Large-scale identification of clinical and genetic predictors of motor progression in patients with newly diagnosed Parkinson's disease: a longitudinal cohort study and validation.

Background: Better understanding and prediction of progression of Parkinson's disease could improve disease management and clinical trial design. We aimed to use longitudinal clinical, molecular, and genetic data to develop predictive models, compare potential biomarkers, and identify novel predictors for motor progression in Parkinson's disease. We also sought to assess the use of these models in the design of treatment trials in Parkinson's disease.

Degraded time-frequency acuity to time-reversed notes.

Time-reversal symmetry breaking is a key feature of many classes of natural sounds, originating in the physics of sound production. While attention has been paid to the response of the auditory system to "natural stimuli," very few psychophysical tests have been performed. We conduct psychophysical measurements of time-frequency acuity for stylized representations of "natural"-like notes (sharp attack, long decay) and the time-reversed versions of these notes (long attack, sharp decay).

Topological phase transition in the Scheidegger model of river networks.

Transport networks are found at the heart of myriad natural systems, yet are poorly understood, except for the case of river networks. The Scheidegger model, in which rivers are convergent random walks, has been studied only in the case of flat topography, ignoring the variety of curved geometries found in nature. Embedding this model on a cone, we find a convergent and a divergent phase, corresponding to few, long basins and many, short basins, respectively, separated by a singularity, indicating a phase transition.