We present a Monte Carlo lookup table (MCLUT)-based inverse model for extracting optical properties from tissue-simulating phantoms. This model is valid for close source-detector separation and highly absorbing tissues. The MCLUT is based entirely on Monte Carlo simulation, which was implemented using a graphics processing unit. We used tissue-simulating phantoms to determine the accuracy of the MCLUT inverse model. Our results show strong agreement between extracted and expected optical properties, with errors rate of 1.74% for extracted reduced scattering values, 0.74% for extracted absorption values, and 2.42% for extracted hemoglobin concentration values.
Seamless and minimally invasive integration of 3D electronic circuitry within host materials could enable the development of materials systems that are self-monitoring and allow for communication with external environments. Here, we report a general strategy for preparing ordered 3D interconnected and addressable macroporous nanoelectronic networks from ordered 2D nanowire nanoelectronic precursors, which are fabricated by conventional lithography. The 3D networks have porosities larger than 99%, contain approximately hundreds of addressable nanowire devices, and have feature sizes from the 10-μm scale (for electrical and structural interconnections) to the 10-nm scale (for device elements). The macroporous nanoelectronic networks were merged with organic gels and polymers to form hybrid materials in which the basic physical and chemical properties of the host were not substantially altered, and electrical measurements further showed a >90% yield of active devices in the hybrid materials. The positions of the nanowire devices were located within 3D hybrid materials with ∼14-nm resolution through simultaneous nanowire device photocurrent/confocal microscopy imaging measurements. In addition, we explored functional properties of these hybrid materials, including (i) mapping time-dependent pH changes throughout a nanowire network/agarose gel sample during external solution pH changes, and (ii) characterizing the strain field in a hybrid nanoelectronic elastomer structures subject to uniaxial and bending forces. The seamless incorporation of active nanoelectronic networks within 3D materials reveals a powerful approach to smart materials in which the capabilities of multifunctional nanoelectronics allow for active monitoring and control of host systems.
The concept of hydrophilic/CO2-philic balance (HCB) was extended to describe stabilization of carbon dioxide-in-water (C/W) foams (also called emulsions) with silica nanoparticles adsorbed at the CO2-water interface. Opaque, white C/W foams (bubble diameter <100 mu m) were generated with either PEG-coated silica or methylsilyl modified silica nanoparticles in a beadpack with CO2 densities between 0.2 and 0.9 g mL(-1). For methylsilyl modified silica nanoparticles, 50% SiOH modification provided an optimal HCB for generation and stabilization of viscous C/W foams with high stability. The apparent viscosity measured with a capillary tube viscometer reached 120-fold that of a CO2-water mixture without nanoparticles, a consequence of the small bubble size and the energy required to deform a high density of aqueous lamellae between CO2 bubbles. Air-in-water (A/W) foams stabilized with nanoparticles were used to gain insight into the relationship between nanoparticle surface properties and adsorption of the nanoparticles at various types of interfaces. With suitable nanoparticles, A/W foams were stable for at least 7 days and C/W foams were stable for at least 23 h. The ability to achieve long term stability for nanoparticle stabilized C/W foams could offer an alternative to conventional surfactants, which are known to have much lower adsorption energies. (C) 2012 Elsevier Inc. All rights reserved.
The dopaminergic system is involved in reward encoding and reinforcement learning. Dopaminergic neurons from this system in the substantia nigra/ventral tegmental area complex (SN/VTA) fire in response to unexpected reinforcing cues. The goal of this study was to investigate whether individuals can gain voluntary control of SN/VTA activity, thereby potentially enhancing dopamine release to target brain regions. Neurofeedback and mental imagery were used to self-regulate the SN/VTA. Real-time functional magnetic resonance imaging (rtfMRI) provided abstract visual feedback of the SN/VTA activity while the subject imagined rewarding scenes. Skin conductance response (SCR) was recorded as a measure of emotional arousal. To examine the effect of neurofeedback, subjects were assigned to either receiving feedback directly proportional (n = 15, veridical feedback) or inversely proportional (n = 17, inverted feedback) to SN/VTA activity. Both groups of subjects were able to up-regulate SN/VTA activity initially without feedback. Veridical feedback improved the ability to up-regulate SN/VTA compared to baseline while inverted feedback did not. Additional dopaminergic regions were activated in both groups. The ability to self-regulate SN/VTA was differentially correlated with SCR depending on the group, suggesting an association between emotional arousal and neurofeedback performance. These findings indicate that SN/VTA can be voluntarily activated by imagery and voluntary activation is further enhanced by neurofeedback. The findings may lead the way towards a non-invasive strategy for endogenous control of dopamine.
We study the variational problem that arises from consideration of large deviations for semimartingale reflected Brownian motion (SRBM) in the positive octant. Due to the difficulty of the general problem, we consider the case in which the SRBM has rotationally symmetric parameters. In this case, we are able to obtain conditions under which the optimal solutions to the variational problem are paths that are gradual (moving through faces of strictly increasing dimension) or that spiral around the boundary of the octant. Furthermore, these results allow us to provide an example for which it can be verified that a spiral path is optimal. For rotationally symmetric SRBM’s, our results facilitate the simplification of computational methods for determining optimal solutions to variational problems and give insight into large deviations behavior of these processes.