Autonomous closed-loop mechanistic investigation of molecular electrochemistry via automation.

Electrochemical research often requires stringent combinations of experimental parameters that are demanding to manually locate. Recent advances in automated instrumentation and machine-learning algorithms unlock the possibility for accelerated studies of electrochemical fundamentals via high-throughput, online decision-making. Here we report an autonomous electrochemical platform that implements an adaptive, closed-loop workflow for mechanistic investigation of molecular electrochemistry.

Ecology and Chronic Wasting Disease Epidemiology Shape Prion Protein Gene Variation in Rocky Mountain Elk (Cervus elaphus nelsoni).

As chronic wasting disease (CWD) continues to spread across North America, the relationship between CWD and host genetics has become of interest. In Rocky Mountain elk (Cervus elaphus nelsoni), one or two copies of a leucine allele at codon 132 of the prion protein gene (132L*) has been shown to prolong the incubation period of CWD. Our study examined the relationship between CWD epidemiology and codon 132 evolution in elk from Wyoming, USA, from 2011 to 2018.

Electrochemical Mechanistic Analysis from Cyclic Voltammograms Based on Deep Learning.

For decades, employing cyclic voltammetry for mechanistic investigation has demanded manual inspection of voltammograms. Here, we report a deep-learning-based algorithm that automatically analyzes cyclic voltammograms and designates a probable electrochemical mechanism among five of the most common ones in homogeneous molecular electrochemistry. The reported algorithm will aid researchers' mechanistic analyses, utilize otherwise elusive features in voltammograms, and experimentally observe the gradual mechanism transitions encountered in electrochemistry.

Behaviour is more important than thermal performance for an Arctic host-parasite system under climate change.

Climate change is affecting Arctic ecosystems, including parasites. Predicting outcomes for host-parasite systems is challenging due to the complexity of multi-species interactions and the numerous, interacting pathways by which climate change can alter dynamics. Increasing temperatures may lead to faster development of free-living parasite stages but also higher mortality.

Machine learning-based inverse design for electrochemically controlled microscopic gradients of O and HO.

A fundamental understanding of extracellular microenvironments of O and reactive oxygen species (ROS) such as HO, ubiquitous in microbiology, demands high-throughput methods of mimicking, controlling, and perturbing gradients of O and HO at microscopic scale with high spatiotemporal precision. However, there is a paucity of high-throughput strategies of microenvironment design, and it remains challenging to achieve O and HO heterogeneities with microbiologically desirable spatiotemporal resolutions. Here, we report the inverse design, based on machine learning (ML), of electrochemically generated microscopic O and HO profiles relevant for microbiology.