Thesis (Ph.D.)--Tufts University, 1988. / Submitted to the Dept. of Engineering Design. Includes bibliographical references. Access restricted to members of the Tufts University community. Also available via the World Wide Web;

Thesis (Ph.D.)--Tufts University, 1998. / Adviser: Steven H. Levine. Submitted to the Dept. of Engineering Design. Includes bibliographical references (leaves 152-167). Access restricted to members of the Tufts University community. Also available via the World Wide Web;

Thesis (Ph.D.)--Tufts University, 2001. / Adviser: Alva Couch. Submitted to the Dept. of Computer Science. Includes bibliographical references (leaves 231-234). Access restricted to members of the Tufts University community. Also available via the World Wide Web;

Thesis (Ph. D.)--Michigan State University. Measurement and Quantitative Methods , 2006. / Title from PDF t.p. (viewed on Mar. 30, 2009) Includes bibliographical references (p. 113-119). Also issued in print.

Judging goodness of fit in multidimensional scaling requires a comprehensive set of diagnostic tools instead of relying on stress rules of thumb. This article elaborates on corresponding strategies and gives practical guidelines for researchers to obtain a clear picture of the goodness of fit of a solution. Special emphasis will be placed on the use of permutation tests. The second part of the article focuses on goodness-of-fit assessment of an important variant of multidimensional scaling called unfolding, which can be applied to a broad range of psychological data settings. Two real-life data sets are presented in order to walk the reader through the entire set of diagnostic measures, tests, and plots. R code is provided as supplementary information that makes the whole goodness-of-fit assessment workflow, as presented in this article, fully reproducible.

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Fragment assembly methods represent the state of the art in computational protein structure prediction. However, one limitation of these methods, particularly for larger protein structures, is inadequate conformational sampling. This thesis describes studies aimed at uncovering potential causes of ineffective sampling, and the development of methods to try and address these problems. To identify behaviours that might lead to poor conformational sampling, we developed measures to study fragment-based sampling trajectories. Applying these measures to the Rosetta Abinitio and EdaFold methods showed similarities and differences in the ways that these methods make predictions, and pointed to common limitations. In both protocols, structural features such as alpha-helices were more frequently altered during the search, as compared with regions such as loops. Analyses of the fragment libraries used by these methods showed that fragments covering loop regions were less likely to possess native-like structural features, and this likely exacerbated the problems of inadequate sampling in these regions. Inadequate loop sampling leads to poor fold-level exploration within individual runs of methods such as Rosetta, and this necessitates the use of many independent runs. Guided by these findings, we developed new heuristic-based search algorithms. These algorithms were designed to facilitate the exploration of multiple energy basins within runs. Over many runs, the enhanced exploration in our protocols produced decoy sets with larger fractions of native-like solutions as compared to runs of Rosetta. Experiments with different fragment sets indicated that our methods could better translate increased fragment set quality into improvements in predictive accuracy distributions. These improvements depend most strongly on the ability of search algorithms to reliably generate native-like structures using a fragment set. In contrast, inadequate retention of native-like decoys when associated with unfavourable score values appears to be less of an issue. This thesis shows that targeted developments in conformational sampling strategies can improve the accuracy and reliability of predictions. With effective conformational sampling methods, developments in methods for fragment set construction and other areas may more reliably enhance predictive ability.

The distinctiveness of a face is a crucial factor for its ability to be discriminated, memorized, and identified correctly. In this thesis, we provided a contribution to face distinctiveness by examining the impact of socially relevant factors such as attractiveness, group and emotional expression on distinctiveness. We show that attractive faces are more similar to each other than unattractive faces, using female Caucasian faces and male faces of a Caucasian and heterogeneous Non-Caucasian group. We also show, using 3D face generated faces of photo-realistic quality, that attractive faces were closer to the prototype of only their specific group, and that hence typicality of attractive faces was group specific. Lastly, we show that assigning an emotional expression to a face that is evaluatively incongruent with its race makes it more psychologically distinctive, as revealed in perceptual discrimination and memory tasks.

The distinctiveness of a face is a crucial factor for its ability to be discriminated, memorized, and identified correctly. In this thesis, we provided a contribution to face distinctiveness by examining the impact of socially relevant factors such as attractiveness, group and emotional expression on distinctiveness. We show that attractive faces are more similar to each other than unattractive faces, using female Caucasian faces and male faces of a Caucasian and heterogeneous Non-Caucasian group. We also show, using 3D face generated faces of photo-realistic quality, that attractive faces were closer to the prototype of only their specific group, and that hence typicality of attractive faces was group specific. Lastly, we show that assigning an emotional expression to a face that is evaluatively incongruent with its race makes it more psychologically distinctive, as revealed in perceptual discrimination and memory tasks.