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DOE OSTI2020

Revisiting Five Years of CASMI Contests with EPA Identification Tools

Software applications for high resolution mass spectrometry (HRMS)-based non-targeted analysis (NTA) continue to enhance chemical identification capabilities. Given the variety of available applications, determining the most fit-for-purpose tools and workflows can be difficult. The Critical Assessment of Small Molecule Identification (CASMI) contests were initiated in 2012 to provide a means to evaluate compound identification tools on a standardized set of blinded tandem mass spectrometry (MS/MS) data. Five CASMI contests have resulted in recommendations, publications, and invaluable datasets for practitioners of HRMS-based screening studies. The US Environmental Protection Agency’s (EPA) CompTox Chemicals Dashboard is now recognized as a valuable resource for compound identification in NTA studies. However, this application was too new and immature in functionality to participate in the five previous CASMI contests. In this work, we performed compound identification on all five CASMI contest datasets using Dashboard tools and data in order to critically evaluate Dashboard performance relative to that of other applications. CASMI data was accessed via the CASMI webpage and processed for use in our spectral matching and identification workflow. Relative to applications used by former contest participants, our tools, data, and workflow performed well, placing more challenge compounds in the top five of ranked candidates than did the winners of three contest years and tying in a fourth. In addition, we conducted an in-depth review of the CASMI structure sets and made these reviewed sets available via the Dashboard. Our results suggest that Dashboard data and tools would enhance chemical identification capabilities for practitioners of HRMS-based NTA.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1983

Identification and stochastic control of helicopter dynamic modes

A general treatment of parameter identification and stochastic control for use on helicopter dynamic systems is presented. Rotor dynamic models, including specific applications to rotor blade flapping and the helicopter ground resonance problem are emphasized. Dynamic systems which are governed by periodic coefficients as well as constant coefficient models are addressed. The dynamic systems are modeled by linear state variable equations which are used in the identification and stochastic control formulation. The pure identification problem as well as the stochastic control problem which includes combined identification and control for dynamic systems is addressed. The stochastic control problem includes the effect of parameter uncertainty on the solution and the concept of learning and how this is affected by the control's duel effect. The identification formulation requires algorithms suitable for on line use and thus recursive identification algorithms are considered. The applications presented use the recursive extended kalman filter for parameter identification which has excellent convergence for systems without process noise.

Molusis, J. A.↗

DOE OSTI2020

Proteomic Characterization of Damaged Single Hairs Recovered after an Explosion for Protein-Based Human Identification

Evidence recovery is challenging where an explosion has occurred. Though hair evidence may be sufficiently robust to be recovered at the site, forensic analysis underutilizes the matrix by relying on morphological analysis. Where DNA is compromised, particularly in hair, protein-based human identification presents a promising alternative. Detection of amino acid polymorphisms in hair proteins as genetically variant peptides (GVPs) permits the inference of individualizing single nucleotide polymorphisms for identification. However, an explosive blast may damage hair proteins and compromise GVP identification. This work evaluates effects of an explosive blast on the hair proteome and GVP identification, investigates microscopy as a predictor of proteome profiling success in recovered hairs to improve analysis throughput, and quantifies discriminative power in damaged hairs. The proteomics dataset has been deposited into the ProteomeXchange Consortium (PXD017427). With the exception of degradation in keratins K75, K80, K40, and keratin-associated protein KAP10-11 as markers of hair cuticular damage, corroborated by scanning electron microscopic analysis, minimal hair proteome degradation following explosion allowed successful proteome profiling of single hairs regardless of morphological damage. Finally, GVP identification remained independent of explosion conditions, permitting similar discriminative power between exploded and undamaged hairs. These findings lend greater confidence to GVP analysis in one-inch hairs for forensic identification and provide information about hair protein localization.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2018

Identification of heavy-flavour jets with the CMS detector in pp collisions at 13 TeV

Many measurements and searches for physics beyond the standard model at the LHC rely on the efficient identification of heavy-flavour jets, i.e. jets originating from bottom or charm quarks. In this paper, the discriminating variables and the algorithms used for heavy-flavour jet identification during the first years of operation of the CMS experiment in proton-proton collisions at a centre-of-mass energy of 13 TeV, are presented. Heavy-flavour jet identification algorithms have been improved compared to those used previously at centre-of-mass energies of 7 and 8 TeV. For jets with transverse momenta in the range expected in simulated events, these new developments result in an efficiency of 68% for the correct identification of a b jet for a probability of 1% of misidentifying a light-flavour jet. The improvement in relative efficiency at this misidentification probability is about 15%, compared to previous CMS algorithms. In addition, for the first time algorithms have been developed to identify jets containing two b hadrons in Lorentz-boosted event topologies, as well as to tag c jets. The large data sample recorded in 2016 at a centre-of-mass energy of 13 TeV has also allowed the development of new methods to measure the efficiency and misidentification probability of heavy-flavour jet identification algorithms. The b jet identification efficiency is measured with a precision of a few per cent at moderate jet transverse momenta (between 30 and 300 GeV) and about 5% at the highest jet transverse momenta (between 500 and 1000 GeV).

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2023

Material Identification From Radiographs Without Energy Resolution

We propose a method for performing material identification from radiographs without energy-resolved measurements. Material identification has a wide variety of applications, including in biomedical imaging, nondestructive testing, and security. While existing techniques for radiographic material identification make use of dual energy sources, energy-resolving detectors, or additional (e.g., neutron) measurements, such setups are not always practical— requiring additional hardware and complicating imaging. We tackle material identification without energy resolution, allowing standard X-ray systems to provide material identification information without requiring additional hardware. Assuming a setting where the geometry of each object in the scene is known and the materials come from a known set of possible materials, we pose the problem as a combinatorial optimization with a loss function that accounts for the presence of scatter and an unknown gain and propose a branch and bound algorithm to efficiently solve it. We present experiments on both synthetic data and real, experimental data with relevance to security applications— thick, dense objects imaged with MeV X-rays. We show that material identification can be efficient and accurate, for example, in a scene with three shells (two copper, one aluminum), our algorithm ran in six minutes on a consumer-level laptop and identified the correct materials as being among the top 10 best matches out of 8,000 possibilities.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Evaluating Software Tools for Lipid Identification from Ion Mobility Spectrometry–Mass Spectrometry Lipidomics Data

The unambiguous identification of lipids is a critical component of lipidomics studies and greatly impacts the interpretation and significance of analyses as well as the ultimate biological understandings derived from measurements. The level of structural detail that is available for lipid identifications is largely determined by the analytical platform being used. Mass spectrometry (MS) coupled with liquid chromatography (LC) is the predominant combination of analytical techniques used for lipidomics studies, and these methods can provide fairly detailed lipid identification. More recently, ion mobility spectrometry (IMS) has begun to see greater adoption in lipidomics studies thanks to the additional dimension of separation that it provides and the added structural information that can support lipid identification. At present, relatively few software tools are available for IMS-MS lipidomics data analysis, which reflects the still limited adoption of IMS as well as the limited software support. This fact is even more pronounced for isomer identifications, such as the determination of double bond positions or integration with MS-based imaging. In this review, we survey the landscape of software tools that are available for the analysis of IMS-MS-based lipidomics data and we evaluate lipid identifications produced by these tools using open-access data sourced from the peer-reviewed lipidomics literature.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

Do Remote Camera Arrangements and Image Capture Settings Improve Individual Identification of Golden Eagles?

Individual identification of animals from camera traps has become an important task in wildlife research, but camera deployment methods often do not facilitate this important undertaking. Identification of individual golden eagles (Aquila chrysaetos) is possible using uniquely marked rectrices, but no studies have explored methods to maximize the rate of individual identification from camera images. Furthermore, our objectives were to assess whether different camera heights (1 m vs. 3 m), image capture settings (one image after a 1-min delay vs. burst of 5 images after a 30 sec delay), and arrangements relative to bait (dorsally vs. ventrally aimed) affected views of rectrices on golden eagles and our ability to identify individuals. We conducted our study from 15 December 2016 to 3 March 2017 on the Savannah River Site, South Carolina. First, we developed a scoring system based on views of rectrices and used a linear mixed-effects model to compare image scores among different camera arrangements and image settings. Next, after identifying individual eagles, we used generalized linear mixed-effects models to compare total individual eagle detections, total days an individual was detected, and probability of obtaining an unknown individual identification among camera arrangements and settings. Overall, we scored a total of 27,499 images, with 8,083 providing views of marked rectrices that allowed identification of 18 individual eagles. Average image scores and proportion of images suitable for individual identification were higher from elevated (3 m) camera arrangements than standard arrangements (1 m) across sites. Regardless of camera height, faster frequency of image capture provided more images that could be used to identify individuals and the most trap days per individual. Researchers and managers should consider deploying elevated cameras traps with faster frequency of image capture to improve data quality and potential for analysis of golden eagle populations and trends across the species’ range.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2023

Improved calorimetric particle identification in NA62 using machine learning techniques

Measurement of the ultra-rare $K^+$ → $π$ +$ν$$\overline{ν}$ decay at the NA62 experiment at CERN requires high-performance particle identification to distinguish muons from pions. Calorimetric identification currently in use, based on a boosted decision tree algorithm, achieves a muon misidentification probability of 1.2×10 -5 for a pion identification efficiency of 75% in the momentum range of 15–40 GeV/c. In this work, calorimetric identification performance is improved by developing an algorithm based on a convolutional neural network classifier augmented by a filter. Muon misidentification probability is reduced by a factor of six with respect to the current value for a fixed pion-identification efficiency of 75%. Alternatively, pion identification efficiency is improved from 72% to 91% for a fixed muon misidentification probability of 10 -5 .

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2020

Deep learning to generate in silico chemical property libraries and candidate molecules for small molecule identification in complex samples

Comprehensive and unambiguous identification of small molecules in complex samples will revolutionize our understanding of the role of metabolites in biological systems. Existing and emerging technologies have enabled measurement of chemical properties of molecules in complex mixtures and, in concert, are sensitive enough to resolve even stereoisomers. Despite these experimental advances, small molecule identification is inhibited by (i) chemical reference libraries (e.g. mass spectra, collision cross section, and other measurable property libraries) representing <1% of known molecules, limiting the number of possible identifications, and (ii) the lack of a method to generate candidate matches directly from experimental features (i.e. without a library). To this end, we developed a variational autoencoder (VAE) to learn a continuous numerical, or latent, representation of molecular structure to expand reference libraries for small molecule identification. We extended the VAE to include a chemical property decoder, trained as a multitask network, in order to shape the latent representation such that it assembles according to desired chemical properties. The approach is unique in its application to metabolomics and small molecule identification, with its focus on properties that can be obtained from experimental measurements (m/z, CCS) paired with its training paradigm, which involved a cascade of transfer learning iterations. First, molecular representation is learned from a large dataset of structures with m/z labels. Next, in silico property values are used to continue training, as experimental property data is limited. Finally, the network is further refined by being trained with the experimental data. This allows the network to learn as much as possible at each stage, enabling success with progressively smaller datasets without overfitting. Once trained, the network can be used to predict chemical properties directly from structure, as well as generate candidate structures with desired chemical properties. Our approach is orders of magnitude faster than first-principles simulation for CCS property prediction. Additionally, the ability to generate novel molecules along manifolds, defined by chemical property analogues, positions DarkChem as highly useful in a number of application areas, including metabolomics and small molecule identification, drug discovery and design, chemical forensics, and beyond.

Colby, Sean M.↗

DOE OSTI2018

Proteomics Goes to Court: A Statistical Foundation for Forensic Toxin/Organism Identification Using Bottom-Up Proteomics

Bottom-up proteomics is increasingly being used to characterize unknown environmental, clinical, and forensic samples. Proteomics-based bacterial identification typically proceeds by tabulating peptide “hits” (i.e. confidently identified peptides) associated with the organisms in a database; those organisms with enough hits are declared present in the sample. This approach has proven successful in laboratory studies, however, important research gaps remain. First, the common-practice reliance on unique peptides for identification is susceptible to a phenomenon known as signal erosion. Second, no general guidelines are available for determining how many hits are needed to make a confident identification. These gaps inhibit the transition of this approach to real-world samples where conditions vary and large databases may be needed. We propose statistical criteria for organism identification that applies regardless of sample quality or data analysis pipeline. These criteria are straightforward, producing a p-value on the result. We test the criteria on LC-MS/MS datasets representing 919 different bacterial and toxin datasets acquired with multiple data collection platforms. Results reveal a >95% correct species-level identification rate, demonstrating the effectiveness and robustness of proteomics-based organism/toxin identification.

Jarman, Kristin H.↗

DOE OSTI2019

A five-level classification system for proteoform identifications

The term proteoform, introduced in these pages in 2013, has rapidly gained acceptance in the proteomics community. The challenge and importance of comprehensively identifying proteoforms in complex samples has been recognized, and reports have begun to appear of new platforms towards that end. However, one interesting central ambiguity has emerged, namely determining precisely what is meant by a “proteoform identification”. At present, the only practical approaches for establishing the exact primary structure of a proteoform employ mass spectrometry (MS), and there is a wide range of MS results that provide “proteoform identifications”. This seemingly small matter has significant impact, as the ambiguity in knowing what is meant by an “identification” makes it difficult to compare results from different laboratories and approaches. This situation hinders the ability of the community to evaluate technological progress and to efficiently expand biological knowledge. To address this issue, we propose a five-level system for classifying proteoform identifications. Here, the classification scheme stems directly from a consideration of the four types of possible ambiguity possible for a proteoform identification, ranging from the most subtle (i.e., precise localization of a post-translational modification, or PTM) to the most dramatic (i.e., ambiguity in the gene of origin). The five classes then correspond to the level of ambiguity present in the identification, ranging from no ambiguity at all (Level 1), to ambiguity of all four types (Level 5). Details of the scheme are provided in Table 1 and Supplementary Table 1, with specific use cases and examples provided in Supplementary Figure 1.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

CFM-ID 4.0 – a web server for accurate MS-based metabolite identification

The CFM-ID 4.0 web server (https://cfmid.wishartlab.com) is an online tool for predicting, annotating and interpreting tandem mass (MS/MS) spectra of small molecules. It is specifically designed to assist researchers pursuing studies in metabolomics, exposomics and analytical chemistry. More specifically, CFM-ID 4.0 supports the: 1) prediction of electrospray ionization quadrupole time-of-flight tandem mass spectra (ESI-QTOF-MS/MS) for small molecules over multiple collision energies (10 eV, 20 eV, and 40 eV); 2) annotation of ESI-QTOF-MS/MS spectra given the structure of the compound; and 3) identification of a small molecule that generated a given ESI-QTOF-MS/MS spectrum at one or more collision energies. The CFM-ID 4.0 web server makes use of a substantially improved MS fragmentation algorithm, a much larger database of experimental and in silico predicted MS/MS spectra and improved scoring methods to offer more accurate MS/MS spectral prediction and MS/MS-based compound identification. Compared to earlier versions of CFM-ID, this new version has an MS/MS spectral prediction performance that is ~22% better and a compound identification accuracy that is ~35% better on a standard (CASMI 2016) testing dataset. CFM-ID 4.0 also features a neutral loss function that allows users to identify similar or substituent compounds where no match can be found using CFM-ID’s regular MS/MS-to-compound identification utility. Finally, the CFM-ID 4.0 web server now offers a much more refined user interface that is easier to use, supports molecular formula identification (from MS/MS data), provides more interactively viewable data (including proposed fragment ion structures) and displays MS mirror plots for comparing predicted with observed MS/MS spectra. These improvements should make CFM-ID 4.0 much more useful to the community and should make small molecule identification much easier, faster, and more accurate.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

Artificial illumination identification from an unmanned aerial vehicle

Artificial illumination identification within images is a useful tool for many applications. Performing such identification allows for an estimation of the illumination source spectrum, which in turn can be used for additional applications ranging from spectral detection and exploitation to statistics about nighttime light usage. Illumination identification has been performed in laboratory settings but not from an unmanned aerial vehicle (UAV) platform. Here, we test the feasibility of using a UAV and commercial off-the-shelf multispectral imaging sensor to perform such artificial illumination identification through linear discriminant analysis using nighttime UAV images. The results are very promising, showing source classification accuracies of 83.3%, 92.3%, 100%, and 100% for the incandescent, light-emitting diode, high pressure sodium, and metal halide illumination sources, respectively. We show that the information gained from the source identification can be further used to inform additional analysis, such as spectral identification. The high resolution of UAV imaging techniques combined with the knowledge of the illumination source can lead to better exploitation of such nighttime data for many applications.

42 ENGINEERING↗

DOE OSTI2019

Assessment of BOLD and GenBank – Their accuracy and reliability for the identification of biological materials

Taxonomic identification of biological materials can be achieved through DNA barcoding, where an unknown “barcode” sequence is compared to a reference database. In many disciplines, obtaining accurate taxonomic identifications can be imperative (e.g., evolutionary biology, food regulatory compliance, forensics). The Barcode of Life DataSystems (BOLD) and GenBank are the main public repositories of DNA barcode sequences. In this study, an assessment of the accuracy and reliability of sequences in these databases was performed. To achieve this, 1) curated reference materials for plants, macro-fungi and insects were obtained from national collections, 2) relevant barcode sequences (rbcL, matK, trnH-psbA, ITS and COI) from these reference samples were generated and used for searching against both databases, and 3) optimal search parameters were determined that ensure the best match to the known species in either database. While GenBank outperformed BOLD for species-level identification of insect taxa (53% and 35%, respectively), both databases performed comparably for plants and macro-fungi (~81% and ~57%, respectively). Results illustrated that using a multi-locus barcode approach increased identification success. This study outlines the utility of the BLAST search tool in GenBank and the BOLD identification engine for taxonomic identifications and identifies some precautions needed when using public sequence repositories in applied scientific disciplines.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2019

Deep learning to generate in silico chemical property libraries and candidate molecules for small molecule identification in complex samples

Colby, Sean M↗

NASA NTRS1987

Systematic identification of IRAS point sources

A large scale program was initiated to identify IRAS point sources. At ROE the ideal facilities are at hand to undertake such a large program, viz. the rapid scanning capabilities of the COSMOS measuring machine to exploit the depth and resolution of the U.K. Schmidt Telescope J survey plates. Sources in 44 Schmidt plate areas were identified including 1300 sources and covering 1100 square degrees. The identification comprise 700 galaxy identifications and 600 stellar identifications. There are also about 40 sources with no obvious identification but which can be most easily explained by cirrus, confusion between two sources or sources just outside the 2 sigma error box. A major aim with the galaxy identification is to provide a data base from which sound statistical analyses can be made. Accurate blue magnitudes and morphological classifications for each identification were produced.

Savage, A.↗

NASA NTRS2003

Star Identification Using a Triplet Algorithm

The stars observed by onboard star trackers must be identified in order for the star tracker information to be used for attitude determination. Star identification associates the observed stars in the body frame with catalog stars, the positions of which in an inertial frame are well known. If the attitude is approximately known (from less accurate sensors) identification is relatively simple, but if there is no prior attitude knowledge (the Lost in Space problem) star identification is quite difficult. A new, reliable, and accurate star identification algorithm has been developed for use in a batch, ground-based attitude determination system. The identification algorithm uses input from one or more star trackers, each of which can observe three or more stars simultaneously. If observations are available at different times, rate data is used to relate them. The algorithm includes the following steps that are automatically performed: 1) Observations are examined to determine the optimal set of simultaneous star observations in a single tracker. 2) The selected optimal observations are identified using a triplet match. 3) The identified stars in the selected tracker at the selected time are used to compute an attitude at that time. 4) The rate data is refined by determination of biases that minimize the dispersion of repeated observations of the same stars. 5) The refined rate data is used to propagate the attitude to the times of each star observation. 6) The attitude at each time is used to associate all stars observed at that time, in each tracker, with catalog stars. The overall algorithm is very dependable at producing accurate star identification with no initial attitude input.

Hashmall, Joseph A.↗

NASA NTRS1998

Word-identification priming for ignored and attended words

Three experiments examined contributions of study phase awareness of word identity to subsequent word-identification priming by manipulating visual attention to words at study. In Experiment 1, word-identification priming was reduced for ignored relative to attended words, even though ignored words were identified sufficiently to produce negative priming in the study phase. Word-identification priming was also reduced after color naming relative to emotional valence rating (Experiment 2) or word reading (Experiment 3), even though an effect of emotional valence upon color naming (Experiment 2) indicated that words were identified at study. Thus, word-identification priming was reduced even when word identification occurred at study. Word-identification priming may depend on awareness of word identity at the time of study.

Mental Recall↗