A single oscillating proto-hypothalamic neuron gates taxis behavior in the primitive chordate Ciona.

Ciona larvae display a number of behaviors, including negative phototaxis. In negative phototaxis, the larvae first perform short spontaneous rhythmic casting swims. As larvae are cast in a light field, their photoreceptors are directionally shaded by an associated pigment cell, providing a phototactic cue. This then evokes an extended negative taxis swim. We report here that the larval forebrain of Ciona has a previously uncharacterized single slow-oscillating inhibitory neuron (neuron cor-assBVIN78) that projects to the midbrain, where it targets key interneurons of the phototaxis circuit known as the photoreceptor relay neurons. The anatomical location, gene expression, and oscillation of cor-assBVIN78 suggest homology to oscillating neurons of the vertebrate hypothalamus. Ablation of cor-assBVIN78 results in larvae showing extended phototaxis-like swims, even in the absence of phototactic cues. These results indicate that cor-assBVIN78 has a gating activity on phototaxis by projecting temporally oscillating inhibition to the photoreceptor relay neurons. However, in intact larvae, the frequency of cor-assBVIN78 oscillation does not match that of the rhythmic spontaneous swims, indicating that the troughs in oscillations do not themselves initiate swims but rather that cor-assBVIN78 may modulate the phototaxis circuit by filtering out low-level inputs while restricting them temporally to the troughs in inhibition.

A single oscillating proto-hypothalamic neuron gates taxis behavior in the primitive chordate Ciona.

Ciona larvae display a number of behaviors, including negative phototaxis. In negative phototaxis, the larvae first perform short spontaneous rhythmic casting swims. As larvae cast in a light field, their photoreceptors are directionally shaded by an associated pigment cell, providing a phototactic cue. This then evokes an extended negative taxis swim. We report here that the larval forebrain of Ciona has a previously uncharacterized single slow-oscillating inhibitory neuron (neuron cor-assBVIN78 ) that projects to the midbrain, where it targets key interneurons of the phototaxis circuit known as the photoreceptor relay neurons . The anatomical location, gene expression and oscillation of cor-assBVIN78 suggest homology to oscillating neurons of the vertebrate hypothalamus. Ablation of cor-assBVIN78 results in larvae showing extended phototaxis-like swims, but which occur in the absence of phototactic cues. These results indicate that cor-assBVIN78 has a gating activity on phototaxis by projecting temporally-oscillating inhibition to the photoreceptor relay neurons. However, in intact larvae the frequency of cor-assBVIN78 oscillation does not match that of the rhythmic spontaneous swims, indicating that the troughs in oscillations do not themselves initiate swims, but rather that cor-assBVIN78 may modulate the phototaxis circuit by filtering out low level inputs while restricting them temporally to the troughs in inhibition.

Erratum: Automatic classification and neurotransmitter prediction of synapses in electron microscopy - CORRIGENDUM.

[This corrects the article DOI: 10.1017/S2633903X2200006X.].

Automatic classification and neurotransmitter prediction of synapses in electron microscopy.

This paper presents a deep-learning-based workflow to detect synapses and predict their neurotransmitter type in the primitive chordate Ciona intestinalis (Ciona) electron microscopic (EM) images. Identifying synapses from EM images to build a full map of connections between neurons is a labor-intensive process and requires significant domain expertise. Automation of synapse classification would hasten the generation and analysis of connectomes. Furthermore, inferences concerning neuron type and function from synapse features are in many cases difficult to make. Finding the connection between synapse structure and function is an important step in fully understanding a connectome. Class Activation Maps derived from the convolutional neural network provide insights on important features of synapses based on cell type and function. The main contribution of this work is in the differentiation of synapses by neurotransmitter type through the structural information in their EM images. This enables the prediction of neurotransmitter types for neurons in Ciona, which were previously unknown. The prediction model with code is available on GitHub.

From Reflection to Action: Combining Machine Learning with Expert Knowledge for Nutrition Goal Recommendations.

Self-tracking can help personalize self-management interventions for chronic conditions like type 2 diabetes (T2D), but reflecting on personal data requires motivation and literacy. Machine learning (ML) methods can identify patterns, but a key challenge is making actionable suggestions based on personal health data. We introduce GlucoGoalie, which combines ML with an expert system to translate ML output into personalized nutrition goal suggestions for individuals with T2D. In a controlled experiment, participants with T2D found that goal suggestions were understandable and actionable. A 4-week in-the-wild deployment study showed that receiving goal suggestions augmented participants' self-discovery, choosing goals highlighted the multifaceted nature of personal preferences, and the experience of following goals demonstrated the importance of feedback and context. However, we identified tensions between abstract goals and concrete eating experiences and found static text too ambiguous for complex concepts. We discuss implications for ML-based interventions and the need for systems that offer more interactivity, feedback, and negotiation.

Phosphoproteomic network analysis in the sea urchin Strongylocentrotus purpuratus reveals new candidates in egg activation.

Fertilization triggers a dynamic symphony of molecular transformations induced by a rapid rise in intracellular calcium. Most prominent are surface alterations, metabolic activation, cytoskeletal reorganization, and cell-cycle reentry. While the activation process appears to be broadly evolutionarily conserved, and protein phosphorylation is known to play a key role, the signaling networks mediating the response to fertilization are not well described. To address this gap, we performed a time course phosphoproteomic analysis of egg activation in the sea urchin Strongylocentrotus purpuratus, a system that offers biochemical tractability coupled with exquisite synchronicity. By coupling large-scale phosphopeptide enrichment with unbiased quantitative MS, we identified striking changes in global phosphoprotein patterns at 2- and 5-min postfertilization as compared to unfertilized eggs. Overall, we mapped 8796 distinct phosphosite modifications on 2833 phosphoproteins, of which 15% were differentially regulated in early egg activation. Activated kinases were identified by phosphosite mapping, while enrichment analyses revealed conserved signaling cascades not previously associated with egg activation. This work represents the most comprehensive study of signaling associated with egg activation to date, suggesting novel mechanisms that can be experimentally tested and providing a valuable resource for the broader research community. All MS data have been deposited in the ProteomeXchange with identifier PXD002239 (http://proteomecentral.proteomexchange.org/dataset/PXD002239).