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OBJECTIVES Report of prevalence and type of ocular disease in a captive population of nondomestic felids. METHODS Medical records of 202 cats from 1993 to 2018 were reviewed. Species, age at diagnosis, sex, ocular examination abnormalities, systemic/physical examination abnormalities, type of examination (visual, sedated, or anesthetized), ocular structures affected, other diagnostics, therapy, and resolution of ocular disease were recorded. RESULTS A total of 202 nondomestic felids including 18 different species (bobcat, caracal, cougar, Fishing cat, Geoffroy's cat, jaguar, jungle cat, leopard, leopard cat, liger, lion, lynx, ocelot, Sand cat, Savannah cat, serval, snow leopard, and tiger) from a rescue facility were evaluated. Forty-six ocular lesions were diagnosed in 33 (16.3%) cats from 8 different species (bobcat, caracal, cougar, leopard, lion, ocelot, serval, and tiger) with a mean age of 16 ± 5.9 years at time of diagnosis. Ocular lesions included corneal disease (37%) (ulcerations, perforations, keratitis, corneal scars), cataracts (23.9%), hyphema (8.7%), lens luxation (6.5%), retinal detachment (6.5%), uveitis (4.3%), conjunctival disease (4.3%), retinal degeneration (2.1%), glaucoma (2.1%), and optic neuritis (2.1%). Therapies included medical (topical antibiotics, anti-inflammatories, serum, etc.) and/or surgical management (enucleation, intracapsular lens extraction, corneoconjunctival transposition, and corneal burr debridement). CONCLUSIONS To the authors' knowledge, this is the first report of the prevalence of ocular disease in a population of captive nondomestic felids. It is difficult to diagnose and treat ocular disease in nondomestic cats due to challenges related to handling, diagnostics, and therapeutics in nondomestic species. Ocular disease seen in this population is similar to that found in domestic cat populations.

International audience; Today, the combination of high angular resolution and high revisit rate is not readily available from space, at least not at a reasonable cost. Many applications in the science, civil or defense domains would benefit from having access to detailed images of the ground as often as possible, in order to study temporal evolutions of specific events. The high angular resolution requires large optics hence large platforms, whereas the revisit rate requires constellations of multiple satellites and therefore small and affordable platforms. We proposed the concept of a deployable telescope onboard a CubeSat, called AZIMOV [1, 3, 5], to address this specific gap. Reaching a diameter of 30 cm once deployed, this concept gives access to a meter resolution on the ground from a Low Earth Orbit, or to a 70 cm resolution on Mars surface from a 400 km polar orbit. We study in this paper the performance of such a telescope in the aggressive thermal environment of space, with respect to the tight optical requirements of the system.

Estimating the remaining useful life (RUL) of an asset lies at the heart of prognostics and health management (PHM) of many operations-critical industries such as aviation. Modern methods of RUL estimation adopt techniques from deep learning (DL). However, most of these contemporary techniques deliver only single-point estimates for the RUL without reporting on the confidence of the prediction. This practice usually provides overly confident predictions that can have severe consequences in operational disruptions or even safety. To address this issue, we propose a technique for uncertainty quantification (UQ) based on Bayesian deep learning (BDL). The hyperparameters of the framework are tuned using a novel bi-objective Bayesian optimization method with objectives the predictive performance and predictive uncertainty. The method also integrates the data pre-processing steps into the hyperparameter optimization (HPO) stage, models the RUL as a Weibull distribution, and returns the survival curves of the monitored assets to allow informed decision-making. We validate this method on the widely used C-MAPSS dataset against a single-objective HPO baseline that aggregates the two objectives through the harmonic mean (HM). We demonstrate the existence of trade-offs between the predictive performance and the predictive uncertainty and observe that the bi-objective HPO returns a larger number of hyperparameter configurations compared to the single-objective baseline. Furthermore, we see that with the proposed approach, it is possible to configure models for RUL estimation that exhibit better or comparable performance to the single-objective baseline when validated on the test sets.

International audience; Deep geological storage is considered by several countries to isolate highly and intermediate radioactive long-lived waste for as long as they are dangerous for humankind and the environment (over 100 000 years) [1]. The French National Waste Management Agency (ANDRA) started building in 2000 an underground laboratory (Bure, Haute-Marne, France) in order to test the feasibility of such a solution. A multibarrier system was designed to confine the radionuclides. The waste is to be calcinated and vitrified before being put in a stainless steel container enclosed in a carbon-steel overpack. The containers are then placed in a horizontal micro-tunnel with a carbon steel casing. The host rock, the callovo-oxfordian claystone (Cox), 500 m underground, was chosen for its low permeability, safe confinement properties and stable pH [2]. To evaluate the degradation of the carbon steel casing over time this study focuses on its corrosion in contact with the Cox as well as with the cement grout used to isolate the casing from the Cox. The resistance of carbon-steel in contact with Cox was tested under aerated conditions, at 50°C, for 7 years in a simplified laboratory experiment. Claystone from Bure and an electrolyte which composition was adjusted to match the Cox poral water were used. The corrosion rate and products are compared to those obtained for the corrosion of carbon-steel in contact with cement in similar conditions as well as archaeological artefacts buried in claystone environments [3]. A multi-scale and multi-technique approach, including SEM-EDX, Raman spectroscopy and STXEM, was used to determine the nature and structure of the corrosion products and to better understand the mechanisms involved in the observed degradation.

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