Netta Cohen

The nematode C. elegans is one of the best studied model organisms in biology. Since the initial description of the C. elegans neuronal connectome by White and colleagues, numerous studies have mapped and analyzed the connectome, and released structured datasets of the connectivity, often merging and adding to previous versions. These datasets now serve as foundational resources that underpin studies into the neuronal basis of C. elegans behavior. In spite of their high utility, heterogeneity in the different datasets and their annotation conventions obscures direct comparison where discrepancies between connectomes frequently stem from differences in coverage or nomenclature rather than genuine biological disagreement. Additionally, datasets describing new connectivity modalities - contact-based (the contactome), extrasynaptic (monoaminergic and neuropeptidergic), and functional connectivity - have also recently become available. Thus, there is a need for a unified framework providing standardized access to these diverse datasets while preserving their individual provenance. Here, we present the C. elegans Connectome Toolbox, a comprehensive framework for consolidating, curating and validating historical and newly published multimodal C. elegans connectivity data. Connectome Toolbox includes an accessible online resource (https://openworm.org/ConnectomeToolbox) for interactive data exploration and is accompanied by a Python library for programmatic access. We demonstrate the utility of the toolbox through a series of analyses that illustrate how connectome data can be explored, visualized, and compared across modalities, including integration of the anatomical connectome with a comprehensive neurotransmitter atlas, and comparison with computational models of C. elegans neural circuits.

Animals must make decisions about how to move through their environment to find food, shelter and mates, yet the general principles underpinning effective search strategies remain poorly understood. Exploration patterns, such as alternating intensive local search with long-range relocation, are observed across many species, but whether these reflect universal organising principles, or are simply imposed by structured environments, is an open question. Here, we reconstruct hours of Caenorhabditis elegans foraging in homogeneous three-dimensional volumes and show that this animal achieves optimal volumetric coverage using a hierarchical search strategy of quasi-planar locomotion patches punctuated by costlier volumetric reorientations. We propose that underlying this cost hierarchy is the worm’s highly-constrained shape-space and show that despite being spanned by only five body-posture modes, it gives rise to effective locomotion, combining non-planar gaits, planar turns and three-dimensional reorientation manoeuvres. Our finding that action frequencies decay with inferred cost in a Boltzmann-like distribution is consistent with the Principle of Maximum Entropy: optimal search emerging from the tendency to maximise information gained subject to embodied constraints. This general principle offers a unifying framework to assess the optimality of animal decision-making, with implications for understanding nervous system function, foraging ecology, and the physics of living active matter.

Background: Alzheimer’s disease (AD) is the most prevalent form of neurodegenerative disease, characterised by the presence of Aβ plaques and neurofibrillary tangles, mediating memory impairments due to loss of neurons and impairments in neuronal plasticity. The most common model to study the toxic effects of Aβ in AD are transgenic rodents. However, these animals develop cognitive impairments and suffer. Thus, to reduce and replace the rodent animals used to study these toxic effects, we are developing a C. elegans neuronal model of human Aβ_1‐42 with a posterior co‐injection marker to allow multi‐colour neuronal imaging without interference from the fluorescence of the co‐injection marker. To achieve this we generated a transcriptional reporter of the antimicrobial peptide gene clec‐60, as the expression of this gene under normal conditions is solely in the posterior intestine.Method: Constructs containing ultra pan‐neuronal Aβ reporter and the clec‐60p::cemOrange co‐injection marker were generated via gateway cloning and micro‐injected into wild type C. elegans. To confirm expression of the co‐injection marker is restricted to posterior intestine confocal images were taken of the injected strains. To confirm presence of Aβ in the neurons, fixed C. elegans were antibody stained using the 4G8 primary and goat anti mouse 488 secondary antibody.Result: We generated the Aβ strain with the clec‐60 fluorescent marker, however, upon expression of the human Aβ_1‐42 in the C. elegans nervous system we observed expression of the fluorescently tagged clec‐60 in the neurons. The presence of the Aβ in the neurons was confirmed with the antibody staining.Conclusion: Expression of human Aβ_1‐42 changes and increases the expression of the innate immunity antimicrobial peptide clec‐60 indicating C. elegans perceive Aβ as a pathogen and elevate innate immune peptide upon its expression.

Introduction: Our work introduces a real-time robotic localization and mapping system for buried pipe networks.Methods: The system integrates non-vision-based exploration and navigation with an active-vision-based localization and topological mapping algorithm. This algorithm is selectively activated at topologically key locations, such as junctions. Non-vision-based sensors are employed to detect junctions, minimizing the use of visual data and limiting the number of images taken within junctions.Results: The primary aim is to provide an accurate and efficient mapping of the pipe network while ensuring real-time performance and reduced computational requirements.Discussion: Simulation results featuring robots with fully autonomous control in a virtual pipe network environment are presented. These simulations effectively demonstrate the feasibility of our approach in principle, offering a practical solution for mapping and localization in buried pipes.

Despite recent advances in robotic technology, sewer pipe inspection is still limited to conventional approaches that use cable-tethered robots. Such commercially available tethered robots lack autonomy, and their operation must be manually controlled via their tethered cables. Consequently, they can only travel to a certain distance in pipe, cannot access small-diameter pipes, and their deployment incurs high costs for highly skilled operators. In this paper, we introduce a miniaturised mobile robot for pipe inspection. We present an autonomous control strategy for this robot that is effective, stable, and requires only low-computational resources. The robots used here can access pipes as small as 75 mm in diameter. Due to their small size, low carrying capacity, and limited battery supply, our robots can only carry simple sensors, a small processor, and miniature wheel-legs for locomotion. Yet, our control method is able to compensate for these limitations. We demonstrate fully autonomous robot mobility in a sewer pipe network, without any visual aid or power-hungry image processing. The control algorithm allows the robot to correctly recognise each local network configuration, and to make appropriate decisions accordingly. The control strategy was tested using the physical micro robot in a laboratory pipe network. In both simulation and experiment, the robot autonomously and exhaustively explored an unknown pipe network without missing any pipe section while avoiding obstacles. This is a significant advance towards fully autonomous inspection robot systems for sewer pipe networks.

This article considers an inverse problem for a Cosserat rod where we are given only the position of the centreline of the rod and must solve for external forces and torques as well as the orientation of the cross sections of the centreline. We formulate the inverse problem as an optimal control problem using the position of the centreline as an objective function with the external force and torque as control variables, with meaningful regularisation of the orientations. A monolithic, implicit numerical scheme is proposed in the sense that primal and adjoint equations are solved in a fully-coupled manner and all the nonlinear coefficients of the governing partial differential equations are updated to the current state variables. The forward formulation, determining rod configuration from external forces and torques, is first validated by a numerical benchmark; the solvability and stability of the inverse problem are then tested using data from forward simulations. The proposed optimal control method is motivated by reconstruction of the orientations of a rod’s cross sections, with its centreline being captured through imaging protocols. As a case study, we take the locomotion of the nematode, Caenorhabditis elegans. In this study we take laboratory data for its centreline and infer its cross-section orientation (muscle locations) with the control force and torque being interpreted as the reaction force, activated by C. elegans’ muscles, from the surrounding fluids. This method thus combines the mathematical modelling and laboratory data to study the locomotion of C. elegans, which gives us insights into the potential anatomical orientation of the worm beyond what can be observed through the laboratory data. The paper is completed with several additional remarks explaining the theoretical and technical details of the model.

A conventional understanding of perception assigns sensory organs the role of capturing the environment. Better sensors result in more accurate encoding of stimuli, allowing for cognitive processing downstream. Here we show that plasticity in sensory neurons mediates a behavioral switch in C. elegans between attraction to NaCl in naïve animals and avoidance of NaCl in preconditioned animals, called gustatory plasticity. Ca²⁺ imaging in ASE and ASH NaCl sensing neurons reveals multiple cell-autonomous and distributed circuit adaptation mechanisms. A computational model quantitatively accounts for observed behaviors and reveals roles for sensory neurons in the control and modulation of motor behaviors, decision making and navigational strategy. Sensory adaptation dynamically alters the encoding of the environment. Rather than encoding the stimulus directly, therefore, we propose that these C. elegans sensors dynamically encode a context-dependent value of the stimulus. Our results demonstrate how adaptive sensory computation can directly control an animal’s behavioral state.

Locomotion is an essential behaviour for the survival of all animals. The neural circuitry underlying locomotion is therefore highly robust to a wide variety of perturbations, including injury and abrupt changes in the environment. In the short term, fault tolerance in neural networks allows locomotion to persist immediately after mild to moderate injury. In the longer term, in many invertebrates and vertebrates, neural reorganization including anatomical regeneration can restore locomotion after severe perturbations that initially caused paralysis. Despite decades of research, very little is known about the mechanisms underlying locomotor resilience at the level of the underlying neural circuits and coordination of central pattern generators (CPGs). Undulatory locomotion is an ideal behaviour for exploring principles of circuit organization, neural control and resilience of locomotion, offering a number of unique advantages including experimental accessibility and modelling tractability. In comparing three well‐characterized undulatory swimmers, lampreys, larval zebrafish and Caenorhabditis elegans, we find similarities in the manifestation of locomotor resilience. To advance our understanding, we propose a comparative approach, integrating experimental and modelling studies, that will allow the field to begin identifying shared and distinct solutions for overcoming perturbations to persist in orchestrating this essential behaviour. image