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20172026
most citedWrinkle force microscopy: a new machine learning based approach to predict cell mechanics from images

1 citations · 1 across the 4 of their papers we have counts for

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physics.bio-ph2026

Elastohydrodynamic coupling enhances flow generation by coordinated ciliary beating

Shota Nakano, Shinji Deguchi, Daiki Matsunaga

Ciliary arrays pump fluid at low Reynolds number through non-reciprocal beating and phase coordination between neighbouring cilia. Previous studies have demonstrated that antiplect…

physics.bio-ph2026

Tuning microswimmer motility by liposome encapsulation: swimming and cargo transport of Chlamydomonas-encapsulating liposome

Koichiro Akiyama, Sota Hamaguchi, Hiromasa Shiraiwa +4

Inspired by biology's use of vesicles for targeted transport, many studies have propelled liposomes with active matter, creating synthetic systems that can be viewed as microscale…

physics.bio-ph2025

Optimal Undulatory Swimming with Constrained Deformation and Actuation Intervals

Fumiya Tokoro, Hideki Takayama, Shinji Deguchi +2

In nature, many unicellular organisms are able to swim with the help of beating filaments, where local energy input leads to cooperative undulatory beating motion. Here, we investi…

physics.bio-ph20211 cited

Wrinkle force microscopy: a new machine learning based approach to predict cell mechanics from images

Honghan Li, Daiki Matsunaga, Tsubasa S. Matsui +4

Combining experiments with artificial intelligence algorithms, we propose a new machine learning based approach to extract the cellular force distributions from the microscope imag…

physics.bio-ph2019

Image based cellular contractile force evaluation with small-world network inspired CNN: SW-UNet

Li Honghan, Daiki Matsunaga, Tsubasa S. Matsui +2

We propose an image-based cellular contractile force evaluation method using a machine learning technique. We use a special substrate that exhibits wrinkles when cells grab the sub…