ToScA North America (6 – 8 June 2017, The University of Texas, Austin, TX) Program

ToScA North America will address key areas of science, including Multi-modal Imaging, Geosciences, Forensics, Increasing Contrast, Educational Outreach, Data, Materials Science and Medical and Biological Science.University of Texas High-Resolution X-ray CT Facility (UTCT); Jackson School of Geosciences, The University of Texas at Austin; Natural History Museum (London); Royal Microscopical Society (Oxford, UK)Geological Science

GiA Roots: Software for the high throughput analysis of plant root system architecture

Background: Characterizing root system architecture (RSA) is essential to understanding the development and function of vascular plants. Identifying RSA-associated genes also represents an underexplored opportunity for crop improvement. Software tools are needed to accelerate the pace at which quantitative traits of RSA are estimated from images of root networks.Results: We have developed GiA Roots (General Image Analysis of Roots), a semi-automated software tool designed specifically for the high-throughput analysis of root system images. GiA Roots includes user-assisted algorithms to distinguish root from background and a fully automated pipeline that extracts dozens of root system phenotypes. Quantitative information on each phenotype, along with intermediate steps for full reproducibility, is returned to the end-user for downstream analysis. GiA Roots has a GUI front end and a command-line interface for interweaving the software into large-scale workflows. GiA Roots can also be extended to estimate novel phenotypes specified by the end-user.Conclusions: We demonstrate the use of GiA Roots on a set of 2393 images of rice roots representing 12 genotypes from the species Oryza sativa. We validate trait measurements against prior analyses of this image set that demonstrated that RSA traits are likely heritable and associated with genotypic differences. Moreover, we demonstrate that GiA Roots is extensible and an end-user can add functionality so that GiA Roots can estimate novel RSA traits. In summary, we show that the software can function as an efficient tool as part of a workflow to move from large numbers of root images to downstream analysis

A virtual world of paleontology

Computer-aided visualization and analysis of fossils has revolutionized the study of extinct organisms. Novel techniques allow fossils to be characterized in three dimensions and in unprecedented detail. This has enabled paleontologists to gain important insights into their anatomy, development, and preservation. New protocols allow more objective reconstructions of fossil organisms, including soft tissues, from incomplete remains. The resulting digital reconstructions can be used in functional analyses, rigorously testing long-standing hypotheses regarding the paleobiology of extinct organisms. These approaches are transforming our understanding of long-studied fossil groups, and of the narratives of organismal and ecological evolution that have been built upon them

On the evaluation of methods for the recovery of plant root systems from X-ray computed tomography images

X-ray micro computed tomography (µCT) allows non-destructive visualisation of plant root systems within their soil environment and thus offers an alternative to commonly used destructive methodologies for the examination of plant roots and their interaction with the surrounding soil. Various methods for the recovery of root system information from X-ray CT image data have been presented in the literature. Detailed, ideally quantitative, evaluation is essential, in order to determine the accuracy and limitations of the proposed methods, and to allow potential users to make informed choices between them. This, however, is a complicated task. Three-dimensional ground truth data is expensive to produce, and the complexity of X-ray CT data means that manually generated ground truth may not be definitive. Similarly, artificially generated data is not entirely representative of real samples. The aims of this work are to raise awareness of the evaluation problem and to propose experimental approaches that allow the performance of root extraction methods to be assessed, ultimately improving the techniques available. To illustrate the issues, tests are conducted using both artificially generated images and real data samples

Visual tracking for the recovery of multiple interacting plant root systems from X-ray μCT images

We propose a visual object tracking framework for the extraction of multiple interacting plant root systems from three-dimensional X-ray micro computed tomography images of plants grown in soil. Our method is based on a level set framework guided by a greyscale intensity distribution model to identify object boundaries in image cross-sections. Root objects are followed through the data volume, while updating the tracker's appearance models to adapt to changing intensity values. In the presence of multiple root systems, multiple trackers can be used, but need to distinguish target objects from one another in order to correctly associate roots with their originating plants. Since root objects are expected to exhibit similar greyscale intensity distributions, shape information is used to constrain the evolving level set interfaces in order to lock trackers to their correct targets. The proposed method is tested on root systems of wheat plants grown in soil

Fully-automated root image analysis (faRIA)

High-throughput root phenotyping in the soil became an indispensable quantitative tool for the assessment of effects of climatic factors and molecular perturbation on plant root morphology, development and function. To efficiently analyse a large amount of structurally complex soil-root images advanced methods for automated image segmentation are required. Due to often unavoidable overlap between the intensity of fore- and background regions simple thresholding methods are, generally, not suitable for the segmentation of root regions. Higher-level cognitive models such as convolutional neural networks (CNN) provide capabilities for segmenting roots from heterogeneous and noisy background structures, however, they require a representative set of manually segmented (ground truth) images. Here, we present a GUI-based tool for fully automated quantitative analysis of root images using a pre-trained CNN model, which relies on an extension of the U-Net architecture. The developed CNN framework was designed to efficiently segment root structures of different size, shape and optical contrast using low budget hardware systems. The CNN model was trained on a set of 6465 masks derived from 182 manually segmented near-infrared (NIR) maize root images. Our experimental results show that the proposed approach achieves a Dice coefficient of 0.87 and outperforms existing tools (e.g., SegRoot) with Dice coefficient of 0.67 by application not only to NIR but also to other imaging modalities and plant species such as barley and arabidopsis soil-root images from LED-rhizotron and UV imaging systems, respectively. In summary, the developed software framework enables users to efficiently analyse soil-root images in an automated manner (i.e. without manual interaction with data and/or parameter tuning) providing quantitative plant scientists with a powerful analytical tool. © 2021, The Author(s)

Pore, live root and necromass quantification in complex heterogeneous wetland soils using X-ray computed tomography

Subsurface structures and especially the interactions between pores, roots and other organic matter elements have a strong impact on ecosystem functioning. Yet despite recent progress in the application of X-ray Computed Microtomography (µCT) to soil structure in agricultural science, applications to the more complex and heterogeneous substrates found in natural soils, specifically wetland soils, remain sparse. We apply X-ray µCT to a complex heterogenous soil and develop a robust segmentation method to quantify the pores, live roots and necromass. This approach significantly improves the detection of the organic matter elements, and gives us unprecedented detail and resolution in the segmentation of pores, live roots and necromass at a high spatial resolution (62.5 µm in this study). We identify several situations where pores and organic matter interact in the soil, including the disconnected air spaces (aerenchyma) that run within the Spartina stem and roots, tubular-shaped pores left behind by decaying roots, and lateral roots deploying within structural fragilities in the sediment. The capacity of X-ray µCT to distinguish the connected live root system from the necromass opens possibilities for applications to determine key wetland soil functions such as soil cohesivity, soil nutrient exchanges and soil carbon dynamics

Modeling, Simulation and Visualization of Plant Growth

Pflanzenmodellierung ist ein interessantes und herausforderndes Thema für die wissenschaftlich interdisziplinäre Forschung im Bereich der Mathematik, Biologie, Botanik, Agrarwirtschaft und Informatik. Im Rahmen dieser Dissertation wird die auf Lindenmayer Systeme (L-Systeme) und Partikel Systeme (PT-Systeme) basierende Modellierung, Simulation und Visualisierung von Pflanzenwachstum präsentiert und anhand von zwei Methoden zur Erzeugung von Pflanzenstruktur vorgestellt. Die erste Methode basiert auf Geklammerten, Stochastischen und Parametrischen L-Systemen. Sie ist für eine präzise Modellierung von bereits bekannten Pflanzenstrukturen geeignet und bietet auch die Möglichkeit, die komplexe Struktur in kleine Bestandteile bezüglich der Produktionsregeln zu zerlegen. In der zweiten Methode wird das PT-System für die Simulation grober Struktur und schneller Produktionsvorgänge eingesetzt, die auf vordefinierter Form und Volumen von Spross und Wurzel der Pflanze basiert. Beide Methoden können für die Modellierung von Pflanzenspross, Wurzel und Blattader eingesetzt werden. Der Prototyp dieser beiden Methoden ist in einer Weise konstruiert, die die physiologischen Daten der Masse realer Pflanzen berücksichtigt wie beispielsweise Länge und Durchmesser des Internodiums, Länge und Durchmesser der Zweige, Länge und Breite des Blattes, Länge und Breite der Wurzel. Diese Daten werden durch Parameterschätzung mit der Anwendung der Levenberg-Marquardt Methode bestimmt, die auf einer N-Puls sigmoidalen Funktion basiert. Alle angepassten Parameter können im Prototyp für die Simulation von Wachstumsverhalten einer Pflanze verwendet werden. Beide vorgeschlagenen Methoden werden für die künstliche Erzeugung bestimmter Pflanzenarten eingesetzt, die mit L-Systemen vertraute Experten von der Natur ablesen und in ein künstliches Modell konvertieren. Auch schlagen wir hier eine Methode für das Umwandeln der erhobenen Daten in ein künstliches Verzweigungsnetzwerk vor, das sogenannte ,,inverse Problem vom L-System". Dieses inverse Problem vom L-System bietet die Möglichkeit, die Struktur eines Verzweigungsnetzwerks mithilfe von Eingabebildern oder Volumendaten der komplexen Struktur zu rekonstruieren. Die tatsächlich wachsende Wurzel im Bodenvolumen kann mit Computer Tomography (CT) gescannt und die Wurzelstruktur aus dem Volumen segmentiert werden. Die endgültige rekonstruierte Struktur wird in L-Systemen basierend auf Geklammerten und Parametrischen L-Systemen für die Weiterverwendung beschrieben. Die Struktur und das Wachstum der Wurzelsysteme sind stark von Umgebungsfaktoren im Boden abhängig. Die Diffusionsgleichung und Richardsgleichung werden verwendet, um die Diffusion der Nährstoffe und den Fluss des Wassers zu beschreiben. Das Wurzelstystem wächst gleichzeitig und abhängig davon, wie die Diffusion der Nährstoffe und der Fluss des Wassers verläuft. Nährstoff- und Wasseraufnahme werden zu jedem Zeitpunkt des Wachstumsprozesses berechnet. Diese Dissertation fördert letztendlich neue Methoden für die Modelierung und Simulierung von Pflanzenwachstum aufgrund von Klimafaktoren, die mit einem von uns neu entwickelten Software Tool durchgeführt werden kann. Ergebnisse, die in dieser Dissertation erreicht werden, können in vielen verwandten Gebieten angewendet werden wie zum Beispiel in der Landwirtschaft, Pflanzenmodellierung, Agrarmanagement, Ökonomie, etc. Die Visualisierung des virtuellen Pflanzenwachstums, das mit L-Systemen, PT-System, inversem Problem, Wasserfluss und Nährstoffdiffusion modelliert wird, kann durch die von uns entwickelte Software PlantVR (Plant Virtual Reality) dargestellt werden

Examining Biogenic and Diagenetic Lead Exposure with Synchrotron Radiation X-ray Fluorescence Imaging of Experimentally Altered Bone

Trace elements, including the toxic trace metal of lead (Pb), have both the potential to provide valuable insights into past human lifeways, as well as a strong affinity for bone and dental tissues, making the analysis of them potentially useful to bioarchaeology. However, trace element analysis of archaeological skeletal remains is constantly hindered by diagenesis, the post-mortem chemical, physical, and biological transformations of skeletal remains, as these processes can interfere with the biogenic (lifetime) chemical composition of bone and teeth. New approaches may aid in overcoming some of these limitations. Synchrotron radiation X-ray fluorescence imaging (SR-XFI) can generate maps of trace metals, including Pb, in bone on a microstructural scale, and it has been proposed that this could be used to distinguish biogenic from diagenetic Pb exposure and provide insights into the individual life histories of Pb exposure. Recent technological improvements in SR-XFI, particularly the use of confocal optics, has permitted higher spatial resolution in element maps and optical, rather than physical, sectioning of fragile archaeological bone samples. The aim of this thesis was to experimentally test whether there are spatial differences in the distribution of Pb for diagenetic and biogenic modes of uptake in bone, and evaluate individual life histories of biogenic Pb exposure in a cadaveric population sampled from Saskatoon, Saskatchewan. To address these aims, this study used inductively coupled plasma-mass spectrometry (ICP-MS) and SR-XFI on bone samples from eighteenth to nineteenth century archaeological sites from Antigua and Lithuania representing biogenic and diagenetic Pb exposure, respectively, and experimentally altered modern bone samples donated to the Body Bequeathal Program (University of Saskatchewan, Saskatoon, SK). Pb concentrations in the cadaveric bone ranged from 1.2 to 7.1 µg/g. By contrast, the bulk Pb concentration of the Antigua sample was 253.94 µg/g and the bulk Pb concentration of the Lithuania individual was 125 µg/g. SR-XFI results demonstrated that there are marked differences in the spatial distribution of Pb corresponding to biogenic versus diagenetic uptake for both archaeological and experimentally altered modern samples. The modern Saskatchewan sample demonstrated a pattern of relatively low Pb exposure with higher levels of Pb exposure occurring in mature bone structures that formed earlier in life, likely during the era of leaded gasoline (pre-1980s)