This study used functional magnetic resonance imaging (fMRI) to examine the association between brain activation during exposure to cocaine-related cues and relapse to drug use in cocaine-dependent (CD) patients. We imaged 17 CD subjects during a 2-week in-patient stay. The subjects then entered a 10-week outpatient placebo-controlled, double-blind randomized clinical trial where urine toxicologies were assessed three times weekly to calculate the treatment effectiveness score (TES). Worse TES correlated with BOLD activation in the left precentral, superior temporal, and posterior cingulate cortices (PCC), and right middle temporal and lingual cortices (R40.65; Po0.005). The left PCC activation also distinguished eight nonrelapsers (TES above mean and completed treatment) from nine relapsers. Cocaine-free urines were significantly greater in the nonrelapsers (92%) than in the relapsers (66%), who also remained in treatment for an average of only 3.2 weeks. Self-reports of craving during fMRI did not differ between nonrelapsers and relapsers and did not correlate with TES. Relapse to cocaine abuse was associated with increased activation in the sensory association cortex, the motor cortex, and PCC while viewing images of cocaine-related cues. These results suggest that relapse to cocaine abuse is associated with increased brain activation to cocaine cues in sensory, motor, and cognitive-emotional processing areas. This physiological activation was a better predictor of relapse than subjective reports of craving, and may be a useful target for treatment development.

In this paper, we analytically and experimentally study the energy harvesting capability of submerged ionic polymer metal composites (IPMCs). We consider base excitation of an IPMC strip that is shunted with an electric impedance and immersed in a fluid environment. We develop a modeling framework to predict the energy scavenged from the IPMC vibration as a function of the excitation frequency range, the constitutive and geometric properties of the IPMC, and the electric shunting load. The mechanical vibration of the IPMC strip is modeled through Kirchhoff-Love plate theory. The effect of the encompassing fluid on the IPMC vibration is described by using a linearized solution of the Navier-Stokes equations, that is traditionally considered in modeling atomic force microscope cantilevers. The dynamic chemo-electric response of the IPMC is described through the Poisson-Nernst-Planck model, in which the effect of mechanical deformations of the backbone polymer is accounted for. We present a closed-form solution for the current flowing through the IPMC strip as a function of the voltage across its electrodes and its deformation. We use modal analysis to establish a handleable expression for the power harvested from the vibrating IPMC and to optimize the shunting impedance for maximum energy harvesting. We validate theoretical findings through experiments conducted on IPMC strips vibrating in aqueous environments.

A three-compartment model is proposed for analyzing magnetic resonance renography (MRR) and computed tomography renography (CTR) data to derive clinically useful parameters such as glomerular filtration rate (GFR) and renal plasma flow (RPF). The model fits the convolution of the measured input and the predefined impulse retention functions to the measured tissue curves. A MRR study of 10 patients showed that relative root mean square errors by the model were significantly lower than errors for a previously reported three-compartmental model (11.6% ؎ 4.9 vs 15.5% ؎ 4.1; P < 0.001). GFR estimates correlated well with reference values by 99m Tc-DTPA scintigraphy (correlation coefficient r ‫؍‬ 0.82), and for RPF, r ‫؍‬ 0.80. Parameter-sensitivity analysis and Monte Carlo simulation indicated that model parameters could be reliably identified. Key words: computed tomography; glomerular filtration rate; impulse retention function; magnetic resonance renography; renal plasma flow MR renography (MRR) and computed tomography renography (CTR) are increasingly used for noninvasive measurement of single-kidney function (1-7). These dynamic imaging techniques record the transit of a tracer, such as Gd-DTPA or iodinated contrast agents, from the aorta through the renal system. Tracer activity versus time curves can then be derived for intrarenal regions such as renal cortex, medulla, and collecting system. Design of an appropriate physiologic model is an essential part of accurate quantification of renal function (1,2).Several models have been proposed to estimate glomerular filtration rate (GFR) from MRR (3-6) and CTR (7). Baumann and Rudin (3) computed the GFR from the medullary uptake of the tracer using the cortical concentration as the input function. Another method (4) used a PatlakRutland plot to estimate GFR from the clearance of the tracer from the vascular compartment. This approach used whole-kidney concentration, obviating the need for regional segmentation of the kidneys. Both of these methods ignored the outflow of the tracer, and the results can be biased by improper selection of the "upslope" interval. Annet et al. (5) extended these techniques to account for tracer leaving the nephron space, thus enabling fitting of the model to measured data over a longer time period. All of these models assume instantaneous mixing of tracer within every compartment.More recently, models have been proposed with the aim of extending physiologic measures beyond GFR. Krier et al. (7) represented the cortex and medulla curves as extended gamma-variate functions with parameters shown to yield renal plasma flow (RPF) and tubular transit times in addition to GFR. GFR and RPF measures were validated against the reference values in pig model using CT renography. Hermoye et al. (8) determined RPF and GFR in rabbits from the cortical impulse response function by numerical deconvolution of renal cortical enhancement curves. The impulse response function exhibited three sequential peaks presumed to reflect the contrast in glomeruli, pro...