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At least 1,063 records · Page 59
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Colorimetric-Luminance Readout for Quantitative Analysis of Fluorescence Signals with a Smartphone CMOS Sensor

Smartphones have shown promise as an enabling technology for portable and distributed point-of-care diagnostic tests. The CMOS camera sensor can be used for detecting optical signals, including fluorescence for applications such as isothermal nucleic acid amplification tests. However, such analysis is typically limited mostly to end point detection of single targets. In this paper, we present a smartphone-based image analysis pipeline that utilizes the CIE xyY (chromaticity-luminance) color space to measure the luminance (in lieu of RGB intensities) of fluorescent signals arising from nucleic acid amplification targets, with a discrimination sensitivity (ratio between the positive to negative signals), which is an order of magnitude more than traditional RGB intensity based analysis. Furthermore, the chromaticity part of the analysis enables reliable multiplexed detection of different targets labeled with spectrally separated fluorophores. We apply this chromaticity-luminance formulation to simultaneously detect Zika and chikungunya viral RNA via end point RT-LAMP (Reverse transcription Loop-Mediated isothermal amplification). We also show real time LAMP detection of Neisseria gonorrhoeae samples down to a copy number of 3.5 copies per 10 μL of reaction volume in our smartphone-operated portable LAMP box. Finally, our chromaticity-luminance analysis is readily adaptable to other types of multiplexed fluorescence measurements using a smartphone camera.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

De Novo Sequencing of Peptides from Top-Down Tandem Mass Spectra

De novo sequencing of proteins and peptides is one of the most important problems in mass spectrometry-driven proteomics. A variety of methods have been developed to accomplish this task from a set of bottom-up tandem (MS/MS) mass spectra. However, a more recently emerged top-down technology, now gaining more and more popularity, opens new perspectives for protein analysis and characterization, implying a need in efficient algorithms for processing this kind of MS/MS data. Here we describe a method that allows to retrieve from a set of top-down MS/MS spectra long and accurate sequence fragments of the proteins contained in a sample. To this end, we outline a strategy for generating high-quality sequence tags from top-down spectra, and introduce the concept of a T-Bruijn graph by adapting to the case of tags the notion of an A-Bruijn graph widely used in genomics. The output of the proposed approach represents the set of amino acid strings spelled out by optimal paths in the connected components of a T-Bruijn graph. We illustrate its performance on top-down datasets acquired from carbonic anhydrase 2 (CAH2) and the Fab region of alemtuzumab.

Environmental Molecular Sciences Laboratory↗
DOE OSTI2023

Large language models generate functional protein sequences across diverse families

Deep-learning language models have shown promise in various biotechnological applications, including protein design and engineering. Here, in this paper, we describe ProGen, a language model that can generate protein sequences with a predictable function across large protein families, akin to generating grammatically and semantically correct natural language sentences on diverse topics. The model was trained on 280 million protein sequences from >19,000 families and is augmented with control tags specifying protein properties. ProGen can be further fine-tuned to curated sequences and tags to improve controllable generation performance of proteins from families with sufficient homologous samples. Artificial proteins fine-tuned to five distinct lysozyme families showed similar catalytic efficiencies as natural lysozymes, with sequence identity to natural proteins as low as 31.4%. ProGen is readily adapted to diverse protein families, as we demonstrate with chorismate mutase and malate dehydrogenase.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Validation of standardized data formats and tools for ground-level particle-based gamma-ray observatories

Context. Ground-based γ-ray astronomy is still a rather young field of research, with strong historical connections to particle physics. This is why most observations are conducted by experiments with proprietary data and analysis software, as is usual in the particle physics field. However, in recent years, this paradigm has been slowly shifting toward the development and use of open-source data formats and tools, driven by upcoming observatories such as the Cherenkov Telescope Array (CTA). In this context, a community-driven, shared data format (the gamma-astro-data-format, or GADF) and analysis tools such as Gammapy and ctools have been developed. So far, these efforts have been led by the Imaging Atmospheric Cherenkov Telescope community, leaving out other types of ground-based γ-ray instruments. Aims. We aim to show that the data from ground particle arrays, such as the High-Altitude Water Cherenkov (HAWC) observatory, are also compatible with the GADF and can thus be fully analyzed using the related tools, in this case, Gammapy. Methods. We reproduced several published HAWC results using Gammapy and data products compliant with GADF standard. We also illustrate the capabilities of the shared format and tools by producing a joint fit of the Crab spectrum including data from six different γ-ray experiments. Results. We find excellent agreement with the reference results, a powerful confirmation of both the published results and the tools involved. Conclusions. The data from particle detector arrays such as the HAWC observatory can be adapted to the GADF and thus analyzed with Gammapy. A common data format and shared analysis tools allow multi-instrument joint analysis and effective data sharing. To emphasize this, a sample of Crab nebula event lists is made public with this paper. Because of the complementary nature of pointing and wide-field instruments, this synergy will be distinctly beneficial for the joint scientific exploitation of future observatories such as the Southern Wide-field Gamma-ray Observatory and CTA.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

High-speed adaptive contact-mode atomic force microscopy imaging with near-minimum-force

In this paper, an adaptive contact-mode imaging approach is proposed to replace the traditional contact-mode imaging by addressing the major concerns in both the speed and the force exerted to the sample. The speed of the traditional contact-mode imaging is largely limited by the need to maintain precision tracking of the sample topography over the entire imaged sample surface, while large image distortion and excessive probe-sample interaction force occur during high-speed imaging. In this work, first, the image distortion caused by the topography tracking error is accounted for in the topography quantification. Second, the quantified sample topography is utilized in a gradient-based optimization method to adjust the cantilever deflection set-point for each scanline closely around the minimal level needed for maintaining stable probe-sample contact, and a data-driven iterative feedforward control that utilizes a prediction of the next-line topography is integrated to the topography feeedback loop to enhance the sample topography tracking. The proposed approach is demonstrated and evaluated through imaging a calibration sample of square pitches at both high speeds (e.g., scan rate of 75 Hz and 130 Hz) and large sizes (e.g., scan size of 30 μm and 80 μm). The experimental results show that compared to the traditional constant-force contact-mode imaging, the imaging speed can be increased by over 30 folds (with the scanning speed at 13 mm/s), and the probe-sample interaction force can be reduced by more than 15% while maintaining the same image quality.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

A deep neural network to search for new long-lived particles decaying to jets

A tagging algorithm to identify jets that are significantly displaced from the proton-proton (pp) collision region in the CMS detector at the LHC is presented. Displaced jets can arise from the decays of long-lived particles (LLPs), which are predicted by several theoretical extensions of the standard model. The tagger is a multiclass classifier based on a deep neural network, which is parameterised according to the proper decay length $\mathrm{c}\tau_0$ of the LLP. A novel scheme is defined to reliably label jets from LLP decays for supervised learning. Samples of pp collision data, recorded by the CMS detector at a centre-of-mass energy of 13 TeV, and simulated events are used to train the neural network. Domain adaptation by backward propagation is performed to improve the simulation modelling of the jet class probability distributions observed in pp collision data. The potential performance of the tagger is demonstrated with a search for long-lived gluinos, a manifestation of split supersymmetric models. The tagger provides a rejection factor of 10 000 for jets from standard model processes, while maintaining an LLP jet tagging efficiency of 30-80% for gluinos with 1 mm $\leq$ $c\tau_0$ $\leq$ 10 m. The expected coverage of the parameter space for split supersymmetry is presented.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

The emergence and transmission dynamics of HIV-1 CRF07_BC in Mainland China

A total of 1155 partial pol gene sequences of human immunodeficiency virus (HIV)-1 CRF07_BC were sampled between 1997 and 2015, spanning 13 provinces in Mainland China and risk groups [heterosexual, injecting drug users (IDU), and men who have sex with men (MSM)] to investigate the evolution, adaptation, spatiotemporal and risk group dynamics, migration patterns, and protein structure of HIV-1 CRF07_BC. Due to the unequal distribution of sequences across time, location, and risk group in the complete dataset (‘full1155’), subsampling methods were used. Maximum-likelihood and Bayesian phylogenetic analysis as well as discrete trait analysis of geographical location and risk group were carried out. To study mutations of a cluster of HIV-1 CRF07_BC (CRF07-1), we performed a comparative analysis of this cluster to the other CRF07_BC sequences (‘backbone_295’) and mapped the mutations observed in the respective protein structure. Our findings showed that HIV-1 CRF07_BC most likely originated among IDU in Yunnan Province between October 1992 to July 1993 [95 per cent hightest posterior density (HPD): May 1989–August 1995] and that IDU in Yunnan Province and MSM in Guangdong Province likely served as the viral sources during the early and more recent spread in Mainland China. We also revealed that HIV-1 CRF07-1 has been spreading for roughly 20 years and continues to cause local transmission in Mainland China and worldwide. Overall, our study sheds light on the dynamics of HIV-1 CRF07_BC distribution patterns in Mainland China. Our research may also be useful in formulating public health policies aimed at controlling acquired immune deficiency syndrome in Mainland China and globally.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

CPU Isosurface Ray Tracing of Adaptive Mesh Refinement Data

Adaptive mesh refinement (AMR) is a key technology for large-scale simulations that allows for adaptively changing the simulation mesh resolution, resulting in significant computational and storage savings. However, visualizing such AMR data poses a significant challenge due to the difficulties introduced by the hierarchical representation when reconstructing continuous field values. Here, we detail a comprehensive solution for interactive isosurface rendering of block-structured AMR data. We contribute a novel reconstruction strategy—the octant method—which is continuous, adaptive and simple to implement. Furthermore, we present a generally applicable hybrid implicit isosurface ray-tracing method, which provides better rendering quality and performance than the built-in sampling-based approach in OSPRay. Finally, we integrate our octant method and hybrid isosurface geometry into OSPRay as a module, providing the ability to create high-quality interactive visualizations combining volume and isosurface representations of BS-AMR data. Lastly, we evaluate the rendering performance, memory consumption and quality of our method on two gigascale block-structured AMR datasets.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Optimized 3D co-registration of ultra-low-field and high-field magnetic resonance images

The prototypes of ultra-low-field (ULF) MRI scanners developed in recent years represent new, innovative, cost-effective and safer systems, which are suitable to be integrated in multi-modal (Magnetoencephalography and MRI) devices. Integrated ULF-MRI and MEG scanners could represent an ideal solution to obtain functional (MEG) and anatomical (ULF MRI) information in the same environment, without errors that may limit source reconstruction accuracy. However, the low resolution and signal-to-noise ratio (SNR) of ULF images, as well as their limited coverage, do not generally allow for the construction of an accurate individual volume conductor model suitable for MEG localization. Thus, for practical usage, a high-field (HF) MRI image is also acquired, and the HF-MRI images are co-registered to the ULF-MRI ones. We address here this issue through an optimized pipeline (SWIM—Sliding WIndow grouping supporting Mutual information). The co-registration is performed by an affine transformation, the parameters of which are estimated using Normalized Mutual Information as the cost function, and Adaptive Simulated Annealing as the minimization algorithm. The sub-voxel resolution of the ULF images is handled by a sliding-window approach applying multiple grouping strategies to down-sample HF MRI to the ULF-MRI resolution. The pipeline has been tested on phantom and real data from different ULF-MRI devices, and comparison with well-known toolboxes for fMRI analysis has been performed. Our pipeline always outperformed the fMRI toolboxes (FSL and SPM). The HF–ULF MRI co-registration obtained by means of our pipeline could lead to an effective integration of ULF MRI with MEG, with the aim of improving localization accuracy, but also to help exploit ULF MRI in tumor imaging.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Missing data in multi-omics integration: Recent advances through artificial intelligence

Biological systems function through complex interactions between various ‘omics (biomolecules), and a more complete understanding of these systems is only possible through an integrated, multi-omic perspective. This has presented the need for the development of integration approaches that are able to capture the complex, often non-linear, interactions that define these biological systems and are adapted to the challenges of combining the heterogenous data across ‘omic views. A principal challenge to multi-omic integration is missing data because all biomolecules are not measured in all samples. Due to either cost, instrument sensitivity, or other experimental factors, data for a biological sample may be missing for one or more ‘omic techologies. Recent methodological developments in artificial intelligence and statistical learning have greatly facilitated the analyses of multi-omics data, however many of these techniques assume access to completely observed data. A subset of these methods incorporate mechanisms for handling partially observed samples, and these methods are the focus of this review. We describe recently developed approaches, noting their primary use cases and highlighting each method's approach to handling missing data. We additionally provide an overview of the more traditional missing data workflows and their limitations; and we discuss potential avenues for further developments as well as how the missing data issue and its current solutions may generalize beyond the multi-omics context.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1980

Analytical design and evaluation of an active control system for helicopter vibration reduction and gust response alleviation

An analytical study was conducted to define the basic configuration of an active control system for helicopter vibration and gust response alleviation. The study culminated in a control system design which has two separate systems: narrow band loop for vibration reduction and wider band loop for gust response alleviation. The narrow band vibration loop utilizes the standard swashplate control configuration to input controller for the vibration loop is based on adaptive optimal control theory and is designed to adapt to any flight condition including maneuvers and transients. The prime characteristics of the vibration control system is its real time capability. The gust alleviation control system studied consists of optimal sampled data feedback gains together with an optimal one-step-ahead prediction. The prediction permits the estimation of the gust disturbance which can then be used to minimize the gust effects on the helicopter.

Taylor, R. B.↗
NASA NTRS1982

High resolution measurements of nightside ion troughs at Venus - Evidence of electrodynamic perturbations

The Bennett rf ion mass spectrometer of the Pioneer Venus Orbiter was expressly designed to provide variable temporal resolution for measurements of thermal ion composition and density. The Explore-Adapt mode is used to obtain priority for measuring the most prominent ion species; in the 2/16 configuration, the two dominant ions within the available range of 16 species are selectively sampled at the highest rate of 0.2 sec/sample. The high-resolution measurements are combined with independent observations from the magnetic field, neutral mass spectrometer, and electron temperature experiments in investigating sharply structured troughs in the low-altitude nightside ion concentrations. The results suggest a close correlation between the structure in the ion distributions and the structured configuration of the magnetic field that is draped about the planet. In the regions of the ion depletions, sharp fluctuations in electron temperature and anomalous increases in the density of neutral gases suggest that the ion depletion may be associated with dynamic perturbation in the ion and neutral flows and/or local joule heating.

Taylor, H. A., Jr.↗
NASA NTRS1987

Solar modulation of galactic antiprotons

Galactic antiproton data of current interest lie in an energy regime heavily influenced by solar modulation. Correcting for it needs to be done more carefully than it has been in the past. The well-known force-field analytic approximation of the spherically-symmetric, steady-state, cosmic-ray transport equation is applied in order to account for modulation down to at least 100 MeV. A sample solution which applies to the currently available antiproton data set (1979-80), and can be used to accurately modulate any possible interstellar antiproton spectrum, is given. The solution is easily adapted for comparison to future measurements. It also shows that boosting the low-energy (less than 600 MeV) side of the interstellar antiproton spectrum will not affect the low-energy spectrum at 1 AU, due to strong adiabatic deceleration during that time.

Perko, J. S.↗
NASA NTRS1989

Analysis of modified SMI method for adaptive array weight control

An adaptive array is applied to the problem of receiving a desired signal in the presence of weak interference signals which need to be suppressed. A modification, suggested by Gupta, of the sample matrix inversion (SMI) algorithm controls the array weights. In the modified SMI algorithm, interference suppression is increased by subtracting a fraction F of the noise power from the diagonal elements of the estimated covariance matrix. Given the true covariance matrix and the desired signal direction, the modified algorithm is shown to maximize a well-defined, intuitive output power ratio criterion. Expressions are derived for the expected value and variance of the array weights and output powers as a function of the fraction F and the number of snapshots used in the covariance matrix estimate. These expressions are compared with computer simulation and good agreement is found. A trade-off is found to exist between the desired level of interference suppression and the number of snapshots required in order to achieve that level with some certainty. The removal of noise eigenvectors from the covariance matrix inverse is also discussed with respect to this application. Finally, the type and severity of errors which occur in the covariance matrix estimate are characterized through simulation.

Dilsavor, R. L.↗
NASA NTRS2012

Compact Two-step Laser Time-of-Flight Mass Spectrometer for in Situ Analyses of Aromatic Organics on Planetary Missions

RATIONALE A miniature time-of-flight mass spectrometer has been adapted to demonstrate two-step laser desorption-ionization (LOI) in a compact instrument package for enhanced organics detection. Two-step LDI decouples the desorption and ionization processes, relative to traditional laser ionization-desorption, in order to produce low-fragmentation conditions for complex organic analytes. Tuning UV ionization laser energy allowed control ofthe degree of fragmentation, which may enable better identification of constituent species. METHODS A reflectron time-of-flight mass spectrometer prototype measuring 20 cm in length was adapted to a two-laser configuration, with IR (1064 nm) desorption followed by UV (266 nm) postionization. A relatively low ion extraction voltage of 5 kV was applied at the sample inlet. Instrument capabilities and performance were demonstrated with analysis of a model polycyclic aromatic hydrocarbon, representing a class of compounds important to the fields of Earth and planetary science. RESULTS L2MS analysis of a model PAH standard, pyrene, has been demonstrated, including parent mass identification and the onset o(tunable fragmentation as a function of ionizing laser energy. Mass resolution m/llm = 380 at full width at half-maximum was achieved which is notable for gas-phase ionization of desorbed neutrals in a highly-compact mass analyzer. CONCLUSIONS Achieving two-step laser mass spectrometry (L2MS) in a highly-miniature instrument enables a powerful approach to the detection and characterization of aromatic organics in remote terrestrial and planetary applications. Tunable detection of parent and fragment ions with high mass resolution, diagnostic of molecular structure, is possible on such a compact L2MS instrument. Selectivity of L2MS against low-mass inorganic salt interferences is a key advantage when working with unprocessed, natural samples, and a mechanism for the observed selectivity is presented.

Getty, Stephanie↗
DOE OSTI2016

SU-F-J-138: An Extension of PCA-Based Respiratory Deformation Modeling Via Multi-Linear Decomposition

Purpose: To address and lift the limited degree of freedom (DoF) of globally bilinear motion components such as those based on principal components analysis (PCA), for encoding and modeling volumetric deformation motion. Methods: We provide a systematic approach to obtaining a multi-linear decomposition (MLD) and associated motion model from deformation vector field (DVF) data. We had previously introduced MLD for capturing multi-way relationships between DVF variables, without being restricted by the bilinear component format of PCA-based models. PCA-based modeling is commonly used for encoding patient-specific deformation as per planning 4D-CT images, and aiding on-board motion estimation during radiotherapy. However, the bilinear space-time decomposition inherently limits the DoF of such models by the small number of respiratory phases. While this limit is not reached in model studies using analytical or digital phantoms with low-rank motion, it compromises modeling power in the presence of relative motion, asymmetries and hysteresis, etc, which are often observed in patient data. Specifically, a low-DoF model will spuriously couple incoherent motion components, compromising its adaptability to on-board deformation changes. By the multi-linear format of extracted motion components, MLD-based models can encode higher-DoF deformation structure. Results: We conduct mathematical and experimental comparisons between PCA- and MLD-based models. A set of temporally-sampled analytical trajectories provides a synthetic, high-rank DVF; trajectories correspond to respiratory and cardiac motion factors, including different relative frequencies and spatial variations. Additionally, a digital XCAT phantom is used to simulate a lung lesion deforming incoherently with respect to the body, which adheres to a simple respiratory trend. In both cases, coupling of incoherent motion components due to a low model DoF is clearly demonstrated. Conclusion: Multi-linear decomposition can enable decoupling of distinct motion factors in high-rank DVF measurements. This may improve motion model expressiveness and adaptability to on-board deformation, aiding model-based image reconstruction for target verification. NIH Grant No. R01-184173.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Acquisition of STEM Images by Adaptive Compressive Sensing

Compressive Sensing (CS) allows a signal to be sparsely measured first and accurately recovered later in software [1]. In scanning transmission electron microscopy (STEM), it is possible to compress an image spatially by reducing the number of measured pixels, which decreases electron dose and increases sensing speed [2,3,4]. The two requirements for CS to work are: (1) sparsity of basis coefficients and (2) incoherence of the sensing system and the representation system. However, when pixels are missing from the image, it is difficult to have an incoherent sensing matrix. Nevertheless, dictionary learning techniques such as Beta-Process Factor Analysis (BPFA) [5] are able to simultaneously discover a basis and the sparse coefficients in the case of missing pixels. On top of CS, we would like to apply active learning [6,7] to further reduce the proportion of pixels being measured, while maintaining image reconstruction quality. Suppose we initially sample 10% of random pixels. We wish to select the next 1% of pixels that are most useful in recovering the image. Now, we have 11% of pixels, and we want to decide the next 1% of “most informative” pixels. Active learning methods are online and sequential in nature. Our goal is to adaptively discover the best sensing mask during acquisition using feedback about the structures in the image. In the end, we hope to recover a high quality reconstruction with a dose reduction relative to the non-adaptive (random) sensing scheme. In doing this, we try three metrics applied to the partial reconstructions for selecting the new set of pixels: (1) variance, (2) Kullback-Leibler (KL) divergence using a Radial Basis Function (RBF) kernel, and (3) entropy. Figs. 1 and 2 display the comparison of Peak Signal-to-Noise (PSNR) using these three different active learning methods at different percentages of sampled pixels. At 20% level, all the three active learning methods underperform the original CS without active learning. However, they all beat the original CS as more of the “most informative” pixels are sampled. One can also argue that CS equipped with active learning requires less sampled pixels to achieve the same value of PSNR than CS with pixels randomly sampled, since all the three PSNR curves with active learning grow at a faster pace than that without active learning. For this particular STEM image, by observing the reconstructed images and the sensing masks, we find that while the method based on RBF kernel acquires samples more uniformly, the one on entropy samples more areas of significant change, thus less uniformly. The KL-divergence method performs the best in terms of reconstruction error (PSNR) for this example [8].

Xie, Weiyi↗
DOE OSTI2024

Adaptation strategies of giant viruses to low-temperature marine ecosystems

Abstract Microbes in marine ecosystems have evolved their gene content to thrive successfully in the cold. Although this process has been reasonably well studied in bacteria and selected eukaryotes, less is known about the impact of cold environments on the genomes of viruses that infect eukaryotes. Here, we analyzed cold adaptations in giant viruses (Nucleocytoviricota and Mirusviricota) from austral marine environments and compared them with their Arctic and temperate counterparts. We recovered giant virus metagenome-assembled genomes (98 Nucleocytoviricota and 12 Mirusviricota MAGs) from 61 newly sequenced metagenomes and metaviromes from sub-Antarctic Patagonian fjords and Antarctic seawater samples. When analyzing our data set alongside Antarctic and Arctic giant viruses MAGs already deposited in the Global Ocean Eukaryotic Viral database, we found that Antarctic and Arctic giant viruses predominantly inhabit sub-10°C environments, featuring a high proportion of unique phylotypes in each ecosystem. In contrast, giant viruses in Patagonian fjords were subject to broader temperature ranges and showed a lower degree of endemicity. However, despite differences in their distribution, giant viruses inhabiting low-temperature marine ecosystems evolved genomic cold-adaptation strategies that led to changes in genetic functions and amino acid frequencies that ultimately affect both gene content and protein structure. Such changes seem to be absent in their mesophilic counterparts. The uniqueness of these cold-adapted marine giant viruses may now be threatened by climate change, leading to a potential reduction in their biodiversity.

Environmental Sciences & Ecology↗