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DOE OSTI2023

Toll-like Receptor Ligands Enhance Vaccine Efficacy against a Virulent Newcastle Disease Virus Challenge in Chickens

To enhance the efficacy of the current Newcastle disease vaccine, we have selected potential adjuvants that target well-characterized pattern recognition receptors: the toll-like receptors (TLRs). Imiquimod is a small-molecule activator of TLR7, which is a sensor of dsDNA. ODN-1826 is a mimetic of CpG DNA and ligates TLR21 (a chicken homologue of TLR9 in mammals). The activation of TLRs leads to antiviral responses, including the induction of type I interferons (IFNs). In this study, birds were vaccinated intranasally with a live LaSota strain with or without imiquimod or ODN-1826 (50 µg/bird). Two weeks after vaccination, the birds were challenged with a virulent Newcastle disease virus (chicken/CA/212676/2002). Both adjuvants (imiquimod or ODN-1826) induced higher and more uniform antibody titers among vaccinated birds compared with the live vaccine-alone group. In addition, adjuvanted vaccines demonstrated greater protective efficacy in terms of the reduction in virus-shedding titer and the number of birds shedding the challenge virus at 2 and 4 days post-challenge. A differential expression of antiviral and immune-related genes was observed among groups from tissues (Harderian gland, trachea, cecal tonsil, and spleen) collected 1 and 3 days after treatment. These results demonstrate the potential of TLR-targeted adjuvants as mucosal vaccine enhancers and warrant a further characterization of immune correlates and optimization for efficacy.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

NuMI Beam Monitoring Simulation and Data Analysis †

Following the decommissioning of the Main Injector Neutrino Oscillation Search (MINOS) experiment, muon and hadron monitors have emerged as vital diagnostic tools for the NuMI Off-axis Appearance (NOvA) experiment at Fermilab. These tools are crucial for overseeing the Neutrinos at the Main Injector (NuMI) beam. This study endeavors to ensure the monitor signal quality and to correlate them with the Neutrino beam profile. Leveraging muon monitor simulations, we systematically explore the monitor responses to variations in proton-beam and lattice parameters. Through the amalgamation of individual pixel data from muon monitors, pattern-recognition algorithms, simulations, and measured data, we devise machine-learning-based models to predict muon monitor responses and Neutrino flux.

43 PARTICLE ACCELERATORS↗
DOE OSTI2023

Inhibition of the C1s Protease and the Classical Complement Pathway by 6-(4-Phenylpiperazin-1-yl)Pyridine-3-Carboximidamide and Chemical Analogs

Abstract The classical pathway (CP) is a potent mechanism for initiating complement activity and is a driver of pathology in many complement-mediated diseases. The CP is initiated via activation of complement component C1, which consists of the pattern recognition molecule C1q bound to a tetrameric assembly of proteases C1r and C1s. Enzymatically active C1s provides the catalytic basis for cleavage of the downstream CP components, C4 and C2, and is therefore an attractive target for therapeutic intervention in CP-driven diseases. Although an anti-C1s mAb has been Food and Drug Administration approved, identifying small-molecule C1s inhibitors remains a priority. In this study, we describe 6-(4-phenylpiperazin-1-yl)pyridine-3-carboximidamide (A1) as a selective, competitive inhibitor of C1s. A1 was identified through a virtual screen for small molecules that interact with the C1s substrate recognition site. Subsequent functional studies revealed that A1 dose-dependently inhibits CP activation by heparin-induced immune complexes, CP-driven lysis of Ab-sensitized sheep erythrocytes, CP activation in a pathway-specific ELISA, and cleavage of C2 by C1s. Biochemical experiments demonstrated that A1 binds directly to C1s with a K d of ∼9.8 μM and competitively inhibits its activity with an inhibition constant (K i) of ∼5.8 μM. A 1.8-Å-resolution crystal structure revealed the physical basis for C1s inhibition by A1 and provided information on the structure–activity relationship of the A1 scaffold, which was supported by evaluating a panel of A1 analogs. Taken together, our work identifies A1 as a new class of small-molecule C1s inhibitor and lays the foundation for development of increasingly potent and selective A1 analogs for both research and therapeutic purposes.

Immunology↗
DOE OSTI2021

A daily, 250 m and real-time gross primary productivity product (2000–present) covering the contiguous United States

Abstract. Gross primary productivity (GPP) quantifies the amount of carbon dioxide (CO2) fixed by plants through photosynthesis. Although as a key quantity of terrestrial ecosystems, there is a lack of high-spatial-and-temporal-resolution, real-time and observation-based GPP products. To address this critical gap, here we leverage a state-of-the-art vegetation index, near-infrared reflectance of vegetation (NIRV), along with accurate photosynthetically active radiation (PAR), to produce a SatelLite Only Photosynthesis Estimation (SLOPE) GPP product for the contiguous United States (CONUS). Compared to existing GPP products, the proposed SLOPE product is advanced in its spatial resolution (250 m versus >500 m), temporal resolution (daily versus 8 d), instantaneity (latency of 1 d versus >2 weeks) and quantitative uncertainty (on a per-pixel and daily basis versus no uncertainty information available). These characteristics are achieved because of several technical innovations employed in this study: (1) SLOPE couples machine learning models with MODIS atmosphere and land products to accurately estimate PAR. (2) SLOPE couples highly efficient and pragmatic gap-filling and filtering algorithms with surface reflectance acquired by both Terra and Aqua MODIS satellites to derive a soil-adjusted NIRV (SANIRV) dataset. (3) SLOPE couples a temporal pattern recognition approach with a long-term Cropland Data Layer (CDL) product to predict dynamic C4 crop fraction. Through developing a parsimonious model with only two slope parameters, the proposed SLOPE product explains 85 % of the spatial and temporal variations in GPP acquired from 49 AmeriFlux eddy-covariance sites (324 site years), with a root-mean-square error (RMSE) of 1.63 gC m−2 d−1. The median R2 over C3 and C4 crop sites reaches 0.87 and 0.94, respectively, indicating great potentials for monitoring crops, in particular bioenergy crops, at the field level. With such a satisfactory performance and its distinct characteristics in spatiotemporal resolution and instantaneity, the proposed SLOPE GPP product is promising for biological and environmental research, carbon cycle research, and a broad range of real-time applications at the regional scale. The archived dataset is available at https://doi.org/10.3334/ORNLDAAC/1786 (download page: https://daac.ornl.gov/daacdata/cms/SLOPE_GPP_CONUS/data/, last access: 20 January 2021) (Jiang and Guan, 2020), and the real-time dataset is available upon request.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Mechanism of B-box 2 domain-mediated higher-order assembly of the retroviral restriction factor TRIM5α

Restriction factors and pattern recognition receptors are important components of intrinsic cellular defenses against viral infection. Mammalian TRIM5α proteins are restriction factors and receptors that target the capsid cores of retroviruses and activate ubiquitin-dependent antiviral responses upon capsid recognition. Here, we report crystallographic and functional studies of the TRIM5α B-box 2 domain, which mediates higher-order assembly of TRIM5 proteins. The B-box can form both dimers and trimers, and the trimers can link multiple TRIM5α proteins into a hexagonal net that matches the lattice arrangement of capsid subunits and enables avid capsid binding. Two modes of conformational flexibility allow TRIM5α to accommodate the variable curvature of retroviral capsids. B-box mediated interactions also modulate TRIM5α’s E3 ubiquitin ligase activity, by stereochemically restricting how the N-terminal RING domain can dimerize. Overall, these studies define important molecular details of cellular recognition of retroviruses, and how recognition links to downstream processes to disable the virus.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2011

Secure VM for Monitoring Industrial Process Controllers

In this paper, we examine the biological immune system as an autonomic system for self-protection, which has evolved over millions of years probably through extensive redesigning, testing, tuning and optimization process. The powerful information processing capabilities of the immune system, such as feature extraction, pattern recognition, learning, memory, and its distributive nature provide rich metaphors for its artificial counterpart. Our study focuses on building an autonomic defense system, using some immunological metaphors for information gathering, analyzing, decision making and launching threat and attack responses. In order to detection Stuxnet like malware, we propose to include a secure VM (or dedicated host) to the SCADA Network to monitor behavior and all software updates. This on-going research effort is not to mimic the nature but to explore and learn valuable lessons useful for self-adaptive cyber defense systems.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2011

The Multi-Isotope Process Monitor: Multivariate Analysis of Gamma Spectra

The International Atomic Energy Agency (IAEA) has established international safeguards standards for fissionable material at spent fuel reprocessing plants to ensure that significant quantities of nuclear material are not diverted from these facilities. Currently, methods to verify material control and accountancy (MC&A) at these facilities require time-consuming and resource-intensive destructive assay (DA). The time delay between sampling and subsequent DA provides a potential opportunity to divert the material out of the appropriate chemical stream. Leveraging new on-line nondestructive assay (NDA) techniques in conjunction with the traditional and highly precise DA methods may provide a more timely, cost-effective and resource efficient means for MC&A verification at such facilities. Pacific Northwest National Laboratory (PNNL) is developing on-line NDA process monitoring technologies, including the Multi-Isotope Process (MIP) Monitor. The MIP Monitor uses gamma spectroscopy and pattern recognition software to identify off-normal conditions in process streams. Recent efforts have been made to explore the basic limits of using multivariate analysis techniques on gamma-ray spectra. This paper will provide an overview of the methods and report our on-going efforts to develop and demonstrate the technology.

Orton, Christopher R.↗
DOE OSTI2012

Image Hashes as Templates for Verification

Imaging systems can provide measurements that confidently assess characteristics of nuclear weapons and dismantled weapon components, and such assessment will be needed in future verification for arms control. Yet imaging is often viewed as too intrusive, raising concern about the ability to protect sensitive information. In particular, the prospect of using image-based templates for verifying the presence or absence of a warhead, or of the declared configuration of fissile material in storage, may be rejected out-of-hand as being too vulnerable to violation of information barrier (IB) principles. Development of a rigorous approach for generating and comparing reduced-information templates from images, and assessing the security, sensitivity, and robustness of verification using such templates, are needed to address these concerns. We discuss our efforts to develop such a rigorous approach based on a combination of image-feature extraction and encryption-utilizing hash functions to confirm proffered declarations, providing strong classified data security while maintaining high confidence for verification. The proposed work is focused on developing secure, robust, tamper-sensitive and automatic techniques that may enable the comparison of non-sensitive hashed image data outside an IB. It is rooted in research on so-called perceptual hash functions for image comparison, at the interface of signal/image processing, pattern recognition, cryptography, and information theory. Such perceptual or robust image hashing—which, strictly speaking, is not truly cryptographic hashing—has extensive application in content authentication and information retrieval, database search, and security assurance. Applying and extending the principles of perceptual hashing to imaging for arms control, we propose techniques that are sensitive to altering, forging and tampering of the imaged object yet robust and tolerant to content-preserving image distortions and noise. Ensuring that the information contained in the hashed image data (available out-of-IB) cannot be used to extract sensitive information about the imaged object is of primary concern. Thus the techniques are characterized by high unpredictability to guarantee security. We will present an assessment of the performance of our techniques with respect to security, sensitivity and robustness on the basis of a methodical and mathematically precise framework.

Janik, Tadeusz↗
DOE OSTI2014

ISMB Conference Funding to Support Attendance of Early Researchers and Students

ISMB Conference Funding for Students and Young Scientists Historical Description The Intelligent Systems for Molecular Biology (ISMB) conference has provided a general forum for disseminating the latest developments in bioinformatics on an annual basis for the past 22 years. ISMB is a multidisciplinary conference that brings together scientists from computer science, molecular biology, mathematics and statistics. The goal of the ISMB meeting is to bring together biologists and computational scientists in a focus on actual biological problems, i.e., not simply theoretical calculations. The combined focus on “intelligent systems” and actual biological data makes ISMB a unique and highly important meeting. 21 years of experience in holding the conference has resulted in a consistently well-organized, well attended, and highly respected annual conference. "Intelligent systems" include any software which goes beyond straightforward, closed-form algorithms or standard database technologies, and encompasses those that view data in a symbolic fashion, learn from examples, consolidate multiple levels of abstraction, or synthesize results to be cognitively tractable to a human, including the development and application of advanced computational methods for biological problems. Relevant computational techniques include, but are not limited to: machine learning, pattern recognition, knowledge representation, databases, combinatorics, stochastic modeling, string and graph algorithms, linguistic methods, robotics, constraint satisfaction, and parallel computation. Biological areas of interest include molecular structure, genomics, molecular sequence analysis, evolution and phylogenetics, molecular interactions, metabolic pathways, regulatory networks, developmental control, and molecular biology generally. Emphasis is placed on the validation of methods using real data sets, on practical applications in the biological sciences, and on development of novel computational techniques. The ISMB conferences are distinguished from many other conferences in computational biology or artificial intelligence by an insistence that the researchers work with real molecular biology data, not theoretical or toy examples; and from many other biological conferences by providing a forum for technical advances as they occur, which otherwise may be shunned until a firm experimental result is published. The resulting intellectual richness and cross-disciplinary diversity provides an important opportunity for both students and senior researchers. ISMB has become the premier conference series in this field with refereed, published proceedings, establishing an infrastructure to promote the growing body of research.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

LLNL Partners with IBM on Brain-Like Computing Chip

Lawrence Livermore National Laboratory (LLNL) will receive a first-of-a-kind brain-inspired supercomputing platform for deep learning developed by IBM Research. Based on a breakthrough neurosynaptic computer chip called IBM TrueNorth, the scalable platform will process the equivalent of 16 million neurons and 4 billion synapses and consume the energy equivalent of a hearing aid battery – a mere 2.5 watts of power. The brain-like, neural network design of the IBM Neuromorphic System is able to infer complex cognitive tasks such as pattern recognition and integrated sensory processing far more efficiently than conventional chips.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

The Liquid Argon Software Toolkit (LArSoft): Goals, Status and Plan

LArSoft is a toolkit that provides a software infrastructure and algorithms for the simulation, reconstruction and analysis of events in Liquid Argon Time Projection Chambers (LArTPCs). It is used by the ArgoNeuT, LArIAT, MicroBooNE, DUNE (including 35ton prototype and ProtoDUNE) and SBND experiments. The LArSoft collaboration provides an environment for the development, use, and sharing of code across experiments. The ultimate goal is to develop fully automatic processes for reconstruction and analysis of LArTPC events. The toolkit is based on the art framework and has a well-defined architecture to interface to other packages, including to GEANT4 and GENIE simulation software and the Pandora software development kit for pattern recognition. It is designed to facilitate and support the evolution of algorithms including their transition to new computing platforms. The development of the toolkit is driven by the scientific stakeholders involved. The core infrastructure includes standard definitions of types and constants, means to input experiment geometries as well as meta and event- data in several formats, and relevant general utilities. Examples of algorithms experiments have contributed to date are: photon-propagation; particle identification; hit finding, track finding and fitting; electromagnetic shower identification and reconstruction. We report on the status of the toolkit and plans for future work.

Pordes, Rush↗
DOE OSTI2017

Funding for the 2ND IAEA technical meeting on fusion data processing, validation and analysis

The International Atomic Energy Agency (IAEA) will organize the second Technical Meeting on Fusion Da Processing, Validation and Analysis from 30 May to 02 June, 2017, in Cambridge, MA USA. The meeting w be hosted by the MIT Plasma Science and Fusion Center (PSFC). The objective of the meeting is to provide a platform where a set of topics relevant to fusion data processing, validation and analysis are discussed with the view of extrapolation needs to next step fusion devices such as ITER. The validation and analysis of experimental data obtained from diagnostics used to characterize fusion plasmas are crucial for a knowledge based understanding of the physical processes governing the dynamics of these plasmas. The meeting will aim at fostering, in particular, discussions of research and development results that set out or underline trends observed in the current major fusion confinement devices. General information on the IAEA, including its mission and organization, can be found at the IAEA websit Uncertainty quantification (UQ) Model selection, validation, and verification (V&V) Probability theory and statistical analysis Inverse problems & equilibrium reconstru ction Integrated data analysis Real time data analysis Machine learning Signal/image proc essing & pattern recognition Experimental design and synthetic diagnostics Data management

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2017

Event Reconstruction in the NOvA Experiment

The NOvA experiment observes oscillations in two channels (electron-neutrino appearance and muon-neutrino disappearance) using a predominantly muon-neutrino NuMI beam. The Near Detector records multiple overlapping neutrino interactions in each event and the Far Detector has a large background of cosmic rays due to being located on the surface. The oscillation analyses rely on the accurate reconstruction of neutrino interactions in order to precisely measure the neutrino energy and identify the neutrino flavor and interaction mode. Similarly, measurements of neutrino cross sections using the Near Detector require accurate identification of the particle content of each interaction. A series of pattern recognition techniques have been developed to split event records into individual spatially and temporally separated interactions, to estimate the interaction vertex, and to isolate and classify individual particles within the event. This combination of methods to achieve full event reconstruction in the NOvA detectors has discussed.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2018

Progress Toward a Deep-Learning-Based Search for Low-Energy Electron Neutrinos in MicroBooNE

MicroBooNE is a neutrino experiment based at Fermilab which consists of a liquid argon time-projection chamber in the Booster Neutrino Beam (BNB). The experiment aims to investigate the excess of electron neutrino-like events seen by the MiniBooNE experiment, also located in the BNB. I will describe the status of our deep-learning-based search for low-energy electron neutrino interactions within the MicroBooNE detector and the muon neutrino interactions by which we will constrain them. This analysis features a novel hybrid approach of traditional reconstruction methods combined with the use of convolutional neural networks, a type of deep learning algorithm highly adept at pattern recognition. I will present work on ongoing studies that will characterize and quantify the systematic uncertainties associated with this analysis.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2018

Particle Flow and PUPPI in the Level-1 Trigger at CMS for the HL-LHC

With the planned addition of tracking information to the Compact Muon Solenoid (CMS) Level-1 trigger for the High-Luminosity Large Hadron Collider (HL-LHC), the trigger algorithms can be completely reconceptualized. We explore the feasibility of using particle flow-like reconstruction and pileup per particle identification (PUPPI) pileup mitigation at the hardware trigger level. This represents a new type of multi-subdetector pattern recognition challenge for the HL-LHC. We present proof-of-principle studies on both physics and hardware-resource performance of a prototype algorithm for use by CMS in the HL-LHC era.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2019

Search for a Low Energy Excess in MicroBooNE

MicroBooNE (the Micro Booster Neutrino Experiment) is a liquid argon time-projection chamber (TPC) experiment designed for short-baseline neutrino physics, currently running at Fermilab. It aims to address the anomalous excess of low-energy events observed by the MiniBooNE experiment. Recent progress towards the search for the low-energy events have brought to develop fully automated event selection algorithm to identify charged-current electron neutrino event candidates with no pions and at least one proton in the final state ($\nu_e~\text{CC} 0\pi \text{N}p$) using the Pandora multi-algorithm pattern recognition. Several cross checks and sidebands have been studied so far to validate the analysis.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2015

The Physics and Nuclear Nonproliferation Goals of WATCHMAN: A WAter CHerenkov Monitor for ANtineutrinos

Herein, this article describes the physics and nonproliferation goals of WATCHMAN, the WAter Cherenkov Monitor for ANtineutrinos. The baseline WATCHMAN design is a kiloton scale gadolinium-doped (Gd) light water Cherenkov detector, placed 13 kilometers from a civil nuclear reactor in the United States. In its first deployment phase, WATCHMAN will be used to remotely detect a change in the operational status of the reactor, providing a first- ever demonstration of the potential of large Gd-doped water detectors for remote reactor monitoring for future international nuclear nonproliferation applications. During its first phase, the detector will provide a critical large-scale test of the ability to tag neutrons and thus distinguish low energy electron neutrinos and antineutrinos. This would make WATCHMAN the only detector capable of providing both direction and flavor identification of supernova neutrinos. It would also be the third largest supernova detector, and the largest underground in the western hemisphere. In a follow-on phase incorporating the IsoDAR neutrino beam, the detector would have world-class sensitivity to sterile neutrino signatures and to non-standard electroweak interactions (NSI). WATCHMAN will also be a major, U.S. based integration platform for a host of technologies relevant for the Long-Baseline Neutrino Facility (LBNF) and other future large detectors. This white paper describes the WATCHMAN conceptual design,and presents the results of detailed simulations of sensitivity for the project's nonproliferation and physics goals. Furthermore, it describes the advanced technologies to be used in WATCHMAN, including high quantum efficiency photomultipliers, Water-Based Liquid Scintillator (WbLS), picosecond light sensors such as the Large Area Picosecond Photo Detector (LAPPD), and advanced pattern recognition and particle identification methods.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Completion design improvement using a deep convolutional network

Maximizing stimulated natural and hydraulic fracture network is one of the primary hydraulic fracturing concerns for economic production from a horizontal shale gas well. Geomechanical facies and preexisting fractures in each stage are identified based on similarities in formation characteristics to optimize the locations of perforation clusters. This often requires analyzing large volumes of drilling, Logging While Drilling (LWD) and Measurement While Drilling (MWD) data. In this paper, we develop a methodology that calculates the mechanical specific energy (MSE) using real-time drill string acceleration signals directly from its definition. High resolution vibration signals have been collected using a tri-axial accerlometer, which was an auxiliary tool included in acoustic borehole imager. This technique provides a cost-efficient solution for engineered completion design. Furthermore, we adopt deep Convolutional Neural Network (CNN) with signal processing to build a data pipeline that effectively extracts patterns from dynamic acceleration signals for rock lateral MSE classification. First, we apply discrete wavelet transform and Short-Time Fourier Transform (STFT) for signal denoising and pattern recognition. Then we construct an image dataset using multi-scale image fusion at pixel level from 3 sensor channels, including axial, lateral acceleration spectrograms and zero-padded revolutions per minute (RPM). The resulted RGB image dataset includes 4,000 images of 5 MSE ranges with various rock strength conditions. Our results demonstrate that the proposed deep learning model can achieve more than 90% classification accuracy. The deep learning results, as a reference source, were applied in selected Marcellus Shale Energy and Environmental Lab (MSEEL) wells engineered completion located in the Marcellus shale gas site.

03 NATURAL GAS↗