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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
NASA NTRS1997

Development and Flight Test of an Emergency Flight Control System Using Only Engine Thrust on an MD-11 Transport Airplane

An emergency flight control system that uses only engine thrust, called the propulsion-controlled aircraft (PCA) system, was developed and flight tested on an MD-11 airplane. The PCA system is a thrust-only control system, which augments pilot flightpath and track commands with aircraft feedback parameters to control engine thrust. The PCA system was implemented on the MD-11 airplane using only software modifications to existing computers. Results of a 25-hr flight test show that the PCA system can be used to fly to an airport and safely land a transport airplane with an inoperative flight control system. In up-and-away operation, the PCA system served as an acceptable autopilot capable of extended flight over a range of speeds, altitudes, and configurations. PCA approaches, go-arounds, and three landings without the use of any normal flight controls were demonstrated, including ILS-coupled hands-off landings. PCA operation was used to recover from an upset condition. The PCA system was also tested at altitude with all three hydraulic systems turned off. This paper reviews the principles of throttles-only flight control, a history of accidents or incidents in which some or all flight controls were lost, the MD-11 airplane and its systems, PCA system development, operation, flight testing, and pilot comments.

Burcham, Frank W., Jr.↗
NASA NTRSDate not supplied

Global SO 2 Data Record from OMPS Instruments on the JPSS Constellation

NASA’s Earth Observing System (EOS) SO 2 climate data record (CDR) started in 2004, with the launch of the Aura/Ozone Monitoring Instrument (OMI) and is now being continued with the SNPP/Ozone Mapping and Profiler Suite (OMPS) launched in 2011. Both OMI and SNPP/OMPS SO 2 CDRs are produced with the Goddard principal component analysis (PCA) spectral fitting algorithm. An advantage of the data-driven PCA retrieval technique is that it enables highly consistent retrievals from different instruments, by inherently accounting for various instrumental factors. To further extend the EOS SO 2 CDR, we are implementing the PCA SO 2 retrieval algorithm with the L1B measurements from OMPS instruments flying on the Joint Polar Satellite System (JPSS) constellation. In this presentation, we will provide an update on our progress in NOAA-20 (launched in 2017) and NOAA-21 (launched in 2022) PCA SO2 retrievals. We will focus on our new NOAA-20/OMPS PCA SO 2 EOS continuity product, to be publicly released in fall of 2023. We will present statistical analyses on the quality of NOAA-20 PCA SO 2 product, including retrieval noise, biases over background areas, and long-term stability. We will compare our PCA SO 2 retrievals from NOAA-20 with those from OMI, SNPP/OMPS, and S5P/TROPOMI (TROPOspheric Monitoring Instrument) for anthropogenic sources as well as large volcanic plumes. We will also discuss the application of a new machine learning technique that helps to further reduce the noise of NOAA-20 SO 2 retrievals. In addition, we will present preliminary PCA SO 2 retrievals from NOAA-21/OMPS, including those from direct readout implementation for aviation disaster avoidance. Finally, we will share some first results applying the PCA algorithm to NASA’s geostationary TEMPO (Tropospheric Emissions: Monitoring of Pollution) instrument to obtain hourly, high resolution SO 2 data over North America.

SO2↗
DOE OSTI2019

Peak selection matters in Principal Component Analysis: A case study of syntrophic microbes

In situ liquid time-of-flight secondary ion mass spectrometry (ToF-SIMS) is a powerful technique to address surface characterization of living biofilms in hydrated conditions. Principal component analysis (PCA) is an important tool to process a large amount of data and interpret the information from ToF-SIMS spectra. However, ToF-SIMS data analysis is still a great challenge in complicated bacterial biofilms, because many interference peaks from the medium may result in inaccurate interpretation of ToF-SIMS spectral analysis. In this study, two syntrophic Geobacter populations were investigated using in situ liquid ToF-SIMS to reveal the biofilm surface changes between them as due to direct interspecies electron transfer (DIET). Peak overlay is an effective strategy to eliminate the matrix effects in handling complex ToF-SIMS data and was used to accomplish peak selection in this work. Both all peak and selected peak spectral PCA showed a distinctive separation among different samples. However, medium samples occupied high scores values in all peak PCA compared with the selected peak PCA, suggesting a strong interference from the medium as the matrix effect. Amino acids and lipids contributed significantly in separating all samples in selected peak PCA, which indicated that surface membrane components such as pili and proteins are important in the co-cultured Geobacter community compared to controls such as the single population planktonic cells and biofilms, respectively. Additionally, the spectral PCA results of high intensity and high resolution data obtained from liquid ToF-SIMS were compared. Selected peak PCA obtained nice separation among samples in both data sets; however, the high resolution data showed better separation between co-culture planktonics and co-culture aggregates, confirming that the higher mass accuracy is useful in analysis of microbial samples. Furthermore, amino acid and water cluster as an important media for microbial metabolite were selected for a more focused selected peak PCA comparison. Consistent scores and loadings plots were achieved. In conclusion, we show that peak selection is critical for acquiring effective microbial information and interpretation of syntrophic Geobacter using spectral data from in situ liquid ToF-SIMS.

spectral overlay, peak selection, matrix effect, P↗
DOE OSTI2014

Inhibition of N-acetylglucosaminyltransferase V enhances sensitivity of radiotherapy in human prostate cancer

Highlights: • We first evaluated the effect of GnT-V on radiation sensitivity of prostate cancer. • Higher level of GnT-V was detected more frequently in the PCa advanced tumors. • Attenuation of GnT-V inhibited cell proliferation, migration and increased apoptosis. • Knockdown of GnT-V could decrease radiation-induced G2/M arrest and NF-κB activity. • Inhibition of GnT-V may be involved in increasing radiation sensitivity of PCa cells. - Abstract: The purpose of this study was to investigate the relationship between N-acetylglucosaminyltransferase V (GnT-V) and radiation sensitivity of prostate cancer (PCa) cells both in vitro and in vivo. Firstly, the GnT-V expression was studied in 84 cases of PCa tissues, in which higher level of GnT-V was detected more frequently in the advanced tumors. Secondly, the GnT-V stably suppressed cell lines PCa/1079 (Lncap/1079 and PC3/1079) were constructed from PCa cell lines (Lncap and PC3) in vitro. Attenuation of GnT-V inhibited cell proliferation, migration and increased apoptosis, which resulted in enhanced radiation sensitivity of PCa cells. The underlying mechanism may be relevant to the increasing ratio of Bax/Bcl-2, the blocking transcription of NF-κB and the reduction of cell cycle G2-M arrest. Finally, in in vivo study, compared with control groups, the irradiated PCa xenograft nude mice of PCa/1079 indicated to reduce tumor-growth rate and enhance survival time. Summary, our studies showed that inhibition of GnT-V probably improved PCa cells’ radiation sensitivity.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

Metformin inhibits epithelial–mesenchymal transition in prostate cancer cells: Involvement of the tumor suppressor miR30a and its target gene SOX4

Highlights: • Metformin inhibits TGF-β-induced EMT in prostate cancer (PCa) cells. • Metformin upregulates tumor suppressor miR30a and downregulates SOX4 in PCa cells. • SOX4 is a target gene of miR30a. - Abstract: Tumor metastasis is the leading cause of mortality and morbidity of prostate cancer (PCa) patients. Epithelial–mesenchymal transition (EMT) plays a critical role in cancer progression and metastasis. Recent evidence suggested that diabetic patients treated with metformin have lower PCa risk and better prognosis. This study was aimed to investigate the effects of metformin on EMT in PCa cells and the possible microRNA (miRNA)-based mechanisms. MiRNAs have been shown to regulate various processes of cancer metastasis. We herein showed that metformin significantly inhibits proliferation of Vcap and PC-3 cells, induces G0/G1 cell cycle arrest and inhibits invasiveness and motility capacity of Vcap cells. Metformin could inhibit TGF-β-induced EMT in Vcap cells, as manifested by inhibition of the increase of N-cadherin (p = 0.013), Vimentin (p = 0.002) and the decrease of E-cadherin (p = 0.0023) and β-catenin (p = 0.034) at mRNA and protein levels. Notably, we demonstrated significant upregulation of miR30a levels by metformin (P < 0.05) and further experiments indicated that miR30a significantly inhibits proliferation and EMT process of Vcap cells. Interestingly, we identified that SOX4, a previously reported oncogenic transcriptional factor and modulator of EMT, is a direct target gene of miR30a. Finally, we screened the expression of miR30a and SOX4 in 84 PCa cases with radical prostatectomy. Of note, SOX4 overexpression is significantly associated with decreased levels of miR30a in PCa cases. In all, our study suggested that inhibition of EMT by metformin in PCa cells may involve upregulation of miR30a and downregulation of SOX4.

60 APPLIED LIFE SCIENCES↗
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↗
NASA NTRS1996

Development and Flight Test of an Augmented Thrust-Only Flight Control System on an MD-11 Transport Airplane

An emergency flight control system using only engine thrust, called Propulsion-Controlled Aircraft (PCA), has been developed and flight tested on an MD-11 airplane. In this thrust-only control system, pilot flight path and track commands and aircraft feedback parameters are used to control the throttles. The PCA system was installed on the MD-11 airplane using software modifications to existing computers. Flight test results show that the PCA system can be used to fly to an airport and safely land a transport airplane with an inoperative flight control system. In up-and-away operation, the PCA system served as an acceptable autopilot capable of extended flight over a range of speeds and altitudes. The PCA approaches, go-arounds, and three landings without the use of any non-nal flight controls have been demonstrated, including instrument landing system-coupled hands-off landings. The PCA operation was used to recover from an upset condition. In addition, PCA was tested at altitude with all three hydraulic systems turned off. This paper reviews the principles of throttles-only flight control; describes the MD-11 airplane and systems; and discusses PCA system development, operation, flight testing, and pilot comments.

Burcham, Frank W., Jr.↗
NASA NTRS1999

Touchdown: The Development of Propulsion Controlled Aircraft at NASA Dryden

This monograph relates the important history of the Propulsion Controlled Aircraft project at NASA's Dryden Flight Research Center. Spurred by a number of airplane crashes caused by the loss of hydraulic flight controls, a NASA-industry team lead by Frank W. Burcham and C. Gordon Fullerton developed a way to land an aircraft safely using only engine thrust to control the airplane. In spite of initial skepticism, the team discovered that, by manually manipulating an airplane's thrust, there was adequate control for extended up-and-away flight. However, there was not adequate control precision for safe runway landings because of the small control forces, slow response, and difficulty in damping the airplane phugoid and Dutch roll oscillations. The team therefore conceived, developed, and tested the first computerized Propulsion Controlled Aircraft (PCA) system. The PCA system takes pilot commands, uses feedback from airplane measurements, and computes commands for the thrust of each engine, yielding much more precise control. Pitch rate and velocity feedback damp the phugoid oscillation, while yaw rate feedback damps the Dutch roll motion. The team tested the PCA system in simulators and conducted flight research in F-15 and MD-11 airplanes. Later, they developed less sophisticated variants of PCA called PCA Lite and PCA Ultralite to make the system cheaper and therefore more attractive to industry. This monograph tells the PCA story in a non- technical way with emphasis on the human aspects of the engineering and flic,ht-research effort. It thereby supplements the extensive technical literature on PCA and makes the development of this technology accessible to a wide audience.

Tucker, Tom↗
DOE OSTI2023

Mallat Scattering Transformation based surrogate for Magnetohydrodynamics

Abstract A Machine and Deep Learning (MLDL) methodology is developed and applied to give a high fidelity, fast surrogate for 2D resistive MagnetoHydroDynamic (MHD) simulations of Magnetic Liner Inertial Fusion (MagLIF) implosions. The resistive MHD code is used to generate an ensemble of implosions with different liner aspect ratios, initial gas preheat temperatures (that is, different adiabats), and different liner perturbations. The liner density and magnetic field as functions of x , y , and z were generated. The Mallat Scattering Transformation (MST) is taken of the logarithm of both fields and a Principal Components Analysis (PCA) is done on the logarithm of the MST of both fields. The fields are projected onto the PCA vectors and a small number of these PCA vector components are kept. Singular Value Decompositions of the cross correlation of the input parameters to the output logarithm of the MST of the fields, and of the cross correlation of the SVD vector components to the PCA vector components are done. This allows the identification of the PCA vectors vis-a-vis the input parameters. Finally, a Multi Layer Perceptron (MLP) neural network with ReLU activation and a simple three layer encoder/decoder architecture is trained on this dataset to predict the PCA vector components of the fields as a function of time. Details of the implosion, stagnation, and the disassembly are well captured. Examination of the PCA vectors and a permutation importance analysis of the MLP show definitive evidence of an inverse turbulent cascade into a dipole emergent behavior. The orientation of the dipole is set by the initial liner perturbation. The analysis is repeated with a version of the MST which includes phase, called Wavelet Phase Harmonics (WPH). While WPH do not give the physical insight of the MST, they can and are inverted to give field configurations as a function of time, including field-to-field correlations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Multiplexed Quantitative Proteomics in Prostate Cancer Biomarker Development

Prostate cancer (PCa) is the most common non-skin cancer among men in the United States. However, the widely used protein biomarker in PCa, prostate-specific antigen (PSA), while useful for initial detection, its use alone cannot detect aggressive PCa and can lead to overtreatment. This chapter provides an overview of PCa protein biomarker development. It reviews the state-of-the-art liquid chromatography-mass spectrometry-based proteomics technologies for PCa biomarker development, such as enhancing the detection sensitivity of low-abundance proteins through antibody-based or antibody-independent protein/peptide enrichment, enriching post-translational modifications such as glycosylation as well as information-rich extracellular vesicles, and increasing accuracy and throughput using advanced data acquisition methodologies. This chapter also summarizes recent PCa biomarker validation studies that applied those techniques in diverse specimen types, including cell lines, tissues, proximal fluids, urine, and blood, developing novel protein biomarkers for various clinical applications, including early detection and diagnosis, prognosis, and therapeutic intervention of PCa.

Prostate cancer, SRM, PRM, DIA, protein biomarker↗
DOE OSTI2014

Adipose-derived stromal cells inhibit prostate cancer cell proliferation inducing apoptosis

Highlights: • AdSC transplantation exhibits inhibitory effect on tumor progressions of PCa cells. • AdSC-induced PCa cell apoptosis may occur via the TGF-β signaling pathway. • High expression of the TGF-β1 gene in AdSCs. - Abstract: Mesenchymal stem cells (MSCs) have generated a great deal of interest in the field of regenerative medicine. Adipose-derived stromal cells (AdSCs) are known to exhibit extensive proliferation potential and can undergo multilineage differentiation, sharing similar characteristics to bone marrow-derived MSCs. However, as the effect of AdSCs on tumor growth has not been studied sufficiently, we assessed the degree to which AdSCs affect the proliferation of prostate cancer (PCa) cell. Human AdSCs exerted an inhibitory effect on the proliferation of androgen-responsive (LNCaP) and androgen-nonresponsive (PC3) human PCa cells, while normal human dermal fibroblasts (NHDFs) did not, and in fact promoted PCa cell proliferation to a degree. Moreover, AdSCs induced apoptosis of LNCaP cells and PC3 cells, activating the caspase3/7 signaling pathway. cDNA microarray analysis suggested that AdSC-induced apoptosis in both LNCaP and PC3 cells was related to the TGF-β signaling pathway. Consistent with our in vitro observations, local transplantation of AdSCs delayed the growth of tumors derived from both LNCaP- and PC3-xenografts in immunodeficient mice. This is the first preclinical study to have directly demonstrated that AdSC-induced PCa cell apoptosis may occur via the TGF-β signaling pathway, irrespective of androgen-responsiveness. Since autologous AdSCs can be easily isolated from adipose tissue without any ethical concerns, we suggest that therapy with these cells could be a novel approach for patients with PCa.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

INPP4B reverses docetaxel resistance and epithelial-to-mesenchymal transition via the PI3K/Akt signaling pathway in prostate cancer

Docetaxel efficiency in the therapy of prostate cancer (PCa) patients is limited due to the development of chemoresistance. Recent studies have implied a role of INPP4B in tumor chemoresistance, while the effects of INPP4B on docetaxel resistance in PCa have not been elucidated. In the present study, the docetaxel-resistant human PCa cell lines PC3-DR and DU-145-DR were established from the parental cell lines PC3 and DU-145, and the expression and role of INPP4B in docetaxel-resistant PCa cells were investigated. The results demonstrated that INPP4B expression was significantly downregulated in docetaxel-resistant cells. Overexpression of INPP4B increased the sensitivity to docetaxel and promoted cell apoptosis in PC3-DR and DU-145-DR cells. In addition, INPP4B overexpression downregulated the expression of the mesenchymal markers fibronectin, N-cadherin, and vimentin, and upregulated the expression level of the epithelial maker E-cadherin. Furthermore, INPP4B overexpression markedly inhibited the PI3K/Akt pathway. We also found that IGF-1, the inhibitor of PI3K/Akt, markedly blocked the change in EMT markers induced by overexpression of INPP4B, and reversed the resistance of PC3-DR and DU-145-DR cells to docetaxel, which is sensitized by Flag-INPP4B. In summary, the presented data indicate that INPP4B is crucial for docetaxel-resistant PCa cell survival, potentially by regulating EMT through the PI3K/Akt signaling pathway. - Highlights: • INPP4B is downregulated in docetaxel-resistant PCa cells. • INPP4B inhibits cell proliferation. • INPP4B induces cell apoptosis. • INPP4B inhibits PCa cell EMT.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

Assembly of three organic–inorganic hybrid supramolecular materials based on reduced molybdenum(V) phosphates

Three supramolecular materials based on (P{sub 4}Mo{sub 6}) polyoxoanions, (Hbbi){sub 2}(H{sub 2}bbi)[Cu{sub 3}Mo{sub 12}{sup V}O{sub 24}(OH){sub 6}(H{sub 2}O){sub 6}(HPO{sub 4}){sub 4}(H{sub 2}PO{sub 4}){sub 2}(PO{sub 4}){sub 2}]·3H{sub 2}O (1), (Hbbi){sub 2}(H{sub 2}bbi)[Ni{sub 3}Mo{sub 12}{sup V}O{sub 24}(OH){sub 6}(H{sub 2}O){sub 2}(HPO{sub 4}){sub 4}(H{sub 2}PO{sub 4}){sub 2}(PO{sub 4}){sub 2}]·9H{sub 2}O (2), (Hbpy)(bpy){sub 3}[Ni{sub 2}(H{sub 2}O){sub 10}Na(PCA){sub 2}][NiMo{sub 12}{sup V}O{sub 24}(OH){sub 6}(H{sub 2}PO{sub 4}){sub 6}(PO{sub 4}){sub 2}]·6H{sub 2}O (3) (bbi=1,1′-(1,4-butanediyl)bis(imidazole), bpy=4,4′-bipyridine, PCA=pyridine-4-carboxylic acid), have been hydrothermally synthesized and structurally characterized by the elemental analysis, TG, IR, UV–vis, PXRD and the single-crystal X-ray diffraction. Compounds 1 and 2 exhibit covalent 1-D chains constructed from M[P{sub 4}Mo{sub 6}]{sub 2} dimeric cluster and (M(H{sub 2}O){sub n}) (M=Cu, n=3 for 1 and M=Ni, n=1 for 2) linker. Compound 3 possesses an unusual POMMOF supramolecular layers based on [Ni(P{sub 4}Mo{sub 6})]{sub 2} dimeric units and 1-D metal–organic strings [Ni(H{sub 2}O){sub 5}Na(PCA)]{sub n}, in which an in situ ligand of PCA from 1,3-bis(4-pyridyl)propane (bpp) precursor was observed. Furthermore, the electrochemical behavior of 1–3-CPE and magnetic properties of 1–3 have been investigated in detail. - Graphical abstract: As new linking unit, Cu(H{sub 2}O){sub 3}, Ni(H{sub 2}O), and (Ni{sub 2}(H{sub 2}O){sub 10}Na(PCA){sub 2}) are introduced into (TM(P{sub 4}Mo{sub 6}){sub 2}) reaction systems to assemble three supramolecular materials under hydrothermal conditions via changing organic ligand and transition metal. - Highlights: • Tree new supramolecular hybrids based on (P{sub 4}Mo{sub 6}) cluster are reported. • Cu(H{sub 2}O){sub 3} and Ni(H{sub 2}O) as linker are introduced into the (TM(P{sub 4}Mo{sub 6}){sub 2}) systems. • 3 shows unusual layers based on [Ni(P{sub 4}Mo{sub 6})]{sub 2} and 1-D chains [Ni(H{sub 2}O){sub 5}Na(PCA)]{sub n}. • An in situ ligand of PCA from bpp precursor was observed in 3. • The electrochemical and magnetic properties of 1–3 have been studied in detail.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2013

IMPROVED SEARCH OF PRINCIPAL COMPONENT ANALYSIS DATABASES FOR SPECTRO-POLARIMETRIC INVERSION

We describe a simple technique for the acceleration of spectro-polarimetric inversions based on principal component analysis (PCA) of Stokes profiles. This technique involves the indexing of the database models based on the sign of the projections (PCA coefficients) of the first few relevant orders of principal components of the four Stokes parameters. In this way, each model in the database can be attributed a distinctive binary number of 2{sup 4n} bits, where n is the number of PCA orders used for the indexing. Each of these binary numbers (indices) identifies a group of ''compatible'' models for the inversion of a given set of observed Stokes profiles sharing the same index. The complete set of the binary numbers so constructed evidently determines a partition of the database. The search of the database for the PCA inversion of spectro-polarimetric data can profit greatly from this indexing. In practical cases it becomes possible to approach the ideal acceleration factor of 2{sup 4n} as compared to the systematic search of a non-indexed database for a traditional PCA inversion. This indexing method relies on the existence of a physical meaning in the sign of the PCA coefficients of a model. For this reason, the presence of model ambiguities and of spectro-polarimetric noise in the observations limits in practice the number n of relevant PCA orders that can be used for the indexing.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

Characterizing skyrmion flow phases with principal component analysis

Principal component analysis (PCA) is a powerful method that can identify patterns in large, complex data sets by constructing low-dimensional order parameters from higher-dimensional feature vectors. There are increasing efforts to use space-and-time-dependent PCA to detect transitions in nonequilibrium systems that are difficult to characterize with equilibrium methods. Here, we demonstrate that feature vectors incorporating the position and velocity information of driven skyrmions moving through random disorder permit PCA to resolve different types of disordered skyrmion motion as a function of driving force and the ratio of the Magnus force to the dissipation. Since the Magnus force creates gyroscopic motion and a finite Hall angle, skyrmions can exhibit a greater range of flow phases than what is observed in overdamped driven systems with quenched disorder. We show that in addition to identifying previously known skyrmion flow phases, PCA detects several additional phases, including different types of channel flow, moving fluids, and partially ordered states. Guided by the PCA analysis, we further characterize the disordered flow phases to elucidate the different microscopic dynamics and show that the changes in the PCA-derived order parameters can be connected to features in bulk transport measures, including the transverse and longitudinal velocity-force curves, differential conductivity, topological defect density, and changes in the skyrmion Hall angle as a function of drive. We discuss how asymmetric feature vectors can be used to improve the resolution of the PCA analysis, and how this technique can be extended to find disordered phases in other nonequilibrium systems with time-dependent dynamics.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Novel principal component analysis tool based on python for analysis of complex spectra of time-of-flight secondary ion mass spectrometry

Time-of-flight secondary ion mass spectrometry (ToF-SIMS) is a powerful surface analysis tool, which can simultaneously provide elemental, isotopic, and molecular information with part per million (ppm) sensitivity. However, each spectrum may be composed of hundreds of ion signals, which makes the spectra data complex. Principal component analysis (PCA) is a multivariate analysis technique that has been widely used to figure out the variances among samples in ToF-SIMS spectra data analysis and is showing great success in the explanation of complex ToF-SIMS spectra. So far, several software tools have been developed for PCA of ToF-SIMS spectra; however, none of them are freely available. Such a situation leads to some difficulties in extending applications of PCA to various research fields. More importantly, it has long been challenging for common researchers to understand PCA plots and extract chemical differences among samples. In this work, we developed a new and flexible software tool (named “advanced spectra pca toolbox”) based on python for PCA of complex ToF-SIMS spectra along with an easy-to-read manual. It can generate data analysis reports automatically to explain chemical differences among samples, allowing less experienced researchers to easily understand tricky PCA results. Moreover, it is expandable and compatible with artificial intelligence/machine learning functions. Pure goethite and different lignin adsorbed goethite samples were used as a model system to demonstrate our new software tool, proving that our software tool can be readily used in complex spectra data processing. Our new software tool is open-source, convenient, flexible, and expandable. We expect this open-source tool will benefit the ToF-SIMS community.

47 OTHER INSTRUMENTATION↗
DOE OSTI2013

Objective models of compressed breast shapes undergoing mammography

Purpose: To develop models of compressed breasts undergoing mammography based on objective analysis, that are capable of accurately representing breast shapes in acquired clinical images and generating new, clinically realistic shapes. Methods: An automated edge detection algorithm was used to catalogue the breast shapes of clinically acquired cranio-caudal (CC) and medio-lateral oblique (MLO) view mammograms from a large database of digital mammography images. Principal component analysis (PCA) was performed on these shapes to reduce the information contained within the shapes to a small number of linearly independent variables. The breast shape models, one of each view, were developed from the identified principal components, and their ability to reproduce the shape of breasts from an independent set of mammograms not used in the PCA, was assessed both visually and quantitatively by calculating the average distance error (ADE). Results: The PCA breast shape models of the CC and MLO mammographic views based on six principal components, in which 99.2% and 98.0%, respectively, of the total variance of the dataset is contained, were found to be able to reproduce breast shapes with strong fidelity (CC view mean ADE = 0.90 mm, MLO view mean ADE = 1.43 mm) and to generate new clinically realistic shapes. The PCA models based on fewer principal components were also successful, but to a lesser degree, as the two-component model exhibited a mean ADE = 2.99 mm for the CC view, and a mean ADE = 4.63 mm for the MLO view. The four-component models exhibited a mean ADE = 1.47 mm for the CC view and a mean ADE = 2.14 mm for the MLO view. Paired t-tests of the ADE values of each image between models showed that these differences were statistically significant (max p-value = 0.0247). Visual examination of modeled breast shapes confirmed these results. Histograms of the PCA parameters associated with the six principal components were fitted with Gaussian distributions. The six-component model was also used to generate CC and MLO view mammogram breast shapes, using the mean PCA parameter values of these distributions and randomly generated values based on the fitted Gaussian distributions, which resemble clinically encountered breasts. A spreadsheet with the data necessary to apply this model is provided as the supplementary material. Conclusions: Our PCA models of breast shapes in both mammographic views successfully reproduce analyzed breast shapes and generate new clinically relevant shapes. This work can aid in research applications which incorporate breast shape modeling, such as x-ray scatter correction, dosimetry, and image registration.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-D-207A-07: The Effects of Inter-Cycle Respiratory Motion Variation On Dose Accumulation in Single Fraction MR-Guided SBRT Treatment of Renal Cell Carcinoma

Purpose: To assess the dose deposition in simulated single-fraction MR-Linac treatments of renal cell carcinoma, when inter-cycle respiratory motion variation is taken into account using online MRI. Methods: Three motion characterization methods, with increasing complexity, were compared to evaluate the effect of inter-cycle motion variation and drifts on the accumulated dose for an SBRT kidney MR-Linac treatment: 1) STATIC, in which static anatomy was assumed, 2) AVG-RESP, in which 4D-MRI phase-volumes were time-weighted, based on the respiratory phase and 3) PCA, in which 3D volumes were generated using a PCA-model, enabling the detection of inter-cycle variations and drifts. An experimental ITV-based kidney treatment was simulated in a 1.5T magnetic field on three volunteer datasets. For each volunteer a retrospectively sorted 4D-MRI (ten respiratory phases) and fast 2D cine-MR images (temporal resolution = 476ms) were acquired to simulate MR-imaging during radiation. For each method, the high spatio-temporal resolution 3D volumes were non-rigidly registered to obtain deformation vector fields (DVFs). Using the DVFs, pseudo-CTs (generated from the 4D-MRI) were deformed and the dose was accumulated for the entire treatment. The accuracies of all methods were independently determined using an additional, orthogonal 2D-MRI slice. Results: Motion was most accurately estimated using the PCA method, which correctly estimated drifts and inter-cycle variations (RMSE=3.2, 2.2, 1.1mm on average for STATIC, AVG-RESP and PCA, compared to the 2DMRI slice). Dose-volume parameters on the ITV showed moderate changes (D99=35.2, 32.5, 33.8Gy for STATIC, AVG-RESP and PCA). AVG-RESP showed distinct hot/cold spots outside the ITV margin, which were more distributed for the PCA scenario, since inter-cycle variations were not modeled by the AVG-RESP method. Conclusion: Dose differences were observed when inter-cycle variations were taken into account. The increased inter-cycle randomness in motion as captured by the PCA model mitigates the local (erroneous) hotspots estimated by the AVG-RESP method.

60 APPLIED LIFE SCIENCES↗