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Type of Publication
4138 Publications
Showing 1791-1800 of 4138 resultsThe brain generates diverse neuron types which express unique homeodomain transcription factors (TFs) and assemble into precise neural circuits. Yet a mechanistic framework is lacking for how homeodomain TFs specify both neuronal fate and synaptic connectivity. We use Drosophila lamina neurons (L1-L5) to show the homeodomain TF Brain-specific homeobox (Bsh) is initiated in lamina precursor cells (LPCs) where it specifies L4/L5 fate and suppresses homeodomain TF Zfh1 to prevent L1/L3 fate. Subsequently, Bsh activates the homeodomain TF Apterous (Ap) in L4 in a feedforward loop to express the synapse recognition molecule DIP-β, in part by Bsh direct binding a DIP-β intron. Thus, homeodomain TFs function hierarchically: primary homeodomain TF (Bsh) first specifies neuronal fate, and subsequently acts with secondary homeodomain TF (Ap) to activate DIP-β, thereby generating precise synaptic connectivity. We speculate that hierarchical homeodomain TF function may represent a general principle for coordinating neuronal fate specification and circuit assembly.
Vision in dim light depends on synapses between rods and rod bipolar cells (RBCs). Here, we find that these synapses exist in multiple configurations, in which single release sites of rods are apposed by one to three postsynaptic densities (PSDs). Single RBCs often form multiple PSDs with one rod; and neighboring RBCs share ~13% of their inputs. Rod-RBC synapses develop while ~7% of RBCs undergo programmed cell death (PCD). Although PCD is common throughout the nervous system, its influences on circuit development and function are not well understood. We generate mice in which ~53 and ~93% of RBCs, respectively, are removed during development. In these mice, dendrites of the remaining RBCs expand in graded fashion independent of light-evoked input. As RBC dendrites expand, they form fewer multi-PSD contacts with rods. Electrophysiological recordings indicate that this homeostatic co-regulation of neurite and synapse development preserves retinal function in dim light.
The ability to automatize the analysis of video for monitoring animals and insects is of great interest for behavior science and ecology [1]. In particular, honeybees play a crucial role in agriculture as natural pollinators. However, recent studies has shown that phenomena such as colony collapse disorder are causing the loss of many colonies [2]. Due to the high number of interacting factors to explain these events, a multi-faceted analysis of the bees in their environment is required. We focus in our work in developing tools to help model and understand their behavior as individuals, in relation with the health and performance of the colony. In this paper, we report the development of a new system for the detection, locali- zation and tracking of honeybee body parts from video on the entrance ramp of the colony. The proposed system builds on the recent advances in Convolutional Neu- ral Networks (CNN) for Human pose estimation and evaluates the suitability for the detection of honeybee pose as shown in Figure 1. This opens the door for novel animal behavior analysis systems that take advantage of the precise detection and tracking of the insect pose.
Expression of Manduca Broad-Complex (BR-C) mRNA in the larval epidermis is under the dual control of ecdysone and juvenile hormone (JH). Immunocytochemistry with antibodies that recognize the core, Z2, and Z4 domains of Manduca BR-C proteins showed that BR-C appearance not only temporally correlates with pupal commitment of the epidermis on day 3 of the fifth (final) larval instar, but also occurs in a strict spatial pattern within the abdominal segment similar to that seen for the loss of sensitivity to JH. Levels of Z2 and Z4 BR-C proteins shift with Z2 predominating at pupal commitment and Z4 dominant during early pupal cuticle synthesis. Both induction of BR-C mRNA in the epidermis by 20-hydroxyecdysone (20E) and its suppression by JH were shown to be independent of new protein synthesis. For suppression JH must be present during the initial exposure to 20E. When JH was given 6 h after 20E, suppression was only seen in those regions that had not yet expressed BR-C. In the wing discs BR-C was first detected earlier 1.5 days after ecdysis, coincident with the pupal commitment of the wing. Our findings suggest that BR-C expression is one of the first molecular events underlying pupal commitment of both epidermis and wing discs.
While we think of neurons as having a fixed identity, many show spectacular plasticity. Metamorphosis drives massive changes in the fly brain; neurons that persist into adulthood often change in response to the steroid hormone ecdysone. Besides driving remodeling, ecdysone signaling can also alter the differentiation status of neurons. The three sequentially born subtypes of mushroom body (MB) Kenyon cells (γ, followed by α'/β', and finally α/β) serve as a model of temporal fating. γ neurons are also used as a model of remodeling during metamorphosis. As γ neurons are the only functional Kenyon cells in the larval brain, they serve the function of all three adult subtypes. Correspondingly, larval γ neurons have a similar morphology to α'/β' and α/β neurons-their axons project dorsally and medially. During metamorphosis, γ neurons remodel to form a single medial projection. Both temporal fate changes and defects in remodeling therefore alter γ-neuron morphology in similar ways. Mamo, a broad-complex, tramtrack, and bric-à-brac/poxvirus and zinc finger (BTB/POZ) transcription factor critical for temporal specification of α'/β' neurons, was recently described as essential for γ remodeling. In a previous study, we noticed a change in the number of adult Kenyon cells expressing γ-specific markers when mamo was manipulated. These data implied a role for Mamo in γ-neuron fate specification, yet mamo is not expressed in γ neurons until pupariation, well past γ specification. This indicates that mamo has a later role in ensuring that γ neurons express the correct Kenyon cell subtype-specific genes in the adult brain.
HortaCloud is a cloud-based, open-source platform designed to facilitate the collaborative reconstruction of long-range projection neurons from whole-brain light microscopy data. By providing virtual environments directly within the cloud, it eliminates the need for costly and time-consuming data downloads, allowing researchers to work efficiently with terabyte- scale volumetric datasets. Standardization of computational resources in the cloud make deployment easier and more predictable. The pay-as-you-go cloud model reduces adoption barriers by eliminating upfront investments in expensive hardware. Finally, HortaCloud’s decentralized architecture enables global collaboration between researchers and between institutions.
Most mammalian cells prevent viral infection and proliferation by expressing various restriction factors and sensors that activate the immune system. While anti-human immunodeficiency virus type 1 (HIV-1) host restriction factors have been identified, most of them are antagonized by viral proteins. This has severely hindered their development in anti-HIV-1 therapy. Here, we describe CCHC-type zinc-finger-containing protein 3 (ZCCHC3) as a novel anti-HIV-1 factor that is not antagonized by viral proteins. ZCCHC3 suppresses production of HIV-1 and other retroviruses. We show that ZCCHC3 acts by binding to Gag nucleocapsid protein via zinc-finger motifs. This prevents interaction between the Gag nucleocapsid protein and viral genome and results in production of genome-deficient virions. ZCCHC3 also binds to the long terminal repeat on the viral genome via the middle-folded domain, sequestering the viral genome to P-bodies, which leads to decreased viral replication and production. Such a dual antiviral mechanism is distinct from that of any other known host restriction factors. Therefore, ZCCHC3 is a novel potential target in anti-HIV-1 therapy.
Machine learning models are only as good as the data to which they are fit. As such, it is always preferable to use as much data as possible in training models. What data can be used for fitting a model depends a lot on the formulation of the task. We introduce Hot-Distance, a novel segmentation target that incorporates the strength of signed boundary distance prediction with the flexibility of one-hot encoding, to increase the amount of usable training data for segmentation of subcellular structures in focused ion beam scanning electron microscopy (FIB-SEM).