SEARCH · Search NASA
Results for “specific correlation”
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.
Structural Basis for Molecular Discrimination by a 3',3'-cGAMP Sensing Riboswitch
Cyclic dinucleotides are second messengers that target the adaptor STING and stimulate the innate immune response in mammals. Besides protein receptors, there are bacterial riboswitches that selectively recognize cyclic dinucleotides. We recently discovered a natural riboswitch that targets 3',3'-cGAMP, which is distinguished from the endogenous mammalian signal 2',3'-cGAMP by its backbone connectivity. Here, we report on structures of the aptamer domain of the 3',3'-cGAMP riboswitch from Geobacter in the 3',3'-cGAMP and c-di-GMP bound states. The riboswitch adopts a tuning forklike architecture with a junctional ligand-binding pocket and different orientations of the arms are correlated with the identity of the bound cyclic dinucleotide. Subsequent biochemical experiments revealed that specificity of ligand recognition can be affected by point mutations outside of the binding pocket, which has implications for both the assignment and reengineering of riboswitches in this structural class.
Direct numerical simulations of turbulent premixed cool flames: Global and local flame dynamics analysis
The cool flame dynamics, especially in turbulent flows, is of great interest for both practical application and fundamental research. Here, in this study, a series of direct numerical simulations of turbulent premixed n-C 7 H 16 /O 2 /O 3 /N 2 cool flames are performed, with the focus on the influence of turbulence intensity (u'/S L , where S L is the laminar flame speed) on the flame structure as well as the global and local cool flame dynamics. It is found that the cool flame front is considerably wrinkled by turbulence at high u'/S L , leading to significantly thickened turbulent cool flame brush and largely altered local reactivity compared with the reference laminar flame. However, the turbulent flame structure in the temperature space is found to be insensitive to u'/S L . Besides, with increasing u'/S L , the normalized turbulent cool flame speed (S L /S L ) is monotonically increased, attributed to substantial augmentation on the flame surface area (A T /A L ), while the stretching factor (I 0 ) remains almost constant and is smaller than 1. The underlying mechanisms for such variations are revealed through local flame dynamics analysis. Specifically, the local flame displacement speed S d is found to be strongly negatively correlated with flame curvature; meanwhile, such negative correlation and the probability distribution function (PDF) of flame curvature are barely influenced by u'/S L , leading to a weak dependence of I 0 on u'/S L . In contrast, the PDF of the tangential strain rate is found to span a much wider range and shift to the positive side as u'/S L increases, suggesting that the enhanced tangential strain rate is the main cause for the increase in surface area of the turbulent premixed cool flame. Finally, the influence of equivalence ratio on above findings is found to be insignificant, indicating that although the local reactivity of turbulent premixed cool flames is altered due to the differential diffusion, the resultant flame- stretch interaction is insensitive to the equivalence ratio. This study presents some unique cool flame dynamics that are distinct from hot flames, which can help improve the understanding and modeling of turbulent cool flames.
Operando Analyses of Solar Fuels Light Absorbers and Catalysts
Operando synchrotron radiation photoelectron spectroscopy in the tender X-ray energy range has been used to obtain information on the energy-band relations of semiconductor and metal-covered semiconductor surfaces while in direct contact with aqueous electrolytes under potentiostatic control. The system that was investigated consists of highly doped Si substrates that were conformally coated with ~70 nm titania films produced by atomic-layer deposition. TiO 2 /electrolyte and Si/TiO 2 /Ni/electrolyte interfaces were then analyzed by synchrotron radiation photoelectron spectroscopy. The PES data allows for determination of the flat-band position and identification of potential regions in which Fermi level pinning, depletion, or accumulation occurred. Operando X-ray absorption spectroscopy (XAS) techniques were additionally used to investigate the properties of heterogeneous electrocatalysts for the oxygen-evolution reaction. Operando XAS including the pre-edge, edge and EXAFS regions allowed the development of a detailed picture of the catalysts under operating conditions, and elucidated the changes in the physical and electronic structure of the catalyst that accompanied increases in the applied potential. Specifically, XAS data, combined with DFT studies, indicated that the activity of the electrocatalyst correlated with the formation of Fe dopant sites in γ-NiOOH.
Machine learning reveals sequence-function relationships in family 7 glycoside hydrolases
Family 7 glycoside hydrolases (GH7) are among the principal enzymes for cellulose degradation in nature and industrially. These important enzymes are often bimodular, including a catalytic domain and carbohydrate binding module (CBM) attached via a flexible linker, and exhibit an active site that binds cello-oligomers of up to ten glucosyl moieties. GH7 cellulases consist of two major subtypes: cellobiohydrolases (CBH) and endoglucanases (EG). Despite the critical importance of GH7 enzymes, there remain gaps in our understanding of how GH7 sequence and structure relate to function. Here, we employed machine learning to gain data-driven insights into relationships between sequence, structure, and function across the GH7 family. Machine-learning models, trained only on the number of residues in the active-site loops as features, were able discriminate GH7 CBHs and EGs with up to 99% accuracy, demonstrating that the lengths of loops A4, B2, B3, and B4 strongly correlate with functional subtype across the GH7 family. Classification rules were derived such that specific residues at 42 different sequence positions each predicted the functional subtype with accuracies surpassing 87%. A random forest model trained on residues at 19 positions in the catalytic domain predicted the presence of a CBM with 89.5% accuracy. Our ML results recapitulate, as top-performing features, a substantial number of the sequence positions determined by previous experimental studies to play vital roles in GH7 activity. We surmise that the yet-to-be-explored sequence positions among the top-performing features also contribute to GH7 functional variation and may be exploited to understand and manipulate function.
Phase transition in two-dimensional tellurene under mechanical strain modulation
Here, we carry out computational simulations based on density functional theory (DFT) to investigate different phases of two-dimensional (2-D) tellurene. These phases are classified by their characteristic space groups and symmetry elements. Correlations of these phases to the bulk crystalline tellurium structure are also illustrated. Our specific interests include mechanical property calculations for different phases and the possible phase transitions between them. Simulation results show that these 2-D Te phases have very different elastic moduli due to their different atomic bonding and relaxed structures. Moreover, compression along the in-plane directions facilitates the α → β phase transition, while in-plane tensile strains always make the α-phase more stable than the β-phase. However, the energy difference between the two phases is comparable to or even much smaller than the thermal energy kT, depending on the in-plane strain direction. We find that further increase of the tensile strain along the chain direction beyond a critical value, ca. 12%, may lead to a possible α → γ phase transition. As the tensile strain is above 15%, the γ-phase will be more stable than the α-phase, accompanied by a further reduced transition energy barrier.
From optical to X-ray ghost imaging
Recent advances in ghost imaging techniques and X-ray sources such as synchrotrons and, more recently, X-ray free-electron lasers (XFEL) have made X-ray ghost imaging a growing topic of interest. One specific type of ghost imaging utilizes thermal radiation and the measurement of intensity fluctuation correlation to form a true image without the need of a lens. This technique allows for much higher resolution than traditional X-ray imaging for a mesoscopic or even a microscopic object. In addition to this benefit of not requiring a lens, a surprising experiment has shown that, when set up correctly, this type of ghost imaging can provide clear images through the measurement of intensity fluctuation correlation when traditional images through measurements of intensity are blurred due to optical turbulence and vibrations. This turbulence-free technique will help maintain the high resolution of X-ray ghost imaging. How is an image formed from fluctuations in light? And what makes it turbulence-free? Using the concept of two-photon interference, this article provides an introduction to these fundamentally interesting concepts and X-ray ghost imaging.
Contributions of biofilm-induced flow heterogeneities to solute retention and anomalous transport features in porous media
We report that microbial biofilms are ubiquitous within porous media and the dynamics of their growth influence surface and subsurface flow patterns which impacts the physical properties of porous media and large-scale transport of solutes. Here, a two-dimensional pore-scale numerical model was used to evaluate the impact of biofilm-induced flow heterogeneities on conservative transport. Our study integrates experimental biofilm images of Paenibacillus 300A strain in a microfluidic device packed with cylindrical grains in a hexagonal distribution, with mathematical modeling. Biofilm is represented as a synthetic porous structure with locally varying physical properties that honors the impact of biofilm on the porous medium. We find that biofilm plays a major role in shaping the observed conservative transport dynamics by enhancing anomalous characteristics. More specifically, when biofilm is present, the pore structure in our geometry becomes more spatially correlated. We observe intermittent behavior in the Lagrangian velocities that switches between fast transport periods and long trapping events. Our results suggest that intermittency enhances solute spreading in breakthrough curves which exhibit extreme anomalous slope at intermediate times and very marked late solute arrival due to solute retention. The efficiency of solute retention by the biofilm is controlled by a transport regime which can extend the tailing in the concentration breakthrough curves. These results indicate that solute retention by the biofilm exerts a strong control on conservative solute transport at pore-scale, a role that to date has not received enough attention.
miR-1207-3p regulates the androgen receptor in prostate cancer via FNDC1/fibronectin
Prostate cancer (PCa) is frequently diagnosed in men, and dysregulation of microRNAs is characteristic of many cancers. MicroRNA-1207-3p is encoded at the non-protein coding gene locus PVT1 on the 8q24 human chromosomal region, an established PCa susceptibility locus. However, the role of microRNA-1207-3p in PCa is unclear. We discovered that microRNA-1207-3p is significantly underexpressed in PCa cell lines in comparison to normal prostate epithelial cells. Increased expression of microRNA-1207-3p in PCa cells significantly inhibits proliferation, migration, and induces apoptosis via direct molecular targeting of FNDC1, a protein which contains a conserved protein domain of fibronectin (FN1). FNDC1, FN1, and the androgen receptor (AR) are significantly overexpressed in PCa cell lines and human PCa, and positively correlate with aggressive PCa. Prostate tumor FN1 expression in patients that experienced PCa-specific death is significantly higher than in patients that remained alive. Furthermore, FNDC1, FN1 and AR are concomitantly overexpressed in metastatic PCa. Consequently, these studies have revealed a novel microRNA-1207-3p/FNDC1/FN1/AR regulatory pathway in PCa. - Graphical abstract: miR-1207-3p/FNDC1/FN1/AR is a novel regulatory pathway in prostate cancer. - Highlights: • Expression of microRNA-1207-3p is significantly lost in prostate cancer (PCa) cells. • MicroRNA-1207-3p regulates proliferation, apoptosis, and migration via direct molecular targeting of the 3′UTR of FNDC1. • MicroRNA-1207-3p regulates proliferation, apoptosis, and migration via direct molecular targeting of the 3′UTR of FNDC1. • FNDC1, FN1, and AR are concurrently overexpressed in metastatic PCa.
AIM2 is a potential therapeutic target in human renal carcinoma and suppresses its invasion and metastasis via enhancing autophagy induction
Highlights: • Low AIM2 expression was correlated with RCC patient survival. • AIM2 suppressed the proliferation, migration and invasion of renal cancer cells. • AIM2 enhanced the expression of autophagy-related genes. • AIM2 exerts the anti-tumor effect via enhancing the induction of autophagy. Absent in melanoma 2 (AIM2) as a tumor suppressor is recently recognized to play an important role in many human solid tumors. However, the biological significance and function of AIM2 in renal cell carcinoma (RCC) remain unknown. In this study, we proposed to investigate the clinicopathological and prognostic significance of AIM2 in RCC patient specimens, to analyze the correlation between AIM2 expression and disease progression, and to further investigate the role and mechanism of AIM2 in malignant behaviors of renal carcinoma by cellular and animal models. The correlation analysis showed that low AIM2 expression was significantly correlated with lymph node metastasis, poor 5-year overall (P < 0.05) and disease-specific survival (P < 0.05) in RCC patients. Over-expression of AIM2 in 786-O or OSRC-2 cells suppressed the cell proliferation, migration and invasion as compared with control. The increased AIM2 expression enhanced the expression of autophagy-related genes (Bcl-2, Beclin-1, LC3-Ⅱ, and ATG-5) and increased the ratio of LC3-Ⅱ/LC3-Ⅰin renal carcinoma cells. Of note, the blockade of autophagy by 3-Methyladenine (3-MA) abrogated the inhibition of cell migration and invasion by over-expressing AIM2, indicated that AIM2 suppressed the malignant behaviors of renal carcinoma by enhancing the induction of autophagy. Furthermore, 3-MA abolished the prevention of tumor growth on nude mouse established by AIM2-OSRC-2 cells. Our results suggested that AIM2 might serve as a novel prognostic indicator as well as a potential therapeutic target for renal carcinoma treatment.
Comparison of Dislocation Characterization by Electron Channeling Contrast Imaging and Cross-Correlation Electron Backscattered Diffraction
The characterization and quantification of dislocations is predominately carried out using transmission electron microscopy (TEM). Nevertheless, the sample preparation necessary for TEM foils can be cumbersome and possibly introduce defects. Two other techniques for characterizing and quantifying dislocations are electron channeling contrast imaging (ECCI) and cross-correlation electron backscattered diffraction (CC-EBSD). ECCI is similar to TEM in that specific imaging (channeling or diffraction) conditions need to be established, but is carried out in a scanning electron microscope on bulk samples, resulting in less complex sample preparation. CC-EBSD measures subtle changes in orientation measured from EBSD patterns to calculate elastic strains and the Nye tensor to deduce and map the total geometrically necessary dislocation (GND) content. Furthermore, this work presented here compares the capabilities of ECCI and CC-EBSD to map dislocations generated from nanoindentation.
Machine Learning-Guided Identification of PET Hydrolases from Natural Diversity
The enzymatic depolymerization of poly(ethylene terephthalate) (PET) is emerging as a leading chemical recycling technology for waste polyester. As part of this endeavor, new candidate enzymes identified from natural diversity can serve as useful starting points for enzyme evolution and engineering. In this study, we improved upon HMM searches by applying an iterative machine learning strategy to identify 400 putative PET-degrading enzymes (PET hydrolases) from naturally occurring homologs. Using high-throughput (HTP) experimental techniques, we successfully expressed and purified >200 enzyme candidates and assayed them for PET hydrolysis activity as a function of pH, temperature, and substrate crystallinity. From this library, we discovered 91 previously unknown PET hydrolases, 35 of which retain activity at pH 4.5 on crystalline material, which are conditions relevant to developing more efficient commercial processes. Notably, four enzymes showed equal to or higher activity than LCC-ICCG, a benchmark PET hydrolase, at this challenging condition in our screening assay, and 11 of which have pH optima <7. Using these data, we identified regions of PETases statistically correlated to activity at lower pH. We additionally investigated the effect of condition-specific activity data on trained machine learning predictors and found a precision (putative hit rate) improvement of up to 30% compared to a Hidden Markov Model alone. Our findings show that by pointing enzyme discovery toward conditions of interest with multiple rounds of experimental and machine learning, we can discover large sets of active enzymes and explore factors associated with activity at those conditions.
Phylogenetically conserved resource partitioning in the coastal microbial loop
Resource availability influences marine microbial community structure, suggesting that population-specific resource partitioning defines discrete niches. Identifying how resources are partitioned among populations, thereby characterizing functional guilds within the communities, remains a challenge for microbial ecologists. We used proteomic stable isotope probing (SIP) and NanoSIMS analysis of phylogenetic microarrays (Chip-SIP) along with 16S rRNA gene amplicon and metagenomic sequencing to characterize the assimilation of six 13 C-labeled common metabolic substrates and changes in the microbial community structure within surface water collected from Monterey Bay, CA. Both sequencing approaches indicated distinct substrate-specific community shifts. However, observed changes in relative abundance for individual populations did not correlate well with directly measured substrate assimilation. The complementary SIP techniques identified assimilation of all six substrates by diverse taxa, but also revealed differential assimilation of substrates into protein and ribonucleotide biomass between taxa. Substrate assimilation trends indicated significantly conserved resource partitioning among populations within the Flavobacteriia, Alphaproteobacteria and Gammaproteobacteria classes, suggesting that functional guilds within marine microbial communities are phylogenetically cohesive. However, populations within these classes exhibited heterogeneity in biosynthetic activity, which distinguished high-activity copiotrophs from low-activity oligotrophs. These results indicate distinct growth responses between populations that is not apparent by genome sequencing alone.
Solvent manipulation of the pre-reduction metal–ligand complex and particle-ligand binding for controlled synthesis of Pd nanoparticles
Understanding how to control the nucleation and growth rates is crucial for designing nanoparticles with specific sizes and shapes. Herein, we show that the nucleation and growth rates are correlated with the thermodynamics of metal–ligand/solvent binding for the pre-reduction complex and the surface of the nanoparticle, respectively. To obtain these correlations, we measured the nucleation and growth rates by in situ small angle X-ray scattering during the synthesis of colloidal Pd nanoparticles in the presence of trioctylphosphine in solvents of varying coordinating ability. The results show that the nucleation rate decreased, while the growth rate increased in the following order, toluene, piperidine, 3,4-lutidine and pyridine, leading to a large increase in the final nanoparticle size (from 1.4 nm in toluene to 5.0 nm in pyridine). Using density functional theory (DFT), complemented by 31 P nuclear magnetic resonance and X-ray absorption spectroscopy, we calculated the reduction Gibbs free energies of the solvent-dependent dominant pre-reduction complex and the solvent-nanoparticle binding energy. The results indicate that lower nucleation rates originate from solvent coordination which stabilizes the pre-reduction complex and increases its reduction free energy. At the same time, DFT calculations suggest that the solvent coordination affects the effective capping of the surface where stronger binding solvents slow the nanoparticle growth by lowering the number of active sites (not already bound by trioctylphosphine). The findings represent a promising advancement towards understanding the microscopic connection between the metal–ligand thermodynamic interactions and the kinetics of nucleation and growth to control the size of colloidal metal nanoparticles.
Making maps of cosmic microwave background polarization for B-mode studies: the POLARBEAR example
Analysis of cosmic microwave background (CMB) datasets typically requires some filtering of the raw time-ordered data. For instance, in the context of ground-based observations, filtering is frequently used to minimize the impact of low frequency noise, atmospheric contributions and/or scan synchronous signals on the resulting maps. In this paper, we have explicitly constructed a general filtering operator, which can unambiguously remove any set of unwanted modes in the data, and then amend the map-making procedure in order to incorporate and correct for it. We show that such an approach is mathematically equivalent to the solution of a problem in which the sky signal and unwanted modes are estimated simultaneously and the latter are marginalized over. We investigated the conditions under which this amended map-making procedure can render an unbiased estimate of the sky signal in realistic circumstances. We then discuss the potential implications of these observations on the choice of map-making and power spectrum estimation approaches in the context of B-mode polarization studies. Specifically, we have studied the effects of time-domain filtering on the noise correlation structure in the map domain, as well as impact it may haveon the performance of the popular pseudo-spectrum estimators. We conclude that although maps produced by the proposed estimators arguably provide the most faithful representation of the sky possible given the data, they may not straightforwardly lead to the best constraints on the power spectra of the underlying sky signal and special care may need to be taken to ensure this is the case. By contrast, simplified map-makers which do not explicitly correct for time-domain filtering, but leave it to subsequent steps in the data analysis, may perform equally well and be easier and faster to implement. We focused on polarization-sensitive measurements targeting the B-mode component of the CMB signal and apply the proposed methods to realistic simulations based on characteristics of an actual CMB polarization experiment, POLARBEAR. Finally, our analysis and conclusions are however more generally applicable.
ARIADNE – a program estimating covariances in detail for neutron experiments
The python program ARIADNE is a tool developed for evaluators to estimate detailed uncertainties and covariances for experimental data in a consistent and efficient manner. Currently, it is designed to aid in the uncertainty quantification of prompt fission neutron spectra, and was employed to estimate experimental covariances for CIELO and ENDF/B-VIII.0 evaluations. It provides a streamlined way to estimate detailed covariances by (1) implementing uncertainty quantification algorithms specific to the observables, (2) defining input quantities for typically encountered uncertainty sources and correlation shapes, and (3) automatically generating plots of data, uncertainties and correlations, GND formatted XML and plain text output files. Covariances of the same and between different datasets can be estimated, and tools are provided to assemble a database of experimental data and covariances for an evaluation based on ARIADNE outputs. The underlying IPython notebook files can be easily stored, including all assumptions on uncertainties, leading to more reproducible inputs for nuclear data evaluations. Here, the key inputs and outputs are shown along with a representative example for the current version of ARIADNE to illustrate its usability and to open a discussion on how it could address further needs of the nuclear data evaluation community.
Measuring and unbiasing the BAO shift in the Ly α forest with AbacusSummit
ABSTRACT The Dark Energy Spectroscopic Instrument (DESI) places sub- per cent constraints on measurements of the Baryon Acoustic Oscillation (BAO) scaling parameters from the Ly $\alpha$ forest. However, no systematic error budget stemming from non-linearities in the three-dimensional clustering of the Ly $\alpha$ forest is included in the DESI-Ly $\alpha$ analysis. In this work, we measure the size of the shift of the BAO peak using large Ly $\alpha$ forest mocks produced on the N-body simulation suite AbacusSummit, which adopt the Fluctuating–Gunn–Peterson Approximation (FGPA). Specifically, we measure the Ly $\alpha$ autocorrelation and the Ly $\alpha$-quasar cross-correlation functions. To mitigate the noise, we adopt a linear control variates technique, reducing the error bars by a factor of up to $\sim \sqrt{50}$ on large scales. From the autocorrelation, we detect a small positive shift in radial direction of $\Delta \alpha _{\parallel }= 0.35~{{\ \rm per\ cent}}$ at the 3$\sigma$ level and virtually no shift in the transverse direction, $\alpha _\perp$. From the cross-correlation, we see a similar shift to $\Delta \alpha _\parallel$, albeit with larger error bars, and a small negative shift, $\Delta \alpha _{\perp }=\sim$0.25 per cent, at the 2$\sigma$ level. We also make a connection with the Ly $\alpha$ forest effective field theory (EFT) framework and find that the one-loop EFT power spectrum yields unbiased measurements of the BAO shift parameters in radial and transverse direction for Ly $\alpha$ auto- and the Ly $\alpha$-quasar cross-correlation measurements. When using the one-loop EFT framework, we find that we can recover the BAO parameters without a shift, which has important implications for future Ly $\alpha$ forest analyses based on EFT. This work paves the way for novel full-shape analyses of the currently observing DESI and future surveys such as the PFS, WEAVE-QSO, and 4MOST.
Galaxy–galaxy lensing estimators and their covariance properties
Here, we study the covariance properties of real space correlation function estimators – primarily galaxy–shear correlations, or galaxy–galaxy lensing – using SDSS data for both shear catalogues and lenses (specifically the BOSS LOWZ sample). Using mock catalogues of lenses and sources, we disentangle the various contributions to the covariance matrix and compare them with a simple analytical model. We show that not subtracting the lensing measurement around random points from the measurement around the lens sample is equivalent to performing the measurement using the lens density field instead of the lens overdensity field. While the measurement using the lens density field is unbiased (in the absence of systematics), its error is significantly larger due to an additional term in the covariance. Therefore, this subtraction should be performed regardless of its beneficial effects on systematics. Comparing the error estimates from data and mocks for estimators that involve the overdensity, we find that the errors are dominated by the shape noise and lens clustering, which empirically estimated covariances (jackknife and standard deviation across mocks) that are consistent with theoretical estimates, and that both the connected parts of the four-point function and the supersample covariance can be neglected for the current levels of noise. While the trade-off between different terms in the covariance depends on the survey configuration (area, source number density), the diagnostics that we use in this work should be useful for future works to test their empirically determined covariances.