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BACKGROUND AND OBJECTIVE: Current methods for imaging reconstruction from high-ratio expansion microscopy (ExM) data are limited by anisotropic optical resolution and the requirement for extensive manual annotation, creating a significant bottleneck in the analysis of complex neuronal structures. METHODS: We devised an innovative approach called the IsoGAN model, which utilizes a contrastive unsupervised generative adversarial network to sidestep these constraints. This model leverages multi-scale and isotropic neuron/protein/blood vessel morphology data to generate high-fidelity 3D representations of these structures, eliminating the need for rigorous manual annotation and supervision. The IsoGAN model introduces simplified structures with idealized morphologies as shape priors to ensure high consistency in the generated neuronal profiles across all points in space and scalability for arbitrarily large volumes. RESULTS: The efficacy of the IsoGAN model in accurately reconstructing complex neuronal structures was quantitatively assessed by examining the consistency between the axial and lateral views and identifying a reduction in erroneous imaging artifacts. The IsoGAN model accurately reconstructed complex neuronal structures, as evidenced by the consistency between the axial and lateral views and a reduction in erroneous imaging artifacts, and can be further applied to various biological samples. CONCLUSION: With its ability to generate detailed 3D neurons/proteins/blood vessel structures using significantly fewer axial view images, IsoGAN can streamline the process of imaging reconstruction while maintaining the necessary detail, offering a transformative solution to the existing limitations in high-throughput morphology analysis across different structures.

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We study the influence of one or multiple thin spots on the flow-induced instabilities of flexible shells of revolution with non-zero Gaussian curvatures. The shell's equation of motion is described by a thin doubly-curved shell theory and is coupled with perturbed flow pressure, calculated based on an inviscid flow model. We show that for shells with positive Gaussian curvatures conveying fluid, the existence of a thin spot results in a localized flow-induced buckling response of the shell in the neighborhood of the thin spot, and significantly reduces the critical flow velocity for buckling instability. For shells with negative Gaussian curvatures, the buckling response is extended along the shell's characteristic lines and the critical flow velocity is only slightly reduced. We also show that the length scale of the localized deformation generated by a thin spot is proportional to the shell's global thickness when the stiffness of the thin spot is negligible compared with the stiffness of the rest of the shell. When two thin spots exist at a distance, their influences are independent from each other for shells with positive Gaussian curvatures, but large-scale deformations can be created due to multiple thin spots on shells with negative curvatures, depending on the thin spots' relative position.

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In this paper, we introduce a method to construct a Reduced-Order Model (ROM) to study the physiological flow and the Wall Shear Stress (WSS) conditions in Abdominal Aortic Aneurysms (AAA). We start the process by running a training case using Computational Fluid Dynamics (CFD) simulations with time-varying flow parameters, such that these parameters cover the range of parameters that we would like to consider in our ROM. We use the inflow angle as the variable parameter in the current study. Then we use the snapshot Proper Orthogonal Decomposition (POD) to construct the reduced-order bases, which are subsequently enhanced using a QR-factorization technique to satisfy the relevant fluid boundary conditions. The resulting ROM enables us to study the flow pattern and the WSS distribution over a range of system parameters computationally very efficiently. We have used this method to show how the WSS varies significantly for an AAA with a simplified geometry, over a range of inflow angles usually considered mild in clinical terms. We have validated the ROM results with CFD results. This approach enables comprehensive analysis of the model system across a range of inflow angles and frequencies without the need to re-compute the simulation for small changes.

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