Body temperature homeostasis is essential and reliant upon the integration of outputs from multiple classes of cooling- and warming-responsive cells. The computations that integrate these outputs are not understood. Here, we discover a set of warming cells (WCs) and show that the outputs of these WCs combine with previously described cooling cells (CCs) in a cross-inhibition computation to drive thermal homeostasis in larval WCs and CCs detect temperature changes using overlapping combinations of ionotropic receptors: Ir68a, Ir93a, and Ir25a for WCs and Ir21a, Ir93a, and Ir25a for CCs. WCs mediate avoidance to warming while cross-inhibiting avoidance to cooling, and CCs mediate avoidance to cooling while cross-inhibiting avoidance to warming. Ambient temperature-dependent regulation of the strength of WC- and CC-mediated cross-inhibition keeps larvae near their homeostatic set point. Using neurophysiology, quantitative behavioral analysis, and connectomics, we demonstrate how flexible integration between warming and cooling pathways can orchestrate homeostatic thermoregulation.

In this paper, we present a novel error measure to compare a computer-generated segmentation of images or volumes against ground truth. This measure, which we call Tolerant Edit Distance (TED), is motivated by two observations that we usually encounter in biomedical image processing: (1) Some errors, like small boundary shifts, are tolerable in practice. Which errors are tolerable is application dependent and should be explicitly expressible in the measure. (2) Non-tolerable errors have to be corrected manually. The effort needed to do so should be reflected by the error measure. Our measure is the minimal weighted sum of split and merge operations to apply to one segmentation such that it resembles another segmentation within specified tolerance bounds. This is in contrast to other commonly used measures like Rand index or variation of information, which integrate small, but tolerable, differences. Additionally, the TED provides intuitive numbers and allows the localization and classification of errors in images or volumes. We demonstrate the applicability of the TED on 3D segmentations of neurons in electron microscopy images where topological correctness is arguable more important than exact boundary locations. Furthermore, we show that the TED is not just limited to evaluation tasks. We use it as the loss function in a max-margin learning framework to find parameters of an automatic neuron segmentation algorithm. We show that training to minimize the TED, i.e., to minimize crucial errors, leads to higher segmentation accuracy compared to other learning methods.

Associating stimuli with positive or negative reinforcement is essential for survival, but a complete wiring diagram of a higher-order circuit supporting associative memory has not been previously available. Here we reconstruct one such circuit at synaptic resolution, the Drosophila larval mushroom body. We find that most Kenyon cells integrate random combinations of inputs but that a subset receives stereotyped inputs from single projection neurons. This organization maximizes performance of a model output neuron on a stimulus discrimination task. We also report a novel canonical circuit in each mushroom body compartment with previously unidentified connections: reciprocal Kenyon cell to modulatory neuron connections, modulatory neuron to output neuron connections, and a surprisingly high number of recurrent connections between Kenyon cells. Stereotyped connections found between output neurons could enhance the selection of learned behaviours. The complete circuit map of the mushroom body should guide future functional studies of this learning and memory centre.

Steroid hormones play key roles in development, growth, and reproduction in various animal phyla [1]. The insect steroid hormone, ecdysteroid, coordinates growth and maturation, represented by molting and metamorphosis [2]. In Drosophila melanogaster, the prothoracicotropic hormone (PTTH)-producing neurons stimulate peak levels of ecdysteroid biosynthesis for maturation [3]. Additionally, recent studies on PTTH signaling indicated that basal levels of ecdysteroid negatively affect systemic growth prior to maturation [4-8]. However, it remains unclear how PTTH signaling is regulated for basal ecdysteroid biosynthesis. Here, we report that Corazonin (Crz)-producing neurons regulate basal ecdysteroid biosynthesis by affecting PTTH neurons. Crz belongs to gonadotropin-releasing hormone (GnRH) superfamily, implying an analogous role in growth and maturation [9]. Inhibition of Crz neuronal activity increased pupal size, whereas it hardly affected pupariation timing. This phenotype resulted from enhanced growth rate and a delay in ecdysteroid elevation during the mid-third instar larval (L3) stage. Interestingly, Crz receptor (CrzR) expression in PTTH neurons was higher during the mid- than the late-L3 stage. Silencing of CrzR in PTTH neurons increased pupal size, phenocopying the inhibition of Crz neuronal activity. When Crz neurons were optogenetically activated, a strong calcium response was observed in PTTH neurons during the mid-L3, but not the late-L3, stage. Furthermore, we found that octopamine neurons contact Crz neurons in the subesophageal zone (SEZ), transmitting signals for systemic growth. Together, our results suggest that the Crz-PTTH neuronal axis modulates ecdysteroid biosynthesis in response to octopamine, uncovering a regulatory neuroendocrine system in the developmental transition from growth to maturation.

The task of the visual system is to translate light into neuronal encoded information. This translation of photons into neuronal signals is achieved by photoreceptor neurons (PRs), specialized sensory neurons, located in the eye. Upon perception of light the PRs will send a signal to target neurons, which represent a first station of visual processing. Increasing complexity of visual processing stems from the number of distinct PR subtypes and their various types of target neurons that are contacted. The visual system of the fruit fly larva represents a simple visual system (larval optic neuropil, LON) that consists of 12 PRs falling into two classes: blue-senstive PRs expressing Rhodopsin 5 (Rh5) and green-sensitive PRs expressing Rhodopsin 6 (Rh6). These afferents contact a small number of target neurons, including optic lobe pioneers (OLPs) and lateral clock neurons (LNs). We combine the use of genetic markers to label both PR subtypes and the distinct, identifiable sets of target neurons with a serial EM reconstruction to generate a high-resolution map of the larval optic neuropil. We find that the larval optic neuropil shows a clear bipartite organization consisting of one domain innervated by PRs and one devoid of PR axons. The topology of PR projections, in particular the relationship between Rh5 and Rh6 afferents, is maintained from the nerve entering the brain to the axon terminals. The target neurons can be subdivided according to neurotransmitter or neuropeptide they use as well as the location within the brain. We further track the larval optic neuropil through development from first larval instar to its location in the adult brain as the accessory medulla.

Triclad flatworms are well studied for their regenerative properties, yet little is known about their embryonic development. We here describe the embryonic development of the triclaty 120d Schmidtea polychroa, using histological and immunocytochemical analysis of whole-mount preparations and sections. During early cleavage (stage 1), yolk cells fuse and enclose the zygote into a syncytium. The zygote divides into blastomeres that dissociate and migrate into the syncytium. During stage 2, a subset of blastomeres differentiate into a transient embryonic epidermis that surrounds the yolk syncytium, and an embryonic pharynx. Other blastomeres divide as a scattered population of cells in the syncytium. During stage 3, the embryonic pharynx imbibes external yolk cells and a gastric cavity is formed in the center of the syncytium. The syncytial yolk and the blastomeres contained within it are compressed into a thin peripheral rind. From a location close to the embryonic pharynx, which defines the posterior pole, bilaterally symmetric ventral nerve cord pioneers extend forward. Stage 4 is characterized by massive proliferation of embryonic cells. Large yolk-filled cells lining the syncytium form the gastrodermis. During stage 5 the external syncytial yolk mantle is resorbed and the embryonic cells contained within differentiate into an irregular scaffold of muscle and nerve cells. Epidermal cells differentiate and replace the transient embryonic epidermis. Through stages 6-8, the embryo adopts its worm-like shape, and loosely scattered populations of differentiating cells consolidate into structurally defined organs. Our analysis reveals a picture of S. polychroa embryogenesis that resembles the morphogenetic events underlying regeneration.

The sense of smell enables animals to react to long-distance cues according to learned and innate valences. Here, we have mapped with electron microscopy the complete wiring diagram of the Drosophila larval antennal lobe, an olfactory neuropil similar to the vertebrate olfactory bulb. We found a canonical circuit with uniglomerular projection neurons (uPNs) relaying gain-controlled ORN activity to the mushroom body and the lateral horn. A second, parallel circuit with multiglomerular projection neurons (mPNs) and hierarchically connected local neurons (LNs) selectively integrates multiple ORN signals already at the first synapse. LN-LN synaptic connections putatively implement a bistable gain control mechanism that either computes odor saliency through panglomerular inhibition, or allows some glomeruli to respond to faint aversive odors in the presence of strong appetitive odors. This complete wiring diagram will support experimental and theoretical studies towards bridging the gap between circuits and behavior.

Summary: State-of-the-art light and electron microscopes are capable of acquiring large image datasets, but quantitatively evaluating the data often involves manually annotating structures of interest. This processis time-consuming and often a major bottleneck in the evaluation pipeline. To overcome this problem, we have introduced the Trainable Weka Segmentation (TWS), a machine learning tool that leveragesa limited number of manual annotations in order to train a classifier and segment the remaining dataautomatically. In addition, TWS can provide unsupervised segmentation learning schemes (clustering) and can be customized to employ user-designed image features or classifiers.

Availability and Implementation: TWS is distributed as open-source software as part of the Fiji image processing distribution of ImageJ at http://imagej.net/Trainable_Weka_Segmentation.

Contact: ignacio.arganda@ehu.eus.

Supplementary information: Supplementary data are available at Bioinformatics online.

A key challenge in neuroscience is the expeditious reconstruction of neuronal circuits. For model systems such as Drosophila and C. elegans, the limiting step is no longer the acquisition of imagery but the extraction of the circuit from images. For this purpose, we designed a software application, TrakEM2, that addresses the systematic reconstruction of neuronal circuits from large electron microscopical and optical image volumes. We address the challenges of image volume composition from individual, deformed images; of the reconstruction of neuronal arbors and annotation of synapses with fast manual and semi-automatic methods; and the management of large collections of both images and annotations. The output is a neural circuit of 3d arbors and synapses, encoded in NeuroML and other formats, ready for analysis.