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An adaptive secondary mirror (ASM) with novel actuator technology is being designed and built for the UH88 telescope as a demonstration of a new generation of ASMs that might be deployed at ground based observatories such as Keck, Subaru, and TMT. Before putting the ASM on the telescope, a set of calibrations and character- izations need to be made in the lab. The crucial lab characterizations of the ASM are to measure its influence functions, and its surface shape when powered and unpowered. To measure these, we develop a novel and inexpensive optical metrology approach using phase measuring deflectometry. This paper describes the simulations we wrote to model the deflectometry method, our data acquisition/analysis pipeline, and a lab prototype sys- tem we built that demonstrates its feasibility on a microelectromechanical systems (MEMS) deformable mirror. Based on the information gained through the deflectometry simulation and the setup prototype, we conclude that phase measuring deflectometry is a reasonable method for obtaining the influence functions but that the absolute surface shape of the ASM will be limited by our knowledge of the placement of components within the deflectometry setup itself. We discuss challenges with extending this approach to larger convex adaptive secondary mirrors. 

3D instance segmentation for unlabeled imaging modalities is a challenging but essential task as collecting expert annotation can be expensive and time-consuming. Existing works segment a new modality by either deploying pre-trained models optimized on diverse training data or sequentially conducting image translation and segmentation with two relatively independent networks. In this work, we propose a novel Cyclic Segmentation Generative Adversarial Network (CySGAN) that conducts image translation and instance segmentation simultaneously using a unified network with weight sharing. Since the image translation layer can be removed at inference time, our proposed model does not introduce additional computational cost upon a standard segmentation model. For optimizing CySGAN, besides the CycleGAN losses for image translation and supervised losses for the annotated source domain, we also utilize self-supervised and segmentation-based adversarial objectives to enhance the model performance by leveraging unlabeled target domain images. We benchmark our approach on the task of 3D neuronal nuclei segmentation with annotated electron microscopy (EM) images and unlabeled expansion microscopy (ExM) data. The proposed CySGAN outperforms pre-trained generalist models, feature-level domain adaptation models, and the baselines that conduct image translation and segmentation sequentially. Our implementation and the newly collected, densely annotated ExM zebrafish brain nuclei dataset, named NucExM, are publicly available at https://connectomics-bazaar.github.io/proj/CySGAN/index.html. 

Abstract Observing cellular physiological histories is key to understanding normal and disease-related processes. Here we describe expression recording islands—a fully genetically encoded approach that enables both continual digital recording of biological information within cells and subsequent high-throughput readout in fixed cells. The information is stored in growing intracellular protein chains made of self-assembling subunits, human-designed filament-forming proteins bearing different epitope tags that each correspond to a different cellular state or function (for example, gene expression downstream of neural activity or pharmacological exposure), allowing the physiological history to be read out along the ordered subunits of protein chains with conventional optical microscopy. We use expression recording islands to record gene expression timecourse downstream of specific pharmacological and physiological stimuli in cultured neurons and in living mouse brain, with a time resolution of a fraction of a day, over periods of days to weeks. 

We report on progress at the University of Hawaii on the integration and testing setups for the adaptive secondary mirror (ASM) for the University of Hawaii 2.2-meter telescope on Maunakea, Hawaii. We report on the development of the handling fixtures and alignment tools we will use along with progress on the optical metrology tools we will use for the lab and on-sky testing of the system.