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DOE OSTI2017

Computed Tomography Radiomics Predicts HPV Status and Local Tumor Control After Definitive Radiochemotherapy in Head and Neck Squamous Cell Carcinoma

Purpose: This study aimed to predict local tumor control (LC) after radiochemotherapy of head and neck squamous cell carcinoma (HNSCC) and human papillomavirus (HPV) status using computed tomography (CT) radiomics. Methods and Materials: HNSCC patients treated with definitive radiochemotherapy were included in the retrospective study approved by the local ethical commission (93 and 56 patients in the training and validation cohorts, respectively). Three hundred seventeen CT radiomic features, including those based on shape, intensity, texture, and wavelet transform, were calculated in the primary tumor region. Cox and logistic regression models were built to predict LC and HPV status, respectively. The best-performing features in the univariable analysis were included in the multivariable analysis after the exclusion of redundant features. The quality of the models was assessed using the concordance index (CI) for modeling of LC and receiver operating characteristics area under the curve (AUC) for HPV status prediction. The radiomics LC model was compared to a model incorporating clinical parameters (tumor stage, volume, and HPV status) and a mixed model. Results: A radiomic signature comprising 3 features was significantly associated with LC (CI{sub training} = 0.75 and CI{sub validation} = 0.78), showing that tumors with a more heterogeneous CT density distribution are at risk for decreased LC. The addition of clinical parameters to the radiomics model slightly improved the model in the training cohort but not in the validation cohort. Another radiomic signature showed good performance in HPV status prediction (AUC{sub training} = 0.85 and AUC{sub validation} = 0.78) and indicated that HPV-positive tumors have a more homogenous CT density distribution. Conclusions: Heterogeneity of HNSCC tumor density, quantified by CT radiomics, is associated with LC after radiochemotherapy and HPV status.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2018

Safety Impacts of Automated Vehicles in Mixed Traffic

Previous research have shown that Automated Vehicles (AV) have the potential to improve transportation thanks to the possibility to perform some control functions without driver input (1). A question that remains is what are the safety impacts of the interaction between human-driven vehicles with different levels of automation at a particular intersection?The main objective of this paper is to build the behavioral framework to simulate different levels of automation and to study the safety impact of these levels under different penetration scenarios. Real-life connected vehicle data were incorporated to revise the Wiedemann car-following model and simulate the behavior of human-driven vehicles, level 3 and level 5 AVs.

Ramin, Arvin↗

DOE OSTI2021

Drosophila models to study causative genes for human rare intractable neurological diseases

Highlights: • Drosophila models targeting some ALS-causing genes are described. • Drosophila models targeting some CMT-causing genes are described. • Drosophila models targeting some Sotos syndrome-causing genes are described. • Genes and pathways that commonly interact with ALS- and/or CMT-causing genes are described. Drosophila is emerging as a convenient model for investigating human diseases. Functional homologues of almost 75% of human disease-related genes are found in Drosophila. Amyotrophic lateral sclerosis (ALS) is a severe neurodegenerative disease that causes defects in motoneurons. Charcot-Marie-Tooth disease (CMT) is one of the most commonly found inherited neuropathies affecting both motor and sensory neurons. No effective therapy has been established for either of these diseases. In this review, after overviewing ALS, Drosophila models targeting several ALS-causing genes, including TDP-43, FUS and Ubiquilin2, are described with their genetic interactants. Then, after overviewing CMT, examples of Drosophila models targeting several CMT-causing genes, including mitochondria-related genes and FIG 4, are also described with their genetic interactants. In addition, we introduce Sotos syndrome caused by mutations in the epigenetic regulator gene NSD1. Lastly, several genes and pathways that commonly interact with ALS- and/or CMT-causing genes are described. In the case of ALS and CMT that have many causative genes, it may be not practical to perform gene therapy for each of the many disease-causing genes. The possible uses of the common genes and pathways as novel diagnosis markers and effective therapeutic targets are discussed.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2021

Automatic identification and quantification of dense microcracks in high-performance fiber-reinforced cementitious composites through deep learning-based computer vision

Highlights: • A method is presented to detect, locate, quantify, and visualize dense microcracks in HPFRCC. • Quantification of dense microcracks is realized using deep learning method for the first time. • The presented method uses deep learning models that are trained using a realistic dataset size. • The presented method has a high computation efficiency for identifying and quantifying cracks. • The presented method provides crack width with errors up to 50 μm and a R{sup 2} value of 0.984. High-performance fiber-reinforced cementitious composites (HPFRCCs) feature high mechanical strengths, crack resistance, and durability. Under excessive loading, HPFRCCs demonstrate dense microcracks that are difficult to identify using existing methods. This study presents a computer vision method for identification, quantification, and visualization of microcracks in HPFRCCs based on deep learning. The presented method integrates multiple deep learning models and computer vision techniques in a hierarchical architecture. The crack pattern (e.g., number, width, and spacing of cracks) are automatically determined from pictures without human intervention. This study shows that the presented method achieves an accuracy of 0.992 for crack detection and an accuracy finer than 50 μm (R{sup 2} > 0.984) for quantification of crack width when deep learning models are trained using only 200 pictures of HPFRCCs and 200 pictures of conventional concrete with incorporation of data augmentation. The presented method is expected to be also applicable to other materials featuring complex cracks.

36 MATERIALS SCIENCE↗

DOE OSTI2019

Small Molecule IL-36γ Antagonist as a Novel Therapeutic Approach for Plaque Psoriasis

IL-36 cytokines are pro-inflammatory members of the IL-1 family that are upregulated in inflammatory disorders. Specifically, IL-36γ is highly expressed in active psoriatic lesions and can drive pro-inflammatory processes in 3D human skin equivalents supporting a role for this target in skin inflammation. Small molecule antagonists of interleukins have been historically challenging to generate. Nevertheless, we performed a small molecule high-throughput screen to identify IL-36 antagonists using a novel TR-FRET binding assay. Several compounds, including 2-oxypyrimidine containing structural analogs of the marketed endothelin receptor A antagonist Ambrisentan, were identified as hits from the screen. A-552 was identified as a the most potent antagonist of human IL-36γ, but not the closely related family member IL-36α, was capable of attenuating IL-36γ induced responses in mouse and human disease models. Additionally, x-ray crystallography studies identified key amino acid residues in the binding pocket present in human IL-36γ that are absent in human IL-36α. A-552 represents a first-in-class small molecule antagonist of IL-36 signaling that could be used as a chemical tool to further investigate the role of this pathway in inflammatory skin diseases such as psoriasis.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2020

Foreword Special issue on the Kilopower Project, Kilowatt Reactor Using Stirling TechnologY (KRUSTY) Test

This special issue of Nuclear Technology contains full-length, peer-reviewed papers describing the design work leading up to and the results of the Kilowatt Reactor Using Stirling TechnologY (KRUSTY) test. KRUSTY was the centerpiece of the National Aeronautics and Space Administration (NASA) Kilopower Project to design, build, and test a space nuclear reactor. This test was the first such test since the end of the Space Nuclear Auxiliary Power (SNAP) project at the end of the 1960s. In this issue, the introduction paper presents the goals of the Kilopower Project and the potential missions this reactor concept could serve in NASA. Kilopower was intended to serve both human exploration needs on planetary surfaces as well as science needs for deep-space exploration. The design work for the experiment by Poston and the power conversion development by Gibson present the pre-work required to perform the eventual KRUSTY test. A paper on regulatory analysis follows, to show the path used to gain approval of the proposed experiment. Then, the early zero-power critical experiments are presented by Sanchez and Grove. These experiments were essential data used to enhance model predictions prior to the high-temperature test. Next, Poston presents the three experiments (warm criticals) that increase the temperature in an incremental fashion prior to the final experiment. These experiments were used to achieve final regulatory approval of the final high-temperature experiment. The last paper by Poston presents the results of the steady-state and transient testing of the reactor at full power and at the design temperature. These results show that the reactor design and as-built experiment met all of the requirements that NASA had developed for the system. Any experiment of this magnitude was accomplished only because of the hard work and dedication of a large number of people at multiple institutions, including the NASA Glenn Research Center, NASA Marshall Space Flight Center, Y-12 National Security Site, Los Alamos National Laboratory, and the Nevada National Security Site prime contractor (formerly National Security Technologies and now the Mission Support and Test Services). The project was jointly funded by the Space Technology Mission Directorate at NASA and the Criticality Safety Program at the National Nuclear Security Administration (NNSA). A special thanks to Jerry McKamy and Angela Chambers at the NNSA and Lee Mason and Don Palac at NASA for their support and guidance. For the authors and multitude of staff that performed this work, the passion for space nuclear reactors was the key to making this experiment a reality.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2024

Assessment of Electric Grid Transmission System Simulator for Human Factors Research

Over the past decades, various technologies have been developed for electric grid operations to support clean energy, meet rising electricity demands, and address infrastructure concerns. However, the human factors aspect is often overlooked during rapid integration. Questions persist about how these technologies impact human performance. Simulators play a critical role in supporting investigation of human factors design concepts and conducting comprehensive usability testing to evaluate human performance and assess human reliability. This paper aims to address human factors research simulator requirements and conduct a comparative study of six different simulators. A detailed evaluation reveals that the evaluated simulators lack the ability to customize user interfaces. Additionally, their user interface designs do not fulfill the basic human factors design principles, potentially leading to increased response variability and reduced statistical power when conducting controlled experimental research. In the future, it is essential to develop scripting tools to integrate customizable user interfaces and simulation models, ensuring meeting research requirements.

Li, Ruixuan↗

DOE OSTI2025

FEDERATED LEARNING ON STOCHASTIC NEURAL NETWORKS

Federated learning is a machine learning paradigm that leverages edge computing on client devices to optimize models while maintaining user privacy by ensuring that local data remain on the device. However, since all data are collected by clients, federated learning is susceptible to latent noise in local datasets. Factors such as limited measurement capabilities or human errors may introduce inaccuracies in client data. To address this challenge, we propose the use of a stochastic neural network as the local model within the federated learning framework. Stochastic neural networks not only facilitate the estimation of the true underlying states of the data but also enable the quantification of latent noise. We refer to our federated learning approach, which incorporates stochastic neural networks as local models, as federated stochastic neural networks. In this work we will present numerical experiments demonstrating the performance and effectiveness of our method, particularly in handling nonindependent and identically distributed data.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Geologic Disposal Safety Assessment (GDSA) Biosphere Model Development

The Spent Fuel and Waste Science and Technology Campaign of the U.S. Department of Energy Office of Nuclear Energy, Office of Spent Fuel and Waste Disposition is conducting research and development on geologic disposal of spent nuclear fuel and high-level nuclear waste. This work includes the Geologic Disposal Safety Assessment (GDSA) program which is charged with development of generic deep geologic repository concepts and system performance assessment models. One part of the GDSA framework is the development of a biosphere model capable of assessing doses to potential receptors exposed to radionuclides released from geologic disposal sites. As part of the GDSA framework, a biosphere model compatible with the PFLOTRAN massively parallel subsurface flow and reactive transport code is under development. The PFLOTRAN model provides the radionuclide source term for the biosphere model. The GDSA Biosphere model then assesses the potential movement of radionuclides through the surface biosphere and the subsequent exposure to a human receptor living in the biosphere. The biosphere model includes pathways originating from the groundwater as well as pathways originating from surface water bodies that have a water exchange with a contaminated groundwater body. The pathways for human exposure include consumption of drinking water, irrigated crops, meat animals, aquatic vegetation, and animals, etc.; external exposure from irrigated ground surfaces, surface water bodies, recreational activities, etc.; and inadvertent exposures such as ingestion of contaminated soils or shower water, etc. The GDSA Biosphere model was designed to be flexible and generic in order to accommodate a variety of different sites and climate states. This presentation will present the on the purpose, design, and development progress of the GDSA Biosphere Model.

GDSA, biosphere, repository↗

DOE OSTI2024

Parking Strategies and Outcomes for Shared Autonomous Vehicle Fleet Operations

Parking spots are a premium commodity, especially in dense downtown settings, so this study examines the service impacts of shared autonomous vehicles (SAVs) parking in legal on- or off-street locations when idle across Travis County in Austin, Texas. Here, using an agent-based activity-based travel demand model with dynamic traffic simulation, two restricted-parking strategies for SAVs were simulated. SAVs either found the nearest available parking spot or the lowest-cost spot (via a tradeoff of parking fees and distance-based costs). Two comparisons were conducted to analyze the impacts of these strategies. First, two restricted parking strategies were compared, where SAVs park without competition with private human-driven vehicles (HVs) for parking locations. Second, a more realistic analysis compared two SAV parking strategies with a scenario where SAVs remain idle in place. Private HVs in all scenarios and strategies of this comparison park at the closest designated location unless they opt for private parking. Using a supply of 8,400 aggregated parking locations in Austin, this study simulated fleet performance under different trip demands, with SAV fares of $\$0.62$ per kilometer ($\$1$ per mile) plus a $\$1$ fixed pickup fee with dynamic ridesharing permitted. Parking costs were negligible in both SAV parking search strategies applied to the Austin network because of the region’s provision of mostly free parking. Requiring SAVs to park on designated on- and off-street parking locations and parking lots (restricted parking) also increased parking costs for HV drivers by up to 22% since SAVs occupied some free parking spaces, especially in the least-cost parking search strategy.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2018

Plaster: An Integration, Benchmark and Development Framework for Heterogeneous Metadata Normalization Methods

The recent advances in the automation of metadata normalization and the invention of a unified schema — Brick — alleviate the metadata normalization challenge for deploying portable applications across buildings. Yet, the lack of compatibility between existing metadata normalization methods precludes the possibility of comparing and combining them. While generic machine learning (ML) frameworks, such as MLJAR and OpenML, provide versatile interfaces for standard ML problems, they cannot easily accommodate the metadata normalization tasks for buildings due to the heterogeneity in the inference scope, type of data required as input, evaluation metric, and the building-specific human-in-the-loop learning procedure. We propose Plaster, an open and modular framework that incorporates existing advances in building metadata normalization. It provides unified programming interfaces for various types of learning methods for metadata normalization and defines standardized data models for building metadata and timeseries data. Thus, it enables the integration of different methods via a workflow, benchmarking of different methods via unified interfaces, and rapid prototyping of new algorithms. With Plaster, we 1) show three examples of the workflow integration, delivering better performance than individual algorithms, 2) benchmark/analyze five algorithms over five common buildings, and 3) exemplify the process of developing a new algorithm involving time series features. We believe Plaster will facilitate the development of new algorithms and expedite the adoption of standard metadata schema such as Brick, in order to enable seamless smart building applications in the future.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2017

NERSC & JGI Seek Elusive Proteins for Gene Editing

A video just released by the Supercomputing 2017 (SC17) conference shows how biologists at UC Berkeley and the Joint Genome Institute (JGI) – a Department of Energy Office of Science User Facility – are using NERSC, the Office of Science’s primary supercomputing center, to identify a class of enzymes capable of editing out genes responsible for disease. This revolutionary work requires petaflops of computing power to sift through billions of DNA sequences in the JGI data portals to identify proteins like Cas9 that, combined with the Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR), can “edit” a genome. First discovered by researchers at UC Berkeley, the CRISPR-Cas9 system has the power to permanently correct disease-causing mutations in cells, animal models, and eventually human patients to cure certain types of cancer, cardiovascular disease, sickle cell disease, and retinal degeneration. Since the CRISPR system was first identified in the early 2000s, it has become the most promising technology for treating genetic disease in humans and enabling plants to survive stress caused by pests and the environment. The annual SC17 conference will take place November 12-17 in Denver, Colorado. The over 12,000 attendees from academia, government and industry are leaders in researching and delivering high performance computing, networking, data storage and analysis in support of scientific discovery. http://sc17.supercomputing.org

Berkeley Lab, LBNL↗

DOE OSTI2017

NERSC & JGI Seek Elusive Proteins for Gene Editing

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

Low-temperature oxidation pathways are critical to thermal incineration of PFAS-laden materials

With growing desire to destroy per- and poly-fluoroalkyl substances (PFAS) now known to be detrimental to human health, a sound understanding of fluorocarbon combustion chemistry is important to efficient thermal destruction within incinerators. While most fluorocarbon combustion models and the sets of reactions contained within them were originally developed for the high temperatures encountered in flame suppression applications, they have often been used to assess PFAS destruction in incinerators, which emphasize a lower range of temperatures. We present results that demonstrate that low-temperature fluorocarbon oxidation pathways—not yet known to play a role in fluorocarbon combustion—impact key incinerator performance metrics, including: PFAS surrogate mole fractions, products of incomplete destruction, and waste destruction efficiencies. The results further point to the utility of NO as a potential additive. The present results show the influence of these pathways for CF 3 O 2 , for which some data are available, but analogous pathways would also occur for other fluoroalkylperoxy radicals, for which little is known. Furthermore, the results demonstrate the need for future work to identify and characterize low-temperature pathways more generally, consider such pathways in kinetic model development, and experimentally probe intermediate temperature conditions to better understand, design, and control thermal destruction technologies for improved PFAS management.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2017

Effects of the distribution and evolution of the coefficient of friction along a fault on the assessment of the seismic activity associated with a hypothetical industrial-scale geologic CO 2 sequestration operation

Carbon capture and storage (CCS) in geological formations is considered as a promising option that could limit CO 2 emissions from human activities into the atmosphere. However, there is a risk that pressure buildup inside the storage formation can induce slip along preexisting faults and create seismic event felt by the population. To prevent this to happen a geomechanical fault stability analysis should be performed, considering uncertainties of input parameters. In this paper, we investigate how the distribution of the coefficient of friction and the applied frictional law could influence the assessment of fault stability and the characteristics of potential injection-induced seismic events. Our modelling study is based on a hypothetical industrial-scale carbon sequestration project located in the Southern San Joaquin Basin in California, USA, where the stability on a major (25 km long) fault that bounds the sequestration site is assessed during 50 years of CO 2 injection. We conduct nine simulations in which the distributions of the coefficients of static and dynamic friction are changed to simulate a hardening and softening phase before and during rupture. Here, our main findings are: (i) variations in friction along the fault have an important effect on the predicted seismic activity, with maximum magnitude ranging from 1.88 to 5.88 and number of seismic events ranging from 338 to 3272; (ii) the extreme values of the coefficient of friction (lowest and highest) present along the rupture area control how much stress is accumulated before rupture; and (iii) an argillaceous caprock can prevent the development of large magnitude seismic events but favor the occurrence of a large number of smaller events.

58 GEOSCIENCES↗

DOE OSTI2017

Targeting S-adenosylmethionine biosynthesis with a novel allosteric inhibitor of Mat2A

S-Adenosyl-L-methionine (SAM) is an enzyme cofactor used in methyl transfer reactions and polyamine biosynthesis. The biosynthesis of SAM from ATP and L-methionine is performed by the methionine adenosyltransferase enzyme family (Mat; EC 2.5.1.6). Human methionine adenosyltransferase 2A (Mat2A), the extrahepatic isoform, is often deregulated in cancer. We identified a Mat2A inhibitor, PF-9366, that binds an allosteric site on Mat2A that overlaps with the binding site for the Mat2A regulator, Mat2B. Studies exploiting PF-9366 suggested a general mode of Mat2A allosteric regulation. Allosteric binding of PF-9366 or Mat2B altered the Mat2A active site, resulting in increased substrate affinity and decreased enzyme turnover. These data support a model whereby Mat2B functions as an inhibitor of Mat2A activity when methionine or SAM levels are high, yet functions as an activator of Mat2A when methionine or SAM levels are low. The ramification of Mat2A activity modulation in cancer cells is also described.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

Sneezing and asymptomatic virus transmission

The novel coronavirus disease (COVID-19) spread pattern continues to show that geographical barriers alone cannot contain a virus. Asymptomatic carriers play a critical role in the nature of this virus quickly escalating into a global pandemic. Asymptomatic carriers may transmit the virus unintentionally through sporadic sneezing. A novel Computational Fluid Dynamics (CFD) approach has been proposed with a realistic modeling of a human sneeze achieved by the combination of state-of-the-art experimental and numerical methods. This modeling approach may be suitable for future engineering analyses aimed at reshaping public spaces and common areas, with the main objective to accurately predict the spread of aerosol and droplets that may contain pathogens. This study shows that the biomechanics of a human sneeze, including complex muscle contractions and relaxations, can be accurately modeled by the angular head motion and the dynamic pressure response during sneezing. These have been considered as the human factors and were implemented in the CFD simulation by imposing a momentum source term to the coupled Eulerian–Lagrangian momentum equations. The momentum source was modeled by the measured dynamic pressure response in conjunction with the angular head motion. This approach eliminated the need to create an ad hoc set of inlet boundary conditions. With this proposed technique, it is easier to add multiple fixed and/or moving sources of sneezes in complex computational domains. Additionally, extensive sensitivity analyses based on different environmental conditions were performed, and their impact was described in terms of potential virus spread.

42 ENGINEERING↗

DOE OSTI2018

Imaging in Diagnostic Nuclear Medicine

Single-photon emission computed tomography (SPECT) and positron emission tomography (PET) are modern methods for visualization in diagnostic nuclear medicine. SPECT is known as a workhorse in cardiology, and PET is the gold standard in oncology. The development of nuclear medicine is provided by cooperation of physicists, mathematicians, biologists, medical doctors, and radio-chemists. In spite of extensive clinical applications, several problems may lead to false diagnoses. In particular, the correction of attenuation of gamma radiation in human organs must be taken into account. For interpretation and evaluation of such an effect on the clinical results, we perform physico-mathematical simulation of the SPECT diagnostics in cardiology in the absence and presence of the correction of attenuation. A brief review of the state-of-the art is presented. The simulation employs the first Russian anthropomorphic mathematical phantom that describes the distribution of radio-pharmacological agent ({sup 99m }Tc-methoxyisobutylisonitrile) in chest organs of a typical male patient. A model for calculation of raw images is developed with allowance for attenuation of radiation in biological tissues and the effect of collimator and detector. The results of the proposed models and calculated images are compared with clinical images obtained at the Meshalkin Institute of Circulation Pathology (Novosibirsk) and Myasnikov Institute of Clinical Cardiology (Moscow). Statistical algorithms are developed for the solution of the inverse problem of image reconstruction based on the entropy principle. The clinical and physico-mathematical approaches are compared in the evaluation of the effect of correction on the quality of reconstructed images of the left ventricle of myocardium.

62 RADIOLOGY AND NUCLEAR MEDICINE↗