Infrared neural stimulation (INS) has been shown to induce neural activity with spatial selectivity without inducing a stimulation artifact or necessitating tissue contact. Most experiments with this technology have been done in mammals, but much is still unknown about INS. Characterization of the experimentally tractable nervous system of the marine mollusk, Aplysia californica, will allow us to answer fundamental questions. In these studies, INS feasibility in Aplysia is shown and characterized with respect to repetition rate, pulse duration, wavelength and temperature. Nerve recordings were taken while stimulating buccal nerve 3 of the buccal ganglion using pulsed infrared light. At each parameter value, the nerve was stimulated at a random radiant exposure level (J/cm2), and observation of stimulation was reported. Stimulation thresholds were calculated as the effective dose (ED50) outputted by a probit regression. No change in stimulation thresholds were investigated at laser repetition rates ranging between 0.5-15 Hz. Stimulation at pulse durations ranging from 3-10 ms showed no change in threshold while it decreased below and increased above that range. Studies showed that a wavelength of 1.875 μm was more efficient for inducing action potentials than at 1.865 μm. Investigations of the ambient temperature challenged previous work by showing that at lower temperatures (0 Celsius) the threshold increased. Complex behavioral patterns were induced using INS in neural networks providing new directions for future clinical devices. Having shown feasibility in Aplysia, we believe this is a useful model for further studies on the physiological mechanism and optimal laser parameters of INS.

With the worlds increasing energy demands, there exists a tremendous need for the development and industrialization of energy-dense biofuels. Metabolically engineered cyanobacteria provide a promising means, as the utility of photosynthesis bypasses the need for harvesting, transporting and deconstructing biomass. The idea proposed is to manipulate circadian pathways in an effort to optimize cyanobacterial isobutyraldehyde (IBA) production. Bioluminescence has proven successful as a real-time reporter of circadian gene activity, so the objective was to determine whether it is a feasible means of measuring IBA levels. The automated high-throughput system developed expands upon the Kondotron system, utilizing a twelve-channel turntable with stepper motor and a CCD-cooled camera. The new system makes use of commercially available parts and is controlled entirely with custom LabView software. It features several software improvements, most notably to colony selection and processing. Luminescence activity was found to increase with increasing IBA vapor concentrations, thereby allowing for efficient screening and monitoring of IBA-producing mutants. It is expected that system performance will only improve when long-chain aldehydes, alcohols, or alkanes are the desired end-product. A similar high-throughput system could be developed for monitoring bacterial fluorescence.

Assessment of the restoration of load-bearing function is the central goal in the study of bone fracture healing. However, bone fracture calluses are inhomogeneous and irregular materials and this complexity has led to considerable uncertainty in the assessment of biomechanical property improvement or impairment during various therapeutic interventions and genetic models of pathological fracture healing. Unfortunately, as a result, arguably one of the most important criteria, mechanical stability, is the least resolved with respect to fracture healing assessment. To address this issue, an inverse finite element analysis (FEA) approach was developed in which biomechanical testing and microCT imaging are integrated through the use of computational modeling to determine mechanical properties of the healing fracture callus tissue. The presented work serves to evaluate the inverse analysis as a functional fracture healing assessment methodology in comparison to more traditional imaging and biomechanical testing measures within the context of normal fracture healing and a therapeutic system involving mesenchymal stem cell (MSC) transplantation. As compared to traditional fracture healing metrics, the results demonstrate that the inverse FEA approach: (1) was the only metric to successfully detect both longitudinal and therapeutic responses, and (2) performed significantly better at late-stage healing time points, where traditional metrics failed. The inverse analysis also added insight to the role of MSCs in fracture healing by demonstrating both accelerated healing and therapeutic benefit at late-stage healing. Additionally, a systems-based approach was developed for the generation of ease-of-use enhancements to the inverse analysis methodology in order to facilitate a wider usage among bone fracture biology groups whom are not experts in computational analysis. This was accomplished by the construction of an online web-enabled model submission system in which bone fracture callus microCT imaging and biomechanical testing data are collected with a minimal amount of pre-processing on a remote user node and submitted to a compute node which builds and executes the inverse model for material property reconstruction. In conclusion, the inverse FEA approach is shown to be a sensitive and functional fracture healing measure and provides a significant first-step towards normalizing the often challenging process of assessing mechanical integrity of healing fractures.

Limitations of commercial RNA extraction kits make them inappropriate for use in a clinical setting. This project develops and evaluates a point-of-care process for simple and rapid extraction of RNA from clinical samples. The novel RNA extraction method does not require laboratory equipment and is simpler and faster. In this thesis the proposed method is compared to three existing commercial extraction kits. Comparisons were made using RT-PCR of extraction solutions obtained after adding a known number of copies of respiratory syncytial virus (RSV) RNA to buffer or negative patient samples and using previously stored RSV positive patient samples. A comparison of viral RNA extraction from buffer found that our initial design of the proposed method achieved extraction efficiency of 80 to 90% of commercial kits. Extraction performance using previously stored RSV positive patient samples ranged from ~15 to 67% of commercial kits. With further optimization this approach would likely have a significant impact on moving genome-based detection methods into a clinical setting.

Current clinical bone diagnostic measures rely predominantly on X-ray-based contrast and are primarily sensitive to bone mineral content. Since bone also contains collagen and water components, which are heavily implicated in fracture resistance, these X-ray measures are micro-anatomically incomplete and do not identify individuals who will fracture. This dissertation aims to improve clinical bone fracture risk assessment through the use of novel magnetic resonance imaging (MRI) methods, which provide quantitative measures of the non-mineral bone components. The overall goal is to advance our understanding of 1H nuclear magnetic resonance (NMR) relaxation in human cortical bone to the point that diagnostically-relevant parameters may be extracted from in vivo bone MRI measurements. To accomplish this, custom NMR hardware was first developed for a rigorous, NMR relaxation-based characterization of ex vivo cortical bone. Such characterization was used to identify the micro-anatomical origins of cortical bone NMR signal components, which included collagen, bound water, and pore water protons. These signal components correlated well with various bone mechanical properties, indicating diagnostic relevance. Using the well-characterized cortical bone relaxation characteristics, novel and clinically practical methods for quantitative, diagnostic bone MRI were developed and validated. Collectively, this work represents a biophysical basis for cortical bone MRI, which stands ready for translation to clinical and research studies.

I reduced the reaction volume in microfabricated suspended-membrane titration calorimeters to nanoliter droplets and improved the sensitivities to below a nanowatt with time constants of around 100ms. The device performance was characterized using exothermic acid-base neutralizations and a detailed numerical model. The finite element based numerical model allowed us to determine the sensitivities within 1% and the temporal dynamics of the temperature rise in neutralization reactions as a function of droplet size. The model was used to determine the optimum calorimeter design (membrane size and thickness, junction area, and thermopile thickness) and sensitivities for sample volumes of 1 nl for silicon nitride and polymer membranes. I obtained a maximum sensitivity of 153 pW/√Hz for a 1 µm SiN membrane and 79 pW/√Hz for a 1 µm polymer membrane. The time constant of the calorimeter system was determined experimentally by using a pulsed laser to increase the temperature of nanoliter sample volumes. For a 2.5 nanoliter sample volume, I experimentally determined a noise equivalent power of 500 pW/√Hz and a 1/e time constant of 110ms for a modified commercially available infrared sensor with a thin-film thermopile. Furthermore, I demonstrated detection of 1.4 nJ reaction energies from injection of 25 pl of 1 mM HCl into a 2.5 nl droplet of 1 mM NaOH.

A statistical analysis of the magnetic resonance imaging-based mcDESPOT method for characterizing two exchanging water proton pools--a seven-dimensional problem that fits to multiple flip angle measurements of both spoiled and refocused gradient echoes--is presented. Theoretical calculations of the Cramer-Rao lower bounds of the variance of fitted model parameters were made using a variety of model system parameters, meant to mimic those expected in human white matter. The results, validated by Monte Carlo simulations, indicated that mcDESPOT signals acquired at feasibly attainable signal to noise ratios cannot provide parameter estimates with useful levels of precision. Precision can be greatly improved by constraining solutions with a priori model information, although this will generally lead to biased parameter estimates with less specificity. These results indicate that previous, apparently successful applications of mcDESPOT to human white matter may have used data fitting methods that implicitly constrained parameter solutions, or that the two-pool model of white matter may not be sufficient to describe the observed water proton signal in mcDESPOT acquisitions. In either case, mcDESPOT-derived estimates of two-pool model parameters cannot yet be unambiguously related to specific tissue characteristics.

Increased main field (B0) strength in MRI leads to increased signal levels but also causes higher field inhomogeneity (delta-B0). This thesis presents the implementation and evaluation of Dynamic Shimming, an advanced field shimming (correction) technique, on a high field human 7 Tesla clinical scanner. In this technique, the shim settings are changed during the acquisition of data from individual sub-volumes leading to better optimized field homogeneity compared to conventional static global shimming. Phantom and invivo experiments have been performed comparing the two shimming techniques. Dynamic shimming was seen to produce lower residual delta- B0 , image distortion and signal losses that static global shimming. In addition, a novel software based prospective method of eddy field compensation applied to higher order shim induced eddy currents has been developed and implemented. The method is shown to significantly reduced eddy fields and related artifacts in dynamic shimming.

In addition to the inherent chemical phase shift between different proton species, fat-water MRI k-space raw data are corrupted by several sources such as magnetic field inhomogeneity; chemical shift phase accumulated during the data readout window and trajectory shifts due to non-ideal gradient performance. NMR signal can be modeled within a single voxel as the mixture of different type protons with all corrupting factors clearly defined. If multiecho data are acquired, the evolving fat-water signal can be described as a linear system which can be unmixed. A reversed readout-based method is investigated in this work to correct the field inhomogeneity for radial fat-water MRI data. In the Cartesian case, the field map can be estimated using an iterative approach when other corrupting factors are precisely modeled. In addition, accurate fat-water signal modeling includes the use of a multipeak fat spectrum, and precise sampling time information. Multipeak fat spectrum information is obtained from nuclear magnetic spectroscopy, and the precise sampling time information is based on the employed pulse sequence. On the other hand, fat-water image reconstruction from radial trajectory data requires non-uniform Fourier transformation including regridding, density correction and interpolation. All these procedures are inserted as a part of radial fat-water separation. The artifact caused by imperfect gradients for radial MRI is also discussed and corrected in this work. Reconstruction results for both Cartesian and radial data with all corrections applied are displayed and compared.

Tunable and sustained drug delivery platforms have great unmet potential to be used for more optimal treatment of human disease. Such delivery devices avoid bolus delivery and its undesirable systemic effects and toxicity. Controlled release can also overcome issues related to insufficient local concentrations of drug for the required timeframe since a single injection of naked drug can result in rapid degradation and subsequent distribution throughout the body. Microspheres offer one route for sustained and controlled release that have great potential as ideal platforms to deliver drugs in an optimized, sustained pattern. Many hydrolytically biodegradable microspheres have been pursued (i.e., PLGA). The focus of this thesis work has been on utilization of smart, stimuli-responsive polymers that release drugs at a rate dictated by the environment rather than hydrolytic degradation mechanisms that act independent of any environmental cues. For example, we have specifically sought applications for delivery to slightly acidic pH (5-7) tissues in cardiac ischemia and chronic diabetic wounds and to tissues laden with cell-damaging reactive oxygen species (in particular hydrogen peroxide) such as in rheumatoid arthritis. With this idea in mind, we formulated, characterized and studied in vitro release profiles of two novel types of smart, stimuli sensitive microspheres. These were pH and temperature-sensitive microspheres made from poly(NIPAAm-co-PAA-co-BA) (NPB microspheres), and Reactive Oxygen Species (ROS)-sensitive microspheres made from poly(propylene) sulfide (PPS microspheres). These intelligent microspheres demonstrated sustained release profile of encapsulated drugs when presented with ischemic pH and hydrogen peroxide as stimuli, indicating their potential for spatio-temporally controlled therapeutic delivery to ischemic and inflammatory environments, respectively. NPB microspheres formulated using a waterinoil-in-water double emulsion method were pursued specifically as candidates to encapsulate hydrophilic drugs (i.e. proteins). The PPS microspheres, on the other hand, were generated using a modified oil-in-water single emulsion method in order to pursue applications for delivery of more hydrophobic (i.e., small molecule) drugs.