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At least 109 records · Page 6

Facile method to stain the bacterial cell surface for super-resolution fluorescence microscopy

A method to fluorescently stain the surfaces of both Gram-negative and Gram-positive bacterial cells compatible with super-resolution fluorescence microscopy is presented. This method utilizes a commercially-available fluorescent probe to label primary amines at the surface of the cell. We demonstrate efficient staining of two bacterial strains, the Gram-negative Shewanella oneidensis MR-1 and the Gram-positive Bacillus subtilis 168. Using structured illumination microscopy and stochastic optical reconstruction microscopy, which require high quantum yield or specialized dyes, we show that this staining method may be used to resolve the bacterial cell surface with sub-diffraction-limited resolution. We further use this method to identify localization patterns of nanomaterials, specifically cadmium selenide quantum dots, following interaction with bacterial cells.

Bacterial↗

Data for reproducing the figures of the paper Multimodal Super-Resolution: Discovering hidden physics and its application to fusion plasmas

This deposit contains the raw data for reproducing research results of the paper Multimodal Super-Resolution: Discovering hidden physics and its application to fusion plasmas. The main contribution of this work is to utilize machine learning techniques to reconstruct and enhance the resolution of a diagnostic measurement from other available diagnostics in a system. The proposed techniques is called Diag2Diag.

diag2diag↗

Super-resolution model for overlapping peak detection and improved spatial resolution in high-energy diffraction microscopy

Reconstruction quality in Far-field High-Energy Diffraction Microscopy (FF-HEDM) is limited by the spatial resolution of area detectors and the frequent occurrence of overlapping diffraction spots. To address these challenges, we developed a super-resolution (SR) framework using convolutional neural networks (CNNs) to recreate 2D diffraction peaks at up to ×8 resolution from raw detector data. A specialized simulation tool was created to generate synthetic training datasets with varying degrees of peak overlap. Integrated into the Microstructural Imaging using Diffraction Analysis Software (MIDAS), the SR model improves the spatial accuracy and precision of 3D grain reconstruction by an order of magnitude. This approach provides a robust solution for investigation of complex micromechanical states and material classes where the analysis is limited by the presence of overlapping peaks. Furthermore, the methodology developed here can potentially be extended to other techniques that require sub-pixel accuracy for high-fidelity data analysis.

High-energy diffraction microscopy↗

Pulse Radiolysis Investigation of Radicals Derived from Water-Soluble Cyanine Dyes: Implications for Super-resolution Microscopy

Light-induced blinking, an inherent feature of many forms of super-resolution microscopy, has been linked to transient reduction of the fluorescent cyanine dye used as an imaging agent. There is, however, only scant literature information related to one-electron reduced cyanine dyes, especially in an aqueous environment. Here, we examine a small series of cyanine dyes, possessing disparate π-conjugation lengths, under selective reducing or oxidizing conditions. The experiment allows recording of both differential absorption spectra and decay kinetics of the resultant one-electron reduced or oxidized transient species in water. Relative to the ground state, absorption transitions for the various radicals are weak and somewhat broadened but do allow correlation with the π-conjugation length. In all cases, absorption maxima lie to the blue of the main ground-state transition. Under anaerobic conditions, the transient species decay on the microsecond to millisecond time scale, with the mean lifetime depending on molecular structure, radiation dose, and dye concentration. The experimental absorption spectra recorded for the one-electron reduced radicals and the presumed dimer cation radical compare well to spectra obtained from time-dependent density functional theory calculations. Furthermore, the results allow conclusions to be drawn regarding the plausibility of the reduced species being responsible for light-induced blinking in direct stochastic optical reconstruction microscopy.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

HiRes: super-resolution for the Spitzer Space Telescope

We present a description of hires, a super-resolution program based upon the Richardson-Lucy algorithm, generalized to the case of redundant coverage, with higher order optical image distortion, implemented for the Sptizer Space Telescope.

image restoration↗

Malic Acid Carbon Dots: From Super-resolution Live-Cell Imaging to Highly Efficient Separation

As-synthesized malic acid carbon dots are found to possess photoblinking properties that are outstanding and superior compared to those of conventional dyes. Considering their excellent biocompatibility, malic acid carbon dots are suitable for super-resolution fluorescence localization microscopy under a variety of conditions, as we demonstrate in fixed and live trout gill epithelial cells. In addition, during imaging experiments, the so-called "excitation wavelength-dependent" emission was not observed for individual as-made malic acid carbon dots, which motivated us to develop a time-saving and high throughput separation technique to isolate malic acid carbon dots into fractions of different particle size distributions using C-18 reversed-phase silica gel column chromatography. This post-treatment allowed us to determine how particle size distribution influences the optical properties of malic acid carbon dot fractions, that is, optical band gap energies and photoluminescence behaviors.

photoblinking, super-resolution localization micro↗

Three Photon Excited Image Scanning Microscopy for in-Depth Super-Resolution Studies of Biological Samples

Multiphoton laser scanning microscopy is a powerful tool for deep imaging of thick biological samples. Image scanning microscopy (ISM) has demonstrated significant improvements in the signal-to-noise ratio in confocal laser scanning microscopy, while at the same time improving upon the effectively attainable resolution. Two-photon excitation (2PE), combined with ISM, has been shown to allow for deep tissue imaging with enhanced resolution compared to 2PE microscopy. Three-photon excitation (3PE) has enabled record imaging depth and contrast for multiphoton imaging, due to the superior suppression of out-of-focus signal generation. In this paper, we demonstrate super-resolution 3PE ISM. This is achieved using a single-photon avalanche detector array, and 1040-nm pulses for 3PE of blue fluorescence. This method enables subdiffraction limited resolution imaging of biological samples stained with blue fluorescent markers, such as mouse myocardial and spinal cord tissues stained with 4 ′ , 6 -diamidino-2-phenylindole. Deconvolution improves the resolving power further and allows for imaging with better than λ / 8 resolution with respect to the 3PE wavelength λ . With the ISM pixel reassignment procedure, we demonstrate a resolution enhancement of ∼ 1.6 laterally, compared to the resolution attained using a photomultiplier tube in a non-descanned detection arrangement, and a factor of ∼ 1.8 enhancement in axial resolution. The experimentally measured three-dimensional point spread function volume is shrunk ∼ 4.4 -fold, which is close to the theoretically expected enhancement. Published by the American Physical Society 2024

47 OTHER INSTRUMENTATION↗

Wavelet Filter Banks for Super-Resolution SAR Imaging

This paper discusses Innovative wavelet-based filter banks designed to enhance the analysis of super resolution Synthetic Aperture Radar (SAR) images using parametric spectral methods and signal classification algorithms, SAR finds applications In many of NASA's earth science fields such as deformation, ecosystem structure, and dynamics of Ice, snow and cold land processes, and surface water and ocean topography. Traditionally, standard methods such as Fast-Fourier Transform (FFT) and Inverse Fast-Fourier Transform (IFFT) have been used to extract Images from SAR radar data, Due to non-parametric features of these methods and their resolution limitations and observation time dependence, use of spectral estimation and signal pre- and post-processing techniques based on wavelets to process SAR radar data has been proposed. Multi-resolution wavelet transforms and advanced spectral estimation techniques have proven to offer efficient solutions to this problem.

Sheybani, Ehsan O.↗

Super‐Resolution Lifetime Imaging of Single Molecules Near Gold Bowtie Nanoparticles

Interactions between light and matter serve as the basis of many technologies, but the quality of these devices is inherently limited by the optical properties of their constituents. Plasmonic nanoparticles are a highly versatile and tunable platform for the enhancement of such optical properties. However, the near-field nature of these effects has made thorough study and understanding of these mechanisms difficult. In this work, we introduce a fully confocal technique combining photoswitching super-resolution microscopy with fluorescence lifetime imaging microscopy to study single-molecule decay rate enhancement. We demonstrate that the technique combines a spatial resolution better than 20 nm, and a 16 ps temporal resolution. Simultaneously, an autocorrelation measurement is also performed to confirm that the data indeed originates from single molecules. This work provides insight into the various mechanisms of plasmon-enhanced emission, and allows the study of the correlation between emission intensity and lifetime enhancement. This complicated relationship is shown to be dependent upon the relative influence of various radiative and nonradiative decay pathways. Here, we provide a platform for further study of emission mislocalization, the position-dependent prominence of different decay pathways, and the direct super-resolved measurement of the local density of states.

36 MATERIALS SCIENCE↗

Correlative nano-imaging of metals and proteins in primary neurons by synchrotron X-ray fluorescence and STED super resolution microscopy: Experimental validation

It is becoming increasingly clear that biological metals such as iron, copper or zinc are involved in synaptic functions, and in particular in the mechanisms of synaptogenesis and subsequent plasticity. Understanding the role of metals on synaptic functions is a difficult challenge due to the very low concentration of these elements in neurons and to the submicrometer size of synaptic compartments. To address this challenge we have developed a correlative nano-imaging approach combining metal and protein detection. First, stimulated emission depletion (STED) microscopy, a super resolution optical microscopy technique, is applied to locate fluorescently labeled proteins. Then, synchrotron radiation induced X-ray fluorescence (SXRF) is performed on the same regions of interest, e.g. synaptic compartments. Here, we present here the principle scheme that allows this correlative nano-imaging and its experimental validation. We applied this correlative nano-imaging to the study of the physiological distribution of metals in synaptic compartments of primary rat hippocampal neurons. We thus compared the nanometric distribution of metals with that of synaptic proteins, such as PSD95 or cytoskeleton proteins. Compared to correlative imaging approaches currently used to characterize synaptic structures, such as electron microscopy correlated with optical fluorescence, our approach allows for ultra-sensitive detection of trace metals using highly focused synchrotron radiation beams. We provide proof-of-principle for correlative imaging of metals and proteins at the synaptic scale and discuss the present limitations and future developments in this area.

59 BASIC BIOLOGICAL SCIENCES↗

Enzyme-Directed Assembly of Nanoparticles in Tumors Monitored by In Vivo Whole Animal and Ex Vivo Super-Resolution Fluorescence Imaging

Matrix metalloproteinase enzymes, overexpressed in HT-1080 human fibrocarcinoma tumors, were used to guide the accumulation and retention of an enzyme-responsive nanoparticle in a xenograft mouse model. The nanoparticles were prepared as micelles from amphiphilic block copolymers bearing a simple hydrophobic block, and a hydrophilic peptide brush. The polymers were end-labeled with Alexa Fluor 647 dyes leading to the formation of labeled micelles upon dialysis of the polymers from DMSO to aqueous buffer. This dye-labeling strategy allowed the presence of the retained material to be visualized via whole animal imaging in vivo, and in ex vivo organ analysis following intratumoral injection into HT-1080 xenograft tumors. We propose that the material is retained by virtue of an enzyme-induced accumulation process whereby particles change morphology from 20 nm spherical micelles to micron-scale aggregates, kinetically trapping them within the tumor. This hypothesis is tested here via an unprecedented super resolution fluorescence analysis of ex vivo tissue slices confirming a particle size increase occurs concomitantly with extended retention of responsive particles compared to unresponsive controls.

Environmental Molecular Sciences Laboratory↗

Super resolution for root imaging

Premise High‐resolution cameras are very helpful for plant phenotyping as their images enable tasks such as target vs. background discrimination and the measurement and analysis of fine above‐ground plant attributes. However, the acquisition of high‐resolution images of plant roots is more challenging than above‐ground data collection. An effective super‐resolution (SR) algorithm is therefore needed for overcoming the resolution limitations of sensors, reducing storage space requirements, and boosting the performance of subsequent analyses. Methods We propose an SR framework for enhancing images of plant roots using convolutional neural networks. We compare three alternatives for training the SR model: (i) training with non‐plant‐root images, (ii) training with plant‐root images, and (iii) pretraining the model with non‐plant‐root images and fine‐tuning with plant‐root images. The architectures of the SR models were based on two state‐of‐the‐art deep learning approaches: a fast SR convolutional neural network and an SR generative adversarial network. Results In our experiments, we observed that the SR models improved the quality of low‐resolution images of plant roots in an unseen data set in terms of the signal‐to‐noise ratio. We used a collection of publicly available data sets to demonstrate that the SR models outperform the basic bicubic interpolation, even when trained with non‐root data sets. Discussion The incorporation of a deep learning–based SR model in the imaging process enhances the quality of low‐resolution images of plant roots. We demonstrate that SR preprocessing boosts the performance of a machine learning system trained to separate plant roots from their background. Our segmentation experiments also show that high performance on this task can be achieved independently of the signal‐to‐noise ratio. We therefore conclude that the quality of the image enhancement depends on the desired application.

Ruiz‐Munoz, Jose F.↗

Super Resolution of Remote Sensing Images Using Edge-Directed Radial Basis Functions

Edge-Directed Radial Basis Functions (EDRBF) are used to compute super resolution(SR) image from a given set of low resolution (LR) images differing in subpixel shifts. The algorithm is tested on remote sensing images and compared for accuracy with other well-known algorithms such as Iterative Back Projection (IBP), Maximum Likelihood (ML) algorithm, interpolation of scattered points using Nearest Neighbor (NN) and Inversed Distance Weighted (IDW) interpolation, and Radial Basis Functin(RBF) . The accuracy of SR depends on various factors besides the algorithm (i) number of subpixel shifted LR images (ii) accuracy with which the LR shifts are estimated by registration algorithms (iii) and the targeted spatial resolution of SR. In our studies, the accuracy of EDRBF is compared with other algorithms keeping these factors constant. The algorithm has two steps: i) registration of low resolution images and (ii) estimating the pixels in High Resolution (HR) grid using EDRBF. Experiments are conducted by simulating LR images from a input HR image with different sub-pixel shifts. The reconstructed SR image is compared with input HR image to measure the accuracy of the algorithm using sum of squared errors (SSE). The algorithm has outperformed all of the algorithms mentioned above. The algorithm is robust and is not overly sensitive to the registration inaccuracies.

Mareboyana, Manohar M.↗

STRUCTURE OF THE INNER JET OF OJ287 FROM VLBA DATA AT 15 GHz IN SUPER-RESOLUTION MODE

In this work we show the results obtained from the Very Long Baseline Array data at 15 GHz of OJ287 in super-resolution mode. The data showed a jet configuration in the form of a 'fork' where superluminal components emerge via stationary components at the northwest and the southeast close to the core to form parallel trajectories along the southwest direction in the plane of sky. This agrees with a source structure of an extended, broad morphology of OJ287.

79 ASTRONOMY AND ASTROPHYSICS↗

Super-resolution demodulation for fiber sensor arrays based on the MUSIC algorithm

This paper studies the use of MUltiple SIgnal Classification (MUSIC) as a super-resolution algorithm to improve demodulation results for intrinsic Fabry–Perot interferometer (IFPI) sensor arrays. Through distinction between noise and signal subspaces in an observation matrix, this paper shows that a 38-fold improvement in the full width at half maximum (FWHM) estimation of IFPI optical path differences (OPD) can be achieved using this algorithm. Based on this improved method, this paper demonstrates that a tunable laser with a 1.3-nm tuning range can achieve the same sensor demodulation performance as a tunable laser with a 50-nm tuning range if a conventional Fourier transform-based algorithm is used. This paper presents a new approach to analyzing optical signals produced by multiple multiplexed interferometers with similar OPDs with potential applications for both single-mode and multiple-mode devices.

Li, Yuqi (ORCID:0000000291643663)↗

High-throughput, super-resolution 3D reconstruction of nano-structured solid oxide fuel cell electrodes and quantification of microstructure-property relationships

Nano-structuring methods are actively applied to solid oxide fuel cell electrodes to reduce the operating temperature while preserving a high electro-catalytic activity. The unique nanoscale microstructure is vital to electrochemical performance, yet not well quantified in three dimensions. Here, with a multi-stage recovering principle of distance correlation functions, the three-dimensional microstructures of La 0.8 Sr 0.2 MnO 3-δ nanoparticles infiltrated porous Y 0.16 Zr 0.84 O 2-δ electrodes are reconstructed with a dimension of 1024 × 1024 × 1024 voxels at a resolution of 7.5 nm from one two-dimensional micrograph. The key geometric characteristics, such as tortuosity factors, active surface/interface areas and three-phase boundary length, are calculated from the reconstructed three-dimensional microstructures at various loadings of La 0.8 Sr 0.2 MnO 3-δ . Combining with the analysis of distribution of relaxation times, the active three-phase boundary length is shown to be the main factor governing the electrode impedance, and is related quantitatively to the electrochemical process at high frequency. The accuracy of capturing nanoscale features is validated by the focused ion beam sectioning dataset of a Ni-Y 0.16 Zr 0.84 O 2-δ electrode at nanoscale resolution. Lastly, this work provides a promising strategy for reconstructing three-dimensional heterogeneous nanostructures from one super-resolution two-dimensional micrograph, and demonstrates a quantitative approach for uncovering processing-structure-property relationships of nanostructured electrodes and beyond.

25 ENERGY STORAGE↗