Manifestations Orl du Rgo chez l’enfant quelle attitude observer ?

Si les manifestations digestives ou respiratoires du reflux gastro-oesophagien sont connues depuis longtemps, la notion de pathologies ORL chroniques ou récidivantes liées au reflux est relativement récente et reste sujette à controverse. A travers une revue de la littérature récente, nous avons tenté de mettre en évidence un lien de causalité entre RGO et manifestations extra digestives, en particulier ORL, et d’en déduire une ligne de conduite thérapeutique.Mots Clés : RGO, Manifestations extra digestives, Pathologie ORL, Traitement médicamenteux, Traitement chirurgical, Traitement coelioscopique

First detection of Waddlia chondrophila in Africa using SYBR Green real-time PCR on veterinary samples.

Waddlia chondrophila is a strict intracellular microorganism belonging to the order Chlamydiales that has been isolated twice from aborted bovine fetuses, once in USA and once in Germany. This bacterium is now considered as an abortigenic agent in cattle. However, no information is available regarding the presence of this bacterium in Africa. Given the low sensitivity of cell culture to recover such an obligate intracellular bacterium, molecular-based diagnostic approaches are warranted. This report describes the development of a quantitative SYBR Green real-time PCR assay targeting the recA gene of W. chondrophila. Analytical sensitivity was 10 copies of control plasmid DNA per reaction. No cross-amplification was observed when testing pathogens that can cause abortion in cattle. The PCR exhibited a good intra-run and inter-run reproducibility. This real-time PCR was then applied to 150 vaginal swabs taken from Tunisian cows that have aborted. Twelve samples revealed to be Waddlia positive, suggesting a possible role of this bacterium in this setting. This new real-time PCR assay represents a diagnostic tool that may be used to further study the prevalence of Waddlia infection

Molecular prevalence of Chlamydia and Chlamydia-like bacteria in Tunisian domestic ruminant farms and their influencing risk factors

Chlamydia and Chlamydia-like bacteria are well known to infect several organisms and may cause a wide range of diseases, particularly in ruminants. To gain insight into the prevalence and diversity of these intracellular bacteria, we applied a pan-Chlamydiales real-time PCR to 1,134 veterinary samples taken from 130 Tunisian ruminant herds. The true adjusted animal population-level prevalence was 12.9% in cattle, against 8.7% in sheep. In addition, the true adjusted herd-level prevalence of Chlamydiae was 80% in cattle and 25.5% in sheep. Chlamydiales from three familylevel lineages were detected indicating a high biodiversity of Chlamydiales in ruminant herds. Our results showed that Parachlamydia acanthamoebae could be responsiblefor bovine and ovine chlamydiosis in central-eastern Tunisia. Multivariable logistic regression analysis at the animal population level indicated that strata and digestive disorders variables were the important risk factors of bovine and ovine chlamydiosis. However, origin and age variables were found to be associated withbovine and ovine chlamydiosis, respectively. At the herd level, risk factors for Chlamydia positivity were as follows: abortion and herd size for cattle against breeding system, cleaning frequency, quarantine, use of disinfectant and floor type for sheep. Paying attention to these risk factors will help improvement of control programs against this harmful zoonotic disease

Deep Burst Denoising

Noise is an inherent issue of low-light image capture, one which is exacerbated on mobile devices due to their narrow apertures and small sensors. One strategy for mitigating noise in a low-light situation is to increase the shutter time of the camera, thus allowing each photosite to integrate more light and decrease noise variance. However, there are two downsides of long exposures: (a) bright regions can exceed the sensor range, and (b) camera and scene motion will result in blurred images. Another way of gathering more light is to capture multiple short (thus noisy) frames in a "burst" and intelligently integrate the content, thus avoiding the above downsides. In this paper, we use the burst-capture strategy and implement the intelligent integration via a recurrent fully convolutional deep neural net (CNN). We build our novel, multiframe architecture to be a simple addition to any single frame denoising model, and design to handle an arbitrary number of noisy input frames. We show that it achieves state of the art denoising results on our burst dataset, improving on the best published multi-frame techniques, such as VBM4D and FlexISP. Finally, we explore other applications of image enhancement by integrating content from multiple frames and demonstrate that our DNN architecture generalizes well to image super-resolution

Interactive mechanism and friction modelling of transient tribological phenomena in metal forming processes: A review

The accurate representation of tribological boundary conditions at the tool-workpiece interface is crucial for analysis and optimization of formability, material flow, and surface quality of components during metal forming processes. It has been found that these tribological conditions vary spatially and historically with process parameters and contact conditions. These time-dependent tribological behaviours are also known as transient tribological phenomena, which are widely observed during forming processes and many other manufacturing application scenarios. However, constant friction values are usually assigned to represent complex and dynamic interfacial conditions, which would introduce deviations in the relevant predictions. In this paper, transient tribological phenomena and the contemporary understanding of the interaction between friction and wear are reviewed, and it has been found that these phenomena are induced by the transitions of friction mechanisms and highly dependent on complex loading conditions at the interface. Friction modelling techniques for transient behaviours for metal forming applications are also reviewed. To accurately describe the evolutionary friction values and corresponding wear during forming, the advanced interactive friction modelling has been established for different application scenarios, including lubricated condition, dry sliding condition (metal-on-metal contact), and coated system

Joint bilateral learning for real-time universal photorealistic style transfer

Photorealistic style transfer is the task of transferring the artistic style of an image onto a content target, producing a result that is plausibly taken with a camera. Recent approaches, based on deep neural networks, produce impressive results but are either too slow to run at practical resolutions, or still contain objectionable artifacts. We propose a new end-to-end model for photorealistic style transfer that is both fast and inherently generates photorealistic results. The core of our approach is a feed-forward neural network that learns local edge-aware a ne transforms that automatically obey the photorealism constraint. When trained on a diverse set of images and a variety of styles, our model can robustly apply style transfer to an arbitrary pair of input images. Compared to the state of the art, our method produces visually superior results and is three orders of magnitude faster, enabling real- time performance at 4K on a mobile phone. We validate our method with ablation and user studies.Published versio

W2S: Microscopy Data with Joint Denoising and Super-Resolution for Widefield to SIM Mapping

In fluorescence microscopy live-cell imaging, there is a critical trade-off between the signal-to-noise ratio and spatial resolution on one side, and the integrity of the biological sample on the other side. To obtain clean high-resolution (HR) images, one can either use microscopy techniques, such as structured-illumination microscopy (SIM), or apply denoising and super-resolution (SR) algorithms. However, the former option requires multiple shots that can damage the samples, and although efficient deep learning based algorithms exist for the latter option, no benchmark exists to evaluate these algorithms on the joint denoising and SR (JDSR) tasks. To study JDSR on microscopy data, we propose such a novel JDSR dataset, Widefield2SIM (W2S), acquired using a conventional fluorescence widefield and SIM imaging. W2S includes 144,000 real fluorescence microscopy images, resulting in a total of 360 sets of images. A set is comprised of noisy low-resolution (LR) widefield images with different noise levels, a noise-free LR image, and a corresponding high-quality HR SIM image. W2S allows us to benchmark the combinations of 6 denoising methods and 6 SR methods. We show that state-of-the-art SR networks perform very poorly on noisy inputs. Our evaluation also reveals that applying the best denoiser in terms of reconstruction error followed by the best SR method does not necessarily yield the best final result. Both quantitative and qualitative results show that SR networks are sensitive to noise and the sequential application of denoising and SR algorithms is sub-optimal. Lastly, we demonstrate that SR networks retrained end-to-end for JDSR outperform any combination of state-of-the-art deep denoising and SR networksComment: ECCVW 2020. Project page: \<https://github.com/ivrl/w2s

Fully Trainable and Interpretable Non-Local Sparse Models for Image Restoration

Non-local self-similarity and sparsity principles have proven to be powerful priors for natural image modeling. We propose a novel differentiable relaxation of joint sparsity that exploits both principles and leads to a general framework for image restoration which is (1) trainable end to end, (2) fully interpretable, and (3) much more compact than competing deep learning architectures. We apply this approach to denoising, jpeg deblocking, and demosaicking, and show that, with as few as 100K parameters, its performance on several standard benchmarks is on par or better than state-of-the-art methods that may have an order of magnitude or more parameters.Comment: ECCV 202

3D color homography model for photo-realistic color transfer re-coding

Color transfer is an image editing process that naturally transfers the color theme of a source image to a target image. In this paper, we propose a 3D color homography model which approximates photo-realistic color transfer algorithm as a combination of a 3D perspective transform and a mean intensity mapping. A key advantage of our approach is that the re-coded color transfer algorithm is simple and accurate. Our evaluation demonstrates that our 3D color homography model delivers leading color transfer re-coding performance. In addition, we also show that our 3D color homography model can be applied to color transfer artifact fixing, complex color transfer acceleration, and color-robust image stitching