A workflow for segmenting soil and plant X-ray computed tomography images with deep learning in Google's Colaboratory.

X-ray micro-computed tomography (X-ray µCT) has enabled the characterization of the properties and processes that take place in plants and soils at the micron scale. Despite the widespread use of this advanced technique, major limitations in both hardware and software limit the speed and accuracy of image processing and data analysis. Recent advances in machine learning, specifically the application of convolutional neural networks to image analysis, have enabled rapid and accurate segmentation of image data. Yet, challenges remain in applying convolutional neural networks to the analysis of environmentally and agriculturally relevant images. Specifically, there is a disconnect between the computer scientists and engineers, who build these AI/ML tools, and the potential end users in agricultural research, who may be unsure of how to apply these tools in their work. Additionally, the computing resources required for training and applying deep learning models are unique, more common to computer gaming systems or graphics design work, than to traditional computational systems. To navigate these challenges, we developed a modular workflow for applying convolutional neural networks to X-ray µCT images, using low-cost resources in Google's Colaboratory web application. Here we present the results of the workflow, illustrating how parameters can be optimized to achieve best results using example scans from walnut leaves, almond flower buds, and a soil aggregate. We expect that this framework will accelerate the adoption and use of emerging deep learning techniques within the plant and soil sciences.

Structural and functional leaf diversity lead to variability in photosynthetic capacity across a range of Juglans regia genotypes.

Similar to other cropping systems, few walnut cultivars are used as scion in commercial production. Germplasm collections can be used to diversify cultivar options and hold potential for improving crop productivity, disease resistance and stress tolerance. In this study, we explored the anatomical and biochemical bases of photosynthetic capacity and response to water stress in 11 Juglans regia accessions in the U.S. department of agriculture, agricultural research service (USDA-ARS) National Clonal Germplasm. Net assimilation rate (An ) differed significantly among accessions and was greater in lower latitudes coincident with higher stomatal and mesophyll conductances, leaf thickness, mesophyll porosity, gas-phase diffusion, leaf nitrogen and lower leaf mass and stomatal density. High CO2 -saturated assimilation rates led to increases in An under diffusional and biochemical limitations. Greater An was found in lower-latitude accessions native to climates with more frost-free days, greater precipitation seasonality and lower temperature seasonality. As expected, water stress consistently impaired photosynthesis with the highest % reductions in lower-latitude accessions (A3, A5 and A9), which had the highest An under well-watered conditions. However, An for A3 and A5 remained among the highest under dehydration. J. regia accessions, which have leaf structural traits and biochemistry that enhance photosynthesis, could be used as commercial scions or breeding parents to enhance productivity.

Surface modification induced cuprous oxide nanoparticle toxicity to duckweed at sub-toxic metal concentrations.

Nanoparticle capping agents are critical for controlling the growth, oxidation state, and final particle size during aqueous synthesis. However, despite the known phytotoxicity of cetyltrimethylammonium bromide (CTAB) to plants, it is used to synthesize metal oxide nanoparticles of uniform size and with mesoporous structure. Among the few studies that have investigated how CTAB influences nanoparticle toxicity, CTAB has never been identified as the primary cause of nanoparticle toxicity in environmental systems; rather nanoparticle surface charge or morphology was identified as the driver of toxicity in environmentally relevant systems. In the current study, CTAB release from CTAB surface modified Cu2O nanoparticles (SM-Cu2O NPs) inhibited duckweed (Landoltia punctata) growth, even when administered at subtoxic Cu concentrations. Organic ligands, such as humic acid (HA) and ethylenediaminetetraacetic acid (EDTA), lessened growth inhibition associated with exposure to SM-Cu2O NPs, likely through electrostatic and hydrophobic interactions with CTAB. Such results highlight the need for a more holistic approach to nanoparticle surface modification and improved communication between toxicologists and synthetic chemists to develop green alternatives for nanoparticle synthesis.

Dissolved organic matter reduces CuO nanoparticle toxicity to duckweed in simulated natural systems.

With increasing demand for recycled wastewater for irrigation purposes, there is a need to evaluate the potential for manufactured nanomaterials in waste water to impact crop production and agroecosystems. Copper oxide nanoparticles (CuO NPs) have previously been shown to negatively impact the growth of duckweed (Landoltia punctata) a model aquatic plant consumed by water fowl and widely found in agricultural runoff ditches in temperate climates. However, prior studies involving CuO NP toxicity to duckweed have focused on systems without the presence of dissolved organic matter (DOM). In the current study, duckweed growth inhibition was shown to be a function of aqueous Cu2+ concentration. Growth inhibition was greatest from aqueous CuCl2 and, for particles, increased with decreasing CuO particle size. The dissolution of CuO NPs in ½ Hoagland's solution was measured to increase with decreasing particle size and in the presence of Suwannee river humic and fulvic acids (HA; FA). However, the current results suggest that HA, and to a lesser extent, FA, decrease the toxicity of both CuO NPs and free ionized Cu to duckweed, likely by inhibiting Cu availability through Cu-DOM complex formation. Such results are consistent with changes to Cu speciation as predicted by speciation modeling software and suggest that DOM changes Cu speciation and therefore toxicity in natural systems.

In Vivo Tracking of Copper-64 Radiolabeled Nanoparticles in Lactuca sativa.

Engineered nanoparticles (NPs) are increasingly used in commercial products including automotive lubricants, clothing, deodorants, sunscreens, and cosmetics and can potentially accumulate in our food supply. Given their size it is difficult to detect and visualize the presence of NPs in environmental samples, including crop plants. New analytical tools are needed to fill the void for detection and visualization of NPs in complex biological and environmental matrices. We aimed to determine whether radiolabeled NPs could be used as a noninvasive, highly sensitive analytical tool to quantitatively track and visualize NP transport and accumulation in vivo in lettuce (Lactuca sativa) and to investigate the effect of NP size on transport and distribution over time using a combination of autoradiography, positron emission tomography (PET)/computed tomography (CT), scanning electron microscopy (SEM), and transition electron microscopy (TEM). Azide functionalized NPs were radiolabeled via a "click" reaction with copper-64 (64Cu)-1,4,7-triazacyclononane triacetic acid (NOTA) azadibenzocyclooctyne (ADIBO) conjugate ([64Cu]-ADIBO-NOTA) via copper-free Huisgen-1,3-dipolar cycloaddition reaction. This yielded radiolabeled [64Cu]-NPs of uniform shape and size with a high radiochemical purity (>99%), specific activity of  2.2 mCi/mg of NP, and high stability (i.e., no detectable dissolution) over 24 h across a pH range of 5-9. Both PET/CT and autoradiography showed that [64Cu]-NPs entered the lettuce seedling roots and were rapidly transported to the cotyledons with the majority of the accumulation inside the roots. Uptake and transport of intact NPs was size-dependent, and in combination with the accumulation within the roots suggests a filtering effect of the plant cell walls at various points along the water transport pathway.