Heimdallr, Baldr, and Solarstein: designing the next generation of VLTI instruments in the Asgard suite.

High angular resolution imaging is an increasingly important capability in contemporary astrophysics. Of particular relevance to emerging fields such as the characterization of exoplanetary systems, imaging at the required spatial scales and contrast levels results in forbidding challenges in the correction of atmospheric phase errors, which in turn drives demanding requirements for precise wavefront sensing. Asgard is the next-generation instrument suite at the European Southern Observatory's Very Large Telescope Interferometer (VLTI), targeting advances in sensitivity, spectral resolution, and nulling interferometry. In this paper, we describe the requirements and designs of three core modules: Heimdallr, a beam combiner for fringe tracking, low order wavefront correction, and visibility science; Baldr, a Zernike wavefront sensor to correct high order atmospheric aberrations; and Solarstein, an alignment and calibration unit. In addition, we draw generalizable insights for designing such system and discuss integration plans.

Real-Time Personalised Pharmacokinetic-Pharmacodynamic Modelling in Propofol Anesthesia through Bayesian Inference.

Pharmacological models describe a patient's response to the administration of a medicinal drug based on parameters derived from population studies. However, considerable inter-patient variability exists, such that population models may underperform when used to predict the actual response of a specific individual. In applications which demand predictive accuracy-such as target-controlled infusion of anesthetic agents-modeling uncertainty may reduce system dependability and introduce clinical risk. Our work investigates the use of Bayesian inference, implemented through a particle filter algorithm, to refine a prior model of propofol pharmacokinetics-pharmacodynamics and estimate patient-specific parameters in real-time. We report here on an observational clinical study conducted on 40 adults undergoing general anesthesia, where we evaluated the performance of Bayesian inference-personalized models in forecasting forward trends of depth of anesthesia (Bispectral Index) measurements and compared it with that of a traditional population-based pharmacological model. Our results show a significant reduction in prediction error metrics for the patient-specific models. Our study demonstrates the viability and practical implementability of Bayesian inference as a tool for real-time intra-operative estimation of personalized pharmacological models in anesthesia applications.