Yi Xin on Muck Rack

Yi Xin

As seen in: Nature, Wiley, Wiley Online Library, PLOS ONE, Frontiers, MDPI, Thorax, China Daily (English), China Daily Global, Science Magazine and
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Articles

High-throughput Raman-activated cell sorting of microalgal genome-wide edited library revealed a regulatory pathway for carotenoid synthesis

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By Qintao Wang, Yanhai Gong, Lianhong Wang, Xuefeng Du, Jiashun Zhang, Yi Xin, Zoran Nikoloski, Yonghua Li-Beisson, Luyang Sun, Bo Ma, Xixian Wang, Jian Xu, Nana Lv
| Nature Verified
Abstract Functional genomics have been hampered by the paucity of efficient methods that connect genotype and metabolic phenotype at single-cell resolution. Using the industrial microalga Nannochloropsis oceanica as a model, we introduced a platform that comprises a genome-wide single-gene-edited mutant library and high-throughput Raman-activated cell sorting (RACS).
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Today in History: A century-long tale of two buildings

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By Yi Xin
| The Daily Princetonian Verified
Lying just past the corner of William Street and Washington Road is a pair of matching buildings in the Collegiate Gothic style, with pointed-arch entrances set centrally in their rectangular ashlar façade: Green Hall, the current home to the Program in Linguistics, and the Julis Romo Rabinowitz Building, housing the Department of Economics and other economics-related research centers.
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Multi-scale data improves performance of machine learning model for long COVID identification

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By Xinmeng Zhang, Srushti Gangireddy, Alyson Dickson, Emily R Pfaff, Hiral Master, Yi Xin, Christopher Chute, Nguyen Tran, Salvatore Mancuso, Toufeeq Ahmed Syed, Zhongming Zhao, QiPing Feng, Melissa Haendel, Paul Harris Verified, Lang Li, Geoffrey Ginsburg, Joshua C. Denny, Dan M. Roden, Wei-Qi Wei, Christopher Guardo, V. Eric Kerchberger, Melissa Basford, Christopher Lunt
| Nature Verified
Abstract Long COVID affects a substantial proportion of the over 778 million individuals infected with SARS-CoV-2, yet predictive models remain limited in scope. While existing efforts, such as the National COVID Cohort Collaborative (N3C), have leveraged electronic health record (EHR) data for risk prediction and identification, accumulating evidence points to additional contributions from social, behavioral, and genetic factors.
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