Frog embryos use multiple levels of temporal pattern in risk assessment for vibration-cued escape hatching.

Stereotyped signals can be a fast, effective means of communicating danger, but animals assessing predation risk must often use more variable incidental cues. Red eyed-treefrog, Agalychnis callidryas, embryos hatch prematurely to escape from egg predators, cued by vibrations in attacks, but benign rain generates vibrations with overlapping properties. Facing high false-alarm costs, embryos use multiple vibration properties to inform hatching, including temporal pattern elements such as pulse durations and inter-pulse intervals. However, measures of snake and rain vibration as simple pulse-interval patterns are a poor match to embryo behavior. We used vibration playbacks to assess if embryos use a second level of temporal pattern, long gaps within a rhythmic pattern, as indicators of risks. Long vibration-free periods are common during snake attacks but absent from hard rain. Long gaps after a few initial vibrations increase the hatching response to a subsequent vibration series. Moreover, vibration patterns as short as three pulses, separated by long periods of silence, can induce as much hatching as rhythmic pulse series with five times more vibration. Embryos can retain information that increases hatching over at least 45 s of silence. This work highlights that embryo behavior is contextually modulated in complex ways. Identical vibration pulses, pulse groups, and periods of silence can be treated as risk cues in some contexts and not in others. Embryos employ a multi-faceted decision-making process to effectively distinguish between risk cues and benign stimuli.

Going the distance for protein function prediction: a new distance metric for protein interaction networks.

In protein-protein interaction (PPI) networks, functional similarity is often inferred based on the function of directly interacting proteins, or more generally, some notion of interaction network proximity among proteins in a local neighborhood. Prior methods typically measure proximity as the shortest-path distance in the network, but this has only a limited ability to capture fine-grained neighborhood distinctions, because most proteins are close to each other, and there are many ties in proximity. We introduce diffusion state distance (DSD), a new metric based on a graph diffusion property, designed to capture finer-grained distinctions in proximity for transfer of functional annotation in PPI networks. We present a tool that, when input a PPI network, will output the DSD distances between every pair of proteins. We show that replacing the shortest-path metric by DSD improves the performance of classical function prediction methods across the board.