Generalized Sparse Convolutional Neural Networks for Semantic Segmentation of Point Clouds Derived from Tri-Stereo Satellite Imagery

We studied the applicability of point clouds derived from tri-stereo satellite imagery for semantic segmentation for generalized sparse convolutional neural networks by the example of an Austrian study area. We examined, in particular, if the distorted geometric information, in addition to color, influences the performance of segmenting clutter, roads, buildings, trees, and vehicles. In this regard, we trained a fully convolutional neural network that uses generalized sparse convolution one time solely on 3D geometric information (i.e., 3D point cloud derived by dense image matching), and twice on 3D geometric as well as color information. In the first experiment, we did not use class weights, whereas in the second we did. We compared the results with a fully convolutional neural network that was trained on a 2D orthophoto, and a decision tree that was once trained on hand-crafted 3D geometric features, and once trained on hand-crafted 3D geometric as well as color features. The decision tree using hand-crafted features has been successfully applied to aerial laser scanning data in the literature. Hence, we compared our main interest of study, a representation learning technique, with another representation learning technique, and a non-representation learning technique. Our study area is located in Waldviertel, a region in Lower Austria. The territory is a hilly region covered mainly by forests, agriculture, and grasslands. Our classes of interest are heavily unbalanced. However, we did not use any data augmentation techniques to counter overfitting. For our study area, we reported that geometric and color information only improves the performance of the Generalized Sparse Convolutional Neural Network (GSCNN) on the dominant class, which leads to a higher overall performance in our case. We also found that training the network with median class weighting partially reverts the effects of adding color. The network also started to learn the classes with lower occurrences. The fully convolutional neural network that was trained on the 2D orthophoto generally outperforms the other two with a kappa score of over 90% and an average per class accuracy of 61%. However, the decision tree trained on colors and hand-crafted geometric features has a 2% higher accuracy for roads

Destroying activity of magnetoferritin on lysozyme amyloid fibrils

Presence of protein amyloid aggregates (oligomers, protofilaments, fibrils) is associated with many diseases as diabetes mellitus or Alzheimer's disease. The interaction between lysozyme amyloid fibrils and magnetoferritin loaded with different amount of iron atoms (168 or 532 atoms) has been investigated by small-angle X-rays scattering and thioflavin T fluorescence measurements. Results suggest that magnetoferritin caused an iron atom-concentration dependent reduction of lysozyme fibril siz

<i>In Silico</i> and <i>in Vitro</i> Study of Binding Affinity of Tripeptides to Amyloid β Fibrils: Implications for Alzheimer’s Disease

Self-assembly of Aβ peptides into amyloid aggregates has been suggested as the major cause of Alzheimer’s disease (AD). Nowadays, there is no medication for AD, but experimental data indicate that reversion of the process of amyloid aggregation reduces the symptoms of disease. In this paper, all 8000 tripeptides were studied for their ability to destroy Aβ fibrils. The docking method and the more sophisticated MM-PBSA (molecular mechanics Poisson–Boltzmann surface area) method were employed to calculate the binding affinity and mode of tripeptides to Aβ fibrils. The ability of these peptides to depolymerize Aβ fibrils was also investigated experimentally using atomic force microscopy and fluorescence spectroscopy (Thioflavin T assay). It was shown that tripeptides prefer to bind to hydrophobic regions of 6Aβ_9–40 fibrils. Tripeptides WWW, WWP, WPW and PWW were found to be the most potent binders. <i>In vitro</i> experiments showed that tight-binding tripeptides have significant depolymerizing activities and their DC₅₀ values determined from dose–response curves were in micromolar range. The ability of nonbinding (GAM, AAM) and weak-binding (IVL and VLA) tripeptides to destroy Aβ fibrils was negligible. <i>In vitro</i> data of tripeptide depolymerizing activities support the predictions obtained by molecular docking and all-atom simulation methods. Our results suggest that presence of multiple complexes of heterocycles forming by tryptophan and proline residues in tripeptides is crucial for their tight binding to Aβ fibrils as well as for extensive fibril depolymerization. We recommend PWW for further studies as it has the lowest experimental binding constant

Binding of Glyco-Acridine Derivatives to Lysozyme Leads to Inhibition of Amyloid Fibrillization

While amyloid-related diseases are at the center of intense research efforts, no feasible cure is currently available for these diseases. The experimental and computational techniques were used to study the ability of glyco-acridines to prevent lysozyme amyloid fibrillization in vitro. Fluorescence spectroscopy and atomic force microscopy have shown that glyco-acridines inhibit amyloid aggregation of lysozyme; the inhibition efficiency characterized by the half-maximal inhibition concentration IC₅₀ was affected by the structure and concentration of the derivative. We next investigated relationship between the binding affinity and the inhibitory activity of the compounds. The semiempirical quantum PM6-DH+ method provided a good correlation pointing to the importance of quantum effects on the binding of glyco-acridine derivatives to lysozyme. The contribution of linkers may be explained by the valence bond theory. Our data provide a basis for the development of new small molecule inhibitors effective in therapy of amyloid-related diseases