Nonreciprocal devices – in which light is transmitted with different efficiencies along opposite directions – are key technologies for modern photonic applications, yet their compact and miniaturized implementation ...
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Aging is a major risk factor for many diseases. Accurate methods for predicting age in specific cell types are essential to understand the heterogeneity of aging and to assess rejuvenation strategies. However, classif...
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Background: Uncertainty quantification in deep learning is an important research topic. For medical image segmentation, the uncertainty measurements are usually reported as the likelihood that each pixel belongs to th...
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Background: Uncertainty quantification in deep learning is an important research topic. For medical image segmentation, the uncertainty measurements are usually reported as the likelihood that each pixel belongs to the predicted segmentation region. In potential clinical applications, the uncertainty result reflects the algorithm's robustness and supports the confidence and trust of the segmentation result when the ground-truth result is absent. For commonly studied deep learning models, novel methods for quantifying segmentation uncertainty are in demand. Purpose: To develop a U-Net segmentation uncertainty quantification method based on spherical image projection of multi-parametric MRI (MP-MRI) in glioma segmentation. Methods: The projection of planar MRI data onto a spherical surface is equivalent to a nonlinear image transformation that retains global anatomical information. By incorporating this image transformation process in our proposed spherical projection-based U-Net (SPU-Net) segmentation model design, multiple independent segmentation predictions can be obtained from a single MRI. The final segmentation is the average of all available results, and the variation can be visualized as a pixel-wise uncertainty map. An uncertainty score was introduced to evaluate and compare the performance of uncertainty measurements. The proposed SPU-Net model was implemented on the basis of 369 glioma patients with MP-MRI scans (T1, T1-Ce, T2, and FLAIR). Three SPU-Net models were trained to segment enhancing tumor (ET), tumor core (TC), and whole tumor (WT), respectively. The SPU-Net model was compared with (1) the classic U-Net model with test-time augmentation (TTA) and (2) linear scaling-based U-Net (LSU-Net) segmentation models in terms of both segmentation accuracy (Dice coefficient, sensitivity, specificity, and accuracy) and segmentation uncertainty (uncertainty map and uncertainty score). Results: The developed SPU-Net model successfully achieved low uncertainty
The spin Seebeck effect (SSE) is sensitive to thermally driven magnetic excitations in magnetic insulators. Vanadium dioxide in its insulating low temperature phase is expected to lack magnetic degrees of freedom, as ...
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We consider a specific class of polynomial systems that arise in parameter identifiability problems of models of ordinary differential equations (ODE) and discover a method for speeding up the Gr¨obner basis comp...
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Blastomere instance segmentation is important for analyzing embryos’ abnormality. To measure the accurate shapes and sizes of blastomeres, their amodal segmentation is necessary. Amodal instance segmentation aims to ...
Blastomere instance segmentation is important for analyzing embryos’ abnormality. To measure the accurate shapes and sizes of blastomeres, their amodal segmentation is necessary. Amodal instance segmentation aims to recover an object’s complete silhouette even when the object is not fully visible. For each detected object, previous methods directly regress the target mask from input features. However, images of an object under different amounts of occlusion should have the same amodal mask output, making it harder to train the regression model. To alleviate the problem, we propose to classify input features into intermediate shape codes and recover complete object shapes. First, we pre-train the Vector Quantized Variational Autoencoder (VQ-VAE) model to learn these discrete shape codes from ground truth amodal masks. Then, we incorporate the VQ-VAE model into the amodal instance segmentation pipeline with an additional refinement module. We also detect an occlusion map to integrate occlusion information with a backbone feature. As such, our network faithfully detects bounding boxes of amodal objects. On an internal embryo cell image benchmark, the proposed method outperforms previous state-of-the-art methods. To show generalizability, we show segmentation results on the public KINS natural image benchmark. Our method would enable accurate measurement of blastomeres in In Vitro Fertilization (IVF) clinics, potentially increasing the IVF success rate.
The purpose of this article is to give a characterization of families of expander graphs via right-angled Artin groups. We prove that a sequence of simplicial graphs {Γi}i∈ forms a family of expander graphs if and o...
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The fabrication of InGaN-based blue 4✕4 array micro-LEDs (μLEDs) with 40 μm ✕40 μm chip size and 2✕2 array μLEDs with 80 μm ✕80 μm chip size etching by the inductive coupled plasma reactive ion etching (ICPRIE) ...
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A non-proteinaceous amino acid called GABA is well-known for its physiological uses and its role as an inhibitory neurotransmitter in the brain of mammals. Apart from its neurological function, GABA has been linked to...
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Limited therapeutic options are available to effectively preventing atherosclerosis. Inflammatory endothelial cells, foamy macrophages, and high protease levels contribute to atherosclerotic plaque formation. Studies ...
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Limited therapeutic options are available to effectively preventing atherosclerosis. Inflammatory endothelial cells, foamy macrophages, and high protease levels contribute to atherosclerotic plaque formation. Studies have shown that catechins effectively scavenge reactive oxygen species (ROS), inhibit monocyte adhesion and reduce cholesterol levels, while nitric oxide (NO) enhances endothelial function. However, due to the poor stability and bioavailability of catechins and the toxicity from the burst release of current synthetic small molecules NO donor, effective delivery of these bioactive compounds to treat atherosclerosis is still a challenge. Herein, a catechin/protein-based NO donor co-delivery nanosystem was designed for combinatorial anti-atherosclerotic therapy. We engineered a (−)-epigallocatechin-3-gallate (EGCG)/NO-releasing protein co-assembled nanocomplex based on specific catechin-protein interactions. Furthermore, the nanocomplex was surface modified with fucoidan (Fu), a sulfated polysaccharide with anti-inflammatory activity. This nanocomplex exhibits sensitivity to ROS, pH, and enzymes. The Fu-functionalized nanoparticles specifically accumulates in atherosclerotic plaques mediated by P-selectin on inflamed endothelial cells and scavenger receptor A (SR-A) on foamy macrophages. Under environmental stimuli that simulate the condition of plaque, the nanoparticles are readily activated to release EGCG and NO in response to excess ROS and high protease levels, exerting the multi-synergistic anti-atherosclerosic effects on reducing monocyte adhesion, promoting NO production to proliferate endothelial cells, lowering ROS levels, and decreasing the foam cell formation in vitro, and reducing lipid accumulation, plaque size, and inflammatory cytokines release in high-fat diet-induced atherosclerosis model in ApoE−/− mice. The integration of plaques targeting ability and multiple therapeutic functions can provide an advanced therapeutic strategy for athero
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