Author

A. Mao

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Animal Behavior & Cognition· 1 Bioengineering· 1 Cell Biology· 1

Signature Assays, Organisms & Methods

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Approach:experimental assay· 2 Approach:biophysical modeling· 1 Research Focus:cancer & tumor microenvironment· 1 Organism:chicken / avian· 1 Approach:deep learning· 1 Research Focus:development & morphogenesis· 1 Research Focus:host-pathogen & immune response· 1 Organism:human· 1

Frequent Co-Authors

A. G. McElligott· 1 shared B. Liu· 1 shared C. Giraudet· 1 shared D. A. Weitz· 1 shared D. Bhatta· 1 shared D. Gao· 1 shared D. J. Mooney· 1 shared F. Li· 1 shared

Activity by Year

2021-2023 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 13 mutual co-author links

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1 shared paperAnimal Behavior & Cognition

A. G. McElligott

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

USP10 strikes down β-catenin by dual-wielding deubiquitinase activity and phase transition potential

Y. Wang, A. Mao, J. Liu, P. Li, S. Zheng, T. Tong, Z. Li, H. Zhang, L. Ma, J. Lin, Z. Pang, Q. Han, F. Li, X. Zhang, M. Chen, , R. Sheng

This study identifies USP10 as a novel regulator of β-catenin through dual mechanisms involving deubiquitination and phase separation, with implications for cancer treatment.

Cell Chemical Biology · Nov 1, 2023Peer-reviewedCell Biology

Synthesis of cell-laden alginate microgels with tunable compositions based on microfluidic pico-injection technique

Y. Zhang, A. Mao, D. J. Mooney, D. A. Weitz

The paper describes a microfluidic method to create cell-laden alginate microgels with tunable compositions by adjusting cross-linker concentration.

bioRxiv · Jan 9, 2022 · PreprintBioengineering

Automated identification of chicken distress vocalisations using deep learning models

A. MAO, C. Giraudet, K. LIU, I. De Almeida Nolasco, Z. Xie, Z. Xie, Y. Gao, J. Theobald, D. Bhatta, R. Stewart, A. G. McElligott

This paper develops a lightweight deep learning model to automatically identify chicken distress calls, achieving high accuracy with reduced computational requirements.

bioRxiv · Dec 15, 2021Animal Behavior & Cognition