91̽»¨

Abstract:

Single-molecule fluorescence resonance energy transfer (smFRET) is a versatile technique for studying biomolecular dynamics and function by detecting nanoscale movements as fluorescence signals. Analysing such signals is a complex exercise, which has recently been the focus of approaches relying on deep learning. Here, we survey such artificial-intelligence-based approaches and compare them with classical methods for smFRET analysis. The use of deep learning has shown potential to enhance precision, accuracy, and speed in analysing massive smFRET datasets.

Abstract:

The present study demonstrates the applicability of the Electrical Impedance Tomography (EIT) technique for real-time monitoring of inspiration and expiration behavior in a respiratory phantom device. The phantom device, which serves as a mechano-electrical simulator of the human respiratory system, is coupled to a real-time monitoring instrument operating based on the EIT technique. This study reveals that the whole system could act as a helpful apparatus for researchers and physicians in improving their ventilation maneuvers for patients. The phantom specifically helps in designing and examining the results of a larger number of experiments, setting up more qualified test environments, and finally more optimal tuning of ventilator devices. The device’s physical appearance and structure resemble the human’s chest cage, making it suitable to be used as a model of the human respiratory system. Experimental results 91̽»¨ the applicability of the phantom and EIT system for real-time monitoring of local aerations in different experimental conditions. Additionally, several recorded and analyzed data leads us to better processing and understanding of the EIT technique and its capabilities in respiration studies. The current work could be considered as a proof of concept and a step towards automatically and intelligently suggesting ventilator settings for optimal adoption of treatment strategies and patient management in hospitals in the future.

Abstract:

OPINION article Front. Comput. Neurosci., 01 October 2013 Volume 7 - 2013 | https://doi.org/10.3389/fncom.2013.00130.