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13,430 results on '"Hyun Seok"'

351. Meta-analysis of the space flight and microgravity response of the Arabidopsis plant transcriptome

Author

Barker, Richard, Kruse, Colin P. S., Johnson, Christina, Saravia-Butler, Amanda, Fogle, Homer, Chang, Hyun-Seok, Trane, Ralph Møller, Kinscherf, Noah, Villacampa, Alicia, Manzano, Aránzazu, Herranz, Raúl, Davin, Laurence B., Lewis, Norman G., Perera, Imara, Wolverton, Chris, Gupta, Parul, Jaiswal, Pankaj, Reinsch, Sigrid S., Wyatt, Sarah, and Gilroy, Simon

Published
2023
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372. Global transpiration data from sap flow measurements: the SAPFLUXNET database

Author

Poyatos, Rafael, Granda, Víctor, Flo, Víctor, Adams, Mark A, Adorján, Balázs, Aguadé, David, Aidar, Marcos PM, Allen, Scott, Alvarado-Barrientos, M Susana, Anderson-Teixeira, Kristina J, Aparecido, Luiza Maria, Arain, M Altaf, Aranda, Ismael, Asbjornsen, Heidi, Baxter, Robert, Beamesderfer, Eric, Berry, Z Carter, Berveiller, Daniel, Blakely, Bethany, Boggs, Johnny, Bohrer, Gil, Bolstad, Paul V, Bonal, Damien, Bracho, Rosvel, Brito, Patricia, Brodeur, Jason, Casanoves, Fernando, Chave, Jérôme, Chen, Hui, Cisneros, Cesar, Clark, Kenneth, Cremonese, Edoardo, Dang, Hongzhong, David, Jorge S, David, Teresa S, Delpierre, Nicolas, Desai, Ankur R, C., Frederic, Dohnal, Michal, Domec, Jean-Christophe, Dzikiti, Sebinasi, Edgar, Colin, Eichstaedt, Rebekka, El-Madany, Tarek S, Elbers, Jan, Eller, Cleiton B, Euskirchen, Eugénie S, Ewers, Brent, Fonti, Patrick, Forner, Alicia, Forrester, David I, Freitas, Helber C, Galvagno, Marta, Garcia-Tejera, Omar, Ghimire, Chandra Prasad, Gimeno, Teresa E, Grace, John, Granier, André, Griebel, Anne, Guangyu, Yan, Gush, Mark B, Hanson, Paul J, Hasselquist, Niles J, Heinrich, Ingo, Hernandez-Santana, Virginia, Herrmann, Valentine, Hölttä, Teemu, Holwerda, Friso, Irvine, James, Ayutthaya, Supat Isarangkool Na, Jarvis, Paul G, Jochheim, Hubert, Joly, Carlos A, Kaplick, Julia, Kim, Hyun Seok, Klemedtsson, Leif, Kropp, Heather, Lagergren, Fredrik, Lane, Patrick, Lang, Petra, Lapenas, Andrei, Lechuga, Víctor, Lee, Minsu, Leuschner, Christoph, Limousin, Jean-Marc, Linares, Juan Carlos, Linderson, Maj-Lena, Lindroth, Anders, Llorens, Pilar, López-Bernal, Álvaro, Loranty, Michael M, Lüttschwager, Dietmar, Macinnis-Ng, Cate, Maréchaux, Isabelle, Martin, Timothy A, Matheny, Ashley, McDowell, Nate, McMahon, Sean, Meir, Patrick, and Mészáros, Ilona

Subjects
Life on Land, Atmospheric Sciences, Geochemistry, Physical Geography and Environmental Geoscience
Abstract

Plant transpiration links physiological responses of vegetation to water supply and demand with hydrological, energy, and carbon budgets at the land-atmosphere interface. However, despite being the main land evaporative flux at the global scale, transpiration and its response to environmental drivers are currently not well constrained by observations. Here we introduce the first global compilation of whole-plant transpiration data from sap flow measurements (SAPFLUXNET, https://sapfluxnet.creaf.cat/, last access: 8 June 2021). We harmonized and quality-controlled individual datasets supplied by contributors worldwide in a semi-automatic data workflow implemented in the R programming language. Datasets include sub-daily time series of sap flow and hydrometeorological drivers for one or more growing seasons, as well as metadata on the stand characteristics, plant attributes, and technical details of the measurements. SAPFLUXNET contains 202 globally distributed datasets with sap flow time series for 2714 plants, mostly trees, of 174 species. SAPFLUXNET has a broad bioclimatic coverage, with woodland/shrubland and temperate forest biomes especially well represented (80% of the datasets). The measurements cover a wide variety of stand structural characteristics and plant sizes. The datasets encompass the period between 1995 and 2018, with 50% of the datasets being at least 3 years long. Accompanying radiation and vapour pressure deficit data are available for most of the datasets, while on-site soil water content is available for 56% of the datasets. Many datasets contain data for species that make up 90% or more of the total stand basal area, allowing the estimation of stand transpiration in diverse ecological settings. SAPFLUXNET adds to existing plant trait datasets, ecosystem flux networks, and remote sensing products to help increase our understanding of plant water use, plant responses to drought, and ecohydrological processes. SAPFLUXNET version 0.1.5 is freely available from the Zenodo repository (10.5281/zenodo.3971689; Poyatos et al., 2020a). The "sapfluxnetr"R package-designed to access, visualize, and process SAPFLUXNET data-is available from CRAN.

Published
2021

387. Deep learning-enabled image quality control in tomographic reconstruction: Robust optical diffraction tomography

Author

Ryu, Donghun, Jo, Youngju, Yoo, Jihyeong, Chang, Taean, Ahn, Daewoong, Kim, Young Seo, Kim, Geon, Min, Hyun-seok, and Park, Yongkeun

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Physics - Optics
Abstract

In tomographic reconstruction, the image quality of the reconstructed images can be significantly degraded by defects in the measured two-dimensional (2D) raw image data. Despite the importance of screening defective 2D images for robust tomographic reconstruction, manual inspection and rule-based automation suffer from low-throughput and insufficient accuracy, respectively. Here, we present deep learning-enabled quality control for holographic data to produce robust and high-throughput optical diffraction tomography (ODT). The key idea is to distill the knowledge of an expert into a deep convolutional neural network. We built an extensive database of optical field images with clean/noisy annotations, and then trained a binary classification network based upon the data. The trained network outperformed visual inspection by non-expert users and a widely used rule-based algorithm, with > 90% test accuracy. Subsequently, we confirmed that the superior screening performance significantly improved the tomogram quality. To further confirm the trained model's performance and generalizability, we evaluated it on unseen biological cell data obtained with a setup that was not used to generate the training dataset. Lastly, we interpreted the trained model using various visualization techniques that provided the saliency map underlying each model inference.

Published
2019
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389. Real-time observations of TRIP-induced ultrahigh strain hardening in a dual-phase CrMnFeCoNi high-entropy alloy.

Author

Chen, Sijing, Oh, Hyun Seok, Gludovatz, Bernd, Kim, Sang Jun, Park, Eun Soo, Zhang, Ze, Ritchie, Robert O, and Yu, Qian

Abstract

Strategies involving metastable phases have been the basis of the design of numerous alloys, yet research on metastable high-entropy alloys is still in its infancy. In dual-phase high-entropy alloys, the combination of local chemical environments and loading-induced crystal structure changes suggests a relationship between deformation mechanisms and chemical atomic distribution, which we examine in here in a Cantor-like Cr20Mn6Fe34Co34Ni6 alloy, comprising both face-centered cubic (fcc) and hexagonal closed packed (hcp) phases. We observe that partial dislocation activities result in stable three-dimensional stacking-fault networks. Additionally, the fraction of the stronger hcp phase progressively increases during plastic deformation by forming at the stacking-fault network boundaries in the fcc phase, serving as the major source of strain hardening. In this context, variations in local chemical composition promote a high density of Lomer-Cottrell locks, which facilitate the construction of the stacking-fault networks to provide nucleation sites for the hcp phase transformation.

Published
2020

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