Theses and Dissertations from UMD
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Item Using CRISPR/Cas9 to functionally dissect Blimp1, a newly identified pair-rule gene in the hemipteran Oncopeltus fasciatus(2024) Reding, Katie; Pick, Leslie; Entomology; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Genetic screens in the fruit fly Drosophila melanogaster identified a class of mutants displaying half as many segments as seen in wild type fly larvae (Nüsslein-Volhard and Wieschaus 1980). Careful examination of the larval cuticle revealed that one out of every two segments were deleted across the anterior-posterior (AP) axis, an unexpected phenotype suggesting that segmentation in Drosophila follows a ‘pair rule’. Thanks in part to the many genetic tools available for this model species, we now have a clear picture of how the AP axis of the Drosophila embryo is polarized and subsequently divided into distinct segments, and how the pair-rule genes (PRGs) define alternate segments during this process. Since all insects share a similar body plan, it is reasonable to expect that the processes regulating establishment of this body plan would be conserved. However, studies of the Drosophila segmentation gene orthologs in non-model insects suggest that this is not always the case. While the use of model organisms enables an unmatched depth of understanding of the mechanisms underlying development, it comes at the expense of understanding the diversity of these mechanisms across taxa. The milkweed bug Oncopeltus fasciatus (Ofas) (Hemiptera) is a particularly useful insect to study in this regard, as none of the orthologs of the Drosophila PRGs have clear PR-function in this species (Liu and Kaufman 2005b; Auman and Chipman 2018; Reding et al. 2019), while the gene E75A, which has no role in segmentation in Drosophila, is expressed in a PR pattern and its knockdown yields PR segmentation defects (Erezyilmaz et al. 2009). These results suggested that PR-regulation of segmentation in Oncopeltus might require a different set of factors than those discovered in Drosophila. To identify other non-canonical PRGs in this species, I conducted an expression pattern-based screen of transcription factor-encoding genes that are co-expressed with E75A during embryogenesis, and have identified the gene Blimp1 as an Oncopeltus PRG. Like the Drosophila PR mutants, Ofas-Blimp1 mutants display loss of alternate segments across the AP axis. No roles of Blimp1 in insect segmentation had been identified prior to this finding. This result suggests that while insect segmentation may be constrained to follow a pair rule, the genes responsible for regulating PR-segmentation are evolutionarily labile. Further, a major barrier to studying gene function in non-models is the lack of genetic tools such as visible markers and established methods for gene editing. Here I will describe deployment of CRISPR/Cas9 technology in Oncopeltus for targeted mutagenesis. While mutation of the ABC transporter-encoding gene white proved to be recessive lethal, I was able to generate a viable visible marker line by disrupting the X-linked gene Ofas-vermilion (v). Of-v is required for production of dark brown eye pigments, thus Ofas-v mutants have bright red eyes, easily discernible from the black eyes of wild type bugs. I show that a co-CRISPR approach using Of-v as a marker of germline mutation is a helpful strategy to identify mutations of interest at unlinked loci, enabling many future genetic manipulations in this species.Item MOVEMENT ECOLOGY OF THE MEXICAN FISH-EATING BAT, MYOTIS VIVESI(2020) Hurme, Edward; Wilkinson, Gerald S; Biology; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Foraging behavior is influenced by the distribution of prey in time and space and the presence of conspecifics. Echolocating bats, which advertise their behavior while vocalizing, provide a unique opportunity for understanding how an organism interacts with conspecifics and the environment to find food. Here I use GPS tracking combined with on-board recording to investigate the foraging movements of lactating Mexican fish-eating bats, Myotis vivesi, in the Gulf of California, Mexico, over a 5-year period. In Chapter 1, I assessed five alternative methods for behavioral state segmentation of GPS tracked foraging paths using on-board audio for validation. While most methods perform well, hidden-Markov model segmentation showed the highest accuracy at predicting foraging movement. In Chapter 2, I evaluated habitat selection across multiple scales for fish-eating bats foraging in the Midriff Islands Region in the Gulf of California. Foraging site use at large scales is most predictive and is associated with dynamic (chlorophyll concentration) and static variables (ocean depth, sea floor slope) consistent with known tidal upwelling regions. In Chapter 3, I examine the function of in-flight social calls recorded from roughly half of all tagged individuals during their foraging flights. Calls contained spectral differences among individuals, were associated with the ends of flights as bats return to their roost, and increased in occurrence with pup age, consistent with directive calls used to communicate with mobile pups. In Chapter 4, I explore how prey distribution impacts social behavior and foraging movements. On-board audio reveals that conspecifics are present during commuting and foraging and playback experiments demonstrate an attraction to foraging call sequences. In collaboration with several colleagues I combined these findings with data from four other bat species ranging in diet and habitat type. Taken together, bat species that frequently encounter conspecifics, such as Myotis vivesi, have ephemeral prey and variable flights (e.g. duration and foraging site location), whereas bats that forage solitarily have predictable or non-shareable prey, such as a congener Myotis myotis, show less variability in their flights. Overall, these results provide new insights into the foraging dynamics and social behavior of bats.Item HEMIPTERAN INSECTS AS MODELS FOR UNDERSTANDING SEGMENTATION(2018) Chen, Mengyao; Pick, Leslie; Entomology; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Although segmentation is highly conserved in arthropods, diverse mechanisms underlie segmentation. Pair-rule genes (PRGs) are a group of genes controlling segmentation in Drosophila melanogaster, a holometabolous insect. While Drosophila are long-germ insects, most insects add segments sequentially. Studying the role of PRGs in sequentially-segmenting species will provide a deeper understanding in terms of developmental biology. Here, I studied two such insects: Halyomorpha halys and Oncopeltus fasciatus, hemimetabolous insects in a sister order to Holometabola. I annotated segmentation genes in the Halyomorpha genome and tested its response to RNA interference which I showed to be effective in this species for the first time. I further showed that three orthologs of Drosophila PRGs are present in the Oncopeltus genome and are expressed during stages at which segments are specified. Surprisingly, only one of these orthologs is expressed in a PR-pattern, indicating that PRG expression and function have changed during insect evolution.Item Seeing Behind The Scene: Using Symmetry To Reason About Objects in Cluttered Environments(2017) Ecins, Aleksandrs; Aloimonos, Yiannis; Computer Science; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Rapid advances in robotic technology are bringing robots out of the controlled environments of assembly lines and factories into the unstructured and unpredictable real-life workspaces of human beings. One of the prerequisites for operating in such environments is the ability to grasp previously unobserved physical objects. To achieve this individual objects have to be delineated from the rest of the environment and their shape properties estimated from incomplete observations of the scene. This remains a challenging task due to the lack of prior information about the shape and pose of the object as well as occlusions in cluttered scenes. We attempt to solve this problem by utilizing the powerful concept of symmetry. Symmetry is ubiquitous in both natural and man-made environments. It reveals redundancies in the structure of the world around us and thus can be used in a variety of visual processing tasks. In this thesis we propose a complete pipeline for detecting symmetric objects and recovering their rotational and reflectional symmetries from 3D reconstructions of natural scenes. We begin by obtaining a multiple-view 3D pointcloud of the scene using the Kinect Fusion algorithm. Additionally a voxelized occupancy map of the scene is extracted in order to reason about occlusions. We propose two classes of algorithms for symmetry detection: curve based and surface based. Curve based algorithm relies on extracting and matching surface normal edge curves in the pointcloud. A more efficient surface based algorithm works by fitting symmetry axes/planes to the geometry of the smooth surfaces of the scene. In order to segment the objects we introduce a segmentation approach that uses symmetry as a global grouping principle. It extracts points of the scene that are consistent with a given symmetry candidate. To evaluate the performance of our symmetry detection and segmentation algorithms we construct a dataset of cluttered tabletop scenes with ground truth object masks and corresponding symmetries. Finally we demonstrate how our pipeline can be used by a mobile robot to detect and grasp objects in a house scenario.Item INVESTIGATING PAIR-RULE GENE ORTHOLOGS IN AN INTERMEDIATE GERM BEETLE, DERMESTES MACULATUS(2017) Xiang, Jie; Pick, Leslie; Entomology; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Insects share a body plan based on repeating segments. Segmentation has been well characterized in Drosophila melanogaster, in which segments are established by a genetic hierarchy including gap, pair-rule and segment polarity genes. Pair-rule genes (PRGs) are a key class of segmentation genes as they are the first cohort of genes expressed in a periodic pattern. Segments are established simultaneously in Drosophila in early embryos, while most other insects add segments sequentially as the embryo elongates. Our goal is to understand molecular mechanisms controlling segment formation and to determine the extent of their conservation during evolution. Here, we established the hide beetle Dermestes maculatus, an intermediate germ developer, as a new model system for studying segmentation patterning. We first established a lab colony and studied early embryogenesis in Dermestes. All nine PRG orthologs were isolated using degenerate PCR and RACE, and their expression patterns were examined with in situ hybridization. Except for opa, all Dermestes PRG orthologs are expressed in PR-like striped patterns. Gene functions were tested using RNA interference (RNAi). We examined both hatched and unhatched larvae to uncover defects with different severities. Both Dmac-prd and -slp knockdown resulted in typical PR defects, suggesting that they are “core” PR genes. Dmac-eve, -run and -odd have dual roles in germ band elongation and in PR segmentation, as severe knockdown caused anterior-only, asegmental embryos while moderate knockdown resulted in PR-like defects. Elongated but asegmental germ bands resulted from Dmac-prd and -slp double knockdown, suggesting decoupling of germ band elongation and PR segmentation. Extensive cell death prefigured the cuticle patterns after knockdowns, seen long ago for Drosophila PR phenotypes, although disrupted cell mitosis was also observed after Dmac-eve knockdown. We propose that PRGs have retained basic roles in PR segmentation during the transition from short-to-long germ development and share evolutionary conserved functions in promoting cell viability. Finally, I also present detailed protocols on Dermestes lab rearing, embryo collection and fixation, in situ hybridization and RNAi. The technical information described here will provide useful information for other genetic studies in this new model system.Item Discrete Optimization Methods for Segmentation and Matching(2012) Liu, Ming-Yu; Chellappa, Rama; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This dissertation studies discrete optimization methods for several computer vision problems. In the first part, a new objective function for superpixel segmentation is proposed. This objective function consists of two components: entropy rate of a random walk on a graph and a balancing term. The entropy rate favors formation of compact and homogeneous clusters, while the balancing function encourages clusters with similar sizes. I present a new graph construction for images and show that this construction induces a matroid. The segmentation is then given by the graph topology which maximizes the objective function under the matroid constraint. By exploiting submodular and monotonic properties of the objective function, I develop an efficient algorithm with a worst-case performance bound of $\frac{1}{2}$ for the superpixel segmentation problem. Extensive experiments on the Berkeley segmentation benchmark show the proposed algorithm outperforms the state of the art in all the standard evaluation metrics. Next, I propose a video segmentation algorithm by maximizing a submodular objective function subject to a matroid constraint. This function is similar to the standard energy function in computer vision with unary terms, pairwise terms from the Potts model, and a novel higher-order term based on appearance histograms. I show that the standard Potts model prior, which becomes non-submodular for multi-label problems, still induces a submodular function in a maximization framework. A new higher-order prior further enforces consistency in the appearance histograms both spatially and temporally across the video. The matroid constraint leads to a simple algorithm with a performance bound of $\frac{1}{2}$. A branch and bound procedure is also presented to improve the solution computed by the algorithm. The last part of the dissertation studies the object localization problem in images given a single hand-drawn example or a gallery of shapes as the object model. Although many shape matching algorithms have been proposed for the problem, chamfer matching remains to be the preferred method when speed and robustness are considered. In this dissertation, I significantly improve the accuracy of chamfer matching while reducing the computational time from linear to sublinear (shown empirically). It is achieved by incorporating edge orientation information in the matching algorithm so the resulting cost function is piecewise smooth and the cost variation is tightly bounded. Moreover, I present a sublinear time algorithm for exact computation of the directional chamfer matching score using techniques from 3D distance transforms and directional integral images. In addition, the smooth cost function allows one to bound the cost distribution of large neighborhoods and skip the bad hypotheses. Experiments show that the proposed approach improves the speed of the original chamfer matching up to an order of 45 times, and it is much faster than many state of art techniques while the accuracy is comparable. I further demonstrate the application of the proposed algorithm in providing seamless operation for a robotic bin picking system.Item USING STATISTICAL METHOD TO REVEAL BIOLOGICAL ASPECT OF HUMAN DISEASE: STUDY OF GLIOBLASTOMA BY USING COMPARATIVE GENOMIC HYBRIDIZATION (CGH) METHOD(2010) Wang, Yonghong; Smith, Paul; Mathematical Statistics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Glioblastoma is a WHO grade IV tumor with high mortality rate. In order to identify the underlying biological causation of this disease, a comparative genomic hybridization dataset generated from 170 patients' tumor samples was analyzed. Of many available segmentation algorithms, I focused mainly on two most acceptable methods: Homogeneous Hidden Markov Models (HHMM) and Circular Binary Segmentation (CBS). Simulations show that CBS tends to give better segmentation result with low false discovery rate. HHMM failed to identify many obvious breakpoints that CBS identified. On the other hand, HHMM succeeds in identifying many single probe aberrations. Applying other statistical algorithms revealed distinct biological fingerprints of Glioblastoma disease, which includes many signature genes and biological pathways. Survival analysis also reveals that several segments actually correlate to the extended survival time of some patients. In summary, this work shows the importance of statistical model or algorithms in the modern genomic research.Item The compositional character of visual correspondence(2004-08-06) Ogale, Abhijit Satishchandra; Aloimonos, Yiannis; Computer Science; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Given two images of a scene, the problem of finding a map relating the points in the two images is known as the correspondence problem. Stereo correspondence is a special case in which corresponding points lie on the same row in the two images; optical flow is the general case. In this thesis, we argue that correspondence is inextricably linked to other problems such as depth segmentation, occlusion detection and shape estimation, and cannot be solved in isolation without solving each of these problems concurrently within a compositional framework. We first demonstrate the relationship between correspondence and segmentation in a world devoid of shape, and propose an algorithm based on connected components which solves these two problems simultaneously by matching image pixels. Occlusions are found by using the uniqueness constraint, which forces one pixel in the first image to match exactly one pixel in the second image. Shape is then introduced into the picture, and it is revealed that a horizontally slanted surface is sampled differently by the two cameras of a stereo pair, creating images of different width. In this scenario, we show that pixel matching must be replaced by interval matching, to allow intervals of different width in the two images to correspond. A new interval uniqueness constraint is proposed to detect occlusions. Vertical slant is shown to have a qualitatively different character than horizontal slant, requiring the role of vertical consistency constraints based on non-horizontal edges. Complexities which arise in optical flow estimation in the presence of slant are also examined. For greater robustness and flexibility, the algorithm based on connected components is generalized into a diffusion-like process, which allows the use of new local matching metrics which we have developed in order to create contrast invariant and noise resistant correspondence algorithms. Ultimately, it is shown that temporal information can be used to assign correspondences to occluded areas, which also yields ordinal depth information about the scene, even in the presence of independently moving objects. This information can be used for motion segmentation to detect new types of independently moving objects, which are missed by state-of-the-art methods.