Statistical inference of the mechanisms driving collective cell movement

Numerous biological processes, many impacting on human health, rely on collective cell movement. We develop nine candidate models, based on advection-diffusion partial differential equations, to describe various alternative mechanisms that may drive cell movement. The parameters of these models were inferred from one-dimensional projections of laboratory observations of Dictyostelium discoideum cells by sampling from the posterior distribution using the delayed rejection adaptive Metropolis algorithm (DRAM). The best model was selected using the Widely Applicable Information Criterion (WAIC). We conclude that cell movement in our study system was driven both by a self-generated gradient in an attractant that the cells could deplete locally, and by chemical interactions between the cells

Skill of managers and the wisdom of herds: examining an alternative approach to grazing management in larkspur habitat, The

2019 Spring.Includes bibliographical references.The many species of larkspur (Delphinium spp. L.) are among the most dangerous poisonous plants on rangelands in the western United States, causing death losses estimated at 2-5% (up to 15%) per year for cattle grazing in larkspur habitat. Research has estimated the value of these losses at $234 million per year. Other effects, such as altered grazing management practices and consequent lost forage quantity and quality, are significant but poorly understood. Current best management practice recommendations stress seasonal avoidance of pastures with larkspur present, with little evidence that this is practical or ultimately effective. Alternative approaches to addressing this complex challenge are difficult to design, test, and apply due to the threat of dead livestock. In this dissertation I explore an alternative approach based on the idea that it may be possible to manage cattle grazing such that no individual consumes a lethal dose, regardless of timing of grazing or larkspur density. This idea was inspired by producers past and present who have reported such success. I examine this hypothesis using agent-based models and a field experiment with Geyer's larkspur (D. geyeri Greene), the focal species throughout this research. Chapter 2 presents a conceptual model that situates this work within the broader context of livestock grazing management and rangeland science. This synthesis also highlights the potential for conceptual models to aid in the design, application, communication, and consilience of research in rangelands. Drawing on a wide range of work, this model challenges the discipline of rangeland science to integrate a broader array of methods and epistemologies to create knowledge sufficient to the complexity of the systems under study. Agent-based models (ABMs) provide an effective method of testing alternate management strategies without risk to livestock. ABMs are especially useful for modeling complex systems such as livestock grazing management and allow for realistic bottom-up encoding of cattle behavior. In Chapter 3, I introduce a spatially-explicit, behavior-based ABM of cattle grazing in a pasture with a dangerous amount of D. geyeri. This model tests the role of herd cohesion and stocking density in larkspur intake, finds that both are key drivers of larkspur-induced toxicosis, and indicates that alteration of these factors within realistic bounds can mitigate risk. Crucially, the model points to herd cohesion, which has received little attention in the discipline, as playing an important role in reducing lethal acute toxicosis. As the first agent-based model to simulate grazing behavior at realistic scales, this study also demonstrates the tremendous potential of ABMs to illuminate grazing management dynamics, including fundamental aspects of livestock behavior amidst ecological heterogeneity. Chapter 3 raises the question of the potential response of larkspur to being grazed. In Chapter 4, I examine the response of D. geyeri to two seasons of 25% or 75% aboveground plant mass removal. The 75% treatment led to significantly lower alkaloid concentrations (mg•g-1) and pools (mg per plant), while the 25% treatment had a lesser effect. Combined with lessons from previous studies, this indicates that Geyer's larkspur plants subject to aboveground mass removal such as may occur via grazing can be expected to become significantly less dangerous to cattle. We suggest that the mechanisms for this reduction are both alkaloid removal and reduced belowground root mass, as significant evidence indicates that alkaloids are synthesized and stored in the roots. The most common explanations for the evolution and persistence of herd behavior in large herbivores relate to decreased risk of predation. However, poisonous plants such as larkspur can present a threat comparable to predation. Chapters 3 and 4 point to the cattle herd itself as the potential solution to this seemingly intractable challenge and suggest that larkspur and forage patchiness may drive deaths. In Chapter 5, I present an agent-based model that incorporates neutral landscape models to assess the interaction between plant patchiness and herd behavior within the context of poisonous plants as predator and cattle as prey. The simulation results indicate that larkspur patchiness is indeed a driver of toxicosis and that highly cohesive herds can greatly reduce the risk of death in even the most dangerous circumstances. By placing the results in context with existing theories about the utility of herds, I demonstrate that grouping in large herbivores can be an adaptive response to patchily distributed poisonous plants. Lastly, the results hold significant management-relevant insight, both for cattle producers managing grazing in larkspur habitat and in general as a call to reconsider the manifold benefits of herd behavior among domestic herbivores. The findings in this dissertation build a strong case for an alternative approach to grazing management in larkspur habitat but fall short of actionable recommendations. For one, this is because a one-size-fits-all solution that would work across the great diversity of habitats and management systems in which larkspur is found is unlikely. Instead, these findings must be placed in context with existing knowledge and the complex multiscale decision-making processes of producers. Future work will thus focus on improving our understanding of the diverse set of management circumstances under which the many species of problematic larkspur are found

Diversification Across a Dynamic Landscape: Phylogeography and Riverscape Genetics of Speckled Dace (Rhinichthys osculus) in Western North America

Evolution occurs at various spatial and temporal scales. For example, speciation may occur in historic time, whereas localized adaptation is more contemporary. Each is required to identify and manage biodiversity. However, the relative abundance of Speckled Dace (Rhinichthys osculus), a small cyprinid fish in western North America (WNA) and the study species for this dissertation, establishes it an atypical conservation target, particularly when contrasted with the profusion of narrowly endemic forms it displays. Yet, the juxtaposition of ubiquity versus endemism provides an ideal model against which to test hypotheses regarding the geomorphic evolution of WNA. More specifically, it also allows the evolutionary history of Speckled Dace to be contrasted at multiple spatial and temporal scales, and interpreted in the context of contemporary anthropogenic pressures and climatic uncertainty. Chapter II dissects the broad distribution of Speckled Dace and quantifies how its evolution has been driven by hybridization/ introgression. Chapter III narrows the geographic focus by interpreting Speckled Dace distribution within two markedly different watersheds: The Colorado River and the Great Basin. The former is a broad riverine habitat whereas the latter is an endorheic basin. Two biogeographic models compare and contrast the tempo and mode of evolution within these geologically disparate habitats. Chapter IV employs a molecular clock to determine origin of Speckled Dace lineages in Death Valley (CA/NV), and to contrast these against estimates for a second endemic species, Devil’s Hole Pupfish (Cyprinodon diabolis). While palaeohydrology served to diversify Rhinichthys, its among-population connectivity occurred contemporaneously. These data also provide guidance for assessing the origin of the Devil’s Hole Pupfish, a topic of considerable contention. The final two chapters present bioinformatic software that facilitates the analysis of single-nucleotide-polymorphism (SNP) DNA data (used herein). Chapter V describes COMP-D, a program designed to assess introgression among lineages, whereas Chapter VI presents programmatic modifications to BAYESASS that allow migration to be quantified from SNP datasets. These five studies provide an in-depth understanding of contemporary and historical processes that shape aquatic biodiversity in environments prone to anthropogenic disturbance. They also highlight the complexities of evolutionary mechanisms and their implications for conservation in a changing world

Analog Spiking Neuromorphic Circuits and Systems for Brain- and Nanotechnology-Inspired Cognitive Computing

Human society is now facing grand challenges to satisfy the growing demand for computing power, at the same time, sustain energy consumption. By the end of CMOS technology scaling, innovations are required to tackle the challenges in a radically different way. Inspired by the emerging understanding of the computing occurring in a brain and nanotechnology-enabled biological plausible synaptic plasticity, neuromorphic computing architectures are being investigated. Such a neuromorphic chip that combines CMOS analog spiking neurons and nanoscale resistive random-access memory (RRAM) using as electronics synapses can provide massive neural network parallelism, high density and online learning capability, and hence, paves the path towards a promising solution to future energy-efficient real-time computing systems. However, existing silicon neuron approaches are designed to faithfully reproduce biological neuron dynamics, and hence they are incompatible with the RRAM synapses, or require extensive peripheral circuitry to modulate a synapse, and are thus deficient in learning capability. As a result, they eliminate most of the density advantages gained by the adoption of nanoscale devices, and fail to realize a functional computing system. This dissertation describes novel hardware architectures and neuron circuit designs that synergistically assemble the fundamental and significant elements for brain-inspired computing. Versatile CMOS spiking neurons that combine integrate-and-fire, passive dense RRAM synapses drive capability, dynamic biasing for adaptive power consumption, in situ spike-timing dependent plasticity (STDP) and competitive learning in compact integrated circuit modules are presented. Real-world pattern learning and recognition tasks using the proposed architecture were demonstrated with circuit-level simulations. A test chip was implemented and fabricated to verify the proposed CMOS neuron and hardware architecture, and the subsequent chip measurement results successfully proved the idea. The work described in this dissertation realizes a key building block for large-scale integration of spiking neural network hardware, and then, serves as a step-stone for the building of next-generation energy-efficient brain-inspired cognitive computing systems

DEEP-LEARNING-ENHANCED MULTIPHYSICS FLOW COMPUTATIONS FOR PROPULSION APPLICATIONS

Numerical simulation is a critical part of research into and development of engineering systems. Engineers often use simulation to explore design settings both analytically and numerically before prototypes are built and tested. Even with the most advanced high performance computing facility, however, high-fidelity numerical simulations are extremely costly in time and resources. For example, a survey of the design parameter space for a single-element injector for a propulsion application (such as the RD-170 rocket engine) using the large eddy simulation technique may require several tens of millions of CPU-hours on a major computer cluster. This is because the flowfields can only be fully characterized by resolving a multitude of strongly coupled fluid dynamic, thermodynamic, transport, multiphase, and combustion processes. The cost is further increased by grid resolution requirements and by the effects of turbulence and high-pressure phenomena, which require treatment of real-fluid physics at supercritical conditions. If such models are used for statistical analysis or design optimization, the total computation time and resource requirements may render the work unfeasible. Recent developments in deep learning techniques offer the possibility of significant advances in dealing with these challenges and significant shortening of the time-to-solution. The general scope of this thesis research is to set the foundations for new paradigms in modeling, simulation, and design by applying deep learning techniques to recent developments in computational science. More specifically, the research aims at developing an integrated suite of data-driven surrogate modeling approaches and software for large-scale simulation problems. The techniques to be put into practice include: (1) deep neural networks for function approximation and solver acceleration, (2) deep autoencoders for nonlinear dimensionality reduction, and (3) spatiotemporal emulators based on multi-level neural networks for simulator approximation and rapid exploration of design spaces. A hierarchy of benchmark cases has been studied to generate databases to enable and support the development and verification of the proposed approaches. Emphasis is placed on canonical examples, as well as on engineering problems for aerospace and automotive applications, including supercritical turbulent flows in a rocket-engine swirl injector, and multiphase cavitating flows in a diesel engine injector.Ph.D

Marshall Space Flight Center Research and Technology Report 2019

Today, our calling to explore is greater than ever before, and here at Marshall Space Flight Centerwe make human deep space exploration possible. A key goal for Artemis is demonstrating and perfecting capabilities on the Moon for technologies needed for humans to get to Mars. This years report features 10 of the Agencys 16 Technology Areas, and I am proud of Marshalls role in creating solutions for so many of these daunting technical challenges. Many of these projects will lead to sustainable in-space architecture for human space exploration that will allow us to travel to the Moon, on to Mars, and beyond. Others are developing new scientific instruments capable of providing an unprecedented glimpse into our universe. NASA has led the charge in space exploration for more than six decades, and through the Artemis program we will help build on our work in low Earth orbit and pave the way to the Moon and Mars. At Marshall, we leverage the skills and interest of the international community to conduct scientific research, develop and demonstrate technology, and train international crews to operate further from Earth for longer periods of time than ever before first at the lunar surface, then on to our next giant leap, human exploration of Mars. While each project in this report seeks to advance new technology and challenge conventions, it is important to recognize the diversity of activities and people supporting our mission. This report not only showcases the Centers capabilities and our partnerships, it also highlights the progress our people have achieved in the past year. These scientists, researchers and innovators are why Marshall and NASA will continue to be a leader in innovation, exploration, and discovery for years to come

Confronting Grand Challenges in environmental fluid mechanics

Environmental fluid mechanics underlies a wealth of natural, industrial and, by extension, societal challenges. In the coming decades, as we strive towards a more sustainable planet, there are a wide range of grand challenge problems that need to be tackled, ranging from fundamental advances in understanding and modeling of stratified turbulence and consequent mixing, to applied studies of pollution transport in the ocean, atmosphere and urban environments. A workshop was organized in the Les Houches School of Physics in France in January 2019 with the objective of gathering leading figures in the field to produce a road map for the scientific community. Five subject areas were addressed: multiphase flow, stratified flow, ocean transport, atmospheric and urban transport, and weather and climate prediction. This article summarizes the discussions and outcomes of the meeting, with the intent of providing a resource for the community going forward