Ultra-high-speed imaging of bubbles interacting with cells and tissue

Ultrasound contrast microbubbles are exploited in molecular imaging, where bubbles are directed to target cells and where their high-scattering cross section to ultrasound allows for the detection of pathologies at a molecular level. In therapeutic applications vibrating bubbles close to cells may alter the permeability of cell membranes, and these systems are therefore highly interesting for drug and gene delivery applications using ultrasound. In a more extreme regime bubbles are driven through shock waves to sonoporate or kill cells through intense stresses or jets following inertial bubble collapse. Here, we elucidate some of the underlying mechanisms using the 25-Mfps camera Brandaris128, resolving the bubble dynamics and its interactions with cells. We quantify acoustic microstreaming around oscillating bubbles close to rigid walls and evaluate the shear stresses on nonadherent cells. In a study on the fluid dynamical interaction of cavitation bubbles with adherent cells, we find that the nonspherical collapse of bubbles is responsible for cell detachment. We also visualized the dynamics of vibrating microbubbles in contact with endothelial cells followed by fluorescent imaging of the transport of propidium iodide, used as a membrane integrity probe, into these cells showing a direct correlation between cell deformation and cell membrane permeability

OVERLAPPED-SPEECH DETECTION WITH APPLICATIONS TO DRIVER ASSESSMENT FOR IN-VEHICLE ACTIVE SAFETY SYSTEMS

ABSTRACT In this study we propose a system for overlapped-speech detection. Spectral harmonicity and envelope features are extracted to represent overlapped and single-speaker speech using Gaussian mixture models (GMM). The system is shown to effectively discriminate the single and overlapped speech classes. We further increase the discrimination by proposing a phoneme selection scheme to generate more reliable artificial overlapped data for model training. Evaluations on artificially generated co-channel data show that the novelty in feature selection and phoneme omission results in a relative improvement of 10% in the detection accuracy compared to baseline. As an example application, we evaluate the effectiveness of overlapped-speech detection for vehicular environments and its potential in assessing driver alertness. Results indicate a good correlation between driver performance and the amount and location of overlapped-speech segments