The objective of this study is to quantify the polycyclic aromatic hydrocarbons (PAHs) of the sediments along the coast of south-western Taiwan and to investigate possible sources. The south-western coastal area is full of heavy industries. There are tens of industrial parks along with export processing zone, steel-making plant, oil refinery, shipbuilding plant, Hsin-ta pelagic fishing port, Hsin-ta thermal power plant and Kaohsiung international port. It is reasonable that the wastewater went into rivers or pipes and eventually enters coast area. Concentrations of total polycyclic aromatic hydrocarbons (PAHs) in the sediments of south-western Taiwan were between 197.3~2498.9 (ng/g ¡Vdw) with the average of 912.0 (ng/g ¡V dw). Among all stations sampled in this study, A3, located at the waterway of Kaohsiung, has the highest value. The wastewater ejected by ships might be the major factor. The second highest value we got is from C2, which located at the area farer from shoreline than A3. Total organic carbon (TOC) versus median particle size has linear relationship, however, total organic carbon (TOC) versus concentrations of polycyclic aromatic hydrocarbons (PAHs) showed no linear relationship. This is probably due to the wide sampling area of this study and sources of polycyclic aromatic hydrocarbons (PAHs) were not the same. Principal component analysis shows three principal components were extracted and up to 78.79% of total variance can be explained. As indicated on rotated loadings, the three major components were characterized by 4-6 rings PAH compounds, 3-4 rings PAH compounds and 2-5 rings PAHs compounds, respectively. Results of Hierachical cluster analysis also show three major groups (Group 1, 2 and 3) can be geographically related. In addition, according to isomer ratios of PAH compounds, pollution sources of each group can be specified. Group 1, stations located northern bound, has mainly pyrogenic pollution sources, while Group2, stations located at central area of this study, has pyrogenic/petrogenic sources. For Group 3, stations located southern bound, was mainly polluted by petroleum. In regards to the sediment quality guidelines, ERL and ERM, there are one third of stations exceed ERL regarding to Acp and Phe, but only station A3 exceeds ERL regarding to F1 and Pyr.

This thesis presents an efficient and biologically informed 3D human body morphing technique through data-driven alteration of standardized 3D models. The anthropometric data is derived from a large empirical database and processed using principal component analysis (PCA). Although techniques using PCA are relatively commonplace in computer graphics, they are mainly used for scientific visualizations and animation. Here we focus on uncovering the underlying mathematical structure of anthropometric data and using it to build an intuitive interface that allows the interactive manipulation of body shape within the normal range of human variation. We achieve weight/gender based body morphing by using PCA. First we calculate the principal vector space of the original data. The data then are transformed into a new orthogonal multidimensional space. Next, we reduce the dimension of the data by only keeping the components of the most significant principal vectors. We then fit a curve through the original data points and are able to generate a new human body shape by inversely transforming the data from principal vector space back to the original measuring data space. Finally, we sort the original data by the body weight, calculating males and females separately. This enables us to use weight and gender as two intuitive controls for body morphing. The Deformer program is implemented using the programming language C++ with OPENGL and FLTK API. 3D and human body models are created using Alias MayaTm.

Integrated circuits manufactured in current technology consist of millions of transistors with dimensions shrinking into the nanometer range. These small transistors have quiescent (leakage) currents that are increasingly sensitive to process variations, which have increased the variation in good-chip quiescent current and consequently reduced the effectiveness of IDDQ testing. This research proposes the use of a multivariate statistical technique known as principal component analysis for the purpose of variance reduction. Outlier analysis is applied to the reduced leakage current values as well as the good chip leakage current estimate, to identify defective chips. The proposed idea is evaluated using IDDQ values from multiple wafers of an industrial chip fabricated in 130 nm technology. It is shown that the proposed method achieves significant variance reduction and identifies many outliers that escape identification by other established techniques. For example, it identifies many of the absolute outliers in bad neighborhoods, which are not detected by Nearest Neighbor Residual and Nearest Current Ratio. It also identifies many of the spatial outliers that pass when using Current Ratio. The proposed method also identifies both active and passive defects.

Gene transcription rates are influenced by proteins, known as Transcription Factors (TFs), that interact with DNA. The locations of TFs on the genome directly influence gene expression and the functional characteristics of a cell. TF binding locations can be estimated for entire genomes using high throughput chromatin immunoprecipitation sequencing (ChIP-Seq). While the analysis of ChIP-Seq binding locations is standardized for a single experiment, complications arise when data sets, taken from different labs and experimental conditions, are combined. In this thesis, I present my method for the simultaneous comparison of multiple ChIP-Seq data sets. My method of comparing multiple ChIP-Seq data sets extends the analysis of a single data set through the addition of two stages, a combination stage, and an extraction stage. Typically, one of two approaches are used to combine information from multiple datasets. Either estimated binding sites are extracted from each dataset and then combined (e.g. by various intersections or unions) or the "raw" genomic signals are analyzed by clustering or dimensionality reduction methods. Both approaches have strengths, but also substantial drawbacks. The method presented here relies both on estimating the binding sites and comparing the “raw” genomic signals between data sets. Once the binding locations have been found, the first step in the combination stage is to define an alternate feature space (AFS). The AFS is the union of all binding locations determined for all data sets. The AFS represents a subset of the genome that is likely to have TF binding in any condition where the protein is active. Once the AFS is defined, the read density is determined from the “raw” genomic signal of each of the data sets. The density is determined for all locations in the AFS resulting in a unified density matrix (UDM). The UDM is the final product of the combination stage of the analysis. After the data sets are homogenized into the UDM, the extraction stage is applied to the matrix. The extraction stage consists of applying machine learning techniques and other methods used to analyze the “raw” genomic signal, to help elucidate underlying similarities and differences between the data sets. I applied this method to the binding locations of the TF TAL1 across 22 ChIP-Seq data sets from the hematopoietic and endothelial lineages. Once the UDM had been generated and normalized, using quantile normalization, hierarchical clustering and principle component analysis (PCA) were applied. Clusters, formed by hematopoietic stem cells (HSCs), Erythroid, and T-cell acute lymphoblastic leukemia (T-ALL), were found using hierarchical clustering. The principle components (PCs) of the UDM provided weights for each peak. Using those weights I could separate groups of cellular conditions including T-ALL, Erythroid, HSC, and Endothelial Colony Forming Cells (ECFCs.) The weights also provided a quantitative measure of importance for each peak in the AFS based on how much weight they provided towards the group of interest. Functional analysis techniques, including de novo motif search and Gene Ontology, were applied to the peak partitions defined using the PCs. Motifs that were enriched in the T-ALL TAL1 partition, and not the Erythroid, were annotated and found to be similar to those that had previously been published, including Runx1 motif and a preference for the CC Ebox (CACCTG). In addition to finding the CC Ebox in T-ALL, I also show that it does not form a composite motif with GATA, indicating an alternative mechanism for the binding of TAL1 in T-ALL. This thesis establishes that heterogeneous collections of ChIP-Seq datasets, from multiple labs and experimental conditions, can be meaningfully combined, and provides an algorithmic template for doing so.

Parametric methods are effective and appropriate when data sets are obtained by well-defined random sampling procedures, the population distribution for responses is well-defined, the null sampling distributions of suitable test statistics do not depend on any unknown entity and well-defined likelihood models are provided for by nuisance parameters. Permutation testing methods, on the other hand, are appropriate and unavoidable when distribution models for responses are not well specified, nonparametric or depend on too many nuisance parameters; when ancillary statistics in well-specified distributional models have a strong influence on inferential results or are confounded with other nuisance entities; when the sample sizes are less than the number of parameters and when data sets are obtained by ill-specified selection-bias procedures. In addition, permutation tests are useful not only when parametric tests are not possible, but also when more importance needs to be given to the observed data set, than to the population model, as is typical for example in biostatistics. The different types of permutation methods for analysis of variance, multiple linear regression and principal component analysis are explored. More specifically, one-way, twoway and three-way ANOVA permutation strategies will be discussed. Approximate and exact permutation tests for the significance of one or more regression coefficients in a multiple linear regression model will be explained next, and lastly, the use of permutation tests used as a means to validate and confirm the results obtained from the exploratory PCA will be described. / Dissertation (MSc)--University of Pretoria, 2012. / Statistics / unrestricted

A comprehensive understanding of sex-specific gait patterns throughout the lifespan is important considering differences between males and females that can manifest biomechanically, and epidemiological evidence of female sex being a risk factor for some age-related pathologies such as osteoarthritis. This thesis aimed to, 1) characterize the differences in lower extremity joint kinematics and kinetics during gait between healthy women and men in different age groups, and 2) define salient inter-joint kinematic coordination strategies in healthy adult gait. Gait data from 154 asymptomatic adult participants was analyzed. Waveform principal component analysis (PCA) was applied to hip, knee and ankle joint angles and net external moments to extract major patterns of variability. Using a two-factor ANOVA, PC scores were examined for significant sex, age and interaction effects. A second series of PCA models were also developed with the PC scores of the kinematic features of each joint to model the inter-joint kinematic coordination. Demographics, anthropometrics and root mean square (RMS) of EMG waveforms for the high and low groups (85th and 15th percentile) of the retained kinematic strategies were statistically compared using a one-way ANOVA analysis. 13 PC features differed between healthy male and female gait patterns and were independent of age category. No PC features significantly differed between the age groups, and there was no significant sex by age interactions. Four different kinematic gait coordination strategies were identified, one with a significant sex-effect. Therefore, both analyses supported sex-differences in gait biomechanics and the importance of using sex-specific normative data in clinical gait studies. Additionally, the results suggest underlying kinematic differences within asymptomatic adults are concentrated in the patterns of their gait mechanics. Understanding how these strategies may link to susceptibility of injury and disease has important implications for patient-centered care and may provide important insight into age-related pathology and disease. / Thesis / Master of Applied Science (MASc) / Since variability in healthy walking gait strategies may provide evidence for early mobility decline, this thesis aimed to identify the primary walking gait strategies in a healthy adult population. This work is distinct from previous work in that it comprehensively investigates the influence of sex and age on walking gait features and simultaneously defines primary walking gait strategies in healthy adults. The results indicate an overall difference in walking strategy between healthy male and female adults but no significant differences with age, indicating that age-matching for gait studies using adult controls is not as critical as sex considerations. Additionally, the results suggest that gait differences within healthy adults are concentrated in the patterns of their gait mechanics. Understanding how these strategies may link to susceptibility of injury and disease may provide important insight into age-related mobility limitations and help improve mobility longevity in the aging population.

No description available.

Principal Components Analysis (PCA) is a statistical procedure commonly used for the purpose of analyzing high dimensional data. It is often used for dimensionality reduction, which is accomplished by determining orthogonal components that contribute most to the underlying variance of the data. While PCA is widely used for identifying patterns and capturing variability of data in lower dimensions, it has some known limitations. In particular, PCA represents its results as linear combinations of data attributes. PCA is therefore, often seen as difficult to interpret and because of the underlying optimization problem that is being solved it is not robust to outliers. In this thesis, we examine extensions to PCA that address these limitations. Specific techniques researched in this thesis include variations of Robust and Sparse PCA as well as novel combinations of these two methods which result in a structured low-rank approximation that is robust to outliers. Our work is inspired by the well known machine learning methods of Least Absolute Shrinkage and Selection Operator (LASSO) as well as pointwise and group matrix norms. Practical applications including robust and non-linear methods for anomaly detection in Domain Name System network data as well as interpretable feature selection with respect to a website classification problem are discussed along with implementation details and techniques for analysis of regularization parameters.

The focus of this research project is the complex of infraspecific taxa that make up the crisp-leaf buckwheat species Eriogonum corymbosum (Polygonaceae), which is distributed widely across southwestern North America. This complex provides an ideal taxonomic group for research into population relationships and speciation. To avoid unnecessary debates about taxonomic validity or contentious issues regarding appropriate species definitions, the historical evolution of the species concept is first reviewed in detail, demythologizing an often-assumed species problem. Following that review, the E. corymbosum complex is examined specifically. Although eight varieties of E. corymbosum are currently recognized based on morphological characters, this group of large, woody shrubs has a history of revisions that demonstrates the uncertainty inherent in circumscriptions based on morphology alone. The apparent rarity of some E. corymbosum varieties also presents conservation and management challenges, demonstrating the need for taxonomic verification. To bring greater resolution to this group, I genetically tested samples from populations of six of the eight varieties of E. corymbosum, as well as a number of related buckwheat species. With 103 AFLP loci and chloroplast sequence data from 397 samples, I found strong support for the designation of the recently named E. corymbosum var. nilesii. This predominantly yellow-flowered variety had previously been considered part of a more common variety, and thus its management had not been of particular concern. But as a separate variety, its known distribution is quite limited, and management for this rare plant is now advised. An examination of the biogeography of the E. corymbosum complex provides further support for the apparent rarity of var. nilesii, as well as var. aureum. Both taxa are found at the periphery of the complex, and both may represent insipient species. While all other varieties appear more closely related to each other than to varieties aureum and nilesii, with overlapping ranges confined mostly to the Colorado Plateau, both var. aureum and var. nilesii appear to have allopatric ranges largely off the Colorado Plateau. It appears these two peripheral varieties may each entail a separate center of origin for two new taxa.

Due to the development of Information Technology and World Wide Web, now sales can understand more about customer's browsing behaviors through the World Wide Web. Compared with now, sales could only get the sale data in the past. There is a huge grown of these data availability compared with the past. As the advance of computer hardware, we have more ability to store these data. However, with these data of buyers' making decision process not being analyzed, decision makers often could not understand the hidden information effectively. The 3C retail chain stores had been selected as a case study for this research. We thought that consumer's need and acceptance toward products had already been reflected in the past consuming choices and searching behavior records. If we can find out the correlation between the searching behaviors and actual consuming records, this will assist business obtain the most appropriate marketing information on the web and increase response effectively. This will be tremendously profitable for marketing decision making. Using above theory, we suggest that data integration analysis model among members is applied to distinguish the marking segmentation for website promotion of company A. We hope that the model will improve current marketing communication and provide the practice value. In the past, we normally apply demographic statistics, geographical distribution or social economy to segment consumers for the research about divining target customers. However, there has been no appropriate segmentation model to assist business of actual brick-and-click to segmentation on the basic behavior of the customer purchase for internet marketing promotions yet. The consumer's behaviors had been applied as segmentation and the buyer's usage and response toward website had been utilized as foundation for this research. Principal components analysis had been applied to extract behavior variable; then Two-Stage Classification Method had applied to divide members into different groups. We divide members into life style groups of the one with similar data points and the one with different data points by the exploratory segmentation model for this research. This will be a nature formation of market segmentation to assist business to pin point what products to be sold on the web and how to differentiate the products. As well as assist business to segment member effectively, distinguish area website service and usage limitation. Business will no longer shoot blind for marketing to members and will be able to make the proper e-marketing communication decision.