Application of Cognitive Engineering Principles to the Redesign of a Dichotomous Identification Key for Parasitology

Dichotomous identification keys are used throughout biology for identification of plants, insects, and parasites. However, correct use of identification keys can be difficult as they are not usually intended for novice users who may not be familiar with the terminology used or with the morphology of the organism being identified. Therefore, we applied cognitive engineering principles to redesign a parasitology identification key for the Internet. We addressed issues of visual clutter and spatial distance by displaying a single question couplet at a time and by switching to the appropriate next couplet after the user made a choice. Our analysis of the original paper-based key versus the Web-based approach found that of 26 applicable cognitive engineering principles, the paper key did not meet 4 (15%) and partially met 11 (42%). In contrast, the redesigned key met 100% of 32 applicable cognitive engineering principles.

Development of automated image analysis tools for verification of radiotherapy field accuracy with an electronic portal imaging device

The successful management of cancer with radiation relies on the accurate deposition of a prescribed dose to a prescribed anatomical volume within the patient. Treatment set-up errors are inevitable because the alignment of field shaping devices with the patient must be repeated daily up to eighty times during the course of a fractionated radiotherapy treatment. With the invention of electronic portal imaging devices (EPIDs), patient's portal images can be visualized daily in real-time after only a small fraction of the radiation dose has been delivered to each treatment field. However, the accuracy of human visual evaluation of low-contrast portal images has been found to be inadequate. The goal of this research is to develop automated image analysis tools to detect both treatment field shape errors and patient anatomy placement errors with an EPID. A moments method has been developed to align treatment field images to compensate for lack of repositioning precision of the image detector. A figure of merit has also been established to verify the shape and rotation of the treatment fields. Following proper alignment of treatment field boundaries, a cross-correlation method has been developed to detect shifts of the patient's anatomy relative to the treatment field boundary. Phantom studies showed that the moments method aligned the radiation fields to within 0.5mm of translation and 0.5$\sp\circ$ of rotation and that the cross-correlation method aligned anatomical structures inside the radiation field to within 1 mm of translation and 1$\sp\circ$ of rotation. A new procedure of generating and using digitally reconstructed radiographs (DRRs) at megavoltage energies as reference images was also investigated. The procedure allowed a direct comparison between a designed treatment portal and the actual patient setup positions detected by an EPID. Phantom studies confirmed the feasibility of the methodology. Both the moments method and the cross-correlation technique were implemented within an experimental radiotherapy picture archival and communication system (RT-PACS) and were used clinically to evaluate the setup variability of two groups of cancer patients treated with and without an alpha-cradle immobilization aid. The tools developed in this project have proven to be very effective and have played an important role in detecting patient alignment errors and field-shape errors in treatment fields formed by a multileaf collimator (MLC). ^

Analysis of recurrent failure times: A time-dependent Yule process approach

Analysis of recurrent events has been widely discussed in medical, health services, insurance, and engineering areas in recent years. This research proposes to use a nonhomogeneous Yule process with the proportional intensity assumption to model the hazard function on recurrent events data and the associated risk factors. This method assumes that repeated events occur for each individual, with given covariates, according to a nonhomogeneous Yule process with intensity function λx(t) = λ 0(t) · exp( x′β). One of the advantages of using a non-homogeneous Yule process for recurrent events is that it assumes that the recurrent rate is proportional to the number of events that occur up to time t. Maximum likelihood estimation is used to provide estimates of the parameters in the model, and a generalized scoring iterative procedure is applied in numerical computation. ^ Model comparisons between the proposed method and other existing recurrent models are addressed by simulation. One example concerning recurrent myocardial infarction events compared between two distinct populations, Mexican-American and Non-Hispanic Whites in the Corpus Christi Heart Project is examined. ^

Application of stellar photometry to the analysis of microarray images

Improvements in the analysis of microarray images are critical for accurately quantifying gene expression levels. The acquisition of accurate spot intensities directly influences the results and interpretation of statistical analyses. This dissertation discusses the implementation of a novel approach to the analysis of cDNA microarray images. We use a stellar photometric model, the Moffat function, to quantify microarray spots from nylon microarray images. The inherent flexibility of the Moffat shape model makes it ideal for quantifying microarray spots. We apply our novel approach to a Wilms' tumor microarray study and compare our results with a fixed-circle segmentation approach for spot quantification. Our results suggest that different spot feature extraction methods can have an impact on the ability of statistical methods to identify differentially expressed genes. We also used the Moffat function to simulate a series of microarray images under various experimental conditions. These simulations were used to validate the performance of various statistical methods for identifying differentially expressed genes. Our simulation results indicate that tests taking into account the dependency between mean spot intensity and variance estimation, such as the smoothened t-test, can better identify differentially expressed genes, especially when the number of replicates and mean fold change are low. The analysis of the simulations also showed that overall, a rank sum test (Mann-Whitney) performed well at identifying differentially expressed genes. Previous work has suggested the strengths of nonparametric approaches for identifying differentially expressed genes. We also show that multivariate approaches, such as hierarchical and k-means cluster analysis along with principal components analysis, are only effective at classifying samples when replicate numbers and mean fold change are high. Finally, we show how our stellar shape model approach can be extended to the analysis of 2D-gel images by adapting the Moffat function to take into account the elliptical nature of spots in such images. Our results indicate that stellar shape models offer a previously unexplored approach for the quantification of 2D-gel spots. ^

Interactions and structural analysis of the zinc -binding motif in decorin, a small leucine rich proteoglycan in extracellular matrix

Decorin, a dermatan/chondroitin sulfate proteoglycan, is ubiquitously distributed in the extracellular matrix (ECM) of mammals. Decorin belongs to the small leucine rich proteoglycan (SLRP) family, a proteoglycan family characterized by a core protein dominated by Leucine Rich Repeat motifs. The decorin core protein appears to mediate the binding of decorin to ECM molecules, such as collagens and fibronectin. It is believed that the interactions of decorin with these ECM molecules contribute to the regulation of ECM assembly, cell adhesions, and cell proliferation. These basic biological processes play critical roles during embryonic development and wound healing and are altered in pathological conditions such as fibrosis and tumorgenesis. ^ In this dissertation, we discover that decorin core protein can bind to Zn2+ ions with high affinity. Zinc is an essential trace element in mammals. Zn2+ ions play a catalytic role in the activation of many enzymes and a structural role in the stabilization of protein conformation. By examining purified recombinant decorin and its core protein fragments for Zn2+ binding activity using Zn2+-chelating column chromatography and Zn2+-equilibrium dialysis approaches, we have located the Zn2+ binding domain to the N-terminal sequence of the decorin core protein. The decorin N-terminal domain appears to contain two Zn2+ binding sites with similar high binding affinity. The sequence of the decorin N-terminal domain does not resemble any other reported zinc-binding motifs and, therefore, represents a novel Zn 2+ binding motif. By investigating the influence of Zn2+ ions on decorin binding interactions, we found a novel Zn2+ dependent interaction with fibrinogen, the major plasma protein in blood clots. Furthermore, a recombinant peptide (MD4) consisting of a 41 amino acid sequence of mouse decorin N-terminal domain can prolong thrombin induced fibrinogen/fibrin clot formation. This suggests that in the presence of Zn2+ the decorin N-terminal domain has an anticoagulation activity. The changed Zn2+-binding activities of the truncated MD4 peptides and site-directed mutagenesis generated mutant peptides revealed that the functional MD4 peptide might contain both a structural zinc-binding site in the cysteine cluster region and a catalytic zinc site that could be created by the flanking sequences of the cysteine cluster region. A model of a loop-like structure for MD4 peptide is proposed. ^