Design and formulation of functional pluripotent stem cell-derived cardiac microtissues.

Access to robust and information-rich human cardiac tissue models would accelerate drug-based strategies for treating heart disease. Despite significant effort, the generation of high-fidelity adult-like human cardiac tissue analogs remains challenging. We used computational modeling of tissue contraction and assembly mechanics in conjunction with microfabricated constraints to guide the design of aligned and functional 3D human pluripotent stem cell (hPSC)-derived cardiac microtissues that we term cardiac microwires (CMWs). Miniaturization of the platform circumvented the need for tissue vascularization and enabled higher-throughput image-based analysis of CMW drug responsiveness. CMW tissue properties could be tuned using electromechanical stimuli and cell composition. Specifically, controlling self-assembly of 3D tissues in aligned collagen, and pacing with point stimulation electrodes, were found to promote cardiac maturation-associated gene expression and in vivo-like electrical signal propagation. Furthermore, screening a range of hPSC-derived cardiac cell ratios identified that 75% NKX2 Homeobox 5 (NKX2-5)+ cardiomyocytes and 25% Cluster of Differentiation 90 OR (CD90)+ nonmyocytes optimized tissue remodeling dynamics and yielded enhanced structural and functional properties. Finally, we demonstrate the utility of the optimized platform in a tachycardic model of arrhythmogenesis, an aspect of cardiac electrophysiology not previously recapitulated in 3D in vitro hPSC-derived cardiac microtissue models. The design criteria identified with our CMW platform should accelerate the development of predictive in vitro assays of human heart tissue function.

Unified analytic model for current-voltage behavior in amorphous oxide semiconductor TFTs

We present a simple and semi-physical analytical description of the current-voltage characteristics of amorphous oxide semiconductor thin-film transistors in the above-threshold and sub-threshold regions. Both regions are described by single unified expression that employs the same set of model parameter values directly extracted from measured terminal characteristics. The model accurately reproduces measured characteristics of amorphous semiconductor thin film transistors in general, yielding a scatter of < 4%. © 1980-2012 IEEE.

Optimal recall from bounded metaplastic synapses: predicting functional adaptations in hippocampal area CA3.

A venerable history of classical work on autoassociative memory has significantly shaped our understanding of several features of the hippocampus, and most prominently of its CA3 area, in relation to memory storage and retrieval. However, existing theories of hippocampal memory processing ignore a key biological constraint affecting memory storage in neural circuits: the bounded dynamical range of synapses. Recent treatments based on the notion of metaplasticity provide a powerful model for individual bounded synapses; however, their implications for the ability of the hippocampus to retrieve memories well and the dynamics of neurons associated with that retrieval are both unknown. Here, we develop a theoretical framework for memory storage and recall with bounded synapses. We formulate the recall of a previously stored pattern from a noisy recall cue and limited-capacity (and therefore lossy) synapses as a probabilistic inference problem, and derive neural dynamics that implement approximate inference algorithms to solve this problem efficiently. In particular, for binary synapses with metaplastic states, we demonstrate for the first time that memories can be efficiently read out with biologically plausible network dynamics that are completely constrained by the synaptic plasticity rule, and the statistics of the stored patterns and of the recall cue. Our theory organises into a coherent framework a wide range of existing data about the regulation of excitability, feedback inhibition, and network oscillations in area CA3, and makes novel and directly testable predictions that can guide future experiments.