An injection and mixing element for delivery and monitoring of inhaled nitric oxide

Background Inhaled nitric oxide (NO) is a selective pulmonary vasodilator used primarily in the critical care setting for patients concurrently supported by invasive or noninvasive positive pressure ventilation. NO delivery devices interface with ventilator breathing circuits to inject NO in proportion with the flow of air/oxygen through the circuit, in order to maintain a constant, target concentration of inhaled NO. Methods In the present article, a NO injection and mixing element is presented. The device borrows from the design of static elements to promote rapid mixing of injected NO-containing gas with breathing circuit gases. Bench experiments are reported to demonstrate the improved mixing afforded by the injection and mixing element, as compared with conventional breathing circuit adapters, for NO injection into breathing circuits. Computational fluid dynamics simulations are also presented to illustrate mixing patterns and nitrogen dioxide production within the element. Results Over the range of air flow rates and target NO concentrations investigated, mixing length, defined as the downstream distance required for NO concentration to reach within ±5 % of the target concentration, was as high as 47 cm for the conventional breathing circuit adapters, but did not exceed 7.8 cm for the injection and mixing element. Conclusion The injection and mixing element has potential to improve ease of use, compatibility and safety of inhaled NO administration with mechanical ventilators and gas delivery devices

Siame-se(3): regression in se(3) for end-to-end visual servoing

International audienceIn this paper we propose a deep architecture and the associated learning strategy for end-to-end direct visual servoing. The considered approach allows to sequentially predict, in se(3), the velocity of a camera mounted on the robot's end-effector for positioning tasks. Positioning is achieved with high precision despite large initial errors in both cartesian and image spaces. Training is fully done in simulation, alleviating the burden of data collection. We demonstrate the efficiency of our method in experiments in both simulated and real-world environments. We also show that the proposed approach is able to handle multiple scenes

Deep metric learning for visual servoing: when pose and image meet in latent space

International audienceWe propose a new visual servoing method that controls a robot's motion in a latent space. We aim to extract the best properties of two previously proposed servoing methods: we seek to obtain the accuracy of photometric methods such as Direct Visual Servoing (DVS), as well as the behavior and convergence of pose-based visual servoing (PBVS). Photometric methods suffer from limited convergence area due to a highly non-linear cost function, while PBVS requires estimating the pose of the camera which may introduce some noise and incurs a loss of accuracy. Our approach relies on shaping (with metric learning) a latent space, in which the representations of camera poses and the embeddings of their respective images are tied together. By leveraging the multimodal aspect of this shared space, our control law minimizes the difference between latent image representations thanks to information obtained from a set of pose embeddings. Experiments in simulation and on a robot validate the strength of our approach, showing that the sought out benefits are effectively found

Visual Servoing in Autoencoder Latent Space

International audienceVisual servoing (VS) is a common way in robotics to control a robot motion using information acquired by a camera. This approach requires to extract visual information from the image to design the control law. The resulting servo loop is built in order to minimize an error expressed in the image space. We consider a direct visual servoing (DVS) from whole images. We propose a new framework to perform VS in the latent space learned by a convolutional autoencoder. We show that this latent space avoids explicit feature extraction and tracking issues and provides a good representation, smoothing the cost function of the VS process. Besides, our experiments show that this unsupervised learning approach allows us to obtain, without labelling cost, an accurate end-positioning, often on par with the best DVS methods in terms of accuracy but with a larger convergence area

Visual Servoing in Autoencoder Latent Space: Supplementary material

This document provides additional information about [1], describing further experiments with Autoencoderbased visual servoing (AEVS), a method for visual servoing in the latent space of an autoencoder. Multiple experiments are described, both in a simulated environment and on a real robot. In this document, we first evaluate the impact of the network specification and dataset sizes on the visual servoing results. Then we conduct experiments to ascertain the applicability and transferability of the method to complex scenes

Antarctic observations at long wavelengths with the IRAIT-ITM Telescope at Dome C

We illustrate the status of the international infra-red telescope IRAIT-ITM, a project developed thanks to an Italian- Spanish-French collaboration and now sited at the Dome C Antarctic base. The telescope and its subsystems were installed at DomeC by a team of Italian and French scientists. The 80 cm telescope is placed on a small snow hill next to a laboratory of astronomy. The operations started in January 2013, with the Nasmyth focal planes equipped with the midinfrared camera AMICA for 1.25 to 25 μm and the sub-millimetre camera CAMISTIC for observation of the sky noise at 200 and 350 μm using a bolometer camera. During 2013 the two winter-overs worked mainly on technological duties, learning how to operate the telescope, while temperatures decreased down to -80°C. The cryogenic systems could be operated respectively at 0.25K and 4K at all times, with satisfactory use of the heat from the compressors of the cryocoolers to the warm-up the laboratory through a closed loop glycol system. The lack of tests and reliability in extreme conditions of some components and difficult access to maintenance hampered regular observations below -50°C. Using the lessons of this first winter, the summer team improves the robustness of the failing systems and ease the access to maintenance. The winter 2014 is the first one with programmed observations. Because of power restrictions, the two instruments are used each one at a time by periods of 2 weeks. The Camistic camera continues to observe the stability of the sky at a fixed altitude in chopping mode and performs skydips. The TCS is being upgraded in order to prepare the next summer season with extensive observations of the sun with Camistic

The Link Between Covid-19 and Biodiversity:A Report Commissioned by the French Public Authorities

L’épidémie COVID-19 pose de nombreuses questions. Quels sont les liens de cette crise sanitaire avec la faune sauvage, quels sont ses liens avec l’érosion de la biodiversité que le dernier rapport de l’Ipbes a souligné, quels sont ses liens avec certains systèmes de production alimentaire et plus généralement avec l’anthropisation de la planète ? Pour les éclairer sur ces sujets, les pouvoirs publics se sont tournés vers la recherche

Mobilisation de la FRB par les pouvoirs publics français sur les liens entre Covid-19 et biodiversité

L’épidémie COVID-19 pose de nombreuses questions. Quels sont les liens de cette crise sanitaire avec la faune sauvage, quels sont ses liens avec l’érosion de la biodiversité que le dernier rapport de l’Ipbes a souligné, quels sont ses liens avec certains systèmes de production alimentaire et plus généralement avec l’anthropisation de la planète ? Pour les éclairer sur ces sujets, les pouvoirs publics se sont tournés vers la recherche

Data infrastructures for AI in medical imaging: a report on the experiences of five EU projects

Abstract Artificial intelligence (AI) is transforming the field of medical imaging and has the potential to bring medicine from the era of ‘sick-care’ to the era of healthcare and prevention. The development of AI requires access to large, complete, and harmonized real-world datasets, representative of the population, and disease diversity. However, to date, efforts are fragmented, based on single–institution, size-limited, and annotation-limited datasets. Available public datasets (e.g., The Cancer Imaging Archive, TCIA, USA) are limited in scope, making model generalizability really difficult. In this direction, five European Union projects are currently working on the development of big data infrastructures that will enable European, ethically and General Data Protection Regulation-compliant, quality-controlled, cancer-related, medical imaging platforms, in which both large-scale data and AI algorithms will coexist. The vision is to create sustainable AI cloud-based platforms for the development, implementation, verification, and validation of trustable, usable, and reliable AI models for addressing specific unmet needs regarding cancer care provision. In this paper, we present an overview of the development efforts highlighting challenges and approaches selected providing valuable feedback to future attempts in the area. Key points • Artificial intelligence models for health imaging require access to large amounts of harmonized imaging data and metadata. • Main infrastructures adopted either collect centrally anonymized data or enable access to pseudonymized distributed data. • Developing a common data model for storing all relevant information is a challenge. • Trust of data providers in data sharing initiatives is essential. • An online European Union meta-tool-repository is a necessity minimizing effort duplication for the various projects in the area