Voltage-Gated Sodium Channels in Drug Discovery

Voltage-gated sodium channels (Nav) control the initiation and propagation of action potential, and thus mediate a broad spectrum of physiological processes, including central and peripheral nervous systems’ function, skeletal muscle contraction, and heart rhythm. Recent advances in elucidating the molecular basis of channelopathies implicating Nav channels are the most appealing druggable targets for pain and many other pathology conditions. This chapter overviews Nav super family from genetic evolution, distribution, human diseases/pathology association, highlighting the most recent structure function breakthrough. The second section will discuss current small and large Nav modulators, including traditional nonselective pore blockers, intracellular modulators, and extracellular modulators

Voltage-Gated Sodium Channel Drug Discovery Technologies and Challenges

Voltage-gated sodium (Nav) channels represent an important class of drug target for pain and many other pathology conditions. Despite the recent advances in channelopathies and structure-function studies, the discovery of Nav channel therapeutics is still facing a major challenge from the limitation of assay technologies. This chapter will focus on advancement and challenge of Nav drug discovery technologies including nonelectrophysiological assays, extracellular electrophysiological assays, and the newly evolved high-throughput automated patch clamp (APC) technologies

Learning Unmanned Aerial Vehicle Control for Autonomous Target Following

While deep reinforcement learning (RL) methods have achieved unprecedented successes in a range of challenging problems, their applicability has been mainly limited to simulation or game domains due to the high sample complexity of the trial-and-error learning process. However, real-world robotic applications often need a data-efficient learning process with safety-critical constraints. In this paper, we consider the challenging problem of learning unmanned aerial vehicle (UAV) control for tracking a moving target. To acquire a strategy that combines perception and control, we represent the policy by a convolutional neural network. We develop a hierarchical approach that combines a model-free policy gradient method with a conventional feedback proportional-integral-derivative (PID) controller to enable stable learning without catastrophic failure. The neural network is trained by a combination of supervised learning from raw images and reinforcement learning from games of self-play. We show that the proposed approach can learn a target following policy in a simulator efficiently and the learned behavior can be successfully transferred to the DJI quadrotor platform for real-world UAV control

Gaussian process tomography for soft x-ray spectroscopy at WEST without equilibrium information

International audienceGaussian process tomography (GPT) is a recently developed tomography method based on the Bayesian probability theory [J. Svensson, JET Internal Report EFDA-JET-PR(11)24, 2011 and Li et al., Rev. Sci. Instrum. 84, 083506 (2013)]. By modeling the soft X-ray (SXR) emissivity field in a poloidal cross section as a Gaussian process, the Bayesian SXR tomography can be carried out in a robust and extremely fast way. Owing to the short execution time of the algorithm, GPT is an important candidate for providing real-time reconstructions with a view to impurity transport and fast magnetohydrodynamic control. In addition, the Bayesian formalism allows quantifying uncertainty on the inferred parameters. In this paper, the GPT technique is validated using a synthetic data set expected from the WEST tokamak, and the results are shown of its application to the reconstruction of SXR emissivity profiles measured on Tore Supra. The method is compared with the standard algorithm based on minimization of the Fisher information

RNA interference for CFTR attenuates lung fluid absorption at birth in rats

<p>Abstract</p> <p>Background</p> <p>Small interfering RNA (siRNA) against αENaC (α-subunit of the epithelial Na channel) and CFTR (cystic fibrosis transmembrane conductance regulator) was used to explore ENaC and CTFR function in newborn rat lungs.</p> <p>Methods</p> <p>Twenty-four hours after trans-thoracic intrapulmonary (ttip) injection of siRNA-generating plasmid DNA (pSi-0, pSi-4, or pSi-C₂), we measured CFTR and ENaC expression, extravascular lung water, and mortality.</p> <p>Results</p> <p>αENaC and CFTR mRNA and protein decreased by ~80% and ~85%, respectively, following αENaC and CFTR silencing. Extravascular lung water and mortality increased after αENaC and CFTR-silencing. In pSi-C₂-transfected isolated DLE cells there were attenuated CFTR mRNA and protein. In pSi-4-transfected DLE cells αENaC mRNA and protein were both reduced. Interestingly, CFTR-silencing also reduced αENaC mRNA and protein. αENaC silencing, on the other hand, only slightly reduced CFTR mRNA and protein.</p> <p>Conclusion</p> <p>Thus, ENaC and CFTR are both involved in the fluid secretion to absorption conversion around at birth.</p

HL-DPoS: An Enhanced Anti-Long-Range Attack DPoS Algorithm

The consensus algorithm is crucial in blockchain for ensuring the validity and security of transactions across the decentralized network. However, achieving consensus among nodes and packaging blocks in blockchain networks is a complex task that requires efficient and secure consensus algorithms. The DPoS consensus algorithm has emerged as a popular choice due to its fast transaction processing and high throughput. Despite these advantages, the algorithm still suffers from weaknesses such as centralization and vulnerability to long-range attacks, which can compromise the integrity of the blockchain network. To combat these problems, we developed an Enhanced Anti-Long-Range Attack DPoS algorithm (HL-DPoS). First, we split nodes into pieces to reduce centralization issues while giving witness nodes the power to report and benefit from malicious node's reports, maintaining high efficiency and high security. Second, we propose a validation method in HL-DPoS that compares consensuses transactions with the longest chain to detect long-range attacks. Algorithm analysis and simulation experiment results demonstrate that our HL-DPoS consensus algorithm improves security while achieving better consensus performance

Nonparametric Generative Modeling with Conditional Sliced-Wasserstein Flows

Sliced-Wasserstein Flow (SWF) is a promising approach to nonparametric generative modeling but has not been widely adopted due to its suboptimal generative quality and lack of conditional modeling capabilities. In this work, we make two major contributions to bridging this gap. First, based on a pleasant observation that (under certain conditions) the SWF of joint distributions coincides with those of conditional distributions, we propose Conditional Sliced-Wasserstein Flow (CSWF), a simple yet effective extension of SWF that enables nonparametric conditional modeling. Second, we introduce appropriate inductive biases of images into SWF with two techniques inspired by local connectivity and multiscale representation in vision research, which greatly improve the efficiency and quality of modeling images. With all the improvements, we achieve generative performance comparable with many deep parametric generative models on both conditional and unconditional tasks in a purely nonparametric fashion, demonstrating its great potential.Comment: ICML 202