Mesoionic Carbenes in Low- to High-Valent Vanadium Chemistry.

We report the synthesis of vanadium(V) oxo complex 1 with a pincer-type dianionic mesoionic carbene (MIC) ligand L1 and the general formula [VOCl(L1)]. A comparison of the structural (SC-XRD), electronic (UV-vis), and electrochemical (cyclic voltammetry) properties of 1 with the benzimidazolinylidene congener 2 (general formula [VOCl(L2)]) shows that the MIC is a stronger donor also for early transition metals with low d-electron population. Since electrochemical studies revealed both complexes to be reversibly reduced, the stronger donor character of MICs was not only demonstrated for the vanadium(V) but also for the vanadium(IV) oxidation state by isolating the reduced vanadium(IV) complexes [Co(Cp*)2][1] and [Co(Cp*)2][2] ([Co(Cp*)2] = decamethylcobaltocenium). The electronic structures of the compounds were investigated by computational methods. Complex 1 was found to be a moderate precursor for salt metathesis reactions, showing selective reactivity toward phenolates or secondary amides, but not toward primary amides and phosphides, thiophenols, or aryls/alkyls donors. Deoxygenation with electron-rich phosphines failed to give the desired vanadium(III) complex. However, treatment of the deprotonated ligand precursor with vanadium(III) trichloride resulted in the clean formation of the corresponding MIC vanadium(III) complex 6, which undergoes a clean two-electron oxidation with organic azides yielding the corresponding imido complexes. The reaction with TMS-N3 did not afford a nitrido complex, but instead the imido complex 10. This study reveals that, contrary to popular belief, MICs are capable of supporting early transition-metal complexes in a variety of oxidation states, thus making them promising candidates for the activation of small molecules and redox catalysis.

Connecting the dots between computational tools to analyse soil-root water relations.

In recent years, many computational tools, such as image analysis, data management, process-based simulation, and upscaling tools, have been developed to help quantify and understand water flow in the soil-root system, at multiple scales (tissue, organ, plant, and population). Several of these tools work together or at least are compatible. However, for the uninformed researcher, they might seem disconnected, forming an unclear and disorganized succession of tools. In this article, we show how different studies can be further developed by connecting them to analyse soil-root water relations in a comprehensive and structured network. This 'explicit network of soil-root computational tools' informs readers about existing tools and helps them understand how their data (past and future) might fit within the network. We also demonstrate the novel possibilities of scale-consistent parameterizations made possible by the network with a set of case studies from the literature. Finally, we discuss existing gaps in the network and how we can move forward to fill them.

CRootBox: a structural-functional modelling framework for root systems.

Background and Aims: Root architecture development determines the sites in soil where roots provide input of carbon and take up water and solutes. However, root architecture is difficult to determine experimentally when grown in opaque soil. Thus, root architecture models have been widely used and been further developed into functional-structural models that simulate the fate of water and solutes in the soil-root system. The root architecture model CRootBox presented here is a flexible framework to model root architecture and its interactions with static and dynamic soil environments. Methods: CRootBox is a C++-based root architecture model with Python binding, so that CRootBox can be included via a shared library into any Python code. Output formats include VTP, DGF, RSML and a plain text file containing coordinates of root nodes. Furthermore, a database of published root architecture parameters was created. The capabilities of CRootBox for the unconfined growth of single root systems, as well as the different parameter sets, are highlighted in a freely available web application. Key results: The capabilities of CRootBox are demonstrated through five different cases: (1) free growth of individual root systems; (2) growth of root systems in containers as a way to mimic experimental setups; (3) field-scale simulation; (4) root growth as affected by heterogeneous, static soil conditions; and (5) coupling CRootBox with code from the book Soil physics with Python to dynamically compute water flow in soil, root water uptake and water flow inside roots. Conclusions: CRootBox is a fast and flexible functional-structural root model that is based on state-of-the-art computational science methods. Its aim is to facilitate modelling of root responses to environmental conditions as well as the impact of roots on soil. In the future, this approach will be extended to the above-ground part of the plant.

Root architecture simulation improves the inference from seedling root phenotyping towards mature root systems.

Root phenotyping provides trait information for plant breeding. A shortcoming of high-throughput root phenotyping is the limitation to seedling plants and failure to make inferences on mature root systems. We suggest root system architecture (RSA) models to predict mature root traits and overcome the inference problem. Sixteen pea genotypes were phenotyped in (i) seedling (Petri dishes) and (ii) mature (sand-filled columns) root phenotyping platforms. The RSA model RootBox was parameterized with seedling traits to simulate the fully developed root systems. Measured and modelled root length, first-order lateral number, and root distribution were compared to determine key traits for model-based prediction. No direct relationship in root traits (tap, lateral length, interbranch distance) was evident between phenotyping systems. RootBox significantly improved the inference over phenotyping platforms. Seedling plant tap and lateral root elongation rates and interbranch distance were sufficient model parameters to predict genotype ranking in total root length with an RSpearman of 0.83. Parameterization including uneven lateral spacing via a scaling function substantially improved the prediction of architectures underlying the differently sized root systems. We conclude that RSA models can solve the inference problem of seedling root phenotyping. RSA models should be included in the phenotyping pipeline to provide reliable information on mature root systems to breeding research.

Root system markup language: toward a unified root architecture description language.

The number of image analysis tools supporting the extraction of architectural features of root systems has increased in recent years. These tools offer a handy set of complementary facilities, yet it is widely accepted that none of these software tools is able to extract in an efficient way the growing array of static and dynamic features for different types of images and species. We describe the Root System Markup Language (RSML), which has been designed to overcome two major challenges: (1) to enable portability of root architecture data between different software tools in an easy and interoperable manner, allowing seamless collaborative work; and (2) to provide a standard format upon which to base central repositories that will soon arise following the expanding worldwide root phenotyping effort. RSML follows the XML standard to store two- or three-dimensional image metadata, plant and root properties and geometries, continuous functions along individual root paths, and a suite of annotations at the image, plant, or root scale at one or several time points. Plant ontologies are used to describe botanical entities that are relevant at the scale of root system architecture. An XML schema describes the features and constraints of RSML, and open-source packages have been developed in several languages (R, Excel, Java, Python, and C#) to enable researchers to integrate RSML files into popular research workflow.

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.

Can diversity in root architecture explain plant water use efficiency? A modeling study.

Drought stress is a dominant constraint to crop production. Breeding crops with adapted root systems for effective uptake of water represents a novel strategy to increase crop drought resistance. Due to complex interaction between root traits and high diversity of hydrological conditions, modeling provides important information for trait based selection. In this work we use a root architecture model combined with a soil-hydrological model to analyze whether there is a root system ideotype of general adaptation to drought or water uptake efficiency of root systems is a function of specific hydrological conditions. This was done by modeling transpiration of 48 root architectures in 16 drought scenarios with distinct soil textures, rainfall distributions, and initial soil moisture availability. We find that the efficiency in water uptake of root architecture is strictly dependent on the hydrological scenario. Even dense and deep root systems are not superior in water uptake under all hydrological scenarios. Our results demonstrate that mere architectural description is insufficient to find root systems of optimum functionality. We find that in environments with sufficient rainfall before the growing season, root depth represents the key trait for the exploration of stored water, especially in fine soils. Root density, instead, especially near the soil surface, becomes the most relevant trait for exploiting soil moisture when plant water supply is mainly provided by rainfall events during the root system development. We therefore concluded that trait based root breeding has to consider root systems with specific adaptation to the hydrology of the target environment.

Expanding the Utility of ß-Diketiminate Ligands in Heavy Group VI Chemistry of Molybdenum and Tungsten.

We report the synthesis of 17 molybdenum and tungsten complexes supported by the ubiquitous BDI ligand framework (BDI = ß-diketiminate). The focal entry point is the synthesis of four molybdenum and tungsten(V) BDI complexes of the general formula [MO(BDIR)Cl2] [M = Mo, R = Dipp (1); M = W, R = Dipp (2); M = Mo, R = Mes (3); M = W, R = Mes (4)] synthesized by the reaction between MoOCl3(THF)2 or WOCl3(THF)2 and LiBDIR. Reactivity studies show that the BDIDipp complexes are excellent precursors toward adduct formation, reacting smoothly with dimethylaminopyridine (DMAP) and triethylphosphine oxide (OPEt3). No reaction with small phosphines has been observed, strongly contrasting the chemistry of previously reported rhenium(V) complexes. Additionally, the complexes 1 and 2 are good precursors for salt metathesis reactions. While 1 can be chemically reduced to the first stable example of a Mo(IV) BDI complex 15, reduction of 2 resulted in degradation of the BDI ligand via a nitrene transfer reaction, leading to MAD (4-((2,6-diisopropylphenyl)imino)pent-2-enide) supported tungsten(V) and tungsten(VI) complexes 16 and 17. All reported complexes have been thoroughly studied by VT-NMR and (heteronuclear) NMR spectroscopy, as well as UV-vis and EPR spectroscopy, IR spectroscopy, and X-ray diffraction analysis.

Curious Case of Cobaltocenium Carbaldehyde.

Cobaltocenium carbaldehyde (formylcobaltocenium) hexafluoridophosphate, a long sought-after functionalized cobaltocenium salt, is accessible from cobaltocenium carboxylic acid by a three-step synthetic sequence involving (i) chlorination to the acid chloride, (ii) copper-borohydride reduction to the hydroxymethyl derivative, and (iii) Dess-Martin oxidation to the title compound. Due to the strongly electron-withdrawing cationic cobaltocenium moiety, no standard aldehyde reactivity is observed. Instead, nucleophilic addition followed by haloform-type cleavage prevails, thereby ruling out common useful aldehyde derivatization. One-electron reduction of cobaltocenium carbaldehyde hexafluoridophosphate affords the deep-blue, isolable cobaltocene carbaldehyde 19-valence-electron radical whose spin density is located fully at cobalt and not at the formyl carbon atom. 1H/13C NMR, IR, EPR spectroscopy, high-resolution mass spectrometry, cyclic voltammetry, single crystal structure analysis (XRD), and density functional theory are applied to characterize these unusual formyl-cobaltocenium/cobaltocene compounds.

Design, Synthesis, Electrochemical, and Biological Evaluation of Fluorescent Chlorido[N,N'-bis(methoxy/hydroxy)salicylidene-1,2-bis(4-methoxyphenyl)ethylenediamine]iron(III) Complexes as Anticancer Agents.

The impact of methoxy and hydroxyl groups at the salicylidene moiety of chlorido[N,N'-bis(methoxy/hydroxy)salicylidene-1,2-bis(4-methoxyphenyl)ethylenediamine]iron(III) complexes was evaluated on human MDA-MB 231 breast cancer and HL-60 leukemia cells. Methoxylated complexes (C1-C3) inhibited proliferation, migration, and metabolic activity in a concentration-dependent manner following the rank order: C2 > C3 > C1. In particular, C2 was highly cytotoxic with an IC50 of 4.2 µM which was 6.6-fold lower than that of cisplatin (IC50 of 27.9 µM). In contrast, hydroxylated complexes C4-C6 were almost inactive up to the highest concentration tested due to lack of cellular uptake. C2 caused a dual mode of cell death, ferroptosis, and necroptosis, whereby at higher concentrations, ferroptosis was the preferred form. Ferroptotic morphology and the presence of ferrous iron and lipid reactive oxygen species proved the involvement of ferroptosis. C2 was identified as a promising lead compound for the design of drug candidates inducing ferroptosis.

Diagnostic Value of DECT-Based Collagen Mapping for Assessing the Distal Tibiofibular Syndesmosis in Patients with Acute Trauma.

Background: Injury to the distal tibiofibular syndesmosis (DTFS) is common in patients with trauma to the ankle, but diagnostic accuracy of conventional X-ray and CT is insufficient. A novel dual energy CT (DECT) post-processing algorithm enables color-coded mapping of collagenous structures, which can be utilized to assess the integrity of the DTFS. Methods: Patients were included in this retrospective study if they underwent third-generation dual-source DECT followed by 3T-MRI or ankle joint surgery within 14 days between January 2016 and December 2021. Three radiologists blinded to all patient data independently evaluated grayscale images and, after 8 weeks, grayscale and collagen mapping images for the presence of ligamentous injury or avulsion fractures of the DTFS. MRI and surgery provided the reference standard. Diagnostic accuracy parameters were calculated for all ratings, and a comparison of ROC curve analysis was performed to evaluate the incremental diagnostic value of color-coded images over grayscale images. Results: A total of 49 patients (median age 49 years; 32 males) were evaluated. Application of collagen mapping significantly increased sensitivity (25/30 [83%] vs. 20/30 [67%]), specificity (110/118 [93%] vs. 70/118 [60%]), positive predictive value (25/33 [76%] vs. 20/67 [30%]), negative predictive value (110/115 [96%] vs. 70/80 [88%]), and accuracy (134/147 [91%] vs. 90/147 [61%]) for the detection of injury to the DTFS (all parameters, p < 0.001). Collagen mapping achieved higher diagnostic confidence, image quality, and noise scores compared to grayscale CT (all parameters, p < 0.001). Conclusions: Collagen mapping yields substantially higher diagnostic accuracy and confidence for assessing the integrity of the distal tibiofibular syndesmosis compared to grayscale CT in patients with acute trauma. The application of this algorithm can accelerate the adequate diagnosis and treatment of DTFS injury in clinical routine.

Root System Scale Models Significantly Overestimate Root Water Uptake at Drying Soil Conditions.

Soil hydraulic conductivity (k soil ) drops significantly in dry soils, resulting in steep soil water potential gradients (ψ s ) near plant roots during water uptake. Coarse soil grid resolutions in root system scale (RSS) models of root water uptake (RWU) generally do not spatially resolve this gradient in drying soils which can lead to a large overestimation of RWU. To quantify this, we consider a benchmark scenario of RWU from drying soil for which a numerical reference solution is available. We analyze this problem using a finite volume scheme and investigate the impact of grid size on the RSS model results. At dry conditions, the cumulative RWU was overestimated by up to 300% for the coarsest soil grid of 4.0 cm and by 30% for the finest soil grid of 0.2 cm, while the computational demand increased from 19 s to 21 h. As an accurate and computationally efficient alternative to the RSS model, we implemented a continuum multi-scale model where we keep a coarse grid resolution for the bulk soil, but in addition, we solve a 1-dimensional radially symmetric soil model at rhizosphere scale around individual root segments. The models at the two scales are coupled in a mass-conservative way. The multi-scale model compares best to the reference solution (-20%) at much lower computational costs of 4 min. Our results demonstrate the need to shift to improved RWU models when simulating dry soil conditions and highlight that results for dry conditions obtained with RSS models of RWU should be interpreted with caution.

Simulating Root Growth as a Function of Soil Strength and Yield With a Field-Scale Crop Model Coupled With a 3D Architectural Root Model.

Accurate prediction of root growth and related resource uptake is crucial to accurately simulate crop growth especially under unfavorable environmental conditions. We coupled a 1D field-scale crop-soil model running in the SIMPLACE modeling framework with the 3D architectural root model CRootbox on a daily time step and implemented a stress function to simulate root elongation as a function of soil bulk density and matric potential. The model was tested with field data collected during two growing seasons of spring barley and winter wheat on Haplic Luvisol. In that experiment, mechanical strip-wise subsoil loosening (30-60 cm) (DL treatment) was tested, and effects on root and shoot growth at the melioration strip as well as in a control treatment were evaluated. At most soil depths, strip-wise deep loosening significantly enhanced observed root length densities (RLDs) of both crops as compared to the control. However, the enhanced root growth had a beneficial effect on crop productivity only in the very dry season in 2018 for spring barley where the observed grain yield at the strip was 18% higher as compared to the control. To understand the underlying processes that led to these yield effects, we simulated spring barley and winter wheat root and shoot growth using the described field data and the model. For comparison, we simulated the scenarios with the simpler 1D conceptual root model. The coupled model showed the ability to simulate the main effects of strip-wise subsoil loosening on root and shoot growth. It was able to simulate the adaptive plasticity of roots to local soil conditions (more and thinner roots in case of dry and loose soil). Additional scenario runs with varying weather conditions were simulated to evaluate the impact of deep loosening on yield under different conditions. The scenarios revealed that higher spring barley yields in DL than in the control occurred in about 50% of the growing seasons. This effect was more pronounced for spring barley than for winter wheat. Different virtual root phenotypes were tested to assess the potential of the coupled model to simulate the effect of varying root traits under different conditions.

A mesoionic carbene complex of manganese in five oxidation states.

Reaction between a carbazole-based mesoionic carbene ligand and manganese(II) iodide results in the formation of a rare air-stable manganese(IV) complex after aerobic workup. Cyclic voltammetry reveals the complex to be stable in five oxidation states. The electronic structure of all five oxidation states is elucidated chemically, spectroscopically (NMR, high-frequency EPR, UV-Vis, MCD), magnetically, and computationally (DFT, CASSCF).

Call for Participation: Collaborative Benchmarking of Functional-Structural Root Architecture Models. The Case of Root Water Uptake.

Three-dimensional models of root growth, architecture and function are becoming important tools that aid the design of agricultural management schemes and the selection of beneficial root traits. However, while benchmarking is common in many disciplines that use numerical models, such as natural and engineering sciences, functional-structural root architecture models have never been systematically compared. The following reasons might induce disagreement between the simulation results of different models: different representation of root growth, sink term of root water and solute uptake and representation of the rhizosphere. Presently, the extent of discrepancies is unknown, and a framework for quantitatively comparing functional-structural root architecture models is required. We propose, in a first step, to define benchmarking scenarios that test individual components of complex models: root architecture, water flow in soil and water flow in roots. While the latter two will focus mainly on comparing numerical aspects, the root architectural models have to be compared at a conceptual level as they generally differ in process representation. Therefore, defining common inputs that allow recreating reference root systems in all models will be a key challenge. In a second step, benchmarking scenarios for the coupled problems are defined. We expect that the results of step 1 will enable us to better interpret differences found in step 2. This benchmarking will result in a better understanding of the different models and contribute toward improving them. Improved models will allow us to simulate various scenarios with greater confidence and avoid bugs, numerical errors or conceptual misunderstandings. This work will set a standard for future model development.

Mechanical and Hydric Stress Effects on Maize Root System Development at Different Soil Compaction Levels.

Soil mechanical resistance, aeration, and water availability directly affect plant root growth. The objective of this work was to identify the contribution of mechanical and hydric stresses on maize root elongation, by modeling root growth while taking the dynamics of these stresses in an Oxisol into consideration. The maize crop was cultivated under four compaction levels (soil chiseling, no-tillage system, areas trafficked by a tractor, and trafficked by a harvester), and we present a new model, which allows to distinguish between mechanical and hydric stresses. Root length density profiles, soil bulk density, and soil water retention curves were determined for four compaction levels up to 50 cm in depth. Furthermore, grain yield and shoot biomass of maize were quantified. The new model described the mechanical and hydric stresses during maize growth with field data for the first time in maize crop. Simulations of root length density in 1D and 2D showed adequate agreement with the values measured under field conditions. Simulation makes it possible to identify the interaction between the soil physical conditions and maize root growth. Compared to the no-tillage system, grain yield was reduced due to compaction caused by harvester traffic and by soil chiseling. The root growth was reduced by the occurrence of mechanical and hydric stresses during the crop cycle, the principal stresses were mechanical in origin for areas with agricultural traffic, and water based in areas with soil chiseling. Including mechanical and hydric stresses in root growth models can help to predict future scenarios, and coupling soil biophysical models with weather, soil, and crop responses will help to improve agricultural management.

Hyperspectral imaging: a novel approach for plant root phenotyping.

BACKGROUND: Root phenotyping aims to characterize root system architecture because of its functional role in resource acquisition. RGB imaging and analysis procedures measure root system traits via colour contrasts between roots and growth media or artificial backgrounds. In the case of plants grown in soil-filled rhizoboxes, where the colour contrast can be poor, it is hypothesized that root imaging based on spectral signatures improves segmentation and provides additional knowledge on physico-chemical root properties. RESULTS: Root systems of Triticum durum grown in soil-filled rhizoboxes were scanned in a spectral range of 1000-1700 nm with 222 narrow bands and a spatial resolution of 0.1 mm. A data processing pipeline was developed for automatic root segmentation and analysis of spectral root signatures. Spectral- and RGB-based root segmentation did not significantly differ in accuracy even for a bright soil background. Best spectral segmentation was obtained from log-linearized and asymptotic least squares corrected images via fuzzy clustering and multilevel thresholding. Root axes revealed major spectral distinction between center and border regions. Root decay was captured by an exponential function of the difference spectra between water and structural carbon absorption regions. CONCLUSIONS: Fundamentals for root phenotyping using hyperspectral imaging have been established by means of an image processing pipeline for automated segmentation of soil-grown plant roots at a high spatial resolution and for the exploration of spectral signatures encoding physico-chemical root zone properties.

RGB and Spectral Root Imaging for Plant Phenotyping and Physiological Research: Experimental Setup and Imaging Protocols.

Better understanding of plant root dynamics is essential to improve resource use efficiency of agricultural systems and increase the resistance of crop cultivars against environmental stresses. An experimental protocol is presented for RGB and hyperspectral imaging of root systems. The approach uses rhizoboxes where plants grow in natural soil over a longer time to observe fully developed root systems. Experimental settings are exemplified for assessing rhizobox plants under water stress and studying the role of roots. An RGB imaging setup is described for cheap and quick quantification of root development over time. Hyperspectral imaging improves root segmentation from the soil background compared to RGB color based thresholding. The particular strength of hyperspectral imaging is the acquisition of chemometric information on the root-soil system for functional understanding. This is demonstrated with high resolution water content mapping. Spectral imaging however is more complex in image acquisition, processing and analysis compared to the RGB approach. A combination of both methods can optimize a comprehensive assessment of the root system. Application examples integrating root and aboveground traits are given for the context of plant phenotyping and plant physiological research. Further improvement of root imaging can be obtained by optimizing RGB image quality with better illumination using different light sources and by extension of image analysis methods to infer on root zone properties from spectral data.

A statistical approach to root system classification.

Plant root systems have a key role in ecology and agronomy. In spite of fast increase in root studies, still there is no classification that allows distinguishing among distinctive characteristics within the diversity of rooting strategies. Our hypothesis is that a multivariate approach for "plant functional type" identification in ecology can be applied to the classification of root systems. The classification method presented is based on a data-defined statistical procedure without a priori decision on the classifiers. The study demonstrates that principal component based rooting types provide efficient and meaningful multi-trait classifiers. The classification method is exemplified with simulated root architectures and morphological field data. Simulated root architectures showed that morphological attributes with spatial distribution parameters capture most distinctive features within root system diversity. While developmental type (tap vs. shoot-borne systems) is a strong, but coarse classifier, topological traits provide the most detailed differentiation among distinctive groups. Adequacy of commonly available morphologic traits for classification is supported by field data. Rooting types emerging from measured data, mainly distinguished by diameter/weight and density dominated types. Similarity of root systems within distinctive groups was the joint result of phylogenetic relation and environmental as well as human selection pressure. We concluded that the data-define classification is appropriate for integration of knowledge obtained with different root measurement methods and at various scales. Currently root morphology is the most promising basis for classification due to widely used common measurement protocols. To capture details of root diversity efforts in architectural measurement techniques are essential.