X-ray micro-CT has increasingly been used for 3D imaging of plant structures. At the micrometer resolution however, limitations in X-ray contrast often lead to datasets with poor qualitative and quantitative measures, especially within dense cell clusters of plant tissue specimens. The current study developed protocols for delivering a cesium based contrast enhancing solution to varying plant tissue specimens for the purpose of improving 3D tissue structure characterization within plant specimens, accompanied by new image processing workflows to extract the additional data generated by the contrast enhanced scans. Following passive delivery of a 10% cesium iodide contrast solution, significant increases of 85.4 and 38.0% in analyzable cell volumes were observed in pear fruit hypanthium and tomato fruit outer mesocarp samples. A significant increase of 139.6% in the number of analyzable cells was observed in the pear fruit samples along the added ability to locate and isolate better brachysclereids and vasculature in the sample volume. Furthermore, contrast enhancement resulted in significant improvement in the definition of collenchyma and parenchyma in the petiolule of tomato leaflets, from which both qualitative and quantitative data can be extracted with respect to cell measures. However, contrast enhancement was not achieved in leaf vasculature and mesophyll tissue due to fundamental limitations. Active contrast delivery to apple fruit hypanthium samples did yield a small but insignificant increase in analyzable volume and cells, but data on vasculature can now be extracted better in correspondence to the pear hypanthium samples. Contrast delivery thus improved visualization and analysis the most in dense tissue types. The cesium based contrast enhancing protocols and workflows can be utilized to obtain detailed 3D data on the internal microstructure of plant samples, and can be adapted to additional samples of interest with minimal effort. The resulting datasets can therefore be utilized for more accurate downstream studies that requires 3D data.

X-ray micro–computed tomography (micro-CT) provides the unique ability to capture intact internal microstructure data without significant preparation of the sample. The fundamentals of micro-CT technology are briefly described along with a short introduction to basic image processing, quantitative analysis, and derivative computational modeling. The applications and limitations of micro-CT in industries such as meat, dairy, postharvest, and bread/confectionary are discussed to serve as a guideline to the plausibility of utilizing the technique for detecting features of interest. Component volume fractions, their respective size/shape distributions, and connectivity, for example, can be utilized for product development, manufacturing process tuning and/or troubleshooting. In addition to determining structure-function relations, micro-CT can be used for foreign material detection to further ensure product quality and safety. In most usage scenarios, micro-CT in its current form is perfectly adequate for determining microstructure in a wide variety of food products. However, in low-contrast and low-stability samples, emphasis is placed on the shortcomings of the current systems to set realistic expectations for the intended users.

Stand-alone and portable lab-on-chips (LOC) can be obtained by exploiting capillary flow in porous media. Polymethylmethacrylate (PMMA) platforms obtained through powder-based three-dimensional (3D) printing are appropriate for capillarity-driven LOCs. However, fluid flow in such platforms needs to be characterized well. For this purpose, a 3D pore network (PN) was extracted from high-resolution $\mu \mathrm{CT}$ images of printed PMMA through a watershed algorithm and a PN model was developed with the final goal of characterizing material permeability. The effect of all parameters involved in the PN extraction and modeling was investigated. The study focused in particular on the effect of the number of seeds for the watershed segmentation, pore sphericity, and pore-to-pore channel shape that was modeled as a bicylindrical or biconical object. The results proved that all PN extraction and modeling parameters influenced the permeability, which was found to be lower the higher the number of seeds and when using sphericity and biconical channels. Eventually, the Calinski-Harabasz index value was used to identify the optimal number of watershed seeds.

Maize (Zea mays) kernels are the largest cereal grains, and their endosperm is severely oxygen deficient during grain fill. The causes, dynamics, and mechanisms of acclimation to hypoxia are minimally understood. Here, we demonstrate that hypoxia develops in the small, growing endosperm, but not the nucellus, and becomes the standard state, regardless of diverse structural and genetic perturbations in modern maize (B73, popcorn, sweet corn), mutants (sweet4c, glossy6, waxy), and non-domesticated wild relatives (teosintes and Tripsacum species). We also uncovered an interconnected void space at the chalazal pericarp, providing superior oxygen supply to the placental tissues and basal endosperm transfer layer. Modeling indicated a very high diffusion resistance inside the endosperm, which, together with internal oxygen consumption, could generate steep oxygen gradients at the endosperm surface. Manipulation of oxygen supply induced reciprocal shifts in gene expression implicated in controlling mitochondrial functions (23.6 kDa Heat-Shock Protein, Voltage-Dependent Anion Channel 2) and multiple signaling pathways (core hypoxia genes, cyclic nucleotide metabolism, ethylene synthesis). Metabolite profiling revealed oxygen-dependent shifts in mitochondrial pathways, ascorbate metabolism, starch synthesis, and auxin degradation. Long-term elevated oxygen supply enhanced the rate of kernel development. Altogether, evidence here supports a mechanistic framework for the establishment of and acclimation to hypoxia in the maize endosperm.