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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Material Science Image Analysis using Quant-CT in ImageJ

We introduce a computational analysis workflow to access properties of solid objects using nondestructive imaging techniques that rely on X-ray imaging. The goal is to process and quantify structures from material science sample cross sections. The algorithms can differentiate the porous media (high density material) from the void (background, low density media) using a Boolean classifier, so that we can extract features, such as volume, surface area, granularity spectrum, porosity, among others. Our workflow, Quant-CT, leverages several algorithms from ImageJ, such as statistical region merging and 3D object counter. It also includes schemes for bilateral filtering that use a 3D kernel, for parallel processing of sub-stacks, and for handling over-segmentation using histogram similarities. The Quant-CT supports fast user interaction, providing the ability for the user to train the algorithm via subsamples to feed its core algorithms with automated parameterization. Quant-CT plugin is currently available for testing by personnel at the Advanced Light Source and Earth Sciences Divisions and Energy Frontier Research Center (EFRC), LBNL, as part of their research on porous materials. The goal is to understand the processes in fluid-rock systems for the geologic sequestration of CO2, and to develop technology for the safe storage of CO2 in deep subsurface rock formations. We describe our implementation, and demonstrate our plugin on porous material images. This paper targets end-users, with relevant information for developers to extend its current capabilities.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2019

A New Look at Aubrites: Investigating 3D Modal Mineralogy with X-Ray Computed Tomography

The aubrites (approximately 30 known meteorites) are a unique group of differentiated meteorites that formed on asteroids with oxygen fugacities (ƒO2) from approximately 2 to approximately 6 log units below the iron-wustite buffer. At these highly reduced conditions, elements deviate from the geochemical behavior exhibited at terrestrial ƒO2, forming FeO-poor silicates and exotic sulfides. While previous studies have described the petrology and 2D modal abundances of aubrites, this work investigates the 3D modal mineralogies of silicate, metal, and sulfide phases in aubrite samples, which are then compared to the available 2D data. In addition to 3D modal mineralogies, we have examined the geochemistry of fourteen aubrites, including mineral major-element compositions, bulk-rock compositions, and oxygen isotopic compositions to understand their formation and evolution at extreme ƒO2 conditions. We utilize X-ray computed tomography (XCT) to non-destructively analyze the distribution and abundances of mineral phases in aubrites and locate composite clasts of sulfide grains for future analytical study. In order to better constrain elemental behavior under reduced conditions, we specifically target minerals phases that comprise moderately volatile elements (i.e. oldhamite [CaS], caswellsilverite [NaCrS2] and djerfisherite [K6Na(Fe,Cu,Ni)25S26Cl]) as it has been shown that their geochemical behavior changes as a function of ƒO2. Currently, we have produced 3D scans of the Norton County aubrite. The results of the XCT data have allowed for the determination of the abundances of silicate groundmass (i.e., enstatite, forsterite, albite, and diopside), light (based on electron density) sulfides (i.e. alabandite [MnS] and daubréelite [FeCr2S4]), heavy (based on electron density) sulfides (i.e., troilite [FeS]), and Fe,Ni metal by segmenting a density histogram in Volume Graphics Studio software. XCT scans of additional aubrites are underway. By combining the 3D representation of the exotic phases found in aubrites with existing 2D characterizations, we are able to better determine modal abundances. By integrating 3D and 2D modal abundances and geochemistry, we can ultimately better constrain aubrite petrogenesis and elemental partitioning under reduced conditions. Furthermore, application of this new 3D approach offers the opportunity to identify and select clasts for future study prior to cutting the sample, which will minimize sample loss of this precious material.

Wilbur, Zoe↗
DOE OSTI2022

Machine learning-assisted upscaling analysis of reservoir rock core properties based on micro-computed tomography imagery

Optimum solutions for geologic modeling and reservoir simulation in industries such as oil and gas recovery and carbon capture and storage require accurate characterization of reservoir properties, which are often heterogeneous. In this study, high-quality micro-computed tomography (CT) images (1.475-μm/pixel resolution) of a sandstone core acquired from the Bell Creek oil field, USA, were used to provide nondestructive analysis of pore- and core-scale heterogeneity across measurement scales of 94–566 μm. In addition to characterizing the as-received sample, the core sample was flooded with brine to evaluate the capacity of the core sample to receive injected fluids. The micro-CT images were systematically segmented into pore spaces and grains via machine learning (ML) steps including image preprocessing, label creation using a traditional ML method based on limited manual image annotation, and finally U-Net segmentation. The segmented image stacks were reconstructed into digital cubes of various scales of voxel lengths. The 3D porosity values were calculated for all the digital cubes, and the fractal dimensions of the cubes were estimated using a box-counting method. The results showed that smaller cubes had greater heterogeneity and that the porosity values could be accurately estimated by fractal dimension and voxel lengths using ML models. For the core sample with brine flooding, the ratio of pores filled by brine to the total pore space was related to the porosity and could also be accurately estimated by porosity, fractal dimension, and voxel lengths using ML models. In conclusion, the results of this study demonstrate that the concept of fractal dimension can be a useful vector to perform upscaling analysis of sandstone rock heterogeneity from the pore to core scale and that fractal dimensions can be used to estimate porosity values and pore space-filling capacity across those scales.

58 GEOSCIENCES↗
DOE OSTI2025

PyCMG-based Simulation of Volumetric Concrete Microstructure

Concrete is a complex, heterogeneous material with a microstructure composed of aggregates, cement paste, and pores spanning multiple length scales. Understanding this microstructure is critical for advancing the performance, durability, and modeling of concrete-based systems. While experimental imaging such as X-ray computed tomography (XCT) provides valuable insights, generating large datasets with detailed ground truth annotations is both costly and labor-intensive due to challenges in segmenting similar phases, such as aggregates and cement paste, that often share similar attenuation properties. To address this, we developed a pipeline to simulate realistic 3D concrete microstructures using the open-source Python package PyCMG. This simulation effort focuses on generating high-fidelity, annotated microstructures that can serve as training or benchmarking datasets for image analysis, segmentation algorithms, and machine learning models, particularly in scenarios where experimental data is scarce.

Ziabari, Amir [Oak Ridge National Laboratory; ORNL↗
DOE OSTI2013

Three-dimensional reconstruction and morphologic characteristics of porous metal fiber sintered sheet

This paper presents an approach to achieve morphological characterizing for complex porous materials based on micro X-ray tomography images, with an example of a novel porous metal fiber sheet produced through solid-state sintering method. The geometrical reconstruction was performed after selection of volume of interest and image processing of anisotropic diffusion smooth. The reconstructed gray level images were then transferred into binary images by adjusting binarization threshold according to the actual porosity. Taking into account the tubular structural feature of the fibers, skeleton extraction algorithm based on the distance transform function was applied and further improved by the scale axis transform method. The skeleton was later pruned and segmented according to the contact points to perform morphological characterizing. Compared with actual manufacturing parameters, the style, length, radius, orientation and tortuosity of fiber segments were discussed. The results show that our proposed method can well describe the actual geometrical and morphological characteristics, which will provide a promising method for the structural description of fibrous networks. - Highlights: • Micro-CT technology was used to achieve the 3D geometrical reconstruction. • Skeleton extraction algorithm was modified to get the medial skeleton. • Skeleton filter operation was adopted to deal with the segmented skeleton. • Useful morphological statistics was obtained through skeleton segments. • Relationship between structure and manufacturing processes was discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2016

TU-H-CAMPUS-IeP3-02: Neurovascular 4D Parametric Imaging Using Co-Registration of Biplane DSA Sequences with 3D Vascular Geometry Obtained From Cone Beam CT

Purpose: To create 4D parametric images using biplane Digital Subtraction Angiography (DSA) sequences co-registered with the 3D vascular geometry obtained from Cone Beam-CT (CBCT). Methods: We investigated a method to derive multiple 4D Parametric Imaging (PI) maps using only one CBCT acquisition. During this procedure a 3D-DSA geometry is stored and used subsequently for all 4D images. Each time a biplane DSA is acquired, we calculate 2D parametric maps of Bolus Arrival Time (BAT), Mean Transit Time (MTT) and Time to Peak (TTP). Arterial segments which are nearly parallel with one of the biplane imaging planes in the 2D parametric maps are co-registered with the 3D geometry. The values in the remaining vascular network are found using spline interpolation since the points chosen for co-registration on the vasculature are discrete and remaining regions need to be interpolated. To evaluate the method we used a patient CT volume data set for 3D printing a neurovascular phantom containing a complete Circle of Willis. We connected the phantom to a flow loop with a peristaltic pump, simulating physiological flow conditions. Contrast media was injected with an automatic injector at 10 ml/sec. Images were acquired with a Toshiba Infinix C-arm and 4D parametric image maps of the vasculature were calculated. Results: 4D BAT, MTT, and TTP parametric image maps of the Circle of Willis were derived. We generated color-coded 3D geometries which avoided artifacts due to vessel overlap or foreshortening in the projection direction. Conclusion: The software was tested successfully and multiple 4D parametric images were obtained from biplane DSA sequences without the need to acquire additional 3D-DSA runs. This can benefit the patient by reducing the contrast media and the radiation dose normally associated with these procedures. Partial support from NIH Grant R01-EB002873 and Toshiba Medical Systems Corp.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

SU-C-213-01: 3D Printed Patient Specific Phantom Composed of Bone and Soft Tissue Substitute Plastics for Radiation Therapy

Purpose: 3D printed phantoms constructed of multiple tissue approximating materials could be useful in both clinical and research aspects of radiotherapy. This work describes a 3D printed phantom constructed with tissue substitute plastics for both bone and soft tissue; air cavities were included as well. Methods: 3D models of an anonymized nasopharynx patient were generated for air cavities, soft tissues, and bone, which were segmented by Hounsfield Unit (HU) thresholds. HU thresholds were chosen to define air-to-soft tissue boundaries of 0.65 g/cc and soft tissue-to-bone boundaries of 1.18 g/cc based on clinical HU to density tables. After evaluation of several composite plastics, a bone tissue substitute was identified as an acceptable material for typical radiotherapy x-ray energies, composed of iron and PLA plastic. PET plastic was determined to be an acceptable soft tissue substitute. 3D printing was performed on a consumer grade dual extrusion fused deposition model 3D printer. Results: MVCT scans of the 3D printed heterogeneous phantom were acquired. Rigid image registration of the patient and the 3D printed phantom scans was performed. The average physical density of the soft tissue and bone regions was 1.02 ± 0.08 g/cc and 1.39 ± 0.14 g/cc, respectively, for the patient kVCT scan. In the 3D printed phantom MVCT scan, the average density of the soft tissue and bone was 1.01 ± 0.09 g/cc and 1.44 ± 0.12 g/cc, respectively. Conclusion: A patient specific phantom, constructed of heterogeneous tissue substitute materials was constructed by 3D printing. MVCT of the 3D printed phantom showed realistic tissue densities were recreated by the 3D printing materials. Funding provided by intra-department grant by University of Minnesota Department of Radiation Oncology.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

Evaluating Quantity of Decommissioning Waste by Cutting Virtual 3D Objects in Large Equipment - 19111

Equipment and piping components contaminated by radioactive materials and/or including irradiated materials as low-level wastes must be cut, segmented, and packed in waste containers. There is a trade-off between cost and exposure dose when packing waste fragments into containers, if the fragments are cut into small sizes, although the number of containers becomes less than the number of larger fragments, the cutting work hours and exposure dose would increase. To avoid excess exposure dose and minimize number of waste containers for many pieces of equipment and piping components, an automatic planning method for virtual cutting of 3D equipment objects under the constraints of container sizes, maximum radioactivity, maximum weight, and maximum dose rate was developed. It was confirmed that the developed method effectively evaluates size, weight, and amount of radioactivity in each container automatically. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2014

Impact of Endografting on the Thoracic Aortic Anatomy: Comparative Analysis of the Aortic Geometry before and after the Endograft Implantation

Purpose: Although the widespread acceptance of thoracic endovascular aortic repair (TEVAR) as a first-line treatment option for a multitude of thoracic aortic diseases, little is known about the consequences of the device implantation on the native aortic anatomy. We propose a comparative analysis of the pre- and postoperative geometry on a clinical series of patients and discuss the potential clinical implications Methods: CT pre- and postoperative acquisitions of 30 consecutive patients treated by TEVAR for different pathologies (20 thoracic aortic aneurysms, 6 false aneurysms, 3 penetrating ulcers, 1 traumatic rupture) were used to model the vascular geometry. Pre- and postoperative geometries were compared for each patient by pairing and matching the 3D models. An implantation site was identified, and focal differences were detected and described. Results: Segmentation of the data sets was successfully performed for all 30 subjects. Geometry differences between the pre- and postoperative meshes were depicted in 23 patients (76 %). Modifications at the upper implantation site were detected in 14 patients (47 %), and among them, the implantation site involved the arch (Z0–3) in 11 (78 %). Conclusion: Modeling the vascular geometry on the basis of imaging data offers an effective tool to perform patient-specific analysis of the vascular geometry before and after the treatment. Future studies will evaluate the consequences of these changes on the aortic function.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2025

Systematic analysis of magnetic equilibrium reconstruction with eddy currents on LTX- β

Magnetic equilibrium reconstruction in the Lithium Tokamak Experiment-Beta (LTX-β) is complicated by strong eddy currents and toroidal asymmetries arising from its segmented, close-fitting conducting shell. In the earlier experiment LTX, these three-dimensional (3D) conductor effects significantly distorted diagnostic signals and challenged conventional axisymmetric reconstruction methods. In this work, we demonstrate that accurate plasma equilibria can be recovered on LTX-β by incorporating a small number of dominant eddy current modes derived from realistic conductor models. Using the open-source $\tt{TokaMaker}$ Grad–Shafranov solver, we reconstruct equilibria across a systematically selected set of LTX-β discharges and validate them against the legacy $\tt{PSI-Tri}$ hybrid 2D-3D code. Our results show that fully 2D $\tt{TokaMaker}$ reconstructions achieve significantly improved agreement with flux loop and Mirnov probe measurements, reducing total chi-squared fitting errors, especially during startup. Finally, we develop new hybrid 2D-3D $\tt{TokaMaker}$ reconstructions by integrating the $\tt{ThinCurr}$ 3D eddy current model, which we optimize for significant further reduction of chi-squared in most scenarios. These findings underscore the importance of realistic wall-current modeling in short-pulse tokamaks, and establish a physics-based reconstruction framework that is extensible to devices with complex passive structures and 3D wall interactions.

Grad–Shafranov solver↗
DOE OSTI2024

Three-Dimensional Ultrastructure of Arabidopsis Cotyledons Infected with Colletotrichum higginsianum

We used serial block-face scanning electron microscopy (SBF-SEM) to study the host–pathogen interface between Arabidopsis cotyledons and the hemibiotrophic fungus Colletotrichum higginsianum. By combining high-pressure freezing and freeze-substitution with SBF-SEM, followed by segmentation and reconstruction of the imaging volume using the freely accessible software IMOD, we created 3D models of the series of cytological events that occur during the Colletotrichum–Arabidopsis susceptible interaction. We found that the host cell membranes underwent massive expansion to accommodate the rapidly growing intracellular hypha. As the fungal infection proceeded from the biotrophic to the necrotrophic stage, the host cell membranes went through increasing levels of disintegration culminating in host cell death. Intriguingly, we documented autophagosomes in proximity to biotrophic hyphae using transmission electron microscopy (TEM) and a concurrent increase in autophagic flux between early to mid/late biotrophic phase of the infection process. Occasionally, we observed osmiophilic bodies in the vicinity of biotrophic hyphae using TEM only and near necrotrophic hyphae under both TEM and SBF-SEM. Overall, we established a method for obtaining serial SBF-SEM images, each with a lateral ( x-y) pixel resolution of 10 nm and an axial ( z) resolution of 40 nm, that can be reconstructed into interactive 3D models using the IMOD. Application of this method to the Colletotrichum–Arabidopsis pathosystem allowed us to more fully understand the spatial arrangement and morphological architecture of the fungal hyphae after they penetrate epidermal cells of Arabidopsis cotyledons and the cytological changes the host cell undergoes as the infection progresses toward necrotrophy. [Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY 4.0 International license .

Biochemistry & Molecular Biology↗
DOE OSTI2017

{sup 18}F-Choline Positron Emission Tomography/Computed Tomography and Multiparametric Magnetic Resonance Imaging for the Detection of Early Local Recurrence of Prostate Cancer Initially Treated by Radiation Therapy: Comparison With Systematic 3-Dimensional Transperineal Mapping Biopsy

Purpose: To compare the diagnostic performance of {sup 18}F-fluorocholine positron emission tomography/computed tomography (FCH-PET/CT), multiparametric prostate magnetic resonance imaging (mpMRI), and a combination of both techniques for the detection of local recurrence of prostate cancer initially treated by radiation therapy. Methods and Materials: This was a retrospective, single-institution study of 32 patients with suspected prostate cancer recurrence who underwent both FCH-PET/CT and 3T mpMRI within 3 months of one another for the detection of recurrence. All included patients had to be cleared for metastatic recurrence. The reference procedure was systematic 3-dimensional (3D)-transperineal prostate biopsy for the final assessment of local recurrence. Both imaging modalities were analyzed by 2 experienced readers blinded to clinical data. The analysis was made per-patient and per-segment using a 4-segment model. Results: The median prostate-specific antigen value at the time of imaging was 2.92 ng/mL. The mean prostate-specific antigen doubling time was 14 months. Of the 32 patients, 31 had a positive 3D-transperineal mapping biopsy for a local relapse. On a patient-based analysis, the detection rate was 71% (22 of 31) for mpMRI and 74% (23 of 31) for FCH-PET/CT. On a segment-based analysis, the sensitivity and specificity were, respectively, 32% and 87% for mpMRI, 34% and 87% for FCH-PET/CT, and 43% and 83% for the combined analysis of both techniques. Accuracy was 64%, 65%, and 66%, respectively. The interobserver agreement was κ = 0.92 for FCH-PET/CT and κ = 0.74 for mpMRI. Conclusions: Both mpMRI and FCH-PET/CT show limited sensitivity but good specificity for the detection of local cancer recurrence after radiation therapy, when compared with 3D-transperineal mapping biopsy. Prostate biopsy still seems to be mandatory to diagnose local relapse and select patients who could benefit from local salvage therapy.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2016

SU-D-201-05: On the Automatic Recognition of Patient Safety Hazards in a Radiotherapy Setup Using a Novel 3D Camera System and a Deep Learning Framework

Purpose: Patient safety hazards such as a wrong patient/site getting treated can lead to catastrophic results. The purpose of this project is to automatically detect potential patient safety hazards during the radiotherapy setup and alert the therapist before the treatment is initiated. Methods: We employed a set of co-located and co-registered 3D cameras placed inside the treatment room. Each camera provided a point-cloud of fraxels (fragment pixels with 3D depth information). Each of the cameras were calibrated using a custom-built calibration target to provide 3D information with less than 2 mm error in the 500 mm neighborhood around the isocenter. To identify potential patient safety hazards, the treatment room components and the patient’s body needed to be identified and tracked in real-time. For feature recognition purposes, we used a graph-cut based feature recognition with principal component analysis (PCA) based feature-to-object correlation to segment the objects in real-time. Changes in the object’s position were tracked using the CamShift algorithm. The 3D object information was then stored for each classified object (e.g. gantry, couch). A deep learning framework was then used to analyze all the classified objects in both 2D and 3D and was then used to fine-tune a convolutional network for object recognition. The number of network layers were optimized to identify the tracked objects with >95% accuracy. Results: Our systematic analyses showed that, the system was effectively able to recognize wrong patient setups and wrong patient accessories. The combined usage of 2D camera information (color + depth) enabled a topology-preserving approach to verify patient safety hazards in an automatic manner and even in scenarios where the depth information is partially available. Conclusion: By utilizing the 3D cameras inside the treatment room and a deep learning based image classification, potential patient safety hazards can be effectively avoided.

60 APPLIED LIFE SCIENCES↗
NASA NTRSDate not supplied

A Markov Random Field Model for Texture-based Segmentation of Small Cracks in Thin InconelTubes

Markov random fields have been used for image segmentation since their introduction in the1980s. This work applies a method from Principal Component Thermography to enhance thecontrast in damage regions in 3D images derived from X-ray computed tomography (CT)inspections. The developed method is applied to sizing of small cracks in thin Inconel tubes designed as probability of detection (POD) samples for radiographic inspection.Misclassification errors arising from artifacts due to beam-hardening are reduced by fitting the boundary of the segmented damage region with the arc of an ellipse. Results are comparedagainst those obtained through manual inspection.

Image segmentation↗
DOE OSTI2025

Subject-specific modeling framework for particle deposition using computational fluid dynamics

Quantifying particle deposition and dose in the respiratory tract requires a physiologically realistic representation and reproducible computational workflows. However, existing modeling frameworks, such as the International Commission on Radiological Protection (ICRP) compartmental models and the Multiple Path Particle Dosimetry (MPPD) tool, lack detailed deposition profiles and subject-specific capabilities. The combination of advances in computer vision algorithms applied to the respiratory tract and Computational Fluid and Particle Dynamics (CFPD) allows high-fidelity simulations of particle behavior in anatomically accurate geometries derived from individual CT scans. The segmentation, preprocessing, and file preparation task for a CFPD simulation was often time-consuming, and no prior studies to-date have yet presented a fully automated framework. This work presents a fully automated workflow to obtain individualized particle deposition profiles in the human respiratory tract. The pipeline starts with segmenting upper and lower airway geometries using morphological and deep learning-based methods, generating three-dimensional (3D) models from CT imaging data. Next, a series of algorithms are presented to quality check and prepare the 3D geometry for a CFD or CFPD simulation. The preprocessing step includes correcting geometric artifacts, enforcing a physically consistent mesh, and automatically identifying and capping multiple outlets, which is required for CFD/CFPD simulations. These processed models are then input into open-source (OpenFOAM) or commercial (StarCCM+) CFD solvers, where flow and transient particle transport equations — including turbulence and particle–wall interactions are solved under realistic breathing conditions. Finally, the resulting particle deposition profiles can be integrated with Monte Carlo radiation transport codes and state-of-the-art computational phantoms to assess organ-specific absorbed doses in scenarios of radioactive aerosol inhalation. The presented work streamlines respiratory tract segmentation, preprocessing for CFD/CFPD simulations, and integration with dose assessment workflows, reducing manual intervention and improving access to high-fidelity, subject-specific modeling. The high precision in predicted particle deposition and dose distributions can improve personalized treatment strategies in respiratory medicine and refine dose estimates for radiation protection.

AI↗
DOE OSTI2014

Electron microscopy and in vitro deneddylation reveal similar architectures and biochemistry of isolated human and Flag-mouse COP9 signalosome complexes

Highlights: • Deneddylation rates of human erythrocyte and mouse fibroblast CSN are very similar. • 3D models of native human and mouse CSN reveal common architectures. • The cryo-structure of native mammalian CSN shows a horseshoe subunit arrangement. - Abstract: The COP9 signalosome (CSN) is a regulator of the ubiquitin (Ub) proteasome system (UPS). In the UPS, proteins are Ub-labeled for degradation by Ub ligases conferring substrate specificity. The CSN controls a large family of Ub ligases called cullin-RING ligases (CRLs), which ubiquitinate cell cycle regulators, transcription factors and DNA damage response proteins. The CSN possesses structural similarities with the 26S proteasome Lid complex and the translation initiation complex 3 (eIF3) indicating similar ancestry and function. Initial structures were obtained 14 years ago by 2D electron microscopy (EM). Recently, first 3D molecular models of the CSN were created on the basis of negative-stain EM and single-particle analysis, mostly with recombinant complexes. Here, we compare deneddylating activity and structural features of CSN complexes purified in an elaborate procedure from human erythrocytes and efficiently pulled down from mouse Flag-CSN2 B8 fibroblasts. In an in vitro deneddylation assay both the human and the mouse CSN complexes deneddylated Nedd8-Cul1 with comparable rates. 3D structural models of the erythrocyte CSN as well as of the mouse Flag-CSN were generated by negative stain EM and by cryo-EM. Both complexes show a central U-shaped segment from which several arms emanate. This structure, called the horseshoe, is formed by the PCI domain subunits. CSN5 and CSN6 point away from the horseshoe. Compared to 3D models of negatively stained CSN complexes, densities assigned to CSN2 and CSN4 are better defined in the cryo-map. Because biochemical and structural results obtained with CSN complexes isolated from human erythrocytes and purified by Flag-CSN pulldown from mouse B8 fibroblasts are very similar, Flag-CSN pulldowns are a proper alternative to CSN preparation from erythrocytes.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Size-Dependent Characteristics of Surface-Rooted Three-Dimensional Convective Objects in Continental Shallow Cumulus Simulations

A segmentation algorithm is applied to high resolution simulations of shallow continental convection to identify individual convective 3D objects within the convective boundary layer, in order to investigate which properties of the objects vary with the object width. The study analyses the geometry of the objects, along with their profiles of vertical velocity and total water, to assess various assumptions often used in spectral mass-flux convection schemes. The methodology of this paper is unique in that we use (a) a novel application of the watershed algorithm to detect individual objects in the constantly evolving continental boundary layer efficiently, and (b) an unprecedentedly large number of simulations being analyzed. In total, 26 days of LASSO simulations at the Atmospheric Radiation Measurement Southern Great Plains site are analyzed, yielding roughly one million objects. Plume-like surface-rooted objects are found to be omnipresent, the vertical extent of which is strongly dependent on the object width. The vertical velocity and moisture anomaly profiles of the widest objects are roughly consistent with the classic buoyancy-driven rising plume model. The kinematic and thermodynamic properties of the objects vary with object width. This width dependence is strongest above cloud base, but much weaker below. Finally the impact of neglecting the contribution of covariances to the vertical moisture flux, which is commonly used in mass-flux parameterizations, is investigated. The average effect of neglecting covariances increases linearly with object width, leading to a 20% flux underestimation for 2 km wide objects. Implications of the results for spectral convection scheme development are briefly discussed.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Single-cell correlations of mRNA and protein content in a human monocytic cell line after LPS stimulation

The heterogeneity of mRNA and protein expression at the single-cell level can reveal fundamental information about cellular response to external stimuli, including the sensitivity, timing, and regulatory interactions of genes. Here we describe a fully automated system to digitally count the intron, mRNA, and protein content of up to five genes of interest simultaneously in single-cells. Full system automation of 3D microscope scans and custom image analysis routines allows hundreds of individual cells to be automatically segmented and the mRNA-protein content to be digitally counted. Single-molecule intron and mRNA content is measured by single-molecule fluorescence in-situ hybridization (smFISH), while protein content is quantified though the use of antibody probes. To mimic immune response to bacterial infection, human monocytic leukemia cells (THP-1) were stimulated with lipopolysaccharide (LPS), and the expression of two inflammatory genes, IL1β (interleukin 1β) and TNF-α (tumor necrosis factor α), were simultaneously quantified by monitoring the intron, mRNA, and protein levels over time. The simultaneous labeling of cellular content allowed for a series of correlations at the single-cell level to be explored, both in the progressive maturation of a single gene (intron-mRNA-protein) and comparative analysis between the two immune response genes. In the absence of LPS stimulation, mRNA expression of IL1β and TNF-α were uncorrelated. Following LPS stimulation, mRNA expression of the two genes became more correlated, consistent with a model in which IL1β and TNF-α upregulation occurs in parallel through independent mechanistic pathways. This smFISH methodology can be applied to different complex biological systems to provide valuable insight into highly dynamic gene mechanisms that determine cell plasticity and heterogeneity of cellular response.

59 BASIC BIOLOGICAL SCIENCES↗