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Zijie Jin

Publications and source records attributed to Zijie Jin.

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Co-Enrichment of Proteins in Extracellular Vesicles

Extracellular vesicles (EVs) are cell-derived secretions that mediate tissue homeostasis and intercellular communication through their diverse cargos, such as proteins. Distinct EV biogenesis pathways suggest specific association and co-enrichment of proteins sharing a biogenesis pathway, and non-association and co-depletion of proteins segregated into distinct pathways. Yet these associations elude conventional protein expression or co-expression measurements. Here, we propose and define pairwise protein co-enrichment (CoEn) to quantify whether a given protein is co-enriched or co-depleted with another protein relative to its overall expression. We measure CoEn, and differential CoEn (dCoEn) between a stimulus and a reference condition, of up to 240 protein pairs in EVs using antibody microarrays. We validate CoEn by modulating well-known EV biogenesis pathways, and find that dCoEn quantifies expected changes between perturbed and reference conditions while uncovering new ones; CoEn and dCoEn in three model cell lines and parental and organotropic breast cancer progeny cell lines reveals both preserved and variable CoEn that may warrant further studies. Collectively, our result suggest that CoEn reflects and illuminates cell physiology and EV biogenies, is readily measurable, and could further serve as quality control in EV biomanufacturing as well as underpin new EV biomarkers.

q-bio.QM

NTIRE 2025 Challenge on RAW Image Restoration and Super-Resolution

This paper reviews the NTIRE 2025 RAW Image Restoration and Super-Resolution Challenge, highlighting the proposed solutions and results. New methods for RAW Restoration and Super-Resolution could be essential in modern Image Signal Processing (ISP) pipelines, however, this problem is not as explored as in the RGB domain. The goal of this challenge is two fold, (i) restore RAW images with blur and noise degradations, (ii) upscale RAW Bayer images by 2x, considering unknown noise and blur. In the challenge, a total of 230 participants registered, and 45 submitted results during thee challenge period. This report presents the current state-of-the-art in RAW Restoration.

eess.IV