Publications by authors named "Eun-Seo Cho"

Nanoscale imaging of whole vertebrates is essential for the systematic understanding of human diseases, yet this goal has not yet been achieved. Expansion microscopy (ExM) is an attractive option for accomplishing this aim; however, the expansion of even mouse embryos at mid- and late-developmental stages, which have fewer calcified body parts than adult mice, is yet to be demonstrated due to the challenges of expanding calcified tissues. Here, we introduce a state-of-the-art ExM technique, termed whole-body ExM, that utilizes cyclic digestion.

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Variations in the proportions of the two major soybean [Glycine max (L.) Merr.] seed globulins, glycinin (11S) and β-conglycinin (7S), significantly affect the nutritional and functional properties of soy-based products, but comprehensive methods for the identification and quantification of individual subunits of these proteins are currently lacking.

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The aim of this study is to describe the general features and eco-friendly biosynthesis of silver nanoparticles (AgNPs) from the marine bacterium F202Z8. To the best of our knowledge, no previous study has reported the biosynthesis of AgNPs using this strain. The formation of AgNPs using F202Z8 was synthesized intracellularly without the addition of any disturbing factors, such as antibiotics, nutrient stress, or electron donors.

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A Gram-negative, pale yellow-pigmented, non-flagellated, motile, rod-shaped and aerobic bacterium, designated strain PG104, was isolated from red algae sp. collected from the coastal area of Pohang, Republic of Korea. Growth of strain PG104 was observed at 15-35 °C (optimum, 30 °C), pH 6.

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Article Synopsis
  • SUPPORT is a self-supervised learning method designed to effectively remove Poisson-Gaussian noise from voltage imaging data by leveraging the spatial and temporal relationships between pixel values.
  • The method utilizes a convolutional neural network that accounts for spatiotemporal dependencies, allowing it to denoise images even when adjacent frames lack useful predictive information.
  • Experimental results demonstrate that SUPPORT not only accurately denoises voltage imaging data but also preserves the essential dynamics of the imaged scene.
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As a vertebrate model animal, larval zebrafish are widely used in neuroscience and provide a unique opportunity to monitor whole-brain activity at the cellular resolution. Here, we provide an optimized protocol for performing whole-brain imaging of larval zebrafish using three-dimensional fluorescence microscopy, including sample preparation and immobilization, sample embedding, image acquisition, and visualization after imaging. The current protocol enables in vivo imaging of the structure and neuronal activity of a larval zebrafish brain at a cellular resolution for over 1 h using confocal microscopy and custom-designed fluorescence microscopy.

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Three-dimensional fluorescence microscopy has an intrinsic performance limit set by the number of photons that can be collected from the sample in a given time interval. Here, we extend our earlier work - a recursive light propagation network (RLP-Net) - which is a computational microscopy technique that overcomes such limitations through virtual refocusing that enables volume reconstruction from two adjacent 2-D wide-field fluorescence images. RLP-Net employs a recursive inference scheme in which the network progressively predicts the subsequent planes along the axial direction.

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Background: Previously, we found that the water extract of Artermisia scoparia Waldst. & Kit suppressed the cytokine production of lipopolysaccharide (LPS)-stimulated macrophages and alleviated carrageenan-induced acute inflammation in mice. Artemisia contains various sesquiterpene lactones and most of them exert immunomodulatory activity.

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We report the development of deep decomposition and deconvolution microscopy (3DM), a computational microscopy method for the volumetric imaging of neural activity. 3DM overcomes the major challenge of deconvolution microscopy, the ill-posed inverse problem. We take advantage of the temporal sparsity of neural activity to reformulate and solve the inverse problem using two neural networks which perform sparse decomposition and deconvolution.

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