Combinatorial Optimization Algorithms for Hypergraph Matching with Application to Posture Identification in Embryonic Caenorhabditis elegans

Point-set matching defines the task in computer vision of identifying a one-to-one alignment between two sets of points. Existing techniques rely on simple relationships between point-sets in order to efficiently find optimal correspondences between larger sets. Modern methodology precludes application to more challenging point-set matching tasks which benefit from interdependent modeling. This thesis addresses a gap in combinatorial optimization literature by enhancing leading methods in both graph matching and multiple object tracking (MOT) to more flexibly use data-driven point-set matching models. Presented contributions are inspired by Caenorhabditis elegans, a transparent free-living roundworm frequently studied in developmental biology and neurobiology. The C. elegans embryo, containing around 550 cells at hatch, can be used for cell tracking studies to understand how cell movement drives the development of specific embryonic tissues and organ functions. The development of muscle cells complicates analyses during late-stage development, as embryos begin twitching due to muscular activity. The sporadic twitches cause cells to move violently and unpredictably, invalidating traditional cell tracking approaches. The embryo possesses seam cells, a set of 20 cells which together act as fiducial markers to approximate the coiled embryo's body. Novel optimization algorithms and data-driven hypergraphical models leveraging the correlated movement among seam cells are used to forward research on C. elegans embryogenesis. We contribute two optimization algorithms applicable in differing conditions to interdependent point-set matching. The first algorithm, Exact Hypergraph Matching (EHGM), exactly solves the n-adic assignment problem by casting the problem as hypergraph matching. The algorithm obtains solutions to highly interdependent seam cell identification models. The second optimization algorithm, Multiple Hypothesis Hypergraph Tracking (MHHT), adapts traditional multiple hypothesis tracking with hypergraphical data association. Results from both studies highlight improved performance over established methods while providing adaptable optimization tools for multiple academic communities. The canonical point-set matching task is solved efficiently under strict assumptions of frame-to-frame transformations. Challenging situations arising from non-rigid displacements between frames will complicate established methods. Particularly, limitations in fluorescence microscopy paired with muscular twitching in late-stage embryonic C. elegans yield adversarial point-set matching tasks. Seam cell identification is cast as an assignment problem; detected cells in images are uniquely identified through a combinatorial optimization algorithm. Existing graph matching methods are underequipped to contextualize the coiled embryonic position in sparsely imaged samples. Both the lack of an effective point-set matching model and an efficient algorithm for solving the resulting optimization problem limit computationally driven solutions to identify seam cells in acquired image volumes. We cast the n-adic problem as hypergraph matching and present an exact algorithm to solve the resulting optimization problem. EHGM adapts the branch-and-bound paradigm to dynamically identify a globally optimal correspondence; it is the first algorithm capable of solving the underlying optimization problem. Our algorithm and accompanying data-driven hypergraphical models identify seam cells more accurately than established point-set matching methods. The final hours of embryogenesis encompass the moments in which C. elegans assumes motor control and begins exhibiting behavior. Rapid imaging of the seam cells provides insight into the embryo’s movement as a proxy for behavior. However, seam cell tracking is especially challenging due to both muscular twitching and the low dose required to gently image the embryo without perturbing development. Current methods in MOT rely on independent object trajectories undergoing smooth motion to effectively track large numbers of objects. Multiple Hypothesis Tracking (MHT) is the foremost method for challenging MOT tasks, yet the method cannot model correlated object movements. We contribute Multiple Hypothesis Hypergraph Tracking (MHHT) as an extension of MHT, which performs interdependent object tracking by jointly representing objects as a hypergraph. We apply MHHT to track seam cell nuclei during late-stage embryogenesis. Data-driven hypergraphical models more accurately track seam cells than traditional MHT based approaches. Analysis of time-lapse embryonic postures and behavioral motifs reveal a stereotyped developmental progression in C. elegans. Further analysis uncovers late-stage motility defects in unc-13 mutants

Age Determination of the Glassy-Winged Sharpshooter, Homalodisca vitripennis, using Wing Pigmentation

A red pigment is contained in the wing veins of the glassy-winged sharpshooter, Homalodisca vitripennis (Hemiptera: Cicadellidae). This insect is the main vector of the plant-pathogenic bacterium Xylella fastidiosa Wells (Xanthomonadales: Xanthomonadaceae), the causal agent of Pierce's disease of grapevines. Over the course of the H. vitripennis lifespan, the red pigment darkens and eventually becomes brown/black in color. These pigments are believed to be pheomelanin and eumelanin, respectively. The age of H. vitripennis can be determined by calculating the amount of red pigment found in the wings by analyzing high resolution wing photographs with image analysis software. In this study, a standard curve for the age determination of H. vitripennis was developed using laboratory—reared insects of known ages varying from 1 to 60 days. The impact of three environmental conditions on these readings was also investigated and found to have little effect on the age determination, and could be easily accounted for. Finally, field collected insects from several Central Texas vineyards were successfully analyzed for age determination suggesting that the annually reported influx of H. vitripennis was composed almost entirely of older insects

Covariance of noncommutative Grassmann star product

Using the Coherent states of many fermionic degrees of freedom labeled by Gra\ss mann variables, we introduce the noncommutative (precisely non anticommutative) Gra\ss mann star product. The covariance of star product under unitary transformations, particularly canonical ones, is studied. The super star product, based on supercoherent states of supersymmetric harmonic oscillator, is also considered

Prospects on Time-Domain Diffuse Optical Tomography Based on Time-Correlated Single Photon Counting for Small Animal Imaging

This paper discusses instrumentation based on multiview parallel high temporal resolution (<50 ps) time-domain (TD) measurements for diffuse optical tomography (DOT) and a prospective view on the steps to undertake as regards such instrumentation to make TD-DOT a viable technology for small animal molecular imaging. TD measurements provide information-richest data, and we briefly review the interaction of light with biological tissues to provide an understanding of this. This data richness is yet to be exploited to its full potential to increase the spatial resolution of DOT imaging and to allow probing, via the fluorescence lifetime, tissue biochemical parameters, and processes that are otherwise not accessible in fluorescence DOT. TD data acquisition time is, however, the main factor that currently compromises the viability of TD-DOT. Current high temporal resolution TD-DOT scanners simply do not integrate sufficient detection channels. Based on our past experience in developing TD-DOT instrumentation, we review and discuss promising technologies to overcome this difficulty. These are single photon avalanche diode (SPAD) detectors and fully parallel highly integrated electronics for time-correlated single photon counting (TCSPC). We present experimental results obtained with such technologies demonstrating the feasibility of next-generation multiview TD-DOT therewith

The Effects of Host-Feeding on Synovigenic Egg Development in An Endoparasitic Wasp, Itoplectis naranyae

Many adult parasitoids feed on host insects, a behavior known as host-feeding. Feeding on hosts is essential to maximizing female fecundity, but its contribution to reproduction varies from species to species. The relationship between fecundity and host-feeding was examined in the solitary endoparasitoid wasp Itoplectis naranyae Ashmead, (Hymenoptera: Ichneumonidae) to assess the significance of host-feeding in female reproduction. Adult female wasps did not respond to hosts when they were 0–1 days old, but subsequently increased their oviposition and host-feeding activities with increasing female age. While newly emerging females had no mature eggs in their ovary, the number of mature eggs increased rapidly thereafter, a process termed synovigeny. Female wasps were capable of maturing eggs without host-feeding, and this suggested that they produced a certain portion of eggs from nutritional reserves that had been stored during the larval stage. Behavioral observations revealed that I. naranyae was a destructive host-feeder as the host was damaged during feeding. Female fecundity was greater in females that had previously fed on hosts than those did not, indicating that host-feeding was involved in egg production. There was a time-delayed relation between host-feeding events and additional egg production; at least 3 days were required to mature eggs from nutrients gained via feeding on hosts. The significance of host-feeding in I. naranyae reproduction is discussed in the context of its life history traits

Python toolbox for Earth Sciences applications

Python v.3.8+ functions and script

A model-based road sign recognition system /

A road sign recognition system poses a real challenge for machine vision. It must recognize a wide variety of road signs under considerable variations in illumination and imaging geometry---all in real-time. This thesis presents a modular road sign recognition system relying on modelling for both detection and recognition. It divides into three main stages of processing. The first, concerned with detection, exploits the specific colors of road signs. The color constancy problem caused by the daylight illumination variations is addressed directly with a physics-based model supplemented by a calibration stage using real data. The second stage of processing, devoted to recognizing road signs in regions of interest found in the detection phase, involves a database containing more than 400 road signs arranged in a tree structure, and uses a novel correlation-based template matching technique relying on a bitwise encoding that accounts for both color labels and affine variations in the image formation process, and which also allows to build templates that are able to represent classes of objects. The content of the database used by the recognition algorithm is generated in a deterministic and automated manner by way of geometrical modelling of the image formation process starting with only model images of the road signs to be recognized. The recognition algorithm exploits color as a first logical classification step to direct the search for a road sign in the database, with the later finer steps being driven by correlation scores obtained from template matching. At the third stage of processing, a scene understanding module exploits constraints on the position of road signs along with the spatial relationships they must have in certain cases to other road signs in the image to filter out false positives. During processing, the system incorporates top-down mechanisms that use data fed back by partial recognitions, which allow to progressively gain more information abou