@article {69602,
	title = {Data Release: High-Resolution Imaging and Segmentation of P7 Mouse Tissue Microarchitecture Using FIB-SEM and Machine Learning},
	journal = {bioRxiv},
	year = {2024},
	month = {2024 Sep 07},
	abstract = {<p>This report presents a comprehensive data release exploring the tissue microarchitecture of P7 aged mice using Focused Ion Beam Scanning Electron Microscopy (FIB-SEM) combined with machine learning-based segmentations of nuclei. The study includes high-resolution 3D volumes and nucleus segmentations for seven vital tissues{\textemdash}pancreas, liver, kidney, heart, thymus, hippocampus, and skin{\textemdash}from a single mouse. The detailed datasets are openly accessible on OpenOrganelle.org, providing a valuable resource for the scientific community to support further research and collaboration.</p>
},
	doi = {10.1101/2024.09.05.611438},
	url = {https://www.doi.org/10.1101/2024.09.05.611438},
	author = {Ackerman, David and Avetissian, Emma and Bleck, Christopher K. E. and Bogovic, John A. and Innerberger, Michael and Korff, Wyatt and Li, Wei-Ping and Lu, Zhiyuan and Petruncio, Alyson and Preibisch, Stephan and Qiu, Wei and Jeff L Rhoades and Saalfeld, Stephan and Silva, Malan and Trautman, Eric T. and Vorimo, Rebecca and Weigel, Aubrey and Yu, Zhiheng and Zubov, Yurii and ,}
}
@article {69567,
	title = {Decomposing heterogeneous dynamical systems with graph neural networks},
	journal = {arXiv},
	year = {2024},
	month = {2024 Jul 27},
	abstract = {<p>Natural physical, chemical, and biological dynamical systems are often complex, with heterogeneous components interacting in diverse ways. We show that graph neural networks can be designed to jointly learn the interaction rules and the structure of the heterogeneity from data alone. The learned latent structure and dynamics can be used to virtually decompose the complex system which is necessary to parameterize and infer the underlying governing equations. We tested the approach with simulation experiments of moving particles and vector fields that interact with each other. While our current aim is to better understand and validate the approach with simulated data, we anticipate it to become a generally applicable tool to uncover the governing rules underlying complex dynamics observed in nature.</p>
},
	doi = {10.48550/arXiv.2407.19160},
	url = {https://doi.org/10.48550/arXiv.2407.19160},
	author = {Allier, C{\'e}dric and Schneider, Magdalena C and Innerberger, Michael and Heinrich, Larissa and Bogovic, John A and Saalfeld, Stephan}
}
