Canopy photosynthesis has typically been estimated using mathematical models that have the following assumptions: the light interception inside the canopy exponentially declines with the canopy depth, and the photosynthetic capacity is affected by light interception as a result of acclimation. However, in actual situations, light interception in the canopy is quite heterogenous depending on environmental factors such as the location, microclimate, leaf area index, and canopy architecture. It is important to apply these factors in an analysis. The objective of the current study is to estimate the canopy photosynthesis of paprika (Capsicum annuum L.) with an analysis of by simulating the intercepted irradiation of the canopy using a 3D ray-tracing and photosynthetic capacity in each layer. By inputting the structural data of an actual plant, the 3D architecture of paprika was reconstructed using graphic software (Houdini FX, FX, Canada). The light curves and A/Ci curve of each layer were measured to parameterize the Farquhar, von Caemmerer and Berry (FvCB) model. The difference in photosynthetic capacity within the canopy was observed. With the intercepted irradiation data and photosynthetic parameters of each layer, the values of an entire plant’s photosynthesis rate were estimated by integrating the calculated photosynthesis rate at each layer. The estimated photosynthesis rate of an entire plant showed good agreement with the measured plant using a closed chamber for validation. From the results, this method was considered as a reliable tool to predict canopy photosynthesis using light interception, and can be extended to analyze the canopy photosynthesis in actual greenhouse conditions.

In existing closed-loop soilless cultures, nutrient solutions are controlled by the electrical conductivity (EC) of the solution. However, the EC of nutrient solutions is affected by both growth environments and crop growth, so it is hard to predict the EC of nutrient solution. The objective of this study was to predict the EC of root-zone nutrient solutions in closed-loop soilless cultures using recurrent neural network (RNN). In a test greenhouse with sweet peppers (Capsicum annuum L.), data were measured every 10 s from October 15 to December 31, 2014. Mean values for every hour were analyzed. Validation accuracy (R²) of a single-layer long short-term memory (LSTM) was 0.92 and root-mean-square error (RMSE) was 0.07, which were the best results among the different RNNs. The trained LSTM predicted the substrate EC accurately at all ranges. Test accuracy (R²) was 0.72 and RMSE was 0.08, which were lower than values for the validation. Deep learning algorithms were more accurate when more data were added for training. The addition of other environmental factors or plant growth data would improve model robustness. A trained LSTM can control the nutrient solutions in closed-loop soilless cultures based on predicted future EC. Therefore, the algorithm can make a planned management of nutrient solutions possible, reducing resource waste.

The genus Acanthamoeba can cause severe infections such as granulomatous amebic encephalitis and amebic keratitis in humans. However, little genomic information of Acanthamoeba has been reported. Here, we constructed Acanthamoeba expressed sequence tags (EST) database (Acanthamoeba EST DB) derived from our 4 kinds of Acanthamoeba cDNA library. The Acanthamoeba EST DB contains 3,897 EST generated from amebae under various conditions of long term in vitro culture, mouse brain passage, or encystation, and downloaded data of Acanthamoeba from National Center for Biotechnology Information (NCBI) and Taxonomically Broad EST Database (TBestDB). The almost reported cDNA/genomic sequences of Acanthamoeba provide stand alone BLAST system with nucleotide (BLAST NT) and amino acid (BLAST AA) sequence database. In BLAST results, each gene links for the significant information including sequence data, gene orthology annotations, relevant references, and a BlastX result. This is the first attempt for construction of Acanthamoeba database with genes expressed in diverse conditions. These data were integrated into a database (http://www.amoeba.or.kr).