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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Random projection using random quantum circuits

The random sampling task performed by Google's Sycamore processor gave us a glimpse of the “quantum supremacy era.” This has definitely shed some light on the power of random quantum circuits in this abstract task of sampling outputs from the (pseudo)random circuits. In this paper, we explore a practical near-term use of local random quantum circuits in dimensional reduction of large low-rank data sets. We make use of the well-studied dimensionality reduction technique called the random projection method. This method has been extensively used in various applications such as image processing, logistic regression, entropy computation of low-rank matrices, etc. We prove that the matrix representations of local random quantum circuits with sufficiently shorter depths [ ∼ O ( n ) ] serve as good candidates for random projection. We demonstrate numerically that their projection abilities are not far off from the computationally expensive classical principal components analysis on MNIST and CIFAR-100 image datasets. We also benchmark the performance of quantum random projection against the commonly used classical random projection in the tasks of dimensionality reduction of image data sets and computing von Neumann entropies of large low-rank density matrices. And finally, using variational quantum singular value decomposition, we demonstrate a near-term implementation of extracting the singular vectors with dominant singular values after quantum random projecting a large low-rank matrix to lower dimensions. All such numerical experiments unequivocally demonstrate the ability of local random circuits to randomize a large Hilbert space at sufficiently shorter depths with robust retention of properties of large data sets in reduced dimensions. Published by the American Physical Society 2024

Kumaran, Keerthi (ORCID:0009000949125721)↗
DOE OSTI2021

Cell-Type-Specific Proteomics Analysis of a Small Number of Plant Cells by Integrating Laser Capture Microdissection with a Nanodroplet Sample Processing Platform

Plant organs and tissues contain multiple cell types, which are well organized in 3-dimensional structure to efficiently perform physiological functions such as homeostasis, response to environmental perturbation, pathogen infection. It is critically important to perform molecular measurements at the cell-type-specific level to discover mechanisms and unique features of cell populations that govern differentiation and respond to external perturbations. Although mass spectrometry-based proteomics has been demonstrated as an enabling discovery tool to study plant physiology, conventional approaches require millions of cells to generate robust biological conclusions. Such requirements mask the cell-to-cell heterogeneities and limit the comprehensive profiling of plant proteins at spatially resolved and cell-type-specific resolutions. This protocol describes a recently-developed proteomics workflow for studying a small number of plant cells by integrating laser capture microdissection, microfluidic nanodroplet-based sample preparation, with ultrasensitive liquid chromatography-mass spectrometry. Using poplar as a model tree species, we provide detailed protocols, including plant tissue harvest, tissue preparation, cryosectioning, laser microdissection, protein digestion, mass spectrometry measurement, and data analysis. We show the workflow enables the precise identification and quantification of thousands of proteins from hundreds of isolated plant root and leaf cells.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Vadose and Saturated Zone Flow and Transport Model Package Report for the Active Trenches of the Low-Level Burial Grounds, Hanford Site, Washington

This model package report (MPR) documents the development of the integrated vadose and saturated zone flow and transport model developed for the performance assessment of Trenches 31 and 34 in the low-level burial grounds (LLBGs). This modeling capability is intended for use in addressing the analysis requirements outlined in DOE O 435.1, Chg 1, Radioactive Waste Management1. The overall objective of the modeling effort is to provide a basis for making informed disposal decisions pertinent to Trenches 31 and 34. The purpose of the MPR is to document the development of the three-dimensional numerical vadose zone and saturated flow and transport model test case and evaluate its adequacy to support the LLBG performance assessment. The purpose is not to present results for DOE 435.1 decision making. The use of the model to perform base case and sensitivity analysis for the LLBG performance assessment, including inputs and results, is documented separately in subsequent environmental calculation files. This report discusses the development and translation of the conceptual model for flow and contaminant transport into the LLBG performance assessment three-dimensional numerical flow and transport model evaluated using the Subsurface Transport Over Multiple Phases (STOMP©) simulator. The development of representative geologic framework is described along with the implementation of waste release models used to represent contaminant release from waste disposed in the trenches. The report also provides the technical basis for specific model parameters and boundary conditions, along with description of modeling assumptions. This MPR includes certain calculations that are necessary to demonstrate the soundness of the model. Results provided by the model include vadose zone and saturated zone flow fields, and estimates of the possible future concentration in groundwater of technetium-99 and iodine-129 as example test cases. As an evaluation of the test cases, the model estimates of current vadose conditions are compared to available and analogous field and laboratory data, and compared to the results from the original performance assessment model (WHC-EP-0645, Performance Assessment for the Disposal of Low-Level Waste in the 200 West Area Burial Grounds 2 ). The features, events, and processes applicable to vadose zone and saturated zone flow and transport model are identified, and representative initial estimates for various parameters are documented. Note that the parameter estimates presented in this MPR are for illustration purposes, and may or may not reflect values selected for eventual performance assessment. Several key topical discussions (i.e., basis for recharge estimates, basis for vadose zone modeling, basis for saturated zone model development, and calibration) are included, which serve as the groundwork for confidence building for the groundwater pathway modeling and results. Numerical simulation results based on an example test case are included to illustrate the use of the combined saturated-unsaturated model in performance assessment calculations. Sensitivity and uncertainty results are not included in this MPR. Those results will be included in future environmental calculation files. The inclusion of the trapezoidal trench geometry and construction details in the finite difference grid introduces some gross simplifications regarding the trench and liner systems. The model test case presented in this MPR includes the assumption that the trench liner system does not affect flow through or around the trenches after the liner system is assumed to fail. This MPR also includes results of other test cases that involve alternate assumptions about how to incorporate the hydraulic effects of the trenches into the vadose zone of the model. These alternate cases do not attempt to account for the presence of the liner system after its assumed failure either. None of these cases is considered to be the base case at this time. Analysis and alternate cases that attempt to account for the presence of the liner system in greater detail, and its effect on flow through and around it, are to be documented in subsequent environmental calculation files. Depending on the results of those analyses and alternate cases, the eventual base case may involve more detailed inclusion of the effects of the liner system hydraulics.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

Block orbital-selective Mott insulators: A spin excitation analysis

We present a comprehensive study of the spin excitations—as measured by the dynamical spin structure factor S(q,ω)—of the so-called block-magnetic state of low-dimensional orbital-selective Mott insulators. We realize this state via both a multi-orbital Hubbard model and a generalized Kondo-Heisenberg Hamiltonian. Due to various competing energy scales present in the models, the system develops periodic ferromagnetic islands of various shapes and sizes, which are antiferromagnetically coupled. The 2×2 particular case was already found experimentally in the ladder material BaFe2Se3 that becomes superconducting under pressure. Here we discuss the electronic density as well as Hubbard and Hund coupling dependence of S(q,ω) using density matrix renormalization group method. Several interesting features were identified: (1) An acoustic (dispersive spin-wave) mode develops. (2) The spin-wave bandwidth establishes a new energy scale that is strongly dependent on the size of the magnetic island and becomes abnormally small for large clusters. (3) Optical (dispersionless spin excitation) modes are present for all block states studied here. In addition, a variety of phenomenological spin Hamiltonians have been investigated but none matches entirely our results that were obtained primarily at intermediate Hubbard U strengths. Our comprehensive analysis provides theoretical guidance and motivation to crystal growers to search for appropriate candidate materials to realize the block states, and to neutron scattering experimentalists to confirm the exotic dynamical magnetic properties unveiled here, with a rich mixture of acoustic and optical features.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

An In Situ , Automated High-Explosives Aging Method Utilizing Two-Dimensional Gas Chromatography–Mass Spectrometry

Understanding chemical changes that occur in high explosives as they age is of great importance to the safe employment and storage of these compounds. Traditional methods of aging high explosives even under accelerated aging conditions are time intensive with durations on the order of months to years. The nature of traditional aging analyses reduces each sample to a snapshot data point often separated widely in time, requiring many assumptions as to how the degradation products develop. Further complicating matters, several analytical techniques are typically employed for each sample analysis in order to ascertain an entire picture of the decomposition pathways. To address these shortcomings with existing methods, a new method of accelerated aging of high explosives utilizing comprehensive two-dimensional gas chromatography coupled to high-resolution mass spectrometry (GC × GC-HRMS) was developed using 2,4,6,8,10,12-hexanitro-2,4,6,8,10,12-hexaazaisowurtzitane (CL-20) as a model compound for method development. This in situ automated method reduces the time scale of aging to a matter of hours using the inlet of the GC × GC as the aging vessel. GC × GC in combination with HRMS allowed for the collection of both evolved gases and other decomposition products produced during the entire aging process in real time with HRMS providing far greater certainty in identification of explosives aging products. Additionally, this method allowed for a higher throughput of samples with greatly simplified sample preparation. Chemometric analysis of the GC × GC-HRMS data set via the alteration analysis (ALA) enabled discovery of statistically significant chemical changes providing insight into the variation of decomposition pathways with varying aging temperatures.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Molecular detection of per- and polyfluoroalkyl substances in water using time-of-flight secondary ion mass spectrometry

Detection of per- and polyfluoroalkyl substances (PFASs) is crucial in environmental mitigation and remediation of these persistent pollutants. We demonstrate that time-of-flight secondary ion mass spectrometry (ToF-SIMS) is a viable technique to analyze and identify these substances at parts per trillion (ppt) level in real field samples without complicated sample preparation due to its superior surface sensitivity. Several representative PFAS compounds, such as perfluorooctanesulfonic acid (PFOS), perfluorobutanoic acid (PFBA), perfluoropentanoic acid (PFPeA), perfluoheptanoic acid (PFHpA), and perfluorononanoic acid (PFNA), and real-world groundwater samples collected from monitoring wells installed around at a municipal wastewater treatment plant located in Southern California were analyzed in this work. ToF-SIMS spectral comparison depicts sensitive identification of pseudo-molecular ions, characteristic of reference PFASs. Additionally, principal component analysis (PCA) shows clear discrimination among real samples and reference compounds. Our results show that characteristic molecular ion and fragments peaks can be used to identify PFASs. Furthermore, SIMS two-dimensional (2D) images directly exhibit the distribution of perfluorocarboxylic acid (PFCA) and PFOS in simulated mixtures and real wastewater samples. Such findings indicate that ToF-SIMS is useable to determine PFAS compounds in complex environmental water samples. In conclusion, ToF-SIMS provides simple sample preparation and high sensitivity in mass spectral imaging, offering an alternative solution for environmental forensic analysis of PFASs in wastewater in the future.

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

Topological data analysis of task-based fMRI data from experiments on schizophrenia

We use methods from computational algebraic topology to study functional brain networks, in which nodes represent brain regions and weighted edges represent similarity of fMRI time series from each region. With these tools, which allow one to characterize topological invariants such as loops in high-dimensional data, we are able to gain understanding into low-dimensional structures in networks in a way that complements traditional approaches based on pairwise interactions. In the present paper, we analyze networks constructed from task-based fMRI data from schizophrenia patients, healthy controls, and healthy siblings of schizophrenia patients using persistent homology, which allows us to explore the persistence of topological structures such as loops at different scales in the networks. We use persistence landscapes, persistence images, and Betti curves to create output summaries from our persistent-homology calculations, and we study the persistence landscapes and images using k-means clustering and community detection. Based on our analysis of persistence landscapes, we find that the members of the sibling cohort have topological features (specifically, their 1-dimensional loops) that are distinct from the other two cohorts. From the persistence images, we are able to distinguish all three subject groups and to determine the brain regions in the loops (with four or more edges) that allow us to make these distinctions.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Analysis of Compressibility Effects and Nonlinear Property Variations in a Supercritical CO2 Mixing Layer

Calculations are performed using the large eddy simulation technique to qualitatively assess the effects of fluid compressibility and thermodynamic nonlinearities on the dynamics of a supercritical flow field. Resulting data are also used to interrogate instantaneous subfilter-scale fields, as modeled by the mixed-dynamic Smagorinsky closure and gradient diffusion model. A three-dimensional wall-resolved calculation of a spatially evolving mixing layer composed solely of carbon dioxide is performed. This is followed by a coarse grid calculation to examine model performance as a function of resolution. Theoretical analysis indicates that the partial derivatives of density with respect to pressure and temperature are important modulators of the temperature and pressure field evolution, respectively. Results pertaining to subfilter velocity-velocity and velocity-temperature fields indicate that the models are most stressed in the regions where compressibility and thermo-fluid nonlinearities dominate the physics.

42 ENGINEERING↗
DOE OSTI2025

Understanding latent timescales in neural ordinary differential equation models of advection-dominated dynamical systems

The neural ordinary differential equation (ODE) framework has shown considerable promise in recent years in developing highly accelerated surrogate models for complex physical systems characterized by partial differential equations (PDEs). For PDE-based systems, state-of-the-art neural ODE strategies leverage a two-step procedure to achieve this acceleration: a nonlinear dimensionality reduction step provided by an autoencoder, and a time integration step provided by a neural-network based model for the resultant latent space dynamics (the neural ODE). This work explores the applicability of such autoencoder-based neural ODE strategies for PDEs in which advection terms play a critical role. More specifically, alongside predictive demonstrations, physical insight into the sources of model acceleration (i.e., how the neural ODE achieves its acceleration) is the scope of the current study. Such investigations are performed by quantifying the effects of both autoencoder and neural ODE components on latent system time-scales using eigenvalue analysis of dynamical system Jacobians. To this end, the sensitivity of various critical training parameters – de-coupled versus end-to-end training, latent space dimensionality, and the role of training trajectory length, for example – to both model accuracy and the discovered latent system timescales is quantified. Furthermore, this work specifically uncovers the key role played by the training trajectory length (the number of rollout steps in the loss function during training) on the latent system timescales: larger trajectory lengths correlate with an increase in limiting neural ODE time-scales, and optimal neural ODEs are found to recover the largest time-scales of the full-order (ground-truth) system. Demonstrations are performed across fundamentally different unsteady fluid dynamics configurations influenced by advection: (1) the Kuramoto–Sivashinsky equations (2) Hydrogen-Air channel detonations (the compressible reacting Navier–Stokes equations with detailed chemistry), and (3) 2D Atmospheric flow.

Advection-dominated dynamical systems↗
DOE OSTI2020

Kepler Data Analysis: Non-Gaussian Noise and Fourier Gaussian Process Analysis of Stellar Variability

We develop a statistical analysis model of Kepler stellar flux data in the presence of planet transits, non-Gaussian noise, and stellar variability. We first develop a model for the Kepler noise probability distribution in the presence of outliers, which make the noise probability distribution non-Gaussian. We develop a signal likelihood analysis based on this probability distribution, in which we model the signal as a sum of the star variability and planetary transits. We argue that these components need to be modeled together if optimal signal is to be extracted from the data. For the stellar variability model we develop an optimal Gaussian process analysis using a Fourier-based Wiener filter approach, where the power spectrum is non-parametric and learned from the data. We develop high dimensional optimization of the objective function, where we jointly optimize all the model parameters, including thousands of star variability modes, and planet transit parameters. We apply the method to Kepler-90 data and show that it gives a better match to the stellar variability than the existing methods, and robustly handles noise outliers. As a consequence, the planet radii have a higher value than what the existing methods give, including splines and celerite.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Search for high-mass resonances in a final state comprising a gluon and two hadronically decaying W bosons in proton-proton collisions at $\sqrt{s}$ = 13 TeV

A search for high-mass resonances decaying into a gluon, g, and two W bosons is presented. A Kaluza-Klein gluon, g$_{KK}$, decaying in cascade via a scalar radion R, g$_{KK}$ → gR → gWW, is considered. The final state studied consists of three large-radius jets, two of which contain the products of hadronically decaying W bosons, and the third one the hadronization products of the gluon. The analysis is performed using proton-proton collision data at $\sqrt{s}$ = 13 TeV collected by the CMS experiment at the CERN LHC during 2016–2018, corresponding to an integrated luminosity of 138 fb$^{−1}$. The masses of the g$_{KK}$ and R candidates are reconstructed as trijet and dijet masses, respectively. These are used for event categorization and signal extraction. No excess of data events above the standard model background expectation is observed. Upper limits are set on the product of the g$_{KK}$ production cross section and its branching fraction via a radion R to gWW. This is the first analysis examining the resonant WW+jet signature and setting limits on the two resonance masses in an extended warped extra-dimensional model.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

A priori analysis on deep learning of subgrid-scale parameterizations for Kraichnan turbulence

In the present study, we investigate different data-driven parameterizations for large eddy simulation of two-dimensional turbulence in the a priori settings. These models utilize resolved flow field variables on the coarser grid to estimate the subgrid-scale stresses. We use data-driven closure models based on localized learning that employs a multilayer feedforward artificial neural network with point-to-point mapping and neighboring stencil data mapping, and convolutional neural network fed by data snapshots of the whole domain. The performance of these data-driven closure models is measured through a probability density function and is compared with the dynamic Smagorinksy model (DSM). The quantitative performance is evaluated using the cross-correlation coefficient between the true and predicted stresses. We analyze different frameworks in terms of the amount of training data, selection of input and output features, their characteristics in modeling with accuracy, and training and deployment computational time. We also demonstrate computational gain that can be achieved using the intelligent eddy viscosity model that learns eddy viscosity computed by the DSM instead of subgrid-scale stresses. Here, we detail the hyperparameters optimization of these models using the grid search algorithm.

42 ENGINEERING↗
DOE OSTI2021

Origins of high ductility exhibited by an extruded magnesium alloy Mg-1.8Zn-0.2Ca: Experiments and crystal plasticity modeling

Low ductility and strength are major bottlenecks against Mg alloys' wide applications. In this work, we systematically design the composition and fabrication process for a low-alloyed Mg-Zn-Ca alloy, showing that it can be extruded at low temperatures (~ 250 degrees C) and high speeds (~2 mm/s). After the extrusion, this alloy exhibits a substantially weakened basal texture, relatively small grain size, very high tensile elongation (~ 30%), and good strength. The origin of the considerably improved ductility was studied using a combination of three-dimensional atom probe tomography (3D-APT), transmission electron microscopy (TEM), electron backscattered diffraction (EBSD) in conjunction with surface slip trace analysis, in-situ synchrotron X-ray diffraction, and elasto-plastic self-consistent (EPSC) modeling. Co-segregation of Zn and Ca atoms at a grain boundary is observed and associated with texture weakening and grain boundary mediated plasticity, both improving the ductility. While basal slip and prismatic slip are identified as the dominant deformation systems in the alloy, the ratio between their slip resistances is substantially reduced relative to pure Mg and most other Mg alloys, significantly contributing to the improved ductility of the alloy. This Mg-Zn-Ca alloy exhibiting excellent mechanical properties and low fabrication cost is a promising candidate for industrial productions.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Electro-osmo-dialysis through nanoporous layers physically conjugated to micro-perforated ion-exchange membranes: Highly selective accumulation of trace coions

This study explores theoretically and numerically the phenomenon of counter-flow accumulation of trace coions during electro-osmo-dialysis through composite membranes containing nanoporous and perforated ion-exchange layers. The combination of charged nanopores and perforations allows electroosmosis, and ion concentration polarization appears near the ion-exchange layer. Accumulation of trace coions occurs because their advective and electromigration flow velocity components have opposite signs and are locally compensating in depletion regions where electric fields are high. This local compensation of flow components can give rise to high accumulation factors (>1000 in some cases) for trace coions at some distance from the interface between the nanoporous and ion-exchange layers. Moreover, the extent of this accumulation depends on the trace-coion diffusion coefficient. The resulting selectivity between different coions might serve as a basis for environmentally friendly separation of ionic species such as synthetic peptides or rare-earth elements. For example, a periodic process might include accumulation followed by species release into smaller volumes of solution. For both rare-earth ions and peptide-like species, the accumulation selectivity between ions with small differences in mobilities depends on system parameters such as nanoporous-layer thickness, distance between the perforations, and the zeta potential of the nanopore surface. Here, the use of optimal values of these membrane parameters may enable tuning of the electro-osmo-dialysis system for separation of specific ions. Modelling reveals that despite inhomogeneous patterns of fluid and ion flows close to the nanoporous/perforated ion-exchange interface, at relatively short distances from this interface (less than half the distance between the perforations) all the flows become practically one-dimensional (1D). Similarly, the concentration profiles of trace coions also become 1D. The flow one-dimensionality at short distances from the current-polarized interface enables a simple 1D approximation that is useful for an approximate analysis of trends in this multi-parameter system.

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

Lift & Learn: Physics-informed machine learning for large-scale nonlinear dynamical systems

In this work, we present Lift & Learn, a physics-informed method for learning low-dimensional models for large-scale dynamical systems. The method exploits knowledge of a system’s governing equations to identify a coordinate transformation in which the system dynamics have quadratic structure. This transformation is called a lifting map because it often adds auxiliary variables to the system state. The lifting map is applied to data obtained by evaluating a model for the original nonlinear system. This lifted data is projected onto its leading principal components, and low-dimensional linear and quadratic matrix operators are fit to the lifted reduced data using a least-squares operator inference procedure. Analysis of our method shows that the Lift & Learn models are able to capture the system physics in the lifted coordinates at least as accurately as traditional intrusive model reduction approaches. This preservation of system physics makes the Lift & Learn models robust to changes in inputs. Numerical experiments on the FitzHugh–Nagumo neuron activation model and the compressible Euler equations demonstrate the generalizability of our model.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Fluctuation cepstral scanning transmission electron microscopy of mixed-phase amorphous materials

Four-dimensional scanning transmission electron microscopy (4D-STEM) is a versatile analytical tool for characterizing materials structural properties. However, extending such analysis to disordered materials is challenging, especially in technologically important samples with mixed ordered and disordered phases. Here, in this work, we present a new 4D-STEM method, called fluctuation cepstral STEM (FC-STEM), based on the fluctuation analysis of cepstral transform of diffraction patterns. The peaks in the associated transformation relate to inter-atomic distances in a thin sample. By varying the real-space range over which fluctuations are calculated, distinct ordered and disordered phases can be mapped in a diffractive image reconstruction. We demonstrate the principles of FC-STEM by characterizing a silicon anode, harvested from a cycled lithium-ion battery. A mixture of amorphous and nanocrystalline silicon, graphitic carbon, and electrolyte by-products is identified and mapped. Comparisons with conventional electron imaging and energy-dispersive X-ray spectroscopy show that FC-STEM is highly effective for the structure determination of mixed-phase amorphous materials.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Bridging cognitive gaps between user and model in interactive dimension reduction

Interactive machine learning (ML) systems are difficult to design because of the "Two Black Boxes" problem that exists at the interface between human and machine. Many algorithms that are used in interactive ML systems are black boxes that are presented to users, while the human cognition represents a second black box that can be difficult for the algorithm to interpret. These two black boxes create cognitive gaps between the user and the interactive ML model when a user interacts with the system. In this paper, we identify several cognitive gaps that exist in a previously-developed interactive visual analytics (VA) system, Andromeda. These cognitive gaps that we are addressing in Andromeda are representative of common problems in other VA systems. Our goal with this work is to open both black boxes and bridge these cognitive gaps by making improvements to the original Andromeda system, including designing new visual features to help people better understand how Andromeda processes and interacts with data and improving the underlying algorithm so that the Andromeda system can better understand the intent of the user during the data exploration process. We evaluate our designs through both qualitative and quantitative analysis (i.e., user study and simulation analysis), and the results confirm that the improved Andromeda system outperforms the original version significantly in a series of high-dimensional data understanding tasks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Tunicamycin-induced endoplasmic reticulum stress inhibits chemoresistance of FaDu hypopharyngeal carcinoma cells in 3D collagen I cultures and in vivo

Highlights: • 3D collagen I culture induces chemoresistance of FaDu hypopharyngeal carcinoma cells. • Tunicamycin-induced ER stress inhibits chemoresistance in collagen I cultures. • Tunicamycin-induced ER stress potentiates anticancer efficacy in vivo. • Tunicamycin-induced ER stress inhibits TGF-β1 and modifies integrin β1 glycosylation. The prognosis in patients with advanced head and neck squamous cell carcinoma (HNSCC) is widely affected by the resistance to chemotherapy. As a culture scaffold, collagen I was showed to promote CSC (cancer stem cell) properties of cancer cells which could be used as in vitro models to study the chemoresistance in HNSCC. Endoplasmic reticulum (ER) stress is a cellular stress condition which could affect tumor progression and promote the anti-tumor effects of certain drugs. However, the impact of ER stress on collagen I induced CSC properties and chemoresistance of HNSCC cells has not been addressed. In this study we investigated the effects of tunicamycin (TM) induced ER stress on the stemness and sensitivity to chemotherapeutic drugs of FaDu hypopharyngeal carcinoma cells in 3D (three-dimensional) collagen I cultures and mouse xenograft models. Our study revealed that Collagen I scaffold promoted CSC properties and increased G1 population of FaDu cells in 3D cultures, accompanied by maturation of integrin β1 and enhanced activated TGF-β1 concentration. Compared to 2D (two-dimensional) cultured cells, cells in 3D Collagen I scaffold exhibited significantly increased resistance to chemotherapeutic drugs of cisplatin and paclitaxel. Further analysis revealed that TM induced ER stress preferentially attenuated chemoresistance of FaDu cells in 3D collagen I, downregulated their CSC properties and TGF-β1 concentration and resulted in deglycosylation of integrin β1. TM was further evaluated in the mouse xenograft models and showed significant tumor growth inhibition in combination with paclitaxel than either TM or paclitaxel alone. Taken together, Our findings suggest that TM-induced ER stress potentiates anticancer efficacy of FaDu cells in 3D cultures and in vivo, and highlight implications for targeting chemotherapy-resistant cancer stem cells under ER stress conditions.

60 APPLIED LIFE SCIENCES↗