Zi Wang

SynSeg generates synthetic datasets to train a U-net model for subcellular segmentation, outperforming traditional methods in vesicle and cytoskeletal filament detection.

The paper identifies translational repression of CYC-2.1 in the germline as a mechanism that extends lifespan through non-autonomous activation of UPRmt and AMPK in C. elegans.

The paper constructs a germline-specific RNAi tool in C. elegans by combining an rde-1 mutation with a germline-specific transgene, demonstrating its efficiency and specificity in RNAi knockdown.

The paper presents a deep learning method using GANs and feature transfer to classify cell shapes with minimal training samples, achieving high accuracy in detecting multicellular rosettes in C. elegans embryos.