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Airborne optical imaging technology: a road map in CIOMP
Airborne optical imaging can flexibly obtain the intuitive information of the observed scene from the air, which plays an important role of modern optical remote sensing technology. Higher resolution, longer imaging distance, and broader coverage are the unwavering pursuits in this research field. Nevertheless, the imaging environment during aerial flights brings about multi-source dynamic interferences such as temperature, air pressure, and complex movements, which forms a serious contradiction with the requirements of precision and relative staticity in optical imaging. As the birthplace of Chinese optical industry, the Changchun Institute of Optics, Fine Mechanics and Physics (CIOMP) has conducted the research on airborne optical imaging for decades, resulting in rich innovative achievements, completed research conditions, and exploring a feasible development path. This article provides an overview of the innovative work of CIOMP in the field of airborne optical imaging, sorts out the milestone nodes, and predicts the future development direction of this discipline, with the aim of providing inspiration for related research.
Segment Anything for Microscopy
Accurate segmentation of objects in microscopy images remains a bottleneck for many researchers despite the number of tools developed for this purpose. Here, we present Segment Anything for Microscopy (μSAM), a tool for segmentation and tracking in multidimensional microscopy data. It is based on Segment Anything, a vision foundation model for image segmentation. We extend it by fine-tuning generalist models for light and electron microscopy that clearly improve segmentation quality for a wide range of imaging conditions. We also implement interactive and automatic segmentation in a napari plugin that can speed up diverse segmentation tasks and provides a unified solution for microscopy annotation across different microscopy modalities. Our work constitutes the application of vision foundation models in microscopy, laying the groundwork for solving image analysis tasks in this domain with a small set of powerful deep learning models.
An integrative data-driven model simulating C. elegans brain, body and environment interactions
The behavior of an organism is influenced by the complex interplay between its brain, body and environment. Existing data-driven models focus on either the brain or the body–environment. Here we present BAAIWorm, an integrative data-driven model of Caenorhabditis elegans, which consists of two submodels: the brain model and the body–environment model. The brain model was built by multicompartment models with realistic morphology, connectome and neural population dynamics based on experimental data. Simultaneously, the body–environment model used a lifelike body and a three-dimensional physical environment. Through the closed-loop interaction between the two submodels, BAAIWorm reproduced the realistic zigzag movement toward attractors observed in C. elegans. Leveraging this model, we investigated the impact of neural system structure on both neural activities and behaviors. Consequently, BAAIWorm can enhance our understanding of how the brain controls the body to interact with its surrounding environment.
Carbohydrate-active enzymes from Akkermansia muciniphila break down mucin O-glycans to completion
Akkermansia muciniphila is a human microbial symbiont residing in the mucosal layer of the large intestine. Its main carbon source is the highly heterogeneous mucin glycoprotein, and it uses an array of carbohydrate-active enzymes and sulfatases to access this complex energy source. Here we describe the biochemical characterization of 54 glycoside hydrolases, 11 sulfatases and 1 polysaccharide lyase from A. muciniphila to provide a holistic understanding of their carbohydrate-degrading activities. This was achieved using a variety of liquid chromatography techniques, mass spectrometry, enzyme kinetics and thin-layer chromatography. These results are supported with A. muciniphila growth and whole-cell assays. We find that these enzymes can act synergistically to degrade the O-glycans on the mucin polypeptide to completion, down to the core N-acetylgalactosaime. In addition, these enzymes can break down human breast milk oligosaccharide, ganglioside and globoside glycan structures, showing their capacity to target a variety of host glycans. These data provide a resource to understand the full degradative capability of the gut microbiome member A. muciniphila.
Oblique line scan illumination enables expansive, accurate and sensitive single-protein measurements in solution and in living cells
An ideal tool for the study of cellular biology would enable the measure of molecular activity nondestructively within living cells. Single-molecule localization microscopy (SMLM) techniques, such as single-molecule tracking (SMT), enable in situ measurements in cells but have historically been limited by a necessary tradeoff between spatiotemporal resolution and throughput. Here we address these limitations using oblique line scan (OLS), a robust single-objective light-sheet-based illumination and detection modality that achieves nanoscale spatial resolution and sub-millisecond temporal resolution across a large field of view. We show that OLS can be used to capture protein motion up to 14 μm2 s−1 in living cells. We further extend the utility of OLS with in-solution SMT for single-molecule measurement of ligand–protein interactions and disruption of protein–protein interactions using purified proteins. We illustrate the versatility of OLS by showcasing two-color SMT, STORM and single-molecule fluorescence recovery after photobleaching. OLS paves the way for robust, high-throughput, single-molecule investigations of protein function required for basic research, drug screening and systems biology studies.
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