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ABSTRACT Nonmuscle myosin II (NMII) generates cytoskeletal forces that drive cell division, embryogenesis, muscle contraction and many other cellular functions. However, at present there is no method that can directly measure the forces generated by myosins in living cells. Here, we describe a Förster resonance energy transfer (FRET)-based tension sensor that can detect myosin-associated force along the filamentous actin network. Fluorescence lifetime imaging microscopy (FLIM)-FRET measurements indicate that the forces generated by NMII isoform B (NMIIB) exhibit significant spatial and temporal heterogeneity as a function of donor lifetime and fluorophore energy exchange. These measurements provide a proxy for inferred forces that vary widely along the actin cytoskeleton. This initial report highlights the potential utility of myosin-based tension sensors in elucidating the roles of cytoskeletal contractility in a wide variety of contexts. 

Abstract The development of high-resolution microscopes has made it possible to investigate cellular processes in 3D and over time. However, observing fast cellular dynamics remains challenging because of photobleaching and phototoxicity. Here we report the implementation of two content-aware frame interpolation (CAFI) deep learning networks, Zooming SlowMo and Depth-Aware Video Frame Interpolation, that are highly suited for accurately predicting images in between image pairs, therefore improving the temporal resolution of image series post-acquisition. We show that CAFI is capable of understanding the motion context of biological structures and can perform better than standard interpolation methods. We benchmark CAFI’s performance on 12 different datasets, obtained from four different microscopy modalities, and demonstrate its capabilities for single-particle tracking and nuclear segmentation. CAFI potentially allows for reduced light exposure and phototoxicity on the sample for improved long-term live-cell imaging. The models and the training and testing data are available via the ZeroCostDL4Mic platform. 

Half-Heusler materials are strong candidates for thermoelectric applications due to their high weighted mobilities and power factors, which is known to be correlated to valley degeneracy in the electronic band structure. However, there are over 50 known semiconducting half-Heusler phases, and it is not clear how the chemical composition affects the electronic structure. While all the n-type electronic structures have their conduction band minimum at either the Γ - or X -point, there is more diversity in the p-type electronic structures, and the valence band maximum can be at either the Γ -, L -, or W -point. Here, we use high throughput computation and machine learning to compare the valence bands of known half-Heusler compounds and discover new chemical guidelines for promoting the highly degenerate W -point to the valence band maximum. We do this by constructing an “orbital phase diagram” to cluster the variety of electronic structures expressed by these phases into groups, based on the atomic orbitals that contribute most to their valence bands. Then, with the aid of machine learning, we develop new chemical rules that predict the location of the valence band maximum in each of the phases. These rules can be used to engineer band structures with band convergence and high valley degeneracy. 

Accurate density functional theory calculations of the interrelated properties of thermoelectric materials entail high computational cost, especially as crystal structures increase in complexity and size. New methods involving ab initio scattering and transport (AMSET) and compressive sensing lattice dynamics are used to compute the transport properties of quaternary CaAl 2 Si 2 -type rare-earth phosphides RECuZnP 2 (RE = Pr, Nd, Er), which were identified to be promising thermoelectrics from high-throughput screening of 20 000 disordered compounds. Experimental measurements of the transport properties agree well with the computed values. Compounds with stiff bulk moduli (>80 GPa) and high speeds of sound (>3500 m s −1 ) such as RECuZnP 2 are typically dismissed as thermoelectric materials because they are expected to exhibit high lattice thermal conductivity. However, RECuZnP 2 exhibits not only low electrical resistivity, but also low lattice thermal conductivity (∼1 W m −1 K −1 ). Contrary to prior assumptions, polar-optical phonon scattering was revealed by AMSET to be the primary mechanism limiting the electronic mobility of these compounds, raising questions about existing assumptions of scattering mechanisms in this class of thermoelectric materials. The resulting thermoelectric performance ( zT of 0.5 for ErCuZnP 2 at 800 K) is among the best observed in phosphides and can likely be improved with further optimization.