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1. Multifrequency Black Hole Imaging for the Next-generation Event Horizon Telescope

   https://doi.org/10.3847/1538-4357/acb7e4  

   Chael, Andrew ; Issaoun, Sara ; Pesce, Dominic W. ; Johnson, Michael D. ; Ricarte, Angelo ; Fromm, Christian M. ; Mizuno, Yosuke ( March 2023 , The Astrophysical Journal)

   The Event Horizon Telescope (EHT) has produced images of the plasma flow around the supermassive black holes in Sgr A* and M87* with a resolution comparable to the projected size of their event horizons. Observations with the next-generation Event Horizon Telescope (ngEHT) will have significantly improved Fourier plane coverage and will be conducted at multiple frequency bands (86, 230, and 345 GHz), each with a wide bandwidth. At these frequencies, both Sgr A* and M87* transition from optically thin to optically thick. Resolved spectral index maps in the near-horizon and jet-launching regions of these supermassive black hole sources can constrain properties of the emitting plasma that are degenerate in single-frequency images. In addition, combining information from data obtained at multiple frequencies is a powerful tool for interferometric image reconstruction, since gaps in spatial scales in single-frequency observations can be filled in with information from other frequencies. Here we present a new method of simultaneously reconstructing interferometric images at multiple frequencies along with their spectral index maps. The method is based on existing regularized maximum likelihood (RML) methods commonly used for EHT imaging and is implemented in theeht-imagingPython software library. We show results of this method on simulated ngEHT datamore »sets as well as on real data from the Very Long Baseline Array and Atacama Large Millimeter/submillimeter Array. These examples demonstrate that simultaneous RML multifrequency image reconstruction produces higher-quality and more scientifically useful results than is possible from combining independent image reconstructions at each frequency.

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2. A Primer on Topological Data Analysis to Support Image Analysis Tasks in Environmental Science

   https://doi.org/10.1175/AIES-D-22-0039.1  

   Ver Hoef, Lander ; Adams, Henry ; King, Emily J. ; Ebert-Uphoff, Imme ( January 2023 , Artificial Intelligence for the Earth Systems)

   Topological data analysis (TDA) is a tool from data science and mathematics that is beginning to make waves in environmental science. In this work, we seek to provide an intuitive and understandable introduction to a tool from TDA that is particularly useful for the analysis of imagery, namely, persistent homology. We briefly discuss the theoretical background but focus primarily on understanding the output of this tool and discussing what information it can glean. To this end, we frame our discussion around a guiding example of classifying satellite images from the sugar, fish, flower, and gravel dataset produced for the study of mesoscale organization of clouds by Rasp et al. We demonstrate how persistent homology and its vectorization, persistence landscapes, can be used in a workflow with a simple machine learning algorithm to obtain good results, and we explore in detail how we can explain this behavior in terms of image-level features. One of the core strengths of persistent homology is how interpretable it can be, so throughout this paper we discuss not just the patterns we find but why those results are to be expected given what we know about the theory of persistent homology. Our goal is that readersmore »of this paper will leave with a better understanding of TDA and persistent homology, will be able to identify problems and datasets of their own for which persistent homology could be helpful, and will gain an understanding of the results they obtain from applying the included GitHub example code.

   Information such as the geometric structure and texture of image data can greatly support the inference of the physical state of an observed Earth system, for example, in remote sensing to determine whether wildfires are active or to identify local climate zones. Persistent homology is a branch of topological data analysis that allows one to extract such information in an interpretable way—unlike black-box methods like deep neural networks. The purpose of this paper is to explain in an intuitive manner what persistent homology is and how researchers in environmental science can use it to create interpretable models. We demonstrate the approach to identify certain cloud patterns from satellite imagery and find that the resulting model is indeed interpretable.

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3. Deep learning extended depth-of-field microscope for fast and slide-free histology

   https://doi.org/10.1073/pnas.2013571117  

   Jin, Lingbo ; Tang, Yubo ; Wu, Yicheng ; Coole, Jackson B. ; Tan, Melody T. ; Zhao, Xuan ; Badaoui, Hawraa ; Robinson, Jacob T. ; Williams, Michelle D. ; Gillenwater, Ann M. ; et al ( December 2020 , Proceedings of the National Academy of Sciences)

   Microscopic evaluation of resected tissue plays a central role in the surgical management of cancer. Because optical microscopes have a limited depth-of-field (DOF), resected tissue is either frozen or preserved with chemical fixatives, sliced into thin sections placed on microscope slides, stained, and imaged to determine whether surgical margins are free of tumor cells—a costly and time- and labor-intensive procedure. Here, we introduce a deep-learning extended DOF (DeepDOF) microscope to quickly image large areas of freshly resected tissue to provide histologic-quality images of surgical margins without physical sectioning. The DeepDOF microscope consists of a conventional fluorescence microscope with the simple addition of an inexpensive (less than $10) phase mask inserted in the pupil plane to encode the light field and enhance the depth-invariance of the point-spread function. When used with a jointly optimized image-reconstruction algorithm, diffraction-limited optical performance to resolve subcellular features can be maintained while significantly extending the DOF (200 µm). Data from resected oral surgical specimens show that the DeepDOF microscope can consistently visualize nuclear morphology and other important diagnostic features across highly irregular resected tissue surfaces without serial refocusing. With the capability to quickly scan intact samples with subcellular detail, the DeepDOF microscope can improve tissue samplingmore »during intraoperative tumor-margin assessment, while offering an affordable tool to provide histological information from resected tissue specimens in resource-limited settings.« less
4. A low-cost smartphone fluorescence microscope for research, life science education, and STEM outreach

   https://doi.org/10.1038/s41598-023-29182-y  

   Schaefer, Madison A. ; Nelson, Heather N. ; Butrum, John L. ; Gronseth, James R. ; Hines, Jacob H. ( March 2023 , Scientific Reports)

   Much of our understanding of cell and tissue development, structure, and function stems from fluorescence microscopy. The acquisition of colorful and glowing images engages and excites users ranging from seasoned microscopists to STEM students. Fluorescence microscopes range in cost from several thousand to several hundred thousand US dollars. Therefore, the use of fluorescence microscopy is typically limited to well-funded institutions and biotechnology companies, research core facilities, and medical laboratories, but is financially impractical at many universities and colleges, primary and secondary schools (K-12), and in science outreach settings. In this study, we developed and characterized components that when used in combination with a smartphone or tablet, perform fluorescence microscopy at a cost of less than $50 US dollars per unit. We re-purposed recreational LED flashlights and theater stage lighting filters to enable viewing of green and red fluorophores including EGFP, DsRed, mRFP, and mCherry on a simple-to-build frame made of wood and plexiglass. These devices, which we refer to as glowscopes, were capable of 10 µm resolution, imaging fluorescence in live specimens, and were compatible with all smartphone and tablet models we tested. In comparison to scientific-grade fluorescence microscopes, glowscopes may have limitations to sensitivity needed to detect dim fluorescencemore »and the inability to resolve subcellular structures. We demonstrate capability of viewing fluorescence within zebrafish embryos, including heart rate, rhythmicity, and regional anatomy of the central nervous system. Due to the low cost of individual glowscope units, we anticipate this device can help to equip K-12, undergraduate, and science outreach classrooms with fleets of fluorescence microscopes that can engage students with hands-on learning activities.

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5. Gene regulation network inference using k-nearest neighbor-based mutual information estimation: revisiting an old DREAM

   https://doi.org/10.1186/s12859-022-05047-5  

   Shachaf, Lior I. ; Roberts, Elijah ; Cahan, Patrick ; Xiao, Jie ( March 2023 , BMC Bioinformatics)

   A cell exhibits a variety of responses to internal and external cues. These responses are possible, in part, due to the presence of an elaborate gene regulatory network (GRN) in every single cell. In the past 20 years, many groups worked on reconstructing the topological structure of GRNs from large-scale gene expression data using a variety of inference algorithms. Insights gained about participating players in GRNs may ultimately lead to therapeutic benefits. Mutual information (MI) is a widely used metric within this inference/reconstruction pipeline as it can detect any correlation (linear and non-linear) between any number of variables (n-dimensions). However, the use of MI with continuous data (for example, normalized fluorescence intensity measurement of gene expression levels) is sensitive to data size, correlation strength and underlying distributions, and often requires laborious and, at times, ad hoc optimization.

   In this work, we first show that estimating MI of a bi- and tri-variate Gaussian distribution usingk-nearest neighbor (kNN) MI estimation results in significant error reduction as compared to commonly used methods based on fixed binning. Second, we demonstrate that implementing the MI-based kNN Kraskov–Stoögbauer–Grassberger (KSG) algorithm leads to a significant improvement in GRN reconstruction for popular inference algorithms, such as Contextmore »Likelihood of Relatedness (CLR). Finally, through extensive in-silico benchmarking we show that a new inference algorithm CMIA (Conditional Mutual Information Augmentation), inspired by CLR, in combination with the KSG-MI estimator, outperforms commonly used methods.

   Using three canonical datasets containing 15 synthetic networks, the newly developed method for GRN reconstruction—which combines CMIA, and the KSG-MI estimator—achieves an improvement of 20–35% in precision-recall measures over the current gold standard in the field. This new method will enable researchers to discover new gene interactions or better choose gene candidates for experimental validations.

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