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All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

ADEPT: a domain independent sequence alignment strategy for gpu architectures

Bioinformatic workflows frequently make use of automated genome assembly and protein clustering tools. At the core of most of these tools, a significant portion of execution time is spent in determining optimal local alignment between two sequences. This task is performed with the Smith-Waterman algorithm, which is a dynamic programming based method. With the advent of modern sequencing technologies and increasing size of both genome and protein databases, a need for faster Smith-Waterman implementations has emerged. Multiple SIMD strategies for the Smith-Waterman algorithm are available for CPUs. However, with the move of HPC facilities towards accelerator based architectures, a need for an efficient GPU accelerated strategy has emerged. Existing GPU based strategies have either been optimized for a specific type of characters (Nucleotides or Amino Acids) or for only a handful of application use-cases. In this paper, we present ADEPT, a new sequence alignment strategy for GPU architectures that is domain independent, supporting alignment of sequences from both genomes and proteins. Our proposed strategy uses GPU specific optimizations that do not rely on the nature of sequence. We demonstrate the feasibility of this strategy by implementing the Smith-Waterman algorithm and comparing it to similar CPU strategies as well as the fastest known GPU methods for each domain. ADEPT’s driver enables it to scale across multiple GPUs and allows easy integration into software pipelines which utilize large scale computational systems. We have shown that the ADEPT based Smith-Waterman algorithm demonstrates a peak performance of 360 GCUPS and 497 GCUPs for protein based and DNA based datasets respectively on a single GPU node (8 GPUs) of the Cori Supercomputer. Overall ADEPT shows 10x faster performance in a node-to-node comparison against a corresponding SIMD CPU implementation. ADEPT demonstrates a performance that is either comparable or better than existing GPU strategies. We demonstrated the efficacy of ADEPT in supporting existing bionformatics software pipelines by integrating ADEPT in MetaHipMer a high-performance denovo metagenome assembler and PASTIS a high-performance protein similarity graph construction pipeline. Our results show 10% and 30% boost of performance in MetaHipMer and PASTIS respectively.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

ADEPT, a dynamic next generation sequencing data error-detection program with trimming

Illumina is the most widely used next generation sequencing technology and produces millions of short reads that contain errors. These sequencing errors constitute a major problem in applications such as de novo genome assembly, metagenomics analysis and single nucleotide polymorphism discovery. In this study, we present ADEPT, a dynamic error detection method, based on the quality scores of each nucleotide and its neighboring nucleotides, together with their positions within the read and compares this to the position-specific quality score distribution of all bases within the sequencing run. This method greatly improves upon other available methods in terms of the true positive rate of error discovery without affecting the false positive rate, particularly within the middle of reads. We conclude that ADEPT is the only tool to date that dynamically assesses errors within reads by comparing position-specific and neighboring base quality scores with the distribution of quality scores for the dataset being analyzed. The result is a method that is less prone to position-dependent under-prediction, which is one of the most prominent issues in error prediction. The outcome is that ADEPT improves upon prior efforts in identifying true errors, primarily within the middle of reads, while reducing the false positive rate.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

ADEPT: A Pedagogical Framework for Integrating Agentic AI with Deterministic Scientific Workflows

The integration of Large Language Models (LLMs) into scientific research promises to accelerate discovery, yet a significant gap remains between the dynamic reasoning of Artificial Intelligence (AI) agents and the static, deterministic nature of canonical scientific workflows. This paper introduces ADEPT (Agentic Discovery and Exploration Platform for Tools), a reference architecture and pedagogical framework explicitly designed to bridge this gap. ADEPT's primary mission is to provide a transparent, "glass-box" environment where researchers and engineers can learn to effectively wrap established scientific software (e.g., BLAST, Nextflow pipelines) and compose it into reliable, agent-driven workflows. We describe its modular, multi-server architecture, which leverages the Model Context Protocol (MCP) for tool serving, LangGraph for robust agentic orchestration, and a secure nsjail-based sandbox for safe code execution. By prioritizing architectural clarity, safety, and modularity, ADEPT serves as an extensible blueprint for building trustworthy AI-augmented systems and fosters the collaborative development necessary to responsibly employ agentic AI for science. We provide practical examples of how to adapt and extend this framework, highlighting its utility in workforce development and AI-readiness capabilities across research and development projects.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2013

ADEPT Final Report: Dynamic Control of Grid Assets Using Direct AC Converter Cells

Seedling funding was secured through the ARPA-E program to develop a robust, utility-scale Power Converter Augmented Transformer (PCAT). The project designed, fabricated, and tested two 13 kV, 1 MW PCATs operating at an effective switching frequency of up to 50 kHz and at an efficiency of greater than 99%. Two versions of the PCAT were fabricated – one using an imputed DC link (IDCL) topology and the other using a back-to-back (BTB) topology. The IDCL avoids the use of electrolytic capacitors for bulk energy storage, thus improving the lifetime and decreasing the size of the converter, while the BTB topology uses an industry-proven topology that is more commercially viable in the short term. To give the PCAT a lifetime and maintenance profile compatible with typical existing utility assets, a novel Passive Thermal Management System (PTMS) was designed and integrated into both PCATs. The PTMS uses a thermosiphon to remove heat from the power electronic devices without forced cooling and reject it to the ambient environment. In addition, a fail-normal feature was implemented to ensure that in the event of PCAT failure the functionality of the underlying transformer is retained. Attainment of project objectives was verified through testing at the Georgia Tech National Electrical Energy Testing Research and Applications Center (NEETRAC). The IDCL-based PCAT had an efficiency of more than 99.10% and the BTB-based PCAT had an efficiency of more than 99.75%. During testing, the PTMS exceeded project requirements by rejecting the 5 kW output of the converters to a 40 °C environment while keeping devices junction temperatures within their tolerances.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

A High-Throughput Computing Infrastructure to Generate Custom, Open Community Geothermal Datasets

The most significant challenge facing geothermal research, development, and deployment is a lack of comprehensive datasets describing the geological and economical properties of North America. Automated knowledge base construction, the process of designing algorithms to analyze text and images to programmatically build new datasets, is one possible solution to this problem. The xDD library of full-text scientific articles (https://xdd.wisc.edu) is one of the largest collections of open and controlled-access scientific documents available for knowledge base construction in the world, but it has been underutilized by experts in geothermal research. The xDD development team attributed the lack of engagement by software developers and geothermal researchers to two perceived shortcomings of the system. First, the workflow for obtaining data from xDD for local development and testing of data mining applications was unnecessarily abstruse and required significant manual intervention by xDD systems administrators. Second, although xDD already held articles from a broad cross-section of scientific literature with an emphasis on the geosciences, it did not have an explicit set of geothermal research documents that could serve as the nucleus of a geothermal data mining application. To address these issues, the Automated Data Extraction PlaTform (ADEPT) was proposed to extend the data distribution capabilities of the xDD system. The ADEPT extension added the following four key features to xDD: 1) integration of National Geothermal Data System (NGDS) documents into the xDD library to provide an explicitly geothermally-themed collection; 2) improved RESTful (i.e., https-protocol driven) web services for external partners to access xDD data for machine learning application development; 3) a web platform for end-users and xDD administrators to coordinate the development of data mining applications from the initial step of browsing available documents to the final stage of deploying a production-quality machine learning application on high-throughput computing infrastructure; and 4) the development of demonstration data mining applications to illustrate the new workflow to potential collaborators. A total of 21,674 geothermal documents from NGDS were fully ingested into the xDD library and the associated metadata is publicly available through the xDD web services; furthermore, the ADEPT web platform is now publicly accessible and fully live at https://xdd.wisc.edu/adept/.

15 GEOTHERMAL ENERGY↗
DOE OSTI2018

Wide Band-Gap Semiconductor Based Power Electronics for Energy Efficiency

Recent advances in wide band-gap (WBG) semiconductor materials, such as silicon carbide (SiC) and gallium nitride (GaN) are enabling a new generation of power semiconductor devices that far exceed the performance of silicon-based devices. Past ARPA-E programs (ADEPT, Solar ADEPT, and SWITCHES) have enabled innovations throughout the power electronics value chain, especially in the area of WBG semiconductors. The two recently launched programs by ARPA-E (CIRCUITS and PNDIODES) continue to investigate the use of WBG semiconductors in power electronics. From materials and devices to modules and circuits to application-ready systems integration, ARPA-E projects have demonstrated the potential of WBG semiconductors to lower the cost of high-efficiency power electronics to enable broad adoption in energy applications.

25 ENERGY STORAGE↗
DOE OSTI2021

Instruction Roofline: An insightful visual performance model for GPUs

The Roofline performance model provides an intuitive approach to identify performance bottlenecks and guide performance optimization. However, the classic FLOP-centric approach is inappropriate for the emerging applications that perform more integer operations than floating point operations. In this article, we reintroduce our Instruction Roofline Model on NVIDIA GPUs and expand our evaluation of it. The Instruction Roofline incorporates instructions and memory transactions across all memory hierarchies together, and provides more performance insights than the FLOP-oriented Roofline Model, that is, instruction throughput, stride memory access patterns, bank conflicts, and thread predication. We use our Instruction Roofline methodology to analyze eight proxy applications: HPGMG from AMReX, Matrix Transpose benchmarks, ADEPT from MetaHipMer's sequence alignment phase, EXTENSION from MetaHipMer's local assembly phase, CUSP, cuSPARSE, cudaTensorCoreGemm, and cuBLAS. We demonstrate the ability of our methodology to understand various aspects of performance and performance bottlenecks on NVIDIA GPUs and motivate code optimizations.

Ding, N↗
DOE OSTI2024

Out-of-Distribution Detection and Radiological Data Monitoring Using Statistical Process Control

Abstract Machine learning (ML) models often fail with data that deviates from their training distribution. This is a significant concern for ML-enabled devices as data drift may lead to unexpected performance. This work introduces a new framework for out of distribution (OOD) detection and data drift monitoring that combines ML and geometric methods with statistical process control (SPC). We investigated different design choices, including methods for extracting feature representations and drift quantification for OOD detection in individual images and as an approach for input data monitoring. We evaluated the framework for both identifying OOD images and demonstrating the ability to detect shifts in data streams over time. We demonstrated a proof-of-concept via the following tasks: 1) differentiating axial vs. non-axial CT images, 2) differentiating CXR vs. other radiographic imaging modalities, and 3) differentiating adult CXR vs. pediatric CXR. For the identification of individual OOD images, our framework achieved high sensitivity in detecting OOD inputs: 0.980 in CT, 0.984 in CXR, and 0.854 in pediatric CXR. Our framework is also adept at monitoring data streams and identifying the time a drift occurred. In our simulations tracking drift over time, it effectively detected a shift from CXR to non-CXR instantly, a transition from axial to non-axial CT within few days, and a drift from adult to pediatric CXRs within a day—all while maintaining a low false positive rate. Through additional experiments, we demonstrate the framework is modality-agnostic and independent from the underlying model structure, making it highly customizable for specific applications and broadly applicable across different imaging modalities and deployed ML models.

Zamzmi, Ghada↗
DOE OSTI2014

A Pair Production Telescope for Medium-Energy Gamma-Ray Polarimetry

We describe the science motivation and development of a pair production telescope for medium-13 energy gamma-ray polarimetry. Our instrument concept, the Advanced Energetic Pair Telescope 14 (AdEPT), takes advantage of the Three-Dimensional Track Imager, a low-density gaseous time 15 projection chamber, to achieve angular resolution within a factor of two of the pair production 16 kinematics limit (~0.6° at 70 MeV), continuum sensitivity comparable with the Fermi-LAT front 17 detector (<3×10-6 MeV cm-2 s-1 at 70 MeV), and minimum detectable polarization less than 10% 18 for a 10 millicrab source in 106 seconds.

Hunter, Stanley D.↗
DOE OSTI2018

Engineering of Escherichia coli β-lactamase TEM-1 variants showing higher activity under acidic conditions than at the neutral pH

Highlights: • Genetic screening is useful for obtaining β-lactamases with altered pH dependency. • A few mutations each suppress the activity of a β-lactamase at pH 7.0. • The engineered β-lactamase variants are more active under the acidic conditions. • The relevant amino acid substitutions are located far from the catalytic site. Escherichia coli β-lactamase TEM-1 is potentially useful in the antibody-directed enzyme/prodrug therapy (ADEPT), converting nontoxic prodrugs to toxic agents. The produced toxin would kill cancer cells, when the enzyme is attached to a tumor-antigen-specific antibody. However, the off-site reaction possibly occurring in the blood or normal tissues raises safety concern. In the present study, we engineered TEM-1 variants preferentially active at pH 5.8–6.2, near the pH of the acidic microenvironment of tumor. A library of randomly mutagenized variants was screened for the ability to confer an antibiotic resistance on E. coli cells in acidic growth media and not in neutral media, to isolate a variant with a Thr-to-Ile substitution at position 160. An extensive mutagenesis study was then conducted in the proximity of this position, to show that a Leu162Glu mutation also causes the acid preference. Kinetic analyses indicated that the overall activity of the wild-type TEM-1 hardly changes over a pH range from 5.8 to 7.0, whereas TEM-1(T160I) is 1.5-times as active at pH 6.2 than pH 7.0, and TEM-1(T160I) is 3.1-fold as active at pH 5.8 than pH 7.0. A further mutagenesis study suggested that a change in the overall structure of the enzyme underlies the pH dependency of the variants.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Uncertainty quantification of graph convolution neural network models of evolving processes

The application of neural network models to scientific machine learning tasks has proliferated in recent years. In particular, neural networks have proved to be adept at modeling processes with spatial–temporal complexity. Nevertheless, these highly parameterized models have garnered skepticism in their ability to produce outputs with quantified error bounds over the regimes of interest. Hence there is a need to find uncertainty quantification methods that are suitable for neural networks. In this work we present comparisons of the parametric uncertainty quantification of neural networks modeling complex spatial–temporal processes with Hamiltonian Monte Carlo and Stein variational gradient descent and its projected variant. Specifically we apply these methods to graph convolutional neural network models of evolving systems modeled with recurrent neural network and neural ordinary differential equations architectures. We show that Stein variational inference is a viable alternative to Monte Carlo methods with some clear advantages for complex neural network models. For our exemplars, Stein variational interference gave similar pushed forward uncertainty profiles through time compared to Hamiltonian Monte Carlo, albeit with generally more generous variance. As a result, projected Stein variational gradient descent also produced similar uncertainty profiles to the non-projected counterpart, but large reductions in the active weight space were confounded by the stability of the neural network predictions and the convoluted likelihood landscape.

36 MATERIALS SCIENCE↗
DOE OSTI2023

A mesoscale crystal plasticity model to predict room-temperature deformation and martensitic transformation of high-strength Quenching and Partitioning (Q&P) Steels and validation with synchrotron X-ray diffraction

Renowned for the superior mechanical properties and adeptness at cold-forming, Quenching and Partitioning (QP) steels have gained prominence as a promising candidate material in fabricating safety-critical components in various industries. The pertinent research on QP steels focus on the martensitic transformation of the Retained Austenite (RA) phase during cold-forming, a crucial mechanism that substantially influences the overall strength and ductility of QP steels. The austenite stability and transformation rate heavily rely on the local strain path and the initial microstructure, which is challenging for analytical prediction. In this paper, a mesoscale model is developed to capture the deformation and transformation kinetics of QP steels inside the microstructure. The model integrates the detailed explicit microstructure, acquired from characterization experiments, into a high-resolution finite element (FE) mesh. It distinctly model the deformation and interaction between the various phases and the effect on the transformation of RA. The model is validated with high energy X-ray diffraction (HEXRD) data, and shows excellent capability in predicting the asymmetric stress-strain behavior under uniaxial tension and compression, as well as the martensitic transformation rate. The model is used to investigate the strain and load partitioning effect of surrounding matrix to the transformation of RA, offering insights into the complex behavior of QP980 and facilitates further material development.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Fission yeast Duf89 and Duf8901 are cobalt/nickel-dependent phosphatase–pyrophosphatases that act via a covalent aspartyl–phosphate intermediate

Domain of Unknown Function 89 (DUF89) proteins are metal-dependent phosphohydrolases. Exemplary DUF89 enzymes differ in their metal and phosphosubstrate preferences. Here, we interrogated the activities and structures of two DUF89 paralogs from fission yeast—Duf89 and Duf8901. We find that Duf89 and Duf8901 are cobalt/nickel-dependent phosphohydrolases adept at hydrolyzing p-nitrophenylphosphate and PP i . Crystal structures of metal-free Duf89 and Co 2+ -bound Duf8901 disclosed two enzyme conformations that differed with respect to the position of a three-helix module, which is either oriented away from the active site in Duf89 or forms a lid over the active site in Duf8901. Lid closure results in a 16 Å movement of Duf8901 Asp195, vis-à-vis Asp199 in Duf89, that brings Asp195 into contact with an octahedrally coordinated cobalt. Reaction of Duf8901 with BeCl 2 and NaF in the presence of divalent cations Co 2+ , Ni 2+ , or Zn 2+ generated covalent Duf8901-(Asp248)–beryllium trifluoride (BeF 3 )•Co 2+ , Duf8901-(Asp248)–BeF 3 •Ni 2+ , or Duf8901-(Asp248)–BeF 3 •Zn 2+ adducts, the structures of which suggest a two-step catalytic mechanism via formation and hydrolysis of an enzyme-(aspartyl)–phosphate intermediate. Alanine mutations of Duf8901 Asp248, Asn249, Lys401, Asp286, and Asp195 that interact with BeF 3 •Co 2+ squelched p-nitrophenylphosphatase activity. A 1.8 Å structure of a Duf8901-(Asp248)–AlF 4 –OH 2 •Co 2+ transition-state mimetic suggests an associative mechanism in which Asp195 and Asp363 orient and activate the water nucleophile. Whereas deletion of the duf89 gene elicited a phenotype in which expression of phosphate homeostasis gene pho1 was derepressed, deleting duf8901 did not, thereby hinting that the DUF89 paralogs have distinct functional repertoires in vivo.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Object detection with deep learning for rare event search in the GADGET II TPC

In the pursuit of identifying rare two-particle events within the GADGET II Time Projection Chamber (TPC), this paper presents a comprehensive approach for leveraging Convolutional Neural Networks (CNNs) and various data processing methods. To address the inherent complexities of 3D TPC track reconstructions, the data is expressed in 2D projections and 1D quantities. This approach capitalizes on the diverse data modalities of the TPC, allowing for the efficient representation of the distinct features of the 3D events, with no loss in topology uniqueness. Additionally, it leverages the computational efficiency of 2D CNNs and benefits from the extensive availability of pre-trained models. Given the scarcity of real training data for the rare events of interest, simulated events are used to train the models to detect real events. To account for potential distribution shifts when predominantly depending on simulations, significant perturbations are embedded within the simulations. This produces a broad parameter space that works to account for potential physics parameter and detector response variations and uncertainties. These parameter-varied simulations are used to train sensitive 2D CNN object detectors. When combined with 1D histogram peak detection algorithms, this multi-modal detection framework is highly adept at identifying rare, two-particle events in data taken during experiment 21072 at the Facility for Rare Isotope Beams (FRIB), demonstrating a 100% recall for events of interest. Here, we present the methods and outcomes of our investigation and discuss the potential future applications of these techniques.

Convolutional neural network↗
DOE OSTI2015

Metallothionein blocks oxidative DNA damage induced by acute inorganic arsenic exposure

We studied how protein metallothionein (MT) impacts arsenic-induced oxidative DNA damage (ODD) using cells that poorly express MT (MT-I/II double knockout embryonic cells; called MT-null cells) and wild-type (WT) MT competent cells. Arsenic (as NaAsO{sub 2}) was less cytolethal over 24 h in WT cells (LC{sub 50} = 11.0 ± 1.3 μM; mean ± SEM) than in MT-null cells (LC{sub 50} = 5.6 ± 1.2 μM). ODD was measured by the immuno-spin trapping method. Arsenic (1 or 5 μM; 24 h) induced much less ODD in WT cells (121% and 141% of control, respectively) than in MT-null cells (202% and 260%). In WT cells arsenic caused concentration-dependent increases in MT expression (transcript and protein), and in the metal-responsive transcription factor-1 (MTF-1), which is required to induce the MT gene. In contrast, basal MT levels were not detectable in MT-null cells and unaltered by arsenic exposure. Transfection of MT-I gene into the MT-null cells markedly reduced arsenic-induced ODD levels. The transport genes, Abcc1 and Abcc2 were increased by arsenic in WT cells but either showed no or very limited increases in MT-null cells. Arsenic caused increases in oxidant stress defense genes HO-1 and GSTα2 in both WT and MT-null cells, but to much higher levels in WT cells. WT cells appear more adept at activating metal transport systems and oxidant response genes, although the role of MT in these responses is unclear. Overall, MT protects against arsenic-induced ODD in MT competent cells by potential sequestration of scavenging oxidant radicals and/or arsenic. - Highlights: • Metallothionein blocks arsenic toxicity. • Metallothionein reduces arsenic-induced DNA damage. • Metallothionein may bind arsenic or radicals produced by arsenic.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2013

Microbial communities and greenhouse gas emissions associated with the biodegradation of specified risk material in compost

Highlights: ► Addition of feathers altered bacterial and fungal communities in compost. ► Microbial communities degrading SRM and compost matrix were distinct. ► Addition of feathers may enrich for microbial communities that degrade SRM. ► Inclusion of feather in compost increased both CH{sub 4} and N{sub 2}O emissions from compost. ► Density of methanogens and methanotrophs were weakly associated with CH{sub 4} emissions. - Abstract: Provided that infectious prions (PrP{sup Sc}) are inactivated, composting of specified risk material (SRM) may be a viable alternative to rendering and landfilling. In this study, bacterial and fungal communities as well as greenhouse gas emissions associated with the degradation of SRM were examined in laboratory composters over two 14 day composting cycles. Chicken feathers were mixed into compost to enrich for microbial communities involved in the degradation of keratin and other recalcitrant proteins such as prions. Feathers altered the composition of bacterial and fungal communities primarily during the first cycle. The bacterial genera Saccharomonospora, Thermobifida, Thermoactinomycetaceae, Thiohalospira, Pseudomonas, Actinomadura, and Enterobacter, and the fungal genera Dothideomycetes, Cladosporium, Chaetomium, and Trichaptum were identified as candidates involved in SRM degradation. Feathers increased (P < 0.05) headspace concentrations of CH{sub 4} primarily during the early stages of the first cycle and N{sub 2}O during the second. Although inclusion of feathers in compost increases greenhouse gas emissions, it may promote the establishment of microbial communities that are more adept at degrading SRM and recalcitrant proteins such as keratin and PrP{sup Sc}.

54 ENVIRONMENTAL SCIENCES↗