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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 757 records · Page 42
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

TH-CD-207A-03: A Surface Deformation Driven Respiratory Model for Organ Motion Tracking in Lung Cancer Radiotherapy

Purpose: To propose and validate a novel real-time surface-mesh-based internal organ-external surface motion and deformation tracking method for lung cancer radiotherapy. Methods: Deformation vector fields (DVFs) which characterizes the internal and external motion are obtained by registering the internal organ and tumor contours and external surface meshes to a reference phase in the 4D CT images using a recent developed local topology preserved non-rigid point matching algorithm (TOP). A composite matrix is constructed by combing the estimated internal and external DVFs. Principle component analysis (PCA) is then applied on the composite matrix to extract principal motion characteristics and finally yield the respiratory motion model parameters which correlates the internal and external motion and deformation. The accuracy of the respiratory motion model is evaluated using a 4D NURBS-based cardiac-torso (NCAT) synthetic phantom and three lung cancer cases. The center of mass (COM) difference is used to measure the tumor motion tracking accuracy, and the Dice’s coefficient (DC), percent error (PE) and Housdourf’s distance (HD) are used to measure the agreement between the predicted and ground truth tumor shape. Results: The mean COM is 0.84±0.49mm and 0.50±0.47mm for the phantom and patient data respectively. The mean DC, PE and HD are 0.93±0.01, 0.13±0.03 and 1.24±0.34 voxels for the phantom, and 0.91±0.04, 0.17±0.07 and 3.93±2.12 voxels for the three lung cancer patients, respectively. Conclusions: We have proposed and validate a real-time surface-mesh-based organ motion and deformation tracking method with an internal-external motion modeling. The preliminary results conducted on a synthetic 4D NCAT phantom and 4D CT images from three lung cancer cases show that the proposed method is reliable and accurate in tracking both the tumor motion trajectory and deformation, which can serve as a potential tool for real-time organ motion and deformation monitoring in lung cancer radiotherapy. This work is supported in part by grant from VARIAN MEDICAL SYSTEMS INC, the National Natural Science Foundation of China (no 81428019 and no 81301940), the Guangdong Natural Science Foundation (2015A030313302)and the 2015 Pearl River S&T Nova Program of Guangzhou (201506010096).

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Synergistic Effects of a Chalkophore, Methanobactin, on Microbial Methylation of Mercury

Microbial production of the neurotoxin methylmercury (MeHg) is a significant health and environmental concern, as it can bioaccumulate and biomagnify in the food web. A chalkophore or a copper-binding compound, termed methanobactin (MB), has been shown to form strong complexes with mercury [as Hg(II)] and also enables some methanotrophs to degrade MeHg. It is unknown, however, if Hg(II) binding with MB can also impede Hg(II) methylation by other microbes. Contrary to expectations, MB produced by the methanotroph Methylosinus trichosporium OB3b (OB3b-MB) enhanced the rate and efficiency of Hg(II) methylation more than that observed with thiol compounds (such as cysteine) by the mercury-methylating bacteria Desulfovibrio desulfuricans ND132 and Geobacter sulfurreducens PCA. Compared to no-MB controls, OB3b-MB decreased the rates of Hg(II) sorption and internalization, but increased methylation by 5- to 7-fold, suggesting that Hg(II) complexation with OB3b-MB facilitated exchange and internal transfer of Hg(II) to the HgcAB proteins required for methylation. Conversely, addition of excess amounts of OB3b-MB or a different form of MB from Methylocystis strain SB2 (SB2-MB) inhibited Hg(II) methylation, likely due to greater binding of Hg(II). Collectively, our results underscore the complex roles of microbial exogenous metal-scavenging compounds in controlling net production and bioaccumulation of MeHg in the environment. Some anaerobic microorganisms convert inorganic mercury (Hg) into the neurotoxin methylmercury, which can bioaccumulate and biomagnify in the food web. While the genetic basis of microbial mercury methylation is known, factors that control net methylmercury production in the environment are still poorly understood. Here, it is shown that mercury methylation can be substantially enhanced by one form of an exogenous copper-binding compound (methanobactin) produced by some methanotrophs, but not by another. This novel finding illustrates that complex interactions exist between microbes and that these interactions can potentially affect the net production of methylmercury in situ .

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2013

Simultaneous Catabolism of Plant-Derived Aromatic Compounds Results in Enhanced Growth for Members of the Roseobacter Lineage

ABSTRACT Plant-derived aromatic compounds are important components of the dissolved organic carbon pool in coastal salt marshes, and their mineralization by resident bacteria contributes to carbon cycling in these systems. Members of the roseobacter lineage of marine bacteria are abundant in coastal salt marshes, and several characterized strains, including Sagittula stellata E-37, utilize aromatic compounds as primary growth substrates. The genome sequence of S. stellata contains multiple, potentially competing, aerobic ring-cleaving pathways. Preferential hierarchies in substrate utilization and complex transcriptional regulation have been demonstrated to be the norm in many soil bacteria that also contain multiple ring-cleaving pathways. The purpose of this study was to ascertain whether substrate preference exists in S. stellata when the organism is provided a mixture of aromatic compounds that proceed through different ring-cleaving pathways. We focused on the protocatechuate ( pca ) and the aerobic benzoyl coenzyme A ( box ) pathways and the substrates known to proceed through them, p -hydroxybenzoate (POB) and benzoate, respectively. When these two substrates were provided at nonlimiting carbon concentrations, temporal patterns of cell density, gene transcript abundance, enzyme activity, and substrate concentrations indicated that S. stellata simultaneously catabolized both substrates. Furthermore, enhanced growth rates were observed when S. stellata was provided both compounds simultaneously compared to the rates of cells grown singly with an equimolar concentration of either substrate alone. This simultaneous-catabolism phenotype was also demonstrated in another lineage member, Ruegeria pomeroyi DSS-3. These findings challenge the paradigm of sequential aromatic catabolism reported for soil bacteria and contribute to the growing body of physiological evidence demonstrating the metabolic versatility of roseobacters.

catalysis (homogeneous), catalysis (heterogeneous)↗
DOE OSTI2025

Visual Instance-aware Prompt Tuning

Visual Prompt Tuning (VPT) has emerged as a parameter-efficient fine-tuning paradigm for vision transformers, with conventional approaches utilizing dataset-level prompts that remain the same across all input instances. We observe that this strategy results in sub-optimal performance due to high variance in downstream datasets. To address this challenge, we propose Visual Instance-aware Prompt Tuning (ViaPT), which generates instance-aware prompts based on each individual input and fuses them with dataset-level prompts, leveraging Principal Component Analysis (PCA) to retain important prompting information. Moreover, we reveal that VPT-Deep and VPT-Shallow represent two corner cases based on a conceptual understanding, in which they fail to effectively capture instance-specific information, while random dimension reduction on prompts only yields performance between the two extremes. Instead, ViaPT overcomes these limitations by balancing dataset-level and instance-level knowledge, while reducing the amount of learnable parameters compared to VPT-Deep. Extensive experiments across 34 diverse datasets demonstrate that our method consistently outperforms state-of-the-art baselines, establishing a new paradigm for analyzing and optimizing visual prompts for vision transformers.

Xiao, Xi [ORNL] (ORCID:0009000009316982)↗
DOE OSTI2014

Dimensionality Reduction Library v 0.2

Dimensionality Reduction Library is a C++ library for dimensionality reduction. In the context of this library, dimensionality reduction is considered to consist of 1)estimation of the intrinsic dimensionality using sampled data, 2) Finding maps that reduce the dimensionality of data (forward map) or increase the dimensionality of data (reverse map) and 3) mapping arbitray coordinates to high and low dimensionalities. The library is intended to provide a consistent interface to multiple dimensionality reduction algorithms with an efficient C++ interface that runs efficiently on multicore architectures. A few routines have been optimized with an option for GPU acceleration or distributed computation. Currently the library offers intrinsic dimensionality estimation using point-PCA reconstruction error and/ residual variance. The following dimensionality reduction methods have been implemented: Principal Component Analysis Multidimensional Scaling Locally Linear Embedding IsoMap Autoencoder Neutral Networks An executable is also supplied that can be built to allow for command-line access to the library routines. A description for an application of the library for molecular structure analysis has been published.

BROWN, WILLIAM↗
DOE OSTI2015

Extreme_SeaState_Contour_v1

This software generates environmental contours of extreme sea states using buoy observations of significant wave height and energy period or peak period. The code transforms these observations using principal component analysis (PCA) to create an uncorrelated representation of the data. The subsequent components are modeled using probability distributions and parameter fitting functions. The inverse first-order reliability method (I-FORM) is then applied to these models in order to generate an extreme event contour based on a given return period (i.e., 100 years).The subsequent contour is then transformed back into the original input space defined by the variables of interest in order to create an environmental contour of extreme sea states.

Eckert, Aubrey↗
DOE OSTI2018

rmd-runs

The RMD_RUNS function will log10 transform peptide peak intensity, that is, peptide abundance data and determine if any LC-MS analyses (ie,runs) in a N x P peptide data set are statistical outliers. The statistical analysis is based on summarizing each LC-MS run as a vector of q=5 summary statistics which describe the peptide abundance distribution for a specific run; a N x q matrix is then analyzed using robust PCA to compute a robust estimate of the covariance matrix used in a the calculation of a robut Mahalanobis distance.

Crowell, Kevin↗
DOE OSTI2020

Multivariate Curve Resolution (MCR) using Principal Components Inputs and Rigorous Equality and Inequality Constraints

This MATLAB pseudocode perform multivariate curve resolution (MCR) using PCA scores & loadings of data as inputs. It employs rigorous least squares equality and inequality constraints for all elements in the solution factor matrices. It also can be used to perform nonnegative matrix factorization. SAND2020-12650 M Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-NA0003525.

Van Benthem, Mark↗
DOE OSTI2022

Baycal

Bayesian Model Calibration (BayCal) toolkit is a software plugin for Risk Analysis Virtual Environment (RAVEN) framework, arming at inversely quantifying the uncertainties associated with simulation model parameters based on available experiment data. BayCal seeks statistical inference of the uncertain input parameters that are consistent with the available measurement data or observed data. The unique feature of BayCal is the capability to be linked with RAVEN to build corresponding calibration workflows for complex multi-physics simulations. In addition to be able to use the machine learning capability of RAVEN to significantly reduce the computational cost of expensive simulation models, another distinctive feature of BayCal is the capability to deal with high-dimensional correlated model outputs, such as time series observations at multiple locations, via principal component analysis (PCA) technique.

Wang, Congjian↗
DOE OSTI2024

Mechanical and Thermal Forcing for Upslope Flows and Cumulus Convection over the Sierras de Córdoba

Abstract The upslope flow processes affecting the vertical extent of orographic cumulus convection are examined using observations from the Cloud, Aerosol, and Complex Terrain Interactions (CACTI) field campaign. Specifically, clear air returns from the U.S. Department of Energy (DOE) second-generation C-band scanning Atmospheric Radiation Measurement (ARM) precipitation radar (CSAPR2) are used to characterize the structure and variability of the ridge-normal (i.e., up/downslope) flow components, which transport mass to the crest of Argentina’s Sierras de Córdoba and contribute to convective initiation. Data are compiled for the entire CACTI period (October–April), including days with clear skies, shallow cumuli, cumulus congestus, and deep convection. To examine shared variability among >70 000 radar scans, we use (i) a principal component analysis (PCA) to isolate modes of variability in the upslope flow and (ii) composite analysis based on convective outcomes, determined from GOES-16 satellite observations. These data are contextualized with observed surface sensible heat fluxes, thermodynamic profiles, and synoptic-scale analysis. Results indicate distinct thermally and mechanically forced upslope flow modes, modulated by diurnal heating and synoptic-scale variations, respectively. In some instances, there is a superposition of thermal and mechanical forcing, yielding either deeper or shallower upslope flow. The composite analyses based on satellite data show that successively deeper convective outcomes are associated with successively deeper upslope flow layers that more readily transport mass to the ridge crest in conjunction with lower lifting condensation levels, facilitating convective initiation. These results help to isolate the forcing mechanisms for orographic convection and thus provide a foundation for parameterizing orographic convective processes in coarse resolution models.

Meteorology & Atmospheric Sciences↗
DOE OSTI2019

Inpatient Opioid Use After Pancreatectomy: Opportunities for Reducing Initial Opioid Exposure in Cancer Surgery Patients

Background: Despite advances in enhanced surgical recovery programs, strategies limiting postoperative inpatient opioid exposure have not been optimized for pancreatic surgery. The primary aims of this study were to analyze the magnitude and variations in post-pancreatectomy opioid administration and to characterize predictors of low and high inpatient use. Methods: Clinical characteristics and inpatient oral morphine equivalents (OMEs) were downloaded from electronic records for consecutive pancreatectomy patients at a high-volume institution between March 2016 and August 2017. Regression analyses identified predictors of total OMEs as well as highest and lowest quartiles. Results: Pancreatectomy was performed for 158 patients (73% pancreaticoduodenectomy). Transversus abdominus plane (TAP) block was performed for 80% (n = 127) of these patients, almost always paired with intravenous patient-controlled analgesia (IV-PCA), whereas 15% received epidural alone. All the patients received scheduled non-opioid analgesics (median, 2). The median total OME administered was 423 mg (range 0–4362 mg). Higher total OME was associated with preoperative opioid prescriptions (p < 0.001), longer hospital length of stay (LOS; p < 0.001), and no epidural (p = 0.006). The lowest and best quartile cutoff was 180 mg of OME or less, whereas the highest and worst quartile cutoff began at 892.5 mg. After adjustment for inpatient team, only epidural use [odds ratio (OR) 0.3; p = 0.04] predicted lowest-quartile OME. Preoperative opioid prescriptions (OR 8.1; p < 0.001), longer operative time (OR 3.4; p = 0.05), and longer LOS (OR 1.1; p = 0.007) predicted highest-quartile OME. Conclusions: Preoperative opioid prescriptions and longer LOS were associated with increased inpatient OME, whereas epidural use reduced inpatient OME. Understanding the predictors of inpatient opioid use and the variables predicting the lowest and highest quartiles can inform decision-making regarding preoperative counseling, regional anesthetic block choice, and novel inpatient opioid weaning strategies to reduce initial postoperative opioid exposure.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2022

Multi-frame, ultrafast, x-ray microscope for imaging shockwave dynamics

Inertial confinement fusion (ICF) holds increasing promise as a potential source of abundant, clean energy, but has been impeded by defects such as micro-voids in the ablator layer of the fuel capsules. It is critical to understand how these micro-voids interact with the laser-driven shock waves that compress the fuel pellet. At the Matter in Extreme Conditions (MEC) instrument at the Linac Coherent Light Source (LCLS), we utilized an x-ray pulse train with ns separation, an x-ray microscope, and an ultrafast x-ray imaging (UXI) detector to image shock wave interactions with micro-voids. To minimize the high- and low-frequency variations of the captured images, we incorporated principal component analysis (PCA) and image alignment for flat-field correction. After applying these techniques we generated phase and attenuation maps from a 2D hydrodynamic radiation code (xRAGE), which were used to simulate XPCI images that we qualitatively compare with experimental images, providing a one-to-one comparison for benchmarking material performance. Moreover, we implement a transport-of-intensity (TIE) based method to obtain the average projected mass density (areal density) of our experimental images, yielding insight into how defect-bearing ablator materials alter microstructural feature evolution, material compression, and shock wave propagation on ICF-relevant time scales.

47 OTHER INSTRUMENTATION↗
DOE OSTI2014

Contrasting Metabolism in Perenniating Structures of Upland and Lowland Switchgrass Plants Late in the Growing Season

Background: Switchgrass (Panicum virgatum L.) is being developed as a bioenergy crop for many temperate regions of the world. One way to increase biomass yields is to move southern adapted lowland cultivars to more northern latitudes. However, many southerly adapted switchgrass germplasm can suffer significant winter kill in northerly climes. Materials and Methods: Here, we have applied next-generation sequencing in combination with biochemical analyses to query the metabolism of crowns and rhizomes obtained from two contrasting switchgrass cultivars. Crowns and rhizomes from field-grown lowland (cv Kanlow) and upland (cv Summer) switchgrass cultivars were collected from three randomly selected post-flowering plants. Summer plants were senescing, whereas Kanlow plants were not at this harvest date. Results: Principal component analysis (PCA) differentiated between both the Summer and Kanlow transcriptomes and metabolomes. Significant differences in transcript abundances were detected for 8,050 genes, including transcription factors such as WRKYs and those associated with phenylpropanoid biosynthesis. Gene-set enrichment analyses showed that a number of pathways were differentially up-regulated in the two populations. For both populations, protein levels and enzyme activities agreed well with transcript abundances for genes involved in the phenylpropanoid pathway that were upregulated in Kanlow crowns and rhizomes. The combination of these datasets suggests that dormancy-related mechanisms had been triggered in the crowns and rhizomes of the Summer plants, whereas the crowns and rhizomes of Kanlow plants had yet to enter dormancy. Conclusions: Delayed establishment of dormancy at more northerly latitudes could be one factor that reduces wintersurvival in the high-yielding Kanlow plants. Understanding the cellular signatures that accompany the transition to dormancy can be used in the future to select plants with improved winter hardiness.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

A Novel Triazolopyridine-Based Spleen Tyrosine Kinase Inhibitor That Arrests Joint Inflammation

Autoantibodies and the immunoreceptors to which they bind can contribute to the pathogenesis of autoimmune diseases such as rheumatoid arthritis (RA). Spleen Tyrosine Kinase (Syk) is a non-receptor tyrosine kinase with a central role in immunoreceptor (FcR) signaling and immune cell functionality. Syk kinase inhibitors have activity in antibody-dependent immune cell activation assays, in preclinical models of arthritis, and have progressed into clinical trials for RA and other autoimmune diseases. Here we describe the characterization of a novel triazolopyridine-based Syk kinase inhibitor, CC-509. This compound is a potent inhibitor of purified Syk enzyme, FcR-dependent and FcR-independent signaling in primary immune cells, and basophil activation in human whole blood. CC-509 is moderately selective across the kinome and against other non-kinase enzymes or receptors. Importantly, CC-509 was optimized away from and has modest activity against cellular KDR and Jak2, kinases that when inhibited in a preclinical and clinical setting may promote hypertension and neutropenia, respectively. In addition, CC-509 is orally bioavailable and displays dose-dependent efficacy in two rodent models of immune-inflammatory disease. In passive cutaneous anaphylaxis (PCA), CC-509 significantly inhibited skin edema. Moreover, CC-509 significantly reduced paw swelling and the tissue levels of pro-inflammatory cytokines RANTES and MIP-1α in the collagen-induced arthritis (CIA) model. In summary, CC-509 is a potent, moderately selective, and efficacious inhibitor of Syk that has a differentiated profile when compared to other Syk compounds that have progressed into the clinic for RA.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

Application of visible and near-infrared spectroscopy to classification of Miscanthus species

Here, the feasibility of visible and near infrared (NIR) spectroscopy as tool to classify Miscanthus samples was explored in this study. Three types of Miscanthus plants, namely, M. sinensis , M. sacchariflorus and M. fIoridulus , were analyzed using a NIR spectrophotometer. Several classification models based on the NIR spectra data were developed using line discriminated analysis (LDA), partial least squares (PLS), least squares support vector machine regression (LSSVR), radial basis function (RBF) and neural network (NN). The principal component analysis (PCA) presented rough classification with overlapping samples, while the models of Line_LSSVR, RBF_LSSVR and RBF_NN presented almost same calibration and validation results. Due to the higher speed of Line_LSSVR than RBF_LSSVR and RBF_NN, we selected the line_LSSVR model as a representative. In our study, the model based on line_LSSVR showed higher accuracy than LDA and PLS models. The total correct classification rates of 87.79 and 96.51% were observed based on LDA and PLS model in the testing set, respectively, while the line_LSSVR showed 99.42% of total correct classification rate. Meanwhile, the lin_LSSVR model in the testing set showed correct classification rate of 100, 100 and 96.77% for M. sinensis , M. sacchariflorus and M. fIoridulus , respectively. The lin_LSSVR model assigned 99.42% of samples to the right groups, except one M. fIoridulus sample. The results demonstrated that NIR spectra combined with a preliminary morphological classification could be an effective and reliable procedure for the classification of Miscanthus species.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Machine Learning to Identify Geologic Factors Associated with Production in Geothermal Fields: A Case-Study Using 3D Geologic Data from Brady Geothermal Field and NMFk

In this paper, we present an analysis using unsupervised machine learning (ML) to identify the key geologic factors that contribute to the geothermal production in Brady geothermal field. Brady is a hydrothermal system in northwestern Nevada that supports both electricity production and direct use of hydrothermal fluids. Transmissive fuid-fow pathways are relatively rare in the subsurface, but are critical components of hydrothermal systems like Brady and many other types of fuid-fow systems in fractured rock. Here, we analyze geologic data with ML methods to unravel the local geologic controls on these pathways. The ML method, non-negative matrix factorization with k-means clustering (NMFk), is applied to a library of 14 3D geologic characteristics hypothesized to control hydrothermal circulation in the Brady geothermal field. Our results indicate that macro-scale faults and a local step-over in the fault system preferentially occur along production wells when compared to injection wells and non-productive wells. We infer that these are the key geologic characteristics that control the through-going hydrothermal transmission pathways at Brady. Our results demonstrate: (1) the specific geologic controls on the Brady hydrothermal system and (2) the efficacy of pairing ML techniques with 3D geologic characterization to enhance the understanding of subsurface processes. This submission includes the published journal article detailing this work, the published 3D geologic map of the Brady Geothermal Area used as a basis to develop structural and geological variables that are hypothesized to control or effect permeability or connectivity, 3D well data, along which geologic data were sampled for PCA analyses, and associated metadata file. This work was done using the GeoThermalCloud framework, which is part of SmartTensors (both are linked below).

15 GEOTHERMAL ENERGY↗
DOE OSTI2017

NMR-based Metabolomics Analysis of Liver from C57BL/6 Mouse Exposed to Ionizing Radiation

The health effects of exposing to ionizing radiation are attracting great interest in the space exploration community and patients considering radiotherapy. However, the impact to metabolism after exposure to high dose radiation has not yet been clearly defined in livers. In the present study, 1H nuclear magnetic resonance (NMR) based metabolomics combined with multivariate data analysis are applied to study the changes of metabolism in the liver of C57BL/6 mouse after whole body exposure to either gamma (3.0 and 7.8 Gy) or proton (3.0 Gy) radiation. Principal component analysis (PCA) and orthogonal projection to latent structures analysis (OPLS) are employed for classification and identification of potential biomarkers associated with gamma and proton irradiation. The results show that the radiation exposed groups can be well separated from the control group. At the same radiation dosage, the group exposed to proton radiation is well separated from the group exposed to gamma radiation, indicating different radiation sources induce different alterations based on metabolic profiling. Common to both gamma and proton radiation at the high radiation doses studied in this work, compared with the control groups the concentrations of choline, O-phosphocholine and trimethylamine N-oxide are decreased statistically, while those of glutamine, glutathione, malate, creatinine, phosphate, betaine and 4-hydroxyphenylacetate are statistically and significantly elevated after exposure to radiation. Since these altered metabolites are associated with multiple biological pathways, the changes suggest that the exposure to radiation induce abnormality in multiple biological pathways. In particular, metabolites such as 4-hydroxyphenylacetate, betaine, glutamine, choline and trimethylamine N-oxide may be good candidates of pre-diagnose biomarkers for ionizing radiation in liver.

Xiao, Xiongjie↗
DOE OSTI2013

Sequential Projection Pursuit Principal Component Analysis – Dealing with Missing Data Associated with New -Omics Technologies

We present a new version of sequential projection pursuit Principal Component Analysis (sppPCA) that has the capability to perform PCA on large multivariate datasets that contain non-random missing values. We demonstrate that sppPCA generates more robust and informative low-dimensional representations of the data than imputation-based approaches and improved downstream statistical analyses, such as clustering or classification. A Java program to run sppPCA is freely available at https://www.biopilot.org/docs/Software/sppPCA.

Webb-Robertson, Bobbie-Jo M.↗