Search NASA⌕ Search

SEARCH · Search NASA

Results for “Data Abstractions and Types”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 667 records · Page 37
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Rationally Designed, Short-Acting RPE65 Inhibitors for Visual Cycle-Associated Retinopathies

Abstract The visual cycle is a metabolic pathway essential for visual function. The bisretinoid byproducts of this pathway can induce retinal toxicity, as occurs in Stargardt disease type 1 (STGD1). Emixustat, which inhibits bisretinoid production, is a visual cycle modulator (VCM) that targets RPE65. However, it causes visual impairment due to its unfavorable duration of action. Here, we report ester-containing analogs of emixustat that are susceptible to hydrolytic clearance and function as short-acting VCMs. We show that the esterase-mediated metabolism of these compounds can be tuned while maintaining high-affinity RPE65 targeting. Compounds 6 (EYE-002) and 7 (EYE-003) containing diethyl acetate and valproate esters, respectively, allowed faster recovery of visual cycle function compared to emixustat. These molecules protected against retinal degeneration in mouse models of photic retinopathy and STGD1. These data demonstrate that shorter attenuation of the visual cycle can therapeutically intervene in retinal diseases with fewer visual side effects compared to emixustat.

Pharmacology & Pharmacy↗
DOE OSTI2021

Evidence of a South Asian Proto‐Monsoon During the Oligocene‐Miocene Transition

Abstract The geological history of the South Asian monsoon (SAM) before the Pleistocene is not well‐constrained, primarily due to a lack of available continuous sediment archives. Previous studies have noted an intensification of SAM precipitation and atmospheric circulation during the middle Miocene (∼14 Ma), but no records are available to test how the monsoon changed prior to this. In order to improve our understanding of monsoonal evolution, geochemical and sedimentological data were generated for the Oligocene‐early Miocene (30–20 Ma) from Indian National Gas Hydrate Expedition 01 Site NGHP‐01‐01A in the eastern Arabian Sea, at 2,674 m water depth. We find the initial glaciation phase (23.7–23.0 Ma) of the Oligocene‐Miocene transition (OMT) to be associated with an increase in water column ventilation and water mass mixing, suggesting an increase in winter monsoon type atmospheric circulation, possibly driven by a relative southward shift of the intertropical convergence zone. During the latter part of the OMT, or “deglaciation” phase (23.0–22.7 Ma), a long‐term decrease in Mn (suggestive of deoxygenation), increase in Ti/Ca and dissolution of the biogenic carbonate fraction suggest an intensification of a proto‐summer SAM system, characterized by the formation of an oxygen minimum zone in the eastern Arabian Sea and a relative increase of terrigenous material delivered by runoff to the site. With no evidence at this site for an active SAM prior to the OMT we suggest that changes in orbital parameters, as well as possibly changing Tethyan/Himalayan tectonics, caused this step change in the proto‐monsoon system at this intermediate‐depth site.

Beasley, C.↗
DOE OSTI2021

Predicting Thermal Performance of an Enhanced Geothermal System From Tracer Tests in a Data Assimilation Framework

Abstract Predicting the thermal performance of an enhanced geothermal system (EGS) requires a comprehensive characterization of the underlying fracture flow patterns from practically available data such as tracer data. However, due to the inherent complexities of subsurface fractures and the generally insufficient geological/geophysical data, interpreting tracer data for fracture flow characterization and thermal prediction remains a challenging task. The present study aims to tackle the challenge by leveraging a data assimilation method to maximize the utilization of information inherently contained in tracer data, and meanwhile maintain the flexibility to handle various uncertainties. A tracer data interpretation framework was proposed with the following three components integrated: (a) We use principal component analysis (PCA) to reduce the dimensionality of model parameter space. (b) We use ES‐MDA (ensemble smoother with multiple data assimilation) to invert for fracture aperture/flow fields and obtain posterior model ensembles for uncertainty quantification. Various data types are assimilated jointly to improve the predictive ability of the posterior ensemble. (c) The inverted fracture aperture fields are then incorporated into reservoir models to predict thermal performance. We developed a field‐scale EGS model to verify the ability of the framework to characterize highly heterogeneous fracture aperture/flow fields and predicting thermal performance. We also applied the framework to a mesoscale field experiment to demonstrate its potential application in real‐world geothermal reservoirs. The results indicate that the proposed framework can effectively retrieve fracture flow information from tracer data for thermal prediction and uncertainty quantification, and thus provide informative guidance for EGS optimization and risk management.

15 GEOTHERMAL ENERGY↗
DOE OSTI2022

X-ray fluorescence microscopy methods for biological tissues

Abstract Synchrotron-based X-ray fluorescence microscopy is a flexible tool for identifying the distribution of trace elements in biological specimens across a broad range of sample sizes. The technique is not particularly limited by sample type and can be performed on ancient fossils, fixed or fresh tissue specimens, and in some cases even live tissue and live cells can be studied. The technique can also be expanded to provide chemical specificity to elemental maps, either at individual points of interest in a map or across a large field of view. While virtually any sample type can be characterized with X-ray fluorescence microscopy, common biological sample preparation methods (often borrowed from other fields, such as histology) can lead to unforeseen pitfalls, resulting in altered element distributions and concentrations. A general overview of sample preparation and data-acquisition methods for X-ray fluorescence microscopy is presented, along with outlining the general approach for applying this technique to a new field of investigation for prospective new users. Considerations for improving data acquisition and quality are reviewed as well as the effects of sample preparation, with a particular focus on soft tissues. The effects of common sample pretreatment steps as well as the underlying factors that govern which, and to what extent, specific elements are likely to be altered are reviewed along with common artifacts observed in X-ray fluorescence microscopy data.

Pushie, M. Jake (ORCID:0000000174945427)↗
DOE OSTI2022

A Statistical Interpolation Code for Ocean Analysis and Forecasting

Abstract We present a data assimilation package for use with ocean circulation models in analysis, forecasting, and system evaluation applications. The basic functionality of the package is centered on a multivariate linear statistical estimation for a given predicted/background ocean state, observations, and error statistics. Novel features of the package include support for multiple covariance models, and the solution of the least squares normal equations either using the covariance matrix or its inverse—the information matrix. The main focus of this paper, however, is on the solution of the analysis equations using the information matrix, which offers several advantages for solving large problems efficiently. Details of the parameterization of the inverse covariance using Markov random fields are provided and its relationship to finite-difference discretizations of diffusion equations are pointed out. The package can assimilate a variety of observation types from both remote sensing and in situ platforms. The performance of the data assimilation methodology implemented in the package is demonstrated with a yearlong global ocean hindcast with a 1/4° ocean model. The code is implemented in modern Fortran, supports distributed memory, shared memory, multicore architectures, and uses climate and forecasts compliant Network Common Data Form for input/output. The package is freely available with an open source license from www.tendral.com/tsis/ .

Srinivasan, Ashwanth↗
DOE OSTI2017

Understanding lattice defects to influence ferromagnetic order of ZnO nanoparticles by Ni, Cu, Ce ions

Future spintronics technologies based on diluted magnetic semiconductors (DMS) will rely heavily on a sound understanding of the microscopic origins of ferromagnetism in such materials. It remains unclear, however, whether the ferromagnetism in DMS is intrinsic - a precondition for spintronics - or due to dopant clustering. For this, we include a simultaneous doping from transition metal (Ni, Cu) and rare earth (Ce) ions in ZnO nanoparticles that increase the antiferromagnetic ordering to achieve high-T{sub c} ferromagnetism. Rietveld refinement of XRD patterns indicate that the dopant ions in ZnO had a wurtzite structure and the dopants, Ni{sup 2+}, Cu{sup 2+}, Ce{sup 3+} ions, are highly influenced the lattice constants to induce lattice defects. The Ni, Cu, Ce ions in ZnO have nanoparticles formation than nanorods was observed in pure sample. FTIR involve some organic groups to induce lattice defects and the metal-oxygen bonding of Zn, Ni, Cu, Ce and O atoms to confirm wurtzite structure. Raman analysis evaluates the crystalline quality, structural disorder and defects in ZnO lattice with doping. Photoluminescence spectra have strong near-band-edge emission and visible emission bands responsible for defects due to oxygen vacancies. The energy band gap is calculated using Tauc relation. Room temperature ferromagnetism has been described due to bound magnetic polarons formation with Ni{sup 2+}, Cu{sup 2+}, Ce{sup 3+} ions in ZnO via oxygen vacancies. The zero field and field cooling SQUID measurement confirm the strength of antiferromagnetism in ZnO. The field cooling magnetization is studied by Curie-Weiss law that include antiferromagnetic interactions up to low temperature. The XPS spectra have involve +3/+4 oxidation states of Ce ions to influence the observed ferromagnetism. - Graphical abstract: The lattice defects/vacancies attributed by Ni and Ce ions in the wurtzite ZnO structure are responsible in high T{sub c} -ferromagnetism due to long-range magnetic interactions with cluster and spin-glass type growth. - Highlights: • Lattice defects/vacancies attributed high T{sub c} –ferromagnetism. • Transition metal and rare earth ions deform the wurtzite ZnO lattice to induce defects. • Oxygen vacancies are more favorable than Zn with Ni, Cu, Ce into ZnO. • Defects assisted long-range ferromagnetism of doped ZnO include cluster and spin-glass growth.

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

Scaffolds of magnetically active 3d metals in the valence electron controlled borides Ti{sub 9−x}M{sub 2+x}Ru{sub 18}B{sub 8} (M=Cr–Ni; x=0.5–1): Structutral, electronic and magnetic properties

Polycrystalline samples of the boride series Ti{sub 9−x}M{sub 2+x}Ru{sub 18}B{sub 8} (M=Cr, Co, Mn, Ni) including single crystals of Ti{sub 8}Co{sub 3}Ru{sub 18}B{sub 8} have been prepared by arc-melting the elements. The phases were characterized by powder X-ray diffraction (Rietveld refinement), single-crystal X-ray diffraction (for M=Co), and energy-dispersive X-ray (EDX) analysis. They are substitutional variants of the Zn{sub 11}Rh{sub 18}B{sub 8} structure type, space group P4/mbm (No. 127) and contain a “scaffold” structural unit (M-ladders interacting with M/Ti-chains) as well as isolated M/Ti-chains. According to DFT calculations, the Ru–X (X=B, Ti, Ti/M) bonding interactions are nearly constant throughout the series and responsible for the structural stability of these phases, whereas the M–M and Ru–M interactions vary significantly with varying valence electron count. Furthermore, density of states (DOS) analyses predict the phases with M=Mn and Ni to develop a total magnetic moment but not the M=Co phase. Susceptibility measurements confirm the Co phase to be paramagnetic and the Mn Phase orders ferrimagnetically below 120 K and thus develops a magnetic moment, as predicted. - Graphical abstract: The crystal structures of the new phases (M=Cr, Mn, Co, Ni) are confirmed by Rietveld refinement of powder diffraction data and single crystal X-ray diffraction (for M=Co) to contain beside the M-ladder also M/Ti-chains. Similar to the series Ti{sub 9}M{sub 2}Ru{sub 18}B{sub 8}, the crystal structure of the new phases are mainly stabilized by the heteroatomic Ru–B and Ru–Ti bonds that remain nearly constant throughout the series, whereas the M-containing bonds vary significantly with varying valence electron count. An experimental finding confirmed and even extended by COHP bonding analyses. In addition, the DOS analyses of the M-elements reveal the development of magnetic moments for the M=Mn, Ni cases but not for M=Co. Indeed, Ti{sub 8}Co{sub 3}Ru{sub 18}B{sub 8} was found experimentally to be a paramagnet and ferrimagnetic ordering below 120 K is found for M=Mn. Highlights: • Synthesis of the valence electron controlled borides Ti{sub 9−x}M{sub 2+x}Ru{sub 18}B{sub 8} (M=Cr–Ni). • Prediction of magnetic properties of Ti{sub 9−x}M{sub 2+x}Ru{sub 18}B{sub 8}. • Ferrimagnetic ordering found in Ti{sub 8}M{sub 3}Ru{sub 18}B{sub 8}. • Chemical bonding studied by density functional theory.

36 MATERIALS SCIENCE↗
DOE OSTI2021

SeismoGen: Seismic Waveform Synthesis Using GAN With Application to Seismic Data Augmentation

Abstract Detecting earthquake arrivals within seismic time series can be a challenging task. Visual, human detection has long been considered the gold standard but requires intensive manual labor that scales poorly to large data sets. In recent years, automatic detection methods based on machine learning have been developed to improve the accuracy and efficiency. However, the accuracy of those methods relies on access to a sufficient amount of high‐quality labeled training data, often tens of thousands of records or more. We aim to resolve this dilemma by answering two questions: (1) provided with a limited amount of reliable labeled data, can we use them to generate additional, realistic synthetic waveform data? and (2) can we use those synthetic data to further enrich the training set through data augmentation, thereby enhancing detection algorithms? To address these questions, we use a generative adversarial network (GAN), a type of machine learning model which has shown supreme capability in generating high‐quality synthetic samples in multiple domains. Once trained, our GAN model is capable of producing realistic seismic waveforms of multiple labels (noise and event classes). Applied to real Earth seismic data sets in Oklahoma, we show that data augmentation from our GAN‐generated synthetic waveforms can be used to improve earthquake detection algorithms in instances when only small amounts of labeled training data are available.

Wang, Tiantong↗
DOE OSTI2022

Towards mapping biodiversity from above: Can fusing lidar and hyperspectral remote sensing predict taxonomic, functional, and phylogenetic tree diversity in temperate forests?

Abstract Aim Rapid global change is impacting the diversity of tree species and essential ecosystem functions and services of forests. It is therefore critical to understand and predict how the diversity of tree species is spatially distributed within and among forest biomes. Satellite remote sensing platforms have been used for decades to map forest structure and function but are limited in their capacity to monitor change by their relatively coarse spatial resolution and the complexity of scales at which different dimensions of biodiversity are observed in the field. Recently, airborne remote sensing platforms making use of passive high spectral resolution (i.e., hyperspectral) and active lidar data have been operationalized, providing an opportunity to disentangle how biodiversity patterns vary across space and time from field observations to larger scales. Most studies to date have focused on single sites and/or one sensor type; here we ask how multiple sensor types from the National Ecological Observatory Network’s Airborne Observation Platform (NEON AOP) perform across multiple sites in a single biome at the NEON field plot scale (i.e., 40 m × 40 m). Location Eastern USA. Time period 2017–2018. Taxa studied Trees. Methods With a fusion of hyperspectral and lidar data from the NEON AOP, we assess the ability of high resolution remotely sensed metrics to measure biodiversity variation across eastern US temperate forests. We examine how taxonomic, functional, and phylogenetic measures of alpha diversity vary spatially and assess to what degree remotely sensed metrics correlate with in situ biodiversity metrics. Results Models using estimates of forest function, canopy structure, and topographic diversity performed better than models containing each category alone. Our results show that canopy structural diversity, and not just spectral reflectance, is critical to predicting biodiversity. Main conclusions We found that an approach that jointly leverages spectral properties related to leaf and canopy functional traits and forest health, lidar derived estimates of forest structure, fine‐resolution topographic diversity, and careful consideration of biogeographical differences within and among biomes is needed to accurately map biodiversity variation from above.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Band Heater Heat Flux Characterization Using Inverse Heat Conduction Problem Models

Abstract An experimental apparatus was constructed to correlate water flowrate and temperature rise under an external band heater. Due to the physical characteristics of the band heater, its transient heating behavior is unknown. This paper investigates the application of inverse heat conduction problem (IHCP) methods to characterize the heat flux from the band heater. Three experiments with different heating times (5, 10, and 20 s) and no flowrate were conducted to measure the transient temperature under the 400 W band heater. Type-T thermocouples measure surface temperature at the centerline of the band heater. The experimental results are computed with five different heat conduction models. The models are chosen to identify how the heat flux response varies from a simplified to a realistic model. Additionally, the results of the experimental heat flux are compared to the manufacturer band heater data (58.9 kW/m2) for each model. The minimum time needed for the heater to fully energize the system is from 10 to 12 s. The residuals for each model are analyzed and used to evaluate the appropriateness of the five different models. The results show that the use of simpler models can achieve results similar to those of complex models, with less time and computational cost.

Engineering↗
DOE OSTI2021

Identification and characterization of proteins of unknown function (PUFs) in Clostridium thermocellum DSM 1313 strains as potential genetic engineering targets

Abstract Background Mass spectrometry-based proteomics can identify and quantify thousands of proteins from individual microbial species, but a significant percentage of these proteins are unannotated and hence classified as proteins of unknown function (PUFs). Due to the difficulty in extracting meaningful metabolic information, PUFs are often overlooked or discarded during data analysis, even though they might be critically important in functional activities, in particular for metabolic engineering research. Results We optimized and employed a pipeline integrating various “guilt-by-association” (GBA) metrics, including differential expression and co-expression analyses of high-throughput mass spectrometry proteome data and phylogenetic coevolution analysis, and sequence homology-based approaches to determine putative functions for PUFs in Clostridium thermocellum . Our various analyses provided putative functional information for over 95% of the PUFs detected by mass spectrometry in a wild-type and/or an engineered strain of C. thermocellum . In particular, we validated a predicted acyltransferase PUF (WP_003519433.1) with functional activity towards 2-phenylethyl alcohol, consistent with our GBA and sequence homology-based predictions. Conclusions This work demonstrates the value of leveraging sequence homology-based annotations with empirical evidence based on the concept of GBA to broadly predict putative functions for PUFs, opening avenues to further interrogation via targeted experiments.

09 BIOMASS FUELS↗
DOE OSTI2024

DASEventNet: AI‐Based Microseismic Detection on Distributed Acoustic Sensing Data From the Utah FORGE Well 16A (78)‐32 Hydraulic Stimulation

Abstract Distributed acoustic sensing (DAS) has emerged as a promising seismic technology for monitoring microearthquakes (MEQs) with high spatial resolution. Efficient algorithms are needed for processing large DAS data volumes. This study introduces a deep learning (DL) model based on a Residual Convolutional Neural Network (ResNet) for detecting MEQs using DAS data, named as DASEventNet. The test data were collected from the Utah FORGE 16A (78)‐32 hydraulic stimulation experiments conducted in April 2022. The DASEventNet model achieves a remarkable accuracy of 100% when discriminating MEQs from noise in the raw test set of 260 examples. Surprisingly, the model identified weak MEQ signatures that have been manually categorized as noise. The decision‐making process with the model is decoded by the classic activation map, which illuminates learning features of the DASEventNet model. These features provide clear illustrations of weak MEQs and varied noise types. Finally, we apply the trained model to the entire period (∼7 days) of continuous DAS recordings and find that it discovers >5,700 new MEQs, previously unregistered in the public Silixa DAS catalog. The DASEventNet model significantly outperforms the traditional seismic method Short‐Term Average/Long‐Term Average (STA/LTA), which detected only 1,307 MEQs. The DASEventNet detection threshold is M w −1.80 compared to the minimum magnitude of M w −1.14 detected by STA/LTA. The spatiotemporal distribution of the newly identified MEQs defines an extensive stimulation zone and more accurately characterizes fracture geometry. Our results highlight the potential of DL for long‐term, real‐time microseismic monitoring that can improve enhanced geothermal systems and other activities that include subsurface hydraulic fracturing.

15 GEOTHERMAL ENERGY↗
DOE OSTI2022

A high spatial resolution land surface phenology dataset for AmeriFlux and NEON sites

Abstract Vegetation phenology is a key control on water, energy, and carbon fluxes in terrestrial ecosystems. Because vegetation canopies are heterogeneous, spatially explicit information related to seasonality in vegetation activity provides valuable information for studies that use eddy covariance measurements to study ecosystem function and land-atmosphere interactions. Here we present a land surface phenology (LSP) dataset derived at 3 m spatial resolution from PlanetScope imagery across a range of plant functional types and climates in North America. The dataset provides spatially explicit information related to the timing of phenophase changes such as the start, peak, and end of vegetation activity, along with vegetation index metrics and associated quality assurance flags for the growing seasons of 2017–2021 for 10 × 10 km windows centred over 104 eddy covariance towers at AmeriFlux and National Ecological Observatory Network (NEON) sites. These LSP data can be used to analyse processes controlling the seasonality of ecosystem-scale carbon, water, and energy fluxes, to evaluate predictions from land surface models, and to assess satellite-based LSP products.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Bootstrapping outperforms community‐weighted approaches for estimating the shapes of phenotypic distributions

Abstract Estimating phenotypic distributions of populations and communities is central to many questions in ecology and evolution. These distributions can be characterized by their moments (mean, variance, skewness and kurtosis) or diversity metrics (e.g. functional richness). Typically, such moments and metrics are calculated using community‐weighted approaches (e.g. abundance‐weighted mean). We propose an alternative bootstrapping approach that allows flexibility in trait sampling and explicit incorporation of intraspecific variation, and show that this approach significantly improves estimation while allowing us to quantify uncertainty. We assess the performance of different approaches for estimating the moments of trait distributions across various sampling scenarios, taxa and datasets by comparing estimates derived from simulated samples with the true values calculated from full datasets. Simulations differ in sampling intensity (individuals per species), sampling biases (abundance, size), trait data source (local vs. global) and estimation method (two types of community‐weighting, two types of bootstrapping). We introduce the traitstrap R package, which contains a modular and extensible set of bootstrapping and weighted‐averaging functions that use community composition and trait data to estimate the moments of community trait distributions with their uncertainty. Importantly, the first function in the workflow, trait_fill , allows the user to specify hierarchical structures (e.g. plot within site, experiment vs. control, species within genus) to assign trait values to each taxon in each community sample. Across all taxa, simulations and metrics, bootstrapping approaches were more accurate and less biased than community‐weighted approaches. With bootstrapping, a sample size of 9 or more measurements per species per trait generally included the true mean within the 95% CI. It reduced average percent errors by 26%–74% relative to community‐weighting. Random sampling across all species outperformed both size‐ and abundance‐biased sampling. Our results suggest randomly sampling ~9 individuals per sampling unit and species, covering all species in the community and analysing the data using nonparametric bootstrapping generally enable reliable inference on trait distributions, including the central moments, of communities. By providing better estimates of community trait distributions, bootstrapping approaches can improve our ability to link traits to both the processes that generate them and their effects on ecosystems.

Maitner, Brian S.↗
DOE OSTI2018

Influence of climate variability, fire and phosphorus limitation on vegetation structure and dynamics of the Amazon–Cerrado border

Abstract. Climate, fire and soil nutrient limitation are important elements that affect vegetation dynamics in areas of the forest–savanna transition. In this paper, we use the dynamic vegetation model INLAND to evaluate the influence of interannual climate variability, fire and phosphorus (P) limitation on Amazon–Cerrado transitional vegetation structure and dynamics. We assess how each environmental factor affects net primary production, leaf area index and aboveground biomass (AGB), and compare the AGB simulations to an observed AGB map. We used two climate data sets (monthly average climate for 1961–1990 and interannual climate variability for 1948–2008), two data sets of total soil P content (one based on regional field measurements and one based on global data), and the INLAND fire module. Our results show that the inclusion of interannual climate variability, P limitation and fire occurrence each contribute to simulating vegetation types that more closely match observations. These effects are spatially heterogeneous and synergistic. In terms of magnitude, the effect of fire is strongest and is the main driver of vegetation changes along the transition. Phosphorus limitation, in turn, has a stronger effect on transitional ecosystem dynamics than interannual climate variability does. Overall, INLAND typically simulates more than 80 % of the AGB variability in the transition zone. Furthermore, the AGB in many places is clearly not well simulated, indicating that important soil and physiological factors in the Amazon–Cerrado border region, such as lithology, water table depth, carbon allocation strategies and mortality rates, still need to be included in the model.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

P44/WDR77 restricts the sensitivity of proliferating cells to TGFβ signaling

Highlights: • P44/WDR77 causes proliferating cells to become non-responsive to TGFβ signaling. • P44/WDR77 down-regulates TβRII and TβR2 expression. • P44/WDR77 down-regulated TGFβ signaling correlates with lung tumorigenesis. - Abstract: We previously reported that a novel WD-40 domain-containing protein, p44/WDR77, drives quiescent epithelial cells to re-enter the cell cycle and plays an essential role for growth of lung and prostate cancer cells. Transforming growth factor beta (TGFβ) signaling is important in the maintenance of non-transformed cells in the quiescent or slowly cycling stage. However, both non-transformed proliferating cells and human cancer cells are non-responsive to endogenous TGFβ signaling. The mechanism by which proliferating cells become refractory to TGFβ inhibition is not well established. Here, we found that silencing p44/WDR77 increased cellular sensitivity to TGFβ signaling and that this was inversely correlated with decreased cell proliferation. Smad2 or 3 phosphorylation, TGFβ-mediated transcription, and TGFβ2 and TGFβ receptor type II (TβRII) expression were dramatically induced by silencing of p44/WDR77. These data support the hypothesis that p44/WDR77 down-regulates the expression of the TGFβ ligand and its receptor, thereby leading to a cellular non-response to TGFβ signaling. Finally, we found that p44/WDR77 expression was correlated with cell proliferation and decreased TGFβ signaling during lung tumorigenesis. Together, these results suggest that p44/WDR77 expression causes the non-sensitivity of proliferating cells to TGFβ signaling, thereby contributing to cellular proliferation during lung tumorigenesis.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

In Situ Characterization of Mesoporous Co/CeO 2 Catalysts for the High-Temperature Water-Gas Shift

Here, mesoporous Co/CeO 2 catalysts were found to exhibit significant activity for the high-temperature water-gas shift (WGS) reaction with cobalt loadings as low as 1 wt %. The catalysts feature a uniform dispersion of cobalt within the CeO 2 fluorite type lattice with no evidence of discrete cobalt phase segregation. In situ XANES and ambient pressure XPS experiments were used to elucidate the active state of the catalysts as partially reduced cerium oxide doped with oxidized cobalt atoms. In situ XRD and DRIFTS experiments suggest facile cerium reduction and oxygen vacancy formation, particularly with lower cobalt loadings. In situ DRIFTS analysis also revealed the presence of surface carbonate and bidentate formate species under reaction conditions, which may be associated with additional mechanistic pathways for the WGS reaction. Deactivation behavior was observed with higher cobalt loadings. XANES data suggest the formation of small metallic cobalt clusters at temperatures above 400 °C may be responsible. Notably, this deactivation was not observed for the 1% cobalt loaded catalyst, which exhibited the highest activity per unit of cobalt.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗