Segmenting root systems in X-ray computed tomography images using level sets

The segmentation of plant roots from soil and other growing media in X-ray computed tomography images is needed to effectively study the root system architecture without excavation. However, segmentation is a challenging problem in this context because the root and non-root regions share similar features. In this paper, we describe a method based on level sets and specifically adapted for this segmentation problem. In particular, we deal with the issues of using a level sets approach on large image volumes for root segmentation, and track active regions of the front using an occupancy grid. This method allows for straightforward modifications to a narrow-band algorithm such that excessive forward and backward movements of the front can be avoided, distance map computations in a narrow band context can be done in linear time through modification of Meijster et al.'s distance transform algorithm, and regions of the image volume are iteratively used to estimate distributions for root versus non-root classes. Results are shown of three plant species of different maturity levels, grown in three different media. Our method compares favorably to a state-of-the-art method for root segmentation in X-ray CT image volumes.Comment: 11 page

DNA Binding of Centromere Protein C (CENPC) Is Stabilized by Single-Stranded RNA

Centromeres are the attachment points between the genome and the cytoskeleton: centromeres bind to kinetochores, which in turn bind to spindles and move chromosomes. Paradoxically, the DNA sequence of centromeres has little or no role in perpetuating kinetochores. As such they are striking examples of genetic information being transmitted in a manner that is independent of DNA sequence (epigenetically). It has been found that RNA transcribed from centromeres remains bound within the kinetochore region, and this local population of RNA is thought to be part of the epigenetic marking system. Here we carried out a genetic and biochemical study of maize CENPC, a key inner kinetochore protein. We show that DNA binding is conferred by a localized region 122 amino acids long, and that the DNA-binding reaction is exquisitely sensitive to single-stranded RNA. Long, single-stranded nucleic acids strongly promote the binding of CENPC to DNA, and the types of RNAs that stabilize DNA binding match in size and character the RNAs present on kinetochores in vivo. Removal or replacement of the binding module with HIV integrase binding domain causes a partial delocalization of CENPC in vivo. The data suggest that centromeric RNA helps to recruit CENPC to the inner kinetochore by altering its DNA binding characteristics

A Statistical Growth Property of Plant Root Architectures.

Numerous types of biological branching networks, with varying shapes and sizes, are used to acquire and distribute resources. Here, we show that plant root and shoot architectures share a fundamental design property. We studied the spatial density function of plant architectures, which specifies the probability of finding a branch at each location in the 3-dimensional volume occupied by the plant. We analyzed 1645 root architectures from four species and discovered that the spatial density functions of all architectures are population-similar. This means that despite their apparent visual diversity, all of the roots studied share the same basic shape, aside from stretching and compression along orthogonal directions. Moreover, the spatial density of all architectures can be described as variations on a single underlying function: a Gaussian density truncated at a boundary of roughly three standard deviations. Thus, the root density of any architecture requires only four parameters to specify: the total mass of the architecture and the standard deviations of the Gaussian in the three (x, y, z) growth directions. Plant shoot architectures also follow this design form, suggesting that two basic plant transport systems may use similar growth strategies

A temporal analysis and response to nitrate availability of 3D root system architecture in diverse pennycress (Thlaspi arvense L.) accessions

IntroductionRoots have a central role in plant resource capture and are the interface between the plant and the soil that affect multiple ecosystem processes. Field pennycress (Thlaspi arvense L.) is a diploid annual cover crop species that has potential utility for reducing soil erosion and nutrient losses; and has rich seeds (30-35% oil) amenable to biofuel production and as a protein animal feed. The objective of this research was to (1) precisely characterize root system architecture and development, (2) understand plastic responses of pennycress roots to nitrate nutrition, (3) and determine genotypic variance available in root development and nitrate plasticity.MethodsUsing a root imaging and analysis pipeline, the 4D architecture of the pennycress root system was characterized under four nitrate regimes, ranging from zero to high nitrate concentrations. These measurements were taken at four time points (days 5, 9, 13, and 17 after sowing).ResultsSignificant nitrate condition response and genotype interactions were identified for many root traits, with the greatest impact observed on lateral root traits. In trace nitrate conditions, a greater lateral root count, length, density, and a steeper lateral root angle was observed compared to high nitrate conditions. Additionally, genotype-by-nitrate condition interaction was observed for root width, width:depth ratio, mean lateral root length, and lateral root density.DiscussionThese findings illustrate root trait variance among pennycress accessions. These traits could serve as targets for breeding programs aimed at developing improved cover crops that are responsive to nitrate, leading to enhanced productivity, resilience, and ecosystem service

A High-Resolution Airborne Color-Infrared Camera Water Mask for the NASA ABoVE Campaign

The airborne AirSWOT instrument suite, consisting of an interferometric Ka-band synthetic aperture radar and color-infrared (CIR) camera, was deployed to northern North America in July and August 2017 as part of the NASA Arctic-Boreal Vulnerability Experiment (ABoVE). We present validated, open (i.e., vegetation-free) surface water masks produced from high-resolution (1 m), co-registered AirSWOT CIR imagery using a semi-automated, object-based water classification. The imagery and resulting high-resolution water masks are available as open-access datasets and support interpretation of AirSWOT radar and other coincident ABoVE image products, including LVIS, UAVSAR, AIRMOSS, AVIRIS-NG, and CFIS. These synergies offer promising potential for multi-sensor analysis of Arctic-Boreal surface water bodies. In total, 3167 km2 of open surface water were mapped from 23,380 km2 of flight lines spanning 23 degrees of latitude and broad environmental gradients. Detected water body sizes range from 0.00004 km2 (40 m2) to 15 km2. Power-law extrapolations are commonly used to estimate the abundance of small lakes from coarser resolution imagery, and our mapped water bodies followed power-law distributions, but only for water bodies greater than 0.34 (±0.13) km2 in area. For water bodies exceeding this size threshold, the coefficients of power-law fits vary for different Arctic-Boreal physiographic terrains (wetland, prairie pothole, lowland river valley, thermokarst, and Canadian Shield). Thus, direct mapping using high-resolution imagery remains the most accurate way to estimate the abundance of small surface water bodies. We conclude that empirical scaling relationships, useful for estimating total trace gas exchange and aquatic habitats on Arctic-Boreal landscapes, are uniquely enabled by high-resolution AirSWOT-like mappings and automated detection methods such as those developed here

Novel mutations support a role for Profilin 1 in the pathogenesis of ALS

AbstractMutations in the gene encoding profilin 1 (PFN1) have recently been shown to cause amyotrophic lateral sclerosis (ALS), a fatal neurodegenerative disorder. We sequenced the PFN1 gene in a cohort of ALS patients (n = 485) and detected 2 novel variants (A20T and Q139L), as well as 4 cases with the previously identified E117G rare variant (∼ 1.2%). A case-control meta-analysis of all published E117G ALS+/− frontotemporal dementia cases including those identified in this report was significant p = 0.001, odds ratio = 3.26 (95% confidence interval, 1.6–6.7), demonstrating this variant to be a susceptibility allele. Postmortem tissue from available patients displayed classic TAR DNA-binding protein 43 pathology. In both transient transfections and in fibroblasts from a patient with the A20T change, we showed that this novel PFN1 mutation causes protein aggregation and the formation of insoluble high molecular weight species which is a hallmark of ALS pathology. Our findings show that PFN1 is a rare cause of ALS and adds further weight to the underlying genetic heterogeneity of this disease

Global Research Alliance N2O chamber methodology guidelines : Summary of modeling approaches

Acknowledgements Funding for this publication was provided by the New Zealand Government to support the objectives of the Livestock Research Group of the Global Research Alliance on Agricultural Greenhouse Gases. Individual authors work contribute to the following projects for which support has been received: Climate smart use of Norwegian organic soils (MYR, 2017-2022) project funded by the Research Council of Norway (decision no. 281109); Scottish Government's Strategic Research Programme, SuperG (under EU Horizon 2020 programme); DEVIL (NE/M021327/1), Soils-R-GRREAT (NE/P019455/1) and the EU H2020 project under Grant Agreement 774378—Coordination of International Research Cooperation on Soil Carbon Sequestration in Agriculture (CIRCASA); to project J-001793, Science and Technology Branch, Agriculture and Agri-Food Canada; and New Zealand Ministry of Business, Innovation and Employment (MBIE) core funding. Thanks to Alasdair Noble and the anonymous reviewers for helpful comments on a draft of this paper and to Anne Austin for editing services.Peer reviewedPublisher PD

Morphological Plant Modeling: Unleashing Geometric and Topological Potential within the Plant Sciences

The geometries and topologies of leaves, flowers, roots, shoots, and their arrangements have fascinated plant biologists and mathematicians alike. As such, plant morphology is inherently mathematical in that it describes plant form and architecture with geometrical and topological techniques. Gaining an understanding of how to modify plant morphology, through molecular biology and breeding, aided by a mathematical perspective, is critical to improving agriculture, and the monitoring of ecosystems is vital to modeling a future with fewer natural resources. In this white paper, we begin with an overview in quantifying the form of plants and mathematical models of patterning in plants. We then explore the fundamental challenges that remain unanswered concerning plant morphology, from the barriers preventing the prediction of phenotype from genotype to modeling the movement of leaves in air streams. We end with a discussion concerning the education of plant morphology synthesizing biological and mathematical approaches and ways to facilitate research advances through outreach, cross-disciplinary training, and open science. Unleashing the potential of geometric and topological approaches in the plant sciences promises to transform our understanding of both plants and mathematics