Surveillance of antibiotic resistance genes (ARGs) has been increasingly conducted in environmental sectors to complement the surveys in human and animal sectors under the "One-Health"framework. However, there are substantial challenges in comparing and synthesizing the results of multiple studies that employ different test methods and approaches in bioinformatic analysis. In this article, we consider the commonly used quantification units (ARG copy per cell, ARG copy per genome, ARG density, ARG copy per 16S rRNA gene, RPKM, coverage, PPM, etc.) for profiling ARGs and suggest a universal unit (ARG copy per cell) for reporting such biological measurements of samples and improving the comparability of different surveillance efforts.

Shape memory polymers (SMPs) are dynamic materials able to recover previously defined shapes when activated by external stimuli. The most common stimulus is thermal energy applied near thermal transitions in polymers, such as glass transition (T_g) and melting (T_m) temperatures. The magnitude of the geometrical changes as well as the amount of force and energy that a SMP can output are critical properties for many applications. While typically deformation steps in the shape memory cycles (SMC) are performed at temperatures well above thermal transitions used to activate shape changes, significantly greater amounts of strain, stress, and mechanical energy can be stored in T_g-based SMPs when deformed near their T_g. Since maximum shape memory storage capacity can be appraised by evaluating the viscoelastic length transitions (VLTs) in a single dynamic mechanical analysis (DMA) experiment, this study correlates VLTs with the measured storage capacities obtained from stress-strain experiments for a broad range of well-defined crosslinked acrylates, epoxies, and polyurethanes. This systematic approach allows for assessment of crosslink/junction density (ν_j), viscoelasticity, and chemical composition effects on maximum deformability, and enables predictions of the magnitude of shape memory properties across a wide variety of polymers. These studies demonstrate that the maximum storable strain (ε-store_max) can be accurately predicted using junction density (ν_j) and shape memory factor (SMF), the latter accounting for the contribution of chemical makeup.