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Description
Continuous monitoring in the adequate temporal and spatial scale is necessary for a better understanding of environmental variations. But field deployments of molecular biological analysis platforms in that scale are currently hindered because of issues with power, throughput and automation. Currently, such analysis is performed by the collection of large

Continuous monitoring in the adequate temporal and spatial scale is necessary for a better understanding of environmental variations. But field deployments of molecular biological analysis platforms in that scale are currently hindered because of issues with power, throughput and automation. Currently, such analysis is performed by the collection of large sample volumes from over a wide area and transporting them to laboratory testing facilities, which fail to provide any real-time information. This dissertation evaluates the systems currently utilized for in-situ field analyses and the issues hampering the successful deployment of such bioanalytial instruments for environmental applications. The design and development of high throughput, low power, and autonomous Polymerase Chain Reaction (PCR) instruments, amenable for portable field operations capable of providing quantitative results is presented here as part of this dissertation. A number of novel innovations have been reported here as part of this work in microfluidic design, PCR thermocycler design, optical design and systems integration. Emulsion microfluidics in conjunction with fluorinated oils and Teflon tubing have been used for the fluidic module that reduces cross-contamination eliminating the need for disposable components or constant cleaning. A cylindrical heater has been designed with the tubing wrapped around fixed temperature zones enabling continuous operation. Fluorescence excitation and detection have been achieved by using a light emitting diode (LED) as the excitation source and a photomultiplier tube (PMT) as the detector. Real-time quantitative PCR results were obtained by using multi-channel fluorescence excitation and detection using LED, optical fibers and a 64-channel multi-anode PMT for measuring continuous real-time fluorescence. The instrument was evaluated by comparing the results obtained with those obtained from a commercial instrument and found to be comparable. To further improve the design and enhance its field portability, this dissertation also presents a framework for the instrumentation necessary for a portable digital PCR platform to achieve higher throughputs with lower power. Both systems were designed such that it can easily couple with any upstream platform capable of providing nucleic acid for analysis using standard fluidic connections. Consequently, these instruments can be used not only in environmental applications, but portable diagnostics applications as well.
ContributorsRay, Tathagata (Author) / Youngbull, Cody (Thesis advisor) / Goryll, Michael (Thesis advisor) / Blain Christen, Jennifer (Committee member) / Yu, Hongyu (Committee member) / Arizona State University (Publisher)
Created2013
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Description
Coronary computed tomography angiography (CTA) has a high negative predictive value for ruling out coronary artery disease with non-invasive evaluation of the coronary arteries. My work has attempted to provide metrics that could increase the positive predictive value of coronary CTA through the use of dual energy CTA imaging. After

Coronary computed tomography angiography (CTA) has a high negative predictive value for ruling out coronary artery disease with non-invasive evaluation of the coronary arteries. My work has attempted to provide metrics that could increase the positive predictive value of coronary CTA through the use of dual energy CTA imaging. After developing an algorithm for obtaining calcium scores from a CTA exam, a dual energy CTA exam was performed on patients at dose levels equivalent to levels for single energy CTA with a calcium scoring exam. Calcium Agatston scores obtained from the dual energy CTA exam were within ±11% of scores obtained with conventional calcium scoring exams. In the presence of highly attenuating coronary calcium plaques, the virtual non-calcium images obtained with dual energy CTA were able to successfully measure percent coronary stenosis within 5% of known stenosis values, which is not possible with single energy CTA images due to the presence of the calcium blooming artifact. After fabricating an anthropomorphic beating heart phantom with coronary plaques, characterization of soft plaque vulnerability to rupture or erosion was demonstrated with measurements of the distance from soft plaque to aortic ostium, percent stenosis, and percent lipid volume in soft plaque. A classification model was developed, with training data from the beating heart phantom and plaques, which utilized support vector machines to classify coronary soft plaque pixels as lipid or fibrous. Lipid versus fibrous classification with single energy CTA images exhibited a 17% error while dual energy CTA images in the classification model developed here only exhibited a 4% error. Combining the calcium blooming correction and the percent lipid volume methods developed in this work will provide physicians with metrics for increasing the positive predictive value of coronary CTA as well as expanding the use of coronary CTA to patients with highly attenuating calcium plaques.
ContributorsBoltz, Thomas (Author) / Frakes, David (Thesis advisor) / Towe, Bruce (Committee member) / Kodibagkar, Vikram (Committee member) / Pavlicek, William (Committee member) / Bouman, Charles (Committee member) / Arizona State University (Publisher)
Created2013
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Description
The efficacy of deep brain stimulation (DBS) in Parkinson's disease (PD) has been convincingly demonstrated in studies that compare motor performance with and without stimulation, but characterization of performance at intermediate stimulation amplitudes has been limited. This study investigated the effects of changing DBS amplitude in order to assess dose-response

The efficacy of deep brain stimulation (DBS) in Parkinson's disease (PD) has been convincingly demonstrated in studies that compare motor performance with and without stimulation, but characterization of performance at intermediate stimulation amplitudes has been limited. This study investigated the effects of changing DBS amplitude in order to assess dose-response characteristics, inter-subject variability, consistency of effect across outcome measures, and day-to-day variability. Eight subjects with PD and bilateral DBS systems were evaluated at their clinically determined stimulation (CDS) and at three reduced amplitude conditions: approximately 70%, 30%, and 0% of the CDS (MOD, LOW, and OFF, respectively). Overall symptom severity and performance on a battery of motor tasks - gait, postural control, single-joint flexion-extension, postural tremor, and tapping - were assessed at each condition using the motor section of the Unified Parkinson's Disease Rating Scale (UPDRS-III) and quantitative measures. Data were analyzed to determine whether subjects demonstrated a threshold response (one decrement in stimulation resulted in ≥ 70% of the maximum change) or a graded response to reduced stimulation. Day-to-day variability was assessed using the CDS data from the three testing sessions. Although the cohort as a whole demonstrated a graded response on several measures, there was high variability across subjects, with subsets exhibiting graded, threshold, or minimal responses. Some subjects experienced greater variability in their CDS performance across the three days than the change induced by reducing stimulation. For several tasks, a subset of subjects exhibited improved performance at one or more of the reduced conditions. Reducing stimulation did not affect all subjects equally, nor did it uniformly affect each subject's performance across tasks. These results indicate that altered recruitment of neural structures can differentially affect motor capabilities and demonstrate the need for clinical consideration of the effects on multiple symptoms across several days when selecting DBS parameters.
ContributorsConovaloff, Alison (Author) / Abbas, James (Thesis advisor) / Krishnamurthi, Narayanan (Committee member) / Mahant, Padma (Committee member) / Jung, Ranu (Committee member) / Helms Tillery, Stephen (Committee member) / Arizona State University (Publisher)
Created2013
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Description
Cancer is the second leading cause of death in the United States and novel methods of treating advanced malignancies are of high importance. Of these deaths, prostate cancer and breast cancer are the second most fatal carcinomas in men and women respectively, while pancreatic cancer is the fourth most fatal

Cancer is the second leading cause of death in the United States and novel methods of treating advanced malignancies are of high importance. Of these deaths, prostate cancer and breast cancer are the second most fatal carcinomas in men and women respectively, while pancreatic cancer is the fourth most fatal in both men and women. Developing new drugs for the treatment of cancer is both a slow and expensive process. It is estimated that it takes an average of 15 years and an expense of $800 million to bring a single new drug to the market. However, it is also estimated that nearly 40% of that cost could be avoided by finding alternative uses for drugs that have already been approved by the Food and Drug Administration (FDA). The research presented in this document describes the testing, identification, and mechanistic evaluation of novel methods for treating many human carcinomas using drugs previously approved by the FDA. A tissue culture plate-based screening of FDA approved drugs will identify compounds that can be used in combination with the protein TRAIL to induce apoptosis selectively in cancer cells. Identified leads will next be optimized using high-throughput microfluidic devices to determine the most effective treatment conditions. Finally, a rigorous mechanistic analysis will be conducted to understand how the FDA-approved drug mitoxantrone, sensitizes cancer cells to TRAIL-mediated apoptosis.
ContributorsTaylor, David (Author) / Rege, Kaushal (Thesis advisor) / Jayaraman, Arul (Committee member) / Nielsen, David (Committee member) / Kodibagkar, Vikram (Committee member) / Dai, Lenore (Committee member) / Arizona State University (Publisher)
Created2013
Description
Intracortical microstimulation (ICMS) within somatosensory cortex can produce artificial sensations including touch, pressure, and vibration. There is significant interest in using ICMS to provide sensory feedback for a prosthetic limb. In such a system, information recorded from sensors on the prosthetic would be translated into electrical stimulation and delivered directly

Intracortical microstimulation (ICMS) within somatosensory cortex can produce artificial sensations including touch, pressure, and vibration. There is significant interest in using ICMS to provide sensory feedback for a prosthetic limb. In such a system, information recorded from sensors on the prosthetic would be translated into electrical stimulation and delivered directly to the brain, providing feedback about features of objects in contact with the prosthetic. To achieve this goal, multiple simultaneous streams of information will need to be encoded by ICMS in a manner that produces robust, reliable, and discriminable sensations. The first segment of this work focuses on the discriminability of sensations elicited by ICMS within somatosensory cortex. Stimulation on multiple single electrodes and near-simultaneous stimulation across multiple electrodes, driven by a multimodal tactile sensor, were both used in these experiments. A SynTouch BioTac sensor was moved across a flat surface in several directions, and a subset of the sensor's electrode impedance channels were used to drive multichannel ICMS in the somatosensory cortex of a non-human primate. The animal performed a behavioral task during this stimulation to indicate the discriminability of sensations evoked by the electrical stimulation. The animal's responses to ICMS were somewhat inconsistent across experimental sessions but indicated that discriminable sensations were evoked by both single and multichannel ICMS. The factors that affect the discriminability of stimulation-induced sensations are not well understood, in part because the relationship between ICMS and the neural activity it induces is poorly defined. The second component of this work was to develop computational models that describe the populations of neurons likely to be activated by ICMS. Models of several neurons were constructed, and their responses to ICMS were calculated. A three-dimensional cortical model was constructed using these cell models and used to identify the populations of neurons likely to be recruited by ICMS. Stimulation activated neurons in a sparse and discontinuous fashion; additionally, the type, number, and location of neurons likely to be activated by stimulation varied with electrode depth.
ContributorsOverstreet, Cynthia K (Author) / Helms Tillery, Stephen I (Thesis advisor) / Santos, Veronica (Committee member) / Buneo, Christopher (Committee member) / Otto, Kevin (Committee member) / Santello, Marco (Committee member) / Arizona State University (Publisher)
Created2013
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Description
This research is focused on two separate but related topics. The first uses an electroencephalographic (EEG) brain-computer interface (BCI) to explore the phenomenon of motor learning transfer. The second takes a closer look at the EEG-BCI itself and tests an alternate way of mapping EEG signals into machine commands. We

This research is focused on two separate but related topics. The first uses an electroencephalographic (EEG) brain-computer interface (BCI) to explore the phenomenon of motor learning transfer. The second takes a closer look at the EEG-BCI itself and tests an alternate way of mapping EEG signals into machine commands. We test whether motor learning transfer is more related to use of shared neural structures between imagery and motor execution or to more generalized cognitive factors. Using an EEG-BCI, we train one group of participants to control the movements of a cursor using embodied motor imagery. A second group is trained to control the cursor using abstract motor imagery. A third control group practices moving the cursor using an arm and finger on a touch screen. We hypothesized that if motor learning transfer is related to the use of shared neural structures then the embodied motor imagery group would show more learning transfer than the abstract imaging group. If, on the other hand, motor learning transfer results from more general cognitive processes, then the abstract motor imagery group should also demonstrate motor learning transfer to the manual performance of the same task. Our findings support that motor learning transfer is due to the use of shared neural structures between imaging and motor execution of a task. The abstract group showed no motor learning transfer despite being better at EEG-BCI control than the embodied group. The fact that more participants were able to learn EEG-BCI control using abstract imagery suggests that abstract imagery may be more suitable for EEG-BCIs for some disabilities, while embodied imagery may be more suitable for others. In Part 2, EEG data collected in the above experiment was used to train an artificial neural network (ANN) to map EEG signals to machine commands. We found that our open-source ANN using spectrograms generated from SFFTs is fundamentally different and in some ways superior to Emotiv's proprietary method. Our use of novel combinations of existing technologies along with abstract and embodied imagery facilitates adaptive customization of EEG-BCI control to meet needs of individual users.
Contributorsda Silva, Flavio J. K (Author) / Mcbeath, Michael K (Thesis advisor) / Helms Tillery, Stephen (Committee member) / Presson, Clark (Committee member) / Sugar, Thomas (Committee member) / Arizona State University (Publisher)
Created2013
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Description
Electrical neural activity detection and tracking have many applications in medical research and brain computer interface technologies. In this thesis, we focus on the development of advanced signal processing algorithms to track neural activity and on the mapping of these algorithms onto hardware to enable real-time tracking. At the heart

Electrical neural activity detection and tracking have many applications in medical research and brain computer interface technologies. In this thesis, we focus on the development of advanced signal processing algorithms to track neural activity and on the mapping of these algorithms onto hardware to enable real-time tracking. At the heart of these algorithms is particle filtering (PF), a sequential Monte Carlo technique used to estimate the unknown parameters of dynamic systems. First, we analyze the bottlenecks in existing PF algorithms, and we propose a new parallel PF (PPF) algorithm based on the independent Metropolis-Hastings (IMH) algorithm. We show that the proposed PPF-IMH algorithm improves the root mean-squared error (RMSE) estimation performance, and we demonstrate that a parallel implementation of the algorithm results in significant reduction in inter-processor communication. We apply our implementation on a Xilinx Virtex-5 field programmable gate array (FPGA) platform to demonstrate that, for a one-dimensional problem, the PPF-IMH architecture with four processing elements and 1,000 particles can process input samples at 170 kHz by using less than 5% FPGA resources. We also apply the proposed PPF-IMH to waveform-agile sensing to achieve real-time tracking of dynamic targets with high RMSE tracking performance. We next integrate the PPF-IMH algorithm to track the dynamic parameters in neural sensing when the number of neural dipole sources is known. We analyze the computational complexity of a PF based method and propose the use of multiple particle filtering (MPF) to reduce the complexity. We demonstrate the improved performance of MPF using numerical simulations with both synthetic and real data. We also propose an FPGA implementation of the MPF algorithm and show that the implementation supports real-time tracking. For the more realistic scenario of automatically estimating an unknown number of time-varying neural dipole sources, we propose a new approach based on the probability hypothesis density filtering (PHDF) algorithm. The PHDF is implemented using particle filtering (PF-PHDF), and it is applied in a closed-loop to first estimate the number of dipole sources and then their corresponding amplitude, location and orientation parameters. We demonstrate the improved tracking performance of the proposed PF-PHDF algorithm and map it onto a Xilinx Virtex-5 FPGA platform to show its real-time implementation potential. Finally, we propose the use of sensor scheduling and compressive sensing techniques to reduce the number of active sensors, and thus overall power consumption, of electroencephalography (EEG) systems. We propose an efficient sensor scheduling algorithm which adaptively configures EEG sensors at each measurement time interval to reduce the number of sensors needed for accurate tracking. We combine the sensor scheduling method with PF-PHDF and implement the system on an FPGA platform to achieve real-time tracking. We also investigate the sparsity of EEG signals and integrate compressive sensing with PF to estimate neural activity. Simulation results show that both sensor scheduling and compressive sensing based methods achieve comparable tracking performance with significantly reduced number of sensors.
ContributorsMiao, Lifeng (Author) / Chakrabarti, Chaitali (Thesis advisor) / Papandreou-Suppappola, Antonia (Thesis advisor) / Zhang, Junshan (Committee member) / Bliss, Daniel (Committee member) / Kovvali, Narayan (Committee member) / Arizona State University (Publisher)
Created2013
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Description
Humans' ability to perform fine object and tool manipulation is a defining feature of their sensorimotor repertoire. How the central nervous system builds and maintains internal representations of such skilled hand-object interactions has attracted significant attention over the past three decades. Nevertheless, two major gaps exist: a) how digit positions

Humans' ability to perform fine object and tool manipulation is a defining feature of their sensorimotor repertoire. How the central nervous system builds and maintains internal representations of such skilled hand-object interactions has attracted significant attention over the past three decades. Nevertheless, two major gaps exist: a) how digit positions and forces are coordinated during natural manipulation tasks, and b) what mechanisms underlie the formation and retention of internal representations of dexterous manipulation. This dissertation addresses these two questions through five experiments that are based on novel grip devices and experimental protocols. It was found that high-level representation of manipulation tasks can be learned in an effector-independent fashion. Specifically, when challenged by trial-to-trial variability in finger positions or using digits that were not previously engaged in learning the task, subjects could adjust finger forces to compensate for this variability, thus leading to consistent task performance. The results from a follow-up experiment conducted in a virtual reality environment indicate that haptic feedback is sufficient to implement the above coordination between digit position and forces. However, it was also found that the generalizability of a learned manipulation is limited across tasks. Specifically, when subjects learned to manipulate the same object across different contexts that require different motor output, interference was found at the time of switching contexts. Data from additional studies provide evidence for parallel learning processes, which are characterized by different rates of decay and learning. These experiments have provided important insight into the neural mechanisms underlying learning and control of object manipulation. The present findings have potential biomedical applications including brain-machine interfaces, rehabilitation of hand function, and prosthetics.
ContributorsFu, Qiushi (Author) / Santello, Marco (Thesis advisor) / Helms Tillery, Stephen (Committee member) / Buneo, Christopher (Committee member) / Santos, Veronica (Committee member) / Artemiadis, Panagiotis (Committee member) / Arizona State University (Publisher)
Created2013
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Description
Image resolution limits the extent to which zooming enhances clarity, restricts the size digital photographs can be printed at, and, in the context of medical images, can prevent a diagnosis. Interpolation is the supplementing of known data with estimated values based on a function or model involving some or all

Image resolution limits the extent to which zooming enhances clarity, restricts the size digital photographs can be printed at, and, in the context of medical images, can prevent a diagnosis. Interpolation is the supplementing of known data with estimated values based on a function or model involving some or all of the known samples. The selection of the contributing data points and the specifics of how they are used to define the interpolated values influences how effectively the interpolation algorithm is able to estimate the underlying, continuous signal. The main contributions of this dissertation are three fold: 1) Reframing edge-directed interpolation of a single image as an intensity-based registration problem. 2) Providing an analytical framework for intensity-based registration using control grid constraints. 3) Quantitative assessment of the new, single-image enlargement algorithm based on analytical intensity-based registration. In addition to single image resizing, the new methods and analytical approaches were extended to address a wide range of applications including volumetric (multi-slice) image interpolation, video deinterlacing, motion detection, and atmospheric distortion correction. Overall, the new approaches generate results that more accurately reflect the underlying signals than less computationally demanding approaches and with lower processing requirements and fewer restrictions than methods with comparable accuracy.
ContributorsZwart, Christine M. (Author) / Frakes, David H (Thesis advisor) / Karam, Lina (Committee member) / Kodibagkar, Vikram (Committee member) / Spanias, Andreas (Committee member) / Towe, Bruce (Committee member) / Arizona State University (Publisher)
Created2013
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Description
Gene manipulation techniques, such as RNA interference (RNAi), offer a powerful method for elucidating gene function and discovery of novel therapeutic targets in a high-throughput fashion. In addition, RNAi is rapidly being adopted for treatment of neurological disorders, such as Alzheimer's disease (AD), Parkinson's disease, etc. However, a major challenge

Gene manipulation techniques, such as RNA interference (RNAi), offer a powerful method for elucidating gene function and discovery of novel therapeutic targets in a high-throughput fashion. In addition, RNAi is rapidly being adopted for treatment of neurological disorders, such as Alzheimer's disease (AD), Parkinson's disease, etc. However, a major challenge in both of the aforementioned applications is the efficient delivery of siRNA molecules, plasmids or transcription factors to primary cells such as neurons. A majority of the current non-viral techniques, including chemical transfection, bulk electroporation and sonoporation fail to deliver with adequate efficiencies and the required spatial and temporal control. In this study, a novel optically transparent biochip is presented that can (a) transfect populations of primary and secondary cells in 2D culture (b) readily scale to realize high-throughput transfections using microscale electroporation and (c) transfect targeted cells in culture with spatial and temporal control. In this study, delivery of genetic payloads of different sizes and molecular characteristics, such as GFP plasmids and siRNA molecules, to precisely targeted locations in primary hippocampal and HeLa cell cultures is demonstrated. In addition to spatio-temporally controlled transfection, the biochip also allowed simultaneous assessment of a) electrical activity of neurons, b) specific proteins using fluorescent immunohistochemistry, and c) sub-cellular structures. Functional silencing of GAPDH in HeLa cells using siRNA demonstrated a 52% reduction in the GAPDH levels. In situ assessment of actin filaments post electroporation indicated a sustained disruption in actin filaments in electroporated cells for up to two hours. Assessment of neural spike activity pre- and post-electroporation indicated a varying response to electroporation. The microarray based nature of the biochip enables multiple independent experiments on the same culture, thereby decreasing culture-to-culture variability, increasing experimental throughput and allowing cell-cell interaction studies. Further development of this technology will provide a cost-effective platform for performing high-throughput genetic screens.
ContributorsPatel, Chetan (Author) / Muthuswamy, Jitendran (Thesis advisor) / Helms Tillery, Stephen (Committee member) / Jain, Tilak (Committee member) / Caplan, Michael (Committee member) / Vernon, Brent (Committee member) / Arizona State University (Publisher)
Created2012