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Graphical abstract, Over recent years, there has been a rapid development of membrane-mimetic systems to encapsulate and stabilize planar segments of phospholipid bilayers in solution. One such system has been the use of amphipathic copolymers to solubilize lipid bilayers into nanodiscs. The attractiveness of this system, in part, stems from the capability of these polymers to solubilize membrane proteins directly from the host cell membrane. The assumption has been that the native lipid annulus remains intact, with nanodiscs providing a snapshot of the lipid environment. Recent studies have provided evidence that phospholipids can exchange from the nanodiscs with either lipids at interfaces, or with other nanodiscs in bulk solution. Here we investigate kinetics of lipid exchange between three recently studied polymer-stabilized nanodiscs and supported lipid bilayers at the silicon-water interface. We show that lipid and polymer exchange occurs in all nanodiscs tested, although the rate and extent differs between different nanodisc types. Furthermore, we observe adsorption of nanodiscs to the supported lipid bilayer for one nanodisc system which used a polymer made using reversible addition-fragmentation chain transfer polymerization. These results have important implications in applications of polymer-stabilized nanodiscs, such as in the fabrication of solid-supported films containing membrane proteins.

A holistic view of racial and gender disparities that simultaneously compares multiple groups can suggest associated underlying contextual factors. Therefore, to more comprehensively understand temporal changes in combined racial and gender disparities, we examine variations in the orders of county-level race-gender specific heart disease death rates by age group from 1973–2015. We estimated county-level heart disease death rates by race, gender, and age group (35–44, 45–54, 55–64, 65–74, 75–84, ≥ 85, and ≥ 35) from the National Vital Statistics System of the National Center for Health Statistics from 1973–2015. We then ordered these rates from lowest to highest for each county and year. The predominant national rate order (i.e., white women (WW) < black women (BW) < white men (WM) < black men (BM)) was most common in younger age groups. Inverted rates for black women and white men (WW, Highlights • Combined racial and gender disparities can suggest underlying contextual factors. • We examined counties’ rate orders of race-gender specific heart disease mortality. • Many counties experienced rate orders that were not observed nationally. • Many counties had higher rates for black women than white men. • Socioeconomic forces may drive heart disease mortality more than biology.

Cardiac electrophysiology models have been developed for over 50 years, and now include detailed descriptions of individual ion currents and sub-cellular calcium handling. It is commonly accepted that there are many uncertainties in these systems, with quantities such as ion channel kinetics or expression levels being difficult to measure or variable between samples. Until recently, the original approach of describing model parameters using single values has been retained, and consequently the majority of mathematical models in use today provide point predictions, with no associated uncertainty. In recent years, statistical techniques have been developed and applied in many scientific areas to capture uncertainties in the quantities that determine model behaviour, and to provide a distribution of predictions which accounts for this uncertainty. In this paper we discuss this concept, which is termed uncertainty quantification, and consider how it might be applied to cardiac electrophysiology models. We present two case studies in which probability distributions, instead of individual numbers, are inferred from data to describe quantities such as maximal current densities. Then we show how these probabilistic representations of model parameters enable probabilities to be placed on predicted behaviours. We demonstrate how changes in these probability distributions across data sets offer insight into which currents cause beat-to-beat variability in canine APs. We conclude with a discussion of the challenges that this approach entails, and how it provides opportunities to improve our understanding of electrophysiology., Highlights • Uncertainty and variability in action potential models can be quantified. • A probabilistic method for inferring maximal current densities is developed and applied. • We use this to infer the currents responsible for canine beat-to-beat variability. • Emulation of mathematical models provides rich information at low computational cost. • The importance of considering uncertainty and variability in future is discussed.

Highlights • Spatial model fit is adequate when network contacts are limited to small distances. • Spatial model fit is poor when network contacts exists over long distances. • Results for spatial models tested were fairly consistent for all contact networks. • Results show the importance of collecting high quality network information., Often, when modeling infectious disease spread, the complex network through which the disease propagates is approximated by simplified spatial information. Here, we simulate epidemic spread through various contact networks and fit spatial-based models in a Bayesian framework using Markov chain Monte Carlo methods. These spatial models are individual-level models which account for the spatio-temporal dynamics of infectious disease. The focus here is on choosing a spatial model which best predicts the true probabilities of infection, as well as determining under which conditions such spatial models fail. Spatial models tend to predict infection probability reasonably well when disease spread is propagated through contact networks in which contacts are only within a certain distance of each other. If contacts exist over long distances, the spatial models tend to perform worse when compared to the network model.