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Unmanned aerial vehicles equipped with hyperspectral imagers have emerged as an essential technology for the characterization of inland water bodies. The high spectral and spatial resolutions of these systems enable the retrieval of a plethora of optically active water quality parameters via band ratio algorithms and machine learning methods. However, fitting and validating these models requires access to sufficient quantities of in situ reference data which are time-consuming and expensive to obtain. In this study, we demonstrate how Generative Topographic Mapping (GTM), a probabilistic realization of the self-organizing map, can be used to visualize high-dimensional hyperspectral imagery and extract spectral signatures corresponding to unique endmembers present in the water. Using data collected across a North Texas pond, we first apply GTM to visualize the distribution of captured reflectance spectra, revealing the small-scale spatial variability of the water composition. Next, we demonstrate how the nodes of the fitted GTM can be interpreted as unique spectral endmembers. Using extracted endmembers together with the normalized spectral similarity score, we are able to efficiently map the abundance of nearshore algae, as well as the evolution of a rhodamine tracer dye used to simulate water contamination by a localized source. [ABSTRACT FROM AUTHOR]

Information about the species composition of a forest is necessary for assessing biodiversity in a particular region and making economic decisions on the management of forest resources. Recognition of the species composition, according to the Earth's remote sensing data, greatly simplifies the work and reduces time and labor costs in comparison with a traditional inventory of the forest, conducted through ground-based observations. This study analyzes the possibilities of tree species discrimination in coniferous–deciduous forests according to Sentinel-2 data using two automated recognition methods: random forest (RF) and generative topographic mapping (GTM). As remote sensing data, Sentinel-2 images of the Raifa section of Volga-Kama State Reserve in the Tatarstan Republic, Russia used: six images for the vegetation period of 2020. The analysis was carried out for the main forest-forming species. The training sample was created based on the cadastral data of the forest fund. The recognition quality was assessed using the F1-score, precision, recall, and accuracy metrics. The RF method showed a higher recognition accuracy. The accuracy of correct recognition by the RF method on the training sample reaches 0.987, F1-score = 0.976, on the control sample, accuracy = 0.764, F1-score = 0.709. [ABSTRACT FROM AUTHOR]

Prediction of molecular properties plays a critical role towards rational drug design. In this study, the Molecular Topographic Map (MTM) is proposed, which is a two-dimensional (2D) map that can be used to represent a molecule. An MTM is generated from the atomic features set of a molecule using generative topographic mapping and is then used as input data for analyzing structure-property/activity relationships. In the visualization and classification of 20 amino acids, differences of the amino acids can be visually confirmed from and revealed by hierarchical clustering with a similarity matrix of their MTMs. The prediction of molecular properties was performed on the basis of convolutional neural networks using MTMs as input data. The performance of the predictive models using MTM was found to be equal to or better than that using Morgan fingerprint or MACCS keys. Furthermore, data augmentation of MTMs using mixup has improved the prediction performance. Since molecules converted to MTMs can be treated like 2D images, they can be easily used with existing neural networks for image recognition and related technologies. MTM can be effectively utilized to predict molecular properties of small molecules to aid drug discovery research. [ABSTRACT FROM AUTHOR]

Following previous efforts to render the Conformational Space (CS) of flexible compounds by Generative Topographic Mapping (GTM), this polyvalent mapping technique is here adapted to the docking problem. Contact fingerprints (CF) characterize ligands from the perspective of the binding site by monitoring protein atoms that are "touched" by those of the ligand. A "Contact" (CF) map was built by GTM-driven dimensionality reduction of the CF vector space. Alternatively, a "Hybrid" (Hy) map used a composite descriptor of CFs concatenated with ligand fragment descriptors. These maps indirectly represent the active site and integrate the binding information of multiple ligands. The concept is illustrated by a docking study into the ATP-binding site of CDK2, using the S4MPLE program to generate thousands of poses for each ligand. Both maps were challenged to (1) Discriminate native from non-native ligand poses, e.g., create RMSD-landscapes "colored" by the conformer ensemble of ligands of known binding modes in order to highlight "native" map zones (poses with RMSD to PDB structures < 2Å). Then, projection of poses of other ligands on such landscapes might serve to predict those falling in native zones as being well-docked. (2) Distinguish ligands–characterized by their ensemble of conformers–by their potency, e.g., testing the hypotheses whether zones privileged by potent binders are clearly separated from the ones preferred by decoys on the maps. Hybrid maps were better in both challenges and outperformed the classical energy and individual contact satisfaction scores in discriminating ligands by potency. Moreover, the intuitive visualization and analysis of docking CS may, as already mentioned, have several applications–from highlighting of key contacts to monitoring docking calculation convergence. [ABSTRACT FROM AUTHOR]