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NASA NTRS2008

Generation of Boundary Manikin Anthropometry

The purpose of this study was to develop 3D digital boundary manikins that are representative of the anthropometry of a unique population. These digital manikins can be used by designers to verify and validate that the components of the spacesuit design satisfy the requirements specified in the Human Systems Integration Requirements (HSIR) document. Currently, the HSIR requires the suit to accommodate the 1st percentile American female to the 99th percentile American male. The manikin anthropometry was derived using two methods: Principal Component Analysis (PCA) and Whole Body Posture Based Analysis (WBPBA). PCA is a statistical method for reducing a multidimensional data set by using eigenvectors and eigenvalues. The goal is to create a reduced data set that encapsulates the majority of the variation in the population. WBPBA is a multivariate analytical approach that was developed by the Anthropometry and Biomechanics Facility (ABF) to identify the extremes of the population for a given body posture. WBPBA is a simulation-based method that finds extremes in a population based on anthropometry and posture whereas PCA is based solely on anthropometry. Both methods yield a list of subjects and their anthropometry from the target population; PCA resulted in 20 female and 22 male subjects anthropometry and WBPBA resulted in 7 subjects' anthropometry representing the extreme subjects in the target population. The subjects anthropometry is then used to 'morph' a baseline digital scan of a person with the same body type to create a 3D digital model that can be used as a tool for designers, the details of which will be discussed in subsequent papers.

Young, Karen S.↗

NASA NTRS2007

Emergency Flight Control of a Twin-Jet Commercial Aircraft using Manual Throttle Manipulation

The Department of Homeland Security (DHS) created the PCAR (Propulsion-Controlled Aircraft Recovery) project in 2005 to mitigate the ManPADS (man-portable air defense systems) threat to the commercial aircraft fleet with near-term, low-cost proven technology. Such an attack could potentially cause a major FCS (flight control system) malfunction or other critical system failure onboard the aircraft, despite the extreme reliability of current systems. For the situations in which nominal flight controls are lost or degraded, engine thrust may be the only remaining means for emergency flight control [ref 1]. A computer-controlled thrust system, known as propulsion-controlled aircraft (PCA), was developed in the mid 1990s with NASA, McDonnell Douglas and Honeywell. PCA's major accomplishment was a demonstration of an automatic landing capability using only engine thrust [ref 11. Despite these promising results, no production aircraft have been equipped with a PCA system, due primarily to the modifications required for implementation. A minimally invasive option is TOC (throttles-only control), which uses the same control principles as PCA, but requires absolutely no hardware, software or other aircraft modifications. TOC is pure piloting technique, and has historically been utilized several times by flight crews, both military and civilian, in emergency situations stemming from a loss of conventional control. Since the 1990s, engineers at NASA Dryden Flight Research Center (DFRC) have studied TOC, in both simulation and flight, for emergency flight control with test pilots in numerous configurations. In general, it was shown that TOC was effective on certain aircraft for making a survivable landing. DHS sponsored both NASA Dryden Flight Research Center (Edwards, CA) and United Airlines (Denver, Colorado) to conduct a flight and simulation study of the TOC characteristics of a twin-jet commercial transport, and assess the ability of a crew to control an aircraft down to a survivable runway landing using TOC. The PCAR project objective was a set of pilot procedures for operation of a specific aircraft without hydraulics that (a) have been validated in both simulation and flight by relevant personnel, and (b) mesh well with existing commercial operations, maintenance, and training at a minimum cost. As a result of this study, a procedure has been developed to assist a crew in making a survivable landing using TOC. In a simulation environment, line pilots with little or no previous TOC experience performed survivable runway landings after a few practice TOC approaches. In-flight evaluations put line pilots in a simulated emergency situation where TOC was used to recover the aircraft, maneuver to a landing site, and perform an approach down to 200 feet AGL. The results of this research, including pilot observations, procedure comments, recommendations, future work and lessons learned, will he discussed. Flight data and video footage of TOC approaches may also be shown.

Cole, Jennifer H.↗

NASA NTRS2012

Application of Principal Component Analysis to NIR Spectra of Phyllosilicates: A Technique for Identifying Phyllosilicates on Mars

Orbital near-infrared (NIR) reflectance spectra of the martian surface from the OMEGA and CRISM instruments have identified a variety of phyllosilicates in Noachian terrains. The types of phyllosilicates present on Mars have important implications for the aqueous environments in which they formed, and, thus, for recognizing locales that may have been habitable. Current identifications of phyllosilicates from martian NIR data are based on the positions of spectral absorptions relative to laboratory data of well-characterized samples and from spectral ratios; however, some phyllosilicates can be difficult to distinguish from one another with these methods (i.e. illite vs. muscovite). Here we employ a multivariate statistical technique, principal component analysis (PCA), to differentiate between spectrally similar phyllosilicate minerals. PCA is commonly used in a variety of industries (pharmaceutical, agricultural, viticultural) to discriminate between samples. Previous work using PCA to analyze raw NIR reflectance data from mineral mixtures has shown that this is a viable technique for identifying mineral types, abundances, and particle sizes. Here, we evaluate PCA of second-derivative NIR reflectance data as a method for classifying phyllosilicates and test whether this method can be used to identify phyllosilicates on Mars.

Rampe, E. B.↗

NASA NTRS2015

Principal Component Analysis of Thermographic Data

Principal Component Analysis (PCA) has been shown effective for reducing thermographic NDE data. While a reliable technique for enhancing the visibility of defects in thermal data, PCA can be computationally intense and time consuming when applied to the large data sets typical in thermography. Additionally, PCA can experience problems when very large defects are present (defects that dominate the field-of-view), since the calculation of the eigenvectors is now governed by the presence of the defect, not the "good" material. To increase the processing speed and to minimize the negative effects of large defects, an alternative method of PCA is being pursued where a fixed set of eigenvectors, generated from an analytic model of the thermal response of the material under examination, is used to process the thermal data from composite materials. This method has been applied for characterization of flaws.

Winfree, William P.↗

NASA NTRS2015

Assessing Wetland Hydroperiod and Soil Moisture With Remote Sensing: A Demonstration for the NASA Plum Brook Station Year 2

Primary Goal: Assist with the evaluation and measuring of wetlands hydroperiod at the PlumBrook Station using multi-source remote sensing data as part of a larger effort on projecting climate change-related impacts on the station's wetland ecosystems. MTRI expanded on the multi-source remote sensing capabilities to help estimate and measure hydroperiod and the relative soil moisture of wetlands at NASA's Plum Brook Station. Multi-source remote sensing capabilities are useful in estimating and measuring hydroperiod and relative soil moisture of wetlands. This is important as a changing regional climate has several potential risks for wetland ecosystem function. The year two analysis built on the first year of the project by acquiring and analyzing remote sensing data for additional dates and types of imagery, combined with focused field work. Five deliverables were planned and completed: 1) Show the relative length of hydroperiod using available remote sensing datasets 2) Date linked table of wetlands extent over time for all feasible non-forested wetlands 3) Utilize LIDAR data to measure topographic height above sea level of all wetlands, wetland to catchment area radio, slope of wetlands, and other useful variables 4) A demonstration of how analyzed results from multiple remote sensing data sources can help with wetlands vulnerability assessment 5) A MTRI style report summarizing year 2 results. This report serves as a descriptive summary of our completion of these our deliverables. Additionally, two formal meetings were held with Larry Liou and Amanda Sprinzl to provide project updates and receive direction on outputs. These were held on 2/26/15 and 9/17/15 at the Plum Brook Station. Principal Component Analysis (PCA) is a multivariate statistical technique used to identify dominant spatial and temporal backscatter signatures. PCA reduces the information contained in the temporal dataset to the first few new Principal Component (PC) images. Some advantages of PCA include the ability to filter out temporal autocorrelation and reduce speckle to the higher order PC images. A PCA was performed using ERDAS Imagine on a time series of PALSAR dates. Hydroperiod maps were created by separating the PALSAR dates into two date ranges, 2006-2008 and 2010, and performing an unsupervised classification on the PCAs.

Brooks, Colin↗

NASA NTRS2019

A New Discrete Wavelength BUV Algorithm for Consistent Volcanic SO2 Retrievals from Multiple Satellite Missions

This paper describes a new discrete wavelength algorithm developed for retrieving volcanic sulfur dioxide (SO2) vertical column density (VCD) from UV observing satellites. The Multi-Satellite SO2 algorithm (MS_SO2) simultaneously retrieves column densities of sulfur dioxide, ozone, and Lambertian effective reflectivity (LER) and its spectral dependence. It is used operationally to process measurements from the heritage Total Ozone Mapping Spectrometer (TOMS) onboard NASA's Nimbus-7 satellite (N7/TOMS: 1978-1993) and from the current Earth Polychromatic Imaging Camera (EPIC) onboard Deep Space Climate Observatory (DSCOVR: 2015-) from the Earth-Sun Lagrange (L1) orbit. Results from MS_SO2 algorithm for several volcanic cases were assessed using the more sensitive principal component analysis (PCA) algorithm. The PCA is an operational algorithm used by NASA to retrieve SO2 from hyperspectral UV spectrometers, such as the Ozone Monitoring Instrument (OMI) onboard NASA's Earth Observing System Aura satellite and Ozone Mapping and Profiling Suite (OMPS) onboard NASA-NOAA Suomi National Polar Partnership (SNPP) satellite. For this comparative study, the PCA algorithm was modified to use the discrete wavelengths of the Nimbus-7/TOMS instrument, described in Sect. S1 of the Supplement. Our results demonstrate good agreement between the two retrievals for the largest volcanic eruptions of the satellite era, such as the 1991 Pinatubo eruption. To estimate SO2 retrieval systematic uncertainties, we use radiative transfer simulations explicitly accounting for volcanic sulfate and ash aerosols. Our results suggest that the discrete-wavelength MS_SO2 algorithm, although less sensitive than hyperspectral PCA algorithm, can be adapted to retrieve volcanic SO2 VCDs from contemporary hyperspectral UV instruments, such as OMI and OMPS, to create consistent, multi-satellite, long-term volcanic SO2 climate data records.

Bradford L Fisher↗

NASA NTRS2020

MSL Telecom Automated Anomaly Detection

The Mars Science Laboratory (MSL) Telecom Operations Team at the Jet Propulsion Laboratory (JPL) has implemented a machine learning system in order to automate the anomaly detection process as a part of daily operations. Machine learning enables reliable detection of anomalies in Telecom-related telemetry and automated reporting of Telecom subsystem status, resulting in an 90% reduction in team workload and improved anomaly detection reliability. At present, machine learning methods are used to detect: 1. Anomalous long-term trends in telemetry data 2. Anomalous time-domain evolution of telemetry values Both types of anomalies pose their own unique challenges that are addressed in different ways. In the first case, long term trending of daily minima, maximum, and mean telemetry values in temperatures, currents, voltages, and radio frequency (RF) power levels is used in addition to hard threshold safety checks to look for changes in long-term equipment health and performance. Long-term trending methods allow for ordinary seasonal variations in these quantities caused by temperature changes over the course of the Martian year while allowing operators to determine whether current performance remains in line with historical values from previous years. Changes in long-term trends can provide important insights into the health and status of the rover's on-board systems as well as valuable early warning if subtle degradation begins to take hold. But while trending of daily statistics is valuable, it does not detect anomalies in the short-term time evolution of data over the course of minutes or hours during a day, and this task is handled with short-term shape analysis. Principal components analysis (PCA) has been found to provide robust detection of short-term anomalies, and several examples of the use of PCA to detect actual anomalous events will be provided here. In using PCA, we use both the percentage of explained variance and also a log likelihood test on the PCA expansion coefficients to flag telemetry data for human review. Previous work in the field of spacecraft anomaly detection includes [1] for MSL and [2] for some other JPL missions.

Mukai, Ryan↗

NASA NTRSDate not supplied

Derivation of Integrated Load Distributions from Resampled Computational Data

Principal component analysis (PCA) has been the center of many surrogate models used to characterize fluid flows in recent years. However, little work has been done to character- ize the uncertainty in the PCA transform itself and its effect on derived surrogate models. To explore the uncertainty, a typical interpolated surrogate model is constructed for a represen- tative aerodynamic body from computational data. The computational data is then resampled to explore the robustness of the PCA transformation. The variations of the model predictions during this resampling are analyzed to get a measure of confidence in the PCA transformation, which is then applied to the interpolated surrogate model to get uncertainty on integrated force predictions. An initial test case has been explored with promising results.

Principal Component Analysis↗

NASA NTRSDate not supplied

Derivation of Integrated Load Distributions from Resampled Computational Data

Computational Fluid Dynamics↗

DOE OSTI2016

An Efficient Sampling-Based Method for Sensitivity and Uncertainty Analysis Through RAVEN

Sensitivity analysis (SA) and uncertainty quantification (UQ) are now widely recognized as essential parts of analyses for complex nuclear systems. Plenty of well-established methodologies are already available for performing SA and UQ, such as Monte Carlo analysis, adjoint-based method, and variance decomposition. The focus of this summary is on developing an efficient sampling-based approach to perform SA and UQ through RAVEN. RAVEN (Risk Analysis and Virtual control Environment) is a software framework able to perform SA and UQ, currently under development at the Idaho National Laboratory. In literature, a number of approaches have been developed to accelerate the process of SA and UQ for large complex systems, such as principal component analysis (PCA) of input space, input-output correlation analysis, and intersection subspace methods. The fundamental idea is to identify the dominant active directions in the parameter space prior to conducting SA and UQ. The premise is that the number of active directions is much less than the dimensionality of input space. In this work, the PCA or Karhunen-Loeve expansion or SVD method is implemented in RAVEN to identify the active directions. This approach is also combined with the forward sampling approaches, such as Monte Carlo, Grid, Latin Hypercube, and Response Surface Design sampling approaches that are currently available in RAVEN, to perform SA and UQ more efficiently. The summary is organized as follows. In the following section, we present the PCA-based forward sampling approach. The effectiveness and efficiency are discussed in the numerical results section. The paper then ends with a concluding summary. In this summary, we have presented PCA-based forward sampling approach to perform SA and UQ. The goal is to reduce the computational overhead for repetitive executions of complex nuclear systems that are generally required when performing SA and UQ. The control of correlations between input parameters is implemented in RAVEN to better represent the input relationships. In addition, we are currently examining our implementations with more realistic models, and we are also focusing on implementation of some new high-dimensional input reduction techniques in RAVEN. (authors)

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2017

Condition monitoring and false alarm reducing of sensors in NPP - 222

With more and more application of digital I and C systems in nuclear power plants (NPP), more and more sensors are also required. Large amount of sensors really can make contribution to the condition monitoring of systems and equipment in NPP, whereas the heavy use also increases the malfunction risk of sensor self. Therefore the reliability and effectiveness of sensors must be guaranteed first. Thus the condition of sensors should be monitored first in a NPP. Since the precise mathematical models of sensors in complex systems are relative difficult to establish, however quantity of operating data can be easily obtained due to the wide application of digital I and C systems, thus this article adopts principal component analysis (PCA) to carry out condition monitoring for sensors. The PCA model developed for sensors is proved to be effective with operating data acquired from a real NPP. It can detect and isolate the fault when an artificial fault is imposed to the normal data. Then the monitoring results are further processed by another control limit (second confidence limit) besides T 2 and Q statistics (first confidence limit) in PCA. The value of second confidence limit is calculated based on the probability statistics of T 2 and Q statistics. And simulation tests show that the application of the second confidence limit can greatly reduce false alarms in the PCA model and the model performance can be greatly improved with the application of false alarms reducing method. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2015

Chemical and anatomical changes in Liquidambar styraciflua L. xylem after long term exposure to elevated CO 2

The anatomical and chemical characteristics of sweetgum were studied after 11 years of elevated CO 2 (544 ppm, ambient at 391 ppm) exposure. Anatomically, branch xylem cells were larger for elevated CO 2 trees, and the cell wall thickness was thinner. Chemically, elevated CO 2 exposure did not impact the structural components of the stem wood, but non-structural components were significantly affected. Principal component analysis (PCA) was employed to detect differences between the CO 2 treatments by considering numerous structural and chemical variables, as well as tree size, and data from previously published sources (for example, root biomass, production and turnover). The PCA results indicated a clear separation between trees exposed to ambient and elevated CO 2 conditions. Lastly, correlation loadings plots of the PCA revealed that stem structural components, ash, Ca, Mg, total phenolics, root biomass, production and turnover were the major responses that contribute to the separation between the elevated and ambient CO 2 treated trees.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2019

A hierarchical analysis of the impact of methodological decisions on statistical downscaling of daily precipitation and air temperatures

Despite the widespread application of statistical downscaling tools, uncertainty remains regarding the role of model formulation in determining model skill for daily maximum and minimum temperature (Tmax and Tmin), and precipitation occurrence and intensity. Impacts of several key aspects of statistical transfer function form on model skill are evaluated using a framework resistant to model overspecification. We focus on: (a) model structure: simple (generalized linear models, GLMs) versus complex (artificial neural networks, ANNs) models. (b) Predictor selection: Fixed number of predictors chosen a priori versus stepwise selection of predictors and inclusion of grid point values versus predictors derived from application of principal components analysis (PCA) to spatial fields. We also examine the influence of domain size on model performance. For precipitation downscaling, we consider the role of the threshold used to characterize a wet day and apply three approaches (Poisson and Gamma distributions in GLM and ANN) to downscale wet-day precipitation amounts. While no downscaling formulation is optimal for all predictands and at 10 locations representing diverse U.S. climates, and due to the exclusion of variance inflation all of the downscaling formulations fail to reproduce the range of observed variability, models with larger suites of prospective predictors generally have higher skill. For temperature downscaling, ANNs generally outperform GLM, with greater improvements for T min than T max . Furthermore, use of PCA-derived predictors does not systematically improve model skill, but does improve skill for temperature extremes. Model skill for precipitation occurrence generally increases as the wet-day threshold increases and models using PCA-derived predictors tend to outperform those based on grid cell predictors. Each model for wet-day precipitation intensity overestimates annual total precipitation and underestimates the proportion derived from extreme precipitation events, but ANN-based models and those with larger predictor suites tend to have the smallest bias.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2018

Nanosized low-temperature phases of titanium(IV) oxide with anatase and η-phase structures: composition, structure, and photocatalytic properties

Nanosized low-temperature modifications of titanium(IV) oxide with the anatase and η-phase (TiO{sub 2–x}•mH{sub 2}O) structures were prepared by the sulfate method (pH <3) and for the first time characterized by a complex of methods (X-ray diffractometry, scanning electron microscopy, IR spectroscopy, Raman spectroscopy, differential scanning calorimetry, low-temperature sorption capacity of samples to nitrogen, and differentiating dissolution). The physicochemical properties of the modifications were compared. Differences in compositions of the surface and volumes of the samples, compositions and structures of the phases (structure of η-phase is superstructure to anatase), and microstructure elements (sizes of crystallites, nanoparticles, and specific surface area; volume of nano- and ultrananopores) were revealed. The region of η-phase stability was found in the coordinates of temperature (T = 50–95 °C) and hydrolysis duration (τ = 10–80 min) and temperature (T≤200 °C) and annealing duration (τ <6 h). The distinctions in the thermal behavior of the nanosized anatase and η-phase were studied. The photocatalytic activity (PCA) is determined by the physicochemical characteristics of the samples and phases and also by the composition and structure of the decomposed substrate. In the case of difenoxazole, the PCA is affected by the compositions of the samples, phases, and surface (the presence of OH groups) and specific surface area, whereas the composition of the samples and specific surface area affect the PCA in the case of thiamethoxam.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

DOE OSTI2019

Impact of image guidance on toxicity and tumour outcome in moderately hypofractionated external-beam radiotherapy for prostate cancer

To report toxicity and efficacy outcome of moderately hypofractionated image-guided external-beam radiotherapy in a large series of patients treated for prostate cancer (PCa). Between 10/2006 and 12/2015, 572 T1-T3N0M0 PCa patients received 70.2 Gy in 26 fractions at 2.7 Gy/fraction: 344 patients (60%) with three-dimensional conformal radiotherapy (3D-CRT) and 228 (40%) with intensity-modulated radiotherapy (IMRT). Radiation Therapy Oncology Group/European Organization for Research and Treatment of Cancer criteria and Houston definition (nadir + 2) were used for toxicity and biochemical failure evaluation, respectively. Median age was 74 years (interquartile range 69–77). Compared with 3D-CRT, in IMRT group more high-risk patients (29% vs 18%; P = 0.002) and more high-volume target (75% vs 60%; P < 0.001) were included. Acute gastro-intestinal (GI) toxicity G > 1 were registered in 8% and in 11% IMRT and 3D-CRT patients, respectively, whereas late GI G > 1 were observed in 2% and 16% IMRT and 3D-CRT patients, respectively. Acute genito-urinary (GU) toxicity G > 1 were registered in 26% and 40% IMRT and 3D-CRT patients, respectively, whereas late GU G > 1 occurred in 5% IMRT and 15% 3D-CRT patients. Multivariate proportional hazard Cox models confirmed significantly greater risk of late toxicity with 3D-CRT compared to IMRT for GU > 1 (P = 0.004) and for GI > 1 (P < 0.001). With a median 4-year follow-up, overall survival (OS), clinical progression-free survival (cPFS) and biochemical PFS (bPFS) for the whole series were 91%, 92% and 91%, respectively. cPFS and bPFS were significantly different by risk groups. Multivariate Cox models for bPFS and cPFS showed no difference between irradiation techniques and a significant impact of risk group and initial PSA. Moderately hypofractionated radiotherapy is a viable treatment option for localized PCa with excellent tumour control and satisfactory toxicity profile. IMRT seems associated with a reduction in toxicity, whereas tumour control was equal between IMRT and 3D-CRT patients and depended mainly on the risk category.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2014

MiR-361-5p acts as a tumor suppressor in prostate cancer by targeting signal transducer and activator of transcription-6(STAT6)

Highlights: • The role of miR-361-5p in prostate cancer (PCa) has not been evaluated until date. • We found that the expression of miR-361-5p in CRPC was lower than in ADPC. • MiR-361-5p suppressed DU145 cell proliferation and triggered apoptosis. • STAT6 is a direct target of miR-361-5p. • STAT6 enhances the expression of Bcl-xL at the transcriptional level. - Abstract: Castration-resistant prostate cancer (CRPC), whose pathogenesis is known to be regulated by microRNAs (miRNAs), has a poor prognosis. In our present study, we found that the expression of miR-361-5p in CRPC was lower than in androgen-dependent prostate cancer (ADPC), indicating that miR-361-5p may play an important role in the progression of ADPC to CRPC. The role of miR-361-5p in prostate cancer (PCa) has not been evaluated until date. Our findings suggest that miR-361-5p is a suppressor in CRPC. Signal transducer and activator of transcription-6 (STAT6), a direct target of miR-361-5p, enhances the expression of B-cell lymphoma-extra large (Bcl-xL), while miR-361-5p inhibits its expression through STAT6. Therefore, miR-361-5p has great clinical significance in preventing the malignant progression of PCa.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

Expression and potential role of the peptide orexin-A in prostate cancer

The peptides orexin-A and orexin-B and their G protein-coupled OX1 and OX2 receptors are involved in multiple physiological processes in the central nervous system and peripheral organs. Altered expression or signaling dysregulation of orexins and their receptors have been associated with a wide range of human diseases including narcolepsy, obesity, drug addiction, and cancer. Although orexin-A, its precursor molecule prepro-orexin and OX1 receptor have been detected in the human normal and hyperplastic prostate tissues, their expression and function in the prostate cancer (PCa) remains to be addressed. Here, we demonstrate for the first time the immunohistochemical localization of orexin-A in human PCa specimens, and the expression of prepro-orexin and OX1 receptor at both protein and mRNA levels in these tissues. Orexin-A administration to the human androgen-dependent prostate carcinoma cells LNCaP up-regulates OX1 receptor expression resulting in a decrease of cell survival. Noteworthy, nanomolar concentrations of the peptide counteract the testosterone-induced nuclear translocation of the androgen receptor in the cells: the orexin-A action is prevented by the addition of the OX1 receptor antagonist SB-408124 to the test system. These findings indicate that orexin-A/OX1 receptor interaction interferes with the activity of the androgen receptor which regulates PCa onset and progression, thus suggesting that orexin-A and its receptor might represent novel therapeutic targets to challenge this aggressive cancer. - Highlights: • Orexin-A and OX1 receptor are present in human cancer prostate tissues. • Orexin-A up-regulates OX1 receptor expression in LNCaP cells. • Orexin-A inhibits testosterone-induced nuclear translocation of androgen receptor.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2018

Exosomes secreted by placental stem cells selectively inhibit growth of aggressive prostate cancer cells

Highlights: • Detailed characterization of exosomes secreted by human placental stem cells (PLSC{sup Exo}) . • Identified anti-cancer efficacy of PLSC{sup Exo} against aggressive prostate cancer cells. • Retinoic acid receptor/Liver x receptor pathway seems central to PLSC{sup Exo} biological effects. The current paradigm in the development of new cancer therapies is the ability to target tumor cells while avoiding harm to noncancerous cells. Furthermore, there is a need to develop novel therapeutic options against drug-resistant cancer cells. Herein, we characterized the placental-derived stem cell (PLSC) exosomes (PLSC{sup Exo}) and evaluated their anti-cancer efficacy in prostate cancer (PCa) cell lines. Nanoparticle tracking analyses revealed the size distribution (average size 131.4 ± 0.9 nm) and concentration of exosomes (5.23 × 10{sup 10}±1.99 × 10{sup 9} per ml) secreted by PLSC. PLSC{sup Exo} treatment strongly inhibited the viability of enzalutamide-sensitive and -resistant PCa cell lines (C4-2B, CWR-R1, and LNCaP cells). Interestingly, PLSC{sup Exo} treatment had no effect on the viability of a non-neoplastic human prostate cell line (PREC-1). Mass spectrometry (MS) analyses showed that PLSC{sup Exo} are loaded with 241 proteins and mainly with saturated fatty acids. Further, Ingenuity Pathway Analysis analyses of proteins loaded in PLSC{sup Exo} suggested the role of retinoic acid receptor/liver x receptor pathways in their biological effects. Together, these results suggest the novel selective anti-cancer effects of PLSC{sup Exo} against aggressive PCa cells.

60 APPLIED LIFE SCIENCES↗