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All sources NASA NTRS DOE OSTI

DOE OSTI2021

Quantifying the hierarchy of structural and mechanical length scales in granular systems

Continuum modeling of granular media is made possible by the existence of a length scale at and above which grain-resolved properties can be meaningfully homogenized. Progress has been made in identifying such length scales relevant to local structural properties such as porosity. However, a systematic analysis of scales above which different mechanical properties can be homogenized has yet to emerge. Here, X-ray tomography and 3D X-ray diffraction data are examined to identify such length scales. The data was obtained in-situ in compressed granular materials with rigid and flexible confinement. The experimental data are supplemented with validated discrete element simulations which examine different system sizes and different boundary conditions. Overall, our study reveals a hierarchy in the length scales of granular solids, with lengths governing structural variables being the shortest, lengths of stress variables being intermediate, and lengths of energy dissipation being the longest. All structural and mechanical length scales obey a power law based on the theory of Geostatistics, implying that the length scales can be found by analyzing samples significantly smaller than the length scales themselves. The length scales are also found to be sensitive to boundary conditions, implying that they are extrinsic features of granular media.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Controlling the corrosion resistance of multi-principal element alloys

Multi-principal element alloys (MPEAs) offer the possibility of many degrees of freedom in the choice of alloying elements to produce either single phase solid solutions or more complex multiphase microstructures. Large ranges of material properties have been observed for MPEAs and mastery of the selection of elements and their compositions can enable novel combinations of properties not possible in traditional alloys. From the aqueous corrosion perspective, optimization of phase stability, control of heterogeneities, passive film identity and its protectiveness, as well as substrate properties such as metal-metal bond strength and activation energy associated with dissolution, can all be controlling factors governed by alloy composition and structure. These factors can mediate the electrochemical reactions controlling spontaneous corrosion. The quest for superior properties based on well-informed element choice is suggested as a path forward guiding MPEA formulations for corrosion performance. However, gaps in fundamental knowledge exist regarding (a) the specific functions of each element, (b) the behavior of elements in unusual combinations, and (c) the formation of complex protective oxides. These issues currently hold back progress in optimization of corrosion properties.

Scully, John R.↗

DOE OSTI2014

From Ag{sub 2}Sb{sub 2}O{sub 6} to Cd{sub 2}Sb{sub 2}O{sub 7}: Investigations on an anion-deficient to ideal pyrochlore solid solution

A complete solid solution between the anion-deficient pyrochlore Ag{sub 2}Sb{sub 2}O{sub 6} and the ideal pyrochlore Cd{sub 2}Sb{sub 2}O{sub 7} has been synthesized through the standard solid state ceramic method. Each composition has been characterized by various different techniques, including powder X-ray diffraction, optical spectroscopy, electron paramagnetic resonance and {sup 121}Sb Mössbauer spectroscopy. Computational methods based on density functional theory complement this investigation. Photocatalytic activity has been studied, and transport properties have been measured on pellets densified by spark plasma sintering. The analysis of the data collected from these various techniques enables a comprehensive characterization of the complete solid solution and revealed an anomalous behavior in the Cd-rich end of the solid solution, which has been proposed to arise from a possible radical O{sup −} species in small concentrations. - Graphical abstract: A complete solid solution between the anion-deficient pyrochlore Ag{sub 2}Sb{sub 2}O{sub 6} and the ideal pyrochlore Cd{sub 2}Sb{sub 2}O{sub 7} has been synthesized and investigated through various techniques including X-ray diffraction, electron paramagnetic spectroscopy, and {sup 121}Sb-Mössbauer spectroscopy. Optical and electrical measurements have been performed, and computational methods have been applied to determine the density of states. Photocatalytic activity was monitored by the degradation of Methylene Blue, and upon cadmium substitution, the degradation amount decreased, which is attributed primarily to the changing optical and electrical properties of the solid solution. Display Omitted - Highlights: • A complete solid solution between Ag{sub 2}Sb{sub 2}O{sub 6} and Cd{sub 2}Sb{sub 2}O{sub 7} has been synthesized. • XRD reveals a relatively constant lattice parameter as the series progresses. • Optical and electrical properties have been measured for the solid solution. • Computational methods have been used to determine the density of states. • Photocatalytic degradation of Methylene Blue decreased upon cadmium substitution.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS1974

Response-produced timeouts under a progressive-ratio schedule with a punished reset option

An attempt is made to extend the generality of timeouts from positive reinforcement to more complex performances. Pigeons were provided with three continuously available options. Responses on one key resulted in food reinforcement under a progressive-ratio schedule, which requires a larger number of responses for successive reinforcements; responses on a second key, which were shocked, reset the progressive-ratio schedule to its first step; and responses on a third key produced a 3-min timeout. Some of these conditions have been studied previously (Dardano, 1968, 1973; Findley, 1958). Questions of general interest included the frequency and stability of response-produced timeouts, changes in the properties of the progressive-ratio performance resulting from the occurrence of timeouts, and the similarity of the relationships between timeouts and the reinforced behavior to relationships found under simpler conditions of reinforcement.

Dardano, J. F.↗

NASA NTRS1999

Progressive Failure Analysis Methodology for Laminated Composite Structures

A progressive failure analysis method has been developed for predicting the failure of laminated composite structures under geometrically nonlinear deformations. The progressive failure analysis uses C(exp 1) shell elements based on classical lamination theory to calculate the in-plane stresses. Several failure criteria, including the maximum strain criterion, Hashin's criterion, and Christensen's criterion, are used to predict the failure mechanisms and several options are available to degrade the material properties after failures. The progressive failure analysis method is implemented in the COMET finite element analysis code and can predict the damage and response of laminated composite structures from initial loading to final failure. The different failure criteria and material degradation methods are compared and assessed by performing analyses of several laminated composite structures. Results from the progressive failure method indicate good correlation with the existing test data except in structural applications where interlaminar stresses are important which may cause failure mechanisms such as debonding or delaminations.

Sleight, David W.↗

NASA NTRS2010

Physics of Life: A Model for Non-Newtonian Properties of Living Systems

This innovation proposes the reconciliation of the evolution of life with the second law of thermodynamics via the introduction of the First Principle for modeling behavior of living systems. The structure of the model is quantum-inspired: it acquires the topology of the Madelung equation in which the quantum potential is replaced with the information potential. As a result, the model captures the most fundamental property of life: the progressive evolution; i.e. the ability to evolve from disorder to order without any external interference. The mathematical structure of the model can be obtained from the Newtonian equations of motion (representing the motor dynamics) coupled with the corresponding Liouville equation (representing the mental dynamics) via information forces. All these specific non-Newtonian properties equip the model with the levels of complexity that matches the complexity of life, and that makes the model applicable for description of behaviors of ecological, social, and economical systems. Rather than addressing the six aspects of life (organization, metabolism, growth, adaptation, response to stimuli, and reproduction), this work focuses only on biosignature ; i.e. the mechanical invariants of life, and in particular, the geometry and kinematics of behavior of living things. Living things obey the First Principles of Newtonian mechanics. One main objective of this model is to extend the First Principles of classical physics to include phenomenological behavior on living systems; to develop a new mathematical formalism within the framework of classical dynamics that would allow one to capture the specific properties of natural or artificial living systems such as formation of the collective mind based upon abstract images of the selves and non-selves; exploitation of this collective mind for communications and predictions of future expected characteristics of evolution; and for making decisions and implementing the corresponding corrections if the expected scenario is different from the originally planned one. This approach postulates that even a primitive living species possesses additional, non-Newtonian properties that are not included in the laws of Newtonian or statistical mechanics. These properties follow from a privileged ability of living systems to possess a self-image (a concept introduced in psychology) and to interact with it. The proposed mathematical system is based on the coupling of the classical dynamical system representing the motor dynamics with the corresponding Liouville equation describing the evolution of initial uncertainties in terms of the probability density and representing the mental dynamics. The coupling is implemented by the information-based supervising forces that can be associated with self-awareness. These forces fundamentally change the pattern of the probability evolution, and therefore, lead to a major departure of the behavior of living systems from the patterns of both Newtonian and statistical mechanics. This innovation is meant to capture the signature of life based only on observable behavior, not on any biochemistry. This will not prevent the use of this model for developing artificial living systems, as well as for studying some general properties of behavior of natural, living systems.

Zak, Michail↗

DOE OSTI2017

Recent Progress in the Understanding of UK Highly Active Liquor Chemistry and Properties - 17144

In the UK Highly Active Liquor (HAL) waste is stored in stainless steel tanks prior to encapsulation in glass in one of three lines in the Waste Vitrification Plant (WVP). The chemistry and physical properties of HAL from the historic and ongoing UK reprocessing operations is well understood, following years of operations and associated research and development programmes using primarily non-active simulants. However the imminent progression to POCO of the Highly Active Storage Tanks (HASTs), where the HAL is stored, requires a more in-depth understanding of the properties of the HAL, in particular the solid phases that are known to exist. Changes in operations are expected, including the addition of new chemical wash reagents, which will result in some fundamental changes to the chemistry in the HASTs. This paper will highlight some of the key findings from recent studies into areas such as solids formation and morphology, chemical dissolution and associated re-precipitation of solids, rheology modification and chemical conversion mechanisms. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2024

Performance Comparison of Machine Learning Models for Ultrasonic Nondestructive Evaluation of Alkali-Silica Reaction in Concrete

Alkali-silica reaction (ASR) causes concrete degradation, leading to cracking, rebar corrosion, and reduced structural integrity, which raises safety concerns. Ultrasonic nondestructive evaluation (NDE) effectively assesses concrete properties and monitors ASR progression. However, its deployment and analysis require specialized expertise and subjective interpretation. As computational power increases, artificial intelligence (AI) and machine learning (ML) algorithms are increasingly being used to automate NDE data analysis across various industries for AI-assisted automation. Regulatory agencies are adapting to this technological shift, prompting a need to evaluate current ML technologies’ capabilities and limitations in assessing concrete material properties and damage. This report presents a comparative analysis of four ML regression models for predicting concrete material damage induced by ASR expansion using long-term ultrasonic data monitoring. The models investigated include linear regression (LR), support vector regression (SVR), shallow neural networks (NN), and deep neural networks (DNN). LR, SVR, and shallow NN models use features extracted from ultrasonic signals, whereas the DNN model processes time-domain ultrasonic signals and frequency spectra directly. The study systematically compared the models’ performance from various perspectives, including model input, prediction performance, and generalization ability. The findings indicate significant variability in model performance, with some ML algorithms achieving very high or very low prediction accuracy depending on the preprocessing and feature engineering (extraction and selection) applied. Key insights include the observation that shallow ML models (LR, SVR, and shallow NNs) require meticulous preprocessing and feature extraction to achieve high accuracy. In contrast, the DNN model, although it bypasses the need for feature engineering, necessitates extensive preprocessing to mitigate noise and computational demands. The SVR model emerged as the top performer among the shallow models, and the DNN model exhibited superior performance on specific datasets but struggled with generalization across specimens from different batches. Additionally, the SVR model is sensitive to temperature variations, whereas the DNN model is robust in this regard. Using recurrent neural networks is recommended for future ASR expansion prediction studies. Recurrent neural networks’ inherent ability to capture temporal dependencies and long-term patterns makes them well suited for analyzing sequential ultrasonic monitoring data. Overall, the results and conclusions of this study could provide insights into the capabilities and effectiveness of ML when applied to ultrasonic NDE data and help identify best practices for using ML for ultrasonic NDE of concrete material properties.

36 MATERIALS SCIENCE↗

NASA NTRS2007

Nondestructive Methods and Special Test Instrumentation Supporting NASA Composite Overwrapped Pressure Vessel Assessments

Many aging composite overwrapped pressure vessels (COPVs), being used by the National Aeronautics and Space Administration (NASA) are currently under evaluation to better quantify their reliability and clarify their likelihood of failure due to stress rupture and age-dependent issues. As a result, some test and analysis programs have been successfully accomplished and other related programs are still in progress at the NASA Johnson Space Center (JSC) White Sands Test Facility (WSTF) and other NASA centers, with assistance from the commercial sector. To support this effort, a group of Nondestructive Evaluation (NDE) experts was assembled to provide NDE competence for pretest evaluation of test articles and for application of NDE technology to real-time testing. Techniques were required to provide assurance that the test article had adequate structural integrity and manufacturing consistency to be considered acceptable for testing and these techniques were successfully applied. Destructive testing is also being accomplished to better understand the physical and chemical property changes associated with progression toward "stress rupture" (SR) failure, and it is being associated with NDE response, so it can potentially be used to help with life prediction. Destructive work also includes the evaluation of residual stresses during dissection of the overwrap, laboratory evaluation of specimens extracted from the overwrap to evaluate physical property changes, and quantitative microscopy to inform the theoretical micromechanics.

Saulsberry, Regor↗

NASA NTRS1994

Failure analysis of woven and braided fabric reinforced composites

A general purpose micromechanics analysis that discretely models the yarn architecture within the textile repeating unit cell was developed to predict overall, three dimensional, thermal and mechanical properties, damage initiation and progression, and strength. This analytical technique was implemented in a user-friendly, personal computer-based, menu-driven code called Textile Composite Analysis for Design (TEXCAD). TEXCAD was used to analyze plain weave and 2x2, 2-D triaxial braided composites. The calculated tension, compression, and shear strengths correlated well with available test data for both woven and braided composites. Parametric studies were performed on both woven and braided architectures to investigate the effects of parameters such as yarn size, yarn spacing, yarn crimp, braid angle, and overall fiber volume fraction on the strength properties of the textile composite.

Naik, Rajiv A.↗

DOE OSTI2024

Assessment of Machine Learning for Ultrasonic Nondestructive Evaluation of Alkali–Silica Reaction in Concrete

Alkali–silica reaction (ASR) is a type of material degradation in concrete structures that leads to concrete cracking and rebar corrosion, thereby reducing the material’s structural integrity and the overall structure’s lifetime and raising safety concerns. Ultrasonic nondestructive evaluation (NDE) has been proven to be a valuable technique for assessing concrete properties and monitoring ASR progression in concrete. However, the deployment and analysis of ultrasonic NDE and its data requires specialized expertise, often relying on the engineer’s subjective interpretation. With the surge in computational power, artificial intelligence (AI) and machine learning (ML) algorithms have become popular in automating NDE data analysis. Various industrial sectors are increasingly adopting ML algorithms for NDE data analysis with a growing emphasis on AI–assisted automation. Regulatory agencies are also preparing for this technological shift, anticipating corresponding revisions in standards. Thus, there is an urgent need to identify the capabilities and limitations of current ML technologies for the evaluation of concrete material properties and damage status. Furthermore, the effects of various factors on ML model performance must be thoroughly investigated. The study summarized herein evaluated the effectiveness of two ML models (i.e., support vector regression (SVR) and deep neural network (DNN)) in predicting concrete material damage induced by ASR based on the long-term ultrasonic monitoring data. Four distinct concrete specimens were cast with artificially induced ASR, and over a period exceeding 500 days, ultrasonic signals and expansion data were continuously collected. For the SVR model, wave velocity and 12 other wave features were extracted from the ultrasonic signals, with 6 out of 13 features selected as input for the model. Different combinations of training and testing datasets were designed to explore factors influencing prediction performance, including the range of data within training and testing sets, in addition to various signal preprocessing methodologies. These findings suggest the importance of using a training dataset with a broader data range compared with testing datasets for improved model performance alongside consistent signal preprocessing across datasets.

36 MATERIALS SCIENCE↗

NASA NTRS1992

Theoretical study of thermodynamic properties and reaction rates of importance in the high-speed research program

One of the primary goals of NASA's high-speed research program is to determine the feasibility of designing an environmentally safe commercial supersonic transport airplane. The largest environmental concern is focused on the amount of ozone destroying nitrogen oxides (NO(x)) that would be injected into the lower stratosphere during the cruise portion of the flight. The limitations placed on NO(x) emission require more than an order of magnitude reduction over current engine designs. To develop strategies to meet this goal requires first gaining a fundamental understanding of the combustion chemistry. To accurately model the combustor requires a computational fluid dynamics approach that includes both turbulence and chemistry. Since many of the important chemical processes in this regime involve highly reactive radicals, an experimental determination of the required thermodynamic data and rate constants is often very difficult. Unlike experimental approaches, theoretical methods are as applicable to highly reactive species as stable ones. Also our approximation of treating the dynamics classically becomes more accurate with increasing temperature. In this article we review recent progress in generating thermodynamic properties and rate constants that are required to understand NO(x) formation in the combustion process. We also describe our one-dimensional modeling efforts to validate an NH3 combustion reaction mechanism. We have been working in collaboration with researchers at LeRC, to ensure that our theoretical work is focused on the most important thermodynamic quantities and rate constants required in the chemical data base.

Langhoff, Stephen↗

DOE OSTI2023

Progress and outlook of Sn–Pb mixed perovskite solar cells

Abstract Organic–inorganic hybrid perovskites have revolutionized solar cell research owing to their excellent material properties. Most previous research has been done on Pb-based perovskites. Recently, efforts to discover a Pb-free or Pb-less perovskite material with an ideal bandgap ranging 1.1–1.3 eV have led researchers to investigate Sn–Pb mixed perovskites. Sn–Pb mixed perovskites have a bandgap of ~ 1.25 eV, which is suitable for high-efficiency single-junction and perovskite/perovskite tandem solar cells. Moreover, the Pb content of Sn–Pb mixed perovskites is 50–60% lower than that of Pb-based perovskites, partially mitigating the Pb toxicity issue. However, incorporating Sn 2+ into the crystal structure also causes various drawbacks, such as inhomogeneous thin film morphologies, easy oxidation of Sn 2+ , and more vulnerable surface properties. Researchers have made substantial progress in addressing these challenges through improvements in compositional design, structural optimization, precursor design, and surface treatments. In this review, we provide a comprehensive overview of the progress in Sn–Pb mixed perovskite solar cells. Furthermore, we analyze the key variables and trends as well as provide an outlook for future directions in the research on Sn–Pb mixed perovskites. Graphical Abstract

14 SOLAR ENERGY↗

DOE OSTI2018

Carbon-Nanotube-Based Thermoelectric Materials and Devices

Conversion of waste heat to voltage has the potential to significantly reduce the carbon footprint of a number of critical energy sectors, such as the transportation and electricity-generation sectors, and manufacturing processes. Thermal energy is also an abundant low-flux source that can be harnessed to power portable/wearable electronic devices and critical components in remote off-grid locations. As such, a number of different inorganic and organic materials are being explored for their potential in thermoelectric-energy-harvesting devices. Carbon-based thermoelectric materials are particularly attractive due to their use of nontoxic, abundant source-materials, their amenability to high-throughput solution-phase fabrication routes, and the high specific energy (i.e., W g-1) enabled by their low mass. Single-walled carbon nanotubes (SWCNTs) represent a unique 1D carbon allotrope with structural, electrical, and thermal properties that enable efficient thermoelectric-energy conversion. Here, the progress made toward understanding the fundamental thermoelectric properties of SWCNTs, nanotube-based composites, and thermoelectric devices prepared from these materials is reviewed in detail. This progress illuminates the tremendous potential that carbon-nanotube-based materials and composites have for producing high-performance next-generation devices for thermoelectric-energy harvesting.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

DOE OSTI2019

Atomic-Scale Manipulation and In Situ Characterization with Scanning Tunneling Microscopy

Scanning tunneling microscope (STM) has presented a revolutionary methodology to nanoscience and nanotechnology. It enables imaging of the topography of surfaces, mapping the distribution of electronic density of states, and manipulating individual atoms and molecules, all at atomic resolutions. In particular, atom manipulation capability has evolved from fabricating individual nanostructures toward the scalable production of the atomic-sized devices bottom-up. The combination of precision synthesis and in situ characterization has enabled direct visualization of many quantum phenomena and fast proof-of-principle testing of quantum device functions with immediate feedback to guide improved synthesis. Several representative examples are reviewed to demonstrate the recent development of atomic-scale manipulation, focusing on progress that addresses quantum properties by design in several technologically relevant materials systems. Integration of several atomically precisely controlled probes in a multiprobe STM system vastly extends the capability of in situ characterization to a new dimension where the charge and spin transport behaviors can be examined from mesoscopic to atomic length scale. The automation of atomic-scale manipulation and the integration with well-established lithographic processes further push this bottom-up approach to a new level that combines reproducible fabrication, extraordinary programmability, and the ability to produce large-scale arrays of quantum structures.

47 OTHER INSTRUMENTATION↗

DOE OSTI2021

An adaptive granular representative volume element model with an evolutionary periodic boundary for hierarchical multiscale analysis

The hierarchical multiscale analysis normally utilizes a microscopic representative volume element (RVE) model to capture path/history-dependent macroscopic responses instead of using phenomenological constitutive models. However, for problems involving large deformation, the current RVE model used in geomechanics may lose representative properties due to the progressive distortion of the RVE box, unless a proper reinitialization is applied. This work develops an adaptive RVE model in conjunction with an evolutionary periodic boundary (EPB) algorithm for hierarchical multiscale analysis of granular materials undergoing large deformation based on a recent RVE model proposed for coupling molecular dynamics and the material point method. The proposed adaptive RVE model avoids the reinitialization of the RVE box that even undergoes extremely large shear deformation; meanwhile, it accounts for the deformation history of the RVE model and treats the interaction between boundary particles and other image particles in a more efficient way. Numerical examples with extremely large deformation are used to illustrate the adaptive granular RVE model enhanced by the proposed EPB algorithm. Furthermore, some key features of this new methodology are further discussed for clarification.

42 ENGINEERING↗

DOE OSTI2023

Synergy between cosmological and laboratory searches in neutrino physics

The intersection of the cosmic and neutrino frontiers is a rich field where much discovery space still remains. Neutrinos play a pivotal role in the hot big bang cosmology, influencing the dynamics of the universe over numerous decades in cosmological history. Recent studies have made tremendous progress in understanding some properties of cosmological neutrinos, primarily their energy density. Upcoming cosmological probes will measure the energy density of relativistic particles with higher precision, but could also start probing other properties of the neutrino spectra. When convolved with results from terrestrial experiments, cosmology can become even more acute at probing new physics related to neutrinos or even Beyond the Standard Model (BSM). Any discordance between laboratory and cosmological data sets may reveal new BSM physics and/or suggest alternative models of cosmology. Here we give examples of the intersection between terrestrial and cosmological probes in the neutrino sector, and briefly discuss the possibilities of what different laboratory experiments may see in conjunction with cosmological observatories.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2018

PRP4 kinase induces actin rearrangement and epithelial-mesenchymal transition through modulation of the actin-binding protein cofilin

Highlights: • PRP4 is involved in pre-mRNA splicing and cell signalling. • PRP4 modulates the actin cytoskeleton of cancer cells via RhoA activity inhibition. • PRP4 induces cofilin dephosphorylation by inhibiting the Rho-ROCK-LIMK-cofilin pathway in HCT116 cells. • Dephosphorylation of cofilin results in F-actin stabilization, re-distribution of cytoplasmic actin, formation of actin stress fibers, and inhibition of cell motility. • PRP4 over-expression induces the expressions of PP1A, which directly or indirectly dephosphorylates cofilin, resulting in actin cytoskeleton rearrangement, downregulation of E-cadherin, and EMT induction. Cofilin activation may be associated with EMT properties, and promotes the progression of human colon cancer. Cell actin cytoskeleton is primarily modulated by Rho family proteins. RhoA regulates several downstream targets, including Rho-associated protein kinase (ROCK), LIM-Kinase (LIMK), and cofilin. Pre-mRNA processing factor 4B (PRP4) modulates the actin cytoskeleton of cancer cells via RhoA activity inhibition. In this study, we discovered that PRP4 over-expression in HCT116 colon cancer cells induces cofilin dephosphorylation by inhibiting the Rho-ROCK-LIMK-cofilin pathway. Two-dimensional gel electrophoresis, and matrix-assisted laser desorption/ionization time-of-flight mass-spectrometry (MALDI-TOF MS) analysis indicated increased expression of protein phosphatase 1A (PP1A) in PRP4-transfected HCT116 cells. The presence of PRP4 increased the expression of PP1A both at the mRNA and protein levels, which possibly activated cofilin through dephosphorylation and subsequently modulated the cell actin cytoskeleton. Furthermore, we found that PRP4 over-expression did not induce cofilin dephosphorylation in the presence of okadaic acid, a potent phosphatase inhibitor. Moreover, we discovered that PRP4 over-expression in HCT116 cells induced dephosphorylation of migration and invasion inhibitory protein (MIIP), and down-regulation of E-cadherin protein levels, which were further restored by the presence of okadaic acid. These findings indicate a possible molecular mechanism of PRP4-induced actin cytoskeleton remodeling and epithelial-mesenchymal transition, and make PRP4 an important target in colon cancer.

60 APPLIED LIFE SCIENCES↗