[Report] Instantaneous ion configurations in the K ion channel selectivity filter revealed by 2D IR spectroscopy


Potassium channels are responsible for the selective permeation of K ions across cell membranes. K ions permeate in single file through the selectivity filter, a narrow pore lined by backbone carbonyls that compose four K binding sites. Here, we report on the two-dimensional infrared (2D IR) spectra of a semisynthetic KcsA channel with site-specific heavy (13C18O) isotope labels in the selectivity filter. The ultrafast time resolution of 2D IR spectroscopy provides an instantaneous snapshot of the multi-ion configurations and structural distributions that occur spontaneously in the filter. Two elongated features are resolved, revealing the statistical weighting of two structural conformations.

Optimizing organic electrosynthesis through controlled voltage dosing and artificial intelligence


Organic electrosynthesis can transform the chemical industry by introducing electricity-driven processes that are more energy efficient and that can be easily integrated with renewable energy sources. However, their deployment is severely hindered by the difficulties of controlling selectivity and achieving a large energy conversion efficiency at high current density due to the low solubility of organic reactants in practical electrolytes. This control can be improved by carefully balancing the mass transport processes and electrocatalytic reaction rates at the electrode diffusion layer through pulsed electrochemical methods. In this study, we explore these methods in the context of the electrosynthesis of adiponitrile (ADN), the largest organic electrochemical process in industry. Systematically exploring voltage pulses in the timescale between 5 and 150 ms led to a 20% increase in production of ADN and a 250% increase in relative selectivity with respect to the state-of-the-art constant voltage process.

A Multi-Domain Evaluation of Scaling in a General Episodic Memory

AAAI Conferences

Episodic memory endows agents with numerous general cognitive capabilities, such as action modeling and virtual sensing. However, for long-lived agents, there are numerous unexplored computational challenges in supporting useful episodic-memory functions while maintaining real-time reactivity. In this paper, we review the implementation of episodic memory in Soar and present an expansive evaluation of that system. We demonstrate useful applications of episodic memory across a variety of domains, including games, mobile robotics, planning, and linguistics. In these domains, we characterize properties of environments, tasks, and episodic cues that affect performance, and evaluate the ability of Soar’s episodic memory to support hours to days of real-time operation.

An Analog VLSI Model of Periodicity Extraction

Neural Information Processing Systems

This paper presents an electronic system that extracts the periodicity of a sound. It uses three analogue VLSI building blocks: a silicon cochlea, two inner-hair-cell circuits and two spiking neuron chips. The silicon cochlea consists of a cascade of filters. Because of the delay between two outputs from the silicon cochlea, spike trains created at these outputs are synchronous only for a narrow range of periodicities. In contrast to traditional bandpass filters,where an increase in' selectivity has to be traded off against a decrease in response time, the proposed system responds quickly, independent of selectivity. 1 Introduction The human ear transduces airborne sounds into a neural signal using three stages in the inner ear's cochlea: (i) the mechanical filtering of the Basilar Membrane (BM), (ii) the transduction of membrane vibration into neurotransmitter release by the Inner Hair Cells (IHCs), and (iii) spike generation by the Spiral Ganglion Cells (SGCs), whose axons form the auditory nerve.