4D flow magnetic resonance imaging (MRI) is a reliable, non-invasive approach for estimating blood flow velocities, vital for cardiovascular diagnostics. Unlike conventional MRI focused on anatomical structures, 4D flow MRI requires high spatiotemporal resolution for early detection of critical conditions such as stenosis or aneurysms. However, achieving such resolution typically results in prolonged scan times, creating a trade-off between acquisition speed and prediction accuracy. Recent studies have leveraged physics-informed neural networks (PINNs) for super-resolution of MRI data, but their practical applicability is limited as the prohibitively slow training process must be performed for each patient. To overcome this limitation, we propose PINGS-X, a novel framework modeling high-resolution flow velocities using axes-aligned spatiotemporal Gaussian representations. Inspired by the effectiveness of 3D Gaussian splatting (3DGS) in novel view synthesis, PINGS-X extends this concept through several non-trivial novel innovations: (i) normalized Gaussian splatting with a formal convergence guarantee, (ii) axes-aligned Gaussians that simplify training for high-dimensional data while preserving accuracy and the convergence guarantee, and (iii) a Gaussian merging procedure to prevent degenerate solutions and boost computational efficiency. Experimental results on computational fluid dynamics (CFD) and real 4D flow MRI datasets demonstrate that PINGS-X substantially reduces training time while achieving superior super-resolution accuracy.

We demonstrated new scheme of artificial life, which conducts the temporal evolution of real-living-cell distribution by giving programmable interactions to the cells. By using optical interlink feedback, two groups of isolated micro-algae cells were artificially interacted each other. The micro-algae cells responded to the illumination pattern produced with artificially designed algorithms, leading to the autonomous evolution of cell distribution in micro-aquariums. Habitat domain separation and autonomous oscillation of cell density were realized with the interlink feedback. In habitat domain separation, the initial fluctuation of cell density distribution grew with the interlink feedback, accompanying clustering of high-density areas. In autonomous oscillation, the photo-responses of two micro- algae determined the period and waveform of the oscillation.

Artificial linking of two isolated culture dishes is a fascinating means of investigating interactions among multiple groups of microbes or fungi. We examined artificial interaction between two isolated dishes containing Euglena cells, which are photophobic to strong blue light. The spatial distribution of swimming Euglena cells in two micro-aquariums in the dishes was evaluated as a set of new measures: the trace momentums (TMs). The blue light patterns next irradiated onto each dish were deduced from the set of TMs using digital or analogue feedback algorithms. In the digital feedback experiment, one of two different pattern-formation rules was imposed on each feedback system. The resultant cell distribution patterns satisfied the two rules with an and operation, showing that cooperative interaction was realized in the interlink feedback. In the analogue experiment, two dishes A and B were interlinked by a feedback algorithm that illuminated dish A (B) with blue light of intensity proportional to the cell distribution in dish B (A). In this case, a distribution pattern and its reverse were autonomously formed in the two dishes. The autonomous formation of a pair of reversal patterns reflects a type of habitat separation realized by competitive interaction through the interlink feedback. According to this study, interlink feedback between two or more separate culture dishes enables artificial interactions between isolated microbial groups, and autonomous cellular distribution patterns will be achieved by correlating various microbial species, despite environmental and spatial scale incompatibilities. The optical interlink feedback is also useful for enhancing the performance of Euglena-based soft biocomputing.