Optical Reconstruction Research Articles

Real-time plasma boundary shape reconstruction using visible camera on EAST Tokamak

Abstract Accurate plasma shape reconstruction is crucial for the stable operation of tokamak devices. In this study, a plasma optical boundary reconstruction system is constructed on the Experimental Advanced Superconducting Tokamak (EAST), and real-time shape reconstruction based on an optical method is realized for the first time on EAST.
First, a boundary extraction algorithm based on gray features is designed to extract the optical boundary from plasma optical images. Then, to reconstruct the optical boundary in the tokamak coordinate system, a camera calibration algorithm is developed based on feature point matching, and a coordinate mapping algorithm is designed based on plasma geometric features. Then, the system reconstructs the plasma boundary shape based on images acquired by cameras. Finally, the system is deployed in real-time on EAST, and its reconstruction rate meets the requirement of the EAST plasma shape control system. This study validates the feasibility of using an optical boundary shape reconstruction method on a tokamak device for real-time plasma shape reconstruction, providing a new shape reconstruction approach during steady-state discharge in future fusion devices.

Ultrastructure Expansion Microscopy (U-ExM) of the Fission Yeast Schizosaccharomyces pombe.

Among widely used super-resolution microscopy techniques, including stimulated emission depletion (STED), photoactivated localization microscopy (PALM), and stochastic optical reconstruction microscopy (STORM), expansion microscopy (ExM) is unique in achieving increased resolution through a physical manipulation of the actual sample rather than optics or postprocessing. Originally developed for applications in neuroscience, ExM now has a solid foothold across many fields and model systems, and has been adapted to work for organisms with cell walls, including budding and fission yeasts, through the inclusion of a pre-expansion enzymatic digestion step. A variant of the ExM technique optimized for preserving the architecture of protein complexes, ultrastructure expansion microscopy (U-ExM), enables super-resolution imaging of full 3D volumes at increased throughput using conventional microscopes and can be readily combined with commonly used antibodies, dyes, and stains. Here, we present its application to the fission yeast Schizosaccharomyces pombe.

Nanosilver-Enhanced Far-Field Fluorescence Fluctuations for Super-Resolution Microscopy.

Fluctuation-based super-resolution microscopy enhances image resolution using signal fluctuations, yet the inherent fluctuations of fluorophores limit its spatiotemporal resolution. In this work, we reveal a far-field enhancement (FFE) effect via a nanosilver film that significantly boosts fluorescence fluctuations of fluorophores positioned up to 10 μm away. The FFE effect arises from the interference of scattered light from the nanosilver film and photothermal-induced refractive index changes in the imaging medium, which create periodic auxiliary illumination on the sample. This phenomenon enabled the development of far-field enhanced super-resolution microscopy (FFE-SRM), a technique compatible with commonly used fluorophores. FFE-SRM improves temporal resolution up to 10-fold and enhances spatial resolution by about 2-fold over various SRM methods, including stochastic optical fluctuation imaging, super-resolution radial fluctuation, mean-shift super-resolution, and direct stochastic optical reconstruction microscopy. We demonstrated the potential of FFE-SRM by revealing mitochondrial dynamics in live-cell imaging, advancing super-resolution imaging, and cellular process exploration.

ASC specks as a single-molecule fluid biomarker of inflammation in neurodegenerative diseases.

Immunotherapeutic strategies for Alzheimer's and Parkinson's disease would be facilitated by better measures of inflammation. Here we established an ultra-sensitive single-molecule pull-down immunoassay combined with direct stochastic optical reconstruction microscopy (dSTORM) to measure the number, size and shape of individual extracellular inflammasome ASC specks. We assayed human post-mortem brain, serum and cerebrospinal fluid of patients with Parkinson's and Alzheimer's as well as healthy elderly. The number of ASC specks increased and showed altered morphology in the blood of early-stage Parkinson's and Alzheimer's patients compared to controls, mimicking those found in the brain and cerebrospinal fluid. In serum samples we also measured the number of Aβ, p-tau and α-syn aggregates and formed a composite biomarker of (ASC + p-tau)/Aβ and (ASC + α-syn)/Aβ ratios that distinguished age-matched healthy controls from patients with early-stage Alzheimer's with AUC of 92% and early-stage Parkinson's with AUC of 97%. Our findings confirm ASC specks as a fluid candidate biomarker of inflammation for neurodegenerative diseases with blood being the main focus for further development as convenient sample for diagnostics and clinical trials.

Phase-only hologram optimization with adaptive constraint strategy for high-quality optical reconstruction

Phase-only hologram optimization with adaptive constraint strategy for high-quality optical reconstruction

Generating high-quality phase-only holograms of binary images using global loss and stochastic homogenization training strategy

Deep learning presents a highly effective for computer-generated holography (CGH). However, existing learning-based CGH algorithms may encounter significant obstacles in generating high-quality holograms for binary images. This study proposes a novel diffraction model-driven neural network, termed load sharing yielding holography (LSY-Holo), which leverages a dual-branch architecture and a global structural similarity index measure (Global_SSIM) loss function to generate phase-only holograms (POHs). LSY-Holo framework facilitates capturing global information, enhancing the network’s performance to address the ill-posed inverse problem. To further optimize the model for binary images, the research introduces a custom Binary dataset alongside a stochastic homogenization preprocessing method (SHPM), which mitigates the prevalent issue of excessive noise in reconstructed images. Additionally, this deep learning approach is integrated with an iterative algorithm, which employs holograms rapidly generated by LSY-Holo as the initial phase of the AdamW Holography (AH) algorithm. This strategy significantly accelerates the convergence of the iterative process. LSY-Holo is further extended to 3D scenes, enabling dynamic defocusing and focusing effects at varying depths. Results from numerical simulations and full-color optical reconstruction experiments demonstrate that the proposed method can enhance image quality and effectively reduce the artifacts in reconstructed images.

Nanoscale Janus particle fabrication and direct observation by super-resolution microscopy

We have developed a new and facile approach for synthesis, labeling, and characterizing amphiphilic nanoscale Janus particles. The aqueous dispersion of the Aerodisp W1226 fumed silica particles were embedded into polycarbonate (P.C.) microspheres surfaces via inverse solvent displacement method and further primary functionalization by an amine group. Subsequently, the polymer was dissolved, and a second modification was preferred to introduce a thiol group on the revealed embedded side of the particles, forming silica-based Janus/amphiphilic particles. We first characterized the Janus particles by laser scanning confocal microscopy and then with direct stochastic optical reconstruction microscopy (dSTORM) to detect the labeling distribution of the different functional groups. Super-resolution imaging results confirmed that the fabricated particles are Janus particles with different chemical properties on each hemisphere. The particles were also characterized by scanning and transmission electron microscopy for size and shape. The results showed aspherical particles with two hemispheres, confirming successful fabrication and chemical modification. Also, this work successfully demonstrated the synthesis, characterization and analysis of nanoscale Janus/amphiphilic particles nanoscale Janus particle.

Inclination information-aided circumferential deformation reconstruction error correction method for CFRP belt integrated with optical fiber sensor

In view of the problem that the distributed optical fiber-based deformation reconstruction method can produce serious accumulated errors, this paper developed a smart fiber optic sensor belt that employs a carbon-fiber composite material as the matrix and strain-sensing fiber optics as the sensing element and is based on the key-point dip angle information acquired by a high-precision tilt sensor. The method of in situ correction for the cumulative error of circumferential deformation was investigated, and the particle swarm optimization algorithm was adopted to perform in situ correction for the reconstructed circumferential deformation field of the sensor belt to reduce the cumulative measurement error of the circumferential deformation field. Simulations and indoor tests were systematically performed. After the correction, the errors of the simulation and indoor tests were reduced from 210.83 and 235.82 mm to 2.17 and 4.04 mm, respectively, which verified the effectiveness and accuracy of the aforementioned method.

Modern Methods of Fluorescence Nanoscopy in Biology

Optical microscopy has undergone significant changes in recent decades due to the breaking of the diffraction limit of optical resolution and the development of high-resolution imaging techniques, which are collectively known as fluorescence nanoscopy. These techniques allow researchers to observe biological structures and processes at a nanoscale level of detail, revealing previously hidden features and aiding in answering fundamental biological questions. Among the advanced methods of fluorescent nanoscopy are: STED (Stimulated Emission Depletion Microscopy), STORM (STochastic Optical Reconstruction Microscopy), PALM (Photo-activated Localization Microscopy), TIRF (Total Internal Reflection Fluorescence), SIM (Structured Illumination Microscopy), MINFLUX (Minimal Photon Fluxes), PAINT (Points Accumulation for Imaging in Nanoscale Topography) и RESOLFT (REversible Saturable Optical Fluorescence Transitions) and others. In addition, most of these methods make it possible to obtain volumetric (3D) images of the objects under study. In this review, we will look at the principles of these methods, their advantages and disadvantages, and their application in biological researches.

Three-dimensional computer holography with phase space tailoring

Computer holography is a prominent technique for reconstructing customized three-dimensional (3D) diffraction fields. However, the quality of optical reconstruction remains a fundamental challenge in 3D computer holography, especially for the 3D diffraction fields with physically continuous and extensive depth range. Here, we propose a 3D computer-generated hologram (CGH) optimization framework with phase space tailoring. Based on phase space analysis of the space and frequency properties in both lateral and axial directions, the intensity of the 3D diffraction field is adequately sampled across a large depth range. This sampling ensures the reconstructed intensity distribution to be comprehensively constrained with physical consistency. A physics-informed loss function is constructed based on the phase space tailoring to optimize the CGH with suppression of vortex stagnation. Numerical and optical experiments demonstrate the proposed method significantly enhances the 3D optical reconstructions with suppressed speckle noise across a continuous and extensive depth range.

Error analysis based on a tunable wave plate polarization interferometric imaging spectrometer

Interference imaging spectroscopy combines modern imaging technology with spectral technology, holding significant importance for object imaging and spectral detection. This article introduces the principle of an adjustable wave plate polarization interferometric imaging spectrometer. The example design specifications are set for an observation wavelength range of 450–780 nm and a maximum resolution of 2 nm at 450 nm, with a 0.5 in detector as the base for calculating the specific dimensions of the Soleil–Babinet compensator. An investigation was conducted on the issues of nonuniform sampling, as well as three types of mechanical errors: flatness, wedge angle tolerance, and optical axis orientation accuracy. Emphasis was placed on discussing the impact of these errors on the instrument’s optical path difference and spectral reconstruction accuracy. This research provides theoretical guidance for the design and engineering of this miniaturized imaging spectrometer.

Unlocking Intracellular Protein Delivery by Harnessing Polymersomes Synthesized at Microliter Volumes using Photo-PISA.

Efficient delivery of therapeutic proteins and vaccine antigens to intracellular targets is challenging due to generally poor cell membrane permeation and endolysosomal entrapment causing degradation. Herein, these challenges are addressed by developing an oxygen-tolerant photoinitiated polymerization-induced self-assembly (Photo-PISA) process, allowing for the microliter-scale (10µL) synthesis of protein-loaded polymersomes directly in 1536-well plates. High-resolution techniques capable of analysis at a single particle level are employed to analyze protein encapsulation and release mechanisms. Using confocal microscopy and super-resolution stochastic optical reconstruction microscopy (STORM) imaging, their ability to deliver proteins into the cytosol following endosomal escape is subsequently visualized. Lastly, the adaptability of these polymersomes is exploited to encapsulate and deliver a prototype vaccine antigen, demonstrating its ability to activate antigen-presenting cells and support antigen cross-presentation for applications in subunit vaccines and cancer immunotherapy. This combination of ultralow volume synthesis and efficient intracellular delivery holds significant promise for unlocking the high throughput screening of a broad range of otherwise cost-prohibitive or early-stage therapeutic protein and vaccine antigen candidates that can be difficult to obtain in large quantities. The versatility of this platform for rapid screening of intracellular protein delivery can result in significant advancements across the fields of nanomedicine and biomedical engineering.

Super-resolution imaging reveals the role of DDR1 cluster in NSCLC proliferation

Discoidin domain receptor 1 (DDR1), a transmembrane protein, is crucial in tumor development. Prior studies have demonstrated a significant correlation between protein cluster distribution on the cell membrane and tumor evolution. However, the precise spatial distribution characteristics of DDR1 on cell membranes and their impact on tumor development remain unclear. In this study, we conducted gene expression analysis to investigate DDR1 expression in non-small cell lung cancer (NSCLC) and its association with patient prognosis. We also employed direct stochastic optical reconstruction microscopy (dSTORM) imaging to examine DDR1's spatial distribution in NSCLC cells and tissues. Our findings indicate that DDR1 forms larger, tighter, and more abundant clusters in cancer cells and tissues compared to their normal counterparts. Notably, we observed that the enhanced aggregation of DDR1 clusters increased the likelihood of interaction with SRC, thereby activating the SRC-STAT3 signaling pathway in NSCLC cells and promoting cell proliferation. This study provides novel insights into the role of DDR1 aggregation in tumor proliferation, confirms DDR1 as a potential tumor marker, and serves as a valuable resource for future drug development.

OGRN: optical-guided residual dense network for SAR image super-resolution reconstruction network

ABSTRACT Although Convolutional Neural Networks have significantly improved the development of SAR image super-resolution (SR) technology in recent years, it is a very challenging problem to reconstruct SAR image with large-scale factors, such as ×4 and ×8 due to limited available information from low-resolution image. The co-registered high-resolution optical image has been successfully applied to enhance the quality of SAR image due to its discriminative characteristics. Compared with single-frame SAR image SR reconstruction technology, optical image-guided SAR image SR reconstruction technology has better performance. This paper proposes an optical-guided residual dense network (OGRN) for SAR image super-resolution reconstruction network. The network is an end-to-end network. Its most important characteristic is that the SR model of SAR images can be obtained through optical image assistance during training, and high-quality SAR images can be generated without optical image assistance during testing. Firstly, a new dense residual backbone network that extracts high-frequency information is constructed. It can extract and propagate high-frequency features of SAR images efficiently to improve the feature representation ability of the network for high-frequency features. Then, the extracted high-frequency features of the SAR image are used to generate an optical image through the designed SAR-to-Optical image translation network. Finally, a reconstruction module is constructed based on the proposed fusion module for optical and SAR images. The fusion module fuses the high-frequency feature information of the extracted optical images with the SAR image features to provide features for SAR image SR reconstruction. Extensive experiments conducted on Sen1-2 and QXS datasets demonstrated that under the guidance of optical image, our OGRN can achieve excellent performance in both quantitative assessment metrics and visual quality.

Nanoscale organization of cardiac calcium channels is dependent on thyroid hormone status.

Thyroid hormone dysfunction is frequently observed in patients with chronic illnesses including heart failure which increases risk of adverse events. This study examined effects of thyroid hormones (TH) on cardiac T-tubule (TT) integrity, Ca2+ sparks, and nanoscale organization of ion channels in excitation-contraction (EC)-coupling, including L-type calcium channel (Cav1.2), ryanodine receptor-type 2 (RyR2), and junctophilin-2 (Jph2). TH deficiency was established in adult female rats by propyl-thiouracil (PTU) ingestion for 8 weeks; followed by randomization to continued PTU without or with oral triiodo-L-thyronine (T3; 10 ug/kg/d) for two additional weeks (PTU+T3). Confocal microscopy of isolated cardiomyocytes (CM) showed significant misalignment of TTs, and increased Ca2+ sparks in thyroid-deficient CMs. Density-Based Spatial Clustering of Applications with Noise (DBSCAN) analysis of STochastic Optical Reconstruction Microscopy (STORM) images showed decreased (p<0.0001) RyR2 cluster number per cell area in PTU CMs compared to euthyroid (EU) control myocytes, and this was normalized by T3-treatment. Cav1.2 channels and Jph2 localized within 210 nm radius of the RyR2 clusters were significantly reduced in PTU myocytes, and these values were increased with T3 treatment. A significant percentage of the RyR2 clusters in the PTU myocytes had neither Cav1.2 or Jph2, suggesting fewer functional clusters in EC-coupling. Nearest neighbor distances between RyR2 clusters were greater (p<0.001) in PTU cells compared to EU and T3-treated CMs that corresponds to disarray of TTs at the sarcomere z-discs. These results support a regulatory role of T3 in the nanoscale organization of RyR2 clusters and co-localization of Cav1.2 and Jph2 in optimizing EC-coupling.

DEEP Q-NETWORK (DQN) PARTIALOCCLUSION SEGMENTATION AND BACKTRACKING SEARCH OPTIMIZATION ALGORITHM (BSOA) WITH OPTICAL FLOW RECONSTRUCTION FOR FACIAL EXPRESSION EMOTION RECOGNITION

Video facial expression recognition (FER) has garnered a lot of attention recently and is helpful for several applications. Although many algorithms demonstrate impressive performance in a controlled environment without occlusion, identification in the presence of partial facial occlusion remains a challenging issue. Solutions based on reconstructing the obscured area of the face have been suggested as a way to deal with occlusions. These options mostly rely on the face’s shape or texture. Nonetheless, the resemblance in facial expressions among individuals appears to be a valuable advantage for the reconstruction. For semantic segmentation based on occlusions, Reinforcement Learning (RL) is introduced as the initial stage. From a pool of unlabeled data, an agent learns a policy to choose a subset of tiny informative image patches to be tagged instead of full images. In the second stage, a trained Backtracking Search Algorithm (BSA) is used to rebuild optical flows that have been distorted by the occlusion. On obtaining optical flows estimated from occluded facial frames, AEs restore optical flows of occluded regions. These recovered optical flows become inputs to anticipate classes f expressions. Optical flux reconstructions then classify stages. This study evaluates classification model’s performances for face expression identification based on Very Deep Convolution Networks (VGGNet). Furthermore, it produces more accurate confusion matrices and proposes approaches for the KMU-FED and CK+ databases, respectively. The results are evaluated using metrics including recall, f-measure, accuracy, and precision.

Comparison of nanoimaging and nanoflow based detection of extracellular vesicles at a single particle resolution.

The characterization of single extracellular vesicle (EV) has been an emerging tool for the early detection of various diseases despite there being challenges regarding how to interpret data with different protocols or instruments. In this work, standard EV particles were characterized for single CD9+, single CD81+ or double CD9+/CD81+ tetraspanin molecule positivity with two single EV analytic technologies in order to optimize their EV sample preparation after antibody labelling and analysis methods: NanoImager for direct stochastic optical reconstruction microscopy (dSTORM)-based EV imaging and characterization, and Flow NanoAnalyzer for flow-based EV quantification and characterization. False positives from antibody aggregates were found during dSTORM-based NanoImager imaging. Analysis of particle radius with lognormal fittings of probability density histogram enabled the removal of antibody aggregates and corrected EV quantification. Furthermore, different machine learning models were trained to differentiate antibody aggregates from EV particles and correct EV quantification with increased double CD9+/CD81+ population. With Flow NanoAnalyzer, EV samples were prepared with different dilution or fractionation methods, which increased the detection rate of CD9+/CD81+ EV population. Comparing the EV phenotype percentages measured by two instruments, differences in double positive and single positive particles existed after percentage correction, which might be due to the different detection limit of each instrument. Our study reveals that the characterization of individual EVs for tetraspanin positivity varies between two platforms-the NanoImager and the Flow NanoAnalyzer-depending on the EV sample preparation methods used after antibody labelling. Additionally, we applied machine learning models to correct for false positive particles identified in imaging-based results by fitting size distribution data.

G-Protein Signaling in Alzheimer's Disease: Spatial Expression Validation of Semi-supervised Deep Learning-Based Computational Framework.

Systemic study of pathogenic pathways and interrelationships underlying genes associated with Alzheimer's disease (AD) facilitates the identification of new targets for effective treatments. Recently available large-scale multiomics datasets provide opportunities to use computational approaches for such studies. Here, we devised a novel disease gene identification (digID) computational framework that consists of a semi-supervised deep learning classifier to predict AD-associated genes and a protein-protein interaction (PPI) network-based analysis to prioritize the importance of these predicted genes in AD. digID predicted 1,529 AD-associated genes and revealed potentially new AD molecular mechanisms and therapeutic targets including GNAI1 and GNB1, two G-protein subunits that regulate cell signaling, and KNG1, an upstream modulator of CDC42 small G-protein signaling and mediator of inflammation and candidate coregulator of amyloid precursor protein (APP). Analysis of mRNA expression validated their dysregulation in AD brains but further revealed the significant spatial patterns in different brain regions as well as among different subregions of the frontal cortex and hippocampi. Super-resolution STochastic Optical Reconstruction Microscopy (STORM) further demonstrated their subcellular colocalization and molecular interactions with APP in a transgenic mouse model of both sexes with AD-like mutations. These studies support the predictions made by digID while highlighting the importance of concurrent biological validation of computationally identified gene clusters as potential new AD therapeutic targets.

STORM image denoising and information extraction.

Stochastic optical reconstruction microscopy (STORM) is extensively utilized in the fields of cell and molecular biology as a super-resolution imaging technique for visualizing cells and molecules. Nonetheless, the imaging process of STORM is frequently susceptible to noise, which can significantly impact the subsequent image analysis. Moreover, there is currently a lack of a comprehensive automated processing approach for analyzing protein aggregation states from a large number of STORM images. This paper initially applies our previously proposed denoising algorithm, UNet-Att, in STORM image denoising. This algorithm was constructed based on attention mechanism and multi-scale features, showcasing a remarkably efficient performance in denoising. Subsequently, we propose a collection of automated image processing algorithms for the ultimate feature extractions and data analyses of the STORM images. The information extraction workflow effectively integrates automated methods of image denoising, objective image segmentation and binarization, and object information extraction, and a novel image information clustering algorithm specifically developed for the morphological analysis of the objects in the STORM images. This automated workflow significantly improves the efficiency of the effective data analysis for large-scale original STORM images.

A novel m-xylylene-diamine/glucose based-supramolecular eutectogels with tissue clearing for three dimensional histological imaging

Hydrogel-based tissue clearing technologies have shown significant promise for deep-tissue imaging and subcellular-level optical 3D reconstruction of whole organs. This study proposes a novel approach utilizing a deep eutectic solvent (DES) formulated with glucose and m-xylylene-diamine (MXDA) to create a highly efficient tissue-clearing hydrogel system named the passive hydrogel clearing system (PHCS). PHCS achieved efficient tissue clearing through a single-step tissue gelation process. The resulting hydrogel-tissue complex exhibited thermoreversible properties, transitioning into a sol state upon heating and vice versa upon cooling. Notably, PHCS enabled media embedding, facilitating immunofluorescence histopathology. Additionally, the system demonstrated compatibility with various fluorescent probes, particularly lipophilic dyes. Our study successfully employed PHCS for the reconstruction of vascular structures within the intestine, enabling the generation of a 3D pathology model. These findings suggest that PHCS is a promising novel method for fabricating hydrogels for tissue clearing and holds great potential for application as a mounting medium for morphological imaging.