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1. 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)

   null (Ed.)

   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 sampling during intraoperative tumor-margin assessment, while offering an affordable tool to provide histological information from resected tissue specimens in resource-limited settings. 

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2. Optimizing cloud motion estimation on the edge with phase correlation and optical flow

   https://doi.org/10.5194/amt-16-1195-2023  

   Raut, Bhupendra A. ; Muradyan, Paytsar ; Sankaran, Rajesh ; Jackson, Robert C. ; Park, Seongha ; Shahkarami, Sean A. ; Dematties, Dario ; Kim, Yongho ; Swantek, Joseph ; Conrad, Neal ; et al ( January 2023 , Atmospheric Measurement Techniques)

   Abstract. Phase correlation (PC) is a well-known method for estimating cloud motion vectors (CMVs) from infrared and visible spectrum images. Commonly, phase shift is computed in the small blocks of the images using the fast Fourier transform. In this study, we investigate the performance and the stability of the blockwise PC method by changing the block size, the frame interval, and combinations of red, green, and blue (RGB) channels from the total sky imager (TSI) at the United States Atmospheric Radiation Measurement user facility's Southern Great Plains site. We find that shorter frame intervals, followed by larger block sizes, are responsible for stable estimates of the CMV, as suggested by the higher autocorrelations. The choice of RGB channels has a limited effect on the quality of CMVs, and the red and the grayscale images are marginally more reliable than the other combinations during rapidly evolving low-level clouds. The stability of CMVs was tested at different image resolutions with an implementation of the optimized algorithm on the Sage cyberinfrastructure test bed. We find that doubling the frame rate outperforms quadrupling the image resolution in achieving CMV stability. The correlations of CMVs with the wind data are significant in the range of 0.38–0.59 with a 95 % confidence interval, despite the uncertainties and limitations of both datasets. A comparison of the PC method with constructed data and the optical flow method suggests that the post-processing of the vector field has a significant effect on the quality of the CMV. The raindrop-contaminated images can be identified by the rotation of the TSI mirror in the motion field. The results of this study are critical to optimizing algorithms for edge-computing sensor systems.

    

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3. 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 data 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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4. Informative community structure revealed using Arabidopsis time series transcriptome data via partitioned local depth

   https://doi.org/10.1093/insilicoplants/diad018  

   Khoury, Maleana G. ; Berenhaut, Kenneth S. ; Moore, Katherine E. ; Allen, Edward E. ; Harkey, Alexandria F. ; Mühlemann, Joëlle K. ; Craven, Courtney N. ; Xu, Jiayi ; Jain, Suchi S. ; John, David J. ; et al ( November 2023 , in silico Plants)

   Transcriptome studies that provide temporal information about transcript abundance facilitate identification of gene regulatory networks (GRNs). Inferring GRNs from time series data using computational modeling remains a central challenge in systems biology. Commonly employed clustering algorithms identify modules of like-responding genes but do not provide information on how these modules are interconnected. These methods also require users to specify parameters such as cluster number and size, adding complexity to the analysis. To address these challenges, we used a recently developed algorithm, partitioned local depth (PaLD), to generate cohesive networks for 4 time series transcriptome datasets (3 hormone and 1 abiotic stress dataset) from the model plant Arabidopsis thaliana. PaLD provided a cohesive network representation of the data, revealing networks with distinct structures and varying numbers of connections between transcripts. We utilized the networks to make predictions about GRNs by examining local neighborhoods of transcripts with highly similar temporal responses. We also partitioned the networks into groups of like-responding transcripts and identified enriched functional and regulatory features in them. Comparison of groups to clusters generated by commonly used approaches indicated that these methods identified modules of transcripts that have similar temporal and biological features, but also identified unique groups, suggesting that a PaLD-based approach (supplemented with a community detection algorithm) can complement existing methods. These results revealed that PaLD could sort like-responding transcripts into biologically meaningful neighborhoods and groups while requiring minimal user input and producing cohesive network structure, offering an additional tool to the systems biology community to predict GRNs.

    

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5. Removing Stripes, Scratches, and Curtaining with Nonrecoverable Compressed Sensing

   https://doi.org/10.1017/S1431927619000254  

   Schwartz, Jonathan ; Jiang, Yi ; Wang, Yongjie ; Aiello, Anthony ; Bhattacharya, Pallab ; Yuan, Hui ; Mi, Zetian ; Bassim, Nabil ; Hovden, Robert ( June 2019 , Microscopy and Microanalysis)

   Abstract Highly-directional image artifacts such as ion mill curtaining, mechanical scratches, or image striping from beam instability degrade the interpretability of micrographs. These unwanted, aperiodic features extend the image along a primary direction and occupy a small wedge of information in Fourier space. Deleting this wedge of data replaces stripes, scratches, or curtaining, with more complex streaking and blurring artifacts—known within the tomography community as “missing wedge” artifacts. Here, we overcome this problem by recovering the missing region using total variation minimization, which leverages image sparsity-based reconstruction techniques—colloquially referred to as compressed sensing (CS)—to reliably restore images corrupted by stripe-like features. Our approach removes beam instability, ion mill curtaining, mechanical scratches, or any stripe features and remains robust at low signal-to-noise. The success of this approach is achieved by exploiting CS's inability to recover directional structures that are highly localized and missing in Fourier Space. 

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