Four-dimensional measurement of root system development using time-series three-dimensional volumetric data analysis by backward prediction.

BackgroundThe root distribution in the soil is one of the elements that comprise the root system architecture (RSA). In monocots, RSA comprises radicle and crown roots, each of which can be basically represented by a single curve with lateral root branches or approximated using a polyline. Moreover, RSA vectorization (polyline conversion) is useful for RSA phenotyping. However, a robust software that can enable RSA vectorization while using noisy three-dimensional (3D) volumes is unavailable.ResultsWe developed RSAtrace3D, which is a robust 3D RSA vectorization software for monocot RSA phenotyping. It manages the single root (radicle or crown root) as a polyline (a vector), and the set of the polylines represents the entire RSA. RSAtrace3D vectorizes root segments between the two ends of a single root. By utilizing several base points on the root, RSAtrace3D suits noisy images if it is difficult to vectorize it using only two end nodes of the root. Additionally, by employing a simple tracking algorithm that uses the center of gravity (COG) of the root voxels to determine the tracking direction, RSAtrace3D efficiently vectorizes the roots. Thus, RSAtrace3D represents the single root shape more precisely than straight lines or spline curves. As a case study, rice (Oryza sativa) RSA was vectorized from X-ray computed tomography (CT) images, and RSA traits were calculated. In addition, varietal differences in RSA traits were observed. The vector data were 32,000 times more compact than raw X-ray CT images. Therefore, this makes it easier to share data and perform re-analyses. For example, using data from previously conducted studies. For monocot plants, the vectorization and phenotyping algorithm are extendable and suitable for numerous applications.ConclusionsRSAtrace3D is an RSA vectorization software for 3D RSA phenotyping for monocots. Owing to the high expandability of the RSA vectorization and phenotyping algorithm, RSAtrace3D can be applied not only to rice in X-ray CT images but also to other monocots in various 3D images. Since this software is written in Python language, it can be easily modified and will be extensively applied by researchers in this field.

Arbuscular mycorrhizal fungus (AMF), forming symbiosis with most terrestrial plants, strongly modulates root system architecture (RSA), which is the main characteristic of root in soil, to improve plant growth and development. So far, the studies of AMF on tea plant seedlings are few and the relevant molecular mechanism is not deciphered. In this study, the 6-month-old cutting seedlings of tea plant cultivar “Wancha No.4” were inoculated with an AMF isolate, Rhizophagus intraradices BGC JX04B and harvested after 6 months of growth. The indexes of RSA and sugar contents in root were determined. The transcriptome data in root tips of mycorrhizal and non-mycorrhizal cutting seedlings were obtained by RNA-sequence (Seq) analysis. The results showed that AMF significantly decreased plant growth, but increased the sucrose content in root and the higher classes of lateral root (LR) formation (third and fourth LR). We identified 2047 differentially expressed genes (DEGs) based on the transcriptome data, and DEGs involved in metabolisms of phosphorus (42 DEGs), sugar (39), lipid (67), and plant hormones (39) were excavated out. Variation partitioning analysis showed all these four categories modulated the RSA. In phosphorus (P) metabolism, the phosphate transport and release (DEGs related to purple acid phosphatase) were promoted by AMF inoculation, while DEGs of sugar transport protein in sugar metabolism were downregulated. Lipid metabolism might not be responsible for root branching but for AMF propagation. With respect to phytohormones, DEGs of auxin (13), ethylene (14), and abscisic acid (5) were extensively affected by AMF inoculation, especially for auxin and ethylene. The further partial least squares structural equation modeling analysis indicated that pathways of P metabolism and auxin, as well as the direct way of AMF inoculation, were of the most important in AMF promoting root branching, while ethylene performed a negative role. Overall, our data revealed the alterations of genome-wide gene expression in tea plant roots after inoculation with AMF and provided a molecular basis for the regulatory mechanism of RSA (mainly root branching) changes induced by AMF.

Root system architecture (RSA) is an important measure of how plants navigate and interact with the soil environment. However, current methods in studying RSA must make tradeoffs between precision of data and proximity to natural conditions, with root growth in germination papers providing accessibility and high data resolution. Functional–structural plant models (FSPMs) can overcome this tradeoff, though parameterization and evaluation of FSPMs are traditionally based in manual measurements and visual comparison. Here, we applied a germination paper system to study the adventitious RSA and root phenology of Populus trichocarpa stem cuttings using time-series image-based phenotyping augmented by FSPM. We found a significant correlation between timing of root initiation and thermal time at cutting collection (P value = 0.0061, R2 = 0.875), but little correlation with RSA. We also present a use of RhizoVision [1] for automatically extracting FSPM parameters from time series images and evaluating FSPM simulations. A high accuracy of the parameterization was achieved in predicting 2D growth with a sensitivity rate of 83.5%. This accuracy was lost when predicting 3D growth with sensitivity rates of 38.5% to 48.7%, while overall accuracy varied with phenotyping methods. Despite this loss in accuracy, the new method is amenable to high throughput FSPM parameterization and bridges the gap between advances in time-series phenotyping and FSPMs.

Effect of the number of projections on dimensional measurements with X-ray computed tomography

Responses of soybean (Glycine max [L.] Merr.) to zinc oxide nanoparticles: Understanding changes in root system architecture, zinc tissue partitioning and soil characteristics

BackgroundThe use of 3D imaging techniques, such as X-ray CT, in root phenotyping has become more widespread in recent years. However, due to the complexity of the root structure, analyzing the resulting 3D volumes to obtain detailed architectural root traits remains a challenging computational problem. When it comes to image-based phenotyping of excavated maize root crowns, two types of root features that are notably missing from existing methods are the whorls and soil line. Whorls refer to the distinct areas located at the base of each stem node from which roots sprout in a circular pattern (Liu S, Barrow CS, Hanlon M, Lynch JP, Bucksch A. Dirt/3D: 3D root phenotyping for field-grown maize (zea mays). Plant Physiol. 2021;187(2):739–57. https://doi.org/10.1093/plphys/kiab311.). The soil line is where the root stem meets the ground. Knowledge of these features would give biologists deeper insights into the root system architecture (RSA) and the below- and above-ground root properties.ResultsWe developed TopoRoot+, a computational pipeline that produces architectural traits from 3D X-ray CT volumes of excavated maize root crowns. Building upon the TopoRoot software (Zeng D, Li M, Jiang N, Ju Y, Schreiber H, Chambers E, et al. Toporoot: A method for computing hierarchy and fine-grained traits of maize roots from 3D imaging. Plant Methods. 2021;17(1). https://doi.org/10.1186/s13007-021-00829-z.) for computing fine-grained root traits, TopoRoot + adds the capability to detect whorls, identify nodal roots at each whorl, and compute the soil line location. The new algorithms in TopoRoot + offer an additional set of fine-grained traits beyond those provided by TopoRoot. The addition includes internode distances, root traits at every hierarchy level associated with a whorl, and root traits specific to above or below the ground. TopoRoot + is validated on a diverse collection of field-grown maize root crowns consisting of nine genotypes and spanning across three years. TopoRoot + runs in minutes for a typical volume size of \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:40{0}^{3}$$\\end{document} on a desktop workstation. Our software and test dataset are freely distributed on Github.ConclusionsTopoRoot + advances the state-of-the-art in image-based phenotyping of excavated maize root crowns by offering more detailed architectural traits related to whorls and soil lines. The efficiency of TopoRoot + makes it well-suited for high-throughput image-based root phenotyping.

The root system architecture (RSA) in solanaceous vegetables has become an exciting area of research, uncovering complex networks essential for plant development, nutrient absorption, and resistance. This review delves into the comprehensive scope of research surrounding roots, shedding light on their dynamic nature and implications for agricultural practices. The Solanaceae family comprises of various vegetables, including tomatoes, potatoes, peppers, and eggplants, each with distinct root systems. Innovative methodologies have uncovered the complex and adaptive nature of these root systems. Roots of solanaceous vegetables have plasticity, reflecting their capacity to adjust to soil conditions, nutrient availability, and stressors. From the taproot structures in potatoes to the fibrous nature of tomato roots, this review synthesizes findings to elucidate the mechanisms behind root development and responses to environmental stimuli. Furthermore, the symbiotic associations between solanaceous crop roots and soil microorganisms have attracted significant interest. Understanding the intricate interactions between root exudates, microbial communities, and nutrient cycling opens avenues for sustainable agriculture, emphasizing the role of root architecture in fostering beneficial soil ecosystems. The implications of many research studies on RSA extend beyond academic interest and play a role in improving crop productivity. Understanding root system architecture enables breeders and agronomists to create cultivars with superior root characteristics, hence enhancing crop output, water-use efficiency, and resilience to abiotic challenges. Nonetheless, certain gaps persist, requiring additional investigation. A deeper investigation into the molecular mechanisms governing root development in solanaceous vegetables, particularly under changing climate scenarios is important for future research.

<p>X-ray computed tomography (CT) is acknowledged as a powerful tool for the study of root system architecture (RSA) of plants grown in soil. The study of the root system properties is however only possible after performing root segmentation, i.e. the binarization of all root voxels. Root segmentation is often regarded as a tedious and difficult task as its success depends on several factors such as the image resolution, the signal to noise during image acquisition and the gray value contrast between the roots and all other surrounding features. Here, we present an improved method for the segmentation of roots from X-Ray computed tomography 3D images. The algorithm Rootine (Gao et al. 2019) does not detect roots by their gray values but by their characteristic tubular shape. This algorithm was further developed in order to improve the root recovery rate and to reduce the number of parameters involved during the segmentation process. This was achieved by adding two key steps: (1) an absolute difference transform and (2) an automatic calculation of the parameters used during the Gaussian smoothing. The first step allows for targeting specific features based on a gray value criteria contained within a user-defined gray value range in order to better distinguish roots from pores whereas the second step allows for targeting root segments of specific diameters. On the benchmark dataset of Gao et al. 2019, the newly called “Rootine v.2” was able to recover 34 % more roots as compared to its preceding version. Moreover, the number of parameters was reduced from 10 down to 5 which allows for a faster calibration and an overall better usability of the algorithm. The presented method also allows for a more reliable estimation of root diameter derived from X-Ray CT images. This work was carried out in the framework of the priority programme 2089 “Rhizosphere spatiotemporal organization - a key to rhizosphere functions” funded by DFG (project number 403640293).</p>

Plant roots change their morphological traits in order to adapt themselves to different environmental conditions, resulting in the alteration of the root system architecture. To understand this mechanism, it is essential to visualize the morphology of the entire root system. To reveal effects of long-term alteration of gravity environment on root system development, we have performed an experiment in the International Space Station using Arabidopsis plants and obtained dried root systems grown in rockwool slabs. The X-ray computed tomography (CT) technique using industrial X-ray scanners has been introduced to visualize the root system architecture of crop species grown in soil in 3D non-invasively. In the case of the present study, however, the root system of Arabidopsis is composed of finer roots compared with typical crop plants and rockwool is also composed of fibers having similar dimension to that of the roots. A higher spatial resolution imaging method is required for distinguishing roots from rockwool. Therefore, in the present study, we tested refraction-contrast X-ray micro-CT using coherent X-ray optics available at the beamline of the synchrotron radiation facility SPring-8 for bio-imaging. We have found that a wide field of view but with low resolution obtained at the experimental Hutch 3 of this beamline provided an overview map of the root systems, while a narrow field of view but with high resolution obtained at the experimental Hutch 1 provided an extended architecture of the secondary roots, by a clear distinction between roots and individual rockwool fibers, resulting in the successful tracing of these roots from their basal regions.

<p id="C3">Root is the main organ of plants that absorbs water and nutrients. The growth of the root system will directly affect the absorption and utilization of nutrients, the resistance to abiotic stress and finally the yield of cotton. In this study, a natural population of 220 upland cotton accessions and a chromosomal segment substitution line (CSSL) population with 325 lines, derived from the crossing and backcrossing of <italic>Gossypium barbadense</italic> acc. 3-79 with <italic>G. hirsutum</italic> cv. ‘Emian 22’ were selected to collect the major root phenotypic traits. Four major traits, namely main root length (MRL), root fresh weight (RFW), root dry weight (RDW) and lateral root angle (LRA) were investigated, and genome-wide association analysis (GWAS) was performed for four root traits in natural populations in combination with genome resequencing. The results showed that the four root traits of the natural population are all in line with normal distribution, and the CSSL population were all in skewed distribution. The mean value of root index of CSSL population was higher than that of natural population. A total of 2,714,140 SNP was obtained from the resequencing data of the natural population. The principal component analysis (PCA) showed that the RFW and MRL could be used as two indexes for cotton root classification, through which the cotton root could be divided into nine types in each population. The analysis of population structure demonstrated that the natural population can be divided into five subgroups. Genome-wide association analysis rrevealed that two association sites were simultaneously associated by RFW and RDW in natural populations. The results of this study provide a theoretical basis for further research on root system architecture (RSA) and its genetic mechanism, and it is also of great significance to cotton breeding of abiotic stress resistance.

Dimensional X-ray Computed Tomography (CT) is a rapidly expanding field of research due to the numerous advantages this technique offers over conventional measurement technologies, most notably, the ability to measure internal features of a component. Tactile and optical Coordinate Measurement Machines (CMM), currently used in the manufacturing production industry, record points on the external surface of a workpiece by measuring the contact point of a physical probe or the reflection of projected light. X-ray CT has the ability to capture full volumetric data, since X-rays are transmitted through the entire object, revealing features which are otherwise invisible. Over the past five years, interest in this field has grown in the UK, with an increasing number of organisations in industry and research having access to X-ray CT machines and the wide range of manufacturers, offering new systems specifically designed for dimensional metrology applications.Despite this, the complexity of data acquisition required for dimensional measurement using X-ray CT has made it difficult to estimate the measurement uncertainty. This has hindered the generation of standards and full-scale adoption of this technique in industry. Due to the nature of X-ray imaging, a number of non-linear influence factors exist which have the potential to cause dimensional measurement error. These influences must be better understood to reduce and ideally, compensate error.In this doctoral thesis, the effects of the influence factors associated with CT data acquisition are studied, specifically, beam hardening and a finite X-ray source size. The effects these have on the quality of X-ray CT data are well understood; typically degrading the achievable contrast and spatial resolution of the CT image. However, the effects on dimensional measurement are less well understood due to the complexity of their interactions before reconstruction of the final image. These influences are modelled in a simulated CT acquisition to quantify any systematic effects on determination of edges in the CT image. The results are then validated by experimentally replicating the simulation set-up.In this work, it is found that beam hardening and a finite source diameter can lead to systematic errors in the edge position within the CT image. Beam hardening generally leads to dilation of the edge; where the edge position moves in the direction of the surface vector. In contrast, a finite source diameter can lead to erosion of the edge; where the edge position moves in an opposing direction to the surface vector.

&amp;lt;p&amp;gt;Root hairs substantially contribute to the acquisition of nutrients and potentially also to water uptake. Hence, they might have a strong impact on plant growth under nutrient- or water-limited conditions. As little information presently exists about differences in matter uptake to plants either with or without root hairs, we hypothesize that the absence of root hairs will be compensated by an increase in root growth to overcome the hair-less handicap. Within the DFG-funded Priority Program 2089, we compare two different genotypes (i.e. &amp;lt;em&amp;gt;Zea mays&amp;lt;/em&amp;gt; &amp;amp;#8220;Wild Type&amp;amp;#8221; and its corresponding hair-less mutant &amp;amp;#8220;&amp;lt;em&amp;gt;rth3&amp;lt;/em&amp;gt;&amp;amp;#8221;) grown in two different substrates (loam and sand) in column experiments. X-ray computed tomography (X-ray CT) was used to investigate the spatial-temporal change of root architecture during growth. Additionally, total root length was measured after destructive sampling at harvest with WinRhizo. Contrary to our expectation, the reduced root surface area available for water and nutrient uptake in case of the hair-less cultivar was not compensated by more intensive root growth. The substrate had a higher impact on root growth than the presence or absence of root-hairs. For shoot growth (shoot biomass), both factors (genotype, substrate) had a significant impact. As a consequence, nutrient uptake efficiency (uptake per unit root length) was clearly increased by the presence of root-hairs, irrespective of the substrate. Water uptake efficiency did not show any difference between genotypes under the well-watered conditions studied. In general, water uptake per unit root length was higher in sand compared to loam. Differences in nutrient uptake efficiency should be reflected in the extent of nutrient depletion gradients around roots. To address such biochemical gradients we develop a new subsampling scheme based on extraction of undisturbed subsamples. Subsamples will be imaged with micro X-ray fluorescence (&amp;amp;#956;XRF) for elemental mapping. The 2D &amp;amp;#181;XRF image will be registered into the 3D X-ray CT image to relate the extent of gradients to the age of the respective root segment.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;&amp;amp;#160;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;This project was carried out in the framework of the priority programme 2089 &amp;amp;#8220;Rhizosphere spatiotemporal organisation - a key to rhizosphere functions&amp;amp;#8221; funded by DFG (project number 403640293).&amp;lt;/p&amp;gt;

Localization and detection of isolated hot spots in single-photon emission computed tomography (SPECT) may be aided by the addition of anatomical information from X-ray computed tomography (CT). A method for registering SPECT and CT volumes was developed, using two planar transmission views of the patient acquired immediately before or after a SPECT scan by placing a sheet source on one head of a dual-head camera system. Corresponding reprojected CT (rCT) images were simultaneously registered to the transmission images, using a region of interest (ROI) containing the lung boundaries. Six parameters (translations in three dimensions and rotations through three Euler angles) were calculated by iteratively transforming and reprojecting until a correlation between the rCT and transmission data within the ROI was maximized. Final values of the transformation parameters were used to register the entire CT volume to the SPECT volume. An experiment was performed using a torso phantom and spherical lesions containing Ga-67, representing the distribution in a lymphoma patient. Accuracy was assessed by calculating the differences between coordinates of centroids of five tumors in the CT and SPECT images. The magnitudes of the differences in x, y and z averaged over the five tumors were 1.55 mm +/-1.30, 036 mm +/-2.23 and 0.13 mm +/-1.47. Precision was assessed by perturbing the orientation of the CT volume over a range of translations and rotations. Effects on the perturbed parameter and on the other parameters that were held constant were examined by calculating the standard deviation about the mean of each parameter's final value following registration. The values varied from 0.06 to 0.62 mm and from 0.05 to 1.68 deg for translation and rotation, respectively. Acceptable accuracy and precision for registration of CT to SPECT volumes were achieved with the use of the paired transmission and rCT images.

This paper describes a method for extracting surfaces from multi-material CT (computed tomography) data. Most contouring methods such as marching cubes algorithm assume that CT data are composed of only two materials. Some extended methods can extract surfaces from the multi-material (non-manifold) implicit representation. However, these methods are not directly applicable to CT data which are composed of three or more materials. There are two major problems that arise from fundamentals of CT. The first problem is that we have to use n(n - l)/2 threshold values for CT data contains n materials and select appropriately one threshold value for each boundary area. The second is that we cannot reconstruct only from CT data in which area three or more materials are adjacent each other. In this paper, we propose a method to solve the problems by using image analysis and demonstrate the effectiveness of the method with application examples construct polygon models from CT data of machine parts.

Impact of ectomycorrhizal symbiosis on root system architecture and nutrient absorption in Chinese chestnut and pecan seedlings