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At least 1,495 records · Page 83
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

LIQUID: an-open source software for identifying lipids in LC-MS/MS-based lipidomics data

We introduce an open-source software, LIQUID, for semi-automated processing and visualization of LC-MS/MS based lipidomics data. LIQUID provides users with the capability to process high throughput data and contains a customizable target library and scoring model per project needs. The graphical user interface provides visualization of multiple lines of spectral evidence for each lipid identification, allowing rapid examination of data for making confident identifications of lipid molecular species.

Environmental Molecular Sciences Laboratory↗
DOE OSTI2023

Identifying strengths and weaknesses of methods for computational network inference from single-cell RNA-seq data

Single-cell RNA-sequencing (scRNA-seq) offers unparalleled insight into the transcriptional programs of different cellular states by measuring the transcriptome of thousands of individual cells. An emerging problem in the analysis of scRNA-seq is the inference of transcriptional gene regulatory networks and a number of methods with different learning frameworks have been developed to address this problem. Here, we present an expanded benchmarking study of eleven recent network inference methods on seven published scRNA-seq datasets in human, mouse, and yeast considering different types of gold standard networks and evaluation metrics. We evaluate methods based on their computing requirements as well as on their ability to recover the network structure. We find that, while most methods have a modest recovery of experimentally derived interactions based on global metrics such as Area Under the Precision Recall curve, methods are able to capture targets of regulators that are relevant to the system under study. Among the top performing methods that use only expression were SCENIC, PIDC, MERLIN or Correlation. Addition of prior biological knowledge and the estimation of transcription factor activities resulted in the best overall performance with the Inferelator and MERLIN methods that use prior knowledge outperforming methods that use expression alone. We found that imputation for network inference did not improve network inference accuracy and could be detrimental. Comparisons of inferred networks for comparable bulk conditions showed that the networks inferred from scRNA-seq datasets are often better or at par with the networks inferred from bulk datasets. Our analysis should be beneficial in selecting methods for network inference. At the same time, this highlights the need for improved methods and better gold standards for regulatory network inference from scRNAseq datasets.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Identifying Sample Provenance From SEM/EDS Automated Particle Analysis via Few-Shot Learning Coupled With Similarity Graph Clustering

Automated particle analysis (APA) provides a vast amount of compositional data via energy-dispersive X-ray spectroscopy along with size and shape data via scanning electron microscopy for individual particles in a sample. In many instances, APA data are leveraged to support identification of the source of a sample based on the detection of particles of a specific composition. Often, the particles that provide context make up a minuscule portion of the sample. Additionally, the interpretation of complex samples can be difficult due to the diversity of compositions both in the mixture and within a particle. In this work, we demonstrate a method to compute and cluster similarity graphs that describe inter-particle relationships within a sample using a multi-modal few-shot learning neural network. Here, as a proof-of-concept, we show that samples known to have been exposed to gunshot residue can be distinguished from samples occasionally mistaken for gunshot residue. Our workflow builds upon standard APA techniques and data processing methods to unveil additional information in a readily interpretable and quantitatively comparable format.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

A GALAH view of the chemical homogeneity and ages of stellar strings identified in Gaia

ABSTRACT The advent of Gaia has led to the discovery of nearly 300 elongated stellar associations (called ‘strings’) spanning hundreds of parsecs in length and mere tens of parsecs in width. These newfound populations present an excellent laboratory for studying the assembly process of the Milky Way thin disc. In this work, we use data from GALAH DR3 to investigate the chemical distributions and ages of 18 newfound stellar populations, 10 of which are strings and 8 of which are compact in morphology. We estimate the intrinsic abundance dispersions in [X/H] of each population and compare them with those of both their local fields and the open cluster (OC) M 67. We find that all but one of these groups are more chemically homogeneous than their local fields. Furthermore, half of the strings, namely Theias 139, 169, 216, 303, and 309, have intrinsic [X/H] dispersions that range between 0.01 and 0.07 dex in most elements, equivalent to those of many OCs. These results provide important new observational constraints on star formation and the chemical homogeneity of the local interstellar medium (ISM). We investigate each population’s Li and chemical clock abundances (e.g. [Sc/Ba], [Ca/Ba], [Ti/Ba], and [Mg/Y]) and find that the ages suggested by chemistry generally support the isochronal ages in all but six structures. This work highlights the unique advantages that chemistry holds in the study of kinematically related stellar groups.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Identifying and characterizing ultracool dwarfs ejected from post-encounter disintegrating systems

ABSTRACT Disintegrating multiple systems have been previously discovered from kinematic studies of the Hipparcos catalogue. They are presumably the result of dynamical encounters taking place in the Galactic disc between single/multiple systems. In this paper, we aim to expand the search for such systems, to study their properties, as well as to characterize possible low-mass ejecta (i.e. brown dwarfs and planets). We have assembled a list of 15 candidate systems using astrometry from the Tycho-Gaia astrometric solution (later upgraded with Gaia DR3), and here we present the discovery and follow-up of five of them. We have obtained DECam imaging for all five systems and by combining near-infrared photometry and proper motion, we searched for ultracool ejected components. We find that the system consisting of TYC 7731-1951-1, TYC 7731-2128 AB, and TYC 7731-1995-1ABC?, contains one very promising ultracool dwarf candidate. Using additional data from the literature, we have found that three out of five disintegrating system candidates are likely to be true disintegrating systems.

Astronomy & Astrophysics↗
DOE OSTI2022

On the parameter combinations that matter and on those that do not: data-driven studies of parameter (non)identifiability

Abstract We present a data-driven approach to characterizing nonidentifiability of a model’s parameters and illustrate it through dynamic as well as steady kinetic models. By employing Diffusion Maps and their extensions, we discover the minimal combinations of parameters required to characterize the output behavior of a chemical system: a set of effective parameters for the model. Furthermore, we introduce and use a Conformal Autoencoder Neural Network technique, as well as a kernel-based Jointly Smooth Function technique, to disentangle the redundant parameter combinations that do not affect the output behavior from the ones that do. We discuss the interpretability of our data-driven effective parameters, and demonstrate the utility of the approach both for behavior prediction and parameter estimation. In the latter task, it becomes important to describe level sets in parameter space that are consistent with a particular output behavior. We validate our approach on a model of multisite phosphorylation, where a reduced set of effective parameters (nonlinear combinations of the physical ones) has previously been established analytically.

Evangelou, Nikolaos↗
DOE OSTI2024

An integrated experimental–modeling approach to identify key processes for carbon mineralization in fractured mafic and ultramafic rocks

Abstract Controlling atmospheric warming requires immediate reduction of carbon dioxide (CO2) emissions, as well as the active removal and sequestration of CO2 from current point sources. One promising proposed strategy to reduce atmospheric CO2 levels is geologic carbon sequestration (GCS), where CO2 is injected into the subsurface and reacts with the formation to precipitate carbonate minerals. Rapid mineralization has recently been reported for field tests in mafic and ultramafic rocks. However, unlike saline aquifers and depleted oil and gas reservoirs historically considered for GCS, these formations can have extremely low porosities and permeabilities, limiting storage volumes and reactive mineral surfaces to the preexisting fracture network. As a result, coupling between geochemical interactions and the fracture network evolution is a critical component of long-term, sustainable carbon storage. In this paper, we summarize recent advances in integrating experimental and modeling approaches to determine the first-order processes for carbon mineralization in a fractured mafic/ultramafic rock system. We observe the critical role of fracture aperture, flow, and surface characteristics in controlling the quantity, identity, and morphology of secondary precipitates and present where the influence of these factors can be reflected in newly developed thermo-hydro-mechanical–chemical models. Our findings provide a roadmap for future work on carbon mineralization, as we present the most important system components and key challenges that we are overcoming to enable GCS in mafic and ultramafic rocks.

58 GEOSCIENCES↗
DOE OSTI2025

Identifying impacts of contact tracing on HIV epidemiological inference from phylogenetic data

Abstract Robust sampling methods are foundational to inferences using phylogenies. Yet the impact of using contact tracing, a type of non-uniform sampling used in public health applications such as infectious disease outbreak investigations, has not been investigated in the molecular epidemiology field. To understand how contact tracing influences a recovered phylogeny, we developed a new simulation tool called SEEPS (Sequence Evolution and Epidemiological Process Simulator) that allows for the simulation of contact tracing and the resulting transmission tree, pathogen phylogeny, and corresponding virus genetic sequences. Importantly, SEEPS takes within-host evolution into account when generating pathogen phylogenies and sequences from transmission histories. Using SEEPS, we demonstrate that contact tracing can significantly impact the structure of the resulting tree, as described by popular tree statistics. Contact tracing generates phylogenies that are less balanced than the underlying transmission process, less representative of the larger epidemiological process, and affects the internal/external branch length ratios that characterize specific epidemiological scenarios. We also examined real data from a 2007–2008 Swedish HIV-1 outbreak and the broader 1998–2010 European HIV-1 epidemic to highlight the differences in contact tracing and expected phylogenies. Aided by SEEPS, we show that the data collection of the Swedish outbreak was strongly influenced by contact tracing even after downsampling, while the broader European Union epidemic showed little evidence of universal contact tracing, agreeing with the known epidemiological information about sampling and spread. Overall, our results highlight the importance of including possible non-uniform sampling schemes when examining phylogenetic trees. For that, SEEPS serves as a useful tool to evaluate such impacts, thereby facilitating better phylogenetic inferences of the characteristics of a disease outbreak. SEEPS is available at https://github.com/MolEvolEpid/SEEPS.

Virology↗
DOE OSTI2023

Genomic analysis of 1710 surveillance-based Neisseria gonorrhoeae isolates from the USA in 2019 identifies predominant strain types and chromosomal antimicrobial-resistance determinants

This study characterized high-quality whole-genome sequences of a sentinel, surveillance-based collection of 1710 Neisseria gonorrhoeae (GC) isolates from 2019 collected in the USA as part of the Gonococcal Isolate Surveillance Project (GISP). It aims to provide a detailed report of strain diversity, phylogenetic relationships and resistance determinant profiles associated with reduced susceptibilities to antibiotics of concern. The 1710 isolates represented 164 multilocus sequence types and 21 predominant phylogenetic clades. Common genomic determinants defined most strains’ phenotypic, reduced susceptibility to current and historic antibiotics (e.g. bla TEM plasmid for penicillin, tetM plasmid for tetracycline, gyrA for ciprofloxacin, 23S rRNA and/or mosaic mtr operon for azithromycin, and mosaic penA for cefixime and ceftriaxone). The most predominant phylogenetic clade accounted for 21 % of the isolates, included a majority of the isolates with low-level elevated MICs to azithromycin (2.0 µg ml –1 ), carried a mosaic mtr operon and variants in PorB, and showed expansion with respect to data previously reported from 2018. The second largest clade predominantly carried the GyrA S91F variant, was largely ciprofloxacin resistant (MIC ≥1.0 µg ml –1 ), and showed significant expansion with respect to 2018. Overall, a low proportion of isolates had medium- to high-level elevated MIC to azithromycin ((≥4.0 µg ml –1 ), based on C2611T or A2059G 23S rRNA variants). One isolate carried the penA 60.001 allele resulting in elevated MICs to cefixime and ceftriaxone of 1.0 µg ml –1 . This high-resolution snapshot of genetic profiles of 1710 GC sequences, through a comparison with 2018 data (1479 GC sequences) within the sentinel system, highlights change in proportions and expansion of select GC strains and the associated genetic mechanisms of resistance. The knowledge gained through molecular surveillance may support rapid identification of outbreaks of concern. Continued monitoring may inform public health responses to limit the development and spread of antibiotic-resistant gonorrhoea.

(3-6) antimicrobial resistance↗
DOE OSTI2026

Identified charged hadron production in Au+Au collisions at $\sqrt{s_{NN}}$ = 54.4 GeV with the STAR detector

Here, we present results on the production of 𝜋±, 𝐾±, 𝑝, and $\bar{𝑝}$ in Au+Au collisions at $\sqrt{𝑠_{N⁢N}}$ = 54.4~GeV using the STAR detector at RHIC, at mid-rapidity (|𝑦|< 0.1). Invariant yields of these particles as a function of transverse momentum are shown. We determine bulk properties such as integrated particle yields (𝑑 ⁢𝑁 /𝑑 ⁢𝑦 ), mean transverse momentum (⟨𝑝 𝑇 ⟩), particle ratios, which provide insight into the particle production mechanisms. Additionally, the kinetic freeze-out parameters (𝑇 kin and ⟨𝛽 𝑇 ⟩), which provide information about the dynamics of the system at the time of freeze-out, are obtained. The Bjorken energy density (𝜖 𝐵⁢𝐽 ), which gives an estimate of the energy density in the central rapidity region of the collision zone at the formation time 𝜏, is calculated and presented as a function of multiplicity for various energies. The results are compared with those from the models such as A Multi-Phase Transport (AMPT) and Heavy Ion Jet INteraction Generator (HIJING) for further insights.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2019

Ultrafast Rydberg-state dissociation in oxygen: Identifying the role of multielectron excitations

We investigated the fragmentation dynamics of highly excited states of molecular oxygen using femtosecond transient photoelectron spectroscopy. An extreme ultraviolet (XUV) pulse populates the autoionizing Rydberg series converging to O$_2^+$c 4 Σ$_u^-$, and a femtosecond near-infrared (IR) pulse was used to photoionize these states as they dissociate. Monitoring the differential photoelectron spectra as a function of time delay allowed us to obtain the relaxation lifetimes of these Rydberg states. We observed a photoelectron signal corresponding to the formation of a 4 p excited atomic oxygen fragment, which is not an expected dissociation product of the (O$_2^+$c 4 Σ$_u^-$) nl σ g Rydberg series. Analysis of the time-dependent photoelectron spectra and photoionization calculations indicate that this fragment results from a previously unexplored (O$_2^+$ 4 II$_g$)4 p repulsive state and that, contrary to expectations, this multielectron excitation pathway presents a substantial cross section. Here, our study demonstrates that two-color time-resolved differential photoelectron spectroscopy is an excellent tool to study the fragmentation dynamics of such multielectron excited states, which are not easily probed by other means.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2019

Local magnetic cluster size identified by neutron total scattering in the site-diluted spin glass S n x F e 4 - x N ( x = 0.88 )

A detailed structure analysis for the site-diluted Sn x Fe 4- x N ( x = 0.25, 0.41, and 0.88) has been carried out through complex modeling of the neutron total scattering data. We present quantitative evidence showing the local ferromagnetic cluster size extending to ~8 Å on average when Sn 0.88 Fe 3.12 N undergoes the spin-glass transition (the other two not showing such a transition). In conclusion, the modeling methodology used in this work involving the corefinement of the nuclear and magnetic structure in both real and reciprocal space can potentially be applied generally to explore a variety of spin-glass material problems.

36 MATERIALS SCIENCE↗