven Brain Institute [Leuven, Belgium] (LBI), GrayNumber Analytics [Lomma, Sweden], Hospices Civils de Lyon (HCL), Laboratoire de Biométrie et Biologie Evolutive - UMR 5558 (LBBE), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS), Université de Lyon, National Space Science Center [Beijing] (NSSC), Chinese Academy of Sciences [Beijing] (CAS), Charité - UniversitätsMedizin = Charité - University Hospital [Berlin], Universitaetsklinikum Hamburg-Eppendorf = University Medical Center Hamburg-Eppendorf [Hamburg] (UKE), University of Glasgow, Cardiovasculaire, métabolisme, diabétologie et nutrition (CarMeN), Université de Lyon-Université de Lyon-Hospices Civils de Lyon (HCL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), Girona Biomedical Research Institute [Girona, Spain] (IDIBGI), Aarhus University Hospital, University of Münster, University of Barcelona, Kreisklinikum Siegen GmbH [Siegen, Germany], Philipps University of Marburg, Guided Development Heidelberg GmbH [Heidelberg, Germany], Lifedatascience Consulting [Schriesheim, Germany] (LdSC), Florey Institute of Neuroscience and Mental Health [Melbourne, Victoria, Australia], Austin Health, Université de Lyon-Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Westfälische Wilhelms-Universität Münster = University of Münster (WWU), Philipps Universität Marburg = Philipps University of Marburg, Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), and CarMeN, laboratoire

Subjects
Male, medicine.medical_treatment, [SDV]Life Sciences [q-bio], diagnostic imaging [Brain Edema], Brain Edema, Fluid-attenuated inversion recovery, Brain Ischemia, Cohort Studies, 0302 clinical medicine, methods [Magnetic Resonance Imaging], Granulocyte Colony-Stimulating Factor, magnetic resonance imaging, Thrombolytic Therapy, humans, Stroke, diagnostic imaging [Ischemic Stroke], 0303 health sciences, therapeutic use [Granulocyte Colony-Stimulating Factor], medicine.diagnostic_test, Penumbra, Thrombolysis, therapy [Ischemic Stroke], Middle Aged, follow-up studies, [SDV] Life Sciences [q-bio], Cerebrovascular Circulation, Cardiology, Female, medicine.symptom, Cardiology and Cardiovascular Medicine, Perfusion, medicine.medical_specialty, physiology [Cerebrovascular Circulation], perfusion, Lesion, 03 medical and health sciences, Internal medicine, medicine, ischemic stroke, Effective diffusion coefficient, Humans, ddc:610, therapy [Brain Edema], 030304 developmental biology, Aged, Advanced and Specialized Nursing, business.industry, Patient Acuity, Magnetic resonance imaging, methods [Thrombolytic Therapy], medicine.disease, diagnostic imaging [Brain Ischemia], therapy [Brain Ischemia], Neurology (clinical), business, 030217 neurology & neurosurgery
Abstract

Background and Purpose: We aimed to investigate fluid-attenuated inversion recovery changes in the penumbra. Methods: We determined core and perfusion lesions in subjects from the WAKE-UP trial (Efficacy and Safety of MRI-Based Thrombolysis in Wake-Up Stroke) and AXIS 2 trial (Granulocyte Colony-Stimulating Factor in Patients With Acute Ischemic Stroke) with perfusion- and diffusion-weighted imaging at baseline. Only subjects with a mismatch volume >15 mL and ratio >1.2 were included. We created voxel-based relative fluid-attenuated inversion recovery signal intensity (rFLAIR SI) maps at baseline and follow-up. We studied rFLAIR SI in 2 regions of interest: baseline penumbra (baseline perfusion lesion−[core lesion+voxels with apparent diffusion coefficient −6 mm 2 /s]) and noninfarcted penumbra (baseline perfusion lesion−follow-up fluid-attenuated inversion recovery lesion) at 24 hours (WAKE-UP) or 30 days (AXIS 2). We analyzed the association between rFLAIR SI and severity of hypoperfusion, defined as time to maximum of the residue function. Results: In the baseline penumbra, rFLAIR SI was elevated (ratio, 1.04; P =1.7×10 − 13 ; n=126) and correlated with severity of hypoperfusion (Pearson r, 0.03; P − 4 ; n=126). In WAKE-UP, imaging at 24 hours revealed a further increase of rFLAIR SI in the noninfarcted penumbra (ratio, 1.05 at 24 hours versus 1.03 at baseline; P =7.1×10 −3 ; n=43). In AXIS 2, imaging at 30 days identified reversibility of the rFLAIR SI (ratio, 1.02 at 30 days versus 1.04 at baseline; P =1.5×10 −3 ; n=26) since it was no longer different from 1 (ratio, 1.01 at 30 days; P =0.099; n=26). Conclusions: Penumbral rFLAIR SI increases appear early after stroke onset, correlate with severity of hypoperfusion, further increase at 24 hours, and are reversible by 30 days. Registration: URL: https://clinicaltrials.gov ; Unique identifier: NCT01525290. URL: https://clinicaltrials.gov ; Unique identifier: NCT00927836.

Published
2021

64. Population-Based Personalization of Geometric Models of Myocardial Infarction

Author

Nicolas Duchateau, Kannara Mom, Patrick Clarysse, Duchateau, Nicolas, Modélisation de la hiérarchie entre descripteurs cardiaques par apprentissage automatique - - MIC-MAC2019 - ANR-19-CE45-0005 - AAPG2019 - VALID, Physique, Radiobiologie, Imagerie Médicale et Simulation - - PRIMES2011 - ANR-11-LABX-0063 - LABX - VALID, Modeling & analysis for medical imaging and Diagnosis (MYRIAD), Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), and ANR-19-CE45-0005,MIC-MAC,Modélisation de la hiérarchie entre descripteurs cardiaques par apprentissage automatique(2019)

Subjects
Matching (graph theory), Computer science, 0206 medical engineering, Population, [INFO.INFO-IM] Computer Science [cs]/Medical Imaging, 02 engineering and technology, Machine learning, computer.software_genre, cardiac magnetic resonance, Synthetic data, 030218 nuclear medicine & medical imaging, Personalization, 03 medical and health sciences, 0302 clinical medicine, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, late Gadolinium enhancement, Relevance (information retrieval), Divergence (statistics), education, Randomness, education.field_of_study, business.industry, Covariance matrix, 020601 biomedical engineering, Model personalization, myocardial infarction, ComputingMethodologies_PATTERNRECOGNITION, Artificial intelligence, business, computer
Abstract

International audience; We propose a strategy to perform population-based personalization of a model, to overcome the limits of case-based personalization for generating virtual populations from models that include randomness. We formulate the problem as matching the synthetic and real populations by minimizing the Kullback-Leibler divergence between their distributions. As an analytical formulation of the models is complex or even impossible, the personalization is addressed by a gradient-free method: the CMA-ES algorithm, whose relevance was demonstrated for the case-based personalization of complex biomechanical cardiac models. The algorithm iteratively adapts the covariance matrix which in our problem encodes the distribution of the synthetic data. We demonstrate the feasibility of this approach on two simple geometrical models of myocardial infarction, in 2D, to better focus on the relevance of the personalization process. Our strategy is able to reproduce the distribution of 2D myocardial infarcts from the segmented late Gadolinium images of 123 subjects with acute myocardial infarction.

Published
2021

65. Biochemical characterization and mutational studies of a novel 3-methlyadenine DNA glycosylase II from the hyperthermophilic Thermococcus gammatolerans

Author

Philippe Oger, Likui Zhang, Kunming Dong, Yong Gong, Donghao Jiang, Yangzhou University, Beijing Synchrotron Radiation Facility, Institute of High Energy Physics, Microbiologie, adaptation et pathogénie (MAP), Centre National de la Recherche Scientifique (CNRS)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon, Microbiology of Extreme Environments (M2E), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université Claude Bernard Lyon 1 (UCBL), and Oger, Phil M.

Subjects
DNA, Bacterial, Hot Temperature, [SDV.BIO]Life Sciences [q-bio]/Biotechnology, DNA Repair, Biochemistry, base excision repair, Divalent, law.invention, DNA Glycosylases, Substrate Specificity, 03 medical and health sciences, chemistry.chemical_compound, 0302 clinical medicine, Bacterial Proteins, law, [SDV.BBM] Life Sciences [q-bio]/Biochemistry, Molecular Biology, [SDV.BBM]Life Sciences [q-bio]/Biochemistry, Molecular Biology, Amino Acid Sequence, Molecular Biology, 030304 developmental biology, chemistry.chemical_classification, 0303 health sciences, Hypoxanthine, biology, alkylated DNA, Adenine, DNA glycosylase, Thermococcus gammatolerans, deaminated DNA, Cell Biology, Base excision repair, Hydrogen-Ion Concentration, biology.organism_classification, [SDV.BIO] Life Sciences [q-bio]/Biotechnology, Thermococcus, Kinetics, Enzyme, chemistry, 030220 oncology & carcinogenesis, Mutation, Recombinant DNA, Sequence Alignment, DNA, DNA Damage
Abstract

International audience; The hyperthermophilic and radioresistant euryarchaeon Thermococcus gammatolerans encodes a putative 3-methlyadenine DNA glycosylase II (Tg-AlkA). Herein, we report biochemical characterization and catalytic mechanism of Tg-AlkA. The recombinant Tg-AlkA can excise hypoxanthine (Hx) and 1-methlyadenine (1-meA) from dsDNA with varied efficiencies at high temperature. Notably, Tg-AlkA is a bi-functional glycosylase, which is sharply distinct from all the reported AlkAs. Biochemical data show that the optimal temperature and pH of Tg-AlkA for removing Hx from dsDNA are ca.70°C and ca.7.0-8.0, respectively. Furthermore, the Tg-AlkA activity is independent of a divalent metal ion, and Mg 2+ stimulates the Tg-AlkA activity whereas other divalent ions inhibit the enzyme activity with varied degrees. Mutational studies show that the Tg-AlkA W204A and D223A mutants abolish completely the excision activity, thereby suggesting that residues W204 and D223 are involved in catalysis. Surprisingly, the mutations of W204, D223, Y139 and W256 to alanine in Tg-AlkA lead to the increased affinity for binding DNA substrate with varied degrees, suggesting that these residues are flexible for conformational change of the enzyme. Therefore, Tg-AlkA is a novel AlkA that can remove Hx and 1-meA from dsDNA, thus providing insights into repair of deaminated and alkylated bases in DNA from hyperthermophilic Thermococcus.

Published
2021

66. Harmonic wideband simultaneous dual‐frequency MR Elastography

Author

Hélène Ratiney, Kevin Tse Ve Koon, Fabien Millioz, Cyrielle Caussy, Olivier Beuf, Pilar Sango Solanas, RMN et optique : De la mesure au biomarqueur, Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Modeling & analysis for medical imaging and Diagnosis (MYRIAD), Cardiovasculaire, métabolisme, diabétologie et nutrition (CarMeN), Université de Lyon-Université de Lyon-Hospices Civils de Lyon (HCL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), ANR-11-INBS-0006,FLI,France Life Imaging(2011), ANR-11-IDEX-0007,Avenir L.S.E.,PROJET AVENIR LYON SAINT-ETIENNE(2011), ANR-10-LABX-0064,Imust,Institut for Multiscale Science and Technology : from Fundamental Physics and Chemistry to Engineering in New Material and Processes and Ecotechnologies(2010), Sango Solanas, Pilar, Infrastructures - France Life Imaging - - FLI2011 - ANR-11-INBS-0006 - INBS - VALID, PROJET AVENIR LYON SAINT-ETIENNE - - Avenir L.S.E.2011 - ANR-11-IDEX-0007 - IDEX - VALID, Institut for Multiscale Science and Technology : from Fundamental Physics and Chemistry to Engineering in New Material and Processes and Ecotechnologies - - Imust2010 - ANR-10-LABX-0064 - LABX - VALID, Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), and Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)

Subjects
Shear waves, [SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, Acoustics, [SDV]Life Sciences [q-bio], Phase (waves), Signal-To-Noise Ratio, Imaging phantom, 030218 nuclear medicine & medical imaging, 03 medical and health sciences, Motion, 0302 clinical medicine, Aliasing, Image Processing, Computer-Assisted, Humans, Radiology, Nuclear Medicine and imaging, Wideband, Spectroscopy, Physics, MR elastography, Phantoms, Imaging, Viscosity, multifrequency, phase offset undersampling, Magnetic Resonance Imaging, Elasticity, Magnetic resonance elastography, Vibration, [SDV] Life Sciences [q-bio], [SDV.IB.IMA] Life Sciences [q-bio]/Bioengineering/Imaging, Harmonic, Molecular Medicine, Elasticity Imaging Techniques, 030217 neurology & neurosurgery
Abstract

International audience; Magnetic resonance elastography (MRE) is used to non‐invasively quantify viscoelastic properties of tissues based on the measurement of propagation characteristics of shear waves. Because some of these viscoelastic parameters show a frequency dependence, multifrequency analysis allows us to measure the wave propagation dispersion, leading to a better characterization of tissue properties. Conventionally, motion encoding gradients (MEGs) oscillating at the same frequency as the mechanical excitation encode motion. Hence, multifrequency data is usually obtained by sequentially repeating monochromatic wave excitations experiments at different frequencies. The result is that the total acquisition time is multiplied by a factor corresponding to the number of repetitions of monofrequency experiments, which is a major limitation of multifrequency MRE. In order to make it more accessible, a novel single‐shot harmonic wideband dual‐frequency MRE method is proposed. Two superposed shear waves of different frequencies are simultaneously generated and propagate in a sample. Trapezoidal oscillating MEGs are used to encode mechanical vibrations having frequencies that are an odd multiple of the MEG frequency. The number of phase offsets is optimized to reduce the acquisition time. For this purpose, a sampling method not respecting the Shannon theorem is used to produce a controlled temporal aliasing that allows us to encode both frequencies without any additional examination time. Phantom experiments were run to compare conventional monofrequency MRE with the single‐shot dual‐frequency MRE method and showed excellent agreement between the reconstructed shear storage moduli G′. In addition, dual‐frequency MRE yielded an increased signal‐to‐noise ratio compared with conventional monofrequency MRE acquisitions when encoding the high frequency component. The novel proposed multifrequency MRE method could be applied to simultaneously acquire more than two frequency components, reducing examination time. Further studies are needed to confirm its applicability in preclinical and clinical models.

Published
2021

67. Identification of a novel bifunctional uracil DNA glycosylase from Thermococcus barophilus Ch5

Author

Philippe Oger, Yong Gong, Qi Gan, Likui Zhang, Donghao Jiang, Haoqiang Shi, Li Miao, Yangzhou University, Institute of High Energy Physics, Chinese Academy of Sciences [Changchun Branch] (CAS), Microbiologie, adaptation et pathogénie (MAP), Centre National de la Recherche Scientifique (CNRS)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon, Microbiology of Extreme Environments (M2E), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS), and Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL)

Subjects
DNA Repair, Deamination, Applied Microbiology and Biotechnology, 03 medical and health sciences, chemistry.chemical_compound, AP site, Uracil, Uracil-DNA Glycosidase, Hyperthermophilic Archaea, Phylogeny, 030304 developmental biology, 0303 health sciences, biology, 030302 biochemistry & molecular biology, [SDV.BBM.BM]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Molecular biology, General Medicine, Base excision repair, biology.organism_classification, Bifunctional glycosylase, Thermococcus, Thermococcus barophilus, [SDV.MP]Life Sciences [q-bio]/Microbiology and Parasitology, chemistry, Biochemistry, DNA glycosylase, Uracil-DNA glycosylase, Uracil DNA glycosylase, Cytosine, Biotechnology
Abstract

Genomes of hyperthermophiles are facing a severe challenge due to increased deamination rates of cytosine induced by high temperature, which could be counteracted by base excision repair mediated by uracil DNA glycosylase (UDG) or other repair pathways. Our previous work has shown that the two UDGs (Tba UDG247 and Tba UDG194) encoded by the genome of the hyperthermophilic euryarchaeon Thermococcus barophilus Ch5 can remove uracil from DNA at high temperature. Herein, we provide evidence that Tba UDG247 is a novel bifunctional glycosylase which can excise uracil from DNA and further cleave the phosphodiester bo nd of the generated apurinic/apyrimidinic (AP) site, which has never been described to date. In addition to cleaving uracil-containing DNA, Tba UDG247 can also cleave AP-containing ssDNA although at lower efficiency, thereby suggesting that the enzyme might be involved in repair of AP site in DNA. Kinetic analyses showed that Tba UDG247 displays a faster rate for uracil excision than for AP cleavage, thus suggesting that cleaving AP site by the enzyme is a rate-limiting step for its bifunctionality. Phylogenetic analysis showed that Tba UDG247 is clustered on a separate branch distant from all the reported UDGs. Overall, we designated Tba UDG247 as the prototype of a novel family of bifunctional UDGs. KEY POINTS: We first reported a novel DNA glycosylase with bifunctionality. Tba UDG247 possesses an AP lyase activity.

Published
2021

68. Méthodes d’identification de sources acoustiques paramétriques par mesures d’antennerie

Author

Baron, Valentin, Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), Université de Lyon, Barbara Nicolas, and Jérôme Mars

Subjects
[SPI.ACOU]Engineering Sciences [physics]/Acoustics [physics.class-ph], Traitement d'antenne, Acoustic imaging, Sound level, Protection, Sources identification, Acoustique sous-Marine, Array, Antennerie, Acoustics, Mining protection, Antenne acoustique, Sound sources, Acoustique, Acoustic arrays, Machine Learning, Sources sonores, Imagerie acoustique, Protection minière, Apprentissage Automatique, Array processing, Underwater acoustics, Identification de sources, Niveau sonore
Abstract

Acoustic sources characterization aims to describe sound emitters through some parameters like their localization in space, the sound level they produce or their identification thanks to their acoustic signature. In this thesis, the objective is to obtain some of these parameters in two industrial application cases, for sources located in far-field and by the use of acoustic arrays. The first application concerns deep-sea mining acoustic impact in the context of Abysound FUI project. Within it, the thesis searches to characterize the excavation machine located on the seabed by assessing their localization and their sound level. First, a design phase has led to the construction of a 3 m size acoustic array. Then, using data from two experimental campaigns conducted in the Mediterranean Sea with this array, the high-resolution method MUSIC accurately localizes the used acoustic sources, either mobile and more than 600 m away from the array, or immersed by 700 m depth. Their sound level is then estimated by beamforming, and the expected levels are verified for monochromatic or wideband signals. In the second application, a complete procedure for the localization and the identification of drones is proposed to protect sensitive areas. It combines array processing and machine learning through three key steps: localization, focalization, and identification. MUSIC localizes again nearby acoustic sources around the industrial array used, then focalization reconstructs each temporal signal, and a SVM model identifies them as drone or not. Experimental validations, inside and outside, establish an important contribution of this thesis work. Acquired data show for instance that the procedure localizes drones with 3° accuracy outside, detects them at 99 %, or identifies them despite the presence of a more powerful source.; La caractérisation de sources acoustiques a pour but de décrire des émetteurs sonores à travers certains paramètres comme leur localisation dans l'espace, le niveau sonore qu'ils produisent ou encore leur identification grâce à leur signature acoustique. Dans cette thèse, l'objectif est d'obtenir certains de ces trois paramètres dans deux cas d'application concrets, pour des sources situées en champ lointain et grâce à des antennes acoustiques. La première application concerne l'impact acoustique de la prospection minière sous-marine par grands fonds dans le cadre du projet FUI Abysound. Au sein du projet, la thèse cherche à caractériser les machines excavatrices placées sur les fonds marins en obtenant leur localisation et leur niveau sonore. Une première phase de design a abouti à la construction d'une antenne conique de 3 m. En s'appuyant ensuite sur les données issues de deux campagnes expérimentales menées en mer Méditerranée avec cette antenne, la méthode haute-résolution MUSIC localise précisément les sources mises en oeuvre, qu'elles soient mobiles et à plus de 600 m de l'antenne, ou immergées par 700 m de fond. Leur niveau sonore est ensuite estimé par formation de voies, et les niveaux attendus sont retrouvés pour des signaux monochromatiques ou à large bande. Dans la seconde application, une procédure complète pour la localisation et l'identification de drones est proposée pour la protection de sites sensibles. Elle combine traitement d'antenne et apprentissage statistique en s'articulant autour de trois étapes clés : la localisation, la focalisation et l'identification. La méthode MUSIC localise à nouveau les sources acoustiques présentes autour de l'antenne industrielle utilisée, puis la focalisation reconstruit le signal temporel de chacune, et un modèle SVM les identifie comme drone ou non. Les validations expérimentales, en intérieur comme en extérieur, constituent une contribution importante de ce travail de thèse. Les données acquises montrent entre autres que la procédure localise des drones à 3° près en extérieur, les détecte à 99 %, ou encore les identifie en présence d'une source perturbatrice plus puissante.

Published
2020

69. Parametric acoustic sources identification methods using array processing

Author

Baron, Valentin, Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), Université de Lyon, Barbara Nicolas, Jérôme Mars, and STAR, ABES

Subjects
[SPI.ACOU]Engineering Sciences [physics]/Acoustics [physics.class-ph], [SPI.ACOU] Engineering Sciences [physics]/Acoustics [physics.class-ph], Traitement d'antenne, Acoustic imaging, Sound level, Protection, Sources identification, Acoustique sous-Marine, Array, Antennerie, Acoustics, Mining protection, Antenne acoustique, Sound sources, Acoustique, Acoustic arrays, Machine Learning, Sources sonores, Imagerie acoustique, Protection minière, Apprentissage Automatique, Array processing, Underwater acoustics, Identification de sources, Niveau sonore
Abstract

Acoustic sources characterization aims to describe sound emitters through some parameters like their localization in space, the sound level they produce or their identification thanks to their acoustic signature. In this thesis, the objective is to obtain some of these parameters in two industrial application cases, for sources located in far-field and by the use of acoustic arrays. The first application concerns deep-sea mining acoustic impact in the context of Abysound FUI project. Within it, the thesis searches to characterize the excavation machine located on the seabed by assessing their localization and their sound level. First, a design phase has led to the construction of a 3 m size acoustic array. Then, using data from two experimental campaigns conducted in the Mediterranean Sea with this array, the high-resolution method MUSIC accurately localizes the used acoustic sources, either mobile and more than 600 m away from the array, or immersed by 700 m depth. Their sound level is then estimated by beamforming, and the expected levels are verified for monochromatic or wideband signals. In the second application, a complete procedure for the localization and the identification of drones is proposed to protect sensitive areas. It combines array processing and machine learning through three key steps: localization, focalization, and identification. MUSIC localizes again nearby acoustic sources around the industrial array used, then focalization reconstructs each temporal signal, and a SVM model identifies them as drone or not. Experimental validations, inside and outside, establish an important contribution of this thesis work. Acquired data show for instance that the procedure localizes drones with 3° accuracy outside, detects them at 99 %, or identifies them despite the presence of a more powerful source., La caractérisation de sources acoustiques a pour but de décrire des émetteurs sonores à travers certains paramètres comme leur localisation dans l'espace, le niveau sonore qu'ils produisent ou encore leur identification grâce à leur signature acoustique. Dans cette thèse, l'objectif est d'obtenir certains de ces trois paramètres dans deux cas d'application concrets, pour des sources situées en champ lointain et grâce à des antennes acoustiques. La première application concerne l'impact acoustique de la prospection minière sous-marine par grands fonds dans le cadre du projet FUI Abysound. Au sein du projet, la thèse cherche à caractériser les machines excavatrices placées sur les fonds marins en obtenant leur localisation et leur niveau sonore. Une première phase de design a abouti à la construction d'une antenne conique de 3 m. En s'appuyant ensuite sur les données issues de deux campagnes expérimentales menées en mer Méditerranée avec cette antenne, la méthode haute-résolution MUSIC localise précisément les sources mises en oeuvre, qu'elles soient mobiles et à plus de 600 m de l'antenne, ou immergées par 700 m de fond. Leur niveau sonore est ensuite estimé par formation de voies, et les niveaux attendus sont retrouvés pour des signaux monochromatiques ou à large bande. Dans la seconde application, une procédure complète pour la localisation et l'identification de drones est proposée pour la protection de sites sensibles. Elle combine traitement d'antenne et apprentissage statistique en s'articulant autour de trois étapes clés : la localisation, la focalisation et l'identification. La méthode MUSIC localise à nouveau les sources acoustiques présentes autour de l'antenne industrielle utilisée, puis la focalisation reconstruit le signal temporel de chacune, et un modèle SVM les identifie comme drone ou non. Les validations expérimentales, en intérieur comme en extérieur, constituent une contribution importante de ce travail de thèse. Les données acquises montrent entre autres que la procédure localise des drones à 3° près en extérieur, les détecte à 99 %, ou encore les identifie en présence d'une source perturbatrice plus puissante.

Published
2020

70. Pisarenko Class Beamformer Applied to Passive Acoustic Mapping of Ultrasound Cavitation

Author

Sivadon, Audrey, Polichetti, Maxime, Béra, Jean-Christophe, Varray, François, Nicolas, Barbara, Gilles, Bruno, Application des ultrasons à la thérapie (LabTAU), Centre Léon Bérard [Lyon]-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de la Santé et de la Recherche Médicale (INSERM), Imagerie Ultrasonore, Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), Inserra, Claude, Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), and Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Adaptive Beamforming, [PHYS.MECA.VIBR]Physics [physics]/Mechanics [physics]/Vibrations [physics.class-ph], [PHYS.MECA.VIBR] Physics [physics]/Mechanics [physics]/Vibrations [physics.class-ph], Therapeutic Ultrasound, [PHYS.MECA.ACOU] Physics [physics]/Mechanics [physics]/Acoustics [physics.class-ph], Passive Cavitation Imaging, [PHYS.MECA.ACOU]Physics [physics]/Mechanics [physics]/Acoustics [physics.class-ph]
Abstract

International audience; Cavitation induced by High Intensity Focused Ultrasound (HIFU) used in therapeutic ultrasound can be imaged passively using linear arrays and beamforming algorithms. A challenging purpose is to image both in spatial and temporal domain cavitation sources that are strongly involved in the tissue-ultrasound interaction, with a long-term objective of high image quality as well as real-time application. Recently, a formalism in the Fourier Domain using the Cross-Spectral Matrix (CSM), i.e. the spatial covariance matrix of the Fourier transform of received data, has been proposed to transpose adaptive algorithms currently used in array processing to this context. The strength of this formalism lies in combining the advantage of adaptive approaches in terms of image quality and enhancement of source localization, with a significant decrease of the numerical cost thanks to the use of Fourier Domain approaches. The present study focuses on an adaptive beamformer making use of the Pisarenko class methods, based on the p-th root compression of the CSM matrix. This method is compared to current state of the art in the context of passive cavitation imaging: The non-adaptive Frequency Domain Passive Cavitation Imaging (FD-PCI) and the adaptive Time Domain Robust Capon Beamformer (TD-RCB). The influence of the parameter p has been investigated on the resolution and the contrast of cavitation maps, and the parameter- free midway approach proposed by Stoica et al. is also considered. The performances of the method are illustrated using the simple case of single point-source simulations. The results show that it approaches the image quality of TD-RCB method, but with a computation time equivalent to FD-PCI. The Pisarenko class beamformer proves to be a promising tool combining good image quality characteristic of the adaptive approach with a significant reduction of the numerical computation time associated to the Fourier Domain implementation.

Published
2020

71. Acoustic localization and identification of drones with a disturbance source

Author

Baron, Valentin, Bouley, Simon, Muschinowski, Matthieu, Mars, Jerome, Nicolas, Barbara, Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), MicrodB, GIPSA-Services (GIPSA-Services), Grenoble Images Parole Signal Automatique (GIPSA-lab), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA), GIPSA - Signal Images Physique (GIPSA-SIGMAPHY), GIPSA Pôle Sciences des Données (GIPSA-PSD), Université Grenoble Alpes (UGA)-Grenoble Images Parole Signal Automatique (GIPSA-lab), Imagerie Ultrasonore, Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Inserra, Claude, Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), and Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon)

Subjects
[PHYS.MECA.VIBR]Physics [physics]/Mechanics [physics]/Vibrations [physics.class-ph], [PHYS.MECA.VIBR] Physics [physics]/Mechanics [physics]/Vibrations [physics.class-ph], array processing, machine learning, drone tracking, drone identification, [PHYS.MECA.ACOU] Physics [physics]/Mechanics [physics]/Acoustics [physics.class-ph], [PHYS.MECA.ACOU]Physics [physics]/Mechanics [physics]/Acoustics [physics.class-ph]
Abstract

International audience; Due to their low cost and their easy use, drones are increasingly used as tools to threaten valuable assets in both military and civil domains. Thus systems have been developed to localize and/or identify them before their neutralization. They can rely on radio frequency, radar signal or even video recording. However these modalities suffer from limitations like near-field detection or weather conditions. Besides, drones do produce acoustic noise during their flight due to their propellers. Consequently, the proposed work uses acoustic waves emitted by drones, array processing and machine learning to localize them and identify them as drones. As multiple acoustic sources can be present within the nearby environment, this work focuses on localization and identification of a drone in the presence of a disturbing acoustic source. An experiment has been performed with a drone flying above a 81-microphones acoustic array of 1 m diameter while a motionless loudspeaker emits noise. Signals recorded on the array are cut into successive blocks of 200 ms to allow an ?instantaneous? identification and tracking of the different sources. MUSIC algorithm is used on each block to estimate the angular directions of arrival of the sources, using local maxima of the localization map. Then the focused signals are built for each source and classified as drone or not thanks to a machine learning model (SVM) learned previously on similar data. Results show that the classification task depends on the signal to noise ratio between the two sources and on their relative position. A final drone trajectory can be estimated thanks to the classification step that allows to tag every detected drone within each block along time.

Published
2020

72. Deep Group-Wise Angular Translation of Cardiac Diffusion MRI in q-space via Manifold Regularized GAN

Author

Patrick Clarysse, Yuemin Zhu, Feng Yang, Lihui Wang, Yunlong He, Modeling & analysis for medical imaging and Diagnosis (MYRIAD), Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), and Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)

Subjects
Image quality, Computer science, super-resolution, 02 engineering and technology, cardiac DTI, Translation (geometry), 030218 nuclear medicine & medical imaging, diffusion MRI, 03 medical and health sciences, 0302 clinical medicine, 0202 electrical engineering, electronic engineering, information engineering, medicine, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Angular resolution, Diffusion (business), spatial-angular information, medicine.diagnostic_test, business.industry, deep learning, Pattern recognition, Magnetic resonance imaging, Manifold, Image translation, 020201 artificial intelligence & image processing, Artificial intelligence, business, image synthesis, Diffusion MRI
Abstract

International audience; Diffusion magnetic resonance imaging (dMRI) has become an indispensable tool for non-invasive characterization of fiber structures of tissues. Clinical applicability of dMRI is often shackled by trade-off between image quality and long acquisition time. We propose a novel group-wise image translation method to improve the angular resolution of cardiac dMRI data. It consists in using a generative adversarial network (GAN) model to estimate a sequence of images from given DW images acquired in a limited number of diffusion gradient directions. We embed a supervised manifold regularized term in the GAN loss function to exploit the correlation between multiple DW images acquired in different gradient directions. Experimental results on cardiac dMRI data demonstrated that our method can significantly improve the quality of diffusion tensor imaging (DTI) reconstruction.

Published
2020

73. Implementation of a Compact Spot-Scanning Proton Therapy System in a GPU Monte Carlo Code to Support Clinical Routine

Author

Jan Gajewski, Angelo Schiavi, Nils Krah, Gloria Vilches-Freixas, Antoni Rucinski, Vincenzo Patera, Ilaria Rinaldi, Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), Institut de Physique des 2 Infinis de Lyon (IP2I Lyon), Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), and Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet [Saint-Étienne] (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)

Subjects
phase space modelling, Aperture, Computer science, Materials Science (miscellaneous), Nozzle, Monte Carlo method, Physics::Medical Physics, Biophysics, General Physics and Astronomy, quality assurance, 030218 nuclear medicine & medical imaging, Computational science, law.invention, 03 medical and health sciences, 0302 clinical medicine, law, proton therapy, GPU-accelerated dose calculation, Physical and Theoretical Chemistry, Pencil-beam scanning, Proton therapy, Mathematical Physics, [PHYS]Physics [physics], Monte Carlo dose calculation, Isocenter, Collimator, pencil beam scanning, lcsh:QC1-999, 030220 oncology & carcinogenesis, Physics::Accelerator Physics, Beam (structure), lcsh:Physics
Abstract

International audience; The purpose of this work was to implement a fast Monte Carlo dose calculation tool, Fred, in the Maastro proton therapy center in Maastricht (Netherlands) to complement the clinical treatment planning system. Fred achieves high accuracy and computation speed by using physics models optimized for radiotherapy and extensive use of GPU technology for parallelization. We implemented the beam model of the Mevion S250i proton beam and validated it against data measured during commissioning and calculated with the clinical TPS. The beam exits the accelerator with a pristine energy of around 230 MeV and then travels through the dynamically extendable nozzle of the device. The nozzle contains the range modulation system and the multi-leaf collimator system named adaptive aperture. The latter trims the spots laterally over the 20 × 20 cm2 area at the isocenter plane. We use a single model to parameterize the longitudinal (energy and energy spread) and transverse (beam shape) phase space of the non-degraded beam in the default nozzle position. The range modulation plates and the adaptive aperture are simulated explicitly and moved in and out of the simulation geometry dynamically by Fred. Patient dose distributions recalculated with Fred were comparable with the TPS and met the clinical criteria. Calculation time was on the order of 10–15 min for typical patient cases, and future optimization of the simulation statistics is likely to improve this further. Already now, Fred is fast enough to be used as a tool for plan verification based on machine log files and daily (on-the-fly) dose recalculations in our facility.

Published
2020

74. Quantitative-analysis of computed tomography in COVID-19 and non COVID-19 ARDS patients: A case-control study

Author

Laure Folliet, Laurent Bitker, Eduardo Enrique Dávila Serrano, Ludmilla Penarrubia, Paul Chabert, Loic Boussel, Mehdi Mezidi, Louis Chauvelot, Judith Provoost, Pierre Lecam, Hodane Yonis, Guillaume David, Jean-Christophe Richard, François Dhelft, Maciej Orkisz, Hospices Civils de Lyon (HCL), Modeling & analysis for medical imaging and Diagnosis (MYRIAD), Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), Service Informatique et développements, CCSD, Accord Elsevier, Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), and Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Adult, Male, ARDS, Coronavirus disease 2019 (COVID-19), [SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, Computed tomography, Body weight, Critical Care and Intensive Care Medicine, Article, Positive-Pressure Respiration, 03 medical and health sciences, 0302 clinical medicine, medicine, Image Processing, Computer-Assisted, Humans, Prospective Studies, Lung, Lung Compliance, Aged, Respiratory Distress Syndrome, medicine.diagnostic_test, Acute respiratory distress syndrome, business.industry, Case-control study, COVID-19, 030208 emergency & critical care medicine, Middle Aged, respiratory system, medicine.disease, Lung weight, 3. Good health, respiratory tract diseases, [SDV.IB.IMA] Life Sciences [q-bio]/Bioengineering/Imaging, Tidal hyperinflation, 030228 respiratory system, Case-Control Studies, Driving pressure, Female, Nuclear medicine, business, Lung tissue, Tomography, X-Ray Computed, Transpulmonary pressure
Abstract

Purpose The aim of this study was to assess whether the computed tomography (CT) features of COVID-19 (COVID+) ARDS differ from those of non-COVID-19 (COVID−) ARDS patients. Materials and methods The study is a single-center prospective observational study performed on adults with ARDS onset ≤72 h and a PaO2/FiO2 ≤ 200 mmHg. CT scans were acquired at PEEP set using a PEEP-FiO2 table with VT adjusted to 6 ml/kg predicted body weight. Results 22 patients were included, of whom 13 presented with COVID-19 ARDS. Lung weight was significantly higher in COVID− patients, but all COVID+ patients presented supranormal lung weight values. Noninflated lung tissue was significantly higher in COVID− patients (36 ± 14% vs. 26 ± 15% of total lung weight at end-expiration, p, Graphical abstract Unlabelled Image, Highlights • Lung weight is significantly increased in all COVID+ ARDS patients. • Lung potential for recruitment of COVID+ ARDS patients is lower than COVID− ARDS. • A substantial proportion of COVID+ patients exhibit large amount of tidal hyperinflation.

Published
2020

75. Contributions à l'adaptation de domaine non supervisée : Fonctions de similarité, transport optimal et garanties théoriques

Author

Dhouib, Sofiane, Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), Modeling & analysis for medical imaging and Diagnosis (MYRIAD), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon, and Carole Lartizien

Subjects
Probabilistic approaches, Domain adaptation, Learning classifiers, Classification par similarité, Information sciences, Benchmarking dataset, Adaptation de domaine, Probabilités, Apprentissage d'un classifieur, Informatique, Jeu de données d'évaluation, Similarity classification, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], Machine Learning, Mathématiques, Transport optimal, Fonction de similarité, Apprentissage Automatique, Similarity functions, Optimal transport, Supervised Learning, Mathematics, Apprentissage Supervisé
Abstract

The surge in the quantity of data produced nowadays made of Machine Learning, a subfield of Artificial Intelligence, a vital tool used to extract valuable patterns from them and allowed it to be integrated into almost every aspect of our everyday activities. Concretely, a machine learning algorithm learns such patterns after being trained on a dataset called the training set, and its performance is assessed on a different set called the testing set. Domain Adaptation is an active research area of machine learning, in which the training and testing sets are not assumed to stem from the same probability distribution, as opposed to Supervised Learning. In this case, the two distributions generating the training and testing data correspond respectively to the source and target domains. Our contributions focus on three theoretical aspects related to domain adaptation for classification tasks. The first one is learning with similarity functions, which deals with classification algorithms based on comparing an instance to other examples in order to decide its class. The second is large-margin classification, which concerns learning classifiers that maximize the separation between classes. The third is Optimal Transport that formalizes the principle of least effort for transporting probability masses between two distributions. At the beginning of the thesis, we were interested in learning with so-called (epsilon,gamma,tau)-good similarity functions in the domain adaptation framework, since these functions have been introduced in the literature in the classical framework of supervised learning. This is the subject of our first contribution in which we theoretically study the performance of a similarity function on a target distribution, given it is suitable for the source one. Then, we tackle the more general topic of large-margin classification in domain adaptation, with weaker assumptions than those adopted in the first contribution. In this context, we proposed a new theoretical study and a domain adaptation algorithm, which is our second contribution. We derive novel bounds taking the classification margin on the target domain into account, that we convexify by leveraging the appealing Optimal Transport theory, in order to derive a domain adaptation algorithm with an adversarial variation of the classic Kantorovich problem. Finally, after noticing that our adversarial formulation can be generalized to include several other cases of interest, we dedicate our last contribution to adversarial or minimax variations of the optimal transport problem, where we demonstrate the versatility of our approach.; L'explosion de la quantité de données produites chaque jour a fait de l' l'Apprentissage Automatique un outil vital pour extraire des motifs de haute valeur à partir de celles-là. Concrètement, un algorithme d'apprentissage automatique apprend de tels motifs après avoir été entraîné sur un jeu de données appelé données d'entraînement, et sa performance est évaluée sur échantillon différent, appelé données de test. L'Adaptation de Domaine est une branche de l'apprentissage automatique, dans lequel les données d'entraînement et de test ne sont plus supposées provenir de la même distribution de probabilité. Dans ce cas, les deux distributions des données d'entraînement et de test correspondent respectivement aux domaines source et cible. Nos contributions se focalisent sur trois aspects théoriques en relation avec l'adaptation de domaine pour les tâches de classification. Le premier est l'apprentissage avec des fonctions de similarité, qui traite les algorithmes de classification basés sur la comparaison d'une instance à d'autres exemples pour décider sa classe. Le deuxième est la classification à vaste marge qui concerne l'apprentissage d'un classifieur maximisant la séparation entre classes. Le troisième aspect est le Transport Optimal qui formalise un principe d'effort minimal pour le transport de masses de probabilité entre distributions. Au début de cette thèse, nous nous intéressions à l'apprentissage avec ce que l'on appelle fonctions de similarités (epsilon,gamma,tau)-bonnes dans le cadre de l'adaptation de domaine, puisque ces fonctions ont été introduites dans la littérature dans le cadre classique de l'apprentissage supervisé. C'est le sujet de notre première contribution dans laquelle nous étudions théoriquement la performance d'une fonction de similarité sur une distribution cible, étant donné qu'elle est adéquate pour la source. Puis, nous abordons plus généralement le thème de la classification à vaste marge pour l'adaptation de domaine, avec des hypothèses de départ plus faibles que celles adoptées dans la première contribution. Dans ce contexte, nous proposons une nouvelle étude théorique et un algorithme d'adaptation de domaine, ce qui constitue notre deuxième contribution. Nous dérivons de nouvelles bornes prenant en compte la marge de classification dans le domaine cible, que nous convexifions en tirant profit de la théorie du Transport Optimal, en vue de dériver un algorithme d'adaptation de domaine présentant une variation adversariale du problème classique de Kantorovitch. Finalement, nous dédions notre dernière contribution aux variations adversariales ou minimax du problème du transport optimal, où nous démontrons l'adaptabilité de notre approche.

Published
2020

76. Identification of a novel type of glucose dehydrogenase involved in the mineral weathering ability of Collimonas pratensis strain PMB3(1)

Author

Marie-Pierre Turpault, Stéphane Uroz, Laura Picard, Philippe Oger, Interactions Arbres-Microorganismes (IAM), Université de Lorraine (UL)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Unité de recherche Biogéochimie des Ecosystèmes Forestiers (BEF), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Microbiologie, adaptation et pathogénie (MAP), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS), Microbiology of Extreme Environments (M2E), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), French Ministere de l'Enseignement Superieur, de la Recherche et de l'Innovation, EC2CO program, ANR-11-LABX-0002,ARBRE,Recherches Avancées sur l'Arbre et les Ecosytèmes Forestiers(2011), Centre National de la Recherche Scientifique (CNRS)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon, and Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National des Sciences Appliquées de Lyon (INSA Lyon)

Subjects
0301 basic medicine, 030106 microbiology, Mutant, Biology, Applied Microbiology and Biotechnology, Microbiology, 03 medical and health sciences, chemistry.chemical_compound, Pyrroloquinoline quinone, Glucose/Methanol/Choline oxidoreductase, Glucose dehydrogenase, Oxidoreductase, Oxalobacteraceae, mineral weathering, [SDV.BBM.GTP]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN], [SDV.BBM]Life Sciences [q-bio]/Biochemistry, Molecular Biology, Weather, chemistry.chemical_classification, Minerals, Ecology, Collimonas, Glucose 1-Dehydrogenase, PQQ-independent glucose dehydrogenase, biology.organism_classification, Complementation, biotite, 030104 developmental biology, Collimonas pratensis PMB3(1), chemistry, Biochemistry, [SDE]Environmental Sciences, Gluconic acid, Bacteria
Abstract

The exact molecular mechanisms as well as the genes involved in the mineral weathering (MW) process by bacteria remain poorly characterized. To date, a single type of glucose dehydrogenase (GDH) depending on a particular co-factor named pyrroloquinoline quinone (PQQ) is known. These enzymes allow the production of gluconic acid through the oxidation of glucose. However, it remains to be determined how bacteria missing PQQ-dependent GDH and/or the related pqq biogenesis genes weather minerals. In this study, we considered the very effective mineral weathering bacterial strain PMB3(1) of Collimonas pratensis. Genome analysis revealed that it does not possess the PQQ based system. The use of random mutagenesis, gene complementation and functional assays allowed us to identify mutants impacted in their ability to weather mineral. Among them, three mutants were strongly altered on their acidification and biotite weathering abilities (58 to 75% of reduction compared to WT) and did not produce gluconic acid. The characterization of the genomic regions allowed noticeably to the identification of a Glucose/Methanol/Choline oxidoreductase. This region appeared very conserved among collimonads and related genera. This study represents the first demonstration of the implication of a PQQ-independent GDH in the mineral weathering process and explains how Collimonas weather minerals.

Published
2020

77. Exploring The Mesoscopic Structure Of The World Air Transportation Network

Author

Issa Moussa Diop, Cherif Diallo, Chantal Cherifi, hocine cherifi, Université Gaston Bergé (Saint-Louis, Sénégal), Décision et Information pour les Systèmes de Production (DISP), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université Lumière - Lyon 2 (UL2), Laboratoire d'Informatique de Bourgogne [Dijon] (LIB), Université de Bourgogne (UB), and Cherifi, Chantal

Subjects
[INFO]Computer Science [cs], [INFO] Computer Science [cs]
Abstract

International audience; Air transportation networks have been extensively studied in the network science literature. Researchers focus on airlines networks, national, regional, continental and worldwide networks using monoplex or multiplex approaches. Inspired by recent results on community-aware centrality measures [1], in this work, an extensive analysis of the macroscopic, mesoscopic and microscopic topological properties of the world air transportation network is performed. Based on the community structure uncovered by the Louvain algorithm, the original network is split into local components and global components. The local components are made of the communities by removing the intercommunity links. The global components are formed by removing all the intracommunity links and subsequent isolated nodes. Results reported in figure 1 shows that the main local components are geographically distributed over seven areas (

Published
2020

78. Semantic Software Capability Profile based on Enterprise Architecture for Software Reuse

Author

Abdelhadi Belfadel, Jannik Laval, Néjib Moalla, Chantal Cherifi, Décision et Information pour les Systèmes de Production (DISP), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université Lumière - Lyon 2 (UL2), and Cherifi, Chantal

Subjects
Service (systems architecture), [INFO.INFO-WB] Computer Science [cs]/Web, Computer science, computer.internet_protocol, business.industry, [INFO.INFO-WB]Computer Science [cs]/Web, Enterprise architecture, Software requirements specification, 020207 software engineering, 02 engineering and technology, Service-oriented architecture, Reuse, Ontology (information science), [INFO] Computer Science [cs], Software, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, [INFO]Computer Science [cs], Orchestration (computing), Software engineering, business, computer, ComputingMilieux_MISCELLANEOUS
Abstract

Open source and software reuse become more and more challenging for companies to save time and development cost. Collecting existing service-oriented solutions and qualifying them helps to reuse them directly or via orchestration. Our objective in this research work targets the consolidation of an advanced service repository able to be directly mapped to end-user requirements for new business application development. In this perspective, we define a model-based capability profile based on Enterprise Architecture offering a wider view qualification for service-oriented software. We propose a meta-model to gather architecture artifacts used to guide the development of existing solutions. The generated capability profiles help to create a repository of software capabilities. Furthermore, an ontology is proposed to exploit the resulted capability profiles to guide the future business needs to reuse the qualified software in an efficient way. This contribution aims to upgrade research on dynamic services consumption and orchestration to the level of end-users’ requirements mapped with advanced service assets as an enabler for accelerating business application development.

Published
2020

79. Sorting cells from fluorescent markers organization in confocal microscopy: 3D versus 2D images

Author

David Rousseau, Ali Ahmad, Carole Frindel, Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), Laboratoire Angevin de Recherche en Ingénierie des Systèmes (LARIS), Université d'Angers (UA), and Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL)

Subjects
[PHYS]Physics [physics], 0301 basic medicine, Point spread function, Microscope, Materials science, Confocal, Sorting, 01 natural sciences, law.invention, Numerical aperture, 010309 optics, [SPI]Engineering Sciences [physics], 03 medical and health sciences, 030104 developmental biology, Confocal microscopy, law, 0103 physical sciences, Microscopy, Biological system, ComputingMilieux_MISCELLANEOUS, Spatial organization
Abstract

We consider the problem of sorting cells from fluorescent markers spatial organization via single cell microscopy. This problem is important in bioimaging since this organization can reflect the healthy or pathological state of cells, the chromatin chain configurations or the spatial organization of DNA during main cell cycle. In this work, we address the question of the detection of spatial organization differences according to fluorescent markers density or distribution using textural features. We compare the performances by simulating 3D images and 2D images of the focal plane with various size of the point spread function of the confocal microscope, that is determined by varying the numerical aperture N A of the used objective lens.

Published
2020

80. Thermal conductivity of irradiated porous silicon down to the oxide limit investigated by Raman thermometry and scanning thermal microscopy

Author

Jean-Marie Bluet, A. M. Massoud, Pierre-Olivier Chapuis, Bruno Canut, Institut des Nanotechnologies de Lyon (INL), École Centrale de Lyon (ECL), Université de Lyon-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École supérieure de Chimie Physique Electronique de Lyon (CPE)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS), Centre d'Energétique et de Thermique de Lyon (CETHIL), Centre National de la Recherche Scientifique (CNRS)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), and Université de Lyon

Subjects
010302 applied physics, Materials science, Silicon, Scanning electron microscope, Analytical chemistry, General Physics and Astronomy, chemistry.chemical_element, 02 engineering and technology, Scanning thermal microscopy, 021001 nanoscience & nanotechnology, Porous silicon, 01 natural sciences, Amorphous solid, Thermal conductivity, chemistry, Transmission electron microscopy, 0103 physical sciences, [SPI.MECA.THER]Engineering Sciences [physics]/Mechanics [physics.med-ph]/Thermics [physics.class-ph], 0210 nano-technology, Porosity
Abstract

International audience; Irradiating porous silicon is expected to reduce thermal conductivity without altering the porous structure and can be studied by optical techniques provided optical properties can be established reliably. Toward this end, meso-porous silicon (PSi), with a porosity of 56%, was prepared from p + Si wafer (0.01-0.02 .cm-1 resistivity) and was partially amorphized by irradiation in the electronic regime with 129 Xe ions at two different energies (29 MeV and 91 MeV) and five fluences ranging from 10 12 cm-2 to 3.10 13 cm-2. The PSi structure is monitored by scanning electron microscopy. High-resolution transmission electron microscopy shows that the amorphous phase is homogeneous in volume and that there is no formation of amorphous-crystalline core-shell structures. An agreement is found between the thermal conductivity results obtained with micro-Raman thermometry, which is an optical contactless technique heating the sample in the depth, and scanning thermal microscopy, which is an electrical technique heating the sample by contact at the sample surface. A linear relation is established between the effective thermal conductivity and the amorphous fraction, predicting the thermal conductivity of fully-amorphous porous Si below 1 W.m-1 .K-1. The obtained values are comparable to that of SiO2, reduced by a factor 6 in comparison to non-irradiated porous samples (~6.5 W.m-1 .K-1) and smaller than bulk silicon by more than two orders of magnitude.

Published
2020

81. Apolar Polyisoprenoids Located in the Midplane of the Bilayer Regulate the Response of an Archaeal-Like Membrane to High Temperature and Pressure

Author

Loricco, Josephine, Salvador-Castell, Marta, Demé, Bruno, Peters, Judith, Oger, Philippe, Microbiology of Extreme Environments (M2E), Microbiologie, adaptation et pathogénie (MAP), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS), Institut Laue-Langevin (ILL), ILL, Université Grenoble Alpes (UGA), Laboratoire Interdisciplinaire de Physique [Saint Martin d’Hères] (LIPhy ), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA), Centre National de la Recherche Scientifique (CNRS)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon, and Oger, Phil M.

Subjects
archaea, [SDV]Life Sciences [q-bio], [SDV.BBM.BP] Life Sciences [q-bio]/Biochemistry, Molecular Biology/Biophysics, phase coexistence, adaptation, [SDV] Life Sciences [q-bio], [SDV.BBM.BP]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Biophysics, Chemistry, high pressure, membrane architecture, archaeal lipids, [PHYS.COND.CM-SCM]Physics [physics]/Condensed Matter [cond-mat]/Soft Condensed Matter [cond-mat.soft], membrane, ComputingMilieux_MISCELLANEOUS, Original Research
Abstract

International audience; Archaea are known to inhabit some of the most extreme environments on Earth. The ability of archaea possessing membrane bilayers to adapt to high temperature (>85 • C) and high pressure (>1,000 bar) environments is proposed to be due to the presence of apolar polyisoprenoids at the midplane of the bilayer. In this work, we study the response of this novel membrane architecture to both high temperature and high hydrostatic pressure using neutron diffraction. A mixture of two diether, phytanyl chain lipids (DoPhPC and DoPhPE) and squalane was used to model this novel architecture. Diffraction data indicate that at high temperatures a stable coexistence of fluid lamellar phases exists within the membrane and that stable coexistence of these phases is also possible at high pressure. Increasing the amount of squalane in the membrane regulates the phase separation with respect to both temperature and pressure, and also leads to an increase in the lamellar repeat spacing. The ability of squalane to regulate the ultrastructure of an archaea-like membrane at high pressure and temperature supports the hypothesis that archaea can use apolar lipids as an adaptive mechanism to extreme conditions.

Published
2020

82. Anthropomorphic lung phantom based validation of in-room proton therapy 4D-CBCT image correction for dose calculation

Author

Christopher Kurz, Arturs Meijers, Julien Dinkel, Florian Kamp, Antje Knopf, Sébastien Brousmiche, David Bondesson, Claus Belka, Guillaume Janssens, Lydia A. den Otter, Katia Parodi, Katharina Niepel, Guillaume Landry, Simon Rit, Stefan Both, Moritz Rabe, Imagerie Tomographique et Radiothérapie, Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet [Saint-Étienne] (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet [Saint-Étienne] (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), and Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)

Subjects
Male, Dose-volume histogram, Lung Neoplasms, Swine, Biophysics, Image registration, Imaging phantom, 030218 nuclear medicine & medical imaging, 03 medical and health sciences, 4d-vct, Cone-beam, Motion, Proton Therapy, Thorax, Tomography, 0302 clinical medicine, Image Processing, Computer-Assisted, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Animals, Humans, Medicine, Radiology, Nuclear Medicine and imaging, Four-Dimensional Computed Tomography, Radiation treatment planning, Lung, Proton therapy, ComputingMilieux_MISCELLANEOUS, Radiological and Ultrasound Technology, Phantoms, Imaging, business.industry, Radiotherapy Planning, Computer-Assisted, Radiotherapy Dosage, Spiral Cone-Beam Computed Tomography, Cone-Beam Computed Tomography, Intensity (physics), medicine.anatomical_structure, business, Nuclear medicine, Chickens
Abstract

Purpose Ventilation-induced tumour motion remains a challenge for the accuracy of proton therapy treatments in lung patients. We investigated the feasibility of using a 4D virtual CT (4D-vCT) approach based on deformable image registration (DIR) and motion-aware 4D CBCT reconstruction (MA-ROOSTER) to enable accurate daily proton dose calculation using a gantry-mounted CBCT scanner tailored to proton therapy. Methods Ventilation correlated data of 10 breathing phases were acquired from a porcine ex-vivo functional lung phantom using CT and CBCT. 4D-vCTs were generated by (1) DIR of the mid-position 4D-CT to the mid-position 4D-CBCT (reconstructed with the MA-ROOSTER) using a diffeomorphic Morphons algorithm and (2) subsequent propagation of the obtained mid-position vCT to the individual 4D-CBCT phases. Proton therapy treatment planning was performed to evaluate dose calculation accuracy of the 4D-vCTs. A robust treatment plan delivering a nominal dose of 60 Gy was generated on the average intensity image of the 4D-CT for an approximated internal target volume (ITV). Dose distributions were then recalculated on individual phases of the 4D-CT and the 4D-vCT based on the optimized plan. Dose accumulation was performed for 4D-vCT and 4D-CT using DIR of each phase to the mid position, which was chosen as reference. Dose based on the 4D-vCT was then evaluated against the dose calculated on 4D-CT both, phase-by-phase as well as accumulated, by comparing dose volume histogram (DVH) values (Dmean, D2%, D98%, D95%) for the ITV, and by a 3D-gamma index analysis (global, 3%/3 mm, 5 Gy, 20 Gy and 30 Gy dose thresholds). Results Good agreement was found between the 4D-CT and 4D-vCT-based ITV-DVH curves. The relative differences ((CT-vCT)/CT) between accumulated values of ITV Dmean, D2%, D95% and D98% for the 4D-CT and 4D-vCT-based dose distributions were −0.2%, 0.0%, −0.1% and −0.1%, respectively. Phase specific values varied between −0.5% and 0.2%, −0.2% and 0.5%, −3.5% and 1.5%, and −5.7% and 2.3%. The relative difference of accumulated Dmean over the lungs was 2.3% and Dmean for the phases varied between −5.4% and 5.8%. The gamma pass-rates with 5 Gy, 20 Gy and 30 Gy thresholds for the accumulated doses were 96.7%, 99.6% and 99.9%, respectively. Phase-by-phase comparison yielded pass-rates between 86% and 97%, 88% and 98%, and 94% and 100%. Conclusions Feasibility of the suggested 4D-vCT workflow using proton therapy specific imaging equipment was shown. Results indicate the potential of the method to be applied for daily 4D proton dose estimation.

Published
2020

83. A Deep Network for Reconstructing Images from Undersampled Poisson data

Author

Lorente Mur, Antonio, Peyrin, F, Ducros, Nicolas, Imagerie Tomographique et Radiothérapie, Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), and Insa Lyon

Subjects
Index Terms-Image reconstruction, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], single-pixel imaging, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Poisson noise, deep learning, convolutional neural network, [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV], undersam- pled data, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, computational optics, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]
Abstract

We consider the reconstruction of an image from a sequence of a few linear measurements corrupted by Poisson noise. This generic problem has many biomedical applications, such as computerized tomography, positron emission tomogra-phy, and optical microscopy. Here, we focus on a computational optics problem where the setup acquires some coefficients of the Hadamard transform of the image of the scene. We formalize this problem in a Bayesian setting where we estimate the missing Hadamard coefficients from those acquired. Then, we propose a deep-learning network that consists of two fully connected layers (FCLs) that map data from the measurement domain to the image domain, followed by convolutional layers that act in the image domain. On the one hand, we set the FCLs so that they compute the best linear solution of the problem. While the first FCL denoises the raw measurements, the second FCL completes the missing measurements from the denoised measurements. The convolutional layers undergo learning through a training phase. We also describe a framework for training the network in the presence of Poisson noise. In particular, our approach includes an estimation of the image intensity, together with a normalization scheme that allows varying noise levels to be handled during training. We compare our network to linear reconstructors and to network variants that do not address the noise issue at all, or that address it implicitly. Finally, we present results from simulated and experimental acquisitions, considering varying noise levels. Our network yields higher reconstruction peak signal-to-noise ratios in scenarios where the actual noise level is higher than that expected and used during the training phase.

Published
2020

84. Cardiac Segmentation With Strong Anatomical Guarantees

Author

Pierre-Marc Jodoin, Youssef Skandarani, Alain Lalande, Nathan Painchaud, Olivier Bernard, Thierry Judge, Modeling & analysis for medical imaging and Diagnosis (MYRIAD), Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), Equipe IFTIM [ImViA - EA7535], Centre Hospitalier Universitaire de Dijon - Hôpital François Mitterrand (CHU Dijon)-Centre Régional de Lutte contre le cancer Georges-François Leclerc [Dijon] (UNICANCER/CRLCC-CGFL), UNICANCER-UNICANCER-Imagerie et Vision Artificielle [Dijon] (ImViA), Université de Bourgogne (UB)-Université de Bourgogne (UB), Service de RMN Spectroscopie, Médecine Nucléaire (CHU de Dijon), and Centre Hospitalier Universitaire de Dijon - Hôpital François Mitterrand (CHU Dijon)

Subjects
FOS: Computer and information sciences, Computer Science - Machine Learning, Computer science, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, Convolutional neural network, 030218 nuclear medicine & medical imaging, Machine Learning (cs.LG), 03 medical and health sciences, 0302 clinical medicine, Medical imaging, Image Processing, Computer-Assisted, FOS: Electrical engineering, electronic engineering, information engineering, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Segmentation, Electrical and Electronic Engineering, Image warping, Representation (mathematics), ComputingMilieux_MISCELLANEOUS, Ultrasonography, Radiological and Ultrasound Technology, Artificial neural network, business.industry, Image and Video Processing (eess.IV), Pattern recognition, Heart, Image segmentation, Electrical Engineering and Systems Science - Image and Video Processing, Autoencoder, Magnetic Resonance Imaging, Computer Science Applications, Artificial intelligence, Neural Networks, Computer, business, Software
Abstract

Convolutional neural networks (CNN) have had unprecedented success in medical imaging and, in particular, in medical image segmentation. However, despite the fact that segmentation results are closer than ever to the inter-expert variability, CNNs are not immune to producing anatomically inaccurate segmentations, even when built upon a shape prior. In this paper, we present a framework for producing cardiac image segmentation maps that are guaranteed to respect pre-defined anatomical criteria, while remaining within the inter-expert variability. The idea behind our method is to use a well-trained CNN, have it process cardiac images, identify the anatomically implausible results and warp these results toward the closest anatomically valid cardiac shape. This warping procedure is carried out with a constrained variational autoencoder (cVAE) trained to learn a representation of valid cardiac shapes through a smooth, yet constrained, latent space. With this cVAE, we can project any implausible shape into the cardiac latent space and steer it toward the closest correct shape. We tested our framework on short-axis MRI as well as apical two and four-chamber view ultrasound images, two modalities for which cardiac shapes are drastically different. With our method, CNNs can now produce results that are both within the inter-expert variability and always anatomically plausible without having to rely on a shape prior., Comment: 11 pages, accepted for publication in IEEE TMI

Published
2020

85. Reconstruction of radiotherapy dose fields shaped with a multileaf collimator from six projections

Author

Odran Pivot, Laurent Desbat, Simon Rit, Rolf Clackdoyle, Patrick Pittet, laboratoire TIMC-IMAG, Université Grenoble Alpes (COMUE) (UGA), Imagerie Tomographique et Radiothérapie, Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), Institut des Nanotechnologies de Lyon (INL), École Centrale de Lyon (ECL), Université de Lyon-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-École supérieure de Chimie Physique Electronique de Lyon (CPE)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), and Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)

Subjects
[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Physics::Medical Physics, [PHYS.PHYS.PHYS-INS-DET]Physics [physics]/Physics [physics]/Instrumentation and Detectors [physics.ins-det]
Abstract

International audience; Quality assurance of small dose fields shaped with a multileaf collimator is crucial in x-ray radiotherapy. Tissue-equivalent scintillating fibers (SciFi) can measure a sinogram of the dose field at the detector plane sampled with a few angles and open up the possibility of real-time quality assurance. We investigate the use of such detectors for estimating dose fields obtained with multileaf collimators (MLC).We propose a forward model of the tomographic projection based on geometrical parameters of the MLC and the beam dose. A least squares minimization is then used to identify these parameters. We validate our approach on real data acquired with a prototype SciFi detector measuring six projections of a field generated with a 6 MeV linear accelerator and shaped by nine pairs of leaves. We observe a good fit with the reference gafchromic film.

Published
2020

86. Numerical investigation of Taylor bubble and development of phase change model

Author

Guo, Longkai, Centre d'Energétique et de Thermique de Lyon (CETHIL), Centre National de la Recherche Scientifique (CNRS)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon, and Shihe Xin

Subjects
Vof, Contraction, Ecoulement diphasique, Mécanique des fluides, Expansion de changement de phase, CFD - Computanional Fluid Dynamics, Méthode numérique, Transition de phase, Numerical method, Bulle de Taylor, [SPI.MECA.MEFL]Engineering Sciences [physics]/Mechanics [physics.med-ph]/Fluids mechanics [physics.class-ph], Taylor bubble, Fluid mechanics, Two-Phase flow, CFD, Phase change material, Phase transition
Abstract

The motion of a nitrogen Taylor bubble in glycerol-water mixed solutions rising through different types of expansions and contractions is investigated by a numerical approach. The CFD procedure is based on an open-source solver Basilisk, which adopts the volume-of-fluid (VOF) method to capture the gas-liquid interface. The results of sudden expansions/contractions are compared with experimental results. The results show that the simulations are in good agreement with experiments. The bubble velocity increases in sudden expansions and decreases in sudden contractions. The bubble break-up pattern is observed in sudden expansions with large expansion ratios, and a bubble blocking pattern is found in sudden contractions with small contraction ratios. In addition, the wall shear stress, the liquid film thickness, and pressure in the simulations are studied to understand the hydrodynamics of the Taylor bubble rising through expansions/contractions. The transient process of the Taylor bubble passing through sudden expansion/contraction is further analyzed for three different singularities: gradual, parabolic convex and parabolic concave. A unique feature in parabolic concave contraction is that the Taylor bubble passes through the contraction even for small contraction ratios. Moreover, a phase change model is developed in the Basilisk solver. In order to use the existed geometric VOF method in Basilisk, a general two-step geometric VOF method is implemented. Mass flux is calculated not in the interfacial cells but transferred to the neighboring cells around the interface. The saturated temperature boundary condition is imposed at the interface by a ghost cell method. The phase change model is validated by droplet evaporation with a constant mass transfer rate, the one-dimensional Stefan problem, the sucking interface problem, and a planar film boiling case. The results show good agreement with analytical solutions or correlations.; Le mouvement d'une bulle d'azote de Taylor dans des solutions mixtes glycérol-eau s'élevant à travers différents types d'expansions et de contractions est étudié par une approche numérique. La procédure CFD est basée sur un solveur open-source Basilisk, qui adopte la méthode du volume de fluide (VOF) pour capturer l'interface gaz-liquide. Les résultats des expansions/contractions soudaines sont comparés aux résultats expérimentaux. Les résultats montrent que les simulations sont en bon accord avec les expériences. La vitesse de la bulle augmente dans les expansions soudaines et diminue dans les contractions soudaines. Le modèle de rupture des bulles est observé dans les expansions soudaines avec de grands taux d'expansion, et un modèle de blocage des bulles est observé dans les contractions soudaines avec de petits rapports de contraction. De plus, la contrainte de cisaillement de la paroi, l'épaisseur du film liquide et la pression dans les simulations sont étudiées pour comprendre l'hydrodynamique de la bulle de Taylor montant par expansions/contractions. Le processus transitoire de la bulle de Taylor passant par une expansion/contraction soudaine est ensuite analysé pour trois singularités différentes: graduelle, parabolique convexe et parabolique concave. Une caractéristique unique de la contraction concave parabolique est que la bulle de Taylor passe par la contraction même pour de petits rapports de contraction. De plus, un modèle de changement de phase est développé dans le solveur Basilisk. Afin d'utiliser la méthode VOF géométrique existante dans Basilisk, une méthode VOF géométrique générale en deux étapes est implémentée. Le flux de masse n'est pas calculé dans les cellules interfaciales mais transféré aux cellules voisines autour de l'interface. La condition aux limites de température saturée est imposée à l'interface par une méthode de cellule fantôme. Le modèle de changement de phase est validé par évaporation de gouttelettes avec un taux de transfert de masse constant, le problème de Stefan unidimensionnel, le problème d'aspiration de l'interface et un cas d'ébullition à film plan. Les résultats montrent un bon accord avec les solutions analytiques ou les corrélations.

Published
2020

87. Iodine‐Transfer Polymerization (ITP) of Ethylene and Copolymerization with Vinyl Acetate

Author

D'Agosto, F, Wolpers, A., Baffie, F., Verrieux, L., Perrin, Lionel, Monteil, V., D'AGOSTO, F., Interface Theory Experiment : Mechanism & Modeling (ITEMM), Institut de Chimie et Biochimie Moléculaires et Supramoléculaires (ICBMS), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut de Chimie du CNRS (INC)-École Supérieure Chimie Physique Électronique de Lyon-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut de Chimie du CNRS (INC)-École Supérieure Chimie Physique Électronique de Lyon-Centre National de la Recherche Scientifique (CNRS), Laboratoire de Chimie, Catalyse, Polymères et Procédés, R 5265 (C2P2), Université de Lyon-Université de Lyon-École supérieure de Chimie Physique Electronique de Lyon (CPE)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-École Supérieure Chimie Physique Électronique de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), and Université de Lyon

Subjects
chemistry.chemical_classification, Ethylene, Molar mass, 010405 organic chemistry, Radical polymerization, General Medicine, [CHIM.MATE]Chemical Sciences/Material chemistry, General Chemistry, Polymer, 010402 general chemistry, 01 natural sciences, Catalysis, 0104 chemical sciences, 3. Good health, chemistry.chemical_compound, Polymerization, chemistry, Polymer chemistry, Copolymer, Vinyl acetate, [CHIM]Chemical Sciences, Reactivity (chemistry), ComputingMilieux_MISCELLANEOUS
Abstract

International audience; The first example of a controlled radical polymerization of ethylene using iodine transfer polymerization (ITP) is performed under mild conditions (≤ 100 °C and ≤ 200 bar). Different commercially available, cheap, and non-toxic iodo alkyls (R-I) were used as chain-transfer agents (CTAs) in combination with 2,2'-azobis(2-methylpropionitrile) (AIBN) to investigate the impact of the R-group on the polymerization behaviour. These systems showed no rate retardation and no significant loss of chain ends. The formed well-defined end-capped polyethylene-iodine (PE-I) species is very stable upon storage. Narrow molar-mass distributions with dispersities around 1.6 were obtained up to number average molar masses Mn of 7300 g mol-1. The copolymerization by ITP (ITcoP) of ethylene with vinyl acetate (VAc) to form poly(ethylene-co-vinyl acetate) (EVA) copolymer was also successful. By fine tuning the ethylene pressure and the vinyl acetate content, a broad range of copolymers containing from 0 to 85 mol% of VAc unit was achieved. By reactivating the iodo chain-end, block copolymer structures composed of ethylene and VAc such as poly(ethylene-co-vinyl acetate)-b-polyethylene (EVA-b-PE) or gradient block copopolymers EVA-b-EVA with different content of VAc in the blocks were obtained for the first time using ITP. This highly versatile synthetic platform provides a straightforward access to a diverse range of well-defined PE based polymer materials. Finally, reactivity trends were explored by a theoretical mechanistic study.

Published
2020

88. Localization of hubs in complex networks with overlapping modular structure

Author

Ghalmane, Zakariya, Cherifi, Chantal, Cherifi, Hocine, El Hassouni, Mohammed, Université Mohammed V de Rabat [Agdal], Décision et Information pour les Systèmes de Production (DISP), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université Lumière - Lyon 2 (UL2), Laboratoire d'Informatique de Bourgogne [Dijon] (LIB), Université de Bourgogne (UB), and Cherifi, Chantal

Subjects
[INFO]Computer Science [cs], [INFO] Computer Science [cs], ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2020

89. Synthèse concomitante de peptides et d'oligonucléotides en présence de lipides et de vésicules lipidiques

Author

Fayolle, Dimitri, Institut de Chimie et Biochimie Moléculaires et Supramoléculaires (ICBMS), Centre National de la Recherche Scientifique (CNRS)-École Supérieure Chimie Physique Électronique de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon, Université Claude Bernard Lyon1, Pierre Strazewski, Michele Fiore (co-encadrant), STAR, ABES, Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), and Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut de Chimie du CNRS (INC)-École Supérieure Chimie Physique Électronique de Lyon-Centre National de la Recherche Scientifique (CNRS)

Subjects
peptido-RNA, phosphoramidate, membranophilic peptides, [CHIM.ORGA]Chemical Sciences/Organic chemistry, peptides membranophiles, vésicules géantes, giant vesicles, peptido-ARN, [CHIM.ORGA] Chemical Sciences/Organic chemistry
Abstract

Translation of nucleic acids into proteins is a key mechanism to all known forms of life. It relies on a complex biological machinery, and chemists are struggling to mimic translation in conditions that may be representative of the emergence of early forms of life from a prebiotic world. Recently, it was suggested that peptido-RNA, a family of conjugates in which a peptide is bound to an oligonucleotide through a N→5’ phosphoramidate, could provide a simple example of ribonucleotide-promoted peptide synthesis. In addition, cellular life is dependent on the formation and growth of compartments primarily based on self-assembled lipid membranes. In this work, we study the interaction of amphiphilic peptido-RNA, in which a lipophilic peptide is bound to a hydrophilic nucleotide, with membranes in three different ways. We found that amphiphilic peptido-RNA of random or defined sequences could be formed in the presence of lipid membranes but that these exerted no significant influence on the reaction networks at play. In contrast, peptido-RNA with short and medium-length membranophilic peptides could durably interact with lipid membranes, providing a localization mechanism. Finally, it is envisioned that, because phosphoramidates are easily cleaved, this localization could be exploited to allow the permeation of nucleotides and short nucleic acids through membranes without the assistance of enzymes. Our results contribute to shed light on a prebiotic peptide-RNA-lipid world, La traduction, synthèse de protéines guidée par les acides nucléiques, est un mécanisme primordial pour toutes les formes de vie connues. Elle repose sur une machinerie biologique complexe, et les chimistes peinent à proposer des alternatives crédibles dans le contexte de l’émergence du vivant. Des travaux récents suggèrent que les peptido-ARN, des conjugués formés d’un peptide et d’un oligonucléotide liés en N→5’ par un phosphoramidate, pourraient représenter un exemple simple de synthèse peptidique promue par un ribonucléotide. D’autre part, la vie cellulaire dépend de la formation et de la croissance de compartiments reposant sur des membranes lipidiques auto-assemblées. Dans cette thèse, nous étudions les interactions de peptido-ARNs amphiphiles, formés d’un peptide lipophile et d’un nucléotide hydrophile, avec des membranes lipidiques selon trois axes. Premièrement, nous avons observé la formation de peptido-ARN amphiphiles à séquences aléatoire ou définie en présence de membranes mais sans observer d’influence significative de ces dernières sur la réactivité. Cependant, des peptido-ARN à séquence peptidique membranophile courte ou moyenne interagissent durablement avec des membranes lipidiques et permettent donc un mécanisme de localisation. En raison de la labilité des phosphoramidates, nous envisageons enfin que cette localisation peut être exploitée pour permettre la pénétration de nucléotides et de petits acides nucléiques à travers des membranes sans mécanisme enzymatique. Nos résultats contribuent à l’élaboration d’un monde prébiotique reposant sur une interaction peptide-ARN-lipides

Published
2020

90. Effect of the COVID-19 pandemic on acute stroke reperfusion therapy: data from the Lyon Stroke Center Network

Author

Michel Ovize, Lucie Rascle, Axel Benhamed, Julia Fontaine, Karim Tazarourte, Laurent Derex, Cécile Plumereau, Tae-Hee Cho, Matteo Cappucci, Marielle Buisson, Elodie Ong, David Schiavo, Norbert Nighoghossian, Thomas Bony, Yves Berthezène, Célia Tuttle, Omer Eker, Roberto Riva, Camille Amaz, Marion Douplat, Laura Mechtouff, Hospices Civils de Lyon (HCL), Service de Neurologie [Lyon], CHU Lyon, Cardiovasculaire, métabolisme, diabétologie et nutrition (CarMeN), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Centre d'Investigation Clinique [Bron] (CIC1407), Hospices Civils de Lyon (HCL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Groupement Hospitalier Est [Bron], Département de Neuroradiologie [Centre Hospitalier Lyon Sud - HCL], Centre Hospitalier Lyon Sud [CHU - HCL] (CHLS), Hospices Civils de Lyon (HCL)-Hospices Civils de Lyon (HCL), Health Service and Performance Research (HESPER), Université de Lyon-Université de Lyon, Université de Lyon, Hôpital Edouard Herriot [CHU - HCL], Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Modeling & analysis for medical imaging and Diagnosis (MYRIAD), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), and CarMeN, laboratoire

Subjects
medicine.medical_specialty, Neurology, Coronavirus disease 2019 (COVID-19), medicine.medical_treatment, [SDV]Life Sciences [q-bio], Clinical Neurology, Brain Ischemia, 03 medical and health sciences, 0302 clinical medicine, Reperfusion therapy, Internal medicine, Pandemic, Medicine, Humans, Thrombolytic Therapy, 030212 general & internal medicine, Prospective Studies, Stroke, Pandemics, Neuroradiology, Retrospective Studies, Thrombectomy, Original Communication, business.industry, Thrombolysis, medicine.disease, 3. Good health, [SDV] Life Sciences [q-bio], Treatment Outcome, Reperfusion, Emergency medical dispatch, Cardiology, Neurology (clinical), France, business, Covid-19, 030217 neurology & neurosurgery
Abstract

BackgroundThe coronavirus disease 2019 (COVID-19) pandemic would have particularly affected acute stroke care. However, its impact is clearly inherent to the local stroke network conditions. We aimed to assess the impact of COVID-19 pandemic on acute stroke care in the Lyon comprehensive stroke center during this period.MethodsWe conducted a prospective data collection of patients with acute ischemic stroke (AIS) treated with intravenous thrombolysis (IVT) and/or mechanical thrombectomy (MT) during the COVID-19 period (from 29/02/2020 to 10/05/2020) and a control period (from 29/02/2019 to 10/05/2019). The volume of reperfusion therapies and pre and intra-hospital delays were compared during both periods.ResultsA total of 208 patients were included. The volume of IVT significantly decreased during the COVID-period (55 (54.5%) vs 74 (69.2%); p=0.03) and was mainly due to time delay among patients treated with MT. The volume of MT remains stable over the two periods (72 (71.3%) vs 65 (60.8%); p=0.14) but the door-to-groin puncture time increased in patients transferred for MT (237 [187-339] vs 210 [163-260]; pConclusionsOur study showed a decrease of the volume of IVT, whereas the volume of MT remained stable although intra-hospital delays increased for transferred patients during the COVID-19 pandemic. These results contrast in part with the national surveys and suggest that the impact of the pandemic may depend on local stroke care networks.

Published
2020
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91. Using Sparse Array for 3D Passive Cavitation Imaging

Author

Audrey Sivadon, Jean-Christophe Béra, Bruno Gilles, Francois Varray, Barbara Nicolas, Application des ultrasons à la thérapie (LabTAU), Centre Léon Bérard [Lyon]-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de la Santé et de la Recherche Médicale (INSERM), Imagerie Ultrasonore, Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), Nicolas, Barbara, Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), and Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Materials science, [INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing, business.industry, Acoustics, Sparse Array, Ultrasound, 3D Imaging, 01 natural sciences, 3. Good health, 030218 nuclear medicine & medical imaging, 03 medical and health sciences, 0302 clinical medicine, Sparse array, [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Cavitation, 0103 physical sciences, Cavitation Imaging, business, 010301 acoustics, ComputingMilieux_MISCELLANEOUS
Abstract

Cavitation is involved in a number of therapeutic applications of ultrasound (US) [1] [2] [3]. Monitoring these treatments requires real-time imaging of the cavitation activity during treatment. For this reason, Passive Acoustic Mapping (PAM) of cavitation has drawn an increasing attention during the past years [4] [5] [6] [7] [8].

Published
2020

92. Futility Risk Model for Predicting Outcome After Transcatheter Aortic Valve Implantation

Author

Arnaud Bisson, Laurent Fauchier, Dominique Babuty, Christophe Saint Etienne, L Quilliet, Anne Bernard, Thibaud Lacour, Julien Herbert, Brahim Harbaoui, Fabrice Ivanes, Pierre Lantelme, Thierry Bourguignon, Denis Angoulvant, Pierre Deharo, Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), CHU Trousseau [Tours], Centre Hospitalier Régional Universitaire de Tours (CHRU Tours), Centre recherche en CardioVasculaire et Nutrition = Center for CardioVascular and Nutrition research (C2VN), Aix Marseille Université (AMU)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Département de Cardiologie [Hôpital de la Timone - APHM], Hôpital de la Timone [CHU - APHM] (TIMONE)-Assistance Publique - Hôpitaux de Marseille (APHM), Amgen,Sanofi, Novartis, AstraZeneca, Bayer, Bristol-MyersSquibb, Boehringer-Ingelheim, MSD, Pfizer, and Servier, Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Assistance Publique - Hôpitaux de Marseille (APHM)- Hôpital de la Timone [CHU - APHM] (TIMONE), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet [Saint-Étienne] (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), and Guieu, régis

Subjects
Male, medicine.medical_specialty, [SDV]Life Sciences [q-bio], 030204 cardiovascular system & hematology, Logistic regression, Risk Assessment, STENOSIS, VALIDATION, DISEASE, Transcatheter Aortic Valve Replacement, 03 medical and health sciences, 0302 clinical medicine, [SDV.MHEP.CSC]Life Sciences [q-bio]/Human health and pathology/Cardiology and cardiovascular system, Internal medicine, medicine, MANAGEMENT, Humans, 030212 general & internal medicine, Stroke, Aged, Retrospective Studies, Aged, 80 and over, Models, Statistical, [SDV.MHEP] Life Sciences [q-bio]/Human health and pathology, business.industry, Vascular disease, MORTALITY, Retrospective cohort study, Atrial fibrillation, Aortic Valve Stenosis, medicine.disease, Prognosis, 3. Good health, [SDV.MHEP.CSC] Life Sciences [q-bio]/Human health and pathology/Cardiology and cardiovascular system, [SDV] Life Sciences [q-bio], REPLACEMENT, Heart failure, Cohort, ATRIAL-FIBRILLATION, Cardiology, Female, Cardiology and Cardiovascular Medicine, Risk assessment, business, Medical Futility, [SDV.MHEP]Life Sciences [q-bio]/Human health and pathology, TASK-FORCE
Abstract

International audience; Risk-benefit assessment for transcatheter aortic valve implantation (TAVI) is still a matter of debate. We aimed to identify patients with a bad outcome within 1 year after TAVI, and to develop a Futile TAVI Simple score (FTS). Based on the administrative hospital-discharge database, all consecutive patients treated with percutaneous TAVI in France between 2010 and 2018 were included. A prediction model was derived and validated for 1-year all-cause death after TAVI (considered as futility) by using split-sample validation: 20,443 patients were included in the analysis (mean age 83 +/- 7 years). 7,039 deaths were recorded (yearly incidence rate 15.5%), among which 3,702 (53%) occurred in first year after TAVI procedure. In the derivation cohort (n = 10,221), the final logistic regression model included male sex, history of hospital stay with heart failure, history of pulmonary oedema, atrial fibrillation, previous stroke, vascular disease, renal disease, liver disease, pulmonary disease, anaemia, history of cancer, metastasis, depression and denutrition. The area under the curve (AUC) for the FTS was 0.674 (95%CI 0.660 to 0.687) in the derivation cohort and 0.651 (95%CI 0.637 to 0.665) in the validation cohort (n = 10,222). The Hosmer-Lemeshow test had a p-value of 0.87 suggesting an accurate calibration. The FTS score outperformed EuroSCORE II, Charlson comorbidity index and frailty index for identifying futility. Based on FTS score, 7% of these patients were categorized at high risk with a 1-year mortality at 43%. In conclusion, the FTS score, established from a large nationwide cohort of patients treated with TAVI, may provide a relevant tool for optimizing healthcare decision.

Published
2020

93. A Framework to Support Value Co-creation in PSS Development

Author

Gilles Neubert, Martha Orellano, Xavier Boucher, Anne Coulon, École des Mines de Saint-Étienne (Mines Saint-Étienne MSE), Institut Mines-Télécom [Paris] (IMT), Institut Henri Fayol (FAYOL-ENSMSE), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Environnement Ville Société (EVS), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-École nationale supérieure d'architecture de Lyon (ENSAL)-École des Mines de Saint-Étienne (Mines Saint-Étienne MSE), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-École Nationale des Travaux Publics de l'État (ENTPE)-Université Jean Monnet [Saint-Étienne] (UJM)-Université Jean Moulin - Lyon 3 (UJML), Université de Lyon-Université Lumière - Lyon 2 (UL2)-École normale supérieure - Lyon (ENS Lyon), Département Génie de l’environnement et des organisations (FAYOL-ENSMSE), Institut Henri Fayol-Ecole Nationale Supérieure des Mines de St Etienne, Laboratoire d'Informatique, de Modélisation et d'Optimisation des Systèmes (LIMOS), Ecole Nationale Supérieure des Mines de St Etienne-Centre National de la Recherche Scientifique (CNRS)-Université Clermont Auvergne [2017-2020] (UCA [2017-2020]), Laboratoire d'Informatique pour l'Entreprise et les Systèmes de Production (LIESP), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université Lumière - Lyon 2 (UL2)-École Centrale de Lyon (ECL), Université de Lyon, Environnement, Ville, Société (EVS), École normale supérieure de Lyon (ENS de Lyon)-École des Mines de Saint-Étienne (Mines Saint-Étienne MSE), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université Lumière - Lyon 2 (UL2)-Université Jean Moulin - Lyon 3 (UJML), Université de Lyon-Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Jean Monnet - Saint-Étienne (UJM)-École Nationale des Travaux Publics de l'État (ENTPE)-École nationale supérieure d'architecture de Lyon (ENSAL)-Centre National de la Recherche Scientifique (CNRS), Institut Henri Fayol-Ecole Nationale Supérieure des Mines de St Etienne (ENSM ST-ETIENNE), Ecole Nationale Supérieure des Mines de St Etienne (ENSM ST-ETIENNE)-Université Clermont Auvergne [2017-2020] (UCA [2017-2020])-Centre National de la Recherche Scientifique (CNRS), Bojan Lalic, Vidosav Majstorovic, Ugljesa Marjanovic, Gregor von Cieminski, David Romero, TC 5, and WG 5,7

Subjects
Value (ethics), 0209 industrial biotechnology, Focus (computing), Process management, Value co-creation, Process (engineering), Interface (Java), Computer science, 05 social sciences, Customer needs, Case study, 02 engineering and technology, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, PSS development, 020901 industrial engineering & automation, Development (topology), Customer value, 0502 economics and business, Co-creation, [INFO]Computer Science [cs], 050203 business & management
Abstract

International audience; The design of innovative offers involves deep collaboration between the provider and the customer to create higher value than in traditional offers. Product-Service Systems (PSS) are bundles of products and services and constitute innovative offers designed to fit complex customer needs. The academic literature on PSS development has a strong focus on the provider perspective, and only a few works address customer involvement in the process of value co-creation. This paper proposes a methodological framework composed of a customer view and a provider view, highlighting the interface between them, in which value co-creation takes place. The proposed framework aims at supporting the collaboration process between the customer and the provider during the entire PSS development. The framework has been built within a R&D project in a French company called Vibratec.

Published
2020

94. Breaking the clinico-radiological paradox in multiple sclerosis using machine learning

Author

Attyé, Arnaud, Cackowski, Stenzel, Tucholka, Alan, Roca, Pauline, Rubini, Pascal, Verclytte, Sebastien, Colas, Lucie, Ding, Juliette, Budzik, Jean-François, Renard, Felix, Barbier, Emmanuel L, Casey, Romain, Vukusic, Sandra, Cotton, François, [GIN] Grenoble Institut des Neurosciences (GIN), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Grenoble Alpes (UGA), Pixyl Medical [Grenoble], Université catholique de Lille (UCL), Laboratoire d'Informatique de Grenoble (LIG), Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ), Université Grenoble Alpes (UGA), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon, Observatoire Français de la Sclérose En Plaques [Lyon] (OFSEP), Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), and ANR-19-P3IA-0003,MIAI,MIAI @ Grenoble Alpes(2019)

Subjects
[INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], [SDV]Life Sciences [q-bio], education
Abstract

International audience; MRI is central to the study of white matter lesions in multiple sclerosis (MS). To date, the distribution of MS lesions, as evaluated on FLAIR imaging, has not been linked to patients’ disability prediction. Based on an international data challenge with 1500 MS patients and ground truth 2-year Expanded Disability Status Scale (EDSS), we have proposed an adaptive machine learning framework to predict the clinical disability. Here, we report the encouraging finding that our algorithm predicts the 2-year EDSS score with an accuracy estimated to 81%, only based on a single initial FLAIR sequence, added to sex and gender information.

Published
2020

95. Surgical Site Infections after glioblastoma surgery: results of a multicentric retrospective study

Author

Elodie Couvé-Deacon, Houda Belmabrouk, Evelyne Emery, Henri Salle, Romuald Seizeur, Alexandre Roux, Jimmy Voirin, Hélène Cebula, Moncef Berhouma, Arnaud Dagain, Pierre-Marie Preux, Matthieu Peyre, Amaury De Barros, Marie-Jeanne Fotso, Johan Pallud, Vincent Jecko, Anne-Claire Beaujeux, Leslie Lemnos, Tuan Le Van, Elise Deluche, Julien Engelhardt, François Caire, Ilyess Zemmoura, Service de Neurochirurgie [CHU Limoges], CHU Limoges, Contrôle de l’Activation Cellulaire, Progression Tumorale et Résistance thérapeutique (CAPTuR), Institut Génomique, Environnement, Immunité, Santé, Thérapeutique (GEIST), Université de Limoges (UNILIM)-Université de Limoges (UNILIM), Service d'Oncologie médicale [CHU Limoges], Service de Bactériologie, Virologie, Hygiène [CHU Limoges], Biomarqueurs en imagerie : neuro développement et pathologies cérébrales (Ima-Brain [Paris]), Institut de psychiatrie et neurosciences de Paris (IPNP - U1266 Inserm), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Paris (UP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Paris (UP), Service de Neurochirurgie [Hôpital Sainte-Anne - APHP], Centre Hospitalier Sainte Anne [Paris], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Département de Neurochirurgie [CHU Toulouse], CHU Toulouse [Toulouse], Hôpitaux Civils Colmar, Service de neurochirurgie [Brest], Hôpital de la Cavale Blanche - CHRU Brest (CHU - BREST ), Laboratoire de Traitement de l'Information Medicale (LaTIM), Institut National de la Santé et de la Recherche Médicale (INSERM)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Centre Hospitalier Régional Universitaire de Brest (CHRU Brest)-Université de Brest (UBO)-Institut Brestois Santé Agro Matière (IBSAM), Université de Brest (UBO), Service de Neurochirurgie [CHU Caen], Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Normandie Université (NU)-CHU Caen, Normandie Université (NU)-Tumorothèque de Caen Basse-Normandie (TCBN)-Tumorothèque de Caen Basse-Normandie (TCBN), Unité Support CYCERON, Normandie Université (NU)-Tumorothèque de Caen Basse-Normandie (TCBN)-Tumorothèque de Caen Basse-Normandie (TCBN)-Centre National de la Recherche Scientifique (CNRS), Service de Neurochirurgie [CHU Saint-Etienne], Centre Hospitalier Universitaire de Saint-Etienne (CHU de Saint-Etienne)-Hôpital Nord (Saint Etienne), Service de Neurochirurgie A [Bordeaux], CHU Bordeaux [Bordeaux], Institut de Neurosciences cognitives et intégratives d'Aquitaine (INCIA), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-SFR Bordeaux Neurosciences-Centre National de la Recherche Scientifique (CNRS), Service de Neurochirurgie [CHRU Tours], Centre Hospitalier Régional Universitaire de Tours (CHRU TOURS), Imagerie et cerveau (iBrain - Inserm U1253 - UNIV Tours ), Université de Tours-Institut National de la Santé et de la Recherche Médicale (INSERM), Service de neurochirurgie [CHU de Dijon], Centre Hospitalier Universitaire de Dijon - Hôpital François Mitterrand (CHU Dijon), Service de Neurochirurgie [Hopital Pierre Wertheimer - HCL], Hôpital neurologique et neurochirurgical Pierre Wertheimer [CHU - HCL], Hospices Civils de Lyon (HCL)-Hospices Civils de Lyon (HCL), Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), Service de Neurochirurgie [HUS, Strasbourg], Les Hôpitaux Universitaires de Strasbourg (HUS), Service de Neurochirurgie [CHU Pitié-Salpêtrière], CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Neuroépidémiologie Tropicale (NET), Université de Limoges (UNILIM)-Université de Limoges (UNILIM)-CHU Limoges-Institut d'Epidémiologie Neurologique et de Neurologie Tropicale-Institut National de la Santé et de la Recherche Médicale (INSERM), Service de l'Information Médicale et de l'Évaluation [CHU Limoges] (SIME), Laboratoire de Biostatistique et d'Informatique Médicale, Université de Limoges (UNILIM), XLIM (XLIM), Université de Limoges (UNILIM)-Centre National de la Recherche Scientifique (CNRS), Université de Brest (UBO)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre Hospitalier Régional Universitaire de Brest (CHRU Brest)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut Brestois Santé Agro Matière (IBSAM), CHU Caen, Normandie Université (NU)-Tumorothèque de Caen Basse-Normandie (TCBN)-Normandie Université (NU)-Tumorothèque de Caen Basse-Normandie (TCBN)-Université de Caen Normandie (UNICAEN), Normandie Université (NU), Centre Hospitalier Régional Universitaire de Tours (CHRU Tours), Université de Tours (UT)-Institut National de la Santé et de la Recherche Médicale (INSERM), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), and CHU Limoges-Institut d'Epidémiologie Neurologique et de Neurologie Tropicale-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut Génomique, Environnement, Immunité, Santé, Thérapeutique (GEIST)

Subjects
Microbiology (medical), medicine.medical_specialty, medicine.medical_treatment, 03 medical and health sciences, 0302 clinical medicine, Risk Factors, Medicine, Humans, Surgical Wound Infection, Medical history, 030212 general & internal medicine, Survival rate, Retrospective Studies, Chemotherapy, business.industry, Medical record, Retrospective cohort study, General Medicine, medicine.disease, 3. Good health, Surgery, Discontinuation, Radiation therapy, Infectious Diseases, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, Neoplasm Recurrence, Local, business, Glioblastoma, 030217 neurology & neurosurgery, Surgical site infection
Abstract

International audience; Background The effects of surgical site infections (SSI) after glioblastoma surgery on patient outcomes are understudied.The aim of this retrospective multicenter study was to evaluate the impact of SSI on the survival of glioblastoma patients.Methods Data from SSI cases after glioblastoma surgeries between 2009 and 2016 were collected from 14 French neurosurgicalcenters. Collected data included patient demographics, previous medical history, risk factors, details of the surgicalprocedure, radiotherapy/chemotherapy, infection characteristics, and infection management. Similar data were collectedfrom gender- and age-paired control individuals.Results We used the medical records of 77 SSI patients and 58 control individuals. 13 were excluded. Our analyses includeddata from 64 SSI cases and 58 non-infected glioblastoma patients. Infections occurred after surgery for primary tumors in 38cases (group I) and after surgery for a recurrent tumor in 26 cases (group II). Median survival was 381, 633, and 547 daysin patients of group I, group II, and the control group, respectively. Patients in group I had significantly shorter survivalcompared to the other two groups (p < 0.05). The one-year survival rate of patients who developed infections after surgeryfor primary tumors was 50%. Additionally, we found that SSIs led to postoperative treatment discontinuation in 30% of thepatients.Discussion Our findings highlighted the severity of SSIs after glioblastoma surgery, as they significantly affect patientsurvival. The establishment of preventive measures, as well as guidelines for the management of SSIs, is of high clinicalimportance.

Published
2020

96. Spatially varying regularization weights for one-step spectral CT with SQS

Author

Pierre-Antoine Rodesch, Si-Mohamed Salim, Simon Rit, Imagerie Tomographique et Radiothérapie, Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), Imagerie et modélisation Vasculaires, Thoraciques et Cérébrales (MOTIVATE), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Rit, Simon, and Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL)

Subjects
[INFO.INFO-IM] Computer Science [cs]/Medical Imaging, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging
Abstract

International audience; Photon-counting detectors provide spectral information for x-ray acquisitions. Taking advantage of this information currently requires iterative algorithms to reconstruct basis material CT images. One-step reconstruction is the simultaneous inversion of the spectral distortion occurring in the detector and the geometrical projection. Separable quadratic surrogate (SQS) algorithms have been applied to this one-step problem with satisfactory convergence and material separation. However, this class of method leads to numerical instabilities stemming from voxels out of the field-of-view (FOV) which need to be included in the forward model for reconstructing the FOV. We aim at improving one-step spectral CT reconstruction by investigating two possible corrections of this effect: replacing the exponential in the forward model by a linear function for negative attenuations and spatially varying regularization depending on the geometrical conditioning. We demonstrate the efficiency of the second method using experimental data acquired on a clinical prototype CT scanner with a photon-counting detector.

Published
2020

97. Dynamic Fluence Modulation using Proton CT for Low-dose Imaging in Particle Therapy

Author

Jannis Dickmann, Christina Sarosiek, George Coutrakon, Simon Rit, Nick Detrich, Victor Rykalin, Mark Pankuch, Robert Johnson, Reinhard Schulte, Katia Parodi, Guillaume Landry, George Dedes, Imagerie Tomographique et Radiothérapie, Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), and Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université Claude Bernard Lyon 1 (UCBL)

Subjects
[INFO.INFO-IM]Computer Science [cs]/Medical Imaging
Abstract

International audience; Dynamic fluence modulation for computed tomography (CT), i.e. the acquisition of tomographic images with variable, patient-and task-specific fluence fields, offers the potential to substantially reduce local imaging dose. In particular, volume-of-interest (VOI) imaging allows to limit imaging dose to a clinically relevant volume and reduce it elsewhere. In the context of particle therapy, where tomographic data is required for treatment planning the VOI is the treatment beam path. VOI imaging is of particular interest for particle therapy given the very low integral out-of-VOI treatment dose. Proton CT imaging allows for a direct measurement of the proton stopping power with an increased accuracy and a decreased imaging dose compared to x-ray-based CT. In addition, frequent imaging is required to verify patient positioning and to monitor potential anatomical changes, which over the course of a treatment may compromise the planned dose. In this work, we evaluate the performance of a fluence-modulated proton CT algorithm for low-dose in-room imaging. This would allow for recalculation or replanning of the treatment dose according to the anatomy of the day with out-of-VOI dose below 1 mGy. We performed a simulation study and acquired experimental data using a prototype proton CT scanner. By employing a bow-tie-like fluence modulation aiming for constant noise, imaging dose was reduced by 9%. For a VOI imaging task, out-of-VOI dose was reduced by 41% and substantially below 1 mGy. This may pave the way for daily imaging prior to every treatment session aiming to eventually reduce safety margins in particle therapy, thus further reducing normal tissue exposure to therapeutic doses.

Published
2020

98. Phenotypic characteristics and prognosis of inpatients with COVID-19 and diabetes: the CORONADO study

Author

Amélie Ducet-Boiffard, Muriel Bourgeon-Ghittori, Claude Chaumeil, Pierre Jean Saulnier, Sandrine Coudol, Bertrand Cariou, Camille Vatier, Jean François Gautier, David Boutoille, Charles Thivolet, Abdallah Al-Salameh, Pierre Gourdy, Sophie Borot, Michael Joubert, Samy Hadjadj, Bruno Laviolle, Matthieu Wargny, Ingrid Allix, Ariane Sultan, Florence Baudoux, Gaëtan Prévost, René Robert, Coralie Amadou, Laurent Meyer, Jean-Pierre Riveline, Matthieu Pichelin, Patrice Darmon, Blandine Tramunt, Bernard Bauduceau, Jean François Thébaut, Gwénaëlle Arnault, Olivier Bourron, Lucien Marchand, Bénédicte Gaborit, Ronan Roussel, Emmanuel Cosson, Emmanuel Disse, Véronique Kerlan, Louis Potier, Centre hospitalier universitaire de Nantes (CHU Nantes), unité de recherche de l'institut du thorax UMR1087 UMR6291 (ITX), Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université de Nantes - UFR de Médecine et des Techniques Médicales (UFR MEDECINE), Université de Nantes (UN)-Université de Nantes (UN), CHU Amiens-Picardie, Centre Hospitalier Universitaire d'Angers (CHU Angers), PRES Université Nantes Angers Le Mans (UNAM), Centre Hospitalier Sud Francilien, CH Evry-Corbeil, Centre hospitalier Bretagne Atlantique (Morbihan) (CHBA), Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille), Hôpital d'Instruction des Armées Begin, Service de Santé des Armées, Service de Diabétologie - Endocrinologie [CHRU Besançon], Centre Hospitalier Régional Universitaire de Besançon (CHRU Besançon), AP-HP - Hôpital Antoine Béclère [Clamart], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Service de Diabétologie [CHU Pitié-Salpétrière], CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Service des maladies infectieuses et tropicales [CHU Nantes], Pharmacologie des anti-infectieux (PHAR), Université de Poitiers-Institut National de la Santé et de la Recherche Médicale (INSERM), Fédération française des diabétiques, Université Sorbonne Paris Cité (USPC), Centre d’Investigation Clinique de Nantes (CIC Nantes), Université de Nantes (UN)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre hospitalier universitaire de Nantes (CHU Nantes), Centre recherche en CardioVasculaire et Nutrition = Center for CardioVascular and Nutrition research (C2VN), Aix Marseille Université (AMU)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Cardiovasculaire, métabolisme, diabétologie et nutrition (CarMeN), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Hospices Civils de Lyon (HCL), Centre Hospitalier Départemental - Hôpital de La Roche-sur-Yon, Service d'endocrinologie, nutrition et maladies métaboliques [Hôpital Nord - APHM], Assistance Publique - Hôpitaux de Marseille (APHM)- Hôpital Nord [CHU - APHM], Centre Hospitalier Régional Universitaire de Brest (CHRU Brest), CHU Pontchaillou [Rennes], Centre d'Investigation Clinique [Rennes] (CIC), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Hôpital Pontchaillou-Institut National de la Santé et de la Recherche Médicale (INSERM), Centre Hospitalier Régional Universitaire [Montpellier] (CHRU Montpellier), Les Hôpitaux Universitaires de Strasbourg (HUS), Service d'endocrinologie, diabétologie, nutrition, Différenciation et communication neuronale et neuroendocrine (DC2N), Université de Rouen Normandie (UNIROUEN), Normandie Université (NU)-Normandie Université (NU)-Institut National de la Santé et de la Recherche Médicale (INSERM), Centre de Recherche des Cordeliers (CRC (UMR_S_1138 / U1138)), École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université de Paris (UP), Service des urgences [Poitiers], Centre hospitalier universitaire de Poitiers (CHU Poitiers), CIC - Poitiers, Université de Poitiers-Centre hospitalier universitaire de Poitiers (CHU Poitiers)-Direction Générale de l'Organisation des Soins (DGOS)-Institut National de la Santé et de la Recherche Médicale (INSERM), Physiologie & médecine expérimentale du Cœur et des Muscles [U 1046] (PhyMedExp), Université de Montpellier (UM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), CHU Saint-Antoine [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Institut des Maladies Métaboliques et Cardiovasculaires (I2MC), Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM), CORONADO Initiative Emergency grant, Air Liquide, CORONADO Initiative Emergency Grant, Fondation Francophone pour la Recherche sur le Diabète, CORONADO Initiative Emergency Grant, Sociéte Francophone du Diabète, Unité de recherche de l'institut du thorax (ITX-lab), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Centre Hospitalier Départemental - Hôpital de La Roche-sur-Yon (CHD Vendée), Université de Rennes (UR)-Hôpital Pontchaillou-Institut National de la Santé et de la Recherche Médicale (INSERM), Service d'endocrinologie, diabétologie et maladies métaboliques [Rouen], CHU Rouen, Normandie Université (NU)-Normandie Université (NU)-Université de Rouen Normandie (UNIROUEN), Normandie Université (NU), École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Paris Cité (UPCité), Service des urgences [CHU Poitiers], Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National de la Santé et de la Recherche Médicale (INSERM), Université de Picardie Jules Verne (UPJV), Service de diabétologie - endocrinologie, Centre Hospitalier Régional Universitaire [Besançon] (CHRU Besançon)-Hôpital Jean Minjoz, Hôpital Antoine Béclère, Service de diabétologie [CHU Pitié-Salpétrière], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-CHU Pitié-Salpêtrière [AP-HP], Unité de Recherche en Epidémiologie Nutritionnelle (UREN), Conservatoire National des Arts et Métiers [CNAM] (CNAM)-Université Sorbonne Paris Cité (USPC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Sorbonne Paris Nord, Centre de Recherche en Nutrition Humaine - Ile de France (CRNH - IDF), Université de Nantes (UN)-Centre hospitalier universitaire de Nantes (CHU Nantes)-Institut National de la Santé et de la Recherche Médicale (INSERM), Service d'endocrinologie, diabète, maladies métaboliques [Hôpital de la Conception - APHM], Aix Marseille Université (AMU), Centre recherche en CardioVasculaire et Nutrition (C2VN), Aix Marseille Université (AMU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut National de la Santé et de la Recherche Médicale (INSERM), Hospices Civils de Lyon (HCL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Groupe d'Etude de la Thrombose de Bretagne Occidentale (GETBO), Université de Brest (UBO)-Institut Brestois Santé Agro Matière (IBSAM), Université de Brest (UBO), École pratique des hautes études (EPHE)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université de Paris (UP), Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Montpellier (UM), Centre Hospitalier Universitaire de Toulouse, Jonchère, Laurent, Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Université de Nantes - UFR de Médecine et des Techniques Médicales (UFR MEDECINE), Université de Nantes (UN)-Université de Nantes (UN)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, and Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL)

Subjects
medicine.medical_specialty, HbA1c, medicine.medical_treatment, Endocrinology, Diabetes and Metabolism, 030209 endocrinology & metabolism, Type 2 diabetes, Logistic regression, Article, 03 medical and health sciences, BMI, 0302 clinical medicine, Mechanical ventilation, [SDV.MHEP.MI]Life Sciences [q-bio]/Human health and pathology/Infectious diseases, Internal medicine, Diabetes mellitus, medicine, Internal Medicine, 030212 general & internal medicine, [SDV.MHEP.ME] Life Sciences [q-bio]/Human health and pathology/Emerging diseases, [SDV.MHEP.EM] Life Sciences [q-bio]/Human health and pathology/Endocrinology and metabolism, Univariate analysis, [SDV.MHEP.ME]Life Sciences [q-bio]/Human health and pathology/Emerging diseases, business.industry, Tracheal intubation, Diabetes, COVID-19, [SDV.MHEP.EM]Life Sciences [q-bio]/Human health and pathology/Endocrinology and metabolism, medicine.disease, Comorbidity, 3. Good health, Death, [SDV.SPEE] Life Sciences [q-bio]/Santé publique et épidémiologie, Hypertension, [SDV.MHEP.MI] Life Sciences [q-bio]/Human health and pathology/Infectious diseases, Observational study, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, business
Abstract

Aims/hypothesis Coronavirus disease-2019 (COVID-19) is a life-threatening infection caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) virus. Diabetes has rapidly emerged as a major comorbidity for COVID-19 severity. However, the phenotypic characteristics of diabetes in COVID-19 patients are unknown. Methods We conducted a nationwide multicentre observational study in people with diabetes hospitalised for COVID-19 in 53 French centres in the period 10–31 March 2020. The primary outcome combined tracheal intubation for mechanical ventilation and/or death within 7 days of admission. Age- and sex-adjusted multivariable logistic regressions were performed to assess the prognostic value of clinical and biological features with the endpoint. ORs are reported for a 1 SD increase after standardisation. Results The current analysis focused on 1317 participants: 64.9% men, mean age 69.8 ± 13.0 years, median BMI 28.4 (25th–75th percentile: 25.0–32.7) kg/m2; with a predominance of type 2 diabetes (88.5%). Microvascular and macrovascular diabetic complications were found in 46.8% and 40.8% of cases, respectively. The primary outcome was encountered in 29.0% (95% CI 26.6, 31.5) of participants, while 10.6% (9.0, 12.4) died and 18.0% (16.0, 20.2) were discharged on day 7. In univariate analysis, characteristics prior to admission significantly associated with the primary outcome were sex, BMI and previous treatment with renin–angiotensin–aldosterone system (RAAS) blockers, but not age, type of diabetes, HbA1c, diabetic complications or glucose-lowering therapies. In multivariable analyses with covariates prior to admission, only BMI remained positively associated with the primary outcome (OR 1.28 [1.10, 1.47]). On admission, dyspnoea (OR 2.10 [1.31, 3.35]), as well as lymphocyte count (OR 0.67 [0.50, 0.88]), C-reactive protein (OR 1.93 [1.43, 2.59]) and AST (OR 2.23 [1.70, 2.93]) levels were independent predictors of the primary outcome. Finally, age (OR 2.48 [1.74, 3.53]), treated obstructive sleep apnoea (OR 2.80 [1.46, 5.38]), and microvascular (OR 2.14 [1.16, 3.94]) and macrovascular complications (OR 2.54 [1.44, 4.50]) were independently associated with the risk of death on day 7. Conclusions/interpretations In people with diabetes hospitalised for COVID-19, BMI, but not long-term glucose control, was positively and independently associated with tracheal intubation and/or death within 7 days. Trial registration clinicaltrials.gov NCT04324736.

Published
2020

99. Using remote sensing to characterize riparian vegetation: A review of available tools and perspectives for managers

Author

Philippe Lejeune, Léo Huylenbroeck, Simon Dufour, Adrien Michez, Marianne Laslier, Blandine Georges, Faculté Gembloux Agro Bio-Tech, Université de Liège, Littoral, Environnement, Télédétection, Géomatique (LETG - Rennes), Littoral, Environnement, Télédétection, Géomatique UMR 6554 (LETG), Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Normandie Université (NU)-Université d'Angers (UA)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Brest (UBO)-Université de Rennes 2 (UR2), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Centre National de la Recherche Scientifique (CNRS)-Institut de Géographie et d'Aménagement Régional de l'Université de Nantes (IGARUN), Université de Nantes (UN)-Université de Nantes (UN)-Université de Caen Normandie (UNICAEN), Université de Nantes (UN)-Université de Nantes (UN), Unité de Microbiologie et génétique (UMG), Centre National de la Recherche Scientifique (CNRS)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon, Université de Liège - Gembloux, and Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL)

Subjects
0106 biological sciences, Environmental Engineering, 010504 meteorology & atmospheric sciences, Transferability, Scientific literature, Management, Monitoring, Policy and Law, 010603 evolutionary biology, 01 natural sciences, Rivers, Waste Management and Disposal, Management practices, Ecosystem, ComputingMilieux_MISCELLANEOUS, 0105 earth and related environmental sciences, Remote sensing, Riparian zone, geography, geography.geographical_feature_category, General Medicine, [SHS.GEO]Humanities and Social Sciences/Geography, 15. Life on land, Continuous data, 13. Climate action, Remote sensing (archaeology), Remote Sensing Technology, Environmental science, Environmental Monitoring
Abstract

Riparian vegetation is a central component of the hydrosystem. As such, it is often subject to management practices that aim to influence its ecological, hydraulic or hydrological functions. Remote sensing has the potential to improve knowledge and management of riparian vegetation by providing cost-effective and spatially continuous data over wide extents. The objectives of this review were twofold: to provide an overview of the use of remote sensing in riparian vegetation studies and to discuss the transferability of remote sensing tools from scientists to managers. We systematically reviewed the scientific literature (428 articles) to identify the objectives and remote sensing data used to characterize riparian vegetation. Overall, results highlight a strong relationship between the tools used, the features of riparian vegetation extracted and the mapping extent. Very high-resolution data are rarely used for rivers longer than 100 km, especially when mapping species composition. Multi-temporality is central in remote sensing riparian studies, but authors use only aerial photographs and relatively coarse resolution satellite images for diachronic analyses. Some remote sensing approaches have reached an operational level and are now used for management purposes. Overall, new opportunities will arise with the increased availability of very high-resolution data in understudied or data-scarce regions, for large extents and as time series. To transfer remote sensing approaches to riparian managers, we suggest mutualizing achievements by producting open-access and robust tools. These tools will then have to be adapted to each specific project, in collaboration with managers.

Published
2020

100. Searching for Influential Nodes in Modular Networks

Author

Zakariya Ghalmane, Chantal Cherifi, hocine cherifi, Mohammed El Hassouni, Université Mohammed V de Rabat [Agdal], Décision et Information pour les Systèmes de Production (DISP), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université Lumière - Lyon 2 (UL2), Laboratoire d'Informatique de Bourgogne [Dijon] (LIB), Université de Bourgogne (UB), and Cherifi, Chantal

Subjects
[INFO]Computer Science [cs], [INFO] Computer Science [cs], ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2020

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