@article {70429, title = {Abstract 5550: Phase diversity wavefront sensing for aberration correction in light sheet microscopy}, journal = {Journal of Biological Chemistry}, volume = {302}, year = {2026}, month = {2026 May }, pages = {112990}, abstract = {

Fluorescence microscopy provides insights into cellular structure and function, but the undesirable bending of light from the sample or imperfect optics ({\textquotedblleft}optical aberrations{\textquotedblright}) often degrade imaging resolution or signal. Adaptive Optics (AO) techniques that sense and subsequently correct the aberrations can restore diffraction-limited imaging, but most implementations remain technically complex and expensive. Wavefront sensing, the key step in AO, remains particularly challenging due to slow speed or additional illumination dose imparted to the sample. Previously we showed that phase diversity (PD), a method for wavefront sensing originally developed for use in astronomy, can rapidly sense and correct aberrations in a widefield fluorescence microscope. Here we extend this approach to light sheet microscopy, showing that the PD-based AO of sample-induced aberrations in live Caenorhabditis elegans embryos restores image contrast and resolution, enabling subcellular imaging throughout the imaging volume. In addition to these preliminary results, the further improvement and extension of this technology to additional samples and microscopes will be discussed.

}, keywords = {Adaptive optics, Fluorescence microscopy, light-sheet microscopy}, issn = {0021-9258}, doi = {https://doi.org/10.1016/j.jbc.2026.112990}, url = {https://www.sciencedirect.com/science/article/pii/S0021925826018624}, author = {Courtney Johnson and Magdalena Schneider and Ryan Christensen and Xuesong Li and Min Guo and Patrick La Riviere and Hari Shroff} } @article {70360, title = {Graph neural networks uncover structure and functions underlying the activity of simulated neural assemblies}, journal = {arXiv}, year = {2026}, month = {2026 Feb 11}, abstract = {

Graph neural networks trained to predict observable dynamics can be used to decompose the temporal activity of complex heterogeneous systems into simple, interpretable representations. Here we apply this framework to simulated neural assemblies with thousands of neurons and demonstrate that it can jointly reveal the connectivity matrix, the neuron types, the signaling functions, and in some cases hidden external stimuli. In contrast to existing machine learning approaches such as recurrent neural networks and transformers, which emphasize predictive accuracy but offer limited interpretability, our method provides both reliable forecasts of neural activity and interpretable decomposition of the mechanisms governing large neural assemblies.

}, doi = {10.48550/arXiv.2602.13325}, url = {https://doi.org/10.48550/arXiv.2602.13325}, author = {C{\'e}dric Allier and Larissa Heinrich and Magdalena Schneider and Stephan Saalfeld} } @conference {70314, title = {Machine learning and neural representations for enhancing phase diversity-based adaptive optics}, booktitle = {Optica Biophotonics Congress 2025}, year = {2025}, month = {2025 Apr 21}, publisher = {Optica Publishing Group}, organization = {Optica Publishing Group}, abstract = {

Using phase-diversity wavefront sensing, machine learning-based deformable mirror control, and neural representations of the unknown sample and phase aberration, we demonstrate precise reconstruction of sample structures in fluorescence microscopy, even under severe, high-order aberrations.Full-text article not available; see video presentation

}, keywords = {Adaptive optics, Deformable mirrors, Fluorescence microscopy, Machine Learning, Phase diversity, Wave front sensing}, doi = {10.1364/NTM.2025.NTu4C.1}, url = {https://opg.optica.org/abstract.cfm?URI=NTM-2025-NTu4C.1}, author = {Magdalena Schneider} }