Concurrent material and structure optimization of multiphase hierarchical systems within a continuum micromechanics framework

We present a concurrent material and structure optimization framework for multiphase hierarchical systems that relies on homogenization estimates based on continuum micromechanics to account for material behavior across many different length scales. We show that the analytical nature of these estimates enables material optimization via a series of inexpensive “discretization-free” constraint optimization problems whose computational cost is independent of the number of hierarchical scales involved. To illustrate the strength of this unique property, we define new benchmark tests with several material scales that for the first time become computationally feasible via our framework. We also outline its potential in engineering applications by reproducing self-optimizing mechanisms in the natural hierarchical system of bamboo culm tissue

Simultaneous submicrometric 3D imaging of the micro-vascular network and the neuronal system in a mouse spinal cord

Defaults in vascular (VN) and neuronal networks of spinal cord are responsible for serious neurodegenerative pathologies. Because of inadequate investigation tools, the lacking knowledge of the complete fine structure of VN and neuronal systems is a crucial problem. Conventional 2D imaging yields incomplete spatial coverage leading to possible data misinterpretation, whereas standard 3D computed tomography imaging achieves insufficient resolution and contrast. We show that X-ray high-resolution phase-contrast tomography allows the simultaneous visualization of three-dimensional VN and neuronal systems of mouse spinal cord at scales spanning from millimeters to hundreds of nanometers, with neither contrast agent nor a destructive sample-preparation. We image both the 3D distribution of micro-capillary network and the micrometric nerve fibers, axon-bundles and neuron soma. Our approach is a crucial tool for pre-clinical investigation of neurodegenerative pathologies and spinal-cord-injuries. In particular, it should be an optimal tool to resolve the entangled relationship between VN and neuronal system.Comment: 15 pages, 6 figure

Nature-Inspired, Computer-Assisted Optimization of Hierarchically Structured Zeolites

Zeolite catalysis is often affected by transport limitations, which significantly influence overall performance. Introducing wide pores as molecular transport highways can reduce transport limitations, control the product distribution, and mitigate effects of catalyst deactivation. Nevertheless, the importance to rationally design the meso‐ and macropore space remains underappreciated. This article reviews multiscale modelling approaches to optimize overall catalytic performance. It provides a general methodology and rules of thumb to guide catalyst synthesis with optimal pore network characteristics. Inspiration is taken from nature, such as the structure of leaves and tissues, with similar requirements and associated features. In optimal hierarchically structured zeolites, the added macro‐/mesopore volume fraction, connectivity, crystal size, and minimum wide pore size are crucial. The broad pore size distribution is secondary. No uncontrolled diffusion limitations should exist within the zeolite crystals. Surface barriers, however, can significantly affect, even dominate overall transport. Understanding their origin and ways to control them is an emergent research area. Synthesis methods to realize hierarchically structured zeolites are briefly reviewed. Significant gaps exist between laboratory synthesis methods and industrial requirements. Zeolite catalysis could benefit from computer‐assisted design of their hierarchical pore network, embracing principles used by natural transport networks for scalable efficiency, selectivity, and robustness

Deep convolutional neural networks for segmenting 3D in vivo multiphoton images of vasculature in Alzheimer disease mouse models

The health and function of tissue rely on its vasculature network to provide reliable blood perfusion. Volumetric imaging approaches, such as multiphoton microscopy, are able to generate detailed 3D images of blood vessels that could contribute to our understanding of the role of vascular structure in normal physiology and in disease mechanisms. The segmentation of vessels, a core image analysis problem, is a bottleneck that has prevented the systematic comparison of 3D vascular architecture across experimental populations. We explored the use of convolutional neural networks to segment 3D vessels within volumetric in vivo images acquired by multiphoton microscopy. We evaluated different network architectures and machine learning techniques in the context of this segmentation problem. We show that our optimized convolutional neural network architecture, which we call DeepVess, yielded a segmentation accuracy that was better than both the current state-of-the-art and a trained human annotator, while also being orders of magnitude faster. To explore the effects of aging and Alzheimer's disease on capillaries, we applied DeepVess to 3D images of cortical blood vessels in young and old mouse models of Alzheimer's disease and wild type littermates. We found little difference in the distribution of capillary diameter or tortuosity between these groups, but did note a decrease in the number of longer capillary segments ($>75\mu m$) in aged animals as compared to young, in both wild type and Alzheimer's disease mouse models.Comment: 34 pages, 9 figure

High resolution 3D visualization of the spinal cord in a post-mortem murine model

A crucial issue in the development of therapies to treat pathologies of the central nervous system is represented by the availability of non-invasive methods to study the three-dimensional morphology of spinal cord, with a resolution able to characterize its complex vascular and neuronal organization. X-ray phase contrast micro-tomography enables a high-quality, 3D visualization of both the vascular and neuronal network simultaneously without the need of contrast agents, destructive sample preparations or sectioning. Until now, high resolution investigations of the post-mortem spinal cord in murine models have mostly been performed in spinal cords removed from the spinal canal. We present here post-mortem phase contrast micro-tomography images reconstructed using advanced computational tools to obtain high-resolution and high-contrast 3D images of the fixed spinal cord without removing the bones and preserving the richness of micro-details available when measuring exposed spinal cords. We believe that it represents a significant step toward the in-vivo application

Ultra-high-resolution 3D imaging of atherosclerosis in mice with synchrotron differential phase contrast: a proof of concept study.

The goal of this study was to investigate the performance of 3D synchrotron differential phase contrast (DPC) imaging for the visualization of both macroscopic and microscopic aspects of atherosclerosis in the mouse vasculature ex vivo. The hearts and aortas of 2 atherosclerotic and 2 wild-type control mice were scanned with DPC imaging with an isotropic resolution of 15 μm. The coronary artery vessel walls were segmented in the DPC datasets to assess their thickness, and histological staining was performed at the level of atherosclerotic plaques. The DPC imaging allowed for the visualization of complex structures such as the coronary arteries and their branches, the thin fibrous cap of atherosclerotic plaques as well as the chordae tendineae. The coronary vessel wall thickness ranged from 37.4 ± 5.6 μm in proximal coronary arteries to 13.6 ± 3.3 μm in distal branches. No consistent differences in coronary vessel wall thickness were detected between the wild-type and atherosclerotic hearts in this proof-of-concept study, although the standard deviation in the atherosclerotic mice was higher in most segments, consistent with the observation of occasional focal vessel wall thickening. Overall, DPC imaging of the cardiovascular system of the mice allowed for a simultaneous detailed 3D morphological assessment of both large structures and microscopic details