Surfactant impairment after mechanical ventilation with large alveolar surface area changes and effects of positive end-expiratory pressure

We have assessed the effects of overinflation on surfactant function and composition in rats undergoing ventilation for 20 min with 100% oxygen at a peak inspiratory pressure of 45 cm H2O, with or without PEEP 10 cm H2O (groups 45/10 and 45/0, respectively). Mean tidal volumes were 48.4 (SEM 0.3) ml kg-1 in group 45/0 and 18.3 (0.1) ml kg-1 in group 45/10. Arterial oxygenation in group 45/0 was reduced after 20 min compared with group 45/10 (305 (71) vs 564 (10) mm Hg); maximal compliance of the P-V curve was decreased (2.09 (0.13) vs 4.16 (0.35) ml cm H2O-1 kg-1); total lung volume at a transpulmonary pressure of 5 cm H2O was reduced (6.5 (1.0) vs 18.8 (1.4) ml kg-1) and the Gruenwald index was less (0.22 (0.02) vs 0.40 (0.05)). Bronchoalveolar lavage fluid from the group of animals who underwent ventilation without PEEP had a greater protein concentration (2.18 (0.11) vs 0.76 (0.22) mg ml-1) and a greater minimal surface tension (37.2 (6.3) vs 24.5 (2.8) mN m-1) than in those who underwent ventilation with PEEP. Group 45/0 had an increase in non-active to active total phosphorus compared with nonventilated controls (0.90 (0.16) vs 0.30 (0.07)). We conclude that ventilation in healthy rats with peak inspiratory pressures of 45 cm H2O without PEEP for 20 min caused severe impairment of pulmonary surfactant composition and function which can be prevented by the use of PEEP 10 cm H2O

Toward morphological thoracic EIT: Major signal sources correspond to respective organ locations in CT

Lung and cardiovascular monitoring applications of electrical impedance tomography (EIT) require localization of relevant functional structures or organs of interest with

Non-invasive monitoring of central blood pressure by electrical impedance tomography: First experimental evidence

There is a strong clinical demand for devices allowing continuous non-invasive monitoring of central blood pressure (BP). In the state of the art a new family of techniques providing BP surrogates based on the measurement of the so-called pulse wave velocity (PWV) has been proposed, eliminating the need for inflation cuffs. PWV is defined as the velocity at which pressure pulses propagate along the arterial wall. However, no technique to assess PWV within central arteries in a fully unsupervised manner has been proposed so far. In this pilot study, we provide first experimenta

Cooperation and communication in multiagent deep reinforcement learning

Reinforcement learning is the area of machine learning concerned with learning which actions to execute in an unknown environment in order to maximize cumulative reward. As agents begin to perform tasks of genuine interest to humans, they will be faced with environments too complex for humans to predetermine the correct actions using hand-designed solutions. Instead, capable learning agents will be necessary to tackle complex real-world domains. However, traditional reinforcement learning algorithms have difficulty with domains featuring 1) high-dimensional continuous state spaces, for example pixels from a camera image, 2) high-dimensional parameterized-continuous action spaces, 3) partial observability, and 4) multiple independent learning agents. We hypothesize that deep neural networks hold the key to scaling reinforcement learning towards complex tasks. This thesis seeks to answer the following two-part question: 1) How can the power of Deep Neural Networks be leveraged to extend Reinforcement Learning to complex environments featuring partial observability, high-dimensional parameterized-continuous state and action spaces, and sparse rewards? 2) How can multiple Deep Reinforcement Learning agents learn to cooperate in a multiagent setting? To address the first part of this question, this thesis explores the idea of using recurrent neural networks to combat partial observability experienced by agents in the domain of Atari 2600 video games. Next, we design a deep reinforcement learning agent capable of discovering effective policies for the parameterized-continuous action space found in the Half Field Offense simulated soccer domain. To address the second part of this question, this thesis investigates architectures and algorithms suited for cooperative multiagent learning. We demonstrate that sharing parameters and memories between deep reinforcement learning agents fosters policy similarity, which can result in cooperative behavior. Additionally, we hypothesize that communication can further aid cooperation, and we present the Grounded Semantic Network (GSN), which learns a communication protocol grounded in the observation space and reward function of the task. In general, we find that the GSN is effective on domains featuring partial observability and asymmetric information. All in all, this thesis demonstrates that reinforcement learning combined with deep neural network function approximation can produce algorithms capable of discovering effective policies for domains with partial observability, parameterized-continuous actions spaces, and sparse rewards. Additionally, we demonstrate that single agent deep reinforcement learning algorithms can be naturally extended towards cooperative multiagent tasks featuring learned communication. These results represent a non-trivial step towards extending agent-based AI towards complex environments.Computer Science

Lieutenant William Douglas Bell

Lieutenant William Douglas Bell, 4th Machine Gun Coy., Canadian Machine Gun Corps, 24 years old, Class of 1915, Remembered at Vimy Memorial. Son of James Anthony Bell and Katerine Bell, of 81 Elm St., St. Thomas, Ontario. Admitted as a law student in 1910 at age 18. Enlisted 2nd overseas contingent, 4 November 1914. Lieutenant 4th Canadian Machine Gun Corps. Killed in action 15 September 1916. Never called to the bar.https://digitalcommons.osgoode.yorku.ca/remembrance_day/1010/thumbnail.jp