Victoria A. Webster‐Wood

Paper Title: Context-dependent mechanical reconfiguration is necessary for multifunctional behavior in an intrinsically confined hydrostat For the most recent version of the paper's code, see the GitHub repository. This repository contains all code and data related to the above-referenced paper. In this paper, we investigate the biomechanics of the Aplysia (sea hare) feeding system (the buccal mass). Specifically, we investigate the role of mechanical reconfiguration on the protraction of the buccal mass's internal grasper, the odontophore. To do this, we combine a kinematic analysis based on in vivo MRI videos of biting and rejection behaviors with a new hybrid kinetic/kinematic biomechanical model of the buccal mass. All results from the paper can be reproduced by running the MATLAB (r2024a) script titled "ComputationalExperiments.m" in the root folder. The outputs of this script can be found in the "Figures" folder. Changes from original version: The title of the repository was updated to reflect a change to the title of the paper. The calculation of the angle in Supplemental Figure S5 was corrected to match the angle reported in the main manuscript (referenced to the jaw line instead of the ventral I3 line). No qualitative changes result from this correction.

Abstract Biologists and engineers often attempt to develop biologically accurate neuromechanical models, but improving these models is challenging due to the high model dimensionality. To overcome this challenge, we present the Reinforcement-Learning-enabled Neuromechanical Model Analysis (RL-NMA) pipeline for targeted, demand-driven-complexity-based model improvements in neurorobotics and neuromechanics. This pipeline is agnostic to the model and system analyzed, allowing it to be broadly applied. We present two case studies of RL-NMA pipeline application. First, we assess a digital twin of a soft robot inspired by the feeding mechanism of the marine mollusk, Aplysia californica . Second, we perform iterative improvement of a computational neuromechanical model of Aplysia feeding to capture in vivo behavior. Third, we assess a different digital twin of a bioinspired soft grasper. Based on the pipeline’s recommendation, targeted model improvements led to improved correlations. These case studies demonstrate iterative application of the RL-NMA pipeline in both neurorobotics and neuromechanics, allowing researchers to achieve demand-driven model improvements in high-dimensional models.

This report provides a summary of the outcomes of the Interdisciplinary Workshop on Mechanical Intelligence held in 2024. Mechanical Intelligence (MI) represents the phenomenon that novel structural features of material/biological/robotic systems can encode intelligence through responsiveness, adaptivity, memory, and learning in the mechanical structure itself. This is in contrast to computational intelligence, wherein the intelligence functions occur through electrical signaling and computer code. The two-day workshop was held at NSF headquarters on May 30-31 and included 38 invited academic researcher participants, and 8 program officers from the NSF. The workshop was structured around active small and large group discussions in groups of 4-5 and 9-10 with the goal of addressing topical questions on MI. Working groups entered notes into shared presentation slides for each discussion session and presented their outcomes in a final presentation on the last day. Here we summarize the overall outcomes of the workshop.

This report provides a summary of the outcomes of the Interdisciplinary Workshop on Mechanical Intelligence held in 2024. Mechanical Intelligence (MI) represents the phenomenon that novel structural features of material/biological/robotic systems can encode intelligence through responsiveness, adaptivity, memory, and learning in the mechanical structure itself. This is in contrast to computational intelligence, wherein the intelligence functions occur through electrical signaling and computer code. The two-day workshop was held at NSF headquarters on May 30-31 and included 38 invited academic researcher participants, and 8 program officers from the NSF. The workshop was structured around active small and large group discussions in groups of 4-5 and 9-10 with the goal of addressing topical questions on MI. Working groups entered notes into shared presentation slides for each discussion session and presented their outcomes in a final presentation on the last day. Here we summarize the overall outcomes of the workshop.

A robot's morphology is pivotal to its functionality, as biological organisms demonstrate through shape adjustments - octopi squeeze through small apertures, and caterpillars use peristaltic transformations to navigate complex environments. While existing robotic systems struggle to achieve precise volumetric transformations, Variable Geometry Trusses offer rich morphing capabilities by coordinating hundreds of actuating beams. However, control complexity scales exponentially with beam count, limiting implementations to trusses with only a handful of beams or to designs where only a subset of beams are actuable. Previous work introduced the metatruss, a truss robot that simplifies control by grouping actuators into interconnected pneumatic control networks, but relies on manual network design and control sequences. Here, we introduce a multi-objective optimization framework based on a tailored genetic algorithm to automate actuator grouping, contraction ratios, and actuation timing. We develop a highly damped dynamic simulator that balances computational efficiency with physical accuracy and validate our approach with experimental prototypes. Across multiple tasks, we demonstrate that the metatruss achieves complex shape adaptations with minimal control units. Our results reveal an optimal number of control networks, beyond which additional networks yield diminishing performance gains.

Ciliated biobots (CiliaBots) are engineered tissues capable of self-actuated propulsion via exterior motile cilia. While correlations have been observed between CiliaBot motility and morphology, direct control of morphological features to deliver desired motility outcomes remains unexplored. Here, we describe the engineering of aggregated CiliaBots (AggreBots) to augment control over CiliaBot structural parameters and, consequently, motility patterns through guided, modular aggregation of human airway epithelial spheroids [referred to as CiliaBot building blocks (CBBs)]. Multi-CBB aggregation generated rod-, triangle-, and diamond-shaped AggreBots, altering tissue geometry without sacrificing surface cilia density or inter-CBB boundary fidelity. The further introduction of CCDC39 -mutated CBBs as cilia-inactive modules enabled the generation of hybrid AggreBots with precision modulation of active cilia distribution, further empowering alterations to motility patterns. Our results demonstrate the potential of AggreBots as living tissue propellers with morphological “levers” by which modifications to tissue motility can be theoretically planned and experimentally verified.

For biohybrid actuators, the properties of synthetic materials interfacing with living cells are crucial due to cellular chemical and mechanical sensitivities. 3D-printable resins exhibit a wide range of properties, but mechanical properties are frequently reported before sterilization. Additionally, biocompatibility must be assessed for specific use cases, including in the context of the cell type and printing procedures. Therefore, this data descriptor details a new dataset of cytotoxicity and material properties of six commercially-available resins (three rigid and three elastomeric) printed on a Phrozen Sonic Mini 8K and sterilized using 70% ethanol exposure or autoclaving. Experiments were designed to model C2C12-biohybrid conditions. Cytotoxicity analyses were conducted by directly culturing C2C12 with sterilized samples. For material characterization, uniaxial tension and compression tests with post-hoc Hookean and Yeoh models and print fidelity assessments were conducted for nonsterile and sterile samples. The mechanical properties were assessed after submersion in phosphate-buffered saline to mimic conditions in biohybrid applications. Overall, this dataset provided comprehensive testing on cytotoxicity and material properties for 3D-printable resins in C2C12-biohybrid applications.

Muscle-based biohybrid systems integrate living muscle with engineered structures to create soft robots, biological models, and regenerative platforms. However, current actuators often lack strength and are difficult to assemble into complex devices. This study presents a suspended compliant skeleton that enhances muscle maturation by providing passive resistance, enabling high-stroke self-exercise without external stimulation. Using immortalized C2C12 cells, the resulting actuators achieved millimeter-scale strokes and millinewton-scale forces, surpassing previous benchmarks. Magnetic interfaces embedded in the skeleton allowed modular assembly into multi-degree-of-freedom devices such as grippers, arms, and positioning stages. These interfaces also support actuator replacement and repair, improving resilience and scalability. This approach significantly boosts engineered muscle performance and offers a robust, modular platform for building high-functioning, repairable biohybrid machines.

To understand how behaviors arise in animals, it is necessary to investigate both the neural circuits and the biomechanics of the periphery. A tractable model system for studying multifunctional control is the feeding apparatus of the marine mollusk Aplysia californica. Previous in silico and in roboto models have investigated how the nervous and muscular systems interact in this system. However, these models are still limited in their ability to match in vivo data both qualitatively and quantitatively. We introduce a new neuromechanical model of Aplysia feeding that combines a modified version of a previously developed neural model with a novel biomechanical model that better reflects the anatomy and kinematics of Aplysia feeding. The model was calibrated using a combination of previously measured biomechanical parameters and hand-tuning to behavioral data. Using this model, simulated feeding experiments were conducted, and the resulting behavioral metrics were compared to animal data. The model successfully produces three key behaviors seen in Aplysia and demonstrates a good quantitative agreement with biting and swallowing behaviors. Additional work is needed to match rejection behavior quantitatively and to reflect qualitative observations related to the relative contributions of two key muscles, the hinge and I3. Future improvements will focus on incorporating the effects of deformable 3D structures in the simulated buccal mass.

Humanity has long sought inspiration from nature to innovate materials and devices. As science advances, nature-inspired materials are becoming part of our lives. Animate materials, characterized by their activity, adaptability, and autonomy, emulate properties of living systems. While only biological materials fully embody these principles, artificial versions are advancing rapidly, promising transformative impacts in the circular economy, health and climate resilience within a generation. This roadmap presents authoritative perspectives on animate materials across different disciplines and scales, highlighting their interdisciplinary nature and potential applications in diverse fields including nanotechnology, robotics and the built environment. It underscores the need for concerted efforts to address shared challenges such as complexity management, scalability, evolvability, interdisciplinary collaboration, and ethical and environmental considerations. The framework defined by classifying materials based on their level of animacy can guide this emerging field to encourage cooperation and responsible development. By unravelling the mysteries of living matter and leveraging its principles, we can design materials and systems that will transform our world in a more sustainable manner.

Abstract Ciliated biobots, or CiliaBots, are a class of engineered multicellular tissues that are capable of self-actuated motility propelled by the motile cilia located on their exterior surface. Correlations have been observed between CiliaBot motility patterns and their morphology and cilia distribution. However, precise control of these structural parameters to generate desired motility patterns predictably remains lacking. Here, we developed a novel Aggregated CiliaBot (AggreBot) platform capable of producing designer motility patterns through spatially controlled aggregation of epithelial spheroids made from human airway cells (referred to as CiliaBot Building Blocks or CBBs), yielding AggreBots with configurable geometry and distribution of active cilia. Guided multi-CBB aggregation led to the production of rod-, triangle-, and diamond-shaped AggreBots, which consistently effected greater motility than traditional single-spheroid CiliaBots. Furthermore, CBBs were found to maintain internal boundaries post-aggregation through the combined action of pathways controlling cellular fluidity and tissue polarity. This boundary fidelity, combined with the use of CBBs with immotile cilia due to mutations in the CCDC39 gene, allowed for the generation of hybrid AggreBots with precision control over the coverage and distribution of active cilia, further empowering control of motility patterns. Our results demonstrate the potential of AggreBots as self-propelling biological tissues through the establishment of morphological “levers” by which alterations to tissue motility can be theoretically planned and experimentally verified.

Neurodevelopmental disorders (NDDs) have arisen as one of the most prevailing chronic diseases within the US. Often associated with severe adverse impacts on the formation of vital central and peripheral nervous systems during the neurodevelopmental process, NDDs are comprised of a broad spectrum of disorders, such as autism spectrum disorder, attention deficit hyperactivity disorder, and epilepsy, characterized by progressive and pervasive detriments to cognitive, speech, memory, motor, and other neurological functions in patients. However, the heterogeneous nature of NDDs poses a significant roadblock to identifying the exact pathogenesis, impeding accurate diagnosis and the development of targeted treatment planning. A computational NDDs model holds immense potential in enhancing our understanding of the multifaceted factors involved and could assist in identifying the root causes to expedite treatment development. To tackle this challenge, we introduce optimal neurotrophin concentration to the driving force and degradation of neurotrophin to the synaptogenesis process of a 2D phase field neuron growth model using isogeometric analysis to simulate neurite retraction and atrophy. The optimal neurotrophin concentration effectively captures the inverse relationship between neurotrophin levels and neuron survival, while its degradation regulates concentration levels. Leveraging dynamic domain expansion, the model efficiently expands the domain based on outgrowth patterns to minimize degrees of freedom. Based on truncated T-splines, our model simulates the evolving process of complex neurite structures by applying local refinement adaptively to the cell/neurite boundary. Furthermore, a thorough parameter investigation is conducted with detailed comparisons against neuron cell cultures in experiments, enhancing our fundamental understanding of the possible mechanisms underlying NDDs.

Abstract Biohybrid devices integrate biological and synthetic materials. The selection of an appropriate synthetic material to interface with living cells and tissues is crucial due to cellular chemical and mechanical sensitivities. As such, the stiffness of the material and its biocompatibility while in direct contact must be considered. In this study, the material properties and biocompatibility of six commercially available, 3D printable resins (three rigid and three elastomeric) were assessed for their suitability for biohybrid actuators. To characterize the material, uniaxial tension and compression tests with post-hoc Hookean and Yeoh model analyses were conducted for both nonsterile and sterilized (ethanol-soaking or autoclaved) samples. The mechanical properties of the elastomeric resins were minimally impacted by the different sterilization techniques. However, both Phrozen AquaGray 8K and Liqcreate Bio-Med Clear rigid resins were significantly softer in tensile tests after sterilization, and AquaGray became far more ductile. Asiga DentaGUIDE was much more stable in its mechanical properties than the other rigid resins. It was also shown that long-term exposure to saline solutions leads to a decrease in the Young’s moduli of these rigid resins before any sterilization has occurred. The print fidelity was also assessed for nonsterile and sterilized samples via manual scoring to determine the impacts of the sterilization processes on the part fidelity. Sterilization techniques had a minimal impact on print fidelity for both elastomeric and ridged resins with two exceptions. In both Formlabs Silicone 40A IPA/BuOAc post-treatment and Phrozen AquaGrey 8K groups, ethanol/UV-sterilization caused more degradation compared to autoclave-sterilization. In addition to the material analyses, cytotoxicity analyses using calcein AM and ethidium homodimer-1 fluorescence markers were conducted by directly culturing C2C12, a common myoblast cell line used in bioactuators, with sterilized resin samples. Of the elastomeric resins, only Formlabs Silicone 40A was shown to have minimal impacts on cell viability. For the rigid resins, Asiga DentaGUIDE, Liqcreate Bio-Med Clear, and ethanol-sterilized Phrozen AquaGray 8K demonstrated minimal impacts on cell viability. Based on these analyses, Asiga DentaGUIDE and Formlabs Silicone 40A demonstrate potential for applications in biohybrid muscle-based actuators when using low-cost 3D printers.

Abstract To understand how behaviors arise in animals, it is necessary to investigate both the neural circuits and the biomechanics of the periphery. A tractable model system for studying multifunctional control is the feeding apparatus of the marine mollusk Aplysia californica . Previous in silico and in roboto models have investigated how the nervous and muscular systems interact in this system. However, these models are still limited in their ability to match in vivo data both qualitatively and quantitatively. We introduce a new neuromechanical model of Aplysia feeding that combines a modified version of a previously developed neural model with a novel biomechanical model that better reflects the anatomy and kinematics of Aplysia feeding. The model was calibrated using a combination of previously measured biomechanical parameters and hand-tuning to behavioral data. Using this model, simulation feeding experiments were conducted, and the resulting behavioral metrics were compared to animal data. The model successfully produces three key behaviors seen in Aplysia and demonstrates a good quantitative agreement with biting and swallowing behaviors. Additional work is needed to match rejection behavior quantitatively and to reflect qualitative observations related to the relative contributions of two key muscles, the hinge and I3. Future improvements will focus on incorporating the effects of deformable 3D structures in the simulated buccal mass. Author summary Animals need to produce a wide array of behaviors so that they can adapt to changes in their environment. To understand how behaviors are performed, we need to understand how the brain and the body work together in their environment. One tractable system in which to study this brain-body relationship is the feeding behavior of the sea slug Aplysia californica . Despite having a small fraction of the number of neurons that humans have, this animal can produce many behaviors, respond to a changing environment, and learn from previous experiences. We have create an improved computer model of the slug’s mouthparts that simulates many of its key muscles and the forces they produce, together with a representation of the network of neurons that control them. With this model, we can recreate the feeding behaviors that we observe in the real animal, including biting, swallowing, and rejection, and use it to make quantitative predictions of how the animal will behave and respond to different stimuli. We found however that some aspects of the system were not well represented by simple 1-dimensional muscles, as has been done in most biomechanical models to date, but requires us to consider more complicated deformations of these soft bodies. Using this model as a tool, we aim to test hypotheses about brain-body interactions in the sea slug to better understand the behavior of small, slowly moving animals.

Animals can accomplish many incredible behavioral feats across a wide range of operational environments and scales that current robots struggle to match. One explanation for this performance gap is the extraordinary properties of the biological materials that comprise animals, such as muscle tissue. Using living muscle tissue as an actuator can endow robotic systems with highly desirable properties such as self-healing, compliance, and biocompatibility. Unlike traditional soft robotic actuators, living muscle biohybrid actuators exhibit unique adaptability, growing stronger with use. The dependency of a muscle's force output on its use history endows muscular organisms the ability to dynamically adapt to their environment, getting better at tasks over time. While muscle adaptability is a benefit to muscular organisms, it currently presents a challenge for biohybrid researchers: how does one design and control a robot whose actuators' force output changes over time? Here, we incorporate muscle adaptability into a many-muscle biohybrid robot design and modeling tool, leveraging reinforcement learning as both a co-design partner and system controller. As a controller, our learning agents coordinated the independent contraction of 42 muscles distributed on a lattice worm structure to successfully steer it towards eight distinct targets while incorporating muscle adaptability. As a co-design tool, our agents enable users to identify which muscles are important to accomplishing a given task. Our results show that adaptive agents outperform non-adaptive agents in terms of maximum rewards and training time. Together, these contributions can both enable the elucidation of muscle actuator adaptation and inform the design and modeling of adaptive, performant, many-muscle robots.

Adipose tissue is a dynamic regulatory organ that has profound effects on the overall health of patients. Unfortunately, inconsistencies in human adipose tissues are extensive and multifactorial, including large variability in cellular sizes, lipid content, inflammation, extracellular matrix components, mechanics, and cytokines secreted. Given the high human variability, and since much of what is known about adipose tissue is from animal models, we sought to establish correlations and patterns between biological, mechanical, and epidemiological properties of human adipose tissues. To do this, twenty-six independent variables were cataloged for twenty patients, which included patient demographics and factors that drive health, obesity, and fibrosis. A factorial analysis for mixed data (FAMD) was used to analyze patterns in the dataset (with BMI > 25), and a correlation matrix was used to identify interactions between quantitative variables. Vascular endothelial growth factor A (VEGFA) and actin alpha 2, smooth muscle (ACTA2) gene expression were the highest loadings in the first two dimensions of the FAMD. The number of adipocytes was also a key driver of patient-related differences, where a decrease in the density of adipocytes was associated with aging. Aging was also correlated with a decrease in overall lipid percentage of subcutaneous tissue, with lipid deposition being favored extracellularly, an increase in transforming growth factor-β1 (TGFβ1), and an increase in M1 macrophage polarization. An important finding was that self-identified race contributed to variance between patients in this study, where Black patients had significantly lower gene expression levels of TGFβ1 and ACTA2. This finding supports the urgent need to account for patient ancestry in biomedical research to develop better therapeutic strategies for all patients. Another important finding was that TGFβ induced factor homeobox 1 (TGIF1), an understudied signaling molecule, which is highly correlated with leptin signaling, was correlated with metabolic inflammation. Furthermore, this study draws attention to what we define as "extracellular lipid droplets", which were consistently found in collagen-rich regions of the obese adipose tissues evaluated here. Reduced levels of TGIF1 were correlated with higher numbers of extracellular lipid droplets and an inability to suppress fibrotic changes in adipose tissue. Finally, this study indicated that M1 and M2 macrophage markers were correlated with each other and leptin in patients with a BMI > 25. This finding supports growing evidence that macrophage polarization in obesity involves a complex, interconnecting network system rather than a full switch in activation patterns from M2 to M1 with increasing body mass. Overall, this study reinforces key findings in animal studies and identifies important areas for future research, where human and animal studies are divergent. Understanding key drivers of human patient variability is required to unravel the complex metabolic health of unique patients.

The industrial revolution of the 19th century marked the onset of an era of machines and robots that transformed societies. Since the beginning of the 21st century, a new generation of robots envisions similar societal transformation. These robots are biohybrid: part living and part engineered. They may self-assemble and emerge from complex interactions between living cells. While this new era of living robots presents unprecedented opportunities for positive societal impact, it also poses a host of ethical challenges. A systematic, nuanced examination of these ethical issues is of paramount importance to guide the evolution of this nascent field. Multidisciplinary fields face the challenge that inertia around collective action to address ethical boundaries may result in unexpected consequences for researchers and societies alike. In this Perspective, we i) clarify the ethical challenges associated with biohybrid robotics, ii) discuss the need for and elements of a potential governance framework tailored to this technology; and iii) propose tangible steps toward ethical compliance and policy formation in the field of biohybrid robotics.

Neurodevelopmental disorders (NDDs) have arisen as one of the most prevailing chronic diseases within the US. Often associated with severe adverse impacts on the formation of vital central and peripheral nervous systems during the neurodevelopmental process, NDDs are comprised of a broad spectrum of disorders, such as autism spectrum disorder, attention deficit hyperactivity disorder, and epilepsy, characterized by progressive and pervasive detriments to cognitive, speech, memory, motor, and other neurological functions in patients. However, the heterogeneous nature of NDDs poses a significant roadblock to identifying the exact pathogenesis, impeding accurate diagnosis and the development of targeted treatment planning. A computational NDDs model holds immense potential in enhancing our understanding of the multifaceted factors involved and could assist in identifying the root causes to expedite treatment development. To tackle this challenge, we introduce optimal neurotrophin concentration to the driving force and degradation of neurotrophin to the synaptogenesis process of a 2D phase field neuron growth model using isogeometric analysis to simulate neurite retraction and atrophy. The optimal neurotrophin concentration effectively captures the inverse relationship between neurotrophin levels and neurite survival, while its degradation regulates concentration levels. Leveraging dynamic domain expansion, the model efficiently expands the domain based on outgrowth patterns to minimize degrees of freedom. Based on truncated T-splines, our model simulates the evolving process of complex neurite structures by applying local refinement adaptively to the cell/neurite boundary. Furthermore, a thorough parameter investigation is conducted with detailed comparisons against neuron cell cultures in experiments, enhancing our fundamental understanding of the mechanisms underlying NDDs.

The coordination of complex behavior requires knowledge of both neural dynamics and the mechanics of the periphery. The feeding system of Aplysia californica is an excellent model for investigating questions in soft body systems' neuromechanics because of its experimental tractability. Prior work has attempted to elucidate the mechanical properties of the periphery by using a Hill-type muscle model to characterize the force generation capabilities of the key protractor muscle responsible for moving Aplysia's grasper anteriorly, the I2 muscle. However, the I1/I3 muscle, which is the main driver of retractions of Aplysia's grasper, has not been characterized. Because of the importance of the musculature's properties in generating functional behavior, understanding the properties of muscles like the I1/I3 complex may help to create more realistic simulations of the feeding behavior of Aplysia, which can aid in greater understanding of the neuromechanics of soft-bodied systems. To bridge this gap, in this work, the I1/I3 muscle complex was characterized using force-frequency, length-tension, and force-velocity experiments and showed that a Hill-type model can accurately predict its force-generation properties. Furthermore, the muscle's peak isometric force and stiffness were found to exceed those of the I2 muscle, and these results were analyzed in the context of prior studies on the I1/I3 complex's kinematics in vivo.

Studying the nervous system underlying animal motor control can shed light on how animals can adapt flexibly to a changing environment. We focus on the neural basis of feeding control in Aplysia californica. Using the Synthetic Nervous System framework, we developed a model of Aplysia feeding neural circuitry that balances neurophysiological plausibility and computational complexity. The circuitry includes neurons, synapses, and feedback pathways identified in existing literature. We organized the neurons into three layers and five subnetworks according to their functional roles. Simulation results demonstrate that the circuitry model can capture the intrinsic dynamics at neuronal and network levels. When combined with a simplified peripheral biomechanical model, it is sufficient to mediate three animal-like feeding behaviors (biting, swallowing, and rejection). The kinematic, dynamic, and neural responses of the model also share similar features with animal data. These results emphasize the functional roles of sensory feedback during feeding.

Neuron growth is a complex, multi-stage process that neurons undergo to develop sophisticated morphologies and interwoven neurite networks. Recent experimental research advances have enabled us to examine the effects of various neuron growth factors and seek potential causes for neurodegenerative diseases, such as Alzheimer’s disease, Parkinson’s disease, and amyotrophic lateral sclerosis. A computational tool that studies the neuron growth process could shed crucial insights on the effects of various factors and potentially help find a cure for neurodegeneration. However, there is a lack of computational tools to accurately and realistically simulate the neuron growth process within reasonable time frames. Bio-phenomenon-based models ignore potential neuron growth factors and cannot generate realistic results, and bio-physics-based models require extensive, high-order governing equations that are computationally expensive. In this paper, we incorporate experimental neurite features into a phase field method-based neuron growth model using an isogeometric analysis collocation (IGA-C) approach. Based on a semi-automated quantitative analysis of neurite morphology, we obtain relative turning angle, average tortuosity, neurite endpoints, average segment length, and the total length of neurites. We use the total neurite length to determine the evolving days in vitro (DIV) and select corresponding neurite features to drive and constrain the neuron growth. This approach archives biomimetic neuron growth patterns with automatic growth stage transitions by incorporating corresponding DIV neurite morphometric data based on the total neurite length of the evolving neurite morphology. Furthermore, we built a convolutional neural network (CNN) to significantly reduce associated computational costs for predicting complex neurite growth patterns. Our CNN model adopts a customized convolutional autoencoder as the backbone that takes neuron growth simulation initializations and target iteration as the input and predicts the corresponding neurite patterns. This approach achieves high prediction accuracy (97.77%) while taking 7 orders of magnitude less computational times when compared with our IGA-C neuron growth solver.

Over the years, efforts in bioinspired soft robotics have led to mobile systems that emulate features of natural animal locomotion. This includes combining mechanisms from multiple organisms to further improve movement. In this work, we seek to improve locomotion in soft, amphibious robots by combining two independent mechanisms: sea star locomotion gait and gecko adhesion. Specifically, we present a sea star-inspired robot with a gecko-inspired adhesive surface that is able to crawl on a variety of surfaces. It is composed of soft and stretchable elastomer and has five limbs that are powered with pneumatic actuation. The gecko-inspired adhesion provides additional grip on wet and dry surfaces, thus enabling the robot to climb on 25° slopes and hold on statically to 51° slopes.

Abstract Despite the impressive performance of recent marine robots, many of their components are non‐biodegradable or even toxic and may negatively impact sensitive ecosystems. To overcome these limitations, biologically‐sourced hydrogels are a candidate material for marine robotics. Recent advances in embedded 3D printing have expanded the design freedom of hydrogel additive manufacturing. However, 3D printing small‐scale hydrogel‐based actuators remains challenging. In this study, Free form reversible embedding of suspended hydrogels (FRESH) printing is applied to fabricate small‐scale biologically‐derived, marine‐sourced hydraulic actuators by printing thin‐wall structures that are water‐tight and pressurizable. Calcium‐alginate hydrogels are used, a sustainable biomaterial sourced from brown seaweed. This process allows actuators to have complex shapes and internal cavities that are difficult to achieve with traditional fabrication techniques. Furthermore, it demonstrates that fabricated components are biodegradable, safely edible, and digestible by marine organisms. Finally, a reversible chelation‐crosslinking mechanism is implemented to dynamically modify alginate actuators' structural stiffness and morphology. This study expands the possible design space for biodegradable marine robots by improving the manufacturability of complex soft devices using biologically‐sourced materials.

Abstract Adipose tissue is a dynamic regulatory organ that has profound effects on the overall health of patients. Unfortunately, inconsistencies in human adipose tissues are extensive and multifactorial including large variability in cellular sizes, lipid content, inflammation, extracellular matrix components, mechanics, and cytokines secreted. Given the high human variability, and since much of what is known about adipose tissue is from animal models, we sought to establish correlations and patterns between biological, mechanical, and epidemiological properties of human adipose tissues. To do this, twenty-six independent variables were cataloged for twenty patients that included patient demographics and factors that drive health, obesity, and fibrosis. A factorial analysis for mixed data (FAMD) was used to analyze patterns in the dataset (with BMI > 25) and a correlation matrix was used to identify interactions between quantitative variables. Vascular endothelial growth factor A (VEGFA) and actin alpha 2, smooth muscle (ACTA2) gene expression were the highest loading in the first two dimensions of the FAMD. The number of adipocytes was also a key driver of patient-related differences, where a decrease in the density of adipocytes was associated with aging. Aging was also correlated with a decrease in overall lipid percentage of subcutaneous tissue (with lipid deposition being favored extracellularly), an increase in transforming growth factor-β1 (TGFβ1), and an increase in M1 macrophage polarization. An important finding was that self-identified race contributed to variance between patients in this study, where Black patients had significantly lower gene expression levels of TGFβ1 and ACTA2. This finding supports the urgent need to account for patient ancestry in biomedical research to develop better therapeutic strategies for all patients. Another important finding was that TGFβ induced factor homeobox 1 (TGIF1), an understudied signaling molecule, is highly correlated with leptin signaling and was correlated with metabolic inflammation. Finally, this study revealed an interesting gene expression pattern where M1 and M2 macrophage markers were correlated with each other, and leptin, in patients with a BMI > 25. This finding supports growing evidence that macrophage polarization in obesity involves a complex, interconnecting network system rather than a full switch in activation patterns from M2 to M1 with increasing body mass. Overall, this study reinforces key findings in animal studies and identifies important areas for future research, where human and animal studies are divergent. Understanding key drivers of human patient variability is required to unravel the complex metabolic health of unique patients.

The Synthetic Nervous System (SNS) is a biologically inspired neural network (NN). Due to its capability of capturing complex mechanisms underlying neural computation, an SNS model is a candidate for building compact and interpretable NN controllers for robots. Previous work on SNSs has focused on applying the model to the control of legged robots and the design of functional subnetworks (FSNs) to realize dynamical systems. However, the FSN approach has previously relied on the analytical solution of the governing equations, which is difficult for designing more complex NN controllers. Incorporating plasticity into SNSs and using learning algorithms to tune the parameters offers a promising solution for systematic design in this situation. In this paper, we theoretically analyze the computational advantages of SNSs compared with other classical artificial neural networks. We then use learning algorithms to develop compact subnetworks for implementing addition, subtraction, division, and multiplication. We also combine the learning-based methodology with a bioinspired architecture to design an interpretable SNS for the pick-and-place control of a simulated gantry system. Finally, we show that the SNS controller is successfully transferred to a real-world robotic platform without further tuning of the parameters, verifying the effectiveness of our approach.

The Synthetic Nervous System (SNS) is a biologically inspired neural network (NN). Due to its capability of capturing complex mechanisms underlying neural computation, an SNS model is a candidate for building compact and interpretable NN controllers for robots. Previous work on SNSs has focused on applying the model to the control of legged robots and the design of functional subnetworks (FSNs) to realize dynamical systems. However, the FSN approach has previously relied on the analytical solution of the governing equations, which is difficult for designing more complex NN controllers. Incorporating plasticity into SNSs and using learning algorithms to tune the parameters offers a promising solution for systematic design in this situation. In this paper, we theoretically analyze the computational advantages of SNSs compared with other classical artificial neural networks. We then use learning algorithms to develop compact subnetworks for implementing addition, subtraction, division, and multiplication. We also combine the learning-based methodology with a bioinspired architecture to design an interpretable SNS for the pick-and-place control of a simulated gantry system. Finally, we show that the SNS controller is successfully transferred to a real-world robotic platform without further tuning of the parameters, verifying the effectiveness of our approach.

Neuron growth is a complex, multi-stage process that develops sophisticated morphologies and interwoven neurite networks. Recent advances have enabled us to examine the effects of neuron growth factors and seek causes for neurodegenerative diseases, such as Alzheimer's disease, Parkinson's disease, and amyotrophic lateral sclerosis. A computational tool that studies neuron growth could shed crucial insights into the effects of various factors and help find a neurodegeneration cure. However, there lacks a computational tool to accurately and realistically simulate neuron growth within reasonable time frames. Bio-phenomenon models ignore potential factors and cannot generate realistic results, and bio-physics models require computationally expensive high-order governing equations. This paper incorporates experimental neurite features into a phase field method-based neuron growth model using an isogeometric analysis collocation (IGA-C) approach. Based on a semi-automated quantitative analysis of neurite morphology, we obtain relative turning angle, average tortuosity, neurite endpoints, average segment length, and the total length of neurites. We use the total neurite length to determine the evolving days in vitro (DIV) and select corresponding neurite features to drive and constrain neuron growth. This approach archives biomimetic neuron growth patterns with automatic growth stage transitions by incorporating corresponding DIV neurite morphometric data based on the total neurite length of the evolving neurite morphology. Furthermore, we built a convolutional neural network (CNN) to significantly reduce computational costs for predicting neurite growth. With a customized convolutional autoencoder as the backbone, our CNN model can predict neurite patterns with a high prediction accuracy, 97.77%, while taking 7 orders of magnitude less computational times than our IGA-C solver.

The McKibben pneumatic artificial muscle is a commonly studied soft robotic actuator, and its quasistatic force-length properties have been well characterized and modeled. However, its damping and force-velocity properties are less well studied. Understanding these properties will allow for more robust dynamic modeling of soft robotic systems. The force-velocity response of these actuators is of particular interest because these actuators are often used as hardware models of skeletal muscles for bioinspired robots, and this force-velocity relationship is fundamental to muscle physiology. In this work, we investigated the force-velocity response of McKibben actuators and the ability to tune this response through the use of viscoelastic polymer sheaths. These viscoelastic McKibben actuators (VMAs) were characterized using iso-velocity experiments inspired by skeletal muscle physiology tests. A simplified 1D model of the actuators was developed to connect the shape of the force-velocity curve to the material parameters of the actuator and sheaths. Using these viscoelastic materials, we were able to modulate the shape and magnitude of the actuators' force-velocity curves, and using the developed model, these changes were connected back to the material properties of the sheaths.

The McKibben pneumatic artificial muscle is a commonly studied soft robotic actuator, and its quasistatic force-length properties have been well characterized and modeled. However, its damping and force-velocity properties are less well studied. Understanding these properties will allow for more robust dynamic modeling of soft robotic systems. The force-velocity response of these actuators is of particular interest because these actuators are often used as hardware models of skeletal muscles for bioinspired robots, and this force-velocity relationship is fundamental to muscle physiology. In this work, we investigated the force-velocity response of McKibben actuators and the ability to tune this response through the use of viscoelastic polymer sheaths. These viscoelastic McKibben actuators (VMAs) were characterized using iso-velocity experiments inspired by skeletal muscle physiology tests. A simplified 1D model of the actuators was developed to connect the shape of the force-velocity curve to the material parameters of the actuator and sheaths. Using these viscoelastic materials, we were able to modulate the shape and magnitude of the actuators' force-velocity curves, and using the developed model, these changes were connected back to the material properties of the sheaths.