Theses and Dissertations from UMD
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New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a give thesis/dissertation in DRUM
More information is available at Theses and Dissertations at University of Maryland Libraries.
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Item MULTISCALE MEASUREMENTS OF ELECTRICAL & MECHANICAL CELLULAR DYNAMICS(2023) Alvarez, Phillip; Losert, Wolfgang; Biophysics (BIPH); Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This dissertation focuses on the study and measurement of coupled electrical and mechanical responses in mammalian cells, tissues, and organs. Cellular biophysics often studies forces and their impact on biochemical pathways. These forces can be electrical, resulting in neuronal action potentials or cardiac cell contractions, or mechanical, driving e.g., a cell’s ability to recognize physical probing or surface texture. These forces and their responses, though, are frequently coupled through interlinked cellular mechanisms which result in emergent responses that take both electrical and mechanical signals into account. One challenge in capturing these emergent responses is that they occur on multiple scales, from the intracellular scale to the organ scale, limiting the ability of commercial microscopes to image these responses simultaneously. In this work I use surface texture, optical imaging, and multiscale-capable image analysis algorithms across these scales to elicit and measure electrical and mechanical responses. To image emergent responses from electrical and mechanical coupling, I developed two custom microscopes that can image at multiple length scales and timescales simultaneously. The Multiscale Microscope can capture slow intracellular mechanical dynamics concurrently with fast tissue scale electrical dynamics, while the BEAMM microscope links fast tissue scale electrical dynamics with both intracellular mechanical dynamics and slower organ-scale mechanical and electrical responses. Finally, I describe ongoing and future studies which exploit these new capabilities for multiscale measurements of electrical and mechanical dynamics.Item Cellular Pattern Quantication and Automatic Bench-marking Data-set Generation on confocal microscopy images(2010) Cui, Chi; JaJa, Joseph; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The distribution, directionality and motility of the actin fibers control cell shape, affect cell function and are different in cancer versus normal cells. Quantification of actin structural changes is important for further understanding differences between cell types and for elucidation the effects and dynamics of drug interactions. We propose an image analysis framework to quantify the F-actin organization patterns in response to different pharmaceutical treatments.The main problems addressed include which features to quantify and what quantification measurements to compute when dealing with unlabeled confocal microscopy images. The resultant numerical features are very effective to profile the functional mechanism and facilitate the comparison of different drugs. The analysis software is originally implemented in Matlab and more recently the most time consuming part in the feature extraction stage is implemented onto the NVIDIA GPU using CUDA where we obtain 15 to 20 speedups for different sizes of image. We also propose a computational framework for generating synthetic images for validation purposes. The validation for the feature extraction is done by visual inspection and the validation for quantification is done by comparing them with well-known biological facts. Future studies will further validate the algorithms, and elucidate the molecular pathways and kinetics underlying the F-actin changes. This is the first study quantifying different structural formations of the same protein in intact cells. Since many anti-cancer drugs target the cytoskeleton, we believe that the quantitative image analysis method reported here will have broad applications to understanding the mechanisms of candidate pharmaceutical.Item Statistical and Geometric Modeling of Spatio-Temporal Patterns for Video Understanding(2009) Turaga, Pavan; Chellappa, Ramalingam; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Spatio-temporal patterns abound in the real world, and understanding them computationally holds the promise of enabling a large class of applications such as video surveillance, biometrics, computer graphics and animation. In this dissertation, we study models and algorithms to describe complex spatio-temporal patterns in videos for a wide range of applications. The spatio-temporal pattern recognition problem involves recognizing an input video as an instance of a known class. For this problem, we show that a first order Gauss-Markov process is an appropriate model to describe the space of primitives. We then show that the space of primitives is not a Euclidean space but a Riemannian manifold. We use the geometric properties of this manifold to define distances and statistics. This then paves the way to model temporal variations of the primitives. We then show applications of these techniques in the problem of activity recognition and pattern discovery from long videos. The pattern discovery problem on the other hand, requires uncovering patterns from large datasets in an unsupervised manner for applications such as automatic indexing and tagging. Most state-of-the-art techniques index videos according to the global content in the scene such as color, texture and brightness. In this dissertation, we discuss the problem of activity based indexing of videos. We examine the various issues involved in such an effort and describe a general framework to address the problem. We then design a cascade of dynamical systems model for clustering videos based on their dynamics. We augment the traditional dynamical systems model in two ways. Firstly, we describe activities as a cascade of dynamical systems. This significantly enhances the expressive power of the model while retaining many of the computational advantages of using dynamical models. Secondly, we also derive methods to incorporate view and rate-invariance into these models so that similar actions are clustered together irrespective of the viewpoint or the rate of execution of the activity. We also derive algorithms to learn the model parameters from a video stream and demonstrate how a given video sequence may be segmented into different clusters where each cluster represents an activity. Finally, we show the broader impact of the algorithms and tools developed in this dissertation for several image-based recognition problems that involve statistical inference over non-Euclidean spaces. We demonstrate how an understanding of the geometry of the underlying space leads to methods that are more accurate than traditional approaches. We present examples in shape analysis, object recognition, video-based face recognition, and age-estimation from facial features to demonstrate these ideas.Item Behavioral and Neuroendocrine Correlates of Sex Change in the Gilthead Seabream (Sparus aurata)(2009) Reyes-Tomassini, Jose J.; Zohar, Yonathan; Marine-Estuarine-Environmental Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Sequential hermaphroditism is the most radical form of environmental sex determination observed in fish: functional adult males or females retain their ability to change sex even as adults. Among the factors that affect sex change in these species, the least understood is the social environment. Here, I studied the influences of social context on sex change in the Gilthead Seabream, Sparus aurata, by using the individual‟s dominance rank as an indicator of social status. To understand the role that the brain might play in sex change, I also studied the two main neuroendocrine factors that serve as the sexually differentiated axes of neural plasticity in most teleost species: AVT and GnRH. To do this, I first developed a set of tools designed to address the challenges associated with observing the behavior of aquacultured species. Using these tools, I provide the first in-depth study of seabream captive behavior, including the results of size-matched and sex-matched paired encounters. I found that females are more aggressive than males, but this difference is influenced by gonadal developmental status. I also showed that small but young males are more aggressive than bigger but older females. I cloned the AVT mRNA in seabream, and validated a quantitative assay to measure total brain AVT levels together with GnRH-1, GnRH-2, and GnRH-3 levels. I found that AVT and GnRH-3 levels rise during the onset of the hypothesized sex-change window, and drop to pre-quiescent levels until spawning, when all of these factors seem to increase their expression levels again. I also show for the first time, that GnRH-2 and dominance rank are strongly correlated in seabream during the spawning season but not during quiescence. GnRH-1 was strongly correlated to rank during quiescence but not during spawning. Finally, neither dominance rank nor size were a good predictor of the outcome of sex change, which seems to contradict what has been documented in sequential hermaphrodite reef fishes. I provide a model that accounts for this apparent contradiction and conclude that the Gilthead seabream remains true to the size-advantage hypothesis of sex allocation theory, if size and dominance are seen as proximate, rather than ultimate, factors.