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Abstract Structural and mechanical cues from the extracellular matrix (ECM) regulate tissue morphogenesis. Tissue development has conventionally been studied withex vivosystems where mechanical properties of the extracellular environment are either poorly controlled in space and time, lack tunability, or do not mimic ECM mechanics. For these reasons, it remains unknown how matrix stress relaxation rate, a time-dependent mechanical property that influences several cellular processes, regulates mammary branching morphogenesis. Here, we systematically investigated the influence of matrix stress relaxation on mammary branching morphogenesis using 3D alginate-collagen matrices and spheroids of human mammary epithelial cells. Slow stress relaxing matrices promoted significantly greater branch formation compared to fast stress relaxing matrices. Branching in slow stress relaxing matrices was accompanied by local collagen fiber alignment, while collagen fibers remained randomly oriented in fast stress relaxing matrices. In slow stress relaxing matrices, branch formation was driven by intermittent pulling contractions applied to the local ECM at the tips of elongating branches, which was accompanied by an abundance of phosphorylated focal adhesion kinase (phospho-FAK) and β1 integrin at the tips of branches. On the contrary, we observed that growing spheroids in fast stress relaxing matrices applied isotropic pushing forces to the ECM. Pharmacological inhibition of both Rac1 and non-muscle myosin II prevented epithelial branch formation, regardless of matrix stress relaxation rate. Interestingly, restricting cellular expansion via increased osmotic pressure was sufficient to impede epithelial branching in slow stress relaxing matrices. This work highlights the importance of stress relaxation in regulating and directing mammary branch elongation. 

Abstract Reconstituted basement membrane (rBM) products like Matrigel are widely used in 3D culture models of epithelial tissues and cancer. However, their utility is hindered by key limitations, including batch variability, xenogenic contaminants, and a lack of tunability. To address these challenges, we engineered a 3D basement membrane (eBM) matrix by conjugating defined extracellular matrix (ECM) adhesion peptides (IKVAV, YIGSR, RGD) to an alginate hydrogel network with precisely tunable stiffness and viscoelasticity. We optimized the mechanical and biochemical properties of the engineered basement membranes (eBMs) to support mammary acinar morphogenesis in MCF10A cells, similar to rBM. We found that IKVAV-modified, fast-relaxing (τ_1/2= 30-150 s), and soft (E = 200 Pa) eBMs best promoted polarized acinar structures. Clusters became invasive and lost polarity only when the IKVAV-modified eBM exhibited both similar stiffness to a malignant breast tumor (E = 4000 Pa) and slow stress relaxation (τ_1/2= 600-1100 s). Notably, tumor-like stiffness alone was not sufficient to drive invasion in fast stress relaxing matrices modified with IKVAV. In contrast, RGD-modified matrices promoted a malignant phenotype regardless of mechanical properties. We also utilized this system to interrogate the mechanism driving acinar and tumorigenic phenotypes in response to microenvironmental parameters. A balance in activity between β1- and β4-integrins was observed in the context of IKVAV-modified eBMs, prompting further investigation into the downstream mechanisms. We found differences in hemidesmosome formation and production of endogenous laminin in response to peptide type, stress relaxation, and stiffness. We also saw that inhibiting either focal adhesion kinase or hemidesmosome signaling in IKVAV eBMs prevented acinus formation. This eBM matrix is a powerful, reductionist, xenogenic-free system, offering a robust platform for both fundamental research and translational applications in tissue engineering and disease modeling. 

Abstract Breast cancer progression is marked by extracellular matrix (ECM) remodeling, including increased stiffness, faster stress relaxation, and elevated collagen levels. In vitro experiments have revealed a role for each of these factors to individually promote malignant behavior, but their combined effects remain unclear. To address this, we developed alginate-collagen hydrogels with independently tunable stiffness, stress relaxation, and collagen density. We show that these combined tumor-mimicking ECM cues reinforced invasive morphologies and promoted spheroid invasion in breast cancer and mammary epithelial cells. High stiffness and low collagen density in slow-relaxing matrices led to the greatest cell migration speed and displacement. RNA-seq revealed Sp1 target gene enrichment in response to both individual and combined ECM cues, with a greater enrichment observed under multiple cues. Notably, high expression of Sp1 target genes upregulated by fast stress relaxation correlated with poor patient survival. Mechanistically, we found that phosphorylated-Sp1 (T453) was increasingly located in the nucleus in stiff and/or fast relaxing matrices, which was regulated by PI3K and ERK1/2 signaling, as well as actomyosin contractility. This study emphasizes how multiple ECM cues in complex microenvironments reinforce malignant traits and supports an emerging role for Sp1 as a mechanoresponsive transcription factor. 

Just-in-time (JIT) compilers make JavaScript run efficiently by replacing slow JavaScript interpreter code with fast machine code. However, this efficiency comes at a cost: bugs in JIT compilers can completely subvert all language-based (memory) safety guarantees, and thereby introduce catastrophic exploitable vulnerabilities. We present Icarus: a new framework for implementing JIT compilers that are automatically, formally verified to be safe, and which can then be converted to C++ that can be linked into browser runtimes. Crucially, we show how to build a JIT with Icarus such that verifying the JIT implementation statically ensures the security of all possible programs that the JIT could ever generate at run-time, via a novel technique called symbolic meta-execution that encodes the behaviors of all possible JIT-generated programs as a single Boogie meta-program which can be efficiently verified by SMT solvers. We evaluate Icarus by using it to re-implement components of Firefox's JavaScript JIT. We show that Icarus can scale up to expressing complex JITs, quickly detects real-world JIT bugs and verifies fixed versions, and yields C++ code that is as fast as hand-written code. 

Upon osmotic compression, rotationally symmetric faceted colloidal particles can form translationally ordered, orientationally disordered rotator mesophases. This study explores the mechanism of rotator-to-crystal phase transitions where orientational order is gained in a translationally ordered phase, using rotator-phase forming truncated cubes as a testbed. Monte Carlo simulations were conducted for two selected truncations (s), one for s = 0.527 where the rotator and crystal lattices are dissimilar and one for s = 0.572 where the two phases have identical lattices. These differences set the stage for a qualitative difference in their rotator–crystal transitions, highlighting the effect of lattice distortion on phase transition kinetics. Our simulations reveal that significant lattice deviatoric effects could hinder the rotator-to-crystal transition and favor arrangements of lower packing fraction instead. Indeed, upon compression, it is found that for s = 0.527, the rotator phase does not spontaneously transition into the stable, densely packed crystal due to the high lattice strains involved but instead transitions into a metastable solid phase to be colloquially referred to as “orientational salt” for short, which has a similar lattice as the rotator phase and exhibits two distinct particle orientations having substitutional order, alternating regularly throughout the system. This study paves the way for further analysis of diffusionless transformations in nanoparticle systems and how lattice-distortion could influence crystallization kinetics. 

Abstract Auxetic materials have a negative Poisson’s ratio and are of significant interest in applications that include impact mitigation, membrane separations and biomedical engineering. While there are numerous examples of structured materials that exhibit auxetic behavior, the examples of engineered auxetic structures is largely limited to periodic lattice structures that are limited to directional or anisotropic auxetic response. Structures that exhibit a three-dimensionally isotropic auxetic response have been, unfortunately, slow to evolve. Here we introduce an inverse design algorithm based on global node optimization to design three-dimensional auxetic metamaterial structures from disordered networks. After specifying the target Poisson’s ratio for a structure, an inverse design algorithm is used to adjust the positions of all nodes in a disordered network structure until the desired mechanical response is achieved. The proposed algorithm allows independent control of shear and bulk moduli, while preserving the density and connectivity of the networks. When the angle bending stiffness in the network is kept low, it is possible to realize optimized structures with a Poisson’s ratios as low as −0.6. During the optimization, the bulk modulus of these networks decreases by almost two orders of magnitude, but the shear modulus remains largely unaltered. The materials designed in this manner are fabricated by dual-material 3D-printing, and are found to exhibit the mechanical responses that were originally encoded in the computational design engine. The approach proposed here provides a materials-by-design platform that could be extended for engineering of optical, acoustic, and electrical properties, beyond the design of auxetic metamaterials. 

Rich variations in gait are generated according to several attributes of the individual and environment, such as age, athleticism, terrain, speed, personal “style”, mood, etc. The effects of these attributes can be hard to quantify explicitly, but relatively straightforward to sample. We seek to generate gait that expresses these attributes, creating synthetic gait samples that exemplify a custom mix of attributes. This is difficult to perform manually, and generally restricted to simple, human-interpretable and handcrafted rules. In this manuscript, we present neural network architectures to learn representations of hard to quantify attributes from data, and generate gait trajectories by composing multiple desirable attributes. We demonstrate this method for the two most commonly desired attribute classes: individual style and walking speed. We show that two methods, cost function design and latent space regularization, can be used individually or combined. We also show two uses of machine learning classifiers that recognize individuals and speeds. Firstly, they can be used as quantitative measures of success; if a synthetic gait fools a classifier, then it is considered to be a good example of that class. Secondly, we show that classifiers can be used in the latent space regularizations and cost functions to improve training beyond a typical squared-error cost.