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Regulating food intake is an important physiological mechanism. Here, the authors use a custom microfluidic device to investigate feeding dynamics in C. elegans, and identify roles of serotonergic neurons in regulating bursts of feeding in response to food availability.
High-throughput time-series data is increasingly available, yet estimating time-derivatives from such data can remain a challenge. Here, the authors provide a non-parametric method for inferring the first and second time-derivatives from multiple replicates of time-series data and for estimating errors in this inference and in any summary statistics.