A Tissue-Engineered Microvascular System to Evaluate Vascular Progenitor Cells for Angiogenic Therapies

The ability of tissue engineered constructs to replace diseased or damaged organs is limited without the incorporation of a functional vascular system. To design microvasculature that recapitulates the vascular niche functions for each tissue in the body, we investigated the following hypotheses: (1) cocultures of human umbilical cord blood-derived endothelial progenitor cells (hCB-EPCs) with mural cells can produce the microenvironmental cues necessary to support physiological microvessel formation in vitro; (2) poly(ethylene glycol) (PEG) hydrogel systems can support 3D microvessel formation by hCB-EPCs in coculture with mural cells; (3) mesenchymal cells, derived from either umbilical cord blood (MPCs) or bone marrow (MSCs), can serve as mural cells upon coculture with hCB-EPCs. Coculture ratios between 0.2 (16,000 cells/cm2) and 0.6 (48,000 cells/cm2) of hCB-EPCs plated upon 3.3 µg/ml of fibronectin-coated tissue culture plastic with (80,000 cells/cm2) of human aortic smooth muscle cells (SMCs), results in robust microvessel structures observable for several weeks in vitro. Endothelial basal media (EBM-2, Lonza) with 9% v/v fetal bovine serum (FBS) could support viability of both hCB-EPCs and SMCs. Coculture spatial arrangement of hCB-EPCs and SMCs significantly affected network formation with mixed systems showing greater connectivity and increased solution levels of angiogenic cytokines than lamellar systems. We extended this model into a 3D system by encapsulation of a 1 to 1 ratio of hCB-EPC and SMCs (30,000 cells/µl) within hydrogels of PEG-conjugated RGDS adhesive peptide (3.5 mM) and PEG-conjugated protease sensitive peptide (6 mM). Robust hCB-EPC microvessels formed within the gel with invasion up to 150 µm depths and parameters of total tubule length (12 mm/mm2), branch points (127/mm2), and average tubule thickness (27 µm). 3D hCB-EPC microvessels showed quiescence of hCB-EPCs (<1% proliferating cells), lumen formation, expression of EC proteins connexin 32 and VE-cadherin, eNOS, basement membrane formation by collagen IV and laminin, and perivascular investment of PDGFR-β+/α-SMA+ cells. MPCs present in <15% of isolations displayed >98% expression for mural markers PDGFR-β, α-SMA, NG2 and supported hCB-EPC by day 14 of coculture with total tubule lengths near 12 mm/mm2. hCB-EPCs cocultured with MSCs underwent cell loss by day 10 with a 4-fold reduction in CD31/PECAM+ cells, in comparison to controls of hCB-EPCs in SMC coculture. Changing the coculture media to endothelial growth media (EBM-2 + 2% v/v FBS + EGM-2 supplement containing VEGF, FGF-2, EGF, hydrocortisone, IGF-1, ascorbic acid, and heparin), promoted stable hCB-EPC network formation in MSC cocultures over 2 weeks in vitro, with total segment length per image area of 9 mm/mm2. Taken together, these findings demonstrate a tissue engineered system that can be utilized to evaluate vascular progenitor cells for angiogenic therapies.

Real-Time and Data-Driven Operation Optimization and Knowledge Discovery for an Enterprise Information System

An enterprise information system (EIS) is an integrated data-applications platform characterized by diverse, heterogeneous, and distributed data sources. For many enterprises, a number of business processes still depend heavily on static rule-based methods and extensive human expertise. Enterprises are faced with the need for optimizing operation scheduling, improving resource utilization, discovering useful knowledge, and making data-driven decisions.

This thesis research is focused on real-time optimization and knowledge discovery that addresses workflow optimization, resource allocation, as well as data-driven predictions of process-execution times, order fulfillment, and enterprise service-level performance. In contrast to prior work on data analytics techniques for enterprise performance optimization, the emphasis here is on realizing scalable and real-time enterprise intelligence based on a combination of heterogeneous system simulation, combinatorial optimization, machine-learning algorithms, and statistical methods.

On-demand digital-print service is a representative enterprise requiring a powerful EIS.We use real-life data from Reischling Press, Inc. (RPI), a digit-print-service provider (PSP), to evaluate our optimization algorithms.

In order to handle the increase in volume and diversity of demands, we first present a high-performance, scalable, and real-time production scheduling algorithm for production automation based on an incremental genetic algorithm (IGA). The objective of this algorithm is to optimize the order dispatching sequence and balance resource utilization. Compared to prior work, this solution is scalable for a high volume of orders and it provides fast scheduling solutions for orders that require complex fulfillment procedures. Experimental results highlight its potential benefit in reducing production inefficiencies and enhancing the productivity of an enterprise.

We next discuss analysis and prediction of different attributes involved in hierarchical components of an enterprise. We start from a study of the fundamental processes related to real-time prediction. Our process-execution time and process status prediction models integrate statistical methods with machine-learning algorithms. In addition to improved prediction accuracy compared to stand-alone machine-learning algorithms, it also performs a probabilistic estimation of the predicted status. An order generally consists of multiple series and parallel processes. We next introduce an order-fulfillment prediction model that combines advantages of multiple classification models by incorporating flexible decision-integration mechanisms. Experimental results show that adopting due dates recommended by the model can significantly reduce enterprise late-delivery ratio. Finally, we investigate service-level attributes that reflect the overall performance of an enterprise. We analyze and decompose time-series data into different components according to their hierarchical periodic nature, perform correlation analysis,

and develop univariate prediction models for each component as well as multivariate models for correlated components. Predictions for the original time series are aggregated from the predictions of its components. In addition to a significant increase in mid-term prediction accuracy, this distributed modeling strategy also improves short-term time-series prediction accuracy.

In summary, this thesis research has led to a set of characterization, optimization, and prediction tools for an EIS to derive insightful knowledge from data and use them as guidance for production management. It is expected to provide solutions for enterprises to increase reconfigurability, accomplish more automated procedures, and obtain data-driven recommendations or effective decisions.

Investigation and Development of a Fully 3D Tilt Capable Hybrid SPECT - CT System for Dedicated Breast Imaging

X-ray mammography has been the gold standard for breast imaging for decades, despite the significant limitations posed by the two dimensional (2D) image acquisitions. Difficulty in diagnosing lesions close to the chest wall and axilla, high amount of structural overlap and patient discomfort due to compression are only some of these limitations. To overcome these drawbacks, three dimensional (3D) breast imaging modalities have been developed including dual modality single photon emission computed tomography (SPECT) and computed tomography (CT) systems. This thesis focuses on the development and integration of the next generation of such a device for dedicated breast imaging. The goals of this dissertation work are to: [1] understand and characterize any effects of fully 3-D trajectories on reconstructed image scatter correction, absorbed dose and Hounsifeld Unit accuracy, and [2] design, develop and implement the fully flexible, third generation hybrid SPECT-CT system capable of traversing complex 3D orbits about a pendant breast volume, without interference from the other. Such a system would overcome artifacts resulting from incompletely sampled divergent cone beam imaging schemes and allow imaging closer to the chest wall, which other systems currently under research and development elsewhere cannot achieve.

The dependence of x-ray scatter radiation on object shape, size, material composition and the CT acquisition trajectory, was investigated with a well-established beam stop array (BSA) scatter correction method. While the 2D scatter to primary ratio (SPR) was the main metric used to characterize total system scatter, a new metric called ‘normalized scatter contribution’ was developed to compare the results of scatter correction on 3D reconstructed volumes. Scatter estimation studies were undertaken with a sinusoidal saddle (±15° polar tilt) orbit and a traditional circular (AZOR) orbit. Clinical studies to acquire data for scatter correction were used to evaluate the 2D SPR on a small set of patients scanned with the AZOR orbit. Clinical SPR results showed clear dependence of scatter on breast composition and glandular tissue distribution, otherwise consistent with the overall phantom-based size and density measurements. Additionally, SPR dependence was also observed on the acquisition trajectory where 2D scatter increased with an increase in the polar tilt angle of the system.

The dose delivered by any imaging system is of primary importance from the patient’s point of view, and therefore trajectory related differences in the dose distribution in a target volume were evaluated. Monte Carlo simulations as well as physical measurements using radiochromic film were undertaken using saddle and AZOR orbits. Results illustrated that both orbits deliver comparable dose to the target volume, and only slightly differ in distribution within the volume. Simulations and measurements showed similar results, and all measured dose values were within the standard screening mammography-specific, 6 mGy dose limit, which is used as a benchmark for dose comparisons.

Hounsfield Units (HU) are used clinically in differentiating tissue types in a reconstructed CT image, and therefore the HU accuracy of a system is very important, especially when using non-traditional trajectories. Uniform phantoms filled with various uniform density fluids were used to investigate differences in HU accuracy between saddle and AZOR orbits. Results illustrate the considerably better performance of the saddle orbit, especially close to the chest and nipple region of what would clinically be a pedant breast volume. The AZOR orbit causes shading artifacts near the nipple, due to insufficient sampling, rendering a major portion of the scanned phantom unusable, whereas the saddle orbit performs exceptionally well and provides a tighter distribution of HU values in reconstructed volumes.

Finally, the third generation, fully-suspended SPECT-CT system was designed in and developed in our lab. A novel mechanical method using a linear motor was developed for tilting the CT system. A new x-ray source and a custom made 40 x 30 cm2 detector were integrated on to this system. The SPECT system was nested, in the center of the gantry, orthogonal to the CT source-detector pair. The SPECT system tilts on a goniometer, and the newly developed CT tilting mechanism allows ±15° maximum polar tilting of the CT system. The entire gantry is mounted on a rotation stage, allowing complex arbitrary trajectories for each system, without interference from the other, while having a common field of view. This hybrid system shows potential to be used clinically as a diagnostic tool for dedicated breast imaging.