Dataset compiled from spreading hot spots, responsible for fire risk in many regions of Indonesian forests, are complex, primarily induced by the large size of the observed regions and high variation of hot spot distribution. The challenge in analyzing this type of dataset is to develop statistical techniques that facilitate the analysis, visualization, and interpretation of the results. Techniques, such as multivariate analysis and artificial neural networks, have been applied to resolve the highdimensional space in such large datasets. Each method uses a different rationale for how the relationship between the input parameters will be preserved during analysis. This study presents the use of a principal component analysis (PCA) and a selforganizing map (SOM) to reduce the high dimensionality of the input variables and, subsequently to visualize the dataset into a two-dimensional (2-D) space. The results indicate that the first two principal components of the PCA provide a large percentage of cumulative variance to explain the data patterns. However, a comparison of the data projection, SOM is better suited than PCA in visualizing the fire-risk distribution in forests. The SOM color-coding and labeling also effectively visualized a classification system of fire risk via node clusters, in such a way that the fire risks level according to their hot spot locations in forest is easily interpreted.
This study investigates the significance of the expression and dynamics of podoplanin in mechanostress and mineralization in cultured murine osteoblasts. Podoplanin increased in osteoblasts subjected to straining in non-mineralization medium, suggesting that the mechanostress alone is a podoplanin induction factor. In osteoblasts subjected to vertical elongation straining in the mineralization medium, the mRNA amounts of podoplanin, osteopontin, and osteocalcin were significantly larger than those in cells not subjected to straining, suggesting that mechanostress is the cause of a synergistic effect in the expression of these proteins. In osteoblasts in the mineralization medium, significant increases in osteocalcin mRNA occurred earlier in cells subjected to straining than in the cells not subjected to straining, suggesting that the mechanostress is a critical factor to enhance the expression of osteocalcin. Western blot and ELISA analysis showed increased podoplanin production in osteoblasts with longer durations of straining. There was significantly less mineralization product in osteoblasts with antibodies for podoplanin, osteopontin, and osteocalcin. There was also less osteopontin and osteocalcin produced in osteoblasts with anti-podoplanin. These findings suggest that mechanostress induces the production of podoplanin in osteoblasts and that podoplanin may play a role in mineralization in cooperation with bone-associated proteins.
Information on rainfall variations is a matter of great importance in agricultural countries. Climate and rainfall are non-linear natural phenomena whose measurement leads to complex data, primarily due to noise patterns and distribution heterogeneity. Therefore, it is difficult to develop an appropriate model in practice by using conventional modeling techniques. This study presents the use of a neuro-fuzzy system for modeling wet season tropical rainfall. The advantage of this technique was the possibility of a modified environment of input parameters for improving the data representation. Two approaches used in the neuro-fuzzy models were classification and prediction. The neuro-fuzzy classification model firstly produced a simple rule base that enables improved interpretability of variation in rainfall rate. The given fuzzy classification rules were then utilized to generate a neuro-fuzzy inference system in order to predict rainfall variation. This approach measured the accuracy of the prediction model according to the root mean square error (RMSE) estimation. The models resulted low values of the RMSE indicated that the prediction models are reliable in representing the recent inter-annual variation of the wet season tropical rainfall.
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