The development of the Flower Constellation theory offers an expanded framework to utilize constellations of satellites for tangible interests. To realize the full potential of this theory, the beta version of Bouquet was developed as a practical computer application that visualizes and edits Flower Constellations in a user-friendly manner. Programmed using C++ and OpenGL within the Qt software development environment for use on Windows systems, this initial version of Bouquet is capable of visualizing numerous user defined satellites in both 3D and 2D, and plot trajectories corresponding to arbitrary coordinate frames. The ultimate goal of Bouquet is to provide a viable open source alternative to commercial satellite orbit analysis programs. As such, the coding of Bouquet puts heavy emphasis on flexibility, upgradability and methods to provide continued support through open source collaboration.

The similarity between two shapes is typically calculated by measuring how well the properties and the spatial organization of the primitives forming the shapes agree. But, when this calculations are done independent from the context, i.e. the whole set of shapes in the experiments, a priori significance to the primitives is assigned, which may cause problematic similarity measures. A possible way of using context information in similarity measure between shape A and shape B is using the category information of shape B in calculations. In this study, shapes are represented as depth-1 shape trees and the dissimilarity between two shapes is computed by using an approximate tree matching algorithm. The category information is created as the union of shape trees that are in the same category and this information guides the matching process between a query shape and a shape whose category is known.

Rendering large terrains on large screens at interactive frame rates is a challenging area of computer graphics. In the last decade, real-time terrain rendering on large screens played a significant role in various simulations and virtual reality systems. To fulfill the demand of these systems, two software tools are developed. The first tool is a Terrain Editor that creates and manipulates large terrains. The second is a Multi-Display Viewer that displays the created terrains on multiple screens. Since the typical large terrains consist of many polygons, graphics boards might have difficulties in rendering the terrain at interactive frame rates. The common solution to this problem is to use terrain simplification without losing image quality. To this purpose, in this study, a paged level of detail mechanism that works with multiple threads is developed and integrated on multiple screen display systems to increase the performance of the high resolution systems.

An efficient method to compute local density-based outliers in high dimensional data was proposed. In our work, we have shown that this type of outlier is present even in any subset of the dataset. This property is used to partition the data set into random subsets to compute the outliers locally. The outliers are then combined from different subsets. Therefore, the local density-based outliers can be computed efficiently. Another challenge in outlier detection in high dimensional data is that the outliers are often suppressed when the majority of dimensions do not exhibit outliers. The contribution of this work is to introduce a filtering method whereby outlier scores are computed in sub-dimensions. The low sub-dimensional scores are filtered out and the high scores are aggregated into the final score. This aggregation with filtering eliminates the effect of accumulating delta deviations in multiple dimensions. Therefore, the outliers are identified correctly. In some cases, the set of outliers that form micro patterns are more interesting than individual outliers. These micro patterns are considered anomalous with respect to the dominant patterns in the dataset. In the area of anomalous pattern detection, there are two challenges. The first challenge is that the anomalous patterns are often overlooked by the dominant patterns using the existing clustering techniques. A common approach is to cluster the dataset using the k-nearest neighbor algorithm. The contribution of this work is to introduce the adaptive nearest neighbor and the concept of dual-neighbor to detect micro patterns more accurately. The next challenge is to compute the anomalous patterns very fast. Our contribution is to compute the patterns based on the correlation between the attributes. The correlation implies that the data can be partitioned into groups based on each attribute to learn the candidate patterns within the groups. Thus, a feature-based method is developed that can compute these patterns efficiently.

This dissertation evaluates the following thesis statement: Program analysis techniques can enable and support the debugging of failures in widely-used applications by (1) capturing, replaying, and, as much as possible, anonymizing failing executions and (2) highlighting subsets of failure-inducing inputs that are likely to be helpful for debugging such failures. To investigate this thesis, I developed techniques for recording, minimizing, and replaying executions captured from users' machines, anonymizing execution recordings, and automatically identifying failure-relevant inputs. I then performed experiments to evaluate the techniques in realistic scenarios using real applications and real failures. The results of these experiments demonstrate that the techniques can reduce the cost and difficulty of debugging.

Thesis (Ed. D.)--West Virginia University, 2003. / Title from document title page. Document formatted into pages; contains iv, 73 p. + HTML presentation (ports., facsims.). Includes an HTML document: A lecture on John Brown by "Porte Crayon" alias David Hunter Strother, an annotated edition with primary materials by Hilary Attfield. Includes abstract. Includes bibliographical references.

Thesis (Ph. D.)--University of Washington, 2003. / Vita. Includes bibliographical references (leaves 117-127).

Mutational analyses of cancer genomes have identified novel candidate cancer genes with hitherto unknown function in cancer. To enable phenotyping of mutations in such genes, we have developed a scalable technology for gene knock-in and knock-out in human somatic cells based on recombination-mediated construct generation and a computational tool to design gene targeting constructs. Using this technology, we have generated somatic cell knock-outs of the putative cancer genes ZBED6 and DIP2C in human colorectal cancer cells. In ZBED6-/- cells complete loss of functional ZBED6 was validated and loss of ZBED6 induced the expression of IGF2. Whole transcriptome and ChIP-seq analyses revealed relative enrichment of ZBED6 binding sites at upregulated genes as compared to downregulated genes. The functional annotation of differentially expressed genes revealed enrichment of genes related to cell cycle and cell proliferation and the transcriptional modulator ZBED6 affected the cell growth and cell cycle of human colorectal cancer cells. In DIP2C-/-cells, transcriptome sequencing revealed 780 differentially expressed genes as compared to their parental cells including the tumour suppressor gene CDKN2A. The DIP2C regulated genes belonged to several cancer related processes such as angiogenesis, cell structure and motility. The DIP2C-/-cells were enlarged and grew slower than their parental cells. To be able to directly compare the phenotypes of mutant KRAS and BRAF in colorectal cancers, we have introduced a KRASG13D allele in RKO BRAFV600E/-/-/ cells. The expression of the mutant KRAS allele was confirmed and anchorage independent growth was restored in KRASG13D cells. The differentially expressed genes both in BRAF and KRAS mutant cells included ERBB, TGFB and histone modification pathways. Together, the isogenic model systems presented here can provide insights to known and novel cancer pathways and can be used for drug discovery.

Recent advances in genome engineering technologies based on the CRISPR-associated RNA-guided endonuclease Cas9 are enabling the systematic interrogation of genome function. Analogous to the search function in modern word processors, Cas9 can be guided to specific locations within complex genomes by a short RNA search string. Using this system, DNA sequences within the endogenous genome and their functional outputs are now easily edited or modulated in virtually any organism of choice. Cas9-mediated genetic perturbation is simple and scalable, empowering researchers to elucidate the functional organization of the genome at the systems level and establish causal linkages between genetic variations and biological phenotypes. To facilitate successful and specific Cas9 targeting, we first optimize the guide RNAs (sgRNA) to significantly enhance gene editing efficiency and consistency. We also systematically characterize Cas9 targeting specificity in human cells to inform the selection of target sites and avoid off-target mutagenesis. We find that SpCas9 tolerates mismatches between guide RNA and target DNA at different positions in a sequence-dependent manner, sensitive to the number, position and distribution of mismatches. We also show that Cas9-mediated cleavage is unaffected by DNA methylation and that the dosage of Cas9 and sgRNA can be titrated to minimize off-target modification. Additionally, we provide a web-based software tool to guide the selection and validation of target sequences as well as off-target analyses. We next demonstrate that Cas9 nickase mutants can be used with paired guide RNAs to introduce targeted double-strand breaks. Because individual nicks in the genome are repaired with high fidelity, simultaneous nicking via appropriately offset guide RNAs can reduce off-target activity by over 1,500-fold in human cells. In collaboration with researchers at the University of Tokyo, we further identified a PAM-interacting domain of the Cas9 nuclease that dictates Cas9 target recognition specificity. Finally, we present protocols that provide experimentally derived guidelines for the selection of target sites, evaluation of cleavage efficiency and analysis of off-target activity. Beginning with target design, gene modifications can be achieved within as little as 1-2 weeks. Taken together, this work enables a variety of genome engineering applications from basic biology to biotechnology and medicine.

Research in Software Product Line Engineering (SPLE) traditionally focuses on product derivation. Prior work has explored the automated derivation of products by module composition. However, it has so far neglected propagating changes (edits) in a product back to the product line definition. A domain-specific product should be possible to update its features locally, and later these changes should be propagated back to the product line definition automatically. Otherwise, the entire product line has to be revised manually in order to make the changes permanent. Although this is the current state, it is a very error-prone process. To address these issues, we present a tool called Paan to create product lines of MS Word documents with back-propagation support. It is a diff-based tool that ignores unchanged fragments and reveals fragments that are changed, added or deleted. Paan takes a document with variation points (VPs) as input, and shreds it into building blocks called tiles. Only those tiles that are new or have changed must be updated in the tile repository. In this way, changes in composed documents can be back-propagated to their original feature module definitions. A document is synthesized by retrieving the appropriate tiles and composing them. / text