Of course!!! Looking forward to reading you again!!

About stress inference
bsky.app/profile/epim...

Speaking of time, @osvaldo-chara.bsky.social 's lab recently developed a new method, ForSys, that uses all available dynamic information. It also tracks the elements of the tissue over time and uses their movement to further inform the stress inference algorithm.

bsky.app/profile/epim...

The code is available at
github.com/borgesaugust... . We welcome any and all feedback 😁!
Stay tuned for more upcoming projects 😉!

(10/10)

I would like to thank my amazing colleagues @borgesaugust.bsky.social, Jeronimo Miranda, Alberto Ceccarelli, Guilherme Ventura, @kuba-sedzinski.bsky.social and @hernanlopez-schier.bsky.social, & reviewers, the editorial team of @cp-iscience.bsky.social and the community for their support.

(9/10)

Finally, we investigated the Polarity Cell Inversion process in the fish’s neuromast. This is a highly dynamic cell rearrangement process, in which ForSys predicts mechanical asymmetries among cell types. You can read more about this process at
doi.org/10.1242/dev....

(8/10)

Then, we applied our tool to the migrating primordium in Zebrafish’s lateral line and were able to pinpoint the location of the rossetogenesis before neuromast deposition by looking at the stresses in the basal plane.

(7/10)

Afterwards, we used ForSys’s static modality to predict stress in Xenopus embryos and found that ForSys could predict the distributions of tensions with good accuracy.

(6/10)

We validated our software in silico and in vivo. First, we generated in silico tissues with Surface Evolver, through the seapipy software, and found that ForSys outperforms existing software, especially for very dynamic tissues.

(5/10)

ForSys is an open source tool that is usable in static (dynamic) conditions to infer the intracellular stresses and intracellular pressures, given segmented (time-lapse) microscopies.

(4/10)

Stress inference methods have been around for about a decade and work mostly in static conditions.
You can read our review at doi.org/10.1042/BST2...

(3/10)