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At least 487 records · Page 27

Position-specific carbon isotopes of Murchison amino acids elucidate extraterrestrial abiotic organic synthesis networks

The Murchison meteorite is a well-studied carbonaceous chondrite with relatively high concentrations of amino acids thought to be endogenous to the meteorite, in part because they are characterized by carbon isotope (δ 13 C) values higher than those typical of terrestrial amino acids. Past studies have proposed that extraterrestrial amino acids in the Murchison meteorite could have formed by Strecker synthesis (for α-amino acids), Michael addition (for β-amino acids), or reductive amination, but a lack of constraints have prevented confident discrimination among these possibilities, or assignment of specific formation pathways to each of several specific amino acids. Position-specific carbon isotope analysis differentiates amongst these mechanisms by relating molecular sites to isotopically distinct carbon sources and by constraining isotope effects associated with elementary chemical reactions. Prior measurements of the position-specific carbon isotopic composition of α-alanine from the Murchison CM chondrite demonstrated that alanine’s high δ 13 C VPDB value is attributable to the amine carbon (δ 13 C VPDB = +142 ± 20‰), consistent with Strecker synthesis drawing on 13 C-rich carbonyl groups in precursors (Chimiak et al., 2021). Here, we measured the δ 13 C composition of fragment ions generated by electron impact ionization of derivatized α-alanine, β-alanine, and aspartic acid from Murchison via gas chromatography-Fourier transform mass spectrometry. α-Alanine’s amine carbon yielded δ 13 C VPDB = +109 ± 21‰, which is consistent with the previously measured value and with formation from 13 C-rich precursors. β-Alanine’s amine carbon presents a lower δ 13 C VPDB = +33 ± 24‰, which supports formation from 13 C-rich precursors but potentially via a Michael addition mechanism rather than Strecker synthesis. Aspartic acid’s amine carbon has δ 13 C VPDB = -14 ± 5‰, suggesting synthesis from precursors distinct from those that generated the alanine isomers. Further, these measurements indicate that Murchison amino acids are a mixture of compounds made from different synthesis mechanisms, though some subsets likely drew on the same substrates; this conclusion highlights the complexity of extraterrestrial organic synthesis networks and the potential of emerging methods of isotope ratio analysis to elucidate the details of those networks.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Investigation of core transport changes in DIII-D discharges with off-axis T e profile peaks

Abstract DIII-D discharges that transition to H-mode solely with off-axis electron cyclotron heating (ECH) often exhibit strong off-axis peaking of electron temperature profiles at the heating location. Electron heat transport properties near these off-axis temperature peaks have been studied using modulated ECH. The Fourier analyzed electron temperature data have been used to infer electron thermal diffusivity. Comparisons with numerical solutions of the time-dependent electron thermal equation find that the data are consistent with a narrow region with electron diffusivity χ e an order of magnitude lower than the average value across the plasma, suggesting an electron internal transport barrier (ITB) near the ECH heating location. Detailed profile analysis and equilibrium reconstructions suggest that the formation of these ITBs are correlated with off-axis values of the safety factor q being near 1. Furthermore, the ECH driven H-mode discharges demonstrate more rapid electron heating rate near the ECH deposition location than L-mode discharges with higher auxiliary ECH heating power. Additional modeling attributes this difference to the modification of electron heat transport in the core at the L-H transition, which also sustains the off-axis electron temperature peaks.

Physics↗

K-space algorithmic reconstruction (KAREN): a robust statistical methodology to separate Bragg and diffuse scattering

Diffuse scattering occurring in the Bragg diffraction pattern of a long-range-ordered structure represents local deviation from the governing regular lattice. However, interpreting the real-space structure from the diffraction pattern presents a significant challenge because of the dramatic difference in intensity between the Bragg and diffuse components of the total scattering function. In contrast to the sharp Bragg diffraction, the diffuse signal has generally been considered to be a weak expansive or continuous background signal. In this paper, using 1D and 2D models, it is demonstrated that diffuse scattering in fact consists of a complex array of high-frequency features that must not be averaged into a low-frequency background signal. To evaluate the actual diffuse scattering effectively, an algorithm has been developed that uses robust statistics and traditional signal processing techniques to identify Bragg peaks as signal outliers which can be removed from the overall scattering data and then replaced by statistically valid fill values. This method, described as a 'K-space algorithmic reconstruction' (KAREN), can identify Bragg reflections independent of prior knowledge of a system's unit cell. KAREN does not alter any data other than that in the immediate vicinity of the Bragg reflections, and reconstructs the diffuse component surrounding the Bragg peaks without introducing discontinuities which induce Fourier ripples or artifacts from underfilling `punched' voids. The KAREN algorithm for reconstructing diffuse scattering provides demonstrably better resolution than can be obtained from previously described punch-and-fill methods. The superior structural resolution obtained using the KAREN method is demonstrated by evaluating the complex ordered diffuse scattering observed from the neutron diffraction of a single plastic crystal of CBr 4 using pair distribution function analysis.

36 MATERIALS SCIENCE↗

Organic amendments change soil organic C structure and microbial community but not total organic matter on sub-decadal scales

Organic C has many benefits for soil, but it is depleted by tillage and crop harvest, and especially so for biofuel crops. Accordingly, strategies such as partially retaining stover or planting a cover crop can help ameliorate the negative effect of C removal. We used a long-term field experiment to study the impacts of stover retention and planting cover crop on soil organic matter (SOM), its extractable components and the soil microbial community. SOM chemical composition characterization was determined by electrospray ionization (ESI) coupled with Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS) in sequential water, methanol (MeOH), and chloroform (CHCl3) extracts. The characteristics of the soil bacterial community were measured by phospholipid fatty acid (PLFA), real-time quantitative PCR, and 16S rRNA gene sequence. The variations in total SOM content, total microbial biomass, and bacterial population were slight among treatments, but SOM chemical compounds, arbuscular mycorrhizal fungi (AMF) biomass, and bacterial structure changed significantly, and especially so in the coupled application of stover retention and cover crop. Specifically, stover retention enriched more lignin-like compounds in soil, whereas cover crop enriched more condensed hydrocarbons, and had more compounds with an aromaticity index (AI) >0.5. The bacterial community was not altered by the cover crop, but the corn stover retention increased the relative abundances of Myxococcales (Deltaproteobacteria) and decreased that of Actinobacteria. Redundancy analysis (RDA) further unveiled that the bacterial community in the stover treatments had significantly positive association with CHCl3-extracted chemical classes, i.e. unsaturated hydrocarbon and lipid with the coupled application (stover and cover crop), and lignin and protein with the single corn stover treatment. Taken together, our study shows how different C addition practices influence the molecular composition of SOM and the structure of soil microbial communities.

Xu, Jiangbin↗

Experimental realization of linearly polarized x-ray detected ferromagnetic resonance

We present the first theoretical and experimental evidence of time-resolved dynamic x-ray magnetic linear dichroism (XMLD) measurements of GHz magnetic precessions driven by ferromagnetic resonance in both metallic and insulating thin films. Our findings show a dynamic XMLD in both ferromagnetic Ni 80 Fe 20 and ferrimagnetic Ni 0.65 Zn 0.35 Al 0.8 Fe 1.2 O 4 for different measurement geometries and linear polarizations. A detailed analysis of the observed signals reveals the importance of separating different harmonic components in the dynamic signal in order to identify the XMLD response without the influence of competing contributions. In particular, RF magnetic resonance elicits a large dynamic XMLD response at the fundamental frequency under experimental geometries with oblique x-ray polarization. The geometric range and experimental sensitivity can be improved by isolating the 2ω Fourier component of the dynamic response. These results illustrate the potential of dynamic XMLD and represent a milestone accomplishment toward the study of GHz spin dynamics in systems beyond ferromagnetic order.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Amplified Mesoscale and Submesoscale Variability and Increased Concentration of Precipitation under Global Warming over Western North America

Abstract Cold-season precipitation statistics in simulations from the storm-resolving WRF Model at 6-km and 1-h resolution over western North America are analyzed. Pseudo–global warming future simulations for the 2041–80 period, constrained by GCMs under the RCP8.5 scenario, are compared to the 1981–2020 historical simulation. The analysis focuses on the dynamical properties of precipitation time series at subdaily scales and on the morphology of storms. The statistical distribution of precipitation intensities in each pixel of the simulation domain is characterized through nonparametric statistical indicators: frequency of wet hours, mean wet-hour precipitation intensity, and Gini coefficient as a measure of the temporal concentration of the precipitation volume. Additionally, the temporal and spatial Fourier power spectra of precipitation time series and precipitation fields are analyzed. The half-power period (HPP) and half-power wavelength (HPW) are defined as spectral measures of the characteristic scales of precipitation’s temporal and spatial patterns. The results show statistically significant increases in the mean wet-hour precipitation intensity and in the Gini coefficient in 99% of the pixels, indicating that the seasonal precipitation volume becomes more concentrated within a smaller number of hours with higher precipitation intensity. The statistics of change in the frequency of wet hours are more contrasted across the simulation domain. The changes are also reflected in the power spectra, which show the spatial and temporal variability increasing proportionally more with finer spatial and temporal scales and the HPW and HPP decreasing. These projected changes are expected to have consequences, not only in terms of hydrologic impacts but also in terms of the predictability of precipitation patterns. Significance Statement The precipitation characteristics of winter storms over the western United States and southwestern Canada are analyzed in future climate simulations for the 2041–80 period. As compared to present-day climate, the most intense parts of the storms are projected to produce a higher rainfall volume, with increased concentration over smaller areas and shorter time intervals. The propensity of rainfall intensity to vary rapidly over time will be enhanced in the future according to the simulations. These model predictions imply an increased risk of rapid flooding in small basins. They also suggest that predicting several hours ahead the time and location at which a storm will produce maximum rainfall may become more challenging in the future.

Climate change↗

Integrated analysis of X-ray diffraction patterns and pair distribution functions for machine-learned phase identification

Abstract To bolster the accuracy of existing methods for automated phase identification from X-ray diffraction (XRD) patterns, we introduce a machine learning approach that uses a dual representation whereby XRD patterns are augmented with simulated pair distribution functions (PDFs). A convolutional neural network is trained directly on XRD patterns calculated using physics-informed data augmentation, which accounts for experimental artifacts such as lattice strain and crystallographic texture. A second network is trained on PDFs generated via Fourier transform of the augmented XRD patterns. At inference, these networks classify unknown samples by aggregating their predictions in a confidence-weighted sum. We show that such an integrated approach to phase identification provides enhanced accuracy by leveraging the benefits of each model’s input representation. Whereas networks trained on XRD patterns provide a reciprocal space representation and can effectively distinguish large diffraction peaks in multi-phase samples, networks trained on PDFs provide a real space representation and perform better when peaks with low intensity become important. These findings underscore the importance of using diverse input representations for machine learning models in materials science and point to new avenues for automating multi-modal characterization.

36 MATERIALS SCIENCE↗

RMCProfile7 : reverse Monte Carlo for multiphase systems

This work introduces a completely rewritten version of the programRMCProfile(version 7), big-box, reverse Monte Carlo modelling software for analysis of total scattering data. The major new feature ofRMCProfile7is the ability to refine multiple phases simultaneously, which is relevant for many current research areas such as energy materials, catalysis and engineering. Other new features include improved support for molecular potentials and rigid-body refinements, as well as multiple different data sets. An empirical resolution correction and calculation of the pair distribution function as a back-Fourier transform are now also available.RMCProfile7is freely available for download at https://rmcprofile.ornl.gov/.

Chemistry↗

Quantitative approaches for multiscale structural analysis with atomic resolution electron microscopy

Atomic-resolution imaging with scanning transmission electron microscopy is a powerful tool for characterizing the nanoscale structure of materials, in particular features such as defects, local strains, and symmetry-breaking distortions. In addition to advanced instrumentation, the effectiveness of the technique depends on computational image analysis to extract meaningful features from complex datasets recorded in experiments, which can be complicated by the presence of noise and artifacts, small or overlapping features, and the need to scale analysis over large representative areas. Here, we present image analysis approaches which synergize real and reciprocal space information to efficiently and reliably obtain meaningful structural information with picometer scale precision across hundreds of nanometers of material from atomic-resolution electron microscope images. Damping superstructure peaks in reciprocal space allows symmetry-breaking structural distortions to be disentangled from other sources of inhomogeneity and measured with high precision. Real-space fitting of the wavelike signals resulting from Fourier filtering enables absolute quantification of lattice parameter variations and strain, as well as the uncertainty associated with these measurements. Implementations of these algorithms are made available as an open source python package.

36 MATERIALS SCIENCE↗

Emergence of liquid following laser melting of gold thin films

X-ray structural science is undergoing a revolution driven by the emergence of X-ray Free-electron Laser (XFEL) facilities. The structures of crystalline solids can now be studied on the picosecond time scale relevant to phonons, atomic vibrations which travel at acoustic velocities. In the work presented here, X-ray diffuse scattering is employed to characterize the time dependence of the liquid phase emerging from femtosecond laser-induced melting of polycrystalline gold thin films using an XFEL. In a previous analysis of Bragg peak profiles, we showed the supersonic disappearance of the solid phase and presented a model of pumped hot electrons carrying energy from the gold surface to scatter at internal grain boundaries. This generates melt fronts propagating relatively slowly into the crystal grains. By conversion of diffuse scattering to a partial X-ray pair distribution function, we demonstrate that it has the characteristic shape obtained by Fourier transformation of the measured F ( Q ). The diffuse signal fraction increases with a characteristic rise-time of 13 ps, roughly independent of the incident pump fluence and consequent final liquid fraction. This suggests the role of further melt-front nucleation processes beyond grain boundaries.

36 MATERIALS SCIENCE↗

Quantum eigenvalue estimation via time series analysis

We present an efficient method for estimating the eigenvalues of a Hamiltonian H from the expectation values of the evolution operator for various times. For a given quantum state ρ, our method outputs a list of eigenvalue estimates and approximate probabilities. Each probability depends on the support of ρ in those eigenstates of H associated with eigenvalues within an arbitrarily small range. The complexity of our method is polynomial in the inverse of a given precision parameter ε, which is the gap between eigenvalue estimates. Unlike the well-known quantum phase estimation algorithm that uses the quantum Fourier transform, our method does not require large ancillary systems, large sequences of controlled operations, or preserving coherence between experiments, and is therefore more attractive for near-term applications. The output of our method can be used to estimate spectral properties of H and other expectation values efficiently, within additive error proportional to ε.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Brighter-fatter Effect in Near-infrared Detectors—III. Fourier-domain Treatment of Flat Field Correlations and Application to WFIRST

Weak gravitational lensing has emerged as a leading probe of the growth of cosmic structure. However, the shear signal is very small and accurate measurement depends critically on our ability to understand how non-ideal instrumental effects affect astronomical images. The Wide-Field Infrared Survey Telescope (WFIRST) will fly a focal plane containing 18 Teledyne H4RG-10 near-infrared detector arrays, which present different instrument calibration challenges from previous weak lensing observations. Previous work [Paper I: Hirata & Choi, PASP, 132, 014501 (2020); and Paper II: Choi & Hirata, PASP, 132, 014502 (2020)] has shown that correlation functions of flat field images, including cross-correlations between different time slices that are enabled by the non-destructive read capability of the infrared detectors, are effective tools for disentangling linear and nonlinear inter-pixel capacitance (IPC) and the brighter-fatter effect (BFE). Here we present a Fourier-domain treatment of the flat field correlations, which allows us to expand the previous formalism to all orders in IPC, BFE, and classical nonlinearity. We show that biases in simulated flat field analyses in Paper I are greatly reduced through the use of this formalism. We then apply this updated formalism to flat field data from three WFIRST flight candidate detectors, and explore the robustness to variations in the analysis. We find that the BFE is present in all three detectors, and that its contribution to the flat field correlations dominates over the nonlinear IPC, in accordance with the results from Paper II on a development detector. The magnitude of the BFE is such that the effective area of a pixel is increased by (3.54 ± 0.03) × 10 -7 for every electron deposited in a neighboring pixel (sensor chip assembly [SCA] 20829, statistical error, not IPC-deconvolved). We compare IPC maps from flat field autocorrelation measurements to those obtained from the single pixel reset method and find a median difference of 0.113% for SCA 20829. After further diagnosis of this difference, we ascribe it largely to an additional source of cross-talk, the vertical trailing pixel effect, and recommend further work to develop a model for this effect. These results represent a significant step toward calibration of the non-ideal effects in WFIRST detectors.

79 ASTRONOMY AND ASTROPHYSICS↗

Role of Intermolecular Interactions in Deep Eutectic Solvents for CO 2 Capture: Vibrational Spectroscopy and Quantum Chemical Studies

Recent research and reviews on CO 2 capture methods, along with advancements in industry, have highlighted high costs and energy-intensive nature as the primary limitations of conventional direct air capture and storage (DACS) methods. In response to these challenges, deep eutectic solvents (DESs) have emerged as promising absorbents due to their scalability, selectivity, and lower environmental impact compared to other absorbents. However, the molecular origins of their enhanced thermal stability and selectivity for DAC applications have not been explored before. Therefore, the current study focuses on a comprehensive investigation into the molecular interactions within an alkaline DES composed of potassium hydroxide (KOH) and ethylene glycol (EG). Combining Fourier transform infrared (FT-IR) and quantum chemical calculations, the study reports structural changes and intermolecular interactions induced in EG upon addition of KOH and its implications on CO 2 capture. Experimental and computational spectroscopic studies confirm the presence of noncovalent interactions (hydrogen bonds) within both EG and the KOH-EG system and point to the aggregation of ions at higher KOH concentrations. Additionally, molecular electrostatic potential (MESP) surface analysis, natural bond orbital (NBO) analysis, quantum theory of atoms-in-molecules (QTAIM) analysis, and reduced density gradient-noncovalent interaction (RDG-NCI) plot analysis elucidate changes in polarizability, charge distribution, hydrogen bond types, noncovalent interactions, and interaction strengths, respectively. Evaluation of explicit and hybrid models assesses their effectiveness in representing intermolecular interactions. This research enhances our understanding of molecular interactions in the KOH-EG system, which are essential for both the absorption and desorption of CO 2 . The study also aids in predicting and selecting DES components, optimizing their ratios with salts, and fine-tuning the properties of similar solvents and salts for enhanced CO 2 capture efficiency.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Spatially resolved x-ray detection with photonic crystal scintillators

We study the self-collimation phenomenon in photonic crystals (PhC) of wide bandgap materials for ultra-fast and high spatial resolution x-ray detection. We work on various heavy inorganic scintillators: BaF 2 , GaN, ZnO, CsI:Tl, NaI:Tl, LYSO, WO 4 compounds, and plastic scintillators. Conventional scintillator detectors do not rely on a direct detection mechanism; hence, they require intricate design and fabrication processes. We offer a PhC design to observe self-collimation phenomena and overcome the ongoing spatial resolution challenges with these types of materials. We investigate the photonic band diagrams and iso-frequency contours. Fourier transforms based on finite-difference time-domain and frequency domain simulations are done for verifying and analyzing the self-collimation with the selected material. Light extraction efficiency at the PhC–air interface, depending on the truncation distance from the excitation point, is measured. Beam divergence values are calculated at 1 mm propagation distance. The vertical field profiles are obtained to observe the confinement. For the spatial resolution analysis, cross-sectional beam profiles have been examined. Furthermore, Gaussian envelopes are fitted to beam profiles for a consistent data analysis, and full-width-at-half-maximum values are considered. As a result, we theoretically prove and demonstrate the spatially resolved x-ray detection at the sub-micrometer level for a wide range of scintillator materials.

36 MATERIALS SCIENCE↗

MetaboDirect: an analytical pipeline for the processing of FT-ICR MS-based metabolomic data

Background: Microbiomes are now recognized as the main drivers of ecosystem function ranging from the oceans and soils to humans and bioreactors. However, a grand challenge in microbiome science is to characterize and quantify the chemical currencies of organic matter (i.e., metabolites) that microbes respond to and alter. Critical to this has been the development of Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS), which has drastically increased molecular characterization of complex organic matter samples, but challenges users with hundreds of millions of data points where readily available, user-friendly, and customizable software tools are lacking. Results: Here, we build on years of analytical experience with diverse sample types to develop MetaboDirect, an open-source, command-line-based pipeline for the analysis (e.g., chemodiversity analysis, multivariate statistics), visualization (e.g., Van Krevelen diagrams, elemental and molecular class composition plots), and presentation of direct injection high-resolution FT-ICR MS data sets after molecular formula assignment has been performed. When compared to other available FT-ICR MS software, MetaboDirect is superior in that it requires a single line of code to launch a fully automated framework for the generation and visualization of a wide range of plots, with minimal coding experience required. Among the tools evaluated, MetaboDirect is also uniquely able to automatically generate biochemical transformation networks (ab initio) based on mass differences (mass difference network-based approach) that provide an experimental assessment of metabolite connections within a given sample or a complex metabolic system, thereby providing important information about the nature of the samples and the set of microbial reactions or pathways that gave rise to them. Finally, for more experienced users, MetaboDirect allows users to customize plots, outputs, and analyses. Conclusion: Application of MetaboDirect to FT-ICR MS-based metabolomic data sets from a marine phage-bacterial infection experiment and a Sphagnum leachate microbiome incubation experiment showcase the exploration capabilities of the pipeline that will enable the research community to evaluate and interpret their data in greater depth and in less time. It will further advance our knowledge of how microbial communities influence and are influenced by the chemical makeup of the surrounding system. The source code and User’s guide of MetaboDirect are freely available through (https://github.com/Coayala/MetaboDirect) and (https://metabodirect.readthedocs.io/en/latest/), respectively.

54 ENVIRONMENTAL SCIENCES↗

Conditioned Simulation of Ground-Motion Time Series at Uninstrumented Sites Using Gaussian Process Regression

Ground-motion time series are essential input data in seismic analysis and performance assessment of the built environment. Because instruments to record free-field ground motions are generally sparse, methods are needed to estimate motions at locations with no available ground-motion recording instrumentation. In this study, given a set of observed motions, ground-motion time series at target sites are constructed using a Gaussian process regression (GPR) approach, which treats the real and imaginary parts of the Fourier spectrum as random Gaussian variables. Model training, verification, and applicability studies are carried out using the physics-based simulated ground motions of the 1906 Mw 7.9 San Francisco earthquake and Mw 7.0 Hayward fault scenario earthquake in northern California. Additionally, the method’s performance is further evaluated using the 2019 Mw 7.1 Ridgecrest earthquake ground motions recorded by the Community Seismic Network stations located in southern California. These evaluations indicate that the trained GPR model is able to adequately estimate the ground-motion time series for frequency ranges that are pertinent for most earthquake engineering applications. The trained GPR model exhibits proper performance in predicting the long-period content of the ground motions as well as directivity pulses.

58 GEOSCIENCES↗

A History of Molecular Level Analysis of Natural Organic Matter by FTICR Mass Spectrometry and The Paradigm Shift in Organic Geochemistry

Natural organic matter (NOM) is a complex mixture of biogenic molecules resulting from the deposition and transformation of plant and animal matter. It has long been recognized that NOM plays an important role in many geological, geochemical, and environmental processes. Of particular concern is the fate of NOM in response to a warming climate in environments that have historically sequestered carbon (e.g., peatlands and swamps) but may transition to net carbon emitters. In this review, we will highlight developments in the application of high-field Fourier transform ion cyclotron resonance mass spectrometry (FTICR MS) in identifying the individual components of complex NOM mixtures, focusing primarily on the fraction that is dissolved in natural waters (dissolved organic matter or DOM). We will first provide some historical perspective on developments in FTICR technology that made molecular-level characterizations of DOM possible. In conclusion, a variety of applications of the technique will then be described, followed by our view of the future of high-field FTICR MS in carbon cycling research, including a particularly exciting metabolomic approach.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS) peak intensity normalization for complex mixture analyses

Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS) is a preferred technique for analyzing complex organic mixtures. Currently, there is no consensus normalization approach, nor an objective method for selecting one, for quantitative analyses of FT-ICR-MS data. We investigate a method to evaluate and score the amount of bias various normalization approaches introduce into the data. We evaluate the ability of the Statistical Procedure for the Analysis of Normalization Strategies (SPANS) to guide selection of appropriate normalization approaches for two different FT-ICR-MS datasets. Further, we test the robustness of SPANS results to changes in SPANS parameter values and assess the impact of using various normalization approaches on downstream statistical analyses. The normalization approach identified by SPANS differed for the two datasets. Normalization methods impacted the statistical significance of peaks differently, underscoring the importance of carefully evaluating potential methods. More consistent SPANS scores resulted when at least 120 significant peaks are used, where larger sets of peaks were obtained by increasing the p-value threshold. Interestingly, we show that Total Sum Scaling and highest peak normalization, used in previous studies, underperformed relative to SPANS-recommended normalization approaches. While there is no single, best normalization method for all datasets, SPANS provides a mechanism to identify an appropriate normalization method for analyzing FT-ICR-MS data quantitatively. As a result, the number of peaks used in the background distributions of SPANS contributes more significantly to the reproducibility of results than the p-value thresholds used to obtain those peaks.

47 OTHER INSTRUMENTATION↗