The mechanics of Arabidopsis seed germination

Germination is defined as the protrusion of the embryonic radicle through the seed coat layers (endosperm and testa). As the radicle elongates, the testa ruptures, followed by rupture of the endosperm. Arabidopsis seeds exhibit a two-step germination process with sequential rupture of the testa and endosperm. We are interested in exploring the physical process of germination. Whilst much effort has previously been placed on genetic networks, a mathematical approach for furthering the understanding of the physical/mechanical properties of germination has not yet been described. The Mathematics in Plant Sciences Study Group helped us to develop a better understanding of the problem. Several different mathematical models were generated for radicle growth and endosperm stretching. These models were developed on multiscale dimensions – looking at the organ, tissue and cellular levels. The outcomes of the study group have heightened our interest in the mechanical aspects of germination, and we are currently progressing with a grant proposal – a collaboration between the Schools of Biosciences and Engineering at the University of Nottingham, and a group from the Department of Biology at the University of Freiburg, Germany

The Adoption of Agile Management Practices in a Traditional Project Environment : An IS/IT Case Study

Hany Wells, Darren Dalcher, Hedley Smyth, ‘The Adoption of Agile Management Practices in a Traditional Project Environment’, paper presented at the 48th Hawaii International Conference on System Sciences (HICSS-48), Kauai, Hawaii, USA, 5-8 January, 2015.Despite the growing interest in the usage and application of Agile Project Management Methodologies (PMMs), there is only scant research examining how and why organisations select agile approaches for managing and delivering Information Technology /Information Systems (IT/IS) projects. This paper reports on the findings of such research conducted within the context of a large technology focused case organisation. The findings identify significant variance between business lines, specifically between product development and software development functions and their ability to follow agile guidelines. Generally across the organisation there was limited evidence of tailoring to context, an important organisational success factor, yet there was a more significant level of tailoring and responsiveness to client needs and wishes. Overall, there was a lack of clarity about the location of the decoupling points following the scoping of the project. Recommendations therefore require further attention and understanding of the implications of new practices employed by organisations, not least by senior management and for additional research underpinning such discovery.Peer reviewedFinal Accepted Versio

An updated protocol for high throughput plant tissue sectioning

Quantification of the tissue and cellular structure of plant material is essential for the study of a variety of plant sciences applications. Currently, many methods for sectioning plant material are either low throughput or involve free-hand sectioning which requires a significant amount of practice. Here, we present an updated method to provide rapid and high-quality cross sections, primarily of root tissue but which can also be readily applied to other tissues such as leaves or stems. To increase the throughput of traditional agarose embedding and sectioning, custom designed 3D printed molds were utilized to embed 5–15 roots in a block for sectioning in a single cut. A single fluorescent stain in combination with laser scanning confocal microscopy was used to obtain high quality images of thick sections. The provided CAD files allow production of the embedding molds described here from a number of online 3D printing services. Although originally developed for roots, this method provides rapid, high quality cross sections of many plant tissue types, making it suitable for use in forward genetic screens for differences in specific cell structures or developmental changes. To demonstrate the utility of the technique, the two parent lines of the wheat (Triticum aestivum) Chinese Spring × Paragon doubled haploid mapping population were phenotyped for root anatomical differences. Significant differences in adventitious cross section area, stele area, xylem, phloem, metaxylem, and cortical cell file count were found

The NASA CYGNSS SmallSat Constellation

The NASA Cyclone Global Navigation Satellite System (CYGNSS) is a constellation of eight microsatellites in low earth orbit at ~525 km altitude and 35 deg inclination. CYGNSS was launched in December 2016 for a planned 2 year mission and 7 of the 8 spacecraft continue to operatue nominally as of May 2023. Each microsatellites carries a bistatic radar receiver to measure reflected GPS signals from the Earth surface. The measurements can be converted to surface wind speed and latent and sensible heat flux over the ocean, and to surface soil moisture and wetland extent over land. Measurements penetrate through all levels of precipitation as well as moderate to heavy vegetation due to the low microwave frequency used by GPS. The number of satellites in the constellation results in sub-daily refresh rates which supports imaging of short time scale weather events such as hurricane rapid intensification, flood inundation dynamics, and sudden soil saturation after major rain events. CYGNSS satellites uses a single string design architecture to reduce the complexity and recurring cost of each unit. Mission redundancy is obtained at the constellation level. Data products are produced by combining measurements from all satellites in such a way that the sampling requirements can be met using only a subset of the satellites. Constellation-level redundancy also permits individual satellites to be switched from their nominal science data taking mode to various engineering test and calibration modes while the overall mission is still able to meet its science requirements

Active Vision and Surface Reconstruction for 3D Plant Shoot Modelling

Plant phenotyping is the quantitative description of a plant’s physiological, biochemical and anatomical status which can be used in trait selection and helps to provide mechanisms to link underlying genetics with yield. Here, an active vision- based pipeline is presented which aims to contribute to reducing the bottleneck associated with phenotyping of architectural traits. The pipeline provides a fully automated response to photometric data acquisition and the recovery of three-dimensional (3D) models of plants without the dependency of botanical expertise, whilst ensuring a non-intrusive and non-destructive approach. Access to complete and accurate 3D models of plants supports computation of a wide variety of structural measurements. An Active Vision Cell (AVC) consisting of a camera-mounted robot arm plus combined software interface and a novel surface reconstruction algorithm is proposed. This pipeline provides a robust, flexible and accurate method for automating the 3D reconstruction of plants. The reconstruction algorithm can reduce noise and provides a promising and extendable framework for high throughput phenotyping, improving current state-of-the-art methods. Furthermore, the pipeline can be applied to any plant species or form due to the application of an active vision framework combined with the automatic selection of key parameters for surface reconstruction

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

Auxin fluxes through plasmodesmata modify root-tip auxin distribution

© 2020. Published by The Company of Biologists Ltd. Auxin is a key signal regulating plant growth and development. It is well established that auxin dynamics depend on the spatial distribution of efflux and influx carriers on the cell membranes. In this study, we employ a systems approach to characterise an alternative symplastic pathway for auxin mobilisation via plasmodesmata, which function as intercellular pores linking the cytoplasm of adjacent cells. To investigate the role of plasmodesmata in auxin patterning, we developed a multicellular model of the Arabidopsis root tip. We tested the model predictions using the DII-VENUS auxin response reporter, comparing the predicted and observed DII-VENUS distributions using genetic and chemical perturbations designed to affect both carrier-mediated and plasmodesmatal auxin fluxes. The model revealed that carrier-mediated transport alone cannot explain the experimentally determined auxin distribution in the root tip. In contrast, a composite model that incorporates both carrier-mediated and plasmodesmatal auxin fluxes re-capitulates the root-tip auxin distribution. We found that auxin fluxes through plasmodesmata enable auxin reflux and increase total root-tip auxin. We conclude that auxin fluxes through plasmodesmata modify the auxin distribution created by efflux and influx carriers

Approaches to three-dimensional reconstruction of plant shoot topology and geometry

There are currently 805 million people classified as chronically undernourished, and yet the World’s population is still increasing. At the same time, global warming is causing more frequent and severe flooding and drought, thus destroying crops and reducing the amount of land available for agriculture. Recent studies show that without crop climate adaption, crop productivity will deteriorate. With access to 3D models of real plants it is possible to acquire detailed morphological and gross developmental data that can be used to study their ecophysiology, leading to an increase in crop yield and stability across hostile and changing environments. Here we review approaches to the reconstruction of 3D models of plant shoots from image data, consider current applications in plant and crop science, and identify remaining challenges. We conclude that although phenotyping is receiving an increasing amount of attention – particularly from computer vision researchers – and numerous vision approaches have been proposed, it still remains a highly interactive process. An automated system capable of producing 3D models of plants would significantly aid phenotyping practice, increasing accuracy and repeatability of measurements

RootNav 2.0: Deep learning for automatic navigation of complex plant root architectures

© The Author(s) 2019. Published by Oxford University Press. BACKGROUND: In recent years quantitative analysis of root growth has become increasingly important as a way to explore the influence of abiotic stress such as high temperature and drought on a plant's ability to take up water and nutrients. Segmentation and feature extraction of plant roots from images presents a significant computer vision challenge. Root images contain complicated structures, variations in size, background, occlusion, clutter and variation in lighting conditions. We present a new image analysis approach that provides fully automatic extraction of complex root system architectures from a range of plant species in varied imaging set-ups. Driven by modern deep-learning approaches, RootNav 2.0 replaces previously manual and semi-automatic feature extraction with an extremely deep multi-task convolutional neural network architecture. The network also locates seeds, first order and second order root tips to drive a search algorithm seeking optimal paths throughout the image, extracting accurate architectures without user interaction. RESULTS: We develop and train a novel deep network architecture to explicitly combine local pixel information with global scene information in order to accurately segment small root features across high-resolution images. The proposed method was evaluated on images of wheat (Triticum aestivum L.) from a seedling assay. Compared with semi-automatic analysis via the original RootNav tool, the proposed method demonstrated comparable accuracy, with a 10-fold increase in speed. The network was able to adapt to different plant species via transfer learning, offering similar accuracy when transferred to an Arabidopsis thaliana plate assay. A final instance of transfer learning, to images of Brassica napus from a hydroponic assay, still demonstrated good accuracy despite many fewer training images. CONCLUSIONS: We present RootNav 2.0, a new approach to root image analysis driven by a deep neural network. The tool can be adapted to new image domains with a reduced number of images, and offers substantial speed improvements over semi-automatic and manual approaches. The tool outputs root architectures in the widely accepted RSML standard, for which numerous analysis packages exist (http://rootsystemml.github.io/), as well as segmentation masks compatible with other automated measurement tools. The tool will provide researchers with the ability to analyse root systems at larget scales than ever before, at a time when large scale genomic studies have made this more important than ever

Three-dimensional reconstruction of plant shoots from multiple images using an active vision system

The reconstruction of 3D models of plant shoots is a challenging problem central to the emerging discipline of plant phenomics – the quantitative measurement of plant structure and function. Current approaches are, however, often limited by the use of static cameras. We propose an automated active phenotyping cell to reconstruct plant shoots from multiple images using a turntable capable of rotating 360 degrees and camera mounted robot arm. To overcome the problem of static camera positions we develop an algorithm capable of analysing the environment and determining viewpoints from which to capture initial images suitable for use by a structure from motion technique