Large-scale in-silico analysis of CSF dynamics within the subarachnoid space of the optic nerve.

BACKGROUND: Impaired cerebrospinal fluid (CSF) dynamics is involved in the pathophysiology of neurodegenerative diseases of the central nervous system and the optic nerve (ON), including Alzheimer's and Parkinson's disease, as well as frontotemporal dementia. The smallness and intricate architecture of the optic nerve subarachnoid space (ONSAS) hamper accurate measurements of CSF dynamics in this space, and effects of geometrical changes due to pathophysiological processes remain unclear. The aim of this study is to investigate CSF dynamics and its response to structural alterations of the ONSAS, from first principles, with supercomputers. METHODS: Large-scale in-silico investigations were performed by means of computational fluid dynamics (CFD) analysis. High-order direct numerical simulations (DNS) have been carried out on ONSAS geometry at a resolution of 1.625 µm/pixel. Morphological changes on the ONSAS microstructure have been examined in relation to CSF pressure gradient (CSFPG) and wall strain rate, a quantitative proxy for mass transfer of solutes. RESULTS: A physiological flow speed of 0.5 mm/s is achieved by imposing a hydrostatic pressure gradient of 0.37-0.67 Pa/mm across the ONSAS structure. At constant volumetric rate, the relationship between pressure gradient and CSF-accessible volume is well captured by an exponential curve. The ONSAS microstructure exhibits superior mass transfer compared to other geometrical shapes considered. An ONSAS featuring no microstructure displays a threefold smaller surface area, and a 17-fold decrease in mass transfer rate. Moreover, ONSAS trabeculae seem key players in mass transfer. CONCLUSIONS: The present analysis suggests that a pressure drop of 0.1-0.2 mmHg over 4 cm is sufficient to steadily drive CSF through the entire subarachnoid space. Despite low hydraulic resistance, great heterogeneity in flow speeds puts certain areas of the ONSAS at risk of stagnation. Alterations of the ONSAS architecture aimed at mimicking pathological conditions highlight direct relationships between CSF volume and drainage capability. Compared to the morphological manipulations considered herein, the original ONSAS architecture seems optimized towards providing maximum mass transfer across a wide range of pressure gradients and volumetric rates, with emphasis on trabecular structures. This might shed light on pathophysiological processes leading to damage associated with insufficient CSF flow in patients with optic nerve compartment syndrome.

Neural networks for large eddy simulations of wall-bounded turbulence: numerical experiments and challenges.

We examine the application of neural network-based methods to improve the accuracy of large eddy simulations of incompressible turbulent flows. The networks are trained to learn a mapping between flow features and the subgrid scales, and applied locally and instantaneously-in the same way as traditional physics-based subgrid closures. Models that use only the local resolved strain rate are poorly correlated with the actual subgrid forces obtained from filtering direct numerical simulation data. We see that highly accurate models in a priori testing are inaccurate in forward calculations, owing to the preponderance of numerical errors in implicitly filtered large eddy simulations. A network that accounts for the discretization errors is trained and found to be unstable in a posteriori testing. We identify a number of challenges that the approach faces, including a distribution shift that affects networks that fail to account for numerical errors.

Toward accelerated data-driven Rayleigh-Bénard convection simulations.

A hybrid data-driven/finite volume method for 2D and 3D thermal convective flows is introduced. The approach relies on a single-step loss, convolutional neural network that is active only in the near-wall region of the flow. We demonstrate that the method significantly reduces errors in the prediction of the heat flux over the long-time horizon and increases pointwise accuracy in coarse simulations, when compared to direct computations on the same grids with and without a traditional subgrid model. We trace the success of our machine learning model to the choice of the training procedure, incorporating both the temporal flow development and distributional bias.

Spinning-enabled wireless amphibious origami millirobot.

Wireless millimeter-scale origami robots have recently been explored with great potential for biomedical applications. Existing millimeter-scale origami devices usually require separate geometrical components for locomotion and functions. Additionally, none of them can achieve both on-ground and in-water locomotion. Here we report a magnetically actuated amphibious origami millirobot that integrates capabilities of spinning-enabled multimodal locomotion, delivery of liquid medicine, and cargo transportation with wireless operation. This millirobot takes full advantage of the geometrical features and folding/unfolding capability of Kresling origami, a triangulated hollow cylinder, to fulfill multifunction: its geometrical features are exploited for generating omnidirectional locomotion in various working environments through rolling, flipping, and spinning-induced propulsion; the folding/unfolding is utilized as a pumping mechanism for controlled delivery of liquid medicine; furthermore, the spinning motion provides a sucking mechanism for targeted solid cargo transportation. We anticipate the amphibious origami millirobots can potentially serve as minimally invasive devices for biomedical diagnoses and treatments.