@article {69902, title = {Chromatix: a differentiable, GPU-accelerated wave-optics library}, journal = {bioRxiv}, year = {2025}, month = {2025 May 2}, abstract = {

Modern microscopy methods incorporate computational modeling of optical systems as an integral part of the imaging process, either to solve inverse problems or enable optimization of the optical system design. These methods often depend on differentiable simulations of optical systems, yet no standardized framework exists{\textemdash}forcing computational optics researchers to repeatedly and independently implement simulations that are prone to errors, difficult to reuse in other applications, and often computationally suboptimal. These common problems limit the potential impact of computational optics as a field. We present Chromatix: an open-source, GPU-accelerated differentiable wave optics library. Chromatix builds on JAX to enable fast simulation of diverse optical systems and inverse problem solving, scaling these simulations from single-CPU laptops to multi-GPU servers. The library implements various optical elements (e.g., lenses, polarizers and spatial light modulators) and multiple light propagation models (e.g., Fresnel approximation, angular spectrum and off-axis propagation) that can be flexibly combined to model various computational optics applications such as snapshot microscopy, holography, and phase retrieval of multiple scattering samples. These simulations can be automatically parallelized to scale across multiple GPUs with a single-line change to the modeling code, enabling simulation and optimization of previously impractical optical system designs. We demonstrate Chromatix{\textquoteright}s capacity to substantially accelerate optics simulation and optimization on existing methods in computational optics, speeding up optical simulation and optimization from 2-6{\texttimes} on a single GPU to up to 22{\texttimes} on 8 GPUs (depending on the particular system being modeled) compared to the original implementations. Chromatix establishes a standard for wave optics simulations, democratizing access to and expanding the design space of computational optics.i

}, doi = {10.1101/2025.04.29.651152}, url = {https://www.biorxiv.org/content/early/2025/05/02/2025.04.29.651152}, author = {Deb, Diptodip and Both, Gert-Jan and Bezzam, Eric and Kohli, Amit and Yang, Siqi and Chaware, Amey and Allier, C{\'e}dric and Cai, Changjia and Anderberg, Geneva and Eybposh, M. Hossein and Schneider, Magdalena C. and Heintzmann, Rainer and Rivera-Sanchez, Fabrizio A. and Simmerer, Corey and Meng, Guanghan and Tormes-Vaquerano, Jovan and Han, SeungYun and Shanmugavel, Sibi Chakravarthy and Maruvada, Teja and Yang, Xi and Kim, Yewon and Diederich, Benedict and Joo, Chulmin and Waller, Laura and Durr, Nicholas J. and P{\'e}gard, Nicolas C. and La Rivi{\`e}re, Patrick J. and Horstmeyer, Roarke and Chowdhury, Shwetadwip and Turaga, Srinivas C.} } @article {69318, title = {Diversification of small RNA pathways underlies germline RNA interference incompetence in wild Caenorhabditis elegans strains.}, journal = {Genetics}, volume = {226}, year = {2024}, month = {2024 Jan 3}, abstract = {

The discovery that experimental delivery of dsRNA can induce gene silencing at target genes revolutionized genetics research, by both uncovering essential biological processes and creating new tools for developmental geneticists. However, the efficacy of exogenous RNA interference (RNAi) varies dramatically within the Caenorhabditis elegans natural population, raising questions about our understanding of RNAi in the lab relative to its activity and significance in nature. Here, we investigate why some wild strains fail to mount a robust RNAi response to germline targets. We observe diversity in mechanism: in some strains, the response is stochastic, either on or off among individuals, while in others, the response is consistent but delayed. Increased activity of the Argonaute PPW-1, which is required for germline RNAi in the laboratory strain N2, rescues the response in some strains but dampens it further in others. Among wild strains, genes known to mediate RNAi exhibited very high expression variation relative to other genes in the genome as well as allelic divergence and strain-specific instances of pseudogenization at the sequence level. Our results demonstrate functional diversification in the small RNA pathways in C. elegans and suggest that RNAi processes are evolving rapidly and dynamically in nature.

}, keywords = {Animals, Caenorhabditis elegans, Caenorhabditis elegans Proteins, Germ Cells, Humans, RNA Interference, RNA, Double-Stranded, RNA, Small Interfering}, issn = {1943-2631}, doi = {10.1093/genetics/iyad191}, author = {Chou, Han Ting and Valencia, Francisco and Alexander, Jacqueline C and Bell, Avery Davis and Deb, Diptodip and Pollard, Daniel A and Paaby, Annalise B} } @article {68564, title = {Local shape descriptors for neuron segmentation.}, journal = {Nature Methods}, volume = {20}, year = {2023}, month = {2023 Feb 01}, pages = {295-303}, abstract = {

We present an auxiliary learning task for the problem of neuron segmentation in electron microscopy volumes. The auxiliary task consists of the prediction of local shape descriptors (LSDs), which we combine with conventional voxel-wise direct neighbor affinities for neuron boundary detection. The shape descriptors capture local statistics about the neuron to be segmented, such as diameter, elongation, and direction. On a study comparing several existing methods across various specimen, imaging techniques, and resolutions, auxiliary learning of LSDs consistently increases segmentation accuracy of affinity-based methods over a range of metrics. Furthermore, the addition of LSDs promotes affinity-based segmentation methods to be on par with the current state of the art for neuron segmentation (flood-filling networks), while being two orders of magnitudes more efficient-a critical requirement for the processing of future petabyte-sized datasets.

}, issn = {1548-7105}, doi = {10.1038/s41592-022-01711-z}, author = {Sheridan, Arlo and Nguyen, Tri M and Deb, Diptodip and Lee, Wei-Chung Allen and Saalfeld, Stephan and Turaga, Srinivas C and Manor, Uri and Funke, Jan} } @article {67299, title = {Algorithms underlying flexible phototaxis in larval zebrafish.}, journal = {Journal of Experimental Biology}, volume = {224}, year = {2021}, month = {2021 May 15}, abstract = {

To thrive, organisms must maintain physiological and environmental variables in suitable ranges. Given that these variables undergo constant fluctuations over varying time scales, how do biological control systems maintain control over these values? We explored this question in the context of phototactic behavior in larval zebrafish. We demonstrate that larval zebrafish use phototaxis to maintain environmental luminance at a set point, that the value of this set point fluctuates on a time scale of seconds when environmental luminance changes, and that it is determined by calculating the mean input across both sides of the visual field. These results expand on previous studies of flexible phototaxis in larval zebrafish; they suggest that larval zebrafish exert homeostatic control over the luminance of their surroundings, and that feedback from the surroundings drives allostatic changes to the luminance set point. As such, we describe a novel behavioral algorithm with which larval zebrafish exert control over a sensory variable.

}, issn = {1477-9145}, doi = {10.1242/jeb.238386}, author = {Chen, Alex B and Deb, Diptodip and Bahl, Armin and Engert, Florian} } @article {67228, title = {Local Shape Descriptors for Neuron Segmentation}, journal = {bioRxiv}, year = {2021}, month = {2021/01}, abstract = {

We present a simple, yet effective, auxiliary learning task for the problem of neuron segmentation in electron microscopy volumes. The auxiliary task consists of the prediction of Local Shape Descriptors (LSDs), which we combine with conventional voxel-wise direct neighbor affinities for neuron boundary detection. The shape descriptors are designed to capture local statistics about the neuron to be segmented, such as diameter, elongation, and direction. On a large study comparing several existing methods across various specimen, imaging techniques, and resolutions, we find that auxiliary learning of LSDs consistently increases segmentation accuracy of affinity-based methods over a range of metrics. Furthermore, the addition of LSDs promotes affinitybased segmentation methods to be on par with the current state of the art for neuron segmentation (Flood-Filling Networks, FFN), while being two orders of magnitudes more efficient{\textemdash}a critical requirement for the processing of future petabyte-sized datasets. Implementations of the new auxiliary learning task, network architectures, training, prediction, and evaluation code, as well as the datasets used in this study are publicly available as a benchmark for future method contributions.Competing Interest StatementThe authors have declared no competing interest.

}, doi = {10.1101/2021.01.18.427039}, url = {https://www.biorxiv.org/content/early/2021/01/18/2021.01.18.427039}, author = {Sheridan, Arlo and Nguyen, Tri and Deb, Diptodip and Lee, Wei-Chung Allen and Saalfeld, Stephan and Turaga, Srini and Manor, Uri and Funke, Jan} } @booklet {67972, title = {Programmable 3D snapshot microscopy with Fourier convolutional networks}, year = {2021}, abstract = {

3D snapshot microscopy enables fast volumetric imaging by capturing a 3D volume in a single 2D camera image and performing computational reconstruction. Fast volumetric imaging has a variety of biological applications such as whole brain imaging of rapid neural activity in larval zebrafish. The optimal microscope design for this optical 3D-to-2D encoding is both sample- and task-dependent, with no general solution known. Deep learning based decoders can be combined with a differentiable simulation of an optical encoder for end-to-end optimization of both the deep learning decoder and optical encoder. This technique has been used to engineer local optical encoders for other problems such as depth estimation, 3D particle localization, and lensless photography. However, 3D snapshot microscopy is known to require a highly non-local optical encoder which existing UNet-based decoders are not able to engineer. We show that a neural network architecture based on global kernel Fourier convolutional neural networks can efficiently decode information from multiple depths in a volume, globally encoded across a 3D snapshot image. We show in simulation that our proposed networks succeed in engineering and reconstructing optical encoders for 3D snapshot microscopy where the existing state-of-the-art UNet architecture fails. We also show that our networks outperform the state-of-the-art learned reconstruction algorithms for a computational photography dataset collected on a prototype lensless camera which also uses a highly non-local optical encoding.

}, 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 = {10.48550/ARXIV.2104.10611}, url = {https://arxiv.org/abs/2104.10611}, author = {Deb, Diptodip and Jiao, Zhenfei and Chen, Alex B. and Broxton, Michael and Ahrens, Misha B. and Podgorski, Kaspar and Turaga, Srinivas C.} }