Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “sampling methods”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Monte Carlo control loops for cosmic shear cosmology with DES Year 1 data

Weak lensing by large-scale structure is a powerful probe of cosmology and of the dark universe. This cosmic shear technique relies on the accurate measurement of the shapes and redshifts of background galaxies and requires precise control of systematic errors. Monte Carlo control loops (MCCL) is a forward modeling method designed to tackle this problem. It relies on the ultra fast image generator (UFig) to produce simulated images tuned to match the target data statistically, followed by calibrations and tolerance loops. Here, we present the first end-to-end application of this method, on the Dark Energy Survey (DES) Year 1 wide field imaging data. We simultaneously measure the shear power spectrum $C_ℓ$ and the redshift distribution $n(z)$ of the background galaxy sample. The method includes maps of the systematic sources, point spread function (PSF), an approximate Bayesian computation (ABC) inference of the simulation model parameters, a shear calibration scheme, and a fast method to estimate the covariance matrix. We find a close statistical agreement between the simulations and the DES Y1 data using an array of diagnostics. In a nontomographic setting, we derive a set of $C_ℓ$ and $n(z)$ curves that encode the cosmic shear measurement, as well as the systematic uncertainty. Following a blinding scheme, we measure the combination of $Ω_m$, $σ_8$, and intrinsic alignment amplitude $A_{IA}$, defined as $S_8D_{IA}=σ_8(Ω_m/0.3)^{0.5}D_{IA}$, where $D_{IA}=1-0.11(A_{IA}-1)$. We find $S_8D_{IA}=0.8954_{-0.039}^{+0.054}$, where systematics are at the level of roughly 60% of the statistical errors. We discuss these results in the context of earlier cosmic shear analyses of the DES Y1 data. Our findings indicate that this method and its fast runtime offer good prospects for cosmic shear measurements with future wide-field surveys.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Evidence of gas phase nucleation of nanodiamond in microwave plasma assisted chemical vapor deposition

The mechanism of ballas-like nanodiamond formation still remains elusive, and this work attempts to analyze its formation in the framework of activation energy (Ea) of nanodiamond films grown from a H2/CH4 plasma in a 2.45 GHz chemical vapor deposition system. The Ea was calculated from the Arrhenius equation corresponding to the thickness growth rate using substrate temperature (∼1000−1300 K) in all the calculations. While the calculated values matched with the Ea for nanodiamond formation throughout the literature, these values of ∼10 kcal/mol were lower compared to ∼15–25 kcal/mol for standard single crystal diamond (SCD) formation, concluding thus far that the energetics and processes involved were different. Further, the substrate preparation and sample collection method were modified while keeping the growth parameters constant. Unseeded Si substrate was physically separated from the plasma discharge by a molybdenum disk with a pinhole drilled in it. Small quantity of a sample substance was collected on the substrate. The sample was characterized by electron microscopy and Raman spectroscopy, confirming it to be nanodiamond, thus suggesting that nanodiamond self-nucleated in the plasma and flowed to the substrate that acted as a mere collection plate. It is hypothesized then, if nanodiamond nucleates in gas phase, gas temperature has to be used in the Arrhenius analysis. The Ea values for all the nanodiamond films were re-calculated using the simulated gas temperature (∼1500−2000 K) obtained from a simple H2/CH4 plasma model, giving new values within the range characteristic to SCD formation. Based on these findings, a unified growth mechanism for nanodiamond and SCD is proposed, concluding that the rate-limiting reactions for nanodiamond and SCD formation are the same.

Materials Science↗
DOE OSTI2021

Performing In Situ Closed-Cell Gas Reactions in the Transmission Electron Microscope

Gas reactions studied by in situ electron microscopy can be used to capture the real-time morphological and microchemical transformations of materials at length scales down to the atomic level. In situ closed-cell gas reaction (CCGR) studies performed using (scanning) transmission electron microscopy (STEM) can separate and identify localized dynamic reactions, which are extremely challenging to capture using other characterization techniques. For these experiments, we used a CCGR holder that utilizes microelectromechanical systems (MEMS)-based heating microchips (hereafter referred to as "E-chips"). The experimental protocol described here details the method for performing in situ gas reactions in dry and wet gases in an aberration-corrected STEM. This method finds relevance in many different materials systems, such as catalysis and high-temperature oxidation of structural materials at atmospheric pressure and in the presence of various gases with or without water vapor. Here, several sample preparation methods are described for various material form factors. During the reaction, mass spectra obtained with a residual gas analyzer (RGA) system with and without water vapor further validates gas exposure conditions during reactions. Integrating an RGA with an in situ CCGR-STEM system can, therefore, provide critical insight to correlate gas composition with the dynamic surface evolution of materials during reactions. In situ/operando studies using this approach allow for detailed investigation of the fundamental reaction mechanisms and kinetics that occur at specific environmental conditions (time, temperature, gas, pressure), in real-time, and at high spatial resolution.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

FMX – the Frontier Microfocusing Macromolecular Crystallography Beamline at the National Synchrotron Light Source II

Two new macromolecular crystallography (MX) beamlines at the National Synchrotron Light Source II, FMX and AMX, opened for general user operation in February 2017 [Schneider et al. (2013). J. Phys. Conf. Ser. 425 , 012003; Fuchs et al. (2014). J. Phys. Conf. Ser. 493 , 012021; Fuchs et al. (2016). AIP Conf. Proc. SRI2015 , 1741 , 030006]. FMX, the micro-focusing Frontier MX beamline in sector 17-ID-2 at NSLS-II, covers a 5–30 keV photon energy range and delivers a flux of 4.0 × 10 12 photons s −1 at 1 Å into a 1 µm × 1.5 µm to 10 µm × 10 µm (V × H) variable focus, expected to reach 5 × 10 12 photons s −1 at final storage-ring current. This flux density surpasses most MX beamlines by nearly two orders of magnitude. The high brightness and microbeam capability of FMX are focused on solving difficult crystallographic challenges. The beamline's flexible design supports a wide range of structure determination methods – serial crystallography on micrometre-sized crystals, raster optimization of diffraction from inhomogeneous crystals, high-resolution data collection from large-unit-cell crystals, room-temperature data collection for crystals that are difficult to freeze and for studying conformational dynamics, and fully automated data collection for sample-screening and ligand-binding studies. FMX's high dose rate reduces data collection times for applications like serial crystallography to minutes rather than hours. With associated sample lifetimes as short as a few milliseconds, new rapid sample-delivery methods have been implemented, such as an ultra-high-speed high-precision piezo scanner goniometer [Gao et al. (2018). J. Synchrotron Rad. 25 , 1362–1370], new microcrystal-optimized micromesh well sample holders [Guo et al. (2018). IUCrJ , 5 , 238–246] and highly viscous media injectors [Weierstall et al. (2014). Nat. Commun. 5 , 3309]. The new beamline pushes the frontier of synchrotron crystallography and enables users to determine structures from difficult-to-crystallize targets like membrane proteins, using previously intractable crystals of a few micrometres in size, and to obtain quality structures from irregular larger crystals.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

A structured framework for predicting sustainable aviation fuel properties using liquid-phase FTIR and machine learning

Sustainable aviation fuels have the potential to improve efficiency, reduce emissions, and enhance energy security. To help identify viable sustainable aviation fuels and accelerate research, machine learning models have been developed to predict relevant physicochemical properties. However, many models have limited applicability, leverage data from complex analytical techniques with confined spectral ranges, or use feature decomposition methods that offer limited interpretability. Using liquid-phase Fourier Transform Infrared (FTIR) spectra, this study presents a structured method for creating accurate and interpretable property prediction models for neat molecules, aviation fuels, and blends. Liquid FTIR spectra can be collected quickly and consistently, offering high reliability, sensitivity, and component specificity using less than 2 ml of sample. The method first decomposes FTIR spectra into fundamental building blocks using non-negative matrix factorization (NMF) to enable scientific analysis of FTIR spectra attributes and fuel properties. The NMF features are then used to create five ensemble models for predicting final boiling point, flash point, freezing point, density at 15°C, and kinematic viscosity at -20°C. All models were trained using experimental property data from neat molecules, aviation fuels, and blends. The models accurately predict key properties across a broad range of neat molecules and representative fuels and blends, while enabling interpretation of relationships between compositional elements, such as functional groups or chemical classes, and their resulting properties. This demonstrates strong potential to support sustainable aviation fuel research and development. The models and data are available on an interactive web tool.

Fourier transform infrared spectroscopy↗
DOE OSTI2025

Biased degenerate ground-state sampling of small Ising models with converged quantum approximate optimization algorithm

The quantum alternating operator ansatz, a generalization of the quantum approximate optimization algorithm (QAOA), is a quantum algorithm used for approximately solving combinatorial optimization problems. QAOA typically uses the transverse field mixer as the driving Hamiltonian. One of the interesting properties of the transverse field driving Hamiltonian is that it results in nonuniform sampling of degenerate ground states of optimization problems. In this study, we numerically examine the fair sampling properties of the transverse field mixer QAOA, and Grover mixer QAOA (GM-QAOA), which provides theoretical guarantees of fair sampling of degenerate optimal solutions, up to a large enough p such that the mean expectation value converges to an optimal approximation ratio of 1. This comparison is performed with high-quality heuristically computed, but not necessarily optimal, QAOA angles, which give strictly monotonically improving solution quality as p increases. These angles are computed using the Julia based numerical simulation software JuliQAOA. Fair sampling of degenerate ground states is quantified using the Shannon entropy of the ground-state amplitudes distribution. The fair sampling properties are reported on several quantum signature Hamiltonians from previous quantum annealing fair sampling studies. Small random fully connected spin glasses are shown, which exhibit exponential suppression of some degenerate ground states with transverse field mixer QAOA. The transverse field mixer QAOA simulations show that some problem instances clearly saturate the Shannon entropy of 0 with a maximally biased distribution that occurs when the learning converges to an approximation ratio of 1 while other problem instances never deviate from a maximum Shannon entropy (uniform distribution) at any p step. Published by the American Physical Society 2025

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Spectral Induced Polarization-Biogeochemical Relationships for Remediation Amendment Monitoring

Geophysical tools such as electrical resistivity (ER) can indirectly monitor subsurface changes in response to remedial injections. These methods exhibit relatively low spatial resolution compared to sediment core characterization but are advantageous due to the ability to collect measurements non-intrusively over time across large volumes of the subsurface. Moreover, along with confirmatory groundwater or core sampling, geophysical methods can be used during active biogeochemical remedies to monitor short-term contaminant transformations and mobility, as well as part of an overall strategy for long-term monitoring of subsurface contaminated sites. The use of alternating current spectral induced polarization (SIP) provides significantly more information than conventional geophysical methods like direct current ER. SIP allows for monitoring changes in both solution and surface conductivity by separation of real and imaginary conductivity, respectively, as well as surface capacitance. In principle, SIP can measure indicators of remedy progression such as precipitation reactions that sequester contaminants, potentially providing a better indication of amendment delivery and reactivity as compared to conventional ER methods. However, multiple processes and material properties have overlapping (interacting) electrical responses across a range of frequencies. Hence, the purpose of this scoping study was to evaluate if SIP can be used to monitor (a) amendment delivery and (b) precipitation and reactivity of amendments under consideration at Hanford. SIP measurements were collected in fully saturated columns packed with sand and Hanford formation sediments containing (a) amendments that were highly conductive with significant capacitance (zero valent iron – ZVI, sulfur modified iron – SMI) and (b) amendments that exhibited low electrical conductivity with a small capacitance (calcite, apatite, bismuth). The sand was a quartz material with homogenous particle size that exhibited a relatively low surface conductivity. It was used as a control for comparison with the sediments from the Hanford Site, which have a greater surface conductivity because of their complex mineralogy and heterogeneous particle size distribution and may have complex interactions with amendments. The amendment mass fraction was varied to represent the different stages and subsurface locations associated with the delivery of a remedy. The primary objective was to identify the solution and solid surface changes associated with the delivery amendments and their secondary reactions within the subsurface. The figure below summarizes results for the amendments tested through this project. The SIP phase shift or imaginary conductivity change for the high conductivity amendments was more than 10 times that of the low conductivity amendments, highlighting the relative ease of detection of ZVI and SMI independent of the background signal from sand or Hanford sediments. The ZVI phase shift and imaginary conductivity changes occur primarily at high frequency (> 100 Hz) whereas SMI changes were at low frequency (0.01 to 10 Hz). SIP signals of SMI also increased over time and the maximum shifted to lower frequencies. The low conductivity amendments (calcite, abiotic and biotic apatite, bismuth subnitrate) exhibited relatively small phase and imaginary conductivity changes when added to sediments. The changes were above minimum detection limits (0.5 mrad for phase shift, 0.03 µS/cm for imaginary conductivity) for the highest concentration except for the commercial bismuth material. The lowest amendment concentration that can be detected is likely sediment specific, as minerals in sediments (clays, magnetite, Fe-oxides) have some capacitance and, therefore, exhibit a variable phase shift.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Identifying common stored product insects using automated deep learning methods

Monitoring stored product insect pests is a common practice for post-harvest management of stored grain and grain-based commodities, which helps ensure product quality from harvest to final consumer. Current methods of sampling and monitoring can be time-consuming, labor-intensive, expensive and require expertise in insect identification. Therefore, this study aims to develop an image-based automated identification system for common stored product insect species using deep-learning methods. Top-down images of the common stored product adult insect species of Rhyzopertha dominica, Cryptolestes ferrugineus, Tribolium castaneum, Sitophilus oryzae, and Oryzaephilus surinamensis were acquired and analyzed. Deep learning-based, state-of-the-art Convolutional Neural Networks (CNN) models (ResNet-50, MobileNet-v2, DarkNet-53, and EfficientNet-b0) were fine-tuned with a transfer learning approach to classify the insect species. All models were able to correctly identify the insect species with at least 96% accuracy and with few misclassifications. One issue with trained CNNs is that they do not explain the reasoning for the classification and are often called a “black box”. Therefore, visualization methods called Gradient-weighted Class Activation Mapping (Grad-CAM) were implemented to explore the black box network. The Grad-CAM uses heat maps to highlight the major image features that the network focused on to make insect species predictions. The Grad-CAM verifies the network's prediction and also helps improve network performance. This study contributes to the overall goal of developing a camera-based system for monitoring stored grain insects. As a result, the developed system would empower warehouse, flour mills, and other food facilities with a tool to quickly and accurately identify insect species in stored product environments and could be implemented as part of a close to real-time monitoring system.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Dynamic Model Agnostic Reliability Evaluation of Machine-Learning Models Integrated in Instrumentation & Control Systems

In recent years, the field of data-driven neural network-based machine learning (ML) algorithms has grown significantly and spurred research in its applicability to instrumentation and control systems. While they are promising in operational contexts, the trustworthiness of such algorithms is not adequately assessed. Failures of ML-integrated systems are poorly understood; the lack of comprehensive risk modeling can degrade the trustworthiness of these systems. In recent reports by the National Institute for Standards and Technology, trustworthiness in ML is a critical barrier to adoption and will play a vital role in intelligent systems' safe and accountable operation. Thus, in this work, we demonstrate a real-time model-agnostic method to evaluate the relative reliability of ML predictions by incorporating out-of-distribution detection on the training dataset. It is well documented that ML algorithms excel at interpolation (or near-interpolation) tasks but significantly degrade at extrapolation. This occurs when new samples are "far" from training samples. The method, referred to as the Laplacian distributed decay for reliability (LADDR), determines the difference between the operational and training datasets, which is used to calculate a prediction's relative reliability. LADDR is demonstrated on a feedforward neural network-based model used to predict safety significant factors during different loss-of-flow transients. LADDR is intended as a "data supervisor" and determines the appropriateness of well-trained ML models in the context of operational conditions. Ultimately, LADDR illustrates how training data can be used as evidence to support the trustworthiness of ML predictions when utilized for conventional interpolation tasks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

NSUF Report EBSD analyses on metallic fuel samples: Tips and improvements in sample preparation

EBSD (Electron Backscatter Diffraction) is an analytic technique used to evaluate the microstructure of a specimen. It can be used to evaluate interesting crystallographic data ranging from grains size to grain orientation, from grain boundaries to stresses. Moreover, in the field of nuclear materials EBSD can aid in estimating properties important for reactor performance, such as dislocation and residual strain [1]. EBSD patterns are generated by focusing a stationary electron gun on the specimen and detecting the backscatter electrons that satisfy Bragg conditions, and thus are diffracted by the crystal planes forming the so called Kikuchi bands. These bands correspond to each of the lattice diffracting crystal planes and are detected on a phosphor screen via a low-light CCD camera [2]. The patterns are generated within a small interaction volume located at a depth of less than 50-100 nm. Because of this, the data quality is extremely sensitive to the integrity of the crystallographic lattice order at the surface of the sample and thus to sample preparation and to oxidation [3]. While conventional sample preparation methods are widely used in materials science, they often do not lead to quality electron backscatter patterns needed for the characterization of nuclear materials [4]. This has been related by many authors to the challenges associated with handling radiological specimens, their rapid oxidation, and artifacts in such samples (e.g. high number of defects, precipitates and/or porosities) [4-8]. EBSD, however, can be an invaluable technique to evaluate microstructural evolution during fabrication and after irradiation at the required mesoscale for model and simulation. It also provides important information on grain structure necessary to understand irradiation effect, such as grain subdivision. Thus, it is of paramount importance to further the application of this technique to nuclear materials and especially irradiated fuels.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Data processing methods and data acquisition for samples larger than the field of view in parallel-beam tomography

Parallel-beam tomography systems at synchrotron facilities have limited field of view (FOV) determined by the available beam size and detector system coverage. Scanning the full size of samples bigger than the FOV requires various data acquisition schemes such as grid scan, 360-degree scan with offset center-of-rotation (COR), helical scan, or combinations of these schemes. Though straightforward to implement, these scanning techniques have not often been used due to the lack of software and methods to process such types of data in an easy and automated fashion. The ease of use and automation is critical at synchrotron facilities where using visual inspection in data processing steps such as image stitching, COR determination, or helical data conversion is impractical due to the large size of datasets. Here, we provide methods and their implementations in a Python package, named Algotom, for not only processing such data types but also with the highest quality possible. The efficiency and ease of use of these tools can help to extend applications of parallel-beam tomography systems.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Learning new physics from data: A symmetrized approach

Thousands of person years have been invested in searches for new physics (NP), the majority of them motivated by theoretical considerations. Yet, no evidence of beyond the Standard Model physics has been found. This suggests that model-agnostic searches might be an important key to explore NP, and help discover unexpected phenomena which can inspire future theoretical developments. A possible strategy for such searches is identifying asymmetries between data samples that are expected to be symmetric within the Standard Model. We propose exploiting neural networks (NNs) to quickly fit and statistically test the differences between two samples. Our method is based on an earlier work, originally designed for inferring the deviations of an observed dataset from that of a much larger reference dataset. We present a symmetric formalism, generalizing the original one, avoiding fine-tuning of the NN parameters and any constraints on the relative sizes of the samples. Our formalism could be used to detect small symmetry violations, extending the discovery potential of current and future particle physics experiments.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Efficient multimode Wigner tomography

Abstract Advancements in quantum system lifetimes and control have enabled the creation of increasingly complex quantum states, such as those on multiple bosonic cavity modes. When characterizing these states, traditional tomography scales exponentially with the number of modes in both computational and experimental measurement requirement, which becomes prohibitive as the system size increases. Here, we implement a state reconstruction method whose sampling requirement instead scales polynomially with system size, and thus mode number, for states that can be represented within such a polynomial subspace. We demonstrate this improved scaling with Wigner tomography of multimode entangled W states of up to 4 modes on a 3D circuit quantum electrodynamics (cQED) system. This approach performs similarly in efficiency to existing matrix inversion methods for 2 modes, and demonstrates a noticeable improvement for 3 and 4 modes, with even greater theoretical gains at higher mode numbers.

Science & Technology - Other Topics↗
DOE OSTI2021

Multisource Data Fusion Outage Location in Distribution Systems via Probabilistic Graphical Models

Efficient outage location is critical to enhancing the resilience of power distribution systems. However, accurate outage location requires combining massive evidence received from diverse data sources, including smart meter (SM) last gasp signals, customer trouble calls, social media messages, weather data, vegetation information, and physical parameters of the network. This is a computationally complex task due to the high dimensionality of data in distribution grids. In this paper, we propose a multi-source data fusion approach to locate outage events in partially observable distribution systems using Bayesian networks (BNs). A novel aspect of the proposed approach is that it takes multi-source evidence and the complex structure of distribution systems into account using a probabilistic graphical method. Our method can radically reduce the computational complexity of outage location inference in high-dimensional spaces. The graphical structure of the proposed BN is established based on the network’s topology and the causal relationship between random variables, such as the states of branches/customers and evidence. Utilizing this graphical model, accurate outage locations are obtained by leveraging a Gibbs sampling (GS) method, to infer the probabilities of de-energization for all branches. Compared with commonly-used exact inference methods that have exponential complexity in the size of the BN, GS quantifies the target conditional probability distributions in a timely manner. As a result, a case study of several real-world distribution systems is presented to validate the proposed method.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

In Vitro Antimicrobial Activity of Volatile Compounds from the Lichen Pseudevernia furfuracea (L.) Zopf. Against Multidrug-Resistant Bacteria and Fish Pathogens

Lichens are symbiotic organisms with unique secondary metabolism. Various metabolites from lichens have shown antimicrobial activity. Nevertheless, very few studies have investigated the antimicrobial potential of the volatile compounds they produce. This study investigates the chemical composition and antimicrobial properties of volatile compounds from Pseudevernia furfuracea collected in two regions of Morocco. Hydrodistillation was used to obtain volatile compounds from samples collected in the High Atlas and Middle Atlas. Gas chromatography–mass spectrometry (GC-MS) analysis identified phenolic cyclic compounds as the primary constituents, with atraric acid and chloroatranol being the most abundant. Additionally, eight compounds were detected in lichens for the first time. The antimicrobial activity of these compounds was assessed using disc diffusion and broth microdilution methods. Both samples demonstrated significant antimicrobial effects against multidrug-resistant human bacteria, reference microorganisms, fish pathogens, and Candida albicans, with minimum inhibitory concentrations (MICs) ranging from 1000 µg/mL to 31.25 µg/mL. This study provides the first report on the volatile compounds from Pseudevernia furfuracea and their antimicrobial effects, particularly against fish pathogens, suggesting their potential as novel antimicrobial agents for human and veterinary use. Further research is warranted to explore these findings in more detail.

Essadki, Yasser (ORCID:0009000648460075)↗
DOE OSTI2022

Solid-State Nuclear Magnetic Resonance as a Tool to Probe the Impact of Mechanical Preprocessing on the Structure and Arrangement of Plant Cell Wall Polymers

Efficient separation of the plant cell wall polymers during lignocellulose processing has been historically challenging due to insolubility of the polymers and their propensity for recalcitrant reassembly. Methods, such as “lignin first” extraction techniques, have advanced efficient biomass use, but the molecular mechanisms for recalcitrance remain enigmatic. Here, we discuss how solid-state Nuclear Magnetic Resonance (NMR) approaches report on the 3D organization of cellulose, xylan, and lignin in the plant cell wall. Recent results illustrate that the organization of these polymers varies across biomass sources and sample preparation methods, with even minimal physical processing causing significant effects. These structural differences contribute to variable extraction efficiencies for bioproducts after downstream processing. We propose that solid-state NMR methods can be applied to follow biomass processing, providing an understanding of the polymer rearrangements that can lead to poor yields for the desired bioproducts. The utility of the technique is illustrated for mechanical processing using lab-scale vibratory ball milling of Sorghum bicolor .

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Methodology for Extracting High-Molecular-Weight DNA from Field Collections of Macrofungi

Many macrofungi are impractical or impossible to culture. Consequently, DNA for long-read sequencing required for the assembly of high-quality genomes must be isolated from samples taken from the environment. Collection is often in remote locations, limiting the options for stabilising samples to methods that do not require refrigeration. Fungi contain species-specific arrays of metabolites that may complicate purification techniques and call for judgement to be made to apply appropriate modifications to the DNA extraction protocol in specific cases. The protocols and commentary we describe are informed by the preparation of DNA from a range of Australasian ectomycorrhizal and saprotrophic macrofungi. We collect samples into isopropanol at ambient temperature and employ a strategy of chromatin isolation followed by the sequential removal of unwanted molecular components to purify DNA.

Burgoyne, Leigh A↗
DOE OSTI2024

AGS-GNN: Attribute-guided Sampling for Graph Neural Networks

We propose AGS-GNN, a novel attribute-guided sampling algorithm for Graph Neural Networks (GNNs) that exploits node features and connectivity structure of a graph while simultaneously adapting for both homophily and heterophily in graphs. (In homophilic graphs vertices of the same class are more likely to be connected, and vertices of different classes tend to be linked in heterophilic graphs.) While GNNs have been successfully applied to homophilic graphs, their application to heterophilic graphs remains challenging. The best-performing GNNs for heterophilic graphs do not fit the sampling paradigm, suffer high computational costs, and are not inductive. We employ samplers based on feature-similarity and feature-diversity to select subsets of neighbors for a node, and adaptively capture information from homophilic and heterophilic neighborhoods using dual channels. Currently, AGS-GNN is the only algorithm that we know of that explicitly controls homophily in the sampled subgraph through similar and diverse neighborhood samples. For diverse neighborhood sampling, we employ submodularity, which was not used in this context prior to our work. The sampling distribution is pre-computed and highly parallel, achieving the desired scalability. Using an extensive dataset consisting of 35 small (<=100K nodes) and large (>100K nodes) homophilic and heterophilic graphs, we demonstrate the superiority of AGS-GNN compare to the current approaches in the literature. AGS-GNN achieves comparable test accuracy to the best-performing heterophilic GNNs, even outperforming methods using the entire graph for node classification. AGS-GNN also converges faster compared to methods that sample neighborhoods randomly, and can be incorporated into existing GNN models that employ node or graph sampling.

artificial intelligence↗