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Rare earths, sometimes called the vitamins of modern materials, captured public attention when their prices increased more than tenfold in 2010 and 2011. As prices fell between 2011 and 2016, rare earths receded from public view, but less visibly, they became a major focus of innovative activity in companies, government laboratories, and universities. Geoscientists worked to better understand the resource base and improve our knowledge about mineral deposits that can be mines in the future. Process engineers carried out research that is making primary production and recycling more efficient. Materials scientists and engineers searched for substitutes that require fewer or no rare earths while providing properties comparable or superior to those of existing materials. As a result, even though global supply chains are not significantly different now than they were before the market disruption, the innovative activity motivated by the disruption will likely have far-reaching, if unpredictable, consequences for supply chains of rare earths in the future.

Stem cells occupy variable environments where they must distinguish stochastic fluctuations from developmental cues. Here, we use optogenetics to investigate how the pluripotency network in embryonic stem (ES) cells achieves a robust response to differentiation cues but not to gene expression fluctuations. We engineered ES cells in which we could quantitatively ontrol the endogenous mechanism of neural differentiation through a light-inducible Brn2 transgene and monitor differentiation status through a genome-integrated Nanog-GFP reporter. By exposing cells to pulses of Brn2, we find that the pluripotency network rejects Brn2 inputs that are below specific magnitude or duration thresholds, but allows rapid differentiation when both thresholds are satisfied. The filtering properties of the network arise through its positive feedback architecture and the intrinsic half-life of Nanog, which determines the duration threshold in the network. Together our results suggest that the dynamic properties of positive-feedback networks might determine how inputs are classified as signal or noise by stem cells.

We describe Quanti.us , a crowd-based image-annotation platform that provides an accurate alternative to computational algorithms for difficult image-analysis problems. We used Quanti.us for a variety of medium-throughput image-analysis tasks and achieved 10-50× savings in analysis time compared with that required for the same task by a single expert annotator. We show equivalent deep learning performance for Quanti.us-derived and expert-derived annotations, which should allow scalable integration with tailored machine learning algorithms.