Bioinspiration & Biomimetics

AO_SCPLOWBSTRACTC_SCPLOWMuscle efficiency decreases with increasing size, largely due to a relative decrease in its mechanical output. Muscle mechanical output depends on its activation, strain, and strain rate and thus varies between different muscles within a limb during locomotion. Distinct muscle coordination patterns are required for efficient cycling, and so we would expect that the coordination patterns for efficient cycling or indeed locomotion would change across animal sizes. We tested whether muscle coordination would change with muscle size using data derived from human cycling: this paradigm allowed for controlled changes in both crank torque and cadence, allowing the multifactorial problem of muscle power output to be decomposed. We used kinematic and pedal data from 12 cyclists undergoing steady pedalling at cadences from 80 to 140 r.p.m. and generated musculoskeletal simulations of their movements. We introduced novel multisegment muscle models in the simulation that incorporated the internal muscle mass and thus accounted for the scaling effects of muscle tissue inertia. We solved the simulations for the muscle activity that was required to minimise the metabolic cost during cycling for each condition. The masses of the muscle models were scaled across five orders of magnitude. The predicted muscle activations were classified by Principal Component analysis to identify whether the coordination of muscle activity was modulated across models with different sized muscles. Analysis of variance revealed significant changes in coordination at the large-scale factors. This study shows how the coordination of muscle activity during locomotion will likely change across a range of body sizes due to the non-linear effects of the inertial mass within the muscle tissues.

Take-off is a fast and energy-efficient strategy for bipedal animals, such as birds, to achieve rapid movement; however, how muscle physiology scales to govern this universal behavior remains unresolved. Research in other species physiologies is not readily applicable. As a result, important questions, whether theropod dinosaurs such as Tyrannosaurus rex were capable of jumping, remain unanswered. In this article, we coupled Lagrangian dynamics with Hills muscle equations and developed new experimental methods to quantify joint rotational stiffness and damping, thereby enabling a systematic description of lower-limb mechanics. The approach establishes a novel kinetic framework that links muscle contractile properties to lower-limb performance without invoking control optimization. Animal observations and tabletop mechanisms validate the framework. The mechanics model reveals that the take-off time of about 0.1 s across body masses of 0.003 to 90 kg is achievable, as heavier birds generate proportionally higher reaction forces. Additionally, Tyrannosaurus rex should be capable of jumping, based on the available physiology data. Beyond evolutionary insights, our framework provides a new methodology for analyzing the mechanical properties of biological joints and informing the design of scalable bio-inspired robots.

Locating, tracking, and intercepting objects is a fundamental behavior for many organisms. For instance, predators must track and capture erratically moving prey for their survival. Using the echolocating bat as a model species, we investigate how short-term changes in target motion predictability affect longer-term motor plans when tracking a prey item. We used a paradigm where prey motion is under experimental control, and then applied computational methods to characterize how target motion predictability influences short- and long-term behavioral control. We find that target motion predictability during the tracking phase of insect capture influences both short-term changes in sonar call control, as well as longer-term behavioral control for transitioning between hunting phases. For changes in immediate behavioral control, bats produce more bursts of calls at a higher rate when tracking unpredictable moving prey, an indication that the bat is collecting more information about the targets motion for unpredictable than predictable trials. In terms of longer-term behavioral control, target motion unpredictability delays the transition from tracking to capture phase behaviors. We suggest that the bat does this to collect more information about target motion to time the transition from tracking to capture behaviors for hunting success. Additionally, we find the effects of target motion unpredictability are first seen as changes in the vocal motor plan and then the auditory motor plan (ear motion), hinting at a sequencing of motor changes that warrant further investigation. SummaryWhen presented with a more challenging hunting task, bats will increase their production of bursts of calls at a higher rate and delay their transition into capture behaviors.

Understanding how biological shape and movement interact with surrounding fluids represents a fundamental challenge at the intersection of biology, physics, and engineering. Fish locomotion exemplifies this challenge: body morphology and swimming kinematics together determine the hydrodynamic forces and flow structures that enable efficient propulsion and maneuverability. Whereas biologists have long sought to connect morphological variation to swimming performance, traditional morphometric approaches provide limited insight into the fluid mechanical consequences of shape differences. Similarly, although computational fluid dynamics can reveal detailed flow physics, simulating hydrodynamics across diverse and dynamic morphologies remains prohibitively expensive for systematic investigation. To bridge this gap, we introduce a data-driven framework that connects fish body shape dynamics to hydro-dynamic performance through compact morphospace parameterization and reduced-order modeling. Using CFD simulations of 15 fish species from the Digital Life Project database (www.digitallife3d.org/3d-model), we generate hydrodynamic datasets capturing the shape-flow relationship. Principal Component Analysis (PCA) extracts four dominant shape parameters from dorsal body profiles, which are then integrated into an Inverse-Design with Dynamic Mode Decomposition (ID-DMD) framework to model the resulting fluid dynamics. The resulting modal analysis suggests that locomotion strategies emerge from specific shape-flow interactions. We further demonstrate the frameworks utility through single- and multi-objective shape optimization, showing how it enables efficient exploration of the morphology-hydrodynamics relationship. This approach offers a novel analysis and design tool for understanding how biological form and motion interact with fluid mechanics, with applications ranging from bio-inspired vehicle development to evolutionary biomechanics.

Biomechanical simulations of complex hand motions remain scarce, due to challenges that span computation and data acquisition. Using a computer vision-based motion capture approach, a 23-degree of freedom musculoskeletal model, and direct collocation optimization, we performed muscle-driven simulations to track hand kinematics from 7 participants performing American Sign Language gestures. While proximal joints were tracked accurately, interphalangeal joint tracking was significantly worse, with a consistent flexion bias. Modifications to finger extensor muscle paths that incorporated the dual-inserting nature of the extensors improved accuracy, suggesting better representation of extensor force distribution across distal joints may be necessary for accurate hand simulations.

This study presents a proof-of-concept cyber-physical architecture integrating a SEIR epidemiological model (Susceptible-Exposed-Infectious-Recovered), implemented in MATLAB, with a simulated Internet of Things (IoT) acquisition and transmission stage based on the ESP32 microcontroller and the ThingSpeak platform. The system generates synthetic biomedical signals of body temperature and peripheral oxygen saturation (SpO2), structured across three levels: circadian variation, scheduled pathological episodes, and Gaussian noise. These signals feed a dual parametric coupling function that dynamically updates the SEIR transmission parameter as a combined function of body temperature and oxygen saturation deviations from their clinical reference values. The proposed architecture is organized into four functional phases: measurement, communication, computational processing, and feedback. Five simulated clinical scenarios were evaluated, ranging from normal conditions (T = 36.5 {degrees}C, SpO2 = 97%) to fever with severe hypoxia (T = 38.5 {degrees}C, SpO2 = 88%), yielding basic reproduction number (R0) values between 4.20 and 5.38, and peak infected proportions between 29.9% and 35.2% of the simulated population (N = 1,000). A sensitivity analysis on the coupling coefficients, with {+/-}50% variation from nominal values, showed that the oxygen saturation coefficient is the most influential parameter on R0 (range = 0.76) compared to the thermal coefficient (range = 0.42), with monotonic and predictable behavior across the entire evaluated parametric space. The primary contribution of this work is system integration: we propose a reproducible platform connecting biomedical simulation, IoT communication, and epidemiological modeling through parametric coupling in a controlled environment. All data used are entirely synthetic; a retrospective calibration with real Colombian data from the first epidemic wave of 2020 confirmed the epidemiological consistency of the model, with a calibrated R0 of 1.85 and a Pearson correlation of 0.930. Results should be interpreted as evidence of architectural feasibility, not as clinical or epidemiological validation. Author SummaryThe COVID-19 pandemic made it clear that epidemiological surveillance systems need tools that combine accessible technology with mathematical models capable of anticipating disease spread. In this work, we built a proof-of-concept platform connecting three elements: a low-cost electronic sensor based on the ESP32 microcontroller, a cloud communication platform (ThingSpeak), and a mathematical model that simulates how an epidemic spreads through a population. The sensor generates synthetic data on body temperature and oxygen saturation that, through a mathematical formula we designed, dynamically modify the rate of contagion in the model. We evaluated five clinical scenarios, ranging from normal conditions to fever with severe hypoxia, and analyzed how sensitive the results are to changes in the system parameters. We found that oxygen saturation has a greater influence on the estimated contagion potential than body temperature. Although all data are synthetic, this platform demonstrates that it is possible to integrate low-cost sensors with epidemiological models in real time, opening a viable pathway for early warning systems in resource-limited settings.

Common terns (Sterna hirundo) are regionally threatened migratory seabirds that form large breeding colonies during the North American summer months. They are highly vocal and serve as important bioindicators of aquatic ecosystems. Historically, acoustic studies on colonial seabirds have proven difficult due to the dense aggregations of individuals and high rate of call overlap. However, as passive acoustic monitoring (PAM) becomes increasingly common for studying seabird colonies, quantitative descriptions of species vocalizations are needed to accurately interpret behavioral information from colony soundscapes and support automated analysis of large acoustic datasets. This study aims to quantify the vocal repertoire of adult common terns. We deployed AudioMoths to collect acoustic data at a tern colony on Seavey Island, New Hampshire, USA from across the breeding season. Using RavenPro, unique call types were identified through visual and aural inspection of the acoustic data in the spectrogram. For each call, we then extracted measurements of peak frequency (Hz), bandwidth 90% (Hz), syllable duration 90% (s), and total bout duration (s) to quantify the characteristics of each call type. Statistical analyses for acoustic parameters by call type were performed using Kruskal-Wallis tests, followed by post-hoc Dunn tests. Our results demonstrate that each call type is significantly different from another by at least one parameter, with the exception of the kek and kip/tjuk calls. These findings present the first quantitative analysis of common tern vocalizations for North America. By defining temporal and spectral characteristics for multiple call types, this work helps translate colony soundscape into biologically meaningful information about tern behavior and colony dynamics. These descriptions also provide key parameters for developing automated tools to detect and classify vocalizations in dense, noisy colonies. Integrating quantified vocal characteristics with PAM offers a promising approach for monitoring colony activity and behavior while minimizing disturbance relative to traditional methods.

Recent advancements in wearable sensors and machine learning show promise for estimating lower-body joint torques outside of laboratory settings. Inertial Measurement Units combined with Convolutional Neural Networks have proven effective for this task. However, the impact of different input data types and formats remains underexplored. This study investigates how variations in input data influence the prediction of lower-body joint torques during walking. Results indicate that while dataset choice causes only minor differences in prediction performance, the overall quality of the dataset plays a more critical role than the specific input variables in achieving accurate torque predictions using wearable sensors.

Multimodal communication plays a central role in animal behavior, particularly when individuals must integrate information from different sensory channels to make rapid decisions. In aquatic environments, chemical and visual cues differ markedly in their spatial and temporal properties, such that chemical signals may be constrained by limited spatial resolution and temporal instability, potentially requiring visual information to reliably guide social decisions. In decapod crustaceans, both cue types are known to mediate reproduction, yet their relative contribution to mate-location behavior remains unclear. Here, we tested how visual and chemical cues from males influence mate-location behavior in females of the prawn Macrobrachium rosenbergii. Females were placed in a central arena and exposed to four stimulus configurations combining visual cues (a life-size photograph of a male or a control background) and chemical cues (water from an aquarium with or without a male). Attraction was quantified as the time spent in each half of the arena. Females showed no directional preference when exposed to chemical cues alone or when visual and chemical cues were spatially incongruent. In contrast, females spent significantly more time near male-associated stimuli only when visual and chemical cues were spatially congruent. These results indicate that mate-location behavior in this species depends on multimodal integration with a strong contextual dependence on visual information, which appears to gate the effectiveness of chemical cues. Spatially congruent multimodal signals are therefore necessary to guide orientation during mate search, suggesting that disruption of visual or chemical information in aquaculture systems may impair mating efficiency.

Various ankle-foot conditions (e.g., fractures, diabetic foot ulcers, and post-surgical recovery) require periods of complete non-weightbearing followed by gradually increasing partial loadings. However, existing assistive devices often provide inconsistent or uncomfortable offloading during gait. Additionally, prolonged proximal leg offloading can contribute to muscle atrophy, reduced bone density, and overuse of other body segments. We present a novel offloading ankle-foot orthosis (OLAFO) designed to overcome these limitations. The OLAFO features a patient-specific load-bearing shank brace, designed through a digital workflow and fabricated from a 3D-printed core reinforced with carbon-fiber composite lamination. Interlocking serrated side struts, adjustable in 2 mm increments, modulate load sharing between the shank and plantar surfaces. Furthermore, the OLAFO incorporates contact plates with a rocker profile informed by roll-over-shape measurements to support forward progression and gait symmetry. Proof-of-concept biomechanical verification in one able-bodied participant evaluated complete offloading, five partial-loading levels, and normal gait using a pressure walkway to compute vertical ground reaction forces and impulses. In complete offloading, the affected foot generated no contact pressures. Across partial-loading levels, the foot impulse increased from 14% to 53% of the total load and scaled linearly with strut height adjustments, supporting clinician-prescribed loading increments. Contralateral stance duration increased only modestly compared to commonly used assistive devices, indicating reduced compensatory loading on the intact limb. These findings demonstrate the proof-of-concept feasibility of the OLAFO, highlighting its potential for verifying full offloading and prescribing partial-loading targets during rehabilitation. Future research will evaluate performance across patient populations and clinical rehabilitation tasks.

CRISPR-Cas genome editing toolkits have expanded the scope of genetic studies in various emerging model organisms. However, their applications are limited mainly to knockout experiments due to technical difficulties in establishing knock-in strains, which enable in vivo molecular tagging-based experiments. Here, we investigated knock-in strategies in the harlequin ladybug Harmonia axyridis, a model insect for evolutionary developmental biology, which shows more than 200 color pattern variations within a species. We tested several knock-in strategies using synthetic DNA templates. We found that ssDNA templates generated founder knock-in strains efficiently (2.5-11%), whereas the 5 regions of ssDNA templates were frequently deleted when the insert length exceeded [~]40 bases. To overcome this limitation, we designed several 3 extended DNA templates. Fast-annealed 3-extended double-stranded DNA templates, which were designed for tagging endogenous proteins with epitope tags, showed high founder generation efficiency (9.9-20.9%) and accuracy (30.8-85.7%). This strategy is also applicable to the two-spotted cricket Gryllus bimaculatus, suggesting that the fast-annealed 3-extended dsDNA template is a versatile DNA template for generating knock-in strains in emerging model insects for developmental genetic studies. Summary statementFast-annealed 3-extended dsDNA templates facilitate efficient CRISPR-Cas9-mediated knock-in in emerging model insects.

As the duration of space flights increases, so does the need to optimize off-planet microbial growth. Microbes can both be unintentionally brought into space and cause human disease or be intentionally harnessed for on-site bioengineering functions. However, optimizing microbial growth is challenging due to an insufficient understanding of how microbial communities are affected by the extraterrestrial environment. To address this gap, we have modified a previously developed model for cell growth in microgravity. By improving the functional form used for cell growth as well as the code usability, we enable further research into how microbial communities are influenced by gravity. Applying this model to isolate individual effects of gravity on cell growth indicates that a lack of gravity-driven flow decreases cell growth in microgravity, while the absence of sedimentation increases cell growth in microgravity. These opposite effects likely contribute to the system-dependent effects of microgravity observed experimentally.

Thin structures such as heart valves and aortic dissection flaps interact dynamically with blood flow in human vessels. Their flexibility and capacity for large deformations generate complex, highly transient hemodynamic patterns over the cardiac cycle. Accurately resolving these interactions remains challenging for conventional boundary-fitted fluid-structure interaction approaches. We present an immersed boundary method for simulating thin structures in incompressible flow on unstructured grids. The method couples a stabilized finite element fluid solver with a nonlinear, rotation-free shell formulation through a direct forcing immersed boundary approach. The framework supports both weak (explicit) and strong (implicit) time-coupling strategies, enabling stable simulations over a wide range of solid-to-fluid density ratios. Hydrodynamic forces acting on thin structures are computed from fluid solutions sampled on both sides of the structure, allowing accurate force reconstruction for zero-thickness shells. To our knowledge, this is the first immersed boundary formulation that couples an unstructured finite element fluid solver with a two-dimensional, rotation-free shell model to simulate interactions between thin structures and incompressible flow. Fluid-structure coupling is achieved using predefined finite element shape functions, which provide consistent projection between Eulerian and Lagrangian fields without additional interpolation procedures. The framework is validated using three-dimensional benchmark problems involving thin structures. Then, valve-like model is used to compare strong and weak coupling strategies. Finally, the method is applied to an idealized type-B aortic dissection model. The proposed approach is implemented within the open-source software CRIMSON, a finite element platform for cardiovascular simulation.

Freediving in rats has emerged as a relevant model to study physiology and neural adaptation underlying submersion mechanisms. However, despite well-established strain-dependent differences in behaviour and physiological responses, most studies about freediving rely on Sprague Dawley rats. As the choice of strain could significantly shape experimental results depending on the field of research, we conducted a behavioural comparative study between Long Evans (LE) rats, genetically closer to the Wild Norway rat, with the commonly used Sprague Dawley (SD) strain. We developed an 11-week progressive voluntary freediving protocol involving four distances (from 5 to 11 meters), and assessed the rats natural willingness to dive and swim, and identified several parameters for evaluation of their confidence (waiting time before diving, speed), performance capacity (freediving time) and population variability. We found that Long Evans rats were naturally more willing to dive and more confident, compared to Sprague Dawley rats: they showed better performance with longer time underwater and slower diving speed. We also uncover differences in their variability, at trial-to-trial intra-individual and population inter-individual levels, which can guide the choice of one strain over the other, depending on the aim of the scientific inquiry. HighlightsO_LILong Evans rats were naturally more willing and confident at the beginning of the freediving training. C_LIO_LILong Evans freedivers showed greater ease in the water during the course of training compared to Sprague Dawleyfreedivers. C_LIO_LILong Evans freedivers demonstrated greater inter- and intra-individual variability. C_LI

Perceptual systems in humans and many other animals are able to segment scenes into regions that are likely to be physically meaningful. This ability depends on having low-level mechanisms that can accurately categorize whether local image patches are samples from the same or different kinds of texture. We find that using spatial proximity as a proxy for same-different ground truth makes it possible to train accurate decision variables and bounds directly from arbitrary natural images with no feedback. We also find that performance can be further improved by using proximity as a ground truth for adjusting the final decision variables and bounds for the current image/scene. These surprising findings result from the simple fact that under a wide range of conditions proximity discrimination (near vs. far) and texture discrimination (same vs. different) have mathematically identical decision bounds if the same image features are used for both tasks. We used the decision variables and bounds trained on natural images as the initial steps in a hierarchical Bayesian observer (HBO) model of texture discrimination [9]. Given the relative simplicity of this HBO model, it did an excellent job of segmenting images having randomly shaped regions containing arbitrary natural textures. We suggest that the proximity proxy is something that natural selection could discover and exploit for any same-different task where the task-relevant stimulus features also vary systematically with distance in space and/or time. For example, natural selection could have created developmental learning/plasticity mechanisms that exploit the proximity proxy.

Humans frequently make decisions in complex, high-dimensional environments, where identifying task-relevant information is critical for rapid behavior optimization. Humans outperform standard reinforcement learning agents in navigating such complexity, yet the cognitive strategies of humans remain unclear. To address this, we developed a novel multi-dimensional learning task in which only a subset of dimensions is reward-related. Crucially, unlike prior studies, subjects are uninformed of the true task dimensionality and have to identify them through exploration. This design closely mimics the ambiguity in real-world tasks. Our results have identified two stereotyped choice patterns that reveal "dimension-guided" strategies in exploration and exploitation. Cross-subject analyses suggest that dimension-guided exploration may promote the efficiency of reward-based learning. These findings indicate that humans leverage task dimensionality to guide exploration, and provide inspiration for improving exploration efficiency in AI agents.

Arboreal insects have developed various strategies to navigate their discontinuous habitats. Many insects, including leafhoppers, katydids, and praying mantises, exhibit the ability to actively leap across their leafy platforms and land on a distant substrate. This behavior is especially important for non-winged insects, including nymphal forms of winged insects, which cannot fly between these substrates. To make a targeted jump, an animal must first orient towards the target, estimate the target distance and angular location, and jump with the appropriate take-off speeds and angles to land on their intended substrate. In three-dimensional space, jumping from one point to another requires estimating distance, as well as azimuthal and elevational angles. Jumping insects such as mantises typically reorient their bodies on the substrate to align with the azimuthal direction of the target. This behavior effectively reduces the task to a two-dimensional problem, in which they must estimate only the distance to the target and its elevational angle. Many insects, including praying mantises, perform rhythmic lateral head movements called peering before performing a targeted jump. Although previous studies suggest that mechanisms such as motion parallax while peering are used for distance estimation, the full repertoire of behaviors that enable mantises to jump to arbitrarily located substrates remains unclear. We hypothesized that mantises have distinct behaviors for distance and elevation angle estimation, which enable them to independently modulate their take-off speeds and angles before jumping. To test this hypothesis, we developed behavioral assays in which mantises were placed on a launch platform and jumped to a target platform positioned at variable distances and angles. Using this apparatus, we filmed the jumps of Giant Asian mantis nymphs (Hierodula spp.) with high-speed videography and tracked body parts to quantify take-off speed and angle. Because mantis jumps are ballistic, their trajectories can be modeled as projectile motion. Our results indicate that mantises estimate target distance and elevation angle using two separate behavioral strategies: distance is assessed through peering maneuvers that generate motion parallax, whereas elevation angle is determined through visual fixation of the target accompanied by specific postural adjustments. By combining these behaviors, mantises modulate the magnitude and direction of propulsive force to achieve successful jumps.

Vertex models are widely used within the field of developmental biology to study tissue morphogenesis. These models are well-suited for modeling deformation at the cellular level where movement is driven by local forces. However, understanding how these microscopic movements coordinate to yield macroscopic phenomena such as the shapes of entire tissues remains a challenge. Here we study a top-down approach using differentiable programming on a simplified vertex model of a laminar tissue, and investigate whether the attributes of individual cells can be tuned to make the mesh as a whole acquire a predefined shape. We let the mesh evolve according to simple rules defined by the input to each polygon, and evaluate the resulting shape against a target boundary. Additionally, we show how the high degeneracy of the output can be reduced by constraining the polygon distributions: first, by adding simple penalties on tissue-wide attributes; and second, by dividing the tissue into regions, within which we bias the attributes toward characteristic values. Our study shows how a simple vertex model can be combined with differentiable programming to model developing tissues, and provides insight into the way individual cells must coordinate to yield macroscopic phenomena such as pre-programmed shapes.

Within species, individuals of the same age can differ in size. Previously, parental genetics, nutrition, space, and social interactions have been suggested to explain different growth rates. However, direct effects of larger individuals on the physiology and growth of smaller individuals are poorly understood. In this study, we investigated how larger individuals of the marine worm Platynereis dumerilii can impact the growth of smaller conspecifics. Comparing growth distributions in communally and individually reared worms, we show that larger worms suppress the growth of smaller ones. Furthermore, we were able to demonstrate that this suppression is chemically mediated. The chemical cue does not originate from faeces but is water soluble, stable for several days and smaller than 3 kDa. Our findings highlight the importance of non-reproduction related chemical signalling, showing evidence that dominant individuals can chemically suppress the growth of their conspecifics. This study provides new insights into how hierarchy can be established and maintained in a population and is particularly relevant for the growing community studying this model species.

Animals continuously evaluate environmental cues to guide approach-avoidance decisions, with internal states like hunger dynamically shaping how stimuli are acted upon. While most studies examine the valence-switching of stimuli from appetitive to aversive using simplified or ambiguous stimuli, we leveraged a system in which a single prey contains both appetitive and aversive features. The nudibranch Berghia stephanieae, is a specialist predator of the sea anemone, Exaiptasia diaphana. These nudibranchs must resolve conflicting signals where chemical cues signal food, while contact can result in injury or death. The danger posed by Exaiptasia was described and quantified through direct counts of nematocysts fired into Berghia and multiple instances where the Berghia was captured and consumed by its prey. To test how internal state influenced the perception of stimuli from prey we recorded predatory behavior of Berghia after different periods of food deprivation. We found that the olfactory cues from prey were attractive to Berghia, even when animals were sated, and usually led to a contact-mediated investigation of prey. Hunger independently modulated olfactory and contact cue valence at different internal states and time scales of food deprivation. Hunger specifically altered the threshold for avoidance following contact with prey, indicating that somatosensory and chemotactile cues are modulated by hunger unlike olfactory cues. Our results highlight how internal state and sensory modality interact to shape decision making in a biologically relevant, high-risk predation context.