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 631 records · Page 35
All sourcesNASA NTRSDOE OSTI
NASA NTRS2013

Detection of Only Viable Bacterial Spores Using a Live/Dead Indicator in Mixed Populations

This method uses a photoaffinity label that recognizes DNA and can be used to distinguish populations of bacterial cells from bacterial spores without the use of heat shocking during conventional culture, and live from dead bacterial spores using molecular-based methods. Biological validation of commercial sterility using traditional and alternative technologies remains challenging. Recovery of viable spores is cumbersome, as the process requires substantial incubation time, and the extended time to results limits the ability to quickly evaluate the efficacy of existing technologies. Nucleic acid amplification approaches such as PCR (polymerase chain reaction) have shown promise for improving time to detection for a wide range of applications. Recent real-time PCR methods are particularly promising, as these methods can be made at least semi-quantitative by correspondence to a standard curve. Nonetheless, PCR-based methods are rarely used for process validation, largely because the DNA from dead bacterial cells is highly stable and hence, DNA-based amplification methods fail to discriminate between live and inactivated microorganisms. Currently, no published method has been shown to effectively distinguish between live and dead bacterial spores. This technology uses a DNA binding photoaffinity label that can be used to distinguish between live and dead bacterial spores with detection limits ranging from 109 to 102 spores/mL. An environmental sample suspected of containing a mixture of live and dead vegetative cells and bacterial endospores is treated with a photoaffinity label. This step will eliminate any vegetative cells (live or dead) and dead endospores present in the sample. To further determine the bacterial spore viability, DNA is extracted from the spores and total population is quantified by real-time PCR. The current NASA standard assay takes 72 hours for results. Part of this procedure requires a heat shock step at 80 degC for 15 minutes before the sample can be plated. Using a photoaffinity label would remove this step from the current assay as the label readily penetrates both live and dead bacterial cells. Secondly, the photoaffinity label can only penetrate dead bacterial spores, leaving behind the viable spore population. This would allow for rapid bacterial spore detection in a matter of hours compared to the several days that it takes for the NASA standard assay.

Behar, Alberto E.↗
NASA NTRS2016

Unsupervised Framework to Monitor Lake Dynamics

A method of reducing processing time when assigning geographic areas to land cover labels using satellite sensor values includes a processor receiving a feature value for each pixel in a time series of frames of satellite sensor values, each frame containing multiple pixels and each frame covering a same geographic location. For each sub-area of the geographic location, the sub-area is assigned to one of at least three land cover labels. The processor determines a fraction function for a first sub-area assigned to a first land cover label. The sub-areas that were assigned to the first land cover label are reassigned to one of the second land cover label and the third land cover label based on the fraction functions of the sub-areas.

Boriah, Shyam↗
NASA NTRS2018

Water Across Synthetic Aperture Radar Data (WASARD): SAR Water Body Classification for the Open Data Cube

The detection of inland water bodies from Synthetic Aperture Radar (SAR) data provides a great advantage over water detection with optical data, since SAR imaging is not impeded by cloud cover. Traditional methods of detecting water from SAR data involves using thresholding methods that can be labor intensive and imprecise. This paper describes Water Across Synthetic Aperture Radar Data (WASARD): a method of water detection from SAR data which automates and simplifies the thresholding process using machine learning on training data created from Geoscience Australia’s WOFS algorithm. Of the machine learning models tested, the Linear Support Vector Machine was determined to be optimal, with the option of training using solely the VH polarization or a combination of the VH and VV polarizations. WASARD was able to identify water in the target area with a correlation of 97% with WOFS. Sentinel-1, Open Data Cube, Earth Observations, Machine Learning, Water Detection 1. INTRODUCTION Water classification is an important function of Earth imaging satellites, as accurate remote classification of land and water can assist in land use analysis, flood prediction, climate change research, as well as a variety of agricultural applications [2]. The ability to identify bodies of water remotely via satellite is immensely cheaper than contracting surveys of the areas in question, meaning that an application that can accurately use satellite data towards this function can make valuable information available to nations which would not be able to afford it otherwise. Highly reliable applications for the remote detection of water currently exist for use with optical satellite data such as that provided by LANDSAT. One such application, Geoscience Australia’s Water Observations from Space (WOFS) has already been ported for use with the Open Data Cube [6]. However, water detection using optical data from Landsat is constrained by its relatively long revisit cycle of 16 days [5], and water detection using any optical data is constrained in that it lacks the ability to make accurate classifications through cloud cover [2]. The alternative solution which solves these problems is water detection using SAR data, which images the Earth using cloud-penetrating microwaves. Because of its advantages over optical data, much research has been done into water detection using SAR data. Traditionally, this has been done using the thresholding method, which involves picking a polarization band and labeling all pixels for which this band’s value is below a certain threshold as containing water. The thresholding method works since water tends to return a much lower backscatter value to the satellite than land [1]. However, this method can be flawed since estimating the proper threshold is often imprecise, complicated, and labor intensive for the end user. Thresholding also tends to use data from only one SAR polarization, when a combination of polarizations can provide insight into whether water is present. [2] In order to alleviate these problems, this paper presents an application for the Open Data Cube to detect water from SAR data using support vector machine (SVM) classification. 2. PLATFORM WASARD is an application for the Open Data Cube, a mechanism which provides a simple yet efficient means of ingesting, storing, and retrieving remote sensing data. Data can be ingested and made analysis ready according to whatever specifications the researcher chooses, and easily resampled to artificially alter a scene’s resolution. Currently WASARD supports water detection on scenes from ESA’s Sentinel-1 and JAXA’s ALOS. When testing WASARD, Sentinel-1 was most commonly used due to its relatively high spatial resolution and its rapid 6 day revisit cycle [5]. With minor alterations to the application's code, however, it could support data from other satellites. 3. METHODOLOGY Using supervised classification, WASARD compares SAR data to a dataset pre-classified by WOFS in order to train an SVM classifier. This classifier is then used to detect water in other SAR scenes outside the training set. Accuracy was measured according to the following metrics:  Precision: a measure of what percentage of the points WASARD labels as water are truly water  Recall: a measure of what percentage of the total water cover WASARD was able to identify.  F1 Score: a harmonic average of the precision and recall scores Both precision and recall are calculated at the end of the training phase, when the trained classifier is compared to a testing dataset. Because the WOFS algorithm’s classifications are used as the truth values when training a WASARD classifier, when precision and recall are mentioned in this paper, they are always with respect to the values produced by WOFS on a similar scene of Landsat data, which themselves have a classification accuracy of 97% [6]. Visual representations of water identified by WASARD in this paper were produced using the function wasard_plot(), which is included in WASARD. 3.1 Algorithm Selection The machine learning model used by WASARD is the Linear Support Vector Machine (SVM). This model uses a supervised learning algorithm to develop a classifier, meaning it creates a vector which can be multiplied by the vector formed by the relevant data bands to determine whether a pixel in a SAR scene contains water. This classifier is trained by comparing data points from selected bands in a SAR scene to their respective labels, which in this case are “water” or “not water” as given by the WOFS algorithm. The SVM was selected over the Random Forest model, which outperformed the SVM in training speed, but had a greater classification time and lower accuracy, and the Multilayer Perceptron Artificial Neural Network, which had a slightly higher average accuracy than the SVM, but much greater training and classification times. Figure 1: Visual representation of the SVM Classifier. Each white point represents a pixel in a SAR scene. In Figure 1, the diagonal line separating pixels determined to be water from those determined not to be water represents the actual classification vector produced by the SVM. It is worth noting that once the model has been trained, classification of pixels is done in a similar manner as in the thresholding method. This is especially true if only one band was used to train the model. 3.1 Feature Selection Sentinel-1 collects data from two bands: the Vertical/Vertical polarization (VV) and the Vertical/Horizontal polarization (VH). When 100 SVM classifiers were created for each polarization individually, and for the combination of the two, the following results were achieved: Figure 2: Accuracy of classifiers trained using different polarization bands. Precision and Recall were measured with respect to the values produced by WOFS. Figure 2 demonstrates that using both the VV and VH bands trades slightly lower recall for significantly greater precision when compared with the VH band alone, and that using the VV band alone is inferior in both metrics. WASARD therefore defaults to using both the VV and VH bands, and includes the option to use solely the VH band. The VV polarization’s lower precision compared to the VH polarization is in contrast to results from previous research and may merit further analysis [4]. 3.2 Training a Classifier The steps in training a classifier with WASARD are 1. Selecting two scenes (one SAR, one optical) with the same spatial extents, and acquired close to each other in time, with a preference that the scenes are taken on the same day. 2. Using the WOFS algorithm to produce an array of the detected water in the scene of optical data, to be used as the labels during supervised learning 3. Data points from the selected bands from the SAR acquisition are bundled together into an array with the corresponding labels gathered from WOFS. A random sample with an equal number of points labeled “Water” and “Not Water” is selected to be partitioned into a training and a testing dataset 4. Using Scikit-Learn’s LinearSVC object, the training dataset is used to produce a classifier, which is then tested against the testing dataset to determine its precision and recall The result is a wasard_classifier object, which has the following attributes: 1. f1, recall, and precision: 3 metrics used to determine the classifier’s accuracy 2. Coefficient: Vector which the SVM uses to make its predictions. The classifier detects water when the dot product of the coefficient and the vector formed by the SAR bands is positive 3. Save(): allows a user to save a classifier to the disk in order to use it without retraining 4. wasard_classify(): Classifies an entire xarray of SAR data using the SVM classifier All of the above steps are performed automatically when the user creates a wasard_classifier object. 3.3 Classifying a Dataset Once the classifier has been created, it can be used to detect water in an xarray of SAR data using wasard_classify(). By taking the dot product of the classifier’s coefficients and the vector formed by the selected bands of SAR data, an array of predictions is constructed. A classifier can effectively be used on the same spatial extents as the ones where it was trained, or on any area with a similar landscape. While

Kreiser, Zachary↗
DOE OSTI2022

Automated Shift Detection in Sensor-Based PV Power and Irradiance Time Series

PV power and irradiance sensor-based measurements are prone to error, resulting in issues such as abrupt time series data shifts. These shifts, which are usually unintentional, may be caused by software or hardware configuration changes on a PV system, and do not reflect an actual change in overall system performance. Locating these shifts and segmenting the associated time series aids in more accurate future PV analysis. In this research, an offline changepoint detection (CPD) algorithm that automatically detects these abrupt data shifts in sensor-based time series is introduced. Data shift periods in 101 daily PV power and irradiance time series were labeled manually by two solar experts. These data streams represent sensor-based measurements, and display a variety of data shift behaviors. A changepoint detection algorithm was tuned using the 101 labeled data streams, with each model configuration's ability to detect labeled changepoints benchmarked using metrics such as F1-score, recall, and Rand Index. Best performing models on seasonality-corrected data streams include the Pruned Exact Linear (PELT) method, the Binary Segmentation method, and the Bottom-Up method, all scoring an average F1-score of 0.76 or greater at detecting labeled changepoints within a 30-day window for the labeled data sets. To promote further research in this space, we are releasing the labeled data shift sets on U.S. Department of Energy's (DOE) DuraMAT Data Hub, and the associated algorithm in the Python PVAnalytics package.

changepoint detection↗
DOE OSTI2022

Automated Shift Detection in Sensor-Based PV Power and Irradiance Time Series

PV power and irradiance sensor-based measurements are prone to error, resulting in issues such as time series data shifts. In this research, a changepoint detection (CPD) algorithm that automatically detects data shifts in sensor-based time series is introduced. Data shift periods in 101 daily PV power and irradiance time series were labeled manually by two solar experts. These data streams represent sensor-based measurements, and display a variety of data shift behaviors. A changepoint detection algorithm was tuned using the 101 labeled data streams, with each model configuration's ability to detect labeled changepoints benchmarked using metrics such as F1-score, recall, and Rand Index. Best performing models on seasonality-corrected data streams include the Pruned Exact Linear (PELT) method, the Binary Segmentation method, and the Bottom-Up method, all scoring an average F1-score of 0.76 or greater at detecting labeled changepoints within a 30-day window across the labeled data sets. Pending approval, we plan to release the labeled data sets for this research on NREL's DuraMAT Data Hub, and the associated algorithm in the Python PVAnalytics package. By supplying the training sets and algorithm, we hope to encourage further development in this research space.

data shift↗
DOE OSTI2017

A novel puromycin decorporation method to quantify skeletal muscle protein breakdown: A proof-of-concept study

Highlights: • Limitations exist in the techniques used to quantify muscle protein breakdown (MPB). • We developed a method for assessing MPB through loss of puromycin labelling in cells. • We validated the method using selected compounds known to dynamically modulate MPB. The precise roles that the major proteolytic pathways play in the regulation of skeletal muscle mass remain incompletely understood, in part due to technical limitations associated with current techniques used to quantify muscle protein breakdown (MPB). We aimed to develop a method to assess MPB in cells, based on loss of puromycin labelling of translated polypeptide chains. Following an initial 24 h incubation period with puromycin (1 μM), loss of puromycin labelling from murine C2C12 myotubes was assessed over 48 h, both in the presence or absence of protein synthesis inhibitor cycloheximide (CHX). To validate the method, loss of puromycin labelling was determined from cells treated with selected compounds known to influence MPB (e.g. serum starvation, Dexamethasone (Dex), tumour necrosis factor alpha (TNF-α) and MG-132)). Reported established (static) markers of MPB were measured following each treatment. Loss of puromycin labelling from cells pre-incubated with puromycin was evident over a 48 h period, both with and without CHX. Treatment with Dex (−14 ± 2% vs. Ctl; P P P P in vitro using an established muscle cell line. It is anticipated this non isotopic-tracer alternative to measuring MPB will facilitate insight into the mechanisms that regulate muscle mass/MPB both in vitro, and perhaps, in vivo.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Kinetics and Optimization of the Lysine–Isopeptide Bond Forming Sortase Enzyme from Corynebacterium diphtheriae

Site-specifically modified protein bioconjugates have important applications in biology, chemistry, and medicine. Functionalizing specific protein side chains with enzymes using mild reaction conditions is of significant interest, but remains challenging. Recently, the lysine–isopeptide bond forming activity of the sortase enzyme that builds surface pili in Corynebacterium diphtheriae ( Cd SrtA) has been reconstituted in vitro. A mutationally activated form of Cd SrtA was shown to be a promising bioconjugating enzyme that can attach Leu-Pro-Leu-Thr-Gly peptide fluorophores to a specific lysine residue within the N-terminal domain of the SpaA protein ( N SpaA), enabling the labeling of target proteins that are fused to N SpaA. Here we present a detailed analysis of the Cd SrtA catalyzed protein labeling reaction. We show that the first step in catalysis is rate limiting, which is the formation of the Cd SrtA-peptide thioacyl intermediate that subsequently reacts with a lysine ε-amine in N SpaA. This intermediate is surprisingly stable, limiting spurious proteolysis of the peptide substrate. We report the discovery of a new enzyme variant ( Cd SrtA Δ ) that has significantly improved transpeptidation activity, because it completely lacks an inhibitory polypeptide appendage (“lid”) that normally masks the active site. We show that the presence of the lid primarily impairs formation of the thioacyl intermediate and not the recognition of the N SpaA substrate. Quantitative measurements reveal that Cd SrtA Δ generates its cross-linked product with a catalytic turnover number of 1.4 ± 0.004 h –1 and that it has apparent K M values of 0.16 ± 0.04 and 1.6 ± 0.3 mM for its N SpaA and peptide substrates, respectively. Cd SrtA Δ is 7-fold more active than previously studied variants, labeling >90% of N SpaA with peptide within 6 h. The results of this study further improve the utility of Cd SrtA as a protein labeling tool and provide insight into the enzyme catalyzed reaction that underpins protein labeling and pilus biogenesis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Increasing the Reproducibility and Replicability of Supervised AI/ML in the Earth Systems Science by Leveraging Social Science Methods

Artificial intelligence (AI) and machine learning (ML) pose a challenge for achieving science that is both reproducible and replicable. The challenge is compounded in supervised models that depend on manually labeled training data, as they introduce additional decision-making and processes that require thorough documentation and reporting. We address these limitations by providing an approach to hand labeling training data for supervised ML that integrates quantitative content analysis (QCA)—a method from social science research. The QCA approach provides a rigorous and well-documented hand labeling procedure to improve the replicability and reproducibility of supervised ML applications in Earth systems science (ESS), as well as the ability to evaluate them. Specifically, the approach requires (a) the articulation and documentation of the exact decision-making process used for assigning hand labels in a “codebook” and (b) an empirical evaluation of the reliability” of the hand labelers. In this paper, we outline the contributions of QCA to the field, along with an overview of the general approach. We then provide a case study to further demonstrate how this framework has and can be applied when developing supervised ML models for applications in ESS. With this approach, we provide an actionable path forward for addressing ethical considerations and goals outlined by recent AGU work on ML ethics in ESS.

58 GEOSCIENCES↗
DOE OSTI2020

Selective Sampling for Sensor Type Classification in Buildings

A key barrier to applying any smart technology to a building is the requirement of locating and connecting to the necessary resources among the thousands of sensing and control points, i.e., the metadata mapping problem. Existing solutions depend on exhaustive manual annotation of sensor metadata --- a laborious, costly, and hardly scalable process. To reduce the amount of manual effort required, this paper presents a multi-oracle selective sampling framework to leverage noisy labels from information sources with unknown reliability such as existing buildings, which we refer to as weak oracles, for metadata mapping. This framework involves an interactive process, where a small set of sensor instances are progressively selected and labeled for it to learn how to aggregate the noisy labels as well as to predict sensor types. Two key challenges arise in designing the framework, namely, weak oracle reliability estimation and instance selection for querying. To address the first challenge, we develop a clustering-based approach for weak oracle reliability estimation to capitalize on the observation that weak oracles perform differently in different groups of instances. For the second challenge, we propose a disagreement-based query selection strategy to combine the potential effect of a labeled instance on both reducing classifier uncertainty and improving the quality of label aggregation. We evaluate our solution on a large collection of real-world building sensor data from 5 buildings with more than 11,000 sensors of 18 different types. The experiment results validate the effectiveness of our solution, which outperforms a set of state-of-the-art baselines.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2018

Learning from crowds with variational Gaussian processes

Solving a supervised learning problem requires to label a training set. This task is traditionally performed by an expert, who provides a label for each sample. The proliferation of social web services (e.g., Amazon Mechanical Turk) has introduced an alternative crowdsourcing approach. Anybody with a computer can register in one of these services and label, either partially or completely, a dataset. The effort of labeling is then shared between a great number of annotators. However, this approach introduces scientifically challenging problems such as combining the unknown expertise of the annotators, handling disagreements on the annotated samples, or detecting the existence of spammer and adversarial annotators. All these problems require probabilistic sound solutions which go beyond the naive use of majority voting plus classical classification methods. In this work we introduce a new crowdsourcing model and inference procedure which trains a Gaussian Process classifier using the noisy labels provided by the annotators. Variational Bayes inference is used to estimate all unknowns. The proposed model can predict the class of new samples and assess the expertise of the involved annotators. Moreover, the Bayesian treatment allows for a solid uncertainty quantification. Since when predicting the class of a new sample we might have access to some annotations for it, we also show how our method can naturally incorporate this additional information. Furthermore, a comprehensive experimental section evaluates the proposed method with synthetic and real experiments, showing that it consistently outperforms other state-of-the-art crowdsourcing approaches.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Self-stabilizing Connected Components

For the problem of computing the connected components of a graph, this paper considers the design of algorithms that are resilient to transient hardware faults, like bit Hips. More specifically, it applies the technique of self-stabilization. A system is self-stabilizing if, when starting from a valid or invalid state, it is guaranteed to reach a valid state after a finite number of steps. Therefore on a machine subject to a transient fault, a self-stabilizing algorithm could recover if that fault caused the system to enter an invalid state. We give a comprehensive analysis of the valid and invalid states during label propagation and derive algorithms to verify and correct the invalid state. The self-stabilizing label-propagation algorithm performs O (V log V) additional computation and requires O (V) additional storage over its conventional counterpart (and, as such, does not increase asymptotic complexity over conventional label propagation). When run against a battery of simulated fault injection tests, the self-stabilizing label propagation algorithm exhibits more resilient behavior than a triple modular redundancy (TMR) based fault-tolerant algorithm in 80% of cases. From a performance perspective, it also outperforms TMR as it requires fewer iterations in total. Beyond the fault tolerance properties of self-stabilizing label-propagation, we believe, they are useful from the theoretical perspective; and may have other use-cases.

Sao, Piyush↗
DOE OSTI2024

The oxidative pentose phosphate pathway in photosynthesis: a tale of two shunts

CO 2 release in the light (R L ) and its presumed source, oxidative pentose phosphate pathways, were found to be insensitive to CO 2 concentration. The oxidative pentose phosphate pathways form glucose 6-phosphate (G6P) shunts that bypass the nonoxidative pentose phosphate reactions of the Calvin–Benson cycle. Using adenosine diphosphate glucose and uridine diphosphate glucose as proxies for labeling of G6P in the stroma and cytosol respectively, it was found that only the cytosolic shunt was active. Uridine diphosphate glucose, a proxy for cytosolic G6P, and 6-phosphogluconate (6PG) were significantly less labeled than Calvin–Benson cycle intermediates in the light. But ADP glucose, a proxy for stromal G6P, is labeled to the same degree as Calvin–Benson cycle intermediates and much greater than 6PG. A metabolically inert pool of sedoheptulose bisphosphate can slowly equilibrate keeping the label in sedoheptulose lower than in other stromal metabolites. Finally, phosphorylation of fructose 6-phosphate (F6P) in the cytosol can allow some unlabeled carbon in cytosolic F6P to dilute label in phosphenolpyruvate. The results clearly show that there is oxidative pentose phosphate pathway activity in the cytosol that provides a shunt around the nonoxidative pentose phosphate pathway reactions of the Calvin–Benson cycle and is not strongly CO 2 -sensitive.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

TH-CD-206-02: BEST IN PHYSICS (IMAGING): 3D Prostate Segmentation in MR Images Using Patch-Based Anatomical Signature

Purpose: MRI has shown promise in identifying prostate tumors with high sensitivity and specificity for the detection of prostate cancer. Accurate segmentation of the prostate plays a key role various tasks: to accurately localize prostate boundaries for biopsy needle placement and radiotherapy, to initialize multi-modal registration algorithms or to obtain the region of interest for computer-aided detection of prostate cancer. However, manual segmentation during biopsy or radiation therapy can be time consuming and subject to inter- and intra-observer variation. This study’s purpose it to develop an automated method to address this technical challenge. Methods: We present an automated multi-atlas segmentation for MR prostate segmentation using patch-based label fusion. After an initial preprocessing for all images, all the atlases are non-rigidly registered to a target image. And then, the resulting transformation is used to propagate the anatomical structure labels of the atlas into the space of the target image. The top L similar atlases are further chosen by measuring intensity and structure difference in the region of interest around prostate. Finally, using voxel weighting based on patch-based anatomical signature, the label that the majority of all warped labels predict for each voxel is used for the final segmentation of the target image. Results: This segmentation technique was validated with a clinical study of 13 patients. The accuracy of our approach was assessed using the manual segmentation (gold standard). The mean volume Dice Overlap Coefficient was 89.5±2.9% between our and manual segmentation, which indicate that the automatic segmentation method works well and could be used for 3D MRI-guided prostate intervention. Conclusion: We have developed a new prostate segmentation approach based on the optimal feature learning label fusion framework, demonstrated its clinical feasibility, and validated its accuracy. This segmentation technique could be a useful tool in image-guided interventions for prostate-cancer diagnosis and treatment.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Self-Supervised Cloud Classification

Abstract Low-level marine clouds play a pivotal role in Earth’s weather and climate through their interactions with radiation, heat and moisture transport, and the hydrological cycle. These interactions depend on a range of dynamical and microphysical processes that result in a broad diversity of cloud types and spatial structures, and a comprehensive understanding of cloud morphology is critical for continued improvement of our atmospheric modeling and prediction capabilities moving forward. Deep learning has recently accelerated our ability to study clouds using satellite remote sensing, and machine learning classifiers have enabled detailed studies of cloud morphology. A major limitation of deep learning approaches to this problem, however, is the large number of hand-labeled samples that are required for training. This work applies a recently developed self-supervised learning scheme to train a deep convolutional neural network (CNN) to map marine cloud imagery to vector embeddings that capture information about mesoscale cloud morphology and can be used for satellite image classification. The model is evaluated against existing cloud classification datasets and several use cases are demonstrated, including training cloud classifiers with very few labeled samples, interrogation of the CNN’s learned internal feature representations, cross-instrument application, and resilience against sensor calibration drift and changing scene brightness. The self-supervised approach learns meaningful internal representations of cloud structures and achieves comparable classification accuracy to supervised deep learning methods without the expense of creating large hand-annotated training datasets. Significance Statement Marine clouds heavily influence Earth’s weather and climate, and improved understanding of marine clouds is required to improve our atmospheric modeling capabilities and physical understanding of the atmosphere. Recently, deep learning has emerged as a powerful research tool that can be used to identify and study specific marine cloud types in the vast number of images collected by Earth-observing satellites. While powerful, these approaches require hand-labeling of training data, which is prohibitively time intensive. This study evaluates a recently developed self-supervised deep learning method that does not require human-labeled training data for processing images of clouds. We show that the trained algorithm performs competitively with algorithms trained on hand-labeled data for image classification tasks. We also discuss potential downstream uses and demonstrate some exciting features of the approach including application to multiple satellite instruments, resilience against changing image brightness, and its learned internal representations of cloud types. The self-supervised technique removes one of the major hurdles for applying deep learning to very large atmospheric datasets.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Soil and soil fraction radiocarbon, C and N from the Enriched Background Isotope Study, Oak Ridge National Laboratory, Tennessee, USA

A large accidental 14CO2 release at the Oak Ridge Reservation in Tennessee, USA provided a strong label pulse into adjacent, well-studied oak forests, resulting in highly elevated Δ14C values in leaf litter (~1000‰) and roots (~260–450‰). A four-year manipulative study was conducted to provide insight into mechanisms underlying soil Organic matter dynamics, specifically to determine the relative contribution of litter versus roots to the bulk mineral soil C pool, as well as to free light, occluded light and heavy fractions. The heavy fraction was further split into fractions with densities of 1.7–2.4 g cm-3 and >2.4 g cm-3 to test the homogeneity of the mineral-associated fraction of C. Substantial concentrations of label were detected in all soil fractions within a year of the 14CO2 release, indicating rapid incorporation of newly fixed photosynthates in all fractions of soil organic C, regardless of differences in stability inferred by previous work. This rapid incorporation of label occurred only in treatments where roots were labeled, indicating that roots are the major source of inputs to mineral soil C stocks at these sites. Separation of the heavy fraction into subfractions of intermediate (1.7–2.4 g cm -3) and high (>2.4 g cm-3) density indicated that both subfractions incorporated label at similar rates, despite significant differences in degree of microbial processing.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Identifying Vehicle Signals in Continuous Seismic Data Using Unsupervised Machine-Learning Techniques

Seismic sensors deployed near roadways effectively capture ground vibrations generated by passing vehicles. Although both traditional and machine‐learning algorithms have been utilized for analyzing such signals, independent validation of detected vehicle events remains limited. We applied two unsupervised machine‐learning algorithms, uniform manifold approximation and projection for dimension reduction, and hierarchical density‐based spatial clustering of applications with noise, to continuous seismic data collected along a road on the main campus of Oak Ridge National Laboratory. The algorithms identified seven distinct cluster labels across the entire dataset. By comparing these cluster labels with precipitation records from a nearby weather station and image‐derived labels from a local camera system, we identified one cluster associated with rainfall and another with vehicle activity. Our algorithms identified a greater number of vehicle‐related labels compared to the camera‐derived labels because seismic data are unaffected by poor lighting conditions. The arrival times of the newly detected vehicle signals corresponded well with the road’s speed limit, supporting our findings. Our algorithm outperformed the short‐term average/long‐term average method and k‐means clustering. Our results suggest that seismic data, when analyzed with machine‐learning algorithms, can complement existing vehicle monitoring systems, particularly under challenging environmental conditions.

Chai, Chengping [Oak Ridge National Laboratory (OR↗
DOE OSTI2016

Chloroplast Dynamics and Photosynthetic Efficiency: Final Technical Report

This project investigated the mechanism by which chloroplasts position themselves to maximize solar energy utilization, to enhance gas exchange, to minimize environmental stress, and to promote efficient exchange of metabolites with other compartments within the plant cell. Chloroplasts move within leaf cells to optimize light levels, moving toward levels of light useful for photosynthesis while moving away from excess light. Plastids sometimes extend their reach by sending out projections (stromules) that can connect anchor chloroplasts in position within the cell or provide close contacts with plasma membrane, mitochondria, peroxisomes, endoplasmic reticulum, and the nucleus. The intracellular location of chloroplasts in relation to other organelles with which they share biosynthetic pathways, such as peroxisomes and mitochondria in photorespiration, affects metabolite flow. This work contributed to the knowledge of the mechanisms of organelle movement and anchoring in specific locations in plant cells and how proteins traffic within the cell. We identified two domains on 12 of the 13 Arabidopsis myosins that were similar to the vacuole-binding (V) domain characterized in yeast and to the DIL domain characterized in yeast and mouse as required for secretory vesicle or melanosome movement, respectively. Because all of the Arabidopsis regions with homology to the V domain contain the amino acid sequence PAL, we refer to this region as the Arabidopsis PAL domain. We have used the yeast Myo2p tail structural information to model the 12 myosin XI tail domains containing the homologous PAL and DIL domains. Eight YFP::DIL domain fusions labeled peroxisomes; none labeled mitochondria or chloroplasts. Six myosin XI Vacuole domains labeled mitochondria and seven labeled Golgi bodies. The Arabidopsis myosin XI-F PAL domain and the homologous myosin XI-F PAL domain from N. benthamiana labels chloroplasts and stromules in N. benthamiana leaves. Using an Arabidopsis line containing hotoconvertible GFP, we observed transfer of protein from one plastid to another and within a stromule from single plastids. We provided time-lapse movies demonstrating movement of both the photoconvertible GFP and standard GFP between plastids. We previously demonstrated the lack of a plastid network within plant cells. We provided protocols explaining how to use fluorescent protein technology to track plastids and stromules within plant cells. We demonstrated that standard GFP unexpectedly could be photoconverted to a red form under certain conditions, allowing the use of GFP lines for studies that require photoconversion.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

Particle integrity, sampling, and application of a DNA-tagged tracer for aerosol transport studies

Aerosols are an ever-present part of our daily environment and have extensive effects on both human and environmental health. Particles in the inhalable range (1-10 μm diameter) are of particular concern because their deposition in the lung can lead to a variety of illnesses including allergic reactions, viral or bacterial infections, and cancer. Understanding the transport of inhalable aerosols across both short and long distances is necessary to predict human exposures to aerosols. To assess the transport of hazardous aerosols, surrogate tracer particles are required to measure their transport through occupied spaces. These tracer particles must not only possess similar transport characteristics to those of interest but also be easily distinguished from the background at low levels and survive the environmental conditions of the testing environment. A previously-developed DNA-tagged particle (DNATrax), composed of food-grade sugar and a DNA oligonucleotide as a “barcode” label, shows promise as a new aerosol tracer. Herein, the use of DNATrax material is validated for use in both indoor and outdoor environments. Utilizing passive samplers made of materials commonly found in indoor environments followed by quantitative polymerase chain reaction (qPCR) assay for endpoint particle detection, particles detection was achieved up to 90 m from the aerosolization location and across shorter distances with high spatial resolution. The unique DNA label and PCR assay specificity were leveraged to perform multiple simultaneous experiments. This allowed the assessment of experimental reproducibility, a rare occurrence among aerosol field tests. To transition to outdoor testing, the solid material provides some protection of the DNA label when exposed to ultraviolet (UV) radiation, with 60% of the DNA remaining intact after 60 minutes under a germicidal lamp and the rate of degradation declining with irradiation time. Additionally, exposure of the DNATrax material using formulations of two different food-grade sugars (maltodextrin and erythritol) to humidity as high as 66% had no significant effect on the DNA label’s degradation or the particle’s aerodynamic diameter, confirming particle stability under such conditions. In summary, confirmation of the DNATrax particles’ size and label integrity under variable conditions combined with experiment multiplexing and high resolution sampling provides a powerful experimental design for modeling aerosol transport through occupied indoor and outdoor locations.

54 ENVIRONMENTAL SCIENCES↗