Biology subjects

Kuhl, E.

Publications and source records attributed to Kuhl, E..

At least 19 recordsLinked to original sources

bioRxiv2026

Grow2Fold: mapping heterogeneous developmental growth to human brain folding

Cortical folding emerges through complex developmental processes, yet what organizes these processes into the characteristic folding patterns of the human brain remains unclear. Here, we developed an imaging-informed whole-brain modeling framework, Grow2Fold, that integrates developmental cortical regionalization, quantitative growth characterization, and mechanics-guided simulation to investigate how cortical growth shapes brain folding patterns. Using 836 fetal and neonatal MRI sessions from 747 participants spanning 21-45 postmenstrual weeks, we identified a reproducible and hierarchical spatiotemporal organization of cortical expansion characterized by coordinated regional growth trajectories. Rapid expansion occurs earlier around peri-central territories and progressively shifts toward frontal, parietal, and temporal association cortices. We encoded these imaging-derived trajectories into region- and age-specific growth laws and incorporated them into anatomically realistic whole-brain mechanical models. The resulting simulations recapitulate major sulcal landmarks and characteristic folding development across global and regional morphological measures. Comparing regional and uniform growth, we find that differential growth drives cortical instability, while heterogeneous developmental growth organizes this instability into anatomically recognizable folding patterns. Combined with subject-specific anatomy, these population-informed growth laws reproduce individualized longitudinal folding trajectories. Together, our results identify spatiotemporally patterned developmental growth as a key spatial constraint on cortical morphogenesis and establish a quantitative bridge from developmental imaging, through mechanical instability, to anatomically organized folding.

biophysics↗

bioRxiv2026

Is shorter faster? The geometry and aerodynamics of crank length in cycling

Crank length affects the geometry of the bicycle-rider system and influences the motion of the lower limb throughout the pedal cycle. Conventional crank selection has largely relied on rider stature and mechanical power, yet mechanical power and cycling economy remain remarkably insensitive to crank length across a broad range. Yet, elite cyclists and triathletes increasingly adopt shorter cranks, particularly in disciplines where aerodynamic drag strongly affects cycling performance. However, the mechanisms that connect crank length to mechanical power and aerodynamic drag remain unclear. Here we show that crank length couples rider anthropometry, hip kinematics, and aerodynamic drag. Population anthropometry of 6,068 individuals reveals that stature alone poorly predicts personalized crank length, while geometric analysis identifies a nearly linear relationship between crank length and minimum lower hip angle: a 10-mm reduction in crank length increases hip-angle margin by approximately 1.42 degrees with little mechanical penalty. This geometric freedom permits more flexion of the torso and a more aerodynamic posture; the resulting aerodynamic advantage grows rapidly with cycling speed because aerodynamic power scales with velocity cubed. Data from 150 elite cyclists and triathletes support this shift toward shorter cranks, with triathletes adopting shorter relative crank lengths than cyclists. Our results establish crank length as a personalized design variable that connects lower-limb geometry to aerodynamics. These insights enable personalized crank selection, integrated rider-bicycle design, and performance optimization for the increased aerodynamic demands of high-speed cycling.

bioengineering↗

bioRxiv2026

Probabilistic model discovery reveals distinct constitutive behavior of kidney cortex and medulla

The kidney is a critical soft-tissue organ responsible for blood filtration. Accurate constitutive models of the kidney are essential to predict tissue deformation and stress, yet existing models prescribe the strain energy function a priori, largely treat tissue variability as deterministic, and do not distinguish between cortex and medulla. Here we use Gaussian constitutive artificial neural networks to discover probabilistic strain energy functions for kidney cortex and medulla from tension, compression, and shear experiments. The medulla is approximately twice as stiff as the cortex across all three modes: The effective Young's moduli are 3.41, 5.04, and 4.48 kPa for the medulla and 1.43, 2.32, and 2.87 kPa for the cortex in tension, compression, and shear. Both regions display pronounced tension--compression asymmetry. When trained on all three modes, the network discovers strain energy functions in the second invariant alone over the tested deformation range. The cortex model selects two exponential I2 terms, while the medulla model selects an additional linear I2 term. Gaussian external weights propagate specimen-to-specimen variability into closed-form probabilistic stress predictions. Our region-specific probabilistic models capture the mechanical heterogeneity and variability of the kidney and enable more realistic simulations of kidney deformation and stress for surgical planning, needle interventions, and renal trauma.

bioengineering↗

bioRxiv2026

Open-source benchmarking of dairy and dairy-free products

Dairy-free foods must reproduce diverse functions of dairy, from foam stabilization in a latte to acid-gel structure in yogurt and melt-stretch behavior in mozzarella. Public consumer benchmarks rarely compare these categories under a common protocol. Here we analyze data from the NECTAR Taste of the Industry 2026 study, a blinded, randomized, in-person evaluation of commercial dairy-free products and dairy benchmarks. The full study included 2,183 consumers and 112 products across ten categories, with 3,220 product evaluations and 79,027 recorded survey responses, including 35,762 product-linked responses. The ten categories included barista milk, butter, cheddar, cream cheese, creamer, ice cream, fluid milk, mozzarella, sour cream, and Greek style yogurt. Within each category, we compare a dairy benchmark with the dairy-free product selected post hoc as the category leader. Participants rated overall liking, flavor, texture and mouthfeel, appearance, similarity to dairy, and purchase intent on seven-point scales and completed check-all-that-apply questions. In exploratory category-specific equivalence tests using a chosen margin of {+/-}0.05 points, barista milk, creamer, and fluid milk meet this margin. Mozzarella has the largest paired deficit of -1.46 points (unadjusted, p < 0.001), followed by Greek style yogurt and butter; all three remain significant after Holm correction across the ten primary comparisons. Descriptive penalty analysis associates off-flavor, artificial or chemical notes, stale or cardboard notes, bitter taste, and lingering aftertaste with the largest reductions in liking. Together, these results establish the first open sensory benchmark for dairy and dairy-free products, demonstrate that sensory parity is already achievable in several beverage categories, and identify the product categories and sensory attributes that offer the greatest opportunities for future innovation.

bioengineering↗

bioRxiv2026

Texture Profile Analysis and Consumer Sensory Evaluation of Plant-Based and Conventional Breaded Shrimp

Plant-based seafood alternatives could reduce pressure on marine ecosystems, yet texture gaps between plant-based and conventional products limit consumer adoption. This study compared the textural properties and consumer acceptance of plant-based and conventional breaded shrimp to evaluate whether instrumental texture differences predict sensory differences. Texture Profile Analysis (TPA) was performed on four breaded shrimp products (n = 14-15 replicates per product), and consumer sensory evaluation (n = 107) used a within-subjects counterbalanced design assessing hedonic ratings, Just-About-Right attributes, Check-All-That-Apply descriptors, and purchase intent. TPA revealed that plant-based shrimp exhibited significantly higher hardness (14.9 {+/-} 1.0 N vs. 9.6 {+/-} 2.8 N), stiffness (592 {+/-} 40 kPa vs. 382 {+/-} 113 kPa), and chewiness (10.5 {+/-} 0.8 N vs. 2.5 {+/-} 1.1 N) compared to conventional shrimp (all p < 0.0001). However, consumer sensory evaluation revealed no significant differences between products for any hedonic attribute, JAR rating, CATA descriptor, or purchase intent (all p > 0.05 after FDR correction). Despite 1.6-fold greater hardness and 4.2-fold greater chewiness, plant-based breaded shrimp received comparable hedonic ratings to conventional shrimp, suggesting that the breaded format masks mechanical texture differences. Achieving TPA parity may not be necessary for consumer acceptance in breaded seafood applications. HighlightsO_LIPlant-based shrimp is 1.6x harder and 4.2x chewier than conventional C_LIO_LIConsumers reported no significant sensory differences between products C_LIO_LIBreaded format masks instrumental texture differences C_LIO_LITPA parity may not be required for consumer acceptance C_LI

bioengineering↗

bioRxiv2026

Constitutive discovery in the living human heart

Constitutive models of myocardial mechanics form a cornerstone of personalized cardiac simulations and cardiac digital twins. Researchers traditionally prescribe these models a priori and calibrate them from ex vivo tissue experiments, even though tissue excision alters loading conditions, removes residual stresses, and eliminates important physiological interactions. Multimodal cardiac MRI now provides subject-specific ventricular geometry, deformation, and myocardial microstructure, yet current inverse approaches still rely on predefined constitutive laws. Here we present the first framework to discover constitutive models of passive myocardial mechanics directly from in vivo cardiac imaging data by embedding a constitutive artificial neural network within a nonlinear finite element model of ventricular filling. Using multimodal cardiac MRI that combines ventricular geometry, deformation, and microstructure from a representative healthy individual, the framework identifies sparse, mechanically admissible strain-energy functions without prescribing their form a priori. The best-performing model contains only two fiber- and two sheet-invariant terms, achieves a mean displacement error of 1.62 mm, and reduces the error of the widely used Guccione and Holzapfel models by 34.14% and 26.01%. The discovered models indicate that fiber- and sheet-related anisotropic mechanisms dominate the passive mechanical response during physiological ventricular filling. More broadly, this work establishes a non-invasive strategy for subject-specific constitutive discovery from cardiac imaging data and lays the foundation for personalized cardiac simulations and cardiac digital twins.

bioengineering↗

bioRxiv2026

The Burger Battle: The Future is Blended

Texture is one of the major barriers to acceptance of sustainable protein products, yet the relationship between instrumental texture and consumer perception remains poorly understood. Here we combined consumer sensory evaluation and texture profile analysis to identify the factors that drive burger acceptance across blended, animal-based, and plant-based formulations. A total of 472 diners evaluated beef-mushroom (n = 171), turkey (n = 100), bean (n = 100), and pea (n = 101) burgers served in Stanford University dining halls, while texture profile analysis quantified physical texture (n = 10 per product). The beef-mushroom burger achieved the highest ratings for meatiness (77.0/100), tastiness (69.4/100), and moistness (61.8/100), and 78% of consumers rated its moistness as just-about-right. Although the pea burger exhibited mechanical properties similar to the beef-mushroom burger, including indistinguishable cohesiveness values (0.53 vs. 0.53), it received substantially lower ratings for meatiness (56.1 vs. 77.0), indicating that mechanical similarity alone does not ensure consumer acceptance. Across products, moistness emerged as the primary sensory limitation, with 58% of turkey consumers and 46% of bean consumers reporting insufficient moisture. Cohesiveness showed the strongest association with perceived meatiness (r = 0.82). These findings demonstrate that successful burger reformulation requires more than mechanical matching; products must reproduce the moisture, flavor, and eating experience associated with meat. Blended burgers may represent the most practical near-term strategy for reducing meat consumption without compromising eating quality.

bioengineering↗

bioRxiv2026

Fiber dispersion in the right ventricle: A comparison of constitutive neural network predictions with experimental data

The mechanical behavior of right ventricular (RV) myocardium is governed by its anisotropic microstructure, yet constitutive models that account for fiber dispersion and enable reliable parameter identification remain limited. In this study, we propose a physics-embedded constitutive neural network framework for automated discovery of strain energy functions and microstructural parameters from experimental data. The model is formulated within an incompressible, orthotropic hyperelastic setting using invariant-based representations. Fiber, sheet, and normal directions are incorporated through a rotated structural basis, and dispersion effects are modeled using a generalized structure tensor approach. The framework is trained on multi-axial mechanical data from ovine RV myocardium, including uniaxial tension-compression and simple shear tests. We investigate two training scenarios: (i) full datasets containing both tensile and compressive regimes and (ii) datasets restricted to tensile loading. In both cases, the model accurately reproduces the measured stress-strain responses and identifies sparse, interpretable constitutive models which involve isotropic, anisotropic, and coupling invariants. However, the identifiability of microstructural parameters strongly depends on the available loading conditions. While tensile-only data yield higher predictive accuracy, they result in non-unique or biased estimates of fiber dispersion. In contrast, inclusion of compressive data enables consistent identification of dispersion parameters by separating fiber and matrix contributions. These results highlight the importance of multi-axial loading data for robust parameter identification and demonstrate the capability of constitutive neural network-based approaches for data-driven modeling of anisotropic soft tissues.

bioengineering↗

bioRxiv2026

The Meatball Matchup: Plant vs. Animal Proteins on Campus

Institutional dining halls increasingly serve plant-based meat products, yet it remains unclear how consumers perceive these products relative to animal-based options in real-world settings. This study compares consumer sensory perceptions of two plant-based meatballs (soy, soy-wheat) and two animal-based meatballs (beef, beef-mushroom) among university dining hall patrons (n = 116), complemented by instrumental Texture Profile Analysis. Animal-based meatballs received significantly higher ratings for moistness, meatiness, fattiness, and tastiness (all p < 0.001), with the meatiness gap being the largest ({Delta} = 1.40 on a 5-point scale). Texture analysis revealed that animal-based samples were significantly harder, more cohesive, and chewier than plant-based samples. In contrast, consumers perceived no differences in chewiness or hardness between categories, revealing a disconnect between instrumental and sensory measures. Just-About-Right penalty analysis identified insufficient savoriness as a universal improvement target across all products, including beef. Flavor and texture emerged as the dominant drivers of dining choice, while sustainability and animal welfare ranked lowest in importance. These findings suggest that sensory parity--particularly in moistness, meatiness, and savoriness--may drive acceptance of plant-based meat more strongly than sustainability messaging. Data and code are available at https://github.com/LivingMatterLab/AI4Food

bioengineering↗

bioRxiv2026

Mechanical, rheological, and sensory characterization of lion's mane mushroom steak

Mushrooms are increasingly recognized as delicious, nutritious, and sustainable foods, with an intrinsic umami flavor and fibrous microstructure that can approximate meat-like texture. Among them, lions mane mushroom has emerged as a promising candidate for whole-cut meat alternatives. Yet, its mechanical, rheological, and sensory properties remain largely unquantified. Here we show that a minimally processed lions mane mushroom steak exhibits distinctive mechanical, rheological, and sensory characteristics that position it favorably among existing meat alternatives. Despite its pronounced fibrous morphology, lions mane steak behaves predominantly as an isotropic material under both mechanical loading and rheological testing, with elastic stiffnesses of E = 33.2 kPa and E = 34.8 kPa in-plane and cross-plane. A fundamental challenge in alternative protein development is to understand how these measurable physical properties relate to human texture perception. In a complementary sensory survey, n = 21 participants ranked lions mane steak as more fatty, fibrous, moist, and meaty than eight animal- and plant-based comparison meats. Strikingly, our perceived sensory softness correlates inversely with our experimentally measured mechanical stiffness ({tau}=-0.60, p = 0.02) and rheological loss modulus ({tau}=-0.56, p = 0.03). Taken together, our results demonstrate that lions mane steak combines favorable mechanical performance with desirable sensory attributes and provide a mechanistic link between physics and taste. Our findings highlight lions mane mushroom as a compelling whole-cut alternative protein and underscore the value of integrated mechanical-sensory characterization for rational food design.

bioengineering↗

bioRxiv2025

The mechanics and physics of tofu: Understanding hydrated soft solids through feature networks

Tofu remains one of the worlds most important plant-based foods, famous for its cultural legacy, nutritional benefits, and environmental sustainability. Made up of only two ingredient-soy beans and water-it has nourished people for centuries; yet, its rheology remains incompletely understood. Here, we use tofu as a minimal model system to study how composition governs the mechanics and physics of hydrated soft solids. We perform over a hundred controlled compression tests on silken, firm, and extrafirm tofu, and observe a strong nonlinearity, a pronounced viscoelasticy, and a more than ten-fold increase in stress with only six percent decrease in water content. We demonstrate that automated model discovery can identify inelastic constitutive laws that accurately capture these unique characteristics across multiple loading magnitudes and rates. The discovered models autonomously separate elastic and inelastic mechanisms and reveal that tofu elasticity depends primarily on the second isochoric invariant, while tofu inelasticity depends on a combination of the second and third deviatoric invariants. This functional form is universal across all three tofu types, but scales in magnitude with water content. Our new water-content-based feature network reveals that this correlation in not linear, as assumed by traditional bi-phasic theories, but rather highly non-linear and sensitive to the individual model terms. These results position tofu as a quantitative benchmark for nonlinear poroviscoelastic solids and demonstrate how physics-informed machine learning can uncover the constitutive structure of hydrated soft materials. Our source code and examples are available at https://doi.org/10.5281/zenodo.16993236.

biophysics↗

bioRxiv2025

Stretching the Limits: From Planar-Biaxial Stress-Stretch to Arterial Pressure-Diameter

Understanding the physiological condition of the vascular system is critical to explain, treat, and manage vascular disease. Numerous experimental and computational studies characterize the mechanical behavior of arterial tissue under controlled laboratory conditions. However, translating this knowledge into physiologically realistic conditions remains challenging. Key difficulties include selecting suitable and relevant test methods, minimizing uncertainty, and ensuring robust model validation. We present a novel integrative approach to translate laboratory experiments on arterial samples into clinically relevant pressure-diameter behavior. We perform controlled planar-biaxial tests on carotid arteries under three stretch ratios and generate axial and circumferential stress-stretch data to calibrate a fiber-reinforced soft tissue model. Using an analytical thick-walled cylindrical model, we predict subject-specific pressure-diameter behavior, informed by arterial prestretches from ring opening experiments. We systematically compare predictions against extension-inflation experiments on tubes from the same artery by applying controlled pairs of axial stretch and inner pressure, while recording outer diameter. We quantify prediction error in absolute and relative stretch regimes and evaluate the importance of the load-free reference dimensions. Results show how planar-biaxial tests probe different stretch regimes compared to extension-inflation deformations, leading to extrapolation of model predictions. We demonstrate how the constitutive material parameters can be fitted to different biomechanical loading conditions and assess the sensitivity of the simulations to axial stretch and circumferential prestretch. Only when key model parameters are accurately captured and their uncertainty propagated, planar-biaxial stress-stretch data can reliably predict arterial pressure-diameter behavior.

bioengineering↗

bioRxiv2025

Discovering dispersion: How robust is automated model discovery for human myocardial tissue?

Computational modeling has become an integral tool for understanding the interaction between structural organization and functional behavior in a wide range of biological tissues, including the human myocardium. Traditional constitutive models, and recent models generated by automated model discovery, are often based on the simplifying assumption of perfectly aligned fiber families. However, experimental evidence suggests that many fibrous tissues exhibit local dispersion, which can significantly influence their mechanical behavior. Here, we integrate the generalized structure tensor (GST) approach into automated material model discovery to represent fibers that are distributed with rotational symmetry around three mean orthogonal directions--fiber, sheet, and normal--by using probabilistic descriptions of the orientation. Using biaxial extension and triaxial shear data from human myocardium, we systematically vary the degree of directional dispersion and stress measurement noise to explore the robustness of the discovered models. Our findings reveal that small dispersion in the fiber direction and arbitrary dispersion in the sheet and normal directions improve the goodness of fit and enable recovery of a previously proposed four-term model in terms of the isotropic second invariant, two dispersed anisotropic invariants and one coupling invariant. Our approach demonstrates strong robustness and consistently identifies similar model terms, even in the presence of up to 7% random noise in the stress data. In summary, our study suggests that automated model discovery based on the powerful generalized structure tensors is robust to noise and captures microstructural uncertainty and heterogeneity in a physiologically meaningful way.

bioengineering↗

bioRxiv2025

Probing mycelium mechanics and taste: The moist and fibrous signature of fungi steak

Fungi-based meat is emerging as a promising class of nutritious, sustainable, and minimally processed biomaterials with the potential to complement or replace traditional animal and plant-based meats. However, its mechanical and sensory properties remain largely unknown. Here we characterize the quasi-static and dynamic mechanical behavior of fungi-based steak using multi-axial mechanical testing, rheology, and texture profile analysis. We find that the rate-independent response under quasi-static compression and shear is isotropic, while the rate-dependent response under dynamic compression is markedly anisotropic with stiffnesses and peak forces four times larger cross-plane than in-plane. Automated model discovery reveals that the exponential Demiray model best explains the rate-independent elastic response upon chewing in both directions. The rate-dependent directional stiffening can be linked, at least in part, to the high water content of mushroom root mycelium and to the restricted fluid flow at higher loading rates. Complementary sensory surveys reveal a strong correlation with mechanical metrics and suggest that we perceive the fungi-based steak as more moist, more viscous, and more fibrous than traditional animal- and plant-based meats. Taken together, our findings position fungi-based steak as an attractive, structurally equivalent, and sensorially compelling alternative protein source that is healthy for people and for the planet.

bioengineering↗

bioRxiv2025

Biaxial testing and sensory texture evaluation of plant-based and animal deli meat

Animal agriculture is one of the largest contributors to global carbon emissions. Plant-based meats offer a sustainable alternative to animal meat; yet, people are reluctant to switch their diets and spending habits, in large due to the taste and texture of plant-based meats. Deli meat is a convenient form of protein commonly used in sandwiches, yet little is known about its material or sensory properties. Here we performed biaxial testing with multiple different stretch ratios of four plant-based and four animal deli meats and fit accurate material models to the resulting stress-stretch data. Strikingly, the plant-based products, turkey, ham, deli, and prosciutto, with stiffnesses of 378 {+/-} 15 kPa, 343 {+/-} 62 kPa, 213 {+/-} 25 kPa, and 113 {+/-} 56 kPa, were more than twice as stiff as their animal counterparts, turkey, chicken, ham, and prosciutto, with 134 {+/-} 46 kPa, 117 {+/-} 17 kPa, 117 {+/-} 21 kPa, and 49 {+/-} 21 kPa. In a complementary sensory texture survey, n = 18 participants were able to correlate the physical stiffness with the sensory brittleness, with Spearmans correlation coefficient of{rho} = 0.857 and p = 0.011, but not with the sensory softness or hardness. Notably, the participants perceived all four plant-based products as less fibrous, less moist, and less meaty than the four animal products. Our study confirms the common belief that plant-based products struggle to meet the physical and sensory signature of animal deli meats. We anticipate that integrating rigorous mechanical testing, physics-based modeling, and sensory texture surveys could shape the path towards designing delicious, nutritious, and environmentally friendly meats that mimic the texture and mouthfeel of animal products and are healthy for people and for the planet. Data and code are freely available at https://github.com/LivingMatterLab/CANN.

bioengineering↗

bioRxiv2024

Constitutive neural networks for main pulmonary arteries: Discovering the undiscovered

Accurate modeling of cardiovascular tissues is crucial for understanding and predicting their behavior in various physiological and pathological conditions. In this study, we specifically focus on the pulmonary artery in the context of the Ross procedure, using neural networks to discover the most suitable material model. The Ross procedure is a complex cardiac surgery where the patients own pulmonary valve is used to replace the diseased aortic valve. Ensuring the successful long-term outcomes of this intervention requires a detailed understanding of the mechanical properties of pulmonary tissue. Constitutive artificial neural networks offer a novel approach to capture such complex stressstrain relationships. Here we design and train different constitutive neural networks to characterize the hyperelastic, anisotropic behavior of the main pulmonary artery. Informed by experimental biaxial testing data under various axial-circumferential loading ratios, these networks automatically discover the inherent material behavior, without the limitations of predefined mathematical models. We regularize the model discovery using cross-sample feature selection and explore its sensitivity to the collagen fiber distribution. Strikingly, we uniformly discover an isotropic exponential first-invariant term and an anisotropic quadratic fifth-invariant term. We show that constitutive models with both these terms can reliably predict arterial responses under diverse loading conditions. Our results provide crucial improvements in experimental data agreement, and enhance our understanding into the biomechanical properties of pulmonary tissue. The model outcomes can be used in a variety of computational frameworks of autograft adaptation, ultimately improving the surgical outcomes after the Ross procedure.

bioengineering↗

bioRxiv2024

Ultra-fast physics-based modeling of the elephant trunk

With more than 90,000 muscle fascicles, the elephant trunk is a complex biological structure and the largest known muscular hydrostat. It achieves an unprecedented control through intricately orchestrated contractions of a wide variety of muscle architectures. Fascinated by the elephant trunks unique performance, scientists of all disciplines are studying its anatomy, function, and mechanics, and use it as an inspiration for biomimetic soft robots. Yet, to date, there is no precise mapping between microstructural muscular activity and macrostructural trunk motion, and our understanding of the elephant trunk remains incomplete. Specifically, no model of the elephant trunk employs formal physics-based arguments that account for its complex muscular architecture, while preserving low computational cost, to enable fast screening of its configuration space. Here we create a reduced-order model of the elephant trunk that can-within a fraction of a second- predict the trunks motion as a result of its muscular activity. To ensure reliable results in the finite deformation regime, we integrate first principles of continuum mechanics and the theory of morphoelasticity for fibrillar activation. We employ dimensional reduction to represent the trunk as an active slender structure, which results in closed-form expressions for its curvatures and extension as functions of muscle activation and anatomy. We create a high-resolution digital representation of the trunk from magnetic resonance images to quantify the effects of different muscle groups. We propose a general solution method for the inverse motion problem and apply it to extract the muscular activations of three representative trunk motions: picking a fruit; lifting a log; and lifting a log asymmetrically. For each task, we identify key features in the muscle activation profiles. Our results suggest that, upon reaching maximum contraction in select muscle groups, the elephant trunk autonomously reorganizes muscle activation to perform specific tasks. Our study provides a complete quantitative characterization of the fundamental science behind elephant trunk biomechanics, with potential applications in the material science of flexible structures, the design of soft robots, and the creation of flexible prosthesis and assist devices.

bioengineering↗

bioRxiv2024

Texture profile analysis and rheology of plant-based and animal meat

Plant-based meat can help combat climate change and health risks associated with high meat consumption. To create adequate mimics of animal meats, plant-based meats must match in mouthfeel, taste, and texture. The gold standard to characterize the texture of meat is the double compression test, but this test suffers from a lack of standardization and reporting inconsistencies. Here we characterize the texture of five plant-based and three animal meats using texture profile analysis and rheology, and report ten mechanical features associated with each products elasticity, viscosity, and loss of integrity. Our findings suggest that, of all ten features, the stiffness, storage, and loss moduli are the most meaningful and consistent parameter to report, while other parameters suffer from a lack of interpretability and inconsistent definitions. We find that the sample stiffness varies by an order of magnitude, from 418.9{+/-}41.7kPa for plant-based turkey to 56.7{+/-}14.1kPa for tofu. Similarly, the storage and loss moduli vary from 50.4{+/-}4.1kPa and 25.3{+/-}3.0kPa for plant-based turkey to 5.7{+/-}0.5kPa and 1.3{+/-}0.1kPa for tofu. All three animal products, animal turkey, sausage, and hotdog, consistently rank in between these two extremes. Our results suggest that-with the right ingredients, additives, and formulation-modern food fabrication techniques can create plant-based meats that successfully replicate the full viscoelastic texture spectrum of processed animal meat.

bioengineering↗