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Results for “functional Principal Component Analysis”

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.

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At least 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

TU-C-17A-03: An Integrated Contour Evaluation Software Tool Using Supervised Pattern Recognition for Radiotherapy

Purpose: Radiotherapy (RT) contours delineated either manually or semiautomatically require verification before clinical usage. Manual evaluation is very time consuming. A new integrated software tool using supervised pattern contour recognition was thus developed to facilitate this process. Methods: The contouring tool was developed using an object-oriented programming language C# and application programming interfaces, e.g. visualization toolkit (VTK). The C# language served as the tool design basis. The Accord.Net scientific computing libraries were utilized for the required statistical data processing and pattern recognition, while the VTK was used to build and render 3-D mesh models from critical RT structures in real-time and 360° visualization. Principal component analysis (PCA) was used for system self-updating geometry variations of normal structures based on physician-approved RT contours as a training dataset. The inhouse design of supervised PCA-based contour recognition method was used for automatically evaluating contour normality/abnormality. The function for reporting the contour evaluation results was implemented by using C# and Windows Form Designer. Results: The software input was RT simulation images and RT structures from commercial clinical treatment planning systems. Several abilities were demonstrated: automatic assessment of RT contours, file loading/saving of various modality medical images and RT contours, and generation/visualization of 3-D images and anatomical models. Moreover, it supported the 360° rendering of the RT structures in a multi-slice view, which allows physicians to visually check and edit abnormally contoured structures. Conclusion: This new software integrates the supervised learning framework with image processing and graphical visualization modules for RT contour verification. This tool has great potential for facilitating treatment planning with the assistance of an automatic contour evaluation module in avoiding unnecessary manual verification for physicians/dosimetrists. In addition, its nature as a compact and stand-alone tool allows for future extensibility to include additional functions for physicians’ clinical needs.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
NASA NTRS2011

Annual Cycle of Surface Longwave Radiation

The annual cycles of upward and downward longwave fluxes at the Earth s surface are investigated by use of the NASA/GEWEX Surface Radiation Budget Data Set. Because of the immense difference between the heat capacity of land and ocean, the surface of Earth is partitioned into these two categories. Principal component analysis is used to quantify the annual cycles. Over land, the first principal component describes over 95% of the variance of the annual cycle of the upward and downward longwave fluxes. Over ocean the first term describes more than 87% of these annual cycles. Empirical orthogonal functions show the corresponding geographical distributions of these cycles. Phase plane diagrams of the annual cycles of upward longwave fluxes as a function of net shortwave flux show the thermal inertia of land and ocean.

Mlynczak, Pamela E.↗
DOE OSTI2020

Probing the Electronic Structure of a Thorium Nitride Complex by Solid-State 15N NMR Spectroscopy

The solid-state 15N NMR powder spectra of the thorium nitride complex, [K(18-crown-6)(THF)2][(R2N)3Th(m-15N)(Th(NR2)3] ([K][1-15N], R = SiMe3), and the thorium amide complex, [Th(NR2)3(15NH2)] (2-15N) were recorded. The spectrum for [K][1-15N] represents the first reported solid-state 15N NMR data for an actinide nitride complex. The experimentally measured tensor spans are 847 ppm for [K][1-15N] and 237 ppm for 2-15N. Both shielding tensors exhibit a near-zero asymmetry parameter, which for [K][1-15N] is consistent with a local rotational symmetry of its 15N-labelled nitride ligand. For 2-15N, the lack of asymmetry can be rationalized by a quasi-free Th-NH2 bond rotation in the solid-state. DFT calculations overestimate the tensor span somewhat for [K][1-15N], but provide isotropic shifts in good agreement with both the solid-state and solution values for both complexes. Natural localized molecular orbital (NLMO) analyses of the nuclear shielding reveal that the larger tensor span in [K][1-15N] vs. 2-15N is primarily a consequence of more pronounced covalency of the N-Th bonds, and large spin-orbit coupling due to significant Th 5f orbital contribution to those bonds, impacting the principal components of the shielding tensor perpendicular to the Th-N-Th axis. Overall, our analysis confirms the involvement of the 5f orbitals in Th-N multiple bonds, and further demonstrates the value of solid-state NMR spectroscopy for interrogating actinide-ligand bonding.

actinides, covalent bonding, Nuclear Magnetic Reso↗
DOE OSTI2013

Life Cycle Analysis of Kidney Gene Expression in Male F344 Rats

Age is a predisposing condition for susceptibility to chronic kidney disease and progression as well as acute kidney injury that may arise due to the adverse effects of some drugs. Age-related differences in kidney biology, therefore, are a key concern in understanding drug safety and disease progression. We hypothesize that the underlying suite of genes expressed in the kidney at various life cycle stages will impact susceptibility to adverse drug reactions. Therefore, establishing changes in baseline expression data between these life stages is the first and necessary step in evaluating this hypothesis. Untreated male F344 rats were sacrificed at 2, 5, 6, 8, 15, 21, 78, and 104 weeks of age. Kidneys were collected for histology and gene expression analysis. Agilent whole-genome rat microarrays were used to query global expression profiles. An ANOVA (p,0.01) coupled with a fold-change.1.5 in relative mRNA expression, was used to identify 3,724 unique differentially expressed genes (DEGs). Principal component analyses of these DEGs revealed three major divisions in life-cycle renal gene expression. K-means cluster analysis identified several groups of genes that shared age-specific patterns of expression. Pathway analysis of these gene groups revealed age-specific gene networks and functions related to renal function and aging, including extracellular matrix turnover, immune cell response, and renal tubular injury. Large age-related changes in expression were also demonstrated for the genes that code for qualified renal injury biomarkers KIM-1, Clu, and Tff3. These results suggest specific groups of genes that may underlie age-specific susceptibilities to adverse drug reactions and disease. This analysis of the basal gene expression patterns of renal genes throughout the life cycle of the rat will improve the use of current and future renal biomarkers and inform our assessments of kidney injury and disease.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Metatranscriptomic Analyses of Diel Metabolic Functions During a Microcystis Bloom in Western Lake Erie (United States)

This study examined diel shifts in metabolic functions of Microcystis spp. during a 48-h Lagrangian survey of a toxin-producing cyanobacterial bloom in western Lake Erie in the aftermath of the 2014 Toledo Water Crisis. Transcripts mapped to the genomes of recently sequenced lower Great Lakes Microcystis isolates showed distinct patterns of gene expression between samples collected across day (10:00 h, 16:00 h) and night (22:00 h, 04:00 h). Daytime transcripts were enriched in functions related to Photosystem II (e.g., psbA), nitrogen and phosphate acquisition, cell division (ftsHZ), heat shock response (dnaK, groEL), and uptake of inorganic carbon (rbc, bicA). Genes transcribed during nighttime included those involved in phycobilisome protein synthesis and Photosystem I core subunits. Hierarchical clustering and principal component analysis (PCA) showed a tightly clustered group of nighttime expressed genes, whereas daytime transcripts were separated from each other over the 48-h duration. Lack of uniform clustering within the daytime transcripts suggested that the partitioning of gene expression in Microcystis is dependent on both circadian regulation and physicochemical changes within the environment.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1994

Principal modes of atmospheric circulation anomalies associated with global angular momentum fluctuations

This paper provides a description of the variability of global atmospheric angular momentum (GAM) and its relationship with principal modes of three-dimensional atmospheric circulation anomalies. The data used are 5-day mean global wind fields from the European Centre for Medium-Range Weather Forecasts initialized dataset for 1980-1989. Significant seasonal variation of GAM is observed with maxima in April and November and a minimum during late July. The amplitude of the annual cycle is largest in the upper troposphere and decreases toward the surface. Although the lower tropospheric contribution to the total angular momentum is relatively small, its annual cycle is out of phase with those of the upper atmosphere and GAM. Also identified is a distinct semiannual component, with double peaks appearing in April and November. This signal is most noticeable in the upper troposphere above the 300-mb level. The principal modes of zonal-mean angular momentum and meridional circulation anomalies and their coupled modes are obtained by using empirical orthogonal function analysis and singular value decomposition. It is shown that the leading modes of the angular momentum and meridional circulation are coupled with each other and are responsible for much of the variability in GAM. The coupled modes represent fluctuations of upper-level subtropical zonal flow, which are linked to the modulation of Hadley circulation intensity in both hemispheres. It is found that GAM is highly correlated with the first eigenvector of upper-level streamfunction anomalies, which consists of a superrotational flow in the tropics and subtropics, except over the central Pacific where a 'blocked' flow with two subtropical anticyclonic circulation cells straddling the equator is found. Much of the blocked flow is due to the establishment of dipole anomalies in the velocity potential with centers over the central Pacific and the Maritime Continent on the interannual time scale. On the intraseasonal time scale, GAM fluctuation is dominated by superrotational flow in the tropics, with the blocked flow present to a much lesser extent. The associated velocity potential anomaly has a weak dipole structure with centers over the Indian Ocean and the eastern Pacific. The implications of the above results on the total angular momentum balance of the earth-atmosphere system are also discussed.

Kang, In-Sik↗
NASA NTRSDate not supplied

Batch Effect Correction Methods for NASA GeneLab Transcriptomic Datasets

RNA sequencing (RNA-seq) data from space biology experiments promise to yield invaluable insights into the effects of spaceflight on terrestrial biology. However, sample numbers from each study are low due to limited crew availability, hardware, and space. To increase statistical power, spaceflight RNA-seq datasets from different missions are often aggregated together. However, this can introduce technical variation or "batch effects", often due to differences in sample handling, sample processing, and sequencing platforms. Several computational methods have been developed to correct for technical batch effects, thereby reducing their impact on true biological signals. In this study, we combined 7 mouse liver RNA-seq datasets from NASA GeneLab (part of the NASA Open Science Data Repository) to evaluate several common batch effect correction methods (ComBat and ComBat-seq from the sva R package, and Median Polish, Empirical Bayes, and ANOVA from the MBatch R package). We quantitatively evaluated the ability of these methods to correct for technical batch variables in space biology RNA-seq data using the following criteria: BatchQC, principal component analysis, dispersion separability criterion, log fold change correlation, and differential gene expression analysis. Each batch variable / correction method combination was then assessed using a custom scoring approach to identify the optimal correction method for the combined dataset, by geometrically probing the space of all allowable scoring functions to yield an aggregate volume-based scoring measure. Finally, we describe the way in which the GeneLab multi-study analysis and visualization portal will allow users to examine the presence or absence of batch effects using multiple metrics. If the user chooses to perform batch effect correction, the scoring approach described here can be implemented to identify the optimal correction method to use for their specific combined dataset prior to analysis.

Lauren M. Sanders↗
DOE OSTI2017

Accurate force field for molybdenum by machine learning large materials data

In this work, we present a highly accurate spectral neighbor analysis potential (SNAP) model for molybdenum (Mo) developed through the rigorous application of machine learning techniques on large materials data sets. Despite Mo's importance as a structural metal, existing force fields for Mo based on the embedded atom and modified embedded atom methods still do not provide satisfactory accuracy on many properties. We will show that by fitting to the energies, forces and stress tensors of a large density functional theory (DFT)-computed dataset on a diverse set of Mo structures, a Mo SNAP model can be developed that achieves close to DFT accuracy in the prediction of a broad range of properties, including energies, forces, stresses, elastic constants, melting point, phonon spectra, surface energies, grain boundary energies, etc. We will outline a systematic model development process, which includes a rigorous approach to structural selection based on principal component analysis, as well as a differential evolution algorithm for optimizing the hyperparameters in the model fitting so that both the model error and the property prediction error can be simultaneously lowered. We expect that this newly developed Mo SNAP model will find broad applications in large-scale, long-time scale simulations.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Maximizing machine learning interatomic potential transferability for the discovery of the novel stellated octadecagon Bi18-Pt24 cage structure

Achieving true transferability remains the central challenge for Machine Learning Interatomic Potentials (ML-IAPs) in modeling complex bimetallic nanoclusters across their vast potential energy surfaces. We systematically investigate data selection strategies to optimize the Chebyshev Interaction Model for Efficient Simulation (ChIMES) potential for the Bi-Pt nanoclusters by comparing three innovative sampling methods: Principal Component Analysis (PCA)/k-means (structural diversity), t-distributedStochasticNeighborEmbedding (t-SNE)/k-means (force-space diversity), and hierarchical clustering. Quantitatively, the PCA/k-means strategy proved most effective for global accuracy, yielding the lowest force errors and achieving energy root mean square errors (RMSE) values competitive with Density Functional Theory (DFT), demonstrating excellent accuracy (19.16meV/atom). Structural validation on 34 unique DFT-optimized isomers further confirmed the potential’s high fidelity, with the best model PCA/k-means reproducing structures with an average root mean square deviation (RMSD) of 0.10 Å. However, the t-SNE methods, by maximizing diversity in the force space, demonstrated superior extrapolative power, leading to the more precise prediction of a novel stellated octadecagon Bi18⁢Pt24 cage structure, demonstrating the potential for exploring previously unseen morphologies. Our results establish a clear methodology for strategic data sampling that successfully maximizes ML-IAP transferability, providing an accurate and computationally efficient tool that accelerates the theoretical discovery of complex bimetallic architectures.

Vangheluwe, Raphaël [Université Paris-Saclay, CNRS↗
DOE OSTI2013

A Mechanistic Understanding of Allosteric Immune Escape Pathways in the HIV-1 Envelope Glycoprotein

The HIV-1 envelope (Env) spike, which consists of a compact, heterodimeric trimer of the glycoproteins gp120 and gp41, is the target of neutralizing antibodies. However, the high mutation rate of HIV-1 and plasticity of Env facilitates viral evasion from neutralizing antibodies through various mechanisms. Mutations that are distant from the antibody binding site can lead to escape, probably by changing the conformation or dynamics of Env; however, these changes are difficult to identify and define mechanistically. Here we describe a network analysis-based approach to identify potential allosteric immune evasion mechanisms using three known HIV-1 Env gp120 protein structures from two different clades, B and C. First, correlation and principal component analyses of molecular dynamics (MD) simulations identified a high degree of longdistance coupled motions that exist between functionally distant regions within the intrinsic dynamics of the gp120 core, supporting the presence of long-distance communication in the protein. Then, by integrating MD simulations with network theory, we identified the optimal and suboptimal communication pathways and modules within the gp120 core. The results unveil both strain-dependent and -independent characteristics of the communication pathways in gp120. We show that within the context of three structurally homologous gp120 cores, the optimal pathway for communication is sequence sensitive, i.e. a suboptimal pathway in one strain becomes the optimal pathway in another strain. Yet the identification of conserved elements within these communication pathways, termed inter-modular hotspots, could present a new opportunity for immunogen design, as this could be an additional mechanism that HIV-1 uses to shield vulnerable antibody targets in Env that induce neutralizing antibody breadth.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2015

Contact- and distance-based principal component analysis of protein dynamics

To interpret molecular dynamics simulations of complex systems, systematic dimensionality reduction methods such as principal component analysis (PCA) represent a well-established and popular approach. Apart from Cartesian coordinates, internal coordinates, e.g., backbone dihedral angles or various kinds of distances, may be used as input data in a PCA. Adopting two well-known model problems, folding of villin headpiece and the functional dynamics of BPTI, a systematic study of PCA using distance-based measures is presented which employs distances between C{sub α}-atoms as well as distances between inter-residue contacts including side chains. While this approach seems prohibitive for larger systems due to the quadratic scaling of the number of distances with the size of the molecule, it is shown that it is sufficient (and sometimes even better) to include only relatively few selected distances in the analysis. The quality of the PCA is assessed by considering the resolution of the resulting free energy landscape (to identify metastable conformational states and barriers) and the decay behavior of the corresponding autocorrelation functions (to test the time scale separation of the PCA). By comparing results obtained with distance-based, dihedral angle, and Cartesian coordinates, the study shows that the choice of input variables may drastically influence the outcome of a PCA.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Detecting DNA Methylation Using Surface-Enhanced Raman Spectroscopy

Among all of the epigenetic events that are responsible for various diseases such as cancer, lupus, and several birth defects, DNA methylation is one of the crucial ones. It occurs because of the alkylation of various bases. These modifications of genes carried through altered creations of proteins ultimately lead to various diseases. For example, a vital cause behind canine lymphoma is found to be DNA methylation in the guanine base. In this work, we analyzed the methylated and nonmethylated guanine structure with the assistance of surface-enhanced Raman spectroscopy and density functional theory (DFT). Because of their vulnerability for causing DNA methylation, the N7 and O6 positions of the guanine structure were the positions of interest with the addition of various adducts such as methyl, hydroxyethyl, and deuterated methyl groups. To distinguish the methylated samples from the nonmethylated ones, principal component analysis was performed, and the same analysis was used to distinguish their methylated positions and added adducts. The experimental results were then explained by structure optimization and frequency calculation of the molecules based on DFT calculations. To understand the charge distribution and detect the possible locations of alkylation of DNA bases, electrostatic potential, highest occupied molecular orbital, and lowest unoccupied molecular orbital for each of the molecules were analyzed, and the reactivity was discussed in the light of electronic structure calculations. The results presented in this study demonstrate a potential label-free technique to examine epigenetic modification of DNA.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

An a priori evaluation of a principal component and artificial neural network based combustion model in diesel engine conditions

A principal component analysis (PCA) and artificial neural network (ANN) based chemistry tabulation approach is presented. ANNs are used to map the thermochemical state onto a low-dimensional manifold consisting of five control variables that have been identified using PCA. Three canonical configurations are considered to train the PCA-ANN model: a series of homogeneous reactors, a nonpremixed flamelet, and a two-dimensional lifted flame. The performance of the model in predicting the thermochemical manifold of a spatially-developing turbulent jet flame in diesel engine thermochemical conditions is a priori evaluated using direct numerical simulation (DNS) data. The PCA-ANN approach is compared with a conventional tabulation approach (tabulation using ad hoc defined control variables and linear interpolation). The PCA-ANN model provides higher accuracy and requires several orders of magnitude less memory. Here, these observations indicate that the PCA-ANN model is superior for chemistry tabulation, especially for modelling complex chemistries that present multiple combustion modes as observed in diesel combustion. The performance of the PCA-ANN model is then compared to the optimal estimator, i.e. the conditional mean from the DNS. The results indicate that the PCA-ANN model gives high prediction accuracy, comparable to the optimal estimator, especially for major species and the thermophysical properties. Higher errors are observed for the minor species and reaction rate predictions when compared to the optimal estimator. It is shown that the prediction of minor species and reaction rates can be improved by using training data that exhibits a variation of parameters as observed in the turbulent flame. The output of the ANN is analysed to assess mass conservation. It is observed that the ANN incurs a mean absolute error of 0.05% in mass conservation. Furthermore, it is demonstrated that this error can be reduced by modifying the cost function of the ANN to penalise for deviation from mass conservation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Measuring Plant Metabolite Abundance in Spearmint ( Mentha spicata L.) with Raman Spectra to Determine Optimal Harvest Time

A fast field-deployable method utilizing Raman spectroscopy to determine the optimal harvest time of plants to extract the highest abundance of target metabolites is presented. Rosmarinic acid is a metabolite extracted from spearmint (Mentha spicata L.). Leaves from commercial “Native” and proprietary clonal line “KI110” spearmint were measured as a function of cell type and age to determine rosmarinic acid abundance. A linear regression model with leave-one-out cross-validation (R 2 CV = 0.61, RMSECV = 11.1 mg/g) was developed between selected Raman peak areas and rosmarinic acid concentrations determined by high-performance liquid chromatography (HPLC). A principal component analysis (PCA) model was also developed to determine rosmarinic acid abundance. The method may be suited to the analysis of many agriculturally relevant plant species and metabolites with distinct Raman peaks.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2011

THE TURBULENCE SPECTRUM OF MOLECULAR CLOUDS IN THE GALACTIC RING SURVEY: A DENSITY-DEPENDENT PRINCIPAL COMPONENT ANALYSIS CALIBRATION

Turbulence plays a major role in the formation and evolution of molecular clouds. Observationally, turbulent velocities are convolved with the density of an observed region. To correct for this convolution, we investigate the relation between the turbulence spectrum of model clouds, and the statistics of their synthetic observations obtained from principal component analysis (PCA). We apply PCA to spectral maps generated from simulated density and velocity fields, obtained from hydrodynamic simulations of supersonic turbulence, and from fractional Brownian motion (fBm) fields with varying velocity, density spectra, and density dispersion. We examine the dependence of the slope of the PCA pseudo-structure function, {alpha}{sub PCA}, on intermittency, on the turbulence velocity ({beta}{sub v}) and density ({beta}{sub n}) spectral indexes, and on density dispersion. We find that PCA is insensitive to {beta}{sub n} and to the log-density dispersion {sigma}{sub s}, provided {sigma}{sub s} {<=} 2. For {sigma}{sub s} > 2, {alpha}{sub PCA} increases with {sigma}{sub s} due to the intermittent sampling of the velocity field by the density field. The PCA calibration also depends on intermittency. We derive a PCA calibration based on fBm structures with {sigma}{sub s} {<=} 2 and apply it to 367 {sup 13}CO spectral maps of molecular clouds in the Galactic Ring Survey. The average slope of the PCA structure function, ({alpha}{sub PCA}) = 0.62 {+-} 0.2, is consistent with the hydrodynamic simulations and leads to a turbulence velocity exponent of ({beta}{sub v}) = 2.06 {+-} 0.6 for a non-intermittent, low density dispersion flow. Accounting for intermittency and density dispersion, the coincidence between the PCA slope of the GRS clouds and the hydrodynamic simulations suggests {beta}{sub v} {approx_equal} 1.9, consistent with both Burgers and compressible intermittent turbulence.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

Big-Data RHEED analysis for understanding epitaxial film growth processes

Reflection high energy electron diffraction (RHEED) has by now become a standard tool for in-situ monitoring of film growth by pulsed laser deposition and molecular beam epitaxy. Yet despite the widespread adoption and wealth of information in RHEED image, most applications are limited to observing intensity oscillations of the specular spot, and much additional information on growth is discarded. With ease of data acquisition and increased computation speeds, statistical methods to rapidly mine the dataset are now feasible. Here, we develop such an approach to the analysis of the fundamental growth processes through multivariate statistical analysis of RHEED image sequence. This approach is illustrated for growth of LaxCa1-xMnO3 films grown on etched (001) SrTiO3 substrates, but is universal. The multivariate methods including principal component analysis and k-means clustering provide insight into the relevant behaviors, the timing and nature of a disordered to ordered growth change, and highlight statistically significant patterns. Fourier analysis yields the harmonic components of the signal and allows separation of the relevant components and baselines, isolating the assymetric nature of the step density function and the transmission spots from the imperfect layer-by-layer (LBL) growth. These studies show the promise of big data approaches to obtaining more insight into film properties during and after epitaxial film growth. Furthermore, these studies open the pathway to use forward prediction methods to potentially allow significantly more control over growth process and hence final film quality.

Big-Data↗
NASA NTRS2005

Principal Component-Based Radiative Transfer Model (PCRTM) for Hyperspectral Sensors: Theoretical Concept - Part I

Modern infrared satellite sensors such as Atmospheric Infrared Sounder (AIRS), Cosmic Ray Isotope Spectrometer (CrIS), Thermal Emission Spectrometer (TES), Geosynchronous Imaging Fourier Transform Spectrometer (GIFTS) and Infrared Atmospheric Sounding Interferometer (IASI) are capable of providing high spatial and spectral resolution infrared spectra. To fully exploit the vast amount of spectral information from these instruments, super fast radiative transfer models are needed. This paper presents a novel radiative transfer model based on principal component analysis. Instead of predicting channel radiance or transmittance spectra directly, the Principal Component-based Radiative Transfer Model (PCRTM) predicts the Principal Component (PC) scores of these quantities. This prediction ability leads to significant savings in computational time. The parameterization of the PCRTM model is derived from properties of PC scores and instrument line shape functions. The PCRTM is very accurate and flexible. Due to its high speed and compressed spectral information format, it has great potential for super fast one-dimensional physical retrievals and for Numerical Weather Prediction (NWP) large volume radiance data assimilation applications. The model has been successfully developed for the National Polar-orbiting Operational Environmental Satellite System Airborne Sounder Testbed - Interferometer (NAST-I) and AIRS instruments. The PCRTM model performs monochromatic radiative transfer calculations and is able to include multiple scattering calculations to account for clouds and aerosols.

Liu, Xu↗
DOE OSTI2023

Probing Molecular Packing of Amorphous Pharmaceutical Solids Using X-ray Atomic Pair Distribution Function and Solid-State NMR

The structural investigation of amorphous pharmaceuticals is of paramount importance in comprehending their physicochemical stability. However, it has remained a relatively underexplored realm primarily due to the limited availability of high-resolution analytical tools. Here in this study, we utilized the combined power of X-ray pair distribution functions (PDFs) and solid-state nuclear magnetic resonance (ssNMR) techniques to probe the molecular packing of amorphous posaconazole and its amorphous solid dispersion at the molecular level. Leveraging synchrotron X-ray PDF data and employing the empirical potential structure refinement (EPSR) methodology, we unraveled the existence of a rigid conformation and discerned short-range intermolecular C–F contacts within amorphous posaconazole. Encouragingly, our ssNMR 19 F– 13 C distance measurements offered corroborative evidence supporting these findings. Furthermore, employing principal component analysis on the X-ray PDF and ssNMR data sets enabled us to gain invaluable insights into the chemical nature of the intermolecular interactions governing the drug–polymer interplay. These outcomes not only furnish crucial structural insights facilitating the comprehension of the underlying mechanisms governing the physicochemical stability but also underscore the efficacy of synergistically harnessing X-ray PDF and ssNMR techniques, complemented by robust modeling strategies, to achieve a high-resolution exploration of amorphous structures.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗