Study of the convergence of the Meshless Lattice Boltzmann Method in Taylor-Green and annular channel flows

The Meshless Lattice Boltzmann Method (MLBM) is a numerical tool that relieves the standard Lattice Boltzmann Method (LBM) from regular lattices and, at the same time, decouples space and velocity discretizations. In this study, we investigate the numerical convergence of MLBM in two benchmark tests: the Taylor-Green vortex and annular (bent) channel flow. We compare our MLBM results to LBM and to the analytical solution of the Navier-Stokes equation. We investigate the method's convergence in terms of the discretization parameter, the interpolation order, and the LBM streaming distance refinement. We observe that MLBM outperforms LBM in terms of the error value for the same number of nodes discretizing the domain. We find that LBM errors at a given streaming distance $\delta x$ and timestep length $\delta t$ are the asymptotic lower bounds of MLBM errors with the same streaming distance and timestep length. Finally, we suggest an expression for the MLBM error that consists of the LBM error and other terms related to the semi-Lagrangian nature of the discussed method itself

Deep learning for diffusion in porous media

We adopt convolutional neural networks (CNN) to predict the basic properties of the porous media. Two different media types are considered: one mimics the sandstone, and the other mimics the systems derived from the extracellular space of biological tissues. The Lattice Boltzmann Method is used to obtain the labeled data necessary for performing supervised learning. We distinguish two tasks. In the first, networks based on the analysis of the system's geometry predict porosity and effective diffusion coefficient. In the second, networks reconstruct the system's geometry and concentration map. In the first task, we propose two types of CNN models: the C-Net and the encoder part of the U-Net. Both networks are modified by adding a self-normalization module. The models predict with reasonable accuracy but only within the data type, they are trained on. For instance, the model trained on sandstone-like samples overshoots or undershoots for biological-like samples. In the second task, we propose the usage of the U-Net architecture. It accurately reconstructs the concentration fields. Moreover, the network trained on one data type works well for the other. For instance, the model trained on sandstone-like samples works perfectly on biological-like samples.Comment: 17 pages, 19 figure

The AI Neuropsychologist: Automatic scoring of memory deficits with deep learning

Memory deficits are a hallmark of many different neurological and psychiatric conditions. The Rey-Osterrieth complex figure (ROCF) is the state–of-the-art assessment tool for neuropsychologists across the globe to assess the degree of non-verbal visual memory deterioration. To obtain a score, a trained clinician inspects a patient’s ROCF drawing and quantifies deviations from the original figure. This manual procedure is time-consuming, slow and scores vary depending on the clinician’s experience, motivation and tiredness. Here, we leverage novel deep learning architectures to automatize the rating of memory deficits. For this, a multi-head convolutional neural network was trained on 20225 ROCF drawings. Unbiased ground truth ROCF scores were obtained from crowdsourced human intelligence. The neural network outperforms both online raters and clinicians. Our AI-powered scoring system provides healthcare institutions worldwide with a digital tool to assess objectively, reliably and time-efficiently the performance in the ROCF test from hand-drawn images

Down-regulation of CBP80 gene expression as a strategy to engineer a drought-tolerant potato

Developing new strategies for crop plants to respond to drought is crucial for their innovative breeding. The down-regulation of nuclear cap-binding proteins in Arabidopsis renders plants drought tolerant. The CBP80 gene in the potato cultivar Desiree was silenced using artificial microRNAs. Transgenic plants displayed a higher tolerance to drought, ABA-hypersensitive stomatal closing, an increase in leaf stomata and trichome density, and compact cuticle structures with a lower number of microchannels. These findings were correlated with a higher tolerance to water stress. The level of miR159 was decreased, and the levels of its target mRNAs MYB33 and MYB101 increased in the transgenic plants subjected to drought. Similar trends were observed in an Arabidopsis cbp80 mutant. The evolutionary conservation of CBP80, a gene that plays a role in the response to drought, suggests that it is a candidate for genetic manipulations that aim to obtain improved water-deficit tolerance of crop plants