Search bioRxiv⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.64898/2026.06.11.731620

Characterization of time-dependent and history-dependent mechanical behaviour of human masseter muscle

Awad, E.·Briot, N.·Chagnon, G.·Challita, R.·De Bengy-Puyvallee, L.·Peric, D.·Hossain, M.
View original source ↗Export metadata ↓

Abstract

The human masseter muscle is one of the primary muscles responsible for mastication and mandibular movement; however, its intrinsic mechanical properties remain insufficiently characterized. In this experimental study, the nonlinear, viscoelastic, and history-dependent behaviour of the human masseter muscle was investigated using ex vivo uniaxial cyclic tensile tests. The masseter muscle samples prepared from fresh and formalin-preserved cadavers were tested under two loading protocols: a continuous stretch protocol with increasing stretch levels and a constant stretch protocol with repeated loading to a fixed maximum stretch. Tests were conducted at two strain rates, and their influences on the mechanical behaviour of the tissue were examined. The effect of formalin preservation was also investigated. The results showed that the stiffness of the tissue increases for formalin-preserved samples. Under cyclic loading, the features including energy dissipation, stress-softening, residual deformation, and cyclic conditioning progressively changed during the initial loading cycles and reached stabilization during the final cycle. These findings provide experimental evidence that the human masseter muscle exhibits nonlinear, viscoelastic, and history-dependent mechanical behaviour under cyclic tensile loading. The experimental data obtained in this study may be used for biomechanical modelling of the human masticatory system and the development of constitutive models for cranio-maxillofacial surgical simulation, prosthetic design, and facial soft-tissue biomechanics. Statement of significanceThe masseter muscle is one of the primary muscles of mastication. To address the current gap in craniofacial biomechanics that has largely focused on the mechanical characterization of the masseter muscle based on imaging techniques or monotonic loading, this study quantifies the nonlinear and viscoelastic mechanical response of masseter tissue under cyclic continuous and constant stretch loading, including strain-rate and preservation effects. The results show that the mechanical behaviour of the masseter muscle, including stiffness, hysteresis, stress-softening, and residual strain behaviour, is strongly influenced by strain-rate and formalin preservation. The experimental results provide mechanical data for constitutive modelling of the masticatory system with applications in cranio-maxillofacial surgical simulation, prosthetic design, and facial soft tissue modelling.

Explore related subjects

bioengineeringPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Awad, E., Briot, N., Chagnon, G., Challita, R., De Bengy-Puyvallee, L., Peric, D., Hossain, M.. 2026-06-16. Characterization of time-dependent and history-dependent mechanical behaviour of human masseter muscle. https://doi.org/10.64898/2026.06.11.731620

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

RNASeek: A Cross-Phyla Generative Foundation Model for Multipurpose RNA Modeling and Reinforcement Learning-Based Design

RNA plays central roles in regulating information flow and provides a versatile substrate for engineering biological functions. While large language models (LLMs) have transformed natural language processing and protein design, a general framework connecting RNA foundation models to functional sequence design remains limited. Here, we present RNASeek, a 1.6-billion-parameter generative foundation model built on a DeepSeek architecture and trained on a cross-phyla transcriptomic corpus for RNA sequence representation and generation. Natural-language tokens enable flexible conditional prediction and sequence design using a unified backbone. RNASeek captures species-specific transcript features and intron-exon boundaries in a zero-shot setting. We then fine-tune RNASeek to predict ribozyme self-cleavage activity and viral mRNA stability, revealing interpretable sequence features associated with function, including ribozyme loop flexibility and stem stability, as well as AU-rich motifs associated with mRNA stability. We use these functional predictors as reward models and apply Group Relative Policy Optimization (GRPO) to update the generation policy of RNASeek toward sequences with desired properties. GRPO-guided generation produces faster-cleaving ribozymes and stability-enhancing 3' UTRs while satisfying user-specified IUPAC constraints. Experimentally validated RNASeek-generated ribozymes achieve wild-type levels of activity, while RNASeek-generated 3' UTR sequences exceed the performance of the training data and benchmarked AI-generated 3' UTRs. Together, RNASeek establishes a unified pretrain-predict-optimize framework that connects learned RNA function to controllable de novo sequence design and provides a general strategy for engineering regulatory RNAs with desired properties.

bioengineering↗
bioRxiv2026

Joint Vector Flow Mapping and Segmentation: Ill-Posedness,Differentiable Bayesian Inference, and Synthetic Vortex-FlowBenchmarks

Vector flow mapping (VFM) reconstructs left-ventricular (LV) blood velocity from color-Doppler echocardiography by combining the measured beamwise component with physical and regularizing constraints. Analysis of the discrete VFM formulation shows that the inverse problem is intrinsically ill posed: the occurrence of singular modes can be predicted from the geometry of the segmented blood-pool domain, the imposed boundary conditions, and the degree of smoothing. These modes can propagate uncertainty along entire transverse bands of the reconstructed velocity field, yet conventional VFM neither quantifies this uncertainty nor allows for correcting the blood-pool segmentation. We introduce Bayesian VFM (B--VFM), a hierarchical framework that jointly infers radial and transverse velocities, a probabilistic blood-pool mask, their spatially resolved uncertainties, and hyperparameters weighting Doppler and segmentation fidelity, mass conservation, boundary conditions, and smoothness. The discretized posterior admits a closed-form gradient and exact Hessian, enabling computationally efficient, gradient-based MAP estimation, sampling, and direct analysis of ill-posed modes. Posterior inference combines Gibbs sampling of conjugate Gamma-distributed hyperparameters with conditional maximum-a-posteriori estimation and a Laplace approximation for the high-dimensional velocity and mask fields. To accommodate systematic departures from planar mass conservation, B-VFM can learn the covariance of the planar divergence residual from an ensemble of flows and incorporate it as a structured model-discrepancy prior. Independent chains converged reproducibly, while covariance priors learned from flow ensembles illustrated how model discrepancies can be incorporated into the inference. B--VFM was evaluated using Lamb-Chaplygin dipoles under ideal conditions and with Doppler corruption, Doppler voids, and segmentation defects, and using the Hicks-Moffatt family of spherical vortices to assess violations of planar mass conservation. The method produced smooth reconstructions, localized uncertainty near unreliable measurements and regions of model inconsistency, and used flow information to correct segmentation errors. Within the tested vortex family, the data-informed planar divergence prior reduced velocity bias and mask distortion. B--VFM thus provides an uncertainty-aware reconstruction method and a flexible foundation for future VFM formulations incorporating additional priors, observations, and physical models. Future work will evaluate the method using clinical data and more complex three-dimensional benchmark flows.

bioengineering↗
bioRxiv2026

Computational design of a versatile, zero-radius proximity labeling enzyme

The ability to map protein interactomes and organelle proteomes is foundational for achieving a molecular understanding of living cells. Proximity labeling (PL) provides a powerful strategy for this, but existing enzymes and photocatalysts are limited by their spatial resolution, reliance on biotin, and/or in vivo compatibility. Here we report FlexID, an engineered promiscuous ligase that catalyzes the rapid attachment of diverse small-molecule probes to proximal endogenous proteins. Critically, FlexID operates through a zero-radius, direct-contact mechanism, offering superior spatial precision compared to existing PL tools. We engineered FlexID by combining the strengths of sequence- and structure-trained computational models to enhance its catalytic activity and structural stability. Biophysical analysis revealed that specific conformational changes in FlexID improve its ability to recognize diverse target proteins while simultaneously preventing the premature release of the reactive intermediate. We demonstrate FlexID's versatility through in vivo proximity labeling, comprehensive organelle proteome mapping, and a high-throughput, fluorescence-based screen for molecular glues. Our work shows that computational methods can be harnessed to create mechanistically distinct PL enzymes and establishes FlexID as a flexible, high-resolution tool for mapping protein interactions and proteomes in living cells.

bioengineering↗