Biology subjects

Daniel, T. L.

Publications and source records attributed to Daniel, T. L..

9 recordsLinked to original sources

bioRxiv2026

Pilifers provide rapid mechanosensory feedback during floral tracking in hawkmoths

Hawkmoths expertly hover over flowers during feeding, accurately tracking floral movements even as flowers gently sway in the wind. They use visual and mechanosensory feedback to adjust their position, while their long, flexible mouthpart, the proboscis, stays inserted inside the nectar spur. Although much is known about how hawkmoths detect and process visual motion, far less is understood about how hawkmoths detect mechanical motion cues. Pilifers are hair bundles at the proboscis-head joint, well-positioned to provide feedback about floral motion. Here, we tested if pilifers could provide mechanosensory feedback essential for successful floral tracking. We computed bending at the proboscis base during floral tracking in freely flying hawkmoths and found that floral movements induce small yet significantly correlated lateral bending. Recordings from pilifer afferents showed that they respond to lateral motions of amplitudes similar to those seen in natural floral tracking behaviors. They respond to different directions and speeds of proboscis motion with an average latency of 1.9 ms. Neural tracing revealed extensive sensory arborizations in the sub-esophageal ganglion of the brain and ventral nerve cord, indicating a feedback pathway to the neck and flight motor system that enables rapid coordination of appendages and body for successful feeding.

neuroscience↗

bioRxiv2023

Intraspecific variation in the placement of campaniform sensilla on the wings of the hawkmoth Manduca sexta

Flight control requires active sensory feedback, and insects have many sensors that help them estimate their current locomotor state, including campaniform sensilla, which are mechanoreceptors that sense strain resulting from deformation of the cuticle. Campaniform sensilla on the wing detect bending and torsional forces encountered during flight, providing input to the flight feedback control system. During flight, wings experience complex spatio-temporal strain patterns. Because campaniform sensilla detect only local strain, their placement on the wing is presumably critical for determining the overall representation of wing deformation; however, how these sensilla are distributed across wings is largely unknown. Here, we test the hypothesis that campaniform sensilla are found in stereotyped locations across individuals of Manduca sexta, a hawkmoth. We found that although campaniform sensilla are consistently found on the same veins or in the same regions of the wings, their total number and distribution can vary extensively. This suggests that there is some robustness to variation in sensory feedback in the insect flight control system. The regions where campaniform sensilla are consistently found provide clues to their functional roles, although some patterns might be reflective of developmental processes. Collectively, our results on intraspecific variation in campaniform sensilla placement on insect wings will help reshape our thinking on the utility of mechanosensory feedback for insect flight control and guide further experimental and comparative studies.

neuroscience↗

bioRxiv2022

Nonuniform structural properties of wings confer sensing advantages

Sensory feedback is essential to both animals and robotic systems for achieving coordinated, precise movements. Mechanosensory feedback, which provides information about body deformation and position in space, depends not only on the properties of sensors but also on the structure in which they are embedded. In insects, wing structure plays a particularly important role in flapping flight: in addition to generating aerodynamic forces, wings provide mechanosensory feedback necessary for guiding flight while undergoing dramatic deformations over the course of each wingbeat. However, the role that wing structure plays in determining mechanosensory information is relatively unexplored. Insect wings exhibit characteristic stiffness gradients, with greatest stiffness at the base and leading edge and lowest stiffness at the tip of the trailing edge. Additionally, wings are subject to both aerodynamic and structural damping. The sensory consequences of stiffness gradients and damping are unknown. Here we examine how both the nonuniform stiffness profile of a wing and its damping impacts sensory performance, using finite elements analysis combined with sensor placement optimization approaches. We show that wings with nonuniform stiffness exhibit several advantages over uniform stiffness wings, resulting in higher accuracy of rotation detection and lower sensitivity to the placement of sensors on the wing. Moreover, we show that higher damping generally improves the accuracy with which body rotations can be detected. These results contribute to our understanding of the evolution of the nonuniform stiffness patterns in insect wings, as well as suggest design principles for robotic systems.

neuroscience↗

bioRxiv2021

Wing structure and neural encoding jointly determine sensing strategies in insect flight

Animals rely on sensory feedback to generate accurate, reliable movements. In many flying insects, strain-sensitive neurons on the wings provide rapid feedback that enables stable flight control. While the impacts of wing structure on aerodynamic performance have been widely studied, the impacts of wing structure on sensing remain unexplored. In this paper, we show how the structural properties of the wing and encoding by mechanosensory neurons interact to jointly determine optimal sensing strategies and performance. Specifically, we examine how neural sensors can be placed effectively over a flapping wing to detect body rotation about different axes, using a computational wing model with varying flexural stiffness inspired by the hawkmoth Manduca sexta. A small set of mechanosensors, conveying strain information at key locations with a single action potential per wingbeat, permit accurate detection of body rotation. Optimal sensor locations are concentrated at either the wing base or the wing tip, and they transition sharply as a function of both wing stiffness and neural threshold. Moreover, the sensing strategy and performance is robust to both external disturbances and sensor loss. Typically, only five sensors are needed to achieve near-peak accuracy, with a single sensor often providing accuracy well above chance. Our results show that small-amplitude, dynamic signals can be extracted efficiently with spatially and temporally sparse sensors in the context of flight. The demonstrated interaction of wing structure and neural encoding properties points to the importance of their joint evolution.

neuroscience↗

bioRxiv2020

From neuron to muscle to movement: a complete biomechanical model of Hydra contractile behaviors

How does neural activity drive muscles to produce behavior? The recent development of genetic lines in Hydra that allow complete calcium imaging of both neuronal and muscle activity, as well as systematic machine learning quantification of behaviors, makes this small Cnidarian an ideal model system to understand and model the complete transformation from neural firing to body movements. As a first step to achieve this, we have built a biomechanical model of Hydra, incorporating its neuronal activity, muscle activity and body column biomechanics, incorporating its fluid-filled hydrostatic skeleton. Our model is based on experimental measurements of neuronal and muscle activity, and assumes gap junctional coupling among muscle cells and calcium-dependent force generation y muscles. With these assumptions, we can robustly reproduce a basic set of Hydras behaviors. We can further explain puzzling experimental observations, including the dual kinetics observed in muscle activation and the different engagement of ecto- and endodermal muscle in different behaviors. This work delineates the spatiotemporal control space of Hydra movement and can serve as a template for future efforts to systematically decipher the transformations in the neural basis of behavior.

biophysics↗

bioRxiv2020

Tactile active sensing in insect-plant pollination

The interaction between insects and the flowers they pollinate has driven the evolutionary diversity of both insects and flowering plants, two groups with the most numerous species on earth. Insects use vision and olfaction to localize their host plants, but to feed from the flower, they must find the tiny nectary opening, which can be well beyond their visual resolution. When vision is limited, the sense of touch becomes especially crucial, yet very few studies have investigated the role of rapid and precise tactile feedback in successful feeding and pollination interactions. In this paper, we study the remarkable feeding behavior of flying insects that use their proboscis, a flexible mouthpart often longer than their entire body length when unfurled, to expertly explore floral surfaces. Specifically, we observed how the crepuscular hawkmoth Manduca sexta interacts with artificial, 3D-printed flowers of varying shapes. We found that moths actively explore the flower for tactile features, systematically sweeping their proboscis from edge to center repeatedly until they locate the nectary. Moreover, naive moths rapidly learn to exploit flowers, and they adopt a tactile search strategy to more directly locate the nectary in as few as three to five consecutive visits. We suggest moths wield their proboscis to extract salient tactile features, such as floral edges and corolla curvature. Our results highlight the proboscis as a unique sensory structure and emphasize the central role of touch in insect-plant pollination interactions.

neuroscience↗

bioRxiv2020

A Data-driven Surrogate Model for Work Computation of a Periodically Forced Half-Sarcomere

Muscle force generation follows from molecular scale interactions that drive macroscopic behaviors and macroscopic processes that influence those at the molecular scale. A particuarly challenging issue is that models at the molecular level of organization are often quite difficult to apply to larger spatial scales. This is particularly true of moleuclar models driven by Monte-Carlo simulations. This challenge of multiscale dynamics requires methods to extract reduced order behaviors from detailed high-dimensional simulations. In this work we present a novel deterministic simulation method yielding accurate predictions of force-length behaviors of contracting muscle sarcomeres undergoing periodic length changes (work loops). The model maintains interpretability by tracking macroscopic state variables throughout the simulation while using data-driven representations of dynamics. Parameters of the data-driven dynamics are learned from trajectories from Monte-Carlo simulations of a half-sarcomere. Our method significantly reduces computational cost by tracking the state of the sarcomere in a course grained set of variables while maintaining accurate prediction of macroscopic level observables and time series for course grained variables. This allows for rapid sampling of the models output and builds towards the ability to scale to multiple-sarcomere simulations. Author SummaryWe develop a data-driven surrogate model for the dynamics of the half-sarcomere. This model achieves the same behavior with respect to force traces as more sophisticated Monte Carlo models at a substantially lower computational cost. The model is built by finding a course grained description of the full state space of the Monte Carlo simulation and learning dynamical models on the course grained space. Data-driven representations of the dynamics in the course grained space are trained using data from the full model. Data-driven models for forcing are also learned, and the result fed back into the dynamics. In doing so, the model seeks to replicate the effects of filament compliance on macro scale dynamics without explicitly tracking micro scale features. We withhold some input parameter regimes and demonstrate accurate reconstruction of course grained state and force traces using the data-driven model and given only knowledge of the initial condition and input. This work allows for faster computation of the forcing behavior of the half-sarcomere, as well as consistent representations of the course grained state variables. It is therefore promising as a step towards multi-sarcomere or even tissue scale models of skeletal muscle.

biophysics↗

bioRxiv2020

In vivo x-ray diffraction and simultaneous EMG reveal the time course of myofilament lattice dilation and filament stretch

Muscles function within an organism depends on the feedback between molecular to meter-scale processes. While the motions of muscles contractile machinery are well described in isolated preparations, only a handful of experiments have documented the kinematics of the lattice occurring when multi-scale interactions are fully intact. We used time-resolved x-ray diffraction to record the kinematics of the myofilament lattice within a normal operating context: the tethered flight of Manduca sexta. Since the primary flight muscles of Manduca sexta are synchronous, we used these results to reveal the timing of in vivo cross-bridge recruitment, which occurred 24 (s.d. 26) ms following activation. In addition, the thick filaments stretched an average of 0.75 (s.d. 0.32)% and thin filaments stretched 1.11 (s.d. 0.65)%. In contrast to other in vivo preparations, lattice spacing changed an average of 2.72 (s.d. 1.47)%. Lattice dilation of this magnitude significantly impacts shortening velocity and force generation, and filament stretching tunes force generation. While kinematics were consistent within individual trials, there was extensive variation between trials. Using a mechanism-free machine learning model we searched for patterns within and across trials. While lattice kinematics were predictable within trials, the model could not create predictions across trials. This indicates that the variability we see across trials may be explained by latent variables occurring in this naturally functioning system. The diverse kinematic combinations we documented mirror muscles adaptability and may facilitate its robust function in unpredictable conditions.

biophysics↗

bioRxiv2018

Conflict of evolutionary interests between plants and pollinators revealed through functional exploration of flower morphospace

The explosive evolutionary diversification of flowering plants traditionally is attributed to the coevolution of plants and their animal pollinators. Plant-pollinator interactions are held as classical examples of mutualisms - beneficial to both parties - in spite of the fact that most other cases of rapid coevolution are the result of conflicts of interest (e.g., predator-prey, host-parasite, sexual conflict, competition for resources). Could the co-diversification of plants and their pollinators be driven by conflict rather than by mutualism? To address this question, we employed the theoretical morphospace paradigm using a combination of 3D printing, electronic sensing, and machine vision technologies to determine the influence of two flower morphological features (corolla curvature and nectary diameter) on the fitness of both parties: the artificial flower and its hawkmoth pollinator. We found that the two parties have almost opposite interests in corolla curvature evolution, with non-overlapping fitness peaks in flower morphospace, suggesting that the evolutionary radiation of flowering plants and their pollinators could be the result of conflict instead of mutualism.

ecology↗