AI-dente: an open machine learning based tool to interpret nano-indentation data of soft tissues and materials.

Nano-indentation is a promising method to identify the constitutive parameters of soft materials, including soft tissues. Especially when materials are very small and heterogeneous, nano-indentation allows mechanical interrogation where traditional methods may fail. However, because nano-indentation does not yield a homogeneous deformation field, interpreting the resulting load-displacement curves is non-trivial and most investigators resort to simplified approaches based on the Hertzian solution. Unfortunately, for small samples and large indentation depths, these solutions are inaccurate. We set out to use machine learning to provide an alternative strategy. We first used the finite element method to create a large synthetic data set. We then used these data to train neural networks to inversely identify material parameters from load-displacement curves. To this end, we took two different approaches. First, we learned the indentation forward problem, which we then applied within an iterative framework to identify material parameters. Second, we learned the inverse problem of directly identifying material parameters. We show that both approaches are effective at identifying the parameters of the neo-Hookean and Gent models. Specifically, when applied to synthetic data, our approaches are accurate even for small sample sizes and at deep indentation. Additionally, our approaches are fast, especially compared to the inverse finite element approach. Finally, our approaches worked on unseen experimental data from thin mouse brain samples. Here, our approaches proved robust to experimental noise across over 1000 samples. By providing open access to our data and code, we hope to support others that conduct nano-indentation on soft materials.

Understanding the mechanical link between oriented cell division and cerebellar morphogenesis.

The cerebellum is a tightly folded structure located at the back of the head. Unlike the folds of the cerebrum, the folds of the cerebellum are aligned such that the external surface appears to be covered in parallel grooves. Experiments have shown that anchoring center initiation drives cerebellar foliation. However, the mechanism guiding the location of these anchoring centers, and subsequently cerebellar morphology, remains poorly understood. In particular, there is no definitive mechanistic explanation for the preferential emergence of parallel folds instead of an irregular folding pattern like in the cerebral cortex. Here we use mechanical modeling on the cellular and tissue scales to show that the oriented granule cell division observed in the experimental setting leads to the characteristic parallel folding pattern of the cerebellum. Specifically, we propose an agent-based model of cell clones, a strategy for propagating information from our in silico cell clones to the tissue scale, and an analytical solution backed by numerical results to understand how differential growth between the cerebellar layers drives geometric instability in three dimensional space on the tissue scale. This proposed mechanical model provides further insight into the process of anchoring center initiation and establishes a framework for future multiscale mechanical analysis of developing organs.

A multiphysics model to predict periventricular white matter hyperintensity growth during healthy brain aging.

Periventricular white matter hyperintensities (WMH) are a common finding in medical images of the aging brain and are associated with white matter damage resulting from cerebral small vessel disease, white matter inflammation, and a degeneration of the lateral ventricular wall. Despite extensive work, the etiology of periventricular WMHs remains unclear. We pose that there is a strong coupling between age-related ventricular expansion and the degeneration of the ventricular wall which leads to a dysregulated fluid exchange across this brain-fluid barrier. Here, we present a multiphysics model that couples cerebral atrophy-driven ventricular wall loading with periventricular WMH formation and progression. We use patient data to create eight 2D finite element models and demonstrate the predictive capabilities of our damage model. Our simulations show that we accurately capture the spatiotemporal features of periventricular WMH growth. For one, we observe that damage appears first in both the anterior and posterior horns and then spreads into deeper white matter tissue. For the other, we note that it takes up to 12 years before periventricular WMHs first appear and derive an average annualized periventricular WMH damage growth rate of 15.2 ± 12.7 mm2/year across our models. A sensitivity analysis demonstrated that our model parameters provide sufficient sensitivity to rationalize subject-specific differences with respect to onset time and damage growth. Moreover, we show that the septum pellucidum, a membrane that separates the left and right lateral ventricles, delays the onset of periventricular WMHs at first, but leads to a higher WMH load in the long-term.

Bacterial mechanosensing of surface stiffness promotes signaling and growth leading to biofilm formation by Pseudomonas aeruginosa.

The attachment of bacteria onto a surface, consequent signaling, and the accumulation and growth of the surface-bound bacterial population are key initial steps in the formation of pathogenic biofilms. While recent reports have hinted that the stiffness of a surface may affect the accumulation of bacteria on that surface, the processes that underlie bacterial perception of and response to surface stiffness are unknown. Furthermore, whether, and how, the surface stiffness impacts biofilm development, after initial accumulation, is not known. We use thin and thick hydrogels to create stiff and soft composite materials, respectively, with the same surface chemistry. Using quantitative microscopy, we find that the accumulation, motility, and growth of the opportunistic human pathogen Pseudomonas aeruginosa respond to surface stiffness, and that these are linked through cyclic-di-GMP signaling that depends on surface stiffness. The mechanical cue stemming from surface stiffness is elucidated using finite-element modeling combined with experiments - adhesion to stiffer surfaces results in greater changes in mechanical stress and strain in the bacterial envelope than does adhesion to softer surfaces with identical surface chemistry. The cell-surface-exposed protein PilY1 acts as a mechanosensor, that upon surface engagement, results in higher cyclic-di-GMP levels, lower motility, and greater accumulation on stiffer surfaces. PilY1 impacts the biofilm lag phase, which is extended for bacteria attaching to stiffer surfaces. This study shows clear evidence that bacteria actively respond to different stiffness of surfaces where they adhere via perceiving varied mechanical stress and strain upon surface engagement.

The accumulation and growth of Pseudomonas aeruginosa on surfaces is modulated by surface mechanics via cyclic-di-GMP signaling.

Attachment of bacteria onto a surface, consequent signaling, and accumulation and growth of the surface-bound bacterial population are key initial steps in the formation of pathogenic biofilms. While recent reports have hinted that surface mechanics may affect the accumulation of bacteria on that surface, the processes that underlie bacterial perception of surface mechanics and modulation of accumulation in response to surface mechanics remain largely unknown. We use thin and thick hydrogels coated on glass to create composite materials with different mechanics (higher elasticity for thin composites; lower elasticity for thick composites) but with the same surface adhesivity and chemistry. The mechanical cue stemming from surface mechanics is elucidated using experiments with the opportunistic human pathogen Pseudomonas aeruginosa combined with finite-element modeling. Adhesion to thin composites results in greater changes in mechanical stress and strain in the bacterial envelope than does adhesion to thick composites with identical surface chemistry. Using quantitative microscopy, we find that adhesion to thin composites also results in higher cyclic-di-GMP levels, which in turn result in lower motility and less detachment, and thus greater accumulation of bacteria on the surface than does adhesion to thick composites. Mechanics-dependent c-di-GMP production is mediated by the cell-surface-exposed protein PilY1. The biofilm lag phase, which is longer for bacterial populations on thin composites than on thick composites, is also mediated by PilY1. This study shows clear evidence that bacteria actively regulate differential accumulation on surfaces of different stiffnesses via perceiving varied mechanical stress and strain upon surface engagement.

Synthetic hydrogels as blood clot mimicking wound healing materials.

Excessive bleeding-or hemorrhage-causes millions of civilian and non-civilian casualties every year. Additionally, wound sequelae, such as infections, are a significant source of chronic morbidity, even if the initial bleeding is successfully stopped. To treat acute and chronic wounds, numerous wound healing materials have been identified, tested, and adopted. Among them are topical dressings, such as gauzes, as well as natural and biomimetic materials. However, none of these materials successfully mimic the complex and dynamic properties of the body's own wound healing material: the blood clot. Specifically, blood clots exhibit complex mechanical and biochemical properties that vary across spatial and temporal scales to guide the wound healing response, which make them the ideal wound healing material. In this manuscript, we review blood clots' complex mechanical and biochemical properties, review current wound healing materials, and identify opportunities where new materials can provide additional functionality, with a specific focus on hydrogels. We highlight recent developments in synthetic hydrogels that make them capable of mimicking a larger subset of blood clot features: as plugs and as stimuli for tissue repair. We conclude that future hydrogel materials designed to mimic blood clot biochemistry, mechanics, and architecture can be combined with exciting platelet-like particles to serve as hemostats that also promote the biological wound healing response. Thus, we believe synthetic hydrogels are ideal candidates to address the clear need for better wound healing materials.

Hyper-viscoelastic damage modeling of whole blood clot under large deformation.

Blood clots play a diametric role in our bodies as they are both vital as a wound sealant, as well as the source for many devastating diseases. In blood clots' physiological and pathological roles, their mechanics play a critical part. These mechanics are non-trivial owing to blood clots' complex nonlinear, viscoelastic behavior. Casting this behavior into mathematical form is a fundamental step toward a better basic scientific understanding of blood clots, as well as toward diagnostic and prognostic computational models. Here, we identify a hyper-viscoelastic damage model that we fit to original data on the nonlinear, viscoelastic behavior of blood clots. Our model combines the classic Ogden hyperelastic constitutive law, a finite viscoelastic model for large deformations, and a non-local, gradient-enhanced damage formulation. By fitting our model to cyclic tensile test data and extension-to-failure data, we inform the model's nine unknown material parameters. We demonstrate the predictability of our model by validating it against unseen cyclic tensile test and stress-relaxation data. Our original data, model formulation, and the identified constitutive parameters of this model are openly available for others to use, which will aid in developing accurate, quantitative simulations of blood clot mechanics.