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At least 487 records · Page 27

Models for randomly distributed nanoscopic domains on spherical vesicles

The existence of lipid domains in the plasma membrane of biological systems has proven controversial, primarily due to their nanoscopic size—a length scale difficult to interrogate with most commonly used experimental techniques. Scattering techniques have recently proven capable of studying nanoscopic lipid domains populating spherical vesicles. However, the development of analytical methods able of predicting and analyzing domain pair correlations from such experiments has not kept pace. In our work, we developed models for the random distribution of monodisperse, circular nanoscopic domains averaged on the surface of a spherical vesicle. Specifically, the models take into account (i) intradomain correlations corresponding to form factors and interdomain correlations corresponding to pair distribution functions, and (ii) the analytical computation of interdomain correlations for cases of two and three domains on a spherical vesicle. In the case of more than three domains, these correlations are treated either by Monte Carlo simulations or by spherical analogs of the Ornstein-Zernike and Percus-Yevick (PY) equations. Importantly, the spherical analog of the PY equation works best in the case of nanoscopic size domains, a length scale that is mostly inaccessible by experimental approaches such as, for example, fluorescent techniques and optical microscopies. The analytical form factors and structure factors of nanoscopic domains populating a spherical vesicle provide a new and important framework for the quantitative analysis of experimental data from commonly studied phase-separated vesicles used in a wide range of biophysical studies.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Analysis of ATS-5 data

Data from the particle detectors on ATS-5 in synchronous orbit and from the meridian line of magnetometers at a ground station are correlated for periods of well defined substorm activity. Specific substorm events are presented whose region of maximum perturbation can be accurately identified using current systems modelling techniques. It was found that the impulsive particle response at ATS-5 is correlated with changes in the auroral electrojet structure during the development of the substorm expansive phase. The response of the particle detectors appears to differ from event to event depending on the geometry of the disturbed region of the magnetosphere. It is concluded that the substorm disturbance typically begins at low latitude and propagates poleward in impulsive steps and that energetic electron enhancements are observed at ATS-5 when the poleward border of the electrojet intensifies in the latitude range of the ATS-5 field line.

Sharp, R. D.↗

maslov-group/FUN-PROSE

mRNA levels of all genes in a genome is a critical piece of information defining the overall state of the cell in a given environmental condition. Being able to reconstruct such condition-specific expression in fungal genomes is particularly important to metabolically engineer these organisms to produce desired chemicals in industrially scalable conditions. Most previous deep learning approaches focused on predicting the average expression levels of a gene based on its promoter sequence, ignoring its variation across different conditions. Here we present FUN-PROSE—a deep learning model trained to predict differential expression of individual genes across various conditions using their promoter sequences and expression levels of all transcription factors. We train and test our model on three fungal species and get the correlation between predicted and observed condition-specific gene expression as high as 0.85. We then interpret our model to extract promoter sequence motifs responsible for variable expression of individual genes. We also carried out input feature importance analysis to connect individual transcription factors to their gene targets. A sizeable fraction of both sequence motifs and TF-gene interactions learned by our model agree with previously known biological information, while the rest corresponds to either novel biological facts or indirect correlations.

Liu, Simon↗

FUN-PROSE: A deep learning approach to predict condition-specific gene expression in fungi

mRNA levels of all genes in a genome is a critical piece of information defining the overall state of the cell in a given environmental condition. Being able to reconstruct such condition-specific expression in fungal genomes is particularly important to metabolically engineer these organisms to produce desired chemicals in industrially scalable conditions. Most previous deep learning approaches focused on predicting the average expression levels of a gene based on its promoter sequence, ignoring its variation across different conditions. Here we present FUN-PROSE—a deep learning model trained to predict differential expression of individual genes across various conditions using their promoter sequences and expression levels of all transcription factors. We train and test our model on three fungal species and get the correlation between predicted and observed condition-specific gene expression as high as 0.85. We then interpret our model to extract promoter sequence motifs responsible for variable expression of individual genes. We also carried out input feature importance analysis to connect individual transcription factors to their gene targets. A sizeable fraction of both sequence motifs and TF-gene interactions learned by our model agree with previously known biological information, while the rest corresponds to either novel biological facts or indirect correlations.

59 BASIC BIOLOGICAL SCIENCES↗

Theory and application of the vector pair correlation function for real-space crystallographic analysis of order/disorder correlations from STEM images

Deviations of local structure and chemistry from the average crystalline unit cell are increasingly recognized to have a significant influence on the properties of many technologically important materials. Here, we present the vector pair correlation function (vPCF) as a new real-space crystallographic analysis method, which can be applied to atomic-resolution scanning transmission electron microscopy (STEM) images to quantify and analyze structural order/disorder correlations. Our STEM-based vPCFs have several advantages over radial PCFs and/or 3D pair distribution functions from x-ray total scattering: vPCFs explicitly retain crystallographic orientation information, are spatially resolved, can be applied directly on a sublattice basis, and are suitable for any material that can be imaged with STEM. To show the utility of our approach, we measure partial vPCFs in Ba 5 SmSn 3 Nb 7 O 30 (BSSN), a tetragonal tungsten bronze (TTB) structured complex oxide. Many TTBs are known to be classical or relaxor ferroelectrics, and these properties have been correlated with the presence of superlattice ordering. BSSN, specifically, exhibits relaxor behavior and an incommensurate structural modulation. From the vPCF data, we observe that, of the cation sites, only the Ba (A2) sublattice is structurally modulated. We then infer the local modulation vector and reveal a marked anisotropy in its correlation length. Finally, short-range correlated polar displacements on the B2 cation sites are observed. This work introduces the vPCF as a powerful real-space crystallography technique, which enables direct, robust quantification of short-to-long range order on a sublattice-specific basis and is applicable to a wide range of complex material types.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Low-Isp derated ion thruster operation

The performance and lifetime expectations of 30 cm xenon ion thruster technology at low values of specific impulse were evaluated, with emphasis on 1000-2500 s operation. Power levels of up to 2.0 kW, appropriate for auxiliary and orbit maneuvering propulsion, were processed at thrust-to-power ratios up to 57 mN/kW. These tests were conducted using a derated 30 cm ion thruster with high-perveance design two-grid ion optics with xenon propellent. Lifetime projections were made based on a simple analysis of critical component erosion rates, and it was found that a strong correlation exists with the ratio of the specific impulse-to-input power. Under all operating conditions for which the projected thruster lifetime is less than 10,000 hrs, the life-limiting component of this technology is erosion of the accelerator grid due to charge-exchange ions. The use of alternative grid materials such as carbon is estimated to increase useful thruster lifetimes by as much as an order of magnitude and may enable long-life high thrust-density, sub-2500 s Isp operation. The performance and life of the derated thruster appears similar to that of the Russian SPT-100 thruster in the 1.0-2.0 kW, 1600-2000 s operational envelope.

Patterson, Michael J.↗

Low-Isp derated ion thruster operation

The performance and lifetime expectations of 30 cm xenon ion thruster technology at low values of specific impulse were evaluated, with emphasis on 1000-2500 s operation. Power levels of up to 2.0 kW, appropriate for auxiliary and orbit maneuvering propulsion, were processed at thrust-to-power ratios up to 57 mN/kW. These tests were conducted using a derated 30 cm ion thruster with high-perveance design two-grid ion optics with xenon propellant. Lifetime projections were made based on a simple analysis of critical component erosion rates, and it was found that a strong correlation exists with the ratio of the specific impulse-to-input power. Under all operating conditions for which the projected thruster lifetime is less than 10,000 hrs, the life-limiting component of this technology is erosion of the accelerator grid due to charge-exchange ions. The use of alternative grid materials such as carbon is estimated to increase useful thruster lifetimes by as much as an order of magnitude and may enable long-life high thrust-density, sub-2500 s Isp operation. The performance and life of the derated thruster appears similar to that of the Russian SPT-100 thruster in the 1.0-2.0 kW, 1600-2000 s operational envelope.

Patterson, Michael J.↗

Structural features of solid-solid phase transitions and lattice dynamics in U 3 O 8

Triuranium octoxide (U 3 O 8 ) undergoes an orthorhombic to hexagonal structural phase transition near Ts=305°C, and a separate nonstructural phase transition at Tc=210°C. The later transition has previously been associated with temperature-induced fluctuations in the uranium oxidation state. A discontinuity in the slope of electrical conductivity versus temperature measurement at 210°C has supported this idea. The orthorhombic phase has three crystallographic sites in two distinct oxidation configurations [2 U(V) and 1 U(VI)], whereas the hexagonal phase has one distinct uranium site. High-resolution x-ray diffraction measurements eliminate the possibility of superlattice Bragg reflections to less than 0.2 e - scattering power and U 3 O 8 is not metallic; consequently, the presence of oxidation fluctuations is required for charge balancing. Interestingly, the order-to-disorder transition occurs at a much lower temperature than the structural transition. Using temperature-dependent x-ray diffraction and Raman spectroscopy, we show anisotropic lattice expansion in the in-plane b and c lattice constants. A specific discontinuity in the temperature derivatives of the b and c lattice constants are the first reported structural signatures of the order-to-disorder transition, suggestive of a change in local U–O coordination. Phonon frequencies of U 3 O 8 measured by Raman spectroscopy show significant temperature-dependent dynamics. Redshifting of several modes between 40 and 300°C cannot be explained by unit cell expansion alone because the unit cell volume decreases in this region. Instead, we show that phonon frequencies are highly correlated with the anisotropic lattice expansion/contraction along specific crystallographic directions.

36 MATERIALS SCIENCE↗

Detection of Delamination in Composite Beams Using Broadband Acoustic Emission Signatures

Delamination in composite structure may be caused by imperfections introduced during the manufacturing process or by impact loads by foreign objects during the operational life. There are some nondestructive evaluation methods to detect delamination in composite structures such as x-radiography, ultrasonic testing, and thermal/infrared inspection. These methods are expensive and hard to use for on line detection. Acoustic emission testing can monitor the material under test even under the presence of noise generated under load. It has been used extensively in proof-testing of fiberglass pressure vessels and beams. In the present work, experimental studies are conducted to investigate the use of broadband acoustic emission signatures to detect delaminations in composite beams. Glass/epoxy beam specimens with full width, prescribed delamination sizes of 2 inches and 4 inches are investigated. The prescribed delamination is produced by inserting Teflon film between laminae during the fabrication of composite laminate. The objectives of this research is to develop a method for predicting delamination size and location in laminated composite beams by combining smart materials concept and broadband AE analysis techniques. More specifically, a piezoceramic (PZT) patch is bonded on the surface of composite beams and used as a pulser. The piezoceramic patch simulates the AE wave source as a 3 cycles, 50KHz, burst sine wave. One broadband AE sensor is fixed near the PZT patch to measure the AE wave near the AE source. A second broadband AE sensor, which is used as a receiver, is scanned along the composite beams at 0.25 inch step to measure propagation of AE wave along the composite beams. The acquired AE waveform is digitized and processed. Signal strength, signal energy, cross-correlation of AE waveforms, and tracking of specific cycle of AE waveforms are used to detect delamination size and location.

Okafor, A. C.↗

DNA2FACE: An approach to correlating 3D facial structure and DNA

In this paper we introduce the concept of correlating genetic variations in an individual's specific genetic code (DNA) and facial morphology. This is the first step in the research effort to estimate facial appearance from DNA samples, which is gaining momentum within intelligence, law enforcement and national security communities. The dataset for the study consisting of genetic data and 3D facial scans (phenotype) data was obtained through the FaceBase Consortium. The proposed approach has three main steps: phenotype feature extraction from 3D face images, genotype feature extraction from a DNA sample, and genome-wide association analysis to determine genetic variations that contribute to facial structure and appearance. Results indicate that there exist significant correlations between genetic information and facial structure. We have identified 30 single nucleotide polymorphisms (SNPs), i.e. genetic variations, that significantly contribute to facial structure and appearance. We conclude with a preliminary attempt at facial reconstruction from the genetic data and emphasize on the complexity of the problem and the challenges encountered.

Srinivas, Nisha↗

Non-destructive evaluation of specific surface area of porous powder systems using experimental and virtual air permeametry

The correlation of flow-permeable surface area (FSSA) of porous initiator powder to its performance is well-established. More specifically, with age, such powders tend to coarsen (i.e., lose surface area), with an accompanying deterioration in performance. Commercially available permeametry apparatuses, such as that from Fisher Scientific or a more modern version from Micromeritics suffers from a few drawbacks, including: (1) the need to work with a fixed mass equivalent of 1 cm 3 of solid material; (2) the need to re-compact the material to monitor aging effects (which can introduce unwanted errors in the derived results); (3) a superfluous design that involves a reference plug, and for the older Fisher Scientific model, the need to eyeball water meniscus height that can lead to inaccuracies; (4) calibrated and tested only for samples packed in cylindrical geometries.

36 MATERIALS SCIENCE↗

Materials informatics: From the atomic-level to the continuum

We report that in recent years materials informatics, which is the application of data science to problems in materials science and engineering, has emerged as a powerful tool for materials discovery and design. This relatively new field is already having a significant impact on the interpretation of data for a variety of materials systems, including those used in thermoelectrics, ferroelectrics, battery anodes and cathodes, hydrogen storage materials, polymer dielectrics, etc. Its practitioners employ the methods of multivariate statistics and machine learning in conjunction with standard computational tools (e.g., density-functional theory) to, for example, visualize and dimensionally reduce large data sets, identify patterns in hyperspectral data, parse microstructural images of polycrystals, characterize vortex structures in ferroelectrics, design batteries and, in general, establish correlations to extract important physics and infer structure-property-processing relationships. In this Overview, we critically examine the role of informatics in several important materials subfields, highlighting significant contributions to date and identifying known shortcomings. We specifically focus attention on the difference between the correlative approach of classical data science and the causative approach of physical sciences. From this perspective, we also outline some potential opportunities and challenges for informatics in the materials realm in this era of big data.

36 MATERIALS SCIENCE↗

Long-term stability of nanostructured thin film electrodes at operating potentials

Long-term stability of nanostructured thin film (NSTF) catalysts at operating potentials has been investigated. Compared to high surface area Pt/C catalysts, NSTF electrodes show 20–50x smaller F – emission rates (FER) because of their high specific activity for oxygen reduction reaction (ORR), but are susceptible to poisoning by the products of membrane degradation because of their low electrochemically active surface area (ECSA). The observed voltage degradation rates at potentials corresponding to 1–1.5 A/cm 2 current density are much higher than the allowable 13–14 μV/h. Although F – is not itself responsible for performance decay, cumulative fluoride release (CFR) is a good marker for catalyst surface contamination. The observed performance decay is not only due to loss of active Pt sites but also adsorbed impurities impeding ORR kinetics. There is a strong correlation between measured CFR and observed decrease in specific ORR activity and limiting current density and increase in mass transfer overpotentials. Furthermore, the correlations indicate that the target of <10% lifetime performance degradation can be achieved by restricting CFR in NSTF electrodes to 0.7 μg/cm 2 , as may be possible with more stable membranes, higher surface area NSTF catalysts, and cell operation at lower temperatures and higher relative humidities.

36 MATERIALS SCIENCE↗

Training, transfer, and retention of three-dimensional spatial memory in virtual environments

Human orientation requires one to remember and visualize spatial arrangements of landmarks from different perspectives. Astronauts have reported difficulties remembering relationships between environmental landmarks when imagined in arbitrary 3D orientations. The present study investigated the effects of strategy training on humans' 1) ability to infer their orientation from landmarks presented ahead and below, 2) performance when subsequently learning a different array, and 3) retention of configurational knowledge over time. On the first experiment day, 24 subjects were tested in a virtual cubic chamber in which a picture of an animal was drawn on each wall. Through trial-by-trial exposures, they had to memorize the spatial relationships among the six pictures around them and learn to predict the direction to a specific picture when facing any view direction, and in any roll orientation. Half of the subjects ("strategy group") were taught methods for remembering picture groupings, while the remainder received no such training ("control group"). After learning one picture array, the procedure was repeated in a second. Accuracy (% correct) and response time learning curves were measured. Performance for the second array and configurational memory of both arrays were also retested 1, 7, and 30 days later. Results showed that subjects "learned how to learn" this generic 3D spatial memory task regardless of their relative orientation to the environment, that ability and configurational knowledge was retained for at least a month, that figure rotation ability and field independence correlate with performance, and that teaching subjects specific strategies in advance significantly improves performance. Training astronauts to perform a similar generic 3D spatial memory task, and suggesting strategies in advance, may help them orient in three dimensions.

Non-NASA Center↗

Integration of transcriptomic and proteomic data identifies biological functions in cell populations from human infant lung

Systems biology uses computational approaches to integrate diverse data types to understand cell and organ behavior. Data derived from complementary technologies, for example transcriptomic and proteomic analyses, are providing new insights into development and disease. We compared mRNA and protein profiles from purified endothelial, epithelial, immune, and mesenchymal cells from normal human lung tissue. Signatures for each cell type were identified and compared at both mRNA and protein levels. Cell specific biological processes and pathways were predicted by analysis of concordant and discordant RNA-protein pairs. Cell clustering and gene set enrichment comparisons identified shared versus unique processes associated with transcriptomic and/or proteomic data. Clear cell-cell correlations between mRNA and protein data were obtained from each cell type. Approximately 40% of RNA-protein pairs were coherently expressed. While the correlation between RNA and their protein products was relatively low (approximately 0.4), cell specific signature genes involved in functional processes characteristic of each cell type, were more highly correlated with their protein products. Consistency of cell specific RNA-protein signatures indicated an essential framework for the function of each cell type. Visualization and reutilization of the protein and RNA profiles are supported by a new web application, "LungProteomics," which is incorporated into the LGEA web portal and freely accessible at (https://research.cchmc.org/pbge/lunggens/lungProteomics/human_sorted_profiles.html).

human lung, sorted cells, integrated omics, Transc↗

Comparison of two software systems for quantification of myocardial blood flow in patients with hypertrophic cardiomyopathy

Backgorund: Quantification of myocardial blood flow (MBF) by positron emission tomography (PET) is important for investigation of angina in hypertrophic cardiomyopathy (HCM). Several software programs exist for MBF quantification, but they have been mostly evaluated in patients (with normal cardiac geometry), referred for evaluation of coronary artery disease (CAD). Software performance has not been evaluated in HCM patients who frequently have hyperdynamic LV function, LV outflow tract (LVOT) obstruction, small LV cavity size, and variation in the degree/location of LV hypertrophy. Aim: We compared results of MBF obtained using PMod, which permits manual segmentation, to those obtained by FDA-approved QPET software which has an automated segmentation algorithm. Methods: {sup 13}N-ammonia PET perfusion data were acquired in list mode at rest and during pharmacologic vasodilation, in 76 HCM patients and 10 non-HCM patients referred for evaluation of CAD (CAD group.) Data were resampled to create static, ECG-gated and 36-frame-dynamic images. Myocardial flow reserve (MFR) and MBF (in ml/min/g) were calculated using QPET and PMod softwares. Results: All HCM patients had asymmetric septal hypertrophy, and 50% had evidence of LVOT obstruction, whereas non-HCM patients (CAD group) had normal wall thickness and ejection fraction. PMod yielded significantly higher values for global and regional stress-MBF and MFR than for QPET in HCM. Reasonably fair correlation was observed for global rest-MBF, stress-MBF, and MFR using these two softwares (rest-MBF: r = 0.78; stress-MBF: r = 0.66.; MFR: r = 0.7) in HCM patients. Agreement between global MBF and MFR values improved when HCM patients with high spillover fractions (> 0.65) were excluded from the analysis (rest-MBF: r = 0.84; stress-MBF: r = 0.72; MFR: r = 0.8.) Regionally, the highest agreement between PMod and QPET was observed in the LAD territory (rest-MBF: r = 0.82, Stress-MBF: r = 0.68) where spillover fraction was the lowest. Unlike HCM patients, the non-HCM patients (CAD group) demonstrated excellent agreement in MBF/MFR values, obtained by the two softwares, when patients with high spillover fractions were excluded (rest-MBF: r = 0.95; stress-MBF: r = 0.92; MFR: r = 0.95). Conclusions: Anatomic characteristics specific to HCM hearts contribute to lower correlations between MBF/MFR values obtained by PMod and QPET, compared with non-HCM patients. These differences indicate that PMod and QPET cannot be used interchangeably for MBF/MFR analyses in HCM patients.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

Multidimensional Distributional Neural Network Output Demonstrated in Super‐Resolution of Surface Wind Speed

Accurate quantification of uncertainty in neural network predictions remains a central challenge for scientific applications involving high-dimensional, correlated data. While existing methods capture either aleatoric or epistemic uncertainty, few offer closed-form, multidimensional distributions that preserve spatial correlation while remaining computationally tractable. In this work, we present a framework for training neural networks with a multidimensional Gaussian loss, generating a closed-form predictive distribution over outputs informed by non-identically distributed training data. Our approach captures aleatoric uncertainty by iteratively estimating the means and covariance matrices, and is demonstrated on a super-resolution example out-of-training-sample. We leverage a Fourier representation of the covariance matrix to stabilize network training and preserve spatial correlation. We introduce a novel regularization strategy—referred to as information sharing—that interpolates between image-specific and global covariance estimates, enabling convergence of the super-resolution downscaling network trained on image-specific distributional loss functions. This framework allows for efficient sampling, explicit correlation modeling, and extensions to more complex distribution families all without disrupting prediction performance. We demonstrate the method on a surface wind speed downscaling task and discuss its broader applicability to uncertainty-aware prediction in scientific models.

17 WIND ENERGY↗

Lattice-scale variations in viscosity are correlated with solution structure at mineral-water interfaces

At solid-liquid interfaces, the viscosity increases markedly from the bulk due to the collective interactions of ions and water molecules, influencing phenomena relevant to nanofluidics, colloidal dynamics, and electrochemistry. Here, in this study, we investigated dissipative forces at the boehmite-water interface using 3D atomic force microscopy. We observed an increase in interfacial solution viscosity, η, by 10-100-fold as the nanoprobe approached the surface in normal direction, with up to four oscillatory features showing average peaks of η/η bulk = 44-71. Moreover, the viscosity showed sub-nanometer variations within 0.5 nm from the interface, templated by the underlying crystal lattice and correlated with the interfacial solution structure. Beyond a near-wall region of approximately 1.2 nm, the dissipative response was comparable to that in bulk solution. Molecular dynamics simulations, along with statistical mechanical analyses, provided details on hydrodynamic structures near the interface. Specifically, the lattice-dependent dissipative responses are correlated with extensive hydrogen bonding by interfacial water molecules, which increased friction, particularly along the [001] direction. These results demonstrate how solution viscosity at mineral-water interfaces is anisotropic and correlated with the local solution structure, providing insights into the dynamics of nanocrystal attachment.

Viscosity↗