@article {69568, title = {DaCapo: a modular deep learning framework for scalable 3D image segmentation}, journal = {arXiv}, year = {2024}, month = {2024 Aug 05}, abstract = {

DaCapo is a specialized deep learning library tailored to expedite the training and application of existing machine learning approaches on large, near-isotropic image data. In this correspondence, we introduce DaCapo{\textquoteright}s unique features optimized for this specific domain, highlighting its modular structure, efficient experiment management tools, and scalable deployment capabilities. We discuss its potential to improve access to large-scale, isotropic image segmentation and invite the community to explore and contribute to this open-source initiative.

}, doi = {10.48550/arXiv.2408.02834}, url = {https://doi.org/10.48550/arXiv.2408.02834}, author = {Patton, William and Jeff L Rhoades and Zouinkhi, Marwan and Ackerman, David G and Malin-Mayor, Caroline and Adjavon, Diane and Heinrich, Larissa and Bennett, Davis and Zubov, Yurii and Team, CellMap Project and Weigel, Aubrey and Funke, Jan} } @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 = {

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.

}, 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 {69518, title = {Hot-Distance: Combining One-Hot and Signed Distance Embeddings for Segmentation}, journal = {arXiv}, year = {2024}, month = {2024 Jun 25}, pages = {2406.17936}, abstract = {

Machine learning models are only as good as the data to which they are fit. As such, it is always preferable to use as much data as possible in training models. What data can be used for fitting a model depends a lot on the formulation of the task. We introduce Hot-Distance, a novel segmentation target that incorporates the strength of signed boundary distance prediction with the flexibility of one-hot encoding, to increase the amount of usable training data for segmentation of subcellular structures in focused ion beam scanning electron microscopy (FIB-SEM).

}, doi = {10.48550/arXiv.2406.17936}, url = {https://doi.org/10.48550/arXiv.2406.17936}, author = {Marwan Zouinkhi and Jeff L Rhoades and Aubrey V. Weigel} } @article {68517, title = {Structured cerebellar connectivity supports resilient pattern separation.}, journal = {Nature}, volume = {613}, year = {2023}, month = {2023 Jan 01}, pages = {543-549}, abstract = {

The cerebellum is thought to help\ detect and correct errors between intended and executed commands and is critical for social behaviours, cognition and emotion. Computations for motor control must be performed quickly to correct errors in real time and should be sensitive to small differences between patterns for fine error correction while being resilient to noise. Influential theories of cerebellar information processing have largely assumed random network connectivity, which increases the encoding capacity of the network{\textquoteright}s first layer. However, maximizing encoding capacity reduces the resilience to noise. To understand how neuronal circuits address this fundamental trade-off, we mapped the feedforward connectivity in the mouse cerebellar cortex using automated large-scale transmission electron microscopy and convolutional neural network-based image segmentation. We found that both the input and output layers of the circuit exhibit redundant and selective connectivity motifs, which contrast with prevailing models. Numerical simulations suggest that these redundant, non-random connectivity motifs increase the resilience to noise at a negligible cost to the overall encoding capacity. This work reveals how neuronal network structure can support a trade-off between encoding capacity and redundancy, unveiling principles of biological network architecture with implications for the design of artificial neural networks.

}, issn = {1476-4687}, doi = {10.1038/s41586-022-05471-w}, author = {Nguyen, Tri M and Thomas, Logan A and Jeff L Rhoades and Ricchi, Ilaria and Yuan, Xintong Cindy and Sheridan, Arlo and Hildebrand, David G C and Funke, Jan and Regehr, Wade G and Lee, Wei-Chung Allen} } @article {49060, title = {A robotic multidimensional directed evolution approach applied to fluorescent voltage reporters.}, journal = {Nature Chemical Biology}, year = {2018}, month = {2018 Feb 26}, abstract = {

We developed a new way to engineer complex proteins toward multidimensional specifications using a simple, yet scalable, directed evolution strategy. By robotically picking mammalian cells that were identified, under a microscope, as expressing proteins that simultaneously exhibit several specific properties, we can screen hundreds of thousands of proteins in a library in just a few hours, evaluating each along multiple performance axes. To demonstrate the power of this approach, we created\ a genetically encoded fluorescent voltage indicator, simultaneously optimizing its\ brightness and membrane localization using our microscopy-guided cell-picking strategy. We produced the high-performance opsin-based fluorescent voltage reporter Archon1 and demonstrated its utility by imaging spiking and millivolt-scale subthreshold and synaptic activity in acute mouse brain slices and in larval zebrafish in vivo. We also measured postsynaptic responses downstream of optogenetically controlled neurons in C. elegans.

}, issn = {1552-4469}, doi = {10.1038/s41589-018-0004-9}, author = {Piatkevich, Kiryl D and Jung, Erica E and Straub, Christoph and Linghu, Changyang and Park, Demian and Suk, Ho-Jun and Hochbaum, Daniel R and Goodwin, Daniel and Pnevmatikakis, Eftychios and Pak, Nikita and Kawashima, Takashi and Yang, Chao-Tsung and Jeff L Rhoades and Shemesh, Or and Asano, Shoh and Yoon, Young-Gyu and Freifeld, Limor and Saulnier, Jessica L and Riegler, Clemens and Engert, Florian and Hughes, Thom and Drobizhev, Mikhail and Szabo, Balint and Ahrens, Misha B and Flavell, Steven W and Sabatini, Bernardo L and Boyden, Edward S} }