Erdheim-Chester disease: look it in the eye. An orbital magnetic resonance imaging study

International audienceErdheim-Chester disease (ECD) is a rare L-group histiocytosis. Orbital involvement is found in a third of cases, but few data are available concerning the radiological features of ECD-related orbital disease (ECD-ROD). Our aim was to characterize the initial radiological phenotype and outcome of patients with ECD-ROD. Initial and follow-up orbital magnetic resonance imaging (MRI) from the patients with histologically proven ECD at a national reference center were reviewed. Pathological orbital findings were recorded for 45 (33%) of the 137 patients included, with bilateral involvement in 38/45 (84%) cases. The mean age (± standard deviation) of these patients was 60 (±11.3) years and 78% were men. Intraconal fat infiltration around the optic nerve sheath adjacent to the eye globe (52%), with intense gadolinium uptake and a fibrous component was the most frequent phenotype described. Optic nerve signal abnormalities were observed in 47% of cases. Two patients had bilateral homogeneous extraocular muscle enlargement suggestive of a myositis-like involvement of ECD-ROD. None had isolated dacryoadenitis but in 17 eyes dacryodenitis was described in association with other types of orbital lesions. Only seven patients (15%) had normal brain MRI findings. ECD-associated paranasal sinus involvement and post-pituitary involvement were detected in 56% and 53% of patients, respectively. A decrease/disappearance of the lesions was observed in 17/24 (71%) of the patients undergoing late (>12 months) followups. Interestingly, ECD-ROD only rarely (7/45; 16%) revealed the disease, with exophthalmos being the most frequently identified feature in this subgroup (3/45; 6%). Even though ECD-ROD can be clinically silent, it comprises a broad array of lesions often resulting in optic nerve signal abnormalities, the functional outcome of which remains to be established. ECD-ROD should thus be assessed initially and subsequently monitored by orbital MRI and ophthalmological follow-up

Pump wavelength-dependent terahertz spin-to-charge conversion in CoFeB/MgO Rashba interface

International audienceSpin/charge interconversion mechanisms provide an essential handle to generate and detect spin currents. Their applications at different timescales are critical in spintronics since they cover a technologically relevant broadband spectrum. While the inverse spin Hall effect is known to be robust from quasi-static to sub-picosecond timescales, the conversion efficiency evolution of the inverse Edelstein effect has not been addressed yet. In this work, we report that while the quasi-static response of the inverse Edelstein effect can be comparable to that of the most efficient inverse spin Hall systems, a drastic drop of efficiency is observed in the terahertz (THz) regime. This behavior at the sub-picosecond timescale is qualitatively understood from the dependence of the inverse Edelstein effect on the energy distribution of spin-carrier entities, which is different between thermalized carriers in the quasi-static regime and hot carriers generated by light pulses. This finding is supported by the pump-laser wavelength dependence in the THz regime for the inverse Edelstein effect, which offers a promising route for tunability of spintronic devices

A Machine Learning Approach to Integral Field Unit Spectroscopy Observations: III. Disentangling Multiple Components in H ii regions

International audienceIn the first two papers of this series (Rhea et al. 2020b; Rhea et al. 2021), we demonstrated the dynamism of machine learning applied to optical spectral analysis by using neural networks to extract kinematic parameters and emission-line ratios directly from the spectra observed by the SITELLE instrument located at the Canada-France-Hawai'i Telescope. In this third installment, we develop a framework using a convolutional neural network trained on synthetic spectra to determine the number of line-of-sight components present in the SN3 filter (656-683nm) spectral range of SITELLE. We compare this methodology to standard practice using Bayesian Inference. Our results demonstrate that a neural network approach returns more accurate results and uses less computational resources over a range of spectral resolutions. Furthermore, we apply the network to SITELLE observations of the merging galaxy system NGC2207/IC2163. We find that the closest interacting sector and the central regions of the galaxies are best characterized by two line-of-sight components while the outskirts and spiral arms are well-constrained by a single component. Determining the number of resolvable components is crucial in disentangling different galactic components in merging systems and properly extracting their respective kinematics

Two-Dimensional Oscillatory Neural Networks for Energy Efficient Neuromorphic Computing

Neuro-inspired computing architectures are one of the leading candidates to solve complex and large-scale associative learning problems for AI applications. The two key building blocks for neuromorphic computing are the neuron and the synapse, which form the distributed computing and memory units. In the NeurONN project, we are proposing a novel neuro-inspired computing architecture where information is encoded in the “phase” of coupled oscillating neurons or oscillatory neural networks (ONN). Specifically, VO2 metal-insulator transition (MIT) devices and 2D memristors will be developed as neurons and synapses for hardware implementations. We predict VO2 MIT devices are up to 250X more energy efficient than state of the art digital CMOS based oscillators, where 2D memristors are up to 330X more energy efficient than state of the art TiO2 memristors. Moreover, the predicted energy efficiency gain of ONN architecture vs spiking neural network (SNN) architecture is up to 40X. Thus, NeurONN will showcase a novel and alternative energy efficient neuromorphic computing paradigm based on energy efficient devices and architectures. Such ONN will demonstrate synchronization and coupling dynamics for establishing collective learning behavior, in addition to desirable characteristics such as scaling, ultra-low power computation, and high computing performance. NeurONN aims to develop the first-ever ONN hardware platform (targeting two demonstrators) and complete with an ONN design methodology toolbox covering aspects from ONN architecture design to algorithms in order to facilitate adoption, testing and experimentation of ONN demonstrator chips by all potential users to unleash the potential of ONN technology.Two-dimensional oscillatory neural networks for energy efficient neuromorphic computing The 4 th and 5 th of February 2020, in Montpellier (France), at the premises of LIRMM, CNRS the Kick-off meeting of NeurONN took place. All the Partners of the NeurONN Consortium met and set the ground for the activities along the three-year duration of the EU Project. NeurONN 1 is a research project funded by H2020 EU's research and innovation programme with core subject "Energy-efficient bio-inspired devices accelerate route to brain-like computing". The project with duration of 36 months (1 January 2020-31 December 2022) brings together leading European research and academic institutions. Neuro-inspired computing employs technologies that enable brain-inspired computing hardware for more efficient and adaptive intelligent systems. Mimicking the human brain and nervous system, these computing architectures are excellent candidates for solving complex and large-scale associative learning problems. The EU-funded NeurONN project will showcase a novel and alternative neuromorphic computing paradigm based on energy-efficient devices and architectures. In the proposed neuro-inspired computing architecture, information will be encoded in the phase of coupled oscillating neurons or oscillatory neural networks (ONN)

Ins Abseits : Vom Genie zur Avantgarde

Die Autonomisierung löste die Kunst zunächst aus kunstexternen Abhängigkeiten, dann von den Ansprüchen von Wirklichkeits- und Wahrheitsbezug, schließlich von ihrem Publikum: Kunst sei nur für Künstler. Diese, hier als fatal betrachtete, Bewegung wird vom Sturm und Drang über die deutsche Romantik, Baudelaire und Mallarmé bis zu Duchamp verfolgt

The PROPKD Score: A New Algorithm to Predict Renal Survival in Autosomal Dominant Polycystic Kidney Disease

International audienceThe course of autosomal dominant polycystic kidney disease (ADPKD) varies among individuals, with some reaching ESRD before 40 years of age and others never requiring RRT. In this study, we developed a prognostic model to predict renal outcomes in patients with ADPKD on the basis of genetic and clinical data. We conducted a cross-sectional study of 1341 patients from the Genkyst cohort and evaluated the influence of clinical and genetic factors on renal survival. Multivariate survival analysis identified four variables that were significantly associated with age at ESRD onset, and a scoring system from 0 to 9 was developed as follows: being male: 1 point; hypertension before 35 years of age: 2 points; first urologic event before 35 years of age: 2 points; PKD2 mutation: 0 points; nontruncating PKD1 mutation: 2 points; and truncating PKD1 mutation: 4 points. Three risk categories were subsequently defined as low risk (0-3 points), intermediate risk (4-6 points), and high risk (7-9 points) of progression to ESRD, with corresponding median ages for ESRD onset of 70.6, 56.9, and 49 years, respectively. Whereas a score ≤3 eliminates evolution to ESRD before 60 years of age with a negative predictive value of 81.4%, a score \textgreater6 forecasts ESRD onset before 60 years of age with a positive predictive value of 90.9%. This new prognostic score accurately predicts renal outcomes in patients with ADPKD and may enable the personalization of therapeutic management of ADPK