The volume of healthy red blood cells is optimal for advective oxygen transport in arterioles.

Red blood cells (RBCs) are vital for transporting oxygen from the lungs to the body's tissues through the intricate circulatory system. They achieve this by binding and releasing oxygen molecules to the abundant hemoglobin within their cytosol. The volume of RBCs affects the amount of oxygen they can carry, yet whether this volume is optimal for transporting oxygen through the circulatory system remains an open question. This study explores, through high-fidelity numerical simulations, the impact of RBC volume on advective oxygen transport efficiency through arterioles, which form the area of greatest flow resistance in the circulatory system. The results show that, strikingly, RBCs with volumes similar to those found in vivo are most efficient to transport oxygen through arterioles. The flow resistance is related to the cell-free layer thickness, which is influenced by the shape and the motion of the RBCs: at low volumes, RBCs deform and fold, while at high volumes, RBCs collide and follow more diffuse trajectories. In contrast, RBCs with a healthy volume maximize the cell-free layer thickness, resulting in a more efficient advective transport of oxygen.

The stress-free state of human erythrocytes: Data-driven inference of a transferable RBC model.

The stress-free state (SFS) of red blood cells (RBCs) is a fundamental reference configuration for the calibration of computational models, yet it remains unknown. Current experimental methods cannot measure the SFS of cells without affecting their mechanical properties, whereas computational postulates are the subject of controversial discussions. Here, we introduce data-driven estimates of the SFS shape and the visco-elastic properties of RBCs. We employ data from single-cell experiments that include measurements of the equilibrium shape of stretched cells and relaxation times of initially stretched RBCs. A hierarchical Bayesian model accounts for these experimental and data heterogeneities. We quantify, for the first time, the SFS of RBCs and use it to introduce a transferable RBC (t-RBC) model. The effectiveness of the proposed model is shown on predictions of unseen experimental conditions during the inference, including the critical stress of transitions between tumbling and tank-treading cells in shear flow. Our findings demonstrate that the proposed t-RBC model provides predictions of blood flows with unprecedented accuracy and quantified uncertainties.

Flow reconstruction by multiresolution optimization of a discrete loss with automatic differentiation.

We present a potent computational method for the solution of inverse problems in fluid mechanics. We consider inverse problems formulated in terms of a deterministic loss function that can accommodate data and regularization terms. We introduce a multigrid decomposition technique that accelerates the convergence of gradient-based methods for optimization problems with parameters on a grid. We incorporate this multigrid technique to the Optimizing a DIscrete Loss (ODIL) framework. The multiresolution ODIL (mODIL) accelerates by an order of magnitude the original formalism and improves the avoidance of local minima. Moreover, mODIL accommodates the use of automatic differentiation for calculating the gradients of the loss function, thus facilitating the implementation of the framework. We demonstrate the capabilities of mODIL on a variety of inverse and flow reconstruction problems: solution reconstruction for the Burgers equation, inferring conductivity from temperature measurements, and inferring the body shape from wake velocity measurements in three dimensions. We also provide a comparative study with the related, popular Physics-Informed Neural Networks (PINNs) method. We demonstrate that mODIL has three to five orders of magnitude lower computational cost than PINNs in benchmark problems including simple PDEs and lid-driven cavity problems. Our results suggest that mODIL is a very potent, fast and consistent method for solving inverse problems in fluid mechanics.

Efficient collective swimming by harnessing vortices through deep reinforcement learning.

Fish in schooling formations navigate complex flow fields replete with mechanical energy in the vortex wakes of their companions. Their schooling behavior has been associated with evolutionary advantages including energy savings, yet the underlying physical mechanisms remain unknown. We show that fish can improve their sustained propulsive efficiency by placing themselves in appropriate locations in the wake of other swimmers and intercepting judiciously their shed vortices. This swimming strategy leads to collective energy savings and is revealed through a combination of high-fidelity flow simulations with a deep reinforcement learning (RL) algorithm. The RL algorithm relies on a policy defined by deep, recurrent neural nets, with long-short-term memory cells, that are essential for capturing the unsteadiness of the two-way interactions between the fish and the vortical flow field. Surprisingly, we find that swimming in-line with a leader is not associated with energetic benefits for the follower. Instead, "smart swimmer(s)" place themselves at off-center positions, with respect to the axis of the leader(s) and deform their body to synchronize with the momentum of the oncoming vortices, thus enhancing their swimming efficiency at no cost to the leader(s). The results confirm that fish may harvest energy deposited in vortices and support the conjecture that swimming in formation is energetically advantageous. Moreover, this study demonstrates that deep RL can produce navigation algorithms for complex unsteady and vortical flow fields, with promising implications for energy savings in autonomous robotic swarms.

S-Leaping: An Adaptive, Accelerated Stochastic Simulation Algorithm, Bridging [Formula: see text]-Leaping and R-Leaping.

We propose the S-leaping algorithm for the acceleration of Gillespie's stochastic simulation algorithm that combines the advantages of the two main accelerated methods; the [Formula: see text]-leaping and R-leaping algorithms. These algorithms are known to be efficient under different conditions; the [Formula: see text]-leaping is efficient for non-stiff systems or systems with partial equilibrium, while the R-leaping performs better in stiff system thanks to an efficient sampling procedure. However, even a small change in a system's set up can critically affect the nature of the simulated system and thus reduce the efficiency of an accelerated algorithm. The proposed algorithm combines the efficient time step selection from the [Formula: see text]-leaping with the effective sampling procedure from the R-leaping algorithm. The S-leaping is shown to maintain its efficiency under different conditions and in the case of large and stiff systems or systems with fast dynamics, the S-leaping outperforms both methods. We demonstrate the performance and the accuracy of the S-leaping in comparison with the [Formula: see text]-leaping and R-leaping on a number of benchmark systems involving biological reaction networks.

Reference Ranges for Fetal Atrioventricular and Ventriculoatrial Time Intervals and Their Ratios during Normal Pregnancy.

BACKGROUND: The diagnostic assessment of fetal arrhythmias relies on the measurements of atrioventricular (AV) and ventriculoatrial (VA) time intervals. Pulsed Doppler over in- and outflow of the left ventricle and tissue Doppler imaging are well-described methods, while Doppler measurements between the left brachiocephalic vein and the aortic arch are less investigated. The aim of this study was to compare these methods of measurement, to find influencing factors on AV and VA times and their ratio, and to create reference ranges. METHODS: Echocardiography was performed between 16 and 40 weeks of gestation in normal singleton pregnancies. Nomograms for the individual measurements were created using quantile regression with Matlab Data Analytics. Statistical analyses were performed with GraphPad version 5.0 for Windows. RESULTS: A total of 329 pregnant women were enrolled. A significant correlation exists between AV and VA times and gestational age (GA) (p = 0.0104 to <0.0001, σ = 0.1412 to 0.3632). No correlation was found between the AV:VA ratio and GA (p = 0.08 to 0.60). All measurements differed significantly amongst the studied methods (p < 0.0001). CONCLUSIONS: AV and VA intervals increase proportionally with GA; no other independent influencing factors could be identified. As significant differences exist between the three methods of assessment, it is crucial to use appropriate reference ranges to diagnose pathologies.

Variability and Constancy in Cellular Growth of Arabidopsis Sepals.

Growth of tissues is highly reproducible; yet, growth of individual cells in a tissue is highly variable, and neighboring cells can grow at different rates. We analyzed the growth of epidermal cell lineages in the Arabidopsis (Arabidopsis thaliana) sepal to determine how the growth curves of individual cell lineages relate to one another in a developing tissue. To identify underlying growth trends, we developed a continuous displacement field to predict spatially averaged growth rates. We showed that this displacement field accurately describes the growth of sepal cell lineages and reveals underlying trends within the variability of in vivo cellular growth. We found that the tissue, individual cell lineages, and cell walls all exhibit growth rates that are initially low, accelerate to a maximum, and decrease again. Accordingly, these growth curves can be represented by sigmoid functions. We examined the relationships among the cell lineage growth curves and surprisingly found that all lineages reach the same maximum growth rate relative to their size. However, the cell lineages are not synchronized; each cell lineage reaches this same maximum relative growth rate but at different times. The heterogeneity in observed growth results from shifting the same underlying sigmoid curve in time and scaling by size. Thus, despite the variability in growth observed in our study and others, individual cell lineages in the developing sepal follow similarly shaped growth curves.

Ultrafast cooling by covalently bonded graphene-carbon nanotube hybrid immersed in water.

The increasing power density and the decreasing dimensions of transistors present severe thermal challenges to the design of modern microprocessors. Furthermore, new technologies such as three-dimensional chip-stack architectures require novel cooling solutions for their thermal management. Here, we demonstrate, through transient heat-dissipation simulations, that a covalently bonded graphene-carbon nanotube (G-CNT) hybrid immersed in water is a promising solution for the ultrafast cooling of such high-temperature and high heat-flux surfaces. The G-CNT hybrid offers a unique platform to integrate the superior axial heat transfer capability of individual CNTs via their parallel arrangement. The immersion of the G-CNT in water enables an additional heat dissipation path via the solid-liquid interaction, allowing for the sustainable cooling of the hot surface under a constant power input of up to 10 000 W cm-2.

Fusing heterogeneous data for the calibration of molecular dynamics force fields using hierarchical Bayesian models.

We propose a hierarchical Bayesian framework to systematically integrate heterogeneous data for the calibration of force fields in Molecular Dynamics (MD) simulations. Our approach enables the fusion of diverse experimental data sets of the physico-chemical properties of a system at different thermodynamic conditions. We demonstrate the value of this framework for the robust calibration of MD force-fields for water using experimental data of its diffusivity, radial distribution function, and density. In order to address the high computational cost associated with the hierarchical Bayesian models, we develop a novel surrogate model based on the empirical interpolation method. Further computational savings are achieved by implementing a highly parallel transitional Markov chain Monte Carlo technique. The present method bypasses possible subjective weightings of the experimental data in identifying MD force-field parameters.

Kapitza Resistance between Few-Layer Graphene and Water: Liquid Layering Effects.

The Kapitza resistance (RK) between few-layer graphene (FLG) and water was studied using molecular dynamics simulations. The RK was found to depend on the number of the layers in the FLG though, surprisingly, not on the water block thickness. This distinct size dependence is attributed to the large difference in the phonon mean free path between the FLG and water. Remarkably, RK is strongly dependent on the layering of water adjacent to the FLG, exhibiting an inverse proportionality relationship to the peak density of the first water layer, which is consistent with better acoustic phonon matching between FLG and water. These findings suggest novel ways to engineer the thermal transport properties of solid-liquid interfaces by controlling and regulating the liquid layering at the interface.

Strain engineering of Kapitza resistance in few-layer graphene.

We demonstrate through molecular dynamics simulations that the Kapitza resistance in few-layer graphene (FLG) can be controlled by applying mechanical strain. For unstrained FLG, the Kapitza resistance decreases with the increase of thickness and reaches an asymptotic value of 6 × 10(-10) m(2)K/W at a thickness about 16 nm. Uniaxial cross-plane strain is found to increase the Kapitza resistance in FLG monotonically, when the applied strain varies from compressive to tensile. Moreover, uniaxial strain couples the in-plane and out-of-plane strain/stress when the surface of FLG is buckled. We find that with a compressive cross-plane stress of 2 GPa, the Kapitza resistance is reduced by about 50%. On the other hand it is almost tripled with a tensile cross-plane stress of 1 GPa. Remarkably, compressive in-plane strain can either increase or reduce the Kapitza resistance, depending on the specific way it is applied. Our study suggests that graphene can be exploited for both heat dissipation and insulation through strain engineering.

Structure and response to flow of the glycocalyx layer.

The glycocalyx is a sugar-rich layer located at the luminal part of the endothelial cells. It is involved in key metabolic processes and its malfunction is related to several diseases. To understand the function of the glycocalyx, a molecular level characterization is necessary. In this article, we present large-scale molecular-dynamics simulations that provide a comprehensive description of the structure and dynamics of the glycocalyx. We introduce the most detailed, to-date, all-atom glycocalyx model, composed of lipid bilayer, proteoglycan dimers, and heparan sulfate chains with realistic sequences. Our results reveal the folding of proteoglycan ectodomain and the extended conformation of heparan sulfate chains. Furthermore, we study the glycocalyx response under shear flow and its role as a flypaper for binding fibroblast growth factors (FGFs), which are involved in diverse functions related to cellular differentiation, including angiogenesis, morphogenesis, and wound healing. The simulations show that the glycocalyx increases the effective concentration of FGFs, leading to FGF oligomerization, and acts as a lever to transfer mechanical stimulus into the cytoplasmic side of endothelial cells.

Barriers to superfast water transport in carbon nanotube membranes.

Carbon nanotube (CNT) membranes hold the promise of extraordinary fast water transport for applications such as energy efficient filtration and molecular level drug delivery. However, experiments and computations have reported flow rate enhancements over continuum hydrodynamics that contradict each other by orders of magnitude. We perform large scale molecular dynamics simulations emulating for the first time the micrometer thick CNTs membranes used in experiments. We find transport enhancement rates that are length dependent due to entrance and exit losses but asymptote to 2 orders of magnitude over the continuum predictions. These rates are far below those reported experimentally. The results suggest that the reported superfast water transport rates cannot be attributed to interactions of water with pristine CNTs alone.

Solving inverse problems in physics by optimizing a discrete loss: Fast and accurate learning without neural networks.

In recent years, advances in computing hardware and computational methods have prompted a wealth of activities for solving inverse problems in physics. These problems are often described by systems of partial differential equations (PDEs). The advent of machine learning has reinvigorated the interest in solving inverse problems using neural networks (NNs). In these efforts, the solution of the PDEs is expressed as NNs trained through the minimization of a loss function involving the PDE. Here, we show how to accelerate this approach by five orders of magnitude by deploying, instead of NNs, conventional PDE approximations. The framework of optimizing a discrete loss (ODIL) minimizes a cost function for discrete approximations of the PDEs using gradient-based and Newton's methods. The framework relies on grid-based discretizations of PDEs and inherits their accuracy, convergence, and conservation properties. The implementation of the method is facilitated by adopting machine-learning tools for automatic differentiation. We also propose a multigrid technique to accelerate the convergence of gradient-based optimizers. We present applications to PDE-constrained optimization, optical flow, system identification, and data assimilation. We compare ODIL with the popular method of physics-informed neural networks and show that it outperforms it by several orders of magnitude in computational speed while having better accuracy and convergence rates. We evaluate ODIL on inverse problems involving linear and nonlinear PDEs including the Navier-Stokes equations for flow reconstruction problems. ODIL bridges numerical methods and machine learning and presents a powerful tool for solving challenging, inverse problems across scientific domains.

Quantitative 3D histochemistry reveals region-specific amyloid-ß reduction by the antidiabetic drug netoglitazone.

A hallmark of Alzheimer's disease (AD) is the extracellular aggregation of toxic amyloid-beta (Aß) peptides in form of plaques. Here, we identify netoglitazone, an antidiabetic compound previously tested in humans, as an Aß aggregation antagonist. Netoglitazone improved cognition and reduced microglia activity in a mouse model of AD. Using quantitative whole-brain three-dimensional histology (Q3D), we precisely identified brain regions where netoglitazone reduced the number and size of Aß plaques. We demonstrate the utility of Q3D in preclinical drug evaluation for AD by providing a high-resolution brain-wide view of drug efficacy. Applying Q3D has the potential to improve pre-clinical drug evaluation by providing information that can help identify mechanisms leading to brain region-specific drug efficacy.

Engineering Toxoplasma gondii secretion systems for intracellular delivery of multiple large therapeutic proteins to neurons.

Delivering macromolecules across biological barriers such as the blood-brain barrier limits their application in vivo. Previous work has demonstrated that Toxoplasma gondii, a parasite that naturally travels from the human gut to the central nervous system (CNS), can deliver proteins to host cells. Here we engineered T. gondii's endogenous secretion systems, the rhoptries and dense granules, to deliver multiple large (>100 kDa) therapeutic proteins into neurons via translational fusions to toxofilin and GRA16. We demonstrate delivery in cultured cells, brain organoids and in vivo, and probe protein activity using imaging, pull-down assays, scRNA-seq and fluorescent reporters. We demonstrate robust delivery after intraperitoneal administration in mice and characterize 3D distribution throughout the brain. As proof of concept, we demonstrate GRA16-mediated brain delivery of the MeCP2 protein, a putative therapeutic target for Rett syndrome. By characterizing the potential and current limitations of the system, we aim to guide future improvements that will be required for broader application.

Bayesian uncertainty quantification and propagation in molecular dynamics simulations: a high performance computing framework.

We present a Bayesian probabilistic framework for quantifying and propagating the uncertainties in the parameters of force fields employed in molecular dynamics (MD) simulations. We propose a highly parallel implementation of the transitional Markov chain Monte Carlo for populating the posterior probability distribution of the MD force-field parameters. Efficient scheduling algorithms are proposed to handle the MD model runs and to distribute the computations in clusters with heterogeneous architectures. Furthermore, adaptive surrogate models are proposed in order to reduce the computational cost associated with the large number of MD model runs. The effectiveness and computational efficiency of the proposed Bayesian framework is demonstrated in MD simulations of liquid and gaseous argon.

Scientific multi-agent reinforcement learning for wall-models of turbulent flows.

The predictive capabilities of turbulent flow simulations, critical for aerodynamic design and weather prediction, hinge on the choice of turbulence models. The abundance of data from experiments and simulations and the advent of machine learning have provided a boost to turbulence modeling efforts. However, simulations of turbulent flows remain hindered by the inability of heuristics and supervised learning to model the near-wall dynamics. We address this challenge by introducing scientific multi-agent reinforcement learning (SciMARL) for the discovery of wall models for large-eddy simulations (LES). In SciMARL, discretization points act also as cooperating agents that learn to supply the LES closure model. The agents self-learn using limited data and generalize to extreme Reynolds numbers and previously unseen geometries. The present simulations reduce by several orders of magnitude the computational cost over fully-resolved simulations while reproducing key flow quantities. We believe that SciMARL creates unprecedented capabilities for the simulation of turbulent flows.

Accelerated Simulations of Molecular Systems through Learning of Effective Dynamics.

Simulations are vital for understanding and predicting the evolution of complex molecular systems. However, despite advances in algorithms and special purpose hardware, accessing the time scales necessary to capture the structural evolution of biomolecules remains a daunting task. In this work, we present a novel framework to advance simulation time scales by up to 3 orders of magnitude by learning the effective dynamics (LED) of molecular systems. LED augments the equation-free methodology by employing a probabilistic mapping between coarse and fine scales using mixture density network (MDN) autoencoders and evolves the non-Markovian latent dynamics using long short-term memory MDNs. We demonstrate the effectiveness of LED in the Müller-Brown potential, the Trp cage protein, and the alanine dipeptide. LED identifies explainable reduced-order representations, i.e., collective variables, and can generate, at any instant, all-atom molecular trajectories consistent with the collective variables. We believe that the proposed framework provides a dramatic increase to simulation capabilities and opens new horizons for the effective modeling of complex molecular systems.

Computing foaming flows across scales: From breaking waves to microfluidics.

Crashing ocean waves, cappuccino froths, and microfluidic bubble crystals are examples of foamy flows. Foamy flows are critical in numerous natural and industrial processes and remain notoriously difficult to compute as they involve coupled, multiscale physical processes. Computations need to resolve the interactions of the bubbles separated by stable thin liquid films. We present the multilayer volume-of-fluid method (Multi-VOF) that advances the state of the art in simulation capabilities of foamy flows. The method introduces a scheme to handle multiple bubbles that do not coalesce. Multi-VOF is verified and validated with experimental results and complemented with open-source software. We demonstrate capturing of crystalline structures of bubbles in realistic microfluidics devices and foamy flows involving tens of thousands of bubbles in a waterfall. The present technique extends the classical volume-of-fluid methodology and allows for large-scale predictive simulations of flows with multiple interfaces.