Jean Pierre Delplanque

TL;DR: This work enables creation of a hydrophobic condenser surface with the directional self-cleaning property that can be used for collection of biological (chemical, environmental) aerosol samples or for condensation enhancement.

TL;DR: This work developed an open source data acquisition platform to acquire VWD, and a modular, multi-algorithm analytic platform (ventMAP) to enable automated detection of off-target ventilation delivery in critically-ill patients, and tested the hypothesis that use of artifact correction logic would improve the specificity of clinical event detection without compromising sensitivity.

TL;DR: The results suggest that it is possible to create a high-performing machine learning-based model for detecting PVA in mechanical ventilator waveform data in spite of both intra-patient, and inter-patient variability in waveform patterns, and the presence of clinical artifacts like cough and suction procedures.

TL;DR: The development of an unobtrusive, open-source, scalable, and user-friendly architecture for collecting, transmitting, and storing mechanical ventilator waveform data that is generalizable to other patient care devices is described.

TL;DR: The intent is to furnish a fully-benchmarked technology that can be widely adopted by researchers and conservationists to spur further developments of breath analysis applications for marine mammal health assessments.