Publications by authors named "Diego Rossinelli"

Article Synopsis
  • The study examines how cerebrospinal fluid (CSF) dynamics are affected by changes in the optic nerve's subarachnoid space, which is important for understanding neurodegenerative diseases.
  • Using advanced computational fluid dynamics, researchers analyzed how structure impacts CSF movement and solute transfer on a microscopic level.
  • Results indicate that minor pressure drops can effectively circulate CSF, but variations in flow speed create potential risks in certain areas of the optic nerve's subarachnoid space.
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Article Synopsis
  • * The new HR-Kidney dataset offers 1.7 TB of high-quality X-ray images of mouse kidneys, including validated segmentations of over 33,000 glomeruli, significantly enlarging existing biomedical datasets.
  • * This dataset provides essential tools for the scientific community, enabling advancements in image processing and machine learning techniques, particularly in unsupervised learning and generative adversarial networks.
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Background: The meninges, formed by dura, arachnoid and pia mater, cover the central nervous system and provide important barrier functions. Located between arachnoid and pia mater, the cerebrospinal fluid (CSF)-filled subarachnoid space (SAS) features a variety of trabeculae, septae and pillars. Like the arachnoid and the pia mater, these structures are covered with leptomeningeal or meningothelial cells (MECs) that form a barrier between CSF and the parenchyma of the optic nerve (ON).

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The rapid increase in medical and biomedical image acquisition rates has opened up new avenues for image analysis, but has also introduced formidable challenges. This is evident, for example, in selective plane illumination microscopy where acquisition rates of about 1-4 GB/s sustained over several days have redefined the scale of I/O bandwidth required by image analysis tools. Although the effective bandwidth could, principally, be increased by lossy-to-lossless data compression, this is of limited value in practice due to the high computational demand of current schemes such as JPEG2000 that reach compression throughput of one order of magnitude below that of image acquisition.

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The coordinated motion by multiple swimmers is a fundamental component in fish schooling. The flow field induced by the motion of each self-propelled swimmer implies non-linear hydrodynamic interactions among the members of a group. How do swimmers compensate for such hydrodynamic interactions in coordinated patterns? We provide an answer to this riddle though simulations of two, self-propelled, fish-like bodies that employ a learning algorithm to synchronise their swimming patterns.

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Particle-mesh interpolations are fundamental operations for particle-in-cell codes, as implemented in vortex methods, plasma dynamics and electrostatics simulations. In these simulations, the mesh is used to solve the field equations and the gradients of the fields are used in order to advance the particles. The time integration of particle trajectories is performed through an extensive resampling of the flow field at the particle locations.

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