Mesenchymal base cellderived exosomes guard trabecular meshwork coming from oxidative tension

Interlaced metallic meshes form a class of three-dimensional metamaterials that exhibit nondispersive, broadband modes at low frequencies, without the low frequency cutoff typical of generic wire grid geometries. However, the experimental observation of these modes has remained an open challenge, both due to the difficulties in fabricating such complex structures and also because the broadband mode is longitudinal and does not couple to free-space radiation (dark mode). Here we report the first experimental observation of the low frequency modes in a block of interlaced meshes fabricated through 3D printing. We demonstrate how the addition of monopole antennas to opposing faces of one of the meshes enables coupling of a plane wave to the low frequency "dark mode" and use this to obtain the dispersion of the mode. In addition, we utilize orthogonal antennas on opposite faces to achieve polarization rotation as well as phase shifting of radiation passing through the structure. Our work paves the way toward further experimental study into interlaced meshes and other complex 3D metamaterials.Nonlinear metasurfaces constitute a key asset in meta-optics, given their ability to scale down nonlinear optics to sub-micrometer thicknesses. To date, nonlinear metasurfaces have been mainly realized using narrow band gap semiconductors, with operation limited to the near-infrared range. Nonlinear meta-optics in the visible range can be realized using transparent materials with high refractive index, such as lithium niobate (LiNbO3). Yet, efficient operation in this strategic spectral window has been so far prevented by the nanofabrication challenges associated with LiNbO3, which considerably limit the aspect ratio and minimum size of the nanostructures (i.e., meta-atoms). Here we demonstrate the first monolithic nonlinear periodic metasurface based on LiNbO3 and operating in the visible range. Realized through ion beam milling, our metasurface features a second-harmonic (SH) conversion efficiency of 2.40 × 10-8 at a pump intensity as low as 0.5 GW/cm2. By tuning the pump polarization, we demonstrate efficient steering and polarization encoding into narrow SH diffraction orders, opening novel opportunities for polarization-encoded nonlinear meta-optics.Rare-earth oxyhydride REO x H3-2x thin films prepared by air-oxidation of reactively sputtered REH2 dihydrides show a color-neutral, reversible photochromic effect at ambient conditions. The present work shows that the O/H anion ratio, as well as the choice of the cation, allow to largely tune the extent of the optical change and its speed. The bleaching time, in particular, can be reduced by an order of magnitude by increasing the O/H ratio, indirectly defined by the deposition pressure of the parent REH2. The influence of the cation (RE = Sc, Y, Gd) under comparable deposition conditions is discussed. Our data suggest that REs of a larger ionic radius form oxyhydrides with a larger optical contrast and faster bleaching speed, hinting to a dependency of the photochromic mechanism on the anion site-hopping.Purpose Automation of organ segmentation, via convolutional neural networks (CNNs), is key to facilitate the work of medical practitioners by ensuring that the adequate radiation dose is delivered to the target area while avoiding harmful exposure of healthy organs. OX04528 The issue with CNNs is that they require large amounts of data transfer and storage which makes the use of image compression a necessity. Compression will affect image quality which in turn affects the segmentation process. We address the dilemma involved with handling large amounts of data while preserving segmentation accuracy. Approach We analyze and improve 2D and 3D U-Net robustness against JPEG 2000 compression for male pelvic organ segmentation. We conduct three experiments on 56 cone beam computed tomography (CT) and 74 CT scans targeting bladder and rectum segmentation. The two objectives of the experiments are to compare the compression robustness of 2D versus 3D U-Net and to improve the 3D U-Net compression tolerance via fine-tuning. Results We show that a 3D U-Net is 50% more robust to compression than a 2D U-Net. Moreover, by fine-tuning the 3D U-Net, we can double its compression tolerance compared to a 2D U-Net. Furthermore, we determine that fine-tuning the network to a compression ratio of 641 will ensure its flexibility to be used at compression ratios equal or lower. Conclusions We reduce the potential risk involved with using image compression on automated organ segmentation. We demonstrate that a 3D U-Net can be fine-tuned to handle high compression ratios while preserving segmentation accuracy.Purpose In clinical practice, positron emission tomography (PET) images are mostly analyzed visually, but the sensitivity and specificity of this approach greatly depend on the observer's experience. Quantitative analysis of PET images would alleviate this problem by helping define an objective limit between normal and pathological findings. We present an anomaly detection framework for the individual analysis of PET images. Approach We created subject-specific abnormality maps that summarize the pathology's topographical distribution in the brain by comparing the subject's PET image to a model of healthy PET appearance that is specific to the subject under investigation. This model was generated from demographically and morphologically matched PET scans from a control dataset. Results We generated abnormality maps for healthy controls, patients at different stages of Alzheimer's disease and with different frontotemporal dementia syndromes. We showed that no anomalies were detected for the healthy controls and that the anomalies detected from the patients with dementia coincided with the regions where abnormal uptake was expected. We also validated the proposed framework using the abnormality maps as inputs of a classifier and obtained higher classification accuracies than when using the PET images themselves as inputs. Conclusions The proposed method was able to automatically locate and characterize the areas characteristic of dementia from PET images. The abnormality maps are expected to (i) help clinicians in their diagnosis by highlighting, in a data-driven fashion, the pathological areas, and (ii) improve the interpretability of subsequent analyses, such as computer-aided diagnosis or spatiotemporal modeling.