Novel 6-hydroxybenzothiazol-2-carboxamides as potent and selective monoamine oxidase B inhibitors endowed with neuroprotective activity.

In neurodegenerative diseases, using a single molecule that can exert multiple effects to modify the disease may have superior activity over the classical "one molecule-one target" approach. Herein, we describe the discovery of 6-hydroxybenzothiazol-2-carboxamides as highly potent and selective MAO-B inhibitors. Variation of the amide substituent led to several potent compounds having diverse side chains with cyclohexylamide 40 displaying the highest potency towards MAO-B (IC50 = 11 nM). To discover new compounds with extended efficacy against neurotoxic mechanisms in neurodegenerative diseases, MAO-B inhibitors were screened against PHF6, R3 tau, cellular tau and α-synuclein (α-syn) aggregation. We identified the phenethylamide 30 as a multipotent inhibitor of MAO-B (IC50 = 41 nM) and α-syn and tau aggregation. It showed no cytotoxic effects on SH-SY5Y neuroblastoma cells, while also providing neuroprotection against toxicities induced by α-syn and tau. The evaluation of key physicochemical and in vitro-ADME properties revealed a great potential as drug-like small molecules with multitarget neuroprotective activity.

High-Fidelity Permeability and Porosity Prediction Using Deep Learning With the Self-Attention Mechanism.

Accurate estimation of reservoir parameters (e.g., permeability and porosity) helps to understand the movement of underground fluids. However, reservoir parameters are usually expensive and time-consuming to obtain through petrophysical experiments of core samples, which makes a fast and reliable prediction method highly demanded. In this article, we propose a deep learning model that combines the 1-D convo- lutional layer and the bidirectional long short-term memory network to predict reservoir permeability and porosity. The mapping relationship between logging data and reservoir parameters is established by training a network with a combination of nonlinear and linear modules. Optimization algorithms, such as layer normalization, recurrent dropout, and early stopping, can help obtain a more accurate training model. Besides, the self-attention mechanism enables the network to better allocate weights to improve the prediction accuracy. The testing results of the well-trained network in blind wells of three different regions show that our proposed method is accurate and robust in the reservoir parameters prediction task.

Self-Attention Fully Convolutional DenseNets for Automatic Salt Segmentation.

3-D salt segmentation is important for many research topics spanning from exploration geophysics to structural geology. In seismic exploration, 3-D salt segmentation is directly related to the velocity modeling building that affects many processing steps, such as seismic migration and full waveform inversion. Manually picking the salt boundary becomes prohibitively time-consuming when the data size is too large. Here, we develop a highly generalized fully convolutional DenseNet for automatic salt segmentation. A squeeze-and-excitation network is used as a self-attention mechanism for guiding the proposed network to extract the most significant information related to the salt signals and discard the others. The proposed framework is a supervised technique and shows robust performance when applied to a new dataset using transfer learning and a small amount of training data. We test the robustness of the proposed framework on the Kaggle TGS salt segmentation dataset. To demonstrate the generalization ability of the framework, we further apply the trained model to an independent dataset synthesized from the 3-D SEAM model. We apply transfer learning to finely tune the trained model from the TGS dataset using only a small percentage of data from the 3-D SEAM dataset and obtain satisfactory results.