Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “data analysis cluster”

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

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 559 records · Page 31

Cleaning Images with Gaussian Process Regression

Many approaches to astronomical data reduction and analysis cannot tolerate missing data: corrupted pixels must first have their values imputed. This paper presents astrofix, a robust and flexible image imputation algorithm based on Gaussian process regression. Through an optimization process, astrofix chooses and applies a different interpolation kernel to each image, using a training set extracted automatically from that image. It naturally handles clusters of bad pixels and image edges and adapts to various instruments and image types. For bright pixels, the mean absolute error of astrofix is several times smaller than that of median replacement and interpolation by a Gaussian kernel. We demonstrate good performance on both imaging and spectroscopic data, including the SBIG 6303 0.4 m telescope and the FLOYDS spectrograph of Las Cumbres Observatory and the CHARIS integral-field spectrograph on the Subaru Telescope.

42 ENGINEERING↗

Search for Spatial Correlations of Neutrinos with Ultra-high-energy Cosmic Rays

For several decades, the origin of ultra-high-energy cosmic rays (UHECRs) has been an unsolved question of high-energy astrophysics. One approach for solving this puzzle is to correlate UHECRs with high-energy neutrinos, since neutrinos are a direct probe of hadronic interactions of cosmic rays and are not deflected by magnetic fields. In this paper, we present three different approaches for correlating the arrival directions of neutrinos with the arrival directions of UHECRs. The neutrino data are provided by the IceCube Neutrino Observatory and ANTARES, while the UHECR data with energies above ∼50 EeV are provided by the Pierre Auger Observatory and the Telescope Array. All experiments provide increased statistics and improved reconstructions with respect to our previous results reported in 2015. The first analysis uses a high-statistics neutrino sample optimized for point-source searches to search for excesses of neutrino clustering in the vicinity of UHECR directions. The second analysis searches for an excess of UHECRs in the direction of the highest-energy neutrinos. The third analysis searches for an excess of pairs of UHECRs and highest-energy neutrinos on different angular scales. None of the analyses have found a significant excess, and previously reported overfluctuations are reduced in significance. Based on these results, we further constrain the neutrino flux spatially correlated with UHECRs.

79 ASTRONOMY AND ASTROPHYSICS↗

Reconstructing the Stripping History of the Sagittarius Stream with Neural Networks

The Sagittarius (Sgr) Stream is produced by the ongoing disruption of the Sgr dwarf spheroidal (dSph) galaxy and is thought to contain multiple wraps that were stripped during different pericentric passages. In this study, we introduce a neural-network–based method trained on N-body simulations to infer the stripping time of Sgr Stream stars directly from their phase-space coordinates. We combine spectroscopic data from SEGUE, APOGEE DR17, and LAMOST DR7 low-resolution spectroscopic (LRS) survey with Gaia EDR3 astrometry and distance estimates from the latest StarHorse catalog to identify high-quality Sgr Stream members. Applying our method to these stars, we measure a clear metallicity gradient with stripping time, well described by a linear relation with slope ∼0.3 dex Gyr −1 . We further predict the stripping times of globular clusters previously suggested to originate from the Sgr dSph. M 54, Terzan 7, Terzan 8, and Arp 2 exhibit stripping times consistent with being currently bound to the Sgr remnant. Pal 12, Whiting 1, and NGC 2419 are inferred to have been stripped 0.9 ± 0.1, 1.1 ± 0.2, and 2.1 ± 0.2 Gyr ago, respectively. For NGC 4147 and NGC 5634, whose membership in the Sgr system remains uncertain, our analysis suggests stripping times of 1.1 ± 0.4 and 1.1 ± 0.1 Gyr, respectively, if they are ultimately confirmed as genuine Sgr members. These results demonstrate that data-driven models of dynamical stripping histories offer a promising approach for reconstructing the formation and chemical evolution of the Sgr Stream.

79 ASTRONOMY AND ASTROPHYSICS↗

59 Co(p,X) spallation reaction cross sections for 250 MeV to 2 GeV protons

Cobalt is an advantageous target for probing the physics of nuclear spallation, because it is a naturally mono-isotopic element ( 59 Co), and its per-nucleon binding energy (BE/A = 8.768 MeV) is near the maximum value for all nuclei. We measured nuclear spallation cross sections for the 59 Co(p,X) reaction at five kinetic energies ranging from 250 MeV to 2 GeV. Cross sections for the production of 58 Co, 57 Co, 57 Mn, 56 Co, 56 Mn, 56 Cr, 55 Fe, 53 Fe, 54 Mn, 52 Mn, 51 Cr, 49 Cr, 48 V, 47 Sc, 46 Sc, 44 Sc, and 44 Scm are reported. Where comparable data exist in the EXFOR reaction database, we find that our measured cross sections generally agree. In many cases, we provide data for reactions or energies not currently reported in EXFOR. Our cross sections also provide evidence for the presence of α-clusters within the 59 Co nucleus, a surprising result given the asymmetry in Z (27) and N (32) for this nucleus. Finally, we use our measurements to evaluate the accuracy of spallation cross section simulations from GEANT4-based radiation transport toolkit, performed with the INCLXX-, Bertini-, and Binary-ion-cascade (BIC) based physics lists. This benchmarking activity revealed that the simulations overestimated the cross sections by a factor of ∼2–4 on average, and that the INCL-XX physics list provides the most reliable results. This evaluation informs the selection of the GEANT4 physics lists used for the analysis of data from NASA’s Psyche mission, which will measure γ rays and neutrons resulting from spallation reactions occurring on the surface of an asteroid whose surface is thought to be rich in iron-nickel metal.

43 PARTICLE ACCELERATORS↗

Correlating and Simulating Socio-Demographically Driven Residential End-Use Activity Schedules

Incorporating socio-demographic and behavioral considerations into decision-support tools is crucial for identifying gaps and addressing consumer needs to ensure reliable and affordable energy solutions. In energy simulation models, the correlation between socio-demographics and time-use behavior is not well-captured. Thus, we developed a large-scale simulation workflow to generate schedules for 10 residential activities across 24 population segments defined by age, income, and employment status. Using pre-pandemic 2015-2019 American Time Use Survey (ATUS) data, we used ANOVA to confirm the correlation between demographic factors and time use. We explored three k-modes clustering methods-backward, forward, and a new hybrid approach-to delineate the occupancy patterns based on demographics. Using the probability of cluster membership for each population segment and a time inhomogeneous Markov chain to generate activity transition probabilities for each cluster, we simulated 50,000 schedules per segment and validated them against the ATUS data. The hybrid method produced the most socio-demographically differentiated clusters while demonstrating comparable performance to other approaches, with an overall root mean square error of 0.12 for both weekday and weekend schedules. Thus, the hybrid method, where each cluster is dominated by certain demographic segments and occupancy patterns, offers more modeling versatility in terms of scenario analysis. The new workflow improves the socio demographic differentiation of energy consumption by considering differences in time use. This approach enables future research on demographically segmented time of use (TOU) energy consumption, including impacts of TOU utility bills and rate analysis, long-run marginal emissions, and energy retrofits.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Photo-z outlier self-calibration in weak lensing surveys

Calibrating photometric redshift errors in weak lensing surveys with external data is extremely challenging. In this work, we show that both Gaussian and outlier photo-z parameters can be self-calibrated from the data alone. This comes at no cost for the neutrino masses, curvature and dark energy equation of state w0, but with a 65% degradation when both w0 and wa are varied. We perform a realistic forecast for the Vera Rubin Observatory (VRO) Legacy Survey of Space and Time (LSST) 3× 2 analysis, combining cosmic shear, projected galaxy clustering and galaxy - galaxy lensing. We confirm the importance of marginalizing over photo-z outliers. We examine a subset of internal cross-correlations, dubbed "null correlations", which are usually ignored in 3× 2 analyses. Despite contributing only ~ 10% of the total signal-to-noise, these null correlations improve the constraints on photo-z parameters by up to an order of magnitude. Using the same galaxy sample as sources and lenses dramatically improves the photo-z uncertainties too. Together, these methods add robustness to any claim of detected new Physics, and reduce the statistical errors on cosmology by 15% and 10% respectively. Finally, including CMB lensing from an experiment like Simons Observatory or CMB-S4 improves the cosmological and photo-z posterior constraints by about 10%, and further improves the robustness to systematics. To give intuition on the Fisher forecasts, we examine in detail several toy models that explain the origin of the photo-z self-calibration. Our Fisher code LaSSI (Large-Scale Structure Information), which includes the effect of Gaussian and outlier photo-z, shear multiplicative bias, linear galaxy bias, and extensions to LCDM, is publicly available at \href{https://github.com/EmmanuelSchaan/LaSSI}{https://github.com/EmmanuelSchaan/LaSSI}.

79 ASTRONOMY AND ASTROPHYSICS↗

Genomics and physiology of Catenibacillus, human gut bacteria capable of polyphenol C-deglycosylation and flavonoid degradation

The genusCatenibacillus(familyLachnospiraceae, phylumBacillota) includes only one cultivated species so far,Catenibacillus scindens,isolated from human faeces and capable of deglycosylating dietary polyphenols and degrading flavonoid aglycones. Another human intestinalCatenibacillusstrain not taxonomically resolved at that time was recently genome-sequenced. We analysed the genome of this novel isolate, designatedCatenibacillus decagia, and showed its ability to deglycosylateC-coupled flavone and xanthone glucosides andO-coupled flavonoid glycosides. Most of the resulting aglycones were further degraded to the corresponding phenolic acids. Including the recently sequenced genome ofC. scindensand ten faecal metagenome-assembled genomes assigned to the genusCatenibacillus, we performed a comparative genome analysis and searched for genes encoding potentialC-glycosidases and other polyphenol-converting enzymes. According to genome data and physiological characterization, the core metabolism ofCatenibacillusstrains is based on a fermentative lifestyle with butyrate production and hydrogen evolution. BothC. scindensandC. decagiaencode a flavonoidO-glycosidase, a flavone reductase, a flavanone/flavanonol-cleaving reductase and a phloretin hydrolase. Several gene clusters encode enzymes similar to those of the flavonoidC-deglycosylation system ofDoreastrain PUE (DgpBC), while separately located genes encode putative polyphenol-glucoside oxidases (DgpA) required forC-deglycosylation. The diversity ofdgpAanddgpBCgene clusters might explain the broadC-glycoside substrate spectrum ofC. scindensandC. decagia. The otherCatenibacillusgenomes encode only a few potential flavonoid-converting enzymes. Our results indicate that severalCatenibacillusspecies are well-equipped to deglycosylate and degrade dietary plant polyphenols and might inhabit a corresponding, specific niche in the gut.

Genetics & Heredity↗

Hydrazinoacetic acid is a biosynthetic precursor of the bacterially produced nitramine, N -nitroglycine

Nitramines [R(R′)N–NO 2 ; R,R′=H or alkyl] are valuable synthetic products, but knowledge of the biosynthetic processes that generate these compounds is limited. This work sought to elucidate the biosynthesis of a nitramine natural product, N-nitroglycine (NNG) by Streptomyces noursei . Stable isotope studies showed that S. noursei cells supplemented with L-(ε- 15 N)lysine, ( 15 N)glycine, or ( 13 C)hydrazinoacetic acid (HAA) incorporated 67%, 88%, and 67% of the isotope label into NNG, respectively, indicating that these compounds are biosynthetic precursors of NNG. Liquid chromatography coupled tandem mass spectrometry (LC-MS/MS) of 15 N-Lys-labeled NNG confirmed that the nitro nitrogen of NNG originates from Lys. Bioinformatics analysis of the S. noursei genome showed evidence for a biosynthetic gene cluster (BGC) that contained machinery for HAA biosynthesis ( nngKLM ), consistent with the results of the isotope labeling. In vitro reconstitution of the gene products produced HAA. The borders of this BGC were defined by cross-referencing the predicted BGC with previously published differential proteomics data. Furthermore, we show that azaserine is produced alongside NNG in S. noursei cultures, linking the two biosynthetic pathways via a proposed nitrosamine biosynthetic intermediate. Finally, the oxygen balance for NNG is −20.2% for the formation of carbon dioxide (CO 2 ), which is comparable to that of hexahydro-1,3,5- trinitro-1,3,5-triazene (common name: RDX; −21.6%). Crystal structure data of NNG indicate that the unit crystalizes as a pure material, not a hydrate, suggesting a favorable energetic crystallization phase. The combined results suggest a route that, with further development, could lead to sustainable production of energetic nitramines via synthetic biology or biocatalytic approaches.

biosynthesis↗

Development of FRET clusters for CBRN Detection

Advanced sensor capabilities for the simultaneous on-site detection of specific chemical, biological, and radiological/nuclear (CBRN) threats is important to significantly limit the risk of exposure to personnel and allow the rapid collection of essential scientific data and critical evidence. Commercially available sensor capabilities are generally complex, and/or require highly specific and ultra-sensitive methods that are often power demanding or require offsite post-analysis for positive detection, leaving personnel vulnerable. The goal of this project is to develop a portable sensor capable of simultaneously detecting CBRN signatures using a multiplexed Förster Resonance Energy Transfer (FRET) based sensor. FRET sensors are tailorable, specific, and highly dependent on the donor-acceptor distance. In this project, nanoparticles were functionalized with aptamers that were designed for the detection of methylphosphonate, a sarin metabolite. FRET was measured between quantum dot donors and dye and metal nanoparticle acceptors using optical spectroscopy.

61 RADIATION PROTECTION AND DOSIMETRY↗

Modeling the Spectral Diversity of Quasars in the Sixteenth Data Release from the Sloan Digital Sky Survey

Abstract We present a new approach to capturing the broad diversity of emission-line and continuum properties in quasar spectra. We identify populations of spectrally similar quasars through pixel-level clustering on 12,968 high signal-to-noise ratio (S/N) spectra from the Sloan Digital Sky Survey (SDSS) in the redshift range of 1.57 < z < 2.4. Our clustering analysis finds 396 quasar spectra that are not assigned to any population, 15 misclassified spectra, and 6 quasars with incorrect redshifts. We compress the quasar populations into a library of 684 high-S/N composite spectra, anchored in redshift space by the Mg ii emission line. Principal component analysis on the library results in an eigenspectrum basis spanning 1067–4007 Å. We model independent samples of SDSS quasar spectra with the eigenbasis, allowing for a free redshift parameter. Our models achieve a median reduced χ 2 on non–broad absorption line quasar spectra that is reduced by 8.5% relative to models using the eigenspectra from the SDSS spectroscopic pipeline. A significant contribution to the relative improvement is from the ability to reconstruct the range of emission-line variation. The redshift estimates from our model are consistent with the Mg ii emission-line redshift with an average offset that displays 51.4% less redshift-dependent variation relative to the SDSS eigenspectra. Our method for developing quasar spectra models can improve automated classification and predict the intrinsic spectrum in regions affected by intervening absorbers such as Ly α , C iv , and Mg ii , thus benefiting studies of large-scale structure.

79 ASTRONOMY AND ASTROPHYSICS↗

Positron annihilation spectroscopy of defects in nuclear and irradiated materials- a review

Positron is the only probe that can detect individual atomic vacancies and small and large vacancy clusters induced by irradiation with remarkable sensitivity, providing information about their size, concentration, and chemical environment. The focus of this review article is to provide guidance to facilitate applications of positron annihilation spectroscopy (PAS) in irradiation-induced defect studies to advance the development of new radiation-tolerant materials. The principle of PAS, its techniques, and data analysis methods are described. PAS studies of defects in nuclear and irradiated materials are reviewed and discussed in depth. Future developments to advance PAS applications in nuclear materials research and studies of materials under extreme environments are presented.

Atomic scale defects↗

Heterointercalation in Chevrel-Phase Sulfides: A Model Periodic Solid for the Investigation of Chain Electron Transfer

Modulation of electron density localization on periodic crystal solids through electron transfer from interstitial cations can directly influence the bonding configurations of small-molecule intermediates at the catalyst binding site. This study presents the microwave-assisted solid-state synthesis of four heterointercalant Chevrel-phase (CP) sulfides with varying metal cation intercalants with compositional and electronic structure investigations of the electron density redistribution as a result of intercalation. The heterointercalant CP sulfides, with the general formula Cu x M y Mo 6 S 8 (where M = Cr, Mn, Fe, Ni; x, y = 1.5−2.5), are presented here for the probe reaction of electrochemical CO 2 reduction. A change in product selectivity is observed toward the production of methanol at low overpotentials of −0.5 V vs reversible hydrogen electrode (RHE), as a result of the intercalant combination present within the CP interstitial cavity. Structural confirmation of all materials was examined through Rietveld refinement of the powder X-ray diffraction (PXRD) data, high-resolution transmission electron microscopy (HR-TEM), and selected-area electron diffraction (SAED). Electron transfer from the intercalated metal cations to the Mo 6 S 8 cluster was investigated via X-ray photoelectron spectroscopy (XPS) of the intercalated metal cations and the chalcogenide cluster. Electron transfer was further confirmed through X-ray absorption analysis (XAS) of the K-edges of Mo and intercalants. Intermediate studies of electrochemical reduction of formaldehyde to methanol resulted in a faradaic efficiency of ∼78% methanol production on Cu x Ni y Mo 6 S 8 . The results presented herein identify distinct principles for materials design that can be utilized in other compositional spaces within the broad families of periodic crystal solids.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Assembly of small silica nanoparticles using lipid-tethered DNA ‘bonds’

Single-stranded DNA molecules modified with cholesterol functional groups are physically tethered to silica nanoparticles (diameter 25 nm) that are encapsulated in a lipid bilayer. Such tethering increases the azimuthal mobility of the DNA molecules across the nanoparticle surface and enables nonspecific bonding, eliminating the need for specialized surface chemistries (such as silane or thiol ligands). To induce assembly, double-stranded DNA ‘bridge’ molecules are then added with complementary nucleotides to the DNA ‘anchor’ molecules that are physically tethered to the lipids on the surface of the particles. Assembly is observed to occur at room temperature and without the need for temperature annealing. Using automated liquid handling tools, assemblies are created in high throughput and rapidly characterized using SAXS. It is determined that the relative concentration of DNA-to-silica and the ionic strength of the solution are important parameters that affect the resulting assembly. Analysis of SAXS data is performed using coarse-grained particle dynamics simulations. The results support the spontaneous formation of semi-crystalline particle assemblies by particle condensation, where the interparticle distance is tuned by the sequence of the DNA ‘bridge’ used to link the particles. Crystallinity analysis performed on the resulting simulations, optimized to match SAXS observations, suggest that particle clusters display increased crystallinity in the center of the clusters, but their maximum size remains relatively small (sub-micron) before settling occurs, which limits the extent of crystallization.

Chiang, Huat Thart [Univ. of Washington, Seattle, ↗

Nearest neighbour distributions: New statistical measures for cosmological clustering

ABSTRACT The use of summary statistics beyond the two-point correlation function to analyse the non-Gaussian clustering on small scales, and thereby, increasing the sensitivity to the underlying cosmological parameters, is an active field of research in cosmology. In this paper, we explore a set of new summary statistics – the k-Nearest Neighbour Cumulative Distribution Functions (kNN-CDF). This is the empirical cumulative distribution function of distances from a set of volume-filling, Poisson distributed random points to the k-nearest data points, and is sensitive to all connected N-point correlations in the data. The kNN-CDF can be used to measure counts in cell, void probability distributions, and higher N-point correlation functions, all using the same formalism exploiting fast searches with spatial tree data structures. We demonstrate how it can be computed efficiently from various data sets – both discrete points, and the generalization for continuous fields. We use data from a large suite of N-body simulations to explore the sensitivity of this new statistic to various cosmological parameters, compared to the two-point correlation function, while using the same range of scales. We demonstrate that the use of kNN-CDF improves the constraints on the cosmological parameters by more than a factor of 2 when applied to the clustering of dark matter in the range of scales between 10 and $40\, h^{-1}\, {\rm Mpc}$. We also show that relative improvement is even greater when applied on the same scales to the clustering of haloes in the simulations at a fixed number density, both in real space, as well as in redshift space. Since the kNN-CDF are sensitive to all higher order connected correlation functions in the data, the gains over traditional two-point analyses are expected to grow as progressively smaller scales are included in the analysis of cosmological data, provided the higher order correlation functions are sensitive to cosmology on the scales of interest.

79 ASTRONOMY AND ASTROPHYSICS↗

A Census of the 32 Ori Association with Gaia*

I have used high-precision photometry and astrometry from the third data release of Gaia (DR3) to identify candidate members of the 32 Ori association. Spectral types and radial velocities have been measured for subsets of the candidates using new and archival spectra. For the candidates that have radial velocity measurements, I have used UVW velocities to further constrain their membership, arriving at a final catalog of 169 candidates. I estimate that the completeness of the survey is ~90% for spectral types of ≲M7 (≳0.06 M ⊙ ). The histogram of spectral types for the 32 Ori candidates exhibits a maximum at M5 (~0.15 M ⊙ ), resembling the distributions measured for other young clusters and associations in the solar neighborhood. The available UVW velocities indicate that the association is expanding, but they do not produce a well-defined kinematic age. Based on their sequences of low-mass stars in color–magnitude diagrams, the 32 Ori association and Upper Centaurus-Lupus/Lower Centaurus-Crux (UCL/LCC) are coeval to within ±1.2 Myr, and they are younger than the β Pic moving group by ~3 Myr, which agrees with results from previous analysis based on the second data release of Gaia. Finally, I have used mid-IR photometry from the Wide-field Infrared Survey Explorer to check for excess emission from circumstellar disks among the 32 Ori candidates. Disks are detected for 18 candidates, half of which are reported for the first time in this work. The fraction of candidates at ≤M6 that have full, transitional, or evolved disks is 10/149 = 0.07$^{+0.03}_{-0.02'}$ which is consistent with the value for UCL/LCC.

79 ASTRONOMY AND ASTROPHYSICS↗

The growth of density perturbations in the last ~10 billion years from tomographic large-scale structure data

In order to investigate the origin of the ongoing tension between the amplitude of matter fluctuations measured by weak lensing experiments at low redshifts and the value inferred from the cosmic microwave background anisotropies, we reconstruct the evolution of this amplitude from z ~ 2 using existing large-scale structure data. To do so, we decouple the linear growth of density inhomogeneities from the background expansion, and constrain its redshift dependence making use of a combination of 6 different data sets, including cosmic shear, galaxy clustering and CMB lensing. We analyze these data under a consistent harmonic-space angular power spectrum-based pipeline. We show that current data constrain the amplitude of fluctuations mostly in the range 0.2 < z < 0.7, where it is lower than predicted by . This difference is mostly driven by current cosmic shear data, although the growth histories reconstructed from different data combinations are consistent with each other, and we find no evidence of systematic deviations in any particular experiment. In spite of the tension with , the data are well-described by the model, albeit with a lower value of S 8 ≡ σ 8 (Ω m /0.3) 0.5 . As part of our analysis, we find constraints on this parameter of S 8 = 0.7781 ± 0.0094 (68% confidence level), reaching almost percent-level errors comparable with CMB measurements, and 3.4σ away from the value found by .

79 ASTRONOMY AND ASTROPHYSICS↗

Investigating Covalent Bonding in f-elements using Gas-phase Ion Chemistry

Introduction In the reprocessing of f-elements present in used nuclear fuels, a variety of diglycolamides (DGA’s) are used as extractants for actinide partitioning. In particular, the Actinide-Lanthanide Separation (ALSEP) process typically utilizes either the N,N,N’,N’-tetraoctyl diglycolamide (TODGA) or N,N,N',N'-tetra-2-ethylhexyl diglycolamide (T2EHDGA) extractant ligands following the partitioning of uranium and plutonium from used nuclear fuel. To better understand fundamental interactions in these processes, covalent bonding of several f-elements with diglycolamides, primarily TODGA, is investigated in the gas phase using nanospray ionization and a quadrupole time-of-flight mass spectrometer. Further, analysis of the identity and relative strength of the cluster is enabled by MS2 isolation and collision induced dissociation. Methods Metal ion cluster analysis was completed using a Bruker mircOTOF-Q II quadrupole time-of-flight mass spectrometer equipped with a CaptiveSpray nanospray ion source. Metal:ligand solutions were prepared as 30 µM europium nitrate, samarium nitrate, cerium nitrate, or holmium nitrate and 3 µM DGA in acetonitrile or a 50:50 mixture of acetonitrile: isopropanol. Cluster mass spectra and collision-induced dissociation experiments were conducted in positive mode. Preliminary data To examine the patterns and relative strength of lanthanide: DGA interactions, MS2 experiments were completed with each lanthanide species listed above. Preliminary analyses of samarium and europium TODGA clusters suggest several combinations of TODGA and nitrate forming. The samarium cluster experiments yielded Sm(TODGA)x clusters with a samarium:TODGA ratio of up to 1:7 able to be isolated and evidence of greater ratios present in the mass spectrum. This is surprising, as metal clusters are not expected to have a coordination space able to accommodate this many ligands as large as TODGA. MS2 experiments show that, at higher ratios and with sufficient collision energy, entire TODGA ligands are removed instead of being fragmented. These experiments show that a lower collision energy is required to remove ligands as the number of bound TODGA’s increases, suggesting that in larger clusters, ligands are more delicately complexed to the metal. In addition to Sm(TODGA)x, several clusters were observed with nitrate ions bound to the metal in addition to TODGA. With a single nitrate ion, clusters with up to six TODGA’s were able to be isolated. In a similar pattern to the samarium clusters with only TODGA, less collision energy is required to eliminate one or more TODGA’s with increasing size. MS2 experiments suggest clusters with one nitrate appear to be of an equivalent or greater stability to clusters which replace the nitrate with a TODGA, as more collision energy is required to remove a TODGA ligand. These species with one nitrate are also in a higher abundance than the equivalent TODGA only cluster. With two nitrate ions, only clusters with a single TODGA were able to be isolated. Analogous europium experiments resulted in very similar clusters. Ratios of up to 1:7 Eu:TODGA were able to be isolated, and clusters with one nitrate and up to five TODGAs were isolated. In clusters with two nitrate ions, one or two TODGA’s could also be bound to the metal. MS2 experiments suggested, similarly to samarium, that larger clusters required less collision energy to eliminate TODGA. Europium clusters with one nitrate are in greater abundance and are stronger than the equivalent cluster which replaces the nitrate with TODGA. Similar analysis with cerium and holmium is ongoing, as well as analysis with other DGA ligands to compare relative strengths of the lanthanide metals with various extractant ligands.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Machine learning analysis of RB-TnSeq fitness data predicts functional gene modules in Pseudomonas putida KT2440

ABSTRACT There is growing interest in engineering Pseudomonas putida KT2440 as a microbial chassis for the conversion of renewable and waste-based feedstocks, and metabolic engineering of P. putida relies on the understanding of the functional relationships between genes. In this work, independent component analysis (ICA) was applied to a compendium of existing fitness data from randomly barcoded transposon insertion sequencing (RB-TnSeq) of P. putida KT2440 grown in 179 unique experimental conditions. ICA identified 84 independent groups of genes, which we call fModules (“functional modules”), where gene members displayed shared functional influence in a specific cellular process. This machine learning-based approach both successfully recapitulated previously characterized functional relationships and established hitherto unknown associations between genes. Selected gene members from fModules for hydroxycinnamate metabolism and stress resistance, acetyl coenzyme A assimilation, and nitrogen metabolism were validated with engineered mutants of P. putida . Additionally, functional gene clusters from ICA of RB-TnSeq data sets were compared with regulatory gene clusters from prior ICA of RNAseq data sets to draw connections between gene regulation and function. Because ICA profiles the functional role of several distinct gene networks simultaneously, it can reduce the time required to annotate gene function relative to manual curation of RB-TnSeq data sets. IMPORTANCE This study demonstrates a rapid, automated approach for elucidating functional modules within complex genetic networks. While Pseudomonas putida randomly barcoded transposon insertion sequencing data were used as a proof of concept, this approach is applicable to any organism with existing functional genomics data sets and may serve as a useful tool for many valuable applications, such as guiding metabolic engineering efforts in other microbes or understanding functional relationships between virulence-associated genes in pathogenic microbes. Furthermore, this work demonstrates that comparison of data obtained from independent component analysis of transcriptomics and gene fitness datasets can elucidate regulatory-functional relationships between genes, which may have utility in a variety of applications, such as metabolic modeling, strain engineering, or identification of antimicrobial drug targets.

09 BIOMASS FUELS↗