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Quality Assurance of ISS LIS lightning Data

Optical lighting detection from space has been ongoing since 1995 from the Optical Transient detector (1995-2000), then later by the Lightning Imaging Sensors (LIS) on the Tropical Rainfall Measuring Mission (TRMM) satellite (1997-2015) and the International Space Station (ISS) (ISS 2017-present). A rapid readout (2 ms) 128x128 pixel CCD (Charge-coupled Device) array detects optical transients from lightning (events) and transmits the observations to ground. Ground processing determines whether individual events are likely due to lightning or noise. Likely lightning events are then grouped into groups, flashes, and areas and their location on the earth are determined. The lightning data are saved in orbit files; each file containing the time, location, and intensity of the lightning events, groups, flashes, and areas detected during one orbit. Even after this processing anomalies may exist in some of the orbits. A manual quality assurance (QA) procedure is performed monthly to assess the data and omit orbits with obvious anomalies from the dataset. These files are not included in the final dataset that has undergone QA. The manual QA was developed during the OTD mission to identify artifacts that made it through ground processing algorithms. The manual QA process was continued for the TRMM and ISS LIS missions. Many anomalies have been identified and software updates now address many of them. However, some anomalies still occur in the processed datasets. The manual QA process examines the data for obvious issues in the lightning files that passed through the processing algorithms. These files are considered anomalous and are not included in the final QA dataset. Some issues that may cause an orbit to be considered anomalous include: obvious noise (excess events that make it through the processing), suspect geolocation, and excessive missing data packets. As an example, one way noise manifests is as “streaks” in lightning climatology plots. These streaks are especially apparent in low lightning rate regions (eg., over oceans). Since there is no method at present to extract these anomalies during processing, orbit files with significant anomalies are identified and removed from the final QA ISS LIS dataset. The final QA dataset can be obtained files from the Global Hydrometeorological Resource Center (GHRC). This presentation will provide examples of the various anomalies and examine how often the various anomalies occur and how much data is lost due to the omission of the anomalous orbits. Comparisons with OTD and TRMM LIS will also be examined.

ISS↗
DOE OSTI2025

Improving ICARUS Track Reconstruction Algorithms

The ICARUS experiment is part of the Short-Baseline Neutrino (SBN) program at Fermilab. The main goal of the experiment is to investigate the possibility of sterile neutrinos in the O(1 eV) mass region and provide clarification of the anomaly detected from the Liquid Scintillator Neutrino Detector (LSND) and MiniBooNE experiments. The ICARUS-T600 detector is a Liquid Argon Time Projection Chamber (LAr-TPC), that can provide excellent 3D imaging and calorimetric reconstruction of any ionizing particles. This detection technique allows a detailed study of neutrino interactions, spanning a wide energy spectrum (from a few keV to several hundreds of GeV). The detector consists of two identical adjacent modules, filled with a total of 760 tons of ultra-pure liquid argon. Each module houses two LAr-TPCs separated by a common cathode with a maximum drift distance of 1.5 m, equivalent to about 1 ms drift time for the nominal $500$ V/m electric drift field. The anode is made of three parallel wire planes positioned 3 mm apart, where the stainless-steel wires are oriented on each plane at a different angle with respect to the horizontal direction ($+60^\degree$,$-60^\degree$,$0^\degree$). The first two planes (Induction 1 and Induction 2) provide a non-destructive charge measurement, whereas the ionization charge is fully collected by the last collection plane. In total, 53248 wires with a 3 mm pitch and length up to 9 m are installed in the detector. In the first stage of the reconstruction, segments of waveforms corresponding to physical signals (hits) are searched for in the deconvolved wire waveform with a threshold-based hit-finding algorithm. Each hit is then fitted with a Gaussian, whose area is proportional to the number of drift electrons generating the signal. In the second stage of the reconstruction, hits are passed as input to Pandora, a framework software composed of different pattern recognition algorithms, that performs a 3D reconstruction of the full image recorded in the collected event, including the identification of interaction vertices and tracks and showers inside the TPC. These are organized into a hierarchical structure (called slice) of particles generated starting from a primary interaction vertex. In some cases, related to the inefficiencies in the hit detection or excessive deflection of the particle trajectory, Pandora breaks the particle's track into two or more smaller pieces and considers each piece as an independent track. We studied this phenomenon focusing on primary muons from ν_μ CC interactions contained in a single module with a track at least 20 cm long, to exclude delta rays. The study determined that about $7-8\%$ of the muon tracks are broken. Approximately $80\%$ of the times, Pandora assigns all segments of the track to the same slice (intra-slice track split), while in the remaining $20\%$ of the cases, one of the segments is associated with another slice (extra-slice track split). To mitigate this phenomenon, we designed an algorithm that detects and stitches the tracks broken by Pandora for the intra-slice split. In Monte Carlo simulations, the algorithm showed an efficiency exceeding $80\%$ and a purity exceeding $93\%$.

Ricci, Alessandro Maria [Pisa U.; INFN, Pisa] (ORC↗
DOE OSTI2022

Identification of Novel Microcystins Using High-Resolution MS and MS n with Python Code

Cyanotoxins called microcystins (MCs) are highly toxic and can be present in drinking water sources. Determining the structure of MCs is paramount because of its effect on toxicity. Though over 300 MC congeners have been discovered, many remain unidentified. In this work, a method is described for the putative identification of MCs using liquid chromatography (LC) coupled with high-resolution (HR) Orbitrap mass spectrometry (MS) and a new bottom-up sequencing strategy. Maumee River water samples were collected during a harmful algal bloom and analyzed by LC–MS with simultaneous HRMS and MS/MS. Unidentified ions with characteristic MC fragments (135 and 213 m/z) were recognized as possible novel MC congeners. An innovative workflow was developed for the putative identification of these ions. Python code was written to generate the potential structures of unidentified MCs and to assign ions after the fragmentation for structural confirmation. The workflow enabled the putative identification of eight previously reported MCs for which standards are not available and two newly discovered congeners, MC-HarR and MC-E(OMe)R.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

A computational modeling framework for pre-clinical evaluation of cardiac mapping systems

There are a variety of difficulties in evaluating clinical cardiac mapping systems, most notably the inability to record the transmembrane potential throughout the entire heart during patient procedures which prevents the comparison to a relevant “gold standard”. Cardiac mapping systems are comprised of hardware and software elements including sophisticated mathematical algorithms, both of which continue to undergo rapid innovation. The purpose of this study is to develop a computational modeling framework to evaluate the performance of cardiac mapping systems. The framework enables rigorous evaluation of a mapping system’s ability to localize and characterize (i.e., focal or reentrant) arrhythmogenic sources in the heart. The main component of our tool is a library of computer simulations of various dynamic patterns throughout the entire heart in which the type and location of the arrhythmogenic sources are known. Our framework allows for performance evaluation for various electrode configurations, heart geometries, arrhythmias, and electrogram noise levels and involves blind comparison of mapping systems against a “silver standard” comprised of computer simulations in which the precise transmembrane potential patterns throughout the heart are known. A feasibility study was performed using simulations of patterns in the human left atria and three hypothetical virtual catheter electrode arrays. Activation times (AcT) and patterns (AcP) were computed for three virtual electrode arrays: two basket arrays with good and poor contact and one high-resolution grid with uniform spacing. The average root mean squared difference of AcTs of electrograms and those of the nearest endocardial action potential was less than 1 ms and therefore appears to be a poor performance metric. In an effort to standardize performance evaluation of mapping systems a novel performance metric is introduced based on the number of AcPs identified correctly and those considered spurious as well as misclassifications of arrhythmia type; spatial and temporal localization accuracy of correctly identified patterns was also quantified. This approach provides a rigorous quantitative analysis of cardiac mapping system performance. Proof of concept of this computational evaluation framework suggests that it could help safeguard that mapping systems perform as expected as well as provide estimates of system accuracy.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Scripts and data associated with a manuscript linking soil and sediment elemental composition with dissolved organic matter chemistry across CONUS

This data package provides scripts and geochemical data for a manuscript titled “Linkages between mineral element composition of soils and sediments with hyporheic zone dissolved organic matter chemistry across the contiguous United States” (preprint: doi: 10.22541/essoar.169447343.31694990/v1). This data is associated with the Worldwide Hydrobiogeochemistry Observation Network for Dynamic River Systems (WHONDRS, https://whondrs.pnnl.gov) and is an extension of the Summer 2019 Sampling campaign which crowdsourced samples from rivers and sediment across the continental United States. Data from this study can be found at https://data.ess-dive.lbl.gov/datasets/doi:10.15485/1603775 and https://data.ess-dive.lbl.gov/datasets/doi:10.15485/1729719. The main objective of this manuscript was to couple sediment water extractable dissolved organic matter chemistry, defined by ultra-high resolution mass spectrometry, with localized sediment elemental composition and watershed scale soil elemental characteristics. This data package contains one main folder with four subfolders. The main data folder contains (1) readme; (2) data dictionary (dd); (3) file-level metadata (flmd); (4) an R markdown to reproduce manuscript figures and analyses; (5) a pdf of instructions to reproduce NGS interpolations with ArcGIS software; and (6) a python script to reproduce NGS extrapolations with python. The four subfolders contain files required to reproduce NGS extrapolations include (1) ‘CONUS_boundaries’ containing boundary layers (.shp) for the Continental United States; (2) ‘ngs_project’ containing files (.shp) with point level NGS soil elemental data (Grossman et al., 2004); (3) ‘raster_outputs’ containing the interpolated raster output files for various soil elements; and (4) ‘NGS_Chemistry_Final’ contain final extracted soil elemental data.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Agilent AgileBioFoundry CRADA (Final Report)

The mission of this CRADA with Agilent was to couple powerful MS platforms (QQQ, IM-QTOF-MS) with Agilent’s novel Ultra-High-Performance Liquid Chromatography (UHPLC) fast metabolomic workflows and perform ABF Machine Learning (ML) to generated datasets. Agilent transferred UHPLC methods to PNNL and LBNL and methods were implemented and demonstrated in both labs, achieving total acquisition times of < 10 min. Metabolites analyzed using Agilent’s shared methods included metabolites from central carbon metabolism, common across hosts, and metabolites unique to engineered strains. Standards were acquired in an UHPLC-Drift Tube Ion Mobility Mass Spectrometer (DTIMS) system for the first time within the context of ABF and methods were optimized based on Agilent’s protocols. Samples from ABF hosts Pseudomonas putida, Aspergillus pseudoterreus, Aspergillus niger and Rhodosporidium toruloides were analyzed using the UHPLC-DTIMS platform for a total of 276 runs. A data analysis workflow compatible with the Experimental Data Depot (EDD) and completely shareable was developed for the acquired UHPLC-DTIMS data. Samples were analyzed using a Data Independent Acquisition Approach (DIA), which for most of the standards provided more transitions therefore increasing detection confidence. Using the data acquired by PNNL, LBNL, and Agilent’s specifications from previous ML projects, SNL applied an ensemble ML strategy to pick the best performing model for automated LC-method selection. Finally, with the contribution of the participant labs and Agilent, SNL developed an Automated Method Selection (AMS) software tool to predict the best liquid chromatography method for analysis of any new molecules of interest. Samples with novel pathways and new metabolite targets of interest are generated at a high pace in the ABF. Therefore, our accomplishment in this CRADA improved the efficiency and accuracy of strain testing by developing and implementing fast analytical methods, robust processing tools, and software for predicting the best methods for UHPLC analysis.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Development and implementation of high-throughput proteomic and metabolomics assays by using advanced chromatographic and mass spectrometric systems (CRADA Final Report)

The mission of this CRADA with Agilent was to couple powerful MS platforms (QQQ, IM-QTOFMS) with Agilent’s novel Ultra-High-Performance Liquid Chromatography (UHPLC) fast metabolomic workflows and perform ABF Machine Learning (ML) to generated datasets. Agilent transferred UHPLC methods to PNNL and LBNL and methods were implemented and demonstrated in both labs, achieving total acquisition times of < 10 min. Metabolites analyzed using Agilent’s shared methods included metabolites from central carbon metabolism, common across hosts, and metabolites unique to engineered strains. Standards were acquired in an UHPLC-Drift Tube Ion Mobility Mass Spectrometer (DTIMS) system for the first time within the context of ABF and methods were optimized based on Agilent’s protocols. Samples from ABF hosts Pseudomonas putida, Aspergillus pseudoterreus, Aspergillus niger and Rhodosporidium toruloides were analyzed using the UHPLC-DTIMS platform for a total of 276 runs. A data analysis workflow compatible with the Experimental Data Depot (EDD) and completely shareable was developed for the acquired UHPLC-DTIMS data. Samples were analyzed using a Data Independent Acquisition Approach (DIA), which for most of the standards provided more transitions therefore increasing detection confidence. Using the data acquired by PNNL, LBNL, and Agilent’s specifications from previous ML projects, SNL applied an ensemble ML strategy to pick the best performing model for automated LC-method selection. Finally, with the contribution of the participant labs and Agilent, SNL developed an Automated Method Selection (AMS) software tool to predict the best liquid chromatography method for analysis of any new molecules of interest. Samples with novel pathways and new metabolite targets of interest are generated at a high pace in the ABF. Overall, the project advanced rapid metabolomics by combining liquid chromatography, ion mobility spectrometry, and data-independent mass spectrometry with machine learning. This multidimensional approach uses retention time, collision cross-section, precursor mass, and fragment-ion information to distinguish chemically similar metabolites that can be difficult to resolve using conventional liquid- or gas-chromatography methods. The resulting workflow also provided automated metabolite-identification error estimates, addressing a recognized need for statistical confidence measures in metabolomics.

Petzold, Christopher [Lawrence Berkeley National L↗
NASA NTRS1999

Aerospace Safety Advisory Panel

This report covers the activities of the Aerospace Safety Advisory Panel (ASAP) for calendar year 1998-a year of sharp contrasts and significant successes at NASA. The year opened with the announcement of large workforce cutbacks. The slip in the schedule for launching the International Space Station (ISS) created a five-month hiatus in Space Shuttle launches. This slack period ended with the successful and highly publicized launch of the STS-95 mission. As the year closed, ISS assembly began with the successful orbiting and joining of the Functional Cargo Block (FGB), Zarya, from Russia and the Unity Node from the United States. Throughout the year, the Panel maintained its scrutiny of NASA's safety processes. Of particular interest were the potential effects on safety of workforce reductions and the continued transition of functions to the Space Flight Operations Contractor. Attention was also given to the risk management plans of the Aero-Space Technology programs, including the X-33, X-34, and X-38. Overall, the Panel concluded that safety is well served for the present. The picture is not as clear for the future. Cutbacks have limited the depth of talent available. In many cases, technical specialties are 'one deep.' The extended hiring freeze has resulted in an older workforce that will inevitably suffer significant departures from retirements in the near future. The resulting 'brain drain' could represent a future safety risk unless appropriate succession planning is started expeditiously. This and other topics are covered in the section addressing workforce. The major NASA programs are also limited in their ability to plan property for the future. This is of particular concern for the Space Shuttle and ISS because these programs are scheduled to operate well into the next century. In the case of the Space Shuttle, beneficial and mandatory safety and operational upgrades are being delayed because of a lack of sufficient present funding. Likewise, the ISS has little flexibility to begin long lead-time items for upgrades or contingency planning. For example, the section on computer hardware and software contains specific findings related to required longer range safety-related actions. NASA can be proud of its accomplishments this past year, but must remain ever vigilant, particularly as ISS assembly begins to accelerate. The Panel will continue to focus on both the short- and long-term aspects of risk management and safety planning. This task continues to be made manageable and productive by the excellent cooperation the Panel receives from both NASA and its contractors. Particular emphasis will continue to be directed to longer term workforce and program planning issues as well as the immediate risks associated with ISS assembly and the initial flights of the X-33 and X-34. Section 2 of this report presents specific findings and recommendations generated by ASAP activities during 1998. Section 3 contains more detailed information in support of these findings and recommendations. Appendix A is a current roster of Panel members, consultants, and staff. Appendix B contains NASA's response to the findings and recommendations from the 1997 ASAP Annual Report. Appendix C details the fact-finding activities of the Panel in 1998. During the year, Mr. Richard D. Blomberg was elected chair of the Panel and Vice Admiral (VADM) Robert F Dunn was elected deputy chair. VADM Bernard M. Kauderer moved from consultant to member. Mr. Charles J. Donlan retired from the Panel after many years of meritorious service. Ms. Shirley C. McCarty and Mr. Robert L. ('Hoot') Gibson joined the Panel as consultants.

Source record↗
NASA NTRS2005

High Frequency QRS ECG Accurately Detects Cardiomyopathy

High frequency (HF, 150-250 Hz) analysis over the entire QRS interval of the ECG is more sensitive than conventional ECG for detecting myocardial ischemia. However, the accuracy of HF QRS ECG for detecting cardiomyopathy is unknown. We obtained simultaneous resting conventional and HF QRS 12-lead ECGs in 66 patients with cardiomyopathy (EF = 23.2 plus or minus 6.l%, mean plus or minus SD) and in 66 age- and gender-matched healthy controls using PC-based ECG software recently developed at NASA. The single most accurate ECG parameter for detecting cardiomyopathy was an HF QRS morphological score that takes into consideration the total number and severity of reduced amplitude zones (RAZs) present plus the clustering of RAZs together in contiguous leads. This RAZ score had an area under the receiver operator curve (ROC) of 0.91, and was 88% sensitive, 82% specific and 85% accurate for identifying cardiomyopathy at optimum score cut-off of 140 points. Although conventional ECG parameters such as the QRS and QTc intervals were also significantly longer in patients than controls (P less than 0.001, BBBs excluded), these conventional parameters were less accurate (area under the ROC = 0.77 and 0.77, respectively) than HF QRS morphological parameters for identifying underlying cardiomyopathy. The total amplitude of the HF QRS complexes, as measured by summed root mean square voltages (RMSVs), also differed between patients and controls (33.8 plus or minus 11.5 vs. 41.5 plus or minus 13.6 mV, respectively, P less than 0.003), but this parameter was even less accurate in distinguishing the two groups (area under ROC = 0.67) than the HF QRS morphologic and conventional ECG parameters. Diagnostic accuracy was optimal (86%) when the RAZ score from the HF QRS ECG and the QTc interval from the conventional ECG were used simultaneously with cut-offs of greater than or equal to 40 points and greater than or equal to 445 ms, respectively. In conclusion 12-lead HF QRS ECG employing RAZ scoring is a simple, accurate and inexpensive screening technique for cardiomyopathy. Although HF QRS ECG is highly sensitive for cardiomyopathy, its specificity may be compromised in patients with cardiac pathologies other than cardiomyopathy, such as uncomplicated coronary artery disease or multiple coronary disease risk factors. Further studies are required to determine whether HF QRS might be useful for monitoring cardiomyopathy severity or the efficacy of therapy in a longitudinal fashion.

Schlegel, Todd T.↗
NASA NTRS1994

Measurement Capabilities of Single-Pulse Planar Doppler Velocimetry

Preliminary investigations are described of a method that is capable of measuring instantaneous, 3-D, velocity vectors everywhere in a light sheet generated by a pulsed laser. The technique, here called Planar Doppler Velocimetry (PDV), is a variation of a new concept for velocity measurements that was called Doppler Global Velocimetry (DGV) in its original disclosure. The concept relies on the use of a narrowband laser and measurements of the Doppler shift of scattered light from particles moving with a flow. The Doppler shift is recorded as a variation in transmission through a sharp-edged spectral filter provided by iodine vapor in a cell. Entire fields of velocity can be determined by using a solid-state camera to record the intensity variations throughout the field of view. However, the implementation of DGV has been centered principally on the use of high power, continuous-wave, ion lasers and measurement times that are determined by the 30-ms framing times of standard video cameras. Hence, they provide velocity fields that are averaged in time at least over that period. On the other hand, the PDV concept described in this presentation incorporates a high energy, repetitively pulsed, Nd-YAG laser that is injection-seeded to make it narrowband and then frequency-doubled to provide light at frequencies absorbed by the iodine vapor. The duration of each pulse is less than 10 nanoseconds. When used in combination with nonstandard, scientific quality, solid state cameras, a sequence of images can be obtained that provides instantaneous velocity vectors everywhere in the field of view. The investigations described in this paper include an accurate characterization of the iodine cell spectral behavior and its influence on the PDV measurements, a derivation of the PDV signal analysis requirements, and the unique aspects of the pulsed laser behavior related to this application. In addition, PDV measurements are to be demonstrated using data from a rotating wheel target and from the flow of a subsonic jet. Initially, single optical fiber light collection and photomultiplier detectors will be substituted for solid state cameras. Those results will allow the determination of the fundamental limitations of the PDV technique without the complications of image acquisition and processing. They will then be used to provide an analysis of the measurement capabilities of PDV both in small aerodynamic research wind tunnels and in large wind tunnels designed for production airframe and propulsion testing. Future plans include the implementation of solid state cameras and the development of the required image acquisition and processing software. Eventually, the PDV technique will be applied to an aerodynamic research program related to transonic wing flutter.

McKenzie, Robert L.↗
DOE OSTI2023

Multivariate regression modelling for gender prediction using volatile organic compounds from hand odor profiles via HS-SPME-GC-MS

The efficacy of using human volatile organic compounds (VOCs) as a form of forensic evidence has been well demonstrated with canines for crime scene response, suspect identification, and location checking. Although the use of human scent evidence in the field is well established, the laboratory evaluation of human VOC profiles has been limited. This study used Headspace-Solid Phase Microextraction-Gas Chromatography-Mass Spectrometry (HS-SPME-GC-MS) to analyze human hand odor samples collected from 60 individuals (30 Females and 30 Males). The human volatiles collected from the palm surfaces of each subject were interpreted for classification and prediction of gender. The volatile organic compound (VOC) signatures from subjects’ hand odor profiles were evaluated with supervised dimensional reduction techniques: Partial Least Squares-Discriminant Analysis (PLS-DA), Orthogonal-Projections to Latent Structures Discriminant Analysis (OPLS-DA), and Linear Discriminant Analysis (LDA). The PLS-DA 2D model demonstrated clustering amongst male and female subjects. The addition of a third component to the PLS-DA model revealed clustering and minimal separation of male and female subjects in the 3D PLS-DA model. The OPLS-DA model displayed discrimination and clustering amongst gender groups with leave one out cross validation (LOOCV) and 95% confidence regions surrounding clustered groups without overlap. The LDA had a 96.67% accuracy rate for female and male subjects. The culminating knowledge establishes a working model for the prediction of donor class characteristics using human scent hand odor profiles.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Ch3MS-RF: a random forest model for chemical characterization and improved quantification of unidentified atmospheric organics detected by chromatography–mass spectrometry techniques

Abstract. The chemical composition of ambient organic aerosols plays a critical role in driving their climate and health-relevant properties and holds important clues to the sources and formation mechanisms of secondary aerosol material. In most ambient atmospheric environments, this composition remains incompletely characterized, with the number of identifiable species consistently outnumbered by those that have no mass spectral matches in the literature or the National Institute of Standards and Technology/National Institutes of Health/Environmental Protection Agency (NIST/NIH/EPA) mass spectral databases, making them nearly impossible to definitively identify. This creates significant challenges in utilizing the full analytical capabilities of techniques which separate and generate spectra for complex environmental samples. In this work, we develop the use of machine learning techniques to quantify and characterize novel, or unidentifiable, organic material. This work introduces Ch3MS-RF (Chemical Characterization by Chromatography–Mass Spectrometry Random Forest Modeling), an open-source, R-based software tool, for efficient machine-learning-enabled characterization of compounds separated in chromatography–mass spectrometry applications but not identifiable by comparison to mass spectral databases. A random forest model is trained and tested on a known 130 component representative external standard to predict the response factors of novel environmental organics based on position in volatility–polarity space and mass spectrum, enabling the reproducible, efficient, and optimized quantification of novel environmental species. Quantification accuracy on a reserved 20 % test set randomly split from the external standard compound list indicates that random forest modeling significantly outperforms the commonly used methods in both precision and accuracy, with a median response factor percent error of −2 %, for modeled response factors, compared to > 15 %, for typically used proxy assignment-based methods. Chemical properties modeling, evaluated on the same reserved 20 % test set and an extrapolation set of species identified in ambient organic aerosol samples collected in the Amazon rainforest, also demonstrate robust performance. Extrapolation set property prediction mean absolute errors for carbon number, oxygen to carbon ratio (O : C), average carbon oxidation state (OSc‾), and vapor pressure are 1.8, 0.15, 0.25, and 1.0 (log(atm)), respectively. Extrapolation set out-of-sample R2 for all properties modeled are above 0.75, with the exception of vapor pressure. While predictive performance for vapor pressure is less robust compared to the other chemical properties modeled, random-forest-based modeling was significantly more accurate than other commonly used methods of vapor pressure prediction, decreasing the mean vapor pressure prediction error to 0.24 (log(atm)) from 0.55 (log(atm)) (chromatography-based vapor pressure prediction) and 1.2 (log(atm)) (chemical formula-based vapor pressure prediction). The random forest model significantly advances an untargeted analysis of the full scope of chemical speciation yielded by two-dimensional gas chromatography (GCxGC-MS) techniques and can be applied to gas chromatography coupled with electron ionization mass spectrometry (GC-MS) as well. It enables the accurate estimation of key chemical properties commonly utilized in the atmospheric chemistry community, which may be used to more efficiently identify important tracers for further individual analysis and to characterize compound populations uniquely formed under specific ambient conditions.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1990

Photochemical cycloaddition reactions of cyanoacetylene and dicyanoacetylene

Photolysis of cyanoacetylene with 185- or 206-nm light yields 1,3,5-tricyanobenzene while 254-nm radiation yields a mixture of tetracyanocyclooctatetraenes, 1,2,4- and 1,3,5-tricyanobenzene. A polymer of cyanoacetylene is the major photoproduct. 1,3,5-Tricarbomethoxybenzene was the only photoproduct identified from the irradiation of methyl propiolate at 254 nm. Mono-, di-, and tricyanobenzenes are formed by irradiation of mixtures of acetylene and cyanoacetylene at 185, 206, and 254 nm along with trace amounts of cyclooctatetraenes. No photoadducts were detected on photolysis of mixtures of cyanoacetylene and CO or HCN. The tetracyanocyclooctatetraene structures were established by UV, MS, and NMR analyses. The 1H NMR of the product mixture exhibited a singlet at delta 7.028 consistent with either 1 or 2 and two singlets at delta 6.85 and 6.91 assigned to 3. Photolysis of mixtures of dicyanoacetylene and acetylene with either 185- or 206-nm light yielded 1,2-dicyanobenzene and (E,Z)-1-buten-3-yne-1,4-dicarbonitrile. These products were also obtained using 254-nm light along with a mixture of tetracyanocyclooctatetraenes. The same three singlets were observed in this product mixture as were observed in the tetracyanocyclooctatetraenes obtained from cyanoacetylene. From this observation it was concluded that the delta 7.02 signal is due to 2 and not 1. The photolysis of cyanoacetylene and dicyanoacetylene in the presence of ethylene with 185-nm light yields 1-cyanocylobutene and 1,2-dicyanocyclobutene, respectively. 2-Cyanobutadiene and 2,3-dicyanobutadiene are the photoproducts with 254-nm light. Reaction pathways are proposed to explain these findings.

NASA Discipline Exobiology↗
DOE OSTI2022

Generating Novel Aroma Phenotypes Using Commercial Wine Samples to Characterize an F1 Population

Due to their disease tolerance and cold hardy nature, interspecific hybrid grapes are widely grown in the Midwestern and Northeastern United States, with additional interest worldwide in the face of increased abiotic and biotic stresses from climate change. However, the aroma profile of these hybrids is unique and generally less popular in comparison with Vitis vinifera grapes. One of the challenges in any phenotyping project is first defining the traits of interest. As wine quality was our ultimate metric of interest, the aroma profile of commercial wines produced from the parents of a breeding population ( Vitis aestivalis derived ‘Norton’ x V. vinifera . ‘Cabernet Sauvignon’) was first assessed for traits of interest. We investigated 11 commercial wines each of Norton, a popular hybrid in Missouri and Cabernet Sauvignon (Cab) for their volatile profiles using the more inclusive metabolomics-based workflow. We then analyzed 21 Norton and 21 Cab grapes from different sites and vintages for the free and bound volatile compounds using HS-SPME-GCMS to validate the differences in wine. The GCMS data was processed using XCMS software to find features that were different between the two cultivars. The two cultivars were found to have differences in their volatile profiles, with 304 features different for wine volatiles, 418 features different for free volatiles, and 302 features different for bound volatiles at 0.05 significance level and with at least a 1.5-fold change between the two cultivars. Those features were used to identify several odor-active compounds in both grapes and wines, including β-damascenone, β-ionone, eugenol, 1,1,6-trimethyl-1,2-dihydronaphthalene (TDN), and methyl salicylate. Some of the identified compounds were higher in Norton than Cab; however, several features were higher in Cab. Using the identified aroma compounds as markers, we phenotyped an F1 population of Norton and Cab. The F1 population was found to be segregating for many aroma compounds with some genotypes demonstrating an even higher concentration of aroma volatiles than either of the parents. Ultimately, using commercially available samples paired with untargeted analysis proved to be an efficient way to determine phenotypes of interest for further analysis and may offer an easy way to choose potential parents with desired traits for breeding.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

A geospatial risk analysis graphical user interface for identifying hazardous chemical emission sources

Background: Performing back trajectory and forward trajectory using the Hybrid Single-Particle Lagrangian Integrated Trajectory Model (HYSPLIT) is a reliable approach for assessing particle transport after release among mid-field atmospheric models. HYSPLIT has an externally facing online interface that allows non-expert users to run the model trajectories without requiring extensive training or programming. However, the existing HYSPLIT interface is limited if simulations have a large amount of meteorological data and timesteps that are not coincident. The objective of this study is to design and develop a more robust tool to rapidly evaluate hazard transport conditions and to perform risk analysis, while still maintaining an intuitive and user-friendly interface. Methods: HYSPLIT calculates forward and backward trajectories of particles based on wind speed, wind direction, and the corresponding location, timestamp, and Pasquill stability classes of the regions of the atmosphere in terms of the wind speed, the amount of solar radiation, and the fractional cloud cover. The computed particle transport trajectories, combined with the online Proton Transfer Reaction-Mass Spectrometry (PTR-MS) data (https://figshare.com/articles/dataset/ARL_Data_from_PROS_station_at_Hanford_site/19993964), can be used to identify and quantify the sources and affected area of the hazardous chemicals’ emission using the potential source distribution function (PSDF). PSDF is an improved statistical function based on the well-known potential source contribution function (PSCF) in establishing the air pollutant source and receptor relationship. Performing this analysis requires a range of meteorological and pollutant concentration measurements to be statistically meaningful. The existing HYSPLIT graphical user interface (GUI) does not easily permit computations of trajectories of a dataset of meteorological data in high temporal frequency. To improve the performance of HYSPLIT computations from a large dataset and enhance risk analysis of the accidental release of material at risk, a geospatial risk analysis tool (GRAT-GUI) is created to allow large data sets to be processed instantaneously and to provide ease of visualization. Results: The GRAT-GUI is a native desktop-based application and can be run in any Windows 10 system without any internet access requirements, thus providing a secure way to process large meteorological datasets even on a standalone computer. GRAT-GUI has features to import, integrate, and convert meteorological data with various formats for hazardous chemical emission source identification and risk analysis as a self-explanatory user interface. The tool is available at https://figshare.com/articles/software/GRAT/19426742.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

UNH TDP - Concurrent Measurements of Inflow, Power Performance, and Loads for a Grid-Synchronized Vertical Axis Cross-Flow Turbine Operating in a Tidal Estuary

This data was collected between October 12 and December 15 of 2021 at the University of New Hampshire (UNH) and Atlantic Marine Energy Center (AMEC) turbine deployment platform (TDP). This data set includes over 29 days of grid connected turbine operation during this 65 day time frame. The priority for this measurement campaign was to collect data while the turbine was electrically connected to the grid by means of a rectifier and inverter. The Fall_2021_UNH_Measurement_Timeline.png highlights when each instrument was functioning and the Fall_2021_UNH_Test_Log.jpg indicates the four main regions for analysis available from this measurement campaign. The TDP is a floating structure moored on the Portsmouth facing side of Memorial Bridge pier #2, which spans the Piscataqua River between Portsmouth, NH and Kittery, ME. The Piscataqua River connects the Great Bay Estuary to the Gulf of Maine and the river currents are dominated by tidal forcing with water velocities exceeding 2.5 m/s during spring ebb tides at this site which were previously characterized by Kaelin Chancey (Assessment Of The Localized Flow And Tidal Energy Conversion System At An Estuarine Bridge - UNH MS Thesis 2019). The turbine under test was a modified New Energy Corporation (Calgary, CA) model EVG-025 4-blade H-Darrius type vertical axis cross flow turbine that rotates in the clockwise direction with a rotor diameter of 3.2m and blade length of 1.7m. The hydro-foil profile was a NACA 0021 with a 10 inch chord length and a blade preset pitch angle of +4deg with a positive angle corresponding with the toe in direction. The standard EVG-025 has a rotor diameter of 3.4m and its rated power output is 25kW at 3 m/s. The rotor diameter was reduced to accommodate the size of the existing TDP moon-pool. This project was pursued to quantify device performance for cross flow turbines operating in a marine environment. Accurate physical models, to characterize cross flow turbine performance, require real operational data sets due to the complexity of blade fluid interactions. This data can help support model development which will help predict turbine performance when analyzing perspective project locations in the future. Instrumentation was deployed to measure; water speed/direction, electrical power output, turbine shaft speed, turbine thrust force, and platform motion. Concurrent measurements of these parameters allow for correlations (cause and affect) to be inferred, allowing for characterization of device performance over a range of operating conditions. Water currents were measured using Acoustic Doppler Current Profilers (ADCP's) and Acoustic Doppler Velocimeters (ADV's) directly upstream and downstream of the turbine for inflow, wake and turbulence measurements. Electrical power output was measured using the Voltsys rectifier and the Shark power meter. Shaft speed was calculated based on the Voltsys measurements of the permanent magnet three phase generator AC generation frequency, coupled directly to the cross flow turbine under test (i.e., no gear box). Platform motions were captured using a Yost IMU (inertial measurement unit). Turbine thrust loading was measured using a reaction arm about the turbine deployment platform spanning beam, where two bi-directional load cells were connected to the system via a pinned connection. This submission includes zipped folders for each instrument containing quality controlled (QC'd) data in daily .csv files for the relevant duration specific to each instrument, along with separate .csv file that contains the units for each variable. Some instrument daily files are quite large and can pose a challenge for a visual spreadsheet editor to open. A processing software like MATLAB or Python is recommended. Note the degree of QC varied between each instrument due to time constraints. Particular time and attention was given to perform quality control tests on the acoustic based instruments that are particularly suscep...

16 TIDAL AND WAVE POWER↗