Jiyoon Kim on Muck Rack

Jiyoon Kim

As seen in: The Korea Daily (English), Nature, Wiley Online Library, PLOS, PLOS ONE, MDPI, Biomed Central (BMC), Medscape, Cell Press, Cell Reports Medicine, Cell Reports Physical Science and
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Articles

TIA-1 promotes FUNDC1-mediated mitophagy to protect against stress-induced cellular senescence

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By Seongho Cha, Myeongwoo Jung, Hyosun Tak, Seungyeon Ryu, Dongwoo Chae, Jiyoon Kim, Seung Jeong, Wook Kim, Eun Kyung Lee, Sukyoung Han
| Nature Verified
Abstract Mitochondrial dysfunction, characterized by reduced mitophagy, excessive mitochondrial elongation, and elevated reactive oxygen species production, is a hallmark of cellular senescence. However, the molecular mechanisms linking impairment of redox balance to mitophagy suppression during senescence remain poorly understood. In this study, we identified TIA-1, an RNA-binding protein, as a positive regulator of FUNDC1 expression, a key receptor for ubiquitin-independent mitophagy.
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Zinc as a master regulator of intracellular organelle homeostasis

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By Sofia Brito, Jiyoon Kim, Bum-Ho Bin
| Nature Verified
Abstract Zinc (Zn²⁺) is an essential trace element that supports a vast array of cellular processes, including enzymatic catalysis, gene expression, immune regulation and signaling. Its unique redox-inert properties and ability to bind diverse proteins make it indispensable for cellular homeostasis.
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AQCat25: unlocking spin-aware, high-fidelity machine learning potentials for heterogeneous catalysis

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By Omar Allam, Brook Wander, SungYeon Kim, Tyler Sours, Jia-Min Chu, Thomas Ludwig, Jiyoon Kim, Shivang Agarwal, Alan E. Rask, Alexandre Fleury Verified, Chuhong Wang, Andrew Reid Wildman, Thomas James Mustard, Kevin Ryczko, Paul Abruzzo, Aayush R. Singh, Rudi Plesch, Rodrigo Wang, AJ Nish
| Nature Verified
Abstract Large-scale datasets have enabled highly accurate machine learning interatomic potentials (MLIPs) for general-purpose heterogeneous catalysis modeling. There are, however, some limitations in what can be treated with these potentials because of gaps in the underlying training data.
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