Memory impairments are common in individuals with temporal lobe epilepsy (TLE). This is understandable given that temporal lobe brain structures involved in TLE play a central role in encoding memories. It is widely accepted that individuals whose seizure focus is in the left temporal lobe (LTLE) tend to have verbal memory impairments, whereas individuals whose seizure focus is in the right temporal lobe (RTLE) tend to have visuospatial memory impairments. However, evidence of functional subdivisions within the left and right temporal lobes in both the animal and human literature suggest that more specific subtypes of memory impairment may exist in TLE based on differences in seizure foci. The aim of this study was to identify more specific subtypes of memory-impairments in patients with intractable TLE using several measures of memory functioning and cluster analysis. Identification of more specific memory subtypes in TLE could have prognostic significance for patients and contribute to our knowledge about the organization of memory systems of the human brain. Four memory subtypes were identified in this sample: 1) patients with mild to moderate figural memory deficits; 2) patients with moderate to severe figural memory deficits, mild facial recognition deficits, and mild attention/concentration deficits; 3) patients with severe figural memory deficits and mild verbal episodic memory deficits; and 4) patients with no episodic or semantic memory deficits. Unexpectedly, the subtypes found did not exhibit the expected pattern of verbal memory impairments with left temporal lobe damage/dysfunction or visuospatial memory impairments with right temporal lobe damage/dysfunction. However, consistent with the literature, there was a trend towards some clusters with better verbal memory having higher left hippocampal volumes; and a trend towards one cluster with facial recognition deficits having lower anterior temporal lobe volumes. Small sample sizes in this study limited the ability to clearly validate many of the cluster differences, particularly differences in brain volumes. Nevertheless, the results of this study support the hypothesis that subtypes of memory impairment do exist in patients with TLE. With larger sample sizes, it is plausible that additional subtypes may be found, or the characteristics of the subtypes found may become clearer.

Many-body perturbation theory and coupled-cluster theory, combined with carefully constructed basis sets, can be used to accurately compute the properties of small molecules. We applied a series of methods and basis sets aimed at reaching the ab initio limit to determine the barrier to planarity for ethylene cation. For potential energy surfaces corresponding to bond dissociation, a single Slater determinant is no longer an appropriate reference, and the single-reference hierarchy breaks down. We computed full configuration interaction benchmark data for calibrating new and existing quantum chemical methods for the accurate description of potential energy surfaces. We used the data to calibrate single-reference configuration interaction, perturbation theory, and coupled-cluster theory and multi-reference configuration interaction and perturbation theory, using various types of molecular orbitals, for breaking single and multiple bonds on ground-state and excited-state surfaces. We developed a determinant-based method which generalizes the formulation of many-body wave functions and energy expectation values. We used the method to calibrate single-reference and multi-reference configuration interaction and coupled-cluster theories, using different types of molecular orbitals, for the symmetric dissociation of water. We extended the determinant-based method to work with general configuration lists, enabling us to study, for the first time, arbitrarily truncated coupled-cluster wave functions. We used this new capability to study the importance of configurations in configuration interaction and coupled-cluster wave functions at different regions of a potential energy surface.

Privacy is defined as the freedom from unauthorized intrusion. The availability of personal information through online databases, such as government records, medical records, and voters and #146; lists, pose a threat to personal privacy. The concern over individual privacy has led to the development of legal codes for safeguarding privacy in several countries. However, the ignorance of individuals as well as loopholes in the systems, have led to information breaches even in the presence of such rules and regulations. Protection against data privacy requires modification of the data itself. The term {em data obfuscation} is used to refer to the class of algorithms that modify the values of the data items without distorting the usefulness of the data. The main goal of this thesis is the development of a data obfuscation technique that provides robust privacy protection with minimal loss in usability of the data. Although medical and financial services are two of the major areas where information privacy is a concern, privacy breaches are not restricted to these domains. One of the areas where the concern over data privacy is of growing interest is collaborative filtering. Collaborative filtering systems are being widely used in E-commerce applications to provide recommendations to users regarding products that might be of interest to them. The prediction accuracy of these systems is dependent on the size and accuracy of the data provided by users. However, the lack of sufficient guidelines governing the use and distribution of user data raises concerns over individual privacy. Users often provide the minimal information that is required for accessing these E-commerce services. The lack of rules governing the use and distribution of data disallows sharing of data among different communities for collaborative filtering. The goals of this thesis are (a) the definition of a standard for classifying DO techniques, (b) the development of a robust cluster preserving data obfuscation algorithm, and (c) the design and implementation of a privacy-preserving shared collaborative filtering framework using the data obfuscation algorithm.

Ophidiidae are major benthopelagic fishes with wide distribution and depth range. Three types of sonic structural patterns have been described in some studied ophidiid fishes, however, the significance of the different types of the sonic apparatus, and the distribution in the subfamilies remain unclear. In addition, although the sonic apparatus and the sonic mechanisms of the high frequency sounds produced by cusk-eels has been explained, the characteristics of extrinsic swimbladder sonic muscle is still unknown. In this study, I investigated three ophidiid subfamilies from Taiwan and reviewed the published data to study the morphological traits of their sonic apparatus. Inaddition, Hoplobrotula armata were related to study the proteomic characterization of the extrinsic swimbladder sonic muscle. Results indicate that the 13 ophidiid species in this study could be grouped into five sonic structural patterns, and three subfamilies could be separated accordingly. Furthermore, because the sister group of the family Ophidiidae-carapidae is soniferous, I suggest that the common ancestor of the three ophidiid subfamilies should also be soniferous. Comparisons made in regarded to environmental factors indicate that diversity of sonic apparatus in three ophidiids subfamiliesis depth dependent. The extrinsic swimbladder sonic muscles could be separated into ventral sonic muscle and intermediate sonic muscle. The histological cross-sections of the ventral sonic muscle fibers show peripheral ring myofibrillar region and central core and they are similar with the sonic muscle. Ventral muscle weights were higher in males than females, but the muscle fibers are smaller in males. Protein values of ventral sonic muscles showed high expression in fast and long duration constructional proteins, and males were higher in protein expression than females. Intermediate sonic muscles, on the other hand, were larger in females than males. The cross-sections of muscle fibers were similar to the white muscle. Expression in the fast constructional related protein in the intermediate sonic muscle was higher than ventral sonic muscles, and the metabolically related protein was lower than ventral sonic muscles. The phylogeny of ophidiiform fishes is not clear at present; the sonic-apparatus diversity in ophidiid fishes observed in this study becomes useful to reveal the relationship phylogenetic of ophidiids fishes. Regard the physiology of sound production, I suggest that the larger ventral sonic muscle in males are be provides a better constructional ability, and their smaller fibers are adaptative for energy metabolism regarded for continuous fast constraction and fatigue resistance. The longer intermediate sonic muscle in females is a better constructional ability than male. In intermediate sonic muscle were be fast constructional related protein, higher in expression than the ventral sonic muscles, the metabolic related protein was lower than the ventral sonic muscles. These results suggest that the intermediate sonic muscle has a hight constructional ability but has a disventage of being easily fatigue.

The aim of strategic group analysis is to find out if clusters of firms that have a similar strategic position exist within an industry or not. In this thesis, by using a conceptual framework that reflects the strategic context, contents and process of construction companies and utilising alternative clustering methods such as traditional cluster analysis, self-organizing maps, and fuzzy C-means technique, a strategic group analysis was conducted for the Turkish construction industry. Results demonstrate that there are three strategic groups among which significant performance differences exist. Self-organising maps provide a visual representation of group composition and help identification of hybrid structures. Fuzzy C-means technique reveals the membership degrees of a firm to each strategic group. It is recommended that real strategic group structure can only be identified by using alternative cluster analysis methods. The positive effect of differentiation strategy on achieving competitive advantage is widely acknowledged in the literature and proved to be valid for the Turkish construction industry as a result of strategic group analysis. In this study, a framework is proposed to model the differentiation process in construction. The relationships between the modes and drivers of differentiation are analyzed by structural equation modeling. The results demonstrate that construction companies can either differentiate on quality or productivity. Project management related factors extensively influence productivity differentiation whereas they influence quality differentiation indirectly. Corporate management related factors only affect quality differentiation. Moreover, resources influence productivity differentiation directly whereas they have an indirect effect on quality differentiation.

This study investigated the concentration distributions of polyclic aromatic hydrocarbons (PAHs) in the sediment cores collected from Fang-Liao submarine canyon. Chemical fingerprinting techniques and statistical analysis were applied to delineate the possible sources of the PAHs in deposited sediment core samples. It is noteworthy that all cores were not dated; therefore the deposition age could not estimate from the depth of deposition directly. The average concentrations of polyclic aromatic hydrocarbons (£U51PAHs) were found ranged from 229 to 638 (ng/g dry wt) in the sediment cores in Fang-Liao submarine canyon. In addition, the low molecular weight PAHs (2-3 ring PAHs) were found dominant in the PAH composition pattern of most samples. Total PAH concentrations were significantly correlated with total organic carbon (TOC) in all the sediment cores. Compared with sediment quality guidelines (SQGs), the PAH concentrations of all sediment samples were lower than those outlined in the criteria, that suggests no evident adverse biological effects caused by PAHs. Results also showed that total PAH concentration of surface sediments (0-2 cm) decreased with the water depth. Identification of PAHs sources suggests that all up-cores were dominated by petrogenic sources, but all down-cores except for S17 and S18 were dominated by pyrogenic sources or mixed sources. In contrast, biogenic sources were found dominant in S17 and S18 as they were characterized by higher ratio of perylene/£Upenta-PAHs(%). Compared with literature, the sediment cores of Fang-Liao submarine canyon were moderately polluted with PAHs. Analysis of diagnostic ratios and hierarchical cluster analysis (HCA) as well as principal component analysis (PCA) all indicate PAHs sources of Fang-Liao submarine canyon were mainly from petroleum and petroleum combustion sources for site of S3,S5,S7,S8 and S17; while pyrogenic or mixed sources for site of S1,S2,S9,S18 and S33.

Most retailers and scientists agree that supporting the buying decisions of individual customers or groups of customers with specific product recommendations holds great promise. Target-oriented promotional campaigns are more profitable in comparison to uniform methods of sale promotion such as discount pricing campaigns. This seems to be particulary true if the promoted products are well matched to the preferences of the customers or customer groups. But how can retailers identify customer groups and determine which products to offer them? To answer this question, this dissertation describes an algorithmic procedure which identifies customer groups with similar preferences for specific product combinations in recorded transaction data. In addition, for each customer group it recommends products which promise higher sales through cross-selling if appropriate promotion techniques are applied. To illustrate the application of this algorithmic approach, an analysis is performed on the transaction database of a supermarket. The identified customer groups are used for a simulation. The results show that appropriate promotional campaigns which implement this algorithmic approach can achieve an increase in profit from 15% to as much as 191% in contrast to uniform discounts on the purchase price of bestsellers. (author's abstract)

The present study examined the extent to which the U.S. STEM labor market is stratified in terms of quality of employment. Through a series of cluster analyses and Chi-square tests on data drawn from the 2008 Survey of Income Program Participation (SIPP), the study found evidence of segmentation in the highly-skilled STEM and non-STEM samples, which included workers with a subbaccalaureate diploma or above. The cluster analyses show a pattern consistent with Labor Market Segmentation theory: Higher wages are associated with other primary employment characteristics, including health insurance and pension benefits, as well as full-time employment. In turn, lower wages showed a tendency to cluster with secondary employment characteristics, such as part-time employment, multiple employment, and restricted access to health insurance and pension benefits. The findings also suggest that women have a higher likelihood of being employed in STEM jobs with secondary characteristics. The findings reveal a far more variegated employment landscape than is usually presented in national reports of the STEM workforce. There is evidence that, while STEM employment may be more resilient than non-STEM employment to labor restructuring trends in the new economy, the former is far from immune to secondary labor characteristics. There is a need for ongoing dialogue between STEM education (at all levels), employers, policymakers, and other stakeholders to truly understand not only the barriers to equity in employment relations, but also the mechanisms that create and maintain segmentation and how they may impact women, underrepresented minorities, and the foreign-born.

As genes with similar expression pattern are very likely having the same biological function, cluster analysis becomes an important tool to understand and predict gene functions from gene expression profi les. In many situations, each gene expression profi le only contains a few data points. Directly applying traditional clustering algorithms to such short gene expression profi les does not yield satisfactory results. Developing clustering algorithms for short gene expression profi les is necessary. In this thesis, two novel methods are developed for clustering short gene expression pro files. The fi rst method, called the network-based clustering method, deals with the defect of short gene expression profi les by generating a gene co-expression network using conditional mutual information (CMI), which measures the non-linear relationship between two genes, as well as considering indirect gene relationships in the presence of other genes. The network-based clustering method consists of two steps. A gene co-expression network is firstly constructed from short gene expression profi les using a path consistency algorithm (PCA) based on the CMI between genes. Then, a gene functional module is identi ed in terms of cluster cohesiveness. The network-based clustering method is evaluated on 10 large scale Arabidopsis thaliana short time-course gene expression profi le datasets in terms of gene ontology (GO) enrichment analysis, and compared with an existing method called Clustering with Over-lapping Neighbourhood Expansion (ClusterONE). Gene functional modules identi ed by the network-based clustering method for 10 datasets returns target GO p-values as low as 10-24, whereas the original ClusterONE yields insigni cant results. In order to more speci cally cluster gene expression profi les, a second clustering method, namely the protein-protein interaction (PPI) integrated clustering method, is developed. It is designed for clustering short gene expression profi les by integrating gene expression profi le patterns and curated PPI data. The method consists of the three following steps: (1) generate a number of prede ned profi le patterns according to the number of data points in the profi les and assign each gene to the prede fined profi le to which its expression profi le is the most similar; (2) integrate curated PPI data to refi ne the initial clustering result from (1); (3) combine the similar clusters from (2) to gradually reduce cluster numbers by a hierarchical clustering method. The PPI-integrated clustering method is evaluated on 10 large scale A. thaliana datasets using GO enrichment analysis, and by comparison with an existing method called Short Time-series Expression Miner (STEM). Target gene functional clusters identi ed by the PPI-integrated clustering method for 10 datasets returns GO p-values as low as 10-62, whereas STEM returns GO p-values as low as 10-38. In addition to the method development, obtained clusters by two proposed methods are further analyzed to identify cross-talk genes under fi ve stress conditions in root and shoot tissues. A list of potential abiotic stress tolerant genes are found.

Clusters in protein interaction networks can potentially help identify functional relationships among proteins. We study the clustering problem by modeling it as graph cut problems. Given an edge weighted graph, the goal is to partition the graph into a prescribed number of subsets obeying some capacity constraints, so as to maximize the total weight of the edges that are within a subset. Identification of a dense subset might shed some light on the biological function of all the proteins in the subset. We study integer programming formulations and exhibit large integrality gaps for various formulations. This is indicative of the difficulty in obtaining constant factor approximation algorithms using the primal-dual schema. We propose three approximation algorithms for the problem. We evaluate the algorithms on the database of interacting proteins and on randomly generated graphs. Our experiments show that the algorithms are fast and have good performance ratio in practice. / xiii, 71 leaves : ill. ; 29 cm.