archiDART: an R package for the automated computation of plant root architectural traits

Background and aims

In order to analyse root system architectures (RSAs) from captured images, a variety of manual (e.g. Data Analysis of Root Tracings, DART), semi-automated and fully automated software packages have been developed. These tools offer complementary approaches to study RSAs and the use of the Root System Markup Language (RSML) to store RSA data makes the comparison of measurements obtained with different (semi-) automated root imaging platforms easier. The throughput of the data analysis process using exported RSA data, however, should benefit greatly from batch analysis in a generic data analysis environment (R software).

Methods

We developed an R package (archiDART) with five functions. It computes global RSA traits, root growth rates, root growth directions and trajectories, and lateral root distribution from DART-generated and/or RSML files. It also has specific plotting functions designed to visualise the dynamics of root system growth.

Results

The results demonstrated the ability of the package’s functions to compute relevant traits for three contrasted RSAs (Brachypodium distachyon [L.] P. Beauv., Hevea brasiliensis Müll. Arg. and Solanum lycopersicum L.).

Conclusions

This work extends the DART software package and other image analysis tools supporting the RSML format, enabling users to easily calculate a number of RSA traits in a generic data analysis environment.

     

Ground-penetrating radar-based automatic reconstruction of three-dimensional coarse root system architecture

Background and aims

The diverse functions of roots set requirements on specific root system architecture (RSA). Investigation on RSA holds potentials for studying the adaptation of plants to environmental stresses and to interspecific competitions. Ground-penetrating radar (GPR) has provided a non-invasive method for studying in situ RSA. However, previous GPR method relied on manually connecting root points detected between radargrams to restore each root branch, resulting in limited accuracy and efficiency of reconstructing RSA. The objective of this study is to improve the effectiveness of 3D RSA reconstruction using GPR root detection data.

Methods

A total of 213 coarse root sections (with diameter >0.5 cm) were extracted from a distribution map of a reference shrub (Arctostaphylos pungens) root system to simulate the coarse roots identified by GPR. An automatic method was established to trace each root point to its optimum growing source point. Connections between discrete root points recovered the topology of the reference RSA. A spline curve smoothing method was applied to restore the 3D morphology of the reference RSA. The proposed protocol was then tested to rebuild the 3D RSA of a shrub (Caragana microphylla) growing in the sandy soils after in situ GPR survey. The accuracy of RSA reconstruction was quantitatively evaluated by a relationship matrix method and qualitatively assessed by direct comparisons between the reconstructed and the actual RSAs after in situ excavation.

Results

For both simulated and field collected GPR detection datasets, the reconstructed RSAs strongly corresponded to the real topology of the actual root systems. When adapting the best strategy, 186 of the 213 (87.32 %) root points on the reference root system of A. pungens were interlinked with correct topology, and the relationship matrix method detected an overall similarity of 82.75 % between the reconstructed and the actual RSAs.

Conclusion

The proposed automatic RSA reconstruction method greatly enhances the interpretation of GPR detection data regarding coarse roots, making in situ non-invasive and long-term mapping and monitoring of RSA possible.     

Water supply and not nitrate concentration determines primary root growth in Arabidopsis

Understanding how root system architecture (RSA) adapts to changing nitrogen and water availability is important for improving acquisition. A sand rhizotron system was developed to study RSA in a porous substrate under tightly regulated nutrient supply. The RSA of Arabidopsis seedlings under differing nitrate (NO₃^‐) and water supplies in agar and sand was described. The hydraulic conductivity of the root environment was manipulated by using altered sand particle size and matric potentials. Ion‐selective microelectrodes were used to quantify NO₃^‐ at the surface of growing primary roots in sands of different particle sizes. Differences in RSA were observed between seedlings grown on agar and sand, and the influence of NO₃^‐ (0.1–10.0 mm ) and water on RSA was determined. Primary root length (PRL) was a function of water flux and independent of NO₃^‐. The percentage of roots with laterals correlated with water flux, whereas NO₃^‐ supply was important for basal root (BR) growth. In agar and sand, the NO₃^‐ activities at the root surface were higher than those supplied in the nutrient solution. The sand rhizotron system is a useful tool for the study of RSA, providing a porous growth environment that can be used to simulate the effects of hydraulic conductivity on growth.     

Evolving technologies for growing,imaging and analyzing 3D root system architecture of crop plants

A plant's ability to maintain or improve its yield under limiting conditions,such as nutrient de ficiency or drought,can be strongly in fluenced by root system architecture(RSA),the three-dimensional distribution of the different root types in the soil. The ability to image,track and quantify these root system attributes in a dynamic fashion is a useful tool in assessing desirable genetic and physiological root traits. Recent advances in imaging technology and phenotyping software have resulted in substantive progress in describing and quantifying RSA. We have designed a hydroponic growth system which retains the three-dimensional RSA of the plant root system,while allowing for aeration,solution replenishment and the imposition of nutrient treatments,as well as high-quality imaging of the root system. The simplicity and flexibility of the system allows for modi fications tailored to the RSA of different crop species and improved throughput. This paper details the recent improvements and innovations in our root growth and imaging system which allows for greater image sensitivity(detection of fine roots and other root details),higher ef ficiency,and a broad array of growing conditions for plants that more closely mimic those found under field conditions.     

Differential effects of sucrose and auxin on localized phosphate deficiency-induced modulation of different traits of root system architecture in Arabidopsis

Phosphorus, one of the essential elements for plants, is often a limiting nutrient in soils. Low phosphate (Pi) availability induces sugar-dependent systemic expression of genes and modulates the root system architecture (RSA). Here, we present the differential effects of sucrose (Suc) and auxin on the Pi deficiency responses of the primary and lateral roots of Arabidopsis (Arabidopsis thaliana). Inhibition of primary root growth and loss of meristematic activity were evident in seedlings grown under Pi deficiency with or without Suc. Although auxin supplementation also inhibited primary root growth, loss of meristematic activity was observed specifically under Pi deficiency with or without Suc. The results suggested that Suc and auxin do not influence the mechanism involved in localized Pi sensing that regulates growth of the primary root and therefore delineates it from sugar-dependent systemic Pi starvation responses. However, the interaction between Pi and Suc was evident on the development of the lateral roots and root hairs in the seedlings grown under varying levels of Pi and Suc. Although the Pi+ Suc- condition suppressed lateral root development, induction of few laterals under the Pi- Suc- condition point to increased sensitivity of the roots to auxin during Pi deprivation. This was supported by expression analyses of DR5uidA, root basipetal transport assay of auxin, and RSA of the pgp19 mutant exhibiting reduced auxin transport. A significant increase in the number of lateral roots under the Pi- Suc- condition in the chalcone synthase mutant (tt4-2) indicated a potential role for flavonoids in auxin-mediated Pi deficiency-induced modulation of RSA. The study thus demonstrated differential roles of Suc and auxin in the developmental responses of ontogenetically distinct root traits during Pi deprivation. In addition, lack of cross talk between local and systemic Pi sensing as revealed by the seedlings grown under either the Pi- Suc- condition or in the heterogeneous Pi environment highlighted the coexistence of Suc-independent and Suc-dependent regulatory mechanisms that constitute Pi starvation responses.     

Root system architecture: opportunities and constraints for genetic improvement of crops

Abiotic stresses increasingly curtail crop yield as a result of global climate change and scarcity of water and nutrients. One way to minimize the negative impact of these factors on yield is to manipulate root system architecture (RSA) towards a distribution of roots in the soil that optimizes water and nutrient uptake. It is now established that most of the genetic variation for RSA is driven by a suite of quantitative trait loci. As we discuss here, marker-assisted selection and quantitative trait loci cloning for RSA are underway, exploiting genomic resources, candidate genes and the knowledge gained from Arabidopsis, rice and other crops. Nonetheless, efficient and accurate phenotyping, modelling and collaboration with breeders remain important challenges, particularly when defining ideal RSA for different crops and target environments.     

Nitrate and phosphate availability and distribution have different effects on root system architecture of Arabidopsis

Plant root systems can respond to nutrient availability and distribution by changing the three-dimensional deployment of their roots: their root system architecture (RSA). We have compared RSA in homogeneous and heterogeneous nitrate and phosphate supply in Arabidopsis. Changes in nitrate and phosphate availability were found to have contrasting effects on primary root length and lateral root density, but similar effects on lateral root length. Relative to shoot dry weight (DW), primary root length decreased with increasing nitrate availability, while it increased with increasing phosphate supply. Lateral root density remained constant across a range of nitrate supplies, but decreased with increasing phosphate supply. In contrast, lateral root elongation was suppressed both by high nitrate and high phosphate supplies. Local supplies of high nitrate or phosphate in a patch also had different effects. Primary root growth was not affected by a high nitrate patch, but growth through a high phosphate patch reduced primary root growth after the root left the patch. A high nitrate patch induced an increase in lateral root density in the patch, whereas lateral root density was unaffected by a high phosphate patch. However, both phosphate- and nitrate-rich patches induced lateral root elongation in the patch and suppressed it outside the patch. This co-ordinated response of lateral roots also occurs in soil-grown plants exposed to a nutrient-rich patch. The auxin-resistant mutants axrl, axr4 and aux1 all showed the wild-type lateral root elongation responses to a nitrate-rich patch, suggesting that auxin is not required for this response.     

An integrated method for quantifying root architecture of field-grown maize

Background and Aims

A number of techniques have recently been developed for studying the root system architecture (RSA) of seedlings grown in various media. In contrast, methods for sampling and analysis of the RSA of field-grown plants, particularly for details of the lateral root components, are generally inadequate.

Methods

An integrated methodology was developed that includes a custom-made root-core sampling system for extracting intact root systems of individual maize plants, a combination of proprietary software and a novel program used for collecting individual RSA information, and software for visualizing the measured individual nodal root architecture.

Key Results

Example experiments show that large root cores can be sampled, and topological and geometrical structure of field-grown maize root systems can be quantified and reconstructed using this method. Second- and higher order laterals are found to contribute substantially to total root number and length. The length of laterals of distinct orders varies significantly. Abundant higher order laterals can arise from a single first-order lateral, and they concentrate in the proximal axile branching zone.

Conclusions

The new method allows more meaningful sampling than conventional methods because of its easily opened, wide corer and sampling machinery, and effective analysis of RSA using the software. This provides a novel technique for quantifying RSA of field-grown maize and also provides a unique evaluation of the contribution of lateral roots. The method also offers valuable potential for parameterization of root architectural models.     

Statistical modeling of nitrogen-dependent modulation of root system architecture in Arabidopsis thaliana

Plant root development is strongly affected by nutrient availability. Despite the importance of structure and function of roots in nutrient acquisition,statistical modeling approaches to evaluate dynamic and temporal modulations of root system architecture in response to nutrient availability have remained as widely open and exploratory areas in root biology. In this study,we developed a statistical modeling approach to investigate modulations of root system architecture in response to nitrogen availability. Mathematical models were designed for quantitative assessment of root growth and root branching phenotypes and their dynamic relationships based on hierarchical con figuration of primary and lateral roots formulating the fishbone-shaped root system architecture in Arabidopsis thaliana. Time-series datasets reporting dynamic changes in root developmental traits on different nitrate or ammonium concentrations were generated for statistical analyses. Regression analyses unraveled key parameters associated with:(i) inhibition of primary root growth under nitrogen limitation or on ammonium;(ii) rapid progression of lateral root emergence in response to ammonium; and(iii) inhibition of lateral root elongation in the presence of excess nitrate or ammonium. This study provides a statistical framework for interpreting dynamic modulation of root system architecture,supported by metaanalysis of datasets displaying morphological responses of roots to diverse nitrogen supplies.     

Root taxa identification in plant mixtures – current techniques and future challenges

Background

Studying root biomass, root system distribution and belowground interactions is essential for understanding the composition of plant communities, the impact of global change, and terrestrial biogeochemistry. Most soil samples and minirhizotron pictures hold roots of more than one species or plant individual. The identification of taxa by their roots would allow species-specific questions to be posed; information about root affiliation to plant individuals could be used to determine intra-specific competition.

Scope

Researchers need to be able to discern plant taxa by roots as well as to quantify abundances in mixed root samples. However, roots show less distinctive features that permit identification than aboveground organs. This review discusses the primary use of available methods, outlining applications, shortcomings and future developments.

Conclusion

Methods are either non-destructive, e.g. visual examination of root morphological criteria in situ, or require excavated and excised root samples. Among the destructive methods are anatomical keys, chemotaxonomic approaches and molecular markers. While some methods allow for discerning the root systems of individual plants, others can distinguish roots on the functional group or plant taxa level; methods such as IR spectroscopy and qPCR allow for quantifying the root biomass proportion of species without manual sorting.     

Root plasticity under abiotic stress

Abiotic stresses increasingly threaten existing ecological and agricultural systems across the globe. Plant roots perceive these stresses in the soil and adapt their architecture accordingly. This review provides insights into recent discoveries showing the importance of root system architecture (RSA) and plasticity for the survival and development of plants under heat, cold, drought, salt, and flooding stress. In addition, we review the molecular regulation and hormonal pathways involved in controlling RSA plasticity, main root growth, branching and lateral root growth, root hair development, and formation of adventitious roots. Several stresses affect root anatomy by causing aerenchyma formation, lignin and suberin deposition, and Casparian strip modulation. Roots can also actively grow toward favorable soil conditions and avoid environments detrimental to their development. Recent advances in understanding the cellular mechanisms behind these different root tropisms are discussed. Understanding root plasticity will be instrumental for the development of crops that are resilient in the face of abiotic stress.

Recent discoveries show the importance of root system architecture plasticity for the survival and growth of plants under several abiotic stresses.     

The arbuscular mycorrhizal fungus Diversispora spurca ameliorates effects of waterlogging on growth,root system architecture and antioxidant enzyme activities of citrus seedlings

Citrus plants are often exposed to heavy rain and subsequent periods of soil waterlogging which severely restrict tree growth. We assessed the effect of one arbuscular mycorrhizal fungus species (Diversispora spurca) on growth, root system architecture (RSA), and antioxidant enzyme activities of young citrus (Citrus junos) seedlings. Waterlogging for 37 d significantly restricted mycorrhizal colonization but increased the number of entry points and vesicles. Compared with non-mycorrhizal controls, mycorrhizal seedlings had significantly greater plant height, fresh mass, total root and taproot lengths, projected and surface root areas, root volume, and numbers of lst, 2nd and 3rd order lateral roots regardless of waterlogging treatment. D. spurca significantly increased root catalase (CAT) activity in non-stressed seedlings and increased root soluble protein concentration and leaf CAT activity in waterlogged seedlings, thereby inducing lower oxidative damage. These results suggest that D. spurca ameliorates effects of waterlogging on growth, RSA and antioxidant enzyme activities.     

Recovering Root System Traits Using Image Analysis Exemplified by Two-Dimensional Neutron Radiography Images of Lupine

Root system traits are important in view of current challenges such as sustainable crop production with reduced fertilizer input or in resource-limited environments. We present a novel approach for recovering root architectural parameters based on image-analysis techniques. It is based on a graph representation of the segmented and skeletonized image of the root system, where individual roots are tracked in a fully automated way. Using a dynamic root architecture model for deciding whether a specific path in the graph is likely to represent a root helps to distinguish root overlaps from branches and favors the analysis of root development over a sequence of images. After the root tracking step, global traits such as topological characteristics as well as root architectural parameters are computed. Analysis of neutron radiographic root system images of lupine (Lupinus albus) grown in mesocosms filled with sandy soil results in a set of root architectural parameters. They are used to simulate the dynamic development of the root system and to compute the corresponding root length densities in the mesocosm. The graph representation of the root system provides global information about connectivity inside the graph. The underlying root growth model helps to determine which path inside the graph is most likely for a given root. This facilitates the systematic investigation of root architectural traits, in particular with respect to the parameterization of dynamic root architecture models.Crucial factors for plant development are light quantity and quality as well as water and nutrient availability in soils. Regarding water and nutrient uptake, root architecture is the main aspect of plant productivity (Lynch, 2007; Smith and De Smet, 2012) and needs to be accurately considered when describing root processes. Currently, understanding the impact of roots and rhizosphere traits on plant resource efficiency is of highest relevance (Hinsinger et al., 2011). Development in this area will increase food security by enabling more sustainable production with reduced fertilizer input by improving cropping systems and cultivars for resource-limited environments (de Dorlodot et al., 2007).Root architectural development includes architectural, morphological, anatomical, as well as physiological traits. For the systematic investigation of such complex biological systems, mathematical modeling is inevitable (Roose and Schnepf, 2008). Ideally, experiments and theoretical models are developed to mutually support each other. In this way, models are created that include state-of-the-art knowledge and have significant parameters. There are various root architectural models incorporating a multitude of processes (Dunbabin et al., 2013) that are originally based on Pagès et al. (1989) and Diggle (1988). Generally, the parameterization of such models is difficult and demands elaborate experimental effort. In this work, we present a novel approach for recovering root system parameters based on image-analysis techniques. In this way, we simplify the systematic investigation of root architectural traits, in particular with respect to the parameterization of root system models.Imaging techniques for the visualization of soil-grown root systems in two and three dimensions include x-ray computed tomography (Heeraman et al., 1997; Tracy et al., 2010; Mooney et al., 2012), neutron radiography (NR; Oswald et al., 2008), and magnetic resonance imaging (Pohlmeier et al., 2008). NR is one of the most suitable techniques to investigate roots grown in soil, because it allows a high throughput, provides a strong contrast between roots and soil, and therefore requires little effort for image processing. A major advantage of NR as well as magnetic resonance imaging is the possibility to monitor water distribution and roots simultaneously (Menon et al., 2007; Oswald et al., 2008; Moradi et al., 2009; Carminati et al., 2010; Stingaciu et al., 2013). This is especially useful as water is a crucial factor ruling root allocation in soil (Hodge, 2010).Images of root architecture contain a huge amount of information, and image analysis helps to recover parameters describing certain root architectural and morphological traits. The majority of imaging systems for root systems are designed for two-dimensional images, such as RootReader2D (Clark et al., 2013), GiA Roots (Galkovskyi et al., 2012), SmartRoot (Lobet et al., 2011), EZ-Rhizo (Armengaud et al., 2009), and Growscreen (Nagel et al., 2012). See also Le Bot et al. (2010) for a review of available software. A starting point for image analysis is commonly a grayscale image of a root system. The first step is to create a binary image by segmentation. Further steps include skeletonization, root tracking, and data analysis. The most common segmentation method is some form of thresholding (e.g. RootReader2D, GiA Roots, SmartRoot, EZ-Rhizo; Stingaciu et al. [2013]). Other methods include the livewire algorithm (Basu and Pal, 2012) or the levelset method (RootTrak; Mairhofer et al., 2012) that determine the borders of each root. The creation of a root system skeleton is either done manually (e.g. DART; Le Bot et al., 2010) or based on morphological operators such as thinning and closing (GiA Roots, RootReader2D, EZ-Rhizo), sometimes with options for the user to correct skeleton points (EZ-Rhizo). The root tracking step can be performed manually (DART) or based on creating a graph representation of the root system combined with Dijkstra’s algorithm, a search algorithm that finds the shortest path between two nodes inside a graph (RootReader2D; Stingaciu et al. [2013]). Furthermore, algorithms can operate on the skeleton (EZ-Rhizo) or directly on the image source (SmartRoot). In SmartRoot, the user selects a root in the original image with a mouse click and then a skeletonization algorithm determines the skeleton of the selected root. The output of all root tracking algorithms is a data structure of a set of roots that stores information such as connectivity between roots and their position in space.Global traits of the root system are obtained directly from the segmented image or the skeleton (GiA Roots, RooTrak). Global traits include convex hull, network depth, network length distribution, maximum number of roots, maximum width of the root system, network length, and specific root length. Furthermore, the data structure from a root tracking procedure is used to obtain individual, local root parameters (DART, RootReader2D, EZ-Rhizo, SmartRoot). The latter are able to obtain root architectural parameters that can be used for model parameterization. An additional aspect is the dealing with dynamic data (i.e. images of the same root system taken at several times). Analysis of such sequences may lead to better insight into the development of the root system (e.g. DART, SmartRoot) or even reveal growth zones and their local growth velocities (Basu and Pal, 2012).Analysis software for two-dimensional images of soil-grown root systems currently work in a semiautomated way with respect to tracing individual roots. This requires considerable user input for larger root systems. We present a new, fully automated approach for recovering root architectural parameters from two-dimensional images of root systems. The software Root System Analyzer is, to our knowledge, the first algorithm for two-dimensional analysis of soil-grown root systems that features fully automated root tracking. Only primary roots have to be initiated manually by the user. The user is also free to initiate any laterals, but this is not mandatory. Further growth of primary roots and laterals is then tracked in a fully automated way. In addition, there is a user interface that allows for manual correction of individual roots if required. In this work, we do not go into the details about the segmentation step but focus on the root tracking step and the parameterization of a root system model (Leitner et al., 2010). The described algorithm starts with a sequence of segmented two-dimensional images showing the dynamic development of a root system. For each image, morphological operators are used for skeletonization. Based on this, a graph representation of the root system is created. A dynamic root architecture model helps to determine which edges of the graph belong to an individual root. The algorithm elongates each root at the root tip and simulates growth confined within the already existing graph representation. The increment of root elongation is calculated assuming constant growth. For each root, the algorithm finds all possible paths and elongates the root in the direction of the optimal path. In this way, each edge of the graph is assigned to one or more coherent roots. The algorithm considers the fact that new branches can only emerge after the apical zone has developed, which helps in the decision of whether the root is branching or two roots are crossing or overlapping. Image sequences of root systems are handled in such a way that the previous image is used as a starting point for the current image. This is helpful in the analysis of complex root systems as well as for retrieving dynamic parameters such as elongation rates. The algorithm is implemented in a set of Matlab m-files, which makes the code flexible so that it can easily be adjusted to specific experimental setups or mathematical models.We exemplify the approach with two-dimensional neutron radiography images of lupine (Lupinus albus) root systems grown in mesocosms filled with a sandy soil. Furthermore, we compare our approach with the approaches of SmartRoot and RootReader2D and demonstrate how our approach can be used to analyze large root systems.     

VAP-RELATED SUPPRESSORS OF TOO MANY MOUTHS (VST) family proteins are regulators of root system architecture

Root system architecture (RSA) is a key factor in the efficiency of nutrient capture and water uptake in plants. Understanding the genetic control of RSA will be useful in minimizing fertilizer and water usage in agricultural cropping systems. Using a hydroponic screen and a gel-based imaging system, we identified a rice (Oryza sativa) gene, VAP-RELATED SUPPRESSOR OF TOO MANY MOUTHS1 (OsVST1), which plays a key role in controlling RSA. This gene encodes a homolog of the VAP-RELATED SUPPRESSORS OF TOO MANY MOUTHS (VST) proteins in Arabidopsis (Arabidopsis thaliana), which promote signaling in stomata by mediating plasma membrane–endoplasmic reticulum contacts. OsVST1 mutants have shorter primary roots, decreased root meristem size, and a more compact RSA. We show that the Arabidopsis VST triple mutants have similar phenotypes, with reduced primary root growth and smaller root meristems. Expression of OsVST1 largely complements the short root length and reduced plant height in the Arabidopsis triple mutant, supporting conservation of function between rice and Arabidopsis VST proteins. In a field trial, mutations in OsVST1 did not adversely affect grain yield, suggesting that modulation of this gene could be used as a way to optimize RSA without an inherent yield penalty.

Root meristem size and root system architecture in both rice and Arabidopsis are regulated by proteins related to mediators plasma membrane–endoplasmic reticulum contact.     

High-throughput imaging and analysis of root system architecture in Brachypodium distachyon under differential nutrient availability

Nitrogen (N) and phosphorus (P) deficiency are primary constraints for plant productivity, and root system architecture (RSA) plays a vital role in the acquisition of these nutrients. The genetic determinants of RSA are poorly understood, primarily owing to the complexity of crop genomes and the lack of sufficient RSA phenotyping methods. The objective of this study was to characterize the RSA of two Brachypodium distachyon accessions under different nutrient availability. To do so, we used a high-throughput plant growth and imaging platform, and developed software that quantified 19 different RSA traits. We found significant differences in RSA between two Brachypodium accessions grown on nutrient-rich, low-N and low-P conditions. More specifically, one accession maintained axile root growth under low N, while the other accession maintained lateral root growth under low P. These traits resemble the RSA of crops adapted to low-N and -P conditions, respectively. Furthermore, we found that a number of these traits were highly heritable. This work lays the foundation for future identification of important genetic components of RSA traits under nutrient limitation using a mapping population derived from these two accessions.     

Shoot phytochrome B modulates reactive oxygen species homeostasis in roots via abscisic acid signaling in Arabidopsis

Underground roots normally reside in darkness. However, they are often exposed to ambient light that penetrates through cracks in the soil layers which can occur due to wind, heavy rain or temperature extremes. In response to light exposure, roots produce reactive oxygen species (ROS) which promote root growth. It is known that ROS‐induced growth promotion facilitates rapid escape of the roots from non‐natural light. Meanwhile, long‐term exposure of the roots to light elicits a ROS burst, which causes oxidative damage to cellular components, necessitating that cellular levels of ROS should be tightly regulated in the roots. Here we demonstrate that the red/far‐red light photoreceptor phytochrome B (phyB) stimulates the biosynthesis of abscisic acid (ABA) in the shoots, and notably the shoot‐derived ABA signals induce a peroxidase‐mediated ROS detoxification reaction in the roots. Accordingly, while ROS accumulate in the roots of the phyb mutant that exhibits reduced primary root growth in the light, such an accumulation of ROS did not occur in the dark‐grown phyb roots that exhibited normal growth. These observations indicate that mobile shoot‐to‐root ABA signaling links shoot phyB‐mediated light perception with root ROS homeostasis to help roots adapt to unfavorable light exposure. We propose that ABA‐mediated shoot‐to‐root phyB signaling contributes to the synchronization of shoot and root growth for optimal propagation and performance in plants.     

A Multiresolution Approach to Discrete Tomography Using DART

In discrete tomography, a scanned object is assumed to consist of only a few different materials. This prior knowledge can be effectively exploited by a specialized discrete reconstruction algorithm such as the Discrete Algebraic Reconstruction Technique (DART), which is capable of providing more accurate reconstructions from limited data compared to conventional reconstruction algorithms. However, like most iterative reconstruction algorithms, DART suffers from long computation times. To increase the computational efficiency as well as the reconstruction quality of DART, a multiresolution version of DART (MDART) is proposed, in which the reconstruction starts on a coarse grid with big pixel (voxel) size. The resulting reconstruction is then resampled on a finer grid and used as an initial point for a subsequent DART reconstruction. This process continues until the target pixel size is reached. Experiments show that MDART can provide a significant speed-up, reduce missing wedge artefacts and improve feature reconstruction in the object compared with DART within the same time, making its use with large datasets more feasible.