Browsing by Type "Dissertation"

This thesis introduces readers to the rich tradition of the ecumenical movementand explores how emerging new strategies can benefit congregations as well as facilitate healing in our fractured and divisive world. It argues that the same principles used in ecumenical dialogue can and should be used in the local church. First, the history and significant steps and missteps of the ecumenical movement are briefly examined, before turning to the contemporary strategies of receptive, spiritual and kenotic ecumenism. Then, the paper considers 21st century examples of thriving ecumenical ministries, including survey feedback that provides an intimate look at how one church (Snowmass Chapel) has committed itself to unity across various denominations. Finally, a process is provided for effective ecumenical leadership both within, and outside of, the local church context. Ecumenical work takes courageous leaders who are willing to acknowledge difference without judgement, listen deeply, and be committed to Christian unity in love. The ecumenical movement has made significant strides in the past century and half, yet it has not made a significant move into the local church. This thesis argues that by introducing the concept of ecumenism to local congregations, leaders can initiate change that has far-reaching impacts across all areas of life.

More than 400,000 anterior cruciate ligament (ACL) injuries occur annually in the United States, 70% of which are non-contact. A severe consequence of ACL injury is the increased risk of early-onset of osteoarthritis (OA). Importantly, the increased risk of OA persists even if the ACL is surgically reconstructed. Thus, due to the long term physical consequences and high financial burden of treatment, injury prevention and improved reconstruction techniques are critical. However, the causes of non-contact ACL injuries remain unclear, which has hindered efforts to develop effective training programs targeted at preventing these injuries. Improved understanding of the knee motions that increase the risk of ACL injury can inform more effective injury prevention strategies. Furthermore, there is presently limited in vivo data to describe the function of ACL under dynamic loading conditions. Understanding how the ACL functions to stabilize the knee joint under physiologic loading conditions can inform design criteria for grafts used in ACL reconstruction. Grafts that more accurately mimic the native function of the ACL may help prevent these severe long term degenerative changes in the knee joint after injury.

To this end, measurements of in vivo ACL function during knee motion are critical to understanding how non-contact ACL injuries occur and the function of the ACL in stabilizing the joint during activities of daily living. Specifically, identifying the knee motions that increase ACL length and strain can elucidate the mechanisms of non-contact ACL injury, as a taut ligament is more likely to fail. Furthermore, measuring ACL elongation patterns during dynamic activity can inform the design criteria for grafts used in reconstructive surgery. To obtain measurements, 3D imaging techniques that can be used to measure dynamic in vivo ACL elongation and strain at high temporal and spatial resolution are needed.

Thus, in this dissertation a method of measuring knee motion and ACL function during dynamic activity in vivo using high-speed biplanar radiography in combination with magnetic resonance (MR) imaging was developed. In this technique, 3D surface models of the knee joint are created from MR images and registered to high-speed biplanar radiographs of knee motion. The use of MR imaging to model the joint allows for visualization of bone and soft tissue anatomy, in particular the attachment site footprints of the ligaments. By registering the bone models to biplanar radiographs using software developed in this dissertation, the relative positions of the bones and associated ligament attachment site footprints at the time of radiographic imaging can be reproduced. Thus, measurements of knee kinematics and ligament function during dynamic activity can be obtained at high spatial and temporal resolution.

We have applied the techniques developed in this dissertation to obtain novel dynamic in vivo measurements of the mechanical function of the knee joint. Specifically, the physiologic elongation and strain behaviors of the ACL during gait and single-legged jumping were measured. Additionally, the dynamic function of the patellar tendon during single legged jumping was measured. The findings of this dissertation have helped to elucidate the knee kinematics that increase ACL injury vulnerability by identifying the dynamic motions that result in elongation and strain in the ACL. Furthermore, the findings of this dissertation have provided critical data to inform design criteria for grafts used in reconstructive surgery such that reconstructive techniques better mimic the physiologic function of the ACL.

The methodologies described in this dissertation can be applied to study the mechanical behavior of other joints such as the spine, and other soft tissues, such as articular cartilage, under various loading conditions. Therefore, these methods may have a significant impact on the field of biomechanics as a whole, and may have applicability to a number of musculoskeletal applications.

Tissue-engineered skeletal muscle presents promising opportunities for developing high-fidelity in vitro models for investigating human muscle biology in the areas of regeneration and disease. In muscle regeneration, satellite cells (SCs) are essential for new muscle fiber formation; however, they lose their native quiescent state upon isolation, making in vitro studies of human SC function challenging. To optimally promote SC quiescence and enable exploration of SC dynamics in vitro, engineered muscle needs to recapitulate the native muscle microenvironment, which is comprised of muscle fibers, extracellular matrix, and other biochemical and mechanical cues. In disease modeling, mechanistic studies and therapeutic development are still extensively evaluated in animal models, which have limited translational relevance to patients. Specifically, Pompe disease is caused by a variety of mutations in the lysosomal enzyme acid alpha-glucosidase (GAA) that varyingly affect residual GAA activity and cannot be captured in the current GAA-/- mouse model. Therefore, human in vitro models are needed to enhance our mechanistic understanding of diseases and stimulate the development of effective therapies. To overcome these limitations, we set the dissertation goals to: 1) generate a pool of quiescent SCs and explore the mechanisms governing their formation and activation using an engineered skeletal muscle microenvironment and 2) develop a high-fidelity tissue-engineered skeletal muscle model for Pompe disease to investigate pathological mechanisms and test candidate therapies. To achieve these goals, we first compared methods for primary human myoblast expansion and found that p38 inhibition significantly increases the formation of Pax7+ cells in engineered 3D skeletal muscle tissues (“myobundles”). Gene expression analysis suggested that within the myobundle environment the Pax7+ cells adopt a quiescent phenotype (3D SCs), characterized by increased Pax7 expression, cell cycle exit, and Notch signaling activation relative to the original 2D expanded myoblasts. We then compared 3D SCs to previously described satellite-like cells that form alongside myotubes in 2D culture, termed reserve cells (RCs). Compared to RCs, 3D SCs showed an advanced quiescent phenotype characterized by a higher Pax7, Spry1, and Notch3 expression, as well as increased functional myogenesis demonstrated by formation of myobundles with higher contractile strength. To examine 3D SC activation, we tested several myobundle injury methods and identified treatment with a bee toxin, melittin, to robustly induce myofiber fragmentation, functional decline, and 3D SC proliferation. To further investigate the transcriptional processes describing how 2D myoblasts acquire 3D SC phenotype (i.e. deactivate) and how 3D SCs respond to injury (i.e. reactivate), we applied single cell RNA-sequencing (scRNA-seq) from which we discovered the existence of two subpools of 3D SCs—“quiescent” (qSC) and “activated” (aSC). The qSC subpool possessed greater expression of quiescence genes Pax7, Spry1, and Hey1, whereas the aSC subpool exhibited increased expression of inflammatory and differentiation markers. Furthermore, we performed trajectory inference along the deactivation process from 2D myoblasts to qSCs and identified deactivation-associated genes, included downregulated genes for proliferation, cytoskeletal reorganization, and myogenic differentiation. In response to tissue injury, we observed a decrease in the proportion of qSCs and an increase in the proportion of aSCs and committed myogenic progenitor cells suggestive of myogenic differentiation. In addition, we observed transcriptional changes within the aSC population reflective of SC activation in vivo, namely increased TNF- signaling, proliferation, and glycolytic and oxidative metabolism. These results strongly suggested that 3D SC heterogeneity and function recapitulate several aspects of native human SCs and could be applied to study human muscle regeneration and disease-associated SC dysfunction. To evaluate the myobundle system in the context of disease modeling, we developed the first 3D tissue-engineered skeletal muscle model of infantile onset Pompe disease (IOPD), the most severe form of Pompe disease. Diseased myobundles demonstrated characteristic GAA enzyme deficiency, accumulation of the GAA target glycogen, and lysosome enlargement. Despite exhibiting these key biochemical and structural hallmarks of disease, IOPD myobundles did not show deficits in contractile force generation or autophagic buildup. We therefore identified metabolic stress conditions that acutely targeted disease-associated abnormalities in the lysosomes and glycogen metabolism, which revealed impairments in contractile function and glycogen mobilization. To further elucidate the biological mechanisms underlying the phenotype of IOPD myobundles, we applied RNA sequencing (RNA-seq) and observed enrichment for terms consistent with Pompe disease phenotype including downregulation of gene sets involved in muscle contraction, increased endoplasmic reticulum stress, and reduced utilization of specific metabolic pathways. We then compared the transcriptomic profiles of GAA-/-¬ and wild-type mice to identify a Pompe disease signature and confirmed the presence of this signature in IOPD myobundles. Finally, treating IOPD myobundles with clinically used recombinant protein (rhGAA) therapy resulted in increased GAA activity, glycogen clearance, and a partial reversal of the disease signature, further confirming the utility of the myobundle system for studies of Pompe disease and therapy. In summary, this dissertation describes novel strategies for the formation and characterization of quiescent human SCs using the myobundle system. We present first-time application of scRNA-seq to engineered skeletal muscle, and uncover transcriptional descriptors of human myoblast deactivation and SC heterogeneity and activation. When utilizing human myobundles as a novel model of Pompe disease, we identified disease hallmarks and responses to therapy consistent with observations in Pompe patients. We anticipate that the findings and methods developed in this work will serve as a useful framework for the future engineering of regenerative human muscle for therapeutic and disease modeling applications.

In this thesis, a 3D microwave imaging method is developed for a microwave imaging system with an arbitrary background medium. In the previous study on the breast cancer detection of our research group, a full wave inverse method, the Diagonal Tensor approximation combined with Born Iterative Method (DTA-BIM), was proposed to reconstruct the electrical profile of the inversion domain in a homogenous background medium and a layered background medium. In order to evaluate the performance of the DTA-BIM method in a realistic microwave imaging system, an experimental prototype of an active 3D microwave imaging system with movable antennas is constructed. For the objects immersed in a homogenous background medium or a layered background medium, the inversion results based on the experimental data show that the resolution of the DTA-BIM method can reach finely to a quarter of wavelength of the background medium, and the system's signal-noise-ratio (SNR) requirement is 10 dB. Moreover, the defects of this system make it difficult to be implemented in a realistic application. Thus, another active 3D microwave imaging system is proposed to overcome the problems in the previous system. The new system employs a fix patch antenna array with electric switch to record the data. However, the antenna array makes the inversion system become a non-canonical inhomogeneous background. The analytical Greens' functions used in the original DTA-BIM method become unavailable. Thus, a modified DTA-BIM method, which use the numerical Green's functions combined with measured voltage, is proposed. This modified DTA-BIM method can be used to the inversion in a non-canonical inhomogeneous background with the measured voltages (or $S_{21}$ parameters). In order to verify the performance of this proposed inversion method, we investigate a prototype 3D microwave imaging system with a fix antenna array. The inversion results from the synthetic data show that this method works well with a fix antenna array, and the resolution of reconstructed images can reach to a quarter wavelength even in the presence of a strongly inhomogeneous background medium and antenna couplings. A time-reversal method is introduced as a pre-processing step to reduce the region of interest (ROI) in our inversion. In addition, a Multi-Domain DTA-BIM method is proposed to fit the discontinue inversion regions. With these improvements, the size of the inversion domain and the computational cost can be significantly reduced, and make the DTA-BIM method more feasible for rapid response applications.

Reasoning about 3D objects is one of the most critical perception problems robots face; outside of navigation, most interactions between a robot and its environment are object-centric. Object-centric robot perception has long relied on maintaining an explicit database of 3D object models with the assumption that encountered objects will be exact copies of entries in the database; however, as robots move into unstructured environments such as human homes, the variation of encountered objects increases and maintaining an explicit object database becomes infeasible. This thesis introduces a general-purpose 3D object representation that allows the joint estimation of a previously unencountered object's class, pose, and 3D shape---crucial foundational tasks for general robot perception.

We present the first method capable of performing all three of these tasks simultaneously, Bayesian Eigenobjects (BEOs), and show that it outperforms competing approaches which estimate only object shape and class given a known object pose. BEOs use an approximate Bayesian version of Principal Component Analysis to learn an explicit low-dimensional subspace containing the 3D shapes of objects of interest, which allows for efficient shape inference at high object resolutions. We then extend BEOs to produce Hybrid Bayesian Eigenobjects (HBEOs), a fusion of linear subspace methods with modern convolutional network approaches, enabling realtime inference from a single depth image. Because HBEOs use a Convolutional Network to project partially observed objects onto the learned subspace, they allow the object to be larger and more expressive without impacting the inductive power of the model. Experimentally, we show that HBEOs offer significantly improved performance on all tasks compared to their BEO predecessors. Finally, we leverage the explicit 3D shape estimate produced by BEOs to further extend the state-of-the-art in category level pose estimation by fusing probabilistic pose predictions with a silhouette-based reconstruction prior. We also illustrate the advantages of combining both probabilistic pose estimation and shape verification, via an ablation study, and show that both portions of the system contribute to its performance. Taken together, these methods comprise a significant step towards creating a general-purpose 3D perceptual foundation for robotics systems, upon which problem-specific systems may be built.

The majority of consumer electronic devices, electric vehicles, and aerospace electronics are powered by lithium ion batteries because of their high energy and power densities. Commercially available lithium ion batteries consist of electrodes, separators and current collectors fabricated in multilayer rolls that are packaged in cylindrical or rectangular cases. The size and shape of the package as well as the composition of the electrode has a significant impact on the battery life and design of the products they power. For example, the battery life and shape of portable electronics such as cell phones or laptops, is governed by the volume that is dedicated to the battery. In the case of electric vehicles, decreasing the size and weight of the battery while increasing capacity is an engineering challenge that affects vehicle range and cost. Therefore, the of my dissertation consists of the development of a novel 3D printable lithium ion battery nanocomposites and the integration of conductive metal nanomaterials into conventional lithium ion anodes. Here, we report the development of PLA-anode, cathode, and separator materials that enable 3D printing of complete lithium ion batteries with a low-cost FFF printer for the first time. The most common 3D printing polymer polylactic acid (PLA) is an insulator. However, our work demonstrates that 3D printed PLA can be infused with a mixture of ethyl methyl carbonate, propylene carbonate, and LiClO4 provides an ionic conductivity of 2.3 x 10−4 S cm−1 which is comparable to that of polymer and hybrid electrolytes (10−3 to 10−4 S cm−1). It was found that up to 12-30 volume % of solids, depending on the filler morphology, could be mixed into PLA without causing it to clog during 3D printing. It was also found that not only is electrical conductivity crucial to the performance of a 3D printed lithium ion battery, but efficient electrical contact to the active materials is as well. To that effect, we investigated the effect of aspect ratio of silver-copper core-shell nanowires on the performance enhancement of a commercially fabricated graphite lithium ion anodes. Currently, carbon is the most common conductive filler used in commercial lithium ion battery anodes. We hypothesize that a more conductive, high aspect ratio would improve the performance of a lithium ion battery. We examined the effect of exchanging carbon with CuAg nanowires as the conductive filler in graphite lithium ion batteries. We tested 4 different aspect ratios and found that not only does aspect ratio matter, diameter and length have profound effect on capacity and energy of the anode at the same volume percent as carbon conductive filler.

In surgical robotics, fully-automated tumor removal is an important topic and it includes three main tasks: tissue classification for cancer diagnosis, pathological mapping for tumor localization and tissue resection by using a laser scalpel. Generating a three-dimensional (3D) pathological tissue model with fully non-contact sensors can provide invaluable information to assist surgeons in decision-making and enable the use of surgical robots for efficient tissue manipulation. To collect the comprehensive information of a biological tissue target, robotic laser systems with complementary sensors (e.g., Optical coherence tomography (OCT) sensor, and stereovision) can play important roles in providing non-contact laser scalpels (i.e., cutting laser scalpel) for tissue removal, applying photonics-based sensors for pathological tissue classification (i.e., laser-based endogenous fluorescence), and aligning multi-sensing information to generate a 3D pathological map. However, there are three main challenges with integrating multiple laser-based sensors into the robotic laser system, which includes: 1) Modelling the laser beam transmission in 3D free-space to achieve accurate laser-tissue manipulation under geometric constraints, 2) Studying the complex physics of laser-tissue interaction for tissue differentiation and 3D shape modelling to ensure safe tissue removal, and 3) Integrating information from multiple sensing devices under sensor noise and uncertainties from system calibration.

Targeting these three research problems separately, a computational framework is proposed to provide kinematics and calibration algorithms to control and direct the 3D laser beam through a system with multiple rotary mirrors (to transmit laser beam in free-space) and laser-based sensor inputs. This framework can serve as a base platform for optics-based robotic system designs and solving the motion planning problems related to laser-based robot systems. Simulation experiments have verified the feasibility of the proposed framework and actual experiments have been conducted with an existing robotic laser system on phantom and ex-vivo biological tissues.

To study the complex physics of laser-tissue interaction, a 3D data-driven method is developed to model the geometric relation between the laser energy distribution, laser incident angles, and the tissue deformation resulting from photoablation. The results of the phantom studies have demonstrated the feasibility of applying the trained model for laser crater shape predictions during the surgical planning.

Finally, a research platform, referred as ``TumorMapping", is developed to collect multimodal sensing information from complementary sensors to build a 3D pathological map of a mice tumor surface. This robot system includes a sensor module attached to a 6-DOF robot arm end-effector, based on the laser-induced fluorescence spectroscopy for tissue classification and a fiber couple cutting laser for tissue resection. A benchtop sensor platform is built with an OCT sensor and a stereovision system with two lens camera to collect the tissue information with a non-contact pattern. The robot-sensor and the complementary sensor sub-systems are integrated in a unified platform for the 3D pathological map reconstruction.

In summary, the research contributions include important advancements in laser-based sensor fusion for surgical decision-making which is enabling new capabilities for the use of 3D pathological mapping combined with intelligent robot planning and control algorithms for robotic surgery.

Prostate cancer is the most common cancer and second-leading cause of cancer death among men in the United States. Early and accurate diagnosis of prostate cancer remains challenging; following an abnormal rectal exam or elevated levels of prostate-specific antigen in serum, clinical guidelines recommend transrectal ultrasound-guided biopsy. However, lesions are often indistinguishable from noncancerous prostate tissue in conventional B-mode ultrasound images, which have a diagnostic sensitivity of about 30%, so the biopsy is not typically targeted to suspicious regions. Instead, the biopsy systematically samples 12 pre-specified regions of the gland. Systematic sampling often fails to detect cancer during the first biopsy, and while multiparametric MRI (mpMRI) techniques have been developed to guide a targeted biopsy, fused with live ultrasound, this approach remains susceptible to registration errors, and is expensive and less accessible.

The goal of this work is to leverage ultrasound elasticity imaging methods, including acoustic radiation force impulse (ARFI) imaging and shear wave elasticity imaging (SWEI), to develop and optimize a robust 3-D elasticity imaging system for ultrasound-guided prostate biopsies and to quantify its performance in prostate cancer detection. Towards that goal, in this dissertation advanced techniques for generating ARFI and SWEI images are developed and evaluated, and a deep learning framework is explored for multiparametric ultrasound (mpUS) imaging, which combines data from different ultrasound-based modalities.

In Chapter 3, an algorithm is implemented that permits the simultaneous imaging of prostate cancer and zonal anatomy using both ARFI and SWEI. This combined sequence involves using closely spaced push beams across the lateral field of view, which enables the collection of higher signal-to-noise (SNR) shear wave data to reconstruct the SWEI volume than is typically acquired. Data from different push locations are combined using an estimated shear wave propagation time between push excitations to align arrival times, resulting in SWEI imaging of prostate cancer with high contrast-to-noise ratio (CNR), enhanced spatial resolution, and reduced reflection artifacts.

In Chapter 4, a fully convolutional neural network (CNN) is used for ARFI displacement estimation in the prostate. A novel method for generating ultrasound training data is described, in which synthetic 3-D displacement volumes with a combination of randomly seeded ellipsoids are used to displace scatterers, from which simulated ultrasonic imaging is performed. The trained network enables the visualization of in vivo prostate cancer and prostate anatomy, providing comparable performance with respect to both accuracy and speed compared to standard time delay estimation approaches.

Chapter 5 explores the application of deep learning for mpUS prostate cancer imaging by evaluating the use of a deep neural network (DNN) to generate an mpUS image volume from four ultrasound-based modalities for the detection of prostate cancer: ARFI, SWEI, quantitative ultrasound, and B-mode. The DNN, which was trained to maximize lesion CNR, outperforms the previous method of using a linear support vector machine to combine the input modalities, and generates mpUS image volumes that provide clear visualization of prostate cancer.

Chapter 6 presents the results of the first in vivo clinical trial that assesses the use of ARFI imaging for targeted prostate biopsy guidance in a single patient visit, comparing its performance with mpMRI-targeted biopsy and systematic sampling. The process of data acquisition, processing, and biopsy targeting is described. The study demonstrates the feasibility of using 3-D ARFI for guiding a targeted biopsy of the prostate, where it is most sensitive to higher-grade cancers. The findings also indicate the potential for using 2-D ARFI imaging to confirm target location during live B-mode imaging, which could improve existing ultrasonic fusion biopsy workflows.

Chapter 7 summarizes the research findings and considers potential directions for future research. By developing advanced ARFI and SWEI imaging techniques for imaging the prostate gland, and combining information from different ultrasound modalities, prostate cancer and zonal anatomy can be imaged with high contrast and resolution. The findings from this work suggest that ultrasound elasticity imaging holds great promise for facilitating image-guided targeted biopsies of clinically significant prostate cancer.

A 3D microwave imaging system suitable for clinical trials has been developed. The anatomy, histology, and pathology of breast cancer were all carefully considered in the development of this system. The central component of this system is a breast imaging chamber with an integrated 3D antenna array containing 36 custom designed bowtie patch antennas that radiate efficiently into human breast tissue. 3D full-wave finite element method models of this imaging chamber, complete with full antenna geometry, have been developed using Ansoft HFSS and verified experimentally. In addition, an electronic switching system using Gallium Arsenide (GaAs) absorptive RF multiplexer chips, a custom hardware control system with a parallel port interface utilizing TTL logic, and a custom software package with graphical user interface using Java and LabVIEW have all been developed. Finally, modeling of the breast (both healthy and malignant) was done using published data of the dielectric properties of human tissue, confirming the feasibility of cancer detection using this system.

Studies of skeletal variation form the basis of our understanding of our species’ history and diversity. This most commonly takes the form of comparative, between- species studies aimed at reconstructing phylogenetic relationships. However, studies of within-species variation can provide insights into relatedness at smaller scales, which can shed light on important microevolutionary processes and be used to identify closely related individuals in the absence of DNA. This dissertation assesses the relationship between genetic and skeletal variation within groups of related and unrelated Homo sapiens and Pan troglodytes, with the aim of better understanding the population sampled by the seemingly homogenous Homo naledi assemblage from the Dinaledi Chamber in Rising Star Cave, South Africa. The hypotheses tested were: 1) closely related individuals exhibit less skeletal variation compared to unrelated individuals, and 2) the Dinaledi assemblage exhibits less morphological variation than H. sapiens and P. troglodytes at the species level, more closely resembling a single population.

Skeletal variation within a subset of H. sapiens from the crypt of Christ Church in Spitalfields, London (17-19th centuries A.D) with documented genealogies and the Gombe population of P. t. schweinfurthii was compared to species-wide baselines and the H. naledi assemblage. The data consisted of 3D models of 556 postcranial elements (first metacarpal (Mc1), proximal femur, talus, calcaneus, and navicular) from 187 individuals that were generated from surface scanners or downloaded from MorphoSource.

This dissertation employed a relatively new 3D geometric morphometric workflow that captures the entire shape of an element through the use of an automated landmarking program and feature-aware registration process. Two hundred pseudolandmarks were automatically and optimally placed on each element. Following alignment, multiple univariate and multivariate statistical analyses were used to quantify shape variation within and between the three species, including distributions of Euclidean distances, Procrustes distances to the mean shapes, Principal Components Analyses (PCA), Between-Group PCA, and Discriminant Function Analyses. The coefficient of relationship was used to represent genetic distance between known genetic relatives within modern humans.

The results of the within-species analyses of skeletal variation support Hypothesis 1 in both modern humans and chimpanzees, though the signal of relatedness is differentially expressed within and across elements. In modern humans, the calcaneus can be used to distinguish known close relatives from distantly related and unrelated individuals. The navicular and femur were also found to be relatively good indicators of relatedness. Within chimpanzees, the talus is the most effective at distinguishing the Gombe population from the species-wide chimpanzee sample, followed by the calcaneus and femur. Within H. naledi, the talus varied the least while the navicular varied the most, though the high levels of variation found in the navicular and Mc1 are likely due to the state of preservation of these elements. The results of the interspecies analyses are more ambiguous. When considering the best preserved elements, the H. naledi talus varies the least within the three species, while the femur varies more than those of either H. sapiens or P. troglodytes at the species level. Hypothesis 2 is thus supported for the talus while rejected in the femur, suggesting that it cannot be rejected as a whole and that the patterns of homogeneity previously observed within H. naledi are more nuanced than previously recognized.

"Cognitive control" describes endogenous guidance of behavior in situations where routine stimulus-response associations are suboptimal for achieving a desired goal. The computational and neural mechanisms underlying this capacity remain poorly understood. The present dissertation examines recent advances stemming from the application of a statistical, Bayesian learner perspective on control processes. An important limitation in current models consists of a lack of a plausible mechanism for the flexible adjustment of control over variable environments. I propose that flexible cognitive control can be achieved by a Bayesian model with a self-adapting, volatility-driven learning scheme, which modulates dynamically the relative dependence on recent (short-term) and remote (long-term) experiences in its prediction of future control demand. Using simulation data, human behavioral data and human brain imaging data, I demonstrate that this Bayesian model does not only account for several classic behavioral phenomena observed from the cognitive control literature, but also facilitates a principled, model-guided investigation of the neural substrates underlying the flexible adjustment of cognitive control. Based on the results, I conclude that the proposed Bayesian model provides a feasible solution for modeling the flexible adjustment of cognitive control.

How did rock and roll, the best music for worshipping the devil, become the finest music for worshipping God? This study narrates the import of rock music into church sanctuaries across America via the rise of contemporary worship music (CWM). While white evangelicals derided rock n' roll as the "devil's music" in the 1950s, it slowly made its way into their churches and beyond over the next fifty years, emerging as a multi-million dollar industry by the twenty-first century.

This study is a cultural history of CWM, chronicling the rise of rock music in the worship life of American Christians. Pulling from several different primary and secondary sources, I argue that three main motivations fueled the rise of CWM in America: the desire to reach the lost, to commune in emotional intimacy with God, and to grow the flock. These three motivations evolved among different actors and movements at different times. In the 1970s, the Jesus People movement anchored in Southern California, adopted the music of the counterculture to attract hippies to church. In the early 1980s, the Vineyard Fellowship combined rock forms with lyrics that spoke of God in the second person in order to facilitate intimate worship with the divine. In the late 1980s, the church growth movement embraced CWM as a tool to attract disaffected baby boomers back to church. By the 1990s, these three motivations had begun to energize an entire industry built around the merger between rock and worship.

Severe constipation can be life-threatening and disproportionately affects patients who may not benefit from conventional treatments. Sacral nerve stimulation (SNS) is an alternative to laxatives and pharmaceuticals, and it modulates propulsive action in the colon. Conventional SNS failed to treat slow-transit constipation. I hypothesized bursts of nerve stimulation interleaved by quiescent periods increase colonic transit more effectively than continuous nerve stimulation. I electrically stimulated the colon directly in computational models and the isolated mouse colon to characterize properties of the colonic motor complex (CMC), and I used optical and fluorescent imaging, electromyography, and manometry to compare the effect of pelvic and sacral nerve stimulation on colonic motility. I developed a computational model of colonic motility and compared the effects of burst and conventional nerve stimulation on pellet velocity and colonic emptying under normal and slow transit conditions. Burst nerve stimulation evoked more frequent calcium and pressure waves, and increased fecal pellet output than continuous nerve stimulation in the isolated mouse colon, anesthetized rat, and computational model, respectively. Burst nerve stimulation with optimized burst frequency, duration, and interval more effectively produced prokinetic motility than continuous nerve stimulation, suggesting that burst SNS may be a viable clinical treatment for severe and slow transit constipation.

Based on a biopsychosocial model of mammography pain, the current study assessed if specific biological and psychosocial factors were associated with higher reported mammography pain in early stage breast cancer survivors. One hundred and twenty-seven women completed questionnaires assessing demographic information, cancer treatment history, ongoing breast pain, mammography-related anxiety, and social support immediately prior to receiving a mammogram. They then completed questionnaires assessing mammography pain and mammography-related pain catastrophizing immediately following the mammogram. Using path modeling and mediation analyses, relations among these variables were examined. Results revealed that mammography-related pain catastrophizing was related to higher mammography pain directly, while ongoing breast pain, lower social support quantity, and lower perceived quality of social support related to higher mammography pain indirectly through mammography-related pain catastrophizing. Moderated mediation analyses found that the mediation effects of mammography-related pain catastrophizing were significantly different at varying levels of perceived quality of social support, with more pronounced negative effects for those with higher quality support than those with lower quality support. The theoretical, clinical, and research implications of these findings are discussed.

Osteoporosis and low bone density affect an estimated 54 million adults of 50 years and over in the United States, resulting in $19 billion in costs for osteoporosis-related bone breaks. Current treatments include the use of antiresorptive and anabolic drugs to decrease the rate of bone resorption and increase the rate of bone formation, respectively. However, these current treatments are unable to completely normalize skeletal integrity. As bone diseases become increasingly prevalent, there is an urgent need to identify novel therapies to improve quality of life and reduce economic burden on the healthcare system.

To identify novel therapeutic targets, we must first begin to understand the cellular complexity of the bone marrow niche and how cellular function is regulated within the bone tissue. Bone-resident cells, such as skeletal progenitors and their descendants, are critically influenced by extrinsic signals derived from the local microenvironment. Previous studies have identified hypoxia as a key microenvironment factor in bone. Thus, the ability to target the hypoxic bone marrow niche presents an attractive and untapped potential for regenerative medicine.

Much of the work investigating the role of hypoxia and HIF signaling have focused on mature osteoblast and chondrocyte populations. In contrast, studies investigating the contribution of HIF signaling on skeletal progenitors and marrow adipocyte populations are scarce. In this dissertation, I investigate the role of hypoxia and HIF signaling in skeletal lineage cells, chiefly skeletal progenitor cells and marrow adipogenic lineage cells. Using cellular, genetic, and pharmacological-based approaches, I characterize the roles of HIF-1α and HIF-2α in both homeostatic and pathological contexts in the aforementioned cell populations.

First, I propose an optimized cell-based system to investigate the function of skeletal progenitors in vitro. Here, I highlight the limitations of current in vitro isolation techniques and introduce a relatively simple method of bone marrow stromal cell purification using hypoxia. Using this system, I assess how skeletal progenitors respond to hypoxic cues and interrogate skeletal progenitor cell differentiation and functional responses in my subsequent research. Next, using genetic and pharmacological approaches, I investigate the role of HIF-2α in bone formation following radiation-injury where I identify HIF-2α as a negative regulator of bone recovery. Additionally, with the assistance of my collaborators, I develop and characterize a bone-targeting nanocarrier to ameliorate radiation-induced bone loss. Lastly, I detail early work I conducted to investigate the role of HIF signaling in marrow adipogenic lineage cells. Here, I establish and characterize animal models to determine how hypoxia and HIF signaling influences adipogenic lineage commitment and expansion in an early and mature marrow adipogenic population.

In summary, this dissertation aims to expand our limited understanding on how the hypoxic bone microenvironment and HIF signaling regulate skeletal lineage cells in vivo, with a special focus on skeletal progenitor and marrow adipogenic populations. In terms of boarder impacts, understanding the signaling networks that regulate bone homeostasis and recovery processes will not only expand our basic understanding of the molecular mechanisms underlying skeletal development, but also provide novel insights for developing therapies to treat bone loss.

This dissertation focuses on a FFT based forward EM solver and its application in inverse problems. The main contributions of this work are two folded. On the one hand, it presents the first scaled lab experiment system in the oil and gas industry for through casing hydraulic fracture evaluation. This system is established to validate the feasibility of contrasts enhanced fractures evaluation. On the other hand, this work proposes a FFT based VIE solver for hydraulic fracture evaluation. This efficient solver is needed for numerical analysis of such problem. The solver is then generalized to accommodate scattering simulations for anisotropic inhomogeneous magnetodielectric objects. The inverse problem on anisotropic objects are also studied.

Before going into details of specific applications, some background knowledge is presented. This dissertation starts with an introduction to inverse problems. Then algorithms for forward and inverse problems are discussed. The discussion on forward problem focuses on the VIE formulation and a frequency domain solver. Discussion on inverse problems focuses on iterative methods.

The rest of the dissertation is organized by the two categories of inverse problems, namely the inverse source problem and the inverse scattering problem.

The inverse source problem is studied via an application in microelectronics. In this application, a FFT based inverse source solver is applied to process near field data obtained by near field scanners. Examples show that, with the help of this inverse source solver, the resolution of unknown current source images on a device under test is greatly improved. Due to the improvement in resolution, more flexibility is given to the near field scan system.

Both the forward and inverse solver for inverse scattering problems are studied in detail. As a forward solver for inverse scattering problems, a fast FFT based method for solving VIE of magnetodielectric objects with large electromagnetic contrasts are presented due to the increasing interest in contrasts enhanced full wave EM imaging. This newly developed VIE solver assigns different basis functions of different orders to expand flux densities and vector potentials. Thus, it is called the mixed ordered BCGS-FFT method. The mixed order BCGS-FFT method maintains benefits of high order basis functions for VIE while keeping correct boundary conditions for flux densities and vector potentials. Examples show that this method has an excellent performance on both isotropic and anisotropic objects with high contrasts. Examples also verify that this method is valid in both high and low frequencies. Based on the mixed order BCGS-FFT method, an inverse scattering solver for anisotropic objects is studied. The inverse solver is formulated and solved by the variational born iterative method. An example given in this section shows a successful inversion on an anisotropic magnetodielectric object.

Finally, a lab scale hydraulic fractures evaluation system for oil/gas reservoir based on previous discussed inverse solver is presented. This system has been setup to verify the numerical results obtained from previously described inverse solvers. These scaled experiments verify the accuracy of the forward solver as well as the performance of the inverse solver. Examples show that the inverse scattering model is able to evaluate contrasts enhanced hydraulic fractures in a shale formation. Furthermore, this system, for the first time in the oil and gas industry, verifies that hydraulic fractures can be imaged through a metallic casing.

In this work, a stabilized finite element framework is developed to simulate small and large deformation solid mechanics problems involving complex geometries and complicated constitutive models. In particular, the focus is on solid dynamics problems involving nearly and fully incompressible materials. The work is divided into three main themes, the first is concerned with the development of stabilized finite element algorithms for hyperelastic materials, the second handles the case of viscoelastic materials, and the third focuses on algorithms for J2-plastic materials. For all three cases, problems in the small and large deformation regime are considered, and for the J2-plasticity case, both quasi-static and dynamic problems are examined.

Some of the key features of the algorithms developed in this work is the simplicity of their implementation into an existing finite element code, and their applicability to problems involving complicated geometries. The former is achieved by using a mixed formulation of the solid mechanics equations where the velocity and pressure unknowns are represented by linear shape functions, whereas the latter is realized by using triangular elements which offer numerous advantages compared to quadrilaterals, when meshing complicated geometries. To achieve the stability of the algorithm, a new approach is proposed in which the variational multiscale approach is applied to the mixed form of the solid mechanics equations written down as a first order system, whereby the pressure equation is cast in rate form.

Through a series of numerical simulations, it is shown that the stability properties of the proposed algorithm is invariant to the constitutive model and the time integrator used. By running convergence tests, the algorithm is shown to be second order accurate, in the $L^2$-nrom, for the displacements, velocities, and pressure. Finally, the robustness of the algorithm is showcased by considering realistic test cases involving complicated geometries and very large deformation.

As vertebrate embryos grow and develop into adults, their organs must acquire mass and mature tissue architecture to maintain proper homeostasis. While juvenile growth encompasses a significant portion of life, relatively little is known about how individual cells proliferate, with respect to one another, to orchestrate this final maturation. For its simplicity and ease of genetic manipulations, the teleost zebrafish (Danio rerio) was used to understand how the proliferative outputs of individual cells generate an organ from embryogenesis into adulthood.

To define the proliferative outputs of individual cells, a multicolor clonal labeling approach was taken that visualized a large number of cardiomyocyte clones within the zebrafish heart. This Brainbow technique utilizes Cre-loxP mediated recombination to assign cells upwards of ~90 unique genetic tags. These tags are comprised of the differential expression of 3 fluorescent proteins, which combine to give rise to spectrally distinct colors that represent these genetic tags. Tagging of individual cardiomyocytes was induced early in development, when the wall of the cardiac ventricle is a single myocyte thick. Single cell cardiomyocyte clones within this layer expanded laterally in a developmentally plastic manner into patches of variable shapes and sizes as animals grew into juveniles. As maturation continued into adulthood, a new lineage of cortical muscle appeared at the base of the ventricle and enveloped the ventricle in a wave of proliferation that fortified the wall to make it several myocytes thick. This outer cortical layer was formed from a small number (~8) of dominant cortical myocyte clones that originated from trabecular myocytes. These trabecular myocytes were found to gain access to the ventricular surface through rare breaches within the single cell thick ventricular wall, before proliferating over the surface of the ventricle.

These results demonstrated an unappreciated dynamic juvenile remodeling event that generated the adult ventricular wall. During adult zebrafish heart regeneration, the primary source of regenerating cardiomyocytes stems from this outer wall of muscle. Regenerating cardiomyocytes within this outer layer of muscle are specifically marked by the cardiac transcription factor gene gata4, which they continue to express as they proliferate into the wound area.

Using heart regeneration to guide investigation of juvenile cortical layer formation, we found that both processes shared similar molecular and tissue specific responses including expression and requirement of gata4. Additional markers suggested that juvenile hearts were under stress and that this stress could play a role to initiate cortical morphogenesis. Indeed, experimental injury or a physiologic increase in stress to juvenile hearts caused the ectopic appearance of cortical muscle, demonstrating that injury could trigger premature morphogenesis.

These studies detail the cardiomyocyte proliferative events that shape the heart and identify molecular parallels that exist between regeneration and cortical layer formation. They show that adult zebrafish heart regeneration utilizes an injury/stress responsive program that was first used to remodel the heart during juvenile growth.

The advent of new genomic approaches, particularly next generation sequencing (NGS) has resulted in explosive growth of biological data. As the size of biological data keeps growing at exponential rates, new methods for data management and data processing are becoming essential in bioinformatics and computational biology. Indeed, data analysis has now become the central challenge in genomics.

NGS has provided rich tools for defining genomic alterations that cause cancer. The processing time and computing requirements have now become a serious bottleneck to the characterization and analysis of these genomic alterations. Moreover, as the adoption of NGS continues to increase, the computing power required often exceeds what any single institution can provide, leading to major restraints in the type and number of analyses that can be performed.

Cloud computing represents a potential solution to this problem. On a cloud platform, computing resources can be available on-demand, thus allowing users to implement scalable and highly parallel methods. However, few centralized frameworks exist to allow the average researcher the ability to apply bioinformatics workflows using cloud resources. Moreover, bioinformatics approaches are associated with multiple processing challenges, such as the variability in the methods or data used and the reproducibility requirements of the research analysis.

Here, we present CloudConductor, a software system that is specifically designed to harness the power of cloud computing to perform complex analysis pipelines on large biological datasets. CloudConductor was designed with five central features in mind: scalability, modularity, parallelism, reproducibility and platform agnosticism.

We demonstrate the processing power afforded by CloudConductor on a real-world genomics problem. Using CloudConductor, we processed and analyzed 101 whole genome tumor-normal paired samples from Burkitt lymphoma subtypes to identify novel genomic alterations. We identified a total of 72 driver genes associated with the disease. Somatic events were identified in both coding and non-coding regions of nearly all driver genes, notably in genes IGLL5, BACH2, SIN3A, and DNMT1. We have developed the analysis framework by implementing a graphical user interface, a back-end database system, a data loader and a workflow management system.

In this thesis, we develop the concepts and describe an implementation of automated cloud-based infrastructure to analyze genomics data, creating a fast and efficient analysis resource for genomics researchers.