The distribution of noble metal nanoparticles (NMNPs) in hydrogels influences their nanoplasmonic response and signals used for biosensor purposes. By controlling the particle distribution in NMNP-nanocomposite hydrogels, it is possible to obtain new nanoplasmonic features with new sensing modalities. Particle positions can be characterized by using volume-imaging methods such as the focused ion beam-scanning electron microscope (FIB-SEM) or the serial block-face scanning electron microscope (SBFSEM) techniques. The pore structures in hydrogels are contained by the water absorbed in the polymer network and may pose challenges for volume-imaging methods based on electron microscope techniques since the sample must be in a vacuum chamber. The structure of the hydrogels can be conserved by choosing appropriate preparation methods, which also depends on the composition of the hydrogel used. In this paper, we have prepared low-weight-percentage hydrogels, with and without gold nanorods (GNRs), for conventional scanning electron microscope (SEM) imaging by using critical point drying (CPD) and hexamethyldisilazane (HMDS) drying. The pore structures and the GNR positions in the hydrogel were characterized. The evaluation of the sample preparation techniques elucidate new aspects concerning the drying of hydrogels for SEM imaging. The results of identifying GNRs positioned in a hydrogel polymer network contribute to the development of mapping metal particle positions with volume imaging methods such as FIB-SEM or SBFSEM for studying nanoplasmonic properties of NMNP-nanocomposite hydrogels.

The glucosinolate-myrosinase system is a well-known plant chemical defence system. Two functional myrosinase-encoding genes, THIOGLUCOSIDASE 1 (TGG1) and THIOGLUCOSIDASE 2 (TGG2), express in aerial tissues of Arabidopsis. TGG1 expresses in guard cells (GCs) and is also a highly abundant protein in GCs. Recently, by studying wild type (WT), tgg single, and double mutants, we showed a novel association between the glucosinolate-myrosinase system defence system, and a physical barrier, the cuticle. In the current study, using imaging techniques, we further analysed stomata and ultrastructure of GCs of WT, tgg1, tgg2 single, and tgg1 tgg2 double mutants. The tgg mutants showed distinctive features of GCs. The GCs of tgg1 and tgg1 tgg2 mutants showed vacuoles that had less electron-dense granular material. Both tgg single mutants had bigger stomata complexes. The WT and tgg mutants also showed variations for cell wall, chloroplasts, and starch grains of GCs. Abscisic acid (ABA)-treated stomata showed that the stomatal aperture was reduced in tgg1 single and tgg1 tgg2 double mutants. The data provides a basis to perform comprehensive further studies to find physiological and molecular mechanisms associated with ultrastructure differences in tgg mutants. We speculate that the absence of myrosinase alters the endogenous chemical composition, hence affecting the physical structure of plants and the plants’ physical defence barriers.

Background: Knowledge discovery in databases (KDD) can contribute to translational research, also known as translational medicine, by bridging the gap between in vitro and in vivo studies, and clinical applications. Here, we propose a ‘systems modeling’ workflow for KDD. Methods: This framework includes the data collection of a composition model (various research models), processing model (proteomics) and analytical model (bioinformatics, artificial intelligence/machine leaning and pattern evaluation), knowledge presentation, and feedback loops for hypothesis generation and validation. We applied this workflow to study pancreatic ductal adenocarcinoma (PDAC). Results: We identified the common proteins between human PDAC and various research models in vitro (cells, spheroids and organoids) and in vivo (mouse mice). Accordingly, we hypothesized potential translational targets on hub proteins and the related signaling pathways, PDAC-specific proteins and signature pathways, and high topological proteins. Conclusions: This systems modeling workflow can be a valuable method for KDD, facilitating knowledge discovery in translational targets in general, and in particular to PADA in this case.