Tolerability of inhaled N-chlorotaurine in an acute pig streptococcal lower airway inflammation model

<p>Abstract</p> <p>Background</p> <p>Inhalation of N-chlorotaurine (NCT), an endogenous new broad spectrum non-antibiotic anti-infective, has been shown to be very well tolerated in the pig model recently. In the present study, inhaled NCT was tested for tolerability and efficacy in the infected bronchopulmonary system using the same model.</p> <p>Methods</p> <p>Anesthetized pigs were inoculated with 20 ml of a solution containing approximately 10⁸CFU/ml <it>Streptococcus pyogenes </it>strain d68 via a duodenal tube placed through the tracheal tube down to the carina. Two hours later, 5 ml of 1% NCT aqueous solution (test group, n = 15) or 5 ml of 0.9% NaCl (control group, n = 16) was inhaled via the tracheal tube connected to a nebulizer. Inhalation was repeated every hour, four times in total. Lung function and haemodynamics were monitored. Bronchoalveolar lavage samples were removed for determination of colony forming units (CFU), and lung samples for histology.</p> <p>Results</p> <p>Arterial pressure of oxygen (PaO₂) decreased rapidly after instillation of the bacteria in all animals and showed only a slight further decrease at the end of the experiment without a difference between both groups. Pulmonary artery pressure increased to a peak 1-1.5 h after application of the bacteria, decreased in the following hour and remained constant during treatment, again similarly in both groups. Histology demonstrated granulocytic infiltration in the central parts of the lung, while this was absent in the periphery. Expression of TNF-alpha, IL-8, and haemoxygenase-1 in lung biopsies was similar in both groups. CFU counts in bronchoalveolar lavage came to 170 (10; 1388) CFU/ml (median and 25 and 75 percentiles) for the NCT treated pigs, and to 250 (10; 5.5 × 10⁵) CFU/ml for NaCl treated pigs (p = 0.4159).</p> <p>Conclusions</p> <p>Inhaled NCT at a concentration of 1% proved to be very well tolerated also in the infected bronchopulmonary system. This study confirms the tolerability in this delicate body region, which has been proven in healthy pigs previously. Regarding efficacy, no conclusions can be drawn, mainly because of the limited test period of the model.</p

The prevalence of metabolic syndrome and metabolically healthy obesity in Europe: a collaborative analysis of ten large cohort studies

Peer reviewe

Motion Cueing Quality Comparison of Driving Simulators using Oracle Motion Cueing

BMW’s new driving simulation center operates multiple motion-base simulators – each with a different kinematic configuration – to serve various experiment use-cases and requirements of simulator users. The selection of a simulator for each experiment should ideally be based on their relative strengths and weaknesses. To support this decision-making process, subjective and objective predictions of motion cueing quality can be used. This paper provides an example comparison of four motion-base driving simulators. The kinematic configurations of the simulators considered differed in the additional presence of a yaw-drive and/or a linear xy-drive. The comparison is made by calculating offline, optimization-based motion cueing with perfect prediction capabilities (the ‘Oracle’) for nine urban drives. A prediction of subjective motion incongruence ratings is made for each simulator. In addition, an error type identification method is used (identifying scaling, missing cue, false cue and false direction cue errors) and evaluated per simulator. As Oracle can fully utilize the available workspace, the employed evaluation methods provide an insight in the fundamental capabilities of each simulator. Both the modelled ratings and the error type analysis show the benefits of adding a xy-drive in urban use-cases: predicted ratings reduce by 19% (i.e., better), while scaling and missing cue errors in the yaw rate are reduced when adding a yaw-drive. The presence of both of these additional motion systems allow for practically one-to-one and therefore error-free motion cueing. The proposed methods provide a straight-forward, yet insightful basis for simulator selection. The presented methods can be extended towards the analysis of multiple motion cueing algorithms and/or other usecases for systematically selecting the best-suited motion cueing method

Quality comparison of motion cueing algorithms for urban driving simulations

When designing driving simulation experiments with motion cueing, it is often necessary to make choices between Motion Cueing Algorithms (MCAs) without being fully able to know how well an MCA will perform during the experiment. Choices between MCAs can therefore be greatly supported by previous measurements or predictions of motion cueing quality. This paper describes a data collection experiment on a nine degree-of-freedom motion-base simulator, in which participants are asked to continuously rate the motion cueing quality during a pre-recorded drive through an urban environment. Three benchmark MCAs are compared: a Model-Predictive Control (MPC) algorithm with infinite prediction horizon, a Classical Washout Algorithm (CWA) tuned for the use-case, and the same algorithm (CWA), but with the tilt-coordination channels turned off. By comparing ratings for the whole scenario, as well as ratings for each maneuver individually, the results show a preference of the presence of tilt-coordination, as well as a preference for the optimization-based MPC algorithm over the CWA condition. The collected data will be used directly for modeling and predicting motion cueing quality for future experiments at BMW, such that the best-suited MCA and parameter setting can be selected before experiments.Control & Simulatio

Motion Cueing Quality Comparison of Driving Simulators using Oracle Motion Cueing

BMW’s new driving simulation center operates multiple motion-base simulators – each with a different kinematic configuration – to serve various experiment use-cases and requirements of simulator users. The selection of a simulator for each experiment should ideally be based on their relative strengths and weaknesses. To support this decision-making process, subjective and objective predictions of motion cueing quality can be used. This paper provides an example comparison of four motion-base driving simulators. The kinematic configurations of the simulators considered differed in the additional presence of a yaw-drive and/or a linear xy-drive. The comparison is made by calculating offline, optimization-based motion cueing with perfect prediction capabilities (the ‘Oracle’) for nine urban drives. A prediction of subjective motion incongruence ratings is made for each simulator. In addition, an error type identification method is used (identifying scaling, missing cue, false cue and false direction cue errors) and evaluated per simulator. As Oracle can fully utilize the available workspace, the employed evaluation methods provide an insight in the fundamental capabilities of each simulator. Both the modelled ratings and the error type analysis show the benefits of adding a xy-drive in urban use-cases: predicted ratings reduce by 19% (i.e., better), while scaling and missing cue errors in the yaw rate are reduced when adding a yaw-drive. The presence of both of these additional motion systems allow for practically one-to-one and therefore error-free motion cueing. The proposed methods provide a straight-forward, yet insightful basis for simulator selection. The presented methods can be extended towards the analysis of multiple motion cueing algorithms and/or other usecases for systematically selecting the best-suited motion cueing method.Control & Simulatio

acados/acados: v0.2.5

&lt;h2&gt;What's Changed&lt;/h2&gt; &lt;ul&gt; &lt;li&gt;update BLASFEO and HPIPM by @FreyJo in https://github.com/acados/acados/pull/969&lt;/li&gt; &lt;li&gt;Fix typo in minimal_example_closed_loop.py by @Federico-PizarroBejarano in https://github.com/acados/acados/pull/970&lt;/li&gt; &lt;li&gt;Fix dependency handling in CMake config file by @Hs293Go in https://github.com/acados/acados/pull/971&lt;/li&gt; &lt;li&gt;Fix sfun sources if hessian is not exact by @asparc in https://github.com/acados/acados/pull/974&lt;/li&gt; &lt;li&gt;update ROADMAP by @sandmaennchen in https://github.com/acados/acados/pull/964&lt;/li&gt; &lt;li&gt;Work on solution sensitivities by @FreyJo in https://github.com/acados/acados/pull/975&lt;/li&gt; &lt;li&gt;Implement cost integration via IRK for Convex-over-nonlinear cost by @FreyJo in https://github.com/acados/acados/pull/976&lt;/li&gt; &lt;li&gt;Add time in IRK, allow to cost integration with time dependent function by @FreyJo in https://github.com/acados/acados/pull/977&lt;/li&gt; &lt;li&gt;Documentation fixes by @FreyJo in https://github.com/acados/acados/pull/979&lt;/li&gt; &lt;li&gt;Formulate constraints as L2 and Huber penalties in Python by @FreyJo in https://github.com/acados/acados/pull/980&lt;/li&gt; &lt;/ul&gt; &lt;h2&gt;New Contributors&lt;/h2&gt; &lt;ul&gt; &lt;li&gt;@Federico-PizarroBejarano made their first contribution in https://github.com/acados/acados/pull/970&lt;/li&gt; &lt;li&gt;@Hs293Go made their first contribution in https://github.com/acados/acados/pull/971&lt;/li&gt; &lt;/ul&gt; &lt;p&gt;&lt;strong&gt;Full Changelog&lt;/strong&gt;: https://github.com/acados/acados/compare/v0.2.4...v0.2.5&lt;/p&gt

acados/acados: v0.2.6

&lt;h2&gt;What's Changed&lt;/h2&gt; &lt;ul&gt; &lt;li&gt;fix CodeQL warnings by @FreyJo in https://github.com/acados/acados/pull/984&lt;/li&gt; &lt;li&gt;fix condition for checking number of stages in detect_dims_ocp.m by @Ajin2305 in https://github.com/acados/acados/pull/989&lt;/li&gt; &lt;li&gt;Codeql review by @FreyJo in https://github.com/acados/acados/pull/988&lt;/li&gt; &lt;li&gt;Rework CasADi requirements in MEX interface by @FreyJo in https://github.com/acados/acados/pull/991&lt;/li&gt; &lt;li&gt;Fix get_optimal_value_gradient, add getter for p from HPIPM by @FreyJo in https://github.com/acados/acados/pull/993&lt;/li&gt; &lt;li&gt;Windows python interface by @asparc in https://github.com/acados/acados/pull/968&lt;/li&gt; &lt;/ul&gt; &lt;h2&gt;New Contributors&lt;/h2&gt; &lt;ul&gt; &lt;li&gt;@Ajin2305 made their first contribution in https://github.com/acados/acados/pull/989&lt;/li&gt; &lt;/ul&gt; &lt;p&gt;&lt;strong&gt;Full Changelog&lt;/strong&gt;: https://github.com/acados/acados/compare/v0.2.5...v0.2.6&lt;/p&gt