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#Forces

13 posts7 participants0 posts today
Public
Rxiv mechanobio

📰 "From Radiation Dose to Cellular Dynamics: A Discrete Model for Simulating Cancer Therapy"
arxiv.org/abs/2504.08499 #Physics.Bio-Ph #Physics.Med-Ph #Dynamics #Forces #Cell

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arXiv.orgFrom Radiation Dose to Cellular Dynamics: A Discrete Model for Simulating Cancer TherapyRadiation therapy is one of the most common cancer treatments, and dose optimization and targeting of radiation are crucial since both cancerous and healthy cells are affected. Different mathematical and computational approaches have been developed for this task. The most common mathematical approach, dating back to the late 1970's, is the linear-quadratic (LQ) model for the survival probability given the radiation dose. Most simulation models consider tissue as a continuum rather than consisting of discrete cells. While reasonable for large scale models, e.g., for human organs, any cellular scale effects become, by necessity, neglected. They do, however, influence growth, morphology, and metastasis of tumors. Here, we propose a method for modeling the effect of radiation on cells based on the mechanobiological \textsc{CellSim3D} simulation model for growth, division, and proliferation of cells. To model the effect of a radiation beam, we incorporate a Monte Carlo procedure into \textsc{CellSim3D} with the LQ model by introducing a survival probability at each beam delivery. Effective removal of dead cells by phagocytosis was also implemented. Systems with two types of cells were simulated: stiff slowly proliferating healthy cells and soft rapidly proliferating cancer cells. For model verification, the results were compared to prostate cancer (PC-3 cell line) data for different doses and we found good agreement. In addition, we simulated proliferating systems and analyzed the probability density of the contact forces. We determined the state of the system with respect to the jamming transition and found very good agreement with experiments.
Public
Rxiv mechanobio

📰 "Implicit Incompressible Porous Flow using SPH"
arxiv.org/abs/2504.07739 #Physics.Flu-Dyn #Adhesion #Forces #Cs.Gr

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arXiv.orgImplicit Incompressible Porous Flow using SPHWe present a novel implicit porous flow solver using SPH, which maintains fluid incompressibility and is able to model a wide range of scenarios, driven by strongly coupled solid-fluid interaction forces. Many previous SPH porous flow methods reduce particle volumes as they transition across the solid-fluid interface, resulting in significant stability issues. We instead allow fluid and solid to overlap by deriving a new density estimation. This further allows us to extend modern SPH pressure solvers to take local porosity into account and results in strict enforcement of incompressibility. As a result, we can simulate porous flow using physically consistent pressure forces between fluid and solid. In contrast to previous SPH porous flow methods, which use explicit forces for internal fluid flow, we employ implicit non-pressure forces. These we solve as a linear system and strongly couple with fluid viscosity and solid elasticity. We capture the most common effects observed in porous flow, namely drag, buoyancy and capillary action due to adhesion. To achieve elastic behavior change based on local fluid saturation, such as bloating or softening, we propose an extension to the elasticity model. We demonstrate the efficacy of our model with various simulations that showcase the different aspects of porous flow behavior. To summarize, our system of strongly coupled non-pressure forces and enforced incompressibility across overlapping phases allows us to naturally model and stably simulate complex porous interactions.
Public
Rxiv mechanobio

📰 "Mechanical Stress dissipation in locally folded epithelia is orchestrated by calcium waves and nuclear tension changes"
biorxiv.org/content/10.1101/20 #Mechanical #Forces #Cell

bioRxiv · Mechanical Stress dissipation in locally folded epithelia is orchestrated by calcium waves and nuclear tension changesEpithelia are continuously exposed to a range of biomechanical forces such as compression, stretch and shear stress arising from their dynamic microenvironments and associated to their function. Changes in tension such as stretch are known to trigger cell rearrangements and divisions, and impact cellular transcription until mechanical stress is dissipated. How cells process, adapt and respond to mechanical stress is being intensively investigated. In here we focus on epithelial folding which is the fundamental process of transformation of flat monolayers into 3D functional tissues. By combining the innovative method for fold generation, live imaging, mechanobiology tools and chemical screening, we uncover the role of calcium waves on mechanical adaptation of folded epithelia that occurs at the tissue and nuclear level. Folding associated tensional load results in the nuclear flattening which is recovered in the time scale of minutes and is dependent on the calcium wave that spread outwards from the channel and across the epithelium. By creating a mutant overexpressing LBR that relaxed nuclear envelope, we demonstrated that despite presence of calcium waves, nuclear tension increase was essential to trigger nuclear shape recovery post folding through the activation of cellular contractility in the cPLA2 dependent manner. Overall our results identify the molecular mechanism for nuclear shape recovery and indicate that mechanical stress dissipation program is activated at the level of nuclei which serve as internal tension sensors. ### Competing Interest Statement The authors have declared no competing interest.
Public
Rxiv mechanobio

📰 "The Granule-In-Cell Method for Simulating Sand--Water Mixtures"
arxiv.org/abs/2504.00745 #Physics.Flu-Dyn #Forces #Cs.Gr #Cell

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arXiv.orgThe Granule-In-Cell Method for Simulating Sand--Water MixturesThe simulation of sand--water mixtures requires capturing the stochastic behavior of individual sand particles within a uniform, continuous fluid medium, such as the characteristic of migration, deposition, and plugging across various scenarios. In this paper, we introduce a Granule-in-Cell (GIC) method for simulating such sand--water interaction. We leverage the Discrete Element Method (DEM) to capture the fine-scale details of individual granules and the Particle-in-Cell (PIC) method for its continuous spatial representation and particle-based structure for density projection. To combine these two frameworks, we treat granules as macroscopic transport flow rather than solid boundaries for the fluid. This bidirectional coupling allows our model to accommodate a range of interphase forces with different discretization schemes, resulting in a more realistic simulation with fully respect to the mass conservation equation. Experimental results demonstrate the effectiveness of our method in simulating complex sand--water interactions, while maintaining volume consistency. Notably, in the dam-breaking experiment, our simulation uniquely captures the distinct physical properties of sand under varying infiltration degree within a single scenario. Our work advances the state of the art in granule--fluid simulation, offering a unified framework that bridges mesoscopic and macroscopic dynamics.
Public
Rxiv mechanobio

📰 "A Deep Learning Framework for the Electronic Structure of Water: Towards a Universal Model"
arxiv.org/abs/2503.24050 #Physics.Atm-Clus #Physics.Comp-Ph #Physics.Chem-Ph #Matrix #Forces

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arXiv.orgA Deep Learning Framework for the Electronic Structure of Water: Towards a Universal ModelAccurately modeling the electronic structure of water across scales, from individual molecules to bulk liquid, remains a grand challenge. Traditional computational methods face a critical trade-off between computational cost and efficiency.We present an enhanced machine-learning Deep Kohn-Sham (DeePKS) method for improved electronic structure, DeePKS-ES, that overcomes this dilemma. By incorporating the Hamiltonian matrix and their eigenvalues and eigenvectors into the loss function, we establish a universal model for water systems, which can reproduce high-level hybrid functional (HSE06) electronic properties from inexpensive generalized gradient approximation (PBE) calculations. Validated across molecular clusters and liquid-phase simulations, our approach reliably predicts key electronic structure properties such as band gaps and density of states, as well as total energy and atomic forces. This work bridges quantum-mechanical precision with scalable computation, offering transformative opportunities for modeling aqueous systems in catalysis, climate science, and energy storage.
Public
Nicomo

#HX-2 #AI-guided strike #drone set to #target #Russian #forces in #Ukraine

The new HX-2 strike drone could soon hurt Russian forces in Ukraine.

Using artificial intelligence, these new German-made weapon systems can strike targets 62 miles away.

More than 6,000 of these one-way uncrewed drones are about to be gifted to Ukraine and will be used against Russian artillery, armour and other military targets.

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