Search

Background: Advances in earth observation and machine learning techniques have created new options for forest monitoring, primarily because of the various possibilities that they provide for classifying forest cover and estimating aboveground biomass (AGB)., Methods: This study aimed to introduce a novel model that incorporates the atom search algorithm (ASO) and adaptive neuro-fuzzy inference system (ANFIS) into mangrove forest classification and AGB estimation. The Ca Mau coastal area was selected as a case study since it has been considered the most preserved mangrove forest area in Vietnam and is being investigated for the impacts of land-use change on forest quality. The model was trained and validated with a set of Sentinel-1A imagery with VH and VV polarizations, and multispectral information from the SPOT image. In addition, feature selection was also carried out to choose the optimal combination of predictor variables. The model performance was benchmarked against conventional methods, such as support vector regression, multilayer perceptron, random subspace, and random forest, by using statistical indicators, namely, root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2)., Results: The results showed that all three indicators of the proposed model were statistically better than those from the benchmarked methods. Specifically, the hybrid model ended up at RMSE = 70.882, MAE = 55.458, R2 = 0.577 for AGB estimation., Conclusion: From the experiments, such hybrid integration can be recommended for use as an alternative solution for biomass estimation. In a broader context, the fast growth of metaheuristic search algorithms has created new scientifically sound solutions for better analysis of forest cover., Competing Interests: The authors have no conflict of interest.

The Arf1 exchange factor GBF1 (Golgi Brefeldin A resistance factor 1) and its effector COPI are required for delivery of ATGL (adipose triglyceride lipase) to lipid droplets (LDs). Using yeast two hybrid, co-immunoprecipitation in mammalian cells and direct protein binding approaches, we report here that GBF1 and ATGL interact directly and in cells, through multiple contact sites on each protein. The C-terminal region of ATGL interacts with N-terminal domains of GBF1, including the catalytic Sec7 domain, but not with full-length GBF1 or its entire N-terminus. The N-terminal lipase domain of ATGL (called the patatin domain) interacts with two C-terminal domains of GBF1, HDS (Homology downstream of Sec7) 1 and HDS2. These two domains of GBF1 localize to lipid droplets when expressed alone in cells, but not to the Golgi, unlike the full-length GBF1 protein, which localizes to both. We suggest that interaction of GBF1 with ATGL may be involved in the membrane trafficking pathway mediated by GBF1, Arf1 and COPI that contributes to the localization of ATGL to lipid droplets.