Sentinel-2 water indexes application for the underground water level analyses in Ovidiopol area of Odessa region

Studied area has a high level of agricultural development. There are different irrigation and drainage systems located there. Significant part of the supplied water losses from the irrigation network because of filtration and reaches the groundwater level, which begins to rise. Control and analyses of groundwater level changes with remote sensing methods for Ovidiopol area is the main goal of that work. The object of study is the groundwater level regime in the territory of Lower Dniester irrigation system in Ovidiopol district, Odessa region. The subject of research is water indexes application for analyses of groundwater level changes. The local system of groundwater observation includes 7 drillholes in Nadlimanskoe village and around. These drillholes located in different geomorphological, hydrogeological and technogenic conditions. The groundwater level was surveyed monthly in 2017.  Sentinel-2 2A images for each month from March 2017 to December 2017 were used for studied area. All satellite images has atmospheric correction. Three water indexes NDWI, MNDWI, NDPI were calculated for drillhole points for each month in 2017 year. Significant correlation coefficients obtained in comparison between groundwater level changes and water indexes in some drillholes points. The highest numbers of correlation connected with free of construction areas and for drillholes, which are located outside of villages

DruGAN: An Advanced Generative Adversarial Autoencoder Model for de Novo Generation of New Molecules with Desired Molecular Properties in Silico

© 2017 American Chemical Society. Deep generative adversarial networks (GANs) are the emerging technology in drug discovery and biomarker development. In our recent work, we demonstrated a proof-of-concept of implementing deep generative adversarial autoencoder (AAE) to identify new molecular fingerprints with predefined anticancer properties. Another popular generative model is the variational autoencoder (VAE), which is based on deep neural architectures. In this work, we developed an advanced AAE model for molecular feature extraction problems, and demonstrated its advantages compared to VAE in terms of (a) adjustability in generating molecular fingerprints; (b) capacity of processing very large molecular data sets; and (c) efficiency in unsupervised pretraining for regression model. Our results suggest that the proposed AAE model significantly enhances the capacity and efficiency of development of the new molecules with specific anticancer properties using the deep generative models

The cornucopia of meaningful leads: Applying deep adversarial autoencoders for new molecule development in oncology

Recent advances in deep learning and specifically in generative adversarial networks have demonstrated surprising results in generating new images and videos upon request even using natural language as input. In this paper we present the first application of generative adversarial autoencoders (AAE) for generating novel molecular fingerprints with a defined set of parameters. We developed a 7-layer AAE architecture with the latent middle layer serving as a discriminator. As an input and output the AAE uses a vector of binary fingerprints and concentration of the molecule. In the latent layer we also introduced a neuron responsible for growth inhibition percentage, which when negative indicates the reduction in the number of tumor cells after the treatment. To train the AAE we used the NCI-60 cell line assay data for 6252 compounds profiled on MCF-7 cell line. The output of the AAE was used to screen 72 million compounds in PubChem and select candidate molecules with potential anticancer properties. This approach is a proof of concept of an artificially-intelligent drug discovery engine, where AAEs are used to generate new molecular fingerprints with the desired molecular properties

The inhibition of functional expression of calcium channels by prion protein demonstrates competition with α2δ for GPI-anchoring pathways.

It has recently been shown that prion protein (PrP) and the calcium channel auxiliary α2δ subunits interact in neurons and expression systems. We examined whether there was an effect of PrP on calcium currents. We show that when PrP is co-expressed with calcium channels formed from CaV2.1/β and α2δ-1 or α2δ-2, this results in a consistent decrease in calcium current density. This reduction was absent when PrP lacked its glycosyl-phosphatidylinositol (GPI) anchor. We have found that α2δ subunits are able to form GPI-anchored proteins [2] and present further evidence here. We have recently characterised a C-terminally truncated α2δ-1 construct, α2δ-1ΔC, and found that, despite loss of its membrane anchor, it still shows partial ability to increase calcium currents. We now find that PrP does not inhibit CaV2.1/β currents formed with α2δ-1ΔC rather than α2δ-1. It is possible that PrP and α2δ-1 compete for GPI-anchor intermediates or trafficking pathways, or that interaction between PrP and α2δ-1 requires association in cholesterol-rich membrane microdomains. Our additional finding that CaV2.1/β1b/α2δ-1 currents were inhibited by GPI-GFP but not by cytosolic GFP, indicates that competition for limited GPI-anchor intermediates or trafficking proteins may be involved in PrP suppression of α2δ subunit function

3D Molecular Representations Based on the Wave Transform for Convolutional Neural Networks

© 2018 American Chemical Society. Convolutional neural networks (CNN) have been successfully used to handle three-dimensional data and are a natural match for data with spatial structure such as 3D molecular structures. However, a direct 3D representation of a molecule with atoms localized at voxels is too sparse, which leads to poor performance of the CNNs. In this work, we present a novel approach where atoms are extended to fill other nearby voxels with a transformation based on the wave transform. Experimenting on 4.5 million molecules from the Zinc database, we show that our proposed representation leads to better performance of CNN-based autoencoders than either the voxel-based representation or the previously used Gaussian blur of atoms and then successfully apply the new representation to classification tasks such as MACCS fingerprint prediction

The Increased Trafficking of the Calcium Channel Subunit α₂δ-1 o Presynaptic Terminals in Neuropathic Pain Is Inhibited by the α₂δ Ligand Pregabalin

Neuropathic pain results from damage to the peripheral sensory nervous system, which may have a number of causes. The calcium channel subunit α₂δ-1 is upregulated in dorsal root ganglion (DRG) neurons in several animal models of neuropathic pain, and this is causally related to the onset of allodynia, in which a non-noxious stimulus becomes painful. The therapeutic drugs gabapentin and pregabalin (PGB), which are both α₂δ ligands, have antiallodynic effects, but their mechanism of action has remained elusive. To investigate this, we used an in vivo rat model of neuropathy, unilateral lumbar spinal nerve ligation (SNL), to characterize the distribution of α₂δ-1 in DRG neurons, both at the light- and electron-microscopic level. We found that, on the side of the ligation, α₂δ-1 was increased in the endoplasmic reticulum of DRG somata, in intracellular vesicular structures within their axons, and in the plasma membrane of their presynaptic terminals in superficial layers of the dorsal horn. Chronic PGB treatment of SNL animals, at a dose that alleviated allodynia, markedly reduced the elevation of α₂δ-1 in the spinal cord and ascending axon tracts. In contrast, it had no effect on the upregulation of α₂δ-1 mRNA and protein in DRGs. In vitro, PGB reduced plasma membrane expression of α₂δ-1 without affecting endocytosis. We conclude that the antiallodynic effect of PGB in vivo is associated with impaired anterograde trafficking of α₂δ-1, resulting in its decrease in presynaptic terminals, which would reduce neurotransmitter release and spinal sensitization, an important factor in the maintenance of neuropathic pain

The Interaction between the First Transmembrane Domain and the Thumb of ASIC1a Is Critical for Its N-Glycosylation and Trafficking

Acid-sensing ion channel-1a (ASIC1a), the primary proton receptor in the brain, contributes to multiple diseases including stroke, epilepsy and multiple sclerosis. Thus, a better understanding of its biogenesis will provide important insights into the regulation of ASIC1a in diseases. Interestingly, ASIC1a contains a large, yet well organized ectodomain, which suggests the hypothesis that correct formation of domain-domain interactions at the extracellular side is a key regulatory step for ASIC1a maturation and trafficking. We tested this hypothesis here by focusing on the interaction between the first transmembrane domain (TM1) and the thumb of ASIC1a, an interaction known to be critical in channel gating. We mutated Tyr71 and Trp287, two key residues involved in the TM1-thumb interaction in mouse ASIC1a, and found that both Y71G and W287G decreased synaptic targeting and surface expression of ASIC1a. These defects were likely due to altered folding; both mutants showed increased resistance to tryptic cleavage, suggesting a change in conformation. Moreover, both mutants lacked the maturation of N-linked glycans through mid to late Golgi. These data suggest that disrupting the interaction between TM1 and thumb alters ASIC1a folding, impedes its glycosylation and reduces its trafficking. Moreover, reducing the culture temperature, an approach commonly used to facilitate protein folding, increased ASIC1a glycosylation, surface expression, current density and slowed the rate of desensitization. These results suggest that correct folding of extracellular ectodomain plays a critical role in ASIC1a biogenesis and function

REVIEW OF IMAGE FEATURE POINTS DETECTION ALGORITHMS

The article raises the problem of navigation and positioning of unmanned aerial vehicles without using satellite navigation  systems, such as GPS and GLONASS. The idea of creating a navigation system using computer vision technology, which  is a subsection of the discipline of artificial intelligence, is described herein. The article gives a brief description of the core  concepts and problems of one of the main subsections of computer vision –  identification of images and finding of specific  points on the image. Also algorithms for determination of local features on the image, that are clearly differing in their  operation principle and purposes, have been reviewed and analysed from the point of view of identifying specific points on  the image. Based on the operation principles of the reviewed algorithms, their material advantages and disadvantages,  which directly affect the efficiency of using these algorithms in non-satellite navigation system, have been identified. In the  conclusion, several key aspects that should be taken into account in selection of appropriate algorithm for development of  a navigation system are highlighted

DruGAN: An Advanced Generative Adversarial Autoencoder Model for de Novo Generation of New Molecules with Desired Molecular Properties in Silico

© 2017 American Chemical Society. Deep generative adversarial networks (GANs) are the emerging technology in drug discovery and biomarker development. In our recent work, we demonstrated a proof-of-concept of implementing deep generative adversarial autoencoder (AAE) to identify new molecular fingerprints with predefined anticancer properties. Another popular generative model is the variational autoencoder (VAE), which is based on deep neural architectures. In this work, we developed an advanced AAE model for molecular feature extraction problems, and demonstrated its advantages compared to VAE in terms of (a) adjustability in generating molecular fingerprints; (b) capacity of processing very large molecular data sets; and (c) efficiency in unsupervised pretraining for regression model. Our results suggest that the proposed AAE model significantly enhances the capacity and efficiency of development of the new molecules with specific anticancer properties using the deep generative models