RESUMO
The comprehensive production of detailed bathymetric maps is important for disaster prevention, resource exploration, safe navigation, marine salvage, and monitoring of marine organisms. However, owing to observation difficulties, the amount of data on the world's seabed topography is scarce. Therefore, it is essential to develop methods that effectively use the limited data. In this study, based on dictionary learning and sparse coding, we modified the super-resolution technique and applied it to seafloor topographical maps. Improving on the conventional method, before dictionary learning, we performed pre-processing to separate the teacher image into a low-frequency component that has a general structure and a high-frequency component that captures the detailed topographical features. We learn the topographical features by training the dictionary. As a result, the root-mean-square error (RMSE) was reduced by 30% compared with bicubic interpolation and accuracy was improved, especially in the rugged part of the terrain. The proposed method, which learns a dictionary to capture topographical features and reconstructs them using a dictionary, produces super-resolution with high interpretability.
Assuntos
Algoritmos , Aprendizagem , Oceanos e MaresRESUMO
Hirondellea species are common inhabitants in the hadal region deeper than 7,000 m. We found that Hirondellea gigas thrived in the Challenger Deep possessed polysaccharide hydrolases as digestive enzymes. To obtain various enzymes of other H. gigas, we captured amphipods from the Japan Trench, and Izu-Ogasawara (Bonin) Trench. A phylogenetic analysis based on the cytochrome oxidase I gene showed close relationships among amphipods, despite the geographic distance between the localities. However, several differences in enzymatic properties were observed in these H. gigas specimens. We also carried out RNA sequencing of H. gigas from the Izu-Ogasawara Trench. The cellulase gene of H. gigas was highly homologous to cellobiohydrolase of Glucosyl Hydrolase family 7 (GH7). On the other hand, enzymatic properties of H. gigas's cellulase were different from those of typical GH7 cellobiohydrolase. Thus, these results indicate that hadal-zone amphipod can be good candidates as the new enzyme resource.