Theses, Dissertations, and Doctoral Papers
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Explore graduate and doctoral level research conducted by graduates of the Indianapolis campus (inclusive of the former institution known as IUPUI) of Indiana University.
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Item 0-GlcNAc selective N-acetyl beta-D glucosaminidase characterization and enzymatic site determination(2008) Lee, Thomas, NathanItem 12-lipoxygenase Promotes Macrophage Infiltration and Pancreatic Islet Dysfunction in the Vertebrate Models of Diabetes Pathogenesis(2020-05) Kulkarni, Abhishek Anant; Harrington, Maureen; Mirmira, Raghavendra; Anderson, Ryan; Goebl, Mark; Mosley, Amber; Marrs, JamesDiabetes is a morbid metabolic disorder that affects almost 500 million people worldwide. Although multiple factors contribute to diabetes pathogenesis, pancreatic islet inflammation and dysfunction are shared characteristics of its major forms. 12- lipoxygenase (12-LOX), an enzyme involved in lipid metabolism, has been implicated in islet inflammation. 12-LOX generates reactive oxygen species (ROS) that activate inflammation and serve as major contributors to islet dysfunction. Importantly, since ROS are transient moieties, they are challenging to study in vivo. Hence, establishing better animal models of ROS-mediated stress is critical to facilitate the discovery and preclinical testing of novel diabetes therapeutics. Here, I have adapted a zebrafish model of conditional β-cell injury, which is regulated by the administration of the prodrug metronidazole (MTZ), to study responses to ROS in vivo. I demonstrate that with MTZ treatment, ROS are generated within β-cells and subsequently exhibit recruitment of macrophages into the islet and induction of β-cell death. I utilized this model to uncover roles for macrophages and 12-LOX during islet injury. Excessive macrophage infiltration exacerbates islet inflammation and dysfunction. Interestingly, on the depletion of macrophages in zebrafish, I observed that β-cells recovered normal function upon cessation of prodrug treatment. This suggests that infiltrating macrophages promote maladaptive inflammation and premature removal of damaged β-cells. Thus, limiting the macrophage infiltration may be a therapeutic approach to restoring β-cell function. Based on the established roles of 12-LOX in other contexts, I hypothesized that its inhibition would prevent the localized infiltration of proinflammatory macrophages. To test this, I used both zebrafish and mouse models and observed a significant reduction in macrophage migration upon loss of 12- LOX activity. Furthermore, I found that expression of CXCR3, a crucial receptor regulating migration, was significantly reduced in 12-LOX loss-of-function macrophages. These data suggest a role for 12-LOX in macrophages, which is conserved across species. Collectively, my study reveals novel roles for 12-LOX in macrophage function and provides testable therapeutic targets for the resolution of inflammation-induced damage in the pancreatic islets.Item 17-Beta estradiol-induced modulation of the immune system(1984) Myers, Michael JosephItem The 1901 Fort Wayne, Indiana City Election: A Political Dialogue of Ethnic Tension(2013) Brown, Nancy Eileen; Wokeck, Marianne Sophia; Snodgrass, Michael; Monroe, Elizabeth Brand, 1947-In 1901, three German American candidates ran for the office of mayor in Fort Wayne, Indiana. The winner, Henry Berghoff, had emigrated from Germany as a teenager. This thesis examines the election discourse in the partisan press for signs of ethnic tension. The first chapter places Fort Wayne in historical context of German immigration and Indiana history. The second and third chapters investigate the editorial pages for evidence of ethnic tension. I also reference a few articles of an editorial nature outside of the editorial pages. The second chapter provides background information about the election and examines indications of the candidates’ ethnicity and references to the German language papers. The third chapter considers the editorial comment about Germany, the intertwining of ethnicity and the issues, and ethnic name-calling. In order to identify underlying bias for or against Germany and to better understand the context of the references to German ethnicity, the fourth chapter explores the portrayal of Germany in the Fort Wayne papers.Item 20S proteasome assembly: alternative pathways and complexes(2017) Hammack, Lindsay J.; Kusmierczyk, Andrew R.; Mosley, Amber L.; Randall, Stephen; Baucum, AJThe ubiquitin-proteasome system is responsible for the targeted degradation of proteins within the cell. The 26S proteasome, which is the protease of this system, is a high molecular weight complex consisting of 33 subunits that arrange to form two smaller complexes the 19S regulatory particle (RP) and the 20S core particle (CP). The 19S RP can bind one or both ends of the 20S CP and is responsible for recognizing the ubiquitinated substrates. After recognition, the 19S RP will subsequently deubiquitinate, unfold, and translocate the substrates into the proteolytic 20S CP. The 20S CP consists of seven unique alpha and seven unique beta subunits that arrange into four stacked rings, with two alpha rings capping two beta rings. Assembly of the alpha(1-7)beta(1-7)beta(1-7)alpha(1-7) structure begins with the formation of an alpha ring and proceeds through specific assembly intermediates. This process is assisted by assembly chaperone proteins that promote on pathway interactions to efficiently construct the 20S CP. In this dissertation, three new findings are described which further characterize the proteasome assembly pathway. First, novel non-canonical complexes comprised of proteasome subunit alpha4 were identified in vivo, revealing proteasome subunits can assemble into complexes outside of the proteasome. Second, Hsp70 proteins, Ssa1/2, were shown to assist in the assembly of 20S CPs, adding to the growing list of proteins guiding proteasome assembly. Third, a novel complex was identified which is believed to represent a new proteasome assembly intermediate.Item A 2D PLUS DEPTH VIDEO CAMERA PROTOTYPE USING DEPTH FROM DEFOCUS IMAGING AND A SINGLE MICROFLUIDIC LENS(2011-08) Li, Weixu; Christopher, Lauren; Rizkalla, Maher E.; Salama, PaulA new method for capturing 3D video from a single imager and lens is introduced in this research. The benefit of this method is that it does not have the calibration and alignment issues associated with binocular 3D video cameras, and allows for a less expensive overall system. The digital imaging technique Depth from Defocus (DfD) has been successfully used in still camera imaging to develop a depth map associated with the image. However, DfD has not been applied in real-time video so far since the focus mechanisms are too slow to produce real-time results. This new research result shows that a Microfluidic lens is capable of the required focal length changes at 2x video frame rate, due to the electrostatic control of the focus. During the processing, two focus settings per output frame are captured using this lens combined with a broadcast video camera prototype. We show that the DfD technique using Bayesian Markov Random Field optimization can produce a valid depth map.Item 3-D Scene Reconstruction for Passive Ranging Using Depth from Defocus and Deep Learning(2019-08) Emerson, David R.; Christopher, Lauren A.; Ben Miled, Zina; King, Brian; Salama, PaulDepth estimation is increasingly becoming more important in computer vision. The requirement for autonomous systems to gauge their surroundings is of the utmost importance in order to avoid obstacles, preventing damage to itself and/or other systems or people. Depth measuring/estimation systems that use multiple cameras from multiple views can be expensive and extremely complex. And as these autonomous systems decrease in size and available power, the supporting sensors required to estimate depth must also shrink in size and power consumption. This research will concentrate on a single passive method known as Depth from Defocus (DfD), which uses an in-focus and out-of-focus image to infer the depth of objects in a scene. The major contribution of this research is the introduction of a new Deep Learning (DL) architecture to process the the in-focus and out-of-focus images to produce a depth map for the scene improving both speed and performance over a range of lighting conditions. Compared to the previous state-of-the-art multi-label graph cuts algorithms applied to the synthetically blurred dataset the DfD-Net produced a 34.30% improvement in the average Normalized Root Mean Square Error (NRMSE). Similarly the DfD-Net architecture produced a 76.69% improvement in the average Normalized Mean Absolute Error (NMAE). Only the Structural Similarity Index (SSIM) had a small average decrease of 2.68% when compared to the graph cuts algorithm. This slight reduction in the SSIM value is a result of the SSIM metric penalizing images that appear to be noisy. In some instances the DfD-Net output is mottled, which is interpreted as noise by the SSIM metric. This research introduces two methods of deep learning architecture optimization. The first method employs the use of a variant of the Particle Swarm Optimization (PSO) algorithm to improve the performance of the DfD-Net architecture. The PSO algorithm was able to find a combination of the number of convolutional filters, the size of the filters, the activation layers used, the use of a batch normalization layer between filters and the size of the input image used during training to produce a network architecture that resulted in an average NRMSE that was approximately 6.25% better than the baseline DfD-Net average NRMSE. This optimized architecture also resulted in an average NMAE that was 5.25% better than the baseline DfD-Net average NMAE. Only the SSIM metric did not see a gain in performance, dropping by 0.26% when compared to the baseline DfD-Net average SSIM value. The second method illustrates the use of a Self Organizing Map clustering method to reduce the number convolutional filters in the DfD-Net to reduce the overall run time of the architecture while still retaining the network performance exhibited prior to the reduction. This method produces a reduced DfD-Net architecture that has a run time decrease of between 14.91% and 44.85% depending on the hardware architecture that is running the network. The final reduced DfD-Net resulted in a network architecture that had an overall decrease in the average NRMSE value of approximately 3.4% when compared to the baseline, unaltered DfD-Net, mean NRMSE value. The NMAE and the SSIM results for the reduced architecture were 0.65% and 0.13% below the baseline results respectively. This illustrates that reducing the network architecture complexity does not necessarily reduce the reduction in performance. Finally, this research introduced a new, real world dataset that was captured using a camera and a voltage controlled microfluidic lens to capture the visual data and a 2-D scanning LIDAR to capture the ground truth data. The visual data consists of images captured at seven different exposure times and 17 discrete voltage steps per exposure time. The objects in this dataset were divided into four repeating scene patterns in which the same surfaces were used. These scenes were located between 1.5 and 2.5 meters from the camera and LIDAR. This was done so any of the deep learning algorithms tested would see the same texture at multiple depths and multiple blurs. The DfD-Net architecture was employed in two separate tests using the real world dataset. The first test was the synthetic blurring of the real world dataset and assessing the performance of the DfD-Net trained on the Middlebury dataset. The results of the real world dataset for the scenes that were between 1.5 and 2.2 meters from the camera the DfD-Net trained on the Middlebury dataset produced an average NRMSE, NMAE and SSIM value that exceeded the test results of the DfD-Net tested on the Middlebury test set. The second test conducted was the training and testing solely on the real world dataset. Analysis of the camera and lens behavior led to an optimal lens voltage step configuration of 141 and 129. Using this configuration, training the DfD-Net resulted in an average NRMSE, NMAE and SSIM of 0.0660, 0.0517 and 0.8028 with a standard deviation of 0.0173, 0.0186 and 0.0641 respectively.Item 3D CBCT analysis of the frontal sinus and its relationship to forensic identification(2014) Krus, Bianaca S.; Wilson, Jeremy John; Starbuck, John M.; Ward, Richard E.The positive identification of human remains that are decomposed, burnt, or otherwise disfigured can prove especially challenging in forensic anthropology, resulting in the need for specialized methods of analysis. Due to the unique morphological characteristics of the frontal sinus, a positive identification can be made in cases of unknown human remains, even when remains are highly cremated or decomposed. This study retrospectively reviews 3D CBCT images of a total of 43 Caucasian patients between the ages of 20-38 from the Indiana University School of Dentistry to quantify frontal sinus differences between adult males and females and investigate the usefulness of frontal sinus morphology for forensic identification. Digit codes with six sections and eleven-digit numbers were created to classify each individual sinus. It was shown that 3D CBCT images of the frontal sinus could be used to make a positive forensic identification. Metric measurements displayed a high degree of variability between sinuses and no two digit codes were identical. However, it was also shown that there were almost no quantifiable and significant sexually dimorphic differences between male and female frontal sinuses. This study confirms that sex determination should not be a primary goal of frontal sinus analysis and highlights the importance of creating a standard method of frontal sinus evaluation based on metric measurements.Item 3D EM/MPM MEDICAL IMAGE SEGMENTATION USING AN FPGA EMBEDDED DESIGN IMPLEMENTATION(2011-08) Liu, Chao; Christopher, Lauren; Rizkalla, Maher E.; Salama, PaulThis thesis presents a Field Programmable Gate Array (FPGA) based embedded system which is used to achieve high speed segmentation of 3D images. Segmenta- tion is performed using Expectation-Maximization with Maximization of Posterior Marginals (EM/MPM) Bayesian algorithm. In this system, the embedded processor controls a custom circuit which performs the MPM and portions of the EM algorithm. The embedded processor completes the EM algorithm and also controls image data transmission between host computer and on-board memory. The whole system has been implemented on Xilinx Virtex 6 FPGA and achieved over 100 times improvement compared to standard desktop computing hardware.Item 3D ENDOSCOPY VIDEO GENERATED USING DEPTH INFERENCE: CONVERTING 2D TO 3D(2013-08-20) Rao, Swetcha; Christopher, Lauren; Rizkalla, Maher E.; Salama, Paul; King, BrianA novel algorithm was developed to convert raw 2-dimensional endoscope videos into 3-dimensional view. Minimally invasive surgeries aided with 3D view of the invivo site have shown to reduce errors and improve training time compared to those with 2D view. The novelty of this algorithm is that two cues in the images have been used to develop the 3D. Illumination is the rst cue used to nd the darkest regions in the endoscopy images in order to locate the vanishing point(s). The second cue is the presence of ridge-like structures in the in-vivo images of the endoscopy image sequence. Edge detection is used to map these ridge-like structures into concentric ellipses with their common center at the darkest spot. Then, these two observations are used to infer the depth of the endoscopy videos; which then serves to convert them from 2D to 3D. The processing time is between 21 seconds to 20 minutes for each frame, on a 2.27GHz CPU. The time depends on the number of edge pixels present in the edge-detection image. The accuracy of ellipse detection was measured to be 98.98% to 99.99%. The algorithm was tested on 3 truth images with known ellipse parameters and also on real bronchoscopy image sequences from two surgical procedures. Out of 1020 frames tested in total, 688 frames had single vanishing point while 332 frames had two vanishing points. Our algorithm detected the single vanishing point in 653 of the 688 frames and two vanishing points in 322 of the 332 frames.