Search NASA⌕ Search

SEARCH · Search NASA

Results for “LABELLING”

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 1,009 records · Page 56
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Optimized anion exchange column isolation of zirconium-89 (89Zr) from yttrium cyclotron target: Method development and implementation on an automated fluidic platform

Zirconium-89 (89Zr), produced by the (p,n) reaction from naturally monoisotopic yttrium (natY), is a promising positron emitting isotope for immunoPET imaging. Its long half-life of 78.4 h is sufficient for evaluating slow physiological processes. A prototype automated fluidic system, coupled to on-line and in-line detectors, has been constructed to facilitate development of new 89Zr purification methodologies. The highly reproducible reagent delivery platform and near-real time monitoring of column effluents allows for efficient method optimization. The separation of Zr from dissolved Y metal targets was evaluated using several anion exchange resins. Each resin was evaluated against its ability to quantitatively capture Zr from a load solution that is high in dissolved Y. The most appropriate anion exchange resin for this application was identified, and the separation method was optimized. The method is capable of a high Y decontamination factor (>105) and has been shown to separate Fe, an abundant contaminant in Y foils, from the 89Zr elution fraction. Finally, the performance of the method was evaluated using cyclotron bombarded Y foil targets. The separation method was shown to achieve >95% recovery of the 89Zr present in the foils. The 89Zr eluent, however, was in a chemical matrix not immediately conducive to labeling onto proteins. The main intent of this study was to develop a tandem column 89Zr purification process, wherein the anion exchange column method described here is the first separation in a dual-column purification process.

Zirconium-89, ImmunoPET, medical isotope, automati↗
DOE OSTI2024

Separable physics-informed DeepONet: Breaking the curse of dimensionality in physics-informed machine learning

The deep operator network (DeepONet) has shown remarkable potential in solving partial differential equations (PDEs) by mapping between infinite-dimensional function spaces using labeled datasets. However, in scenarios lacking labeled data, the physics-informed DeepONet (PI-DeepONet) approach, which utilizes the residual loss of the governing PDE to optimize the network parameters, faces significant computational challenges, particularly due to the curse of dimensionality. This limitation has hindered its application to high-dimensional problems, making even standard 3D spatial with 1D temporal problems computationally prohibitive. Additionally, the computational requirement increases exponentially with the discretization density of the domain. Here, to address these challenges and enhance scalability for high-dimensional PDEs, we introduce the Separable physics-informed DeepONet (Sep-PI-DeepONet). This framework employs a factorization technique, utilizing sub-networks for individual one-dimensional coordinates, thereby reducing the number of forward passes and the size of the Jacobian matrix required for gradient computations. By incorporating forward-mode automatic differentiation (AD), we further optimize computational efficiency, achieving linear scaling of computational cost with discretization density and dimensionality, making our approach highly suitable for high-dimensional PDEs. We demonstrate the effectiveness of Sep-PI-DeepONet through three benchmark PDE models: the viscous Burgers’ equation, Biot’s consolidation theory, and a parameterized heat equation. Our framework maintains accuracy comparable to the conventional PI-DeepONet while reducing training time by two orders of magnitude. Notably, for the heat equation solved as a 4D problem, the conventional PI-DeepONet was computationally infeasible (estimated 289.35 h), while the Sep-PI-DeepONet completed training in just 2.5 h. These results underscore the potential of Sep-PI-DeepONet in efficiently solving complex, high-dimensional PDEs, marking a significant advancement in physics-informed machine learning.

Neural operator↗
DOE OSTI2020

Overcoming small minirhizotron datasets using transfer learning

Minirhizotron technology is widely used to study root growth and development. Yet, standard approaches for tracing roots in minirhiztron imagery is extremely tedious and time consuming. Machine learning approaches can help to automate this task. However, lack of enough annotated training data is a major limitation for the application of machine learning methods. Transfer learning is a useful technique to help with training when available datasets are limited. In this paper, we investigated the effect of pre-trained features from the massives-cale, irrelevant ImageNet dataset and a relatively moderate-scale, but relevant peanut root dataset on switchgrass root imagery segmentation applications. We compiled two minirhizotron image datasets to accomplish this study: one with 17,550 peanut root images and another with 28 switchgrass root images. Both datasets were paired with manually labeled ground truth masks. Deep neural networks based on the U-net architecture were used with different pre-trained features as initialization for automated, precise pixel-wise root segmentation in minirhizotron imagery. We observed that features pre-trained on a closely related but relatively moderate size dataset like our peanut dataset were more effective than features pre-trained on the large but unrelated ImageNet dataset. Here, we achieved high quality segmentation on peanut root dataset with 99.04% accuracy at the pixel-level and overcame errors in human-labeled ground truth masks. By applying transfer learning technique on limited switchgrass dataset with features pre-trained on peanut dataset, we obtained 99% segmentation accuracy in switchgrass imagery using only 21 images for training (fine tuning). Furthermore, the peanut pre-trained features can help the model converge faster and have much more stable performance.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Generative learning for slow manifolds and bifurcation diagrams

In dynamical systems characterized by separation of time scales, the approximation of so called “slow manifolds”, on which the long term dynamics lie, is a useful step for model reduction. Initializing on such slow manifolds is a useful step in modeling, since it circumvents fast transients, and is crucial in multiscale algorithms (like the equation-free approach) alternating between fine scale (fast) and coarser scale (slow) simulations. In a similar spirit, when one studies the infinite time dynamics of systems depending on parameters, the system attractors (e.g., its steady states) lie on bifurcation diagrams (curves for one-parameter continuation, and more generally, on manifolds in state parameter space. Sampling these manifolds gives us representative attractors (here, steady states of ODEs or PDEs) at different parameter values. Algorithms for the systematic construction of these manifolds (slow manifolds, bifurcation diagrams) are required parts of the “traditional” numerical nonlinear dynamics toolkit. In more recent years, as the field of Machine Learning develops, conditional score-based generative models (cSGMs) have been demonstrated to exhibit remarkable capabilities in generating plausible data from target distributions that are conditioned on some given label. It is tempting to exploit such generative models to produce samples of data distributions (points on a slow manifold, steady states on a bifurcation surface) conditioned on (consistent with) some quantity of interest (QoI, observable). In this work, we present a framework for using cSGMs to quickly (a) initialize on a low-dimensional (reduced-order) slow manifold of a multi-time-scale system consistent with desired value(s) of a QoI (a “label”) on the manifold, and (b) approximate steady states in a bifurcation diagram consistent with a (new, out-of-sample) parameter value. This conditional sampling can help uncover the geometry of the reduced slow-manifold and/or approximately “fill in” missing segments of steady states in a bifurcation diagram. Finally, the quantity of interest, which determines how the sampling is conditioned, is either known a priori or identified using manifold learning-based dimensionality reduction techniques applied to the training data.

Dynamical systems↗
DOE OSTI2021

Influence of PRE-emergence herbicides on soybean development, root nodulation and symbiotic nitrogen fixation

We report widespread occurrence of herbicide-resistant weeds has further limited effective POST-emergence herbicide options in soybean (Glycine max (L.) Merr.) leading to an increased adoption of PRE-emergence herbicides. The objective of this study was to investigate the influence of 11 commonly used PRE herbicides on soybean development, root nodulation, and symbiotic N fixation. Soybean plants were grown under greenhouse conditions in pots (10 L; 4 plants per pot) filled with silt loam soil and treated one day after planting with a labeled field rate of imazethapyr, chlorimuron-ethyl, cloransulam-methyl, metribuzin, sulfentrazone, flumioxazin, saflufenacil, acetochlor, S-metolachlor, dimethenamid-P, pyroxasulfone and no herbicide (nontreated control). Sulfentrazone reduced soybean canopy at the VC growth stage but no canopy reduction was observed at the V2 growth stage from any of the herbicide treatments. At the R2 growth stage, herbicides had no effect on soybean development (root and shoot biomass), root nodulation (# nodule per plant, nodule diameter, and nodule biomass) and symbiotic N fixation (acetylene reduction assay and 15 N natural abundance). According to our findings, although PRE herbicides may slightly affect early-season soybean development, the impacts on plant growth, root nodulation, and symbiotic N fixation were negligible. Thus, when sprayed according to the label, the benefits of PRE herbicides for weed control likely outweigh any potential concern regarding soybean development, root nodulation, and N fixation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Energy efficiency outlook in China’s urban buildings sector through 2030

Here, this study uses bottom-up modeling framework in order to quantify potential energy savings and emission reduction impacts from the implementation of energy efficiency programs in the building sector in China. Policies considered include (1) accelerated building codes in residential and commercial buildings, (2) increased penetration of district heat metering and controls, (3) district heating efficiency improvement, (4) building energy efficiency labeling programs and (5) retrofits of existing commercial buildings. Among these programs, we found that the implementation of building codes provide by far the largest savings opportunity, leading to an overall 17% reduction in overall space heating and cooling demand relative to the baseline. Second are energy efficiency labels with 6%, followed by reductions of losses associated with district heating representing 4% reduction and finally, retrofits representing only about a 1% savings.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2018

Ingested microplastic as a two-way transporter for PBDEs in Talitrus saltator

Highlights: • Ingestion of contaminated MP by T. saltator moves organic pollutants to its tissues. • Uncontaminated MP ingested by contaminated T. saltator removes organic pollutants. • MP double role as carrier and scavenger of chemicals is demonstrated. The presence and accumulation of plastic waste into the marine environment are well known environmental issues. Microplastics (MPs) end up in sea waters and, due to their hydrophobicity and high surface/volume ratio, POPs tend to sorb and accumulate to their surface. The supralittoral amphipod Talitrus saltator (T. saltator) was selected to study the role of MPs in the transfer of organic pollutants and to investigate if ingested MPs could either transfer contaminants to biota or clean it adsorbing pollutants taken from the diet. T. saltator is an established POPs (Persistent Organic Pollutants) biomonitor in coastal environments and it is able to swallow microplastics in natural condition. Two laboratory experiments were performed and T. saltator was exposed to a labelled polybrominated diphenyl ether ({sup 13}C-labelled BDE-47) to investigate the opposite gradient role of MPs. X Ray Micro-CT (Micro-Computed Tomography) analyses were also performed on sandhopper samples to evaluate the uptake of MPs via digestive tract. The results showed that MPs ingestion could whether transfer and remove contaminants from T. saltator, indicating a partial balance among positive and negative effects. This study has underlined MP potential double role demonstrating that MP can act both as a carrier and scavenger for the bioaccumulation of organic pollutants (i.e. PBDEs), suggesting that chemicals leaching from MPs could have a limited impact to biota.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Bio-distribution and deposition of wildfire smoke chemicals into olfactory bulb and brain of rats after intranasal instillation

Epidemiological and experimental studies suggest wildfire smoke is a potential contributor to neurological dysfunction and associated with neuroinflammation. Using doses comparable to those encountered during intense wildfire events (200-300 μg/m 3 ), we explore the absorption, distribution, metabolism, and elimination (ADME) and pharmacokinetics of representative members of major chemical classes (acid, phenol, PAH, aldehyde) in inhaled wood smoke condensates. Male Sprague Dawley rats were intranasally instilled with smoldering eucalyptus woodsmoke extract (WSE) reconstituted in saline spiked with 14 C-labeled palmitic acid (PA), benzo[a]pyrene (B[a]P), catechol (CAT) or benzaldehyde (BZ). Serum was collected from 5 min to 2 weeks after exposure and tissues were collected at 0.5, 2, 4, 24 h and 2 weeks after exposure. Urine was collected over the 24 h exposure. Tissues were collected, rinsed in PBS and analyzed by accelerator mass spectrometry (AMS) for 14C-labeled chemicals. PA and B[a]P entered circulation slowly, reached maximum concentration (C max ) near 15 ng/mL at 2 h, and had circulating concentrations near 1/3 C max 24 h after exposure. CAT and BZ rapidly entered circulation and were mostly cleared at 2 h. Excess 14 C from all four chemicals was detected in olfactory bulb and brain over the first 24 h but only PA (or its metabolites) was retained in olfactory bulb, brain and kidney at 2 weeks post exposure. All excess 14 C was cleared from the lung at 2 weeks. Metabolite analysis of urine (CAT, BZ and B(a)P) dosed samples did not detect any parent compound. CAT and BZ were rapidly cleared. The slower uptake and clearance of PA or B[a]P or their reactive metabolites when dosed with WSE in brain and olfactory bulb potentially provide greater opportunity for inflammatory response. This study suggests that wildfire smoke chemicals can enter the brain directly from the nasal cavity to the olfactory bulb and via systemic circulation.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
DOE OSTI2023

Machine learning and deep learning for mineralogy interpretation and CO 2 saturation estimation in geological carbon Storage: A case study in the Illinois Basin

Carbon capture and storage (CCS) is a promising approach to simultaneously maintaining energy security and reducing carbon dioxide (CO 2 ) emissions under the current energy portfolio that is dominated by fossil fuel energy. Pre-injection formation characterization and post-injection CO 2 monitoring are two critical tasks to guarantee storage efficiency in CCS. The CCS projects in the Illinois Basin, the first large-scale CO 2 injection into saline aquifers in the United States, employed conventional and the latest pulsed neutron logging (PNL) tools for mineralogy interpretation and CO 2 saturation estimation, which provide valuable references for future CCS projects. Because of the inherent fuzziness of petrophysical measurements and complex subsurface heterogeneity, interpreting well-logging data is time-consuming, and its accuracy can be user-biased. In recent years, data-driven methods have been widely used to capture the non-linear patterns between input features and interpretation results. This work applied and evaluated four commonly used machine learning (ML) models, including ridge regression (RR), random forest (RF), gradient boosting regression (GBR), support vector regression (SVR), and one deep learning (DL) model, the artificial neural network (ANN). We optimized the hyperparameters of the four ML models and the DL model using the simulated annealing algorithm and the grid search strategy, respectively. The input features of the mineralogy interpretation models were eleven conventional well-logging parameters, and the label data (i.e., ground truth) were the porosity and volumetric fractions of six minerals, including quartz, feldspar, dolomite, calcite, clay, and iron minerals. The results demonstrated that the GBR and RF models were superior in predicting volumetric fractions of minerals and porosity; label data with low coefficient of variation (CV) values tended to yield better performance. For CO 2 saturation estimation, the RF was the best-performing model, followed by SVR, ANN, GBR, and RR. Furthermore, we conducted feature importance ranking using the permutation importance algorithm and found that the formation sigma and well pressure were the most important features in this study. In conclusion, the study of CCS projects in the Illinois Basin bridges the gap between the limited knowledge and understanding of geological carbon storage and the increasing demand for reliable, cost-effective, and sustainable energy solutions.

58 GEOSCIENCES↗
DOE OSTI2016

Data catalog project—A browsable, searchable, metadata system

Modern experiments are typically conducted by large, extended groups, where researchers rely on other team members to produce much of the data they use. The experiments record very large numbers of measurements that can be difficult for users to find, access and understand. Here. we are developing a system for users to annotate their data products with structured metadata, providing data consumers with a discoverable, browsable data index. Machine understandable metadata captures the underlying semantics of the recorded data, which can then be consumed by both programs, and interactively by users. Collaborators can use these metadata to select and understand recorded measurements. The data catalog project is a data dictionary and index which enables users to record general descriptive metadata, use cases and rendering information as well as providing them a transparent data access mechanism (URI). Users describe their diagnostic including references, text descriptions, units, labels, example data instances, author contact information and data access URIs. The list of possible attribute labels is extensible, but limiting the vocabulary of names increases the utility of the system. The data catalog is focused on the data products and complements process-based systems like the Metadata Ontology Provenance project [Greenwald, 2012; Schissel, 2015]. This system can be coupled with MDSplus to provide a simple platform for data driven display and analysis programs. Sites which use MDSplus can describe tree branches, and if desired create ‘processed data trees’ with homogeneous node structures for measurements. Sites not currently using MDSplus can either use the database to reference local data stores, or construct an MDSplus tree whose leaves reference the local data store. A data catalog system can provide a useful roadmap of data acquired from experiments or simulations making it easier for researchers to find and access important data and understand the meaning of the data and how it was obtained. This is particularly useful in research facilities that study the results of many different experiments or simulations and may not know the intricacies of the data organization in use where the data was generated. It is also possible to store a local copy of key data items in local MDSplus trees and then add processed data to the local catalog.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2018

Bayesian classification of falls risk

Background. Prior research in falls risk prediction often relies on qualitative and/or clinical methods. There are two challenges with these methods. First, qualitative methods typically use falls history to determine falls risk. Second, clinical methods do not quantify the uncertainty in the classification decision. In this paper, we propose using Bayesian classification to predict falls risk using vectors of gait variables shown to contribute to falls risk. Research Questions: (1) Using a vector of risk ratios for specific gait variables shown to contribute to falls risk, how can older adults be classified as low or high falls risk? and (2) how can the uncertainty in the classifier decision be quantified when using a vector of gait variables? Methods. Using a pressure sensitive walkway, biomechanical measurements of gait were collected from 854 adults over the age of 65. In our method, we first determine low and high falls risk labels for vectors of risk ratios using the k-means algorithm. Next, the posterior probability of low or high falls risk class membership is obtained from a two component Gaussian mixture model (GMM) of gait vectors, which enables risk assessment directly from the underlying biomechanics. We classify the gait vectors using a threshold based on Youden's J statistic. Results. Through a Monte Carlo simulation and an analysis of the receiver operating characteristic (ROC), we demonstrate that our Bayesian classifier, when compared to the k-means falls risk labels, achieves an accuracy greater than 96% at predicting low or high falls risk. Significance. Our analysis indicates that our approach based on a Bayesian framework and an individual's underlying biomechanics can predict falls risk while quantifying uncertainty in the classification decision.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Recurrent neural networks for short-term and long-term prediction of geothermal reservoirs

Accurate prediction of geothermal reservoir responses to alternative energy production scenarios is critical for optimizing the development of the underlying resources. While the conventional physics-based models offer a comprehensive prediction tool, data-driven models provide an efficient alternative to build fit-for-purpose predictive models by extracting and using the statistical patterns in the collected data to make predictions. The recurrent neural network (RNN) is a data-driven model that is commonly applied to predict time series sequences. This paper presents a variant of RNN that also utilizes the efficiency of convolutional neural networks (CNN) for the prediction of energy production from geothermal reservoirs. Specifically, a CNN–RNN architecture is developed that takes historical well controls as input (features) and their corresponding production response data as output (labels) to learn an input-output mapping that can predict the future well production responses/performance for any given future well control inputs. The model is paired with a labeling scheme to handle real field disturbances that create data gaps. In addition to the model structure, we introduce a thorough workflow for applying the model, which includes data pre-processing, feature selection, as well as different training strategies for short-term and long-term prediction. Finally, the performance and accuracy of the model are evaluated by applying it to multiple datasets, including a field reservoir model.

15 GEOTHERMAL ENERGY↗
DOE OSTI2020

Losses of CO and CO 2 upon collision-activated dissociation of substituted 2-methoxyphenoxides after methyl radical loss

Some deprotonated, substituted 2-methoxyphenols fragment by methyl radical loss followed by competing losses of CO and CO 2 upon collisional activation in linear quadrupole ion trap mass spectrometers. These reactions were examined experimentally and computationally in order to determine their mechanisms. For deprotonated vanillin, the CO loss was found to involve ring contraction, with the highest free energy barrier of 58.0 kcal mol -1 (for the syn rotamer; here, syn refers to the relationship between the aldehyde oxygen atom and the oxygen atom of the methoxy group) for the entire process. The atoms lost in this fragmentation are the oxygen atom that was bound to the first eliminated methyl group and the aromatic carbon atom bound to this oxygen. Examination of carbon-13 labeled vanillin supports these assignments. Examination of several model compounds revealed that this reaction requires the presence of an electron-withdrawing substituent in the para-position relative to the phenol moiety. In contrast, the CO 2 loss from deprotonated vanillin occurs via ring opening followed by re-cyclization and then ring contraction, leading to the loss of CO 2 in a process wherein the highest free energy barrier is 84.5 kcal mol -1 (for the syn isomer). The atoms lost in this fragmentation are the carbon and oxygen atoms from the phenoxide group and the oxygen atom that was bound to the first eliminated methyl group, which is supported by examination of carbon-13 labeled vanillin. Despite the higher total free energy requirement, the CO 2 loss is competitive with CO loss, possibly due to favorable entropy and low activation energy (free energy barrier of 48.9 kcal mol -1 ) for the first reaction step (the analogous value for CO loss is 58.0 kcal mol -1 ). The extent of CO 2 loss is strongly affected by substituents – it either is not observed or is very slow for the other compounds studied here. For example, it is substantially less favorable than CO loss (highest free energy barrier 60.9 kcal mol -1 ) for deprotonated acetovanillone for which the first reaction step for CO 2 loss has a substantially greater free energy barrier (60.6 kcal mol-1) than for vanillin.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Developing a peptide concatemer (PepCon) as a process control for LC-MS based proteomics

Quantitative concatenated peptide (QconCAT) technology is currently used for the absolute quantification of proteins of interest in a biological sample. It relies on artificially created proteins that are concatenations of different, isotopically labeled peptides. Because these isotopically labeled peptides are at a 1:1 ratio and correspond to naturally occurring peptides in the biological sample, each peptide can be used as a standard for the absolute quantitation of all proteins of interest at once. Although the QconCAT technology has utility for quantitation of known peptides in a mixture, it is not helpful for scientists who need a proteomics standard (1) that can be spiked into a protein mixture at an extremely low level, (2) that can be co-purified during sample fractionation, and (3) that is optimized for ESI used in mass spectrometry. Thus, there exists a need for an ideal standard protein that is large enough to behave as a protein but consists of multiple, concatenated copies of the same peptide, which, upon digestion, amplifies (e.g., >10-fold) into a detectable peptide species. Here, this technical report describes the development of “PepCon”, a peptide concatemer that fulfills this unmet need.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Predictive Parameters of Symptomatic Hematochezia Following 5-Fraction Gantry-Based SABR in Prostate Cancer

Purpose: This study identified predictors of high-grade late hematochezia (HH) following 5-fraction gantry-based stereotactic ablative radiation therapy (SABR). Methods and Materials: Hematochezia data for 258 patients who received 35 to 40 Gy SABR in 5-fractions as part of sequential phase 2 prospective trials was retrieved. Grade 2 or higher late rectal bleeding was labeled HH. Hematochezia needing steroid suppositories, 4% formalin, or 1 to 2 sessions of argon plasma coagulation (APC) was labeled grade 2. More than 2 sessions of APC, blood transfusion, or a course of hyperbaric oxygen was grade 3 and development of visceral fistula, grade 4. Various dosimetric and clinical factors were analyzed using univariate and multivariate analyses. Receiver operating characteristic (ROC) curve analysis and recursive partitioning analysis were used to determine clinically valid cut-off points and identify risk groups, respectively. Results: HH was observed in 19.4%, grade ≥3 toxicity in 3.1%. Median follow-up was 29.7 months (interquartile range [IQR]: 20.6-61.7) Median time to develop HH was 11.7 months (IQR: 9.0-15.2) from the start of radiation. At 2 years, cumulative HH was 4.9%, 27.2%, and 42.1% in patients who received 35 Gy to prostate (4-mm planning target volume [PTV] margin), 40 Gy to prostate (5-mm PTV margin), and 40 Gy to prostate/seminal vesicles (5-mm PTV margin), respectively (P<.0001). In the ROC analysis, volume of rectum receiving radiation dose of 38 Gy (V38) was a strong predictor of HH with an area under the curve of 0.65. In multivariate analysis, rectal V38 (≥2.0 cm{sup 3}; odds ratio [OR]: 4.7); use of anticoagulants in the follow-up period (OR: 6.5) and presence of hemorrhoids (OR: 2.7) were the strongest predictors. Recursive partitioning analysis showed rectal V38 < 2.0 cm{sup 3}, and use of anticoagulants or rectal V38 ≥ 2.0 cm{sup 3} plus 1 other risk factor resulted in an HH risk of >30%. Conclusions: Rectal V38 and 2 clinical factors were strong predictors of HH following 5-fraction SABR. Planning constraints should keep rectal V38 below 2.0 cm{sup 3}.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2018

Micro-imaging of Brain Cancer Radiation Therapy Using Phase-contrast Computed Tomography

Experimental neuroimaging provides a wide range of methods for the visualization of brain anatomic morphology down to subcellular detail. Still, each technique-specific detection mechanism presents compromises among the achievable field-of-view size, spatial resolution, and nervous tissue sensitivity, leading to partial sample coverage, unresolved morphologic structures, or sparse labeling of neuronal populations and often also to obligatory sample dissection or other sample invasive manipulations. X-ray phase-contrast imaging computed tomography (PCI-CT) is an experimental imaging method that simultaneously provides micrometric spatial resolution, high soft-tissue sensitivity, and ex vivo full organ rodent brain coverage without any need for sample dissection, staining or labeling, or contrast agent injection. In the present study, we explored the benefits and limitations of PCI-CT use for in vitro imaging of normal and cancerous brain neuromorphology after in vivo treatment with synchrotron-generated x-ray microbeam radiation therapy (MRT), a spatially fractionated experimental high-dose radiosurgery. The goals were visualization of the MRT effects on nervous tissue and a qualitative comparison of the results to the histologic and high-field magnetic resonance imaging findings.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2024

Automated 3D cytoplasm segmentation in soft X-ray tomography

Cells’ structure is key to understanding cellular function, diagnostics, and therapy development. Soft X-ray tomography (SXT) is a unique tool to image cellular structure without fixation or labeling at high spatial resolution and throughput. Fast acquisition times increase demand for accelerated image analysis, like segmentation. Currently, segmenting cellular structures is done manually and is a major bottleneck in the SXT data analysis. This paper introduces ACSeg, an automated 3D cytoplasm segmentation model. ACSeg is generated using semi-automated labels and 3D U-Net and is trained on 43 SXT tomograms of immune T cells, rapidly converging to high-accuracy segmentation, therefore reducing time and labor. Furthermore, adding only 6 SXT tomograms of other cell types diversifies the model, showing potential for optimal experimental design. ACSeg successfully segmented unseen tomograms and is published on Biomedisa, enabling high-throughput analysis of cell volume and structure of cytoplasm in diverse cell types.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

An international survey of building energy codes and their implementation

Buildings are key to low-carbon development everywhere, and many countries have introduced building energy codes to improve energy efficiency in buildings. Yet, building energy codes can only deliver results when the codes are implemented. For this reason, studies of building energy codes need to consider implementation of building energy codes in a consistent and comprehensive way. This research identifies elements and practices in implementing building energy codes, covering codes in 22 countries that account for 70% of global energy demand from buildings. Access to benefits of building energy codes depends on comprehensive coverage of buildings by type, age, size, and geographic location; an implementation framework that involves a certified agency to inspect construction at critical stages; and independently tested, rated, and labeled building energy materials. Training and supporting tools are another element of successful code implementation, and their role is growing in importance, given the increasing flexibility and complexity of building energy codes. Some countries have also introduced compliance evaluation and compliance checking protocols to improve implementation. This article provides examples of practices that countries have adopted to assist with implementation of building energy codes.

54 ENVIRONMENTAL SCIENCES↗