Concealing information, one of the many forms of deception, is a pervasive phenomenon as it is present in virtually every facet of interpersonal communication. In some cases, information concealment can have profound implications (e.g., insider threats in organizations, security screening at the border, and criminal interviews). New technologies are under development to aid in identifying concealed information, however, additional research is needed in three key areas to increase the feasibility of using these technologies in real-world credibility assessment contexts. First, research is needed to investigate the accuracy of new credibility assessment technologies relative to existing deception-detection systems. Demonstrating that new technologies meet or exceed detection accuracies of existing systems (e.g., the polygraph) is critical. Second, research is needed to determine if a targetless Concealed Information Test (CIT) is feasible. Existing CIT research supports the presence of main effect differences between persons concealing information and the control group. These behaviors may permit the detection of concealed information without the use of customized sets of stimuli. Eliminating the need to create customized sets of stimuli for each examinee would drastically increase the ease with which an automated system can be used to conduct a CIT. Finally, research is needed to illuminate various elements of the human-computer interaction that occurs during automated credibility assessments. This is a new domain of human-computer interaction as system users in this context are not instigating the interaction, and in many cases, they may be seeking to limit the effectiveness of the system. Before novel systems designed to conduct credibility assessments can be adopted, further research is needed to illuminate how users perceive, respond to, and strategically manage their behaviors when interacting with systems of this nature. This dissertation contains the results of a research program designed to address each of these areas. First, an experiment was designed to investigate the accuracy rates of two promising noncontact measures of concealed information (oculometrics and vocalics) relative to electrodermal activity (EDA). Second, an experiment was designed to evaluate the feasibility of using a targetless CIT to elicit main effect differences between concealers and the control group to identify concealed information. And third, a thorough analysis of examinees' general perceptions, self-reported stress and arousal, perceived effort and performance, and use of countermeasures within the context of an automated credibility assessment interview was conducted. This research effort has yielded the following findings. First, eye tracking and vocalics can be used to identify significant differences in the behaviors and physiology of examinees concealing information, however, the accuracy with which truth tellers and information concealers can be classified remains impractical for an applied setting. Second, there are main effect differences between persons concealing information and telling the truth, however, the use of countermeasures may limit the accuracy with which concealers can be identified. Finally, the presence of concealed information and the use of crime-relevant questions alter how examinees perceive and react to a system designed to identify concealed information. The limitations of this research, as well as directions for future research, are discussed.

Abstract Malignancy Associated Changes are subtle alterations in the morphology and nuclear texture of cells in the vicinity of a malignant lesion. The phenomenon was first described in 1959, and has been the subject of considerable research in the four intervening decades, due to its potential utility to cancer screening programs. In this thesis the history of research into malignancy associated changes is reviewed, and the major findings of previous workers summarized. Original work aimed at improving the accuracy of classification of Pap smear slides is described in detail. A novel algorithm, which incorporates a genetic algorithm for feature selection and training of a neural network, is described. The algorithm was tested upon a large artificial dataset consisting of points from nested spheres in multiple dimensions. It was able to select the most discriminatory features and classify data with 99% accuracy on 80% of runs for two dimensional data, and on 90% of runs for three-dimensional data. The algorithm was also tested on two real data sets from the UCI Machine Learning Repository, the “sonar” data and the “ionosphere” data. On both of these datasets the algorithm produced a classifier using a subset of features which performed as well as previously reported classifiers using the full feature set. This algorithm was then tested on a large dataset of cell images, and its performance compared with that of the standard stepwise linear discriminant analysis approach. Both of these approaches produced similar results, which are comparable to those of previous workers in this field. Interestingly, runs of the genetic algorithm with different random number seeds tended to select different feature subsets, which produced approximately equivalent performance. This finding indicates that amongst the features used, which were selected from those previously identified in the literature as useful for MACs detection, many subsets exist which are equally discriminatory.

<p>Aminoglycoside antibiotics are commonly used to treat bacterial infections but are highly susceptible to chemical modification, leading to resistance. Chemical modification can be hindered through the use of small molecule inhibitors that target bacterial enzymes involved in resistance, most notably kinases. Current methods for the discovery of small molecule inhibitors of kinases and related “kinase-like” enzymes are limited in throughput and utilize slow, tedious, and expensive assays. This thesis is focused on the development of highly versatile and scaleable kinase and “kinase-like” screening platforms for the discovery of small molecule inhibitors of these drug targets. The work begins with the validation of a matrix-assisted laser desorption/ionization tandem mass spectrometry (MALDI-MS/MS) platform utilizing phosphorylation of kanamycin, an aminoglycoside antibiotic, by aminoglycoside phosphotransferase 3ʹIIIa (APH 3ʹIIIa) as a model system. Using a product-to-substrate signal ratio as an internal standard, the assay was used to functionally screen over 200 compounds, combined into mixtures to enhance assay throughput. Moreover, the assay was used to determine inhibitory dissocation constants for newly discovered modulators. Throughput was further increased to a novel dual-kinase assay targeting a bacterial enzyme, APH 3ʹIIIa and a human kinase, protein kinase A (PKA), which was validated using the previous small molecule library. Alternative assay development platforms were also studied using imaging mass spectrometry of reaction microarrays and the fabrication of sol-gel derived bioaffinity chromatography columns. The MS-based kinase assays developed herein are highly amenable to high throughput screening, and have the potential to be extended to other important therapeutic targets.</p> / Doctor of Philosophy (PhD)

This thesis describes a set of image analysis tools developed for the purpose of quantifying the distribution of chromatin in (light) microscope images of cell nuclei. The distribution or pattern of chromatin is influenced by both external and internal variations of the cell environment, including variations associated with the cell cycle, neoplasia, apoptosis, and malignancy associated changes (MACs). The quantitative characterisation of this pattern makes possible the prediction of the biological state of a cell, or the detection of subtle changes in a population of cells. This has important application to automated cancer screening. The majority of existing methods for quantifying chromatin distribution (texture) are based on the stochastic approach to defining texture. However, it is the premise of this thesis that the structural approach is more appropriate because pathologists use terms such as clumping, margination, granulation, condensation, and clearing to describe chromatin texture, and refer to the regions of condensed chromatin as granules, particles, and blobs. The key to the structural approach is the segmentation of the chromatin into its texture primitives. Unfortunately all of the chromatin segmentation algorithms published in the literature suffer from one or both of the following drawbacks: (i) a segmentation that is not consistent with a human's perception of blobs, particles, or granules; and (ii) the need to specify, a priori, one or more subjective operating parameters. The latter drawback limits the robustness of the algorithm to variations in illumination and staining quality. The structural model developed in this thesis is based on several novel low-, med-ium-, and high-level image analysis tools. These tools include: a class of non-linear self-dual filters, called folding induced self-dual filters, for filtering impulse noise; an algorithm, based on seeded region growing, for robustly segmenting chromatin; an improved seeded region growing algorithm that is independent of the order of pixel processing; a fast priority queue implementation suitable for implementing the watershed transform (special case of seeded region growing); the adjacency graph attribute co-occurrence matrix (AGACM) method for quantifying blob and mosaic patterns in the plane; a simple and fast algorithm for computing the exact Euclidean distance transform for the purpose of deriving contextual features (measurements) and constructing geometric adjacency graphs for disjoint connected components; a theoretical result establishing an equivalence between the distance transform of a binary image and the grey-scale erosion of its characteristic function by an elliptic poweroid structuring element; and a host of chromatin features that can be related to qualitative descriptions of chromatin distribution used by pathologists. In addition, this thesis demonstrates the application of this new structural model to automated cervical cancer screening. The results provide empirical evidence that it is possible to detect differences in the pattern of nuclear chromatin between samples of cells from a normal Papanicolaou-stained cervical smear and those from an abnormal smear. These differences are supportive of the existence of the MACs phenomenon. Moreover the results compare favourably with those reported in the literature for other stains developed specifically for automated cytometry. To the author's knowledge this is the first time, based on a sizable and uncontaminated data set, that MACs have been demonstrated in Papanicolaou stain. This is an important finding because the primary screening test for cervical cancer, the Papanicolaou test, is based on this stain.