Fluctuations in tumor blood flow are common and attributed to factors such as vasomotion or local vascular structure, yet, because vessel structure and physiology are host-derived, animal strain of tumor propagation may further determine blood flow characteristics. In the present report, baseline and stress-altered tumor hemodynamics as a function of murine strain were studied using radiation-induced fibrosacomas (RIF) grown in C3H or nude mice. Fluctuations in tumor blood flow during one hour of baseline monitoring or during vascular stress induced by photodynamic therapy (PDT) were measured by diffuse correlation spectroscopy. Baseline monitoring revealed fluctuating tumor blood flow highly correlated with heart rate and with similar median periods (i.e., ∼9 and 14 min in C3H and nudes, respectively). However, tumor blood flow in C3H animals was more sensitive to physiologic or stress-induced perturbations. Specifically, PDT-induced vascular insults produced greater decreases in blood flow in the tumors of C3H versus nude mice; similarly, during baseline monitoring, fluctuations in blood flow were more regular and more prevalent within the tumors of C3H mice versus nude mice; finally, the vasoconstrictor L-NNA reduced tumor blood flow in C3H mice but did not affect tumor blood flow in nudes. Underlying differences in vascular structure, such as smaller tumor blood vessels in C3H versus nude animals, may contribute to strain-dependent variation in vascular function. These data thus identify clear effects of mouse strain on tumor hemodynamics with consequences to PDT and potentially other vascular-mediated therapies.

The dispersion pattern of a received signal is critical for understanding physical properties of the propagation medium. The objective of this work is to estimate accurately sediment sound speed using modal arrival times obtained from dispersion curves extracted via time-frequency analysis of acoustic signals. A particle filter is used that estimates probability density functions of modal frequencies arriving at specific times. Employing this information, probability density functions of arrival times for modal frequencies are constructed. Samples of arrival time differences are then obtained and are propagated backwards through an inverse acoustic model. As a result, probability density functions of sediment sound speed are estimated. Maximum a posteriori estimates indicate that inversion is successful. It is also demonstrated that multiple frequency processing offers an advantage over inversion at a single frequency, producing results with reduced variance.

Matched-field processing is applied to source localization and detection of sound sources in the ocean. The source spectrum is included in the set of unknown parameters and is estimated in the localization/detection process. Bayesian broadband (multi-tonal) incoherent and coherent processors are developed, integrating the source spectrum estimation using a Gibbs sampler and are first evaluated in source localization via point estimates and probability density functions obtained from synthetic signals. The coherent performance is superior to the incoherent one both in terms of source location estimates and density spread. The two processors are also applied to real data from the Hudson Canyon experiment. Subsequently, using Receiver Operating Characteristic (ROC) curves, the two processors are evaluated and compared in the task of joint detection and localization. The coherent detector/localization processor is superior to the incoherent one, especially as the number of frequencies increases. Joint detection and localization performance is evaluated with Localization-ROC curves.

No abstract

Sequential Bayesian filtering methods have been previously used in dispersion curve tracking for long range sound propagation in the ocean. Modal frequency probability density functions were extracted for sound speed inversion. Here, we calculate modal arrival time densities, instead, and employ them for inversion for sediment sound speed and thickness and water column depth. Bayesian mode identification is performed to this end. We investigate two methods for describing the statistical errors in power spectra, which we use in the arrival time density calculation using normal modes. We then link these densities to the parameters of interest. The approaches are tested with synthetic data as well as data collected in the Gulf of Mexico. [Work supported by ONR.]