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Abstract Cellular quiescence is a state of reversible proliferative arrest that plays essential roles in development, resistance to stress, aging, and longevity of organisms. Here we report that rapid depletion of RNase MRP, a deeply conserved RNA-based enzyme required for rRNA biosynthesis, induces a long-term yet reversible proliferative arrest in human cells. Severely compromised biogenesis of rRNAs along with acute transcriptional reprogramming precede a gradual decline of the critical cellular functions. Unexpectedly, many arresting cells show increased levels of histone mRNAs, which accumulate locally in the cytoplasm, and S-phase DNA amount. The ensuing proliferative arrest is entered from multiple stages of the cell cycle and can last for several weeks with uncompromised cell viability. Strikingly, restoring expression of RNase MRP leads to a complete reversal of the arrested state with resumed cell proliferation at the speed of control cells. We suggest that targeting rRNA biogenesis may provide a general strategy for rapid induction of a reversible proliferative arrest, with implications for understanding and manipulating cellular quiescence. 

Abstract High entropy alloys (HEAs) are an important material class in the development of next-generation structural materials, but the astronomically large composition space cannot be efficiently explored by experiments or first-principles calculations. Machine learning (ML) methods might address this challenge, but ML of HEAs has been hindered by the scarcity of HEA property data. In this work, the EMTO-CPA method was used to generate a large HEA dataset (spanning a composition space of 14 elements) containing 7086 cubic HEA structures with structural properties, 1911 of which have the complete elastic tensor calculated. The elastic property dataset was used to train a ML model with the Deep Sets architecture. The Deep Sets model has better predictive performance and generalizability compared to other ML models. Association rule mining was applied to the model predictions to describe the compositional dependence of HEA elastic properties and to demonstrate the potential for data-driven alloy design. 

Higher energy density batteries continue to be pursued by researchers. One general route to increase energy density is to increase electrode thickness, which reduces the relative fraction of the cell allocated to inactive components. One route to fabricate thick electrodes is to use mildly thermally treated, or sintered, electrodes comprising only electroactive materials. In this report, the concept of sintered electrodes comprising two different electroactive components will be reported. Conventional composite electrodes with multiple electroactive materials have previously been investigated with the goal of combining desirable attributes of the different components. Sintered electrodes have additional complexity relative to composite electrodes in that interfaces can be formed during processing, and consideration of the location of the different component materials must be taken into account due to the need for electronic conduction through the electrode matrix to proceed through the electroactive materials themselves. Both additional considerations and outcomes will be discussed in this report where multicomponent sintered electrodes of LiCoO 2 and LiMn 2 O 4 were fabricated and characterized. 

For batteries, thicker electrodes increase energy density, however, molecular transport limits the rate of charge/discharge for extracting large fractions of available energy. Mitigating transport limitations by increasing electrolyte conductivity and aligning the pores in the electrode microstructure are described.