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This document is a short summary of the steps required for MicroCT evaluation of a specimen. This includes data acquisition through image analysis, for the Explosives Threat Assessment (ETA) program. Expected outputs for each stage are outlined. Information and data shall be shipped to LLNL as described herein.
Crops with deeper roots would have multiple benefits, including better drought tolerance, reduced requirement for nitrogen fertilizer, and better sequestration of atmospheric CO 2 . DEEPER is an integrated platform of phenomic, genomic, and in silico technologies to generate maize lines with deeper roots. DEEPER is: LEADER (Leaf Elemental Accumulation from Deep Roots) is a breakthrough technology to nondestructively measure rooting depth by using the plant itself as a sensor. LEADER uses handheld X-ray Fluorescence spectrometry to quantify foliar accumulation of elements that are differentially distributed in the soil profile. LEADER is nondestructive and is orders of magnitude cheaper, faster, and more precise than any competing assay of rooting depth in the field. LEADER is able to distinguish deep-rooted from shallow-rooted maize lines in the field without the need for costly and noisy soil coring. RootRobot/DIRT3D, to automatically phenotype root architecture in any field, combining RootRobot, a mechatronics platform to excavate, clean, section, and image mature root crowns, with DIRT3D, software to quantify architectural traits in 3D. Anatomics, a high-throughput platform to phenotype root anatomy, combining LAT 2.0, a technology for 3D imaging of root anatomy and composition, with RootScan3D, software to automatically extract 3D anatomical and cell wall composition metrics from LAT 2.0 output. Using this platform we discovered two novel root traits, parenchyma cell wall thickness and multiseriate cortical sclerenchyma, that improve rooting depth and drought tolerance in maize and wheat. OpenSimRoot/Deep, software to simulate root interaction with hard subsoils. Using this platform we discovered novel concepts regarding how to increase crop rooting depth by modulating how individual root axes respond to hard soil. DeepGenes, a toolkit of genes, parent lines, and genomic selection strategies to enable breeding hybrids with deeper roots. We discovered 3 novel root genes that increase rooting depth in maize and wheat. DEEPER discovered novel root phenotypes for deeper rooting, and delivered validated ideotypes for deeper-rooted maize; novel technologies to rapidly assess root depth, root architecture and anatomy in field-grown plants; novel software tools for root modeling and 3D image analysis of root architecture and anatomy; and validated genes and genomic selection models to deploy traits for deeper rooting in maize breeding. Each DEEPER technology is transformative in its own right, and exceeds existing technologies. They are mutually synergistic, deployable for field-grown plants, and are ready for application. The phenotyping and modeling technologies are readily applicable to many crops, and genetic leads in maize may have utility in other grasses. Taken as a whole they represent a transformative platform to develop deeper-rooted crops, with greater drought tolerance, reduced fertilizer requirement, and greater carbon sequestration.
This is the poster presentation prepared for 2024 Geothermal Rising Conference, October 27-30, 2024, Waikoloa, HI. The poster is part of the LDRD project CS-GE^2 to demonstrate the multiple-tier fracture imaging analysis for geothermal reservoir applications.
This project addresses the challenge of processing corn stover by fractionating this biomass feedstock to both streamline processing and generate new potential co-products. Additionally, the project developed new field-deployable analytical tools that can be coupled with empirical models that were used to predict feedstock properties and processing performance. The overall scope of this project was: (1) identify conditions for optimal corn stover fractionation using a two- stage physical fractionation, (2) assess how physical fractionation impacts properties, partitioning of biomass, and response to processing, (3) further adapt, develop, and validate several advanced characterization tools for assessing biomass properties that can be linked to processing behavior, and (4) develop and validate predictive models based on measurements that can be performed “in the field” or “at the biorefinery gate” to predict feedstock processing behavior (preprocessing and deconstruction). The first objective employed pre-separation processing (size reduction) which was next subjected to enhanced separations to yield fractions enriched or depleted in select compositional components or properties. For the second objective, fractions were screened for their response to post-separation processing (pretreatment and enzymatic hydrolysis). Detailed characterization profiles were developed and dynamic image analysis to assess distribution of particle size and morphology. For the final objective, we utilized these tools to develop empirical models to assess the relative abundance of tissue type in order to assess fractionation efficacy and to predict fraction performance during pretreatment and enzymatic hydrolysis.
This SAND report summarizes work supported by an Engineering Sciences Research Foundation (ESRF) Lab Directed Research and Development (LDRD) project entitled “Beyond Magic Barrels: Digital manufacturing for crystallization, process development and optimization of explosive materials.” This SAND report is written in two parts with Part 1 discusses recrystallization of our explosive exemplar and Part 2 summarizing our work with recrystallization of resveratrol. We have studied resveratrol recrystallization with a multiscale approach combining experiments, modeling and simulation. At the single crystal scale, microscopy experiments illuminate crystal time-dependent growth rates using advanced image analysis. Bench scale experiments were carried out to look at growth of multiple particles in a small reactor creating thousands of particles and analyzing the results with microscopy and μCT. For the modeling we combine kinetic Monte Carlo (kMC) models with subscale information from density functional theory (DFT) or molecular dynamics. This work is discussed in Part 1 and can also be found in a paper from the project discussing a coarse-grained kMC model specifically developed for resveratrol. For well-mixed systems, we have population balance equations (PBE) linked with species mass conservation forming a set of ordinary differential equations that can be solved quickly. For more complicated geometries, such as the vat crystallization used throughout the complex, a coupled computational fluid dynamic (CFD)/PBE method was developed to account for gradients in temperature and concentration and differences in crystallization rates throughout the domain. These simulations are more complex and require high performance computing. We present results for two cases: 5% seed fast cool with parameters fit to the well-mixed case and 5% seed slow cool using the same parameters. We show reasonable agreement with experiments though are particles are significantly larger than the experiments.
Plant biomass represents an abundant and increasingly important natural resource and it mainly consists of a number of cell types that have undergone extensive secondary cell wall (SCW) formation. These cell types are abundant in the stems of Arabidopsis, a well-studied model system for hardwood, the wood of eudicot plants. The main constituents of hardwood include cellulose, lignin, and xylan, the latter in the form of glucuronoxylan (GX). The binding of GX to cellulose in the eudicot SCW represents one of the best-understood molecular interactions within plant cell walls. The evenly spaced acetylation and 4-O-methyl glucuronic acid (MeGlcA) substitutions of the xylan polymer backbone facilitates binding in a linear two-fold screw conformation to the hydrophilic side of cellulose and signifies a high level of molecular specificity. However, the wider implications of GX–cellulose interactions for cellulose network formation and SCW architecture have remained less explored. In this study, we seek to expand our knowledge on this by characterizing the cellulose microfibril organization in three well-characterized GX mutants. The selected mutants display a range of GX deficiency from mild to severe, with findings indicating even the weakest mutant having significant perturbations of the cellulose network, as visualized by both scanning electron microscopy (SEM) and atomic force microscopy (AFM). We show by image analysis that microfibril width is increased by as much as three times in the severe mutants compared to the wild type and that the degree of directional dispersion of the fibrils is approximately doubled in all the three mutants. Further, we find that these changes correlate with both altered nanomechanical properties of the SCW, as observed by AFM, and with increases in enzymatic hydrolysis. Results from this study indicate the critical role that normal GX composition has on cellulose bundle formation and cellulose organization as a whole within the SCWs.
Unmanned aerial vehicles (UAVs) provide an intermediate scale of spatial and spectral data collection that yields increased accuracy and consistency in data collection for morphological and physiological traits than satellites and expanded flexibility and high-throughput compared to ground-based data collection. In this study, we used UAV-based remote sensing for automated phenotyping of field-grown switchgrass (Panicum virgatum), a leading bioenergy feedstock. Using vegetation indices calculated from a UAV-based multispectral camera, statistical models were developed for rust disease caused by Puccinia novopanici, leaf chlorophyll, nitrogen, and lignin contents. For the first time, UAV remote sensing technology was used to explore the potentials for multiple traits associated with sustainable production of switchgrass, and one statistical model was developed for each individual trait based on the statistical correlation between vegetation indices and the corresponding trait. Also, for the first time, lignin content was estimated in switchgrass shoots via UAV-based multispectral image analysis and statistical analysis. The UAV-based models were verified by ground-truthing via correlation analysis between the traits measured manually on the ground-based with UAV-based data. The normalized difference red edge (NDRE) vegetation index outperformed the normalized difference vegetation index (NDVI) for rust disease and nitrogen content, while NDVI performed better than NDRE for chlorophyll and lignin content. Overall, linear models were sufficient for rust disease and chlorophyll analysis, but for nitrogen and lignin contents, nonlinear models achieved better results. As the first comprehensive study to model switchgrass sustainability traits from UAV-based remote sensing, these results suggest that this methodology can be utilized for switchgrass high-throughput phenotyping in the field.
Machine learning methodologies have played remarkable roles in solving complex systems with large data, well-defined input–output pairs, and clearly definable goals and metrics. The methodologies are effective in image analysis, classification, and systems without long chains of logic. Recently, machine-learning methodologies have been widely applied to inertial confinement fusion (ICF) capsules and the design optimization of OMEGA (Omega Laser Facility) capsule implosion and NIF (National Ignition Facility) ignition capsules, leading to significant progress. As machine learning is being increasingly applied, concerns arise regarding its capabilities and limitations in the context of ICF. ICF is a complicated physical system that relies on physics knowledge and human judgment to guide machine learning. Additionally, the experimental database for ICF ignition is not large enough to provide credible training data. Most researchers in the field of ICF use simulations, or a mix of simulations and experimental results, instead of real data to train machine learning models and related tools. They then use the trained learning model to predict future events. This methodology can be successful, subject to a careful choice of data and simulations. However, because of the extreme sensitivity of the neutron yield to the input implosion parameters, physics-guided machine learning for ICF is extremely important and necessary, especially when the database is small, the uncertain-domain knowledge is large, and the physical capabilities of the learning models are still being developed. In this work, we identify problems in ICF that are suitable for machine learning and circumstances where machine learning is less likely to be successful. This study investigates the applications of machine learning and highlights fundamental research challenges and directions associated with machine learning in ICF.
Medical implants are routinely tracked and monitored using different techniques, such as MRI, X-ray, and ultrasound. Due to the need for ionizing radiation, the two former methods pose a significant risk to tissue. Ultrasound imaging, however, is non-invasive and presents no known risk to human tissue. Aerogels are an emerging material with great potential in biomedical implants. While qualitative observation of ultrasound images by experts can already provide a lot of information about the implants and the surrounding structures, this paper describes the development and study of two simple B-Mode image analysis techniques based on attenuation measurements and echogenicity comparisons, which can further enhance the study of the biological tissues and implants, especially of different types of biocompatible aerogels.
Abstract We present a study of the potential for convolutional neural networks (CNNs) to enable separation of astrophysical transients from image artifacts, a task known as “real–bogus” classification, without requiring a template-subtracted (or difference) image, which requires a computationally expensive process to generate, involving image matching on small spatial scales in large volumes of data. Using data from the Dark Energy Survey, we explore the use of CNNs to (1) automate the real–bogus classification and (2) reduce the computational costs of transient discovery. We compare the efficiency of two CNNs with similar architectures, one that uses “image triplets” (templates, search, and difference image) and one that takes as input the template and search only. We measure the decrease in efficiency associated with the loss of information in input, finding that the testing accuracy is reduced from ∼96% to ∼91.1%. We further investigate how the latter model learns the required information from the template and search by exploring the saliency maps. Our work (1) confirms that CNNs are excellent models for real–bogus classification that rely exclusively on the imaging data and require no feature engineering task and (2) demonstrates that high-accuracy (>90%) models can be built without the need to construct difference images, but some accuracy is lost. Because, once trained, neural networks can generate predictions at minimal computational costs, we argue that future implementations of this methodology could dramatically reduce the computational costs in the detection of transients in synoptic surveys like Rubin Observatory's Legacy Survey of Space and Time by bypassing the difference image analysis entirely.
Here, we search for signatures of gravitational lensing in the gravitational-wave signals from compact binary coalescences detected by Advanced Laser Interferometer Gravitational-wave Observatory (LIGO) and Advanced Virgo during O3a, the first half of their third observing run. We study: (1) the expected rate of lensing at current detector sensitivity and the implications of a non-observation of strong lensing or a stochastic gravitational-wave background on the merger-rate density at high redshift; (2) how the interpretation of individual high-mass events would change if they were found to be lensed; (3) the possibility of multiple images due to strong lensing by galaxies or galaxy clusters; and (4) possible wave-optics effects due to point-mass microlenses. Several pairs of signals in the multiple-image analysis show similar parameters and, in this sense, are nominally consistent with the strong lensing hypothesis. However, taking into account population priors, selection effects, and the prior odds against lensing, these events do not provide sufficient evidence for lensing. Overall, we find no compelling evidence for lensing in the observed gravitational-wave signals from any of these analyses.
Simulated galaxy distributions are suitable for developing filament detection algorithms. However, samples of observed galaxies, being of limited size, cause difficulties that lead to a discontinuous distribution of filaments. We created a new galaxy filament catalog composed of a continuous cosmic web with no lone filaments. The core of our approach is a ridge filter used within the framework of image analysis. We considered galaxies from the HyperLeda database with redshifts 0.02 ≤ z ≤ 0.1, and in the solid angle 120° ≤ R.A. ≤ 240°, 0° ≤ decl. ≤ 60°. We divided the sample into 16 two-dimensional celestial projections with redshift bin Δ z = 0.005, and compared our continuous filament network with a similar recent catalog covering the same region of the sky. We tested our catalog on two application scenarios. First, we compared the distributions of the distances to the nearest filament of various astrophysical sources (Seyfert galaxies and other active galactic nuclei, radio galaxies, low-surface-brightness galaxies, and dwarf galaxies), and found that all source types trace the filaments well, with no systematic differences. Next, among the HyperLeda galaxies, we investigated the dependence of the g - r color distribution on the distance to the nearest filament, and confirmed that early-type galaxies are located on average further from the filaments than late-type ones.
The Vera C. Rubin Observatory will soon survey the southern sky, delivering a depth and sky coverage that is unprecedented in time-domain astronomy. As part of commissioning, Data Preview 1 (DP1) has been released. It comprises a Legacy Survey of Space and Time (LSST) Commissioning Camera observing campaign between 2024 November and December with multiband imaging of seven fields, covering roughly 0.4 deg 2 each, providing a first glimpse into the data products that will become available once the LSST begins. In this work, we search three fields for extragalactic transients. We identify eight new likely supernovae (SNe), and three known ones from a sample of 369,644 difference image analysis objects. Photometric classification using Superphot+ assigns subclasses with >95% confidence to only one SN Ia and one SN II in this sample. Our findings are in agreement with SN detection rate predictions of 15 ± 4 SNe from simulations using simsurvey. The SN detection rate in the data is possibly affected by the lack of suitable templates. Nevertheless, this work demonstrates the quality of the data products delivered in DP1 and indicates that the Rubin Observatory’s LSST is well placed to fulfill its discovery potential in time-domain astronomy.
Understanding a plant's root system architecture (RSA) is crucial for a variety of plant science problem domains including sustainability and climate adaptation. Minirhizotron (MR) technology is a widely-used approach for phenotyping RSA non-destructively by capturing root imagery over time. Precisely segmenting roots from the soil in MR imagery is a critical step in studying RSA features. In this paper, we introduce a large-scale dataset of plant root images captured by MR technology. In total, there are over 72K RGB root images across six different species including cotton, papaya, peanut, sesame, sunflower, and switchgrass in the dataset. The images span a variety of conditions including varied root age, root structures, soil types, and depths under the soil surface. All of the images have been annotated with weak image-level labels indicating whether each image contains roots or not. The image-level labels can be used to support weakly supervised learning in plant root segmentation tasks. In addition, 63K images have been manually annotated to generate pixel-level binary masks indicating whether each pixel corresponds to root or not. These pixel-level binary masks can be used as ground truth for supervised learning in semantic segmentation tasks. By introducing this dataset, we aim to facilitate the automatic segmentation of roots and the research of RSA with deep learning and other image analysis algorithms.
This document reviews five options for alert production in LSST Operations Year 1 (LOY1), taking into account any implications on LSST formal requirements including up-scopes, down-scopes or explicit violations. The Data Management System Science Team's preferred option for maximizing LSST science is to generate template images from as much of the data from the commissioning and/or science verification phases as possible and use them to run Difference Image Analysis and alert production during LOY1. A proposal to increase the sky area covered during commissioning via a "filler" scheduler program is also presented. As a potential moderate up-scope, this study presents an option to build interim templates on a $\sim$monthly basis during LOY1, which could increase the accessible sky area by ~1000-2000 deg**2 per month, and should be reconsidered closer to the start of Operations.
The plant kingdom contains a stunning array of complex morphologies easily observed above-ground, but more challenging to visualize below-ground. Understanding the magnitude of diversity in root distribution within the soil, termed root system architecture (RSA), is fundamental in determining how this trait contributes to species adaptation in local environments. Roots are the interface between the soil environment and the shoot system and therefore play a key role in anchorage, resource uptake, and stress resilience. Previously, we presented the GLO-Roots (Growth and Luminescence Observatory for Roots) system to study the RSA of soil-grown Arabidopsis thaliana plants from germination to maturity (Rellán-Álvarez et al., 2015). In this study, we present the automation of GLO-Roots using robotics and the development of image analysis pipelines in order to examine the temporal dynamic regulation of RSA and the broader natural variation of RSA in Arabidopsis , over time. These datasets describe the developmental dynamics of two independent panels of accessions and reveal highly complex and polygenic RSA traits that show significant correlation with climate variables of the accessions’ respective origins.
Mosaic construction and image analysis for Surveyor TV pictures
Digital image processing applied to picture generation, intensity and geometric manipulation, spatial frequency operations and image analysis