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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.

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At least 541 records · Page 30
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Identification of In Situ Generated High-Spin Fe(II)-N 4 Active Sites for Acidic Oxygen Reduction Reaction via Operando 57 Fe Mössbauer Spectroscopy

Iron-nitrogen-carbon (FeNC) catalysts are considered among the most promising alternative to Pt catalysts in acidic oxygen reduction reaction (ORR), yet the geometric and electronic structures of the true active site under reaction conditions have not been clearly elucidated. Herein, we synthesized a representative FeNC catalyst by pyrolyzing Fe 3+ absorbed on ZIF-8-derived N-doped carbon at a mild temperature under the H 2 /Ar atmosphere, where a formation mechanism of FeN 4 sites through a Zn-mediated Fe nanoparticle atomization process was proposed. The resulting FeNC-750 catalyst shows high acidic ORR activity with a half-wave potential of 0.838 V and a peak power density of 1 W cm -2 in proton-exchange membrane fuel cell (PEMFC). By using operando 57 Fe Mössbauer spectroscopy on FeNC-750, it was revealed that the pyrrolic N-coordinated high-spin Fe 2+ N 4 sites, which are in situ generated from high-spin Fe 3+ N 4 during ORR, are identified as catalytic active states. Density functional theory calculations further verified that, compared to the pyridinic N-coordinated low-spin Fe 2+ N 4 , the pyrrolic N-coordinated high-spin Fe 2+ exhibits optimized adsorption energy for reaction intermediates, thereby lowering the energy barrier of the rate-determining step (RDS) and facilitating OH* desorption. In conclusion, this work provides both experimental and theoretical evidence of the true active site in the FeNC during acidic ORR, offering significant insights for the rational design of high-performance materials for acidic fuel cells.

FeNC catalyst↗
DOE OSTI2026

Combining ToF‐SIMS and Multivariate Analysis to Resolve Active Sites on Ni‐Based HER Catalysts

Unambiguous identification of active sites in heterogeneous catalysis remains a major challenge, particularly for materials with ultrathin, chemically mixed surface layers. Here, we demonstrate a generalizable approach that combines time-of-flight secondary ion mass spectrometry (ToF-SIMS) with multivariate statistical analysis (principal component analysis [PCA] and multivariate curve resolution [MCR]) to resolve catalytically relevant motifs at the nanoscale. Using Ni electrodes as a model system, PCA distinguished hydroxide-enriched domains from oxide- and metal-rich regions, while MCR decomposed depth profiles and 3D images into hydroxide, oxide, and metallic layers with nanometer resolution. A unique secondary-ion fragment, NiO 3 H 3 − (m/z 108.94), emerged as a marker of hydroxide-rich environments and correlated with hydrogen evolution reaction (HER) activity across a series of Ni electrodes. Complementary density functional theory (DFT) calculations revealed that Ni(OH) 2 clusters adjacent to metallic Ni offer the most favorable water dissociation energetics, establishing the structural origin of the marker. While illustrated here for Ni-based HER, this workflow provides a broadly applicable framework to isolate and rank near-surface patterns that govern catalytic activity, thereby extending ToF-SIMS from a qualitative probe to a predictive tool for active site identification.

HER active sites↗
DOE OSTI2023

Learning effective stochastic differential equations from microscopic simulations: Linking stochastic numerics to deep learning

We identify effective stochastic differential equations (SDEs) for coarse observables of fine-grained particle- or agent-based simulations; these SDEs then provide useful coarse surrogate models of the fine scale dynamics. We approximate the drift and diffusivity functions in these effective SDEs through neural networks, which can be thought of as effective stochastic ResNets. The loss function is inspired by, and embodies, the structure of established stochastic numerical integrators (here, Euler–Maruyama and Milstein); our approximations can thus benefit from backward error analysis of these underlying numerical schemes. They also lend themselves naturally to “physics-informed” gray-box identification when approximate coarse models, such as mean field equations, are available. Existing numerical integration schemes for Langevin-type equations and for stochastic partial differential equations can also be used for training; we demonstrate this on a stochastically forced oscillator and the stochastic wave equation. Our approach does not require long trajectories, works on scattered snapshot data, and is designed to naturally handle different time steps per snapshot. We consider both the case where the coarse collective observables are known in advance, as well as the case where they must be found in a data-driven manner.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Molten Salt Reactor Fundamental Safety Function PIRT

The phenomenon identification and ranking table (PIRT) process was employed to evaluate the current capability to assess the ability of liquid-salt–fueled molten salt reactors to achieve their fundamental safety functions (FSFs). The PIRT process provides a structured mechanism to elicit and document expert opinions on the most important phenomena and the corresponding level of knowledge with regard to achieving the FSFs. The PIRT panel included reactor developers, accident progression evaluation tool developers, US Department of Energy (DOE) national laboratory technical staff, university researchers, and US Nuclear Regulatory Commission (NRC) staff. The information-gathering process concentrated on phenomena related to accidents in which the fuel salt—including any in the cover gas—has been released from the first barrier layer. This information-gathering process included identifying all potential accidents that could result in the release of a substantial amount of radionuclides that could breach or bypass the first barrier. The PIRT did not identify any previously unrecognized systemic vulnerabilities. However, the elicitation process identified several areas with low levels of knowledge and significant potential impact on accident progression. These areas either require additional research to improve the state of knowledge or additional design conservatism to accommodate the remaining unknowns.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2024

Facial Named Entity Recognition by Attention-Based Graph Convolutional Neural Network

In the realm of facial recognition and analysis, the ability to accurately cluster large datasets of facial images stands as a cornerstone for various applications, ranging from security surveillance to user biometric identification. This project evolves a novel approach to facial data clustering by embedding facial images into a high-dimensional vector space using an advanced embedding model trained on separate data and assumes a graph-like structure on the high-dimensional vectors. We find our method works significantly better than common shallow methods.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Identification of a secretory heme‐binding protein from Nocardia seriolae involved in cell apoptosis

Abstract According to the whole‐genome bioinformatics analysis, a heme‐binding protein from Nocardia seriolae (HBP) was found. HBP was predicted to be a bacterial secretory protein, located at mitochondrial membrane in eukaryotic cells and have a similar protein structure with the heme‐binding protein of Mycobacterium tuberculosis , Rv0203. In this study, HBP was found to be a secretory protein and co‐localized with mitochondria in FHM cells. Quantitative analysis of mitochondrial membrane potential value, caspase‐3 activity, and transcription level of apoptosis‐related genes suggested that overexpression of HBP protein can induce cell apoptosis. In conclusion, HBP was a secretory protein which may target to mitochondria and involve in cell apoptosis in host cells. This research will promote the function study of HBP and deepen the comprehension of the virulence factors and pathogenic mechanisms of N. seriolae .

Wen, Yiming↗
DOE OSTI2022

Identification of efficient and stable solid-solid electrochemical interfaces for Li-S batteries. Final Report

The objective of this work is to develop a better understanding of the interactions between battery components that drive the observed battery performance. Using computational tools at the atomistic level, we elucidate molecular, crystal, and amorphous structures, interfacial structures and reactions, and electrochemical behavior. We focus on Li metal anodes, liquid electrolytes, solid electrolytes, S cathodes, and metal-oxide cathodes, and their corresponding interfaces. Within liquid electrolytes, we study their bulk structure and dynamics, as a function of salt concentration. The analysis allows to understand the formation of localized high concentrated regions as the solvent is substituted by a weakly interacting diluent species. Analysis of the electron distribution yields the reactive behavior at interfaces. Moreover, we employ tools that allow the characterization of ion and electron transport at interfaces, during the processes of solvation and desolvation occurring while cycling. For Sulfur cathodes we elucidate synthesis processes and the role of carbon-sulfur and nitrogen-sulfur interactions not only to provide electronic conductivity but also to define alternative reaction pathways during discharge of Li-S batteries. Full-cell simulations offer new insights regarding the integration of all the battery components. Our studies also include solid electrolytes and their interfaces with Li metal, S-C, and metal-oxide electrodes.

25 ENERGY STORAGE↗
DOE OSTI2026

Highlighting the Importance of Authentic Reference Chemicals in the Unambiguous Identification of Unknowns during Routine Sample Analysis─An OPCW Proficiency Test Case

The unequivocal identification of unknown chemicals during routine sample analysis is a constant occurrence in the field of analytical chemistry. The process can be painstakingly tedious, particularly in situations where a tentatively identified unknown may possess isomeric counterparts yielding identical accurate mass values and very similar mass spectra. In this work, we present the experimental process involved in the correct identification of 1,4-oxathiane 4,4-dioxide and differentiation from its constitutional isomer, 2-hydroxyethyl vinyl sulfone, when present in a silica gel matrix featured in the 54th Environmental Organisation for the Prohibition of Chemical Weapons (OPCW) proficiency test. After discovering that the library-generated mass spectrum for 2-hydroxyethyl vinyl sulfone in a preliminary gas chromatography–mass spectrometry (GC-MS) analysis did not match the one from an authentic reference chemical, we embarked on an unknown structure determination campaign involving GC-MS, liquid chromatography-tandem mass spectrometry (LC-MS-MS), and nuclear magnetic resonance (NMR) spectroscopy. All of the information obtained, coupled to the synthesis of an authentic reference chemical, was used to determine the identity of the unknown as 1,4-oxathiane 4,4-dioxide. The process described in this work highlights the important role played by authentic reference chemicals in the identification of unknown chemicals during routine sample analysis.

Chemistry↗
DOE OSTI2025

Reorganization of Water at Aqueous Aluminum Chloride (AlCl 3 ) Interfaces: Vibrational Sum Frequency Generation and Molecular Dynamics Simulations

AlCl 3 hydration states and complexation are not well understood both in solutions and at the air–aqueous interface despite their potential significance in natural waters and their industrial and energy-related applications. Here, we investigated Al 3+ and Cl – ion behaviors in an AlCl 3 aqueous bulk solution and at the air–aqueous interface using interface-selective vibrational sum frequency generation (SFG), Raman and infrared spectroscopies, molecular dynamics (MD) simulation, as well as molecular-informed reduced modeling. Our reduced modeling reveals relatively long-range effects for Al 3+ as compared to monovalent ions such as Na + indicating that the interfacial depth of trivalent ions can be significantly larger than that of monovalent ions at the air–water interface. MD simulations reveal interfacial stratification and multiple layering of the ions. Compression of the Al 3+ and Cl – distributions with increasing concentrations from 0.5 to 2.5 m is also observed in the subsurface regions. Significant SSP- and PPP-polarized SFG OH spectral intensity increases are observed from 0.5 to 1.5 m and 0.5 to 2.5 m, respectively, indicative of interfacial depth increases and a change in average orientation above 1.5 m. Extensive evaluation of SFG spectra, Fresnel-corrected using several approaches, shows the same trends. The nonmonotonic trend points to a changing structure in surface and subsurface water orientation and hydrogen bonding environment generally consistent with the MD simulation of stratification and water orientation changes. Furthermore, solvent-shared ion pairing is implicated with MD simulation radial distribution analysis and consistent with infrared spectral identification of the hexaaqua aluminum ion in the solution phase. Spectral evidence of a strong Al 3+ hydration shell and the acidic behavior of the Al 3+ ions is obvious in the Raman and infrared spectra of the bulk solution. In conclusion, we show that the MD dipole potential is directly related to the MD second-order susceptibility of the interface, χ SFG–MD (2) , both of which correlate up to ∼35 Å with the spectral observations of increasing and then saturating intensities, suggesting that both ion stratification and interfacial depth determine the water orientations at an air–water interface of 1-3 electrolyte solutions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

XANES reflects coordination change and underlying surface disorder of zinc adsorbed to silica

In this work, zinc K -edge X-ray absorption near-edge structure (XANES) spectroscopy of Zn adsorbed to silica and Zn-bearing minerals, salts and solutions was conducted to explore how XANES spectra reflect coordination environment and disorder in the surface to which a metal ion is sorbed. Specifically, XANES spectra for five distinct Zn adsorption complexes (Zn ads ) on quartz and amorphous silica [SiO 2(am) ] are presented from the Zn–water–silica surface system: outer-sphere octahedral Zn ads on quartz, inner-sphere octahedral Zn ads on quartz, inner-sphere tetrahedral Zn ads on quartz, inner-sphere octahedral Zn ads on SiO 2(am) and inner-sphere tetrahedral Zn ads on SiO 2(am) . XANES spectral analysis of these complexes on quartz versus SiO 2(am) reveals that normalized peak absorbance and K -edge energy position generally decrease with increasing surface disorder and decreasing Zn–O coordination. On quartz, the absorption-edge energy of Zn ads ranges from 9663.0 to 9664.1 eV for samples dominated by tetrahedrally versus octahedrally coordinated species, respectively. On SiO 2(am) , the absorption-edge energy of Zn ads ranges from 9662.3 to 9663.4 eV for samples dominated by tetrahedrally versus octahedrally coordinated species, respectively. On both silica substrates, octahedral Zn ads presents a single K -edge peak feature, whereas tetrahedral Zn ads presents two absorbance features. The energy space between the two absorbance peak features of the XANES K -edge of tetrahedral Zn ads is 2.4 eV for Zn on quartz and 3.2 eV for Zn on SiO 2(am) . Linear combination fitting of samples with a mixture of Zn ads complex types demonstrates that the XANES spectra of octahedral and tetrahedral Zn ads on silica are distinct enough for quantitative identification. These results suggest caution when deciphering Zn speciation in natural samples via linear combination approaches using a single Zn ads standard to represent sorption on a particular mineral surface. Correlation between XANES spectral features and prior extended X-ray absorption fine structure (EXAFS) derived coordination environments for these Zn ads on silica samples provides insight into Zn speciation in natural systems with XANES compatible Zn concentrations too low for EXAFS analysis.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

In cellulo crystallization of Trypanosoma brucei IMP dehydrogenase enables the identification of genuine co-factors

Sleeping sickness is a fatal disease caused by the protozoan parasite Trypanosoma brucei (Tb). Inosine-5’-monophosphate dehydrogenase (IMPDH) has been proposed as a potential drug target, since it maintains the balance between guanylate deoxynucleotide and ribonucleotide levels that is pivotal for the parasite. Here we report the structure of TbIMPDH at room temperature utilizing free-electron laser radiation on crystals grown in living insect cells. The 2.80 Å resolution structure reveals the presence of ATP and GMP at the canonical sites of the Bateman domains, the latter in a so far unknown coordination mode. Consistent with previously reported IMPDH complexes harboring guanosine nucleotides at the second canonical site, TbIMPDH forms a compact oligomer structure, supporting a nucleotide-controlled conformational switch that allosterically modulates the catalytic activity. The oligomeric TbIMPDH structure we present here reveals the potential of in cellulo crystallization to identify genuine allosteric co-factors from a natural reservoir of specific compounds.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Multiple Hypothesis Tracking for Opportunistic Transmitter Identification

The exploitation of radio frequency (RF) signals from Low Earth Orbit (LEO) communication satellites for the purposes of navigation is an active field of research. In this alternative navigation system, navigation observables are passively extracted from existing RF signals. Because these signals are not designed for navigation purposes and often have proprietary signal structures that are unknown to the receiver, they lack much of the information required for navigation systems or such information is unavailable to the receiver.

42 ENGINEERING↗
DOE OSTI2025

Towards Content Authenticity: Multimodal Fake News Detection and AI-Generated Text Identification

In today’s digital world, the spread of fake news and the rise of AI-generated text have become major threats to content authenticity and public trust. This thesis addresses both challenges through two complementary research directions: detecting fake news using multimodal features, and identifying AI-generated text using semantic and structural reasoning. The first part of the work focuses on fake news detection by introducing a novel model that combines text and image features through a unique rotational attention mechanism. Unlike traditional attention methods, this approach rotates the roles of query, key, and value across modalities to capture deeper interactions. Additionally, the model incorporates external domain information by linking news posts to top-ranked websites from Google search results, which helps assess the credibility of content based on its broader web context. This results in a more reliable and accurate fake news detection system that outperforms existing state-of-the-art methods. The second part presents SGG-ATD, a new framework for detecting AI-generated text. It uses masked language modeling to measure sentence coherence, followed by constructing a graph where keywords—both original and predicted—are connected based on semantic and contextual similarity. A Graph Convolutional Network (GCN) is then used to learn structural relationships within the text for final classification. Experimental results demonstrate that SGG-ATD achieves high F1-scores and consistently outperforms strong baselines. This method contributes to robust AI text detection, supporting accountability and resilience against AI-driven misinformation.

Gupta, Nidhi↗
DOE OSTI2024

Multi-Level Structural Damage Characterization Using Sparse Acoustic Sensor Networks and Knowledge Transferred Deep Learning

Standard structural health monitoring techniques face well-known difficulties for comprehensive defect diagnosis in real-world structures that have structural, material, or geometric complexity. This motivates the exploration of machine-learning-based structural health monitoring methods in complex structures. However, creating sufficient training data sets with various defects is an ongoing challenge for data-driven machine (deep) learning algorithms. The ability to transfer the knowledge of a trained neural network from one component to another or to other sections of the same component would drastically reduce the required training data set. Also, it would facilitate computationally inexpensive machine learning based inspection systems. In this work, a machine-learning-based multi-level damage characterization is demonstrated with the ability to transfer trained knowledge within the sparse sensor network. A novel network spatial assistance and an adaptive convolution technique are proposed for efficient knowledge transfer within the deep learning algorithm. Proposed structural health monitoring method is experimentally evaluated on an aluminum plate with artificially induced defects. It was observed that the method improves the performance of knowledge transferred damage characterization by 50% during localization and 24% during severity assessment. Further, experiments using time windows with and without multiple edge reflections are studied. Results reveal that multiply scattered waves contain rich and deterministic defect signatures that can be mined using deep learning neural networks, improving the accuracy of both identification and quantification. In the case of a fixed sensor network, using multiply scattered waves shows 100% prediction accuracy at all levels of damage characterization.

36 MATERIALS SCIENCE↗
DOE OSTI2023

CEGANN: Crystal Edge Graph Attention Neural Network for multiscale classification of materials environment

Abstract We introduce Crystal Edge Graph Attention Neural Network (CEGANN) workflow that uses graph attention-based architecture to learn unique feature representations and perform classification of materials across multiple scales (from atomic to mesoscale) and diverse classes ranging from metals, oxides, non-metals to hierarchical materials such as zeolites and semi-ordered mesophases. CEGANN can classify based on a global, structure-level representation such as space group and dimensionality (e.g., bulk, 2D, clusters, etc.). Using representative materials such as polycrystals and zeolites, we demonstrate its transferability in performing local atom-level classification tasks, such as grain boundary identification and other heterointerfaces. CEGANN classifies in (thermal) noisy dynamical environments as demonstrated for representative zeolite nucleation and growth from an amorphous mixture. Finally, we use CEGANN to classify multicomponent systems with thermal noise and compositional diversity. Overall, our approach is material agnostic and allows for multiscale feature classification ranging from atomic-scale crystals to heterointerfaces to microscale grain boundaries.

36 MATERIALS SCIENCE↗
DOE OSTI2022

On the use of vibronic coherence to identify reaction coordinates for ultrafast excited-state dynamics of transition metal-based chromophores

In this study, the question of whether one can use information from quantum coherence as a means of identifying vibrational degrees of freedom that are active along an excited-state reaction coordinate is discussed. Specifically, we are exploring the notion of whether quantum oscillations observed in single-wavelength kinetics data exhibiting coherence dephasing times that are intermediate between that expected for either pure electronic or pure vibrational dephasing are vibronic in nature and therefore may be coupled to electronic state-to-state evolution. In the case of a previously published Fe(ιι) polypyridyl complex, coherences observed subsequent to 1 A 1 → 1 MLCT excitation were linked to large-amplitude motion of a portion of the ligand framework; dephasing times on the order of 200–300 fs suggested that these degrees of freedom could be associated with ultrafast (~100 fs) conversion from the initially formed MLCT excited state to lower-energy, metal-centered ligand-field excited state(s) of the compound. Incorporation of an electronically benign but sterically restrictive Cu(ι) ion into the superstructure designed to interfere with this motion yielded a compound exhibiting a ~25-fold increase in the compound’s MLCT lifetime, a result that was interpreted as confirmation of the initial hypothesis. However, new data acquired on a different chemical system – Cr(acac') 3 (where acac' represents various derivatives of acetylacetonate) – yielded results that call into question this same hypothesis. Coherences observed subsequent to 4 A 2 → 4 T 2 ligand-field excitation on a series of molecules implicated similar vibrational degrees of freedom across the series, but exhibited dephasing times ranging from 340 fs to 2.5 ps without any clear correlation to the dynamics of excited-state evolution in the system. Taken together, the results obtained on both of these chemical platforms suggest that while identification of coherences can indeed point to degrees of freedom that should be considered as candidate modes for defining reaction trajectories, our understanding of the factors that determine the interplay across coherences, dephasing times, and electronic and geometric structure is insufficient at the present time to view this parameter as a robust metric for differentiating active versus spectator modes for ultrafast dynamics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Machine Learning–Augmented Laser-Induced Breakdown Spectroscopy for Spectral Discrimination of Iron Oxalates

Enhanced characterization and phase identification of post-PUREX Pu Oxalates (PuOXA) are pivotal for nonproliferation and pre-detonation nuclear forensics. Despite significant advances in the characterization of PuO 2 samples, little is known about the impact of both the chemical structure and oxidation states of PuOXA (i.e., Pu(III) and Pu(IV)) have on optical emission signatures. Here, we demonstrate the analytical capabilities of laser-induced breakdown spectroscopy (LIBS) applied to Fe(II) and Fe(III) oxalate samples as surrogates for PuOXA, highlighting the discriminating features in the LIBS emission spectra arising from differences in the oxidation states within mixed FeOXA samples. We report the enhancement of spectral feature selection using Principal Component Analysis (PCA), which enables the analytical superiority of machine learning algorithms such as Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), and Random Forest Regression (RFR) over conventional univariate techniques for phase discrimination and chemometric analysis. Cluster analysis revealed how both matrix effects and laser ablation influence cluster separability by introducing spectral artifacts that misdirect the maximization of variance. PCA-selected emission lines were used in the regression models, demonstrating that both univariate and multivariate linear regression models (i.e., PLSR and SVR) can achieve acceptable performance, with machine learning models outperforming conventional calibration regressions. Furthermore, the application of non-linearly activated PCA-selected emission lines illustrates how simplifying the data while retaining captured variance enables the use of less complex and more computationally efficient models. Furthermore, this is particularly evident in the underperformance of RFR, which suffers from increased computational costs and overfitting owing to its high complexity.

Oxalates↗
DOE OSTI2022

MINE 2.0: enhanced biochemical coverage for peak identification in untargeted metabolomics

Abstract Summary Although advances in untargeted metabolomics have made it possible to gather data on thousands of cellular metabolites in parallel, identification of novel metabolites from these datasets remains challenging. To address this need, Metabolic in silico Network Expansions (MINEs) were developed. A MINE is an expansion of known biochemistry which can be used as a list of potential structures for unannotated metabolomics peaks. Here, we present MINE 2.0, which utilizes a new set of biochemical transformation rules that covers 93% of MetaCyc reactions (compared to 25% in MINE 1.0). This results in a 17-fold increase in database size and a 40% increase in MINE database compounds matching unannotated peaks from an untargeted metabolomics dataset. MINE 2.0 is thus a significant improvement to this community resource. Availability and implementation The MINE 2.0 website can be accessed at https://minedatabase.ci.northwestern.edu. The MINE 2.0 web API documentation can be accessed at https://mine-api.readthedocs.io/en/latest/. The data and code underlying this article are available in the MINE-2.0-Paper repository at https://github.com/tyo-nu/MINE-2.0-Paper. MINE 2.0 source code can be accessed at https://github.com/tyo-nu/MINE-Database (MINE construction), https://github.com/tyo-nu/MINE-Server (backend web API) and https://github.com/tyo-nu/MINE-app (web app). Supplementary information Supplementary data are available at Bioinformatics online.

59 BASIC BIOLOGICAL SCIENCES↗