Hybrid Signal-Processing Method Based on Neural Network for Prediction of NO3, K, Ca, and Mg Ions in Hydroponic Solutions Using an Array of Ion-Selective Electrodes.

In closed hydroponics, fast and continuous measurement of individual nutrient concentrations is necessary to improve water- and nutrient-use efficiencies and crop production. Ion-selective electrodes (ISEs) could be one of the most attractive tools for hydroponic applications. However, signal drifts over time and interferences from other ions present in hydroponic solutions make it difficult to use the ISEs in hydroponic solutions. In this study, hybrid signal processing combining a two-point normalization (TPN) method for the effective compensation of the drifts and a back propagation artificial neural network (ANN) algorithm for the interpretation of the interferences was developed. In addition, the ANN-based approach for the prediction of Mg concentration which had no feasible ISE was conducted by interpreting the signals from a sensor array consisting of electrical conductivity (EC) and ion-selective electrodes (NO3, K, and Ca). From the application test using 8 samples from real greenhouses, the hybrid method based on a combination of the TPN and ANN methods showed relatively low root mean square errors of 47.2, 13.2, and 18.9 mg∙L-1 with coefficients of variation (CVs) below 10% for NO3, K, and Ca, respectively, compared to those obtained by separate use of the two methods. Furthermore, the Mg prediction results with a root mean square error (RMSE) of 14.6 mg∙L-1 over the range of 10-60 mg∙L-1 showed potential as an approximate diagnostic tool to measure Mg in hydroponic solutions. These results demonstrate that the hybrid method can improve the accuracy and feasibility of ISEs in hydroponic applications.

Computational identification and phylogenetic analysis of the oil-body structural proteins, oleosin and caleosin, in castor bean and flax.

Oil bodies (OBs) are the intracellular particles derived from oilseeds. These OBs store lipids as a carbon resource, and have been exploited for a variety of industrial applications including biofuels. Oleosin and caleosin are the common OB structural proteins which are enabling biotechnological enhancement of oil content and OB-based pharmaceutical formations via stabilizing OBs. Although the draft whole genome sequence information for Ricinus communis L. (castor bean) and Linum usitatissimum L. (flax), important oil seed plants, is available in public database, OB-structural proteins in these plants are poorly indentified. Therefore, in this study, we performed a comprehensive bioinformatic analysis including analysis of the genome sequence, conserved domains and phylogenetic relationships to identify OB structural proteins in castor bean and flax genomes. Using comprehensive analysis, we have identified 6 and 15 OB-structural proteins from castor bean and flax, respectively. A complete overview of this gene family in castor bean and flax is presented, including the gene structures, phylogeny and conserved motifs, resulting in the presence of central hydrophobic regions with proline knot motif, providing an evolutionary proof that this central hydrophobic region had evolved from duplications in the primitive eukaryotes. In addition, expression analysis of L-oleosin and caleosin genes using quantitative real-time PCR demonstrated that seed contained their maximum expression, except that RcCLO-1 expressed maximum in cotyledon. Thus, our comparative genomics analysis of oleosin and caleosin genes and their putatively encoded proteins in two non-model plant species provides insights into the prospective usage of gene resources for improving OB-stability.