@article {69422,
	title = {Fast, Accurate, and Versatile Data Analysis Platform for the Quantification of Molecular Spatiotemporal Signals},
	journal = {bioRxiv},
	year = {2024},
	month = {2024 May 07},
	abstract = {<p>Optical recording of intricate molecular dynamics is becoming an indispensable technique for biological studies, accelerated by the development of new or improved biosensors and microscopy technology. This creates major computational challenges to extract and quantify biologically meaningful patterns embedded within complex and rich data sources. Here, we introduce Activity Quantification and Analysis (AQuA2), a fast, accurate and versatile data analysis platform built upon advanced machine learning techniques. It decomposes complex live imaging-based datasets into elementary signaling events, allowing accurate and unbiased quantification of molecular activities and identification of consensus functional units. We demonstrate applications across a range of biosensors (calcium, norepinephrine, ATP, acetylcholine, dopamine), cell types (astrocytes, oligodendrocytes, microglia, neurons), organs (brains and spinal cords), animal models (zebrafish and mouse), and imaging modalities (confocal, two-photon, light sheet). As exemplar findings, we show how AQuA2 identified drug-dependent interactions between neurons and astroglia, and distinct sensorimotor signal propagation patterns in the mouse spinal cord.</p>
},
	doi = {10.1101/2024.05.02.592259},
	url = {https://www.biorxiv.org/content/early/2024/05/06/2024.05.02.592259},
	author = {Xuelong Mi and Alex Bo-Yuan Chen and Daniela Duarte and Erin Carey and Charlotte R. Taylor and Philipp N. Braaker and Mark Bright and Rafael G. Almeida and Jing-Xuan Lim and Virginia M. Rutten and Wei Zheng and Mengfan Wang and Michael E. Reitman and Yizhi Wang and Kira E. Poskanzer and David A. Lyons and Axel Nimmerjahn and Misha B. Ahrens and Guoqiang Yu}
}
@conference {68678,
	title = {FourierNets enable the design of highly non-local optical encoders for computational imaging},
	booktitle = {Advances in Neural Information Processing Systems},
	year = {2022},
	month = {10/2022},
	abstract = {<p>Differentiable simulations of optical systems can be combined with deep learning-based reconstruction networks to enable high performance computational imaging via end-to-end (E2E) optimization of both the optical encoder and the deep decoder. This has enabled imaging applications such as 3D localization microscopy, depth estimation, and lensless photography via the optimization of local optical encoders. More challenging computational imaging applications, such as 3D snapshot microscopy which compresses 3D volumes into single 2D images, require a highly non-local optical encoder. We show that existing deep network decoders have a locality bias which prevents the optimization of such highly non-local optical encoders. We address this with a decoder based on a shallow neural network architecture using global kernel Fourier convolutional neural networks (FourierNets). We show that FourierNets surpass existing deep network based decoders at reconstructing photographs captured by the highly non-local DiffuserCam optical encoder. Further, we show that FourierNets enable E2E optimization of highly non-local optical encoders for 3D snapshot microscopy. By combining FourierNets with a large-scale multi-GPU differentiable optical simulation, we are able to optimize non-local optical encoders 170{\texttimes}\&nbsp;to 7372{\texttimes}\&nbsp;larger than prior state of the art, and demonstrate the potential for ROI-type specific optical encoding with a programmable microscope.</p>
},
	keywords = {Computer Vision and Pattern Recognition (cs.CV), electronic engineering, FOS: Computer and information sciences, FOS: Electrical engineering, Image and Video Processing (eess.IV), information engineering, Machine Learning (cs.LG)},
	doi = {https://doi.org/10.48550/arXiv.2104.10611},
	url = {https://openreview.net/forum?id=Kx1VCs1treH},
	author = {Diptodip Deb and Zhenfei Jiao and Ruth R Sims and Alex Bo-Yuan Chen and Michael Broxton and Misha Ahrens and Kaspar Podgorski and Srinivas C Turaga},
	editor = {Alice H. Oh and Alekh Agarwal and Danielle Belgrave and Kyunghyun Cho}
}
