Soft X-ray Imaging Research Articles

Quantifying the Growth and Evolution of Particle Cracking in Mo2C Anode with Different Cycles Using Soft X-ray Imaging

Quantifying the Growth and Evolution of Particle Cracking in Mo₂C Anode with Different Cycles Using Soft X-ray Imaging

Three-Dimensional Remodeling of SARS-CoV2-Infected Cells Revealed by Cryogenic Soft X-ray Tomography.

Plus-strand RNA viruses are proficient at remodeling host cell membranes for optimal viral genome replication and the production of infectious progeny. These ultrastructural alterations result in the formation of viral membranous organelles and may be observed by different imaging techniques, providing nanometric resolution. Guided by confocal and electron microscopy, this study describes the generation of wide-field volumes using cryogenic soft-X-ray tomography (cryo-SXT) on SARS-CoV-2-infected human lung adenocarcinoma cells. Confocal microscopy showed accumulation of double-stranded RNA (dsRNA) and nucleocapsid (N) protein in compact perinuclear structures, preferentially found around centrosomes at late stages of the infection. Transmission electron microscopy (TEM) showed accumulation of membranous structures in the vicinity of the infected cell nucleus, forming a viral replication organelle containing characteristic double-membrane vesicles and virus-like particles within larger vesicular structures. Cryo-SXT revealed viral replication organelles very similar to those observed by TEM but indicated that the vesicular organelle observed in TEM sections is indeed a vesiculo-tubular network that is enlarged and elongated at late stages of the infection. Overall, our data provide additional insight into the molecular architecture of the SARS-CoV-2 replication organelle.

Limited-angle artifacts removal and jitter correction in soft x-ray tomography via physical model-driven deep learning

Soft x-ray nanoscale tomography provides high-resolution three-dimensional visualization of the imaged objects and promotes the development of multiple research fields. However, the current challenges lie in the presence of limited-angle artifacts and projection jitter, which degrade the imaging resolution and quality. To address these issues, we propose a physical model-driven deep learning including forward and backward CT models. Combing with the iterative algorithm, the proposed method simultaneously suppresses the limited-angle and jitter artifacts. Furthermore, the physical model generates plenty of data to overcome the requirement of abundant experimental datasets. Both simulation and experiment demonstrate the feasibility and validity of the proposed reconstruction algorithm.

Kinetic modelling of sterol transport between plasma membrane and endo-lysosomes based on quantitative fluorescence and X-ray imaging data.

Niemann Pick type C1 and C2 (NPC1 and NPC2) are two sterol-binding proteins which, together, orchestrate cholesterol transport through late endosomes and lysosomes (LE/LYSs). NPC2 can facilitate sterol exchange between model membranes severalfold, but how this is connected to its function in cells is poorly understood. Using fluorescent analogs of cholesterol and quantitative fluorescence microscopy, we have recently measured the transport kinetics of sterol between plasma membrane (PM), recycling endosomes (REs) and LE/LYSs in control and NPC2 deficient fibroblasts. Here, we use kinetic modeling of this data to determine rate constants for sterol transport between intracellular compartments. Our model predicts that sterol is trapped in intraluminal vesicles (ILVs) of LE/LYSs in the absence of NPC2, causing delayed sterol export from LE/LYSs in NPC2 deficient fibroblasts. Using soft X-ray tomography, we confirm, that LE/LYSs of NPC2 deficient cells but not of control cells contain enlarged, carbon-rich intraluminal vesicular structures, supporting our model prediction of lipid accumulation in ILVs. By including sterol export via exocytosis of ILVs as exosomes and by release of vesicles-ectosomes-from the PM, we can reconcile measured sterol efflux kinetics and show that both pathways can be reciprocally regulated by the intraluminal sterol transfer activity of NPC2 inside LE/LYSs. Our results thereby connect the in vitro function of NPC2 as sterol transfer protein between membranes with its in vivo function.

An Automatic Non-Destructive External and Internal Quality Evaluation of Mango Fruits based on Color and X-ray Imaging with Machine Learning and Deep Learning Based Classification Models

Quality evaluation of food products, agricultural produce to be specific, has gained momentum from past few decades due to the increased awareness among consumers across the world. This has resulted in the increased emphasis on the development and use of quality assessment techniques in food industry. Moreover, there is a need to automate the quality monitoring of agricultural produce like fruits and vegetables which is otherwise done manually in developing countries hence labor intensive, time consuming and subjective in nature. This paper presents an empirical analysis to build a rapid, robust, real-time, non-destructive computer vision based quality assessment model for mango fruits. The work employs the automatic disease classification of mango fruits based on machine and deep learning models. Firstly, the dataset of colored mango fruits images with 2279 images falling into three classes and another dataset of soft X-ray images of mango fruits with 572 images belonging to two quality classes are developed for detecting external and internal defects, respectively. The multilayer perceptron neural network (MLP NN) with two hidden layers, which may be considered as the starting point for deep learning technique, is proposed as machine learning model to classify the color images of mango fruits into one of three external quality classes with 95.1% accuracy and also to classify the soft X-ray images into two internal quality classes with 97.5% accuracy. In order to step out of feature engineering, actual deep learning convolutional neural network (CNN) models, a customized CNN model and pre-trained CNN models, VGGNet (VGG16) and DenseNet121 were also explored for mango disease classification. The maximum validation accuracy of custom CNN was found to be with 91.52% and 98.7% for color and augmented X-ray images, respectively. The classification accuracy of pre-trained models were found to be reasonably good for the color images but exhibited high variability in results and made it difficult to draw a general conclusion for the proposed datasets. However, the proposed MLP NN model based on few basic intensity and geometric features and also the proposed customized CNN model were found to be the best models and they outperform the state of the art reported in the literature.

Soft X-ray tomography using the optimized regularization method in Alvand Tokamak

The detection of Soft X-ray (SXR) radiation emitted during plasma discharge gives valuable information on tokamak plasma physics, position and shape of the plasma. Intensity of SXR radiation is routinely measured with SXR detectors. In this research, the arrangement and performance of such a system was investigated in Alvand tokamak. According to the diagnostic ports available on the system, the optimal placement has been obtained for the maximum coverage of the detectors. By choosing three synthetic plasma emissivity profiles, including Gaussian, Hollow and Banana profiles, the lined-integrated data reached to detector were simulated. Two-dimensional reconstruction of plasma emissivity was done using the Tikhonov regularization method. In this method, the regularization term and parameter were calculated using the Laplace matrix and iteration method, respectively. The minimum number of pixels for discretizing the poloidal cross section of plasma was 400. For this number of pixels, the root mean square derivation of reconstruction emissivity was obtained for Gaussian, Hallow and Banana models 0.016, 0.029 and 0.031, respectively. In addition, the convergence and stability of the reconstruction method was investigated by adding random noise to the primary data. For Gaussian and Hallow profiles, adding noise up to about 20 % has not significantly affected on reconstruction results. Meanwhile, in the Banana model, the reconstruction process will lose its effectiveness only for noise more than 2 %.

Ultrastructural features mirror metabolic derangement in human endothelial cells exposed to high glucose

High glucose-induced endothelial dysfunction is the early event that initiates diabetes-induced vascular disease. Here we employed Cryo Soft X-ray Tomography to obtain three-dimensional maps of high d-glucose-treated endothelial cells and their controls at nanometric spatial resolution. We then correlated ultrastructural differences with metabolic rewiring. While the total mitochondrial mass does not change, high d-glucose promotes mitochondrial fragmentation, as confirmed by the modulation of fission–fusion markers, and dysfunction, as demonstrated by the drop of membrane potential, the decreased oxygen consumption and the increased production of reactive oxygen species. The 3D ultrastructural analysis also indicates the accumulation of lipid droplets in cells cultured in high d-glucose. Indeed, because of the decrease of fatty acid β-oxidation induced by high d-glucose concentration, triglycerides are esterified into fatty acids and then stored into lipid droplets. We propose that the increase of lipid droplets represents an adaptive mechanism to cope with the overload of glucose and associated oxidative stress and metabolic dysregulation.

An approach of filtering simulated magnetospheric X-ray images based on self-supervised network and random forest

Imaging is an important method for observing the Earth’s space environment. Future missions, such as the Solar wind Magnetosphere Ionosphere Link Explorer (SMILE), aim to explore the interaction between the solar wind and the Earth’s magnetosphere via soft x-ray imaging. With the advent of these missions, a large number of magnetospheric images may be acquired. However, as the viewing geometry and solar wind conditions change, satellites sometimes fail to capture the magnetopause inside the field of view. We propose an approach that blends machine learning and deep learning to filter the simulated x-ray images for the SMILE mission, aiming to achieve automatic classification of the detected images. First, we performed magnetohydrodynamic simulations to derive the predicted SMILE x-ray images. Then, we used a self-supervised contrast feature extraction network to study the features of the images. Using this network, the random forest classifier can distinguish whether the subsolar point at the magnetopause has been detected. Finally, we designed the magnetopause filter to obtain the subsolar magnetopause images with observation positions outside the magnetosphere. As a result, the prediction accuracy of the classifier is up to 93%. And the F1 score is up to 95.5%. The stratified predictions allow an automatic screening of whether satellite magnetospheric images cover the subsolar magnetopause. These images, which have observation positions outside the magnetosphere, can be used to invert the three-dimensional magnetopause.

Dynamic Characterization of Plasma Boundaries with Soft X-Ray Imagers

Dynamic Characterization of Plasma Boundaries with Soft X-Ray Imagers

Soft X-ray Tomography Enables New Insights into the Coordinated Division of Organelle-like Symbiont in a Globally Distributed Unicellular Marine Haptophyte Alga.

Soft X-ray Tomography Enables New Insights into the Coordinated Division of Organelle-like Symbiont in a Globally Distributed Unicellular Marine Haptophyte Alga.

The Role of Soft X-ray Tomography in Generating Whole-cell Models.

The Role of Soft X-ray Tomography in Generating Whole-cell Models.

Dehydration: an Alternative Specimen Preparation for Soft X-ray Tomography.

Dehydration: an Alternative Specimen Preparation for Soft X-ray Tomography.

Large Volume Imaging Soft X-Ray Tomography of Infected Cells.

Large Volume Imaging Soft X-Ray Tomography of Infected Cells.

Correlative Cryo Soft X-ray Tomography and Spectromicroscopy to Study Ca Biomineralization Processes in Frozen Hydrated Whole Cells.

Correlative Cryo Soft X-ray Tomography and Spectromicroscopy to Study Ca Biomineralization Processes in Frozen Hydrated Whole Cells.

Applications of Soft X-ray Tomography for the Direct Observation of Native Cellular Events.

Applications of Soft X-ray Tomography for the Direct Observation of Native Cellular Events.

Integration of Laboratory Cryo Soft X-ray Tomography into CLEM Workflows for Multimodal Multiscale Imaging of Bulk Samples.

Integration of Laboratory Cryo Soft X-ray Tomography into CLEM Workflows for Multimodal Multiscale Imaging of Bulk Samples.

Soft X-ray Tomography for Mapping and Quantifying Intracellular Organelle Interactions.

Soft X-ray Tomography for Mapping and Quantifying Intracellular Organelle Interactions.

First Experimental Monitoring of the Three-Dimensional Structure of Toroidal Plasmas Using Multiple Soft X-Ray Imaging Techniques

In this study, a previously proposed three-dimensional (3D) tomography method using a multiple-pinhole camera is applied experimentally to detect 3D structures in reversed-field pinch plasmas. The 3D structure of the toroidal plasma was successfully reconstructed and found to be consistent with magnetic field measurements at the plasma’s edge. The results also indicate that toroidal mode number estimation may be possible using the images from the multiple-pinhole camera.

Magnetopause Detection under Low Solar Wind Density Based on Deep Learning

Extracting the peak value of the X-ray signal in the original magnetopause detection method of soft X-ray imaging (SXI) for the SMILE satellite is problematic because of the unclear interface of the magnetosphere system under low solar wind density and the short integration time. Herein, we propose a segmentation algorithm for soft X-ray images based on depth learning, we construct an SXI simulation dataset, and we segment the magnetospheric system by learning the spatial structure characteristics of the magnetospheric system image. Then, we extract the maximum position of the X-ray signal and calculate the spatial configuration of the magnetopause using the tangent fitting approach. Under a uniform universe condition, we achieved a pixel accuracy of the maximum position of the photon number detected by the network as high as 90.94% and contained the position error of the sunset point of the 3D magnetopause below 0.2 RE. This result demonstrates that the proposed method can detect the peak photon number of magnetospheric soft X-ray images with low solar wind density. As such, its use improves the segmentation accuracy of magnetospheric soft X-ray images and reduces the imaging time requirements of the input image.

Experimental study of core MHD events in thousand-second improved confinement plasma on the EAST tokamak

Recently, stationary plasma with a world-record pulse length of 1056 s was achieved on the Experimental Advanced Superconducting Tokamak (EAST). In this work, the core magnetohydrodynamics (MHD) events as well as mode coupling processes have been investigated in EAST long pulse operation with electron heating dominant and pure radio frequency wave heating low collisionality, by using several diagnostics and the nonlinear numerical code M3D together. A saturated m/n = 1/1 kink mode was observed in the core region, where a stable internal transport barrier was found in the electron temperature channel. The frequencies and 2D structures of these modes were studied by a combination of soft X-ray (SXR) imaging and electron cyclotron emission diagnostics. The frequency of the m/n = 1/1 mode exhibited a feature of chirping down with time, and this chirping rate corresponds to the rate of electron diamagnetic drift frequency change. A twisted pattern (‘Taichi structure in shape’) was reconstructed by SXR tomography of the m/n = 1/1 mode. The perturbations of electron temperature and density caused by m/n = 1/1 are different in size, the latter one being much smaller. The destabilization of m/n = 1/1 was due to the strong central heating combined with electron cyclotron resonance heating and lower hybrid current drive. In the presence of the m/n = 1/1 mode, a negative current was generated on the magnetic axis, which anomalously broadened the core current profile. A m/n = 3/2 tearing mode triggered by m/n = 1/1 mode was also observed. The m/n = 3/2 mode has a smaller frequency than the m/n = 1/1 mode, and carries an m/n = 3/2 island with detectable size. A novel 3D MHD model that separately evolves the plasma density and temperature is applied to the m/n = 3/2 triggered by m/n = 1/1. It is found that a toroidal current density at the q = 1.5 surface, caused by non-axisymmetric density perturbation during the m/n = 1/1 nonlinear growth phase, was generated and hence the destabilization of the m/n = 3/2 tearing mode. Both the modeled electron temperature and density perturbations agree well with experimental observations. In the last section of this paper, the interaction between the m/n = 1/1 mode and fast electrons as well as the active control of this mode are also presented.