The present review examines recent experimental findings in root transport phenomena in terms of the composite transport model (CTM). It has been a well-accepted conceptual model to explain the complex water and solute flows across the root that has been related to the composite anatomical structure. There are three parallel pathways involved in the transport of water and solutes in roots – apoplast, symplast, and transcellular paths. The role of aquaporins (AQPs), which facilitate water flows through the transcellular path, and root apoplast is examined in terms of the CTM. The contribution of the plasma membrane bound AQPs for the overall water transport in the whole plant level was varying depending on the plant species, age of roots with varying developmental stages of apoplastic barriers, and driving forces (hydrostatic vs. osmotic). Many studies have demonstrated that the apoplastic barriers, such as Casparian bands in the primary anticlinal walls and suberin lamellae in the secondary cell walls, in the endo- and exodermis are not perfect barriers and unable to completely block the transport of water and some solute transport into the stele. Recent research on water and solute transport of roots with and without exodermis triggered the importance of the extension of conventional CTM adding resistances that arrange in series (epidermis, exodermis, mid-cortex, endodermis, and pericycle). The extension of the model may answer current questions about the applicability of CTM for composite water and solute transport of roots that contain complex anatomical structures with heterogeneous cell layers.

In general, greenhouse cultivation involves the rampant application of chemical fertilizers, with the aim of achieving high yields. Oversaturation with mineral nutrients that aid plant growth, development, and yield may lead to abiotic stress conditions. We explore the effects of excess magnesium on tomato plant metabolism, as well as tomato fruit quality using non-targeted mass spectrometry (MS)-based metabolomic approaches. Tomato plants were subjected to three different experiments, including high magnesium stress (MgH), extremely high magnesium stress (MgEH), and a control with optimal nutrient levels. Leaves, roots, and fruits were harvested at 16 weeks following the treatment. A metabolic pathway analysis showed that the metabolism induced by Mg oversupply was remarkably different between the leaf and root. Tomato plants allocated more resources to roots by upregulating carbohydrate and polyamine metabolism, while these pathways were downregulated in leaves. Mg oversupply affects the fruit metabolome in plants. In particular, the relative abundance of threonic acid, xylose, fucose, glucose, fumaric acid, malic acid, citric acid, oxoglutaric acid, threonine, glutamic acid, phenylalanine, and asparagine responsible for the flavor of tomato fruits was significantly decreased in the presence of Mg oversupply. Altogether, we concluded that Mg oversupply leads to drastically higher metabolite transport from sources (fully expanded leaves) to sinks (young leaves and roots), and thus, produces unfavorable outcomes in fruit quality and development.

The application of a machine learning (ML) model to bio-electrochemical anaerobic digestion (BEAD) is a future-oriented approach for improving process stability by predicting performances that have nonlinear relationships with various operational parameters. Five ML models, which included tree-, regression-, and neural network-based algorithms, were applied to predict the methane yield in BEAD reactor. The results showed that various 1-step ahead ML models, which utilized prior data of BEAD performances, could enhance prediction accuracy. In addition, 1-step ahead with retraining algorithm could improve prediction accuracy by 37.3% compared with the conventional multi-step ahead algorithm. The improvement was particularly noteworthy in tree- and regression-based ML models. Moreover, 1-step ahead with retraining algorithm showed high potential of achieving efficient prediction using pH as a single input data, which is plausibly an easier monitoring parameter compared with the other parameters required in bioprocess models.