Future Trends in Alternative Sustainable Materials for Low-Temperature Thermoelectric Applications.

In the evolution of pervasive electronics, it is imperative to significantly reduce the energy consumption of power systems and embrace sustainable materials and fabrication processes with minimal carbon footprint. Within this context, thermoelectric generators (TEGs) have garnered substantial attention in recent years because of the readily available thermal gradients in the environment, making them a promising energy-harvesting technology. Current commercial room-temperature thermoelectrics are based on scarce, expensive, and/or toxic V-VI chalcogenide materials, which limit their widespread use.

Neural dynamics of social verb processing: An MEG study.

Human vocabularies include specific words to communicate interpersonal behaviors, a core linguistic function mainly afforded by social verbs (SVs). This skill has been proposed to engage dedicated systems subserving social knowledge. Yet, neurocognitive evidence is scarce, and no study has examined spectro-temporal and spatial signatures of SV access.

Cell Architecture and Dynamics of Human Induced Pluripotent Stem Cell-Derived Cardiomyocytes (hiPSC-CMs) on Hydrogels with Spatially Patterned Laminin and N-Cadherin.

Controlling cellular shape with micropatterning extracellular matrix (ECM) proteins on hydrogels has been shown to improve the reproducibility of the cell structure, enhancing our ability to collect statistics on single-cell behaviors. Patterning methods have advanced efforts in developing human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) as a promising human model for studies of the heart structure, function, and disease. Patterned single hiPSC-CMs have exhibited phenotypes closer to mature, primary CMs across several metrics, including sarcomere alignment and contractility, area and aspect ratio, and force production.

Multiscale effective connectivity analysis of brain activity using neural ordinary differential equations.

Neural mechanisms and underlying directionality of signaling among brain regions depend on neural dynamics spanning multiple spatiotemporal scales of population activity. Despite recent advances in multimodal measurements of brain activity, there is no broadly accepted multiscale dynamical models for the collective activity represented in neural signals. Here we introduce a neurobiological-driven deep learning model, termed multiscale neural dynamics neural ordinary differential equation (msDyNODE), to describe multiscale brain communications governing cognition and behavior.