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

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At least 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Physics-based modeling and information-theoretic sensor and settings selection for tool wear detection in precision machining

Precision machining of metals is an energy intensive process with applications and impacts across the manufacturing industry. The energy efficiency, product yield, and maintenance of the precision machine require a digital twin that can assist with prognostics and health management. In this report a physics-based model is developed and validated against face milling data, and then used for the timely and precise inference of machining faults that cannot be measured directly. Computer numerical control (CNC) measurements of power and force are used through this physics-based machining model to predict deviations of the outputs of power consumption and cutting forces during normal operation. A model-based fault detection and isolation methodology is applied to determine the optimal (traditional and available) sensor suite and the test settings (admissible input values) that improve the inference of tool wear in face milling. The optimal sensor suite and input test settings are obtained by solving a mixed integer non-linear program that optimizes information-theoretic metrics relevant to the detection and isolation of tool wear from steady-state or transient machining measurements. Dynamic time warping and k—NN classification are then used to validate the robustness of the optimal design for fault detection test design, including the optimal sensor suite.

42 ENGINEERING↗
DOE OSTI2020

Data-driven selection of stiff chemistry ODE solver in operator-splitting schemes

Most computational fluid dynamics simulations of practical combustion applications employ operator-splitting schemes, where chemistry and transport are separated and integrated with distinct numerical methods. The changes in composition due to chemistry are evaluated by solving ordinary differential equations (ODE) in each cell of the computational domain, which typically dominates the computational cost when detailed chemistry is considered. In this work, a data-driven approach for the selection of chemistry ODE solvers in operator-splitting schemes is presented. Neural networks are used to predict the ODE solvers CPU times and errors for a given thermochemical state. This allows the selection of an optimal ODE solver on a cell-by-cell, timestep-by-timestep basis. The models are trained using a wide set of thermochemical states generated through partially-stirred reactors and flames simulations. The methodology is validated by quantifying the prediction errors, the classification accuracy, and the computational speedup. The model predicts the optimal ODE solver for 70 to 95% of the validation cases and decreases the computional cost by a factor of 3 or more. The generalizability of the methodology to different chemical mechanisms and different fuels is assessed and it is shown that the model’s performance is only slightly degraded and its applicability is significantly enhanced if the inputs to the neural networks are restricted to a small set of thermochemical state variables present in most chemical mechanisms. In conclusion, the models are used in an homogeneous reactor case and a multi-dimensional CFD simulation of a diesel spray at high pressure where a speedup of more than 3 is achieved.

42 ENGINEERING↗
DOE OSTI2022

Leveraging Nitrogen Linkages in the Formation of a Porous Thorium–Organic Nanotube Suitable for Iodine Capture

We report the synthesis, characterization, and iodine capture application of a novel thorium–organic nanotube, TSN-626, [Th 6 O 4 (OH) 4 (C 6 H 4 NO 2 ) 7 (CHO 2 ) 5 (H 2 O) 3 ]·3H 2 O. The classification as a metal–organic nanotube (MONT) distinguishes it as a rare and reduced dimensionality subset of metal–organic frameworks (MOFs); the structure is additionally hallmarked by low node connectivity. TSN-626 is composed of hexameric thorium secondary building units and mixed O/N-donor isonicotinate ligands that demonstrate selective ditopicity, yielding both terminating and bridging moieties. Because hard Lewis acid tetravalent metals have a propensity to bind with electron donors of rival hardness (e.g., carboxylate groups), such Th–N coordination in a MOF is uncommon. However, the formation of key structural Th–N bonds in TSN-626 cap some of the square antiprismatic metal centers, a position usually occupied by terminal water ligands. TSN-626 was characterized by using complementary analytical and computational techniques: X-ray diffraction, vibrational spectroscopy, N 2 physisorption isotherms, and density functional theory. TSN-626 satisfies design aspects for the chemisorption of iodine. The synergy between accessibility through pores, vacancies at the metal–oxo nodes, and pendent N-donor sites allowed a saturated iodine loading of 955 mg g –1 by vapor methods. Furthermore, the crystallization of TSN-626 diversifies actinide–MOF linker selection to include soft electron donors, and these Th–N linkages can be leveraged for the investigation of metal-to-ligand bonding and unconventional topological expressions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Band gap predictions of double perovskite oxides using machine learning

Abstract The compositional and structural variety inherent to oxide perovskites spawn wide-ranging applications. In perovskites, the band gap E g , a key material parameter for these applications, can be optimally controlled by varying the composition. Here, we implement a hierarchical screening process in which two cross-validated and predictive machine learning models for band gap classification and regression, trained using exhaustive datasets that span 68 elements of the periodic table, are applied sequentially. The classification model separates wide band gap materials, with E g ≥ 0.5 eV, from materials which have zero or relatively small band gaps, namely E g < 0.5 eV, and the second regression model quantitatively predicts the gap value of the wide band gap compounds. The study down-selects 13,589 cubic oxide perovskite compositions that are predicted to be experimentally formable, thermodynamically stable, and have a wide band gap. Of these, a subset of 310 compounds, which are predicted to be stable and formable with a confidence greater than 90%, are identified for further investigation. Our models are methodically analyzed via performance metrics and inter-dependence of model features to gain physical insight into the band gap prediction problem. Design maps to identify the variation of band gap with substitution of different elements are also presented.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Species-Selective Detection of Volatile Organic Compounds by Ionic Liquid-Based Electrolyte Using Electrochemical Methods

The detection of volatile organic compounds (VOCs) is an important topic for environmental safety and public health. However, the current commercial VOC detectors suffer from cross-sensitivity and low reproducibility. In this work, we present species-selective detection for VOCs using an electrochemical cell based on ionic liquid (IL) electrolytes with features of high selectivity and reliability. The voltammograms measured with the IL-based electrolyte absorbing different VOCs exhibited species-selective features that were extracted and classified by linear discriminant analysis (LDA). The detection system could identify as many as four types of VOCs, including methanol, ethanol, acetone, formaldehyde, and additional water. A mixture of methanol and formaldehyde was detected as well. The sample required for the VOCs classification system was 50 μL, or 1.164 mmol, on average. The response time for each VOC measurement is as fast as 24 s. The volume of VOCs such as formaldehyde in solution could also be quantified by LDA and electrochemical impedance spectroscopy techniques, respectively. The system showed a tunable detection range for 1.6 and 16% (w/v) CH 2 O solution by adjusting the composition of the electrolyte. The limit of detection was as low as 1 μL. For the 1.6% CH 2 O solution, the linearity calibration range was determined to be from 5.30 to 53.00 μmol with a limit of detection at 0.53 μmol. The mechanisms for VOCs determination and quantification are also thoroughly discussed. Finally, it is expected that this work could provide a new insight into the concept of electrochemical detection of VOCs with machine learning analysis and be applied to both VOCs gas monitoring and fluid detection.

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

The DESI Early Data Release white dwarf catalogue

The Early Data Release (EDR) of the Dark Energy Spectroscopic Instrument (DESI) comprises spectroscopy obtained from 2020 December 14 to 2021 June 10. White dwarfs were targeted by DESI both as calibration sources and as science targets and were selected based on Gaia photometry and astrometry. Here, we present the DESI EDR white dwarf catalogue, which includes 2706 spectroscopically confirmed white dwarfs of which approximately 60 per cent have been spectroscopically observed for the first time, as well as 66 white dwarf binary systems. We provide spectral classifications for all white dwarfs, and discuss their distribution within the Gaia Hertzsprung–Russell diagram. We provide atmospheric parameters derived from spectroscopic and photometric fits for white dwarfs with pure hydrogen or helium photospheres, a mixture of those two, and white dwarfs displaying carbon features in their spectra. We also discuss the less abundant systems in the sample, such as those with magnetic fields, and cataclysmic variables. The DESI EDR white dwarf sample is significantly less biased than the sample observed by the Sloan Digital Sky Survey, which is skewed to bluer and therefore hotter white dwarfs, making DESI more complete and suitable for performing statistical studies of white dwarfs.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Impact of including second and later cancers in cause-specific survival estimates using population-based registry data

Background. Second or later primary cancers account for approximately 20% of incident cases in the United States. Currently, cause-specific survival (CSS) analyses exclude these cancers because the cause of death (COD) classification algorithm was available only for first cancers. The authors added rules for later cancers to the Surveillance, Epidemiology, and End Results cause-specific death classification algorithm and evaluated CSS to include individuals with prior tumors. Methods. The authors constructed 2 cohorts: 1) the first ever primary cohort, including patients whose first cancer was diagnosed during 2000 through 2016) and 2) the earliest matching primary cohort, including patients with any cancer who matched the selection criteria irrespective of whether it was the first or a later cancer diagnosed during 2000 through 2016. The cohorts' CSS estimates were compared using follow-up through December 31, 2017. The new rules were used in the second cohort for patients whose first cancers during 2000 through 2016 were their second or later cancers. Results. Overall, there were no statistically significant differences in CSS estimates between the 2 cohorts. Estimates were similar by age, stage, race, and time since diagnosis, except for patients with leukemia and those aged 65 to 74 years (3.4 percentage point absolute difference). Conclusions. The absolute difference in CSS estimates for the first cancer ever cohort versus earliest of any cancers cohort in the study period was small for most cancer types. As the number of newly diagnosed patients with prior cancers increases, the algorithm will make CSS more inclusive and enable estimating survival for a group of patients with cancer for whom life tables are not available or life tables are available but do not capture other-cause mortality appropriately.

Survival Analysis↗
DOE OSTI2025

One-shot gas detection with transformer paired neural networks in Mako collected longwave infrared hyperspectral imagery

To date, careful data treatment workflows and statistical detectors are used to perform hyperspectral image (HSI) detection of any gas contained in a spectral library, which is often expanded with physics models to incorporate different spectral characteristics. In general, surrounding evidence or known gas-release parameters are used to provide confidence in or confirm detection capability, respectively. This makes quantifying detection performance difficult as it is nearly impossible to develop an absolute ground truth for gas target pixel presence in collected HSI. Consequently, developing and comparing new detection methods, especially machine learning (ML)-based methods, is susceptible to subjectivity in derived detection map quality. Here, in this work, we demonstrate the first use of transformer-based paired neural networks (PNNs) for one-shot gas target detection for multiple gases while providing quantitative classification and detection metrics for their use on labeled data. Terabytes of training data are generated from a database of long-wave infrared HSI obtained from historical Mako sensor campaigns over Los Angeles. By incorporating labels, singular signature representations, and a model development pipeline, we can tune and select PNNs to detect multiple gas targets that are not seen in training on a quantitative basis. We additionally assess our test set detections using interpretability techniques widely employed with ML-based predictors, but less common with detection methods relying on learned latent spaces.

Hyperspectral imaging↗
DOE OSTI2020

Deep Learning for Automated Identification of Eels in Sonar Data

Freshwater eels, such as the American eel (Anguilla rostrata) present numerous challenges related to safe downstream fish passage at hydroelectric facilities. One of those challenges is effective monitoring of their abundance, movements, and behavior to facilitate design and operation of eel protection and passage facilities. A previous EPRI study documented the ability of human analysts to reliably identify American eels in data obtained with a 1100/1800 kHz, multibeam sonar. This report describes a project to develop deep learning (a subset of artificial intelligence) tools to automate the time-consuming, subjective process of eel identification in multibeam sonar data. The project exploited new data collected in the laboratory and the existing data from the prior EPRI field study to develop and test deep learning and other data analytic tools, including wavelet filtering, differencing for static object removal, and convolutional neural network analysis. The analysis of the laboratory data demonstrated feasibility of the approach, revealed object characteristics observed with the sonar that distinguish eels from similarly sized and shaped acoustic targets, and provided additional data for algorithm selection and training. Deep learning algorithms trained and tested on the laboratory data alone achieved accuracy rates of greater than 98% when classifying acoustic images of eels and similar-sized neutrally buoyant sticks. The algorithm trained and tested on the pre-existing field data alone, and yielded classification accuracy of 9.3% false positives and 13.3% false negatives when distinguishing between eels and sticks/PVC pipes based on video clips (i.e., multiple, consecutive images). This performance is comparable to the classification accuracy achieved by human analysts in the prior study. The deep learning algorithm trained on a combination of video clips obtained in the laboratory and the field and tested on video clips from the field, was able to distinguish eels from sticks and PVC pipes (a river debris analog) of similar size with 100% accuracy. Outreach to the hardware, software, and end-user communities early in the project helped to identify needs and specify the application space. Outreach to those communities at the end of the project communicated project results and opportunities for further development. The project achieved proof of concept for automated identification of eel in multibeam sonar data. Future work should focus on acquisition of additional data for more robust algorithm training and testing; modification of the software tools to accommodate multiple acoustic targets in the acoustic field at a given time; identification of additional object classes; incorporation of motion in the object identification and classification algorithms; operationalizing the software tools, including integration with other existing sonar data analysis tools; and partnering with hardware and software providers for distribution of the software tools with their commercial products.

13 HYDRO ENERGY↗
DOE OSTI2022

Ionic Liquid Engineering in Perovskite Photovoltaics

Over the past decade, perovskite photovoltaics have approached other currently available technologies and proven to be the most prospective type of solar cells. Although the many-sided research in this very active field has generated consistent results with regard to their undisputed consistently increasing power conversion efficiency, it also produced several rather contradictory opinions. Among other important details, debate surrounding their proneness to surface degradation and poor mechanical robustness, as well as the environmental footprint of this materials class, remains a moot point. The application of ionic liquids appears as one of the potential remedies to some of these challenges due to their high conductivity, the opportunities for chemical “tuning” of the structure, and relatively lower environmental footprint. This article provides an overview, classification, and applications of ionic liquids in perovskite solar cells. Here, we summarize the use and role of ionic liquids as versatile additives, solvents, and modifiers in perovskite precursor solution, in charge transport layer, and in interfacial and stability engineering. Finally, challenges and the future prospects for the design and/or selection of ionic liquids with a specific profile that meets the requirements for next-generation highly efficient and stable perovskite solar cells are proposed.

14 SOLAR ENERGY↗
DOE OSTI2026

Dark Energy Survey Year 6 Results: Photometric Dataset for Cosmology

We describe the photometric dataset assembled from the full 6 yr of observations by the Dark Energy Survey (DES) in support of static-sky cosmology analyses. DES Y6 Gold is a curated dataset derived from DES Data Release 2 (DR2) that incorporates improved measurement, photometric calibration, object classification and value-added information. Y6 Gold comprises nearly 5000 deg$^{2}$ of grizY imaging in the south Galactic cap and includes 669 million objects with a depth of i$_{AB}$ ∼ 23.4 mag at a signal-to-noise ratio ∼ 10 for extended objects and a top-of-the-atmosphere photometric uniformity <2 mmag. Y6 Gold augments DES DR2 with simultaneous fits to multiepoch photometry for more robust galaxy shapes, colors, and photometric redshift estimates. Y6 Gold features improved morphological star–galaxy classification with an efficiency of 98.6% and a contamination of 0.8% for galaxies with 17.5 < i$_{AB}$ < 22.5. Additionally, it includes per-object quality information, and accompanying maps of the footprint coverage, masked regions, imaging depth, survey conditions, and astrophysical foregrounds that are used for cosmology analyses. After quality selections, benchmark samples contain 448 million galaxies and 120 million stars. This publication is complemented by data access and documentation.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

LANL Regular Employee Population and Demographics, FY15-21

This document summarizes an effort to compile and analyze the LANL regular employee population’s demographic attributes and trends between FY15-FY21 using MicroStrategy, a web-based data analytics and visualization tool. LANL’s HR Division collected and prepared data for the Weapons Program’s annual reporting activities for the 2017 through 2023 NNSA Stockpile Stewardship and Management Plan (SSMP). Los Alamos workforce data represent the fiscal year-end snapshot of the permanent employee population who are categorized by the Common Occupational Classification System (COCS). This work enables further analyses, including trends for all workforce attributes presented in the SSMPs as well as a definition of a crosswalk between COCS categorization and the internal laboratory job classification hierarchy. Data for other sites and the federal workforce, as shown in the SSMPs, are included and corresponding figures can be visualized within the MicroStrategy tool. A portable, Excel-based version of the tool was developed and shared with the multi-site workforce working group. This summary highlights selected aspects of the LANL regular workforce. The data analyses and visualizations communicate insights within three major themes regarding permanent LANL employees: 1) demographic transformation, 2) attrition, and 3) skills. Regarding demographic transformation, and consistent with the general trend across the enterprise, early career regular employees are the fastest growing group, with growth ranging between 12-34% year-to-year over the seven-year period analyzed. The data also reflect effects of internal and external factors on attrition; the large spike in separations in FY18 aligns with the contract transition from LANS to Triad and the dip in attrition between FY20-FY21 is likely a result of the global pandemic. The population of regular employees in general management, engineer, and operator COCS categories have the fastest growth rates amongst all the occupational categories. The MicroStrategy and Excel tools provide additional details and trends.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Predictive analytics of selections of russet potatoes

We explore the application of machine learning algorithms specifically to enhance the selection process of Russet potato (Solanum tuberosum L.) clones in breeding trials by predicting their suitability for advancement. This study addresses the challenge of efficiently identifying high-yield, disease-resistant, and climate-resilient potato varieties that meet processing industry standards. Leveraging manually collected data from trials in the state of Oregon, we investigate the potential of a wide variety of state-of-the-art binary classification models. The dataset includes 1086 clones, with data on 38 attributes recorded for each clone, focusing on yield, size, appearance, and frying characteristics, with several control varieties planted consistently across four Oregon regions from 2013 to 2021. We conduct a comprehensive analysis of the dataset that includes preprocessing, feature engineering, and imputation to address missing values. We focus on several key metrics such as accuracy, F1-score, and Matthews correlation coefficient (MCC) for model evaluation. The top-performing models, namely a feedforward neural network classifier (Neural Net), a histogram-based gradient boosting classifier (HGBC), and a support vector machine classifier (SVM), demonstrate consistent and significant results. To further validate our findings, we conducted a simulation study using the aims, data-generating mechanisms, estimands, methods, and performance measures (ADEMP) framework, simulating different data-generating scenarios to assess model robustness and performance through true positive, true negative, false positive, and false negative distributions, area under the receiver operating characteristic curve (AUC-ROC) and MCC. The simulation results highlight that non-linear models like SVM and HGBC consistently show higher AUC-ROC and MCC than logistic regression, thus outperforming the traditional linear model across various distributions, and emphasizing the importance of model selection and tuning in agricultural trials. Variable selection further enhances model performance and identifies influential features in predicting trial outcomes. The findings emphasize the potential of machine learning in streamlining the selection process for potato varieties, offering benefits such as increased efficiency, substantial cost savings, and judicious resource utilization. Our study contributes insights into precision agriculture and showcases the relevance of advanced technologies for informed decision-making in breeding programs.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

An Accuracy-Maximization Approach for Claims Classifiers in Document Content Analytics for Cybersecurity

This paper presents our research approach and findings towards maximizing the accuracy of our classifier of feature claims for cybersecurity literature analytics, and introduces the resulting model ClaimsBERT. Its architecture, after extensive evaluations of different approaches, introduces a feature map concatenated with a Bidirectional Encoder Representation from Transformers (BERT) model. We discuss deployment of this new concept and the research insights that resulted in the selection of Convolution Neural Networks for its feature mapping aspects. We also present our results showing ClaimsBERT to outperform all other evaluated approaches. This new claims classifier represents an essential processing stage within our vetting framework aiming to improve the cybersecurity of industrial control systems (ICS). Furthermore, in order to maximize the accuracy of our new ClaimsBERT classifier, we propose an approach for optimal architecture selection and determination of optimized hyperparameters, in particular the best learning rate, number of convolutions, filter sizes, activation function, the number of dense layers, as well as the number of neurons and the drop-out rate for each layer. Fine-tuning these hyperparameters within our model led to an increase in classification accuracy from 76% obtained with BertForSequenceClassification’s original model to a 97% accuracy obtained with ClaimsBERT.

Ameri, Kimia (ORCID:0000000328791871)↗
DOE OSTI2022

Newly developed sugarbeet lines with altered postharvest respiration rates differ in transcription factor and glycolytic enzyme expression

Respiration is the principal cause for postharvest sucrose loss in sugarbeet (Beta vulgaris L.) roots. Although reductions in respiration rate could mitigate these losses, developing sugarbeet cultivars and storage procedures that reduce respiration are hindered by a lack of knowledge of the genetic and metabolic factors that control storage respiration rate. Research was conducted to identify genes and gene products that affect storage respiration rate by creating two sugarbeet lines that differ in respiration rate and characterizing gene expression differences between these lines. Sugarbeet lines F1056 and F1057, which differ by up to 42% in respiration rate, were created by divergent selection of a sugarbeet population using root respiration rate after 30 d in storage as the principal selection criterion. RNA sequencing identified 287 differentially expressed genes (DEGs) between these lines on the day of harvest and after 28-d storage. Of these DEGs, nine encoded transcription factors and five encoded enzymes involved in the respiratory pathway. Other DEGs contributed to a variety of biological and molecular functions based on gene ontology classifications. Of respiratory pathway DEGS, genes for NAD+- and NADP+-dependent forms of glyceraldehyde-3-phosphate dehydrogenase were of note due to their high upregulation in the high respiring line and for their established role in glycolysis, a pathway identified as a likely bottleneck in respiratory substrate production. Overall, lines F1056 and F1057 provide new tools for investigating genetic and physiological differences in storage respiration rate, and their DEGs identify candidates for genes affecting sugarbeet root respiration rate.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Using Data Science Tools to Reveal and Understand Subtle Relationships of Inhibitor Structure in Frontal Ring-Opening Metathesis Polymerization

The rate of frontal ring-opening metathesis polymerization (FROMP) using the Grubbs generation II catalyst is impacted by both the concentration and choice of monomers and inhibitors, usually organophosphorus derivatives. Herein we report a data-science-driven workflow to evaluate how these factors impact both the rate of FROMP and how long the formulation of the mixture is stable (pot life). Using this workflow, we built a classification model using a single-node decision tree to determine how a simple phosphine structural descriptor (V bur-near ) can bin long versus short pot life. Additionally, we applied a nonlinear kernel ridge regression model to predict how the inhibitor and selection/concentration of comonomers impact the FROMP rate. Furthermore, the analysis provides selection criteria for material network structures that span from highly cross-linked thermosets to non-cross-linked thermoplastics as well as degradable and nondegradable materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

A Southern Photometric Quasar Catalog from the Dark Energy Survey Data Release 2

We present a catalog of 1.4 million photometrically selected quasar candidates in the southern hemisphere over the ~5000 deg 2 Dark Energy Survey (DES) wide survey area. We combine optical photometry from the DES second data release (DR2) with available near-infrared (NIR) and the all-sky unWISE mid-infrared photometry in the selection. We build models of quasars, galaxies, and stars with multivariate skew-t distributions in the multidimensional space of relative fluxes as functions of redshift (or color for stars) and magnitude. Our selection algorithm assigns probabilities for quasars, galaxies, and stars and simultaneously calculates photometric redshifts (photo-z) for quasar and galaxy candidates. Benchmarking on spectroscopically confirmed objects, we successfully classify (with photometry) 94.7% of quasars, 99.3% of galaxies, and 96.3% of stars when all IR bands (NIR YJHK and WISE W1W2) are available. The classification and photo-z regression success rates decrease when fewer bands are available. Our quasar (galaxy) photo-z quality, defined as the fraction of objects with the difference between the photo-z z p and the spectroscopic redshift z s , |Δz| ≡ |z s - z p |/(1 + z s ) ≤ 0.1, is 92.2% (98.1%) when all IR bands are available, decreasing to 72.2% (90.0%) using optical DES data only. Our photometric quasar catalog achieves an estimated completeness of 89% and purity of 79% at r < 21.5 (0.68 million quasar candidates), with reduced completeness and purity at 21.5 < r ≲ 24. Among the 1.4 million quasar candidates, 87,857 have existing spectra, and 84,978 (96.7%) of them are spectroscopically confirmed quasars. Finally, we provide quasar, galaxy, and star probabilities for all (0.69 billion) photometric sources in the DES DR2 coadded photometric catalog.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

cTULIP: application of a human-based RNA-seq primary tumor classification tool for cross-species primary tumor classification in canine

The domestic dog, Canis familiaris, is quickly gaining traction as an advantageous model for use in the study of cancer, one of the leading causes of death worldwide. Naturally occurring canine cancers share clinical, histological, and molecular characteristics with the corresponding human diseases. In this study, we take a deep-learning approach to test how similar the gene expression profile of canine glioma and bladder cancer (BLCA) tumors are to the corresponding human tumors. We likewise develop a tool for identifying misclassified or outlier samples in large canine oncological datasets, analogous to that which was developed for human datasets. We test a number of machine learning algorithms and found that a convolutional neural network outperformed logistic regression and random forest approaches. We use a recently developed RNA-seq-based convolutional neural network, TULIP, to test the robustness of a human-data-trained primary tumor classification tool on cross-species primary tumor prediction. Our study ultimately highlights the molecular similarities between canine and human BLCA and glioma tumors, showing that protein-coding one-to-one homologs shared between humans and canines, are sufficient to distinguish between BLCA and gliomas. The results of this study indicate that using protein-coding one-to-one homologs as the features in the input layer of TULIP performs good primary tumor prediction in both humans and canines. Furthermore, our analysis shows that our selected features also contain the majority of features with known clinical relevance in BLCA and gliomas. Our success in using a human-data-trained model for cross-species primary tumor prediction also sheds light on the conservation of oncological pathways in humans and canines, further underscoring the importance of the canine model system in the study of human disease.

60 APPLIED LIFE SCIENCES↗