Smartphone-based gaze estimation for in-home autism research.

Atypical gaze patterns are a promising biomarker of autism spectrum disorder. To measure gaze accurately, however, it typically requires highly controlled studies in the laboratory using specialized equipment that is often expensive, thereby limiting the scalability of these approaches. Here we test whether a recently developed smartphone-based gaze estimation method could overcome such limitations and take advantage of the ubiquity of smartphones. As a proof-of-principle, we measured gaze while a small sample of well-assessed autistic participants and controls watched videos on a smartphone, both in the laboratory (with lab personnel) and in remote home settings (alone). We demonstrate that gaze data can be efficiently collected, in-home and longitudinally by participants themselves, with sufficiently high accuracy (gaze estimation error below 1° visual angle on average) for quantitative, feature-based analysis. Using this approach, we show that autistic individuals have reduced gaze time on human faces and longer gaze time on non-social features in the background, thereby reproducing established findings in autism using just smartphones and no additional hardware. Our approach provides a foundation for scaling future research with larger and more representative participant groups at vastly reduced cost, also enabling better inclusion of underserved communities.

A kinetic ensemble of the Alzheimer's Aß peptide.

The conformational and thermodynamic properties of disordered proteins are commonly described in terms of structural ensembles and free energy landscapes. To provide information on the transition rates between the different states populated by these proteins, it would be desirable to generalize this description to kinetic ensembles. Approaches based on the theory of stochastic processes can be particularly suitable for this purpose. Here, we develop a Markov state model and apply it to determine a kinetic ensemble of Aß42, a disordered peptide associated with Alzheimer's disease. Through the Google Compute Engine, we generated 315-µs all-atom molecular dynamics trajectories. Using a probabilistic-based definition of conformational states in a neural network approach, we found that Aß42 is characterized by inter-state transitions on the microsecond timescale, exhibiting only fully unfolded or short-lived, partially folded states. Our results illustrate how kinetic ensembles provide effective information about the structure, thermodynamics and kinetics of disordered proteins.