Inhibitory modulation of action potentials in crayfish motor axons by fluoxetine.

The goal of this report is to explore how K2P channels modulate axonal excitability by using the crayfish ventral superficial flexor preparation. This preparation allows for simultaneous recording of motor nerve extracellular action potentials (eAP) and intracellular excitatory junctional potential (EJP) from a muscle fiber. Previous pharmacological studies have demonstrated the presence of K2P-like channels in crayfish. Fluoxetine (50 µM) was used to block K2P channels in this study. The blocker caused a gradual decline, and eventually complete block, of motor axon action potentials. At an intermediate stage of the block, when the peak-to-peak amplitude of eAP decreased to ∼60%-80% of the control value, the amplitude of the initial positive component of eAP declined at a faster rate than that of the negative peak representing sodium influx. Furthermore, the second positive peak following this sodium influx, which corresponds to the after-hyperpolarizing phase of intracellularly recorded action potentials (iAP), became larger during the intermediate stage of eAP block. Finally, EJP recorded simultaneously with eAP showed no change in amplitude, but did show a significant increase in synaptic delay. These changes in eAP shape and EJP delay are interpreted as the consequence of depolarized resting membrane potential after K2P channel block. In addition to providing insights to possible functions of K2P channels in unmyelinated axons, results presented here also serve as an example of how changes in eAP shape contain information that can be used to infer alterations in intracellular events. This type of eAP-iAP cross-inference is valuable for gaining mechanistic insights here and may also be applicable to other model systems.

Calculations of the effect of catalyst size and structure on the electrocatalytic reduction of CO2 on Cu nanoclusters.

The structure and catalytic properties of Cu nanoclusters of sizes between 55 and 147 atoms were examined to understand if small Cu clusters could provide enhancement over traditional catalysts for the electrocatalysis of CO2 to CO and carbon-based fuels, such as CH4 and CH3OH, compared to bulk Cu surfaces and large Cu nanoparticles. Clusters studied included Cu55, Cu78, Cu101, Cu124, and Cu147, the structures of which were determined using global optimisation. The majority of Cu clusters examined were icosahedral, including the perfect closed-shell, partial-shell, elongated and distorted icosahedral clusters. Free energy diagrams for the reduction of CO2 showed the potential required for the formation of CO is notably smaller for all cluster sizes considered, relative to Cu(111). Less variation is observed for the limiting potential for the formation of CH4 and CH3OH. However, it was found that clusters that are either a distorted motif or contain vacancy defects yielded the best activity and provide an interesting synthesis target for future experiments.

Calculating incidence of Influenza-like and COVID-like symptoms from Flutracking participatory survey data.

This article describes a new method for estimating weekly incidence (new onset) of symptoms consistent with Influenza and COVID-19, using data from the Flutracking survey. The method mitigates some of the known self-selection and symptom-reporting biases present in existing approaches to this type of participatory longitudinal survey data. The key novel steps in the analysis are: 1) Identifying new onset of symptoms for three different Symptom Groupings: COVID-like illness (CLI1+, CLI2+), and Influenza-like illness (ILI), for responses reported in the Flutracking survey. 2) Adjusting for symptom reporting bias by restricting the analysis to a sub-set of responses from those participants who have consistently responded for a number of weeks prior to the analysis week. 3) Weighting responses by age to adjust for self-selection bias in order to account for the under- and over-representation of different age groups amongst the survey participants. This uses the survey package [22] in R [30]. 4) Constructing 95% point-wise confidence bands for incidence estimates using weighted logistic regression from the survey package [21] in R [28]. In addition to describing these steps, the article demonstrates an application of this method to Flutracking data for the 12 months from 27th April 2020 until 25th April 2021.

Enhancing the hydrogen evolution activity of MoS2 basal planes and edges using tunable carbon-based supports.

The hydrogen adsorption free energy (ΔGHads) on the basal plane and edges of MoS2 is studied using periodic density functional theory, with the catalyst supported by a range of two-dimensional carbon-based materials. Understanding how ΔGHads can be tuned with support gives insight into MoS2 as a catalyst for the hydrogen evolution reaction. The supports studied here include graphene oxide materials, heteroatom doped (S, B, and N) graphene, and some insulator materials (hexagonal boron nitride and graphitic carbon nitride). For the basal plane of MoS2, a wide range of values for ΔGHads are observed (between 1.4 and 2.2 eV) depending on the support material used. It is found that ΔGHads relates directly to the energy of occupied p-orbital states in the MoS2 catalyst, which is modified by the support material. On the Mo-edge of MoS2, different supports induce smaller variations in ΔGHads, with values ranging between -0.27 and 0.09 eV. However, a graphene support doped with graphitic N atoms produces a ΔGHads value of exactly 0 eV, which is thermodynamically ideal for hydrogen evolution. Furthermore, ΔGHads is found to relate closely and linearly to the amount of charge transfer between MoS2 and support when they adhere together. The support-induced tuning of ΔGHads on MoS2 observed here provides a useful tool for improving current MoS2 catalysts, and the discovery of variables which mediate changes in ΔGHads contributes to the rational design of new hydrogen evolution catalysts.