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DOE OSTI2020

Better understanding and prediction of antiviral peptides through primary and secondary structure feature importance

The emergence of viral epidemics throughout the world is of concern due to the scarcity of available effective antiviral therapeutics. The discovery of new antiviral therapies is imperative to address this challenge, and antiviral peptides (AVPs) represent a valuable resource for the development of novel therapies to combat viral infection. We present a new machine learning model to distinguish AVPs from non-AVPs using the most informative features derived from the physicochemical and structural properties of their amino acid sequences. To focus on those features that are most likely to contribute to antiviral performance, we filter potential features based on their importance for classification. These feature selection analyses suggest that secondary structure is the most important peptide sequence feature for predicting AVPs. Our Feature-Informed Reduced Machine Learning for Antiviral Peptide Prediction (FIRM-AVP) approach achieves a higher accuracy than either the model with all features or current state-of-the-art single classifiers. Understanding the features that are associated with AVP activity is a core need to identify and design new AVPs in novel systems. The FIRM-AVP code and standalone software package are available at https://github.com/pmartR/FIRM-AVP with an accompanying web application at https://msc-viz.emsl.pnnl.gov/AVPR.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

IMG/VR v4: an expanded database of uncultivated virus genomes within a framework of extensive functional, taxonomic, and ecological metadata

Viruses are widely recognized as critical members of all microbiomes. Metagenomics enables large-scale exploration of the global virosphere, progressively revealing the extensive genomic diversity of viruses on Earth and highlighting the myriad of ways by which viruses impact biological processes. IMG/VR provides access to the largest collection of viral sequences obtained from (meta)genomes, along with functional annotation and rich metadata. A web interface enables users to efficiently browse and search viruses based on genome features and/or sequence similarity. Here, for this work, we present the fourth version of IMG/VR, composed of >15 million virus genomes and genome fragments, a ≈6-fold increase in size compared to the previous version. These clustered into 8.7 million viral operational taxonomic units, including 231 408 with at least one high-quality representative. Viral sequences in IMG/VR are now systematically identified from genomes, metagenomes, and metatranscriptomes using a new detection approach (geNomad), and IMG standard annotation are complemented with genome quality estimation using CheckV, taxonomic classification reflecting the latest taxonomic standards, and microbial host taxonomy prediction. IMG/VR v4 is available at https://img.jgi.doe.gov/vr, and the underlying data are available to download at https://genome.jgi.doe.gov/portal/IMG_VR.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS1990

An investigation of chaotic Kolmogorov flows

A two dimensional flow governed by the incompressible Navier-Stokes equations with a steady spatially periodic forcing (known as the Kolmogorov flow) is numerically simulated. The behavior of the flow and its transition states as the Reynolds number (Re) varies is investigated in detail, as well as a number of the flow features. A sequence of bifurcations is shown to take place in the flow as Re varied. Two main regimes of the flow were observed: small and large scale structure regimes corresponding to different ranges of Re. Each of the regimes includes a number of quasiperiodic, chaotic, and relaminarization windows. In addition, each range contains a chaotic window with non-ergodic chaotic attractors. Spatially disordered, but temporally steady states were discovered in large scale structure regime. Features of the diverse cases are displayed in terms of the temporal power spectrum, Poincare sections and, where possible, Lyapunov exponents and Kaplan-Yorke dimension.

Platt, N.↗

NASA NTRS1991

An investigation of chaotic Kolmogorov flows

Platt, N.↗

DOE OSTI2025

Prediction of Specificity of α-Conotoxins to Subtypes of Human Nicotinic Acetylcholine Receptors with Semi-supervised Machine Learning

Conotoxins are a family of highly toxic neurotoxins composed of cysteine-rich peptides produced by marine cone snails. The most lethal cone snail species to humans is Conus geographus, with fatality rates of up to ∼65% from a single sting, which is caused mostly by the activity of α-conotoxins against human nicotinic acetylcholine receptors (nAChRs). While sequence-based machine learning (ML) classifiers have been trained to identify targets of conotoxins binding voltage-gated ion channels, no ML model has been built to predict the subtype-specific nAChR targets of α-conotoxins. Here, we trained an ML model in a semi-supervised manner to predict the specificity of α-conotoxin binding toward different human nAChR subtypes to overcome the challenge of limited data in subtype-specific nAChR targets of α-conotoxins and the issue that one α-conotoxin can bind multiple nAChR subtypes with high selectivity. We considered additional features of sequences of α-conotoxins in training our ML model, including the secondary structure propensities and electrostatic properties, which resulted in better prediction capability for the ML model. Notably, we identify that most α-conotoxins bind to α3β2, α1γδ, and α7 subtypes of human nAChRs. Our findings from this study provide a framework for predicting targets of various kinds of toxins.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS1996

Fibronectin regulates calvarial osteoblast differentiation

The secretion of fibronectin by differentiating osteoblasts and its accumulation at sites of osteogenesis suggest that fibronectin participates in bone formation. To test this directly, we determined whether fibronectin-cell interactions regulate progressive differentiation of cultured fetal rat calvarial osteoblasts. Spatial distributions of alpha 5 integrin subunit, fibronectin, osteopontin (bone sialoprotein I) and osteocalcin (bone Gla-protein) were similar in fetal rat calvaria and mineralized, bone-like nodules formed by cultured osteoblasts. Addition of anti-fibronectin antibodies to cultures at confluence reduced subsequent formation of nodules to less than 10% of control values, showing that fibronectin is required for normal nodule morphogenesis. Anti-fibronectin antibodies selectively inhibited steady-state expression of mRNA for genes associated with osteoblast differentiation; mRNA levels for alkaline phosphatase and osteocalcin were suppressed, whereas fibronectin, type I collagen and osteopontin were unaffected. To identify functionally relevant domains of fibronectin, we treated cells with soluble fibronectin fragments and peptides. Cell-binding fibronectin fragments (type III repeats 6-10) containing the Arg-Gly-Asp (RGD) sequence blocked both nodule initiation and maturation, whether or not they contained a functional synergy site. In contrast, addition of the RGD-containing peptide GRGDSPK alone did not inhibit nodule initiation, although it did block nodule maturation. Thus, in addition to the RGD sequence, other features of the large cell-binding fragments contribute to the full osteogenic effects of fibronectin. Nodule formation and osteoblast differentiation resumed after anti-fibronectin antibodies or GRGDSPK peptides were omitted from the media, showing that the inhibition was reversible and the treatments were not cytotoxic. Outside the central cell-binding domain, peptides from the IIICS region and antibodies to the N terminus did not inhibit nodule formation. We conclude that osteoblasts interact with the central cell-binding domain of endogenously produced fibronectin during early stages of differentiation, and that these interactions regulate both normal morphogenesis and gene expression.

NASA Program Space Physiology and Countermeasures↗

DOE OSTI2019

India National Gas Hydrate Program Expedition 02 Summary of Scientific Results: Gas Hydrate Systems Along the Eastern Continental Margin of India

The India National Gas Hydrate Program Expedition 02 (NGHP-02) discovered gas hydrate at high saturation in sand reservoirs at several sites in the deepwater Bay of Bengal. To assess the potential response of those deposits to scientific depressurization experiments, comprehensive geologic models were constructed to enable numerical simulation for two sites. Both sites (NGHP-02-09 and NGHP-02-16) feature thick sequences of thinly-interbedded reservoir and non-reservoir facies at sub-seafloor depths less than 300m and sub-sea depths of 2400m or more. These settings pose significant challenges to current modeling capabilities. First, the thinly-interbedded reservoir architecture complicates the determination of basic reservoir parameters from both log and core data due to measurement resolution issues. Secondly, the fine scale variation in sediment properties imparts great contrasts in key parameters over very short distances, creating high gradients at multiple scales and varying orientations that necessitate careful design of high-definition simulation grids. Thirdly, the deposits include internal sources of water, as well as a range of complex boundary conditions, including variable permeability within the overlying mud-rich “seals,” that complicate reservoir depressurization. Lastly, because of the unique combination of great water depth and relatively shallow sub-seafloor depth: models designed to maximize the dissociation rate impose large pressure drawdowns on relatively low-strength sediments. This condition renders the proper evaluation and integration of the geomechanical response to hydrate dissociation critical. In this report, we review the history of gas hydrate reservoir simulation, discuss methods for creating geologic input models, and summarize the key findings and implications of the collaborative NGHP-02 numerical simulation effort. Together, the studies confirm the viability of the modeled accumulations for scientific testing and identify key challenges related to the selection of specific test sites and the design of test wells.

Collett, Tim↗

DOE OSTI2022

G4Boost: a machine learning-based tool for quadruplex identification and stability prediction

Background: G-quadruplexes (G4s), formed within guanine-rich nucleic acids, are secondary structures involved in important biological processes. Although every G4 motif has the potential to form a stable G4 structure, not every G4 motif would, and accurate energy-based methods are needed to assess their structural stability. Here, we present a decision tree-based prediction tool, G4Boost, to identify G4 motifs and predict their secondary structure folding probability and thermodynamic stability based on their sequences, nucleotide compositions, and estimated structural topologies. Results: G4Boost predicted the quadruplex folding state with an accuracy greater then 93% and an F1-score of 0.96, and the folding energy with an RMSE of 4.28 and R 2 of 0.95 only by the means of sequence intrinsic feature. G4Boost was successfully applied and validated to predict the stability of experimentally-determined G4 structures, including for plants and humans. Conclusion: G4Boost outperformed the three machine-learning based prediction tools, DeepG4, Quadron, and G4RNA Screener, in terms of both accuracy and F1-score, and can be highly useful for G4 prediction to understand gene regulation across species including plants and humans.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2018

A tale of two tools: mcres.py, a stochastic resonance generator, and grokres.py, a resonance quality assurance tool

We detail two software tools, now integrated into the fudge code system. The first tool, mcres.py, can be used to generate stochastic ensembles of resonances which are both consistent with the expectations of the Gaussian Orthogonal Ensemble of Random Matrix Theory and with the level densities and widths encoded in ENDF formatted files. The second tool, grokres.py, can be used to assess global and local features of sequences of resonances found in ENDF files and make comparisons to known results from Random Matrix Theory. We apply these tools to 54 Fe and other nuclei.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

NASA NTRS1984

Observation of an oscillating magnetic field shell at three locations

The complex magnetic-pulsation event near magnetic shell L = 4.5 at 1830-1930 UT on July 14, 1982, is characterized on the basis of observations obtained by the DE-1 satellite near the equator and by magnetic stations at Siple, Antarctica, and Roberval, Quebec, Canada, at opposite ends of the L = 4.2 field line. The data are presented graphically and discussed in the light of theoretical models. The main features (in sequence) observed were a brief 120-sec compressional pulsation, a 5-nT 180-sec transverse pulsation, a weak 44-sec transverse pulsation, and a rapidly developing 240-sec azimuthal pulsation. The 180-sec and 240-sec pulsations are found to be large-scale toroidal oscillations on one and two resonant shells, respectively, while the 44-sec pulsation appears to be more localized.

Cahill, L. J.↗

NASA NTRS1990

Fabry-Perot observations of comet Austin

Preliminary results of a program to observe Comet Austin (1990c1) from 16 April to 4 May and from 11 May to 27 May 1990 using the West Auxiliary of the McMath Solar Telescope on Kitt Peak, Arizona were presetned. The observations were made with a 15 cm duel-etalon Fabry-Perot scanning and imaging spectrometer with two modes of operation: a high resolution mode with a velocity resolution of 1.2 km/s and a medium resolution mode with a velocity resolution 10 km/s. Scanning data was obtained with an RCA C31034A photomultiplier tube and imaging data was obtained with a Photometrics LN2 cooled CCD camera with a 516 by 516 Ford chip. The results include: (1) information on the coma outflow velocity from high resolution spectral profiles of (OI)6300 and NH2 emissions, (2) gaseous water production rates from medium resolution observation of (OI)6300, (3) spectra of H2O(+) emissions in order to study the ionized component of the coma, (4) spatial distribution of H2O(+) emission features from sequences of velocity resolved images (data cubes), and (5) spatial distribution of (OI)6300 and NH2 emissions from medium resolution images. The field of view on the sky was 10.5 arcminutes in diameter. In the imaging mode the CCD was binned 4 by 4 resulting in 7.6 sec power pixel and a subarray readout for a field of view of 10.5 min.

Schultz, David↗

NASA NTRS1995

Shielded, Automated Umbilical Mechanism

Umbilical mechanism automatically connects and disconnects various fluid couplings and/or electrical contacts while shielding mating parts from debris. Reacts mating and demating loads internally, without additional supporting structures. All functions - extension of plug, mating, and movement of debris shields - actuated by single motor. If mechanism jams or fails at any point in sequence, override feature in drive train allows manual operation. Designed for service in outer space, where its shields protect against micrometeoroids, debris, ultraviolet radiation, and atomic oxygen. Used on Earth to connect or disconnect fluid or electrical utilities in harsh environments like those of nuclear powerplants or undersea construction sites, or in presence of radioactive, chemical, or biological hazards, for example.

Barron, Daniel R.↗

NASA NTRS1976

Kinematics and spectra of planetary nebulae with O VI-sequence nuclei

Spectral features of NGC 5189 and NGC 6905 are tabulated. Fabry-Perot profiles around H alpha and O III lambda 5007 of NGC 5189, NGC 6905, NGC 246, and NGC 1535, are illustrated. The latter planetary nebula is a non-O VI-sequence, comparison object of high excitation. The kinematics of the four planetary nebulae are simply analyzed. Discussion of these data is motivated by the possibility of collisional excitation by high-speed ejecta from broad-lined O VI-sequence nuclei, and by the opportunity to make a comparison with conditions in the supernova remnant or ring nebula, G2.4 + 1.4, which contains an O VI-sequence nucleus of Population I.

Johnson, H. M.↗

DOE OSTI2024

Reconstructing Richtmyer–Meshkov instabilities from noisy radiographs using low dimensional features and attention-based neural networks

We develop an ML-based approach for density reconstruction based on transformer neural networks. This approach is demonstrated in the setting of ICF-like double shell hydrodynamic simulations wherein the parameters related to material properties and initial conditions are varied. The new method can robustly recover the complex topologies given by the Richtmyer-Meshkoff instability (RMI) from a sequence of hydrodynamic features derived from radiographic images corrupted with blur, scatter, and noise. A noise model is developed to characterize errors in extracting features from synthetic radiographs of the simulated density field. The key component of the network is a transformer encoder that acts on a sequence of features extracted from noisy radiographs. This encoder includes numerous self-attention layers that act to learn temporal dependencies in the input sequences and increase the expressiveness of the model. This approach is shown to exhibit an excellent ability to accurately recover the RMI growth rates, despite the gas-metal interface being greatly obscured by radiographic noise. Our approach can be applied in a broad array of fields involving shock physics and material science.

47 OTHER INSTRUMENTATION↗

NASA NTRS1994

The Sequence of Events generator: A powerful tool for mission operations

The functions and features of the sequence of events (SOE) and flight operations procedures (FOP) generator developed and used at DLR/GSOC for the positioning of EUTELSAT 2 satellites are presented. The SOE and FOP are the main operational documents that are prepared for nominal as well as for non-nominal mission execution. Their structure and application are described. Both of these documents are generated, validated, and maintained by a common software tool. Its main features and advantages are demonstrated. The tool has been improved continuously over the last 5 years. Due to its flexibility it can easily be applied to other projects and new features may be added.

Wobbe, Hubertus↗

DOE OSTI2024

Machine Learning Framework for Conotoxin Class and Molecular Target Prediction

Conotoxins are small and highly potent neurotoxic peptides derived from the venom of marine cone snails which have captured the interest of the scientific community due to their pharmacological potential. These toxins display significant sequence and structure diversity, which results in a wide range of specificities for several different ion channels and receptors. Despite the recognized importance of these compounds, our ability to determine their binding targets and toxicities remains a significant challenge. Predicting the target receptors of conotoxins, based solely on their amino acid sequence, remains a challenge due to the intricate relationships between structure, function, target specificity, and the significant conformational heterogeneity observed in conotoxins with the same primary sequence. We have previously demonstrated that the inclusion of post-translational modifications, collisional cross sections values, and other structural features, when added to the standard primary sequence features, improves the prediction accuracy of conotoxins against non-toxic and other toxic peptides across varied datasets and several different commonly used machine learning classifiers. Here, we present the effects of these features on conotoxin class and molecular target predictions, in particular, predicting conotoxins that bind to nicotinic acetylcholine receptors (nAChRs). We also demonstrate the use of the Synthetic Minority Oversampling Technique (SMOTE)-Tomek in balancing the datasets while simultaneously making the different classes more distinct by reducing the number of ambiguous samples which nearly overlap between the classes. In predicting the alpha, mu, and omega conotoxin classes, the SMOTE-Tomek PCA PLR model, using the combination of the SS and P feature sets establishes the best performance with an overall accuracy (OA) of 95.95%, with an average accuracy (AA) of 93.04%, and an f1 score of 0.959. Using this model, we obtained sensitivities of 98.98%, 89.66%, and 90.48% when predicting alpha, mu, and omega conotoxin classes, respectively. Similarly, in predicting conotoxins that bind to nAChRs, the SMOTE-Tomek PCA SVM model, which used the collisional cross sections (CCSs) and the P feature sets, demonstrated the highest performance with 91.3% OA, 91.32% AA, and an f1 score of 0.9131. The sensitivity when predicting conotoxins that bind to nAChRs is 91.46% with a 91.18% sensitivity when predicting conotoxins that do not bind to nAChRs.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2018

RNAcentral: a hub of information for non-coding RNA sequences

RNAcentral is a comprehensive database of non-coding RNA (ncRNA) sequences, collating information on ncRNA sequences of all types from a broad range of organisms. We have recently added a new genome mapping pipeline that identifies genomic locations for ncRNA sequences in 296 species. We have also added several new types of functional annotations, such as tRNA secondary structures, Gene Ontology annotations, and miRNA-target interactions. A new quality control mechanism based on Rfam family assignments identifies potential contamination, incomplete sequences, and more. The RNAcentral database has become a vital component of many workflows in the RNA community, serving as both the primary source of sequence data for academic and commercial groups, as well as a source of stable accessions for the annotation of genomic and functional features. These examples are facilitated by an improved RNAcentral web interface, which features an updated genome browser, a new sequence feature viewer, and improved text search functionality.

Sweeney, Blake A.↗

DOE OSTI2023

Data-driven prediction of geometry- and toolpath sequence-dependent intra-layer process conditions variations in laser powder bed fusion

Geometrical features and toolpath sequence are two important factors that cause process condition variations, such as variations in the meltpool temperature or meltpool size, that might lead to undesired material properties in the laser powder bed fusion (LPBF) process. Due to the high dynamics and complex physics of the LPBF process, it is difficult to predict variations in process conditions with simulations alone. Advances in measurement technology and computational technologies open up new possibilities for smart manufacturing. In this paper, a data-driven method to predict intra-layer variations in the processing conditions that source from the toolpath sequence and part geometry is presented. The approach is demonstrated using two-color on-axis pyrometer measurements. Three demonstration cases are presented in which it is demonstrated (1) how the trained predictive model can be used as a filter to ease the interpretation of process variations and discover patterns related to toolpath and part geometry, and (2) how to generate predictions that can be used for feedforward control, i.e., for adjusting laser power or scanning speed along the toolpath using a meltpool temperature prediction model generated based on on-axis measurements. Results show that the developed prediction model is able to meaningfully predict process variations resulted from toolpath sequence and geometry. Predictions are aligned with the results from the related work of others and for the case of 180° laser path turnarounds in our high-speed X-ray imaging experiments. In conclusion, the potential issues related to the current maturity status of the process and measuring equipment that could in practice affect the performance of the proposed solutions are also discussed.

42 ENGINEERING↗