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The Role of Connectivity in Structuring Community Composition and Diversity at Hydrothermal Vents Across the Northwest Pacific
Full text linkOkinawa Institute of Science and Technology Graduate UniversityDoctor of PhilosophyConnectivity, or the movement of individuals among isolated habitat patches, promotes local and regional biodiversity, and its resilience to disturbances both natural and anthropogenic. Species associated with seafloor hydrothermal vent habitats are distinctly reliant on connectivity due to their spatial restriction to the point source of chemical energy from vent chimneys that fuels their chemosynthetic food web. Measuring connectivity among hydrothermal vents is particularly urgent in regions where mining of these ecosystems is imminent. Our understanding of connectivity is limited by the scarcity of observational data from these inaccessible deep-sea ecosystems. Modelling is a viable alternative to the study of connectivity as the dispersal that facilitates connectivity is mostly dictated by predictable ocean currents, which can be reliably simulated. This thesis combines empirical observations of species’ distributions and environmental conditions at hydrothermal vents with simulations of dispersal, to model connectivity among vent sites in the Northwest Pacific. First, I curate the most comprehensive regional dataset of hydrothermal vent species distributions to infer connectivity in the form of a species assemblage network (Chapter 1). I then simulate how the planktonic larvae of vent species disperse among the vent sites in this region using Lagrangian particle tracking methods within an Ocean General Circulation Model (Chapter 2). Finally, I combine the among-site dispersal estimates with observations of local environmental parameters to create a simulated species assemblage network using a metacommunity model (Chapter 3). This metacommunity model accurately recreated the empirical observations from chapter 1 and gives crucial insight into the interacting effects of dispersal barriers and environmental niche on driving diversity and community composition patterns at hydrothermal vents. Furthermore, I used the combination of observed and simulated connectivity results to quantitatively evaluate the relative role each individual hydrothermal vent plays in maintaining connectivity and biodiversity in the region. Such an evaluation has critical and timely implications for proposed mining and the spatial management of hydrothermal vents in this region. Lastly, we demonstrate that hydrothermal vents are natural laboratories for the advancement of metacommunity theory and conservation ecology due to their characteristic isolation and discrete nature
The Landau–Levich Problem for a Uniaxial Nematic Liquid Crystal
Full text linkOkinawa Institute of Science and Technology Graduate UniversityDoctor of PhilosophyThis thesis considers numerical simulation and experimental measurement of a dipcoating process with nematic liquid crystals. In the first part of the thesis, we consider the specialization of Ericksen–Leslie theory for dip-coating of a plate withdrawn from a liquid crystal reservoir and introduce a numerical algorithm for solving the free surface flow of nematic liquid crystals. We study the most significant features of the flow, such as the stagnation point location and the liquid crystal film thickness systematically against the controlling parameters. Our results give significant insight into the role of bulk elasticity and surface anchoring energy on the nematic liquid crystal film thickness in the dip-coating process. Depending on the interplay between bulk elasticity and viscous forces viscoelastic and viscous regimes are identified. At low plate velocities where the viscous and elastic forces remain in the same order of magnitude, both viscous stresses and bulk elasticity control the nematic liquid crystal film thickness. In this regime, the elastic forces in bulk result in a film-thinning behavior and resist the entrainment of the liquid with the plate. On the other hand, increasing the plate velocity results in the domination of viscous forces over the elastic and capillary forces. In this regime, the liquid crystal film thickness converges to the predictions for Newtonian liquids. In the second part of the thesis, we experimentally measure the liquid crystal film thickness to validate the predictions from the numerical simulations. The experimental measurements confirm the predicted viscoelastic and viscous regimes and the threshold for the plate velocity where the transition occurs
Machine Learning Guided Exploration of an Empirical Ribozyme Fitness Landscape
Full text linkOkinawa Institute of Science and Technology Graduate UniversityDoctor of PhilosophyFitness landscape of a biomolecule is a representation of its activity as a function of its sequence. Properties of a fitness landscape determine how evolution proceeds. Therefore, the distribution of functional variants and more importantly, the connectivity of these variants within the sequence space are important scientific questions. Exploration of these spaces, however, is impeded by the combinatorial explosion of the sequence space. High-throughput experimental methods have recently reduced this impediment but only modestly. Better computational methods are needed to fully utilize the rich information from these experimental data to better understand the properties of the fitness landscape. In this work, I seek to improve this exploration process by combining data from massively parallel experimental assay with smart library design using advanced computational techniques. I focus on an artificial RNA enzyme or ribozyme that can catalyze a ligation reaction between two RNA fragments. This chemistry is analogous to that of the modern RNA polymeraseenzymes, therefore, represents an important reaction in the origin of life. In the first chapter, I discuss the background to this work in the context of evolutionary theory of fitness landscape and its implications in biotechnology. In chapter 2, I explore the use of processes borrowed from the field of evolutionary computation to solve optimization problems using real experimental sequence-activity data. In chapter 3, I investigate the use of supervised machine learning models to extract information on epistatic interactions from the dataset collected during multiple rounds of directed evolution. I investigate and experimentally validate the extent to which a deep learning model can be used to guide a completely computational evolutionary algorithm towards distant regions of the fitness landscape. In the final chapter, I perform a comprehensive experimental assay of the combinatorial region explored by the deep learning-guided evolutionary algorithm. Using this dataset, I analyze higher-order epistasis and attempt to explain the increased predictability of the region sampled by the algorithm. Finally, I provide the first experimental evidence of a large RNA ‘neutral network’. Altogether, this work represents the most comprehensive experimental and computational study of the RNA ligase ribozyme fitness landscape to date, providing important insights into the evolutionary search space possibly explored during the earliest stages of life
Plasma Membrane Damage-Dependent Senescent Cells Accelerate Wound Healing In Vitro via Soluble Molecules and Increased Extracellular Vesicles
Full text linkOkinawa Institute of Science and Technology Graduate UniversityDoctor of PhilosophyCellular senescence is a sustained cell cycle arrest that contributes to physiological and pathological processes in vivo. These range from deleterious processes such as organismal aging and cancer progression to essential processes such as embryonic development and wound healing. While senescence subtypes triggered by telomere shortening, DNA damage, and oncogene activation are well-known, recent studies have identified plasma membrane damage (PMD) as a novel trigger of cellular senescence. The functions of PMD-dependent senescence (PMD-Sen) remain unknown. Here, I focus on paracrine signaling to predict such functions and show the importance of extracellular vesicles (EVs) in paracrine signaling by PMD-Sen cells. I found that PMD significantly increases EV production acutely and that EV production after senescence induction is higher than in DNA damage response-dependent senescence (DDR-Sen). From proteomic analysis of cells and their EVs, I showed many similarities between PMD-Sen and DDR-Sen; however, differentially regulated PMD-Sen EV proteins are more significantly involved in wound healing pathways, and depletion of EVs from conditioned media reduces wound healing of recipient cells in vitro. This study shows one potential function of PMD-Sen in vivo and provides proteomic characterization to support future mechanistic studies or biomarker identification of senescence subtypes
Diffraction-Based Experiments in Transmission Electron Microscopy: Lensing, Charging, and Amorphous Structures
Full text linkOkinawa Institute of Science and Technology Graduate UniversityDoctor of PhilosophyThe interaction between electrons with electromagnetic potentials is the basis for electron microscopy. To build electron microscopes and interpret the data collected through them, it is necessary to understand how electrons are influenced by potentials and how they influence the potentials within samples. By further understanding the information encoded in an electron wave after passing through a sample, new techniques for analyzing materials can be developed. Here, several methods to extend the capabilities of electron microscopy are proposed. It is demonstrated that a new form of electromagnetic lens can be produced by a torus-shaped lens with a poloidal current flow. The lensing effect is due to the magnetic vector potential in the absence of electromagnetic fields and can produce convex and concave lensing. The dynamics of charge buildup on insulating samples from the moment they are exposed to an electron beam are measured. These measurements reveal a non-uniform charge distribution in the illuminated area whose temporal development is dose-rate dependent. These results may be used to improve the resolution achievable in protein structure reconstructions. Finally, a new method to experimentally determine the structure of dominant short-range order structural motifs in amorphous materials is explored
Detection of the Rydberg States of Electrons on Superfluid Helium Confined in Microchannel Devices
Full text linkOkinawa Institute of Science and Technology Graduate UniversityDoctor of PhilosophyThe potential for quantum information processing with surface-state electrons (SSE) on liquid helium has been pointed out in one of the historically first proposals for quantum computing [1]. The quantized Rydberg states of the vertical motion of SSE, as well as the spin states of SSE, present two promising candidates for the experimental implementation of qubit states. However, the lack of a sensitive state-readout method for a single electron has so far prevented much progress in implementation of these states for quantum computing. In order to overcome this obstacle, this PhD project seeks to lay the experimental groundwork for the realization of Rydberg state detection of SSE on liquid helium confined in microchannel devices, as well as their potential use for spin state detection. We start from the transport measurements of SSE in a microchannel setup, since well-understood transport behavior of SSE can help us to detect the Rydberg transition. An unusual transport effect is reported and discussed. By employing the time-resolved measurements, we show that the effect is due to the dynamical interaction of the electron crystal with the surface excitations of the liquid substrate. Next, the feasibility of detecting the transition between Rydberg states in a 4-µm deep channel device is demonstrated using two different methods, the conductivity method and the image-charge method. We find that the observed transition frequency for the two lowest Rydberg states, which is in the range of 0.4 − 0.5 THz, is determined by the image charges induced by SSE in the conducting electrodes of the microchannel device and the applied potentials, and is in a good agreement with our analytical and numerical calculations. Owing to the low sensitivity of this method, the number of SSE in the device is required to be large, on the order of 104, that is the sensitivity is far below the final goal of single electron readout. Taking advantage of the LC (tank) resonating circuits, we significantly improve the measurement sensitivity by employing a resonator albeit with a relatively low quality factor. Finally, we present our ongoing experimental efforts to optimize the resonator setup, in particularly increasing its quality factor, which is an important step towards realizing an ultra-sensitive readout of the single-electron Rydberg states
Soap Film Mediated 3D Self-Assembly: Suspended and Displacement Driven Geometries Using Centimeter-Scale Tiles
Full text linkOkinawa Institute of Science and Technology Graduate UniversityDoctor of PhilosophyThe shapes of soap films and their properties have been studied for centuries, and scientific literature mentioning the self-assembly of structures across length scales is becoming increasingly prevalent. While research regarding self-assembly is often focused on the nanoscopic regime, multiple recent reviews aptly state that self-assembly is not limited to molecules. To better understand self-assembly systems in generality, pushing the boundaries of scale and methodology is paramount. This thesis demonstrates the self-assembly of multiple geometries on centimeter length scales by use of a novel medium, namely soap films. Gravity-mediated and displacement-driven assemblies demonstrate platonic solid geometries as well as prismatic and pyramidal structures of varying heights within a catenoid-like soap film membrane. The edge-toedge alignment of any two tiles is similar to the self-assembly processes described in research regarding the rotation of microstructures. An axes-symmetric model of soap film deflection within the context of gravity-mediated structures is derived and compared to experiments with excellent accordance found. The model is derived by energy methods, using a calculus of variations approach. For the structures constructed using the displacement-driven method, two effective radii predicting the pinch-off of the soap film were discovered and used to compare experimental results to the predictions. Self-folding within the context of displacement-driven assemblies is also presented to reiterate the versatility and robustness of the assembly method. Numerical simulations using Surface Evolver are presented throughout to provide an additional method of analysis of the equilibrium states of the assemblies. The characterization of fundamental geometries assembled by the methods presented in this thesis enables future research and applications in packaging, robotics, and three-dimensional electronics
Designing a Tractable Behavioral Paradigm for Investigating Olfactory Figure-ground Segregation
Full text linkOkinawa Institute of Science and Technology Graduate UniversityDoctor of PhilosophyOdors naturally exist as mixtures in the environment. Detecting relevant cues amidst other signals and noise, a task called figure-ground segregation, is important for survival. Understanding mechanisms that enable animals to solve such a challenging task requires a paradigm that recapitulates key features of the task, yet ideally should be simple enough that allows these mechanistic bases to be studied experimentally. In my PhD, I developed a behavioral paradigm using only binary mixtures as a model for olfactory figure-ground segregation in mice. Ethyl butyrate (EB) was assigned as target odor and ten other background odors with differing degrees of chemical similarity to EB were included as part of a Go/No-Go task. The fact that the mixtures comprised only two odors made the number of possible odor combinations limited, which therefore made the paradigm tractable and ensured that all combinations can be presented exhaustively per behavioral session. Despite its simplicity, I demonstrate that the experimental paradigm can still impose a degree of challenge for mice through the use of a highly similar background odor. This captures recent findings that the degree of overlap between odor-evoked neural representations underlies figure-ground segregation difficulty. Additionally, it was determined that mice performing the binary mixture task can easily generalize when presented with a novel odor, which suggests that demixing is likely involved. Finally, two example cases are presented as examples how the experimental paradigm can be applied to investigate possible neural mechanisms of olfactory figure-ground segregation. Overall, despite its simplicity, the experimental paradigm using only binary mixtures may be used to probe neural mechanisms of olfactory figure-ground segregation
Wave Propagation and Light-Matter Interactions in Optical Nanofibers and Discrete Media
Full text linkOkinawa Institute of Science and Technology Graduate UniversityDoctor of PhilosophyBuilding on more than 50 years of sustained progress, artificial systems of atoms and photons are now routinely controllable down to the nanoscale, which paves the way for simulators and processors powered by the light-matter interaction. In particular the rapid experimental progress made in platforms of nanoscale photonics and neutral atoms demands fresh computational studies along with more powerful theoretical tools in order to simulate these increasingly complex (quantum) optical systems. In this thesis I contribute to (1) the understanding of current state-of-the-art in experimental optical nanofiber systems on one hand, and to (2) the general theory of emission into photonic lattices together with (3) quantum metrology in light-matter platforms. In the former I (1) systematically investigate light propagation in coupled optical nanofibers fibers and dispersion potential mediated through these nanofibers for experimentally relevant parameters, shedding light on effects that may be observed in near-future setups. In the latter I (2) study hyperbolic lattices exhibiting strongly anisotropic emission, with results that may have applications in transporting and storing photons in nanoscale platforms. Additionally, in a collaborative work (3) a proposal is made for a metrological protocol consisting of quenching through a quantum phase transition to obtain quantum-limited precision in system measurements
Automated segmentation of micro-CT images by deep learning and its application to comparative morphology
Full text linkOkinawa Institute of Science and Technology Graduate UniversityDoctor of PhilosophyImage segmentation is one of the most fascinating challenges of computer vision. A field of potential application is organismal biology, where researchers are increasingly using three-dimensional (3D) scanning which produces data-rich volumetric images for precise and comprehensive anatomical characterization. To date, the segmentation of anatomical structures remains a bottleneck to research, as it is commonly performed with highly tedious and time-consuming manual work. During recent years, however, machine learning methods are an emerging approach to overcoming this limitation, especially with the use of deep learning techniques such as convolutional neural networks (CNNs), which proved to be very efficient and, as such, promising candidates for image segmentation. The main objective of this PhD project was to develop a pipeline for the fully-automated segmentation of anatomical structures in micro-computed tomography (micro-CT) images of insects using state-of-the-art deep learning methods. The restricted number of available high-resolution 3D labeled images necessitated the use of a CNN architecture that performs segmentation satisfactorily even with limited data; the U-Net architecture is such a CNN that has shown good performance in medical images using few annotated images. Ant brains were selected as the test case. Since no dataset of micro-CT images of ant brains existed for the current case study, a new extensive dataset was created across a wide variation of 94 ant species. Its existence can be of importance, as brain images of ants are similar to those of other insects; therefore, our dataset can act as a starting point for the development of a substantial library of micro-CT images of insects, and work as a pre-training dataset for future CNNs. Also, our network is generalizable for segmenting the whole neural system in full-body scans, and works in tests of distantly related and morphologically divergent insects (e.g., fruit flies). The latter suggests that algorithms such as our network can be applied generally across diverse taxa. The chosen species set was designed to be interesting for further evolutionary morphology analysis. Therefore, we used it to test the social brain hypothesis for ants, i.e., whether there is a connection between the brain investment and the sociality of each species. Volumetric statistical analysis was performed, also considering phylogenetic data; its results, however, did not validate the hypothesis