Diversification of small RNA pathways underlies germline RNA interference incompetence in wild Caenorhabditis elegans strains.

The discovery that experimental delivery of dsRNA can induce gene silencing at target genes revolutionized genetics research, by both uncovering essential biological processes and creating new tools for developmental geneticists. However, the efficacy of exogenous RNA interference (RNAi) varies dramatically within the Caenorhabditis elegans natural population, raising questions about our understanding of RNAi in the lab relative to its activity and significance in nature. Here, we investigate why some wild strains fail to mount a robust RNAi response to germline targets. We observe diversity in mechanism: in some strains, the response is stochastic, either on or off among individuals, while in others, the response is consistent but delayed. Increased activity of the Argonaute PPW-1, which is required for germline RNAi in the laboratory strain N2, rescues the response in some strains but dampens it further in others. Among wild strains, genes known to mediate RNAi exhibited very high expression variation relative to other genes in the genome as well as allelic divergence and strain-specific instances of pseudogenization at the sequence level. Our results demonstrate functional diversification in the small RNA pathways in C. elegans and suggest that RNAi processes are evolving rapidly and dynamically in nature.

Local shape descriptors for neuron segmentation.

We present an auxiliary learning task for the problem of neuron segmentation in electron microscopy volumes. The auxiliary task consists of the prediction of local shape descriptors (LSDs), which we combine with conventional voxel-wise direct neighbor affinities for neuron boundary detection. The shape descriptors capture local statistics about the neuron to be segmented, such as diameter, elongation, and direction. On a study comparing several existing methods across various specimen, imaging techniques, and resolutions, auxiliary learning of LSDs consistently increases segmentation accuracy of affinity-based methods over a range of metrics. Furthermore, the addition of LSDs promotes affinity-based segmentation methods to be on par with the current state of the art for neuron segmentation (flood-filling networks), while being two orders of magnitudes more efficient-a critical requirement for the processing of future petabyte-sized datasets.

Algorithms underlying flexible phototaxis in larval zebrafish.

To thrive, organisms must maintain physiological and environmental variables in suitable ranges. Given that these variables undergo constant fluctuations over varying time scales, how do biological control systems maintain control over these values? We explored this question in the context of phototactic behavior in larval zebrafish. We demonstrate that larval zebrafish use phototaxis to maintain environmental luminance at a set point, that the value of this set point fluctuates on a time scale of seconds when environmental luminance changes, and that it is determined by calculating the mean input across both sides of the visual field. These results expand on previous studies of flexible phototaxis in larval zebrafish; they suggest that larval zebrafish exert homeostatic control over the luminance of their surroundings, and that feedback from the surroundings drives allostatic changes to the luminance set point. As such, we describe a novel behavioral algorithm with which larval zebrafish exert control over a sensory variable.